Advances in Space Research最新文献

{"title":"Low-thrust transfer with Theory of Functional Connections: Application to 243 Ida with a solar sail","authors":"A.K. de Almeida ,&nbsp;T. Vaillant ,&nbsp;L.B.T. Santos ,&nbsp;D. Maia","doi":"10.1016/j.asr.2024.09.069","DOIUrl":"10.1016/j.asr.2024.09.069","url":null,"abstract":"<div><div>A spacecraft needs to perform maneuvers in the neighborhood of the body that it orbits in order to achieve the targets of its mission. These maneuvers are usually performed with propellant based engines. Solar sails, as low-thrust propulsion systems, represent a way to perform them without fuel consumption, but it necessitates to change their orientation in order to generate the desired thrust, which is a complex procedure. In this paper, we propose a new method to perform propellant-free maneuvers with a solar sail using the Theory of Functional Connections. This method is applied to the asteroid Ida in the context of transfers between artificial equilibrium points.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 2","pages":"Pages 2108-2125"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of rubber membrane on lunar regolith mechanical properties under low effective confining pressure","authors":"Siyuan Wang ,&nbsp;Mingjing Jiang ,&nbsp;Jiayu Lin","doi":"10.1016/j.asr.2024.10.048","DOIUrl":"10.1016/j.asr.2024.10.048","url":null,"abstract":"<div><div>The existing lunar exploration activities and associated equipment interactions are limited to the surface environment, where the stress state of the lunar regolith is significantly lower than that in laboratory tests conducted on Earth. To address this, this paper proposes a new framework for discrete modeling of large-scale triaxial tests on lunar regolith under low confining pressure. The framework incorporates particle shapes from the Chang’E-5 mission (CE-5) and flexible boundary conditions. Firstly, the shape characteristics of the lunar regolith particles were adopted in the Discrete Element Method (DEM) model to reproduce the mechanical properties of the lunar regolith as accurately as possible. Then, experiments with varying membrane particle stiffness ratios were conducted to explore the effect of the rubber membrane’s properties on the mechanical characteristics of lunar regolith under low effective confining pressure. Topological Data Analysis (TDA) tools from persistent homology were utilized to quantify the dynamic response of particles during the onset and development of strain localization. The results indicate that under low effective confining pressure, selecting appropriate rubber membrane types is crucial for accurately determining the mechanical properties of lunar regolith.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 2","pages":"Pages 2340-2360"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conductive paths generation using topology optimization for worst-case thermal design in space systems","authors":"Carlos Arroyo-Ruiz ,&nbsp;David González-Bárcena ,&nbsp;Javier González-Monge ,&nbsp;Ángel Sanz-Andrés","doi":"10.1016/j.asr.2024.10.029","DOIUrl":"10.1016/j.asr.2024.10.029","url":null,"abstract":"<div><div>Designing thermal systems for space platforms is a challenging task due to the wide variability in thermal conditions during the orbit and the strict constraints imposed by other subsystems. Several strategies have been developed to address these challenges while minimizing the thermal control subsystem’s signature, encompassing different algorithms optimize components positions in the platform. When relocation is not possible, thermal couplings between components can be modified to enhance heat transfer and achieve more uniform temperature distribution. To design optimal thermal paths in 2D space structures, in this paper a topology optimization framework based on the Heaviside Projection Method is introduced, using the Lumped Parameter Method (LPM) and taking the radiation heat transfer into account. The algorithm’s performance is tested on a Printed Circuit Board (PCB) by designing an optimal copper layer to meet specific thermal requirements under both extreme hot and cold conditions. Results show a significant improvement in thermal compliance for all components with the addition of this high-conductivity layer. Additionally, a reduction method is introduced to transfer the material distribution from the optimization to a coarser mesh of any size, which is useful to facilitate its implementation in existing software, where a large number of degrees of freedom can be a limitation.