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Abstract Accurate and timely inland waterbody extent and location data are foundational information to support a variety of hydrological applications and water resources management. Recently, the Cyclone Global Navigation Satellite System (CYGNSS) has emerged as a promising tool for delineating inland water due to distinct surface reflectivity characteristics over dry versus wet land which are observable by CYGNSS’s eight microsatellites with passive bistatic radars that acquire reflected L-band signals from the Global Positioning System (GPS) (i.e., signals of opportunity). This study conducts a baseline 1-km comparison of water masks for the contiguous United States between latitudes of 24°N-37°N for 2019 using three Earth observation systems: CYGNSS (i.e., our baseline water mask data), the Moderate Resolution Imaging Spectroradiometer (MODIS) (i.e., land water mask data), and the Landsat Global Surface Water product (i.e., Pekel data). Spatial performance of the 1-km comparison water mask was assessed using confusion matrix statistics and optical high-resolution commercial satellite imagery. When a mosaic of binary thresholds for 8 sub-basins for CYGNSS data were employed, confusion matrix statistics were improved such as up to a 34% increase in F1-score. Further, a performance metric of ratio of inland water to catchment area showed that inland water area estimates from CYGNSS, MODIS, and Landsat were within 2.3% of each other regardless of the sub-basin observed. Overall, this study provides valuable insight into the spatial similarities and discrepancies of inland water masks derived from optical (visible) versus radar (Global Navigation Satellite System Reflectometry, GNSS-R) based satellite Earth observations. 

J. Integer Seq. 27 (2024), no. 7, Art. 24.7.7, 18 pp. 

Because cloud storage services have been broadly used in enterprises for online sharing and collaboration, sensitive information in images or documents may be easily leaked outside the trust enterprise on-premises due to such cloud services. Existing solutions to this problem have not fully explored the tradeoffs among application performance, service scalability, and user data privacy. Therefore, we propose CloudDLP, a generic approach for enterprises to automatically sanitize sensitive data in images and documents in browser-based cloud storage. To the best of our knowledge, CloudDLP is the first system that automatically and transparently detects and sanitizes both sensitive images and textual documents without compromising user experience or application functionality on browser-based cloud storage. To prevent sensitive information escaping from on-premises, CloudDLP utilizes deep learning methods to detect sensitive information in both images and textual documents. We have evaluated the proposed method on a number of typical cloud applications. Our experimental results show that it can achieve transparent and automatic data sanitization on the cloud storage services with relatively low overheads, while preserving most application functionalities. 

A<sc>bstract</sc> A comprehensive study of the local and nonlocal amplitudes contributing to the decayB⁰→K^*0(→K⁺π⁻)μ⁺μ⁻is performed by analysing the phase-space distribution of the decay products. The analysis is based onppcollision data corresponding to an integrated luminosity of 8.4 fb⁻¹collected by the LHCb experiment. This measurement employs for the first time a model of both one-particle and two-particle nonlocal amplitudes, and utilises the complete dimuon mass spectrum without any veto regions around the narrow charmonium resonances. In this way it is possible to explicitly isolate the local and nonlocal contributions and capture the interference between them. The results show that interference with nonlocal contributions, although larger than predicted, only has a minor impact on the Wilson Coefficients determined from the fit to the data. For the local contributions, the Wilson Coefficient$$ {\mathcal{C}}_9 $$ $C_9$, responsible for vector dimuon currents, exhibits a 2.1σdeviation from the Standard Model expectation. The Wilson Coefficients$$ {\mathcal{C}}_{10} $$ $C_{10}$,$$ {\mathcal{C}}_9^{\prime } $$ $C_9^{\prime }$and$$ {\mathcal{C}}_{10}^{\prime } $$ $C_{10}^{\prime }$are all in better agreement than$$ {\mathcal{C}}_9 $$ $C_9$with the Standard Model and the global significance is at the level of 1.5σ. The model used also accounts for nonlocal contributions fromB⁰→ K^*0[τ⁺τ⁻→ μ⁺μ⁻] rescattering, resulting in the first direct measurement of thebsττvector effective-coupling$$ {\mathcal{C}}_{9\tau } $$ $C_{9\tau }$. 

A search for hidden-charm pentaquark states decaying to a range of ${\mathrm{\Sigma }}_c\overline{D}$and ${\mathrm{\Lambda }}_c^{+}\overline{D}$final states, as well as doubly charmed pentaquark states to ${\mathrm{\Sigma }}_{c}D$and ${\mathrm{\Lambda }}_c^{+}D$, is made using samples of proton-proton collision data corresponding to an integrated luminosity of $5.7{\mathrm{fb}}^{-1}$recorded by the LHCb detector at $\sqrt{s}=13\mathrm{TeV}$. Since no significant signals are found, upper limits are set on the pentaquark yields relative to that of the ${\mathrm{\Lambda }}_c^{+}$baryon in the ${\mathrm{\Lambda }}_c^{+}\to p{K}^{-}{\pi }^{+}$decay mode. The known pentaquark states are also investigated, and their signal yields are found to be consistent with zero in all cases. © 2024 CERN, for the LHCb Collaboration2024CERN