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Integrated communication–sensing–computing (ICSC) satellites, which integrate edge computing servers on Earth observation satellites to process collected data directly in orbit, are attracting growing attention. Nevertheless, some monitoring tasks involve sequential sub-tasks like target observation and movement prediction, leading to data dependencies. Moreover, the limited energy supply on satellites requires the sequential execution of sub-tasks. Therefore, inappropriate assignments can cause circular waiting among satellites, resulting in deadlocks. This paper formulates task offloading in ICSC satellites with data-dependent constraints as a mixed-integer linear programming (MILP) problem, aiming to minimize service latency and energy consumption simultaneously. Given the non-centrality of ICSC satellites, we propose a distributed deadlock-free task offloading (DDFTO) algorithm. DDFTO operates in parallel on each satellite, alternating between sub-task inclusion and consensus and sub-task removal until a common offloading assignment is reached. To avoid deadlocks arising from sub-task inclusion, we introduce the deadlock-free insertion mechanism (DFIM), which strategically restricts the insertion positions of sub-tasks based on interval relationships, ensuring deadlock-free assignments. Extensive experiments demonstrate the effectiveness of DFIM in avoiding deadlocks and show that the DDFTO algorithm outperforms benchmark algorithms in achieving deadlock-free offloading assignments. [ABSTRACT FROM AUTHOR]

The number of real-time dynamic satellite observation missions has been rapidly increasing recently, while little attention has been paid to the dynamic mission-scheduling problem. It is crucial to reduce perturbations to the initial scheduling plan for the dynamic mission-scheduling as the perturbations have a significant impact on the stability of the Earth observation satellites (EOSs). In this paper, we focus on the EOS dynamic mission-scheduling problem, where the observation profit and perturbation are considered simultaneously. A multi-objective dynamic mission-scheduling mathematical model is first formulated. Then, we propose a multi-objective dynamic mission-scheduling algorithm (MODMSA) based on the improved Strength Pareto Evolutionary Algorithm (SPEA2). In the MODMSA, a novel two-stage individual representation, a minimum perturbation random initialization, multi-point crossover, and greedy mutation are designed to expand the search scope and improve the search efficiency. In addition, a profit-oriented local search algorithm is introduced into the SPEA2 to improve the convergence speed. Furthermore, an adaptive perturbation control strategy is adopted to improve the diversity of non−dominated solutions. Extensive experiments are conducted to evaluate the performance of the MODMSA. The simulation results show that the MODMSA outperforms other comparison algorithms in terms of solution quality and diversity, which demonstrates that the MODMSA is promising for practical EOS systems. [ABSTRACT FROM AUTHOR]

Satellite edge computing (SEC) plays an increasing role in earth observation, due to its global coverage and low-latency computing service. In SEC, it is pivotal to offload diverse observation-data-processing tasks to the appropriate satellites. Nevertheless, due to the sparse intersatellite link (ISL) connections, it is hard to gather complete information from all satellites. Moreover, the dynamic arriving tasks will also influence the obtained offloading assignment. Therefore, one daunting challenge in SEC is achieving optimal offloading assignments with consideration of the dynamic delay-sensitive tasks. In this paper, we formulate task offloading in SEC with delay-sensitive tasks as a mixed-integer linear programming problem, aiming to minimize the weighted sum of deadline violations and energy consumption. Due to the limited ISLs, we propose a fully-decentralized method, called the PI-based task offloading (PITO) algorithm. The PITO operates on each satellite in parallel and only relies on local communication via ISLs. Tasks can be directly offloaded on board without depending on any central server. To further handle the dynamic arriving tasks, we propose a re-offloading mechanism based on the match-up strategy, which reduces the tasks involved and avoids unnecessary insertion attempts by pruning. Finally, extensive experiments demonstrate that PITO outperforms state-of-the-art algorithms when solving task offloading in SEC, and the proposed re-offloading mechanism is significantly more efficient than existing methods. [ABSTRACT FROM AUTHOR]

The purpose of constructing onboard observation mission queues is to improve the execution efficiency of onboard tasks and reduce energy consumption, representing a significant challenge in achieving efficient global military reconnaissance and target tracking. Existing research often focuses on the aspect of task scheduling, aiming at optimizing the efficiency of single-task execution, while neglecting the complex dependencies that might exist between multiple tasks and payloads. Moreover, traditional task scheduling schemes are no longer suitable for large-scale tasks. To effectively reduce the number of tasks within the network, we introduce a network aggregation graph model based on multiple satellites and tasks, and propose a task aggregation priority dynamic calculation algorithm based on graph computations. Subsequently, we present a dynamic merging-based method for multi-satellite, multi-task aggregation, a novel approach for constructing onboard mission queues that can dynamically optimize the task queue according to real-time task demands and resource status. Simulation experiments demonstrate that, compared to baseline algorithms, our proposed task aggregation method significantly reduces the task size by approximately 25% and effectively increases the utilization rate of onboard resources. [ABSTRACT FROM AUTHOR]

