[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123350-en":3,"doc-seo-123350-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123350,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","High-Latitude Ionospheric Irregularities - Characterized Through Machine Learning Methods - Dissertation","Doctoral dissertation on high-latitude ionospheric irregularities and how machine learning methods can characterize them. The work is presented as a PhD thesis in Engineering Physics at Embry-Riddle Aeronautical University, with committee direction and formal approval documented. Support details include an NSF grant, and acknowledgments recognize contributions from committee members and collaborating scientists. The manuscript positions atmospheric/ionospheric research questions within a data-driven analytical framework using advanced computational learning approaches.","Doctoral Dissertations and Master's Theses  \nSpring 2025  \nHigh-Latitude Ionospheric Irregularities Characterized Through Machine Learning Methods  \nAnna-Marie Bals  \nEmbry-Riddle Aeronautical University, [balsa@my.erau.edu](balsa@my.erau.edu)  \nFollow this and additional works at: [https://commons.erau.edu/edt](https://commons.erau.edu/edt)  \n Part of the Engineering Physics Commons, Other Physics Commons, and the Plasma and Beam Physics Commons  \nScholarly Commons Citation  \nBals, Anna-Marie, \"High-Latitude Ionospheric Irregularities Characterized Through Machine Learning Methods\" (2025) . Doctoral Dissertations and Master 's Theses. 900.  \n[https://commons.erau.edu/edt/900](https://commons.erau.edu/edt/900)  \nThis Dissertation-Open Access is brought to you for free and open access by Scholarly Commons. It has been accepted for inclusion in Doctoral Dissertations and Master's Theses by an authorized administrator of Scholarly Commons. For more information, please contact [commons@erau.edu](commons@erau.edu).  \nHIGH-LATITUDE IONOSPHERIC IRREGULARITIES  \nCHARACTERIZED THROUGH MACHINE LEARNING METHODS  \nBy  \nA. MARIE BALS  \nA dissertation submitted in partial fulfillment of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nEMBRY-RIDDLE AERONAUTICAL UNIVERSITY College of Arts and Sciences  \nAPRIL 2025  \n© Copyright by A. MARIE BALS, 2025 All Rights Reserved  \n© Copyright by A. MARIE BALS, 2025 All Rights Reserved  \nHIGH-LATIUDE IONOSPHERIC IRREGULARITIES CHARACTERIZED THROUGH MACHINE LEARNING METHODS  \nBy  \nAnna-Marie Bals  \nThis Dissertation was prepared under the direction of the candidate’s Dissertation Committee Chair, Dr. Kshitija B. Deshpande, and has been approved by the members of the Dissertation committee. It was submitted to the College of Arts and Sciences and was accepted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Engineering Physics  \nKshitija B. Deshpande, Ph.D.  \nMatthew Zettergren  \nCommittee Chair  \nDigitally signed by Matthew Zettergren  \nDate: 2025.04.23 15:15:00-04'00'  \n\n| Matthew D. Zettergren, Ph.D. Committee Member\u003Cbr> |  |\n| --- | --- |\n| Shantanab Debchoudhury, Ph.D. Committee Member |  |\n| Edwin\u003Cbr>Mierkiewicz | \u003Cbr>Digitally signed by Edwin Mierkiewicz\u003Cbr>Date: 2025.04.23 17:26:48-04'00' |\n| Edwin J. Mierkiewicz, Ph.D. Graduate Program Chair, Ph.D. Engineering Physics\u003Cbr>\u003Cbr>Digitally signed by Peter\u003Cbr>Peter Hoffmann Hoffmann\u003Cbr>Date: 2025.04.24 16:17:29\u003Cbr>-04'00' |  |\n\nPeter M. Hoffmann, Ph.D.  \nDean, College of Arts and Sciences  \n􀀆􀀌􀀃􀀂 􀀊􀀉􀀈􀀄􀀆􀀅􀀑􀀒􀀎􀀏􀀓􀀎􀀑􀀏􀀑􀀔 􀀐􀀖􀀍􀀔􀀓􀀍􀀐􀀐􀀄􀀇􀀋􀀕􀀏􀀑􀀍􀀏􀀏  \nLuca Spogli, Ph.D. Committee Member  \nJohn Hughes 2025.04.23 20:55:26-04'00'  \nJohn M. Hughes, Ph.D.  \nDepartment Chair, Physical Sciences  \nACKNOWLEDGMENT  \nThis work was supported by Dr. Deshpande’s NSF Grant AGS 1848207 .  \nI recently came across a study concluding that teachers enforce learning in their students only up to 40 % by their words. This is certainly true for my Ph.D. advisor, Dr. Kshitija Deshpande, who offered me this unique opportunity on the verge of a global pandemic. Despite all obstacles, unusual circumstances, and uncertain and heartbreaking events, and fully virtually-she motivated and challenged me to become the best researcher I could, guided me through my first academic years, and was patient with my initial writing attempts. Her professionalism and care have left a lasting mark, even more so because she showed me how to manage several big roles at once, most notably as a scientist AND a Mum.  \nThe research giants who offered me invaluable support are: Dr. Shantanab Debchoudhury, who always takes time for a beverage of choice to explain, discuss, and share wise and handson advice whenever needed-life and science alike. He is also brave enough to try Bavarian food. Dr. Matt Zettergren, brave enough to be a Red Sox fan, for briefly describing to me his bird’s eye view on our field and the possibilities in the future and for initiating many discussions tha","cbCaibW7c6NUygBT","https://ap.wps.com/l/cbCaibW7c6NUygBT","pdf",25808001,1,211,"English","en",105,"# Acknowledgment\n## Funding and support\n## Committee and collaborators","[{\"question\":\"What is the dissertation topic?\",\"answer\":\"The dissertation examines high-latitude ionospheric irregularities and characterizes them using machine learning methods.\"},{\"question\":\"Where was the dissertation submitted and under what degree?\",\"answer\":\"It was submitted to Embry-Riddle Aeronautical University’s College of Arts and Sciences in partial fulfillment of the requirements for a Doctor of Philosophy in Engineering Physics.\"},{\"question\":\"Who supported and guided the research?\",\"answer\":\"The work was supported through an NSF grant and directed by the dissertation committee chair, Dr. Kshitija B. Deshpande, with additional committee and collaborator contributions acknowledged.\"}]","High-Latitude Ionospheric Irregularities - Characterized Through Machine Learning Methods - Dissertation | PDF",1785816072,532,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"high-latitude-ionospheric-irregularities-characterized-through-machine-learning-methods-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/high-latitude-ionospheric-irregularities-characterized-through-machine-learning-methods-dissertation/123350/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the dissertation topic?","Question",{"text":75,"@type":76},"The dissertation examines high-latitude ionospheric irregularities and characterizes them using machine learning methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Where was the dissertation submitted and under what degree?",{"text":80,"@type":76},"It was submitted to Embry-Riddle Aeronautical University’s College of Arts and Sciences in partial fulfillment of the requirements for a Doctor of Philosophy in Engineering Physics.",{"name":82,"@type":73,"acceptedAnswer":83},"Who supported and guided the research?",{"text":84,"@type":76},"The work was supported through an NSF grant and directed by the dissertation committee chair, Dr. Kshitija B. 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