Satellite Deployment Research Articles

Expanded Linear Responsivity for Earth and Planetary Radiometry

Abstract Earth and planetary radiometry requires spectrally dependent observations spanning an expansive range in signal flux due to variability in celestial illumination, spectral albedo, and attenuation. Insufficient dynamic range inhibits contemporaneous measurements of dissimilar signal levels and restricts potential environments, time periods, target types, or spectral ranges that instruments observe. Next-generation (NG) advances in temporal, spectral, and spatial resolution also require further increases in detector sensitivity and dynamic range corresponding to increased sampling rate and decreased field of view (FOV), both of which capture greater intrapixel variability (i.e., variability within the spatial and temporal integration of a pixel observation). Optical detectors typically must support expansive linear radiometric responsivity, while simultaneously enduring the inherent stressors of field, airborne, or satellite deployment. Rationales for significantly improving radiometric observations of nominally dark targets are described herein, along with demonstrations of state-of-the-art (SOTA) capabilities and NG strategies for advancing SOTA. An evaluation of linear dynamic range and efficacy of optical data products is presented based on representative sampling scenarios. Low-illumination (twilight or total lunar eclipse) observations are demonstrated using a SOTA prototype. Finally, a ruggedized and miniaturized commercial-off-the-shelf (COTS) NG capability to obtain absolute radiometric observations spanning an expanded range in target brightness and illumination is presented. The presented NG technology combines a multipixel photon counter (MPPC) with a silicon photodetector (SiPD) to form a dyad optical sensing component supporting expansive dynamic range sensing, i.e., exceeding a nominal 10 decades in usable dynamic range documented for SOTA instruments.

Influence of Gamma irradiation on shape memory polymer nano-composite for satellite deployment mechanism

This research investigates the impact of gamma irradiation on epoxy-MWCNT nanocomposites for satellite deployment mechanisms. Nanocomposites, enhanced with surfactants, were meticulously prepared and subjected to controlled gamma irradiation (250–1000 kGy) utilizing the Cobalt-60 facility Industrial Mega Gamma-1 at NCRRT in Egypt. Surface tension measurements explored surfactant effects on epoxy-MWCNT composites in acetone. Acetone reduced tension from 26.7 to be 24.2 (mN/m). Surfactants (Tween 80, SDS) effectively lowered tension (24.4 mN/m), while surfactant-free systems had higher tension (25.1 mN/m). Cationic surfactant (CTAB) slightly increased tension (25.4 mN/m) but aided MWCNT dispersion. Nonionic and anionic surfactants showed superior dispersing power, aligning with MWCNTs and enhancing dispersion. Thermogravimetric analysis (TGA) unveiled alterations in the thermal stability of epoxy-MWCNT nanocomposites induced by radiation, particularly evident at elevated doses (500 and 1000 kGy). Notably, surfactant-modified specimens exhibited discernible effects on various thermal stability parameters. DMA analysis revealed radiation-induced changes in viscoelastic properties. Unirradiated epoxy exhibited a Tg of 58 °C, while 250 kGy irradiation enhanced crosslinking (Tg: 64 °C). Higher doses (500 kGy, 1000 kGy) caused marginal Tg changes. Surfactant-modified samples showed varied effects, with Tween 80 emphasizing its role in phase separation. Results highlighted radiation’s influence on stiffness and energy dissipation. Shape memory behavior indicated increased recovery time with higher doses, except at 250 kGy. Epoxy-MWCNT exhibited a stable recovery time, suggesting a MWCNT stabilizing effect. Fixation rates consistently reached 100%, indicating improved shape recovery influenced by MWCNTs and surfactants. This study provides insights into optimizing nanocomposites for satellite deployment applications.

