diff --git a/femmt/optimization/sto.py b/femmt/optimization/sto.py
index 3c846d56..b8ddcd10 100644
--- a/femmt/optimization/sto.py
+++ b/femmt/optimization/sto.py
@@ -116,6 +116,11 @@ def objective(trial, config: StoSingleInputConfig,
         :type show_geometries: bool
         :param process_number: process number. Important for parallel computing, each simulation works in a separate folder
         :type process_number: int
+        :return: returns volume, loss and absolute error of inductances in %
+            if number_objectives == 3:
+                total_volume, total_loss, 100 * (abs(difference_l_h / config.l_h_target) + abs(difference_l_s12 / config.l_s12_target))
+            elif number_objectives == 4:
+                return total_volume, total_loss, 100 * abs(difference_l_h), 100 * abs(difference_l_s12)
         """
         # suggest core geometry
         core_inner_diameter = trial.suggest_float("core_inner_diameter", config.core_inner_diameter_min_max_list[0], config.core_inner_diameter_min_max_list[1])
@@ -282,10 +287,10 @@ def objective(trial, config: StoSingleInputConfig,
             # norm_total_loss, norm_difference_l_h, norm_difference_l_s12 = total_loss/10, abs(difference_l_h/config.l_h_target), abs(difference_l_s12/config.l_s12_target)
             # return norm_total_loss, norm_difference_l_h, norm_difference_l_s12
             if number_objectives == 3:
-                return total_volume, total_loss, abs(difference_l_h / config.l_h_target) + abs(
-                    difference_l_s12 / config.l_s12_target)
+                return total_volume, total_loss, 100 * (abs(difference_l_h / config.l_h_target) + abs(
+                    difference_l_s12 / config.l_s12_target))
             elif number_objectives == 4:
-                return total_volume, total_loss, abs(difference_l_h), abs(difference_l_s12)
+                return total_volume, total_loss, 100 * abs(difference_l_h), 100 * abs(difference_l_s12)
 
         except Exception as e:
             print(e)
@@ -524,7 +529,7 @@ def show_study_results(study_name: str, config: StoSingleInputConfig,
 
     @staticmethod
     def show_study_results3(study_name: str, config: StoSingleInputConfig,
-                            error_difference_inductance_sum, storage: str = 'sqlite') -> None:
+                            error_difference_inductance_sum_percent, storage: str = 'sqlite') -> None:
         """
         Show the results of a study.
 
@@ -534,8 +539,8 @@ def show_study_results3(study_name: str, config: StoSingleInputConfig,
         :type study_name: str
         :param config: Integrated transformer configuration file
         :type config: ItoSingleInputConfig
-        :param error_difference_inductance_sum: e.g. 0.05 for 5%
-        :type error_difference_inductance_sum: float
+        :param error_difference_inductance_sum_percent: |err(L_s12) + err(L_h)| in %
+        :type error_difference_inductance_sum_percent: float
         :param storage: storage, e.g. 'sqlite' or path to postgresql-database
         :type storage: str
 
@@ -552,16 +557,16 @@ def show_study_results3(study_name: str, config: StoSingleInputConfig,
         time_stop = datetime.datetime.now()
         print(f"Finished loading study {study_name} from database in time: {time_stop - time_start}")
 
-        print(f"{error_difference_inductance_sum = }")
+        print(f"{error_difference_inductance_sum_percent = }")
 
         time_start = datetime.datetime.now()
         print(f"start generating Pareto front....")
-        fig = optuna.visualization.plot_pareto_front(study, targets=lambda t: (t.values[0] if error_difference_inductance_sum > t.values[2] else None, t.values[1] if error_difference_inductance_sum > t.values[2] else None), target_names=["volume in m³", "loss in W"])
-        fig.update_layout(title=f"{study_name}: Filtering {error_difference_inductance_sum * 100} % of |err(Ls_12)| + |err(L_h)|")
+        fig = optuna.visualization.plot_pareto_front(study, targets=lambda t: (t.values[0] if error_difference_inductance_sum_percent > t.values[2] else None, t.values[1] if error_difference_inductance_sum_percent > t.values[2] else None), target_names=["volume in m³", "loss in W"])
+        fig.update_layout(title=f"{study_name}: Filtering {error_difference_inductance_sum_percent} % of |err(Ls_12)| + |err(L_h)|")
         time_stop = datetime.datetime.now()
         print(f"Finished generating Pareto front in time: {time_stop - time_start}")
 
