This paper presents the results of an attenuation model for remote depth estimation of buried radioactive wastes using a Cadmium Zinc Telluride (CZT) detector. Previous research using an organic liquid scintillator detector system showed that the model is able to estimate the depth of a 329-kBq Cs-137 radioactive source buried up to 12 cm in sand with an average count rate of 100 cps. The results presented in this paper showed that the use of the CZT detector extended the maximum detectable depth of the same radioactive source to 18 cm in sand with a significantly lower average count rate of 14 cps. Furthermore, the model also successfully estimated the depth of a 9-kBq Co-60 source buried up to 3 cm in sand. This confirms that this remote depth estimation method can be used with other radionuclides and wastes with very low activity. Finally, the paper proposes a performance parameter for evaluating radiation detection systems that implement this remote depth estimation method.
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http://dx.doi.org/10.3390/s18051612 | DOI Listing |
Nowadays, spaceborne LiDAR technology, particularly ICESat-2, has become a transformative tool in marine environmental research. Unlike traditional passive optical remote sensing methods, ICESat-2 offers detailed vertical structure mapping of oceanic optical properties. Despite the potential of ICESat-2 for observing the optical vertical structure, its application in the East China Sea with complex hydrological conditions and dynamic ecosystems remains limited.
View Article and Find Full Text PDFFaint-light imaging plays an important role in applications including fluorescence-lifetime microscopy and remote sensing. Superconducting nanowire single-photon detectors (SNSPDs) outperform other single-photon detectors in terms of comprehensive performance, however, large-format SNSPD imagers with many pixels remain an outstanding technological challenge. Here, as an alternative route, we use a multimode-fiber-coupled fractal SNSPD as the light-sensing element to perform three-dimensional single-pixel imaging at the wavelength of 1560 nm.
View Article and Find Full Text PDFThe repetitive observations of satellites provide rich multi-temporal information for coastal remote sensing, making it possible to improve the accuracy of bathymetric inversion through multi-temporal satellite data. This study takes Culebra, Puerto Rico, as the study area and attempts multi-temporal bathymetric inversion using 193 Sentinel-2 images and eight tracks of ICESat-2 ATL03 data. Two widely used machine-learning models, CatBoost and Random Forest (RF), were employed to construct bathymetric inversion models, and the Fusion followed by Inversion (FI) strategy and inversion followed by Fusion (IF) strategy were also compared.
View Article and Find Full Text PDFAppl Ergon
January 2025
Centre for Human Factors and Sociotechnical Systems, University of the Sunshine Coast, Australia; School of Health, University of the Sunshine Coast, Australia.
Remotely Piloted Aircraft Systems (RPAS) are a rapidly expanding technology that operates within the larger complex aviation system. As a result, protective frameworks and risk controls for supporting safe operation are still developing. Adverse events are occurring, yet it is unclear what systemic factors interact to create them.
View Article and Find Full Text PDFInnovation (Camb)
January 2025
Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China.
Urban sensing has become increasingly important as cities evolve into the centers of human activities. Large language models (LLMs) offer new opportunities for urban sensing based on commonsense and worldview that emerged through their language-centric framework. This paper illustrates the transformative impact of LLMs, particularly in the potential of advancing next-generation urban sensing for exploring urban mechanisms.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!