feat: ADR-024 Contrastive CSI Embedding Model — all 7 phases (#52)

Full implementation of Project AETHER — Contrastive CSI Embedding Model.

## Phases Delivered
1. ProjectionHead (64→128→128) + L2 normalization
2. CsiAugmenter (5 physically-motivated augmentations)
3. InfoNCE contrastive loss + SimCLR pretraining
4. FingerprintIndex (4 index types: env, activity, temporal, person)
5. RVF SEG_EMBED (0x0C) + CLI integration
6. Cross-modal alignment (PoseEncoder + InfoNCE)
7. Deep RuVector: MicroLoRA, EWC++, drift detection, hard-negative mining, SEG_LORA

## Stats
- 276 tests passing (191 lib + 51 bin + 16 rvf + 18 vitals)
- 3,342 additions across 8 files
- Zero unsafe/unwrap/panic/todo stubs
- ~55KB INT8 model for ESP32 edge deployment

Also fixes deprecated GitHub Actions (v3→v4) and adds feat/* branch CI triggers.

Closes #50

rust-port/wifi-densepose-rs/crates/wifi-densepose-train/Cargo.toml

@@ -2,10 +2,14 @@
 name = "wifi-densepose-train"
 version = "0.1.0"
 edition = "2021"
-authors = ["WiFi-DensePose Contributors"]
+authors = ["rUv <ruv@ruv.net>", "WiFi-DensePose Contributors"]
 license = "MIT OR Apache-2.0"
 description = "Training pipeline for WiFi-DensePose pose estimation"
+repository = "https://github.com/ruvnet/wifi-densepose"
+documentation = "https://docs.rs/wifi-densepose-train"
 keywords = ["wifi", "training", "pose-estimation", "deep-learning"]
+categories = ["science", "computer-vision"]
+readme = "README.md"
 
 [[bin]]
 name = "train"
@@ -23,8 +27,8 @@ cuda = ["tch-backend"]
 
 [dependencies]
 # Internal crates
-wifi-densepose-signal = { path = "../wifi-densepose-signal" }
-wifi-densepose-nn = { path = "../wifi-densepose-nn" }
+wifi-densepose-signal = { version = "0.1.0", path = "../wifi-densepose-signal" }
+wifi-densepose-nn = { version = "0.1.0", path = "../wifi-densepose-nn" }
 
 # Core
 thiserror.workspace = true