Fluorescence spectra of atmospheric aerosols by J. Reichardt, F. Lauermann, O. Behrendt

“…The fluorescence maxima are below 500 <span class="inline-formula">nm</span>, and a linear decrease in the spectral backscatter coefficient can be seen at longer wavelengths; the spectral fluorescence capacity is low (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>&lt;</mo><mn mathvariant="normal">1</mn><mo>×</mo><msup><mn mathvariant="normal">10</mn><mrow><mo>-</mo><mn mathvariant="normal">6</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="fcbbb2805ab9534794f91444ae9ac33e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-25-5857-2025-ie00002.svg" width="52pt" height="14pt" src="acp-25-5857-2025-ie00002.png"/></svg:svg></span></span> <span class="inline-formula">nm⁻¹</span>). …”
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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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Machine Learning-Driven Rapid Flood Mapping for Tropical Storm Imelda Using Sentinel-1 SAR Imagery by Reda Amer

“…The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. …”
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Integrating GIS and remote sensing for soil attributes mapping in degraded pastures of the Brazilian Cerrado by Rômullo Oliveira Louzada, Ivan Bergier, Édson Luis Bolfe, Jayme Garcia Arnal Barbedo

“…The variables include spectral bands, vegetation and soil indices, gray-level co-occurrence matrices (GLCM), backscatter coefficients, polarimetric decompositions, and topographic indices. …”
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Automated system for high-throughput process-structure-property dataset generation of structural materials: A γ/γ′ superalloy case study by Thomas Hoefler, Ayako Ikeda, Toshio Osada, Toru Hara, Kyoko Kawagishi, Takahito Ohmura

“…While the necessary topographic data is typically acquired using atomic force microscopy, a significant speedup was achieved by automatic indent detection and scanning using Angular selective Backscatter FE-SEM analysis. As a method to rapidly assemble comprehensive and consistent P-S-P datasets, we expect it to facilitate efficient alloy design, given a vast majority of modeling approaches still heavily rely on empirical data.…”
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Self-supervised feature learning for acoustic data analysis by Ahmet Pala, Anna Oleynik, Ketil Malde, Nils Olav Handegard

“…The proposed model is trained with three sampling methods: random sampling, which ignores class imbalance present in the acoustic survey data; class-balanced sampling, which ensures equal representation of known categories; and intensity-based sampling, which selects data to capture backscatter variations. The quality of extracted features is then evaluated and compared. …”
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Raman Spectroscopy Principles for in vivo Diagnostic by Ellipsoidal Reflectors by Bezuglyi Mikhail, Bezuglaya Natalia

“…Additionally, the investigation demonstrates the efficiency of ellipsoidal photometry method for registration of Raman scattering signal on test-solutions. …”
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Kinetic‐Controlled Crystallization of α‐FAPbI3 Inducing Preferred Crystallographic Orientation Enhances Photovoltaic Performance by Sooeun Shin, Seongrok Seo, Seonghwa Jeong, Anir S. Sharbirin, Jeongyong Kim, Hyungju Ahn, Nam‐Gyu Park, Hyunjung Shin

“…With microscopic observations, for example, electron backscatter diffraction and selected area electron diffraction, it is examined that higher concentration of MACl induces slower crystallization kinetics, resulting in larger grain size and [100] preferred orientation. …”
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