Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Application of Machine Learning in Space Engineering
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Application of Machine Learning in Space Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Aerospace Science and Engineering".

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 4241

Special Issue Editor

Dr. Zhongcheng Mu

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: spacecraft system design; mission schedule; cooperative control for space complex cluster system

Special Issue Information

Dear Colleagues,

Completing complex missions in orbit intelligently and automatically is the main developing trend in space engineering. The combination of machine learning and space engineering is one of the key technologies to solve the issue above. From the subsystem level to the whole satellite system, to formation missions, and to constellation missions, especially for the mega LEO satellite network, machine learning algorithms have presented some advantages when facing uncertain space environments. Machine learning in space engineering will bring a powerful approach to solving complex problems and making decisions. Meanwhile, new challenges and issues have also emerged to attract more and more attention.  

Dr. Zhongcheng Mu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • space engineering
  • autonomous operation in orbit
  • intelligent decision

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

24 pages, 2402 KB  
Article
A Hybrid Approach to Enhanced SGP4 for Galileo Constellations
by Edna Segura, Rosario López, Iván Pérez, Martín Lara and Juan Félix San-Juan
Appl. Sci. 2026, 16(5), 2214; https://doi.org/10.3390/app16052214 - 25 Feb 2026
Viewed by 538
Abstract
An up-to-date catalog of residents space objects orbiting Earth requires a critical balance between computational efficiency and orbital prediction precision. This work presents HSGP4, a hybrid orbit propagator specifically tailored for Galileo-type orbits that enhances the classical SGP4 analytical model using Artificial Neural [...] Read more.
An up-to-date catalog of residents space objects orbiting Earth requires a critical balance between computational efficiency and orbital prediction precision. This work presents HSGP4, a hybrid orbit propagator specifically tailored for Galileo-type orbits that enhances the classical SGP4 analytical model using Artificial Neural Networks. The methodology centers on a non-invasive hybridization process that utilizes high-fidelity pseudo-observations to forecast SGP4 error residuals. A core contribution is the introduction of the Hybrid Two-Line Element format, which encapsulates neural model parameters alongside traditional orbital elements, ensuring seamless integration with existing catalog infrastructure. The development process involved comprehensive Exploratory Data Analysis and sensitivity analysis, which identified the argument of latitude as the most influential variable for correcting SGP4 errors in the MEO region. To ensure statistical robustness, a hierarchical selection strategy was implemented. This reduced an exhaustive search space of 32,256 candidate architectures to a final subset of optimized configurations. Validated against a decade of TLE data, the results confirm that HSGP4 effectively captures missing dynamic patterns and significantly improves ephemeris accuracy. By forecasting SGP4 error residuals, this hybrid approach provides a high-fidelity correction layer. It compensates for the limitations of analytical theories without requiring complex numerical integration. Full article
(This article belongs to the Special Issue Application of Machine Learning in Space Engineering)
►▼ Show Figures

Figure 1

19 pages, 2776 KB  
Article
Implementation of the Stack-CNN Algorithm for Space Debris Detection on FPGA Board
by Matteo Abrate, Federico Reynaud, Mario Edoardo Bertaina, Antonio Giulio Coretti, Andrea Frasson, Antonio Montanaro, Raffaella Bonino and Roberta Sirovich
Appl. Sci. 2025, 15(17), 9268; https://doi.org/10.3390/app15179268 - 23 Aug 2025
Cited by 1 | Viewed by 1801
Abstract
The detection of faint, fast-moving objects such as space debris, in optical data is a major challenge due to their low signal-to-background ratio and short visibility time. This work addresses this issue by implementing the Stack-CNN algorithm, originally designed for offline analysis, on [...] Read more.
The detection of faint, fast-moving objects such as space debris, in optical data is a major challenge due to their low signal-to-background ratio and short visibility time. This work addresses this issue by implementing the Stack-CNN algorithm, originally designed for offline analysis, on an FPGA-based platform to enable real-time triggering capabilities in constrained space hardware environments. The Stack-CNN combines a stacking method to enhance the signal-to-noise ratio of moving objects across multiple frames with a lightweight convolutional neural network optimized for embedded inference. The FPGA implementation was developed using a Xilinx Zynq Ultrascale+ platform and achieves low-latency, power-efficient inference compatible with CubeSat systems. Performance was evaluated using both a physics-based simulation framework and data acquired during outdoor experimental campaigns. The trigger maintains high detection efficiency for 10 cm-class targets up to 30–40 km distance and reliably detects real satellite tracks with signal levels as low as 1% above background. These results validate the feasibility of on-board real-time debris detection using embedded AI, and demonstrate the robustness of the algorithm under realistic operational conditions. The study was conducted in the context of a broader technology demonstration project, called DISCARD, aimed at increasing space situational awareness capabilities on small platforms. Full article
(This article belongs to the Special Issue Application of Machine Learning in Space Engineering)
►▼ Show Figures

Figure 1

Review

Jump to: Research

29 pages, 2499 KB  
Review
Data Mining for Early Fault Detection in Artificial Satellites: A Review
by Victor Manuel Macias Martinez, Ingrid Xiomara Bejarano Cifuentes, Santiago Muñoz Giraldo, Mario Andrés Córdoba Gonzalez, Andrés Felipe Solis Pino and Cesar Alberto Collazos Ordónez
Appl. Sci. 2026, 16(1), 528; https://doi.org/10.3390/app16010528 - 5 Jan 2026
Viewed by 1110
Abstract
Early detection of faults in artificial satellites is crucial for the success of missions, but it is hindered by the scarcity of data on faults and the limitations of traditional monitoring methods. As an alternative, technological trends such as data mining have emerged. [...] Read more.
Early detection of faults in artificial satellites is crucial for the success of missions, but it is hindered by the scarcity of data on faults and the limitations of traditional monitoring methods. As an alternative, technological trends such as data mining have emerged. This study presents a literature review to provide an in-depth examination of the landscape of data mining applications for early fault detection in satellites. Following the PRISMA protocol, the available scientific corpus from seven scientific databases was reviewed, and 52 primary studies were selected from an initial set of 2726 records published between 2011 and 2024. The results indicate that this is a rapidly expanding field, with an annual growth rate of 35.72%, characterized by a significant geopolitical concentration of research and funding led by China. From a methodological point of view, unsupervised approaches (~40%) predominate, a response to the lack of labeled in-flight data. However, supervised and hybrid models demonstrate superior performance, achieving F1 scores above 97% when selected or simulated data are available. A misalignment was identified in the domain, although research clearly favors the EPS due to the availability of data. Operational statistics, however, confirm that the ADCS system is the primary cause of mission failure. It is essential to note that the limited availability of public datasets and models, with less than 15% of studies providing access, is the main obstacle to reproducibility and progress. The primary conclusion of this work is that the field is expanding, and all stakeholders must contribute to its continued growth. Key actions include establishing public benchmarks that enable transparent evaluation, exploring physics-based models that account for uncertainty to address data scarcity, and concerted efforts to bridge the transfer gap from academic satellite operations to the real world. Full article
(This article belongs to the Special Issue Application of Machine Learning in Space Engineering)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop