Initial public dataset release

CHECKSUMS_SHA256.txt

@@ -0,0 +1,27 @@
+50b4cc17b3d993aa03673b0e54c872c01512306630711808763c3aeae621c13a  data/audio/vehicle_audio_20250812_105042_node_01_respeaker.flac
+ddf66945e6f8342b6fa464b5dbb654511f06bf317416b6837df1725ea786a1bf  data/audio/vehicle_audio_20250812_105042_node_02_respeaker.flac
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+ef32375fae0bdec981f173bc618360bea4e9a2c399fa57265c86d57fb1766ac3  data/processed/gdf_cleaned.parquet
+8b4b2237e13b4a39a8d948ddd9c9bbe9c6cb68f32a6b58c67a4093bca1d353e0  data/processed/normalized_cleaned.parquet
+a037706bee3eae6ddebc709c2343a91dbf3c2d28be67ea2012a180391de74639  data/processed/valid_indices.csv
+96ada0dcfef13f89e1ab3b096e8d77ac129d3905421f1a5621bd82142b027dd0  LICENSE_OR_USAGE_NOTES.md
+29a0c67c8e0d91e96f9cced88592839b350c15c4b412b170ac29c234e30096b5  MANIFEST.csv
+8179da9fefc54ad559192d9ef097f5c5299e6240262d249dbcd9b63f00156b92  metadata/active_experiment_config.json
+ab23f1170985390b129b6ffa0d5ba7528111938ba1a5684f3b453a3129ac0753  metadata/bandit_instances/cyclic_5s__final_embedding_divisor_hidden_512__linearized__f7d2fe4d2f94833d.json
+b832d1e4ed75c0917ceda28aaeed00fee827cbdcd3679cbf20d40568298f5f3e  metadata/cyclic_replay_5s_config_with_beta.json
+7f04eb36a1c8538ab3fe0a0277dae5991e96cdbbfe164288800749cb9e5118ff  metadata/cyclic_replay_5s_indices.npy
+2d602a9be9af403c322d2af27aafed56cbcc842b4d272d175e11eb20afae844d  metadata/cyclic_replay_5s_K20_state_labels_one_period.npy
+83677be3aa7398d150b3387a62a73f60180e415edce1f02d4b76eaf62740c274  metadata/cyclic_replay_trim_points.json
+4d3d4271e407cf724fba8e8af10955e3b8c8f4a1a47832fc377a3966ad8dc683  metadata/final_embedding_divisor_hidden_512/learned_embeddings.npy
+548fbbf3398c4073dca3d1ea7a91ff08cfeebc6c9c3a5e1fec2c0ae35291368d  metadata/final_embedding_divisor_hidden_512/meta.json
+8e5105aad6ff69e73eeac88ec8f1ce64debc8d2d81c57cfd558b231a57d79f25  provenance_public.md
+a9a0bf290bb1a90f5ff82a258fb5b7b417f956b927c35977f7eb3df24bdd712a  README.md
+a2cd894dffef44b771d09703242bd68e7f91bded083132ed9660b66f8d850348  sensor_locations/gq_sensor_locations_active_nodes.csv
+9c7575c6a1396beab04a180327fca783ca4d87b8e96d14a2af7d3e9ba42b1843  sensor_locations/gq_sensor_locations_source.csv

LICENSE_OR_USAGE_NOTES.md

@@ -0,0 +1,14 @@
+# License and Usage Notes
+
+Distribution Statement A. Approved for public release; distribution is unlimited.
+
+The raw audio, GPS, sensor-location data, processed tables, and derived metadata
+in this repository are provided for research use. Public filenames have been
+normalized to avoid local-machine naming conventions. Scientific values and
+copied file bytes are unchanged except where `MANIFEST.csv` explicitly marks a
+generated or filtered artifact.
+
+Large Python pickle caches are intentionally excluded because they are
+implementation-specific executable serialization formats. The released
+portable tables, NumPy arrays, and JSON metadata are sufficient to reconstruct
+those caches when combined with an experiment implementation.

