Nanosatellites are proliferating as low-cost, dedicated remote sensing opportunities for small nations. However, nanosatellites’ performance as remote sensing platforms is impaired by low downlink speeds, which typically range from 1200 to 9600 bps. Additionally, an estimated 67% of downloaded data are unusable for further applications due to excess cloud cover. To alleviate this issue, we propose an image segmentation and prioritization algorithm to classify and segment the contents of captured images onboard the nanosatellite. This algorithm prioritizes images with clear captures of water bodies and vegetated areas with high downlink priority. This in-orbit organization of images will aid ground station operators with downlinking images suitable for further ground-based remote sensing analysis. The proposed algorithm uses Convolutional Neural Network (CNN) models to classify and segment captured image data. In this study, we compare various model architectures and backbone designs for segmentation and assess their performance. The models are trained on a dataset that simulates captured data from nanosatellites and transferred to the satellite hardware to conduct inferences. Ground testing for the satellite has achieved a peak Mean IoU of 75% and an F1 Score of 0.85 for multi-class segmentation. The proposed algorithm is expected to improve data budget downlink efficiency by up to 42% based on validation testing.

This paper describes the “2017 and 2018 KOSEN Space Camps.” These camps offer introductory space technology programs for students enrolled in the various KOSEN technology colleges in Japan. Since 2015, we have hosted this camp at the Marine Park Niihama. Over the last two years, we evolved and refined the camp's contents to develop a more effective space technology and science education program. In 2017, we successfully held two CanSat development courses: One for beginners to learn how to use onboard computers and sensors and one that is driven more by the participants' imaginations. We tried to make our camps stand out in terms of content variations and load balances including competition. In 2018, we attempted to introduce advanced content such as model CubeSat development and competition. This new trial included online preparation materials from KOSEN's space academia. We will continue to improve the Space Camp content so that in the future, this program may be established as an annual space event in Niihama City.

This work presents a spiral-loop sequential-phase (SLSP)-fed radial-sector patch circularly polarized (CP) antenna for S-band CubeSat platforms. The architecture stacks three RO4003C substrates in an aluminum enclosure: a lower layer with tapered-blade parasitic elements, a middle layer with the SLSP feed and four radial-sector patches, and an upper tilted hexagonal metasurface superstrate separated by an air-gap. Characteristic mode analysis is used to realize an orthogonal modal pair. A prototype integrated on a CubeSat structure was measured in an anechoic chamber and validated under vibration and thermal-vacuum testing per ECSS/NASA practices. The antenna achieves a measured return loss bandwidth of 2–2.34 GHz, an axial ratio bandwidth of 2.04–2.25 GHz, and a maximum gain of 7.24 dBic at 2.18 GHz. The metasurface and parasitic elements enhance bandwidth while maintaining boresight CP. The novelty lies in the integration of SLSP-fed radial-sector patches with a tilted hexagonal metasurface superstrate and tapered-blade parasitic elements within a compact stacked configuration, making the proposed antenna well suited for CubeSat S-band applications.

This review paper examines battery management system (BMS) for nanosatellites in low earth orbit, addressing a significant knowledge gap at the intersection of space power systems and battery technology. Through systematic analysis of 108 high-impact journal publications spanning 2010–2024, a comprehensive framework addressing nine critical design factors affecting BMS implementation is established. The review presents the first comprehensive classification of 50+ commercially available BMS integrated circuits across five functional categories (authentication, charger, fuel gauge, monitor/balancer, and protector) specifically evaluated for nanosatellite applications, offering valuable selection guidance for mission designers. Comparative analysis demonstrates that Kalman filter-based state of charge estimation methods achieve ± 3% accuracy across the − 40 to + 85 °C operational range, compared to ± 8% for open circuit voltage methods under thermal cycling conditions, while requiring only 2–5% of typical nanosatellite processing resources making the accuracy-computation trade-off favourable for mission-critical applications. The systematic framework reveals that lithium-ion batteries currently dominate nanosatellite applications, while significant opportunities exist in developing flexible, software-defined BMS architectures capable of in-orbit reconfiguration. Key findings emphasize the need for radiation-tolerant implementations, adaptive thermal management solutions, and simplified autonomous control algorithms suitable for resource-constrained platforms. This work provides essential guidance for researchers and engineers developing next-generation power systems for nanosatellite missions, with broader implications for advancing miniaturized space technology in challenging orbital environments.

Electrostatic discharge and potential differences from surface charging during satellite operations can cause serious damage and control malfunctions. Detecting and preventing such charging events is therefore critical for safe and reliable spacecraft servicing. This study developed an automatic, high‐accuracy detection method for surface charging of Defense Meteorological Satellite Program (DMSP) using a Convolutional Neural Network (CNN), which identifies the “ion‐line structure” in ion energy‐time diagrams observed by the plasma instrument during charging. Unlike the previous conventional threshold‐based approach using fixed criteria for auroral electron flux and plasma density, our CNN employs Grad‐CAM visualization to extract ion‐line regions continuously, enabling event detection independent of thresholds. Using ion‐line events detected between 2009 and 2019 as training data, the CNN achieved 99.6% accuracy in cross‐validation. Applying the model to DMSP F16 ion flux data between 2005 and 2023 automatically detected 2,955 events, of which 1,323 exhibited charging potentials exceeding 100 V. Detected events were concentrated between 17 and 03 magnetic local time (MLT), with strong (>100 V) negative charging frequently occurring between 18 and 24 MLT. About 86% corresponded to monoenergetic electron fluxes precipitations. As the energy peak of these fluxes shifted toward higher energies, the corresponding current density increased from 4.71 to 14.4 μA/m ² . However, when plasma density exceeded 10 ⁴ cm ⁻³ , the ion current became dominant, preventing surface charging—confirming the validity of conventional thresholds from a current balance perspective. Moreover, the mean energy of electrons contributing to charging varied with plasma density, indicating that conditions previously discussed independently can be integratively explained by considering plasma density effects.