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At present, solar remote sensing instruments face problems regarding the short detection time for solar activity and the need to preheat the electronics before detection. The accurate orbit prediction function ensures that the maximum error of the payload electronics system preheating time is less than 8 s, enabling the electronics to obtain stable and reliable solar remote sensing data, and allowing the prediction function to adapt to the changes in preheating time and the limit position of turntable rotation. To achieve the complete detection of solar activity by a payload in orbit, a turntable with two directions of rotation was accurately controlled to achieve rapid capture and track the Sun's activity, and the delay time for tracking the Sun successfully was less than 8 s to ensure that the payload made full use of the solar detection time for full detection. The turntable's tracking error was less than 0.002°, allowing the payload to obtain high-precision solar remote sensing data in orbit. The above methods enable the payload to realize the complete, stable, and high-precision detection of solar activity in orbit for wavelengths ranging approximately from 165 nm to 2400 nm. [ABSTRACT FROM AUTHOR]

Using convolutional neural network (CNN) methods and satellite images for landslide identification and classification is a very efficient and popular task in geological hazard investigations. However, traditional CNNs have two disadvantages: (1) insufficient training images from the study area and (2) uneven distribution of the training set and validation set. In this paper, we introduced distant domain transfer learning (DDTL) methods for landslide detection and classification. We first introduce scene classification satellite imagery into the landslide detection task. In addition, in order to more effectively extract information from satellite images, we innovatively add an attention mechanism to DDTL (AM-DDTL). In this paper, the Longgang study area, a district in Shenzhen City, Guangdong Province, has only 177 samples as the landslide target domain. We examine the effect of DDTL by comparing three methods: the convolutional CNN, pretrained model and DDTL. We compare different attention mechanisms based on the DDTL. The experimental results show that the DDTL method has better detection performance than the normal CNN, and the AM-DDTL models achieve 94% classification accuracy, which is 7% higher than the conventional DDTL method. The requirements for the detection and classification of potential landslides at different disaster zones can be met by applying the AM-DDTL algorithm, which outperforms traditional CNN methods. [ABSTRACT FROM AUTHOR]