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 2","pages":"Pages 2323-2339"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A GIRS-based analysis of urban green space losses with land-use changes and its relationship with surface urban heat island in the city of Tabriz","authors":"Firouz Aghazadeh ,&nbsp;Hojjatollah Mashayekh ,&nbsp;Mahsa Asadzadeh Akbari ,&nbsp;Shahram Boroukanlou ,&nbsp;Nader Habibzadeh ,&nbsp;Mohammad Ghasemi ,&nbsp;Ajanta Goswami","doi":"10.1016/j.asr.2024.10.018","DOIUrl":"10.1016/j.asr.2024.10.018","url":null,"abstract":"<div><div>This research investigates the relationship between urbanization, land-use land-cover (LULC) changes, and surface urban heat islands (SUHIs) in Tabriz, Iran. Using Landsat satellite data from 2000 to 2021, the study analyzed changes in urban green space (UGS) and their impact on SUHIs. The maximum likelihood classification algorithm was employed to assess LULC changes, while the perceptron neural network model and cellular automaton Markov chain model were used to simulate and predict future LULC dynamics. Various indices, including the UGS Change Intensity Index (CII), Land Use Dynamic Degree Index (LUDD), and UGS Land Index, were used to quantify urban expansion, green space changes, and per capita green space (PCG) availability. The findings revealed a significant increase in built-up area and a subsequent decline in green space. While green space initially increased, it later decreased, leading to a reduction in both UGS area and PCG. Surprisingly, the analysis of heat islands associated with green space and built-up areas showed that built-up areas had a more pronounced mitigating effect on SUHIs compared to green space. Consequently, the green space heat island effect intensified, while the built-up area heat island effect decreased over time.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 2","pages":"Pages 1804-1824"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning-based approach for flash flood susceptibility mapping considering rainfall extremes in the northeast region of Bangladesh","authors":"Md Enayet Chowdhury,&nbsp;A.K.M. Saiful Islam,&nbsp;Rashed Uz Zzaman,&nbsp;Sharfaraj Khadem","doi":"10.1016/j.asr.2024.10.047","DOIUrl":"10.1016/j.asr.2024.10.047","url":null,"abstract":"<div><div>Flash floods are catastrophic global events, especially in northeast Bangladesh, and assessing flash flood susceptibility is crucial for preparedness and mitigation. Traditional geographic system-based flash flood susceptibility mapping struggles to capture flash floods’ non-linear and complex nature. However, machine learning models have recently emerged as an efficient alternative to address these limitations. This study evaluates four machine learning models—Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Random Forest – Gradient Boosting (RFGB) Hybrid, and Categorical Boosting (CatBoost)—for flash flood susceptibility. It categorizes areas into five susceptibility levels: very low, low, moderate, high, and very high. Covering 24,424.25 km² across eight districts, the study uses 400 points (200 flood and 200 non-flood) for training and validation, based on field investigation, historical flood information, Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar data using Google Earth Engine, and insights from the local people. The models’ predictive performances are evaluated by incorporating topographical attributes and rainfall indices and using accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) score, with 70 % of the data for training and 30 % for testing. The ANN model performs best with rainfall indices, achieving high accuracy (maximum AUC = 0.802). The RFGB hybrid model shows excellent training accuracy (AUC ≥ 0.971) but suffers from overfitting during validation (AUC ≤ 0.674), requiring careful hyperparameter adjustment. The CatBoost model effectively uses both rainfall indices and terrain features, achieving AUC = 0.701 in training and AUC = 0.667 in validation. The ANN model conservatively includes the largest area (2198.3 km²) under ’very high’ susceptibility. This study’s flash flood susceptibility maps, which include rainfall extremes, are more robust than those without, helping local administrative authorities and national flood practitioners prepare for flash floods.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 2","pages":"Pages 1990-2017"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-triggered MPC with adaptive prediction horizon for nano-satellite attitude control system","authors":"Taihe Huang ,&nbsp;Jinxiu Zhang ,&nbsp;Minghao Li ,&nbsp;Yan Shen ,&nbsp;Jianing Wu ,&nbsp;Hui Wang","doi":"10.1016/j.asr.2024.10.022","DOIUrl":"10.1016/j.asr.2024.10.022","url":null,"abstract":"<div><div>Nano-satellites are essential tools for various applications, including scientific experiments, deep space exploration and astronomical observation. Achieving precise model predictions is crucial for their successful operation. To address the intricate constraints of nano-satellites and enhance control performance, the Model Predictive