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    <responseDate>2026-10-14T14:23:09Z</responseDate>
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    <identifier>10.57760/sciencedb.10775</identifier>
    <datestamp>2024-12-06T13:34:30Z</datestamp>
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  <dc:date>2024-12-06</dc:date>
  <dc:title>Raw Seismic Waveform Datasets for Real-time Discrimination</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.10775</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Six raw seismic waveform datasets have been compiled&amp;nbsp;from a portion of the localized University of Utah Seismograph Stations (UUSS) data for modeling and testing, the global Stanford Earthquake Dataset (STEAD) for generalization testing, and the Russia-Ukraine conflict data for case study analysis.Additionally, a code package is provided to demonstrate the application of tiny deep learning on microcontroller units. The model, named MCU-Quake, can distinguish seismological signals from ambient noise and explosions in real-time using low-cost microcontrollers and short-duration data. This capability is particularly valuable for monitoring catastrophic natural hazards.</dc:description>
  <dc:subject>Seismic signals; Earthquake; Quarry blast; Seismic waveforms;  IoT devices; Deep learning</dc:subject>
  <dc:creator>Zhi Geng</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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