Earth Observation Satellite has facilitated the study of the earth’s environment and become powerful in various applications such as: Land temperature, Land use, urban monitoring, defense, etc. Additionally, the transmission of this image captured from the EOS to the earth must be confidential and secure against illegal access, and without data corruption. However, this task remains challenging due to the space radiation environment that could affect the satellite’s hardware, and lead to corrupted data. These issues motivate our study to provide an enhanced approach to securing the satellite and ground station link. In this paper, we propose a secure implementation-based radiation-hardened Virtex-4QV FPGA. The secure implementation utilizes a combined architecture between the AES algorithm and Hamming code to ensure security. The most significant advantage of the proposed implementation is the use of the three keys 128/192/256 bits with the pipelined architecture which leads to a high throughput and a high level of security. Based on several security criteria, the suggested cryptosystem findings demonstrate the system’s effectiveness in terms of security. Therefore, this proposed solution can be used and utilized in the EOS application.

The number of real-time dynamic satellite observation missions has been rapidly increasing recently, while little attention has been paid to the dynamic mission-scheduling problem. It is crucial to reduce perturbations to the initial scheduling plan for the dynamic mission-scheduling as the perturbations have a significant impact on the stability of the Earth observation satellites (EOSs). In this paper, we focus on the EOS dynamic mission-scheduling problem, where the observation profit and perturbation are considered simultaneously. A multi-objective dynamic mission-scheduling mathematical model is first formulated. Then, we propose a multi-objective dynamic mission-scheduling algorithm (MODMSA) based on the improved Strength Pareto Evolutionary Algorithm (SPEA2). In the MODMSA, a novel two-stage individual representation, a minimum perturbation random initialization, multi-point crossover, and greedy mutation are designed to expand the search scope and improve the search efficiency. In addition, a profit-oriented local search algorithm is introduced into the SPEA2 to improve the convergence speed. Furthermore, an adaptive perturbation control strategy is adopted to improve the diversity of non−dominated solutions. Extensive experiments are conducted to evaluate the performance of the MODMSA. The simulation results show that the MODMSA outperforms other comparison algorithms in terms of solution quality and diversity, which demonstrates that the MODMSA is promising for practical EOS systems.

With the development of satellite cluster technology, the earth observation capability of satellite clusters has been greatly enhanced, along with the improvement of satellite earth observation and inter-satellite data transmission ability. Nevertheless, it is difficult to coordinate satellite observation, inter-satellite data transmission, and satellite–ground data download to satisfy the constraints of satellite multi-subsystems. In this article, the multi-satellite observation-relay transmission-downloading coupling scheduling problem is described. Based on the conventional tabu search algorithm for multi-satellite earth observation, the data transmission path planning algorithm is integrated to carry out the entire coupling process of multi-satellite observation, inter-satellite data transmission, and satellite–ground data downloading. Referring to the idea of the artificial potential field method, the satellite cluster profit-state evaluation function is introduced to enhance the local search process within the tabu search framework. Moreover, in the data transmission planning algorithm, the rule-based Dijkstra data transmission path planning method is proposed based on two data transmission path planning strategies and the satellite cluster state-strategy selection rules. The simulation results show that the proposed method can realize the entire process of scheduling satellite cluster observation, relay transmission, and downloading and enhance the ability of the satellite cluster to obtain observation data. The improved Dijkstra method enhances the adaptability of the data transmission path planning method to the multi-subsystem coupled problem, and the improved local search in the tabu search method elevates the searching capability of the algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Integrated scheduling data transmission and observation tasks to achieve better schedule results. • In order to make more flexible scheduling, a time-continuous model for the data transmission process is established to achieve accurate time decisions. • A hybrid Actor-Critic reinforcement learning method is designed for solving single-satellite observation and transmission task scheduling problems, which performs well under intensive observation scenarios. • Different training methods are applied to meet various scheduling requirements and random scenarios. Earth observation satellites(EOS) generate a large amount of observation data in intensive observation scenarios, while the ability of data storage on the EOS is limited. It makes integrating satellite observation and data transmission tasks for EOS imperative. This paper establishes a time-continuous model for the single EOS integrated scheduling problem, which considers data transmission and observation simultaneously. A hybrid Actor-Critic reinforcement learning method is adopted to solve the EOS integrated scheduling problem for a more efficient solution in intensive observation scenarios. Furthermore, the algorithm can flexibly determine the start and end time of the data transmission task. Experimental results show that the hybrid Actor-Critic reinforcement learning method deals with a large scale of problems with high efficiency and good results. [ABSTRACT FROM AUTHOR]

