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排序方式: 共有906条查询结果,搜索用时 15 毫秒
901.
902.
《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2023,71(1):286-297
This paper investigates the ionospheric storm of December 19–21, 2015, which was initiated by two successive CME eruptions that caused a G3 space weather event. We used the in situ electron density (Ne) and electron temperature (Te) and the Total Electron Content (TEC) measurements from SWARM-A satellite, as well as the O/N2 observations from TIMED/GUVI to study the ionospheric impact. The observations reveal the longitudinal and hemispherical differences in the ionospheric response to the storm event. A positive ionospheric storm was observed over the American, African and Asian regions on 20 December, and the next day showed a negative storm. Both these exhibited hemispheric differences. A positive storm was observed over the East Pacific region on 21 December. It is seen that the net effect of both the disturbance dynamo electric field and composition differences become important in explaining the observed variability in topside ionospheric densities. In addition, we also discuss the Te variations that occurred as a consequence of the space weather event. 相似文献
903.
《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2023,71(6):2566-2574
Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty). 相似文献
904.
905.
906.
提出了一种针对多光谱图像中桥梁的识别算法。首先,根据水体和背景地物在不同光谱波段的亮度差异,计算多光谱图像的水体指数得到水体增强图,搜索其具有明显双峰的直方图得到最优阈值,实现河流的完整提取;其次,利用桥梁的存在会导致局部水体的光谱异常,沿河流中间线进行潜在桥梁区域的快速提取;再进一步利用桥梁长度以及与河流的空间关系进行鉴别,有效剔除虚警。利用 SPOT4遥感影像进行实验,结果表明本文算法运算量小,对于多个桥梁的识别具有很好的实用性。 相似文献