发表机构
Universidad de Alcalá; Institute of Technology, Universidad San Pablo-CEU, CEU Universities; Daza de Valdés Institute of Optics (IO-CSIC)（阿尔卡拉大学; CEU大学联盟圣巴勃罗-CEU大学理工学院; 达萨·德·巴尔德斯光学研究所（西班牙国家研究委员会））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究发布Marlinks-NS DAS数据集，含74771个标记实例，用于海底电缆保护的船舶检测与定位，支持相关机器学习任务的可复现研究。
AI 中文摘要
近期海底电信及电力电缆（尤其是波罗的海区域）发生的意外损坏与疑似破坏事件，凸显了其脆弱性及对持续监测方案的需求。应用于海底光缆的分布式声学传感（DAS）可实现水下声活动的广域监测。我们提出Marlinks-NS DAS数据集，包含经处理的海底DAS测量数据及从AIS获取的船舶信息，专为电缆保护研究定制。该数据集定义了两项机器学习任务：船舶检测及船到电缆的距离估计，支持在真实海洋条件下开展可复现研究。数据集包含10天连续记录中来自北海一段28公里长埋地光缆的2554米段的74771个标记数据实例，每个实例包含250个传感通道的谱能量特征，以及来自AIS信息的匿名距离测量值和元数据。发布的HDF5数据、文档、处理说明及示例代码，支持基于DAS的海底电缆保护船舶监测方法的可复现开发与评估。
英文摘要
Recent incidents of accidental damage and suspected sabotage to submarine telecommunication and power cables, particularly in the Baltic Sea, have underscored their vulnerability and the need for continuous monitoring solutions. Distributed acoustic sensing (DAS) applied to submarine optical-fiber cables enables wide-area monitoring of underwater acoustic activity. We present the Marlinks-NS DAS dataset, comprising processed submarine DAS measurements and AIS-derived vessel information curated for cable-protection research. The dataset defines two machine-learning tasks (vessel detection and vessel-to-cable distance estimation) allowing reproducible research under realistic marine conditions. The dataset contains 74,771 labeled data instances from ten days of continuous recording along a 2,554 m segment in a 28 km buried fiber-optic cable in the North Sea. Each instance includes spectral-energy features from 250 sensing channels, together with anonymized distance measurements and metadata from AIS information. The released HDF5 data, documentation, processing description, and example code support reproducible development and evaluation of DAS-based vessel-monitoring methods for submarine cable protection.
Comments19 pages, 8 figures, 3 tables. Submitted to be considered for publication as a Data Descriptor in the Scientific Data Journal
