diff --git a/.gitignore b/.gitignore index a9b24ab..54f8757 100644 --- a/.gitignore +++ b/.gitignore @@ -49,4 +49,5 @@ __pycache__* */jupyter-lab.log jupyter-lab.log +**/*.pkl *.dat diff --git a/clean_py_notebooks.py b/clean_py_notebooks.py deleted file mode 100644 index 3a4f825..0000000 --- a/clean_py_notebooks.py +++ /dev/null @@ -1,72 +0,0 @@ -""" -Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and -binary forms, with or without modification, are permitted exclusively -under the terms of the Modified BSD license. You should have received -a copy of the license with this file. If not, please or visit: -http://tudat.tudelft.nl/LICENSE. -""" -# PLEASE NOTE: -# This script is NOT a tudatpy example. -# It is a script to clean the .py files that are generated from Jupyter notebooks, using the following option: File > Download as > Python (.py) -# Running it will automatically edit all the .py example files (please check the changes made before pushing them to the repository). - -import glob - -examples_list = glob.glob("*/*.py") -examples_list += glob.glob("*/*/*.py") - -for example_path in examples_list: - print("Cleaning example at %s" % example_path) - - with open(example_path, "r+") as file: - example_content = file.readlines() - - # Remove file type and encoding - if "!/usr/bin/env python" in example_content[0]: - example_content = example_content[3:] - - checking_comment_end = False - skip_next = False - # Go trough each line in the example - for i, line in enumerate(example_content): - if skip_next: - skip_next = False - continue - # Remove the "In[x]" notebook inputs - if "In[" in line: - # Also remove the two lines after - [example_content.pop(i) for _ in range(3)] - - # Check for the end of a markdown cell - elif checking_comment_end: - # If the line is empty, the markdown cell is finished - if line == "\n": - # Add """ to close the string comment, then an empty line - example_content[i] = "\"\"\"\n" - example_content.insert(i+1, "\n") - checking_comment_end = False - # Detect if we have a second title in the same markdown cell, and mark the separation - elif "##" in line: - example_content[i] = line.replace("# ", "", 1) - example_content.insert(i-1, "\"\"\"\n\n") - example_content.insert(i+2, "\"\"\"\n") - skip_next = True - # If we are still in the markdown cell, remove the simple # that indicates a comment line - else: - example_content[i] = line.replace("# ", "", 1) - - # If the line starts with # #, we are in a markdown cell that starts with a title - elif "# #" in line: - # Replace the first line to keep the title with a comment # - example_content[i] = line.replace("# ", "", 1) - example_content.insert(i+1, "\"\"\"\n") - checking_comment_end = True # Start looking for the end of the cell - - # Remove the lines that made the plots interactive - elif "# make plots interactive" in line: - [example_content.pop(i) for _ in range(2)] - - - file.seek(0) - file.writelines(example_content) - file.truncate() diff --git a/convert_py_to_ipynb.py b/convert_py_to_ipynb.py deleted file mode 100644 index 3322453..0000000 --- a/convert_py_to_ipynb.py +++ /dev/null @@ -1,41 +0,0 @@ -# This script found on stackoverflow -# https://stackoverflow.com/questions/23292242/converting-to-not-from-ipython-notebook-format -# -# Be sure to **leave a space** after every first-column comment character. -# -# Add relevant markers to your input py file as desired (followed by an empty line), eg.: -# # markdowncells are embedded in comments -# -# -# "$ \\Delta = \\theta $" # for latex math mode; double backslashes may not be necessary -# "---" # to insert a horizontal line -# -# Possibly obsolete: -# -# - -# from IPython.nbformat import v3, v4 -from nbformat import v3, v4 -import glob - -examples_list = glob.glob("*/*.py") -examples_list += glob.glob("*/*/*.py") - -for example_path in examples_list: - print("Cleaning example at %s" % example_path) - new_example_path = example_path.split('.')[0] + ".ipynb" - with open(example_path) as fpin: - text = fpin.read() - - text += """ -# - -# If you can read this, reads_py() is no longer broken! - """ - - nbook = v3.reads_py(text) - nbook = v4.upgrade(nbook) # upgrade v3 to v4 - - jsonform = v4.writes(nbook) + "\n" - with open(new_example_path, "w") as fpout: - fpout.write(jsonform) diff --git a/create_scripts.py b/create_scripts.py new file mode 100644 index 0000000..4fc1793 --- /dev/null +++ b/create_scripts.py @@ -0,0 +1,164 @@ +""" +Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and +binary forms, with or without modification, are permitted exclusively +under the terms of the Modified BSD license. You should have received +a copy of the license with this file. If not, please or visit: +http://tudat.tudelft.nl/LICENSE. +""" +# PLEASE NOTE: +# This script is NOT a tudatpy example. +# It is a script to clean the .py files that are generated from Jupyter notebooks, using the following option: File > Download as > Python (.py) +# Running it will automatically edit all the .py example files (please check the changes made before pushing them to the repository). + +# Standard library imports +import re +import glob +import subprocess + +# Other imports +from tqdm import tqdm + +# Utilities +def request_confirmation(message): + message = f'{message} [y/N]' + width = max(60, len(message)+20) + return input( + f'{"="*width}\n{message:^{width}}\n{"="*width}\n' + ).strip().lower() == 'y' + +""" +Use the find command line utility to find the paths of all +notebooks in this repository and store them in a list +""" +example_notebooks = glob.glob('**/*.ipynb', recursive=True) +example_scripts = [notebook.replace('.ipynb', '.py') for notebook in example_notebooks] +all_python_files = glob.glob('**/*.py', recursive=True) + +""" +Transform each notebook into a python script using +the jupyter nbconvert command line utility +""" + +def generate_script(notebook): + subprocess.run(['jupyter', 'nbconvert', '--to', 'script', notebook]) + +if request_confirmation('Regenerate Python scripts from Jupyter notebooks?'): + # Generate the python scripts + for notebook in tqdm(example_notebooks): generate_script(notebook) + # Assert that all the notebooks were converted to python scripts + assert all([script in all_python_files for script in example_scripts]), \ + f'Unsuccessful: not all notebooks were converted to python scripts. Failed conversions:\n' + \ + '\n'.join([script for script in example_scripts if script not in all_python_files]) +else: + # If there are missing scripts + if not all([script in all_python_files for script in example_scripts]): + # Generate the missing python scripts + for script in [script for script in example_scripts if script not in all_python_files]: + generate_script(script) + +""" +Clean up the python scripts +""" + +for example_python_script in example_scripts: + + print(f'Cleaning example: {example_python_script}') + + with open(example_python_script, "r+") as file: + + # Read example + example_content = file.readlines() + + # Remove file type and encoding + if "!/usr/bin/env python" in example_content[0]: + example_content = example_content[3:] + + # State + checking_comment_end = False + skip_next = False + + # Indentation + indentation = '' + + # Go trough each line in the example + for i, line in enumerate(example_content): + + if skip_next: + skip_next = False + continue + + # --> Remove the "In[x]" notebook inputs + if "In[" in line: + # Also remove the two lines after + [example_content.pop(i) for _ in range(3)] + + # --> End of MD cell + elif checking_comment_end: + # --> End of cell: if the line is empty, the markdown cell is finished + if line == "\n": + # Add """ to close the string comment, then an empty line + example_content[i] = "\"\"\"\n" + example_content.insert(i+1, "\n") + checking_comment_end = False + # --> Second title: detect if we have a second title in the same markdown cell, and mark the separation + elif "##" in line: + example_content[i] = line.replace("# ", "", 1) + example_content.insert(i, "\"\"\"\n\n") + example_content.insert(i+2, "\"\"\"\n") + skip_next = True + # If we are still in the markdown cell, remove the simple # that indicates a comment line + else: + example_content[i] = line.replace("# ", "", 1) + + # --> Start of MD cell: if the line starts with # #, we are in a markdown cell that starts with a title + elif "# #" in line: + # Replace the first line to keep the title with a comment # + example_content[i] = line.replace("# ", "", 1) + example_content.insert(i+1, "\"\"\"\n") + if example_content[i+2] == "\n": + example_content.pop(i+2) + checking_comment_end = True # Start looking for the end of the cell + + # --> Remove the lines that made the plots interactive + elif "# make plots interactive" in line: + [example_content.pop(i) for _ in range(2)] + + # We're in a code cell, so we record the indentation level + else: + + # Retrieve the last non-empty line + last_nonempty_line = next(line for line in example_content[i-1::-1] if re.match(r'^( {4}){0,2}\S', line)) + + # Keep track of current indentation + indentation = ' ' * (len(last_nonempty_line) - len(last_nonempty_line.lstrip())) + + file.seek(0) + file.writelines(example_content) + file.truncate() + +""" +Check Python scripts for syntax errors +""" +print('\nChecking Python scripts for syntax errors\n') +for example_python_script in example_scripts: + + # Test the example + result = subprocess.run(['python', '-m', 'py_compile', example_python_script], + stdout=subprocess.DEVNULL, + stderr=subprocess.STDOUT) + + if result.returncode != 0: + print(f'Unsuccessful: syntax error in example: {example_python_script}') + example_scripts.remove(example_python_script) +print('') + +""" +Test python scripts +""" +if request_confirmation('Test generated Python scripts?'): + for example_python_script in example_scripts: + + print(f'Testing example: {example_python_script}') + + # Test the example + subprocess.run(['python', example_python_script]) \ No newline at end of file diff --git a/estimation/covariance_estimated_parameters.py b/estimation/covariance_estimated_parameters.py index 9ad4da8..e58b42a 100644 --- a/estimation/covariance_estimated_parameters.py +++ b/estimation/covariance_estimated_parameters.py @@ -1,8 +1,8 @@ # DELFI-C3 - Covariance Analysis """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -40,7 +40,6 @@ from tudatpy.astro.time_conversion import DateTime from tudatpy.astro import element_conversion - ## Configuration """ First, NAIF's `SPICE` kernels are loaded, to make the positions of various bodies such as the Earth, the Sun, or the Moon known to `tudatpy`. @@ -57,12 +56,11 @@ simulation_start_epoch = DateTime(2000, 1, 1).epoch() simulation_end_epoch = DateTime(2000, 1, 4).epoch() - ## Set up the environment """ We will now create and define the settings for the environment of our simulation. In particular, this covers the creation of (celestial) bodies, vehicle(s), and environment interfaces. -""" +""" ### Create the main bodies """ @@ -85,7 +83,6 @@ # Create system of bodies bodies = environment_setup.create_system_of_bodies(body_settings) - ### Create the vehicle and its environment interface """ We will now create the satellite - called Delfi-C3 - for which an orbit will be simulated. Using an `empty_body` as a blank canvas for the satellite, we define mass of 400kg, a reference area (used both for aerodynamic and radiation pressure) of 4m$^2$, and a aerodynamic drag coefficient of 1.2. Idem for the radiation pressure coefficient. Finally, when setting up the radiation pressure interface, the Earth is set as a body that can occult the radiation emitted by the Sun. @@ -105,7 +102,7 @@ environment_setup.add_aerodynamic_coefficient_interface(bodies, "Delfi-C3", aero_coefficient_settings) # Create radiation pressure settings -reference_area_radiation = 4.0 +reference_area_radiation = (4*0.3*0.1+2*0.1*0.1)/4 # Average projection area of a 3U CubeSat radiation_pressure_coefficient = 1.2 occulting_bodies = ["Earth"] radiation_pressure_settings = environment_setup.radiation_pressure.cannonball( @@ -114,7 +111,6 @@ # Add the radiation pressure interface to the environment environment_setup.add_radiation_pressure_interface(bodies, "Delfi-C3", radiation_pressure_settings) - ## Set up the propagation """ Having the environment created, we will define the settings for the propagation of the spacecraft. First, we have to define the body to be propagated - here, the spacecraft - and the central body - here, Earth - with respect to which the state of the propagated body is defined. @@ -126,7 +122,6 @@ # Define central bodies of propagation central_bodies = ["Earth"] - ### Create the acceleration model """ Subsequently, all accelerations (and there settings) that act on `Delfi-C3` have to be defined. In particular, we will consider: @@ -168,7 +163,6 @@ bodies_to_propagate, central_bodies) - ### Define the initial state """ Realise that the initial state of the spacecraft always has to be provided as a cartesian state - i.e. in the form of a list with the first three elements representing the initial position, and the three remaining elements representing the initial velocity. @@ -184,7 +178,6 @@ delfi_ephemeris = environment.TleEphemeris( "Earth", "J2000", delfi_tle, False ) initial_state = delfi_ephemeris.cartesian_state( simulation_start_epoch ) - ### Create the integrator settings """ For the problem at hand, we will use an RKF78 integrator with a fixed step-size of 60 seconds. This can be achieved by tweaking the implemented RKF78 integrator with variable step-size such that both the minimum and maximum step-size is equal to 60 seconds and a tolerance of 1.0 @@ -195,7 +188,6 @@ runge_kutta_fixed_step_size(initial_time_step=60.0, coefficient_set=propagation_setup.integrator.CoefficientSets.rkdp_87) - ### Create the propagator settings """ By combining all of the above-defined settings we can define the settings for the propagator to simulate the orbit of `Delfi-C3` around Earth. A termination condition needs to be defined so that the propagation stops as soon as the specified end epoch is reached. Finally, the translational propagator's settings are created. @@ -215,12 +207,11 @@ termination_condition ) - ## Set up the observations """ Having set the underlying dynamical model of the simulated orbit, we can define the observational model. Generally, this entails the addition all required ground stations, the definition of the observation links and types, as well as the precise simulation settings. -""" +""" ### Add a ground station """ @@ -241,7 +232,6 @@ [station_altitude, delft_latitude, delft_longitude], element_conversion.geodetic_position_type) - ### Define Observation Links and Types """ To establish the links between our ground station and `Delfi-C3`, we will make use of the [observation module](https://py.api.tudat.space/en/latest/observation.html#observation) of tudat. During th link definition, each member is assigned a certain function within the link, for instance as "transmitter", "receiver", or "reflector". Once two (or more) members are connected to a link, they can be used to simulate observations along this particular link. The precise type of observation made along this link - e.g., range, range-rate, angular position, etc. - is then determined by the chosen observable type. @@ -260,7 +250,6 @@ link_definition = observation.LinkDefinition(link_ends) observation_settings_list = [observation.one_way_doppler_instantaneous(link_definition)] - ### Define Observation Simulation Settings """ We now have to define the times at which observations are to be simulated. To this end, we will define the settings for the simulation of the individual observations from the previously defined observation models. Bear in mind that these observation simulation settings are not to be confused with the ones to be used when setting up the estimator object, as done just above. @@ -293,12 +282,11 @@ [viability_setting] ) - ## Set up the estimation """ Using the defined models for the environment, the propagator, and the observations, we can finally set the actual presentation up. In particular, this consists of defining all parameter that should be estimated, the creation of the estimator, and the simulation of the observations. -""" +""" ### Defining the parameters to estimate """ @@ -315,7 +303,6 @@ # Create the parameters that will be estimated parameters_to_estimate = estimation_setup.create_parameter_set(parameter_settings, bodies) - ### Creating the Estimator object """ Ultimately, the `Estimator` object consolidates all relevant information required for the estimation of any system parameter: @@ -334,7 +321,6 @@ observation_settings_list, propagator_settings) - ### Perform the observations simulation """ Using the created `Estimator` object, we can perform the simulation of observations by calling its [`simulation_observations()`](https://py.api.tudat.space/en/latest/estimation.html#tudatpy.numerical_simulation.estimation.simulate_observations) function. Note that to know about the time settings for the individual types of observations, this function makes use of the earlier defined observation simulation settings. @@ -346,14 +332,13 @@ estimator.observation_simulators, bodies) - # ## Perform the covariance analysis """ Having simulated the observations and created the `Estimator` object - containing the variational equations for the parameters to estimate - we have defined everything to conduct the actual estimation. Realise that up to this point, we have not yet specified whether we want to perform a covariance analysis or the full estimation of all parameters. It should be stressed that the general setup for either path to be followed is entirely identical. -""" +""" ### Set up the inversion """ @@ -372,7 +357,6 @@ weights_per_observable = {estimation_setup.observation.one_way_instantaneous_doppler_type: noise_level ** -2} covariance_input.set_constant_weight_per_observable(weights_per_observable) - ### Propagate the covariance matrix """ Using the just defined inputs, we can ultimately run the computation of our covariance matrix. Printing the resulting formal errors will give us the diagonal entries of the matrix - while the first six entries represent the uncertainties in the (cartesian) initial state, the seventh and eighth are the errors associated with the gravitational parameter of Earth and the aerodynamic drag coefficient, respectively. @@ -381,16 +365,14 @@ # Perform the covariance analysis covariance_output = estimator.compute_covariance(covariance_input) - # Print the covariance matrix print(covariance_output.formal_errors) - ## Results post-processing """ Finally, to further process the obtained data, one can - exemplary - plot the correlation between the individual estimated parameters, or the behaviour of the formal error over time. -""" +""" ### Correlation """ @@ -409,14 +391,12 @@ plt.tight_layout() plt.show() - ### Propagated Formal Errors """ -""" - initial_covariance = covariance_output.covariance state_transition_interface = estimator.state_transition_interface output_times = observation_times +""" # Propagate formal errors over the course of the orbit propagated_formal_errors = estimation.propagate_formal_errors_split_output( @@ -440,4 +420,3 @@ plt.tight_layout() plt.show() - diff --git a/estimation/estimation_dynamical_models.py b/estimation/estimation_dynamical_models.py index f3109a4..27d0361 100644 --- a/estimation/estimation_dynamical_models.py +++ b/estimation/estimation_dynamical_models.py @@ -1,8 +1,8 @@ # MARS EXPRESS - Using Different Dynamical Models for the Simulation of Observations and the Estimation """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -42,7 +42,6 @@ # Retrieve current directory current_directory = os.getcwd() - ## Simulation Settings """ After having defined the general configuration of our simulation (i.e. importing required `SPICE` kernels, defining start and end epoch of the simulation) we will create the main celestial bodies involved in the simulation (mainly Mars, its two moons, the two neighbouring planets, and the Sun), the spacecraft itself, and its environment interface. @@ -75,7 +74,7 @@ bodies.get("MEX").mass = 1000.0 # Create radiation pressure settings -reference_area_radiation = 4.0 +reference_area_radiation = (4*0.3*0.1+2*0.1*0.1)/4 # Average projection area of a 3U CubeSat radiation_pressure_coefficient = 1.2 occulting_bodies = ["Mars"] radiation_pressure_settings = environment_setup.radiation_pressure.cannonball( @@ -90,7 +89,6 @@ # Define central bodies of propagation central_bodies = ["Mars"] - time2plt = np.arange(simulation_start_epoch, simulation_end_epoch, 60) mex2plt = list() for epoch in time2plt: @@ -119,7 +117,6 @@ plt.tight_layout() plt.show() - ## Set Up the Observations """ Having set the underlying environment model of the simulated orbit, we can define the observational model. This entails the addition all required ground stations, the definition of the observation links and types, as well as the precise simulation settings. @@ -142,7 +139,6 @@ [station_altitude, new_norcia_latitude, new_norcia_longitude], element_conversion.geodetic_position_type) - ### Define Observation Model Settings """ Within this example - as it is common practice when tracking deep-space missions using the ESTRACK system - Mars Express will not be tracked using a set of one-way signal path, but a n-way one (realised as two-way link ends in this example). For our example at hand this means that the signal travels from Earth to the spacecraft where it gets re-transmitted and subsequently has to travel back to Earth where it is recorded and processed. In particular, we will model two-way range and range-rate (Doppler) observables. @@ -172,7 +168,6 @@ one_way_nno_mex_link_definition, light_time_correction_settings = [light_time_correction_settings])) - ### Define Observation Simulation Settings """ Finally, for each above-defined observation model, we will define the noise of the observation-type and several, general viability criteria. We impose the spacecraft to be at a certain minimum angle (15 degrees) of elevation above the horizon as seen from the ground station, as well as introduce Mars as a body that can potentially occult the line-of-sight between Mars Express and New Norcia (i.e. when the spacecraft dives 'behind' Mars, we will not simulate any observations). @@ -215,7 +210,6 @@ viability_settings ) - ## Define the Dynamical Model(s) """ Note that unlike it has usually been the case so far - be it with examples dealing with propagation or the prior estimation ones - we have always defined a mere single dynamical model. The modular structure of tudat, however, enables us to simulate the observations using a dynamical model that is (theoretically entirely) different from the one used to perform the estimation. Hence, we will now first define the model that will be used during the simulation of observations. In particular, we will consider: @@ -250,7 +244,6 @@ propagation_setup.acceleration.cannonball_radiation_pressure() ]) - ### Perform the observations simulation """ However, following the known - trivial - estimation pipeline, the observations are simulated using the `simulation_observations()` function of the respective `Estimator` object. However, to avoid having to create two distinct estimators, we will manually implement a set of observation simulators upfront, before altering the dynamical model and creating the actual estimator. @@ -310,7 +303,6 @@ observation_simulators, bodies) - ### Alter the Dynamical Model for Mars """ We will now re-purpose the previously defined dynamical model of accelerations acting on `MEX` by altering the perceived gravitational acceleration of Mars onto the spacecraft. In particular, we will remove the gravitational pull of both of Mars' moons - Phobos and Deimos - from the acceleration settings of the spacecraft. All remaining settings, however, remain untouched. @@ -341,7 +333,6 @@ integrator_settings=integrator_settings, termination_settings=termination_settings) - ## Perform the estimation """ Having altered the dynamical model as well as the propagator settings, we create the `Estimator` object and subsequently set up the inversion of the problem - in particular, one has to define which parameters are to be estimated, could potentially include any a-priori information in the form of an a-priori covariance matrix, and define the weights associated with the individual types of observations. @@ -380,7 +371,6 @@ estimation_setup.observation.one_way_range_type: noise_level_range ** -2} estimation_input.set_constant_weight_per_observable(weights_per_observable) - ### Estimate the individual parameters """ Finally, the actual estimation can be performed - ideally having reached a sufficient level of convergence, the least squares estimator will have found the most suitable parameters for the problem at hand. @@ -391,12 +381,10 @@ # Perform the covariance analysis estimation_output = estimator.perform_estimation(estimation_input) - # Print the covariance matrix print(estimation_output.formal_errors) print(truth_parameters - parameters_to_estimate.parameter_vector) - ## Post-processing """ Finally, to further illustrate the impact certain differences between the applied dynamical models have, we will first plot the behaviour of the simulated observations over time, as well as show how the discrepancy between our estimated solution and the 'ground truth' builds up over time. @@ -416,7 +404,6 @@ plt.tight_layout() plt.show() - simulator_object = estimation_output.simulation_results_per_iteration[-1] state_history = simulator_object.dynamics_results.state_history @@ -440,4 +427,3 @@ plt.tight_layout() plt.show() - diff --git a/estimation/estimation_with_mpc.py b/estimation/estimation_with_mpc.py index f29976b..564acae 100644 --- a/estimation/estimation_with_mpc.py +++ b/estimation/estimation_with_mpc.py @@ -1,21 +1,16 @@ -# %% [markdown] # Initial state estimation with Minor Planet Center Observations """ Copyright (c) 2010-2023, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ This example highlights a simple orbit estimation routine using real, angular observation data from the [Minor Planet Center](https://www.minorplanetcenter.net/) (MPC). We will estimate the initial state of [Eros](https://en.wikipedia.org/wiki/433_Eros) a near-Earth asteroid visited by the NEAR Shoemaker probe in 1998. We will use the Tudat BatchMPC interface to retrieve and process the data. For a more in depth explanation of this interface we recommend first checking out the [Retrieving observation data from the Minor Planet Centre](https://docs.tudat.space/en/latest/_src_getting_started/_src_examples/notebooks/estimation/retrieving_mpc_observation_data.html) example. We will also briefly use the SBDBquery class which interfaces JPL's [Small Body DataBase (SBDB)](https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html). """ -# %% [markdown] ## Import statements """ -""" - -# %% # Tudat imports for propagation and estimation from tudatpy.kernel.interface import spice from tudatpy.kernel import numerical_simulation @@ -23,6 +18,7 @@ from tudatpy.kernel.numerical_simulation import propagation_setup from tudatpy.kernel.numerical_simulation import estimation, estimation_setup from tudatpy.kernel.numerical_simulation.estimation_setup import observation +""" # import MPC interface from tudatpy.data.mpc import BatchMPC @@ -38,22 +34,19 @@ from matplotlib.lines import Line2D import matplotlib.cm as cm -# %% [markdown] ## Preparing the environment and observations """ -""" +""" ### Loading Spice Kernels. """ We use SPICE kernels to retrieve the ephemerides the planets as well as to verify our results for Eros. The ephemerides for Eros and other asteroids are loaded in with the `codes_300ast_20100725.bsp` kernel included with Tudat's standard kernels. """ -# %% # SPICE KERNELS spice.load_standard_kernels() -# %% [markdown] ### Setting some constants """ Let's setup some constants that are used throughout the tutorial. The MPC code for Eros is 433. We also set a start and end date for our observations, the number of iterations for our estimation, a timestep for our integrator and a 1 month buffer to avoid interpolation errors in our analysis. @@ -63,10 +56,8 @@ For our frame origin we use the Solar System Barycentre. The data from MPC is presented in the J2000 reference frame, currently BatchMPC does not support conversion to other reference frames and as such we match it in our environment. """ -# %% [markdown] # Direct inputs: -# %% target_mpc_code = 433 observations_start = datetime.datetime(2018, 1, 1) @@ -85,10 +76,8 @@ global_frame_origin = "SSB" global_frame_orientation = "J2000" -# %% [markdown] # Derived inputs: -# %% target_sbdb = SBDBquery(target_mpc_code) mpc_codes = [target_mpc_code] # the BatchMPC interface requires a list. @@ -97,13 +86,11 @@ print(f"SPK ID for {target_name} is: {target_spkid}") -# %% [markdown] ### Retrieving the observations """ We retrieve the observation data using the BatchMPC interface. By default all observation data is retrieved, even the first observations from Witt in 1898. We filter to only include data between our start and end dates. """ -# %% batch = BatchMPC() batch.get_observations(mpc_codes) batch.filter( @@ -113,10 +100,8 @@ batch.summary() -# %% [markdown] # Our batch includes many observations from space telescopes, lets take a closer look at that data. -# %% print("Summary of space telescopes in batch:") print(batch.observatories_table(only_space_telescopes=True)) obs_by_WISE = ( @@ -128,10 +113,8 @@ print("\nInitial and Final Observations by WISE:") print(obs_by_WISE) -# %% [markdown] # While the observations from WISE appear to be useful, including them requires setting up the dynamics for the WISE spacecraft which is too advanced for this tutorial and its observations will be excluded later on in this example. The observations can also be filtered out explicitly by excluding the observatories with the .filter() method, specifying their codes (C57 etc.). Note that all the observations are given in an angular format, Right Ascension (RA) and Declination (DEC) in radians. -# %% [markdown] ### Set up the environment """ We now set up the environment, including the bodies to use, the reference frame and frame origin. The epherides for all major planets as well as the Earth's Moon are retrieved using spice. @@ -139,7 +122,6 @@ BatchMPC will automatically generate the body object for Eros, but we still need to specify the bodies to propagate and their central bodies. We can retrieve the list from the BatchMPC object. """ -# %% # List the bodies for our environment bodies_to_create = [ "Sun", @@ -165,13 +147,11 @@ bodies_to_propagate = batch.MPC_objects central_bodies = [global_frame_origin] -# %% [markdown] ### Convert the observations to Tudat """ Now that our system of bodies is ready we can retrieve the observation collection from the observations batch using the `to_tudat()` method. Note that by setting the included_satellites to `None`, space telescope observations are filtered out. From the observation collection we can also retrieve observation links. We use the links to define our observations settings this is where you would also add bias settings. For the purpose of this example, we will keep it simple and use the plain angular position settings, which can process observations with Right Ascension and Declination. We can also retrieve the times for the first and final observations from the batch object in seconds since J2000 TDB, which is what tudat uses internally. We here add our buffer, set previously, to avoid interpolation errors down the line. """ -# %% # Transform the MPC observations into a tudat compatible format. # note that we explicitly exlude all satellite observations in this step by setting included satellites to None. observation_collection = batch.to_tudat(bodies=bodies, included_satellites=None) @@ -196,13 +176,11 @@ epoch_start_buffer = epoch_start_nobuffer - time_buffer epoch_end_buffer = epoch_end_nobuffer + time_buffer -# %% [markdown] ### Creating the acceleration settings """ Eros will be propagated and as such we need to define the settings of the forces acting on it. We will include point mass gravity accelerations for each of the bodies defined before, as well as Schwarzschild relativistic corrections for the Sun. With these accelerations we can generate our acceleration model for the propagation. A more realistic acceleration model will yield better results but this is outside the scope of this example. """ -# %% # Define accelerations accelerations = { "Sun": [ @@ -230,13 +208,11 @@ bodies, acceleration_settings, bodies_to_propagate, central_bodies ) -# %% [markdown] ### Retrieving an initial guess for Eros' position """ We use the SPICE ephemeris to retrieve a 'benchmark' initial state for Eros at the first epoch. We can also use this initial state as our initial guess for the estimation. We add a random uniform offset of +/- 1 million kilometers for the position and 100 m/s for the velocity. Adding this random offset should not have a strong influence on the final results, it is added in to keep the tutorial representative. In real-world cases we might not have such a good initial guess. """ -# %% # benchmark state for later comparison retrieved from SPICE initial_states = spice.get_body_cartesian_state_at_epoch( target_spkid, @@ -259,13 +235,11 @@ print("Error between the real initial state and our initial guess:") print(initial_guess - initial_states) -# %% [markdown] ### Finalising the propagation setup """ For the integrator we use the fixed timestep RKF-7(8) setting our initial time to the time of the batch's final observation - buffer. We then set the termination to stop at the time of the batch's oldest observation plus buffer. These two settings are then the final pieces to create our propagation settings. """ -# %% # Create numerical integrator settings integrator_settings = propagation_setup.integrator.runge_kutta_variable_step_size( epoch_start_buffer, @@ -292,7 +266,6 @@ termination_settings=termination_condition, ) -# %% [markdown] ## Setting Up the estimation """ With the observation collection, the environment and propagations settings ready we can now begin setting up our estimation. @@ -300,7 +273,6 @@ In this example we will simply estimate the position of Eros and as such only include an initial states parameter. """ -# %% # Setup parameters settings to propagate the state transition matrix parameter_settings = estimation_setup.parameter.initial_states( propagator_settings, bodies @@ -311,10 +283,8 @@ parameter_settings, bodies, propagator_settings ) -# %% [markdown] # The `Estimator` object collects the environment, observation settings and propagation settings. We also create an `EstimationInput` object and provide it our observation collection retrieved from `.to_tudat()`. Our maximum iterations steps was previously set to 6. -# %% # Set up the estimator estimator = numerical_simulation.Estimator( bodies=bodies, @@ -335,21 +305,17 @@ # Set methodological options pod_input.define_estimation_settings(reintegrate_variational_equations=True) -# %% [markdown] ## Performing the estimation """ With everything set up we can now perform the estimation. """ -# %% # Perform the estimation pod_output = estimator.perform_estimation(pod_input) -# %% [markdown] # The estimator appears to converge within ~4 steps. Lets check how close our initial guess and final estimate are compared to the benchmark initial state. -# %% # retrieve the estimated initial state. results_final = pod_output.parameter_history[:, -1] @@ -366,18 +332,16 @@ f"{target_name} final radial error to spice: {round(error_magnitude_final, 2)} km" ) -# %% [markdown] ## Visualising the results """ -""" +""" #### Change in residuals per iteration """ We want to visualise the residuals, splitting them between Right Ascension and Declination. Internally, `concatentated_observations` orders the observations alternating RA, DEC, RA, DEC,... This allows us to map the colors accordingly by taking every other item in the `residual_history`/`concatentated_observations`, i.e. by slicing [::2]. """ -# %% residual_history = pod_output.residual_history # Number of columns and rows for our plot @@ -440,16 +404,13 @@ plt.show() -# %% [markdown] # As seen previously, the estimation converges around iteration 4. -# %% [markdown] #### Residuals Corellations Matrix """ Lets check out the corellation of the estimated parameters. """ -# %% # Corellation can be retrieved using the CovarianceAnalysisInput class: covariance_input = estimation.CovarianceAnalysisInput(observation_collection) covariance_output = estimator.compute_covariance(covariance_input) @@ -481,7 +442,6 @@ fig.set_tight_layout(True) -# %% [markdown] #### Orbit error vs spice over time """ Next, lets take a look at the error of the orbit over time, using spice as a reference. @@ -489,7 +449,6 @@ We saw in the residuals graph that there are two large gaps in observations, for 2022 and around Jan 2020. Lets collect those gaps and overlay them on to our error plot. """ -# %% # lets get ranges for all gaps larger than 6 months: gap_in_months = 6 @@ -508,7 +467,6 @@ print(f"Largest gap = {round(max(gaps), 3)} years") print(gap_ranges) -# %% # Now lets plot the orbit error fig, ax = plt.subplots(1, 1, figsize=(9, 5)) @@ -560,16 +518,13 @@ plt.show() -# %% [markdown] # Please note that a lack of observations in an area of time does not necessarily result in a bad fit in that area. Lets look at the observatories next. -# %% [markdown] #### Final residuals highlighted per observatory """ This plot shows the final iteration of the residuals, highlighting the 10 observatories with the most observations. """ -# %% # 10 observatories with most observations num_observatories = 10 @@ -577,7 +532,6 @@ # if you would like to check the iteration 1 residuals, use: # finalresiduals = np.array(residual_history[:, 0]) -# %% # This piece of code collects the 10 largest observatories observatory_names = ( batch.observatories_table(exclude_space_telescopes=True) @@ -614,7 +568,6 @@ # (divide by two because the observations are concatenated RA,DEC in this list) n_obs_not_top = int(sum(mask_not_top) / 2) -# %% fig, axs = plt.subplots(2, 1, figsize=(13, 9)) # Plot remaining observatories first @@ -675,13 +628,11 @@ plt.show() -# %% [markdown] #### Residual Boxplots per observatory """ Let's visualise these residuals as boxplots as well, again splitting for right ascension and declination. Note that some low level Matplotlib is used for this plot. Consider using the simplified [seaborn boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) implementation if this format is relevant to your use case. """ -# %% num_observatories = 6 data_per_observatory_list_RA = [] @@ -767,19 +718,16 @@ fig.set_tight_layout(True) plt.show() -# %% [markdown] #### Histograms per observatory """ Finally, lets get the residual histogram for the top 6 observatories, splitting again for right ascension and declination. """ -# %% num_observatories = 6 nbins = 20 number_of_columns = 2 transparency = 0.6 -# %% number_of_rows = ( int(num_observatories / number_of_columns) if num_observatories % number_of_columns == 0 @@ -834,9 +782,6 @@ fig.set_tight_layout(True) plt.show() -# %% [markdown] # That's it for this tutorial! The final estimation result is quite close to spice at times, but there is clearly plenty of room for improvement in both the dynamical model and the estimation settings. Consider for example adding weights and biases on observations and links as well as improved integrator settings and perturbations. # # Consider rerunning the script for some other object by changing the `target_mpc_code` variable and seeing how the results change. - - diff --git a/estimation/full_estimation_example.py b/estimation/full_estimation_example.py index ca82fbb..d808fe5 100644 --- a/estimation/full_estimation_example.py +++ b/estimation/full_estimation_example.py @@ -1,8 +1,8 @@ # DELFI-C3 - Parameter Estimation """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -40,7 +40,6 @@ from tudatpy.astro.time_conversion import DateTime from tudatpy.astro import element_conversion - ## Configuration """ First, NAIF's `SPICE` kernels are loaded, to make the positions of various bodies such as the Earth, the Sun, or the Moon known to `tudatpy`. @@ -57,12 +56,11 @@ simulation_start_epoch = DateTime(2000, 1, 1).epoch() simulation_end_epoch = DateTime(2000, 1, 4).epoch() - ## Set up the environment """ We will now create and define the settings for the environment of our simulation. In particular, this covers the creation of (celestial) bodies, vehicle(s), and environment interfaces. -""" +""" ### Create the main bodies """ @@ -85,7 +83,6 @@ # Create system of bodies bodies = environment_setup.create_system_of_bodies(body_settings) - ### Create the vehicle and its environment interface """ We will now create the satellite - called Delfi-C3 - for which an orbit will be simulated. Using an `empty_body` as a blank canvas for the satellite, we define mass of 400kg, a reference area (used both for aerodynamic and radiation pressure) of 4m$^2$, and a aerodynamic drag coefficient of 1.2. Idem for the radiation pressure coefficient. Finally, when setting up the radiation pressure interface, the Earth is set as a body that can occult the radiation emitted by the Sun. @@ -105,7 +102,7 @@ environment_setup.add_aerodynamic_coefficient_interface(bodies, "Delfi-C3", aero_coefficient_settings) # Create radiation pressure settings -reference_area_radiation = 4.0 +reference_area = (4*0.3*0.1+2*0.1*0.1)/4 # Average projection area of a 3U CubeSat radiation_pressure_coefficient = 1.2 occulting_bodies = ["Earth"] radiation_pressure_settings = environment_setup.radiation_pressure.cannonball( @@ -114,7 +111,6 @@ # Add the radiation pressure interface to the environment environment_setup.add_radiation_pressure_interface(bodies, "Delfi-C3", radiation_pressure_settings) - ## Set up the propagation """ Having the environment created, we will define the settings for the propagation of the spacecraft. First, we have to define the body to be propagated - here, the spacecraft - and the central body - here, Earth - with respect to which the state of the propagated body is defined. @@ -126,7 +122,6 @@ # Define central bodies of propagation central_bodies = ["Earth"] - ### Create the acceleration model """ Subsequently, all accelerations (and there settings) that act on `Delfi-C3` have to be defined. In particular, we will consider: @@ -168,7 +163,6 @@ bodies_to_propagate, central_bodies) - ### Define the initial state """ Realise that the initial state of the spacecraft always has to be provided as a cartesian state - i.e. in the form of a list with the first three elements representing the initial position, and the three remaining elements representing the initial velocity. @@ -184,7 +178,6 @@ delfi_ephemeris = environment.TleEphemeris( "Earth", "J2000", delfi_tle, False ) initial_state = delfi_ephemeris.cartesian_state( simulation_start_epoch ) - ### Create the integrator settings """ For the problem at hand, we will use an RKF78 integrator with a fixed step-size of 60 seconds. This can be achieved by tweaking the implemented RKF78 integrator with variable step-size such that both the minimum and maximum step-size is equal to 60 seconds and a tolerance of 1.0 @@ -195,7 +188,6 @@ runge_kutta_fixed_step_size(initial_time_step=60.0, coefficient_set=propagation_setup.integrator.CoefficientSets.rkdp_87) - ### Create the propagator settings """ By combining all of the above-defined settings we can define the settings for the propagator to simulate the orbit of `Delfi-C3` around Earth. A termination condition needs to be defined so that the propagation stops as soon as the specified end epoch is reached. Finally, the translational propagator's settings are created. @@ -215,12 +207,11 @@ termination_condition ) - ## Set up the observations """ Having set the underlying dynamical model of the simulated orbit, we can define the observational model. Generally, this entails the addition all required ground stations, the definition of the observation links and types, as well as the precise simulation settings. -""" +""" ### Add a ground station """ @@ -241,7 +232,6 @@ [station_altitude, delft_latitude, delft_longitude], element_conversion.geodetic_position_type) - ### Define Observation Links and Types """ To establish the links between our ground station and `Delfi-C3`, we will make use of the [observation module](https://py.api.tudat.space/en/latest/observation.html#observation) of tudat. During th link definition, each member is assigned a certain function within the link, for instance as "transmitter", "receiver", or "reflector". Once two (or more) members are connected to a link, they can be used to simulate observations along this particular link. The precise type of observation made along this link - e.g., range, range-rate, angular position, etc. - is then determined by the chosen observable type. @@ -260,7 +250,6 @@ link_definition = observation.LinkDefinition(link_ends) observation_settings_list = [observation.one_way_doppler_instantaneous(link_definition)] - ### Define Observation Simulation Settings """ We now have to define the times at which observations are to be simulated. To this end, we will define the settings for the simulation of the individual observations from the previously defined observation models. Bear in mind that these observation simulation settings are not to be confused with the ones to be used when setting up the estimator object, as done just above. @@ -293,12 +282,11 @@ [viability_setting] ) - ## Set up the estimation """ Using the defined models for the environment, the propagator, and the observations, we can finally set the actual presentation up. In particular, this consists of defining all parameter that should be estimated, the creation of the estimator, and the simulation of the observations. -""" +""" ### Defining the parameters to estimate """ @@ -315,7 +303,6 @@ # Create the parameters that will be estimated parameters_to_estimate = estimation_setup.create_parameter_set(parameter_settings, bodies) - ### Creating the Estimator object """ Ultimately, the `Estimator` object consolidates all relevant information required for the estimation of any system parameter: @@ -334,7 +321,6 @@ observation_settings_list, propagator_settings) - ### Perform the observations simulation """ Using the created `Estimator` object, we can perform the simulation of observations by calling its [`simulation_observations()`](https://py.api.tudat.space/en/latest/estimation.html#tudatpy.numerical_simulation.estimation.simulate_observations) function. Note that to know about the time settings for the individual types of observations, this function makes use of the earlier defined observation simulation settings. @@ -346,14 +332,13 @@ estimator.observation_simulators, bodies) - # ## Perform the estimation """ Having simulated the observations and created the `Estimator` object - containing the variational equations for the parameters to estimate - we have defined everything to conduct the actual estimation. Realise that up to this point, we have not yet specified whether we want to perform a covariance analysis or the full estimation of all parameters. It should be stressed that the general setup for either path to be followed is entirely identical. -""" +""" ### Set up the inversion """ @@ -370,7 +355,6 @@ perturbed_parameters[i+3] += 0.01 parameters_to_estimate.parameter_vector = perturbed_parameters - # Create input object for the estimation convergence_checker = estimation.estimation_convergence_checker(maximum_iterations=4) estimation_input = estimation.EstimationInput( @@ -385,7 +369,6 @@ weights_per_observable = {estimation_setup.observation.one_way_instantaneous_doppler_type: noise_level ** -2} estimation_input.set_constant_weight_per_observable(weights_per_observable) - ### Estimate the individual parameters """ Using the just defined inputs, we can ultimately run the estimation of the selected parameters. After a pre-defined maximum number of iterations (the default value is set to a total of five), the least squares estimator - ideally having reached a sufficient level of convergence - will stop with the process of iterating over the problem and updating the parameters. @@ -396,17 +379,15 @@ # Perform the estimation estimation_output = estimator.perform_estimation(estimation_input) - # Print the covariance matrix print(estimation_output.formal_errors) print(truth_parameters - parameters_to_estimate.parameter_vector) - ## Results post-processing """ Finally, to further process the obtained data, one can - exemplary - plot the behaviour of the simulated observations over time, the history of the residuals, or the statistical interpretation of the final residuals. -""" +""" ### Range-rate over time """ @@ -418,7 +399,7 @@ plt.figure(figsize=(9, 5)) plt.title("Observations as a function of time") -plt.scatter(observation_times / 3600.0, observations_list ) +plt.scatter(observation_times / 3600.0, observations_list) plt.xlabel("Time [hr]") plt.ylabel("Range rate [m/s]") @@ -427,7 +408,6 @@ plt.tight_layout() plt.show() - ### Residuals history """ One might also opt to instead plot the behaviour of the residuals per iteration of the estimator. To this end, we have thus plotted the residuals of the individual observations as a function of time. Note that we can observe a seemingly equal spread around zero. As expected - since we have not defined it this way - the observation is thus not biased. @@ -435,7 +415,7 @@ residual_history = estimation_output.residual_history -fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(9, 6), sharex=True) +fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(9, 6)) subplots_list = [ax1, ax2, ax3, ax4] for i in range(4): @@ -443,13 +423,14 @@ subplots_list[i].set_ylabel("Observation Residual [m/s]") subplots_list[i].set_title("Iteration "+str(i+1)) + ax3.set_xlabel("Time since J2000 [s]") ax4.set_xlabel("Time since J2000 [s]") + plt.tight_layout() plt.show() - ### Final residuals """ Finally, one can also analyse the residuals of the last iteration. Hence, for each of the estimated parameters, we have calculated the true-to-formal-error rate, as well as plotted the statistical distribution of the final residuals between the simulated observations and the estimated orbit. Ideally, given the type of observable we have used (i.e. free of any bias) as well as a statistically sufficient high number of observations, we would expect to see a Gaussian distribution with zero mean here. @@ -468,6 +449,6 @@ plt.xlabel('Final iteration range-rate residual [m/s]') plt.ylabel('Occurences [-]') plt.title('Histogram of residuals on final iteration') + plt.tight_layout() plt.show() - diff --git a/estimation/galilean_moons_state_estimation.py b/estimation/galilean_moons_state_estimation.py index c5f1973..61e884a 100644 --- a/estimation/galilean_moons_state_estimation.py +++ b/estimation/galilean_moons_state_estimation.py @@ -1,8 +1,8 @@ # Galilean Satellites - Initial State Estimation """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -35,12 +35,11 @@ from tudatpy.numerical_simulation import estimation, estimation_setup from tudatpy.astro.time_conversion import DateTime - ## Orbital Simulation """ Entirely independent of the upcoming estimation-process, we first have to define the general settings of the simulation, create the environment, and define all relevant settings of the propagation. -""" +""" ### Simulation Settings """ @@ -54,7 +53,6 @@ simulation_start_epoch = DateTime(2031, 7, 2).epoch() simulation_end_epoch = DateTime(2035, 4, 20).epoch() - ### Create the Environment """ For the problem at hand, the environment consists of the Jovian system with its four largest moons - Io, Europa, Ganymede, and Callisto - as well as Saturn and the Sun which will be relevant when creating some perturbing accelerations afterwards. While slightly altering the standard settings of the moons, such that their rotation around their own main axis resembles a synchronous rotation, we will also apply a tabulated ephemeris based on every current (standard) ephemeris to the moons' settings. While, at first glance, this does not add any value to the simulation, this step is crucial in order to later be able to simulate the moons states purely based on their ephemerides without having to propagate their states. @@ -95,7 +93,6 @@ # Create system of selected bodies bodies = environment_setup.create_system_of_bodies(body_settings) - ### Create Propagator Settings """ Trivially, in order to estimate 'better' initial states for the Galilean moons we have to include all four of them in our propagation. Acceleration-wise - apart from the individual numbers - they are moreover modelled entirely identical: mutual spherical harmonic acceleration due to Jupiter, tidal dissipation on both the moons and the primary, mutual spherical harmonic acceleration due to the remaining three moons, and point mass gravity attraction by both Saturn and the Sun. @@ -206,12 +203,11 @@ termination_settings=termination_condition, output_variables=dependent_variables_to_save) - ## Orbital Estimation """ Having defined all settings required for the simulation of the moons' orbits, the orbital estimation can finally be discussed - we will have to create the required link ends for the Galilean moons, define the observation model and simulation settings, simulate the states of the moons based on their associated ephemerides, define the estimable parameters, and finally perform the estimation itself. -""" +""" ### Create Link Ends for the Moons """ @@ -245,7 +241,6 @@ 'Callisto': link_definition_callisto, } - ### Observation Model Settings """ As mentioned above, we will 'observe' the state of the moons at every epoch as being perfectly cartesian and handily available to the user. However, note that the `cartesian_position` observable is typically not realized in reality but mainly serves verification or analysis purposes. @@ -256,7 +251,6 @@ estimation_setup.observation.cartesian_position(link_definition_ganymede), estimation_setup.observation.cartesian_position(link_definition_callisto)] - ### Observation Simulation Settings """ To simulate the states of the moons at every given epochs, we will have to define the simulation settings for all moons. For the problem at hand, they will be entirely identical - we have to define the correct `observable_type` that is associated with the `cartesian_position` observable, give the above-realised `link_definition`, and finally define the epochs at which we want to take the states from the respective ephemerides. @@ -276,7 +270,6 @@ observation_times, reference_link_end_type=estimation_setup.observation.observed_body)) - ### Simulate Ephemeris' States of Satellites """ In a nutshell, what we want to do is to check the ephemeris every three hours - as defined just above - and take the associated (cartesian) state of all four moons at that moment as our observable. However, in order to automatically satisfy all requirements in terms of inputs to the estimator, we have to manually create an `observation_simulator` object, since we explicitly do not want to use the (propagating) simulators that get created alongside the estimator. @@ -294,7 +287,6 @@ ephemeris_observation_simulators, bodies) - ### Define Estimable Parameters """ Given the problem at hand - minimising the discrepancy between the NOE-5 ephemeris and the states of the moons when propagated under the influence of the above-defined accelerations - we are mainly interested in an improved initial state of all four Galilean moons. We will thus restrict the set of estimable parameters to the moons' initial states. @@ -304,7 +296,6 @@ parameters_to_estimate = estimation_setup.create_parameter_set(parameters_to_estimate_settings, bodies) original_parameter_vector = parameters_to_estimate.parameter_vector - ### Perform the Estimation """ Using the set of artificial cartesian 'observations' of the moons' ephemerides we are finally able to estimate improved initial states for each of the four Galilean satellites. To this end we will make use of the known estimation functionality of tudat - nevertheless, note that in order to easily post-process the results we have changed the associated settings such that the moons' state histories will be saved for every iteration of the estimation. All other settings remain unchanged and thus equal to their default values (for more details see [here](https://py.api.tudat.space/en/latest/estimation.html#tudatpy.numerical_simulation.estimation.EstimationInput.define_estimation_settings)). @@ -315,7 +306,6 @@ estimator = numerical_simulation.Estimator(bodies, parameters_to_estimate, position_observation_settings, propagator_settings) - # Create input object for the estimation estimation_input = estimation.EstimationInput(ephemeris_satellite_states) # Set methodological options @@ -324,14 +314,12 @@ print('Performing the estimation...') print(f'Original initial states: {original_parameter_vector}') - with util.redirect_std(redirect_out=False): estimation_output = estimator.perform_estimation(estimation_input) initial_states_updated = parameters_to_estimate.parameter_vector print('Done with the estimation...') print(f'Updated initial states: {initial_states_updated}') - ## Post-Processing """ With the initial states updated, the estimation is finished. In the following we will thus be left with analysing how well the propagation of the improved initial states performs compared to the ephemeris solution. @@ -399,7 +387,6 @@ ax1.set_ylabel(r'Difference [km]') ax1.legend(); - # Overall, for the inner three moons trapped in resonance (for more details see below) the above results lie within the expected range of achievable accuracy given the rather rudimentary set-up of the environment and especially associated acceleration models. However, what is striking is that the performance of Callisto falls short compared to the other satellites. Thus, hypothetically, to enhance the estimated solution of the orbit of Callisto with respect to the underlying ephemeris, one could opt to estimate its gravity field alongside the initial state, which could lead to significantly improved results. However, this path left as an adventure to be followed and explored by the reader. def calculate_mean_longitude(kepler_elements: dict): @@ -429,7 +416,6 @@ def calculate_mean_longitude(kepler_elements: dict): return mean_longitude_dict - ### LAPLACE STABILITY ### ephemeris_kepler_elements = np.vstack(list(ephemeris_keplerian_states.values())) propagation_kepler_elements = np.vstack(list(dependent_variable_history.values())) @@ -484,4 +470,3 @@ def calculate_mean_longitude(kepler_elements: dict): ax2.xaxis.set_minor_locator(mdates.MonthLocator()) ax2.xaxis.set_major_formatter(mdates.DateFormatter('%b-%Y')) ax2.set_ylabel(r'Laplace $\Delta\Phi_L$ [deg]'); - diff --git a/estimation/retrieving_mpc_observation_data.ipynb b/estimation/retrieving_mpc_observation_data.ipynb index 30e6372..fcbd963 100644 --- a/estimation/retrieving_mpc_observation_data.ipynb +++ b/estimation/retrieving_mpc_observation_data.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -80,11 +80,11 @@ " Batch Summary:\n", "1. Batch includes 2 minor planets:\n", " ['433', '329']\n", - "2. Batch includes 18274 observations, including 1994 observations from space telescopes\n", - "3. The observations range from 1892-03-21 21:00:12.096012 to 2023-08-30 10:04:37.718416\n", - " In seconds TDB since J2000: -3401189955.718365 to 746661946.9011023\n", - " In Julian Days: 2412179.37514 to 2460186.919881\n", - "4. The batch contains observations from 378 observatories, including 3 space telescopes\n", + "2. Batch includes 19643 observations, including 2057 observations from space telescopes\n", + "3. The observations range from 1892-03-21 21:00:12.096012 to 2024-02-18 03:09:15.379204\n", + " In seconds TDB since J2000: -3401189955.718365 to 761497824.5643451\n", + " In Julian Days: 2412179.37514 to 2460358.631428\n", + "4. The batch contains observations from 391 observatories, including 5 space telescopes\n", "\n", " Code Name count\n", "0 000 Greenwich 230.0\n", @@ -93,18 +93,20 @@ "8 008 Algiers-Bouzareah 556.0\n", "12 012 Uccle 68.0\n", "... ... ... ...\n", - "2369 Z22 MASTER-IAC Observatory, Tenerife 55.0\n", - "2381 Z34 NNHS Drummonds Observatory 5.0\n", - "2399 Z52 The Studios Observatory, Grantham 12.0\n", - "2420 Z73 Observatorio Nuevos Horizontes, Camas 5.0\n", - "2427 Z80 Northolt Branch Observatory 54.0\n", + "2409 Z22 MASTER-IAC Observatory, Tenerife 55.0\n", + "2421 Z34 NNHS Drummonds Observatory 5.0\n", + "2439 Z52 The Studios Observatory, Grantham 12.0\n", + "2460 Z73 Observatorio Nuevos Horizontes, Camas 5.0\n", + "2467 Z80 Northolt Branch Observatory 54.0\n", "\n", - "[378 rows x 3 columns]\n", + "[391 rows x 3 columns]\n", "Space Telescopes:\n", - " Code Name count\n", - "1225 C51 WISE 372.0\n", - "1231 C57 TESS 1620.0\n", - "1232 C59 Yangwang-1 2.0\n" + " Code Name count\n", + "267 270 Unistellar Network, Roving Observer 32.0\n", + "270 275 Non-geocentric Occultation Observation 5.0\n", + "1226 C51 WISE 398.0\n", + "1232 C57 TESS 1620.0\n", + "1233 C59 Yangwang-1 2.0\n" ] } ], @@ -130,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -139,12 +141,12 @@ "text": [ "Initial and Final Observations by TESS\n", " number epochUTC RA DEC\n", - "11601 433 2021-06-06 21:34:01.804817 4.753241 -0.722587\n", - "13220 433 2021-06-24 04:44:01.103987 4.588734 -0.674985\n", + "11902 433 2021-06-06 21:34:01.804817 4.753241 -0.722587\n", + "13521 433 2021-06-24 04:44:01.103987 4.588734 -0.674985\n", "Initial and Final Observations by WISE\n", " number epochUTC RA DEC\n", - "9530 433 2014-04-03 09:20:06.403193 4.944692 -0.634497\n", - "4468 329 2022-12-04 04:24:29.951981 0.087998 -0.125128\n" + "9829 433 2014-04-03 09:20:06.403193 4.944692 -0.634497\n", + "4631 329 2023-11-01 14:10:14.880002 2.222389 0.034132\n" ] } ], @@ -174,24 +176,24 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Size before filter: 18274\n", - "Size after filter: 5425\n", + "Size before filter: 19643\n", + "Size after filter: 5487\n", "\n", " Batch Summary:\n", "1. Batch includes 2 minor planets:\n", " ['433', '329']\n", - "2. Batch includes 5425 observations, including 1853 observations from space telescopes\n", + "2. Batch includes 5487 observations, including 1855 observations from space telescopes\n", "3. The observations range from 2018-05-01 03:22:18.336012 to 2023-08-22 22:03:05.184015\n", " In seconds TDB since J2000: 578417007.5214744 to 746013854.3668225\n", " In Julian Days: 2458239.64049 to 2460179.41881\n", - "4. The batch contains observations from 78 observatories, including 2 space telescopes\n", + "4. The batch contains observations from 80 observatories, including 3 space telescopes\n", "\n" ] } @@ -216,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -260,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -276,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -294,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -337,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -377,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -388,11 +390,11 @@ " Batch Summary:\n", "1. Batch includes 1 minor planets:\n", " ['238']\n", - "2. Batch includes 4288 observations, including 235 observations from space telescopes\n", - "3. The observations range from 1892-03-18 22:48:06.047982 to 2023-09-09 08:26:57.379211\n", - " In seconds TDB since J2000: -3401442681.766395 to 747520086.5617365\n", - " In Julian Days: 2412176.45007 to 2460196.852053\n", - "4. The batch contains observations from 106 observatories, including 1 space telescopes\n", + "2. Batch includes 4508 observations, including 256 observations from space telescopes\n", + "3. The observations range from 1892-03-18 22:48:06.047982 to 2024-01-23 05:43:31.411196\n", + " In seconds TDB since J2000: -3401442681.766395 to 759260680.595693\n", + " In Julian Days: 2412176.45007 to 2460332.738558\n", + "4. The batch contains observations from 107 observatories, including 1 space telescopes\n", "\n" ] } @@ -433,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -444,11 +446,11 @@ " Batch Summary:\n", "1. Batch includes 3 minor planets:\n", " ['238', '433', '329']\n", - "2. Batch includes 9713 observations, including 2088 observations from space telescopes\n", - "3. The observations range from 1892-03-18 22:48:06.047982 to 2023-09-09 08:26:57.379211\n", - " In seconds TDB since J2000: -3401442681.766395 to 747520086.5617365\n", - " In Julian Days: 2412176.45007 to 2460196.852053\n", - "4. The batch contains observations from 156 observatories, including 2 space telescopes\n", + "2. Batch includes 9995 observations, including 2111 observations from space telescopes\n", + "3. The observations range from 1892-03-18 22:48:06.047982 to 2024-01-23 05:43:31.411196\n", + " In seconds TDB since J2000: -3401442681.766395 to 759260680.595693\n", + " In Julian Days: 2412176.45007 to 2460332.738558\n", + "4. The batch contains observations from 158 observatories, including 3 space telescopes\n", "\n" ] } @@ -468,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -479,21 +481,21 @@ " Batch Summary:\n", "1. Batch includes 2 minor planets:\n", " ['433', '329']\n", - "2. Batch includes 322 observations, including 28 observations from space telescopes\n", + "2. Batch includes 378 observations, including 28 observations from space telescopes\n", "3. The observations range from 2023-01-04 19:17:22.271984 to 2023-08-22 22:03:05.184015\n", " In seconds TDB since J2000: 726131911.4559577 to 746013854.3668225\n", " In Julian Days: 2459949.30373 to 2460179.41881\n", - "4. The batch contains observations from 18 observatories, including 1 space telescopes\n", + "4. The batch contains observations from 20 observatories, including 1 space telescopes\n", "\n", "\n", " Batch Summary:\n", "1. Batch includes 2 minor planets:\n", " ['433', '329']\n", - "2. Batch includes 322 observations, including 28 observations from space telescopes\n", + "2. Batch includes 378 observations, including 28 observations from space telescopes\n", "3. The observations range from 2023-01-04 19:17:22.271984 to 2023-08-22 22:03:05.184015\n", " In seconds TDB since J2000: 726131911.4559577 to 746013854.3668225\n", " In Julian Days: 2459949.30373 to 2460179.41881\n", - "4. The batch contains observations from 18 observatories, including 1 space telescopes\n", + "4. The batch contains observations from 20 observatories, including 1 space telescopes\n", "\n" ] } @@ -597,7 +599,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.13" }, "orig_nbformat": 4 }, diff --git a/estimation/retrieving_mpc_observation_data.py b/estimation/retrieving_mpc_observation_data.py index ea0ebff..a14f4ec 100644 --- a/estimation/retrieving_mpc_observation_data.py +++ b/estimation/retrieving_mpc_observation_data.py @@ -1,9 +1,8 @@ -# %% [markdown] # Retrieving observation data from the Minor Planet Centre """ Copyright (c) 2010-2023, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -18,18 +17,15 @@ - [329 Svea](https://en.wikipedia.org/wiki/329_Svea) """ -# %% [markdown] ## Basic Usage """ """ -# %% [markdown] ### Import statements """ In this example we do not perform an estimation, as such we only need the batchMPC class from data, environment_setup and observation to convert our observations to Tudat and optionally datetime to filter our batch. We also load the standard SPICE kernels. """ -# %% from tudatpy.data.mpc import BatchMPC from tudatpy.kernel.numerical_simulation import environment_setup from tudatpy.kernel.numerical_simulation.estimation_setup import observation @@ -41,17 +37,13 @@ spice.load_standard_kernels() -# %% [markdown] ### Retrieval """ -""" +We initialise a `BatchMPC` object, create a list with the objects we want and use `.get_observations()` to retrieve the observations. `.get_observations()` uses [astroquery](https://astroquery.readthedocs.io/en/latest/mpc/mpc.html) to retrieve data from MPC and requires an internet connection. The observations are cached for faster retrieval in subsequent runs. The `BatchMPC` object removes duplicates if `.get_observations()` is ran twice. -# %% [markdown] -# We initialise a `BatchMPC` object, create a list with the objects we want and use `.get_observations()` to retrieve the observations. `.get_observations()` uses [astroquery](https://astroquery.readthedocs.io/en/latest/mpc/mpc.html) to retrieve data from MPC and requires an internet connection. The observations are cached for faster retrieval in subsequent runs. The `BatchMPC` object removes duplicates if `.get_observations()` is ran twice. -# -# Tudat's estimation tools allow for multiple Objects to be analysed at the same time. BatchMPC can process multiple objects into a single observation collection automatically. For now lets retrieve the observations for Eros and Svea. BatchMPC uses MPC codes for objects and observatories. To get an overview of the batch we can use the `summary()` method. Let's also get some details on some of the observatories that retrieved the data using the `observatories_table()` method. +Tudat's estimation tools allow for multiple Objects to be analysed at the same time. BatchMPC can process multiple objects into a single observation collection automatically. For now lets retrieve the observations for Eros and Svea. BatchMPC uses MPC codes for objects and observatories. To get an overview of the batch we can use the `summary()` method. Let's also get some details on some of the observatories that retrieved the data using the `observatories_table()` method. +""" -# %% asteroid_MPC_codes = [433, 329] # Eros and Svea batch1 = BatchMPC() @@ -63,10 +55,8 @@ print("Space Telescopes:") print(batch1.observatories_table(only_in_batch=True, only_space_telescopes=True, include_positions=False)) -# %% [markdown] # We can also directly have a look at the the observations themselves, for example, lets take a look at the first and final observations from TESS and WISE. The table property allows for read only access to the observations in pandas dataframe format. -# %% obs_by_TESS = batch1.table.query("observatory == 'C57'").loc[:, ["number", "epochUTC", "RA", "DEC"]].iloc[[0, -1]] obs_by_WISE = batch1.table.query("observatory == 'C51'").loc[:, ["number", "epochUTC", "RA", "DEC"]].iloc[[0, -1]] @@ -75,15 +65,11 @@ print("Initial and Final Observations by WISE") print(obs_by_WISE) -# %% [markdown] ### Filtering """ +From the summary we can see that even the first observations from the 1890s are included. This is not ideal. We might also want to exclude some observatories. To fix this we use the `.filter()` method. Dates can be filtered using the standard seconds since J2000 TDB format or through python's datetime standard library in UTC for simplicity. Additionally, specific bands can be selected and observatories can explicitly be included or excluded. The `.filter()` method alters the original batch in place, an alternative is shown in the Additional Features section. """ -# %% [markdown] -# From the summary we can see that even the first observations from the 1890s are included. This is not ideal. We might also want to exclude some observatories. To fix this we use the `.filter()` method. Dates can be filtered using the standard seconds since J2000 TDB format or through python's datetime standard library in UTC for simplicity. Additionally, specific bands can be selected and observatories can explicitly be included or excluded. The `.filter()` method alters the original batch in place, an alternative is shown in the Additional Features section. - -# %% observatories_to_exlude = ["000", "C59"] # chosen as an example print(f"Size before filter: {batch1.size}") @@ -92,13 +78,11 @@ batch1.summary() -# %% [markdown] ### Set up the system of bodies """ A system of bodies must be created to keep observatories' positions consistent with Earth's shape model and to allow the attachment of these observatories to Earth. For the purposes of this example, we keep it as simple as possible. See the [Estimation with MPC](https://docs.tudat.space/en/latest/_src_getting_started/_src_examples/notebooks/estimation/estimation_with_mpc.html) for a more complete setup and explanation appropriate for estimation. For our bodies, we only use Earth and the Sun. We set our origin to `"SSB"`, the solar system barycenter. We use the default body settings from the `SPICE` kernel to initialise the planet and use it to create a system of bodies. This system of bodies is used in the `to_tudat()` method. """ -# %% bodies_to_create = ["Sun", "Earth"] # Create default body settings @@ -110,13 +94,11 @@ # Create system of bodies bodies = environment_setup.create_system_of_bodies(body_settings) -# %% [markdown] ### Retrieve Observation Collection """ """ -# %% [markdown] # Now that our batch is ready, we can transform it to a Tudat `ObservationCollection` object using the `to_tudat()` method. # # The `.to_tudat()` does the following for us: @@ -131,21 +113,16 @@ # # If our batch includes space telescopes like WISE and TESS we must either link their Tudat name or exclude them. For now we exclude them by setting `included_satellites` to `None`. The additional features section shows an example of how to link satellites to the `to_tudat()` method. The '.to_tudat()' method does not alter the batch object itself. -# %% observation_collection = batch1.to_tudat(bodies, included_satellites=None) -# %% [markdown] # The names of the bodies added to the system of bodies object as well as the dates of the oldest and latest observations can be retrieved from the batch: -# %% epoch_start = batch1.epoch_start # in seconds since J2000 TDB (Tudat default) epoch_end = batch1.epoch_end object_names = batch1.MPC_objects -# %% [markdown] # We can now retrieve the links from the ObservationCollection we got from `to_tudat()` and we can now create settings for these links. This is where link biases would be set, for now we just keep the settings default. -# %% observation_settings_list = list() link_list = list( @@ -160,21 +137,17 @@ observation.angular_position(link, bias_settings=None) ) -# %% [markdown] # With the `observation_collection` and `observation_settings_list` ready, we have all the observation inputs we need to perform an estimation. Next, lets verify the observations. Next, check out the [Estimation with MPC](https://docs.tudat.space/en/latest/_src_getting_started/_src_examples/notebooks/estimation/estimation_with_mpc.html) example to try estimation with the observations we have retrieved here. The remainder of the example discusses additional features. -# %% [markdown] ## Additional Features """ """ -# %% [markdown] ### Using satellite observations. """ Space Telescopes in Tudat are treated as bodies instead of stations. To use their observations, their motion should be known to Tudat. A user may for example retrieve their ephemirides from a SPICE kernel or propagate the satellite. This body must then be linked to the MPC code for that space telescope when calling the `to_tudat()` method. The MPC code for TESS can be obtained using the `observatories_table()` method as used previously. Bellow is an example using a spice kernel. """ -# %% # Note that we are using the add_empty_settings() method instead of add_empty_body(). # This allows us to add ephemeris settings, # which tudat uses to create an ephemeris which is consistent with the rest of the environment. @@ -200,13 +173,11 @@ observation_collection = batch1.to_tudat(bodies, included_satellites=sats_dict) -# %% [markdown] ### Manual retrieval from astroquery """ Those familiar with astroquery (or those who have existing filitering/ retrieval processes) may use the `from_astropy()` and `from_pandas()` methods to still use `to_tudat()` functionality. The input must meet some requirements which can be found in the API documentation, the default format from astroquery fits these requirements. """ -# %% from astroquery.mpc import MPC mpc_code_hypatia = 238 @@ -225,25 +196,19 @@ batch2.summary() -# %% [markdown] ### Combining batches """ +Batches can be combined using the `+` operator, duplicates are removed. """ -# %% [markdown] -# Batches can be combined using the `+` operator, duplicates are removed. - -# %% batch3 = batch2 + batch1 batch3.summary() -# %% [markdown] ### Copying and non in-place filtering """ We may want to compare results between batches. In that case it is usefull to copy a batch or perform non-destructive filtering: """ -# %% # Copying existing batches: import copy batch1_copy = copy.copy(batch1) @@ -258,13 +223,11 @@ batch1_copy.summary() batch1_copy2.summary() -# %% [markdown] ### Plotting observations """ The `.plot_observations_sky()` method can be used to view a projection of the observations. Similarly, `.plot_observations_temporal()` shows the declination and right ascension of a batch's bodies over time. """ -# %% import matplotlib.pyplot as plt # Try some of the other projections: 'hammer', 'mollweide' and 'lambert' @@ -274,8 +237,5 @@ plt.show() -# %% # Similar to the sky plot, specific bodies can be chosen to be plotted with the objects argument fig = batch1.plot_observations_temporal() - - diff --git a/mission_design/cassini1_mga_optimization.py b/mission_design/cassini1_mga_optimization.py index 191007e..ec067fa 100644 --- a/mission_design/cassini1_mga_optimization.py +++ b/mission_design/cassini1_mga_optimization.py @@ -1,9 +1,8 @@ # Cassini 1 (MGA transfer) optimization with PyGMO """ +Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. """ -# Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. - ## Context """ @@ -42,13 +41,11 @@ # Pygmo imports import pygmo as pg - ## Helpers """ +First of all, let us define a helper function which is used troughout this example. """ -# First of all, let us define a helper function which is used troughout this example. - # The design variables in the current optimization problem are the departure time and the time of flight between transfer nodes. However, to evaluate an MGA trajectory in Tudat it is necessary to specify a different set of parameters: node times, node free parameters, leg free parameters. This function converts a vector of design variables to the parameters which are used as input to the MGA trajectory object. # # The node times are easily computed based on the departure time and the time of flight between nodes. Since an MGA transfer with unpowered legs is used, no node and leg free parameters are required; thus, these are defined as empty lists. @@ -84,20 +81,18 @@ def convert_trajectory_parameters (transfer_trajectory_object: tudatpy.kernel.tr return node_times, leg_free_parameters, node_free_parameters - ## Optimisation problem """ +The core of the optimization process is realized by PyGMO, which requires the definition of a problem class. +This definition has to be done in a class that is compatible with what the PyGMO library expects from a User Defined Problem (UDP). See [this page](https://esa.github.io/pygmo2/tutorials/coding_udp_simple.html) from the PyGMO's documentation as a reference. In this example, this class is called `TransferTrajectoryProblem`. + +There are four mandatory methods that must be implemented in the class: +* `__init__()`: This is the constructor for the PyGMO problem class. It is used to save all the variables required to setup the evaluation of the transfer trajectory. +* `get_number_of_parameters(self)`: Returns the number of optimized parameters. In this case, that is the same as the number of flyby bodies (i.e. 6). +* `get_bounds(self)`: Returns the bounds for each optimized parameter. These are provided as an input to `__init__()`. Their values are defined later in this example. +* `fitness(self, x)`: Returns the cost associated with a vector of design parameters. Here, the fitness is the $\Delta V$ required to execute the transfer. """ -# The core of the optimization process is realized by PyGMO, which requires the definition of a problem class. -# This definition has to be done in a class that is compatible with what the PyGMO library expects from a User Defined Problem (UDP). See [this page](https://esa.github.io/pygmo2/tutorials/coding_udp_simple.html) from the PyGMO's documentation as a reference. In this example, this class is called `TransferTrajectoryProblem`. -# -# There are four mandatory methods that must be implemented in the class: -# * `__init__()`: This is the constructor for the PyGMO problem class. It is used to save all the variables required to setup the evaluation of the transfer trajectory. -# * `get_number_of_parameters(self)`: Returns the number of optimized parameters. In this case, that is the same as the number of flyby bodies (i.e. 6). -# * `get_bounds(self)`: Returns the bounds for each optimized parameter. These are provided as an input to `__init__()`. Their values are defined later in this example. -# * `fitness(self, x)`: Returns the cost associated with a vector of design parameters. Here, the fitness is the $\Delta V$ required to execute the transfer. - ########################################################################### # CREATE PROBLEM CLASS #################################################### ########################################################################### @@ -186,13 +181,11 @@ def fitness(self, trajectory_parameters: List[float]) -> list: return [delta_v] - ## Simulation Setup """ +Before running the optimisation, it is first necessary to setup the simulation. In this case, this consists of creating an MGA object. This object is created according to the procedure described in the [MGA trajectory example](https://docs.tudat.space/en/stable/_src_getting_started/_src_examples/notebooks/propagation/mga_dsm_analysis.html). The object is created using the central body, transfer bodies order, departure orbit, and arrival orbit specified in the Cassini 1 problem statement (presented above). """ -# Before running the optimisation, it is first necessary to setup the simulation. In this case, this consists of creating an MGA object. This object is created according to the procedure described in the [MGA trajectory example](https://docs.tudat.space/en/stable/_src_getting_started/_src_examples/notebooks/propagation/mga_dsm_analysis.html). The object is created using the central body, transfer bodies order, departure orbit, and arrival orbit specified in the Cassini 1 problem statement (presented above). - ########################################################################### # Define transfer trajectory properties ########################################################################### @@ -228,7 +221,6 @@ def fitness(self, trajectory_parameters: List[float]) -> list: transfer_body_order, central_body) - ## Optimization """ """ @@ -262,7 +254,6 @@ def fitness(self, trajectory_parameters: List[float]) -> list: legs_tof_lb[4] = 1000 * constants.JULIAN_DAY legs_tof_ub[4] = 6000 * constants.JULIAN_DAY - # To setup the optimization, it is first necessary to initialize the optimization problem. This problem, defined through the class `TransferTrajectoryProblem`, is given to PyGMO trough the `pg.problem()` method. # # The optimiser is selected to be the Differential Evolution (DE) algorithm (its documentation can be found [here](https://esa.github.io/pygmo2/algorithms.html#pygmo.de)). When selecting the algorithm, here the coefficient F is selected to have the value 0.5, instead of the default 0.8. Additionaly, a fixed seed is selected; since PyGMO uses a random number generator, this ensures that PyGMO's results are reproducible. @@ -303,14 +294,12 @@ def fitness(self, trajectory_parameters: List[float]) -> list: # Create population pop = pg.population(prob, size=population_size, seed=optimization_seed) - ### Run Optimization """ -""" +Finally, the optimization can be executed by successively evolving the defined population. -# Finally, the optimization can be executed by successively evolving the defined population. -# -# A total number of evolutions of 800 is selected. Thus, the method `algo.evolve()` is called 800 times inside a loop. After each evolution, the best fitness and the list with the best design variables are saved. +A total number of evolutions of 800 is selected. Thus, the method `algo.evolve()` is called 800 times inside a loop. After each evolution, the best fitness and the list with the best design variables are saved. +""" ########################################################################### # Run optimization @@ -333,18 +322,16 @@ def fitness(self, trajectory_parameters: List[float]) -> list: print('The optimization has finished') - ## Results Analysis """ -""" +Having finished the optimisation, it is now possible to analyse the results. -# Having finished the optimisation, it is now possible to analyse the results. -# -# According to [Vinkó et al (2007)](https://www.esa.int/gsp/ACT/doc/MAD/pub/ACT-RPR-MAD-2007-BenchmarkingDifferentGlobalOptimisationTechniques.pdf), the best known solution for the Cassini 1 problem has a final objective function value of 4.93 km/s. -# -# The executed optimization process results in a final objective function value of 4933.17 m/s, with a slightly different decision vector from the one presented by Vinkó et al. (2017). This marginal difference can be explained by an inperfect convergence of the used optimizer, which is expected, considering that DE is a global optimizer. -# -# The evolution of the minimum $\Delta V$ throughout the optimization process can be plotted. +According to [Vinkó et al (2007)](https://www.esa.int/gsp/ACT/doc/MAD/pub/ACT-RPR-MAD-2007-BenchmarkingDifferentGlobalOptimisationTechniques.pdf), the best known solution for the Cassini 1 problem has a final objective function value of 4.93 km/s. + +The executed optimization process results in a final objective function value of 4933.17 m/s, with a slightly different decision vector from the one presented by Vinkó et al. (2017). This marginal difference can be explained by an inperfect convergence of the used optimizer, which is expected, considering that DE is a global optimizer. + +The evolution of the minimum $\Delta V$ throughout the optimization process can be plotted. +""" ########################################################################### # Results post-processing @@ -411,4 +398,3 @@ def fitness(self, trajectory_parameters: List[float]) -> list: ax.set_aspect('equal') ax.legend(bbox_to_anchor=[1, 1]) plt.show() - diff --git a/mission_design/earth_mars_transfer_window.py b/mission_design/earth_mars_transfer_window.py index 9018ab8..446b170 100644 --- a/mission_design/earth_mars_transfer_window.py +++ b/mission_design/earth_mars_transfer_window.py @@ -7,8 +7,8 @@ under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Summary """ diff --git a/mission_design/hodographic_shaping_mga_optimization.py b/mission_design/hodographic_shaping_mga_optimization.py index 89714c3..0460982 100644 --- a/mission_design/hodographic_shaping_mga_optimization.py +++ b/mission_design/hodographic_shaping_mga_optimization.py @@ -1,9 +1,8 @@ # Hodographic-shaping MGA transfer optimization with PyGMO """ +Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. """ -# Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. - ## Context """ @@ -48,16 +47,14 @@ from tudatpy.numerical_simulation import environment_setup from tudatpy.trajectory_design import shape_based_thrust, transfer_trajectory - ## Helpers """ -""" +First of all, let us define a helper function to create the MGA transfer object. Given some inputs, this function creates the transfer legs and nodes settings, and then uses them to create the transfer trajectory object. The function creates a transfer constituted by hodographic-shaping legs, selecting the shaping functions such that there are two free shaping coefficients per velocity direction per leg. -# First of all, let us define a helper function to create the MGA transfer object. Given some inputs, this function creates the transfer legs and nodes settings, and then uses them to create the transfer trajectory object. The function creates a transfer constituted by hodographic-shaping legs, selecting the shaping functions such that there are two free shaping coefficients per velocity direction per leg. -# -# The function takes as inputs the usual parameters necessary for creating an MGA transfer object: the transfer body order, the departure orbit (semi-major axis and eccentricity), the arrival orbit (semi-major axis and eccentricity), the used system of bodies, and the name of the central body. Additionally, the function takes some more arguments used when defining the shaping functions: the time of flight per leg and the number of revolutions per leg. -# -# The function returns the created transfer trajectory object. +The function takes as inputs the usual parameters necessary for creating an MGA transfer object: the transfer body order, the departure orbit (semi-major axis and eccentricity), the arrival orbit (semi-major axis and eccentricity), the used system of bodies, and the name of the central body. Additionally, the function takes some more arguments used when defining the shaping functions: the time of flight per leg and the number of revolutions per leg. + +The function returns the created transfer trajectory object. +""" def create_low_thrust_transfer_object(transfer_body_order: list, times_of_flight: np.ndarray, @@ -134,24 +131,22 @@ def create_low_thrust_transfer_object(transfer_body_order: list, return transfer_trajectory_object - ## Optimisation problem """ +The core of the optimization process is realized by PyGMO, which requires the definition of a problem class. +This definition has to be done in a class that is compatible with what the PyGMO library expects from a User Defined Problem (UDP). See [this page](https://esa.github.io/pygmo2/tutorials/coding_udp_simple.html) from the PyGMO's documentation as a reference. In this example, this class is called `MGAHodographicShapingTrajectoryOptimizationProblem`. + +The following methods are implemented: +* `__init__()`: This is the constructor for the PyGMO problem class. It is used to save all the variables required to setup the creation and evaluation of the transfer trajectory. +* `get_bounds()`: Returns the bounds for each optimized parameter. These are provided as an input to `__init__()`. Their values are defined later in this example. +* `swingby_periapsis_to_optim_parameter()`: When it comes to the swingby periapsis altitude, the parameter that is optimized is the logarithm of the altitude, not the altitude itself. As such, this function converts the altitude to the optimized altitude parameter. This function can be modified to use a different scaling function (i.e. other than the logarithm). +* `optim_parameter_to_swingby_periapsis()`: This function executes the inverse of the transformation done by the `swingby_periapsis_to_optim_parameter` function. That is, it converts the optimized altitude parameter to the periapsis altitude. +* `get_nix()`: Returns the number of design variables which are to be optimized as integers. In this case, the only integer parameters are the number of revolutions per leg. +* `get_transfer_trajectory_object()`: For a given vector of design parameters, the function returns the evaluated transfer trajectory object. This is useful to further analyze the transfer after the optimization, allowing e.g. to retrieve the state history. +* `get_node_times()`: For a given vector of design parameters, the function returns the transfer node times. +* `fitness()`: Returns the cost associated with a vector of design parameters. Here, the fitness is the $\Delta V$ required to execute the transfer. Each time this function is called, a new transfer trajectory object is created and evaluated using the specified design parameters. """ -# The core of the optimization process is realized by PyGMO, which requires the definition of a problem class. -# This definition has to be done in a class that is compatible with what the PyGMO library expects from a User Defined Problem (UDP). See [this page](https://esa.github.io/pygmo2/tutorials/coding_udp_simple.html) from the PyGMO's documentation as a reference. In this example, this class is called `MGAHodographicShapingTrajectoryOptimizationProblem`. -# -# The following methods are implemented: -# * `__init__()`: This is the constructor for the PyGMO problem class. It is used to save all the variables required to setup the creation and evaluation of the transfer trajectory. -# * `get_bounds()`: Returns the bounds for each optimized parameter. These are provided as an input to `__init__()`. Their values are defined later in this example. -# * `swingby_periapsis_to_optim_parameter()`: When it comes to the swingby periapsis altitude, the parameter that is optimized is the logarithm of the altitude, not the altitude itself. As such, this function converts the altitude to the optimized altitude parameter. This function can be modified to use a different scaling function (i.e. other than the logarithm). -# * `optim_parameter_to_swingby_periapsis()`: This function executes the inverse of the transformation done by the `swingby_periapsis_to_optim_parameter` function. That is, it converts the optimized altitude parameter to the periapsis altitude. -# * `get_nix()`: Returns the number of design variables which are to be optimized as integers. In this case, the only integer parameters are the number of revolutions per leg. -# * `get_transfer_trajectory_object()`: For a given vector of design parameters, the function returns the evaluated transfer trajectory object. This is useful to further analyze the transfer after the optimization, allowing e.g. to retrieve the state history. -# * `get_node_times()`: For a given vector of design parameters, the function returns the transfer node times. -# * `fitness()`: Returns the cost associated with a vector of design parameters. Here, the fitness is the $\Delta V$ required to execute the transfer. Each time this function is called, a new transfer trajectory object is created and evaluated using the specified design parameters. - ####################################################################### # Pygmo problem class ####################################################################### @@ -471,7 +466,6 @@ def fitness(self, # Return the value of the objective function return [objective] - ## Optimization """ """ @@ -500,22 +494,20 @@ def fitness(self, arrival_semi_major_axis = np.inf arrival_eccentricity = 0.0 - ### Optimization Setup - """ - """ +""" +Next, the bounds of the optimized parameters are selected. The bounds used here were not tuned, therefore further tuning them might allow better optimization results to be found. -# Next, the bounds of the optimized parameters are selected. The bounds used here were not tuned, therefore further tuning them might allow better optimization results to be found. -# -# The lower and upper bounds for the departure and arrival velocities are all taken to have the value 0; as such, these free variables are not optimized. +The lower and upper bounds for the departure and arrival velocities are all taken to have the value 0; as such, these free variables are not optimized. +""" -########################################################################### -# Select optimization bounds -########################################################################### + ########################################################################### + # Select optimization bounds + ########################################################################### julian_day = constants.JULIAN_DAY -# Select bounds + # Select bounds departure_date_lb = 9000 * julian_day # s departure_date_ub = 9200 * julian_day # s departure_velocity_lb = 0.0 # m/s @@ -539,52 +531,49 @@ def fitness(self, [departure_date_ub, departure_velocity_ub, arrival_velocity_ub, leg_tof_ub, swingby_incoming_velocity_ub, swingby_periapsis_altitude_ub, leg_free_coefficient_ub, leg_number_of_revolutions_ub]] - # To setup the optimization, it is first necessary to initialize the optimization problem. This problem, defined through the class `MGAHodographicShapingTrajectoryOptimizationProblem`, is given to PyGMO trough the `pg.problem()` method. # # The optimiser is selected to be the Simple Genetic Algorithm (SGA) algorithm (its documentation can be found [here](https://esa.github.io/pygmo2/algorithms.html#pygmo.sga)), and is created by calling `pg.algorithm()`. A fixed seed is used to ensure that the results are reproducible. # # Since a large population is being optimized (1000 individuals), the `pg.island` class is used instead of `pg.population`. The island serves as a wrapper to the population, allowing the population to be evolved simultaneously in multiple threads, multiple processes, or even multiple machines. Here, as the type os island is not selected explicitly, usually the evolution will occur in multiple processes (though it depends on the operating systems), meaning that multiple CPUs will be used simultaneously. A seed is also specified when creating the island (this seed is used when creating the initial population). -########################################################################### -# Setup optimization -########################################################################### + ########################################################################### + # Setup optimization + ########################################################################### seed = 42 pop_size = 1000 -# Create Pygmo problem + # Create Pygmo problem transfer_optimization_problem = MGAHodographicShapingTrajectoryOptimizationProblem( central_body, transfer_body_order, bounds, departure_semi_major_axis, departure_eccentricity, arrival_semi_major_axis, arrival_eccentricity) problem = pg.problem(transfer_optimization_problem) -# Create algorithm and define its seed + # Create algorithm and define its seed algorithm = pg.algorithm(pg.sga(gen=1)) algorithm.set_seed(seed) -# Create island + # Create island island = pg.island(algo=algorithm, prob=problem, size=pop_size, seed=seed) - ### Run Optimization - """ - """ - -# Finally, the optimization can be executed by successively evolving the island. To do so, the method `island.evolve()` is called the desired number of times inside a loop. After starting each evolution of the island, the method `island.wait_check()` is called, which makes the program wait for all the evolutions running in parallel to finish. After each evolution is finished, the best fitness and parameters vector are saved. +""" +Finally, the optimization can be executed by successively evolving the island. To do so, the method `island.evolve()` is called the desired number of times inside a loop. After starting each evolution of the island, the method `island.wait_check()` is called, which makes the program wait for all the evolutions running in parallel to finish. After each evolution is finished, the best fitness and parameters vector are saved. +""" -########################################################################### -# Run optimization -########################################################################### + ########################################################################### + # Run optimization + ########################################################################### num_gen = 40 -# Initialize lists with the best individual per generation + # Initialize lists with the best individual per generation list_of_champion_f = [island.get_population().champion_f] list_of_champion_x = [island.get_population().champion_x] -# freeze_support needs to be called when using multiprocessing on windows -# If called from other operating systems, freeze_support doesn't have any effect + # freeze_support needs to be called when using multiprocessing on windows + # If called from other operating systems, freeze_support doesn't have any effect mp.freeze_support() for i in range(num_gen): @@ -598,26 +587,24 @@ def fitness(self, list_of_champion_f.append(island.get_population().champion_f) print('Evolution finished') - ## Results Analysis - """ - """ - -# Having finished the optimisation, it is now possible to analyse the results. An optimum of 41.7 km/s was found, which is a very significant improvement with respect to the best fitness in the initial population (above 200 km/s). The evolution of the minimum $\Delta V$ throughout the optimization is plotted. +""" +Having finished the optimisation, it is now possible to analyse the results. An optimum of 41.7 km/s was found, which is a very significant improvement with respect to the best fitness in the initial population (above 200 km/s). The evolution of the minimum $\Delta V$ throughout the optimization is plotted. +""" -########################################################################### -# Extract the best individual and plot fitness evolution -########################################################################### + ########################################################################### + # Extract the best individual and plot fitness evolution + ########################################################################### print('\n########### CHAMPION INDIVIDUAL ###########\n') print('Total Delta V [m/s]: ', island.get_population().champion_f[0]) print("Parameters vector [various]: ", island.get_population().champion_x) -# Plot fitness over generations + # Plot fitness over generations fig, ax = plt.subplots(figsize=(8, 4), constrained_layout=True) ax.plot(np.arange(0, num_gen+1), np.float_(list_of_champion_f) / 1000) -# Prettify + # Prettify ax.set_xlim((0, num_gen)) ax.grid('major') ax.set_title('Best individual over generations') @@ -626,20 +613,20 @@ def fitness(self, #### Plot the transfer - """ +""" - The transfer trajectory object associated with a given design parameter vector can be retrieved from the PyGMO problem class through the `get_transfer_trajectory_object` object. The returned object is already evaluated. Using the transfer trajectory object one can, for example, retrieve the state and thrust acceleration history throughout the transfer and plot them. - """ +The transfer trajectory object associated with a given design parameter vector can be retrieved from the PyGMO problem class through the `get_transfer_trajectory_object` object. The returned object is already evaluated. Using the transfer trajectory object one can, for example, retrieve the state and thrust acceleration history throughout the transfer and plot them. +""" -########################################################################### -# Extract the champion trajectory object and plot trajectory -########################################################################### + ########################################################################### + # Extract the champion trajectory object and plot trajectory + ########################################################################### design_parameters = island.get_population().champion_x champion_transfer_trajectory_object = transfer_optimization_problem.get_transfer_trajectory_object(design_parameters) champion_node_times = transfer_optimization_problem.get_node_times(design_parameters) -# Extract the state history + # Extract the state history state_history = champion_transfer_trajectory_object.states_along_trajectory(100) fly_by_states = np.array([state_history[champion_node_times[i]] for i in range(len(champion_node_times))]) state_history = result2array(state_history) @@ -647,7 +634,7 @@ def fitness(self, acceleration_history = result2array(acceleration_history) au = 1.5e11 -# Plot the state history + # Plot the state history fig, ax = plt.subplots(figsize=(8,5), constrained_layout=True) ax.plot(state_history[:, 1] / au, state_history[:, 2] / au) ax.quiver(state_history[:, 1] / au, state_history[:, 2] / au, @@ -662,4 +649,3 @@ def fitness(self, ax.set_ylabel('y [AU]') ax.set_aspect('equal') ax.legend(bbox_to_anchor=[1, 1]) - diff --git a/mission_design/low_thrust_earth_mars_transfer_window.ipynb b/mission_design/low_thrust_earth_mars_transfer_window.ipynb new file mode 100644 index 0000000..74f04d3 --- /dev/null +++ b/mission_design/low_thrust_earth_mars_transfer_window.ipynb @@ -0,0 +1,1214 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5cd7cb3f", + "metadata": {}, + "source": [ + "# Earth-Mars transfer window design using Porkchop Plots\n", + "\n", + "Copyright (c) 2010-2024, Delft University of Technology\n", + "All rigths reserved\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE." + ] + }, + { + "cell_type": "markdown", + "id": "2da28d08", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "This example demonstrates the usage of the tudatpy `porkchop` module to determine an optimal launch window (departure and arrival date) for a **low-thrust** Earth-Mars transfer mission.\n", + "By default, the porkchop module uses a Lambert arc to compute the $\\Delta V$ required to depart from the departure body (Earth in this case) and be captured by the target body (in this case Mars).\n", + "Users can provide a custom function to calculate the $\\Delta V$ required for any given transfer. This can be done by supplying a `callable` (a function) to the `porkchop` function via the argument\n", + "\n", + " function_to_calculate_delta_v\n", + "\n", + "In this example, this option will be used to choose (make a preliminary choice, that is) the optimal departure and arrival date of a low-thrust transfer from the Earth to Mars." + ] + }, + { + "cell_type": "markdown", + "id": "53388f52", + "metadata": {}, + "source": [ + "## Import statements\n", + "\n", + "The required import statements are made here, starting with standard imports (`os`, `pickle` from the Python Standard Library), followed by tudatpy imports." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3c187402", + "metadata": {}, + "outputs": [], + "source": [ + "# General imports\n", + "import os\n", + "import pickle\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Tudatpy imports\n", + "import tudatpy\n", + "from tudatpy import constants\n", + "from tudatpy import numerical_simulation\n", + "from tudatpy.interface import spice_interface\n", + "from tudatpy.astro.time_conversion import DateTime\n", + "from tudatpy.trajectory_design import shape_based_thrust\n", + "from tudatpy.trajectory_design import transfer_trajectory\n", + "from tudatpy.numerical_simulation import environment_setup\n", + "from tudatpy.numerical_simulation import propagation_setup\n", + "from tudatpy.trajectory_design.porkchop import porkchop, plot_porkchop\n", + "\n", + "# Tudatpy data processing utilities\n", + "from tudatpy.numerical_simulation.propagation import create_dependent_variable_dictionary\n", + "from tudatpy.util import result2array" + ] + }, + { + "cell_type": "markdown", + "id": "6a41e8c0", + "metadata": {}, + "source": [ + "## Environment setup\n", + "\n", + "The simulation environment is set up here: the standard Spice kernels are loaded, the origin of the global frame is defined, and all necessary bodies are created. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "335917f7", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Load spice kernels\n", + "spice_interface.load_standard_kernels( )\n", + "\n", + "# Define global frame orientation\n", + "global_frame_orientation = 'ECLIPJ2000'\n", + "\n", + "# Create bodies\n", + "bodies_to_create = ['Sun', 'Venus', 'Earth', 'Moon', 'Mars', 'Jupiter', 'Saturn']\n", + "global_frame_origin = 'Sun'\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation)\n", + "\n", + "# Create environment model\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "# Create vehicle object and add it to the existing system of bodies\n", + "vehicle_mass = 4.0E3\n", + "specific_impulse = 3000.0\n", + "bodies.create_empty_body('Vehicle')\n", + "bodies.get_body('Vehicle').mass = vehicle_mass\n", + "\n", + "# Create vehicle thrust settings \n", + "thrust_magnitude_settings = (\n", + "propagation_setup.thrust.custom_thrust_magnitude_fixed_isp( lambda time : 0.0, specific_impulse ) )\n", + "environment_setup.add_engine_model(\n", + " 'Vehicle', 'LowThrustEngine', thrust_magnitude_settings, bodies )\n", + "environment_setup.add_rotation_model(\n", + " bodies, 'Vehicle', environment_setup.rotation_model.custom_inertial_direction_based(\n", + " lambda time : np.array([1,0,0] ), global_frame_orientation, 'VehcleFixed' ) )" + ] + }, + { + "cell_type": "markdown", + "id": "0db92e4d", + "metadata": {}, + "source": [ + "## Shape-based low-thrust trajectory optimization\n", + "\n", + "Define the necessary parameters of the low-thrust trajectory:\n", + "\n", + "- Number of revolutions around the Sun\n", + "- Free parameters for radial shaping functions\n", + "- Free parameters for normal shaping functions\n", + "- Free parameters for axial shaping functions\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d5f38f3e", + "metadata": {}, + "outputs": [], + "source": [ + "number_of_revolutions = 2\n", + "\n", + "radial_velocity_shaping_free_coefficients = [\n", + " 2471.19649906354,\n", + " 4207.587982407276\n", + "]\n", + "normal_velocity_shaping_free_coefficients = [\n", + " -5594.040587888714,\n", + " 8748.139268525232,\n", + "]\n", + "axial_velocity_shaping_free_coefficients = [\n", + " -3449.838496679572,\n", + " 0.0\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "a0ed8bd3", + "metadata": {}, + "source": [ + "### Velocity shaping functions\n", + "\n", + "Define a factory function to obtain the radial velocity shaping functions\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8e0c8a02", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def get_radial_velocity_shaping_functions(trajectory_parameters: list,\n", + " frequency: float,\n", + " scale_factor: float,\n", + " time_of_flight: float,\n", + " number_of_revolutions: int) -> tuple:\n", + " \"\"\"\n", + " Retrieves the radial velocity shaping functions (lowest and highest order in Gondelach and Noomen, 2015) and returns\n", + " them together with the free coefficients.