Spacecraft Informatics

Abstract

Spacecraft informatics is one of the most exciting and contemporary research topics in recent years. Many countries are deploying related technologies such as AI, robotics, machine learning, etc., in the deep-space explorations. Moreover, considering the high-complexity, high cost and high risk involved in spacecraft, advanced technologies in information modelling, simulation, optimisation and decision support methods are required to improve the effectiveness, efficiencies, reliabilities and safety of the space operations (Du et al. 2017; Rui et al. 2014). The emerging informatics approach offers the benefit to the area of spacecraft regarding in-orbit spacecraft, satellites, space-stations of any types in deep-space exploration missions from ground control, user payload, space weather and conditions, remote sensing and telemetry, and many more spaceflight missions and activities of designing, forecasting, planning and control. To contribute the present and future space exploration and spacecraft development, in this special issue, we have collected excellent papers of research in spacecraft informatics. Each paper underwent a double-blind peer review by independent, anonymous expert referees. After the reviewing processes, eight highquality papers were accepted and are published in this issue. The first paper is ‘Optimisation problems and resolution methods in satellite scheduling and spacecraft operation: a survey’ by Xhafa and Ip (2019). This paper aims to study the state of the art in the satellite scheduling regarding the spacecraft design, operation and satellite deployment system. With heuristics methods, the constraint features in satellite mission planning, including window accessibility and visibility requirements can be addressed for producing smalland low-cost satellites. The second paper, entitled ‘Moon image segmentation with a new mixture histogram model’ by Hsu et al. (2019) is related to an application of image processing technology in the spacecraft. This paper aims to develop a histogram mixture model with genetic algorithm for improving the effectiveness in segmenting the moon surface image. Instead of the manual parameters measurement, the parameters can be obtained by a genetic algorithm. The results show that the proposed algorithm improved the drawbacks of previous non-parametric methods for moon image segmentation. In the papers, entitled ‘Blockchain adoption for information sharing: risk decisionmaking in spacecraft supply chain’ by Zheng et al. (2019) and ‘A framework for rocket and satellite launch information management systems based on blockchain technology’ by Li, Wang, and Zhang (2019), they employed blockchain technology for information management and sharing in the spacecraft supply chain. The use of blockchain technology allows the stakeholders in the spacecraft to (i) reduce transaction cost and risks, and (ii) improve the reliability and traceability of the spacecraft information to enhance the overall effectiveness and efficiency of the supply chain. The paper ‘Health condition estimation of spacecraft key components using belief rule base’ by Tang et al. (2019) developed a semi-quantitative method to examine the ENTERPRISE INFORMATION SYSTEMS 2021, VOL. 15, NO. 8, 1019–1021 https://doi.org/10.1080/17517575.2021.1886331