China's first precipitation measurement satellite has been successfully launched in April 2023, combining active and passive microwave instruments dual-frequency precipitation measurement radar and microwave imager to carry out high precision precipitation detection. In view of the limited coverage of precipitation measurement radar, the radar reflectivity factor data of wide swath radar is developed by using the microwave radiometer imager data of the same platform and the deep learning model, and the coverage of 2.67 times the width of precipitation measurement radar is achieved, which significantly increases the observation range of precipitation measurement radar. Taking Typhoon "Tali" in 2023 and extreme short-time heavy precipitation in North China as examples, the application potential of the wide swath results generated by the service is analyzed. The calculation result of the radar re-flectivity factor of wide swath radar is consistent with that of ground based radar, which has a good reference in practice. In the ground system operation scheduling, in order to support the continuous high-timeliness calculation of wide swath radar reflectivity factor data, this paper designs the wide swath radar reflectivity factor operation flow, comprehensively considers the resource con-sumption, timeliness and reliability of the operation implementation, and proposes the best data-driven operation scheduling strategy,which can effectively reduce the running time and improve the timeliness.

As one of the earth orientation parameters, UT1-UTC characterizes the irregularities of the earth rotation speed, and plays an important role in many space exploration activities such as space autonomoμs navigation, deep space probe orbit determination. Yet China has not established the relatively stable independent guarantee ability of UT1-UTC, it is of great reference signifi-cance to carry out UT1-UTC determination simulation at the early stage of system planning and construction. In this paper, the Mon-te Carlo simulation analysis of UT1-UTC solution is carried out by μsing VieSked++ software, and the measured data is utilized to verify the simulation conclμsions. Firstly, the IVS conventional observation mode is simulated, and subsequently about 100 times of IVS conventional observation data are used for comparison and verification. The results show that the actual solution accuracy is about 1.4 times of the repeatability factor. Secondly, the UT1-UTC intensive observation simulation is carried out for the single base-line of the Chinese deep space network, which is located in the northern and southern hemispheres, respectively. The simulation re-sults show that the UT1-UTC solution accuracy of the JM-KS baseline and the NM-AG baseline of the deep space network is expect-ed to be about 18 μs and 22.4 μs, respectively. Furthermore, the real observation data by JM-KS baseline of China deep space net-work are used for resolving UT1-UTC to verify the simulation result. Finally, on the basis of deep space network station resources,the simulation of multi-station and multi-baseline UT1-UTC monitoring capability is carried out. The results of this paper effectively evaluate the ability of UT1-UTC monitoring based on single baseline of China deep space network, and provide reference for subse-quent system construction and UT1-UTC solution ability evaluation.

This paper introduces an MSK non-coherent demodulation algorithm that integrates the soft spreading spectrum of CCSK, addressing the issue of signal demodulation in noisy environments. MSK modulation is widely used due to its efficient spec-tral utilization and low bit error rate, but traditional coherent demodulation relies on accurate carrier phase information, which is not suitable for short-burst communication and high-dynamic environments. The algorithm combines CCSK spreading technology, using base functions with strong periodic auto-correlation characteristics and their cyclic shift sequences, and proposes a waveform-matching-based MSK non-coherent demodulation algorithm, which significantly enhances the anti-interference capability, simplifies the receiver design, and maintains good demodulation performance under low signal-to-noise ratio conditions, with a high degree of tolerance to the sampling point drift. The simulation results show that under a signal-to-noise ratio of -5.5 dB, the system's bit error rate (BER) can be kept below 10^-6, demonstrating excellent noise resistance. Additionally, simulation experiments analyzed the im-pact of sampling point drift, revealing that a 1-sample point drift can cause 2-3 dB decrease in demodulation performance, while a 2-sample point drift can contribute to a 5-6 dB decrease, yet the demodulation performance remains within an operational range. The algorithm demonstrates outstanding demodulation performance under low signal-to-noise ratios and in high-dynamic scenarios. In conclusion, the waveform-matching-based MSK non-coherent demodulation algorithm proposed in this paper offers an efficient and reliable solution for wireless communication in complex environments.

In order to achieve high-precision remote sensing inversion of suspended particulate matter concentration in the seas surrounding the Yellow River Estuary, this paper constructs seasonal models for spring, summer, and autumn, as well as a cross-seasonal model, utilizing GOCI-I image data and based on the WOA-BP algorithm. These models are compared with multiple algo-rithms such as Catboost, RF, KNN, BP and so on. The results reveal that within each seasonal model, the WOA-BP algorithm exhib-its superior performance on both the training and testing sets, with the average relative errors for the respective seasonal testing sets being 24.18%, 25.97%, and 29.42%. When the cross-seasonal testing set is employed to evaluate the three models, and their accura-cy is found to be significantly lacking, which indicates that seasonal models are not applicable across different seasons. In the cross-seasonal model, the WOA-BP algorithm again demonstrates the highest accuracy, with an overall average relative error of 26.96%. The average relative errors when testing with the three seasonal testing sets are 25.80%, 21.90%, and 37.17%, respectively. While the accuracy for summer is improved, the accuracy for the other two seasons falls below that of the corresponding seasonal models,with autumn experiencing the greatest decline in precision. Therefore, it is suggested that the cross-seasonal model be employed for spring and summer, whereas the appropriate seasonal models are recommended for autumn.

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 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.

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.

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.

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.

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.