\n", + "\n", + " Parameters\n", + " ----------\n", + " trajectory_parameters : list\n", + " List of trajectory parameters to optimize.\n", + " frequency: float\n", + " Frequency of the highest-order methods.\n", + " scale_factor: float\n", + " Scale factor of the highest-order methods.\n", + " time_of_flight: float\n", + " Time of flight of the trajectory.\n", + " number_of_revolutions: int\n", + " Number of revolutions around the Sun (currently unused).\n", + "\n", + " Returns\n", + " -------\n", + " tuple\n", + " A tuple composed by two lists: the radial velocity shaping functions and their free coefficients.\n", + " \"\"\"\n", + " # Retrieve default methods (lowest-order in Gondelach and Noomen, 2015)\n", + " radial_velocity_shaping_functions = shape_based_thrust.recommended_radial_hodograph_functions(time_of_flight)\n", + " # Add degrees of freedom (highest-order in Gondelach and Noomen, 2015)\n", + " radial_velocity_shaping_functions.append(shape_based_thrust.hodograph_scaled_power_sine(\n", + " exponent=1.0,\n", + " frequency=0.5 * frequency,\n", + " scale_factor=scale_factor))\n", + " radial_velocity_shaping_functions.append(shape_based_thrust.hodograph_scaled_power_cosine(\n", + " exponent=1.0,\n", + " frequency=0.5 * frequency,\n", + " scale_factor=scale_factor))\n", + " # Set free parameters\n", + " free_coefficients = trajectory_parameters[3:5]\n", + " return (radial_velocity_shaping_functions,\n", + " free_coefficients)" + ] + }, + { + "cell_type": "markdown", + "id": "f58e0413", + "metadata": {}, + "source": [ + "Define a factory function to obtain the normal velocity shaping functions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "406d0e5d", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def get_normal_velocity_shaping_functions(trajectory_parameters: list,\n", + " frequency: float,\n", + " scale_factor: float,\n", + " time_of_flight: float,\n", + " number_of_revolutions: int) -> tuple:\n", + " \"\"\"\n", + " Retrieves the normal velocity shaping functions (lowest and highest order in Gondelach and Noomen, 2015) and returns\n", + " them together with the free coefficients.\n", + "\n", + " Parameters\n", + " ----------\n", + " trajectory_parameters : list\n", + " List of trajectory parameters to optimize.\n", + " frequency: float\n", + " Frequency of the highest-order methods.\n", + " scale_factor: float\n", + " Scale factor of the highest-order methods.\n", + " time_of_flight: float\n", + " Time of flight of the trajectory.\n", + " number_of_revolutions: int\n", + " Number of revolutions around the Sun (currently unused).\n", + "\n", + " Returns\n", + " -------\n", + " tuple\n", + " A tuple composed by two lists: the normal velocity shaping functions and their free coefficients.\n", + " \"\"\"\n", + " # Retrieve default methods (lowest-order in Gondelach and Noomen, 2015)\n", + " normal_velocity_shaping_functions = shape_based_thrust.recommended_normal_hodograph_functions(time_of_flight)\n", + " # Add degrees of freedom (highest-order in Gondelach and Noomen, 2015)\n", + " normal_velocity_shaping_functions.append(shape_based_thrust.hodograph_scaled_power_sine(\n", + " exponent=1.0,\n", + " frequency=0.5 * frequency,\n", + " scale_factor=scale_factor))\n", + " normal_velocity_shaping_functions.append(shape_based_thrust.hodograph_scaled_power_cosine(\n", + " exponent=1.0,\n", + " frequency=0.5 * frequency,\n", + " scale_factor=scale_factor))\n", + " # Set free parameters\n", + " free_coefficients = trajectory_parameters[5:7]\n", + " return (normal_velocity_shaping_functions,\n", + " free_coefficients)" + ] + }, + { + "cell_type": "markdown", + "id": "e069c004", + "metadata": {}, + "source": [ + "Define a factory function to obtain the axial velocity shaping functions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f9a4c0ea", + "metadata": {}, + "outputs": [], + "source": [ + "def get_axial_velocity_shaping_functions(trajectory_parameters: list,\n", + " frequency: float,\n", + " scale_factor: float,\n", + " time_of_flight: float,\n", + " number_of_revolutions: int) -> tuple:\n", + " \"\"\"\n", + " Retrieves the axial velocity shaping functions (lowest and highest order in Gondelach and Noomen, 2015) and returns\n", + " them together with the free coefficients.\n", + "\n", + " Parameters\n", + " ----------\n", + " trajectory_parameters : list[ float ]\n", + " List of trajectory parameters to optimize.\n", + " frequency: float\n", + " Frequency of the highest-order methods.\n", + " scale_factor: float\n", + " Scale factor of the highest-order methods.\n", + " time_of_flight: float\n", + " Time of flight of the trajectory.\n", + " number_of_revolutions: int\n", + " Number of revolutions around the Sun.\n", + "\n", + " Returns\n", + " -------\n", + " tuple\n", + " A tuple composed by two lists: the axial velocity shaping functions and their free coefficients.\n", + " \"\"\"\n", + " # Retrieve default methods (lowest-order in Gondelach and Noomen, 2015)\n", + " axial_velocity_shaping_functions = shape_based_thrust.recommended_axial_hodograph_functions(\n", + " time_of_flight,\n", + " number_of_revolutions)\n", + " # Add degrees of freedom (highest-order in Gondelach and Noomen, 2015)\n", + " exponent = 4.0\n", + " axial_velocity_shaping_functions.append(shape_based_thrust.hodograph_scaled_power_cosine(\n", + " exponent=exponent,\n", + " frequency=(number_of_revolutions + 0.5) * frequency,\n", + " scale_factor=scale_factor ** exponent))\n", + " axial_velocity_shaping_functions.append(shape_based_thrust.hodograph_scaled_power_sine(\n", + " exponent=exponent,\n", + " frequency=(number_of_revolutions + 0.5) * frequency,\n", + " scale_factor=scale_factor ** exponent))\n", + " # Set free parameters\n", + " free_coefficients = trajectory_parameters[7:9]\n", + " return (axial_velocity_shaping_functions,\n", + " free_coefficients)" + ] + }, + { + "cell_type": "markdown", + "id": "e69ae539", + "metadata": {}, + "source": [ + "### Low-thrust Trajectory Optimization solution\n", + "\n", + "Define a function to obtain the LTTO solution" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "57fd8850", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def create_hodographic_trajectory(\n", + " trajectory_parameters: list,\n", + " bodies: tudatpy.numerical_simulation.environment.SystemOfBodies,\n", + " departure_body: str,\n", + " target_body: str,\n", + " central_body: str) \\\n", + " -> tudatpy.trajectory_design.transfer_trajectory.TransferTrajectory:\n", + " \"\"\"\n", + " It creates and returns the hodographic shaping object, based on the trajectory parameters.\n", + "\n", + " Parameters\n", + " ----------\n", + " trajectory_parameters : list\n", + " List of trajectory parameters to be optimized.\n", + " bodies : tudatpy.numerical_simulation.environment.SystemOfBodies\n", + " System of bodies present in the simulation.\n", + "\n", + " Returns\n", + " -------\n", + " hodographic_shaping_object : tudatpy.trajectory_design.shape_based_thrust.HodographicShaping\n", + " Hodographic shaping object.\n", + " \"\"\"\n", + "\n", + " # Time settings\n", + " initial_time = trajectory_parameters[0] * constants.JULIAN_DAY\n", + " time_of_flight = trajectory_parameters[1] * constants.JULIAN_DAY\n", + " final_time = initial_time + time_of_flight\n", + " \n", + " # Number of revolutions\n", + " number_of_revolutions = int(trajectory_parameters[2])\n", + " \n", + " # Compute relevant frequency and scale factor for shaping functions\n", + " frequency = 2.0 * np.pi / time_of_flight\n", + " scale_factor = 1.0 / time_of_flight\n", + " \n", + " # Retrieve shaping functions and free parameters\n", + " radial_velocity_shaping_functions, radial_free_coefficients = get_radial_velocity_shaping_functions(\n", + " trajectory_parameters,\n", + " frequency,\n", + " scale_factor,\n", + " time_of_flight,\n", + " number_of_revolutions)\n", + " normal_velocity_shaping_functions, normal_free_coefficients = get_normal_velocity_shaping_functions(\n", + " trajectory_parameters,\n", + " frequency,\n", + " scale_factor,\n", + " time_of_flight,\n", + " number_of_revolutions)\n", + " axial_velocity_shaping_functions, axial_free_coefficients = get_axial_velocity_shaping_functions(\n", + " trajectory_parameters,\n", + " frequency,\n", + " scale_factor,\n", + " time_of_flight,\n", + " number_of_revolutions)\n", + "\n", + " # Create settings for transfer trajectory (zero excess velocity on departure and arrival)\n", + " hodographic_leg_settings = transfer_trajectory.hodographic_shaping_leg(\n", + " radial_velocity_shaping_functions,\n", + " normal_velocity_shaping_functions,\n", + " axial_velocity_shaping_functions )\n", + " node_settings = list()\n", + " node_settings.append( transfer_trajectory.departure_node( 1.0E8, 0.0 ) )\n", + " node_settings.append( transfer_trajectory.capture_node( 1.0E8, 0.0 ) )\n", + "\n", + " # Create and return transfer trajectory\n", + " trajectory_object = transfer_trajectory.create_transfer_trajectory(\n", + " bodies, [hodographic_leg_settings], node_settings, [departure_body, target_body], central_body )\n", + "\n", + " # Extract node times\n", + " node_times = list( )\n", + " node_times.append( initial_time )\n", + " node_times.append( final_time )\n", + "\n", + " #transfer_trajectory.print_parameter_definitions( [hodographic_leg_settings], node_settings )\n", + " hodograph_free_parameters = trajectory_parameters[2:9]\n", + "\n", + " # Depart and arrive with 0 excess velocity\n", + " node_parameters = list()\n", + " node_parameters.append( np.zeros([3,1]))\n", + " node_parameters.append( np.zeros([3,1]))\n", + "\n", + " # Update trajectory to given times, node settings, and hodograph parameters\n", + " trajectory_object.evaluate( node_times, [hodograph_free_parameters], node_parameters )\n", + "\n", + " return trajectory_object" + ] + }, + { + "cell_type": "markdown", + "id": "343f7546", + "metadata": {}, + "source": [ + "Create function to obtain transfer ΔV" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "652cfc1e", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def hodographic_low_thrust_trajectory_delta_v(\n", + " bodies: tudatpy.numerical_simulation.environment.SystemOfBodies,\n", + " departure_body: str,\n", + " target_body: str,\n", + " departure_epoch: float,\n", + " arrival_epoch: float,\n", + " central_body: str = 'Sun') \\\n", + " -> [tudatpy.trajectory_design.transfer_trajectory.TransferTrajectory, float]:\n", + " \"\"\"\n", + " Function to calculate the required ΔV of an Earth-Mars transfer\n", + "\n", + " Parameters\n", + " ----------\n", + " bodies : tudatpy.numerical_simulation.environment.SystemOfBodies\n", + " The system of bodies containing the celestial bodies involved in the transfer.\n", + " departure_body : str\n", + " The name of the departure celestial body.\n", + " target_body : str\n", + " The name of the target celestial body.\n", + " departure_epoch : float\n", + " The departure epoch in seconds since J2000.\n", + " arrival_epoch : float\n", + " The arrival epoch in seconds since J2000.\n", + " central_body : str, optional\n", + " The name of the central celestial body (default is 'Sun').\n", + "\n", + " Returns\n", + " -------\n", + " [tudatpy.trajectory_design.transfer_trajectory.TransferTrajectory, float]\n", + " A tuple containing the transfer trajectory object and the required ΔV.\n", + " \"\"\"\n", + " \n", + " # The entries of the vector 'trajectory_parameters' contains the following:\n", + " # * Entry 0: Departure time (from Earth's center-of-mass) in Julian days since J2000\n", + " # * Entry 1: Time-of-flight from Earth's center-of-mass to Mars' center-of-mass, in Julian days\n", + " # * Entry 2: Number of revolutions around the Sun\n", + " # * Entry 3,4: Free parameters for radial shaping functions\n", + " # * Entry 5,6: Free parameters for normal shaping functions\n", + " # * Entry 7,8: Free parameters for axial shaping functions\n", + " \n", + " trajectory_parameters = [\n", + " departure_epoch / constants.JULIAN_DAY,\n", + " (arrival_epoch - departure_epoch) / constants.JULIAN_DAY,\n", + " number_of_revolutions,\n", + " *radial_velocity_shaping_free_coefficients,\n", + " *normal_velocity_shaping_free_coefficients,\n", + " *axial_velocity_shaping_free_coefficients\n", + " ]\n", + "\n", + " hodographic_shaping_object = create_hodographic_trajectory(\n", + " trajectory_parameters,\n", + " bodies,\n", + " departure_body,\n", + " target_body,\n", + " central_body)\n", + "\n", + " # Retrieve delta V\n", + " ΔV = hodographic_shaping_object.delta_v\n", + "\n", + " return ΔV\n" + ] + }, + { + "cell_type": "markdown", + "id": "963cf649", + "metadata": {}, + "source": [ + "## Porkchop Plots\n", + "\n", + "The departure and target bodies and the time window for the transfer are then defined using tudatpy `astro.time_conversion.DateTime` objects." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1a30217b", + "metadata": {}, + "outputs": [], + "source": [ + "departure_body = 'Earth'\n", + "target_body = 'Mars'\n", + "\n", + "earliest_departure_time = DateTime(2016, 9, 1)\n", + "latest_departure_time = DateTime(2017, 7, 1)\n", + "\n", + "earliest_arrival_time = DateTime(2019, 11, 1)\n", + "latest_arrival_time = DateTime(2021, 9, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "371604d6", + "metadata": {}, + "source": [ + "To ensure the porkchop plot is rendered with good resolution, the time resolution of the plot is defined as 0.5% of the smallest time window (either the arrival or the departure window):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bc532a49", + "metadata": {}, + "outputs": [], + "source": [ + "time_window_percentage = 0.5\n", + "time_resolution = time_resolution = min(\n", + " latest_departure_time.epoch() - earliest_departure_time.epoch(),\n", + " latest_arrival_time.epoch() - earliest_arrival_time.epoch()\n", + ") / constants.JULIAN_DAY * time_window_percentage / 100" + ] + }, + { + "cell_type": "markdown", + "id": "75def91b", + "metadata": {}, + "source": [ + "Generating a high-resolution plot may be time-consuming: reusing saved data might be desirable; we proceed to ask the user whether to reuse saved data or generate the plot from scratch." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ddd8ed7b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# File\n", + "data_file = 'porkchop.pkl'\n", + "\n", + "# Whether to recalculate the porkchop plot or use saved data\n", + "RECALCULATE_delta_v = input(\n", + " '\\n Recalculate ΔV for porkchop plot? [y/N] '\n", + ").strip().lower() == 'y'\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "id": "9248e585", + "metadata": {}, + "source": [ + "Lastly, we call the `porkchop` function, which will calculate the $\\Delta V$ required at each departure-arrival coordinate and display the plot, giving us\n", + " \n", + "- The optimal departure-arrival date combination\n", + "- The constant time-of-flight isochrones\n", + "- And more" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8991c2f8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "if not os.path.isfile(data_file) or RECALCULATE_delta_v:\n", + " # Regenerate plot\n", + " [departure_epochs, arrival_epochs, ΔV] = porkchop(\n", + " bodies,\n", + " departure_body,\n", + " target_body,\n", + " earliest_departure_time,\n", + " latest_departure_time,\n", + " earliest_arrival_time,\n", + " latest_arrival_time,\n", + " time_resolution,\n", + " function_to_calculate_delta_v=hodographic_low_thrust_trajectory_delta_v\n", + " )\n", + " # Save data\n", + " pickle.dump(\n", + " [departure_epochs, arrival_epochs, ΔV],\n", + " open(data_file, 'wb')\n", + " )\n", + "else:\n", + " # Read saved data\n", + " [departure_epochs, arrival_epochs, ΔV] = pickle.load(\n", + " open(data_file, 'rb')\n", + " )\n", + " # Plot saved data\n", + " plot_porkchop(\n", + " departure_body = departure_body,\n", + " target_body = target_body,\n", + " departure_epochs = departure_epochs, \n", + " arrival_epochs = arrival_epochs, \n", + " delta_v = ΔV,\n", + " threshold = 60\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "9dbad462", + "metadata": {}, + "source": [ + "### Variations\n", + "\n", + "The Tudat `porkchop` module allows us to\n", + "\n", + "- Save the $\\Delta V$ porkchop returned by `porkchop` and plot it again without recalculating with the `plot_porkchop` function\n", + "- Plot $\\Delta V$ (default) or C3 (specific energy), as well as choose whether to plot departure and arrival $\\Delta V$ together as the total $\\Delta V$ required for the transfer (default), or separately (in those cases in which the manoeuvre is performed in two burns, one at departure and one at arrival to the target planet).\n", + "\n", + "Let's make use of `plot_porkchop` to see all four combinations!" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a04fc515", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cases = [\n", + " {'C3': False, 'total': True, 'threshold': 80, 'filename': 'figures/Δ_tot.png'},\n", + " {'C3': True, 'total': True, 'threshold': 3000, 'filename': 'figures/C3_tot.png'}\n", + "]\n", + "\n", + "for case in cases:\n", + " plot_porkchop(\n", + " departure_body = departure_body,\n", + " target_body = target_body,\n", + " departure_epochs = departure_epochs, \n", + " arrival_epochs = arrival_epochs, \n", + " delta_v = ΔV,\n", + " save = False,\n", + " **case\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "id": "3097d419", + "metadata": {}, + "source": [ + "# Verification\n", + "\n", + "Interestingly, discontinuities appear in the $\\Delta V$/$C_3$ porkchop. To ensure that the porkchop can be trusted -that it is possible to choose a transfer window based on our porkchop-, we will proceed to investigate two transfers:\n", + "\n", + "- The (approximately) minimum $\\Delta V$ transfer: `2016-11-16`-`2020-6-11`\n", + "- A high $\\Delta V$ transfer in the dark red region of the porkchop: `2016-10-22`-`2021-01-21`" + ] + }, + { + "cell_type": "markdown", + "id": "61096838", + "metadata": {}, + "source": [ + "## Trajectory visualization\n", + "\n", + "Provided with a transfer window, the following function will obtain the shape-based low thrust trajectory from the Earth to Mars, numerically propagate a trajectory using a low-thrust thrust model for our spacecraft, and plot the: \n", + "\n", + "- Cartesian coordinates of the spacecraft, as a function of time, both for the analytical and integrated trajectory\n", + "- The Cartesian coordinates as a function of time of the Earth and Mars\n", + "- The thrust acceleration on the spacecraft as a function of time\n", + "- And a 3D plot showing the complete manoeuvre" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "58d8d844", + "metadata": {}, + "outputs": [], + "source": [ + "def inspect_low_thrust_trajectory(\n", + " departure_date: DateTime,\n", + " arrival_date: DateTime\n", + " ):\n", + " \"\"\"\n", + " This function has the following sections:\n", + "\n", + " 1. Define transfer parameters\n", + " 2. Obtain low-thrust shape-based semi-analytical trajectory\n", + " 3. Create vehicle thrust settings\n", + " 4. Create termination settings\n", + " 5. Propagator settings\n", + " 6. Integrator settings\n", + " 7. Propagate dynamics\n", + " 8. Process simulation output\n", + " 9. Retrieve ephemeris of astronomical bodies\n", + " 10. Plot trajectory \n", + " \"\"\"\n", + "\n", + " ###########################################################################\n", + " # DEFINE TRANSFER PARAMETERS ##############################################\n", + " ###########################################################################\n", + "\n", + " trajectory_parameters = [\n", + " departure_date.epoch() / constants.JULIAN_DAY,\n", + " (arrival_date.epoch() - departure_date.epoch()) / constants.JULIAN_DAY,\n", + " number_of_revolutions,\n", + " *radial_velocity_shaping_free_coefficients,\n", + " *normal_velocity_shaping_free_coefficients,\n", + " *axial_velocity_shaping_free_coefficients\n", + " ]\n", + "\n", + " # Fixed parameters\n", + " minimum_mars_distance = 5.0E7\n", + " # Time since 'departure from Earth CoM' at which propagation starts (and similar\n", + " # for arrival time)\n", + " time_buffer = 30.0 * constants.JULIAN_DAY\n", + "\n", + " # Propagation time settings\n", + " initial_propagation_time = departure_date.epoch() + time_buffer\n", + " final_propagation_time = arrival_date.epoch() - time_buffer\n", + "\n", + " ###########################################################################\n", + " # OBTAIN LOW-THRUST SHAPE-BASED SEMI-ANALYTICAL TRAJECTORY ################\n", + " ###########################################################################\n", + "\n", + " # Create problem without propagating\n", + " hodographic_shaping_object = create_hodographic_trajectory(\n", + " trajectory_parameters, \n", + " bodies,\n", + " 'Earth',\n", + " 'Mars',\n", + " 'Sun'\n", + " )\n", + "\n", + " # Retrieves analytical results and write them to a file\n", + " analytical_trajectory = lambda n: result2array(hodographic_shaping_object.states_along_trajectory(n))[:, 1:]\n", + "\n", + " # Report transfer ΔV\n", + " print(f'{hodographic_shaping_object.delta_v/1000:.2f} km/s')\n", + "\n", + " ###########################################################################\n", + " # CREATE TERMINATION SETTINGS #############################################\n", + " ###########################################################################\n", + "\n", + " # Time settings\n", + " time_termination_settings = propagation_setup.propagator.time_termination(\n", + " final_propagation_time,\n", + " terminate_exactly_on_final_condition=False)\n", + " # Altitude\n", + " relative_distance_termination_settings = propagation_setup.propagator.dependent_variable_termination(\n", + " dependent_variable_settings=propagation_setup.dependent_variable.relative_distance('Vehicle', 'Mars'),\n", + " limit_value=minimum_mars_distance,\n", + " use_as_lower_limit=True,\n", + " terminate_exactly_on_final_condition=False)\n", + " # Define list of termination settings\n", + " termination_settings_list = [time_termination_settings,\n", + " relative_distance_termination_settings]\n", + " # Create termination settings object\n", + " termination_settings = propagation_setup.propagator.hybrid_termination(termination_settings_list,\n", + " fulfill_single_condition=True)\n", + " \n", + " # Retrieve dependent variables to save\n", + " dependent_variables_to_save = [\n", + " propagation_setup.dependent_variable.relative_distance('Vehicle', 'Earth'),\n", + " propagation_setup.dependent_variable.relative_distance('Vehicle', 'Sun'),\n", + " propagation_setup.dependent_variable.relative_distance('Vehicle', 'Mars'),\n", + " propagation_setup.dependent_variable.single_acceleration_norm(\n", + " propagation_setup.acceleration.thrust_acceleration_type,'Vehicle','Vehicle')\n", + " ]\n", + "\n", + " ###########################################################################\n", + " # PROPAGATOR SETTINGS #####################################################\n", + " ###########################################################################\n", + "\n", + " current_propagator = propagation_setup.propagator.unified_state_model_quaternions\n", + "\n", + " # Define propagation settings\n", + " # Define bodies that are propagated and their central bodies of propagation\n", + " bodies_to_propagate = ['Vehicle']\n", + " central_bodies = ['Sun']\n", + "\n", + " # Update vehicle rotation model and thrust magnitude model\n", + " transfer_trajectory.set_low_thrust_acceleration( hodographic_shaping_object.legs[ 0 ], bodies, 'Vehicle', 'LowThrustEngine' )\n", + " \n", + " # Define accelerations acting on capsule\n", + " acceleration_settings_on_vehicle = {\n", + " 'Sun': [propagation_setup.acceleration.point_mass_gravity()],\n", + " 'Vehicle': [propagation_setup.acceleration.thrust_from_engine('LowThrustEngine')]\n", + " }\n", + "\n", + " # Create global accelerations dictionary\n", + " acceleration_settings = {'Vehicle': acceleration_settings_on_vehicle}\n", + " acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies,\n", + " acceleration_settings,\n", + " bodies_to_propagate,\n", + " central_bodies)\n", + "\n", + " # Retrieve initial state\n", + " initial_state = hodographic_shaping_object.legs[ 0 ].state_along_trajectory( initial_propagation_time )\n", + "\n", + " # Create propagation settings for the translational dynamics\n", + " translational_propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " initial_state,\n", + " initial_propagation_time,\n", + " None,\n", + " termination_settings,\n", + " current_propagator,\n", + " output_variables=dependent_variables_to_save)\n", + "\n", + " # Create mass rate model\n", + " mass_rate_settings_on_vehicle = {'Vehicle': [propagation_setup.mass_rate.from_thrust()]}\n", + " mass_rate_models = propagation_setup.create_mass_rate_models(bodies,\n", + " mass_rate_settings_on_vehicle,\n", + " acceleration_models)\n", + " # Create mass propagator settings\n", + " mass_propagator_settings = propagation_setup.propagator.mass(bodies_to_propagate,\n", + " mass_rate_models,\n", + " np.array([vehicle_mass]),\n", + " initial_propagation_time,\n", + " None,\n", + " termination_settings)\n", + "\n", + " # Create multi-type propagation settings list\n", + " propagator_settings_list = [translational_propagator_settings,\n", + " mass_propagator_settings]\n", + "\n", + " # Create multi-type propagation settings object for translational dynamics and mass\n", + " propagator_settings = propagation_setup.propagator.multitype(propagator_settings_list,\n", + " None,\n", + " initial_propagation_time,\n", + " termination_settings,\n", + " dependent_variables_to_save)\n", + " \n", + " ###########################################################################\n", + " # INTEGRATOR SETTINGS #####################################################\n", + " ###########################################################################\n", + "\n", + " # Create integrator settings\n", + " current_tolerance = 10.0 ** (-10.0)\n", + " # Create integrator settings\n", + " integrator = propagation_setup.integrator\n", + " # Define fixed step size\n", + " step_size = constants.JULIAN_DAY\n", + " # Here (epsilon, inf) are set as respectively min and max step sizes\n", + " # also note that the relative and absolute tolerances are the same value\n", + " integrator_settings = integrator.runge_kutta_variable_step_size(\n", + " step_size,\n", + " propagation_setup.integrator.CoefficientSets.rkdp_87,\n", + " step_size,\n", + " step_size,\n", + " current_tolerance,\n", + " current_tolerance)\n", + " propagator_settings.integrator_settings = integrator_settings\n", + "\n", + " ###########################################################################\n", + " # PROPAGATE DYNAMICS ######################################################\n", + " ###########################################################################\n", + " dynamics_simulator = numerical_simulation.create_dynamics_simulator(\n", + " bodies, propagator_settings )\n", + "\n", + " ###########################################################################\n", + " # PROCESS SIMULATION OUTPUT ###############################################\n", + " ###########################################################################\n", + " # Retrieve propagated state and dependent variables\n", + " state_history = dynamics_simulator.state_history\n", + "\n", + " # Create dependent variable dictionary\n", + " dv_dict = create_dependent_variable_dictionary(dynamics_simulator)\n", + "\n", + " # Create state history array\n", + " state_history_array = result2array(state_history)\n", + "\n", + " # Retrieve propagation buffer time\n", + " buffer = time_buffer / constants.JULIAN_DAY\n", + "\n", + " # Retrieve time vector\n", + " t = state_history_array[:, 0] / constants.JULIAN_DAY\n", + " t = (t - t[0]) + buffer\n", + "\n", + " # Retrieve analytical time vector\n", + " t_analytical = np.arange(0, max(t)+buffer+1, step_size/constants.JULIAN_DAY)\n", + "\n", + " # Retrieve x, y and z position history\n", + " x = state_history_array[:, 1]\n", + " y = state_history_array[:, 2]\n", + " z = state_history_array[:, 3]\n", + "\n", + " # Retrieve analytical trajectory\n", + " x_analytical = analytical_trajectory(len(t_analytical))[:, 0]\n", + " y_analytical = analytical_trajectory(len(t_analytical))[:, 1]\n", + " z_analytical = analytical_trajectory(len(t_analytical))[:, 2]\n", + "\n", + " # Retrieve thrust magnitude\n", + " thrust_magnitude = dv_dict.asarray('Single acceleration norm of type thrust , acting on Vehicle')\n", + "\n", + " ###########################################################################\n", + " # RETRIEVE EPHEMERIS OF ASTRONOMICAL BODIES ###############################\n", + " ###########################################################################\n", + "\n", + " retrieve_ephemeris = lambda body: np.vstack([spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=body,\n", + " observer_body_name=\"SSB\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time=epoch_in_days * constants.JULIAN_DAY\n", + " )[:3] for epoch_in_days in np.linspace(trajectory_parameters[0], trajectory_parameters[0] + trajectory_parameters[1], len(t_analytical))])\n", + "\n", + " # Retrieve trajectory of the Earth\n", + " earth_trajectory = retrieve_ephemeris('Earth')\n", + " earth_x = earth_trajectory[:, 0]\n", + " earth_y = earth_trajectory[:, 1]\n", + " earth_z = earth_trajectory[:, 2]\n", + "\n", + " # Retrieve trajectory of mars\n", + " mars_trajectory = retrieve_ephemeris('Mars')\n", + " mars_x = mars_trajectory[:, 0]\n", + " mars_y = mars_trajectory[:, 1]\n", + " mars_z = mars_trajectory[:, 2]\n", + "\n", + " ###########################################################################\n", + " # PLOT TRAJECTORY #########################################################\n", + " ###########################################################################\n", + "\n", + " # Create figure\n", + " fig = plt.figure(figsize=(15, 7))\n", + "\n", + " # Create 3 subplots for x, y, z coordinates\n", + " ax1 = plt.subplot2grid((4, 2), (0, 0))\n", + " ax2 = plt.subplot2grid((4, 2), (1, 0), sharex=ax1)\n", + " ax3 = plt.subplot2grid((4, 2), (2, 0), sharex=ax1)\n", + " ax4 = plt.subplot2grid((4, 2), (3, 0), sharex=ax1)\n", + "\n", + " # Plot analytical and numerical coordinates\n", + " ax1.plot(t_analytical, x_analytical, label='Analytical')\n", + " ax1.plot(t, x, label='Numerical')\n", + " ax1.plot(t_analytical, earth_x, label='Earth', color='#78c4ff', zorder=1, alpha=0.6)\n", + " ax1.plot(t_analytical, mars_x, label='Mars', color='#f04848', zorder=1, alpha=0.6)\n", + " ax1.set_ylabel('X [km]')\n", + "\n", + " ax2.plot(t_analytical, y_analytical)\n", + " ax2.plot(t, y)\n", + " ax2.plot(t_analytical, earth_y, color='#78c4ff', zorder=1, alpha=0.6)\n", + " ax2.plot(t_analytical, mars_y, color='#f04848', zorder=1, alpha=0.6)\n", + " ax2.set_ylabel('Y [km]')\n", + "\n", + " ax3.plot(t_analytical, z_analytical)\n", + " ax3.plot(t, z)\n", + " ax3.plot(t_analytical, earth_z, color='#78c4ff', zorder=1, alpha=0.6)\n", + " ax3.plot(t_analytical, mars_z, color='#f04848', zorder=1, alpha=0.6)\n", + " ax3.set_ylabel('Z [km]')\n", + "\n", + " ax4.plot(t, thrust_magnitude / vehicle_mass, color='green', label='Thrust acceleration')\n", + " ax4.set_ylabel('$||a_T||$ [$m/s^2$]')\n", + " ax4.set_xlabel('Time [days]')\n", + "\n", + " # Legend\n", + " fig.legend(loc='lower center', ncol=5)\n", + "\n", + " # Create a 3D subplot for the trajectories\n", + " ax5 = plt.subplot2grid((3, 2), (0, 1), rowspan=3, projection='3d')\n", + " ax5.view_init(elev=32, azim=-66)\n", + "\n", + " # Plot Earth trajectory\n", + " ax5.plot(earth_x, earth_y, earth_z, color='#78c4ff')\n", + " ax5.scatter(earth_x[0], earth_y[0], earth_z[0], label='Earth, $t_0$', marker='$\\Lambda$', s=100, color='#1f9eff')\n", + "\n", + " # Plot Mars trajectory\n", + " ax5.plot(mars_x, mars_y, mars_z, color='#f04848')\n", + " ax5.scatter(mars_x[-1], mars_y[-1], mars_z[-1], label='Mars, $t_f$', marker='$\\Gamma$', s=100, color='#c93232')\n", + "\n", + " # Plot the 3D trajectories\n", + " ax5.plot(x_analytical, y_analytical, z_analytical)\n", + " ax5.plot(x, y, z)\n", + "\n", + " # Set labels\n", + " ax5.set_xlabel('X')\n", + " ax5.set_ylabel('Y')\n", + " ax5.set_zlabel('Z')\n", + "\n", + " # Show the legend\n", + " ax5.legend()\n", + "\n", + " # Grid\n", + " for ax in [ax1, ax2, ax3, ax4]: ax.grid(\n", + " True,\n", + " which='major',\n", + " color='grey',\n", + " linestyle='--'\n", + " )\n", + "\n", + " # Layout\n", + " plt.subplots_adjust(\n", + " top=0.95,\n", + " bottom=0.127,\n", + " left=0.043,\n", + " right=0.99,\n", + " hspace=0.335,\n", + " wspace=0.0\n", + " )\n", + "\n", + " # Show the plot\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d39304fa", + "metadata": {}, + "source": [ + "## Observations and conclusions\n", + "\n", + "Notice that there is a change in the number of revolutions around the Sun between the two transfers: the low $\\Delta V$ transfer has close to 3 revolutions around the Sun, while the high $\\Delta V$ transfer greatly \"stretches\" its second revolution around the Sun to reach Mars. This is expected as the chosen number of revolutions (2) allows trajectories with between 2 and 3 revolutions around the Sun: thus, a discontinuity appears as trajectories approach 3 revolutions, and are from that point on forced to use 2; this is the case for the high $\\Delta V$ trajectory.\n", + "The thrust acceleration of the high $\\Delta V$ trajectory is 2 orders of magnitude higher than that for the low $\\Delta V$ trajectory. This is expected as the orbit of the spacecraft in the high $\\Delta V$ trajectory is distorted considerably more than in the low $\\Delta V$ case.\n", + "\n", + "The discontinuities observed in the $\\Delta V$ porkchops are explained by a discontinuous change in the number of revolutions required for the low-thrust transfer. The porkchops generated are thus reasonable, and can be used to identify a preliminary transfer window for our low-thrust spacecraft, as well as obtain a preliminary estimate of the $\\Delta V$ required for the mission.\n", + "Next steps in mission design should be the optimization of the shaping function parameters (`radial_velocity_shaping_free_coefficients`, `normal_velocity_shaping_free_coefficients` and `axial_velocity_shaping_free_coefficients`) and number of revolutions around the Sun, which were fixed in this example, analyzing disturbing perturbations along the trajectory, analyzing the impact of navigation uncertainties on the trajectory, et cétera.\n" + ] + }, + { + "cell_type": "markdown", + "id": "fea7ccda", + "metadata": {}, + "source": [ + "**Low $\\Delta V$ trajectory**" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0b714e57", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12.00 km/s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "inspect_low_thrust_trajectory(\n", + " DateTime(2016,11,16),\n", + " DateTime(2020,6,11)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "708814a8", + "metadata": {}, + "source": [ + "**High $\\Delta V$ trajectory**" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "06ccf481", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "526.24 km/s\n" + ] + }, + { + "data": { + "image/png": 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96e/zYkmSJEmSfM/lcnH8+HESEhKIjY31d3MkL6uurqagoIABAwZ0aa2ZHh1s1jSN73//++zbt4/169cT08EvCv48qdZcLpzbtuHcv19k9igK+sGDMUyY4NfgrlpejnPPHlzHjok6rIqCfuRIjJMmoQQH+61dkiR1nWa349y9G+e+fSLYoygi0DJxIjo/nVhoLheu48dx7tzZtAihEhaGYfp09AMHyqvqkuRDLoed4ztXYzmwkrTSTaRoxS3uzyeR/Khx6NNn0mvsNST26n/Z59RcLlwHDuDYuRMas3x1KSkYJk3y2sWt+joLJ3eupi57FQMrNhBH9fl90CWT3/s2BkSNIjiv8Py5zpAhGKdORQkN9Xh7ugsZbJYkSZKkK4/VauX06dP07duXYBnz6fEaGhrIzc2lX79+bZYgbo8eHWz+0Y9+xJo1a1i5ciXx8fFNt8fHx6PXX2Kl8kZ+DTZrGrVLl/Lp8ePcPGQIoTNnomu2D/6mlpfj2LoVNTdX3BAUhHHGDPRDhgRk8Mdut7NkyRJuv/32LteekWR/elKg9KUrNxfH+vVojfXfdWlpGGfMQBcX57c2Nae5XLiOHMGZmdmyjbNmNWUcBkpf9gSyLz2rO/eny+nk0PZV1O9dwuDKjURT03SfTTNyPGQMdX3mkTxhPr3Th3boHEAtLMS+bh1aRQUASnw8xunT0TfWDG+Np/vSbrdzeNsKHPs+YljVJkIVUUqjTjNzOPoakuJnElfeWF7DZMI4aRL6UaOaFhLszjralzLYLEmSJElXHnewuavBR6l78NTr3TPmcrbhn//8JwAZGRktbndflQlkiqJgmDqV3MOHMd10E7rGmpmBQhcbi3nBAlznzuHYtAmtvBzHV1/hOnwY45w5AVdaQ9M0Tp06RQ++tuJTsj89x999qVmtODZvFvVRASUiQgSZ09MD6sKRotdjGDEC/ZAhOPfswblnD2peHrb338cwYQKGCRP83pc9iexLz+pu/ampKqf2rqNi54ekl35NBlVN91UQwYmoGRiH3cDQqfMZGRbZ8ed3OHDu2IEzK0tkDgcHY5w6Ff3QoZcN4nq6L00mE6Nn3wKzb6GqqpKta94g5fh/6KeeYULVCtTKlWSFziQ0ag69rXYcmzfjPHIE09y56BJbXe6w2+hu41KSJEmSJEnqHnp0sLm7nzwHSkbhpeh79UL37W/jzMrCuXMnan4+tg8+wDhtGvqMjIAKVkmS1JIrLw/72rWiJrKiYBg9GsPkyShdqM3kbYrBIDILhwzBsXkzak4OzsxMXDk5aLNm+bt5ktStFZw5Se76N0k7+xkDtPN1i6sJ5UTMVQSPuY3Bk29gkrHzGcVqcTH2NWvQqkXpCv3QoRhnzEAJgEyZqKhopn37l2jqLzi4bRX2LX9nnHU7Y+s3Qf0mssyTiQqeQWIZ2JYswTB+PIaJE1HaMVtOkiRJkiRJkq4UPTrYLPmGotdjHDcO/cCBONavRz17FsemTbhOn8Y0bx5KWJi/myhJUjOaquLMzMS5axdoGkpUFMZ589CnpPi7ae2mi4zEfNNNOI8fx7FxI1pZGbZPPgG6/4VGSfIlu7We7PUfoj/wPiMa9pCiiPdPnWYmO2ImptG3MXz6QsabuxYM1jQN5759OLduFcedsDCMc+agD8CZZopOx8jpN8H0mzi+fzs1Xz3LmJpvGG3bgcu6kz1Bc0g1TSRm1y5xrnP11QFV6kySJEmSJCkQaRrk14FTBYMOUkPhSshPvOeeeygrK2PlypX+borPdP+Ccz2YwWBg/vz53Wblel1EBKaFCzHOng0GA+rZs1j/+19c7rrOftTd+jLQyf70HF/3pVZXh/2zz3BmZoKmoR8xAvMdd3SrQHNzhkGDCLrrLnT9+2PQNK4NCUH94gu0ujp/N61bk+9xzwrE/jx7PIvM135Iw3MDGZv5CKOsu9ErGkdNI9g96k9ojx1n0i+WMmbutzB1NdDc0IB9+XKcW7aI486AAZjvuqtTgWZf9+WgUVMY99hyTt32FbuCp6FXNCba1hFleZkDNbtwlBRhW7IE58GD3e5CVyCOS0mSJEmSeq7VZ2D6xzD7E/Hvl2e8v8177rkHRVEu+vnwww89up3c3FwURWH37t0efd7u1gY3eXYZwPR6PWPHjvV3MzpEURQMGRnoevXCvnYtWkkJ9hUrRE3ViRP9tqBOd+zLQCb703N82ZdqcTG2lStF2QyjEeNVV2EYMsQn2/YmJTQU0w03oD9yhFEbN0J+PtYPPsB07bWXXGhMapt8j3tWoPSnw2Fn/7qPMO19kwz7Pno33l5MLDmpC+gz5wcM6T/co9tUy8qwr1yJZrGAwSAWEx4xotNltvzVlwNHTIARq8jeugrz+t8y0HWCSY7l5FXtoipoIQM2bEDNz8c4Zw5KN1kEMlDGpSRJkiRJV4aVp0GngKqBXhH/v66v97c7b948/vOf/7S4LSoqyvsbvoLJzOYAZrfbefXVV7Hb7f5uSofpYmIw33Yb+owM0DScmZnYP/8crb7eL+3pzn3ZFrsLyhog1wLHK+FoBRyrhDMWKG0Ah+rFbffA/vQXX/Wl8/hxUWairg4lJgbzt751yUCzSwOLDQpqxZjKqRb/FtWBxS6mQAUSRVFQBwzgLZcLR3Q01Ndj/+wzHLt3d7tsw0Dgi3Fpd0GFFYrrxTgrrocqGzhcXtuk3/j7mFlRnMeOd56k/JmhjN/xUzLs+1A1hazgKeyd8S9inzrGlB/8hRQPB5pdOTnYPv4YzWJBiYzEfNttGEaO7NJ6Du3tS1WDOgdU28Q4s9jB5uz6sWvEtBvo/+udbMt4llKiSKOQkdbXOVixiurD+7F9+CFqaWnXNuIj/h6XkiRJkiRdOewu2JAnztFAfN9cf07c7m1ms5mkpKQWP0GN64WsWbOG6dOnExUVRWhoKNdeey1Hjhxp8fdPP/00I0aMYNmyZQwcOJCgoCAmT54MnM8m7tevHwATJkxoyp6+0OOPP05kZCR9+vTxaEmNjrTBV2RmcwDTNI3S0tJuGyhR9HpMs2fjTE7GsW4dal4eto8+wnTTTT6vbdjd+9Ktxg5na6CkMShzOZFmSA6FXmEQ7sFEq57Sn4HA232paRrOnTtF2QxA16cPpuuvbzXzrsEJeY3jq9x6/kSgNQYdxARBSqj4MQfAp4mmaZRVVGC69150O3bgOnwY57ZtaGVlGOfODeiFDwONN8alQ4XCOhFULmsAq7Ptx4YZITZYjK34EJH50J3565h5Mmsz1Rv+ysiq9UxWxJl8JRGcSF1M32t/wujeg72yXU3TcO7ahXPHDgB0aWniuOOBRQDb6ktVExdaS+rFvzX21o9hRh1EmSGucXyFmzpeK1Cn1zP15gepnHUb299/jEnlnzNR2U5FdTbHrLcwaGkdpmuuQd+/fxf21PvkZ7kkSZIkSb6yswgaLggs1zshsxim+7GiY1FREffeey+TJ09Gp9Pxm9/8hgULFnDs2DF0zWbmFxcX8+KLL/LWW2+RmJjImjVrAEhLS6OwsJC8vDwmTpzImjVrGDVq1EXbWb9+PVOnTmX37t388Y9/5P777+fcuXMeKWfW3jb4UgCEB6SezjB4MLq4OOxffIFWVYVt6VJM112HvvGqi3RpmiaCM8crRQCwuRCjCMoE6cV0FJcGNpf4kt3gFBld1TaR9RwfAgOjICH4yijCL4HmcuH4+mtcx44BYBgzBsO0aReVsylrgONVUHxBmWODTowvs14E+5yN46vWIRZ1KKkXP/vLIC0MBkSJCxz+phiNmObNw5mYiGPjRlzHj6NWVmK+6SaU8HB/N++KY7HByWo4VwuuC2ZcBBvA5B5fqhhf7jFW6xDZ9CY99IuA9EgIkmctl+Vyudj39YcE736N4Y6D4kYFjhoGUzPyHjKu/R4Tg0K9tn1NVXGsX4/r8GEA9BkZGGfMQNHrvbK9Buf5mRe2C77A6BQRXNbrxMUOh0v8W9ogfo5UQIQJ+keJY5i+g/P9omPjmfLQuxza+RXBax4lnbPE2N8hs3gq/ZfXETF9Fobx4/2aVSJJkiRJkhQI1p4V5/wKoHK+zMLXZ70fbF6zZg1hYWEtbjtw4ADp6encc889LW7/3e9+R0ZGBidPnmTQoEFNt5eXl/Puu+/SvzGZwH2fXq8nKSkJq1UEa2JjY0lKSrqoDampqfz6178G4JFHHuE///kPeXl5TdnIXdHeNrz33nv8/ve/x2Aw8O677zJx4sQub7st8mub5BO62FjMt9+OffVq1Lw87CtXYpw+Hf3o0fJL2CWUN8DBcqhsFmROCoXUMBE0vlTgxeoUQer8xunppY0/ccEwMk5kdkk9l2a3i/fbmTOgKBjnzMEwvOUU+SobZJeJoItbbLBYFTghRASaW3t7qpq4oOEeX1U2kXF/tgbSwmFYjLgQ4m+GkSNRYmKwr1qFVlqK9aOPMM+fjy4x0d9NuyLUOuBwuRgjbmEmkU2aEALRZnFB40I2J1TaoKixvIbNJUoEnagSF8wGRosAotSStb6WrJWvk3zkLcZr+QA4ND1ZkXMIm/UQQ8bO8PrnreZwYF+zBvX0aXHcmT0bw8iRXtmW1QlHLaKUlDuD2awXn5EJweIzLsQoAs5ursZjV6UVChs/Ey122FciAs9DY6B3eMu/aY/hk66mfuQOtrzzGFOLP2Ciso2CqhPkrC+jX0WFmFkhF+GTJEmSJOkKpWmw5ow4FwO4azC8L/KhWH0GfjfJuwlxM2fO5J///GeL29Ia1/bJycnhqaeeYseOHZSVlaGqIjumtra2xeNTUlKaAs2d0fxvo6OjAaioqPBIsLk9bDYbv//978nMzOTUqVM8+uijfPPNN17bnjzzDWCaomfugm9h7CFTv5WgIEwLFuDYtAlXdjaOzZtRKysxzp7t9YUDjUYjd911V7fpS7sLsstFphaIbKv0CJF9FdzOd22QAfpEiJ86B5yqgtMWkcW6IU881/CY9mdy2W02Ck4doCr/BA2lOcyKzOXAq9/B4KjF4KzDpNlQ0eFS9GiKAbsxAkdQNFpQLIboXoQkDyauz1ASkvv4baHIQOSNsak1NGBbvhytuBgMBrF4Xt++Tfc7VRFYOVUtPvgVBfqEi8zk9pRb0SkigznSDIOiReDmRJUIKubViH+HxUL/yI4Hbbqitb7Up6Zi/ta3xAJlZWXYPvnkov6QLtaVcenS4ESlCBC7g4ApYWI8xAZd/kTSbIAkgwgaZsSJ0hsnqsQ4O1Ypgouj48Vzdhfe/AyqKsnn6IqXGZz3EZMRHxoWQjiacgv9bnyUCanpHt9mazSrFfvy5ahFReK4c9116NM9v22DwcjcRXexqdCIO5E5NliMr+TQSx9z9IoIQkeZoV+k+KzNbaxJ3+AUQedTVTA2AaI7WPEjJCSU6Q++RvbW+cR+9RApSinO+n+wN7OAjMoqzAsXoAQHd3a3vaK7nRtJkiRJktQ9Ha44n+Bk0MEjY+DD4+J7Q3E9HKkUCUveEhISwoABA1q9b/78+SQlJfHOO++QkpLC6dOnufbaa5uCzm5dXVCwtXIZvixllpmZyZgxY4iOjmb8+PHk5eVRX19PSEiIV7Yng80B7KmVJ1h78BzPB8Vz3dBYfzfHIxS9HuNVV6GLjsaxZQuu7Gy0+npM113n1awfnU7X5sEl0JQ2wO7i8/VM+0SIoHBXauKGGiEjXgSYD5eL6eynqkTZhAlJrWc5lxadJTdzFdqZ7URVH6aP4zR9FUf7N2oD3BcDzwGNs7lrCOasaSA1MSMx9h5Pn1FXEZd65ZZU8fTY1GpqsC1bhlZVBUFBmBcsQNdsCk2FFXYVQ33jS5kaBsNjxRjprOggmJgkgoEHy0VGfnYZFNbCuMSuPXdHtNWXuogIzLfein3VKtSzZ7GvWIHxqqswjBjhm4Z1Q50dlxabqLtW07jeWHzjTIrOllfRKWKMpoSKoHN2ubh4trNI1KIfHQ9G71Rn8ChvfAYV5B4jf8UzZJStYnLjsbmQeM4OvoeRN/2EieHRHt3epWi1tdg++wytogLMZkzz56NP8fx8yDoH7CnWUR4q+jLKLI5f8Z0sD2XSiwtm/aPgdLUoOWWxw6Z8GBQFQ2I6fsFsxLQbqBg6iR1v3sfkug1MdH3OvqNn6F1XRfRtd6CLiOh4Q72kO50bSZIkSZLUfX11VpxTaRpMTBSzrccnQmaRuP3rs94NNrelvLycw4cP88orrzBr1iwA9u3b16nnMjWuieR0XmJhGi+7VBuKiopITEzkxRdfZMSIESQmJlJcXOy1zGoZbA5QdTYXO0+WUNNg58F/7+S+2YN58up0dL5ME/QSRVEwjBmDEhkppvnn5GD/7DNMN93kkcWDWmOz2XjppZd49NFHMZsDs36EqomsvaMV4v9hJhgTLw7EnhJqFMHltDrYVyqmuW86B2MSIC1U5VT2Dsp2fEBC8WbSXadpsYyjAjVaMEWGVCxByZytNRDfbwTG0GgMwREoxiDQVFBduBxWXHWVUF+G0lBOUF0+sbZzJKklhCsNDLcfgKIDUPQ+ZMIZXRqFsVMIGjKXIVNuJCjkyqmr68mxqdbUYP/kEzSLBSUsDNOiRehixKe2pom6uYfKxe8hRhGoS/TghczoIJiRIjIFD5aLGuMbzokTigTvXDBt4VJ9qZhMmObPF7VkjxzBsX49Wm0thkmTZCmfVnRmXJ6xQFapOJaZ9TAiTtTB9UT3KorIZE4MgaOVoob9ucYSLpOSICIwD+tNPPk+P35kP2Wrn2VC9VpSFBcocEw/iJqxDzD6mu+QbPTgarDt0PwC14XHHU8qrIM9xWC12Tiy7CVuve9RBieYPTJ7Qq+ImR1pYXCgTIytY43rJExI7Hit8JiYWCb94lO2fvS/TDj6PGOULArO5pP7dil97/yRzxdJbkt3ODeSJEmSJKn7W3NGfEfQAXN6idvm9oJdRSK7efUZeGi097Zvs9koKipqcVt4eDjR0dHExcXx7rvv0rt3b06ePMkf/vCHTm0jMTGRsLAwli1bxrBhwzCZTAR1ML51zz338O6773L69Gn6dmIm7qXaoGkaiqLwi1/8AhC1qb35PVgGmwNUqFnPZw9M4Fv/8zGnXHH8a8NRss9V8+Z3RhFi6gZpXO2gT0/HtGgR9hUrUAsKsH3yCeaFC1HCvDM32m63e+V5PcGpimzTosYF2vpEiOnjrdUz9YSkUJgbBHtK4MzpExxY80/UslUM0M7RPMfplD6dkrhJGHuPJ3nIZFL6DWWgTo/NZmPlc8/xxB1PdOgLqsNu5dTx/ZQf3wH5e4ipyqafM4c+ah59SvOgdAn135jZGz4ZdehChsy8hbDwKI/vf6DxxNhULRbsn34qAs0REZhuvrkpg86litfaXTs3NUxcyPBGRqiiiOnpCSHiSnWVDbYWiMDjgEjvL055qb5U9HqM8+ahhIfjzMzEmZmJVl+P8aqrZMC5Fe0dl6oG+0vFRQYQi5FOSOjabIy26HUikzU5VIyvWgdszIdxCWJcB7Kuvs+PZO+lYvWzTKr9mkGKCgocCBqHMuMxRky5zi/liS513PEUTYNDFaI0C4ha36rDTnqU58v0mA3igmxKLewtaSw7dU5c0Ijp4LVwRadj2h1PcGzvVMKX30eKUkJDxV858HY5I+94FH1jnUB/C+RzI0mSJEmSur/COpEsAmJhwFmNweZZveDPu8XvRyrE45K9tIb1119/TXJycovbnn32WZ544gk++ugjHnroIYYNG8bw4cN55plnWLRoUYe3odfree211/jNb37DCy+8gKqqHS6TUVdXR1BQUKdLdlyqDcnJyS0C7sXFxSQkJHRqO+0hg80BLNysZ4YxlxtmT+fVDcfYfqKQxa/beP++8cT5al66l+lTUzHfcgu2zz9HKy/HtnSpyIrqYj2c7qTBCTsKRVBOp4gs495eTuzVVJUT21YQtP0f3Fa3Db0iDkA2zcihsCm4htxIv4k30T+xF50vgX8xoymI/iMm0X/EpKbbqsuLyclchePEenpXbCNJKWNs7SbYtYmGzCfYEzkL86TvMWzyDej0PeNCi6epVVXYly1Dq6lBiYrCvHgxSrgYRDYn7CgS5TMURVzE6Bfh/aBvqBFmpkJWGZy1iLIaDU4YGev9bV+KoigYJ09GCQvDsWEDruxscDgwXn21rCXeCQ5VBH1L6sX/h8bA4Gjvv8YxQXBVL3GRrrRBtGFUPKRHene7/nD4wC6qvnyWSbXrxbFagUOhkwiZ92syxsz2W7taHHciIzHffHPTccdTLrxQNiAK+ofBFo9u5WKpYRBhEuVaauywOV8EnJM68QVo8NiZlPbaTNa/vs1oxz5G17/FjndKGX/H0xhkCQtJkiRJknq4r8+CAmiI0mcDGs/XB0VBXBCUWcX96/Lg7iGe3/4777zDO++80+b9c+bMITs7u8VtFwaJn376aZ5++unLbuvuu+/m7rvvbrUNzfXt27fVQPTmzZt58MEHu1Qfuq02TJw4kaysLKqqqsjJySEtLc1r9ZpBBpsDnqLAT2f2YmhKJI9+uIdjBRUs+Ps2PvzBJHp3NM0mQOni4zHfdhv2zz9Hq6rCtnQp5ptv9so03EBT54AtBaJ+rkkPk5PEQkfeorpc7PvyXaJ3/5Xhaq64UYGDplEc7fUtGHUHyXExTEoGo4/ibpGxiYy5/l64/l40VeV41hbKd31M76IvSaWYcZav4KuvyP86kTO9b2bQdT8mLrmPbxrXDahVVSKzsLZWBJpvvrlpdkCDUwRJ6hzi9ZyY5JtyFm56HYyNh0gTHCwTdcLtLrH4lr8rAhlGjACjEcfatbiOHUNzOkXteHlBo92sTthaKOo063Wi3IC3shFaYzbA1BRR9uB0tciutjpFwLsnJKofytqBZe2zTKrbhK4xyJwdNpXo655i+Ijpfm2bWlWF7ZNPoK7uouOOp9hdsL3w/IWysY0XYm02j26mTeEmmN1LXMgorhcX7cbEi5lHHRWfkETEL79ky+s/ZnrFJ0x2rWDPe0UMu/VFgkeM8nzjJUmSJEmSAsSXjcFmvQJz086fpysKzEmDT0+KjOcvz3gn2NxdHDp0iPr6ep588kmvPL/ZbOa3v/0tEyZMwGAw8O6773plO26K5svlD7sZi8VCZGQk1dXVRPhhQRdVVSkrKyMuLg6dTkfmGQs//PcuquqsJEaG8vGPJveYgDOAVlcnFhgqL4fgYMyLF6OLi/PIc1/Yl4Gg1i4CzQ1OkQU6NQXCvJSwrrpcZK15i9g9f6WPmgdAnRbEwfgbSJ77U/oMHUdJ45dplyqyBqeltF3Gwxf9qakqR/duwrLtLYaXf0WYIpavtWt69kfNI3ruwwzImOqVbftSV/pSq6nB5q7RHBMjMppDRbSv+YWMECNMSRaZev5ytkZMS9c0UXt3QqLnA86d6UvX6dPYV60Clwtd796YbrwRxdgzZo50xeX60uqEzQXiOGbWi/EV7aePI00TU/Pc9e4HRolSG4EUcO7I2Mzeu5W6r55lUsPm87eFzyDm+qdIGTbF2029LK2mBtvSpSKj+YLjjqfYXeL4VW0Tn0OTk0R5FvD957mqwb4ScQwDUeu+Xycz6DVNY/tHzzPhyHMYFRcntP4k3vR/REyY5rkGd0BH+9Lf58WSJEmSJPme1Wrl9OnT9OvXr8M1iGvsMPq/oi4zwOtXwXV9z9+/Khce3CB+NyiQdZf3YiJS+3Tl9W5OBpsvwd8n1ZqmYbfbMZlMTfVEjxbXc9cbOyivaSAhMoSlP5pM7xgvpsL6mGa1ioWGSkshKEgEnD2wkE5rfelPFruoY2t1ioUAZ6R0fAGi9jq6cw26r37DIOdxsW1COJx2F8MWPU5EbMsaPZVW0S6HKhYmnJosMhYv5Ov+bKi1cGjdfwjN/g9DHUeabj9kGoljwoOMmvstFF33zEjtbF9qdXUi0FxVJTILb7mlKeBz4YWMGakQHADzWIrqxLR0VYNeYTDOwwHnzvalKy8P+8qV4HCgS0nBtGABismPkfkAcKm+bJ4xH2yA6SniOOZvOY3ZzSBKeQyL9W97mmvP2Dyw6xus655jonXr+dsiZhN/w/8jecgEXzX1ki513PEUW2Og2WITFzKmpUBks6UB/PF5rmmQXQ4nq8T/uxJwBti9YRkDNz5IpFLPLm0kvW78O8kTR3uiqR3S0b7093mxJEmSJEm+15Xg4xen4Scbxe86BbLubJkAVW2DMR+I74cAr14FN/T1SLOlTvJUsDkwUjylVtntdp577rkWi7cMSQzhvz+YTGx4MCXV9dz6+g4Kqqx+bKVnKY0BZiUxEaxWbJ9+ilpc3OXnba0v/aXOcT7QHGGCmV4KNBedPU7WC/MZsvpbDHIep04LYnvvB9AezmbyfS9cFGgGkZk4tTGjuayhMdO5lctRvu7P4LAIxi/8CUOf2sGx+Z+xO3wuDk3PcPtBRm/9Mbl/GsueVW+julw+aY8ndaYvNatV1DmvqkIJD8fULLOw3nE+0BwWQIFmEPVOJyaJjNNztecznT2ls+NSn5aGedEiMJlQCwqwL1+OFgDHCn9qqy+tzQLNIY3jKxACzSDqNWc0ToY51izTORC01Z+apnFgx3p2P3sNGV/MZ6J1K6qmsD9yLkV3bSDj0c8DJ9BstYrZR1VVKBER3stozj8faJ6e2jLQDP75PFcUGBErakYDZJXCGUvnn2/8VYvJv2U52Vo6j9nv5dbPczm+aZdH2toRgXRuJEmSJElSz3O62fnShMSLZ9pGmsXtbjnVvmmX5H0y2NwNDU4I4b8/mCICzpZ6vv1GJlX1Dn83y2OUoCDMixahS0oCmw3bsmWozVbN7M6szvOB5nCT+CJt9nAg0OlwsP39PxL+5nRG136DS1PYGbOAhh/vZsr3/4fIqEun+8UEiSnxep1Y9GtPsWcDgl01eNxVjP/Fp1T+YDc7Ur5LjRZMPzWXcZkPc/aZUexe+U9cTqe/m+k1mt2ObflytLIyCAnBtHgxumaLAW51B5pNgRVodksOhYmJIniTVwOHAiQgqEtOxrx48fmA84oVV3zA+UJ2l6jR7A40T08RmfOBpH8UjGgMOB+pCNwTVk3T2L/9K/Y9O4+MNYsZb9uJS1PIir6Gku9uZNQjn5I0cKy/m9lEs9vPl7kKDcW0aJHHFwN0qqJGs8UuLsDOSPVv6Z8LuQPO/RszmveVitkanTUsYwKhP/kGQlLIdxn59peFHFyf6ZnGSpIkSZIkBYAHM2DjLeLnnatbf8w7V59/zIMZvm2f5D0y2NxNDU4I5u17JxMebOZsWQ13vrkbq737ZXW2RTGbMS1ahC4lBRq/5KolJf5uVpc4XLCtWaBmWorI3PKk09nbyP2fyUw58QKhio0jxuGcvu1LJj30H+KS0tr9PHHBokamokB+beAEBJtL6JXO5B/+H+rPD7A97QdYCKGvmsf43b8k/88Z7FvzLpqq+ruZHqU5ndhXrkQrKjpfZqZxpVqHKsZXbbPSBt4qzdJVKWFioS2AE5WBExDUJSaez3DOzxcBZ0fPuZDXFS5VzHRoXtog0ALNbgOjYEjj+rL7S6GwCwFBT9M0jX1b17D/2TmM+vJWxtp349R07Iu5nrJ7tjD65x+T1H+0v5vZguZwiGz/kpKLjjue4tJEiZ0Kq1jMdFqKuCAbaBQFRsZBWri4CJtZLMpPdVa/hHA+efgqhkXqqVD13PVVEbvX+z7DWZIkSZIkyRt0CvSNED9tJUEFG84/xt+LyEueI4PN3VhGSgh/v3sCQUYDh/Mr+P5/9uFSAygFtYsUkwnTggUtA85lZf5uVqe4NBGoqXYHapI9m3HqdNjZ9tav6PXxTQxwnsRCCLtG/o4hT25mwIhJnXrOhBAY21hp40QlnA6QgOCFImMSmHLfC/BwNjv6PEA1ofRW8xmz4yGO/3kyh7av9ncTPUJTVexr1qCeOwcmE+aFC9HFiix1VYPMIqiygbExEBhoGc0X6hMBQ5sFBAtq/dseN11SEuaFC88HnFeuvOIDzmpjUK28QZTYmebFxUw9ZUi0GGMAu7oYEPQETdMIdlVy7MWrGfPVtxht34tD07M39iYqv7+NMQ99SGK/Ef5tZCs0VcX+5ZeoBQXiuLNoEbqYGM9uQxMldUrqxYyaqSmBldF8IUWBMQliwUJXYzZ2bRcOEXHhQXzw8zmMjdZj0fR896sitq7b7bkGS5IkSZIkSZKPyQUCL8HfC6G0d+GWZQfK+eVHmThdKrePTeF/bxstvg31EJrdLhYNLC6G4GDMt9zS4S+7/lwgUNPEdNszFhGomZEKUebL/117FZw+guW/9zKkceG8PaEz6H3XK8Sn9PHI8x+pELVPFUWU10gMCbwFF5uzVFdw6ONnyMj7D6GKDYCs4EmE3/hH+ncy8O5N7elLTdNwbNqE68AB0OsxLVyIvlevpvuzSsXFAL1OLDYZ3fk6/j6laaLtuRbR9lmt1Gft2PN5blyqhYXYPvtMLBqYloZp/nwUQ4BH8D2oeV8eKlc4USUyDaaliJkP3YGqiUBgSb24yDe7l5hV4kuaqrLvm+WYtjzPCGc2AHZNz8H4m+iz8Cni0gb7tkEdoGkajg0bcGVng8GAadEi9CkpHt9Oa58xl2tXIHz+OFRRv7zaJrKwZ/USWdmdVWt18IP/W8f2chdmVF6dk8Tca7xbr1suEChJkiRJ0uV4asE4qXuQCwReATRNo7q6mstdD1icEctjN44mkQruzL6P7e//wUct9A2lMYtTiY+HhgZRw7mqqkPP0d6+9IZT1ecXEpqY5NlA894VrxH5zmyGOI5QowWzZ9xzjP3Fco8FmkFkCPZunDK8q0iUAfFnf15ORGQMU+5/kYYHdrMzdhFOTcfohp30+/hadv31TsqL8/zdxBba05fO3btFoFlRMF17bYtA86mq81nnExK7T6AZRHBpVPz5DMEdRWDrQjUgT45LXXIypoULwWhEzcvD/sUXaN1wAcrOcvfl6WqNE1XitnEJ3SfQDCI4PjEJIsxiXO0sEuPMFzRVZc+6pRz+8zTGbvweI5zZ2DUDu+NvxvLDTMb99N8BHWgGcGZmikCzomC65hqvBJrzas4v5Dgm/vKBZgiczx+jDqYmi3JFNXbY3cX1DcKCjLz983nMjtdjQ8eP1xex4es9nmtwKwKlLyVJkiRJkqSeRQabA5jD4eC1117D0Y4p3D+ckszjAwsYrTvFxBMvk7XxEx+00HfciwYqsbFQV4d92TJUS/uXgu9IX3pSUR0cbKz8MTKufV+k26Ohvo4df72bsXueIFSxcsg4nJp7NzFu/o9RdJ59WysKjE4QQUxHY0Cwweaf/uyIuOTeTPrZuxTevYm9YbPQKRoTKr/A9OoEdrz3NHabn+fVN7rc2HQeOYJz+3YAjDNnoh8woOm+4no40Di+hseKxfe6G50iFgwMMUK9Q1zQ6Gw1IE+/z/UpKZgWLACDAfXMGRxr1/a4OuBtcfflvkLRl0NioJdn14PzCaNO1J836kWZmX2l3l3wVHWp7P7qI44+M5lxm+9juPMwNs1IZvytvKD8gJH3v05c6oDLP5GfObOzce7cCYBx1qwWxx1PqbSK8hkg6mz3aWeirL8+z1sTZBDjS6eIz/sjXVzfIMhk4B8PXc3cBD12dDzwdQGbN2Z5pK2tCaS+lCRJkiRJknoOnwSb9+7d2+EfeeLbMToFbvruL9kQegN6RSN9w0/JPbbf383yKCU4WASco6LQamqwf/opWm2AFHpthcUuaoWCKHbvXsG+qwpOH+XcizOYXLkCVVPYlvYDBj++iZS+3suS0yswKUlMRbfYROmD7iJtYAZjH1vOkRuWclw/kHClgcknX6b4uTHsX/9hQGd0uc6cwfH11wAYxo3DMGpU030Wu6jTDNA7QgRruiuTHqYkiVIapQ2QXe7vFp2nT03FdOONoNPhOnECx/r1AT1mPKWu8SNYA1LDxAyH7irUKC5ogMik9caClKpLZfeX73PyzxMYv/WHDHUdw6oZ2Z30beof3Muo+1/FoXSPtHDX6dM4NmwAwDBhAoYMzy8L3uAUFy5VDZJCYVisxzfhM9FBooYzwLFKsahuV5iNel752Txmx+mwoeOHX+axfduhrjdUkiRJkiRJknzEJwUox48fj6Io7f6CrtPpOH78OOnp6V5uWc9iNuoY+cM3yf7bXEa4DlP54R1UPfQNUdFx/m6axyihoZhvvhnb0qVoFgu2zz/HfMstKAFWO8ihwo5CcKpi2nlGvGfKaB/c8DG9N/2cFOqoIpyCuX9j6sybu/7E7RBsEFPStxRAYZ1PNulRQydejTpuDpmfv0L6gRdJ0wpI++ZH7M98g+ibX6D3oNH+bmILanEx9lWrQNPQDx6MYerUpvucqgg0O1WIDYbRHhpf/hRhhvEJotTBqSqINkNagGTS6vv0wXTdddhXr8Z1+DCYTBhnzAi4euWe4lLPXyiLNImFQrv7riaEiNklB8vgYLmoDe6JkiAul4u9a98jatdfGK/mAFCvmTmUchsDFz3B+MQ0AGw2W9c35gNqYSH21avFcWfoUAyTJ3t8Gy5NvM+tTlHreHxi9195vHe4qN18sgr2lIgFDsO7sMhhkNHA6z+bxw/+spbNlTruW5HD20YDkyYEdukVSZIkSZKk1ljPnsVV13YQQR8aSlDv3j5skeRtPiujsXPnTk6fPn3Zn5ycHFl0vBmTqWPfVuIiQ9Df9RFFxNJHyyfn9TtxOZ1eap1/KGFhmG6+GUJD0crLsS1fjma3X/bvOtqXnaVpsL9UZAa6A7T6Ln6R1lSVbf/+HcM3/oBI6jhmGITt+xsY5qNAs1tcMGQ0XrvQGU1UBkYlinbT6fVMvPkhzI/sY0fSXdg1PaOsu0h6fw473ngYa71/MuUvHJtqdTW25cubFqczzpvXIrB5oEzUCDXrYVJi18dXoEgJg0GNGbT7SqH28m/ri3jrfa4fMADjvHkAuLKymkoM9EQHy8X40hlNTEgSC5v2BP0jxQUMTRPBdFsXPhpdLheZX7xF7jNjmbDzIQaqOdRjJjP1uzh/msWEH71CVGOg2c1Xn0GdpVZVYVuxApxOdH36YJwzxysXVA6XixIaRr1YELAzi+oFYl8OjxWfkS4VMou7Xh88yGzknz+/mqmRGvWaju9/epx9+3M809hmArEvJUmSJEnqOaxnz3Jg8WIO3X13mz8HFi/Gevasx7f9zjvvoCgKt99+e9NtW7ZsQVEURowY4fHteVtubi6KorB7925/N+WyFM0H84Gvuuoqli1bRlRUVLsef8MNN/Dmm2+SnJzs3YZdRndedXv9ls1M/WoxQYqD7SnfY8oP/+bvJnmcWlGBbelSsFrRpaVhWrAARa/3d7M4YxF1KBUFZqSIzNOusNkayHr1+0yqXgXAjuj5jP7hPwkK9lAB6A7SNLEQ0rlaEUyfkybKIHRHeScPUr70UUZbMwEoUBIpnfknRl11+2X+0ns0mw3bxx+jVVSgxMeLzP1mwYC8GtH/ANNSRMZmT6JqInu+vEFkO89OFeU1AoVz/34cmzYBYJwxA8OYMX5ukWcV1IqsU4CpKZ6rMx8onCpsOCcuZCSEiAXeOhJPdToc7F3zNgn7/kZfVSw2Wkswh9PuYMiiXxERm+SllnuXZrViW7IEraoKJSEB8803tzjueEpRHWwvFL9PTu6edeYvxeqE9XliQco+EWJWQFfV19u45+W1ZNboiNSpfHTPGIYM6nX5P/SC7nxeLEmSJElS51itVk6fPk2/fv06lRhad+QIh+6++7KPG/7ee4QOHdqZJrbpnXfe4cEHHyQ+Pp7Dhw8TGhrKQw89xGeffUZERATZ2dke3Z635ebm0q9fP3bt2sX48eO9so2uvt5uPvkKv2HDhnYHmgFWrVrl90BzIFBVlZMnT6J2YkGqWVNnsHrIMwBMKXiXrLX/8XTz/E4XE4N5wQIwGlHz8nB8+WWbi3d1pS87osYO+xsXbBsa0/VAc1VZESdfvJpJ1atwaQq7hvyKST/7t98CzSACMxlxKo6Sk9TbVfaWeHfBLW9KGzCSUY9/yd7Jf6OYWFK0YkZt+gF7n59PUd4pn7Sh+djUVBX76tVoFRUQGop5/vwWAZ9ax/l62YOje16gGcR0+gmJ5+uDuxdAbA9fvM8No0Y1lRZwbN6M81DPqaVa5zi/YFv/CJWaAu8fM33NoBP1m3UKlNTDiar2/Z3TYWfn56+T/+fRTNzzS/qqedQQQmbvH6A+fJCJ9718yUCzrz6DOkNzucRxp6oKJSzsouOOpzQ4Ybd7fEV2PtAcyH0ZZBBlQUBceD5b0/XnDAkx8+ZDc8kIcVGt6vjOu3s5c6a4609MYPelJEmSJElXFn2od7IQdDod1157LStXrkRVVZYvX86iRYua7l+zZg3Tp08nKiqK0NBQrr32Wo4cOdLiOZ5++mlGjBjBsmXLGDhwIEFBQUxuVm5O0zR+85vfkJaWRlBQEAMHDuTvf/+7x/bBndHcr18/ACZMmICiKAFd1jGA8sWkCzkcDt5///1OLZao18HVt/6IVZF3ANB/6y85d/Kgp5vod7qkpPOLd508iWPjxlZrg3elL9ur+dTZ+OCuL9h27sR+6l6ZxXD7QWq1YA5f9QYTvv1rFF0AvG1dDo589T64HBTWeWfBLV9RdDrGXvc9Qn+xlx2Jd+DUdIyt+4aIN6aw/b3f43B0opZDB7jHpt1ux7FxI+rZs2AwYF6wACUsrOlxLg12ues0B8GQGK82y6+CDTCuMWCTaxHZ3O3hi/c5NC6aNnas2Ob69bhO+ebChDepjTMWHKpY8GxghG/60h8izTAqXvx+uEJk0bfF4bCzc9nfKfzzKCbt+xV9tHNUE8rOvg+gPJLNxO+/QERU/GW36aux2VGapuHYtAk1Lw+MRkzz56N44US/aXy5RP8P78JSEoHal24JIeePz1mlYjHXrgoPD+Hdn17FQLOL1CCICPfMlcZA70tJkiRJknqe9D/+keHvvdfiJ2PZMq/WbL7tttv46KOP2Lx5M0OGDCEm5vyX6aKiIu699162bt3K7t27CQ8PZ8GCBRddjC8uLubFF1/krbfe4sCBA9x5551N93366ae88MILvPbaaxw9epR//OMfhDX7Lt9VaWlpFBYWkpkpZmSvWbOGwsJCCgsLPbYNT/PJAoGtKSsro6Gh4aLAYG9ZFNxjwoww7Ht/5cArB8hwHaL0v3dT/4vNhIT2rKmP+t69zy/elZ2NEhSEsdliar5ysFxkYpr1XV/w6HDmOlJWfZcoailQErDd/gEjh3lnmkRXDI2BozWQXQ4xQSJI1V2FhUcx+cevk5N9L/bPH2aI4zBTTr5EzrOfYrvuBYZOvNqr23cdOICWnQ2Kgum669DFtwxgHS6HKpuoczohqfsvqHU5iSEie/tYpQjYxARBqNHfrRIURcEwbRqa1Yrr8GHsa9ZgWrQIfWqqv5vWaUcqoMIqMn8nJIKuhyc69gmH0npRDmhX8cXlgGy2BvateJ20Q68xSRNZpFWEcyz9e4xY9AsmRfSMqz2urCxclzjueMqxSihrEBfCJ/agOvNtGRwtLmKUNojxNbtX1/c5Oiac9x+cQUiImTAPBZslSZJ8RdM0VFXFarWi1+vR6/XodDp0Ol1AZ+ZJkuR5wf36ebxcxuXMmTOH++67j7feeovbb7+ds83qQ99zzz0tHvu73/2OjIwMTp48yaBBg5puLy8v591336V///4ALe7Lzc0lKiqK6667DoPBQN++fT3afr1eT1JSElarWDQrNjaWpKSLZ1Vu2rSJBx54ALPZzKpVq0hJSfFoOzrC5ymSf/zjHwkLCyMhIYE+ffrQt2/fph93SrjkOX1jzFQv/i9lRJGu5nLw9fvaLDXRnekHDMB41VUAOHfvxrF3r0+3X1ALpxuze8cliqm0nXVg/Uf0++LbRFHLccMgjA+sp18ABpoB+jVOhVYbF9xy9oChlT5iEoOe2MKujN9TRRjpai5DV93Kzr99h+qKEq9t17FjBwCGadPQp6e3uK+oDk5Wid/HJYjM3yvBkBiRxe1URVakGkDlWhRFwThnDrr0dHC5sK9YgVpa6u9mdUpxPRyvFL+PTQicoL43KQqMbtzXBifsaywHZG2oZ8dH/0vFsyOYnP00qVoxFUSws//PMf3iIJO++wyhPSXQfPo0ji1bADBOn47eS+dgpQ1wtEL8Pjoewq6A9eh0irjobGosB3S43DPPm5AYLQPNkiR1O5qm4XK5sNlsWK1WGhoaqK2tpaamBovFQl1dHTabDYfDIcrKddf6fJIkBSy9Xs/111/Phx9+yOLFi1vcl5OTwx133EG/fv0IDw9vKo9RW1vb4nEpKSlNgeYLuZ9z4MCB/PCHP+S9997DbvfuDOnWfPDBB/z2t78lKyvLr4Fm8EOw+S9/+Qv//e9/aWhoQFXVFj8ul8vXzQloiqIQHx/f5au9U4b1ZfOE13BqOibVrGXbkhc91MLAYhgxAkNjRrNzyxachw833eepvmxN8zqnA6O7tqDWwZWvMGzTAwQrdg4GTyDt4a+JT0zzTEM9yN2fOp3C2MbgZ50DDnagvm4g0+n1TLj5YfjJbnZF3QDApIrlOP82nl3LX/foBRutvJxYvR5F09CPGHHRgnMt6pxG9bwFtS5Fp4iLNwadyLp1B0Tb4s33eavb0+lENmhKCtjt2JYvR63uXjVlrE7Y01j+tW8EpDbO9vJ1X/qDUQcTk0TgOa+8hg3//hOW/xnO5CPPkEwZ5USROfBRQn55iEnf+QMh4dGd3lag9adaWop9zRpoPO7oR4/2ynZsrvMLmvaOgN7hXX/OQOvLtgQZzi8QeLJK1AgPNN2lLyVJ6r40TcPpdDaV6zEYDBgMBvR6PYqioGkaNpuNuro6amtrsVgs1NTUUF9fj81mw+l0yuCzJEke8eijj/L2228THd3ynH7+/PmUlJTwzjvvsHfvXpYtWwZwURmNS61Dl56ezqlTp/jb3/5GeHg4P/vZz1i4cKHH96EtDQ0NjB49miVLlvDUU08xevRoCgoKfLb91vg8P+7ee+9l06ZNBAcHYzabW9ynKAozZszwdZMClslk4sEHH+zy8xh0MHvuDazO/xnzC/7K+CPPcWTPBIaOm931RgYYw7hxYLXi3LsXx7p1KEFB6NPTPdaXF7qwzumwzia8aRqHljzNyCN/AQV2hF/D2J++h+mC90iguLA/xyXAlgJRXzcptOcERKPik5nw8Acc2b6akK9+SR81j9i9v+LgoQ+JvPVv9B6Y0aXn1+rqYM0a7ouIQJeWhnHWrBZf+t0Z4w4XRJlheGxX96j7CTWK+rp7iuFopaiHGtNGuRZvvc8vRTEYMN10E7ZPPkErL8f++eeYb70VJSTwsw81TVzIsLkgwgQZzero+qMv/cForyJs00uMOfsOsYgLBSXEkDv0h2TM/xkTQzxTay2Q+lOrq8O+YgU4HK0edzy2HQ32lIgLGmFGGNWFOs3NBVJfXk5yqJgBdLpa9MXcC8q1+Ft36ktJkrofd6DZ6XQCtPisab6wlV6vb3q8Owva6XQ23a8oSlOAunkJDnmhTJKkjhg8eDCDBw9ucVt5eTmHDx/mlVdeYdasWQDs27evU88fEhLC/PnzmT9/PpMmTeJb3/oWVquVoCDP1Ro1NS7i7T6uugUHB5OVlcU999zDrbfeyk033eSxbXaWzzOb8/Pz+etf/8p3v/tdvve97130I53ncrnYu3evRzK+o4Ng0C1PsyNoOmbFSeSK+ykrCdxi4p3lrqWqHzoUNA37mjW48vM92pfNXVTntDPnPKrK0Xd/yvAjfwFgQ+wdTHj4o4ANNMPFYzM+5PyCiHsbgws9ydAp15P8q93s6PsTrJqRkbZ9JL43h21v/hJrQ+fS1TSHA9uKFThrajig16O/5hoUfcsoxLFKUfdT3zi+enqd07akhUGvsMbgaOPFndZ4631+OUpQEOaFC1EiItCqqrAtX47mh2lTHXW8StQt1utEHXB9szMCf/Wlr9RUlbPjnSexvziCeWf/SizV5JPAyvTfEPGrw0z81pMEeSjQDIHTn5rTiW3lSrTaWpSoKEzXX3/RccdTTlVDcZ34XJyYJD4nPSFQ+rK9RsRCuEl8Lu5tLNcSKLpbX0qS1H1cGGjWtWOBc0VR0Ol0GAwGjEZjU2AZxIKm7vIbFosFi8VCbW0tVqsVh8OBy+WSGdCSJHVYdHQ0cXFxvPvuu+Tk5LB27Vr+8Ic/dPh5/v3vf/PGG29w+PBhjh49ykcffcTAgQMvCjTfc889KIpCbm5up9qbmJhIWFgYy5Ytw2KxNNVwDkQ+DzZv2LCB7du3U1hYyOnTp1v85OTk+Lo5Ac3pdLJixYqLrlp01sAYHQ2L/0OekkwKpZx7826PPXcgURQF49y5opaq04l9xQrsRUUe7UsQU2K7XOdUdXHsX/cwJPc9AFanPsSsn7yGXu/zt2aHtDY2h8ZChBnsrsD7Qu0JJnMQk+/5MxXf+4aDQeMwKw6m5v2Tkv8dz8EtKzv0XJqmYV+7Fq2kBKfZzJqyMlyGlhNNrsQ6p21RFJHd7C7XcqCNci2ePmZ2hBIWhmnhQggORispwf7FF2gBHMApbxAXy0BkNEdcML782ZfeZKkoYeebj6H9ZSSTc18lmhrylBS2j/wTu+/MxjH1MU43BHt8u4HQn5qm4Vi7Fq24GIKCMC1YgOLBTIvmKq1i4ViAkXEQ6cFrp4HQlx1h0In6zYoChXViBlCg6G59KUlS99CZQHNr3NnPer2+KQBtMBjQ6XTiM83hoL6+/qL6z+4AtKz/LEnS5eh0Oj766CN27drFsGHDePLJJ3nmmWc6/DxRUVG88cYbTJ48mUmTJlFTU8Onn3560ePq6uoICgq6ZEmOS9Hr9bz22mssWbKE6OhogoM9/73FU3xeRmP69Om89NJLXHXVVa2mk3/3u9/1dZOuGDoFJvePYfXMd4nfeCOjbbvZ/M6TzLj/eX83zePctVTtn32GWlCA7YsvPPr8Vuf5OpTN65x2hOa0c/wfdzG4dC0uTWFV/99y03ce6bZTwvQKTEiADefEgmOnLZAe6e9WeV5K+jCSH/+avavfos+uP9Jby4ev7yIz81rS73qZuMTUyz6Hc9s21FOnQK/HdN118O67Le63OT1f57S7M+lF/eYt+XDWAkkhnXvfeZMuOhrzggXYPv0UNS8Px9q1GK+7LuDe03aXKM+iadArHPpcAeOrvCSfE589x8iCJUxCZACc0fWidMxDjL7uXtKMJvJqxPvuWKWovd9WuZbuyrljB66TJ0Gnw3TDDeg6eZJ7OQ4VMhvHV0oY9Ivwyma6FXcZpOwyOFgOccEi21mSJKmn8VSguS3Ny28036b7x263NwWYdTpdU6Z08/Ibnm6TJEmB65577uGee+656Pann36ap59+GoA5c+aQnZ3d4v4LL1Q1f3xrFixYwIIFCy7bns2bN/Pggw92OtgMcPfdd3P33Xd3+u99xedH2vLycoqKivjggw94++23W/y88847Ht+epmn85je/ISEhgbCwMO644w6qu9kCTp4UbIBJEyaxaoCYGjAt71/sWbfUz63yDnctVSUuDhoagMYauV3UvM5p+AV1Ttv9HA4rJ165hcGla7Frer4c9ly3DjS7RZjFlGEQiwVaAr+SQKcoOh1jb7wf48N7yIxbjKopTLR8ieG1iexY+jLqJTJanYcP49yzBwDj3Lnok5Nb3N+izqnJc3VOe4L4YBjUuJ7DvhKxeGKg0SUmYrrxRtDpcJ04gWPTpoDKqtE0MfOgwSlmY4yOFxmXPVXBmRNse+UHBL8yhskF/yYUK6d0/dg98S/0euoA4+f/CINRRP3SwluWa3F6bh1Qv3MePYpz1y4AjHPmoO/Vyyvb0TTIKoF6B4QYYUwPH18dMSBSHMNcqhhfauAcFiRJkjzC24HmtrjLb1yYAa0oCqqqYrVaqaura8p+di9AaLfb5QKEkiT5zKFDh6ivr+fJJ5/0d1N8wueZzRs2bGjzvvLyco9v79VXX+Xll1/m/fffJzU1le985zs88MADfPDBBx7flqcpikL//v09HoBMDYM+1zzIhve2c1XNctI3P0xe+kjS+g2+/B93M+5aqvaPPqJvbS321avF4l1dqIfsrnPqrkPZ0YoXqq2OnL8vYlBNJjbNyIbRL3LD4u5Vr/xSYzM9EorqRZmRPcUws1fPrTUcERXHxJ++w8m969F/8Qj9XLlMzn6aQ8c+Jmjx3+g/bHyLx7vOncOxbh0AhgkTMAwZgmq3t+jLk9UiM1ynwMREz9U57SmGxIixVWUTQflpyeeDWd46ZnaUvndvjFdfjWPtWlwHDqCEhGCcONGvbXI7bRFT+RVF1AE3tjG+AqUvOyv36F5KVv8PY6q+IkVxgQInDAOpmfQIo+fcQf82Dtyj4qHcer5cy9gEz7THn/3pKig4f9wZNw7DsGFe29aZGjhXe358eWMxvO46NhVFzM5Yd1Ycv45U+H/R1+7al5IkBR5/BZrbcuEChO6AcvN2Wq3WiwLV7t/lAoSSJHna8OHDsVi8W0/NGwm8naVoPr6U984777Saxr5q1Sruv/9+CgoKPLq9jIwMrr32Wp5/XpSK+Pzzz7nlllsoLi4mNvbSZ/kWi4XIyEiqq6uJiOhZ80AdKqw7VU/6BzMYpJ7kiH4wfR/bFNA1X7pCrarC9vHH0NCALjUV08KFKIaOX2upsMI3+SJ7a0yCKKHREc76as78fT796/dTp5nZMekV5t5wW4fbEeganLAuDxwukYnq7y/UvuC029j78bOMOP4qIYoNu6ZnV8rdjLn7GUJCw8UYXLIErFb0Awe2Wl6h0gqbGsfX6Hjo1wPLkHhCjR3W54nMwJFxMCDK3y1qnXP/fhybNgEim9QwYoRf21Nlg03nAr/fuuLo7nU0rH+RMfVbm247ZB4N0x5m2PSFKO348lvaIMq1AExKEqUguiu1ulocdxoa0PXvj+mGG7z25dliE2WUVE0c892zEKSW8mshs0j8PiNVlNToLnryebEkSZ3X3kCzqqrY7Xa/B6LdmpffcP+4PyPbKr8hA9DSlchqtXL69Gn69evXaincy6k7coRDzco+DH/vPUKHDvVkEyUP6urr7ebzzOZf/OIXAE0B54aGBh5++GHeeustfvnLX3p0WzabjUOHDrVYTXLmzJm4XC727dvHvHnzLnq8zWZr+r/7qkPz23U6HUajsWnRATf31dDmdaKApg+pC283Go3odLoW23PfrihK07Se7du3M2XKFEJCQpoWQmjObDajqmqL2xVFwWQy4XK5Wiz64r7d6XSiulyMitfzzex3SFx/HUNdx9j8jx8z4YHXW1wF9vQ+NWcymTy6T81XU7/wdXIajWxLTGT8uXME5edT+8UXGK+5pinw0J59srtgez44ndA70kjvMAWbrf37ZK0uo/DVG+lvO0qNFsyuqf9g7rULO71Pbv54nZxOJ7t27WLq1KktntvddpPiYkSEk90lcKQEYgwKyZGBvU/Q9bE36tYnKS/4Fic/foSM+u1MK3yX/OfXkD3xtwwrdaG3WnHEx6PMnNmiTdu3b2fMuIlsK9LjdEJyKPQOMwL+3ycIvNcp3GxmRIzKviIHBwohQgdRQSIz5ZtvvmHSpEkYGi8m+XOf9BkZWC0WnHv3Ylu/HpNeT/CQIX55ndAZ2JFnx+HQSAyBXkHgcrW9T6qqsnHjRqZMmdLUl4E69lSXiyPbVmDc+X+MsB8AQNUUskKnETz7EQaMmiHa3s6xF6GDAVEmTlRo7Mp3MLsXBBm6tk82m63p89xkMvnk/WSrqcH2+edo9fUosbGEz5vXVM/S06+Tho7MYiNOh4O4IJXewWCzdb9zI18cI+KMkBwEBfVGdhcrTE+wY2yWAe7Tc6PGvpw+fTpBQUGX3acLXxdJkqRAy2juiPbUf3bfdmH9Z3cGdGvPIUlSS/rQ0Ev+X+qZfB5sXrNmDddffz2apjFs2DDuvvtu9Ho9mzdvZvLkyR7dVnl5OaqqEhcXx69+9SvWrVvHrl270Ov1lJaWXvT4Z599lt///vcX3f7SSy81RfTHjBnDggULWL16Nfv27Wt6zKxZs5g9ezZLlizh1KlTTbfPnz+fsWPH8sYbb7TY5l133cWAAQN46aWXWnzx+/GPf0xkZCTPPfdc021bt27liSeeoLq6mtdee63pdpPJxJNPPklOTg7vv/9+0+3x8fE8+OCD7N+/nxUrVjTd3r9/f+6++262bNnCpsZsO4DaqB9xX9WLzKhaxt//bKRc38vr+wR4dZ/aep3IyGB6QQGfZmeTm5XV6X2a+qMf43C0f58e+fG9FP3fdfRznaZSC+Nv2t3En8pnDnR5n/z1OtntdhITE1myZEnT7W29TudS+vOT79/N1gDfJ4+NvV+u5pNXf8e0kn+TqhSTmvkTNqvj6ZO0kE12OzkvvND0+Ouvv55Nmzaxe/8h6qrKADgEJAbaPgXY66SW5XBoiding0BcXDz3338fmzdvZvPmzQGzTy+uX39+h5Ys4Zff+Q61YWE+f53Chs7m0Pol1Bae4hCw/jL7lJaWxtatW9m69XyGcKCNvRnTp7P0/37N2Oo1jNHlAWDX9GRFX8tudTilNS5YvRVWb+3w2Pvlr54gt6CavcteI8uD+7R161afjL2I8HD+5+WXz+9QVRVPANVlZV55nXoPGUPUuAUU7VlN9sl9bPTCPvn63Mjbr1PGwh9T64rkxef9/34CmDdv3mX3yWq1IkmS5NY80NxTgq6XCkC76z+XlZVhtVrp3bt3U+Zz85+e0A+S5ElBvXuTsWwZrro69KGhBPXu7e8mST7g8zIaAPv27ePaa6+lurqaBx54gOeee84r5Rvy8/Pp1asXmzdvZvv27Wzfvp1PPvkEo9HIf/7zH+64444Wj28tszktLY2SkpKm6YK+zPCz2Wy8/PLLPPLII4SHh3sl00XVYFuRDscXv+K60jep18zkLPyMgcPHddvs0tZeJ3dfPvbYYwQVFlK7ahWapmEYMwZjYxbkpfbpaImN7HJQgGkpkBDe/n2qLz9H3VuL6OU8S6kWyYlr/s248VMCJsuqM6+Tuz8ff/zxFhkMF+6TQxXT9q0uhT7RJkbHBu4+gefHXk11Bcff+jkT6tagVzQsWgj7Bz3E2EUPNWWLulwunn/+eYYsfhSD0cS0FIgOCtx9CqTXqbbBwaZzYFOhf6TCsFiN5557jkceeQRzY132QNgnTVWxr12LmpsrMv9vvhnXBSsQe/N1KqjXk1VuQHXamZKkERt8+X1yOBwX9WWgjL26WgtH175J7+Nv00sT9QjqNDNZCYvod9NjpPQZ5JGxV23TWH/agQoMj4H0qM7vU319fdPneXBwsFfHnsFgwLlpEw0HD4Jej3nRInTx8V57nfJrYF+ZDp3ByKR4B7FB3f/cCLx/jLA4jWwuUFCddsbGQ2q4uN1f50ahoaGX3SeLxUJCQoIsoyFJUqcCzYFWRqOzzp49S3V1NcOHD0dV1abjprsfmpffkPWfpZ7AU2UVpO6hW5XRqKioaPH/Pn368NFHH3HzzTczevRoGhoaaGhoACAmJsZj242Li0On01FWVtZUoqOyshKXy0V8fPxFjzebzU1fqi93u9FobHWbJpOpQ7e3tr0LbzebzU0fXq09XqfTtXq7+wPuQgaDoSnQBTAxBdZf879kfbyP0fa9hK24n/p+W4iOjvHaPrl5a5/cLnydDAYD+gEDCJ0zB8f69ZCVhT48HP2YMW3uU7UNjtWY0RtFndOkZnV0L7dPNSW5WN+8iV6uAgq1WIoXf8TU0RM8uk9ugfg6mYHJvUSd67waSAox0Cu8e+/ThS71OkXkFzEqaCqnlIHorKsY4DrJjBPPcfSlZTD/LwwZPZVSiwhA6I0mRiaZW4yvQNyn1vjrdYoINTO+F+wohNx6iA+2NT3PhX/j730y33gj9s8+Qy0owLFiBebbbkMX2fLF9sbrVGOHg5Xi9+EJJlKi2r9P7vZf2CZ/jb3SgjOc+OJlhuV/zFRqAagknMNpdzJs4S+YFpd42X3qyNiLClIYlWzmQBkcr4XUaDB3cp+axoHZ3PT6eGvsOffvR83OxqzTYbr+evS9ejXd5+nXqc4BhyygM4gazSmRPefcyM1br1O8WSx4eqzSzGELJEdBcOPm/XFu1J59aut1kSTpyuK+IOZyuTqV0dy8NnJ31Lyusztw7g44uy8Kui9iX7gAoaz/LEnSlcInlxXj4uKIj49v8TNv3jyqq6u57777iI+Pb3qMJ5nNZoYPH95iGvA333yDXq9nTGNwMZDpdDrGjBnj9au/4SbISDBw6rr3KSaWvlo+x/91Dy6Xevk/7iYu7EvDiBEYpkwBwLF5M86jR1v9O6cqFvJRNUgKhf4dWLCtKv8Eda9fQ7KrgHziqbj9M0ZfEGjurjoyNmODxRdqgKxSsXjglcB15gyOb74BoN+cRaQ/uZPdw56klmCGuI4xYNmNbHnlAXacsRDdfwwJoToGRvm3zd1Rcuj5hTr3l+sYNdr7x8zOUAwGTDfdhBIXB/X12Btr6XqTS4XMYvFvfHDHFmzz1edPe+Rk7yTz5W8T+Y+xTM1/myhqyVcSyRz6BEGPHWbaff9LdLNAsyelR0JCiPgM2F0Mrk7OBfNVf7pyc5uOO4apU9H37++9bWmwq1h8TsYEwVDP5QpcUiCNza4aEiNmsjhUMb58PdewJ/WlJEm+0dVAc09x4eRwd1+4g8pGoxGDwYBOp2vqs/r6empra6mpqcFisVBXV4fNZmuabeKHCeeS1CFyjF4ZPPU6+6SMRvN6cZcza9Ysj277lVde4Ve/+hX//e9/SUlJ4Tvf+Q6jR4/mgw8+uOzfXkmrbmsa7CiC/P0bWLj9FoyKi43pv2D2d3/r76Z5jaZpODZvxpWVBYqCaf589H37tnjMnhI4axGLQ81JA/PFiUOtKj9zGNc780nQyjhLEtY7P2fQoCEe34fuQtVEdnOlFeKCYXoK9ORzU7WiAtuSJWC3ox86FOO8eU0n4xVFZzj7/kOMrtkIQCFxbBryexbdchdBxh7cKV7kVGFDHtQ6ICUMJiYG7vjS6uqwffwxmsWCEh+P+ZZbUC6RWdwVWaVwuloct65KO5812R1oqkr2N5+ibP87I2zna8seNQ6jYcKDZMy5A30rGZveYHXC13ngcMHAaBgR65PNdphaVoZt6VJx3Bk2DOPcuV4NAmSXwYkqMOrE52NI6wnA0mXU2mH9OXFRaEQcAX3R8Uo6L5Yk6WJdDTTX1tZy+vRpQkNDiYiIICwsrFte7Dp37hwVFRVkZGR0+G+bL0Do/nH3ZfMFCJtnQEuSv7lcLo4fP05CQgKxsQF6Iix5THV1NQUFBQwYMKDNGX7t4Zeazb6kaRq/+c1v+Mc//kF9fT3z58/n9ddfJ+qCepmt8fdJtcPhYPXq1Vx//fVdepHby+aEdXmgbnyWm848h0PTc/Dq/zJ2+nVe37a3tdWXmqbhWLsW17FjYDBgWrQIfUoKAGdrYE+xeNz0VJEZ2B5Fp7IwvLeIOK2S06TC95bTr98AT++SX3VmbHanL9RdodXXY1uyBM1iQZeSgmnxYpRWpjdvX/sxvbf9mlRKANgdNJWkb/+VXn0H+brJPUKlFTaccXBu12puvOF6+scEbuRLrarC9vHH0NCALi0N04IFrY6RriiohZ2inDFTUyAxpGN/7+vPHzdrQx0HV/2ThENv0kcVi/65NIWs8FmEzf45g8fP8Vlbmmvenx35PHDzdn9qdXXiuFNTg65XL0wLF3p8TDVXVAfbC8Xvk5LERR5f8dfY9KbT1eLikE6B2b0g0kfVKjral/4+L5YkyX+6Gmiur69n586dhIWFoaoqNTU1qKpKWFgYERERTT/BwcEBny197tw5ysvLGTVqlEeer/kChM1LjLgzpd3Z0u5yHIHeP1LPVFhYSFVVFQkJCYSEhMhx2EOpqkpBQQFGo5HevXt36XXuRnlOnaMoCn/605/405/+5O+mdJiqqk2LKfqC2QBjE2D79CfYXprJlPr1pH79Ywr6biSlVx+ftMFb2upLRVEwzpuHZrWinjmDfcUKzLfeSl14LFmNi8kPiWl/YOHc0d2EfHgzMVRzSumN+fsr6ZXWvfuuNZ0Zm2EmGBkrvlAfLoeEYN99ofYVzeXC/sUXIms1IgLTjTe2GvCptUPZgNsoTL2OmE2/Zlrx+4y3bqP+7ens6Hsvo7/1G4JCfBi96QGig2BQpMrBU/s4UHItSeEQGqBxKF1UFOYFC7B9+ilqXh6OL7/EeN11KB7KXqlzwF5xDYOBUR0PNIPvP38Kz54gZ83/MbRgGROwAFCrBXMgcSF9b3iUcX0H+6QdbUkJgz4RcMYiLkLOSQNTB2K53uxPzenEtnIlWk0NSlQUphtu8GqgucF5fnylR/o20Ay+H5u+0DcCiupFEH9XMVzVC/Q+SGbriX0pSZLneSLQnJmZSVJSEn379m3K1m1oaMBisWCxWDh37hw1NTXo9foWweeIiIhLri3hD54OsjXPbIaW9Z/dizBardaL6j+7f5f1nyVfSEpKAqCkpMTPLZG8TafTdTnQDFdAsFnqmKRQSI9SyLnhbc58Oo0+6jkOv/NtYh9bhzmoExGLbkDR6zFdf71YvKuoCOuyz9gz7TZcxgjigmFwO+uc5h7cRtQntxFFLSd06YT/cAVJSb0u/4dXEH99ofYFTdNwrFuHWlgIJpPIVg2++CqFu46uU4W4yHAmfP8FXn7WxALTdobYDzH5zOsUPf8JBROeZMx193osAHkl6B8l/nU11tedkSoyBQORLjER0403Yl++HNfJk/DNNxhnzeryh7q7jq5DFQH4YQE8001TVQ5tX4V96+tk1G0lWRHrBBQRT07/7zD8pp8yNTpwdiAjDsoaRDB/fylMSPJ3i87PztGKiyEoSBx3vLhKuNo4vmwucbEwUEuKdDeKAmPjYZ1VLOp5qBwyPLuMiSRJUqd4MtA8aNAgHA4HIAKsISEhhISENAWxVFWltra2KQBdUlJCfX09QUFBLYLP4eHhrS6K6kvenBzu7uPm+9i87IZ7AcLmCxU2L7/hzn6WAWjJkxRFITk5mYSEhKb3sdQzmUwmj5Tw8UmwOSMjg02bNhEd3b6o3eTJk/nkk09ITU31csuk1gyPhdKGKHbN/oDo9dcyzHmY3f+4j/EP/TdwC6F2kdIYHLQtXUpZfgVRaz/Dds2tjO8b0q5g1cm9m0hYfgcR1HFUP4i4B1YSF++dBau6s4u+UFeIAE5P4NyzB9fRo6L+9w03oItpfbWsg+VQbRNZkROSQOeCBl0EfR9Zx66v3yNtz3MkaaUkZT7K0X1voL/hfxg4ZqaP96Z7cr9XDQpUWOF45fnFKQORvndvjNdcg+PLL3EdOIASEoJx4sQuPefhclFSxKiDCYmBGWyvr60ie/W/SDjyb0aoZ8WNChwyj8Ix9n5Gzr2DJEPgpaUbdDA+UdSfP1cLSTWQFu7fNjl37BAXK3Q6cdxpR4mwrjhaAeUN4iLhxMSec7EwEDTNLiuEU9WQGNq5WQmSJEme4slA8+DBl5+hpNPpmgLKbk6nsyn4XFVVxdmzZ7Hb7YSFhREeHt70+NDQUJ/VN/ZHELe1/m8egLbb7U23uWs9uwPQzTOgJamr3Bc0JOlyfBJszs7OZs+ePcS0EXxp7fE2m83LrQp8er2eWbNm+fzN7P5CvdGRwaqMv3Pb/vsYX7mKzA/+yMQ7u+eCge3pSyUoiNK5C6n4YCmmmirG7V5OUP+bgUtP3Tqycy1pq75LmNLAYcMwUn6ykqgAysbzhq6MzRZfqKvEl+nu/oXadfIkzm3bADDOmoW+d+9WH3euRtTmBPEeCzaAE9GXBqORCfN/QP2cb7Htwz8y+uy7DHEcRv1sAZkbr6Xv7f9LQmrPK8niSe5x2SdRT1Y5HK0UYyvae4meXWYYNAisVhwbN+LcsQMlOBjDyJGdeq7COjhZJX4fl9i1MiLe+Pw5e+IAhV/9naElK5hIPQB1mpnsuOtJnPczhg8d77FteUtMkJjtcrRClASKCWpfP3ujP51HjuDctQsA49y56Ht5dyZNcT0cqxS/j4kXpZH8wV/nRr6QFAr9IsXnxJ5imNu7/QsTd0ZP7ktJkrrG04FmRVE6lQ1sMBiIiYlpEUew2WxYLBaqq6spLi7m5MmTaJrWIvgcERFBUFCQ1wLDgbDs1aUC0KqqYrVaWzxO1n+WJMmXfLJAoLuOUHs3pSgKJ06cID093cstu7QrfSGU45ViKqe6/o/cUvACLk0he9Y/GTXndn83zStq7LDhHOiqKhm9ZSmxXH7xrv0bP2XghgcIUWxkmzLo89PlhEe0s+7GFS6rVHyhNutF/dOgblrURy0uxvbJJ+B0os/IwDR7dquPq20cX04VBkWLGQSXUnzuFHlLfsV4y1cA1GlBHOjzXUbf/v8IDov08F70LFrjVP/8WhEInJMmLqIFMseOHTgzM0Vm/PXXox/QsUVF6xywIU+UzxgQBSMDZMaAzdbAgXUfYt7/bzJse5tuz1NSODfgLobd8ACR0QHS2HZSNdicL7LnY4L8U67FlZ+PfdkyUFUM48djnDrVq9trcIrxZXOJckhjEry6uSuaU4WN58Q5SXKoWIAxUGIBV/p5sSRdKTRNw2KxcPLkSYYPH97hgGRdXR27du1qEWh2P687A9eTQU5N06ivr2/KgLZYLNTW1mIwGC6q/+yJhWULCwspKipizJgxHmi99124ACGcD0BfWH5D1n+WJMlTfPL1+/Tp0+Tk5HD69Ol2/eTk5NCnj8zgs9vtvPfee00fyr42IArigkGZ/RQbwm5Cr2ikb3qIU4d3+6U9XXG5vnTX0XWpEJkYTdLtC8BoRM3Lw/7ll2iqetHf7Fn9NkM33E+IYuNg0Hj6/3zVFRNo9sTYHBkLEWYRvNhVLAI43Y1aXY1t+XJwOtH16YNxZuvlLprXaY4NgqHNJnm01ZeJvfoz/tGlHJ+/jOOGwYQqVqac/Sf1L4wk86PncNit3ty1bsndlw6HndHxInO8ziEubARAAsolGSZNQj9iBGga9i+/xHXuXLv/1ht1mrv6Hj938iDb//ETap8dzITMh8mw7UXVFLKCJ5M1601S/182U+76f90u0AwisDwuUVzAqLDC4YrL/40nP8/VqirsX3wBqopuwAAMU6Z0+Tkvub3GGug2lzhm+7v0kb/PjbzNPbtMUcRshVyL97bV0/tSkqSOc2c022w2ioqKPBZo9iZFUQgNDSU5OZnBgwczYcIEZs6cSUZGBjExMdTX13P8+HE2b97M9u3bOXToEHl5eVRXV+NyuTq1zUDIbG4v98KCBoMBo9HYFGAGcDgcNDQ0tKiVXVtbi9Vqbcps7077KklS4PBJLqEMHHeOpmmcOnXKbwd4nSK+8KzL01F+w5tkL5vLCEc2liV3UvbA18QltV4qIBBdqi81DfaXgcUmsmzHJ4LBkIjSuHiXevIkjo0bMV51VdMJ065P/sLYA0+jVzT2hs1ixM+WYDIH8Fx9D/PE2HTX/NxwTiy6dbQisBczu5BmtWL//HNoaECJj8d0/fVtLuZ3oOyCOs3Nzrsv15eDxs1BG7OdPWveJnHX8/TSiog98iz5R9+icNxjjL3+PnRyCjTQsi/d7+UtBZBXI4L8/QI4IVxRFIyzZ6M1NKCeOoV95UrMt9yCLv7yq4QduqBOs94D3+s68x63WxvIXvc+5gP/ZrhtP+6iDqVEc6rXYvpe/SNG9xnS9cYFgDCjKAeUWQQnKsX4Sg5t+/Ge+jzXrFbsy5eD1YqSmIjp6qu9/kX+SIU4Rut1MCkA6jT7+9zIF6LMMCxGvLcPlImLSFFmz2/nSuhLSZLar3npDL1e3+Fjgz8CzW3R6/VERkYSGXn+5M/hcDQFVCsqKsjNzcXpdBIaGkpkZGRTGY7Q0NAend3bkQUIm2dAX1h+oyf3kSRJXddNJ65LvhJsEMGLbQVBHL1+KdErZpOqFXHqX4sIeXgdIeHdP5M31wJnGjOHxjXW0YULFu/KzhaLd02ezM73fsekk38BBTJj5jPuwXfQG+RbqTPCTaL25+5iUQs0Nrh71G/WXC7sK1eiVVWhhIVhnj8fxdR6AdNcy/nMtPHNxldHKDo94264H/vc77Djs78y4MgrpGrFpO7+JSf3vUbt9KcYNevmNoPdV6q4YN8EbDxF0ekwXXst9s8/R83Px/b555hvvfWSi76drRG1z6HrdZo7K+fIHko2/oshxSsZSw0AqqZwMGQC6pjvMfKq25ls9FOBXy9KDYP0SMhprK97VZp3+19zOlsed266CcUD04EvpaBWlNQC/9ZpvhINjIJyKxTVwc4iuKqXuGApSZLkDRfWaNbpdKitzOxsSyAFmttiNBqJjY0lNlZkt2iahtVqpaamBovFQlFREcePH0dRlIvqP5vN5qZ96mz96UDWngUIbTYbeXl5xMXFER4e3qIEh3tRQkmSJDcZIZMuKzEEhsTAUZLZNmcpc9fdQH/XKbJfuZlBj6zu1hm95Q0iqxlEVu2Fgc7mi3c5duxkz45/MblqKQDbk7/D5B/8TQb4uigtXGTN5VpE0HlOWucCsr6iaRqOr75CLSgAkwnTwoUoYWGtPrbCCvtLxe9DY7oeSDeZzUz+1uPU1/6QHUufY0TuOwxw5cCm+zi27QUc0x9n+IxFckw20zxgk9kYsDEGcMBGMRgw3XQTtk8/RSstxb5sGaZbb0UXHn7RYyutsK9E/D4o+tKZtZ5WWVbEsa/fJubEJwxyncC9wkIxsZzqtZh+V/+IUX0G+a5BfjIiDipt4rXYVSzqN3sis/xCmqbhWLv2/HFnwQKUUO++4BYb7G4cXwOixLFa8h1FgXEJYvZPvQP2lgRW/WZJknqO1hYD1Ol0TYHGywWOOxJoDqRAraIoBAcHExwcTEKCWIxAVdUW9Z9zc3Opq6vDaDQ2BZ6dTmfA7IM3tRaALi0tJSwsjJCQEJxOZ4sAvHvxQXfwWS5AKElXNp8sENhd+XshFJfLxf79+xk1apTfVwpXNdhWCKX1oOVs5NqttxOq2NgdPo+xDy8J+Gn8rfVl8wWPUsNEBndbn4d1W7dw4Ks/M4WtAGzr9zOmfPePV+wHqKfHpkuFTfmi1ERMEEz3UsDGExzbtuHcvVss5LZoEfq0tFYf1+AUizxZnZASChPbCBJ0pS+rywo5tvRpMgo/IUhxAHDMMBjr1F+SMfuWKy7o3FZf2l3nAzaXei0CiVZXh+2TT0QWa1QU5ltuaRFctDnFPjU4ITEUJid5dpG61vrSabdx6JulqPv+y/Da7ZgUUefQoek5HDoJbez3GD7rZow9MIv5Upovztg/qvWaxl09Zjo2b8a5bx/odJgWLmzzuOMpdpc4ftU5ID4Ypqb4fhHEtgTSuZEvVFrhm3xxHjYiTlxA85SO9qW/z4slSfI8d6DZ6XS2WBzOZrOxYcMGrrnmmktmrHYk0OzeVnsC2IHE5XI1ZT9bLBYqKytxOByEhIS0yH4OCwvr8dm9O3fupH///sTFiZMddyip+eKD7tfXHXBuXn5DLkAoSVcOGWy+BHlS3ZLNCesbg2emw0u5Zs8PMSoudsTfyqQf/6tbBbZcGmzOF1/iIkwwq5dYlKc1luoKzrx6CyNte3FpCpmm25l491PoZS1yj6q1i+CZU4U+EWLKdqCdizizs3GsXw+A8eqrMQwd2urjXBpsyReZzeGN48voxbdHacEZTn72DKOLlxGsiIWejusHUTv5UUbP+RY6fxdZDQCVVnFBQ9NElvmQmMv/jb9pNTUi4GyxoMTGioBzUBCqJmpRlzeI2sGzvDi9XlNVTh7YSuW2fzOwZA3RnF+t7KS+P+UDbmbQ3HuJTkj1TgO6icI62FEofh+XCL09mAXs3LcPx+bNABivvRbD4MGee/JWqJrYl+J6CDHC7F5iPQPJf3KqxSwZRYFpKeICgD/I82JJ6lnaCjSDqG+8bt065s2bh6GNcoF1dXVkZmY2Lcx3uSBidw02X6ikpITc3FzS09ObAtAWiwWXy0VYWFiLAHRISEi33tcLXRhsbk3z8hsXZse7Fyp0l9+Q9Z8lqeeSEYgAZrfbefXVVwNmlXCzQUzh1ClgH3Yrm0Y8A8Dk0qXs+tdP0DpQ18vXmvelpsHe4vMLak1ObjvQXHzuFOV/vYqRtr3Ua2YOpD7G6PAR2FetQi0q8u1OBBBvjM0wk8g4BVFDO6faY0/tEa7cXBwbNgBgmDixzUCzponSBhXu8ZV06UCzJ/oyPqUPUx78J7U/3sOOpDtp0EwMch1n7NYHOPPMGPZ8/ioOu7XTz99dXKovo4NgdONae0cqRC3aQKeEh2NavBhCQ9HKy7F99hmq1cb+UhFoNuhgUrJ3As0nD+1i6R/v5MwfRzLws5uYWLKEaCyUEcX2xDs4detaBvxmL5Pu+H9XfKAZRAmTwY1LGLjf/8119n3uOnkSx5YtABimTfN6oBlEjfPievFZPykp8ALNgXZu5Av9IkQZE02DXUViRoMnXIl9KUmScKlAM5xfRK6tvLSOBpp7GkVRiIuLIz09ndGjRzNjxgwmTZpEWloaOp2OgoICdu3axebNm9m3bx+nTp2itLQUm83m76Z32eVe6wuzmo1GY1NWs7v+c319fVO2eE1NDfX19dhsNpxOZ4dqhUuSFLgCuDKqpGkapaWlAVUTKiYIxiSIxZBqxvyYdfY65p74IxML/8uOfylM+sHfAzLDuXlfHq6Ac7UiQ2hiUtsLOp08sI3IT++iHxWUEUXV4vcYPXIq9uXLUfPyxOJdixeja6zxdSXx1thMDBHThLPL4GC5yApOCIAFA9XCQuyrVoGmoR86FMOkSW0+9mgl5NWI8TUh6fILanmyL+OTehP/wGtUlvw/Dn72Z4bnL6Wfmku/fU9Ssu95cvp/h6Hzf05kVGyXtxWILteXfSNEHdpT1aIW7SwjRAbwgoEAushIzIsXiwznkhIKliznzISFYDQxPlHMzPCUojPHyN30HvFnVjLAlcOAxtttmpHs8Gkoo+9k5KzFTLnCymS019AYqLaL+uA7CsWCge768515n7sKCrB/+aU47mRkYBg71kstPy+nGk5Wid/HJgTmgpqBeG7kbYoiLpZZ7GDSea6kyZXYl5IkXT7QDDSVg2gt8CcDzRfvr6IohISEEBISQlKSyJ5RVZW6urqmzOecnBzq6uowm80tsp/di+11B539vLjUAoSqqmK1Wls8zp313LwEx5U2ziSpu+seRzUpoPQOF194TlRC7eTH+EZxMfP4n5lc+D5b/6lj6g8Dd9G83Go43jgLfEx824HM3Sv/xfBdvyZYsZOrS8P03U8Y0FdklJluvBH755+jFhZiW7YM8803o4uP99Ee9HwDIkXt5rwasaDbzF6eDah1lFpejm35cnA60fXpg3HOnDZPdnItcLRC/D46vusLAnZWdEIqE3/4CtUVT7Nt+V8YlPseCVSQcOqv1L78T7YlLaLvdQ+T0m+IfxroRyPiwOIQ9ee3F4oSAUEB/kmoi4nBvGgRZR9+iuVsIb2sXxB983ySQ7ve8IK8U5zd/AExp1cwyHGUxskFODQ9u7Qh6MfcwfC5dzEuohvUHfEzRYHxibDpHNTYRcB5RmrbM2cuRa2owL5iBbhc6NLTMc6c6fUvWYV15xc0HRYrFwQMNAYdTEsWC5wGSv1sSZK6n/YEmqHtzOYrPdDs1p6gq06nIzw8nPDwcFJTxSwwp9PZov7zuXPnsNlshIaGEh4eTmRkJBEREYSGhvb4+s/NA9B6vb5FzWen04nD4WjxOIPBIOs/S1I3EuBfsaVANSxGfJkuqoPaKb9iGypTjz/HtKL/sOvVGsY+8Cb6ALxCm10OOqOo19qnlXKDLqeTXW8+xOTC90GBg0Hj6f2jj4iMPl+XSjGZMC1ciP2zz1CLis4HnC9Ru0pqP0URFwJqHaLUybYCUZM22A/DSa2pwfbZZ2CzoUtKwnT99ShtLKJUXA9ZjYGawdEii9bfImPimXrPM1gbnmLX6n+RkP0v+qh5TC3+APWdDzkQMhFt4v2MmHFzQL5fvUGnwMTGgGCtQyx8OiPVuzW1PaE8LJ59ExeSumEZSVV5xG9fjXbDDW2Ox0s5ezyL/O0fE5O3lsHO46Q03q5qCofMo6gbtIC+U27jqzfe5okbfojZHIDprQHKqIMpyaL+fJUNMotFKZ2OUGtqsDc/7lx7rdcv4FZaYVex+L1PBAyK8urmpE4yXxmHaUmSvKS9gWY4H+BrntnsqUBzd6/Z3JW2GwwGoqOjiY6ObrrNZrM1BZ9LSko4deoUqqpeVP85ODg4IPrNW21wP6+7DAe0XIDQ4XA0lX26sFSHO/gsA9CSFDjkAoGX4O+FUFRVJScnh/T09IC8sulQxSJoVTYRCIza+TwTDz+DTtHYEzqT4T/9iKDgAKiBABTVqny1L4fQxHT6ROkY28ricxUlBeS/dTcjrXsA2JHyXSZ8/+U2g3CazYbts8/QioshOBjzLbegi7kysv98MTZtLvimMSAYYRIBQW8tgtYaraEB29KlaJWVKDExmG+9FSUoqNXHljWIoKVLFdmA4xLav7ihL9/nmuoie9MnaDv/QYZ1d9PtBUoiuX2/xaDrfkxcYsolniGwdaQva+3wTb4YZ/HBMCUF9AF6blppFQsCOlXoU3uOIVuXo7ic6NLTL3kBxE1TVXIObKFs1yckF35Nb/Vci/uPGodR2W8+6bPuJDG1LxD4nz+BrrxBvGaqBr0jYHSsyunTl+9Prb5eHHeqqlCio8VxJ9i7q8FZbPBNAThcYrbPlOTAzpyVY9NzOtqX/j4vliSp8zoSaHZbu3YtU6dOJSwszCOBZncbVFXt1sfvsrIyTp06xaRLlNXrCk3TaGhoaLH4YE1NDXq9vkXwOSIiApPJt9M/d+zYwaBBg4jx43feCxcfdGsrA1ouQChJ/hFQweaGhgaCvfylqiPkSfXl2ZwiYFPrEPV1ww+8yZhdv8SkuDhkHEmvH31MZFyyX9tY3gBbGwOBSaGiTvOFQaVDW78g/qufkkAF9ZqZwxOfZfyN9132uTWrFduyZWilpRASIjKcr5CAsy/UOUQGqs0FscEw9RKLOXqSZreL17W4GCUsDPNtt6GEtz6nvKIxEOhSRaBmcnLgBi2bO3P8AAVfv8rwkuVEUAeAXdNzKHwq+jF3MWzGzRhMPTujtdIKmxtfu9QwUQIh0IJsVTZxUc+hivfAtGQg7wz2lSubSiy0FnCuq6nm2I4vsB35kv4V35BARdN9dk3P0eAxWAdcT/9ptxOb3NvHe3VlKKyDnUViUbeB0TDiMqXSNZsN26efopWWooSHi0BzG8cdT6mxw+bGiy7RQTAtJfCz/CX/kefFktQ9dSbQDPD1118zadIkdDqdxzKae0Kwuby8nJMnT3ot2NwaVVWpra1tEYCur68nKCjoovrP+k7Memuv7du3M3jwYL8Gm1vTPPjszsZ3B5l1Ol1T9rP7RwafJcn7fHaUX7x4MUVFRW3ev2LFCoYNG+ar5nQLNpuNZ599NqBXrTUbYGqKqHlaY4f6UfdxdN7b1BHEcMdB6l+ZSU72Tr+1r8IqMk7tNhuHljzLqChbi0Cg02FnxxuPMHTtXSRQwVldKiW3r2hXoBlACQrCvHgxSlwc1NdjW7YMtarKOzsTQHw1NkONIvhh0ImLBtsLRXanN2lOJ/YvvhAZ60FBmBYtajPgU2WDrY3ByvhgMV2+o4Fmf73P+wzKYMqDr2N6/Bi7Rv2Jk4YBmBQXY2o3k7H5ASx/HsCOV3/Iif3bu83iUR3ty+gg8ZopCuTXwt4SkYkaKCyN48uhisVZpySDXgf6Pn0w3XQT6PWoOTnYV61CczrJP5HFzvf/QPazszG+kM7YrT9mSsVnJFBBnWZmd+gsdo37XxoeOUHGE+uYeOtjbQaau8PnT6BLDhW12wGOlth45s/PYrW23p+aw4F9xQpx4TI4+JLHHU+pc4jxZXNBhFlczOsOgWY5Nj1H9qUk9XydDTSDCNbV19fLGs2t8PW5sU6nIyIigl69ejFs2DAmT57MzJkzGTJkCGFhYVRVVZGdnc2mTZvIzMzkyJEj5OfnU1NT0+oijz1N87IaRqMRo9HYFFR2uVxYrVbq6+upqamhurqa2tpaGhoasNvtuFyubvNdR5K6E59Vf6usrGTo0KE8//zz3H///U23nz17lp/97GesXbuWRx991FfN6TbcdYkCWahRZNttKRDBN7XvQsy3pBD66d300oqo/3g+mbnPMPGmH/i0XSX1sKNIBAJjg8DlsKNv9kU653Amjk9/wmTncVAgM+oGRtz/OiFhkR3ajjvgbPv0U7TycmyffIJ58eIen+Hsq7EZaRYB560F58tVeCvDWXO5sK9ejZqXB0Yj5gUL2nwdy5oFv2ODGjOaO9kmf77Pg0LCmbD4Z7D4Z5w8uIPSzW8zqGQ1sVQzueQjWPYRpz/vTUnadSRPvYPeg8f6ra3t0dG+TAiBCYmiXm1eDWiIMij+znCudF8oc0FUK4FAfZ8+1M+cyskv/oO65yv67nmEVEpJdT9AgUIlgbzY6QQPv56Bk29kfHBoh9rQHT5/Al3fCHGxYH+huLh5tBJGJbUss6O5XNhXrUItKACTCfOiReia1XL0BotdHFOtTjEraXqyb8sUdZUcm54j+1KSeq6uBJrdDh06RGpqKoMGDZKB5kaB0g8Gg4GYmJimLGNN01rUfy4uLubkyZNomkZ4eHiLDOigoKBO70eg7P/lNM9shovrPzscjqb64bL+syR5ns+CzRs3buS9997j8ccf5/333+fVV19l+fLl/PGPf+S6667jyJEj9O3b11fNkTwswgzTU8V0b4sN8iInMPyBbzj49rcZad3LxN2Psefk1wz+/muERXg/CFtQK4JHqiYyTsfEwDeN99mtDex9/zeMPfsWJsVFDcEcG/8HJt70w05vTwkOFgHnZcvOB5wXLUIXH++ZHbrCxQSdDziXN4h/p3g4OKKpKvYvv0Q9fRoMBkzz56NLan1lr8I6yCwS4ys2WLTFF+U9vG3AyMkMGDkZh93G/s3LcO37LyNqttJPPUu/M/+EM//kjC6NgpRriZ98O/2HTfD6wmW+kBom/t1VDOdqxOs6PqHzFw+6qvmFsqjGiy1GPTTUVHFy91fUHdtAXOkO0p05TFLOZ2LYNQNHgzKo730VKRMWkDYgg+Qe8Pp0dwOjwGmDQ8DJKjAYYXisCDhrqorjq69Qz5wRx50FC7z+uVFpFaWlHK7GQHOKXHhOkiSpp+lqoLmurg6Hw0FycrLHAs09qXZuIGbCKopCUFAQQUFBJCQkAKKd9fX1TQHos2fPUltbi8FguKj+s9FovOw2AnG/28s99pqXGWlefsO9AKF7nLrrP19YfqOnjGFJ8jaffr24++67WbhwIffeey8jRowgIiKClStXMnv2bF82Q/IS9yJuWwpESY19rkQm/nQtOz58nAl5bzOuag0FL08id95LjJg23ytt0DQ4UQWHysX/U0JhfBI47eLOQ998Ssz2PzFZPQcKZIVMpdddrzI+tV+Xt6001my2ffYZWmkptmXLMC9ciC4xscvPLYmA8/QUMb4qrLDxnCjhEnb586LLagr4nDwJej2mG29E36vXxY/TIKcaDpaL35NCYWKi/4KS3mI0mRk199sw99tYqkrZt+EjTMdWMLxhN33UPPqcewOWvkGBksi52GkEDbuOgZNuIDjUu9P+vSk1DBQgs1hcrNri9PwFjfY4WwP7Gst5hNkKCT+9id2rthFTmskAxzFGKs2mQipwVteLovAxKLZE+ppTGJw+ANONN6K0sbCp5B/pUed/P1EFDU4YE6eirv8a1/HjoCiYbrgBfYp3F+gsrBMXVVyqKCMztZtlNEuSJEmXp2laU3mAzgaaMzMzMRgMpKamyuDaBbpTfyiKQmhoKKGhoSQni3WUXC5Xi/rPRUVFTWtnNQ8+h4WFebX+cyBoLXjcPABtt9tbZETX19cTFxfXFHx2Z0BLknQxny4QWF9fz5/+9Cdefvll5s+fz6ZNmxg8eDD/+Mc/GDp0qK+a0W7+XghFVVXKysqIi4vrVgexeocoL2CxiyDc2HiozF5L7NqfkqIVA7AnYi69vvUiiR4I8ro5VRGkOVcr/t83AkbFi+nwJ/Zvp/6LXzPKvheACiI4NeF3jL/++x7PzNSsVuzLl6MWFYHJhGnBAq8HEHzNn2PTYhPlBRqcIkgyMRHiQzr/fJqm4Vi3DtfhwyLgc+ON6NPTL3qcS4WsMjhrEf/vHQFj4rtebqE7vc+rq8o58c0SDEeXM7RuF2bF0XSfTTNyLHgU9WmzSBw1jz5DJqLzccDTE31Z2phV7FRFiaDJSWLmhrc5nS627d1P6eFNRJdmkl5/gL7auYsel08i56LGo+8/i77jriUupS8Arrw87CtWgNOJLjUV0/z5KF1Yobw7jcvuwN2fdaY49pfr0FSN/vu+Jr3gCHq9gun669EPGOC17WsaHKuEI43rRMaHwKSk7lGj+UJybHpOR/vS3+fFkiRdnqcCzSkpKZSUlDBkyBDiPTjjxuFwNLWtu6qsrOTIkSNMnTrV303xGIfD0WLxQYvFgtPpJCwsrGnhwYiICPbv38/w4cOJioryd5N9StM0qqqqOHToUNPCkO5AtTvwbDAYmspxdKcLEpLkLT4LNn/44Yc89thjREVF8frrrzN9+nQqKir4+c9/zscff8xjjz3Gb37zG8xmH3yrbyd/n1S7TxZMJlO3O2A5VFFmoKRe/L9vBKSbKjjy3qNMLP0MnaJRr5k5mHIbAxY9SWzixVmkHVFphT0lIqNaUSAjDvpFwIk9X1O/7nlGN+wAwK7p2Zv8bYZ9+w9ERMV1dTfbpNnt2FesQM3PbyrJoE9L89r2fM3fY9PqFBc0qhrXNBoSA4OjOx741TQNx8aNuA4eBEXBeO21GAYNuuhxFhvsLoHqxu2NjIP+kS3rrnaWv/uys+prqzmybRWOY2voU76VZEpb3G8hlJyQDBqSJxM9/Cr6j5iM0eTd47un+tJiF+Or3iHG1Oh46B3umdcbQHU6yTt5kNLjO3HkZxFWeZg+thNEKPUXPfasLpWSyFFovaeSNOoaevVreyqr69w57CtXgt2OkpCAeeFClODgTrWxu47LQNW8P0vr4fTydYSfOoxepxA7/zriRg702ratTthXCkV14v/pkeIY5u+65J0lx6bndLQv/X1eLEnSpXky0Dxo0CC2bdvGwIEDm0oyeILT6Wwq7dFdVVVVcfjw4R4VbL6QpmlYrVZqampaBKBVVSUsLIyYmJimDGiz2XxFfB67g83Tpk1rynbWNK1pAcbW6j+7f5f1n6Urkc+CzSEhITz11FM8/vjjF9UDWrt2LQ888AB6vZ4TJ074ojnt4u+TapvNxnPPPccTTzwRUEH49lI1kUV1vFL8P9wkMkErj2/BtfIXDHYcBRBB5/gbiZv5A/pndOxD26XC8SqRsaVpYNbDiLBKir95l/AjHzLEeaSxLQobtTH0vfNl0oeM9uBetk1zOLB/8QXq2bMi4Hzdda1mzHZHgTA2nSocKIMzjZnGMUEwJkGUc2kPTVVxbNiA69AhEWieNw/DBTMsVE3UWD1SIX43NmZSJ3Qhk/pCgdCXXaWpKmeP76doz3KC8jbTvyGbMKWhxWNsmpEzxnQqo0eipI4lYcgU0gaO8uj0PE/2pc0Ju0pEpjOIMhsZcRDUgWRtTVUpL8mn6GQWNecOoZQeJdpylDR7DiGK7aLH12tmTpkGUxM/lpD+U+g3ejaRsa3XDW+LWlyM7fPPwWpFiYnBvGgRSlhYh54Desa4DCTu/vzVr36Fbts2Gg5kU9KgcGbStdT0HsSgaBgS7fmSPIV1YsaPzSWCyxlx0K9ja+AGHDk2Paejfenv82JJktrm6UCzoihs376dfv36kdTGGiad0RMym5sHHa8kqqqydetWkpOTcTqdWCwWamtrMZlMLcpvhIeHt6v+c3dzuYz25uU23GU43O9DnU7Xov6zXIBQuhL4bI7zwYMH6d+/f6v3XXPNNRw8eJDf/va3vmqO5AM6RSyCFB8Mu4tF1vE3+dA7eTqDH9vO3m8+Inzniwx0nmBS2afw6aec+Lw/5alziR49nwEZU9G3MQ1f06CgDrLLoc6mYi8+SsTZL0k6t5a4+j30bgzk2DU9+6KvJf7qR9m8dBXT+vmuXItiNGKaPx/76tWoOTnYv/gC41VXYRgxwmdt6MkMOhibAHHBkFUq6jivz4MBUTAo6tJ1SDVVxfH117iOHm010KxpUNIAB8vEuAVRn3lMfMeCjVcKRaejz5Ax9BkyBgCnw87xg9upOLyB4ILt9Ks/SIRSxyDnMSg9BqVLIQsaNBP5hjQqw/rjjB2MOXk4cf0ySOk9AIOXs6Avx2yAacniYtbh8v/P3nmHx1Gd+/8z24t6s9y7cbflItuAbVpCJwVCgBCSkFwIufcmQBIgNwGSewMhIYGbRurNj3RKQggETMfY4CYXyb3KsiSrS5ZWu5K2zJzfH+Md76qX1WpXOp/n0WPv7O7MOe+cmd39nvd8Xzjt1VdqzMuCaelgjvhu6Gmup+7UEVqqjuOvP4ml+ThpraWMD54iBx9d1lAo4BN2TlpmUuWejyd7Ka7pK7lgeQGL3EPrt2ncOOw33EDgxRcRTU34//Y3bB/9KKYxttwxUQm+9x6WI0ewmRWmfPTDtGbPodWjT8pWemFJDoxzDT2L3huAvY1QezabOc0GK8ZButRmJRKJZNQxHEIz6DYByVwQbrgYqwJhWCDNzc0lPV2fuVZVNSr7uaqqio6ODlwuVxf/52SeYAD9OuutD30VIAwEAlH7iRSgI+03xur4kow+4iab9CQ0h3G73fzoRz+KU2sk8STPBZdN0YXhUx7d87ai1cSUJTeTfcEn2bfzZYI7/o+Fre8zWz3B7PITUP5r2v9po9w6nZaUmWjuXIQzB8Vsps2v4m9vxearIqejisLQMTJpPXdABcqVCZyefgNzPvRvrBo/Bb/fD7wa974rZjO2q64i+M47qAcPEnznHYTPh6WwUH6QxIgpqZDjgJIGfZn4sTNwskVfKj4tTffdjUSoKsE33kA9dqyLdYYqoNoLx1t0axbQs5kXZsPUGNoojHYsVhtzlq2HZesB0FSVUycOUH9kK6GKXWSc2cfUwHGcSoBZ6gloOQEtb0Ap8AGEhIlqUzZN1vH4XJMIpU3GnDkFd1Y+rszxpGZPID1nArZhzmr0B/xkd9Qy23Oa0orTdDRXsd9XzQl/HZmBGrICVeSpNaTRRk85fppQqDKNo9ExjY6MWQTyFuPJK6Q9eyFmixWzCZZk6bYssbI1MGVlYQsLzs3N+P/2N71YaQw9FyUDQ6gqAOrhw1hMprMTXOexDMh36fevcL2DLIduC5TnGviYaPHrBQgrvfqkmaLoE3DzhiFrWiKRSCQjz3AJzaCLZ2GLAEk0UoTXMZvNZGRkRHk4BwIBQ3xuaGigtLQUVVUN/+fwn8vlSqrfw5GZyv2ltwKEmqbR0dER9bpw5nPkXzLFSCKJROboSeKCzaxnoU5L07ME69t14fmUx0TqtI8wbv5HONVWRe2253CfepM5vp24lQ7OCx2B5iPQ3Pv+A8LMSessmiZczLiVH2X6glVMSZDZU8VkwnrppShuN6GiIkLbtyN8PqwXXRTz4oRjFZcV1ozXl4wfbNI9lo+e0f+ynXqmYI4DUswqvPka2okTYDJhvuIK2qfMoskDDe16tnzo7Hdqk6IL1udl9p4lLekbk9nM1DmLmTpnsbFNDQU5VXqQxpMl+KsOYGs6SlZbKRNCp7ErQcaLesYH6iGwV7/+y7vut0W4aTWlEjC58Jtd+E0u1oZC7HnqMIrFhmKyIBQTmmJGKGZQTJhQUdQgqAGU8J8WxKa2YVe9OFQvTs1HivDhUvw4gBygr/UIjaTTaMnH65pIIG0apry5ZE1bTO60RVgtKZjb4IxXL2wJYEYvMjk/C5zD8ElsSkvDfv31+P/5T0RDA/6//10vQjd1auwPJukVEQoReOMN/YGi6JZKs895NE9I0Qv2HWmCEy36Ko2t1fq4mJQKeU7IdnQvFmtC9xivb4fK1nM+9qDf9xbl6BZWEolEIhl9DKfQDHomayxFVU3TOHDgAIFAgLS0NNLT00lLS0s6y4WxLP71ZzzYbDZycnLIyckx3tPe3o7H46G1tZWqqiqOHDmCyWQyCg9G+j8nKoMRm7sjUoAOZ0GHBehQKEQwGIx6XaT9hvR/liQTcfNsTkZG2ptuNBfBaWjXs0+rfPqP5c5oapBA9QEsVTuxnTmGw9+IO9io3+RNZrA4UNInYMuaQuqURUxfUIjd0bORbqLEMrR3L8H33gMhMM2Yge3yy1GS7AsWJE48u0MIXXQ+6TlXoBLAFAwwcfMruOsqwGymeu1VtORP7/J+h+VsQcs03Uph+NubuLEcCTRVpabqFGdOH6OtthS1qQyLpxx7ew2uwBnStTNkCA8WJT6ZNqpQaFLSaTFn4bNm43fk0m7Pw+MYj9c9FbJmYRk3B4szHZOiC4QmBQS673OwUzPNJj1LfmYGpMTh0hcdHQRefRWtslLP5O+nlY8cl7FBBAIEXnkFtbycgNlMytVXY5ne9b4TpiOkZyaf8kSPHeXs2HJFjK+OELSF9NoFka8b79athDIdw9WrkUWOzdghCwRKJMnLcAvNALt27SI3N5cpU6YMub2aprFnzx78fj95eXmG9UJ7eztOp9MQG9PT0xPecsHj8VBSUsLatWtHuilx5/3332fRokWGjcZg0TQNn88XVXzQ5/Nht9u7+D9berDVjDf19fWUlZWxcuXKYT9Wd/7PQJcChNL/WZLIJMaVK+kWIQQtLS3k5OSMuptHjlP/C6hQ26ZnZTV1gC+oi88msxXHpKUwaSlWM6TY9aXF+S7IsA/cziBRYmlZvBjF5SLw+utopaX4X3gB+9VXD6qA10iSKPHsDkXRMwUnpOjjqbYNGhp9ON99CXNTParFyukLr6YtX//ibFL0MZXl0EWabEd87TISOZYjgclsZsLkGUyY3HMxTU1VaTlTh6ehCl9LI+2+FtQOL1qHhw5PI3YCaKEgmqaiCBWTUFHQUISKigVhtqGYbWCxYbLYMVmsCFsKJkc6jtRMHKlZuNKySMnIJiU1i1yLhc4GFELAGb8+sVHfrmeUakIfc51xW/XxNcGtZ5vG085AcTiwfeQjBN9+G/XwYd3Kx+PBsmZNr+NNjsuhIzo6CLz0ElpNDcJqpWPtWtL6yCx3WPRs5PnZui1QeHx1hHSbjbZuxpfFdO7+NdEdn0mykUSOzdghYymRJCfxEJohdp7NmqZRXFyM3++noKDAEMxALxjo8XhoaWmhsbGRkydPoqpql4xXp9OZMPepRGnHSBGL/oezmlNTU5k4cSIAoVAoyv+5srISv9+P2+2OGgtut3tEJiM0TYvbue/L/zkYDBIIBIzs53AGtMVikf7PkoRhlP8kSW6CwSC/+MUvRnXFdZsZJqfqf6ALOAHtXLazzRxdiGuwJFIszbNmYXM6CbzyCqK2lo5nn8V+7bWY8vJGtF0DIZHi2RtuK0wTzUzY9CIi5EHLc6JddR1T8saB0IUdq2lkvZiTJZaJhMlsJj1nPOk546O2+/1+HnvsMR544DvDHktF0QW+rLPZo5rQs0w7QufuXw4LOM267/dIopjNWD/0IZS0NEI7dhDauRPh8ejbzN03To7LoaG1tBD45z8Rzc1gt2O66ip+9bvf8cCcOf2Kp1mBiSn6nxDQoeoTGe0hPasZwGHWs53d1tj5fScDcmzGDhlLiST5iJfQDLogOFTPZk3TKCkpoaOjg5UrV6IoCqFQyHjearWSnZ1NdnY2EG25EBYcW1tbsVgsUYLjSNtvjNXF4cPZb4vFQmZmJpmZmcY2v99vjIW6ujpOnDiBpmld/J/jMRkRKxuNwdKb/3P4vuD3+43XRRYgjMyAlkjihRSbJQmFooB9DPjjmidOxH7jjQRefhlx5gz+v/0N24c/jHnWrJFu2qhCq63F/9JL0N6OkpaG86MfxRRRwEIiiRUmRbfFiIc1xmBQFAXr6tUoaWl6lvPRowiPB9tVVyXdyopEJ+q+k5qqZ5a73YPeX9hCYzi8vSUSiUSSPIQFJU3Thl1ohqFnNmuaxt69e2lra2PlypVYrVbUswVzezumy+XC5XKRn59v7Ccy47Wmpob29nZcLleU4Bgv+w2ZLRo/7HY7ubm55J4tci2EoK2tzfB/Dk9GmM3mLpMRNltsi1aMtNjcHb0J0KqqEgqFjOcjCxB2zoCWSIYD+dNFIhkhTBkZuuC8YQNaeTmBV1/Fsno1lrOz/pKhETp6lOBbb0EohJKbi/2661CGIPhIJKMBy/z5KCkp+n2npoaOZ57BfvXVmMaP7/vNkj5RS0sJvPZa1/uO39/3myUSiUQi6YFIoXkwy+MHKjTD0DKbw0Kz1+ulsLBwSMKfyWQiPT09yic4EAgY4nNDQwOlpaVdMl7T09NxOBzD8rtqrGY2w8iK7Yqi4Ha7cbvdjD/73VXTNLxeb1QGdFtbGw6Ho4v/s7mHFX39IRHF5u6IzGyGaP/nUChEKBSio6Oji/9z+P/S/1kSK6TYnODEekZuLJOIsVTsdmzXXUdw0ybUvXsJbduGVluL7UMfQnEkdnWnRIwn6B+ooe3bCe3YAYBp6lRsV16JkqDthcSNZTIiY9k35ilTsH/yk7qVT2Mj/r//HetFF3UpHChj2X+EEIR27ya0ZYteALab+46MZ+yQsYwdMpYSSeIzEkIzDD6zWQjB/v378Xq9rFy5Muo+E1nsbCjYbDZycnLIyckx9tve3k5LSwsej4eKigoOHTo0LPYbUohLLEwmk3FuwwSDQSMbvrm5mfLycgKBACkpKVF+4APxf04WsbkzA/F/BrrYb0j/Z8lgUcRYnpbrA1l1WxJPQgcOENy4EVQVJS0N29VXY8rtXJZM0hsiGCT45puox48DYCkowHLBBSjSn0oi6YIIBAi8+SbaiRMAmBcuxLpuHUqCVP1OFkQwSPCdd1CPHAHOxvGii+R9RzLqkN+LJZL4o2kawWAw7kIzwMGDBzGbzZx33nn9fo8Qgn379tHS0kJhYWEXP/hQKEQwGBxShml/UVU1KuO1paWFjo6OIdtv+Hw+ioqKuOiii4av8QnKpk2bKCgoIDU1daSbMiCEEFH+z2EbDiFEl2KUPWXDV1ZW0tTUxOLFi0egB8NPpAAdlgiFEIbXc1iAlgUIJf1F/qJMYDRNo7S0lBkzZkgz9yGSDLG0LFiAKTeXwKuvIjwe/M8/j3X9eszz5yfcjTwR46k1N+uxa2gAkwnrJZdgmT9/pJvVJ4kYy2RFxnJgKDYbtquuIrRzJ6Ft21D379dXVlxxBaSny1j2A83j0TPE6+tBUfR79qJFXe7ZcmzGDhnL2CFjKZEkNpqmUVVVhcPhICUlJa5CM+gZkQOx0RBCcODAAZqbm7sVmuON2Wzut/1GfwVHkJnNyYiiKDgcDhwOB3l5eUC0/7PH46G8vByv19tjNnyyZjb3l978nzVNo6Ojg8rKSmw2G/n5+VGZz5EZ0BJJGPnNMoEJBoP8+c9/JhgMjnRTkp5kiaUpLw/7TTdhmjYNQiGCb79NcMMGREfHSDctikSLp3r8OP5nntGFZqcT28c+lhRCMyReLJMZGcuBoygK1pUrsV13HTidiPp6/M88Q/v+/TKWfaCWl+N/9lldaA7fdxYv7vaLthybsUPGMnbIWEokiUs4o7msrIwzZ87EXWgGfTl9fxdBh4XmpqYmCgsLcSSoHWDYfmPGjBksXbqUtWvXsnLlSiZMmICmaZSXl7Nt2zbef/999u7dS1lZGU1NTYRCoaj9jOXF4aNFUAz7P48fP57zzjuPlStXsm7dOhYvXkxWVhZtbW0cPXqUzZs3s3XrVqqrqw2rlr4KXY4WIn2drVYrbW1tBINBFEUxvJ99Ph+tra20tLTg9Xppb28nEAigquqYvk4koziz+b333uPRRx9l9+7ddHR0sGTJEh555BHWr18/0k2TSHpFcTiwXXvtuWzD48fRamqwfvjDmCdNGunmJRRCVQl+8AFqcTEApgkTsF1xBUpKysg2TCJJMsxTp+K4+WYCr7+Odvo0wXffBXSrDUY4MynREKpKaNs2Qrt2AaDk5mK75hpMSbakVCKRSCSJSaR1htlsHrBgEwuhGfqf2SyE4NChQzQ2NvYpNCeaUNldwbmw/UbY/7mqqirKfsPpdBrZnnJVyOiiu2z4YDBoZD77fD727t1LKBQyilGGs+LdbnfCje9YE74nha01ILoAYdj/GaKF6kgLDlmAcOwwasXm7du3s2bNGr7zne+Qk5PDU089xZVXXklxcTFz5swZ6eZJJL0SzjY0T5lC4PXXEc3NBP7xDyzLlmFZtUp6qgJaYyOBN97QswoBy/LlWNaskT6pEskgUVJSsH3sY4R27sS/bRsA/ueew/qhD2GeMmWEW5cYaC0t+j25pgaQPtcSiUQiiS2aphEIBIwl+yaTaUBWFrESmqF/mc1CCA4fPkxdXR2rVq3C6XQO+niJQm/2Gy0tLTQ1NQG6f/FA7DdGA2MxU9VqtZKdnY3H48FutzNv3jw6OjoM+43q6mqOHj2KoihdxoPdbh9V4yHsHR/JQAoQhv8sFgsWi0X6P49yRu2vo/vuuy/q8Y9+9CN+97vfsWHDhqQRmxVFITc3V154MSBZY2kaNw77TTcRfP991P37Ce3ahXriBNZLLhnRLOeRjKcQArW4mOCWLaCq+vL1Sy/FPGNG3NsSC5J1bCYiMpZDRzGZsBYWYs/LI/svfwGvl8CLL+qi6oUXokRUlB9LCCFQDx4kuHkznM32tl16KeZZs/r1fjk2Y4eMZeyQsZRIEouw0BzOmB2o2BxLoRn6zmwOC821tbUUFhb2S2hO1vtN2H4jJyeHjo4OtmzZwsqVKw3B8dSpU/h8PqxWaxe/X8som5BO1nM4VMITQIqi4HQ6cTqdjBs3DtCv3Uj/57KyMrxeLzabLWospKamYrVaR7gng6e/2fy9+T8LIQgEAvj9fuN1kQUIO2dOS5IXRYyR6anW1laysrL4v//7P2677bZuX+P3+/H7/cZjj8fD5MmTqaurM6pum0wmrFarsbQpTHh5QHgmOkz4oum83Wq1YjKZoo4X3q4oirH8IIzNZjNmhiKx2+3GkoUwiqJgs9lQVTXKXyq8PRQKRfkMyT4lR5/EqVOIzZsJer1ogHnePKyrVmFxu5O2TwM9T6bWVrT33qOjshIBmKZOxbZuHbaMjKTt02g8T7JPo6NPHa2tBLdvRz1wQG97aiq2iy9GnTAhafs0mPOktbYSfO89tMpK7IoC+flw8cWYzn4vSMY+RTJazpPs0/D3yePxkJeXR0tLi/G9WCKRxIbuhGaA/fv3Y7fbmT17dq/vj7XQDHDy5ElaWlpYunRpl+eEEBw9epSqqipWrVqFy+Xq1z41TcPv90dlQSYbYbH54osvjoqzqqq0trYagqPH46GjowO32x0lOLrd7qQV0t577z1WrFiB2+0e6abEnRMnThAMBpk7d26/Xt/TeAjbsYT/UlJSkmY87Nq1i4kTJ5Kfnx+T/UUWIIwswKgoShf7DVmAMPkYXdNsvfDoo48yceJEbrjhhh5f873vfY/vfOc7XbY/8cQThvdUQUEB1113HRs2bGDPnj3Ga9avX89FF13Ec889x4kTJ4zt1157LcuWLeO3v/0t9WeX+wN86lOfYtasWTzxxBNRPzTuuusu0tPTeeyxx6La8MADD9DS0sIvfvELY5vNZuMb3/gGpaWl/PnPfza25+bm8qUvfYmSkhJefvllY/vMmTO59dZbef/993nvvfeM7WOpT2vXruWSSy5J2j5d+6lP8frTT1NSWwtbt8LWraydP5+Lb7gh7n26/PLLSUlJ4a9//WvMz1NPY+/ylBSWWK38sbWVRlWF5mYoKUm48zSQPl199dWYTCa2bt1KQ0NDzM/TWLpH3Hnnnbz00kvs3bt31PRppM7T9OnT+dFPfhIlYN2uaaS+9BI/bm5Oyj4N9Dw1nznDL3/1q3N9UhS+dvXVlKWl8ZennkrKPo3G8yT7FL/vRh0JVqxYIhkt9CQ0A/3KbPZ6vRQVFcVUaIaeM5uFEBw7doyqqioKCwv7LTRH7nekEZqm/45obISmJmhtRTQ1QW0t1NXp/3b6vhPGBlwEiF/+ksisPQVIc7lIy8yE7GzIy0MdN462YBBvaystp09TCqgWi2G3kJ6enlR2C2MkT7FbIsXQ/mA2m8nIyCAjI8PYFrZj8Xg8NDQ0UFpaiqqqxngI/+tyuRJyPMTapzwysxmi/Z9DoRChUCjKUigsQIf/L/2fE5ukzWz+5S9/yV133dXtcxdccAHvv/++8fiZZ57hjjvuYOPGjSxbtqzHfSZaZrPf7+fJJ5/knnvuITU1NWEzXQbSp0jimb0TjuXXvvY13G530vcpWFlJ8L33EC0tmAFrfj5i9WpMEbOMw9mncDzvu+++qA+c4Rh7oVOnCH7wAaKlBQtgnTYNcf75KBFZVYl6nvpzPamqyuOPP869996LLcKiIJn7NFLXkxCCxx57jHvuuQf72aJ2yd6nkTpPwWAwKpYiEEApLkbds4eAEGA2YykowLJ4MfaUlKTo00DOk7mhgcB77xE4Kxqaxo3DetFFOPPzB9WntrY24/Pc6XTKsSe/GyXEeRrodyOZ2SyRxJ7ehGaAw4cPI4Rg3rx53b5/uIRmgPLycurr61m+fHnU9mPHjlFRUUFhYSEpAyzKHe5vPDI5harqonFVFVRWIvbtg337hv24/UWYzXgmT6YhP5/28eOxTJmCa8IE0s4K0Ilov7Fx40ZWrlw5JjObjx07hhAippasQgja29vxeDxGFnRraysmk6lb/+eRZvv27cycOZOcnJy4HTPSfiP8F77PdbbfkP7PiUXi3cH6yU033cRFF13U7XORs6svvfQSd9xxBy+++GKvQjPoX+S7u4i7296T106kWNSf7T3dNCK3h2c6FUXp9vUmk6nb7eELrjNhQ/bOxLNPYeLdp/Brkr1P1unTEZMnEyouJlRUhKithX/+E2X2bCyrVmHKykq6PnVGq69HfPABSnk5NoCUFGzr1mGaNavHD5BE7xN0PR9hAcJms/V4/+mORO5TX9uHq0/hWHZ3z07WPsHInSeIiKXdDmvXoi1YgHnjRrTKSti1C3HoEGphIeYFC5KiT32dJ9HaSnDLFoJHjqCc3W5dswbzokVG8dHB9Cn8ervdbvRFjj353ain7Yn63SgRfuhKJKOJvoRm0K/7zpNPYcJC88SJE5k9e3bMBZbusqqPHz8+aKF5OBGBAJSXQ2kpYudOKC6O7QGsVnA6wWpFKAqB9nbsZjMEg3oth4iJvf6iqCrpZWWkl5V1ea49LY3q+fNR58/HNmcOqbm5CWO/MVaFvIFmNvcHRVFwuVy4XC7DmkLTNHw+n5EBXVpais/nw263d/F/jveERKwzm/tDf/yfw9vCXs/S/zkxSFqxufOShO547bXXuPXWW3n22We55JJL4tMwiSROKBYL1hUrsMybR3DrVtRDh1CPHUM9fhzznDlYCgsxZWaOdDMHjNbcTGjHDtQjR0AIMJkwL16MddUqFPlDVyIZUUxZWdg+9jHUY8cIbdmC8HgIbtxIaM8eLKtWYZ4zxxBlkwnh8xHctQt13z79B6OiYF6wAOvq1SgDXB4skUgkEklf9Edohp5tNIZbaIZzq8bCnDhxglOnTiWE0Cy8XjhyBFFcDK+/PrA3p6bC/PkoM2dCTg5kZup/GRngdPYZy0AgwNb332f9+vW9ek8LIcDn0+04Wlr0f6urEUeOwIEDELEipTNOj4eJ27bBtm3GtqDdTuOKFYQKCnDMmUNaRoZh9SkZfsJi5nATzmpOTU1l4sSJAIRCoSj/58rKSvx+f9z9wEdCbO6O3gRoTdMM26/w68LCc6QFx1idNIknSSs298U777zD9ddfz5NPPklBQQE1NTUApKSkjPiHY39RFIWZM2fKCyEGjOZYKm43tssuQ1uyhOD27WilpahHjqAePYp51iwsS5ei5OfHtO/DEU+toYHQzp2ox47pIjNgnj0by5o1mPqYWEpmRvPYjDcylrGjt1gqioJlzhzMM2ei7t9PcMcOREsLwTfeILRtG5aCAszz56MkQbVt4fXqK0T27jV+9JkmTMC6di2msxXGY4Ecm7FDxjJ2yFhKJCNDf4Vm6F5sjofQ3PnYpaWllJWVUVhYSGpq6rAcrzeEqsLRo4gdO+CVV/r3pvPOQ1m5EqZMgQkTICcnJhPi/Y23oiiQkqL/TZp0bnun1wmvV7f7KCtDFBXB7t3d7s/q95P/wQfwwQfGtprzzsNXWIhl/nzSsrKGPds1SR1YY8JwZDb3F4vFQmZmJpkRiWR+v98Qn+vq6jhx4gSappGSkhIlQDv7MYHSXxJFbO6OSAHabDYbY1UIQSgUMlaIhF/X2X5D+j/HnqT1bO6Lz372s/z+97/vsv3hhx/m29/+dr/24fF4SE9Pl950kqRCq6sjuGMHWmmpsU3Jy8OyZAnm2bNREsj/S2gaWmkpoX370CoqjO2madOwrloVU7FHIpEMDyIQ0AXbkhJob9c3OhxYFi7EvGABpvT0kW1gN2h1dYT27EE9etSY3FLGjcO6Zg2myZPll02JpBvk92KJZOgMRGgGKCsr48yZMxQUFADxE5oBampqOHnyJPn5+ZSWlrJy5cohX/sD8WwWPh/s3Il47TWIKF7aLbNno1x4IcyZA5Mno/RiETZUgsEgmzdvZt26dcMr7La36wL07t3w9tvg9fb5nopFi2gpKMA6YwZp6emkp6fjdrtjNk7effddVq1aNeCikKOBQ4cO4XA4mD59+kg3pVuEELS1tXXxfzabzVHic1paWq8Wer3x3nvvsXz58qRJ3uxMZAHCcCY00KUAYaT9hvxNMHhGrdgcC0b6S3UoFOL999/nwgsvTMgCAcnEWIylVl9PqKREt6MI+4jZbJhnz8Z83nmYJk4c9M1zKPEUQiAaGvRl+IcO6cvLQF+2PnMmlpUrMeXmDqpdychYHJvDhYxl7BhMLEUwiHroEKHduxEej7HdNHky5vnzMU+fPqw//vpsX3s76tGjhA4eRJwt/AdgmjgRy7JlmKZNG7YvlHJsxg4Zy9gx0FiO9PdiiSTZGajQDNFF+uIpNAPU1dVx4MABVFVl5cqVpMdg8rgvsVn4fFBUhPj73/Vs355YsgRl3TqYNw8ljsXKQL93btq0adjF5u4QPp9uH/Lmm7BrV6+vDaSmcuL882maNg13ZqYhNKanpw/ag//dd99l9erVOJ3OQb0/mTl48CAul4tp06aNdFP6jaZpeL1eIwPa4/HQ1taGw+Ho4v/cmyVMmI0bN1JYWDiqJht6KkAYmQHd2X5DCtD9Q35LT2BUVeW9995jzZo18gfVEBmLsTTl5mK77DLE+ecTOnAAdf9+RGsr6oEDqAcOgNuNedo0zNOnY5o0aUAi0EDjKVQVrbYW7dQp1OPHEWfOnHvS6cQyfz7mhQsTMgNyuBmLY3O4kLGMHYOJpWK1Ylm8GPPChfqKhf370SoqjL+gxYJpyhTMs2ZhnjIlLl7IwutFPXkStbRUXz0RXopsMuk2QwUFcVlBIcdm7JCxjB0ylhJJ/BiM0AznrCziLTQD1NfX4/f7Wb16dUyE5p4QqgrFxYgXXoBjx7p/kc2GcuONsHSpnrmcAGLPSOTsKW43LFuGsmyZ3oZAAI4fR2zYANu3R73W1trKvAg/67oPf5jyWbPwaNqgi82N5TzFkbTRGCwmk8k4x2GCwaCR+dzc3Ex5eTmBQICUlBRSU1NJT08nLS0Nl8sVNSkU9kNOVBuNwdJXAUK/34/f7+9WgA5bcCTbuIgX8pulRDLKUVwurCtXYlmxAu30adTDh1GPHwef75zwrCgoeXmYxo/HlJ+PKScHJT0dpR8znN0hfD60+nq0ujpdZD59Wq/UHMZsxjR1Kuazvq+DPY5EIkk8lLNCrnnWLLSWFr146dGjiOZmtNJStNJSgoCSna1nPY8fj5Kbq99zhvBlTQiBaG1Fq67W/6qqEA0N0W3LzdUnt+bMQRmDWTkSiUQiiT+DFZpBF4uCwWDcheby8nKqqqqw2+1kDFPtFFFVpVtkvPZa9y/IzUW5/npYsQIlgVZTJJKwpNhserHD+fOBs/7PxcWIv/4VIlZxAeS98QZ5b7wBQPv119O4ZAktHg8VFRWG2Ni52Fwi9XWk0TRtVMTDarWSlZVFVlYWgCGohjOfa2pqOHbsGEIIUlNTjfEQts4YbWJzd3T2f4ZzAnQoFGLjxo2sXr0am83Wxf9ZFiA8hxSbJZIxgqIomCdNwjxpEuLii3Xh+eRJtJMnER4PorYWtbYW9dwbUDIyUNLSUJxOPQvR4QCTidBZW47gnj2YNA0RCCB8Pn0/Hg/4/V0b4HBgnjwZ07RpusA8gsvpJRJJfDClp2NavRrLqlW6fc7x46gnTyIaGhCNjaiNjajFxfqLbTZMWVn6PSc1FSU1Fex2/V4RLjaoaaBpCL8f2toQ7e0IrxetqUlfMXG2+IeBougTaNOnY54xA9PZL9YSiUQikcSDoQjNAIFAgNbWVqZPnx43obmiooIjR44wZ84cTp48GdN9C02D3bvRfv/77m0y3G6UW2+F1av1LN4EJhGzfJWUFLjwQpQLL9Tbd/Ik4rnnuhQddP7970z6+9+ZBChf/CKBwkJazvr9hsVGICrTNZwdO1ZFtGTMbO4PiqLgcDhwOBzk5eUB0f7PHo+H8vJyvGc9ww8ePBg1JqxJUBA8FkRmNgPYbDbMZjOaphEMBgkGg8YYkf7POlJsTmBMJhMFBQVjYvZouJGxjEYxmzFPmYJ5yhTEunXR2YA1NYZoI86ciba8OIsqBItsNrTt2wl1d9M8K1Sb8vL0v4kT9czFMXaD7Q9ybMYOGcvYEetYKoqCkpuLKTcX65o1iLY21MpKtMpKtPp6PQM5EECrqYGamqEcSD/OhAmYJ0zANGFCXOw6+kKOzdghYxk7ZCwlkuFlqEKz1+vl+PHjWK3WuAnNlZWVHD58mOXLl2MymWImqIr2dtQ33yT0u991/4KPfxzl4otRkqA4eLL8nlEUBWbMQHngAQBETQ3ixRfhnXeiXid++Uusv/wlOXl55N59NxQUGGJjS0sLHo+H0tJSfGfr7Bw+fJjMCA/o/nj9jgZGq9jcHYqi4Ha7cbvdjB8/HoD29na2bt1KZmYmXq+Xmpoa2tvbcTqdURnxKSkpo3pMqGeT7sLZy5F9jbTfCAaDBAIBY8yYTKaoDOix4P8sCwT2giyEIhmrCCEgnC3o8yHa2qC9HdHRcS6zUAgUqxWsVhSbDcXt1jMSw1mJMnNZIpH0E6GqiKYmREsLWksLorVVXwoaDOp+hIEAKIr+ZzKhWK26iOxyobjdmDIzUbKyhmT/I5FIekd+L5ZI+k8shOaioiIyMzPxeDysW7dumFp6jtOnT3Pw4EGWLVtGdnY2LS0t7Ny5k0svvXTQ+xReL6GXXkJ97rmuT06YgPLpT0NBAUoSTXppmsbGjRu54IILBl1ob6QR1dWIv/yli8+zwZVXonziE3qm9FmCwSCbN29mypQpRtZrMBjE7XaPCfuN4uJicnNzmThx4kg3ZUTo6Ohgy5YtXHzxxcb5DQaDUcUHPR4PoVDIsGQJ23CMpjHh8/koKirioosu6tfruytACBjZzp0tOEZTAoDMbE5ggsEgGzZs4MorrxwzyxOGCxnLgaEoCqSmYk5N7fb5YDDIaxs2cOWHPiTjOUTk2IwdMpaxI96xVMxmlNxcyM1lNErFcmzGDhnL2CFjKZEMD7ESmidOnEhWVhYHDhwYppaeo6qqKkpoBoaU2Syamwn9/e+oL7/c9clLL0X5yEdQ8vOH0uQRYzSIZsr48Shf/ap+fk+cQDz1FFRWnnvBhg2IDRsQDgfKgw+izJ5tFBCcPHkydru9i9dvdXU1R48eRVGUKPE5LS0taUX5SMZSZnN3hD2rI2NgtVrJzs427hlCCDo6OrodE5H+z+ExkYzx1DRtQJnbvRUg1DSNjo6OqNeFhWeLxZL0/s9SbE5gNE1jz549XH755SPdlKRHxjK2yHjGDhnL2CFjGTtkLGOLjGfskLGMHTKWEknsiaXQPHv2bJqbm9E0bZhaq1NdXc2BAwcoKCgwRCPQxY+BHlu0t+si89/+1uU5y803o334wzS0t5Oamkqyy4+jYYG4oigwaxbKE08ggkF47z3Er3997gUdHYhvfhMB8OlPozgcUe/t7PWraRo+n88QG8P2G3a7PcrnNzU1NemsFqTYrPWZdasoCk6nE6fTybizljiapkX5P5eVleH1erHZbFHic2pqalJMfKuqOuSx27kAYfheEi5AGAqF6OjoQFEU6urq+OpXv8pLL72UdFnPUmyWSCQSiUQikUgkEolkCMRaaA4XmhpOsbmmpob9+/ezdOlScnJyop4bSGazCAZRX3uN0P/9X5fnLF/4AuYPfQjNYqFk925aWloIhUJRAmR6enrSeL2Gz+toEJsjUaxWuOwylMsuQ9TXI375S9i379wL/vhH1gPa7t2If//3KIuNMCaTidTUVFJTUw27iVAoZAiNzc3NlJeXG/YbkQK0y+VKaDFXis19i83dYTKZSElJISUlhQkTJgC6YNva2mqMi6qqKjo6OnC5XF38nxNNYI2F2NyZ8LgK3/Ph3P2lsbGRTZs2xfR48UKKzb0QPsEej2dEju/3+41lCKNh6clIImMZW2Q8Y4eMZeyQsYwdMpaxRcYzdshYxo6BxjL8fXi0CSwSSSwYDqEZGFaxuba2ln379rFkyRJyc3O7PB8Wm3sT2YQQaDt2EPze97o8Z/nc5zBfeSWKzYamaRTv2UMwGGT16tUoimKITc3NzZw6dcrweo0UoB0OR0IKfInYplii5OaiPPggIhSC115D/OEPxnOmXbsQt9+OyM9H+da3UM5mNfeExWIhKyuLrKwsgCj7jZaWFqqqqjhy5IghVIeFxvT0dGwJVANICJFwwmc8GazY3B1ms5mMjAwyMjKMbYFAwBCfGxoaKC0tRVVVY0yE/x3pSQlVVeMyDsJ99Pl8pKSkJOU9RxYI7IXKykomT5480s2QSCQSiUQikUgSgoqKCiZNmjTSzZBIEobhEppBFxref//9mNvd1NXVUVJSwuLFi43l7p3x+/28++67fPjDH+5WXNGqqgh+//uIU6eitptvvBHLxz6G4nTqr9M0SkpKaG9vZ/ny5YZ4HdnPsNdrS0uLIUJ6vV4sFoshPIZFyLB38Ejy7rvvsmrVKlwu10g3JW5ohw4hHn6Y7ka38oMfoEybNvh9R9hvhMdAW1sbDoeji9XCSGW/FxUVMW3atG4nZsYCTU1NHDlyhDVr1sTleEII2tvb8Xg8xsRUa2trl0mJeHuCV1dXU11dzbJly+JyvLfffpuvf/3rhvd1MjHyd+oEZsKECVRUVJCamjoiJ9bj8TB58mQqKipk1e8hImMZW2Q8Y4eMZeyQsYwdMpaxRcYzdshYxo6BxlIIQWtrq7EMViKRDK/QDP3LLh4o9fX1lJSUsGjRoh6F5vCxoWtGo/D7Cf31r6gvvhj9+rVrsX7+8ygR2YpCCPbt24fP56OwsBCLxUIgEOhyrEiv1/yzhQNVVcXr9Rri4+nTp/H7/bjd7igB2u12x/23erKJPrFAzJnDe1/8IhfOn4/lRz+CiEkGcd99CEB59FGUWbMGvO/+2G9EZr9HCo3xynSVNhqxy2zuD4qi4HK5cLlcxj2hN0/wzpMSwzUpNdACgUPF5/ONyD0uFkixuRdMJlNCZG6ELxrJ0JGxjC0ynrFDxjJ2yFjGDhnL2CLjGTtkLGPHQGKZnp4+zK2RSJIHVVUJBoPDJjQDUf6dsRAb6uvrKS4uZtGiRYaA0xPdeROr+/YRfPDB6Bemp2N78EFMnURGIQT79+/H4/FQWFiIzWYbkA2P2WwmPT096r4Tab9QU1NjZPtFWi+kpaXFxX5hzC4Qz8rC9PjjCJ9PLya4davxlPiv/9JF50ceQZk9e0iH6c5+I2z9FPb5jbTfiMx+H47zL8Xm+IrN3dHTpESk/3NlZWXUpFT4z+12x6T98bLRCOP1eknpxh89GZBis0QikUgkEolEIpFIJP0kLDSHfVwHIzTv2LGDSZMm9Sg0Q8/ZxYOhoaGB4uJiFixY0KfQ3PnYor2d4K9+hbZxY9RrrHffjWn9+i7tF0Jw8OBBzpw5Q2FhYdQy96EIdna7ndzcXMPKoLP9wvHjxw37hc7FB2MpEI1l0dEoZuZ2o9xzD+Lf/133dH7jDeM14pvf1EXnH/4QZcqUmB03nP0ezsgPn/9w9ntdXV2U/UZ4DMSi+KSmaWP6vCeC2NwdFouFzMxMMjMzjW3hSanwmDhx4gSapnXJinc6nQM+p8NRILA3wpnNyYgUmyUSiUQikUgkEolEIukHkUJzZ+/h/tBfoRmiBd+h0NjYyJ49e1iwYEG/rXCMzOZ9+/B/97vR7SosxPof/4HSzaoIIQSHDx+mvr6eVatW4XA4htT23ugu0zEYDBpCU1NTEydPnjSEpkgB2m63D0k8HLOZzZ1QbDaUL3wB8ZnPIP74R3jtNeM58bWvIaxWlB//GCUnJ+bHjjz/YYLBIK2trbS0tNDU1ERZWVlM7DdkZnNiis3d0XlSSghBW1ub4ftcWVlJa2srZrM5akz0Jys+3mKz1+uNGt/JhBSbExi73c7DDz8sq63HABnL2CLjGTtkLGOHjGXskLGMLTKesUPGMnbIWEokAydWQvPkyZOZNWtWn++Phdjc1NTE7t27mT9//sA811WVmVu3QklJ1Gbrww9jLijo9i1CCI4ePUpNTQ2rVq3CebZIYDyxWq1kZ2eTnZ1ttKm9vd3Ifi0vL8fr9WK1WrsUH+yviDQWRce+xHXFakW5/XbErbcinn4a3npLfyIYRHzpS4ipU1EefhhlmC0BrFZrr/Ybp0+fNuw3BiI0jnWxObyKIxlRFAW3243b7Wb8+PGAfk/1er1RGdD9KUo5Ep7NyWqjoQg5JSeRSCQSiUQikUgkEkmPxFtoDvP666+zdu1aXC7XgNt85swZdu7cybx58wZUi0irribw1a9CW5uxzXTBBXo2cy8C8rFjx6ioqGDVqlXdLv0WQhgFAkdSuFNV1ch+DVtwBAIBI/s1LED3lP26adMmCgoKkjbjcDCEQiE2bdrE2rVrsVqtfb5eBAKIJ5+EXbuin7joIpQ77kAZpgJu/aGz0OjxeLrYr4SFxrDA+v7777No0aIxW7ugoqKC5uZmFi1aNNJNGTbCWfGR4yIYDEb5Pzc0NOB2u5k5c2Zc2vTAAw8A8POf/zwux4slMrNZIpFIJBKJRCKRSCSSHhgpoRn07ObBZDafOXOGXbt2MXfu3AEJzermzQR/9KOobdb77sN8/vm9vu/EiROUl5dTWFiY8B6jZrOZjIwMMjIyjG3h7NeWlpao4nOdiw9ardYxneHa374rNhvK/fcjWlsRDz0Ep0/rT2zciNi4Eb70JZSLLhquZvZK5HkN09l+JdJ+Iz09HVVV8fv9YzbDeSx4VneXFR/p/1xTU0NLSwuNjY00NzdHZUA7HI5hiY/P5zM8ypMNKTZLJBKJRCKRSCQSiUTSDSMpNMPgxObm5mZ27drFnDlzmDx5cr/eI0IhQr/7Heqrrxrb2jMyMD38MI7p03t9b1lZGWVlZaxcuTJps30dDgcOh4O8vDwgOvu1paWF2tpa2tvbcblchEIh6urqAHC73UlrLxAPlNRUlCefRNTUIL78ZWO7eOopxFNPoTz+OMrUqSPYQp2e7FfCQqOqqhw4cCDK5zdyAmK0k0yezbFCUZQu94Xi4mJSU1NxOp20trYatjwWi6WLLUssxkUy22hIsVkikUgkEolEIpFIJJJOBINB/H4/ZrN5RIRmGLjY3NLSws6dO5k1axZTpkzp13tEczOB++9H1NYa28yf+ATF+fkUZGb2+t7y8nKOHz/OypUrozJFk53I7NdwZnggEMDj8bB//37OnDlDZWUlQghSU1O7FB8cbQzVfVXJz0d57jlESQnikUfO7ffrX9f9nL/7XZQEipuiKLhcLlwuF/n5+VRXV7Ns2TKEEF0mIJxOZxef39EmzI5Fsbk7NE3D5XIZ3s+gT0hG2rLU1NR0Oy5SUlIG7PcsxWaJRCKRSCQSiUQikUhGCaqqUllZSXl5OatWrRoRoRkGJjZ7PB5DaJ42bVq/3qOdOKH7M0dgffBBzMuXo2zc2OuxKysrOXLkCCtWrOi3l62iKEMWLkcKm81GTk4OFouFOXPmkJqaSltbm+H9XFZWhtfrxW63R2W+di4ylswM1SpAWbIEnnkG8dxz8MIL+sZTpxCf/jTcfjvKFVfEoJWxRwiB2WzG5XJFTUB0tt84efIkqqqSmpoaJTQ6nc6ktqGQYrNOdwUCzWYz6enpUffA3mxZwveEtLQ03G53r+PC6/UOq9jc3NzMvffeyyuvvEJbWxsFBQX88Ic/pLCwcMj7lqMlQRFC8OCDD5KXl0dKSgo333wzLS0tI92shOORRx5h0aJFuN1uJkyYwBe+8AUaGhqiXrNx40YWL16M3W5n8eLFbNq0Ker5M2fOcNNNN5GSkkJeXh4PPfRQPLuQsHz5y19GURT+9re/GdtkLAfGyy+/zPLly3E4HIwbN44HH3zQeE7Gsv80Nzdz++23M27cOFJTU1m3bh07duwwnpex7JmDBw9y/fXXM3HixC7XM8Qmdj/72c+YPHkyTqeTK664gsrKymHt00jRWyzfe+89Lr/8cnJzc0lNTeXCCy/kvffei3q/jGU0fY3NMEVFRVitVq655pqo7TKe5+grlpWVlXzyk58kMzOTlJQU1qxZg8fjMZ6XsZRIuhK2zlAUZVBepbESmqH/YrPH46GoqIgZM2b0W2hWd+yIFprtdmy/+hXm5cuNY/ckDFdVVXHo0CGWL19OZh/Zz6ORsK1K+Hfo3LlzKSwsZN26dcyfP5/U1FSam5vZt28fmzZtoqioiCNHjlBTU0NbW1vSCu6xQDGZMN10E8rTT8PEicZ28bvfod14I6KmZuQa1wM9eTWH7TemT5/OkiVLWLt2LatWrTLE6MrKSrZv3877779PSUkJJ0+epLGxkWAwGO8uDAkpNuuoqtqvOHQeFxdeeCGrV69mypQpmEwmqqur2blzJ5s2bWL37t0cP36curo6Ojo6ou4NPp9vWFeM3HvvvWzZsoV//vOf7N69m+nTp3PVVVfR0dEx5H3L0ZKgPPXUUzz55JP85je/YePGjRQXF/PFL35xpJuVcGzdupX777+fXbt28cILL7Bz504++clPGs/X1tZy7bXXcvHFF7Nnzx7Wr1/PtddeS319vfGaO+64g3379rFx40Z+85vf8KMf/Yhf/epXI9GdhOHNN99k7969UdtkLAfGW2+9xQ033MDHP/5xiouLefvtt1m7di0gYzlQevsQlLHsHa/Xy4wZM/jxj3/c5blYxO6VV17h7rvv5jvf+Q7btm2jvb2dG2+8MS59ize9xXL79u2sWbOGl19+mT179lBYWMiVV17J0aNHjdfIWEbTWzzDtLW18fnPf55ly5Z1eU7G8xy9xbKjo4NLL72UxsZGXnnlFfbu3csDDzwQlZUjYymRRBPp0Ww2mwfslxxLoRn6Jza3trZSVFTEtGnTmN6Hv3KY0EsvEXz00XPHWbIE+x/+gCmiGFVPx66pqeHAgQMUFBQYxbTGEr2dU4vFQmZmJtOmTWPx4sVRIpOiKN2Kj01NTYRCoTj2YOAMhziuuFyYnnwS5Xvfiz7Wl7+M9r//ixhEYczhQAjR78KAYfuN/Px85syZw4oVK1i/fj2LFy8mKyuLtrY2jh49yubNm9m6dSsHDhygsrISj8czqEKg8UKKzTqqqg5qlYKiKDidTsaNG8fs2bNZvnw569atY/ny5eTn5xMKhSgrK2PLli3827/9G1dffTXf+ta3qI2wNhoOtm/fzs0338zq1auZPXs29913H42NjZw6dWrI+1bEWJ5SS2AWL17M5ZdfzuOPPw7AP//5T66//npqa2sN03pJV1544QWuv/56mpubSU9P54knnuD73/8+1dXVmEwmVFVl3LhxfOtb3+Luu++moaGBcePG8dJLL3H11VcDcM899/Duu+9SXFw8sp0ZIZqamli1ahWvvvoqc+bM4fnnn+eGG26QsRwgF110EXPmzOHXv/51l+dkLAfGggULuPHGG3n44YcBOHDgAAsXLuTw4cO88sorMpb9RFEU43qG2IzD6667DrvdzvPPPw9ASUkJS5cuZc+ePSxdunQkuhkXOseyM0IIMjMz+c53vsNXvvIVGcs+6Cmed911F5MmTeLYsWM0NDTwr3/9C0DGsxc6x/Lpp5/m3nvvpby8vNtlmDKWEkk0qqoSCAQA/Xo6c+YM+/btY/369f16f6yFZtCTa6ZPn05+fn6vx5wyZQqzZs3qc39C0wj9+teor71mbDN/7GNYPv1plE5i0gcffMDs2bON4lgAdXV1lJSUsGTJkqjt/UEIEVVsMVn54IMPWLBgARkZGYN6v6ZptLa2Gt6/Ho+Hjo4O3G53VPG5vpbYx5NAIMD777/P+vXrh8USRGga4k9/grOf9WGU730PZebMmB9vIGiaxsaNG7ngggti5scdtlkIn/+w2By2WQiPAYfDkRBj4MCBA7jd7n6vmhitbN68mSVLlgxbtrGqqmzZsoV3332XPXv28O677yKE4LzzzqOwsND4C69MHSpf/OIX2bt3Ly+//DLp6en893//N88//zx79+4dcoFDOTWRgPj9fg4cOMAFF1xgbFu3bh2qqrJnz54RbFniU1tbi9PpNC68Xbt2sWbNGmMWzmw2c8EFF7Bz504A9uzZg6ZpXWK9b98+/H5//DuQANx1113ceeedzJ49O2q7jGX/CQaDbNmyhTlz5rB+/XrGjRvHZZddZmSLy1gOjLVr1/L666/T2NhIKBTi2WefZe7cucyYMUPGcgjEIna7du2Kej785Su8j7GK1+vF5/MZy4plLAfOq6++yvbt27n//vu7PCfj2X/CP44ffPBB8vPzWbRoET//+c+N52UsJZJzdBaaFUUZkF/ycAjN0Htmc+dj9oUIhQg+9liU0Gz9ylewfuYzXYTm7o5dX19PSUkJixYtGrDQPJoYqu+0yWQiPT2dyZMns3DhQs4//3wuvPBCZsyYgc1mo7a2ll27dhlL7E+cOEF9fb0xPkeS4RI+FZMJ0223ofzmN1HbxTe+gfbIIwhVHZbj9ofwuY5l38M2CzNmzGDp0qWsXbuWwsJCw36joqKCbdu2JYz9hsxs1unOszmWmM1m1q5dy0MPPcQLL7xAWloar732Gj/84Q+ZNm0aL7/8MpdffjkXXnhhTI734x//mDlz5pCTk4PD4eDZZ59lw4YNQxaaQYrNCUljYyOappGTk8P999/PihUryMjIwGw2Ry1vlkTj9Xp58skn+fd//3ccDgegfyHKyclhz549ZGVlsWPHDnJycow41tfXY7FYyMjIYPXq1Xz1q18lJycHTdNoamoaye6MCH/84x8pKyvjnnvu6fKcjGX/aWhoIBgM8oMf/IBPf/rTbNiwgby8PC6//HK8Xq+M5QDp7UNQxnLwxCJ24X089dRTTJs2jba2tqh9jFUeffRRJk6caGSXylgOjIaGBu68807+7//+D4ulay1rGc/+U11dzaZNm6irq+PVV1/l7rvv5p577jF8nWUsJRKd7oRm0H/4q/0QuYZLaIaexWafz0dRURGTJk3qn9AcCBD41rfQIupeWL/9bcwXX9zjeyJF1cbGRoqLi1m4cGGPWdaSwWOz2cjNzWXmzJksW7bMWGI/btw4AoEApaWlvP/++2zZsoX9+/dTUVFBS0tLQlsvDAYlPR3Tc8+h3HXXuY0lJYibb0ZE2JPFk+EQmzvT2X5j5cqVrFu3Lsp+48iRI2zevJlt27Zx8ODBuNpvSLFZHwf99WyOFT6fj6lTp3L11Vfzne98h9dee43GxkZei5gwHApPPfUUu3fv5q233mLHjh2sWbOGa665hvb29iHvu+s3eMmIEzlLmpOTw5QpU0awNclBKBTilltuYcKECTzyyCPG9nAsnU4nU6ZMwe12R8U38v+TJk0iLy9vzBZrqKio4Ktf/Srvvvtut7N1Mpb9J/yBf+211/KFL3wBgF/96ldkZWXx1ltvyVgOkMgPwczMTH7yk59wzTXXUFRUJGM5BGIZu4yMDKZMmYLZbB7z8X3mmWf4+c9/zsaNG3G5XICM5UC54447+MxnPkNBQUG3z8t49h9N09A0jd/85je4XC6WLVvGG2+8wZ/+9CduuOEGGUuJhJ6FZuifX/JwCs09taGtrY0dO3YwYcIEZs+e3ecxRXs7ga99DXH6tLHN9oMfYJozp1/HPnPmDLt372b+/PmMHz9+8J0JtyfJbTSGmtnc32OkpKSQkpLCxLNF9EKhkGG90NTURFlZGaFQiNTUVMN2IS0tbVisF+J931cuvhhWr0bccQecXWkjvvUtxLJlKPfd120m/nARD7G5O8xmM+np6aSnpxvbAoGAYbvR0NBAaWmpYb8RPv/DMQak2HzuN/5wZjZHEggECAaDpKamRm1XFCUm1rp+v59vfOMb/P3vf+fSSy8F4Le//S0ZGRm8+OKL3HzzzUPavxSbE5CcnBxMJhMNDQ18/etfB/RK4aqqkpubO8KtSzw0TeO2226jurqat99+G5vNZjyXl5dHQ0MDc+fONbwHGxsbjTjm5eURCoVobm42snz+8Y9/YDKZxlyxi127dtHQ0MDys9Wnw9xyyy288MILMpYDICsrC0VRmBPxBT41NZWcnBxOnz4tYzkA+voQlLEcPLGIXW5uLg0NDdx9993ccsstXfYx1njppZe44447ePHFF6OK2slYDox33nmHV155hR/+8IcAxpJRh8NBY2OjjOcAyMnJYdKkScbEB8CMGTN46623ADk2JZLehGY4J7b2JI4Ot9Ac2YYwYaF5/PjxzJkzp39C81e+gqirM7bZfvpTTJMn93lsRVHw+XwcOnSIuXPnGqLnWGekhHKLxUJWVpZxfxZC0N7eboiP5eXleL1eLBaLITymp6eTmpra7UqhREdxOlH++EfE5s2In/5U37h7N+Kmm+AnP0GJU4b9SInN3WGz2cjJySEnJwfQ29bW1tbtGIj0fk5NTR2SNYIUmzFWucRLbPZ6vQDd1tyIBW1tbfj9/i4TrIqixCSzeWyPlgTFbrezYMECPvjgA2Pbpk2bMJvNPWb5jFWEEHz+85/n4MGDvP76612M2pcvX87WrVuNL2hhw/UVK1YAUFBQgMlk6hLrRYsWxcz8P1m47LLLOHjwIMXFxcYfwA9/+EN++MMfylgOAKfTyXnnnUdpaamxra2tjcbGRiZOnChjOQD6+hCUsRw8sYjd8uXLo54vKSnB4/EY+xhLvPbaa9x66608++yzXHLJJVHPyVgOjKKiIkpKSozPouuuu45169ZRXFyMy+WS8RwAS5cu5fTp01Ee9eXl5YZgJGMpGcv0JTTDOVGhu+zmeAjNEC02t7e3s2PHDsaNG8d5553Xt9Dc0UHg3nujheZf/apfQjPoMSorK2P27NlM7ud7xgqJsMKjJ+uFRYsWkZGRgdfr5eDBg2zatInt27dz+PBhqqqq8Pl8g27/SAiuytq1KL//PUQcW3z5y2jPPBOX4yeS2NwZRVFwu92MHz+e8847L8p+IzMzE5/Px+HDh4dsvyGEkGLzWbE5XnHwer3G+R0OMjMzKSws5Fvf+hbbtm3j2LFjfOUrX0HTNC7uxV6pvyTf9NYY4c477+T+++9n7dq1TJgwgQceeIBPfOITMUmXH0188Ytf5J133uFf//oXgUCAmpoaQM/CMZvN3HLLLTz88MPcc8893HnnnfziF78gEAjwqU99CtAzfj7+8Y9z3333MW7cOE6fPs2vf/1rnnjiiZHs1oiQkpLC3Llzu2yfMGECEyZMkLEcIJ///Od56KGHuPTSSykoKODxxx8nKyuLSy+9lNWrV8tY9pPID8GsrCyys7P5yU9+YnwIOp1OGcteCAQCHDx40HhcVlZGcXEx+fn5Mbmm77zzTj7ykY/w//7f/2P58uV8+ctfZvXq1SxdujTeXR12eovlwYMHuf7663nyyScpKCgwPovCy19lLLvSWzw7F6hNT08nGAwan1EyntH0dZ1/+9vf5j//8z/5+te/TnFxMS+88AJ/+MMfABlLydilP0IznBMVOheFipfQHG6DpmmG0JyXl8fcuXP7Fpr9ft06o7ra2Gb71a8wjRvXr+O2trbS0tJCTk4OU6dOHVIfRhuJKDqGMZvNZGRkkJGRYWzz+/20tLTg8Xioqanh6NGjKIoSZb2RlpYWtUK4MyMtritOJ8qzzyL+9S/E2c8wXngB7YUXUJ5+GiViBU+sSWSxuTv6st+or6837DfCFix92W/IzOZznwPxGgc+n4+UlJRhPd7f/vY3vva1r3HdddfR3t7OokWL+Ne//sX06dOHvG9FjPRdQ9ItQggefPBBfvWrX9HW1sa1117LL3/5y6gPDUnPN/yTJ08ybdo0QK/E/p//+Z8cPXqU8847j5/97GesW7fOeO2ZM2f44he/yL/+9S9cLhd33XUX//3f/x2P5ic8iqLw/PPPG0WuZCz7j6ZpfOtb3+J3v/sdXq+XZcuW8eMf/9hYnSBj2X8qKir42te+xrvvvmt8CD7yyCPGjKuMZc+UlZV1+2Xh4Ycf5tvf/nZMYvfTn/6U73//+zQ0NHDRRRfx29/+1qikPZroLZZlZWX8/ve/7/a5b3/724CMZWf6GpuRfPazn6WhoYF//etfxjYZz3P0Fcu33nqLr33taxw+fJhJkybxn//5n3zlK18xXidjKRlrhEIhw56nN6EZ9N9kr7/+OhdddJFRgDyeQjPAgQMHUBSFhoYGsrOzmT9/ft9CczBI4IEHECdOGNsGIjSH+2iz2Zg4cWJMhAfQ4xkMBpNeuNq+fTszZ840rAySDU3T8Pl8hgDt8Xhoa2vD6XRGCdApKSnGeero6GDLli1dVm+NBKKxERFZQBBQvvlNlCVLhuV47e3tbNu2LSbZnolCZ/sNj8eD1+vFarVGic9paWlYLBa2bdvG7Nmzx3Tyo8fjoaSkhLVr18bleNu3b+fTn/40VVVVSTPREYkUmyUSiUQikUgkEolEMuoZiNAc5vXXX2ft2rW4XK64C80A+/bto7a2lvz8fBYsWNC30KxpBB99FG3nTmPbQIRmn8/Hjh07mDhxIu3t7aSkpDBz5swh9SGSYDCIqqpJLTbv2LGDGTNmJK3Y3B3BYNAoPhgWHyMzX51OJ0ePHuXiiy9OCOFLCIH48Y9hy5ZzG9esQbn77pi3L+yTftFFF8V0v4mGqqq0trZGCdAdHR24XC78fj8TJkwgPz8ft9ud1NfvYDlz5gyHDh3i/PPPj8vx3nnnHb761a9y7NixhLjmBoq00ZBIJBKJRCKRSCQSyagmUmgeiFBiMplQVXVEhGa/309dXZ1R06dPoVkIQr/5TbTQ/LOf9Vtobmtro6ioiPHjxzN79mz2798/4vYJicpoi4vVaiU7O9vIXO2c+VpZWQnABx980KX4YLwKpkWiKArK3XcjPvxhRHhF1NatiK1b4Xe/Q4lhUbWeCoSONrqzYAkEArS0tHDgwAGam5upqqpCCGFMQoTHgt1uH/UxUlU1rmPd5/PhdruTNq5SbJZIJBKJRCKRSCQSyahlsEIz6AKM1+vl0KFDTJ48uYuv/HDh9/vZsWMHdrud9PT0fgkO6j/+gbphg/HY9vjjmPppedPR0UFRURF5eXlG8UFFUQZURGyskKziz0AIFyYLF59rb29n69atLFiwwBCgKyoqCAaDuN3uKAHa6XTGLUbK/Pnwhz8gbrvN2CZuvx0efBBl0aKYHEPTtDFxzrvDZrORm5uLyWRi7ty5pKSk0NbWRktLC62trZSVleHz+Xq03xhNdPbuH268Xi8pMZw0iTej6+xLJBKJRCKRSCQSiURylqEIzWEOHDjA1KlT4yY0BwIBioqKSEtLw+1209bW1ud71M2bCYULpwHW73wHUz/b29HRwY4dO8jOzmbevHmGsGYymWKewatpWtJ7NsPoy2zuD4qikJmZSWZmJqDHILL4YFVVFUeOHMFkMnUpPmi1WoevXQ4HPPss4re/hTff1Nv2P/+DuOIKTLffPuT9j5XM5t4IX7ORkxBhOttvnD59Gr/fj9vtjhKfk91+I972P+ECgcmKFJv7ycGDB3nwwQfZtm0bVVVVUUXT+sOLL77Iz3/+c3bs2GFUAI30eKqpqeHee+9l+/btlJaW8vjjj/O1r31tOLoikUgkEolEIpFIJKMaIQSqqg5JaG5tbSUQCBi2EvEgLDSnpKSwaNEiTp061Wd2sXbsGMEf/ch4bL33Xsz9LJYWCATYuXMnGRkZXaw6Yp3Z7PV62b59O6qqkpqaGpUNa7fbY3ac4WasC49hFEXB4XDgcDgYd9aqRdM0vF6vIUDX1NTQ3t6Oy+WKEqBjLTwqioLyb/+GuOCCc7Yar72G9s47KE8/jTKELNuxLjYLIXqdIOrOfsPv9xvic11dHcePHzfsNyInIZLJfiPeNhperzdK1E82pNjcT7xeLzNmzODmm2/mE5/4xIDf7/F4WLduHZdccgn/9V//1eX59vZ2cnJy+O///m8pMkskEolEIpFIJBLJIImV0FxUVITNZmP8+PGxbmK3hIVml8vF4sWLjUzC3gRf0dRE4OtfNx6bb7wR87p1AzpeSkoKCxcu7CL6hP2qY0HYD3rixInk5eXR2tpKS0sLJ0+exOfzGXYhYSEqNTU1obMgx1pmc3/7G85qTktLM7YFAgGj+GCk8Bh+XaTv71BR5s+Hp59GfPaz4YMjbrkFfv5zlNzcQe1Tis36uR/I9Wi328nNzSX3bMyFEPh8PkOALisrw+v1YrPZosZBampqwtpvjIRnc2pqatyOF2sS8ywmIIWFhRQWFvb4fElJCffeey9bt24lNzeXz3zmMzz00EPGhXLbWQ+hjRs3dvv+6dOn85Of/ASAb37zm7FtvEQikUgkEolEIpGMAWIpNE+ePJmGhoaYCa69EQwG2blzJ06nkyVLlhjtNplMPYrNwu/H/5WvGI9Ny5djufnmAR8vLGx3xmQyGXEcCmE/6Pz8fGbMmIGmaaSkpBgifigUMsTIpqYmysrKUFWVlJSUKAHa4XAMuS2xYKwKj4Ptt81mIycnx1jZHSk8trS0UFpaGjXhEBYeU1JSBiXuKS4XPPMM4sEH4dgx/Zj//u9w//0oy5cPeH9jXWwO33+GMvmjKAopKSmkpKQwYcIEQBdvw+JzT/Yb6enpCVMkL96ezeECgcmKFJtjQGNjI5dccglf+MIX+OUvf0l1dTWf+9znSElJ4b777hvp5kkkEolEIpFIJBLJqCfWQvPs2bM5c+bMsBfJCwu/drudpUuXRrW7J7FZCEHwxz+G1lZ9Q1oa1vvu65coEwqF2LVrFzabrcvxIlEUZcgZvH6/n6KiIrKzs5k7dy6hUKjLaywWC1lZWWRlZQF639rb2w0rhnAWZGcxciSzn8daZnMs6U54DE84eDwempubOXXqFKFQyJhwCIuP/S0+qJhMKI88gnj1VcTTTwMgvv99xHXXYbr11gG1VwiR0Fn2w00sxObuMJvNUR7gcM5+o6WlhdraWo4fPw5AampqlP/zSEw8jYSNRu4gs/ETASk2x4Cf/exnzJo1i+9///sAzJ49m69+9av85Cc/kWKzRCKRSCQSiUQikQwzwyE0h/cznJnNoVCInTt3YrPZKCgo6NLunsRm9a230LZsMR7b//d/UfphQ6CqKrt27cJsNnd7vP4cu7+E/aDT0tIMP+h+CYWKgsvlwuVyRWU/h603IsXI7ryfhzsLMhGyLOPNcIvr3U04dHR0GBMOlZWVtLa2YrFYuhQf7M12QbnqKpg7F/HAA/qGl15CKy5Gefzxfp/HsZ7ZPBgbjcEyEPuNyDEQD/uNkRCbZ8yYEbfjxRopNseAvXv3snv37qhKkaqqxmW5lUQikUgkEolEIpGMZYZLaAY9+264MpvDQrPFYukxw7g7wVcrKyP0858bj23f/S7KWZGuN1RVZffu3QAUFBT0KZwMJbM5nD3tdDpZtGjRkMU6i8USlQUZzn4OZ0GeOnUKn8+H1Wrt4v08HAKRzGweXhRFwel04nQ6yc/PB/TxG1l8sKqqio6ODsN2IbL4YFShyxkz4P/9P8TnPqdvKC9HfPKT8Mc/9muCZqyLzaqq9nuiKNb0lAXf2tpqCNCVlZVR9hs9jYOhoqoqNpstZvvri7a2NmmjIYFrrrmGxx9/fKSbIZFIJBKJRCKRSCRjhuEUmsP7G44korAYazabWbZsWY+CaGexWbS1Ebj7buOx5bbbMC1c2OfxNE2juLiYUCjEypUr+5UFONjM5nD2dG8i+lCJzH6OFCPD4nN3VgyR3s9DEaHGqvA40v02m83GeQwTabtQU1PD0aNHURSlS/FBm9ut+zh/7nPQ3g6A+PSn4Re/QMnO7vW4Y11sTjQbkc4TT6CPg/AkRE1NDcfOenWH7TdiUYQy3p7NXq83KqE12ZBicwxYtGgRzz77LDNmzEioi1AikUgkEolEIpFIRitCCEKhkOEDHGuhGYYnszmcYawoSq9CM3QVfIP/+7/G/5X58zF/7GN9Hk/TNEpKSvD7/f0WmmFwmc2aprFnzx4Ali1b1uWcDKdo19kDtrMVQ3l5OV6v18h+jvR+HqiIJDObE4POtguapkUVHzx+/DhtbW04HA79nH/3u+Q9+yzWHTsAEHfdBY88gtLNtR9mrIvNmqYlvM5lt9vJy8sjLy8POGe/Eb72I4tQRno/p6Wl9fvaj7eNhs/nIzU1NW7HizVSbO4ngUCAgwcPGo/LysooLi4mPz+f//iP/+CnP/0pn//857n77ruxWq1s2bKFkydP8sgjjwDQ1NREeXm5YXC+f/9+MjIymDVrljFbUVxcbByrqqqK4uJisrKymDJlSnw7K5FIJBKJRCKRSCQJTDyE5vB+Yyk2h4VmIQTLly/vU7yIPL66aRPaWZEMwPZf/9WnCCaEYO/evbS1tbFy5UqsVmu/2zrQvoezp4PBICtXroyrMNMdPVkxhL2fPR4PFRUVBIPBqEJ06enpvWY/j0XhMVnEdZPJRGpqKqmpqUycOBHQC3CGLReampo4WVhIvt3OrM2bARDf/CaBL30J2/r13Z5bTdPG5DkPkwxic2ci7TfC46Cz/Ub42g/bb4T/erLfUFU1rnHw+Xwys3ksUFVVRUFBgfH461//OgAPP/ww3/72t3n77be5//77ueCCCzCbzSxcuJAvfelLxutfeuklPhf2CAIuvvhiAN59910uuugigKj9P/nkkzz55JN85jOf4emz1VMlEolEIpFIJBKJZKwTL6EZ9GzZWNloqKrKnj17UFWVFStW9NvKQgiBaGgg+MQTxnbbd7+L0ocQIYRg3759tLa2UlhYOGC/UUVR+i02h4/V3t7ea/a0EGJEhUuz2UxGRgYZGRlGezo6OoxM2IqKCg4dOoTFYokSnztnQCaL+CoBq9VKdnY22WftMoQQtK9YQdPixWSd9T63PvUUp3bvxnPZZVF+3xaLRWY2jxKxvTv7jfC1H7bfCNuwRBYeDdtvxDOzOZyZLTObxwDTpk3r9QNl6dKlvP766z0+/9nPfpbPfvazvR5DfmBJJBKJRCKRSCQSSc/EUmieMmUKs2bN6vW1JpPJ8IMeCp2zfvtrZWEymdBUlcB3vmNsM3/0o336NAshOHDgAM3NzRQWFg7KqzQsdPdF+Fgej2dQovZIEpn9PG7cOOBc9nNYgK6srDQyINPT0wkEAgQCgTEnQo6Wvhp+3+vXI2bPRpz1QJ+ybRttHR2cvPZaKioqCAQCpKSkYLFYCAQC+Hw+XC7XqIlDf0nGzOb+4nA4cDgchv1GpA1LZ/uNUChEY2MjNptt2AqPRuL1emWBQIlEIpFIJBKJRCKRSIaTeAvN4WMMNbM57GMcCAT6ndEcefz8khJERYW+weHA8qlP9foeIQSHDx+msbGRwsJCHA7HoNrdn8zmyGOtWrVqSAW4EoXusp8jC5B1dHRw8uRJKisru3g/D+TcJhOjNTFOmTABfvtbxBe+AICruJgFLS2Yvv99I+v19OnTBAIBioqKMJlMXYoPDsSaJhkZzWJzZ7qzYQmFQng8Hvbv309bWxv79+/vYr+Rnp4e84kImdkskUgkEolEIpFIJBLJMDISQjMMvUBgOKM5XJxvoMKUqbGRmWe9ZQFsjz+O0ss+hBAcPXqU2tpaCgsLcTqdg257fzKbjx8/Tk1NDatWrRq0qJ3oKIpiZECOGzeO9vZ2MjIySE9PNwTo06dP4/f7u3g/O53OMZcJm2woaWnwpz8hbr1V33DyJNpnPoP96afJy8sjEAhgMplYtGgRXq/XyHqtra2lvb0dp9MZdc7dbveoEmfHktjcHRaLhaysLBRFYfbs2aSmpuL3+42VD9XV1Yb9RqT3c3p6+qBXeYRXT0ixeZSiaRpVVVWkpqbKDwiJRCKRSCQSyZhFCEFraysTJkwY0z86JSPDSAnN4WMNVmzWNI2SkhLDx3igQrMQAtOPf2w8ttxyC6bJk3t9z/Hjx6mqqqKwsBCXyzWodofpK7O5tLSUioqKAR1rtPyuVhSF9PR00tPTjW2R3s+nT5/m8OHDWCyWLpmwyZr9PFrOXXcoNhs88wzippv0De3tiE9+Ut921i4lMqs5TCAQMMTn+vp6SktL0TQtyvM3PT09qTP+x7rYHCbs2Rw5+dSX/YbD4YgSoPtrv+Hz+QCGtUDgyy+/zLe//W0OHDhAeno6d9xxB//zP/8Ts/0n510uTlRVVTG5jw9ziUQikUgkEolkrFBRUcGkSZNGuhmSMcRICs0w+AKBmqaxd+9e2traWLly5aAy3LT334fSUv2B1Yr54x/v9fUnTpwwxN9YeH32ltl86tQpTp48ycqVK4dVEElEehJduxOgIr2fq6qq8Pv9hvdzWIwciz7AiYhiMsGzzyLuuANaWgAQN92EeOyxHs+PzWYjJyeHnJwc/fVC0NbWZmS8l5WV4fV6sdvtURMO8fD8jRVSbNbPq6ZpPZ6z3uw3PB4Pzc3NlJeXR3m/hwXo7q5/r9cLMGyezW+99RY33HADDz30EH/+858JhUJUVVXF9BhSbO6FcMp6RUVF1OxVvPD7/TzxxBPce++9ST0TlgjIWMYWGc/YIWMZO2QsY4eMZWyR8YwdMpaxY6Cx9Hg8TJ48OamXdEqSj5EWmsPHHGhmsxCCffv24fV6B10wT7S2EvzRj86147vfReklI/bkyZOUlZVRWFgYM/G3p75XVlZy7NgxVqxYMeDfyaNFVO2Ph7HJZDJE5XASW6T3c1VVFUeOHDFel8jZz6PVs7kziqKg/OY3aA88YEz0THrgAbzf/Ga/3+92u3G73UyYMAHQRcfW1lZaWlqiRMeUlJSoc56olitSbMa4Dw5kgiBsv5GVlQXo11B49UPn6z81NZW0tDQOHDjA8uXL8fl8pKSkDFvcv/vd7/KZz3yGb0aM64V9FJ0dKIl1B0swwhd656US8cLv9xtp9/IH1dCQsYwtMp6xQ8YydshYxg4Zy9gi4xk7ZCxjx2BjmYg/hCWjk0QQmmHgmc1hodnj8QxaaAYI/b//Z/y/cuFCJk+b1uNrT506xYkTJ1i5cmVMJ4QURekiMlZXV3Po0CGWL19uFNAbawzlPmi328nLy4vKfvZ6vYYAXV1dTUdHh1F8LCxWy+zn+GJ67DG0738fdu0CYO4jjyD++EeUQXz3sFgsZGZmkpmZCXQVHSsrK2ltbe1iuZKampoQxQel2IzxGTCUOCiKgtPpxOl0Mm7cOOCc/UZLSwtNTU1885vfpLy8nMzMTILBIP/7v//L6tWrKSgoGJL/fiTBYJAtW7ZwzTXXsH79eg4fPsyiRYt44oknWLx4cUyOAVJsTmhMJhMFBQVj/sKOBZ1jKYQAVYVQSP8zmcBiAbMZJUmWs4wkcmzGDhnL2JHIsRRC6N5vwaB+z1FVMJv1e47VCk6nvnQvQUjkWCYjMp