Control (MPC) algorithm is an effective solution. However, implementing an MPC-based attitude control system in actual engineering scenarios presents significant challenges, primarily due to the substantial computational burden, especially given the limited onboard computing resources of nano-satellites. In this paper, we introduce a modified adaptive self-triggered model predictive control (ST-MPC) algorithm designed to stabilize the attitude of nano-satellites, while simultaneously reducing communication and computational overhead compared to traditional MPC methods. The proposed self-triggered mechanism dynamically determines the next trigger time according to the system state. Moreover, we incorporate considerations for the efficiency of actuators to address the constraints imposed by the magnetic torque characteristics within the modified self-triggered mechanism. Additionally, a strategy for adaptive prediction horizon is proposed to balance computation load and control accuracy. The results of our simulations demonstrate the effectiveness of the modified ST-MPC algorithm in comparison to both traditional MPC and standard ST-MPC approaches. This algorithm may have the potential to significantly impact attitude control applications for nano-satellites.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 2","pages":"Pages 2251-2270"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intention inference for space targets using deep convolutional neural network","authors":"Jiasheng Li ,&nbsp;Zhen Yang ,&nbsp;Yazhong Luo","doi":"10.1016/j.asr.2024.10.006","DOIUrl":"10.1016/j.asr.2024.10.006","url":null,"abstract":"<div><div>Intention inference for space targets is crucial for space situational awareness. This paper introduces a rapid and precise method for recognizing the intentions of non-cooperative space targets using a deep convolutional neural network (CNN). By employing a relative orbital dynamics model, an analysis of relative motion was performed, resulting in the identification of 11 distinct motion intentions for space targets. This study also describes how to generate relative motion trajectory images to create a training set for intention inference, effectively converting the problem into one of image recognition and classification. Extensive simulations were carried out to fine-tune the network hyperparameters, and the results highlight the exceptional performance of the proposed CNN-based method, which achieved an accuracy of 99.682%. This method significantly enhances recognition accuracy over other neural network-based methods for space objects and offers considerable potential for applications like spacecraft collision avoidance and strategic maneuvers among space targets.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 2","pages":"Pages 2184-2200"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding northeastern tropical atlantic ocean dynamics in relation to climate indices","authors":"Isabel Cardoso ,&nbsp;Isabel Iglesias ,&nbsp;M. Nieves Lorenzo ,&nbsp;Fabiola N. Amorim ,&nbsp;M. Joana Fernandes ,&nbsp;Clara Lázaro","doi":"10.1016/j.asr.2024.11.032","DOIUrl":"10.1016/j.asr.2024.11.032","url":null,"abstract":"<div><div>Since 1993, Satellite Altimetry greatly enhanced the ability to study and understand ocean dynamics, particularly in the context of climate change. Though relatively low-energy and understudied, the Northeast Tropical Atlantic Ocean (NTAO) plays a crucial role in the Earth’s climate system. This study aims to deepen understanding of the NTAO region using the satellite altimetry-derived daily sea level gridded data set provided by the Copernicus Climate Change Service (C3S).</div><div>The analysis of long-term regional Sea Level Anomaly (SLA) signals in the NTAO reveals a higher rate of sea level rise compared to the global average. The same analysis for regional Eddy Kinetic Energy (EKE) per unit mass and surface geostrophic currents shows declining rates, in contrast to global counterparts.</div><div>Correlation analysis between SLA and climate indices (CI) uncovered significant links with the North Atlantic Oscillation, Tropical North/South Atlantic, Western Hemisphere Warm Pool, and Southern Oscillation Index. Composite maps of sea surface temperature (SST), sea level pressure (SLP), and wind anomalies, as well as complementary maps with anomalies of SLA, EKE, and ocean circulation, were examined to understand the primary mechanisms behind these correlations. SST emerged as the main forcing factor, with SLP and wind anomalies also contributing to specific regional and index correlations. EKE anomalies further elucidate differences in the anomalies of the surface geostrophic currents in the areas influenced by the key currents in the study region. The findings of this study show an intricate interplay between oceanic dynamics and climate phenomena, shedding light on the complex mechanisms driving changes in the Northeast Tropical Atlantic Ocean.