Quantum cascade technology, based on resonant tunneling and intersubband transitions in a multiple-quantum well or superlattice structure can generate light source and detect optical signals, it is the theoretical cornerstone of quantum cascade laser(QCL) and quantum cascade detector (QCD), which has wide application prospect in detection, remote sensing, communication, ra-dar, and other fields. After three decades of research, quantum cascade technology has made significant progress in basic research,product performance, application system research and scene testing. In this paper, the principle and development history of quantum cascade technology are briefly introduced first. Subsequently, the calculation approaches of intersubband energy level structure and electron transport dynamics in quantum cascade devices are elaborated. Next, the research progress of quantum cascade technology is mainly reviewed, including mid- and far-infrared high power QCL, mid-and far-infrared widely tunable QCL, terahertz QCL, high performance QCD, and single-chip photonic integration of QCL and QCD. Finally, the commercially available QCL and QCD products, as well as their application status, are introduced.

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.

The traditional deep learning-based Polarimetric Synthetic Aperture Radar (PolSAR) feature classification method extracts image local features by stacking convolutional layers, which makes it difficult to establish long-range dependencies. It is not-ed that Transformer, a deep learning model based on a self-attention mechanism that captures global pixel-to-pixel correlations, has achieved success in image classification tasks. Meanwhile, the PolSAR feature classification task has demonstrated better classification results in the complex domain compared to the real domain. Therefore, Transformer is introduced into the complex domain, and a hybrid model of Transformer and Unet based on the complex domain (CT-Unet) is proposed for PolSAR feature classification. This model combines Transformer with CNN for feature extraction on PolSAR data of complex type. The experimental results of PolSAR feature classification using the Xi'an dataset and German dataset show that the proposed model can effectively improve the accuracy of PolSAR feature classification. Transformer is expected to make up for the shortcomings of convolutional neural net-works in the PolSAR feature classification task.

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.

This paper introduces a novel design for a miniaturized wide-stopband hairpin filter that aims to overcome the chal-lenges of parasitic passbands at the harmonic frequencies encountered in traditional microstrip hairpin filters. By incorporating three 1/4-wavelength open-circuit microstrip lines at the input and output of the conventional hairpin filter, the design effectively suppresses parasitic passbands. Additionally, the application of cross-coupling techniques facilitates the miniaturization of the device. The design, fabrication, and testing phases culminated in the development of a bandpass filter with a 400 MHz bandwidth and a center frequency of 3 GHz. The filter demonstrates an insertion loss of 2.5 dB and achieves effective suppression of parasitic passbands be-low 13 GHz, with an out-of-band suppression of 24 dB. The new filter design is characterized by its simplicity and low complexity,with dimensions of just 23 mm by 27.7 mm, meeting the current demands in the communications field for high-performance, minia-turized filters, and demonstrating significant potential for future applications.

Commercial IP core is currently used in the development of high-speed memory device to achieve date error correction,the maximum achievable encoding and decoding speed is 800Mbps， which can only rely on multiple IP cores working simulta-neously to meet the requirements of gigabit high-speed data access rate. In view of the bit error characteristics of spaceborne storage data, this paper proposes an improved RS decoding algorithm, which reduces the number of iterations and the amount of computation in the decoding process by downgrading the residual polynomial in the encoding algorithm and the syndrome polynomial in the decoding algorithm, and adopts the sub-term multiplexing technology for the finite field multiplier of the basic operation unit in the decoding algorithm. The implementation results indicate that the maximum speed the encoder and decoder can achieve 10.5 Gbps，while the encoder resources is reduced by 15% and decoder resources reduced by 40% compared to a single commercial IP core,which can meet the application needs of high-speed of memory platform.

In order to meet the demand for distributed joint timekeeping system, a remote time comparison system was de-signed, which includes satellite co-visibility, satellite bi-directional, and fiber bi-directional time comparison means. The domestic equipment used in the system has the same time comparison accuracy as imported equipment, achieving high-precision clock differ-ence measurement between laboratories using multiple means. The relative zero value calibration of the time comparison equipment was completed using a mobile calibration station, and a 20-day time comparison was conducted between laboratories that were 50 km apart. The experimental results show that the mean clock difference measured by the three comparison means at the two locations is less than 1 ns, the trend is the same, and the amplitude is similar, and all can accurately measure the clock difference between the two locations.

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.

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 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.

By utilizing Unmanned Aerial Vehicle (UAV) Hyper-Spectral Imaging (HSI) and Light Detection and Ranging, this study aims to investigate the classification methods of wetland vegetation in the Yellow River estuary using LiDAR data. However,due to the high spatial resolution HSI spectral variability and uneven LiDAR point cloud density, the classification results exhibit a"pepper and salt" phenomenon. To address these issues, this paper proposes a two-branch convolutional neural network (SSF-C-DBCNN) that integrates empty spectrum feature fusion and channel attention mechanism. The spectral attention mechanism miti-gates the impact of spectral variability by assigning different weights to each band. Meanwhile, the spatial attention mechanism fo-cuses on learning and emphasizing dense point cloud regions with strong feature expression ability in order to alleviate the influence of uneven LiDAR point cloud density on the results. Finally, the channel attention mechanism is introduced for extracting deeper fea-tures after two-branch feature fusion. Experimental verification using HSI and LiDAR data collected by UAV demonstrates that the proposed method outperforms random forest as well as five deep learning methods, yielding more suitable classification results for actual land cover while effectively suppressing the "pepper and salt" phenomenon.

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.