PORTO @ 
Archivio Istituzionale della Ricerca
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishIn this paper, we present a bagged tree model able to detect phase scintillation at high latitudes with 95% accuracy, 5% scintillation miss-detection and 5% scintillation false alarm. The input to the model is a series of 3 minutes of the Total Electron Content (TEC), 3 minutes of the change in TEC (dTEC), and the satellite elevation. These values are extracted from Ionospheric Scintillation Monitoring Records (ISMR) logged by Ionospheric Scintillation Monitoring (ISM) receivers. We compare the performance of this model to Support Vector Machine (SVM) models, k-Nearest Neighbors (k-NN) models, and also to other decision tree models. Furthermore, we assess the ability of the TEC and dTEC features to detect scintillation independently of the scintillation indexes. For this, we compare the above decision trees, kNN and SVM models to the same models but trained using scintillation indexes as additional inputs. Moreover, we show the results of testing the proposed model using a novel data set. Finally, we compare the accuracy of the machine learning model to the performance of a detector based on the phase scintillation index σ ϕ threshold.
Detecting Phase Scintillation at High Latitudes Using Ionospheric Scintillation Monitoring Records and Machine Learning Techniques / Imam, Rayan; Savas, Caner; Dovis, Fabio. - ELETTRONICO. - (2021), pp. 37-42. ((Intervento presentato al convegno 2021 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE) tenutosi a Cleveland, OH, USA nel 12-14 Oct. 2021 [10.1109/WiSEE50203.2021.9613840].
| Titolo: | Detecting Phase Scintillation at High Latitudes Using Ionospheric Scintillation Monitoring Records and Machine Learning Techniques | |
| Autori: | ||
| Data di pubblicazione: | 2021 | |
| Abstract: | In this paper, we present a bagged tree model able to detect phase scintillation at high latitude...s with 95% accuracy, 5% scintillation miss-detection and 5% scintillation false alarm. The input to the model is a series of 3 minutes of the Total Electron Content (TEC), 3 minutes of the change in TEC (dTEC), and the satellite elevation. These values are extracted from Ionospheric Scintillation Monitoring Records (ISMR) logged by Ionospheric Scintillation Monitoring (ISM) receivers. We compare the performance of this model to Support Vector Machine (SVM) models, k-Nearest Neighbors (k-NN) models, and also to other decision tree models. Furthermore, we assess the ability of the TEC and dTEC features to detect scintillation independently of the scintillation indexes. For this, we compare the above decision trees, kNN and SVM models to the same models but trained using scintillation indexes as additional inputs. Moreover, we show the results of testing the proposed model using a novel data set. Finally, we compare the accuracy of the machine learning model to the performance of a detector based on the phase scintillation index σ ϕ threshold. | |
| ISBN: | 978-1-6654-0371-9 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| WiSEE21_conferenceFullPaper_pdfexpress.pdf | Final version of the Submitted manuscript. | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
| Detecting_Phase_Scintillation_at_High_Latitudes_Using_Ionospheric_Scintillation_Monitoring_Records_and_Machine_Learning_Techniques.pdf | Online version | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2951313
Copyright © 2022