Advances in Space Research最新文献

{"title":"Ionospheric slant total electron content mapping algorithm: IONOLAB-SMAP","authors":"Meltem Koroglu,&nbsp;Ozan Koroglu,&nbsp;Feza Arikan,&nbsp;Orhan Arikan","doi":"10.1016/j.asr.2025.09.027","DOIUrl":"10.1016/j.asr.2025.09.027","url":null,"abstract":"<div><div>Global Positioning System (GPS) technology has significantly reshaped navigation and positioning services, providing essential insights across various sectors such as transportation, defense, and scientific research. Total Electron Content (TEC) within the ionosphere, a key parameter for evaluating the influence of ionospheric variations on GPS signal accuracy. These variations, influenced by temporal, geographic, and solar conditions, can cause notable positioning errors, emphasizing the need for precise monitoring and detailed analysis to improve the reliability of GPS-enabled services. TEC values are estimated using dual-frequency GPS receiver measurements that are stored as Receiver INdependent EXchange Format (RINEX) records by using IONOLAB-STEC method. This method, which provides updates every 30 s, incorporates corrections for ionospheric and tropospheric delays, along with timing inaccuracies from satellites and receivers, enhancing the accuracy of GPS positioning. Current Global Ionospheric Maps (GIM) suffer from inadequate temporal and spatial resolution. This research presents an advanced interpolation-based approach for generating high-resolution TEC maps, with a particular emphasis on a preprocessing step applied to the STEC values to reduce the influence of outliers and enhance data quality. This approach allows for the generation of STEC maps with a spatial resolution of 0.48° and a temporal resolution of 5 min, which corresponds to the assumed duration of the ionosphere’s wide-sense stationary period on disturbed days. Moreover, the output maps produced by the proposed method are capable of capturing finer-scale variations in the ionospheric structure, as the STEC data utilized in this study are considered to be closer to raw measurements compared to JPL-TEC and TEC estimates. Our findings highlight the superior performance of the STEC mapping technique over traditional TEC mapping methods, marking a significant advancement in geospatial applications. This methodological innovation promises to enhance GPS reliability and precision across various high-stakes fields, setting a new standard for ionospheric monitoring and supporting advancements in navigation technology and space weather prediction.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 1","pages":"Pages 374-392"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528300","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":"Pre-seismic ionospheric disturbances following the 2025 Mw 7.7 Mandalay, Burma (Myanmar) Earthquake from GNSS observations","authors":"Husan Eshkuvatov,&nbsp;Shuanggen Jin,&nbsp;Bobomurat Ahmedov,&nbsp;Shukhrat Mardonov,&nbsp;Shahzod Numonjonov","doi":"10.1016/j.asr.2025.10.068","DOIUrl":"10.1016/j.asr.2025.10.068","url":null,"abstract":"<div><div>Accurately predicting ionospheric behavior and mitigating the effects of its rapid disturbances are essential for ensuring the reliability of satellite communications, positioning, and navigation systems. Ionospheric disturbances can result from geomagnetic storms, seismic activity, solar events, volcanic eruptions, and other geophysical phenomena. Space weather monitoring relies on analyzing Vertical Total Electron Content (VTEC) and its anomalies, using time series data, spatial maps, and various derived parameters to detect and understand these variations. This study examines the feasibility of predicting Total Electron Content (TEC) at the CMUM and CUSV stations in Thailand during earthquake events. TEC measurements and solar parameters were collected for a selected Myanmar earthquake, covering the period surrounding the event. The Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) model was applied to forecast TEC over 31 days, with input parameters sourced from the OMNIWeb database. Continuous earthquake monitoring via ionospheric analysis offers promising insights into precursory seismic signals. Here, we investigate the 7.7 magnitude Myanmar earthquake by analyzing ionospheric disturbances using GPS-derived TEC data from two International GNSS Service (IGS) stations. We detect anomalous TEC variations 3–13 days before the earthquake, suggesting a potential link between pre-seismic activity and ionospheric perturbations. These findings underscore the utility of space-based geodetic tools for earthquake precursor studies and contribute to growing evidence of lithosphere-atmosphere–ionosphere coupling.