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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> Thepp→W^±(→μ^±ν_μ)Xcross-sections are measured at a proton-proton centre-of-mass energy$$ \sqrt{s}=5.02 $$ $\sqrt{s}=5.02$TeV using a dataset corresponding to an integrated luminosity of 100 pb⁻¹recorded by the LHCb experiment. Considering muons in the pseudorapidity range 2.2< η <4.4, the cross-sections are measured differentially in twelve intervals of muon transverse momentum between 28< p_T<52 GeV. Integrated overp_T, the measured cross-sections are$$ {\displaystyle \begin{array}{c}{\sigma}_{W^{+}\to {\mu}^{+}{\nu}_{\mu }}=300.9\pm 2.4\pm 3.8\pm 6.0\ \textrm{pb},\\ {}{\sigma}_{W^{-}\to {\mu}^{-}{\overline{\nu}}_{\mu }}=236.9\pm 2.1\pm 2.7\pm 4.7\ \textrm{pb},\end{array}} $$ $\begin{array}{c}{\sigma }_{W^{+}\to {\mu }^{+}{\nu }_{\mu }}=300.9\pm 2.4\pm 3.8\pm 6.0\mathrm{pb},\\ {\sigma }_{W^{-}\to {\mu }^{-}{\overline{\nu }}_{\mu }}=236.9\pm 2.1\pm 2.7\pm 4.7\mathrm{pb},\end{array}$ where the first uncertainties are statistical, the second are systematic, and the third are associated with the luminosity calibration. These integrated results are consistent with theoretical predictions. This analysis introduces a new method to determine theW-boson mass using the measured differential cross-sections corrected for detector effects. The measurement is performed on this statistically limited dataset as a proof of principle and yields$$ {m}_W=80369\pm 130\pm 33\ \textrm{MeV}, $$ $m_W=80369\pm 130\pm 33\mathrm{MeV},$ where the first uncertainty is experimental and the second is theoretical. 

The first dedicated $Z$-boson mass measurement at the LHC with $Z\to {\mu }^{+}{\mu }^{-}$decays is reported. The dataset uses proton-proton collisions at a center-of-mass energy of 13 TeV, recorded in 2016 by the LHCb experiment, and corresponds to an integrated luminosity of $1.7{\mathrm{fb}}^{-1}$. A template fit to the ${\mu }^{+}{\mu }^{-}$mass distribution yields the following result for the $Z$-boson mass: $m_Z=91,185.7\pm 8.3\pm 3.9\mathrm{MeV}$, where the first uncertainty is statistical and the second systematic. This result is consistent with previous measurements and predictions from global electroweak fits.