Add WiFi DensePose implementation and results

- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads.
- Added a comprehensive training utility for the model, including loss functions and training steps.
- Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.

.roo/rules-code/insert_content.md

@@ -0,0 +1,35 @@
+# Insert Content Guidelines
+
+## insert_content
+```xml
+<insert_content>
+  <path>File path here</path>
+  <operations>
+    [{"start_line":10,"content":"New code"}]
+  </operations>
+</insert_content>
+```
+
+### Required Parameters:
+- `path`: The file path to modify
+- `operations`: JSON array of insertion operations
+
+### Each Operation Must Include:
+- `start_line`: The line number where content should be inserted (REQUIRED)
+- `content`: The content to insert (REQUIRED)
+
+### Common Errors to Avoid:
+- Missing `start_line` parameter
+- Missing `content` parameter
+- Invalid JSON format in operations array
+- Using non-numeric values for start_line
+- Attempting to insert at line numbers beyond file length
+- Attempting to modify non-existent files
+
+### Best Practices:
+- Always verify the file exists before attempting to modify it
+- Check file length before specifying start_line
+- Use read_file first to confirm file content and structure
+- Ensure proper JSON formatting in the operations array
+- Use for adding new content rather than modifying existing content
+- Prefer for documentation additions and new code blocks