Increasing attention is being given to the effect of climate change on schistosomiasis, but the impact is currently unknown. As the intermediate snail host (Neotricula aperta) of Schistosoma mekongi inhabits the Mekong River, it is thought that environmental factors affecting the area of water will have an impact on the occurrence of schistosomiasis mekongi. The aim of the present study was to assess the impact of precipitation on the prevalence of human schistosomiasis mekongi using epidemiological data and Earth observation satellite data in Khong district, Champasak province, Lao PDR. Structural equation modelling (SEM) using epidemiological data and Earth observation satellite data was conducted to determine the factors associated with the number of schistosomiasis mekongi patients. As a result, SEM identified 3 significant factors independently associated with schistosomiasis mekongi: (1) a negative association with mass drug administration (MDA); (2) negative association with total precipitation per year; and (3) positive association with precipitation during the dry season. Precisely, regardless of MDA, the increase in total yearly precipitation was suggested to decrease the number of schistosomiasis patients, whereas an increase in precipitation in the dry season increased the number of schistosomiasis patients. This is probably because when total precipitation increases, the water level of the Mekong River rises, thus decreasing the density of infected larvae, cercaria, in the water, and the frequency of humans entering the river would also decrease. In contrast, when precipitation in the dry season is higher, the water level of the Mekong River also rises, which expands the snail habitant, and thus water contact between humans and the snails would also increase. The present study results suggest that increasing precipitation would impact the prevalence of schistosomiasis both positively and negatively, and precipitation should also be considered in the policy to eliminate schistosomiasis mekongi in Lao PDR.

Satellite onboard autonomous task planning is one of the key technologies for the operation of earth observation satellites,which has received great attention from researchers in recent years.Considering the limited computing resources,as well as the dynamic changes of observation tasks and resource onboard,the heuristic search algorithms are mainly used to solve the satellite onboard task planning problem,and the optimization of solution needs to be improved.Firstly,a new sequential decision-ma-king framework for observation tasks is constructed in this paper.Based on this framework,an earth observation satellite can decide the observation task to be performed in real-time,without generating a plan in advance.Then,an observation task decision model based on attention mechanism,and the corresponding input feature representation method and model training method are designed.An observation task sequence algorithm based on attention neural network is proposed.Finally,based on a set of random data,the performance of the proposed algorithm,two deep learning algorithms and two heuristic online search algorithms are compared.Experimental results show that the response time of the proposed method is less than one-fifth of the existing deep learning algorithm,and the profit gap is much smaller than that of the heuristic search algorithms,which confirm the feasibility and effectiveness of our method.

The imaging results of Earth Observation Satellite (EOS) with optical camera are usually affected by meteorological parameters, such as clouds that block the ground target and make the images useless. In order to achieve effective images, research on minimizing the impact of cloud coverage has been conducted for several years, and many methods have been proposed. Cloud detection and mission planning are two typical approaches to be considered. However, results of cloud detection are influenced by cloud status, such as cloud movement. Moreover, performance of mission planning algorithms is related to the computation environment, especially for those implemented onboard. By considering the above limitations, an autonomous mission planning method for the EOS based on onboard cloud detection is proposed in this paper. In order to detect the cloud coverage above the ground target, the cloud-detecting sensor is utilized before satellite taking images. The approach to analyze the cloud detection area of the ground target is introduced. Then, the effective observation window of the ground target is edited according to the cloud detection results. Moreover, the imaging plan of the ground target is re-planned based on the edited effective observation window accordingly. Mathematical simulations are implemented. And the result of simulations shows that the proposed method is able to update accurate effective observation window of the ground target, and the imaging plans are adjusted appropriately which increases the validity of images taken by the satellite. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the transmission properties of optical windows intentionally contaminated and exposed to simulated solar radiation simulating the Meteosat Third Generation mission's conditions. The testing campaign consisted of three test phases: first, an outgassing test on space-qualified paints, used as contamination sources in this study, to derive input parameters for long-term outgassing predictions; the second phase, in which optical windows are contaminated to match the predictions; and the third phase, during which the contaminated optical windows were subjected to the vacuum UV/UV illumination exposure, as per mission profile, after the contamination and irradiation exposure. Transmission losses of the optical material were measured in the UV/near infrared and Fourier-transform infrared range. The findings demonstrated that UV exposure of contaminated surfaces, depending on the contamination source type, can result in chemical reactions and bonding of contaminants to the substrate or nearby molecules, or in contrary can act as a cleaning agent, causing reemission of contaminants. This study shows that performance loss based only on total surface contamination may lead to erroneous estimation and that outgassing and radiation should be treated as synergistic effects in the mission preparation phase. [ABSTRACT FROM AUTHOR]