MANIFEST.csv

@@ -0,0 +1,27 @@
+path,category,role,size_bytes,sha256,mtime_utc,source_basename,exact_byte_copy,notes
+data/audio/vehicle_audio_20250812_105042_node_01_respeaker.flac,raw_audio,Raw FLAC audio for active sensor node 1.,109925591,50b4cc17b3d993aa03673b0e54c872c01512306630711808763c3aeae621c13a,2025-08-13T12:43:51+00:00,20250812_105042_dvpg_gq_orin_1_respeaker.flac,yes,
+data/audio/vehicle_audio_20250812_105042_node_02_respeaker.flac,raw_audio,Raw FLAC audio for active sensor node 2.,111586404,ddf66945e6f8342b6fa464b5dbb654511f06bf317416b6837df1725ea786a1bf,2025-08-13T12:44:47+00:00,20250812_105042_dvpg_gq_orin_2_respeaker.flac,yes,
+data/audio/vehicle_audio_20250812_105042_node_03_respeaker.flac,raw_audio,Raw FLAC audio for active sensor node 3.,109457488,422dd1e9e5f6464c3bc499a252416451f28f17ffeeeb78dce4faf5374926b17f,2025-08-13T12:45:04+00:00,20250812_105042_dvpg_gq_orin_3_respeaker.flac,yes,
+data/audio/vehicle_audio_20250812_105042_node_04_respeaker.flac,raw_audio,Raw FLAC audio for active sensor node 4.,112500953,b1a2d153561b118caf6dbb32158b22a702813018dd26a20a7fd20dfb11e25795,2025-08-13T12:45:24+00:00,20250812_105042_dvpg_gq_orin_4_respeaker.flac,yes,
+data/audio/vehicle_audio_20250812_105042_node_05_respeaker.flac,raw_audio,Raw FLAC audio for active sensor node 5.,115489245,c1e4185eda168e2594bd95ac013bc39a9b94a065b3ff887456ae3302c99d7f25,2025-08-13T12:45:40+00:00,20250812_105042_dvpg_gq_orin_5_respeaker.flac,yes,
+data/audio/vehicle_audio_20250812_105042_node_07_respeaker.flac,raw_audio,Raw FLAC audio for active sensor node 7.,112732450,ed9a56e4325fae0fb93f02a819c0452c753c936fc21e642573db51d183df997e,2025-08-13T12:46:42+00:00,20250812_105042_dvpg_gq_orin_7_respeaker.flac,yes,
+data/audio/vehicle_audio_20250812_105042_node_08_respeaker.flac,raw_audio,Raw FLAC audio for active sensor node 8.,112973939,77ae7a6a9b5621c5efd7f5f6c2abee554d4628df524ec281f672b5edd98d7334,2025-08-13T12:57:18+00:00,20250812_105042_dvpg_gq_orin_8_respeaker.flac,yes,
+data/audio/vehicle_audio_20250812_105042_node_09_respeaker.flac,raw_audio,Raw FLAC audio for active sensor node 9.,102137182,f9ba0505c90f0b5108b9af37ce8c572bb4bc70d74af8dc65c41a74896402881c,2025-08-13T12:47:01+00:00,20250812_105042_dvpg_gq_orin_9_respeaker.flac,yes,
+data/audio/vehicle_audio_20250812_105042_node_10_respeaker.flac,raw_audio,Raw FLAC audio for active sensor node 10.,101762967,c10290d892e1877625b3100fc462a8049481e147140ad2680ca1ff868aacfaa4,2025-08-13T12:47:28+00:00,20250812_105042_dvpg_gq_orin_10_respeaker.flac,yes,
+data/gps/vehicle_gps_20250812_105042.csv,raw_gps,Raw vehicle GPS trajectory used to align the vehicle context sequence.,772058,72d7720180d318ce07f29f0b90b139f0dd3f617882750f3041a0eea7c2eae233,2025-08-13T12:50:37+00:00,ATV2-20250812-105042.csv,yes,
+data/processed/gdf_cleaned.parquet,processed_data,Processed vehicle bandit table produced from the included raw GQ audio/GPS data.,1112916,ef32375fae0bdec981f173bc618360bea4e9a2c399fa57265c86d57fb1766ac3,2026-04-27T02:49:27.668691+00:00,gdf_cleaned.parquet,yes,
+data/processed/normalized_cleaned.parquet,processed_data,Processed vehicle bandit table produced from the included raw GQ audio/GPS data.,1242844,8b4b2237e13b4a39a8d948ddd9c9bbe9c6cb68f32a6b58c67a4093bca1d353e0,2026-04-27T02:49:27.692793+00:00,normalized_cleaned.parquet,yes,
+data/processed/valid_indices.csv,processed_data,Processed vehicle bandit table produced from the included raw GQ audio/GPS data.,43873,a037706bee3eae6ddebc709c2343a91dbf3c2d28be67ea2012a180391de74639,2026-04-27T02:49:27.703796+00:00,valid_indices.csv,yes,
