The space-borne laser detection system has the capability to detect the vertical profile of clouds and aerosols. Exist-ing payloads, such as the Caliop radar on the US Calipso satellite, the multi-beam LiDAR on the domestic "Goumang" satellite, and the atmospheric detection lLiDAR on air pollution monitoring satellites, are limited to single-beam cloud-aerosol detection with a narrow detection area. A proposed solution is a multi-beam cloud-aerosol detection LiDAR system that operates in a satellite orbit of 800 km and utilizes a multi-beam detection system to extend radar coverage to 30 km. The central beam employs dual-wavelength polarization detection for obtaining vertical profiles and particle species of atmospheric aerosols and clouds, while the edge beam uses single-wavelength detection for cloud profiling. This approach significantly improves data acquisition efficiency through simulation-based verification of dynamic range and high sensitivity single photon detections which reduce laser energy require-ments, as well as radar weight and power consumption. Finally, through simulation, the detection effect of space-borne multi-beam cloud-aerosol detection radar on typical clouds and aerosols is verified.

With the widespread use of network encryption protocols, traditional network traffic classification technology has been challenged. The current method has the following limitations: first, the model is highly dependent on the depth feature, which requires the labeled training data set to be large enough in scale, otherwise the model will have difficulty generalizing to new data;second, the model only focuses on one modal feature of traffic, and the feature differentiation of the same mode of traffic from differ-ent categories may not be obvious. To solve these problems, a deep learning-based encryption traffic classification model called Par-allel Transformer Net (PTNet) is proposed in this paper. Based on the semi-supervised idea of pre-training and fine-tuning, the mod-el makes full use of a large amount of unlabeled traffic data on the network for pre-training, and then fine-tunes on the basis of a small amount of labeled data. Additionally, the model extracts the flow characteristics of load and packet length sequences in parallel to carry out multi-mode feature fusion. Three different traffic classification tasks and their corresponding datasets (Android, USTC-TFC, and CSTNET-TLS1.3) show good results, with classification accuracies reaching 95%, 98%, and 97%, respectively.

The chemical oxygen demand (COD) and chlorophyll a concentration, which are typical water quality parameters re-lated to the spectrum, serve as important indicators for reflecting the degree of water pollution and eutrophication. Support Vector Regression (SVR) is suitable for small sample sizes and widely utilized in remote sensing retrieval of typical offshore water quality parameters; however, it faces challenges in model parameter selection and may easily fall into local optimal solutions. To address this issue, an Improved Sparrow Search Algorithm (ISSA) is developed by integrating reverse learning and simulated annealing. An enhanced support vector regression model (ISA-SVR) is proposed by refining the Sparrow algorithm to optimize the penalty coeffi-cient and kernel parameters of the SVR model. Inversion models for COD and Chl-a concentrations are established using measured water spectra and data on water quality parameters. The accuracy of the model is validated using Sentinel-2 satellite remote sensing spectral data, yielding inversion accuracies for each water quality parameter concentration. The mean relative error (MRE) of the COD concentration prediction model and Chl-a concentration prediction model based on ISSA algorithm optimized SVR are 20.02%and 30.17%, respectively, outperforming other models such as linear regression, SVR, and SSA-SVR models. Experimental results demonstrate that ISA-SVR algorithm represents an effective approach for remotely sensed retrieval of COD and Chl-a concentrations while offering valuable insights for subsequent scientific management of offshore water quality.

The complex electromagnetic environment of modern battlefields requires strong robustness of satellite navigation anti-jamming receivers. A polarization-sensitive array uses polarization information to increase the dimension of processing signals and can be applied in navigation to improve the anti-interference performance of the receiver. In this paper, the anti-jamming of the dual-polarization array in a complex electromagnetic environment is studied. The signal model and the dual-polarization array are in-troduced, and the amplitude and phase information obtained by finite element simulation on HFSS are then used to synthesize the steering vector and simulate it in MATLAB to prepare the anti-jamming ability of the ordinary array and the dual-polarization array. The real data is measured in a darkroom to verify the simulation results. The satellite collection positioning test was carried out on the roof to compare the positioning results of the ordinary array and the Dual-polarization array. The simulation and experiment shows that the dual-polarization array can suppress super-degree-of-freedom jamming and has good robustness under low elevation angle jamming. The dual-polarization antenna does not change the antenna size and can maintain the receiver miniaturization while keeping good robustness.

In the radar imaging equipment test, the traditional real scene test method is difficult to construct, the scene is limited, and the test risk is high, so it is urgent to solve the problems of insufficient testing and incomplete evaluation of the target recog-nition algorithm. Aiming at the existing problems, this paper designs a test system for the target recognition algorithm, which can provide the processing and labeling of SAR image and inverse SAR image, as well as the automatic operation, environment configu-ration and performance evaluation of target recognition algorithm. Compared with the traditional test method, the system has the ad-vantages of low cost, short test time, strong controllability and extensibility.