Neural Network-Based Fusion of InSAR and Optical Digital Elevation Models with Consideration of Local Terrain Features

InSAR and optical techniques represent two principal approaches for the generation of large-scale Digital Elevation Models (DEMs). Due to the inherent limitations of each technology, a single data source is insufficient to produce high-quality DEM products. The increasing deployment of satellites has generated vast amounts of InSAR and optical DEM data, thereby providing opportunities to enhance the quality of final DEM products through the more effective utilization of the existing data. Previous research has established that complete DEMs generated by InSAR technology can be combined with optical DEMs to produce a fused DEM with enhanced accuracy and reduced noise. Traditional DEM fusion methods typically employ weighted averaging to compute the fusion results. Theoretically, if the weights are appropriately selected, the fusion outcome can be optimized. However, in practical scenarios, DEMs frequently lack prior information on weights, particularly precise weight data. To address this issue, this study adopts a fully connected artificial neural network for elevation fusion prediction. This approach represents an advancement over existing neural network models by integrating local elevation and terrain as input features and incorporating curvature as an additional terrain characteristic to enhance the representation of terrain features. We also investigate the impact of terrain factors and local terrain feature as training features on the fused elevation outputs. Finally, three representative study areas located in Oregon, USA, and Macao, China, were selected for empirical validation. The terrain data comprise InSAR DEM, AW3D30 DEM, and Lidar DEM. The results indicate that compared to traditional neural network methods, the proposed approach improves the Root-Mean-Squared Error (RMSE) ranges, from 5.0% to 12.3%, and the Normalized Median Absolute Deviation (NMAD) ranges, from 10.3% to 26.6%, in the test areas, thereby validating the effectiveness of the proposed method.

Optimal Launch Manifesting for Space Freight Forwarders

This paper proposes a novel framework to optimize a launch vehicle manifesting plan for small satellites from the perspective of a space freight forwarder. The forwarder in space logistics coordinates individual shipment orders from the launch customers and launch service providers like a freight forwarder in ground logistics does. An optimal launch manifesting problem is formulated as a variant of the vehicle routing problem involving the decisions on carriers, launch strategies (dedicated or multi-payload), launch dates, and satellite deployment sequences. A solution procedure based on the column generation method is proposed to address the challenges associated with the large-scale combinatorial optimization problem. A case study demonstrates the effectiveness of the proposed framework and solution procedure.

Detection and Analysis of Anomalous Behavior in On-Orbit Satellites Using AI Algorithms

The increasing deployment of satellites for essential applications necessitates robust anomaly detection to maintain their operational integrity. Traditional methods, which depend on manual monitoring and predefined thresholds, often prove inadequate in the complex space environment. This paper investigates the application of Artificial Intelligence (AI) algorithms to improve the detection and analysis of anomalous behavior in on-orbit satellites. AI, especially through machine learning (ML) and deep learning (DL), provides advanced capabilities for processing extensive telemetry data and identifying intricate patterns. By leveraging historical data, AI systems can establish normal operational parameters and detect deviations indicating potential anomalies. Techniques such as supervised and unsupervised learning are employed to develop models with high predictive accuracy. Furthermore, AI facilitates root cause analysis by correlating anomalies with operational conditions or external factors, enabling effective corrective measures. The integration of AI also promotes autonomous satellite operations, which are crucial for deep-space missions. This advancement enhances satellite reliability and safety, supporting sustainable and progressive space exploration. In this research, machine learning algorithms were employed to develop the proposed anomaly detection system. The system aims to detect subtle failures in the spacecraft’s attitude dynamics system, particularly in the reaction wheel subsystem, by learning solely from the spacecraft's nominal behavioral data. The system was developed from a small satellite's attitude dynamics control system, which may exhibit bearing failures in the reaction wheels. Two types of anomaly detection systems were introduced: a two-sided learning anomaly detection system and a one-sided learning anomaly detection system. For this study, a two-sided learning anomaly detection system was developed using the Logistic Regression (LR) method. This provided a foundation for the training process using a machine learning approach. By learning from both nominal and failure behaviors of the satellite, the system was designed to detect small reaction wheel friction failures effectively.

