With the rapid growth of multimodal remote sensing (RS) data, there is an increasing demand for intelligent onboard computing to alleviate the transmission and latency bottlenecks of traditional orbit-to-ground downlinking workflows. While many lightweight AI algorithms have been widely developed and deployed for onboard inference, their limited generalization capability restricts performance under the diverse and dynamic conditions of advanced Earth observation. Recent advances in remote sensing foundation models (RSFMs) offer a promising solution by providing pretrained representations with strong adaptability across diverse tasks and modalities. However, the deployment of RSFMs onboard resource-constrained devices such as nano satellites remains a significant challenge due to strict limitations in memory, energy, computation, and radiation tolerance. To this end, this review proposes the first comprehensive survey of onboard RSFMs deployment, where a unified deployment pipeline including RSFMs development, model compression techniques, and hardware optimization is introduced and surveyed in detail. Available hardware platforms are also discussed and compared, based on which some typical case studies for low Earth orbit (LEO) CubeSats are presented to analyze the feasibility of onboard RSFMs’ deployment. To conclude, this review aims to serve as a practical roadmap for future research on the deployment of RSFMs on edge devices, bridging the gap between the large-scale RSFMs and the resource constraints of spaceborne platforms for onboard computing.

Research on the scientific basis for selecting platform for Low Earth Orbit remote sensing satellite in Vietnam

Abstract High-precision real-time satellite orbit products are prerequisite for achieving high-performance navigation and positioning services. This study develops an integrated precise orbit determination approach that combines GPS and low Earth orbit (LEO) satellites using the Square Root Information Filtering (SRIF) algorithm to improve the convergence time and accuracy of GPS real-time satellite orbits. By collecting data from varying numbers of global ground stations and LEO satellites (Sentinel-3A/B, GRACE-C/D, Swarm-A/B/C), GPS and LEO satellite orbits were determined synchronously. Orbit convergence time and accuracy were evaluated against the official reference products. Results show, with 9 ground stations, adding 7 LEO satellites decreases the GPS orbit convergence time in along-track, cross-track, and radial directions from 28.0, 28.3, and 21.8 h to 1.9, 3.9, and 14.3 h, respectively, achieving remarkable improvements of 93%, 86%, and 34%. Increasing the number of ground stations to 79 further reduces the convergence time to 0.7, 1.4, and 10.9 h in three directions. For LEO satellites, increasing ground stations from 9 to 79 decreases the average convergence time across all three directions by 44-70%. In terms of GPS orbit accuracy after convergence, the configuration of 7 LEO satellites with 9 ground stations reaches accuracies of 6.5, 3.8, and 3.0 cm in the along-track, cross-track, and radial directions, which is improved by 64%, 66%, and 48%, respectively, compared to the results without LEO satellites. When 79 ground stations are used, the GPS orbit accuracy is further improved to 5.0, 2.5, and 2.6 cm in the three directions. Notably, the three-dimensional GPS orbit accuracy reaches 6.8 cm with 19 ground stations and 7 LEO satellites, which outperforms the accuracy level of 7.4 cm obtained utilizing only 79 ground stations. These results indicate the optimal deployment of LEO satellites can effectively compensate for ground network limitations while maintaining high-precision real-time orbit determination.

In recent years, there has been increasing investment in the deployment of massive commercial Low Earth Orbit (LEO) constellations to provide global Internet connectivity. These constellations, now equipped with inter-satellite links, can serve as low-latency Internet backbones, requiring LEO satellites to act not only as access nodes for ground stations, but also as in-orbit core routers. Due to their high velocity and the resulting frequent handovers of ground gateways, LEO networks highly stress mobility procedures at both the sender and receiver endpoints. On the other hand, a growing trend in networking is the use of technologies based on the Information Centric Networking (ICN) paradigm for servicing IoT networks and sensor networks in general, as its addressing, storage, and security mechanisms are usually a good match for IoT needs. Furthermore, ICN networks possess additional characteristics that are beneficial for the massive LEO scenario. For instance, the mobility of the receiver is helped by the inherent data-forwarding procedures in their architectures. However, the mobility of the senders remains an open problem. This paper proposes a comprehensive solution to the mobility problem for massive LEO constellations using the Named-Data Networking (NDN) architecture, as it is probably the most mature ICN proposal. Our solution includes a scalable method to relate content to ground gateways and a way to address traffic to the gateway that does not require cooperation from the network routing algorithm. Moreover, our solution works without requiring modifications to the actual NDN protocol itself, so it is easy to test and deploy. Our results indicate that, for long enough handover lengths, traffic losses are negligible even for ground stations with just one satellite in sight.

