This paper mainly examines the response of variation of the TEC, foF2, and hmF2 obtained from observations (GPS and digisondes) and models (IRI 2016 and IRI-Plas 2017) across low-to-high latitudes during various geomagnetic storm time conditions in different solar activity years. The 19 February 2014, 17 March 2015, and 4 November 2021 geomagnetic storm cases caused positive storm effects (particularly at low latitudes), while the 8 September 2017, and 26 August 2018 geomagnetic storm cases resulted in negative storm effects, especially at mid and high latitudes. Furthermore, during the 19 February 2014 storm, the sharp increase (peak) diurnal digisondes TEC values are observed, on average, when the hmF2 values reach about 360, 282, and 312 km, in the low, mid and high latitudes, respectively. During the 26 August 2018 storm, the peak TEC values are observed, on average, when the hmF2 values reach about 313, 258, and 268 km in the low, mid and high latitudes, respectively. Hence, the digisonde-derived peak TEC in mid latitudes typically coincides with a decrease in hmF2, while in low latitudes, it is associated with an increase in hmF2. Additionally, during low solar activity periods, digisonde-derived peak TEC values were observed when hmF2 decreased, contrasting with patterns seen during high solar activity. Both the IRI 2016 and IRI-Plas 2017 models performed well, with the models peak TEC values being observed when the hmF2 variations attain similar values with the observations, reinforcing the models' reliability in capturing ionospheric responses during geomagnetic storms.
{"title":"The geomagnetic storm time responses of the TEC, foF2, and hmF2 in different solar activity during solar cycle 24 and 25","authors":"Yekoye Asmare Tariku","doi":"10.1029/2024RS007961","DOIUrl":"https://doi.org/10.1029/2024RS007961","url":null,"abstract":"This paper mainly examines the response of variation of the TEC, foF2, and hmF2 obtained from observations (GPS and digisondes) and models (IRI 2016 and IRI-Plas 2017) across low-to-high latitudes during various geomagnetic storm time conditions in different solar activity years. The 19 February 2014, 17 March 2015, and 4 November 2021 geomagnetic storm cases caused positive storm effects (particularly at low latitudes), while the 8 September 2017, and 26 August 2018 geomagnetic storm cases resulted in negative storm effects, especially at mid and high latitudes. Furthermore, during the 19 February 2014 storm, the sharp increase (peak) diurnal digisondes TEC values are observed, on average, when the hmF2 values reach about 360, 282, and 312 km, in the low, mid and high latitudes, respectively. During the 26 August 2018 storm, the peak TEC values are observed, on average, when the hmF2 values reach about 313, 258, and 268 km in the low, mid and high latitudes, respectively. Hence, the digisonde-derived peak TEC in mid latitudes typically coincides with a decrease in hmF2, while in low latitudes, it is associated with an increase in hmF2. Additionally, during low solar activity periods, digisonde-derived peak TEC values were observed when hmF2 decreased, contrasting with patterns seen during high solar activity. Both the IRI 2016 and IRI-Plas 2017 models performed well, with the models peak TEC values being observed when the hmF2 variations attain similar values with the observations, reinforcing the models' reliability in capturing ionospheric responses during geomagnetic storms.","PeriodicalId":49638,"journal":{"name":"Radio Science","volume":"59 12","pages":"1-18"},"PeriodicalIF":1.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2024-12-31DOI: 10.1109/mnet.2024.3524611
Liyan Sui, Ke Zhang, Fan Wu, Xiaoyan Huang
{"title":"Large Models for Resource Allocation in Edge Computing Power Networks","authors":"Liyan Sui, Ke Zhang, Fan Wu, Xiaoyan Huang","doi":"10.1109/mnet.2024.3524611","DOIUrl":"https://doi.org/10.1109/mnet.2024.3524611","url":null,"abstract":"","PeriodicalId":55022,"journal":{"name":"IEEE Network","volume":"192 1","pages":""},"PeriodicalIF":9.3,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142908404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Link prediction infers the likelihood of a connection between two nodes based on network structural information, aiming to foresee potential latent relationships within the network. In social networks, nodes typically represent users, and links denote the relationships between users. However, some user nodes in social networks are hidden due to unknown or incomplete link information. The prediction of implicit links between these nodes and other user nodes is hampered by incomplete network structures and partial node information, affecting the accuracy of link prediction. To address these issues, this paper introduces an implicit link prediction algorithm based on extended social graph (ILP-ESG). The algorithm completes user attribute information through a multi-task fusion attribute inference framework built on associative learning. Subsequently, an extended social graph is constructed based on user attribute relations, social relations, and discourse interaction relations, enriching user nodes with comprehensive representational information. A semi-supervised graph autoencoder is then employed to extract features from the three types of relationships in the extended social graph, obtaining feature vectors that effectively represent the multidimensional relationship information of users. This facilitates the inference of potential implicit links between nodes and the prediction of hidden user relationships with others. This algorithm is validated on real datasets, and the results show that under the Facebook dataset, the algorithm improves the AUC and Precision metrics by an average of 5.17(%) and 9.25(%) compared to the baseline method, and under the Instagram dataset, it improves by 7.71(%) and 16.16(%), respectively. Good stability and robustness are exhibited, ensuring the accuracy of link prediction.
{"title":"Implicit link prediction based on extended social graph","authors":"Ling Xing, Jinxin Liu, Qi Zhang, Honghai Wu, Huahong Ma, Xiaohui Zhang","doi":"10.1007/s40747-024-01736-1","DOIUrl":"https://doi.org/10.1007/s40747-024-01736-1","url":null,"abstract":"<p>Link prediction infers the likelihood of a connection between two nodes based on network structural information, aiming to foresee potential latent relationships within the network. In social networks, nodes typically represent users, and links denote the relationships between users. However, some user nodes in social networks are hidden due to unknown or incomplete link information. The prediction of implicit links between these nodes and other user nodes is hampered by incomplete network structures and partial node information, affecting the accuracy of link prediction. To address these issues, this paper introduces an implicit link prediction algorithm based on extended social graph (ILP-ESG). The algorithm completes user attribute information through a multi-task fusion attribute inference framework built on associative learning. Subsequently, an extended social graph is constructed based on user attribute relations, social relations, and discourse interaction relations, enriching user nodes with comprehensive representational information. A semi-supervised graph autoencoder is then employed to extract features from the three types of relationships in the extended social graph, obtaining feature vectors that effectively represent the multidimensional relationship information of users. This facilitates the inference of potential implicit links between nodes and the prediction of hidden user relationships with others. This algorithm is validated on real datasets, and the results show that under the Facebook dataset, the algorithm improves the AUC and Precision metrics by an average of 5.17<span>(%)</span> and 9.25<span>(%)</span> compared to the baseline method, and under the Instagram dataset, it improves by 7.71<span>(%)</span> and 16.16<span>(%)</span>, respectively. Good stability and robustness are exhibited, ensuring the accuracy of link prediction.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"178 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/jiot.2024.3524389
Jinghong Tan, Zhian Liu, Kun Guo, Mingxiong Zhao
{"title":"Long-Term Client Selection for Federated Learning With Non-IID Data: A Truthful Auction Approach","authors":"Jinghong Tan, Zhian Liu, Kun Guo, Mingxiong Zhao","doi":"10.1109/jiot.2024.3524389","DOIUrl":"https://doi.org/10.1109/jiot.2024.3524389","url":null,"abstract":"","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"202 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142908395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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