Enhanced hybrid energy storage system combining battery and supercapacitor to extend nanosatellite lifespan

The power limitations of nanosatellites, particularly during peak demand periods such as data transmission and maneuvering, hinder their operational capabilities. Traditional Electrical Power Subsystems relying solely on batteries face challenges in delivering the necessary power density and maintaining battery life, especially during eclipses. This study proposes an innovative Hybrid Energy Storage System for a 3U nanosatellite, integrating high-energy-density batteries with high-power-density supercapacitors, using an active parallel hybrid topology with two bidirectional converters and an optimal power management strategy. Using MATLAB and Simulink models, the study optimizes the Hybrid Energy Storage System by focusing on minimizing the capacity rate and depth of discharge to extend battery life. Simulation results show a 53.42% reduction in depth of discharge compared to a battery-only system, indicating a significant extension of battery life. Additionally, the proposed system allows for a minimal 2.08% increase in overall mass while maintaining enhanced performance. This research fills a critical gap in the literature by exploring active Hybrid Energy Storage System topologies for spacecraft applications, beyond the traditional passive and semi-active configurations. The innovative power management strategy ensures efficient energy utilization, significantly enhancing the reliability and efficiency of nanosatellite missions. The findings offer a practical solution to improve mission performance and extend the operational lifespan of nanosatellites, paving the way for more robust and capable small satellite deployments.

Democratizing Direct-to-Cell Low Earth Orbit Satellite Networks

The emergent Direct-to-cell Low Earth Orbit (LEO) satellites promise to eliminate cellular coverage dead zones by enabling direct network access to our regular phones and IoTs everywhere on Earth via 4G, 5G, and beyond. However, their large-scale deployment can be impeded by scarce orbital slots in space, a lack of licensed cellular spectrums, and prohibitive capital costs for satellite deployment and operation. This article explores how to enable multi-tenant direct-to-cell LEO satellites as a cost-effective win-win solution for satellite operators, mobile operators, and users. We demonstrate that the state-of-the-art cellular networks' stateful hop-by-hop session-based architecture restricts the LEO satellite multi-tenancy. To remove this technical barrier, we propose MOSAIC [1], an alternative scheme to shift from the hop-by-hop session-based cellular service to pay-as-you-go satellite self-service. MOSAIC lets mobile operators supply their users with one-time digitally signed tokens as "coins" to pay for local satellite access on demand. Any satellite operator's authentic satellite can accept these tokens to serve this user without pre-establishing sessions or contacting mobile operators. Akin to cloud computing, this paradigm lets satellite operators flexibly lease their satellites to more mobile operators or higher revenues and return on investment, and allows mobile operators and users to enjoy competitive satellites for complementary coverage and cost optimizations. Our evaluations with the real satellite data and commodity 3GPP NTN protocol stack validate MOSAIC's scalability and performance advantages compared to the state-of-the-art.

Comprehensive Analysis of Satellite Deployment Dynamics using MATLAB

A crucial section of area missions is satellite placement, which requires proper management and analysis to guarantee undertaking fulfilment. This paper presents a comprehensive framework for evaluation and simulation of satellite deployment dynamics based on MATLAB. The proposed framework combines visualization processes, dynamics of satellite deployment, and numerical methods to accurately model satellite behaviour all through deployment ranges. The simulation platform uses MATLAB's capability which includes thrust, aerodynamic drag, and gravitational forces to represent the complicated dynamics of satellite deployment. Furthermore, the platform permits multi-satellite deployment situations and parameter-customizable analysis of unmarried and multi-satellite deployments. The performance and acceptability of the MATLAB-primarily based platform is illustrated through case research and overall performance reviews, providing insightful data for planning satellite missions, design optimization, and manipulate strategies. For engineers and academics running on satellite improvement area assignment layout, the proposed framework is a useful resource to better apprehend and examine deployment dynamics underneath numerous operational eventualities.

5G Radiation Source Location Based on Passive Virtual Aperture Technology by Single-Satellite

With the development of 5th-Generation Mobile Communication (5G) technology and the deployment of low-Earth orbit satellites, using satellites to locate 5G radiation sources is of great significance in commerce and the military as an important task of integrated sensing and communication. Recently, passive virtual aperture technology has been introduced into passive location to improve accuracy, but the existing method, using matched filters to search the Doppler information to realize the location, has the disadvantages of high complexity and poor range resolution. In this paper, an improved 5G radiation source location based on a virtual aperture is proposed, which uses the improved Golden Section search-fractional Fourier algorithm (GSS-FRFT) to improve the existing passive virtual aperture location methods. First, the received signals are coherently accumulated to convert the time gain into spatial gain, and the subcarrier phase information is extracted by Fast Fourier Transform based on the 5G signal characteristics to obtain the azimuth signal. Then, an improved high-order GSS-FRFT algorithm is proposed to analyze the Doppler information, and signal focusing and satellite ephemeris data are used to estimate the effective velocity and solve the radiation source location. The simulation results show that the proposed method can improve the location accuracy compared with other single-satellite location methods and has high resolution, high accuracy and low complexity compared with the existing passive virtual aperture location method.

