{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Tan DY"],"funding":["Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)","National Natural Science Foundation of China (National Science Foundation of China)"],"pagination":["156"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12357945"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["4(1)"],"pubmed_abstract":["Large-diameter gravity aqueducts are essential for water supply systems but face performance and safety risks from complex flow conditions. Effective flow-state monitoring is critical for hydraulic performance and infrastructure safety. However, conventional monitoring techniques like closed-circuit television (CCTV) inspection and ultrasonic sensing have limited real-time accuracy in distinguishing flow states. Here we show a real-time, distributed flow monitoring framework based on distributed acoustic sensing (DAS). A hierarchical clustering model, called DAS-Hydro HierarchyNet, was developed to analyze low-frequency acoustic signals and classify water flow states using a multi-level approach. The framework enables continuous flow monitoring along large aqueducts, overcoming point-based"],"journal":["Communications engineering"],"pubmed_title":["Real-time monitoring of water states in large-diameter aqueducts - learning from distributed acoustic sensing signals."],"pmcid":["PMC12357945"],"funding_grant_id":["42225702","BK20241211","42407250"],"pubmed_authors":["Zhang W","Huang JW","Wang P","Tan DY","Shi B","Tang ZY","Duan HF","Yan ZR","Zhu HH","Wang J","Yuan Z"],"additional_accession":[]},"is_claimable":false,"name":"Real-time monitoring of water states in large-diameter aqueducts - learning from distributed acoustic sensing signals.","description":"Large-diameter gravity aqueducts are essential for water supply systems but face performance and safety risks from complex flow conditions. Effective flow-state monitoring is critical for hydraulic performance and infrastructure safety. However, conventional monitoring techniques like closed-circuit television (CCTV) inspection and ultrasonic sensing have limited real-time accuracy in distinguishing flow states. Here we show a real-time, distributed flow monitoring framework based on distributed acoustic sensing (DAS). A hierarchical clustering model, called DAS-Hydro HierarchyNet, was developed to analyze low-frequency acoustic signals and classify water flow states using a multi-level approach. The framework enables continuous flow monitoring along large aqueducts, overcoming point-based","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-04-13T17:11:33.107Z","creation":"2026-04-07T13:34:25.842Z"},"accession":"S-EPMC12357945","cross_references":{"pubmed":["40819185"],"doi":["10.1038/s44172-025-00483-6"]}}