6xQ8YydshYShKZ8FL0w4cPs2DBgkGJbLEQmmFgmc1CCPbv309LSwuFhYWDnhTTTp5Efecd4/HJwkIm9tCGiooKjh49yooVK6L8g2NB577X1dWxf/9+li5damTrDYbRIJrGKtO3Ox/g7oqPRb4unAkbbyFyNJy3gWC6/360n/0MNm0CQHz60zBIwTmSnkTHsOVK5KSDy+XqUnww3udBis2xEZu7I9J+Y9KkSezbt4+GhgZ++ctf8utf/5p3332Xxx57jDNnzrBkyRIKCwu5/fbbWbFixaCP2dDQQDAY5Ac/+AGPPvooy5Yt44c//CGXX345x44di9nKGCk2JzBWq5XrrrtupJuR1AivF62+HhoauMJuR/vHP+hoa0N4vdDTl0a7HSUlBcXtRklLw5STg5KTo/87yMyE0YYcm7FDxjJ2JEIsRSiEVluLaGhAa2xENDYiWlsRPh/09qNEUcDlQnG7MWVmGvccU04OyjB5dfVGIsRyNCHjGTtkLGOHjKUkUQlnNAcCASorK5k/f/6AxZVYCc2giwH9yWwWQnDgwAHOnDkzJKFZCEHwe98zHlvvvRfR0dGt4B0uQrd8+XIjazKWRGY2NzQ0UFJSwuLFi8nNzY35sZKJ4Rb77HY7ubm5RpzD2c9hAbqmpiZKiAyLkSMhRI52TP/xHzT7/aRt3w6cFZz/8AcUhyO2x4mwXAkTCASMjPe6ujqOHz8OEFV8MB4rvaTYHF0ccLjJyclhyZIlTJ48mZdffhkhBKdOnWL79u1s377d8HMeLOHPkmuvvZYvfOELAPzqV78iKyuLt956i49+9KND7QIgxeaEJhgMsmHDBq688sqEWD6RDIhAAPXkSbTycrTTpxEeDwBBIXirrY3LXC6snW8QihItAvn9CL8f0dgIgBrxOiUnB/OkSZjO/ilj9LzIsRk7ZCxjx0jEUgiBaGhAPXECrbISrbZWz1ruCYvFWEWBpp1bXSEE+HwInw+1rg6OHDHeomRlYZo0Sb/3TJkSl0kvOS5ji4xn7JCxjB0ylpJERAhBMBhEVVXDs3agQkdrays7duxg6tSpQxaaQbfR6CuzWQjBwYMHaWxsZNWqVTiGIERpW7Yg6uoAUMaNw7R2Laa33+7Shurqag4ePEhBQcGQsox7Q1EUNE2jqamJPXv2sGDBAiMTc6wTTw/jyKzmcIHWSCGypqaGo0ePoihKlPVGLLOfx4pnc3c0fOQjaEKQsWMHAOK224ZFcO6MzWaLmnQQQhiWCx6Ph9LSUnw+n2GJFVl8MJbisBBizIvNvRUHHA7Cns2g34enTZvGtGnT+OQnPznkfWdlZaEoCnPmzDG2paamkpOTw+nTp4e8/zBSbE5gNE1jz549XH755SPdlIRGBAKox4+jHjuGVlkZLfQoCkpWFkpGBvt27uSKj34Ue0YGpKToy18sFpRwleWzthribOaz8HoRZ86gNTTo2YleL6K+nlB9PezZAxYLpqlTMc+ahXn69DGV9SzHZuyQsYwd8Yyl1tSEevAg6vHjxqSWgcuFKT8fU3a2LhRnZKC4XOB2d2uVITRNt9g4e9/Rmpr0zOiGBkRzM6KpCbWpCXXvXjCbMU2ZgnnmTMwzZgzbl1w5LmOLjGfsGKuxFEJQsvckJyobuf7qlTHZ51iNpSRxiRSaFUUxxOZI4bkvYi00Q982GkIIDh06RENDA4WFhUMSmkUgQPDxx43H1m98A0VRurShtrbWsLPIyckZ9PH6Inzc3bt3M3fuXKPg2VgnEbKHOwuRkd6vHo+H2tpa2tvbcblcUQL0ULKfE6HfI4EQgobrriMjKwtee03fdttt8Kc/xVUDUBSFlJQUUlJSmDhxIqAXHwxnvDc1NVFWVkYoFDIKzoXPvcPhGPT5k5nN+udQPGPg9XpxD9PqVqfTyXnnnUfp2QKYAG1tbTQ2NhrjKhZIsVmStGj19YT27UM9ehQCAWO7kpGBecYMPft4/HgUux3F74edO/Xt3SwzURTFyDhUHA7oJjtAeL2op0/r2YsVFQiPB+3ECbQTJwhaLJhnz8a8YIF+zDH6QSyRjGaEqqIePYq6fz9adfW5JywWXQCeNk1f8ZCePqB7gGIy6UK02w3jxmGeOfPcMTs60Cor9XvPqVOI5ma0kyfRTp7U7zuzZmFeuFDedySSUUqbr4N/vLGHP5bUc7jDjEvRuGJ9O+6U2BSJkUgShc5Cc+Rff4vzDYfQDL0XCBRCcPjwYerq6igsLBxyASf11VeN/5vWr8d0tihgpNhcX1/P3r1742Jn0dbWBsDMmTOZPHnysB4r2Ui0TN9I79cwgUDAECJra2sNG4ZIETI9PV2ubukDIYQ+6XP77WhwTnC+9Vb4y19Q+jkZNhxYLBaysrKM1Q1CCNrb2w3v54qKCg4dOoTFYomy3khLS+v3JJ6maf1+7WglbKMRLyIzm4eDz3/+8zz00ENceumlFBQU8Pjjj5OVlcWll14as2OM7REjSTqEEGiVlYSKivQs5rMoGRmY587FPGsWpuFaRpaSguW88+C88/Sl8/X1qCdOoB47hmhuRj10CPXQIZSsLCxLl2KeO3dEP3gkEklsEIEAof37Ce3ZAz6fvlFRME2fjmXuXExTpw6bpY7icOiC8qxZ+n2nqclYySGamlAPH0Y9fFi/7xQU6PcdWeRUIkl6autb+PVLu/jbCR8tmgkwY0NwSa4Zj6dNis2SUUV3QnOY3oTeSIZLaAZdxBNCGIJTZLuPHDlCbW0thYWFuFyuIR1HtLURevpp47H1M5+JaoOmaTQ2NlJcXMzChQuH3c7C5/NRUlICwNSpU4f1WMlGskzw22w2cnJyjOx3IUSU9/Px48dpa2vD6XR28X7unMWZaOJ6PIm0kTDdfjtaMAhvv60/d8st8MwzCVPoW1EUXC4XLpeL/Px8QBdKvV6vkfV++vRp/H4/brc7qvigy+XqdmxrmpY0Y364GAmxOXLiKNbce++9NDU18eUvfxmv18uyZcvYsGFDTI85qpWwRx55hGeeeYbS0lLS09O56qqreOyxx4Z1qVEsMZvNrF+/Pq6DOlERQqCdOkVwxw5ETY2+UVHOZfVNmtTrDTDWsVQUBSUvD1NeHpbVq9Gqq1EPHDAEoOA77xDctg3LkiVYFi8ecsXaREOOzdghYxk7Yh1LEQwSKi4mtHs3+P36Rrcby+LFWObNQxnG2ebuUBQFJTsbU3Y2lsJCRE0NoQMHUI8e1e87b7+t33eWLsWyaNGQlvXJcRlbZDxjx2iPZWVNM794sYjny/wEUAATk60qn5qfyScvX0JmVlrMjjXaYylJDnoTmqF/YvNwCs2AITJF2nkIITh69CjV1dUxEZoBQi+8YPzffP31KBEJNCaTiZaWFk6cOMGCBQsYP378kI/XG21tbRQVFTFhwgROnToll9F3QzKKr4qiGNnP4eXywWCw1yJ0YTFyLNN5osl0551oPh9s26Y/f9NNCSU4d8ZsNncpPuj3+3v1/A5nP9tstrj7FScioy2z2WQy8eijj/Loo48O2zEUkYx3yX5yzTXXcNNNN7FixQqam5v54he/SHZ2Nm+fnYXqC4/HQ3p6Oi0tLWP+BjuSaPX1BN9/H62iQt9gsWCePx/r8uUowzjbMxiE34968CChPXsQ4SqhdjuWFSt00VkuUZJIEh6haaiHDxPcutXIZFYyMrAsX56QmcPC79czr4uLz2VeO51YV6zAvGiRXGEhkSQBdc1tPPn8dv52wkcQ/QftcleIL54/mUsuWoLZMvL3Hfm9WBJr+hKaATZt2sSCBQvIzs7udh/DLTSDLjK8+eabXHLJJdhsNoQQHDt2jNOnT1NYWBgTX03R3Iz/s581Htv/8he93sNZNm3aREdHB/PmzRt2O4uOjg62b99Obm4us2fP5u233zb6Hgs0TSMQCCS1eH348GFsNhszZswY6abEnM5F6FpaWmhra8NutxMMBpk1a5bh/ZzM53AgHDp0CLvd3uV8a9/+Nhw8aDxWnn02aTOAw57f4XPu8XiMrHdN00hLS2Pq1KmkpKSMmfMeSWVlJU1NTSxevDgux/vyl79MTk4Oj0d4+Ccbo/oX6L/+9a+oxw899BDXX389LS0tUbM6iUogEOC5557jxhtvjNmHezIhOjoIfvAB6sGDIASYzXpG4bJlurfpAIhXLBW7XV/Kvngx6rFjhHbuRDQ1EfrgA0LFxVhXrsS8cGHCznr2l7E+NmOJjGXsiEUstdpaAu+8g6ivB0BJS8OyejXmOXMS9rpV7Hasy5djWboU9ehRQkVFiOZmgps3E9qzB8uqVZjnzRtQ++W4jC0ynrFjtMWyPaDy63/t4Vc7a2jTFEBhlTvIly+eyZrzFwzrD7rRFktJctEfoRl6z2yOh9AM5zKbw57JJ06coLKyMmZCM0Do+eeN/1s+97kooTks9k2cOHHYheZAIEBRURFZWVnMmzfPyN4dxflpg2a0xqS7InTBYJCqqirKysqor6/nxIkTCCG6eD+P1s+SzpnNYUzf/jbaPffA6dP66770JZRf/CLOrYsNkZ7fkefd4/Fw5MgR2traKC4uRtM0o/hgOAPabrcnrcjeX+JdINDn8zF9+vS4HW84GNVic2dqa2txOp3Ye7A08Pv9+MNLpdEzODpvN5lMWK1WgsFgVEVgs9mMxWIhEAhEffBYLBbMZnOX7VarFZPJFHW88HZFUQgEAvj9fk6cOEFHRwdWq9X4UhaJ3W5H07So7YqiYLPZUFWVUCjUZXsoFIr60hbPPkUSzgzo3CebzUbw8GHaNm2Cjg79mDNm4Fq3DpGSQiAUMpa097dP4VgGg0FsNtvw9ykUgunTYepUtGPHMO/ejdrSgvfdd1FKSrBecAGWSZOS9jyF46mqatR+kn3sjcT1pKoqJ06cwO/3j5o+jdR5EkIY98zwcfvbJxEIENy+HQ4exAIErFbMBQVYFi1CtVhQhMAMiT/25s1Dmz6d4KFDhHbtQrS2Yn7rLaz79sH556NE+Dv2dp66i6Uce4PvU0dHhxFPRVFGRZ9G6jyNlu9GFrOJXf/4OV/dN5WKgA1QWGgP8I21k1h9UQEhVY1q/3D0aaDfjTr3QSIZLP0VmkEft92JzfESmkG/psLtOHHiBOXl5axcuTJmS5yFx4P6yivGY/MVVxj/b21tZefOnTidTqMA2HARDAYpKioiNTWVhQsXdvGnjhXhAmY9+cMmA8na7sFitVpJS0vDarVSUFBgZD+Hs2BLS0vx+Xw4HI4o643RkgXbk9gMoDzxBOK223R9orER7X/+B9ODD8a5hcOD1WolOzsbh8PB+PHjyc/Pp62tzSg+WFZWhs/nw2q1dik+ONpsN+Jto9HW1hazycyRYsyIzV6vlyeffJJ///d/x+FwdPua733ve3znO9/psv2JJ54w3lNQUMB1113Hhg0b2LNnj/Ga9evXc9FFF/Hcc89x4sQJY/u1117LsmXL+O1vf0v92Uw5gE996lPMmjWLJ554IuoH4V133UV6ejqPPfaYse3JJ5/kgQceoKWlhV9EzJTZbDa+8Y1vUFpayp///Gdje25uLl/60pcoKSnh5ZdfNrbPnDmTW2+9lffff5/33nvP2D4SfQJ67NO98+dz4vhxng/bUAC5FRV8KT2d3bt3D6lPW7du5bLLLot7nx647z6OvvMOz2zZAs3NUFpKjtPJl+66i5Jjx5LyPAGUlZXx3HPPGduTfeyNxPV05ZVXAvCHP/yBhoaGUdGnkTpPn//85wH9njnYPp3vcLC+oICXqqspff11eP31Ee3TYM7Ta2+8Ed0nt5sL6+p47o9/pCxCkOqtT+HMqchYyrE39D49+eSTo65PIL8bDbRPKVoz15o3Uagd5wbt4/xGuZbllkqmK2dYsfYqGpua4tqn/n436jibACCRDIWBCM3QfWZzPIXmMCaTifLycqqqqigsLIxpEaXQiy8a/7d89rNGrRev10tRURHTpk3jzJkzURNfsSYUChmi9uLFi43zEj5HsTq2EIK9e/dSW1uLxWKJyopNS0szPLGTgdGa2dwfIrOfJ0yYAJzLgvV4PDQ0NFBaWmpkwUae454S/xKZyAKBnVEUBf7wB8QnP6lv2LcP7emnMUXY4iQ7Yc92RVFwu9243W7DN15VVeO8ezweKioqCAaDUcUH09LSknpyCeIvNnu93mH1bI4Ho9qzOUwoFOLjH/84Ho+HN954o8flHd1lNk+ePJm6ujrDmy7e2TtPPvkk99xzD6mpqUmbvdPfLCu1tJTAxo3Yg0E0kwlRUICloADFbI5J9s6TTz7J1772Ndxu94hljgVaWwkWFaEePIgiBDabDdOaNYi5c6O+1CXyeQKMeN53331RH7zJOvbCjFRm8+OPP869994bdW9K5j6NZGbzY489xj333GN8ke2tTxZFoW3zZoJ79+qvTUvDcdFF2KdPT5g+xeI8mTo6EDt20H7wIEJvBNYLLsA+f3635y+8j86xTKQ+JdrY66tPbW1txue50+kcFX0ayczmZP1u1NHWyqG/fpOVtc9iVgRe4WC783qWf/J+nBPy436eBvrdyOPxkJeXJz2bJYNmoEIzwK5du8jNzWXKlCnAyAjNAG+++SaKolBYWBjT8S98Pvyf+pTx2P7MMygOBz6fjx07djBp0iRmz57N7t27yc7OZurUqTE7dhhVVdm1axeKorBs2bIugsqbb77JmjVrhix8CCE4cOAAZ86cYfHixVGF6VpaWujo6CAlJcUQqNLT03E6nQkpUB09ehSTyRTXMTjSNDU1ceTIEdasWdOv1wshaGtrizrH4eznyEmGZMh+3rdvHxkZGb3a2AhN0wsFnkW5/XaUiFUKycyOHTuYPn06ubm5fb42vBIq0vu5tbUVk8nUpfigNYnqWR05cgSLxcLMmTPjcrxLLrmEe+65h1tuuSUuxxsOkmfqcJBomsZtt91GdXU1b7/9dq8+Qna7vduZtu6293Rh9LT/nrb3NLNnt9uxWCxce+21uN1u4wtZd683mUzdbjebzd3Ovlgslm5njePRp84oioJNUQhu2oRy8CB2QBk3DueHPoSpm6Vig+1TOJbhDPXh7lNP58mRno7jssvQliwhuHEjWnU12qZNmE6cwHrppZgyMvrdp/62fTj6FI6nzWbr9nwky9hLhOtJVVWuvfZaXC5Xt/tPxj71tX24+hSOpdvt7tKvzn3SGhvxv/YapsZG7IqCefFirBdcYBTxTJQ+QQzOk90OH/oQloULCWzcqPtRb9yIevIk5ksvxdbNj8fIz5/Ox06IPp0lWc6T2+3uEs9k79NInadk/W50dOdbZL55L6upAQW2s5IJ59/NJR+6qouferz6NNDvRsmYjSZJHAYjNEN0ZnNYaJ42bVrcfvCDvpJPVVUWLFgQ84kW9dVXjf9bbr0VxeGgra2NoqIiJkyYYIiZJpNpWDKbNU1jz549CCG6FZrh3IT+UBBCcOTIERoaGigsLMRkMuF0OqPi6ff7aWlpoaWlhdOnT3P48GEsFkuU+JxIy/PHQM7ekIjMgg1nP4dCIUOEbGpq4uTJk108gNPT0xPu86Y3G40wismkZzjfdpv+nt/9DvLyUJYti0cTh5VwZnN/UBQFh8OBw+EgLy/PeL/X6zXOfU1NjWGlE3ltJ3LRSVVV4+pJ7vP5kt5GY1RnNgshuP3229mzZw/vvPPOgH2uZNXt4UdraiLwr38hmptBUbAsX45l1SqUBPkSMZwIIVD37iW4ZQsEg2CxYF2zBvPSpQk5gy+RjBZCR44QfPttCIXA6cT2oQ9hnjZtpJsVF4SmEdq1i9COHaCqepbz2rWY58+X9x2JZBhobz3Dvt/fTWHDiwDUiUzK0m5m+S1fxnx2CWqyIL8XSwbLYIVm0DMKnU4n48aNGxGh+dSpUxw7dgyLxcKCBQv6ldnXX0QwiP8TnzAe2//6VzrQswhzc3OZN2+eEau9e/fidrtj2ndN0ygpKaG9vZ3CwsIeLSzefvttVqxYQXp6+qCPFfa6LiwsxOl0EggE+hSVNE2jtbXVEKBbWloIBoNG9nNYoHI4HHH/DnPs2DEAZs+eHdfjjiRNTU0cPXqU1atXx2yf4eznyCxYr9eL3W6POsepqakjKkIWFxeTm5trFM7rDXHmDOLOO43Hyo9+hDLMhT2Hmy1btjBv3jwyMzNjts9AIBB13j0eT5eik4lku7Jv3z7S09ONVTbDiRCCRYsW8fTTT3PxxRcP+/GGi8ScNogRX/ziF3nnnXf44x//SCAQoKamhpqamh4rGicagUCAp556qstS2NGCevw4/mef1YVmtxvbxz6G9fzzh0VoTsRYKoqCZckS7LfcZqLDzwABAABJREFUgmnyZAiFCG7eTODFFxE+30g3r1cSMZ7Jioxl7OgrlkJVCbz3HsHXX4dQCNPkyTg+9akxIzSDnnVhXbkS+803o+TnQyBA8O23CWzYgIjwY5XjMrbIeMaOZIrl4R1v0PLESkNo3s6FWJc/ycr//E5CCM3JFEtJ8jIUoRn0zOb29vYREZrLy8s5duwYK1aswGazxTyzWNu82fi/+Zpr8CsKRUVFZGdnRwnNEPvMZiEE+/fvx+fzsWLFil69kk0m05CyeMvKyigrK2PFihVRmXp97dNkMhnizqJFi7jgggtYvXq1YWVQXl7Otm3b+OCDD9i3bx/l5eW0tLQMq7d1JKM4Zy9uhLOfx48fz9y5cyksLGTdunXMmzePlJQUmpqa2Lt3L++99x47d+7k2LFj1NXVxb2GQH8ym8MomZkoP/zhufd+9auIiFpUychAMpv7i81mIycnh5kzZ1JQUMC6detYsWIF+fn5BINBSktL+eCDD/jggw/Yv3+/cX2PlJY3Ep7NyZ7ZPKptNH79618DsHjx4qjtJ0+eZFoSiAtCCOrr60fdB5nQNELbthHauRMA08SJ2K68EsXlGr5jJnAsTenp2D76UdT9+wlu3oxWUUHHX/6C7bLLME+fPtLN65ZEjmeyIWMZO3qLpfD5CGzYgFZVBYBl5Up9FUWCLtUabkxZWdhvuIHQnj2Etm5FO34cf00N1iuuwDxhghyXMUbGM3YkQyz9/g52/eEBVlU+jVkRnBa51DhvYPlVt2BetChhVhEkQywlyU1YaA6FQkZxqYGiqirV1dXMnDkzrkJzRUUFR48eZfny5WRkZGAymWIqcgghCP70p8Zj9corKSoqIiMjgwULFnSJVSzFZiEEBw8epLm5mVWrVvW5NHwoBQIrKys5duwYK1euHHJRRUVRcDqdOJ1O8vN1n/twcbKWlhaam5s5deoUoVAoKjtyOGwZYmEtkmzEq78Wi4WsrCxjVboQgvb2diMDtqyszMh+7uz9PFxi4EDEZgBlyhS47z7ED36gv//22+Gvf03a1du9FUiMFd0VnQzbrng8nqjrO9LbPS0tLS7e7vEWm30+X0wL0Y4Eo1psHmsfAMmACAQIvP462smTAJiXLtV9UpP0xhsrFEXBsmgRpokTCbz2GqKhgcDLL2NesgTrhReO+fhIJENBa2oi8NJLCI8HbDZsH/4w5hkzRrpZI45iMmFdvhzzpEkEXn8d0dxM4O9/x1JYiOg0SSuRSPpHxfF9tP31c5yvHjvrzbyGaeOvo+AjH8OUANnMEkm8iIXQ3NraSnV1dcztI/qisrKSw4cPs2LFCmPZuNlsjm1m8cGDEP6tungxO0+eJDU1lUU9TEgNRfCNOq4QHD58mPr6elatWtUvEXawmc01NTUcOnSIZcuWkRFRlyaWmM1mMjMzjfMUKUy2tLQYwqTD4Yjyfo5FUTqpNcQHRVFwuVy4XC7Gn/0cDYVChsVKpAjZnfdzLETIgYrNAMqKFXDjjYjnntP3cfPNKGf/n2wMR2Zzf+hu4qGjo8OYeKisrKS1tRWLxdLFfqO31RqDQdO0uInNwWAQv98/5KKsI82oFpsliYXw+fC//DKirk73J77kEixz5450sxIKU1YW9htvJLhlC2pxMWpJCVpNDfarrkJJ8pktiWQkUCsqCLzyCgQCKBkZ2K69FlMM/cZGA6Zx47DfdBPBjRtRDx8mtH07wdOnR7pZEklyIQS7//lT5u75LpMVPy3CzVH79SydtRbb1VejJPlSSIlkIMRKaN6xYwdZWVkxFw164/Tp0xw6dIjly5dH+ZPG2sYi+Oc/G/8/vGQJbrebxYsX9xgrk8lEKBQa8nGPHz9OTU0Nq1atwul09us9g+l7Q0MD+/btY8mSJWRnZw+mqYOiJ2EynP3c2NhoFKWLFCXT0tIGVPwrUVaoxJtE6bfFYul2kiF8nsvLy/F6vVit1i7ez4MRDAeb2avccAPi0CHYtw8A7eGHCf3bv6G2tfX4HrPLhW3SpAEfazgZKbG5Mz2tbogsPlhdXU1HRwdut7tL8cGhjF9VVeMWA+9Z2xWZ2SwZNqxWK5/61Kd6rISeTERlFjqd2K+5Jq4ZPskUS8ViwbZuHerkyQTeeANRW0vHM89gu/JKzAnywZNM8Ux0ZCxjR+dYhg4e1AsBCoFp/Hhs11yD0s8fVmMN5WzGd2jyZILvvou5ooJP5OVhbm6GceNGunlJj7zOY0cixrLN28LB397BiubXQIF9Yi4ZaR9hybylWD/8YZQ4CmUDIRFjKUl+Yik0T5s2DavVSn19/TC0tCtVVVUcPHiQZcuWdSksbzabY2ajIRob9cxmQLVaUadNo2DJkl6FjFiI3aWlpZSXl7Nq1SpcA7AvHKhlxJkzZ9izZw8LFiwgLy+v2/3Fk+6yI9va2ozs5+PHj9PW1obT6TSEyfT09D7FKZnZnDhETjJ0tljpzYIhPT29XwUmB5PZHMb04INoN94IQGDfPk7eemuf75n5pz8ljOAshIiLjcZgMZvNxjUb9nP3+/3Gua+pqTEKenbOfh7IBFM8bTR8Z+t3Sc9mybBhMpmYNWvWSDdjyKhVVQRefhn8fj2z8LrrMA3TUqqeSMZYmqdPx37TTQRefRVRX0/gH//AeuGFmJcuHfFZ5WSMZ6IiYxk7ImMZ3LWL0AcfAGCeMwfrZZclrOCTSFjmzcOUk0PglVeY7vEQ/Pvf4eKLscybN9JNS2rkdR47Ei2WpYd2oTz/WVZo5ahC4QPzNSxLW4G9sBDLmjUj/nndG4kWS0nyE2uheebMmVRWVsalIFR1dTUHDhygoKCg20zcWGY2q2+/fe64H/4wS5cu7VPIGerxT506RWlpKYWFhQNemj2QY3s8Hnbt2sV5551n+K52x0jeG8NF6dxut9HGYDBoLM2vq6vj+PHjAF2yn8OTc9KzOfHpzmIl0oKhoqKCQ4cOYbVaowTItLS0LqLiUMRmAOWZZxA33YTWzxj2lvkcb8LXfqKKzd1ht9vJzc0lNzcX0Pvg8/kMATo8wTQQe514i80ulyuuHtHDQfKMmDGI3+/ne9/7Hn6/f6SbMmjU8nICL74Ifj+m8eOxf+ITcReaIXljaUpPx37DDZjnzgUhCG7eTPD11xExWEY3FJI1nomIjGXsCMfSu2mTITRbli/HevnlUmgeAKbcXPjYx/jflhb8wSDBN98ksHEjIk7V3Ucj8jqPHYkUyx3//CXjnrmS6Vo5DSKDfc47WZm5Csdll2E9//yEFpohsWIpSX6GQ2iG2GYU90RNTQ379+9n6dKl5OTkdPuaWBUIFJpG6C9/MR5Pu+WWfgkKQxGbKysrOXr0KCtWrCAtLW3A7++vX7TX62Xnzp3MmDGDKVOmDKapI4bVaiUnJ4cZM2ZQUFDA2rVrWbZsGbm5ubS3t3P06FE2b97M9u3bOXToEK2trYRCoaQTYIdKon+u9UbYgiE/P585c+awcuVK1q1bx8KFC0lPT8fj8XDgwAE2bdpEUVERR44coaamhvb2djRNG5rYbDKh/Pa3/X69eQArD4ab8LWfzOfeZDKRmprKxIkTmTdvHqtXr2bt2rWcd955uFwumpqaKC4uZtOmTezcuZNjx45RW1tLe3u7cY3H07PZ6/WSkpKS1DEHmdmc8AQCgZjuL6RBnTdA6ZG9tFfsJdBSiy1wBk2A2ebElJZPzqTZzJi3jJT0rL532AtqaSmBDRtAVTFNnYrtqqtQRnCpZqxjGS8UqxXrhz6Eadw4gps2oR49iubxYB9hD8ju4ukLQlMHNPuhIwT+s9/JzSZwmCHNBpkOyLCDKbnvnTElWcdmPBECWoPQ0A6eALQFIXj2d49JAZcV7JogEAgQ2LMHp6JgueACrMuXj2zDkxTF4SCgaViWL4fdu1H37kW0tGC78kqUASw5SxaE0MdVYwe0+KE9BAENFM6NrxQr5Dj0e9hg7l/yOo8dIx3LQEcbxb/5IoWN/wQFDpgWkOW+jvPsKdguvxzz7NlRrxcCvEF9fHkC+vgKqoACZgXcEeMr1Qbx/G0x0rGUjA6GS2iG4Reba2pq2LdvH0uXLjWy4LojVgUC1bPerQDKxRdj6ed3+cGKzdXV1UMu0tefAoHt7e3s3LmTSZMmMWMUFGEOi1OpqalMOmtnEAgEDOuNpqYm/H4/mzdvjsp8Ho7CZJLhw2w2k5GRYVwb4eznsP9vZWUlhw4dQgjByZMnaW1tNc71QIVHJS0N5a674NFHjW3j/+u/sE+bFt2mBPNsTsbM5v5gtVrJzs42VrJEFhf1eDxRvt9paWlomobX68Vmsw37Ne7z+ZLeQgOk2DwmCGlwrKKaExv/RN7pN1jkL+ZCpZcfF/tB3aBwzDyD+rzzyVn1SWYvuQBlADeY0JEjBN94Q/dKnTkT2xVXoCT5MoCRRFEULEuWoGRn67YaNTX4n39eL3YWx6Ib3dERglOtUOkFTz+To6xmGO+G6WmQaY/vD2tJcuEN6OOrolUXaHrEp5G77R0AKlsVTOsuZty8ReQIOb6GgnXlSiz5+QTeeAPt1Cn9vnPddZiSvGBFGE8Ayj1Q4dXvZT3Sfu6/FhNMSJH3r7FKXeUJWp++kcLQcTShsN15HYsdyzDZbNiuvhrz1KnGa73Bc+OrLdi//TssMCkFpqZCmn2YOiGRxJDhFJohdiJvd9TW1hpF7HoTmiE2mc2aptH8+98TNrGwXnddv987GLG5rq7OyNgeSpG+viwj/H4/RUVF5OXlMbvTZNtowmazGUvzzWYzbW1tTJ482RCgT58+jd/vNzyBw3/98QROBsZCFndkAbpxZ2uWqKrK1q1bSUlJobW1lcrKSoLBIG63O8qCwel09n2eOwnLtt//HmdEsdBEZLSKzZ3prrioqqq0trZy5swZGhoaOHr0KIFAgJSUlCj/Z5fLFdNrvLW1dcgFDRMBKTaPYgIqbN+xBW3zj1jt28g85ewvaQV8ws4py3S89nH4ren6j+VgB6mBGsYFT5OvNDJbO8HsmhPwzz9y6uVJnJ5zGwXX3IUzpfflV5FFucxz5+peqaP85hQvzJMmYb/xRr3YYnOzLvxceWXUj9t4UlIP1X7Qzn73UBRIt0GWQ8/Uspn1zMCQBm0hXdhpaNczuso9+l+mA+ZlQZ5TijaSc3j8cPgMnPae22ZSINupC3xuK9hM+pgJhjTE228SOK0Xf6hacQntExZyrErPop+bBfkuOb4Gi3nWLOypqfhffhnR2Ij/2WexX3stpiQuHNjshyNNUOU7t81s0u9dWfazmfJn50eDmr5qwxOAenn/GtPs3/4m+Ru+wEyaOUMqJ3L/jaVaJths2K69FvPEiYCeHX+km/tXpl0fMy6L/vkoOPv5GITmADS265Mex5v1vzwXzM/S3yORJCLDLTTD8GU219XVsXfvXhYvXtxtEbvu2jGUlQCaprF/+3bmlJYa20zTp/f7/QMVmxsaGigpKWHRokV9CulDOXYgEKCoqIiMjAzmzZuX9OJIfwn3Myw4hQuTdZcVa7FYosTn1NTUpPdiHUuYzWYURSE/P9/IgI70fj59+jSHDx/GYrF08X7uKwP29+45fPSJnzP+3n+PQ08Gh6Zpo15o7olw5rvT6eTkyZOcf/75BAIBw/u5urqao0ePYjKZjHMeHgNDKb7s8/kG7K2fiEixOYGxWq3cddddAx6omoDdJSX4N3yTtf7N+kYFjppmUj/lKsYtv4YZ81cyv4cPuZAGh0+e5NjON8g6+QrL2rcyVatk6uFHaTn8E4qnf5YlNzyAy901sy10+PA5oXnRIqwXXZQQXzoGG8tExJSRoQvOr7yCdvo0gZdewrp+PZbFi+Ny/JAGx1utnHfNXVS2WVFM+g/haWkwwa3/gO4NTehLiU959B/iZzpgSxXkOmFJrr6EONEJBALUVBynteoYHfUnCXrq0HwNWAPN2APNmNV2TFoAiwhhIaT7bStWVJMVYbISMjvpsGQQcmSiuLK5dHYqhz/4Jyn5M8mfMgd3yujIGh0MARUONUFpy7lteS59fOW7dEEwEqFpBN96C7XyCJpb4Y4PXYN1/nwqvHo2YbMftlVDztnxlZYE4ysR6HzPNI0bh+OTn9QF54YG/H//O7YPfxhzkhUX86twsBHKPOe25bv1LNJxbt3SoDc0oVsFnWqFytaI+5cLluZASg/jazR9Bo00IxFLIQSbnv8xqw/8D3YlRKlpGsrkL7Gg1Q92O/aPfhTTuHEEVDjYBCc73b+mpOqreSx9/FZTNahr1z8fa9qg7uzfxBRYnKNnPccSOS4lQyEeQjMMj9hcX19vCLHj+jlxOhTPZCEE+/fvx1pSYmyzfO5zA9rHQI5/5swZ9uzZw4IFC8jPzx/QcbqjJ8/mUCjErl27cLlcLFy4MCF+8400DocDh8NhTGCEMyPDAnRFRQXBYJDU1NSo4oMOR3LMKo7Vc9y5QGD4PIfvH5qm0draGiVAh7PcI8+z0ik7fEPmeXzQoPC74hJsS5fEtU/9ZSyLzWHCnt0mk6nLNR4uPhg+92GvZ6fTGTXx0Fvxwc54vd642Gh8+ctf5qc//SnPP/88N9xwQ8z3L8XmBEZRFP2mNICbelNrG1v/8BCX1f0/7EoITSjsSL2E7EvvZk7BOub0Yx8WE8ydOZ25M+/EF7yTPeVN1G38JSsqnmYStaw5+TNqH/8rh5d/nYKr7zSylkNHjxJ8882EE5phcLFMZBSHA9tHP0rwnXdQDx0iuHEjwufDsnr1sPbxTAcU1YI3oGBxpZPjUpifDdmO/mf1mRRdWM51wqJsONqsC4v17fBOhZ6FOjsjQTydhaChqpRTB7bhryjG2XSQvLbjjNPqmKIM4gdH599KEYIXR8/9t4EMGqwTaEmZiZozl7SpS5gydwVpOeMH04ukobYNdtWe8/qe4NbHQ3oPS8mFEPo1cPgwKAr2K64ga8oUbDaFHBfMzz6XIdgQHl+ZMCczQcZXAtPdPVNJTcV+ww0EXnsNrayMwIYNcZ3oGirVPthVd9YrF92q4LysgU1AmBR94iLHCQuy4Fjz2ftXG7xdoWc5z87oej8cbZ9BI0m8Y9neEeCDX/0Hl515FhTY676QKZM/hb2uAWw2Q2iu9sGeuoj7V4p+v+np/tUdZpMuSo936xYcR5qgvFWfmK1tg0U5+sRIrLoux6VksMRCaPZ4PBQVFfUqNEPsxeaGhgaKi4tZuHDhgITYwdpoCCE4cOAAzc3NFBYXG9vNa9cOaD/98U0GaGlpYdeuXcydO5cJEyYMtLn9PraqquzevRuLxcKSJUvGnBjV3zHfkydw2Hoj7Atrs9mivJ9TU1PHXEwTmc5ic2dMJpNx/sL4/X7jPFdVVXHkyBFsNTVkRrzPpQUoSZvGk0+/x32PzkZJoMKAYaTYrN/velqNEOnvHiYYDBric0NDA6WlpWiaRmpqapT1it3e/ZfEeGQ2v/nmm+zdu3dYjyHF5gQmEAjw2GOP8cADD/Q4ECPZtW0jWa//B1eLClBgt30lWR99jNXzVgy6DW4rnD8zC8+k/2Jn9dfZuuk3XFj6BOOpZ9yuB9i7/zlybvoF41QLwddf14Xm+fMTSmiGgccyGVDMZt2iJD2d0LZthIqKED4f1ksuibltiRD6kuDDZ/T/20SAXc89xjX3P4DDMfh42i36j+cZ6VBcr2dwHWzUhZuV4/Tn44oQnC7dT/muN7BUbGFq6x7yaKRLXXIF/MJKjXkcZ2zj8TtywZmNcGUhnFlYnanY7HYw2xBmm15kTA0QCvoJBTpQO7wo7U2ItkZoayR45jQTLK3kabWk0E4OzeQEm+HMQTjzMhwD3oI6sqlwz6dj3DIy51zAjMXn43Alfxa0KvTzfrxZf5xi1bOQ83r5viWEIPjuu6gHD4KiYL38ctQpU6Kuc7sZFmTrWdF7G6DGp2dN17fDinHglJ+APdLTPVOx2bBdcw3BjRtR9+/XJ7ra2rCsWpVQ9/xIVE0//+Fs5jSbPr5ynEPbr+Ps/Wv62ftXfRscaDw7vvKi71+j8TNopIhnLGvq6qj4zc1cFtwJQPG0LzA39XzE6dO60PyRjyByx1Fcfy6bOfXs+Mod4vhKscLycTAzQxexm/36v7VtUJDb9yqi/iDHpWQwCKEX41VVddiFZtDFOiFETASPxsZGI+M37MnZXwbjHS2E4NChQzQ2NrJq7lyoqQFAGTcOJWtghdj7k9nc2trKzp07mTVrlmHrEAs6ZzZrmkZJSQmqqrJy5cpBWUL05QOd6Ay2/ZGewOHJDlVVjcznpqYmTp48GSVMhcWpkb5PJ/P5Gip9ic3dYbfbycvLi8qAbSopoT7iNTebKvkW0/hT7lIKvvI/XPC/34q5/+9QkWIzxuddf7FareTk5JCTo6sIQgja2toMAbqsrAyv14vdbo8Snt1uNykpKcMuNjc1NfGlL32JV199lTlz+pOOOjjkT+1RgCbg8bfLOfLuFn5nraCBDMoKH2b5FZ+NmeiYZoeLp1o5+ZEvseX0ZzBvfIQran/NYv9u2p5ex3bbx1jqno9l/jxd7EygG+RoRlEUrIWFKC6XIbyJjg69IGOMqqQGNdhZqwt1oC/nnZcGu4hddpXbCueP1zO4ShrOZjlXQuE43aN3OAn4/RzctoGO/f9iasN7TBR1TIx4PijMVJgn05R2HsHcRbimLCF/xkLyxk9lqsnMUN2y/X6//mP//gew22x4muqpLj+Cp/Iwau1BHE1HyGk/wQStljylkTzfZijdDKU/JrjBzFHrLBry1uA87xLmrrwMpyu5Ktf6Q7CtRrcmAH3iYWF2V7uMSIQQBN97D3X/fl1o/tCHsMyZg+rvvkKl2wprzo6v4no9y/ndCn2b9EIdOIrJhPXii1HcbkLbtxPasQPR1qZPMibYl9H2kG6j0nx2aMzOgHnZfdtlDIQUK1wwXrfWKDk7afZOpT6+MqR+l7Qc2L8H599uZSWVdGCj4sIfMM+XhXbqFFit2K67jkDueLaePje+ZmXoKypiOb4y7LB+kj4Zd7BJH19BLTZis0QyUOItNAOGkDnQH/udaWpqYvfu3cyfP39QGb8DzWwWQnDkyBHq6uooLCzEsnEj4Tq0lptuGtTxexObfT4fRUVFTJ06lWmdipANlcjM5rAlSFtbG6tWrerTk1bSN2azmczMTDIz9ZzXsDAVFqBLS0vx+Xw4HI4o72e32z3mRcB4MRixuTMmkwm32x0lNl/86ev47G/e4um8ZTw0+VJ+d/+3abjx2ijv5/T09BG9zqTY3Htmc39QFAW3+/+zd95hcVznHn5nK7D0DkIdSUggBIiiXmy5SLZs2bKd4lTHie304sRObpKb4lw7vdtxilMdJ7ZlybbcLcvqEk0CCSGJ3jvswgJbZ+4fw65AoiywuyyI93l4hJbdnTNnvjkz8zvf+X06dDqd8/pjs9mc1it6vZ63336bH/3oRyxevBhBEIiLi6OsrIzExES3a2sPPfQQDzzwgMcLus5eHaY5vRaRL79wnnfO1gCreDrqK9zx4YfIjHZ/4SZBkIWgCD8dubr/Y1/1h1h+6EFWiedYZ/0PecZ1LM/+KJprfDCaClQpKQh+fljeeguxshLLvn1obr0VYZL+X71WWajptsjLx9OiZO/JSdRHGRFBgPnBsviX2ww9FjjaKGc4x7t5Ys9qtVJ67BWsp//DEsNR0uhz/s0iqSjXLqcnJoeApZtZkLaZRUEhLHJvE4ZHEAiOiCY4IhrShy6vNBi6qCs5SW/lSTRNBST0niNK6GKp7SJLGy9C498wvafmnF8KfXM2EJ21i/nLMnxO/BtMtwVONMnFsdQKOYsvzgWt3Jabi724WBaat21DlZTk0vbmBcnF3041y9s+3ACroyFh+ieHex1BEFDn5CD4+zuFf6m/H81NN7ltomuy6M1yfJlsoFbKk1ejZctPBkGQM+jDtXCqBYwD8ZUdI3tCzzK9OPLWi6Qc/xJhgpF2IRzbPc8yv6Ybe80lUKnQ7NxJT0Q8J+vlCQ21Ur5WxXgovhSCbP8T5Q8muzyBNsss3mYqhGbAKXLY7fYJ+4t3dnZSUFDA8uXLmTNnztgfGIbxZjaXl5fT1NREdnY2AQEBmJ57zvk3RU7OuLc/mtjc399PXl4ec+bMcblfx4Mjs1mSJM6fP49erycnJ+ea9nv3ZGb2YGHKkYFvs9mclgzt7e1UVFQADMl+nmxRMlfbdi3iDrF5ONQaDV/4yWcp/t4eCgPn8J3QtfzJbKU3IsJZgM5kMqHT6YZ4P3sz+1mSpGtebBZF0e1FPVUq1ZBJppSUFG677TaOHj3K73//ey5evEhKSgpBQUHk5OSQk5PDmjVr2LJlCxrNxIsQ/fOf/6S6upp///vf7tqVEfGNJ8JZJkR7r42P/jWf0voOBOCBrcv4zI07PD7whAxk2RR2x4LqXt41nWCLsJ8s23HqfrOBjnv+xfykDI+2YZarUSYmovH3x/Lqq4iNjZj37EG7axfCBM3lHWKvyQZaJayJg3AvZIEGa2BLguwN3dwrC4OpkfJS4slSW5pLw/vPkNjyJql0OV/vIITK8I2olu8gcc2trAgKGeVbpoaQkDBC1m2HddsBuTBefW0ZjaffQVlzmHn6PKKETlLMp6HyNFT+lnpiqI3aTPCq20jKvhGVxnfSLDv64XiTXHDSkXnsSnFIW3ExtlOnAFBv2oRq+fJxbTdQA5sG4qulV/7XIsoTabOMH1VqKkJAgDzRVVGB5eWX0ezciTCJmyB30NYvC812UY6rtXHeEeiCtbB5jjxh5mhDejTE+c6pN8soiKLEO//4EddX/RyVIFKpTSLm/hdQl1Riv3QJBAHNLbfQGZHAiQY5vgIH4ivQC/E1uxJjlqliqoRmwLm9ifo2d3V1OT2MExISJvQdjna4KjZXVFRQV1dHdnY2Op0OyWCAnh4AhPnzJ+TLOtL2TSYTeXl5REdHs3TpUo88Bzoym8vKymhrayMnJ2fKLR2uNVQqFREREURERADyOekoSmYwGCgvL6evr4+AgIAhGbE6nc5tMXGt22h4SnDVaDX85P6N3PPPc1zyj+RXb5Tw/bU5CANWOGaz2Znl3tzczKVLl1AoFM7Cc47j7amJhtnM5slnNruCQqFg6dKlLF26lOPHj7NixQoeffRRzpw5w6lTpzh58iTPPvssZwZ5/4+Xuro6vva1r3Hw4EGP7w+AIF3Lo8YYdHd3ExISgsFgIDg42Ovbd9zYaTSaqy4SDQYL9/4ll+pWA1q1kh/flc6uVe7PZh4J0WDA9MILtHf00RoaT3lcIFsKP0ssHfRI/lRu+T2rtu72WnvGYrS+nGmIbW2YX34Z+voQQkPR3HEHiqDxpW4azHCsUS50FKSB9fFD/W290Z+iJC9Jd/isJkfImV3jxWw2UfzOvwgseobl1hLn610EcTHyBgIzP8TyrOu9MuAOh7v6UhJFai+doeH0WwTUvMfy/tNoBavz793oKA9Zh2LlbpI27MLPz8P+JKMwWAgM95MnMrQudL+9rAzLm2+CJKHKzka9Zs2Qv4+nL0UJzrbLxd1g4vE1UxlvXNrr6rC89hpYLAixsWhvu23SKysmSkufvCJDlORM0JxYOfPUm4iSbNlSMzB+pUZKJPhfG9cgT+Op64+xr5+8pz7N1p5XATgfeTNJn/4r9qJz2E6eBEB90010zF02JL6yY6evpcV4+3Kq74tnmRqmUmh2cODAAbKzs4cUYHIFvV5Pfn4+S5cuZd68eePe7mA6OjooKSlh06ZNo76vqqqKysrKIe21HTiA7be/BUD1uc+huuGGcW+/p6eHU6dOsW3bNudrFouF3NxcQkJCSElJ8dj1pbS0FIPBQF9fH9nZ2ZP2EnUUmPRUtqg3qKuro6uri1QfKpJssVicoqTDG9YhSg72fp6oJUNLSwt1dXVkZk68HtR0RJIkDh48yLp16/Cb5L1t/6VLVH/mM87/L/jjH/Ef8Mw9+Y9X+ExtCJIg8ETNW9zyxx8N+x2iKGI0Gocca5PJ5LGJhubmZhobG8nIuHaTCRsbG2lpaSE9Pd0r2/vQhz7Eli1bePjhh936vfv27ePOO+8ckhltNptRq9Xcddddbs92ns1s9mEkScJgMBAZGTlkoKjqNPHhP52iqcuIzk/NXz+RQ/YC76XlSX19WF5+GaGvj+iESLq33Iq2349DcUdJeftukq3nSH7/fnJby8n+wCNea9dojNSXMxFFVBTau+/Gsncvkl6PZc8eNLt2oRiogjwWXSZZaLaKslfkuvirhUBv9KfDtsNPBRc65cJb4Log2N3ezIVXf8HCmufJGshitkpKinXrUGV8mORNd7JGM/VpYu7qS0GhYH5SxsCqgm9i6u2m+MQrWM7tZ7H+GGF0k2F4B46+g+HI1ygK20JAxj2sWHcrSi/aHrT2yUKzKEFUAKyJBZULk+X2ujosb78tFyFNSUE1zBLU8fSlQpAz5tUKufhlSYfcpqTx1euZsYw3LpVz56K94w7ML7+M1NyMee9etLff7vWq2k29claxKEGMDnJiRvf/9hQKQS7iplbIXrtFrRJdSgMZC2f+NcjTeOL609BQT9szH2KrvRhREriQ/GVW3P2/2M4NEpo3baItYRm5TVMfX+7iWro3mmVi+ILQDHLGl81mG/uNgzAYDOTn57NkyZJJC82ONoyV2VxTU0NFRQVZWVlDhHH7a685f1dmZbll+1arlfz8fAIDAz0qNAMYjUYMBgNr1671aNGq6YQvjpkajWZIUTJRFIdkPw+2ZBhsveHv7+/y/vjifnsLT+/7mo/dxqe/8DP+GJvNDxO2svKHP2Xed75+1fsGZzU7VmtYLBbnBENLSwtlZWUIgnCV9/NEsp9FUbymjzt4J7N5MJ4qELht2zbOnz8/5LXly5fzs5/9jLvuusvt25vGt8gzH6vVylNPPYXVejk7sdFgcQrNoTo//vuZtd4Vmi0WzK+8gqTXIwQHo739NpLj/VgWBpqweC7c8S7Hg7ajEkSyS/+P409/AdE+vsrNnmC4vpzJKEJC0O7ejRAaitTdjXnPHsTOzjE/122GY02y0BzuJ2c0D5dx6q3+FARYHi7/gCwIlulH/0xzfRUnnnwQ1W9Tya55mii6aCeUU/M+TednClj9jf2s2vZhVD4gNIPn+tJPF0zqto+Q+eX/EPQ/lZy76b+cjLqHNsIIEXrJ0b/Gyvc+jv6xxeQ++SnKzxxGGmeV9fHS0X9ZaI7RwVoXhWaxpQXL/v1gt6NITJQL0Q1z0zPevhQEuZjXCnlFIqWdUKEfxw7NYCYSl4qYGLR33AH+/khtbZhfegmpt9eDrRxKa99loTleJ2c0T6UQKAhysculYSDarbz2r6co77g2rkGexN1jZlHhScQ/XUeavZhe/Ki+8c+suOd72CsqsL7/PgCqrCw6l6Zxyofiyx1ca/dGs4wPXxGaYfx+yQaDgby8PBITE5k/f7KlnGXGKhBYV1dHWVkZmZmZhIRcfjaTzGakykr5P/7+CC4mfwy3fYdvss1mo6CgAK1WS2pqqkeFoMbGRjo7O4mMjHT7iobpvsDa19uvUCgICgoiISGB5ORk1q1bx/r161m0aBEqlYrGxkZyc3M5evQoRUVFVFdX09XVNWKc+/r+egrH2OOO80x5RRLGlf9/8OdfIt3YSK9SwyP987FcuOjS92o0GqKioli8eDEZGRls2rSJjIwMoqKiMJlMlJWVceTIEU6ePMn58+dpaGjAaDS6dEw94Vc83ZgKsXm8K3lcITAwkKSkpCE/APHx8RMqnDsWXklnKywsHPdnVq5ceU0XHRiOjl4rH/zzZaH5xQfWkuipakfDINntWF57Dam1Ffz90dx+O8LAjItDrLnY5U/zzn/z3uHvcF3971jX9A9O/s5A1uee8Wr25CwgBAWh3b0b8759SB0dTg9nRVTUsO/vtQ4IzXbZF3J9vGtCoDdwZJuWdsK5dlkAn3fF+NveUEHVS99nVftrxAo2EKBcuYjOVQ+SdtPHydH6hrg8FajUalLW3gxrb0ay27iY+zaG/P+wpOM9ItAT0foi7HuRylfm07xoN0tvuJ/ImIkV0BkJg/my0BwdMCDUuHDPJnZ3Y37lFbBaUcydKxegc7Nv2LIwuV0XOqG4XbZcuDK+ZnENRVSUPO7s3YvU2SmPO3fcgeCBG6bBdJngpEMIDJSLtSl8IAlDEGBFuFxUtQQ5vgL93V/0dJaJcWj/v1id9zCBQj8tihiU9/6XRYvTsTc2YnnrLedKCmPaGk42geRj8TXLLJ7Cl4RmkMVmVz2bu7u7yc/PZ/HixSxYsGBS272yDSMJ3g0NDVy4cIHMzExCrxCTxeJi5++qD3xgwtt3eKbabDZOnz6NUqkkLS3No16qra2tlJSUEBcXd81nNl7JdO0PrVZLVFQUUQPPg6Io0tPT48yKbWhowGKxEBgYOCQjdrL2EdMZhyDrjmOuSUhg8b/+hb2vD2VAAJorfOTVGjVP3Luau/dUcU4Xy2+fep2v/nzxuItvOyYaBguWg21WWltbKS8vB7jK+/nK4nOzmc3eF9x7e3vRTbDuli/hFfUvMzNzXBVbFQoFly5dYtGiRR5u2fTBYLLxwT/nUdvWTaCfhn9+Kse7QrMkYT14ELGuDtRqtLfdhiJsqJ/B8nDZ47e6W0H31h9xpCCSdSXfZ03XyxT8+i5Sv/Af1D6STXqtIOh0aO+8Uxac29qcS9sVMUP9vU022TrDZJM9mtfF+Y7Q7CApXM64LtdDYassOMcEQGdbM5f2/ID0pufJEqwgwAX1Cqzrv0bKpjtJvMYLGlyJoFSxbO0OWLsDi9lM4dGXsZ95jtTuIywSa1hU/gusZb/mdOBalKs/QvKmu1CqJjfx12uViwE6MuZdFZolkwnLK69Afz9CVBSaW25B8NCFPikMLHbZw7mwFTQKiJ3+1/gpQREejnb3biz79iHp9Zj37JG940M8swqn2yJPlNlF2UM308eEQEGA5WHwysD/81pgnVJu6yxTg81m572/fodt9U+iECTK/Fcx98EX8QuJRtTrL6+kWLSI/nVbONYkyPEV4HvxNcss7kaSJLq7uzl69CjXXXfdlAvN4LrY3NPTQ15eHgsXLmThwoWT3u5gRrLRaGpq4vz582RkZBB2xbMRgDhgxQOgWL16UtsHOHPmDKIokpmZ6VHxo6Ojg6KiIlauXElfXx89AwUOZ7nMTMj0VSgUTjsNByaTyWm9UVdXR2lpKWq1Gq1Wi9VqxWAwEBgYeM1ku7pTbAauEpivJD5lCd977TBftS3hb9GryfnMI2x45ueT3+4VNiuSJA3xfnYUmfT39x8y0eCYdLyW8WYfOIp/eqs2hifHMa+lmp46dco5gzYakiSRkpLihRZNDzQaDTa7xKeePUNZUxf+GhV/vS+HlV5Oi7IVFmI/f16uxL59+1ViJcgP1KuiZEGpwQhdmV8hNyCU1XlfY3XPQc78ahdJX9w7ZYXJrpylu1YQ/P3R3nEHlldeQXR4qe7ahSI2FpAFmpPNsiAYoJYzml0pdjQV/ZkSIU9o1PXAieoewk8+QVrNM6wR+kCAEnUK0pb/IWX9Dq+3bTJMVWxqtFoyrr8Hrr+HHn0bZ9/5G8EXn2ep7RLpvUfh8FHaDn+Tirm7mX/DQ8TNSxz3Nix2OD4wkRGsgbUuTmRIdjuWN95A6uwEnQ7tzp0ILvTTRPtSGPBwtopyfOW2wOY5EHINF1ufTFwqQkPR7N7t9I43v/gi2t27XfaOdxWzTc6Yd6zIyIlzbSLD2wiC3J+xAdBmlQsYbkmQJ/dmGT+TiU1Ddw9n//AJbux7FwQ4F3sHyfc/jaDSXp7gMpkQoqMRr7+RE80KZ3ytcXGibDpxrd4bzTI8joxmkDNoRVEc9wO2u4VmcE1sdgjNCxYs8EjCklKpRJKkIX3S0tLCuXPnSEtLIyIiYtjP2Q8ccP4ujCEyuYLFYiE7O3vCRd5cQa/XU1hYyIoVK4iNjaWqqmpGCKvuZDyJdNMNPz8//Pz8iBl43rfb7fT09FBfX09/fz/FxcXYbDaCgoKGeD9rtTPzptndYrMrbPvGfdzz1Sd5PnIl3563jRdfeYPI27a7dRuCIDizn+fMkVe0OiYTuru7aWtro6KiAlEUUavVVFRUOAXoa+3ewW63e9V1wVOezd5GkLwwSm7dupW9e/detaxoJHbs2MFf/vIX4uLiPNuwMfCVqttf3VPKS3mVqJQK/vjxHK5b6t0KVvbyciyvvw6AevNmVKtWjf5+SRaX2vvBXwURFXtYefgB/AQrp/3XkfzlfWhm6MXIl5EsFllwbmwEjUZe2h4dQ16LPDmgVsKWORDo49cOm13kzZf+RmbJY8QiVw2sUi6ge/3/kLrlLrdbLFyLVJ7PpeX9v7Cs9XXC6QbAJiko1q1FlfNpUjbchsKFbAZxYCxoGxgLtiTIBR/HQpIkrO+9h72kRF5JcdddI9q/uJuJtnmW4ZF6e52WGkJgIJrdu92W4WwX4WgjdJpAp4bNCcN7zPsSV7Z5S4Jrk3uzuIfamip6/v4BksWL2CQFl9L/hxW3fw0EQZ7g2rsXsbERISgI1d33cFSvo8sEgWrYNA3iy9P4yn3xLJ5hsHUGwDvvvMOWLVvGtXzeE0IzyJaMERERI/ovG41GcnNzmTdvHomJ458YdwWr1cqBAwfYtm0bKpWK1tZWioqKWLVqFdHR0cN+Rmprw/zpTwOgWLcOzTe+MaFtS5LE2bNnaWxsZN26dR49/3p6esjNzR3id11TU0NHRwcZGRlu2YYoilRXVwMQGhqKTqebdsv0GxsbaWlpIT09faqb4jWamppoamoiPT2d/v5+Z0aswWDAaDSi1WqHiM+BgYEzIiPWbDZz7NgxtmzZ4tX96e/Uc+8v3qPMP5L13TU8+Z07UHhZgJQkidLSUkwmE/7+/hgMBmf2s0N4DgkJQafTzYhjPRIlJSXodDq3WjONhCRJxMTEUFBQwIoVKzy+PU/ilUfogwcPjuv9rw8Im9c6oijyo32FvJTXAsC3bk31utAstrRgefttAJSrVo0pNIOc9ZMTC+/Xy9myfct2U6ZWsvTA/aT3H+f0b3az8kt7UGm8JziLokhlZSWLFi2a0QPhaAgaDZrbbnMKzua9e6nbegcNyhgEQc7YclVonqr+LD+Xi+mVh7nVUgRAA1EULP0a2+/6NGrN9FQEfTE2F63IZtGKbEymfvLefRZd8d9ZYSkmo+8YHDxG/ftxNCR+mOU7HiQ4bIQHLAmK2mTRVqmQM5pdFW1thYWy0CwIaG6+2WWh2R19qRAge9D4daoZNsRP/0Jg48VdcSnodGjvuEMuFtjVheWll9DceeekBWdJgtNtsmirHogvXxYCB/fnmlgF7zfI8ZXbDOviZ20ZxsNEY7Po1PvEvHEfyXTQjY6uW/7EiuxbgIEJrgMHnJOx6p23UdgrC83TIb4mii9ef2aZGhxCsyNrVxCEMQviXYlDaF64cKHbM4tHy2w2Go3k5eUxd+5ctwrcw7UB5Aw3vV7vtJgYSWgGsJ85c/nz69dPaLuSJHH+/Hn0ej2ARzOae3t7nZMFg4V9d2fxlpWV0djYiL+/P+Xl5SgUiiHCVXBwsEf30x3M5MzmsRAEgYCAAAICAogdWClrs9mc4nNnZydVVVWIonhV9vN0zIidisxmAP/wUH6SEcAHS2wcC57P81//BR986rtebYMgCKhUKoKCgliyZAkgT7w5jnV7ezuVlZWIojjE+3m6HuuR8KZns81mw2QyzYjM5tk7Sx/m1eIWnsltAuAj65dw31r3FuwaC7G7G/Orr4LNhmLBAtQbN7r8WY1SfjhTK2QxwLxsFxe3/AGLpCK99whnf3sPdpv3qp9brVaeffbZa77iukNwVsTHY+y1YN63D21nK+lREDkOdxNv92dfTyennnqABS/cRIqlCJOk5kjCZ8i95zT27Ico6lIxXe/3fDk2/fz8ybr1flZ86wh1H3yP3Ki7MEr+JEhN5JT9HM2vVpD3m49QdT7/qs9WGqBaToomK8Z1Owp7eTm2Y8cAUG/ahHIcnovu6ssrx6/CNqZtfE0Ud8alwzteCA1F6umRs0e7uyf1nZf0st2JMDA54Ot2FIP7U6sasGNQyJMxRddgfE2GicTm4X1/YunrdxNLB/XKBKz3vcv8AaEZwJaXh/3CBadVWJkigvpB8eXrK34mii9ff2bxHoOFZkEQnILKeIvyeUpoHq0tDnE0Pj6exMREj4pBju/u7Ozk9OnTJCcnO4W2kbAPSrhSuJCwcyWSJHHx4kXa2trIysoatUjhZOnv73f25ZXHcCS/6olQVVVFQ0MDGRkZpKWlsWnTJqcNSW9vL6WlpRw+fJjc3FwuXbpEc3Mz/f3916yw62uMdI6pVCrCw8NZuHAhq1atYuPGjWRlZREXF4fVaqWyspKjR49y4sQJzp8/T319PT09PdPiuEqSNGRs9CaJd97Ml5qOA/DzuPVU/v0Fr7fhSjsltVpNREQEixYtIi0tjY0bN5KZmUlsbOyQY338+HFKSkqoq6uju7vbY2OXN/CmZ7PRaAQYUtxxujJlU4bt7e3DXjjmzZs3RS3yLXrNdr7z8jkkFKxbEssPblni1e1LZjOWV1+Fvj6EyEg0N988bnuCII38kHa8SRYFVqbfRYloJ/nwQ6T3vE/+7z5Cxhf+7dJy/Fnch6DRYLrpNuqefRm/9iZST+wlfuGdgHdsCsZL0cHniT30KDl0gABndBuJvefnbJy/jNa+y/EVrIGlV9dlmcVNzE1azdykv9Dbo+fkm38hsvSfJIpVZHW+Cs+/SrE2A1vWg6RtvYsOi5LidvlzyREQ52KhvYmspPAUg8ev+h4I18Li0ClrzrTHWaz0pZeQ9Ho5w3n3bhQTuJFq7oXzsoMOqZHgxVq5biNEK0/CnGySJ2XC/GDBrCuB27HZbBz989fZ0vwMCFCqy2bhg//FL+jyKjHbxYvYBgp4qbdupTVyPqXyPD+rpml8zTKLq4iiiNVqvUpoBlk8stlsY36Hp4VmYNgs676+PvLy8oiLi2Pp0qUeF4Ic/XPu3DmWL19OfHz8qO+XJAnp/PnLn59AllpFRQVNTU1kZ2fj7+/vVtF3MBaLhfz8fCIjI1m2bNlVfSkIglu2W19fT0VFBVlZWQQEBDgFHEdG5Ny5c4HhC9RpNBqfsmi4FjObx7O/giCg0+nQ6XTOc2VwRqzDDxi4Kqvdm964ruAQm6eKD//sqxz6wV5OBc3lfy5K/KOzC3W49x56x/LuFwSBwMBAAgMDrzrW3d3ddHR0XJXp7jjm08Xn2263ey2zube3F2BGZDZ7XWz+4Q9/yI9//GP6+vqu+psgCONarjWT0WmV/HR3Kj/69zv8/p6tKLy4xlYSRSxvvonU0TGuwlzDER0AKyOguB3OdcDGdR+g2GYj7fjnydS/yfGnP8vaB5+a9dn1IhY7nOrU0L/5dpYf20d430DRwDvu8Jovriu0tzRS9a8vkNXzLgD1Qiwdm/6PtK27ne8ZHF8lHbJgEzU19SevGXRBoay5+2tI4lc4e+ptrEd/xyrjUVLNhXD0M9Qc+wGFcz+BPftBFkQFsSTUte+VjEbM+/fLKykWLhzXSgpPER0gF6U82w5nOyBUCxGz8TVhhMDAoYLznj1od+9GGIfg3GuFfNlZigXBsMg99s9TQpwOVkTIwnlRmxxfodPjnntaYDDoufiHe9nSfxSAMwn3suqTv0FQXr71FVtasL4rX2NUGRmYlqWQXyf/bWGI/DPLLDOV0YRmcC2z2RtCs6Mtg4Xvvr4+cnNziYmJGVYc9QQGgwFJkpg/fz4JLhT6k5qanL8rt4+/sFdVVRU1NTXk5OSg08mz9p4Qm61WK/n5+QQFBZGcnDxsXyoUikkLq83NzZSWlrJ69WpCQkJGncgYrkDdcBYNg0XKkJAQnxMpZxmKIyPWUUxTkiR6e3udEwstLS309/cTEBAw5LgGBARMqdg71WKzMiCAH26dw+5TZs7pYvnzd//EQ7+bmP/7RJhIodjhjnVfX5+z+GBVVRW9vb34+fkNOY+nehJpJLwtNvv7+3tte57E62Lzr371K/79739z0003TZuZjKli69IwKhK60Wm9e5hsx48j1tSASoX21lvHJQQMx6IQeSl6vRFyW+C66++lyGRg9elvsq71P5z4ZyRrP/4jN7V+eARBICoqatoVn3A3kgQFrbJgExCgYf6Hb0fa/zJS8/gEZ4/2pyRR8MZfWJj7fbLoxi4J5MZ+iNSP/piEwKtT/xaFgN4MtT2Q1wzXzZ1eBd2ma2wKCgUr194Ma2+mueYCtW/8ihXN+5gv1TO/9jH0Nb+idM5ddNz6ZSLjR7fCkOx2zK+/Dr29CBERaG66aUITUJ7oy8UD41eDY/xKAC8PyVOCp+JSCAy87OFsMGB+6SXZYsOF64xdkj2OraI8sZTqO3NjYzJSfy4NleOruVf2B986WzBwTFyJzbqqi5j++QGyxSoskpKLWT8k7dbPDXmP1NsrT3DZ7SgWLUJYu57cRrCJEO4HKyM9vSdTz3S9/swyecYSmuFqgfdKvCU0O9piNpuBy3YP0dHRJCUleSV+u7u7yc/PR6lUEuVqHYnSUufv47XQqK2tdWYAD85uc7fYbLPZKCgoQKvVkpqaOmJfTjazub29nbNnz7Jq1SrCw+WVJeMRr5VKJWFhYYSFhTk/6xCuDAYD5eXl9PX1eVWkvBYzm93N4IzYOXNku1CLxeKcWGhububSpUsoFIoh2bDe9vSearEZIO6mLXxrz7f55vwbeTo2mw1/eZ6Vn7rHK9uWJGnSAvBwme6j+XwPFqB9QTP0pmez0WgkMDBwymPOHQiSl0fJhx9+GEmSuPnmm68KHEEQ2OgD2WwOrsWq27YLF7AOLGPXbN+Ocol77DtsIhysB6NFzjxdFw/5z/2A7Es/ByA35X/JvuurbtnWLCNzqUvOAFYIsGmOLNhIZjPmffuQWlrAzw/t7t0oBmYhvY2ho5mqv36aNONhAKoV87Hd+lsSMzaP+jmbKBd06xkUX7MFt7xPQXUnte88SVbDP0hATj+1SkqKQ7cRfuPDLEzOvuozjsJc9vPnQatF+4EPoAgN9XLLR8c6EF/G2fhyG1JPD+Y9e5C6uxFCQ+UMZ93ofiunW2XLCbVSFmV1MySByWKXr499VojVyX7OM+D+cso4e+It4t/6NBEY6CQE/c5nWLR625D3SHY7lpdeQmxqQggLQ/uBD3Bar6GmWxb7r5sL/tfApNJ4uRbvi2cioihisVjG9CF1WFQMl8XrTaEZ5Cxfg8HAsmXLyM3NJTIykhUrVnjlYdxoNHLq1CkWLlxIfX09ycnJzmy90bA8/jjiqVMAaP/+dwQXC+M2NDRw/vx5MjMzneKqgyNHjrB8+XIiIyc/GyaKIgUFBYiiSGZm5qhCSmtrK2VlZayfQJFDvV5PXl4eK1ascAqKIGdUu9MH1SFS6vV6DAYDPT09TpFysEWDuwSj1tZWamtryczMdMv3TQcaGxtpbW0lLS3Na9sURRGj0eicWOju7nYWTxssQPv7+3tsPOjp6eHMmTNTrlOJfX18/X+f4+3QJSwwdfHfr24lIMzz1+KioiIiIyOHnL+ewDGJ5BCgu7u7MRqNaLXaIcc6KCjI69nPx44dIyUlhZBJFjh3hcOHD/P5z3+eysrKaS84e/1WuqGhgRdeeIF///vf+Pn5DfmbIAhUVlZ6u0k+i91up6ioiFWrVnllJkVsacF64AAAqqwstwnNACoF5MTKgk1bP1zohOwPf5eTf2pnTcPfWX32BxQFRbDqpo+7bZuD8XZf+iJt/bLQDLLPadjA6SdotWhvv10WnFtb5Qznu+4aVfDzRH8WH3qJuINfJY0urJKSvHn3kfmRx9Bo/cb8rCO+Dg6KrxVTo5ePm5kSm41GqLWFw9ZvI0U+SuHJf6MteJpk6zlWG96CF97izKvZKDZ8iZXrdjgzl+1nz8pCsyCgufnmSQnNnupL9TDj13SJr4ni6bgUgoLQ7N6NZc8eJL1eHnd270bwH96npLZnaMHJ6SY0j9afGiVkx8DhBjnDuUw/6z8/GqP15Yk9v2F18ffRCDaqlIsIuu95Fs25+l7GeugQYlMTaDRobr2VWpMsNIMcX9eK0DxTrj+zuI6rQjOMnNnsbaHZ0RaLxUJeXh4RERFeE5odBQjnzZvHokWLaGxsdDnD1yE0Ay4Lzc3NzZw/f56MjIyrhGZwX2azKIoUFRVhtVrJzs4e8/yfaGZzT08PBQUFLFmyxONClUajITIy0inEi6JIT0+PU6Ssr6/HarU6RcrQ0NBJZ01ea5nNU7G/w3l6m81m53FtaGjgwoULqFSqIZMK7pxY8IXMZgBFQADfuXExp4/1Uu0Xxu++91e+8esveXy7E7HRmAiDs5/j4uKAy9nP3d3ddHV1UVNTg81mu8r7+Upd0d14s0BgT08POp3OJ2Jusnj9dvrgwYOcOHGCrKwsb2962mGz2Xj11VdJTk72+EPAkOWkCxeiWrPG7dsI1kB6lOy3ebELIv0h51O/4tRvO8npepXlx7/KhdAYknJudvu2vdmXvojJJltMAMwNuroQleDnh3bXLnlpe3u7XLzrrrtQjJC55M7+7O81cvqvX2Jd+4sA1AgJmG7/A+vSxzd7HHRFfEX4Q8w0KOw0E2LTaJXtWQASQ2FuqIq5N38Mbv4YpQWH6Dv4c9J6DpNmyoV37+XiwaX0ZH6O1BVbsR86BIBq3TqU8+dPqh2e7Mvhxq+ZXDjMG3GpCApCc8cdmF98EamzE/O+fWjvuAPhihvGbrOc1QyQFD49zusrGas/w/zkScAzbXC+U46vcM/eN09bhutLm9VC/p8+z9rW/4IARYGbWPbQv/DTXS3w2M6dw37unDzBddNN9ASEcaZe/ltS+Mw+r69kJlx/ZnEdh9DsEA7GepBVqVRXeTZPhdAMctv1ej1xcXEj+gq7G4cvdHx8PImJicDwhQqHQ+rudv6ucPGZqrW1lbNnz5KWljZi5rQ7xGZJkigpKaG3t5fs7GyX7Agm4tnc19dHfn4+8+bNY8GCBRNs7cQZnNUM8n4PLjxYXV3tzJp0CM8hISHodDqXRKWZIARNV7RaLdHR0URHRwNXTyzU1dUNmVgYbMcwkePmsBvyBUJv2ML//ufrfH7RTv4VlcaNe98l7Y5tY39wEkzl/qtUKsLDw4fY7/T39zszn2tqapzn8ZXez+68r/G2Z/NMKA4IUyA2b9iwgV/84hds3bp12BmIj33sY95u0jWPZLdjcfilhofLfqkeGlDmBkF7v5yhVtAK181VsPqzf6Xgl7tY3XeUuDfuoyp4PwuXZ3hk+9cikgSFrWC2y4JsWtTwS7SdgvOLL8rFuxyZhh4c7KrPnYSX7medKFdkOhV1F6mf+BX+uon5hM8Ngg4TVBmgoGX6+TdPR0RJFmAdPqfJVzwfLV+9GVZvpqmyhPrXf8rKtv0ss1+CU1+i7mQMdeotrFx1C34Zvn/Ozw2SM5trnOMXaGf1mUmhCAmRPZz37EFqa8Pyyitodu1yFqW1i7JXtihBVAAsm8EZvwuC5etjvRHyBvzB1bPxNSbdXa3UPP0B1pgKAcib/2kyP/5jBMXVnWdvbMT6/vsA8qT6/IXk1cvxFR0ASTM4vma5thmv0AxXFwicKqHZbDZTVVWFUqkkJSXFK6KHwxc6NjaWpUuXOrepVCpdEnvFigrn74r09DHf39HRQVFREStXrhzVE3qyYrMkSVy4cIHOzk5ycnLQuFgAfrzbNZvN5OfnExMT4xTqr8Tb4pUgCPj7++Pv709sbCww1DO2vb2dioHjNli0Cg4OHrHw4LWW2Qy+KbKPNrHQ3d1NbW0tRqMRtVo9RHx21Y7BVzKbHWz65XfY+f3/8Gp4Ev9baOS/N5nwC/BchoI3/YrHQhAEAgICCAgIGJL97Jhs0Ov1Q7KfB/t8+/n5TXiyQZIkr3o268awFpwueF2G6eiQ1/E/99xzV/1NEIRZsdnLSJKE9f335eWkWi2aW291PuR7ipWRsiDYY5FF0DWxalZ87j9c/NU2llkv0PvfD9DymXeJiZ9cluMsMpUGaOmTPWazY2TLiZEQAgLQ3nmnLDgbDHKm4Z13IgS4N9VLEkXy9/yMVed+jEaw0UEozVt+Ts6Wuyb93SsjoKMfui2yILgubtb/1JNc6IQukxxXWTEjexnHLUom7vN/Q9/aQNErPyWp7nnmCi3Mtf2X9oK3KOnMZcXtXyY41LervqUOjF9G5/g1G1+TRREe7lxZITY3Y9m/H81ttyGoVJR0yNcKrRIyo2e2V7YgyJOBnWbZv/lMG2TGzMbXaNRePI3iPx9ipdREn6Tl4tqfkHXzJ4Z9r2Q0yhProogiMRFVZiZF7UPja7avZ5mJTERoBjmjzGGjMZVCc15eHjqdDrPZ7BXBx2QykZeXR2Rk5FUFCF3ObC4vv/yZEcRWB11dXRQWFrJixQqnCDoSkxWby8vLaW5uJicnZ1zLzsdTDM9qtZKfn09ISAjLly/3KZHuSobLmuzt7XVmyLa0tNDf349OpxsiUnrSH3iWyTPcxILdbndOLDgESbvd7lIxOl8Tm4XgYL6eEsDxml4q/cJ5+tEn+dJvPFf7ypcyu4dDpVJdVUC0v7/febyvnGwY7P3sioDsGHNnM5vHz5TYaIyEQ4ieRUYQBBYvXuzRk9t+9iz2khK3+KW6ikOUer9e9qes6oZFIUHEPrCX+ievI0Fs4tJf7sTvi+8SEuKeNCNv9KUvYjDDuYHTKiUCgl2wJRMCA4cubX/55auWtk+mP3v0HZT9+ZNkGQ+BAKf91zDvk8+QHO0eLzflQHwdrIfWPqgwyNYOvsp0js32ftlSAmSLiQAXfHRDouJJn7udnp4EjveXssjyPrG0E1n1O3p/+Wdy4+9k0c6vExk//odZb/SlSiFP2jjGr0oDLA712OamDG/HpSIqShac9+5FrK/H8tprdG29lQqDfGOXET29Vym42p9qpTx+HW6QM5xjdDBvYgs9ZiyOviw59CJLj32NQKGfJqLo3f1P0lPXDvsZyWbD/Npr0NeHEBGBZts2mvsEqgzy31fHgHYax9dEmc7Xn1lcY6JCM1z2SZ4qodnh0RwYGMjcuXM5d+6cx7fpELfDwsKG9YV2NbPZfuKE83dh3rwR32cwGCgoKGDZsmUueRpPRmyuqqqitraWnJwcAsaZROLqdu12O4WFhfj5+bFy5cppN7YIgkBgYCCBgYHO42GxWJzic2Njo9Mf2N/fH7PZjF6vd1m0mu5M50xupVI5rCB5pa2Kn5/fkKz2wMBAnxObAUI/cS//8+lv8tWFO/hrdAbbTpwlee1Kj2zLW57N7mJw9vOVkw0OAfpKq5XB3s9XHmvHBKO3+mAmic2C5OVR429/+xuf+MQnrnr99ddf5/7776exsdGbzRmVmV51215fj2XvXpAkVOvXo1692qvbr9BDcbucqbYlAUK00FRVgt/fbyaMbgq02aR8bT9azcQLN1zL2EVZcO2xyB6na8eZ4St2dWF+8UXo70cRGztkaftEuXTmKAEvf4oEqVkuApj4JdZ8+DsolO4fvCsNUNQ2NL5mcR8WO7xXB/02WQxbHePa52ynT2M9ckSe4LrjDsToKM68/hcii//AQrFG/m5JSXH4TcRtf4Q5S9M8txOTYLjxa5bJY29owPLyy9gsNi6FJ1K75mYWhSlY5dsJ727nQieUdsqTZ9clQKBnFxxNKyRR5OQ/v0NO5e9RCBIl6pXE3P9fImOGF2okScL67rvYS0tBq0X7wQ9i0YVwoE4exxJD5RVXs4zNTL8vnmlMRmgGWZxsb2+nu7t7yoRmnU5Hamqqs9Dcdddd59Ft5ubmEhQURGpq6rD9debMGUJCQli4cOGo32Xatcv5u9++fcO+p6enh9zcXBYtWjTm9zk4ffo0YWFh4/ZArq+v58KFC2RlZTmtBsZDT08Pp06dYtu2kb1hRVHk9OnT2Gw2MjMzxxRf7XY7Vqt1WolYILfbaDTS2NhIa2srCoViSMGy0TJkpzv19fV0dnaSmpo61U3xCINtVRwWHKIo4u/vj8ViYfny5QQHB7tsP+NppMZGHv7lG7wduoSl/e385/u7UHvgmfrYsWMkJycT6oWkRG9xpdWKwWBwZj8Ptt4IDg7GbDZz6tQptm7d6pW2ffvb38ZkMvH00097ZXuexOuj+9e+9jX+9re/Of/f39/PAw88wO233z6sCH0tY7PZeP/994etBD1ZxO5uLG+8AZKEctkyVFPgl7ooRM7aEiXZn9IuQtzCZPS3/xOTpGa1OZfc338K0T75qsue7Etf5dyg5eerJ7A8WBEWhnbXLtBqnUvbpYH+G29/SqLIif/+hPl7d5EgNdNMFBU7X2DdR//XI0IzwMJgiB0UX7bJh5FHmI6xKUnyEv9+G+jUkOqiEGivq8N69CgA6o0bUSYkoNZoydr1Web/zxkKN/6JEnUKGsFOZtfrzPn3Zop+egsVhSOviBmMN/vyyvHLV+NrokxVXCrnzEF9yy20W5T415azsOBdksOnbyaNg/H259IwucipXRy4Pk7/LnALvcZu8n6xm7VVv0MhSJyK2MWShw+MKDQD2IuLZaFZENBs344QHEJ+qyw0B2thxfB1uK4JpuP1ZxbXmKzQDLIdQmdnp9eFZocNQ0BAAKmpqSgUiqv8oz21TZ1ON2pGrivtkIxG5++KjcMXu+7t7SU/P5/58+e7LDSDnLE33szmpqYmSktLycjImJDQDGNnNkuSxNmzZzGZTGRkZMzoLF+lUklISAjR0dFotVo2bNhATk4Oc+bMwW63U1VVxbFjxzh+/DglJSXU19djNBqndVbwtYLDVmXhwoWkpaWxceNGsrKynCJreXk5R48e5cSJE5w/f56GhoYpPbZCfDyPSuWE2fq55B/Jn361xyPbmW6Zza7gsFpx+PJnZWWxadMmUlJSCAkJobu7m5KSEg4fPkxxcTEAzc3N9PX1efx49/b2EhQ0M5Y1ej1q3nzzTR5++GH++te/curUKVJTUzl06BBHjhzh//7v/7zdHJ/Gbrdz6NAht99cSRYLlv37ob8fIToa9fXXT8nSEEGA1VGyGNpjgbMDdg8L07dQtfnXiJLARsOrvP/Mtya9LU/1pa/iWN4P8vLziS4PdixtR6NxLm2X7PZx9aep18CZX93F2tIfoRWsFAWsJeALx0nKvH5ijXIRQbi89L7HctlOxNeYjrFZ2wMNRrmPs2JA7cKVRDQYLk9wLV+OctWqIX9XKBVkXH8PK751lJIdL1Hgvx6AVb1HWfzKLkof38j5w3uQRnnY8WZfOsYvR3ydbff4Jr3KVMZlTch8yrO3gyCwsPkC4qGD0/4hbbz9qRh0bunNcpbztU5jTRmNv9xCtvE9rJKSk0nfIucLf0czSvaYvb4e6+HDAKjWr0c5bx4VBmjru9zHSt9aGetVpuP1Z5axcYfQ3N3dTU1NDX5+fl4XmvPy8vDz82PVqlVOgcPhleyJa4HNZiM/Px+tVjtkm8Phip2EWFV1+f0pKVf93VF8MC4ujsWLF4+rreO10Whra+PcuXOkpaU5fYknwmiezZIkUVpaisFgIDMzc8RiesN9bjpf2x3nlWPJflxcHElJSeTk5LBx40aWLVuGv78/bW1tFBQUcPjwYU6fPk1VVRWdnZ3TcpJvOh+viSAIAjqdjrCwMPz9/VmzZg0bN25kyZIl+Pn50draOuTYVlZW0tHRgdVq9VobI//v+zzSIN/n/Lkvkso69z/wzkSxeTiUSiWhoaHMnz+flStXsn79etasWUNMTAwKhYL6+npOnTrF0aNHKS4uprq6mq6uLrefyzPJRsPrDnVZWVm888473HTTTRgMBh588EGeeOIJ/P39vd2UaxJJkrC+9x5SezsEBKC95RYE1dQZFWpVcgGkY41QZZDtHuJ0sPy6eynuqif17P9xXcNTHHxhLlvv/tyUtXM6YbLJhcsAFofI2b2TQRETg2bnTiwvv4xYU4PlrbeQXFxG0lxVgvlfHyLdXoNVUlK45Itkf/i7CF66YDmyuq+Mr1kmjtECRQPC6vJwCHOhvoxksWB57TUwmRBiYlBv3Triw68gCCRnXw/Z11NeUkDH2z8hQ/8Oy83F8N59VB56jJ7Vn2PljR9HoXLtgcZTaFWX46u6Wz7XZuNrcjh85sWERehuvhHN8bexnzuHoFaj2rDB5zzzPIm/CtKjIbcZyrog2h+i3Vurddpw7ugrxL/7WeLpoYtg/irdwefu+PKon7lqBVd6OnozlAw8h6VGQrBvrISdZRa3IknSpIXmvLw8YmJi6Onp8UALh2ew6JuWljZE3HBkyrrbO9Vms1FQUIBarSY9PX1MQcWVAoFSXd3l98+dO+RvjuKDUVFRLFu2bNz7Mh6xubOzkzNnzpCSkkJU1OS8qBQKhVMcvrLN5eXltLS0kJOTMyOtI0ZjJPFVrVYTERFBRIS8dEYUxSGFB5uamjCZTE6/WMfPcH6xs0w9g+NerVYTGRlJZKTsv3XlsR1cVHJw4cGAgACPHFtBo2H73Vt59d0ajgXP50e/f5M/P/5ht27rWhGbr8SR/eywxcnMzHTa6DiOd0NDA2az+Srv58kUETUajeh0M+OB0itR09nZOeRn/vz5/Pe//yUgIIC0tDT6+/udf3M3kiTxne98h+joaAIDA/nQhz6EwWBw+3amC7bCQuyXLsnLSXfsQPCBFP3oAFgSKv9e2CqLpQCpux+haO5HAFh/7jscf/elqWngNEKS5D402+WH6GQ3LQ9WzpmD5tZbQalELC/H+t57Y36m9NAL6P6+jfn2GtoI48LNz5Hzke95TWh2MDi+CgbF1yzjR5Qgv1Ve2h/pf7lfR0OSJKwHDlye4Nqxw+UJrsTk1eR85b+03XeKY1EfoFfSssheyarcr9H8o2QKXvwplv7eye3UJIkOuFyAsnA2viaFwzJClOSJoXkZy1AP+HPaTp/GdurUFLfQ+8wJhPkD1rgFA9YP1xKSKJL7r++y/J2PEU4PFcrF9H30DXqUo1/cJKtVnuDq70eIikJ9/fXYJcEZX7E6WDBrOTzLDEUQhEkLzQsXLnTaAngDh9CsVquvEprhstjszvY4itkJguCS0Oxox5iZzWVlzt8HFwe0WCzk5+cTGho6bPFBV3BVbDYYDBQWFpKUlERcXNy4t3MljrZeKa5WV1dTW1tLZmbmuIsOTnfGc/wUCgVBQUEkJCSQnJzMunXrWL9+PQsWLHBmTJ48eZJjx45x9uxZamtrMRgMEy4G6UmuRTFcFMUR9/vKY7t27Vo2bNjAokWLUKvVNDU1kZeXx5EjRygqKvJINqyw7Xq+1XAIrWgjN2gur7512m3f7ZhkuhbFZgd2u925/w4bnXnz5jmzn9etW+c8lxsaGpzZz47jPd6VDJ7KbP7Rj37EypUr0el0xMfHc//999Pe7tmluV5JaY2MjBz2BJUkiU996lPcf//9zhkjd9/UPPnkk/zyl7/k2WefZc6cOXz0ox/lwQcf5LnnnnPrdjyBQqFw+ebHFey1tdiOHwdAvXkzyvh4t3yvO1geAa39clZbQSusGyhml/rJ31Dym0aS9e+x8shnKQqNYVXm+nF/v7v70lepNEDL4OXBbtxd5bx5aLZvl600ystZFRMz/Hkt2jn9r/8hreIPKASJ86rlhH7iOVYmuO5J525Gii9fYDrFZmkndJnkpf2ZMXKcjYWtsBB7WRkoFBOe4Iqfv4T4z/2R9tYfcPrVn7Oi7jnipRbizz1G57nfUrn4oyy/7atoAoKnpC9XRECbj8bXRJmKuBzOZ16VnAw2G9ZDh7Dl5oJa7fVitu5gMv2ZGgkd/WC0wuk2yI6Z/vHlCsbuTsr++DGyjUdAgNzQHaR+5k8o1dpR+9K5gqutDfz90dx6K4JKxdlWeWWGn0q2WLoW+nAsptP1ZxbXEQRh0kLzokWL0Ov1XhGbHdnFSqWS9PT0Yf1+B4vNrto0jIajmJ0oii4Vs3PgSmazeOKE83dhQDAY7AmdkpIyYcHOFbHZaDSSn5/P4sWLmXtFZvVEcYwRgzMcGxsbKSsrIysra8b4i46XydhKaLVaoqOjiY6OBuTYdhQq0+v11NTUYLfbryo86CvF6a4lxruiQqPREBUV5VxRIIriqNmwk81sFwSBuT/5AQ/83z/5Tdxafnaylc2brYT4u2esBK7p+wS73T7qNcLPzw8/Pz/nuSyKIj09Pc7ig4OP9+DigyNlu3tKbD5x4gSPPPIImZmZ6PV6HnzwQT7wgQ9w4MABt2/LgVfE5oMHXSvu5AmefvppHnroIW6//XYAnnjiCXbv3s3vfvc759IWX0WtVnPbbbe55btEgwHLm29e9ktdudIt3+sulIIsXh2sg9Y+qDDI2YKCQknSZ/9N+S9uINF0lpj9H6U89A0SE5eN6/vd2Ze+imP5OUBKhFz4yN0oFy1CfeON8Pbb3GSxIJw6hTRoaXt/dxflf7yXDOMxEOBo2C4yH/gDfn5Ta5MzUnz5AtMlNtv64VKX/Ht6tLzEfyzsNTVuneCKjI5lw6d+Sk/Ptzn6yu9YVPZX4mkjvOJ39P7yzxTF38XG2x9xy4PoePDl+Joo3o7LwT7zq2OG+syrVq1CslqxHT+O7dgx2VJjmlVCn0x/qgYmdw41QKMRagJmflZudWkBihc+SrrYgEVSkr/im6y9+2vOlTGj9aWtsBD7xYvOgoCKoCCaemWrG5AnMrQzt3bVuJgu159ZPM+VQjPIxbI87SvrEJoFQRi1sJwjW9sd4rcoipw5cwar1UpWVhaqcdgJupLZjMkk/ztgEenYR1c8ocdCoVCMekz6+vrIy8tj3rx54yo8OBaO+3zHvre2tlJSUkJ6erqzeNpEv3O64u72K5VKwsLCCAsLA2SBs6+vzylQVlRU0NfX51zW7/jR6XRe68trzbPZwWTtexQKBcHBwQQHBzsngEwmk3Nyob6+ntLSUtRq9RDrjaCgIJcnwoT4eD4m1bPf1EGlXwS/+OM7fP9LOybcZgeOYz4rNrt+46hQKJzH0IHZbHaey42NjVy8eNH5vuDgYPr6+oiLiyM8PNxjYvP+/fuH/P+73/0uu3fvxmAwTLh47Fh4RWzevHmzNzZzFWazmZKSEn7wgx84X9u0aRN2u53Tp0+zbdu2q95vNpud/+/u7r7qdYVCgVqtxmq1DrnZUCqVqFQqLBbLkIFYpVKhVCqvel2tVqNQKIZsz/G6IAhYLBasVivvvPMON9xwAzqdTs7WucJwXqvVIorikNcFQUCj0WC327HZbEhWK+ZXX4X+frSxsQgbN2KxWJzv9+Y+DUaj0QzZJy2QFATnu7WcaxMJVlgJ0QIoiLn/eeqfupEEex1lz95FzX2vExsd7dxXm8025Ab0yn1y9OXNN9+Mv7+/1/ZpPMfpytfH2qfBx0lQqDhZb8FqkYgOgDl+YLd7aJ8WLMC8bh3vvf021xUUoBIE1FlZtFQWI7zwcVaKDZglNSeXf5OcXZ9zfud498ndseeIr7MdcK5dQ4RWIkDh3eM03D5JksTbb7/N9ddfP+SBx5dizyYpKGhRI9qsJASKRKrBbB79OAk9PRhffx1JFFEuX45i6VIUAxkxk92noKAQ1n3om/Qav8DRd/5B/LmnWSTVktn4LyxP/of88JuIvelhohYmu+04jRV7WsnsjK/iJgjXqgnz850xYrz7ZLfb2b9/PzfccINTvPdU7JlskNcggELDgiAboUo7jhBx7BOrVmHv78dWWIj54EH8FAr8UlKmzVhuMpmc13OtVjvu4xTmpyQx0MKFTonTjRAkQJjOd8YId8ZewRt/ZUXuN9EJZlqIoPXmP7AmZxsIAmazedR7I3ttLZZjx9ACyg0bsEVF0dNrJrce7KJAUpSGCK0ds9m7++Srx2m890ZX7sMsMwODwUB+fj6LFi0aIlAqlUpnUT5PiFkOGwtgVKH5yvZMBlEUKS4upr+/n+zs7HEJzTB2ZrHUe9naS7l2rfOZU6FQDGsPMl5G277DDzo2NpbExMRJbWe47YJ8z9rZ2UlRURErV650+tZeq3hSfHUUp3MseQc5Q36wN3BZWdkQwSo0NJTg4OBxCWOzjI0nxsArs2HtdvuQbNi6ujqsVqszs90hQvv5jVwsR/Pzn/Kdhx7hk4m7eckYyG3VnaxeMPHCoDCb2QxyH0z2nLpyJcOV2e6PP/44+/fvZ/78+bS1tXHs2DGSkpJYtmyZx/q+paUFf39/j3rtT11lOC/Q0dGBKIpERkbyyCOPcODAAfLy8lAqlbS1tV31/scff5zvf//7V73+i1/8wnlip6enc9ttt/HGG29w+vRlP5zNmzezZcsWnn/+eSoqKpyv79y5k4yMDP785z8P2ea9995LYmIiv/jFL4Y8aDz00EOEhITwxBNPOF8rLi7m0UcfxWAw8NRTTzlf12g0fPOb36SyspJnn33W+XpUVBSf/exnKSoq4tVXX3W+vkCr5WM7dnDo5EkOHTrkfH0q9gkYcZ+2fOKblJVV8tS/h+7TR+7fR/vT17NEquXkn+7mz4rtLEtczEc+8hGOHj3q0j4FBQWxbds2r+/TeI7T4sXj26fNmzcTlryFcweex9hUQQlw0Av7ZLFYWKxWs+/AAaLf/jsfE15BJ5hoJoKCld8lv6SG93/84wnvkydjL/meRzlyycDpfd49TsPt0/bt2zl9+jR1dXVDfJN8KfYSktIJX30brYVvcK7sNG+OsU+3bt9O8qVL/LO9nQ5RhBMn4MQJj+2TKO0m3q+X9bajpNjOk9n1GuJzr3NMSqU29mbu/ey3vB57ql0Pcf3SEH72E98YI8a7T3PnzqW4uJji4mK3HafR9ikwbjFp2z9C1/mjPHF47H1a99prXOfnx/MnT06rsby4uHjCx+nQnj/TPrBPxcCHP3wvS5ZM/T6NdpzGE3uSJLIppJbrDC+BAPniMt5QbEN8+wQxi9NcvzcCvrZuHTWBgfx70PsDQqO444ufpei0988nX4i90fbJ1XsjkyNjcxafZjyiyEhCMwy1rhivKDsWDqFZkiRWr17t0vdPVmyWJImzZ89iNBrJzs6e0CqosdogNTZe/s/ixZw5cwa73T4uq47RGElsdvhBh4eHk5SU5HZhzPF9BoOB4uJikpKSiI2Ndct3Tlemov3DFacbLFg1NjZisViGtWdwF9P9uE0ET024DUapVBIaGupcKSBJEiaTyXlsa2pq6O3tRaPRDDm2gYGBTjFS8PMj4/os7igsYW9EMj/8+3Fe+PYO1JPw1XSMN9ficXcw2LPZXVyZ7f7ss89SU1PDkSNH+OpXv8qBAwf4/e9/j5+fHzk5Oaxdu5Zt27axfv347WSHw2g08stf/pLPfe5zbh0frkSQZvB6iIaGBhISEjhy5AgnTpzgxIkT7NmzB7VazT//+U8+9KEPDXn/cJnNc+fOpbW1leBgec2qNzNdzGYzv/zlL/nKV75CUFDQhDJdTAUFWE+cAEFAe9tt+C9Y4PPZOyi1vFsj0m+xsiAYVkZe3qfmCycJ+s/t6DBx2G8r6Z//N0GBAWPuk6MvH374YXQ63YzISHLQblKS26ZCtFnIjpEzmz29T47+/PJ1W7h48Hdki28DcFaVQuR9zxEdM8fr2XCu7pPZBkdaNJhtEvN0VlYOSsqYigw/u93OT3/6U7761a8O8WHzldir7YbiDgVKtZr10VaCNaPvkyRJiG+/jVBdjcXfH+3u3QgDFXU9vU+iKPKb/3uU6/zPk2m+XEyu1C8dxYYvsSBzu9Oo1VOxZ7bBoXqwCmoWhgqsCJn6MWIi+2S1WnniiSf4yle+4pzx9kTsVeqhpBMUCoFtCzQEKEffJ0mSsB46BBcuoFIo4OabUQwqwOSrY3lfX5/zeu7v7z/h49RnlThUB1YJkiLVpERN/RgBk4+91vpyep/7JEm2iwCcjLuX1I/8BKXq6qz64e6NLL29mPftQ+rsRIiOJvjuu5EUCkpbrZR2ydWwN88ViAicmvPJF8by4fZpvPdG3d3dREdHYzAYnPfFs/gmrmShjyY0gywyvP3222zdutWtmU+iKFJYWIjNZiMzM9NlIfvIkSMsX758Qtm0kiRRUlJCV1cX2dnZE96fxsZG6urqyMnJGfbv9qNHsf7sZwDU3XsvbXPmkJWV5TZ7L0dxsfT0dOdrNpuN3Nxc/P39J23TMRpvvvkmarV6iNXKZHCMQdM1C9dgMHD27Fk2bNgw1U0ZwmCB0mAwYDQaRxUox0NNTQ09PT2kpKR4oOW+i6Ng48optiG12WzO7GfHjyiKQ329g4IwfvIz3J50L3qVP49siOMjm5dMeJt9fX3k5uayZcsW9+3INKOiogKr1UpSUpLHtyVJEnFxceTm5rJ06VKKioo4efIkJ06cYM6cOfzkJz+Z9DZsNht33nkn3d3dvP322x71gfdKZnNqaiqHDh1yehCNxZo1a9izZw9z5syZ1HYjIyNRKBS0t7fz9a9/HYCuri7sdrvTsH0wWq122JuP4V4f6aZhpIM10usj3ewMfl2r1TqLfQz3foVCMfz3NDaiOHUKrSCg3rwZ1YIFgPzwMNyNnTf3ycFI+5QZp+B4o5a6fkiwyZXjAWKT1lC7409oX/s4m0wHefMvD3PjF37v8j453jMV+zTScVIqlcPeaLmyTyYbFHXKvy+J1DB3mFPMU/uklCzUn3iMbDEXgPe5jsyd3yMwfp5zvyayT660fXLnE2TGwvFGgbp+7ZD4As8cp9Ha7ngg1Gg0I44/Y+2TA3fHntECpT2gUMHycIgKGnufrLm5SNXVoFQStHMnivCrl295ap/MZjP9yjBWfvVVLlwsouvtn5LZc4DlptPw7ieoen8xfTlfZMV1H0FQysfM3bGn1ULOXDjWKHvExuq0xOmGvtebY4QrbR/tJmO465+7Ys9oU3LJCEo1rIpy+MyPvU/aG27AKknYL12Cd95BddttKBMSXNqnqTqfLseHdogtyXCMdpw0GshMgNxmKDNAbCBE+vvm9enKto/0etE7/2LhsW8wl166CaBm/Y9Zc8PHhn3/cPdGAMKRI2i6ukCnw2/nTgSVCoMZynq1KNWQFgURgd7bp7HaPtbr3j5Ort4beXKp5SzuRRCEUZf4jyU0gxxv7i7e7ijMN1G/5Im0RZIkSktL6ezsnJTQDGMXCJRaW52/67VaMjMz3VpH4srMZrvdTkFBAWq12qNCs2NVQ3R0tFuEZgfTOVvSV9vusGeIiYkBhgqUnZ2dVFVVIYriEG/gkJAQr9c7mU54I7PZFVQq1bC+3g7v58rKSnp7e4nbvo3PHz/JY3O38uSham5ePY/IwImNe4OLgl6rjNezebLb6uvrIzAwELVaTWZmJpmZmXz+8593y/eLosjHPvYxmpqaOHDggEeF5gULFnhHbD537hwFBQWEDyM6jPR+d/jCabVakpOTOXbsGLt27QLg8OHDzmrHvo5SqWTz5s0TCm6xu/tyQcCkJJTTrJhSTAAsDoUKPRS2wnVz5QryAPOyb6O863ESTzzCzV3Psv9v8dzyyf8Z9SIwmb70VSQJClrBbJdFmuTJWTKNi9aqYj7BCyT0tdAvaTgV/RDZtkiEw4exBwSgXDLxGVRvcGV8XT93aEEyb+KrsWmXIK8F7CJE+sOSUBc+U1mJ7eRJANRbt6KY5BLL8TK4L5NSsyH1BaorLlL3+k9Y3f4qC20VcOxLNJ54grbUB0je/gAqbYDb2xEdIBcILNdfji+/aWZa5em4tItyfImSPNmzcBxJkoJCgfqGG5BsNsTKSiyvvop21y4UcXEeaas7cGd/zgmEecHyqoP8Fvn6qPGt4cMlzKY+Tj/zJda0Pg/ARdVSgu79JysXjp45cmVf2vLyEMvLQaFAs2MHQmAgNhHymuXrZLxu5hdUnCi+ev2ZxbO4IjQ7cGeRQEdhPrPZPG6hGSYmNkuSxIULF2htbSUnJ2fSy4XHKhAolpU5f1+5ZYvbH+QHi82O/pQkifT0dI8JQg6LDkEQmD9/vke2MV2ZDgvEhxMoe3t7nZmxZWVl9Pf3ExAQMER8DggIGPbZ2hdEV2/jK2LzlQz29Y4buAe22WwYDAbu3P9ZXohM4aJ/FN/720HuWxc9ockFURR9ct+9id1u99pku9FoBGR7M3cjSRKf+tSnOH/+PO+9957HV6jl5eV5z7P5pptucnlAdmdAP/DAAzzyyCNs3LiR+Ph4Hn30Ue6++24iIiLctg1PoVKpJrRkQbLZsLz2GvT3I0RHo77uumk5SCSHQ1s/dJtlwWZtnHMFPIk3PUhpVx3LL/yO7TU/4+29c7jpzk+O+F0T7UtfpsIArX2gECArBiZhxzQuSg48y4IjX0WHiUai6Nz5Vzav3oD1vfewl5RgefNNNCoVSjdWwfYEyeFy//VYoKAN1sZeji9v4quxeaET9GZQKyAzRo6z0RA7O7G8LdupKFNTUa1Y4YVWDmW4vlyweBkLvvAXmpsbyHvlF6xq+A/xYhPxZ75Hx5lfUbv04yzf+RX8glxbeeMqKyKgdWD8KmiFdXFTE18TxdNxebZDPve0SsiIGn/fCEolmu3bsbz6KmJtLeaXX0a7ezeKYVYt+QLu7s/USOjoh14rnGmTrwHTKb5qy89hfu7jrLGXA3Ai5kNk3PdLtFr/MT87uC/tVVXYTsmWOeotW1AOFFE62w5GqzzJkx49vfrGm/jq9WcWzzEeoRncU5QPZMGiqKgIk8k0YVuJsbKKr0SSJMrKymhubiYnJwd//7HHF1faMJLYLEkS1qIiHFM3fh54kHdsX5IkiouLMZlMEyp06Co2m42CggJ0Oh1ms3laiKveYjo+W4Pc7sDAQAIDA52ryC0Wi1N8bmpq4tKlSyiVyiHZz8HBwdfs8fdVsXk4VCoVERERSD/7KY/+78/5ZOJuDuv9uUsZip/ZRHl5OX19fc7JBccx1ul0w+7jbGazd/ugd6DIbGBgoNu/+8EHH+S9995j//79WCwWmpubAbn+hyeSDqKiovBKr1VVVVFZWUlVVZVLP5WVlW6bOf3sZz/Ll7/8ZT71qU+xefNmVq1aNaToii9jsVj417/+dZWf32hIkoT1wAGktjbw90dzyy0IHroB8TRKBWRFyyJXSx9UGob+ffkHHuNC3C6UgsSmom9w+OAbI37XRPrSl9Gb4VyH/HtqJAR7bgWEE0m0k//Xh0k+8ll0mChgOeJ975KSuRFBEFBv3Ypy6VKQJCyvv469rs7zjZoESoUs0CgEaOmFqu6paYcvxmZbH1zqkn/PiAb/MYYQyWTCsn8/WCwo4uNRb9zo+UYOw2h9GRs7h82f+Tl85RyHFj1MExFEoCf90q+x/XwFhX/5Ij3t7otZpXA5vlr75Mmh6YQn47KpF6oG+mN1zMRXFQhKJZpbbkERHw8WC+Z9+xA7O93XUDfi7v50TAIJAjQYoc7olq/1Cqde+SNh/9zGEns5egIp2vAH1j70B5eEZrjcl6aWFixvvSWv4EpJQTXgIdlolC1sAFZHT8+sb2/hi9efWdzDcKLBeIVmcE9msyiKFBcX09fXNylbifEK3xUVFdTX15OVlUVAgHtWMY3WhoqKCpR9fW7Zzkg4BPeSkhJ6enrcbtMxGIfliUqlclp0XKti40jMlP7QaDRERUWRmJjI6tWr2bRpE6mpqYSFhdHT00NJSQmHDx+moaGBnp4eWltb3bIKfbogSdK0E1yFefPI6G1iR9dFJEHgL0cbWb58OWvWrGHDhg0kJiai0WhoaWkhPz+fI0eOcObMGaqqqujs7HSO+7Nis3dtNHp7e/Hz8/PIBOIf//hHamtrSU1NJS4uzvlT50HNxisq5FQuuREEgccee4zHHntsytowUSRJoqKiYlwXMntREfaLF0EQ0GzfjsIDKfjeJFgLKRFQ3C6Lq1H+Dl9PQBBY9qk/UfabFpZ0nyDl/fs5HfQi6Zlrr/qeifSlr2IbWH4uDSw/98by4N7uTiqevpfM3uMAHA7bzYGuuXwr5vKy9auWtu/fj+b2252ZZr5IyKD4Otsu20V4Q7gfjK/FpsUO+QOWg/ODIX6MiVVJFLG89RaSXo8QFCQvY5+iJdmu9GVoaBibP/Yd+vof5sj+PzHn/B9ZJNWRUfd3LL99ltPRtxK34xvELkyedHuCNXKB06I2KBkYv0KmieWpp+LSZJNXqoBsZRMzyed/Qa1Gc9ttmF96Cam1FfPevXKG80A1b1/BE/0Z7gdJYVDaKWc3h/tBoA9bLhoNHVz4ywPkdL8DAlzQJBPx8X+yas7icX2Poy/NNhsaxwTX5s0A9NugsE1+35JQnAVzZxkeX7v+zOI5JiI0w+Qzmx1Cs9FoJDs7e1K2EmNZWAymsrKSmpoasrOz3ZohNlJmc1VVFTXV1TiqBwiLxzeuuYogCPT392OxWMjJyfHY0m5HJrrNZiMrKwuFQjFqVvdEt2EymfD39582WaODmY5tdhWFQuHMaAb5WmEyyVmxRqOR6upqjEYjfn5+Q6wZdDrdjBQmp1Nm82CEp57iy194mIPBCzmj17D/XCs7V8ag0WiIjIx0FlsVRXGItUpTUxMmkwmdTodWq3X6CE/Xc3WyeFNsNhqNI2aZT5apuNebeaPBNYy9vh7rkSMAqDdsuKpg0nRlUYgsSIiDPGQdCCoNiz77AlXaJMIFI3Ne/RDlF89OXWO9wNl2MFrk5cEZXlgeXF9WRMevNpLaexyzpOb4yh+S/cCTKISrhw/H0nbFvHlgtWJ55RXEQcVSfJFFIbIgIUqy/6n9Gn7mliQ43SoLgoFqOWt+LGwnTiDW1IBKhebWWxHclD3kaQL8/dl49xeZ+z9nOJ79O0qUSWgEG+lt+4j+23pO//x2ys4cmfR2FgbLk0LDjV/XGg6feYubfeYFjQbtrl0IERHQ24tl3z6knh73fLmPszQMIvzkuCoY8MD2RS6cfB3jr3LI7H4HuySQO+9+lnzjEFHjFJpBnuACkAwGhMBANNu3IyiVcny1gNUOoVpY7vtuabPM4hUmKjSDLPBONLNZkiTOnTuH0WgkKytr0v7Frgrf1dXVVFVVkZWV5Xbfy+GsPOrq6qioqCBzYHUFgMJDYnNLS4tTAHaHLchwSJLE+fPn6e3tZfXq1c4Mu7GKTo4Hh9/0yZMnOXbsGGfPnqW2tpbu7m63Ctqe5lqZqBMEAX9/f4KCgggJCSE7O5tNmzaRlJREQEAA7e3tFBYWcuTIEU6fPk1lZSUdHR1YrdapbrpbmLZic0QEMYFqPt2aD0jkv/FvzJ31V71PoVAQFBREQkICycnJrFu3jvXr17No0SLnmJebm8vRo0cpKiqiurqarq4utxaP9WW8LTZ7wkJjqpie/gqzXIXY04PljTfk5aTLlqFMS5vqJrkNQZBF1ffqoNsCJZ1DRTClXxBxn3uN2t9czzxbNZbndtN0/1vEJfi2Z/BEuHJ5sNbD417Re/9h0eEvE0Q/LUTQsfMvrMvcOurSKcfSdsvLLyM2NmLet0/ONPRRn3RBkPvyQB0YzFDaASkuiKwzkZoeaOyV+yQrFlRjTEfaLl7EVlAAgPr6633WL3c01CoV63Z8FPHmj1Bw7HWEo78iw5xLes/7sO99il9Pw5r9EGlb757QjYYgyJ7EB0yyR/G5Dlg1/brJLXjSZ17w80O7axfmPXuQ9HpnhrOg07lvIz6IQpCtSN6rg04TXOyC5V4sFjsWpv4+Tv/j6+Q0PotCkGgQYuje8STZ2dsm/J223Fz5l4FrjeMYl+nlOg/KAYsR5fR7LpxlFrfhEEYmIzSDbKMxEUFBkiTOnj2LwWAgOzvbLRm4rojNtbW1lJeXk5WV5ZHiR47saof41NjYyIULF8jMzCTIZMJhSCN4YFVfTU0NLS0taDQadB68tl26dIn29nbWrFkzZILAXZnNjkkIs9nMunXrMJlM6PV6urq6qK6uRhTFIV7B4ylm5k2mo/g4WQaL6yqVivDwcMLDw51/c2TH6vV6mpubndmxg4/ldMyOnc5F8oSf/pSPfOZB4uNL+aDyAOf2X2D+x54e83NarZaoqCjsdjtWq5X09HR6enowGAx0d3fT0NCAxWIhMDBwiPezn5/ftO2rkRBF0as2Gp7KbJ4KZsVmH0alUrFz584xPVski0X2S+3vR4iKmrYFAUfDkcV7ogkq9HKm8+Dl137BkYR+Zj8NT20jQWqm+pmddH/2XYIjYwHX+9KX6R+0/HxJmGeXB4t2kZP/+BZrqv+AQpA4r04m6r7/sCJuHjB2fzqXtu/di9TScllw9rGl7Q4c8XWySRYsogO8t/zaV2Kz2yLbiQCsCJczA0dDbG3FeuAAAKrVq1EtW+bhFo7NZPpSoRBYvfEW2HgL5edOoX/np6TpD5BqOQNHH6D22PdpSvoEK295gIDA0HF9t1YlC4LHG2Xv+ZgAOdvZl3F3XHaZLvvMr/SQz7yg06G9447LgvPAuCP4+bl/Y+PEk+e5Tg1pUfLKjAudEO0PEZ5JeBsX5edy4aUHWCtWggCnQm9hxSd/x5yQiavhtosX4cwZbgoIwP+661DExACy0H5+wK57ZQQEedkOabriK9efWTzDZIVmmJiNhiRJlJSUoNfr3SY0O9oyWqZkfX09Fy9eJDMz07n83904LAJEUaStrY2SkhIyMjIICwtDLC11vk8Ic2/R4YaGBi5dukRSUhLl5eVu/e7BVFZW0tDQQE5ODn5XXDvdkdksSRIXLlygq6uL7OxsBEHAz8+P0IHnA0mS6OvrQ6/XYzAYKCsro7+/f4hgGRoa6jOC1rWS2ewKwxUeNJvNTmuGhoYGLly4gEqlGiI+BwUFeU3ImyjT0bPZgRASgnZpIuHNrTAfVrS+TlNtMcHzUl36vMOz+UprFQCTyeQ8vnV1dZSWlqJWq4cc38DAQJ8/vmNht9u9dvxnUmZza2vrrNjsyyiVSjIyMkZ9jyRJWN99d2hBQB+c/XUHsTrZ8qDSIC+VvX7u0MJSwdFz6fvEK7T+9WYWiHWU/eFW+MLbBIeEu9SXvowoQW4zWMWB5cEezFwz6Dsp++PHWNd3BAQ4GXkHGZ/+Axrt5ZtOV/pT0GjQ3n67LPx0dGDZuxfNXXf5rI94nA4WhsiFywpa5fjyRmEpX4hNmyjHl12EqADZ63Q0pL4+eYLLZkMxfz6qtVf7pE8F7urLxJQcSHmR1royql//BUlN+5gnNTKv9P/oLv01p+LuYOGOrxA9N9Hl74wJkD2KK/TypNF1c+VJDl/FnXFpsUPugM98nE62FvEUQlAQGkeGc0cH5pdfRnvHHQiTXL49WTx9ns8Nkgvp1vXInuvXJYB6iu7tbRYz+f/+PhlVT6MRbOgJonrd4+Tc+NFJfa/Y1ob1wAGUgsDqtWtRr1gBDMRXsxxf8YHeqWMwU/CF688snsEdQjOM30bDITR3dnaSnZ19lWA5GZRKJSaTadi/NTY2Ulpa6hR+PYVDNGltbeXcuXOsWrWKiIGVe1JX1+U3hrvvRr2lpYXz58+TkZGBWq32mM1EXV0dlZWVZGdnD5s57Y7M5qqqKpqamlizZg1arfaq4qSCIKDT6dDpdE7B0mKxOMVnh2DpELRCQ0Odgpa3xUBfELungvHst1arJTo6mujoaEAW7RzZsQaDgdraWmw2m9Oew3E8J2u5426mq42GA+HRR8n65Cc5EpvGRu0Zet74PsEP7HHps6MVCPTz88PPz4+YgYn/wcdXr9dTU1Mz5Pg6fjzlNe8ppsKzeSbw+c9/ftaz2ZexWCw8+eSTo1YJt+XmYi8vB4UCzS23oPDAkjFfImUgY8lsl4sAXTmhHDt/GcYPvEgXQSyxlVH/21vpNnS61Je+zPkOOXNLpZDtDTy1PLjqfD6GX28gs+8IFklJXur3WPP5vw0RmsG12ITLS9uF0FCknh4se/ci9fZ6pvFuICUCAjWXi5h5I2FhqmNTkuTiYj0W2ZYlcwwfcMlux/L660hGI0JoKJqbbkLwkdl+d/dl9NwlZD/wFKqvnSc36VHqhTiC6SWn6V+E/zmLMz+/nUv577ocKMnhslex2e69+Joo7upLhw94nxUC1N7xmVeEhqK94w7w90dqacHyyitIU+wb6I3zfFWU3Md9Vihq99hmRqX6/ClqfrKONdW/RyPYOBuQg/jgcdImKTQPnuCyJSTwp6IiLBaL0we83yZneGdEeT6+ZhJTff2ZxXO0t7dPWmiG8dloSJJEaWkpHR0dZGVluVVohpGzrJubmykpKSE9Pd0p/HoKh+hy9uxZUlJSnCIagKTXO38X3LSSr729neLiYlJTU4mIiHB7kT4Hzc3NXLhwgYyMjBHtRwRBmNS26+vrqaysJDMzk4Bx1PfQaDRER0ezZMkSMjMz2bRpE8nJyQQFBdHZ2cmZM2c4fPgwhYWFTq/gifqMj5drLbN5svurVCoJDQ1l/vz5pKamsmHDBnJycpgzZw52u53KykqOHj3KiRMnOH/+PA0NDRiNxinv52kvNut0KDIzUdTEYZWUJBlP0Xb2XZc+O5rYfCWjHV9RFKmurubYsWMcO3aMc+fOUVdXNy182r1tozFTMptfeOGF2cxmX0aSJNra2kYcYO3l5dhOnQJAvXUrSg/4g/kaSoXs9fl+PTT3ylnOi0OHvmfR8tVU3PkfVC/dzQpbCRd+u4Poz+wbtS99maZe2doBZG/hQA8lrp/e/zTL8r5DgGCmhQh6bv8LWRlbh33vWLE5GF9e2n4lqkHx1dQL5Yaxs3wny3j60hPU9MjZkCBPZIyVbWs9cgSxsRE0GrkgoA8dR0/1ZUBQGNkf/CZ2+zcofO95VHl/INVyhrSe92H/+1x6cyn61E+x6qaPo9WO7F+gVEBWNBysl7NQhxu/fAV39WWFQfYBVwiQHeOd1QIAivBwp4ez2NiI5bXX0OzciTBFS/m8cZ6rFfJk0eEG+ZyODoB5XlpIYjb3U/iv75BZ+wxqwU63pONSxv+weudDk56Mck5w9fQghIaivv562n71KyRJokwv3wsoBMiOnbps7unKVF9/ZvEciYmJbhHclErlqHU6HDjsEVpbW8nJyfFI8brhxObW1lbOnj3LqlWriIz0fMGN7m65cEpiYiJxcXFD/jZEbHbDKr6uri5Onz5NcnKyM3PQE2Jze3u7sw/DR8nIVigUEx4rWlpaKC0tZfXq1ZP20lYqlYSFhTkz2Ad7BRsMhiFewY5MWU94yU5n8dFXEASBgIAAAgICnOeT1Wqlu7sbg8FAa2ur0zZmcGZscHCwV+2fprvYDCB84QukfPzjHOzP5saAEygOPgYp14Ew+j3aeMTmq7Y5zPG12WzO49vZ2UlVVZXTp32wV7svZbd7M7N5JonNkiTNis3TFbGtDcvbbwOgTEtDlZw8xS3yHiFaOQO1uB3Odsi2Elf6Uy5O3UC5+Dzivg+QZCvl4h9vQyndMDUNngS9VtmHE2RRKt4DY4/F1E/RXz5LVttLIMBZbQYJ9z9LYpT7Ji+uWtq+b5+8tN0Hl9GEauUClEVtUNIBYVqI9AH/U09gMMv7CbAiAqLG2E/b2bPYi4tBENDcdBMKNy4TnQ4olUoybvgQ3PAhLhWfpPO935De9TZLbZeg8BHaCp/gUsJdJG7/HDFzhs8mCx40fp3rgHA/CPMdvd6tDPZpTonw/n4qoqJkK599+xBra7G88Qaa7dunTHD2BhH+kBQuezefboUQjXzN9CSlBe+jfe2LrBVrQIDCgPUk3PsUmXPmT/q7JUnCeujQkAku68AEV2f/ZZ/m1MixfeZnmWWW8aNSqegdY0WaQ2huaWkhOzvbI0IzXC02t7e3U1RUxMqVK4dkGHuK7u5u8vPzEQRh2O1JbW2X/zPJZdDd3d0UFBSwbNky4gclEzkEX3eJX3q93iloj9WHE81s7uzsdGZnjyZmT5SxvILr6+udXrKDxWd3WG/MTtS5H7VaTUREhHOVgiiKQyYTmpqaMJlMzsJ0nppMGMyMEJv9/ZHS04m7eJGeFD8WWMspPfYcCRvuHfVz7varHq6wZF9fn/P4VlZW0tvbi7+//5DJhcDAwCk5Bo6CsLOezRNjVmyehgzxS507F/WGDVPdJK+zKAQ6TNBglL1AtyZcnZGZmLaRcuEFpL33sMx2kTtFE4bOh4iOS5iaRo8T+4CPrk2UBalkD6wMbKy+iPFfHyHLdgmAo3M+xZpP/BiVB3y/HUvbzXv2ILW2Ynn5ZTS33+6TgvPCYNm2pK5HPga+7q87EawD8SVKsp/w0tDR32+vrcX6/vsAqNasQTnJpbnTnaWpayB1DR2tDZx+/bckVj9HFF1E1f8J2x//QkHgBvzXP8DyNTuuyuxcFAJt/XL2/KlmefzSzrD4Mg/yaY4PlPd5KlDExaHZuRPLK68gVlZieest2fplBgvOy8Lk8au1T46vLQmeySg3Gjoo+efDZLXtRSFIdBFMVfb3SL/5k26z1rGfOYP93Dl5guvGG+UJroEsy4JWkBSQMOvTPMssV+Guh/KxCgRKksSlS5dobm4mJydnXPYI40WhUDjb0tHR4RRJY2NjPbZNB0ajkby8PBYtWkRVVdWwfSK1tjp/n0z9nN7eXqff9rx584b8bXCBwslm2vX09FBQUMDSpUuHCNojMZHM5u7ubgoLC0lKSnJmZ3uD4byCh8umHCxWhoSEjCtbdrqLjxPF2/utUCgICgoiKCiIhAT5Gd5kMtHd3Y1erx+xMF1QUJDbBMLpXCBwMMIXv8i8T36SI9057Ag5RGj+r5DW3oOgHHm8mkxms0ttGuTT7hiHRspuH5z5HBwcjNoLdcocY703M5snu/rDl5hhj7czC7Vazb333jvkRJLsdiyvveZcTqrZvt1n/FK9iSBAejQYLGC0QF4LrI+Xl9IOJnHVesp4EXHv3axQ1FD+p1sRPv0KUXHzhv9iH0GS5MxHvVleFpztAZ/mgrf/zeLjXyceI3oCqdz4SzZcf49Lnx0uNl3BubR9717E5mafFZwFAdKi5P7vscii2YZh4ssdTLQvJ4MkyRnzRiv4q2B1zOg+p2JnJ5bXXwdJQpmUhCoz02ttHQ9T0ZcR0XOI+MQT2Czf48y7/8TvzF9JspSwuvcwvH2Y2ncTaFn2EVbc/AC6EHkWXxDkPn+/Tj4GuSOMX1PJZPpSlCCvWfYO9gUfXWVCApodO7C89hpieTkW8Lrg7M3YVAiyHdDBenl1TEErrIl13zGQRJHTr/+Z+fk/Ige9nM0cso3FH/0tGZHuWxFjr6rCevQoAKp161AuWgSAUqVm1c33YkFNkBrSvOADPlOZijFzlumFSqUa0Y5DkiTKyspobGwkOzvbo0IzyA/7oijS1dVFYWEhy5cvd0kknSx9fX3k5eUxb948Fi1aRG1t7bAZvoPF5onS399PXl4ec+bMYdHAmDcYd4nNfX195OfnM2/ePObPd20VyngtPPr6+igoKGDhwoXMnTt3ok11C6NZb+j1eqf1xkSyZWdC1qur+Eomt6Mw3XCTCV1dXVRXVyOK4lWF6SZqzTBTjrGg0yGtWMGSigo6VwURZ2/i3KF/Mv+6+0b8jKfF5uG4Mrv9SquclpYW+vv7CQgIGHJ8AwIC3H6cHGOeN8Vmb1zXvMWs2OzDKBQKEhMTnf+XJAnre+8hNjXJy0l37vQpv1Rvo1ZATqzsr9s+sJw2ZZjs3yWr1lKt3gvP7yZRrKLxj9to+Mge5ixe6f1Gu0hVN1TLtnBkxciCoLswm3o58+cvkNMuV6EtUy0h8KPPkjF/mcvfcWVsjgdFVJSc4TwgOJtffhmtDwrOqkHx1dEvW2qs9IAd4GT6cqKUdg71OdWOcv2U+vqwvPIKWCwo4uNRX3+9z95wTUVfOlBp/Ejb8WnY8WmqS07R8t6TpLS/yTyxnnmlT9B3/pfkRtxE+JaHSExdJ49fcYPGrw5I8bzdpMtMpi/PdciZ20qFLHL6go+ucsECNLfcMmWCs7djU6OUPbIPN8jn+iW9nPE8WaounMH40pfIsJwBoFaIx3DdE2RsvH3yXz4Isa0Ny5tvyhNcycmoMjKcfzvXqUCKSHSeQ+prb77dbUzlmDnL9GC0zOby8nIaGhrIyspCN0nbCFfbYrFYnPYSjixHT9Lf309ubi5xcXHOc2VwhvUQBttoTACz2UxeXh5RUVEsXbp02PcMFpsns538/HxiYmLGdf4LguCy2OjYRmxs7LCi+WCmQsgby3rDkS2r0WiGiFmDrTd89V74WmS4yYTB1gwVFRX09fVNWJycKWIzgPDVrxJ9//0c02eyI+wg0UW/Rdr4YQT18JqSN4vjjcRw56vFYsFgMNDd3U1zczOXLl1CoVA4s57d5e1tt9tRKBReO/5Go9Er11Nv4bVb9PXr13P69GlvbW5GYDabefzxx52FOWx5edhLS+XlpNu3owhzw5PjNCdYIxfNAyjrulzo7EriFqfyjOoj1AuxxEstBPxzBxVnDnuvoeOgtU/OagbZOiPGjYkiNefzaPrpWqfQfCr2Qyz4+hHixiE0w9WxOV4cgjN+fkgDgrM0we/yJEGD4qtcD7UjxNdkmGxfjpcGI1zskn9Pj5YtWkbCuZKiuxshJATNLbf4tP2At/tyJBYk55Dzhb9j+0opx5d9k2rFXAIEM9mdr5D40nZKH8vh5J5fozAbLo9feqj3QHxNlIn2ZU03VOjl3zOjZY9qX8EhOKNUyoLzW28hjbI83J1MRWyG+cGqKPn38x2ybctE6TF0cuwPn2fOc9ez0nIGk6Tm+LwHif5GASvdLDRLvb1YXn0VrFbZKmzLFudNfnU3lLebOfffx0kNNRPsO/VjpiW+MmbO4n7caaMxXGZzeXk5dXV1ZGVlec1f0mQyYbFYSExMvMpewhMMFn+XLVvm7FNHhrU7sVqt5OfnExISwooVK0Y8fo7XJ7p9x3ZCQ0NZvnz5uOLE1cxmm81GQUEBISEhJCUlTRuRzmG9sWTJErKysti0aRMrVqxAp9PR0dHBmTNnOHLkCKdPn6ayspKuLvlm2leyfb3FdDieDmuG+Ph4li9fzpo1a9i4cSOJiYloNBqam5vJy8vjyJEjFBUVUV1dTVdX14gTazNKbA4OhoQElldJtIqhRIttVB98ZsT3T0VmsytoNBqioqJYvHgxGRkZbNq0ibS0NMLDw+nt7eXChQscPnyY3NxcLl68SFNTE319feM+Xx1is7eYSQUCwYuZzdHR0WRnZ3P//ffzox/9yCMFAmYiFosFANuFC9hOngRAvXkzSheXPF0LzAmEJaGyWFPYKi/bHk5A67VrUXzydcr+fjdL7BVo995FYftvydj2AW83eUR6rZd9TucGyfvlDkS7yMnnf0LGhZ/hJ1jpIIS6zT8nZ+vdE/5OR2xOlMEZzpIPZzjHB8LSMLjUJcdXgMr9BQMn25euYjDLS+oBEkNh3ijF0iVJwvruu0NXUnio6I878VZfukJIaDjrPvQokvgNzp18C/OJp0ntPsxy2wU4+12MxY/TELGNsPT76YzZREGrgoARxq+pYLx92WmCMwOJXUnhniloOlmmMsN5KmJzQbBcqLG6W7ab2jxnfAUD7TYbp1/5HQuLf8F6DHIRWf9sIu75DesWLnd7eyWrFfP+/UhG42WrsIFj09F/Ob5Em4XYmZP4MaX40pg5i++hUqmuEmAqKiqoqakhOzvbaw/FPT09nD9/HkEQWLBggce3Z7FYyMvLIzQ09Crxd0zRdZz3sTabjfz8fPz9/Vm5cuWoopYgCBMu1Ge32ykoKMDPz4+UlJRxi2euZDbb7XYKCwvRaDRj7ouvM5L1hl6vdxaqA5xxEhISQmhoKH4zeNXxdBbW1Wo1kZGRREbKywhFUcRoNA4pJGm1Woe1UhFFcVrH8pUI3/kO4Q88wLGuDHZEvMecc08hbvkECs3VGW6+KjZfiUKhIDg4mODgYKdtz+DVCg0NDVy4cAGVSjXE9zk4OHjUzG273e7VzO5ZsXmC7N27l2PHjvH1r3+dpUuX8sMf/pAHH3xwRp24nsLe0IB44AAAqowMVKmpU9wi32NFBPRY5eXCJ5tgc4IsOl9JREwCgV98l7NP7maluZC0Iw9wqukc2fd+f8q9ry12ONEEVruckZbuJp/T5rpyWv71GdaZC0CAIr8s4j/+DGk+4Ft9leC8bx/aXbt8TnBeES576zYa4eRAwa3AaWZx2W+T48suQlTA2AUnbXl52C9elFdS7NghF+aaZUIICgUp67bDuu10tdRS9vYfmVP5AnNoJrvzVTjwKpWK+ZyNv4ejWfdz/YrYYccvX8ZokeNLlCBWB0k+vPDmKsFZktDcfLNPZ+1PhlVR8kRmW798jLYMU1B3OIqPvILu/e+Saa8CoE6IR7/hu6y87oMeMUmWJAnLO+8gtbSAnx+a225zWoX1WOBEszwRG6eDErdvfZZZZhmOK200Kisrqa6uJjs7m6CgUWas3YijOF9CQgLV1dUezzJ0ZP/qdLphRdmxiiYyDqHAbrdz+vRplEolq1atcknUGa93MsiC0ZkzZxAEgbS0tAmJR2NtV5IkiouLsdvtZGRkTAuBajwMXsqfkJCAxWLh6NGjzJ8/n+7u7mGtN0JDQ9HpdDOuL2YCV4qTkiRhNpvR6/V0d3dTU1OD0WhEq9UiiiIajcZ5/Kf78RTCwpCAlGo7DWERzFF0UPTOUyy65WtXvXe6iM3DcWWhUFEU6enpGWKXM9wEg1ardY77s2Lz5PCqZ/P69es5fvw4L774Il/4whd4/PHHCQ0NHfKe4uJibzZpWmB56y20djuKxERU69dPdXN8EkdBpMMNcvbmiSY5g2s4r9DgkHCWf+0N8p6+n6yOV8mp+A2FvzrP8gf+jr9uak5uuyiLmD0WWQTIiZX9TieDJIoU7Ps1ScU/JpZ+zJKa4qQvk/mBbyIofEdUGSI4t7TIgvPtt/uUH7kgyHYafVa5aKAjvjS+042jYrXD8UZZcA7UyF6uoxWjs50/f3klxdatKL2wXPVaISxmHtkffQxJ/AHnT75J78lnWGl4n0ViDYvqf4q57pfkv7kBbebHSNt0G6ppULjLbIPjTfKEWahWHot9fR55iOBcUYHlzTdnrODs8GY/VC9Pmp1ogo1zZF/64agqOUXX/v8lo/8EAN3oOJv4IJl3f525Ws+sbpAkCeuRI4jl5aBQoLnlFhQD94emgfhyTMSmRcC7HmnFLLPMHNxtoyFJEtXV1VRVVZGVleU1odlRnC8hIYH58+c7C3956uHfZrM5M3NHEn/HEl0FF/02RVGkqKgIm81GVlaWy/s0XrFZkiTOnj2LyWQiOzt7wn03WmazJEmUlJRgNBrJyclxySd1uiecOdofFRVFbGwsIMePo1BdR0cHlZWVAE4P2dDQULf4yE4l0/24jYQgCPj5+REbG3vV8SwtLcVoNHLmzBlEUXQeT8fPdCyyK/zsZ4Q8/DDHOtOZE/ku8y4+g3jDQ1dlN09nsflKHJ7OISEhgDxumUwmp/dzbW0tRqMRjUbjPMaSJHlt/x2rJ7x1ffUGXh/pqqureeGFF+ju7ua+++5zLmWY5WpUViv3xcWh7u9HEReH5sYbZ+wA7w5UClg7UHCrxyKLt+viZNFWrVbz0EMPOS8GKo0fmZ/7Byee/wmZpT8ho/tdyn6+Ee2H/s68Jd7NHBclyG+VlwirFHKbJ1sQsL7yAp3/fYhMcyEAF1RJBNzzNFlL0ybfYK7uz8lyleD80ktyhrOHK5uPB5UC1gzElyOLc338yIKNq7i7L6/ELsnnQrdFLgS4Lm50kdxeXY3VsZJi9WpUKSkeaZcn8HRfuhNBoWDFuh2wbgf6jjYK3nmG6Ev/ZYlYwfr+9+HI+7QcCaciZjuxGz/OopQcr7bP1b60DUyU9VohQC2PwZM9J7zFVYLz/v1oduxA8ED8THVsapSwNl4ev/Rm2a5pTezQSafGyvM0v/xd0vTvslCQsEkKCqPvYOk9P2J9VJxH22crLMR+5gwA6htuQDlQAMYmymNtnyO+YkGtmD7nua8z1XE5i+/jEMWqq6upqKggKyuL4OBgr2x7cHG+JUuWOAVWT2WaObKMBUEgPT19RIFhzMxmF8RmSZI4d+4cfX19ZGdnj0t8HI/YLEkSpaWlGAwGcnJyJnWuj7bd8vJy2tvbycnJQaNx3Ux/JjzXDhbgVSoV4eHhTrtQSZKGWDU0NzdjMpkIDAx0Wm84rBpm8T0cx1Or1TJ37lyio6Pp7e11Hs+ysjL6+/vR6XRDJhT8/f19PraFefOQgNRqC/XhkSQo2ik+8GcWbv/ikPfNJLH5SgRBwN/fH39/f+cEg91ud04Y6fV6p5d3QUHBkEkGrYdWYc+KzRPEYDDw2GOP8dvf/pZbbrmFkpISr3huTVckmw3La68R1N+PIjgYza23IkzjWVBv4a+SxY4jDdDeLz9Q58TKg0lISMiQgV9QKFj7wUc5dyyFOe88xBKxkr5/baNw9f+SsfOzXknNkyS5GGCjUX7oXxM7Pj/NK7Ga+yl47gesqvozCYIFk6SmMPHzZH3wf9z6MDlcf04WRVQU2jvvxLxvH1J7O+YXX0R7xx0IPjTg+qtksfZwg+xPm9sMOXGgnEQ3eKIvHYgS5LfI54JSAevih7eXcb6/pQXL66+DJKFMSkK1bp3b2+RJPNmXniQ0Ior1H3wESfoGxYXHaT38F7IMbxFDJzEtz8KLz1KxdyHti3ax5PpPEh7rec9+V/rSsSKj0wTqgYkyV+wZfAnlggVodu7Esn8/Yk0Nlpdflu0bxvGw7Aq+EJuBA2Lt0UZo6YWCFlgdAy0NldS89H1Wd7xKvGAHAQoDNxNz+w/IXpLm8XbZLlzAduwYAKoNG1AtkwvW2kXZFktvllcprYsDrQokaer7cqbgC3E5i+dwxWN3LBwiqENodmSEeRqTyUReXh7R0dHO4nwOwWNUoXeCjCfLeLKZzQ4BWK/Xj1ucdWX7gykvL6elpYWcnJxJiyMjeUXX1NRQW1tLTk4O/tOgtoe7cGXcFASBoKAggoKCSEhIAHBaNRgMhiGZlA6hMiQkhMDAQJ8cl6ezZ/NkcFj3DLZSmTMwKW6xWJzic1NTE5cuXUKpVA7JfA4KCvKqFYOrCN/+NkGPPcaxrlUkRBwgrvQZpBseQFBdHitmstg8HFd6tdfX19PS0kJ8fDwGg4Hq6mqMRiN+fn5DjrG77HJmmo2G1yJn8eLFvPHGG+zfv589e/bMCs1joVRij4vj13o9bN/uUxmevk6oVs5AVQiyh3NhK5jNFp544olhC+GkrL8V+2cOUaJJJUAwk1H4LfJ/cSdd7S0ebackwdkOqDLI/18dLXvpTuy7JM4c3EPLExmsqX4Sf8FCqTaVjo8eYN1Hv+f2rCWLZeT+nAyKyEi0d92FEBSEpNdj3rMHUa936zYmS4hWFj0UArT0yYKNOIl7L0/1pSjJbXNMZOTEyufGiO/v6sL8yitgs6GYPx/19df75I3uaHiqL72FIAikrl7Pqgee4eAHy3l++e/I9duARVKy2F5FTtkvCXlqFWef2Mqpfb+nu7vLY20Zqy/tEpxqgbY+eSJjbRwEuVef9RrKefPQ7NoFGg1iYyPml15CMpncug1fic0If8cELFRVlfP+rz9F2J+yWdO5D7Vgp8gvk9LbXiXj4VeY4wWh2V5Tg/WddwBQpaejzsiQX5fgVLPsM60cmMhwxJev9OVMYLYvZxmL+vp6AFJSUrwmNJvNZvLy8ggPD2f58uXOexFBEMbOKp4ADq/h/v5+MjMzx8wyVigUV7VhiAg3xjNbWVkZra2tZGVlTUgAdlVsrq6upra2lqysLALc8Bw53HYd4trq1atnlEAyHka0FhFFTP29dLbU0VxzkYbyYmov5NN4qYCehhLQVxIqdREfaCVI6qK3+SLlZw5z9K0XeXvfvzl68A1KivJpbmrAOjtGTymj+cRrNBqioqJITExk9erVbNq0idTUVEJDQ+nu7ubcuXMcPnyY/Px857lvNpu9vAcjsHIlACuq7bSIoURJ7VQf+seQt1xrYvOVOPy64+LiSEpKIjs7m02bNpGUlERAQADt7e2cPn2aw4cPU1hYSEVFBe3t7RO6rxJFccaJzV7LQfr2t7/NF77wBZ+c1fFFBEFAnZMDBw+iCPPhSks+SpS/7FF5qhnqesBmHf39kfGLCP36exz953dZU/MHMnveo+N3WZzO/l/Sbv6k24sHShKc64AKvfz/9GhImGACb/W5Uxj2f4s0Uz4AbYRSlfFNsm79zJQXPZwIitBQNHfdhWXvXllwHshwVkSMUdHOi0T4yxMaJ5qgwQgCcobgaD7I3kSU5EmWeqMsKmXHQswozxlSby+Wl1+G/n6E6Gg027fPSO/a6UJUAKyZ689J9UdpWP1RLGILitP/ILTsJVbYzrPSVAhnCuk//T0KAtcgJd/Bik27CQj0jhAgSpDfLGfHKgRZaI6Y5slMyvh4eWXFyy8jtbZi3rNHtvJx0XtzOmFtOkvsq4+zWv8m6oFM5ouaZMTrvsOqNdu91o4hKymWLkW1YYP8uiSvGmnpk+NrXRyEz64wnmWWcTPZzOa6ujouXryIUqlE56Wx0GKxkJeXR0hICMnJyVcJPMMJvZPBYWfR09Pjss2EUqm8Wuwd9P/RrJgqKyupr68nOzt7wlnArojNDQ0NlJWVkZWV5Tbh4srM5vb2ds6dO0daWtpVNZhmMv19RtrqLmFsrcZWWURJ8/uo+tvwM7USYO3CXzSik3oJknoJFmxu2aZdEujDjz7BH6MQRJ8qBLMmDIs2DNEvDPwjUAZF4R8eT3D0fMJi5qJSecYeaboloriD8fj2juYLPFpm7FRkswuCAPfdR9gzz3DckM72sINEFf8RacsnEJRy/FzrYvNwtk3D2eX09fU5j3F5eTl9fX0EBAQMsd7Q6XSjHmOj0QjgMRsNSZL47ne/y9NPP01fXx87d+7kD3/4g0cnkr0mNn/5y1/21qZmmQWQK9avjpZtBOp75Nfso9xzq9RqNtz3OOWFN6De/0Xmi3VE5H6VM8X/IeyOnzJ/WZpb2uXIaHYIzWlRsGAC9nf1laW0vPwd0vXvohAkrJKS07F3k/Thx8kOCXdLW6cKRVAQ2t27ZUuNjg5Z+Ln9dhQxMVPdNCcxAXIhtLwWWdS1S5AVOzlLDXdgl6CwZZDQHCOfCyMhmUxyP3d3I4SEoN250+0WArOMn1idHF+5zdCmiGHO5q+z7J6vU1tRQsORfzC37hUSxEZW9x6G3MP0nXqUwuB1KFN2kbRpN1p/z9yo2MShQuCaOHlybyagiI6Wx529e+Vx58UX0dxxBwov+ZN6mounj9Jz4Gek97zPXEECAU6rVnEp5WES1+wkPcZ7g5fY0YH55ZfBakUxdy7qG25AEISr4mttHETOkPiaZZbpRH19PRcuXGD16tUUFxdjs7lHNBsNq9VKXl4egYGBpKSkDPtQ7s7MZoedRWdn57jsLIYVeweL+iMIM7W1tVRWVpKdnT0pAXgssbm1tZXz58+Tnp7uVhFYoVA4Jy/0ej2nT58mOTmZqKgot23Dl+jubKWpLB9T/VmUXZUE9dUSZW0ghk6iB96TA2AY4QsGwtcuCZjQYBXU2FBhRY1NUGEXZBlGkEQUyD+CJKHEjgYLAZIJrSBnSykFiSD6CaKfGKkTrMg/vcNv2i4JtAthdKmiMWpjMAfEQugC/GISCZ+7jPDY+Sh8qFi8rzNaZvNYDOcLbLPZnMJkW1sbFRUVAFcVHvRKIcnrr4dnnmFpNXSGBhEnNlFy/HnmbbwXGJ/QPhNxpSCtIAjodDp0Oh3x8fGAfD1zHOPW1lbKy8uBocc4ODh4yARnb698Qnsqs/nJJ5/kl7/8Jc8++yxz5szhox/9KA8++CDPPfecR7YHU1AgcJZZvMncIFn8O1En/z+/BdbPlZfljkRixnWYV+Ry7F//S1bdM6SZcrH9eyt5UbezYPcPiIqbN+H2DBYCQRaaF45zMqm2NJ/WN39Cmv4dEgQRBMgL3Er8HY+RvXj6FHMbC0Gnk4WfV15Bam7G/NJLaHbsQDnf8361rjInUBZEcpuhqVf2F82JnboCaVZxYOl5nyw0Z8VA/CjXK8lsdgr66HRobr99RmZyTlfmBMpZ6Xktcga9VYScRcnMS/wxkvg4VSUnaD3xHxKa3mIOLWT0HIQTB+k7/g0KgtchLt9F0oZdBAWHuqU9Fruczd9pumzNMlrG/HREER6O1rGywmDA/MILaG+7DcU0fZi222yUHPw3qrw/ssJyVn5RgDP+a1BveZjw5dvQtgnUGsEGZMZ4fsJM1Osx790LJhNCTAyaW25BUCqviq81cRA9w+JrllmmAw0NDZSWlpKRkUF4eLhHrCuuxGazkZ+fj7+/P6mpqRMvzjcOHEvas7Ozx1WgTalUXr0MfgwhqrGxkYsXL5KZmTnpAoujic2dnZ0UFRWxcuVKIiMjJ7WdK3FkNhuNRgoKCliyZIlTWJnumPp7qTt7jL7K4wR0nieu/xJzaGfOCO83oKNNEUMnwdiD4rHrYhCC4lAFx6DRhaEJDCUgKJSAoHD8dcEICgVKQAm4apxiAfpsVsz9Rky9PVj6ezH3GTB3t2PubsNsaEE0tqE0daK16AkUu4mQOomSOtEIdrnuh60TbBdkUboNKJO/u1/SUK+Io1k9j07/BXSHLscSk0ZAwkr8tX5olRJ+SgmtUiJAKRGolvBXSlhFAZVw7fk2T0ZsHg6VSkVERAQRA6t2RyokqdPphojPnig8KKjVSOnpRJ4+zYnudG4OOUxQ4ZOw4cMgCNjt9mtabJ7o/qvVaiIjI53jsMMiY7hj/Pzzz7NgwQIWLFiARqPxWNHmp59+moceeojbb78dgCeeeILdu3fzu9/9zhmL7kaQrlWndxfo7u4mJCQEg8HgtcrLg5EkCYvFgkajuSaXrLiT5l6J47UWUGkI8xNYEycXexuLhrIztO19lLS+EwD0SVqKo29j0c6vEz1v2bjaYLEPZCn2y/ekGdEwz8XkQ9Fm4+zhvUi5fyLNdMr5+jm/1Whu+j5L0zeOqy2TxZuxKVksWF57DbGuDgQB9bZtqJYv9+g2x0trn1wozS5CsFYu9DhaIb7BuKsv+6yy0Kw3y5MpYwmBksWC5eWXEZuawN8f7e7dKMKnd0b8TB0zW/rkY2sXZc/wNbEQMCi+JFGkvPgYbSf/w4Lmt4mn1fk3s6Sm1D8Dc+LNLF6/m8g41yZrruzLHosc40aLXAxwJlhnjIZkNMqWGh0doNGgueUWlHPnTvz7vBybXR1tXHj9dyyofJY4qQ0Aq6TkXMgWwm58hAUpOc73NhrlCQ1Rku0qcmI9V+hR7O7G8uKLSEYjQmQk2jvvRPDzk+OrCYzWgWKT8SNbZ8zU83wqGG9fTvV98Szjw2q1ulxIzkFjYyMlJSVkZGQ4Hz6PHz/OokWLnFl57sYhNKtUKjIyMkZ9sD9+/DiLFy8mZpIr3SoqKqipqSEnJ2fcFiEVFRX09vaSmprqfE0SRcx33gmActs21J//vPNvra2tFBUVkZaW5pYs4Ly8PGJjY5l7xTXJYDCQl5dHUlKSsxidO6mpqaG1tZXe3l7i4+NZunTppL7PMf6A960ZrBYTVacP0l9+hPD2fBKtl9AMY3lRL8TS7L8EU8hiFBGL0MUuIXLuMoLDoxFFkffff5/169dPuvjiWFhEMFgU9FgF2s0KWvuV1Pcp6DQPk3Ep2lEb6/EzVKHrrSawr44wUx3R5hrm2GpJkFpkC61hsEpKqoS5VGsSadQl0x66Cn1MNvagse9/BCQSg+wsCLIRH2AnSiuimSHJ08eOHSM5OdmrdjFms9kpTBoMBnp6elCr1VcVHnSHECy1tyN99rM0af1IWHWcQMHEpev+TFz6zRw9epTU1NRr9ppfWlqKVqtl0aJFbv9ui8WCXq/nRz/6Efn5+ZSWlmK329m+fTvr1q1j7dq1k14J48BsNhMQEMCePXvYtWsXAF1dXYSHh/POO++wbdu2SW9jOGYzm30YSZIwGAxERkbOPlBNkmh/ieRAA5cskejNAu/Xy4JN2BiJDHOWpDHnG29SfPQ1/N//Hktsl1jT9gK2v+whP3Aj2qyPkbzhdhRj+GLpzbJY1Gd1TQh0UFtWTMOhv7O4YS+rpA4AREmgULeR4G1fJyVjk6td4Fa8GZuCRoPmttuwvvsu9osXsb7zDlJPD6qsLJ85L6IDYEO8LJZ0m+FQvZyR6sryb3f0ZVsf5LbIExoapexxOlpsSzabLOA3NYFWi3bXrmkvNMPMHTNjBsWXwQzvXxFfgkLBkrSNLEnbiGgXuXjmMPr8F5jb/A7xtMgTVOdOwbnvc0m1DP3c64nJupN5SatH9HUf3JctfQJ5LbKFhr9KFppDPPtcNeUIgYFod++Wz5OGBiwvv4z6hhtQLRvfJKMDb8SmJIqUFx6g68ifSdEfZK0gZ951EcT5uDtZfMuXSU+4+mY5PhDWKuTJ0E6THF9r4kYvKDqh9hmNcsa40YgQGip7Yvv50dzLkPhaFydP2o34PTP0PJ8KZvtyZjPeY9rU1ERJSQnp6elDspxUKpXHMpvtdjuFhYUoFArS09PHFE7ckdlcXV1NdXU12dnZE/KiHtY3enBfD8rj6ujocGYau8tuYrjM5t7eXgoKCli8eLFHhGaQM/P0ej1xcXEsWbLELd85WV/x8aBvb6Iu/zW0Ve+yrLeA1UL/oIZAK+HU6FIxx6QRMHcVcUtWExISzkgLUN05ZkoSdFsFWk0KaowqKnqUdAwnJF/5OVFEae5CY6xD09+O1iz/BJg78Ld2EmDvRmPvQyP2IyDRoYiiX/JnoViHv3B1ATO1YGcp1Sy1VIPlXegCqoa+p4z5nAvIplsbh1kVjFkTSr9fNOaAeKqMCZQZokEx/EPIPJ2NVeFWFgba0amksRYE+Azuzmx2Ba1WS3R0NNHRsmmL3W6np6cHg8GAXq+npqYGm812lfWGq3ZAgxEiI5GAOLOJk31pbNOdxH7yD5B+86xn8zCeze5Co9EQHR3Nr3/9awAOHDjAAw88wLZt2zh58iR/+MMfaGxsJDU1lV/84hds3bp1wtvq6OhAFEUiIyN55JFHOHDgAHl5eSiVStra2ty1S1cxKzb7MFarlaeeeopHH33U4zOmMx2r1cp/nnmKL3ztUQo7tfRY4FADrAiHxNCxC7ulbrgFad12zh59FfHor1hlKSSz9xC8f4j2979GWeQ2glK2k5h9E34Bl9OVJQmqu6G4Xc4aC1DLQvNID/FWcx9lpw+jP/sm8U0HWCDW4jDt0BPIpegdJNz4RTITV7qlXyaKt2NTUCpR33gjQmAgtoICbCdPIhmNqLds8ZkiiOF+sCUBTjTLgvORBkgKh2Vho8fXZPpSlOBil/wjSbIAmDNGVrVktV7OFFerZS/saWoRcCUzecwM94PNCXJ2cbcZjjbKsXVlfCmUCpat3gKrt4AkUXuhgMZTLxFe/w5LbZdYarsIVReh6kkahBhqwzegTbqBZWu2owsKdX6Poy93fvpRavrkvozwk0VuT2W9+hqCnx+aXbuwvv029rIyrG+9hWQ0osrIGPdDhydjs625jvJ3/sycqhdYIjYMNB6qlfNpW3EfKTffz3rd6FkR0QED8TWQXXyoHpIjYHHImKvDXUIyGjG/9BKSwYAQHIz2zjsR/QI43w7levk9Ef6yx/xY8TWTz3NvM9uXszhobm52Fnu70n7BUzYaDqFZkiRWr17t0gP9ZNtSV1dHeXk5WVlZEy7CNFyBwOGuCXq9nsLCQpYvX+7WrPArxeb+/n7y8vKYM2cOCxcudNt2BmOz2aitrUWpVLJixYppMznVb+ym/NgedGUvk2wqZK4w0G8CtBNKRVAW9oQ1RCVvJmbuUpIm8EwxHrHcISo39im52K3ivH70ZCVJtKHVVxD4/+y9d3hc5Zn3/znTNDMa9WLJsmRJtmzZqrZVjI0xNUBCCQFSSNsNLJBsCqSRvL+wJO++SXY3CaRsCAlJlk02GwiEEJohMWCMbWzUJVuSZXXJ6mWkKZp6zu+P8Yw1qiNpRs3nc12+bE85z/Pc5zlnZr7P/XxvYz1R4+eIsnUR7egjztlPnDhIojRMuGCf8xgzsoTTl0UHWdYOsM78vFNSMiJEMSzEMKJMZEiTwoh2M+OGTIYit/JK9DZEnX+CS4LWzf5EB1mRLtSr42edHyshNk9FqVQSHR3ty66WJImJiQlf5nNraysWiwWdTucnPs9XlM6L8K1vIf2//0dkVyTu7QI7rOV0ddRe8mJzIJ7NwcLpdBIfH8+DDz7oe6yrq4t3332X9PT0JR178n0qPj6etLTF28IuhEvkJ6OMjIdwtecHdeWAZ+vwmWHPFvVdCWCYZyFQUCjIu+JWuOJWOs6cpO/Ik2wbfJ14jMQPPQdHnsP+lpp6dRbmmB04E3IZCd/KRHgaUkQSSdEGShJVSG43w/0jWEYHGB3oxt7XCINnMRgb2eo4y85J27ickpKzukJsuR9j59Ufo0QfGsP4tYAgCKj370cwGHAePYr79GkkiwXN9devmoJ2ejUcTIHqQegyQeOIx2KjMCH4maBjdqgY8PwNHn/yXQlz+5FLDgeOl15CPH8eVCo0N9+MIkTbYmWCT/gM88t7/5pxfgkCaTuKSNtRBHyP/vPttB7/M9rWv7FjooIU+kkZ/jMc/zOOY0rOhOVg3nSQpN0fQJNSAEDrGCjVHm/5vPiVL4C53AhKJeobbvAsdFVV4Tp+HMlkQn3gAMIyffmcCZtljPq3nkE482dyrWVcdmFLrFUKoy76GqIP3M223VeTvoAfCBEaz+djxQD0WaBuyPN3YQCfj3Mhjo/jeP55TxFSgwHNbbdhVBmo6AbThcSqS3V+ycisBvr7+6mrq6OgoGDG7FulUhn0AoGiKFJdXY3L5aK4uDjgQlhLEZt7enpobGykqKiIqKgFFkyZxHwF+iSXC5PJ5PM1Dnam8eT2HQ4H5eXlxMfHL9nWYja850qhUBAWFrbqhSdJFGmtPYbjvd+SO/4O+7xirADNigz6NhwkMu/9bM4pZecSiuQFIuCZnAKdZiUVw2q6rbPPccFuInyoitiRahJNZ0iyt7HR1U2K1IdmFssLzxsvtCPpGFdEYlFEYFNFYldH4dREI4ZFgVoPaj0KjQ5Bo0ep0aMI0yEIShxOFxM2OzabjQmbHZfbhVajJkyjRqtWolEJSLYxlOfL2TRexSapd94xq71+0dIIuFo8BSGswMjF14xKEbQr0zivyaQ/fDsjUTm80leAIzLDl0SkVUrcmGJjW6Rrzt82y8FqEJunIggCer0evV5PcnIy4BErx8fHMRqN9Pf3c+7cORQKxbSidDPeb/M8iWzbx8ycdOSxP6yW4bf+EzHto6tu7MvJcnpWm83mabttUlNTp1kmLYb4+HgUCgVDQ0N87WtfAzw2Gm63O6RFXmWxWeaSQ63wZE916D0Zx0MT8EaXJ8M5K5qA/KU25+xlc85e7PYJao+9wMSZV0kbOUGyMMROVz0M1sPgn2d9vx6IA6atKQkwRBRdEbtwZ3k8VnPjEqcf4BJGVVCAEB6O429/Q2xrw/7ss2huugnFEn44BBOVwlNka4PeIwqO2OCtbsiI9GShLjUr1OaChhFPxjx45nNBAmwyzJ2B6OfRfMGaRLlOCrtcSkydX6M2eLPLI9btiIGwOebXhpR0Nnz4K8BXmDCPU3fyZWyNr5My/C4b6SfHUQuttdD6M4aJYp+YhfFkBFtKbiAvLhtBWN0/MEOFIAgecdlgwHnsGO7aWqTRUTQ33oiwgKJSS8VqNVP/9vMIZ/7MTtMJdnu3wApwTrWd0eyPkn3dP1AatXhLHI3SYzHVPg51w54aA290wZZo2BYd2OfjZESj0WOdYTIhREYi3vIhKm2RdF3YsRem9NQvSJLrksrIBJ1ABIKBgQFqa2vJz8/3bdeeSrBtNERRpKamBrvdviChGRYvNvf39/ssQmJiYhb8/oX0wW0yUV5ezubNm5ecjTYTXrHZ63VtMBjIyckJiSAkSRKnT5/GbreTkZFBd3d30NsIFrYJC+fe+h8Sz/6BQrHV86AA3UIy7ck3kLj3LpIzdrA0t29/JtuASBIM2hRUjaipGJ59hVZpGSSq7x2Shk+Raj1DurOFNKkXxUyF9wRwSCp6FEmMhqUwoU9BjEhGEZmENiYFQ3wq0RtS0ekNRAJLcdWVJAmbzebnEzxoNqONSiUqbT/m6GjeHR1Fq9USHa5moPEkrs6TbBipINPdNmP/HZKS88oURtUb0DuNJLr7iBVMxAgmYsQz7LKdAdtLMAy0glnS0aTcQpsul56o3RzqK+UvcXlwYVGgON7BvkQH4arlLTm2GsXmmVCr1X6FB70FPb3ns6enB7vdjsFg8Mt+1mq1nrn8wQ/CCy/g6EmGjFp2Dv+NzsQbli2zdzUSShuNqZjN5qD4M89EWFgYOTk5HD9+3OfZfPToUZRKJbt27QpJmyCLzauexfjuyMzM5FgKAqRHQrwWaoY82adNo54svswojzCoD6DAW1iYjvxrPgbXfIwJh8jJ06fpbjiBbrCWJEsjsa4BYsURogSL3/tESWAMA2ZlJCNhqVgit6DduJP4nVewKTOX+JVewg2AlZybyq1bCTMYsL/yCtLwMPZnnkHz/vejDJFP3WJIjfB46tYOebLoW8c8Ak56pEcYjJwUvkBiOe6AFiN0mjz2GQAbwz1C83wCtmSzeYqd9fdf9GheYnGd1cqlcs+cOr/axqBjHDZHQmbk3J63ADpDJHnX3gXX3oXoFqlvPEP7ey8T23uEXHs1ccIY1ynKoaMcOr5DP3F0R+1GStvPxsLrSM7YuWosbJYL1a5dCFFROF5/HbGry3PfufnmgP3OFzM3jYM9tL77F4Szh9hmLqNIsHmeEKBLSKZ7442kHvwUWduC90VREDz3qATdxc/Hc97Px0hIjwJDAJ+P4sgI9r/8BcliwRERTdsVt9ExGuGzM02NgPz4hQvYcOlc58uBHMtLl8HBQZ+f8FwF94KZ2SxJEnV1dVgsFkpKSlCrA6ymPKkvCxWbBwcHqa2tpaCgYJpFyGKYL7PZ1N9PcnIyW7ZsWXJbs7XvcrmorKxErVZTUFAQMqG5sbGR0dFR9u7di9FoXHDByflwu90IgrC0AtkWE+dee5zs9t+zDyPgKY5cHXUV2pJ/JCNvPwUh+L4iSWBRRvD2oIFK4yyFeEQ34X2nSOp/m7TxCrIcZ8igZ/rrBE99hfPqDMYjsxDjstBt2EpMyjZiN6QRp1IRN/1dQUUQBHQ6HTqdzmf74nK5fELlwMAARqMRAHNMDNEZpUQVvo/IyEj6zEbO1x3F0XaC+OFKtrjOoRJENIKbDLGTFFsPZ8NyqU/9FBHbLkel0THe3YCr/yyasRYSbG2kir0YhAl2i6fZbTkNlqehxyNAn1VupTl8N21xB6jYdI3PhuPABjuXJThQhfjr6FoRm6fizWqOjIz0ZcdOXlDo6uqioaHBV3gwdvdukl94gYIBM3WpmeSpWtF1vYkg3LzCI1k5llNstlgsi6ojECj33XcfDz30EAcOHGDjxo184xvf4M477/Sr0RBsBGm5XPnXIHLV7UsDSYI+q8dSwzSpVkK8zpM9mKDzCINT9V9R8lgYjNigx+LJkPYSrvb4XW4M9/xwdztsTNgmcLrchKkEtPooFAvI5JCZGclsxv7yy0gDAyAIqK+8ElXeyvpZz8SAFepHPFmoXqLDPNl88VrP/NIoL2YmS5Kn2J/RcXF+jU+yZIvRQm5cYAUIxfFxHC++iDQyAlqtR2ieJXtJZm0y0/yKCoMkvccHN1ID2knzS5TA7vYULh22Qa/Z49PrJVo5QUTXW7ib/kZU/ykyHWenbeMcJIZ2QwHOpD3EbNtHRt5laHWXRnqqODSE4+WXkcbHPbsErr8eZZB8MkWXi5bT7zJQ/Rqx599ku6PBL1uoX4ijfcP1xJR8jKzCy0Mu+Hs/H+uHPQteXmK1ns/HeJ3HfkOj8L9/2bp7sL74MjaLjTF9LM0Hb8N9YX7E6zz3r/kK9MqsPuTvxWsLt9s9q0g8ODhIdXU1eXl58/oJnzt3DrvdTm5u7pL6482SNRqNlJSULMonvKGhAYAdO3YE9PqRkREqKirIzc31bTVfKoODgzQ2NnLgwAG/x20XssXsGzYQ+cQTIROn6uvrGRwcRKPRLDgzfCG0trbS3t7O3r170ev1DAwMcO7cOfbv3x+U44+MjFBeXu7zoo2KiiI6OhqDwRDQtnXbhIWzr/yUnI7fEYMJgD7iad78EdKv+gxRccFPqnC4oWlcxaFuLU5phvN7QVxO7nuDrLET5DtriJ6ScATQLSTRG74De0Ie2o05JGYWEh2fvOoX8RsaGpAkiYiICF+hOqfTicFg8J3DqKgonDYLnVWHEVveYrPxFBsZ8DtOt5BER/xB9LkfICP/CpQqFU6Hjd62eoytlbh7aog1NbDV1YJWcPq91y0JnFVk0qgvojN2P0OpN+AO95zrT2+1kKIP7oIIwFtvvUVpaSl6/SyLCmsYt9vN+Pi4T4DO++53ATi5OZzrk/7OMFG47zuBzrA6dhAvNydOnGDHjh1L3hETCP/xH/9Bc3Mzf/zjH0NyfEmSePjhh/nlL3+J1Wrl5ptv5oknnvD5gIcCWWyeg5X+Ui2KIq2trWRmZq56f6zVTiCx9P6objb6C8detCpQCZ4f1A63R6yZSqzWY8eRHD5/0cG1zGqam5LLhfPwYdxNTQAoc3JQHzyIsMrEfEnybElvGfP47HrvvJIoYu5rJWpjJmqlwje/xCl3ZkHwiDtboz0CdSC/YcTBQewvvggWi8cr9dZbUYRw9XKlWU3zcrmRJM99q3nK/PKiEC4umLnEmZ/fGO6ZXzFa/1jaJyw0V76JqfEIUQPvkeVoRCP4CxgOSUmHOpPRmHwUqUXEb99P6tbcdbv1TrJacbz6KmJPDwgCqpISVMXFs/5QnG1uim6RjqYaBmpfR9N1jExzFVGY/d7brMikP/kqNhR/kC35+xGW4DG5WLyfj61jnsWNqSgVFz8fNR0tJB1/HUF0YYvdQPcVNyPp9CRfmF+xSxSZL+XrPNgsNJYr/b1YZmGIoojT6Zz2+NDQEFVVVeTk5LAxADut1tZWTCYTBQUFi+6LJEnU19czPDxMSUkJ2kVaEDU1NeFwOAISvo1GI+Xl5WRnZwfVN3lkZIS6ujoOHjzoe8zpdOK+807Pf+Li0P7mN0FrbzKSJHH8+HHsdjsHDhwI2c6E7u5uGhsbKSkp8V3rs4nsi8FsNnPy5EkyMzMJDw/3+cyOjY0hiqJPtIyOjp7mMSuJImfe/APpNT8imSEAzgsbaM/+J3Zc+w+oNcFdyRxzCFQMazg5OHOslZY+NrT/leyhv1FkP0WMYPJ73iJpadbmYIorJCxtNxt3XEZk7NpM+mhsbEStVvuy9mey3jCbzR6rjQvic2REBOODHQzV/R1D9zvssFUTNklAHiaKc1H7UWbfSGbRDWi0FzNpXC4nPS11DJ97D+F8GZvNNaTR59cntyRwWpnN6YgraN/4AcwbLweFkptTJ8iNdgWlyPGbb77JZZddhk4XQJbPGkesqoLvfx+TUoVmdy2JCiMvb/gcYVuv9rPe0Ov1azLbe6EcO3aM/Pz8ZfnO88gjj2AymXjyySdD3tZysbrUGBk/nE4nf/jDH+Qq4UEgkFgKgkckTg4HixN6LR5xcHgCnKLHK3cqaoVHmEnQQYrBk9F8KbCa5qagUqG+/nqEuDhcJ0/iPnMGsb8fzfvfjyKEK3ULRRAgUe/5Y3dBrxUGrTBgclL31h/I+fA3EPGPZbjaM78SdZ55uZDt5u6uLhyvvAIOB0JcHGG33IKwyMrra4XVNC+XG0GABL3nj8N9cbfFiM1zPxMlEN3+rw9XQ9yF+1dSOH4VwCfHUhceQd6BW+HArYAnm6ix5m3Gzx5D019FqvUMccIYWa5zMHjO41df6dn62KXJYDwqG0VyLrGZe9iYtRudYe2LVIJej+a223C+/Tbu06dxnTqF2NPjKVg6Q+aLN56f/9xn6Wt6D0vLCcL6Kkm1niGDcSbnRZslHS3hBTgyriV1721sTd3K1uUb2oxM/ny0uS7Or2Gb5/9uEdxA9LlaNlS8DUg4UzOQrr2ewkgNyVPm11K4lK/zYCPH8tJjeHh4QUIzeDybl2Kj4bVjGBoaWpLQDIHbaIyPj4e0QN/kPrjdbiorK/FJ8ZbpmazBQJIkzp49i81mIyEhIWRC88DAAA0NDezevdtPYFEoFAQjR81ms/k8rTdt2oQoisTExLB582YkScJisfiyZvv6+rDZbERERBAVFYV9tJuYk9/jMlcj4Mlkbs35Ajuu/RT5quD9CDM5BU4OaigbmjnGuv4KMjqfJX/8bXLdDSi9O5AEj7h8TpuLOamUyG0HSN1ZSmYQ+7bSTBYY57PemFykLirjGpSFH6JdrWCk8RjKlr+RbXqXOMaIG3sVTr3K+KlwzkQdRJN/B5m7r0alUpO2fTdp23cD9wPQ1ttB7+mjODpOkTpWwRahiwKxgYKxBhj7JSP1kVSElVCWeBMvZ34IKSyKmzZNkBezOOHZO+cvlcVtoaAACYhwuzhlzuV9kcfIMh5Ds/VexsbG6Ovro6mpyXNOJ4nPkZGR6zLBZLkLBIbKs3mlkMVmGZkZCFd7MrC2RnsyupyiR7BxSx7RRqP0bEsPUwaWYSoTWgRBQF1cjGLDBhyvv440NIT96afRXHMNyqysle7eNMJUHu/m9Eiwx0A5cNUmUGlAwiPKaJXTrVsCxVVXh/PIEZAkFCkpaD7wgWUtZCazsmiUF+cXeO5ZEy7P/Qs88ytMufjdF1pdONl73w973w94sozOd5zj/JmjuDvLiBmtI8NxDoMwwQ5nPQzVw9DzUOfxq+9UJDOky8AelYkqIYuo1J1s3JKHIXburdyrDUGpRHP11biSk3G+9RZiVxe2P/4RzQ03oEhOZqC3g4FzFUx01yL0n+Fj7kai//Mx4qYU0bFLapq1OYwn7SNy5zVk7bqCglXso6tVeWobZF7YUekWwWJ3I504htRYg8IAmtwcwq6+atVvCZaRuZQYGRmhsrKSnTt3Biw0w+KL8oFHqGlqaqK/v5+SkpIlZwYqlcp5fYPNZjPl5eVkZGSwefPmJbU3Xx9EUaSqqsr/BTbbDO9aOq2trfT09JCSkjJjxnowGBkZoaamhvz8/Gk+nvN5VQeC0+mkvLyc+Ph4tmzZMm0RQxAEDAYDBoOBlJQUwCNODw0O0v23H3Pl6HOoBTcWKYx3Ym4n+cp/IjNpI0rl0iUNqwsqhzUc7Z954U3XX8GWjqcpHXuNrXRO6jS0KNLpS9iPfsf1bM7bzxbN+ly8C2SxQaVSzVqkzpu97lQkE7H785wN/zqWnjOoO46wbfQoCYyyd+xVeOdV+t+JpTnhOiL33MnmHSW+7xPxyZuJT/4k8EkA2npa6az4G+qOI+TZKogVxrnOcZjrug8z0fV1TmlKqUy8hVcz70TUxvCJTCtphsDvZ94xXwpZvACCQoF0+eVw7BgJ3TpcOxRst9fSbe1jyxbPjpKp57S7u9tnpzK18OBaRpKkZfdsns/Waq0hi80yMvMgCB7xZjFFjGSWF2VaGtqPfQzHa68h9vTgOHQIZXc36v37EVaxeANg0MBSE8skt9uXaQmg3LYN9bXXrjpLEZnlRSGEdteFoFCQkrGdlIztwD8B4HY66GyuZbC5HMf5WsJHG9lobyZeGCNN6iHN2gPW49AL1HqOY8RAn2oT47pNuCJSUMWkootPJyopg+iNmURExqyqL/uiW2RosIdhSy+WBCeu5nKUI/0YfvVzkuhng2D2r3p/QXftI4FuQw6OpN3EZe8nM28fOWFr1wdQ4bAR9tohxK4uUIKqtBRVScmqOlcyMpcik6/B0dFRKioq2LFjh0/EC5SlFAhsaWmhp6eHkpKSoPidzid8W61WysrK2LRpE5mZmUtubya8mc2SJFFTU4PD4aCkpITglFCcmc7OTtra2igpKWF4eBi73T7/mxbI+Pg4lZWVZGdnz1gwUhCEJWU2ezPA9Xo9O3fuDPgzYqD9DPpDX+A6sRMEqNDtQ3XNwySrwzEajXR2laFQKPx8nyMiIgLKRhQlaDEpebZ95rmpNraQde43XGZ8ka10+R53SErqdXto1eax/eBHSNu6k7VpjBF6phapm2q94TKkMZZ+O6btH0McaSXy/BHyzcfZIIywYfAZeO0Z2l/fRPfm29h88FNEx/t7r8dvzCR+4/3A/TgddiprjzNy+jW2D79BqtDPlc53uPL8O9i7/w+n1MX8vfGj9G/9CJE6Dfdut6CZZ5pcamIzgPDRjyIdO0amyUKZI4fLwuoYPPoroj7xU2D+c9rR0YHZbCYsLMxPfA7Uj3214F1cWy8FAlcCWYFYxQiCQEJCwiV1cwsVciyDy2qOp2AwoPnQh3C9+y6uigrcdXWIXV1orrsORZCKwwSTYMVSMptxHDqE2Nvr8ZC97DJUe/asynMUKlbzvFxrLDWWSrWGtB1FpO0o8j0mSRLDA90MnKvAdL4BabAJvamNeHsXyQwRjZloVyOYGsEEUwu2j0t6jIpoLKpobJpY3No4XLo4JF0cQngsGn0kGn0kuvAoNPpIVDoDam0Eao2GMI0ajVqDQqlCQkBCQBTdiC4nosuOy+nAZpvANjGB3TqO0zKK0zqGyzKK3TyKy2pEaR0gzDZIuH2QKNcQseIoiYJr+g/MCyFzSwLdihQGwrNwxGZzdtDBTXd9jqTUrayXvAVxcBDHK694iiWq1Wiuuw7l1tCafsjXefCQY3lp4BWaF+tdrFKpFpXZ3NraSkdHB6WlpUH7AT2X2Gyz2SgrKyMpKYmsEO5qUyqVvmKHZrOZ0tJSj9VIdDQYjUFvr7e3l7Nnz1JUVERkZCSjo6NLzjCeitVqpaKigoyMDFJTU2d8zVIym73CvCRJFBQUBGTJIYkidYd+yZ6G/0ArOBkhkrO7vkXOlR/12zUjiiImk8kndHV2duJ2u4mIiPArWqdWX1x1NzsFjvSFUTs6fSVecFhIbv5fivv+SImr0lek1yswW7d8gPTLPsiWqDjOHz1KbFLaomKyVlnq58VM1htOp/OCb3cyYym5vDnyMZx9daSMvMtuRznpdJPe/jMcbY9TG74PCu5ia8mNKKck06g1YWQVXQ1FV3vmT0M5/RUvsHXwMOlCD1e4TnBF1wlGOr/FkfAbeLzzbqzJe7kz3UpW5Mz3lUtSbE5MxHt1WgaSIbWObf2HcNrMKLXTbR5ms1PxFh4cHh6mtbUVSZKIjIz082OffF2uNpZbbF6PNhpygcA5kAuhyMisbdydnTgPH0Yymz0CbFGRJ+NunXlKudvacBw+DBMToNGgueEGlOnpK90tGZmAMZnGGGhvwHS+HvtQB4x1ozGfJ8LeR7x7kGjBPP9BFoBbEi56LC6RAeIY1GzErEvFFZ1OmCaeqH4TiYoINBod6ssvR5mXt65+qEiShLumBufx4+B2I0RGorn55nVdgFRG/l681pAkiYGBAcrLy9m2bRtpaYsTxUZHR6mpqeHKK68M+D3t7e20tLRQXFwc1LnS399PS0sL+/bt83vcbrdz6tQpYmNjycnJCen91m6389Zbb6HT6SgtLfVtFbd//etIF4pVa194IShtDQ4OUl1dTWFhIQkJCQB0dXXR399PUVHRPO8ODG/sEhISyM7OnjV2JpOJkydPct111y3o+N7ikCMjI5SWlvp5TTudzhk9UW0TFtp+/8+UmA4DUB1WRNxHfzEtq3W29iYmJnyWDUajkYmJCcLDwxEjUzjq3I6b6dmVYUOnyW/6KdeYXyZKuOi7fUadizHrQ2Tsu52IKP/PuKNHj7Jr1y4i1nlNFC/19fXodDoyMjLmf/ES8No0GI1GBvp6MJ55je0jb7GTFt9reomnJfV20q+5l6i46Zn4k5FEkc6mKnrefZbC4VdIFEZ9zzUKmRyN/xjt2feyLTGcD222+VlkOhwOjh07xsGDB9elJ/FsSC+/jPS732EXFEwUNbNZ0Uftrn8l4+p/XNzxJvmxe/94r8vJ2c86nW7VfF+22WycOHGCq666aln69L73vY/777+fT3/60yFva7mQM5tXMW63m5qaGgoKCi6pm1sokGMZXNZKPJVpaSjuustjLXH2LK6yMtwtLaivugrlAreRhoqlxFJyu3EeP467uhoAISHB4xcbExOCnq5+1sq8XAssdywjIqKIyNsLeXtnfH7CPMZobzvjwz1YR/uwjw0gmgdR2oYJs4+gcY6hdlnRiFbCxAl02NBLE2iEmTNVZhKa3ZKAExUTgg6LEI5VYcCmDMeljkQMi8CtTwBDEsrIZHRxKcQmpZGQlEZimG5adrNkMuE4fBixqwvnkSM4GhtpSEqicP/+NT83JavVM7b2dgAUmZlorr122Xzh5es8eMixXN94C7Ft3bp10UIzLLxAYGdnJ83NzUEXmmHmzGaHw0F5eTlRUVEhF5oBOjo6ACgsLPTzJBUSE31isyRJS+7H6Ogo1dXV5OTk+IRmCI53sheXy0VFRQVRUVFzCs3edheTo9bS0sLAwAB79+4NqKihcagXyx8+QYnrLG5J4N3N95F3+zdRKAK7RwmCgF6vR6/Xs3HjRiQJ6obh5Z4ImGp1LbqJbfsrl3c9QanjPU8Ws+ApPNic9AE2XP5J0jZnM9vVs1pEsfXGZJuGtLQ0pOJSbLaHOFZzHGf1s+w2v0WyMERy1y+x/9dvKTNcie6ye8jK3zfjOREUCjZn72Fz9h5crn/l3XdfxVX9DKX242TTSvbgdxkb+DF/N9zCj7IfxBG7na/nmlApLs3MZgAOHoTf/Y4wSaRqfBubo/vQNvwJFik2z+TH7nA4fMJzT08PjY2NqFQqP/E5IiJixb6feBfCluvcWyyWdZfZLIvNqxiXy8VLL71ETk6O/CNgicixDC5rKZ6CVovm+utxZ2biOHIEaWQEx5//jHLHDtQHDqx44bzFxlLs7cXx5ptIw8MAKAsLPd7Uq/x8hJK1NC9XO6stljpDFLqsAjZmFSzofZLLgdPpxOFy4nQ4cbmdKCQ3SiQUSjWoNChUGlTqMMI0arSCgBZY6nKNEBGB5oMfxF1bi/PECRy9vbzc0MB2SSJ83z6EVbxtcDYkScJ97hzOt9/27KJQKlEfOLDsWdurbW6uZeRYrm90Oh1FRUVER0cv6ThegTcQAbW7u9tn+RAVFbWkdufqixevWKrT6chbhntRe3s7XV0e796wKYU2hMRJy44Ox5IKcZhMJiorK9m2bdu0Yo7BEpu9HsoajSag2AmCsOB2u7q6aG9vp7S0NKDikD2tZwh/4dNkS32MEU7LFT+joOR9C2rTiyhB9Yia187P8D3fZSet4Zfc0PcEGZz3PCZAuWoXo1vvZGvJjeTGxgZ0X5Q3iYcer01Dwd5rYe+12CYsHDvyRxIbf882sYXLLX+Hw3+n+o2ddKXeStqeG4iNjZtRqFSp1OQeuBUO3MrA8ABn3vgfsrv+yGahlzssf+S28qc5ptnPLzu+wNjmG/j8VpuvD5cSQkSEz0ojoTcMMUpgm62O3r5mDEnBsUvTaDQkJCT4FtNmssRxuVxERET4CdBT772hYjmLA3ozv9fbLol1Kza//fbbfO9736OyshKbzUZBQQHf/e53OXjw4Ep3TUZGZgVQZmWhTU3FeeIE7tOncTc04G5rQ11S4hFL1sgPbcnh8Iyhrg4kCXQ6NNdeizLEW9pkZNYigkqDRqVhJcqDCoKAqqAARWYmzjffhOpqXNXV2NvaUO3di3L79jXz40UymXAcOYLY1gaAEBeH5n3vQzEp205GRmb1ERMTs2QxTKVSIUnSvGJzT08PDQ0N7Nmzh5gQ7bCaLDa73W4qKipQq9UUFhaGvPBUd3e3L2P75MmT0zKshfj4i/8xmxctNlutVsrLy9m8eTObN2+e9nwwxGZJkqitrcXtdrN79+6AYud9TaBZ2/39/TQ2NrJnz55ZBZTJx+k6W0XSyx8nlnHOCxsw3foUWVvyAhzRRUQJqkbUvD6DyCw4LGTW/5z3D/6KjQwCYJZ01MS/n/CST6KP2IBzbIzGs2dxOp3TfJ+nZmavlc/wYLIaxqzVhVNw4z1I13+G6tqjuE4+SaH5HQqpp7CznrbO31MWfwuKTSVERUXPeg6j4hLZ9+EvI4pf4tjRF1FX/xel7koOOo9xsOkYlc15/KHt64xsvpkDkgX1yg99WRn/9KeJ/O//Zsu4hUpxB0XKenre+T3b7vxOSNpTKBS+8wQXLXG84nNbWxsWiwWtVjut8GAo5uVyis0gZzavKU6dOsVll13Gd77zHeLj43n88ce58cYbqa6uZtu2bSvdPRkZmRVA0GrRXH017uxsnG+9hTQ8jPPoUVzV1aj27UOZlbUqvkTNhCRJuBsbcb37rseDGlBmZ6O+4ooVz86WkZGZHUVEBGE33ADV1Qjh4Ujj4zj/9jdcVVWo9+9HuYTt7aFGcrlwVVXhKi8HpxMUClTFxaiKitbMAp2MjMzS8P7Ydrlcs9og9PX1cebMGXbt2kVsbGzI+qJQKHC73YiiSGVlJQC7du0KudDc19fnE9KjoqJQKpXTBF9hkqAqjY0hLMLD3m63U15eTlJSElu2bJnxNUsVm70eypOLGwaCN8aiKM4rwIyOjlJbW0t+fv6c88G7EDJZaD6n3ILmrv8lJXFhdneSBI1jKv7SOUMGtdNKXsOjXNf/axLw+PUOEUVj+ifJet/95EdE+16ampqKJEnYbDaf73NLSwtWqxW9Xu8TLqOjo32LMJcKq22sgkLBlsIrofBKunra6XnzF+T1/YUMoZuMocc5P/Qc9al3MZF9HcPDw1gsFnQ6nZ/4rNfrUSiUFFx5G1x5G6ebauh64wmuthxit1jH7uZPUt26k9+0fY3hjA/yzQILitX5UzHoTOTk4DVCGhnaCBvqSe16EaRHQAjtPRf8LXGSkz1+7S6Xyyc+Dw4O0tLi8fH2Fh70/gn0vjYXgdzrgomc2byG+PrXv+73/x/96Ef89re/5dChQ2tGbBYEgS1btqxa8WstIccyuKz1eCo3bkTxsY/hrq/HefKkR/x57TVcFRWoi4pQbNniV+k6lMwXS0mSEM+fx3nsGNLAgOc9kZEe3+kZMl4uZdb6vFxNyLEMLt54am+7DUV9Pa6KCqTBQRwvvIBi0yZURUUoUlNXTbwlUcTd1ITrxAnf4pYiKQn1NdeseBFAeW4GDzmW6x9BEJYsEHlFxqmZvF4GBgaoq6ujsLCQ+MnZvSFApVIhiiLV1dW4XC6Ki4tDLgYMDg5SV1dHQUGBTzidUfCdZFciGY0LbsfpdFJeXk50dPSc/slLFZubm5sZHBycVqxvPrz9mW8+eS1Atm/fzoYNcxduA+hpPe0nNOs//WciYxY2j3qtCv6rOXz6E6KbnNYnuartUVLoBy74MW/9R7a/7x4KdDO8h4u2DTqdzidyTfaXPX/+PI2NjUiSRGtrK/Hx8URHRxMeHh7yhQ+ZmYnfmE78J/4d89hD1L7+ODu7/pcUBkjp+jEDXb+jaes95F37j1gn7IyNjdHX10dTUxNKpdJPpNy0JZfN235Bd087TS//mKvH/0qhWE9h6z9S2/bv/KLr/xK18xo+sWVipYccckS1GlGpROF2s73byXiinkQGaap9k+SCa1ekTyqViri4OOIufBcVRdGv8GBfXx82mw2DweB3XrVa7YK/68xUvDRUeMex3jKbBWm1LVGFCJPJRGxsLL/5zW/41Kc+NeNr7HY7drvd9//x8XFSU1MZGBjwFbhQKBSo1WqcTqffB71SqUSlUuFwOPw+hFUqFUqlctrjarUahULh1573cUEQcDgcfo9rNBokScLp9K9sEBYWhiiKfo8LgoBGo8HtdvsV9PA+7nK5/L4wymOSx3Qpj0lyOHDV1uKqribM7UaUJFxRUagKClBu3YoiLGxFxqRSqaCrC8vJk4j9/d4BoSsqQr1rF84pPzTW+3mSxySPaT2NSeV0Yj15Eufp0550LECVkIC2qAh3aipM+nK7nGOS3G7cZ8/irqlBPT6OW5Jwh4ejKi1FmZWFQqG4pM6TPCb/MY2Pj5OYmMjY2FjQC7/JhIap53qxHD58mNLS0mlZV4ODg1RXV5Ofnx+QsLhU7HY7b731FgaDgZKSkgWJpYthZGSEiooKcnNzfaIjwJEjR6Zl7Yrd3Tg+/3kAVF/4Aqprrgm4HZfLRXl5OWq1et5M7dHRUWpqarjyyisXPJ6Ojg6am5spLS1dsKjhcrk4fPgwV1999axxn5iY4NSpU6SkpJCVlTXvMfvPt6P63U1sZGBRQrPFJfD7Fh0j9ukLDlk9f+VA43fIcnsyH4eIpnHb/ey8/h7UmqXvCHS73Zw4cYKEhATsdjvGCwsMkZGRfpmz68kP/8yZMxgMhhntXVYbE1YzTX/7FVkt/80GPPVtzgsb6Mz9Ijuv/QQKhXKaR7DRaMTlcvllyZpGB2h740mut7yITvB8Xh9RH+DtnO9zU9FWtkUFXjx1rdHV1YX73XdJe+45AKr2wJWqE1TFfYCt//DLFe7d7Njtdt85HRsbw2QyoVarpxUenE9I7u3tpbe3l927d4e8z+Pj42zatInBwcGQL9ouJ+s2s3kq3/ve90hJSeGOO+6Y9TXf//73+c53pnvQPProo75qw7t27eKWW27h0KFDVFVV+V5z8OBBrrzySv70pz/50vkBbr75Znbv3s2vf/1rBgcHfY9//OMfZ+vWrTz66KN+PzQ++9nPEhUVxb/927/59eEb3/gGY2Nj/OIXv/A9ptFo+OY3v0lrayt/+MMffI8nJCTwuc99jpqaGl566SXf41u2bOETn/gEx44d4+233/Y9fimN6cCBA1x99dXrakwrdZ4uu+wykpOTefrpp9fNmL561VWce+89nu3ogAvVxuP1ej776U9T090dkjG9//3vx2KxcObMGYaGhnyP35mcTIbNxs9HR5ksRXw2PZ0oUbyk595sY7r33nt59tlnabpQDX49jGmlzlN6ejo/+MEP/ISqtT6m1Xae3jCbqRod9T2+z2bj8qEh/mS10j5JmFyOMbWcPs3//uUvvsfjFAru3rCBhvh4Xq2rg+efD2hM6/E8XQpjCvS7kc1mQ+bSZGphPoDh4WGqq6vJzc1dFqFZkiTf53thYWHIheaxsTEqKyvJzs72E5qBmW00JvlUe3eiBYI3U1sQhIC8pxeb2dzb20tTUxPFxcWLyp6b7Nk8E06nk4qKCuLj49m6df4CYhMWExN/+ATbGeC8sAHtJ54OWGiWJDg2oOGd/um+2HHWNq6ofoASyxHA48lcnfpJsm56kILw4G1RVyqVKJVKNmzY4PNGt1gsPuuNnp4eHA4HBoPBz3oj1PM2lKylHEWd3kDBB7+M0/E5jh36FdnNvyRF6iel7v+j5cyTjBR/he37bp3VI9hoNPrsU4Ttt3BC9yEmKp/mxomXuNL5DvurDvLiuQ/z/O7v8+UiFZp1mNAuiiKWSYtGUo8B0mDL8BEklx1BtTyF+hZKWFgYiYmJJF4o2up2u32LCkajkY6ODtxu97TCg1OvzeX0bLZYLAByZvNq4YknnuCzn/3sjM/t37+fY8eO+f7/9NNPc++993LkyJE5VyZWW2az3W7nscce48EHHyQiImLVZrosZEyTWc7sHW8sv/rVrxIeHr4uxuRlJc6TN55f//rX/b4Ur+Uxgec8uW02bLW1uM6cQRofRwA0goAYFYWUno4yMxMhISFoGX5ut5sf/OAHfPHWW1F1deHu6AC3GzWg0GhwZ2ejKihACA9f9JjW09yba0ySJPFv//ZvPPjgg75KxWt9TCt1npxO57RYrvUxreR5slqtvs9znU43bUzSxARSfT00NOAwm30VwIW4OMK2bEGTlYVzSkbhUsckmc2429tRdXbi6urC6Y2XXo+msBBdQQGiUrnqzpP83WjlvhvJmc1rj6lzYLEcPXqUnTt3+jKuRkdHKS8vZ+fOnaSkLMxbdzFIkkRjYyMDAwNMTExw8OBBdLoZvHmDhNls5tSpU2RmZpIxQwHmEydOsGXLFj+RXZIk7LfdBoCipATN//k/87YjSRI1NTVYrVaKi4tRq9XzvsdkMnHq1CmuvTbwbexDQ0NUVVVRWFhIwiILu0qSxOuvvz5j7N1uty8zOxDBXBJF6h7/OMWmNzBioP+2P7MxMyewsdgU/KppBvsLl4Pr2r9PSesvMAgTuCWBU3G3knrz/0d0fPL01weBEydOsGPHjlkLYk72fTYajdM8g6Ojo9HpdGvGyuj06dNERESsiczmqVgtJs698mPyO39PpGAFoE5TiOqG/8emrMJZ3zcyMsLp06dJSUnBaDTS29lEWudz7HedAqCfWJ7e9C/Eld7FremOWY+zFmlra2NiYoLsb38bAKtCibKojkTBSMMVv2BT8c0r28FF4l1U8F6bY2NjWK1WdDqdn/g8PDyMyWQiNzc35H1qbm5m7969TExMrCsrnjWb2fzRj3501u1Der3e9+8XX3yRe++9lxdeeGHeFPiwsDDfj+r5Hp/ty8Bsq5WzPT5Te1MfDwsLQxAEBEGY8fUKhWLGx70rrlNRqVQzmqYv55i8LPeYvK9ZT2PyIo8pOGNSarWEl5QgFRcjdnfjPnMGd0sLirExqKmBmhoknQ4pORlXSgpCYiKauLhpRfpmG5NarQazGXF4GLGnB3tXl6c/b7+NShBQ4alorsrLQ7lt26zF/y718zTTmLxizkz37LU6Jli58wQzx3Itj2mlzpP39WFhYb6x+I0pLAz27UPauxdNezuu06cROzpgZARpZAR7WRmEh3v85lNSEOLjEWJjQasNaEyS241kNCIODCD09iL29CANDaEAREAB6NLSUOXlocjI8BX/U8KqPU/yd6OLLNd3o9nGILP+UalUPtHaaDRSUVFBdnb2sgjNAOfOnaOvr4/S0lKOHz8eFAF9NqxWK2VlZaSlpc0oNMPM2cWTBUOxrW3edryF+sbHxyktLQ1IaPa2s5DMZqPRSFVVFTk5OYsWmr3tzuQBLooiNTU1SJJEQUFBQEJJ1Ys/5zLTG7gkBa0Hf0ZWAEKzKMHhnjDKh6ffp64W3yX96OfJcjeDAGeV23Bc9z3ycvYGPsBFMJ8nularJSkpiaSkJMCT/e0Vt7yZ5kql0k98NhgMq1psWivC+FT04REUfPhhxkfvp+7l/6Co/znyHNW4/noL78V/iPTbHiYianpNCu/nq7dgp7hrFybT+3nhnUPsPvtD0ujjS90P8G7v//DDXU/wsWwVCTGRazqD3YsoiigUCoQvfAHpZz9DL7qpcOdzteoo9trnYY2KzZMLD27cuBHwvzb7+/s5d+4c4Pnsa2trIyoqisjIyKAUHpwJs9lMeHj4mr2+ZmPNis3R0dFETyrEMBOvvfYan/jEJ3jmmWe4+uqrl6djMjIy6wJBEFCmpqJMTUWy23G3tyO2tuJub4eJCcTWVsTW1otv0OkQIiIQdDoEvR4m//B3OJDsdiSr1VM0ZlJmmnjhS6oQFYUqKwvl1q0IiYnr7sNGRkZmfgSFAmVmJsrMTCSrFXdbG+7WVsTOTrBYcJ87h/vCF2AA9HoEg8Fz39HpPF7PCgWIIpLdDjYbksWCNDbm84a+2JiAIikJRWamx5/+wjZSGRkZmal4bTTGxsYoLy8nKyuL1NTUZWm7paWF7u5uSkpK0Ov1M1p6BAubzUZ5eTkbNmyY0wpCoVDM3YdJVjqzMblQ30IWcrxCtyRJ835XNJvNVFRUkJWV5RNVlsJUkd0rmFutVkpKSgLact5eX8au+n8DAU6mf5a8PfN7W485BH7eOH17eZreTm7Ndylpe5wwwck44dRu+wK5N96PMkSi0GQW+l1drVYTHx/v2yHg9Qw2Go0YjUba29sRRXGa73OoBK5LkciYBPI/+QO6uv6JsZceZs/EcfYNP8for1+ndueD5F1/j1+ReFEU/c6zQqEgKiqKa276KLZrbuFP//sf3Dr631zmLie37AC/7/4/uDLexw5Fl+/8rbUMdi9esZk9e3yPqbq1kA5bjcdwO6woNPrZD7CGmOnabGhowGazYbFY/GxxvMJzdHS0L/FhqXjF5vXGur1zvfnmm9x+++089thj7Nq1i76+PsDjg7JWvFAUCsW8RSJkAkOOZXC51OIphIWh2r4dtm9HcrsR+/sRvdmBw8NI4+MwMeHZCh/QAQWEqCgUycmEbdhA4dmz6D/4wYCzWmRm5lKbl6FEjmVwWUw8Bb0eVU4OqpwcJJcLsa8PsacHsbfXc98xm8Fq9SxiBXJAtRohJgbFxo2eDOnkZJ89z1pCnpvBQ47l+idY4oZKpcJsNtPQ0MCWLVuWbRt9e3s77e3tlJSU+H6/hUpsdjgclJeXEx0dzY4dO+aM3UyezQAkJAQkNLe3t9PZ2UlpaemC7UAmeyfP1UevcJ6amkp6evqC2piNqZm8LS0tDA4Osnfv3oAyOZ0OO+qX/hmt4KRaW0Lhnd/E7Z47S7t2RMXL3dNj9KGIOvQvfZYcZx0IUB1WRPQdP6UgKW3hA1sCS3Ek9QqXkz2DrVarb3t/X18fNpttmu/zSu0yWaPuqzOyIXUbGz73DOUnXyH+xL+SLnWzv/7/Utv8IrpbfkTS5u3A3NeZVqfnxru/TX3TnShf/hL5NPK5voc5NvQKf9/zJLeqhunt7eXs2bOoVCrf+Qu0QN1KI4oiSqUSQa/3fc/cMWChZ3M8G4UhTlccYvNlt69oH0OFQqHwnTPvwqPNZvNlP3d1ddHQ0IBGo/Gz3ljszgSLxUJERMSaW5CYj3UrNv/ud7/DarVy3333cd999/kef+SRR/j2Bd+Z1Y5areaWW25Z6W6sC+RYBpdLOZ6CUonygljjXemVHA4ko9GTQWi1Itls4Hb7MgkFjQa0WgStFiE6GiEy0rdNHeDW/PwVGct641Kel8FGjmVwWWo8BZUK5aZNKDdt8j0mORxIo6Oee473viOKnvuOICCEhXnuO3q9J2vZYFgXX2LluRk85FjKBIokSXR0dMzqYRwKuru7aW5upqioiIhJnvWhEJtdLhcVFRWEh4eTm5s7771ytiJ9itxcxLfeAjz2RcIMmb7nz5/n3LlzfgL6QvAKGb6swxnwCufx8fFkTSrwtVQmj7urq4v29vYFCea1z36PfWI7o0SQ/IknUSiUs4rNbgn+2Kqj0+IvV2RGuCjsf46tf/8qMZiwSGFUZ3+ZvPd/1i8jdTkI9meqIAiEh4cTHh7us6ix2+0+z+fOzk7OnDmDVqv1Ey7X4/b75WL73g/g3H0tx158lKL2X5HvqMb67PupzLyP3Fu+HNAOgq3bcnB94TX+9w+PcvvQ41zuOsmpk//Gf+7+V/718jAMKrdfBvvUAnXe87jako5EUfT1SfjMZ5B++1s0kkStUMhGDuNsOATrVGwGz/gnL6JptVq0Wq3Pq9/tdjM+Ps7Y2BgjIyO0tbX5diZMFqADOa8Wi2VZMpvffvttvve971FZWYnNZqOgoIDvfve7HDx4MCTtrVux+amnnuKpp55a6W4sCafTyaFDh7jxxhtX3c1nrSHHMrjI8fRH0GgQLlS8XShyLIOHHMvgIccyuIQinoJGgzCpONWlgjw3g4ccS5lAsFgsjIyMEBUV5fMtDTU9PT00NDSwZ8+eabaJwRab3W43lZWVqFSqgD2HZ+uDYvt2n9jM8DBM+W44MDBAfX09u3fv9mWzLpTJYvNMeMej1+vZuXNnUEVIr190f38/jY2N0xYC5qK7uZY9Hb8GAZoKv0lhQjIul2vGbFmLS+An9dOF+I+mmzAd+r/s6XoShSBxTrkVbnuC/M3ZSx7bYgl1tm9YWBiJiYkkXphLLpeL8fFxjEYjAwMDnDt3zpch7RUtIyMjQ5Y1ux5FbbUmjII7vklX+23YX3yQXGcdl7f9lMaf/w3z1d8PaMwqtYqb/+HrVJ6+nt6/PcYvbNfjfvck949s40sHN1GaMD2D3Zsl29zcjNVqRa/X+53Hlbbe8FvQKi6G3/4WAEWHAjZDxuhxJLcLQbk+JUW32z3ndaRUKomJifEVCJ18Xo1G47Tz6v2j1+unnVeTybQs7gunTp3isssu4zvf+Q7x8fE8/vjj3HjjjVRXV7Nt27agt7c+Z8Y6QRRFqqqquP7661e6K2seOZbBRY5n8JBjGTzkWAYPOZbBRY5n8JBjGTzkWK5/lipUTExMUFZWhsFgIDIyMki9mpv+/n7OnDlDYWEhsbGx056f1y95AYiiSHV1NaIoUlRUFLBAN1tm8+QFQGlw0C8RYXh4mJqaGvLz84mLm16ILFDmEpu94xEEIWDhfKFtj4+P09TURH5+vk9kCQTTS/+HMMFJTVgRBdd/ZtbX9VkV/LZ5eobfP6f3MPA/n+HyiXdBgHejPkDWJx4jTLtynrErIQSqVCpiY2N914YoipjNZp/1Rmdnpy9rdrLvczAWFNeTjcZMJKVnI33+FY4f+iX5DY+S7WrE/PpdHE66B0pLAzpGbm4B6Vm/IfWZDtq6uqk728S3x8a4ujifr+V7CplPzmD3eqk7HA6f+NzT00NjYyMqlcrvHC639YYkSb72hLg4n5XG9gEbxrRwogUTzY3H2ZATmqzYlcbtdgfkQ+9lvvPqLQrqXRwKCwujvb2dyy+/fNkym7/+9a/7/f9HP/oRv/3tbzl06JAsNsvIyMjIyMjIyMjIyMiEFpvNxnvvvUdiYiJKpRKXyxXyNoeGhqitrSU/P5+EhIQZX6NSqYIiNkuSRF1dHTabjZKSkgUVYZtN8PYTmwcGICcHgLGxMaqqqtixY4dvC/ZimU1sliSJ06dP+8azEJEkUCRJoqmpie3bty9oHA3vvkqh7T2ckpKID/5gVruL5nElf2r3F4+zo5xcqW/F+tuPs9t9DrukpmLnN8h7/30zHuNSQ6FQEBkZ6VsMkiSJiYkJn/jsza4MDw/3Ey61Wu26zFJeKoJCQf4HPstAwfvo+vNn2emq54P9P+O9X54h9aM/xhA1fQFsKoYwJS9+KpMvvRbDkaozdPf18+LbpzBai/ju3pnvoxqNhoSEBN99z+32WG94s2QnW28EexFhNqZZ9Vx5JRw5Qrjoola1iwPuYxhrXpHF5jmYel69RUG9nwlf/OIXGR8fJzY2lqioKJ577jn27dsXlIKugWA2m7FYLAtaOFwIstg8B97Vu/Hx8RVp3263Y7PZGB8fX7FCAOsFOZbBRY5n8JBjGTzkWAYPOZbBRY5n8JBjGTwWGkvv9+H1nt0m45kb7733HnFxcezYsYO2tjZsNltI2xwZGaGqqoqcnJw5hcxgZDZLkkR9fT1jY2OUlpYuWLCZzUZDmCSQi+fOobzqKsxmM+Xl5WzZsoVNk3z3l8LUzGpJkmhsbGR0dJS9e/eGRICamJjAbreTlJREWlrgRfhEtxvDO/8KQHn8rezOzPF/XhQRBIHa0TBePa/1e+6mTRMkjNWh+v0n2Cz1M0oEHdf+grzCK5c8nmAwtWDiakAQBPR6PXq9flp2pdFopLu726+wmVe4NARY1+FSEagTN23B9c+v8Ob/PsIVA/9DiflNOn9zA903P8mmrIKAjvGTG2L4c9ouvv9KHcbxcf527Dh2ZzE/PBCYVU90dLTPRmiq9ca5c+eYmJggPDzcz6IhmNYbU8Vm4corkY4cAcDeb4B4SO5/OyhtrUa8BRKDyeSioGlpadx8882cO3eOhx56iJ6eHr773e9SW1tLamoq+/btY//+/dx9991otdr5D74Ivve975GSksIdd9wRkuML0mq7Q64iuru7SU1NXeluyMjIyMjIyMjIyKwKurq6giaayYQWSZJwOBwLeo/D4eC9994jMjKSvLw8BEGgo6OD4eFhdu/eHZJ+jo2NUVZWxvbt2+f97VVbW0t4ePii/aO92bm9vb0LKm43mXPnzmGz2cjLy5v2nO2DHwQ8Wc7ij3/MqVOn2LhxY1C3KB8+fJiSkhJfNmtrayvt7e3s3bsXvT74thLeOWG328nNzV1QVnPdm09TdOqLmCUd5nveJSbRI4BKkoTT6cThcFA2rOXtQf8t5J/eakE6X0Xii3cRxxjnhQ2YP/g7Nk4Rq1eSsrIy0tPTZ83CX614C5t5s5/HxsYApvk+TxXaamtriY2NvaTu/319fTS+93eKz/2IJIawSFpOF3+PnQc/HPAx6gft3Pd0A8bxcdQqFQdLinn0SjVL1YQnWzQYjUZMJhNqtdpPfF6K9UZlZSXJyckkJycDILlcSHfdBcBgWBhpBUfRCG56PvYmERuDb8Gw0pw6dYotW7YQHx8f8ra++tWvotPp+PGPf4zJZOLUqVOcOHGCiooKnn/++YBF7yeeeILPfvazMz63f/9+jh075vv/008/zb333suRI0dC9tkuZzbPwcaNG+nq6iIiImJFVvHGx8dJTU2lq6tr2XzS1ityLIOLHM/gIccyeMixDB5yLIOLHM/gIccyeCw0lpIkYTKZlm17p8zy43A4fB7Nubm5vt8/obTRMJlMlJeXs3Xr1oCSfJbal9bWVs6fP79oodnbh9kK9HmR+vspLy8nISGBrKysRbUzG5Mzm7u7u2ltbaWkpCQkQvPkgoMKhWJBmbySKGKoeByA2uTb2TNJaBZFEUmSqB4L5+1B/6y9ezJHmeisJemVTxLHGM2KTNQf/yMbE1OCN7AgsBozmwNhamEzURSxWCw+8bm7uxun0znNsmEtjnWpSJJEVGouzn2HOP2/nyHXWUdp+Zc51l1D3sf+LwrF/CLgzoQw/vKZXD78u0YGR0Z46+Qpvugu4qfXaJckOM9mvWE0GhkdHaW9vR1RFImMjPQToAPd+TA1s1dQqZA0GnA4SLDbqVdmUyieoa/mb+tSbA6GjUagWCwW33mMiIjg2muv5dprr13wcT760Y9y5ZVXzvjc5M+HF198kXvvvZcXXnghZEIzyGLznCgUilWxcjfZh0lmacixDC5yPIOHHMvgIccyeMixDC5yPIOHHMvgsZBYRkVFhbg3MiuF0+mkvLwcvV5Pfn6+XzZcsHySp2I2m33Zoenp6QG9JxChdzY6Ojpoa2ujtLR0ScWYZisQCKDIy0OsqwMgwmBg586dQU9a8rY/MDBAQ0MDu3fvDsn9UBRFampqACgoKKCsrGxBsW+qeJN8VyMOScXmGx8APOKd2+32FDMcDeNwr7+FzwM7zYy215P0yqeIZ4xmRQaqu57GEB2P0+lEEAQUCsWyFkpb7ygUCiIiIoiIiCA1NRVJkrDZbD7xubW1FYvFglKpxOl0olQqg27ZsFqRJAlBEIiOTyb8/hc4/sdvsH/oWS7v+2/Knuwk89O/QqOdf9EqPlzFXz+zgzt+10TPwCBHy8r5kqKYn14dPDuwuaw3jEbjjNYb0dHRs/p3ey1uJiN8+tNITz4JwKC0BTiDuvMd4PNBG8dqYbnFZoPBsOTjTD7/s/Haa6/xiU98gmeeeYarr756yW3OhSw2y8jIyMjIyMjIyMjIrDMCFYJcLhcVFRWEhYVRUFAwTcibzaN4KVitVsrKyti0adOCLDGUSiV2u33B7Z0/f56mpiaKi4uJiIhY8PsnM6dv9J49cEFszktNDYkYp1AofMXf8vPziYuLC3obXl9rq9XqKzi40Mxm57tPAFAVewOFSWl+QnOLSc2hbn+h7St5VmwjvUS++A/EY6RFkYH+H54nPCrOlw3t/eNFoVAgCIJPhF5O1mpm83wIgoBOp0On0/ksFJxOJ9XV1ahUKnp6emhsbPRZNkz2fV5viwCTBVe1Joz8Tz/GiddyKD79rxSb36LuyTuI/9TvAyocGBGm5K//sJ0P/reC8/39vPNeOV9WlPLolaGR5ARBIDw8nPDw8Bn9u8+fP+93Hr3n0nsepxUIBCi46Feta5mATEg3VyG5nQjK0BUrXAlC4dk8G2azOShi83y8+eab3H777Tz22GPs2rWLvr4+AAwGQ0jal8VmGRkZGRkZGRkZGRmZSxC3201FRQVKpZLCwsIZxSKVShVUGw2bzUZZWRlJSUkLtphYjPDd399PfX09u3btmjfrK9A+zJThK4oirW436Rf+L/T1QQj8PiVJorm5mezs7AX5Jy+E5uZmhoaGKC0tRaPRAB7xKtDMZuNgL3nm4yBA9BX3+1lnDNqVPNPmb53x5VwrgsOM5fd3sV0aoFtIRvcPfyY6PsnvdZMFZ0mSfMf1stzi83oUm2dCrVaj0WiIj48nJSXFz7JhZGSEtrY2JEkiMjLSz/dZpVrbcpM3s3kyeTfcQ010MjuOfYk8Rw0tv70V1yf/RHR88rzH06oV/PlTWXzwv9z0DQ1x5NR7PKTex7/vD9UI/JnJemN8fJyxsbFp1ht2ux2TyURkZKTPekNITMQ747cM2zBmhBMtWGhuKmPDjn3LM4hlwLswtlyLJ8HKbJ6P3/3ud1itVu677z7uu+8+3+OPPPII3/72t4Pe3tq++tc5YWFhPPLII3K19SAgxzK4yPEMHnIsg4ccy+AhxzK4yPEMHnIsg4ccy0uDubIvvX68ALt37541iyuYmc12u52ysjLi4uLIzs5ecObvQvsyNDREbW0t+fn5QSv0NFNmsyRJnDlzBuskew6xvR1Fbm5Q2vRitVqx2WwkJSWFrJB9Z2cnnZ2dlJSU+PlaLySzueWtp9gnuDmn3Er6zhKfQGxxwZNn/b2lv7BzAr1SpPGXn2GPuwkjBmy3/56NM4h3Uy00vKKz2+2eUXz2is6hEJ/Xu4XEVCaf+5ksGyb7Pvf09OBwODAYDH6+z2vt82YmsRlg+94P0BaZQNKhf2SL2Ebr7+/A+MnnAhKcwzUK/vjJbO586gxDo6Mcfa+cZxJL+EjW4uyBlsJU/26v9YbRaGR8fJzz58/T2tpKeHi47zzGZ2SgaGtDL7ppUOex13WS0fo31pXY7L2HrDUbjfl46qmneOqpp0LejhdZbF7FhIWFhWSF4VJEjmVwkeMZPORYBg85lsFDjmVwkeMZPORYBg85lpc2oihSXV2N2+2mqKhozh/VwSoQ6PWFjoiIICcnZ1Fi3ULE5tHRUaqqqsjJyQlqBvDUzGZJkjh79iwjIyOUHjwIT3jsI8STJ+Gmm4LWrt1up7y8HLVaHTThfCr9/f2cPXuWoqKiaXYjc3lVT2VD2wsADG69k02ieEEMhh+f8RdU/nGbjegwiarnfsh+6zs4JCUdVz9OVmZOQO14BWTv/PWKz5OznyfPl2CLz5dKZvN8CILg24rvrXk12fe5vb0ds9mMTqfzs97Q6/WrWrSfTWwG2LyzhB7tnxD/8jEyxQ7afn87o598jpj4+YvoxusV/OTOndz7P5WYrVZ+9UYtlyfnkhJ6vXFOJltvtLS0kJ+fj0ajYWxszFc8cjgjgx1tbQAYrYmggfC+91a240HGe89YDrHZu1CzVHun1YgsNsvIyMjIyMjIyMjIyFwieAu/2e12iouL593qrlKpfALeYgU6l8tFeXk5Op2O/Pz8RQtMgYrN4+PjVFRUsH37dp9fabCYmtnc2tpKT08PpaWlaHU6bBceF0+fDlqbXl/tqKgobDZbSETO0dFRXxa4N9NxMoF6FPe2NbBFbMMlKdhy8C6fQP33Hv+s1hs3OdgULtJ++iTFLT8FAcq2fZldxdctegwzic+AX+bzVPF5svC8kPm9mkXSUDCX8DoTWq2WpKQkkpI8VihOp5Px8XGMRiN9fX00NTX5ig16xeeIiIhV5fs835g3ZubQ+6Fn4PmPkCF20vz7j2C550XCI6ZfP1PJT1Byz7W7+Pmr7zEwPMIDL3Xxp4+mslqmlfd+HxYWRmJiIomJiQC409LgzTcBiOqYgCzYPNFAedl7RMfF+7LY17KFivcesVxzcbk8m5ebtTsDZGRkZGRkZGRkZGRkZGZlqkAoSRK1tbVYrVaKi4t9Xpxz4RXuFuth6fWFVqlUMxYgXAiBiM0Wi4Xy8nIyMjJIS0tbdFtz9cErYnZ2dtLW1kZpaSnhFyw0hB07kBoagIULdDPhtTvRaDTk5eVRWVkZcIZxoJhMJiorK+f0gQ40s7nn1J9JBxrD8siIikMURXonVJQNXZxrSTqRogQXVouJiEOfQyO4qdTvp/DWB4IzoEl9nvw3eEQ0r/jszX6enLm/EN9nObM5cNRqNXFxcb6ClqIoYjKZfAXrOjo6cLvdREZG+gnQKylaBnL9JmfsoPdDz6B8/g62im3UPvVJNt33HGqNds73AdyTp6Kyt4B3yipobG3ji28k8LNr53/fciBJ0ozzX5mYiPcukG60YZJ0RAgTGCQjNpuBvr4+bDYbBoPBJzxHRUWh1WrXzAKNtzjgcvV3uWw0lhtZbJaRkZGRkZGRkZGRkVnnSJJEXV0dZrOZkpISX+G3+fCKzS6XKyBxejKiKFJVVQXM7QsdKDP5JU9mYmKCsrIyUlJSyMzMXFJbc/VBFEV6e3tntJxQHjiA64LYzNAQXCjGtRi8iwNut5vdu3f7PIuDKTZPTExQUVHB5s2b5/SBDrRAYEz3YQDG0q7ziLko+K9zOr/X3L3dk/999rnvsF/qpZ84kj/+C4RlyCScyfd5NRUdvFRQKBQ+ITItLc3nF+wVn5uampiYmPCJll7xWatdPjE20MWi5IwdtN/wG7SHPk6+o5r3nrqf7Ht+G9B8/s/rwrljcDPn2js4WV3D29klHNy0sqKs91qYdb5v2QItLehFN02q7exxV8NgIztL3gd4LH+81htdXV00NDSgVqv9/LsNBsOqvZ6WszigbKMhIyMjIyMjIyMjIyMjsybxFrAzGo0LEprBIzIupkig167D4XBQUlISFP/LqX7Jk/EWH0xISGDbtm0hy0pTKBS4XC5Onz5NYWHhNMsJRVaW799iWxvKRYrNkiRRX1+P2WymtLTUl+G5kEJ98+FwOCgvLyc+Pp4tW7bM+dpA2jWbjGQ5GkGApN03oVAoeL3bf679884JFAJ0naumpO9pEKBr73fYGbN4UX4pzFZ0cLIAPVPRQbj0MptDmek52S/Ya33jFS2NRiOdnZ2YTCa0Wq2f+BweHh6yfs0puE4hPWcv9WOPsevE5ykxHebYX35Awe0Pzfs+hQA/ujmNTz41zJjJzA/faOWKT21ZUTsN77yebezCddchtbQAMOpOAapR9pT7np9mveF2Mz4+ztjYGCMjI7S1tSFJki+LfbVZb7jd7mUtDihJ0roUm1fnUoIMkiTx8MMPk5iYiMFg4GMf+xhjY2Mr3a1Vx3e/+13y8vJ8H0r33HMPQ0NDfq85cuQI+fn5hIWFkZ+fz9GjR/2eHx0d5aMf/SgGg4HExET+5V/+ZTmHsGr54he/iCAIPPfcc77H5FgujJdeeok9e/ag1WrZsGEDDz/8sO85OZaBYzQa+cxnPsOGDRuIiIjgiiuu4L33LhaikGM5O/X19dx+++2kpKRMu54hOLH7z//8T1JTU9HpdNxwww10d3eHdEwrxVyxfPvtt7n++utJSEggIiKCyy+/nLffftvv/XIs/ZlvbnopKytDrVZz05QiW3I8LzJfLLu7u/nIRz5CTEwMBoOByy67jPHxcd/zcizXN14bjcbGRoaHhykpKVlUZqBKpVpQkUBvFrXFYqGoqChoIsJsore3+GBUVBQ7d+4MqShmNpsRRZHc3FwSZhCShUnWHWJl5aLbaW5uZnBwkKKiIr/FgWBlNnvtOcLDwwMq2BiIZ3NH1ZuoBJHzwgaSNm9j3Knws88ojHMRGyYhiSLOlx9CLbip1F3GzituX/J4goVCoUCpVKJWqwkLC0Oj0aDRaFCpVH5Z/k6nE7fbjdPpxOVyBd3aZLWxEsK6V7Tctm0bxcXFXHHFFWRnZ6PT6RgYGKC8vJx33nmHmpoa2tvbMRqNC14Um4uF2uBk77uFk1lfAWBv2885V3E4oPdlRAqUFOQD0N59ngfesi+8s0HEO5dnFdq3b/f9U93p6WuyaXaPeqVSSUxMDOnp6RQUFHDgwAH27NlDYmIiExMTnD17lqNHj/Lee+9x9uxZ+vr6mJiYWLHFnOUWm4F1aaMhi82rlMcff5zHHnuMJ598kiNHjlBdXc3999+/0t1adbz77rs89NBDVFRU8Pzzz1NeXs5HPvIR3/P9/f3cfPPNXHXVVVRVVXHw4EFuvvlmBgcHfa+59957qaur48iRIzz55JP86Ec/4pe//OVKDGfV8Pe//53a2lq/x+RYLozDhw9zxx138KEPfYjq6mreeOMNDhw4AMixXChf/vKXOXHiBH/961+prKwkIyOD97///dhsNjmW82A2m8nMzOQnP/nJtOeCEbtXXnmFBx54gO985zucPHmSiYkJPvzhDy/L2JabuWJ56tQpLrvsMl566SWqqqooKSnhxhtvpKmpyfcaOZb+zBVPL1arlbvvvpvdu3dPe06O50XmiqXNZuOaa65heHiYV155hdraWr7xjW/4/YiSY7m+kSSJpqYm+vv7KS4uXvQW9IVkNnuzqMfGxiguLl5QFvVi+uEtnqfVasnLywup0GwymThz5gzArN7GQtjFQnjuw4GJTVPp6Oigs7OToqIidDp/C4pgiM3erHNBECgoKAgoZvO1K0kSE+c8i9bdEbsQBIH/bfEvCnhTqgOAxhMvkeusxSapifngvy9hJKFnsvjsFZ67urpwu92+zE1RFHG5XDgcDj/xeb0L0MuNSqUiNjaWzMxMdu3axRVXXEFhYSGxsbGYTCZOnz7N0aNHqaiooLm5maGhIZxO56LbW4znev7NX+A9wzWoBJG0I19idOB8QO/7wQEVWzd7FqrK605jcqxc1rx33s469uRk3z8TjZ7XbpAGcFlGAjq+IAgYDAZSUlLIyclh37597N+/n/T0dARBoKuri5MnT3LixAlOnz5NV1cXJpNp2a4nr2fzcmCxWHwLW+sNQbrU9n6sEfLz87n++uv5wQ9+AMBf//pXbr/9dvr7+32m+jLTef7557n99tsxGo1ERUXx6KOP8u///u/09vb6PN42bNjAt771LR544AGGhobYsGEDL774Ih/4wAcAePDBB3nrrbeorq5e2cGsECMjI5SWlvLqq6+ybds2nn32We644w45lgvkyiuvZNu2bfzqV7+a9pwcy4WRk5PDhz/8YR555BEAzpw5Q25uLo2NjbzyyityLANEEATf9QzBmYe33HILYWFhPPvsswDU1NRQWFhIVVUVhYWFKzHMZWFqLKciSRIxMTF85zvf4Utf+pIcy3mYLZ6f/exn2bRpE+fOnWNoaIiXX34ZQI7nHEyN5VNPPcWXv/xlOjs7Z8yakWO5/mloaKCjo4OSkhJfAbvFcPz4cbKysnzi2mx4s6gHBgYoLS0Nur+qzWbjyJEjXH/99T4P4YqKCiRJYs+ePSEVCKxWK6dOnSIlJYXW1lauvfbaWTO2bffc4/FrBrQvvLCgdnp7ezl9+jTFxWI0BfIAAQAASURBVMVER0dPe76+vh6lUsn2SdmFC2GypUppaWnAPtxnz57F7Xazc+fOGY8piiIdj17NDlcD7+Z+h41X3ccTjReF8o9l2tgaJSKJIucfu5Jt7nOcSPgohf/42KLGsVJ0d3fT3NxMcXGx75qayfd5ssyyHnyfKysrSU5OJnmS0LjakCSJiYkJn/XG2NgYVquV8PDwab7PgYjITU1NKBQKtm7duqB+WC0mrL+6ngyxk0rdPrbe/3RA/s3PtSj4/p+P43A6KS3M59cfiF5Qu8FiYmKCkydPctVVV836GvHCorMEmEua2SQM0HDNU2wqvDYoffBab3jPo3eX/+QCkpGRkSGx3ujt7aW3t3fGZIdgU1NTw80338zIyMiaKaAYKGvzTrfOsdvtnDlzhv379/seu+KKK3C73b4CGzIz09/fj06n860MVVRUcNlll/k+1JVKJfv376e83OMpVFVVhSiK02JdV1eH3b6y21dWis9+9rPcd999ZE3ymwM5lgvB6XRy4sQJtm3bxsGDB9mwYQPXXnutL1tcjuXCOHDgAK+//jrDw8O4XC6eeeYZsrOzyczMlGO5BIIRu4qKCr/nCwoKiIyM9B3jUsVsNmOxWHw+nnIsF86rr77KqVOneOih6X6HcjwD58iRI+zfv5+HH36YpKQk8vLy+PnPf+57Xo7l+ichIcFPFFssgdpoNDc309fXt6Qs6rnwislut9uXnetyuYJSfHAubDYbZWVlJCUl+QoPzpVlp7r+et+/pZHAsv3AswDk9YKeSWiGpWc2ezM+9+zZs6CCj3O1K0kSLpeTNGcrADFbinm2zT9Tb2uU571NZa+zzX0OqxTG5pu+tshRrAxDQ0OcPXuWgoICv2tKoVCgUqnQaDR+1htKpdLnde12u/2yn71zeC2x2sUwQRDQ6/UkJyezY8cO9u7dy+WXX05mZiZKpZLu7m6/jNnu7m5MJtOsdg2LyWwG0IdHYLn+JzglJbsnTnDmzT8E9L7bM0V2bvV4p9c1naNpVKLTLNBjEVjOFFFRFOdfFPnQhwAQgG6FJyPb3FkdtD54rTcyMjIoLCzkiiuuYM+ePSQkJDAxMUFjY+M06w2bzRaUtpfTRsNsNi/583m1IovNq5Dh4WFEUSQ+Pp6HHnqIoqIioqOjUSqVftubZfwxm8089thj/PM//7Pvy+3g4CDx8fFUVVURGxvLe++9R3x8vC+Og4ODqFQqoqOj2bt3L1/5yleIj49HFEVGFvDFcL3w+9//nvb2dh588MFpz8mxDBzvlq3/+I//4JOf/CSHDh0iMTGR66+/HrPZLMdygfzkJz9h27ZtxMfHo9VqeeaZZzh06BBqtVqO5RIIRuy8x3j88cdJT0/HarX6HeNS5Xvf+x4pKSm+7FI5lgtjaGiI++67j9/85jczZqzI8Qyc3t5ejh49ysDAAK+++ioPPPAADz74oM/XWY7l+sfr1b1UArHRaG1tpbOzk+LiYvR6/ZLbnK0fgK9An8ViYc+ePSEtLOX1g46JiSE7O9tP8J4NRUGB79/iJEuluRgbG6OqqoqcnJwZvaB9x16C2NzZ2TmrPcd8eDPJpyKKIm63m772RsIFOzZJTWRaDsP2i1LD3dsuikBS2W8AqIm7gZiEjYsax0pgMpmoq6tj586d04pCTsVrvTFVfFapVD4Rb62Jz2t1Q7xGoyEhIYGsrCyKioq44oor2LlzJ+Hh4QwNDVFZWcnRo0eprq6mra2N0dFR37UtiuKiBfbNO4s5lfIPAGTXfI+x4f553yMI8MmSZLRhYVgVUdz+RiQfeM3A9YcMvNGzfMXzAhm3kJPj+7fZGgmAauBMyPrktd7YtGmTn/XG5s2bfdYb7777LsePHw9oIWEultuz2WAwrPqFnMWwOso9yvgx+YKIj48nbVKRCZmZcblc3HXXXWzcuJHvfve7vse9sdTpdKSlpREeHu4X38n/3rRpE4mJiWv2g3SpdHV18ZWvfIW33nprxpurHMvA8X5RvPnmm7nnnnsA+OUvf0lsbCyHDx+WY7lAHn/8cSorKzl8+DAxMTH89Kc/5aabbqKsrEyO5RIIZuyio6NJS0tDqVRe8vF9+umn+fnPf86RI0d8Yoscy4Vx77338ulPf5pdu3bN+Lwcz8Dxbut+8skn0ev17N69m7/97W/8z//8D3fccYccS5mAUSqVc2Y2d3R00NraSklJSUgLHXl/kDc1NWE0GikpKQmqJ/RUvH7Qer2e3NxcX/vzCb5CRobv3+6jR1Hu3TtnO2azmfLycrKysti4cW4BdrFic39/P2fPnqWoqGhR58iboTsZr30GwEibZwdupyqd2iH/TL2N4Rde09dFgeUECBB7cO3UI7Lb7VRXV5OWlrYoG4nJu8gAn93G5L8nL15MtdxYq9YbqxFvxqx3wUCSJMxms8964/z58zidTiIiInC5XBgMBhwOx6LuMztv/yatPz9MpthBw4vfJe8ffzrve65JcfO7zAxqjDqQJBAEFILE610qrk0JvEjrUggos3nLFt8/lcMCbIJYc2ALa8EiLCyMDRs2+PzzXS4X4+PjjI2NMTQ0REtLC+Cx3vBaqARivbESYvN6RBabVyHx8fEoFAqGhob42tc8W4u8K2xzrXJfqoiiyKc+9Sl6e3t54403/D4IEhMTGRoaIjs72+c9ODw87ItjYmIiLpcLo9Hoy/L5y1/+gkKhIDY2dtnHspJUVFT4ttRN5q677uL555+XY7kAYmNjEQSBbdu2+R6LiIggPj6e8+fPy7FcAHa7nW9+85v8+c9/5pprrgHg17/+NdHR0bzwwgtyLJdAMGKXkJDA0NAQDzzwAHfddde0Y1xqvPjii9x777288MILfj5vciwXxptvvskrr7zCD3/4QwBfcR+tVsvw8LAczwUQHx/Ppk2b/LJMMzMzOXyhaJkcy/VPsLKlVCrVrJm83d3dNDU1UVxcTGRkZFDamw2vCDc0NMTevXtDYtXhRRRFqqurfUX0Josv82V6C5PsKcQTJ+Zsx2azUV5eTmpqKunp6fP2azFi88jICLW1teTn58+blTsbUzObvdYQXmHKPegRdkbDM6kYujj+W9Iu2pZ1HH+GTYJEoyqbtKzCRfVjufFaWcbExPgsVJbKVPEZLmaIe8XnqQUGV4Pv83rMvhQEgYiICCIiIti0aROSJGGz2RgbG/NlOh87dgy9Xu/n+6zT6eaNh1qjZXTft+DYP1Ey/ALNLf9Eypa8Od+jFGBb2iZqXEpPqjMgSgJHe1U4RVAvw6kPRGwW9Hq8S0/RJk8/k9w9OEQ3KJZHqJ2Kt4Ck9/vL5IWEsbExent7sdlsGAwG33n0enhPZjkLBJrN5nUrNstLZKuQsLAwcnJyOH78uO+xo0ePolQqZ83yuVSRJIm7776b+vp6Xn/99WlfcPfs2cO7777r+6B2u92cOHGCoqIiAHbt2oVCoZgW67y8vHVZEXQurr32Wurr66murvb9AfjhD3/ID3/4QzmWC0Cn07F9+3ZaW1t9j1mtVoaHh0lJSZFjuQCsVit2u93vy5z3y/bExIQcyyUQjNjt2bPH7/mamhrGx8d9x7iUeO211/jEJz7BM888w9VXX+33nBzLhVFWVkZNTY3vs+iWW27hiiuuoLq6Gr1eL8dzARQWFnL+/Hk/j/rOzk5SUlIAeW7KBM5s4mpvby8NDQ3s3r17Vo/hYNLW1oYkSezYsSNkVh3g+Y1RW1uLw+GYsfBgIIKvIu+iqCTN8lqHw0F5eTnx8fHT6qXMetwFis0mk4nKykqys7N9GYCLYXJm81ShWRAEVKZOACwG/125+bEX501sxyEAhje/f9H9WE4kSaKurg6VSsXOnTtDKrYqFArUajUajQatVktYWBhqtXpW32eXy7Ws1huXyo4WQRDQ6XQkJSURERFBWloaBw4cYOvWrajVanp6ejh16hTHjx+nrq6Orq4uxsfHZz0P20pvpEpbikoQMf393wPqw94kERT+eaFWt0D54PIIoAFlNgPExQGQZLZjl9SE4cQ82BHi3gWOdyFhsvXGvn372Lx5M+D5PnTixAmOHz/OmTNnfNYbLpdr2RZ01rNns5zZvEq57777eOihhzhw4AAbN27kG9/4BnfeeSdxFy5oGQ/3338/b775Ji+//DIOh4O+vj7Ak4WjVCq56667eOSRR3jwwQe57777+MUvfoHD4eDjH/844Mn4+dCHPsTXv/51NmzYwPnz5/nVr37Fo48+upLDWhEMBgPZ2dnTHt+4cSMbN26UY7lA7r77bv7lX/6Fa665hl27dvGDH/yA2NhYrrnmGvbu3SvHMkBiYmIoKSnhW9/6FrGxscTFxfHTn/4UURS56qqr0Ol0ciznwOFwUF9f7/t/e3s71dXVJCUlBeWavu+++7j11lv5r//6L/bs2cMXv/hF9u7dS2Fh4XIPNeTMFcv6+npuv/12HnvsMXbt2uX7LDIYDBgMBjmWMzBXPKcKLlFRUTidTt9nlBxPf+a7zr/97W/zhS98ga997WtUV1fz/PPP87vf/Q6QYykTOCqVyrfLwMvAwICvmN1y/Ebp6uqipaXF54EbKiRJor6+nvHxcUpLS2fccq1QKOb1sFZeey1iXZ3nmJ2dCFOylt1uN5WVlej1+gUJmQsRmycmJigvLyc9PZ3U1NSA3jMb3szmyZm3XqEZINzaA0Cv5mL2r04peZMzMY+NsM3ZAAKklH5oSX1ZLpqamrBYLJSUlCx7NrFCofBr0xt7b/bzZAsTwJfxvJKZz+sNSZJ8iwDx8fHEx8cDnmvXZDJhNBoZGRmhra0NURR9mbLR0dF+dg3qq74Bh25jt/ltWtsbSUqf/pt7MmUDSgSkCwK/gPLC6TzSq+KyDXPfd4JBoGKzcM01SH/6E3rRTRcb2Eo3w50NGDYEZwdAKNBqtWi12hmtNwYHB2lpaUEURbRaLUqlMmDrjcUi22jILDuf+9zn6O3t5e6778ZqtXLzzTfzi1/8YqW7ter41a9+BUB+fr7f421tbaSnp5OUlMRLL73EF77wBZ544gm2b9/OSy+95Lf181e/+hX3338/Bw8eRK/X85WvfIX77rtvWcexFpBjuTC+/OUvMzIywhe/+EXMZjO7d+/m0KFDvm1aciwD57nnnuOrX/0qt9xyCxMTE+Tl5fHyyy+TccEPUY7l7PT09PjtiPFaMz3yyCN8+9vfXnLsPvCBD/DYY4/x8MMPMzQ0xJVXXskf/hBYxe21xlyxbG9vx2q1ct999/nFxxtnkGM5lfnm5nzI8bxIINf5V7/6VfLy8ti0aRP/9m//xp133ul7vRzL9U2wMjGVSiU228Uib0NDQ9TU1JCfn78sliq9vb00NjZSVFTEmTNn5hV6l8K5c+cYHByktLR01l1QSqVy/szmSQW0xLIyFJPE5rksOuYjULHZmzWdmJjIlkn+qovF2+7UjGYvsS7PQmtX2FbfY3dmTNpVUfMWiYJEl5BM4qal9yfUdHV10dvbS0lJCepJtigrxVy+z5OFaC+TLTeCJT6vRxuNuZAkacYxK5VKoqOjfbs5JEnCYrH4fJ/7+vqw2+0YDAaP+By/meqwIgrt5Qy9+VOSPvP4HG3C4R4VEoLHRsPYgTvak4l7uFvFNwrshPo0BJzZPMlWZtgVy1Z1NxP950LYs+Azk/VGdXU1SqUSq9VKT08PDofDdy69iwnB2iG7nsVmQbpU9kPIyMjIyMjIyMjIyMhcQkiShMPhWPJx2tvbGRkZYffu3YyOjlJeXk5OTs68xeyCwcDAADU1NRQWFpKQkMC7775LRkYGSUlJQW+rra2N1tZWSktL5xQATpw4wZYtW+a1pbB98IOef2i1aJ9+GrhozWAymRYlZPb09NDV1UVpaemsr3G73ZSVlaHRaNi1a1dQRMLz58/T1dXF7t27pwnNANK/pxMu2Pn2ruPY43MB+Fah1SeMVf3+G+zv/W9ORb2fvPt+s+T+hJLBwUHq6uqWzR4mGEy2RZtcfNDLUosOlpeXk5aWRmJiYvA6vcqpqakhPj7eZz21ELy+z14Beqj5PT7S+2+YJB2tt73KhqSN6PX6addRo1HBnYcv2CpIIrS8AVuu9fk3P3ethe3RobVO6e3tpbe316/+yExIo6NIFxanK3ZIXB35LuXxt7H90z8Laf9CzdTzPvVcms1mtFqtT3yOiorCYDAs6j77ta99DY1Gw09/On/xyLWGnNksIyMjIyMjIyMjIyMjMyveAoFjY2NUVFSQnZ29LELz8PAwNTU15OXl+TKo5yvOt1i6u7tpaWmhuLh43kyzQPsgbNyI1NMDNptP+Dt79iyjo6Ps3bt3URmz82U2i6JITU2NL2s6GEKz107AZDJRX19PTEwMMTExPrHMYZsgWvBkMbt0FwX4yU3rjWc9zyfksJoxmUzU1dWxc+fONSM0AzOKyJMLDXrFZ5fL5feeQIsOXoo5irNlNgfCVLsGR2EBPT/7DRuFQTpOvkBnSrHPpsGbLRsREcFbPRoUgoQkQaTKiSE+BrPKwbhbgyDAWz0qtkcvfQFxLgK20YiJ8RUJlOyeTN8wa18Ie7Y8uN1uv/HPZr1hNBp91huAn/gcFRUVUJFBi8WyrEXry8rK2LdvH9dffz0vv/xySNuSxWYZGRkZGRkZGRkZGZl1SDBtNOx2O+Xl5WzdunXJ/r+BMDY2RlVVFTt27PDLYg6F2Nzf3+8rdBgVFTXv6wO1slDedhuun/8cAKmnh3a7nZ6eHvbu3bvobdhzte31m7ZarZSWlgYkdgSCJEnExcWRl5fH2NgYfX19nD17FqVSSUxMDCrJRjHglgTcOo9wkqL3P0cJtnYAwjetXrHZZrNRVVUVssz55Wahvs8LEZ8vBURRDNo9VKMJo2HjLWzs+Q2pxpNkfuwrmEwmX7ZsZ2cnbrebl8avQpQ0CEBmbBjZW4poGIHaIQlRgsPnVdy/M7Ris3dxaSGo7J57TbhjIBRdWlbcbvec986p1huiKPrZqEy13oiOjiYqKmrGe77FYiEiIiJkY5mM1Wrl7rvvnjdjPVjIYnOA1NfX8/DDD3Py5El6enp49tlnueOOOwJ+/3vvvcdXvvIVXyX1j3/84/zHf/xHyIzGZWRkZGRkZGRkZGRkBEFYclaiy+XCYrGwdetW0qcUugsFJpPJJ2xv2rTJ77lgi83Dw8PU1taSn58fcKHDQAoEAigLC/HmkY4cPkxrSgolJSXo9fpF99dbqG8mmpubGRoaWnTW9Ex4fZoFQSAhIcGXYS6KIuPj44yOjtLR2AzAOAZQeESabP04oqhGoVAgiSLx4jAIELkhPSj9CjYul4vq6mri4uKWZY6vBIH4Ps9WdND7/0uJpWQ2z0TMrpuh5zdsn6hmwmH3ZcCmpaUhSRLtwza6jnh2VUhA8oXbRHI4VA95+nF2TEmfVSBJH7pM84A9mwEuvxyOHUN3wdI/xjUYsn4tF/OJzVNRKBS+ukzezyubzYbRaGRsbIz29nY/6w2z2YwgCOzevXtZxeavfOUrfOQjH+HcuXMMDQ2FvD15uSpAzGYzmZmZ/OQnP1nwe0dHR/nABz7Azp07qays5H/+53945pln+M53vhOCnsrIyMjIyMjIyMjIyAQHq9XKuXPnUCqVZE4qCBXK9srLy9m8efOMol8wxeaxsTEqKyvZsWPHvP7LU/sQSGazMKl4Ytirr7Jr1y4iIyMX1Vcvs2U2d3Z20tnZSVFREVqtdklteJksPk4V3RQKBdHR0WRkZJCe6vE2tQsXBe7x1ireeustysvLOVNXjVZwAhCduHD/21AjiiJ1dXWo1Wp27NhxyYiqCoUCpVKJWq0mLCwMjUaDRqNBpVL5xDa3243T6cTtduNyuXC5XNP8oNcrwRabU7ftZoBY9IKdtpq3/J4TBIFTY5EIF4wp4rUimw2epaooDYQrvMtWEk9XDtDd3Y3ZbA7JeViI2CxkZwMQafX0I4ZxJOdE0Pu0nCxUbJ4JrVZLUlIS27dvp6SkhCuuuILt27ej0+n4+9//zk033cSmTZt49913efPNN3nrrbewWCxBGsF0Xn31VU6dOsVDDz0UsjamIovNAVJSUsIPfvCDWbOZa2pquOaaa9Dr9WzevJl/+Zd/8fkhvfvuuwwPD/PYY4+RlZXFddddxwMPPMCTTz65nEOQkZGRkZGRkZGRkZEJGJvNRnl5OTExMUDoMxttNhtlZWUkJSWxZcuWGV8TLLHZbDbPmj09H4FmNgOIFwqqqe12Yi/EcSnMJDb39/dz9uxZdu/ePa/fdKB47Ra8VgJznXu32/O7181FgeZ9B0rZu3cvSUlJWExGz/OSQE3tGZqamhgYGAhK8cqlIkkSTU1NTExMkJ+ff0nbR0wWnzUaDWFhYYSFhdHf34/T6SQyMhJRFHG5XDidThwOBy6XyzdP1hvBFpsFhYJ2QyEAto6Kac+/cV6FACgFiYNJLjYbvAs9kBcvoBQkFECFJYHBwUEqKip45513qKmpob29HaPRGJR744LsQy4U0Yuz2bFJnsUmy3DPkvuwkoiiGDQLIi8qlYq4uDgyMzP51re+RXd3N88//zzh4eH09/fzyU9+kqioKIqLi3nggQd47bXXgtb20NAQ9913H7/5zW+W1Vnh0r2TBpHh4WGuvvpqioqKqKmp4fe//z1/+MMfePTRRwHPlhxBEPy2Mmm1Wvr7++nt7V2pbsvIyMjIyMjIyMjIrHMWK5Y4HA7KysqIiYkhKysr5GKSt73Y2Fiys7Nn7XcwxOaJiQnKy8tJTU0lIyNjwe8PNLPZZDLRnJ/v+7/U3r7gtqYyVWweGRmhtraWgoIC36LAUpksNE+2UZgN0eURjV2TXDoVCoHw8HA2bdpERvpm3/NpaWm4XC6am5t5++23OXHiBA0NDfT29mKz2YLS/4XQ1dVFf38/u3btCpr1yHpBEARGR0dpbW2lsLCQyMhIwsLCUKvVKJVKj0XKhbnicrnWnfgcbLEZwBHv8SzXDZ/xe9zshLJBJSICbkng8mQ3etXFrOXNERJuSUBE4LTZwLbcXRw4cIDCwkJiY2MxmUycPn2ao0ePUlFR4bPUcTqdC+7jgsTWC2KzRpIYlTx2EJbx4QW3uZqYWiAwFGg0Gi677DIMBgPf+ta36OrqoqWlhQcffBCn08k777wT8LGeeOIJ34Lg1D+XX3459957L5/+9KfZtWtXCEc0HdkwOAj853/+J1u3buXf//3fAcjKyuIrX/kKP/3pT/n6179OcXExYWFh/PCHP+RrX/sa58+f59e//jUAg4ODJCcnr2T3ZWRkZGRkZGRkZGRkfDidTsrKyoiIiCA3Nxe73e7zdQ3Fj3Cn00l5ebmvvbkEHoVCsaSMWIfDQXl5OQkJCWRlZS3qGIFkNnvtQNKvuAIOHwbA/eabKJZoRTJZbDaZTFRWVpKdnU3ihQzqpTLZwzcQoRlAupDZLM6Sy6ZUeURcJW6SNmzw/f51OBwYjUaP73NHB6dPn0an0xEdHU1MTAzR0dHo9fqQZdQPDAzQ3NzMnj170Ol0IWljLWOxWKitrWXHjh1ER0cDCy86ONn3ea1ljYdCbNZtyod2SJxo8Xv8eL8Kt+RpS4FEaaKLmhGN7/kEnYACCREBlyRwvF/F+za5iIyMJDIyktTUVCRJYmJiwleorrm5GavVSnh4uM8fOjo6Gq1WO+e4FpTZPKmgqlXy2Pc4zKMBRmP14Z2/wc5sng2z2UxERASCILB582Y2b97MXXfdtaBjfPSjH+XKK6+c8Tm9Xk9+fj6vvPIKP/zhDwF8CxBarZbh4WHCw8OXNIbZkMXmIFBbW0tlZaXfliW32+37ApKcnMx//dd/8fnPf56HH36Y8PBwvvSlL1FbW7vmbrgyMjIyMjIyMjIyMusXl8tFeXk5Wq2W/Px8BEHwbb0NRcaX2+2msrISjUbja28uVCrVojObvWOLiIhg586dixaSFAqFzzJxJux2O+Xl5SQlJZGenY39wuPul19Gfc89i2pzctuiKPqys9PT00lNTV3SMb0sNKPZ1ye1RxRTMXNMtAaPIKUSRIwTFvThngxIjUZDYmKiTyh3uVwYjUaMRiM9PT00NDSgVqt9wnNMTAwGgyEoAuD4+DinT58mJyeHqEmCmYwHp9NJTU0NKSkpbNy4cdbXBVJ0cPL1upbE51CIzRGJ6QDEiiO4RRHhQgw6TBdjsTveTYQaFJOa1qsldse7KR/y3IvbTdNjJwgCer0evV7vt6AzNjbG2NgY58+fp7GxEbVaTXR0tE98nnpNiaIYcJa/IHhdpsEqakEJDsvaFZu9c3W5xGaLxbJk66Po6GjfYtBMlJWV+V2D3/zmNzEajfziF79YUrHa+ZDF5iBx00038YMf/GDW5z/ykY/w4Q9/mL6+PmJiYjh06BCAnNUsIyMjIyMjIyMjIxMyFiKWeIVflUpFYWHhNCHJ7XYH1WpAFEWqqqoA2LVrV0Di00L8kifjdrupqKgIWNSeC6VSOWt2tcvloqKigqioKJ8diGL/fsTjxwGQ7HaEsLBFt+21LigvLycxMXFWb+uFslihGUAd5smMC5PsMz6v10fgkFRoBBfjQ70+sXkqKpWK+Ph44uPjAc85Gx8fZ3R0lKGhIZqbmxEEwS/zOTIycsGi5cTEBFVVVWRmZi6oMOSlgiRJ1NXVodVqF5z9P5P4DPhlPk8VnycLz6tJgA6F2ByV6PGHDxfs9JqMGKJiAbgn28ENqZ6M0wStR741Oi62rVNKPH65jUGb57FN4YEVBtRoNCQkJJBwoVip95oaGxtjeHiY1tZWT78mZT67XC7CFnKP2r4dzp7FJnoym13WscDfu8rwztflmIeSJGGxWIiImPl+GCymXsNRUVE4nU6yLxR3DBWy2BwE8vLyeOaZZ8jMzJxzUgqC4BOXn376afLy8oiLi1uubsrIyMjIyMjIyMjIyMyIKIpUV1cjiiJFRUV+mV2CIKBUKufM5l0okiRRW1uLw+GguLg44EyyQP2SJyOKIjU1NUiS5CeiL5bZBO/JWdp5eXk+oUp10004LojNYm0tyuLiRbctSR6RKTw8fEnZ2VOP6c1CXajQDKDWerLjtMwswAsKBX2KDaRJ5zH2NJO0eVtAx1UqlcTExPi8qEVRxGQy+aw32tvbcbvdvsy+mJgYoqKi5pxLLpeL6upqEhIS2Lx584LGealw7tw5JiYmKCkpWfL8mklEFkXRJz57593ke4t3Dq509rP3eggm+vAIXJIClSBis5p8YrNCgDSDv4A8bL/Ytl4loVNNf81CmXpNSZKE2Wz2WW/09PRgt9sJCwvD5XL5BGiNRjP7QXfuhLNncYqehUhxYnxJfVxJvPf15Zh3ExMTiKIYcrF5pZDF5gBxOBzU19f7/t/e3k51dTVJSUl8/vOf52c/+xl33303DzzwAGq1mhMnTtDW1sZ3v/tdAJ599lnS09OJj4/nmWee4dlnn+XZZ59dqeHIyMjIyMjIyMjIyMgAF8VYu91OcXHxjBXrg1GYz4skSZw5cwaTyURpaemCsqUX2g9Jkjh9+jRWq5XS0tIZx7ZQZhK8veK52+1m9+7dfmKFsO2iuOp6+ulFi82iKPp+kwZLaAb8fHYXc0yN1rMNXMfFzGa3CMpJes1IWApptvPY+puA9y+qnwqFwpeBuXnzZl9m4OjoKEajkfPnz+NwOIiMjPTLfvbOL1EUqa2tJSwsbM4ilJcyPT09nD9/npKSkpAVTJzJ93my7cZU3+eVEp9DkdkMXm9zEaS572M91ouLJin64Nx7pyIIAhEREURERLBp0yYkSaK6uhqNRoMoirS1tWGxWHxe6l7xWafT+WIjJCcjAaJ0ob/iwosSrhbcbjdKpXJZ7g1msxlgyTYaC+Wpp55alnZksTlAenp6/Ko3fu1rXwPgkUce4dvf/jZvvPEGDz30EPv370epVJKbm8vnPvc53+tbW1v553/+Z8bGxti2bRt/+MMfuP3225d9HDIyMjIyMjIyMjIyMl68YqzFYplTYApWZrMkSZw9e5bh4WFKS0vnzpibpR+Bis2SJNHY2Mjo6OiCRe25mJrZLEkS9fX1mM3mGQVtQakEjQYcDqSWlkWJWN42bDab55hBEkO8Waaw+Gw+XWQ0AGrBjWAzImmj6Z1QsCn8omBoid0JPe+h6qteapd9CIKAwWDAYDD4CqTZbDaf+NzU1ITVasVgMBAdHY3FYsFut1NaWrqq7BpWC0ajkYaGBgoLC0NWNGwmZis6OFmAXomig6EQmyVRBALLTvYWDATYqF/Ybo7F4hX1o6OjSUlJATz+3V7f597eXpqamlAqlT7xOU6vRwdI3v66177YvBxYLBZUKtXCLEvWELLYHCDp6em+LUszUVhYyOuvvz7r8w899BAPPfRQKLomIyMjIyMjIyMjIyMzI3OJJV4B02g0ziv8LqUw32RaWlro7e2ltLQUrVa74PcvRGxubW2lr69v0W3N1YfJ4ldzczODg4NzxlB1zz24Hn8cAKm9HSEjY0FtNjc3MzQ0RGlpKW+//faCrURmYqkZzV7CDVFYpDDCBTsaczd2bTQVQyo2hV+01QhLK4aep0g21S6537MhCAI6nQ6dTucrame32zEajXR2djI2NoYkSZw8edJnJTA1S/NSxWazUVNTQ1ZW1opbfc5VdND792y+z8EUn0VRDPq8mLCMEyl4+q6PSpj1da4pl3eUZnnEZsDn2+5FrVb7ealPtrMxGo30DgxQDEh43uO0T+ByuYKyi2S5mTr2UGI2mwkPD1+3C19r7+zLyMjIyMjIyMjIyMjILAlvhrFXJJ0vuyoYNhrt7e10dHRQUlKCXq9f1DEC7UdnZyft7e1Lams2FAqFT6Tt6Oigs7OT0tJSdDrdrO9R7tvnE5tdL76I5ktfCri9zs5OvzYEQViy2LyUgoAzYVREEy71o7X2YCeX2hEVt26+KDanFVyJ810lqfTS0t5Icnpoi1N58c5rk8lESUkJOp3OJ5J1d3dTX1+PRqPx2W7ExMQQHh5+SYnPbreb6upq4uPjSU1NXenuTGO+ooOTbTggeEUHQ5HZPD7SxwbAIoXNWigToN/m32fNMuqR8wmuk+1sAMSdO+F3v0O8kNlsNZs4evSob0eB13pjLWTwLmdms1dsXq/IYrOMjIyMjIyMjIyMjMw6ZTaxpLm5md7eXp8ANx9LtdHo7u6mubmZ4uLiJRVECkRs7unp4ezZsxQVFYWk+JLXRsO7pby4uHhe301h0vPiW29BgGJzX1+fbyzeNiaL3Ysh2EIzwLgylhRXPzprD2MzPG+IiuVsWA65jlr6Kl5aNrF5bGyMM2fOkJubS2RkJAAJCQkkJHiySt1uN2NjY4yOjjIwMOBnEeAVoCMiItZt9qHXP12pVLJjx441IbLPVnRwqu/zUosOhkJsHjt/DoAhRTyxc7yudiQ0ftmBsNDCiAqVChHwRioqKpJd+/djNBoZGxujo6ODM2fOoNVq/cRnvV6/6ubbcttoGAyGVReDYCGLzXMgiiI9PT1ERESs2wkgIyMjIyMjIyMjMx+SJGEymdi4ceO6FV0uJdra2nyZsoFmVi3FRqOvr4+GhgZ2797ty4ZbLPOJzYODg5w5c4bCwkJiYmKW1NZcfXA6nZw+fZrCwkKio6MDe9+dd+K+UCReGhlBiJ1LboKRkRHq6uooKCjwG8tSxOZQCM0AVk08uMBg7fI95hRBPel2MZZ6HbTUktD+IvC1oLQ7FxMTE1RXV7NlyxYSExNnfI1SqSQ2NpbYC+fCaxHg9X1ua2tDkiSioqJ8thtRUVHLJkiFmra2NsbGxta8j/Vsvs/e7OfF+D6HQmy29ZwGYECfNafYXDVy0Y6nNN4xxyuDz2KtJBQX7EEkQU1YWBgbNmxgw4YNALhcLp/vc39/P+fOnUOhUPiJz6thUWclxOb1iiw2z0FPT8+q3EYiIyMjIyMjIyMjsxJ0dXWxadOmle6GzBLo7OykpaUloGzcySzWRmNwcNAnmAbDC1ahUPiEo6nCxOjoKNXV1eTm5voyV0OB1WrFbreTn5+/oHZU73ufT2x2//3vqD7ykVlfazKZqKysJDs7e5pQulixOVRCM4AtcjNY3yHD2Uzzhcde6dTwwfSLQlnGwY/jbP4x29zNNDZWkJa9J2jtT8XpdFJVVcWGDRtIS0sL+H1TLQIkScJisTA6Osro6ChdXV04nU4iIyN94nN0dHTQik8uJwMDA7S3t1NcXLzgQp2rnbl8nycL0V5mEp9DITZrh+oAsMfOntlvcvq3WZqwBsTmtDQUwlnPv6XpMVOpVMTFxfk+A0RRxGw2+7KfOzs7cbvdRERE+AToqKioZb+uRFFcVhsNWWy+RPFuuerq6vJtuVlO7HY7jz76KF/+8pfXhL/NakaOZXCR4xk85FgGDzmWwUOOZXCR4xk85FgGj4XGcnx8