Micro-Satellite Systems Design, Integration, and Flight.

Within the past decade, the aerospace engineering industry has evolved beyond the constraints of using single, large, custom satellites. Due to the increased reliability and robustness of commercial, off-the-shelf printed circuit board components, missions have instead transitioned towards deploying swarms of smaller satellites. Such an approach significantly decreases the mission cost by reducing custom engineering and deployment expenses. Nanosatellites can be quickly developed with a more modular design at lower risk. The Alpha mission at the Cornell University Space Systems Studio is fabricated in this manner. However, for the purpose of development, the initial proof of concept included a two-satellite system. The manuscript will discuss system engineering approaches used to model and mature the design of the pilot satellite. The two systems that will be primarily focused on are the attitude control system of the carrier nanosatellite and the radio frequency communications on the excreted femto-satellites. Milestones achieved include ChipSat to ChipSat communication, ChipSat to ground station communication, packet creation, error correction, appending a preamble, and filtering the signal. Other achievements include controller traceability/verification and validation, software rigidity tests, hardware endurance testing, Kane damper, and inertial measurement unit tuning. These developments matured the technological readiness level (TRL) of systems in preparation for satellite deployment.

Static and Modal Analysis of a Box Structured Satellite Deployment Mechanism with self-actuated Torsion Joint

A brand-new satellite deployment mechanism with boxed structure and self-actuated torsion joints is proposed and an effective method to verify the feasibility of this mechanism is established in this paper. This mechanism has the characteristics of high base frequency, high ratio of deployed and folded space occupation ratio. In order to meet the design requirements, the related analysis and optimization need to be conducted, and several conclusions are obtained. Firstly, the modal analysis of deployment mechanism at folded and fully deployed state is analyzed, and the result showed that the proper wall thickness is the key parameter to satisfy the design requirement and an optimized value could be obtained; Secondly, the heat deformation analysis result showed that the material plays an more important roll on affecting the thermal deformation than structure parameter; Thirdly, Under the torsional moment of the joints, the stress distribution of the deployment mechanism under different folding angle is investigated, it could be clearly found that the maximum stress is always located on the bonded area of the rod and joint, and the maximum stress is increased with the opening angle generally. By combined analysis including thermal, static, and modal, the related characteristics are obtained which could be used for structure optimization and provide an effective solution for the design of box structured satellite unfolding mechanism with self-actuated torsional joints.

Tracking an untracked space debris after an inelastic collision using physics informed neural network

With the sustained rise in satellite deployment in Low Earth Orbits, the collision risk from untracked space debris is also increasing. Often small-sized space debris (below 10 cm) are hard to track using the existing state-of-the-art methods. However, knowing such space debris’ trajectory is crucial to avoid future collisions. We present a Physics Informed Neural Network (PINN)—based approach for estimation of the trajectory of space debris after a collision event between active satellite and space debris. In this work, we have simulated 8565 inelastic collision events between active satellites and space debris. To obtain the states of the active satellite, we use the TLE data of 1647 Starlink and 66 LEMUR satellites obtained from space-track.org. The velocity of space debris is initialized using our proposed velocity sampling method, and the coefficient of restitution is sampled from our proposed Gaussian mixture-based probability density function. Using the velocities of the colliding objects before the collision, we calculate the post-collision velocities and record the observations. The state (position and velocity), coefficient of restitution, and mass estimation of un-tracked space debris after an inelastic collision event along with the tracked active satellite can be posed as an optimization problem by observing the deviation of the active satellite from the trajectory. We have applied the classical optimization method, the Lagrange multiplier approach, for solving the above optimization problem and observed that its state estimation is not satisfactory as the system is under-determined. Subsequently, we have designed Deep Neural network-based methods and Physics Informed Neural Network (PINN) based methods for solving the above optimization problem. We have compared the performance of the models using root mean square error (RMSE) and interquartile range of the predictions. It has been observed that the PINN-based methods provide a better estimation performance for position, velocity, mass and coefficient of restitution of the space debris compared to other methods.