As satellite observation technology develops and the number of Earth observation (EO) satellites increases, satellite observations have become essential to developments in the understanding of the Earth and its environment. However, the current impacts to the remote sensing community of different EO satellite data and possible future trends of EO satellite data applications have not been systematically examined. In this paper, we review the impacts of and future trends in the use of EO satellite data based on an analysis of data from 15 EO satellites whose data are widely used. Articles that reference EO satellite missions included in the Web of Science core collection for 2020 were analyzed using scientometric analysis and meta-analysis. We found the following: (1) the number of publications and citations referencing EO satellites is increasing exponentially; however, the number of articles referencing AVHRR, SPOT, and TerraSAR is tending to decrease; (2) papers related to EO satellites are concentrated in a small number of journals: 43.79% of the articles that were reviewed were published in only 13 journals; and (3) remote sensing impact factor (RSIF), a new impact index, was constructed to measure the impacts of EO satellites and to predict future trends in applications of their data. Landsat, Sentinel, MODIS, Gaofen, and WorldView were found to be the most significant current EO satellite missions and MODIS data to have the widest range of applications. Over the next five years (2021–2025), it is expected that Sentinel will become the satellite mission with the greatest influence. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We present a comparison study of state-of-the-art classical optimization methods to a D-Wave 2000Q quantum annealer for the scheduling of agile Earth observation satellites. The problem is to acquire high-value images while obeying the attitude maneuvering constraint of the satellite. In order to investigate close to real-world problems, we created benchmark problems by simulating realistic scenarios. Our results show that a tuned quantum annealing approach can run faster when used to find the optimal solution than a classical exact solver for some of the problem instances. Moreover, we find that the solution quality of the quantum annealer is comparable to the heuristic method used operationally for small problem instances, but degrades rapidly due to the limited precision of the quantum annealer. [ABSTRACT FROM AUTHOR]

We report on an unusual failure pattern resulting from thermally driven fracture of laser amplifier single crystals. The pattern led to some confusion with regard to the point of initiation due to the coalescence of hackle lines to a region within the fracture surface, rather than the more common divergence of hackle away from the origin. The pattern leads to new fracture terms: Hackle node - the coalescence of hackle marks to a point of prior compression. The feature is produced as a thermally-generated, centrally-located compressive region transforms to tension thereby drawing crack propagation and hackle to the compressive region, forming an internal terminus. Hackle terminus – a hackle node formed by final crack propagation within the component. As part of the analysis, the fracture toughness and slow crack growth constants of neodymium doped, yttrium ortho-vanadate (Nd:YVO 4) were measured. Nd:YVO 4 exhibits slow crack growth and is very brittle with a fracture toughness of only 0.48 MPa√m. [ABSTRACT FROM AUTHOR]

Earth observation satellites (EOSs) are taking a large number of pictures with increasing resolution which produce massive image data. Satellite data transmission becomes the bottleneck part in the process of EOS resource management. In this paper, we study the earth observation satellite integrated scheduling problem (EOSIS) where the imaging activities and download activities are considered integratively. We propose an integer linear programming model to formulate the problem. Due to the NP-hardness of the problem, we propose an efficient local search heuristic (ELSH) to solve problems of large size. ELSH uses a dedicated local search method to guarantee algorithm performance and efficient constraint handling mechanisms to guarantee algorithm efficiency. Numerical experimental results show that the algorithm demonstrates excellent performance on a set of benchmark instances. The ELSH achieves optimal results for all small-size instances (with 50 targets, two satellites, and three ground stations), and is very robust for large instances with up to 2000 targets. Moreover, the proposed ELSH easily dominates the state-of-the-art algorithm. [ABSTRACT FROM AUTHOR]

