Determination of Flood Subsidy (2023/2024) Based on SAR Images for Agricultural Land in Lower Saxony, Germany by C.-H. Yang, C. Stemmler, C. Röttger, C. Büker

“…The workflow is designed to integrate modules from SNAP and custom algorithms on a cloud-computing platform, generating backscatter coefficients to distinguish flooded and non-flooded areas. …”
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Evaluation on pipeline steel adaptability for extended hydrogen-contacting service by Yuanxing NING, Cuiwei LIU, Yuxing LI, Ming SONG, Huimin ZHANG, Qu'er CHEN, Mengze ZHU, Shiyao PENG, Junwen CHEN

“…Due to minimal pressure fluctuations and the insignificant effect of alternating loads, no flaw waves were detected in the pipeline segment, and no internal surface or buried cracks were found. …”
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Sample-Adjusted Threshold Calculation (SATC) for Optimizing Flood Mapping From a Trustworthy and Explainable Perspective by Zhijun Jiao, Biyan Chen, Zhimei Zhang, Lixin Wu

“…However, in cross-regional flood monitoring, challenges in accurately detecting floodwater pixels arise from interference factors affecting SAR backscatter, which are inherently present in various geo-environments (e.g., wind waves, vegetation, thick clouds, and high-relief terrain). …”
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Dust Aerosol Classification in Northwest China Using CALIPSO Data and an Enhanced 1D U-Net Network by Xin Gong, Delong Xiu, Xiaoling Sun, Ruizhao Zhang, Jiandong Mao, Hu Zhao, Zhimin Rao

“…Using CALIPSO Level 1B and Level 2 Vertical Feature Mask (VFM) data from 2015 to 2020, the model processed backscatter coefficients, polarization characteristics, and color ratios at 532 nm and 1064 nm to classify aerosol types. …”
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Factorial-experimental investigation of LPBF regimes for VZh159 nickel superalloy grain structure and structural strength optimization by Rustam R. Kyarimov, Eugene S. Statnik, Eugene S. Statnik, Iuliia A. Sadykova, Alexander A. Frantsuzov, Alexey I. Salimon, Alexey I. Salimon, Alexander M. Korsunsky, Alexander M. Korsunsky, Alexander M. Korsunsky

“…Metallurgically sound samples (based on hydrostatic weighing data and microscopy, with practically no pores detected) were obtained with nine combinations of power and scanning speed, showing significant variation in the tensile strength (in the 1,040–1,220 MPa range) and yield strength (in the 560–1,100 MPa range), which correlated with the cross-sectional area of the single scan line (for example, the depth of the melt pool varied in the range 410–530 µm), while the average grain size (deduced from Electron Backscatter Diffraction (EBSD) images) remained statistically unchanged. …”
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Surface morphology, electronic defects and passivation strategies at the p–n junction of Cu(In,Ga)(S,Se)2 solar cells by Amala Elizabeth, Andreas May, Finnegan Volkamer, Florian Giesl, Hossam Elanzeery, Thomas Dalibor, Daniel Abou-Ras, Harry Mönig

“…Based on the high power conversion efficiencies and compatibility toward large-area deposition techniques, Cu(In,Ga)(S,Se) _2 (CIGSSe) phototvoltaic absorbers are currently at the forefront of chalcopyrite thin-film solar cell technology. …”
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