-        fig.write_html(f"{config.working_directory}/{study_name}_error_diff_{error_difference_inductance_sum}_{datetime.datetime.now().isoformat(timespec='minutes')}.html")
+        fig.write_html(f"{config.working_directory}/{study_name}_error_diff_{error_difference_inductance_sum_percent}%_{datetime.datetime.now().isoformat(timespec='minutes')}.html")
         fig.show()
 
     @staticmethod
@@ -906,18 +911,18 @@ def thermal_simulation_from_geo(geo, thermal_config: ThermalConfig, flag_insulat
                                colors_geometry=femmt.colors_geometry_ba_jonas, flag_insulation=flag_insulation)
 
     @staticmethod
-    def df_plot_pareto_front(df: pd.DataFrame, sum_inductance_error: float):
+    def df_plot_pareto_front(df: pd.DataFrame, sum_inductance_error_percent: float):
         """
         Plots an interactive Pareto diagram (losses vs. volume) to select the transformers to re-simulate.
 
         :param df: Dataframe, generated from an optuna study (exported by optuna)
         :type df: pd.Dataframe
-        :param sum_inductance_error: maximum allowed error of |error(L_s12)| + |error(L_h)|
-        :type sum_inductance_error: float
+        :param sum_inductance_error_percent: maximum allowed error of |error(L_s12)| + |error(L_h)| in %
+        :type sum_inductance_error_percent: float
         """
 
         print(df.head())
-        df_pareto = df[df["values_2"] < sum_inductance_error]
+        df_pareto = df[df["values_2"] < sum_inductance_error_percent]
 
         names = df_pareto["number"].to_numpy()
         fig, ax = plt.subplots()
@@ -952,7 +957,7 @@ def hover(event):
 
         plt.xlabel('Volume in m³')
         plt.ylabel('Losses in W')
-        plt.title(f"|error(L_s12)| +  |error(L_h)| < {sum_inductance_error} %")
+        plt.title(f"|error(L_s12)| +  |error(L_h)| < {sum_inductance_error_percent} %")
         plt.grid()
         plt.show()
 