Dynamic analysis of a box-structured satellite deployment mechanism with self-actuated torsion joints

Abstract. This paper designed a kind of satellite deployment mechanism with a boxed structure and passive torsion joints. This deployment mechanism has significant strengths, including a high base frequency and stiffness, a high ratio of deployed and folded space occupation ratios, and self-actuated joints without needing any external power to drive. In order to analyze the dynamic characteristics of this mechanism, a simplified governing equation is proposed and dynamic behavior is studied systematically, including impact response, harmonic response, and modal analysis. Through systematic research, several conclusions are drawn. Firstly, when the deployment mechanism reached the ending stage of unfolding driven by passive torsional joints, the load base installed on top of the deployment mechanism generated a first-order sharp reaction force and multiple low-order shocks followed, and the system entered a stable state after a certain time of vibration. Secondly, the system can generate a high vibration magnitude at low-frequency excitation when the mechanism is in a fully deployed state and at high-frequency excitation when the mechanism is in a folded state. Thirdly, the first sixth-order natural frequency and vibration shape with different wall thicknesses are obtained by modal analysis. The result shows that only with a 2.5 mm wall thickness can the connecting rod satisfy the design requirement. Through dynamic behavior research, the structure characteristics are obtained which can be used for structure optimization and to provide an effective solution for the design of a box-structured satellite-unfolding mechanism with self-actuated torsion joints.

Radiation effect on silicon photonics chips for space quantum key distribution.

Quantum communication satellites have potential for applications in future quantum networks. Photonics integrated chips, due to their compact and lightweight nature, are well-suited for satellite deployment. However, the harsh radiation environment of space can cause permanent damage to these chips, resulting in degraded performance or complete loss of functionality. In this work, we conducted a series of radiation experiments to evaluate the effects of γ rays and high energy protons on quantum key distribution transmitter chips. The results suggest that the insertion loss of the chip is slightly reduced by about 1.5 dB after 100 krad (Si) γ ray irradiation, and further reduced by about 0.5 to 1 dB after 2.39 × 1011/cm2 proton radiation. The half-wave voltages, extinction ratios, and polarization angles are not changed significantly within the measurement error range. Our work proves the feasibility of deploying quantum constellations utilizing terminals based on photonics chips.

Distributed Model Predictive Control via Separable Optimization in Multiagent Networks

We present a distributed model predictive control method which enables a group of agents to compute their control inputs locally, while communicating with their neighbors over a communication network. While many distributed model predictive control methods require a central station for some coordination or computation of the optimization variables, our method does not require a central station, making our approach applicable to a variety of communication network topologies. With our method, each agent solves for its control inputs without solving for the control inputs of other agents, allowing for efficient optimization by each agent, unlike some other distributed methods. Further, our method attains linear convergence to the optimal control inputs in convex model predictive control problems, improving upon the sub-linear convergence rates provided by some other distributed methods such as dual decomposition methods. Moreover, our algorithm provides a closed-loop controller for convex model predictive control problems with affine constraints. We demonstrate our method in both convex and non-convex model predictive control problems in wireless transceiver alignment and satellite deployment, where we show robustness of our method to time delays.

Navigating the Challenges of 3D Printing Personalized Medicine in Space Explorations: A Comprehensive Review.

Space exploration has undergone a paradigm shift in recent years, with a growing emphasis on long-duration missions and human habitation on other celestial bodies. Private aerospace businesses are at the forefront of advancing the next iteration of spacecraft, encompassing a wide range of applications such as deep space exploration (e.g., SpaceX) and cost-effective satellite deployments (e.g., Rocketlab). One of the critical challenges associated with prolonged space missions is the provision of personalized medical care. 3D printing technology has emerged as a potential solution, enabling the on-demand production of personalized medical devices and medications. However, the unique conditions of space pose substantial challenges to the successful implementation of 3D printing for personalized medicine. Tremendous scope for research exists in terms of resource utilization and waste management in space ecosystem, robotic and artificial intelligence (AI) enabled tool utilization, remote operability, interplanetary travel, space education and training tools, digital twins, space tourism and in many other aspects of 3D printing for personalized medicine in space explorations.