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 1","pages":"Pages 232-245"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528741","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":"Forecasting ionospheric foF2 based on a machine learning CNN-BiLSTM-MHA model","authors":"Jie Wu,&nbsp;Yuyang Huang,&nbsp;Shunzu Gao,&nbsp;Qian Pan,&nbsp;Yuhao Zheng,&nbsp;Qihui Jiang,&nbsp;Chao Xiong","doi":"10.1016/j.asr.2025.11.056","DOIUrl":"10.1016/j.asr.2025.11.056","url":null,"abstract":"<div><div>The critical frequency of the ionospheric F2 layer (foF2) is a key parameter for describing the state of the ionosphere, and its short-term forecast is particularly challenging because of its nonlinear and non-stationary characteristics. In this study, we proposed a hybrid model architecture integrating Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM) and Multi-Head Attention (MHA) networks to predict the short-term variations of foF2. The model incorporated 15 years (2008–2022) ionospheric measurements from 10 available digital ionosonde stations located at middle latitudes. The input parameters included local time, day of year, geomagnetic and solar activity indices, and historical ionospheric foF2 observations. The forecasting performance of the CNN-BiLSTM-MHA model was systematically analyzed and evaluated by comparing with other machine learning approaches (e.g., BiLSTM and LSTM), as well as estimates from the International Reference Ionosphere model (IRI-2020). The results show that our CNN-BiLSTM-MHA model performs better than other models. The average root mean square error (RMSE), correlation coefficient (<em>R</em>) and percent deviation (PD) of the CNN-BiLSTM-MHA model across all stations are 0.581 MHz, 0.936 and 9.29 %, respectively. In terms of RMSE, it is reduced by 9.48 %, 23.34 % and 30.61 % compared to the BiLSTM, LSTM and IRI2020 model, respectively. The results show that the CNN-BiLSTM-MHA model greatly avoids the apparent overestimation or underestimation observed in other models, particularly during the winter nights. It is also found that our CNN-BiLSTM-MHA model has superior performance during geomagnetic disturbed period. Compared to the IRI2020 model, RMSE is reduced by an average of 17.65 %.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 1","pages":"Pages 361-373"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528299","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":"Reconstruction of ionospheric TEC maps using singular value decomposition in both space and time","authors":"Furkan Ardic,&nbsp;Feza Arikan,&nbsp;Orhan Arikan","doi":"10.1016/j.asr.2025.11.087","DOIUrl":"10.1016/j.asr.2025.11.087","url":null,"abstract":"<div><div>The ionosphere affects the performance of shortwave and satellite communications, and space-based navigation and positioning systems. Understanding the ionospheric structure is crucial for enhancing the performance of these systems. Total Electron Content (TEC) is a key parameter for characterizing the ionospheric structure. TEC can typically be estimated at only a limited number of spatial points for most parts of the globe. Consequently, accurate, reliable, and robust TEC mapping/interpolation methods are required. In this study, a basis expansion algorithm is proposed to represent the underlying physics of TEC using singular value decomposition (SVD) for 2-D reconstruction. Extracting the significant singular values corresponding to signal subspace allows a reduced number of contributing basis vectors. Utilizing the TEC estimated from 32% of the grid, the least squares optimization based algorithm allows reconstruction in computationally efficient closed form. For the preparation of Model Matrices, interpolated Global Ionospheric Maps (GIM) can be utilized for user defined space–time resolution for the two-dimensional (2-D) reconstruction of TEC even in regions where GPS receivers are either non-existent or sparse such as over oceans and deserts. In this study, an example is provided for reconstruction of TEC for European region where Model Matrices are generated considering diverse solar activity levels, hourly-monthly trend variations, and geomagnetic indices. We provide examples of reconstruction over a wide range of geomagnetic activity from 2000 to 2024, covering almost two solar cycles. Of the 145,152 reconstructed TEC values, 99.757% demonstrate an error below 3 TECU relative to Jet Propulsion Laboratory (JPL) TEC data. The Probability Density Function (PDF) of the differences between JPL-TEC and the reconstructed maps is estimated. The estimated PDF parameters can be used to determine the optimal signal subspace for practical applications. The proposed reconstruction algorithm is suitable for near-real time estimation and prediction of TEC maps.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 1","pages":"Pages 299-315"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528744","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":"Two-stage modeling of ionospheric TEC for earthquake precursors: forecasting with LSTM, GRU, RNN and classification via Random Forest based on the 2023 Türkiye earthquakes","authors":"Cafer Budak","doi":"10.1016/j.asr.2025.12.018","DOIUrl":"10.1016/j.asr.2025.12.018","url":null,"abstract":"<div><div>Earthquake prediction remains a major scientific challenge despite advances in seismic monitoring. Recent research based on the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) hypothesis investigates ionospheric Total Electron Content (TEC) anomalies as potential earthquake precursors. This study presents a novel two-stage hybrid machine learning framework for detecting TEC-based earthquake precursors, validated using the 6 February 2023 Türkiye earthquake sequence. The methodology integrates deep learning forecasting models with ensemble classification techniques