nNTU1JBYEsgsH+fPn/dZMiw0w1ilUi3YRsMr/ubk5MyaWbpQvEWn3G63nyBiMpmoqKhg+/btJCcnB6WtmTCbzZw9exaFQuErQhcowiRh2vXHP84qNk9MTFBeXk56evqMyU+LEZtDKTQDCHFboA/ibR2+x+pGVX5ic3R8MrXhl7HHeoyxt38O2b8Nah+8iKJIbW0tOp2Obdu2LWmsgiBgMBgwGAykpqYiSRITExMYjUZGR0c5e/YsExMTPpHM6/u82sVbk8nE6dOnycnJuSTu6wsVnycTrKJxLpeTLEsFAIas/bO+rmzIf+4Y1NKS214IixpvZiaK0XoABPv89yaFQkFkZKRPZ5t8XY2NjXHu3DkmJiYIDw/3y34OZqHXmZj6uRJKZM/mSxjvh9Lki2A5sdvtaLVaIiMj5R9US0SOZXCR4xk85FgGDzmWwUOOZXCR4xk85FgGj8XGUraWW3t4z1lfXx/19fXs3r17UfYSC81sHhsbo6Kiguzs7AWLsnPhFQLcbrcv681isVBWVkZGRsaCslgXis1mo7y8nKSkJHp7exd1DOW11+I+fBgAyWhEmGLB4XA4KC8vJzExkS1btsx4jIWKzV5RzevFGorrODwpC85AvPO83+NWF+gnqQ7q/f8Mfz/GLuPf6O1pI2FjRlD7IUkSDQ0NOBwOiouLgy4cCYKAXq9Hr9f75rXNZvMVHWxtbcVsNqPX632ZzzExMQH5oi8XDoeD6upqNm/e7LM5uNSYKj6DR2T12m4MDg76XuNdZFuM7/NkOk4fpxALY4SzOe/yGV8jSXBy8KLYnBPtXHA7S2UxYrOQlITKeGEx0mJbcJszXVcOh8MnPnd1ddHQ0OAr5ukVn4NdzFO20Qgesti8ilEoFOzatWvFfWvWA3Isg4scz+AhxzJ4yLEMHnIsg4scz+AhxzJ4yLG8tBgYGKCuro7CwsJFW1moVCrsdntArzWbzZSXl7Nly5ag2xJ6BR+v2OoVgDdu3EhmZmZQ25qMVwSOj48nIyOD7u7uRW2zV95+u09sdr/+ul92s9vtprKyEoPBwM6dO2c9ttdKJFAm+9WGasEoPm0HAEniIB9KGuT5Pk8W94/q9Dy8y+p73dZdV3LmSC45ztOcf+1REj7zs6D2o729neHhYUpKSnxZ8KFGq9WSlJREUlIS4LHw8GY+d3d3U19fT1hYmE94DoVIFiiiKFJTU0NkZGRIr5e1iNf32Wq10tTUxI4dO9BqtX5FBycv8ixUfLZWPQ9Ak6GEbNXM9hAtJn+h87qNgd1zg4koigufm9HR6JQTAKhMwbH90Gg0JCYm+nbFeIt5jo2NMTg4SEtLC4Ig+Cw3oqKiiIyMXJJYvJxis2yjIbNiqNVqbrnllpXuxrpgLcVScrvBakXy/nE4wO32/AFQqRDUatBoEAwGz59l3qa1luK52pFjGTzkWC4OSRSRxsaQLBYkqxUmJsBu58YNG6C8HKckgVrtuc9oNAjh4QiRkQgREQjrpDhOqJHnZvCQYxk85FheOpjNZmpqasjLy1uSj3Ggmc1Wq5WysjJSU1PJyAhu1urUvngF4NjYWLZv3x4y8c4rAuv1enbu3InT6ck2XIzYrJhk8THZSkMURaqrqxEEgfz8/DmPu5DMZm+2pvd9oSImYSODxJAgjKLpfQ+ED1zsgwSKScOxl34Jjv0TRYN/obP1fjZm5gSlD319fbS1tVFUVBTy7fZzoVarSUhI8F1vbrfbl/nc19fH2bNnUSqVfpnPBoMh5It/kiTR2NiI2+0mNzdX3qkyA263m5qaGjZu3DhtR8ZSig7aJizkjrzueV3eh2dt/0/ter//61XLa6EBi7TRiI4mQuFZVAobMYegVzMX87RYLL7s5+7ubpxO5zTf54VY2oiiuGyLVBaLRbbRkFkZnE4nhw4d4sYbb1yTBQdWE6s1ltLEBGJPD+LAAOLwMNLICNLYmGf/zEIIC0OIiUERH+/5k5iIkJAQMiFotcZzLSLHMnjIsZwfyeXy3G96exH7+z33HKMRpvxgdUoSh61WrtXrUc/2Q0QQPIJzfDyKhAQUiYkoNm5EkK0NpiHPzeAhxzJ4yLG8dDAYDJSWli7ZFlCpVM7r2ezNMt6wYQNZWVlLam++vjgcDk6fPo1erycnJydkwtlkEbigoACFQuHLfFusv6fy/e/H/eqrgMdKg6go6uvrmZiYoLS0dN7MukDF5uXIaJ7MeW0WCbb3MLdXUVT8PsqHPPeWH5/W8eW8Cd/rsvfdRHV5MYW2MswvfxO++OKS2zYajdTX15OXl7ciFphzoVQqpxVHGx8fx2g0MjIyQmtrK5Ik+YoNxsTELDlDcya6u7sZHBykpKRk2bI31xKSJFFfX49KpZrx/rWYooNe4fnc20+zDwu9xLO15MYZ2+8w+5+TT26xzvi6UOIV0Rd6X5OioogSPCKzbtgUiq5NQ6FQEBERQUREhM9P3WtpMzY2RktLC1arFb1eP833ebb7odvtXja/dYvFQkpKyrK0tRLIYvMqRhRFqqqquP7661e6K2ue1RJLye1G7O7G3daG2N2NNDIy8wsFAfR6TwZhWBgoFOD9QuB0gsuF5HAgmUzgcIDdjtTXh7uvD9/Hm1qNYuNGFJs2oczIQHFhBTAYrJZ4rgfkWAYPOZYzI46OIra3425tRezru7hLYjIqlWeXhF7vufcoldSdPMl1xcUo1WpwOj27LOx2Twb0+LjnPjQ+jjQ+jtja6jmOICAkJqJMTUWRno4iOVnOmkGem8FEjmXwkGN56eDdZrwQ24WZUKlUc2Y2e7OMo6Oj2bFjR0jv/wqFgrNnz6JSqXwCcCiQJInTp09js9n8BDpvewst0udFddttPrHZ9cortJeUMDQ0xN69ewNa/AlEbA51QcCZsMTmQs97qAZquWGT0yc2W1wCbgmUk7pguOn7OJ69nkJbGe+99TS5V3100e1arVaqq6vZunXrkrL3lwuFQuETltPT05EkCbPZzOjoKEajka6uLlwuF5GRkb7s5+jo6CVlXI6MjNDU1MSePXtWlX/0aqKrq4vR0VFKS0sDuqfMJD4DvsxnrwjtdDhIqf8VAM2pt5N/4fqd3IYowR9a/bOaU8MD98gPFt4xLPSealZrSBEsABhsi7svLhVBENDpdOh0Ol+RWIfD4bPeOH/+PI2NjajV6mm+z5NrAciezcFBFptlZEKMJIqIHR24Gxtxd3R4xOFJCHFxKJKSUMTFef4dEwML8PDyis7i8DDS0BDi4CBifz/YbIgdHYgdHbiOH0eIjUW5ZQvKbdtQLNKrT0ZGZm0gmky4Gxpwnz2LNDrq/6RO51mISkpCER+PEBPjyVCefM+x2+HkSTQHDqCZIVNZkiSYmEAcGUEaHPRkS/f3IxmNSP39uPr7obwcwsNRbt3K/8/ef8dHdd35//jz3mnqo96QEKIjgRCgZoMN7hV3J3HidCexnWTTN85305P9xcnuxrtJ1kk25ZPiFtu4gWtcMNiYIlAB9d5712javff8/hjNICEBKjOq83yYhzV3yj3nPefeufd13uf11q1b5xee/fjx42eRcyEbDUVROHnyJEFBQT5fni+EwOl0IkmSTzM0hRBUVFTQ19c3QQR29286BRPHIo0RRNVnnqExPp7c3NwpWz9cTGyeD6EZwJC0FVohdrgMSYJL45wc6XDF7f9XON67OXF1OicS7mFn+2OsPfEDetMvIzJ2+ll+TqeTgoICEhISfFoc0pdIkuTJ0Fy5ciVCCEZGRjy+z+Xl5dhsNkJCQoiIiPAI0FPNwBwZGaG4uJiNGzcSfk5BSj8u+vr6qKqqYseOHTMuQOwWLMeJyJpG8cEnuFS00E8IqVc/MG6FiPv4PNk7fp+fWmuZURtmy0zF5t6BTlYANmHA5Fw41/tGo3GCpc3g4CADAwP09vZSV1eHEIKwsDDCw8Ox2WxzdoxYLJYFtwrDm/jFZj9+fITW14daUoJSXg4jY5bABAejS01Fl5LiWnI+y5llyWh0idRRUbB+PTC6/KW725VF3diI1tSE6O1F6e1FOXECKS4OfXo6uvXr59zv2Y8fP75BaBpabS3K6dNozc1n7XhkGXnFCtcKh1WrkMzmWd90SqOrL3RBQZCU5NmuDQ2hNTWhNTai1teDxYJaVIRaVIQUGek672zahDSPPop+/Pjx42dmnM9Gw+1nrNfryczM9KnvrBCCkpISNE0jNTXVp96adXV1tLa2kpeXN0F8kiQJnU4348xmGC0UuG8fADuSkqaV4XYhsXm+hGaAFem74Dikao209bRzVWK8R2wGaB+RiA86m2Gf/qEfU/ubw6zWGmj4x5eI+OI+pGmMH3exu+DgYNaP3gctBSRJIjg4mODgYM8ye5vN5sl8rq6u9vi9um03IiIiJp2sUBSFwsJCEhISlvSS/dlgs9koLi5m/fr1XhcabVYLqaf/G4AzyR8jMypmgu/zsEPwZtt4TSA+QAHmvoDvTMXmwf5WADq1SMwsHLH5XNxe6REREYDrfDnW93l4eJiqqira29vH+T7PdALiQgwPD/s9m/3MDzqdjt27d/v9lLzAXMVSCIHW0oJSWIhWV3dW7AkMRL9xo0vcjY31+UWfJElIMTHIMTHot21D2O2o9fWoVVVodXWIjg6cHR04Dx9Gl56OPjMTeRqzav6x6T38sfQeyzWWwm5HKSlBLS522VuMIq9YgW7TJnRr1kzbR3mmsZRDQ5HT0iAtzWUb1NiIWlWFWl2N6O3FefgwziNH0G3ciH77dtdKjmXAch2bvsAfS+/hj+XyQpIkn9houP2MNU0jKyvL5wXOqqqq6O7uJiwszKfX083NzdTW1pKTk0NQUNCkr5lOkb7JGLrsMoJGxeagF1+Ef/3XKb/3fPueT6EZXEUC6+VkVmlNNBW9g/nKe/jseht/qnSJoH+oCByX3WwMCMR+029wvHQ726xHeX/fL9h290NT2pfbX1dRFLZt27bkV08FBASQkJAwzh7Anfnc2NhISUkJJpNpXNHBwMBATp8+TUBAgE891BczmqZRXFxMVFQUSWMSKLxFxb4fs5Nu2ohh063fnGC9oaoaf6saf465f00viuI6X1+o6KAvmKnHu6OnHoB+ZxhmbzfKh0iSREhICCEhISQlJTEyMkJiYiI6nY6BgQHq6+sZHh4mMDDQY7thNpsJCgqa9TlneHiY0NBQL/Vk4eEXmxcwer2ePXv2zHczlgS+jqUQAq2xEefRo4iODs92edUq9Js3I6ek+KxY31SQTCb0Gzag37ABMTKCUl6OWlKC6OtDLSxELSpCt24d+h07kKfgc+Yfm97DH0vvsdxiKRwOlMJClIICl+0FuCa2Nm9Gl54+rQmkc/FGLCWdzrWKIzUVsWcPakUFypkziK4u1JIS1NJS5NWrMWRlIcfFzWpfC53lNjZ9iT+W3sMfSz/T5VwbDSEEp0+f9vgZ+zLLGFyZxk1NTeTm5lJZWTljC4uL0dnZSVlZGdu3b7/gEucL2YpcjKGhIU5VV7Nr9LF25AhCiCmLF5OJzfMtNLtpC89iVW8TSt17wD0kBo9v508KxttppGzK4kTZV9lZ/Z/k1f6KwvfT2LTzlovup66ujr6+PrKzs5flpJnRaCQ2NpbY2FjAlcE8MDBAX18fbW1tVFRUAC4xLTU11SNsLXVRfrpUVlaiaZpPfObrS4+R0/YkSNCc8z3SgyauXijqMzCsnBWR82IcRAYbxhUfPF/RQZh+BvLFmOn5Q+6vB8DqWNwexKqqEhAQQFRUFPHx8YDLqsdd0LO9vZ3Kykp0Ot048Tk0NHRa34U7o3opZzbPfV6+nynjcDh47LHHcJzj8etn+vgylmpLC459+3C8+KJLaNbr0W3ejOnjH8d0yy3oVq+eV6H5XKSgIAzbt2O6916Mt96KnJwMQqBWVmJ/8kkcr76K1t9/wc/wj03v4Y+l91gusRROJ86TJ7H95S8oR4+C3Y4UEYHhyisJ+PSnMVxyyayEZvB+LCWjEf2WLQTccw/Gu+5CTk0FIdBqarD/4x/YDxxAO1/B1CXAchmbc4E/lt7DH0s/08Wd2exe+l1aWsrAwABZWVlTKmo3G5qbm6mpqSErK4uQkBBkWfaJ2Nzb20tRURFbtmwh6iI1Tmaa2Wy1WsnPzyc1NRXD177m2S5KS6f8Gefu2y1KCSHmVWgGkFftBCChL9+z7buZI+NeU94//t4o87avccx8PTpJsO79r9NSc+aC+2hra6OhoYHMzMwp+1wvdfR6PVFRUaxdu5bs7Gw2btyIJEmsWLGCvr4+8vPzOXjwIAUFBdTV1dHf3z+rzPylQGtrK+3t7WzdutXrExYjliFCXv4iBknlVOClpO+5e8Jrum0SLzeNX3141QoFnU6HwWDAaDQSEBCAyWRCr9ePK0SoKAqKouBwOFAUxTPRNFvc55DpEjZYBYA04uqPWKTXFpMVCDQYDERFRbFmzRq2b9/O5ZdfTkZGBmazmf7+foqKijh06BCnTp2itraWnp6eSS2nzsViscxJZnNzczMf/vCHiYiIICQkhEsuuYTBMathfYU/s3kBI4SgpqZm1kve/Pgmllp/P85Dh9Dq610bdDr0GRnod+xAOs9yu4WEJEnoUlLQpaSgdXWh5Oe7lruPLnnXpadjyMlBmsQ/zj82vYc/lt5jqcdSCIFWXY3z8GHE8DAAUng4+txcdOvWTcvjcCr78lUsdYmJ6BIT0Xp6UE6eRK2oQKutxV5Xh27TJgy5uUhLbEnZUh+bc4k/lt7DH8vlhTfEx7PLvlVqamro6uoiNzfXJ16WY+no6KCsrIwdO3ZgNps9bfG22Dw0NMSpU6fYuHGjJ6PtQsxE8HY4HOTn5xMXF8fq1ath5Up45BHXc488QsAf/zilzzmf2Aze+a5nw8pt16CelEjVGqlrqiYueS2SBPdtsPLHCpcv7TN1Jr6VMULAqKYjyTIbPvko5Y/eyEalHMu+e+j82IvErlg94fP7+vooLS1l69atS3oJ+mwYGBigoqKCjIwMoqOjAdcYGRoa8lhvNDQ0oKqqJzszIiICs9ns8xUKC4WhoSHKy8vJyMggcJY1lCaj5rGvkCta6CSShI/+74TnbSr8tmz8fv8l3Yo8yeEry/KEooPuf+7Jv7HnA/eE00ysN9yZzdN6jxAkO2pAgtAhHSDAZoNFWBtqMrH5XGRZ9ng5A+MKeg4MDNDe3u4p6OnOfA4PD5/wW2mxWKbl1T8TbDYbV111FcnJybz88svEx8dz+vTpOVkNsjzOJH78eBHhcKDk57uWrqsqSNIFhdnFgBwTg/GGG9CysnB+8AFafT3qmTOoFRXoc3LQZ2YuqOxsP36WG1pPD85333UV/gOk0FCXyLxxo1dF5rlEjorCeO21rvPOkSNotbWopaWolZXoc3P95x0/fvz4WWC4b05rampoaWkhNzfXJyLNWHp6eiguLmbr1q1ERkaOa4s3szJHRkY82cbJyclTes9026CqKidPniQkJOTskn2DASklBdHQAN3dCLt9SnUWxgrdY5fZz4Wn68UwR8VSYUwjzVlC64mXiEv+OgAJQYI1oSo1Q65x9B/FQXw3cwS3Nm4MCCT044/T+Je9rBStND5xN32f2k9ETKLnsy0WC0VFRWzYsMEjovoZj81mo7CwkDVr1oyLkSRJhIWFERYWxsqVKz0CmbvoYGlpKXa7ndDQ0HG+z75etTAfOJ1OioqKWLVqlU/GUcFz/8XOgVfRhETLnv9iQ9T4yStNwK/OjD933rLSjtk4tcnfycTnc4sOjj03Tcf3eSZic3tLIxukAVQhEduvAjIs0sxmTdOmLcROVtDTbrczMDBAf3+/x1M9ICCAQ4cOYTAYuPzyyxkeHva52PzUU0/R1dXl+e0BXBOdc8D8/xr58bOIUGtrsT/2GEp+PqgqckqKy47iyisXrdA8FjkmBtMtt2C86y6k+HhwOlHefx/7k0+iNjbOd/P8+Fl2CEXB+f772J94wiU06/Xoc3Iw3Xsv+rS0RSs0j0WOjMR0882Y7r4bOSEBFMV13nn8cdSmpvlunh8/fvz4GcUtVjQ1NZGVleVzr8mBgQFOnTpFWlqax5fWjTczm+12O/n5+cTHx0/rJnw6mc3uQoqyLJORkTEu+9jw5S97/lbfeGPK+x7r6Qrzn9E8lr4VVwIQ1vjmuO0fXWsf9/inheNXg0bEJKJ99BnaiGalaMX+1zvp7WwBXFnhhYWFJCYm+qSQ21JAVVWKioqIjo5m5cqVF3ytWyBLSkpi8+bNXHbZZezcuZOVK1eiKArV1dUcPHiQDz74gLKyMk+25mLH7TUfEhJCamqq1z+/9PDz5FX9FwAfrHqADbnXn7N/+Hu1Cbt29nhNDVHZGjXz85ksy+h0OoxGIyaTCaPRiNFoRK/Xe4RjVVU9thtOp/O8thszEZs7q08C0KLFoddG37sIxWb3+dQbWb8mk4nY2FjWr19PdnY2l19+ORs3bgTgwIED3HjjjWiaxoMPPsjDDz/Me++955Pj6+DBg+zcuZPvfe97xMfHs2XLFv73fydm2vuCxX+XuoTR6/Xs3bt32Sxl8SWzjaWw2XC8/jqOAwcQw8NIYWEYb74Z4y23IEdEeLm1848uMRHT3XdjuPpqCAxE9PbieOEFHG+8gbDZ/GPTi/hj6T2WWiy1tjbsTz6JcvIkCIG8Zg2me+/FkJeH5OMsk/mIpZyQgPGuuzBcc43rvNPfj+P553G8/jpikd/cLLWxOZ/4Y+k9/LFcXnhDiGxtbUUIwcaNGy9YOM8bDA8Pk5+fz7p16zyZYmPxltisKAonT57EbDZ7PG6nylQzm4UQlJSUYLVa2b59+wQhQ1qz5mx7/vSnKe3bLTa7BSP3kvmFQlzWrQBstBczNNA97rmxxQHBVTBwLLErVmO56ym6CWe1Vo/4617aG6ooKioiJCSEdevW+bbxixS3j7okSTMudhcYGEhCQgJpaWlceuml7N69m9WrVyPLMvX19Rw+fJj33nuPkpISWlpaGBkZWXRWTLW1tVitVtLT071+zFQXHGTTka+jkwTHzDeQefe/TXjN/kYjjcPjzwEfO2cSZra4xWeDweARn8f6PkuSNEF8VhTF4wU9XbFZrf8AgA51zLl6EYrNvlwhotfriYyM5KGHHuKdd96hoKAAgGuuuYb8/HzuvPNOzGYzu3bt4vXXX/faftva2jh06BCdnZ288sorfPWrX+VrX/sazz77rNf2cT4ksdjODnPI4OAgZrOZgYEBn19Q+Vm4qHV1ON56C0ZGQJLQb9+OPjcXaZncnAmbDefRo6inT7umYoOCMF55Jbo5Wn7hx89yQ6gqypEjKIWFy/aYEzYbzmPHUIuLl20M/PhZaPivixc3bmFhpnR0dFBcXIxOpyMzM3OcpYW3sVqtHD16lKSkpPMKi3V1dQwMDJCZmTnj/WiaRn5+PrIss3379mkLDKdOnSIyMpJVq1Zd8HVVVVW0tLSQl5d33mJ2zieeQH36aQCMv/oV8kWyUmtra+nr62Pz5s0e8Wih0fmfOazSmjiS/gMyb7p/3HOagH8/J6v5XBG6tbaE4GfvIYEu2onkRNoP2XPDHXPiNboYqauro6mpyac+6oqieDyf3f60BoOBiIgIj/VGSEjIghyPAF1dXZw+fZqcnByv2xfUFL3Hqtc+Qahk5bQxk+QHn8doHH+8/7PZwNGu8QkjD20dwTAPKaDuyaqxqyOEEHR1ddHS0sL27dun7Pvc/MgVbNKqOOG4hfUFrskl6cc/RhrN5F0s2O123n//ffbs2eNzS6La2lp27NiB3W5Hp9N56mi89957bN++nYyMjCl9zu9+9zseeOCBSZ/buXMngYGBHD16lI6ODoJG64p9+MMfxm6388ILL3irO5Piz2xewDgcDh599FF/lXAvMJNYCkXB8fbbOPbvh5ERpIgIV7bvzp3LRmgGkAICMO7Zg+muu5AiImBkhOH9+/nfX/wC+xxUMV3q+I9z77EUYqn19WF/+mmXJ7wQ6DZuJODee+dcZJ3vWEoBARh378Z0992e847jwIFFm+U83/FcSvhj6T38sfQzVdy+yRkZGRiNRq8X5huL3W7nxIkTxMXFsXbt2vO+braZzUIIioqKUFWVzMzMGQkL5xbpm4zGxkYaGxvJyso6r9AMoL/9ds/fzl/+8oKfKYQgICCA7u5ujhw5wpkzZ2hubsZisSyoLNPmFTcCEFb1/ITnZAm+ueXCGc6Jq9Nx3rufelYQTy+7Sx6i8ujLvmvwIqarq4u6ujoyMzN9WrBTr9cTHR3NunXryM7O5oorriAjI4OQkBC6uro4ceIEBw8epKCggPr6evr7+73qrT4bRkZGOHPmDGlpaT4RmlNe/xShkpUSw2YSPvf0OKFZCDjQaJwgNH998/wIzeA6fxkMBoxGIwEBARiNRmRZpqmpicjISI8HtDv7WVGUSa03+vp6WKfWABCvH+NNbbXOZXe8gqqqMyqqOBMsFgvBwcGefUmSxNq1a/nUpz41ZaEZ4CMf+QhlZWWT/nviiSeIjo4mKSnJIzSDy7O5paXF6306l+WjmC1C3DNLC+miYbEy3Vhqvb04Xn0V0dPjymbetg19Xt6yEpnPRU5IwHTPPSjHjmE/eZJuqxXbs8+iv/56dImJF/8AP5PiP869x2KOpRACtawM58GDoCgQGIjx6qvR+cBLbqrtWQixlOPjPecd5dQp1IoKtLY2jNdd5/J3XiQslHguBfyx9B7+WC4vZppp2N/f7/FNjouLo7a2dlYZ0hfC6XRy8uRJwsLCLmoDMBux2W03MDw8TG5u7oytZC5mo9He3k5FRQVZWVkXFbekwECIiYGuLkR9PcLhQDIaJ227qqpER0ezZ88eBgcH6evr8+xLr9d7skwjIiIIDg6etyzTFbs+Bk/+njT7aRpbaoldMX7iPFAPX908wn+fOSuC/KQgaFyGswMDpzd/D0v5r0hXSsl8736OtpWTecc3l0TdCm8wPDzsEVHnetWJTqfzjDVwZcsODQ15sp/r6+tRVZXw8HBPwUGz2Tzn2eluL+vExETi4+Mv/oZpUHroOdI++DpBkp0yfRqxn3uWoOBQz/NCwL56I2X9488zX908QvACqr0oyzLV1dUYjUbPihJ31vO5/vBwtuhgfcFbrJQ02qQ4wkzhZz/Q7l1rkLnAW37NU8FbxQHdx9b5yMzM5OWXX8Zut3smohobGye1p/I2/jO0Hz/noJSVYX/qKZfQHBiI8dZbMezatayFZjeSXo9h505Mt90GgBgawrFvH87jxxELZNbaj5/FhnA6cb7xBs433wRFQU5OJuCee+ZNaF5oeM47d92FFBaGGBzE/uyzOE+e9Itkfvz48eNDhoaGOHnyJOvXr/fcmOr1ep9kNquqyqlTpzAajRMK6E3GbMTm6upqurq6yMrKwjiJoDtVLlQgsLe3l9OnT7N161aPEHcxjN/+tudv9ZVXJjzvFprdBbzcHqBr1qwhKytrXJZpZ2cnx44d491336WoqIiGhgYGBwfn9HczLnkdJYbNyJKg5fDjk74m1ABf3Tw+A/InBUEI4YphWVkZWbm7WPHgAY6Zr0cnCXbWPkLJ7z/JyFD/HPRiYeMumpicnOx1EXUmyLKM2WwmJSWFzMxMdu/eTW5uLrGxsZ7M4nfeeYfjx49TVVVFV1cXTqfTp21yTy4ZDAav+30X7v9fMj/4MkGSnWLTDmI+t4/gELPneUWDR8sCJgjNX0m3ErqAhGZwefJ3dXWxZcsWZFme1PfZXXTQLciqqopc5fIXbozaiRirl/hoUtKXqKo6J1nNcDaz2dd89KMfxel08uUvf5mqqiqeeeYZnnvuOT72sY/5fN9+9cyPn1GEquI8eBC1pAQAOTkZ47XXIs3BSWCxIcfFAaBbswZqa1GOHkVrbsZ43XX+ePnxMw20wUEcL7+M6OpyraLIy0O/Y4c/W2cS3KsrnG+/jVpVhfL++2iNjf7ztB8/fvz4AIvFwokTJ1i1ahUpKSme7TqdzuuZzZqmUVhYiBCCbdu2Telmf6Zic0NDA42NjeTm5hIYGDiT5o5rw2SZzUNDQ5w6dYpNmzYRGxs75c+Tx9iGKH/5C/rR5A44m2EohECW5UnFeFmWJ2SZujOfe3p6qKmpQZIkT4ZpREQEoaGhPhVXBtfdDqVnSGl6Hk37/5DliVmDoQbBN7aM8F+nz2Y4/7QwiEv6D7FxwwaioqIA2Py5P/H+c/9BXs3/kDP0Jk2/u5KOG39Lanquz9q/kNE0jeLiYkJDQ1kzpsjkQkKSJEJCQggJCSE5ORkhBDabjb6+Pvr6+qisrGRkZISQkJBxvs/etAJpbGykr6+P3Nxcr411h8NG2d+/zqU9z4MEx8OuY8Nnfo/BeLbdFif88kzQhPd+fYFlNIMry7a8vJyMjIzz2v24Y+cWmjVNw+FwkjZ0BCQISLsBkV+P+8ykKooriWb0fXMl4s4GVVXnLLPZLTb7euVJcnIy+/fv55vf/CZbtmwhKSmJhx9+mLvvvtun+wW/2LygMRgMfOxjH8NgWGBno0XIxWIpLBYcr7yC1tbmEnxyc9FnZfkFn/PgjmdQaiqishLnwYNozc3YnnoK0403Lqrl7fON/zj3HostlmpTE45XXwWbzbWK4oYb0CUlzXezgIUbS8lkwnD99cgrV+J89120pibs//gHxptu8kyCLUQWajwXI/5Yeg9/LJcX07mhtVqtnDhxghUrVrD6nJoB3s5sFkJw5swZbDYbOTk5U77Rv1BW8floa2ujsrJySrYWU23DuZ7nVquV/Px8UlNTSZrBb7r+E59A+dvfANDKy5FHC2yNXcI+1e9SlmXPEuvU1FQ0TWN4eNgj9NXV1SGEGCc+h4WFeVUUWrvnYwyW/BdJtHHyyAE27bp10tcF6eH/2zrC/6/orDj3Qfj1pEXagNF+yzLb7vo2Z05mEf/WV0gWbTgO3MWJsi+x9Y5vTipkL2UqKipwOp1kZmYu2IJ85yJJEoGBgQQGBpI4asNot9s9tht1dXUMDQ0RFBQ0blwGBATMqI99fX1UV1ezY8cOrwnYPe2NDD/xKS5RygB4P/EzZH70J+N0g7YRiT9WTJzM+teMEUwLbJiqqkpxcTHJyclER0dP+X2yLFNdeJAcaYgBQliTdS3aqT/iWTshBEKIcZOT7omyufJFni5zKTYPDw8TGhp68Rd6gauvvprCwsI52ddY/GLzAkaW5QsWxvAzdS4US62jw5VZODwMRqNL8BmTweFnIuPiuWkTcny8K4a9vdj37cOweze6zZsXzYXPfOI/zr3HYomlEAK1sBDne++BEEixsZhuuglpji44psJCjqUkSejT05ETEnAcOIDo78f+7LMYrrgCfVrafDdvUhZyPBcb/lh6D38s/UyGu0BfdHQ069evn3AtN9vCfGMRQlBeXk5/fz+5ubnTmvi4mF/yuXR3d3PmzBkyMzOnbGsx3TY4HA7y8/OJi4ubINJP+TNvuskjNju+9z0CnnkGTdM8MZ+NQCPLMmFhYYSFhZGSkoIQYpz43NDQ4HV/3eAQM4UxN3Fp9zPoTv0ZziM2A+hk+M6WIX52+uz10J8qAtgSoXDbqrOi/rodVzGw6m0KnvgC26xH2Vn735T99xsYbvsViavTZ9zWxURTUxMdHR2z8hxfKJhMJuLi4ogbTRpwOp309/fT399Pc3MzpaWlGI1GT9bzVL3IbTYbxcXFrF+//oKettOh9NA+Uo/+G8kMMCiCqMj7Odt23+V5Xgg40qnn7dbx9jwheo0vp9vQLzx9lfLycgwGw4yy4x2FzwBQHb6TTXoj2pjvxKDTgdHosf8Ro+LzZL7PC0V8no/M5qXM/H+jPuZTn/qUZ/bE/e+HP/zhfDdrStjtdn72s59hX4Tm6guN88VSqazEvm8fYngYKTwc04c/7Beap8C58ZQjIjB96EOu5X+ahvOdd3C+9RZiEXo1zTX+49x7LIZYCvfxcfgwCIFu0yaXF/ECEpphccRSjozE9JGPIK9eDaqK8803cRw8iPCBl+hsWQzxXCz4Y+k9/LH0cy5Op5P8/HzCwsJIT0+fVMzR6/Ves9Goqamhvb2drKysaWcdTsfOY2BggIKCAtLT04mJiZlJUydFlmWPcKKqKidPniQkJOSixQ0vhGQyIW/f7nrgdKL19k47o3nK+5IkQkNDWblyJVu3bh3nr2uxWDh9+jTvvPMOJ06coLq6mp6enhlNNMTu+QIAGdYTtNWXn/d1Lm/dEq5zHiRYf1aQOt2n9/g4uzFHxbL+i/t4f8NDWEQAm5RSkp65gVNP/ztOx9I+p/X29lJZWcnWrVtnbQWzEDEYDMTExLBu3TpycnK44oor2Lx5M0FBQeO8yAsLC6mvr2dgYGDCxJPbYiQ6OnpGKwzOxTI8QPH/3UfW0S8RxQC1cgqdd79E+hih2a66/JnPFZpzY5x8dfPCFJrP9WmeDoNDg2QOvA1AYNao/+/Yg3TU8ken02E0Gif4Prv3p6oqiqLgcDhwOp0eX/r5wO2HPxd4q0DgQmZxT4NNkauvvpq///3vnseL6Us9d2mWn5kzNpZCCJRTp1Defx8AOSUF4/XXI3nRH2qpc+7YlEazwpVTp1COHEEtLUX09WG86SakoIl+VX7O4j/OvcdCjqVwOHC8+ipaQwNIEoZdu9At4KWPCzmWbiSjEeNNN6EcP45y7BhqcTGip8d13jmP59x8sRjiuVjwx9J7+GO5fLjYb42iKJw8eZKAgIALFujT6XReGTcNDQ00NDSQk5ND0AyuE91ZxUKIC/ZteHiY/Px81q1b51m27y3cVh5uz2lZlqdU3PBiGL70Jeyf+QwAzt/8Bumhh87r0+xNJvPXHRkZ8VgclJaWYrfbCQsLG+eve7HM2sTV6RQFZLPVdoLON/+HhPt+O+nrampqGBwcJCcnhxyjjYIeHQcaz96b/bQwiI+vtbEqdIytxq1foWP7TQw99y9k2AvYVf8b6n/1In2X/ZgNudd7LzgLhJGREYqLi9mwYYPXMvQXOjqdjsjISCIjIwGXIDg0NDTBDsZsNnvGZHt7O5qmsXHjxlkfN+VHDhD3/nfJEx1oQuJo7IfYdM+/Ywo4m5VaNyTzWPXE684Pr7az3rzwkiBgaj7NF6Ly3ae4XLLSKsWRknmla+PYYo+TnBcm8312Zzu7/x47oXWu5YavheC5ttFYTLrkTFgWYrPJZFoQ1Vn9LAyEpuE8dAi1uBgA3datGC67zO/P7AUkScKwYwdydLRLVGtrw/700xhvvRV5mVwQ+fEzGWJ4GPv+/a5CgHo9xmuvRedfvu4VJEnCkJuLHBuL4/XX0Vpazp53zOaLf4AfP378LHM0TaOgoABZlsnMzLzgDb03CgS2trZ6vJNn6lmp0+k8y7LPJybZbDby8/NJTk5m1apVs2jx+dugqiolJSVYrVZyc3O9IlRIkZEuoUZREKdOIasq0hwJIOPaIUkEBwcTHBzMihUrAJcntVvkKy8vx2azERoaOs5fdzI7FJH3RTj4Kbb3vExneyNR8SvHPd/S0kJTUxM5OTkYja7M0G1RKuvDRsYVWfv7qKD33cwR3F97XPJaYr98gCOv/J5NpY+wSmti1bufJj9/N9G3/juxSQuzeN50URSFoqIi4uPjvZKtu1iRZRmz2YzZbGbVqlUeOxj3pEhDQwOKohAWFkZtba3HDma69Ql6Optof/bbZA+/A0A70bTt+Q8yx0xiODV4ts5E9eDE4/Orm62EGsSE7QuBmfo0uxFCEFXxJACNybeQ4fZLHzsROYV4nys+Ax7boLFFUefK99lvo+FdloXY/N577xEbG0tUVBS33XYbP/jBDyadvbHb7eOWEg4ODk7YLssyBoMBp9M5Lr1fp9Oh1+txOByIMcsH9Hq9JwNg7HaDwYAsyxOWLhoMBiRJwuFweJ6z2+0YR/1unGNni3AJ6ZqmjdsuSRJGo9GzJOHc7YqijJsxmss+jWUu++Ruk9NmgzffxFZdjQAMl1yCvHUrmhDoYFH1yc18fE/u946N7di2k5QEt92G45VXEAMD2J96ipC9exHx8Qu2TzA/35P7vee2cTH3ab6+JzeTjcn57JPW1+fxhTcGBmK8+WaUqCiUMe9ZaN/TZLFc8GMvMRH97bejvvwy9r4+V8HS669Hjo+f9+Np7O/5YjmeFuo5wn9t5P1rI0VRMJlMF+2T325j8SNJ0rjvGFzXckVFRSiKQnZ29kVvtGdbILCrq4uSkhK2bds2q8xMdztVVZ1UcHD7J0dHR7Nu3boZ7+dCyLKMxWJhZGSEvLw8rxXbFEKg+8lPUL/zHdfjAwfgjju88tmz5dzibjabzSM+V1dXY7FYCAkJ8QjPERERGI1G1mVdQ9l7aWxSSml69ZdEffq/PZ/Z09NDeXk527ZtmyC+BBtcwvLPiwNxamev9X5aGMQ9a2ysDTub5Zx58wMM7LyLD/b9gJyeF8iyvIv98Ss4GXsnqbd8B3NUrO8D5CPchTSNRiPr16+f7+YsKNx2MKGhoZjNZrq6ukhPT0cIQX9/P+Xl5VitVkJDQ8f5PrsnNc7F4bBR8tKv2FLze5KlEVQhcSz6Dtbd/RM2hJ09Z1UNyDxVO1FT2hqpsHelA2lhLlwEZufTDFB28l2y1HIcQs+qqz5/9omx1zQzXGEoy/K4c7o769ktPJ/r++xN8XmuxeaEhIQ52dd8seTF5htvvJGPfOQjrFixglOnTvGtb32Lzs5O/vSnP0147c9+9jN+9KMfTdj+y1/+0iNOb9u2jVtuuYVXX32VgoICz2t2797Nnj17ePrpp6mpqfFs37t3L9u3b+ePf/wjXV1dnu0f+9jHWLt2Lb/85S/H3Wg88MADmM1mHn74Yc+2Rx55hIceeoiBgQF++9uzy46MRiPf+c53qK2t5fHHH/dsj4mJ4cEHH6SoqIj9+/d7tq9Zs4Z7772X9957j3fffdezfT76BMxLnz547DF2qSovWCzUO53w6qvw6quLuk/z9T098MADNDc388QTT1y0T6v0ej70wgt8EBfH4dLSBdun+fiebr75Zh544AH+9re/0d3dvST6NF/f0/3338/u3bt55JFHFmyf/vWee+gFfjtm+0L8nlavXo3BYBgXy8Uy9rZ96EP88de/pqe/H/7yF0+fFsLx9Mgjjyya42mhnyP810be69Px48e54oorLtonm82Gn6WFW8CyWCzk5ORMqdDYbAoE9vX1UVhYyObNm2eUTXduO8AlDpwr8qqqyqlTpwgKCiItLc1n9hO9vb04HA527tw5o2Xok+EppjVGBNIefxz59tsXpO1WQEAACQkJHtHEbrd7Mkxra2sZHh4mODiYiIgIhtM+w6bib5LZ+QL93d8mPDqB4eFhiouL2bRpk8cm4VwkCR7aaqVpWOYvVWfj/GSN6++Hto5gGNWYzBExbL3vUSpLP436+nfZ7Czm0q6nGPrji5xa+Qk23vxVgkLDfRoTX1BTU8Pw8DC5ubkLopDaQsTpdFJcXMzq1as9kyHujHybzTbpuHQLz+Hh4QSYTJx56zGSCn/JTtEBElTq1uK89hdkbrnEs58hp8QfygOwKBOPx0+ts5EcMj9+w1PF7dOcl5c347GkffC/ABRHXENazJgse4vl7N9eOidOJj77qujgXHo2uyfmljKSOHd6e4nz+OOP8/GPf5yhoaEJM6eTZTYnJyfT2dlJWFgYMLeZLkIIHA6Hx1Ddn70z8z5pIyMM7d9PQG8vhoAAuP565DEzSYuxT27m43tyL1k8t0jMZH0SioLyzjvoamtRhEDKzcWwbduC6xPMz/d0vpu2xdyn+fqeDAYDVqt1nK/hfPZJa2tj+MUXwelEiorCdNNNmMLDF8X3JEkSQ0NDGI1GTywX09izDw9jf/NNlz82ELRrF8YdO+bteHIXPjEajeh0ukVxPC3Uc4T/2sh7fXLHMjAwEIPBcNE+DQ4OEhsby8DAgOe62M/iYux3LISgrKyMrq4ucnNzpyyWdnZ2UlVVxc6dO6e178HBQY4fP86GDRtITk6edtsn44033mDnzp3j7uncliCKopCVleWzLLX29naKi4sxmUzs3r3bK5/pFlDcgod4913UX/8aAN1Xv4p82WVe2c9c4nA4PCJfb08PK977Jhup4+2QvSTe/F3q6+tJTExk7RStxTQB/1440eM7NUTl3nXjz7NC0yg/sp/woz9njVYHwADBnFlxD2tu/BfCIrxXLNKXtLW1UV5eTnZ29pIXp2aKEMJjBbR169aLTsw4nU7PuOzr7aWz4ghbOp8jTbgmXLsJp2Ljg2Tc+AC60Uk4RYO3Wg0c75q4giEtXOG2FAe6BT4PMDw8zPHjx8nIyJjxhF995Wk2PH8dOklQc+erJKzJ9Dzn/PKXobUVAP0vfoE0w8zp6TDW93ns/92MzXq+mJA824zv6fChD32IG264gX/5l3/x+b7mi2UnNpeWlpKenk5paSmbNm264GsHBwcxm83zdlFtt9t5+OGHeeihh6ZdodnPWcTwMIPPPcd/19Xxlfh4wu64A9mLlaiXI9Mdm0IIlCNHUE6eBEC/fTv6nTsXZIbGXOM/zr3HQoql2tCA4+WXQVGQExMx7t27qAqQLqRYzpRz/fn1OTnoc3Pn5byzFOK5UPDH0ntMN5bzfV3sZ/aMnTypqqqiubmZ3NzcaRXo6+npoaSkhMsvv3zK77FYLBw7doyUlBSv3sS/9dZbZGdne8ajEILTp08zNDRETk6O12wtzqWnp4dTp06xevVqmpubvSI2jy2S5RZHhKah3H235zWGfftmvZ/5pvS9l8g68gUcQseL636BMTSGgICAcbYbgYGBF/2t7rJK/K48cML2G5IcZMWM9xTXNJUzb/6dFUW/Ilm0ATAsAimKv4Pka75MdGKK9zroZQYGBjh58iRbtmwhxn//el6qq6vp6OiY1nEvNI3SQ/swn/wV69VqACzCxGHzbVhTrsEUGDzq9xxOsy6Rd7onrwPyuQ1W4oMWvqSmqirHjx+ftbXQmd98mJyRQxQFXcK6Lz037jnnpz8No1a0+v/5H6R58BZ3/8ad6/vs5kJFB0tKSggODvaJx/+53HjjjXzqU5/is5/9rM/3NV8seRuNcykvL0eSJFauXHnxF/tZ9GiDgziefx7R3w+A6dZb/ULzPCBJEoadOyEwEOW991BOnUJYrRiuuspfmNHPkkOtrsbx2mugacgpKRhvvBHJRze8fs6PJMsYdu9GCgpCOXoU5fhxhM3m2uaf6PLjx88ypq6ujsbGxmkLzTB9Gw13kb7ExERWr1493aZOuS1CCCoqKujr6yM3N9dnQvPQ0BAFBQVs2rSJkJAQGhsbvfK5Y5eCe1ZnyTLyvfeiPfYYANrp08hbtnhlf/NF2q5bKD75f2TYT7Ki4Rk2PfgUQ0ND9Pf309raSllZGUajcZz4HBQUNOF3OyZQ8N3MEV5vMXBiTKbpq81GXm028pHVNtaZXfGUZR0Z134K9cp7OfbOE8QU/ZbVWj07Ox7H+fenKAq9DEPu51i7bc+Cui+x2+0UFRWxevVqv9B8ATo7O2lsbJyy0OywWSl7+zFiS/8f2aMZ7yPCRGHsbSTf9A0uj01G0zQGBgY53anwUt/kutE1iXZyY9UF7c08lvLycvR6/awm/CqLjpAzcghNSIRc972JLxgaOvv3eTyxfc1kIvJY3+cLFR2cS8/m4eHhGRfIXSwsabF5eHiY733ve9x9990kJiZy5swZvva1r/GZz3xmyVd+9ANafz+O555DDA8jhYZCfz/yLAqR+Jk9hu3bkQICcL71FmpZGcJux3j99UhT8Aj042cxoFZVuYRmIdCtW4fh2mvnpYK8HxeSJGHIyXGdd95915XlbLNhuOYa//fix4+fZUlTUxM1NTUzXpJ/roXahXAX6YuMjGTDhg1en+iTZdkjNtfV1dHa2jotS5DpYrVayc/PJzU1laSkJIaGhmZVLNGNpmmezzl3mbe8d69HbFZ/+EPkJZDd3L3lC6gnvsClzqMUlxxh7fYrPEv6VVVlYGCAvr4+2tvbqaioQK/XjxOfg4ODR7MT4fokJ1cnOvlZ0fhJE3fhtrFFBHV6PVuu+QTiqns5cfg5gk/9jjRnCVnDB+Gtg1QfXE3nxo+z4Yp7CQyaX7sKVVUpKioiMjKSlJSFm3k931gsFkpKSkhPT7/o+ayns4nGN37HhtbnuIR+1/tFAIUJd5F649fJjHZZbAoBFYMGnq1PnPRzYuhn3eBxLL128sPCPOPSbDZPyfd+Pmhra5u1T7PQNKS3fwJAgfkqNq3bNsmLxmR4z5PYPBlT9X222+0eK7OZ+j5PBSEEFotlyWuSC/No8BI6nY6ioiL++te/Mjw8THJyMvfeey/f/e5357tpfnyM1t+Pfd8+sFiQIiIw3Xgj/OY3890sP4A+LQ0pIADHa6+h1dbieOEFjLfcgrSAfpD8+JkJnoxmIdBt3Ijh6qsXVIbMckafkQEmE8433kCtrHRNdPkzzv348bPMcHu/7tixA7N58iXhF8OdTeyu33E+FEXh5MmTBAcHk56e7pMVJTqdDk3TaG5upra2lpycHJ/dvLuF87i4OE+GtizL4zzdZ8JkGc1jkfR65BtvRHvlFdfr6+uR5mCJt69obm7GJody3Hwdlwy+RvDb/4aScRC93nUfoNPpiIyM9BQLdGWYusTnzs5OKisr0el0HoEvPDyc0NBQvrdthGEnPHJmvOjsLiJ4a4qdjEiXoC/JMum774Ldd1Faeoyhw//H1v5/slatZW3Jjxg68wuKI64kcMc9rN12xZxfy7n91AE2bdrkX411HhRFobi4mBUrVhAXFzfpa1RFofL4q4jCJ8gYeo9kyTVR1kkklcl3s+b6L7Jt1LtbE3C6V8dLjZPbSoUaND693o7ZaESInYyMjHh8n0tLS7Hb7YSGho4rOmhcAPe3FouFsrIytmzZMquJuIJ/Ps4uZyF2YSDm5kmyms9lAfT9fLgFZHcWs6ZptLW1YbVaiYqKAhg3kTibooPnw2KxLPnM5mXn2Twd5tubbmwRHP+PzNTRBgZw7NvnymiOjMR0xx0QGOiPpRfxxthUW1pw7N8PDgdyfDzGW29dVJ623sJ/nHuP+YylWl2N49VXl4zQvFTH5Tgv7eRkjDfdNCcTXUs1nvOBP5beY7qxnO/rYj+zp6SkBLPZPKsl+U6nk7feeourr776vFl8mqZx8uRJhBDs2LHDZ8uSjx49itlsprm5me3bt3tEAm+jKAonTpwgICCAzMxMz/FitVp59913ue6662Z0Pjq3IOD5PkPY7Sgf/ajn8WL1bu7u7qaoqIjt27cjqTaC/nw54Qzz/qovs+1D/9+UPkPTNAYHB12F3fr66O/vR5Ikj8AXERGBYgzj0fLJJx22RSnclOyYYH0w0NNJ7Zv/R2rjMySKTs/2ZimBhhU3k7DzXuJT1s+479Ohvr7eY3Pjr00wOW5/dofDwfbt2ycIgO0NFbQd/gtrWg8QT7dne7l+IwNbPs3G3R/GYHTF1q7C4XYDH3SePwHh/k1WYgIuLJvZbDbPmOzr6/NkrrqF54iICJ+tujgf3vJpHhrsQ/rdTmLp41jyZ9lyz08nfZ3zzjs9f+ufeWbR3AtZrVaOHj1Keno6sbGx0yo6CBNXpFwMIQQrV67krbfeYseOHV7ty0JiSWc2L3aEEAwMDBAdHe2/oZoiHo/m4WFXRvPttyMFBXlmxf2x9A7eGJu6FSsw3X479hdeQGtvx/7CC5huvRVpjn+E5xv/ce495iuWS01ohqU7LnUpKRhvuw3Hiy+iNTXh2L/fVbzRx4LzUo3nfOCPpffwx3L5sWHDhlnbPriFY1VVJxWbhRAUFRWhKArZ2dk+9b/UNI2mpiYyMjJ8JjRrmkZRURGyLJORkTHuWBmbFTfdfk5VaAaQTCakSy5BfPCB671NTUjJyTPozfwxNDTE6dOnSUtLI2LU1rAw/RtcWvIjttX9Hx1NdxOXfHExTJZlwsPDCQ8PJzU1FU3TGB4e9ojPdXV1CCG4KTwcOSyO/UPjP7OgR09Bj2vcfm3zCCGj+qI5KpZtH/4umvYdCvPfxH7qSTYPHCSJNpKa/wD/+ANVujV0JF5NbPYdJK3N8G6ARunq6qK2tpYdO3b4heYL0NjYSH9//zhbiM7mGlqO/IOYptdYr1bhdogfIJjSqGsx536CVZvzPJ/RbZN4scFI68jkx65BFnxmvY3YwKnlZgYEBJCQkEBCgsuOw+FweITnxsZGSkpKMJlM48TnyfzIvUlFRQU6nW7WhVmrnv43dtFHqxTHhtu/M+lrhNM57vFiuRcSQnDmzBni4uKIjY0FJmY+w1m7o7HFXMeubHGfx6eS/ey20Vjqmc2LYwQsU5xOJ7/97W9xnnPg+pkcMTTk8mgeHEQKD8d0xx1Io0vp/LH0Lt6KpxwX58o8DwhAdHRgf/55hM3mpVYuDvxj03vMRyzVmpqzQvOGDUtCaIalPS51iYmYbrsNjEa0lhYcL72EcDh8us+lHM+5xh9L7+GP5fLDG6KG+4Z6Mt9mIQQlJSUMDw+zY8cOn/qXDg0NMTQ0RGxsLPHx8T7Zh7s/VquV7du3TxCU3YLCdAX86QjNbnQPPuj5W/nqV6e1v/nGbrdTWFjIypUrPUIcwNYbPk+JYTNBkp3B576OmIEliSzLhIWFkZKSQmZmJnv27CE7O9s1iWbp5rKBV9jR989J3/vImSB+UhDEiS69x2pWlnWsz7mOLff/BeuXTnN0y084bdyKKiTWqTXsavo965+7js7/zKHgse9QU3gIdYoe5hdjeHiYM2fOkJaWNmObm+VAb28v1dXVZGzZQkdDKaee/ncaf7mHVU/sYmf9r1mvVqEKiWLTNo5mPoz2L0Vs/eyjrNqch6LByW49PykI4rdlgZMKzdEBGl9Ks/LQVuuUhebJMBqNxMbGsmHDBnJzc9mzZw9paWkEBATQ1tbG0aNHOXToEEVFRTQ2NjI0NDQue3a2tLW10dnZSUZGxqysH8689xK7ep8HoPuyn2AMOI9V0djigIuIuro6HA4HGzZsuODrZFnGYDBgNBoJCAjAZDJhMBjQ6XTIsuw5ryuKgsPhQFEUz3n+XOx2O4qiLPlVYnOS2Xzq1Klpv2fLli0+qyLsZ+khhoexu4Vms3mc0OxnYSPHxHgynEVXF/bnnnNlpAcGznfT/Pi5IGpDw3ih+ZprloTQvByQExJc553nn0drbXV5x992m9873s+Cxi1M+fEz3+j1+kkF1srKSrq7u8nLy/OpV+nIyAj5+fkEBwf79Ga9qqqKnp4e8vLyJr0vdR+P0/FtnonQDCAFBY3Pbm5sRFq5csr7nS9UVaWgoICIiAiP17UbSZYx7P0v7PtuZqs9nyMv/YrM2746q/1JkkRoaCihoaGsXLnSk0G4pa+Czt5B3nOsZVA/vmD8a81GXmt2jdcvbDwrMAYFh5Jxw31ww320d7XScPR5gmtfY5OtgFVaE6ta/wKtf6H/jRAqQrJwpOwmeceNRMVP/3txOp0UFhaSnJzss8mTpUB/Xw9HX/k7scMlcOobbBAduCVCTUiUGrcwmHojKTvvZm2Mq8CfENAwLPNig5EBx/l/QzMjFa5a4SDIRwqZXq8nKirKswrDvfK6v7+f7u5uqqurPZYw7sznsLCwGf3ue8unua+nk1VHHgLgePTtbM7be/4XDw7OeD/zxcDAAHV1dTNahTPVooNuxvo+WywWgBkV6V1MzInYnJWVhSRJU56pkWWZysrKCT9IfvxMhrDZXELlwABSWJhLaF7iB+5SQ46JwXTHHa7M5u7us4JzUNDF3+zHzzygtra6vH81DXntWr/QvAiR4+ImWvnccsuys/LxszjQNI0f/9+bRAYb+fK9u/22F37mFXeRwLHU1dXR0tJCbm6uT31J7Xa7p1CfqqqztgU5Hw0NDTQ1NV2wP27hYKpi80yFZje6L34RZVRsVr72tQXv3ez21dXr9aSlpU3a36S1GeSv/TI7a37J9opf0lh1BUnrtnqtDZIkERISQkhICMnJsF0ILJYujrepvD88UdT9fbkr2SXCqPHxdXbMRpd+ERGTSMTeLwJfpG+gm9oPXsRY/TrrLCcJl4bJGj4IJQeh5EfUySm0h29HWplLwubLiUlMvWAbNU2juLiYkJCQWdsdLDUcNiv1xYexVL5LeNdx1jnKuUs6m0luFwbKA7YyvPIKVu68m9WxLnsZIaDTKvFWq5HqwQuLiLestLMlUkWe459VWZY9PuPnWsL09/fT0NCAqqqYzWaP9UZ4ePhFRVFVVSkuLiY5OXlW/vyqqtL++BfYQR/NUiLr7vn5BV8vFpnYrCgKp0+fZs2aNV6ZtJys6OBYyw33+R+gs7MTSZKWfHLtnHk2Hzt2bEqDXQjB5s2b56BFi4OFUMF0ISMcDuwvvojo7YXgYIx33IF0Hu8bfyy9i7fjKUdFnRWce3pcws8ddywL4cc/Nr3HXMRS6+rC8dJLriJzKSkYr7tuSQrNy2Fcuq187M8/j2hvx/7ii66JLh/0fTnEc65YjrH8z78e5C/1TsDJnjP1ZGy5sHgxVZZjLJcz3pqk0Ov142w0mpubqampITs7m2AfrixUFIWTJ09iNpvZtGkT5eXlPhGb29vbqaysJDs7+6KZZ7IsT6kNY4tMzURoBpACA5F27UK8957rMxd4dnNlZSUWi4WcnJwLZmduvf3rFP/6IBn2U8gvPYjjy29hNPrm+t8lPgdz5Tq4khEGHBJ/qjBhUca3r88h86sSl/AcblT56BoHUaMF4kLN0Wy9/rPAZ1EUB0VF7zFc9k+iO46wTqkiVWsgtbcBep+HQmiVYmkOycCZmIU5dQcr1m/DNMaKoLKyEofDQXZ29rKfSOxubaC97H2UpnzMfadZ46ggUxpj9SRBB1HUhV+Kbv01pGZfz7pg172/ENBskXmrxUCj5cKC7LowleuSHESYvGdbMVvcljBuWxghBCMjIx4/8tbWVux2O2FhYZ7M5/Dw8AmCpbd8mk899WN2247iEHqGb/oNkYEX8RceKzYvgvuiiooKAgMDSUlJ8cnnTyY+g0vE//3vf78srHIk4U1jmPNwxRVX8PzzzxMeHj6l199444386U9/GufpNB/4q24vbISq4njpJbSmJggIwHTXXciRkfPdLD+zROvrw75vH4yMIMXGuoQff4EMPwsErb8f+zPPgNWKnJCA8dZb/dYLSwCtqwv788+DzYacmIjxllv836ufBcNvnjzMfxa5buJ+sCOcT9+9c17a4b8uXvxomuYVj+4PPviA1NRU4uPjaW9v5/Tp0+zYsYNIH16Ha5pGfn4+kiSxY8cOz0pYp9NJenq61/bT09PDqVOn2Lp1q6dY1IV4++232bFjx0WFA3dxKWBWdjjCbkf56Ec9jxdqdnNTUxM1NTXk5OQQNIWVij3tjYT87WoiGOKDyNvZet+jc9DKswgBNYMyT9ZeXOS+PdlCepTEZLrwQE8nTYVvojR8QExfIauVGnTSeLnFKXQ06lbSHbqBoZC19MtRZF9xMzHxSd7qzoJH01TaGyrorSvC2XqGwL5ykqzlxNI74bXdhFMdmEFbwDo2XHYnKeu3epI87CqU9+v4Z4sRq3phoV5CcHeqg3Xmuc9i9hZWq9VTdLC/vx+LxUJISIhHfHY4HNTW1pKXlzerFSbFh54n54MvIkuC45u/z+YbH7joe9RXXkH7059cD1JSMPzylzPev69pb2+nvLx81nGaCYcOHeLOO+/k6NGjbNmyZU73PdfMidi8WJnvi2pN06itrWX16tV+j75zEJqG47XX0KqrwWDAdMcdyHFx5329P5bexdfx1Hp7XYLzMhD0/GPTe/g6lmJoCPuzzyKGhpBGvcaXaub9chyX2miRUhwO5ORkjHv3InmpuNVyjKevWG6x/P2+o/zsRA8A30oP4osfv8Jrnz3dWM73dbGf2eMtsfn48eMkJiYSEBBAQUHBlIXZmSKEoLCwEJvNRnZ2tqfwYE1NDRaLhYyMDK/sZ2hoiGPHjrFx40aSkqYm/B08eJCMjIwLCu3eEprdKL/+NeLgQQB0P/sZ8vr1s/5Mb9LV1cXp06fZvn37lJPNAEoP7SPr6JcA+GDzj9l64+d81MILo2pQ1Kvj5aaLJ7skm0a4NslJYtjky+FHhvppPP0e1tr3Ce0pItleTQSTF1LrJpx2w0qGQlPRItcREL8ec/xqohNXYwxYnLVsLIN9dDVWMNRWidJVg2GwHvNIA8nOeoIk+4TXq0KiXreKLvNmtMTtRG+4lNDolZwcnQAKj4yibUTmWKee0v6pXaPdstJOWoSKYQleMjgcDo/w3NPTg8ViwWg0Eh0d7RGgAwMDp5UxX336KGteuYdgycbxiJvZ/Lk/TOl96hNPoI1OfklXXYV+TFHThYTNZuODDz4gLS2NuAvoR76gv7+fSy65hK985St8/etfn9N9zwdzZqPhZ/o4nU4ef/xxHnroIUz+zE4PQgicb7/tEpp1Oow333xBoRn8sfQ2vo6nHBmJ6bbbsD/3HFpbG479+12ZhkvQ18g/Nr2HL2MprFaX1cLQEFJ4OKZbb12yQjMsz3Epx8VhuvVWl4dzUxOOV17BeNNNSNMsGDIZyzGevmI5xfIPL530CM1fXGPgwXv3ePXzl1Ms/bjwpo3G8PAwZWVlpKWl+VxoLi0tZXh4mNzcXI/QDFO3sJgK7qKDqampUxaawbVE+kKezWOLRHkr/rrPfx5lVGxWv/Md5AWU3Tw0NMTp06dJS0ubltAMkHb5nbzfeIqdrX9m2+mfUJOSQWp6rm8aegF0MmyPVtkePYKiQUmfjpcaJz9HNtmD+FPN2cfJJgt7EhRSwg1IEgSFhrPx0pvh0psBV8JUfWsdrWVH6at4n3hnE0nOOuLoIZp+op390FsMvUD12c/tIoIefSxDAYk4ghMhOBZdWCzG0FiCI+MJjUogNDwGnZcmyS+G3WZhqLcbS38ntqFu7H1tqP0tyMNtmKzthDo6iVK7iGGQSY1UJbAJA036FHpD1qJEbSIkZRsr0vJICA7Fvb7dYnPw5qkqemMu5/3GMGi8eNt0kuDmlQ42mlWMs7+EW9AYjUbi4uKIjo6mt7eX5ORkoqOjPbYbZWVlGAwGjzd0eHg4ISEh5z0XtTZUkvDqpwmWbJSYMtnwyV9PuS2ipcXzt7x27az75guEEJw5c4a4uLg5F5qFEHzrW99i7dq1fOUrX5nTfc8X8yY2d3d3Y7VaJxQNXLmAfaf8LAyUI0dQS0tBkjBedx265OT5bpIfHyDHxLgE5+efR2tpwfHyyxhvvtlrmYZ+/EwVoSg4DhxA9PcjhYRg9BevXLLICQkY9+51WTTV1+N4/XWM11+/JD25/Sxs/vjaaf79SDsAX0yR+MZnr172Xp5+Fg6aptHY2Mj69etZsWKFT/dVXV1NZ2cneXl5EzzGJytUOBMcDgcnT54kLi5u2gXqZVk+r9g824KA50MymZDvvNOTRagdO4acO/ei7LnYbDYKCgo8FiszYetHf0zhb06TaTtB1Mv30RP1MlHx86cP6GXYGqWyNWoEIaDJIvNig5F+x+TXBU32YP5eP+b9ksZlMSNsjZEINUpIskxkfAo1TZ3EX7WdTZs2AdA91E973RmGmsvQuqsIHKgh0tZIrNZFkGQnhj5ilD4YroDhyduqColhKYgRgrDKQdjlYBz6YJy6IDRdAJqsR8gGhGwAWY/QjR5PQkMSKmgaCBWEiqw6kdUR9IoVvWbFqFoxajYCNQthYpAIyU7EFGPYg5lO/QoGg5JxmldhiFlLZGom8SkbWaHX4z6DaAK6bRLFnTpOdevpsctAEARmg3KhPcDKEJUrEpwkBWuL1iJjNrh9mtevX48sy0RHRwMuf+CBgQH6+/vp7OykqqoKSZI8Wc8RERGEhoYiyzIdrQ2Ynv4IMfRTp0sh7jOPY5iGd7qoqjr7YIHqM/X19djtdjIzM+d8388//zyvvvoqhYWFFy3yuFSYc9XmJz/5CT//+c8ZGRmZ8JwkST6rKOxnaaAUFqKcPAmA4cor0S3QWTM/3kGOi8N4yy04XnwRrbERx6uvYrzxRq9kGvrxMxWEEDjffBOtrQ2MRoy33YZ8niKkfpYGuqQkjDfdhOPAAbTqapz//CeGa67xC85+5ow/v13OTw+60rceSFD4xuduWhaWIX4WB+6CVWFhYaxatcqn+2poaKCxsZHc3FwCAyfaCHhDbHYXHQwJCWHTpk3TFoTPl13tK6HZs98Pf9gjNqu/+AXSs8/O64SUoigUFhYSFRU1q3EhyzriP/4nGv94AytFC9WPf4SRz79CUGi419o6UyQJVoZofDndBoBNhdO9el5rPr/VnyJk3ukM4Z3OMZ8jBGsMq7kqJRZNgDyaAb06Yxdk7Br3fqFptPV10ttag6WzAWdvI9JgM0Z7DwGOXkKUPsxaPxEMoZMEZiyYsYCG699FRNqZBeJs3wakUIbkMIb1EYwExOMMigdzIqaIJIKik4hesY4QcyTnlmCzKdBml6ntkjnZrWdYmd5vXKBOcPUKBxvMKoHLPA+pra2Nzs5OcnNzJ1wr6HQ6IiMjPTY/mqYxNDTk8X2ur69HVVX0kkJa/ndIpo02KQbDx54iKHSaHvw9PZ4/paioWffL2wwMDFBbW0tWVta4FTJzQVtbG1/5ylf4zW9+s6ySa+f80Pzv//5vnnjiCa677jr/kr2LIEkSMTEx/kyWUdS6OpyHDwOg37kT/TSKgfhj6V3mMp66xMSzmYZ1dTjffBPDtdcume/SPza9hy9iqRw7hlpZ6VpJceONy6YI6XIfl7qUFIw33ojjwAHUigowGDBcccWM47Hc4+lNlnos/3K4mh+/4VqT/YVoK9/6/E3IPropWuqx9DOR2X7Xdrud/Px8goKCfO7b3dbWRmVlJVlZWYSEhEz6motZWFwMTdMoKipClmUyMjJmFJ/J2uBroRlA0unQPfgg6qOuQnrayy+ju/lmr+9nKgghOH36NAaDYUaC/bmERcTQ/qEn6PnHLaxV6yj+872kPLAPg3FhaQcBOsiOUciOcSm6FicU9+p5s/XCdWaEJFGtS6W6YuJzWyMVNphVEoM1QvQCSZYxR8VjjooHzl8cdsBhZ6i3k5HhPuyWQZwjAyjWQVTrIJptCBQbqE7QFNAcSKoTSXOMtkcHkg4kGSHJrr91RjAGIxmDkEzB6EzB6E0hGILCCAqPJSQimpDQSAJlmUBgMiMdhwqDTonmfpmqQR0lfTqc2szGRrhRIy9WYb1ZxWz0lxxzY7FYKCsrY8uWLZNOyJ2LLMuYzWbMZjMpKSkIIaivKiHkhU+SQhttRPP+2u8Q3dhJxJCT8PBwwsPDMVzEylKcO+G2wO6XFEXhzJkzrF69+qLFXL2Npmncf//9XHvttdxzzz1zuu/5Zs4LBH7zm99ECMH1118/QWyWJInLLrtsLptzQfyFUBYOWmcn9mefBUVBt3nzrG76/SxO1Pp6HPv3gxDoMjIw7N7tHwN+fIpSVobzn/8EwHDVVdOa4PKzNFAqK3G+/joIgT4rC8Oll853k/wsYX57sJqfv+ZSHz4fPsS3778e3TQ9T32J/7p4aeBwOCbYGE4Fp9PJ8ePHCQkJITAwELvdzpYtW3zQQpfdYkFBAZmZmcTETOr4CrgK0VVUVLBr167zvuZ8uL07BwcHycnJuaiYcj7y8/OJjY31ZKvNhdDsRgiBctddnsf6f/xjzu3mhBBUVFTQ29tLdnb2jOM4GXWn3yf1lY8RJNk5EXIFGz//tznzJPYGigYtIzKnuvWc6fNOu9eFKawI1ogOEEQYNYL0EKgX6CVX5rWvEQKcGowoEiOKRL9Doscu0T4iUzukwzFDMflcYhzN5Cbo2JxkXpKF/byBqqocP36cqKgo1s+wSGhdWT7R+z9JHL10EM3IR/YRFpNMX1+fp/DgyMgIoaGhHuuN8PDwCTqe6O5G+cIXPI8NC8hHHqC0tJSRkRF27Ngx5/rB7373Ox555BGKioouWEh2KTLnZ+uWlhaeeeYZnnjiCQLOKa4kSRK1tbVz3aQFi6qqFBUVsXXr1mXj6zIZYmgI+/79oCjIyckzEhn9sfQu8xFP3apVGK69Fucbb6AWFyMFBGDIy5uTffsS/9j0Ht6MpdrcjPOttwDQZ2UtO6HZPy5d6NevB4cD59tvo+TnIwUGot+2bdqf44+n91iKsRRC8B+vV/DowdGM5rAB/vUTe3wuNC/FWPrxDaqqcurUKQICAtiyZQsNDQ2TWiJ6g4GBAQoKCkhPT7+g0Ayzs9Goqqqip6eHvLy8WQmkYzOb51JoBte9s+4HP0D90Y8AUP/wB/QPPODTfZ5LU1MTHR0dsxLsz0fqlp2U9j/C1iNfJnv4HY7/8bOkff7PyPLiOF/pZUgJ0UgJcXBVZDf5+flsSM9ACY6lol/Hsa7px6tqUE/V4CzaJAkkyeWEIUkgj/5f1cDuJaF4OgTpBduiFNaGqcQGauiFwvHjx4mOjmb9ypkJqMsFt0/z2hnaipYde511Bx8kTBqhQU5G/uhTxCW6POsDAwNJTEwEXCta3LYbtbW1DA8PExQUNM732dTZeaFdzSsdHR10dHRwySWXzLnQXF5ezve//31eeOGFZSc0wzyIze+88w4ffPAB2dnZc73rRYeiKOzfv5/09PRlexMgHA6X0GyxIEVFzdiv1x9L7zJf8dRv2AB2O86DB1GOH0cKCEA/Dwb/3sQ/Nr2Ht2Kp9fbiePll0DR069ahv+QSL7ZyceAfl2fRb96MsFpRPvjAZeUUEIB+tKDPVPHH03sstVhqmuD7L53hsaMuj+ZvhPbw4N2XoBu9yfMlSy2WfnyDpmkUFhYCkJmZiSzLXivMdy7Dw8Pk5+ezbt06j9BxIWbajoaGBpqamsjNzZ2Q/DRd3J7NQgg0TUMIMSdCs2f/GRm4IyDefBNx771Ic1RborOzk+rqanbs2DGlJfwzIe2y2ylw2th+4pvkDL7B0T9+gc33/X7RCM7gEusKCwtJTU1lRVw0oJESonFtktPzGosTumwyDcM6Kgdk2q2+6Z8iJJgHF4o1oSqrw1QSgzQiTMJlEXLOIeKyYynFaDTOWEBdLlzIp/liCCE4tf9RcssexigplBnSifr0k4SETz65ZzKZiIuLIy4uDnCtcunv76e/v5/m5mZKS0tZUV2N+xsTej1CiAWxAtlms1FaWkpaWtqsz/XTxeFwcN9993Hfffdx1VVXzem+FwpzLjbv2rWLX/7yl1xxxRWTfuGf+MQn5rpJfhYoQtNwvPoqorsbgoIw7t2L5Pf5XvboMzJcws+xYzgPHXIJPxs3znez/CwRxMgIjpdeArsdOT7eVRhuAVws+Zlf9FlZYLOhFBTgfPNNJJMJ3erV890sP4scp6rxrWeKeKGwFQnBD81dfPTaDHTr1s130/wsYSRJmrKNhtuL12azkZOT45mU0Ov1KIp3K4/ZbDby8/NJSkqacoG5mYjN7e3tVFZWkp2dfV4v6Ongzmx2i80we2/s6aL/3/9F+eIXAVC++U0Mv/+9z/c5ODjImTNnSE9P97kH6uYr7yFfcZBd8B3y+l/mxO8+yfr7/rTgPJwnw+0LHhERccFxHWyAYIPGqlCN3Qnjn3OqgrZ+K+39FlqHVLptOoZ0rqJ8C4EVQSoJQRoJQRqRJkGIwSUmG6eplzc2NtLf309eXp6/KO4FmK5P81hs1hHK//YVLhs4ABKcCr6M1Z/5fxgDg6f8GQaDgZiYGM/KE1VVsdbVeZ7vSU6m4t13x9luhIaGzvl3KoSgpKSE2NhYj1A+l/t++OGHsdvt/OxnP1u295JzLjb3jFapfPLJJyc8J0mSX2z2A7gOUOehQ2gNDaDXY9q7F9nvD+hnFH1ODsJmQy0qwvnPf7qEn9TU+W6Wn0WOUBQcBw4gBgeRwsIw3nzznHsf+lmYSJKEftcu13mnrAzHq69ivPVWdElJ8900P4sUy/Agb//ft3mt8xr0GHk4vJNbslPQb98+303z4wdwXYuXlZUxMDBAbm7uOIsEb2c2OxwO8vPzp+096m7HVLPoenp6OH36NFu3biXcSzY17sxmdzzmQyST4uORNmxAVFRAdzeithbJhxOiVquVgoICVq9ePWciTsa1n+QEgh0F3yV7+C0Kf/chku97nMCg2U8Y+Ar3MSSEIC0tbcaCk0EnsTIqiJVRQZ7PtVgs9PVVeLx1FUUhLCyMiIgIzOERBIWFo6DHroIqQBMSmnD97fonoWigCZeNhkEWGGXQSa5/siSQJTDqwCQLTDqQfaiX9fb2Ul1dTVZWFkbjhQssLmdUVaW4uJikpKSL2gydS1tDFcqz93GpWokmJI6tup+Mu/8NaZarBHQ6HQHNzZ6E+ZhrriFo2zb6+vro7e2ltrYWIYSn2GBERARhYWE+X1HV0NCA1Wpl69atPt3PZBw7doxf//rXHDp0yGerPhYD82KjcT7cQrQfF5IksWbNmmU5E6IWFqIWF4MkYbz2WuRZXsgs51j6gvmOpyRJGC6/HGw21IqKs8LPihXz0p7ZMN+xXErMJpZCCJz//CdaezuYTBhvvRUpKMgHrVwc+MflRCRJwnDVVQi7Ha22FseBA5juvBN5Chf7/nh6j6UQy662Rnr/eCd71UokQx1y6F1ctS4Kw549c9qvpRBLP76jpqaGjo4OcnNzJxSD0ul0XstsdvtBBwUFkZ6ePq3x6BYrNE27qHAxODhIQUEBmzZtIjY2dlZtHossy1itVhRF8bpn8XTQff/7KB/7GADKt77lswJdiqJQWFhITEwMKSkpPtnH+ci49lMUBZrZfOQrZNqOU/a7Wwn/5OOYo+LntB1TpbGxke7ubnJzc70qrEmSREhICCEhISQnJyOEYGRkxFPQra2sFLvd7hGf3dml+gWaQGGz2SguLmbDhg0+z5Jf7MzEp1loGgWv/IHMkp8TIlkZIJi6yx9h6yV7vdYuceaM52/dunWYzWbMZjOrVq1CCMHw8LDH97mpqQmn04nZbPaMTW+Pz8HBQWpqasjKyprzcT80NMTnPvc5vvvd77Jjx4453fdCQxIzKUc8C/7yl7/wqU99asL2V155hfvuu4/W1ta5bM4F8Vfdnh/U2lqXX6oQ6HfuxLDMD1I/50eoKo6XX0arrwejccrCjx8/5+I8cgQlPx9kGeNtt/kzVv2cF6EoOF54Aa21FQIDMd19N7KPC7n5WTrUluYT8PQ9JNJJvwihJejjrEnagumuuxa8VZj/unhpoCjKRbOSGxoaqK6uJicnh9BJ/H/7+/spKCjgiiuumFVbNE2joKAARVHIysqatiCnKApvvvkmV1555QWzIUdGRjh27BgrV65kzZo1s2rzWIQQtLe3U1FRsSDEPfXpp9H+8Q8AdA88gHz11V79/Mn8u+eDqpNvkfrm5wmTRmiR4hi65f+xcsP0i/f6ku7uboqLi9mxY8ecC6hCCGw2myfrua+vD5vNRmho6LjxOZ+TI240TSM/P5/g4OBZZX8vB9ra2igvLycvL2/K2bJ93e20Pvllsq3vAVBuSCPgrt8Rm+w9uy7hdKJ85COex/rHHkO6QPuEEFitVs/kSF9fH1ar1TM+3dnPM81wV1WVo0ePkpCQwOo5trwTQvDlL3+Zqqoq3nnnnQU7wTNXzPkvxDe+8Q3+8pe/eB5brVa+8IUvcOutt04qQi9nFEXh4MGDXvdEW8hoHR04XnsNhEC3ebPXlpMux1j6koUST0mnw3jDDciJieBwYH/pJbTBWZRongcWSiyXAjONpXLmjEtoBgxXXeUXmvGPywsh6fWuGgIxMWC14njhBYTFcsH3+OPpPRZzLE8ffpHof+wlkU6aiaMv5Ausjlo7bzUpFnMs/fiO1tZWKisr2bFjx6RCM3jHRkMIwZkzZ7DZbGzfvn1GmZ/u91yoLW6Ljri4OK8KD0IIVFUlKiqKXbt2sXPnTpKSknA4HJSXl3Pw4EGOHz9OVVUV3d3dc3KcyXff7flb/e1vEU7nBV49PYQQlJeXY7fbycjImFdP3XU7rqL1tmdpleJYITpIeuFOSg89N2/tOReLxcLp06fZtGnTvGTqSpJEYGAgiYmJpKens2vXLnbt2sXKlStRFIXKykoOHjzIBx98QHl5OR0dHTgcjjlvJ7gydYUQbNy40S80XwC3T/PmzZunJDRrmsapA78n8M+Xk219D4fQcSTlQZL+5XWvCs0AtLePe3ghoRlc4zMoKIgVK1Z4xudll13GqlWr0DSN2tpa3n33XY4cOUJpaSltbW1YrdYpN6eiogKj0UjqPFhsvvzyy+zbt4+//vWvy15ohnmw0Xjttde44YYbPN5F9957LzqdjsOHD5OXlzfXzVnQqKrKu+++yyWXXLIsBqsYGsJ+4AAoCvLKlRh27/baj85yi6WvWUjxlAwGjDffjH3fPkRPD44XX8R0991Ic1xxdqYspFj6AqFpDA70MtTThqW/A+tAN4p1ENU6hGYfAvswOIaRnRZ0Tgs6dQRZU9AJJ7JQXX+jjP5fRSCjSTIaMkKS0dAhJAlN0qHIAYgRhVMFcQhDEJohCAzBSKZg5KBIjGExBIRFExwRR1hkPKHmSERLC85Reyd9Tg76TZvmOWILg6U+LmeLZDJhuuUW7M88gxgcxP7SS5juvBPpPFkY/nh6j8UYS6FpHH3y38mu/CV6SaNc3kB4yJ1EBYTOa02KxRhLP76ls7OTkpIStm3bdkFPY3eBwKl6JU9GRYXLb/ZcP+jpIEmSxzN5MhRF4eTJk4SFhbFp0yav3Ve4hWZN05Bl2SPuuQU+wJO519fXR3l5OTabzeeZz5IkofvhD1F/+EMA1H//d/Sjf8+WhoYGurq6yMnJWRDni5UbtjHwmdco+dvHSXeeYfsHX+KDxpNkfORH6OaxfU6nk8LCQpKSkkhISLj4G+aIgIAAEhISPG2y2+2ezNLa2lqGh4cJDg72jM+IiIgJ9jnepqWlxWPV42v/3sXMdH2aGyuLUV7+JrucpwGok1Ow3/g/ZKbl+qR9orl51p8REBBAfHw88fEuSxyn0+kZn42NjZSUlGAymcZlPgcFBU04p3d2dtLR0UFeXt6cT150dnbypS99iUceeWTOM6oXKnN+Js7Ozuaf//wn1113HQMDA9x///08/PDDy9o42w8IhwP7/v1gsSBFRWG84QYk/4+OnykiBQScFX76+nDs34/x9tv9xd18yNBAL71tDQx1NWLrbUbpb0UebkNv7cLk6CdY6SdEG8QshjBLKnOW1yEDA1N7qSokBgihj3CGdFHYj59AK30OffgKAqKSCYtNISZ5LUEhfv84PxORgoMx3nab67zT1YXjlVdcGar+3y4/Y7Bahjjz+09zyeA/QYL8gN2sC9iNUWfEeN11s65J4cfPdDnfDXhvby9FRUVs2bKF6OjoC37GdLySJ6Ouro7W1lZyc3MJmGVygE6nQ9O0Cds1TaOoqAidTseWLVt8KjRPxnyJz/KWLajBwWCxIE6fRjQ3I81yxVZHRwe1tbVkZWUtqHt2c1Q8gV/cz9G//gt5ffvZ2fpnzvw6n/B7/khkfPKct0fTNIqLiwkKCpqWp+58YDKZxol7DofDY2lQX1/P6dOnCQoKGic+z/ZYHcvg4CDl5eVkZmYuqDG1EKmsrJyST3N/Twd1L/yU7O7nMUoqI8JE0erPk37bN9AbfDdxIGprPX9LGzZ45TMNBgOxsbEef31FURgYGKCvr89jXaTT6SbYwpSWlrJp06Y5H1OapvGlL32JXbt2+d0axjAnSkxvb++4xykpKfzjH//gjjvuIDMzE6vV6kmNj4yMnIsm+VlACE3D8eqriO5uCAqat+WkfhY3Umgoxltvxf7ss2htbTheew3jjTcizeMyv8WK02Gnq7mGvpYqRjprUHsb0A+1EGjrJMzZRaTWS6hkY/LFtecweg82LAIZkMMYkUNx6IJx6oNR9EGohhCEIRiMIWAKQTIGI+mNyDoDkt6ErDcg6/XIehOSrENoAqEpCE31/EOoaIoTp3WI0uKTbFi9ElmxIjlHkJwj6JwWjM4BAp39BGuDhGlDhEhWdJIgkiEiGQKtCXoLoRdoHN+FbsLpMqxgOCgZJXwVhujVhCasIz41HXOUXyhazsjh4a6Jrn370Bobcb71FoZrrvEvBfUDQGt9BSN/v4dstQZFyOQn3U/GSAKSBIZdu9B50TvWj5/ZMDg4yKlTp9i4caNHfLoQblFUUZRpi83Nzc3U1NSQk5NDcHDwjNo7lsksPYQQlJSUYLPZyMnJ8VrW5FSF5smYS/FZ/+ijKJ/8JADKV76C/tlnZ/y7NDAwQElJCZs3b16QXu1GYwAZn/s/jr72Z7YU/ZjNzmJ6/nYNJbk/JX33XXPalqqqKux2Ozk5OYvuOsBoNI4T95xOp0d8dmeWBgQEjBufgYGBM+qnw+GgqKiI1atXExUV5e2uLCna29tpb28nLy/vvNY1NquFkpf+m631f2anNAISFAbmEn77f7I1yfeTHqK42PO3tHu3T/ah1+uJioryjBdN0xgcHKSvr4+enh6qq6vRNA2j0YjVaqW/v5+wsLA5s/v529/+xsmTJykuLp5Xi6GFxpyIzdHR0ZOeiIQQfPazn+W+++7zLMOarf/XUkKWZbZt27akB6wQAue776I1NIBe77PlpMshlnPJQo2nHBWF8eabXcW7amtxHjyI4YorFvQF33zEUlMUutsb6WmpxNJeg9JTj36wkaCRFiId7cSIbhIlQeL5PmA0nIMiiF5dFEOGGKwBsSjB8cihcRjCYjCGxRIYHktoZBzmyDhCAoMJ8XG/nE4nncZXybnhhgsuyRVOJ4NP/4PB9iYsATL29SnYBttQ+1vQDbcRYO0g1NFJlNZFGCNE00+0sx8GSlxZ0w1nP6ubcNpNq7CErYWYjYQmbyZx3TbCoxdmZfSpslCP8YWIHBeH8cYbcezfj1pejhQcjGHnzvGv8cfTayyWWJ765+Osfv9fSWSYPsJovfQ/2FrTC5KCLiMDXWbmfDdx0cTSj2+xWCzk5+ezevVqkpOnlg0qSdKM7ts6OzspKytj+/btXhMuJxObq6qq6OnpIS8vz2tF0IQQaJqGEGLaQvNkXEh8LisrG1dwMDIyErPZPGXxWQoJQb77brRnngFAe+kldLfeOu02Wq1WCgsLWbNmjUeEXKhkXP8ZWtbloHv+PlZrDUQd+zLHS19i5Uf+i7AI3xcPb2lpoa2tbcHYjMwWg8FATEyMx7ZBURSP+Nzc3ExpaSlGo3Fc5vNktgbnIoTg9OnThIWFsWrVqjnoyeLFYrFQWlp6Xp9mq3WY0ld+x/qa/8dl9IIENbpUhnb+G+vzbpqTNgohENXVnsdyWtqc7FeWZcLDwz12T/X19TQ2NrJy5UoGBgZobGxEURTMZrPHdmM659DpUFNTw0MPPcSTTz45JZuT5YQkhBC+3sm777475dfu9tFsyEzwV932PUpBAc7Dh0GSMN5wA7oFvuTIz+JAra7G8eqrIAT6vDwMOTnz3aQ5RWgafV2t9DRXMdhRg7OrHnmggUBLMxGONmK1TozShW8QbcJAhy6OfmMC1uAkhDkZQ/gKAqOSCItNJjJ+FcGhi89eQgiB45VX0GpqICAA04c+hHwBX8qBng46GsoZbKvE2VWDrr+BkJEmoh0txNJ73vf1YKbduIqh8I3oErcStTaL5PWZGIz+VRtLFaW0FOebbwJg2L0b/dat89wiP/OBzWqh8E9fJq97HwDV+rUE3/Vnwt87ASMjyCkprhVci1Dg9V8XLw00TcM5WjjOZrNx9OhREhIS2DDN5c9vvvkmubm55y0ieC59fX3k5+ezZcuWKWVPT5UjR46wZs0a4kYtaRoaGqiuriY3N5eQEO9Nc2ua5hG152KCZqz43NvbO23xWQiBctfZzF79Y49dtHDXWJxOJydOnCAyMpINGzYs6MSNsdhtFsqe+h65HU+hkwRdRFCX/QPSd9/ts/Nuf38/J0+eZNu2bctmlbaqqh5bg76+PgYGBtDr9ePE5+Dg4Anjprq6ms7OziUjyvsKVVU5fvy45/gbi8UySPkrj7Kp7q9E0w9AJ5HUbvkKm6/9DLJu7uIqurpQ7r/f81j/9NNzbic3NDTE8ePH2bFjh0d8FkIwMjLi8X3u6+vDbrcTGhrqEZ/Dw8MxnqfWylRRFIVrr72WzMxMfvvb3y6a8+RcMSdi83wihOD73/8+v//97xkZGWHv3r387ne/m1Jl2Pm+qHY6nbz66qvccJEsvcWKWluL4+WXXYLgrl0Ytm/32b6WeiznmsUQT6W4GOfBgwAYrroKfXr6/DboPMw0lkMDvXQ2VjLYVo29qxapv4GA4WbM9lZi1Q6CJPsF368ImU45hl5DAiPBK1DDVqKPXkVI3Fqik9YRFZeMrFtcYshUYul87z2UU6dAp8N4++3oEs+bv31Rhgf7aK0upr+hGK2jjMCBKmJs9SSKzklfbxcGGvUp9IVtQMRnYF61neS0HIJDw2fcBl+xGI7xhYjz+HGUo0ddE6jXX49unavitz+e3mMhx7K+7CTaM59htVYPwLH4e8j86M8R+w8genqQoqMx3XXXeQtJzjXTjeV8Xxf78Q5usdnhcHD8+HHCw8NJT0+f9k3ywYMH2bp1KxERERd97dDQEMeOHWPDhg1Tzp6eKseOHSM5OZnExETa2to4c+YM2dnZFyxwOF3mWmiejIuJz+Hh4RPsQkR9Pco3vuF6EBmJ4Q9/mNK+NE2joKAAWZbZunXrolz9UF1wkPA3v8FK0QpAUUA2wXsfJjHVu5mXVquV48ePT2tlwFJEVVWPrYFb4BvrqRsREcHIyAglJSXk5OR4dSJoKVJWVsbQ0BBZWVme46+ztZ7mN39LWvvzLgtAoJ1o6jd+jo3XfQ6jae69r7Vjx1B/8QvPY8O+fXO6f1VVOXbsGHFxcay5iDWZzWYbNz4tFounKKZbgJ6uL/nPf/5znnzySU6dOuUf05Ow5KeTHn30UR555BEef/xxVqxYwcc//nHuv/9+nnzyyflu2kVx/9Bfd911890Ur6N1dOB47TUQAt3mzei3bfPt/pZwLOeDxRBPfUYGYngYJT8f59tvIwUFoUtNne9mTeB8sbRZLXQ0VtLfUo2tqxbR14BxqIkwWwsxajtmLOf3TJZAExLdUgQ9hgSGA1eghCWji1xFUNxqIlesI3ZFKokG4/mtMhYhFxuXyunTLqEZMFx99ayEZoCQsAjWb98N28evyLEM9dNaXUxffSFaazFhA+Uk26sJlaysU6uhrxr6XoYy0F6RaNQl0hmShpKwjfC1uaxKzyMgaH4vWBbDMb4Q0WdnI0ZGUIuLcbzxBsbAQHRJSf54epGFGEvF6eDEEz9ie+3vMUlOegmjZc8j5Fx2O46XXkL09EBwMKa9exeM0AwLM5Z+5gZFUTh58iTBwcGkpaXNKBtrMvuKyRgZGSE/P5/U1FSfiHHudvT09HDmzBm2bt3qVaHZbZ8B5y+uOBecz3ajt7eX0tJSj/gcGRnpEU90q1YhrVuHqKqC3l60U6eQL5LcI4SgrKwMh8NBdnb2ohSaAdZu24Nt47u8/8yPyG57kq22Ezievo4TiR9jw20PEeSFiX5VVSksLCQmJoakWRZhXOyMFZZhvKdud3c31dXVqKpKaGgo3d3dnr8X6/jyJWN9miWg6tQ72D/4P7YNH2bV6MrUFimOprQvsOmaT5Nh9F7hxukiamrOPkhImPP9V1ZWotfrSZ3CPX5AQAAJCQkkjLZzbFHMsb7kYzOfL2QNc+rUKf7jP/6Dt99+2y80n4clLzb//ve/54EHHuDWUZ+qhx9+mDvvvJPf/OY3fkP6eUIbGsK+fz8oCnJKCoY9e/xLDvz4BP0llyAsFtSyMhyvvorpjjuQvbh0c6Y4nQ562hrob69jsL2WVKWcgj88iMnaTrC9k0ilk2j6SQFSLvA5fYTSrY9nKCARe2gyUsQqAmNXE7FiLbFJ64gNDGJhO+zNHWpDgyfTXZ+Xh95L1ZInIzg0nHXbLodtl3u2aapKS0M5HRXHsTcXEdRbQqK1ihipj5VaCysHW2Dwn1ABzgM6avQp9JjTIXE7UevzWLlxh9+CYxEgSRKGyy9HWCxoNTU4DhzAdPfd4L8IXbLUlhxHee5BLlGrQILigGwSP/knNsevxPnWW2hNTWAwYLrlFqQp2g348eNL3JMMer1+VlmrOp0ORVEu+Bq73U5+fj5xcXGsXr16RvuZSjssFgsVFRVs2rTJq97CsykI6GumLD5/6lOk/Nu/AaD++78jPfUU0gVWMtTX19PT07MkbA4CAkPY9on/oKHmk1gPfJsM+yl2tv2V3t8+T8Gaz5F+0xcxBswsG1QIwZkzZzAYDGzcuHFBjY2FwFhP3eTkZI4dO4bZbCYkJIS+vj7q6uoQQniEvYiIiDkt6LZQsVgslJWVsXJFHGWv/C/xdc+xVatzPSlBqWEzlq2fYePld5Ghn//VXdqxY56/5RtvnNN9d3V1XbR44oU4tyimoigea5jW1lbKy8s91jDugq1xcXHodDpGRka47777+Na3vkVubq63u7ZkmJNfkIyMDN59990pLbMCyMvLY9++faxYsWJW+7Xb7ZSUlPDjH//Ys+3yyy9HVVUKCgq4+uqrJ7zebj+79HxwcHDCdlmWMRgMOJ1Ozyw3uC509Ho9DoeDsc4ker0enU43YbvBYECW5XH7c2+XJAmHw8E/TrZSoUTz64P1mAICQQg0VcX1WyYhSaDXGxBCQ9NUgu1drOxzLd/V6XRoQuDapeT6T5KQZb3LyF2AkABJRpYkJFk3ul2AJAMSsuza7uqnhGvHEpIsI+t0qOpo/0d/XHU6HZKsQ1VU17bR7bJejyTJqKqGpKoYSouRRkaQg4NxhhjQDr6BQHLVHJNBbzAiBKOf7267jF5vQHNnF0gyruZL6PQGhCZQNeHaLRKSTkanN6BqKkIDVXFi0obJLzqNKSAIRXVdHEuj/ZL1ru9DUVTX+0fbIuuNrovpUW87dwx0BgOSLKM6FRCA7IqZ3mhEIKEpyuhFh+vzDSaTKzNCUUZjIyPpTQQEBrouYsdcrEuyhN5gRFUUtDEZI5LOFQNFcSLUs2NP1unQ6fUoTgdCE55myqNjz+lwII0Ze7rRsec8Z+zpRsee4nCM2643GkGIszEAnA7Xe6t7NWT57OdIkoTBaETTVNRz+mQwGFGcyrgsGHlMnzR1/PGk0+txjumTeyy5+8RF+iTcfdp+BfrOIXQtTYh/vID1htuQo+NcNxBj+gRgMJlcSyXHbpdc3+tUvifFbsXS34Yy2Imjrxkx2IZs6cRg7SLA3kWYo5MotYso0Ue8JHDL3jk6oJsJWEQA7bo4eo2JWAKTcIStRIpagyl2LQExqzAFhbv6D4QZRr8nxUkf0NcP9NsxGF19UpSzfZJGx6qmqqjqmD5Jo31Sx489WR49npTx5z1Zp0OnG/2exn4fOj2yToficCAYs330OHOPHzd6vQEkCcXpOPvl4ToXuPs07nsa7ZN6Tp/ceypqs2NwJw9KEqahAYIPvILq1HCsXs/IigzkducF+6TMok9isj4BBKxFn5VG0A5XnxqB8p5mhmuPYWgvIKK3iFX2cqKkAdaotazprYXe/XDG5aFdpV9DR+hmbHHbCVq7k5AVmzyxElP4ns7bp3O2y3oD7mFxsvVsLCft03S/p9Exdu7Yg5mNvan2acpjb5I+jfUam3Kf0vcQ3j5MYHcHzsefp+NK1wV4fosdY8DF+6R5s09TPJ6UKXxPMz1HzPZ70o3pkzL63LFmOwHBY/o05ouaztg7X5+ki/RpZKAb+z9/zBVdj2GUVAZEMO9v+A7mnZ+jwikR9NoxQgpL0EsSg1ddz7A1DBrP9ut8Y083+j2p53xP+tE+aYqT7bEgj17XGUd/n8aKfu7tinLOb+4517Dua1BFUTCZTBe9hj33mtXP4mRgYABN09ixY8eshB29Xn/BzGZ39rTZbGbTpk0+E+OEEDQ1NbF69WqvZpcuZKF5MsaKz0IIbDYbvb299PX1UX7rrWx88UUALN/8Jvbvf39S24329nbq6urIysqa9nLyhcyKNZsRX97P8XefJiH/FySLNnbW/Bdtv/obpekPkn7Np6c9oV9bW8vg4CC5ubnLXiC9EEIISktLCQgIIC0tDVmWWbVqFUIIhoeHPbYGDQ0NqKo6QXw+d4wuZWw2K++99GfiOw6y4fhxjJLrd90uDBSFX0XY5Q+watPCqUMknE5obvY8luew8LFb59u4ceOkxRNngl6vJyoqypOQ6raG6e/vp7u7m4cffthjH2W1WpFlmX/9139d8L8N88mceDbLsswbb7wxZcP8yy+/nOLi4lnPgLe2trJixQoOHz7M/v37eeuttzhx4gQGg4G///3v3HPPPeNe/8Mf/pAf/ehHEz7noYce8vzgbtu2jVtuuYWXXnqJgoICz2t2797Nnj17eOyxx6gZs5xg7969bN++nUcffZSuri7P9o997GOsXbuWn/3sZzjGiHsPPPAAZrOZhx9+mFfsG+jQpp4Fc7lcxN+MP5/y6/3MP6qQcKJHQTfu/6pkwIkORZNwokNFhyqbEIYAHIrAqWooo9sxhiAFhGIbseBQVFRkFPQYwuLQhUQx2NmCU3GiIqOhIyR5MwZzLB2lR1E1gYYOIUnEbbsefUgk9YefRZNkQEZDYsNtX0Ox26l67Y+edst6I9GbLiEwMoGGd5/ybDeZY9hw84P0VJ+i5dh+z/aQhDWsvvJe2osP0nn6bMHQiDXbSM67haajL9FXc/Z4it2ym/iMPdS+/RjDbWePpxW5e4lau52KA49iHzh7PKVe8TFCE9dy5h8/Q1POHk/rb3oAQ7CZkqcfHhf3jL1fxqYpVL7823F92vzh7zDUWk3d238HNPTCSWBgAAkbtuFoK8PRXkagsBMoWQnRqYQZBQGOHsxqP1HSAGZpZMrfvUPo6CSCbjmGPjmSPjWYfi0AC8FYpSBi8z5M2JpsSp75+ZT6lP6hh3BaBs7fp3ce92xfLN/TdPu07obP03D4GYZaKj3bQ2NTuHpEo8LSRamwLfg+1b79GAZhwyx6SdD1sVrXyVpnBaGSlXMZFEFUimTqSaSDWLSkHFbs+QzNxw/Muk/BcasoefphhHZWSPCPven1KTRuFVc6dFT1tyyKsbdcv6fp9ikm/TKiHA1cXvETEqQeAN7XtlCS8W/EZN4woU+b11+CnH2tT/oUExPDgw8+yKlTp9i//2yf1qxZw7333svBgwfHFek+3zXsZZddxpVXXnnRa1ibzcbDDz/s92xe5LgnDmZ7g3zy5EliYmJYuXLlhOc0TSM/Px9JkmYtal8Ih8PBoUOHCAoK4pJLLvHaTf9iE5ovhhAC5+c/j9TrKmxccvfddEdHYzabx1kfFBUVsWXLFmJiYuazuT7F6bBT8tofWFv+W0+h5zaiqV37KTZe/wUCp2Bj1tHRQUlJCdnZ2VMukLlcqa+vp6mpidzc3AsWYxNCYLFYPOJzX18fiqJ4fMk91jBLTHx2Oh1UHX8d55kX2dj/LhHSsOe5at0autbcQerujxMWsfCOSa2yEvU73/E81j/77JycK4UQFBQUYDAY2LJli8/358bhcHDs2DH+/Oc/s2/fPsLCwrDZbOTl5XHZZZdx+eWXk5eXR3Bw8Jy1aaEzZ2KzJElMdVeSJFFVVTVrsbmlpYWkpCQOHz7MBx98wAcffMC+ffvOKzZPltmcnJxMZ2en56J6LjOb/+1ANXVdFlcWneS6SBOahgDPZ0mS7MqYFYI1zkrutfzVtd31aiQ0JOH6e/x24ckMlSTh2i5Gt3tee77teB57cgmFO6tYjL4ez2e4XydpGhJns6QlaexnM+7/0tjPHrdtbF/Gb3Nn70pjnpvs/ZP1ZfLPHv85EiBLS7qe5gVxC+NO9Dglw6g4bkCR9CiSHqdkREWPKhtQJCOq5BLNNdmAKhsROuPoYyOabEDIBoTeBLoANFnveqwzIulN4N6uKTCacS8hkCUJZAmhqgihnR1vkowQGsJpRVIdSKoDNAeypiBrDlBsyIodw0A3suZAJ6vojBIGzYpJGyFAsxIgbARiJVDYCJQcFw/IeXAIHb1SOP1yJMP6CCzGaOwB0WjBcUjhK5HCVmCMTiEgIhFZ1k3I8HP/Rk81a9F9rE2WtShxToaf5N4+vcxm3UyzFmea2Tza9ulmNk/ok9NB6BsvEdjXgyPMzMD1tyFMronDmWZiqrPoEzPsk+p00FtfhK3+GKaOAuIGz7BaqSFAGv8ZAP2EUG9YT495M0p8JkGrczEnrkdvME7re9LpJs8ulc7TJzFJJuZkfQLX6gdVVdGmmF3qWdHh9szk/N+TrBuz+mEK39NU++Q+zs63+sFwntUPBrudgJf+gbBYUOLisV59M5LROKVVKu59TmdFh3v7+VapTGdFx/n65MoOvvjKG8/3dM7Km4XWJ3nMKpWLrbzpKDtCxPs/ZKuzEIBWYijb+m8k5H0EvdG1EkptbiTg9RdB03Bu2oJ26Z7JzxGjfXKe0yfDaJ+Uc/pkHO2T4nSyKsy9gGx2mc2e+E7xGnZwcJDY2Fi/2LzIEUKMS3SZKUVFRYSGhk64XxNCUFhYiM1mIzs722dWDIqicOLECZxOJ9HR0aSleafw21ITmt0Iux3lox/1PFb+9jd6R/1Ke3p6cDgcBAYGEh8fv2SFvbFYR4YpO/A/bKx/jGj6AZc9XWni3ay8+gtExU+cRAFXscsTJ06wefNmr1q2LEV6e3spKCggKysLs9k8rfcKIRgZGfEUc+vr6xtXFHOstcFiw2a1UHPyTZSS/WwcOETEaLE/gF7CqIi+lohLPsHKTdnz2MqLo77wAtrf/+55PFfFARsbG2loaCAvL2/OC0X39PSQm5vL9773Pe6//37Ky8s5dOiQ598tt9zC//7v/85pmxYycyI2NzQ0TPs9SUlJs/6Bs9vtBAUFsW/fPm677TYA+vr6iIyM5J///OcEG41zme+q2w6Hg6effpoPfehDF5wJXAwoBQU4Dx8GScJ4443oLlIt1Nt4I5Zu+5Gzf48K/5pLvBZCc92kuW1K0DxCqecf4uw2pwNFcaA67a6lwU47quJEddpRnU40xY6qONAUJ5riQFMcCMWBpjoRigOhOkE9+39G/y+pTpfQqjqQNCeS5kRWncjCgU5zImtOdMKJXjjRCQX96N96FPQoGIUTA4pn6c5yxi4MDEohWORQRnRh2A1hOI3hqCYzIiAcOSgSQ0gEAeEJhEQlEB6TRFhEDNI0MniW0nE+34yNpUGvx/Hyy2h1dRAYiOlDH0Ke5oXuQsbpsNNYfpKeyqOItkIi+0tIUeowShOXNPcRRlPAeixRWzCt3EFi2iXErVh9wXHqH5feQ+vuZuiZZ3iht5c7tmwh5Oabp3WO8DOe+Rib9eWn6N3/A7ZbDgGu34aClZ8i854fEBB0NqtNGxjA/vTTYLUip6ZivOmmBf1dTzeW831d7Md7eMMS5cyZM5hMJtatW+fZ5l4y39vbe9FMxtng9p1WVRWz2YzT6WTz5s2z/tylKjS70d5/H/WXvwRAyshA/4Mf4HQ6OXbsmEe8c/s+OxyOcZnPS1V8tlmHKXvjT6RW/j8SRQcATqGjKGQn+uzPsC7rGs953J3ZuGLFCp95kC8VbDYbR48eZe3atV6xt3Fbw4zNfLbZbISGho4bo3MtPk6VjqZqWk++TFDjO2y0FoxLbOoljAJjNqbNt7J1z20u67BFgPLDHyJOnwZAvuYadPff7/N9Dg0Ncfz4cbZv3z5li15voWkan/jEJ7DZbBw4cGDCih33RK7J5K+x42ZOpoJSUi5U4sp3mEwm0tPTef/99z1i86FDh9DpdGzbtm1e2jQdhBDU1NRMOSN8oaLW1OB87z0ADLt2zbnQDN6JpeTOxnY9AhbuDaQ30FQNp2LHYbehOFxiuNNuRXHasY5YeOG5Z7jx+uuQhIbmtKE67WNEcZtHHEdxIFTHqCDueixpo0K46kAeFcR1qgNZuMRwWTgxCHdW12h+uXQ2L12Cs/no0mjuuSSjSq6s6bP/jAidASEbz2ZOayC6B5DQI4fHYFyXhj4wFENgKMagUExBZkxBoYSEmQkIDCYG8OXCpaVynC8ExsbS+d57LqFZp8N0881LSmgGV9bwmoxLWZNxqWeb3TZCVflJequOIbUVEjlQSopST4Q0SIQtH1ryoeX/wQfQg5mWgHWMhG9An7iFiNRtJK3LwBQQBPjHpTeRo6MxXHcd9X//O2pdHc5DhzDs3r3kRIy5Yi7HZm3pcXpe/0+297/BKkmgCYmT4dew4rafkJe6cXy7bDYcL74IVitSTAzG665b0EIz+I/z5cx0Vpyej8kKBFZXV9PZ2UleXp7PhGZ3YTabzUZOTg6NjY1eEc/FaF0YIcSSFJoB5J07UR99FGw2RHExakkJRVYrISEhpKWlIUkSK1asQAjhKTjY19dHSUnJOPE5MjISs9m8JMTngMAQtt36FRTlAY6+/SRhZ/5OmlJCluUQHDxE47uJNK24ifhLP0pz1xBms5nU1NT5bvaCRtM0ioqKiI2N9ZqPuiRJE4pijhWfKysrGRkZITQ0dJzv83wlTAz0ddFU+A5K3WESeo+TqjXiGTUSdBJJbcROjJtvpU+YiYyKYYMPi5d7G6FpHqEZQLriCp/vU1VVTp8+TUpKypwLzQBPPfUUhw8fpri4eFJrKEmS/ELzOSy+dQfT5Atf+ALf/va3ueyyy0hMTOShhx7i7rvv9hh/+/EtWkcHjtdfByHQbdmCbg6N4/3MDlknY9IFYjJNNN232+3Y5DdYv33Pojypqg0NOF56CTSBLjAFw2WXLcmbiuWKcvo0cmEhAMZrr0VOSJjfBs0RpoAg1mVeBpmXebbZrBbqyvLpq3YJ0FGDpaQoDURJA0TZ8qE9H9qBU65MnnrdCrqD1+GIXE+4OkhHUxVJqZuQl8AN5Xyicxc8liTU4mKkoCAMOQunyIufswhNo+TIKyjv/Q+ZtuOsBpCgIHgXETf9kOy0ictahariePllRH8/UkgIpr17kfwrAvwscfR6/TiRt6GhgcbGRnJzc71WsGkyqqqq6O3t9Syh1ul0FyxUOFXcYjOwpK8J9X/8I8q99wKgff/7qF/7GtvO8buWJImgoCCCgoImFZ/PnDmz5MRnvd5IxrWfhGs/SVnpCQbe/wNbet9gJa2sbP4DPP0HRqQNDKy7nYHEGMKjl8e15UwoLy8H8Ll4GhAQQEJCAgmj1/l2u90zRmtqarBYLAQHB3uE54iICJ/dtw70ddFY9A5K7fvE9Z5gtVpPwhj7TVVIVBjT6Eu4nKitN5K8YTsZskxZWRny4OC4FSKLgnOcC6Q5yPSvqqpCr9fPy6qChoYGvvnNb/KnP/3JM978XJwlLzY/+OCDtLW18dnPfpaRkRH27t3Lb3/724u/0c+s0YaGsO/fD4qCnJLiz+Tys2DQpaRguOYanG+8gVpYiBQcjGHHjvlulh8v4TxyBBOg37kT3WK7ePMyAYHBrN++G7bv9myzjQxTU3qc/roC6DhD6EAlSc46wiQLq7RGVg01wtBbXCoDjz2OTRho062gL2gldvNq9DHrCU3aSMLqDMyRC69gyULGcOmlcOQIytGjSEFB6L2w7NuPdxjo7aTs9T8RW/UUm7V6wHVzWBR6OaFXfZNt2y6f9H1CCJxvv43W0gJGI8a9e5FCLl5gyo+fxc7YzOa2tjYqKyvJysoixIfjv6GhwVNszF283Rtis6Zpns/wVTHDhYIUGIju299G/bmrqPy2xx5Dt2vXhd+zzMTnlLRsSMtmZKif4sNPE1S+jy3O02yhAiofRq34OeXGNPqTriIu61YSU73jF74UaGlpobOzk9zc3Dn//k0mE/Hx8cTHxwMu2xO333N9fT2nT58mKChonPjsPo9MB4fDTnPFSQZqjqFvLyB+qJQU0cI4CVKCBimJtogdSKt2krLjelZFxLBqzEva29tpb28nLy9v0Z13tDFFhgEkH9uXdHV10draOi+xUlWVL3zhC9x+++3ccccdc7rvxc6SF5slSeKnP/0pP/3pT+e7KdNGr9ezd+/eRWl8LxwOV+boyAhSVBTGG26Y1+WkizmWC5GlEE/9xo1gteI8fBjl/fddws+mTXPfjiUQy4WC3NfHdaGh6IVAt3kz+u3b57tJC5KAoBA2ZF0JWVd6tglNo72llo6qU4w0FWHoLiNyuJIkrZUAyUmqVk/qcD0MH4IWoND1vj5C6dbFMRSQgCNkBZiTMUWvIjQulZiktdP2MF+quI/zgK1b0ZxOlBMncL7zjuumfx6spRYz3jxnOuw2yt7fj6PwH2wZOEjeaNFNmzBQHHMzK274JtvXXHhCQDlxArWszFWT4oYbkGMWzwSM//dn+eINGw29Xo+qqnR3d3PmzBkyMzN9urTZLWhnZ2ePE7RnKzYvl4zmsXQkJxMYFUVwTw9SVxfa++8j79w55fefT3zu7e2dID5HRkYSERGxKMXnoNBwknLvpNC0Bn1yLP2F+4lrepm1ai3pzhKoK4G6X1EvJ9MamYdx7R5WbbuSoNDw+W76vDAwMEB5eTmZmZk+Xd0wVYxGI7GxsZ5Cjk6n0yM+NzY2UlJSQkBAwATxeex5wGYdprWqiMGGIugsIWKgnFRnFZsnKdLdKCXSGr4DadUuVmy9kpjYpPNaMY6MjFBWVkZ6evqCiNV00d591/O37GMB1m63U1paysaNGwkKCvLpvibjN7/5Dc3Nzezfv3/Z/EZ4izkpELhY8RdCmRlCVXEcOIDW0ABBQZg+/GHk0NCLv9GPn3nA+d57KKdOuYSCvXvRrVo1303yMwPE0BC2p58GiwU5ORnjLbcgLbKbmoWI4nTQ0VRFd30J1rZypN4aQobqiHE0EUvvRd9vFUb65HCGdBGMGKNwBEQjgmKQQmMxhMVhCI4kIDSCgNAIgkIjCA2PxmiafpaJNxGahtPpwGa14LBZcFhHcNiGcdpGcNpHUO0jKPYRVKcVYR9Bc1oRTivCaQPF5irSqo73pZdHferdBVplhw2d5nC50Ov0IJ//4lVDhyobUEd96DXP/40InRGhD0AYQ5BMIUimMHRBYRgCzRiCwjAFhxMYEk5wmCu2fjsUsAz1U3XsVRynX2DjwGHCsHieq9Otomvdh9lw7eemlLWvVFTgfP11AAxXXIF+yxaftXsh4L8uXjo4nU6PwDpTWlpaaGhowGKxkJ6e7vFR9QU9PT2cOnWKzMxMYs6Z0Ono6KCmpoZLL730PO8+P+6CgEKI0dosS19I6Ovr49SpU2zdvBnzgw96tuv//nckLwk554rPfX19i1J8tlgsHD9+nA0bNowb353NNbQcf5GwxjfZaC/GMKY4s1PoqDaspzc2l4DUS0jafCmh5uj5aP6c4i6emJyczKpFci+lKIpHfO7p7qK7tQ6dtZNgayvh1gbi7XUkay3opIly2SDB1Js2MhS1FVNKFklplxIWFTel/aqqyokTJ4iIiFhUPs1u/v/snXd8W9X5/99X09uy45E4cfZyEmd6ZJEBYRUClFVmC2W3UGihDZSy+oUWym4plJbSBb+WWcpeWZCQ2LETj9gZduwMj3jJtjw17j2/PxyJKHESD1mS7fPm5RfRlXTvOY/Ovbr6nOd8HtHWhuv73/c8Nvz+9yhuuzhfH0sI8vLy0Ov1pKam+v0aXVhYyOmnn84nn3zCsmXdr3CTnBiZyhDEOBwOXnnlFW688caAmdv3FiEEzo0bu4RmgwHz6tVBITQPxlgGM0MpnoYlSxBtbah79uD4+GPMF1+M7sjyK38wlGIZKITDgf2DD3C0tvKvtjZuWrVKCs395OhxOXriTEZPnHnca9pamjhcXoztcBn2hgPQdAhTayWR9mpGuGqIxUao4iBU1IKrFlxAO5xKo+4URlqVcDqVEFyKCadiwqUzoXr+zGg6E99WbP22gCieoqEKinCh01womuvIv53ohAv9ke16XOiEC6NwYNIcmLFjFnbMODApggE/G93N1478DTCqULAqkbToomjXR9NptOA0W1BDRqCEx6ILj8MUFU9YzEgiYkYSHTeSsPCooMtM7+01s7Ojjf07t9BYvI6oyq+ZYi9i7lHCQAMWSuNOJ2bxD5gydxkTethftbIS55dfAmCYP39QCs3y+0fSH5xOJy0tLccJcb7GZrOxY8cOZsyYcZzQDF22F33JbHYLzZqmDdmCgMfS1tZGfn4+06ZNIy4xEe2JJ1DXrAHAde21GN95xyfHOTrzecyYMceJz5WVlUEvPjudTvLy8hg9evRx4zthzCQSxvwM+BnW5nrKsz+B8q9Jbs5mNDWkuHZB1S6o+jtshv3KGGqiUlGT5mOZkMaoyamYQ8ID0q+BQAhBYWEhUVFRjBs3LtDNOSFCCGxN9dTtL6a1ag+ivoQQWzkj7AdJUasxKa7j36SAVURyyDiBluipGJJmEzd1EaMmpDBZ17fxWlJSgqIog8+n+QiioMB7wwBe/w8dOkRraysLFy70+zW6s7OTG2+8kTvuuIPTTjvt1G+QHIcUm4MYIQR1dXWDqkq4KzcXdefOrizRc85Bl9izGb6BZjDGMpgZSvFUFAXjqlWIzk60Awewv/8+5ssuQ+enKrdDKZaBQKgqjk8+QdTXI0JDaWhqAima9JuejMvwSAuTZi+G2d1nk3W0tWCtOUhLfRXtjYdxNh9Ga61F11aLqbOeEIeVELWNMK2VcNFOhNIBQIjiJIQmEHT9AfS/9lPPOOY+VhMKnZiwKyYcmHAoZhw6My7FjEtnxqU3o+pC0PRmVEMoQm8GfVfGMXoT6I0oBhOaYqB4915mzZ6HwRyKzmACnQGxtwTR2goGI/qZM1C6KVwjVBeasxPNaUeoDoTL7vnD5QCXHZ2zFb2jBb2rDZOrDZPaRojWTqhoJ0x0EKbY0SuCWGzEajbQKsBJl/h/EjqEiWYlmhaDhXaDBYc5FjU0DsJGoIuMxxydQKhlJBGxo7DEjSQsItp3n8UJONnYbLbWU70vH9uhIkTVDmIbCxnnKmP6UeIyClQpCRyKW0bU/EuZmn4mmb20kdCsVhwffgiqim7SJAy9WH4eTMjvn+FLf3+0d3Z2UlZWhsFgGNAsxvb2dnJzc5k4cSKjT5A557bz6A1HZzQPF6HZ4XCQl5dHUlISY8aMAUA3eTLa0qWITZsAUP/1L/TXXuvzY3cnPre3t3uynisqKnC5XB7P50CLz27xNCws7JSCYGR0HLPPvBboilv5oRIO53+B/tA3JLUWMUYcZryoYHxzBTR/ArvAJXRU6JKoD59MZ8w0QkanYklOISF5MgbD4LuHLS0txW63M2fOnICfSy3NDTRU7qO1Zj9O6wGU5kOEtFUSba8mXq1hlNJOt+XdlK5khyp9EtawidhHpGBKmklY4lQ0XQi25maamprQ6/WIVhVXRSUxMTFERET0qs81NTVUV1cPSp9mN1pu7rcPEhMH7DNvbW2lpKSE+fPnYxxgT+hjEULwyCOPYDAYeOSRRwI+rgcrUmyW+AzXnj24vvkGAOOyZegDUClUIukLil6P6dxzsf/3v4iaGhz/+x/mSy+VRZ6CHCEEzg0bvl1Jce658OqrgW6W5Aih4ZFdGdHdZEV3h+py0WprpM1mpcPWgLOzDZejA9XRiebs+r9wdqK57F2WFQgQRynSouuxABAait4AOiOKvusPvRGd3ohiMKIzmNEZDOj0JvSmUAzmMIwhYZhCwjCFhGMKjSAkNAyTKYQwnY7+Liy22+188fjjXLv6Nq9K6GJ5J/Z33kE0NKDUK5gvuxBlALz7HPZObA01tDQepr2pHrutFmdLPVpbPUqHFUNnIyZHI6HOJiLVJiyimRDF2ZWZTh0jXXVdmemdQPOJj9MhTDQp0bQaLLQbY3CYYlFDR0B4HLqIeMzR8YREJWAKi/w21uZQzKHhmEPCUHQ6hKahCYGmqQhNw+HopKOlifaWJuxtzbTbGohTK8h987cobTUY22oI66whwVlBHE0cJ3cr0EA0B8Nn4Ri3nDELzmP0xJkk9fGHg2hr66pJYbejGzkS01lnyR8hkmGFw+EgJyeH6OhobDbbgB9n5MiRTJgw4YSv621m83DMaNY0jfz8fCIiIo4TT/V33YXriNisvfceuqVLUU4Sb1+gKArh4eGEh4cHpfhcUlJCZ2cn6enpvR4ficlTSEyeAnRZlFTVV1NZtBnHgW1EWfNJduzDorR2CdCtFdC6AQ4BW7ssOGp0CTSYRtMWMRZhmYApbjxhcclYEscSHZsYdAJlbW2tp2jnQPn/C02jraUZW0MVrQ1VdDZW4WquRmmtwdRRS6i9nihXPbFaI4lKBydMdTvyUdYSS60pmdaI8aixkwlNnErM2BnEj57ASJ2eE61v1TQNm81GY2Mj9fX1lJaWoigKFovFM04jIyNP+Bm1t7dTXFw8aH2aoeuzEOvXex7rr7pqQI6jqiqFhYWMGzduQGsBnIivv/6aV155haysLK/7dknvkGKzxCeoVVWe5aT6uXMxzJkT4BZJJL1DMZkwr16N/e23EU1N2P/3P8yXXILShyrFEv/gyslBLSryFOZyHSkAIhmc6A0GomPje+SVO1RQQkIwX3AB9rfe6rruvP8+5u9+F8XH2fkmcwhxSeOIS+rZ8lahabS2NtNcf5hWazUdTTU4bbW4WutR2urQdzZgtlsJczYSoTYTI5oxHyVO4xanOzipON0dCqA/8gdgAo6d9purA/Z1//5aYqk1j6PVMg3juHSSZp7GyOQpjPDBD3ThdGL/8EOEzYYSHY3p/PMHvAK7RBJMqKrK9u3bCQsLY/LkyWRnZw/IcVwuF7m5uURFRTF9+vSTCn56vb7H/tPDUWgWQlBUVISmacyaNeu4PiuKguHVV3H98IcAuO65B8N//uPXa1swic9VVVVUVlaSkZHhk2xKS9woLMsvBS4Fur5fD9VWUFu6g87KQozW3cS1lZKkdhVkHiOqGWOvBnsONOD1XWcXRup1sTQZ4mk3x+M0x6CFxKKExWCIiMMQEUdo9AjCLQmERVgICYtEP4AFYNva2igqKmLmzJmEh5/cFkTTVDpabbS3NmFvs2Fvb8HZ3oyrswW1vRmt3Qodjeg7rRgdzYQ4mwhTbURqNqJFC+GKyinv8o8MbXfxaltIEvbwMWBJxjRiHFEjJzBi9CQiwiKPu6/oCTqdDovFgsViYcKECWiaRktLi2eclpeXI4TwEp+joqI8E2IFBQUkJSV5ChYORkRJiddjZcGCATlOaWkpOp2OiQFIXmxqauLmm2/m0UcfZdaskxeJHoy4axQc+++BQBYIPAmBLoSiaRplZWVMnDgx6GYxj0ZrbMT+1lvQ2Ylu0iRM554bdB6PgyWWg4WhHE+tublrPLe3o0tKwnTRRSgDeKM2lGM5kLh278b5+ecAGFeswDB7toylD5Gx9C2niqdmtWJ/++2u79Fx47pEzCDyrjwVQtNoa23GVl9Ni7WajqZanM01qK310F6PoaMek6PxiDhtw4wdk3BgxoFBObVIpAqFNiWMdsLo1IXSpoTTGToSZ/hIlMiRGCyjiRo9lVGTUomIih2wPjo+/hitrAxCQvxqtzRQ9PY8D/R9scR3qKqKy9WNP+lJ0DSNHTt24HK5SEtLw2638/XXX3OWj7P7NU1j+/btaJpGWlraKcdmR0cHGzdu5Oyzzz5pO4QQaJqGpmnDphggwL59+6iqqiIjI+OkGXra5s2ozzzT9SA8HOM//+mnFp6aY8Vnq9U6IOJzU1MTubm5zJ07lxEjRvio9T1D01QaDh+ksWIv7TWlYC3D3HKQSPthYtV6YkUzum4K1Z2KdmGmQwmhUwmhUwnDoQvFqTejKUY0xXCk6LABoTMiFCNCb0Ao7t89364gUxDfWpshQHPhaLdhUjRMOoFBONBpDgyaE4NwYBBd/w8VHYSKDsIVe79j1CbMWHWx2AxxtJvjcIYmICIS0UePIsQyioi4MVgSkgmLCMz3kxCC1tZWzzhtbGxEVVUsFgsulwuXy+WzSYxAof7tb2gffuh57Cuf96Opr6+noKCAzMzMU05i+BohBDfddBO1tbV89tlnQeUj7yv+8Y9/sGjRIqZOnQoMrOAsxeaTIG+qT41ob8f+5ptdWT6JiZgvvlhm+UgGPVpdHfZ33gGHI2gnUIYzakUFjvfeA03DMH8+xqVLA90kiaTfaNXV2P/7X3C50E+fjvHMM4eFGOJy2OnsbMfe0QZwJNtQh6LTo+gUjAYTIWERAb8GOzZuRM3PB70e03e/i34AC+IEK/K+eOjQW7HZ7WHb0tLiEUscDgfr1q3jrLPO8tmkZHfHORXudpx55pknFQY0TcPlcqEoyrCZRK2qqmLPnj2kp6cT0QNrONcvf4nYswcA3bXXor/oogFuYd84lfgcGxtLdHR0rz7nzs5OsrKymDBhAmPHjh3A1vcNh6OTxpoKbLUH6bRW4GquhnYr+s5GjI6mI5nAzZ5MYKPir2IXvcMp9LQrIUcmj8Ow60Jx6MPpNFlwmSyI0FiUsFh04SMwRYwg5EimdmRMPCGhg8veUAhBW1sbBw4coLq62uMvHxUV5ZkksVgsg0bQFJqG67LLPI91N9yA/jvf8ekxHA4HW7ZsYfLkySf06R9I3nnnHe68807y8vKC8jrQX37yk5/wwgsvcP3113PNNdewdOlSz/fsQIjOfrPROOOMM7jyyiu58cYbu32+vr6ejIwMysrK/NWkoMdut/PMM8/ws5/9LCi9YoTLhcO9nDQqCnMQLycN9lgONoZ6PHXx8ZjOPx/H//6Htm8fzrVrMa5aNSDCz1CPpa/RrFYcH30EmoZ+8mSvwlwylr5DxtK39CSeulGjMH3nOzg++AB1924wmTAuXz7kBWeDyUyEyUxEVM+yhAMxNl07dnQJzYDprLOGjNAsz3NJT9mzZw+NjY1kZmZ6fpi6BRKXy4XJR9Y/e/fuPe44p8LdDlVVTyjaaJqGqqrDSmi2Wq3s2rWLuXPn9khoBtA/+qhHTNL+9S90aWkoR4oJBhMns92wWq3H2W6cSnxWVZW8vDzi4+NJTk72c296hskUQmLyZBKTJ5/ytQ5No9XRQWdbCx2tNhwdLTjabTg7W1E7W1Ed7QjVCS4HQnWB6gDNCaqz6/+aeyJK6fpz34coCqCjs7OTToeTqNh4dMYQMJjQGUJQjOYjNTHM6Iwh6E1mjKERmMOiCQmPIiwyGpMpFKNORzQcX2NhiOG+3tTW1jJ79mzi4+M947SpqYni4mLsdvtx4vNAeV/3F1Fa6vVYd9ppvt3/Ecsfi8VCUgDus6qqqrjzzjt54YUXhqTQnJ+fzxdffMEdd9zB2rVrKSgo4MYbb2T16tUkJSWhKIrPBWe/jeT169ezefNm3n33XV555ZXjBpCqqhw4cMBfzRk0OByOQDehW4Sm4fjsM7TDh8FsxnTBBSh+XubQW4I1loOVoR5P/ZgxmM45B8fHH6Pu2gVG44AJP0M9lr7CqzDXqFEYu1m6K2PpO2QsfUtP4qkfPx7jmWfi/Pxz1IICFJMJ4+LFfmjd4MKfY1MtLcV5pHiWYckS9McU1xrsyPN8eNKbe5ny8nKqqqrIzMwk5Kg6Fm7hzldi8/79+6moqGDhwoVexzkV7nacqEig2z7j6NcOddra2sjPz2fatGm9soNQdDoMf/4zrptvBsB1551+92/uC/0Rn90Cl16vP6U/+GBB0ekwh4RjDgknesSJyt31DavVyo4dO0hLSyM6eqjLxf1D0zQKCgoYNWqUx6f52HHa2dnpydDfvXs3nZ2dREZGeonPwWK7Ib75xuuxEhnp0/1XVFTQ0tLCokWL/H4eaprGrbfeyjnnnMOVV17p12P7i8bGRlJSUvjFL37Bb37zG48vdWFhIddccw0LFy5EURTKyspwOp1Mmzat38f067TJ+vXruf/++5k5cybPPfccP/jBD/x5eImPEELg3LABbd++ruWk552HLnZgPBIlkkCinzTJW/gxmzEuWhToZg1LhMOB/f33u1ZSWCxdnrZBOvMvkfQHw/Tp4HDg3LABV05OV4ZzWlqgmzUsUSsrcXz+OQiBfvZsDPPnB7pJEolfqaioYN++faSnpx/nnakoimdZeH+prq6mpKSk2+OcCkVR0Ov13bbDXRBwoIsgBRMOh4MdO3YwZswYxvQhK1kZMQL9HXeg/uEPALiuuGJAfFkHkhOJz1ar1avgoMVi8VgdZGZmDpvJiL7S2dlJQUEB06dPl0JzD9i7dy+Koni8cY9FURRCQ0MJDQ31JGIeLT7v3buX9vb248RnX60k6Q1CCLQPPvA81l19tU/339raSklJCXPnzg2IuP7yyy+zZ88e3nzzzSH7XbFs2TKSkpI89iSvv/46r776Kv/3f//Hzp07ufHGG8nMzOT73/8+l19++eATmydNmsS6dev485//zJ133sk777zDn//8Z0aO9O2Mm2RgcWVloe7cCYqC6eyz0Qfh8iqJxFd4CT/btnUJPwNUeVfSPR7Lnro6CA3tWkkRGhroZkkkA4Zh9myE04lr82Zc33yDYjRimDMn0M0aVmh1dTg++ABcLnQTJ2JctmzI/gCRSLqjtraWXbt2MX/+/BMKSycSeXtDQ0MDO3fuZO7cuVgslj7to7t2uIVmTdOOeMEP/fPXbQcRFRXF5Mmntls4EboVK9A2bULs2AGA649/xPDjH/uqmX7naPE5OTnZIz4fOHCAqqoqDAYDmzdvxmKxeBUclOLzt6iqSn5+PgkJCQHx0h1s1NTUUF1d3etJjJCQEEaNGsWoUaOALrsrt/hcWlpKW1sb4eHhnnEaExPjFxssUVzs9Vh3xhk+27emaezcuZPk5GRiA5DAuGvXLh566CH+97//BeT4/kKn03kmPpxOJ0ajkR/+8IesWLGC22+/nf/7v/9Dr9fT3NzMT3/6U58c028FAnU6HYcPH/YsIaioqODWW29ly5Yt/P73v2fVqlUkJSX5ZHbcVwS6EIqmadTX1xMXFxc0X3auggKcGzYAYFyxAsPs2YFtUA8JxlgOZoZjPJ25ubg2bwbAuHIlhtRUn+x3OMayNwhNw/HJJ10rKUwmzBdfjO7I98ixyFj6DhlL39LXeDq3bOma5AKMq1ZhmDFjoJo4aPDH2NSamrC//Ta0t6NLSsJ00UVDciVFb2MZ6Ptiie8QQpzUQqWxsZGcnBxSU1NPmhT01VdfMWPGDOLi4vrUDpvNRnZ2NikpKf0SsDZu3EhqaqpHKDg2o3k4CM3u4oqdnZ0sWLCg30XHhBC4Lr3U81j/y1+iG0LJFi0tLWzbto2ZM2eSkJDglfnc2NjoyXyW4nMXxcXFtLS0kJ6ePqzj0BPa29vJyspixowZJCYm+nTfDofD4/nc2NhIS0sLYWFhXuJzb2yIeorr+ecRX33leezL1Q579+7FarWSkZHh97HlcDg4/fTTWbZsGc8++6xfvisee+wx/vOf/1BWVkZ0dDTf+c53ePzxx/v8PdoT3JOuR+O2mHJ/V1xzzTX8v//3/9i+fTtz5871yXEDduc8ZswYPvzwQ1577TXuuusuUn0k3AwlFEUhOjo6aG6Q1NJSnBs3AmDIyBg0QjMEXywHO8MxnsYFC8DhwLVtW9eEi9HYlfXcT4ZjLHvKcZY9559/QqEZZCx9iYylb+lrPA0LFyKcTtS8PJxr16IYjUPOM7i3DPTYFG1tON57D9rbUeLiMK1ePSSFZpDnuaR7WlpayM3NZdq0aadcfdofG4329nZyc3OZOHFivzMlj85sHo4ZzQD79u3DZrORkZHRb6EZjtikvPYarmuuAUD9zW9QXnkFJaZnxVyDGYfDQV5eHuPGjfOIgcdmPre1tXmE50OHDg1r8bmyspLa2loWLlw4bPrcV472afa10AxgMplITEz07NvpdHqE54MHD1JUVERISMhx4nN/roOio8NLaNbfeWe/++GmoaGBioqKgNjYCCH47W9/i8Ph4PHHH/fbd8WWLVtYs2YNaWlpNDU1ceutt/K9732PtWvX+vxYTzzxBFdffXW3lkpHW1DV1tby4Ycf8sADD/hMaAbw2yc6bty4br/4rrnmGoqKioiLi8NPSdaDBvfAD4biLWpFBY7PPuvyLZw1C0NmZqCb1CuCKZZDgeEaT8PChehnzwYhcH7xBa69e/u9z+Eay57g2rKlV5Y9Mpa+Q8bSt/Q1noqiYDztNPQzZoAQOD79FLW8fIBaOTgYyLEpOjux/+9/Xd7wUVGYL7wQxQ/LUwOFPM8lx9Le3k5OTg4TJkxg7Nixp3x9X200HA4HOTk5jBw5kgkTJvSlqV7odDpPJvNwFJorKys5dOgQ8+bN86mfqxIaiv6JJzyPXTfeiAiiVch9QdM08vPziY6OZuLEid2+RlEUIiIiSE5OZvbs2SxbtozMzEwSEhJobW2loKCA9evXk5ubS1lZGY2NjZ4ilEON5uZmdu/ezezZswckY3aocSqfZl9jNBqJj49n6tSpZGZmsmLFCqZPn47JZKKiooLNmzezadMmdu7cSUVFBW1tbb3W3MSRlb1uFB/VL3I4HOzcuZOpU6f22qvfF2RlZfHCCy/wz3/+069j+8MPP+Saa65h+vTpLFy4kAcffJB169bR3Nzs0+M899xz3HfffcybN4//9//+n2f7sZ+/Xq+nvr6ejIwMHnnkEZ+2wW+pGuUn+XGUkJDAm2++6a+mSHqJVlOD48MPQVXRTZqEccWKYXPzJpEcjaIoGJcvB6cTddcunJ99BoDBTzcUwwnXjh1dxdHosi3R98N7UCIZzCiKgvH008HlQt27F8dHH2E67zz0PhBoJN/i8Yavr4ewsC7rjAD8+JFI/EF39/F2u52cnBwSExNPKMIdi8FgwOVy9erYLpeL3NxcoqKimD59uk9+U7hFb03TEEIMK6G5oaGB3bt3M2/evAERbHSTJyOuvhrt9dcBcF15JcZB+rtdCMHu3btRVZWZM2f2eIy4xWe3AH1s5vPBgwdRVdWT+RwbG0tUVNSgzwJ2OBwUFBQwadKkIe1l6yv66tPsSwwGA3FxcR5LBlVVPZnP1dXV7NmzB4PB4JX5HB4eftJzQT2qMCDjx6P4oICfEILi4mIsFktAPMBbWlq48cYbeeCBB5gf4OLPNTU1hIaG+tR7u7a2ljfeeIN7772Xzs5Ovv/97/PRRx/x8ssvExERcZytRmpqKp9//rnPju9maK4LlPgMra4O+3vvgcOBbvRoTGefjTLIvzglkv6gKArGI0URpOA8MLiKinB+/TUAhkWLMMyaFeAWSSSBRdHpMJ55Jmgaamlpl+D8ne+g76EgJDk5QlVxfPQRWlVVlzf8hRei62OhMolkMHK0AJySktJjEa63mc2appGXl4der2f27Nk+E4T1ej0ulwtVVYeNRzPgybJNSUkZUDFQf/HFiKwsRGkpqCqup57CcM89A3a8geLQoUPU1dX122pkOIjPmqZRWFhIVFQU48aNC3Rzgp729naKi4uZMWMGYWFhgW6OB71ez4gRIxgxYgTQJT7bbDYaGxupra1l79696PV6L/E5IiLCcw0VlZVQUfHt/m65xSftqqysxGazsXDhQr9fr4UQ3HfffYwZM4Z7Anwda21t5dlnn+XHP/6xT7OrGxoaWLp0KatWreL0009n+fLl3HPPPaSkpPDXv/6Vs846C/jWIic1NRXDAFjGSbFZckI0q7VLaLbb0Y0cOaR9CyWS3qDodFJwHiBcu3bhXLcOAMO8eRjS0gLcIokkOFD0eoznnAOffYZaUoLj44+l4OwDhKri+PhjtAMHwGDAtHo1uvj4QDdLIhlwFEXxFAjavn07RqOx1wJwbzKbhRDs3LkTu93u88w/nU5HW1sbmqZh9EHW3WDAbrezY8cOkpOTSUpKGvDj6R9/3FMwUGzZgvr55+iPCBaDgYaGBkpKSliwYAGhoaE+3ffJxGer1eolPsfGxhITExP04vO+ffuw2+3MmTNn2Eze9JWB9mn2JUcLy9DVdrf4XF9fT2lpKYqieCZKEj7+mKOvqIoP6oa0tbWxd+9e5syZ41Pbn57y0Ucf8c4777Bjx44BEVh7isvl4qqrriIpKYnHHnvMp/tOSUnhhz/8ISkpKQCsXr2aWbNm8etf/5pzzjmHn/zkJzz22GNccsklTJ8+nb///e8+Pb4bRUij5BMS6Krb7krRJpPJ7xd5rakJ+zvvQFsbSnw85u9+F2UQ+zQFMpZDERnPLoQQOL/8EnXXLlAUjGef3WvBWcbyW1x793YJ90KgT03ttWWPjKXvkLH0Lb6Mp9A0nEcEZ3S6YSc4+zSWqorj00+7ipAeEZr1yck+amnw09tYBvq+WOJbHA6HJ9O4s7OT9PT0Xv/w3rVrF4qiML0HBZP37NnD4cOHyczM9GkGlxCCgwcPUlZWhtPpJDo62iPoDdUibqqqkpOTQ1hYGLNmzfLb97RwOHBdeaXnsf6JJ9ANApuz9vZ2srKymDp1akCW7B8rPjc2Nga1+FxTU0NxcTEZGRkB8dIdbOzevZumpiYyMjKC5jPsK5qm0dLSQmNjI82HDzPjySc9zzWddx7Giy/u11jVNI3s7GxiY2P95mt9NDU1NWRmZvLEE09w/fXX+/34bjRN45prrqGkpIS1a9f69J5KCOH1nXC0ZUZraytvv/02999/PzabDYPBwP79+4mOjvbZ8Y9GpqkGMUIImpubiYuL8+uPfc1mw/Hf/3YJzSNGDHqhGQIXy6GKjGcXiqJgXLUKOCrDWQgM06b1eB8yll2opaXfCs0zZ/bJG17G0nfIWPoWX8ZT0ekwnn02KMq3Hs7nnjtsfM19FUuhaTi/+KJLaNbru3ywh5HQDPI8H+64PTNbW1vJzMzsU4aXXq/vUYHJ/fv3U1FRwcKFC30uNKuqSlJSEklJSXR0dHjEPHc2qTuLLzY2lsjIyEEvBrkzxHU6HTNmzPDruauYTBheegnXbbcBoK5Zg/K3v6EE8eST0+kkLy+PpKSkgAjNcOrM5wMHDgSN+Nza2kpRURGzZs2SQnMPCAafZl+i0+mIjo4mOjoadedOji57WZ2aijUvz8sixj1We2pLs2/fPgAmB+CeVdM0br/9dpYuXcoPfvADvx/fjRCCG264geLiYtatW+fzyftjvxN0Oh0ulwuDwUBERATXXXcder2eH/zgB3z88ccDJjQDDP4zYgjjdDp56aWXcDqdfjum1tyM4913ES0tKBYL5osuGvRCMwQmlkMZGc9vcQvO+hkzQAicn3+Oq6Cgx++XsQS1vBzHp592Cc3Tp2M8/fQ+/XiSsfQdMpa+xdfxVHQ6jGedhX7aNBACxyef4Coq8sm+gx1fxFJoWteqlL17v80OH4aelPI8H96UlpZSW1tLWlpan5cyGwyGU3o2V1dXe+wLfCleuYVmTdNQFAWdTkd4eDjJycnMnj2b5cuXk5GRQVxcHM3NzWzfvp2NGzeyY8cODhw4QEtLC4NxgW9paSktLS3MmTOnX77DfUVJSED/q195Hruuvx4RpNcQtzAfEhLCFB8s//cVbvE5OTmZOXPmsHz5cjIzM4mPj8dms5GXl8f69evZvn075eXlNDU1oWnaqXfcT1wuFwUFBSQnJ5OQkDDgxxvsBKtPsy8QquopCgqgzJjB7PR0z1hNSEjweMZv2LCBnJwc9u3bR0NDwwm/E6xWK4cOHWLWrFkBEeb/8Y9/sH37dl5++eWATgzceuutrFu3jn/96184HA4OHz7M4cOHe1X/oDvc76+rq2Pjxo385z//YePGjQBek8kdHR08/vjjXHzxxZxzzjn9OuapkJnNEg9aU1OX0Nza2iU0X3yxrMQukfQAT9FAgwG1oADnhg0Iux1DWprMFjsFaknJt0Lz1KkYV62SMZNIeoCnaKDRiLpzJ861axGdnRgXLAh004IaoapdNiSlpaAomM45B/2ECYFulkTiV4QQ2O120tLS+uVfe6oCgQ0NDezcuZO5c+di8WHRTbfQLIRAp9N1e9+gKAqRkZFERkYyduxYhBC0tLRgtVqxWq3s27cPnU7nlfkcHh4e1PcgFRUVVFZWkp6eHhCvUze6efMQV16J9u9/A+C64goMb78ddLErLS2lvb096O0Njs58do/VtrY2T5b+gQMH0DTtuGxSX/ZJCEFRURFmszkgWaeDDXcBxcHg09wXxPbtcNSqFf2R1QzdZem3t7d7imNWVlbicDiIioryjFWLxeKZ+JkyZQoRERF+709paSn33Xcf//73v4kPcF2OP//5zwDMnj3ba3t5eTnjx4/v0z41TfNMPl5++eVYrVYSExP56quvuOWWW3j++ec9r62rq+Pyyy/noYce6lsHeoEUmyXAkWKA774L7e0osbFd1hlSaJZIeoyiKBiXL0cJCcGVnY1ryxaw2zEsWRJ0N9/BgmvXLpxffvmt0HzWWShB/GNAIgk2FJ0O48qVKGYzrtxcXJs3d113Fi2S151uEC4Xjk8+QSsv78poPuecYWM/IpEcjaIopKam9jtbUq/Xn7BAoM1mY8eOHcyYMcOnP+7dRQ3dPpQ9vdYpikJUVBRRUVGMHz/eqzBWXV0dJSUlGAwGj/AcGxtLaGho0FxL6+vr2bNnD/Pnzw8KewP9pZci9u1DZGcD4LrxRox//WuAW/UtVVVVVFRUkJGRMeiKRgZCfD5w4AA2m43MzMygGfPBzN69exFCBMR32B9ob73l9Vg5QRFSRVEIDw8nPDycMWPGIISgs7PTIz7v3r2bzs5O9Ho9RqMRs9mM0+n06znpdDq56aabuPrqq/nOd77jt+OeiIFYUeM+92+99VY6OzvJycmhsrKS+fPnExcXB3RlNIeGhjJ27Fh++ctf+rwN3SHF5iDHH7PWWl0d9vfeg44OlLi4LuuMIbYUBPwTy+GEjOfxKIqCceFCFLMZ59df49q+vSvT8PTTTyqiDsdYuo5kgAPoZ8w4ZYx6ynCM5UAhY+lbBiqeiqJgXLIEQkJwbd6MKyen67qzYsWQnbzpSyyFw4Hjo4/QDh3qKgZ43nnD0jrjWOR5PnzxhaB0IhuN9vZ2cnJymDhxok99co+2zuiN0NwdOp0Oi8WCxWJhwoQJaJpGc3MzVquV6upqdu/ejclk8gjPMTEx/coC7w8tLS0UFhYyY8YMYmJiAtKG7jCsWYPzxhuhsRGamnA98QSGNWsC3Syam5vZtWsXc+fODQphvr90Jz63trZ6BL3+is8NDQ3s27cv4Bnzg4Xa2toh5dN8LFpJCeKItzKA/v77e/xeRVEIDQ0lNDSUpCMC9f79+ykrK8NisVBSUkJ7ezuRkZFemc8DOe6eeeYZmpqaePLJJ4f0REpLSwulpaXceeedGI1G7r//fubOncv999+Ppmk8/fTTnH766SxevNhvYr8iBqNZlZ8YDlW31cpKHB9+CHY7SkLCkPFolkgCjau4GOfatSAEunHjMJ17Loq8gUMIgWv79q4MTEA/Zw7GZcuG9Je/ROIvXDt34ly/Xl53jkF0duL44AO06mowGjGtXo1+zJhAN2vQMRzui4cTLper3x6RdXV17Nmzh6VLl3q2ORwOtm7dSnx8PNOnT/fZ9/vRGc2Kogz4fYOqqjQ1NXmKuNlsNkJCQjzCc2xsLGazeUDbAGC328nOziYpKYlJkyYN+PF6ixAC16WXeh7rvvc99JdfHrD2dHZ2kpWVxfjx4xk3TCYUjxWfGxsbvcTnkxXH7OjoICsriylTpgSsgOJgoqOjg61btzJjxowhaZ8B4Pr1rxH5+Z7H/bHIaWtrIysrizlz5jBixAig65p29Fhta2sjPDzcIz7HxMT47Nqam5vL2Wefzbp161i4cKFP9hmsCCFYvHgxV111FbNmzeKyyy7jq6++YsaMGdTX1/OjH/2Ic889l+uvv95vbZKZzUGMpmmUlZUxceLEAZk1U0tLcXz2GagqulGjMK1ePWSF5oGO5XBDxvPUGGbMQAkJwfHpp2gHDmB/+23Mq1ejREZ6vW44xVJoGs6vv0Y9cgNjSEvz6XL/4RTLgUbG0rf4K56GWbO6rjuff37S685gprex1Jqbcfzvf4imJjCbMV94IbqRIwe+oYMAeZ5L+ovBYPCy0XC5XOTm5hIVFeVToRm+FZvBN1nZp0Kv1zNixAiPQOJyubwySXfu3OkRSNwCtK+z81RVZceOHcTExDBx4kSf7ttXKIqC4Y03cH3vewBob7yBkpiIbvlyv7dFVVXy8/OJi4tj7Nixfj9+oOjOn/xkmc9u8VkIQUFBAQkJCVJo7gGaplFQUDBkfZoBRFmZl9Csv+uuPl9vNU1j586djB492nMdBTCbzYwcOZKRR+7FHA6HZ6zu37+fwsJCwsLCvMTnkD5oVO3t7dx444384he/GJJCs6qqXkViFUXhhhtu4Pnnn6ekpISnn36aGTNmALBlyxa++uornnrqKb+2ccjeWW7YsMEz6+3+66vhdqBwOp28/vrrA1Il3FVQgOOTT7qE5okTMX33u0NWaIaBjeVwRMazZ+gnTsR8ySUQFoaor6fzzTfRamu9XjNcYun2SvUIzUuWYFy82Kc/GIdLLP2BjKVv8Wc89ZMnd113QkO/ve7U1Q34cf1Fb2Kp1dRgf+stRFMTSkQE5ksvlULzUcjzfHjji+/fowsEappGXl4eer2e2bNn+/T7XdM0z3ECNTFiMBiIj49n6tSpLFy4kBUrVjB58mQURaGsrIyNGzeyZcsW9uzZQ21tbb/PKyEEhYWFGAwGZsyYEdQrwBSDAcM//uF5rP7+92g7dvi1DUIIiouLURSFlJSUoI7XQOMWn8eOHcucOXNYvnw56enpxMXFebzUN2zYwObNm7Hb7YwaNarf/u3DgZKSkiHt0wygvvmm12NlyZI+76usrAxN005ZcNJkMpGYmMj06dM919apU6diMBg4ePAgX3/9NZs2baKoqIiqqio6OjpO6XsshOBXv/oVFouF+3thAzKYePzxx9mwYQOOowo5rly5ktmzZ5OcnMzu3bv57LPPePHFF7ntttu4++67/T4JN+Qzm4uKioiNjQXwUv6HK0LTcH3zDa7t2wHQz5o1pH0dJZJAo0tMxHz55Tg++ADR0ID9nXcwnXnmsCpKJTo6upawHz4Mej3GM8/EMIRv1CSSQKNLTMT8ve/heP99hNWK/e23Ma1ahX7KlEA3zW+o5eVdk+ouV1c9igsuQAlABXSJZCjjzmwWQrBz507sdrvPfUyPzmgOpgx8o9FIQkICCQkJwLfZeVar1eNLGhUV5ckktVgsGAw9/+m9d+9e2trayMjICKp+nwglIgLDiy/i+tGPAFAffRR++1t0frrf279/P01NTYMmXv6ku8znffv2ceDAAWJiYsjLy0MIgcVi8WTpn8h2Y7hSW1tLVVXVkPVpBhAHDiC2bfM81t1wQ581IqvVysGDB8nIyOi1Bmc0GomPj/cUlnW5XB5Lo4qKCoqLizGbzR6/55iYGMLCwrwmmL788ktef/11cnJyBl2B0J7w0EMP8X//938kJydz3333cckllxAfH8+kSZN46qmneOGFF8jOzuZvf/sbaWlpXH/99fz85z/3ezuHvNickJDgqcA43BGdnTg++wztwAEADAsXYkhPH9YzvxKJP9BFRWG+9FIcH3+MdugQjo8/xjB/PobFiwPdtAFHq6vD8dFHCJsNzGZM55+PXi7Vk0gGHM9155NPuq47n3yCoaYGw+LFQ3qCWQiBKzcX15YtXd7VycmYzjtPeldLJAOAXq9HCMGePXtobGxk4cKFvRJUT4W7IKAQIuh/r7iz89zL6zs7Oz3i8+7du+ns7CQqKspTcDA6OvqEIsyhQ4eorq4mIyNjUAklSmIihqefxnX33QCo992H8txzKMnJA3rc2tpaysvLSUtL84uP9mDHZrNx4MAB5s2bR2xsrJfthtVqpby8XIrPR9HR0UFRUREzZswgLCws0M0ZMNR//9vrse6ss/q0H6fTSVFREVOmTCHCB5P8BoOBuLg4j6Z3tJ9+dXU1e/bs4eDBg7z33nssXryYtLQ0br31Vn73u98xbdq0fh8/2Dh06BAfffQRf/rTn9i5cyc/+tGP2LRpE/fccw8zZsxg1KhRPPbYY9TX12MymQgPDw/ciqCAHNWPpKenY7fbSUtL43e/+x3Tp08/4Wvtdjt2u93z2GazHbddp9NhNBpxOp1ey030ej0GgwGHw+GV1m8wGNDr9cdtNxqN6HQ6r+O5tyuKgsPhwOFwMGLECBwOByaTCSHEcUuyzGYzmqZ5bVcUBZPJhKqqHh81rbER56efYrTZcOn16JYvR5syBYfD4dc+HU1/+3T09mOLnBzbJ3cs3a8ZCn1yE4jPyeFwEB8fjxDCaz+DuU8w8J+T6cILaf/6a5x5edhzc9EdPoxh5Uri4+OPa8tg6dPJPqf2oiIc69eDqqJERhJ+wQXo4+IGrE+KohAXF+e1Hzn2+tYnRVE83z9DpU+B/JyO/j73a58UBXHOOajZ2ejy8nDk5tJRXY1p1SqUIz+YBtvndLJ7I+Fw4NywAUN5OZoQaCkpGJcuxSEEypHXB2OfjiaY742O7YNkcOMrGw2AyspKFi5c6FOhzy00a5qGTqcLerH5WEJCQhg1ahSjRo0CugQrq9VKY2MjO3fuxOFweHnoRkdHo9PpqKuro6SkhPnz5w9KYUsZPx79//0f6gMPAOC66y4Mf/oTypEsRV/T2trKzp07mTlzpixc2gMcDgcFBQVMmjTJs/r7ZJ7Pw118Hg4+zQBaSYl3VvMPf4jSh4lDIQS7du0iIiKCMQNUiPlYP31VVSkuLmbXrl189NFHPProo+j1er744gucTifLli1j9uzZQ2q8nnXWWaSkpHDzzTdz0UUXcdVVV7FlyxYee+wxzjrrLGJjY7HZbBQWFnLeeecFrJ2KOJXhySBlz549bN68mfnz59PU1MSjjz5KQUEBu3fv9lxYj+Xhhx/mkUceOW77vffe6zElnzdvHhdccAHvv/8+O47yolq+fDkrVqzgtddeY9++fZ7tq1evZv78+bz44ovUHeWZePXVVzN58mR++9vfev3QuO2224iOjubxxx8/rg3Nzc289NJLnm0mk4n77ruP0tJSXn/9dc/2+Ph4fvSjH7F9+3Y++OADz/bxBgPfGzWKLQkJfJWb69k+mPs0adIkrrnmGjZs2MDGjRtln2SfBl2fzomJIe3ii3n5/feHTJ/OP+88ZtvtvLxuHQ1HiTSDuU9DcezJPg2vPv388stp+PJLXrVah0yfuv2cgLtGjODg9On8Z/PmodGnIPmcOjs7efzxx2lubpaizhDg2MmLvlBVVUVBQQHz5s3zqRBzbEbzYBOaT4UQgvb2do+YZ7VaUVWVyMhIbDYbEydOZPz48YNaHNG2bUM96vpmePVVlOhonx7D4XCQnZ3NqFGjmDRpkk/3PRTRNI0dO3ZgNBpJTU3t8XnlFp/dkyWNjY0IIbwKuA1V8dm9aiM9PX3IWrIKIVDvuw9RUuLZZnjzTZQ+9LeqqoqSkhIWLVrk84KpPeH111/nvvvu49VXX6WoqIiNGzeyefNmDAYDp512Gm+++Wafig0GGw0NDV5FFzVN46qrruLNN9/k9ttv57rrruP6669nxYoVPP/88wFr56AVm//0pz9x2223dfvckiVL2LRpk9e21tZWkpOTeeyxx/jRES+pY+kuszk5OZna2lrPTbU/M11UVaWwsJDU1FRCQ0N7nenistvp+Ppr1KKirjaOHEnE+eejms3DLnvHHcu5c+diNpuHRJ/cBOJzUlWV3bt3k5qa6tWWwdwn8O/npFmtOD77DJqa2OV0MiMtDVNmpueLfTD2CUBrbkZbvx5dTQ0OIdDPnYshIwPlyD4Gsk96vZ4dO3YwY8YMzw2hHHt965MQgpycHFJTUz2xHOx9CnRms/v73Gg0BqxPakMDbR9/jGhs7Nr37NmEn3YaQqcbNJ/TsfdGmqbRWViIc9MmUFUIDSXy/PNRRo4cNH069nMK1nsjm81GQkKCFJuHCP0VmxsaGti+fTtCCBYvXuyT5dLwrUezqqqDMqO5LwghsFqtFBQUYDabsdvtXmJebGwskZGRgy4W2rp1qH/8o+ex4W9/Q/HRtUPTNLZv347RaPR5QcqhSklJCXV1dWRkZPTL7ma4iM+1tbUUFRWRmZk5KFcZ9BRt+3bUxx7zPNbfcQe6FSt6vZ/29na2bt3K7NmzA2Jje+DAARYtWsSrr77KxRdf7NnucrnYsWMHubm53HrrrX5v10Djcrk85/N//vMfbrvtNhwOB5GRkVRWVgZ0kmTQ2mhcccUVrDjBSdDdxSAiIoIpU6Zw4IhfcXeYzeZul391t/1E/lknmsE50fYTLTdz32h8+umnzJ071zOr393rdTrdcdu1xkZcn36Koa4Og6JgmDevy6dRr8cA3X7B+KNPx9KbPkHXj7zuThiDwXDSPh0dy5O1fTD16Vj82Se73c4HH3zAzJkzh0yf3Pjtcxo1ipCrrqJ13To+y8piekEBHD6M8eyz0R21+mIw9cm1Z0+X0OxwgMlExKpV3RZCHKg+2e12PvroI1JTU497To693vXp6GvmsW0arH2CwH1Oqqp64unuSyD6ZIiLI+rKK3Fu3oxaUACFhdirqjCddRbmbpY4B+Pn5PV9brejrl+PrqQEM6AbOxbTmWd6CgHKsefbeyPphTq06I84Z7PZPJO7e/fu9Zrs6A+D3Tqjr6iqSklJCQkJCcyYMQOAlpYWj5hXVlaGoigeC4PY2FjCw8ODPj66009HtLej/e1vALiuvx7D3/+OEhnZ733v2bMHp9PJvHnzgj4OwUBNTQ0VFRX9FprB23Zj3LhxCCFoaWnxCM/l5eUAHpuYwSg+u32aU1JShrTQLFTVa0IIQFm2rNf70TSNnTt3kpSUFBChWVVVbrnlFi6++GK++93vej1nMBhIT08nPT3d7+3yB+7fGXq9niuuuAKz2cwll1zCF198EfBs/EErNlssFiwWS49fb7fbKS8v5/rrrx+4RgUBQtNw7diBa+tWT4aP6cwz0Y8fH+imSSSSY1BMJkwrV0JWFpjNiLo67P/+N4a0NAxpaX1avhQIRFsbjo0b0UpLAdCNGtUlmsvMN4kk6FCMRkwrVqCOG4fjyy8RDQ3Y//OfrqKlGRkog6QYlVpWRufmzdDeDoqCYfFiDPPnS9FBIhlg2tvbycnJYeLEiYwePZp9+/Z5ZdD3FXdGsxBiWAnNQggKCwsxGo2kpKR4+h0VFUVUVBTjx49H0zSP+Oz2dDYYDB7hOSYmhrCwsKCMmf7880FV0f75TwBc113Xb8H50KFD1NTUkJmZGXAxZTDQ2tpKUVERs2bNIjw83Of7VxTFM157Kj5HRUUF5XiFb32aR44cyciRIwPdnAFF++ILaGryPNY/+GCfikiXlZWhqipTpkzxYet6zh/+8AcqKir48MMPg3ZcDSTu62BdXR233HILP/zhD1m8eHGAWzWIxeZT8cwzzzB+/Hhmz55Na2srv/nNb9DpdFx++eWBbtqAodXV4Vi7FlFbC4Bu3DhMZ5zhyfCRSCTBS8hll6Fs2oR24ACurCzUvXsxrlyJfoCKK/gCIQRqUVHX8vUjxcgM6eke2wyJRBK86CdMIOSqq3Bs2IC2bx+u3FzUkhKMK1YE9QS1aG0FwPH555gVBSUmBtNZZ6EbwoV7JJJgwW63k5OTw6hRo5gwYQLQ9SPXl2Iz+KZ44WBhz549dHR0kJ6efsLMT51OR3R0NNHR0UyYMAFN02hubsZqtXL48GF2796NyWTyynwODQ31c09OjP7CC0EItH/9C+if4Gy1Wtm7dy/z588Pqj4GKy6Xi/z8fJKTk0lISPDLMU8mPrsLDgJeBTKDySampKQEIQRTp04NdFMGFNHSgvaXv3y7ITwc3Zw5vd5PY2MjBw4cICMjIyCTP4WFhTz22GN88sknw97iKyoqijVr1nD33XcHuinAEBabHQ4Hd911F7W1tURERLBo0SI2bNjgZaQd7CiKwqRJk0554RXt7TizslB37gQhwGzGuGwZ+unTg+aiHWh6GktJz5Dx9B3uWOoiIzFecAHq3r04v/4a0diI49130U2ahHHxYnQxMYFuqhdadTXOr79GO3wYACUhAdMZZ6AboGrjPUGOS98hY+lbgjWeSng45vPOQy0rw7lhA8Jmw/H+++iSkzEuXRrQ8/lYhMOBa8cO7Dk5jDcYUHS6rsmttLQ+VUyXBO+4lPiH3n7uLpeL7du3ExUVxfSjfmMYDIZ+22i4PZqBQbXUvr8cPHiQmpoaMjIyTmjZ0x06nc6TIQpdS8ibmppobGyksrKSXbt2ERIS4pX5HOiiWPqLLgLwFpx76eHc3t5OQUEB06dP9/RdcmKEEBQVFRESEsLkbmzt/MVgEp9ra2upqqoaFlnz2v/7f16PDccULO4JTqeTnTt3MmXKFCJ9YI/TWzo7O7nhhhu44447OO200/x+/GDDbDYHjdAMg7hAoD+w2WxER0cHbSEU4XDgKizEtW1bV1YhoJs8GdPy5SgDsERGIpH4B9HZifObb7qKewoBioI+NRVjWlrAVypojY24tm5FdVcsNhgwLlqEfs4cmc0skQxihMOBKysLV0FBlw0XoJ82DUN6upePvN/bpaqoxcU4s7OhrQ04YtVz+unoBlECwVAg2O+LJb3n2KKQJ8JdjE0IwYIFC7wE4ZycHBITE0lOTu5TG9w+zW77jOFCbW0tO3fuZMGCBURHR/t03y6Xi6amJo/ns81mIywsjNjYWI/4fCKP9oFG/e9/0V57zfPY8Je/oPTgO8blcpGdnU1sbCzTp08fyCYOGcrLy6moqCAzMzNgn3dPOFZ8bjpi6+Bv8bmjo4OtW7eSkpIy5O0zRFkZrp//3PNYWbwYQx9EysLCwoB5pwsh+OUvf8nXX3/N1q1bg3qMD1ek2HwSAn1T7XK52LRpE0uXLvUy8hcOB678fFw7dkBnJwBKfDzG004L6iX3geREsZT0DRlP33GyWGoNDTg3b0bbv79rg06Hfvp0DPPn+1380erqcOXkoJaWfiuAp6RgXLgw4AK4GzkufYeMpW8ZTPHUmpu7JpT27PFs002c2HXdGTXKbz8mhMPRZdOzfbtHZFaiolAWLuSb6mpOO+20oI9lsNPbcRno+2KJ7+mJ2Oz2FG5tbe22uFheXp7H3qG3HC00u4uhDwdsNhs5OTnMnDmTRD9YADmdTo9/rtVqpbW1lYiICK/M595kVvcX9b33PBnOAIYXXkAZNeqErxdCkJeXh6ZpzJs3b1hNSvSVhoYG8vLySE9PH3TXa7f47J4sOVp8do9XX4vPmqaxbds2oqKiSElJ8dl+gxGhqrjuuANqajzbDP/8Z6+TFaurq9mzZw+LFi0KSAHhr776iksvvZStW7cya9Ysvx9fcmrkXXoQo6oqGzduZNGiRRgMBrTaWlw7d3b9AHQ6AVCiozGkp3dZZsgv3hNybCwl/UPG03ecLJa6ESMwX3ABakUFrq1b0aqqUIuLUYuL0SUno58xA/2kSQO2hFw4nailpahFRWhVVd+2a8IEjAsXBtUSe5Dj0pfIWPqWwRRPXXQ0prPPRps7F+e2bWhlZWhlZTjKylBGjMAwY0bXPccAeGUKIRCHD+MqKupaPXHkXofwcIzz56OfPRuHy8VXb7zB4sWLgz6Wwc5gGpeSwLF3714aGxtZuHBht+Okr57NbqFZ07RhVRCwo6ODHTt2MHHiRL8IzQBGo5GEhASPZ6/D4fAIz6WlpbS1tREZGenJfLZYLAN6TdBfdBGEhaG9/DIArttvx/DUUygnmLBwtzEzM1MKzT2go6ODwsJCpk+fPuiEZvC23Rg/fryX+Gy1WikrKwN8Kz4PF59mAO3DD72EZv1PftJrobmjo4Pdu3cza9asgAjNTU1N3HzzzTz66KNSaA5i5J1lEOPONHBu24Y4cADR0OB5TomNxZCWhn7qVCkySyRDHP2YMegvvRS1qgrX9u1d4s+hQ2iHDuE0m9GPH49uwgT048ah9PMLXzgcqPv3o5WXo5aXeyx6UBT0U6ZgWLAg6ERmiUTie3SJiZjPPx/NasW1fTvq3r2IhgacX3+Nc9MmdElJ6CdORDduHEpMTJ9/5AmnE+3wYdSyMrTycoTN5nlOiYnBMH9+l7jt9k7spzesRCL5FkVRTprZvH//fiorK8nMzDyhoKDX63vt2XysdcZwEZpdLhd5eXnEx8czbty4gLXDZDKRmJjoEbs7Ozs94vPu3bvp7OwkKirKI+RZLBaf+9fqzzoLJSIC9emnAXDdcw/6X/8a3cyZXq+rrq6moqKi177WwxVVVcnPzycxMZHRo0cHujk+oafis9vHvLfis9unOVAF7vyJOHwY7Z///HaD0YiybFmv9qFpGoWFhYwcOZL4APwmFEJw9913M3XqVO644w6/H1/Sc6TYHKQIIbC//TYArtxc9IrStYR+8mT0s2ahGz162NyYSSSSLvRJSeiTktBsNk+Gs2htRd2zB3XPHpyKgjJiBLqRI9ElJKBYLCjR0Sjh4cdNSglNg7Y2NJsN0dSEVleHVl2NqK/vssk4ghIVhX7GDAwzZgSNXYZEIvEfuthYTKtWIZYuRd27F1dREaKuDq2yEq2yEr7+GszmrutOXFzXNSc6GiUsrOtHjMEAQiCcTnA6ES0tiOZmtKYmtJoaRF2d1zUHg+Hbex0/2nZIJBJvqqurKSkpISMjg/CTZL0ZDAac7lUIPUAIgaZpwy6jWdM0CgoKMJvNXgUWg4GQkBBGjRrFqCNWFh0dHR7xuaioCIfDQXR0tCfzOTo62icZxrrFiyE8HPXXvwZAffBBWLMGXUYGAM3NzezatYvZs2efdAxKuhBCsHv3bnQ6HdOmTQt0cwaMY8VnTdO8PJ/37duHoig9Ep87OjooKioiJSVlyI8xoWmov/+91zbDc8/1+lpUXl6Oy+UKWBb4O++8w2effUZ+fv6QnxwY7EixOUhRFAXDiBGkVlZimDAB45Qp6CdMQAlwJeHBik6nkx5+hVlaAADTiElEQVRfPkTG03f0JZa6qCh0CxdiyMhAO3y4Kwt53z5EUxOivh61vp7jFrQaDGA0dok6LtdJswMVi6UrY3HixEEl9shx6TtkLH3LUIinEhKCYfZsDLNnozU3e1Y/aNXVYLejHTiAduBA33YeFoZ+3Dj0kyahS05GOUn22lCIZbAgYyk5EfX19ezcuZO5c+eesnidXq+n80gNmVMxXK0z3CKg3W4nPT096M+50NBQQkNDSUpKQghBR0eHJ4u0oqICl8vlZWEQFRXV5z7p5syBxx9HvfdeANQnnkD88Ic4zziD/Px8Jk6cSFxcnC+7N2SprKykvr5+2NmN6HQ6oqOjiY6O7pH4HBsbS0REhMePfuTIkUO+ICCA9umniKPrcVx6KUov+93U1MT+/fsDlgVeVVXFXXfdxR//+Mc+F6WV+A9ZIPAkBLoQimhtBZMJRVbWlEgkPUC0tnaJz9XVaFYrorm5a0m6pnX/BkVBiYxEiY5GFxeHbuRIlJEj0UVG+rfhEolk0CJUFVFfj1ZT8+11p7kZYbd3eS67XKAongkvJSICXVRUV/ZzfHzXhFZExLARnQYzgb4vlvgel8t1nN+yzWYjOzublJSUHi3D379/P1arlfnz55/0dUdnNA+nYoDQFaMDBw6QkZFB6AD43fsTIQRtbW2e4m2NjY1omuYl5PXFP1dUVOC6807P4/rZs2m45BJmzpw5rMZKX2lubiYnJ4d58+YR6+ci4sHO0eKz+09RFIxGI5qmMXv2bKKjo4f0OBOVlbh+8hOvbYY33/zWoqwHuFwutm7dSnJyckBsgDRN48ILL2TkyJG89tprQ/rzGirIzOYgxmU288knn3DuuedKj6p+4nQ6ZSx9iIyn7/BlLJWIiK7l55Mne7YJTQOHA+FwdAk/R0QfxWCAkJAh5fkux6XvkLH0LUM5nopej5KYiO4Eha6EpnVNbPnoR8FQjqW/kbGUHEt7ezs5OTlMmjSpx36vBoOhRwUC3WIzMKxEgpqaGsrKykhLSxv0QjN0fXYRERFEREQwduxYhBC0trZ6Mp/Ly8sBPMJzbGws4eHhp/zMlTFjMLzyCq4bbwQgrqCAuJYWlKeeGvA+DXYcDgf5+flMnjxZCs3d0F3m88GDByktLSU6Oprt27d3m/k8VK5TwuXC9fjjXtsMf/hDr4RmgN27dxMaGsrYsWN92bwe8/LLL1NSUsLbb789ZD6boc7QURmGIJqmsWPHDs+NmaTvyFj6FhlP3zHQsVR0OpSQkC7rjREj0MXGdmUVhoUNKaEZ5Lj0JTKWvmU4x1Px8VL54RxLXyNjKTkau91OTk4Oo0aNYvz48T1+X08KBGqa5hGkh9Py/ubmZoqKipg1a9aQXQ2gKAqRkZGMGzeOefPmsXz5cubPn090dDT19fVkZWWxceNGCgoKOHToEG1tbScsSqnExFB5tChWXo7zkku6Ji0l3eL2ArdYLAETAQcbdrud8vJyZs2aRXp6OitWrGD+/PlYLBYaGhrYtm0bGzZsIC8vj4MHD9LS0nLSQqrBjvbWW1BV5Xmsu/JKlKSkXu2jurqa+vr6gK002LVrFw899BB/+9vfiImJ8fvxJX1DZjZLJBKJRCKRSCQSyTDDLRq4XC5yc3OJiorqdfG6U2U2u60zYHgJzR0dHeTl5TFp0iQSEhIC3Ry/cXQW6YQJE9A0jebmZhobG6mpqWHv3r0YjUaP33NsbKwn47uuro6yQ4eIffVVQn/4Q88+XZddhuG111CGQGa4ryktLcXhcDB37lyZ7dkDNE07zqf5ZJ7P9fX1lJaWejKf3eN2sGQ+a/n5aG+//e0GsxndxRf3ah8dHR3s3r2bmTNnEhKA+mEOh4ObbrqJm266idNPP93vx5f0HSk2nwT3DJbNZgvI8e12O52dndhsNsxmc0DaMFSQsfQtMp6+Q8bSd8hY+g4ZS98i4+k7ZCx9R29j6b4fHswZXpLj0TSNvLw8jEYjs2fP7rWAotfrTyg2u60zhBCDQpjxFU6nkx07dpCYmDjss011Op3HnmDixImoqkpzczNWq5Wqqip27dqF2WwmMjKShoYGpk6dSlR0NLzzDq5f/tJT0Mx1zTUYfv97lB7auwwHampqqKysJCMjA4NByjo9obS0FFVVmTp16glf0xPxWafTeRXJDEbxWTQ2ov76117bDH/8Y69Wtgoh2LlzJ4mJiQGZNBNC8Jvf/AaHw8Fvf/vboIux5OTIAoEnoaKiQla5lEgkEolEIpFIjnDo0CHGjBkT6GZIfIDL5WLHjh20trb2WbCy2Wxs27aNM844w2u7EAJVVdE0DZ2P7XSCGbc9jU6nY86cOcMqm7svuFwu6uvr2bVrFzqdDofDQVhY2Leezx99hPLxx57X6+++G93ixQFscXDQ2tpKdnY2s2bNGlaZ8/2htraWnTt3kpmZSXh4eJ/34xaf3UUym5qavCZVgkF8FqqKev/9iJISzzb9vfeiS0/v1X7Kysqorq5m4cKF6Hvp8ewLtmzZwurVq9m0adMpi9D6iuLiYh544AG2bt1KVVUVb731Fpdeeqlfjj3UkFNgJyEpKYlDhw71qaKuL7DZbCQnJ3Po0KEh6/PlL2QsfYuMp++QsfQdMpa+Q8bSt8h4+g4ZS9/R21gKIWhpaSGpl16PkuDlwIEDNDY2snDhwj5nRnaX2ewWmoUQw0poFkKwa9cuHA4H6enpUmjuATqdjsrKSmJiYpgzZw4ul4umpiZPscHCsWNJXr2aSR98AID69NOIoiL0N90U4JYHDqfTSX5+PmPHjpVCcw/p6OigqKiIGTNm9Etohu6tYtzi89GZz4EUn7X/9/+8hGZl6dJeC83Nzc2Ul5eTnp4eEKG5paWFm266iQcffNBvQjN0TeRMnDiRK6+8kssuu8xvxx2KSLH5JOh0uqDI3IiKipI/qHyEjKVvkfH0HTKWvkPG0nfIWPoWGU/fIWPpO3oTy+jo6AFujcSfJCcnM2LEiH5Z0uj1eo8vs06n81hnDLeMZoD9+/fT0NAgbQ16wd69ez3ivKIoGI1G4uPjiY+PB7r8WhsbGzkwcSLjnn8eAO3TT3F88w2tTzxBTGzssIq1EIKioiJCQ0OZNGlSoJszKHD7NCcmJnp8mn1JsInP2tdfo733ntc2/e2392ofLpeLwsJCJk2aFJB7LSEE9957L8nJyfz85z/367EzMjLIyMjw6zGHKsPnyiyRSCQSiUQikUgkEqCruF9oP4uuuYU+VVVRFGVYWmdAl39ueXk5aWlpASmiNRipqKjg8OHDZGZmnlAwNplMJCYmkpiYiMjIwHX11QAYbDYst93Gph/+kLD4eI/thsViCUgWpr/Yv38/LS0tZGZmDqvzqz+4fZqnTZvml+N1Jz7bbLbjPJ8HQnwWZWWozz3ntc3wyisoRmOv9rNnzx5CQ0MZN25cv9vUFz788EP++9//sn379iF9Pg91pNgskUgkEolEIpFIJMMMX4gbbiHA5XJ5MpuHm9Dc1NREUVERqampcsVFD7FarezZs4d58+b1eMJDCQnB8PbbuH78Y6ipAWDJq6/SdPvt1ISFUVxcjN1uJzo62lO4zWKxDBk7k4aGBs+EhslkCnRzBgV1dXVUVFSQmZkZMNHSXUzQYrH0SHyOjY0lPDy819dQ0diI65gsYP2jj6LExPRqP4cPH6auro6FCxcG5DpeU1PD7bffznPPPcfEiRP9fnyJ75BicxBjNpt56KGHZLV1HyBj6VtkPH2HjKXvkLH0HTKWvkXG03fIWPoOGUuJL1AUBb1ej8vl8mSnDiehub29nby8PCZPnuyxfpCcnI6ODgoKCpg2bRqxsbG9eq+iKBhffBH19dfR3n0XAMsLLxB77rnobriBjo4OT+G2iooKXC4XFovFIz5HRUUNSvG5o6ODwsJCpk+fLic0ekhHRwc7d+4kJSWl3z7NvuRU4nNJSQl6vb5X4rPo7MT14IPex/nBD9ClpPSqbR0dHezatYsZM2YEZIWGpmn8+Mc/ZtmyZXz/+9/3+/ElvkURQohAN0IikUgkEolEIpFIJP5DCIHD4ej3ftatW8ecOXMGrZDXV5xOJ9u2bWPEiBF+W6I/2HG5XGzbto2YmBimT5/er31pu3ej3n+/1zbDG2+gHJn0EELQ1tZGY2OjR4DWNM0jPsfGxhIZGRn0kyOqqrJt2zaio6NJ6aV4OFzRNI2cnBwiIiKYMWNGoJvTK44Wn91/JxOfhaqi/uY3iLw8zz6UhQsx9NLrWAhBbm4uYWFhAYvZq6++ymOPPUZBQUFQTN4pisJbb73FpZdeGuimDEqk2CyRSCQSiUQikUgkwxC73d6v92uaRlZWFq2trR4hJDY2dkALYAUDmqZ5/ETnzp07pPvqK4QQ5Ofno6oq8+bN88nEhOjowHXNNV7bDM8/jzJmTLfHb21txWq1YrVaaWpqAvCM24Eu3NYXhBAUFxfT1tZGWlrasJrM6Q979+71FOsc7J6/R4vP7nHrFp9jY2KIf/999OvXf/sGiwXDn/+M0st+l5eXU1VVdVIP9YGktLSUJUuW8J///IfzzjvP78fvDik29w8pNkskEolEIpFIJBLJMKQ/YrMQwlMQ8NgMUp1O5xGeY2NjCQ0NDSoRrz8IISgqKqK1tZW0tLSACDODkdLSUg4fPkxGRoZPPYeFEKi//jWioMCzTXfFFegvu+yk79M0jZaWFi8Rzz1u3QJ0WFhYQMdtRUUF+/btIzMzUxae7CF1dXUUFhaSmZkZVPYZvuJo8dn43nskbtrk9bz9T38iPC6uV+O2ubmZnJwc0tLSiI6O9nWTT4nT6eSss85i/vz5vPjiiwE95xwOB8XFxQDMmzePJ598klWrVjFy5EhGjhwZsHYNRqTYLJFIJBKJRCKRSCTDEIfDQV9+Dh4tNB9bENAthjQ0NGC1WmlubsZsNnuJz4PZK7ysrIzKykrS09OlANhDDh8+zK5du0hPTyciImJAjqFt3Ij6+997bTP8+98oPRS23ePWnfnc3NyMwWA4btLEXzQ1NZGbm8u8efN67W09XOno6CArK4tp06YxatSoQDdnQFHffx/tH//w2lZ8xx3Uqyp6vd4zaRITE3NSz2eXy0VWVhZJSUlMmDDBH00/jscff5w33niD3NzcAbs+9JT9+/d3G4eHHnqIhx9+2P8NGsRIsVkikUgkEolEIpFIhiF9EZtPJjR3h6qqnuxRq9VKS0sL4eHhHgEvJiYGo9HYn274jerqanbv3k1aWhqRkZGBbs6gwGazkZOTQ2pq6oD7sIrGRlw33ui1Tf/oo70ulAZd47a5udmTrX/0pIk783mgJhvsdjtZWVmMGzeOcePGDcgxhhqD2ae5t2iff4768ste2wx/+ANKUpLXpEljY6PHduNE4nNRUREdHR0sWLAgIBnFOTk5nHPOOaxfv57MzEy/H18ycEjTnyBFCMEDDzxAQkICERERXHnllTQ3Nwe6WUHHY489RmpqKuHh4SQlJXHjjTdSX1/v9ZoNGzYwe/ZszGYzs2fP5quvvvJ6vrGxkSuuuIKIiAgSEhJ48JhKrsOVn/zkJyiKwttvv+3ZJmPZOz744AMWLFhASEgIiYmJPPDAA57nZCx7TlNTEz/84Q9JTEwkMjKSZcuWkZ2d7XlexvLEFBcXc8kllzB69OjjzmfwTexeeOEFkpOTCQ0N5ZxzzqGiomJA+xQoThbLjRs3cvbZZxMfH09kZCRLly5l48aNXu+XsfTmVGPTzbZt2zAajZx//vle22U8v+VUsayoqOB73/uex4900aJF2Gw2z/MylpLeIIRA07QeC80Aer2euLg4pk6dysKFC1mxYgWTJk1CCEFJSQkbNmwgKyuLkpISGhoaUFXVDz3pPY2NjRQXF5OamiqF5h5it9vJy8tj4sSJfin4pcTEYHj7bZQFCzzb1F/9Ctezz/Z6UsUt0E2ePJn09HRWrlzJjBkzMJlMHDp0iK+//prNmzdTXFzM4cOH++1/7kbTNAoLC7FYLIwdO9Yn+xwOlJaWoqrqkC/Wqa1ff7zQ/OSTKElJAOh0OiwWCxMnTmTBggWsXLmSuXPnEhERQW1tLVlZWXz11VcUFBRQVFRETU0NM2fODIjQ3NbWxk033cSaNWuk0DwEkWJzkPLiiy/y7LPP8pe//IUNGzaQl5fHrbfeGuhmBR1btmxhzZo15Obm8u6775KTk8P3vvc9z/M1NTWsXr2alStXsmPHDpYvX87q1aupq6vzvObmm2+msLCQDRs28Je//IWnn36al4+5gA83vvjiCwqO8j0DGcve8uWXX3LppZdy8cUXk5eXx9q1aznttNMAGcve8rOf/YxvvvmG//3vf2zfvp0JEybwne98h87OThnLU9Da2srEiRN5/vnnj3vOF7H76KOPuOuuu3jkkUfYunUrHR0dXH755X7pm785WSyzsrJYtGgRH3zwATt27CAjI4Nzzz2XvXv3el4jY+nNyeLppr29nRtuuIH58+cf95yM57ecLJadnZ2cccYZNDQ08NFHH1FQUMC9997rVTBJxnJ40xuBwZ3RrKpqj4Xm7jAajSQmJpKSksKSJUs47bTTSE5Oxm63U1RUxIYNG8jJyaGsrIympiY0TevTcXxJW1sb+fn5TJs2jbi4uEA3Z1Cgqir5+fnExsb6NTtXURQMv/wl+qOSPMSmTbguvRRx1D1Ob9Hr9YwYMYIpU6aQmZnJihUrmDp1Knq9nv379/PVV1/xzTffsHv3bmpra3E6nX06TmlpKQ6HgxkzZgwZn/OBpq6ujoqKCmbPnj3oCwKeDO2LL1BfeMFrm/6RR1AmTjzhe7oTn2fPnk1ISAjV1dVomkZ2djYFBQUcOnSI1tbWPlkr9RYhBL/61a+IiYnhl7/85YAfT+J/pI1GkDJ79mzOPvtsnnzySQD+97//cckll1BTU8OIESMC3Lrg5d133+WSSy6hqamJ6OhonnnmGZ544gmqq6vR6XSoqkpiYiK/+tWvuOuuu6ivrycxMZH333/fU/X0pz/9KevXrycvLy+wnQkQVquVzMxMPv74Y6ZOneqpwCpj2TvcN6B//vOfj3tOxrJ3zJw5k8svv5yHHnoI6FruNWvWLHbv3s1HH30kY9lDjq2o7ItxeMEFF2A2m3nrrbcAyM/PZ+7cuezYsYO5c+cGopt+4VTVqYUQxMTE8Mgjj3DnnXfKWJ6CE8XztttuY8yYMZSUlFBfX8+HH34IION5Eo6N5d///nd+9rOfcfDgwW59EGUsJU6ns0di7tEZzYqiDJgIJoSgvb3dY7nR2NiIEAKLxcKIESM8Gfr+FOEcDgfbtm0jPj6eqVOn+u24gxl3EcW2tjbS0tICJgCKzk5cV1/ttU130UXor73W58dyOBw0NjZ6LGPa2tqIjIz0WG7ExMScspik29s6IyNjSBa3GwiGi0+z+umnaH/5i9c2/UMPoZs9u9f7EkKQm5tLaGgoKSkpNDc3e8ZuU1MTBoPBY7lxKs/nvvLZZ5/x/e9/n9zcXHldHaLIzOYgxD2rv2TJEs+2ZcuWoaoqO3bsCGDLgp+amhpCQ0M9RUdyc3NZtGgROl3XUNfr9SxZsoScnBwAduzYgaZpx8W6sLDQZ0uhBhu33XYbt9xyC1OmTPHaLmPZc5xOJ9988w1Tp05l+fLlJCYmsmrVKk+2uIxl7zjttNP47LPPaGhowOVy8cYbbzB9+nQmTpwoY9kPfBG73Nxcr+fnzJlDVFSUZx/DldbWVtra2oiJiQFkLPvCxx9/TFZWFmvWrDnuORnPnrNhwwaWLFnCAw88wMiRI0lNTeWPf/yj53kZS0lPcYvN0Lts6N6iKArh4eEkJyczZ84cli9fzoIFC4iJiaG+vp7s7GzPEvCKigo6OjoGrC3QZWmQn59PRETEcffGkhNz4MABrFYrc+bMCWimqRISgvGdd9BddZVnm/beezgvuQTR2OjTY5lMJhITE5k+fTqLFy9m2bJljB8/HlVV2bNnzyntYlpbWykuLmbWrFlSaO4hbsuRhISEoS00v//+8ULzgw/2SWiGrvOzs7OTadOmodPpiImJOS7zuTvbjUOHDtHW1tbvzOf6+np+9KMf8eSTT0qheQgjxeYgpKGhAU3TiIuLY82aNaSlpWGxWNDr9V7LmyXetLa28uyzz/LjH//YU6yhrq6OuLg4duzYQWxsLNnZ2cTFxXniWFdXh8FgwGKxsHDhQu6++27i4uLQNA2r1RrI7gSEf/3rX+zfv5+f/vSnxz0nY9lz6uvrcTqd/O53v+Paa6/lk08+ISEhgbPPPpvW1lYZy17y/PPPM3XqVOLi4ggJCeGNN97gk08+wWg0ylj2A1/Ezr2PF198kfHjx9Pe3u61j+HKb37zG0aPHu3JLpWx7B319fXccsst/PWvf+02C0zGs+dUV1fz1VdfUVtby8cff8xdd93FT3/6U4+vs4ylpCfCsaZpHmHMPUHpLxRFISoqivHjxzN//nyPEBIeHk51dTWbN29m06ZNHt9ch8Phs2O7s3M1TWPWrFnS0qCH1NfXU1ZWxpw5cwasgF5v0V9yCYZXXvHa5rrxRtQT1AvwBWazmZEjRzJjxgyWLl3K0qVLSU5OxuFwUFxczPr169m2bRv79u2jtraWvLw8xo4d6xdv66HCvn37hrRPs9A01H/8A+0f//Darn/wQXRz5vRpnzabjX379pGamtrtPdapxOetW7f2S3zWNI2f/OQnLFiwgJtvvrlPfZAMDk6+jkMSEI4+WePi4mRhgB7gcrm46qqrSEpK4rHHHvNsd8cyNDSUsWPHEh4e7hXfo/89ZswYEhIS/OJRFIwcOnSIu+++m/Xr13ebgSBj2XPcmT+rV6/mxiMVsV9++WViY2P58ssvZSx7yYsvvsj27dv58ssviYmJ4fe//z3nn38+27Ztk7HsB76MnbuIjV6vH/bx/c9//sMf//hHNmzYQFhYGCBj2VtuvvlmfvCDHzBv3rxun5fx7Dlu24O//OUvhIWFMX/+fD7//HNee+01Lr30UhlLySkJpNDcHW4hJCYmhkmTJuFyuWhqasJqtbJ//34KCwuJiIggNja2x9YFJ8LtGZ2RkTGkfWB9SVtbG4WFhaSkpBAdHR3o5nihxMRgfOcd1H/9C+299wDQ/v1vtH//G8Of/oQywCJvSEgISUlJJCUlIYSgo6PDY7mxf/9+NE2jqamJsrIyYmNjiYqKCopzLlipq6vj0KFDZGZmDsnzU7hcqC+8gPj6a6/t+kcfRZeS0qd9qqpKYWEhEyZM6PH5efQ1F7q+E9y2G7W1tezdu9fLdiM2NpawsLATTs79+9//ZvPmzRQWFsrxPcSRYnMQEhcXh06no76+np///OdAV/VjVVXlTGc3aJrG97//faqrq1m7di0mk8nzXEJCAvX19UyfPt3jPdjQ0OCJY0JCgucm1Z3l89///hedTkdsbKzf+xJIcnNzqa+vZ8FR1ZsBrrrqKt59910Zy14QGxuLoihey4IiIyOJi4ujsrJSxrIX2O127rvvPt555x3OOOMMAF555RUsFgvvvfeejGU/8EXs4uPjqa+v56677uKqI0tUj97HcOP999/n5ptv5r333vMqaidj2TvWrVvHRx99xFNPPQXgKbIUEhJCQ0ODjGcviIuLY8yYMZ6JD4CJEyfy5ZdfAnJsSk6Ov6wz+oPBYCAuLs5TtM/tm2u1Wtm7dy8dHR1ERUV5xOfo6OgeCVNVVVUcPHiQ9PR0jz2f5OQ4nU7y8vIYM2ZMUFsa6K+9Ft055+C69VbPNtett6IsW4b+jjtQ/CCAKYpCWFgYYWFhOBwOmpqaSE1NpaWlBavVysGDB9E0DYvF4pk0iYqKCtrz0N90dnZSVFRESkrKkLQcEW1tqL/9LWLXLq/thqefRhk/vs/73bNnDyaTiQkTJvR5H8eKz6qqYrPZsFqt1NTUeInPsbGxOJ1Oxo0bh06nY//+/fz85z/nb3/7GyNHjuxzGySDAzmVEISYzWZmzpzJ5s2bPdu++uor9Hr9CbN8hitCCG644QaKi4v57LPPiIqK8np+wYIFbNmyxXOjrKoq33zzDWlpaQDMmzcPnU53XKxTU1OH3Y3lqlWrKC4uJi8vz/MH8NRTT/HUU0/JWPaC0NBQpk2bRllZmWdbe3s7DQ0NjB49WsayF7S3t2O3271urnU6HYqi0NHRIWPZD3wRuwULFng9n5+fj81m8+xjOPHpp59yzTXX8MYbb3D66ad7PSdj2Tu2bdtGfn6+57voggsuYNmyZeTl5REWFibj2Qvmzp1LZWWll0f9wYMHGT16NCDHpuTEIrIQAlVVB7wgoK9x++ampKSwZMkSlixZwpgxY+js7KSwsJANGzaQm5tLeXk5zc3N3WbpW61Wdu3a5Vk6Ljk1mqZRUFBAWFgYkydPDnRzTokSH9/l5XzxxZ5t4quvcF12GVpxsd/aUV9fT3l5OXPmzMFisXh5laenpxMXF0dTUxPbt29nw4YN5OXlceDAAVpaWobtChP3WBuqPs3i8GFct99+vND8wgv9Eppra2upqanxuSWQXq/3rDRJS0tjxYoVHqujw4cPc/bZZzN27FguvPBCzjvvPFatWsWFF17os+NLgheZ2Ryk3HLLLaxZs4bTTjuNpKQk7r33Xi677DJGjBgR6KYFFbfeeivr1q3jww8/xOFwcPjwYaArC0ev13PVVVfx0EMP8dOf/pRbbrmFl156CYfDwdVHqhLHxcVx8cUX84tf/ILExEQqKyv585//zDPPPBPIbgWEiIgIpk+fftx293IvGcveccMNN/Dggw9yxhlnMG/ePJ588kliY2M544wzWLhwoYxlD4mJiSEjI4Nf/epXxMbGMmLECH7/+9+jaRorV64kNDRUxvIkuH0B3ezfv5+8vDxGjhzpk3P6lltu4cILL+Rvf/sbCxYs4Cc/+QkLFy5k7ty5/u7qgHOyWBYXF3PJJZfw7LPPMm/ePM93UUREBBERETKW3XCyeB5bhCs6Ohqn0+n5jpLx9OZU5/nDDz/MHXfcwc9//nPy8vJ49913+ec//wnIWEq652ih2T3BO1gJDQ0lNDTUY13Q1taG1WqlsbGR/fv3A3gy8NzZ/Pn5+UybNk3+7uoFJSUl2O12MjIyBtV40V99NboLLsB13XWebeoDD6DGx2N4/nmUAUxM6OjoYOfOnUyfPv24hClFUYiMjCQyMpKxY8cihPBkPTc0NLBv3z5Phql77J7MumAoMZR9mrWdO1Efeui47YZXXkE5kkncFzo7OykuLiYlJYXQ0ND+NPGUuMXno4tkf/XVVzz99NNUV1fz4YcfMnr0aFasWOH5mzp16rAYu8MNRQzXKbEgRwjBAw88wMsvv0x7ezurV6/mT3/6ExaLJdBNCypOdFEqLy9n/JGZvw0bNnDHHXewd+9epk2bxgsvvMCyZcs8r21sbOTWW2/lww8/JCwsjNtuu41f//rX/mh+0KMoCm+99ZanyJWMZc/RNI1f/epXvPrqq7S2tjJ//nyef/55z+oEGcuec+jQIe655x7Wr19PR0cHqampPPbYY6xcuRKQsTwZ+/fv73ap3EMPPcTDDz/sk9j94Q9/4IknnqC+vp4VK1bwyiuvMGbMmAHvm785WSz379/PP44p3uJ+7uGHHwZkLI/lVGPzaK677jrq6+v58MMPPdtkPL/lVLH88ssvueeee9i9ezdjxozhjjvu4M477/S8TsZyeKOqKi6Xy/PYbZ2hquqgF5pPhRDCs/zbarXS1NSEpmmEh4czbtw4YmNjB1yYGQpUVlZSUlJCRkaGl2XPYEPbtAn12We9tukuvRTdFVf4/DxQVZVt27YRHR1NSh/8dzVN84zdxsZGmpqaMBgMHssN99gdaudvXV0dhYWFZGZmDin7DCEE2iefoP31r95PjB2L4fHH+zXpIYRg+/btmM1mZs2a1c+W9o2CggJOP/10PvvsM9LT08nOzmbDhg1s2LCBb775hrvuuovHH388IG2TDBxSbJZIJBKJRCKRSCSSYYimaR5f9KGU0dxbVFUlJycHvV6PxWLBarVis9kICQnxZI7GxsZ61YaRdE1Wbd++nXnz5g2JWhjC6UT91a8QpaVe2/UPP4wuNdU3xxCCoqIi2tvbSUtL80mRNFVVPUXbrFYrzc3NmM1mr8znkJAQH7Q+cHR2drJ161amTZs2pOwzREcH6h//iNiyxWu77qyz0N10U789xA8cOMChQ4dYuHBhn4ul9oeOjg6WL1/O6tWr+c1vfnPc90pnZydtbW1yJckQRIrNEolEIpFIJBKJRDIMcYvN7ozmwebR7AuEEBQWFtLZ2cmCBQs8BQRdLpdHvLNarbS2thIREeER72JiYgIi3gQLHR0dZGVlMWnSJJKTkwPdHJ8iKipwHbUCxE1/7Qyga8VeWVkZmZmZAyYAq6pKU1OTJ/P52ImTmJiYQVW7RNM0cnJyiIiIYMaMGYFujs8QBw/iuv9+aG/32q6/8050R6007CstLS1kZ2ezYMGCgKyQF0Jw3333sWnTJrZu3Son64YZUmyWSCQSiUQikUgkkmGIW2x2W2cAPsm0HEyUlpZy+PBhMjIyTiqGOBwOj/BstVrp7OwkKirKI+BZLJZhEzuXy8W2bduwWCx9soEYLGgbNqD+4Q9e25TZs9Hfdx9KH4SzpqYmcnNzmT9/vsfT1h+4J07ckyctLS2Eh4d7hOeYmJigFgJLSkqor68nIyPDMxk0mBFCoH3xBdrLLx/3nOGZZ1DGjev3MVRVJSsri8TERCZNmtTv/fWFjRs3cumll5Kdnc3MmTMD0gZJ4JBis0QikUgkEolEIpEMQ4QQdHZ2DluhubKykr1795KRkdFrD9iOjg4v8dnlcnnZFkRGRg7JDHEhBAUFBTidTubPnz/kx4xQVdTnn0ds3uy1XXf22ehuuAGlh+Kn3W4nKyuLcePGMc4HYmJ/cDqdXln7bW1tREZGesavxWLBaDQGtI1uhppPs2hu7hpP+fneTyQldfkz+6iPu3btoqWlxWdWLb2lqamJzMxM7r77bu666y6/H18SeKTY3EOKi4t54IEH2Lp1K1VVVV5F03zFN998w5o1a8jNzSU0NJSLLrqIvx5rEi+RSCQSiUQikUgkPmDLli20tbWxYMGCQbWs3hc0NDSQl5fnE79hIQRtbW0e8a6xsRHAy+85LCxsSIjP+/bto7q6+pSZ4EMNYbPhuuMOaG312q675hp0F1100s9W0zS2b9+OyWQiNTU16MbB0Vn7jY2NdHR0EBkZ6ZX5HIiM4qHm06xt24baTSG8noyh3lBXV8fOnTtZuHBhQIqcCiG44YYbaGho4LPPPhvyE1KS7hm+JlO9pLW1lYkTJ3LllVdy2WWX+Xz/RUVFrFq1iltuuYUXX3wRo9HI3r17fX4ciUQikUgkEolEIgH4+uuveeKJJ3A6nSxdupTly5dz+umnk5KSMqQFgtbWVgoKCkhJSfFJYTtFUYiIiCAiIoKxY8eiaRotLS1YrVZqa2vZu3cvRqPRS3wejAXbampqOHDgwLATmgGUqCiM//gHorIS109+4tmuvfYa2muvob/9dpQVK7oVDEtKSnA6ncybNy/ohGYAk8nEyJEjGTlyJNAl8rozn3ft2oXdbic6OtqT+RwdHT3g4rOmaRQWFhIfHz/ohWbR3Iz6l78cVwQQwPDUUygTJvjsWHa7naKiIqZPnx4QoRng7bff5vPPPyc/P39If49ITo7MbO4DiqIcl9mcn5/Pz372M7Zs2UJ8fDw/+MEPePDBB3tcNOK6666jqqqKzz//fKCaLZFIJBKJRCKRSCReqKrK9u3bWbt2LWvXrmXz5s1ERUWxfPlyVq5cyYoVK0hOTg5Kkawv2O12srOzGTVqFJMnT/bLMVVVpbm52ZM9arPZCA0N9SrYFuzirc1mIycnh1mzZpGQkBDo5gQcbfdu1PvvP267/pZbUM4803O+HD58mF27dpGZmUlYWJi/m+kTjraMaWxsxOl0Eh0d7Rm/UVFRPhcVh4JPsxACsXHjcb7fAEpmJvqf/ATFh5NOQgh27NiB0WgkNTXVZ/vtDZWVlWRkZPDSSy9xxRVXBKQNkuBAis194FixuaGhgalTp3LjjTdy4403Ul1dzfXXX88tt9zCL37xix7tc/z48Vx99dUUFBSQlZXFlClTeOyxx1ixYsUA9kQikUgkEolEIpFIvqWjo4MtW7bwxRdfsH79enJycpgwYQIrVqxgxYoVLF++nJiYmEEpPquqSk5ODmFhYcyaNStgfXB75rqzR1tbWz22BW7xOZgENrdAP2bMGCb4MAtzKKB98w3q008ft1133XV0LF9Odk4OqampxMfHB6B1vkcIQXt7u0d4tlqtaJqGxWLxjN2oqKh+nVv19fUUFBSQkZFBRESED1vvP8TBg6gvvogoKTnuOf3DD6MbADH44MGDHDhwgIULFwbEc1tVVS666CJGjhzJa6+9Nii/IyS+Q4rNfeBYsfmRRx7h448/Jisry/OaF198kd///vfs3r27R/s0m82EhIRw//33c+aZZ/Kvf/2Ll156ib1795KcnDwg/ZBIJBKJRCKRSCSSEyGEoKmpifXr17N27VrWrVvH3r17mTt3rsdyY9GiRYSEhAS9sOAubOdwOJg/f37QiblHFxt02xa4xefo6OiALUfXNI2cnBxCQ0MDKtAHM0IIxKZNqM89d9xzTatWEXfzzT0uJDjYEELQ2trqEZ7dfuVu8Tk2NpaIiIgejxu3T/PUqVNJSkoayKYPCKKtDe2NN9A++ui455RFi9D/+McoA2Bv0dLSQnZ2NvPnzycmJsbn++8Jbg0sPz8/YG2QBA9SbO4Dx4rNl1xyCe+//75XUQ1VVVFVFYfDwRVXXMEbb7zR7b7uv/9+Hn30UYxGI8uWLWPt2rVA15f6mDFj+MUvfiGrd0okEolEIpFIJJKAI4SgoqLCY7mxbt06rFYrmZmZrFy5kpUrVzJ37tweWwn6k5KSEmpqagaF37A7c9T9d3TmaGxsLJGRkX4RfYUQFBcX09raSlpaWlAJ9MGKlpWF+rvfHbddycjo8nUODw9Aq/yHEMLjV+7O3tfpdB6/55iYGMLDw7sdv5qmkZubS1hYGDNnzgxA6/uOcLnQ1q5F+/OfvZ9QFPT33AORkegGqE+qqpKVlUVCQoLfrIGOpbi4mBUrVvD+++9z+umnB6QNkuBCis19oDuxWdM0nnzyyeNeO3nyZKqqqrDZbN3uKy4ujri4OEaNGsVFF13ESy+95Hlu0aJFLF26tNv9SiQSiUQikUgkEkkg0TSN3bt388UXX7Bu3To2btwIwLJly1ixYgUrV65kypQpAS8SVVFRQWlpKenp6YQPMrHPnTl6tGfu0eJdbGwsYWFhAyI+HzhwgP3795OZmTkoCxoGirKyMto2b2b6e+8d/2RUFIZf/xplmKxe1jQNm83myXxuamrCYDB4hOfY2FhCQ0NRFIWSkhLq6urIzMwcNBMbQtMQW7ag/v734HJ5Pae78kp0q1ejHJWUOBDs3r0bm81GWlpaQK61drud008/nZUrV/L000/L1Q8SQIrNfeJYsfnhhx/mjTfeoKioqM8n97nnnoumaXz22WdA103F2LFjufvuu2Vms0QikUgkEolEIgl6XC4X2dnZrFu3ji+//JKtW7cSFxfn8XteuXIlI0eO9KsY0dDQQF5eXkCXl/sSt3jnFp+bmpowm81e4rMvhOH6+nry8/NJS0sjOjraBy0fHrj9htPT04mMjESUleFaswY07bjX6u+6C2XJEpQAT8b4E03TvIplNjc3YzabCQsLo7GxkXnz5jFixIhAN/OUCCEQ27ej/ulPYLV6PacsWYL+Bz9A8UM/6urqKCwsZOHChQEpQCmE4OGHH+aTTz4hJydHTkpJPEixuYc4HA6Ki4sBmDdvHk8++SSrVq1i5MiRGAwGpk2bxgUXXMBdd92F0Wjkm2++oby8nMcee6xH+3/77be5/PLLefHFF1m1ahX/+te/+N3vfseePXsYO3bsQHZNIpFIJBKJRCKRSHyKu5DYpk2b+PLLL1m3bh15eXlMmzbNIz6fdtpp/S4mdjJaW1vZtm0b06dPZ9SoUQNyjECjqipNTU0e8c5msxEeHu5VbLC3xcLa2trIzs4e0nEbCDo6Oti6dSvTpk07zm9YNDWhPvUUYteu496nzJ2L/kc/8os4GWyoqkptbS3FxcWYzWY6OzsJCQnxynw2D3BmcG8QmobIzkb961+PF5mnTEF3443o/GRlYbfb2bp1K1OmTAmYv/U333zDhRdeyKZNm5g3b15A2iAJTqTY3EP279/fbeXdhx56iIcffpi8vDzWrFnD5s2b0ev1zJo1ix/96EdcffXVPT7Gc889x7PPPktdXR0pKSn87ne/44wzzvBlNyQSiUQikUgkEonE7wghaGhoYN26dR6/5/LychYsWODJes7MzPSZsGS328nOziYpKYlJkyb5ZJ+DAafT6bEssFqttLW1ERUV5RGfLRbLSS0KnE4n2dnZJCQkMGXKFD+2fHCjqirbtm3DYrEwffr0E75O2O1ob76J1p3FBnT5Op92GkoQ+p4PBMf6NLtcLo/Xs9VqpaWlhfDwcC/P50B4rguHo6sI5F/+Ag6H95MjR6K/7TZ0s2b5rz1CkJeXh16vJzU1NSDWFTabjUWLFnHrrbeyZs0avx9fEtxIsVkikUgkEolEIpFIJH5FCMH+/ftZu3YtX375JevXr6e1tZXFixezcuVKVqxYQWpqap+8W1VVJScnh/DwcGbOnDmsPUTtdrtXsUG73e5VbDAqKspjBalpGnl5eSiKwty5c4d13HqDEIKioiLa29t77JsrhEAUF6P++tfHef0CkJSE/sc/Rpk2bUh/DqfyaT568qSxsZHW1lYiIiK8Jk96m7nfG0RjI9pnn6G99dbxT44ahf7661Hmz/f7Z3Tw4EEOHDjAwoULB7T/J0IIwY9//GPKy8tZt27doPHYlvgPKTZLJBKJRCKRSCQSiSSgaJpGQUEBa9euZe3atXz99deYzWaWL1/uyXyeMGHCKUUdIQT5+fm4XC7mz58f8OKEwYQQgo6ODi/xWdM0T9aozWbDZrORmZmJYZhk1vqCQ4cOUVZW1udCiqKtDe0//0H7+ONun1dSU9H/8IcoQ8xe0+1vnZGRQURERI/e43A4vDL329vbPZn7MTExWCyWfo9doWmIoiK0Tz5BZGUd97wybRq6a65BSUkJyERAa2srWVlZAfWh/+CDD7jlllvYsWNHtw4AEokUmyUSiUQikUgkEolEElTY7XaysrI8fs/Z2dmMHj2a5cuXezKf4+LijhN7nnvuOebMmcPSpUsDkvE3mBBC0NraSkNDA9XV1bS2tmI0Gj1Zo7GxsYSGhg7pzNr+0tTURG5urk+EPyEEYs8e1Oefh9rabl+jpKejv+IKGDduUH8unZ2dbN26lalTp/bLb7izs9Mr87mzs9PLNiY6OrrHWbeioQFt/Xq0f/+72+eVZcvQX3ghyvjxfW5vf1FVlezsbOLj45nsJ2/oYzl8+DCZmZk8+eSTXHfddX47rhCCBx98kJdffpn29nZWr17Nn/70J1nANEiRYvNJ0DSNqqoqIiMjB/WFXCKRSCQSiUQi6Q9CCFpaWkhKSpKZohK/4x5/Gzdu9Pg9FxUVMXPmTJYvX87pp5/OkiVLeOGFF3j++edZv379SX1zJd64BdM5c+ZgMBhoaGjAarXS3NyM2Wz2Ep+DqVhboHFPiIwfP56xPs46FqraVYjumWdA07p/0Zgx6L//fZTZs1EG0cTKsT7NvsSdue8WoJ1OJ9HR0Z7M5+joaK/vMNHcjLZlC9qHH0J1dbf71F17LbrTT0eJivJpW/vCnj17aGpqIj09PSDfxZqmcfnllxMWFsabb77p1zb88Y9/ZM2aNbz++uuMHj2aa6+9lrlz5/LvE0wOSAKLFJtPQkVFBcnJyYFuhkQikUgkEolEEhQcOnSIMWPGBLoZkmGOEILDhw97FRusqKhA0zS+973vceONN5KWliYzm3tAR0cH2dnZTJgw4TjBVFVVL8sCd7E2t/AcExMzbGPsFkxDQkKYNWvWgCaniY4OtA0b0F555aSv0115JbqlSyExMaiT5UpLS6mtrT2hT7OvEELQ3t7uNYZVVSVBpyOxooLo7Gx0VVXdvldZsADd2WejzJ2LEiR+xG7bkczMTMLDwwPShr/+9a/89re/paCggLi4OL8ee/bs2Zx99tk8+eSTAPzvf//jkksuoaamhhEjRvi1LZJTI8Xmk9Dc3IzFYuHQoUNEBWAWy26388wzz/Czn/1MziD3ExlL3yLj6TtkLH2HjKXvkLH0LTKevkPG0nf0NpY2m43k5GSamprkklVJ0JGXl8fSpUu57LLLaGlpYf369TidTpYuXerJfE5JSZFZ+cegqirbtm0jKiqKlB74zzqdTi+/546ODiIjI72KtQ2XQmF79uzBarWSkZHh1z4Lux2Rk4P6yitgs534hRYLuu99D92CBShBJMT1xae5vwinE7F3L9qOHYj//vfEr4uJQX/ppeiWLEGJjPRL23qKw+Fgy5YtTJ48mdGjRwekDSUlJSxdupQ333yTc88916/HttvthIWF8c4773DRRRcB0NjYSGxsLF988QWrVq3ya3skpyaoXf+Li4t54IEH2Lp1K1VVVbz11ltceumlPX7/+PHjOXDggNe29evXs2LFih693/1lGxUVFTCxOSQkhKioKPmDqp/IWPoWGU/fIWPpO2QsfYeMpW+R8fQdMpa+o6+xDOZsOcnwpLKykvPPP59f/vKX/PKXvwS6RNTt27ezdu1avvzySx555BGioqK8/J6Tk5OH9XgWQlBUVIRer2f69Ok9ioXRaCQxMZHExESgyy/XLTwXFRV5WRbExsYSFRU1JAX+6upqqqqqBjwztzsUsxllyRJ0S5Z0WW3s3o32xhuIoiLvFzY1ob38Mh4DDrMZ3XnnoaSloUyYgGIy+bXd0DVedu7cyfTp0wdUaBYOB6KsDFFcjLZ2LRw+fOLXRkXRvHIlNWPHUqtp6HQ6YsrKPEUzw8PDA36dcJ+rFoulX/7W/cHpdHLTTTdx7bXXcs455/j9+A0NDWiaRlxcHGvWrGHt2rVs27YNvV5PXV2d39sjOTVBLTa3trYyceJErrzySi677LI+7ePBBx/ktttu8zyOjY31VfMGHJ1Ox7x584bkF7S/kbH0LTKevkPG0nfIWPoOGUvfIuPpO2QsfYeMpWQo0NrayurVqznrrLO47777PNv1ej3p6emkp6dz77330tHRwZYtW/jiiy/4+9//zu23386ECRNYsWIFK1asYPny5cTExARcVPIn5eXlNDc3k5mZ2efrQEhICElJSSQlJXksC9zi88GDBxFCYLFYGDFiRNAId/2lpaWFXbt2kZqaSlhYWEDbouj1KDNnovv1r4Eu/2GRk4P6xhvQ0OD9Yrsd7d134d13v902bhy6ZctQpk5FSU4e0GxeTdMoLCwkPj7ep4KpcLmgqgpRXo7YtatLXD6Rx/URlJQUlGXLPFnf8UD8kTa2tLRgtVqpq6ujpKQEg8HgEZ5jYmIICwvz+xiuqKigpaWFRYsWBeT8EULw1FNP0dzczO9+97uAtcFNXFyczz3SJb5n0NhoKIrSp8zm22+/nXvuuadPx7TZbERHR9Pc3ByQzGaJRDK4cKgOGtobqG+vp769noaOBmx2G62OVq+/FkeL598O1YFTdeLUnLg0l+ffTrXrsU7Ref3pdfpv/63oCTGEEGYMI9QYSqghlFBjKGGGbx9HmaOwhFi8/mJCY7CEWIg2R2PUD0+fPYlEIpH0DnlfLAlG7HY7zz33HD/96U8x9TBTUwhBU1MT69ev9/g97927l7lz53osNxYtWkRISMigF0ZPRE1NDUVFRaSnpxM5QAKju6ijW3xubGz0Eu5GjBhBaGjogBx7oHA6nWRlZTFq1CgmTZoU6OacFCEEVFejbd+O9tFHUFvb4/cqqaldXsXJySjx8RAfj9LPz6q/Ps2irQ1qahCHD3f9FRYiCgp69uawMHRnnokybx7K9Ok9LqaoaRrNzc2e8dvU1ITJZPLyLB/oMdza2kp2djZz584NWOJkTk4O55xzDuvXryczMzMgbZA2GoOPIS822+12nE6nR3i+7rrrTvh6u92O3W73PHZ709XW1npuqnU6HUajEafTiXbUjJler8dgMOBwOLxmXQwGA3q9/rjtRqMRnU7ndTz3dkVRcDgcOJ1OvvjiC84880zCw8MRQuB0Or1ebzab0TTNa7uiKJhMJlRVxeVyHbfd5XKhqqpnuz/7dDQmk8lvfXLH8pxzziE0NHRI9MlNID4np9PJunXrOPvss71eO5j7BN1/TgCdopODTQc50HSAqpaqrr/WKqrbqqluqaa+vR5rh5UWRwuDjXBjOPFh8SSEJ5AQnsDIiJHEh8UTFxrXtS0sgVGRo5gwYgJmxRy0n5OiKCiKwkcffcSqVas8xWoG89gL5PmkqioffvghZ555pieWg71PgfycOjs7Pd/nZrN5SPQpUJ+TvDcK3L2RzWYjISFBis2SIYcQgoqKCtauXesRn61WK5mZmaxcuZKVK1cyd+5cDIagXhTcY1paWti2bRuzZs0iISHBb8c9WrizWq00NzcTEhLiEe5iY2N7PGEQCIQQ5OXlATB37txBOREh2tq6vIs3b0asX9+3nZjNKOPHo4wdC2PGoERHQ2QkhIaihIRASAiEhoLRCDod6HQ0NDZSUFhIRno64SEh4HKB09n1/46OLiG5vR3a2hCtrdDQgDh0CLF/P/TFImHUqC7f5RkzUKZN62qXD1BVlaamJk/BQZvNRkhIiGcCJTY21qcWX5qmkZWVRVxcHFOmTPHZfntDW1sbS5Ys4eqrr+ahhx4KSBvcyAKBg4uh8Y15An72s58xf/58wsPD+fDDD7nhhhvQ6/Vce+213b7+t7/9LY888shx25955hlCjlyg5s2bxwUXXMAnn3zCjh07PK9Zvnw5K1as4M0332Tfvn2e7atXr2b+/Pm88sorXl4yV199NZMnT+aZZ57x+qFx2223ER0dzeOPP+7ZVlBQwL333ktzczMvvfSSZ7vJZOK+++6jrKyM119/3bM9Pj6eH/3oR+Tn5/PBBx94tk+aNIlrrrmGTZs2sXHjRs/2QPQJCEifIiMjWbVq1ZDqU6A+J4fDwZQpU3jzzTcHfZ9UVDqMHZx9xdls2bOFT7M/pZFGrFhpVprpFJ30FB06RoSNwKyaUewKIYRgwsS4keOYMm4K5XvKaW9qx4QJI0YWzFlAYX4hMZExtLa0okOHHj3nnHkO48eO5x//+AcOlwMNDYHg/NXnExoWyn/e+A8aGk6cuHBxxjln0NjSyIbNG3DhwokTTacxZdYUKhsqKa0spfPIfw7F4elTm7ONtuY29jfvP2XfwvXhhKvhRBFFNNHMGT+H0+acxp7sPbRVtxFNNAYMATufbrjhBvLz88nPz/dsD/axd6o+Bep8Sk5OpqCggIKjMkYGe5+C4XMqKCgYcn0CeW/kqz5BcN8bdXb2/LtQIhlMKIpCcnIy1113Hddddx2aprF7926++OIL1q1bx7PPPgvAsmXLWLFiBStXrmTKlCmD0oLG4XCQl5fH+PHj/So0Q9eEV0xMDDExMUyaNAmXy0VTUxNWq5X9+/dTWFhIRESEV9ZoMAn85eXltLW1kZmZOSiFZgAlPBxl3jx08+bB7bcDIFpaEAcPdnk/b94Mx9S8Og67HbFnD2LPnh4fNxo47ci/XSd7YV8YOxbd/PkokyahTJoECQkD9vno9XpGjBjhETaPHsMHDx5k586dhIeHe9lu9GcCpbS0FEVRApZFL4Tg/vvvZ8SIER4//EByyy23sGbNGk477TSSkpK49957ueyyy6TQHKQM6czmY7npppvIz88nOzu72+eDLbPZbrfz7LPP8tOf/pTIyEiZvdOPPrljec899xAeHj4k+uQmEJ+TO56/+MUvvG60g71PdpedEmsJJc0lFNUWUVRbRHF9Mfsa9+HSTn7rYwmxkBSRxOjI0SRFdv1/rGUsCWEJxIbEEhcaR2xoLLFhsZhN5h73SVVVnnzySX72s5953YwM9NjTGXQ0tjdS11pHbVstde111LbX0tDZwOGWw9S01lDbXkttWy2H2w5js5+k2rV7vyiMiRrDpJhJTIqdxPio8Yy3jGeiZSITYyaSEJkwoH0SQvD444/z05/+1JNVECxjr699CtT55HQ6j4vlYO9TID+n9vZ2z/d5aGjokOhToD4neW8UuHsjmdksGa64XC6ys7NZt24dX375JVu3biU+Pt4z+bRy5UpGjhwZ9AKkpmnk5uZiNptJTU0NuvY6HA5PxqjVaqWjo4OoqCiP+BwdHe33Ynxu6uvrKSgoGFDbkWBDtLZ22VbU1HT5Ie/cidi7NzCNGTGiy9rjiK2HMno0jBqFEmSFgp1OJ42NjZ5x3Nra6plAcU+0uFcMnoqGhgby8/PJzMwkPDx8gFvePZ9++inXXXcdOTk5TJ06NSBtOBohBA888AAvv/wy7e3trF69mj/96U9YLJZ+7ffZZ5/l0UcfpbS0lJiYGM/27373uxw+fJhvvvkm6K6Xg4E+TRW+e7SpfA8599xzA+7JNG/ePN5///0TPm82m7td9tDd9hNdJE40c3Wi7SdaZnH0drPZ7Fki3t3rdTpdt9v1en23X8gGg6HbWWJ/9smNv/vkfs1Q6pMb2Sfvtte11bH90HZyq3PZXr2dnbU7KbWWogq1+/fqzUyMmcik2ElMtHT93y2ajo0eS5ixdwVAetontwBhMplOeP3ptr0++JziI+KJj4hnBjO6PcbR2Ow2KmwVHGo+xCHboeP+faD5AO3Odg7ZurZtOLDhuH1YQixMiZ3C9LjpTI+bTkpcCtPjpjMpdhImXff9702f3LHs7potz6fe9wm6j+Vg7lOgPif3690WGjD4+xToz0neG32Lv+6NfLk0WCIZTBgMBhYvXszixYu5//77aW9vZ9OmTXzxxRe89NJL3HLLLUybNo0VR4oNnnbaaURFRQWVOCGEYPfu3aiqysyZM4OqbW5MJhOJiYkkJiYC0NHR4RHtCgsLcblcWCwWj/jsrxi3t7dTWFhISkrKsBGaAZSICIiI6MoWXrz4uOeFqoLNBs3N39pgtLcj2tuhowM6OmiqraXTZiPBYkHRNDAYUEwmMJvBZOqy2wgJ6fKDPmK/oURHwxFrDiWIMtt7gtFoJCEhwbNq4OgJlNLSUtra2oiMjPSMYYvF0u33tMPhYOfOnUydOjVgQnN9fT0//vGPefLJJ4NCaIau+6NHH32URx991Kf7veOOO/jLX/7Co48+ytNPPw3A5s2bef/998nKygrK6+VgoE9nb2+zixVFoaSkhIkTJ/blcCels7OTw4cPY7FYTjmjsXv3bsaPH+/zNgwUer2e5cuXB2wGdyghY+lbgi2e9e31ZFdmk1uVy/bD28mtyuWQ7VC3r40yRzEzfiYz42cyI34GMxNmkhKXwuio0egU/y+HDLZYnogocxQz4mcwI757YVoIQW1bLWWNZexr3Mc+6z7Kmsq6/t9YRnVrNU2dTWyr2sa2qm1e79UreibFTuoSoUccEaLjU5gZP5NIc89v6gdLLAcDMpa+RcbTd8hY+g4ZS4mk9yiKQnh4OGeffTZnn302QggaGho8Xs/3338/5eXlLFiwwJP1nJmZGfDJmkOHDlFXV9fn4myBIDQ0lNDQUJKSkhBC0NbW5inUtn//fgAvr9zw8HCfi0KqqpKfn8+oUaMYNWqUT/c92FH0eoiJgZgYuou6OzM3IyMDY0SE39sXDBw7gdLZ2ekRn3fv3k1nZ6cnez8mJgaLxYJOp6O4uPj/s3fncVHV3+PHX8O+LwqIIioq4AKKK64sapm5ZpZaplSm6cfKLEu/lUtl9anUtNxatEVNTcut0gQU9x0UxR0QF0hZBJGdub8//DEfySWBC5flPB8PHsq9M/eee+Yyc+fMe84bBwcH3NzcNIlbr9fz6quv0q5dO1566SVNYqhIJiYmzJs3j379+vGf//yHxo0b8+abb/Liiy/Svn17rcOrskrVRsPIyIikpKSH7vNka2vLsWPHSlxszsvLIyYmBrg9Kvmzzz6jV69euLq64urqCsCOHTsIDg5m+vTpzJgxw3DfvXv3snfvXnr27ImdnR3btm3jtdde47vvvmPEiBEPtX+ZdVuIykev6DmTfIa9l/ay59Ie9lzaw9mUe3+ly6u2F23rtqWta1tau7ampXNL6tnWk08nNZCVn0VsWiznUs5xKvkUp5NPG34eNLGih4MHvnV8aeXS6va/dVrRtFZTTIyq1kgHIYSo6uS6WIh/pygK8fHxhIWFERoayvbt28nMzKRLly4EBwcTFBSEr69vhRZ8U1JSiIqKol27dmX+unllUdTap6jlxo0bNzAxMSk22WBZv1WtKAonT54kOzubdu3aVcke3VrJyclh//79eHl5Ua9ePa3DqbTuHL2fmppKXl4elpaW5Obm4uPjg5OTkybn3fLly3n33Xc5fvy4oe5WEzzxxBOYmpry1FNPMXbsWM6ePYuTk5PWYVVZpSo2P//888yfP/+hv0Yybtw4PvjggxI/UPHx8Xh4eNy1/M7C8v2KzUePHmX06NGcO3eOgoICvLy8mDRpEqNGjXro/Wt9UZ2Xl8eaNWt4+umnK/XMvFWB5FJdFZnPAn0BRxOPEh4Xzu6E3ey7vI/U7NS7budd25v29drTrm472tVrh5+rH3bmlf/NcE0/NxVFITEzkVPX7yhAp5wm5noMV29eved9zI3NaenSEl+X28VnXxdf2tRtg52JXY3OpZpq+nmpNsmneiSX6ilpLrW+LhaiKtLr9Rw/fpywsDDCwsLYtWsX5ubmxfo9e3h4lNtAiFu3bnHw4EG8vb2rddFPr9cbJmpLTU0lIyMDCwuLYsXnkr5mXLp0idjYWDp16qT5yPSqRK/Xc/ToUSwsLPDx8dE6nCqj6FsSUVFRODg4cOvWrbtax9ja2pZ78Tk+Pp7OnTvz/fff88QTT5TrviqbuLg4WrZsia2tLe+99x4T/v8kmqJ0SjU0bNmyZSW6/Z0zapdEo0aN+LdaeFBQ0D1v07ZtW44ePVqq/VYWiqJw4cKFf82B+HeSS3WVZz4VReHk9ZOExYYRHh/Ojvgdd01OZ2liSUe3jnRx70JX9650qt+J2lZVcxbamn5u6nQ66tnWo55tPXo27llsXUpWCtHXoon+O5rjfx+//f9r0WTlZ3E08ShHE4s/x7vbuWOdYc253efwd/enbd22uNrUnE/j1VTTz0u1ST7VI7lUj+RSiPJnZGSEn58ffn5+vPHGG+Tm5nLgwAFCQ0NZvXo1b7zxBm5ubgQGBhpGPjs5OalSfM7Pz+fYsWO4ublV60Iz3M5zUUEObk/qWDRiNC4ujujoaMNEbUUtC+7VK7fIjRs3OHv2LG3btpVCcwnFxsaSl5dHmzZttA6lSlEUhfPnz9OgQQO8vLwMrWOKzuOLFy+i1+sNEw0WFZ/V/KCqoKCAMWPGMGTIEAYNGqTadqsKDw8PXnjhBdavX8+4ceO0DqfKk+8hCyE0l5SZxJbzW9h6YSvhceFcu3Wt2HoHCweCGgUR2DCQru5d8XP1w9T44WbyFVVXbavaBDUKIqhRkGGZXtETlxZnKD4f//s4x/8+zrnUc4Y+3e/vet9w+3q29QytVNrVa0fbum1xs3WTVipCCCGEqHDm5uYEBAQQEBDAzJkzuXnzJhEREYSFhTF79mxeeOEFWrZsaRj13LVr11L1IlYUhejoaCwsLPD09Cyno6m8TExMcHZ2xtnZGbj9TY6iUc9nzpwp1iu3aKK2ohGjubm5HDt2DE9PTxwdHbU8jConJSWFhIQEOnbsWGV6g1cWFy5cAKBp06bA7QE5NjY22NjY4O7ujqIoZGZmGs7juLg4AMOHJ2r0LZ8/fz6JiYn88ccfNfa9kpOTEw4ODnL+qkCKzUKICleoL+Tw1cP8fu53/jj3B0cSjxRbb2VqRfcG3enh0YOeHj3xc/XD2Eie8AUY6YxoUqsJTWo14Ynm//tqV0ZuBgcTDvL5ys+p1bIWUX9HcTr5NFdvXuXqzatsPrvZcFsXaxc61OtAR7eO+Lv509GtI46W8mZCCCGEEBVHp9NhZ2dH//796d+/P4qikJSURHh4OGFhYbz++utcvXqVjh07GorP7du3x9T03wdcnDt3juzsbDp27Fhji0Z3MjMzKzbvU3Z2tqFoFx0dTUFBgWHEaFJSEo6Ojri7u2scddWSk5NDdHQ03t7e2NTQCQFLKzU1lUuXLtGxY8f7tsnQ6XTY2tpia2tLw4YN0ev13Lx5k9TUVJKTkzl37hwmJiaGwrOjoyNWVlYP/fd/7NgxPvroI7Zu3SqtsoQqStSzOTo6mvHjx3Pt2jUeeeQRPvzwQ8MkA507d2bfvn3lFacmtO5NVzT7bevWreWTlTKSXKqrNPnMyM3gj3N/8Pu539lyfgvJWcnF1rev154+TfvwSONH8K/vj5lxzejFKeemev6Zy8y8TI4lHeNo4lGOJB7haOJRYq7HUKgU3nVfr9pe+Lv53/6p70+rOq1qzDl4L3JeqkvyqR7JpXpKmkutr4uFqGmKvla/bds2wsLC2LFjB/n5+XTr1o3AwEB69OhB8+bN7ypO/fjjj5iamjJgwACsra01ir7qKGpXkJqaSkJCAtnZ2XdNNliSol1NdGef5pYtW0quSiAvL4/9+/fj4eFRpg849Ho96enphrYb6enpmJqaFhv5fL9JM7OzswkICGDgwIHMmjWrRj9+M2bMYO3atZw4cULrUKq8EhWbu3btyty5c/H29mbNmjUsXLiQb7/9lnbt2tGmTRsiIyPLM9YKJxfVQpRNclYyG89s5NdTv7Itdht5hXmGdXbmdvRu0pvHPR+nT9M+1LGpo2GkoqbIzs/m2N/HOHjlIAeuHODA5QNcSLtw1+3Mjc1pW7etofjs7+ZPI4dGNfriSwhRs8l1sRDaKiws5OjRo4bJBvfs2YOdnV2xfs/nzp3jqaee4rvvvqtxk3uVVWJiIqdPn6Zjx44UFBQYRj7fuHHDULQr+rGwsNA63Erl/PnzXLt2DX9/f/kguAQUReH48eMoikLr1q1VfZ9RWFhIeno6qamppKWlkZ6ejoWFhaFfuampKY0bN0ZRFKZMmcLevXvZt29fjZ98WYrN6ilRsdnX15fo6GjD77GxsQwfPpzXXnuNzz//vMpPyPdPWl9U5+Xl8e233zJ69Oga/0dfVpJLdT0on1cyrrD+9Hp+Pf0rEfERxUaRetbyZFCzQfT17EsX9y7Sdxk5N9VU2lwmZyXfLj5fPsCBKwc4eOUgaTlpd93OxdqFru5dDRNTtq3bFnOT6jlpjJyX6pJ8qkdyqZ6S5lLr62IhRHHZ2dns27ePbdu2sX37dg4dOgRA69ateeONNwgMDMTR0VE+KH8IN2/e5NChQ/j6+hr6PBe5s2iXmppKRkYGlpaWxSYbrMmvRykpKRw7doyOHTtK+4wSunLlCufPn6dz587lfg4VFBRw48YN0tLSWL9+Pe+//z716tXDw8ODgwcPsm3bNrp3716uMYiapUQ9m+3t7YmJiaFFixYANG7cmPDwcJ577jlOnTpVLgHWZIqicP36dZklXAWSS3X9M5/JWcmsjVnLyuiV7ErYVey2fq5+DG42mMHNB9PCuYVc8P6DnJvqKW0unayceNzzcR73fNywnXOp5zhw+YBhBHRUUhTXbl3jt9O/8dvp34Dbo587uHWgq3tXQxG6tlVt1Y9LC3JeqkvyqR7JpXokl0JUbZaWlvTo0YMePXpw69YtunXrRu3atfHy8uLDDz9k5MiR+Pn5GVpudO7cGQsLC7kW/4f8/HyOHTtGw4YN7yo0AxgbGxsKy0W3L2pVcOHCBW7duoWtrW2x4nNNGd0rfZpL79atW5w5c4bWrVtXyIcVJiYmODk54eTkxOTJkwkJCWHDhg1MnTqV2rVrExgYSMuWLenRowfBwcGGD6uEKK0SFZvXrFmDiUnxu1hbW/Prr7+yZ88eVQMTQlRuueSy6uQqfjn9C1svbKVAX2BY17l+ZwY3v11gbuzYWMMohSg5nU6HV20vvGp78Vzr5wDIKcjhyNUj7Lm0hz2X9rD30l6Ss5LZnbCb3Qm7Dfdt5tTMUHzu2qArnrU85U2dEEIIIcqNoiiMHj0aa2trfv/9d8zNzVEUhcuXLxtabowdO5bU1FT8/f0JDg4mODgYPz+/u97b1zSKonDixAmsra1p3Pjh3rOYmpri4uKCi4sLALm5uYZRz6dOnSI3Nxd7e3tD8dne3v6+k75VZUW5c3Jyol69elqHU6Xo9XpOnDiBm5sbtWtrM1DFycmJvXv30qVLF7Zu3UpKSgoRERFs376dqVOncvbsWaKjow0DTYUoqRK9ujzoSaRr165lDkYIUbnlF+bz14W/+PHYj/zGb+Rvyjesa1u3Lc/4PMNQn6HUt6uvYZRCqM/CxIKuDW4XkOH2BfbZlLPsvbTXUIA+nXza8PNd5HfA7VHTXdy70M29G4GNAmnj2kbaxwghhBBCNR999BF79+7l0KFDmJvfbu+l0+lwd3cnJCSEkJAQ9Ho9p0+fZtu2bYSHhzN37lwAAgICCAoKIjg4GE9Pz2pZFH2Q2NhYbt26hb+/f6kHB5ibm1O3bl3q1q0LQFZWlqH4fOnSJfR6PQ4ODobis62tbbUYiBAbG0teXh5+fn7V4ngq0oULF9Dr9TRt2lSzGNauXctff/3FsWPHMDIywtnZmSFDhjBkyBDgdg/zog9UhCiNEvVsflh5eXnVom+R1r3p9Ho9sbGxNG7cuMa98KtNclk2p66fYmnkUn48/iPXbl0zLG/i2IRnfZ9luO9wmjk10zDCqkvOTfVoncvkrGT2XdpnKD4funKI3MLcYrexNrWma4OuBDQIILBRIB3qdaiUfZ+1zmV1I/lUj+RSPSXNpdbXxUKIe/vhhx9o3bo1fn5+D32fgoICDh48SHh4OKGhoezfvx9nZ2cCAwMNxWdXV9dqXUS8fv060dHRdOjQAVtb23LZh6IoZGZmGorPaWlpGBkZ4ejoaCg+W1lZVbk8S5/m0ktNTSUyMhJ/f3/NcnflyhU6duzIokWLGDZsmCYxiOqvzMXmhQsXYmNjw8iRI8nPz2fQoEFs2bIFLy8v1q1bV6WH3ctFtajJMnIzWH1iNUujlrL/8n7DchdrF4b7DOdZ32dpX699lbs4EqKi5BbkcjTxKHsu7WFXwi52Xdx118SDFiYWdKrfyVB87lS/E1amVhpFLIQQ9yfXxUJUT4qikJWVxe7duw2TDUZFReHt7U1QUBBBQUF0794dOzu7anPdn5WVxYEDB2jWrJlhRHJF0Ov1ZGRkGIrPN27cwNzc3NDruVatWlhYWFRYPKWRm5vL/v37adq0KW5ublqHU6Xk5+ezb98+PDw8cHd31ySGwsJCBg4cSL169fjpp5+qzd+0qHzKXGz29vZmyZIlBAUFsWLFCl555RWWLFnCqlWruHXrFlu2bFEr1gqn9UV1bm4uc+bMYdKkSYavRInSkVw+HEVR2HlxJ0ujlvLLyV/ILsgGwFhnTD+vfrzQ5gX6NO2DvkAv+VSJnJvqqey51Ct6Tlw7QUR8BDsTdhIRH8H1rOvFbmNqZEoHtw4ENgwkoGEAXdy7YGcurz9VneRTPZJL9ZQ0l1pfFwshKoaiKKSkpBAWFkZ4eDjh4eHExcXRrl07w6hnf3//KvscXFhYyMGDB3F0dKRZM22/mVlYWMiNGzcMxeeMjAysra2LTTZoalp52q8pisKRI0ewsLCgZcuWUqgsAUVRiI6OprCwUNPWIwsWLOCrr74iKipKJgAU5arMMwIkJCQYmunv3LmTF198kaeeegpfX186d+5c5gBrury8PK1DqDYkl/d3I+cGPx77kUWHF3E6+bRheXOn5rzQ5gVGtBqBq42rYXluQa7kU0WSS/VU5lwa6YxoVacVreq04hX/V1AUhTMpZ4oVn6/cvMLeS3vZe2kvH+/+GCOdEW3rtiWgQQBBjYIIaBiAvYV9hcRbmXNZFUk+1SO5VI/kUgjxTzqdDicnJ4YOHcrQoUNRFIX4+HjCwsIIDQ1l1KhRZGZm0qVLF4KDgwkKCsLX1xdjY2OtQ/9XiqIQExODiYkJXl5eWoeDsbExtWvXNkwSl5+fT1paGqmpqZw/f55bt25hZ2dnKD47ODhomufY2Fhyc3OlT3MpJCYmkpaWRufOnTXLXUxMDDNmzGDTpk1SaBblrszFZmdnZ06ePEn9+vUJDQ1l/vz5wO0n8nJoBy2EUFFkYiQLDy1k5YmVZOVnAWBjZsNwn+G80OYF/N1KP1mGEOLBdDodzZya0cypGWPbj0VRFOJuxBERH0HExQh2XtxJ3I04Dl89zOGrh5mzfw5GOiPa1W1HcKNgenj0oFuDblibWWt9KEIIIUSFiIiI4KOPPuLo0aPk5OTQunVrZs2aRWBgoNahVVs6nQ4PDw9Gjx7N6NGj0ev1HD9+nNDQUMLDw/n4448xNzcv1u/Zw8OjUr6HuHTpEqmpqXTq1KlS9v03NTXFxcXFMDFbTk6OofgcExNDbm5usckG7ezsKuw4UlJSuHjxIh06dMDEpMxlpBolKyuL06dP06pVK83mNsvNzWX06NGMHTuW4OBgTWIQNUuZnyVGjRrFk08+afhkpGfPngDs3buX5s2bl3XzQgiV5RTksObkGhYdXlSsF7OPiw/j249nRKsR2JqXzyQZQoj70+l0NHZsTGPHxjzf5nkALqVfYufFnURcjGBH/A7OpZ7j0NVDHLp6iE/3foqpkSkd3TrSw6MHwY2C6ezeGQuTyt3rTwghhCitAwcO0LlzZ2bOnImTkxMLFy6kT58+REVFVYqRqjWBkZERfn5++Pn58eabb5Kbm8uBAwcIDQ1l9erVvPHGG7i5uREYGGgY+ezk5KR58TktLY1z587Rrl27KtMCxMLCgrp161K3bl0URSE7O9vQciMhIQG9Xl9sskEbG5tyyXNubi4nTpzA29u73CZTrK70ej0nTpygXr16ODk5aRKDoijMmjWLwsJCPvroI83/FkXNUOaezQDr1q3j4sWLPPHEE3h4eADw008/4eDgQP/+/cscpFa07k2n1+tJTk7GycmpUn7yWpVILuHqzassOLiAJUeWkJKdAtzuDzukxRDGdxhPV/euD/3CI/lUj+RSPTUhl5czLrM9bjvh8eGEx4WTkJ5QbL25sTld3LsYis8d3TpialzyXn81IZcVSfKpHsmlekqaS62vi4W4F0VRcHR0ZObMmbz22mtah1PjKYrCzZs3iYiIMPR8PnnyJC1btjSMeu7atSvW1tYVWvAqmtTOw8ODBg0aVNh+y5OiKGRmZpKSkkJaWhppaWkYGRkZCs+1atXC0tKyzHmWPs1lc/78ea5fv07Hjh01a4GyZ88eBg0axJ49e/Dz89MkBlHzqFJsrq60vqhWFIW8vDzMzMzkSb2ManIuIxMjmbt/LqtOrCJfnw9AA/sGvNzuZV5o8wJ1bOqUeJs1OZ9qk1yqp6blsqjtRnhcONvjtxMeF05SZlKx21ibWtO9YXdD2402rm0wNvr3C92alsvyJvlUj+RSPSXNpdbXxULcy82bN6lVqxbfffcdI0eO1Doc8Q+KopCUlER4eLih+Hz16lX8/f0NI5/bt29frpPg6fV6jhw5gqWlZbUulur1ejIyMkhJSSE1NZX09HTMzc2LFZ9LM6L7woULJCUl4e/vL+0zSigtLY2jR4/SsWNHzUaEZ2Rk0LlzZ8aNG8dbb72lSQyiZip1sTkpKQlXV9d/v2EVpvVFdW5uLp988glTpkypMl/1qaxqWi71ip7NZzczd/9cdsTvMCzv1qAbkzpNYoD3gIcqON1PTctneZJcqqem57JowsGi4vP2uO2GbzEUsTe3J7BRIMGNgunp0RMfF597vumq6blUm+RTPZJL9ZQ0l1pfFwtxL1OnTuXnn38mJiYGKysrrcMR/0JRFM6fP8+2bdsICwtjx44d5Ofn061bNwIDA+nRowfNmzdX9Zsrp0+fJi0tTdORpVooLCw09HtOTU3l5s2bWFtbGwrPjo6O/1rkT0lJ4dixY3To0EHaZ5RQfn4++/fvp2HDhpqNplcUhfHjxxMfH094eHiNOv+F9kr90VSjRo0YOXIkkydPxtPTU82YhBCldCvvFt9Hfc8XB77gfOp5AEyMTHi65dO83ul12tdrr3GEQojycueEg+M7jEev6Dlx7YSh+LwjfgfpuelsPLORjWc2AlDHug49G/ekl0cvejXuhbu9u8ZHIYQQoiZbvHgx48aNu+e6rl27snv3bsPvq1atYsGCBezYsUMKzVWETqfD09MTT09Pxo8fT2FhIUePHiUsLIzQ0FBmzpyJnZ1dsX7P7u7upR6NnJiYSGJiIv7+/jWu0GZsbIyTk5OhT3B+fr6h8Hzu3Dmys7OxtbU1FJ8dHByK5aioT7OXl5cUmktIURROnz6NtbU17u7aXVtv2rSJDRs2EBkZWePOf6G9Uheb9+zZw6effkrLli3p168fb7/9Nv7+/mrGJoR4SKnZqXx54EvmH5xPanYqAA4WDoxtN5YJHSdQ366+xhEKISqakc6IVnVa0apOKyZ2mkihvpDIpEjC4273e96VsIu/b/3NyuiVrIxeCYB3bW96Ne5FoHsg2WRrfARCCCEe1tWrV8nPz6dhw4Zah1Imw4YNIygo6J7r7iwob9y4kTFjxrB+/Xratm1bQdEJtRkbG9OhQwc6dOjAlClTyM7OZt++fWzbto1ly5YxYcIEPDw8CAoKIigoiMDAQBwdHR+q+Hzz5k1OnTpFq1at5MMIwNTUlDp16lCnzu0Wijk5OYbi88mTJ8nPz8fe3t5QfD5//jy1atXCzc1N48irnqSkJFJSUujcubNmbVuSkpJ45ZVXmDdvnmFeNSEqUqmLze3atWP16tWcP3+ezz//nODgYMOLRJ8+fdSMUQhxH1cyrjBn3xyWHFnCrfxbADRxbMLrnV5nlN8obMxsNI5QCFFZGBsZ075ee9rXa89bXd8ityCX/Zf3ExobSmhcKAevHORMyhnOpJxhwaEF6NCx/cftPNrkUXo17kWn+p0wN5G2BUIIUdnk5OTwww8/sGrVKuLi4nj55Zd59913q2S7EwcHBxwcHB54my1btjBixAhWr15Njx49KiYwUSEsLS3p0aMHPXr0QFEUbty4wfbt2wkNDeXDDz9k5MiR+Pn5GVpudO7cGQsLi7sKetevXyciIoI2bdoYRvaK4iwsLKhXrx716tVDURSysrIMxee4uDj0ej21a9fm0qVL1KpVq8IndayqsrKyOH36ND4+Ppq1+9Lr9fznP/8hMDCQ5557TpMYhFBtgsBr167xxRdfsHjxYtzc3Hj77bcZPnx4lR6ur3VvOpkERz3VLZfnUs7x6Z5P+fH4j+QV5gHQuk5rpnabypAWQ8rUj/lhVLd8aklyqR7JZdncyLnBjvgdt4vPsaGcSTlTbL2VqRUBDQMMLTd86/hipFOvp2J1JuemeiSX6pEJAquPnJwc/vrrL2rXrs1zzz1H69at+e6776hVq5bWoakuPDyc/v37M3fuXAYMGGBYbmNjg42NDLKozhRF4fLly4SFhRkmG0xNTcXf35/g4GCCg4Px8/NDp9PRu3dv7OzsWLdunbxWlFBqaiqRkZG0bNnSMPo5LS0NExOTYpMNWlpaah1qpaPX6zl8+DC2trY0b95cszi+/fZbPvnkE44fPy4ftgjNqFZsLpKZmcnixYuZN28eRkZGTJo0iZdeeqlKfnVF64tqvV5PcnIyTk5Oqk6SUBNVl1xGJUXx8e6P+eXkLyjc/tPt3qA7U7tN5bGmj1XYxVR1yWdlILlUj+RSPXq9nmPxxzh64yjh8eGExoZy7da1YrdxsXahp0dPejW+XXxuYK/N5CdVgZyb6pFcqqekudT6ulg8HDs7Oz766CPGjx9/z8e1sLAQnU5XZf9+QkJC+OGHH+5aPn36dGbMmFHxAQnN6PV6Tp8+zbZt2wgPDyciIgIAJycnkpOT+fXXX+nYsWOVPde1kJuby/79+2nSpAn16/+vDaNeryc9Pd0w8jk9PR0LC4tixWczMzMNI68cLly4wN9//61pj/Bz587RrVs31qxZIx0HhKZUf+a1sbHhzTffJDY2lunTp7NkyRJmz56t9m5qhPz8fBYtWkR+fr7WoVR5VT2XkYmRPLH6CdosacOak2tQUOjn1Y/dz+9m5/M76ePZp0I/ta/q+axMJJfqkVyqJz8/n40/bWREyxGsGLyCpDeSOP7yceY8Ooc+TftgZWrFtVvX+PnEz7y48UUaftEQry+9GP/7eH479Rtp2WlaH0KlIuemeiSX6pFcVh+FhYUAHD9+nFu3buHn51eswFZQUMDx48cpKCjA2Nj4ruLb9evXuXjxYoXGXFrff/89iqLc9SOF5prHyMiIFi1a8Nprr7FhwwaSk5N59913uXTpEk2aNOHxxx+nefPmjBkzhpUrV5KYmIjK4+yqFUVROHHixD37NBsZGeHo6EiTJk3o0KEDQUFBNGvWDBMTE+Lj44mIiGDfvn2cOXOG69evU1BQoNFRaOfGjRvEx8fj6+urWaE5Pz+f0aNHM3LkSB577DFNYhCiSKl7Nv8bU1NTXnjhBV544QWSkpLKazdCVGtRSVHMjJjJ+tPrgdsTfg1tOZSp3abiW8dX2+CEEDWGTqfDt44vvnV8eb3z6+QV5v2v33Ps7X7P51LPcS71HIsOL8JIZ0RHt4482vhRejftTUe3jpgYldslhxBC1GhFBbQNGzbQuHFjGjT43zdN9u/fz8cff8yZM2dISEjg0Ucf5eOPPy72Fe8NGzawfPlyzp49S9OmTfntt9+oXbt2hR+HEGVx8eJFZs2axffff8+wYcPIyspi9+7dbNu2jUWLFjF27Fi8vb0Nkw12794dOzs7abPx/8XGxpKTk0Pr1q3/NScmJiY4OTkZWjTk5eWRlpZGamoqZ8+eJTs7Gzs7O8OoZ3t7+yrdXvXfFBQUEB0dTdOmTbG1tdUkBkVR+Oyzz7h58yaffvqpZuf1xIkTiYiIIDo6mscee4zNmzdrEofQXoW883N1da2I3QhRbdyryDzcZzjvBrxLM6dm2gYnhKjxzIzNCGgYQEDDAN4Pfp/0nPT/9XuOC+V08mn2X97P/sv7eX/n+9ib29OzcU96N+nNo00epZFDI60PQQghqp1NmzbRtWtXHB0dAdi5cyczZszA09OTxYsXk5eXx6uvvsrChQv58ssvURSFgoICvL29GTNmDKGhofz000/Y29sX225+fr6heCREZZSVlcXgwYMJCQlh+PDhAFhbW9O7d2969+6NoiikpKQYej2/8847xMXF0b59ewIDAwkODsbf31+zCd20lpqaysWLF+nQoQMmJiUvEZmZmVGnTh3q1KkDQHZ2tqH4HB0dTUFBAQ4ODobic3Ur8p8+fRorK6tiH/RVtMOHDzNnzhx27NiBtbW1ZnEUFBQwZswYNmzYoFkMonIoc7H5ypUrrFixgoSEBLKzs+/6asrSpUvLuosaTXofqacq5PKfRWYdOp7xfaZSFpmrQj6rCsmleiSX6ilJLu0t7BnYbCADmw0E4FL6Jf668BdbL2wlNDaUtJw0fj31K7+e+hUAr9pehsJzUKMgbMyq/6ROcm6qR3KpHsll9VBUHIqKiuLll182zJWzYcMGoqKi6Nq1K7m5uTRq1IgJEybw4YcfEhMTQ4sWLTA1NaV79+6G2z/66KOYmJig1+sN7Tb27NnDjBkzSEtLIyAggLfeegt3d3dtDlaIezh06BCurq58+umn91yv0+lwcnJi6NChDB06FEVRiI+PJywsjNDQUEaNGkVmZiZdunQhODiYoKAgTdshVKTc3Fyio6Px8vJSbVSupaUllpaW1KtXD0VRuHXrlqHfc3x8PACOjo6G4rO1tXWVLT4nJiaSnJxMp06dNDuGW7du8dJLLzFlyhQ6duyoSQxFvvrqKwAOHDhAcnKyprEIbZV5gsDmzZvj6upKp06dsLCwuGv99OnTy7J5TclEKKKinLp+ine3v2soxOjQMdx3OO8FvFfpisxCCFEShfpCDl89zNYLW/nrwl/sv7yfQqXQsN7UyJSuDboais9+rn4Y6WQyHyEqG7kurpzy8/MxNTVlz5499OjRg927d9OhQwcyMjIYOnQomZmZAMTExGBsbEzDhg05cuQICQkJ1K9f31BUTkpKokOHDkyePJlXX30VRVHQ6XQoikJoaCjnzp1j27Zt7N27lxUrVtCrVy+Nj1yI4orO2dLQ6/UcP36c0NBQwsPD2bVrF+bm5gQGBhIUFERwcDAeHh5VtiB6P4qicPToUczMzPDx8amQ41MUhYyMDEPx+caNG5iYmBSbbNDS0rLc41BDdnY2+/fvp2XLlri4uGgSg6IovP7660RHR7Nz505MTU01ieOfQkJCSE5OljYaNViZi83e3t6sXbsWX9/q1z9W64tqvV5PbGwsjRs3lll0y6iy5jIhPYEZO2bww7Ef0Ct6Q5H53e7v0ty5+b9vQCOVNZ9VkeRSPZJL9ZRnLm/k3GB73Ha2XtjK1gtbib8RX2y9i7ULjzR+hEebPMqjTR7F1abqt+KSc1M9kkv1lDSXWl8Xi3ubOHEimZmZXLlyhcuXL/PHH3/g7u7OlStXGDVqFN26dWPGjBmkpKRw7Ngx/vrrLy5fvszy5cuLFef27t1Lt27dOH36NF5eXvfc17PPPktWVhbz58/H3d29TMU9ISqz3Nxc9u/fb2i7cfDgQdzc3AwtN4KCgnBycqry5/+FCxdISkrC39+/VO0z1KDX67lx44ah+JyRkYGFhUWx4nNl/BaOXq/nyJEjWFtb06JFC83i2LJlCyEhIRw5cgRPT0/N4vgnKTaLMl+lv/766wQGBtKlSxd69Ohx109ZxMTE8OSTT+Lm5oZOp2Pt2rUlun9aWhrDhg3DxsYGFxcXpk2bVqZ4Klp+fj4rVqyQWcJVUNlyee3WNSZumYjnl54si1qGXtEzqNkgosdFs2LwikpdaIbKl8+qTHKpHsmlesozlw4WDjzR/AkW91tM7KuxnJ1wli/7fEl/r/5Ym1pz7dY1VkSvYNT6UdSdXZfWi1vz1ra3CIsNI6cgR/V4KoKcm+qRXKpHclk9dOrUiQsXLnD8+HFOnjxJcHAw33zzDW5ubtja2rJ//34AateuTY8ePfjkk09Yvnx5sW0oisKePXtwcXG5Z6G5aGxSZGQkLVq0MIzgq+qFNiHup2hU8/vvv8+uXbtITk5m/vz52NvbM3v2bBo3bkznzp2ZMmUKW7duJTMz8652opVdUUuLVq1aaVZoBjAyMqJWrVo0bdqUjh07EhQUhLe3N8bGxsTFxREREcG+ffs4c+YM169fp6CgQLNY7xQfH09eXh7e3t6axZCcnMz48eP57LPPKqTQvHjxYnQ63T1/unXrVu77F1VLmZ9VZs2axeOPP0737t3v2UajLDIzM2ncuDHDhw/nqaeeKvH9x4wZQ0xMDDt27ODKlSs888wzuLm5MXbsWFXjFOJhpeekM3vfbObun0tm3u2vNQY3Cubjnh/jX99f4+iEEKJi6XQ6PGt74lnbkwkdJ5BXmMfeS3sN/Z6PJh7l+N/HOf73cT7b+xmWJpYENQri0SaP8ljTx/Cu7S3FDiFEjTZs2DCGDRsG3B6luHbtWnJycsjLy+Ott97i1VdfZerUqTz33HPk5ORw9epVgoODi/VIzc3NJTw8nICAAIBi/ZqLRi+fOHGC9PR0WrZsWWMnUassJk6cSEREBNHR0Tz22GMycrCc6XQ67Ozs6N+/P/3790dRFJKSkgyjnl9//XWuXr2Kv7+/YeRz+/btK007g3sp6tPs7e2tWp9mtZiYmODs7IyzszMAeXl5hlHPZ86cIScnB3t7e2rVqoWjoyMODg4V/k2nGzduEBcXR4cOHTTr663X63nllVfo0KEDL730UoXsc9iwYQQFBd1zXdFcAUIUKXOxuVWrVvznP/+hc+fOasRTTMeOHUvd4Dw5OZlff/2VjRs30r59e9q3b8+YMWNYtGiRFJtFhcvOz2bhoYV8vPtjUrJTAGhXtx0f9/yYXo17SbFECCEAM2MzghoFEdQoiI96fsT1W9fZFrvNUHxOykziz/N/8uf5P3l96+s0cmhEn6Z96NO0Dz08emBtpt3s20IIobUmTZrw9ttvG37v1KkTkyZNYs6cOSxevBgvLy+6d+9Oz549ASgsLMTY2JjU1FQOHTrE3LlzAYoVbgoLCzExMSEsLIzatWsbRs9JCw3tFBQUMGbMGDZs2KB1KDWSTqejbt26jBgxghEjRqAoCufPn2fbtm2EhYWxaNEi8vPz6datm6Hfc/PmzStN6ydFUThx4gS1atXCzc1N63D+lZmZGa6urri63m6rlp2dbSg+X758mYKCgmKTDdra2pbrc1NBQQEnTpygSZMmmraUWrFiBfv27eP48eMVdm45ODjg4OBQIfsSVV+Zi81RUVF0794dFxeXe45sjo2NLesuSiUyMhK9Xk/Xrl0NywICApg/fz65ubn3/EQ+NzeX3Nxcw+8ZGRl3LTcyMsLU1JT8/Hz0er3htsbGxpiYmJCXl1fsKzQmJiYYGxvftdzU1BQjI6Ni+ytartPpyMvLIy8vj9q1a5OXl4eZmRmKotz1VUdzc3P0en2x5TqdDjMzMwoLC4t9zaRoeUFBAYWF/5ucqSKP6U4VeUxFuSy6TUUdk17Rs+rkKqbvnM6ljEsAeNXyYmbATJ5o9gTm5uZV8nHKy8vD2dkZRVGKbUfOvZIfk16vx9nZ+a5YqvIxafU4Fc00fud2qvoxafU46XQ6w+uPlsfkYObAM77P8FSzpygsLOTE9ROExoUSGh/KroRdxN+IZ9HhRSw6vAgzYzMCGgTwuOfj9GzUEy9HL8ObDa0fpztfz+Xck2ujfx7TnSrztdE/j0FUfjqdjuHDhzN8+HAUReHUqVPY2dlhaWlJfn4+P/74I1u3bsXd3Z0bN27c82vQRUWMsLAwmjVrhru7u2HbQhtfffUVAAcOHCA5OVnjaIROp8PT0xNPT0/Gjx9PYWEhR48eJSwsjG3btjFjxgzs7OyK9Xt2d3fX7G8oLi6OnJwcWrduXSX/ji0tLXFzc8PNzQ1FUbh165ah+BwXFwdQrN+zlZWVqsd5+vRpLC0tadiwoWrbLKm4uDjeeustfvjhB0MRvrI4f/48mZmZhv7bUVFRmJmZadrXWmijzMXmlStXqhGH6q5fv46JiQkODg506tSJrl27MmjQIPR6PampqdStW/eu+3z88cfMnDnzruVz5swxFNLbtGnDgAED+PPPP4mMjDTcpmim2jVr1nDhwgXD8v79+9O2bVu+/fZbrl+/blj+7LPP0rRpU+bMmVPsjca4ceOwt7fnk08+Kbb/KVOmkJ6ezqJFiwzLzczMmDp1KrGxsaxYscKw3NnZmfHjx3Ps2DE2bdpkWN6kSRNGjBjB7t27iYiIMCzX4pgATY7p4MGDFXZMccTxF3+RSCIArlaudMzqSOvU1pxZf4bvnL+r8o/T+fPn5dxT4ZjGjx/PwoULq9UxafU4de7cmTlz5lSrY9Lqcbp582axXFamY3o38F3aD2vPtGXT2JW0i/Oc50bhjduF6LhQABxwwBNPmtKUKUOn0KpZK80fpzlz5si5p9IxybVRxV8b5eRUzZ7p4jadTlfszb6pqSk+Pj78+uuvrF27Fr1eT8+ePXn66ad55513DF+tLyo2nzx5khdeeMHQr1kIcW/GxsZ06NCBDh06MGXKFLKzs9m3bx/btm1j2bJlTJgwAQ8PD4KCgggKCiIwMBBHR8cKKfwWFWQ7duyoaZ9mteh0OmxsbLCxsaFBgwbo9Xpu3rxJamoq165d4+zZs5iamhYrPpel9WtSUhLJycl06tRJs0J90TcbnnrqKQYOHKhJDA8yevTou649GjZsSHx8vHZBCU3olCrSyV6n0/HLL78wZMiQh7r9ihUrCAkJIT8/nyFDhtChQwc6d+5MYGAgV69evWex+V4jm93d3bl27ZrhKxIVOdKlsLCQ6OhofH19sbS0lNE7ZTimolz6+flhbm5ersd0+vpp3tr2FpvP3+6fZmtmy9RuU3m146uY3PH5TlV+nAoLCzl9+jS+vr7FYqnKxwTa/D0V9UH859frqvIxafU4GRsbGyYvKuqfVtWPSavHSVEUDh8+jK+vryGXlfmYFEXhTMoZtsVvY2vsVnZe3Ele4f/iNDc2J6BhAI96PErvxr3xrOWJTqer0JHNRa/npqamcu7JtVGleJxKem2UkZGBi4sL6enpmn51WJSPpKQkVq5cSVpaGq+++irOzs5s376dTZs24eHhwWeffcb8+fMZNGiQ1qGK/y8kJITk5GTp2VyFKIrCjRs32L59O6GhoWzfvp2zZ8/i5+dHYGAgPXr0oHPnzlhYWKhezMzNzWX//v00adKE+vXrq7rtyqqwsJD09HTDyOeMjAwsLS2LFZ8ftrd2dnY2+/fvp0WLFtSpU6ecI7+/2bNn88MPP3D06FF5LRaVWqk+zkpNTaVWrVoPffu0tDQcHR1Ls6tSc3FxoaCggBs3brB27VoAfvvtN8Nsp/dibm5+z/Ya91p+vyclMzOzEi2/3wQb5ubm5ObmsmXLFvz8/AxfEb/X7Y2MjO653NjY+J4N601MTO75SWZFHNM/VdQx3ZnLB8VelmO6fus6M8NmsvjwYgqVQox1xoxtN5bpQdNxsb7/KJCq+Djl5uayadOm+04SUxWPqUhF/z39Wy6r4jH92/LyOqbc3Fx+//13fH1971pXVY8JtHmc7nzO/GdMlfWYWru1prVba97s+iaZeZlsj9vOH+f+4M/zf3Ix/SLbYrexLXYbk8Mm4+HgcbvXs2cfghsFY21+d69nNY+psLDQkM+iY5FzT66N7re8sl4byaRw1ZurqyuTJk0qtiwrK4vffvuNlJQUMjMzeffdd7l27RqDBw/GyclJo0irt8WLFzNu3Lh7ruvatSu7d++u4IiEmnQ6HY6OjgwePJjBgwejKAqXL18mLCyMsLAwxo4dS2pqKv7+/gQHBxMcHIyfn1+ZRyEX9Wl2dHSsEn2a1WJsbGwoKgPk5+eTlpZGamoqFy5c4Pjx49ja2hpu4+joeM/X06L81alTR9NC87Fjx/j444/566+/pNAsKr1SPWs5OzuTmJj40F+jatiwIVFRUTRu3Lg0u3ugnJwckpKS7mpW3qZNG4yMjNizZw99+/YFYOfOnfcsQghRFjkFOczbP4+Pdn9ERu7tPt/9vfrz317/pblzc42jE0KImsfGzIb+3v3p73171vjTyacNEwvuvLiTuBtxLDy8kIWHF2JubE5go0DDRINetb1UH00khBBVUd++fQ3vow4dOsTy5cvZunUrXbt2lWJzORk2bBhBQUH3XGdlZVWxwYhyp9PpcHd3JyQkhJCQEPR6PadPnzZMNlg0aWdAQABB/3+yQU9PzxJPCFfV+zSrxdTUFBcXF0MdKzc31zDq+dSpU+Tm5mJvb28oPtvb22NkZER8fDx5eXm0adNGs9izs7MZPXo0EydOLDYvmRCVVamKzYqisHjxYmxsbB7q9v/8KuDDysvLIyYmxvB7fHw8UVFRxWYj3b9/P8HBwUyfPp0ZM2YYbuvk5MTgwYN56623qFOnDleuXOHrr78u1n9SiLJQFIVfYn7hrW1vcTH9IgBt67bl80c+J9gjWOPohBBCwO03cs2dm9PcuTmTOk8iMy+T8Lhw/jz3p2HU818X/uKvC3/x+tbXaeLYhH5e/ejn1Y+AhgGYGd97tKcQQtQkRT1oRfn65wAqUbMYGRnRokULWrRowWuvvUZ+fj6HDh0iPDycjRs3MnXqVFxcXAzzBwQHB+Pq6vrAAnJ169OsJnNzc+rWrWtosZqVlWUoPl+6dAm9Xo+NjQ0ZGRnF2vRVNEVRmDFjBhYWFsyYMaNGf2Agqo5SPds0aNCAZcuWPfTtXV1dH7oXzp2uXr1a7NOjyZMnA9xVWL6fr7/+mpdffpnAwECsrKx44403GDt2bInj0IpOp6NJkybyZKICtXN5LOkYr255lZ0XdwJQ364+H/X4iGdbPYuRrmSfNFdFcm6qR3KpHsmleqpzLm3MbBjgPYAB3gPuOer5QtoF5h2Yx7wD87A1s6V309708+xHH88+D2yJ9CDVOZ8VTXKpHsmlEFXT+fPnyczMNPSgjYqKwszMrNgEkKJ6MDU1pUuXLnTp0oV33nmHrKwsdu/ezbZt21i0aBFjx47F29vbMNlg9+7dsbOzMzyvX7p0iRdeeIH58+cbJv0U92dlZYWVlRX169dHURTS09OJiorC0tKS06dPc/bsWRwdHQ0jn62srCrkNXTHjh0sW7aMgwcP3rfllRCVTZWZIFALGRkZ2Nvby0QowiA5K5n3wt/j66Nfo1f0WJpYMqXbFN7s8iZWpvLVNiGEqMoy8zIJjQ1l05lN/H7ud/6+9bdhnQ4d/vX96e/Vn35e/fB18ZUinahR5LpYiMohKCiIiIiIYssaNmxIfHy8NgEJTSiKQkpKCmFhYYSHhxMeHk5cXBzt27cnMDCQwMBApk2bhqOjo2HuKlEyJ0+eJDs7m3bt2hkmyi0a+Xzjxg3Mzc2L9Xu2sLBQPYa0tDQ6derE5MmTefXVV1XfvhDlRYrND6D1RXVBQQG7d++mW7du8pWXMiprLgv0BSw+vJhp26eRlpMGwNCWQ/n0kU9pYN9A7XArPTk31SO5VI/kUj2SS9Areo5cPcKms5vYfHYzkUmRxdY3sG9AP8/b7TaCPYKxMLn/GwzJp3okl+opaS61vi4WQghxf4qiEB8fT1hYGKGhoWzevJmsrCwCAgJ49NFHCQoKwtfXV7NWEFXN33//TUxMDJ07d75nEbmwsJAbN24Yis8ZGRlYW1sXKz6X5tv9d1IUhRdeeIG0tDS2bNkiHxiIKkXO1kqssLCQiIgICgsLtQ6lyitLLsPjwmmzpA2v/PkKaTlptK7Tmh2jdrBqyKoaWWgGOTfVJLlUj+RSPZJLMNIZ0cGtA+8Hv8/RsUe59PollvRbQj+vfliYWJCQnsDCwwt5fOXj1P60NoNWDeLbo9+SeDPxrm1JPtUjuVSP5FIIIaoPnU6Hh4cHo0ePZty4cSiKwsqVK+nXrx+7d++md+/eeHh48Nxzz/Hdd98RGxuLjDu8t5ycHGJiYmjRosV9RysbGxtTu3ZtPD098ff3JygoiKZNmwK3W93s2LGDAwcOcO7cOVJSUkr1WvvLL7+wbds2li5dKoVmUeXIkBAh7iP+Rjxv/vUm606tA6CWZS1m9ZjFS21fwthIPhEWQoiapL5dfca0G8OYdmPIys9ie9x2Np/dzOZzm7mccZkNZzaw4cwGANrVbUd/r/4M8B6An6uftoELIYQQosa4du0aw4cPZ/bs2QwbNgyAN998k9zcXPbv309YWBirV6/mjTfewM3NjcDAQIKDgwkKCsLJyanGtwhTFIUTJ07g4uJCnTp1Hvp+pqamuLi44OJye36PnJwc0tLSSE1NJSYmhtzcXBwcHAwjn+3s7B5YQL58+TKvv/46ixcvpn79+mU+LiEqmhSbhfiHnIIcPt3zKR/v/picghyMdcaMaz+OmcEzqWVZS+vwhBBCaMzK1Iq+Xn3p69WXhcpCjv19jM1nN7Pp7CYOXjnIkcQjHEk8woyIGTSwb0D/pv25yU3yC/Mxx1zr8IUQQghRDen1ekaMGEFAQABjx44tts7c3NzQy3nmzJncvHmTiIgIQkNDmT17Ni+88AItW7YkKCiI4OBgunbtirW1dY0rPl+8eJGcnBz8/PzKtB0LCwvq1q1L3bp1URSF7OxsQ8uNhIQE9Hp9sckGbWxsDLkuLCxk7Nix9OvXj6efflqFoxKi4kmxuRIzMjKiTZs28pUJFTxsLree38qEPydwPvU8AMGNgpnfZz4+Lj4VEWaVIeemeiSX6pFcqkdy+fB0Oh1+rn74ufrxbsC7/J35N3+c+4ONZzey9fxWEtITWHBkAQCbvtxEP69+DPQeSO+mvbExs9E4+qpHzk31SC6FEKJ60el0jBo1iv79+z+wSKzT6bCzs6N///70798fRVFISkoyTDY4ceJEEhMT8ff3N4x8bt++fZl7EFd2GRkZXLhwgfbt26s6L4ROp8PKygorKyvq16+PoihkZmaSkpJCamoqFy5c4IMPPsDGxobAwEBu3LjBhQsX+PXXX2tcsV9UHzJB4APIRCg1x+WMy7y+9XXWxqwFoK5NXb547AueavGUPMELIYQolaz8LEJjQ9lwegMbz24kOSvZsM7c2JxHmjzCIO9B9Pfuj4u1i4aRCvHv5LpYCCFqBr1ez/nz5wkNDSUsLIwdO3aQn59Pt27dDCOfmzdvXq0+rCwsLGT//v3UrVuXxo0bV+i+9Xo94eHhbN26lbCwMM6cOYOLiwt9+/alZ8+e9OzZE1dX1wqNSYiykmLzA2h9UZ2fn8+ff/5Jnz59qv2niOXtfrnML8zny4NfMn3HdDLzMjHWGfOq/6vMCJqBnbm8kbofOTfVI7lUj+RSPZJLdeXn57P5j804+Diw+fxm1p9ZT2xarGG9Dh1d3LswqNkgBnoPxLO2p4bRVm5ybqqnpLnU+rpYCCGENgoLCzl69CihoaGEh4ezZ88e7OzsCAoKMvy4u7tX6UFaMTEx3Lp1i/bt22t2HLm5uQQFBREYGMjjjz/O9u3bCQsLIzIykmbNmjFo0CBmzZqlSWxClFT1+SiqGtLr9URGRqLX67UOpcq7Vy53J+ym3dfteOOvN8jMy6SLexeOjDnCnN5zpND8L+TcVI/kUj2SS/VILtWl1+s5HnWcLvW7MLv3bM6/cp7ocdF8EPwB7eq2Q0Fhz6U9TN42Ga+vvGi5sCXvhL3DoSuHZKb4f5BzUz2SSyFEVaIoCu+99x4uLi7Y2NgwfPhw0tPTtQ6rRjA2NqZDhw5MnTqVbdu2kZKSwsqVK/Hw8GDZsmX4+Pjg5+fHa6+9xvr160lNTa1S1y/Xrl3j77//xsfHR7NCs6IozJo1C0VR+PTTT3nsscf473//y+HDh7l+/ToffPCBTBQoqhTp2SxqnOu3rvNW6Ft8H/U9ALUta/PpI58S4heCkU4+fxFCCFG+dDodPi4++Lj48G7AuySkJ7DxzEY2nNnAjvgdxFyPIeZ6DB/t/ogG9g14svmTDGkxhE71O8nrlBBCiBpp4cKFzJ07lxUrVuDm5sZzzz3Hyy+/zM8//6x1aDWOpaUlPXr0oEePHiiKwo0bN9i+fTuhoaF88MEHnD17Fj8/PwIDA+nRowedO3fGwsKiUo58zsnJISYmhubNm2NpaalZHHv37mXRokXs3bsXCwuLYutq1arF4MGDNYpMiNKRYrOoMfTo+TbqW97b8R5pOWkAvNT2JT7u+TG1rWprHJ0QQoiaqoF9AyZ0nMCEjhNIy07jj3N/sP7Mev489ycJ6QnM3T+XufvnUs+2HoObDWZIiyF0a9ANYyNjrUMXQgghKsSSJUsYN24cAwcOBOCTTz7hySef5KuvvqJ2bXkvpxWdToejoyODBw9m8ODBKIrC5cuXCQsLIywsjLFjx5Kamoq/vz/BwcEEBwfj5+en6gR8paUoCidPnsTJyUnTnsgZGRm89NJLTJ8+ndatW2sWhxBqkp7ND6B1b7qCggJ2795Nt27dKsWTcVUWnRTNsz8/S3RGNAB+rn4s6ruITvU7aRxZ1STnpnokl+qRXKpHcqmu0uYzOz+brRe2sjZmLRvPbORm3k3DOhdrF55o9gRDWgwhqFEQJkY143GSc1M9Jc2l1tfFQoiaKzc3FysrK9atW8egQYMASEtLo1atWmzbto1evXppG6C4L71ez+nTp9m2bRthYWHs3LkTgICAAMNkg56enppMNhgfH8/ly5fp1KmTZtcUiqIwbtw4Ll68SHh4OMbGMpBAVA9SbH4Auaiu+nILcvl498d8tOsj8vX5WJta82GPD5nQcUKNeWMuhBCi6sstyCU0NpS1p9ay4fQGwzd04HY7qIHeAxnSYgg9G/fEzNhMw0hFdSXXxUIIrVy9ehU3Nzd27drFpk2bCAsL49ChQ5iamvLTTz8xfPhwrUMUDyk/P59Dhw4RHh5OaGgo+/btw8XFhcDAQEPx2dXVtdxbbty8eZODBw/Srl07HBwcynVfD7JhwwbGjRtHVFQUjRo10iwOIdQmjf8qsby8PJYvX05eXp7WoVRJOy/uxG+JHzMjZpKvz8fPyo+ol6KY2GmiFJrLSM5N9Ugu1SO5VI/kUl1q5NPcxJy+Xn1ZNnAZf7/5N1tHbOWlti/hbOVMSnYKS6OW8vjKx3H5zIWRv41k45mN5BbkqngUlYOcm+qRXAohqoo7x8c5OTnRoEEDDaMRZWFqakqXLl1499132b59O6mpqXz77be4urqycOFCvLy86NChA2+++SabN28mPT1d9ckGCwsLiY6OplGjRpoWmpOSknjllVeYP3++FJpFtSMVt0pMURQuXLhQpWZyrQzSstN4O/Rtvjn6DQB1rOsw55E5nF1/Fnc7d42jqx7k3FSP5FI9kkv1SC7VpXY+TY1NebTJozza5FEW9l3Irou7WHdqHetOrSMpM4mfjv/ET8d/wt7cnsHNBzPMZxg9PHpUiw9a5dxUj+RSCFFVODk5YWRkRHJyMpMnTwZut9EoLCzE2dlZ4+hEael0Oqytrenduze9e/dGURRSUlIM/Z7feecd4uLiaN++PYGBgQQHB+Pv74+5uXmZ9nv27FlMTEzw8PBQ6UhKTq/XM378eIKDgxkxYoRmcQhRXkr1riM4OLhUX2sICQlh5MiRpdmlEP9KURR+ifmFV/98lb9v/Q3AmLZj+KTXJ1gZWfEJn2gcoRBCCKEuEyMTgj2CCfYIZn6f+ey9tJe1MWtZG7OWKzevsCxqGcuiluFk5cRTLZ5imM8wujXohpFOvtwmhBCiajA3N6dly5bs2bPH0LN5586dGBsb06ZNG22DE6rR6XQ4OTkxdOhQhg4diqIoxMfHExYWRmhoKKNGjSIzM5MuXboYJhv08fEpUZ/j69evk5SURKdOnTTpE11k6dKlREdHc+zYMU3jEKK8lKrYHBISUqqdycyaorwkpCcw/vfx/H7udwCaOTXj635f071hd+D2pBJCCCFEdWakM6Jbg250a9CNOb3nsCdhD6tOrOKXmF+4nnWdRYcXsejwIurZ1mNoy6EM8xlGh3odyr0vohBCCFFWY8eO5e2336Z79+7Uq1ePKVOm8NRTT1G7dm2tQxPlRKfT4eHhwejRoxk9ejR6vZ7jx48TGhpKeHg4H3/8Mebm5sX6PXt4eNz3uubWrVucPHmSZs2aYWlpWcFH8z9nz57lnXfeYc2aNTg5OWkWhxDlSSYIfACtJ0IpLCzk2LFjtG7dWmYlvY9CfSFfHfyKd8Lf4Vb+LcyMzfi/bv/HlG5TMDf539drJJfqknyqR3KpHsmleiSX6tI6nwX6ArbHbWfViVWsO7WO9Nx0wzoPBw9D4blVnVaVvvCsdS6rk5LmUuvrYiFEzaYoCu+99x5LliwhKyuL/v37s3jxYk177gpt5ebmsn//fsLCwggPD+fgwYO4ubkZWm4EBQXh5OSETqejsLCQRx55hMcee4y33npLs5jz8/Pp1asXHTt25Kuvvqr0111ClJYUmx9ALqort9PJp3lhwwvsu7wPgG4NuvF1v69p7txc48iEEEKIyim3IJe/LvzFqpOr2HB6A7fybxnWNXNqxrCWw3jG9xk8a3tqGKWojOS6WAghRGWlKAo3b94kIiKC0NBQtm/fzsmTJ2nZsiVBQUEkJSWxY8cODh8+rNloYkVR+Oijj/j11185fPgw1tbWmsQhREWQ5jCVWF5eHgsXLpRZwv+hQF/AJ7s/wW+xH/su78PWzJbFfRcTERJx30Kz5FJdkk/1SC7VI7lUj+RSXZUpn+Ym5vT37s+KwSu4Nvkaa4asYXDzwZgbm3M6+TQzImbg9ZUXnb/rzIKDC0jOStY65GIqUy6rOsmlEEKI6kKn02FnZ0f//v2ZN28ex44d4/Lly7z11lskJCSwdu1aUlNTefbZZ/noo4/Yt28f+fn5FRrj4cOHmTt3Lj/88IMUmkW1J8XmSkxRFK5fvy6zhN/h+N/H6fRtJ6aGTSW3MJc+TftwcvxJxrYf+8DJjiSX6pJ8qkdyqR7JpXokl+qqrPm0MrXiqZZPse7pdVybfI0fB/1I7ya9MdIZsf/yfib8OYG6s+sy4OcBrDm5huz8bK1DrrS5rIokl0IIIaornU5H3bp1efLJJzl//jzvvPMOMTExDB8+nFOnTvH000/j7u7OkCFD+Oqrrzh58iR6vb7c4snMzGT06NFMnTqVjh07ltt+hKgsSjVBoBAVLa8wj492fcSsXbMo0BfgYOHAvMfm8Vyr56TPkRBCCFFGduZ2PNf6OZ5r/RxJmUmsOrGK5ceXcyTxCJvObmLT2U3YmdsxpPkQnmv9HAENAx74Ia8QQgghhNbefvttbGxsmDZtGqampnh5eTF+/HgKCws5evQooaGhbNu2jRkzZmBnZ0dQUJDhx93dXZVag6IovPPOOzg7OzN16lQVjkqIyk+KzaLSO3L1CM9veJ7oa9EADGo2iIWPL6SubV2NIxNCCCGqH1cbVyZ2msjEThOJuR7DiuMrWB69nIT0BJZGLWVp1FLc7dx51vdZRrQaQUuXllqHLIQQQghRTGhoKN9//z2RkZGYmpoWW2dsbEyHDh3o0KEDU6dOJTs7m7179xIaGsqyZcuYMGECHh4eBAUFERwcTEBAAI6OjqUqPm/ZsoXVq1dz5MgRTEykBCdqhlJNEBgcHFyqP7KQkBBGjhxZ4vtpReuJUPR6PbGxsTRu3Bgjo5o3eiinIIeZO2by2d7PKFQKcbJyYsHjC3iqxVMlPv9qei7VJvlUj+RSPZJL9Ugu1VUd8qlX9OxO2M1Px37il5hfSM9NN6zzc/VjVOtRPOv7LM7WzuUbRzXIZWVR0lxqfV0shBBClERWVhZRUVF06dKlRPdTFIUbN26wfft2w2SDZ8+exc/Pj8DAQHr06EHnzp2xsLD417rE9evX8ff35/3332fMmDFlORwhqpRSFZt/+OGHUu3Mz8+P1q1bl+q+WpCLau3svbSXFza8wJmUMwAM9xnOvMfmlfubWCGEEEI8WE5BDr+f/Z2fjv/EH+f+IF9/e4IdEyMT+nv153m/5+nj2QcTIxm9U53IdbEQQoiaSFEULl++TFhYGGFhYYSHh5Oamoq/vz/BwcEEBwfj5+d316hlvV7PM888A8D69evlQ3JRo5Sq2FxTaH1RnZuby5w5c5g0aRLm5uYVvn8tZOVn8U7YO8w7MA8FBVcbVxb3XczAZgPLtN2amMvyJPlUj+RSPZJL9Ugu1VWd85mSlcLqk6tZFrWMw1cPG5bXsa7Dc62e4/k2z9PCuYVq+6vOuaxoJc2l1tfFQojK7fz58/j6+vLdd98ZCmwAQUFB2Nvbs2HDBg2jE0I9er2e06dPs23bNsLCwti5cycAAQEBhrYbnp6eLF++nGnTphEdHU2dOnU0iTUiIoKPPvqIo0ePkpOTQ+vWrZk1axaBgYGaxCNqDvlopZLLy8vTOoQKs+/SPlovbs0XB75AQSHEL4SY8TFlLjQXqUm5rAiST/VILtUjuVSP5FJd1TWfta1qM77DeA69dIjocdFM6jQJF2sX/r71N5/v+5yWC1vi/60/iw8v5kbODVX2WV1zqQXJpRBCLU2bNuX//u//mDx5MpmZmQAsX76cyMhIFi5cqHF01UdMTAxPPvkkbm5u6HQ61q5dq3VINY6RkREtWrTgtddeY+PGjVy/fp0//viDjh07snHjRjp16oSnpyevvvoqX3/9tWaFZoADBw7QuXNnNm3aRGRkJB07dqRPnz6cPXtWs5hEzSDFZqG53IJcpoZOpduybpxPPY+brRt/PPMHywYuw9HSUevwhBBCCPEQfFx8mN17Npdfv8z6oesZ6D0QEyMTDl45yLjfx1F3dl2eWfcM2y5so1BfqHW4QgghVPb2229jb2/Phx9+SEZGBpMnT+bTTz/Fzc1N69CqjczMTBo3bsy8efO0DkX8f6ampnTp0oV3332X7du3k5qaypw5cxg8eDADB6ozcK603nrrLWbMmEGnTp1o2rQps2fPxszMjD///FPTuET1p2ozvffee48PPvhAzU2Kai4qKYqRv40k+lo0AM+1eo75febjYOGgbWBCCCGEKBVTY1MGNhvIwGYDuXbrGsuPL2dZ1DJOXDvBzyd+5ucTP+Nu506IXwgvtnmRhg4NtQ5ZCCGECszMzFiyZAmPPPII586dw8vLSyZFU1nHjh3p2LGj1mGI+9DpdFhbWzN8+HCGDx+udTh3yczM5NatWzg6yqA+Ub5U7dnctm1bjh49qtbmUBSFadOmsWTJErKysujfvz+LFy/G3t7+oe7fqFEjLl68WGzZ9u3bCQoKeqj7a92bTq/Xk5ycjJOTU7VrJl+gL+CT3Z8wM2ImBfoCnK2cWdJvCU80f6Jc9ledc6kFyad6JJfqkVyqR3KpLsnn7Wu6I4lHWBa5jJUnVhpaaujQ8WiTRxnTbgz9vfpjamz6wO1ILtVT0lxqfV0shKg6nnrqKdauXcuJEydo2bKl1uFUWzqdjl9++YUhQ4ZoHYqoIqZOncrPP/9MTEwMVlZWWocjqjFVRzZfv379gf2Yxo8fX6LtLVy4kLlz57JixQrc3Nx47rnnePnll/n5558fehvTpk1j3Lhxht9r1apVohi0pNPpsLe3R6fTaR2Kqk4nn2bU+lEcvHIQgCeaPcHifotxsXYpt31W11xqRfKpHsmleiSX6pFcqkvyeTsH7eu1p3299szuPZv1p9fz7dFvCYsLY+uFrWy9sJU61nUI8QthdNvRNK3V9L7bqem5VIvkUghRHlJSUoiIiMDOzo5NmzZJsVmIcrR48eJi9a47de3ald27dxt+X7VqFQsWLGDHjh1SaBblTtUhIQUFBSQnJ3P9+vV7/pTUkiVLGDduHAMHDqR9+/Z88skn/PLLL6SkpDz0NmxtbXF1dTX8mJmZlTgOreTl5fHJJ59Um8lb9Iqeefvn0WZJGw5eOYi9uT0/PfET655eV66FZqh+udSa5FM9kkv1SC7VI7lUl+SzOAsTC4b5DCN0ZCjnXznPlK5TqGNdh79v/c1/9/wXzy896fljT1adWEVuQW6x+0ou1SO5FEKUh4kTJ+Ll5cVvv/3GBx98QHx8vNYhCVFtDRs2jFOnTt3zZ+XKlYbbbdy4kTFjxrB+/Xratm2rYcSiplB1ZLOrqyvTpk1TZVu5ubmcPHmS999/37AsICCAwsJCIiMj6dWr10NtZ/bs2XzyySc0atSICRMmEBIS8sB95ub+701NRkbGXcuNjIwwNTUlPz8fvV5vuK2xsTEmJibk5eVxZ2cSExMTjI2N71puamqKkZFRsf0VLdfpdOTl5RnW5ebmYmZmhqIo5OfnF7u9ubk5er2+2HKdToeZmRmFhYUUFBTctbygoIDCwv9NzFMRx3Qx/SIh60PYmbATgF4evVg6cClutm53vckpj2MqiqmgoABzc3NVH6c7VfXH6WGPqei+d+a2qh8TaPM4Fd33nzFW5WPS6nEqcq9zsqoek1aP071yWdWPScvH6c7X8+pyTGo9TvWt6zOj+wze6fIOW+O38m3kt2w9v5XwuHDC48KpbVmbka1H8lLbl2hs17haXRtp/TiV9Nron8cghBD/9Ndff7F69WqOHj2Kj48Pjz32GK+++iobN27UOjQhqiUHBwccHBweeJstW7YwYsQIVq9eTY8ePSomMFHjqVps7tq1q2rbSklJQa/X4+TkxNtvv01YWBiHDh3C2Nj4oUdJT5o0ibZt22Jtbc3mzZt58cUXMTY25rnnnrvn7T/++GNmzpx51/I5c+ZgYWEBQJs2bRgwYAB//vknkZGRhtsEBgYSFBTEmjVruHDhgmF5//79adu2Ld9++22xuJ999lmaNm3KnDlzir3RGDduHPb29nzyySeGZXPnzmXKlCmkp6ezaNEiw3IzMzOmTp1KbGwsK1asMCx3dnZm/PjxHDt2jE2bNhmWN2nShBEjRrB7924iIiIMy8vzmBQU7ALtmL5vOpl5mZhiyqM8Svu49rhYuJCcnFyhx7Rv3z569epVLo8TUGUfp9IcE0B8fDxr1qypNsekxePUp08fAH788UeSk5OrxTFp9Ti9+OKLwO3nzOpyTFo9Tu7u7nflsqofU2V4nObOnVvtjgnUfZz+fPZP3vroLQ7kHyCSSFKyU5i7fy5z98+lAQ1oRzta0KJKXxtVtsfpYa+NcnJyEEKI+7l16xZjx47ljTfewMfHB4DPP/+c5s2bs2HDBgYOHKhxhELUPOHh4Tz55JOGa9CkpCQAbGxssLGx0Tg6UZ2pOkGgmq5cuUL9+vXZtWsX+/btY9++faxbtw5TU1N++umnUs3s+dJLL3Hs2DEOHjx4z/X3Gtns7u7OtWvXDBOhVPTI5rlz5/L6669ja2tb5UbvJGYmMv7P8fx54U8AutTvwjd9v6GJYxOg4kfvzJ07lzfffBNra+tqMSKpiFYjm+fOnctbb71VbFKhqnxMoN3I5s8++4xJkyYVa/NTlY9Jq8dJURQ++eQTXn/9dczNzavFMWn1OOXn59+Vy6p+TFo+TllZWYbXc0tLy2pxTBXxOBXoC9h6YSvLji/jj/N/oFdub9sCC0L8QpjQeQLNnZpXqWO6c7nWj1NJr40yMjJwcXGRCQKFEPc0adIk1q9fz8mTJ7G0tDQs/7//+z9WrFhBTEwM1tbWGkZYPeTl5RETEwPc/hDxs88+o1evXoa2oULcKSQkhB9++OGu5dOnT2fGjBkVH5CoMVQrNp8/f57MzMxiF+RAqfvB5ObmYmVlxbp16xg0aBAAaWlp1KpVi23btj10G407LVy4kJkzZ/L3338/1O21nnVbURTy8vIwMzOrcpO3rDqxivG/jyctJw0zYzNm9ZjF651ex9jIWJN4qnIuKyPJp3okl+qRXKpHcqkuyWfZXc64zLLIZXwX+R0X0y8alndr0I0xbccwpMUQLE0tH7AF8U8lPS+1vi4WQghx+5ulHh4edy2X4qEQojIpcxuN06dPM2DAgGJfu7vTnSMqSsLc3JyWLVuyZ88eQ7F5586dGBsb06ZNG8PtcnJySEpKeqheNadPn6ZRo0alikcLiqKQnp6Ok5NTlXlzmpyVzH/++A9rTt5urdC2blt+HPQjLV20nYW4KuayMpN8qkdyqR7JpXokl+qSfJZdfbv6vBf4HlO6TmFt5FpWX1jN5rOb2Z2wm90Ju3lty2uMbD2Sse3G0ty5udbhVglyXgohRNXTqFEjVBovKIQQ5cbo32/yYBMnTsTPz4+oqCgsLS3ZsmULP//8My1atGD9+vVl2vbYsWNZtGgRGzdu5PDhw0yZMoWnnnqK2rVrG26zf/9+PDw8+OKLL4rdd+/evXz++edERkZy4cIFFi9ezJIlS3jllVfKFFNFys/PZ9GiRXd9lbKy2nx2Mz4LfVhzcg3GOmOmB05n/4v7NS80Q9XLZWUn+VSP5FI9kkv1SC7VJflUj75Qz9k/zrL6idVcnHiRD4I/oIF9A9Jy0ph3YB4tFrYgYFkAy48vJ6dAegw/iJyXQgghhBCiPJR5ZPPu3bs5cuQI3t7eGBkZ0bhxY5o0aYKDgwPvvPMO/fv3L/W2x48fT2JiIi+++CJZWVn079+/2IQpD2JhYcHKlSuZOXMmBQUFeHl58fXXXzNixIhSxyPuLSM3g9e3vM7SqKUANHdqzo9P/Ej7eu01jkwIIYQQ1ZWbnRvvBrzL1G5T+evCXyw5soTNZzezK2EXuxJ23R7t3GokY9qNkdHOQgghhBBCVJAyF5tNTU0NX+NwcnLi6tWrNGnSBE9PT06ePFmmbet0Oj788EM+/PDD+94mKCjonl8jadu2LUePHi3T/sW/2x63nZANISSkJ6BDx6TOk/iwx4dYmFhoHZoQQgghagBjI2P6ePahj2cfrmRcYWnkUr45+g2XMi7xxYEv+OLAFwQ0DGBM2zE82eJJuUYRQgghhBCiHJW5jYaPjw/79u0DwN/fn/fff5+dO3fy4Ycf0rRp0zIHWNOZmZlpHcI9ZeVn8dqfr9Hjxx4kpCfg4eDBjpAdfP7o55X2TVxlzWVVJflUj+RSPZJL9Ugu1SX5VM+Dculm58Z7ge8R91ocvz/zOwO8B2CkM2LnxZ2M+G0E9efU542tb3A6+XQFRlx5yXkphBBCCCHUplPK2F1+69atJCUlMWrUKOLi4ujXrx+nTp3CycmJNWvWEBQUpFKoFU9m3b63/Zf3M2r9KM6mnAVgbLuxfP7o59iY2WgcmRBCCCFEcZczLrM0cinfHv2WSxmXDMsDGwYypt0Ynmz+JOYm5hpGWDXIdbEQQgghhHgYZS4230tqaiqOjo5VfmZrrS+q9Xo9sbGxNG7cGCOjMg9CL7O8wjxm7pjJJ3s+Qa/oqWdbj+8GfMdjTR/TOrR/VdlyWdVJPtUjuVSP5FI9kkt1ST7VU5ZcFuoL2XJ+C0uOLOH3c7+jV/QA1LaszajWoxjTbgzeTt7lEXalVNJcan1dLIQQQgghqoZyecdTq1atKl9orgzy8/NZsWJFpZgl/FjSMTp+05GPdn+EXtHzrO+znBh3okoUmqFy5bI6kHyqR3KpHsmleiSX6pJ8qqcsuTQ2MqavV182Dt/IxYkXmRE4g/p29UnJTmHO/jk0W9CM4B+C+Tn6Z3ILcssh+spFzkshhBBCCFEeZHiNeKACfQGzds6iwzcdOPb3MZysnFj71FqWD16Oo6Wj1uEJIYQQQpRYfbv6TA+aTtxrcWwavol+Xv0w0hmxI34Hz/z6DPXn1mfyX5MNLcOEEEIIUbnMmjULX19frK2tqVevHqNHjyY5OVnrsIQQSLFZPMDp5NN0+a4L725/l3x9PoOaDeLEuBM82eJJrUMTQgghhCgzEyMT+nn1Y9PwTcS/Fs/0wOm42bqRnJXM5/s+x/srb3r80INVJ1bViNHOQgghRFWxb98+3n77bY4cOcKvv/7K4cOHGTp0qNZhCSEAk9LcKTg4uFRtMkJCQhg5cmRpdlkj6XQ6nJ2dK7wlSaG+kHkH5vFO+DvkFORgb27Pl32+ZESrEVW2PYpWuayuJJ/qkVyqR3KpHsmluiSf6inPXLrbuzMjaAbvBrzLn+f+ZMmRJfx5/k+2x29ne/x2nKyceN7veV5q+xKetT1V339Fk/NSCCFEVbZ58+Ziv0+bNo0nn3yS9PR07O3tNYpKCAGlnCDwhx9+KNXO/Pz8aN26danuq4WaOBHKhdQLPL/heXYl7AKgd5PefDvgW+rb1dc4MiGEEEKIipWQnsB3R7/ju8jvuHLzimF5D48ejGk7hieaP4GZsZmGEVacmnhdLIQQoupYtGgRb7zxBqmpqVhYWGgdjhA1WqmKzTWF1hfVhYWFHDt2jNatW2NsbFyu+1IUhcWHFzN522Ru5d/CxsyGOY/OYXTb0dVixEtF5rImkHyqR3KpHsmleiSX6pJ8qkerXBboC/jj3B98feRr/jj3Bwq3L5+drZwJ8QvhWd9naVWnVZW6ZippLrW+LhZCCCHuJzMzk7Zt2zJw4EA+++wzrcMRosaTns2VWEFBAZs2baKgoKBc93Mp/RK9l/dm/B/juZV/i6BGQUSPi+aldi9VqTdND1JRuawpJJ/qkVyqR3KpHsmluiSf6tEqlyZGJgzwHsDmZzYTPzGe9wLeo55tPa5nXeezvZ/ht8SP5guaM2PHDE5dP1WhsZWWnJdCCCGqg4KCAp555hnq1avHrFmztA5HCIEUm2s0vaJn0aFFtFzYkm2x27AwsWDeY/MIGxlGI4dGWocnhBBCCFHpNLBvwPvB73Nx4kXWD13P4OaDMTc250zKGWZGzKTFwha0XtyaWTtncSzpGPIlQiGEEKJ86PV6Ro4cSWJiIhs3bsTMrGa0thKisivVBIGi6jubcpbRG0cbejN3rt+ZZQOX4e3krXFkQgghhBCVn4mRCQObDWRgs4Fk5Gaw8cxGVp1YxdYLWzn+93GO/32cd7e/S327+jze9HH6evWlp0dPrM2stQ5dCCGEqPIUReHFF18kJiaG8PBwafEkRCUixeZKTKfT0aRJE1VbWeQX5jN732xm7JhBbmEu1qbWfNTzI/7T4T8YG1XfPpLlkcuaTPKpHsmleiSX6pFcqkvyqZ7Kmks7cztGtBrBiFYjSM1O5bdTv7H+zHrCYsO4nHGZr49+zddHv8bUyJQObh0IbBhIQMMAurh3wc5cmzfHlTWXQgghxMN4+eWXCQ8PZ/PmzeTl5ZGUlASAs7OzzJEhhMZkgsAHqG4ToRy5eoTRm0YTlRQFQO8mvVnSbwkNHRpqG5gQQgghRDWUnZ/Njvgd/H7ud34/9zvxN+KLrR/ZeiQ/DPpBm+BKqLpdFwshhKja7vdhaVxcHI0aNarYYIQQxZRqZHNwcHCpRkGEhIQwcuTI0uyyRiooKGD37t1069YNE5PSD0JPyUrhnfB3+PrI1ygo1LKsxRe9v2BEqxE1ZjSLWrkUt0k+1SO5VI/kUj2SS3VJPtVT1XJpaWpJH88+9PHsw5fKl8TdiGPnxZ3svLiTiIsRBDQI0Cy2qpZLIYQQ4k4yblKIyqtUV5YhISGl2lnr1q1Ldb+aqrCwkIiICDp37lyqNwGF+kK+Pfot/xf+f6RmpwLwrO+zzOk9BxdrF7XDrdTKmktRnORTPZJL9Ugu1SO5VJfkUz1VOZc6nY7Gjo1p7NiYEL8Q4PZkzVqpyrkUQgghhBCVV6muLEeNGqV2HEJFiqKw5fwW/i/8/wwtM3xdfPnq8a8IaKjdCBohhBBCCPE/RjojrUMQQgghhBBCVTKMoZrZk7CHqWFT2ZWwCwB7c3s+CP6AcR3GYWIkD7cQQgghhBBCCCGEEKJ8SPWxEjMyMqJNmzYYGT3cqJfFhxcz7vdxAFiYWDChwwSmdJtCbava5RlmlVDSXIoHk3yqR3KpHsmleiSX6pJ8qkdyqR7JpRBCCCGEKA86Rbqq31dVm3U7KTOJZl814+mWTzMtcBr17eprHZIQQgghhKgGqtp1sRBCCCGE0IYMZajE8vPz2bhxI/n5+Q91e1cbVy5OvMjX/b+WQvM/lDSX4sEkn+qRXKpHcqkeyaW6JJ/qkVyqR3IphBBCCCHKgxSbKzG9Xk9kZCR6/cPPVG5vYV+OEVVdpcmluD/Jp3okl+qRXKpHcqkuyad6JJfqkVwKIYQQQojyIMVmIYQQQgghhBBCCCGEEGUmEwQ+QFE764yMDE32n5ubS05ODhkZGZibm2sSQ3UhuVSX5FM9kkv1SC7VI7lUl+RTPZJL9ZQ0l0XXwzLdixBCCCGEeBCZIPABLl++jLu7u9ZhCCGEEEIIUSlcunSJ+vVlbhAhhBBCCHFvUmx+AL1ez9WrV7G1tUWn01X4/jMyMnB3d+fSpUsy63cZSS7VJflUj+RSPZJL9Ugu1SX5VI/kUj0lzaWiKNy8eZN69ephZCSd+IQQQgghxL1JG40HMDIyqhQjN+zs7OQNlUokl+qSfKpHcqkeyaV6JJfqknyqR3KpnpLk0t5eJqIWQgghhBAPJsMShBBCCCGEEEIIIYQQQpSZFJuFEEIIIYQQQgghhBBClJkUmysxc3Nzpk+fLrOtq0ByqS7Jp3okl+qRXKpHcqkuyad6JJfqkVwKIYQQQojyIBMECiGEEEIIIYQQQgghhCgzGdkshBBCCCGEEEIIIYQQosyk2CyEEEIIIYQQQgghhBCizKTYLIQQQgghhBBCCCGEEKLMpNgshBBCCCGEEEIIIYQQosyk2FxJKYrCe++9h4uLCzY2NgwfPpz09HStw6p0Zs2aha+vL9bW1tSrV4/Ro0eTnJxc7DY7duygVatWmJub06pVK3bu3FlsfVpaGsOGDcPGxgYXFxemTZtWkYdQab366qvodDrWrl1rWCa5LJlNmzbRrl07LCwsqFOnDu+9955hneTy4d24cYMXXniBOnXqYGtrS0BAAAcPHjSsl1zeX0xMDE8++SRubm53/T2DOrn76quvcHd3x9LSkscee4zLly+X6zFp5UG5jIiIoHfv3jg7O2Nra0u3bt2IiIgodn/JZXH/dm4WOXToEKampvTr16/Ycsnn//xbLi9fvszQoUNxdHTExsaGzp07k5GRYVgvuRRCCCGEEGqSYnMltXDhQubOncs333zDjh07iIqK4uWXX9Y6rEpn3759vP322xw5coRff/2Vw4cPM3ToUMP6v//+m/79+xMcHExkZCSBgYH079+f69evG24zZswYoqOj2bFjB9988w2zZ89myZIlWhxOpbFt2zaOHz9ebJnksmRCQ0MZMmQIgwcPJioqirCwMLp37w5ILktq0qRJ7N27lw0bNnD06FE8PDx4/PHHycnJkVz+i8zMTBo3bsy8efPuWqdG7n7//XcmTpzIzJkz2b9/P9nZ2Tz99NMVcmwV7UG5PHDgAJ07d2bTpk1ERkbSsWNH+vTpw9mzZw23kVwW96B8FsnKyuLFF1+kbdu2d62TfP7Pg3KZk5NDz549SUlJ4ffff+f48eNMmTIFY2Njw20kl0IIIYQQQlWKqJR8fX2VN9980/D7+vXrFWNjYyU5OVnDqCq/devWKYBy48YNRVEUZfbs2YqLi4tSWFioKIqiFBQUKLVr11bmzp2rKIqiXL9+XTEyMlI2b95s2MbEiROV1q1bV3TolUZKSorStGlT5ezZswqg/PLLL4qiSC5LKjAwUHnppZfuuU5yWTItWrRQZsyYYfj9xIkTCqCcPn1aclkCd/49K4o652H//v2VIUOGGH6PiopSACUyMrJcj0Vr/8zlP+n1esXe3l754osvFEWRXP6b++Xz5ZdfVj788ENl1KhRSt++fQ3LJZ/3989cLlu2THF0dFRu3rx5z9tLLoUQQgghhNpkZHMllJuby8mTJ+natathWUBAAIWFhURGRmoYWeX3999/Y2lpibm5OQBHjhyhc+fOGBndPtWNjY3p2rUrhw8fBiAyMhK9Xn9XrqOjo8nNza34A6gExo0bx9ixY/H09Cy2XHL58PLz89m7dy9eXl4EBgZSp04devXqZRgtLrksme7du7N161ZSUlIoKChg9erVNGvWjMaNG0suy0CN3B05cqTY+tatW2NnZ2fYRk2VmZnJrVu3cHR0BCSXpfHHH39w4MAB3n777bvWST4f3o4dO+jatSvvvfcerq6u+Pr6smDBAsN6yaUQQgghhFCbFJsroZSUFPR6PU5OTrz99tu0b98eBwcHjI2Ni329WRSXmZnJ3Llz+c9//oOFhQUA169fx8nJicjISGrVqsXBgwdxcnIy5PH69euYmJjg4OBAp06deOONN3ByckKv15Oamqrl4Wjip59+Ij4+ntdff/2udZLLh5ecnEx+fj6ffvopzz33HH/++ScuLi707t2bzMxMyWUJzZs3Dy8vL5ycnLCwsGD16tX8+eefmJqaSi7LQI3cFW1j4cKFNGrUiKysrGLbqKk++ugj3NzcGDJkCCC5LKnk5GTGjh3Ld999h4mJyV3rJZ8PLzExkZ07d3Lt2jX++OMPJk6cyOuvv27o6yy5FEIIIYQQarv7Cl5oTlEUw/+dnJxo0KCBhtFUDQUFBTzzzDPUq1ePWbNmGZYX5dLS0pIGDRpgbW1dLL93/r9+/fq4uLgUW1aTXLp0iTfeeIPt27cX6+VYRHL58PR6PQD9+/dn9OjRACxZsoRatWoRGhoquSyhhQsXcvToUUJDQ3F0dGT+/Pn069ePQ4cOSS7LQM3cOTg40KBBA4yNjWt8fletWsWCBQvYsWMHVlZWgOSypMaMGcOoUaNo06bNPddLPh+eXq9Hr9fzzTffYGVlRdu2bfnrr79Yvnw5Q4YMkVwKIYQQQgjVSbG5EnJycsLIyIjk5GQmT54M3J4pvLCwEGdnZ42jq3z0ej0jR44kMTGRsLAwzMzMDOtcXFxITk6mWbNmREVFAbdHjhfl0cXFhYKCAm7cuGEY5fPbb79hZGRErVq1KvxYtHTkyBGSk5Np165dseXPPPMMv/76q+SyBGrVqoVOp8PLy8uwzNbWFicnJ65cuSK5LIHc3FymTp3KunXr6NmzJwDffvstDg4OrF+/XnJZBmrkztnZmeTkZCZOnMgzzzxz1zZqmo0bNzJmzBjWr19fbFI7yWXJhIeH8/vvv/P5558Dt1sTAVhYWJCSkiL5LAEnJyfq169v+OADoHHjxoSGhgJybgohhBBCCPVJG41KyNzcnJYtW7Jnzx7Dsp07d2JsbHzfUT41laIovPjii8TExLB161bs7OyKrW/Xrh379u0zjDQtLCxk7969tG/fHoA2bdpgZGR0V659fX0NfZ9ril69ehETE0NUVJThB+Dzzz/n888/l1yWgKWlJd7e3sTGxhqWZWVlkZKSgpubm+SyBLKyssjNzUWn0xmWGRkZodPpyM7OllyWgRq5a9euXbH1x44dIyMjw7CNmmTLli2MGDGC1atX06NHj2LrJJclc+jQIY4dO2Z4LRowYAABAQFERUVhZWUl+SwBPz8/rly5UqxHfUJCAm5uboCcm0IIIYQQohyU/xyEojS++uorxdraWtmwYYNy6NAhpVmzZsqwYcO0DqvSGTNmjNKgQQPl+PHjSmJiouGnoKBAURRFSUxMVGxsbJRXX31VOXnypDJhwgTFzs5OuXbtmmEbQ4YMUVq0aKEcOnRIWb9+vWJlZaUsXrxYq0OqVLhjVnvJZcl89tlniqWlpbJ69Wrl7NmzyksvvaTUqVNHycjIkFyWUMeOHZU2bdoo+/btU86ePatMmDBBsbKyUmJjYyWX/yI3N1eJjIxUIiMjFUD57LPPlMjISMNzZVlzt3nzZsXY2FhZunSpcuzYMSUgIEDp1KmTFoda7h6Uy7CwMMXKykpZsmRJsdeimzdvGu4vuSzuQfn8p1GjRil9+/Yttkzy+T8PymVCQoJiYWGhvPTSS8rZs2eVNWvWKBYWFsqaNWsM95dcCiGEEEIINUmxuZLS6/XKO++8ozg5OSlWVlbK0KFDlbS0NK3DqnSAe/7ExcUZbrN9+3bFx8dHMTMzU3x9fZWIiIhi20hNTVWefvppxcrKSnFyclLee++9Cj6KyuvOYrOiSC5LorCwUJk6dapSp04dxdraWunevbty9OhRw3rJ5cNLSEhQnn76acXZ2VmxsbFROnfurISHhxvWSy7vLy4u7p7PkdOnT1cURZ3czZ8/X3Fzc1PMzc2V3r17K5cuXaqIQ6twD8rlqFGjHphnRZFc/tO/nZt3ulexWfL5P/+Wy23btimtW7dWzM3NlSZNmihffPFFsftLLoUQQgghhJp0iiIzfAghhBBCCCGEEEIIIYQoG+nZLIQQQgghhBBCCCGEEKLMpNgshBBCCCGEEEIIIYQQosyk2CyEEEIIIYQQQgghhBCizKTYLIQQQgghhBBCCCGEEKLMpNgshBBCCCGEEEIIIYQQosyk2CyEEEIIIYQQQgghhBCizKTYLIQQQgghhBBCCCGEEKLMpNgshBBCCCGEEEIIIYQQosyk2CyEEOK+bGxs+P777zWNQafTodPpsLGx+dfbahVvo0aNDHEmJydX+P6FEEIIIYQQQojKQIrNQghRg4SEhBiKovf6iY+PL3b72NhYhg4dqk2wd/jmm2+IjY3VOoz7OnToEOvWrdM6DCGEEEIIIYQQQlNSbBZCiBpk3rx5JCYmkpiYyLRp06hfv77h98TERNzd3Yvd3sXFBUtLS42i/R8HBwdcXFy0DuO+nJ2dqVWrltZhCCGEEEIIIYQQmpJisxBC1CD29va4urri6uqKra0txsbGht9dXV0xNjYGoGnTpobRzv9sSzFjxgwCAwPx8vKiTZs2zJ8/nzp16tC3b18KCwsNt/vll1/w8fHB0tKSli1bsmbNGtWPZ9asWYZC75w5c+5a//bbb+Pl5YWFhQV169bljTfeID8/H4CEhASMjIw4dOhQsfsMHjyYESNGGH4PDw+nffv2WFlZ4ezszIABA8jLy1P9WIQQQgghhBBCiKpOis1CCCHusn//fhITE7Gysrrn+itXrrBhwwaysrJYs2YNYWFh7N2711C43b59O6NGjeKtt97i5MmTvPvuu4wcOZIDBw6oFuPmzZt5//33mT9/Prt27WLLli1kZWUVu012djZfffUVp06dYvny5axcuZJPPvkEgAYNGhAcHMxPP/1kuH16ejp//PEHo0aNAqCwsJAnn3ySTp06ceLECUJDQ+ncuTN6vV614xBCCCGEEEIIIaoLE60DEEIIUfk4OTkBtyfnu5f27dvTvHlz2rRpQ6NGjfDx8cHb25uEhAQ6derE+++/zyuvvMLIkSMBaNy4Mb/99hvLli3D399flRiXLl3KoEGDGD58OABz5szB19e32G3mz59v+L+HhwfPPPMMf/75J++99x5wu4f1m2++yZw5czAxMWHt2rU4OTnRs2dPAG7cuMGNGzd47LHHaNy4MQCtW7dWJX4hhBBCCCGEEKK6kZHNQgghSszCwsLw753/z87OBuD48ePMmzcPGxsbw8/69etVneTvwoULtGjRwvB7ixYtDG1Aiqxdu5YuXbpQp04dbGxs+PLLL8nMzDSsf/LJJ8nOzuavv/4CYMWKFYwYMQIjo9svj7Vr1+app55i6NChDBo0iP/+979cvHhRtWMQQgghhBBCCCGqEyk2CyGEUI2iKIb/z5gxg6ioKMNPTEwMy5YtU3V//xx5fef+Dxw4wNChQ+nTpw9//fUXUVFRvPDCC8VaYFhZWTFkyBCWL1/OlStXiIiIMIzGLrJmzRp27txJt27dWLNmDT4+Ppw/f17V4xBCCCGEEEIIIaoDaaMhhBBCdb6+vsTGxtK0adNy20fTpk05efKk4feYmJhiheTdu3fj7u5uaJkBtycF/KeQkBD69OmDp6cnbdu2LTZauki7du1o164dr732Gs7OzoSFhZXrsQkhhBBCCCGEEFWRFJuFEEIUk52dTXp6OnB7pHB6ejpJSUkYGxvj7Oz8UNuYNm0ajz32GF5eXgwYMID09HS2bNmCh4cHI0aMUCXO0aNHM2jQIH7++WdatWrFpEmTio109vT05PLly6xbtw4/Pz/WrVvH7t27adCgQbHtdO/eHVdXVz7++GNmz55dbF1CQgILFixg0KBB1KtXj9DQUDIyMu7qDS2EEEIIIYQQQghpoyGEEOIfVq9eTd26dalbty5ZWVlMnDiRunXr0qFDh4feRo8ePVi5ciUrVqzA19eXvn37cvDgQby9vVWLs0+fPkyfPp1XXnmF7t2789hjj2FlZWVYP2DAACZPnszLL79MmzZtiImJ4ZVXXrlrOzqdjueeew5FURg2bFixdVZWVpw9e5bBgwfj7e3NZ599xpIlS+jSpYtqxyGEEEIIIYQQQlQXOuXOBpdCCCFEJaPT6fjll18YMmRIue1jzJgxJCYmsmnTplJvY8eOHQQHB3P9+nWcnJxUjE4IIYQQQgghhKgapNgshBCiUtPpdFhYWGBvb09SUpKq205PT+f48eP07t2b1atX079//1Jtp2XLlsTGxpKTkyPFZiGEEEIIIYQQNZb0bBZCCFGpnTt3DgAjI/U7Pw0cOJCDBw/y8ssvl7rQDPDHH3+Qn58PQK1atdQKTwghhBBCCCGEqFJkZLMQQgghhBBCCCGEEEKIMpMJAoUQQgghhBBCCCGEEEKUmRSbhRBCCCGEEEIIIYQQQpSZFJuFEEIIIYQQQgghhBBClJkUm4UQQgghhBBCCCGEEEKUmRSbhRBCCCGEEEIIIYQQQpSZFJuFEEIIIYQQQgghhBBClJkUm4UQQgghhBBCCCGEEEKUmRSbhRBCCCGEEEIIIYQQQpSZFJuFEEIIIYQQQgghhBBClJkUm4UQQgghhBBCCCGEEEKUmRSbhRBCCCGEEEIIIYQQQpSZFJuFEEIIIYQQQgghhBBClJkUm4UQQgghhBBCCCGEEEKUmRSbhRBCCCGEEEIIIYQQQpSZidYBCCGEeDh6vZ68vDytwxBCCCGEEKLCmZqaYmxsrHUYQggh/oUUm4UQogrIy8sjLi4OvV6vdShCCCGEEEJowsHBAVdXV3Q6ndahCCGEuA8pNgshRCWnKAqJiYkYGxvj7u6OkZF0QBJCCCGEEDWHoihkZWVx7do1AOrWratxREIIIe5His1CCFHJFRQUkJWVRb169bCystI6HCGEEEIIISqcpaUlANeuXcPFxUVaagghRCUlw+OEEKKSKywsBMDMzEzjSIQQQgghhNBO0cCL/Px8jSMRQghxP1JsFkKIKkJ60wkhhBBCiJpMroeFEKLyk2KzEEIIIYQQQgghhBBCiDKTYrMQQogqKSgoCJ1Oh06nY8KECeW6r+TkZHQ6HTt27FB92zNmzDAch4+Pj+rbF9r6/PPPadSoUblsu1GjRoZz5/PPPy+XfQj1xMfHo9PpOHz4sNahCKG6HTt2oNPpSE5O1jqUaqEir3HUsHbtWhlxLIQQwkCKzUIIIcqVn58f5ubmnDx5UtXt/vrrryQmJtK5c+cybedh3iDXrl2bxMREunTpUqZ93cubb75JYmIib7zxhurbro50Oh0uLi7k5eUZlvn4+DBjxgztgnqA8ePHc+jQoXLZ9qFDh0hMTKR+/frlsv3qJiQkxFC8ufNn1apVqu5Hp9Oxdu1aVbdZE33//ffodDqefvppw7Ldu3fLB3MauN/fTtFPfHy81iEWUx0K32pd46hhxowZ//o3179/fxITEysoIiGEEJWdFJuFEEKUm4sXLxIfH8/w4cPZsGGDqtuuVasWrq6uFTJxok6nK7d92djY4Orqio2Njerbrq5SUlL45ZdftA7joVhZWeHs7Fwu23Z2dsbV1RVjY+Ny2X511KtXLxITE4v9DBo0SOuwxH1YWlpy4MABbt26BcCaNWtwd3fXOKqaZ968eYa/l2nTplG/fv1if0PymKivIq9x1GBubo6rq6vWYQghhKgkpNgshBCi3GzYsIHg4GD69OlTrNhcNOpo1apVeHl5YW9vz8svv4xerzfcZs6cOfj6+mJlZYWTkxMvvPACGRkZD73vF198kb59+xZbFh0djU6n4/z584YYgoODgduFO51Od1fLAxMTE8Porfu10Vi9ejWtW7fGwsKCBg0aMH36dMO6LVu20K1bNxwcHLC2tqZ3796cOnXqoY9D3K1v374sWLDgnuvuNaItKCjI8DXkGTNmEBgYiJeXF23atGH+/PnUqVOHvn37UlhYaLjPL7/8go+PD5aWlrRs2ZI1a9bctS+dTsfSpUsZNmwYNjY2ODk5GW63fPlyw3lzvzYaWVlZvPbaa9SrVw8rKys6duxIRESEYf3bb7+Nl5cXFhYW1K1blzfeeIP8/PwS50v8T1FB5M4fCwsLUlJSGD58OG5ubpibm+Pt7c1333131/2LzqWZM2fi4uKCtbU1b775JoDh8QZ46qmn7jvqMyoqijZt2mBjY8OQIUPIysoq9+OuqoyMjOjduzebN29Gr9ezceNGw4cDajxmAF999RVNmjTBwsKChg0b8s4771TU4VUZ9vb2hr8XW1tbjI2Ni/0N3fmBV2ho6H1f1+HBj8e9RtD+85sCUVFRBAYGYmtrS61atejVqxdJSUkAD/26/m8e5rk3NjaWwYMHY29vj729Pf369ePy5cuG9QUFBcyYMYNGjRphYWGBr68v69atM6zPzs7m1VdfpU6dOtjb29O3b98SjxCPj49n0KBB2NraUrduXf7zn//c9XwSEhJCv379WLJkCe7u7lhaWhq+LXDhwgUGDhxInTp1sLCwoHXr1mzatMlw36I2XzNnzuTkyZOG57SQkBDDbYq+bXDn898/rV69Gm9vb8zMzPD29r7rw+JGjRrxzjvvMGDAAKysrGjfvj3nz58vUS6EEEJULlJsFkKIKkZRFLLyCjT5URSlRLFu2LCBRx55hF69enHkyBGuXr1abP2yZcv49ddfWbp0KV9//TV//PGHYV1aWhoffvgh0dHRbN68mQMHDvD6668/9L5DQkL466+/uHbtmmHZihUr6NKlC02bNqVLly4kJiYa3vydPHmSxMTEu1oeXLly5YEtQLZt28azzz7L8OHDOXbsGD///DM3btwwrE9KSuL5559nz549HD58GFtbWwYMGHDXG/BKQVEg71bF/5TwvBo6dCjnzp0jMjKyVId55coVNmzYQFZWFmvWrCEsLIy9e/caHvvt27czatQo3nrrLU6ePMm7777LyJEjOXDgwF3b+uCDD2jXrh1Hjx7ll19+oVatWgAMGTLEMArwfl566SU2bdrEd999x/Hjx5k4cSIXLlwwrM/Ozuarr77i1KlTLF++nJUrV/LJJ5+U6pjLk6JAgb7if0p42jxQZmYm9evXZ+3atZw+fZq3336bMWPGsHPnzrtuu2HDBmJjYwkNDWXPnj20bt0awDDKE+Cbb76576jPRYsW8c0337Bp0ya2bNnC0qVL1TuQh6AoCkpenjY/pXjQnnrqKVavXs2uXbto1qyZ4W9MjcfsyJEjvPLKK0ybNo0zZ86wcuXKCh+dqSgKt/JuafJTmsfj3zzodb3I/R6Ph/Hcc8/h7OzM4cOH2b17N4MGDTIUgh/2df3f/Ntzb25uLo888gg3b95k27Zt7Nu3j27duhW7xpk+fToLFizgv//9LydOnGDWrFnExcUZ1o8bN479+/ezfv16Dh06hIuLC/379y/2oeeD5OXl0bt3b+zt7Tl48CCbNm3i4MGDxT5IKRIVFcX69ev59ddfOXLkCL169QJuz0fRoUMHfv/9d2JiYhg6dChPPPGEIc4723x5e3sbntPmzZtn2La/vz+JiYl8880394zz1KlTPPPMMzz//POcOHGC559/nuHDh3PmzJlit/vmm2944YUXOHToEPn5+UyZMuWh8iCEEKJyMtE6ACGEECWTnV9Ii2lbNdl3zPu9sTJ7uJeOtLQ0du7cyeLFi6lduzZt2rRh06ZNjB071nCbadOm4ePjg4+PD76+vhw6dIh+/foBt4t4RZo0acLLL7/Mxx9//NCxdu/enYYNG7Jq1SpeffVVFEXh559/NoxaMzMzw9XV1VC4cHFxwcnJ6a7t1KlT54FtCj788EOeffZZwxsjb29vunbtalh/5wgguP0GtFWrVpw/fx4vL6+HPp4KkZ8FH9Wr+P3+31Uws37om5ubm/Piiy+yYMECvv322xLvrn379jRv3pw2bdrQqFEjfHx88Pb2JiEhgU6dOvH+++/zyiuvMHLkSAAaN27Mb7/9xrJly/D39y+2rYCAACZPngxQ7PG0sLAwjAK8l9jYWFauXEl4eLhhFF7Tpk2L3Wb+/PmG/3t4ePDMM8/w559/8t5775X4mMtToQKbYit+v/0bg0kJ56PasmXLXS1rjh8/TuPGjfnss88Myzw8PFiwYAFbtmwhICCg2O3NzMxYunSp4XnBz88PoFiR0sHB4b5Fy0mTJtG+fXsAevbsWW49ve8rP5+cxYsrdp//n8XLL0MJ2wL06NGDF198kaVLl/L000+TkJAAQMOGDcv8mMXHx2NsbMygQYOwt7enYcOGxZ6/K0JWfhY2H2vTRilzaibWJXjufRgPel0vcr/H42HEx8czduxYvL29AWjRokWx7T7M6/q/+bfn3pUrV5KcnMyRI0dwcHC4K47s7Gxmz57NokWLGDp0KFD8+T0+Pp4ff/yRkydP0rx5cwC+/PJL7OzsOHToEJ06dfrXGH/++WcyMzOL5XHmzJk8+eSTLFiwoNgo45s3b7Jq1Srs7e2Lxerv71/sNe3//u//mD17Ntu2bWPMmDHY2NgYfkxMTO75nGZqaoqrq6shD//07bff4ufnZ7hGmjJlCmvWrOGbb74pNrHtwIEDDd9aCAkJ4YsvvvjXHAghhKi8ZGSzEEKIcvH777/j5uaGp6cnAI888gjr168vdpsmTZoY/u/o6Ehqaqrh9/DwcHr27Em9evWwsbFh8uTJZGZmliiGUaNGsXz5cgB27drF33//XWyyKTWcOHGC7t2733d9bGwsw4cPx8PDA1tbW8ObyJIeiyju5ZdfZvXq1aSlpZX4vhYWFoZ/7/x/dnY2cLv4OG/evGJvtNevX09s7N0V1Qc99g9y8uRJjIyMHljYWrt2LV26dKFOnTrY2Njw5ZdfynlTRgEBAURFRRX7cXd3p7CwkFmzZuHr64ujoyM2NjZERUXdM9+dO3cuU5/sBz3vibsZGxvTp08fVq1axRNPPGFYrsZj1rNnT5o0aUKzZs0YNWoU33zzDTdv3izX46nuHub8Lsvf0Lhx45g0aRKPPfYYM2fOJCYmptSx3s+/PfeeOHECX1/f+xZYz58/T25u7n1fH6Kjo1EUhQ4dOhheY1xdXVEU5Z6vM/dy/PhxkpKSsLe3N2xjyJAh5OTk3DVRn6+vr6HQfKdbt24xefJkmjdvjoODAzY2NqSlpan6OnP+/Hl8fX2LLSv6wP1O8rwohBDVi4xsFkKIKsbS1JiY93trtu+HtWHDBi5dumQo5hUWFmJkZFTsjbyJSfGXoaKv9CYkJNCvXz9efPFFPv30U+zt7fn555/573//W6J4R44cyYwZMzh79iwrVqxg4MCB931zWBb361MIt2dod3V15fvvv6devXrExcXRu3fvytlGw9Tq9ihjLfZbQo0aNSI4OJhly5YVW36vx+Jhc33nV8pnzJjBkCFDiq23tLS86z6lPZ/+7evrBw4cYOjQocyYMYMBAwZgbW3N559/zu7du0u1v/JkrLs9yliL/ZaUlZXVXSPIAT777DM+//xzvvrqK9q0aYOZmRlPPfXUPc+dsj6H3O95r8KYmt4eYawFU9NS3W3SpEkEBgbi6OhoWDZnzpwyP2YODg5ER0cTERFBREQEM2bM4Msvv+To0aN3PU7lxcrUisyp2nyIZFWK595/8zDn9/0ej38+f9/rsfz00095/vnnCQsLY926dcyaNYvw8HC6detW+qDv8DDPvYqiPPB1v+iYH3QbIyMjDh8+fFe+6tSp89CxtmvXjpUrV9613MXFpdjv98v3W2+9xebNm5k3bx7NmjXDxMSEzp07q3598s883Ct/mj8vCiGEUJUUm4UQoorR6XQP3cpCK7m5uWzdupUFCxYQFBQE3H7j0KlTJ7Zs2YKzs/MD73/o0CFyc3P54osvDKOf/jlSp4iNjY1hROo/NWzYkMDAQJYuXcratWv58ccf77pN0UzvBQUFD3t4xfj4+LBr1y5efPHFu9alpKQQExPDggULCAwMBLhvn+EHHUeF0elK1M5Ca//5z3+YMGGC4TGE/72pzszMNHx9+vLly7Rq1eqht+vr60tsbOw9i5Jq8fHxQa/Xs3fvXsPfyJ12796Nu7t7sZYZRe0D/knrc0enK3k7i8pm586dDBw4kGeffRaAnJwcEhISStVSwdTUtNTPJxVBp9OVuJWF1ry9vQ1tE4qo9ZiZmZnxyCOP8MgjjzBgwAD8/f25cOHCXfsrLzqdTvVWFlWVg4NDsVG1ly5duuftmjdvTvPmzZkwYQItW7bk999/L1ZsLsvr+sM89/r6+vLdd9+Rnp5+zxHDnp6emJubs2vXrmIjdov4+PigKAqpqal06dLlgfHc7/nd19eX77//HldX17taAz2snTt38vzzzxvaVyQlJZGSknLX7czMzEr9nNakSZNiE9/C7ZHdPXr0KNX2hBBCVA3SRkMIIYTqwsPDuXXrFkOHDqVZs2Y0a9aM5s2b0717dzZs2PCv92/atCl6vZ7/197dhUT1rXEc/2kOpmBpSLQlEkPD1NBGpKvASosKgpTeTCPLIAsJe5GiF8jKLKSULhIhS1DDKHsRy6yLuujCsRTJgoacwfBiyEiiFDW0/0W4z/GYpn/rqPX9wNzM3sysNWuzZ++1n/U8hYWFcjgcunLlypDq5QOio6NVW1ur5uZmuVyuITdE27dv14ULF+Th4aFVq4ZGhAcFBcnd3V03b95UZ2enenp6zG1fvnyRy+Uyiwx+/PhRLpdr0PLOY8eOqaysTOfPn5fdbpfNZlNWVpak70tB/f39VVJSIofDodraWmVnZw/bD6fTqerqarlcriEV5THUypUr5ebmNqjQUEhIiHx8fMzj5fr162ptbR3T5544cULXrl1TXl6e7Ha76uvrderUKTMly2i0t7fL5XLp8+fP6uvrk8vlksvlMicN5s+fr6SkJKWlpenBgwdqaWlRZWWlSkpKzH60tbXp1q1bamlp0fnz54eNao6OjlZlZaXevn0rl8tFRNgIenp6zLEYeHV2diokJERPnjyRzWbTq1evlJqaqt7e3n/1HcHBwaqqqlJHR4e6u7sZj9/kV4zZ/fv3VVBQoKamJrW0tKikpEQzZ87UvHnzflOrMRKr1ap3797JZrOpv79/UE5f6XtRvL179+rp06dqbW3VnTt35HQ6h6RpGOl//WdGc+5NSkqSv7+/Nm7cqPr6er1580YFBQV6/vy5pO+rYA4cOKCsrCzduHFDDodDDx8+NPMQBwUFKTk5WampqaqpqZHD4VBNTY22bt06JDXUcNc4W7Zs0axZs7Rp0ybV19fLbrervLxc6enpY+prVVWVXr58qYaGBm3bts1cjfbfgoOD5XQ6VVdXp+7ubrMgo/Sf66KBwsgD59WBhwY7d+5UY2OjcnNzZbfblZubq6amJqWlpY26nQCAqYfJZgDAL3f37l1FRUUNWvIsfc+PWV1d/dMImcjISOXn5ysnJ0cRERG6d++eWdjvf2VmZspqtSomJkaGYai5uXnQ9sTERFksFm3evPmHy6INw9C5c+d09uxZ+fj4DIpmy8vLk2EYCg8PNz/LMAwlJCSY+8THx6u0tFRlZWVatGiREhMTzZs1d3d3VVRUqL6+XmFhYTpy5IjOnDnzw34sW7ZMBw8eVEpKigzDUFFR0Yi/Eb5HBKanp6uvr898z9vbW5cvX1Z+fr4CAgJUV1c35kjH5cuXq7y83BzTtWvXymazjSnSceB4zM7OVltbmwzDkGEYqqioMPcpKirSmjVrtGPHDkVERCgnJ0eBgYGSpHXr1unQoUPavXu3Fi9erNevXysjI+OH33X69GnNmDFDYWFhMgxDnz59GlN//yaPHz82x2LgdenSJR0/flxWq1UrVqxQXFycrFbrkGKQo5Wfn6+GhgbNnj1bXl5eY37YgdH5FWPm5+en27dvKzY2VpGRkWpsbFRVVdUPU+bg91u6dKn27dunVatWKTQ0VNHR0YO2u7u7q6OjQykpKVqwYIEyMzN19OhRJSUlDdpvpP/1nxnNudfT01OPHj2St7e34uLiFBMTo5qamkEF9E6ePKk9e/aYOZH379+vuXPnmtsLCwvN8//ChQuVkZEhPz+/IcfecNc4np6eqq2t1fTp0xUfHy+r1aq8vDyFhoaOuq8XL16Ur6+vlixZooSEBCUnJysgYGiR4PXr12vDhg1avXq1vLy8tGvXLnNbQkKCDMMw3xs4rw48KAgPD1dpaamKi4sVHh6u4uJilZeXj6mdAICpx+0b4RYAMKl1d3fL6XQqKCjohxEnGFlbW5sCAwNls9mG3LgCAABg6uC6GAAmv8md9BMAgH/p69ev+vDhgw4fPqyoqCgmmgEAAAAA+M1IowEA+CM9e/ZMAQEBevHiha5evTrRzQEAAAAA4I9HZDMA4I8UGxtLYS4AAAAAAP6PiGwGAAAAAAAAAIwbk80AMEUQpQsAAIC/GdfDADD5MdkMAJPctGnTJEm9vb0T3BIAAABg4nR1dUmSLBbLBLcEADAccjYDwCTn4eEhb29vtbe3y2KxyN2d54QAAAD4e3z79k1dXV16//69fH19zWAMAMDk4/aNdSgAMOn19vbK6XSqv79/opsCAAAATAhfX1/NmTNHbm5uE90UAMAwmGwGgCmiv7+fVBoAAAD4K1ksFiKaAWAKYLIZAAAAAAAAADBuJP4EAAAAAAAAAIwbk80AAAAAAAAAgHFjshkAAAAAAAAAMG5MNgMAAAAAAAAAxu0fyta9jt9J56cAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "inspect_low_thrust_trajectory(\n", + " DateTime(2016,10,22),\n", + " DateTime(2021,1,21)\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "practical-astro", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/mission_design/low_thrust_earth_mars_transfer_window.py b/mission_design/low_thrust_earth_mars_transfer_window.py new file mode 100644 index 0000000..16070ca --- /dev/null +++ b/mission_design/low_thrust_earth_mars_transfer_window.py @@ -0,0 +1,894 @@ +# Earth-Mars transfer window design using Porkchop Plots +""" + +Copyright (c) 2010-2024, Delft University of Technology +All rigths reserved +This file is part of the Tudat. Redistribution and use in source and +binary forms, with or without modification, are permitted exclusively +under the terms of the Modified BSD license. You should have received +a copy of the license with this file. If not, please or visit: +http://tudat.tudelft.nl/LICENSE. +""" + +## Summary +""" + +This example demonstrates the usage of the tudatpy `porkchop` module to determine an optimal launch window (departure and arrival date) for a **low-thrust** Earth-Mars transfer mission. +By default, the porkchop module uses a Lambert arc to compute the $\Delta V$ required to depart from the departure body (Earth in this case) and be captured by the target body (in this case Mars). +Users can provide a custom function to calculate the $\Delta V$ required for any given transfer. This can be done by supplying a `callable` (a function) to the `porkchop` function via the argument + + function_to_calculate_delta_v + +In this example, this option will be used to choose (make a preliminary choice, that is) the optimal departure and arrival date of a low-thrust transfer from the Earth to Mars. +""" + +## Import statements +""" + +The required import statements are made here, starting with standard imports (`os`, `pickle` from the Python Standard Library), followed by tudatpy imports. +""" + +# General imports +import os +import pickle +import numpy as np +import matplotlib.pyplot as plt + +# Tudatpy imports +import tudatpy +from tudatpy import constants +from tudatpy import numerical_simulation +from tudatpy.interface import spice_interface +from tudatpy.astro.time_conversion import DateTime +from tudatpy.trajectory_design import shape_based_thrust +from tudatpy.trajectory_design import transfer_trajectory +from tudatpy.numerical_simulation import environment_setup +from tudatpy.numerical_simulation import propagation_setup +from tudatpy.trajectory_design.porkchop import porkchop, plot_porkchop + +# Tudatpy data processing utilities +from tudatpy.numerical_simulation.propagation import create_dependent_variable_dictionary +from tudatpy.util import result2array + +## Environment setup +""" + +The simulation environment is set up here: the standard Spice kernels are loaded, the origin of the global frame is defined, and all necessary bodies are created. + +""" + + +# Load spice kernels +spice_interface.load_standard_kernels( ) + +# Define global frame orientation +global_frame_orientation = 'ECLIPJ2000' + +# Create bodies +bodies_to_create = ['Sun', 'Venus', 'Earth', 'Moon', 'Mars', 'Jupiter', 'Saturn'] +global_frame_origin = 'Sun' +body_settings = environment_setup.get_default_body_settings( + bodies_to_create, global_frame_origin, global_frame_orientation) + +# Create environment model +bodies = environment_setup.create_system_of_bodies(body_settings) + +# Create vehicle object and add it to the existing system of bodies +vehicle_mass = 4.0E3 +specific_impulse = 3000.0 +bodies.create_empty_body('Vehicle') +bodies.get_body('Vehicle').mass = vehicle_mass + +# Create vehicle thrust settings +thrust_magnitude_settings = ( +propagation_setup.thrust.custom_thrust_magnitude_fixed_isp( lambda time : 0.0, specific_impulse ) ) +environment_setup.add_engine_model( + 'Vehicle', 'LowThrustEngine', thrust_magnitude_settings, bodies ) +environment_setup.add_rotation_model( + bodies, 'Vehicle', environment_setup.rotation_model.custom_inertial_direction_based( + lambda time : np.array([1,0,0] ), global_frame_orientation, 'VehcleFixed' ) ) + +## Shape-based low-thrust trajectory optimization +""" + +Define the necessary parameters of the low-thrust trajectory: + +- Number of revolutions around the Sun +- Free parameters for radial shaping functions +- Free parameters for normal shaping functions +- Free parameters for axial shaping functions + +""" + +number_of_revolutions = 2 + +radial_velocity_shaping_free_coefficients = [ + 2471.19649906354, + 4207.587982407276 +] +normal_velocity_shaping_free_coefficients = [ + -5594.040587888714, + 8748.139268525232, +] +axial_velocity_shaping_free_coefficients = [ + -3449.838496679572, + 0.0 +] + +### Velocity shaping functions +""" + +Define a factory function to obtain the radial velocity shaping functions + +""" + + +def get_radial_velocity_shaping_functions(trajectory_parameters: list, + frequency: float, + scale_factor: float, + time_of_flight: float, + number_of_revolutions: int) -> tuple: + """ + Retrieves the radial velocity shaping functions (lowest and highest order in Gondelach and Noomen, 2015) and returns + them together with the free coefficients. + + Parameters + ---------- + trajectory_parameters : list + List of trajectory parameters to optimize. + frequency: float + Frequency of the highest-order methods. + scale_factor: float + Scale factor of the highest-order methods. + time_of_flight: float + Time of flight of the trajectory. + number_of_revolutions: int + Number of revolutions around the Sun (currently unused). + + Returns + ------- + tuple + A tuple composed by two lists: the radial velocity shaping functions and their free coefficients. + """ + # Retrieve default methods (lowest-order in Gondelach and Noomen, 2015) + radial_velocity_shaping_functions = shape_based_thrust.recommended_radial_hodograph_functions(time_of_flight) + # Add degrees of freedom (highest-order in Gondelach and Noomen, 2015) + radial_velocity_shaping_functions.append(shape_based_thrust.hodograph_scaled_power_sine( + exponent=1.0, + frequency=0.5 * frequency, + scale_factor=scale_factor)) + radial_velocity_shaping_functions.append(shape_based_thrust.hodograph_scaled_power_cosine( + exponent=1.0, + frequency=0.5 * frequency, + scale_factor=scale_factor)) + # Set free parameters + free_coefficients = trajectory_parameters[3:5] + return (radial_velocity_shaping_functions, + free_coefficients) + +# Define a factory function to obtain the normal velocity shaping functions + + +def get_normal_velocity_shaping_functions(trajectory_parameters: list, + frequency: float, + scale_factor: float, + time_of_flight: float, + number_of_revolutions: int) -> tuple: + """ + Retrieves the normal velocity shaping functions (lowest and highest order in Gondelach and Noomen, 2015) and returns + them together with the free coefficients. + + Parameters + ---------- + trajectory_parameters : list + List of trajectory parameters to optimize. + frequency: float + Frequency of the highest-order methods. + scale_factor: float + Scale factor of the highest-order methods. + time_of_flight: float + Time of flight of the trajectory. + number_of_revolutions: int + Number of revolutions around the Sun (currently unused). + + Returns + ------- + tuple + A tuple composed by two lists: the normal velocity shaping functions and their free coefficients. + """ + # Retrieve default methods (lowest-order in Gondelach and Noomen, 2015) + normal_velocity_shaping_functions = shape_based_thrust.recommended_normal_hodograph_functions(time_of_flight) + # Add degrees of freedom (highest-order in Gondelach and Noomen, 2015) + normal_velocity_shaping_functions.append(shape_based_thrust.hodograph_scaled_power_sine( + exponent=1.0, + frequency=0.5 * frequency, + scale_factor=scale_factor)) + normal_velocity_shaping_functions.append(shape_based_thrust.hodograph_scaled_power_cosine( + exponent=1.0, + frequency=0.5 * frequency, + scale_factor=scale_factor)) + # Set free parameters + free_coefficients = trajectory_parameters[5:7] + return (normal_velocity_shaping_functions, + free_coefficients) + +# Define a factory function to obtain the axial velocity shaping functions + +def get_axial_velocity_shaping_functions(trajectory_parameters: list, + frequency: float, + scale_factor: float, + time_of_flight: float, + number_of_revolutions: int) -> tuple: + """ + Retrieves the axial velocity shaping functions (lowest and highest order in Gondelach and Noomen, 2015) and returns + them together with the free coefficients. + + Parameters + ---------- + trajectory_parameters : list[ float ] + List of trajectory parameters to optimize. + frequency: float + Frequency of the highest-order methods. + scale_factor: float + Scale factor of the highest-order methods. + time_of_flight: float + Time of flight of the trajectory. + number_of_revolutions: int + Number of revolutions around the Sun. + + Returns + ------- + tuple + A tuple composed by two lists: the axial velocity shaping functions and their free coefficients. + """ + # Retrieve default methods (lowest-order in Gondelach and Noomen, 2015) + axial_velocity_shaping_functions = shape_based_thrust.recommended_axial_hodograph_functions( + time_of_flight, + number_of_revolutions) + # Add degrees of freedom (highest-order in Gondelach and Noomen, 2015) + exponent = 4.0 + axial_velocity_shaping_functions.append(shape_based_thrust.hodograph_scaled_power_cosine( + exponent=exponent, + frequency=(number_of_revolutions + 0.5) * frequency, + scale_factor=scale_factor ** exponent)) + axial_velocity_shaping_functions.append(shape_based_thrust.hodograph_scaled_power_sine( + exponent=exponent, + frequency=(number_of_revolutions + 0.5) * frequency, + scale_factor=scale_factor ** exponent)) + # Set free parameters + free_coefficients = trajectory_parameters[7:9] + return (axial_velocity_shaping_functions, + free_coefficients) + +### Low-thrust Trajectory Optimization solution +""" + +Define a function to obtain the LTTO solution +""" + + +def create_hodographic_trajectory( + trajectory_parameters: list, + bodies: tudatpy.numerical_simulation.environment.SystemOfBodies, + departure_body: str, + target_body: str, + central_body: str) \ + -> tudatpy.trajectory_design.transfer_trajectory.TransferTrajectory: + """ + It creates and returns the hodographic shaping object, based on the trajectory parameters. + + Parameters + ---------- + trajectory_parameters : list + List of trajectory parameters to be optimized. + bodies : tudatpy.numerical_simulation.environment.SystemOfBodies + System of bodies present in the simulation. + + Returns + ------- + hodographic_shaping_object : tudatpy.trajectory_design.shape_based_thrust.HodographicShaping + Hodographic shaping object. + """ + + # Time settings + initial_time = trajectory_parameters[0] * constants.JULIAN_DAY + time_of_flight = trajectory_parameters[1] * constants.JULIAN_DAY + final_time = initial_time + time_of_flight + + # Number of revolutions + number_of_revolutions = int(trajectory_parameters[2]) + + # Compute relevant frequency and scale factor for shaping functions + frequency = 2.0 * np.pi / time_of_flight + scale_factor = 1.0 / time_of_flight + + # Retrieve shaping functions and free parameters + radial_velocity_shaping_functions, radial_free_coefficients = get_radial_velocity_shaping_functions( + trajectory_parameters, + frequency, + scale_factor, + time_of_flight, + number_of_revolutions) + normal_velocity_shaping_functions, normal_free_coefficients = get_normal_velocity_shaping_functions( + trajectory_parameters, + frequency, + scale_factor, + time_of_flight, + number_of_revolutions) + axial_velocity_shaping_functions, axial_free_coefficients = get_axial_velocity_shaping_functions( + trajectory_parameters, + frequency, + scale_factor, + time_of_flight, + number_of_revolutions) + + # Create settings for transfer trajectory (zero excess velocity on departure and arrival) + hodographic_leg_settings = transfer_trajectory.hodographic_shaping_leg( + radial_velocity_shaping_functions, + normal_velocity_shaping_functions, + axial_velocity_shaping_functions ) + node_settings = list() + node_settings.append( transfer_trajectory.departure_node( 1.0E8, 0.0 ) ) + node_settings.append( transfer_trajectory.capture_node( 1.0E8, 0.0 ) ) + + # Create and return transfer trajectory + trajectory_object = transfer_trajectory.create_transfer_trajectory( + bodies, [hodographic_leg_settings], node_settings, [departure_body, target_body], central_body ) + + # Extract node times + node_times = list( ) + node_times.append( initial_time ) + node_times.append( final_time ) + + #transfer_trajectory.print_parameter_definitions( [hodographic_leg_settings], node_settings ) + hodograph_free_parameters = trajectory_parameters[2:9] + + # Depart and arrive with 0 excess velocity + node_parameters = list() + node_parameters.append( np.zeros([3,1])) + node_parameters.append( np.zeros([3,1])) + + # Update trajectory to given times, node settings, and hodograph parameters + trajectory_object.evaluate( node_times, [hodograph_free_parameters], node_parameters ) + + return trajectory_object + +# Create function to obtain transfer ΔV + + +def hodographic_low_thrust_trajectory_delta_v( + bodies: tudatpy.numerical_simulation.environment.SystemOfBodies, + departure_body: str, + target_body: str, + departure_epoch: float, + arrival_epoch: float, + central_body: str = 'Sun') \ + -> [tudatpy.trajectory_design.transfer_trajectory.TransferTrajectory, float]: + """ + Function to calculate the required ΔV of an Earth-Mars transfer + + Parameters + ---------- + bodies : tudatpy.numerical_simulation.environment.SystemOfBodies + The system of bodies containing the celestial bodies involved in the transfer. + departure_body : str + The name of the departure celestial body. + target_body : str + The name of the target celestial body. + departure_epoch : float + The departure epoch in seconds since J2000. + arrival_epoch : float + The arrival epoch in seconds since J2000. + central_body : str, optional + The name of the central celestial body (default is 'Sun'). + + Returns + ------- + [tudatpy.trajectory_design.transfer_trajectory.TransferTrajectory, float] + A tuple containing the transfer trajectory object and the required ΔV. + """ + + # The entries of the vector 'trajectory_parameters' contains the following: + # * Entry 0: Departure time (from Earth's center-of-mass) in Julian days since J2000 + # * Entry 1: Time-of-flight from Earth's center-of-mass to Mars' center-of-mass, in Julian days + # * Entry 2: Number of revolutions around the Sun + # * Entry 3,4: Free parameters for radial shaping functions + # * Entry 5,6: Free parameters for normal shaping functions + # * Entry 7,8: Free parameters for axial shaping functions + + trajectory_parameters = [ + departure_epoch / constants.JULIAN_DAY, + (arrival_epoch - departure_epoch) / constants.JULIAN_DAY, + number_of_revolutions, + *radial_velocity_shaping_free_coefficients, + *normal_velocity_shaping_free_coefficients, + *axial_velocity_shaping_free_coefficients + ] + + hodographic_shaping_object = create_hodographic_trajectory( + trajectory_parameters, + bodies, + departure_body, + target_body, + central_body) + + # Retrieve delta V + ΔV = hodographic_shaping_object.delta_v + + return ΔV + + +## Porkchop Plots +""" + +The departure and target bodies and the time window for the transfer are then defined using tudatpy `astro.time_conversion.DateTime` objects. +""" + +departure_body = 'Earth' +target_body = 'Mars' + +earliest_departure_time = DateTime(2016, 9, 1) +latest_departure_time = DateTime(2017, 7, 1) + +earliest_arrival_time = DateTime(2019, 11, 1) +latest_arrival_time = DateTime(2021, 9, 1) + +# To ensure the porkchop plot is rendered with good resolution, the time resolution of the plot is defined as 0.5% of the smallest time window (either the arrival or the departure window): + +time_window_percentage = 0.5 +time_resolution = time_resolution = min( + latest_departure_time.epoch() - earliest_departure_time.epoch(), + latest_arrival_time.epoch() - earliest_arrival_time.epoch() +) / constants.JULIAN_DAY * time_window_percentage / 100 + +# Generating a high-resolution plot may be time-consuming: reusing saved data might be desirable; we proceed to ask the user whether to reuse saved data or generate the plot from scratch. + +# File +data_file = 'porkchop.pkl' + +# Whether to recalculate the porkchop plot or use saved data +RECALCULATE_delta_v = input( + '\n Recalculate ΔV for porkchop plot? [y/N] ' +).strip().lower() == 'y' +print() + +# Lastly, we call the `porkchop` function, which will calculate the $\Delta V$ required at each departure-arrival coordinate and display the plot, giving us +# +# - The optimal departure-arrival date combination +# - The constant time-of-flight isochrones +# - And more + + +if not os.path.isfile(data_file) or RECALCULATE_delta_v: + # Regenerate plot + [departure_epochs, arrival_epochs, ΔV] = porkchop( + bodies, + departure_body, + target_body, + earliest_departure_time, + latest_departure_time, + earliest_arrival_time, + latest_arrival_time, + time_resolution, + function_to_calculate_delta_v=hodographic_low_thrust_trajectory_delta_v + ) + # Save data + pickle.dump( + [departure_epochs, arrival_epochs, ΔV], + open(data_file, 'wb') + ) +else: + # Read saved data + [departure_epochs, arrival_epochs, ΔV] = pickle.load( + open(data_file, 'rb') + ) + # Plot saved data + plot_porkchop( + departure_body = departure_body, + target_body = target_body, + departure_epochs = departure_epochs, + arrival_epochs = arrival_epochs, + delta_v = ΔV, + threshold = 60 + ) + +### Variations +""" + +The Tudat `porkchop` module allows us to + +- Save the $\Delta V$ porkchop returned by `porkchop` and plot it again without recalculating with the `plot_porkchop` function +- Plot $\Delta V$ (default) or C3 (specific energy), as well as choose whether to plot departure and arrival $\Delta V$ together as the total $\Delta V$ required for the transfer (default), or separately (in those cases in which the manoeuvre is performed in two burns, one at departure and one at arrival to the target planet). + +Let's make use of `plot_porkchop` to see all four combinations! +""" + +cases = [ + {'C3': False, 'total': True, 'threshold': 80, 'filename': 'figures/Δ_tot.png'}, + {'C3': True, 'total': True, 'threshold': 3000, 'filename': 'figures/C3_tot.png'} +] + +for case in cases: + plot_porkchop( + departure_body = departure_body, + target_body = target_body, + departure_epochs = departure_epochs, + arrival_epochs = arrival_epochs, + delta_v = ΔV, + save = False, + **case + ) + + +# Verification +""" + +Interestingly, discontinuities appear in the $\Delta V$/$C_3$ porkchop. To ensure that the porkchop can be trusted -that it is possible to choose a transfer window based on our porkchop-, we will proceed to investigate two transfers: + +- The (approximately) minimum $\Delta V$ transfer: `2016-11-16`-`2020-6-11` +- A high $\Delta V$ transfer in the dark red region of the porkchop: `2016-10-22`-`2021-01-21` +""" + +## Trajectory visualization +""" + +Provided with a transfer window, the following function will obtain the shape-based low thrust trajectory from the Earth to Mars, numerically propagate a trajectory using a low-thrust thrust model for our spacecraft, and plot the: + +- Cartesian coordinates of the spacecraft, as a function of time, both for the analytical and integrated trajectory +- The Cartesian coordinates as a function of time of the Earth and Mars +- The thrust acceleration on the spacecraft as a function of time +- And a 3D plot showing the complete manoeuvre +""" + +def inspect_low_thrust_trajectory( + departure_date: DateTime, + arrival_date: DateTime + ): + """ + This function has the following sections: + + 1. Define transfer parameters + 2. Obtain low-thrust shape-based semi-analytical trajectory + 3. Create vehicle thrust settings + 4. Create termination settings + 5. Propagator settings + 6. Integrator settings + 7. Propagate dynamics + 8. Process simulation output + 9. Retrieve ephemeris of astronomical bodies + 10. Plot trajectory + """ + + ########################################################################### + # DEFINE TRANSFER PARAMETERS ############################################## + ########################################################################### + + trajectory_parameters = [ + departure_date.epoch() / constants.JULIAN_DAY, + (arrival_date.epoch() - departure_date.epoch()) / constants.JULIAN_DAY, + number_of_revolutions, + *radial_velocity_shaping_free_coefficients, + *normal_velocity_shaping_free_coefficients, + *axial_velocity_shaping_free_coefficients + ] + + # Fixed parameters + minimum_mars_distance = 5.0E7 + # Time since 'departure from Earth CoM' at which propagation starts (and similar + # for arrival time) + time_buffer = 30.0 * constants.JULIAN_DAY + + # Propagation time settings + initial_propagation_time = departure_date.epoch() + time_buffer + final_propagation_time = arrival_date.epoch() - time_buffer + + ########################################################################### + # OBTAIN LOW-THRUST SHAPE-BASED SEMI-ANALYTICAL TRAJECTORY ################ + ########################################################################### + + # Create problem without propagating + hodographic_shaping_object = create_hodographic_trajectory( + trajectory_parameters, + bodies, + 'Earth', + 'Mars', + 'Sun' + ) + + # Retrieves analytical results and write them to a file + analytical_trajectory = lambda n: result2array(hodographic_shaping_object.states_along_trajectory(n))[:, 1:] + + # Report transfer ΔV + print(f'{hodographic_shaping_object.delta_v/1000:.2f} km/s') + + ########################################################################### + # CREATE TERMINATION SETTINGS ############################################# + ########################################################################### + + # Time settings + time_termination_settings = propagation_setup.propagator.time_termination( + final_propagation_time, + terminate_exactly_on_final_condition=False) + # Altitude + relative_distance_termination_settings = propagation_setup.propagator.dependent_variable_termination( + dependent_variable_settings=propagation_setup.dependent_variable.relative_distance('Vehicle', 'Mars'), + limit_value=minimum_mars_distance, + use_as_lower_limit=True, + terminate_exactly_on_final_condition=False) + # Define list of termination settings + termination_settings_list = [time_termination_settings, + relative_distance_termination_settings] + # Create termination settings object + termination_settings = propagation_setup.propagator.hybrid_termination(termination_settings_list, + fulfill_single_condition=True) + + # Retrieve dependent variables to save + dependent_variables_to_save = [ + propagation_setup.dependent_variable.relative_distance('Vehicle', 'Earth'), + propagation_setup.dependent_variable.relative_distance('Vehicle', 'Sun'), + propagation_setup.dependent_variable.relative_distance('Vehicle', 'Mars'), + propagation_setup.dependent_variable.single_acceleration_norm( + propagation_setup.acceleration.thrust_acceleration_type,'Vehicle','Vehicle') + ] + + ########################################################################### + # PROPAGATOR SETTINGS ##################################################### + ########################################################################### + + current_propagator = propagation_setup.propagator.unified_state_model_quaternions + + # Define propagation settings + # Define bodies that are propagated and their central bodies of propagation + bodies_to_propagate = ['Vehicle'] + central_bodies = ['Sun'] + + # Update vehicle rotation model and thrust magnitude model + transfer_trajectory.set_low_thrust_acceleration( hodographic_shaping_object.legs[ 0 ], bodies, 'Vehicle', 'LowThrustEngine' ) + + # Define accelerations acting on capsule + acceleration_settings_on_vehicle = { + 'Sun': [propagation_setup.acceleration.point_mass_gravity()], + 'Vehicle': [propagation_setup.acceleration.thrust_from_engine('LowThrustEngine')] + } + + # Create global accelerations dictionary + acceleration_settings = {'Vehicle': acceleration_settings_on_vehicle} + acceleration_models = propagation_setup.create_acceleration_models( + bodies, + acceleration_settings, + bodies_to_propagate, + central_bodies) + + # Retrieve initial state + initial_state = hodographic_shaping_object.legs[ 0 ].state_along_trajectory( initial_propagation_time ) + + # Create propagation settings for the translational dynamics + translational_propagator_settings = propagation_setup.propagator.translational( + central_bodies, + acceleration_models, + bodies_to_propagate, + initial_state, + initial_propagation_time, + None, + termination_settings, + current_propagator, + output_variables=dependent_variables_to_save) + + # Create mass rate model + mass_rate_settings_on_vehicle = {'Vehicle': [propagation_setup.mass_rate.from_thrust()]} + mass_rate_models = propagation_setup.create_mass_rate_models(bodies, + mass_rate_settings_on_vehicle, + acceleration_models) + # Create mass propagator settings + mass_propagator_settings = propagation_setup.propagator.mass(bodies_to_propagate, + mass_rate_models, + np.array([vehicle_mass]), + initial_propagation_time, + None, + termination_settings) + + # Create multi-type propagation settings list + propagator_settings_list = [translational_propagator_settings, + mass_propagator_settings] + + # Create multi-type propagation settings object for translational dynamics and mass + propagator_settings = propagation_setup.propagator.multitype(propagator_settings_list, + None, + initial_propagation_time, + termination_settings, + dependent_variables_to_save) + + ########################################################################### + # INTEGRATOR SETTINGS ##################################################### + ########################################################################### + + # Create integrator settings + current_tolerance = 10.0 ** (-10.0) + # Create integrator settings + integrator = propagation_setup.integrator + # Define fixed step size + step_size = constants.JULIAN_DAY + # Here (epsilon, inf) are set as respectively min and max step sizes + # also note that the relative and absolute tolerances are the same value + integrator_settings = integrator.runge_kutta_variable_step_size( + step_size, + propagation_setup.integrator.CoefficientSets.rkdp_87, + step_size, + step_size, + current_tolerance, + current_tolerance) + propagator_settings.integrator_settings = integrator_settings + + ########################################################################### + # PROPAGATE DYNAMICS ###################################################### + ########################################################################### + dynamics_simulator = numerical_simulation.create_dynamics_simulator( + bodies, propagator_settings ) + + ########################################################################### + # PROCESS SIMULATION OUTPUT ############################################### + ########################################################################### + # Retrieve propagated state and dependent variables + state_history = dynamics_simulator.state_history + + # Create dependent variable dictionary + dv_dict = create_dependent_variable_dictionary(dynamics_simulator) + + # Create state history array + state_history_array = result2array(state_history) + + # Retrieve propagation buffer time + buffer = time_buffer / constants.JULIAN_DAY + + # Retrieve time vector + t = state_history_array[:, 0] / constants.JULIAN_DAY + t = (t - t[0]) + buffer + + # Retrieve analytical time vector + t_analytical = np.arange(0, max(t)+buffer+1, step_size/constants.JULIAN_DAY) + + # Retrieve x, y and z position history + x = state_history_array[:, 1] + y = state_history_array[:, 2] + z = state_history_array[:, 3] + + # Retrieve analytical trajectory + x_analytical = analytical_trajectory(len(t_analytical))[:, 0] + y_analytical = analytical_trajectory(len(t_analytical))[:, 1] + z_analytical = analytical_trajectory(len(t_analytical))[:, 2] + + # Retrieve thrust magnitude + thrust_magnitude = dv_dict.asarray('Single acceleration norm of type thrust , acting on Vehicle') + + ########################################################################### + # RETRIEVE EPHEMERIS OF ASTRONOMICAL BODIES ############################### + ########################################################################### + + retrieve_ephemeris = lambda body: np.vstack([spice_interface.get_body_cartesian_state_at_epoch( + target_body_name=body, + observer_body_name="SSB", + reference_frame_name="ECLIPJ2000", + aberration_corrections="NONE", + ephemeris_time=epoch_in_days * constants.JULIAN_DAY + )[:3] for epoch_in_days in np.linspace(trajectory_parameters[0], trajectory_parameters[0] + trajectory_parameters[1], len(t_analytical))]) + + # Retrieve trajectory of the Earth + earth_trajectory = retrieve_ephemeris('Earth') + earth_x = earth_trajectory[:, 0] + earth_y = earth_trajectory[:, 1] + earth_z = earth_trajectory[:, 2] + + # Retrieve trajectory of mars + mars_trajectory = retrieve_ephemeris('Mars') + mars_x = mars_trajectory[:, 0] + mars_y = mars_trajectory[:, 1] + mars_z = mars_trajectory[:, 2] + + ########################################################################### + # PLOT TRAJECTORY ######################################################### + ########################################################################### + + # Create figure + fig = plt.figure(figsize=(15, 7)) + + # Create 3 subplots for x, y, z coordinates + ax1 = plt.subplot2grid((4, 2), (0, 0)) + ax2 = plt.subplot2grid((4, 2), (1, 0), sharex=ax1) + ax3 = plt.subplot2grid((4, 2), (2, 0), sharex=ax1) + ax4 = plt.subplot2grid((4, 2), (3, 0), sharex=ax1) + + # Plot analytical and numerical coordinates + ax1.plot(t_analytical, x_analytical, label='Analytical') + ax1.plot(t, x, label='Numerical') + ax1.plot(t_analytical, earth_x, label='Earth', color='#78c4ff', zorder=1, alpha=0.6) + ax1.plot(t_analytical, mars_x, label='Mars', color='#f04848', zorder=1, alpha=0.6) + ax1.set_ylabel('X [km]') + + ax2.plot(t_analytical, y_analytical) + ax2.plot(t, y) + ax2.plot(t_analytical, earth_y, color='#78c4ff', zorder=1, alpha=0.6) + ax2.plot(t_analytical, mars_y, color='#f04848', zorder=1, alpha=0.6) + ax2.set_ylabel('Y [km]') + + ax3.plot(t_analytical, z_analytical) + ax3.plot(t, z) + ax3.plot(t_analytical, earth_z, color='#78c4ff', zorder=1, alpha=0.6) + ax3.plot(t_analytical, mars_z, color='#f04848', zorder=1, alpha=0.6) + ax3.set_ylabel('Z [km]') + + ax4.plot(t, thrust_magnitude / vehicle_mass, color='green', label='Thrust acceleration') + ax4.set_ylabel('$||a_T||$ [$m/s^2$]') + ax4.set_xlabel('Time [days]') + + # Legend + fig.legend(loc='lower center', ncol=5) + + # Create a 3D subplot for the trajectories + ax5 = plt.subplot2grid((3, 2), (0, 1), rowspan=3, projection='3d') + ax5.view_init(elev=32, azim=-66) + + # Plot Earth trajectory + ax5.plot(earth_x, earth_y, earth_z, color='#78c4ff') + ax5.scatter(earth_x[0], earth_y[0], earth_z[0], label='Earth, $t_0$', marker='$\Lambda$', s=100, color='#1f9eff') + + # Plot Mars trajectory + ax5.plot(mars_x, mars_y, mars_z, color='#f04848') + ax5.scatter(mars_x[-1], mars_y[-1], mars_z[-1], label='Mars, $t_f$', marker='$\Gamma$', s=100, color='#c93232') + + # Plot the 3D trajectories + ax5.plot(x_analytical, y_analytical, z_analytical) + ax5.plot(x, y, z) + + # Set labels + ax5.set_xlabel('X') + ax5.set_ylabel('Y') + ax5.set_zlabel('Z') + + # Show the legend + ax5.legend() + + # Grid + for ax in [ax1, ax2, ax3, ax4]: ax.grid( + True, + which='major', + color='grey', + linestyle='--' + ) + + # Layout + plt.subplots_adjust( + top=0.95, + bottom=0.127, + left=0.043, + right=0.99, + hspace=0.335, + wspace=0.0 + ) + + # Show the plot + plt.show() + +## Observations and conclusions +""" + +Notice that there is a change in the number of revolutions around the Sun between the two transfers: the low $\Delta V$ transfer has close to 3 revolutions around the Sun, while the high $\Delta V$ transfer greatly "stretches" its second revolution around the Sun to reach Mars. This is expected as the chosen number of revolutions (2) allows trajectories with between 2 and 3 revolutions around the Sun: thus, a discontinuity appears as trajectories approach 3 revolutions, and are from that point on forced to use 2; this is the case for the high $\Delta V$ trajectory. +The thrust acceleration of the high $\Delta V$ trajectory is 2 orders of magnitude higher than that for the low $\Delta V$ trajectory. This is expected as the orbit of the spacecraft in the high $\Delta V$ trajectory is distorted considerably more than in the low $\Delta V$ case. + +The discontinuities observed in the $\Delta V$ porkchops are explained by a discontinuous change in the number of revolutions required for the low-thrust transfer. The porkchops generated are thus reasonable, and can be used to identify a preliminary transfer window for our low-thrust spacecraft, as well as obtain a preliminary estimate of the $\Delta V$ required for the mission. +Next steps in mission design should be the optimization of the shaping function parameters (`radial_velocity_shaping_free_coefficients`, `normal_velocity_shaping_free_coefficients` and `axial_velocity_shaping_free_coefficients`) and number of revolutions around the Sun, which were fixed in this example, analyzing disturbing perturbations along the trajectory, analyzing the impact of navigation uncertainties on the trajectory, et cétera. + +""" + +# **Low $\Delta V$ trajectory** + +inspect_low_thrust_trajectory( + DateTime(2016,11,16), + DateTime(2020,6,11) +) + +# **High $\Delta V$ trajectory** + +inspect_low_thrust_trajectory( + DateTime(2016,10,22), + DateTime(2021,1,21) +) diff --git a/propagation/coupled_translational_rotational_dynamics.py b/propagation/coupled_translational_rotational_dynamics.py index 1195c43..f319ea5 100644 --- a/propagation/coupled_translational_rotational_dynamics.py +++ b/propagation/coupled_translational_rotational_dynamics.py @@ -1,23 +1,19 @@ # Propagation of coupled translational-rotational dynamics """ -""" +The present example will demonstrate the use of a multi-type propagator in Tudat. For that, we will consider the problem of simulating the coupled translational-rotational dynamics of Phobos around Mars, including the definition of a realistic initial state. Let's begin by talking about the forces and torques that we'll be considering as part of our dynamical problem. -## Disclaimer -""" -The presented code requires the developer versions of tudat and tudatpy. Check whether your tudat-space environment fulfills this condition by opening a terminal, activating the envrionment with `conda activate tudat-space` and then running `conda list | grep dev`. If there is no output to this command, you don't have the developer versions of tudat and tudatpy. Check [this documentation](https://docs.tudat.space/en/latest/_src_getting_started/installation.html) for instructions on how to acquire these versions. """ - -## Context -""" -The present example will demonstrate the use of a multi-type propagator in Tudat. For that, we will consider the problem of simulating the coupled translational-rotational dynamics of Phobos around Mars. Let's begin by talking about the forces and torques that ww'll be considering as part of our dynamical problem. -""" - - ## Phobos' dynamics in the Martian system """ -A complete description of the state vector and the equations of motion can be found in [reference to sections 2.4.1 and 2.4.2 of my literature study], with a derivation of the equations in the preceding sections and chapters. However, it is useful to write down Newon's second law of translation and rotational motion here, and include in the process all the accelerations and torques that we will model in this example. These two equations read as follow: +Due to the relative complexity of this example, it is useful to provide the explicit equations of motion. More details can be found in the [Tudat mathematical model description](https://github.com/tudat-team/tudat-space/raw/master/Tudat_mathematical_model_definition.pdf), and a number of sources in literature, most notably: +* [Rambaux et al. (2012).](https://www.aanda.org/articles/aa/abs/2012/12/aa19710-12/aa19710-12.html) Rotational motion of Phobos. Astronomy & Astrophysics, 548, A14. +* [Jacobson, R. A. (2010)](https://iopscience.iop.org/article/10.1088/0004-6256/139/2/668/meta) The orbits and masses of the Martian satellites and the libration of Phobos. The Astronomical Journal 139.2:668. +* [Jacobson, R. A., and V. Lainey. (2014)](https://www.sciencedirect.com/science/article/abs/pii/S003206331300144X) Martian satellite orbits and ephemerides. Planetary and space science 102 (2014): 35-44. +* [Le Maistre, S. et al. (2019)](https://www.sciencedirect.com/science/article/abs/pii/S0019103518304019). Signature of Phobos’ interior structure in its gravity field and libration. Icarus, 321, 272-290. + +The equations we will use here for the translational and rotational dynamics are: \begin{equation} \frac{\text{d}\vec v}{\text{d}t}=(\mu_{\text{M}}+\mu_{\text{P}}) \left(-\frac{\vec r}{r^3}+\frac{1}{\mu_{\text{M}}}\mathbf{R^{\mathcal{I/M}}}\nabla_{\mathcal M}U_{\text{M}}(\vec\rho_M^{\ P}) - \frac{1}{\mu_{\text{P}}}\mathbf{R^{\mathcal{I/P}}}\nabla_{\mathcal P}U_{\text{P}}(\vec\rho_P^{\ M})\right) - \sum_{i=1}^N\mu_i\left(\frac{\vec r_{iP}}{r_{iP}^3}-\frac{\vec r_i}{r_i^3}\right) @@ -26,7 +22,7 @@ \mathbf I\frac{\text d\vec\omega}{\text dt} + \vec\omega\times\left(\mathbf I\vec\omega\right) = -M\vec\rho_P^{\ M}\times\left(\mathbf{R^{\mathcal{I/P}}}\nabla_{\mathcal P}U_{\text{P}}(\vec\rho_P^{\ M})\right) - \sum_{i=1}^NM_i\vec\rho_P^{\ i}\times\left(\mathbf{R^{\mathcal{I/P}}}\nabla_{\mathcal P}U_{\text{P}}(\vec\rho_P^{\ i})\right) \end{equation} -Here: \ +where: \ · Subindices/superindices $P$, $M$ and $i$ refer to Phobos, Mars and the $i$-th third body. Superindex $I$ refers to an _inertial_ reference frame. \ · $\vec r_{AB}$ is a _position_ vector going from body A to body B. No subindices mean the vector goes from Mars to Phobos. \ · $\vec\rho_A^B$ is a vector going from A to B and expressed in the vector basis attached to the reference frame of body A. \ @@ -40,24 +36,24 @@ · $M_A$ is the mass of body A. As you can see, our example simulation will include the following accelerations/torques: \ -· Mutual spherical harmonic acceleration between Mars and Phobos. Mars' gravity field will be expanded to D/O 10/10; we will use Phobos' quadrupole gravity field, i.e. including only the central term, and coefficients $C_{2,0}$ and $C_{2,2}$. \ +· Mutual spherical harmonic acceleration between Mars and Phobos. Mars' gravity field will be expanded to D/O 10/10, while Phobos' will be expanded to D/O 4/4. \ · Third body forces will include those of the Sun, the Earth, Deimos and Jupiter. \ -· The torque that the center of mass of Mars exerts on Phobos' quadrupole gravity field. \ -· The torque exerted on Phobos' quadrupole gravity field by these bodies: the Sun, the Earth, Deimos and Jupiter (the same bodies as in the accelerations). -""" +· The torque that the center of mass of Mars exerts on Phobos' gravity field. \ +· The torque exerted on Phobos' gravity field by these bodies: the Sun, the Earth, Deimos and Jupiter (the same bodies as in the accelerations). +""" ## Where is each term defined in Tudat? """ -When implementing the dynamics in tudat, it is always usefule to keep in mind the a-priori knowledge that we need in order to integrate the equations. In other words, what _environments_ we will have to define and/or use. In this case, the full state of Phobos will be propagated, so its position, velocity, orientation and angular velocity are **all** propagated states. As an example, in a simple translational propagation, the orientation and angular velocity are given by the **environment** through the body's _rotation model_. In this coupled propagation, the environment will have to define: \ +When implementing the dynamics in tudat, it is important to be specific in the environment models that we need in order to integrate the equations. In this case, the full state of Phobos will be propagated, so its position, velocity, orientation and angular velocity are **all** propagated states. As an example, in a simple translational propagation, the orientation and angular velocity are given by the **environment** through the body's _rotation model_. In this coupled propagation, the environment will have to define: \ · The gravity field of all bodies (either spherical harmonic gravity field or central gravity field). \ · A rotation model for Mars. \ · The _ephemeris_ of all third bodies. \ · The inertia tensor of Phobos. -For most of these, we will use the defaults provided by spice. Phobos, however, is very poorly implemented in Tudat. For this detailed of a propagation, we will have to create it ourselves from scratch. -""" +For most of these, we will use the defaults provided by spice. For Phobos, however, we will create it ourselves from scratch, since the default settings (point mass gravity field; no inertia tensor) are insufficient for our purposes. +""" ## Import statements """ @@ -77,38 +73,50 @@ from tudatpy.astro.element_conversion import rotation_matrix_to_quaternion_entries from tudatpy.astro.frame_conversion import inertial_to_rsw_rotation_matrix from matplotlib import pyplot as plt -TWOPI = 2.0*PI spice.load_standard_kernels([]) - ## Auxiliary functions """ -In order to keep the main code neat and clean, several auxiliary functions will be used that need to be defined before the main code. Feel free to skip them now and come back to them when they are used in the script. +In order to keep the main code neat and clean, several auxiliary functions will be used that need to be defined before the main code. Feel free to skip them now and come back to them when they are used in the script. [TODO, add proper comments to each function (specifying what it does, what goes in, what goes out) before the function ... ... DONE]. """ + ### Gravitational field definition """ -Tudat's environment defaults don't include much information about Phobos, so we will have to create it ourselves from scratch. Part of this process is assigning Phobos a gravitational field, which takes a few lines. Thus, the corresponding code will be separated from the main script into the function below. This function returns Phobos' gravity field settings. +Tudat's environment defaults don't include much information about Phobos, so we will have to create it ourselves from scratch. Part of this process is assigning Phobos a gravitational field, which takes a few lines. Thus, the corresponding code will be separated from the main script into the function below. """ def get_gravitational_field(frame_name: str) -> environment_setup.gravity_field.GravityFieldSettings: - # The gravitational field implemented here is that by Le Maistre et al. (2019). + """Retrieve Phobos gravity field settings + This function returns gravity fields settings for Phobos' gravity field, with + coefficients taken from Le Maistre et al. 2019. + + Parameters + ---------- + frame_name: str + Name of the Phobos-fixed reference frame to which to fix the gravity field + + Returns + ------- + settings_to_return : GravityFieldSettings + Gravity field settings for Phobos + """ phobos_gravitational_parameter = 1.06e16*constants.GRAVITATIONAL_CONSTANT phobos_reference_radius = 14e3 - phobos_normalized_cosine_coefficients = np.array([[ 1.0, 0.0, 0.0, 0.0, 0.0], - [ 0.0, 0.0, 0.0, 0.0, 0.0], - [-0.029243, 0.0, 0.015664, 0.0, 0.0], - [ 0.0, 0.0, 0.0, 0.0, 0.0], - [ 0.0, 0.0, 0.0, 0.0, 0.0]]) - phobos_normalized_sine_coefficients = np.array([[0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0], - [0.0, 0.0, 0.0, 0.0, 0.0]]) - + # [TODO: check if coefficients are from Le Maistre ... ... DONE] + phobos_normalized_cosine_coefficients = np.array([[ 1.0, 0.0, 0.0, 0.0, 0.0 ], + [ 0.0, 0.0, 0.0, 0.0, 0.0 ], + [-0.029243, 0.000084, 0.015664, 0.0, 0.0 ], + [-0.002222, -0.002450, 0.004268, 0.000917, 0.0 ], + [ 0.002693, -0.001469, -0.000920, 0.001263, -0.000032]]) + phobos_normalized_sine_coefficients = np.array([[0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.000072, -0.000020, 0.0, 0.0], + [0.0, 0.001399, -0.000537, -0.006642, 0.0], + [0.0, 0.000402, -0.000555, -0.001218, 0.000088]]) settings_to_return = environment_setup.gravity_field.spherical_harmonic( phobos_gravitational_parameter, phobos_reference_radius, @@ -118,50 +126,92 @@ def get_gravitational_field(frame_name: str) -> environment_setup.gravity_field. return settings_to_return - ### Initial rotational state generation """ -Part of the propagation setup is coming up with an initial rotational state. There are two parts to this: coming up with a rotation quaternion and coming up with an angular velocity vector. This is usually not trivial. In this case, however, we can exploit certain properties of Phobos to make the task easier. Phobos is in synchronous rotation around Mars, meaning that Phobos' $x$ points towards the planet, its $z$ axis is parallel to the orbital angular momentum vector and the $y$ axis completes the right-handed basis. This is just a slightly modified version of the RSW frame. On the other hand, the angular velocity is to be expressed in body-axes, which makes its definition trivial. The creation of a rotational state with these characteristics is given in the function below. +The numerical propagation of the rotational dynamics requires an initial state (rotation quaternion and angular velocity vector). We assume that Phobos is in synchronous rotation around Mars, meaning that Phobos' $x$ axis points towards the planet, its $z$ axis is parallel to the orbital angular momentum vector and the $y$ axis completes the right-handed basis (similar to the axes along its RSW frame, with only a flipping of signs). The angular velocity is expressed in body-fixed axes, which makes its definition much easier: we only define non-zero angular velocity about the z-axis. The creation of a rotational state with these characteristics is given in the function below. """ -def get_initial_rotational_state_at_epoch(epoch: float) -> np.ndarray: +def get_initial_rotational_state_at_epoch(epoch: float, rotational_rate_around_z_axis: float) -> np.ndarray: + + """Retrieve Phobos initial rotational state (from fully locked model) + This function returns the Phobos initial state by specifying that its + body-fixed x-axis points towards Mars' center of mass (with its state + from Spice), and a user-defined value for its angular velocity + + Parameters + ---------- + epoch: float + Epoch at which to compute initial rotational state + + rotational_rate_around_z_axis: float + The angular velocity of the body about its z axis. + + Returns + ------- + np.array + Vector of initial rotational state (quaternion and angular velocity) + """ translational_state = spice.get_body_cartesian_state_at_epoch('Phobos', 'Mars', 'J2000', 'None', epoch) synchronous_rotation_matrix = inertial_to_rsw_rotation_matrix(translational_state).T synchronous_rotation_matrix[:,:2] = -1.0*synchronous_rotation_matrix[:,:2] phobos_rotation_quaternion = rotation_matrix_to_quaternion_entries(synchronous_rotation_matrix) - angular_rate = 0.000228035245 # In rad/s - angular_velocity = np.array([0.0, 0.0, angular_rate]) + angular_velocity = np.array([0.0, 0.0, rotational_rate_around_z_axis]) return np.concatenate((phobos_rotation_quaternion, angular_velocity)) - ### Generic logistic functions """ -There are two functionalities that will be required in this example but have no direct native implementation: -- Often times, one wants angles to be given in the interval $[0,2\pi)$; other times, one wants the angles in the interval $(-\pi,\pi]$. Since there is no native implementation of these functionalities, the `bring_inside_bounds` family of functions below allow to comfortably select the range in which the elements of an array are to be expressed. -- It is usually of interest to study the frequency components of periodic quantities, like many of those we will encounter in this example. Python provides functions to compute the [fast fourier transform](https://numpy.org/doc/stable/reference/routines.fft.html) of these quantities, but there exists a whole range of ifs and buts, details and subtleties that one has to be aware of. Thus, in order not to bring all these considerations in the middle of our code, we will create a function devoted to it. +There are two functions that will be required in this example but have no direct native implementation (yet): + +- Often times, one wants angles to be given in the interval $[0,2\pi)$; other times, one wants the angles in the interval $(-\pi,\pi]$. The `bring_inside_bounds` family of functions below allows the user to select the range in which the elements of an array are to be expressed. +- It is usually of interest to study the frequency components of periodic quantities, like many of those we will encounter in this example. Python provides functions to compute the [fast fourier transform](https://numpy.org/doc/stable/reference/routines.fft.html) of these quantities, but there exists a range of details and subtleties that one has to be aware of. Thus, in order not to bring all these considerations in the middle of our code, we create a function devoted to it. Additional functions required in this process are also defined below. - To aid in visualization, the longitudinal normal mode of Phobos will be shown in the FFT plots. A function to compute it will be defined. """ -# ANGLE INTERVAL MANAGEMENT def bring_inside_bounds(original: float | np.ndarray, lower_bound: float, upper_bound: float, include: str = 'lower') -> float | np.ndarray: + """This function brings a number inside the given bounds, assuming the interval defined by the bounds can periodically extend the whole real line (e.g. an angle of 9$\pi$ is equivalent to an angle of $\pi$ and at the same time equivalent to an angle of $-\pi$). If a (multidimensional) array is passed, the operation is performed on all its entries. It returns the same object and of the same dimension as it was given. + *Note:* This function's support of arrays is limited to one-dimensional and two-dimensional arrays. + + Parameters + ---------- + original: float | np.ndarray + The original number or array of numbers. + + lower_bound: float + The lower bound of the periodic interval. + + upper_bound: float + The upper bound of the periodic interval. + + include: str + The bound that is to be kept. It can be 'upper' or 'lower'. Anything else will result in an error. + + Returns + ------- + float | np.array + The number of array of numbers, all inside the interval. + + """ + if include not in ['upper', 'lower']: raise ValueError('(bring_inside_bounds): Invalid value for argument "include". Only "upper" and "lower" are allowed. Provided: ' + include) - scalar_types = [float, np.float32, np.float64, np.float128] - if type(original) in scalar_types: - return bring_inside_bounds_scalar(original, lower_bound, upper_bound, include) - - dim_num = len(original.shape) + if type(original) in [float, np.float32, np.float64, np.float128]: + to_return = bring_inside_bounds_scalar(original, lower_bound, upper_bound, include) + else: + dim_num = len(original.shape) - if dim_num == 1: to_return = bring_inside_bounds_single_dim(original, lower_bound, upper_bound, include) - elif dim_num == 2: to_return = bring_inside_bounds_double_dim(original, lower_bound, upper_bound, include) - else: raise ValueError('(bring_inside_bounds): Invalid input array.') + if dim_num == 1: + to_return = bring_inside_bounds_single_dim(original, lower_bound, upper_bound, include) + elif dim_num == 2: + to_return = bring_inside_bounds_double_dim(original, lower_bound, upper_bound, include) + else: + raise ValueError('(bring_inside_bounds): Invalid input array.') return to_return @@ -169,6 +219,29 @@ def bring_inside_bounds(original: float | np.ndarray, lower_bound: float, def bring_inside_bounds_single_dim(original: np.ndarray, lower_bound: float, upper_bound: float, include: str = 'lower') -> np.ndarray: + """This function brings the entries of a one-dimensional array inside the given bounds, assuming the interval defined by the bounds can periodically extend the whole real line (e.g. an angle of 9$\pi$ is equivalent to an angle of $\pi$ and at the same time equivalent to an angle of $-\pi$). It returns another one-dimensional array. + + Parameters + ---------- + original: np.ndarray + The original array. + + lower_bound: float + The lower bound of the periodic interval. + + upper_bound: float + The upper bound of the periodic interval. + + include: str + The bound that is to be kept. It can be 'upper' or 'lower'. Anything else will result in an error. + + Returns + ------- + np.array + The array of numbers, all inside the interval. + + """ + new = np.zeros_like(original) for idx in range(len(new)): new[idx] = bring_inside_bounds_scalar(original[idx], lower_bound, upper_bound, include) @@ -179,6 +252,29 @@ def bring_inside_bounds_single_dim(original: np.ndarray, lower_bound: float, def bring_inside_bounds_double_dim(original: np.ndarray, lower_bound: float, upper_bound: float, include: str = 'lower') -> np.ndarray: + """This function brings the entries of a two-dimensional array inside the given bounds, assuming the interval defined by the bounds can periodically extend the whole real line (e.g. an angle of 9$\pi$ is equivalent to an angle of $\pi$ and at the same time equivalent to an angle of $-\pi$). It returns another two-dimensional array. + + Parameters + ---------- + original: np.ndarray + The original array. + + lower_bound: float + The lower bound of the periodic interval. + + upper_bound: float + The upper bound of the periodic interval. + + include: str + The bound that is to be kept. It can be 'upper' or 'lower'. Anything else will result in an error. + + Returns + ------- + np.array + The array of numbers, all inside the interval. + + """ + lengths = original.shape new = np.zeros_like(original) for idx0 in range(lengths[0]): @@ -191,6 +287,31 @@ def bring_inside_bounds_double_dim(original: np.ndarray, lower_bound: float, def bring_inside_bounds_scalar(original: float, lower_bound: float, upper_bound: float, include: str = 'lower') -> float: + """This function brings a scalar inside the given bounds, assuming the interval defined by the bounds can periodically extend the whole real line (e.g. an angle of 9$\pi$ is equivalent to an angle of $\pi$ and at the same time equivalent to an angle of $-\pi$). It returns another scalar. + + Parameters + ---------- + original: float + The original number. + + lower_bound: float + The lower bound of the periodic interval. + + upper_bound: float + The upper bound of the periodic interval. + + include: str + The bound that is to be kept. It can be 'upper' or 'lower'. Anything else will result in an error. + + Returns + ------- + float + The number, now inside the interval. + + """ + + # EXPLAIN THINGS HERE. MAKE CLEAR WHAT VARIABLES REPRESENT. + if original == upper_bound or original == lower_bound: if include == 'lower': return lower_bound @@ -202,28 +323,61 @@ def bring_inside_bounds_scalar(original: float, lower_bound: float, center = (upper_bound + lower_bound) / 2.0 - # if original < lower_bound: - # reflect = True - # else: - # reflect = False - # - # if reflect: original = 2.0 * center - original + if original < lower_bound: + reflect = True + else: + reflect = False + + if reflect: + original = 2.0 * center - original dividend = original - lower_bound divisor = upper_bound - lower_bound remainder = dividend % divisor new = lower_bound + remainder - # if reflect: new = 2.0 * center - new + if reflect: new = 2.0 * center - new - if new == lower_bound and include == 'upper': new = upper_bound - if new == upper_bound and include == 'lower': new = lower_bound + if new == lower_bound and include == 'upper': + new = upper_bound + if new == upper_bound and include == 'lower': + new = lower_bound return new # FAST FOURIER TRANSFORM FUNCTIONALITIES def get_fourier(time_history: np.ndarray, clean_signal: list = [0.0, 0]) -> tuple: + """This function computes the fast fourier transform of a provided time history. It assumes that the quantity of the time history is real, and calls Numpy's rfft function to compute it. This function complements Numpy's rfft in the following ways: + + · It accounts for a time history with an odd number of entries and removes the last entry to make it of even length. + · It allows to clean the signal. This encompasses two things: + - Some quantities present jumps because they are by definition bounded inside an interval, but their evolution is secular. This function removes this jumps and works with a continuous signal. + - Sometimes one is interested in the residuals of the signal when a predefined polynomial is removed from it. This function allows to remove this polynomial and return the fft of the residuals. The coefficients of the polynomial are computed using Numpy's polyfit. + · Numpy's rfft returns a complex arrays of coefficients, usually not useful. This function returns the amplitude domain, attending to the fact that (a) the norm of the coefficients is to be taken and (b) the actual amplitude of the sinusoid is twice the norm of the complex coefficient. + · Numpy's rfftfreq returns a frequency array that is in cycles / unit_of_time. This function returns the frequencies in rad / unit_of_time. + + Parameters + ---------- + time_history: np.ndarray + A two-dimensional array with two columns: the first column is the time, the second is the quantity whose frequency content is to be computed. + clean_signal: list[float] + This determines (a) whether the signal is to be removed of jumps and (b) whether a polynomial is to be removed from the signal. The first entry of clean_signal is the value of the jumps, and the second entry is the degree of the polynomial. + + Returns + ------- + tuple + There are two returns: the array of frequencies (in rad / unit_of_time) and the array of amplitudes. + + """ + + if type(clean_signal[1]) != int: + raise TypeError('(get_fourier): Invalid input. The second entry in clean_signal should be of type "int". A type ' + str(type(clean_signal[1])) + 'was provided.') + if clean_signal[1] < 0: + raise ValueError('(get_fourier): Invalid input. The second entry in clean_signal cannot be negative. Current values is ' + str(clean_signal[1]) + '.') + if clean_signal[0] < 0.0: + raise ValueError('(get_fourier): Invalid input. The first entry in clean_signal cannot be negative. Current values is ' + str(clean_signal[1]) + '.') + sample_times = time_history[:,0] signal = time_history[:,1] @@ -231,29 +385,79 @@ def get_fourier(time_history: np.ndarray, clean_signal: list = [0.0, 0]) -> tupl sample_times = sample_times[:-1] signal = signal[:-1] - if clean_signal[0] != 0.0: signal = remove_jumps(signal, clean_signal[0]) + if clean_signal[0] != 0.0: + signal = remove_jumps(signal, clean_signal[0]) if clean_signal[1] != 0: coeffs = polyfit(sample_times, signal, clean_signal[1]) - signal = signal - coeffs[0] - coeffs[1] * sample_times + for idx in len(coeffs): + current_coeff = coeffs[idx] + exponent = idx + signal = signal - current_coeff*sample_times**exponent n = len(sample_times) dt = sample_times[1] - sample_times[0] - frequencies = TWOPI * rfftfreq(n, dt) + frequencies = 2.0*PI * rfftfreq(n, dt) amplitudes = 2*abs(rfft(signal, norm = 'forward')) return frequencies, amplitudes def remove_jumps(original: np.ndarray, jump_height: float, margin: float = 0.03) -> np.ndarray: + """This function removes discontinuities from a signal of a quantity that is periodic (like angles), so that a continuous signal is obtained in the end. Since data points are discrete, and it may happen that a jump occurs between two points that are not the **full** length of the jump apart, a margin is used to define how close two points can be while still considering that there is a jump between them. This function supports two-dimensional inputs. Then, each column will be assumed to contain one signal from which the jumps are to be removed. + + The particular mathematics are as follows. Consider a signal contained in an interval of length L, from which a continuous signal is required that extends beyond the bounds of this interval. By definition, the distance between any two points in the signal will be $|x_i - x_j| <= L$. However, if two (consecutive) points meet the condition $mL < |x_i - x_{i+1}| <= L$ a jump is considered to exist between them and will therefore be removed. Here, $m$ is the user defined margin (the default is 3%). + + Parameters + ---------- + original: np.ndarray + The original signal with all the elements between the bounds. + + jump_height: float + It is the value $L$ as used in the previous explanation. + + margin: float + It is the value $m$ as used in the previous explanation. + + Returns + ------- + np.ndarray + An array of the same shape as the provided one. Each column is now a continuous signal. + + """ + dim_num = len(original.shape) - if dim_num == 1: return remove_jumps_single_dim(original, jump_height, margin) - elif dim_num == 2: return remove_jumps_double_dim(original, jump_height, margin) + if dim_num == 1: + return remove_jumps_single_dim(original, jump_height, margin) + elif dim_num == 2: + return remove_jumps_double_dim(original, jump_height, margin) else: raise ValueError('(remove_jumps): Invalid input array.') def remove_jumps_single_dim(original: np.ndarray, jump_height: float, margin: float = 0.03) -> np.ndarray: + """This function removes discontinuities from a signal of a quantity that is periodic (like angles), so that a continuous signal is obtained in the end. Since data points are discrete, and it may happen that a jump occurs between two points that are not the **full** length of the jump apart, a margin is used to define how close two points can be while still considering that there is a jump between them. This function is the one-dimensional implementation of *remove_jumps*. + + The particular mathematics are as follows. Consider a signal contained in an interval of length L, from which a continuous signal is required that extends beyond the bounds of this interval. By definition, the distance between any two points in the signal will be $|x_i - x_j| <= L$. However, if two (consecutive) points meet the condition $mL < |x_i - x_{i+1}| <= L$ a jump is considered to exist between them and will therefore be removed. Here, $m$ is the user defined margin (the default is 3%). + + Parameters + ---------- + original: np.ndarray + The original (one-dimensional) signal with all the elements between the bounds. + + jump_height: float + It is the value $L$ as used in the previous explanation. + + margin: float + It is the value $m$ as used in the previous explanation. + + Returns + ------- + np.ndarray + The continuous signal. + + """ + new = original.copy() u = 1.0 - margin l = -1.0 + margin @@ -267,6 +471,28 @@ def remove_jumps_single_dim(original: np.ndarray, jump_height: float, margin: fl def remove_jumps_double_dim(original: np.array, jump_height: float, margin: float = 0.03) -> np.ndarray: + """This function removes discontinuities from a signal of a quantity that is periodic (like angles), so that a continuous signal is obtained in the end. Since data points are discrete, and it may happen that a jump occurs between two points that are not the **full** length of the jump apart, a margin is used to define how close two points can be while still considering that there is a jump between them. This function is the two-dimensional implementation of *remove_jumps*. + + The particular mathematics are as follows. Consider a signal contained in an interval of length L, from which a continuous signal is required that extends beyond the bounds of this interval. By definition, the distance between any two points in the signal will be $|x_i - x_j| <= L$. However, if two (consecutive) points meet the condition $mL < |x_i - x_{i+1}| <= L$ a jump is considered to exist between them and will therefore be removed. Here, $m$ is the user defined margin (the default is 3%). + + Parameters + ---------- + original: np.ndarray + The original (two-dimensional) signal with all the elements between the bounds. Each of the columns of this array is interpreted as an independent signal. + + jump_height: float + It is the value $L$ as used in the previous explanation. + + margin: float + It is the value $m$ as used in the previous explanation. + + Returns + ------- + np.ndarray + The continuous signal. + + """ + new = original.copy() u = 1.0 - margin l = -1.0 + margin @@ -281,6 +507,23 @@ def remove_jumps_double_dim(original: np.array, jump_height: float, margin: floa # NORMAL MODE COMPUTATION def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, mean_motion: float) -> float: + """This function computes the frequency of the longitudinal normal mode of a body from the components of its inertia tensor. The equation is provided in Rambaux, 2012. + + Parameters + ---------- + inertia_tensor: np.ndarray + The tensor of inertia of the body. + + mean_motion: float + The mean motion of the body's orbit. + + Returns + ------- + float + The frequency of the longitudinal normal mode of the body (in rad/s). + + """ + # From Rambaux (2012) "Rotational motion of Phobos". A = inertia_tensor[0,0] @@ -290,7 +533,6 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, return mean_motion * np.sqrt(3*gamma) - ## Generating the environment """ We will begin by creating our Solar System. We will begin by creating the `body_settings` for all bodies except Phobos. These settings will be used to create the `bodies` object. **Note:** Be aware of the modules you need to import. @@ -312,62 +554,57 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, # AND NOW WE CREATE THE BODIES OBJECT bodies = environment_setup.create_system_of_bodies(body_settings) -""" -Although both the translation and rotation of Phobos will be numerically integrated, it is advantegous to assign the body object with a-priori ephemeris and rotation models. One might want to access these attributes - for instance to retrieve an initial state - and pre-existing ephemeris and rotation models prevent potential internal inconsistencies within tudat. - -If you read through the lines of the `body_settings`, you might have noticed that we are assigning the `scaled_moment_of_inertia` of Phobos to its `gravity_field_settings`, while no other mention to Phobos' inertia tensor is made when creating this environment. As it turns, there exists a relationship between the components of the inertia tensor and the degree 1 and 2 harmonic coefficients (see [reference to sections 2.2.4 of my literature study, or any of the sources I reference there]), completed by the scaled mean moment of ienrtia. When calling the function `create_system_of_bodies()`, Tudat will automatically compute the inertia tensor of Phobos using the information of its gravity field. Thus, information on the inertia tensor has been unified inside the `gravity_field_settings`. - -Now that our environment is complete. It is time to start defining the dynamics themselves. -""" +# Although both the translation and rotation of Phobos will be numerically integrated, it is advantageous to assign the body object with a-priori ephemeris and rotation models. One might want to access these attributes - for instance to retrieve an initial state - and pre-existing ephemeris and rotation models prevent potential internal inconsistencies within tudat. +# +# Above, we are assigning the `scaled_moment_of_inertia` of Phobos to its `gravity_field_settings`, while no other mention to Phobos' inertia tensor is made when creating this environment. There exists a relationship between the components of the inertia tensor and the degree 2 spherical harmonic coefficients (see [`spherical_harmonic_coefficients_from_inertia`](https://py.api.tudat.space/en/latest/gravitation.html#tudatpy.astro.gravitation.spherical_harmonic_coefficients_from_inertia)), completed by the scaled mean moment of inertia. When calling the function `create_system_of_bodies()`, Tudat will automatically compute the inertia tensor of Phobos using the information of its gravity field. Thus, information on the inertia tensor has been unified inside the `gravity_field_settings`. Note that, for case where the inertia tensor is defined 'manually' by the user, the attribute `body_settings.get('Phobos').rigid_body_settings` has to be set. +# +# Now that our environment is complete. It is time to start defining the dynamics themselves. +# ## Coupled dynamics """ -If you have used Tudat before, you are most probably familiar with what _translational propagators_ are. Possibly, you are also familiar with combined translational-mass propagations. These are just an example of a **multi-type propagation**, and the combined translational-rotational is another example of this mult-type propagation. The way Tudat deals with these multi-type propagations is by keeping all different propagations separate and creating the appropriate "single-type" propagators for each type of dynamics, and then putting them all together at then end in this _multi-type propagator_. Thus, we will follow the same process here. +If you have used Tudat before, you are most probably familiar with what _translational propagators_ are. Possibly, you are also familiar with combined translational-mass propagations. These are just an example of a **multi-type propagation**, and the combined translational-rotational is another example of this mult-type propagation. The way Tudat deals with these multi-type propagations is by creating the appropriate "single-type" propagation settings for each type of dynamics, and then putting them all together at then end in the _multi-type propagator settings_. Thus, we will follow the same process here. For more details, see [multi-type propagation documentation](https://docs.tudat.space/en/latest/_src_user_guide/state_propagation/propagation_setup/multi_type.html). -As you will see in the code below - and can be deduced comparing the APIs for the [translational](https://py.api.tudat.space/en/latest/propagator.html#tudatpy.numerical_simulation.propagation_setup.propagator.translational), [rotational](https://py.api.tudat.space/en/latest/propagator.html#tudatpy.numerical_simulation.propagation_setup.propagator.rotational) and [multi-type](https://py.api.tudat.space/en/latest/propagator.html#tudatpy.numerical_simulation.propagation_setup.propagator.multitype) propagators - some of the inputs (namely the integrator settings, the initial time, the termination settings and the output variables) are identical between all three propagators - the two single-type and the one multi-type. In these overlaps, tudat will only read the "top level" arguments, i.e. those passed to the multi-type propagator and will ignore the rest. This means that these inputs can be left empty (`0`, `NaN` or `None`) for the single-type propagators. However, it is good practice to be self consistent and pass the same inputs to all propagators. This facilitates the use of the single-type propagators for the simulation of only one type of dynamics while being consistent with the inputs of the multi-type simulation. +As you will see in the code below - and can be deduced comparing the APIs for the [translational](https://py.api.tudat.space/en/latest/propagator.html#tudatpy.numerical_simulation.propagation_setup.propagator.translational), [rotational](https://py.api.tudat.space/en/latest/propagator.html#tudatpy.numerical_simulation.propagation_setup.propagator.rotational) and [multi-type](https://py.api.tudat.space/en/latest/propagator.html#tudatpy.numerical_simulation.propagation_setup.propagator.multitype) propagators - some of the inputs (namely the integrator settings, the initial time, the termination settings and the output variables) are identical between all three propagators - the two single-type and the one multi-type. In these overlaps, tudat will only read the "top level" arguments, i.e. those passed to the multi-type propagator and will ignore the rest. This means that these inputs can be left empty (`0`, `NaN` or `None`) for the single-type propagators. However, it is good practice to be self-consistent and pass the same inputs to all propagators. This facilitates the use of the single-type propagators for the simulation of only one type of dynamics while being consistent with the inputs of the multi-type simulation. -Below, we will begin by creating these common inputs and will then move on to the propagator-specific inputs. -""" +Below, we will begin by creating these common inputs and will then move on to the propagator-specific inputs. To analyze our output, we will save Phobos' Kepler elements, Phobos-fixed spherical position of Mars and Phobos' 3-1-3 Euler angles (inertial to body-fixed). +""" ### Common inputs +""" +""" # INTEGRATOR SETTINGS # Here, we will select an RKDP7(8) integrator working in a fixed-step regime with a step size of 5 minutes. (For example, why not.) time_step = 300.0 # This is 5 minutes in seconds. coefficients = propagation_setup.integrator.CoefficientSets.rkdp_87 -integrator_settings = propagation_setup.integrator.runge_kutta_variable_step_size(time_step, - coefficients, - time_step, - time_step, - np.inf, np.inf) +integrator_settings = propagation_setup.integrator.runge_kutta_fixed_step( time_step, + coefficients, + propagation_setup.integrator.higher ) # INITIAL TIME initial_epoch = DateTime(2000, 1, 1).epoch() - # TERMINATION CONDITION # We will run a simulation of 30 days. simulation_time = 30.0*constants.JULIAN_DAY termination_condition = propagation_setup.propagator.time_termination(initial_epoch + simulation_time, terminate_exactly_on_final_condition = True) - # DEPENDENT VARIABLES dependent_variables = [ propagation_setup.dependent_variable.keplerian_state('Phobos', 'Mars'), propagation_setup.dependent_variable.central_body_fixed_spherical_position('Mars', 'Phobos'), propagation_setup.dependent_variable.inertial_to_body_fixed_313_euler_angles('Phobos') ] - ### Translational dynamics """ -Let's start by defining our translational propagator. You might have already done this in the past, as it is the most common type of propagator. This propagator is created using the `propagation_setup.propagator.translational()` function. Here, we will create all the inputs required by the function one by