Advances in Space Research最新文献

{"title":"Corrigendum to “Investigating the heliosphere, magnetosphere, atmosphere, and properties of cosmic rays during the 2018 Aug 25-26 strong geomagnetic storm” [Adv. Space Res. 73 (2024) 4363–4377/AISR-D-23-01163]","authors":"","doi":"10.1016/j.asr.2024.09.046","DOIUrl":"10.1016/j.asr.2024.09.046","url":null,"abstract":"","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539453","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":"Single track orbit determination analysis for low Earth orbit with approximated J2 dynamics","authors":"","doi":"10.1016/j.asr.2024.09.035","DOIUrl":"10.1016/j.asr.2024.09.035","url":null,"abstract":"<div><div>In the domain of Space Situational Awareness (SSA), the challenges related to orbit determination and catalog correlation are notably pronounced, exacerbated by data scarcity. This study introduces an initial orbit determination methodology that relies on data obtained from a single surveillance radar, with the need for fast algorithms within an operational context serving as the main design driver. The result is a linearized least-squares fitting procedure incorporating an analytically formulated approximation of the dynamics under the <span><math><mrow><msub><mrow><mi>J</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span> perturbation, valid for short-term propagation. This algorithm utilizes all available observables, including range-rate, distinguishing it from other similar methods. A significant contribution of this paper is the enhancement of estimation quality by incorporating information about the object’s predicted orbital plane into the methodology, a method denoted as OPOD. The proposed methods are evaluated through a series of simulations against a classical range and angles fitting method (GTDS) to examine the effects of track length and measurement density on the quality of full state estimation, including the impact of using arcs that are too short. The OPOD methodology shows promising results throughout a wide range of scenarios.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538909","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":"Comment on “Analysis and diagnosis of abnormal SLR validation results for BeiDou-3 SECM-B MEO C225 and C226 satellite orbits”","authors":"","doi":"10.1016/j.asr.2024.09.020","DOIUrl":"10.1016/j.asr.2024.09.020","url":null,"abstract":"","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538907","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":"Tailored accelerometer calibration by POD for thermospheric density retrieval with GRACE and GRACE-FO","authors":"","doi":"10.1016/j.asr.2024.09.021","DOIUrl":"10.1016/j.asr.2024.09.021","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The density of the upper atmosphere can be determined by orbit and accelerometer data from low Earth orbit satellites as insitu measurements along the orbit. One main challenge therein is the estimation of physical accelerometer calibration parameters, meaning that these parameters should not absorb other effects and model deficiencies in the Precise Orbit Determination (POD) process. The accelerometers of all geodetic satellites like GRACE and GRACE-FO are affected by time dependent bias and scale factors. Therefore a calibration of the data is indispensable.&lt;/div&gt;&lt;div&gt;A dynamic POD based physical accelerometer calibration is developed for the complete GRACE and GRACE-FO missions. We investigate different parametrization strategies and utilize different observation data, as the accurate inter-satellite ranging additionally to GPS orbit data. For the estimation parameters we distinguish between offset and scale, furthermore, cross-track and radial directions are significantly less sensitive than along-track and require a different evaluation. For the offset, constant and time dependent parameters are investigated. Furthermore, a continuous offset calibration over arc boundaries is implemented and tested. The sensitivity of the scale factor is lower, although, in contrast to the offset, it increases with higher total accelerations. This means that it needs to be estimated over longer time periods. We investigate periods between three hours and one month as well as results from Gravity Field Recovery (GFR). Monthly scale factors give valuable results, at least for x-axis and when the Solar activity is not very low. Nevertheless, we also estimate weighted constant scale factors from the monthly results and use these in a subsequent POD, giving more realistic offset results for most periods and cross-track and radial directions.&lt;/div&gt;&lt;div&gt;From the used background models in the POD, Earth’s gravitational model has a noticeable influence on the estimated calibration parameters, especially the scale factors. We utilized several different models. Results with monthly ITSG solutions are distinctly better than the ones with the time dependent GOCO06s model.&lt;/div&gt;&lt;div&gt;We show that the validation with usual metrics, like post-fit POD residuals, is not able to reflect the quality of the different estimated calibration parameters. For a quantitative validation we introduce an approach based on the modeled non-gravitational accelerations. Therefore, the uncertainty of the models is evaluated first. The influence of main error sources in the models is assessed and propagated to the results.&lt;/div&gt;&lt;div&gt;We compare our scale parameters to available references and the complete calibration to TU Delft’s latest results. Finally we show the effect of different calibration options on the retrieved density.&lt;/div&gt;&lt;div&gt;The estimated calibration parameters and non-gravitational accelerations for the whole GRACE and GRACE-FO missions are available on our data server","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538983","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":"Reply to the comment on ”Analysis and diagnosis of abnormal SLR validation results for BeiDou-3 SECM-B MEO C225 and C226 satellite orbits“","authors":"","doi":"10.1016/j.asr.2024.09.019","DOIUrl":"10.1016/j.asr.2024.09.019","url":null,"abstract":"","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538908","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":"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":null,"pages":null},"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":"","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":null,"pages":null},"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":"On equatorial spread F occurrence: A multi-dimensional quantitative assessment","authors":"T.V. Sruthi, G. Manju, K.S. Vishnupriya","doi":"10.1016/j.asr.2024.09.005","DOIUrl":"https://doi.org/10.1016/j.asr.2024.09.005","url":null,"abstract":"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 foF 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.","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":null,"pages":null},"PeriodicalIF":2.6,"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, Anurag Ohri, Prabhat Kumar Singh, Shishir Gaur, Rajarshi Bhattacharjee","doi":"10.1016/j.asr.2024.09.004","DOIUrl":"https://doi.org/10.1016/j.asr.2024.09.004","url":null,"abstract":"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 (R = 0.91, MAE = 1.13 and MAPE=7.76 % during pre-MS and R = 0.93, MAE = 0.88 and MAPE=2.29 % during post-MS) and for Chl-a (R = 0.97, MAE = 0.59, and MAPE=2.07 % during pre-MS and R = 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.","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":null,"pages":null},"PeriodicalIF":2.6,"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, R. Umar, N.H. Sabri, S. Safei, C. Monstein, C.C. Lau, S.N.A. Syed Zafar","doi":"10.1016/j.asr.2024.09.001","DOIUrl":"https://doi.org/10.1016/j.asr.2024.09.001","url":null,"abstract":"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.","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":null,"pages":null},"PeriodicalIF":2.6,"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}