feat: Sensing-only UI mode with Gaussian splat visualization and Rust migration ADR

- Add Python WebSocket sensing server (ws_server.py) with ESP32 UDP CSI
  and Windows RSSI auto-detect collectors on port 8765
- Add Three.js Gaussian splat renderer with custom GLSL shaders for
  real-time WiFi signal field visualization (blue→green→red gradient)
- Add SensingTab component with RSSI sparkline, feature meters, and
  motion classification badge
- Add sensing.service.js WebSocket client with reconnect and simulation fallback
- Implement sensing-only mode: suppress all DensePose API calls when
  FastAPI backend (port 8000) is not running, clean console output
- ADR-019: Document sensing-only UI architecture and data flow
- ADR-020: Migrate AI/model inference to Rust with RuVector ONNX Runtime,
  replacing ~2.7GB Python stack with ~50MB static binary
- Add ruvnet/ruvector as upstream remote for RuVector crate ecosystem

Co-Authored-By: claude-flow <ruv@ruv.net>

ui/index.html

@@ -27,6 +27,7 @@
             <button class="nav-tab" data-tab="architecture">Architecture</button>
             <button class="nav-tab" data-tab="performance">Performance</button>
             <button class="nav-tab" data-tab="applications">Applications</button>
+            <button class="nav-tab" data-tab="sensing">Sensing</button>
         </nav>
 
         <!-- Dashboard Tab -->
@@ -478,6 +479,9 @@
                 <p>While WiFi DensePose offers revolutionary capabilities, successful implementation requires careful consideration of environment setup, data privacy regulations, and system calibration for optimal performance.</p>
             </div>
         </section>
+
+        <!-- Sensing Tab -->
+        <section id="sensing" class="tab-content"></section>
     </div>
 
     <!-- Error Toast -->