to enable robust detection of potential anomalies associated with earthquakes. In the first stage, next-day TEC values are forecasted using three recurrent neural network architectures: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN). We tested the framework using GNSS-derived TEC data from 10 TNPGN-Active stations within 500 km of the earthquake epicenters. The results show no single architecture consistently outperforms others across all stations. LSTM achieved the highest R² values in 6 out of 10 stations, with the best performance recorded at the ANTE station (R² = 0.9280, MSE = 17.6107, RMSE = 4.1965, MAE = 3.1000, and MAPE = 9.12 %). GRU outperformed the other models in 4 stations, while RNN did not achieve the top performance in any station. In the second stage, the TEC values forecasted in the first stage were used as inputs to the Random Forest classifier. The model successfully distinguished normal ionospheric variations from potential earthquake precursors, achieving 98.87 % accuracy, 97.06 % balanced accuracy, and 98.85 % F1-score, with an average inference time of 0.64 s. The system successfully detected pre-seismic TEC anomalies 1–3 days before the Mw 7.7 Pazarcık earthquake (04:17 local time, depth 8.6 km) and the Mw 7.6 Elbistan earthquake (13:24 local time, depth 7.6 km). The proposed two-stage hybrid framework consistently identified precursor signals prior to these major earthquakes, achieved high classification accuracy with sub-second inference times, and demonstrated promising potential for future operational applications in GNSS-based earthquake monitoring systems.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 1","pages":"Pages 279-298"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148502393","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":"Effect of geomagnetic storm intensity on the occurrence rate of large-scale ionospheric irregularities across various latitudes","authors":"Mukulika Mondal,&nbsp;Jitesh Barman,&nbsp;A.K. Singh","doi":"10.1016/j.asr.2025.09.064","DOIUrl":"10.1016/j.asr.2025.09.064","url":null,"abstract":"<div><div>In the present study, we have investigated the effect of geomagnetic storms of varying intensity [moderate (−100 nT &lt; disturbance storm time index (Dst) &lt; -70 nT), intense (-150nT &lt; Dst &lt; -100 nT), severe (-150nT &lt; Dst &lt; -250nT), and super (Dst &lt; -250nT)] recorded in the increasing phase of the 25th solar cycle between January 2021 and February 2025 on the occurrence of the large-scale ionospheric irregularities across low latitude to sub-auroral regions [low latitude, IISC (13⁰N, 77.57⁰ E; dip latitude: 4.9⁰N); beyond EIA station, Varanasi (25.31⁰N, 82.97⁰ E; dip latitude: 16.2⁰N); mid-latitude, POL2 (42.68⁰N, 74.69⁰ E; dip latitude: 34.5⁰N); sub auroral, NOVM (55⁰N, 82.90⁰ E; dip latitude: 46.2⁰N)] using rate of change of Total electron content (TEC) index (ROTI). At low latitudes, the generation of irregularities was observed when the Dst minimum occurs at local post-midnight hours, irrespective of storm intensity, due to the upward F-layer drift under the influence of the eastward prompt penetration electric field (PPEF). Over mid-latitudes, the irregularities were suppressed during the main phase, which could be associated with the effect of the westward disturbance dynamo electric field (DDEF) induced by the change in the thermospheric O/N₂ ratio. Irregularity generations are more frequent over high latitudes during the main and recovery phases of storms due to the formation of polar patches related to particle precipitation. From the overall analysis, the ROTI occurrence rate shows an increasing pattern with increasing storm intensity levels over IISC, but decreases during highly disturbed geomagnetic conditions (superstorms). Over Varanasi and POL2, the storm intensity does not exhibit significant variation in the ROTI occurrence rate. Contrastingly, the ROTI occurrence rate increases with increasing storm intensity over NOVM.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 1","pages":"Pages 177-195"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148502398","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":"Ionospheric gradients in multi-constellation global navigation satellite system signals onboard UAV using GIM and Klobuchar model over Thailand region","authors":"Kutubuddin Ansari,&nbsp;Sampad Kumar Panda,&nbsp;Kavutarapu Venkatesh,&nbsp;Punyawi Jamjareegulgarn","doi":"10.1016/j.asr.2025.07.092","DOIUrl":"10.1016/j.asr.2025.07.092","url":null,"abstract":"<div><div>The effects of the ionosphere on Global Navigation Satellite System (GNSS) signals have been a focal point of research nowadays. During adverse ionospheric conditions, ionospheric gradients become more pronounced and disruptive compared to quiet days, potentially leading to increased positioning errors or loss of satellite signal lock. We introduce an ionospheric spatial gradient estimation method to detect the anomalous gradients from multi-constellation GNSS signals (i.e., GPS, GLONASS, and Galileo) signals recorded by the onboard sensor of flying real-time kinematic unmanned aerial vehicle (RTK UAV) over the Thailand region. We employ the Klobuchar model and global ionospheric maps (GIMs) for estimating the slant total