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相关概念视频

Precipitation Gravimetry01:03

Precipitation Gravimetry

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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Topographic Surveying and Contours01:29

Topographic Surveying and Contours

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Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
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Methods of Obtaining Topography01:25

Methods of Obtaining Topography

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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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Precipitation Processes01:12

Precipitation Processes

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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
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相关实验视频

Updated: Jan 13, 2026

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
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在Landsat交叉轨道区域使用深度合奏学习与不确定性量化进行每日陆地表面温度重建.

Shengjie Liu1, Siqin Wang1, Lu Zhang2

  • 1Spatial Sciences Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA 90089, USA.

IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society
|January 7, 2026
PubMed
概括
此摘要是机器生成的。

我们开发了DELAG,一种深度学习方法,使用Landsat数据重建城市地区的高分辨率地表温度 (LST). 这种方法提高了数据的可用性和准确性,即使在阴天条件下.

关键词:
斯过程是高斯过程.陆地卫星 (Landsat) 是一个地球卫星.年度温度周期的周期.深度集体学习 (Deep Ensemble Learning) 是一种深度集体学习.土地表面的温度 地表的温度重建的重建的重建.不确定性量化不确定性量化

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科学领域:

  • 地球和环境科学 地球和环境科学
  • 遥感 遥感 遥感 遥感
  • 气候科学 气候科学

背景情况:

  • 高时空分辨率的土地表面温度 (LST) 对城市环境研究至关重要.
  • 陆地卫星数据提供高空间分辨率,但受到不经常的重新访问和云干扰的影响,限制了其在复杂的城市景观中的实用性.

研究的目的:

  • 开发和验证DELAG,这是一个深度集体学习方法,用于复杂的城市地区重建Landsat LST.
  • 通过Landsat的交叉轨道能力和双卫星运行来提高LST数据的可用性.
  • 评估重建的LST的可靠性和不确定性及其在估计近地表空气温度中的应用.

主要方法:

  • 实施了DELAG,这是一种集体深度学习方法,结合了年度温度周期和高斯过程.
  • 利用Landsat数据,增强交叉轨道特性和双卫星操作,将数据频率提高到每16天4个场景.
  • 在不同云覆盖条件下,在纽约市,伦敦和香港验证了DELAG.

主要成果:

  • 德拉格成功地在RMSE值低的复杂城市地区重建了Landsat LST (0.73-0.96 K晴天,0.84-1.62 K阴天).
  • 该方法提供了不确定性量化,提高了LST重建的可靠性.
  • 重建的LST给出了准确的近地气温估计 (RMSE = 1.48-2.11 K),与清天空LST结果相似.

结论:

  • 德拉格为复杂的城市环境中的Landsat LST高分辨率重建提供了一种新而实用的解决方案.
  • 增强的数据可用性和准确性解决了传统LST数据采集的局限性.
  • 这种方法提高了研究复杂气候事件的潜力,并提高了在高时空分辨率下对空气温度的估计.