Lithological identification and classification constitute indispensable facets of geology, resource exploration, and re-lated disciplines. The emergence of hyperspectral remote sensing has ushered in novel perspectives for lithological identification. The utilization of machine learning to extract information from hyperspectral rock images, thereby enabling accurate lithological identification, holds paramount practical significance. Currently, the application of machine learning methods for the classification of hyperspectral rock images lacks a comprehensive exploitation of spatial and spectral information. Therefore, this paper introduces a three-dimensional convolutional residual network structure augmented with an attention mechanism, capable of effectively extracting spatial, spectral, and joint spatial-spectral features from hyperspectral rock images. In this experiment, images of 10 different types of rock samples were collected using a drone equipped with a hyperspectral sensor. The algorithm proposed in this study was applied to classify hyperspectral rock images. Experimental results indicate that, in comparison to traditional machine learning algo-rithms such as SVM and RF, as well as deep learning algorithms like ResNet, 3DCNN, and SSRN, the proposed algorithm exhibits higher accuracy.

The development of stealth technology and high-speed aircraft has brought serious challenges to radar target detection, and the long time accumulation technology is an effective method to improve the signal-to-noise ratio (SNR) in the field of signal processing. For high speed and high maneuvering targets, signal accumulation over a long period of time will cause the problem of moving across the range unit and moving across the Doppler unit. The "two-migration" problem will seriously affect the accumulation gain, and the traditional MTD (Moving Target Detection) method has been unable to accumulate the energy of the signal. In order to solve the "two-migration" problem and improve radar detection performance of high-speed targets, many methods have emerged in recent years. Based on the target motion model, this paper will analyze long-term accumulation into three categories:long-term accumulation under uniform radial motion, long-term accumulation under accelerated radial motion, and long-term accumulation under complex radial high-order motion. This paper summarizes and presents the "two-migration" problems and solutions under the corresponding model, aiming to provide reference for subsequent research.

Trellis coding modulation technology can improve the signal-to-noise ratio, reduce the transmission power, and lower the bit error rate without changing the bandwidth of the information transmission spectrum. The realization method is to divide the constellation map to form a subset and gradually increase the minimum Euclidean distance between the signal points in the constellation map. In this paper, the demodulation performance of 8PSK and 16QAM-TCM modulation techniques is simulated on the simulation platform of a relay satellite communication system developed by the project team. The simulation curve shows the relationship between the required bit error rate and signal-to-noise ratio. The simulation results demonstrate: ① under the channel conditions of an ideal channel, I/Q amplitude and phase imbalance, amplitude and frequency characteristics, group delay, phase noise, power amplifier saturation, if the required BER is 1E-7, compared to 8PSK modulation technology, the signal-to-noise ratio of 16QAM-TCM technology saves 8.85 dB, 9.04 dB, 8.45 dB, 10.2 dB, 8.5 dB, and 14.6 dB, respectively; ② under the channel conditions of non-linear, correspondingly along with increasing of the signal-to-noise ratio, the BER of 8PSK modulation technology fluctuates around the order of 1E-3; ③ if the required BER is 1E-7, compared with the ideal channel SNR simulation result, the non-linear channel SNR simulation result of 16QAM-TCM technology loses 4.8 dB.

With the rapid development of remote sensing technology, the intelligent decoding of weak targets in optical remote sensing images has become one of the research hotspots in remote sensing information processing. The feature targets of remote sensing images are often characterized by small scale, many types, a large number, fast moving speed of some key small targets, and are easily affected by the complex background environment and noise, which makes it a great challenge extract information from weak targets in remote sensing images. Early research on weak target segmentation, detection, and tracking algorithms in intelligent interpretation algorithms mostly relied on template matching and a priori knowledge, and such algorithms need to consume a lot of resources, arithmetic, and expert knowledge costs, and there were problems of large computational volume and poor generalization ability. In recent years, with the rapid development of deep learning and other artificial intelligence technologies, the information of weak targets can be accurately obtained in massive remote sensing data, and the features of weak targets can be quickly extracted by combining deep learning algorithms to provide efficient and accurate decoding information. This paper summarizes the research progress of intelligent interpretation algorithms for weak targets in remote sensing images, including weak target segmentation, detection, and tracking algorithms based on traditional image processing methods, as well as typical related algorithms based on deep learning. By analyzing the advantages and limitations of these methods, it is of great significance to improve the information acquisition ability of relevant targets, enhance the situational awareness level of observation, and future applications.

Common-view time transfer involves exchanging the common-view data of two locations to complete long-distance and high-precision time comparison. This paper proposes a method for compressing and transmitting common-view data based on Beidou short message for areas without ground communication network. The method compresses the common-view data and uses the short message function of Beidou to transmit the compressed data in real time. A comparison test has been conducted in an area without a ground communication network. The test results demonstrate that the common-view data compression method designed in this paper has a significant compression effect and can achieve real-time common-view time transfer without a ground network through Beidou short message.