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    Towards an RFI Mitigation Algorithm in Synthetic Aperture Interferometer Radiometry: Application to the SMOS Space Mission by Ali Khazaal

    Published 2025-01-01
    “…The second step employs a modified version of the previous algorithm, incorporating a dynamic, source-specific search to detect and correct residual interferences. …”
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  3. 1083

    Investigation on the Radar Scattering and Doppler Spectrum From Trimaran Based on the Motion of Six Degrees of Freedom by Yuxin Deng, Jinxing Li, Wangqiang Jiang, Min Zhang

    Published 2023-01-01
    “…Second, an accelerated geometrical optics and physical optics algorithm using the light source function (GO-PO/LSF) is proposed, which uses the light source function to realize the visibility judgment of surfaces and greatly reduces the computation time. …”
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    A Hybrid Mangrove Identification Method by Combining the Time-Frequency Threshold of the Mangrove Index With a Random Forest Binary Classifier by Zhuokai Jian, Bin Ai, Jiali Zeng, Yuchao Sun

    Published 2025-01-01
    “…Specifically, the method utilized a small sample size to successfully obtain pure and accurate mangrove classification. …”
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    Estimation of Above-Ground Biomass for <italic>Dendrocalamus Giganteus</italic> Utilizing Spaceborne LiDAR GEDI Data by Huanfen Yang, Zhen Qin, Qingtai Shu, Li Xu, Jinge Yu, Shaolong Luo, Zaikun Wu, Cuifen Xia, Zhengdao Yang

    Published 2025-01-01
    “…The outcomes reveal that 1) the results showed that the power function emerged as the most efficacious model, with coefficient of determination (<italic>R</italic><sup>2</sup>) &#x003D; 0.87 and root mean square error (RMSE) &#x003D; 0.00051 Mg, in estimating the AGB of <italic>Dendrocalamus giganteus</italic>. 2) Based on the feature importance ranking of Random Forest, five variables were selected from the 40 extracted from GEDI, achieving RMSE &#x003D; 8.21 Mg&#x002F;ha and mean absolute error (MAE) &#x003D; 6.12 Mg&#x002F;ha. …”
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    Gene-Environment Interactions in Stress Response Contribute Additively to a Genotype-Environment Interaction. by Takeshi Matsui, Ian M Ehrenreich

    Published 2016-07-01
    “…To shed light on this problem, we genetically dissected an environment-specific poor growth phenotype in a cross of two budding yeast strains. This phenotype is detectable when certain segregants are grown on ethanol at 37°C ('E37'), a condition that differs from the standard culturing environment in both its carbon source (ethanol as opposed to glucose) and temperature (37°C as opposed to 30°C). …”
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    Bioluminescence and environmental light drive the visual evolution of deep-sea shrimp (Oplophoroidea) by Danielle M. DeLeo, Heather D. Bracken-Grissom

    Published 2025-02-01
    “…In pelagic environments, vertically migrating animals can experience drastic changes to their lighting environment across depth, subjecting them to unique selective pressures, possibly to distinguish between changes in ambient light and bioluminescent sources. …”
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