Low Earth Orbit Research Articles

Challenges and opportunities offered by geostationary space observations for air quality research and emission monitoring

Abstract Space-borne remote sensing of atmospheric chemical constituents is crucial for monitoring and better understanding global and regional air quality. Since the 1990s, the continuous development of instruments onboard low-Earth orbit (LEO) satellites has led to major advances in air quality research by providing daily global measurements of atmospheric chemical species. The next generation of atmospheric composition satellites measures from the geostationary Earth orbit (GEO) with hourly temporal resolution, allowing the observation of diurnal variations of air pollutants. The first two instruments of the GEO constellation coordinated by the Committee on Earth Observation Satellites (CEOS), the Geostationary Environment Monitoring Spectrometer (GEMS) for Asia and the Tropospheric Emissions: Monitoring of Pollution (TEMPO) for North America, were successfully launched in 2020 and 2023, respectively. The European component, Sentinel-4, is planned for launch in 2025. This work provides an overview of satellite missions for atmospheric composition monitoring and the state of the science in air quality research. We cover recent advances in retrieval algorithms, the modeling of emissions and atmospheric chemistry, data assimilation, and the application of machine learning based on satellite data. We discuss the challenges and opportunities in air quality research in the era of GEO satellites, and provide recommendations on research priorities for the near future.

Influence of Proton Irradiation on Thin Films of AZO and ITO Transparent Conductive Oxides—Simulation of Space Environment

Transparent conductive oxides are essential materials for many optoelectronic applications. For new devices for aerospace and space applications, it is crucial to know how they respond to the space environment. The most important issue in commonly used low-Earth orbits is proton radiation. This study examines the effects of high-energy proton irradiation (226.5 MeV) on thin films of aluminium-doped zinc oxide (AZO) and indium tin oxide (ITO). We use X-ray diffraction and electron microscopy observations to see the changes in the structure and microstructure of the films. The optical properties and homogeneity of the materials are determined by spectrophotometry and spectroscopic ellipsometry (SE). Analysis of the chemical states of the elements with X-ray photoelectron spectroscopy (XPS) gives insight into what proton irradiation changes at the surface of the oxides. All measurements show that ITO is less influenced than AZO. The proton energy and fluence used in this study simulate about a hundred years in low Earth orbit. This research demonstrates that both transparent conductive oxide thin films can function under simulated space conditions, with ITO showing superior resilience. The ITO film was more homogenous in terms of the total thickness measured with SE, had fewer defects and adsorbates present on the surface, as XPS analysis proved, and did not show a difference after irradiation regarding its optical properties, transmission, refractive index, or extinction coefficient.

Secrecy Analysis of LEO Satellite-to-Ground Station Communication System Influenced by Gamma-Shadowed Ricean Fading

The Low Earth Orbit (LEO) small satellites are extensively used for global connectivity to enable services in underpopulated, remote or underdeveloped areas. Their inherent broadcast nature exposes LEO–terrestrial communication links to severe security threats, which always reveal new challenges. The secrecy performance of the satellite-to-ground user link in the presence of a ground eavesdropper is studied in this paper. We observe both scenarios of the eavesdropper’s channel state information (CSI) being known or unknown to the satellite. Throughout the analysis, we consider that locations of the intended and unauthorized user are both arbitrary in the satellite’s footprint. On the other hand, we analyze the case when the user is in the center of the satellite’s central beam. In order to achieve realistic physical layer security features of the system, the satellite channels are assumed to undergo Gamma-shadowed Ricean fading, where both line-of-site and scattering components are influenced by shadowing effect. In addition, some practical effects, such as satellite multi-beam pattern and free space loss, are considered in the analysis. Capitalizing on the aforementioned scenarios, we derive the novel analytical expressions for the average secrecy capacity, secrecy outage probability, probability of non-zero secrecy capacity, and probability of intercept events in the form of Meijer’s G functions. In addition, novel asymptotic expressions are derived from previously mentioned metrics. Numerical results are presented to illustrate the effects of beam radius, satellite altitude, receivers’ position, as well as the interplay of the fading or/and shadowing impacts over main and wiretap channels on the system security. Analytical results are confirmed by Monte Carlo simulations.

RF System Advancements for Satellite and Space Communications: Integrating AI with High-Frequency and Hybrid Systems

With the rapid evolution of satellite communications, the use of advanced radio frequency (RF) systems has become pivotal in addressing emerging challenges. High-frequency bands, such as millimeter-wave (mmWave) and terahertz (THz), enable high-throughput data transmission but introduce new complexities such as Doppler shifts and signal degradation. Hybrid satellite-terrestrial networks are expanding the possibilities for 5G and 6G backhauls, while low Earth orbit (LEO) constellations improve latency- sensitive applications. This paper explores RF system advancements, including beamforming, MIMO, and AI-driven optimizations. Additionally, it discusses challenges like energy efficiency, atmospheric attenuation, and spectrum management. The paper concludes with insights into future trends such as quantum RF systems, deep- space communications, and reconfigurable intelligent surfaces. Keywords – Satellite Communications, RF Systems, Millimeter-Wave, Terahertz, Beamforming, AI-Driven Optimization, Hybrid Networks, Non-Terrestrial Networks (NTN), Cognitive Radios, Network Slicing, LEO Satellites, 5G/6G Backhaul, Doppler Mitigation, Energy-Efficient RF.

On the first test of the Weak Equivalence Principle in low Earth orbit

On the first test of the Weak Equivalence Principle in low Earth orbit

Ground and Space (LEO) Based GNSS Ionospheric Detection Using Chapman Function Constraint

AbstractLimited by the number and location distribution of ground stations, the ground based ionospheric detection using Global Navigation Satellite Systems (GNSS) technology suffers from low accuracy and poor reliability in some regions. To improve the performance of the global ionospheric model, this study combines the Continuously Operating Reference Station and the Low Earth Orbit (LEO) Satellite observation to conduct the ground and space‐based (G/SBased) joint ionospheric detection technique. With reference to the ionospheric radar data of the global ionospheric radio observatory, the performance of the G/SBased GNSS joint ionospheric detection technique was tested in quiet and disturbed geomagnetic environments. Results show that in the ground and space based joint mode, the distribution of ionospheric puncture points (IPPs) is uniform, thus effectively avoiding the problem of IPPs blank in the absence of ground stations. In the quiet geomagnetic environment, the ionospheric detection of the G/SBased joint mode has a remarkable performance improvement compared with the ground GNSS mode, with an average improvement of 55.51%. In the geomagnetically disturbed environment, the G/SBased joint mode still has high consistency with the ionospheric radar results, and the detection performance is better than that of the ground GNSS mode. This research shows that combining with ground GNSS and LEO satellite observation data can substantially provide the performance of GNSS ionospheric total electron content detection and can provide good data support for the construction of a global ionospheric refinement model.

Bridging Maritime Communication Gaps: The Role of LEO Satellites in Expanding Global Connectivity

ABSTRACT Direct device -to-device is the latest and potentially the biggest opportunity in the telecom business as the gap between satellite and ground communication is narrowing. Respectively, the telecom companies and mobile network operators are interested in increasing the coverage of their telecom networks which will boost the quality of services and will increase the revenues as well. In this regard, Low-earth-orbit (LEO) satellite mega-constellations, such as SpaceX Starlink, are under fast deployments and promise broadband Internet to remote areas that terrestrial networks cannot reach. This paper will investigate the opportunities and challenges facing the LEO mega-constellation and satellite networks in the maritime industry.

Acquisition Performance for Inter-Satellite Optical Communication Between Low Earth Orbit and Geostationary Orbit Satellites with Vibration and Asymmetric Initial Pointing Errors

Acquisition Performance for Inter-Satellite Optical Communication Between Low Earth Orbit and Geostationary Orbit Satellites with Vibration and Asymmetric Initial Pointing Errors

MODRS: A Multi-Objective Deep Learning Algorithm for Optimizing Routing and Scheduling in LEO Satellite Networks

The demand for high-quality Direct-to-Home (D2H) television broadcasting services delivered via Low Earth Orbit (LEO) satellite constellations has surged in recent years. To address the growing needs of viewers, satellite communication must optimize the scheduling and routing of signals while balancing conflicting objectives. This research presents a novel approach named as Multi-Objective Deep Routing and Scheduling (MODRS) algorithm that is designed to tackle the challenges of signal latency minimization, bandwidth utilization maximization, and viewer demand satisfaction. The Multi-Objective Deep Neural Network (MODNN) is implemented in this paper to make intelligent routing and scheduling decisions for balancing multiple objectives. To enhance the learning process and provide training stability, the experience replay is used and the epsilon-greedy strategy is included to balance exploitation and exploration strategies. The Pareto-front concept is used for efficient D2H television broadcasting in the LEO satellite constellation. The experimental validation is conducted based on low-latency broadcasting, high-bandwidth utilization, viewer demand flexibility, adaptive signal strength and resource allocation efficiency. Using a series of simulated scenarios, this paper explores the versatility and robustness of MODRS, showcasing its exceptional performance in real-time, resource-efficient, disaster recovery, and rural broadcasting contexts. The findings indicate that MODRS is well-suited for a wide range of real-world applications, from low-latency broadcasting and disaster recovery to cost-effective rural expansion, enhancing the quality and accessibility of D2H television services. The MODRS algorithm emerges as a transformative solution for satellite communication optimization, ensuring viewer satisfaction and operational efficiency.