diff --git a/tests/integration/fixtures/results/thermal_transformer_5_windings.json b/tests/integration/fixtures/results/thermal_transformer_5_windings.json
index af7edf09..4c643c0f 100644
--- a/tests/integration/fixtures/results/thermal_transformer_5_windings.json
+++ b/tests/integration/fixtures/results/thermal_transformer_5_windings.json
@@ -1,210 +1,215 @@
 {
   "core_parts": {
     "core_part_1": {
-      "min": 22.92825004550824,
-      "max": 43.27512987849097,
-      "mean": 34.60804139121339
+      "min": 22.95433725837616,
+      "max": 43.89662651821714,
+      "mean": 34.881033758318985
     },
     "total": {
-      "min": 22.92825004550824,
-      "max": 43.27512987849097,
-      "mean": 34.60804139121339
+      "min": 22.95433725837616,
+      "max": 43.89662651821714,
+      "mean": 34.881033758318985
     }
   },
   "windings": {
     "winding_0_0": {
-      "min": 2464.622987622317,
-      "max": 2464.625267910403,
-      "mean": 2464.624166582381
+      "min": 32.46969088518171,
+      "max": 32.47228956383554,
+      "mean": 32.471278101733795
     },
     "winding_0_1": {
-      "min": 2470.371956026039,
-      "max": 2470.374532744722,
-      "mean": 2470.373158084463
+      "min": 36.5775538369669,
+      "max": 36.57901602206849,
+      "mean": 36.57834066944631
     },
     "winding_0_2": {
-      "min": 2477.248374770164,
-      "max": 2477.251139602545,
-      "mean": 2477.2497544339285
+      "min": 39.51619974051405,
+      "max": 39.51731053236967,
+      "mean": 39.516780843014345
     },
     "winding_0_3": {
-      "min": 2483.905606963324,
-      "max": 2483.908037920709,
-      "mean": 2483.906888560786
+      "min": 41.74011979248609,
+      "max": 41.74135137029185,
+      "mean": 41.74079942493729
     },
     "winding_0_4": {
-      "min": 2489.218620076731,
-      "max": 2489.220336221918,
-      "mean": 2489.219589118021
+      "min": 43.43350684920263,
+      "max": 43.43496031767983,
+      "mean": 43.43431283987183
     },
     "winding_0_5": {
-      "min": 2492.39300449571,
-      "max": 2492.393823717064,
-      "mean": 2492.393522147772
+      "min": 44.68179623459834,
+      "max": 44.68337132301919,
+      "mean": 44.68268021154097
     },
     "winding_0_6": {
-      "min": 2492.691452686723,
-      "max": 2492.692445454184,
-      "mean": 2492.6920301767764
+      "min": 45.48009496592619,
+      "max": 45.48170770433286,
+      "mean": 45.48100554030618
     },
     "winding_0_7": {
-      "min": 2488.976319010141,
-      "max": 2488.978470883644,
-      "mean": 2488.9775431427506
+      "min": 45.63856152963693,
+      "max": 45.63962312265074,
+      "mean": 45.63925092352275
     },
     "winding_0_8": {
-      "min": 2482.223762850147,
-      "max": 2482.227091591252,
-      "mean": 2482.225566952801
+      "min": 45.75380277290472,
+      "max": 45.75485856688088,
+      "mean": 45.754462591788766
     },
     "winding_0_9": {
-      "min": 2472.741819118157,
-      "max": 2472.746150525667,
-      "mean": 2472.7440990433806
+      "min": 45.61736126311623,
+      "max": 45.61837180835362,
+      "mean": 45.6179734743146
     },
     "winding_0_10": {
-      "min": 2461.001969326565,
-      "max": 2461.007084814581,
-      "mean": 2461.0046119740814
+      "min": 45.2328782987868,
+      "max": 45.23378069012698,
+      "mean": 45.23341331272183
     },
