feat: Training mode, ADR docs, vitals and wifiscan crates

- Add --train CLI flag with dataset loading, graph transformer training,
  cosine-scheduled SGD, PCK/OKS validation, and checkpoint saving
- Refactor main.rs to import training modules from lib.rs instead of
  duplicating mod declarations
- Add ADR-021 (vital sign detection), ADR-022 (Windows WiFi enhanced
  fidelity), ADR-023 (trained DensePose pipeline) documentation
- Add wifi-densepose-vitals crate: breathing, heartrate, anomaly
  detection, preprocessor, and temporal store
- Add wifi-densepose-wifiscan crate: 8-stage signal intelligence
  pipeline with netsh/wlanapi adapters, multi-BSSID registry,
  attention weighting, spatial correlation, and breathing extraction

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

rust-port/wifi-densepose-rs/crates/wifi-densepose-sensing-server/src/graph_transformer.rs

@@ -745,4 +745,94 @@ mod tests {
         assert!((sum - 1.0).abs() < 1e-5);
         for &wi in &w3 { assert!(wi.is_finite()); }
     }
+
+    // ── Weight serialization integration tests ────────────────────────
+
+    #[test]
+    fn linear_flatten_unflatten_roundtrip() {
+        let lin = Linear::with_seed(8, 4, 42);
+        let mut flat = Vec::new();
+        lin.flatten_into(&mut flat);
+        assert_eq!(flat.len(), lin.param_count());
+        let (restored, consumed) = Linear::unflatten_from(&flat, 8, 4);
+        assert_eq!(consumed, flat.len());
+        let inp = vec![1.0f32; 8];
+        assert_eq!(lin.forward(&inp), restored.forward(&inp));
+    }
+
+    #[test]
+    fn cross_attention_flatten_unflatten_roundtrip() {
+        let ca = CrossAttention::new(16, 4);
+        let mut flat = Vec::new();
+        ca.flatten_into(&mut flat);
+        assert_eq!(flat.len(), ca.param_count());
+        let (restored, consumed) = CrossAttention::unflatten_from(&flat, 16, 4);
+        assert_eq!(consumed, flat.len());
+        let q = vec![vec![0.5f32; 16]; 3];
+        let k = vec![vec![0.3f32; 16]; 5];
+        let v = vec![vec![0.7f32; 16]; 5];
+        let orig = ca.forward(&q, &k, &v);
+        let rest = restored.forward(&q, &k, &v);
+        for (a, b) in orig.iter().zip(rest.iter()) {
+            for (x, y) in a.iter().zip(b.iter()) {
+                assert!((x - y).abs() < 1e-6, "mismatch: {x} vs {y}");
+            }
+        }
+    }
+
+    #[test]
+    fn transformer_weight_roundtrip() {
+        let config = TransformerConfig {
+            n_subcarriers: 16, n_keypoints: 17, d_model: 8, n_heads: 2, n_gnn_layers: 1,
+        };
+        let t = CsiToPoseTransformer::new(config.clone());
+        let weights = t.flatten_weights();
+        assert_eq!(weights.len(), t.param_count());
+
+        let mut t2 = CsiToPoseTransformer::new(config);
+        t2.unflatten_weights(&weights).expect("unflatten should succeed");
+
+        // Forward pass should produce identical results
+        let csi = vec![vec![0.5f32; 16]; 4];
+        let out1 = t.forward(&csi);
+        let out2 = t2.forward(&csi);
+        for (a, b) in out1.keypoints.iter().zip(out2.keypoints.iter()) {
+            assert!((a.0 - b.0).abs() < 1e-6);
+            assert!((a.1 - b.1).abs() < 1e-6);
+            assert!((a.2 - b.2).abs() < 1e-6);
+        }
+        for (a, b) in out1.confidences.iter().zip(out2.confidences.iter()) {
+            assert!((a - b).abs() < 1e-6);
+        }
+    }
+
+    #[test]
+    fn transformer_param_count_positive() {
+        let t = CsiToPoseTransformer::new(TransformerConfig::default());
+        assert!(t.param_count() > 1000, "expected many params, got {}", t.param_count());
+        let flat = t.flatten_weights();
+        assert_eq!(flat.len(), t.param_count());
+    }
+
+    #[test]
+    fn gnn_stack_flatten_unflatten() {
+        let bg = BodyGraph::new();
+        let gnn = GnnStack::new(8, 8, 2, &bg);
+        let mut flat = Vec::new();
+        gnn.flatten_into(&mut flat);
+        assert_eq!(flat.len(), gnn.param_count());
+
+        let mut gnn2 = GnnStack::new(8, 8, 2, &bg);
+        let consumed = gnn2.unflatten_from(&flat);
+        assert_eq!(consumed, flat.len());
+
+        let feats = vec![vec![1.0f32; 8]; 17];
+        let o1 = gnn.forward(&feats);
+        let o2 = gnn2.forward(&feats);
+        for (a, b) in o1.iter().zip(o2.iter()) {
+            for (x, y) in a.iter().zip(b.iter()) {
+                assert!((x - y).abs() < 1e-6);
+            }
+        }
+    }
 }