Intelligent Beam-Hopping-Based Grant-Free Random Access in Secure IoT-Oriented Satellite Networks

This research presents an intelligent beam-hopping-based grant-free random access (GFRA) architecture designed for secure Internet of Things (IoT) communications in Low Earth Orbit (LEO) satellite networks. In light of the difficulties associated with facilitating extensive device connectivity while ensuring low latency and high reliability, we present a beam-hopping GFRA (BH-GFRA) scheme that enhances access efficiency and reduces resource collisions. Three distinct resource-hopping schemes, random hopping, group hopping, and orthogonal group hopping, are examined and utilized within the framework. This technique utilizes orthogonal resource allocation algorithms to facilitate efficient resource sharing, effectively tackling the irregular and dynamic traffic. Also, a kind of activity mechanism is proposed based on the constraints of the spatio-temporal distribution of devices. We assess the system’s performance through a thorough mathematical analysis. Furthermore, we ascertain the access delay and success rate to evaluate its capability to serve a substantial number of IoT devices under satellite–terrestrial delay and interference of massive connections. The suggested method demonstrably improves connection, stability, and access efficiency in 6G IoT satellite networks, meeting the rigorous demands of next-generation IoT applications.

Passive Inter-Satellite Localization Accuracy Optimization in Low Earth Orbit Satellite Networks

Passive Inter-Satellite Localization Accuracy Optimization in Low Earth Orbit Satellite Networks

Design And Development of a Robotic Arm for Space Debris Mitigation Using Electrostatic and Gecko-Inspired Gripping Technologies

As space debris poses a growing threat to satellite operations, there is an urgent need for advanced robotic systems capable of effective debris capture in low Earth orbit. This paper presents the development and optimization of a robotic arm equipped with an electrostatic adhesion mechanism, designed specifically for microgravity environments. The primary objective is to engineer a versatile, lightweight robotic arm that can securely capture and hold various types of debris, including non-magnetic and composite materials. Key features include electrostatic adhesive pads for adaptable grip, a telescopic extension arm to increase reach with minimal mechanical complexity, and a retractable storage profile to streamline debris retrieval and handling. Through detailed calculations, we establish the required adhesive force to counter both inertial and gravitational forces acting on debris, ensuring secure capture even during minor satellite manoeuvres. An electrostatic charging system is designed to induce sufficient adhesive force, with calculations for charge requirements and pad dimensions optimized for secure adhesion. This paper details the design, force calculations, and component selection that make the robotic arm efficient, lightweight, and adaptable, contributing to safer, more effective space debris removal.

Federated deep reinforcement learning based computation offloading in a low Earth orbit satellite edge computing system

Federated deep reinforcement learning based computation offloading in a low Earth orbit satellite edge computing system

Enhanced detection and identification of satellites using an all-sky multi-frequency survey with prototype SKA-Low stations

Abstract With the low Earth orbit environment becoming increasingly populated with artificial satellites, rockets, and debris, it is important to understand the effects they have on radio astronomy. In this work, we undertake a multi-frequency, multi-epoch survey with two SKA-Low station prototypes located at the SKA-Low site, to identify and characterise radio frequency emission from orbiting objects and consider their impact on radio astronomy observations. We identified 152 unique satellites across multiple passes in low and medium Earth orbits from 1.6 million full-sky images across 13 selected ≈ 1 MHz frequency bands in the SKA-Low frequency range, acquired over almost 20 days of data collection. Our algorithms significantly reduce the rate of satellite misidentification, compared to previous work, validated through simulations to be < 1%. Notably, multiple satellites were detected transmitting unintended electromagnetic radiation, as well as several decommissioned satellites likely transmitting when the Sun illuminates their solar panels. We test alternative methods of processing data, which will be deployed for a larger, more systematic survey at SKA-Low frequencies in the near future. The current work establishes a baseline for monitoring satellite transmissions, which will be repeated in future years to assess their evolving impact on radio astronomy observations.

A dataset of tracked mesoscale precipitation systems in the tropics

AbstractMesoscale Convective Systems (MCSs) are often quantified via surface‐based radar network, geostationary satellite, or low earth orbit satellite observations. However, each of these has drawbacks for detecting cloud systems such as a lack of global coverage, a lack of variables to quantify deep convective cloud and precipitation properties, and a lack of continuous observations of individual MCSs, respectively. To generate a dataset of tropical Tracked IMERG Mesoscale Precipitation Systems (TIMPS), we use the Forward in Time tracking algorithm to track precipitation systems in the Integrated Multi‐satellitE Retrievals for the Global Precipitation Mission (IMERG). IMERG is a global gridded precipitation product that incorporates observations from a constellation of satellites with passive microwave sensors and other sources, allowing the TIMPS dataset to have continuous temporal precipitation information for MCSs in a global tropical strip with data every 30 min in time and 0.1° in space. TIMPS are provided in a publicly available data base with a variety of variables including MCS size, motion, and precipitation properties, estimations of MCS life cycle stages, and their proximity to the nearest tropical cyclone. By combining the TIMPS dataset with the University of Washington Convective Features database, we also provide snapshots of information from more spatially detailed space‐borne radar coverage. The TIMPS dataset provides the means for detailed long‐term and large‐scale study of MCSs in all regions of the tropics with applications such as composite studies of MCS life cycles and the evaluation of model performance.