     "winding_0_11": {
-      "min": 2447.702835199451,
-      "max": 2447.708361365698,
-      "mean": 2447.7056138058474
+      "min": 44.55687818695807,
+      "max": 44.55764094993858,
+      "mean": 44.557314793118834
     },
     "winding_0_12": {
-      "min": 2433.963551025305,
-      "max": 2433.96898851516,
-      "mean": 2433.966194290475
+      "min": 43.53204889650706,
+      "max": 43.53263648297864,
+      "mean": 43.53235522896999
     },
     "winding_0_13": {
-      "min": 2421.411361748649,
-      "max": 2421.415935241971,
-      "mean": 2421.4134703125815
+      "min": 42.12037292746778,
+      "max": 42.12107785647889,
+      "mean": 42.12074040030956
     },
     "winding_0_14": {
-      "min": 2411.658586503392,
-      "max": 2411.661596644385,
-      "mean": 2411.6597593717233
+      "min": 40.24862692846933,
+      "max": 40.24946910894522,
+      "mean": 40.24904215657611
     },
     "winding_0_15": {
-      "min": 2462.589952915303,
-      "max": 2462.592362272446,
-      "mean": 2462.591343167411
+      "min": 31.33131603269113,
+      "max": 31.3342286105505,
+      "mean": 31.332980326017747
     },
     "winding_0_16": {
-      "min": 2468.524174438603,
-      "max": 2468.527140408961,
-      "mean": 2468.5255320946685
+      "min": 36.97510546533365,
+      "max": 36.97711201957079,
+      "mean": 36.97618895530999
     },
     "winding_0_17": {
-      "min": 2476.667320604713,
-      "max": 2476.670672492523,
-      "mean": 2476.6690209640046
+      "min": 41.10981353812058,
+      "max": 41.11131874633569,
+      "mean": 41.11060441036244
     },
     "winding_0_18": {
-      "min": 2484.309530469007,
-      "max": 2484.312419414024,
-      "mean": 2484.311058369641
+      "min": 44.08917239724664,
+      "max": 44.09029784074088,
+      "mean": 44.08975083369402
     },
     "winding_0_19": {
-      "min": 2490.251716424458,
-      "max": 2490.253713351461,
-      "mean": 2490.252824340566
+      "min": 46.22191675862916,
+      "max": 46.22290486519357,
+      "mean": 46.222444184047326
     },
     "winding_0_20": {
-      "min": 2493.919383707341,
-      "max": 2493.920496235528,
-      "mean": 2493.9200028737614
+      "min": 47.68187375095892,
+      "max": 47.68283899879056,
+      "mean": 47.68240871473582
     },
     "winding_0_21": {
-      "min": 2494.9620626245,
-      "max": 2494.963101824575,
-      "mean": 2494.9625286071987
+      "min": 48.50807546332715,
+      "max": 48.5090644104509,
+      "mean": 48.50861849512869
     },
     "winding_1_0": {
-      "min": 2449.331542327825,
-      "max": 2449.340423543304,
-      "mean": 2449.3372504112526
+      "min": 31.7560262724195,
+      "max": 31.76085077765494,
+      "mean": 31.758764977802375
     },
     "winding_1_1": {
-      "min": 2463.198764014782,
-      "max": 2463.206425958142,
-      "mean": 2463.2024048091594
+      "min": 39.3761325820728,
+      "max": 39.37935821642702,
+      "mean": 39.377740103981374
     },
     "winding_1_2": {
-      "min": 2479.628037600093,
-      "max": 2479.634536202971,
-      "mean": 2479.631518469704
+      "min": 45.26577009223739,
+      "max": 45.26795501715243,
+      "mean": 45.26689774417915
     },
     "winding_1_3": {
-      "min": 2491.016577494271,
-      "max": 2491.020736376096,
-      "mean": 2491.0188580729964
+      "min": 48.79634625041055,
+      "max": 48.79762282455627,