+LICENSE_OR_USAGE_NOTES.md,documentation,Public dataset documentation.,739,96ada0dcfef13f89e1ab3b096e8d77ac129d3905421f1a5621bd82142b027dd0,2026-09-12T18:00:05.088814+00:00,,no,Generated/sanitized public metadata.
+metadata/active_experiment_config.json,metadata,Configuration or portable replay/mixing metadata.,900,8179da9fefc54ad559192d9ef097f5c5299e6240262d249dbcd9b63f00156b92,2026-09-12T17:58:49.210588+00:00,,no,Generated/sanitized public metadata.
+metadata/bandit_instances/cyclic_5s__final_embedding_divisor_hidden_512__linearized__f7d2fe4d2f94833d.json,metadata,Configuration or portable replay/mixing metadata.,728,ab23f1170985390b129b6ffa0d5ba7528111938ba1a5684f3b453a3129ac0753,2026-09-12T18:04:08.153156+00:00,cyclic_5s__final_embedding_divisor_hidden_512__linearized__f7d2fe4d2f94833d.json,no,Generated/sanitized public metadata.
+metadata/cyclic_replay_5s_config_with_beta.json,metadata,Configuration or portable replay/mixing metadata.,1538,b832d1e4ed75c0917ceda28aaeed00fee827cbdcd3679cbf20d40568298f5f3e,2026-09-12T18:04:01.823033+00:00,cyclic_replay_5s_config_with_beta.json,no,Generated/sanitized public metadata.
+metadata/cyclic_replay_5s_indices.npy,metadata,Replay/Markov-chain metadata for the paper instance.,8000128,7f04eb36a1c8538ab3fe0a0277dae5991e96cdbbfe164288800749cb9e5118ff,2026-05-06T08:05:30.453203+00:00,cyclic_replay_5s_indices.npy,yes,
+metadata/cyclic_replay_5s_K20_state_labels_one_period.npy,metadata,Replay/Markov-chain metadata for the paper instance.,28960,2d602a9be9af403c322d2af27aafed56cbcc842b4d272d175e11eb20afae844d,2026-05-06T08:05:30.473855+00:00,cyclic_replay_5s_K20_state_labels_one_period.npy,yes,
+metadata/cyclic_replay_trim_points.json,metadata,Replay/Markov-chain metadata for the paper instance.,346,83677be3aa7398d150b3387a62a73f60180e415edce1f02d4b76eaf62740c274,2026-04-27T02:58:39.574180+00:00,cyclic_replay_trim_points.json,yes,
+metadata/final_embedding_divisor_hidden_512/learned_embeddings.npy,derived_model,Derived embedding/reward model artifact used to construct the bandit rewards.,92545760,4d3d4271e407cf724fba8e8af10955e3b8c8f4a1a47832fc377a3966ad8dc683,2026-04-26T04:27:24.939331+00:00,learned_embeddings.npy,yes,
+metadata/final_embedding_divisor_hidden_512/meta.json,derived_model,Configuration or portable replay/mixing metadata.,1386,548fbbf3398c4073dca3d1ea7a91ff08cfeebc6c9c3a5e1fec2c0ae35291368d,2026-09-12T18:04:14.495842+00:00,meta.json,no,Generated/sanitized public metadata.
+provenance_public.md,documentation,Public dataset documentation.,3562,8e5105aad6ff69e73eeac88ec8f1ce64debc8d2d81c57cfd558b231a57d79f25,2026-09-12T17:59:56.682455+00:00,,no,Generated/sanitized public metadata.
+README.md,documentation,Public dataset documentation.,6426,a9a0bf290bb1a90f5ff82a258fb5b7b417f956b927c35977f7eb3df24bdd712a,2026-09-12T18:09:10.144825+00:00,,no,Generated/sanitized public metadata.
+sensor_locations/gq_sensor_locations_active_nodes.csv,sensor_locations,Active 9-node sensor GPS table after applying DROP_NODES=[6].,374,a2cd894dffef44b771d09703242bd68e7f91bded083132ed9660b66f8d850348,2026-09-12T15:33:21.411245+00:00,nodes_gps.csv,filtered_rows,Rows filtered to active paper nodes only; coordinate values unchanged.
+sensor_locations/gq_sensor_locations_source.csv,sensor_locations,Source sensor GPS table used by preprocessing; node 6 is recorded but excluded from the paper instance.,419,9c7575c6a1396beab04a180327fca783ca4d87b8e96d14a2af7d3e9ba42b1843,2025-08-13T13:19:05.629519+00:00,nodes_gps.csv,yes,

