Piece 3 of the native-JNA DNN path. Adds a local backend that runs the SAME
L1+L2 inference as CuasDnnRemote but in-process via LibTorch (libtpdnn.so / JNA),
so the CUAS pipeline runs without the DGX or any Python server:
  - CuasDnnBackend  : shared interface (upload/getNFrames/inferBatch->BatchResult/close)
  - TpDnnJna        : JNA Library binding libtpdnn.so's C-ABI
  - CuasDnnLocal    : wraps it; reads N/P/vr/l2_ch from each model's bundled .meta.json
                      (single source of truth), float[][]<->float[], builds BatchResult
  - CuasDnnRemote   : now implements CuasDnnBackend (signatures unchanged)
  - CuasDetectRT    : DNN path gate now fires on (curt_dnn_remote || curt_dnn_local);
                      backend = local? CuasDnnLocal : CuasDnnRemote; ensureServer skipped
                      when local; local-CPU-ORT gate also excludes curt_dnn_local (no
                      double-run). runDnnRemote loop unchanged.
  - IntersceneMatchParameters: curt_dnn_local (flag) + curt_dnn_local_dir (model dir
                      override; empty = bundled /cuas_dnn resource) + GUI labels/persist.

Validated: full Java->JNA->libtpdnn vs the Python-server oracle = EXACT
(offset5=0.0, roi=0.0, nch=6). mvn -DskipTests package OK.

Runtime: -Djna.library.path=<dir with libtpdnn.so>; libtpdnn.so finds libtorch via
its rpath. Model resolution mirrors CuasDnnRemote's bundled-vs-override scheme.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>