The radiation damage effect is relatively serious during spacecraft operation in orbit. To know the real cumulative dose in orbit accurately, a radiation dose detector is carried on the FY-4B satellite. It comprises one electric control unit and three dose detectors, with a total of four payloads. Each dose detector includes five dose-monitoring points corresponding to different shielding thicknesses, which are mainly used for the measurement of the dose depth. Three dose detectors with the same function are installed in the X, Y and Z positions and can detect the total dose in different directions during the operation of the satellite. Each detector has the characteristics of small size, high integration and low power consumption. At present, the FY-4B satellite is in orbit. The detectors have been working normally since they were started up, and good preliminary detection results have been obtained. From the power-up of the detectors to April 2022, the maximum dose has reached 2.7 × 10³ rad(Si). We conducted a preliminary analysis of the relationship between total dose and shielding thickness. In addition, the relationship between daily average dose and shielding thickness was further analyzed. These studies have application value for radiation protection design.

Low-frequency harmonic interference is an important factor that affects the performance of low-speed direct-drive servo systems. In order to improve the low-speed smoothness of direct-drive servo, firstly, the causes of the first and second harmonics of electromagnetic torque and tooth harmonics are analyzed based on the mathematical model of PMSM (permanent magnet synchronous motor) and the principle of vector control. Accordingly, the CC-EUMA (Electrical angle Update and Mechanical angle Assignment algorithm for Center Current) and SL-DQPR (Double Quasi-Proportional Resonant control algorithm for Speed Loop) algorithm are proposed. Second, to confirm the algorithm’s efficacy, the harmonic environment is simulated using Matlab/Simulink, and the built harmonic suppression module is simulated and analyzed. Then, a miniaturized, fully digital drive control system is built based on the architecture of the Zynq-7000 series chips. Finally, the proposed suppression algorithm is verified at the board level. According to the experimental results, the speed ripple decreases to roughly one-third of its initial value after the algorithm is included. This effectively delays the speed ripple’s low-speed deterioration and provides a new idea for the low-speed control of the space direct-drive servo system.

Particle identification is a critical component of space environment detection. In this study, we propose a CNN-LSTM based multi-class automatic particle classification network, which employs 1-D CNN to extract more discriminative local features of waveforms produced by particle detector, and utilizes the ability of LSTM to capture long-range dependencies for extracting global features with stronger generalization capability. The proposed network can effectively classify the waveforms from multi-type with multi-energy charged particles, achieving an accuracy of 91.72% in simultaneously classifying H, B, C, O, and Al ions across three distinct energy levels. Furthermore, the proposed network achieves an accuracy of 73.60% in classifying particles with unseen energy levels during training, significantly surpassing the accuracy of previous methods. Extensive analysis experiments demonstrate the robustness and effectiveness of the proposed network.

This article aims to conduct an in-depth investigation into the environmental impact of Hall thruster plumes on spacecraft surface charging. The non-uniform plasma plume generated by Hall thrusters may trigger charging and discharging effects, making the assessment of surface charging risks crucial. Through numerical simulations using SPIS system, this study evaluates the surface charging characteristics of a complex spacecraft in orbit, simulating the effects of turning on and off the thrusters, as well as varying distances between the thrusters and the spacecraft. The simulation demonstrates that turning on the thrusters significantly affects spacecraft charging, reducing the potential difference between spacecraft surfaces from 3740 V to 19.2 V, effectively alleviating electrostatic discharge on the spacecraft surface. The closer the thruster is to the spacecraft, the more CEX ions are collected on the surface, influenced by the beam ions, resulting in a surface potential change of 1.3 V, with minor effects on surface potential but contributing to increased deposition contamination on the spacecraft surface.

This work introduces the instrument design of the medium-energy proton detector (MEPD, detection range: 30 keV–5 MeV) mounted on the Chinese Fengyun-4B (FY-4B) satellite. Compared to a similar detector on the Fengyun-3E (FY-3E) satellite, this instrument has undergone significant changes due to the different orbital radiation environment and solar lighting conditions. Based on the calculation of the radiation model AP8, the geometrical factor is reduced to 0.002 cm²sr, while that of the MEPD on the FY-3E satellite is 0.005 cm²sr. Another difference is that the sensors in some directions are exposed to direct sunlight for 80 min every day on this orbit, depending on the attitude angle of the satellite, which is much worse than that on the FY-3E satellite. According to the calculation results of transmittance of photons through different materials, a 100 nm thickness nickel film is added in front of the sensors to eliminate light pollution completely. The test using a solar simulator shows that the measure is effective and the detector has no error count when the solar irradiance coefficient is 1.0. In addition, the Geant4 software is applied to simulate the particle transportation process under complete machine condition to check the contamination of electrons in the sensors in all directions after magnetic deflection. The data obtained in orbit show that the instrument works properly, and the data are in good agreement with the AP8 model. The observations of the MEPD on board the FY-4B satellite can provide important support for the safety of spacecraft and theoretical research related to space weather.

Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA × TOF), time-of-flight energy (TOF × E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm’s FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications.