Pith Number

pith:6ROSRYNH

pith:2025:6ROSRYNH5WOH7ATA4S3B3L3UMK

not attested not anchored not stored refs pending

Vessel Detection and Localization Using Distributed Acoustic Sensing in Submarine Optical Fiber Cables

Daniel Pizarro-Perez, Erick Eduardo Ramirez-Torres, Javier Macias-Guarasa, Javier Tejedor, Miguel Gonzalez-Herraez, Pedro J. Vidal-Moreno, Roel Vanthillo, Sira Elena Palazuelos-Cagigas, Sonia Martin-Lopez

Distributed acoustic sensing on submarine cables detects vessels with over 90% F1-score and 141 m average distance error.

arxiv:2509.11614 v5 · 2025-09-15 · physics.geo-ph

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\pithnumber{6ROSRYNH5WOH7ATA4S3B3L3UMK}

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Results demonstrate DAS as a practical tool for maritime surveillance, with an overall F1-score of over 90% in vessel detection, and a mean average error of 141 m for vessel distance estimation, bridging the gap between experimental research and real-world deployment.

C2weakest assumption

The acoustic signatures produced by vessels remain sufficiently distinct from background noise and other maritime sources across the full range of sea states and ship types encountered during the ten-day period, allowing the chosen ML models to generalize without substantial post-hoc tuning or data exclusion.

C3one line summary

The study applies DAS on submarine cables with ML models to achieve over 90% F1-score vessel detection and 141 m mean distance error over a 10-day real-world test, releasing the full dataset.

Formal links

2 machine-checked theorem links

Cited by

1 paper in Pith

Sea-Scan: High-Accuracy, ML-based Dark Vessel Detection and Localisation via Weakly Supervised DAS Monitoring

Receipt and verification

First computed	2026-08-04T01:57:53.034798Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f45d28e1a7ed9c7f8260e4b61daf74628971164f6c7efc1e325776405de4af13

Aliases

arxiv: 2509.11614 · arxiv_version: 2509.11614v5 · doi: 10.48550/arxiv.2509.11614 · pith_short_12: 6ROSRYNH5WOH · pith_short_16: 6ROSRYNH5WOH7ATA · pith_short_8: 6ROSRYNH

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/6ROSRYNH5WOH7ATA4S3B3L3UMK \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f45d28e1a7ed9c7f8260e4b61daf74628971164f6c7efc1e325776405de4af13

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "1c326b09dcde8f92b78eb5206ebe2c61bf512f98ac345a0e35a2a804020c4f55",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.geo-ph",
    "submitted_at": "2025-09-15T06:14:13Z",
    "title_canon_sha256": "d9d44c5fea80fb53049eadf1441058f74ae1337b3fc3b6ba5f5641810226cf46"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2509.11614",
    "kind": "arxiv",
    "version": 5
  }
}