Robust Finite-Time Control Approach for Spacecraft Rendezvous and Formation Reconfiguration in Earth Orbits

Refined modeling of effective visible magnitudes for optical observations of low Earth orbit satellites

Case studies of ion focusing in the downstream region of a modeled low Earth orbit spacecraft

Examining the effects of simulated low earth orbit on the viscoelasticity and strain behaviour of deployable polybenzoxazine nanocomposites

Abstract Ionospheric plasma interaction with charged satellite surfaces can lead to a substantial increase in orbital drag. In this context, natural events related to solar activity can have a detrimental effect on a satellite's lifespan and introduce uncertainty in orbital trajectory predictions. This work investigates the variations in charge drag coefficient during solar minimum and solar maximum conditions for Low Earth Orbits with altitudes in the range 300–1,000 [km]. Two months, August 2008 and January 2014, near the solar minimum and solar maximum activities are selected to represent variations of ion and electron densities and temperatures during the solar cycle. The simulations are performed using pdFOAM, an electrostatic particle‐in‐cell code. The results show that the charge drag coefficient increases with altitude. Variations are more pronounced during solar minimum than maximum. The variation of the charge drag coefficient depends significantly on the total ion density as well as the percent fraction of lighter ions . In the lower part of the ionosphere (300 [km]), dominated by , the charge drag coefficient , varies in the range . At higher altitudes 1,000 [km], dominated by , especially during the night, it varies in the range , with the extreme values being related to the solar minimum. For both solar minimum and solar maximum conditions, the charge drag force can become significant compared to the neutral drag force and it is 2 times higher at altitude 1,000 [km].

Novel concept of a front-intake solar array wing for atmospheric drag compensation in very low Earth orbits using air-breathing electric propulsion

The ionizing particle shielding capability of multi-functional high-strength and low-Z composites in low earth orbit

Activating the principle of ‘taking into account the special needs of the developing countries’ in the distribution of low Earth orbit frequency/orbit resources

Orbital capacity and maximum energy output of a space-based solar power constellation in Geosynchronous Earth Orbit

Reinforcement learning and genetic algorithm-based dynamic controller deployment for Low Earth Orbit satellite networks

Research on the design of low earth orbit mega-constellations with space-based defense capabilities

Updating urban cartography is essential for territorial development planning and management in the Dominican Republic, where municipalities lack Geographic Information Systems (GIS) that detail local productive components. To address this need, a municipal cartographic model was proposed using digital photogrammetry with a UAV and GIS, focusing on the municipality of San José de Ocoa. The study area was delimited using Google Earth satellite images and a flight was conducted with a Phantom 4 RTK drone, employing RTK/PPK techniques to ensure the accuracy of the geographic coordinates. GNSS observations were stored in RINEX 3.02 format for possible subsequent adjustments.Nine flights were conducted, obtaining 4 985 georeferenced photos with accuracies of less than 2 cm. Image processing was performed in three stages using Agisoft Metashape, generating digital terrain models and contour lines with Global Mapper. Subsequently, urban map elements, such as buildings and street axes, were vectorized using Civil 3D and orthophotos as reference.The result was a georeferenced plan including the footprints of buildings and bodies of water, exported in shapefile format. Finally, the cartography was validated by a walk through the municipality, updating the vectorized information with data from the UAV survey

The research focused on creating a Geographic Information System (GIS) for the municipality of San José de Ocoa, Dominican Republic, using digital photogrammetry with drones. The study area was delimited using Google Earth satellite images, and 4 985 georeferenced photos with accuracies of less than 2 cm were obtained using drones. Image processing was carried out in three stages using Agisoft Metashape, generating digital terrain models and contour lines. Urban elements such as buildings and streets were subsequently vectorized using Civil 3D and orthophotos, resulting in a georeferenced plan in shapefile format. The cartography was validated through a walking tour of the municipality, updating the information with data from the UAV survey. The GIS incorporated various layers, including satellite orthoimages from 2013 and 2023, digital models, and shapefiles of buildings and roads. The WGS 1984 datum and the UTM projection system, Zone 19 N, were used with ArcGIS Pro and ArcGIS Online software. The results showed a significant expansion of the urban area between 2015 and 2023, with a 376,97 % increase in educational use and a 245,20 % increase in occupation of risk areas. The research generated a complete GIS, an interactive web application, and a 1:5 000 scale cartographic map. It was concluded that the methodology is replicable at the national level, optimizing territorial management in the municipalities of the Dominican Republic

