Pub Date : 2026-02-01DOI: 10.1016/j.asr.2025.11.112
Jorge Rubio , Adrián de Andrés , Carlos Paulete , Ángel Gallego , Diego Escobar
The increasing congestion of Earth’s orbital environment necessitates advancements in traditional Space Surveillance and Tracking (SST) methods to ensure the safe and sustainable use of space. In this context, accurately estimating the attitude of uncontrolled space objects is essential for developing effective space debris mitigation strategies and improving key predictions, such as atmospheric re-entries and collision probabilities. This study introduces the AISwarm-UKF method, a novel approach for attitude estimation of uncontrolled space objects with known geometric and optical characteristics using light curve data. The method integrates different estimation, optimisation and data analysis techniques, namely Adaptive Importance Sampling (AIS), Systematic Resampling, Particle Swarm Optimisation (PSO), Clustering and the Unscented Kalman Filter (UKF), to improve the performance of the Bayesian inference process. Applied to a realistic operational scenario, the AISwarm-UKF method demonstrates high accuracy, robustness, and computational efficiency, offering a viable solution for space situational awareness.
{"title":"Attitude estimation of uncontrolled space objects: A Bayesian-informed swarm intelligence approach","authors":"Jorge Rubio , Adrián de Andrés , Carlos Paulete , Ángel Gallego , Diego Escobar","doi":"10.1016/j.asr.2025.11.112","DOIUrl":"10.1016/j.asr.2025.11.112","url":null,"abstract":"<div><div>The increasing congestion of Earth’s orbital environment necessitates advancements in traditional Space Surveillance and Tracking (SST) methods to ensure the safe and sustainable use of space. In this context, accurately estimating the attitude of uncontrolled space objects is essential for developing effective space debris mitigation strategies and improving key predictions, such as atmospheric re-entries and collision probabilities. This study introduces the AISwarm-UKF method, a novel approach for attitude estimation of uncontrolled space objects with known geometric and optical characteristics using light curve data. The method integrates different estimation, optimisation and data analysis techniques, namely Adaptive Importance Sampling (AIS), Systematic Resampling, Particle Swarm Optimisation (PSO), Clustering and the Unscented Kalman Filter (UKF), to improve the performance of the Bayesian inference process. Applied to a realistic operational scenario, the AISwarm-UKF method demonstrates high accuracy, robustness, and computational efficiency, offering a viable solution for space situational awareness.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 3","pages":"Pages 3791-3814"},"PeriodicalIF":2.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081528","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}
Pub Date : 2026-02-01DOI: 10.1016/j.asr.2025.11.081
Somaiyeh Sabri , Stefaan Poedts
Coronal Mass Ejections (CMEs) are among the primary drivers of space weather disturbances, with the potential to trigger severe geomagnetic storms and pose risks to satellite operations, navigation systems, and astronaut safety. While initial observational parameters such as CME speed and angular width, commonly derived from coronagraphs like SOHO/LASCO, are essential for early detection, they are often insufficient to reliably predict a CME’s geoeffectiveness. In this study, we investigate two CMEs from June 21 and June 25, 2015, using both remote-sensing observations and a data-driven simulation approach based on the EUHFORIA MHD model using the cone model.
Our analysis reveals that, despite their comparable initial speeds, the two CMEs produced markedly different geomagnetic responses. CME1 led to a major geomagnetic storm (), while CME2 resulted in only moderate activity (). EUHFORIA simulations indicate that CME1’s enhanced geoeffectiveness was likely amplified by CME–CME interactions, this factor not discernible from coronagraph observations alone. In contrast, CME2 appears to have dissipated energy during propagation, possibly due to solar wind drag or lack of interaction-driven compression.
By comparing model-derived indices with in situ data, we demonstrate the importance of heliospheric modeling in capturing CME propagation dynamics and magnetic field evolution. Our findings highlight that background solar wind conditions, and CME–CME interactions are critical to assessing a CME’s space weather impact. This underscores the need for integrated modeling frameworks like EUHFORIA to improve the accuracy of arrival time predictions and geomagnetic storm forecasting. The research emphasizes that the interactions of CMEs are crucial in shaping their effects on Earth, indicating that their initial speeds, while comparable, have a lesser impact. In addition, the EUHFORIA numerical model aligns with the values determined by the GFZ German research centre; this implies that EUHFORIA can also compute and potentially forecast the impact of CMEs on the Earth. While CMEs remain primary drivers of geomagnetic storms, this work underscores that their space weather impacts are governed by complex interplay between intrinsic properties and evolving heliospheric conditions.
