Simplify model: shared DINOv3 WEB + MONA in last 12/24 blocks

Three related architecture changes, driven by a cost/simplicity trade-off:

1. **Shared encoder**: one DINOv3 LVD-1689M (WEB) processes both drone
   and satellite images. Previously asymmetric — separate WEB (drone) and
   SAT-493M (satellite) encoders. Saves ~303M frozen params and halves
   VRAM for the image tower. Expected to lose some satellite-domain
   inductive bias; MONA adapters pick up the slack.

2. **MONA in last 12/24 blocks**: adapters injected only in the top half
   of the ViT. The lowest 12 blocks keep their pretrained features
   untouched. Trainable MONA count drops from 14.0M (48 adapters × 2
   encoders) to 3.5M (24 adapters × 1 encoder).

3. **No DINO_SAT**: `nn_models/DINO_SAT` is no longer loaded by the
   default config. It stays on disk and the path param is kept for
   backward compat with asymmetric checkpoints.

Parameter counts (with text fusion + LoRA + gates):
  Before: 17.6M trainable / 733M total (2.35%)
  After:   7.06M trainable / 434M total (1.63%)

Also fixes a pre-existing resume bug: checkpoints now record
`shared_encoder`, `baseline_mode`, `mona_bottleneck`, `mona_last_n_blocks`
so `AsymmetricEncoder.load_checkpoint` can rebuild the right architecture.
Old checkpoints still load (missing keys fall back to asymmetric defaults
via `ckpt.get(..., <default>)`).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/models/asymmetric_encoder.py

@@ -540,6 +540,8 @@ class AsymmetricEncoder(nn.Module):
             lrsclip_path=lrsclip_path,
             baseline_mode=ckpt.get("baseline_mode", False),
             shared_encoder=ckpt.get("shared_encoder", False),
+            mona_bottleneck=ckpt.get("mona_bottleneck", 64),
+            mona_last_n_blocks=ckpt.get("mona_last_n_blocks", 24),
             device=device,
         )
         model.load_state_dict(ckpt["model_state"], strict=False)