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Space radiation particles will cause a variety of radiation effects on orbiting satellites, such as single event effects, total ionizing dose, displacement damage, etc. In response to these space radiation effects, BeiDou Navigation satellites M15/M16 operating in medium earth orbit (MEO) developed the first integrated monitor for high-energy proton and particle radiation effects, based on space particle radiation detection technology. This payload realizes the joint observation of the high-energy proton environment, particle radiation linear energy transfer spectrum, and total radiation dose in the MEO. According to the detection data, research on the characteristics and laws of the space particle radiation effects can be carried out, and the problem of spacecraft reliability verification in orbit can be solved from the causal chain. In this article, we have introduced the high-energy proton detector (energy range 3–300 MeV), including technical indicators, working principles, instrument design, and ground calibration.

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. [ABSTRACT FROM AUTHOR]

Based on the characteristics of space particle radiation in the Sun-synchronous orbit (SSO), a space particle radiation effect comprehensive measuring instrument (SPRECMI) was installed on the orbital platform of the upper stage of the Chinese CZ-4C carrier rocket, which can acquire the high-energy proton energy spectra, linear energy transfer (LET) spectra of particles, and radiation dose rate. The particle radiation detection data were obtained at 1000 km altitude for the first time, which can be used mainly for scientific research of the space environment, in-orbit fault analysis, and the operational control management of spacecraft, and can also serve as reference data for component validation tests. After SPRECMI's development, accelerator calibration and simulations were conducted, and the results demonstrated that all the measured indicators, including the high-energy proton spectra (energy range: 21.8–275.0 MeV, precision: <3.3%), total radiation dose (dose range: 0–1.04 × 106 rad, sensitivity: 6.2 µrad/h), and the LET spectra (range: 0.001–37.20 MeV/(mg/cm2), >37.2 MeV/(mg/cm2)), met the relevant requirements. Furthermore, the in-orbit flight test revealed that the detection results of the load components were consistent with the physical characteristics of the particle radiation environment of the spacecraft's orbit. [ABSTRACT FROM AUTHOR]

Featured Application: The present study can be applied to the study of the influence of a Hall thruster plume on the spacecraft surface. 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. [ABSTRACT FROM AUTHOR]

The global navigation satellite system radio occultation (GNSS-RO) is an important means of space-based meteorological observation. It is necessary to test the Global Navigation Satellite System Occultation signal receiver on the ground before the deployment of space-based occultation detection systems. The current approach of testing the GNSS signal receiver on the ground is mainly the mountaintop-based testing approach, which has problems such as high cost and large simulation error. In order to overcome the limitations of the mountaintop-based test approach, this paper proposes an accurate, repeatable, and controllable GNSS atmospheric occultation simulation system and builds a load performance evaluation approach based on the ground-based GNSS atmospheric occultation simulation system on the basis of it. The GNSS atmospheric occultation simulation system consists of the visualization and interaction module, the GNSS-RO simulation signal generation module, the GNSS-RO simulator module, the GNSS-RO signal receiver module, and the GNSS-RO inversion and evaluation module, combined with the preset atmospheric model to generate GNSS-RO simulation signals with a high degree of simulation, and comparing the atmospheric parameters of the inversion performance of the GNSS-RO signal receiver with the parameters of the preset atmospheric model to obtain the error data. The overall performance of the GNSS-RO signal receiver can be evaluated based on the error information. The novel approach to evaluate the GNSS-RO signal receiver performance proposed in this paper is validated by using the FY-3E (FengYun-3E) receiver qualification parts that have been verified in orbit, and the results confirm that the approach can meet the requirements of the GNSS-RO receiver performance test. This study shows that the novel approach to evaluate the GNSS-RO signal receiver performance in terms of the ground-based atmospheric occultation simulation system can efficiently and accurately be used to carry out the receiver test and provides an effective solution for the ground-based test of GNSS-RO signal receivers. [ABSTRACT FROM AUTHOR]

Based on delay-Doppler maps (DDMs) in raw counts generated by spaceborne global navigation satellite system reflectometry (GNSS-R) receivers, retrieving ocean surface wind speeds is feasible, so several spaceborne GNSS-R missions have been carried out. However, it is currently troubled by global data latency of several hours or even more due to the bottleneck in the satellite downlink. Consequently, this article, for the first time, presents an algorithm for spaceborne GNSS-R receivers to conduct the DDM calibration in orbit and then to retrieve ocean surface wind speeds in real time, which contributes to not only lightening the burden on downloading a wealth of scientific data but also broadcasting real-time ocean surface wind speeds to users. Since there is a power correlation between direct and reflected signals from the same GNSS satellite with respect to the GNSS-R receiver, this algorithm calibrates direct signal power first, and then it estimates the real-time GNSS transmitter effective isotropic radiated power at the reflected signal according to the normalized antenna pattern of the corresponding GNSS satellite. Afterward, DDMs in raw counts are calibrated. Finally, ocean surface wind speeds are computed using pretrained geophysical model functions. Exploiting the scientific data from the GNOS-II onboard China's FY-3E satellite, this algorithm is validated carefully, and final retrieved ocean surface wind speeds against collocated European Centre for Medium-Range Weather Forecasts wind speeds have an overall root-mean-square error of 1.68 m/s and 1.50 m/s for GPS-R and BDS-R, respectively. [ABSTRACT FROM AUTHOR]

This paper tackles pose tracking and model refinement, one of the fundamental work for 3D photogrammetry. The researches belong to the videometrics, an interdisciplinewhich combines computer vision, digital image processing, photogrammetry and optical measurement. Related works are summarized briefly in this paper. This paper studies the problem of pose tracking for target with 3D model. For the target with accurate 3D model, line model based pose tracking methods are proposed for target which is rich in line features. Experimental results indicate that the proposed methods track the target pose accurately. Normal distance iterative reweighted least squares and distance image iterative least squares methods are proposed to process more general targets. This paper adopts bundle adjustment to tackle pose tracking in image sequence for target with inaccurate 3D line model. The proposed method optimizes the model line parameters and the pose parameters simultaneously. The model line orientation, position and mean angle error, mean position error of the pose are 0.3°,3.5mm and 0.12°,20.1mm in simulation experiments of satellite pose tracking. Line features are used to track target pose with unknown 3D model through image sequence. The model line parameters and pose parameters are optimized under the framework of SFM. In simulation experiments, the reconstructed line orientation, position error and mean angle error, mean position error of pose are 0.4°,7.5mm and 0.16°,23.5mm.

The objective of this work is to study the characteristics of the hydrostatic bearing with nanolubricant. To investigate the effect of nanoparticle additives on the performance of hydrostatic bearings, a coupled heat transfer finite-element model of the hydrostatic bearing is developed, and the results of the static and dynamic performance of the hydrostatic bearing with lubricant and nanolubricant are evaluated based on computational fluid dynamics methods at different eccentricity ratios. The results show that the rotation error of the bearing–rotor system is reduced by using nanolubricant at a high eccentricity ratio. [ABSTRACT FROM AUTHOR]