{"title":"Quantifying the role of CME–CME interactions in geomagnetic storm severity: A case study using EUHFORIA","authors":"Somaiyeh Sabri , Stefaan Poedts","doi":"10.1016/j.asr.2025.11.081","DOIUrl":"10.1016/j.asr.2025.11.081","url":null,"abstract":"<div><div>Coronal Mass Ejections (CMEs) are among the primary drivers of space weather disturbances, with the potential to trigger severe geomagnetic storms and pose risks to satellite operations, navigation systems, and astronaut safety. While initial observational parameters such as CME speed and angular width, commonly derived from coronagraphs like SOHO/LASCO, are essential for early detection, they are often insufficient to reliably predict a CME’s geoeffectiveness. In this study, we investigate two CMEs from June 21 and June 25, 2015, using both remote-sensing observations and a data-driven simulation approach based on the EUHFORIA MHD model using the cone model.</div><div>Our analysis reveals that, despite their comparable initial speeds, the two CMEs produced markedly different geomagnetic responses. CME1 led to a major geomagnetic storm (<span><math><mrow><msub><mrow><mi>K</mi></mrow><mrow><mi>p</mi></mrow></msub><mo>=</mo><mn>9</mn></mrow></math></span>), while CME2 resulted in only moderate activity (<span><math><mrow><msub><mrow><mi>K</mi></mrow><mrow><mi>p</mi></mrow></msub><mo>≈</mo><mn>3</mn></mrow></math></span>). EUHFORIA simulations indicate that CME1’s enhanced geoeffectiveness was likely amplified by CME–CME interactions, this factor not discernible from coronagraph observations alone. In contrast, CME2 appears to have dissipated energy during propagation, possibly due to solar wind drag or lack of interaction-driven compression.</div><div>By comparing model-derived <span><math><mrow><msub><mrow><mi>K</mi></mrow><mrow><mi>p</mi></mrow></msub></mrow></math></span> indices with in situ data, we demonstrate the importance of heliospheric modeling in capturing CME propagation dynamics and magnetic field evolution. Our findings highlight that background solar wind conditions, and CME–CME interactions are critical to assessing a CME’s space weather impact. This underscores the need for integrated modeling frameworks like EUHFORIA to improve the accuracy of arrival time predictions and geomagnetic storm forecasting. The research emphasizes that the interactions of CMEs are crucial in shaping their effects on Earth, indicating that their initial speeds, while comparable, have a lesser impact. In addition, the EUHFORIA numerical model aligns with the values determined by the GFZ German research centre; this implies that EUHFORIA can also compute and potentially forecast the impact of CMEs on the Earth. While CMEs remain primary drivers of geomagnetic storms, this work underscores that their space weather impacts are governed by complex interplay between intrinsic properties and evolving heliospheric conditions.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 3","pages":"Pages 4000-4017"},"PeriodicalIF":2.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081626","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}
Pub Date : 2026-02-01DOI: 10.1016/j.asr.2025.11.017
Yan Liu , Qing Liu , Bowen Bai , Jiayi Du , Donglong Yang
In rapidly urbanizing coastal regions, land reclamation has become a critical spatial resource and a key driver of socio-economic development. However, the long-term spatio-temporal patterns of reclamation and their large-scale environmental consequences remain insufficiently understood, representing a major gap in current research. Addressing this gap, this study takes Qingdao, one of China’s most intensively reclaimed coastal cities, as a case study. By synthesizing multi-source geospatial data on the Google Earth Engine (GEE) platform, we analyze the evolution of reclamation types and spatial patterns over the past four decades. Furthermore, we develop a quantitative, remote sensing–based environmental assessment system that integrates NDVI, NDWI, NDBI, and LST to comprehensively evaluate vegetation dynamics, water conditions, built-up intensity, and surface temperature in reclaimed areas. Our results show that: (1) from 1980 to 2020, Qingdao’s reclaimed land expanded by 230 %, with the proportion of fishery land decreasing from 90 % to 40 % while industrial and urban land increased significantly; (2) the spatial focus of reclamation shifted from Jiaozhou Bay to the northern and southern flanks, with the West Coast New Area emerging as a new growth pole; and (3) reclamation has produced notable environmental trade-offs: reclaimed areas became greener (higher vegetation indices) but also hotter (mean surface temperature +7 °C) and drier (declining water indices), indicating water conditions deterioration. By combining methodological innovation with long-term spatial analysis, this study provides a comprehensive understanding of reclamation-induced landscape transformation and its environmental effects, offering valuable insights for sustainable land-use policy, coastal management, and urban planning.