README.md

@@ -0,0 +1,145 @@
+---
+license: other
+task_categories:
+- audio-classification
+- time-series-forecasting
+tags:
+- audio
+- geospatial
+- vehicle-tracking
+- sensor-network
+- contextual-bandits
+pretty_name: Grace's Quarters Acoustic Vehicle Tracking Dataset
+---
+
+# Grace's Quarters Acoustic Vehicle Tracking Dataset
+
+Distribution Statement A. Approved for public release; distribution is unlimited.
+
+## Dataset Summary
+
+This repository contains synchronized outdoor acoustic recordings, vehicle GPS,
+sensor locations, processed 200 ms observations, and portable derived metadata
+for one Grace's Quarters vehicle-tracking sequence. It contains dataset files and
+dataset documentation only. Research algorithms, experiment drivers, training
+code, plotting code, notebooks, and manuscript sources are not included.
+
+The sequence uses nine active acoustic nodes: `1, 2, 3, 4, 5, 7, 8, 9, 10`.
+Node 6 is retained in the source sensor-location table for provenance but was
+excluded before constructing the associated experimental instance.
+
+## Contents
+
+| Path | Description |
+| --- | --- |
+| `data/audio/` | Nine lossless multichannel FLAC recordings, one per active node. |
+| `data/gps/` | The raw vehicle trajectory for sequence `20250812_1050`. |
+| `sensor_locations/` | Source and active-node sensor coordinate tables. |
+| `data/processed/` | Aligned acoustic power/distance tables and valid row indices. |
+| `metadata/` | Active configuration, portable learned embeddings, and replay/mixing metadata. |
+| `MANIFEST.csv` | Per-file role, size, source basename, and SHA-256 digest. |
+| `CHECKSUMS_SHA256.txt` | Checksums in standard verification format. |
+
+There are no `examples_with_embed.pkl` or bandit-instance pickle caches in this
+release. Those objects are implementation caches reconstructible from the
+released tables, arrays, metadata, and an experiment implementation.
+
+## Raw Formats
+
+### Audio
+
+Each `vehicle_audio_..._node_NN_respeaker.flac` file is FLAC, 16 kHz,
+six-channel, signed 16-bit PCM. The files are lossless byte copies of the
+approved source recordings under neutral public filenames. Audio starts at the
+timestamp encoded in each filename. The associated preprocessing uses 10 ms
+mean-power chunks and resamples them into 200 ms observations.
+
+### Vehicle GPS
+
+`vehicle_gps_20250812_105042.csv` is a headerless 11-field trajectory with
+7,599 rows. Field 0 is local wall-clock time, fields 1 and 2 are latitude and
+longitude in decimal degrees (WGS 84 / EPSG:4326), and the remaining receiver
+fields are preserved verbatim. The associated preprocessing consumes the first
+three fields, removes duplicate timestamps, sorts chronologically, and aligns
+the trajectory with 200 ms audio observations. For exact compatibility, note
+that the original preprocessing called `pandas.read_csv` with its default
+header handling, so the first raw record supplied column labels and the
+resulting aligned table has 7,598 rows. Timestamps carry no encoded UTC offset;
+interpret them in the acquisition clock used by the synchronized sensor files.
+
+### Sensor Locations
+
+The sensor CSVs contain `Node #`, `Lat`, `Lon`, and `Acc`. Latitude and
+longitude are WGS 84 decimal degrees. `Acc` is a source text field. During
+preprocessing, vehicle and sensor coordinates are projected to local UTM zone
+18N (EPSG:32618), so `distance_to_N` values are in meters.
+
+## Processed Tables
+
+`gdf_cleaned.parquet` has 7,598 timestamped rows and 18 columns: nine `rpiN`
+acoustic power columns in dB and nine `distance_to_N` columns in meters.
+
+`normalized_cleaned.parquet` has the same 7,598-row datetime index and adds
+the aligned `Latitude` and `Longitude` columns, for 20 columns total. The name
+is retained for compatibility with the original processing output.
+
+`valid_indices.csv` contains 7,473 zero-based row positions. A row is valid
+when it follows the preceding row by exactly 200 ms and projected vehicle
+movement is at least `0.0416667` m; the downstream context builder then uses
+five historical observations. The released cleaned tables contain no missing
+values. Use `valid_indices.csv` for the associated experimental sample set.
+
+```python
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+root = Path(".")
+processed = pd.read_parquet(root / "data/processed/gdf_cleaned.parquet")
+valid = pd.read_csv(root / "data/processed/valid_indices.csv")["valid_index"]
+embeddings = np.load(
+    root / "metadata/final_embedding_divisor_hidden_512/learned_embeddings.npy",
+    allow_pickle=False,
+)
+```
+
+## Derived Metadata
+
+`learned_embeddings.npy` is a portable `float32` array with shape
+`(964017, 24)`. Rows follow chronological valid-time order and, within each
+time, action order by subset size and canonical subset string. There are 129
+actions per time. Keeping this array makes the learned representation stable
+across hardware; direct checkpoint inference was independently observed to
+agree within `7.2e-7` maximum absolute error.
+
+The replay metadata records a circular modular-stride sequence with stride 25
+(5 seconds at 200 ms sampling), a 7,208-step period, 20 clustered states, and
+the fitted mixing coefficient used by the associated experiment. See
+`provenance_public.md` for the complete dependency chain.
+
+## Filename Compatibility
+
+Public filenames are intentionally neutral. `MANIFEST.csv` records each raw
+file's original `source_basename`. Legacy loaders can construct temporary
+aliases from that column without modifying this dataset. Such aliases are an
+implementation compatibility detail and are not included here.
+
+## Integrity And Limitations
+
+Use `CHECKSUMS_SHA256.txt` to verify every released artifact. The release
+contains one vehicle pass at one outdoor site and should not be treated as a
+general benchmark for all vehicles, weather, terrain, microphones, or acoustic
+conditions. GPS accuracy, multipath, clock alignment, and node-specific audio
+conditions may affect downstream analyses. The nine recordings are
+multichannel; channel selection and aggregation are analysis choices.
+
+## Acknowledgement
+
+This research reported in this paper was sponsored in part by:
+the IoBT Collaborative Research Alliance funded by the Army Research
+Laboratory (ARL) under Cooperative Agreement W911NF-17-2-0196.
+The views and conclusions contained in this document are those of
+the authors and should not be interpreted as representing the
+official policies, either expressed or implied, of the funding
+agencies.

data/audio/vehicle_audio_20250812_105042_node_01_respeaker.flac

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data/audio/vehicle_audio_20250812_105042_node_02_respeaker.flac

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data/processed/valid_indices.csv

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metadata/active_experiment_config.json