A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks.

This paper proposes a green computing strategy for low Earth orbit (LEO) satellite networks (LSNs), addressing energy efficiency and delay optimization in dynamic and energy-constrained environments. By integrating a Markov Decision Process (MDP) with a Double Deep Q-Network (Double DQN) and introducing the Energy-Delay Ratio (EDR) metric, this study effectively quantifies and balances energy savings with delay costs. Simulations demonstrate significant energy savings, with reductions of up to 47.87% under low business volumes, accompanied by a minimal delay increase of only 0.0161 s. For medium business volumes, energy savings reach 26.75%, with a delay increase of 0.0189 s, while high business volumes achieve a 4.36% energy reduction and a delay increase of 0.0299 s. These results highlight the proposed strategy's ability to effectively balance energy efficiency and delay, showcasing its adaptability and suitability for sustainable operations in LEO satellite networks under varying traffic loads.

AI-driven modeling and control of low earth orbit satellites

AI-driven modeling and control of low earth orbit satellites

Communication Link Analysis of a Low-Earth Orbit Satellites Considering Interference Sources Moving Along Various Parabola-Curved Paths.

We analyze the communication link of an LEO satellite considering interference sources moving along various parabola-curved paths. In this situation, the location of the ground station, airborne interference source paths, and the satellite's trajectory were expressed in the East-North-Up (ENU) coordinate system. The airborne interference source path is designed using a parabola equation with a directrix parallel to the satellite's trajectory to analyze the interference situation for more diverse interference source paths, rather than using a straight path. To investigate critical interference situations where the J/S ratio is maintained above -20 dB with a small deviation during the communication time, we investigate interference situations by changing the parameters of the interference source path. The genetic algorithm (GA) is used to easily find an airborne interference source path that maintains the J/S ratio above -20 dB with a small deviation. A cost function for the GA is then defined as the average difference between the J/S ratio and the reference value (-10 dB and -20 dB) during the communication time. The optimum parameters of the interference source path are determined at a minimum cost in the GA. These results demonstrate that more significant interference situations for the communication link can be easily found by using parabola-curved paths and the GA. As a result, previous studies investigated the basic tendency of the J/S ratio using straight paths. However, this study provides a database for operating an anti-jamming system based on the obtained optimized path.

GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks

This paper addresses the issue of onboard inference for AI-based routing algorithms in dynamic LEO (Low Earth Orbit) satellite networks. In dynamic LEO networks, it is essential to maintain communication performance across varying topologies while considering link disconnections and overcoming computational constraints for real-time inference on embedded boards. This paper proposes a GPU-based inference acceleration method to reduce the computation time required for real-time onboard inference of a Dueling DQN (Deep Q-Network)-based routing algorithm in dynamic LEO satellite networks. The approach is composed of memory management, low-level operations, and efficient indexing methods, which collectively enhance computational efficiency. As a result, the proposed method achieves approximately 2.4 times faster inference compared to conventional CPU-based approaches. Additionally, the kernel performance analysis reveals that the proposed method reaches 10% of the peak computational performance and 20% of the peak memory performance. This demonstrates the compatibility of the proposed method for integration with additional applications in the multitasking systems of LEO satellites.

Development of Piezoelectric Inertial Rotary Motor for Free-Space Optical Communication Systems.

This paper presents the design, development, and investigation of a novel piezoelectric inertial motor whose target application is the low Earth orbit (LEO) temperature conditions. The motor utilizes the inertial stick-slip principle, driven by the first bending mode of three piezoelectric bimorph plates, and is compact and lightweight, with a total volume of 443 cm3 and a mass of 28.14 g. Numerical simulations and experimental investigations were conducted to assess the mechanical and electromechanical performance of the motor in a temperature range from -20 °C to 40 °C. The results show that the motor's resonant frequency decreases from 12,810 Hz at -20 °C to 12,640 Hz at 40 °C, with a total deviation of 170 Hz. The displacement amplitude increased from 12.61 μm to 13.31 μm across the same temperature range, indicating an improved mechanical response at higher temperatures. The motor achieved a maximum angular speed up to 1200 RPM and a stall torque of 13.1 N·mm at an excitation voltage amplitude of 180 Vp-p. The simple and scalable design, combined with its stability under varying temperature conditions, makes it well suited for small satellite applications, particularly in precision positioning tasks such as satellite orientation and free-space optical (FSO) communications.