{"title":"Spatiotemporal variations of coastal land reclamation and its environmental indicators in rapid urbanization areas over 40 years: Qingdao, China (1980–2023)","authors":"Yan Liu , Qing Liu , Bowen Bai , Jiayi Du , Donglong Yang","doi":"10.1016/j.asr.2025.11.017","DOIUrl":"10.1016/j.asr.2025.11.017","url":null,"abstract":"<div><div>In rapidly urbanizing coastal regions, land reclamation has become a critical spatial resource and a key driver of socio-economic development. However, the long-term spatio-temporal patterns of reclamation and their large-scale environmental consequences remain insufficiently understood, representing a major gap in current research. Addressing this gap, this study takes Qingdao, one of China’s most intensively reclaimed coastal cities, as a case study. By synthesizing multi-source geospatial data on the Google Earth Engine (GEE) platform, we analyze the evolution of reclamation types and spatial patterns over the past four decades. Furthermore, we develop a quantitative, remote sensing–based environmental assessment system that integrates NDVI, NDWI, NDBI, and LST to comprehensively evaluate vegetation dynamics, water conditions, built-up intensity, and surface temperature in reclaimed areas. Our results show that: (1) from 1980 to 2020, Qingdao’s reclaimed land expanded by 230 %, with the proportion of fishery land decreasing from 90 % to 40 % while industrial and urban land increased significantly; (2) the spatial focus of reclamation shifted from Jiaozhou Bay to the northern and southern flanks, with the West Coast New Area emerging as a new growth pole; and (3) reclamation has produced notable environmental trade-offs: reclaimed areas became greener (higher vegetation indices) but also hotter (mean surface temperature +7 °C) and drier (declining water indices), indicating water conditions deterioration. By combining methodological innovation with long-term spatial analysis, this study provides a comprehensive understanding of reclamation-induced landscape transformation and its environmental effects, offering valuable insights for sustainable land-use policy, coastal management, and urban planning.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 3","pages":"Pages 2831-2854"},"PeriodicalIF":2.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081826","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}
Pub Date : 2026-02-01DOI: 10.1016/j.asr.2025.11.085
Hana Bobáľová , Šimon Opravil
Land surface temperature (LST) data derived from satellite images are important for various applications, including mapping urban heat islands, analysing temporal and spatial temperature patterns, assessing the cooling effect of urban greenery, and developing population vulnerability indices for heat waves. Thermal sensors aboard Landsat satellites provide the most spatially detailed data with the longest temporal continuity. Landsat Surface Temperature (ST) is already available as a standard product, and a code for estimating the Landsat LST using the empirical statistical mono-window method has been implemented in the Google Earth Engine (GEE). However, these approaches rely on the ASTER Global Emissivity Dataset, which has certain limitations, including missing values. In GEE, we developed an approach to calculate land surface emissivity using various NDVI-based methods, combined with the statistical mono-window and radiative transfer equation methods for LST calculation. Developed code provides the creation of seamless LST products without unnatural block artifacts, reflecting the current land cover and vegetation condition. Validation against in situ measurements from the SURFRAD network revealed that the statistical mono-window method proved to be more accurate than the Landsat ST product and radiative transfer equation methods, regardless of the emissivity data source. The NDVI-based emissivity combined with the statistical mono-window method yielded higher LST precision than the approach using ASTER GED emissivity. These results were consistent across all Landsat missions. Furthermore, we demonstrate that the lowest accuracy is achieved in calculating LST on mixed surfaces and the highest on bare soil. The overestimation of satellite LST measurements at high temperatures was only apparent on mixed and vegetated surfaces, while it was more pronounced in the Landsat ST product and other radiative transfer equation methods. These findings and the publicly available GEE code can lead to more accurate LST mapping and analysis results.
{"title":"Improving Landsat land surface temperature estimation in Google Earth Engine using NDVI-based emissivity","authors":"Hana Bobáľová , Šimon Opravil","doi":"10.1016/j.asr.2025.11.085","DOIUrl":"10.1016/j.asr.2025.11.085","url":null,"abstract":"<div><div>Land surface temperature (LST) data derived from satellite images are important for various applications, including mapping urban heat islands, analysing temporal and spatial temperature patterns, assessing the cooling effect of urban greenery, and developing population vulnerability indices for heat waves. Thermal sensors aboard Landsat satellites provide the most spatially detailed data with the longest temporal continuity. Landsat Surface Temperature (ST) is already available as a standard product, and a code for estimating the Landsat LST using the empirical statistical mono-window method has been implemented in the Google Earth Engine (GEE). However, these approaches rely on the ASTER Global Emissivity Dataset, which has certain limitations, including missing values. In GEE, we developed an approach to calculate land surface emissivity using various NDVI-based methods, combined with the statistical mono-window and radiative transfer equation methods for LST calculation. Developed code provides the creation of seamless LST products without unnatural block artifacts, reflecting the current land cover and vegetation condition. Validation against in situ measurements from the SURFRAD network revealed that the statistical mono-window method proved to be more accurate than the Landsat ST product and radiative transfer equation methods, regardless of the emissivity data source. The NDVI-based emissivity combined with the statistical mono-window method yielded higher LST precision than the approach using ASTER GED emissivity. These results were consistent across all Landsat missions. Furthermore, we demonstrate that the lowest accuracy is achieved in calculating LST on mixed surfaces and the highest on bare soil. The overestimation of satellite LST measurements at high temperatures was only apparent on mixed and vegetated surfaces, while it was more pronounced in the Landsat ST product and other radiative transfer equation methods. These findings and the publicly available GEE code can lead to more accurate LST mapping and analysis results.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 3","pages":"Pages 3281-3299"},"PeriodicalIF":2.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081954","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}
Pub Date : 2026-02-01DOI: 10.1016/j.asr.2025.11.048
Emrehan Kutlug Sahin , Selçuk Demir , Mert Ozturk , Mehmet Serhan Duzce
In recent years, many technological innovations have marked the 21st century. One of the most rapid and unpredictable is the Artificial Intelligence (AI) revolution. The integration of AI systems, particularly generative AI, has just started manifesting itself in geoscience applications. This study investigates the potential benefits and limitations of the state-of-the-art generative AI framework, Google-Gemini, in improving the accuracy and efficiency of landslide susceptibility maps (LSMs). The research aims to shed light on the efficacy of Gemini AI and its implications for enhancing geoscience applications beyond LSM through empirical trials and comparative analysis. Furthermore, a web-based, user-friendly interface called Geo[AI]-LSM has been produced and is freely available to all users for producing LSMs. In the proposed framework, two distinct tools play critical roles: the Data Preparation tool, which prepares the landslide conditioning factor dataset, and the Geo[AI]-LSM tool, which constructs model architecture based on the provided prompt, applies the model training strategies, displays the accuracy values, and finally plots the LSM. In this study, Geo[AI]-LSM is employed to estimate the landslide susceptibility of Mudurnu district in Bolu Province, Türkiye to demonstrate the generative AI’s capabilities. The current work develops models using various machine learning (ML) pipelines, each more sophisticated than the previous one. For this purpose, five alternative prompts (i.e., Prompts [1], [2], [3], [4], [5]) ranging from relatively simple to complex, were employed to generate ML models using the well-known Random Forest (RF) algorithm. The findings are evaluated using various performance metrics, including accuracy, Kappa, precision, recall, and F1 statistics. Experiments with datasets from the study area showed that the proposed Geo[AI]-LSM approach achieved an accuracy of about 89 % for the Prompt [5] model. Ultimately, it is believed that this research’s findings will make a substantial contribution to the current conversation about using AI technology to address geoscience challenges and improve landslide risk assessment and management.
