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Research development and technical difficulties of ultra-LEO spacecraft
, Available online  , doi: 10.11728/cjss2022-0010
Ultra LEO spacecraft has become a hot research field for a wide range of application in military, remote sensing, scientific research, etc. Due to the special space environment of ultra-low orbit, many technical difficulties need to be solved, mainly focusing on atmospheric environment prediction, aerodynamic, aerothermal and so on. This paper introduces the typical mission of ultra LEO spacecraft, the main atmospheric model and inversion method, the aerodynamic design of ultra LEO spacecraft and the stability control method under aerodynamic interference, the aerodynamic thermal protective material and the variable switching technology of thermal insulation and heat dissipation. This review is helpful to promote the key technology research and test demonstration of ultra LEO spacecraft, turning the ultra LEO spacecraft from test mission to space application mission as soon as possible.
Comparative Study between the deriving ionospheric foF2 from nighttime OI 135.6nm emission and ionosonde observations
, Available online  , doi: 10.11728/cjss2022-0018
During the nighttime, the 135.6nm spectral line is excited by the radiation recombination process of F region O+ and e- and the mutual neutralization process of O+ and O- in the ionosphere. There is a strong correlation between the intensity of the spectral line and the maximum electronic density of ionospheric F2 layer (NmF2). Based on the physical model in which the OI 135.6 nm emission is proportional to the square of NmF2, we establish a retrieval algorithm suitable for different longitude and latitude, local time, season and solar activity. In this paper, the critical frequency of ionospheric F2 region (foF2) was retrieved from 135.6nm emission observed by the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) instrument on board the Defense Meteorological Satellite Program (DMSP), and then the estimated results were compared with the detection results of ground-based ionosonde. As the results show, during the high-solar activity year (2013), the data with relative error less than or equal to 20% accounted for 92.99%, and the average relative error was about 7.18%. During the low-solar activity years (2017), the data with relative error less than or equal to 20% accounted for 80.76%, and the average relative error was about 13.02%. Finally, we analyzed the difference of retrieval accuracy of the algorithm during the high and low solar activity years.
Spectrum Sensing Algorithm for Cognitive Satellite Communication Based on Bi-LSTM and Bayesian Likelihood Ratio Test
, Available online  , doi: 10.11728/cjss2022-0017
With LEO mega satellites constellation coming into operation, the available spectrum resources are more overcrowded. To improve spectrum utilization, cognitive satellite communication technology composed of GEO relay satellites and LEO satellites has become an important choice. The most critical step in the cognitive satellite communication scenario is the spectrum sensing technology used to quickly determine the presence or absence of the primary user. Since most current spectrum sensing algorithms are model-driven, they rely heavily on the assumed statistical model for their detection performance, which makes it more difficult to model and deploy in variable satellite communication scenarios. In this paper, we firstly analyze the signal-to-noise ratio fluctuations during LEO satellite transit, and secondly propose a spectrum sensing algorithm combining bidirectional long short-term memory network and Bayesian likelihood ratio test for this variable channel environment. The algorithm does not require any a priori knowledge of PU signals and can automatically learn features from PU signal data and make decisions. Simulation results show that the proposed algorithm still achieves 83% detection performance at a signal-to-noise ratio of -14 dB and consistently outperforms convolutional neural networks, multi-layer perceptron, and model-driven energy detection-based algorithms.
A Time-Varying Volume Data Transfer Function for Interplanetary Numerical Simulation Data
, Available online  , doi: 10.11728/cjss2022-0011
Understanding the interplanetary propagation of solar storms is the foundation of space environmental forecasting and services. The visualization of numerical model results is an important method to analyze the propagation dynamics process and verify the validity of the model. In order to facilitate the visual analysis of the model results, a transfer function design algorithm for volume rendering of time-varying data(TVTF) is proposed. The algorithm is based on the KNN background subtractor method to extract images including motion regions and motion subsets, and then use frequency-tuned (FT) salient region detection algorithm to detect coronal mass ejection(CME) in motion area images, and according to the CME detection results, a color inverse mapping algorithm is designed to find the boundary threshold between the CME and the background. Finally, the transfer function is adaptively adjusted based on the threshold to realize the fast 3D visualization of CME in the motion region at each time step. The experimental results show that the transfer function can adapt to the numerical model results in static and dynamic backgrounds. Compared with the linear transfer function, the occlusion of the line of sight direction is effectively avoided, the change of relative momentum is intuitively and automatically displayed, and the evolution process of CME in interplanetary space is traced. The extraction of local regions reduces data redundancy, and the process of adaptively adjusting the transfer function by automatically analyzing the data with the help of algorithms avoids the inefficiency of manual adjustment.
Responses of the middle and upper atmospheric wind to geomagnetic activities
, Available online  , doi: 10.117282022-0016

Responses of the middle and upper atmospheric (80-100 km ) wind to geomagnetic activities have been investigated using neutral wind data from 2012 to 2018 years, which were observed by Mohe, Beijing and Wuhan Meteor radars. Daily averaged wind data for geomagnetic quiet condition (Kp ≤ 2) and geomagnetic disturb condition (Kp ≥ 4) were chosen for comparison, and the variation characteristics of wind during geomagnetic disturbances were obtained. The observations show that the influence of geomagnetic activity on zonal wind varied with seasons and latitudes. For zonal wind, the effect of geomagnetic activity at higher latitudes tended to be more westerly wind in the upper mesosphere and more easterly wind in the lower thermosphere, and the differences between disturbed and quiet conditions were on the order of 3 m/s; while for the lower latitudes, it tended to be more easterly wind in the 80-100 km region, and the influence were about 5 m/s. In spring, the three stations had similar tendencies, and had no latitude differences. But the easterly wind in the middle atmosphere became stronger with the decrease of latitude in summer/winter. The effect of geomagnetic activities on the meridional wind had seasonal differences. The influence of geomagnetic activities in spring and winter was stronger than that in summer and autumn. In winter, the effect of geomagnetic activity on the meridional wind in middle and low latitudes was stronger than that in higher latitudes. According to the calculation results, the influence on zonal wind was about 5 m/s to 10 m/s, and on meridional wind was about 3 m/s to 5 m/s. The impact of geomagnetic activities on MLT wind can penetrate down to about 80 km. At this height, the influence on zonal wind was the strongest in spring, reaching 8 m/s, and on meridional wind was the strongest in spring/winter, reaching 5 m/s.

An FPGA-implemented method for real-time multi-dimensional feature extraction of sequence image targets
, Available online  , doi: 10.117282022-0014

The prerequisite of target detection and tracking is to model and represent the target based on multi-dimensional features extracted from the target region. The traditional target feature extraction needs to connect the target region first and then must calculate the target feature, which has still room for improvement in real-time. An advantage of the new method based on the synchronous calculation of pixel-connected domain markers and target features is that the target features can be output when the target region is connected. The process establishes a feature transfer mechanism, creates a marker table, a marker mapping table, and a feature attribute table simultaneously when scanning images, and uses the marker mapping table to associate the marker table and the feature attribute table. The marker merging and feature attribute transfer calculation are performed synchronously at the time of region adjacency, which ensures the real-time target feature extraction. As a result, the method proved to be effective because of an implementation scheme based on an FPGA system design. Simulation test results show that this method has several outstanding features: the time of connected domain markers is close to the theoretical minimum; the storage of images utilizes the circular buffer with low resource consumption; marking and computation are processed in parallel flow to improve detection and tracking efficiency; multi-target features are tested and verified to be accurate, which can effectively support subsequent target tracking detection; and it has theoretical and practical values.