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 2","pages":"Pages 1616-1635"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Method of generating potential evapotranspiration with high precision and resolution","authors":"Qingzhi Zhao ,&nbsp;Lulu Chang ,&nbsp;Hongwu Guo ,&nbsp;Liangliang Wang ,&nbsp;Yibin Yao ,&nbsp;Wenjie Peng ,&nbsp;Zufeng Li ,&nbsp;Ningbo Wang","doi":"10.1016/j.asr.2024.10.014","DOIUrl":"10.1016/j.asr.2024.10.014","url":null,"abstract":"<div><div>Potential evapotranspiration (PET) is a key factor in hydrological cycle and energy balance and plays an important role in drought and global climate change response. Existing observational and modeling methods for PET retrieval have their limitations, such as low precision and poor spatial resolution, which becomes the focus of this study. A hybrid PET fusion (HPF) method is proposed by fusing station- and grid-based PET, in which the PET expression is determined by considering the factors of location, temperature, and zenith total delay (ZTD). In addition, an improved Helmert variance component estimation method is introduced to determine the optimal weights of the HPF model. Corresponding data, which include monthly Thornthwaite (TH)-derived PET data with a spatial resolution of 0.25° × 0.25° and Penman–Monteith (PM)-derived PET data at 704 meteorological stations, over the past 60 years from 1959 to 2018 in China are selected. The 10-fold cross-validation method is introduced to evaluate the internal and external accuracies of the proposed HPF method. Statistical result shows that the average root mean square (RMS) of the proposed HPF method is 13.98 mm, with an average RMS improvement rate (IR) of 46.71 % compared with TH-derived PET, when PM-derived PET is regarded as a reference. Moreover, the performance of the HPF-derived standardized precipitation evapotranspiration index (SPEI) is evaluated at different time scales, and the average RMS is 0.3, with an average RMS IR of 26.33 % compared with TH-derived SPEI. Such results verify the good performance of the proposed HPF model and enrich the methods for obtaining PET with high precision and resolution.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 2","pages":"Pages 1759-1774"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of vegetation indices for mapping burned areas using a deep learning method and a comprehensive forest fire dataset from Landsat collection","authors":"Gabriel E. Suárez-Fernández,&nbsp;Joaquín Martínez-Sánchez,&nbsp;Pedro Arias","doi":"10.1016/j.asr.2024.12.001","DOIUrl":"10.1016/j.asr.2024.12.001","url":null,"abstract":"<div><div>Forested areas, crucial for their multifunctional roles, face significant risks from wildfires. Recent methods have enabled accurate delineation of burned areas (BA) using satellite images, which is essential for assessing impacts and recovery. Nevertheless, many such techniques rely on short time series for training data and utilize many predictors without thorough analysis of efficient computational frameworks. Therefore, this paper presents a novel method that builds a forest fire dataset while minimizing technical and technological costs, while also assessing the benefits of incorporating vegetation spectral indices (VIs) versus specific spectral bands in a Convolutional Neural Network (CNN) detector. The methodology involves creating a dataset of real forest fires from 1985 to 2021 using two consecutive Landsat images and applying VIs and unsupervised clustering techniques. The dataset is then used to explore the feasibility of integrating VIs from single-temporal Landsat images into a U-Net-based CNN, measuring performance across multiple settings. A real dataset was successfully acquired, achieving a 73.68 % level of agreement with the limited years available in the Spanish Ministry’s official records, while the analysis of the introduction of VIs into a CNN revealed that using single-temporal images with the Landsat blue band and VIs achieved a 73 % Intersection over Union (IoU) metric, leading to a 46 % reduction in storage requirements. In contrast, using the green band with the blue band and VIs resulted in a 75 % IoU and a 31 % reduction in storage, comparable to configurations using visible and infrared bands without VIs. Consequently, this methodology successfully collects a dataset of BA from wildfires, extending the digitized time scale while demonstrating the advantages of applying VIs, rather than specific spectral bands, in a CNN for delineating burned areas, achieving data optimization and normalization.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 2","pages":"Pages 1665-1685"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}