one, as listed in the API, except the ones we already created before. +Let's start by defining our translational propagator. Here, we will create all the inputs required by the function one by one, as listed in the API, except the ones we already created before. """ # CENTRAL BODIES AND BODIES TO PROPAGATE central_bodies =['Mars'] bodies_to_propagate = ['Phobos'] - # ACCELERATION MODEL third_body_force = propagation_setup.acceleration.point_mass_gravity() acceleration_settings_on_phobos = dict( Mars = [propagation_setup.acceleration.mutual_spherical_harmonic_gravity(12, 12, 4, 4)], @@ -379,11 +616,9 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, acceleration_settings = {'Phobos': acceleration_settings_on_phobos} acceleration_model = propagation_setup.create_acceleration_models(bodies, acceleration_settings, bodies_to_propagate, central_bodies) - # INITIAL STATE initial_translational_state = spice.get_body_cartesian_state_at_epoch('Phobos', 'Mars', 'J2000', 'NONE', initial_epoch) # We will just pick the closest to a default. - # PROPAGATION SETTINGS translational_propagator_settings = propagation_setup.propagator.translational( central_bodies, acceleration_model, @@ -393,21 +628,16 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, integrator_settings, termination_condition ) - ### Rotational dynamics """ -This part might be new to quite some people, and an explanation on "how tudat interprets rotational dynamics" can be found in the corresponding [Tudat documentation](https://docs.tudat.space/en/latest/_src_user_guide/state_propagation/propagation_setup/rotational.html). On the bright side, the setup a rotational propagation is pretty much the same as the setup of a translational propagation. On the not-so-bright side, rotational dynamics present one "teeny tiny" complication: coming up with an initial state. If you think that the _orientation_ of Phobos always has to be referred to some (preferably inertial) reference frame, you might be wondering where in our code we have to tell Tudat what this reference frame is. And the answer is, nowhere. Tudat will assume that **all orientations are given as referred to the `global_frame_orientation` defined at the beginning**. Thus, an appropriate rotation quaternion $\mathbf q$ and angular velocity vector $\vec\omega$ should be computed to represent a sensible orientation and angular velocity. - -**Note:** Keep in mind that the angular velocity vector should be given in body axes. \ -**Note:** At the moment, Tudat is very limited in the choice of this global frame orientation, and the only choices are `ECLIPJ200` (with reference plane at the ecliptic of epoch) and `J2000` (the reference frame attached to Earth at epoch, where the "reference horizontal" is the equator). Either of those to options are quite inconvenient to define the orientation of Phobos, whose orientation could be more comfortably given as referred to the Martian equator. +An explanation on how tudat defines rotational dynamics can be found in the corresponding [Tudat documentation](https://docs.tudat.space/en/latest/_src_user_guide/state_propagation/propagation_setup/rotational.html), and follows the structure of translational dynamics closely. One difference, and potential complication, is defining an initial state. If you think that the _orientation_ of Phobos always has to be referred to some (preferably inertial) reference frame, you might be wondering where in our code we have to tell Tudat what this reference frame is. And the answer is, nowhere. Tudat will assume that **all orientations are given as referred to the `global_frame_orientation` defined at the beginning** (at the moment, the only options are J2000 and ECLIPJ2000). Thus, an appropriate rotation quaternion $\mathbf q$ (inertial to body-fixed) and angular velocity vector $\vec\omega$ (w.r.t. the inertial frame, expressed in the body-fixed frame) has to be defined. -As mentioned earlier, the process of creating a rotational propagator mimics quite well that of creating a translational propagator. In this case, the function that creates the propagator object is `propagation_setup.propagator.rotational()`, so we will go over all the inputs listed in the API and create them all one by one. +Below, we create the settings for the rotational dynamics. """ # BODIES TO PROPAGATE bodies_to_propagate = ['Phobos'] - # TORQUE SETTINGS torque_settings_on_phobos = dict( Mars = [propagation_setup.torque.spherical_harmonic_gravitational(4,4)], Sun = [propagation_setup.torque.spherical_harmonic_gravitational(4,4)], @@ -419,8 +649,8 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, torque_model = propagation_setup.create_torque_models(bodies, torque_settings, bodies_to_propagate) # INITIAL STATE -initial_rotational_state = get_initial_rotational_state_at_epoch(initial_epoch) - +angular_rate = 0.000228035245 +initial_rotational_state = get_initial_rotational_state_at_epoch(initial_epoch, angular_rate) # PROPAGATION SETTINGS rotational_propagator_settings = propagation_setup.propagator.rotational( torque_model, @@ -430,12 +660,11 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, integrator_settings, termination_condition ) - -# You might be wondering what this `get_initial_rotational_state_at_epoch` function is. Given the hassle of coming up with a suitable initial rotational state, a user-defined function has been created to do just that, while keeping this part of the code fairly simple. The definition of this function can be found a couple of sections below. +# The `get_initial_rotational_state_at_epoch` is defined at the top of this file. # ### Combined propagator """ -With the translational and rotational propagators defined, the only thing left is to create the multi-type propagator, which is extremely simple at this point. +With the translational and rotational propagators defined, the only thing left is to create the multi-type propagator, which is done as follows. Note that the `integrator_settings`, `initial_epoch`, `termination_condition` and `dependent_variables` provided here will override any such settings provided to the constituent single-type propagator settings. """ # MULTI-TYPE PROPAGATOR @@ -446,10 +675,9 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, termination_condition, output_variables = dependent_variables) - ## Simulating the dynamics """ -At this point, one has everything they need to simulate. This is always done through the `numerical_simulation.create_dynamics_simulator` function, which only requires the `bodies` object and a propagator settings object. And both of those are available now. +At this point, one has everything they need to simulate. This is always done through the `numerical_simulation.create_dynamics_simulator` function, regardless of the type of dynamics that is considered """ # DYNAMICS SIMULATION @@ -457,17 +685,16 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, state_history = simulator.state_history dependent_variable_history = simulator.dependent_variable_history - ## Let's look at plots """ -We are now ready to do some post-processing. Here, we will be looking at how Phobos moves and rotates. In order to better interpret the results, it is good to gather some facts about its situation in the Martian system. Useful information can be: \ +We are now ready to do some post-processing, and look at our results! Here, we will be looking at how Phobos moves and rotates. In order to better interpret the results, it is good to gather some facts about its situation in the Martian system. Useful information on Phobos is: \ · It has a semimajor axis of ~9500km. \ -· It has an orbital period of ~5h, which means that in 30 days it completes ~140 orbits around Mars. \ +· It has an orbital period of ~7h, which means that in 30 days it completes ~140 orbits around Mars. \ · It is in a near-circular, near-equatorial orbit ($e\approx0.0151$, $i\approx1.1º$). \ · It is locked in synchronous rotation, which means that Mars should be fixed at $0º$ latitude and longitude in the Phobian sky. \ -· On top of this synchronous rotation, there exist so-called _physical librations_ (see [reference to section 2.5 in my literature study]), so that Mars does oscillate periodically around $0º$. +· On top of this synchronous rotation, there exist so-called _physical librations_, see Rambaux et al. (2010). -Here, we will plot the Keplerian elements, the coordinates of Mars in Phobos' sky, and Phobos' Euler angles. +Here, we will plot the Keplerian elements, the coordinates of Mars in Phobos' sky, and Phobos' Euler angles. The latter two both provide information on Phobos' orientation. [TODO: add some comments in the part below ... ... IN THE CODE! WHAT DOES EACH PLOT REPRESENT] """ ## PLOTS @@ -481,7 +708,6 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, epochs = states_array[:,0] / constants.JULIAN_DAY time_label = 'Time since J2000 [days]' - plt.figure() plt.plot(epochs, dependents_array[:,1] / 1e3) plt.grid() @@ -528,7 +754,6 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, plt.figure() plt.plot(epochs, np.degrees(bring_inside_bounds(dependents_array[:,10], -PI, PI, include = 'upper')), label = r'$\Psi$') plt.plot(epochs, np.degrees(dependents_array[:,11]), label = r'$\theta$') -# plt.plot(epochs, np.degrees(dependents_array[:,12]), label = r'$\varphi$') plt.grid() plt.legend() plt.xlabel(time_label) @@ -538,11 +763,10 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, mean_motion = 0.0002278563609852602 normal_mode = get_longitudinal_normal_mode_from_inertia_tensor(bodies.get('Phobos').inertia_tensor, mean_motion) librations = bring_inside_bounds(dependents_array[:,8:10], -PI, PI, 'upper') -lon_lib_freq, lon_lib_amp = get_fourier(np.hstack((np.atleast_2d(dependents_array[:,0]).T, np.atleast_2d(librations[:,1]).T)), [TWOPI, 1]) -lat_lib_freq, lat_lib_amp = get_fourier(np.hstack((np.atleast_2d(dependents_array[:,0]).T, np.atleast_2d(librations[:,0]).T)), [TWOPI, 1]) +lon_lib_freq, lon_lib_amp = get_fourier(np.hstack((np.atleast_2d(dependents_array[:,0]).T, np.atleast_2d(librations[:,1]).T)), [2.0*PI, 1]) +lat_lib_freq, lat_lib_amp = get_fourier(np.hstack((np.atleast_2d(dependents_array[:,0]).T, np.atleast_2d(librations[:,0]).T)), [2.0*PI, 1]) plt.figure() plt.loglog(lon_lib_freq * 86400.0, np.degrees(lon_lib_amp), marker='.', label='Lon') -# plt.loglog(lat_lib_freq * 86400.0, np.degrees(lat_lib_amp), marker='.', label='Lat') plt.gca().set_ylim(bottom=1e-8) plt.axvline(mean_motion * 86400.0, ls='dashed', c='r', linewidth = 1.0, label='Phobos\' mean motion (and integer multiples)') plt.axvline(normal_mode * 86400.0, ls='dashed', c='k', linewidth = 1.0, label='Longitudinal normal mode') @@ -556,29 +780,21 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, plt.show() -""" -**Note:** The third Euler angle of Phobos, $\varphi$, has been commented out of the plot. This is because it represents Phobos' rotation about its $z$ axis, and therefore it will increase secularly and overlap with $\Psi$. - -Here, there are two things that immediately stand out: Mars' coordinates in Phobos' sky are a mess (i.e. Phobos' orientation is not what we expected) and the inclination of Phobos is nowhere near 0. We will start by addressing the latter. Notice that "the inclination of Phobos" is usually thought of as measured from the Martian equator. Here, however, the Euler angles of the orbit are computed with respect to inertial space, i.e. the equator of the Earth in this case. The Martian equator is inclined by about $35.5º$ with respect to that of the Earth, and that is why we see Phobos' inclination oscillate around that value rather than $0º$. - -Explaining the orientation of Phobos is a bit lengthier. The rotational equations of motion presented at the beginning of this example, also known as Euler equations, give rise to non-trivial oscillatory solutions when their homogeneous form are solved. This means that, even when setting all torques to 0, Phobos' rotational motion will be oscillatory around all three axes. These solutions are called _proper modes_ or _normal modes_ and have a particular frequency associated to them - which is often times referred to as _normal mode_ as well - which is determined by Phobos' physical properties and the mean motion of its orbit. Although an in-depth discussion of normal modes will not be provided here - the interested reader is referred to [reference to Appendix B of my literature study] for that - the two properties that one should know about them are: -* They **always** vanish in the presence of damping -* They **never** vanish in the absence of damping. - -Phobos is not observed to contain any normal modes in its present day rotational motion. However, in the dynamics we have modelled in this example, damping does not exist and therefore the normal modes will naturally arise. This can be very well seen in the frequency content of the longitudinal libration. A similar plot is obtained for the Fourier transform of the Euler angles. Thus, this _undamped_ dynamics os not a realistic representation of Phobos' motion. Nevertheless, the presence of these normal modes do not affect Phobos' translation in a significant way: both the semi-major axis and eccentricity oscillate around a constant value in a similar way as they would in a purely translational simulation, and the orbit's Euler angles show the expected secularity in the arguments of periapsis (fast) and the nodes (slow). - -In any case, a representative coupled simulation needs to eliminate the normal modes of these dynamics. - -**Note:** If you are wondering why the FFT peak at the normal mode does not *exactly* coincide with the reference line, it is because this reference line has been computed with an approximate expression. - -""" +# **Note:** The third Euler angle of Phobos, $\varphi$, is omitted from plot. This is because it represents Phobos' rotation about its $z$ axis, and therefore it will increase secularly and overlap with $\Psi$. +# +# Here, there are two things that immediately stand out: Mars' coordinates in Phobos' sky are a mess (i.e. Phobos' orientation is not what we expected) and the inclination of Phobos is nowhere near 0. We will start by addressing the latter. Notice that "the inclination of Phobos" is usually thought of as measured from the Martian equator. Here, however, the angles of the orbit are computed with respect to global frame orientation, which is here set to J2000. The Martian equator is inclined by about $35.5º$ with respect to that of the Earth, and that is why we see Phobos' inclination oscillate around that value rather than $0º$. +# +# The reason behind the strange behaviour of Phobos' orientation is quite a bit lengthier. The rotational equations of motion presented at the beginning of this example, also known as Euler equations, give rise to non-zero periodic solutions even in the absence of any forcing (e.g. homogeneous solution). This means that, even when setting all torques to 0, Phobos' rotational motion will be oscillatory around all three axes. These solutions are called _proper modes_ or _normal modes_ and have a particular frequency associated to them - which is often times referred to as _normal mode_ as well. These are determined by Phobos' physical properties and the mean motion of its orbit. Although an in-depth discussion of normal modes will not be provided here - the interested reader is referred to (Rambaux et al. 2010). +# +# In reality, Phobos' free modes have been damped due to dissipative effect. However, in the dynamics we have modelled in this example, this damping is not present, nor eas the initial state consistent with a damped situation, and therefore the normal modes will naturally arise. This can be very well seen in the frequency content of the longitudinal libration above, where a strong peak is observed at Phobos' proper mode. Thus, the _undamped_ dynamics we have simulated here is not realistic representation of Phobos' motion. +# +# **Note:** If you are wondering why the FFT peak at the normal mode does not *exactly* coincide with the reference line, it is because this reference line has been computed with an approximate expression. +# ## Getting rid of the normal modes """ -As we just saw, Phobos does not quite spin the way we would expect it to due to the excitation of its normal modes. Two questions that may arise are (1) why we don't see the normal modes in real life and (2) how to we get rid of the normal modes in the simulation. The answer to both lies in **damping**. A mathematical discussion of how damping and external forcings affect normal modes is given in [reference to appendix B of my literature study]. Phobos has been spinning up there for about 4.5 thousands of millions of years (that is not a made up number). In that time, any little amount of damping that the body was subjected to - be it for structural reasons, or additional torques we are not considering - has been able to make the normal modes vanish, and that is why Phobos does not present these rotations anymore. This presents two complications in getting rid of normal modes in the simulation: we cannot really simulate all $4.5\times10^9$ years that Phobos has been orbiting Mars, and we don't really know if any of the torques that we have included in our rotational dynamics have a damping effect. - -The solution to these problems is to introduce a _virtual torque_ ("virtual" is just the scientific word for "made up") that we know has a strong damping effect, and we propagate the dynamics forward. At one point, we will reach a state in which the normal modes have been eliminated. Now, we remove this damping torque and we propagate the dynamics backward for the same amount of time. This new rotational state that we have found, call it _damped initial state_ will not excite the normal modes of Phobos when propagated forward in the original dynamics, by definition, because that is how it was obtained. In practice, this can be done in several ways, and decisions are to be made with respect to, for instance, (a) how to compute this torque or (b) how long to propagate back and forth for. Moreover, the question still remains of what would happen if we apply this torque for, say, 1 year, and then try to use the new damped initial state to propagate a 2 year long arc. Would the normal modes still be removed after that 1 year. If not, how do they come back: suddenly or gradually? Answers to all these questions will not be covered here (it is not the point of this example), but the interested reader is referred to [reference to the appendix of my soon-to-exist master thesis] for an algorithm of the computation of the virtual damping torque. +As we just saw, Phobos does not rotate the way we would expect it to, due to the presence of excited normal modes. The fact that the normal mode is excited is a result of the initial rotational state, which is not consistent with rotational dynamics in which the normal modes are damped. We solve this problem by determining an initial state where these normal modes **are** damped, following the method used by (Rambaux et al. 2010). Specifically, we introduce a _virtual torque_ ("virtual" is a scientific word for "made up") that we know has a strong damping effect, and we propagate the dynamics forward in time. At one point, we will reach a state in which the normal modes have been eliminated from the dynamics (or 'damped'). Now, we remove this damping torque and we propagate the dynamics backward in time from this point onwards, until we reach our original initial time. This new rotational state that we have found, and will call the _damped initial state_ would ideally not excite the normal modes of Phobos when propagated forward again using our 'normal' (e.g. without the virtual torque) dynamical model. -Here, we will cover how to obtain the damped state in Tudat. In Tudat, the forward-backward propagation scheme is performed several times. A _damping time_ is selected, which is a measure of how quick the torque will eliminate the normal modes, and it is used to compute the torque. The propagation is performed forwards with the torque for 10 times the specified damping time, and backwards without it, and the new state is used in another iteration with a new and larger damping time. Luckily, Tudat provides a function that performs all the iterations for us. It is called `get_zero_proper_mode_rotational_state`. The implementation of this function has been somewhat particularized to bodies that rotate uniformly around their Z axis. Thus, the damping torque will act to prevent the excitation of the normal modes while preserving that uniform rotation. The function thus requires as inputs: (1) the `bodies` object, (2) the `propagatotupler_settings` object, (3) the mean rotational rate of the body around its Z axis, and (4) a list of dissipation times or damping times in order to compute the virtual torques for the different iteration. The code looks as below. +In practice, the normal mode will be excited much less than initially, but some excitation still remains. This is further resolved by repeating the above procedure many times, with increasing 'damping times'. In Tudat, this approach is automated by the `get_zero_proper_mode_rotational_state` function [TODO: add API docs], where additional details of the algorithm are described. In short, a _damping time_ :math:`\tau_{d}` is selected, which is a measure of how quick the virtual torque will eliminate the normal modes, and it is used to compute the torque. The propagation is performed forwards in time with the virtual torque enabled, for :math:`10 \tau_{d}`, and backwards to the original time without it. The new initial state is used in another iteration with a new and larger damping time. The implementation of the virtual torque in this algorithm is very specific to bodies in spin-orbit resonance, which will (averaged over long time periods) rotate uniformly around their body-fixed :math:`z` axis, with a period equal to the orbital period. The damping torque will act to prevent the excitation of the normal modes while preserving that uniform rotation. The function thus requires as inputs: (1) the `bodies` object, (2) the `propagator_settings` object, (3) the mean rotational rate of the body around its Z axis, and (4) a list of dissipation times or damping times in order to compute the virtual torques for the different iteration. The above is implemented as follows: """ phobos_mean_rotational_rate = 0.000228035245 # In rad/s @@ -591,14 +807,13 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, damped_state_history = damping_results.forward_backward_states[-1][1] damped_dependent_variable_history = damping_results.forward_backward_dependent_variables[-1][1] -""" -As you can see, we are using the `bodies` and the `propagator_settings` that we had already created earlier. The output of this function is this `damping_results` object. It contains quite a few things, the most important of which are: \ -· **The damped initial state.** This state is of the same type and size as the one provided in the `propagator_settings`. In this case of the coupled dynamics, a 13-dimensional vector. \ -· **The forward-backward states.** It is a list of tuples. Each tuple contains the results of one iteration. In each of these tuples, there are two dictionaries: one of them is the state history of the forward propagation, i.e. with the damping torque; the other is the state history of the backward propagation, i.e. without the torque. There is one more tuple than iterations. The tuple in index 0 contains the undamped states, i.e. the histories of the forward and backward states when no torque is applied. **Note:** In this tuple, the two dictionaries are in principle the same, becuase the dynamics of both propagations are identical. The small errors that might be encountered are fully integration errors. \ -· **The forward-backward dependent variables.** It is the exact same thing as the forward-backward states, but with the dependent variables provided in the `propagator_settings`. - -Notice that `dapming_results` already contains the propagated states of the "fully" damped dynamics, which means there is no need to repropagate them again with the obtained damped initial state. The damped trjectory is readily available to us, although it spans the 40960h of the final damping time. To aid in comparison with the undamped dynamics from above, we will only plot the first 30 days. -""" +# As you can see, we are using the `bodies` and the `propagator_settings` that we had already created earlier. The output of this function is the `damping_results` object, of type [TODO, insert link to class here]. This object contains: \ +# · **The damped initial state.** This state is of the same type and size as the one provided in the `propagator_settings`. In this case of the coupled dynamics, a 13-dimensional vector. This is the initial state that the algorithm finds after its iterations, for which the normal modes should be (almost) entirely damped. \ +# · **The forward-backward states.** It is a list of tuples. Each tuple contains the results of one iteration. In each of these tuples, there are two dictionaries: one of them is the state history of the forward propagation, i.e. with the damping torque; the other is the state history of the backward propagation, i.e. without the torque. There is one more tuple than iterations. The tuple in index 0 contains the undamped states, i.e. the histories of the forward and backward states when no torque is applied. **Note:** In this tuple, the two dictionaries are in principle the same, because the dynamics of both propagations are identical. The small errors that might be encountered are fully integration errors. \ +# · **The forward-backward dependent variables.** It is the exact same thing as the forward-backward states, but with the dependent variables provided in the `propagator_settings`. +# +# Notice that `dapming_results` already contains the propagated states of the "fully" damped dynamics, which means there is no need to re-propagate them again with the obtained damped initial state. The damped trjectory is readily available to us, although it spans the 40960h of the final damping time. To aid in comparison with the undamped dynamics from above, we will only plot the first 30 days. +# ## Let's look at plots (again) """ With the new damped states and dependent variables, we can perform the same kind of post-processing that we did earlier. @@ -677,8 +892,8 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, mean_motion = 0.0002278563609852602 normal_mode = get_longitudinal_normal_mode_from_inertia_tensor(bodies.get('Phobos').inertia_tensor, mean_motion) damped_librations = bring_inside_bounds(damped_dependents_array[:,8:10], -PI, PI, 'upper') -damped_lon_lib_freq, damped_lon_lib_amp = get_fourier(np.hstack((np.atleast_2d(damped_dependents_array[:,0]).T, np.atleast_2d(damped_librations[:,1]).T)), [TWOPI, 1]) -damped_lat_lib_freq, damped_lat_lib_amp = get_fourier(np.hstack((np.atleast_2d(damped_dependents_array[:,0]).T, np.atleast_2d(damped_librations[:,0]).T)), [TWOPI, 1]) +damped_lon_lib_freq, damped_lon_lib_amp = get_fourier(np.hstack((np.atleast_2d(damped_dependents_array[:,0]).T, np.atleast_2d(damped_librations[:,1]).T)), [2.0*PI, 1]) +damped_lat_lib_freq, damped_lat_lib_amp = get_fourier(np.hstack((np.atleast_2d(damped_dependents_array[:,0]).T, np.atleast_2d(damped_librations[:,0]).T)), [2.0*PI, 1]) plt.figure() plt.loglog(damped_lon_lib_freq * 86400.0, np.degrees(damped_lon_lib_amp), marker='.', label='Lon') # plt.loglog(lat_lib_freq * 86400.0, np.degrees(lat_lib_amp), marker='.', label='Lat') @@ -695,6 +910,4 @@ def get_longitudinal_normal_mode_from_inertia_tensor(inertia_tensor: np.ndarray, plt.show() -""" -In these new damped dynamics, Mars does oscillate periodically around $0º$ of latitude and longitude in Phobos' sky, and the Fourier transform shows that the frequency at the normal mode is gone. Note that we would see the same thing in the FFT of Phobos' Euler angles. This is now a faithful representation of Phobos' motion. -""" \ No newline at end of file +# In these new damped dynamics, Mars does oscillate periodically around $0º$ of latitude and longitude in Phobos' sky, and the Fourier transform shows that the frequency at the normal mode is gone. Note that we would see the same thing in the FFT of Phobos' Euler angles. This is now a faithful representation of Phobos' motion. Note that, even though the libration at the frequency of the normal mode is removed, the fact that Phobos' normal mode is very close to its orbital period (strongest forcing frequency), there are numerous small forcing terms close to the normal mode that result in observable effects in Phobos' libration frequency spectrum. These are discussed and tabulated in detail by (Rambaux et al. 2010). diff --git a/propagation/impact_manifolds_lpo_cr3bp.py b/propagation/impact_manifolds_lpo_cr3bp.py index 2b4f14c..7f2b748 100644 --- a/propagation/impact_manifolds_lpo_cr3bp.py +++ b/propagation/impact_manifolds_lpo_cr3bp.py @@ -6,8 +6,8 @@ under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -43,13 +43,11 @@ from tudatpy.math import interpolators, root_finders from tudatpy.astro.time_conversion import DateTime - ## Auxiliary Functions """ +Since the CR3BP is being used, functions to compute the units of length and time used to make the CR3BP dimensionless are first defined. """ -# Since the CR3BP is being used, functions to compute the units of length and time used to make the CR3BP dimensionless are first defined. - ######################################################################################################################## # Compute unit of length of the CR3BP def cr3bp_unit_of_length (distance_between_primaries: float) -> float: @@ -67,7 +65,6 @@ def cr3bp_unit_of_time (gravitational_parameter_primary: float, return unit - # In the CR3BP, trajectories are usually analyzed with respect to a synodic frame, i.e. a frame centered on the barycenter that rotates with the primaries (Mars and Phobos in this case). Instead of the usual barycentric synodic frame, here the synodic frame is considered to be Phobos centered (since orbits in the proximity of Phobos are being analyzed); therefore, the synodic frame coincides with Phobos' body-fixed frame. # # Since Tudat propagates the trajectories with respect to a frame with inertial origin and orientation, it is necessary to define functions to convert the state and state transition matrix (STM) to/from the body-fixed frame. The functions implemented here are valid for the conversion between an inertial frame and any **uniformly-rotating** (i.e. constant angular velocity) body-fixed frame. @@ -170,7 +167,6 @@ def convert_stm_inertial_to_body_fixed( return stm_synodic - # Finally, two functions are defined to create the propagator settings. # # The `create_time_termination_propagator_settings` function creates the settings for an orbit propagation that terminates at an exact time. @@ -278,14 +274,12 @@ def create_hybrid_termination_propagator_settings(central_bodies, return hybrid_termination_propagator_settings - ## Model and Propagation Setup """ -""" +To setup the used model (CR3BP with polyhedral secondary), it is first necessary to define a series of parameters. These include the gravitational parameters of Mars and Phobos, the semi-major axis of Phobos, the polyhedron of Phobos (coordinates of the vertices and vertices defining each facet), and the initial state and period of the used Lagrange point orbit. This periodic orbit was determined via continuation, which is currently not available via Tudat. This and other functionalities for the computation of periodic orbits will be added to Tudat in (near-ish) future. -# To setup the used model (CR3BP with polyhedral secondary), it is first necessary to define a series of parameters. These include the gravitational parameters of Mars and Phobos, the semi-major axis of Phobos, the polyhedron of Phobos (coordinates of the vertices and vertices defining each facet), and the initial state and period of the used Lagrange point orbit. This periodic orbit was determined via continuation, which is currently not available via Tudat. This and other functionalities for the computation of periodic orbits will be added to Tudat in (near-ish) future. -# -# Since all the trajectories are here propagated in dimensionless coordinates, all the dimensional parameters are made dimensionless using the units of time and length of the CR3BP. +Since all the trajectories are here propagated in dimensionless coordinates, all the dimensional parameters are made dimensionless using the units of time and length of the CR3BP. +""" #################################################################################################################### # Define dimensional model parameters and then make them dimensionless @@ -350,7 +344,6 @@ def create_hybrid_termination_propagator_settings(central_bodies, # Compute dimensionless volume volume_secondary = polyhedron_utilities.volume(vertices_coordinates, vertices_defining_each_facet) - # Next, the used system of bodies is created according to the assumptions of the CR3BP with polyhedral secondary: # # * Mars: located at origin of reference frame and having point mass gravity @@ -417,7 +410,6 @@ def create_hybrid_termination_propagator_settings(central_bodies, # Create system of selected celestial bodies bodies = environment_setup.create_system_of_bodies(body_settings) - # The acceleration models are now created. As mentioned, the primary (Mars) has point mass gravity and the secondary (Phobos) has polyhedral gravity. #################################################################################################################### @@ -441,7 +433,6 @@ def create_hybrid_termination_propagator_settings(central_bodies, acceleration_models = propagation_setup.create_acceleration_models( bodies, acceleration_settings, bodies_to_propagate, central_bodies) - # Next, the settings of the integrator are defined. A variable-step RKDP8(7) integrator is used. #################################################################################################################### @@ -452,13 +443,9 @@ def create_hybrid_termination_propagator_settings(central_bodies, current_tolerance = 1e-12 initial_time_step = 1e-6 # Maximum step size: inf; minimum step size: eps -integrator_settings = propagation_setup.integrator.runge_kutta_variable_step_size(initial_time_step, - current_coefficient_set, - np.finfo(float).eps, - np.inf, - current_tolerance, - current_tolerance) - +integrator_settings = propagation_setup.integrator.runge_kutta_variable_step_size( + initial_time_step, current_coefficient_set, np.finfo(float).eps, np.inf, + current_tolerance, current_tolerance) # Finally, the dependent variables to save during the propagation are selected. Here, no dependent variable is saved, though the code can be modified to do so if desired. @@ -467,7 +454,6 @@ def create_hybrid_termination_propagator_settings(central_bodies, dependent_variables_to_save = [] - ## Propagation of the Lagrange point orbit """ @@ -475,7 +461,7 @@ def create_hybrid_termination_propagator_settings(central_bodies, Next, the propagator settings are created using the `create_time_termination_propagator_settings` function: these settings define the propagation of the orbit to an exact final time (the period of the orbit). -Having the propagator settings and the initial state in the inertial frame, it is now possible to propagate the orbit. The `create_variational_equations_solver` function is used, to allow the propagation of the STM (necessary for computing the invariant manifolds). +Having the propagator settings and the initial state in the inertial frame, it is now possible to propagate the orbit. The `create_variational_equations_solver` is used, to allow the propagation of the STM (necessary for computing the invariant manifolds). After the propagation is finished, the state and STM histories with respect to the inertial frame are retrieved and converted to the body-fixed frame. """ @@ -488,19 +474,15 @@ def create_hybrid_termination_propagator_settings(central_bodies, bodies, name_secondary, {simulation_start_epoch: initial_state_lpo_body_fixed}) # Create propagator settings -time_propagator_settings = create_time_termination_propagator_settings(central_bodies, - acceleration_models, - bodies_to_propagate, - state_history_lpo_inertial[simulation_start_epoch], - simulation_start_epoch, - integrator_settings, - period_lpo, - dependent_variables_to_save) +time_propagator_settings = create_time_termination_propagator_settings( + central_bodies, acceleration_models, bodies_to_propagate, state_history_lpo_inertial[simulation_start_epoch], + simulation_start_epoch, integrator_settings, period_lpo, dependent_variables_to_save) # Propagate variational equations, propagating just the STM parameter_settings = estimation_setup.parameter.initial_states(time_propagator_settings, bodies) lpo_single_arc_solver = numerical_simulation.create_variational_equations_solver( - bodies, time_propagator_settings, estimation_setup.create_parameter_set(parameter_settings, bodies), + bodies, time_propagator_settings, + estimation_setup.create_parameter_set(parameter_settings, bodies), simulate_dynamics_on_creation=True) # Retrieve state and STM history and convert them to body-fixed frame @@ -512,16 +494,14 @@ def create_hybrid_termination_propagator_settings(central_bodies, stm_history_lpo_body_fixed = convert_stm_history_inertial_to_body_fixed( bodies, name_secondary, stm_history_lpo_inertial) - ## Propagation of the invariant manifolds """ -""" +Having propagated an unstable Lagrange point orbit, its invariant manifolds are now computed. Only the unstable invariant manifolds are propagated; the stable ones can be obtained in a similar way, but unsing a negative time step instead (backward propagation). -# Having propagated an unstable Lagrange point orbit, its invariant manifolds are now computed. Only the unstable invariant manifolds are propagated; the stable ones can be obtained in a similar way, but unsing a negative time step instead (backward propagation). -# -# The initial state of each manifold branch can be obtained by perturbing the state at a node of the orbit with the most unstable eigenvector of the monodromy matrix associated with that node, which corresponds to the eigenvector associated with the eigenvalue with the largest norm. -# -# Since the linearized dynamics are being considered via the STM, one can instead use the monodromy matrix just to determine the unstable eigenvector at the 1st node. The unstable eigenvectors at the remaining nodes of the orbit can then be obtained using the STM. However, to do that, one needs to know the state and STM at each node of the orbit, which here is done using an interpolator. This allow having nodes equally spaced in time along the orbit. A 4th order Lagrange interpolator is used. +The initial state of each manifold branch can be obtained by perturbing the state at a node of the orbit with the most unstable eigenvector of the monodromy matrix associated with that node, which corresponds to the eigenvector associated with the eigenvalue with the largest norm. + +Since the linearized dynamics are being considered via the STM, one can instead use the monodromy matrix just to determine the unstable eigenvector at the 1st node. The unstable eigenvectors at the remaining nodes of the orbit can then be obtained using the STM. However, to do that, one needs to know the state and STM at each node of the orbit, which here is done using an interpolator. This allow having nodes equally spaced in time along the orbit. A 4th order Lagrange interpolator is used. +""" #################################################################################################################### # Propagate the invariant manifolds @@ -544,7 +524,6 @@ def create_hybrid_termination_propagator_settings(central_bodies, state_history_lpo_body_fixed_interpolator = interpolators.create_one_dimensional_vector_interpolator( state_history_lpo_body_fixed, interpolator_settings) - # Having defined the interpolators, it is now possible to loop over the nodes of the Lagrange point orbit and determine the initial state of the unstable invariant manifold at each of them. This initial state is defined with respect to Phobos' body-fixed frame, so it needs to be converted to the inertial frame before executing the propagation. # # Next, the propagator settings are created. Hybrid propagator settings are used, which terminate the propagation after a maximum time or maximum distance to Phobos is reached, or after the spacecraft impacts Phobos (whatever happens first). Finally, the `create_dynamics_simulator` function is called to propagate each manifold. @@ -583,37 +562,26 @@ def create_hybrid_termination_propagator_settings(central_bodies, {simulation_start_epoch: manifold_initial_state_body_fixed}) # Create propagator settings - hybrid_propagator_settings = create_hybrid_termination_propagator_settings(central_bodies, - acceleration_models, - bodies_to_propagate, - state_history_manifold_inertial[simulation_start_epoch], - simulation_start_epoch, - integrator_settings, - dependent_variables_to_save, - name_spacecraft, name_secondary, - gravitational_parameter_secondary, - volume_secondary, - hybrid_termination_max_distance, - hybrid_termination_max_time) + hybrid_propagator_settings = create_hybrid_termination_propagator_settings( + central_bodies, acceleration_models, bodies_to_propagate, state_history_manifold_inertial[simulation_start_epoch], + simulation_start_epoch, integrator_settings, dependent_variables_to_save, name_spacecraft, name_secondary, gravitational_parameter_secondary, + volume_secondary, hybrid_termination_max_distance, hybrid_termination_max_time) # Propagate manifold - manifold_single_arc_solver = numerical_simulation.create_dynamics_simulator(bodies, - hybrid_propagator_settings) - + manifold_single_arc_solver = numerical_simulation.create_dynamics_simulator( + bodies, hybrid_propagator_settings) if manifold_direction_to_propagate == -1: manifold_single_arc_solvers[0].append(manifold_single_arc_solver) else: manifold_single_arc_solvers[1].append(manifold_single_arc_solver) - ## Plotting the results """ -""" +Finally, we can plot the computed orbit and its manifolds. Before plotting the manifolds, their state history is retrieved from the single arc solver and converted to the body-fixed frame. -# Finally, we can plot the computed orbit and its manifolds. Before plotting the manifolds, their state history is retrieved from the single arc solver and converted to the body-fixed frame. -# -# Phobos' shape is also plotted, using the `tricontourf` function. +Phobos' shape is also plotted, using the `tricontourf` function. +""" #################################################################################################################### # Make plot: x vs y, x vs z @@ -681,8 +649,3 @@ def create_hybrid_termination_propagator_settings(central_bodies, ax[0].legend() ax[0].set_ylabel('y [km]') ax[1].set_ylabel('z [km]') - - -plt.show() - - diff --git a/propagation/keplerian_satellite_orbit.py b/propagation/keplerian_satellite_orbit.py index 7b78d9c..24e545b 100644 --- a/propagation/keplerian_satellite_orbit.py +++ b/propagation/keplerian_satellite_orbit.py @@ -1,6 +1,7 @@ # Keplerian satellite orbit """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. + """ ## Context @@ -36,7 +37,6 @@ from tudatpy.util import result2array from tudatpy.astro.time_conversion import DateTime - ## Configuration """ NAIF's `SPICE` kernels are first loaded, so that the position of various bodies such as the Earth can be make known to `tudatpy`. @@ -54,12 +54,11 @@ simulation_start_epoch = DateTime(2000, 1, 1).epoch() simulation_end_epoch = DateTime(2000, 1, 2).epoch() - ## Environment setup """ Let’s create the environment for our simulation. This setup covers the creation of (celestial) bodies, vehicle(s), and environment interfaces. -""" +""" ### Create the bodies """ @@ -84,7 +83,6 @@ # Create system of bodies (in this case only Earth) bodies = environment_setup.create_system_of_bodies(body_settings) - ### Create the vehicle """ Let's now create the massless satellite for which the orbit around Earth will be propagated. @@ -93,7 +91,6 @@ # Add vehicle object to system of bodies bodies.create_empty_body("Delfi-C3") - ## Propagation setup """ Now that the environment is created, the propagation setup is defined. @@ -108,7 +105,6 @@ # Define central bodies of propagation central_bodies = ["Earth"] - ### Create the acceleration model """ First off, the acceleration settings that act on `Delfi-C3` are to be defined. @@ -131,7 +127,6 @@ bodies, acceleration_settings, bodies_to_propagate, central_bodies ) - ### Define the initial state """ The initial state of the vehicle that will be propagated is now defined. @@ -184,15 +179,12 @@ termination_settings ) - - - ## Propagate the orbit """ The orbit is now ready to be propagated. -This is done by calling the `create_dynamics_simulator()` function of the `numerical_simulation` module. -This function requires the `bodies` and `propagator_settings` that have been defined earlier. +This is done by calling the `create_dynamics_simulator()` function of the `numerical_simulation module`. +This function requires the `bodies` and `propagator_settings` that have all been defined earlier. After this, the history of the propagated state over time, containing both the position and velocity history, is extracted. This history, taking the form of a dictionary, is then converted to an array containing 7 columns: @@ -210,12 +202,11 @@ states = dynamics_simulator.state_history states_array = result2array(states) - ## Post-process the propagation results """ The results of the propagation are then processed to a more user-friendly form. -""" +""" ### Print initial and final states """ @@ -236,7 +227,6 @@ """ ) - ### Visualise the trajectory """ Finally, let's plot the trajectory of `Delfi-C3` around Earth in 3D. @@ -257,7 +247,3 @@ ax.set_ylabel('y [m]') ax.set_zlabel('z [m]') plt.show() - - - - diff --git a/propagation/linear_sensitivity_analysis.py b/propagation/linear_sensitivity_analysis.py index 6280339..beb4781 100644 --- a/propagation/linear_sensitivity_analysis.py +++ b/propagation/linear_sensitivity_analysis.py @@ -1,8 +1,8 @@ # Linear sensitivity analysis of perturbed orbit """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -10,7 +10,8 @@ The script demonstrates how the basic numerical simulation setup (aiming to propagate the state of the system) can swiftly be extended to enable a study of the system's sensitivity. -Via the `estimation_setup.parameter module`, the system parameters w.r.t. which the sensitivity is to be studied are defined and a create_variational_equations_solver function from the numerical_simulation module is used in order to setup and integrate the system's variational equations. After obtaining the state transition matrices from the integrated variational equations, the system's response to small perturbations can be tested via simple matrix multiplication. +Via the `estimation_setup.parameter module`, the system parameters w.r.t. which the sensitivity is to be studied are defined and a `create_variational_equations_solver` function from the numerical_simulation module is used in order to setup and integrate the system's variational equations. After obtaining the state transition matrices from the integrated variational equations, the system's response to small perturbations can be tested via simple matrix multiplication. + The availability of variational equations in tudat enables many more, advanced functionalities, such as covariance analysis and precise orbit determination. """ @@ -38,7 +39,6 @@ from tudatpy.util import result2array from tudatpy.astro.time_conversion import DateTime - ## Configuration """ NAIF's `SPICE` kernels are first loaded, so that the position of various bodies such as the Earth, the Sun, the Moon, Venus, or Mars, can be make known to `tudatpy`. @@ -54,12 +54,11 @@ simulation_start_epoch = DateTime(2000, 1, 1).epoch() simulation_end_epoch = DateTime(2000, 1, 2).epoch() - ## Environment setup """ Let’s create the environment for our simulation. This setup covers the creation of (celestial) bodies, vehicle(s), and environment interfaces. -""" +""" ### Create the bodies """ @@ -84,7 +83,6 @@ # Create the system of bodies bodies = environment_setup.create_system_of_bodies(body_settings) - ### Create the vehicle and its environment interface """ Let's now create the satellite for which an orbit will be simulated. @@ -96,7 +94,7 @@ # Create vehicle objects. bodies.create_empty_body("Delfi-C3") -bodies.get("Delfi-C3").mass = 2.2 +bodies.get("Delfi-C3").mass = 400.0 # Create aerodynamic coefficient interface settings reference_area = (4*0.3*0.1+2*0.1*0.1)/4 # Average projection area of a 3U CubeSat @@ -119,13 +117,11 @@ environment_setup.add_radiation_pressure_interface( bodies, "Delfi-C3", radiation_pressure_settings) - ## Propagation setup """ Now that the environment is created, the propagation setup is defined. First, the bodies to be propagated and the central bodies will be defined. -Subsequently, the integrator settings are defined using a RK4 integrator with the fixed step size of 10 seconds. Central bodies are the bodies with respect to which the state of the respective propagated bodies is defined. """ @@ -135,7 +131,6 @@ # Define central bodies of propagation central_bodies = ["Earth"] - ### Create the acceleration model """ First off, the acceleration settings that act on `Delfi-C3` are to be defined. @@ -180,14 +175,13 @@ bodies_to_propagate, central_bodies) - ### Define the initial state """ The initial state of the vehicle that will be propagated is now defined. This initial state always has to be provided as a cartesian state, in the form of a list with the first three elements reprensenting the initial position, and the three remaining elements representing the initial velocity. -In this casWithin this example, we will retrieve the initial state of Delfi-C3 using its Two-Line-Elements (TLE) the date of its launch (April the 28th, 2008). The TLE strings are obtained from [space-track.org](https://www.space-track.org). +Within this example, we will retrieve the initial state of Delfi-C3 using its Two-Line-Elements (TLE) the date of its launch (April the 28th, 2008). The TLE strings are obtained from [space-track.org](https://www.space-track.org). """ # Retrieve the initial state of Delfi-C3 using Two-Line-Elements (TLEs) @@ -198,31 +192,24 @@ delfi_ephemeris = environment.TleEphemeris( "Earth", "J2000", delfi_tle, False ) initial_state = delfi_ephemeris.cartesian_state( simulation_start_epoch ) - -### Create the integrator settings -""" -The last step before starting the simulation is to setup the integrator that will be used. - -In this case, a RK4 integrator is used with a step fixed at 10 seconds. -""" - -# Create numerical integrator settings -fixed_step_size = 10.0 -integrator_settings = propagation_setup.integrator.runge_kutta_4(fixed_step_size) - - ### Create the propagator settings """ The propagator is finally setup. First, a termination condition is defined so that the propagation will stop when the end epochs that was defined is reached. +Subsequently, the integrator settings are defined using a RK4 integrator with the fixed step size of 10 seconds. + Then, the translational propagator settings are defined. These are used to simulate the orbit of `Delfi-C3` around Earth. """ # Create termination settings termination_condition = propagation_setup.propagator.time_termination(simulation_end_epoch) +# Create numerical integrator settings +fixed_step_size = 10.0 +integrator_settings = propagation_setup.integrator.runge_kutta_4(fixed_step_size) + # Create propagation settings propagator_settings = propagation_setup.propagator.translational( central_bodies, @@ -234,7 +221,6 @@ termination_condition ) - ### Setup the variational equations """ In addition to the state of the satellite, variation equations will also be propagated. @@ -254,11 +240,9 @@ # Create the parameters that will be estimated parameters_to_estimate = estimation_setup.create_parameter_set(parameter_settings, bodies) - - ## Propagate the dynamics """ -In this example, since we wish to propagate the variational equations in addition to the satellite state, we use the `create_variational_equations_solver()` function (instead of the `SingleArcSimulator()` function that we would normally use). +In this example, since we wish to propagate the variational equations in addition to the satellite state, we use the `create_variational_equations_solver()` function (instead of the `create_dynamics_simulator()` function that we would normally use). This function takes additional arguments: the parameters that have to be estimated, and a boolean to specify that the parameters will be intergrated immidiately when the function is called. """ @@ -273,7 +257,6 @@ state_transition_matrices = variational_equations_solver.state_transition_matrix_history sensitivity_matrices = variational_equations_solver.sensitivity_matrix_history - ## Perform the sensitivity analysis """ Now that the state transition matrix history and sensitivity matrix history are known, we can perform the actual sensitivity analysis by varying the estimated parameters. @@ -297,7 +280,6 @@ earth_standard_param_dict[epoch] = np.dot(sensitivity_matrices[epoch], earth_standard_param_variation) delta_drag_coeff_dict[epoch] = np.dot(sensitivity_matrices[epoch], drag_coeff_variation) - ## Post-process the results """ First, extract the time, and deviation in position and velocity associated with the system response to each variation. @@ -324,7 +306,6 @@ delta_r3 = np.linalg.norm(delta_drag_coefficient_array[:, 1:4], axis=1) delta_v3 = np.linalg.norm(delta_drag_coefficient_array[:, 4:8], axis=1) - ### Plot the deviation in position """ Make a plot of the deivation in position over time, in response to all parameter variations. @@ -345,7 +326,6 @@ plt.tight_layout() plt.show() - ### Plot the deviation in velocity """ Make a plot of the deivation in velocity over time, in response to all parameter variations. @@ -365,7 +345,3 @@ plt.legend() plt.tight_layout() plt.show() - - - - diff --git a/propagation/mga_trajectories.ipynb b/propagation/mga_trajectories.ipynb index 1f1c0f2..06675ab 100644 --- a/propagation/mga_trajectories.ipynb +++ b/propagation/mga_trajectories.ipynb @@ -350,7 +350,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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" ] @@ -1081,11 +1081,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total Delta V of 49934.658 m/s and total Time of flight of 2390.712 days\n", + "Total Delta V of 49190.988 m/s and total Time of flight of 2390.712 days\n", "\n", "Delta V per leg: \n", " - between Earth and Mars: 10165.234 m/s\n", - " - between Mars and Earth: 8233.303 m/s\n", + " - between Mars and Earth: 7489.632 m/s\n", " - between Earth and Jupiter: 31536.122 m/s\n", "\n", "Delta V per node : \n", @@ -1134,7 +1134,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1185,7 +1185,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/propagation/mga_trajectories.py b/propagation/mga_trajectories.py index a6f9542..65495dc 100644 --- a/propagation/mga_trajectories.py +++ b/propagation/mga_trajectories.py @@ -6,8 +6,8 @@ under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -48,7 +48,6 @@ from tudatpy.util import result2array from tudatpy import constants - # First, let's explore an MGA transfer trajectory with no thrust applied during the transfer legs. In this case, the impulsive $\Delta$Vs are only applied during the gravity assists. ### Setup and inputs @@ -76,14 +75,12 @@ arrival_semi_major_axis = 1.0895e8 / 0.02 arrival_eccentricity = 0.98 - ### Create transfer settings and transfer object """ +The specified inputs can not be used directly, but they have to be translated to distinct settings, relating to either the nodes (departure, gravity assist, and arrival planets) or legs (trajectories in between planets). The fact that unpowered legs are used is indicated by the creation of unpowered and unperturbed leg settings. +These settings are, in turn, used to create the transfer trajectory object. """ -# The specified inputs can not be used directly, but they have to be translated to distinct settings, relating to either the nodes (departure, gravity assist, and arrival planets) or legs (trajectories in between planets). The fact that unpowered legs are used is indicated by the creation of unpowered and unperturbed leg settings. -# These settings are, in turn, used to create the transfer trajectory object. - # Define the trajectory settings for both the legs and at the nodes transfer_leg_settings, transfer_node_settings = transfer_trajectory.mga_settings_unpowered_unperturbed_legs( transfer_body_order, @@ -98,19 +95,16 @@ transfer_body_order, central_body) - ### Define transfer parameters """ +Next, it is necessary to specify the parameters which define the transfer. The advantage of having a transfer trajectory object is that it allows analyzing many different sets of transfer parameters using the same transfer trajectory object. +The definition of the parameters that need to be specified for this transfer can be printed using the `transfer_trajectory.print_parameter_definitions()` function. """ -# Next, it is necessary to specify the parameters which define the transfer. The advantage of having a transfer trajectory object is that it allows analyzing many different sets of transfer parameters using the same transfer trajectory object. -# The definition of the parameters that need to be specified for this transfer can be printed using the `transfer_trajectory.print_parameter_definitions()` function. - # Print transfer parameter definitions print("Transfer parameter definitions:") transfer_trajectory.print_parameter_definitions(transfer_leg_settings, transfer_node_settings) - # For this transfer with unpowered legs, the transfer parameters only constitute the times at which the powered gravity assists are executed, i.e. at the nodes. # This type of legs does not require any node free parameters or leg free parameters to be specified. Thus, they are defined as lists containing empty arrays. @@ -134,17 +128,14 @@ for i in transfer_node_settings: node_free_parameters.append( np.zeros(0)) - ### Evaluate transfer """ +The transfer parameters are now used to evaluate the transfer trajectory, which means that the semi-analytical methods used to determine the $\Delta$V of each leg are now applied. """ -# The transfer parameters are now used to evaluate the transfer trajectory, which means that the semi-analytical methods used to determine the $\Delta$V of each leg are now applied. - # Evaluate the transfer with given parameters transfer_trajectory_object.evaluate( node_times, leg_free_parameters, node_free_parameters ) - ### Extract results and plot trajectory """ Last but not least, with the transfer trajectory computed, we can now analyse it. @@ -172,7 +163,6 @@ print(" - at %s: %.3f m/s" % \ (transfer_body_order[i], transfer_trajectory_object.delta_v_per_node[i])) - #### Plot the transfer """ The state throughout the transfer can be retrieved from the transfer trajectory object, here at 500 instances per leg, to visualize the transfer. @@ -210,13 +200,11 @@ plt.tight_layout() plt.show() - ## MGA transfer with DSMs and manually-created settings """ +This next part of the example now makes use of DSMs in between the nodes. The general approach is similar to the example without DSMs, with some modifications to the inputs and transfer parameters. Additionaly, the manual creation of the nodes and legs settings is here exemplified, instead of using a factory function to get them. """ -# This next part of the example now makes use of DSMs in between the nodes. The general approach is similar to the example without DSMs, with some modifications to the inputs and transfer parameters. Additionaly, the manual creation of the nodes and legs settings is here exemplified, instead of using a factory function to get them. - ### Setup and inputs """ Again, a simplified system of bodies suffices. In this case, a transfer to Mercury is considered with gravity assists at Earth and Venus. As before, the departure and arrival orbits, and the central body (Sun) are also specified: the transfer is considered to start at the edge of Earth's SOI and end at the edge of Mercury's SOI. @@ -236,7 +224,6 @@ arrival_semi_major_axis = np.inf arrival_eccentricity = 0.0 - ### Create transfer settings and transfer object """ @@ -287,7 +274,6 @@ # Final node: capture_node transfer_node_settings.append( transfer_trajectory.capture_node(arrival_semi_major_axis, arrival_eccentricity) ) - # Having created the nodes and legs settings, either manually or using the factory function, it is then possible to use them to create the transfer trajectory object. # Create the transfer calculation object @@ -298,7 +284,6 @@ transfer_body_order, central_body) - ### Define transfer parameters """ @@ -309,7 +294,6 @@ print("Transfer parameter definitions:") transfer_trajectory.print_parameter_definitions(transfer_leg_settings, transfer_node_settings) - # The legs with velocity-based DSMs require more transfer parameters than the unpowered legs. In particular, for legs with DSMs it is necessary to specify the leg free and node free parameters. # # There is a free parameter for each leg, representing the leg's time-of-flight fraction at which the DSM takes place. There are three free parameters for the departure node and each swingby node. The node free parameters represent the following: @@ -347,7 +331,6 @@ node_free_parameters.append(np.array([1.10000000891 * 6.052e6, 1.34317576594, 0.0])) node_free_parameters.append(np.array([])) - ### Evaluate transfer """ The same approach is used to evaluate the transfer trajectory with the transfer parameters. @@ -356,12 +339,11 @@ # Evaluate the transfer with the given parameters transfer_trajectory_object.evaluate( node_times, leg_free_parameters, node_free_parameters) - ### Extract results and plot trajectory """ Finally, the results are extracted and used to visualize the transfer trajectory. -""" +""" #### Print results """ @@ -385,7 +367,6 @@ print(" - at %s: %.3f m/s" % \ (transfer_body_order[i], transfer_trajectory_object.delta_v_per_node[i])) - #### Plot the transfer """ The state throughout the transfer can be retrieved from the transfer trajectory object, here at 500 instances per leg, to visualize the transfer. @@ -413,13 +394,11 @@ ax.legend(bbox_to_anchor=[1, 1]) plt.show() - ## MGA transfer with hodographic-shaping legs """ +This final example shows how to setup a low-thrust MGA transfer, with the thrust profile modeled using hodographic shaping. The approach is similar to the previous examples, with only some small differences when selecting the shaping functions. """ -# This final example shows how to setup a low-thrust MGA transfer, with the thrust profile modeled using hodographic shaping. The approach is similar to the previous examples, with only some small differences when selecting the shaping functions. - ### Setup and inputs """ Again, a simplified system of bodies is used. A transfer between the Earth and Jupiter is considered, with gravity assists at Mars and the Earth. The transfer is considered to start at the edge of Earth's SOI and end at the edge of Jupiter's SOI. @@ -439,7 +418,6 @@ arrival_semi_major_axis = np.inf arrival_eccentricity = 0.0 - ### Create transfer settings and transfer object """ @@ -515,7 +493,6 @@ normal_velocity_function_components_per_leg.append(normal_velocity_functions) axial_velocity_function_components_per_leg.append(axial_velocity_functions) - # Having selected the shaping functions, it is now possible to create the legs and nodes settings and after that to create the transfer trajectory object. # Get legs and nodes settings @@ -535,7 +512,6 @@ transfer_body_order, central_body) - ### Define transfer parameters """ @@ -546,7 +522,6 @@ print("Transfer parameter definitions:") transfer_trajectory.print_parameter_definitions(transfer_leg_settings, transfer_node_settings) - # In this case, there is a much larger list of free parameters than in the previous cases. As before, the first parameters are the node times, corresponding to the times when the spacecraft encounters each planet. Next, the node free parameters are required. These include the selection of the departure velocity at the departure node (3 parameters), the arrival velocity at each swingby node (3 parameters per node), the characteristics of each swingby (3 parameters per node), and the arrival velocity at the arrival node (3 parameters). Finally, it is necessary to specify the leg free parameters. These consist of the number of revolutions of each leg (1 parameter per leg), and of two free coefficients per velocity component per leg (6 parameters per leg). # # The parameters used in this example were determined via optimization, using PyGMO. The code used to optimize the transfer is analyzed in [this example](https://tudat-space.readthedocs.io/en/latest/src_getting_started/_src_examples/notebooks/pygmo/hodographic_shaping_mga_optimization.ipynb). @@ -570,7 +545,6 @@ node_free_parameters.append([3074.131807921915, 0.0, 0.0, 10**8.129202206701256, 0.0, 0.0]) node_free_parameters.append(np.zeros(3)) - ### Evaluate transfer """ Having selected the transfer parameters, it is now possible to evaluate the transfer. @@ -579,12 +553,11 @@ # Evaluate the transfer with the given parameters transfer_trajectory_object.evaluate( node_times, leg_free_parameters, node_free_parameters) - ### Extract results and plot trajectory """ Finally, the results are extracted and used to analyze the transfer trajectory. -""" +""" #### Print results """ @@ -608,7 +581,6 @@ print(" - at %s: %.3f m/s" % \ (transfer_body_order[i], transfer_trajectory_object.delta_v_per_node[i])) - #### Plot the transfer """ Similarly to the previous cases, the state history throughout the transfer can be retrieved with `states_along_trajectory`. Furthermore, it is possible to retrieve the thrust acceleration history with respect to different reference frames (inertial, TNW, and RSW). Below, the thrust acceleration is retrieved with respect to a TNW frame (through `tnw_thrust_accelerations_along_trajectory`) and plotted. @@ -636,4 +608,3 @@ ax.set_axisbelow(True) ax.legend(bbox_to_anchor=[1, 1]) plt.show() - diff --git a/propagation/perturbed_satellite_orbit.ipynb b/propagation/perturbed_satellite_orbit.ipynb index 2ef04d9..47dda05 100644 --- a/propagation/perturbed_satellite_orbit.ipynb +++ b/propagation/perturbed_satellite_orbit.ipynb @@ -197,7 +197,6 @@ "occulting_bodies_dict[ \"Sun\" ] = [ \"Earth\" ]\n", "vehicle_target_settings = environment_setup.radiation_pressure.cannonball_radiation_target(\n", " reference_area_radiation, radiation_pressure_coefficient, occulting_bodies_dict )\n", - "\n", "environment_setup.add_radiation_pressure_target_model(\n", " bodies, \"Delfi-C3\", vehicle_target_settings)" ] diff --git a/propagation/perturbed_satellite_orbit.py b/propagation/perturbed_satellite_orbit.py index 83846f9..87762d4 100644 --- a/propagation/perturbed_satellite_orbit.py +++ b/propagation/perturbed_satellite_orbit.py @@ -1,8 +1,8 @@ # Perturbed satellite orbit """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -24,6 +24,7 @@ # Load standard modules import numpy as np + import matplotlib from matplotlib import pyplot as plt @@ -37,7 +38,6 @@ from tudatpy.util import result2array from tudatpy.astro.time_conversion import DateTime - ## Configuration """ NAIF's `SPICE` kernels are first loaded, so that the position of various bodies such as the Earth can be make known to `tudatpy`. @@ -53,12 +53,11 @@ simulation_start_epoch = DateTime(2000, 1, 1).epoch() simulation_end_epoch = DateTime(2000, 1, 2).epoch() - ## Environment setup """ Let’s create the environment for our simulation. This setup covers the creation of (celestial) bodies, vehicle(s), and environment interfaces. -""" +""" ### Create the bodies """ @@ -87,7 +86,6 @@ # Create system of selected celestial bodies bodies = environment_setup.create_system_of_bodies(body_settings) - ### Create the vehicle """ Let's now create the 400kg satellite for which the perturbed orbit around Earth will be propagated. @@ -98,15 +96,14 @@ bodies.get("Delfi-C3").mass = 2.2 - # To account for the aerodynamic of the satellite, let's add an aerodynamic interface and add it to the environment setup, taking the followings into account: # - A constant drag coefficient of 1.2. -# - A reference area of 4m$^2$. +# - A reference area of 0.035m$^2$. # - No sideslip or lift coefficient (equal to 0). # - No moment coefficient. # Create aerodynamic coefficient interface settings, and add to vehicle -reference_area = (4*0.3*0.1+2*0.1*0.1)/4 # Average projection +reference_area = (4*0.3*0.1+2*0.1*0.1)/4 # Average projection area of a 3U CubeSat drag_coefficient = 1.2 aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant( reference_area, [drag_coefficient, 0, 0] @@ -114,7 +111,6 @@ environment_setup.add_aerodynamic_coefficient_interface( bodies, "Delfi-C3", aero_coefficient_settings) - # To account for the pressure of the solar radiation on the satellite, let's add another interface. This takes a radiation pressure coefficient of 1.2, and a radiation area of 4m$^2$. This interface also accounts for the variation in pressure cause by the shadow of Earth. # Create radiation pressure settings, and add to vehicle @@ -124,7 +120,6 @@ occulting_bodies_dict[ "Sun" ] = [ "Earth" ] vehicle_target_settings = environment_setup.radiation_pressure.cannonball_radiation_target( reference_area_radiation, radiation_pressure_coefficient, occulting_bodies_dict ) - environment_setup.add_radiation_pressure_target_model( bodies, "Delfi-C3", vehicle_target_settings) @@ -142,7 +137,6 @@ # Define central bodies of propagation central_bodies = ["Earth"] - ### Create the acceleration model """ First off, the acceleration settings that act on `Delfi-C3` are to be defined. @@ -188,7 +182,6 @@ bodies_to_propagate, central_bodies) - ### Define the initial state """ The initial state of the vehicle that will be propagated is now defined. @@ -206,7 +199,6 @@ delfi_ephemeris = environment.TleEphemeris( "Earth", "J2000", delfi_tle, False ) initial_state = delfi_ephemeris.cartesian_state( simulation_start_epoch ) - ### Define dependent variables to save """ In this example, we are interested in saving not only the propagated state of the satellite over time, but also a set of so-called dependent variables, that are to be computed (or extracted and saved) at each integration step. @@ -273,13 +265,11 @@ output_variables=dependent_variables_to_save ) - - ## Propagate the orbit """ The orbit is now ready to be propagated. -This is done by calling the `create_dynamics_simulator()` function of the `numerical_simulation` module. +This is done by calling the `create_dynamics_simulator()` function of the `numerical_simulation module`. This function requires the `bodies` and `propagator_settings` that have all been defined earlier. After this, the history of the propagated state over time, containing both the position and velocity history, is extracted. @@ -288,8 +278,7 @@ - Columns 1 to 3: Position history, in meters, in the frame that was specified in the `body_settings`. - Columns 4 to 6: Velocity history, in meters per second, in the frame that was specified in the `body_settings`. -The same is done with the dependent variable history. The column indexes corresponding to a given dependent variable in -the `dep_vars` variable are printed when the simulation is run, when `create_dynamics_simulator()` is called. +The same is done with the dependent variable history. The column indexes corresponding to a given dependent variable in the `dep_vars` variable are printed when the simulation is run, when `create_dynamics_simulator()` is called. Do mind that converting to an ndarray using the `result2array()` utility will shift these indexes, since the first column (index 0) will then be the times. """ @@ -304,12 +293,11 @@ dep_vars = dynamics_simulator.dependent_variable_history dep_vars_array = result2array(dep_vars) - ## Post-process the propagation results """ The results of the propagation are then processed to a more user-friendly form. -""" +""" ### Total acceleration over time """ @@ -327,8 +315,6 @@ plt.xlim([min(time_hours), max(time_hours)]) plt.grid() plt.tight_layout() -plt.show() - ### Ground track """ @@ -351,8 +337,6 @@ plt.yticks(np.arange(-90, 91, step=45)) plt.grid() plt.tight_layout() -plt.show() - ### Kepler elements over time """ @@ -400,8 +384,6 @@ ax.set_xlim([min(time_hours), max(time_hours)]) ax.grid() plt.tight_layout() -plt.show() - ### Accelerations over time """ @@ -447,4 +429,3 @@ plt.yscale('log') plt.grid() plt.tight_layout() -plt.show() diff --git a/propagation/reentry_trajectory.ipynb b/propagation/reentry_trajectory.ipynb index 4b6d9b2..6ec7e12 100644 --- a/propagation/reentry_trajectory.ipynb +++ b/propagation/reentry_trajectory.ipynb @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 19, "id": "ff69831a", "metadata": { "collapsed": false, @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 20, "id": "c46fdfff", "metadata": { "collapsed": false, @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 21, "id": "c18b2ffe", "metadata": { "collapsed": false, @@ -228,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 22, "id": "6ea394a1", "metadata": { "collapsed": false, @@ -242,7 +242,7 @@ "spice.load_standard_kernels()\n", "\n", "# Set simulation start epoch (January the 1st, 2000 plus 6000s)\n", - "simulation_start_epoch = DateTime(2000, 1, 1, 1, 40)\n", + "simulation_start_epoch = DateTime(2000, 1, 1, 1, 40).epoch()\n", "\n", "# Set the maximum simulation time (avoid very long skipping re-entry)\n", "max_simulation_time = 3*constants.JULIAN_DAY\n", @@ -272,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 23, "id": "06dd4f2c", "metadata": { "collapsed": false, @@ -307,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 24, "id": "e75afb5e", "metadata": { "collapsed": false, @@ -334,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "id": "b1611884", "metadata": { "collapsed": false, @@ -362,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 26, "id": "c7a6dfb7", "metadata": { "collapsed": false, @@ -396,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 27, "id": "55c21079", "metadata": { "collapsed": false, @@ -430,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, "id": "5b5139ec", "metadata": { "collapsed": false, @@ -474,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 29, "id": "bb67e486", "metadata": { "collapsed": false, @@ -518,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 30, "id": "9c4bf3ba", "metadata": { "collapsed": false, @@ -563,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 31, "id": "9d21dfa3", "metadata": { "collapsed": false, @@ -622,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 32, "id": "40cd08f9", "metadata": { "collapsed": false, @@ -665,7 +665,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 33, "id": "d8dc6404", "metadata": { "collapsed": false, @@ -676,7 +676,7 @@ "outputs": [ { "data": { - "image/png": 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5Ps3saAAAAMBlUfCAv+HlatMbNzfTF3e2UVUvFx06eVY3fRirN+ftUc55juYBAACgfKHgAUVwXcMgLXiiW+G6eR8sO6jBU1ZpZ0K62dEAAACAQhQ8oIh83S+sm/fRyFYK8HDWnqRMDZ6ySu8t3q+8/AKz4wEAAAAUPKC4+kVU0/wnuql/RLDOF9g1ceE+Df0gVvtOZJodDQAAAJUcBQ+4CoGeLvrg9lZ697YW8nGzafvxdA2cHKPPY+JUUGA3Ox4AAAAqKQoecJUMw9DgFtW14IluimxQRbnnC/SvX3dp5OdrlXA62+x4AAAAqIQoeMA1CvJ21Zd3ttWrQyLkZrMq9uAp9YteoZ+3HDc7GgAAACoZCh5QAgzD0MgONfXbY13UPNRXGefO6/EZW/Tot5uVnpVndjwAAABUEhQ8oATVqeKpnx7oqCd61ZfVYuiXrQnqG71CMftTzI4GAACASoCCB5QwJ6tFj/eqp58e7KTagR5KyjinkZ+vVdScnTqXx+LoAAAAKD0UPKCUtAj11W+PddGoDjUlSVNjD2vg5BjtOM7i6AAAACgdFDygFLk7O+lfQyL05V1tVcXLRQeSz2jI+6v0/tIDOs/i6AAAAChhFDygDPRoUFXzx/7/4uhvzd+rWz9ZoyOnzpodDQAAAA6EggeUEX8PZ31weyu9M6y5PF2ctPFImq5/d6W+Wx8vu53F0QEAAHDtKHhAGTIMQze1rqG5j3dVu9r+Opubr2d/2q77/rNRKWdyzI4HAACACo6CB5gg1N9d397XQf+4vqGcrRYt2n1C/aJXaMmeE2ZHAwAAQAVGwQNMYrUYur9buGY/3Fn1gzyVciZXd0/doH/M2q6s3PNmxwMAAEAFRMEDTNY4xFtzHumie7rUliRNXxuvAe/FaOvR0+YGAwAAQIVDwQPKAVebVS8NbKxv7m2vYG9XxaWc1dAPY/Xe4v0spwAAAIAio+AB5UjnuoGaN7arBjSrpvwCuyYu3KdhH69mOQUAAAAUCQUPKGd83Z01ZXhLTbq1ubxcnLQ5/rT6s5wCAAAAioCCB5RDhmHoxpY1NHdsV7Wv7a+s/y6nMOarjTrFcgoAAAD4C6YWvBUrVuiGG25QSEiIDMPQ7NmzL/q83W5XVFSUQkJC5ObmpsjISO3cufOix+Tk5OjRRx9VYGCgPDw8NGjQIB07dqwM3wVQemr4uWv6fR30fP+GslkNLdh1Qn2jV2rpnmSzowEAAKAcMrXgnT17Vs2bN9eUKVMu+/k333xTEydO1JQpU7R+/XoFBwerd+/eyszMLHzM2LFjNWvWLM2YMUMxMTE6c+aMBg4cqPz8/LJ6G0CpsloMjen+5+UUcnTX1PV6afYOZefycw4AAID/Z2rB69+/v1599VUNHTr0ks/Z7XZFR0frhRde0NChQxUREaFp06YpKytL06dPlySlp6fr888/1zvvvKNevXqpZcuW+vrrr7V9+3YtWrSorN8OUKqahPhoziNddFfnWpKkr9Yc0YDJK7Xt2GlTcwEAAKD8cDI7wF+Ji4tTUlKS+vTpU7jNxcVF3bt3V2xsrMaMGaONGzcqLy/voseEhIQoIiJCsbGx6tu372WfOycnRzk5/38dU0ZGhiQpLy9PeXl5pfSOiuePHOUlT2VSnmdvlfSPfvXVrW6Anpu5Q4dOntXQD2L1aI9w3d+1lpysFfuy2vI8+8qA+ZuH2ZuH2ZuL+ZuH2ZunuLMv7veo3Ba8pKQkSVJQUNBF24OCgnTkyJHCxzg7O8vPz++Sx/zx9Zfz2muvafz48ZdsX7Bggdzd3a81eolauHCh2REqrfI++8cbSN/HWbTllEWTFh/Q7HX7NbJuvgJdzU527cr77B0d8zcPszcPszcX8zcPszdPUWeflZVVrOcttwXvD4ZhXPSx3W6/ZNv/utJjnn/+eT355JOFH2dkZCg0NFR9+vSRt7f3tQUuIXl5eVq4cKF69+4tm81mdpxKpSLN/ma7XT9vTVTUr7sVl5mviTtd9ML1DXVzq5Ar/n9SHlWk2Tsi5m8eZm8eZm8u5m8eZm+e4s7+j7MNi6rcFrzg4GBJF47SVatWrXB7cnJy4VG94OBg5ebmKi0t7aKjeMnJyerUqdNfPreLi4tcXFwu2W6z2crdD3h5zFRZVJTZD2tbUx3Cq+ip77dq3eFU/WP2Ti3fn6LXhjaTv4ez2fGuSkWZvaNi/uZh9uZh9uZi/uZh9uYp6uyL+/0ptxfs1K5dW8HBwRcduszNzdXy5csLy1vr1q1ls9kuekxiYqJ27NjxtwUPcDSh/u769v4OerbfheUU5u88ob7RK7R0L8spAAAAVCamHsE7c+aMDhw4UPhxXFyctmzZIn9/f4WFhWns2LGaMGGC6tWrp3r16mnChAlyd3fXiBEjJEk+Pj6655579NRTTykgIED+/v4aN26cmjZtql69epn1tgBTWC2GHowMV9d6gRr73RYdSD6ju75crzs61tTz/RvJzdlqdkQAAACUMlML3oYNG9SjR4/Cj/+4Lm706NGaOnWqnnnmGWVnZ+uhhx5SWlqa2rdvrwULFsjLy6vwayZNmiQnJyfdcsstys7OVs+ePTV16lRZrfxjFpVTRHUf/fpoF70+d4+mxh7Wf1Yf0aoDKXr3tpaKqO5jdjwAAACUIlMLXmRkpOx2+19+3jAMRUVFKSoq6i8f4+rqqsmTJ2vy5MmlkBComFxtVkUNaqLrGlbVuB+26uDJsxry/io90bu+HugeLqul4t2ABQAAAFdWbq/BA3DtutWvovlju+n6psE6X2DXW/P36taPV+toavFutwsAAICKgYIHODg/D2e9P6KV3hnWXJ4uTtpwJE39312pHzce+9sj6AAAAKh4KHhAJWAYhm5qXUNzH++qNjX9dCbnvMb9sFUPfbNJaWdzzY4HAACAEkLBAyqRUH93fTemo57u20BOFkNzdySpb/QKLd930uxoAAAAKAEUPKCSsVoMPdyjrmY/3FnhVTyUnJmj0V+sU9ScnTqXl292PAAAAFwDCh5QSV1YTqGrRnesKUmaGntYAyfHaMfxdJOTAQAA4GpR8IBKzM3ZqvGDIzT1rraq4uWiA8lndOMHq/TBsgPKL+AGLAAAABUNBQ+AIhtU1fyx3dSvSbDy8u16c95eDf9kDcspAAAAVDAUPACSJH8PZ304spXeurmZPJytWnc4Vf3fXamfWE4BAACgwqDgAShkGIaGtQnV3Me7FS6n8NQPW/XI9M06ncVyCgAAAOUdBQ/AJcICLl5O4bftieobvUIr97OcAgAAQHlGwQNwWX8spzDzoU6qU8VDJzJyNOpzllMAAAAozyh4AP5Wsxq++u3RrrrjT8sp3DA5RjsTWE4BAACgvKHgAbgiN2erXhkcoS/vbKtATxftTz6jIe+v0kfLD7KcAgAAQDlCwQNQZD0aVtX8sV3Vp3GQ8vLten3uHg3/dI2OpbGcAgAAQHlAwQNQLAGeLvp4VGu9edN/l1OIS1X/6JWatZnlFAAAAMxGwQNQbIZh6Ja2ofr98a5qFearzJzzeuK7rXrkW5ZTAAAAMBMFD8BVqxngoe/HdNRTvevLajH027ZE9YteqZj9KWZHAwAAqJQoeACuiZPVokd71tPMBzupTqCHkjLOaeTna/WvX3exnAIAAEAZo+ABKBHNQ33162NdNLJDmCTp85g4DZ6ySrsSMkxOBgAAUHlQ8ACUGHdnJ706pKm+uLONAj2dtfdEpoa8v0qfrDioApZTAAAAKHUUPAAl7rqGQZo/tpt6Nw5Sbn6BJvy+RyM+W6Pjp7PNjgYAAODQKHgASkWAp4s+GdVarw9tKndnq9YcSlW/6BX6ectxs6MBAAA4LAoegFJjGIZuaxem3x/rqpZhvso8d16Pz9iiR7/drPSsPLPjAQAAOBwKHoBSVyvQQz+M6agnel1YTuGXrQnq9+4KxR5gOQUAAICSRMEDUCacrBY93quefnygo2oHeigx/ZxGfLZWr7KcAgAAQImh4AEoUy3D/PTbY100ov2F5RQ+i4nTkPdXaU8SyykAAABcKwoegDLn7uykCTc21Wd3XFhOYU9SpgZNXqXPVx0WqykAAABcPQoeANP0ahykeWO7qVejqsrNL9Dr8/bpg10WJaafMzsaAABAhUTBA2CqQE8XfXpHG702tKncbBbtz7Bo4JRYzdmaYHY0AACACoeCB8B0hmFoeLswzXm4o2p62pVx7rwe+3azHp+xWenZLKcAAABQVBQ8AOVGrQAPPR6Rr8d6hMtqMfTzlgT1j16hVSynAAAAUCQUPADlitWQHr0uXD8+0FG1AtyVkH5Ot3+2VuN/2clyCgAAAFdAwQNQLl1YTqGrRna4sJzCl6sOa8B7K7X16GlzgwEAAJRjFDwA5ZaHi5NeHdJUU+9qq6peLjp48qyGfhiriQv3KS+/wOx4AAAA5Q4FD0C5F9mgqhY80U2Dmocov8Cu9xbv140frNL+E5lmRwMAAChXKHgAKgRfd2e9N7ylJg9vKR83m3Ycz9CAyTH6bOUhFbA6OgAAgCQKHoAK5obmIVrwRDdFNqii3PMFevW33Rr+6RodTc0yOxoAAIDpKHgAKpwgb1d9eWdbTbixqdydrVobl6r+767U9xuOym7naB4AAKi8KHgAKiTDMDSifZjmPt5VbWr66UzOeT3z4zbd95+NOpmZY3Y8AAAAU1DwAFRoNQM89N2Yjnquf0M5Wy1atPuE+kav0LwdiWZHAwAAKHMUPAAVntVi6IHu4fr5kc5qGOyl1LO5euDrTXryuy1Kz84zOx4AAECZoeABcBiNqnnr50c666HIcFkMaebm4+oXvUIx+1PMjgYAAFAmKHgAHIqLk1XP9GuoHx7oqJoB7kpMP6eRn69V1Jydys7NNzseAABAqaLgAXBIrWv6a+7jXTWyQ5gkaWrsYQ14b6W2HD1tbjAAAIBSRMED4LDcnZ306pCmmnZ3OwV5u+hQylnd9GGsJi7Yq9zzBWbHAwAAKHEUPAAOr3v9KlowtrsGtwhRfoFd7y05oKEfrtK+E5lmRwMAAChRFDwAlYKPu03v3tZSU0a0lK+7TTuOZ2jg5Bh9vPyg8gtYHB0AADgGCh6ASmVgsxAtGNtNPRpUUe75Ar02d4+GfRSrgyfPmB0NAADgmlHwAFQ6Vb1d9cWdbfXmTc3k6eKkTfGndf27K/XZykMczQMAABUaBQ9ApWQYhm5pG6r5T3RT13qByjlfoFd/263bPlmtwylnzY4HAABwVSh4ACq16r5u+s/d7TThxqbycLZq/eE09X93pabFHlYBR/MAAEAFQ8EDUOkZhqER7cM0b2w3dawToOy8fL08Z6dGfLZGR1OzzI4HAABQZBQ8APivUH93fXNve/1rcBO52axacyhVfaNX6Os1R2S3czQPAACUfxQ8APgTi8XQqI61NG9sV7Wr5a+s3Hy9OHuHRn2+TsfSOJoHAADKNwoeAFxGzQAPzbi/g/45sLFcbRbFHEhRv+iVmrEunqN5AACg3KLgAcBfsFgM3d2ltn5/rKta1/TTmZzzem7mdo3+cr0S07PNjgcAAHAJCh4AXEGdKp76fkxHvXB9Izk7WbRi30n1mbRCP2w4ytE8AABQrlDwAKAIrBZD93Wro98f66rmob7KPHdeT/+4TfdO26Ck9HNmxwMAAJBEwQOAYqlb1VM/PdBRz/RrIGerRYv3JKv3pOX6bj3X5gEAAPNR8ACgmJysFj0UWVe/PNpFzWv4KPPceT3703aN+nwd6+YBAABTORXlQdu2bSv2Ezdu3FhOTkV6egCokBoEe+mnBzvpi1VxemfBPsUcSFHf6BV6pm8D3dGxliwWw+yIAACgkilSA2vRooUMwyjy6UcWi0X79u1TnTp1rikcAJR3TlaL7u8Wrt6Ng/Xsj9u07nCqon7Zpd+2J+r1m5opvIqn2REBAEAlUuRDbGvXrlWVKlWu+Di73a6IiIhrCgUAFU3twAvr5n2z9ohen7tH6w+nqf+7K/VEr/q6r2ttOVk5Ix4AAJS+IhW87t27q27duvL19S3Sk3br1k1ubm7XkgsAKhyLxdCojrXUo2FVPT9zu1buT9Eb8/bo9+2JevPmZmpUzdvsiAAAwMEV6VfKS5cuLXK5k6Tff/9d1apVu9pMAFCh1fBz13/ubqe3bm4mb1cnbT+erhsmx2jiwn3KPV9gdjwAAODAOGcIAEqBYRga1iZUi57srj6Ng3S+wK73Fu/XwMkrteXoabPjAQAAB1Xs21za7Xb9+OOPWrp0qZKTk1VQcPFvo2fOnFli4QCgoqvq7aqPR7XWb9sT9fLPO7XvxBkN/WCV7u1aR0/0qi83Z6vZEQEAgAMp9hG8xx9/XKNGjVJcXJw8PT3l4+Nz0R8AwMUMw9DAZiFa+GR3DWkRogK79MmKQ+oTvVwr9p00Ox4AAHAgxT6C9/XXX2vmzJm6/vrrSyMPADgsfw9nRd/WUjc0D9GLs3foaGq27vhinW5sWV0vDmikAE8XsyMCAIAKrthH8Hx8fMpsfbvz58/rxRdfVO3ateXm5qY6derolVdeuei0ULvdrqioKIWEhMjNzU2RkZHauXNnmeQDgKvRs1GQFj7ZXXd1riXDkGZtPq6eE5frx43HirzeKAAAwOUUu+BFRUVp/Pjxys7OLo08F3njjTf00UcfacqUKdq9e7fefPNNvfXWW5o8eXLhY958801NnDhRU6ZM0fr16xUcHKzevXsrMzOz1PMBwNXydHHSyzc00ayHOqthsJdOZ+Vp3A9bdftna3U45azZ8QAAQAVV7II3bNgwpaWlqWrVqmratKlatWp10Z+StHr1ag0ePFgDBgxQrVq1dPPNN6tPnz7asGGDpAtH76Kjo/XCCy9o6NChioiI0LRp05SVlaXp06eXaBYAKA0tQn31y6Nd9Fz/hnJxsij24Cn1jV6h95ceUF4+SyoAAIDiKfY1eHfeeac2btyokSNHKigoSIZhlEYuSVKXLl300Ucfad++fapfv762bt2qmJgYRUdHS5Li4uKUlJSkPn36FH6Ni4uLunfvrtjYWI0ZM6bUsgFASbFZLXqge7j6RwTrxdk7tHJ/it6av1dztiTotZuaqlWYn9kRAQBABVHsgvfbb79p/vz56tKlS2nkucizzz6r9PR0NWzYUFarVfn5+fr3v/+t4cOHS5KSkpIkSUFBQRd9XVBQkI4cOfKXz5uTk6OcnJzCjzMyMiRJeXl5ysvLK+m3cVX+yFFe8lQmzN48lX32Id7O+nxUS83Zmqh/z92rvScyddOHsbq9Xaie7FVPXq7F/iu7WCr7/M3E7M3D7M3F/M3D7M1T3NkX93tU7H8thIaGytvbu7hfdlW+++47ff3115o+fbqaNGmiLVu2aOzYsQoJCdHo0aMLH/e/RxHtdvvfHll87bXXNH78+Eu2L1iwQO7u7iX3BkrAwoULzY5QaTF781T22dskjWss/XzEonUnLfp67VH9sjleN9UqUDN/u0rxxAlJzN9MzN48zN5czN88zN48RZ19VlZWsZ7XsBfzlm2//fabJk+erI8++ki1atUq1osVV2hoqJ577jk9/PDDhdteffVVff3119qzZ48OHTqk8PBwbdq0SS1btix8zODBg+Xr66tp06Zd9nkvdwQvNDRUKSkpZVZeryQvL08LFy5U7969ZbPZzI5TqTB78zD7S8UePKWX5uxSfOqFG1t1rxeolwY2VE3/kv9lFPM3D7M3D7M3F/M3D7M3T3Fnn5GRocDAQKWnpxepqxT7CN7IkSOVlZWl8PBwubu7XxIqNTW1uE/5l7KysmSxXHwfGKvVWrhMQu3atRUcHKyFCxcWFrzc3FwtX75cb7zxxl8+r4uLi1xcLl1vymazlbsf8PKYqbJg9uZh9v+ve8NgLQivoilLDuiTFYe0fH+KVk+O1UOR4Xqge7hcbdYSf03mbx5mbx5mby7mbx5mb56izr64359iF7xJkyaV6o1V/uyGG27Qv//9b4WFhalJkybavHmzJk6cqLvvvlvShVMzx44dqwkTJqhevXqqV6+eJkyYIHd3d40YMaJMMgJAaXO1WTWubwPd2Kq6Xv55p2IOpCh60X7N2nxcrwyOUPf6VcyOCAAAyomruovmXynptfEmT56sl156SQ899JCSk5MVEhKiMWPG6J///GfhY5555hllZ2froYceUlpamtq3b68FCxbIy8urRLMAgNnCq3jqq3va6ddtifrXr7t05FSWRn+xTtc3DdZLAxurmo+b2REBAIDJir0O3p+vh/uzs2fPqn///tcc6M+8vLwUHR2tI0eOKDs7WwcPHtSrr74qZ2fnwscYhqGoqCglJibq3LlzWr58uSIiIko0BwCUF4Zh6IbmIVr8VHfd06W2rBZDv29PUs93luvTFYdYOw8AgEqu2AVvwYIFevHFFy/advbsWfXr10/5+fklFgwA8Ne8XG16aWBj/fJIF7Wu6aes3Hz9+/fdGvhejNbFldy10AAAoGK5qoL35ZdfatKkSZKkzMxM9e7dW4ZhaN68eSUeEADw1xqHeOuHMR315k3N5Odu094Tmbrl49UaO2OzktLPmR0PAACUsWJfg1e7dm3Nnz9fkZGRslgsmjFjhlxcXPTbb7/Jw8OjNDICAP6GxWLolrah6t04SG/O36sZ6+M1e0uCFuw6oYd71NU9XWqXyt02AQBA+VPsI3iSFBERoV9//VUvvPCC3N3dNXfuXModAJjMz8NZrw1tqjkPd1GrMF9l5ebrrfl71WfSCi3cdULFXPYUAABUQEU6gteyZcvLLo3g4uKihIQEde7cuXDbpk2bSi4dAKDYmtbw0U8PdtLPWxL02tzdik/N0n3/2aCu9QL18g2NVbcqdxkGAMBRFangDRkypJRjAABKkmEYGtKyuno3DtL7Sw/os5VxWrk/Rf2iV+qOjrX0eK968nFjYVsAABxNkQreyy+/XNo5AAClwMPFSc/0a6hb24bqX7/u1qLdJ/TFqjj9vOW4nu7bQMPahMpqufQMDQAAUDFd1TV4AICKpWaAhz4b3Ub/ubudwqt46NTZXD03c7sGvLdSK/efNDseAAAoIUUqeP7+/kpJSSnyk4aFhenIkSNXHQoAUDq61a+ieWO76aWBjeXt6qQ9SZka9fk6jf5infadyDQ7HgAAuEZFOkXz9OnTmjt3rnx8fIr0pKdOnWLRcwAop2xWi+7pUls3taqu9xYf0FdrDmv5vpNauf+k2lexqG1mjkL8uT4PAICKqMjr4I0ePbo0cwAAypivu7P+eUNj3dGxpt6Yt0dzdyRpdbJFvaJj9ED3cN3btbbcnYu9XCoAADBRkU7RLCgoKPafOnXqlHZ2AEAJqBXooQ9HttaMe9uqpqddWbn5mrhwn3q8vUw/bDiq/ALWzwMAoKLgJisAAElS65p+eiIiX9G3NFMNPzedyMjR0z9u04D3VmrxbhZKBwCgIqDgAQAKGYY0oGmwFj3ZXf+4vqG8/nsjlnumbdDNH63WmkOnzI4IAAD+BgUPAHAJV5tV93cL18pneuiB7uFytVm08Uiabvtkje74Yp12HE83OyIAALgMCh4A4C/5ujvruf4NtfzpHhrZIUxOFkMr9p3UwMkxenj6Jh06ecbsiAAA4E8oeACAKwrydtWrQ5pq8VPdNaRFiAxD+m1bonpPWqHnftqmhNPZZkcEAAC6yoJ38OBBvfjiixo+fLiSk5MlSfPmzdPOnTtLNBwAoHypGeCh6Nta6vfHuqpXo6rKL7BrxvqjinxrmV6avYOiBwCAyYpd8JYvX66mTZtq7dq1mjlzps6cuXB6zrZt2/Tyyy+XeEAAQPnTqJq3PhvdVj892FHta/srN79AX605osi3lunF2dspegAAmKTYBe+5557Tq6++qoULF8rZ2blwe48ePbR69eoSDQcAKN9a1/TXd2M66tv7OqhDnQtF7+s18er+1lK9MGu7jlP0AAAoU8UueNu3b9eNN954yfYqVaro1Clunw0AlVHH8ADNuP//i15evl3frI1X5FtL9Y9Z23UsLcvsiAAAVArFLni+vr5KTEy8ZPvmzZtVvXr1EgkFAKiY/ih6M+7voI51ApSXb9f0tfHq8fYyPfvjNu66CQBAKSt2wRsxYoSeffZZJSUlyTAMFRQUaNWqVRo3bpzuuOOO0sgIAKhgOtQJ0Lf3d9B393dQp/ALRe+7DUfVc+JyPfTNRm07dtrsiAAAOKRiF7x///vfCgsLU/Xq1XXmzBk1btxY3bp1U6dOnfTiiy+WRkYAQAXVvk6Apt/XQT8+0FG9GlWV3S79vj1Jg6as0sjP1mrVgRTZ7XazYwIA4DCcivsFNptN33zzjV555RVt3rxZBQUFatmyperVq1ca+QAADqBNLX99Vstfe5My9fHyg/p5a4JiDqQo5kCKmtXw0YPdw9WnSbCsFsPsqAAAVGjFLnh/CA8PV3h4eElmAQA4uAbBXpp4aws90bu+Po+J04z18dp2LF0PfrNJtQM9dFfnWrqpVQ15uFz17gkAgEqtSHvQJ598sshPOHHixKsOAwCoHEL93RU1qIkeva6upsUe1tTYw4pLOat//rxTb8/fq+HtwjS6Uy2F+LqZHRUAgAqlSAVv8+bNF328ceNG5efnq0GDBpKkffv2yWq1qnXr1iWfEADgsAI8XfRknwYa0z1cP206pi9i4nT4VJY+XnFIn8XEqX9EsO7uUlutwvzMjgoAQIVQpIK3dOnSwv+eOHGivLy8NG3aNPn5XdjhpqWl6a677lLXrl1LJyUAwKF5uDjpjo61NLJ9TS3Zk6wvVsUp9uAp/botUb9uS1TLMF/d3bm2+kUEy2Yt9v3BAACoNIp9kcM777yjBQsWFJY7SfLz89Orr76qPn366KmnnirRgACAysNiMdSrcZB6NQ7SroQMfbEqTnO2JGhz/Gk9Gr9ZVbxcdFvbUA1vF8bpmwAAXEaxfw2akZGhEydOXLI9OTlZmZmZJRIKAIDGId56e1hzrXruOj3es54CPV10MjNHk5ccUJc3lui+/2zQ8n0nVVDAMgsAAPyh2EfwbrzxRt11111655131KFDB0nSmjVr9PTTT2vo0KElHhAAULlV8XLRE73r6+EedbVgV5K+XnNEaw6lauGuE1q464RqBrjr9vZhGtY6VH4ezmbHBQDAVMUueB999JHGjRunkSNHKi8v78KTODnpnnvu0VtvvVXiAQEAkCRnJ4sGNgvRwGYh2n8iU9+sjddPG4/pyKksTfh9j95esE/9I4J1S5tQdawTIAtr6gEAKqFiFzx3d3d98MEHeuutt3Tw4EHZ7XbVrVtXHh4epZEPAIBL1AvyUtSgJnqmXwPN2ZKgr9Yc0c6EDP28JUE/b0lQdV83DWtTQze3rqEafu5mxwUAoMxc9UqyHh4eatasWUlmAQCgWNydnXRbuzDd2jZU246l6/sNRzVnS4KOn85W9KL9enfxfnUOD9SwNjXUt0mwXG1WsyMDAFCqil3wevToIcP469NelixZck2BAAAoLsMw1DzUV81DffXigMaavzNJ3284qtiDpxRzIEUxB1Lk5eqkQc1DNKRldbUO8+MUTgCAQyp2wWvRosVFH+fl5WnLli3asWOHRo8eXVK5AAC4Km7OVg1pWV1DWlbX0dQs/bjxmH7ceEzHT2frm7Xx+mZtvKr7umlQixANaVFdDYK9zI4MAECJKXbBmzRp0mW3R0VF6cyZM9ccCACAkhLq764netfX4z3rKfbgKc3afFzzdybp+OlsfbjsoD5cdlANg700pGV1DWoewtp6AIAK76qvwftfI0eOVLt27fT222+X1FMCAFAiLBZDXeoFqku9QP07L0KLdp/Qz1sStGxvsvYkZer1uXv0+tw9alfbX4Oah6hvk2BV8XIxOzYAAMVWYgVv9erVcnV1LamnAwCgVLjarIXLLZzOytXv25M0e8txrYtLLfzz0s871LaWvwY0raZ+EcEK8mb/BgCoGIpd8P53MXO73a7ExERt2LBBL730UokFAwCgtPm6O2tE+zCNaB+mhNPZ+mVrgn7fkaStR08Xlr2X5+xU65p+6h8RrP5Nq6k6p3ECAMqxYhc8b2/vi+6iabFY1KBBA73yyivq06dPiYYDAKCshPi6aUz3cI3pHq5jaVmatyNJc3ckaeORtMI/r/62W81DfdWncZB6NqqqBkFef3tnaQAAylqxC97UqVNLIQYAAOVHDT933du1ju7tWkdJ6ec0f2eSft+eqHWHU7X16GltPXpab83fqxp+burV6ELZa187QM5OFrOjAwAquWIXvDp16mj9+vUKCAi4aPvp06fVqlUrHTp0qMTCAQBgtmAfV43uVEujO9XSycwcLdx1Qot3n1DMgRQdS8vW1NjDmhp7WJ4uTupWP1C9GgWpR4Oq8vNwNjs6AKASKnbBO3z4sPLz8y/ZnpOTo+PHj5dIKAAAyqMqXi6F1+xl5+Yr5kCKFu8+oUW7k5VyJke/b0/S79uTZDGkFqG+6la/irrXr6JmNXxlZWF1AEAZKHLBmzNnTuF/z58/Xz4+PoUf5+fna/HixapVq1aJhgMAoLxyc7aqd+Mg9W4cpIICu7YdTy8se7sTM7Qp/rQ2xZ9W9KL98nW3qUvdwMLCx105AQClpcgFb8iQIZIkwzA0evToiz5ns9lUq1YtvfPOOyUaDgCAisBiMdQi1FctQn31VJ8GSjidrRX7Tmr5vpOKOZCi01l5+nVbon7dlihJahjsVVj22tTyE1fuAQBKSpELXkFBgSSpdu3aWr9+vQIDA0stFAAAFVmIr5tuaxem29qF6Xx+gbYcPX2h8O1P0bZjp7UnKVN7kjL1yYpDcrVZ1DrMT355hmocS1eLmgGczgkAuGrFvgYvLi6uNHIAAOCQnKwWtanlrza1/PVknwZKPZurmAMpWr73pFbsP6mTmTladfCUJKt+/XitvF2d1KFOgDrXDVSn8ADVrerJUgwAgCIrUsF77733dP/998vV1VXvvffe3z72scceK5FgAAA4In8PZw1qHqJBzUNkt9u1P/mMVuw9odmrd+twlrMyzp3Xgl0ntGDXCUlSVS8XdQoPUKe6gepcN5CF1gEAf6tIBW/SpEm6/fbb5erqqkmTJv3l4wzDoOABAFBEhmGofpCXavu7qkraTvXpG6m9J7O16kCKYg+maMPhNCVn5mj2lgTN3pIgSaoZ4K5O4YHqXDdAHeoEKNDTxeR3AQAoT4pU8P58WianaAIAUDqcrJbCm7U83KOuzuXla9ORNK06mKJVB05p27HTOnIqS0dOxevbdfGSLtywpWN4gDqFB6pdbX/5uNlMfhcAADMV+xq8V155RePGjZO7u/tF27Ozs/XWW2/pn//8Z4mFAwCgMnO1WdWpbqA61Q3U032ljHN5WncoVasOpmj1wVOFN2vZk5SpL1cdlsWQmlb3UcfwC9fvtanlJ3fnYu/qAQAVWLH/1h8/frweeOCBSwpeVlaWxo8fT8EDAKCUeLva1KtxkHo1DpIknTqTozWHUhX738J3KOWsth5L19Zj6fpo+UHZrIZahvr99whfgFqE+crFyWryuwAAlKZiFzy73X7Zu3lt3bpV/v7+JRIKAABcWYCniwY0q6YBzapJkhLTs7X64CnFHjyl2AMpSkg/p3WHU7XucKreXbxfrjaL2tbyLzylMyLEW05WVuEDAEdS5ILn5+cnwzAuXBBev/5FJS8/P19nzpzRAw88UCohAQDAlVXzcdPQVjU0tFUN2e12xadmXSh7B09p9cEUpZzJ1cr9KVq5P0XSXnm5OKl9Hf/CUzobBHnJwhp8AFChFbngRUdHy2636+6779b48ePl4+NT+DlnZ2fVqlVLHTt2LJWQAACgeAzDUM0AD9UM8NDwdmGFSzLEHkhR7MFTWnPolDLOndei3clatDtZ0oUlHDrWCSg8pbN2oAdr8AFABVPkgjd69GhJUu3atdWpUyfZbNylCwCAiuKPJRnqB3npzs61lV9g166EDMUevFD41h9OVerZXP22PVG/bU+UJAV7u6pT3QD1bRKs7vWryNXG9XsAUN4VqeBlZGQU/nfLli2VnZ2t7Ozsyz7W29u7ZJIBAIBSY7UYalrDR01r+GhM93Dlni/QtmOn/3tKZ4o2HTmtpIxzmrnpuGZuOi5PFyf1bhykgc2qqWu9KnJ24to9ACiPilTwfH19r3iKxh83X8nPzy+RYAAAoOw4O1nUppa/2tTy12M96+lcXr42HknT0j3J+m17ohLTz2nW5uOatfm4fNxsGtqquoa3C1P9IC+zowMA/qRIBW/p0qWlnQMAAJQjrjarOtcNVOe6gfrH9Y20KT5Nv267cPrmycwcfbnqsL5cdVitwnx1W7swDWoewimcAFAOFKngde/evUhPtmXLlmvJAgAAyiGLxSg8uvfSwMZasf+kZqyL16LdydoUf1qb4k/r9bl7dEfHmhrVoaYCPF3MjgwAlVax18H7X+np6frmm2/02WefaevWrZyiCQCAA7NaDPVoUFU9GlRVcsY5/bDxmKavjdfx09mKXrRfHy47qJtb19AD3cMV6u9udlwAqHSu+grpJUuWaOTIkapWrZomT56s66+/Xhs2bCjJbAAAoByr6u2qh3vU1fKnIzV5eEs1re6jnPMF+mZtvHq8vUzPz9yu46cvf1M2AEDpKNYRvGPHjmnq1Kn64osvdPbsWd1yyy3Ky8vTTz/9pMaNG5dWRgAAUI45WS26oXmIBjarprVxqXp/6QGt3J+ib9fF68eNR3Vb2zA92rOuqnq5mh0VABxekY/gXX/99WrcuLF27dqlyZMnKyEhQZMnTy7NbAAAoAIxDEMd6gToq3va64cHOqpjnQDl5dv11Zoj6vHWMr2/9IDO5XEpBwCUpiIXvAULFujee+/V+PHjNWDAAFmt3CkLAABcXtta/vr2/g6afl97Na/ho7O5+Xpr/l71mrhcv21LlN1uNzsiADikIhe8lStXKjMzU23atFH79u01ZcoUnTx5sjSzAQCACq5TeKBmPdRZk25trmBvVx1Ly9bD0zfptk/W6EDyGbPjAYDDKXLB69ixoz799FMlJiZqzJgxmjFjhqpXr66CggItXLhQmZmZpZkTAABUUBaLoRtb1tCScd01tlc9udosWhuXquvfXal3F+1XznlO2wSAklLsu2i6u7vr7rvvVkxMjLZv366nnnpKr7/+uqpWrapBgwaVRkYAAOAA3J2dNLZXfS18orsiG1RRbn6BJi3ap+vfXal1calmxwMAh3DVyyRIUoMGDfTmm2/q2LFj+vbbb0sqEwAAcGCh/u768s62mjy8pQI9nXXw5Fnd8vFqvfLLLm7CAgDX6JoK3h+sVquGDBmiOXPmlMTTXeT48eMaOXKkAgIC5O7urhYtWmjjxo2Fn7fb7YqKilJISIjc3NwUGRmpnTt3lngOAABQcgzD0A3NQ7Toye66tU2oJOmLVXG6YXKMdhxPNzkdAFRcJVLwSktaWpo6d+4sm82muXPnateuXXrnnXfk6+tb+Jg333xTEydO1JQpU7R+/XoFBwerd+/eXBMIAEAF4OvurDdubqYv72yrKl4u2p98RkPeX6UpS/brfH6B2fEAoMIp1wXvjTfeUGhoqL788ku1a9dOtWrVUs+ePRUeHi7pwtG76OhovfDCCxo6dKgiIiI0bdo0ZWVlafr06SanBwAARdWjYVXNH9tN/SOCdb7ArrcX7NMtH6/WsbQss6MBQIXiZHaAvzNnzhz17dtXw4YN0/Lly1W9enU99NBDuu+++yRJcXFxSkpKUp8+fQq/xsXFRd27d1dsbKzGjBlz2efNyclRTk5O4ccZGRmSpLy8POXl5ZXiOyq6P3KUlzyVCbM3D7M3F/M3D7O/wMvZ0Lu3NFXPBoEa/9sebYo/rQHvrdQbQyPUs2HVUnlNZm8u5m8eZm+e4s6+uN8jw16OVxp1dXWVJD355JMaNmyY1q1bp7Fjx+rjjz/WHXfcodjYWHXu3FnHjx9XSEhI4dfdf//9OnLkiObPn3/Z542KitL48eMv2T59+nS5u7uXzpsBAABFduqcNG2/VUfOGJKkHtUKdENYgazl+twjACh5WVlZGjFihNLT0+Xt7X3Fx5frgufs7Kw2bdooNja2cNtjjz2m9evXa/Xq1YUFLyEhQdWqVSt8zH333aejR49q3rx5l33eyx3BCw0NVUpKSpGGVhby8vK0cOFC9e7dWzabzew4lQqzNw+zNxfzNw+zv7zc8wV6e+F+fRl7RJLUItRH0bc0U3VftxJ7DWZvLuZvHmZvnuLOPiMjQ4GBgUUueOX6FM1q1aqpcePGF21r1KiRfvrpJ0lScHCwJCkpKemigpecnKygoKC/fF4XFxe5uLhcst1ms5W7H/DymKmyYPbmYfbmYv7mYfYXs9mklwdFqEN4oJ7+Yau2HE3X4A/W6L3hLdW9fpUSfi1mbybmbx5mb56izr64359yfaJD586dtXfv3ou27du3TzVr1pQk1a5dW8HBwVq4cGHh53Nzc7V8+XJ16tSpTLMCAIDS0bdJsH57rKuah/oqPTtPd325Th8tP6hyfBISAJimXBe8J554QmvWrNGECRN04MABTZ8+XZ988okefvhhSRfW0Bk7dqwmTJigWbNmaceOHbrzzjvl7u6uESNGmJweAACUlFB/d30/poNuaxuqArv0+tw9evTbzcrKPW92NAAoV8r1KZpt27bVrFmz9Pzzz+uVV15R7dq1FR0drdtvv73wMc8884yys7P10EMPKS0tTe3bt9eCBQvk5eVlYnIAAFDSXJysem1oU0VU91HUnJ36dVuiDiSf0Sej2igsgJukAYBUzgueJA0cOFADBw78y88bhqGoqChFRUWVXSgAAGAKwzA0skNNNQj20oNfb9KepEwNej9Gk4e3VNd6JXtdHgBUROX6FE0AAIDLaVvLX7882lnNQ311OitPo79Yp6mr4rguD0ClR8EDAAAVUjUfN313fwfd3LqGCuxS1C+79NLPO5SXX2B2NAAwDQUPAABUWK42q966uZme799QhiF9vSZed325XulZeWZHAwBTUPAAAECFZhiGxnQP1yej2sjd2aqYAym68cNViks5a3Y0AChzFDwAAOAQejcO0g8PdFSIj6sOnTyrIe+v0uqDp8yOBQBlioIHAAAcRpMQH81+pHPhouijPl+rGevizY4FAGWGggcAABxKVS9XfXd/B93QPETnC+x6buZ2vfrrLuUXcIdNAI6PggcAAByOq82q925robG96kmSPouJ0/3/2aDMc9x8BYBjo+ABAACHZBiGxvaqr8nDW8rFyaLFe5J184erdTQ1y+xoAFBqKHgAAMCh3dA8RN+N6agqXi7aeyJTg99fpfWHU82OBQClgoIHAAAcXotQX815pLMiqnsr9WyuRny6Rt9vOGp2LAAocRQ8AABQKVTzcdP3Yzrq+qbBysu365kft+n1eXvFvVcAOBIKHgAAqDTcnZ00ZXgrPXZdXUnS56uO6LO9FmWeO29yMgAoGRQ8AABQqVgshp7s06Dw5is70yy69dO13HwFgEOg4AEAgErphuYhmn5PW3nb7NqffFaD31+ldXHcfAVAxUbBAwAAlVazGj56qmm+IkIu3Hzl9s/W6Pv13HwFQMVFwQMAAJWar4s0/Z62GtC02oWbr/y0Ta/+ukvn8wvMjgYAxUbBAwAAlZ6bs1VTRrTU2F71JEmfxcTpzi/XK+1srsnJAKB4KHgAAACSDMPQ2F719cHtreRmsyrmQIoGvR+jXQkZZkcDgCKj4AEAAPzJ9U2radbDnRTm766jqdm66cNY/botwexYAFAkFDwAAID/0TDYW3Me6ayu9QKVnZevR6Zv1utz9yifVdEBlHMUPAAAgMvwdXfW1LvaaUz3OpKkj5Yf1F1T1+t0FtflASi/KHgAAAB/wWox9Hz/RnpveEu52ixase+kBk1ZpT1JXJcHoHyi4AEAAFzBoOYhmvlgZ9Xwc1N8apaGfhCr37cnmh0LAC5BwQMAACiCxiHe+uWRLupcN0BZufl66JtNen3uHtbLA1CuUPAAAACKyM/DWdPuaqd7u9SWdOG6vJGfr9XJzByTkwHABRQ8AACAYnCyWvTiwMaaMqKlPJytWnMoVQPeW6l1calmRwMACh4AAMDVGNgsRD8/0kX1qnoqOTNHwz9do09XHJLdzlIKAMxDwQMAALhKdat6avbDnTW4RYjyC+z69++79eDXm5RxLs/saAAqKQoeAADANfBwcVL0rS30r8FNZLMamrczSYNZSgGASSh4AAAA18gwDI3qWEvfj+moEB9XxaWc1ZD3V+mnjcfMjgagkqHgAQAAlJCWYX769bGu6la/is7lFeipH7bq6R+2Kiv3vNnRAFQSFDwAAIAS5O/hrC/vbKuxverJMKQfNh7TIE7ZBFBGKHgAAAAlzGoxNLZXfU2/t4OCvF10IPmMBk9Zpa/XHOEumwBKFQUPAACglHQMD9Dvj3VVjwZVlHO+QC/O3qGHp29SejZ32QRQOih4AAAApSjA00Wfj26rFwc0ks1q6PftSRrw3kptjk8zOxoAB0TBAwAAKGUWi6F7u9bRjw90Uqi/m46lZWvYR6v18fKDKijglE0AJYeCBwAAUEaah/rqt8e6akCzajpfYNdrc/do9JfrlJR+zuxoABwEBQ8AAKAMebvaNGV4S702tKlcbRat3J+ivtEr9Pv2RLOjAXAAFDwAAIAyZhiGhrcL02+PdVXT6j5Kz87TQ99s0pPfb1HmOW7AAuDqUfAAAABMEl7FUzMf6qRHetSVxZBmbjqu/u+u1Lq4VLOjAaigKHgAAAAmslktGte3gb4f07HwBiy3frJab87bo9zzBWbHA1DBUPAAAADKgTa1/PX7Y111c+sastulD5Yd1NAPV2lPUobZ0QBUIBQ8AACAcsLL1aa3hzXXh7e3kq+7TTuOZ+iGyTGavHi/8vI5mgfgyih4AAAA5Uz/ptW0YGw39WoUpLx8u95ZuE9D3l+l3YkczQPw9yh4AAAA5VBVb1d9ekdrRd/aQj5uNu1MyNCgKTF6dxFH8wD8NQoeAABAOWUYhoa0rK6FT3ZT78YXjuZNWrRPg6es0q4EjuYBuBQFDwAAoJyr6uWqT0a11ru3tZCvu027Ei8czZu4YK/O5eWbHQ9AOULBAwAAqAAMw9DgFtW14Ilu6tM4SOcL7HpvyQH1f3elYg+kmB0PQDlBwQMAAKhAqnq56uNRrfXB7a1U1ctFcSlnNeKztXrq+61KPZtrdjwAJqPgAQAAVDCGYej6ptW06KnuGtWhpgxD+mnTMfV8Z5l+2nhMdrvd7IgATELBAwAAqKC8XW3615AI/fhAJzUM9lJaVp6e+mGrbv9srQ6dPGN2PAAmoOABAABUcK1r+umXR7vo2X4N5WqzKPbgKfWNXqHX5+7R2ZzzZscDUIYoeAAAAA7AZrXowchwLRjbXZENqigv366Plh/Ude8s089bjnPaJlBJUPAAAAAcSFiAu768s60+u6ONwvzddSIjR4/P2KJbP17D2nlAJUDBAwAAcDCGYahX4yAteKKbxvWpLzebVesOp2rg5JV6cfZ2pXG3TcBhUfAAAAAclKvNqkeuq6fFT3XXwGbVVGCXvl4Tr25vLdXHyw+ySDrggCh4AAAADi7E101TRrTSt/d1UKNq3so8d16vzd2jnu8s1+zNx1VQwPV5gKOg4AEAAFQSHcMD9OujXfT2sOaq5uOq46ezNfa7LRr0foxiD6aYHQ9ACaDgAQAAVCJWi6GbW9fQ0nGRerpvA3m6OGnH8QyN+HSt7p66nhuxABUcBQ8AAKAScrVZ9XCPulr2dKTu6FhTThZDS/Yk6/r3VurhbzZp/4lMsyMCuAoUPAAAgEos0NNFrwyO0IInuumG5iEyDOm37YnqE71CY2dsVlzKWbMjAigGCh4AAABUp4qnJg9vqbmPd1W/JsGy26XZWxLUa+JyPf3DVh1NzTI7IoAioOABAACgUMNgb300qrV+fbSLejasqvwCu37YeEyRby/Tk99t4dRNoJyj4AEAAOASEdV99PmdbTXroU7qWi9Q+QV2zdx8XL0nrdD9/9mgLUdPmx0RwGVQ8AAAAPCXWob56at72mvOI53Vr0mwDENasOuEhry/SiM+XaOY/Smy21lHDygvnMwOAAAAgPKvWQ1ffTSqtQ4kZ+qj5Yc0e/NxxR48pdiDp9Somrfu7lxLNzQPkavNanZUoFLjCB4AAACKrG5VL709rLmWP9NDd3aqJTebVbsTM/T0j9vU5Y0lmrRwn5Izz5kdE6i0KHgAAAAotuq+booa1ESrn79Oz/VvqGo+rko5k6t3F+9Xl9eX6snvt2jbsdNmxwQqHU7RBAAAwFXzdXfWA93DdU+X2pq/M0lfxMRpU/xpzdx0XDM3HVdEdW8NbxemwS2qy9OFf3oCpa1CHcF77bXXZBiGxo4dW7jNbrcrKipKISEhcnNzU2RkpHbu3GleSAAAgErIZrVoYLMQzXyos2Y/3FlDWoTI2WrRjuMZemHWDrX/9yI9P3O7th9LNzsq4NAqTMFbv369PvnkEzVr1uyi7W+++aYmTpyoKVOmaP369QoODlbv3r2VmckaLQAAAGZoEeqr6Ntaas0/eurFAY1Up4qHzubm69t18bphSowGTl6pL2LidOpMjtlRAYdTIQremTNndPvtt+vTTz+Vn59f4Xa73a7o6Gi98MILGjp0qCIiIjRt2jRlZWVp+vTpJiYGAACAv4ez7u1aR4uf7K4Z93fQoOb/f1TvlV93qfNbK/Txbot+256kc3n5ZscFHEKFKHgPP/ywBgwYoF69el20PS4uTklJSerTp0/hNhcXF3Xv3l2xsbFlHRMAAACXYRiGOtQJ0HvDLxzVGz+oiZqH+iq/wK5dpy0a+/02tX11kZ75catiD6TofH6B2ZGBCqvcX+k6Y8YMbdq0SevXr7/kc0lJSZKkoKCgi7YHBQXpyJEjf/mcOTk5ysn5/1MCMjIyJEl5eXnKy8sridjX7I8c5SVPZcLszcPszcX8zcPszcPsy56Xs6ERbatrRNvq2pt4Wu/9vEbbz7gpMT1H3284pu83HJO/h019GgepX5Mgta/lJydrhTgmUaHws2+e4s6+uN8jw26324udqowcPXpUbdq00YIFC9S8eXNJUmRkpFq0aKHo6GjFxsaqc+fOSkhIULVq1Qq/7r777tPRo0c1b968yz5vVFSUxo8ff8n26dOny93dvXTeDAAAAC6rwC4dypA2pFi0NdVQ1nmj8HMeTnY187ereYBd9b3touuhssnKytKIESOUnp4ub2/vKz6+XBe82bNn68Ybb5TVai3clp+fL8MwZLFYtHfvXtWtW1ebNm1Sy5YtCx8zePBg+fr6atq0aZd93ssdwQsNDVVKSkqRhlYW8vLytHDhQvXu3Vs2m83sOJUKszcPszcX8zcPszcPszfX5eafl1+gtXFpmrfzhBbsOqG0rP8/euHl6qRudQPVo0GgutUPlJ+7s1nRKzx+9s1T3NlnZGQoMDCwyAWvXJ+i2bNnT23fvv2ibXfddZcaNmyoZ599VnXq1FFwcLAWLlxYWPByc3O1fPlyvfHGG3/5vC4uLnJxcblku81mK3c/4OUxU2XB7M3D7M3F/M3D7M3D7M315/nbbFKPRsHq0ShY/84v0Lq4VP22PVHzdyYp5UyuftuRpN92JMliSK1r+um6hkHq2aiq6lX1lGEYV3gl/C9+9s1T1NkX9/tTrguel5eXIiIiLtrm4eGhgICAwu1jx47VhAkTVK9ePdWrV08TJkyQu7u7RowYYUZkAAAAlBAnq0Wd6gaqU91AvTI4QluOntaSPSe0eHey9iRlav3hNK0/nKY35u1RDT83datfRV3qBqpTeIB8ObqHSqpcF7yieOaZZ5Sdna2HHnpIaWlpat++vRYsWCAvLy+zowEAAKCEWC2GWtf0U+uafnq6b0MdP52tJXuStWT3Ca06eErH0rI1fW28pq+Nl2FITav7qHPdQHWtG6hWNf3karNe+UUAB1DhCt6yZcsu+tgwDEVFRSkqKsqUPAAAACh71X3dNKpDTY3qUFNZuee1+uApxRxIUcz+FO1PPqNtx9K17Vi6Plx2UK42i9rW8leHOgFqW8tfzWr4UPjgsCpcwQMAAAD+zN3ZST0bBalnowtLZ53IOKdV/y17MQdSlJyZo5X7U7Ryf4okydnJohY1fNWutr/a1vZX65p+8nThn8VwDPwkAwAAwKEEebtqaKsaGtqqhux2uw4kn9GqAyladzhV6+LSlHIm58J/H06VlkoWQ2oS4qPWNf3UMsxXLUJ9Febvzk1bUCFR8AAAAOCwDMNQvSAv1Qvy0p2da8tutysu5azWH07V2rhUrT+cqqOp2dp+PF3bj6drauyFr/P3cFbzGj5qEeqnFmG+alHDVz7u3G0S5R8FDwAAAJWGYRiqU8VTdap46ta2YZKkxPRsrYtL1eb409py9LR2JWQo9Wyulu49qaV7TxZ+bZ1AD7UI9VWzGj5qHuqrRtW8uZYP5Q4FDwAAAJVaNR83DW5RXYNbVJck5ZzP1+7ETG2JT9OWoxdK3+FTWTqUclaHUs5q5ubjkiSb1VDDYO/Cwte8hq/qVvWU1cKpnTAPBQ8AAAD4Excnq1qEXrgW7w9pZ3O15dhpbTuarq3HTmvr0dM6dTa38NTOb9bGS5Lcna2KqO7z/0f6aviqhp8b1/OhzFDwAAAAgCvw83BWjwZV1aNBVUmS3W7X8dPZ2no0XduOXTjKt+N4us7m5mtdXKrWxaUWfu0f1/M1D/VVxzoBahnmJ2cni1lvBQ6OggcAAAAUk2EYquHnrhp+7hrQrJokKb/AroMnz2jr0dP/PcqXrj1JF1/PF639cne2qkOdAHWpG6gu9QJVr6onR/hQYih4AAAAQAmwWgzVD/JS/SAvDWsTKkk6l5ev3YkZ2nYsXRuOpCn2QIpOnc3Vkj3JWrInWZIU6u+mvo2D1S8iWK3C/GThGj5cAwoeAAAAUEpcbVa1DPNTyzA/je5USwUFdu1OyihchH1d3IVlGj6LidNnMXEK9HRRnyZBGtKiutrW8uPIHoqNggcAAACUEYvFUJMQHzUJ8dGY7uHKyj2vFftOat6OJC3ek6yUMzmavjZe09fGK8zfXUNbVdfQljUUFuBudnRUEBQ8AAAAwCTuzk7qF1FN/SKqKfd8gdYcOqVftibo9+2Jik/NUvSi/YpetF/tavtrVIea6hcRLJuVG7Tgr1HwAAAAgHLA2cmibvWrqFv9Kho/uIkW7DyhnzYdKzyVc11cqqp6uWhE+zCNaBemqt6uZkdGOUTBAwAAAMoZd2cnDWlZXUNaVldierZmrDuq6evilZyZo+hF+zVlyQENaFZND3QPV6Nq3mbHRTnC8V0AAACgHKvm46YnetfXqmev07u3tVCbmn46X2DXz1sS1P/dlbp76nqtP5x65SdCpcARPAAAAKACcHayaHCL6hrcorq2H0vXRysO6vftiYVLLrSp6aeHe9RVZIMq3H2zEuMIHgAAAFDBNK3ho/dHtNKSpyI1vF2onK0WbTiSprumrtfQD2MVsz9Fdrvd7JgwAQUPAAAAqKBqB3rotaHNtPLZHrqva2252izaHH9aIz9fq9s+WcOpm5UQBQ8AAACo4IK8XfXCgMZa8UwP3dmplpytFq2NS9Wwj1Zr9BfrtO3YabMjooxQ8AAAAAAHUdXLVVGDmmjZ05Ea3i5MThZDy/ed1KApq/Tot5t1NDXL7IgoZRQ8AAAAwMGE+LrptaFNtfip7rqxZXUZhvTL1gT1fGe5/v3bLqVn55kdEaWEggcAAAA4qJoBHpp0awv98kgXda4boNz8An26Mk49J63U0gRDOecLzI6IEkbBAwAAABxcRHUffX1Pe315V1s1CPJSevZ5zT5iVb/3VmnO1gTuuOlAKHgAAABAJWAYhno0qKrfH++qCUMay9tm17G0bD327WYNeX+V1h46ZXZElAAKHgAAAFCJWC2GhrWuoRdb5uvx68Ll4WzV1mPpuvWTNbr/Pxt06OQZsyPiGlDwAAAAgErIxSo90iNcy57uodvbh8liSAt2nVCfSSv08s87dOpMjtkRcRUoeAAAAEAlVsXLRf++sanmj+2mng2r6nyBXdNWH1HkW8v0wbIDOpeXb3ZEFAMFDwAAAIDqBXnp8zvbavq97dUkxFuZOef15ry9uu7tZZq1+ZgKCrgRS0VAwQMAAABQqFPdQP3ySBdNvKW5qvm4KiH9nJ74bqsGvR+j2IMpZsfDFVDwAAAAAFzEYjE0tFUNLR0XqWf6NZCni5N2HM/QiE/X6t5p63UgOdPsiPgLFDwAAAAAl+Vqs+qhyLpa9nSk7uhYU1aLoUW7k9U3eqVemLVdJzO5EUt5Q8EDAAAA8LcCPV30yuAILXiim3o3DlJ+gV3frI1X5FtLNXnxfp3JOW92RPwXBQ8AAABAkYRX8dSnd7TRd/d3ULMaPjqbm693Fu5TtzeX6tMVh5Sdyx03zUbBAwAAAFAs7esEaPZDnfXubS1UK8BdqWdz9e/fd6vbW0s1Lfawcs5T9MxCwQMAAABQbBaLocEtqmvRk9315s3NVN3XTSczc/TynJ3q8dYyzVgXr7z8ArNjVjoUPAAAAABXzclq0S1tQrV0XKT+NSRCQd4uSkg/p+dmblfPd5br+w1HKXpliIIHAAAA4Jo5O1k0qkNNLX+6h14a2FiBns6KT83SMz9uU+Rby/Sf1Yd1Lo9TN0sbBQ8AAABAiXG1WXVPl9pa8UwPPd+/oQI9XXT8dLb++fNOdXljqT5efpC7bpYiCh4AAACAEufu7KQx3cMV82wPvTK4iar7uinlTI5em7tHnV9fouhF+3Q6K9fsmA6HggcAAACg1LjarLqjYy0tezpSb93cTHUCPZSenafoRfvV8bUlevnnHTpy6qzZMR0GBQ8AAABAqbNZLRrWJlQLn+yuKSNaqnE1b2Xn5Wva6iOKfHuZxny1QRsOp8put5sdtUJzMjsAAAAAgMrDajE0sFmIBjStptUHT+nTlYe0dO9Jzd95QvN3nlCLUF/d27W2+jUJlpOV41HFRcEDAAAAUOYMw1CnuoHqVDdQ+09k6vOYOM3cfFxbjp7WI9M3q4afm0Z3rKVb2oTKx91mdtwKg0oMAAAAwFT1grz0+k3NtOrZ6/RYz3ry93DWsbRs/fv33Wr/2iI9P3ObdidmmB2zQqDgAQAAACgXqni56Mne9RX73HV6bWhTNQz20rm8An277qj6v7tSt3y8Wr9tS2Th9L/BKZoAAAAAyhVXm1XD24XptrahWn84TdNiD2veziSti0vVurhUBXu76vb2YRrePkyBni5mxy1XKHgAAAAAyiXDMNSutr/a1fZXUvo5TV97RNPXxSsp45zeWbhPk5cc0IBm1TSqY021DPWVYRhmRzYdBQ8AAABAuRfs46on+zTQw9fV1dztSZq2+rA2x5/WrM3HNWvzcTUJ8dbt7WtqcIsQebhU3ppTed85AAAAgArHxcmqIS2ra0jL6tp27LSmxR7RL9sStDMhQ/+YtV0Tft+tG1tW1+0dwtQw2NvsuGWOggcAAACgQmpWw1fv3OKrlwY20o8bj+mbtfGKSzmrr9Yc0VdrjqhNTT/d3iFM/SOqydVmNTtumaDgAQAAAKjQfN2ddW/XOrqnS23FHjylb9Ye0YKdJ7ThSJo2HEnTK7/s0rA2oRrRLky1Aj3MjluqKHgAAAAAHIJhGOpcN1Cd6wYqOeOcvlt/VN+ui1dC+jl9suKQPllxSF3rBer29jXVq1FVOVkdb9U4Ch4AAAAAh1PV21WP9qynh3rU1dI9yfpm7REt23dSK/enaOX+FAV5u+i2tmG6rV2oqvm4mR23xFDwAAAAADgsq8VQr8ZB6tU4SEdTs/Ttunh9v+GoTmTk6N3F+zVl6QH1bFhVt3eoqa51A2WxVOylFih4AAAAACqFUH93PdOvocb2qq95O5P0zZojWhuXqgW7TmjBrhMK83fX8HZheqB7nQq7pp7jnXQKAAAAAH/D2cmiQc1D9N2Yjlr0ZDfd2amWvFydFJ+apVUHUipsuZM4ggcAAACgEqtb1UtRg5ro2X4N9eu2BFX3q9jX41HwAAAAAFR6bs5WDWsTanaMa8YpmgAAAADgICh4AAAAAOAgKHgAAAAA4CAoeAAAAADgICh4AAAAAOAgKHgAAAAA4CAoeAAAAADgICh4AAAAAOAgKHgAAAAA4CAoeAAAAADgICh4AAAAAOAgKHgAAAAA4CAoeAAAAADgICh4AAAAAOAgKHgAAAAA4CAoeAAAAADgIJzMDlAe2O12SVJGRobJSf5fXl6esrKylJGRIZvNZnacSoXZm4fZm4v5m4fZm4fZm4v5m4fZm6e4s/+jo/zRWa6EgicpMzNTkhQaGmpyEgAAAAC4VGZmpnx8fK74OMNe1CrowAoKCpSQkCAvLy8ZhmF2HEkXmnpoaKiOHj0qb29vs+NUKszePMzeXMzfPMzePMzeXMzfPMzePMWdvd1uV2ZmpkJCQmSxXPkKO47gSbJYLKpRo4bZMS7L29ub/+lMwuzNw+zNxfzNw+zNw+zNxfzNw+zNU5zZF+XI3R+4yQoAAAAAOAgKHgAAAAA4CApeOeXi4qKXX35ZLi4uZkepdJi9eZi9uZi/eZi9eZi9uZi/eZi9eUp79txkBQAAAAAcBEfwAAAAAMBBUPAAAAAAwEFQ8AAAAADAQVDwytCKFSt0ww03KCQkRIZhaPbs2Rd93m63KyoqSiEhIXJzc1NkZKR27tx50WNycnL06KOPKjAwUB4eHho0aJCOHTtWhu+iYrrW2aempurRRx9VgwYN5O7urrCwMD322GNKT08v43dSMZXEz/6fH9u/f//LPg8uVVKzX716ta677jp5eHjI19dXkZGRys7OLqN3UTGVxOyTkpI0atQoBQcHy8PDQ61atdKPP/5Yhu+iYrrS7GfOnKm+ffsqMDBQhmFoy5YtlzwH+9urd63zZ5979UriZ/8P7G+Lp6RmXxL7WwpeGTp79qyaN2+uKVOmXPbzb775piZOnKgpU6Zo/fr1Cg4OVu/evZWZmVn4mLFjx2rWrFmaMWOGYmJidObMGQ0cOFD5+fll9TYqpGudfUJCghISEvT2229r+/btmjp1qubNm6d77rmnLN9GhVUSP/t/iI6OlmEYpR3ZYZTE7FevXq1+/fqpT58+WrdundavX69HHnlEFgu7kL9TErMfNWqU9u7dqzlz5mj79u0aOnSobr31Vm3evLms3kaFdKXZnz17Vp07d9brr7/+l8/B/vbqXev82edevZL42f8D+9viKYnZl9j+1g5TSLLPmjWr8OOCggJ7cHCw/fXXXy/cdu7cObuPj4/9o48+stvtdvvp06ftNpvNPmPGjMLHHD9+3G6xWOzz5s0rs+wV3dXM/nK+//57u7Ozsz0vL6804zqca5n/li1b7DVq1LAnJiZe8jy4squdffv27e0vvvhiWUZ1OFc7ew8PD/t//vOfi57L39/f/tlnn5V6Zkfxd39XxMXF2SXZN2/efNF29rcl52rmfznsc4vvWmbP/vbaXO3sS2p/y69fy4m4uDglJSWpT58+hdtcXFzUvXt3xcbGSpI2btyovLy8ix4TEhKiiIiIwseg+Ioy+8tJT0+Xt7e3nJycyiKmwyrq/LOysjR8+HBNmTJFwcHBZkR1OEWZfXJystauXauqVauqU6dOCgoKUvfu3RUTE2NWbIdQ1J/7Ll266LvvvlNqaqoKCgo0Y8YM5eTkKDIy0oTUlQf72/KHfW7ZYX9rjpLc31LwyomkpCRJUlBQ0EXbg4KCCj+XlJQkZ2dn+fn5/eVjUHxFmf3/OnXqlP71r39pzJgxpZ7P0RV1/k888YQ6deqkwYMHl2k+R1aU2R86dEiSFBUVpfvuu0/z5s1Tq1at1LNnT+3fv79sAzuQov7cf/fddzp//rwCAgLk4uKiMWPGaNasWQoPDy/TvJUN+9vyhX1u2WJ/a46S3N/ya5By5n/Pdbbb7Vc8/7koj8GVFXX2GRkZGjBggBo3bqyXX365rOI5vL+b/5w5c7RkyRKuOyolfzf7goICSdKYMWN01113SZJatmypxYsX64svvtBrr71WtmEdzJX+3nnxxReVlpamRYsWKTAwULNnz9awYcO0cuVKNW3atKzjVnrsb8se+9yyxf7WPCW5v+UIXjnxxyHw//3NYHJycuFveIODg5Wbm6u0tLS/fAyKryiz/0NmZqb69esnT09PzZo1SzabrcxyOqqizH/JkiU6ePCgfH195eTkVHiKzk033cSpategKLOvVq2aJKlx48YXPaZRo0aKj48vg5SOqSizP3jwoKZMmaIvvvhCPXv2VPPmzfXyyy+rTZs2ev/998s8c2XC/rZ8YJ9b9tjfmqck97cUvHKidu3aCg4O1sKFCwu35ebmavny5erUqZMkqXXr1rLZbBc9JjExUTt27Ch8DIqvKLOXLvwWsU+fPnJ2dtacOXPk6upqRlyHU5T5P/fcc9q2bZu2bNlS+EeSJk2apC+//NKM2A6hKLOvVauWQkJCtHfv3ou+dt++fapZs2aZ5nUkRZl9VlaWJF1y9zSr1Vr4m16UDva35mOfaw72t+Ypyf0tp2iWoTNnzujAgQOFH8fFxWnLli3y9/dXWFiYxo4dqwkTJqhevXqqV6+eJkyYIHd3d40YMUKS5OPjo3vuuUdPPfWUAgIC5O/vr3Hjxqlp06bq1auXWW+rQrjW2WdmZqpPnz7KysrS119/rYyMDGVkZEiSqlSpIqvVasr7qiiudf7BwcGXvdA7LCxMtWvXLrP3URFd6+wNw9DTTz+tl19+Wc2bN1eLFi00bdo07dmzh/XYruBaZ9+wYUPVrVtXY8aM0dtvv62AgADNnj1bCxcu1K+//mrW26oQrjT71NRUxcfHKyEhQZIK/0H1x9817G+vzbXOn33u1bvW2bO/vXrXOvsS3d9e8304UWRLly61S7rkz+jRo+12+4XbZr/88sv24OBgu4uLi71bt2727du3X/Qc2dnZ9kceecTu7+9vd3Nzsw8cONAeHx9vwrupWK519n/19ZLscXFx5rypCqQkfvb/l7htc5GU1Oxfe+01e40aNezu7u72jh072leuXFnG76TiKYnZ79u3zz506FB71apV7e7u7vZmzZpdsmwCLnWl2X/55ZeX/fzLL79c+Bzsb6/etc6ffe7VK4mf/f/F/rZoSmr2JbG/Nex2u714lRAAAAAAUB5xDR4AAAAAOAgKHgAAAAA4CAoeAAAAADgICh4AAAAAOAgKHgAAAAA4CAoeAAAAADgICh4AAAAAOAgKHgAAAAA4CAoeAMChRUVFqUWLFmX+usuWLZNhGDIMQ0OGDCmR5zp9+nSRvyYqKqrw9aOjo6/p9QEAFQcFDwBQYf1RYP7qz5133qlx48Zp8eLFpmXcu3evpk6dek3P0alTJyUmJsrHx6fIXzNu3DglJiaqRo0a1/TaAICKxcnsAAAAXK3ExMTC//7uu+/0z3/+U3v37i3c5ubmJk9PT3l6epoRT5JUtWpV+fr6XtNzODs7Kzg4uFhf88f7tlqt1/TaAICKhSN4AIAKKzg4uPCPj4+PDMO4ZNv/nqJ55513asiQIZowYYKCgoLk6+ur8ePH6/z583r66afl7++vGjVq6IsvvrjotY4fP65bb71Vfn5+CggI0ODBg3X48OFiZ46MjNSjjz6qsWPHys/PT0FBQfrkk0909uxZ3XXXXfLy8lJ4eLjmzp1b+DX/e4rm1KlT5evrq/nz56tRo0by9PRUv379Liq8AIDKiYIHAKh0lixZooSEBK1YsUITJ05UVFSUBg4cKD8/P61du1YPPPCAHnjgAR09elSSlJWVpR49esjT01MrVqxQTExMYanKzc0t9utPmzZNgYGBWrdunR599FE9+OCDGjZsmDp16qRNmzapb9++GjVqlLKysv7yObKysvT222/rq6++0ooVKxQfH69x48Zd9UwAAI6BggcAqHT8/f313nvvqUGDBrr77rvVoEEDZWVl6R//+Ifq1aun559/Xs7Ozlq1apUkacaMGbJYLPrss8/UtGlTNWrUSF9++aXi4+O1bNmyYr9+8+bN9eKLLxa+lpubmwIDA3XfffepXr16+uc//6lTp05p27Ztf/kceXl5+uijj9SmTRu1atVKjzzyiKnXGgIAygeuwQMAVDpNmjSRxfL/v+MMCgpSRERE4cdWq1UBAQFKTk6WJG3cuFEHDhyQl5fXRc9z7tw5HTx4sNiv36xZs0teq2nTphflkVT4+pfj7u6u8PDwwo+rVav2t48HAFQOFDwAQKVjs9ku+tgwjMtuKygokCQVFBSodevW+uabby55ripVqpT46xuGUfi6xXkOu91e7CwAAMdCwQMA4ApatWql7777TlWrVpW3t7fZcQAA+EtcgwcAwBXcfvvtCgwM1ODBg7Vy5UrFxcVp+fLlevzxx3Xs2DGz4wEAUIiCBwDAFbi7u2vFihUKCwvT0KFD1ahRI919993Kzs7miB4AoFwx7JywDwBAiVu2bJl69OihtLS0a17o/FrUqlVLY8eO1dixY03LAAAoOxzBAwCgFNWoUUPDhw8v89edMGGCPD09FR8fX+avDQAwD0fwAAAoBdnZ2Tp+/LgkydPTU8HBwWX6+qmpqUpNTZV04U6fPj4+Zfr6AABzUPAAAAAAwEFwiiYAAAAAOAgKHgAAAAA4CAoeAAAAADgICh4AAAAAOAgKHgAAAAA4CAoeAAAAADgICh4AAAAAOAgKHgAAAAA4CAoeAAAAADiI/wP34XTCgU790gAAAABJRU5ErkJggg==", + "image/png": 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" ] @@ -714,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 34, "id": "00436c5f", "metadata": { "collapsed": false, @@ -725,7 +725,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -756,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 35, "id": "629b9b44", "metadata": { "collapsed": false, @@ -767,7 +767,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -798,7 +798,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 36, "id": "cc405878", "metadata": { "collapsed": false, @@ -809,7 +809,7 @@ "outputs": [ { "data": { - "image/png": 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", 