+      "mean": 48.796968166708666
     },
     "winding_1_4": {
-      "min": 2497.55133211461,
-      "max": 2497.553355853142,
-      "mean": 2497.552520478547
+      "min": 50.73675682742959,
+      "max": 50.73766944432063,
+      "mean": 50.73730938292935
     },
     "winding_1_5": {
-      "min": 2499.637235344508,
-      "max": 2499.637680009987,
-      "mean": 2499.637530356642
+      "min": 51.39153990347307,
+      "max": 51.39233900227744,
+      "mean": 51.39214310201355
     },
     "winding_2_0": {
-      "min": 2492.744284306458,
-      "max": 2492.745644665979,
-      "mean": 2492.7451399813813
+      "min": 55.61754251676955,
+      "max": 55.61838609459338,
+      "mean": 55.618034926590575
     },
     "winding_2_1": {
-      "min": 2486.653296707275,
-      "max": 2486.656380397912,
-      "mean": 2486.6549576485745
+      "min": 56.6392961701875,
+      "max": 56.63990987555569,
+      "mean": 56.63974287868686
     },
     "winding_2_2": {
-      "min": 2476.359005538744,
-      "max": 2476.363658407392,
-      "mean": 2476.3614666315134
+      "min": 56.60291683313284,
+      "max": 56.60352999026215,
+      "mean": 56.60335849232446
     },
     "winding_2_3": {
-      "min": 2462.028992409631,
-      "max": 2462.035177366829,
-      "mean": 2462.0321954857895
+      "min": 55.54399805170948,
+      "max": 55.54479542365297,
+      "mean": 55.54444303952271
     },
     "winding_2_4": {
-      "min": 2443.548306836435,
-      "max": 2443.556103820271,
-      "mean": 2443.5523283104512
+      "min": 53.28001078696334,
+      "max": 53.2811854459856,
+      "mean": 53.28059655397327
     },
     "winding_2_5": {
-      "min": 2420.648276806302,
-      "max": 2420.658056319677,
-      "mean": 2420.653384911554
+      "min": 49.3243606000016,
+      "max": 49.3264268971334,
+      "mean": 49.32547154152814
     },
     "winding_3_0": {
-      "min": 2396.816412593339,
-      "max": 2396.827067049417,
-      "mean": 2396.8212290000383
+      "min": 43.45287651978335,
+      "max": 43.45500318562692,
+      "mean": 43.453681749016724
     },
     "winding_4_0": {
-      "min": 2413.164399948718,
-      "max": 2413.180792950681,
-      "mean": 2413.1742922436774
+      "min": 27.40032878084305,
+      "max": 27.4019323331449,
+      "mean": 27.401238193999237
     },
     "winding_4_1": {
-      "min": 2436.567908962225,
-      "max": 2436.575005521358,
-      "mean": 2436.571856769563
+      "min": 30.23908770222705,
+      "max": 30.24077981904784,
+      "mean": 30.2397760835027
     },
     "total": {
-      "min": 2396.816412593339,
-      "max": 2499.637680009987,
-      "mean": 2466.765816540429
+      "min": 27.40032878084305,
+      "max": 56.63990987555569,
+      "mean": 43.99915982076293
     }
   },
   "misc": {
     "case_volume": 0.00010625000000000001,
     "case_weight": -1
+  },
+  "insulations": {
+    "min": 23.74415143054456,
+    "max": 45.17151765974866,
+    "mean": 36.693880802270165
   }
 }
\ No newline at end of file
diff --git a/tests/integration/test_femmt.py b/tests/integration/test_femmt.py
index c427fd37..b4d00d34 100644
--- a/tests/integration/test_femmt.py
+++ b/tests/integration/test_femmt.py
@@ -1287,9 +1287,6 @@ def femmt_simulation_transformer_5_windings(temp_folder):
         # Set onelab path manually
         geo.file_data.onelab_folder_path = onelab_folder
 