@@ -0,0 +1,38 @@
+{
+  "dataset": "Grace's Quarters vehicle bandit instance",
+  "raw_sequence": "20250812_1050",
+  "vehicle_gps_public_file": "data/gps/vehicle_gps_20250812_105042.csv",
+  "active_nodes": [
+    1,
+    2,
+    3,
+    4,
+    5,
+    7,
+    8,
+    9,
+    10
+  ],
+  "excluded_nodes": [
+    6
+  ],
+  "sample_ms": 200,
+  "history_steps": 5,
+  "max_subset_size": 3,
+  "num_actions": 129,
+  "utility_second_weight": 0.45,
+  "utility_third_weight": 0.2,
+  "rho_note": "The utility builder was called with rho=None; the realized median nearest-sensor distance over the valid five-step context chain is 10.316986163806751 meters.",
+  "replay": {
+    "target_seconds": 5.0,
+    "stride": 25,
+    "T_replay": 1000000,
+    "cycle_start_idx": 265,
+    "cycle_end_idx": 7472,
+    "period_len": 7208,
+    "K_states": 20,
+    "pca_dim": 10,
+    "beta_hat_used": 0.8075480035772411
+  },
+  "implementation_included": false
+}

metadata/bandit_instances/cyclic_5s__final_embedding_divisor_hidden_512__linearized__f7d2fe4d2f94833d.json

@@ -0,0 +1,25 @@
+{
+  "cache_version": "vehicle_bandit_instance_cache_v1",
+  "cache_type": "cyclic_5s_bandit_instance_with_beta",
+  "run_name": "final_embedding_divisor_hidden_512",
+  "embedding_artifact_dir": "metadata/final_embedding_divisor_hidden_512",
+  "reward_mode": "linearized",
+  "include_intercept": true,
+  "sort_rows": true,
+  "sample_ms": 200,
+  "target_seconds": 5.0,
+  "stride": 25,
+  "T_replay": 1000000,
+  "trim_path": "metadata/cyclic_replay_trim_points.json",
+  "cycle_start_idx": 265,
+  "cycle_end_idx": 7472,
+  "base_cycle_len": 7208,
+  "period_len": 7208,
+  "K_states": 20,
+  "pca_dim": 10,
+  "random_state": 0,
+  "max_tv_lag": 150,
+  "tv_fit_min_lag": 1,
+  "tv_fit_max_lag": 80,
+  "smoothing": 0.0
+}

metadata/cyclic_replay_5s_K20_state_labels_one_period.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2d602a9be9af403c322d2af27aafed56cbcc842b4d272d175e11eb20afae844d
+size 28960

metadata/cyclic_replay_5s_config_with_beta.json

@@ -0,0 +1,60 @@
+{
+  "cyclic_replay": true,
+  "replay_type": "modular_circular_stride",
+  "sample_ms": 200,
+  "target_seconds": 5.0,
+  "stride": 25,
+  "effective_seconds_per_bandit_step": 5.0,
+  "cycle_start_idx": 265,
+  "cycle_end_idx": 7472,
+  "base_cycle_len": 7208,
+  "gcd_base_cycle_len_stride": 1,
+  "period_len": 7208,
+  "T_replay": 1000000,
+  "cache_included": false,
+  "cache_metadata_path": "metadata/bandit_instances/cyclic_5s__final_embedding_divisor_hidden_512__linearized__f7d2fe4d2f94833d.json",
+  "beta_estimation": {
+    "state_definition": "KMeans(PCA(vec(X_t))) on one unique modular-stride period",
+    "K_states": 20,
+    "pca_dim": 10,
+    "beta_hat_used": 0.8075480035772411,
+    "beta_tv_fit": 0.8075480035772411,
+    "beta_slem": 0.8095157557221683,
+    "Cmix_tv_fit": 1.290213574715908,
+    "Cmix_emp_envelope": 1.6515513407406872,
+    "tv_fit_r2": 0.9998146678428504,
+    "irreducible": true,
+    "has_self_loop": true,
+    "visited_states": 20,
+    "state_counts_min": 162,
+    "state_counts_median": 344.5,
+    "state_counts_max": 758
+  },
+  "algorithm1_delay_candidates": [
+    {
+      "c_tau": 0.1,
+      "tau": 8,
+      "tau_over_T": 8e-06
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+    {
+      "c_tau": 0.25,
+      "tau": 18,
+      "tau_over_T": 1.8e-05
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+    {
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+      "tau": 36,
+      "tau_over_T": 3.6e-05
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+      "tau": 54,
+      "tau_over_T": 5.4e-05
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+    {
+      "c_tau": 1.0,
+      "tau": 72,
+      "tau_over_T": 7.2e-05
+    }
+  ]
+}

