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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> An angular analysis ofB⁰→ K^*0e⁺e⁻decays is presented using proton-proton collision data collected by the LHCb experiment at centre-of-mass energies of 7, 8 and 13 TeV, corresponding to an integrated luminosity of 9 fb⁻¹. The analysis is performed in the region of the dilepton invariant mass squared of 1.1–6.0 GeV²/c⁴. In addition, a test of lepton flavour universality is performed by comparing the obtained angular observables with those measured inB⁰→ K^*0μ⁺μ⁻decays. In general, the angular observables are found to be consistent with the Standard Model expectations as well as with global analyses of otherb → sℓ⁺ℓ⁻processes, whereℓis either a muon or an electron. No sign of lepton-flavour-violating effects is observed. 

A<sc>bstract</sc> A search for the decay$$ {B}_c^{+} $$ $B_c^{+}$→ χ_c1(3872)π⁺is reported using proton-proton collision data collected with the LHCb detector between 2011 and 2018 at centre-of-mass energies of 7, 8, and 13 TeV, corresponding to an integrated luminosity of 9 fb⁻¹. No significant signal is observed. Using the decay$$ {B}_c^{+} $$ $B_c^{+}$→ψ(2S)π⁺as a normalisation channel, an upper limit for the ratio of branching fractions$$ {\mathcal{R}}_{\psi (2S)}^{\chi_{c1}(3872)}=\frac{{\mathcal{B}}_{B_c^{+}\to {\chi}_{c1}(3872){\pi}^{+}}}{{\mathcal{B}}_{B_c^{+}\to \psi (2S){\pi}^{+}}}\times \frac{{\mathcal{B}}_{\chi_{c1}(3872)\to J/\psi {\pi}^{+}{\pi}^{-}}}{{\mathcal{B}}_{\psi (2S)\to J/\psi {\pi}^{+}{\pi}^{-}}}<0.05(0.06), $$ $R_{\psi \left(2S\right)}^{{\chi }_{c1}\left(3872\right)}=\frac{B_{B_c^{+}\to {\chi }_{c1}\left(3872\right){\pi }^{+}}}{B_{B_c^{+}\to \psi \left(2S\right){\pi }^{+}}}\times \frac{B_{{\chi }_{c1}\left(3872\right)\to J/\psi {\pi }^{+}{\pi }^{-}}}{B_{\psi \left(2S\right)\to J/\psi {\pi }^{+}{\pi }^{-}}}<0.05\left(0.06\right),$ is set at the 90 (95)% confidence level.