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8/5z6M7csvYWowCgWX7YYm7V8IxK87cvdX/LwTw8TExLDF5d+UWXiOh39vJtDeTeH8m4Od/OemZlJnTp1yMjIKFODuKNa9uAZhsFbb73Ftddee8biDsBqtXLeeeexY8eO07YJDAwkMDCwzHZ/f/8yH4bD4cBisWC1WktfC+dlxUMli88t7rNarVgsllN+rmdTkX3k3Cnv5lDezVHevK9IcS2P0L9Rf4ICq84EYsMThvP8hudJzU3lp7SfGBA3wOyQykU/7+ZQ3s2hvJujvHn31GdTLSuFH374gZ07dzJhwoSztjUMg6SkJGJjYyshMhEREe8xDIPvk78HYGDjgSZHU1qgLZDRzUcDMH/bfHODERGpwUwt8LKzs0lKSiIpKQmAPXv2kJSURHJyMgBTpkzhuuuuK7Pfm2++Sc+ePWnfvn2Z16ZNm8a3337L7t27SUpKYsKECSQlJXHLLbd49b2IiIh42/YT2zmUc4hAWyC9Y3ubHU4ZV7ZyLVv048Ef2Z+53+RoRERqJlMLvLVr19KlS5eSGS4nT55Mly5dePjhhwHXRCrFxV6xjIwMFixYcNreu/T0dG6++WbatGnDkCFDOHjwIMuXL6dHjx7efTMiIiJetnT/UgB6N+hNSCUtk+OO+Ih4+jbsi4HB21veNjscEZEaydRr8AYMGMCZ5nhJTEwssy0yMvKMM8zMmDGDGTNmeCI8ERGRKqV4eOaFcReaHMnpjWs7jh8P/sinOz/ltk63ERUUZXZIIiI1SrW8Bk9ERKSmSc1JZcvxLViw0K9RP7PDOa1esb1oHd2avKI8Ptj+gdnhiIjUOCrwREREqoGlya7hmZ3rda7SC4lbLBbGtRsHwHtb3qPAUXCWPURExJNU4ImIiFQD3+79FoBBjQeZHMnZDW0ylPoh9TmWf4yvdn9ldjgiIjWKCjwREZEq7nDOYTakbQBgSJMhJkdzdv5Wf65tey0Aib8n4jScJkckIlJzqMATwDWk5tNPPy15vnXrVnr16kVQUBCdO3c2LS4REYFF+xZhYNClXhdiQmPMDqdcLm9xOWH+YezJ2MOKAyvMDkdEpMYwdRZNqVzXX3896enppQq5YikpKdSqVavk+dSpUwkNDWXbtm2EhYWRmJjIXXfdRXp6+imPPWDAAK655houvvhimjZtWrI9LCyMxo0bM2DAAO666y5atGjh6bclIuLziodnDm0y1ORIyi8sIIwrW17JnN/nkPh7Iv3j+psdklQxm49tJvH3RI7nHwcDDE4/s7qcG6fh5Fj2MT5d8ilWi/p3/mx8+/H0bdjX7DA8SgWeABATU/ovwrt27WLEiBHEx8efdd/jx4+zatUq3n33Xex2OwDfffcd7dq1Izc3l02bNjFr1iw6derEF198wUUXXXTK49jtdvz9/c/9zYiI+JCU7BQ2HtmIBQuD4webHY5bxrYZy9ub32bt4bX8dvQ32tdpb3ZIUgVkOjN5cNWDLNy70OxQapw9h/eYHUKVc2nzS80OweNU4AngGqL5ySefMHr0aCwWCwDr1q3j0UcfpX///vzwww8l7cDVw/fII48A8NVXX9GpUycaNmzI3r17Aahdu3ZJ0disWTNGjhzJRRddxIQJE9i1axc2m41HHnmETz/9lIkTJ/L444+zd+9eHA4H3377LY8//ji//fYbNpuN3r17M2vWLJo3b14S76pVq7jtttvYunUr7du358EHH+Syyy5jw4YNGlIqIj6luPeuW/1u1AupZ3I07okJjWF4s+F8vutzEn9P5Ln+z5kdkpiowFHAW7+9xeuZr2PPdP1BeESzEZzf8HxsFhsWLCZH6LuKHEUkbUiic5fO+Nn06/+f+eIfnvQJSxkpKSkMGjSIiy++mHvuuYeQkBDmzJnDww8/zLZt2wDX0Mtin3/+OaNGjTrjMa1WK5MmTeKyyy5j3bp19OjRA4CdO3fywQcfsGDBAmw2GwA5OTlMnjyZDh06kJOTw8MPP8xll11GUlISVquVrKwsRo4cyfDhw3nvvffYt28fd911l3eSISJisu/3uxY3r07DM//surbX8fmuz1m8bzHJmck0jmhsdkhSyQzDYGnyUp5d+ywHsw8C0KlOJ6b0nEK7Ou1Mjq5msNvtOH53MDR+qEZL1QAq8DzAMAzyivK8eg6n00leUR5+dj+s1j/GTgf7BZf0qnlKTEwMfn5+hIWFlfTCRUZGYrFYygzlLCgo4Ntvv+Xhhx8+63Fbt24NwN69e0sKvMLCQt5++23q1q1b0u7yyy8vtd+bb75JvXr12Lx5M+3bt+fdd9/FYrHw+uuvExQURNu2bTl48CA33XTTOb1vEZGqxuF0sOX4FgB6xPQwOZqKaRXdin6N+rH8wHLe/O1NpvWZZnZIUolSc1KZvmY6y/YvA6BecD36W/pz/+D7CQgIMDU2EV+lAs8D8ory6PleT1POvWbsGkL8Q0w5N8DSpUupXbs2HTp0OGtbw3BdPP3ngjQ+Pr5UcQeu6/8eeughfvrpJ44ePYrT6ZpeOzk5mfbt27Nt2zY6duxIUFBQyT7FBaOIiC9JzkomryiPYL9g4iPOfk10VXVTh5tYfmA5n+/6nFs73VptZgKVinMaTj7Y9gEz188kx56Dn9WPG9rdwPVtruf7Rd97/I/TIvIHTaMj56Q8wzOLbdni+iv0n2fZDA0NLdNu5MiRHDt2jNdff501a9awZs0awNXbB65C8a9fDMXFo4iIL9mftR+AxuGNsVltJkdTcZ3rdaZHTA+KnEXM+W2O2eGIl+1O383131zP9DXTybHn0LFuRz645AMmdp1IsF+w2eGJ+Dz14HlAsF8wa8au8eo5nE4nWVlZhIeHlxmiWRkCAgJwOBylthmGwRdffMHcuXPPur/T6WT27Nk0bdqULl26nLbdsWPH2LJlC//973+54IILAFi5cmWpNq1bt+bdd9+loKCAwMBAANauXevuWxIRqfKKr1dqGNbQ5EjO3U0db+Ln1J9ZsGMBN3W8iTrBdcwOSTzMMAze2/oeM9bNoMBRQIhfCJO6TuLqVldX6z9QiFQ3KvA8wGKxeH2YpNPppMiviBD/kFIFnrsyMjJISkoqtS06Ovqs+zVp0oTs7GyWLFlCp06dCAkJYfPmzeTk5NCvX78y7Y8dO0Zqaiq5ubn89ttvzJw5k59//pmvvvqqZDKVU6lVqxa1a9fmtddeIzY2luTkZO6///5SbcaOHcu///1vbr75Zu6//36Sk5N57jnXzGwa8iEivuRA1gEAGoZX/wKvZ0xPOtbpyK9Hf2Xu5rlM7jbZ7JDEg47kHuGhHx/ix0M/AtC3QV+m9p5KbFisyZGJ1Dwq8GqYZcuWlelBGzdu3Fn369OnD7fccgtXX301x44dY+rUqTgcDkaMGIGfX9kfo0GDBgEQEhJCfHw8AwcO5LXXXiMhIeGM57Farbz//vtMnDiR9u3b06pVK2bPns2AAQNK2kRERPDFF19w66230rlzZzp06MDDDz/M2LFjS12XJyJS3RUP0YwLjzM5knNnsVi4qeNN3Ln0TuZvnc+E9hOIDIw0OyzxgCX7lvDI6kdIL0gn0BbI5G6TGdN6jP7oKmISFXg1SGJiIomJiad97c/+2ssH8Morr/DKK6+UPO/YsSMPPvhgqTZNmjQp9/VwjzzySMlaen82aNAgNm/eXGrbX4/Zp08fNm7cWPL83Xffxd/fn8aNNf22iPgOXyrwAPo36k/LWi3ZfmI77215j1s732p2SHIOChwFPPPzM3yw/QMA2kS34akLnqJZVDOTIxOp2TTJilRIYWEhl19+OcOGDTPl/HPnzmXlypXs2bOHTz/9lPvuu4+rrrqK4GBdvC0ivsEwjJIhmo3DfeOPVxaLhZs6uJa0eXvL22QWZpockVTUgawDXLvwWj7Y/gEWLIxvP553h7+r4k6kClAPnlRIQEAAU6dONe38qampPPzww6SmphIbG8uVV17J9OnTTYtHRMTTjuQdId+Rj81i86nrmAbHDyYhKoGd6Tv53+//484ud5odkrjp++Tv+feP/yarMIuowCievuBp+jTsY3ZYInKSevCkWrr33nvZu3cv+fn57NmzhxkzZhASYt56gCIinpacmQxATGgM/lZ/k6PxHJvVxu2dbwfgnc3vcDz/uMkRSXkVOYt4Yd0LTPx+IlmFWXSq24kPR36o4k6kilGBJyIiUgX9eQ08X3NR44toE92G3KJc3tr0ltnhSDkcyT3CjYtuLFnH8B9t/sGcoXO0aL1IFaQCT0REpArytQlW/sxisZQMzXx/2/uk5aaZHJGcydrUtVz15VWsO7yOUP9Qnu//PPf1uA9/m+/0LIv4EhV4FVTemSKl6tBnJiLVSUkPXoTv9eABnN/wfLrU60KBo4DXfn3N7HDkFJyGkzc2vcGERRM4mneUhKgE3h/xPkOaDDE7NBE5AxV4bvL3d/21Kjc31+RIxF3Fn1nxZygiUpUVF3iNwhuZHIl3/LkXb8GOBRzMPmhyRPJnJ/JPcPuS25m1fhZOw8mlzS/l3eHv0iSyidmhichZaBZNN9lsNqKiokhLcw0nCQkJqZSFPJ1OJ4WFheTn52O1qi53h2EY5ObmkpaWRlRUFDabzeyQRETOKjnLNcmKL16DV+y8mPPoFduLn1J+4tWNr/JY38fMDkmApLQk7vnhHg7nHibQFsi/e/6b0QmjtXC5SDWhAq8CYmJcFxQXF3mVwTAM8vLyCA4O1n+wFRQVFVXy2YmIVGUZBRlkFWYBvtuDV+zOLnfyU8pPfL7rc25od4PWUTORYRj87/f/MWv9LIqMIppENOG5/s/RKrqV2aGJiBtU4FWAxWIhNjaWevXqYbfbK+Wcdrud5cuX069fPw0xrAB/f3/13IlItVG8RELd4LoE+wWbHI13dazbkYFxA/l+//c8v+55/nPRf8wOqUbKKMjgwR8fZNn+ZQAMazqMqb2nEuofampcIuI+FXjnwGazVVrRYLPZKCoqIigoSAWeiIiP8+UZNE9lcrfJrDiwguUHlrP60Gp6N+htdkg1yvYT25m0dBIHsg/gb/Xn/h73c2XLKzViSKSa0sVcIiIiVUzx9Xc1pcBrEtmEq1tfDcBza5/D4XSYHFHNsXjfYv6x8B8cyD5Aw7CGvDP8Ha5qdZWKO5FqTAWeiIhIFbM3cy9AjZqx8JaOtxAeEM72E9v5fNfnZofj85yGk9nrZzN52WTyivLoGduT90e8T9vabc0OTUTOkQo8ERGRKmZPxh4AmkY2NTmSyhMVFMU/O/4TgNkbZpNr13JE3pJVmMWdS+/k9U2vAzCu7TheHfQqUUFR5gYmIh6hAk9ERKQKMQyjRhZ4AGNajyEuPI6jeUd5deOrZofjk3Zn7GbsV2NZfmA5gbZAnjj/Ce457x78rJqWQcRXqMATERGpQg7nHiavKA8/i1+NuQavWIAtgPvOuw+Atze/zY4TO0yOyLcs27+MsV+NZW/mXmJCY/jfsP8xsvlIs8MSEQ9TgSciIlKFFPfeNQpvhL+15s2a3D+uPxfGXUiRUcTjPz2O03CaHVK15zScvLrxVe5ceic59hy61e/G+yPep13tdmaHJiJeoAJPRESkCqmpwzP/7P4e9xPsF8z6tPV8tvMzs8Op1nLsOUxeNpn/JLnWFxzTegyvD3md2sG1TY5MRLxFBZ6IiEgVogIPYsNiubXTrQC8sO4F0vPTzQ2omkrOTObvX/2dJclL8Lf682ifR3mg5wM1smdYpCZRgSciIlKFFC+RUJMLPIB/tP0HCVEJpBekM2P9DLPDqXZWHlzJNV9dw66MXdQLrkfixYlc1uIys8MSkUqgAk9ERKQKUQ+ei7/Vn4d6PQTAxzs+ZuXBlSZHVD0YhsGbm97ktu9uI6swi051O/H+Je/TsW5Hs0MTkUqiAk9ERKSKyLHncDj3MABNIpqYG0wV0LV+V/7R5h8ATP1xKhkFGSZHVLXtzdjL+G/HM3P9TAwMLm9xOW8NfYu6IXXNDk1EKpEKPBERkSqieHhm7aDaRAZGmhtMFTGp6ySaRDQhLS+NJ9Y8YXY4VZLdYee/G//L5Z9fztrDawn2C+ahXg/xSJ9HCLAFmB2eiFQyFXgiIiJVhIZnlhXkF8T086djtVhZuGchi/YuMjukKiUpLYmrvryKl5JeotBZSN8Gffn40o+5qtVVZocmIibxMzsAERERcdmdvhtQgfdXHet2ZEL7Cby+6XUe/elROtTpQGxYrNlhmep4/nFmr5/Nxzs+xsAgOiiae8+7l+FNh2OxWMwOT0RMpB48ERGRKmLHiR0AJEQlmBxJ1XNrp1tpV7sdGQUZ3PPDPdgddrNDMoXdaefdLe9yyceXsGDHAgwMRjUfxWejPmNEsxEq7kREBZ6IiEhVsSPdVeC1qNXC5EiqHn+bP8/1f47wgHB+PforL6x7weyQKt1PKT9x1RdX8dTPT5Flz6JNdBvmDpvL4+c/TlRQlNnhiUgVoSGaIiIiVUCOPYeD2QcBaFmrpcnRVE2NwhvxxPlPcOfSO3lnyzt0qteJi5tcbHZYXrc/az8z1s1g8b7FAEQFRjGx60T+lvA3bFabydGJSFVjag/e8uXLGTlyJA0aNMBisfDpp5+esf2yZcuwWCxlblu3bi3VbsGCBbRt25bAwEDatm3LJ5984sV3ISIicu52pu8EoF5wPc2geQYD4gYwvv14AB5a+RC/H/3d5Ii8J6swixfWvsCoT0exeN9irBYrY1uP5cvLvuTKllequBORUzK1wMvJyaFTp0689NJLbu23bds2UlJSSm4tWvwxlGX16tVcffXVXHvttWzcuJFrr72Wq666ijVr1ng6fBEREY/ZmeEq8DQ88+zu7HIn5zc8n3xHPncsvYPUnFSzQ/Ioh9PBh9s/5JJPLmHO73OwO+30jO3JhyM/ZErPKfoDgIickalDNIcNG8awYcPc3q9evXpERUWd8rWZM2cyePBgpkyZAsCUKVP44YcfmDlzJvPmzTuXcEVERLymuAdPBd7Z+Vn9eLbfs1z3zXXsOLGD25bcxpyhc3yi8Fl/eD1P/fwUW45vAVwL3v/fef/HBQ0v0AQqIlIu1fIavC5dupCfn0/btm158MEHGThwYMlrq1ev5u677y7VfujQocycOfO0xysoKKCgoKDkeWZmJgB2ux27vWrM0lUcR1WJp6ZQ3s2hvJtDeTdHcb63H98OQLPwZvoMyiHQEsjMfjMZ9+04dpzYwS2Lb+HlC18mzD+sXPtXtZ/3w7mHmbVhFt/s+waAMP8wbulwC1e2vBJ/qz9FRUUmR+gZVS3vNYXybg538+6pz8diGIbhkSOdI4vFwieffMLo0aNP22bbtm0sX76cbt26UVBQwNtvv82rr77KsmXL6NevHwABAQEkJiYyduzYkv3ee+89brjhhlJF3J898sgjTJs2rcz29957j5CQkHN7YyIiImdhGAZPZD5BnpHHbWG30cCvgdkhVRuHHYd5M/tNco1c4m3xjAsbR4AlwOywyq3AKODHgh9Zkb8CO3YsWOgW0I3BQYMJtYaaHZ6IVKLc3FzGjh1LRkYGERERFT5OterBa9WqFa1atSp53rt3b/bv389zzz1XUuABZYYwGIZxxmENU6ZMYfLkySXPMzMziYuLY8iQIeeUXE+y2+0sXryYwYMH4+/vb3Y4NYbybg7l3RzKuznsdjsfffsReUYeNouNfwz/B0F+QWaHVa30Ot6Lfy75J/vs+/jE7xNmDZhFVGDUGfcx++e90FHIRzs+4o3f3yC9IB2ATnU68X/d/4+20W0rPZ7KYnbeayrl3Rzu5r14FOG5qlYF3qn06tWLd955p+R5TEwMqamlL7ZOS0ujfv36pz1GYGAggYGBZbb7+/tXuX8EVTGmmkB5N4fybg7lvfIdchwCoFlUM8KDw02OpvrpWL8j/x38X2797lY2HdvEjd/dyH8H/5eY0Jiz7lvZP+92p50vd33JKxtfISUnBYD4iHju6HIHQ+OH1pjr7PT/jDmUd3OUN++e+myq/ULnGzZsIDY2tuR57969Wbx4cak2ixYtok+fPpUdmoiISLkcKnIVeL7cc+NtHet25H8X/496IfXYnbGba768hl9SfzE7rBL5Rfm8t+U9Rnw8godXPUxKTgr1gusxtfdUPhn1CRc3ubjGFHci4l2m9uBlZ2ezc+fOkud79uwhKSmJ6OhoGjduzJQpUzh48CBz584FXDNkNmnShHbt2lFYWMg777zDggULWLBgQckxJk2aRL9+/Xj66acZNWoUn332Gd999x0rV66s9PcnIiJSHsU9eG1qtzE5kuotoVYC7wx7hzuW3sH2E9u5adFNTOw6kXFtx5m2ZtzRvKN8tP0j5m2dx/H84wDUDqrN9e2u5+rWVxPsF2xKXCLiu0wt8NauXVtqBszi6+DGjRtHYmIiKSkpJCcnl7xeWFjIPffcw8GDBwkODqZdu3Z89dVXDB8+vKRNnz59eP/993nwwQd56KGHaN68OfPnz6dnz56V98ZERETcUFzgta2tHrxzFRsWyzvD3+Gx1Y/xxe4vmLFuBt8nf8+0PtNoFtWsUmIwDIN1h9cxf9t8vtv3HUWGawbMhmENGd9+PKMSRhFoK3tpiIiIJ5ha4A0YMIAzTeKZmJhY6vm9997Lvffee9bjXnHFFVxxxRXnGp6IiIjXHck7QpaRhQULrWq1OvsOclbBfsFMP386Xet35bm1z5F0JIkrvriCsa3HMqHDBGoF1fL4OQ3DYNPRTXy791sW7VtUavH1znU7c03raxjSZAj+Vl3/JCLeVe0nWREREanOth3fBrgWtA7x19I8nmKxWLii5RWc3/B8pq2exsqDK/nf5v/x4fYPubrV1YxuNvqcz2EYBr8f+91V1O1dxKGcQyWvhfiFMKzpMK5pfQ2to1uf87lERMpLBZ6IiIiJtp7YCqAiwEtiQmN4+aKX+fHQj8xeP5stx7cw5/c5zPl9Dk39mnJ8y3H6N+5Pk8gmWC1nnnvOaThJzkzmt2O/sfrQalYdWsXRvKMlrwf7BTMgbgBDmwylb4O+Wu5CREyhAk9ERMREuzJ2AdAyqqXJkfgui8XC+Q3Pp0+DPvyw/wfmb5/PqoOr2FO0hxc2vMALG14gxC+EhFoJNAxtSGRgJCH+IRQ5iyhwFHA07yiHcw6zO2M3uUW5pY4d7BdM/0b9GdpkKOc3PF9FnYiYTgWeiIiIiQ5mHwSgUVgjkyPxfVaLlYGNBzKw8UD2ndjHy4tf5njkcdanrSe3KJdfj/zKr0d+PeMxgmxBtKzVkm4x3ejboC9d6nUhwBZQSe9AROTsVOCJiIiYqHix6wZhDUyOpGZpENaAPoF9GH7hcCw2C8mZyWw/sZ0jeUdIL0gn156Lv9Uff5s/dYLrUC+kHvHh8TSJbIKfVb8+iUjVpf+hRERETJJXlMex/GMANAhVgWcWP6sfzaKaVdoyCiIi3nTmq4lFRETEa1KyXb13gQQSERBhcjQiIuILVOCJiIiY5ED2AQBqWWthsVhMjkZERHyBCjwRERGTFE+wUsvq+YW3RUSkZlKBJyIiYpJD2a6FsWvZVOCJiIhnqMATERExSXEPXpQ1ytxARETEZ6jAExERMYmGaIqIiKepwBMRETGJCjwREfE0FXgiIiImyC7MJqMgA9AQTRER8RwVeCIiIiYo7r2LDIgkyBJkcjQiIuIrVOCJiIiYIDkrGYC48DiTIxEREV/iV55Gs2fPdvvAN9xwA+Hh4W7vJyIiUhMkZ/6pwEs3NxYREfEd5Srw7rrrLho1aoTNZivXQffv388ll1yiAk9EROQ0SnrwwlTgiYiI55SrwANYu3Yt9erVK1dbFXYiIiJnVtyD1zi8scmRiIiILynXNXhTp04lLCys3Ad94IEHiI6OrnBQIiIivk7X4ImIiDeUqwdv6tSpbh10ypQpFQpGRESkJsgryiMtNw1w9eDtZ7/JEYmIiK84p1k0n3rqKdLT0z0UioiISM2wP8tV0EUERBAZGGlyNCIi4kvOqcB74oknOH78uKdiERERqRF0/Z2IiHjLORV4hmF4Kg4REZEao/j6u8YRKvBERMSztNC5iIhIJSvpwVOBJyIiHlbuZRJOZfPmzTRo0MBTsYiIiNQI+zL3ARqiKSIinndOBV5cnKZ2FhERcdeu9F0ANItqZnIkIiLia8o1RDM6OpqjR4+W+6CNGzdm3759FQ5KRETEVx3LO8aJghNYsNAsUgWeiIh4Vrl68NLT0/n666+JjCzfVM7Hjh3D4XCcU2AiIiK+aGf6TgAahTci2C8Yu91uckQiIuJLyj1Ec9y4cd6MQ0REpEYoLvCaRzU3ORIREfFF5SrwnE6nt+MQERGpEYqvv0uISjA5EhER8UVaJkFERKQSFRd46sETERFvUIEnIiJSSQzDYEf6DgBaRLUwORoREfFFKvBEREQqyZG8I2QVZmG1WGkS2cTscERExAepwBMREakkxROsNA5vTKAt0ORoRETEF6nAExERqSRbj28FoGWtliZHIiIivsrtAs9ms5GWllZm+7Fjx7DZbB4JSkRExBdtPrYZgLa125ociYiI+Cq3CzzDME65vaCggICAgHMOSERExFcVF3htarcxORIREfFV5V7ofPbs2QBYLBbeeOMNwsLCSl5zOBwsX76c1q1bez5CERERH5BZmMn+rP0AtI1WD56IiHhHuQu8GTNmAK4evFdffbXUcMyAgACaNGnCq6++6vkIRUREfMDWY67r7xqGNSQqKMrcYERExGeVu8Dbs2cPAAMHDuTjjz+mVq1aXgtKRETE1/x+7HdA19+JiIh3lbvAK/b99997Iw4RERGflpSWBECHOh3MDURERHya2wWew+EgMTGRJUuWkJaWhtPpLPX60qVLPRaciIiILzAMg6QjSQB0qdfF3GBERMSnuV3gTZo0icTEREaMGEH79u2xWCzeiEtERMRnJGclczz/OP5Wfw3RFBERr3K7wHv//ff54IMPGD58+DmffPny5Tz77LOsW7eOlJQUPvnkE0aPHn3a9h9//DGvvPIKSUlJFBQU0K5dOx555BGGDh1a0iYxMZEbbrihzL55eXkEBQWdc8wiIiLu2pC2AYB2tdsRYNOSQiIi4j1ur4MXEBBAQkKCR06ek5NDp06deOmll8rVfvny5QwePJiFCxeybt06Bg4cyMiRI9mwYUOpdhEREaSkpJS6qbgTERGzFF9/p+GZIiLibW734P3rX/9i1qxZvPTSS+c8PHPYsGEMGzas3O1nzpxZ6vkTTzzBZ599xhdffEGXLn98aVosFmJiYs4pNhEREU/ZdnwbAO3rtDc5EhER8XVuF3grV67k+++/5+uvv6Zdu3b4+/uXev3jjz/2WHBn43Q6ycrKIjo6utT27Oxs4uPjcTgcdO7cmccee6xUAfhXBQUFFBQUlDzPzMwEwG63Y7fbvRO8m4rjqCrx1BTKuzmUd3Mo797hcDrYmb4TgKbhTcvkV3k3h/JuDuXdHMq7OdzNu6c+H4thGIY7O5zq+rY/mzNnTsUCsVjOeg3eXz377LM89dRTbNmyhXr16gHw008/sXPnTjp06EBmZiazZs1i4cKFbNy4kRYtWpzyOI888gjTpk0rs/29994jJCSkQu9HREQE4KjjKDOzZuKHHw9HPozV4vbVESIiUgPk5uYyduxYMjIyiIiIqPBx3C7wvMXdAm/evHnceOONfPbZZwwaNOi07ZxOJ127dqVfv37Mnj37lG1O1YMXFxfH0aNHzym5nmS321m8eDGDBw8u02sq3qO8m0N5N4fy7h1L9i/h/1b8H22i2/Duxe+WeV15N4fybg7l3RzKuznczXtmZiZ16tQ55wLP7SGaAEVFRSxbtoxdu3YxduxYwsPDOXToEBEREYSFhVU4mPKaP38+EyZM4MMPPzxjcQdgtVo577zz2LFjx2nbBAYGEhgYWGa7v79/lftHUBVjqgmUd3Mo7+ZQ3j1rT+YeAFrWannGvCrv5lDezaG8m0N5N0d58+6pz8btAm/fvn1cfPHFJCcnU1BQwODBgwkPD+eZZ54hPz+fV1991SOBnc68efMYP3488+bNY8SIEWdtbxgGSUlJdOjQwatxiYiInMq2E64JVlrWamlyJCIiUhO4fSHApEmT6N69OydOnCA4OLhk+2WXXcaSJUvcOlZ2djZJSUkkJSUBsGfPHpKSkkhOTgZgypQpXHfddSXt582bx3XXXcfzzz9Pr169SE1NJTU1lYyMjJI206ZN49tvv2X37t0kJSUxYcIEkpKSuOWWW9x9qyIiIuds+4ntALSKbmVyJCIiUhNUaBbNH3/8kYCA0gu1xsfHc/DgQbeOtXbtWgYOHFjyfPLkyQCMGzeOxMREUlJSSoo9gP/+978UFRVx++23c/vtt5dsL24PkJ6ezs0330xqaiqRkZF06dKF5cuX06NHD3ffqoiIyDnJteeyP2s/AC1qnXqiLxEREU9yu8BzOp04HI4y2w8cOEB4eLhbxxowYABnmuOluGgrtmzZsrMec8aMGcyYMcOtOERERLxhR7rr+u+6wXWJDoo+S2sREZFz5/YQzcGDB5dacNxisZCdnc3UqVMZPny4J2MTERGp1oqHZ+r6OxERqSxu9+DNmDGDgQMH0rZtW/Lz8xk7diw7duygTp06zJs3zxsxioiIVEvbjmuCFRERqVxuF3gNGjQgKSmJefPmsX79epxOJxMmTODvf/97qUlXREREarrfj/4OQJvabUyOREREaooKrYMXHBzM+PHjGT9+vKfjERER8QkFjgK2ntgKQIc6WqpHREQqR7kKvM8//5xhw4bh7+/P559/fsa2l156qUcCExERqc62Ht9KkbOI6KBoGoY1NDscERGpIcpV4I0ePZrU1FTq1avH6NGjT9vOYrGccoZNERGRmmbTkU0AtK/THovFYnI0IiJSU5SrwHM6nad8LCIiIqe26airwNPwTBERqUxuL5MgIiIiZ2YYBklpSQB0rNvR3GBERKRGcbvAmzhxIrNnzy6z/aWXXuKuu+7yREwiIiLV2oHsAxzKOYSfxY/OdTubHY6IiNQgbhd4CxYsoG/fvmW29+nTh48++sgjQYmIiFRnP6f8DLh670L8Q0yORkREahK3C7xjx44RGRlZZntERARHjx71SFAiIiLV2c+prgKvR2wPkyMREZGaxu0CLyEhgW+++abM9q+//ppmzZp5JCgREZHqymk4/yjwYlTgiYhI5XJ7ofPJkydzxx13cOTIES688EIAlixZwvPPP8/MmTM9HZ+IiEi1svnYZo7mHSXEL4ROdTuZHY6IiNQwbhd448ePp6CggOnTp/PYY48B0KRJE1555RWuu+46jwcoIiJSnSxNXgpA34Z9CbAFmByNiIjUNG4XeAC33nort956K0eOHCE4OJiwsDBPxyUiIlItfb//ewAubHyhyZGIiEhNVKECr1jdunU9FYeIiEi1tydjDzvTd2Kz2Lig4QVmhyMiIjVQuQq8rl27smTJEmrVqkWXLl2wWCynbbt+/XqPBSciIlKdfLbzM8A1PDMysOyM0yIiIt5WrgJv1KhRBAYGAjB69GhvxiMiIlItOZwOvtj1BQCjE0abG4yIiNRY5SrwatWqhdXqWlHhhhtuoFGjRiXPRUREBFYcXEFaXhpRgVEMaDTA7HBERKSGKleVNnnyZDIzMwFo2rSpFjQXERH5izm/zQFcvXf+Nn+ToxERkZqqXD14DRo0YMGCBQwfPhzDMDhw4AD5+fmnbNu4cWOPBigiIlLVJaUlsT5tPX5WP/7R5h9mhyMiIjVYuQq8Bx98kDvvvJM77rgDi8XCeeedV6aNYRhYLBYcDofHgxQREamqDMNg5vqZAIxsNpL6ofXNDUhERGq0chV4N998M2PGjGHfvn107NiR7777jtq1a3s7NhERkSpv8b7FrDu8jiBbELd2utXscEREpIYrV4E3e/Zsbr75Ztq3b8+cOXPo3bs3wcHB3o5NRESkSjuWd4zpa6YDcH3764kNizU5IhERqencnmRl/PjxZGVleTUoERGRqs7utPPAygc4nn+chKgEbuxwo9khiYiIaJIVERERdzmcDqatmsaqQ6sIsgXxdL+nCbQFmh2WiIiIJlkRERFxR1ZhFg/9+BBLkpdgtVh5tv+ztKzV0uywREREAE2yIiIiUi6GYbA0eSlP/fIUqTmp+Fv9efKCJxkQN8Ds0EREREqUq8ADCA8PL5lkpW/fvgQGaiiKiIj4vpTsFBbtW8Rnuz5jx4kdADQOb8z086fTuV5nc4MTERH5i3IXeMXGjRtHeno6b7/9Nrt27eL//u//iI6OZv369dSvX5+GDRt6I04REamCDMNwrz3lb+/NYxcfP7col1x7bsl9ekE6KTkppOSksC9zH78e+ZXDuYdL9gn1D2Vs67Hc2OFGQvxD3DqfiIhIZXC7wPv1118ZNGgQkZGR7N27l5tuuono6Gg++eQT9u3bx9y5c70RZ4329Z6vefrnpykoKGDGxzPO2t7dX3Lc5c4vXW7/wlWBX9C8ffwiexFPffgUFiwePX5Ve6/u8urPgWHgdDp59P1HvXN8d3PjdvPq/XPw4HsPur2PL7NarHSu25mLm17M8KbDiQyMNDskERGR03K7wLv77ru5/vrreeaZZwgPDy/ZPmzYMMaOHevR4MQlvyifY/nHAMjOzzY5mhrKbnYANZTT7ACkJvC3+hPqH0qIXwgRgRHEhMQQExpDw7CGtKvTjna126m3TkREqg23C7y1a9fy2muvldnesGFDUlNTPRKUlHZh4wtpFdWKFStWcMEFF+Dnd/aPrTy9Tee6j8XiZnt3Y3L/Lbj/Hs7SvqioiB9++IH+/fvj5+fn/fdckX3cbu7dz7lC5/hLe3uRne+//56BAwfi7+fvkZjcVdX+PVTGe3YUOfjuu+8YNGhQ+f6fqYL/HtyNKcQvBH9b2Z8xERGR6srtAi8oKKhk0fM/27ZtG3Xr1vVIUFJaZGAkIVEh7LDtoEVUC/z99ctIZbHb7dSx1SE+Il55r0R2u50oaxSxobHKeyWy2+2EWcOIDopW3kVERKopq7s7jBo1ikcffRS73TVmzWKxkJyczP3338/ll1/u8QBFRERERESkfNwu8J577jmOHDlCvXr1yMvLo3///iQkJBAeHs706dO9EaOIiIiIiIiUg9tDNCMiIli5ciVLly5l/fr1OJ1OunbtyqBBg7wRn4iIiIiIiJST2wVesQsvvJALL7zQk7GIiIiIiIjIOXB7iCbADz/8wMiRI0lISKBFixZceumlrFixwtOxiYiIiIiIiBvcLvDeeecdBg0aREhICBMnTuSOO+4gODiYiy66iPfee88bMYqIiIiIiEg5uD1Ec/r06TzzzDPcfffdJdsmTZrECy+8wGOPPabFzkVEREREREzidg/e7t27GTlyZJntl156KXv27PFIUCIiIiIiIuI+twu8uLg4lixZUmb7kiVLiIuL80hQIiIiIiIi4j63C7x//etfTJw4kVtvvZW3336bd955h1tuuYVJkyZxzz33uHWs5cuXM3LkSBo0aIDFYuHTTz896z4//PAD3bp1IygoiGbNmvHqq6+WabNgwQLatm1LYGAgbdu25ZNPPnErLhERERERkerI7QLv1ltv5f3332fTpk3cddddTJo0id9++4358+fzz3/+061j5eTk0KlTJ1566aVytd+zZw/Dhw/nggsuYMOGDTzwwANMnDiRBQsWlLRZvXo1V199Nddeey0bN27k2muv5aqrrmLNmjVuxSYiIiIiIlLdVGgdvMsuu4zLLrvsnE8+bNgwhg0bVu72r776Ko0bN2bmzJkAtGnThrVr1/Lcc89x+eWXAzBz5kwGDx7MlClTAJgyZQo//PADM2fOZN68eeccs4iIiIiISFXldg/eL7/8csresDVr1rB27VqPBHU6q1evZsiQIaW2DR06lLVr12K328/YZtWqVV6NTURERERExGxu9+Ddfvvt3HvvvfTs2bPU9oMHD/L00097dShkamoq9evXL7Wtfv36FBUVcfToUWJjY0/bJjU19bTHLSgooKCgoOR5ZmYmAHa7vaRwNFtxHFUlnppCeTeH8m4O5d0cyrs5lHdzKO/mUN7N4W7ePfX5uF3gbd68ma5du5bZ3qVLFzZv3uyRoM7EYrGUem4YRpntp2rz121/9uSTTzJt2rQy2xctWkRISMi5hOtxixcvNjuEGkl5N4fybg7l3RzKuzmUd3Mo7+ZQ3s1R3rzn5uZ65HxuF3iBgYEcPnyYZs2aldqekpKCn1+FLukrt5iYmDI9cWlpafj5+VG7du0ztvlrr96fTZkyhcmTJ5c8z8zMJC4ujiFDhhAREeHBd1BxdrudxYsXM3jwYPz9/c0Op8ZQ3s2hvJtDeTeH8m4O5d0cyrs5lHdzuJv34lGE58rtiqx4ApPPPvuMyMhIANLT03nggQcYPHiwR4I6nd69e/PFF1+U2rZo0SK6d+9ekrTevXuzePFi7r777lJt+vTpc9rjBgYGEhgYWGa7v79/lftHUBVjqgmUd3Mo7+ZQ3s2hvJtDeTeH8m4O5d0c5c27pz4btwu8559/nn79+hEfH0+XLl0ASEpKon79+rz99ttuHSs7O5udO3eWPN+zZw9JSUlER0fTuHFjpkyZwsGDB5k7dy4At9xyCy+99BKTJ0/mpptuYvXq1bz55pulZsecNGkS/fr14+mnn2bUqFF89tlnfPfdd6xcudLdtyoiIiIiIlKtuF3gNWzYkF9//ZV3332XjRs3EhwczA033MCYMWPcrjrXrl3LwIEDS54XD5McN24ciYmJpKSkkJycXPJ606ZNWbhwIXfffTf/+c9/aNCgAbNnzy5ZIgGgT58+vP/++zz44IM89NBDNG/enPnz55eZFEZERERERMTXVOiiudDQUG6++eZzPvmAAQNKJkk5lcTExDLb+vfvz/r168943CuuuIIrrrjiXMMTERERERGpVtxeB09ERERERESqJhV4IiIiIiIiPkIFnoiIiIiIiI9QgSciIiIiIuIj3C7wfvnlF9asWVNm+5o1a1i7dq1HghIRERERERH3uV3g3X777ezfv7/M9oMHD3L77bd7JCgRERERERFxn9sF3ubNm+natWuZ7V26dGHz5s0eCUpERERERETc53aBFxgYyOHDh8tsT0lJwc+vQsvqiYiIiIiIiAe4XeANHjyYKVOmkJGRUbItPT2dBx54gMGDB3s0OBERERERESk/t7vcnn/+efr160d8fDxdunQBICkpifr16/P22297PEAREREREREpH7cLvIYNG/Lrr7/y7rvvsnHjRoKDg7nhhhsYM2YM/v7+3ohRREREREREyqFCF82FhoZy8803ezoWEREREREROQflKvA+//xzhg0bhr+/P59//vkZ21566aUeCUxERERERETcU64Cb/To0aSmplKvXj1Gjx592nYWiwWHw+Gp2ERERERERMQN5SrwnE7nKR+LiIiIiIhI1eH2MgkiIiIiIiJSNVVokpWff/6ZZcuWkZaWVqZH74UXXvBIYCIiIiIiIuIetwu8J554ggcffJBWrVpRv359LBZLyWt/fiwiIiIiIiKVy+0Cb9asWbz11ltcf/31XghHTmnvj9jW/Y8uBw9g+/wrsJZ3ZG0FCm63i/SKnMP9Xcx6Lzank07792P7avGp816hP2pU1c+l6rwXq9NJh/17sX67HKw2r5zjFDtVwnkq63OpAIsFq9NJ24O7sS5ZU47/Z6roz1hFPhP/YAgIPXkLg8BwCI+FiIYQEl15n4GIiIgHuF3gWa1W+vbt641Y5HRO7MG6aT6NAY6bHUzNYgWaABwzN46axgY0AzhqciA1jA1oAZBmciBViV8w1G8LsZ0hvg8kDILgKLOjEhEROS23C7y7776b//znP8ycOdML4cgpNeiK46JH2LJlK23atMZ21h4Nw/1zGO7u42Z7t49fGec4e3uHw8n27dtp2bIltnL3nLp3jtLNvfw5VMY5PHB8h8PJzp07SEhogc12irxXufdQGefw/ntwOB3s3r2bZs2ale/nvUJ5cpPX8+SEonwozDl5y4a8dMhKgZwjUJQHB9e5bmvfBKsftBgCvW6FJheod09ERKoctwu8e+65hxEjRtC8eXPatm2Lv79/qdc//vhjjwUnJ9VvizO6BbuOL6RVr+HY/pJz8R6n3c72zIUknK+8Vyan3c7W3IU0G6C8Vyan3c7mgoU0uUh5B6CoANL3Q+pGOLgediyGo9tg20LXrdlAGPYM1G1pdqQiIiIl3O6SuPPOO/n+++9p2bIltWvXJjIystRNRETEJ/gFQp0EaH85DJ0Od/wMt62B7hPAFgC7v4f/XgBJ75kdqYiISAm3e/Dmzp3LggULGDFihDfiERERqbrqtYZLXoA+d8BX/4JdS+HTW+HodrhoqoZsioiI6dzuwYuOjqZ58+beiEVERKR6iG4Gf18A/e93PV85A5Y+bm5MIiIiVKDAe+SRR5g6dSq5ubneiEdERKR6sFph4BS4+GnX8xXPwfq55sYkIiI1nttDNGfPns2uXbuoX78+TZo0KTPJyvr16z0WnIiISJXX6xbIz4BlT7iGbcZ2ct1ERERM4HaBN3r0aC+EISIiUo31+z9ISXLNrvnZ7XDT92DTTKQiIlL53C7wpk6d6o04REREqi+rFUbOguSfIHUT/DjTVfSJiIhUsoqs3CwiIiJ/FVYPhp28Hm/5c5Bx0Nx4RESkRnK7wHM4HDz33HP06NGDmJgYoqOjS91ERERqrA5XQuM+UJQPy540OxoREamB3C7wpk2bxgsvvMBVV11FRkYGkydP5m9/+xtWq5VHHnnECyGKiIhUExYLDJ7mepz0LhzZZm48IiJS47h9Dd67777L66+/zogRI5g2bRpjxoyhefPmdOzYkZ9++omJEyd6I84a7ZvfUnl+0Vaysmy8tOtHLOVcSNeCewvuVtb6vOWNv6R9hc7hmX0MwyAjw8Zr+1aXidvd/FY4rko4ibt7ePt9GIbBiRM2/nfwZ7d+XirtZ8XdM1XG54777+Wv78MwnBw7ZmVe6i9YLKf++5+n/m25E5c3zhFgsxIS6EdYoI3QAD9CA/2oHxFEg6ggGkYFE187lAC/Cl7FENcDWo2AbV/Biufhb69V7DgiIiIV4HaBl5qaSocOHQAICwsjIyMDgEsuuYSHHnrIs9EJAJn5dnak5QAWUvNyzA6nBrJwICfL7CBqIAt7s9PNDqIGsrIj84TZQZguwM9K+wYRnNckmuEdYunYKNK9P071/z9XgffbArjoYYhs5L1gRURE/sTtAq9Ro0akpKTQuHFjEhISWLRoEV27duWXX34hMDDQGzHWeANa1uXtG7qzZs0aevbsic3PduYdDPfP4e4uhps7GG6ewf3ju9m+nCdwOBz88vMvnNfjPKy2s+T9HIPyeo4q5efCzfdwmu2OIgfr1q+jW9dupX7e3X8P7r9p7//suXv8yvu34ygqIikpic6dO2PzO/3Xg7ufs7sqI0eFRU6yC4rILXSQU1BEZn4RaZn5HEzP48CJPLILilifnM765HT+u3w3zeuGcvvABC7t1AA/Wzl69hp0gSYXwN4V8NMrMHS6e29KRESkgtwu8C677DKWLFlCz549mTRpEmPGjOHNN98kOTmZu+++2xsx1nj1IoKoFWzj+FaDXs2iyywuL95jt9vJ3mHQr0Ud5b0S2e127HsNBretp7xXIrvdjvXABoZ3jK3ReTcMg73Hcknaf4IlW9L4bsthdh3JYfIHG5nz415euKoTLeqHn/1AfSe5Crx1ia4lE4KjvB26iIiI+wXeU089VfL4iiuuoFGjRqxatYqEhAQuvfRSjwYnIiJS2SwWC03rhNK0TiiXdWlEdkERb6/exyvLdrLpYAaXvLiSGVd3ZniH2DMfKGEQ1GsLaZth/Vzoq2vURUTE+855HbxevXoxefJkFXciIuKTwgL9uHVAcxZP7s8FLepQUOTk9vfW8/H6A2fe0WKBnre4Hq9LBKfT67GKiIi43YMHsH37dpYtW0ZaWhrOv3xhPfzwwx4JTEREpCqpHxFE4g09eOiz33hvTTL3fvQrdcIC6dey7ul3an85fPtvOL4L9i6HZgMqLV4REamZ3C7wXn/9dW699Vbq1KlDTExMqVnFLBaLCjwREfFZNquFx0e1J7egiE+TDnHX/CS+mXQB9SKCTr1DYBh0uhp+eQPWzlGBJyIiXuf2EM3HH3+c6dOnk5qaSlJSEhs2bCi5rV+/3hsxioiIVBlWq4Wnr+hIm9gIjucUcu+CX888s2i3G1z3W7+EvPRKiVFERGoutwu8EydOcOWVV3ojFhERkWoh0M/Gi2M6E2CzsmzbERZvPnz6xjHtoXYLcBbB7mWVFqOIiNRMbhd4V155JYsWLfJGLCIiItVGQr1wbrygKQCPf7UFu+MMk6i0GOy637m4EiITEZGazO1r8BISEnjooYf46aef6NChQ5m1kiZO1DTQIiJSM9w+MIEP1h4g+Xgun244yJXd407dMOEi+Oll2PW9a6X1P12/LiIi4klu9+C99tprhIWF8cMPP/DSSy8xY8aMktvMmTPdDuDll1+madOmBAUF0a1bN1asWHHattdffz0Wi6XMrV27diVtEhMTT9kmPz/f7dhERETOJDTQj5tO9uK9vGwXDudprsVr3BusfpB5EE7srbwARUSkxnG7B2/Pnj0eO/n8+fO56667ePnll+nbty///e9/GTZsGJs3b6Zx48Zl2s+aNavUQutFRUV06tSpzDWBERERbNu2rdS2oKDTzHAmIiJyDv7RK55XftjFnqM5LN2axuC29cs2CgiFht1g/xrYuxKim1Z+oCIiUiO43YO3bNkyj538hRdeYMKECdx44420adOGmTNnEhcXxyuvvHLK9pGRkcTExJTc1q5dy4kTJ7jhhhtKtbNYLKXaxcTEeCxmERGRPwsN9OOqk0Mz31uz7/QNm5zvut/3YyVEJSIiNZXbBd7FF19M8+bNefzxx9m/f3+FT1xYWMi6desYMmRIqe1Dhgxh1apV5TrGm2++yaBBg4iPjy+1PTs7m/j4eBo1asQll1zChg0bKhyniIjI2Yzp4Rp1smz7EQ6cyD11o+ICb+9K13V4IiIiXuD2EM1Dhw7xzjvvkJiYyCOPPMJFF13EhAkTGD16NAEBAeU+ztGjR3E4HNSvX3ooS/369UlNTT3r/ikpKXz99de89957pba3bt2axMREOnToQGZmJrNmzaJv375s3LiRFi1anPJYBQUFFBQUlDzPzMwEwG63Y7fby/2evKk4jqoST02hvJtDeTeH8l5xjSID6N0smtW7j7Ng7X5uG9CsbKOYrvhZ/bBk7Md+dBdEuf44qbybQ3k3h/JuDuXdHO7m3VOfj8U44+qsZ5aUlMRbb73FvHnzcDqd/P3vf2fChAl06tTprPseOnSIhg0bsmrVKnr37l2yffr06bz99tts3br1jPs/+eSTPP/88xw6dOiMhaXT6aRr167069eP2bNnn7LNI488wrRp08psf++99wgJCTnrexEREfkpzcK8XTZiQwzu7+Q4ZZvztz9G7ZwdbGh8I8m1+1VyhCIiUpXl5uYyduxYMjIyiIiIqPBx3O7B+7POnTtz//33Ex0dzVNPPcVbb73Fyy+/TO/evXn11VdLzW75V3Xq1MFms5XprUtLSyvTq/dXhmHw1ltvce21156119BqtXLeeeexY8eO07aZMmUKkydPLnmemZlJXFwcQ4YMOafkepLdbmfx4sUMHjy4zNIU4j3KuzmUd3Mo7+emb56dj55eRkoutOzej4R6YWXaWIM3wKoZdIrMov3w4YDybhbl3RzKuzmUd3O4m/fiUYTnqkIFnt1u57PPPuOtt95i8eLFdO/enZdeeokxY8Zw/Phx7rvvPq688ko2b9582mMEBATQrVs3Fi9ezGWXXVayffHixYwaNeqM5//hhx/YuXMnEyZMOGushmGQlJREhw4dTtsmMDCQwMDAMtv9/f2r3D+CqhhTTaC8m0N5N4fyXjF1/P3p16IuS7am8e2WI7RpWKtso+b9YNUMrMmrsPr5lVoPT3k3h/JuDuXdHMq7Ocqbd099Nm5PsnLnnXcSGxvLLbfcQsuWLdmwYQOrV6/mxhtvJDQ0lLi4OJ566qmzDrEEmDx5Mm+88QZvvfUWW7Zs4e677yY5OZlbbrkFcPWsXXfddWX2e/PNN+nZsyft27cv89q0adP49ttv2b17N0lJSUyYMIGkpKSSY4qIiHjL0PauWZuXbk07dYO4nq718DL2Q/oZZtwUERGpILd78DZv3syLL77I5ZdfftrhkQ0aNOD7778/67Guvvpqjh07xqOPPkpKSgrt27dn4cKFJbNipqSkkJycXGqfjIwMFixYwKxZs055zPT0dG6++WZSU1OJjIykS5cuLF++nB49erj5TkVERNwzoFVdAH49kEFaVj71wv+yBmtAKDToCgd+hn2roFaTyg9SRER8mtsF3pIlS85+UD8/+vfvX67j3Xbbbdx2222nfC0xMbHMtsjISHJzTzMFNTBjxgxmzJhRrnOLiIh4Ur3wIDo2iuTXAxks23akZH28UuL7nCzwfoTOYys/SBER8WnlKvA+//zzch/w0ksvrXAwIiIi1d3AVvVOFnhppynw+sKPM109eCIiIh5WrgJv9OjRpZ5bLBb+vLqC5U8XiTscp54aWkREpCbo17IOs5bs4Kfdx3E6DaxWS+kGjXsCFji+GzJTILiOKXGKiIhvKtckK06ns+S2aNEiOnfuzNdff016ejoZGRksXLiQrl278s0333g7XhERkSqtQ8Mogv1tHM8pZEdadtkGQZEQc3Jm52T14omIiGe5PYvmXXfdxaxZsxg6dCgRERGEh4czdOhQXnjhBSZOnOiNGEVERKqNAD8r3Zu4lkhYs+fYqRvF93Xda5imiIh4mNsF3q5du4iMjCyzPTIykr1793oiJhERkWqtV7PaAPy0+3QFXh/XvQo8ERHxMLcLvPPOO4+77rqLlJSUkm2pqan861//0lIEIiIiQM+m0QD8tPt4qWvWSxQXeGmbIfd4JUYmIiK+zu0C76233iItLY34+HgSEhJISEigcePGpKSk8Oabb3ojRhERkWqlY6Mogvytp78OL7QO1GkFgGX/T5UcnYiI+DK318FLSEjg119/ZfHixWzduhXDMGjbti2DBg0qNZumiIhITRXgZ6Vr41qs2nWMdftO0LJ+eNlGTfrC0W1Y9q8Geld6jCIi4pvcLvDAtSzCkCFDGDJkiKfjERER8Qmd46JYtesYScnpjOnRuGyD+L6w9i0s+1ZBrAo8ERHxDLeHaAL88MMPjBw5koSEBFq0aMGll17KihUrPB2biIhItdU5LgqApP3pp27Q2FXUWQ5vws+RVzlBiYiIz3O7wHvnnXcYNGgQISEhTJw4kTvuuIPg4GAuuugi3nvvPW/EKCIiUu10bhwFwPa0LLLy7WUbRDaEWk2wGE6ic3ZUbnAiIuKz3B6iOX36dJ555hnuvvvukm2TJk3ihRde4LHHHmPs2LEeDVBERKQ6qhceRMOoYA6m57HpQAZ9EuqUbRTfF07spXb2tsoPUEREfJLbPXi7d+9m5MiRZbZfeuml7NmzxyNBiYiI+ILiYZobTjdM8+RyCbWzt1ZOQCIi4vPcLvDi4uJYsmRJme1LliwhLi7OI0GJiIj4grNeh3eywKuVuxvsug5PRETOndtDNP/1r38xceJEkpKS6NOnDxaLhZUrV5KYmMisWbO8EaOIiEi11OXkdXhJ+9MxDKPsckK1mmKExWDNTsV5aB0kDKz8IEVExKe4XeDdeuutxMTE8Pzzz/PBBx8A0KZNG+bPn8+oUaM8HqCIiEh11b5hJH5WC0eyCjiUkU/DqODSDSwWjMa9sWz+BEvyahV4IiJyztwq8IqKipg+fTrjx49n5cqV3opJRETEJwT522gVE87vhzLZdCC9bIEHGI17Q3GBJyIico7cugbPz8+PZ599FofD4a14REREfEq7BhEA/H4o85SvOxu7rsOzHPgFigorLS4REfFNbk+yMmjQIJYtW+aFUERERHxPuwaRwOkLPOq0pMAWhqUoD1I2VmJkIiLii9y+Bm/YsGFMmTKF3377jW7duhEaGlrq9UsvvdRjwYmIiFR3f/TgZZy6gcXK8bBWxGasg30/Qtx5lRidiIj4mgpNsgLwwgsvlHnNYrFo+KaIiMiftI6NwGKBw5kFHM0uoE5YYJk2R0sKvFVw/l2VH6SIiPgMt4doOp3O095U3ImIiJQWFuhHk9qu0S6bTzNM81hYa9eD5J/Aqe9SERGpOLcLvD/Lz8/3VBwiIiI+q+1ZJlrJCG6MERAGBRlw+PfKDE1ERHyM2wWew+Hgscceo2HDhoSFhbF7924AHnroId58802PBygiIlLdlec6PCOul+vxvh8rKSoREfFFbhd406dPJzExkWeeeYaAgICS7R06dOCNN97waHAiIiK+oHgmzdMN0YST6+GBCjwRETknbhd4c+fO5bXXXuPvf/87NputZHvHjh3ZunWrR4MTERHxBcU9eHuO5ZBTUHTKNn8UeKvAMCorNBER8TFuF3gHDx4kISGhzHan04ndbvdIUCIiIr6kTlgg9SMCMQzYknLqXjwjtjP4BUHuMTi6vXIDFBERn+F2gdeuXTtWrFhRZvuHH35Ily5dPBKUiIiIrznrgue2AGh0cg08DdMUEZEKcnsdvKlTp3Lttddy8OBBnE4nH3/8Mdu2bWPu3Ll8+eWX3ohRRESk2mvXIIKlW9POeB0e8X1h7wrXMM3u4ysvOBER8Rlu9+CNHDmS+fPns3DhQiwWCw8//DBbtmzhiy++YPDgwd6IUUREpNormUkz5TQzaQLE93Hd7/1R1+GJiEiFuN2DBzB06FCGDh1aZntSUhKdO3c+15hERER8TttY1xDN7anZ2B1O/G2n+Btro/PA6gdZhyB9H9RqUrlBiohItXdOC50DZGRk8PLLL9O1a1e6devmiZhERER8Tlx0MOFBfhQ6nOw4nH3qRgEh0KCr6/G+VZUXnIiI+IwKF3hLly7l73//O7Gxsbz44osMHz6ctWvXejI2ERERn2GxWGgbe5YFzwGa9HXda6IVERGpALcKvAMHDvD444/TrFkzxowZQ3R0NHa7nQULFvD4449rFk0REZEzOOtMmuCaaAXUgyciIhVS7gJv+PDhtG3bls2bN/Piiy9y6NAhXnzxRW/GJiIi4lOKJ1o540yacT3AYoXjuyEzpZIiExERX1HuAm/RokXceOONTJs2jREjRmCz2bwZl4iIiM9p1/BkgZeSidN5mlkygyIhpoPrcbJ68URExD3lLvBWrFhBVlYW3bt3p2fPnrz00kscOXLEm7GJiIj4lOZ1wwjws5JdUETy8dzTNyweprlnReUEJiIiPqPcBV7v3r15/fXXSUlJ4Z///Cfvv/8+DRs2xOl0snjxYrKysrwZp4iISLXnb7PSqn44AFtTzzBMs2l/1/2upVoPT0RE3OL2LJohISGMHz+elStXsmnTJv71r3/x1FNPUa9ePS699FJvxCgiIuIzWse4CrzNKWf4w2iT88Hq71oL7/juSopMRER8wTmtg9eqVSueeeYZDhw4wLx58zwVk4iIiM9qc3KphC0pZ+jBCwyDxr1cj3ctrYSoRETEV5zzQucANpuN0aNH8/nnn3vicCIiIj6rXAUeQPMLXfc7l3g5IhER8SUeKfBERESkfIoXOz9wIo/MfPvpGyZc5LrfuwKKCishMhER8QUq8ERERCpRZIg/DSKDANh6puvw6neA0LpQmA0Hfq6k6EREpLpTgSciIlLJyjVM02qFZgNdjzVMU0REysn0Au/ll1+madOmBAUF0a1bN1asOP2aP8uWLcNisZS5bd26tVS7BQsW0LZtWwIDA2nbti2ffPKJt9+GiIhIuZX7OrziYZqaaEVERMrJ1AJv/vz53HXXXfz73/9mw4YNXHDBBQwbNozk5OQz7rdt2zZSUlJKbi1atCh5bfXq1Vx99dVce+21bNy4kWuvvZarrrqKNWvWePvtiIiIlEvrWNdSCWct8Ip78FI2Qs5RL0clIiK+wNQC74UXXmDChAnceOONtGnThpkzZxIXF8crr7xyxv3q1atHTExMyc1ms5W8NnPmTAYPHsyUKVNo3bo1U6ZM4aKLLmLmzJlefjciIiLlU9yDt+1wFg7nGRYyD6/vuhYPQ714IiJSLqYVeIWFhaxbt44hQ4aU2j5kyBBWrVp1xn27dOlCbGwsF110Ed9//32p11avXl3mmEOHDj3rMUVERCpLk9qhBPlbybc72Xss98yNWwxy3e9Y7P3ARESk2vMz68RHjx7F4XBQv379Utvr169PamrqKfeJjY3ltddeo1u3bhQUFPD2229z0UUXsWzZMvr16wdAamqqW8cEKCgooKCgoOR5ZqZryIzdbsduP8MU1pWoOI6qEk9NobybQ3k3h/JeuVrWD+PXA5n8fjAdP06fd0vTgfitnIGxawlFBflgtZ2ynbhHP+/mUN7Nobybw928e+rzMa3AK2axWEo9NwyjzLZirVq1olWrViXPe/fuzf79+3nuuedKCjx3jwnw5JNPMm3atDLbFy1aREhISLneR2VZvFh/wTWD8m4O5d0cynvlCC20Ala++WkTlzQ+fd4tRhHDrMH45x5j9YKXORHavHID9XH6eTeH8m4O5d0c5c17bu5ZRnSUk2kFXp06dbDZbGV61tLS0sr0wJ1Jr169eOedd0qex8TEuH3MKVOmMHny5JLnmZmZxMXFMWTIECIiIsodizfZ7XYWL17M4MGD8ff3NzucGkN5N4fybg7lvXIdX5PM6i+3UhhSFzh8xrzb8j+FrZ/Tt34uzn7DKzVOX6Wfd3Mo7+ZQ3s3hbt6LRxGeK9MKvICAALp168bixYu57LLLSrYvXryYUaNGlfs4GzZsIDY2tuR57969Wbx4MXfffXfJtkWLFtGnT5/THiMwMJDAwMAy2/39/avcP4KqGFNNoLybQ3k3h/JeOdo3qgXAtsPZUOcseW85BLZ+jm33UmwXPViJUfo+/bybQ3k3h/JujvLm3VOfjalDNCdPnsy1115L9+7d6d27N6+99hrJycnccsstgKtn7eDBg8ydOxdwzZDZpEkT2rVrR2FhIe+88w4LFixgwYIFJcecNGkS/fr14+mnn2bUqFF89tlnfPfdd6xcudKU9ygiInIqrWJcSyWkZhaQc7bLLhJOTrRycL1ruYTQOt4NTkREqi1TC7yrr76aY8eO8eijj5KSkkL79u1ZuHAh8fHxAKSkpJRaE6+wsJB77rmHgwcPEhwcTLt27fjqq68YPvyP4Sp9+vTh/fff58EHH+Shhx6iefPmzJ8/n549e1b6+xMRETmdiCB/GtUK5sCJPA7lnv46cVfjWNdyCYc3wc4l0OnqyglSRESqHdMnWbntttu47bbbTvlaYmJiqef33nsv995771mPecUVV3DFFVd4IjwRERGvaRMbwYETeRwsz3X1LQa7Crwd36rAExGR0zJ1oXMREZGarHjB84M5Z+nBA2g1zHW/YzEUFXoxKhERqc5U4ImIiJikbazrOryzDtEEaNgdwmKgIBP2LPdyZCIiUl2pwBMRETFJcQ9eSi7YHc4zN7ZaofXJa863fuHlyEREpLpSgSciImKSuFohhAbYcBgW9hzNOfsOrS9x3W9dCE6Hd4MTEZFqSQWeiIiISaxWS8lyCVtSs8++Q5MLIDASctLgwC9ejk5ERKojFXgiIiImalU/DIBtqVlnb+wXAC2Huh5v0TBNEREpSwWeiIiIiVqf7MHbWp4CD6BN8TDNL8EwvBSViIhUVyrwRERETNTm5Eyavx3KxChPwZYwCPyC4cReOLTBu8GJiEi1owJPRETERG1jI/CzGJzItbPrSDkmWgkI/WM2zU0feTc4ERGpdlTgiYiImCjQz0q86zI8ftl7vHw7dbjSdf/bAs2mKSIipajAExERMVnzCNfQzHIXeM0vgqAoyE6FvSu8F5iIiFQ7KvBERERM1izczQLPLwDajXY93vShd4ISEZFqSQWeiIiIyZqGG1gtsP94HqkZ+eXbqXiY5uYvwF7OfURExOepwBMRETFZkN8fyyWUuxevcR+IaAgFGbDjWy9GJyIi1YkKPBERkSqgW3wtwI0Cz2qFjle5Hq+f66WoRESkulGBJyIiUgWcFx8FwM97ylngAXS9znW/cwmc2Of5oEREpNpRgSciIlIFdD/Zg7ftcBYZefby7RTdDJr2BwzY8I73ghMRkWpDBZ6IiEgVUDc8kCa1QzAMWL/vRPl37DbOdb/hHXAUeSc4ERGpNlTgiYiIVBHdm0QDblyHB9D6EgiOhqxDsPM7L0UmIiLVhQo8ERGRKqJHRQo8v0DoPNb1eO1bXohKRESqExV4IiIiVcR5TV0F3sb9GeTbHeXfsft41/2Ob+HoTi9EJiIi1YUKPBERkSqiSe0Q6oQFUOhwsulgRvl3rN0cWl7serzmVe8EJyIi1YIKPBERkSrCYrHQ42Qv3k+7jrm3c6/bXPdJ70KeG5O0iIiIT1GBJyIiUoX0blYbgNW73SzwmvaD+u3BngvrEj0fmIiIVAsq8ERERKqQ3s3rALB23wn3rsOzWP7oxVv1IuRneiE6ERGp6lTgiYiIVCHN64ZSPyKQwiKne+vhAXS8GmonQO4xWP2SdwIUEZEqTQWeiIhIFWKxWOhzshdvlbvX4dn84MKHXI9XvQTZaR6OTkREqjoVeCIiIlVM7+au6/BW7Trq/s5tR0GDrmDPgeXPejgyERGp6lTgiYiIVDF9ThZ4Gw9kkF1Q5N7OFgsMesT1eO0cOL7Hs8GJiEiVpgJPRESkimlUK4T42iE4nAa/7Dnu/gGa9YfmF4LTDose9HyAIiJSZanAExERqYL6nMswTYChT4DFBlu/hF1LPRiZiIhUZSrwREREqqDi5RJW7KhggVevDfS42fX46/ugqNBDkYmISFWmAk9ERKQKuiChDlYLbE3NIiUjr2IHGXA/hNSBo9thzaueDVBERKokFXgiIiJVUK3QADrHRQGwbNuRih0kOAoGTXU9XvYknNjnkdhERKTqUoEnIiJSRQ1oVQ+AZdvOYT27zv+A+L5gz4UvJoFheCg6ERGpilTgiYiIVFEDTxZ4K3ccpbDIWbGDWK0wcjb4BcHu7yHpPQ9GKCIiVY0KPBERkSqqXYMI6oQFklPoYM2eYxU/UJ0E1/V4AN9OgazDnglQRESqHBV4IiIiVZTVamFou/oAfLTuwLkdrPedENsJ8jPg8zs0VFNExEepwBMREanCrjmvMQBf/5ZKeu45LHVg84PRr4AtEHYsgp9f91CEIiJSlajAExERqcLaN4ygbWwEhUVOPt1w8NwOVr8dDH7U9XjRg3D493MPUEREqhQVeCIiIlWYxWLhmh5xALz/y36Mcx1a2fOfkDAYHAXw0QSwV3CNPRERqZJU4ImIiFRxozo1JNDPytbULH49kHFuB7NYXEM1Q+vBkS3w5d26Hk9ExIeowBMREaniIkP8Gd4hFoB5Pyef+wHD6sLlb4DFChvnwc+vnfsxRUSkSlCBJyIiUg2M6eGabOXTpIPnNtlKsWb9YfBjrsffPgD7Vp37MUVExHSmF3gvv/wyTZs2JSgoiG7durFixYrTtv34448ZPHgwdevWJSIigt69e/Ptt9+WapOYmIjFYilzy8/P9/ZbERER8ZrzmtSibWwE+XYn7/+y3zMH7X07tL8CnEXwwXVwYq9njisiIqYxtcCbP38+d911F//+97/ZsGEDF1xwAcOGDSM5+dTDT5YvX87gwYNZuHAh69atY+DAgYwcOZINGzaUahcREUFKSkqpW1BQUGW8JREREa+wWCzc0LcJAHNX7aXI4fTEQeHSFyGmA+QcgXevhLwT535cERExjakF3gsvvMCECRO48cYbadOmDTNnziQuLo5XXnnllO1nzpzJvffey3nnnUeLFi144oknaNGiBV988UWpdhaLhZiYmFI3ERGR6m5kpwZEhwZwKCOfxZsPe+agASEw9gOIaAhHt8P7/4CiAs8cW0REKp1pBV5hYSHr1q1jyJAhpbYPGTKEVavKdx2A0+kkKyuL6OjoUtuzs7OJj4+nUaNGXHLJJWV6+ERERKqjIH8bY09eizfnx72eO3BEA1eRFxAO+1bCp7eC0+G544uISKXxM+vER48exeFwUL9+/VLb69evT2pqarmO8fzzz5OTk8NVV11Vsq1169YkJibSoUMHMjMzmTVrFn379mXjxo20aNHilMcpKCigoOCPv1ZmZmYCYLfbsdvt7r41ryiOo6rEU1Mo7+ZQ3s2hvJvD3bxf3b0Br/6wi5/3Hufn3UfoEhflmUBqt8Jy+VvY5o/B8tsCnLYgHCNmuGba9EH6eTeH8m4O5d0c7ubdU5+PxTjnFVMr5tChQzRs2JBVq1bRu3fvku3Tp0/n7bffZuvWrWfcf968edx444189tlnDBo06LTtnE4nXbt2pV+/fsyePfuUbR555BGmTZtWZvt7771HSEhIOd+RiIhI5Xhvp5U1R6y0r+XkptYeuBbvTxqc+Jnue/+DBYNddYfwW8O/u67VExERr8rNzWXs2LFkZGQQERFR4eOY1oNXp04dbDZbmd66tLS0Mr16fzV//nwmTJjAhx9+eMbiDsBqtXLeeeexY8eO07aZMmUKkydPLnmemZlJXFwcQ4YMOafkepLdbmfx4sUMHjwYf39/s8OpMZR3cyjv5lDezVGRvLc+ksPFL/7IbyesNOval9Yx4R6MaDiOX1vj98UdND+yiCYt2uAc8KDPFXn6eTeH8m4O5d0c7ua9eBThuTKtwAsICKBbt24sXryYyy67rGT74sWLGTVq1Gn3mzdvHuPHj2fevHmMGDHirOcxDIOkpCQ6dOhw2jaBgYEEBgaW2e7v71/l/hFUxZhqAuXdHMq7OZR3c7iT91YNohjeIZavfk3hvyv28tLYrp4Nptu14MiHhfdgWzULm7MIhk73uSIP9PNuFuXdHMq7Ocqbd099NqYVeACTJ0/m2muvpXv37vTu3ZvXXnuN5ORkbrnlFsDVs3bw4EHmzp0LuIq76667jlmzZtGrV6+S3r/g4GAiIyMBmDZtGr169aJFixZkZmYye/ZskpKS+M9//mPOmxQREfGC2wck8NWvKXy1KYXJR7JpVjfMsyfocRMYBnz9f/DTf8CeCyNeAKtvXpMnJjGMP03oY8pVQzWDw47FKAKHvQqsgl3FWGw+9/+aqQXe1VdfzbFjx3j00UdJSUmhffv2LFy4kPj4eABSUlJKrYn33//+l6KiIm6//XZuv/32ku3jxo0jMTERgPT0dG6++WZSU1OJjIykS5cuLF++nB49elTqexMREfGmtg0iuKh1PZZsTeOFxds934sH0PNm8A+Gz++EdXNca+WNfhmCIj1/LqlZ0rZA0lz49QMoyDA7Gp/nD1wKkGRuHFXS6Feg81izo/AoUws8gNtuu43bbrvtlK8VF23Fli1bdtbjzZgxgxkzZnggMhERkartX0NasXRbGl/+msI/+2XQoZEXCq+u17qKvE9uga1fwmub4aq5rsXRRdzhKMKy+VPO3/4M/hu2mx2NiM8yvcATERGRimnbIILRnRvyyYaDPP3NVt65sad3TtThCqjVFD4cB8d3wxuDYNgz0PU6n7wuTzws9zisS4Rf3sAv8yC1AcNiw9J6BHQfD7GdXO0sFkA/T95gLypi0aJFDBkyBH8//fpfir/vzZivT1hERKQamzy4JV/9msLKnUdZseMIF7So650TNeoG/1wOH98MOxfDFxNd9yNnQ0i0d84p1VvaFljzKmycD0V5ABghddge0ZdmVz2Of3RjkwOsQex2ivxCXcOrNcmKz/OtKwpFRERqmLjoEP7Ry3Xt+vSvtlDk8Oy6eKWERMPYD2DQNLD6w5Yv4JU+sHuZ984p1YvTCdu+gbmj4OVerp67ojzXkN5RL1N0ZxJbYy+H8FizIxXxWSrwREREqrk7L0ygVog/W1OzSFy117sns1rh/Lvgxu+gdgvISnH9Mv/1/VCY491zS9VVkAVr/gsvdYd5V7uKfosV2oyE6xfCP1dAl7+DX5DZkYr4PBV4IiIi1Vyt0ADuu7g1ADO/28HhzHzvn7RBZ9eQze7jXc/XvOLqsdn1vffPLVXH8T3wzRR4oS18fS8c3wWBkdD7DpiYBFe/A0366lpNkUqkAk9ERMQHXNU9ji6No8guKGLS+xvIK3ScfadzFRACl8yAfyyAyDhIT4a3R8Ont0PWYe+fX8xhGLBnOcwbC7O7wE8vQ0Em1E6A4c/B5M0wdDrUijc7UpEaSQWeiIiID7BaLTxxWQdCA2z8tPs4NyT+TG5hUeWcPGEQ3LYaetwMWCDpHdcv/t8/4Rq6J77Bngfr58Kr58P/RsK2rwADml8Ef/8Ibv8FetwEgWFmRypSo6nAExER8RFtYiOYO6EHYYF+/LT7ONfP+YXsgkoq8gLDYfizMP5baNgd7Dnww9OuQu/n16GosHLiEM9yOmDvj65hmDPauRa9P/yba2r57hPg9p/h2o+hxWDX9ZkiYjr9SxQREfEh3eKjmTuhB+GBfvy85zhjXvuJI1kFlRdA456uCViu/B9EN4ecI7DwHleht+a/rl4gqdrsebDta9dQ2+daQOJw1zDM3GOuobiDH3UNw7zkBajbyuxoReQvtA6eiIiIj+nauBbv3NiTGxJ/YdPBDC5/ZRVzx/egSZ3QygnAYoF2o6H1CNc0+cufhcwDrkk4lj8LvW939f4ERVROPHJ2eSdg+yLY+gXsXAL23D9eC4qCVsNcM2K2GAo2/fooUpXpX6iIiIgP6hQXxUe39GbcnJ9JPp7L315Zxct/70qvZrUrLwibv+uarC7Xuq7LWzkLMpLhu0dgxQzo8g84bwLUbl55MckfMg7A1oWw9UvYuxKMP03ME9HIVaC3HgHxfVyfpYhUCyrwREREfFSzumF8fGtfxp/syfvHG2t46JK2XNc7HktlTlvvHwTn3Qhdx8Gmj2DlC3B0O/z0H9ctYRCcd9PJ67hslRdXTeN0QupGV0/d9q/h0IbSr9dr90dRF9tJSxuIVFMq8ERERHxY3fBAPvhnb+5b8CufbzzE1M9/Z9PBDB4b1Z7ggEoupmz+0HkMdLwadi1xTb6yYxHs/M51C4+FDldCp2ugfrvKjc1X5We6Fh3f8S3sWAzZf16+wgKNe7kKulbD1ZMq4iNU4ImIiPi44AAbs67pTMdGkTyxcAsfrTtA0v50Zl/ThbYNTLgOzmp19da1GOxaKHvtm7DhHchKgVWzXbf6HaDdKGg1Auq1UW9SeTkdcHSHq2De8S3sWw1O+x+vB4RBswHQcii0vBjC6pkWqoh4hwo8ERGRGsBisXDjBc1oGxvBXfOT2JmWzej//Mi9F7difN+mWK0mFVDRTWHI43Dhw67evI3zYPu3cHiT67b0cajV1NXL1GIwxPUE/2BzYq0KDAPy012Lyqfvd91nnLw/vgeO7QTHX2ZNrZ0ALYa4bvF9wC/QlNBFpHKowBMREalB+iTU4Zu7+nHvR7/y3ZbDPP7VFr79PZUn/9aBhHrh5gXmFwBtLnHdco/Dli9g20LY9T2c2AOrX3LdbAGuIq9pP4jv67pWzJcW1jYM13IE6fvKFnDFzwvPsni8XxA07u3qpWsxREMvRWoYFXgiIiI1THRoAK9f14131yTzxMIt/LL3BMNnreT2gQncOqA5AX4mL5MbEg3dxrluBdmu6/W2fe26liwrBfaucN0AsLjWYmvQFRp0dj2u09J1PV9VG9bpKHJdA5eV4rplpvzp8SHXffp+KCrHWoGhdV1r0kU1hqg4iIp33eq0cG3TZDUiNZYKPBERkRrIYrHwj17xDGhVl4c+/Y3vtx1hxnfb+WzjQR4c0YaBrepV7kybpxMYBm1HuW6GAcd2wZ4fYM9yOPALZB6EI1tdt43v/bFfQJhraGJUY4hoCBGxrvvwGAiuBX6h+DnyXMd0h2FAUT4UZJ28ZbomMinIgvwMV+9b3nHXfe7xk7eT23KOAuU5n8UVZ1TjvxRxjSGyMUQ2goAQ9+IWkRpDBZ6IiEgN1qhWCG9dfx5f/JrCo1/8zu4jOYxPXMsFLerw7xFtaB1ThRYjt1igToLrdt4E17asw67p/g9tgNRfXcsvHN8DhdmQkuS6nYI/MAIwNt0K/qGuxbttAWD1dz222FyTkziKTt7bXROYFOWBs6ji78HqB2ExroIzPAbCG7juI07eR8a5CjgfvU7O6TRIy4PPkg7xW0o2vx3MIKfQcfYd5ZwYhkFWpo1X9qyuGn+4qUImD27J4Lb1zQ7Do1TgiYiI1HAWi4VLOzVgQKu6/GfpTub8uJcVO44yfNYKruwWx50XJdCoVhXtMQqvD60udt2KFRW6rts7usPVw5d56I9bdirkZ2DkpWNx2rEYzrNf03ZKFggM/9MtAoIiIKQ2BEe77kNqnbw/uS20rutmNXkIbCU6nJlP0v50Nu5P59cDGWw8kE5Wvh8k/WZ2aDWQBXIr8rPu2zLy7GdvVM2owBMREREAIoL8mTK8DX/vGc9T32xh4aZU5q/dz8cbDnDNeY25snsj2jWIxGbWjJvl5RfguhavbqvTNikqLOSbrz7j4v698DcKXb1yjsKTPXVFrt46m7+rx83m7+rZs9pcE5gERbh6/WpQoVYeGXl2Np0s4jbuT2fjgXQOZxaUaedvMegQV4vOcbXoFBdJdGiACdHWLEVFDn7++Wd69OiBn5+uz/yzlvVNnFzKS1TgiYiISCmNa4fw8t+7sW7fcZ5ftJ1Vu47x9k/7ePunfUQE+dGzWW16No2me5No2jWIwN9WDQsdiwWnNcA1LNLf3+xoqp18u4MtKZknCzlXUbf7SE6ZdlaL6xfoznFRdGwURbvYUHatW8nIS3rgr7xXGrvdTuZ2g/MTaivvNYAKPBERETmlbvHRvHdTL1bvOsacH/ewevcxMvOLWLz5MIs3HwYg2N9Gp7hIzmsSTbf4WnSJq0VkiH6B9BWGYXA4s4BdR7LZdSSbbalZ/Hogg62pmdgdZSeMaRwdQsdGkXSOi6JTXBTtGkQQEvDHr5t2u5291fDvASLViQo8EREROaPezWvTu3ltihxOfj+Uyapdx1i79zjrkk+Qnmvnp93H+Wn38ZL2jWoF0zY2gnYNImkVE05CvTDia4dUz56+GiKnoIjk47nsPpJTUsztPpLD7iPZp50EpXZoAJ3ioujUKIqOcZF0ahSl4ZYiVYAKPBERESkXP5vV9Qt9XBTQHKfTYNeRbNbuO8Eve4+zbt8J9h3L5cCJPA6cyGPRyV4+AD+rhfjaITSvG0bzemEk1A2jWd1QGkeHEB0aoJn9vMwwDE7k2tl7LIfkY7nsO5bLvuM5rvtjuRzNLnutXDGb1UJ8dAjN6obSvF4YHRtG0SkukoZRwfrcRKogFXgiIiJSIVarhRb1w2lRP5wxPRoDkJFrZ3NKJptTMvn9UAY707LZlebqBdp1JIddR3LgT4UfuIZ5NqoVTKNawcRFh5x8HEJMZBB1wwKpGx5IkL8mhjgVp9Mgz+4gt9BBdkERJ3ILOZFTyJGsAvYdzyX5WG5JUZdVcOblHaJC/GlaJ5TmJ4vv5nXDaF43jMbRIQT4qfdVpLpQgSciIiIeExniXzKks5hhGKRm5pcUezuPZLMrLYfdR7NJyyogz+5gR1o2O9KyT3vciCA/6oYHUi88iLrhgUSHBhAR7E/kyVtEkJ/rcYg/EUH+BPvbCA6wEehnrfReJsMwsDsMCh1OCotct4Iix8l7Z8n2fLuDvEJXcZb3p8e59qI/thc6yC0sKmlTvM31uIh8u9Ot2GIigoivHXLy5upBbVI7lMa1Q4gM1rWTIr5ABZ6IiIh4lcViITYymNjIYC5oUbfUawVFDg6l53PgRPHQTtf9/uO5HM4s4Eh2AYVFTjLzi8jML3L1ALopyN/qKvj8bQQF2AiwWbFaICvTxlv71+Bns2KzWLBaXcMRrScLQofTwOE0cBoGToOSx3/eXlzAFTpOFm8nHxtl5x/xKosFQgP8qBXqT3RIANGhAcRFu4q4+GhXQRcXHaKeUJEaQAWeiIiImCbQz0bTOqE0rRN6ytcNwyAzv4gjWQWkZeVzJKuAI1kFnMgtJCPPTkZe0cl7O5kn77Py7aVmeMy3O8m3OznBXxc0trA/J8OL787Fz2ohwM9KoJ+VgOKbzUqgn42QAFdPY0iAjZAAP9djf9fzoJLHfiVtgk+2CwlwFazF+wX5V35PpYhUTSrwREREpMqyWCwlwzAT6oWVe78ih5P8Iid5hQ7y7a5bnt1Bvv3kcEl7EWt+/oWuXbuB1VbSM1d8b7GA1WLBZrVgs1iwFD+2lt7u/+fCzfZHARfo5xoeGmCzYq3qC8OLiE9RgSciIiI+x89mJcxmJSzw1L/q2O12sncYXNSmnhZ+FhGfoimRREREREREfIQKPBERERERER+hAk9ERERERMRHqMATERERERHxESrwREREREREfIQKPBERERERER+hAk9ERERERMRHqMATERERERHxESrwREREREREfIQKPBERERERER+hAk9ERERERMRHqMATERERERHxESrwREREREREfIQKPBERERERER+hAk9ERERERMRHqMATERERERHxEX5mB1AVGYYBQGZmpsmR/MFut5Obm0tmZib+/v5mh1NjKO/mUN7NobybQ3k3h/JuDuXdHMq7OdzNe3HtUVyLVJQKvFPIysoCIC4uzuRIRERERESkJsnKyiIyMrLC+1uMcy0RfZDT6eTQoUOEh4djsVjMDgdwVfRxcXHs37+fiIgIs8OpMZR3cyjv5lDezaG8m0N5N4fybg7l3Rzu5t0wDLKysmjQoAFWa8WvpFMP3ilYrVYaNWpkdhinFBERoX+YJlDezaG8m0N5N4fybg7l3RzKuzmUd3O4k/dz6bkrpklWREREREREfIQKPBERERERER+hAq+aCAwMZOrUqQQGBpodSo2ivJtDeTeH8m4O5d0cyrs5lHdzKO/mMCvvmmRFRERERETER6gHT0RERERExEeowBMREREREfERKvBERERERER8hAq8Kuirr76iZ8+eBAcHU6dOHf72t7+VvLZx40bGjBlDXFwcwcHBtGnThlmzZpU5xqZNm+jfvz/BwcE0bNiQRx99FF1ueWZnyjvApEmT6NatG4GBgXTu3PmUx/j222/p1asX4eHh1K1bl8svv5w9e/ZUQvTVlyfybhgGzz33HC1btiQwMJC4uDieeOKJSoi++vJE3ovt3LmT8PBwoqKivBewjzjXvC9btoxRo0YRGxtLaGgonTt35t13362k6Ks3T/zM67vVfWfLe3JyMiNHjiQ0NJQ6deowceJECgsLS7XRd6v7PJF3fbe6zxN5L3Yu361a6LyKWbBgATfddBNPPPEEF154IYZhsGnTppLX161bR926dXnnnXeIi4tj1apV3HzzzdhsNu644w4AMjMzGTx4MAMHDuSXX35h+/btXH/99YSGhvKvf/3LrLdWpZ0t7+D6j278+PGsWbOGX3/9tcwxdu/ezahRo5g8eTLvvvsuGRkZ3H333fztb39jw4YNlfVWqhVP5B1cv5gtWrSI5557jg4dOpCRkcHRo0cr4y1US57KO4DdbmfMmDFccMEFrFq1ytuhV2ueyPuqVavo2LEj9913H/Xr1+err77iuuuuIyIigpEjR1bWW6l2PJF7fbe672x5dzgcjBgxgrp167Jy5UqOHTvGuHHjMAyDF198EdB3a0V4Iu+g71Z3eSrv4IHvVkOqDLvdbjRs2NB444033NrvtttuMwYOHFjy/OWXXzYiIyON/Pz8km1PPvmk0aBBA8PpdHosXl/hbt6nTp1qdOrUqcz2Dz/80PDz8zMcDkfJts8//9ywWCxGYWGhp8L1GZ7K++bNmw0/Pz9j69atHo7QN3kq78Xuvfde4x//+IcxZ84cIzIy0jNB+iBP5/3Phg8fbtxwww3nEJ1v81Tu9d3qnvLkfeHChYbVajUOHjxYsm3evHlGYGCgkZGRYRiGvlvd5am867vVPZ7Ke7Fz/W7VEM0qZP369Rw8eBCr1UqXLl2IjY1l2LBh/P7772fcLyMjg+jo6JLnq1evpn///qXW3Bg6dCiHDh1i79693gq/2qpo3v+qe/fu2Gw25syZg8PhICMjg7fffpshQ4bg7+/vpeirL0/l/YsvvqBZs2Z8+eWXNG3alCZNmnDjjTdy/PhxL0VevXkq7wBLly7lww8/5D//+Y8XIvUtnsz7X/31O0BK81Tu9d3qnvLkffXq1bRv354GDRqUbBs6dCgFBQWsW7cO0HeruzyVd323usdTeQfPfLeqwKtCdu/eDcAjjzzCgw8+yJdffkmtWrXo37//af9BrV69mg8++IB//vOfJdtSU1OpX79+qXbFz1NTU70UffVVkbyfSpMmTVi0aBEPPPAAgYGBREVFceDAAd5//31vhV6teSrvu3fvZt++fXz44YfMnTuXxMRE1q1bxxVXXOGt0Ks1T+X92LFjXH/99SQmJhIREeGtcH2Gp/L+Vx999BG//PILN9xwg6dC9Tmeyr2+W91TnryfKqe1atUiICCgJKf6bnWPp/Ku71b3eCrvnvpuVYFXCR555BEsFssZb2vXrsXpdALw73//m8svv5xu3boxZ84cLBYLH374YZnj/v7774waNYqHH36YwYMHl3rNYrGUem6cvAj8r9t9mbfyfjqpqanceOONjBs3jl9++YUffviBgIAArrjiihp1EX5l593pdFJQUMDcuXO54IILGDBgAG+++Sbff/8927Zt89bbrHIqO+833XQTY8eOpV+/ft56S9VCZef9z5YtW8b111/P66+/Trt27Tz5tqoFM3Kv71bP5/1UuTMMo2S7vltdKjvv+m51qey8e+q7VZOsVII77riDa6655oxtmjRpQlZWFgBt27Yt2R4YGEizZs1ITk4u1X7z5s1ceOGF3HTTTTz44IOlXouJiSnz18S0tDSAMn858GXeyPuZ/Oc//yEiIoJnnnmmZFvxZDhr1qyhV69ebr6D6qmy8x4bG4ufnx8tW7Ys2damTRvANVtVq1at3Am/2qrsvC9dupTPP/+c5557DnB9QTmdTvz8/HjttdcYP358Bd5F9VPZeS/2ww8/MHLkSF544QWuu+46t/f3BZWde323ungy7zExMaxZs6bUvidOnMBut5fkVN+tLpWdd323ulR23j313aoCrxLUqVOHOnXqnLVd8fTM27Zt4/zzzwdcs+js3buX+Pj4kna///47F154IePGjWP69OlljtO7d28eeOABCgsLCQgIAGDRokU0aNCAJk2aeOZNVQOezvvZ5ObmYrPZSm0rfl78l52aoLLz3rdvX4qKiti1axfNmzcHYPv27QBuHae6q+y8r169GofDUfL8s88+4+mnn2bVqlU0bNjQ/TdQTVV23sHVc3fJJZfw9NNPc/PNN1cobl9Q2bnXd6uLJ/Peu3dvpk+fTkpKCrGxsYArp4GBgXTr1g3Qd2uxys67vltdKjvvHvtudXtaFvGqSZMmGQ0bNjS+/fZbY+vWrcaECROMevXqGcePHzcMwzB+++03o27dusbf//53IyUlpeSWlpZWcoz09HSjfv36xpgxY4xNmzYZH3/8sREREWE899xzZr2tKu9seTcMw9ixY4exYcMG45///KfRsmVLY8OGDcaGDRuMgoICwzAMY8mSJYbFYjGmTZtmbN++3Vi3bp0xdOhQIz4+3sjNzTXrrVVpnsi7w+EwunbtavTr189Yv369sXbtWqNnz57G4MGDzXpbVZ4n8v5XmkXz7DyR9++//94ICQkxpkyZUuo74NixY2a9rWrBE7nXd6v7zpb3oqKi/2/v/l6a+uM4jr+m34zZWfvh3BaMCkRkpP2wroQwKSgoUoSIiGgJgcGkQeuiCFs3XkVEVxFRRkT5F0RQsVZdFOSFBCEU1moNgqybZlj4+V58ad8sy5bL5fH5gAM7Zzuf9+d8GHx8eX7MNDY2mo0bN5rBwUFz8+ZNEw6HTSwWK7TB3Fq8Uow7c2vxSjHu3/rduZWA95cZHx83hw4dMoFAwLhcLrNp0ybz+PHjwvvHjx83kr5bli1bNqmdoaEhs379erNw4UITCoVMMpnkMc4/Md24G2NMa2vrlGM/MjJS+MzVq1fNmjVrzKJFi0xtba3Zvn27efLkySwfzdxRqnHPZrOms7PTWJZlgsGgiUaj/MH7E6Ua968R8KZXinHfu3fvlO+3trbO/gHNIaX6zjO3FudXxv3Fixdm69atxul0Gp/PZ2Kx2KSfojCGubVYpRp35tbilGrcv/a7c6vDmHl0hyoAAAAA2BhP0QQAAAAAmyDgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AAAAALAJAh4AAAAA2AQBDwAAAABsgoAHAAAAADZBwAMA2FoymdTq1atnvW4qlZLD4ZDD4VBHR0dJ2nr//v0v75NMJgv1T58+PaP6AIC5g4AHAJizvgSYHy3RaFSJREK3bt0qWx+Hh4fV398/ozZaWlqUy+Xkdrt/eZ9EIqFcLqdwODyj2gCAueWfcncAAIDflcvlCq8HBgbU29ur4eHhwjan0ynLsmRZVjm6J0kKBALyeDwzaqOqqkqhUKiofb4cd2Vl5YxqAwDmFs7gAQDmrFAoVFjcbrccDsd32769RDMajaqjo0N9fX0KBoPyeDw6ceKEPn/+rMOHD8vn8ykcDuvChQuTamWzWe3cuVNer1c1NTVqb2/X8+fPi+7zhg0b1NPTo3g8Lq/Xq2AwqHPnzunDhw/at2+fXC6X6urqdP369cI+316i2d/fL4/Hoxs3bigSiciyLG3ZsmVS4AUAzE8EPADAvHP79m29fv1a6XRap06dUjKZ1LZt2+T1evXgwQN1d3eru7tbL1++lCTl83m1tbXJsiyl02ndu3evEKrGx8eLrn/p0iX5/X49fPhQPT09OnDggHbs2KGWlhYNDg5q8+bN2rNnj/L5/A/byOfzOnnypC5fvqx0Oq1MJqNEIvHbYwIAsAcCHgBg3vH5fDpz5owaGhrU1dWlhoYG5fN5HT16VPX19Tpy5Iiqqqp0//59SdK1a9dUUVGh8+fPq6mpSZFIRBcvXlQmk1EqlSq6/qpVq3Ts2LFCLafTKb/fr/3796u+vl69vb16+/athoaGftjGp0+fdPbsWa1bt07Nzc2KxWJlvdcQAPB34B48AMC8s2LFClVU/P8/zmAwqMbGxsJ6ZWWlampq9ObNG0nSo0eP9PTpU7lcrkntfPz4Uc+ePSu6/sqVK7+r1dTUNKk/kgr1p1JdXa26urrC+pIlS376eQDA/EDAAwDMOwsWLJi07nA4ptw2MTEhSZqYmNDatWt15cqV79qqra0teX2Hw1GoW0wbxpii+wIAsBcCHgAA02hubtbAwIACgYAWL15c7u4AAPBD3IMHAMA0du/eLb/fr/b2dt29e1cjIyO6c+eODh48qFevXpW7ewAAFBDwAACYRnV1tdLptJYuXarOzk5FIhF1dXVpbGyMM3oAgL+Kw3DBPgAAJZdKpdTW1qZ3797N+IfOZ2L58uWKx+OKx+Nl6wMAYPZwBg8AgD8oHA5r165ds163r69PlmUpk8nMem0AQPlwBg8AgD9gbGxM2WxWkmRZlkKh0KzWHx0d1ejoqKT/nvTpdrtntT4AoDwIeAAAAABgE1yiCQAAAAA2QcADAAAAAJsg4AEAAACATRDwAAAAAMAmCHgAAAAAYBMEPAAAAACwCQIeAAAAANgEAQ8AAAAAbIKABwAAAAA28S/hHkPV4FCVTgAAAABJRU5ErkJggg==", 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" ] @@ -844,7 +844,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 37, "id": "e289aa3e", "metadata": { "collapsed": false, @@ -855,7 +855,7 @@ "outputs": [ { "data": { - "image/png": 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", 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Cb1o0nU4nf/vb33jqqaewWq3Y7XYee+wx7rvvPgAWL17MqFGj2L9/P23btvXc78YbbyQzM5Pvv697g8tp06bV2rjR7b333iMqSnNKRPzJgVL4fr+FtbkGTlwVo/RYJ6emOjgpyYm2f2sZRVWQUWiwMd9gc4FBhf1I9S7UcNI70cngVk76JjgJa7nFRkUkCISEhJCamkqHDh0IC1OHhvieqqoq9u7dS3Z2NtXV1bWuKysr47LLLgueFs0PP/yQd999l/fee4++ffuyZs0apk6dStu2bbn66qs9t/v1KjxOp/O4+3rcd9993HHHHZ6fi4qK6NChA+PHj/eZOXhz5sxh3LhxmiPQgjTu5mnK2O/NL+Nfc3fw5fosz/y6sT1bceMp6QztVP8GqnJEcz3mq6odLM/M58cth/hxSw77CipYl2ewLg+iwqyc0as1Z/dP5ZRurYJyA3a91phHY2+O5h73iooK9u7dS0xMjObgHcXpdFJcXExsbOxx3xeLd9U17hUVFURGRnLqqafWOQfPG/wmwfvrX//KvffeyyWXXAJA//79yczM5IknnuDqq68mNTUVwLPCpltOTg5t2rSp97jh4eGEhx+7x1NoaKhPveD7WjzBQuNunoaMfWGZjX/+sJWZSzM9rZhn90/j1jO60SvV/A9o/JG3H/OhoTCmVypjeqUy3elk44Eivl6XxVdrD7C/oJyv1mXz1bpsEqNC+eOg9lw8rAM9Uxu2KFYg0WuNeTT25miucbfb7Z6943xpMRGzudsD3WMjLaOucbdYLBiGUedzwFvPCb9J8MrKyo55QB7dr5uenk5qaipz5sxh0KBBgKsEOn/+fJ566qkWj1dEmo/D4eR/K/fx5HdbyCutAuCU7q24e0Iv+rePNzk6qY9hGPRrF0+/dvHcM7Enq/cW8NXaA3yzLouc4kreWLSLNxbtYmCHBC4Z1oFzB7YjUj2cIiIijeI3Cd7vf/97HnvsMTp27Ejfvn1ZvXo1zz77LNdeey3geuMwdepUHn/8cbp370737t15/PHHiYqK4rLLLjM5ehHxls1ZRfzts/Ws3lMAQLeUGKb9vi+ju7cyNzBpFMMwGFyzcfoDZ/dhwdZDfLB8D3M357BmbwFr9hbw5HdbuGx4R64a0ZnUeLVaiYiINITfJHjPPfccDz74IFOmTCEnJ4e2bdty00038dBDD3luc/fdd1NeXs6UKVM8G53Pnj27wXvgiYjvqrY7eHHeDv794zZsdifRYVamntmDyaM6E2pVu4k/s1oMxvZKYWyvFA4VV/LJqn3MXJrJ3rxyXpi3g1cW7OSck9K4dnQ6J7VPMDtcERERn+Y374piY2OZMWMGmZmZlJeXs2PHDh599NFaKyQZhsG0adPIysqioqKC+fPn069fPxOjFhFv2HawmPNfXMwzc7ZiszsZ36cNc+8cww2ndlFyF2Bax4Zz82ldmXfXWF66YgjDOydR7XDy+ZoD/OH5RVz88i8s3HYYP1kAWkTEJ02ePJnzzjuv2c/z+eef061bN6xWK1OnTm32852o3bt3YxgGa9asMTuUE6J3RiLis5xOJ+8t3cPZzy1k3b5C4iJC+OfFA3j5yiFq2QtwVovBxH6pfHTzCL66ZTR/HNSOEIvB0l15XPH6Us5/cTE/bclRoiciAWfx4sVYrVYmTpxodign7KabbuJPf/oTe/fu5ZFHHmnSMd566y0SEhKOubxz586e/bClNiV4IuKTSiuruf3DNfzts/VUVTs4rUdrZt9+Gn8c1F5LPAeZ/u3j+efFA/n5nrFMHtmZ8BALq/cUcM1by/n98wuZvTFbiZ6IBIw33niDW2+9lYULF7Jnzx6zw2mykpIScnJymDBhAm3bttWUqRakBE9EfM7Bcjj/paV8vuYAVovBvWf14s3Jw1S1C3Jp8ZFM+0Nffr5nLDee2oWoMCsb9hdx4zsrueDFxSzdmWt2iCIiJ6S0tJSPPvqIP//5z5xzzjm89dZbta6fN28ehmEwd+5chg4dSlRUFCNHjiQjI6PW7R599FFSUlKIjY3l+uuv57777uOUU06p97xOp5Onn36aLl26EBkZyYABA/jf//533Fjz8/O56qqrSExMJCoqirPOOott27Z54nQndKeffjqGYTBv3rw6j/Pss8/Sv39/oqOj6dChA1OmTKGkpMRznGuuuYbCwkIMw/BMxxozZgyZmZncfvvtnssBcnNzufTSS2nfvj1RUVH079+f999/v9b5HA4HTz31FN26dSM8PJyOHTvy2GOP1Rmbw+HghhtuoEePHmRmZh53PHyJEjwR8SmLd+Tyz/VWdh4upU1cOB/c+DtuPq0rFouqduKSEhvB3yb1ZuE9pzNlTFciQi2s2lPAxa8s4Zo3l7E5yzsbxYqItLQPP/yQnj170rNnT6644grefPPNOjsU7r//fp555hlWrFhBSEiIZ1V5gJkzZ/LYY4/x1FNPsXLlSjp27MhLL7103PM+8MADvPnmm7z44ots3LiR22+/nSuuuIL58+fXe5/JkyezYsUKvvzyS3755RecTieTJk3CZrPVSjo/+eQTsrKyGDlyZJ3HsVgs/Pvf/2bDhg28/fbb/Pjjj9x9990AjBw5khkzZhAXF0dWVhZZWVncddddfPrpp7Rv356HH37Yczm4NhEfMmQIX3/9NRs2bODGG2/kyiuvZOnSpZ7z3XfffTz11FM8+OCDbNq0iffee6/OPbOrqqq46KKLWLFiBQsXLqRTp07HHUNf4jeraIpI4Ju5NJOHvtiI3WEwuGMCr1w1lFYx4WaHJT4qKTqMuyf2YvLIzvxr7jY+WL6XnzIOMW/rIf44qB33TOxFmzhVfUWCntMJtjJzzh0aBY2YVvD6669zxRVXADBx4kRKSkqYO3cuZ555Zq3bPfbYY5x22mkA3HvvvZx99tlUVFQQERHBc889x3XXXcc111wDwEMPPcTs2bMpLCys85ylpaU8++yz/Pjjj4wYMQKALl26sHDhQl5++WXPeY62bds2vvzySxYtWuRJ3GbOnEmHDh34/PPPufDCC0lJSQEgKSmJ1NTUen/noxdfSU9P55FHHuHPf/4zL7zwAmFhYcTHx2MYxjHHsFqtxMbG1rq8Xbt23HXXXZ6fb731Vr777js+/vhjTj75ZIqLi/nXv/7F888/z9VXXw1A165dGT16dK1jl5SUcPbZZ1NeXs68efOIj/evPXaV4ImI6ZxOJ09/n8GL83YAMKSVg7cnDyEmSsmd/LaUuAge+2N/rj+lC/+YncE367L4dNV+vt+Qza1ndOfaUemEhahhRSRo2crg8bbmnPtvByAsukE3zcjIYNmyZXz66acAhISEcPHFF/PGG28ck+CddNJJnu/T0tIAyMnJoWPHjmRkZDBlypRatx82bBg//PBDnefdtGkTFRUVjBs3rtblVVVVDBo0qM77bN68mZCQEE4++WTPZcnJyfTs2ZPNmzc36Pd1++mnn3j88cfZtGkTRUVFVFdXU1FRQWlpKdHRDRs7N7vdzpNPPsmHH37I/v37qayspLKy0nOczZs3U1lZyRlnnHHc47jbPOfOnUtUVFSjYvAFSvBExFQOh5OHvtzAu0tcE8lvO70r6WUZhIdaTY5M/E16q2j+c9lgbjylgGlfbWT1ngKe/HYLHy3fy4O/78PYnilmhygiUq/XX3+d6upq2rVr57nM6XQSGhpKfn4+iYmJnstDQ0M937vnnzkcjmMuO/o49XHf75tvvql1boDw8Lo/aK3veE6ns1ELoWVmZjJp0iRuvvlmHnnkEZKSkli4cCHXXXcdNputwcdxe+aZZ/jnP//JjBkzPPP6pk6dSlVVFQCRkZENOs6kSZN49913WbJkCaeffnqj4zCbEjwRMY3N7uCvH6/l8zUHMAx47Lz+XDg4jVmzMn77ziL1GNAhgU9uHsmnq/fz5Ldb2Hm4lGveXM6Zvdvw8Ll9aZvQsD/wIhIgQqNclTSzzt0A1dXV/Pe//+WZZ55h/Pjxta674IILmDlzJrfcckuDjtWzZ0+WLVvGlVde6bls5cqV9d6+T58+hIeHs2fPnjrbMeu7T3V1NUuXLvW0aObm5rJ161Z69+7doGMArFixgurqap555hksFlenxUcffVTrNmFhYdjt9mPuW9flP//8M+eee66nzdXhcLBt2zZPTN27dycyMpK5c+dy/fXX1xvXn//8Z/r168cf/vAHvvnmmwaPi69Qgicipqi2O7jtg9XMWp9NiMXgmYsGcO7Adk36xE7k1ywWgz8Nac+Evm3499xtvLloNz9sPsgvOw5zz1m9uOLkTlq4RyRYGEaD2yTN8vXXX5Ofn8911113zHyvP/3pT7z++usNTvBuvfVWbrjhBoYOHcrIkSP58MMPWbduXb2LhMTGxnLXXXdx++2343A4GD16NEVFRSxevJiYmBjPXLWjde/enXPPPZcbbriBl19+mdjYWO69917atWvHueee2+Dfu2vXrlRXV/Pcc8/x+9//nkWLFh2zIEznzp09cxEHDBhAVFQUUVFRdO7cmQULFnDJJZcQHh5Oq1at6NatG5988gmLFy8mMTGRZ599luzsbE+CFxERwT333MPdd99NWFgYo0aN4tChQ2zcuJHrrrvumHG02+2cc845fPvtt8fM0/NlmpQgIi3O4XBy9yfrmLU+mzCrhZeuGMK5A9v99h1FGik2IpT7z+7Dt7edwtBOiZRW2Xnoi41c9PIvbM8pMTs8ERHA1Z555pln1rmYxwUXXMCaNWtYtWpVg451+eWXc99993HXXXcxePBgdu3axdVXX01ERP2LTj3yyCM89NBDPPHEE/Tu3ZsJEybw1VdfkZ6eXu993nzzTYYMGcI555zDiBEjcDqdzJo1q1b76G8ZOHAgzz77LE899RT9+vVj5syZPPHEE7VuM3LkSG6++WYuvvhiWrduzdNPPw3Aww8/zO7du+natSutW7cG4MEHH2Tw4MFMmDCBMWPGkJqaynnnnVfreA8++CB33nknDz30EL179+biiy8mJyenzvimTp3K9OnTmTRpEosXL27w72U2w6ndYWspKioiPj6ewsJC4uLizA4Hm83GrFmzmDRpUqOeMHJiNO7Nx+l08uAXrjl3VovBC5cPZkLfIytgaezNEQzj7nA4eXdpJk99u4XSKjthVgu3ndmdm0/ritWkal4wjLuv0tibo7nHvaKigl27dpGenn7chCbYnHnmmSQnJ/P+++97WiGl+TkcDoqKioiLi/OM+/Eeo97KQ/Q/LCIt6u/fZ/Dukj0YBjx70YBayZ1Ic7JYDK4a0ZnZd5zG2J6tqbI7+Pv3GVz88i/szTNpCXURES8rKyvj2WefZePGjWzZsoX/+7//Y+7cuVx66aVmhyYtRAmeiLSY95ft4YWarRAeO6+/2jLFFO0SInlj8jCevWgAseEhrMjM56x//cz/Vu477kpzIiL+wDAMZs2axSmnnMKQIUP46quv+PjjjxkzZozZoUkL0SIrItIift52iAc+3wDA1DO7c9nJHU2OSIKZYRicP7g9wzoncedHa1m2O4+7Pl7LT1tyeOKC/sRFqGVPRPxTZGTkMXveuVsFJTiogicizW7rwWKmvLsKu8PJHwe147YzupsdkggAHZKieP/G33H3xJ6EWAy+WZ/FH55byKYDeiMkIiL+SQmeiDSrogobN72zkuLKaoanJ/HkBf0btQmqSHOzWgymjOnGJ38eSbuESHbnlvHHFxbxv5X7zA5NRESk0ZTgiUizcTqd/PXjtew6XEq7hEheumII4SFWs8MSqdOADgl8fetoxvRsTWW1g7s+Xst9n66nqtphdmgiIiINpgRPRJrNywt28v3Gg4RZLbxw+WCSosPMDknkuBKjw3jj6mHcMa4HhuFaGOjqN5ZRWGYzOzQREZEGUYInIs1i2a48nv5uCwD/94c+DOiQYG5AIg1ksRj8vzO688bkYcSEh/DLzlzOf3ERe3K1lYKIiPg+JXgi4nVFFTZu/3ANDiecP7gdlw3Xipnif8b2TOHjm0eQFh/BjkOl/PGFRazek292WCIiIselBE9EvO6hzzewv6CcjklRPHxuPy2qIn6rd1ocn/9lFP3axZFbWsUVry1lyc5cs8MSkSA0ZswYpk6d6vm5c+fOzJgxo8H337NnD1arlTVr1ng9Nm8zDIPPP//c7DDqNHnyZM477zyzwzguJXgi4lVfrNnP52sOYLUY/PPigcSEa7tN8W9t4iL46KYRjO7WitIqO5PfXMaCrYfMDktEAszkyZMxDOOYr+3bt9d5++XLl3PjjTd6NYa33nqLhIQErx7zeKZNm8bAgQNb7HzBQgmeiHjNwaIKHqzZzPyWsd0Y0inR5IhEvCMqLITXrh7K6b1SqLA5uP7tFUryRMTrJk6cSFZWVq2v9PT0Om/bunVroqKiWjhC8QdK8ETEa/7vi40UVVRzUvt4bj29m9nhiHhVRKiVl64YwsS+qVTZHdz0zkrNyRMRrwoPDyc1NbXWl9Va9/ZCv27R3LJlC6NHjyYiIoI+ffrwww8/1NnquHPnTsaOHUtUVBQDBgzgl19+AWDevHlcc801FBYWeqqH06ZNqzdWwzB48cUXOeuss4iMjCQ9PZ2PP/641m3uueceevToQVRUFF26dOHBBx/EZnOtSvzWW28xffp01q5d6znfW2+95bnv4cOH+eMf/0hUVBTdu3fnyy+/PO7YvfvuuwwdOpTY2FhSU1O57LLLyMnJ8Vw/b948DMNg7ty5DB06lKioKEaOHElGRkat4zz66KOkpKQQGxvL9ddfz7333nvcKqPT6eTpp5+mS5cuREZGMmDAAP73v/8dN9bmpgRPRLzi+43ZfLcxmxCLwZPnn0SIVS8vEnjCQiz8+9JBnNqjNeU2O9e8tZxtB4vNDktEgpzD4eC8884jKiqKpUuX8sorr3D//ffXedv777+fu+66izVr1tCjRw8uvfRSqqurGTlyJDNmzCAuLs5TPbzrrruOe94HH3yQCy64gLVr13LFFVdw6aWXsnnzZs/1sbGxvPXWW2zatIl//etfvPrqq/zzn/8E4OKLL+bOO++kb9++nvNdfPHFnvtOnz6diy66iHXr1jFp0iQuv/xy8vLy6o2lqqqKRx55hLVr1/L555+za9cuJk+eXOfv/8wzz7BixQpCQkK49tprPdfNnDmTxx57jKeeeoqVK1fSsWNHXnzxxeOOwQMPPMCbb77Jiy++yMaNG7n99tu54oormD9//nHv15w0OUZETlhRhY2HvnC1Zt54ahf6tI0zOSKR5hMWYuGlKwZz2atLWbO3gGveWs5Xt4wmUfs8ivgkp9NJeXW5KeeODIls1EJjX3/9NTExMZ6fzzrrrGOqYnWZPXs2O3bsYN68eaSmpgLw2GOPMW7cuGNue9ddd3H22WcDriSqb9++bN++nV69ehEfH49hGJ5j/JYLL7yQ66+/HoBHHnmEOXPm8Nxzz/HCCy8AruTHrXPnztx55518+OGH3H333URGRhITE0NISEid55s8eTKXXnopAI8//jjPPfccy5YtY+LEiXXGcnSi1qVLF/79738zfPhwSkpKao3pY489xmmnnQbAvffey9lnn01FRQURERE899xzXHfddVxzzTUAPPTQQ8yePZuSkpI6z1laWsqzzz7Ljz/+yIgRIzznXrhwIS+//DKnnHJKg8bR25TgicgJe/q7LRwsqqRzchT/74zuZocj0uyiwkJ4c/IwznthEZm5Zdzy/irevma4KtciPqi8upyT3zvZlHMvvWwpUaENnyc3duzYWhWj6OjoBt0vIyODDh061EqUhg8fXudtTzrpJM/3aWlpAOTk5NCrV686b//444/z+OOPe37etGkTHTu6tj9yJzVuI0aMqLVK5//+9z9mzJjB9u3bKSkpobq6mri4hn0IfHSc0dHRxMbG1mq5/LXVq1czbdo01qxZQ15eHg6HA3CtHtqnT586j3v079+xY0cyMjKYMmVKreMOHz6cH3/8sc5zbtq0iYqKimMS6aqqKgYNGtSg37M5KMETkROy8UAhM5fuAeDx8/sTEVr3XAGRQJMYHcYrVw7ljy8sYtH2XJ76bgv3n93nt+8oIlKP6OhounVr/Bx2p9PZ4EphaGio53v3fdzJUF1uvvlmLrroIs/Pbdu2Pe7x3cdcsmQJl1xyCdOnT2fChAnEx8fzwQcf8MwzzzQ6Tvdx64uztLSU8ePHM378eN59911at27Nnj17mDBhAlVVVfUet67f/9fj6HQ6643Rfb9vvvmGdu3a1bouPDy83vs1NyV4ItJkTqeT6V9twumEc05KY2TXVmaHJNKieqbG8o8LBzBl5ipe/XkXv+uSzBm925gdlogcJTIkkqWXLTXt3C2hV69e7Nmzh4MHD9Kmjes1aPny5Y0+TlhYGHa7vdZlSUlJJCUl1Xn7JUuWcNVVV9X62V25WrRoEZ06dao1FzAzM/M3z9cUW7Zs4fDhwzz55JN06NABgBUrVjT6OD179mTZsmVceeWVnsuOd5w+ffoQHh7Onj17PG2fRzte4tyclOCJSJPNWp/Nsl15RIRauG9Sb7PDETHFpP5pXDc6ndcX7uKeT9bx3dRTaRVj3ie3IlKbYRiNapP0R+PGjaNr165cffXVPP300xQXF3sSq8bMAezcuTMlJSXMnTuXAQMGEBUVddytGD7++GOGDh3K6NGjmTlzJsuWLeP1118HoFu3buzZs4cPPviAYcOG8c033/DZZ58dc75du3axZs0a2rdvT2xsbJMqXx07diQsLIznnnuOm2++mQ0bNvDII480+ji33norN9xwA0OHDmXkyJF8+OGHrFu3ji5dutR5+9jYWO666y5uv/12HA4Ho0ePpqioiMWLFxMTE1MrUWxJmiwgIk1SYbPz+CzXSlk3n9aVdgkt8ymliC/664Se9GwTy+GSKu79ZP1xW3pERLzNarXy+eefU1JSwrBhw7j++us9C5xEREQ0+DgjR47k5ptv5uKLL6Z169Y8/fTTx7399OnT+eCDDzjppJN4++23mTlzpme+27nnnsvtt9/OLbfcwsCBA1m8eDEPPvhgrftfcMEFTJw4kbFjx9K6dWvef//9Rv7mLq1bt+att97i448/pk+fPjz55JP84x//aPRxLr/8cu677z7uuusuBg8e7FmJ83hj+Mgjj/DQQw/xxBNP0Lt3byZMmMBXX31V7/6FLcFw6q9QLUVFRcTHx1NYWNjgSaDNyWazMWvWLCZNmnRML7I0H437b3v+x238Y/ZW2sZHMPfOMUSGeWfuncbeHBr3E7fpQBHn/WcRVXYHz182iHNOOv48FdC4m0ljb47mHveKigp27dpFenp6oxKbQLRo0SJGjx7N9u3bSU9Pp6ioiLi4OCwW79R3DMPgs88+47zzzvPK8XzVuHHjSE1N5Z133mn0fR0OxzHjfrzHqLfyELVoikijFZRV8fL8nQDcc1YvryV3Iv6sT9s4/jymK/+au42Hv9rEaT1aExuhxEFEWsZnn31GTEwM3bt3Z/v27dx2222MGjWKrl27mjYXzN+UlZXx0ksvMWHCBKxWK++//z4//PADc+bMMTu0RlGLpog02kvzd1JcWU2v1Fh+34AqhUiw+POYrnROjiKnuJJ/ztlmdjgiEkSKi4uZMmUKvXr1YvLkyQwbNowvvvjC7LD8imEYzJo1i1NOOYUhQ4bw1Vdf8cknn3DmmWeaHVqjqIInIo2SU1TBW4t3Aa55RxZLwydviwS6iFArD5/bj6veWMZbi3dx6fAOdG8Ta3ZYIhIErrrqqlorWja3QJzlFRkZyQ8//GB2GCdMFTwRaZTnf9pOhc3BoI4JnN4rxexwRHzOqT1aM65PGxxOeOq7DLPDERGRIKMET0QabF9+Ge8vc21q/tcJPRu19LJIMLlnYi+sFoMfNh9k2a48s8MREZEgogRPRBrs5fk7sdmdjOqWrE3NRY6jW0oMFw9zbbb7+KzNAdnKJOLL9JwTX9USj00leCLSIIeKK/loxV4Abhnb3eRoRHzf1DO7ExVmZc3eAr7fmG12OCJBwb31QllZmcmRiNTN/dhszu1ZtMiKiDTIG4t2UVntYGCHBH7XJcnscER8XkpsBNeNTue5H7fz/E/bmdA3VW3NIs3MarWSkJBATk4OAFFRUXre4dqPraqqioqKCq/tgye/7ehxNwyDsrIycnJySEhIwGptvi2mlOCJyG8qqrDx7i+ZAEwZ01V/LEUa6JpR6bz28y427C/i522HObVHa7NDEgl4qampAJ4kT1xtgeXl5URGRupveAuqa9wTEhI8j9HmogRPRH7Tu0syKa6spntKDGf2bmN2OCJ+Iyk6jEuGd+DNRbv57y+ZSvBEWoBhGKSlpZGSkoLNZjM7HJ9gs9lYsGABp556arO2Bkptvx730NDQZq3cuSnBE5HjqrDZeWPhbsC1ibP2vRNpnEuHd+TNRbuZvzWHgrIqEqLCzA5JJChYrdYWeTPtD6xWK9XV1URERCjBa0FmjbuacEXkuL5el8Xhkkraxkfw+wFtzQ5HxO/0aBNL77Q4bHYns9ZrsRUREWleSvBEpF5Op5M3F+0C4MoRnQm16iVDpCnOHej6cOSLNftNjkRERAKd3q2JSL1WZuaz8UAR4SEWLqnZ00tEGs9d/V66K48DBeUmRyMiIoFMCZ6I1OutxbsBOG9gOxKjNW9IpKnaJUQyvLNre5Gv1h4wORoREQlkSvBEpE5ZheV8u8E1X+jqkZ3NDUYkAPy+pk3zO216LiIizUgJnojU6YNle7E7nAxPT6JP2zizwxHxe+P7uLYYWb2ngJyiCpOjERGRQKUET0SOYXc4+XjFXgCu+F0nk6MRCQxt4iIY2CEBgDmbD5objIiIBCwleCJyjAXbDnGgsIKEqFBP1UFETtz4vq7n0+yNSvBERKR5aKNzETnGh8tc1bs/DmpHRKg2iRXxlvF9Unn6uwwW7zhMcYWNCD29xMc4HE725JWxJ6+M7MIKym12qh1O7A4HBWU2dueWcri4iiq7g1CrQauYcDokRdG/XTzDOieRGh9h9q8gEvSU4IlILYeKK/mhpn3skmEdTY5GJLB0S4mhS+todh4qZV7GISb2aW12SCLkllQye9NB5mw6yMrMfArLbU0+1oAOCfxhQFsuHNqeuIhQL0YpIg2lBE9Eavlk1T6qHU4GdUygZ2qs2eGIBJxxfdrw8vydzN18UAmemGrVnnzeXLSbb9dnUe1wei4PD7HQKTmKtgmRRIVZCbFYCLEaRIeF0Ck5itT4CMJDrFRW2zlcXMn2QyWs3VvIhgOFrN1bwNq9BTwzO4NLh3fklrHdtM2OSAtTgiciHk7nkcVVLh6qjc1FmsPYnim8PH8nP287jOOoN9UiLWVzVhFPf7eFnzIOeS7r1y6Os/qlcUr3VvROiyPU2vhlGnKKK/h+QzbvLMlk68ESXl+4i49W7OW2M7pzzah0rBbDm7+GiNRDCZ6IeGzYX8SOQ6WEh1g4+6Q0s8MRCUhDOiUSEx5CbmkVG7OKzA5Hgkh5lZ1n52Tw+sJdOJxgtRicN7Ad14zqTL928Sd8/JTYCK4c0ZkrfteJ+VsP8dR3GWzOKuLRbzbz7YZs/nHhANrHq5on0tyU4ImIx+dr9gNwZp82xGruhEizCLVaGNUtme83HmTBtlzSzQ5IgsKG/YXc8t4qdueWAXBWv1T+OqEnXVrHeP1chmEwpmcKp3RvzUcr9vLYN5tZmZnPWf9awBPn9dUS7iLNTM8xEQFce999tfYAAOcNbGdyNCKB7bQeKQD8vO2wyZFIMPhw+R7Of3Exu3PLSI2L4I3JQ3nxiiHNktwdzWoxuHR4R76begojuyZTYXNw+8fr+SrTgl3tySLNRgmeiADwy45ccoorSYgK5bQeWvhBpDmd2qMVAKv3FlBWbXIwErAcDifTvtzIPZ+sp6rawRm9Uvh+6qmc3qtl9zdtnxjFO9edzE2ndQHghwMWpn60jqpqR4vGIRIslOCJCACfrXa1Z57dP42wEL00iDSn9olRdEuJweGErYVaeEK8r6rawdQP1/DW4t0A3DW+B69eNZT4KHPa760Wg/vO6s0zf+qP1XDy3caD3PTOCipsdlPiEQlkehcnIlTY7Hy/MRuA8wapPVOkJbgr5ZsLlOCJd1VVO7jpnRV8ufYAIRaDf10ykFtO747FB1ax/MOANG7o5SA8xMJPGYe47u3lSvJEvEwJnogwL+MQJZXVtEuIZEjHRLPDEQkKY3oeSfCcTs1HEu+wO5xM/XA1P2UcIiLUwqtXD+VcH5tX3TvByetXDSY6zMqi7bm8smCn2SGJBBS/SvD279/PFVdcQXJyMlFRUQwcOJCVK1d6rnc6nUybNo22bdsSGRnJmDFj2Lhxo4kRi/iH7zZkATCxX6pPfMIrEgyGdU4i1GpQWGWQmVdmdjgSAOwOJ/d9uo5Z67MJs1p45cqhjO2ZYnZYdTo5PYnH/tgfgNcX7qK4wmZyRCKBw28SvPz8fEaNGkVoaCjffvstmzZt4plnniEhIcFzm6effppnn32W559/nuXLl5Oamsq4ceMoLi42L3ARH1dZbWfu5hzAtWy2iLSMiFArAzskALBkZ765wYjf219QziWv/MJHK/ZhMeDflw7kVB9fMOv3A9rStXU0heU2/vtLptnhiAQMv0nwnnrqKTp06MCbb77J8OHD6dy5M2eccQZdu3YFXNW7GTNmcP/993P++efTr18/3n77bcrKynjvvfdMjl7Edy3enktxZTUpseEMVnumSIv6XbrrObdkZ57JkYg/+25DFmfNWMDy3fnEhIfw3KWDmdgvzeywfpPVYnDr6d0BePXnnZRUaklZEW/wm43Ov/zySyZMmMCFF17I/PnzadeuHVOmTOGGG24AYNeuXWRnZzN+/HjPfcLDwznttNNYvHgxN910U53HrayspLKy0vNzUVERADabDZvN/HYBdwy+EEswCaZx/2ada++7cb1TsNursZs81z2Yxt6XaNzNMbRDHABLduVRVVWFYahFuqUEwmPe6XTy/E87+fdPOwA4qX0cz154Ep2Sonz29/r1uE/s05r05Ch25ZbxzuJdXD+6s4nRBa5AeLz7o8aOu7f+fwynn8zsjoiIAOCOO+7gwgsvZNmyZUydOpWXX36Zq666isWLFzNq1Cj2799P27ZtPfe78cYbyczM5Pvvv6/zuNOmTWP69OnHXP7ee+8RFRXVPL+MiI+wO+HBFVZKqw3+0sdOj3i/eDkQCRjVDrh3mRWb0+C+AdWk6s+ONFC1A2Zut7Aq19WMNSbNwR86OrD6TW/WEb8cNPhgp5X4MCcPDbKjnXokWJWVlXHZZZdRWFhIXFxck4/jNxU8h8PB0KFDefzxxwEYNGgQGzdu5MUXX+Sqq67y3O7Xn346nc7jfiJ63333cccdd3h+LioqokOHDowfP/6EBtZbbDYbc+bMYdy4cYSGmrN3TTAKlnFfvCOX0iUrSYwK5ZaLziTEB94ZBMvY+xqNuzlsNhsvbZ7LtiKDsA79mHRyR7NDChr+/Jgvr7Iz5f01rMrNJcRiMP33vbloaHuzw2qQusb9jGoHc59ZwKGSKuztBvKHQW1/4yjSWP78ePdnjR13dyfhifKbBC8tLY0+ffrUuqx379588sknAKSmuhaHyM7OJi3tSN95Tk4Obdq0qfe44eHhhIeHH3N5aGioTz0BfC2eYBHo4/5jxmEAxvVpQ2TEsc8DMwX62PsqjXvL6x7vZFsRLNtdwDWju5odTtDxt8d8aWU1N85czZKdeUSGWnn1qqGM7t7K7LAa7ehxDw2Fa0an8/R3Gby+KJMLh3VUu3Iz8bfHe6Bo6Lh76//G/I/rG2jUqFFkZGTUumzr1q106tQJgPT0dFJTU5kzZ47n+qqqKubPn8/IkSNbNFYRf+B0Ovkxw7V65pm96/8QRESaV/c4V2v0sl152g9PjqvCZufat5azZGceMeEh/Pe64X6Z3NXl8pM7ER1mJeNgMfO2HjI7HBG/5jcJ3u23386SJUt4/PHH2b59O++99x6vvPIKf/nLXwBXa+bUqVN5/PHH+eyzz9iwYQOTJ08mKiqKyy67zOToRXzPjkMl7M0rJ8xqYVS3wHiDIOKPOsZAqNUgt7SK3bnaD0/qZnc4ueOjNSzd5Uru3rluOMM6J5kdltfER4Zy6XBXi/LL83eYHI2If/ObBG/YsGF89tlnvP/++/Tr149HHnmEGTNmcPnll3tuc/fddzN16lSmTJnC0KFD2b9/P7NnzyY2NtbEyEV8049bXNW7k7skER3uN93aIgEnxAL928UDsDJT++HJsZxOJw9/tZFZ67MJtRq8cuUQBgXgtjbXjk4nxGKwZGcea/cWmB2OiN/ymwQP4JxzzmH9+vVUVFSwefNmzxYJboZhMG3aNLKysqioqGD+/Pn069fPpGhFfJs7wTu9V4rJkYjI4I4JAKzM1H54cqzXF+7i7ZqNwJ+9aCAjA7Trom1CJL8f4Fpg5c1Fu0yORsR/+VWCJyLeUVRhY8VuV6VACZ6I+QZ3SADwPC9F3H7ZkcsT324B4IGze3sSoEB17ah0AL5Zn8Wh4srfuLWI1EUJnkgQ+nnrYaodTrq0jqZTcrTZ4YgEvcEdXS2a23JKKCzTRsTiklVYzq3vr8LucPLHQe24bnS62SE1u/7t4xnUMQGb3cn7y/aYHY6IX1KCJxKEPO2ZPVW9E/EFyTHhdE527XK+ao+qeAJV1Q6mzFzF4ZIqeqfF8fgf+wfN1gFXj+gMwMylmdjsDnODEfFDSvBEgozT6WR+zRLUas8U8R1DOrlWRNRCKwLwyNebWL2ngLiIEF66YjCRYVazQ2oxZ/VPpVVMGAeLKpm98aDZ4Yj4HSV4IkEm42Axh0sqiQi1MKRz4K3CJuKvhnRyPR9XaKGVoPfJyn28syQTw4B/XTIo6Frpw0Osni0T3v5lt7nBiPghJXgiQWbR9lwAhqcnEx4SPJ8Ii/i6oTUfuKzdW6i2tCC2JbuIv322HoDbzujO2CDttLj85E5YLQbLduWxOavI7HBE/IoSPJEgs2j7YQBGd0s2ORIROVq31jHERYRQbrOzJavY7HDEBBU2O//v/dVUVjsY07M1/+/07maHZJrU+Agm9k0F4L81W0SISMMowRMJIja7gyU7XRW8UQG6j5KIv7JYDAbUbJewZl+BqbGIOR77ZjNbD5bQKiacf1w4AIslOBZVqc8Vv+sEwJdr9lNaWW1yNCL+QwmeSBBZs7eAsio7SdFh9E6NMzscEfmVAe0TAFi3t8DUOKTl/bDpIO8scW9mPoBWMeEmR2S+33VJonNyFKVVdr5Zn2V2OCJ+QwmeSBBxt2eO7Joc9J8Mi/iik9q79sNbqwpeUCkst3nm3V0/Op1Te7Q2OSLfYBgGFw3rAMCHy/eaHI2I/1CCJxJE3Ame2jNFfNPAmhbN7TklakkLIk/M2kxOcSVdWkVz14SeZofjU/40uD1Wi8HKzHy2HdTcVJGGUIInEiRKKqtZvacAgNFK8ER8UkpcBKlxETicsGF/odnhSAtYvOMwH9RUp544vz8RoVrd+GgpcRGePVtVxRNpGCV4IkFixe48qh1OOiRF0iEpyuxwRKQe7jbNdfuU4AW6ymo7f/vU1Zp5xe86cnIXrW5cl0tq2jQ/WbWPymq7ydGI+D4leCJBYvlu1+bJwzvrDYSIL9NKmsHjjYW72Z1bRuvYcO6Z2MvscHzWaT1a0yYunPwyG3M2HTQ7HBGfpwRPJEgs35UPwPD0RJMjEZHj8aykqQQvoOUUVfD8j9sAuHdiL2IjQk2OyHeFWC1cMLg9AJ+t2m9yNCK+TwmeSBCorLZ7qgHDOieZG4yIHFf/mhbNvXnl5JVWmRyNNJcnv9tCaZWdgR0S+OOgdmaH4/PcYzR/6yFySypNjkbEtynBEwkC6/cVUlXtoFVMGOmtos0OR0SOIz4y1PM81XYJgWnt3gI+ralETftDX21b0wDd28TSr10c1Q4nX609YHY4Ij5NCZ5IEFhWM/9uWOckDENvJER8nWehlb1aaCUQ/WN2BgDnD2rn2RpDftsfB9W0aa5Wm6bI8SjBEwkCy3e5Eryhas8U8Quahxe4ftmRy8/bDhNqNbh9XA+zw/ErfxjQFqvFYO2+QnYcKjE7HBGfpQRPJMDZHU5WZNYssKIET8QvDOjgquCt3VeI0+k0ORrxFqfT6aneXTKso7asaaTWseGc0t21j+vnquKJ1EsJnkiAy8gupriimugwK73TYs0OR0QaoE9aPBYDDpdUklOsBSUCxU8ZOazMzCc8xMItp3czOxy/5F5s5bPV+3E49OGHSF2U4IkEOPf+d4M7JRJi1VNexB9Ehlnp0joGgE0HikyORrzB4XDy9++3AjB5ZGfaxEWYHJF/Gt8nlZjwEPbll7NqT77Z4Yj4JL3bEwlwK2vaM7U9goh/6ds2DoCNB7TQSiD4KSOHzVlFRIdZufm0rmaH47ciw6yM69MGgG/WZ5kcjYhvUoInEuDcy6xrpTYR/9InzZXgbcpSBS8QvDR/BwBX/K4TidFhJkfj387unwbArPVZatMUqYMSPJEAlldaRWZuGQADlOCJ+JW+bV0LrWxUi6bfW747j+W78wmzWrh2dLrZ4fi9U3q0IjY8hINFlaxUm6bIMZTgiQSwtXsLAOjSOpr4yFBzgxGRRulT06KZmVtGcYXN5GjkRLw0z1W9O39wO82984LwECvj+ta0aa5Tm6bIrynBEwlgq2sSPLVnivifpOgw0uJdycCW7GKTo5Gm2nqwmLlbcjAMuPHULmaHEzDOOelIm6ZdbZoitSjBEwlg7greICV4In7Js9DKfi204q/eWLgLgIl9Uz0ro8qJG92tNbERIeQUV7KiZrVoEXFRgicSoJxOp2eBFc2/E/FPWmjFvxWUVfH5GteG3NeM0tw7bwoLsTChbyqg1TRFfk0JnkiA2p1bRkGZjbAQC71S48wOR0SaoI8WWvFrHy7fS4XNQe+0OIZ1TjQ7nIBztqdNM1uraYocRQmeSIByt2f2axtHWIie6iL+yF3B23awhGq7w+RopDHsDifvLMkEYPLIThiGYXJEgWdUV9dqmodLKj1zzkVECZ5IwFJ7poj/a58YSWSolSq7g8y8MrPDkUaYu/kg+/LLSYgK5dyB7cwOJyCFhVgY0ysFgDmbDpocjYjvUIInEqA27ne1dPVvF29yJCLSVBaLQbcU18Ic2w5qJU1/4q7eXTysAxGhVpOjCVzj+ri2S5i9KdvkSER8hxI8kQDkcDjZeMC16p57s2QR8U/d27gTvBKTI5GG2pdfxsLthwG4fHgnk6MJbGN6tibUarDzUCnbc/QcEQEleCIBKTOvjNIqO+EhFrq2jjY7HBE5AT3axAKwVW9e/cb/Vu7D6YSRXZPpmBxldjgBLS4ilN91SQbUpinipgRPJAC5q3e90uIIseppLuLPerRRi6Y/cTicfLxiH+Bqz5TmN75mu4Q5atMUAZTgiQSkDTXz7/q11fYIIv6ue4qrgrfzUKlW0vQDi3fksr+gnNiIEM8+bdK8xvV2zcNbvbeAnOIKk6MRMZ8SPJEApPl3IoGjXYJW0vQnH67YC8B5A9tpcZUWkhofwYD28TidMHdzjtnhiJhOCZ5IgHE6nWyq2RS5ryp4In7PYjGOWmhFbZq+rLDMxvcbXW2CFw1Ve2ZLcq+m+YPm4YkowRMJNNlFFeSWVmG1GPRMjTU7HBHxAvdWCVu1kqZP+25jFlXVDnq2iaVfO33A1pLG1uyHt3hHLhU2u8nRiJhLCZ5IgHHvf9c9JUbtQSIBwrOSpip4Pu3LtQcA+MPAthiGYXI0waVPWhwpseGU2+ws351ndjgiplKCJxJgNta0Z/ZRe6ZIwHCvpKl9vnxXTlEFi3fkAvCHAW1Njib4GIbBaT1aAzAv45DJ0YiYSwmeSIBxf8LfJ00Jnkig0Eqavu/rdVk4nTC4YwIdkrT3nRnG9HS1ac7L0EIrEtyU4IkEmIyaBM/d0iUi/u/olTR352olTV/0hbs9U9U704zu3gqrxWDHoVL2asVZCWJK8EQCSGW1nV2HSwG0wIpIADl6Jc3tOZqH52syc0tZu7cAiwFnn6QEzyzxkaEM7pgAwLytatOU4KUETySA7Mgpxe5wEh8ZSkpsuNnhiIgXdWkVDcCOQ6UmRyK/9vW6LABGdWtFa732msrdpjlfbZoSxJTgiQQQ9/y7nm1itYKbSIDp0tpVwdupBM/nfLvBleCd3T/N5EhkTE/XQiuLd+RSWa3tEiQ4KcETCSCe+XepMSZHIiLe1qW1q4K387BW0vQle3LL2LC/CItxZLNtMY97u4SyKjvLd+WbHY6IKZTgiQSQrdlHKngiEli6tHJ9cOOeZyu+4buNrurd77okkxyj9kyzGYbBqTXbJfy8XfPwJDgpwRMJIFpBUyRwpdfMwSsos5FXWmVyNOL27YZsAM7ql2pyJOI2qlsyAIu355ociYg5lOCJBIiSymr25ZcDWkFTJBBFhllplxAJwM5DatP0BVmF5azeU4BhwIS+SvB8xciurQDYcKCQwjKbydGItDwleCIBYltN9a5NXDgJUWEmRyMizcFdxdNCK77hu5rq3ZCOiaTERZgcjbi1iYuga+tonE74ZaeqeBJ8lOCJBIitas8UCXjuhVZ2aKEVn+Buz5yo9kyfM6qbq4q3eMdhkyMRaXlK8EQCxLaDrjd83VOU4IkEqi6q4PmMQ8WVLN+dB8BZ2h7B57jbNBdtV4InwUcJnkiA2Fmzsp77E34RCTxH9sJTBc9s32/MxumEAe3jPXMjxXf8rksShgE7DpVysKjC7HBEWpQSPJEA4X7DpwRPJHC5n9978sqotjtMjia4fedpz1T1zhclRIXRr208oDZNCT5K8EQCQFW1g701K2h2ba1NzkUCVdv4SCJCLdjsTs+qudLy8kurPIt3aHsE3zWyZruERdouQYKMEjyRALAnrxS7w0l0mJWUWG20KxKoLBaDzsk18/C00Ippfth8ELvDSa/UWDq3UteEr3LPw1u8/TBOp9PkaERajhI8kQCw45B7/l0MhmGYHI2INKeunnl4WmjFLHM35wAwXnvf+bRhnRMJtRocKKwgM7fM7HBEWowSPJEAsPOQFlgRCRaerRKU4JmistrOz9sOAXBm7xSTo5HjiQoL4aT2CQAsq1nxVCQYKMETCQCeBVZaaf6dSKBzt2hm5irBM8PSnXmUVtlpHRvuWcRDfNewzkkALN+lBE+ChxI8kQCgLRJEgken5CgAtZyZ5MctrvbMM3qlYLGoJd7XDU9PBPDsWSgSDPw2wXviiScwDIOpU6d6LnM6nUybNo22bdsSGRnJmDFj2Lhxo3lBirQQdwUvXZP9RQJep5oK3oHCciqr7SZHE1ycTidztxwE4PReas/0B0M6ufbD251bRk6x9sOT4OCXCd7y5ct55ZVXOOmkk2pd/vTTT/Pss8/y/PPPs3z5clJTUxk3bhzFxcUmRSrS/PJLq8gvswGq4IkEg1YxYUSFWXE60VYJLWxbTgl788oJC7Ewunsrs8ORBoiPDKVnm1gAlu/KNzkakZbhdwleSUkJl19+Oa+++iqJiYmey51OJzNmzOD+++/n/PPPp1+/frz99tuUlZXx3nvvmRixSPNyL5WeFh9BVFiIydGISHMzDMNTxdM8vJblXj1zZNdkvd76keHpNfPw1KYpQcLvXp3+8pe/cPbZZ3PmmWfy6KOPei7ftWsX2dnZjB8/3nNZeHg4p512GosXL+amm24yI9wTZmz5mrb5yzA2VYLV2tB7NfIkTZlD0NznaEJMXjyHYbeTWrASI4Mj4+6j41S2/RCnW3bTJyYWMmzNco7G3fzExsmwV5NStA5jRzhY63mJavQpzH08eef4zXsOo7qa5OLNGJlx9Y/7CZ6j5g6NvHkzj5NhQEgEhMdCWLTrKzQaLL79+WenpCg2ZxVpHl4L++mo+XfiP4Z2TuK/v2QqwZOg4VcJ3gcffMCqVatYvnz5MddlZ2cD0KZNm1qXt2nThszMzHqPWVlZSWVlpefnoqIiAGw2GzZbA94sN7OQr29jWGUh7DY7kuASApwMsMvkQBrgFOCUMCAXeN/kYLwgBBgBsMPkQIJMCDAaYLvJgfgAp2GBmDY4Y9tCfHucrXvjTD0JZ/thEJn42wdoBPffmcb+vWmfGAHArkMlPvG3yh81duxLKqtZtcfV4jeyS6LGvYma+pg/EYPau1o0N2cVkVdcTmyEX7399Qozxl0aP+7e+v/xm0f43r17ue2225g9ezYRERH13u7Xmzw7nc7jbvz8xBNPMH369GMunz17NlFRUU0P2EtODk/HGlr52zesYeBs5Bkae/vG36VFYmqkRsfk9MGYahyugNJqSAyDuLDfunXjzmG0wO/d+P/vRv4OjTy66xTN+3s37f/aB2MKhHFyOrE6bYTYKwhxVGDgxHA6oDgLozgLDqyEzV/UHNkgN6YH+xN+x96kUdit9f8taqw5c+Y06vbFBw3Ayootu5ll7PRaHMGooWO/Id+g2mElOdzJhiXz2NDMcQW6xj7mT1RyuJXcSoNXPplD78SW+Nvmm1p63MWloeNeVuadrowGJXhJSUmNOqhhGKxatYpOnTo1Kai6rFy5kpycHIYMGeK5zG63s2DBAp5//nkyMjIAVyUvLS3Nc5ucnJxjqnpHu++++7jjjjs8PxcVFdGhQwfGjx9PXFyc1+JvKpttHHPmzGHcuHGEhoaaHU7QsNlsfjPuV7+8hHX7inj+/AFM6Fv/Y91f+NPYBxKNO1Q7nVBdARUFruSuKAujMBMjez1G1mqM3O20KsmgVUkGJx36FMeI23AMvwlCI5t8zqaOe8KOXD7cuZKKkBgmTRrd5PMHs8aO/cpvtgB7GHdSByZN6tP8AQYos15r5pWv57M1WRhtujHpzO4tdl5fodd4czR23N2dhCeqQQleQUEBM2bMID7+tzf0dDqdTJkyBbvdu0s3n3HGGaxfv77WZddccw29evXinnvuoUuXLqSmpjJnzhwGDRoEQFVVFfPnz+epp56q97jh4eGEh4cfc3loaKhPPQF8LZ5g4Q/jvi/ftexzekqsz8faGP4w9oEo6Mc9LAyi4iCp47HX5WfC5q9gxRsYeTuwznsU67r34E9vQtuBJ3Taxo57lxTXB5D78iuwWEOwaj+2Jmvo2C/e6Zq/dVqPlOB+jnhJS7/WDOmczGdrsli3vyio//+C/jXeJA0dd2/93zS4RfOSSy4hJaVhk4pvvfXWJgdUn9jYWPr161frsujoaJKTkz2XT506lccff5zu3bvTvXt3Hn/8caKiorjsssu8Ho+ILyiusJFXWgVAxyTzW4pFAlpiJxh5C/xuCqz/GH6YBnk74fVx8IfnYMAlLRZK24RIQq0GVXYHWYXltE/U8785ZRWWsz2nBMOAkV21PYI/GtQxAYB1ewtxOJzapF4CWoMSPIfD0aiDmrXv3N133015eTlTpkwhPz+fk08+mdmzZxMbG2tKPCLNbW+eaw+spOgwYiP0iZxIi7BYYMDF0H0cfHELZHwDn90EVaUw7LoWCcFqMeiQGMXOw6XsyS1TgtfMFm47DMBJ7eKJj9JrrT/q2SaWyFArxZXV7DhUQvc2em8ogcu314H+DfPmzWPGjBmenw3DYNq0aWRlZVFRUcH8+fOPqfqJBJI9ea7JuB1UvRNpeVFJcPG7MLxmG55v7oCNn7fY6Tsmu573u7VVQrNbuN2V4Glzc/8VYrXQv71rqtHqvQXmBiPSzBq9iuaXX35Z5+WGYRAREUG3bt1IT08/4cBE5LftyXNtcqz2TBGTWCxw1lOAE5a9Ap/dDEnpkDag2U/dOTkaOERmnjY7b05Op5NF7gSvW2uTo5ETMahDAst25bF6TwEXDe1gdjgizabRCd55552HYRg4f7U8tvsywzAYPXo0n3/+OYmJ3t0vSERqc1fwOinBEzGPYcCEJyB3B+yYCx9PhpsXQVjzPi/dlfu9eargNadtOSUcLqkiItTC4E4JZocjJ8A9D2+NKngS4BrdojlnzhyGDRvGnDlzKCwspLCwkDlz5jB8+HC+/vprFixYQG5uLnfddVdzxCsiR9lTMwdPFTwRk1lD4E+vQ1w718Irc4/dX9XbOiS6tmfYl1/e7OcKZkt3uVbPHNIpkfAQq8nRyIkY2MFVeMjILqKsqtrkaESaT6MreLfddhuvvPIKI0eO9Fx2xhlnEBERwY033sjGjRuZMWMG1157rVcDFZFj7dUcPBHfEZkIf/g3vHsBLH0J+p4PHU9uttO5F1ZRgte8ltUkeMM7J5sciZyo1PgIUuMiyC6qYN2+Qn7XRf+nEpgaXcHbsWNHnRuAx8XFsXPnTgC6d+/O4cOHTzw6EamX3eFkX74rwXMvtiAiJut2Jgy6wvX9d/dCI1ehboz2Sa4KXl5pFaWVqkY0B6fTybJduQAMT08yORrxBrVpSjBodII3ZMgQ/vrXv3Lo0CHPZYcOHeLuu+9m2LBhAGzbto327dt7L0oROUZWYTk2u5Mwq4XUuAizwxERt9MfgrAYOLDKtV9eM4mLCCU+0rVkv6p4zWNPXhkHiyoJtRqexED828AOCQCs2VNgahwizanRCd7rr7/Orl27aN++Pd26daN79+60b9+e3bt389prrwFQUlLCgw8+6PVgReQI9wIr7RMjsWrDVhHfEdsGTrnT9f2Pj0B1VbOdqr1nHp4WWmkO7vbMk9onEBGq+XeBwJPgqYInAazRc/B69uzJ5s2b+f7779m6dStOp5NevXoxbtw4LBZXvnjeeed5O04R+RX3/Lv2mn8n4nt+NwWWvgyFe2Ht+zDk6mY5TYfEKDYeKFIFr5l45t+pPTNg9GsXj2FAdlEFh0sqaRUTbnZIIl7X6AQPXFsiTJw4kTFjxhAeHo5hqHog0tL2F1QARz7BFxEfEhoBo26D7++Dn5+BgZeBNdTrp3E//7VVQvNYtlsJXqCJDg+hS6todhwqZcP+Qsb0TDE7JBGva3SLpsPh4JFHHqFdu3bExMSwa9cuAB588EFef/11rwcoInXbX/OJfbsEJXgiPmnIZIhuDQWZsP5/zXKK9toqodlkF1aQmVuGxXBtkSCBo1+7eAA2HigyORKR5tHoBO/RRx/lrbfe4umnnyYsLMxzef/+/T1z8ESk+R0oUIIn4tPColytmgBLXgCn0+uncG+Rsq9AFTxvc1fv+rSNIy7C+9VXMU+/tq4Eb8P+QpMjEWkejU7w/vvf//LKK69w+eWXY7UemXB80kknsWXLFq8GJyL1O1DoSvDaKsET8V1DJkNIJGSvg8zFXj+89sJrPqsy8wEY2kntmYGmbzvXdl/rleBJgGp0grd//366det2zOUOhwObzeaVoETk+BwOJ1k1c/DaJmiLBBGfFZUEAy52fb/0Ra8f3t2iWVBmo7hCf4O9aXXNKovaHiHw9K2p4O3LL6egrPlWuRUxS6MTvL59+/Lzzz8fc/nHH3/MoEGDvBKUiBzf4dJKquwOLAa00R54Ir7t5Jtd/275BvIzvXro6PAQkqJd0yVUxfOeCpudTQdc1Z3BHTX/LtDER4bSKdlV/dY8PAlEjV5F8//+7/+48sor2b9/Pw6Hg08//ZSMjAz++9//8vXXXzdHjCLyK+4FVlLjIgi1NvpzGhFpSSm9ocsY2DkPVr0NZzzk1cO3T4wkr7SKffnl9E6L8+qxg9WmrCJsdifJ0WFaqThA9WsbT2ZuGev3FzKqWyuzwxHxqka/M/z973/Phx9+yKxZszAMg4ceeojNmzfz1VdfMW7cuOaIUUR+5YCnPVNvPET8wtBrXf+ungn2aq8eWpude9/qPQWAqz1TW0EFJvc8PC20IoGoSfvgTZgwgQkTJng7FhFpIPcKmkrwRPxEj7MgqhWUZMO22dBrktcO7V5oZW+eWjS9ZU3N/LuBHRJMjUOaj3slTbVoSiBSb5eIH9qvBE/Ev4SEwcBLXd+v+q9XD9023jUPN6tQCZ63rN7jWkFzkObfBSx3O/Pu3FLKq+wmRyPiXQ2q4CUmJja4RSEvL++EAhKR33ZkDzwtsCLiNwZfDYufg23fQ9EBiGvrlcOm1XzQc6CwwivHC3aHiivZl1+OYcBJ7ePNDkeaSevYcJKjw8gtrWJbTjEntU8wOyQRr2lQgjdjxgzP97m5uTz66KNMmDCBESNGAPDLL7/w/fff8+CDDzZLkCJSm7uC106T/0X8R6vu0HEk7FkMa2bCqX/1ymHbxrteB7JVwfMKd3tm95QYYrXBeUDrlRbLou25bMlSgieBpUEJ3tVXX+35/oILLuDhhx/mlltu8Vz2//7f/+P555/nhx9+4Pbbb/d+lCJSi+bgifipwVe5ErxV78DoO71yyNSaFs2c4kpsdodW1j1B6/cVADBAb/gDXs82ca4EL7vY7FBEvKrRfwW+//57Jk6ceMzlEyZM4IcffvBKUCJSvwqbnfwy14bGqdoDT8S/9DkXwuOhIBN2L/DKIZOjwwizWnA64WCR2jRP1IaaRTf6qz0z4PVKjQUg46AWWpHA0ugELzk5mc8+++yYyz///HOSk5O9EpSI1O9QcSUA4SEW4iPVPiTiV8KioP+fXN+vescrh7RYDE8VL1vz8E6Ye9n8vm2V4AW6XmmuBG9Llip4ElgavU3C9OnTue6665g3b55nDt6SJUv47rvveO2117weoIjU5v6Evk1chPZnEvFHg66AFa/D5q9g/JNeOWRqfAR78sq00MoJyimuIKe4EosBvWve/Evg6p4Si2FAbmkVh4oraR0bbnZIIl7R6Are5MmTWbx4MQkJCXz66ad88sknxMfHs2jRIiZPntwMIYrI0Q4WuSp4beL0h0jEL7UdBG36gb0Sy8ZPvHNI91YJBVpo5US490Tr0jqGqLAmbRUsfiQyzErn5GgAtmSrTVMCR5NevU4++WRmzpzp7VhEpAGOruCJiB8yDBh0JXx3D5Y170LbE19N071VQpYqeCdkY017Zr+2cSZHIi2lV2osuw6XkpFdzCndW5sdjohXNKiCV1TUuE81iovVyyzSXJTgiQSAky4CaxjGwfXEl+0+4cOlabNzr9iw3/V+p187zb8LFj1rFlrRSpoSSBqU4CUmJpKTk9Pgg7Zr146dO3c2OSgRqd+RBE8tmiJ+KyoJep0NQMfcE19NMy1eFTxv2HBAC6wEG89KmkrwJIA0qEXT6XTy2muvERMT06CD2my2EwpKROp3ZA6eKngifm3QlbDxM9rnL4bqCght+qq47gregQIleE1VUFbFvnxXBbSPWjSDRrcUV4K341AJTqdTi5dJQGhQgtexY0deffXVBh80NTWV0BP4QyUi9TtY7HoDlxKrBE/Er3UZizOuPWFF+6jO+AYGXtLkQ7kTvMMllVRVOwgL0WbnjbWpZoGVTslR2oImiHRKjiLEYlBWZSe7qMJTDRfxZw1K8Hbv3t3MYYhIQ+VoFU2RwGCx4BhwKdaf/45lzcwTSvCSosMID7FQWe3gYFEFHZKivBhocNiU5Urw+qSpehdMQq0WOiVHseNQKTtySpXgSUDQR3wifqSkspqSymoAUtSiKeL3HCddihMDy+4FkL+7yccxDOOoNk0ttNIU7jlY7kU3JHh0be2agrQ9R/PwJDAowRPxIzk1C6zEhIcQE649mkT8XkJHDsX2cX2/5r0TOlRqTYKXXaR5eE2x9WBNgtdGCV6w6ZriSvB2HCo1ORIR71CCJ+JH3AuspKg9UyRg7Ek+zfXN6pngsDf5OG1rWsu00ErjORxOth4sAaCHKnhBx13B23GoxORIRLxDCZ6IH8mpWWCljRZYEQkYWfGDcUYkQNE+2PlTk4/jqeBpL7xG25dfTrnNTliIhU6avxh0uraOBpTgSeBQgifiR7QHnkjgcVjCcPT7k+uHVe80+TjuBM9d6ZeGy6hpz+zWOoYQq94aBRt3i+bBokqKK7TVl/i/Jr2K/fzzz1xxxRWMGDGC/fv3A/DOO++wcOFCrwYnIrXleFo0VcETCSSOAZe7vtnyDZTmNukY7r0xNQev8Tzz79SeGZTiIkJJiXV9cLpT8/AkADQ6wfvkk0+YMGECkZGRrF69mspK1xvO4uJiHn/8ca8HKCJH5JZWAdAqJszkSETEq1L7Q9oAcNhg7ftNOoQ7wTuoBK/R3Cto9tACK0FL8/AkkDQ6wXv00Ud56aWXePXVV2ttZj5y5EhWrVrl1eBEpLbDJa4PVJKj1aIpEnCGXOP6d/lr4HA0+u6pNQleTnElDofTm5EFvCMVvBiTIxGzdE3RPDwJHI1O8DIyMjj11FOPuTwuLo6CggJvxCQi9cgtcVXwklXBEwk8J10EEfGQvwu2z2n03VvFhGExwO5wcrhU8/AaymZ3eN7Uq4IXvI7shacET/xfoxO8tLQ0tm/ffszlCxcupEuXLl4JSkTqllvzpq1VjCp4IgEnLBoGXen6funLjb57iNXieW3I0UIrDbY7twyb3Ul0mJV2CZFmhyMm6VKT4O0+XGZyJCInrtEJ3k033cRtt93G0qVLMQyDAwcOMHPmTO666y6mTJnSHDGKCOB0OlXBEwl0w64HDNgxFw5va/TdPQutFGoeXkNtq9n/rnubWAzDMDkaMYt7e4zMvFKcTrU4i38Laewd7r77bgoLCxk7diwVFRWceuqphIeHc9ddd3HLLbc0R4wiAhSVV1NdM68mKVoJnkhASkqHHhNh67ew7FWY9HSj7t4mLoL1+wu1kmYj7DzsWjWxe4rm3wWzdomRWC0GFTYHOcWVng9LRPxRk7ZJeOyxxzh8+DDLli1jyZIlHDp0iEceecTbsYnIUdxzamIjQggPsZocjYg0m5NvdP275j2oKGrUXVPj3S2aSvAaaldNS156zWbXEpxCrRbaJriSusxctWmKf2vybp5RUVEMHTqU4cOHExOjT71Empu7PVPz70QCXJex0KonVBXDijcaddc2sdoLr7F257oqeF1a6b1MsOuU5EryM3O1F574twa1aJ5//vkNPuCnn37a5GBEpH65ni0S1J4pEtAMA0bdBl9MgSUvwMk3Q2jD2sXaxLv3wtMiKw3hdMLOmgpeF1Xwgl6n5CgWboc9eargiX9rUIIXHx/f3HGIyG84XLPJuebfiQSB/hfCT49D0T5Y+x4MvbZBd9Nm541TbIOSymoMw/XmXoKb+zGwWy2a4ucalOC9+eabzR2HiPwGTwVPLZoigS8kDEbeCt/dA4v+BYOuAutv/8l2b3auFs2GyakZpvaJkZrbLHSsadHcoxZN8XNNnoMnIi3ryBw8VfBEgsLgKyEyCfJ3w8bPGnQXd4JXUGajwmZvxuACQ065a1sEzb8TOFLBy1SLpvi5Rm+TMGjQoDr3iTEMg4iICLp168bkyZMZO3asVwIUERf3JueagycSJMKi4XdT4KdHYf6T0PePv1nFi4sMITzEQmW1g5yiSjqq7fC4DrkTPM2/E6BjzV54BWU2CsttxEeGmhyRSNM0uoI3ceJEdu7cSXR0NGPHjmXMmDHExMSwY8cOhg0bRlZWFmeeeSZffPFFc8QrErTyaubgJSrBEwkev7sZopIhdzusff83b24YBqnxatNsKHeLZpdWSvAEosNDPCtV79E8PPFjjU7wDh8+zJ133snPP//MM888w7PPPsuCBQu46667KC0tZfbs2TzwwAPaF0/EywrKbAAkRinBEwka4bEw+nbX9/OehOrfXh3TvVWCFlr5bZ4WzdZq0RSXI22amocn/qvRCd5HH33EpZdeeszll1xyCR999BEAl156KRkZGScenYh4FJa7EryEKLWMiASVYddDbJprRc0Vv73o2ZGtEpTgHY/d4SS3Jl/WCpri1j4xEoADBeUmRyLSdI1O8CIiIli8ePExly9evJiICNcfFYfDQXi4VvoT8SZ3BS8hUhU8kaASGgmn3e36fsHfoaLwuDdPjXP9/VWCd3wHiyqwOw1CrQZp8ZFmhyM+om2CO8HT80f8V6MXWbn11lu5+eabWblyJcOGDcMwDJYtW8Zrr73G3/72NwC+//57Bg0a5PVgRYJVhc1Oec2KePGq4IkEn0FXwi8vQO42mP80THis3pu28WyVoM3Oj2dvvqtC0zY+Eqvl2MXjJDi5E7x9+argif9qdIL3wAMPkJ6ezvPPP88777wDQM+ePXn11Ve57LLLALj55pv585//7N1IRYJYUU17psWA2PBGP21FxN9ZQ2HiEzDzT7D0ZdfG58ld67ypZ7PzQlUgjsf9Bt7dkicC0D5BLZri/5r0TvHyyy/n8ssvr/f6yEi9WIp4U0FNghcfGYpFnzSLBKfu46DbONg+B76/Hy77oM6btdFm5w3iruB1SNJ7FjnC06JZqARP/FeTSwFVVVXk5OTgcDhqXd6xY8cTDkpEanPPv9OePCJBbsLjsPMn2PotbP0eekw45iYpsa45eLklatE8Hk8FL0EJnhzRNsH1AUlBmY3Symqi1TUjfqjRi6xs27aNU045hcjISDp16kR6ejrp6el07tyZ9PT05ohRJOgVlLn2wIvXFgkiwa11D/hdzRSIr++AyuJjbpIc43qdKK2yU15lb8no/IqngqcWTTlKbEQosRGupE5tmuKvGv2xxOTJkwkJCeHrr78mLS0Nw1C7mEhzc7doJqiCJyJj/gabv4L83TD3YZj091pXx4SHEBZioaraQW5pJe3DtAVAXfapRVPq0S4hki3ZxewvKKd7m1izwxFptEYneGvWrGHlypX06tWrOeIRkToUaQ88EXELi4JzZsA758GyV6Hfn6DjyZ6rDcOgVXQYBworyC2pon2iErxfq7DZySl2tbBqkRX5NXeCp60SxF81ukWzT58+HD58uDliEZF6HNkDTwmeiABdx8LAKwAnfHbTMa2ayTE18/BKNQ+vLvvyywAItzr1uirHaKuVNMXPNTrBe+qpp7j77ruZN28eubm5FBUV1foSEe8rKNccPBH5lQmPQXwHyN8Fs/5a6yr3PLzDJVVmRObz9ua53ri3CkdTTeQY7WqquvuV4ImfanSCd+aZZ7JkyRLOOOMMUlJSSExMJDExkYSEBBITE5sjRgCeeOIJhg0bRmxsLCkpKZx33nlkZGTUuo3T6WTatGm0bduWyMhIxowZw8aNG5stJpGWogqeiBwjMgHOfxUMC6x9H9Z97LkqKdqV4OWVKsGry96aCl5SuNPkSMQXuSt4SvDEXzV6Dt5PP/3UHHH8pvnz5/OXv/yFYcOGUV1dzf3338/48ePZtGkT0dHRADz99NM8++yzvPXWW/To0YNHH32UcePGkZGRQWysJsmK/yrUHDwRqUunEXDq3TD/Sfh6KqT2h5RetIrRVgnHs79mgZWkCJMDEZ/UrmarBPfjRMTfNDrBO+200+q9bs2aNScSy3F99913tX5+8803SUlJYeXKlZx66qk4nU5mzJjB/fffz/nnnw/A22+/TZs2bXjvvfe46aabmi02kebmqeApwRORXzv1r5C5CHb/DB9cCjf8SHJNBS9XLZp1OlDoWjwjMUwVPDlWmzhXgneouBKn06k2XvE7J7x7Y2FhITNnzuS1115j7dq12O0ts+dOYWEhAElJSQDs2rWL7Oxsxo8f77lNeHg4p512GosXL643wausrKSy8sgnnO55hDabDZvN1lzhN5g7Bl+IJZj42rjn1+yDFxNq8ZmYmouvjX2w0Libw2vjft6rhLw5DiNvJ46PryGx5zMAHCqu0P9pHbIKXC2aCeF6zLc0f3itSYiwAlBld3CoqIzEAJj/7g/jHogaO+7e+v8xnE5nkz6++vHHH3njjTf49NNP6dSpExdccAEXXHABgwYN8kpgx+N0Ojn33HPJz8/n559/BmDx4sWMGjWK/fv307ZtW89tb7zxRjIzM/n+++/rPNa0adOYPn36MZe/9957REVpaWnxDfcts1JmN7hvQDWpeliKSB3iyjI5ZesjhDirWBY3gYtyrqZ9tJO/nqTNzn9t+ioreZUGU/tVk64ZHFKHvy23UlptcM+Aatrq7660kLKyMi677DIKCwuJi4tr8nEaVcHbt28fb731Fm+88QalpaVcdNFF2Gw2PvnkE/r06dPkIBrrlltuYd26dSxcuPCY635dRv+t0vp9993HHXfc4fm5qKiIDh06MH78+BMaWG+x2WzMmTOHcePGERqq9ryW4kvj7nQ6uWPpD4CTSeNPJzUusCeN+NLYBxONuzm8Pu6b0uCz6xle9D03WpP4wvonJk2qf2pFMHI4nNy1zPWamhCGHvMtzF9ea/6zYzFbc0roOXA4p3RrZXY4J8xfxj3QNHbcvbUjQYMTvEmTJrFw4ULOOeccnnvuOSZOnIjVauWll17ySiANdeutt/Lll1+yYMEC2rdv77k8NTUVgOzsbNLS0jyX5+Tk0KZNm3qPFx4eTnh4+DGXh4aG+tQTwNfiCRa+MO7lVXbsDlehPTEmktDQE+6s9gu+MPbBSONuDq+N+4ALoTQbZj/A30Lfp7AsgZCQMzSH6Cg5xRXY7E4sBsSF6TFvFl8f9zbxEWzNKSGvzO7TcTaWr497oGrouHvr/6bB2yTMnj2b66+/nunTp3P22WdjtVq9EkBDOZ1ObrnlFj799FN+/PFH0tPTa12fnp5Oamoqc+bM8VxWVVXF/PnzGTlyZIvGKuJNxZWufmzDgKjQln3eiYgfGnkr1b+7FYDHrC9TtuFrkwPyLdk1C6y0jg3HqrxX6pES6+qWySmuMDkSkcZrcIL3888/U1xczNChQzn55JN5/vnnOXToUHPGVstf/vIX3n33Xd577z1iY2PJzs4mOzub8nLXEraGYTB16lQef/xxPvvsMzZs2MDkyZOJiorisssua7E4RbytpKIagJiwECwWvRsRkd8WMuERPnOeRojhIOqzayHju9++U5A4UOB6wx7o7e5yYlLiXN1dOUXaakT8T4MTvBEjRvDqq6+SlZXFTTfdxAcffEC7du1wOBzMmTOH4uLi5oyTF198kcLCQsaMGUNaWprn68MPP/Tc5u6772bq1KlMmTKFoUOHsn//fmbPnq098MSvlVS6ErzYiOBozRQRLzAMngn/C7PswzEcVfDhFbD5K7Oj8gnZha4PhtPileBJ/drE1iR4quCJH2pwgucWFRXFtddey8KFC1m/fj133nknTz75JCkpKfzhD39ojhgBV4tmXV+TJ0/23MYwDKZNm0ZWVhYVFRXMnz+ffv36NVtMIi2h2F3BU4InIo0QExXJrbZbyel0Djhs8NHVsOETs8MyXVaRu4J37Px7EbeUmgqvKnjijxqd4B2tZ8+ePP300+zbt4/333/fWzGJyFE8CV64EjwRabj4yFDsWFk66Ek46RJw2uF/18EvL5gdmqncc/BSVcGT40ipqeAdVAVP/NAJJXhuVquV8847jy+//NIbhxORo7hbNGMitOqViDRcQpTrNaOwwgHnvQBDrwOc8P198O094AjO/fHcFRn3G3iRurQ5qoLXxC2jRUzjlQRPRJpPSYVrFc1YVfBEpBESIsMAKCy3gcUKZz8D4x52Xbn0Jde8vArv7LnkTw6VuBK8VjFhJkcivqx1zQcAldUOimo6aUT8hRI8ER/nqeApwRORRoh3V/DKXR8SYRgw6ja48C2whkPGLHh1LORsNi9IExz2JHiq4En9IkKtng9Wc0s0D0/8ixI8ER9XrFU0RaQJ4iNdCV5BWVXtK/r+Ea6ZBXHtIHc7vHo6rPvYhAhbXlW1g4IyV8KrCp78lqSax0huadVv3FLEtyjBE/FxJVpFU0Sa4EiCZzv2yvZD4aYF0GUM2Mrg0+vhsz9DRWHLBtnCcktdlZhQq0G85jXLb0iKrknwSpTgiX9Rgifi49SiKSJNkfDrFs1fi24FV3wKp/4VMGDte/DiaNi9qOWCbGGHil0JXnJ0OBaLYXI04uuSo11tvHmq4ImfUYIn4uNKaxK8aCV4ItII7gpevQkeuBZfOf0BuOZbSOgEhXvgrbPhu79BZUkLRdpyPPPvYtWeKb8t2VPB0xw88S9K8ER8XFmVaynzqDCryZGIiD+ptYrmb+k0Av68CAZdCThhyX/gPydDxrfNG2QLc1fwWmuBFWkAzcETf6UET8THldtcCV5kqBI8EWk4d4tmnXPw6hIeC+c+D5f/DxI6QtE+eP8S+OByyN/dfIG2oMM1c6m0gqY0hKeCpwRP/IwSPBEfV15TwYtUBU9EGiGupkWz3GansroRm5p3HwdTlsKoqWAJgS1fw/PDYPYDUJ7fPMG2EE8FT5ucSwMk11Tw8krVoin+RQmeiI9TBU9EmiI2PAT3OiINatM8WlgUjJvuWmkz/TSwV8Hi5+Dfg2DJS1DtnxWNQ9oDTxohqWaRFa2iKf5GCZ6Ij1MFT0SawmIxPFW8woa2af5am75w1Reuts3WvVwVvO/ugeeHwKr/gr2JxzWJu4LXShU8aQB3i2b+r/eSFPFxSvBEfJwqeCLSVAnuvfAaW8E7mmG42jZvXgTnzICYNlCwB768FZ4fCqtngr3aOwE3M88qmtrkXBqgQSvRivggJXgiPk4VPBFpqtiazbxLKryQgFlDYOg18P/WwITHIbq1a/GVL6bAf4bBijfAVn7i52lG+TWLZbj3NxM5nviahYoqbA4qbI2YxypiMiV4Ij7MZndQ7XACEBWqffBEpHGiw10fDBVXerHCFhYFI/4Ct62FcY9AVDLk7YSvb4d/9oN5T0FprvfO5yUOh9NTiUmseeMucjwxYUfmsRapiid+RAmeiA8rP+oTw4gwPV1FpHFiwl0fDJV6M8FzC4uGUf8PblsHE5+E+I5QdhjmPQ7/7ONK+HI2e/+8TVRcUU3N52WeyozI8dSax6oET/yI3jGK+DB3e6bVYhBm1dNVRBonujkTPLfwGPjdn+H/rYY/vQFpA6G6wtWy+cLv4I2zYN3HUG3uUvMF5a72zKgwK+EhanmXhvHKPFaRFqaeLxEf5pl/F2rFMAyToxERf+NO8EqaM8Fzs4ZAvwug7/mQuQiWvAgZ38Kexa6v75Jh4GUw4FLX6pwtLL9mJVH3G3aRhog/0ZVoRUygBE/Eh7lbNCO0gqaINEGztmjWxzCg82jXV9EBWPUOrHobiva79tJb/By06Q8nXQT9L4S4tBYJq6BmqfuEKK2gKQ0XX/N4UQVP/Il6vkR8WFlNBS9KK2iKSBNEh7kreCatABjXFsbc45qnd8l70OscsITCwfUw50F4tje8/QdY/hoUZzdrKAU1FZjEaFXwpOG0VYL4I1XwRHxYhfbAE5ET4F5Fs0UreHWxhkCvs11fZXmw6XNY9xHs+QV2zXd9fXMntB/mSgJ7/x6Su3o1BE8FL1IVPGm4BCV44oeU4In4MPccvAhV8ESkCUxp0fwtUUkw9FrXV94u2PQFbPka9i0/8vXD/0FiOnQdC11Ph86nQGTCCZ3WMwdPK2hKIxyZg1dlciQiDacET8SHldVU8KJUwRORJmjRRVaaIikdRk91fRVlQcY3sPlr2P0z5O+CFbtcq3EaVmg3BDqNhA4nu76ikxt1KncFRgmeNIZaNMUfKcET8WEV7lU0VcETkSbwVPCqfDTBO1pcGgy73vVVWQy7F8GOH11fudtg3zLXl1tyN+jwO2g/FFJPgpTerk3Y66EWTWmKmAiT57GKNIESPBEfVlbzpkxz8ESkKY7sg+dnb07DY6HnRNcXQMFe2LUA9i6BPUvhcAbkbnd9rXnXdRvDAsndIe0kaNMPWvd0JYEJnSAkzFPFjI3QWx9puBbZS1LEy/QqJ+LDym0OQNskiEjTuBdZ8dkWzYZK6ACDLnd9gWuhlr3LXAnfgTWQvR7KDrsSv8MZsP7jI/c1rJDQkb+UtWJUSBL99wyAqN4Y0alEVOWCoxpQ26bULbqmg6bMH6rgIjWU4In4sKpqV4IXHqodTUSk8dwtmiUVAfbmNCqpdoXP6XRts5C9HrLXwcGNNRW+HWArhfxdDGIXg0KADd/DBtcboAmAc9NdEJsK0a0hKtn1Fd3KdY6oZIhqBRFxEBbrqiyGx0BYzZdFr82BLirM3ebsZ1VwCWpK8ER8WJXd9QclzKo3ESLSeO4Er9xmx+5wYrUYJkfUTAzDNYcvLg16jD9yuTvxy93OP97/hpiyvVzY3UKy/RDOwn04Cw9gcdpdm7AX7W/8ed2JXmgkWMMgJMz1rzUMrKFgDT/q+7DatzGsrrgtVld7qVHzr+dny1E/13WdARhHfn/XN7/6ua7LjrruhO9Xz8/HYdjttM1fjbGpEqy+353SLreMcywZJJSFwobDZofTZP427i2q40jXa0cAUYIn4sNsdicAYSFK8ESk8dzzh8DVYhYbEWStiEclfh84KjlcXclp408hOS2OapuNWd98zaRThxBadsjV4ll6GMpyXd+X5bpaQUsPQ2URVJZAVbHrX2dNNaeqxPUlDRYCDAPYbW4cDdUReD4MqAb+Z3IwJ8Dfxr1FXfqhEjwRaTnuFk1V8ESkKcKP+nCowuYgNsLEYExWUula5j7mqKQXwwKxaZDUseEHcjqhusKV6FUWuRI8WwXYq2q+bGCvrPn3qMuqK2tf73SAw+761/3l+dle873T9f2vr3M6XNe543F98xs/N+Q2TblP4zicDnJz80hOTsJi+P7ftspqO6syCzAMOLlLEgb+WQX3t3FvUVFJZkfgdUrwRHxYpTvBUwVPRJrAMAwiQi1U2BxU2IJ3DlG13UFFzaJVtRK8pjAMV0tmaCTEtPZCdMHFbrOxeNYsJk2ahCXU9yvKVRU2Lp02G4Atl0/020XP/G3c5cToXaOID7PZXW9IQlXBE5Emcr8hrawO3gTv6G0iok80wZOg4l5kBaBMC62In9C7RhEfVqUKnoicoIgQV4LnrmAFo5KaJe7DQix6PZVGsVpcVXDQXnjiP/QqJ+LDlOCJyIlyvzktD+IWTfc2ESfcnilByf24KdVeeOIn9K5RxIe5WzTDrP45qVtEzOdu0QzmOXjujd7dG7+LNIZnL7zK4H0OiX9Rgifiw6rsquCJyIkJD1WLpjvBiwnX4hLSeO55m2rRFH+hd40iPsyziqY2JRWRJoqsadEM5gpeqSfB02upNF50mOtxU6YWTfETSvBEfNiRVTTVoikiTaMWzaMreJqDJ40XVfO4KVGLpvgJJXgiPkyLrIjIifKsolkdvC2a7gpelBI8aQJ35VctmuIv9K5RxIcpwRORExVe06JZGcQVPHe7uzvZFWkMVcHF3+hdo4gPO7KKpp6qItI0VourxdvhdJociXkqaxaYcSe7Io0RXvMha1UQV8HFv+iVTsSH2eyuN2QhSvBEpIlCahK8akfwJnhVdlflRR+WSVOE11R+K5XgiZ/QK52ID7PXvCFzv0ETEWksq8X1p95uD94ETxU8ORHuCl5ltVo0xT/olU7Eh7k/cQ/RKpoi0kSq4B3ZUzRcFTxpgjC1aIqf0XJSIj7M7nD9MVEFT0Sayj0Hzx7ECd6RCp4WWZHGO1LBU4JXn52HSnhlwU4qqx1cNzqdfu3izQ4pqCnBE/Fh7k/c3S1WIiKNZVUFz1PB0xw8aQr3HDxV8Oq2YnceV7+xjNIqVwvr1+sO8MbkYZzSvbXJkQUvvdKJ+DDNwRORExXiqeAF75tT99wpzcGTpghTBa9eldV2bvtgDaVVdgZ3TGBUt2Rsdie3fbCGogqb2eEFLb3SifiwIxU8JXgi0jSq4B21p6gqeNIEWmSlft+uz2Z/QTlt4sJ59/qTeWPyMLq2jiavtIpX5u80O7ygpVc6ER+mCp6InCj364cjiBM8d+VFFTxpClXw6vfesj0AXDa8E1FhIYSHWLlrfE8APli+17Ofr7QsvdKJ+Cin0+lJ8FTBE5Gmcs/hDeYKXqWngqdFVqTxtA9e3XJLKlm+Ow+AC4e291x+Zp82tIoJ43BJJfMzDpkVXlBTgifio45e8S5Ei6yISBO5t1kJ5lU03S2a7lY7kcYI1zYJdVq4/TBOJ/RKjaVtQqTn8lCrhXNOagvATxk5ZoUX1PRKJ+Kjjv603ap98ESkiTQH76gKnhI8aQK1aNZtwdbDAJzW49jVMkd2TQbgl525LRqTuOiVTsRH1a7gKcETkaYJ0T54VLlX0VSCJ02gRVbqtvFAIQDD05OOue7kLslYDNh5qJSDRRUtHVrQ0yudiI+qVcFTgiciTWQxVMFTBU9ORHio9sH7tWq7g52HSgHo0Sb2mOvjI0PpmRoHwNq9BS0ZmqAET8RnVR+18pTVUIInIk1zZA5e8L45PTIHT4usSOO5t9dQi+YRe/LKqLI7iAi10O6o+XdH653mSvw2ZxW3ZGiCEjwRn+Vup7IYYFEFT0SayKoWTVXw5IS4t9eotKlF021bTgkA3VJi6n2P0rOmsrf9UEmLxSUueqUT8VHVnj3w9DQVkabTHDytoiknxl3Bq9Kebh47apK2bq1j6r1Nx6QoAPbll7VITHKEXulEfJT2wBMRb9A+eEcWx1CCJ03hqeBVO3A6g/d5dLRDxZUApNXTngnQPtGd4JW3SExyhF7pRHzUkQqeEjwRabpgr+BV2x24f3XNwZOmcD9unM7g/qDkaPmlVQAkRYXVe5t2ia7k71BxJTZVP1uUEjwRH+VeECFEe+CJyAnw7INnD843pkcvjKE5eNIUR1d+tdCKS16ZDYDE6PoTvNiIEM/3JRXVzR6THKFXOhEfVe1p0dTTVESaLtgreFVK8OQEuefggRZacfNU8KJD671NqNVCZM0WEyWVSvBaUkC+0r3wwgukp6cTERHBkCFD+Pnnn80OSaTR3J+2q0VTRE6Ee4W76iDdJsFdcQmxGJrTLE1isRhaaOVX8moSvITjtGgCxNRU8YoqbM0ekxwRcAnehx9+yNSpU7n//vtZvXo1p5xyCmeddRZ79uwxOzSRRtEiKyLiDZ4KXnAW8LSCpniFu/pbaVOC53A4PYustImLOO5t3W2aatFsWQH3avfss89y3XXXcf3119O7d29mzJhBhw4dePHFF80OTaRRPIusaA6eiJyAI/vgBecbU/cKmmrPlBPh/oBAFTzIK6uiyu7AMCAlNvy4t40NdyV4xUrwWlTIb9/Ef1RVVbFy5UruvffeWpePHz+exYsX13mfyspKKisrPT8XFRUBYLPZsNnMLyf/9X/r2L3XwjcFq7FoLlaLcTgc5Bw0d9zzy1ztD1YDn3gsthT37xpMv7Mv0Libo0XG3el6Q7r7cBk3v7Oi+c7jo9ytYWFWS61x1mPeHP467qE1H7ZO+2IDcZH1zzvzVd58X+OeT9c6JhwcdmyO+uclRoe75uA99+M2/rdy7wmdt7ncdEo6/drFNcuxG/t499bzIqASvMOHD2O322nTpk2ty9u0aUN2dnad93niiSeYPn36MZfPnj2bqKioZomzMWZvsFJmt0DeIbNDCUK+Me5GZSmzZs0yO4wWN2fOHLNDCEoad3M057gfKAUIoaSymu82Hmy28/i6MEdFna+lesybw9/GPdxhBQwW78wzO5QT4N33NfFG+W++P3EUWwALa/cVsnZfodfO7U0d7AfYk9i8PewNfbyXlXlnU/iASvDcDKN2S5vT6TzmMrf77ruPO+64w/NzUVERHTp0YPz48cTFNU823xhzQ55jX9YBUlNSCbGEYBhWLFixGJaaf601l1mweK6zYtT8bNS63HUfw7DWXHbkuvqOUd+4BTq73cHmLZvp3as3Vqt5lVPDMBjToxVtj7ORaKCx2WzMmTOHcePGERrqf5+S+iuNuzlaaty7DzgU3JsNGwandEumY9KRD271mDeHv457/xFlLNiW69oMzw95+32NYRiM7dmatPjjz8EbUVbFnE05Pr0P3mk9WtM+sXneZzX28e7uJDxRAZXgtWrVCqvVeky1Licn55iqnlt4eDjh4cf2D4eGhvrEC8+Kwv9RHFrMtnxzzm81rK4vi5UQIwSrpZ6fay6zGlZCLCENus/Rtzvu/Rpz7Aacx2rUJLvH+bJX24nKK2X80CTCQsOwGlYMw/D860qGgzcBbgm+8hwMNhp3czT3uJ/Zt22zHdvf6TFvDn8b9y4p8XRJiTc7jCaz2WzMKtjEpJGdW3TcU+JDuXxEeoudz1c19PHurf+bgErwwsLCGDJkCHPmzOGPf/yj5/I5c+Zw7rnnmhhZ001Kn8S23dtIa5uGw3Bgd9ixO+3YHXaqndWen6sd1Z7L7c7jX1frdjXXOZx1f7Livj2++8FLs3r8f48f93oDo+4kEQsWi+vfWomhYXF9j3FMcmjwq5/rSB5/fZtjrv+NYzbkGPWdu6kaeix3XE6nk+KiYt6e9XaTxqAp5/QGb56zoXE15JwNPZbT6aSguIAPv/8Q4zgrt/pq/A3lzXN641gOp4Pckly+mPsFFqMBn6o3ILSGxB9iCSEpIomkiCSSI5JpFdWKDrEd6BTbifjweH14JSLixwIqwQO44447uPLKKxk6dCgjRozglVdeYc+ePdx8881mh9Yk9wy9h1k5s5g0clKzfuLicDrqTPzq+rm+5NF9ucPpOH7y2YBj1fq5riS1jkS2wcdz2HHgwOE88uV0OrE77Uf+pWEtGE6cR5Jg8arsgrrnzUrz2pvrm5PgA92ug7vMDsEjNiyWTrGd6J7YnZ5JPemd1JueST2JDo02OzQREWmAgEvwLr74YnJzc3n44YfJysqiX79+zJo1i06dOpkdmk9zV55CLf7TLtGcqqqq+GbWN0w4awIhISGe5M+dCB+dCB6dKB59u1qX/ep2dVVM60oqnXX0+p/I7RqirmM15dgNTpJ/dT673c7SpUs5+eSTsVqtDT9Wg27S8MT9N2/jpXFqKK/9v9RzHLvdzoqVKxg6ZCiWBs7PaMn/l6Y+npr1fA387z3esezVdlavWc3AgQM9j/cTiqmBt7PZbeRV5Hm+skuz2Vu8l4NlBymuKmZD7gY25G6odZ9OcZ0Y0HoAA1MGMrD1QLomdG1Y1VFERFpUwCV4AFOmTGHKlClmhyF+zN1OGWoJJdSqpLcl2Ww2ckNzOTn1ZL+an+HvbDYbpetKOa39aRr3FmSz2bBvsjOx80SfGPeK6gr2Fu9ld9FuMvIy2JK3hS15WzhYdpDMokwyizL5cseXAMSGxnJSykkMbD2QoW2GclLrkwizhpn8G4iISEAmeCIiItJ4ESERdE/sTvfE7ozrNM5zeV5FHhsPb2TNoTWszVnLusPrKLYVs2j/IhbtX+S6rzWCASkDGJ46nOGpw+nbqq+6QkRETKAET0RERI4rKSKJU9qfwintTwGg2lHN1vytrMlZw6qcVSzPXk5eRR5Ls5ayNGspAJEhkQxuM5jhqcM5OfVkeiX1wmo5fhuqiIicOCV4IiIi0ighlhD6JPehT3IfLut9GU6nk52FO1mWvYxlWctYfnA5hZWFtSp8saGxDG4zmKFthjI0dSi9knoRYtHbEBERb9Mrq4iIiJwQwzDomtCVrgldubTXpTicDrblb3MlfNnLWJm9kmJbMfP3zWf+vvkARIdGMzDFNX9vaJuhaukUEfESJXgiIiLiVRbDQs+knvRM6smVfa7E7rCzJW8LKw6uYEX2ClbmrKS4qvYcvsiQSAa0HsCQNkMY0HoAfVv1JS4szuTfRETE/yjBExERkWZltVjp26ovfVv15eq+V2N32NlWsI0V2StYcXAFKw+upKCygCVZS1iStcRzv85xnenXqh/9WvWjb3Jfuid21358IiK/QQmeiIiItCirxUqvpF70SurFFX2uwOF0sLNgpyfZW394PftL9rO7aDe7i3bz9c6vPfdNi06jS0IXusZ3pUt8F9rFtiM1KpXU6FQiQiJM/K1ERHyDEjwRERExlcWw0C2xG90Su3FJr0sAyK/IZ2PuRtYfXs/GwxvZlLuJQ+WHyCrNIqs0y9PaebTE8ESSI5OJCY0hNiyWmLAYwq3hWAwLBq79Td2bszscDvaU7WHt8rVYLEc2bDcw6ozRMOq+3Nv3OZ76jne8Y3k9tnquamgMDoeDHeU72LFmB1Zr/auqttj4NPI+YdYw0uPS6d+6PylRKY2OUaQlKMETERERn5MYkcjodqMZ3W6057LCykJ2Fu5kR8EOdhTsYFfhLrJKs8guzaasuoz8ynzyK/MbdZ6l25Z6O3RpgAWbFpgdwgnrndSbP3b/I3/o+ge1DotPUYInIiIifiE+PJ5BKYMYlDKo1uVOp5OiqiKyS7MpqCyguKqY4qpiiqqKsDlsOJ1OHE4HDhw4nU4A7HY727Zto3v37p5KkhNnned136fO65pwn8Ye63jHO+596rvuOKF5cwzqus5hd7Br9y46d+5cq3LaEF4fnybcp8xWxtb8rWzN38rmvM1sXrqZf6/6N9f2u5ar+l5FuDW83vuKtBQleCIiIuLXDMMgPjye+PD4Bt/HZrMxa98sJp00idBQbc/QUmw2G7MOzmLSEP8e99zyXL7f/T3vb3mf3UW7+ffqf/PJtk/4vxH/x4i2I8wOT4Jc4z46EREREREJcsmRyVzW+zK+OO8LnjjlCVKiUthfsp8b59zIP5b/A5vdZnaIEsSU4ImIiIiINIHFsHBOl3P46ryvuKjHRQC8veltbphzA/kVjZsPKuItSvBERERERE5AVGgUD454kH+N/RcxoTGsPLiSy765jMyiTLNDkyCkBE9ERERExAtO73g67056l3Yx7dhXso9rvruGHQU7zA5LgowSPBERERERL+ma0JV3J71L98TuHCo/xLXfX0tGXobZYUkQUYInIiIiIuJFrSJb8cb4N+id1Ju8ijxunHMje4r2mB2WBAkleCIiIiIiXpYQkcBrE16rleQdKjtkdlgSBJTgiYiIiIg0g7iwOF448wU6xHZgf8l+/vzDnym1lZodlgQ4JXgiIiIiIs2kVWQrXh73Mq0iW5GRn8G9C+7F7rCbHZYEMCV4IiIiIiLNqENsB/499t+EWcKYt28ez61+zuyQJIApwRMRERERaWb9W/fn4VEPA/D6htf5eufXJkckgUoJnoiIiIhICzi7y9lc3/96AKYvns7W/K0mRySBSAmeiIiIiEgLuXXQrYxqN4oKewV3zrtTi66I1ynBExERERFpIRbDwhOjnyAlKoXdRbuZ/st0nE6n2WFJAFGCJyIiIiLSghIjEnnmtGcIMUL4dte3fLz1Y7NDkgCiBE9EREREpIUNTBnIbYNvA+DJZU+yJW+LyRFJoFCCJyIiIiJigqv7Xs2YDmOwOWzcs+AeyqvLzQ5JAoASPBERERERExiGwcMjH6Z1ZGt2Fu7kmRXPmB2SBAAleCIiIiIiJkmMSOTR0Y8C8GHGh8zfO9/kiMTfKcETERERETHRyLYjubLPlQA8tPghDpcfNjki8WdK8ERERERETHbb4NvontidvIo8Hlr0kLZOkCZTgiciIiIiYrJwazhPnfIUYZYwft7/Mx9kfGB2SOKnlOCJiIiIiPiA7onduWPoHQA8s+IZtudvNzki8UdK8EREREREfMRlvS5jVLtRVNoruffne6myV5kdkvgZJXgiIiIiIj7CMAweHfUoieGJZORn8O9V/zY7JPEzSvBERERERHxIq8hWTB85HYC3N73NLwd+MTki8SdK8EREREREfMzYjmO5qMdFADyw8AEKKgrMDUj8hhI8EREREREfdNewu+gc15mc8hym/TJNWydIgyjBExERERHxQZEhkTx16lOEWEKYu2cun23/zOyQxA8owRMRERER8VF9kvtw66BbAXhy2ZNkFmWaHJH4OiV4IiIiIiI+bHLfyQxPHU55dTn3LrgXm8Nmdkjiw5TgiYiIiIj4MIth4bHRjxEXFseG3A28uOZFs0MSH6YET0RERETEx6VGp/LQiIcAeG39a6zIXmFyROKrlOCJiIiIiPiBCZ0ncG7Xc3Hi5O4Fd3O4/LDZIYkPUoInIiIiIuIn/nby3+gS34VD5Ye4d8G92B12s0MSH6MET0RERETET0SFRvHPMf8kMiSSpdlL+c+a/5gdkvgYJXgiIiIiIn6kS0IXpo+cDsCr619l/t75JkckvkQJnoiIiIiInzkr/Swu7XUpAPctvI/dhbvNDUh8hhI8ERERERE/9Nehf2VA6wEUVxVzy4+3UFhZaHZI4gOU4P3/9u49Oor67uP4Z3NbQu5hQy4kEqDAiYAioOIFQ6gREBGKl4qoRJSLHgQqtFbKgWgLPdroo1IvpVQoFhGxWhGkxIKACKUY8nAtQUAIJEEkQBIJ5ra/5488WQ0gsLDJZDfv1zl7DjM785vvfCT++GZ2ZgEAAAAvFOgfqJfSXlJCSIIOlh7UpE8nqaqGL0Fv7mjwAAAAAC/lCHbojz/9o0ICQ/TF11/o2X8/K2OM1WXBQjR4AAAAgBfrGNVRWalZ8rP56R97/6FXcl+xuiRYiAYPAAAA8HI3t7lZ03pPkyTN3T5Xc7bNsbgiWIUGDwAAAPAB93S6R1N6TZEkzc6drQU7F1hcEaxAgwcAAAD4iJFdRurx7o9Lkv7wxR/0xtY3uCevmQmwugAAAAAAnjPuqnEyxuj1ra/r1f99VcfLjyvFpFhdFhoJV/AAAAAAH2Kz2fR498f16+t+LUlatGeRFp5aqLLKMosrQ2OgwQMAAAB80IiUEXr+ludl97crrzpPI/45QnnH86wuCw2MBg8AAADwUQPbDdSb6W8q0hapw98e1n3L79Pr//s6X4juw2jwAAAAAB+WEp2ix8IeU9/Evqp2Vuu1ra/p3mX36rPDn/EAFh9EgwcAAAD4uBC/EL3Q5wU9f8vzirRHau/JvXp81ePK+GeGPjn4iaqd1VaXCA/xigbvwIEDeuSRR9SuXTsFBwerQ4cOmjFjhiorK+ttl5+fr8GDByskJEQOh0MTJkw4axsAAACgObLZbBrYbqCW/WyZMrpkKMgvSFuObtGTa57UgL8P0B82/0G5R3NV46yxulRcBq/4moTdu3fL6XTqT3/6k37yk59ox44dGj16tE6dOqWsrCxJUk1NjQYNGqSYmBitX79excXFGjlypIwxmj17tsVnAAAAADQNEfYITe41WQ+kPKDFeYv13p739HX511qwa4EW7FqglgEt1S2mm7q06qKksCQlhSUpukW0QgNDFRIUoiC/oHrj2Wy2est+8pO/n7/8bF5xLcnneEWDN2DAAA0YMMC13L59e+Xl5en11193NXjZ2dnatWuXDh06pISEBEnSCy+8oIyMDM2cOVPh4eGW1A4AAAA0RbEhsZrQY4LGXj1Wnx3+TKvyV2ntobUqqyrTpqJN2lS06bKPEWALkJ+ttuEL8AtQdItoOYIdah3cWm0j2qpjZEd1ju6sK8KuOKtRxKXxigbvXEpKShQdHe1a3rhxo7p27epq7iSpf//+qqioUE5OjtLS0qwoEwAAAGjS7P523dr2Vt3a9lbVOGu09+Rebf1mq/ae3KvDZYd1+NvDKqko0beV36rS6d7tT9WmWjKSnLXLZZVlOlh68KztHMEOXRt7rXon9FZaUpqiWkR54MyaJ69s8Pbt26fZs2frhRdecK07cuSIYmNj620XFRWloKAgHTly5EfHqqioUEVFhWu5tLRUklRVVaWqKusfH1tXQ1OopTkhd+uQvTXI3Rrkbh2ytwa5W8Pd3NuHtVf7sPbnHqumSlXO2nGMvn8CZ92fjTEyMqpx1qjG1MhpnKoxtX+urKnUie9O6NjpY/r69NfaX7Jf+07u05cnv9Sx08e04sAKrTiwQs/antW1sddqYPJApV+RrhYBLS7n9C3jbu6e+rmwGQufjZqZmalnnnnmvNts3rxZvXr1ci0XFhYqNTVVqampmjt3rmv9mDFjdPDgQa1cubLe/kFBQVqwYIHuu+8+t2p4++231bJlS3dOBwAAAICbqkyVDtcc1v6q/dpdvVtFNUWu94JtweoR1EPXBV2nVv6tLKyy4ZWXl+v+++9XSUnJZd1eZmmDd+zYMR07duy82yQnJ6tFi9quvbCwUGlpabr++us1f/58+fl9f+Pm9OnT9eGHH2rr1q2udSdOnFB0dLRWr179ox/RPNcVvKSkJB07dqxJ3LdXVVWlTz75ROnp6QoMDLS6nGaD3K1D9tYgd2uQu3XI3hrkbg1vyz2/LF/ZB7P1wb4PVHSqttnzs/lpQNsBeqTLI2oX0c7iCi+Ou7mXlpbK4XBcdoNn6Uc0HQ6HHA7HRW1bUFCgtLQ09ezZU/PmzavX3EnSDTfcoJkzZ6qoqEjx8fGSah+8Yrfb1bNnzx8d1263y263n7U+MDCwSf0ANLV6mgtytw7ZW4PcrUHu1iF7a5C7Nbwl9w7RHfRY9GMac/UYrS9Yr0W7F+nzws/18YGPteLACvVP7q/Huj+m9hHn/hhpU3OxuXvqv41XPLu0sLBQffv2VVJSkrKysvTNN9/oyJEj9e6tu+2223TllVfqwQcfVG5urlatWqUpU6Zo9OjRTeJKHAAAAICL5+/nr9SkVL2R/obeueMd9UvqJyOjfx74p4Z9OEy/+/fvVHy62OoymxyvaPCys7O1d+9erV69WomJiYqPj3e96vj7+2v58uVq0aKFbrrpJt17770aOnSo62sUAAAAAHinLq266OV+L+u9we+pb1Jf1ZgaLc5brEEfDNKcbXN0uvq01SU2GV7R4GVkZNQ+keccrx+64oortGzZMpWXl6u4uFizZ88+58cvAQAAAHifztGdNbvfbL3Z/011adVFp6pOaXbubA3+YLA+2veRnMZpdYmW84oGDwAAAADqXBt3rd4e9Lae6/OcEkIS9HX515q6fqpGLB+h3KO5VpdnKRo8AAAAAF7Hz+an29vfrqU/W6qJPSYqJDBEO4p36KEVD2nymsk6XHbY6hItQYMHAAAAwGvZ/e16tNujWvazZbqr413ys/kp+2C27vzHnfqfnP/Rt5XfWl1io6LBAwAAAOD1HMEOZd6YqXfveFe943urylmlN3e8qUEfDNK7ee+q2lltdYmNggYPAAAAgM/oHN1Zc9Ln6I/9/qjk8GQd/+64fvvv3+qej+7RhsINVpfX4GjwAAAAAPgUm82m1KRUvT/kff36ul8rPChce0/u1dhPxmrcv8Zp2zfbrC6xwdDgAQAAAPBJgX6BGpEyQh8P+1gPpDygAFuAPi/4XCM+HqFxn4zTruJdVpfocTR4AAAAAHxahD1CT133lJYOXaqhPxkqf5u/Pi/8XIfKDlldmscFWF0AAAAAADSGpPAk/fam32rMVWP0wZcfKL1tutUleRwNHgAAAIBmJSksSRN6TLC6jAbBRzQBAAAAwEfQ4AEAAACAj6DBAwAAAAAfQYMHAAAAAD6CBg8AAAAAfAQNHgAAAAD4CBo8AAAAAPARNHgAAAAA4CNo8AAAAADAR9DgAQAAAICPoMEDAAAAAB9BgwcAAAAAPoIGDwAAAAB8BA0eAAAAAPgIGjwAAAAA8BE0eAAAAADgIwKsLqCpMcZIkkpLSy2upFZVVZXKy8tVWlqqwMBAq8tpNsjdOmRvDXK3Brlbh+ytQe7WIHdruJt7Xf9R149cKhq8M5SVlUmSkpKSLK4EAAAAQHNTVlamiIiIS97fZi63RfQxTqdThYWFCgsLk81ms7oclZaWKikpSYcOHVJ4eLjV5TQb5G4dsrcGuVuD3K1D9tYgd2uQuzXczd0Yo7KyMiUkJMjP79LvpOMK3hn8/PyUmJhodRlnCQ8P5wfSAuRuHbK3Brlbg9ytQ/bWIHdrkLs13Mn9cq7c1eEhKwAAAADgI2jwAAAAAMBH0OA1cXa7XTNmzJDdbre6lGaF3K1D9tYgd2uQu3XI3hrkbg1yt4ZVufOQFQAAAADwEVzBAwAAAAAfQYMHAAAAAD6CBg8AAAAAfAQNXhOyfPlyXX/99QoODpbD4dCwYcNc723dulXDhw9XUlKSgoODlZKSopdffvmsMbZv367U1FQFBwerTZs2evbZZ8Vtlhd2vuwlaeLEierZs6fsdru6d+9+zjFWrlyp3r17KywsTDExMbrrrrv01VdfNUL13ssTuRtjlJWVpU6dOslutyspKUmzZs1qhOq9lydyr7N3716FhYUpMjKy4Qr2IZeb/Zo1azRkyBDFx8crJCRE3bt318KFCxupeu/lib/zzK/uu1Du+fn5Gjx4sEJCQuRwODRhwgRVVlbW24a51X2eyJ259dJ4Ivs6lzO/8kXnTcTf//53jR49WrNmzVK/fv1kjNH27dtd7+fk5CgmJkZ/+9vflJSUpA0bNmjMmDHy9/fX+PHjJUmlpaVKT09XWlqaNm/erD179igjI0MhISGaPHmyVafW5F0oe6n2f3SjRo3Spk2btG3btrPG2L9/v4YMGaInn3xSCxcuVElJiX7xi19o2LBhys3NbaxT8SqeyF2q/YdZdna2srKy1K1bN5WUlOjYsWONcQpeyVO5S1JVVZWGDx+uPn36aMOGDQ1dutfzRPYbNmzQVVddpaeeekqxsbFavny5HnroIYWHh2vw4MGNdSpexRO5M7+670K519TUaNCgQYqJidH69etVXFyskSNHyhij2bNnS2JuvRSeyF1ibr0Unspe8sD8amC5qqoq06ZNGzN37ly39nv88cdNWlqaa/m1114zERER5rvvvnOt+/3vf28SEhKM0+n0WL2+xN3sZ8yYYa6++uqz1i9ZssQEBASYmpoa17qlS5cam81mKisrPVWuz/BU7rt27TIBAQFm9+7dHq7QN3kq9zq/+tWvzAMPPGDmzZtnIiIiPFOkj/J09j90++23m4cffvgyqvNdnsqd+dU9F5P7xx9/bPz8/ExBQYFr3aJFi4zdbjclJSXGGOZWd3kqd+ZW93kq+zqXO7/yEc0mYMuWLSooKJCfn5+uueYaxcfHa+DAgdq5c+d59yspKVF0dLRreePGjUpNTa33XRv9+/dXYWGhDhw40FDle7VLzf5MvXr1kr+/v+bNm6eamhqVlJTorbfe0m233abAwMAGqt57eSr3jz76SO3bt9eyZcvUrl07JScn69FHH9Xx48cbqHLv5qncJWn16tVasmSJXn311Qao1Pd4MvsznTkX4Hueyp351T0Xk/vGjRvVtWtXJSQkuNb1799fFRUVysnJkcTc6i5P5c7c6j5PZS95Zn6lwWsC9u/fL0nKzMzUtGnTtGzZMkVFRSk1NfVHf5g2btyod999V2PHjnWtO3LkiGJjY+ttV7d85MiRBqreu11K9ueSnJys7OxsTZ06VXa7XZGRkTp8+LDeeeedhirdq3kq9/379+vgwYNasmSJFixYoPnz5ysnJ0d33313Q5Xu1TyVe3FxsTIyMjR//nyFh4c3VLk+xVPZn+m9997T5s2b9fDDD3uqVJ/iqdyZX91zMbmfK9OoqCgFBQW5MmVudY+ncmdudZ+nsvfU/EqD14AyMzNls9nO+/riiy/kdDolSb/5zW901113qWfPnpo3b55sNpuWLFly1rg7d+7UkCFDNH36dKWnp9d7z2az1Vs2/38D+JnrfV1DZf9jjhw5okcffVQjR47U5s2btXbtWgUFBenuu+9uVjfhN3buTqdTFRUVWrBggfr06aO+ffvqL3/5iz799FPl5eU11Gk2OY2d++jRo3X//ffrlltuaahT8hqNnf0PrVmzRhkZGfrzn/+sLl26ePK0mjwrcmd+9Xzu58rOGONaz9xaq7FzZ279XmNn76n5lYesNKDx48frvvvuO+82ycnJKisrkyRdeeWVrvV2u13t27dXfn5+ve137dqlfv36afTo0Zo2bVq99+Li4s76TeLRo0cl6azfGPi6hsj+fF599VWFh4fr+eefd62reyDOpk2b1Lt3bzfPwDs1du7x8fEKCAhQp06dXOtSUlIk1T6pqnPnzu6U77UaO/fVq1dr6dKlysrKklQ7OTmdTgUEBGjOnDkaNWrUJZyFd2rs7OusXbtWgwcP1osvvqiHHnrI7f29XWPnzvxay5O5x8XFadOmTfX2PXHihKqqqlyZMrfWauzcmVu/19jZe2p+pcFrQA6HQw6H44Lb1T2aOS8vTzfffLOk2qfnHDhwQG3btnVtt3PnTvXr108jR47UzJkzzxrnhhtu0NSpU1VZWamgoCBJUnZ2thISEpScnOyZk/ISns7+QsrLy+Xv719vXd1y3W91moPGzv2mm25SdXW19u3bpw4dOkiS9uzZI0lujePtGjv3jRs3qqamxrX84Ycf6rnnntOGDRvUpk0b90/AizV29lLtlbs77rhDzz33nMaMGXNJdXu7xs6d+bWWJ3O/4YYbNHPmTBUVFSk+Pl5SbaZ2u109e/aUxNxap7FzZ279XmNn77H51e3HsqBBTJw40bRp08asXLnS7N692zzyyCOmdevW5vjx48YYY3bs2GFiYmLMiBEjTFFRket19OhR1xgnT540sbGxZvjw4Wb79u3m/fffN+Hh4SYrK8uq0/IKF8reGGO+/PJLk5uba8aOHWs6depkcnNzTW5urqmoqDDGGLNq1Spjs9nMM888Y/bs2WNycnJM//79Tdu2bU15eblVp9akeSL3mpoa06NHD3PLLbeYLVu2mC+++MJcf/31Jj093arTavI8kfuZeIrmxfFE9p9++qlp2bKlefrpp+vNBcXFxVadVpPnidyZX913odyrq6tN165dzU9/+lOzZcsW869//cskJiaa8ePHu8ZgbnWfJ3Jnbr00nsj+TJc6v9LgNRGVlZVm8uTJpnXr1iYsLMzceuutZseOHa73Z8yYYSSd9Wrbtm29cbZt22b69Olj7Ha7iYuLM5mZmTzC+QIulL0xxqSmpp4z/6+++sq1zaJFi8w111xjQkJCTExMjLnzzjvNf//730Y+G+/hqdwLCgrMsGHDTGhoqImNjTUZGRn8Y/c8PJX7D9HgXRxPZD9y5Mhzvp+amtr4J+QlPPV3nvnVPReT+8GDB82gQYNMcHCwiY6ONuPHj6/3VRTGMLe6y1O5M7e6z1PZ/9Clzq82Y5rRXaoAAAAA4MN4iiYAAAAA+AgaPAAAAADwETR4AAAAAOAjaPAAAAAAwEfQ4AEAAACAj6DBAwAAAAAfQYMHAAAAAD6CBg8AAAAAfAQNHgDAZ2VmZqp79+6Nftw1a9bIZrPJZrNp6NChHhnr5MmTF71PZmam6/gvvfTSZR0fAOBdaPAAAF6proH5sVdGRoamTJmiVatWWVZjXl6e5s+ff1lj3HjjjSoqKlJERMRF7zNlyhQVFRUpMTHxso4NAPA+AVYXAADApSgqKnL9efHixZo+fbry8vJc64KDgxUaGqrQ0FArypMktW7dWpGRkZc1RlBQkOLi4tzap+68/f39L+vYAADvwxU8AIBXiouLc70iIiJks9nOWnfmRzQzMjI0dOhQzZo1S7GxsYqMjNQzzzyj6upq/fKXv1R0dLQSExP15ptv1jtWQUGBfv7znysqKkqtWrXSkCFDdODAAbdr7tu3r5544glNmjRJUVFRio2N1Zw5c3Tq1Ck9/PDDCgsLU4cOHbRixQrXPmd+RHP+/PmKjIzUypUrlZKSotDQUA0YMKBewwsAaL5o8AAAzcrq1atVWFiodevW6cUXX1RmZqbuuOMORUVFadOmTRo3bpzGjRunQ4cOSZLKy8uVlpam0NBQrVu3TuvXr3c1VZWVlW4f/69//ascDof+85//6IknntBjjz2me+65RzfeeKO2bNmi/v3768EHH1R5efmPjlFeXq6srCy99dZbWrdunfLz8zVlypRLzgQA4Dto8AAAzUp0dLReeeUVde7cWaNGjVLnzp1VXl6uqVOnqmPHjnr66acVFBSkzz//XJL0zjvvyM/PT3PnzlW3bt2UkpKiefPmKT8/X2vWrHH7+FdffbWmTZvmOlZwcLAcDodGjx6tjh07avr06SouLta2bdt+dIyqqiq98cYb6tWrl3r06KHx48dbeq8hAKDp4B48AECz0qVLF/n5ff/7zdjYWHXt2tW17O/vr1atWuno0aOSpJycHO3du1dhYWH1xvnuu++0b98+t49/1VVXnXWsbt261atHkuv459KyZUt16NDBtRwfH3/e7QEAzQcNHgCgWQkMDKy3bLPZzrnO6XRKkpxOp3r27KmFCxeeNVZMTIzHj2+z2VzHdWcMY4zbtQAAfA8NHgAA59GjRw8tXrxYrVu3Vnh4uNXlAABwXtyDBwDAeYwYMUIOh0NDhgzRZ599pq+++kpr167VxIkTdfjwYavLAwCgHho8AADOo2XLllq3bp2uuOIKDRs2TCkpKRo1apROnz7NFT0AQJNjM3xoHwAAj1qzZo3S0tJ04sSJy/6i88uRnJysSZMmadKkSZbVAABoXFzBAwCggSQmJmr48OGNftxZs2YpNDRU+fn5jX5sAIC1uIIHAICHnT59WgUFBZKk0NBQxcXFNerxjx8/ruPHj0uqfdJnREREox4fAGAdGjwAAAAA8BF8RBMAAAAAfAQNHgAAAAD4CBo8AAAAAPARNHgAAAAA4CNo8AAAAADAR9DgAQAAAICPoMEDAAAAAB9BgwcAAAAAPoIGDwAAAAB8xP8BMtVmUkAo0MkAAAAASUVORK5CYII=", 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" ] @@ -889,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 38, "id": "2954e9fb", "metadata": { "collapsed": false, @@ -900,7 +900,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -933,7 +933,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 39, "id": "a0c10a1a", "metadata": { "collapsed": false, @@ -944,7 +944,7 @@ "outputs": [ { "data": { - "image/png": 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", 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ERKR4oigit7QGhwsNOF1Sg9Ml1SivNaG2wYwaowVWUYRGLUCjUiFMr0FsmA5dw/XoFhmMvnFh6Bsfhm4RQZ3mgpk6J1EU8W1jgTe+b6zM0XhOqF6D307sg6X/PYw1209h1qhEaF1YY84Tjl+owktfngQAPDUtFVd2l3+W+kDltwXehAkTcLkl+ubPn4/58+f7KCIiIiJqTX55Lb48UoydJ0qQfbYc5bUdW7A3NkyP0SkxuColBtcPiENiDNebJf9yurQGxVVG6DUqv10c3F2/uCoJL391EgUVdfjfT4WYNryn11/TahWx+KOf0GCxYtKAOMwY4f3X7Mz8tsAjIiIi/1VSZcTm/QX49/4CHCl0Xr9Jp1FhYPcI9IkLQ++uoegapkeYXoMQvQZqQYDJaoXJbEW10YySKiNKqow4V16Lk8XVOFNWi9JqI/53sBD/O1iIJz85hGGJUbh1aA/cPiIBkV5es4rIFQfOVQAABveMRJBWLW8wHhakVeO+sb3wfNZxvL7zNG4b1sPrLeqfHDiPnHMVCNNr8PT0wWzB7yAWeEREROSysmojntt2DB/uzYfFautpoxKAkckxuP7KOIxOicGgHpHQadzr1lVvsuCn/ErsyS3DzhOl2HPmInLOVSDnXAWe33YMt6cl4FfX9kZCNFv1SD4/5VcCAIYkRMkbiJf88upkvPzVSRw6b8DBgkqvvs96kwUrtx4DAPx6whXoFhnktddSChZ4REREdFmiKOLjfQVY/t9DMNSbAQDDEqNwx8gEZKR2R3SoZyZDCNKqcVVj98zfXt8XxVX1+OxgET7Yk4ejRVV49/uz+NeP5zB3bC/89vo+nGGPZHEgvwIAMDQxUt5AvCQ6VIf0wd3wn5zz+GBPnlcLvH/uPouCijp0jwzCA9ekeO11lMRvZ9EkIiIi/1BjNOOxDw9g0cYDMNSbMbB7BDbOG4PNvxmHu0cne6y4a01ceBDmjO2FzxaMx/oHR2NM7y5oMFvx2o7TuGnVTnx3stRrr03UGqtVxNHCKgBAas/OWeABwF1XJQEAPsk5j2qj2SuvYbJY8ea3uQCABZP6drrurnJhgUdERERtKq024q7Xd+Pf+wugVglYNKUf/vu7azCqV4xP4xAEAWP7xGL9Q6Px1n0jkRgTjIKKOtz9xg94YdsxWK3tT8xG5CkXqupRZ7JAoxKQ1IknABqdEoPeXUNR02DBFi8tfP6/nwpRWFmP2DA9pnNiFY9hgUdEREStKq6qxx2vfo+f8isRE6rDBw9djd9e3xdqlXwTIAiCgOsHxOPzBdfi7tG2FoaXvjqJ332wH/Umi2xxkXKcLqkBACR1CfHZEgJyEAQB04bZii5vFHiiKGLtjtMAgPvGJkOvYeudp3Teo5KIiIjcVllnwpy3fkRuaQ16RgXjo3ljcFWKb1vt2hPaONveytuHQKsW8L+DhXjo3b0wmlnkkXedLrUVeL1jQ2WOxPsyBncHAHx3shSVHVz+5FI55ypwuNCAIK0Kd4/mOtaexAKPiIiInFitIh75YD+OFBrQNVyPDx66Gr27hskdVqvuGJmId+8fjWCtGjtPlOK36/c7Zvck8obTJdUAgBQFFHh94sLQPz4cJouIbYeLPLrvj/flAwBuGtTNq+N4lYgFHhERETlZu/M0vjleAr1GhbfvG4WkLv49zmjMFV3w5pyR0GtUyDp8AX//4rjcIVEndu5iLQCglwIKPAC4KbUbAODrY8Ue26fRbMF/D9i6fc5MS/DYfsmGBR4RERE5HD5vwHONa1ItnTooYGYJHNsnFn+dOQSAbUzeV0cvyBwRdVYlVUYAQHy4MtZru65/VwDAtydKYbZYPbLPncdLUVlnQreIIIy9ItYj+6QmLPCIiIgIgG3Sgyc/+Rlmq4gbB8XjF6MS5Q5JkmnDe+K+sb0AAI9/fBCVdZ4dM0QENBV4seF6mSPxjaEJUYgM1sJQb3as/9dRXxyx3YC5cVC8rJM2dVYs8IiIiAgA8OlPhfjxTDmCtWo8eesgCELgXXg9nj4AvWNDUVxlxN8+Pyp3ONTJiKKI0uoGAEBsmDLGjalVAq7pa2tl++Z4x9edtFpFfHnU1t1z0pXxHd4ftcQCj4iIiGC1inj5qxMAgIev640eUcEyR+SeIK0az8wYDAD414/ncKpxQgwiTzDUm9HQ2E0xNkwZLXgAcG1jgbf7VFmH9/VTQSVKqowI02swurf/zMzbmbDAIyIiImw7fAHHL1QjXK/B3HEpcofTIVf37oIbroyDxSrihW2ccIU8x949MzxIgyCtctZtS0uOBgD8VFDR4XF4O4+XAADG943l2ndewgKPiIiI8P4PZwEA94xJRmSwVuZoOm7Rjf0BAFt+LnTMekjUUaXVtgKvq4Ja7wCgd2wYwoM0qDdZcbSoqkP72nPmIgDb7LfkHSzwiIiIFO58RR2+PWkbW3PXVUkyR+MZA7pFYHzfWIgi8M/dZ+UOhzoJe4GnpO6ZAKBSCRiWGAUA2H+uwu39mC1WZJ8tBwBclcLumd7CAo+IiEjh/r0vH6IIXN07Bokx/r3mnRRzxvQCYBuLV2+yyBsMdQqljhk0lTHBSnPDk2zdNPfnlbu9j0PnDahtsCAyWIt+ceGeCo0uodgC79y5c5gwYQIGDhyIIUOGYOPGjXKHREREJIv/HSwCAMwY0bkWHJ44IA7dI4NQWWfCN43jfog6orLODACIDFZggdfYgnegAy14e3Jt3TNH9YqGissjeI1iCzyNRoNVq1bh8OHD+OKLL/Doo4+ipqZG7rCIiIh8qqiyHkcKDRAEYNKAOLnD8Si1SsDNg7sDsC0BQdRRNQ22Ai9Mr7zJQQb2iAAAnCmrdbtF3L6O3ojGSVvIOxRb4HXv3h3Dhg0DAMTFxSEmJgYXL16UNygiIiIf237Mth7VsMQodOmE44oyhtgKvO3Hijs8+x9RtdFW4IXqNTJH4ntx4XpEBGlgsYo4XeJeo8iRQgMAYFCPSE+GRpfw2wJvx44duPXWW9GjRw8IgoDNmze3eM7q1auRkpKCoKAgpKWlYefOnW691t69e2G1WpGYmNjBqImIiAKLvcvU+D6xMkfiHUMTohAVokVVvdnRekDkrlqjvQVPeQWeIAjo3802bu5EsfSZNOtNFuSW2grDK7tz/J03+e3RWVNTg6FDh2Lu3LmYOXNmi8c3bNiAhQsXYvXq1Rg3bhxee+01pKen4/Dhw0hKss0AlpaWBqPR2OJnt23bhh49egAAysrKcO+99+KNN95oNx6j0ei0L4PBdgfCZDLBZDK5/T49xR6DP8SiFMy57zHnvsec+56vc773rK3AG5YQ0Wl/z2N7x2DLzxfw9ZELGNKj5YUlj3PfC9ScV9Xb4tWrhYCLHeh43q/oGoofz5Tj6PlKZAyS1qX7UH4lrCLQJVSH6CB1QObPHa7m3JP5EERRFD22Ny8RBAGbNm3CtGnTHNtGjx6NESNGYM2aNY5tV155JaZNm4YVK1a4tF+j0YjJkyfjoYcewj333NPuc5cuXYply5a12L5+/XqEhHSeGceIiEg5DA3An7I1ECDimVEWhPjtbd+O2XVBwIbTavSLtOI3A9lNk9z3yiEVThhUuLevBWmxfn8J7XHbCwVsOqPGsBgr5vaX9ln6oVjA+lP8HLaltrYWs2fPRmVlJSIiIjq0r4A8lTc0NCA7OxuPP/640/YpU6Zg165dLu1DFEXcd999uP766y9b3AHAkiVLkJmZ6fjeYDAgMTERU6ZM6fAvwRNMJhOysrIwefJkaLWBv0BtIGDOfY859z3m3Pd8mfOvj5UA2ftxRdcw3D51nFdfS04phVXYsPp7FBp1uOmmiS1m7+Nx7nuBmvM383YDBgPGjR6J6/t3lTscyTqad/2RYmw6kwNzUBQyMq6W9LNHvzgBnMrFyP5JyMgYKPm1A5WrObf3DvSEgCzwSktLYbFYEB8f77Q9Pj4eRUVFLu3ju+++w4YNGzBkyBDH+L733nsPgwcPbvX5er0een3LwedardavTkz+Fo8SMOe+x5z7HnPue77IeW5ZHQBgQPeITv37HdgzCkFaFarqzThX2YA+cWGtPo/Hue8FWs5rGmyzR0YE6wMq7ku5m/dejWvX5VfUSf75c+X1AICU2PCAzp27LpdzT+YkIAs8O0FwvgMnimKLbW255pprYLWyeZiIiJTrZHE1AKBvJ19wWKNWIbVHJPaeLcdP+RVtFnhEl9PQOBOrXuu38xR6VWK0bVhSRa0JVfUmhAe5XpTkXawFACR34dAmbwvIozM2NhZqtbpFa11xcXGLVj0iIiJq3Ql7gRff+Qse++x/p0qqZY6EApnJbBt3p1UF5CV0h4XqNYgJtS3yfu5incs/J4qiYwbN5C6hXomNmgTk0anT6ZCWloasrCyn7VlZWRg7dqxMUREREQUOURQdLXhKaNFKibVdVNovMoncYW7s/aXVuNZjrDNKjA4GAJwrr3X5Z2wtfrYlJpJi2ILnbX7bRbO6uhonT550fJ+bm4ucnBzExMQgKSkJmZmZuOeeezBy5EiMGTMGa9euRV5eHubNmydj1ERERIGhvNbkWLRZCV2mene1FXjuLtBMBAAmi60FT6PQFjwA6BkdjAP5lThf4XoLXpHBNv4uJlSHYJ3aW6FRI78t8Pbu3YuJEyc6vrfPYDlnzhysW7cOs2bNQllZGZYvX47CwkKkpqZiy5YtSE5OlitkIiKigFFc1XTBpdd0/guulFhbK+WZshpYrWKLmTSJXGFuHIOnVSv3+IkLDwIAFFe1XGu6LSWNz40LbzlhIXme3xZ4EyZMwOWW6Js/fz7mz5/vo4iIiIg6jwsGZV1wJUQHQ6MSUG+yotBQj55RwXKHRAHIZG1swVMrtwWva+M5o9jgeoFXWm17bmyYMs43clPu0UlERKRgFxq7TMVHBMkciW9o1Sp0j7K9Vyldy4iac7TgKbgF2H5TyN4LwBX2FryuCrmhJDcWeERERApU7CjwlHPB1a2xmLUXt0RSWKwiGhvwFN2CF9tYpJVVN7j8MyzwfEu5RycREZGCNXXRVEYLHtDUWllUyQKPpDNZmtZP1ih4DF50iG2ZhMo6k8s/U9LYRbMru2j6BAs8IiIiBbJ3r2ILHpFrzNamuSF0Cm7Biwq2LW5eXut6C55jDF64zisxkTO/nWSFXFdQUYf73voBtdVqvH52NzRqFTQqAWqVAI1agFrV7Hunr43b1W1st3+vdt6uUQvQqlXQa1TQqVXQaRr/b/Zv22PqpscaH9eqBQiCcu96ERH5C3sLXlcFteDFhNkuLi/WuN7yQGRnbt6Cp+AxePYWvNoGC4xmi0uz8Dq6aIYp53wjJxZ4nUC9yYITxTUABBTUGuQOp12CYBvorleroNeqEaKz/R9s/6rVOLaF6DROj4Xo1AjTaxEZrEVEsMb2NUiLiGAt1Ao+0RIRucPevSomVDl31O0XphUSWh6I7Oxr4AFQ9HVHeJAGggCIIlBZa0JcxOULvIs1ts+cks43cnKpwHvppZck73ju3LkIDw+X/HMkXffIILw3dyR27f4BaSNHAoIaZqsIi1WE2Wpt/Co2fbVYnb93fG3cbmlje+P3JrMVDRYrGsyN/zf7t7GVxyzNujSIIhyPVTUusOsJYfrGgi9Yi4ggDWLD9Ogabvs/Nkxn+3dYEGLDdegaplf04GgiIgCoqrcVeOFByrnXGx0ivWsZkZ3Z2rQGnpJ7I6lUAiKDtaioNaGizoQ4F2biNdTbrvkigpVzvpGTS1leuHAhEhISoFa7thDquXPncMstt7DA85EQnQZX947BxaMiruvXFVqtVu6QnFisYlMBaLE4/l1vsqLOZEZtgwW1DRbUNX6tbTCjrsGCmgYL6hoaHzfZHq+uN6OyzoTKOhMM9SbUNlgAANVGM6qNZhS4MPW1WiWgW0QQekYHIyE6GAnRIUiIsv07qUsIekQGcwFcIur0qhovuML0yrnginK04LGLJklnbmzB06h4kzhUp0FFrQk1LtysN5pt134AEB7kX9eonZXLZ/W9e/ciLi7OpeeysKPm1CoBwY1dLQHPfrAbzFZU1dsLPlvxV1HbgLLqBpRWG1FSZURJ49fSaiNKqxtgsYooqKhDQUUd9uS23GewVo3eXUPRJy4MV3QNQ5+4MPSLD0NKbJiiu2QQUedh73EBABEKuuCyd9G8yBY8coN9Fk0lz6BpF6q3NfrYb7S3x34zCVDWDSU5uZTlJ598EmFhYS7v9I9//CNiYmLcDorIVTqNCl3C9Oji4rS7VquIkmoj8svrkF9ei/xyW6GXX16H/Iu1OFdeizqTBYfOG3DovPN4xhCdGgO7RyC1ZyQG94zEoO5hEMU2XoiIyI/Zu2cCQJiCumjau6O60upAdCn7GDwlz6BpF6KzfZZcKfCqGwu8UJ2aN8p9xOUCT4olS5a4FQyRt6lUAuIjghAfEYS05OgWj5ssVpy7WIuTxdU4VVKDk8XVOFlSjRMXqlDbYMHes+XYe7bc8fwwrRpbqw5gbJ9YXN27C/rEhSm6Xz4RBYbqxgInRGEXXKGNF6Umi23ogE7DC3VyHVvwmjS14F3+Zom9BY/dM31H8m27uro6iKKIkJAQAMDZs2exadMmDBw4EFOmTPF4gES+pFWr0LtrGHp3dW6xtlhFnC6pxsGCStv/+bav1SYrPjt0AZ8dugAASIgOxg1XxmPKwHiMSomBlnf5iMgPKXH8HYDGoQI2dQ0WFngkiX0dPI7Ba2rBqzG60kVTeRM6yU1ypm+77TbMmDED8+bNQ0VFBUaPHg2tVovS0lK88MIL+PWvf+2NOIlkpVYJ6Bsfjr7x4ZgxIgEAUF1nxNqPPoeq2wD8eLYce8+UI7+8Dut2ncG6XWcQGazFLUO6446RiRiaEMmWPSLyG/bxd0Fa1yZP6yx0Gtt6rCaLiFqTGZEeHhdOnZt9HTwtW/AQqnO9Ba/OZCsCQ3TKOt/ISXKBt2/fPrz44osAgI8++gjx8fHYv38/Pv74Y/z5z39mgUeKodeocEUEkDGhN7RaLeoaLNh5ogRZhy/gq6PFKKtpwPs/5OH9H/LQJy4Mc8Yk4/a0RKc7yEREcrDPaKfEFqxgrRomi9mllgei5uzLPnGmbSBE73oLnv2GkisLopNnSC7wamtrHbNkbtu2DTNmzIBKpcLVV1+Ns2fPejxAokARrFNjyqBumDKoGyxWEd+fKsPH+/Lx2c+FOFlcjT/95xCezzqOe69OxgPjeyMymHeOiUgeDY0tEUqcLCJUr4Gh3rYcD5EU9nnVWN41teDVuNCCZzTbPmt6rfLON3KRnOk+ffpg8+bNOHfuHLZu3eoYd1dcXIyIiAiPB0gUiNQqAdf0jcWLs4bhxyduwLKpg5AUE4KKWhNe+uokrlv5Nd7YeRr1Jl5gEJHv2VvwtEpswZPQtYyoNRxy0dQaZz+XtMdoUu4NJblIzvSf//xnLFq0CL169cLo0aMxZswYALbWvOHDh3s8QKJAFx6kxZyxvfD1oglYc/cI9I0LQ0WtCU/97whuXLUDu0+XyR0iESmMfTZAvQIvuEJ0rq/fRdQcl0ZqYp9J1H4uaY+9xwBb8HxHcqZvv/125OXlYe/evfj8888d2ydNmuQYmxcIqqqqMGrUKAwbNgyDBw/G66+/LndI1MmpVQLSB3fH5wuvxd9mDkF8hB5ny2rxi7W78afNP7M1j4h8Rslj8EK0rq/fRdQatt/BMUu42XL5qtfegscxeL7j8hi8Hj164LbbbsPUqVMxadIkdOvWzenxq666yuPBeVNISAi++eYbhISEoLa2FqmpqZgxYwa6dOkid2jUyalVAu4clYibBnfDii1H8cGePLy3+yz25ZXj1V+mITEmRO4QiaiTU3KBZ295MFsv3/JA1JwINuHZaRonmjG58DlyjMFT4PlGLi5nev369QgJCcEjjzyC2NhY3HHHHXjvvfdw8eJFb8bnNWq12rGWX319PSwWC0S2vZMPRQRpsWLGYLz3wFWICdXh0HkDbn3lW+Scq5A7NCLq5IwKnmTFvrC7fUZEIqk4BK+pBc/kSguegm8oycXlTE+YMAHPP/88Tpw4ge+//x4jRozAP/7xD3Tv3h0TJkzAiy++iFOnTnkssB07duDWW29Fjx49IAgCNm/e3OI5q1evRkpKCoKCgpCWloadO3dKeo2KigoMHToUCQkJWLx4MWJjYz0UPZHrxvftiv/+7hoMSYhERa0Jd7++m+PyiMirlDzJir3lwcwCj6TiIeNgXwvQ7MoYPMcyCco738jFrUwPGjQIS5Yswe7du5GXl4e7774bX331FQYPHozU1FT873//63BgNTU1GDp0KF555ZVWH9+wYQMWLlyIJ554Avv378f48eORnp6OvLw8x3PS0tKQmpra4v/z588DAKKionDgwAHk5uZi/fr1uHDhQofjJnJHz6hgfPDQ1Rh7RRfUNFhw39t7sD+vXO6wiKiTMim6Bc/2ntmCR+4SOAoPGjda8DgGz3ckr4N3qfj4eDz00EN46KGHUFNTg23btkGn03U4sPT0dKSnp7f5+AsvvIAHHngADz74IABg1apV2Lp1K9asWYMVK1YAALKzs11+D0OGDMGOHTtwxx13tPoco9EIo9Ho+N5gMAAATCYTTCaTS6/jTfYY/CEWpfB0znUqYO3dw/Dr9TnYebIMD7zzIz781Wgkc0yeA49z32POfc8XOa8z2vatUSnvd6sSbBekRpO5Ra6Vlgs5BWLOTWbb0hqiKAZU3M15Ku8q2Iq2BrP5svuqa7A9rhYCN28d4WrOPZkbQZQ48Mxe2LTYkSBAr9d7pLhrbd+bNm3CtGnTAAANDQ0ICQnBxo0bMX36dMfzFixYgJycHHzzzTeX3eeFCxcQHByMiIgIGAwGjBkzBh988AGGDBnS6vOXLl2KZcuWtdhuH5tI5ClGC/DSITXyawT0DBHx6GALOLMwEXnS//JU2FagwrXdrJiZoqzJRtYdV2F/mQoze1lwbXe24pHrjlUKWH1Yje4hIh4fquxZWPeWCHjvpBr9Iq34zcD2zyH/PKHCj6UqTE2yYFJPfubaUltbi9mzZ6OysrLDa4tLbsGLiopqd4HHhIQE3HfffXjyySehUnnnqrS0tBQWiwXx8fFO2+Pj41FUVOTSPvLz8/HAAw9AFEWIoojf/va3bRZ3ALBkyRJkZmY6vjcYDEhMTMSUKVP8YoF3k8mErKwsTJ48GVqtVu5wFMGbOR97XT2mrv4eBTUm/KzqjScyBnh0/4GKx7nvMee+54uc//T5MaDgLPpekYKMm/p75TX81Rc1P2F/WRH6XzkQGWOTAfA4l0Mg5jzyVBlWH85GRHg4MjLGyh2OWzyVd+HnIrx38idERndBRsaodp+7dcMBoPQChqQORMaYZLdfM1C5mvO2GtHcIbnAW7duHZ544gncd999uOqqqyCKIn788Ue88847+L//+z+UlJTgueeeg16vxx//+EePBdqaSwtNURTbLT6bS0tLQ05OjsuvpdfrodfrW2zXarV+dWLyt3iUwBs5T+iixXN3DMX96/Zi3fd5uGlwD1zdm0t42PE49z3m3Pe8mXOLaPtbGaTTKO73qm0cByRCaPHeeZz7XiDlXKO2XTYLQstjJ9B0NO9BOtvPWqziZfdjbTzf6HWB87v2hsvl3JO5kVzgvfPOO3j++edx5513OrZNnToVgwcPxmuvvYYvv/wSSUlJePrpp71W4MXGxkKtVrdorSsuLm7RqkcUqK4fEI+7rkrCB3vysPSTQ/j0d9c4BjUTEXWEfQ04jZd62vgzzqJJ7uI6eE2kLJNgaRwNpuLcND4j+cz+/fffY/jw4S22Dx8+HN9//z0A4JprrnGazdLTdDod0tLSkJWV5bQ9KysLY8cGZpM5UWsW39gfUSFaHC2qwsbsfLnDIaJOwl7bqBS4oBdn0SR32WetcLW3WGemaVwmweTCMgmio8Bj3nxFcoGXkJCAN998s8X2N998E4mJiQCAsrIyREdHdyiw6upq5OTkOLpR5ubmIicnx1E4ZmZm4o033sBbb72FI0eO4NFHH0VeXh7mzZvXodcl8ifRoTr87vq+AIDV20+6tN4MEdHlNF2oyhuHHNiCR9Rx9tZ/Vz5H9qeolXjCkYnkLprPPfcc7rjjDnz22WcYNWoUBEHAjz/+iKNHj+Kjjz4CAPz444+YNWtWhwLbu3cvJk6c6PjePsHJnDlzsG7dOsyaNQtlZWVYvnw5CgsLkZqaii1btiA5WXmDN6lzm31VElZ/fRLnLtbhkwPnMWNEgtwhEVHAU26XKXXjm7ZYecOMpLGXMgr82LSg07jegmdtvKPE+s53JBd4U6dOxbFjx/Dqq6/i+PHjEEUR6enp2Lx5M3r16gUA+PWvf93hwCZMmIDLreAwf/58zJ8/v8OvReTPgnVq3H9NClZuPYb3f8hjgUdEHWavbZTY1YwteNRRCvzYtOBowXNlDJ6VXTR9za2Fznv16oVnn33W07EQURvuSEvAC1nHkX22HCeLq9EnLkzukIgogCl5sgh149ghiwsXpkTNSVw6ulOTNgbP9lWtxC4DMnFr+qydO3fil7/8JcaOHYuCggIAwHvvvYdvv/3Wo8ERkU1cRBAm9u8KANiYfU7maIgo0HEMHlvwyH1K/NxcSqeWMgaPXTR9TXKB9/HHH+PGG29EcHAw9u3bB6PRCACoqqrCM8884/EAichmZmPXzM8OFvEuIhF1CGfR5CyaJB2PmCb2ZZtMZtfH4CnxfCMXyQXeU089hVdffRWvv/6604J8Y8eOxb59+zwaHBE1ua5/V+g0KuRdrMXJ4mq5wyGiAGbvoqnEyy224FFHCYr85Diz97Z05VOk5BtKcpFc4B07dgzXXntti+0RERGoqKjwRExE1IoQnQZjr+gCAPjyaLHM0RBRQFNwF03Ooklu4z2BFlzpUWRtrPDUbg0MI3dITnX37t1x8uTJFtu//fZb9O7d2yNBEVHrxve1jcPbk3tR5kiIKJApucsUW/CooxT4sWnB3orpWguefQweE+crkgu8hx9+GAsWLMAPP/wAQRBw/vx5vP/++1i0aBGXLCDyslG9ogEAe89cdNwRIyKSSslnj6YWPCVngdyh5NlnLyWlVmMXTd+TvEzC4sWLUVlZiYkTJ6K+vh7XXnst9Ho9Fi1ahN/+9rfeiJGIGg3sHoEQnRqGejOOF1dhQLcIuUMiogDUNIum8i642IJHHaW8T03bXJnzTXT0GPByMOTgVm/Yp59+GqWlpdizZw92796NkpIS/OUvf/F0bER0CY1ahcE9IwEAhwoMMkdDRIHKquALLnXjQCCug0dScQLrllxp1bTYzzdKPOHIxK2FzgEgJCQEI0eO9GQsROSCAd3C8UPuRRy7UCV3KEQUoOyXZEq83GILHrnLUeApsOX7UpK6aDbOZ8Qumr7jUoE3Y8YMl3f473//2+1giOjy+jd2yzxWxAKPiNyk4C6anEWTyHNcadVUco8BubjURTMyMtLxf0REBL788kvs3bvX8Xh2dja+/PJLREZGei1QIrLp3y0MAAs8InKfki+41AJb8Mg9Sm75vpT95pArnyJ7EahW4A0lubjUgvf22287/v2HP/wBd955J1599VWo1WoAgMViwfz58xERwQkfiLytX3w4AKDIUA9DvQkRQVqZIyKiQKPkrmYKfMtEHiflY2ThMgk+J3mSlbfeeguLFi1yFHcAoFarkZmZibfeesujwRFRS+FBWnQJ1QEAzl2slTkaIgpE9okReLlF5DrRUajIHIg/YRdNvyS5wDObzThy5EiL7UeOHIGV/dmJfCIhOhgAcO5incyREFEgUvK6VAp8y+RhPISaPkeuzKJp7zHAWTR9R/IsmnPnzsX999+PkydP4uqrrwYA7N69G88++yzmzp3r8QCJqKWEmBAcyK9Efjlb8IhIuqZ18OSNQ06c8p6k4iHTRGgsc6VNsqLgE46PSS7wnnvuOXTr1g0vvvgiCgsLAQDdu3fH4sWL8dhjj3k8QCJqqVtEEACguMoocyREFJiU20VTUOS7Jk/iWDJpN4csVnbR9DXJXTRVKhUWL16MgoICVFRUoKKiAgUFBVi8eLHTuLxAUVtbi+TkZCxatEjuUIhcFheuBwAUG+pljoSIApGSu2jaudK1jKg5tvq2JGUWTSWfb3zN7YXOAXSKWTOffvppjB49Wu4wiCSJi2gs8NiCR0RuEB19NOWNQw68xqSO4iHUlAPRhaqXXTR9z6UWvBEjRqC8vNzlnV5zzTUoKChwOyhfOXHiBI4ePYqMjAy5QyGSpGuYrYtmCQs8InID1/Niawy5gweNg4STh6PAk9xvkNzlUgteTk4ODhw4gJiYGJd2mpOTA6OxYxeeO3bswMqVK5GdnY3CwkJs2rQJ06ZNc3rO6tWrsXLlShQWFmLQoEFYtWoVxo8f7/JrLFq0CCtXrsSuXbs6FCuRr0WF2Na+q6gzyRwJEQUidtEkch8/Nk1cKXktjZPs83zjOy530Zw0aZJLzbCAZwaf1tTUYOjQoZg7dy5mzpzZ4vENGzZg4cKFWL16NcaNG4fXXnsN6enpOHz4MJKSkgAAaWlprRaa27Ztw48//oh+/fqhX79+LPAo4NgLvMo6E0RR5IBvIpKE63mxBY+k4zHTRMosmiK7aPqcSwVebm6u5B0nJCRI/pnm0tPTkZ6e3ubjL7zwAh544AE8+OCDAIBVq1Zh69atWLNmDVasWAEAyM7ObvPnd+/ejX/961/YuHEjqqurYTKZEBERgT//+c+tPt9oNDoViwaDAQBgMplgMsnfimKPwR9iUQo5cx6isZ0kG8xWVNUaEawLvAmO3MHj3PeYc9/zRc6tjU14VotFcb9bq8UCABBFa4tcKy0XcgrEnJvMZgC2giWQ4m7OU3m3mJt+/nL7snfRtFrMAZu3jnA1557MjSC62iwnI0EQnLpoNjQ0ICQkBBs3bsT06dMdz1uwYAFycnLwzTffSNr/unXr8PPPP+O5555r8zlLly7FsmXLWmxfv349QkJCJL0eUUeJIpC5Ww0rBCwbYUaUXu6IiCiQ/OOwCscrVbinjwUju/r9ZYBH7SsV8M4JNfpEWPG7QVa5w6EAklMm4O3jalwRLuKRVIvc4ciqygT8315bO9Hfx5jbfe6SPWrUWgT8cZgZ8cG+iC4w1dbWYvbs2aisrOzwRJYdmkVTLqWlpbBYLIiPj3faHh8fj6KiIq+85pIlS5CZmen43mAwIDExEVOmTPGL2URNJhOysrIwefJkaLVaucNRBLlzvuynr1Fea0LamPHo3y3c568vB7lzrkTMue/5Iuf/urAXqLyI4cOHIWNId6+8hr8SDxbhnRM/oUuXLsjIGAWAx7kcAjHnws9FePv4T4jpEuM4dgKNp/JeVtOA/9u7HYCt1117Q0We2PcVYDFj4oTr0KtLqNuvGahczbm9d6AnBGSBZ3fpweTuWKT77rvvss/R6/XQ61s2k2i1Wr86MflbPEogV84jg7UorzWhzgLF/c55nPsec+573s257W+lRqNR3O+1ac1eocV753Hue4GUc7XadtksCC2PnUDT0bzrtE0t/1qttt3rb3tnQV0A/a694XI592RuAnLC0tjYWKjV6hatdcXFxS1a9Yg6qyCt7SKl3qTsbiJEJJ19TIwSpzzgPA/kLhHK/dy053KDvThrr+8FZIGn0+mQlpaGrKwsp+1ZWVkYO3asTFER+Za9wKtrYIFHRNLYr8eUfMGlrJGHRJ7V/Mxxuc9S0zp4yj3f+JpbXTQrKirw0Ucf4dSpU/j973+PmJgY7Nu3D/Hx8ejZs6dHAquursbJkycd3+fm5iInJwcxMTFISkpCZmYm7rnnHowcORJjxozB2rVrkZeXh3nz5nnk9Yn8XbC9Bc/MSQKISKLGKzIl1ncC21/ITaKCPzeXkpIDR4HHvPmM5ALvp59+wg033IDIyEicOXMGDz30EGJiYrBp0yacPXsW7777rkcC27t3LyZOnOj43j7ByZw5c7Bu3TrMmjULZWVlWL58OQoLC5GamootW7YgOTnZI69P5O/sSyPUswWPiCRSchdNBzbhkZt4k8CZbYxd2zlhF03fk1zgZWZm4r777sPf/vY3hIc3zdyXnp6O2bNneyywCRMmXHZh9fnz52P+/Pkee02iQGJvwavjGDwiksj+19WdickCnQLfMnkI7wk0aV7kutxFkx8+n5E8Bu/HH3/Eww8/3GJ7z549vbZEARG1pNfaPr6cZIWIpLLfQFXy9ZbIy3Vyk5I/Nw4u5kAURUfXVnbR9B3JBV5QUFCr6zQcO3YMXbt29UhQRHR5bMEjInfZu0wp8XpLie+ZPONyPcuUqr20WJs9xhY835Fc4N12221Yvnw5TCYTAFv3jry8PDz++OOYOXOmxwMkotaxwCMid3EWzctP7U7UFgV/bBya56C91nBrsw8aZ9H0HckF3nPPPYeSkhLExcWhrq4O1113Hfr06YPw8HA8/fTT3oiRiFrBSVaIyG0K7qKpxPdM5GmufoycCjx+9nxG8iQrERER+Pbbb/HVV19h3759sFqtGDFiBG644QZvxEdEbdBrbPdnjFwmgYgksip6unfbm2YDHknlWCaBHX2dtNcaLrKLpiwkF3jvvvsuZs2aheuvvx7XX3+9Y3tDQwP+9a9/4d577/VogETUOntXB4uVlylEJI29S5USZ9Ekoo5z9dzR/BqFBZ7vSO6iOXfuXFRWVrbYXlVVhblz53okKCK6PHXjidLCgSREJJGo5ElWGt80J8wgqZpujMgciB9wp4sm8+Y7kgs8URRbrdrz8/MRGRnpkaCI6PLUjS14VrbgEZFETV00ecVFRB3TbhfNZv9mC57vuNxFc/jw4RAEAYIgYNKkSdBomn7UYrEgNzcXN910k1eCJKKWVI4WPJkDIaKAIzoWHpY5EBnY3zJPnSQVG32buDqLJnMmD5cLvGnTpgEAcnJycOONNyIsLMzxmE6nQ69evbhMApEP2S/MrDx7EpGbOFkEEbmj+bmj3cuQZo+xAc93XC7wnnzySQBAr169MGvWLAQFBXktKCK6PHbRJCJ3WRW9TELjLJo8dZJEIrs2O7iTAmbNdyTPojlnzhxvxEFEEnEWTSJyV9OFqrxxEFHga78Bj9cocpBc4FksFrz44ov48MMPkZeXh4aGBqfHL1686LHgiKht9lk02UWTiKSynzWU2EWTY/DIXU2fG2quvRlpRacumsycr0ieRXPZsmV44YUXcOedd6KyshKZmZmYMWMGVCoVli5d6oUQiag1bMEjIncpuYsmUUfxc+N6DppfoTBtviO5wHv//ffx+uuvY9GiRdBoNLjrrrvwxhtv4M9//jN2797tjRiJqBWcRZOI3MZ18DgIjyTj2omtczUrLIx9R3KBV1RUhMGDBwMAwsLCHIue33LLLfjf//7n2eiIqE3qxk8v/+AQkVT2s4ZKieskEHUQPzWuz6LJaxR5SC7wEhISUFhYCADo06cPtm3bBgD48ccfodfrPRsdEbXJ0YLHLppEJJGji6bMccjB3orAMydJxWOmiVtdNNmE5zOSC7zp06fjyy+/BAAsWLAAf/rTn9C3b1/ce++9uP/++z0eoDdpNBoMGzYMw4YNw4MPPih3OESSqDkGj4jcxFk0idzAZRJax8sQvyN5Fs1nn33W8e/bb78diYmJ+O6779CnTx9MnTrVo8F5W1RUFHJycuQOg8gtnEWTiNxln7pciReq9q5lPHUSua/5maO9pRD4OZOHpALPZDLhV7/6Ff70pz+hd+/eAIDRo0dj9OjRXgmOiNomsIsmEbnJarV9VV55R+Q+x40RmePwB67eHGq6meTNaOhSkrpoarVabNq0yVuxONmxYwduvfVW9OjRA4IgYPPmzS2es3r1aqSkpCAoKAhpaWnYuXOnpNcwGAxIS0vDNddcg2+++cZDkRP5hr2LJus7InKXElvw4BiDx5MnkSe40kqnwDONrNwag9daseVpNTU1GDp0KF555ZVWH9+wYQMWLlyIJ554Avv378f48eORnp6OvLw8x3PS0tKQmpra4v/z588DAM6cOYPs7Gy8+uqruPfee2EwGLz+vog8xT6LJrtoEpFU9pntlDyJJk+dJBXHrjZx7qLZDn7OZCF5DF6fPn3wl7/8Bbt27UJaWhpCQ0OdHn/kkUc8Elh6ejrS09PbfPyFF17AAw884JgcZdWqVdi6dSvWrFmDFStWAACys7PbfY0ePXoAAFJTUzFw4EAcP34cI0eObPW5RqMRRqPR8b29GDSZTDCZTK6/MS+xx+APsSiF3DkXG/tYmS1Wxfze5c65EjHnvueLnFsar1QtZovifrdWiwWArci9NNdKy4WcAjHnlsZjx2oVAyru5jyV9+bLH9iuhVuvehsaX0cQhIDNWUe5mnNP5kcQJS5QkZKS0vbOBAGnT5/ucFCt7XfTpk2YNm0aAKChoQEhISHYuHEjpk+f7njeggULkJOT41J3y/LycoSEhECv1yM/Px/jxo3D/v37ERMT0+rzly5dimXLlrXYvn79eoSEhLj3xog64GiFgDVH1OgRIuIPQy1yh0NEAeTPe9WoNAlYNNiMxDC5o/GtI+UCXj2qRs8QEYt57iQJdl0QsOG0GqnRVjw0wCp3OLISRWDhbls70dMjzQjTtv68CiPw5D4NVIKIF6/m5609tbW1mD17NiorKxEREdGhfUluwcvNze3QC3pCaWkpLBYL4uPjnbbHx8ejqKjIpX0cOXIEDz/8MFQqFQRBwN///vc2izsAWLJkCTIzMx3fGwwGJCYmYsqUKR3+JXiCyWRCVlYWJk+eDK22jU8ZeZTcOY86VYY1R7IRGhaGjIxxPn99OcidcyVizn3PFzl/+udvAJMR48dfg4Hd5f8b5kvhJ0rx6tF9iIiIQEbGGAA8zuUQiDmv2puPDacPIz4+HhkZw+UOxy2ezPvC3ba1sCdNmoQuYa2vhV1kqMeT+3ZAJaiQkXFjh14vULmac08OFZNc4PmTSweHi6Lo8oDxsWPH4uDBgy6/ll6vb3Uhd61W61cnJn+LRwnkyrmu8TWtIhT3O+dx7nvMue95M+f2rjtajfJ+r2pN46WPILR47zzOfS+Qcq5SqRu/qgIm5rZ4Mu+adval0ZgB2MYtBnrOOupyOfdkfiRPsuIPYmNjoVarW7TWFRcXt2jVI+qs7LNocqIAIpLKPjpDiZNFKPAtE3lVe9chjolp+MnzqYAs8HQ6HdLS0pCVleW0PSsrC2PHjpUpKiLfss+iaWGFR0QS2U8bKiVWeI0kTkFAxHXwLiFIWXKESfMpv+2iWV1djZMnTzq+z83NRU5ODmJiYpCUlITMzEzcc889GDlyJMaMGYO1a9ciLy8P8+bNkzFqIt/hQudE5C77WUOJ9Z0S3zN5Fo8hGwGXXwWBVyjy8NsCb+/evZg4caLje/sEJ3PmzMG6deswa9YslJWVYfny5SgsLERqaiq2bNmC5ORkuUIm8il1418YKws8IpLIvn4mr1OJXMdG3za020WT5xo5uFXg7dy5E6+99hpOnTqFjz76CD179sR7772HlJQUXHPNNR4JbMKECZftPjF//nzMnz/fI69HFGjsY/DYRZOIpGpasFl5l10cC0TucrR88xgC0Hj+EF3qoMlWTx+TPAbv448/xo033ojg4GDs37/fsfh3VVUVnnnmGY8HSESts4+dYQMeEUml5ElW7HhvjKhjXDl98HMmD8kF3lNPPYVXX30Vr7/+utN0nmPHjsW+ffs8GhwRtc3egscumkQkVdPMdsqj5KKWOog3RlrlShHHVk/fklzgHTt2DNdee22L7REREaioqPBETETkgsb6jl00iUgy+1lD0bNocvoHog6RMoumgk81spBc4HXv3t1pdku7b7/9Fr179/ZIUER0eSoVZ9EkIvcouYumAt8yeYiSZ59tjb1VzpV18Mi3JBd4Dz/8MBYsWIAffvgBgiDg/PnzeP/997Fo0SJOeELkQ5xFk4jcZXV00VTulSovPIk6yIXTB9cOlIfkWTQXL16MyspKTJw4EfX19bj22muh1+uxaNEi/Pa3v/VGjETUCs6iSUTuclx0KfGqS4nvmTxC5I2RVrV3FaLkGXvl5NYyCU8//TSeeOIJHD58GFarFQMHDkRYWJinYyOidti7aLIBj4ikarrokjcOOfHUSdQx9tPH5ZY1a/5c8g23FzoPCQnByJEjPRkLEUlgn2SFXTSJSCol31Vn6wu5S1Ty9LOtcOX0wSsUebhU4M2YMcPlHf773/92Oxgicp19DB67aBKRVBwX41qrAxFdXvuTrLAoloNLBV5kZKS34yAiiex33nmNQkRSKbmLphLfM3mGYxZNWaPwH1Jaw5kz33KpwHv77be9HQcRERH5SNOFqnIvu3hvjKhj2EXTf0leJoGI/EPzEyu7GhGRO5TYmqXAt0weouSxq+1xZR085sy3JE+yMnz48FZ/SYIgICgoCH369MF9992HiRMneiRAImodT5VE5C7eFAKbFkgydtF05phF04UPE+s735LcgnfTTTfh9OnTCA0NxcSJEzFhwgSEhYXh1KlTGDVqFAoLC3HDDTfgP//5jzfiJaJW8FqNiKRQ8oUqWxKIPMO1zxIvUOQguQWvtLQUjz32GP70pz85bX/qqadw9uxZbNu2DU8++ST+8pe/4LbbbvNYoETkrPmJladPIpJCVHKF14jnTZLK3vLNewTOXOqi6ZtQqJHkFrwPP/wQd911V4vtv/jFL/Dhhx8CAO666y4cO3as49ERUZt4siSijlLiJCu8OCfyjKYumm1z3EviB8+nJBd4QUFB2LVrV4vtu3btQlBQEADAarVCr9d3PDoicgnH0xARScPzJrmLpUojCYlgznxLchfN3/3ud5g3bx6ys7MxatQoCIKAPXv24I033sAf//hHAMDWrVsxfPhwjwfrabm5ubj//vtx4cIFqNVq7N69G6GhoXKHReQSp1k05QuDiAJM88JGiTfVFfiWibyqvZslvI8iD8kF3v/93/8hJSUFr7zyCt577z0AQP/+/fH6669j9uzZAIB58+bh17/+tWcj9YL77rsPTz31FMaPH4+LFy+y1ZECihK7VhGRZynxLGIvanndSVJxyn9nrnXR5LhFOUgu8ADg7rvvxt13393m48HBwW4H5CuHDh2CVqvF+PHjAQAxMTEyR0QkkdM6ePKFQUSBpfn5gheqROQuaecPnmt8ye2FzhsaGpCfn4+8vDyn/z1lx44duPXWW9GjRw8IgoDNmze3eM7q1auRkpKCoKAgpKWlYefOnS7v/8SJEwgLC8PUqVMxYsQIPPPMMx6LncjXXFmDhogIYMuV/UKTN8ZIKkdrlMxx+BtXZtEk35LcgnfixAncf//9LSZaEUURgiDAYrF4JLCamhoMHToUc+fOxcyZM1s8vmHDBixcuBCrV6/GuHHj8NprryE9PR2HDx9GUlISACAtLQ1Go7HFz27btg0mkwk7d+5ETk4O4uLicNNNN2HUqFGYPHmyR+In8jaBLXhE5AanMXgyxkFEga3pOuTyY/DYWcC3JBd49913HzQaDT799FN0797da9070tPTkZ6e3ubjL7zwAh544AE8+OCDAIBVq1Zh69atWLNmDVasWAEAyM7ObvPnExISMGrUKCQmJgIAMjIykJOT02aBZzQanYpFg8EAADCZTDCZTNLenBfYY/CHWJRC7pybTeZm/zbBBKsscfiS3DlXIubc97ydc7Ol6VxhNpthMinrystisZ07raLYItc8zn0nEHNubmzEEEVrQMXdnCfzbj9zmEzmNvdnNpsdzw3UnHWUqzn3ZH4kF3g5OTnIzs7GgAEDPBaEVA0NDcjOzsbjjz/utH3KlCmtLuHQmlGjRuHChQsoLy9HZGQkduzYgYcffrjN569YsQLLli1rsX3btm0ICQmR9ga8KCsrS+4QFEeunNebAftH+LPPt0KnliUMWfA49z3m3Pe8lXNbfWc7d3zxRRZC3BqNH7jOVAGABnW1tdiyZYvTYzzOfS+Qcn60QACgRn5BAbZsOSd3OB3iibw3NKgBCNixYweOt3EpnF8DABrU19e3+LwpzeVyXltb67HXknxaHzhwIEpLSz0WgDtKS0thsVgQHx/vtD0+Ph5FRUUu7UOj0eCZZ57BtddeC1EUMWXKFNxyyy1tPn/JkiXIzMx0fG8wGJCYmIgpU6YgIiLCvTfiQSaTCVlZWZg8eTK0Wq3c4SiC3DmvNprxhx+/AgDceOONCFZAhSd3zpWIOfc9b+e8wWwFfvgCADB58mREBivr95pzrgIv/rwHwcHByMi4FgCPczkEYs7zd+YCeSeQkJCAjIxUucNxiyfzvvTA16gxmzB+/LXoGx/W6nMOnTdg5U+7ERwUhIyM6zr0eoHK1Zzbewd6guQC769//SsWL16MZ555BoMHD24RqC+LnUu7h9rHAbrqct1Am9Pr9a0uo6DVav3qxORv8SiBXDnXWZuOdVsMnb/As+Nx7nvMue95K+dWoWmsvBJ/rxpN46WPILR470rMh9wCKecqle3vrEpQBUzMbfFE3lWN19warabNfdk/b0IrnzeluVzOPZkfyQXeDTfcAACYNGmS03ZPT7LSntjYWKjV6hatdcXFxS1a9YiUgLNoEpE7lDjxgf1GMCenIncp8XPTHlc+S8yZb0ku8L7++mtvxCGJTqdDWloasrKyMH36dMf2rKws3HbbbTJGRuQ7nEWTiNzhtA6efGEQBRwuk+DMfh3S3k1mXp/IQ3KBd911vuk/W11djZMnTzq+z83NRU5ODmJiYpCUlITMzEzcc889GDlyJMaMGYO1a9ciLy8P8+bN80l8RHIT+CeGiEgynjmJPOXyreEsiuXh9txZtbW1yMvLQ0NDg9P2IUOGdDgoANi7dy8mTpzo+N4+wcmcOXOwbt06zJo1C2VlZVi+fDkKCwuRmpqKLVu2IDk52SOvT+TvnFrw5AuDiAKMUwse+00RuYxrujmTkgeea3xLcoFXUlKCuXPn4rPPPmv1cU+NwZswYYLTYqytmT9/PubPn++R1yMKZJf7rBARtUaJl1yObmU8bxJ5RLstePyYyUIl9QcWLlyI8vJy7N69G8HBwfj888/xzjvvoG/fvvjkk0+8ESMRERF5SPPxMrypTiQdh0jY2LPQ7hg834RCl5DcgvfVV1/hP//5D0aNGgWVSoXk5GRMnjwZERERWLFiBW6++WZvxElEl2AXTSJyh9LvqNsvzhWeBqIOk9ZF03txUEuSW/BqamoQFxcHAIiJiUFJSQkAYPDgwdi3b59noyMilyj9go2IXNf8dMGWCCLX2bv1slhx1n4XTV6gyEFygde/f38cO3YMADBs2DC89tprKCgowKuvvoru3bt7PEAiah0vzIioo5R4odo0Bk/eOIgCnSvXIfaPmRLPNXKS3EVz4cKFKCwsBAA8+eSTuPHGG/H+++9Dp9Nh3bp1no6PiNrgdLLkhQoRuYh31Incw1k0nbmSB0fOeFPapyQXeHfffbfj38OHD8eZM2dw9OhRJCUlITY21qPBEZFr2hvgTETUHM8WNjxvEnmGK/eMWBT7ltvr4NmFhIRgxIgRnoiFiCRwasDjdQoRuch5HTz54iAKNM3mn5UxCv/hyiyavKUkD8lj8IjIP3DRUCLqKCV2m+IYPHIXjxlnrlyHNHXRJF9igUcUoDgEj4jcwhY8og7h58aZa100mTRfYoFH1Alw0gQicpXSx55xHTxyl9I/O21hB03/wwKPKEDxZhgRucNpDJ58YRAFLH5ubKTNokm+5NYkKxUVFdizZw+Ki4thtVqdHrv33ns9EhgRta95dwfeISMidyix2xTH4JG7eMy0zqVeRMo71chKcoH33//+F3fffTdqamoQHh7u9MdBEAQWeEQy4B8dInJV89MFr7mIpFPgfZFWOW6WtPMcDiGRh+Qumo899hjuv/9+VFVVoaKiAuXl5Y7/L1686I0YiYiIyEOUfsHVdHGu7DyQdPYjRomzz7bGlTw05Yx8SXKBV1BQgEceeQQhISHeiIeIJGi6e8YLFSJyjVMLHq+6iKiD2rtn5BiDx5ONT0ku8G688Ubs3bvXG7EQkbtY3xGRG5R40eWYRZPnTZKq8aBR4MemVWwN918ujcH75JNPHP+++eab8fvf/x6HDx/G4MGDodVqnZ47depUz0ZIRG0SwNMqEUnDwoaIPMGVOtfew4g1sW+5VOBNmzatxbbly5e32CYIAiwWS4eDIiLXCIIAiOygSUSuU/oZw5WJIYhaw/FkrWv3ppGji6ZPQqFGLnXRtFqtLv0fSMXdsWPHMGzYMMf/wcHB2Lx5s9xhEbmFd+SJyGW84CIiD7B38XblEoQT0/iW5DF47777LoxGY4vtDQ0NePfddz0SlC/0798fOTk5yMnJwbfffovQ0FBMnjxZ7rCIJOHpkojcpdTzh/19K302UZKOE4Y4c62LJslBcoE3d+5cVFZWttheVVWFuXPneiQoX/vkk08wadIkhIaGyh0KkSScRZOIpHJ0M+NFKpEk/FvbOtdm0fRNLGQjucATRbHVPwr5+fmIjIz0SFAAsGPHDtx6663o0aMHBEFotfvk6tWrkZKSgqCgIKSlpWHnzp1uvdaHH36IWbNmdTBiIt/jbHBEJJXSzxccg0fkIfbPktJPKn7IpUlWAGD48OEQBAGCIGDSpEnQaJp+1GKxIDc3FzfddJPHAqupqcHQoUMxd+5czJw5s8XjGzZswMKFC7F69WqMGzcOr732GtLT03H48GEkJSUBANLS0lrtTrpt2zb06NEDAGAwGPDdd9/hX//6V7vxGI1Gp30ZDAYAgMlkgslkcvt9eoo9Bn+IRSn8Ief2u4m249Dlj3PA8oecKw1z7nveznlD434FL76GPzOZzLZ/iC1zrcR8yCUQc26xWAHY5qYIpLib82jeG+s6k9nc5v5MZvvnTQzYnHWUqzn3ZH4E0cWye9myZY6vjz32GMLCwhyP6XQ69OrVCzNnzoROp/NYcI4gBQGbNm1yms1z9OjRGDFiBNasWePYduWVV2LatGlYsWKFy/t+7733sHXrVvzzn/9s93lLly515KC59evXc9F3ks1ju9UwiwKeHGFGjF7uaIgoEFQYgSf3aaAWRLxwdeBMjuYpF+qAZ3I0CFGLWHGV8t4/ue+/eSp8UaDCdd2smJFilTsc2T2To8aFOgG/G2hGnzY68R2tELDmiBo9Q0QsHsrPW3tqa2sxe/ZsVFZWIiIiokP7cvmW/5NPPgkA6NWrF2bNmoWgoKAOvXBHNDQ0IDs7G48//rjT9ilTpmDXrl2S9vXhhx/iV7/61WWft2TJEmRmZjq+NxgMSExMxJQpUzr8S/AEk8mErKwsTJ48ucXahOQd/pDzxT9+AbPZiokTJ6JnVLAsMfiSP+RcaZhz3/N2zgsr6/Hkvh1QqVTIyLjR4/v3d6dKavBMznfQ6rSO98/j3PcCMeeHt53AFwW56JXSCxkZA+QOxy2ezPtLJ7/DhboajL76aoxOiWn1OeEnSrHmyD5EREQgI2NMh14vULmac3vvQE+Q3Kdrzpw5Hntxd5WWlsJisSA+Pt5pe3x8PIqKilzeT2VlJfbs2YOPP/74ss/V6/XQ61s2kWi1Wr86MflbPErgDzlXqzWyx+BL/pBzpWHOfc9bOVdrbF2mBAiK/J1qtbZLH1FEi/fP49z3AinnKrVt6gq1Sh0wMbfFE3lXNQ5oVanbzoe6cUiXICjzfNPc5XLuyfxILvAsFgtefPFFfPjhh8jLy0NDQ4PT4xcvXvRYcJdz6WQvbU0A05bIyEhcuHDB02ER+QxnpSIiqRwjM3j+IJKEc4k4c+UaxH6+4fWKb0meRXPZsmV44YUXcOedd6KyshKZmZmYMWMGVCoVli5d6oUQW4qNjYVarW7RWldcXNyiVY+oM+PCoUTkLqWePRzr4MkaBQUyFiuXaG+ZhMavzJlvSS7w3n//fbz++utYtGgRNBoN7rrrLrzxxhv485//jN27d3sjxhZ0Oh3S0tKQlZXltD0rKwtjx471SQxE/oR3FYnIVVyXisg99pmr+dGxcSzVJOG55BuSu2gWFRVh8ODBAICwsDDHoue33HIL/vSnP3kssOrqapw8edLxfW5uLnJychATE4OkpCRkZmbinnvuwciRIzFmzBisXbsWeXl5mDdvnsdiIPJ3vEAjIpJG4EJ4RB7h0jUIP2eykFzgJSQkoLCwEElJSejTpw+2bduGESNG4Mcff2x1EhJ37d27FxMnTnR8b5/Bcs6cOVi3bh1mzZqFsrIyLF++HIWFhUhNTcWWLVuQnJzssRiI/F1TVyOeQYnINU1D8HiHiEgStn63qr1eRI5WT+bMpyQXeNOnT8eXX36J0aNHY8GCBbjrrrvw5ptvIi8vD48++qjHApswYQIut0Tf/PnzMX/+fI+9JlGgsd+JZhdNIpJKqRdcHINH5Fmu3GRW6OlGNpILvGeffdbx79tvvx0JCQnYtWsX+vTpg6lTp3o0OCIiIvIspY8jUmphSx3XNGEIDyLAtTzwBrQ8JBd4l7r66qtx9dVXeyIWIpKId6KJSCpecNlcrpcQ0aV4zLSu3S6anEZTFm4VeMeOHcPLL7+MI0eOQBAEDBgwAL/73e/Qv39/T8dHRO2xzxXAPzpEJJFSWyE49pA6ikeQjZSbzMyZb0leJuGjjz5CamoqsrOzMXToUAwZMgT79u1DamoqNm7c6I0YiYiIyEMcN9RljUJ+vC1GUvFeqjOXFjr3fhjUCskteIsXL8aSJUuwfPlyp+1PPvkk/vCHP+COO+7wWHBE1D520SQiqcSmaTQVSaENl+RJPIactNeLyP4YP3e+JbkFr6ioCPfee2+L7b/85S9RVFTkkaCIyDWcRZOIpOLpwobnTZKKh4wzV5aUZI8BeUgu8CZMmICdO3e22P7tt99i/PjxHgmKiKTinx0ikoYXXETu4ThOGyl5UOqYX7lI7qI5depU/OEPf0B2drZj9szdu3dj48aNWLZsGT755BOn5xKR9/B8SURSOXpoKvwE4sraXUTNsdW3Da7Mokk+JbnAsy8svnr1aqxevbrVxwDbHw6LxdLB8IioPY4xeDyBEpHLlD0mRqnvmzyHx5BNUxfNy3fSZMp8S3KBZ7VavREHEXUA6zsichVvCNkwDyQVW32dSSnaWBT7luQxeM3l5+ez4COSkdK7WBGR+5R69nBMTiVzHBS4lPrZaYtLC52TT3WowBs4cCDOnDnjoVCISCp20SQiqRyz2vEGEZEkTeNX5Y3Db7gwk3fTLJpMmi91qMBrb90LIvI+1/q/ExE1UfgyeE3vm6dNog6RdA5R6glHJh0q8IiIiCiw8IYQUcewNcpZu1Os8HQjiw4VeH/84x8RExPjqViISDIudE5E7lFqNzP2fCB3seeaM8dnqZ28iJxFUxaSC7zly5ejtrYWALBkyRJERUUBAOrq6rB8+XKPBkdE7Ws6ucobBxEFjqbzBS+5iNyh1Jsjl3LMA9DOczhuUR6SC7xly5ahurq6xfba2losW7bMI0ERERGRdyj9gktgzwdyEw8ZZ1ImamK3Vt+SXOCJotjqL/TAgQMB113zxRdfxKBBgzBw4EA88sgjbHqngNN094zHLhG5hucLoo5hqeLMlVk0ybdcXug8OjoagiBAEAT069fPqcizWCyorq7GvHnzvBKkN5SUlOCVV17BoUOHoNVqce2112L37t0YM2aM3KERuYxdNInIXUq9SG0ag0ckDf/WOms6h7QzBq8xaUrtMSAXlwu8VatWQRRF3H///Vi2bBkiIyMdj+l0OvTq1SvgiiOz2Yz6+noAgMlkQlxcnMwREREReZfSu2gSdRg/PACkpYEp8y2XC7w5c+YAAFJSUjB27FhotVqvBQUAO3bswMqVK5GdnY3CwkJs2rQJ06ZNc3rO6tWrsXLlShQWFmLQoEFYtWoVxo8f79L+u3btikWLFiEpKQkajQbz5s3DFVdc4YV3QuQ97NNORO5S6vnD0bWdzTEkEbs3t44fJf/jcoFnd91118FqteL48eMoLi6G1Wp1evzaa6/1SGA1NTUYOnQo5s6di5kzZ7Z4fMOGDVi4cCFWr16NcePG4bXXXkN6ejoOHz6MpKQkAEBaWhqMRmOLn922bRuCg4Px6aef4syZMwgODkZ6ejp27NjRZvxGo9FpXwaDAYCt5c9kMnniLXeIPQZ/iEUp/CPnYmMMZkX87v0j58rCnPuet3NuMpkB2C5Wlfh7NZnNTf++JNdKzIdcAjHn9mte0WoJqLib82Te7TdJTOa2r0FMZovjuYGas45yNeeezI8gSryFtXv3bsyePRtnz55tcfdLEARYLBaPBdd8v5e24I0ePRojRozAmjVrHNuuvPJKTJs2DStWrLjsPjdu3Ijt27fjH//4BwBg5cqVEEURixcvbvX5S5cubXWW0PXr1yMkJETiOyLyjCez1ahoEPDYYDOSwuSOhogCwblq4LmDGkTpRCxL8/zfbH9naAD+lK2BABGrxijv/ZP7PjytwncXVLgpwYr0ROvlf6CTe+lnNU5VCZjbz4JhXVovJ/aWCHjvpBr9I62YP5A5a09tbS1mz56NyspKREREdGhfklvw5s2bh5EjR+J///sfunfvLmmKVE9paGhAdnY2Hn/8caftU6ZMwa5du1zaR2JiInbt2oX6+npotVps374dv/rVr9p8/pIlS5CZmen43mAwIDExEVOmTOnwL8ETTCYTsrKyMHnyZK93nyUbf8j5s4d3oKKhHuPGjcPgnpGX/4EA5w85Vxrm3Pe8nfODBZXAwR8QHByMjAzP9LoJJCVVRvwp+xtAEJCRkQGAx7kcAjHnuz85jO8u5KNf377IuD4wh/V4Mu/vF/6IU1XlGDZsODIGd2v99XLO472TPyM2tisyMtI69HqBytWc23sHeoLkAu/EiRP46KOP0KdPH48FIVVpaSksFgvi4+OdtsfHx6OoqMilfVx99dXIyMjA8OHDoVKpMGnSJEydOrXN5+v1euj1+hbbtVqtX52Y/C0eJZAz56rGGyxqtUZRv3ce577HnPuet3KuVtv+9KsEQZG/U622sZudiBbvn8e57wVSzgWVbXUxlVoVMDG3xRN512nUAABRaDsfKrXtOSqVMs83zV0u557Mj+QCb/To0Th58qSsBZ7dpa2Hba3R15ann34aTz/9tKfDIvI5jm8mIlfxfEHUMUqdoOhSWrUtDw2WtrteNs3ay5z5kksF3k8//eT49+9+9zs89thjKCoqwuDBg1tUm0OGDPFshK2IjY2FWq1u0VpXXFzcolWPiIiIyI7XmeQuLjHiTKO2tWiaLbxt5G9cKvCGDRsGQRCcJlW5//77Hf+2P+atSVYupdPpkJaWhqysLEyfPt2xPSsrC7fddpvXX5/IXzQtdM6TKxG5hgsPE7mLf2ub09kLPGs7LXiNX3m68S2XCrzc3Fxvx9FCdXU1Tp486RRDTk4OYmJikJSUhMzMTNxzzz0YOXIkxowZg7Vr1yIvLw/z5s3zeaxEcnEUePKGQUQBxHHBpdArruZvW+rQDiKAxYqdxt5F09xeF03eUJKDSwVecnKyt+NoYe/evZg4caLje/sMlnPmzMG6deswa9YslJWVYfny5SgsLERqaiq2bNkiS6xERESBgg3+RO7hZ8eZRmVvwbt8Yljf+ZbkSVY++eSTVrcLgoCgoCD06dMHKSkpHQ5swoQJl+12Nn/+fMyfP7/Dr0UUqOwDvflHh4ikUupEEc1b7ESRLQskHY8ZG53GlghTey14vgqGnEgu8KZNm9ZiPB7gPA7vmmuuwebNmxEdHe2xQImopaY/MjyFEpGr2GWKyB28merM3oJnaq8Fj7NoykIl9QeysrIwatQoZGVlobKyEpWVlcjKysJVV12FTz/9FDt27EBZWRkWLVrkjXiJqBmeLolIKsdMgPKGIRunMXiyRUGBjMWKjbZxkhVTO8sk2DFjviW5BW/BggVYu3Ytxo4d69g2adIkBAUF4Ve/+hUOHTqEVatWOc2ySUTexbuKROQqni6I3CPy0+PEvg6eub118JgzWUhuwTt16hQiIiJabI+IiMDp06cBAH379kVpaWnHoyOidtnvIvL0SURSKbUVovnb5hIzRO6zz6JpamcdPK4dKA/JBV5aWhp+//vfo6SkxLGtpKQEixcvxqhRowAAJ06cQEJCgueiJKJW8XxJRFIpvYsmkbt4P8CZK100m1LGM44vSe6i+eabb+K2225DQkICEhMTIQgC8vLy0Lt3b/znP/8BYFvD7k9/+pPHgyWi1vGPDhG5SlR4hdd89lCeOskdbI2ysRd45nZa8Egekgu8/v3748iRI9i6dSuOHz8OURQxYMAATJ48GarG2XSmTZvm6TiJqDX2hc5Z4RGRi3i2IHIPPzvOtI4umu0tdG77yqLYtyQXeICt3/5NN92Em266ydPxEJEE9vMl/+gQkVSKvd5yGoMnXxgUuJS6huSlXFkmwT7JCjPmWy4VeC+99BJ+9atfISgoCC+99FK7z33kkUc8EhgRERF5nsh1qYjcwhsCzlyZRdOOpxvfcqnAe/HFF3H33XcjKCgIL774YpvPEwSBBR6RDzlm0eQfHSJykdLvqDvNosn+DySB47Oj1A/PJVyaZIUfMVm4VODl5ua2+m8ikldTF02eQYnIRTxdEHUI6zsbe4HX0N4yCY1f2a3VtyQvk0BERESBT6mtEM3fNlsXSBIeL05CdGoAQK3RfNnnKvV8IxeXWvAyMzNd3uELL7zgdjBEJI3AWVaISCLeUSdyj7XxjoBaxc8OAIQF2cqI6vYKPN5FkYVLBd7+/ftd2hkHbBP5lv0CjadPInKV0qct57UKuctsZYHXXKj+8gWe44YSU+ZTLhV4f//73zFo0CCo1Wpvx0NEEvCESURSKX3MLk+b5C5LY4GnYYEHAAhrLPBq2ivw7DeU+MnzKZfG4A0fPhwXL14EAPTu3RtlZWVeDYqIpGEPCCIi6XjuJCmaWvA4hQXQvMCzyBwJXcqlIzQqKgqnT58GAJw5cwZW6+XXuyAi31H6HXkicp3S18FT6NsmD7A6CjyZA/ET9i6aDRYrjObWizyxqQmPfMilLpozZ87Eddddh+7du0MQBIwcObLN7pr2QjAQPPfcc3j77bchCAIef/xx/PKXv5Q7JCJJlHqBRkTua5pkhXhzjKRgC54zewseAFTXm6EPa1kb8HwjD5cKvLVr12LGjBk4efIkHnnkETz00EMIDw/3dmxedfDgQaxfvx7Z2dkAgEmTJuGWW25BVFSUvIERuYHdjIhIKqXeH+JYIHIXx+A5U6sEhOs1qDKaUVFnQpcwfZvP5Q1p33KpwAOAm266CQCQnZ2NBQsWBHyBd+TIEYwdOxZBQUEAgGHDhuHzzz/HL37xC5kjI3IdV0kgIqlE3hFyYCpICnPjECXOotkkNlyPKqMZpVVGXNE1rMXj/IzJQ3Ib89tvv+2T4m7Hjh249dZb0aNHDwiCgM2bN7d4zurVq5GSkoKgoCCkpaVh586dLu8/NTUVX3/9NSoqKlBRUYGvvvoKBQUFHnwHRN7HG2JEJJXSpy1X6vumjrNPQcECr0lsmA4AUFrd0Orj7KIpD5db8HytpqYGQ4cOxdy5czFz5swWj2/YsAELFy7E6tWrMW7cOLz22mtIT0/H4cOHkZSUBABIS0uD0Whs8bPbtm3DwIED8cgjj+D6669HZGQkRo0aBY2m7XQYjUanfRkMBgCAyWSCyWTq6NvtMHsM/hCLUvhDzu134s1msyJ+9/6Qc6Vhzn3P2zk3mxunNBeV+Xs1mZomg2gwmaBTiTzOZRCIOTdZGo8dqzWg4m7O03mPCdECAIoqa1vdp6UxZ6IYuDnrKFdz7sn8CGIA9NUQBAGbNm3CtGnTHNtGjx6NESNGYM2aNY5tV155JaZNm4YVK1ZIfo0HH3wQ06dPx80339zq40uXLsWyZctabF+/fj1CQkIkvx6RJ6z8SY38GgEPD7BgYLTff5SJyA8cKhew9qgaiaEiFg1R3vTmJiuw6AfbDd2/XmVGEJf4JRc9/5MaeTUCHhpgQSr/5gIAPjqtws4LKkzpacXNSS1n2f/6vIDNZ9VIi7Xi3r6chb89tbW1mD17NiorKxEREdGhffltC157GhoakJ2djccff9xp+5QpU7Br1y6X91NcXIy4uDgcO3YMe/bswauvvtrmc5csWYLMzEzH9waDAYmJiZgyZUqHfwmeYDKZkJWVhcmTJ0Or1codjiL4Q85fP7sb+TUGjBw1EhP6dZUlBl/yh5wrDXPue97OefCxEqw9uh9RUZHIyLja4/v3d0aTBYt++BIAMHnyFIQHaXicyyAQc/7ame+BmipcfdUoXNs3Vu5w3OLpvJ/++hR2XjiFqG6JyMgY1OLx89+eAc4eR0LPnsjIGNzh1wtErubc3jvQEwKywCstLYXFYkF8fLzT9vj4eBQVFbm8n2nTpqGiogKhoaF4++232+2iqdfrode3nB1Iq9X61YnJ3+JRAjlzrmocB6BRaxT1e+dx7nvMue95K+f2ZY5UgqDI36lVaJp+QKt1PnfyOPe9QMp54ySa0AdQzG3xVN7jI2292MpqTK3uz9C4CHp0qD7gc9ZRl8u5J/PjVoH33nvv4dVXX0Vubi6+//57JCcnY9WqVUhJScFtt93mseAu59IpV0VRlDQNq5TWPiJ/xrWciMhVotJnWWmGZ06Swr5MApfBa9I13Nb4UVrdcs4LAChr3G6fjIV8Q/IhumbNGmRmZiIjIwMVFRWOwZNRUVFYtWqVp+NrVWxsLNRqdYvWuuLi4hatekSdmWOZBF6lEJFESi3vuA4euatpHTxWeHb2Aq+4qq0Czza7Zmw7a+SR50k+Ql9++WW8/vrreOKJJxzdPABg5MiROHjwoEeDa4tOp0NaWhqysrKctmdlZWHs2LE+iYHIL/AOPBFJxPtBTXhzjKQwNxZ4XCahSUJ0MACgyFCPelPLSZvsLXvtLYJOnie5i2Zubi6GDx/eYrter0dNTY1HggKA6upqnDx50ul1c3JyEBMTg6SkJGRmZuKee+7ByJEjMWbMGKxduxZ5eXmYN2+ex2IgChS8SCEiV9knz1bq/SGlvm/quKYWPB5Edl1CdQjXa1BlNOPcxVr0jXdeK9u+Pl4XdtH0KckFXkpKCnJycpCcnOy0/bPPPsPAgQM9FtjevXsxceJEx/f2GSznzJmDdevWYdasWSgrK8Py5ctRWFiI1NRUbNmypUVcRJ2Zo4umrFEQUSDhwsPN8ORJEljYgteCIAhIjg3BzwUGnClzLvBEUURZja0Frytb8HxKcoH3+9//Hr/5zW9QX18PURSxZ88efPDBB1ixYgXeeOMNjwU2YcIEXG6Jvvnz52P+/Pkee02iQMM70UTkLimTknUmynzX5Ansotm65C6h+LnAgLNlzj35ahosqDfZ1r5jC55vSS7w5s6dC7PZjMWLFzsW5OvZsyf+/ve/4xe/+IU3YiSiy7jczRAiIjueLppwBmKSwmK1FSvsoukspUsoAODMJQWefQbNEJ0aIbqAXJktYLmV7YceeggPPfQQSktLYbVaERcX5+m4iMgF7KJJRNI1jsGTOQq5KLXlkjqOLXitS+5iWwvvTGmt0/amCVbYeudrkmfRvP7661FRUQHAtlyBvbgzGAy4/vrrPRocEbWPFypEJJW9BY+nD7ZmkjRWLpPQql6xrbfgOSZYCeX4O1+TfIRu374dDQ0NLbbX19dj586dHgmKiKThRQoRSaXU9eCU+a7JE8xc6LxVvRq7aJ6vqEOD2erYzjXw5ONyF82ffvrJ8e/Dhw87LTJusVjw+eefo2fPnp6Njoja1XShwgqPiFzDs0UT5oKk4ELnrYsN00GnVqHBYkVxVT0Som1dNu1dNGPZRdPnXC7whg0bBkEQIAhCq10xg4OD8fLLL3s0OCJqH7tYEZFUosLXSeB5k9zFMXitEwQBcRF65JfX4YLB6CjwyjgGTzYuF3i5ubkQRRG9e/fGnj170LVrV8djOp0OcXFxUKvVXgmSiFpn72LFLppE5CpR4ZOsNMcZiMlV9vF3AGfRbE18RBDyy+tQUlXv2FbKLpqycbnAsy8gbrVaL/NMIvI1XqIQkVRKbcni5FTkjgZL0/WvVsMumpeKj7AVcRcMRse2plk0WeD5muRlEt599912H7/33nvdDoaIJGq8TuFNaCJyFc8XTZgKclWN0ez4d7CWPdYuFRceBAC4YGhqwSursbfgsYumr0ku8BYsWOD0vclkQm1tLXQ6HUJCQljgEfkQ70MTkVRNQ/B4BiFyVW2DBQAQpFVxDF4r4tppwWMXTd+T3MZcXl7u9H91dTWOHTuGa665Bh988IE3YiSiyxB5H5qIXGQfd8aeimzNJNfZC7xQneS2EUWIb2zBK24cg2eyWFFRawIAdAllC56veaQTcd++ffHss8+2aN0jIu8S2EWTiNyk5AJPye/9ckRRhNUqwmyxOpYFIKCmwdZFM0TP7pmt6R5pK/Dsi51fbOyeqRKA6BAWeL7msdsQarUa58+f99TuiMgF7GJFROS+QO39YLJYUVlnQmWdCRW1JhjqTKg2mlHbYEa10YJaoxnVDWbUGi2oMZpR02BGbYPF9hyjxfHcmgYLrFZbFqyi2OrNQo1KgF6jQpBWjSCtGnqNCnqtGiE6NWLDdIgN06NruN7xNSE6GCmxoQjpZC1dtUZbC16ItnO9L08ZnBAJtUrAuYt1OF9Rh/JaW4EXE6qHil1afU7yUfrJJ584fS+KIgoLC/HKK69g3LhxHguMiFwXmJcoRCQH+0W8km8QCZD/vCmKIqqNZlTUNhVqFXUNju8r60yobNzWvJCrrDOhprG7oC+YrSLMDRbJr9k9MggpsaGO/1N7RmJwz0iE6gOzQKplC167woO0GNAtHIfOG3CwoNIxEQ0nWJGH5E/ZtGnTnL4XBAFdu3bF9ddfj+eff95TcRGRC5q6aMp9qUJEgcKxDp5y67smHTh1Gs0WVNWbG/83wVBn+1pVb4ah8av9sap6M6qMztsqak2OhbPdFRGkQWSIFpHBWoTpNQjVaRCq1yBUr0aoToMQvQZhejVCdE3bbI9rEKpTI1inhkalgkoAIAAqwVb2qwQBgmC7GWA0W1FvsqDebIHRZP+3FTVGM8qqjSipMqKkuqHxqxF5ZTUorzWhsLIehZX12HWqzBGvSgD6xYdjaEIUhidGwGhs8635HfsYvBAdC7y29G8s8I4XVaFndDAALnIuF8kFHtfBI/IfvEAj6pxEEahrsKC8zgJDvQmGejMMdbavzYsJQ70J1fVmNFisaDBb0WARYTJb0WCxwuTYZvu3ySzCZLE6pi5XMntZ9dqO0wjWqlFrNOH4aRV2bPoZJgtQb7I4Chv71wZzU3FTbTSjweyZ6yGdRoXoEC2ignWIDNEiKliLqMaiLTJYi8gQHSKDbdsjmz0WHqT129kcy2sakFtWg9ySGpwurcbJ4mr8lF+Jwsp6HC2qwtGiKmzYCwAavHvuO1zbryuignXQagRMGhCP/t3C5X4LLVQ3LpPQ2bqeetIVXcMAAGfKahHS2FIbE8oZNOXAo5QogCm5ixVRIKsxmnGmrAZny2qRW1qD8xV1uGCoR5GhHhcq63GxRg3L7i+9GoN9UgQlsnd6ePPb3GZbVcAF6XMJhOk1CA+y/6+95KsGEc3+Ha5vejw61FbUBWlVnW7x9ehQHaJDdRiRFO20/YKhHjnnKrA/rwK7TpbgYEElTpXU4FRJjeM5f/v8GPrHh2PqsB6YOrQHEmNCfB1+q8obb4xEh2hljsR/2WfLLK9tQGXjGLyoYOZLDi4VeJmZmS7v8IUXXnA7GG+ZPn06tm/fjkmTJuGjjz5yeuzTTz/FY489BqvVij/84Q948MEHZYqSyH3soUnkn0RRREFFHQ6dN+DweQMOnTfgSKEBBRV1l/lJ2wW/SrCNbYkIbioUbF9t28KDtAjXa6DXqqBTq6BVq6DV2P6t0wjQqhu3O7apoFEJqDFa0Dc+zPsJCABzxiRDpxZw7sxppF7ZHyF6LfRaFfQaNYLa+BqqVyM8yNYt0l9b0fxRfEQQbhzUDTcO6gaT6Qps/M8WhF0xAjtPXsTx4moEa1XYd7YCxy5UYeXWY3hu2zFMGhCP+8f1wpgrushaCNtbvrtwTbc2xTQWeBdrGlBRZ1siIYoFsSxcKvD279/v0s789Q7UI488gvvvvx/vvPOO03az2YzMzEx8/fXXiIiIwIgRIzBjxgzExMTIFCmRNH76kSNSJKtVxJmyGvyUX4mDBZU4dL4Sh88bYKg3t/r86BAtesWGIqVLKBKigxEfGYRuEUHoEqLBgR++xdSMKYgKDfLbv62dwV9uG4R7xvSCyWTCli0nkXFtCrRaXpD6SqgWSE/thqnDEx3bKmtN+PxQITbvP4/vT5fhiyMX8MWRCxjQLRwPX9cbU4f2lKWotk/7zzXd2hbTrAXPvgZeJFvwZOFSgff11197Ow6vmjhxIrZv395i+549ezBo0CD07NkTAJCRkYGtW7firrvu8nGERB0TqFN9EwWyGqMZ+/MqsOfMRew7W44D+RWoaqWY06gE9I0Px6AeERjYPQKDekSgf7dwRLWxNpTJZMJZva3rH4s77+oRFSx3CHSJyBAtZo1KwqxRSThZXI11u3LxcXYBjhZV4dENB/CPr0/hscn9cFNqN59+PuwFXgwLvDZFt9qCx3zJoUNj8PLz8yEIgqNAcseOHTuwcuVKZGdno7CwEJs2bWoxU+fq1auxcuVKFBYWYtCgQVi1ahXGjx/fkdABAOfPn3eKPSEhAQUFBR3eL5GvsYsmkfeVVBmx98xF/HimHD+euYjDhYYWC0HrNSoM7BGBIT0jMahnJAZ2j0Df+DDoNZx5z5/0jw/H8eIqjO7dRe5QqB194sLw1LTB+P2UAXhv9xm8vjMXJ4ur8ev392FM7y5Ydtsg9Iv3zYQsJVW2KT9j2UWzTfbxdlX1ZlysMTptI99yaxbNp556Cs8//zyqq6sBAOHh4XjsscfwxBNPQKVSSdpfTU0Nhg4dirlz52LmzJktHt+wYQMWLlyI1atXY9y4cXjttdeQnp6Ow4cPIykpCQCQlpYGYytz7W7btg09evRo87Vbm1qed0spkPB4JfKeosp67DxRgj25F7H3bDlyS2taPCchOhijesVgZK9oDEuMQr/4cGjV0v4Oku998rtxsFhFzogYICJDtPjt9X1x79heeH3HaazdcRrfny5D+t934qHxvfHo5L5evYkiiiLyLtYCgN9M+uKPwoKaPk/nK+oBcAyeXCSf2Z544gm8+eabePbZZzFu3DiIoojvvvsOS5cuRX19PZ5++mlJ+0tPT0d6enqbj7/wwgt44IEHHJOfrFq1Clu3bsWaNWuwYsUKAEB2drbUtwEA6Nmzp1OLXX5+PkaPHt3qc41Go1MRaTAYANi60phMJrde35PsMfhDLErhFzkXbdN0m80WRfzu/SLnCqOknBtNFuzNq8DOE6XYeaIMx4urnR4XBKB/XBjSkqMxMjkKacnRLWeitFpgsnZsEWol5VwuKtgmsLk018y577iT82A18MjE3pg+rBtWfHYcWUeK8eo3p7D96AU8d/tgry2vUFptRJ3JAkEA4kI1AX2cePNYVwHQqgWYLKKjS2uoVgjofHmCqzn3ZJ4EUeIKyT169MCrr76KqVOnOm3/z3/+g/nz53eoi6MgCE5dNBsaGhASEoKNGzdi+vTpjuctWLAAOTk5+Oabb1ze9/bt2/HKK684zaJpNptx5ZVXYvv27Y5JVnbv3o0uXVp22Vi6dCmWLVvWYvv69esREsK7OSSPV4+ocKRChbuvsOCqOPbTJJLKZAWOVAjYXyrg53IBDdamVnEBIpLCgD4RIq6IEJESLiKEDT5EfuOniwL+dUqFGrMAtSDijhQrxsR7/m9hbhWw6mcNonUilqZ17AZOZ/fHH9WoMTedR/+SZkYEh+G5pLa2FrNnz0ZlZSUiIiI6tC/Jf6ouXryIAQMGtNg+YMAAXLx4sUPBXKq0tBQWiwXx8fFO2+Pj41FUVOTyfm688Ubs27cPNTU1SEhIwKZNmzBq1ChoNBo8//zzmDhxIqxWKxYvXtxqcQcAS5YscVouwmAwIDExEVOmTOnwL8ETTCYTsrKyMHnyZM4A5iP+kPN/l+7DkYpSDB4yBBkj3B8LGyj8IedK0xlzbrJY8d2pMmw5WISsIyWOBYwBIC5cj2v6dMG1fWMx9ooYRMswQUBnzLm/Y859zxM5zwDwYLURf9x8CF8fK8W/TqsRFJ+EP9zYDxoPdpX+z4FC4OeD6NczBhkZozy2Xzl4+1hfeXQnasqbloGZcctN0GmU3W3d1Zzbewd6guQCb+jQoXjllVfw0ksvOW1/5ZVXMHToUI8F1tyl44xEUZQ09mjr1q1tPjZ16tQWrZGt0ev10OtbDqzVarV+9cfA3+JRAjlzrmqcKlqtVivq987j3PcCPedmixW7T1/Epz+dx+eHihxTeANAt4gg3DykO24Z0h3DEqP8ZmxroOc8EDHnvtfRnHeP1uKt+67CS1+exItfHMe67/Nwrrwe/7h7BIK0nhmXd77SNkQnuUtopzk+vHWsh+mbSosQnRqhwZyUxu5yOffk70Nygfe3v/0NN998M7744guMGTMGgiBg165dOHfuHLZs2eKxwAAgNjYWarW6RWtdcXFxi1Y9IiWyX4j+68dz2H36IgTBNq5EgACr6Lx4QvNLVpUgQBBsPy8ItseExp+z7UNo3L9tm0pA0/PR9HNqQYBKJdi+CraCUyUIUKts+1C12C44fkartn2vUamgVTftR6MSoGlckNm+MLNeY1vAWRAtqLcADWYrNBppN3pImQ6cq8BH2fn47OdClFY3OLbHhumQMbg7bhnSAyOTox03S4go8AiCgAU39EXf+DA8uiEHXx4txn1v78Gbc0YhVN/xftUnGsfj9ooN7fC+OrvwZhOteCL35B7Jmb/uuutw/Phx/OMf/8DRo0chiiJmzJiB+fPntztjpTt0Oh3S0tKQlZXlNAYvKysLt912m0dfiygQ2acfzj5bjuyz5TJH4ysa/GHPFwDgKADtxaBWIzRuUzduE5wKRZ1GjWCtCqF6DcL0GoQ2/h+mVyNYq0aYXouIYA0igrQID9IgPEir+K4lgajBbMWWg4VYt+sMcs5VOLZHhWiRntoNtw7pgdG9u8iyWDIReU/G4O7oEqrDA+/sxe7TFzHvn9l4c86oDp/HDxVUAgAG9Yj0RJidWvOiLthDLagknVuldY8ePSTPltmW6upqnDx50vF9bm4ucnJyEBMTg6SkJGRmZuKee+7ByJEjMWbMGKxduxZ5eXmYN2+eR16fKJA9nj4AgxMi0WC2wiraFjwXxaZuzPaWuuZTKTV/jlW0PdZ8mwjbNmuzf4sQ0fif089ZRRFma+NriyIsVttjVqsIa+PzLKLoeMxitf2M7d8iTBYrzFYRZosVFtH2uMVqhdkiwmi2osFiRYPZ9r/9uc01WGzPQctVUjwmWKtGeJAGEcFaRDR+jQrWokuYHl3CdOgSqkN0iA5dwnSIjwhCt4ggj479INcVG+rx/g95eP+HPJRW2w4KrVpAxuDumDa8J67pE8slDIg6udG9u+DdB67C3a//gJ0nSrFo4wGsmjXM7Vb6aqMZuWW2JVIG9ZB/zgV/F6JrKupY4MlHcoH3+eefIywsDNdccw0A4B//+Adef/11DBw4EP/4xz8QHR0taX979+7FxIkTHd/bJzKZM2cO1q1bh1mzZqGsrAzLly9HYWEhUlNTsWXLFiQnJ0sNnajTiYsIwtxxKXKH4TP1xgb893+f4fobJsMqqGGyF4CNX42NhaC9KGy45PEGsxV1JguqjWbUGM2OrzVGC2obbF8N9SZU1ZsdE2/UmSyoM1lQXOVaFalWCegeGYSE6GAkRIcgMTqk8d/BSIgJQbeIILYcedi5i7V45auT+HhfvuMmQFy4Hr+8Ohl3XZWEruEcA0KkJCOSovHqPWl4YN2P+OTAefSNC8PvJvV1a19HCg0QRdtYXS5yfnnNxz0GaXlDTS6SC7zf//73+Otf/woAOHjwIDIzM/HYY4/hq6++QmZmJt5++21J+5swYUKrC443N3/+fMyfP19qqETUyahVAnRqICLY+xMhmC1WVBvNMNSZYag32f5v/HdFbQPKqhtQUm1ERa0JF2sacLGmAUWV9WiwWJFfXof88joALWcW1qgE9IgKbir6HAVgCBJjghEfHsTxYC4qqKjDK1+dxMa95xyFXVpyNO4b2ws3pXZjax2Rgl3XryuemTEYiz/6CS98cRzDkqIwvm9XyfvZn2cb/pDak90zXRHsVOCxBU8ukgu83NxcDBw4EADw8ccf49Zbb8UzzzyDffv2ISMjw+MBEhHJQaNWISpEhygJ0+RbrSJKqo3IL69Ffnkdzl2sdRR7+eW1KKiog8kiIu9iLfIu1ra6jyCtCv3jwzGoZyRSe0QitWcE+sWH8w9lM4WVtsLuw73nYLLYCrvxfWOx8Ia+SEuOkTk6IvIXd45MxL6z5fjXj+fw6IYD+CLzWknndAD44bTtRt3oFJ5bXOHURVPHv1tykVzg6XQ61NbaLky++OIL3HvvvQCAmJgYj67fQEQUaFQqAfERQYiPCEJaK73ILVYRxVX1joIv/6Kt+DvXWBCer6hDvcmKA/mVOJBf6fg5jUrAld0jkJYcjbTkaIzqFYNukUE+fGf+ocFsxRvfnsZLX55AvckKABh7RRc8OrkfRvXixRcRtbR06iDsPVuOk8XVWP7pYbxw5zCXf9ZiFbHnTGOB15vnGFc4teBpWODJRXKBd8011yAzMxPjxo3Dnj17sGHDBgDA8ePHkZCQ4PEAiYg6C9v4vGB0jwxutSAxW6zIu1iLw4UG/FxgwKHzlfi5oBLltSYcLKjEwYJKrNt1BgCQFBOCcX26YGL/OIzrE9vpp6P+4XQZ/m/zz47pykf1isZjU/rj6t5dZI6MiPxZkFaNv90+BDPX7MK/9xXgtmE9cV0/17pqHi0yoKrejDC9BgO7c4IVVwSxBc8vSL4ieOWVVzB//nx89NFHWLNmDXr27AkA+Oyzz3DTTTd5PEAiIqXQqFXo3TUMvbuG4ZYhtmVnRFFEQUUd9uVVYN/Zcuw9exGHzxts3Tz31OKDPeegU6swuncMJvSPw/UD4pDSidZquljTgBVbjmBjdj4AoEuoDk/cfCWmD+/JdRCJyCUjkmxjc9/+7gz+8ulhjFsw3qXZju3dM0f2iubsyC4K4SQrfkFygZeUlIRPP/20xfYXX3zRIwEREVETQRAaJ2IJwdShtqKvqt6EvWfKsf1YMb46VoxzF+uw80Qpdp4oxV8+PYzUnhGYNqwnpg7tgbiIwO3K+dnBQizZdBAVtSYAwF1XJeEPN/WXPIaGiGjhDf2weX8BThZX45MD5zFjxOV7nf2QWwYAuIrj71zWvNWOY8fl41afHovFgk2bNuHIkSMQBAEDBgzAtGnToNF07i5CRET+IDxIi4kD4jBxQByWiiJOl9bg66PF+PpYMXafvoifC2xdPJ/ZcgTXD4jHg+NTMDolJmBavIxmC1ZsOerojjqgWzienj4YacnSluEhIrKLDNbiwfG9sXLrMazefgrThvVsd8ZiURSxJ9c+wQq7gruqeVGnY6unbCRXZD///DOmTp2KCxcuoH///gBs4++6du2KTz75BIMHD/Z4kERE1DpBEHBF1zBc0TUMD47vjbJqI/53sBCb9xdgX14FvjhyAV8cuYBBPSLwwDUpuGVID+g0/vtHN7+8Fr/+5z4cLLBNMvPwdb2xaEp/LnlARB12z5hkvPrNKZwsrsbWQ0VIH9y9zeeeKK5Gea0JwVo1BnOJBJfpm/194ZI/8pH8F/PBBx9Eamoq8vPzsW/fPuzbtw/nzp3DkCFD8Ktf/cobMRIRkYu6hOlx75he+Pf8cfgi8zrcPToJQVoVDp03IPPDA7jmr19h9faTqGuwyB1qC8eKqjBzzS4cLKhEVIgWb983CkvSr2RxR0QeERGkxX1jewEA3vg2t93n/nDa1j1zRHKUX98U8zfNe4qwvpOP5CP2wIEDWLFiBaKjm7rKREdH4+mnn0ZOTo4nYyMiog7oExeGp6cPxvePT8Lvb+yPuHA9iquM+NvnxzDxue349758WBsXCJdb9tly3Pna97hgMKJffBj+98h4TBwQJ3dYRNTJ/PLqZKgE2zknt7SmzeftOWNb4JzdM6VRORV4rPDkIrnA69+/Py5cuNBie3FxMfr06eORoIiIyHOiQ3X4zcQ++PYP1+O5O4aiZ1Qwigz1yPzwAG77x3fYn1cua3zfHC/BL9/4AZV1JgxPisKHD49Bz6hgWWMios4pPiII4/valkn49778Np+376ztvDiSY38lad7hIlDGfXdGLhV4BoPB8f8zzzyDRx55BB999BHy8/ORn5+Pjz76CAsXLsRf//pXb8dLRERu0mlUuD0tAV8+dh3+cNMAhOk1OFhQiZlrduGvnx+F0ez7bpv/PXAeD77zI+pMFlzbryvef3A0Z8kkIq+6Pc02g+bmnAKIYsteDBcM9SioqINKAIYmRvk4usDGLpr+waVJVqKiopx+YaIo4s4773Rss384br31Vlgs/jeug4iImgRp1fj1hCtwx8gEPPXpYWzOOY81209h18lSrP5lms9az/69Lx+PbTwAUQRuGdIdL9w5jGNdiMjrJl0ZB51ahXMX63CqpBp94sKdHrf3aujfLQKhes4QLwW7aPoHl47ar7/+2ttxEBGRj8WG6bHqF8NxU2p3PP7vn3AgvxK3vLQTL981Atf0jfXqa+ecq8DjHx+EKAK/vDoJy6amQs3bvUTkAyE6DUb3jsHOE6XYfqyklQKvAgAwPCnK98EFuOancZ7S5eNSgXfddde5tDNOskJEFHhuSu2GQT0i8Ov3s/FzgQH3vvUDnrtjqEsLAbujpMqIee9lo8FixZSB8Vg+NZXTaRORT03oH4edJ0rx9bFiPDi+t9Nj9gJvRBLH30nVvNWOY/Dk0+G+MJWVlVi9ejVGjBiBtLQ0T8REREQ+lhgTgo/mjcXMEQmwisCijQfwn5wCj7+OyWLFb97fhyJDPa7oGorn7xzK4o6IfG5if9tEK3tyLzotGyOKIg4XGgCA69+5QXBqweO5XS5uF3hfffUVfvnLX6J79+54+eWXkZGRgb1793oyNiIi8qEgrRorbx+Cu65KglUEHt2Qg09/KvToazz9vyPYc+YiwvQarL13JMKDtB7dPxGRK1JiQ9E1XA+TRcTP5ysd2wsq6lBtNEOrFpASGypjhIGpeVHHJUzlIyn1+fn5eOqpp9C7d2/cddddiI6Ohslkwscff4ynnnoKw4cP91acRETkAyqVgKenpeIXoxJtLXkf/4ycMs/chd11shTrdp0BALw4axiu6Brmkf0SEUklCAKGNc6QmdPYJRMAjl+oAgD0jg3jpE9u4CQr/sHlIzcjIwMDBw7E4cOH8fLLL+P8+fN4+eWXvRmbx0yfPh3R0dG4/fbbJT1GRKREKpWAZ6YPxh1pCbBYRbx/UoXTJW0vCOwKk8WKJz85BAC45+pkTB4Y74lQiYjc5ijwzlU4th0rqgYA9OsW3spP0OU073HPMXjycbnA27ZtGx588EEsW7YMN998M9RqtTfj8qhHHnkE7777ruTHiIiUSqUS8OzMIRjbOwYNVgGPfXQQDWar2/t7Z9cZnCiuRkyoDo9N6efBSImI3GMv8A4166J5otjWgtcvjj0M3MF18PyDywXezp07UVVVhZEjR2L06NF45ZVXUFJS4s3YPGbixIkID2/9Tkx7jxERKZlaJeCvM1MRohHx83kDns865tZ+iqvqseqLEwCAxTf250LmROQX+jQWcefK6xw3sPIv1gEAkrqEyBZXIFNxkhW/4HKBN2bMGLz++usoLCzEww8/jH/961/o2bMnrFYrsrKyUFVV5VYAO3bswK233ooePXpAEARs3ry5xXNWr16NlJQUBAUFIS0tDTt37nTrtYiISJpuEUH4RW/bhc/aHaed7nS76m+fH0O10YyhCZG4c2Sip0MkInJLXLgeITo1LFYR58prAcDxNTGGBZ47VGzB8wuSR4+GhITg/vvvx7fffouDBw/isccew7PPPou4uDhMnTpVcgA1NTUYOnQoXnnllVYf37BhAxYuXIgnnngC+/fvx/jx45Geno68vDzHc9LS0pCamtri//Pnz0uOh4iInA3tIuLm1G4QRWDlVmmteOcr6rBpv225hSenDuKSCETkNwShaabM0yU1MJotKDLUAwCSWOC5hevg+QeXFjpvS//+/fG3v/0NK1aswH//+1+89dZbkveRnp6O9PT0Nh9/4YUX8MADD+DBBx8EAKxatQpbt27FmjVrsGLFCgBAdna2e29AAqPRCKPR6PjeYLCtkWIymWAymbz++pdjj8EfYlEK5tz3mHPfs+f6txOSsfXwBWw/VoJvj1/A6JQYl37+rW9Pw2IVcXVKNAZ3D+PvzgU8zn2POfc9f8l5ry4hOHTegJMXDEiO1kMUgWCtChE6QfbYvMHbebdYzI5/i1Zrp8yhVK7m3JO56lCBZ6dWqzFt2jRMmzbNE7tzaGhoQHZ2Nh5//HGn7VOmTMGuXbs8+lqXs2LFCixbtqzF9m3btiEkxH/u8mRlZckdguIw577HnPve8ezvMLqrCt9dUOH/PvwRC1MtuNzN2QYL8H62GoCAwfpSbNmyxSexdhY8zn2POfc9uXNuLlcBUGHn/qOozDsCQI1IjQWfffaZrHF5m7fyXlAD2MuLQz8fxJaSn7zyOoHocjmvra312Gt5pMDzltLSUlgsFsTHO0+nHR8fj6KiIpf3c+ONN2Lfvn2oqalBQkICNm3ahFGjRl32seaWLFmCzMxMx/cGgwGJiYmYMmUKIiIi3HyHnmMymZCVlYXJkydDq+XCwb7AnPsec+57zXM+qt6K61/ciTPVVnQddDWu6tV+K94nBwpRt+cgekYFYdFd49k900U8zn2POfc9f8l5TXYBtuYfgiayKxL7xQNHDqN/YldkZIyQLSZv8nbej1+owt9++h4AMGTIEGSk9fT4awQaV3Nu7x3oCX5d4Nld2odXFEVJ/Xq3bt3q1mPN6fV66PX6Ftu1Wq1f/THwt3iUgDn3Pebc97RaLXqEaDF9eAI+2JOH9XsKMK5v+2vZbcopBADcnpYIvZ4zZ0rF49z3mHPfkzvniV1sY/CKDEZUGi0AgNiwoE5/HHgr77pm+9Rq1J0+j1JcLueezJXkSVZ8KTY2Fmq1ukVrXXFxcYtWPSIi8r57xyQDAD4/VISiyvo2n3fBUI/vTpUCAG5PS/BJbEREUnWPDAYAnK+oR2WtbQxUdAiLEnc5r4PHXhty8esCT6fTIS0trUWf1aysLIwdO1amqIiIlOvK7hG4KiUGFquI9Xvy2nzeN8dLIIrA0MQoTjdORH4rLsLWO6vaaHbMoBnFAs9tTuvg+XWV0bnJnvrq6mrk5OQgJycHAJCbm4ucnBzHMgiZmZl444038NZbb+HIkSN49NFHkZeXh3nz5skYNRGRcs2+KgkA8PnPhW0+Z+cJW+vddX1jfRITEZE7wvUaaBqrktzSGgBAVAi7lLtLxRY8vyD7GLy9e/di4sSJju/tE5nMmTMH69atw6xZs1BWVobly5ejsLAQqamp2LJlC5KTk+UKmYhI0Sb07wqVABy/UI1zF2tbtNBZrSK+PVECABjfr6scIRIRuUQQBESH6lBSZcTpEnuBxxY8d3EdPP8ge4E3YcIEiKLY7nPmz5+P+fPn+ygiIiJqT1SIDiOTY7DnzEV8fawY947p5fT4ofMGlNeaEKbXYFhilCwxEhG5KjpEi5IqI6qN5sbv2YLnruY1HSdOlo/sXTSJiCjwXH9lHADgq6PFLR7b0dh6N+aKLtCq+WeGiPzbpQUdW/Dc13w5HHbRlA//8hIRkWTX9LGNrdufV9GiF8bOxgLvWo6/I6IAEBHsXNBxDJ77VGzB8wss8IiISLJ+8eHQqVWorDMhv7zOsb2uwYLss+UAgPF9Of6OiPxfqE7t9D2XSXAfx+D5BxZ4REQkmU6jQv9u4QCAgwWVju0niqtgsojoEqpDchcuj0BE/i9U3zQlhU6tQrBW3c6zqT3OY/BY4MmFBR4REbkltWckAOcC72RxNQCgb3wY794SUUAIa1bghQdpeO7qAKcWPBnjUDoWeERE5JbB9gIvv3kLnq3A6xMXJktMRERSheiaCrwgtt51iHMXTRkDUTgWeERE5BZ7gffz+UrHRCsnLjS24MWFyxYXEZEUofqmok6v4aVxR6hZ1fkFHsVEROQWWzdMoKLWhLKaBgDA6RK24BFRYGneRVPHAq9DBKbPL/DXQEREbgnSqpEQHQwAOFVcDatVdMyoyQlWiChQhDQr8PTsotkhnFjFP7DAIyIit13R1dZSd6qkBiXVRjRYrFCrBHSLCJI5MiIi1+jUTZfD7KLZMc3XvrtkiVTyIR7FRETktt6xtgLvdEk18strAQDdI4OgUfPPCxEFBq26qSphgdcxbMHzDzyKiYjIbVfEhQIATpfWOLpn9owKljMkIiJJNE4teOyi2RGs7/wDCzwiInJb90hbV8ziqnpHgZcQzfF3RBQ4tM36Feq1vDTuCLbg+QcexURE5LaYUD0A4GJ1g6OLZs9otuARUeDQcAyex7DA8w88iomIyG1dQnUAgNKaBpRV25ZKiAvXyxkSEZEkmmZj8LjQeceoWN/5BRZ4RETkti5htgKvwWzF+UpbF83IYK2cIRERSaJVsQXPUwS24PkFHsVEROS2EJ0GwY13vE+X1AAAokJY4BFR4NA4zaLJFjwKfIoo8KZPn47o6GjcfvvtTtvPnTuHCRMmYODAgRgyZAg2btwoU4RERIErprGbZm2DBQBb8IgosHCZBO9gY558FHEUP/LII3j33XdbbNdoNFi1ahUOHz6ML774Ao8++ihqampkiJCIKHDZu2naRQXr2ngmEZH/0TTvoslZNKkTUMRRPHHiRISHh7fY3r17dwwbNgwAEBcXh5iYGFy8eNHH0RERBTb7RCt2EcEamSIhIpKOXTSps5G9wNuxYwduvfVW9OjRA4IgYPPmzS2es3r1aqSkpCAoKAhpaWnYuXOnx+PYu3cvrFYrEhMTPb5vIqLOzL5Ugl2ongUeEQUOLZdJ8ApRlDsC5ZL9KK6pqcHQoUPxyiuvtPr4hg0bsHDhQjzxxBPYv38/xo8fj/T0dOTl5f1/e3cfHFV973H8s3naBvJsQhJIJMoIkxoQCB0ExRBaSFExFnUstZCAoJQbC0VqC2UgOCOMToax7dh2qC2plFFKH26t0gkoBqrhIiRUECuIgNEQhmvQBI0mIfndP7hZEwIhy56ckz15v2Z2hnN295zv+STsL989T77XZGdnKysrq8vj1KlTPaqhrq5Oc+bM0YYNGyzZJgDoT6K/9lVDFxEW0umPJQDo68I6XNs/gs8vuIDjX7NOnz5d06dPv+zz69ev14MPPqj58+dLkp5++mmVlZXp17/+tdatWydJqqysvOr1NzU16Tvf+Y6WL1+uiRMndvu6pqYm33RDQ4MkqaWlRS0tLVe9fqu019AXaukvyNx+ZG6/nmTe8ZSVgRGh/HwCxO+5/cjcfn0q87ZW3z+Nae0bNfUSW3N3eZY91dPMrczK8QavO83NzaqsrNRPf/rTTvOnTZumioqKgJdvjFFhYaGmTJmi2bNnd/vadevWac2aNV3mb9++XQMGDAi4Fqvs2LHD6RL6HTK3H5nbr7vMq6tD1H5AiKe1Wdu2bbOpKnfj99x+ZG6/vpB5U6vU/ifxoYMH9bXatxytxw69mXtOaohOfS41vLdP24712mqCzpUyb2xstGxdfbrB+/jjj9Xa2qrk5ORO85OTk3X69OkeLycvL09VVVX6/PPPlZaWpr/97W/6xje+oTfeeENbtmzRqFGjfOf+bdq0SSNHjuyyjOXLl2vp0qW+6YaGBqWnp2vatGmKiYm5ug20UEtLi3bs2KGpU6cqPJxLlNuBzO1H5vbrSeYny4+rrObCKJ4YG63bb7/80RC4Mn7P7Ufm9utLmTefb9Njb74iSRo7ZrRuH5XqaD29yY7cb++VpQavnmbefnSgFfp0g9fOc9GNNIwxXeZ1p6ys7JLzb731VrW1tfVoGV6vV16vt8v88PBwxz+YOupr9fQHZG4/Mrdfd5lHdrioStTXwvjZWITfc/uRuf36QuZhYV9dDSQivH98hvWF3PubK2Vu5c+jT59JmpiYqNDQ0C57686cOdNlrx4AwBkdL0rAFTQBBJuOOw1CuTs3XKBPN3gRERHKzs7ucszqjh07ur0gCgDAPt7wr+4bFRnOPaQABK+QEBo8BD/Hv2r97LPPdOzYV2dgnjhxQv/+97+VkJCga6+9VkuXLtXs2bM1btw4TZgwQRs2bFB1dbUWLlzoYNUAgHYd9+BFcA8pAEEsNpLDFhH8HG/w9u/fr9zcXN90+4VMCgoKVFpaqvvvv191dXV6/PHHVVtbq6ysLG3btk1Dhw51qmQAQAcdmzrugQcgGK2560Yd/9/PNP66BKdLAQLmeIM3efJkmSvc6n7RokVatGiRTRUBAPzRscEL5fAmAEGoYGKG0yUAluGrVgBAQLyd9uDR4AEA4CQaPABAQDruwQsLYVgBAMBJjMQAgIB03IMXxh48AAAcRYMHAAhIROhXt0YI4xw8AAAcRYMHAAiIN7zjHjyGFQAAnMRIDAAISMf74IWzBw8AAEfR4AEAAtLpIivswQMAwFGMxACAgHAfPAAA+g4aPABAQDo2eMYYBysBAAA0eACAgHS8TUIb/R0AAI6iwQMABKTjRVZa6fAAAHAUDR4AICAez1fn3bVxiCYAAI6iwQMAWIYGDwAAZ9HgAQAswxGaAAA4iwYPAGCZNjo8AAAcRYMHALAMF1kBAMBZNHgAAMvQ3wEA4CwaPACAZYYnRzldAgAA/VqY0wUAAILff//XLfqf43W6b1y606UAANCv0eABAAI2Oj1Oo9PjnC4DAIB+j0M0AQAAAMAlaPAAAAAAwCVo8AAAAADAJWjwAAAAAMAlaPAAAAAAwCVo8AAAAADAJWjwAAAAAMAlaPAAAAAAwCVo8AAAAADAJWjwAAAAAMAlaPAAAAAAwCVo8AAAAADAJWjwAAAAAMAlaPAAAAAAwCVo8AAAAADAJWjwAAAAAMAlwpwuIFgZYyRJDQ0NDldyQUtLixobG9XQ0KDw8HCny+kXyNx+ZG4/MrcfmduPzO1H5s4gd/v1NPP2nqK9xwgEDd5VOnfunCQpPT3d4UoAAAAAuMG5c+cUGxsb0DI8xoo2sR9qa2vTqVOnFB0dLY/H43Q5amhoUHp6uj788EPFxMQ4XU6/QOb2I3P7kbn9yNx+ZG4/MncGuduvp5kbY3Tu3DkNHjxYISGBnUXHHryrFBISorS0NKfL6CImJob/sDYjc/uRuf3I3H5kbj8ytx+ZO4Pc7deTzAPdc9eOi6wAAAAAgEvQ4AEAAACAS9DguYTX69Xq1avl9XqdLqXfIHP7kbn9yNx+ZG4/MrcfmTuD3O3nROZcZAUAAAAAXII9eAAAAADgEjR4AAAAAOASNHgAAAAA4BI0eAAAAADgEjR4Qejll1/W+PHjFRkZqcTERM2cOdP33FtvvaVZs2YpPT1dkZGRyszM1M9//vMuyzh06JBycnIUGRmpIUOG6PHHHxfX27m87jKXpMWLFys7O1ter1ejR4++5DLKysp08803Kzo6WklJSbrnnnt04sQJG6oPTlZkboxRSUmJhg8fLq/Xq/T0dK1du9aG6oOXFbm3O3bsmKKjoxUXF9d7BbtAoJmXl5crPz9fqampGjhwoEaPHq3NmzfbVH1wsuL3nHHUP1fKvLq6WjNmzNDAgQOVmJioH/7wh2pubu70GsZR/1iROeOof6zIvF0gY2jY1RQP5/zlL3/RggULtHbtWk2ZMkXGGB06dMj3fGVlpZKSkvTHP/5R6enpqqio0EMPPaTQ0FAVFRVJkhoaGjR16lTl5uZq3759Onr0qAoLCzVw4EA9+uijTm1an3WlzKULH4Dz5s3T3r17dfDgwS7LOH78uPLz87V06VJt3rxZ9fX1+tGPfqSZM2fqwIEDdm1K0LAic+nCH2nbt29XSUmJRo4cqfr6en388cd2bEJQsip3SWppadGsWbM0adIkVVRU9HbpQcuKzCsqKjRq1Cj95Cc/UXJysl5++WXNmTNHMTExmjFjhl2bEjSsyJxx1D9Xyry1tVV33HGHkpKS9Prrr6uurk4FBQUyxuiXv/ylJMZRf1mRucQ46g+rMpcsGEMNgkZLS4sZMmSIefbZZ/1636JFi0xubq5v+le/+pWJjY01X375pW/eunXrzODBg01bW5tl9bqBv5mvXr3a3HTTTV3mb9261YSFhZnW1lbfvBdffNF4PB7T3NxsVbmuYFXm77zzjgkLCzPvvvuuxRW6k1W5t3vsscfM97//fbNx40YTGxtrTZEuY3XmHd1+++1m7ty5AVTnTlZlzjjacz3JfNu2bSYkJMTU1NT45j3//PPG6/Wa+vp6YwzjqD+sypxxtOesyrxdoGMoh2gGkaqqKtXU1CgkJERjxoxRamqqpk+frsOHD3f7vvr6eiUkJPim9+zZo5ycnE43XMzLy9OpU6d08uTJ3io/KF1t5hcbN26cQkNDtXHjRrW2tqq+vl6bNm3StGnTFB4e3kvVByerMv/HP/6h66+/Xi+99JKuu+46ZWRkaP78+Tp79mwvVR7crMpdknbu3KmtW7fqmWee6YVK3cPKzC928ec+LrAqc8bRnutJ5nv27FFWVpYGDx7sm5eXl6empiZVVlZKYhz1h1WZM472nFWZS9aMoTR4QeT48eOSpOLiYq1cuVIvvfSS4uPjlZOTc9n/bHv27NGf/vQnPfzww755p0+fVnJycqfXtU+fPn26l6oPTleT+aVkZGRo+/btWrFihbxer+Li4vTRRx/phRde6K3Sg5ZVmR8/flwffPCBtm7dqueee06lpaWqrKzUvffe21ulBzWrcq+rq1NhYaFKS0sVExPTW+W6glWZX+zPf/6z9u3bp7lz51pVqmtYlTnjaM/1JPNL5RkfH6+IiAhfnoyjPWdV5oyjPWdV5laNoTR4fUBxcbE8Hk+3j/3796utrU2S9LOf/Uz33HOPsrOztXHjRnk8Hm3durXLcg8fPqz8/HytWrVKU6dO7fScx+PpNG3+/8Twi+e7VW9lfjmnT5/W/PnzVVBQoH379mnXrl2KiIjQvffe229Oyrc787a2NjU1Nem5557TpEmTNHnyZP3ud7/Ta6+9piNHjvTWZvY5due+YMECfe9739Ntt93WW5vU59mdeUfl5eUqLCzUb3/7W914441Wblaf5kTmjKPWZn6p3IwxvvmMo/Znzjhqf+ZWjaFcZKUPKCoq0ne/+91uX5ORkaFz585Jkr7+9a/75nu9Xl1//fWqrq7u9Pp33nlHU6ZM0YIFC7Ry5cpOz6WkpHT5hvHMmTOS1OWbBbfqjcy788wzzygmJkZPPfWUb177hXD27t2rm2++2c8tCD52Z56amqqwsDANHz7cNy8zM1PShatYjRgxwp/yg5bdue/cuVMvvviiSkpKJF0YuNra2hQWFqYNGzZo3rx5V7EVwcXuzNvt2rVLM2bM0Pr16zVnzhy/3x/M7M6ccdTazFNSUrR3795O7/3kk0/U0tLiy5Nx1P7MGUftz9yqMZQGrw9ITExUYmLiFV/XfsnmI0eO6NZbb5V04So7J0+e1NChQ32vO3z4sKZMmaKCggI98cQTXZYzYcIErVixQs3NzYqIiJAkbd++XYMHD1ZGRoY1G9XHWZ35lTQ2Nio0NLTTvPbp9m993M7uzG+55RadP39e77//voYNGyZJOnr0qCT5tZxgZ3fue/bsUWtrq2/673//u5588klVVFRoyJAh/m9AELI7c+nCnrs777xTTz75pB566KGrqjuY2Z0546i1mU+YMEFPPPGEamtrlZqaKulCnl6vV9nZ2ZIYRyX7M2cctT9zy8ZQvy/LAkctXrzYDBkyxJSVlZl3333XPPjgg2bQoEHm7Nmzxhhj3n77bZOUlGQeeOABU1tb63ucOXPGt4xPP/3UJCcnm1mzZplDhw6Zv/71ryYmJsaUlJQ4tVl92pUyN8aY9957zxw4cMA8/PDDZvjw4ebAgQPmwIEDpqmpyRhjzKuvvmo8Ho9Zs2aNOXr0qKmsrDR5eXlm6NChprGx0alN67OsyLy1tdWMHTvW3Hbbbaaqqsrs37/fjB8/3kydOtWpzerzrMj9YlxFs3tWZP7aa6+ZAQMGmOXLl3f63K+rq3Nqs/o0KzJnHPXPlTI/f/68ycrKMt/85jdNVVWVeeWVV0xaWpopKiryLYNx1D9WZM446h8rMr/Y1Y6hNHhBprm52Tz66KNm0KBBJjo62nzrW98yb7/9tu/51atXG0ldHkOHDu20nIMHD5pJkyYZr9drUlJSTHFxMZd2vowrZW6MMTk5OZfM/cSJE77XPP/882bMmDFm4MCBJikpydx1113mP//5j81bExysyrympsbMnDnTREVFmeTkZFNYWMgfvd2wKveOaPC6Z0XmBQUFl3w+JyfH/g0KAlb9njOO9lxPMv/ggw/MHXfcYSIjI01CQoIpKirqdBsKYxhH/WFV5oyjPWdV5h1d7RjqMaafnJkKAAAAAC7HVTQBAAAAwCVo8AAAAADAJWjwAAAAAMAlaPAAAAAAwCVo8AAAAADAJWjwAAAAAMAlaPAAAAAAwCVo8AAAAADAJWjwAAD9SnFxsUaPHm37esvLy+XxeOTxeHT33XdbsqxPP/20x+8pLi72rf/pp58OaP0AgL6LBg8A4BrtDczlHoWFhVq2bJleffVVx2o8cuSISktLA1rGxIkTVVtbq9jY2B6/Z9myZaqtrVVaWlpA6wYA9G1hThcAAIBVamtrff/esmWLVq1apSNHjvjmRUZGKioqSlFRUU6UJ0kaNGiQ4uLiAlpGRESEUlJS/HpP+3aHhoYGtG4AQN/GHjwAgGukpKT4HrGxsfJ4PF3mXXyIZmFhoe6++26tXbtWycnJiouL05o1a3T+/Hn9+Mc/VkJCgtLS0vT73/++07pqamp0//33Kz4+Xtdcc43y8/N18uRJv2uePHmyHnnkES1ZskTx8fFKTk7Whg0b9Pnnn2vu3LmKjo7WsGHD9M9//tP3nosP0SwtLVVcXJzKysqUmZmpqKgoffvb3+7U8AIA+gcaPABAv7dz506dOnVKu3fv1vr161VcXKw777xT8fHx2rt3rxYuXKiFCxfqww8/lCQ1NjYqNzdXUVFR2r17t15//XVfU9Xc3Oz3+v/whz8oMTFRb775ph555BH94Ac/0H333aeJEyeqqqpKeXl5mj17thobGy+7jMbGRpWUlGjTpk3avXu3qqurtWzZsqvOBAAQnGjwAAD9XkJCgn7xi19oxIgRmjdvnkaMGKHGxkatWLFCN9xwg5YvX66IiAi98cYbkqQXXnhBISEhevbZZzVy5EhlZmZq48aNqq6uVnl5ud/rv+mmm7Ry5UrfuiIjI5WYmKgFCxbohhtu0KpVq1RXV6eDBw9edhktLS36zW9+o3Hjxmns2LEqKipy9FxDAIAzOAcPANDv3XjjjQoJ+eo7z+TkZGVlZfmmQ0NDdc011+jMmTOSpMrKSh07dkzR0dGdlvPll1/q/fff93v9o0aN6rKukSNHdqpHkm/9lzJgwAANGzbMN52amtrt6wEA7kSDBwDo98LDwztNezyeS85ra2uTJLW1tSk7O1ubN2/usqykpCTL1+/xeHzr9WcZxhi/awEABDcaPAAA/DR27Fht2bJFgwYNUkxMjNPlAADgwzl4AAD46YEHHlBiYqLy8/P1r3/9SydOnNCuXbu0ePFiffTRR06XBwDox2jwAADw04ABA7R7925de+21mjlzpjIzMzVv3jx98cUX7NEDADjKYzhAHwCAXldeXq7c3Fx98sknAd/oPBAZGRlasmSJlixZ4lgNAIDewx48AABslJaWplmzZtm+3rVr1yoqKkrV1dW2rxsAYB/24AEAYIMvvvhCNTU1kqSoqCilpKTYuv6zZ8/q7Nmzki5c6TM2NtbW9QMA7EGDBwAAAAAuwSGaAAAAAOASNHgAAAAA4BI0eAAAAADgEjR4AAAAAOASNHgAAAAA4BI0eAAAAADgEjR4AAAAAOASNHgAAAAA4BL/B6MIwLf6pqVqAAAAAElFTkSuQmCC", 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" ] @@ -987,7 +987,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/propagation/reentry_trajectory.py b/propagation/reentry_trajectory.py index 595b886..9308564 100644 --- a/propagation/reentry_trajectory.py +++ b/propagation/reentry_trajectory.py @@ -34,7 +34,6 @@ import numpy as np from matplotlib import pyplot as plt - # Load tudatpy modules from tudatpy.interface import spice from tudatpy import numerical_simulation @@ -44,7 +43,6 @@ from tudatpy.util import result2array from tudatpy.astro.time_conversion import DateTime - ## Aerodynamic guidance class """ @@ -158,7 +156,6 @@ def updateGuidance(self, current_time: float): self.bank_angle = np.arccos(cosine_of_bank_angle) self.current_time = current_time - ## Configuration """ @@ -172,7 +169,7 @@ def updateGuidance(self, current_time: float): spice.load_standard_kernels() # Set simulation start epoch (January the 1st, 2000 plus 6000s) -simulation_start_epoch = DateTime(2000, 1, 1, 1, 40) +simulation_start_epoch = DateTime(2000, 1, 1, 1, 40).epoch() # Set the maximum simulation time (avoid very long skipping re-entry) max_simulation_time = 3*constants.JULIAN_DAY @@ -185,7 +182,6 @@ def updateGuidance(self, current_time: float): """ - ### Create the bodies """ @@ -210,7 +206,6 @@ def updateGuidance(self, current_time: float): # Create system of bodies (in this case only Earth) bodies = environment_setup.create_system_of_bodies(body_settings) - ### Create the vehicle """ @@ -221,7 +216,6 @@ def updateGuidance(self, current_time: float): bodies.create_empty_body("STS") bodies.get_body( "STS" ).set_constant_mass(5.0e3) - ### Add an aerodynamic coefficient interface """ @@ -243,17 +237,14 @@ def updateGuidance(self, current_time: float): # Add predefined aerodynamic coefficients database to the body environment_setup.add_aerodynamic_coefficient_interface(bodies, "STS", coefficient_settings) - ### Add rotation model based on aerodynamic guidance """ -""" - -# Create the aerodynamic guidance object +Create the aerodynamic guidance object aerodynamic_guidance_object = STSAerodynamicGuidance(bodies) rotation_model_settings = environment_setup.rotation_model.aerodynamic_angle_based( 'Earth', '', 'STS_Fixed', aerodynamic_guidance_object.getAerodynamicAngles ) environment_setup.add_rotation_model( bodies, 'STS', rotation_model_settings ) - +""" ## Propagation setup """ @@ -270,7 +261,6 @@ def updateGuidance(self, current_time: float): # Define central bodies of propagation central_bodies = ["Earth"] - ### Create the acceleration model """ @@ -297,7 +287,6 @@ def updateGuidance(self, current_time: float): bodies, acceleration_settings, bodies_to_propagate, central_bodies ) - ### Define the initial state """ @@ -329,7 +318,6 @@ def updateGuidance(self, current_time: float): initial_earth_fixed_state, simulation_start_epoch, earth_rotation_model ) - ### Define the dependent variables to save """ @@ -350,7 +338,6 @@ def updateGuidance(self, current_time: float): propagation_setup.dependent_variable.mach_number("STS", "Earth") ] - ### Create the propagator settings """ @@ -362,6 +349,8 @@ def updateGuidance(self, current_time: float): Combinated termination settings are then needed, which can be done using the `propagation_setup.propagator.hybrid_termination()` function. +Subsequently, the integrator settings are defined using a RK4 integrator with the fixed step size of 0.5 seconds. + Then, the translational propagator settings are defined. These are used to simulate the orbit of `Delfi-C3` around Earth. """ @@ -392,17 +381,6 @@ def updateGuidance(self, current_time: float): output_variables=dependent_variables_to_save ) - -### Create the integrator settings -""" - -The last step before starting the simulation is to setup the integrator that will be used. - -In this case, a RK4 integrator is used with a step fixed at 0.5 seconds. -""" - - - ## Propagate the trajectory """ @@ -428,7 +406,6 @@ def updateGuidance(self, current_time: float): # Convert the dependent variables from a dictionary to a numpy array dependent_variables_array = result2array(dependent_variables) - ## Post-process the propagation results """ @@ -456,7 +433,6 @@ def updateGuidance(self, current_time: float): plt.tight_layout() plt.show() - ### Airspeed vs altitude """ @@ -471,7 +447,6 @@ def updateGuidance(self, current_time: float): plt.tight_layout() plt.show() - ### g-load over time """ @@ -486,7 +461,6 @@ def updateGuidance(self, current_time: float): plt.tight_layout() plt.show() - ### Aerodynamic coefficient over time """ @@ -505,7 +479,6 @@ def updateGuidance(self, current_time: float): plt.tight_layout() plt.show() - ### Angles over time """ @@ -523,7 +496,6 @@ def updateGuidance(self, current_time: float): plt.tight_layout() plt.show() - ### Angle of attack vs Mach number """ @@ -540,7 +512,6 @@ def updateGuidance(self, current_time: float): plt.tight_layout() plt.show() - ### Derivative of flight path angle over time """ @@ -561,4 +532,3 @@ def updateGuidance(self, current_time: float): plt.grid() plt.tight_layout() plt.show() - diff --git a/propagation/separation_satellites_diff_drag.py b/propagation/separation_satellites_diff_drag.py index 3d84a04..dfdc7fe 100644 --- a/propagation/separation_satellites_diff_drag.py +++ b/propagation/separation_satellites_diff_drag.py @@ -2,8 +2,8 @@ """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -26,6 +26,7 @@ # Load standard modules import numpy as np + from matplotlib import pyplot as plt from scipy import interpolate @@ -35,18 +36,16 @@ from tudatpy.astro import element_conversion from tudatpy.interface import spice_interface from tudatpy.numerical_simulation import environment_setup, propagation_setup, propagation -from tudatpy.numerical_simulation.environment import SystemOfBodies from tudatpy.astro.time_conversion import DateTime # Load spice kernels spice_interface.load_standard_kernels() - ## Creation of the environment """ This includes both the vehicles and the celestial bodies. -""" +""" ### Creation of celestial bodies """ @@ -76,7 +75,6 @@ # Create system of selected celestial bodies bodies = environment_setup.create_system_of_bodies(body_settings) - ### Creation of vehicle settings """ @@ -113,11 +111,10 @@ environment_setup.add_aerodynamic_coefficient_interface( bodies, "obelix", aero_coefficient_settings) - ## Creation of propagation settings """ -""" +""" ### Creation of acceleration settings """ @@ -160,7 +157,6 @@ bodies_to_propagate, central_bodies) - ### Set initial state """ @@ -185,7 +181,6 @@ # Both satellites have the same inital state initial_states = np.concatenate((initial_state, initial_state)) - ### Set dependent variables to save """ These include (for both satellites): @@ -205,7 +200,6 @@ ), ] - ### Define termination settings """ @@ -227,6 +221,7 @@ where $\mathbf{r_1}$ and $\mathbf{r_2}$ are the position vectors of the first and second satellites respectively. """ +from tudatpy.numerical_simulation.environment import SystemOfBodies class AngleSeparationTermination: @@ -270,11 +265,13 @@ def compute_angular_separation(self, state_1: np.ndarray, state_2: np.ndarray): """ # Check input for state 1 if state_1.shape != (6, ): - err_msg = "Input must be a cartesian state vector of 6 components, but the one provided has shape " + str(state_1.shape) + err_msg = "Input must be a cartesian state vector of 6 components, but the one provided has shape " \ + + str(state_1.shape) raise ValueError(err_msg) # Check input for state 2 if state_2.shape != (6, ): - err_msg = "Input must be a cartesian state vector of 6 components, but the one provided has shape " + str(state_2.shape) + err_msg = "Input must be a cartesian state vector of 6 components, but the one provided has shape " \ + + str(state_2.shape) raise ValueError(err_msg) # Get scalar product of position vector scalar_product = np.dot(state_1[:3], state_2[:3]) @@ -322,7 +319,6 @@ def terminate_propagation(self, time: float): stop_propagation = False return stop_propagation - # Now the termination settings can be created. # Set simulation start and end epochs @@ -342,12 +338,8 @@ def terminate_propagation(self, time: float): termination_list = [time_termination_condition, angle_termination_condition] hybrid_termination = propagation_setup.propagator.hybrid_termination(termination_list, fulfill_single_condition=True) -## Creation of integration settings -""" - -We use a variable step size Runge-Kutta-Fehlberg 7(8) integrator with relative and absolute tolerances equal to -$10^{-10}$. -""" +# We use a variable step size Runge-Kutta-Fehlberg 7(8) integrator with relative and absolute tolerances equal to +# $10^{-10}$. # Create numerical integrator settings initial_step_size = 10.0 @@ -362,7 +354,6 @@ def terminate_propagation(self, time: float): tolerance, tolerance) - # The translational propagation settings are created here. # Create propagation settings @@ -377,8 +368,6 @@ def terminate_propagation(self, time: float): output_variables=dependent_variables_to_save ) - - ## Execute simulation """ With these commands, we execute the simulation and retrieve the output. @@ -393,7 +382,6 @@ def terminate_propagation(self, time: float): # Check which termination setting triggered the termination of the propagation print("Termination reason:" + angular_separation.termination_reason) - ## Post processing """ @@ -435,7 +423,6 @@ def return_sparse_output(time_history, variable_history, datapoints=200): interpolated_values = [interp_function(epoch) for epoch in time_interp] return time_interp, interpolated_values - # We retrieve the output and convert it to `numpy` arrays. # Get time and transform it in days @@ -446,7 +433,6 @@ def return_sparse_output(time_history, variable_history, datapoints=200): states_list = np.vstack(list(states.values())) dependent_variable_list = np.vstack(list(dependent_variables.values())) - ### Kepler elements """ @@ -502,9 +488,7 @@ def return_sparse_output(time_history, variable_history, datapoints=200): if element_number == 0: current_ax.legend() -plt.tight_layout() -plt.show() - +plt.tight_layout() ### Drag acceleration norm """ @@ -533,8 +517,6 @@ def return_sparse_output(time_history, variable_history, datapoints=200): ax.set_title("Drag acceleration") ax.legend() plt.tight_layout() -plt.show() - # As expected, the drag acceleration experienced by the satellite orbiting at a higher altitude (Asterix) is lower than # the other satellite's drag acceleration. This happens because Obelix has 3 times the drag surface area of Asterix. @@ -582,8 +564,6 @@ def return_sparse_output(time_history, variable_history, datapoints=200): ax.grid() ax.legend(loc='lower left') plt.tight_layout() -plt.show() - # Due to the larger drag acceleration experienced by Obelix, the satellites decays at a faster rate. # @@ -607,8 +587,3 @@ def return_sparse_output(time_history, variable_history, datapoints=200): ax.set_title("Angular separation between two satellites") ax.grid() plt.tight_layout() -plt.show() - - - - diff --git a/propagation/solar_system_propagation.py b/propagation/solar_system_propagation.py index e0a14f8..24cdb3e 100644 --- a/propagation/solar_system_propagation.py +++ b/propagation/solar_system_propagation.py @@ -1,8 +1,8 @@ # Solar System Propagation """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -40,7 +40,6 @@ from tudatpy.util import result2array from tudatpy.astro.time_conversion import DateTime - ## Configuration """ NAIF's `SPICE` kernels are first loaded, so that the position of various bodies such as the Earth can be make known to `tudatpy`. @@ -58,12 +57,11 @@ simulation_start_epoch = DateTime(2000, 4, 25).epoch() simulation_end_epoch = simulation_start_epoch + 5 * constants.JULIAN_YEAR - ## Environment setup """ Let’s create the environment for our simulation. This setup covers the creation of (celestial) bodies, vehicle(s), and environment interfaces. -""" +""" ### Create the bodies """ @@ -91,7 +89,6 @@ body_settings = environment_setup.get_default_body_settings(bodies_to_create) body_system = environment_setup.create_system_of_bodies(body_settings) - ## Propagation setup """ Now that the environment is created, the propagation setup is defined. @@ -114,7 +111,6 @@ else: central_bodies_hierarchical.append("Sun") - ### Create the acceleration model """ The acceleration settings that act each body are first defined. These accelerations only consist in the gravitational effect of each other body modeled as a Point Mass. @@ -149,7 +145,6 @@ else: acceleration_models_hierarchical = acceleration_models - ### Define the initial state """ The initial state of each body now has to be defined. @@ -184,7 +179,7 @@ """ # Create termination settings -termination_condition = propagation_setup.propagator.time_termination(simulation_end_epoch) +termination_settings = propagation_setup.propagator.time_termination(simulation_end_epoch) # Create numerical integrator settings fixed_step_size = 3600.0 @@ -201,7 +196,7 @@ system_initial_state_barycentric, simulation_start_epoch, integrator_settings, - termination_condition + termination_settings ) else: propagator_settings_hierarchical = propagation_setup.propagator.translational( @@ -211,7 +206,7 @@ system_initial_state_hierarchical, simulation_start_epoch, integrator_settings, - termination_condition + termination_settings ) ## Propagate the bodies @@ -219,7 +214,7 @@ Each of the bodies can now be simulated. This is done by calling the `create_dynamics_simulator()` function of the `numerical_simulation` module. -This function requires the `body_system`, `integrator_settings`, and the appropriate `propagator_settings_X` (with X being the propagation varian), that have all been defined earlier. +This function requires the `body_system` and the appropriate `propagator_settings_X` (with X being the propagation varian), that have all been defined earlier. In the same step, the history of the propagated states over time, containing both the position and velocity history, is extracted. This history takes the form of a dictionary. The keys are the simulated epochs, and the values are an array containing the states of all of the bodies one after another. @@ -235,12 +230,11 @@ results_hierarchical = numerical_simulation.create_dynamics_simulator( body_system, propagator_settings_hierarchical).state_history - ## Post-process the propagation results """ The results of the propagation are then processed to a more user-friendly form. -""" +""" ### Plot the barycentric system evolution in 3D """ @@ -276,8 +270,6 @@ ax1.set_zlabel('z [m]') ax1.set_zlim([-2.5E11, 2.5E11]) plt.tight_layout() -plt.show() - ### Plot the hierarchical system evolution in 3D """ @@ -331,8 +323,3 @@ ax.set_zlim(ax_lim) plt.tight_layout() -plt.show() - - - - diff --git a/propagation/thrust_between_Earth_Moon.py b/propagation/thrust_between_Earth_Moon.py index a44c128..97e6186 100644 --- a/propagation/thrust_between_Earth_Moon.py +++ b/propagation/thrust_between_Earth_Moon.py @@ -39,13 +39,14 @@ """ # Load tudatpy modules +""" from tudatpy.interface import spice from tudatpy import numerical_simulation from tudatpy.numerical_simulation import environment_setup, propagation_setup from tudatpy import constants from tudatpy.util import result2array from tudatpy.astro.time_conversion import DateTime - +""" ## Configuration """ @@ -63,14 +64,12 @@ spice.load_standard_kernels() """ - # Set simulation start and end epochs (total simulation time of 30 days) """ simulation_start_epoch = DateTime(2000, 4, 25).epoch() simulation_end_epoch = simulation_start_epoch + 30 * constants.JULIAN_DAY """ - ## Environment setup """ @@ -95,9 +94,10 @@ """ # Create bodies in simulation +""" body_settings = environment_setup.get_default_body_settings(bodies_to_create) system_of_bodies = environment_setup.create_system_of_bodies(body_settings) - +""" ### Create the vehicle """ @@ -111,7 +111,6 @@ system_of_bodies.get_body("Vehicle").set_constant_mass(5e3) """ - ### Define the thrust guidance settings """ @@ -136,7 +135,6 @@ environment_setup.add_engine_model( 'Vehicle', 'MainEngine', thrust_magnitude_settings, system_of_bodies ) - ## Propagation setup """ @@ -152,8 +150,9 @@ """ # Define central bodies of propagation +""" central_bodies = ["Earth"] - +""" ### Create the acceleration model """ @@ -183,16 +182,19 @@ """ # Compile the accelerations acting on the vehicle +""" acceleration_dict = dict(Vehicle=acceleration_on_vehicle) +""" # Create the acceleration models from the acceleration mapping dictionary +""" acceleration_models = propagation_setup.create_acceleration_models( body_system=system_of_bodies, selected_acceleration_per_body=acceleration_dict, bodies_to_propagate=bodies_to_propagate, central_bodies=central_bodies ) - +""" ### Define the initial state """ @@ -209,7 +211,6 @@ system_initial_state = np.array([8.0e6, 0, 0, 0, 7.5e3, 0]) """ - ### Define dependent variables to save """ @@ -224,18 +225,21 @@ """ # Create a dependent variable to save the mass of the vehicle over time +""" vehicle_mass_dep_var = propagation_setup.dependent_variable.body_mass( "Vehicle" ) +""" # Define list of dependent variables to save +""" dependent_variables_to_save = [vehicle_altitude_dep_var, vehicle_mass_dep_var] - +""" ### Create the termination settings """ -Let's now define a set of termination conditions. In this setup, once any single one of them is fulfilled, the propagation stops. +Let's now define a set of termination settings. In this setup, once any single one of them is fulfilled, the propagation stops. -These conditions are the following: +These settings are the following: - Stop when the altitude get above 100,000 km. - Stop when the Vehicle has a mass of 4,000 kg (burned 1,000 kg of propellant). - Stop when the Vehicle reaches the specified end epoch (after 30 days). @@ -261,6 +265,7 @@ [termination_distance_settings, termination_mass_settings, termination_time_settings], fulfill_single_condition = True) + ### Create the integrator settings """ @@ -277,7 +282,9 @@ """ # Setup the tolerance of the variable step integrator +""" tolerance = 1e-10 +""" # Create numerical integrator settings (using a RKF7(8) coefficient set) """ @@ -290,7 +297,6 @@ absolute_error_tolerance=tolerance) """ - ### Create the propagator settings """ @@ -319,38 +325,42 @@ """ # Create a mass rate model so that the vehicle loses mass according to how much thrust acts on it +""" mass_rate_settings = dict(Vehicle=[propagation_setup.mass_rate.from_thrust()]) mass_rate_models = propagation_setup.create_mass_rate_models( system_of_bodies, mass_rate_settings, acceleration_models ) +""" # Create the mass propagation settings +""" mass_propagator_settings = propagation_setup.propagator.mass( bodies_to_propagate, mass_rate_models, - [5e3], #initial_vehicle_mass + [5e3], initial vehicle mass simulation_start_epoch, integrator_settings, termination_settings ) +""" # Combine the translational and mass propagator settings +""" propagator_settings = propagation_setup.propagator.multitype( [translational_propagator_settings, mass_propagator_settings], integrator_settings, simulation_start_epoch, termination_settings, [vehicle_altitude_dep_var, vehicle_mass_dep_var]) - - +""" ## Propagate the orbit """ The orbit from the Earth to the Moon is now ready to be propagated. -This is done by calling the `create_dynamics_simulator()` function of the `numerical_simulation` module. +This is done by calling the `create_dynamics_simulator()` function of the `numerical_simulation module`. This function requires the `system_of_bodies` and `propagator_settings` that have all been defined earlier. After this, the history of the propagated state over time, containing both the position and velocity history, is extracted. @@ -366,18 +376,20 @@ # Instantiate the dynamics simulator and run the simulation """ dynamics_simulator = numerical_simulation.create_dynamics_simulator( - system_of_bodies, propagator_settings -) + system_of_bodies, propagator_settings) """ # Extract the state and dependent variable history +""" state_history = dynamics_simulator.state_history dependent_variable_history = dynamics_simulator.dependent_variable_history +""" # Convert the dictionaries to multi-dimensional arrays +""" vehicle_array = result2array(state_history) dep_var_array = result2array(dependent_variable_history) - +""" ### Get Moon state from SPICE """ @@ -392,9 +404,10 @@ for epoch in list(state_history.keys()) } Convert the dictionary to a mutli-dimensional array +""" moon_array = result2array(moon_states_from_spice) """ - +""" ## Post-process the propagation results """ @@ -414,19 +427,26 @@ """ # Create a figure for the altitude of the vehicle above Earth +""" fig1 = plt.figure(figsize=(9, 5)) ax1 = fig1.add_subplot(111) ax1.set_title(f"Vehicle altitude above Earth") +""" # Plot the altitude of the vehicle over time +""" ax1.plot(time_days, dep_var_array[:,1]/1e3) +""" # Add a grid and axis labels to the plot +""" ax1.grid(), ax1.set_xlabel("Simulation time [day]"), ax1.set_ylabel("Vehicle altitude [km]") +""" # Use a tight layout for the figure (do last to avoid trimming axis) +""" fig1.tight_layout() - +""" ### Vehicle mass over time """ @@ -442,14 +462,19 @@ """ # Plot the mass of the vehicle over time +""" ax2.plot(time_days, dep_var_array[:,2]) +""" # Add a grid and axis labels to the plot +""" ax2.grid(), ax2.set_xlabel("Simulation time [day]"), ax2.set_ylabel("Vehicle mass [kg]") +""" # Use a tight layout for the figure (do last to avoid trimming axis) +""" fig2.tight_layout() - +""" ### Plot trajectories in a 3D Projection """ @@ -465,17 +490,22 @@ """ # Plot the vehicle and Moon positions as curve, and the Earth as a marker +""" ax3.plot(vehicle_array[:,1], vehicle_array[:,2], vehicle_array[:,3], label="Vehicle", linestyle="-", color="green") ax3.plot(moon_array[:,1], moon_array[:,2], moon_array[:,3], label="Moon", linestyle="-", color="grey") ax3.scatter(0.0, 0.0, 0.0, label="Earth", marker="o", color="blue") +""" # Add a legend, set the plot limits, and add axis labels +""" ax3.legend() ax3.set_xlim([-3E8, 3E8]), ax3.set_ylim([-3E8, 3E8]), ax3.set_zlim([-3E8, 3E8]) ax3.set_xlabel("x [m]"), ax3.set_ylabel("y [m]"), ax3.set_zlabel("z [m]") +""" # Use a tight layout for the figure (do last to avoid trimming axis) +""" fig3.tight_layout() +""" plt.show() - diff --git a/propagation/two_stage_rocket_ascent.py b/propagation/two_stage_rocket_ascent.py index 99ef340..84f8256 100644 --- a/propagation/two_stage_rocket_ascent.py +++ b/propagation/two_stage_rocket_ascent.py @@ -1,11 +1,8 @@ -import sys -sys.path.insert(0, '/home/dominic/Software/tudat-bundle/build-tudat-bundle-Desktop-Default/tudatpy/') - # Two-stage rocket ascent """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -29,18 +26,18 @@ # Load standard modules import numpy as np + from matplotlib import pyplot as plt from datetime import datetime # Load tudatpy modules -from tudatpy.kernel.interface import spice -from tudatpy.kernel import numerical_simulation -from tudatpy.kernel.numerical_simulation import environment, environment_setup, propagation, propagation_setup -from tudatpy.kernel.astro import element_conversion, time_conversion -from tudatpy.kernel import constants +from tudatpy.interface import spice +from tudatpy import numerical_simulation +from tudatpy.numerical_simulation import environment, environment_setup, propagation, propagation_setup +from tudatpy.astro import element_conversion, time_conversion +from tudatpy import constants from tudatpy.util import result2array - ## Configuration """ NAIF's `SPICE` kernels are first loaded, so that the position of various bodies such as the Earth can be make known to `tudatpy`. @@ -58,12 +55,11 @@ # Convert simulation start to seconds since J2000 simulation_start_epoch = time_conversion.julian_day_to_seconds_since_epoch(simulation_start_JD) - ## Environment setup """ Let’s create the environment for our simulation. This setup covers the creation of (celestial) bodies, vehicle(s), and environment interfaces. -""" +""" ### Create the bodies """ @@ -99,11 +95,10 @@ def create_bodies(): # Return the system of selected celestial bodies return environment_setup.create_system_of_bodies(body_settings) - + # Create the system of selected celestial bodies bodies = create_bodies() - ## First section simulation """ In this example, our rocket consists of two stages. Do note that, further, the nomenclature of rocket **sections** is used, rather than rocket **stages**. The first rocket section contains both the first and the second stage. The second rocket section contains only the second stage. Section 1 is thus before stage separation, and section 2 after. @@ -152,6 +147,21 @@ def add_aero_coefficients(section_name, CD, CL, ref_area=0.25): # Create an aerodynamic coefficient interface for the first rocket section add_aero_coefficients("Section 1", 0.85, 0.4) +### Propagation setup +""" +Now that the environment is created, the propagation setup is defined. + +First, the bodies to be propagated and the central bodies will be defined. +Central bodies are the bodies with respect to which the state of the respective propagated bodies is defined. + +The body to be propagated in the first part of this example is the first rocket section. +""" + +# Define bodies that are propagated +bodies_to_propagate = ["Section 1"] + +# Define central bodies of propagation +central_bodies = ["Mars"] #### Thrust model """ @@ -198,8 +208,6 @@ def is_thrust_on(self, time): def get_thrust_magnitude(self, time): # If we are in the 15 first seconds, return 1.75 times the magnitude - if( not self.is_thrust_on( time ) ): - return 0.0 if self.t0 is None: self.t0 = time if time - self.t0 < 15: @@ -213,22 +221,21 @@ def get_specific_impulse(self, time): def get_thrust_direction(self, time): # Get aerodynamic angle calculator aerodynamic_angle_calculator = self.propagated_body.flight_conditions.aerodynamic_angle_calculator - + # Set thrust in vertical frame and transpose it thrust_direction_vertical_frame = np.array([[0, np.sin(self.vertical_angle), - np.cos(self.vertical_angle)]]).T - + # Retrieve rotation matrix from vertical to inertial frame from the aerodynamic angle calculator vertical_to_inertial_frame = aerodynamic_angle_calculator.get_rotation_matrix_between_frames( environment.AerodynamicsReferenceFrames.vertical_frame, environment.AerodynamicsReferenceFrames.inertial_frame) - + # Compute the thrust in the inertial frame thrust_inertial_frame = np.dot(vertical_to_inertial_frame, thrust_direction_vertical_frame) # Return the thrust direction in the inertial frame return thrust_inertial_frame - #### Define the thrust settings """ Using the `thrust_model` class that was defined earlier, we can now define thrust acceleration settings for the first rocket section. @@ -242,50 +249,33 @@ def get_thrust_direction(self, time): # Define a function to create acceleration settings based on the direction and magnitude from the custom thrust class def create_body_settings_for_thrust(current_thrust_model, bodies, body_name): - - # Define body rotation model according to the required thrust direction settings + # Define the thrust direction settings for the first section from the custom direction function rotation_model_settings = environment_setup.rotation_model.custom_inertial_direction_based( current_thrust_model.get_thrust_direction, - "J2000", "VehicleFixed" + "J2000", "VehicleFixed" ) + environment_setup.add_rotation_model( bodies, body_name, rotation_model_settings ) + # Define the thrust magnitude settings for the first section from the custom functions thrust_magnitude_settings = propagation_setup.thrust.custom_thrust_magnitude( current_thrust_model.get_thrust_magnitude, - current_thrust_model.get_specific_impulse, + current_thrust_model.get_specific_impulse ) - + environment_setup.add_engine_model( - body_name, - "MainEngine", - thrust_magnitude_settings, - bodies ) + body_name, + "MainEngine", + thrust_magnitude_settings, + bodies ) + # Setup the thrust model for the first section current_thrust_model = thrust_model(4250, 275, np.deg2rad(40), bodies.get("Section 1"), 185) create_body_settings_for_thrust(current_thrust_model, bodies, "Section 1") - -### Propagation setup -""" -Now that the environment is created, the propagation setup is defined. - -First, the bodies to be propagated and the central bodies will be defined. -Central bodies are the bodies with respect to which the state of the respective propagated bodies is defined. - -The body to be propagated in the first part of this example is the first rocket section. -""" - -# Define bodies that are propagated -bodies_to_propagate = ["Section 1"] - -# Define central bodies of propagation -central_bodies = ["Mars"] - - - #### Create the accelerations model """ First off, the acceleration settings from the environment that act on the rocket are to be defined. @@ -323,32 +313,6 @@ def create_section_accelerations(section_name): # Define the acceleration models for the first rocket section acceleration_models = create_section_accelerations("Section 1") -# -#### Aerodynamic model -""" -""" -A very basic aerodynamic model is now defined, to update the angle of attack of our vehicle as a function of time. -It is encouraged for this model to be improved. For now, it ensures some slight variation in the aerodynamic acceleration over time, since it varies the angle of attack between -2 deg and 2 deg, using the following equation for the angle of attack $\alpha$ in radians over the time $t$ in seconds: -$$ -\alpha(t) = \frac{2*\pi}{180} \cdot \sin \left( \frac{t \cdot \pi}{750} \right) -$$ -""" -# -class AeroGuidance(propagation.AerodynamicGuidance): -# - def __init__(self): - # Call the base class constructor - propagation.AerodynamicGuidance.__init__(self) -# - def updateGuidance(self, current_time): - # Update angle of attack as a function of time - self.angle_of_attack = np.deg2rad(2) * np.sin(current_time*np.pi/750) -# -# Set the aerodynamic guidance of the first section -guidance_object = AeroGuidance() -environment_setup.set_aerodynamic_guidance(guidance_object, bodies.get("Section 1"), silence_warnings=True) -""" - ### Define the initial state """ @@ -376,7 +340,6 @@ def updateGuidance(self, current_time): initial_mars_fixed_state, simulation_start_epoch, bodies.get_body("Mars").rotation_model ) - ### Define dependent variables to save """ Different dependent variables can be saved alongside the state of the vehicle during the propagation. In this example, we are particularily interested in saving the altitude, airspeed, dynamic pressure, and mass of the first rocket section. In addition, various acceleration norms are defined to be saved as dependent variables. @@ -397,11 +360,10 @@ def define_dependent_variables_to_save(section_name): propagation_setup.dependent_variable.single_acceleration_norm( propagation_setup.acceleration.aerodynamic_type, section_name, "Mars") ] - + # Define the dependent variables to save for the first rocket section dependent_variables_to_save = define_dependent_variables_to_save("Section 1") - ### Define termination settings """ Termination settings define the conditions that, once reached, will stop the propagation. @@ -412,13 +374,13 @@ def define_dependent_variables_to_save(section_name): """ class vehicle_falling: - + def __init__(self, body, initial_time): # Initialise the class used to compute wether a body is falling or not self.body = body self.last_h = -np.inf self.init_t = initial_time - + def is_it_falling(self, time): # Compute the difference in altitude since this function was last called dh = self.body.flight_conditions.altitude - self.last_h @@ -442,7 +404,6 @@ def is_it_falling(self, time): fulfill_single_condition=True ) - ### Create integrator settings """ Let's now create integrator settings. These use a RK78 intergration scheme with a variable step size. The following settings are used: @@ -468,7 +429,6 @@ def define_integrator_settings(): # Define the integrator settings integrator_settings = define_integrator_settings() - ### Create propagator settings """ The acceleration models, the initial state, the dependent variables, and the termination settings can now all be combined to define translational propagation settings. @@ -517,11 +477,9 @@ def create_propagator_settings(section_name, initial_state, simulation_start_epo termination_settings, dependent_variables_to_save ) - + # Define the translational and mass propagator settings for the first rocket section -propagator_settings = create_propagator_settings("Section 1", initial_inertial_state, simulation_start_epoch, 370, - combined_termination_settings, integrator_settings) - +propagator_settings = create_propagator_settings("Section 1", initial_inertial_state, simulation_start_epoch, 370, combined_termination_settings, integrator_settings) ### Run the first section ascent """ @@ -541,7 +499,6 @@ def create_propagator_settings(section_name, initial_state, simulation_start_epo dep_vars = dynamics_simulator.dependent_variable_history dep_vars_array_section_1 = result2array(dep_vars) - ### Save section 1 final state """ Because we now want to simulate the second section from our rocket, we need to save what was the last state from the first section. This way, we can start a new propagation, simulating the remaining ascent of the second section (being the second stage) only, starting from where the first section ended. @@ -550,7 +507,6 @@ def create_propagator_settings(section_name, initial_state, simulation_start_epo final_state_section_1 = states_array_section_1[-1,1:7] final_epoch_section_1 = states_array_section_1[-1,0] - ## Second section simulation """ With the first section simulation finished, the resulting states and dependant variables saved as the `states_array_section_1` and `dep_vars_array_section_1` arrays, and the final first section state saved as `final_state_section_1`, we can now propagate our second rocket section. @@ -591,17 +547,6 @@ def create_propagator_settings(section_name, initial_state, simulation_start_epo # Define the acceleration models for the second rocket section acceleration_models = create_section_accelerations("Section 2") -# -### Add aerodynamic model -""" -""" -The same aerodynamic model as for the first section is used for the second section, ensuring some variation in the angle of attack. -""" -# -guidance_object = AeroGuidance() -environment_setup.set_aerodynamic_guidance(guidance_object, bodies.get("Section 2"), silence_warnings=True) -# -""" ### Define dependent variables """ @@ -610,7 +555,6 @@ def create_propagator_settings(section_name, initial_state, simulation_start_epo dependent_variables_to_save = define_dependent_variables_to_save("Section 2") - ### Define integrator settings """ A RK4 integration scheme is also used for this second rocket section, with a half a second time step. However, this initial integration epoch is now setup as the final epoch of the first section. @@ -618,7 +562,6 @@ def create_propagator_settings(section_name, initial_state, simulation_start_epo integrator_settings = define_integrator_settings() - ### Define propagator settings """ New translational and mass propagator settings are now defined for the second section. @@ -628,9 +571,7 @@ def create_propagator_settings(section_name, initial_state, simulation_start_epo """ # Define the translational and mass propagator settings for the first rocket section -propagator_settings = create_propagator_settings("Section 2", final_state_section_1, final_epoch_section_1, 85, - termination_max_time_settings, integrator_settings) - +propagator_settings = create_propagator_settings("Section 2", final_state_section_1, final_epoch_section_1, 85, termination_max_time_settings, integrator_settings) ### Run second section simulation """ @@ -650,7 +591,6 @@ def create_propagator_settings(section_name, initial_state, simulation_start_epo dep_vars = dynamics_simulator.dependent_variable_history dep_vars_array_section_2 = result2array(dep_vars) - ## Results analysis """ With the ascent simulation of both sections completed, we can now analyse the results. Most importantly, this consists in plotting the various dependent variables that has been saved over time. @@ -663,7 +603,6 @@ def create_propagator_settings(section_name, initial_state, simulation_start_epo dep_vars_array = np.concatenate((dep_vars_array_section_1, dep_vars_array_section_2)) - ### Extract the data """ Let's now extract each of the relevant data array from the dependent variables multi-dimensional array. @@ -741,41 +680,39 @@ def create_propagator_settings(section_name, initial_state, simulation_start_epo plt.tight_layout() plt.show() - ### Results analysis """ Finally, we can analyse the plot that we produce, giving various insights in our numerical simulation of a two-stage rocket ascent on Mars. -""" +""" #### Altitude over time """ In the first plot on the top left, we can see the altitude plotted as a function of time. This shows that the rocket progressively gains more and more altitude, as its accelerates. After stage separation, at around 9min, when the second stage ignites, the rocket does not gain significantly more altitude. This is because the second stage fires only horizontally. Because it does so at apoapsis, it fires only prograde, increasing the prograde (horizontal) velocity of the rocket, without increasing its radial (vertical) velocity. Afterwards, we see oscillations between around 150km and 900km. This shows that our vehicle is now in orbit. -""" +""" #### Airspeed over time """ The second plot on the top right clearly shows that the rocket gains considerable speed in the first seconds, when the first stage fires. At first stage burnout, the velocity starts decreasing. This is because we loose velocity for altitude, until we reach apoapsis. At apoapsis, the second stage ignites, and the velocity increases considerably again. Afterwards, we see oscillations between around 3.4 and 2.7 km/s, showing that our vehicle is at orbital velocity around Mars. -""" +""" #### Rocket mass over time """ The thrist plot in the middle left shows the mass of our rocket over time in its first 10 minutes. We can clearly identify the two moments where the rocket burns propellant, indicated by the two sections where the mass linearly decreases, from 0min to 2min, and from 9min to 10min. During the burns, since the rocket has a thrust magnitude that is 1.75 times higher in the first 15 seconds, it also looses propellant 1.75 times faster. This can be seen by the higher mass rate at the beginning of the ignition of both stages. Finally, we can see at around 9min that the two stages separate, because our rocket mass instantaneously changes from 185kg to 85kg. -""" +""" #### Dynamic pressure over time """ The fourth plot, in the middle right, shows the dynamic pressure over time in front of our rocket. While the model used to compute it is rather simplistic, it still gives a good indication as when max-q (moment of maximum dynamic pressure) is reached, and of its magnitude. This maximum dynamic pressure may be lower than expected, since we are flying on Mars and not the Earth. -""" +""" #### Accelerations over time """ Finally, the plot at the bottom shows various accelerations over time. Clearly, thrust gives the acceleration of the highest magnitude. Once again, we can see that the thrust was each time of a mgnitude 1.75 times higher in the first 15 seconds. Also, we can see that the thrust acceleration increases over time furing each of the burn. This can be expected: the acceleration that results in the thrust force becomes higher over time as our rocket mass becomes lower. The Martian gravitational acceleration comes second in magnitude. While it appears constant, because its magnitude is much lower than the one of the thrst, this gravitational acceleration decreases as altitude increases. Finally, the aerodynamic acceleration is only significant in the first 2min of the ascent. One may realise that this acceleration follows a similar shape as the dynamic pressure over time. This is because the aerodynamic acceleration relies purely on the aerodynamic coefficients (which are constant in this case), the angle of attack (that varies only between -2deg and 2deg), and the dynamic pressure. -""" diff --git a/pygmo/asteroid_orbit_optimization/aoo_custom_environment.py b/pygmo/asteroid_orbit_optimization/aoo_custom_environment.py index 9632fa6..5eff5aa 100644 --- a/pygmo/asteroid_orbit_optimization/aoo_custom_environment.py +++ b/pygmo/asteroid_orbit_optimization/aoo_custom_environment.py @@ -1,8 +1,8 @@ # Asteroid orbit optimization with PyGMO - Custom Environment """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -10,8 +10,8 @@ **This part of the example is focussed on the creation of a custom environment, using manually defined rotation, ephemeris, gravity field, and shape settings**. A PyGMO compatible Problem class is also created for the next parts of the example. Using this problem class, a propagation is conducted to show a possible trajectory orbiting Itokawa. -""" +""" ### NOTE """ @@ -57,7 +57,6 @@ current_dir = os.path.abspath('') - ## Creation of Custom Environment """ @@ -108,7 +107,6 @@ def get_itokawa_rotation_settings(itokawa_body_frame_name): return environment_setup.rotation_model.simple( "ECLIPJ2000", itokawa_body_frame_name, initial_orientation_eclipj2000, 0.0, rotation_rate) - ### Itokawa ephemeris settings """ The next helper function defined, `get_itokawa_ephemeris_settings()`, that can be used to set the ephemeris of Itokawa. @@ -156,7 +154,6 @@ def get_itokawa_ephemeris_settings(sun_gravitational_parameter): "Sun", "ECLIPJ2000") - ### Itokawa gravity field settings """ The `get_itokawa_gravity_field_settings()` helper function can be used to get the gravity field settings of Itokawa. @@ -185,7 +182,6 @@ def get_itokawa_gravity_field_settings(itokawa_body_fixed_frame, itokawa_radius) normalized_sine_coefficients=normalized_sine_coefficients, associated_reference_frame=itokawa_body_fixed_frame) - ### Itokawa shape settings """ The next helper function defined, `get_itokawa_shape_settings()` return the shape settings object for Itokawa. It uses a simple spherical model, and take the radius of Itokawa as input. @@ -195,7 +191,6 @@ def get_itokawa_shape_settings(itokawa_radius): # Creates spherical shape settings return environment_setup.shape.spherical(itokawa_radius) - ### Simulation bodies """ Next, the `create_simulation_bodies()` function is setup, that returns an [environment.SystemOfBodies](https://tudatpy.readthedocs.io/en/latest/environment.html#tudatpy.numerical_simulation.environment.SystemOfBodies) object. This object contains all the body settings and body objects required by the simulation. Only one input is required to this function: the radius of Itokawa. @@ -242,7 +237,7 @@ def create_simulation_bodies(itokawa_radius): bodies.get("Spacecraft").set_constant_mass(400.0) # Create radiation pressure settings, and add to vehicle - reference_area_radiation = 4.0 + reference_area_radiation = (4*0.3*0.1+2*0.1*0.1)/4 # Average projection area of a 3U CubeSat radiation_pressure_coefficient = 1.2 radiation_pressure_settings = environment_setup.radiation_pressure.cannonball( "Sun", @@ -255,7 +250,6 @@ def create_simulation_bodies(itokawa_radius): return bodies - ### Acceleration models """ The `get_acceleration_models()` helper function returns the acceleration models to be used during the astrodynamic simulation. The following accelerations are included: @@ -289,7 +283,6 @@ def get_acceleration_models(bodies_to_propagate, central_bodies, bodies): bodies_to_propagate, central_bodies) - ### Termination settings """ The termination settings for the simulation are defined by the `get_termination_settings()` helper. @@ -331,7 +324,6 @@ def get_termination_settings(mission_initial_time, return propagation_setup.propagator.hybrid_termination(termination_settings_list, fulfill_single_condition=True) - ### Dependent variables to save """ Finally, the `get_dependent_variables_to_save()` helper function returns a pre-defined list of dependent variables to save during the propagation, alongside the propagated state. This function can be expanded, but contains by default only the position of the spacecraft with respect to the Itokawa asteroid expressed in spherical coordinates. @@ -345,7 +337,6 @@ def get_dependent_variables_to_save(): ] return dependent_variables_to_save - ## Optimisation problem formulation """ The optimisation problem can now be defined. This has to be done in a class that is compatible to what the PyGMO library can expect from this User Defined Problem (UDP). See [this page](https://esa.github.io/pygmo2/problem.html#pygmo.problem) from the PyGMO documentation as a reference. In this example, this class is called `AsteroidOrbitProblem`. @@ -446,7 +437,6 @@ def fitness(self, def get_last_run_dynamics_simulator(self): return self.dynamics_simulator_function() - ### Setup orbital simulation """ Before running the optimisation, some aspect of the orbital simulation around Itokawa still need to be setup. @@ -501,7 +491,6 @@ def get_last_run_dynamics_simulator(self): # Create acceleration models acceleration_models = get_acceleration_models(bodies_to_propagate, central_bodies, bodies) - #### Dependent variables, termination settings, and orbit parameters """ To define the propagator settings in the subsequent sections, we first call the `get_dependent_variables_to_save()` and `get_termination_settings()` helpers to define the dependent variables and termination settings. @@ -518,7 +507,6 @@ def get_last_run_dynamics_simulator(self): orbit_parameters = [1.20940330e+03, 2.61526215e-01, 7.53126558e+01, 2.60280587e+02] - #### Integrator and Propagator settings """ Let's now define the integrator settings. In this case, a variable step integration scheme is used, with the followings: @@ -554,7 +542,6 @@ def get_last_run_dynamics_simulator(self): propagator, dependent_variables_to_save) - ## Propagating Orbit """ @@ -577,7 +564,6 @@ def get_last_run_dynamics_simulator(self): design_variable_vector = np.array([1.20940330e+03, 2.61526215e-01, 7.53126558e+01, 2.60280587e+02]) orbitProblem.fitness(design_variable_vector) - ### Visualizing orbit """ With a little bit of post-processing, the orbit can be plotted. You can see that with only 2 full orbits that the trajectory is already very perturbed. This has to do with all the perturbations that are in the model, which poses a challenge for the optimization in the next two parts of the example. @@ -604,4 +590,3 @@ def get_last_run_dynamics_simulator(self): ax.set_ylim([-1000,1000]) ax.set_zlim([-1000,1000]) ax.grid() - diff --git a/pygmo/asteroid_orbit_optimization/aoo_design_space_exploration.py b/pygmo/asteroid_orbit_optimization/aoo_design_space_exploration.py index 7824e02..645e644 100644 --- a/pygmo/asteroid_orbit_optimization/aoo_design_space_exploration.py +++ b/pygmo/asteroid_orbit_optimization/aoo_design_space_exploration.py @@ -1,14 +1,14 @@ # Asteroid orbit optimization with PyGMO - Design Space Exploration """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ This tutorial is the second part of the Asteroid Orbit Optimization example. **This page reuses the** [Custom environment](https://tudat-space.readthedocs.io/en/latest/_src_getting_started/_src_examples/notebooks/pygmo/asteroid_orbit_optimization/aoo_custom_environment.html) **part of the example, without the explanation, after which a Design Space Exploration (DSE) is done**. The DSE is collection of methods with which an optimization problem can be analyzed, and better understood, without having to execute an optimization or test randomly. -""" +""" ## Problem definition """ @@ -26,8 +26,8 @@ - good resolution (the mean value of the distance should be minimized). The constraints are set on the altitude: all the sets of design variables leading to an orbit. -""" +""" #### NOTE """ @@ -38,15 +38,14 @@ ## Import statements """ -""" - # Load standard modules import os import numpy as np -# Uncomment the following to make plots interactive -# %matplotlib widget +Uncomment the following to make plots interactive +%matplotlib widget from matplotlib import pyplot as plt from itertools import combinations as comb +""" # Load tudatpy modules @@ -65,7 +64,6 @@ current_dir = os.path.abspath('') - ## Creation of Custom Environment """ """ @@ -103,13 +101,10 @@ def get_itokawa_rotation_settings(itokawa_body_frame_name): return environment_setup.rotation_model.simple( "ECLIPJ2000", itokawa_body_frame_name, initial_orientation_eclipj2000, 0.0, rotation_rate) - ### Itokawa ephemeris settings """ -""" - def get_itokawa_ephemeris_settings(sun_gravitational_parameter): - # Define Itokawa initial Kepler elements + Define Itokawa initial Kepler elements itokawa_kepler_elements = np.array([ 1.324118017407799 * constants.ASTRONOMICAL_UNIT, 0.2801166461882852, @@ -118,31 +113,29 @@ def get_itokawa_ephemeris_settings(sun_gravitational_parameter): np.deg2rad(69.0803904880264), np.deg2rad(187.6327516838828)]) - # Convert mean anomaly to true anomaly + Convert mean anomaly to true anomaly itokawa_kepler_elements[5] = element_conversion.mean_to_true_anomaly( eccentricity=itokawa_kepler_elements[1], mean_anomaly=itokawa_kepler_elements[5]) - # Get epoch of initial Kepler elements (in Julian Days) + Get epoch of initial Kepler elements (in Julian Days) kepler_elements_reference_julian_day = 2459000.5 - # Sets new reference epoch for Itokawa ephemerides (different from J2000) + Sets new reference epoch for Itokawa ephemerides (different from J2000) kepler_elements_reference_epoch = (kepler_elements_reference_julian_day - constants.JULIAN_DAY_ON_J2000) \ * constants.JULIAN_DAY - # Sets the ephemeris model + Sets the ephemeris model return environment_setup.ephemeris.keplerian( itokawa_kepler_elements, kepler_elements_reference_epoch, sun_gravitational_parameter, "Sun", "ECLIPJ2000") - +""" ### Itokawa gravity field settings """ -""" - def get_itokawa_gravity_field_settings(itokawa_body_fixed_frame, itokawa_radius): itokawa_gravitational_parameter = 2.36 normalized_cosine_coefficients = np.array([ @@ -163,25 +156,25 @@ def get_itokawa_gravity_field_settings(itokawa_body_fixed_frame, itokawa_radius) normalized_cosine_coefficients=normalized_cosine_coefficients, normalized_sine_coefficients=normalized_sine_coefficients, associated_reference_frame=itokawa_body_fixed_frame) - +""" ### Itokawa shape settings """ -""" - def get_itokawa_shape_settings(itokawa_radius): - # Creates spherical shape settings + Creates spherical shape settings return environment_setup.shape.spherical(itokawa_radius) - +""" ### Simulation bodies """ +def create_simulation_bodies(itokawa_radius): """ -def create_simulation_bodies(itokawa_radius): - ### CELESTIAL BODIES ### - # Define Itokawa body frame name + ##CELESTIAL BODIES ### +""" + Define Itokawa body frame name itokawa_body_frame_name = "Itokawa_Frame" +""" # Create default body settings for selected celestial bodies bodies_to_create = ["Sun", "Earth", "Jupiter", "Saturn", "Mars"] @@ -217,7 +210,7 @@ def create_simulation_bodies(itokawa_radius): bodies.get("Spacecraft").set_constant_mass(400.0) # Create radiation pressure settings, and add to vehicle - reference_area_radiation = 4.0 + reference_area_radiation = (4*0.3*0.1+2*0.1*0.1)/4 # Average projection area of a 3U CubeSat radiation_pressure_coefficient = 1.2 radiation_pressure_settings = environment_setup.radiation_pressure.cannonball( "Sun", @@ -230,13 +223,10 @@ def create_simulation_bodies(itokawa_radius): return bodies - ### Acceleration models """ -""" - def get_acceleration_models(bodies_to_propagate, central_bodies, bodies): - # Define accelerations acting on Spacecraft + Define accelerations acting on Spacecraft accelerations_settings_spacecraft = dict( Sun = [ propagation_setup.acceleration.cannonball_radiation_pressure(), propagation_setup.acceleration.point_mass_gravity() ], @@ -246,6 +236,7 @@ def get_acceleration_models(bodies_to_propagate, central_bodies, bodies): Mars = [ propagation_setup.acceleration.point_mass_gravity() ], Earth = [ propagation_setup.acceleration.point_mass_gravity() ] ) +""" # Create global accelerations settings dictionary acceleration_settings = {"Spacecraft": accelerations_settings_spacecraft} @@ -257,34 +248,32 @@ def get_acceleration_models(bodies_to_propagate, central_bodies, bodies): bodies_to_propagate, central_bodies) - ### Termination settings """ -""" - def get_termination_settings(mission_initial_time, mission_duration, minimum_distance_from_com, maximum_distance_from_com): - # Mission duration + Mission duration time_termination_settings = propagation_setup.propagator.time_termination( mission_initial_time + mission_duration, terminate_exactly_on_final_condition=False ) - # Upper altitude + Upper altitude upper_altitude_termination_settings = propagation_setup.propagator.dependent_variable_termination( dependent_variable_settings=propagation_setup.dependent_variable.relative_distance('Spacecraft', 'Itokawa'), limit_value=maximum_distance_from_com, use_as_lower_limit=False, terminate_exactly_on_final_condition=False ) - # Lower altitude + Lower altitude lower_altitude_termination_settings = propagation_setup.propagator.dependent_variable_termination( dependent_variable_settings=propagation_setup.dependent_variable.altitude('Spacecraft', 'Itokawa'), limit_value=minimum_distance_from_com, use_as_lower_limit=True, terminate_exactly_on_final_condition=False ) +""" # Define list of termination settings termination_settings_list = [time_termination_settings, @@ -294,11 +283,8 @@ def get_termination_settings(mission_initial_time, return propagation_setup.propagator.hybrid_termination(termination_settings_list, fulfill_single_condition=True) - ### Dependent variables to save """ -""" - def get_dependent_variables_to_save(): dependent_variables_to_save = [ propagation_setup.dependent_variable.central_body_fixed_spherical_position( @@ -306,12 +292,10 @@ def get_dependent_variables_to_save(): ) ] return dependent_variables_to_save - +""" ## Optimisation problem formulation """ -""" - class AsteroidOrbitProblem: def __init__(self, @@ -323,21 +307,22 @@ def __init__(self, design_variable_lower_boundaries, design_variable_upper_boundaries): - # Sets input arguments as lambda function attributes - # NOTE: this is done so that the class is "pickable", i.e., can be serialized by pygmo + Sets input arguments as lambda function attributes + NOTE: this is done so that the class is "pickable", i.e., can be serialized by pygmo self.bodies_function = lambda: bodies self.integrator_settings_function = lambda: integrator_settings self.propagator_settings_function = lambda: propagator_settings - # Initialize empty dynamics simulator + Initialize empty dynamics simulator self.dynamics_simulator_function = lambda: None - # Set other input arguments as regular attributes + Set other input arguments as regular attributes self.mission_initial_time = mission_initial_time self.mission_duration = mission_duration self.mission_final_time = mission_initial_time + mission_duration self.design_variable_lower_boundaries = design_variable_lower_boundaries self.design_variable_upper_boundaries = design_variable_upper_boundaries +""" def get_bounds(self): return (list(self.design_variable_lower_boundaries), list(self.design_variable_upper_boundaries)) @@ -403,7 +388,6 @@ def fitness(self, def get_last_run_dynamics_simulator(self): return self.dynamics_simulator_function() - ### Setup orbital simulation """ @@ -411,10 +395,9 @@ def get_last_run_dynamics_simulator(self): #### Simulation settings """ -""" - # Load spice kernels spice.load_standard_kernels() +""" # Set simulation start and end epochs mission_initial_time = 0.0 @@ -441,13 +424,11 @@ def get_last_run_dynamics_simulator(self): # Create acceleration models acceleration_models = get_acceleration_models(bodies_to_propagate, central_bodies, bodies) - #### Dependent variables, termination settings, and orbit parameters """ -""" - # Define list of dependent variables to save dependent_variables_to_save = get_dependent_variables_to_save() +""" # Create propagation settings termination_settings = get_termination_settings( @@ -455,11 +436,8 @@ def get_last_run_dynamics_simulator(self): orbit_parameters = [1.20940330e+03, 2.61526215e-01, 7.53126558e+01, 2.60280587e+02] - #### Integrator and Propagator settings """ -""" - # Create numerical integrator settings integrator_settings = propagation_setup.integrator.runge_kutta_variable_step_size( initial_time_step=1.0, @@ -468,6 +446,7 @@ def get_last_run_dynamics_simulator(self): maximum_step_size=constants.JULIAN_DAY, relative_error_tolerance=1.0E-8, absolute_error_tolerance=1.0E-8) +""" # Get current propagator, and define translational state propagation settings propagator = propagation_setup.propagator.cowell @@ -483,7 +462,6 @@ def get_last_run_dynamics_simulator(self): propagator, dependent_variables_to_save) - ## Design Space Exploration """ @@ -520,7 +498,6 @@ def get_last_run_dynamics_simulator(self): constraint_values = np.zeros((no_of_runs, len(orbit_param_names))) parameters = np.zeros((no_of_runs, len(orbit_param_names))) - #### Monte Carlo loop """ @@ -615,7 +592,6 @@ def get_last_run_dynamics_simulator(self): 2: r' deg', 3: r' deg'} - obj_arrays = [mean_latitude_all_param, mean_distance_all_param] objective_names = ['Latitude', 'Distance'] for obj in range(2): #number of objectives @@ -634,19 +610,18 @@ def get_last_run_dynamics_simulator(self): #For more verbose results, remove the 'break' below. break - ### Fractional Factorial Design """ The Fractional Factorial Design (FFD) method has a number of pros and cons relative to the Monte Carlo method. The concept is based on orthogonality of a design matrix, with which you can extract information efficiently without running a ton of simulations. In other words, a selection of corners of the design space hypercube are explored. The advantage of the orthogonal array, based on Latin Squares, is that it is computationally very light, thereby of course sacrificing knowledge about your design space. The information per run is high with FFD. -""" +""" #### Orthogonal Array """ A function, `get_orthogonal_array()`, is used that calculates the orthogonal array depending on the number of levels (2 or 3) and number of factors (design variables) that any specific problem has. The algorithm is based on the Latin Square and the array is systematically built from there. The content of this array can sometimes be confusing, but it is quite straightforward; the rows represent experiments, the columns represent the factors, and the entries represent the discretized value of the factor — in the two-level case -1 becomes the minimum bound and 1 becomes the maximum bound. If you print the array that rolls out, you can get a feel for the structure of the method and the reason why it is efficient. -""" +""" #### Fractional Factorial Design Loop """ @@ -699,7 +674,6 @@ def get_last_run_dynamics_simulator(self): mean_dependent_variables_list[i, 0] = mean_distance mean_dependent_variables_list[i, 1] = mean_latitude - #### Post-processing FFD """ As not many runs are done, plotting any data is not sensible. An Analysis of Variance (ANOVA) can be done to determine percentage contributions of each parameter. For example, one would find that the eccentricity—one of the design variables—has a x% contribution to the distance objective. @@ -711,14 +685,14 @@ def get_last_run_dynamics_simulator(self): """ Factorial design (FD) is another systematic approach to exploring the design space. It can be very useful as far fewer assumptions are made about the results; FD is complete in that all corners—and potentially intermediate points—of the hypercube are tested. Whereas with FFD an orthogonal array was created with Latin Squares, here the array is built using Yates algorithm. Some information can be found [here](https://www.itl.nist.gov/div898/handbook/eda/section3/eda35i.htm). -""" +""" #### Yates Array """ The Yates array is similar to the orthogonal array in that it is orthogonal, and the rows, columns, and entries correspond to the same things (experiments, factors, and discretised values, respectively). The Yates array has significantly more rows, because it is complete, as mentioned before. -""" +""" #### FD loop """ @@ -785,7 +759,6 @@ def get_last_run_dynamics_simulator(self): mean_distances[i] = mean_distance mean_latitudes[i] = mean_latitude - #### Anova Analysis """ @@ -799,7 +772,6 @@ def get_last_run_dynamics_simulator(self): no_of_levels, level_of_interactions=2) - #### ANOVA Results """ In the tables below, the individual, linear, and quadratic contributions to the distance objective can be found in percentages, which follows from the anova_analysis function. NOTE: These results were made with the 2-level yates array, because the interaction columns are calculated with -1 and 1. This doesn't work with 7 levels. diff --git a/pygmo/asteroid_orbit_optimization/aoo_optimization.py b/pygmo/asteroid_orbit_optimization/aoo_optimization.py index 3cf94a1..d2c3e4a 100644 --- a/pygmo/asteroid_orbit_optimization/aoo_optimization.py +++ b/pygmo/asteroid_orbit_optimization/aoo_optimization.py @@ -1,14 +1,14 @@ # Asteroid orbit optimization with PyGMO - Optimization """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ This tutorial is the third part of the Asteroid Orbit Optimization example. **This page reuses the** [Custom environment](https://tudat-space.readthedocs.io/en/latest/_src_getting_started/_src_examples/notebooks/pygmo/asteroid_orbit_optimization/aoo_custom_environment.html) **part of the example, without the explanation, after which an optimization is executed.** -""" +""" ## Problem recap """ @@ -27,8 +27,8 @@ - good resolution (the mean value of the distance should be minimized). The constraints are set on the altitude: all the sets of design variables leading to an orbit. -""" +""" #### NOTE """ @@ -39,15 +39,14 @@ ## Import statements """ -""" - # Load standard modules import os import numpy as np -# Uncomment the following to make plots interactive -# %matplotlib widget +Uncomment the following to make plots interactive +%matplotlib widget from matplotlib import pyplot as plt from itertools import combinations as comb +""" # Load tudatpy modules @@ -66,7 +65,6 @@ current_dir = os.path.abspath('') - ## Creation of Custom Environment """ """ @@ -104,13 +102,10 @@ def get_itokawa_rotation_settings(itokawa_body_frame_name): return environment_setup.rotation_model.simple( "ECLIPJ2000", itokawa_body_frame_name, initial_orientation_eclipj2000, 0.0, rotation_rate) - ### Itokawa ephemeris settings """ -""" - def get_itokawa_ephemeris_settings(sun_gravitational_parameter): - # Define Itokawa initial Kepler elements + Define Itokawa initial Kepler elements itokawa_kepler_elements = np.array([ 1.324118017407799 * constants.ASTRONOMICAL_UNIT, 0.2801166461882852, @@ -119,31 +114,29 @@ def get_itokawa_ephemeris_settings(sun_gravitational_parameter): np.deg2rad(69.0803904880264), np.deg2rad(187.6327516838828)]) - # Convert mean anomaly to true anomaly + Convert mean anomaly to true anomaly itokawa_kepler_elements[5] = element_conversion.mean_to_true_anomaly( eccentricity=itokawa_kepler_elements[1], mean_anomaly=itokawa_kepler_elements[5]) - # Get epoch of initial Kepler elements (in Julian Days) + Get epoch of initial Kepler elements (in Julian Days) kepler_elements_reference_julian_day = 2459000.5 - # Sets new reference epoch for Itokawa ephemerides (different from J2000) + Sets new reference epoch for Itokawa ephemerides (different from J2000) kepler_elements_reference_epoch = (kepler_elements_reference_julian_day - constants.JULIAN_DAY_ON_J2000) \ * constants.JULIAN_DAY - # Sets the ephemeris model + Sets the ephemeris model return environment_setup.ephemeris.keplerian( itokawa_kepler_elements, kepler_elements_reference_epoch, sun_gravitational_parameter, "Sun", "ECLIPJ2000") - +""" ### Itokawa gravity field settings """ -""" - def get_itokawa_gravity_field_settings(itokawa_body_fixed_frame, itokawa_radius): itokawa_gravitational_parameter = 2.36 normalized_cosine_coefficients = np.array([ @@ -164,25 +157,25 @@ def get_itokawa_gravity_field_settings(itokawa_body_fixed_frame, itokawa_radius) normalized_cosine_coefficients=normalized_cosine_coefficients, normalized_sine_coefficients=normalized_sine_coefficients, associated_reference_frame=itokawa_body_fixed_frame) - +""" ### Itokawa shape settings """ -""" - def get_itokawa_shape_settings(itokawa_radius): - # Creates spherical shape settings + Creates spherical shape settings return environment_setup.shape.spherical(itokawa_radius) - +""" ### Simulation bodies """ +def create_simulation_bodies(itokawa_radius): """ -def create_simulation_bodies(itokawa_radius): - ### CELESTIAL BODIES ### - # Define Itokawa body frame name + ##CELESTIAL BODIES ### +""" + Define Itokawa body frame name itokawa_body_frame_name = "Itokawa_Frame" +""" # Create default body settings for selected celestial bodies bodies_to_create = ["Sun", "Earth", "Jupiter", "Saturn", "Mars"] @@ -218,7 +211,7 @@ def create_simulation_bodies(itokawa_radius): bodies.get("Spacecraft").set_constant_mass(400.0) # Create radiation pressure settings, and add to vehicle - reference_area_radiation = 4.0 + reference_area_radiation = (4*0.3*0.1+2*0.1*0.1)/4 # Average projection area of a 3U CubeSat radiation_pressure_coefficient = 1.2 radiation_pressure_settings = environment_setup.radiation_pressure.cannonball( "Sun", @@ -231,13 +224,10 @@ def create_simulation_bodies(itokawa_radius): return bodies - ### Acceleration models """ -""" - def get_acceleration_models(bodies_to_propagate, central_bodies, bodies): - # Define accelerations acting on Spacecraft + Define accelerations acting on Spacecraft accelerations_settings_spacecraft = dict( Sun = [ propagation_setup.acceleration.cannonball_radiation_pressure(), propagation_setup.acceleration.point_mass_gravity() ], @@ -247,6 +237,7 @@ def get_acceleration_models(bodies_to_propagate, central_bodies, bodies): Mars = [ propagation_setup.acceleration.point_mass_gravity() ], Earth = [ propagation_setup.acceleration.point_mass_gravity() ] ) +""" # Create global accelerations settings dictionary acceleration_settings = {"Spacecraft": accelerations_settings_spacecraft} @@ -258,34 +249,32 @@ def get_acceleration_models(bodies_to_propagate, central_bodies, bodies): bodies_to_propagate, central_bodies) - ### Termination settings """ -""" - def get_termination_settings(mission_initial_time, mission_duration, minimum_distance_from_com, maximum_distance_from_com): - # Mission duration + Mission duration time_termination_settings = propagation_setup.propagator.time_termination( mission_initial_time + mission_duration, terminate_exactly_on_final_condition=False ) - # Upper altitude + Upper altitude upper_altitude_termination_settings = propagation_setup.propagator.dependent_variable_termination( dependent_variable_settings=propagation_setup.dependent_variable.relative_distance('Spacecraft', 'Itokawa'), limit_value=maximum_distance_from_com, use_as_lower_limit=False, terminate_exactly_on_final_condition=False ) - # Lower altitude + Lower altitude lower_altitude_termination_settings = propagation_setup.propagator.dependent_variable_termination( dependent_variable_settings=propagation_setup.dependent_variable.altitude('Spacecraft', 'Itokawa'), limit_value=minimum_distance_from_com, use_as_lower_limit=True, terminate_exactly_on_final_condition=False ) +""" # Define list of termination settings termination_settings_list = [time_termination_settings, @@ -295,11 +284,8 @@ def get_termination_settings(mission_initial_time, return propagation_setup.propagator.hybrid_termination(termination_settings_list, fulfill_single_condition=True) - ### Dependent variables to save """ -""" - def get_dependent_variables_to_save(): dependent_variables_to_save = [ propagation_setup.dependent_variable.central_body_fixed_spherical_position( @@ -307,12 +293,10 @@ def get_dependent_variables_to_save(): ) ] return dependent_variables_to_save - +""" ## Optimisation problem formulation """ -""" - class AsteroidOrbitProblem: def __init__(self, @@ -323,20 +307,21 @@ def __init__(self, design_variable_lower_boundaries, design_variable_upper_boundaries): - # Sets input arguments as lambda function attributes - # NOTE: this is done so that the class is "pickable", i.e., can be serialized by pygmo + Sets input arguments as lambda function attributes + NOTE: this is done so that the class is "pickable", i.e., can be serialized by pygmo self.bodies_function = lambda: bodies self.propagator_settings_function = lambda: propagator_settings - # Initialize empty dynamics simulator + Initialize empty dynamics simulator self.dynamics_simulator_function = lambda: None - # Set other input arguments as regular attributes + Set other input arguments as regular attributes self.mission_initial_time = mission_initial_time self.mission_duration = mission_duration self.mission_final_time = mission_initial_time + mission_duration self.design_variable_lower_boundaries = design_variable_lower_boundaries self.design_variable_upper_boundaries = design_variable_upper_boundaries +""" def get_bounds(self): return (list(self.design_variable_lower_boundaries), list(self.design_variable_upper_boundaries)) @@ -399,7 +384,6 @@ def fitness(self, def get_last_run_dynamics_simulator(self): return self.dynamics_simulator_function() - ### Setup orbital simulation """ @@ -407,10 +391,9 @@ def get_last_run_dynamics_simulator(self): #### Simulation settings """ -""" - # Load spice kernels spice.load_standard_kernels() +""" # Set simulation start and end epochs mission_initial_time = 0.0 @@ -437,13 +420,11 @@ def get_last_run_dynamics_simulator(self): # Create acceleration models acceleration_models = get_acceleration_models(bodies_to_propagate, central_bodies, bodies) - #### Dependent variables, termination settings, and orbit parameters """ -""" - # Define list of dependent variables to save dependent_variables_to_save = get_dependent_variables_to_save() +""" # Create propagation settings termination_settings = get_termination_settings( @@ -451,11 +432,8 @@ def get_last_run_dynamics_simulator(self): orbit_parameters = [1.20940330e+03, 2.61526215e-01, 7.53126558e+01, 2.60280587e+02] - #### Integrator and Propagator settings """ -""" - # Create numerical integrator settings integrator_settings = propagation_setup.integrator.runge_kutta_variable_step_size( initial_time_step=1.0, @@ -464,6 +442,7 @@ def get_last_run_dynamics_simulator(self): maximum_step_size=constants.JULIAN_DAY, relative_error_tolerance=1.0E-8, absolute_error_tolerance=1.0E-8) +""" # Get current propagator, and define translational state propagation settings propagator = propagation_setup.propagator.cowell @@ -479,7 +458,6 @@ def get_last_run_dynamics_simulator(self): propagator, dependent_variables_to_save) - ## Optimisation run """ @@ -514,7 +492,6 @@ def get_last_run_dynamics_simulator(self): # Select Moead algorithm from pygmo, with one generation algo = pg.algorithm(pg.nsga2(gen=1, seed=fixed_seed)) - ### Initial population """ An initial population is now going to be generated by PyGMO, of a size of 48 individuals. This means that 48 orbital simulations will be run, and the fitness corresponding to the 48 individuals will be computed using the UDP. @@ -524,7 +501,6 @@ def get_last_run_dynamics_simulator(self): population_size = 48 pop = pg.population(prob, size=population_size, seed=fixed_seed) - ### Evolve population """ We now want to make this population evolve, as to (hopefully) get closer to optimum solutions. @@ -552,12 +528,11 @@ def get_last_run_dynamics_simulator(self): print("Evolving population is finished") - ### Results analysis """ With the population evolved, the optimization is finished. We can now analyse the results to see how our optimization was carried, and what our optimum solutions are. -""" +""" #### Extract results """ @@ -601,7 +576,6 @@ def get_last_run_dynamics_simulator(self): fitness_list[population_index], population_list[population_index]] - #### Pareto fronts """ As a first analysis of the optimization results, let's plot the Pareto fronts, to represent the optimums. @@ -632,7 +606,6 @@ def get_last_run_dynamics_simulator(self): 2: r' deg', 3: r' deg'} - # Loop over populations for population_index in simulation_output.keys(): @@ -678,7 +651,6 @@ def get_last_run_dynamics_simulator(self): plt.tight_layout() plt.show() - #### Design variables histogram """ Plotting the histogram of the design variables for the final generation gives insights into what set of orbital parameters lead to optimum solutions. Possible optimum design variables values can then be detected by looking at the number of population members that use them. A high number of occurences in the final generation **could** indicate a better design variable. At least, this offers some leads into what to investigate further. @@ -701,7 +673,6 @@ def get_last_run_dynamics_simulator(self): plt.tight_layout() plt.show() - #### Initial and final orbits visualisation """ One may now want to see how much better the optimized orbits are compared to the ones of the random initial population. This can be done by plotting the orbit bundles from the initial and final generations. @@ -744,7 +715,6 @@ def get_last_run_dynamics_simulator(self): plt.tight_layout() plt.show() - #### Orbits visualization by design variable """ Finally, we can visualize what range of design variables lead to which type of orbits. This is done by plotting the bundle of orbits for the last generation. @@ -809,4 +779,3 @@ def get_last_run_dynamics_simulator(self): # Show the figure plt.tight_layout() plt.show() - diff --git a/pygmo/himmelblau_minimization.py b/pygmo/himmelblau_minimization.py index 760acaa..5f0fbc0 100644 --- a/pygmo/himmelblau_minimization.py +++ b/pygmo/himmelblau_minimization.py @@ -1,8 +1,8 @@ # Himmelblau minimization with PyGMO """ Copyright (c) 2010-2022, Delft University of Technology. All rights reserved. This file is part of the Tudat. Redistribution and use in source and binary forms, with or without modification, are permitted exclusively under the terms of the Modified BSD license. You should have received a copy of the license with this file. If not, please or visit: http://tudat.tudelft.nl/LICENSE. -""" +""" ## Context """ @@ -40,7 +40,6 @@ # Load pygmo library import pygmo as pg - ## Create user-defined problem """ A PyGMO-compatible problem class is now defined. This is known in PyGMO terminology as a User-Defined Problem (UDP). @@ -78,7 +77,6 @@ def fitness(self, x): # Return list return [function_value] - ## Create problem """ With the custom problem class defined, we can now setup the optimisation. @@ -96,7 +94,6 @@ def fitness(self, x): # Print the problem's information print(prob) - ## Create algorithm """ As a second step, we have to create an algorithm to solve the problem. Many different algorithms are available through PyGMO, including heuristic methods and local optimizers. @@ -121,7 +118,6 @@ def fitness(self, x): # Print the algorithm's information print(algo) - ## Initialise population """ A population in PyGMO is essentially a container for multiple individuals. Each individual has an associated decision vector which can change (evolution), the resulting fitness vector, and an unique ID to allow their tracking. The population is initialized starting from a specific problem to ensure that all individuals are compatible with the UDP. The default population size is 0. @@ -138,7 +134,6 @@ def fitness(self, x): if inspect_pop: print(pop) - ## Evolve population """ We now want to make this population evolve, as to (hopefully) get closer to optimum solutions. @@ -168,12 +163,11 @@ def fitness(self, x): print('Number of function evaluations: ', pop.problem.get_fevals()) print('Difference wrt the minimum: ', pop.champion_x - np.array([3,2])) - ## Visualise optimisation """ We can now visualise how our optimisation was carried trough different ways. -""" +""" ### Fitness history """ @@ -210,7 +204,6 @@ def fitness(self, x): # Show the figure plt.show() - ### Himmelblau function """ Then, let's plot the Himmelblau function with the best individual of each generation. @@ -248,7 +241,6 @@ def fitness(self, x): # Show the plot plt.show() - ### Visualise vicinity of minimum """ Let's make the same plot as before, but zooming in on the optimum at (3, 2). @@ -288,7 +280,6 @@ def fitness(self, x): # Show the figure plt.show() - ## Grid search """ To investigate how well the Differential Evolution algorithm performed, let's now run the optimisation with a grid search of 1000x1000 nodes. @@ -320,7 +311,6 @@ def fitness(self, x): print('Number of function evaluations: ', number_of_nodes**2) print('Difference wrt the minimum: ', best_x_GS - np.array([3, 2])) - ## Monte Carlo search """ Finally, let's perform our optimisation again with a Monte Carlo search, also with 1000x1000 nodes. @@ -356,7 +346,3 @@ def fitness(self, x): print('Decision variable vector: ', best_x_MC) print('Number of function evaluations: ', number_of_points**2) print('Difference wrt the minimum: ', best_x_MC - np.array([3, 2])) - - - -