metadata/cyclic_replay_5s_indices.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7f04eb36a1c8538ab3fe0a0277dae5991e96cdbbfe164288800749cb9e5118ff
+size 8000128

metadata/cyclic_replay_trim_points.json

@@ -0,0 +1,13 @@
+{
+  "cycle_start_idx": 265,
+  "cycle_end_idx": 7472,
+  "cycle_len": 7208,
+  "start_trim": 265,
+  "end_trim": 0,
+  "total_trim": 265,
+  "loc_boundary_m": 0.9493070013771703,
+  "loc_threshold_m": 1.0,
+  "min_len_constraint": 6500,
+  "X_boundary_over_p99_step": 2.0670854261895935,
+  "reward_boundary_over_p99_step": 3.3047650358054774
+}

metadata/final_embedding_divisor_hidden_512/learned_embeddings.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d3d4271e407cf724fba8e8af10955e3b8c8f4a1a47832fc377a3966ad8dc683
+size 92545760

metadata/final_embedding_divisor_hidden_512/meta.json

@@ -0,0 +1,51 @@
+{
+  "public_note": "The config name below is a retained legacy label; config.hidden=512 is the authoritative hidden width.",
+  "config": {
+    "name": "full_universe_divisor_hidden_1024",
+    "hidden": 512,
+    "emb_dim": 8,
+    "loss_name": "mse",
+    "combine_mode": "mul_only",
+    "seed": 22,
+    "max_epochs": 80,
+    "patience": 20,
+    "ridge_alphas": [
+      0.01,
+      0.1,
+      1.0,
+      10.0,
+      100.0
+    ]
+  },
+  "best_epoch": 28,
+  "best_alpha": 1.0,
+  "metrics": {
+    "train": {
+      "r2": 0.9884165525436401,
+      "rmse": 0.01818079274081761,
+      "top1": 0.6216819094356457,
+      "top3": 0.9065358019183583,
+      "mean_rank": 1.8717376756636182,
+      "avg_regret": 0.002546895841336426,
+      "avg_norm_regret": 0.004971643355790649
+    },
+    "val": {
+      "r2": 0.934257984161377,
+      "rmse": 0.04802778696411822,
+      "top1": 0.6164658634538153,
+      "top3": 0.8868808567603749,
+      "mean_rank": 2.2978580990629185,
+      "avg_regret": 0.004179782018444465,
+      "avg_norm_regret": 0.00779921198437386
+    },
+    "test": {
+      "r2": 0.9439240097999573,
+      "rmse": 0.04407126866588067,
+      "top1": 0.6136363636363636,
+      "top3": 0.9030748663101604,
+      "mean_rank": 2.1009358288770055,
+      "avg_regret": 0.004372186161936286,
+      "avg_norm_regret": 0.008189854974875068
+    }
+  }
+}

