fix(train): wire wifi-densepose-signal into the pipeline; correct MODEL_CARD env-sensor claim (#536)

Addresses three findings from the 2026-05-11 training-pipeline audit:

#1/#2 — `wifi-densepose-signal` was a phantom dependency of `wifi-densepose-train`
(listed in Cargo.toml, never imported), and vitals/CSI signal features were
absent from the pipeline. New module `wifi_densepose_train::signal_features`:
`extract_signal_features(&Array4<f32>, &Array4<f32>) -> Array1<f32>` (and the
convenience method `CsiSample::signal_features()`) runs a windowed observation's
centre frame through `wifi_densepose_signal::features::FeatureExtractor`,
producing a fixed-length (FEATURE_LEN=12) amplitude / phase-coherence / PSD
feature vector — the hook for a future vitals / multi-task supervision head
(breathing- and heart-rate-band power are read off the PSD summary). The vector
is produced on demand and is not yet fed back into the loss; wiring it as a
training target is the documented follow-up. `wifi-densepose-signal` is now an
actually-used dependency. 5 new tests (2 unit in signal_features.rs, 3
integration in tests/test_dataset.rs); existing wifi-densepose-train tests
unchanged and green.

#3 — `docs/huggingface/MODEL_CARD.md` presented PIR/BME280 environmental-sensor
weak-label fine-tuning as a current capability; there is no env-sensor
ingestion in the training pipeline. Marked that path as planned/not-implemented
in the training-steps list and the data-provenance section.

(#5 — README's "92.9% PCK@20" overclaim — fixed separately in PR #535.)

CHANGELOG updated.

docs/huggingface/MODEL_CARD.md

@@ -168,14 +168,14 @@ The training process works like this:
 1. **Collect** raw CSI frames from ESP32-S3 nodes placed in a room
 2. **Extract** 8-dimensional feature vectors from sliding windows of CSI data
 3. **Contrast** -- the model learns that features from nearby time windows should produce similar embeddings, while features from different scenarios should produce different embeddings
-4. **Fine-tune** task heads using weak labels from environmental sensors (PIR motion, temperature, pressure) on the Cognitum Seed companion device
+4. **Fine-tune** task heads — *planned:* weak labels from environmental sensors (PIR motion, temperature, pressure) on the Cognitum Seed companion device. **This environmental-sensor ground-truth path is not yet implemented** (no PIR/BME280 ingestion in the training pipeline today); current task-head supervision uses the proxy/camera labels described elsewhere.
 
 ### Data provenance
 
 - **Source:** Live CSI from 2x ESP32-S3 nodes (802.11n, HT40, 114 subcarriers)
 - **Volume:** ~360,000 CSI frames (~3,600 feature vectors) per collection run
 - **Environment:** Residential room, ~4x5 meters
-- **Ground truth:** Environmental sensors on Cognitum Seed (PIR, BME280, light)
+- **Ground truth:** *Planned* — environmental sensors on the Cognitum Seed (PIR, BME280, light). Not yet wired into training; treat the PIR/BME280 references in this card as the intended design, not a current capability.
 - **Attestation:** Every collection run produces a cryptographic witness chain (`collection-witness.json`) that proves data provenance and integrity
 
 ### Witness chain
@@ -208,7 +208,7 @@ Add a second ESP32-S3 to enable cross-node signal fusion for better accuracy and
 | USB-C cables (x3) | Power + data | ~$9 |
 | **Total** | | **~$27** |
 
-The Cognitum Seed runs the ONNX models on-device, orchestrates the ESP32 nodes over USB serial, and provides environmental ground truth via its onboard PIR and BME280 sensors.
+The Cognitum Seed runs the ONNX models on-device and orchestrates the ESP32 nodes over USB serial. (Using its onboard PIR/BME280 sensors as training ground truth is planned but not yet implemented — see "Data provenance" above.)
 
 ---