Abstract Rate-track imagery−in which a telescope tracks an object’s proper motion instead of the stellar background−has long been used to enhance the signal-to-noise ratio of faint solar system bodies such as asteroids, comets, and artificial satellites. Images from such observations are characterized by point source targets and streaked stars. While astrometric plate solving−matching ubiquitous and unique star fields to a world coordinate system−is a mature field of research, the complexities of detecting and measuring the centroid of star streaks in rate track imagery decreases astrometric accuracy. This is unfortunate, given that defining and refining orbits of bodies of the solar system require accurate astrometric positions. In this work, we propose a novel observing methodology for experiments requiring the enhanced signal-to-noise ratio of rate track imagery and the astrometrics of orbiting solar system bodies by capping rate track sequences with a sidereal observation at the tracked object’s position. The resulting data provide high-precision astrometry and a natural filter for detecting star streaks in the rate-track imagery. We demonstrate astrometric uncertainties in right ascension and declination of ≤1 . ″ 38 and ≤0 . ″ 96, respectively, on observations of calibration satellites using telescopes with pixel scales (instantaneous field’s of view) of 0 . ″ 90–1 . ″ 68. This technique−Sidereal ENriched Precision Astrometric Intelligence (SENPAI)—is designed to run automatically, requires no tuning or calibration frames on a per sensor basis, and throughput exceeds 85% on most sensors tested. SENPAI far exceeds the threshold requirement of ≤3 . ″ 0 of uncertainty in object positioning for initial determination of—and state updates to—the orbits of artificial satellites.

Communication infrastructure in remote areas struggles to deliver stable, high-quality services for power systems. Low Earth Orbit (LEO) satellite networks offer an effective solution through their low latency and extensive coverage. Nevertheless, the high orbital velocity of LEO satellites combined with massive user access frequently leads to signaling congestion and degradation of service quality. To address these challenges, this paper proposes a LEO satellite handover strategy based on Quality of Service (QoS)-constrained K-Means clustering and Deep Q-Network (DQN) learning. The proposed framework first partitions users into groups via the K-Means algorithm and then imposes an intra-group QoS fairness constraint to refine clustering and designate a cluster head for each group. These cluster heads act as proxies that execute unified DQN-driven handover decisions on behalf of all group members, thereby enabling coordinated multi-user handover. Simulation results demonstrate that, compared with conventional handover schemes, the proposed strategy achieves an optimal balance between performance and signaling overhead, significantly enhances system scalability while ensuring long-term QoS gains, and provides an efficient solution for mobility management in future large-scale LEO satellite networks.

Abstract The integration of high-temperature superconductors (HTS) into electric propulsion systems, particularly Magneto-Plasma-Dynamic Thrusters (MPDTs), has recently garnered significant attention. However, research on low-power HTS-based MPDTs, which are crucial for small satellites and CubeSats, remains limited. The increasing demand for compact, high-efficiency propulsion in Low Earth orbit underscores the need for scalable HTS-AF-MPDT systems operating below 15 kW. Despite this, challenges such as the lack of detailed theoretical models, limited plasma diagnostics, and excessive Joule heating in conventional copper magnets persist. In this work, using a downscaled version of a 25 kW high-temperature superconducting (HTS) based applied-field magneto-plasma-dynamic thruster, we address these limitations by developing and experimentally validating a theoretical MHD-based plasma acceleration model for an AF-MPDT equipped with a conduction-cooled HTS magnet.The system achieves a specific impulse of 3265s at an input power of 12 kW, more than eight times higher than traditional chemical propulsion, alongside a thrust of 320mN and an efficiency of 25% at sub 12kW. The HTS magnet reduces magnetic power consumption from 285 kW to under 1 kW and lowers magnet mass from 220 kg to 60 kg, enabling substantial improvements in system miniaturization and efficiency. These results represent the first reported demonstration of a 12kW high-temperature superconducting AF-MPDT, bridging theoretical predictions with experimental outcomes and laying the groundwork for in-orbit demonstration of high-performance propulsion for small satellites.