Advances in Space Research最新文献

{"title":"Treatment of epistemic uncertainty in conjunction analysis with Dempster-Shafer theory","authors":"Luis Sánchez ,&nbsp;Massimiliano Vasile ,&nbsp;Silvia Sanvido ,&nbsp;Klaus Merz ,&nbsp;Christophe Taillan","doi":"10.1016/j.asr.2024.09.014","DOIUrl":"10.1016/j.asr.2024.09.014","url":null,"abstract":"<div><div>The paper presents an approach to the modelling of epistemic uncertainty in Conjunction Data Messages (CDM) and the classification of conjunction events according to the confidence in the probability of collision. The approach proposed in this paper is based on Dempster-Shafer Theory (DSt) of evidence and starts from the assumption that the observed CDMs are drawn from a family of unknown distributions. The Dvoretzky–Kiefer–Wolfowitz (DKW) inequality is used to construct robust bounds on such a family of unknown distributions starting from a time series of CDMs. A DSt structure is then derived from the probability boxes constructed with DKW inequality. The DSt structure encapsulates the uncertainty in the CDMs at every point along the time series and allows the computation of the belief and plausibility in the realisation of a given probability of collision. The methodology proposed in this paper is tested on a number of real events and compared against existing practices in the European and French Space Agencies. We will show that the classification system proposed in this paper is more conservative than the approach taken by the European Space Agency but provides an added quantification of uncertainty in the probability of collision.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"74 11","pages":"Pages 5639-5686"},"PeriodicalIF":2.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655983","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":"Preface: Information theory and machine learning for geospace research","authors":"Simon Wing, Georgios Balasis","doi":"10.1016/j.asr.2024.09.007","DOIUrl":"https://doi.org/10.1016/j.asr.2024.09.007","url":null,"abstract":"","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"31 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258475","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":"Remote sensing framework for geological mapping via stacked autoencoders and clustering","authors":"Sandeep Nagar ,&nbsp;Ehsan Farahbakhsh ,&nbsp;Joseph Awange ,&nbsp;Rohitash Chandra","doi":"10.1016/j.asr.2024.09.013","DOIUrl":"10.1016/j.asr.2024.09.013","url":null,"abstract":"<div><div>Supervised machine learning methods for geological mapping via remote sensing face limitations due to the scarcity of accurately labelled training data that can be addressed by unsupervised learning, such as dimensionality reduction and clustering. Dimensionality reduction methods have the potential to play a crucial role in improving the accuracy of geological maps. Although conventional dimensionality reduction methods may struggle with nonlinear data, unsupervised deep learning models such as autoencoders can model non-linear relationships. Stacked autoencoders feature multiple interconnected layers to capture hierarchical data representations useful for remote sensing data. We present an unsupervised machine learning-based framework for processing remote sensing data using stacked autoencoders for dimensionality reduction and <em>k</em>-means clustering for mapping geological units. We use Landsat 8, ASTER, and Sentinel-2 datasets to evaluate the framework for geological mapping of the Mutawintji region in Western New South Wales, Australia. We also compare stacked autoencoders with principal component analysis (PCA) and canonical autoencoders. Our results reveal that the framework produces accurate and interpretable geological maps, efficiently discriminating rock units. The results reveal that the combination of stacked autoencoders with Sentinel-2 data yields the best performance accuracy when compared to other combinations. We find that stacked autoencoders enable better extraction of complex and hierarchical representation of the input data when compared to canonical autoencoders and PCA. We also find that the generated maps align with prior geological knowledge of the study area while providing novel insights into geological structures.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"74 10","pages":"Pages 4502-4516"},"PeriodicalIF":2.