漂移壳追踪方法与内辐射带的长期变化

地磁场的长期变化发迹辐射带结构,如何在当前年代下恰当地使用ＡＥ－８／ＡＰ－８模型,目前国内外对此问题存在着很大的争议。我们在物理分析的基础上给出了当前年代下使用辐射带模型ＡＰ－８的漂移壳追踪方法ＤＳＴＭ（Ｄｒｉｆｔ Ｓｈｅｌｌ Ｔｒａｃｉｎｇ Ｍｅｔｈｏｄ）。计算结果表明地磁场的长期变化对内辐射带的通量分布有着显著的影响。３０－４０年来,南大西洋通量异常区中心向丁和向北发生偏移,高粒子辐射区域的范     

Understanding the mineralogy of a compositional anomaly north of the Korolev basin using Moon mineralogy mapper

The work attempts to understand the mineralogy of the reported geochemical anomaly located in the north – northeast region of the Korolev basin using Moon Mineralogy Mapper (M³) onboard Chandrayaan ?1 and other lunar datasets. To understand the mineralogy, colour composite images using integrated band depth parameters and mineral indices were prepared, and the M³ spectral signatures corresponding to the unique colours of these colours composites were investigated. Further, Modified Gaussian Model (MGM) deconvolution was applied to these spectra. The results of spectra studies reveal that the area is made up of heterogeneous lithologies, predominantly composed of anorthosite along with minor occurrences of pyroxene-bearing (both low-calcium and high-calcium variety) and spinel-bearing lithologies. Correlation of spectral studies with the morphology revealed that pyroxene was typically associated with fresh craters and their ejecta. Spinel was found to be ubiquitous and is well-dispersed, possibly distributed along with the ejecta blanket of large impact craters. This association hints that the pyroxene-hosted layer most likely occurs beneath the spinel-bearing layer. Such observed assemblages may have resulted from physical mixing during the impact cratering events. This mixed lithology could arise due to the mafic mineral-bearing ejecta of the South-Pole Aitken (SPA) basin and spinel-bearing Orientale ejecta. Korolev most likely impacted on a thick layer of SPA ejecta, and impact basins such as Hertzsprung and Orientale have occurred post-Korolev formation. Orientale being the youngest of the large impact basins, its ejecta carrying the light plain material would have overprinted the older SPA ejecta. Smaller impact craters would have churned the ejecta so that presently we observe a composite lithology with a variable abundance of pyroxene and spinel.     

数据驱动的运载火箭氧涡轮泵异常分析方法

基于模糊聚类和LSTM网络，提出了一种数据驱动的运载火箭发动机氧涡轮泵数据异常分析方法。通过模糊聚类对工况复杂，标签不完整的数据样本进行预分类，得到完整的标签并且分析特征贡献度，为LSTM网络的特征筛选和训练打下基础；通过LSTM网络对氧涡轮泵数据进行预测，并计算预测结果与原始数据之间的平均误差，再根据非参数阈值计算方法计算的阈值判据来判断设备是否异常，最终实现了氧涡轮泵数据驱动的故障检测报警，相较于红线阈值检测方法准确率提升7%。     

A method for satellite time series anomaly detection based on fast-DTW and improved-KNN

In satellite anomaly detection, there are some problems such as unbalanced sample distribution, fewer fault samples, and unobvious anomaly characteristics. These problems cause the extisted anomaly detection methods are difficult to train accurate classification model, and the accuracy of anomaly detection is hard to improve. At the same time, the monitoring data of satellite has high dimension and is difficult to extract effective features. Based on the DTW over-sampling method, this paper realizes the over-sampling of fault samples in satellite time series, and constructs a distributed and balanced time series data set. The Fast-DTW method is applied to calculate the distance between different time series, which can improve the speed of similarity calculation. KNN (K-Nearest Neighbor) method is applied for classification and the best classification result is obtained by search the optimal hyper-parameters k. The results show that the proposed method has high anomaly detection accuracy and consumes short calculation time.     

机器视觉在军事领域的应用现状及发展趋势

机器视觉技术凭借其非接触测量、实时性好、可持续工作等优点，在军事领域中有着广阔的应用前景。在对机器视觉光学照明系统、成像系统、视觉信息处理系统等关键技术进行概述的基础上，详细分析了机器视觉技术在军事领域进行典型目标物识别、人员识别、装备缺陷检测等典型场景以及典型军事装备上的应用现状。在此基础上，指出了机器视觉在军事领域的应用，仍然存在视觉传感器硬件系统难以适应极端环境、复杂的军事目标适应性不足、目标识别的实时性难以保证、多传感器融合获取军事目标信息能力缺乏等问题。同时，对机器视觉技术在军事领域应用的未来发展趋势进行了展望，研究分析结果可为机器视觉在军事领域的进一步实用化提供参考。     

Detection of surface temperature anomaly of the Sea of Marmara

Monitoring sea surface temperature (SST) over a long-term and detecting the anomalies highly contribute to understanding the prevailing water quality of the sea. Earth observation satellite images are the key data sources that offer the long-term SST detection in a cost and time effective way. Since the Sea of Marmara in Türkiye is surrounded by the highly populated provinces, the water quality of the sea has gained importance for scientific and public communities over the years. This article emphasizes on the significance of detecting SST trend and corresponding anomalies of the Sea of Marmara over the past 32 years. To address the SST variations of the Sea of Marmara in time, a comprehensive set of both field and satellite data regarding SSTs were obtained within the context of this study. The SST trend and its anomalies between the years 1990 and 2021 were detected by applying Seasonal-Trend decomposition procedure based on LOESS (STL) method to NOAA OISST V2 data. On the other hand, spatial SST distribution was detected with Landsat-8, Sentinel-3 and NOAA OISST V2 satellite data. SST results were verified with the in-situ data within the scope of accuracy assessment. The results showed that SST time-series data performed an increasing trend and had anomalies mostly during the spring months in the recent years.