{"title":"Geoscience in the era of generative artificial intelligence (Geo[AI]-LSM): understanding the potential benefits of Google Gemini in producing landslide susceptibility mapping","authors":"Emrehan Kutlug Sahin , Selçuk Demir , Mert Ozturk , Mehmet Serhan Duzce","doi":"10.1016/j.asr.2025.11.048","DOIUrl":"10.1016/j.asr.2025.11.048","url":null,"abstract":"<div><div>In recent years, many technological innovations have marked the 21st century. One of the most rapid and unpredictable is the Artificial Intelligence (AI) revolution. The integration of AI systems, particularly generative AI, has just started manifesting itself in geoscience applications. This study investigates the potential benefits and limitations of the state-of-the-art generative AI framework, Google-Gemini, in improving the accuracy and efficiency of landslide susceptibility maps (LSMs). The research aims to shed light on the efficacy of Gemini AI and its implications for enhancing geoscience applications beyond LSM through empirical trials and comparative analysis. Furthermore, a web-based, user-friendly interface called Geo[AI]-LSM has been produced and is freely available to all users for producing LSMs. In the proposed framework, two distinct tools play critical roles: the Data Preparation tool, which prepares the landslide conditioning factor dataset, and the Geo[AI]-LSM tool, which constructs model architecture based on the provided prompt, applies the model training strategies, displays the accuracy values, and finally plots the LSM. In this study, Geo[AI]-LSM is employed to estimate the landslide susceptibility of Mudurnu district in Bolu Province, Türkiye to demonstrate the generative AI’s capabilities. The current work develops models using various machine learning (ML) pipelines, each more sophisticated than the previous one. For this purpose, five alternative prompts (i.e., Prompts [1], [2], [3], [4], [5]) ranging from relatively simple to complex, were employed to generate ML models using the well-known Random Forest (RF) algorithm. The findings are evaluated using various performance metrics, including accuracy, Kappa, precision, recall, and F1 statistics. Experiments with datasets from the study area showed that the proposed Geo[AI]-LSM approach achieved an accuracy of about 89 % for the Prompt [5] model. Ultimately, it is believed that this research’s findings will make a substantial contribution to the current conversation about using AI technology to address geoscience challenges and improve landslide risk assessment and management.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 3","pages":"Pages 3061-3085"},"PeriodicalIF":2.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081924","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}
Pub Date : 2026-02-01DOI: 10.1016/j.asr.2025.11.080
Yuxing Li , Guangyun Li , Mingjian Chen , Xingyu Shi , Shuai Tong
Aiming at the issue of poor positioning accuracy of smartphones in obstructed environments due to the scarcity of available satellites, a GPS/Galileo/BDS tightly combined pseudorange differential positioning model is put forward. This model employs the single-point positioning inter-station differential estimation method to calculate the differential inter-system pseudorange bias (DISPB) in real-time and make corrections. Firstly, the pseudorange noises of different systems are evaluated. Secondly, the stabilities of the DISPB obtained by the proposed single-point positioning inter-station differential estimation method and the traditional tightly combined estimation method are compared. Finally, the usability of the algorithm is verified through experiments. The experimental results indicate that the pseudorange noise of smartphones is over ten times that of geodetic receivers, and the pseudorange noise of GPS is smaller than that of Galileo and BDS. Hence, it is recommended to utilize GPS satellites as the reference satellites for the tightly combined model. The DISPB obtained by the two methods are relatively stable and approximately equal, and the DISPB obtained by the new method can be employed as the correction value for the tightly combined model. By raising the cut-off elevation angle, a simulation experiment on shading is conducted in an open environment. The positioning accuracies of the tightly combined model corrected by the DISPB obtained by the two methods are the same. Compared with the loosely combined model, the planar positioning accuracy of the tightly combined model is enhanced by 9.8–39 %. In the actual obstructed experiment, the planar positioning accuracy of the tightly combined model is improved by 9.1–25.6 %. This method enhances the positioning accuracy and reliability of smartphones in obstructed environments.
{"title":"Analysis on the performance of single-frequency tightly combined pseudorange differential positioning for smartphones with GPS/Galileo/BDS","authors":"Yuxing Li , Guangyun Li , Mingjian Chen , Xingyu Shi , Shuai Tong","doi":"10.1016/j.asr.2025.11.080","DOIUrl":"10.1016/j.asr.2025.11.080","url":null,"abstract":"<div><div>Aiming at the issue of poor positioning accuracy of smartphones in obstructed environments due to the scarcity of available satellites, a GPS/Galileo/BDS tightly combined pseudorange differential positioning model is put forward. This model employs the single-point positioning inter-station differential estimation method to calculate the differential inter-system pseudorange bias (DISPB) in real-time and make corrections. Firstly, the pseudorange noises of different systems are evaluated. Secondly, the stabilities of the DISPB obtained by the proposed single-point positioning inter-station differential estimation method and the traditional tightly combined estimation method are compared. Finally, the usability of the algorithm is verified through experiments. The experimental results indicate that the pseudorange noise of smartphones is over ten times that of geodetic receivers, and the pseudorange noise of GPS is smaller than that of Galileo and BDS. Hence, it is recommended to utilize GPS satellites as the reference satellites for the tightly combined model. The DISPB obtained by the two methods are relatively stable and approximately equal, and the DISPB obtained by the new method can be employed as the correction value for the tightly combined model. By raising the cut-off elevation angle, a simulation experiment on shading is conducted in an open environment. The positioning accuracies of the tightly combined model corrected by the DISPB obtained by the two methods are the same. Compared with the loosely combined model, the planar positioning accuracy of the tightly combined model is enhanced by 9.8–39 %. In the actual obstructed experiment, the planar positioning accuracy of the tightly combined model is improved by 9.1–25.6 %. This method enhances the positioning accuracy and reliability of smartphones in obstructed environments.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 3","pages":"Pages 3239-3257"},"PeriodicalIF":2.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081343","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}
Pub Date : 2026-02-01DOI: 10.1016/j.asr.2024.10.046
Zlatko Majlinger , Milan S. Dimitrijević , Vladimir A. Srećković
Stark widths for eight spectral lines of singly charged tellurium ion (Te II) have been calculated with the help of the modified semiempirical method. The calculations have been performed for a temperature range from 5 000 K up to 100 000 K and electron density of cm−3. Employing the obtained results, we investigated the influence of Stark broadening of Te II spectral lines to test the importance of the broadening by electron impacts in stellar spectra. Examples of the importance of Stark broadening in comparison with thermal Doppler broadening in atmospheres of spectral class DA, DB white dwarfs as well as A-type stars are presented. The newly acquired data will be important also for various modeling and laboratory plasma analysis.
{"title":"Dataset on Stark broadening of Te II spectral lines","authors":"Zlatko Majlinger , Milan S. Dimitrijević , Vladimir A. Srećković","doi":"10.1016/j.asr.2024.10.046","DOIUrl":"10.1016/j.asr.2024.10.046","url":null,"abstract":"<div><div><span>Stark widths for eight spectral lines of singly charged tellurium ion (Te II) have been calculated with the help of the modified semiempirical method. The calculations have been performed for a temperature range from 5 000 K up to 100 000 K and electron density of </span><span><math><mrow><msub><mrow><mi>N</mi></mrow><mrow><mi>e</mi></mrow></msub><mo>=</mo><msup><mrow><mn>10</mn></mrow><mrow><mn>17</mn></mrow></msup></mrow></math></span> cm<sup>−3</sup><span><span><span>. Employing the obtained results, we investigated the influence of Stark broadening of Te II spectral lines to test the importance of the broadening by </span>electron impacts in </span>stellar spectra<span>. Examples of the importance of Stark broadening in comparison with thermal Doppler broadening in atmospheres of spectral class DA, DB white dwarfs as well as A-type stars are presented. The newly acquired data will be important also for various modeling and laboratory plasma analysis.</span></span></div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 3","pages":"Pages 4092-4097"},"PeriodicalIF":2.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081449","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}
Pub Date : 2026-02-01DOI: 10.1016/j.asr.2025.11.041
Zimu Zhang , Qing Li , Zhaoguo Zhang , Lei Liu , Wei Li
Aerospace vehicles serve as high-value platforms for space missions, requiring ultra-quiet micro-vibration environment to ensure the operational precision of sensitive payloads. However, onboard disturbance sources, such as flywheels, cryocoolers, and thrusters, induce cross-band micro-vibrations spanning 0.1–300 Hz, severely degrading payload performance. To solve this challenge, this paper first develops an integrated modeling method of multi-source disturbances using finite element model (FEM) analysis, quantifying disturbance transmission characteristics and acceleration responses. Simulation results reveal that the coupled effects of these disturbances excite broadband micro-vibrations, significantly degrading the micro-vibration environment at the payload interface. Additionally, on this basis, a hybrid controller integrating PI feedback control with the Least-mean-square (LMS) feedforward control is designed for cross-band micro-vibration suppression. Furthermore, an eight-leg redundant active vibration isolation platform is developed for experimental validation. Results demonstrate the hybrid controller’s efficiency in suppressing cross-band disturbances arising from multi-source coupling. The work of this paper provides a more comprehensive framework for analyzing multi-source disturbances in aerospace vehicles and presents an effective hybrid control strategy for suppressing cross-band micro-vibrations in high-precision aerospace vehicles.