Deployment of dual-purpose satellites for military applications. Part 1. Planet’s and Blacksky’s next-generation imagery surveillance satellites

Deployment of dual-purpose satellites for military applications. Part 1. Planet’s and Blacksky’s next-generation imagery surveillance satellites

Gravity-based network traffic abstraction and laser ON/OFF control in optical satellite networks

As the cost of launching low Earth orbit satellites continuously decreases, satellite-based communications networks are emerging as a new area for both academia and industry. Lasers are already employed for building inter-satellite links, forming optical satellite networks. The orbiting nature of satellites determines that the optical satellite networks are usually uniformly distributed around the Earth, to provide seamless coverage to any place at all times. However, the end users on the Earth are non-uniformly distributed. As a result, many satellites with laser links might not be utilized efficiently. From the energy perspective, this work studies the energy-efficiency issues of the inter-satellite laser links. We first model the optical satellite networks by presenting the satellite constellation with inter-satellite laser design principles and introduce the laser ON/OFF control problem accordingly. To explore the possibility of saving energy in the massive satellite deployment, we further introduce the gravity-based network traffic model and propose a gravity-based network traffic abstraction (GNTA) model to evaluate the importance of each laser link. Accordingly, we further propose a GNTA-based ON/OFF control (GOOC) algorithm to improve the energy efficiency of inter-satellite laser links by switching OFF parts of the laser links that are less utilized. We evaluate the GOOC’s performance using simulation, and results show that switching OFF 20% of the laser terminals in the full-gridmesh topology can improve the energy efficiency by about 10%, with an acceptable cost of network performance degradation.

A Legal Evaluation of the Impact of Artificial Intelligence on Outer Space Asset-Financing

The space industry has grown significantly in importance, with more and more private companies aiming to provide services within the space environment. These include space tourism and the extensive deployment of satellites for earth monitoring, communication, and space exploration. Technological developments have accelerated the ability of private companies to provide services and establish businesses in the space area, with several new businesses providing services worldwide. With the technological advances in AI, the space area has been an essential area for AI to be deployed and the challenges it may face. The challenges with AI in the space sector and regulations in the space sector overall is the global regulatory nature of the environment. This is incredibly challenging given the significant discussion regarding national AI regulations to deal with this fast-developing area. Based on the challenging regulatory environment and associated risks, financing these new business models has presented new complexities that must be taken care of. Asset-based financing of such operations represents vital opportunities to deal with the intricate complexities of such operations and the various legal environments. While liability and other challenges have to be considered both in light of national and international regulations that may have to be taken into account, asset financing represents a very attractive option given the priority and security of the interest in the space asset. Specifically, there are various remedies given that it reduces the risk of various non-compatible regulations in order to secure their concerning asset rights. Furthermore, pre-existing third-party interests can be looked up via online registries, reducing potential risks.

Can Yield Prediction Be Fully Digitilized? A Systematic Review

Going beyond previous work, this paper presents a systematic literature review that explores the deployment of satellites, drones, and ground-based sensors for yield prediction in agriculture. It covers multiple aspects of the topic, including crop types, key sensor platforms, data analysis techniques, and performance in estimating yield. To this end, datasets from Scopus and Web of Science were analyzed, resulting in the full review of 269 out of 1429 retrieved publications. Our study revealed that China (93 articles, >1800 citations) and the USA (58 articles, >1600 citations) are prominent contributors in this field; while satellites were the primary remote sensing platform (62%), followed by airborne (30%) and proximal sensors (27%). Additionally, statistical methods were used in 157 articles, and model-based approaches were utilized in 60 articles, while machine learning and deep learning were employed in 142 articles and 62 articles, respectively. When comparing methods, machine learning and deep learning methods exhibited high accuracy in crop yield prediction, while other techniques also demonstrated success, contingent on the specific crop platform and method employed. The findings of this study serve as a comprehensive roadmap for researchers and farmers, enabling them to make data-driven decisions and optimize agricultural practices, paving the way towards a fully digitized yield prediction.