fixed linking issues due to updated filenames

modules/med-about-customizing-pattern.adoc

@@ -2,17 +2,17 @@
 :imagesdir: ../../images
 
 [id="about-customizing-pattern-med"]
-= About customizing the pattern {med-pattern}
+= About customizing the {med-pattern}
 
 One of the major goals of the {solution-name-upstream} development process is to create modular and customizable demos. The {med-pattern} is just an example of how AI/ML workloads built for object detection and classification can be run on OpenShift clusters. Consider your workloads for a moment - how would your workload best consume the pattern framework? Do your consumers require on-demand or near real-time responses when using your application? Is your application processing images or data that is protected by either Government Privacy Laws or HIPAA?
 The {med-pattern} can answer the call to either of these requirements by using {serverless-short} and {ocp-data-short}.
 
 [id="understanding-different-ways-to-use-med-pattern"]
 == Understanding different ways to use the {med-pattern}
 
-. The {med-pattern} is scanning X-Ray images to determine the probability that a patient might or might not have Pneumonia. Continuing with the medical path, the pattern could be used for other early detection scenarios that use object detection and classification. For example, the pattern could be used to scan C/T images for anomalies in the body such as Sepsis, Cancer, or even benign tumors. Additionally, the pattern could be used for detecting blood clots, some heart disease, and bowel disorders like Crohn's disease.
-. The Transportation Security Agency (TSA) could use the {med-pattern} in a way that enhances their existing scanning capabilities to detect with a higher probability restricted items carried on a person or hidden away in a piece of luggage. With Machine Learning Operations (MLOps), the model is constantly training and learning to better detect those items that are dangerous but which are not necessarily metallic, such as a firearm or a knife. The model is also training to dismiss those items that are authorized; ultimately saving passengers from being stopped and searched at security checkpoints.
-. Militaries could use images collected from drones, satellites, or other platforms to identify objects and determine with probability what that object is. For example, the model could be trained to determine a type of ship, potentially its country of origin, and other such identifying characteristics.
-. Manufacturing companies could use the pattern to inspect finished products as they roll off a production line. An image of the item, including using different types of light, could be analyzed to help expose defects before packaging and distributing. The item could be routed to a defect area.
+* The {med-pattern} is scanning X-Ray images to determine the probability that a patient might or might not have Pneumonia. Continuing with the medical path, the pattern could be used for other early detection scenarios that use object detection and classification. For example, the pattern could be used to scan C/T images for anomalies in the body such as Sepsis, Cancer, or even benign tumors. Additionally, the pattern could be used for detecting blood clots, some heart disease, and bowel disorders like Crohn's disease.
+* The Transportation Security Agency (TSA) could use the {med-pattern} in a way that enhances their existing scanning capabilities to detect with a higher probability restricted items carried on a person or hidden away in a piece of luggage. With Machine Learning Operations (MLOps), the model is constantly training and learning to better detect those items that are dangerous but which are not necessarily metallic, such as a firearm or a knife. The model is also training to dismiss those items that are authorized; ultimately saving passengers from being stopped and searched at security checkpoints.
+* Militaries could use images collected from drones, satellites, or other platforms to identify objects and determine with probability what that object is. For example, the model could be trained to determine a type of ship, potentially its country of origin, and other such identifying characteristics.
+* Manufacturing companies could use the pattern to inspect finished products as they roll off a production line. An image of the item, including using different types of light, could be analyzed to help expose defects before packaging and distributing. The item could be routed to a defect area.
 
 These are just a few ideas to help you understand how you could use the {med-pattern} as a framework for your application.

modules/med-about-medical-diagnosis.adoc

@@ -43,4 +43,4 @@ The {med-pattern} uses the following products and technologies:
 * {rh-serverless-first} for event-driven applications
 * {rh-ocp-data-first} for cloud native storage capabilities
 * {grafana-op} to manage and share Grafana dashboards, data sources, and so on
-* S3 storage
+* Storage, such as AWS S3 buckets

modules/med-ocp-cluster-sizing.adoc

@@ -2,7 +2,7 @@
 :imagesdir: ../../images
 
 [id="med-openshift-cluster-size"]
-== About {med-pattern} OpenShift cluster size
+== About OpenShift cluster size for the {med-pattern}
 
 The {med-pattern} has been tested with a defined set of configurations that represent the most common combinations that {ocp} customers are using for the x86_64 architecture.
 

modules/med-setup-aws-s3-bucket-with-utilities.adoc

@@ -9,21 +9,21 @@ To use the link:https://github.com/validatedpatterns/utilities/tree/main/aws-too
 .Procedure
 
 . Export the following environment variables for AWS. Ensure that you replace the values with your keys:
-
++
 [source,terminal]
 ----
 export AWS_ACCESS_KEY_ID=AKXXXXXXXXXXXXX
 export AWS_SECRET_ACCESS_KEY=gkXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
 ----
 
 . Create the S3 bucket and copy over the data from the {solution-name-upstream} public bucket to the created bucket for your demo. You can do this on the cloud providers console or you can use the scripts that are provided in link:https://github.com/validatedpatterns/utilities[utilities] repository:
-
++
 [source,terminal]
 ----
 $ python s3-create.py -b mytest-bucket -r us-west-2 -p
 $ python s3-sync-buckets.py -s validated-patterns-md-xray -t mytest-bucket -r us-west-2
 ----
-
++
 .Example output
 
 image:/videos/bucket-setup.svg[Bucket setup]

modules/med-troubleshooting-deployment.adoc

@@ -1,8 +1,8 @@
 :_content-type: REFERENCE
-:imagesdir: ../../images
+:imagesdir: ../../../images
 
 [id="troubleshooting-the-pattern-deployment-troubleshooting"]
-=== Troubleshooting the Pattern Deployment
+=== Troubleshooting the pattern deployment
 
 Occasionally the pattern will encounter issues during the deployment. This can happen for any number of reasons, but most often it is because of either a change within the operator itself or something has changed in the {olm-first} which determines which operators are available in the operator catalog. Generally, when an issue occurs with the {olm-short}, the operator is unavailable for installation. To ensure that the operator is in the catalog, run the following command: