Pub Date : 2025-11-26DOI: 10.1109/JMASS.2025.3637574
Tibor Herman;Károly Kazi;Levente Dudás
PocketQubes are an emerging class of picosatellites that offer affordable access to space for educational and technology demonstration missions. However, a significant proportion of PocketQube missions fail due to design limitations and lack of subsystem redundancy. This article presents the HUNITY platform core—a modular, redundant module designed to handle critical satellite functions and improve mission reliability. We describe the system architecture, key implementation and integration challenges, and the testing methodology. The module has undergone vibration and thermal vacuum testing using the flight model hardware, achieving Technology Readiness Level 7 (TRL 7) in accordance with standard environmental qualification procedures.
{"title":"Experimental Results for a Modular and Redundant PocketQube Platform Core: Design, Implementation, and Testing","authors":"Tibor Herman;Károly Kazi;Levente Dudás","doi":"10.1109/JMASS.2025.3637574","DOIUrl":"https://doi.org/10.1109/JMASS.2025.3637574","url":null,"abstract":"PocketQubes are an emerging class of picosatellites that offer affordable access to space for educational and technology demonstration missions. However, a significant proportion of PocketQube missions fail due to design limitations and lack of subsystem redundancy. This article presents the HUNITY platform core—a modular, redundant module designed to handle critical satellite functions and improve mission reliability. We describe the system architecture, key implementation and integration challenges, and the testing methodology. The module has undergone vibration and thermal vacuum testing using the flight model hardware, achieving Technology Readiness Level 7 (TRL 7) in accordance with standard environmental qualification procedures.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"7 1","pages":"47-56"},"PeriodicalIF":2.1,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-24DOI: 10.1109/JMASS.2025.3636624
{"title":"2025 Index IEEE Journal on Miniaturization for Air and Space Systems","authors":"","doi":"10.1109/JMASS.2025.3636624","DOIUrl":"https://doi.org/10.1109/JMASS.2025.3636624","url":null,"abstract":"","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"6 4","pages":"417-429"},"PeriodicalIF":2.1,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11266960","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1109/JMASS.2025.3627820
{"title":"The Journal of Miniaturized Air and Space Systems","authors":"","doi":"10.1109/JMASS.2025.3627820","DOIUrl":"https://doi.org/10.1109/JMASS.2025.3627820","url":null,"abstract":"","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"6 4","pages":"C2-C2"},"PeriodicalIF":2.1,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11261885","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145555458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Research on remote sensing image super-resolution (RSISR) based on deep neural network has made significant progress. However, complex network architectures and high computational costs conflict with the resource limitations of small edge devices. To alleviate this problem, in this article, we propose a multilevel variance feature modulation network (MVFMNet), which can effectively utilize both local and nonlocal information for better super-resolution reconstruction of remote sensing images. Specifically, we propose a local variance-aware spatial attention (LVSA) module, which employs adaptive max-pooling to extract nonlocal features and introduces local variance to represent local structure. Building upon LVSA, we design a multilevel variance feature modulation block (MVFMB) by integrating two LVSA branches with distinct downsampling scales, enabling adaptive selection of multiscale representative features. To further enhance the features modulated by MVFMB, we introduce a symmetric gated feed-forward network to fuse more local contextual information. Comparison experiments conducted on several benchmark datasets demonstrate that MVFMNet can achieve a better tradeoff between reconstruction accuracy and computational efficiency in remote sensing image SR (RSISR). The code of MVFMNet will be released at https://github.com/AHUT-MILAGroup/MVFMNet.
{"title":"MVFMNet: A Lightweight Network for Remote Sensing Image Super-Resolution","authors":"Wei Xue;Tiancheng Shao;Mingyang Du;Jing Zhou;Xiao Zheng","doi":"10.1109/JMASS.2025.3635049","DOIUrl":"https://doi.org/10.1109/JMASS.2025.3635049","url":null,"abstract":"Research on remote sensing image super-resolution (RSISR) based on deep neural network has made significant progress. However, complex network architectures and high computational costs conflict with the resource limitations of small edge devices. To alleviate this problem, in this article, we propose a multilevel variance feature modulation network (MVFMNet), which can effectively utilize both local and nonlocal information for better super-resolution reconstruction of remote sensing images. Specifically, we propose a local variance-aware spatial attention (LVSA) module, which employs adaptive max-pooling to extract nonlocal features and introduces local variance to represent local structure. Building upon LVSA, we design a multilevel variance feature modulation block (MVFMB) by integrating two LVSA branches with distinct downsampling scales, enabling adaptive selection of multiscale representative features. To further enhance the features modulated by MVFMB, we introduce a symmetric gated feed-forward network to fuse more local contextual information. Comparison experiments conducted on several benchmark datasets demonstrate that MVFMNet can achieve a better tradeoff between reconstruction accuracy and computational efficiency in remote sensing image SR (RSISR). The code of MVFMNet will be released at <uri>https://github.com/AHUT-MILAGroup/MVFMNet</uri>.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"7 1","pages":"36-46"},"PeriodicalIF":2.1,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Time series synthetic aperture radar (SAR) data have shown significant potential for crop recognition in cloudy and rainy regions. However, time series SAR data often form a high-dimensional feature set, which can reduce accuracy and even lead to the dimensional disaster phenomenon. This challenge highlights the need for effective feature optimization techniques to enhance the performance of crop recognition models. Deep learning models, such as U-Net, have become a cornerstone for crop recognition using remote sensing data. This study investigates whether feature optimization is necessary for U-Net models when using time series SAR data, and explores which feature optimization methods and features are most suitable for improving model performance. Using 15 sets of Sentinel-1A data collected in Jiexi County during the second half of 2021, this study derived seven time series features, including backscattering (VV and VH), interference (VV and VH), and polarization characteristics (Alpha, Entropy, and Anisotropy), resulting in 103 variables. Feature selection (random forest (RF)) and feature fusion [principal component analysis (PCA), and minimum noise fraction transform, MNF] were employed to optimize all feature variables and extract the first seven optimized components. Each time series feature was also individually optimized using PCA and MNF, and the first optimized component was extracted. The results indicate that: 1) the U-Net crop recognition model constructed using the first seven optimized components extracted from all 103 original variables achieved similar accuracy to models constructed using all 103 original variables, with Kappa difference of 0.1. However, the computation time for all feature variables was significantly longer than for the optimized components. This suggests that feature optimization is necessary for U-Net models from computational efficiency; 2) the U-Net crop recognition model constructed using the top seven optimized components derived from all feature variables through PCA and MNF outperformed the model constructed using the first seven selected components derived through RF, with overall accuracy (OA) and Kappa values higher by 0.19 and 0.29, respectively. This suggests that feature fusion may be more suitable than feature selection for optimizing time series SAR data in crop recognition; and 3) the first component of the time series backscattering feature achieved the highest accuracy, with OA and Kappa values as high as 0.79 and 0.60, respectively, outperforming that of time series polarization and interference features. A U-Net crop recognition model constructed by combining the first component achieved similar accuracy with that constructed by all features variables, with OA and Kappa values of 0.81 and 0.65, respectively. This suggests that backscattering, polarization, and interference features are complementary in crop recognition, and their combination can significantly improve recognition accuracy. The
{"title":"Effect of Different Feature Optimization Method on Deep Learning Model for Crop Recognition With Time Series SAR Data","authors":"Jiayi Zheng;Wanjun Xia;Huaxiang Ding;Xiaojun Wang;Hongfei Xu;Xinjian Wen;Rui Zhang;Weixing Xue;Chaolong Yao;Feng Xue;Changwei Wang;Yufang Liu","doi":"10.1109/JMASS.2025.3629032","DOIUrl":"https://doi.org/10.1109/JMASS.2025.3629032","url":null,"abstract":"Time series synthetic aperture radar (SAR) data have shown significant potential for crop recognition in cloudy and rainy regions. However, time series SAR data often form a high-dimensional feature set, which can reduce accuracy and even lead to the dimensional disaster phenomenon. This challenge highlights the need for effective feature optimization techniques to enhance the performance of crop recognition models. Deep learning models, such as U-Net, have become a cornerstone for crop recognition using remote sensing data. This study investigates whether feature optimization is necessary for U-Net models when using time series SAR data, and explores which feature optimization methods and features are most suitable for improving model performance. Using 15 sets of Sentinel-1A data collected in Jiexi County during the second half of 2021, this study derived seven time series features, including backscattering (VV and VH), interference (VV and VH), and polarization characteristics (Alpha, Entropy, and Anisotropy), resulting in 103 variables. Feature selection (random forest (RF)) and feature fusion [principal component analysis (PCA), and minimum noise fraction transform, MNF] were employed to optimize all feature variables and extract the first seven optimized components. Each time series feature was also individually optimized using PCA and MNF, and the first optimized component was extracted. The results indicate that: 1) the U-Net crop recognition model constructed using the first seven optimized components extracted from all 103 original variables achieved similar accuracy to models constructed using all 103 original variables, with Kappa difference of 0.1. However, the computation time for all feature variables was significantly longer than for the optimized components. This suggests that feature optimization is necessary for U-Net models from computational efficiency; 2) the U-Net crop recognition model constructed using the top seven optimized components derived from all feature variables through PCA and MNF outperformed the model constructed using the first seven selected components derived through RF, with overall accuracy (OA) and Kappa values higher by 0.19 and 0.29, respectively. This suggests that feature fusion may be more suitable than feature selection for optimizing time series SAR data in crop recognition; and 3) the first component of the time series backscattering feature achieved the highest accuracy, with OA and Kappa values as high as 0.79 and 0.60, respectively, outperforming that of time series polarization and interference features. A U-Net crop recognition model constructed by combining the first component achieved similar accuracy with that constructed by all features variables, with OA and Kappa values of 0.81 and 0.65, respectively. This suggests that backscattering, polarization, and interference features are complementary in crop recognition, and their combination can significantly improve recognition accuracy. The","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"7 1","pages":"25-35"},"PeriodicalIF":2.1,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-04DOI: 10.1109/JMASS.2025.3628658
Rishi Raj Singh;Akhilesh Mohan
The empty substrate integrated waveguide (ESIW) is a popular technology that has gained widespread attention in the past decade. It is basically a substrate integrated waveguide with no dielectric that have minimal losses along the direction of wave propagation. This article presents an improved microstrip to empty SIW (ESIW) transition, aiming to alleviate fabrication complexity with enhanced performance characteristics. The transition includes a microstrip line, a tapered region, and a $lambda $ /4 long stepped structure protruding into the ESIW. The taper region eliminates discontinuity effects and ensures good impedance matching. While, the stepped profile offers ease of design and manufacturing compared to previously reported transitions. The back-to-back Transition I is designed, fabricated and its characteristics are measured. The fabricated Transition I offers a fractional bandwidth (FBW) of 77.40% for a frequency range of 7.6–17.2 GHz (X and Ku band) with return loss (RL) and insertion loss (IL) better than 14.4 and 1.5 dB, respectively. In order to validate its usability at higher frequency bands, Transition II is designed for K/Ka band applications. It offers a FBW of 70% for a frequency range of 18.9–39.3 GHz (K/Ka band) with RL and IL better than 20 and 1.5 dB, respectively.
{"title":"A Wideband Microstrip Line to Empty Substrate Integrated Waveguide (ESIW) Transition","authors":"Rishi Raj Singh;Akhilesh Mohan","doi":"10.1109/JMASS.2025.3628658","DOIUrl":"https://doi.org/10.1109/JMASS.2025.3628658","url":null,"abstract":"The empty substrate integrated waveguide (ESIW) is a popular technology that has gained widespread attention in the past decade. It is basically a substrate integrated waveguide with no dielectric that have minimal losses along the direction of wave propagation. This article presents an improved microstrip to empty SIW (ESIW) transition, aiming to alleviate fabrication complexity with enhanced performance characteristics. The transition includes a microstrip line, a tapered region, and a <inline-formula> <tex-math>$lambda $ </tex-math></inline-formula>/4 long stepped structure protruding into the ESIW. The taper region eliminates discontinuity effects and ensures good impedance matching. While, the stepped profile offers ease of design and manufacturing compared to previously reported transitions. The back-to-back Transition I is designed, fabricated and its characteristics are measured. The fabricated Transition I offers a fractional bandwidth (FBW) of 77.40% for a frequency range of 7.6–17.2 GHz (X and Ku band) with return loss (RL) and insertion loss (IL) better than 14.4 and 1.5 dB, respectively. In order to validate its usability at higher frequency bands, Transition II is designed for K/Ka band applications. It offers a FBW of 70% for a frequency range of 18.9–39.3 GHz (K/Ka band) with RL and IL better than 20 and 1.5 dB, respectively.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"7 1","pages":"18-24"},"PeriodicalIF":2.1,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147268759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To provide services during large-scale natural disasters, it is crucial for network operators to have adaptive and intelligent solutions. With this in mind, new solutions need to be developed, as conventional ground base stations (GBSs) may not be suitable or fast enough to provide services in such emergency situations. Hence, a research gap for emergency communications networks (ECNs) is the deployment of uncrewed aerial vehicles (UAVs) in emergency areas. To address this research gap, this article focuses on the efficient and optimal 3-D deployment of UAVs in scenarios characterized by high user density and heterogeneous distributions. The purpose of this study is to address real-world challenges, including the spatial distribution of users and the simultaneous presence of multiple GBSs. This study attempted to develop a novel data clustering approach for wireless networks, based on the affinity propagation algorithm, referred to as deep-embedded AP clustering (DEAPC). By integrating deep learning to map data into a latent feature space, this method enhances the clustering algorithm’s capability to handle scattered and noisy data. Moreover, a novel mechanism is designed to accommodate the presence of multiple GBS and to assign users to them. Simulation outcomes demonstrate that the new approach outperforms current leading clustering algorithms, reducing the required number of UAVs whereas also increasing system sumrate and decreasing computational time. This research presents a new method for creating intelligent, resilient, and adaptive UAV-based wireless networks in disaster scenarios.
{"title":"Optimized Intelligence-Based 3-D Deployment of Uncrewed Aerial Vehicles in Emergency Areas","authors":"Nooshin Boroumand Jazi;Farhad Faghani;Mahmoud Daneshvar Farzanegan","doi":"10.1109/JMASS.2025.3627256","DOIUrl":"https://doi.org/10.1109/JMASS.2025.3627256","url":null,"abstract":"To provide services during large-scale natural disasters, it is crucial for network operators to have adaptive and intelligent solutions. With this in mind, new solutions need to be developed, as conventional ground base stations (GBSs) may not be suitable or fast enough to provide services in such emergency situations. Hence, a research gap for emergency communications networks (ECNs) is the deployment of uncrewed aerial vehicles (UAVs) in emergency areas. To address this research gap, this article focuses on the efficient and optimal 3-D deployment of UAVs in scenarios characterized by high user density and heterogeneous distributions. The purpose of this study is to address real-world challenges, including the spatial distribution of users and the simultaneous presence of multiple GBSs. This study attempted to develop a novel data clustering approach for wireless networks, based on the affinity propagation algorithm, referred to as deep-embedded AP clustering (DEAPC). By integrating deep learning to map data into a latent feature space, this method enhances the clustering algorithm’s capability to handle scattered and noisy data. Moreover, a novel mechanism is designed to accommodate the presence of multiple GBS and to assign users to them. Simulation outcomes demonstrate that the new approach outperforms current leading clustering algorithms, reducing the required number of UAVs whereas also increasing system sumrate and decreasing computational time. This research presents a new method for creating intelligent, resilient, and adaptive UAV-based wireless networks in disaster scenarios.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"7 1","pages":"10-17"},"PeriodicalIF":2.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-28DOI: 10.1109/JMASS.2025.3626122
Madasu Venkateswara Rao;G. Challa Ram;S. Yuvaraj;Jagannath Malik
This article investigates the potential use of electromagnetic waves with orbital angular momentum (OAM) modes for simultaneous wireless information and power transfer (SWIPT). Due to the use of the same frequency, traditional SWIPT approaches have been hindered by interference between power and data signals. OAM modes have attracted interest in SWIPT owing to their mode orthogonality property, reducing the interference between power and data signals. A uniform circular array antenna generating OAM modes + 1, −1, and 0 at 2.4 GHz is utilized in the preliminary experimental study of SWIPT. This investigation explores using OAM modes for SWIPT and quantifies the isolation level that can be achieved at the same frequency over the different transmission distances. The experimental results show a minimum isolation of 11 dB between Mode + 1 and Mode −1, and 7 dB for Mode 0 is achieved. The findings demonstrate the feasibility of the OAM-based SWIPT advantageous approach over traditional methods, providing valuable insights into the use of OAM antennas for reliable energy harvesting and large-scale IoT connectivity in future 6G networks.
{"title":"Design and Experimental Analysis of UCA Antennas for Enhanced SWIPT Using OAM Modes","authors":"Madasu Venkateswara Rao;G. Challa Ram;S. Yuvaraj;Jagannath Malik","doi":"10.1109/JMASS.2025.3626122","DOIUrl":"https://doi.org/10.1109/JMASS.2025.3626122","url":null,"abstract":"This article investigates the potential use of electromagnetic waves with orbital angular momentum (OAM) modes for simultaneous wireless information and power transfer (SWIPT). Due to the use of the same frequency, traditional SWIPT approaches have been hindered by interference between power and data signals. OAM modes have attracted interest in SWIPT owing to their mode orthogonality property, reducing the interference between power and data signals. A uniform circular array antenna generating OAM modes + 1, −1, and 0 at 2.4 GHz is utilized in the preliminary experimental study of SWIPT. This investigation explores using OAM modes for SWIPT and quantifies the isolation level that can be achieved at the same frequency over the different transmission distances. The experimental results show a minimum isolation of 11 dB between Mode + 1 and Mode −1, and 7 dB for Mode 0 is achieved. The findings demonstrate the feasibility of the OAM-based SWIPT advantageous approach over traditional methods, providing valuable insights into the use of OAM antennas for reliable energy harvesting and large-scale IoT connectivity in future 6G networks.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"7 1","pages":"3-9"},"PeriodicalIF":2.1,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-24DOI: 10.1109/JMASS.2025.3613723
Matheus R. Torres;Diego A. Coutinho;Evandro C. Vilas Boas
This work presents a compact and modular electrical power subsystem (EPS) tailored for CubeSats operating in low Earth orbit (LEO). The design integrates off-the-shelf components, including a microcontroller-based battery management system, analog solar panel regulators, and a digitally reconfigurable step-up converter. These components manage the charging and discharging of lithium-ion batteries, optimize solar energy harvesting, and deliver multiple regulated voltage outputs to mission-critical subsystems. Experimental validation demonstrates reliable power regulation, efficient battery management, and robust solar panel integration, confirming the system’s effectiveness under varying load and input conditions. The architecture’s hybrid nature, combining analog and digital control, provides a flexible and scalable solution for small satellite missions with strict resource constraints and evolving power demands.
{"title":"A Hybrid Electrical Power Subsystem for CubeSats: Design and Experimental Validation","authors":"Matheus R. Torres;Diego A. Coutinho;Evandro C. Vilas Boas","doi":"10.1109/JMASS.2025.3613723","DOIUrl":"https://doi.org/10.1109/JMASS.2025.3613723","url":null,"abstract":"This work presents a compact and modular electrical power subsystem (EPS) tailored for CubeSats operating in low Earth orbit (LEO). The design integrates off-the-shelf components, including a microcontroller-based battery management system, analog solar panel regulators, and a digitally reconfigurable step-up converter. These components manage the charging and discharging of lithium-ion batteries, optimize solar energy harvesting, and deliver multiple regulated voltage outputs to mission-critical subsystems. Experimental validation demonstrates reliable power regulation, efficient battery management, and robust solar panel integration, confirming the system’s effectiveness under varying load and input conditions. The architecture’s hybrid nature, combining analog and digital control, provides a flexible and scalable solution for small satellite missions with strict resource constraints and evolving power demands.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"6 4","pages":"392-403"},"PeriodicalIF":2.1,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145555459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-15DOI: 10.1109/JMASS.2025.3609830
Anastasios N. Bikos;Sathish A. P. Kumar
This article introduces SAT-IOTA, a lightweight and artificial intelligence (AI)-driven cybersecurity framework designed for blockchain-powered satellite infrastructures. Unlike traditional detection approaches, SAT-IOTA employs predictive anomaly analytics combined with a sliding window (SW) machine learning mechanism to proactively identify and mitigate security threats in SAGIN. The proposed framework integrates IOTA distributed ledger technology (DLT) for secure, decentralized telemetry data management, tokenized satellite components, and resilience against cyber–physical attacks. Through a custom-built testbed with Hornet nodes, we evaluate the framework’s performance under Denial of Service (DoS) scenarios, achieving 97% prediction accuracy and an F-measure of 80%. The results confirm that SAT-IOTA enhances space system security by combining blockchain-driven trust with AI-based anomaly prediction, offering a scalable and resource-efficient solution for next-generation satellite communications.
{"title":"SAT-IOTA: A Cybersecurity Reinforcement Framework for Blockchain-Driven Space Satellites Utilizing Anomaly Prediction","authors":"Anastasios N. Bikos;Sathish A. P. Kumar","doi":"10.1109/JMASS.2025.3609830","DOIUrl":"https://doi.org/10.1109/JMASS.2025.3609830","url":null,"abstract":"This article introduces SAT-IOTA, a lightweight and artificial intelligence (AI)-driven cybersecurity framework designed for blockchain-powered satellite infrastructures. Unlike traditional detection approaches, SAT-IOTA employs predictive anomaly analytics combined with a sliding window (SW) machine learning mechanism to proactively identify and mitigate security threats in SAGIN. The proposed framework integrates IOTA distributed ledger technology (DLT) for secure, decentralized telemetry data management, tokenized satellite components, and resilience against cyber–physical attacks. Through a custom-built testbed with Hornet nodes, we evaluate the framework’s performance under Denial of Service (DoS) scenarios, achieving 97% prediction accuracy and an F-measure of 80%. The results confirm that SAT-IOTA enhances space system security by combining blockchain-driven trust with AI-based anomaly prediction, offering a scalable and resource-efficient solution for next-generation satellite communications.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"6 4","pages":"378-391"},"PeriodicalIF":2.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145555454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：