{"title":"Integrated modeling and active suppression of multi-source micro-vibrations in aerospace vehicles","authors":"Zimu Zhang , Qing Li , Zhaoguo Zhang , Lei Liu , Wei Li","doi":"10.1016/j.asr.2025.11.041","DOIUrl":"10.1016/j.asr.2025.11.041","url":null,"abstract":"<div><div>Aerospace vehicles serve as high-value platforms for space missions, requiring ultra-quiet micro-vibration environment to ensure the operational precision of sensitive payloads. However, onboard disturbance sources, such as flywheels, cryocoolers, and thrusters, induce cross-band micro-vibrations spanning 0.1–300 Hz, severely degrading payload performance. To solve this challenge, this paper first develops an integrated modeling method of multi-source disturbances using finite element model (FEM) analysis, quantifying disturbance transmission characteristics and acceleration responses. Simulation results reveal that the coupled effects of these disturbances excite broadband micro-vibrations, significantly degrading the micro-vibration environment at the payload interface. Additionally, on this basis, a hybrid controller integrating PI feedback control with the Least-mean-square (LMS) feedforward control is designed for cross-band micro-vibration suppression. Furthermore, an eight-leg redundant active vibration isolation platform is developed for experimental validation. Results demonstrate the hybrid controller’s efficiency in suppressing cross-band disturbances arising from multi-source coupling. The work of this paper provides a more comprehensive framework for analyzing multi-source disturbances in aerospace vehicles and presents an effective hybrid control strategy for suppressing cross-band micro-vibrations in high-precision aerospace vehicles.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 3","pages":"Pages 3670-3683"},"PeriodicalIF":2.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081534","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}
Pub Date : 2026-02-01DOI: 10.1016/j.asr.2025.10.095
Geng Gao , Wei Zheng , Yongjin Sun , Jiankang Du , Yongqi Zhao , Minxing Zhao
The Gravity Recovery And Climate Experiment (GRACE) series missions have revolutionized our understanding of Earth gravity field by combining Global Positioning System (GPS) tracking and inter-satellite K-band ranging (KBR) to monitor global mass transport with unprecedented resolution and continuity. Within the Celestial Mechanics Approach (CMA), GPS-based kinematic orbits—together with their stochastic characteristics—are treated as pseudo-observations to simultaneously reconstruct satellite orbits and recover Earth gravity field. Nevertheless, ambiguity-float kinematic orbits and epoch-wise covariance models, which are simplified versions of the fully populated covariance matrices derived from GPS observation noise propagation, remain widely used, thereby limiting further improvements in the accuracy of CMA solutions. This study investigates the impact of applying integer ambiguity resolution (IAR) to enhance kinematic orbit precision and reduce temporal correlations in the stochastic model. These refinements enable the use of simplified epoch-wise covariance matrices without compromising solution consistency. Using GRACE Follow-On (GFO) data from November 2019, we evaluate the effects of IAR-based orbits and decorrelated covariance structures within a least squares framework incorporating variance component estimation (VCE). Monthly gravity fields are estimated up to degree and order 60 using GPS-only observations, and up to 96 when K-band range-rate (KRR) data are incorporated. The reconstructed IAR-based orbits exhibit three-dimensional root mean square (3D RMS) errors close to 1 cm, a significant improvement over float solutions (∼2.5 cm). In GPS-only solutions, gravity fields based on IAR and float orbits remain consistent up to degree and order 45, diverging beyond this—despite the nominal resolution limit of ∼1300 km. In joint GPS and KRR solutions, discrepancies appear beyond degree and order 25 between IAR- and float-based models, as well as with the GFO Science Data System (SDS) RL06.1 products—manifested as more pronounced north–south striping artifacts in global mass distributions. To address this issue, we apply a fixed-weight strategy to kinematic ambiguity-fixed orbits and KRR observations, which substantially improves the consistency of the resulting gravity field with SDS models and outperforms both float and IAR solutions derived under VCE. This suggests that the superior precision of IAR-based orbits leads to relatively higher weighting of the kinematic positions, which in turn reduces the effective contribution of KBR observations to gravity field recovery and biases the estimates toward the polar-orbit-dominated sensitivity of the GFO constellation. These results highlight the importance of an adequate stochastic description of kinematic positions, which depends not only on observation quality but also on the underlying modeling, including ambiguity resolution and background force models.