Earth observation satellite remote sensing can provide reliable wide-ranging spatial information and is believed as one of the key technologies for flood risk identification and dynamic modelling. Aiming at illustrating how satellite remote sensing technology promotes flood studies,this paper reviewed the development and technical demands of flood risk identification and dynamic modelling research and analyzed the historical contribution and periodic utility of spatial remote sensing information in flood studies based on the 3-stage evolution trajectory of earth observation satellite remote sensing. Application progresses in 3 typical methods in flood studies,which are risk zoning,hydrological modelling and microwave remote sensing monitoring,respectively,have been discussed and summarized. Focuses of the future research on flood risk identification and dynamic modelling have also been pointed out,including the integration of remote sensing spatial information and model algorithms,the development and application of remote sensing algorithms and systems,the development and application of an integrated system for typical methods and the application of big data methodology and technology,in expectation of providing valuable references for improving the response capacity and risk management level of flood disasters. [ABSTRACT FROM AUTHOR]

GaoFen-4(GF-4) is the highest spatial resolution Earth observation satellite operating in geosynchronous orbit. Its fixed Earth observation location, rapid responsiveness, and wide observation range make it popular in disaster and emergency monitoring. To evaluate the GF-4 image quality in detail on a long-term basis, this study analyzes the image quality after the commissioning phase by focusing on ground sample distance (GSD) and geometric and radiometric quality. The theoretical calculation, geometric and radiometric measurements, and on-site experiments results show that (1) the GSD of the GF-4 image is ~50 m at the nadir point and increases gradually with the distance away from the nadir point, (2) most external geometric errors are within the design requirements of 4 km despite some exceeding the limit, and the internal geometric errors are tested within 1 pixel, and (3) image sharpness is generally stable but varies with the atmosphere condition and imaging time, and the radiometric response gradually degrades at the rate of less than 5.5% per year.

Earth observation satellite task scheduling research plays a key role in space-based remote sensing services. An effective task scheduling strategy can maximize the utilization of satellite resources and obtain larger objective observation profits. In this paper, inspired by the success of deep reinforcement learning in optimization domains, the deep deterministic policy gradient algorithm is adopted to solve a time-continuous satellite task scheduling problem. Moreover, an improved graph-based minimum clique partition algorithm is proposed for preprocessing in the task clustering phase by considering the maximum task priority and the minimum observation slewing angle under constraint conditions. Experimental simulation results demonstrate that the deep reinforcement learning-based task scheduling method is feasible and performs much better than traditional metaheuristic optimization algorithms, especially in large-scale problems.

Orbital parameters are raised as one of the main inputs of electrical power subsystem and thermal control subsystem of a satellite. Some variables such as eclipse time, sun incidence angle to the panels, albedo and thermal radiation of Earth are dependent on satellite orbital features. In this paper, all of the parameters above are simulated using precise simulation methods within the satellite mission. That is, the process of optimal orbital design is performed with the aid of evolutionary optimization methods. This process is aimed at minimizing number of mission days that misses the assurance of satellite power supply in addition to minimizing thermal load exerted to satellite and also its standard deviation. At last, a pair of optimal solutions for two different configurations is compared.

Earth observation satellites (EOSs) are taking a large number of pictures with increasing resolution which produce massive image data. Satellite data transmission becomes the bottleneck part in the process of EOS resource management. In this paper, we study the earth observation satellite integrated scheduling problem (EOSIS) where the imaging activities and download activities are considered integratively. We propose an integer linear programming model to formulate the problem. Due to the NP-hardness of the problem, we propose an efficient local search heuristic (ELSH) to solve problems of large size. ELSH uses a dedicated local search method to guarantee algorithm performance and efficient constraint handling mechanisms to guarantee algorithm efficiency. Numerical experimental results show that the algorithm demonstrates excellent performance on a set of benchmark instances. The ELSH achieves optimal results for all small-size instances (with 50 targets, two satellites, and three ground stations), and is very robust for large instances with up to 2000 targets. Moreover, the proposed ELSH easily dominates the state-of-the-art algorithm.

Low-altitude satellite orbits, while enabling high-resolution imagery of the Earth surface, have limited coverage properties. This disadvantage of the remote sensing satellites can be offset by the low-thrust electric propulsion capability, which permits to combine high-resolution with short revisit times. The purpose of this work is to present a simple closed-loop control function that allows the direct control of the orbit, making the spacecraft to pass over prescribed sites on the terrestrial surface without the need of precomputed reference trajectories. The simplicity of application contributes to the autonomy of the remote sensing satellites. The performance of the new control algorithm is illustrated by representative numerical simulation results. [ABSTRACT FROM AUTHOR]