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538982","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":"Light curve attitude estimation using particle swarm optimizers","authors":"Alexander Burton,&nbsp;Liam Robinson,&nbsp;Carolin Frueh","doi":"10.1016/j.asr.2024.09.008","DOIUrl":"10.1016/j.asr.2024.09.008","url":null,"abstract":"<div><div>Knowledge of the attitude of a space object is useful in space situational awareness for independently evaluating a satellite’s health and characterizing unknown objects. In cases where only non-resolved optical observations are available, the object’s attitude may be estimated using a time sequence of brightness observations, also known as the light curve. This attitude estimation problem is plagued with multiple difficulties: even in the absence of noise and when all other relevant factors are perfectly known, the non-uniqueness of the problem means that multiple attitude time histories may fit the light curve equally well. In addition, there is often insufficient information about the object to generate an initial state guess for an estimator. This paper presents a method that estimates an observed object’s attitude and angular velocity while accounting for ambiguities and without needing any initial state guess. The only inputs are the light curve, the object’s albedo shape, and the object’s position relative to the Sun and the observer. The ability of the estimator to resolve attitude time histories is demonstrated using simulated light curves by comparing state estimates against known true states.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"74 11","pages":"Pages 5619-5638"},"PeriodicalIF":2.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655494","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":"On equatorial spread F occurrence: A multi-dimensional quantitative assessment","authors":"T.V. Sruthi ,&nbsp;G. Manju ,&nbsp;K.S. Vishnupriya","doi":"10.1016/j.asr.2024.09.005","DOIUrl":"10.1016/j.asr.2024.09.005","url":null,"abstract":"<div><div>The present study investigates the role of gravity wave induced seed perturbations in the occurrence of Equatorial Spread F (ESF) under the influence of the post sunset background conditions modulated by prevailing electrodynamics and neutral wind. Ionospheric foF₂ data sets over geomagnetic equatorial station Trivandrum (8.5°N, 77°E and magnetic dip 0.68°N-corresponding to the period of study) corresponding to vernal and autumnal equinoctial periods encompassing high, low and moderate solar activity years, are used for the study. Meridional wind data is obtained either from ESA’s sun-synchronous satellite GOCE (Gravity field and steady-state Ocean Circulation Explorer) or derived using ionosonde h’F (base height of ionosphere at 2.5 MHz) data from Trivandrum (TVM- 8.5°N, 77°E and magnetic dip 0.68°N) and Sriharikota (SHAR−13.7°N, 80.2°E and magnetic dip 6.9°N-for period of study). This particular study is carried out for geomagnetically quiet days of Vernal Equinox (VE) and Autumnal Equinox (AE) seasons, which are most favoured for ESF occurrence over Indian longitudes. Considering thermospheric wind, ion-neutral collisions, and electric field effects in association with gravity wave seed, threshold curve is generated, which clearly demarcates ESF and NSF (Non spread F) days. Previous studies have addressed ESF variability in electrodynamical domain alone (wherein the layer is above a threshold level). The present study, for the first time, succeeds in demarcating ESF and NSF days by incorporating effects of electric field, neutral wind, collisional RT instability term, and gravity wave seed perturbations simultaneously irrespective of threshold height.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"74 11","pages":"Pages 6074-6084"},"PeriodicalIF":2.8,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258476","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":"Water quality hotspot identification using a remote sensing and machine learning approach: A case study of the River Ganga near Varanasi","authors":"Anurag Mishra,&nbsp;Anurag Ohri,&nbsp;Prabhat Kumar Singh,&nbsp;Shishir Gaur,&nbsp;Rajarshi Bhattacharjee","doi":"10.1016/j.asr.2024.09.004","DOIUrl":"10.1016/j.asr.2024.09.004","url":null,"abstract":"<div><div>Turbidity (Turb) and Chlorophyll-a (Chl-a) are crucial indicators of water quality because they can reveal the presence of suspended particles and algae, respectively. Understanding the health of rivers and spotting long-term water quality changes can both benefit from monitoring these measures. Traditional methods of monitoring these parameters, like in-situ measurements, is