-        # This line is for automated pytest running on github only. Please ignore this line!
-        if onelab_folder is not None: geo.file_data.onelab_folder_path = onelab_folder
-
         # 2. set core parameters
         core_dimensions = fmt.dtos.SingleCoreDimensions(window_h=16.1e-3, window_w=(22.5 - 12) / 2 * 1e-3,
                                                         core_inner_diameter=12e-3, core_h=22e-3)
@@ -1416,119 +1413,7 @@ def femmt_simulation_transformer_5_windings(temp_folder):
                                case_gap_right, case_gap_bot, show_thermal_simulation_results=False,
                                color_scheme=fmt.colors_ba_jonas,
                                colors_geometry=fmt.colors_geometry_ba_jonas,
-                               flag_insulation=False)
-
-    except Exception as e:
-        print("An error occurred while creating the femmt mesh files:", e)
-    except KeyboardInterrupt:
-        print("Keyboard interrupt..")
-
-    electromagnetoquasistatic_result = os.path.join(temp_folder_path, "results", "log_electro_magnetic.json")
-    thermal_result = os.path.join(temp_folder_path, "results", "results_thermal.json")
-
-    return electromagnetoquasistatic_result, thermal_result
-
-
-@pytest.fixture
-def thermal_simulation(temp_folder):
-    temp_folder_path, onelab_folder = temp_folder
-
-    # Create new temp folder, build model and simulate
-    try:
-        working_directory = temp_folder_path
-        if not os.path.exists(working_directory):
-            os.mkdir(working_directory)
-
-        geo = fmt.MagneticComponent(component_type=fmt.ComponentType.Inductor,
-                                    working_directory=working_directory, verbosity=fmt.Verbosity.Silent, is_gui=True)
-
-        # Set onelab path manually
-        geo.file_data.onelab_folder_path = onelab_folder
-
-        core_db = fmt.core_database()["PQ 40/40"]
-        core_dimensions = fmt.dtos.SingleCoreDimensions(core_inner_diameter=core_db["core_inner_diameter"],
-                                                        window_w=core_db["window_w"],
-                                                        window_h=core_db["window_h"],
-                                                        core_h=core_db["core_h"])
-        core = fmt.Core(core_type=fmt.CoreType.Single, core_dimensions=core_dimensions,
-                        mu_r_abs=3100, phi_mu_deg=12,
-                        sigma=0.,
-                        non_linear=False,
-                        permeability_datasource=fmt.MaterialDataSource.Custom,
-                        permittivity_datasource=fmt.MaterialDataSource.Custom
-                        )
-        geo.set_core(core)
-
-        air_gaps = fmt.AirGaps(fmt.AirGapMethod.Center, core)
-        air_gaps.add_air_gap(fmt.AirGapLegPosition.CenterLeg, 0.0005)
-        geo.set_air_gaps(air_gaps)
-
-        insulation = fmt.Insulation()
-        insulation.add_core_insulations(0.001, 0.001, 0.002, 0.001)
-        insulation.add_winding_insulations([[0.0001]])
-        geo.set_insulation(insulation)
-
-        winding_window = fmt.WindingWindow(core, insulation)
-        vww = winding_window.split_window(fmt.WindingWindowSplit.NoSplit)
-
-        winding = fmt.Conductor(0, fmt.Conductivity.Copper, winding_material_temperature=25)
-        winding.set_solid_round_conductor(conductor_radius=0.0015,
-                                          conductor_arrangement=fmt.ConductorArrangement.Square)
-
-        vww.set_winding(winding, 8, None)
-        geo.set_winding_windows([winding_window])
-
-        geo.create_model(freq=100000, pre_visualize_geometry=False, save_png=False)
-        geo.single_simulation(freq=100000, current=[3], show_fem_simulation_results=False)
-
-        thermal_conductivity_dict = {
-            "air": 1.57,  # potting epoxy resign
-            "case": {
-                "top": 1.57,
-                "top_right": 1.57,
-                "right": 1.57,
-                "bot_right": 1.57,
-                "bot": 1.57
-            },
-            "core": 5,  # ferrite
-            "winding": 400,  # copper
-            "air_gaps": 1.57,
-            "insulation": 1.57
-        }
-
-        case_gap_top = 0.002
-        case_gap_right = 0.0025
-        case_gap_bot = 0.002
-
-        boundary_temperatures = {
-            "value_boundary_top": 20,
-            "value_boundary_top_right": 20,
-            "value_boundary_right_top": 20,
-            "value_boundary_right": 20,
-            "value_boundary_right_bottom": 20,
-            "value_boundary_bottom_right": 20,
-            "value_boundary_bottom": 20
-        }
-
-        boundary_flags = {
-            "flag_boundary_top": 1,
-            "flag_boundary_top_right": 1,
-            "flag_boundary_right_top": 1,
-            "flag_boundary_right": 1,
-            "flag_boundary_right_bottom": 1,
-            "flag_boundary_bottom_right": 1,
-            "flag_boundary_bottom": 1
-        }
-
-        # color_scheme = fmt.colors_ba_jonas
-        # colors_geometry = fmt.colors_geometry_ba_jonas
-        color_scheme = fmt.colors_ba_jonas
-        colors_geometry = fmt.colors_geometry_draw_only_lines
-
-        geo.thermal_simulation(thermal_conductivity_dict, boundary_temperatures, boundary_flags, case_gap_top,
-                               case_gap_right,
-                               case_gap_bot, show_thermal_simulation_results=False, pre_visualize_geometry=False, color_scheme=color_scheme,
-                               colors_geometry=colors_geometry)
+                               flag_insulation=True)
 
     except Exception as e:
         print("An error occurred while creating the femmt mesh files:", e)
@@ -1726,7 +1611,7 @@ def test_simulation_transformer_5_windings(femmt_simulation_transformer_5_windin
     # check thermal simulation results
     # assert os.path.exists(thermal_result_log), "Thermal simulation did not work!"
     # fixture_result_log = os.path.join(os.path.dirname(__file__), "fixtures", "results", "thermal_transformer_5_windings.json")
-    # compare_thermal_result_logs(thermal_result_log, fixture_result_log, significant_digits=3)
+    # compare_thermal_result_logs(thermal_result_log, fixture_result_log, significant_digits=2)
 
 
 def test_load_files(temp_folder, femmt_simulation_inductor_core_material_database,