Showing 4,661 - 4,680 results of 4,914 for search '"Earth"', query time: 0.10s Refine Results
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    Reverse osmosis for the treatment of well water and wastewater for reuse by A. Mélendez- Rivera, J. Musa-Wasil, D. Almestica-Martinez, K. Malave-Llamas

    Published 2025-01-01
    “…BACKGROUND AND OBJECTIVES: Freshwater is a scarce resource, constituting only 2.5% of Earth's total water. Approximately 90% of freshwater is concentrated near the South Pole of the Antarctic Circle, exacerbating its scarcity for living organisms. …”
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  4. 4664

    Machine Learning para la Clasificación y Análisis de los Índices de Biomasa y su relación con el Cambio Climático, Desierto de Atacama by Santos Gómez, Edwin Pino-Vargas, Germán Huayna, Jorge Espinoza-Molina, Karina Acosta-Caipa, Fredy Cabrera-Olivera4

    Published 2024-04-01
    “…Fue importante el análisis geoespacial basado en Google Earth Engine (GEE) y el procesamiento de imágenes Landsat 5 ETM y Landsat 8 OLI/TIRS, para el período 1985 - 2022, lo que permitió caracterizar el cambio climático. …”
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  5. 4665

    Epsilon Canis Majoris: The Brightest Extreme-ultraviolet Source with Surprisingly Low Interstellar Absorption by J. Michael Shull, Rachel M. Curran, Michael W. Topping

    Published 2025-01-01
    “…The observed extreme-ultraviolet spectrum yields a hydrogen photoionization rate Γ _HI ≈ 10 ^−15 s ^−1 (at Earth). The total flux decrement factor at the Lyman limit (Δ _LL = 5000 ± 500) is a combination of attenuation in the stellar atmosphere (Δ _star = 110 ± 10) and interstellar medium (Δ _ISM = 45 ± 5) with optical depth τ _LL = 3.8 ± 0.1. …”
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    Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral images by N.A. Firsov, V.V. Podlipnov, N.A. Ivliev, D.D. Ryskova, A.V. Pirogov, A.A. Muzyka, A.R. Makarov, V.E. Lobanov, V.I. Platonov, A.N. Babichev, V.A. Monastyrskiy, V.I. Olgarenko, D.P. Nikolaev, R.V. Skidanov, A.V. Nikonorov, N.L. Kazanskiy, V.A. Soyfer

    Published 2023-10-01
    “…The paper presents a study of various approaches to the classification of soil covers based on neural network algorithms using hyperspectral remote and proximal sensing of the Earth. The spectral distributions were recorded in the laboratory using an Offner imaging scanning hyperspectrometer. …”
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  10. 4670

    Rabies re-emergence after long-term disease freedom (Amur Oblast, Russia) by A. D. Botvinkin, I. D. Zarva, I. V. Meltsоv, S. A. Chupin, E. M. Poleshchuk, N. G. Zinyakov, S. V. Samokhvalov, I. V. Solovey, N. V. Yakovleva, G. N. Sidorov, I. A. Boyko, V. G. Yudin, E. I. Andaev, A. Ye. Metlin

    Published 2022-12-01
    “…Genetically closest rabies virus isolates have been found in Heilongjiang Province (China, 2011, 2018) and Jewish Autonomous Oblast (Russia, 1980). GIS and open Earth remote sensing data were used to map the rabies cases. …”
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  11. 4671

    La ruta del cacique Llampilanguen (1804): la reconstrucción geográfica de un camino. histórico by Walter Daniel Melo, Juan Francisco Jiménez, Sebastián Leandro Alioto

    Published 2016-12-01
    “…En la metodología se utilizaron fuentes históricas y documentos cartográficos con la antigua toponimia mapuche, que en parte y afortunadamente se ha conservado durante un lapso considerable; la base cartográfica fue un modelo de elevación SRTM y tomas de Google Earth; y el Sistema de Información Geográfico (SIG) utilizado fue ArcGis 10.0, que con sus herramientas de buffer conectó los parajes relevantes. …”
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  12. 4672

    Historical and projected forest cover changes in the Mount Kenya Ecosystem: Implications for sustainable forest management by Brian Rotich, Abdalrahman Ahmed, Benjamin Kinyili, Harison Kipkulei

    Published 2025-06-01
    “…Land Use Land Cover (LULC) maps for 2000, 2014, and 2023 were classified using Random Forest (RF) in Google Earth Engine (GEE). Explanatory factors of LULC change (slope, aspect, population density, proximity to rivers, roads, and towns) were used to project LULC for 2035 using Cellular Automata and Markov Chain Analysis (CA-MCA).Six LULC types (open forest, closed forest, cropland, bareland, built-up, shrubland and grassland) were successfully classified with accuracies exceeding 82.5% and Kappa coefficients above 0.77. …”
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  13. 4673

    بررسی اثر شاخص‌های مورفولوژیکی رودخانه گرگانرود بر پهنه‌های سیلاب با استفاده از داده‌های سنجش از دور و تحلیل‌های مکانی(منطقه مطالعاتی: شهر آق‌قلا)... by کامران گنجی, سعید قره چلو, احمد احمدی

    Published 2020-11-01
    “…پارامتر ضریب سینوزیته و تعداد و میانگین شعاع پیچانرودها در چهار بازه در حدفاصل روستای سلاق‌یلقی تا روستای دوگونچی با استفاده از نرم‌افزار Google Earth و AutoCAD محاسبه گردید. مساحت پهنه‌های به دست آمده با استفاده از شاخص MNDWI تقریباً 88 درصد از مساحت پهنه‌های بدست آمده از شاخص NDWI بیشتر بود. …”
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  14. 4674

    Fingerprints of surface flows on solid substrates ablated by phase change: from laboratory experiments to planetary landscapes by Carpy, Sabrina, Berhanu, Michael, Chaigne, Martin, Courrech du Pont, Sylvain

    Published 2025-02-01
    “…In this case, the fluid mechanics associated with such phase changes play a key role in the evolution of terrestrial and planetary landscapes, observed by probes orbiting planets and moons. On Earth, sea ice, glaciers and karst plateaus extend over meters or kilometers. …”
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    Analysis of Radio Science Data from the KaT Instrument of the 3GM Experiment During JUICE’s Early Cruise Phase by Paolo Cappuccio, Andrea Sesta, Mauro Di Benedetto, Daniele Durante, Umberto De Filippis, Ivan di Stefano, Luciano Iess, Ruaraidh Mackenzie, Bernard Godard

    Published 2025-01-01
    “…This paper analyzes KaT data collected at the ESA/ESTRACK ground station in Malargüe, Argentina, during the Near-Earth Commissioning Phase (NECP) in May 2023 and the first in-cruise payload checkout (PC01) in January 2024. …”
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    Algorithm for Cloud Particle Phase Identification Based on Bayesian Random Forest Method by Fu Tao, Yang Zhipeng, Tao Fa, Hu Shuzhen, Lu Yuxiang, Fu Changqing

    Published 2025-01-01
    “…The study of cloud particle phase states is regarded as crucial for a deep understanding of the impact of clouds on the earth's climate, environment, and sustainable development, providing a scientific foundation for addressing climate change and improving environmental quality. …”
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