time-consuming, expensive, and inconvenient in some places. Sentinel-2, a multispectral satellite, might offer a more workable and economical option for monitoring water quality, though. This study used 100 in-situ data collected from the Ganga River near Varanasi in the pre-monsoon season (pre-MS) and post-monsoon season (post-MS) in order to create a model for the prediction of optically active water quality parameters by combining Multispectral Instrument (MSI) data and machine learning method (Random Forest). To create spatial distribution maps for Chl-a and Turb, 14 spectral indices and band ratios were employed as independent variables. The results showed that the prediction accuracy for Turb (R² = 0.91, MAE = 1.13 and MAPE=7.76 % during pre-MS and R² = 0.93, MAE = 0.88 and MAPE=2.29 % during post-MS) and for Chl-a (R² = 0.97, MAE = 0.59, and MAPE=2.07 % during pre-MS and R² = 0.95, MAE = 0.61, and MAPE = 2.71 % during post-MS). The Ganga near Varanasi abruptly turned green due to an increase in algal bloom in May and June 2021. This study not only revealed the reasons behind the green appearance but also identified potential areas of concern or hotspots. In order to identify hotspot locations, drainage networks, point source discharge locations and LU-LC were used.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"74 11","pages":"Pages 5604-5618"},"PeriodicalIF":2.8,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258495","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":"Burst-classifier: Automated classification of solar radio burst type II, III and IV for CALLISTO spectra using physical properties during maximum of solar cycle 24","authors":"N.Z. Mohd Afandi ,&nbsp;R. Umar ,&nbsp;N.H. Sabri ,&nbsp;S. Safei ,&nbsp;C. Monstein ,&nbsp;C.C. Lau ,&nbsp;S.N.A. Syed Zafar","doi":"10.1016/j.asr.2024.09.001","DOIUrl":"10.1016/j.asr.2024.09.001","url":null,"abstract":"<div><div>Continuous observation of solar radio bursts (SRBs) throughout the year using the CALLISTO spectrometer generates a huge volume of spectral data. This study introduces a burst-classifier algorithm, which is an automated algorithm, to classify the SRB spectrum into three solar radio bursts, namely Type II (SRBT II), Type III (SRBT III) and Type IV (SRBT IV). The proposed algorithm was designed using four characteristic parameters derived from a collection of training dataset files. The characteristic parameters were derived from the intensity bursts observed on frequency channels and timesteps of the spectrum. This dataset consisted of 50 spectra of SRBT II and SRBT III, along with 40 spectra for SRBT IV, collected during the solar maximum of 2014 (Solar Cycle 24). After observations and analysis of the training dataset, each burst type was set up with a threshold. A training dataset of 80 data spectra from 2013 to 2016 was used to test the algorithm. Accuracy of the proposed algorithm was calculated using the percentage of true positives (TP) and false positives (FP). Findings demonstrate an accuracy of ∼74 % with 57 out of 80 spectra classified as TP and 23 spectra as FP.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"74 11","pages":"Pages 6104-6123"},"PeriodicalIF":2.8,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258496","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":"Stratospheric airship trajectory planning in wind field using deep reinforcement learning","authors":"Lele Qi, Xixiang Yang, Fangchao Bai, Xiaolong Deng, Yuelong Pan","doi":"10.1016/j.asr.2024.08.057","DOIUrl":"https://doi.org/10.1016/j.asr.2024.08.057","url":null,"abstract":"Stratospheric airships, with their long endurance, high flight altitude, and large payload capacity, show promise in earth observation and mobile internet applications. However, challenges arise due to their low flight speed, limited maneuverability and energy constraints when planning trajectories in dynamic wind fields. This paper proposes a deep reinforcement learning-based method for trajectory planning of stratospheric airships. The model considers the motion characteristics of stratospheric airships and environmental factors like wind fields and solar radiation. The soft actor-critic (SAC) algorithm is utilized to assess the effectiveness of the method in various scenarios. A comparison between time-optimized and energy-optimized trajectories reveals that time-optimized trajectories are smoother with a higher speed, while energy-optimized trajectories can save up to 10% energy by utilizing wind fields