Arthur Hartog

Dr. Arthur Hartog


Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 April 2025 in JASA Express Letters
Verified
Journal Article
Open access
LBLéa Bouffaut
Léa Bouffaut
QGQuentin Goestchel
Quentin Goestchel
RRRobin André Rørstadbotnen
Robin André Rørstadbotnen
ASAnthony Sladen
Anthony Sladen
AHArthur Hartog
Arthur Hartog
HKHolger Klinck
Holger Klinck
Distributed acoustic sensing (DAS) is a promising technology for underwater acoustics, but its instrumental response is still being investigated to enable quantitative measurements. We use fin whale 20 Hz calls to estimate the conversion between DAS-recorded strain and acoustic pressure. Our method is tested across three deployments on varied seafloor telecommunication cables and ocean basins. Results show that after accounting for well-established DAS response factors, a unique value for water compressibility provides a good estimate for the conversion. This work represents a significant step forward in characterizing DAS for marine monitoring and highlights potential limitations related to instrument noise floor.

4 July 2025 in Sensors
Verified
Journal Article
Open access
PDF file
TDThurian Le Du
Thurian Le Du
AHArthur Hartog
Arthur Hartog
GHGraeme J Hilton
Graeme J Hilton
RDRoman Didelet
Roman Didelet
Distributed acoustic sensing (DAS) is a highly effective method of monitoring all kinds of intrusions on railway tracks. These intrusions represent a real problem in the railway sector, as they can lead to human deaths or damage to railway tracks, and these intrusions may be human or animal. A fiber was deployed along 12 km of track in a railway test center, enabling us to acquire data day and night. A data acquisition campaign was carried out in April 2023 to capture the signatures of several scenarios (walking, digging, falling rocks, etc.) in order to train machine learning models and prevent any intrusion by detecting and classify these intrusion. The study shows the diversity of signals that fiber can acquire in the rail sector and the machine learning model performance. Signals associated with the presence of animals are also presented.