{"title":"GRACE-FO gravity field recovery from integer ambiguity resolved kinematic orbits and decorrelated stochastic model","authors":"Geng Gao , Wei Zheng , Yongjin Sun , Jiankang Du , Yongqi Zhao , Minxing Zhao","doi":"10.1016/j.asr.2025.10.095","DOIUrl":"10.1016/j.asr.2025.10.095","url":null,"abstract":"<div><div>The Gravity Recovery And Climate Experiment (GRACE) series missions have revolutionized our understanding of Earth gravity field by combining Global Positioning System (GPS) tracking and inter-satellite K-band ranging (KBR) to monitor global mass transport with unprecedented resolution and continuity. Within the Celestial Mechanics Approach (CMA), GPS-based kinematic orbits—together with their stochastic characteristics—are treated as pseudo-observations to simultaneously reconstruct satellite orbits and recover Earth gravity field. Nevertheless, ambiguity-float kinematic orbits and epoch-wise covariance models, which are simplified versions of the fully populated covariance matrices derived from GPS observation noise propagation, remain widely used, thereby limiting further improvements in the accuracy of CMA solutions. This study investigates the impact of applying integer ambiguity resolution (IAR) to enhance kinematic orbit precision and reduce temporal correlations in the stochastic model. These refinements enable the use of simplified epoch-wise covariance matrices without compromising solution consistency. Using GRACE Follow-On (GFO) data from November 2019, we evaluate the effects of IAR-based orbits and decorrelated covariance structures within a least squares framework incorporating variance component estimation (VCE). Monthly gravity fields are estimated up to degree and order 60 using GPS-only observations, and up to 96 when K-band range-rate (KRR) data are incorporated. The reconstructed IAR-based orbits exhibit three-dimensional root mean square (3D RMS) errors close to 1 cm, a significant improvement over float solutions (∼2.5 cm). In GPS-only solutions, gravity fields based on IAR and float orbits remain consistent up to degree and order 45, diverging beyond this—despite the nominal resolution limit of ∼1300 km. In joint GPS and KRR solutions, discrepancies appear beyond degree and order 25 between IAR- and float-based models, as well as with the GFO Science Data System (SDS) RL06.1 products—manifested as more pronounced north–south striping artifacts in global mass distributions. To address this issue, we apply a fixed-weight strategy to kinematic ambiguity-fixed orbits and KRR observations, which substantially improves the consistency of the resulting gravity field with SDS models and outperforms both float and IAR solutions derived under VCE. This suggests that the superior precision of IAR-based orbits leads to relatively higher weighting of the kinematic positions, which in turn reduces the effective contribution of KBR observations to gravity field recovery and biases the estimates toward the polar-orbit-dominated sensitivity of the GFO constellation. These results highlight the importance of an adequate stochastic description of kinematic positions, which depends not only on observation quality but also on the underlying modeling, including ambiguity resolution and background force models.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 3","pages":"Pages 3844-3857"},"PeriodicalIF":2.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081568","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}
Pub Date : 2026-02-01DOI: 10.1016/j.asr.2025.11.064
Chengqian Wu , Caisheng Wei , Jianhua Wang , Pengfei Guo , Chuan Ma , Xia Wu
To address the strong dependence of existing satellite fault detection methods on large labeled telemetry datasets, together with the scarcity of fault samples and the severe class imbalance in real telemetry that lead to missed detections and limited generalization, this paper proposes a self supervised fault detection framework for satellite telemetry. The framework adopts a bidirectional long short-term memory backbone and performs dual temporal adversarial self supervised pretraining to reduce reliance on labeled data. During supervised fine tuning, cost sensitive learning is introduced to adaptively reweight fault samples, thereby mitigating the high false negative rate caused by class imbalance. Experiments on public satellite telemetry datasets demonstrate that the proposed model offers clear advantages over mainstream satellite fault detection methods.
{"title":"Dual-temporal adversarial self-supervised BiLSTM for satellite telemetry fault detection with cost-sensitive learning","authors":"Chengqian Wu , Caisheng Wei , Jianhua Wang , Pengfei Guo , Chuan Ma , Xia Wu","doi":"10.1016/j.asr.2025.11.064","DOIUrl":"10.1016/j.asr.2025.11.064","url":null,"abstract":"<div><div>To address the strong dependence of existing satellite fault detection methods on large labeled telemetry datasets, together with the scarcity of fault samples and the severe class imbalance in real telemetry that lead to missed detections and limited generalization, this paper proposes a self supervised fault detection framework for satellite telemetry. The framework adopts a bidirectional long short-term memory backbone and performs dual temporal adversarial self supervised pretraining to reduce reliance on labeled data. During supervised fine tuning, cost sensitive learning is introduced to adaptively reweight fault samples, thereby mitigating the high false negative rate caused by class imbalance. Experiments on public satellite telemetry datasets demonstrate that the proposed model offers clear advantages over mainstream satellite fault detection methods.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 3","pages":"Pages 3922-3933"},"PeriodicalIF":2.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081625","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}
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