provenance_public.md

@@ -0,0 +1,74 @@
+# Public Data Provenance
+
+Distribution Statement A. Approved for public release; distribution is unlimited.
+
+## Dependency Graph
+
+The dataset supports the following reconstruction when combined with the
+separately maintained experiment implementation:
+
+```text
+public FLAC + vehicle GPS + sensor coordinates
+  -> 10 ms audio mean power and timestamp alignment
+  -> 200 ms resampling and WGS84-to-UTM distance calculation
+  -> gdf_cleaned + normalized_cleaned + valid_indices
+  -> five-step contexts and all node subsets of size 1, 2, or 3
+  -> 964,017 ordered context/action/utility examples
+  -> attach the released 24-dimensional learned_embeddings.npy rows
+  -> fit the declared alpha=1 ridge reward model
+  -> linearized bandit instance
+  -> 5-second modular circular replay
+  -> PCA(10) + KMeans(20) state process and transition estimate
+  -> delayed contextual-bandit experiment
+```
+
+The intermediate `examples_with_embed.pkl` is a dataframe cache. It contains
+timestamps, sensor subsets, subset strings and sizes, 85-dimensional contexts,
+42-dimensional raw action vectors, utility and distance diagnostics, nearest
+sensor labels, and 24-dimensional learned embeddings. It is not source data.
+
+The intermediate cyclic bandit-instance pickle is also a cache. It contains
+the ordered feature/action arrays, observed and linearized rewards, oracle
+values, replay indices, clustered state sequence, transition counts and
+matrix, stationary distribution, total-variation diagnostics, and mixing-fit
+parameters. The release stores its portable replay/state/config components,
+but not the pickle object.
+
+## Active Construction
+
+- Raw sequence: `20250812_1050`
+- Active nodes: `[1, 2, 3, 4, 5, 7, 8, 9, 10]`
+- Excluded node: `6`
+- Observation interval: 200 ms
+- Context history: 5 observations
+- Action subsets: all active-node subsets of size 1, 2, or 3 (129 actions)
+- Utility weights for second/third selected nodes: `0.45` and `0.20`
+- Realized utility distance scale on the valid chain: `10.316986163806751` m
+- Chronological time split: 60% train, 20% validation, 20% test
+- Embedding seed: `22`
+- Embedding architecture: two towers, hidden width 512, tower width 8,
+  multiplicative combined representation of width 24
+- Reward fit: ridge regression with alpha `1.0`
+- Replay trim: inclusive row positions 265 through 7,472
+- Replay stride: 25 observations; period 7,208; horizon 1,000,000
+- State model: PCA dimension 10 and KMeans with 20 states,
+  `random_state=0`, `n_init=20`
+- Mixing fit: lags 1 through 80 from a TV-distance curve computed through lag
+  150; fitted beta `0.8075480035772411`
+
+## Clean-Rebuild Validation
+
+An isolated validation rebuilt the processed tables from the released raw
+files. Shapes, columns, row order, valid indices, and every numeric value
+matched the retained processed artifacts exactly (`max_abs_diff = 0`). The
+964,017 example rows were then reconstructed without an old example cache.
+Direct model inference differed from the retained embedding array by at most
+`7.2e-7`; attaching the portable array produced exact scientific equality.
+The resulting cyclic instance, including rewards, replay indices, states,
+transition matrix, and mixing estimates, matched the prior cache exactly.
+
+No original/private Grace's Quarters scientific file was read during the clean
+build. The public data therefore contain sufficient scientific information to
+reconstruct the experimental instance when combined with the experiment
+implementation. Private validation logs and source filesystem paths are not
+part of this release.

sensor_locations/gq_sensor_locations_active_nodes.csv

@@ -0,0 +1,10 @@
+Node #,Lat,Lon,Acc
+1,39.35080630466828,-76.34492396920828,~4cm
+2,39.35093662815,-76.34493841541,~4cm
+3,39.3510127177,-76.34486595198,~4cm
+4,39.35114617104999,-76.34477154187002,~4cm
+5,39.35090170367,-76.34465868084001,~4cm
+7,39.35099059792,-76.34509,~4cm
+8,39.35086821719,-76.34506581606999,~4cm
+9,39.350875,-76.3453,~4cm
+10,39.35071333732,-76.34520813482001,~4cm

sensor_locations/gq_sensor_locations_source.csv

@@ -0,0 +1,11 @@
+Node #,Lat,Lon,Acc
+1,39.350806304668275,-76.34492396920828,~4cm
+2,39.35093662815,-76.34493841541,~4cm
+3,39.3510127177,-76.34486595198,~4cm
+4,39.35114617104999,-76.34477154187002,~4cm
+5,39.350901703670004,-76.34465868084001,~4cm
+6,39.35113808296,-76.34451156931999,~4cm
+7,39.35099059792,-76.34509,~4cm
+8,39.35086821719,-76.34506581606999,~4cm
+9,39.350875,-76.3453,~4cm
+10,39.35071333732,-76.34520813482001,~4cm