Accurate Solar Wind Speed Prediction with Multimodality Information

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Journal profile

Open Access journal Space: Science & Technology, published in association with BIT, promotes the interplay of science and technology for the benefit of all application domains of space activities. It particularly welcomes articles illustrating successful synergies in space programs and missions.

Editorial board

Space: Science & Technology’s editorial board is led by Peijian Ye (China Academy of Space Technology), and it includes experts who have been carefully selected to include all domains of sciences and technologies covered by space missions of different types.

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Research Article

Overview of Activities: ARES-III and LEARN Analog Missions in the LunAres Hab

Progress is being made on human Lunar and Martian missions by space agencies and private organisations around the world, with the aims of establishing reliable long-duration architectures. Complementing this, research is being carried out under controlled and isolated conditions within simulated space habitats, to gain insights into the effects of such conditions on the research subjects and their impacts on crews’ wellbeing and success. This paper provides an overview of the experiments conducted during two separate 15-day missions—one Martian and one Lunar—conducted in the LunAres Research Base in Piła, Poland, in 2018. Some activities were common between the two crews; others were only carried out by one. Using the same methodology, both collected cognitive function, environmental, physiological, and inventory data, resulting in a larger dataset allowing comparisons between the two missions in terms of varying human factors. Experiments conducted by the Lunar crew included the following: effects of consuming lyophilised food on oral health and saliva production, influence of isolation on hearing capability, feelings on security in the isolated habitat, and research into earthworm growth in different soil compositions. The Mars mission analysed physical performances of the crew and compared them to performances realised during similar activities in Mars Research Desert Station missions and the impact of confinement on their efficiency performing a remote operation of a rover. For each piece of research, an overview of the background, methodology, results, and conclusions is given, referencing the resulting papers. In addition, nonresearch activities are included for completeness and context.

Review Article

LEO Mega Constellations: Review of Development, Impact, Surveillance, and Governance

The rapid development of Low Earth Orbit (LEO) mega constellations has significantly contributed to several aspects of human scientific progress, such as communication, navigation, and remote sensing. However, unrestrained deployment of constellations has also strained orbital resources and increased spacecraft congestion in LEO, which seriously affects the safety of in-orbit operations of many space assets. For the long-term and sustainable development of space activities in LEO regions, space environment stability must be maintained using more rational surveillance and governance mechanisms. This review contributes to the research gap and facilitates the development of LEO mega constellations. First, the current development of typical LEO mega constellations is reviewed, followed by the analysis of the impact of LEO mega constellations in terms of astronomical observation, spacecraft safety in orbit, and space environment evolution. Then, two main solutions to conduct the challenges raised by LEO mage constellations are elaborated: one is to ensure the safety operation of spacecraft using space surveillance infrastructures and space situational awareness technologies, and the other is to accelerate the deorbit of constellation satellites at the end of life based on postmission disposal and active removal methods. Finally, the future development and potential research directions of LEO mega constellations are prospected.

Research Article

A Surrogate-Assisted Evolutionary Algorithm for Space Component Thermal Layout Optimization

In space engineering, the electronic component layout has a very important impact on the centroid stability and heat dissipation of devices. However, the expensive thermodynamic simulations in the component thermal layout optimization problems bring great challenges to the current optimization algorithms. To reduce the cost, a surrogate-assisted evolutionary algorithm with restart strategy is proposed in this work. The algorithm is consisted of the local search and global search. A restart strategy is designed to make the local search jump out of the local optimum promptly and speed up the population convergence. The proposed algorithm is compared with two state-of-the-art algorithms on the CEC2006, CEC2010, and CEC2017 benchmark problems. The experiment results show that the proposed algorithm has a high convergence speed and excellent ability to find the optimum in the expensive constrained optimization problems under the very limited computation budget. The proposed algorithm is also applied to solve an electronic component layout optimization problem. The final results demonstrate the good performance of the proposed algorithm, which is of great significance to the component layout optimization.

Research Article

Centered Error Entropy-Based Sigma-Point Kalman Filter for Spacecraft State Estimation with Non-Gaussian Noise

The classical sigma-point Kalman filter (SPKF) is widely used in a spacecraft state estimation area with the Gaussian white noise hypothesis. The actual sensor noise is often disturbed by outliers in the harsh space environment, and the SPKF algorithm will reduce the filtering accuracy or even diverge. In this study, to enhance the robustness under non-Gaussian noise condition, the outlier-robust SPKF algorithm based on a centered error entropy (CEE) criterion is derived. Unscented Kalman filter (UKF) is typical of SPKF; combining the deterministic sampling criterion with the centered error entropy criterion, a robust centered error entropy UKF (CEEUKF) algorithm is proposed. The CEEUKF uses the unscented transformation (UT) method to perform time update step and then uses the robust regression model and CEE criterion to reconstruct the measurement update step. The effectiveness of the proposed CEEUKF is verified by a spacecraft attitude determination system.

Research Article

The Effect of Martian Ionospheric Dispersion on SAR Imaging

When passing through the ionosphere, the high-frequency (HF) pulse signal of the Mars Exploration Radar is affected by the dispersion effect error, which results in signal attenuation and time delay and brings about a phase advance in such a way that the echo cannot be matched and filtered. In this paper, a high-order phase model is built to overcome the above problems and enable echo matching and filtering. Most studies on the dispersion effect approximate the additional phase after the effect, assuming that the ionosphere is a thin-layer structure. In this paper, an effective model for the HF waveband is constructed to analyze the change of signal propagation paths in the ionosphere. The additional phase is expanded in a Taylor series and retained these expansions as high-order terms to calculate the cumulative additional phase along the path. We show the range-offset variables of signal frequency, bandwidth, and electron density, simulate the effects of the ionosphere under different conditions, and conclude that the model can effectively estimate Mars without considering the effects of magnetic fields and anomalous solar activity and the effect of the ionosphere on synthetic aperture radar (SAR) echoes. The results obtained using ray tracing calculations are different from those obtained by simplifying assumptions, and we can simulate the Martian ionospheric effects by the former.

Research Article

Two-Stage Solar Flare Forecasting Based on Convolutional Neural Networks

Solar flares are solar storm events driven by the magnetic field in the solar activity area. Solar flare, often associated with solar proton event or CME, has a negative impact on ratio communication, aviation, and aerospace. Therefore, its forecasting has attracted much attention from the academic community. Due to the limitation of the unbalanced distribution of the observation data, most techniques failed to effectively learn complex magnetic field characteristics, leading to poor forecasting performance. Through the statistical analysis of solar flare magnetic map data observed by SDO/HMI from 2010 to 2019, we find that unsupervised clustering algorithms have high accuracy in identifying the sunspot group in which the positive samples account for the majority. Furthermore, for these identified sunspot groups, the ensemble model that integrates the capability of boosting and convolutional neural network (CNN) achieves high-precision prediction of whether the solar flares will occur in the next 48 hours. Based on the above findings, a two-stage solar flare early warning system is established in this paper. The F1 score of our method is 0.5639, which shows that it is superior to the traditional methods such as logistic regression and support vector machine (SVM).