and solar energy absorption. Overall, the deep reinforcement learning approach proves effective in trajectory planning for stratospheric airships in deterministic and dynamic wind fields, offering valuable insights for flight design and optimization.","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"21 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258500","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":"Fragmentation characterization in the circular restricted three body problem for cislunar space domain awareness","authors":"Arly Black, Carolin Frueh","doi":"10.1016/j.asr.2024.08.076","DOIUrl":"https://doi.org/10.1016/j.asr.2024.08.076","url":null,"abstract":"With heightened international interest in spacecraft activities in the vicinity of the Moon, cislunar space debris is likely to follow. Even one fragmentation event can have catastrophic and far-reaching consequences, which drives the need for appropriate debris characterization tools. How a single fragmentation plays out is highly dependent on any given initial condition in the near-chaotic cislunar region. This paper offers a means of structuring the cislunar region in terms of dynamical flow, which enables global characterization of fragmentation events without propagation of every possible case. This work investigates patterns in fragment behaviour as a function of energy, , and orbit location, and explores emergent dynamic structures in the vicinity of the Earth-Moon Lagrange point. Subsequent findings are applied to analysis of a realistic breakup event for a 500 kg satellite on an Lyapunov orbit with a Jacobi constant of 3.0165, modeled using an in–house modified version of the NASA Standard Breakup Model.","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"23 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258497","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":"Dependence of the main ionospheric trough position on local time, longitude and geomagnetic activity in the southern winter hemisphere","authors":"A.T. Karpachev","doi":"10.1016/j.asr.2024.08.075","DOIUrl":"10.1016/j.asr.2024.08.075","url":null,"abstract":"<div><div>Based on the meticulous identification of ionization troughs, performed earlier from the CHAMP satellite data, two<!--> <!-->additional issues<!--> <!-->were<!--> <!-->resolved: (1) the longitudinal effect characteristics in the position of the main ionospheric trough (MIT) were corrected, and (2) for the first time,<!--> <!-->the dependence of the<!--> <!-->MIT<!--> <!-->position on geomagnetic activity was determined for<!--> <!-->all<!--> <!-->local<!--> <!-->time hours. A large dataset from the CHAMP satellite in the southern winter hemisphere under high solar activity was utilized. According to the refined data the amplitude of the longitudinal effect in the MIT<!--> <!-->position changes from ∼ 3° to ∼ 5° in the course of the day. The shape of the longitudinal effect<!--> <!-->varies<!--> <!-->with local time, however, the MIT in the eastern hemisphere is<!--> <!-->consistently<!--> <!-->located at higher latitudes than in the western hemisphere. The main reason for the longitudinal effect is the dependence of the equatorward boundary of auroral diffuse precipitation on the tilt angle of the Earth’s dipole. The dependence on geomagnetic activity<!--> <!-->was determined as a linear regression Λ_T = Λ₀ − <strong><em>a</em></strong>Kp, where Λ is the geomagnetic latitude, and the Kp index<!--> <!-->is<!--> <!-->considered for the previous<!--> <!-->6<!--> <!--> h. The latitude Λ₀ and coefficient <strong><em>a</em></strong> exhibited pronounced dependence on local time, with Λ₀ increasing and <strong><em>a</em></strong> decreasing when moving from night to day. Because the amplitude of the longitudinal effect decreases with increasing magnetic activity, the value of <strong><em>a</em></strong> also<!--> <!-->depends<!--> <!-->on longitude. Consequently, coefficient<!--> <strong><em>a</em></strong> was<!--> <!-->determined separately<!--> <!-->for<!--> <!-->the eastern and western hemispheres. The<!--> <!-->average<!--> <!-->values<!--> <!-->of <strong><em>a</em></strong> vary from 1.3 − 1.4° during the day to 1.8 − 1.9° at night. The<!--> <!-->difference between the<!--> <!-->eastern and western hemispheres is ∼ 0.3°.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"74 11","pages":"Pages 6065-6073"},"PeriodicalIF":2.8,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258501","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}