electron contents (STECs) and the corresponding ionospheric spatial gradients between base station and rover (RTK UAV) receivers among the studied multi-constellation systems. The results show that the STEC values estimated from IGS-GIM are larger than those computed by Klobuchar model. Such kind of gradient variation cannot show a perfect correlation due to limited accuracy of Klobuchar model parameters. As for our analysis, the ionospheric spatial gradients estimated from GPS satellites are higher than those calculated from GLONASS and Galileo satellites due to the smallest differences between the two successive positions of flying rover estimated from GPS satellites. The outcomes from this study complement the multi-GNSS cooperative strategy for monitoring ionospheric gradients, thereby mitigating the adverse effects in dynamic positioning and navigation solutions over low-latitude regions.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 1","pages":"Pages 331-343"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528746","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":"Flight demonstration of a CubeSat Sporadic-E detector","authors":"Bruce A. Fritz,&nbsp;Kenneth F. Dymond,&nbsp;Ellen J. Wagner,&nbsp;Andrew C. Nicholas,&nbsp;Scott A. Budzien,&nbsp;Andrew W. Stephan","doi":"10.1016/j.asr.2025.12.017","DOIUrl":"10.1016/j.asr.2025.12.017","url":null,"abstract":"<div><div>The Triple Magnesium Ionospheric Photometer (Tri-MIP) has been developed as a 1U CubeSat-compatible sensor to detect a mid-ultraviolet (MUV), Mg⁺ doublet emission near 280 nm, produced by solar resonant excitation, as a tracer of dayside Sporadic-E (Es). The initial flight of Tri-MIP on the Slingshot-1 spacecraft launched into a circular orbit at 500 km altitude and 45<span><math><mrow><mi>°</mi></mrow></math></span> inclination. Paired with a 1U scanning ultraviolet mirror (SUVM) on the Slingshot-1 spacecraft, Tri-MIP provides altitude profiles of Mg⁺ airglow emissions through limb scans of Earth’s ionosphere along the wake direction of the orbit. Tri-MIP is especially well-suited for Es detection and may allow for the observation of faint signatures of Es that are otherwise not visible with other commonly used remote plasma detection methods such as ground-based ionosondes or GPS radio occultation experiments. This manuscript presents a highlight of results from a year of on-orbit operation for the Slingshot-1 Tri-MIP. The following results demonstrate a new technological development that could provide future insight into Es formation or dynamics.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 1","pages":"Pages 393-399"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528297","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":"Recent developments in equatorial, low- and mid-latitude mesosphere, thermosphere and ionosphere studies","authors":"Venkatesh Kavutarapu (Guest Editor),&nbsp;Michael Pezzopane (Guest Editor)","doi":"10.1016/j.asr.2026.05.006","DOIUrl":"10.1016/j.asr.2026.05.006","url":null,"abstract":"","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 1","pages":"Pages 1-2"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528747","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 global neural network model of 3D ionospheric electron density based on COSMIC radio occultation measurements","authors":"Qihui Jiang,&nbsp;Shunzu Gao,&nbsp;Chao Xiong,&nbsp;Qian Pan,&nbsp;Yuyang Huang,&nbsp;Jie Wu,&nbsp;Hong Zhang","doi":"10.1016/j.asr.2025.12.011","DOIUrl":"10.1016/j.asr.2025.12.011","url":null,"abstract":"<div><div>Developing more accurate ionospheric empirical models, especially for representing the 3-dementional distribution of electron density (<em>Ne</em>), is important for both ionospheric studies and radio wave propagation applications. In this study, an accurate <em>Ne</em> profile model, named Deep Learning Ionosphere Model (DLIM) was built by utilizing over 3 million <em>Ne</em> profiles derived from radio occultation measured by the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC). We used the α-Chapman function with five parameters to describe each profile, including the peak density of the F2 layer (<em>NmF2</em>), the corresponding altitude of the peak (<em>hmF2</em>), scale height at F2 peak height (<em>H</em>₀), the scale height gradients <em>α</em>₁ (above and equals <em>hmF2</em>) and <em>α</em>₂ (below <em>hmF2</em>). We then built a sub-model based on deep convolutional neural network for each parameter, incorporating latitude, longitude, day-of-year (DoY), local time, solar activity indices, and geomagnetic indices as inputs. By combining these five sub-models, we constructed this DLIM for predicting the 3-D distributions of electron density. The model shows excellent agreement with observations from COSMIC-1 as well as the independent dataset from COSMIC-2. It also performances well in capturing the ionospheric climatology, such as solar activity dependence, local time variation, and global pattern, e.g., the equatorial ionization anomaly (EIA) and longitudinal four-wave patterns.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"78 1","pages":"Pages 344-360"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148528296","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}