The precise insertion of CubeSats into designated orbits is a complex task, primarily due to the limited propulsion capabilities and constrained fuel reserves onboard, which severely restrict the scope for large orbital corrections. This limitation necessitates the development of more efficient maneuvering techniques to ensure mission success. In this paper, we propose a maneuvering sequence that exploits the natural J2 perturbation caused by the Earth's oblateness. By utilizing the secular effects of this perturbation, it is possible to passively influence key orbital parameters such as the argument of perigee and the right ascension of the ascending node, thereby reducing the need for extensive propulsion-based corrections. The approach is designed to optimize the CubeSat's orbital insertion and minimize the total fuel required for trajectory adjustments, making it particularly suitable for fuel-constrained missions. The proposed methodology is validated through comprehensive numerical simulations that examine different initial orbital conditions and perturbation environments. Case studies are presented to demonstrate the effectiveness of the J2-augmented strategy in achieving accurate orbital insertion, showing a major reduction in fuel consumption compared to traditional methods. The results underscore the potential of this approach to extend the operational life and capabilities of CubeSats, offering a viable solution for future low-Earth orbit missions.

CubeSats offer a low-cost platform for space research, particularly for Earth observation. However, their resource-constrained nature and being in space, challenge the flexibility and complexity of the deployed image processing pipelines and their orchestration. This paper introduces two novel systems, DIPP and DISH, to address these challenges. DIPP is a modular and configurable image processing pipeline framework that allows for adaptability to changing mission goals even after deployment, while preserving robustness. DISH is a domain-specific language (DSL) and runtime system designed to schedule complex imaging workloads on low-power and memory-constrained processors. Our experiments demonstrate that DIPP's decomposition of the processing pipelines adds negligible overhead, while significantly reducing the network requirements of updating pipelines and being robust against erroneous module uploads. Furthermore, we compare DISH to Lua, a general purpose scripting language, and demonstrate its comparable expressiveness and lower memory requirement.

Nanosatellites are proliferating as low-cost dedicated sensing systems with lean development cycles. Kyushu Institute of Technology and collaborators have launched a joint venture for a nanosatellite mission, VERTECS. The primary mission is to elucidate the formation history of stars by observing the optical-wavelength cosmic background radiation. The VERTECS satellite will be equipped with a small-aperture telescope and a high-precision attitude control system to capture the cosmic data for analysis on the ground. However, nanosatellites are limited by their onboard memory resources and downlink speed capabilities. Additionally, due to a limited number of ground stations, the satellite mission will face issues meeting the required data budget for mission success. To alleviate this issue, we propose an on-orbit system to autonomously classify and then compress desirable image data for data downlink prioritization and optimization. The system comprises a prototype Camera Controller Board (CCB) which carries a Raspberry Pi Compute Module 4 which is used for classification and compression. The system uses a lightweight Convolutional Neural Network (CNN) model to classify and determine the desirability of captured image data. The model is designed to be lean and robust to reduce the computational and memory load on the satellite. The model is trained and tested on a novel star field dataset consisting of data captured by the Sloan Digital Sky Survey (SDSS). The dataset is meant to simulate the expected data produced by the 6U satellite. The compression step implements GZip, RICE or HCOMPRESS compression, which are standards for astronomical data. Preliminary testing on the proposed CNN model results in a classification accuracy of about 100\% on the star field dataset, with compression ratios of 3.99, 5.16 and 5.43 for GZip, RICE and HCOMPRESS that were achieved on tested FITS image data.

Achieving global space-air-ground integrated network (SAGIN) access only with CubeSats presents significant challenges such as the access sustainability limitations in specific regions (e.g., polar regions) and the energy efficiency limitations in CubeSats. To tackle these problems, high-altitude long-endurance unmanned aerial vehicles (HALE-UAVs) can complement these CubeSat shortcomings for providing cooperatively global access sustainability and energy efficiency. However, as the number of CubeSats and HALE-UAVs, increases, the scheduling dimension of each ground station (GS) increases. As a result, each GS can fall into the curse of dimensionality, and this challenge becomes one major hurdle for efficient global access. Therefore, this paper provides a quantum multi-agent reinforcement Learning (QMARL)-based method for scheduling between GSs and CubeSats/HALE-UAVs in order to improve global access availability and energy efficiency. The main reason why the QMARL-based scheduler can be beneficial is that the algorithm facilitates a logarithmic-scale reduction in scheduling action dimensions, which is one critical feature as the number of CubeSats and HALE-UAVs expands. Additionally, individual GSs have different traffic demands depending on their locations and characteristics, thus it is essential to provide differentiated access services. The superiority of the proposed scheduler is validated through data-intensive experiments in realistic CubeSat/HALE-UAV settings.

Abstract-- This paper presents a Hierarchical Reinforcement Learning (HierRL) methodology tailored for optimizing CubeSat task scheduling in Low Earth Orbits (LEO). Integrating this mechanism creates a safe and fault-tolerant system for CubeSat task scheduling. I. INTRODUCTION CubeSats have transformed the space industry, providing a cost-effective and efficient way to conduct diverse space A rising focus is on equipping involves a constrained optimization [8]. However, the inherent spacecraft with advanced autonomous decision-making uncertainties and complexities of space environments, capabilities [3, 4]. Achieving this relies on using automated combined with task variability and unpredictability, often planning tools to reduce human involvement and effectively surpass the capabilities of traditional tools [9]. Implementing on-board planning mechanisms in spacecraft missions brings One promising solution gaining attention involves substantial benefits, including increased spacecraft applying artificial intelligence to dynamic task scheduling availability, heightened reliability, and reduced ground [10].

This work presents a large-scale simulation study investigating the deployment and operation of distributed swarms of CubeSats for interplanetary missions to small celestial bodies. Utilizing Taylor numerical integration and advanced collision detection techniques, we explore the potential of large CubeSat swarms in capturing gravity signals and reconstructing the internal mass distribution of a small celestial body while minimizing risks and Delta V budget. Our results offer insight into the applicability of this approach for future deep space exploration missions. Introduction In the last decade CubeSats have emerged as an innovative and cost-effective platform for testing new satellite technologies, with applications ranging from Earth observation to deep space missions. For instance, the HERA interplanetary mission that will explore the Didymos binary system in 2025 will embark two CubeSats to perform a detailed exploration of the system.

CHILLING AI satellite swarms that hunt down and destroy enemy targets have been unveiled by China in another terrifying step in the space race. Chinese scientists said they could now launch hundreds of mini satellites - dubbed "cubesats" - from a large motherboard in space with deadly precision and speed. Weighing in at 2.2lbs, these tiny satellites are so complex they can only be controlled by Artificial Intelligence (AI). According to researchers, the complexity of a large scale space battle would be so immense that it's beyond the human brain and even beyond some powerful algorithms, the South China Morning Post reports. The study, published in the peer-reviewed journal Chinese Space Science and Technology, said unlocking the right AI to control the motherboard and cubesats would have "strong economic and military value".

Swarms of drones have gotten a lot of time in the spotlight lately, mostly for their use in potential military operations. The U.S. military is testing out swarm operations in simulations, while the British Army is using live drones operating in swarms during actual training operations. Other militaries are also interested in deploying swarms. One of the biggest advantages a swarm of drones has when performing military operations is its resiliency. If a swarm enters combat and several individual drones get shot down or otherwise incapacitated, it really doesn't reduce the combat effectiveness of the swarm, nor the tactics that it uses.

Researchers at the Image Processing Laboratory (IPL) of the University of Valencia, in collaboration with the University of Oxford and the Phi-Lab of the European Space Agency (ESA), have developed a model for flood detection based on neural networks. It's called WorldFloods and has been launched into space by aerospace company D-Orbit from Cape Canaveral. In terms of flooding, observing the Earth from space provides valuable information for decision-making on the ground. Large constellations of small nanosatellites--the CubeSats--are a promising solution to reduce revisitation time from days to hours--as long as it takes a sensor to re-cover a location--in disaster areas. However, data transmission to terrestrial receivers is limited by the power and bandwidth restrictions of the cubes.

A NASA-operated satellite designed to hunt for distant planets may be gone for good. According to the agency, NASA lost contact with its Arcsecond Space Telescope Enabling Research in Astrophysics (ASTERIA), a briefcase-sized spacecraft designed to study planets outside our solar system. Communications were lost on December 5 according to NASA and the agency will continue trying to reach it until March 2020. 'The ASTERIA project achieved outstanding results during its three -month prime mission and its nearly two-year-long extended mission,' said JPL's Lorraine Fesq, current ASTERIA program manager. 'Although we are disappointed that we lost contact with the spacecraft, we are thrilled with all that we have accomplished with this impressive CubeSat.'