{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105832"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105832","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Decision tree application to satellite measurement and analysis of exospheric neutral densities","abstract":"There are generally two types of models to simulate space science parameters: physics-based models and statistics-based models. The first type of model makes predictions based on physical assumptions and mathematical expressions. The second type does so based on past data, applying linear regression algorithms along with polynomial and wavelet functions. The project investigates the mechanism of small-scale changes in near- Earth space, and the interaction between the charged particles of the solar wind and Earth’s magnetosphere. We have developed a decision tree-based machine learning model that has the capability to make predictions about various physical parameters of the Earth’s magnetosphere. The training data set is provided by the Cluster II mission from ESA, courtesy of Dr. Elena Kornberg. The Cluster II is a space mission of the European Space Agency (ESA) with NASA collaboration, comprising four satellites flying in a tetrahedral formation while collecting the most detailed data yet on small- scale changes in near-Earth space, and on the interaction between the charged particles of the solar wind and Earth’s magnetosphere for a continuous period of two solar cycles (22 years). We also investigate the role of neutral dynamics in the evolution of ring current and the terrestrial magnetosphere as a whole. We study the role of the geocoronal density distribution in the ring current loss by incorporating different geocoronal models with the Hot Electron and Ion Drift Integrator (HEIDI) model coupled with the Space Weather Modeling Framework (SWMF). This approach provides insight into the role of neutral constituents of Earth’s exosphere in the overall magnetosphere dynamics.","abstract_html":"There are generally two types of models to simulate space science parameters: physics-based models and statistics-based models. The first type of model makes predictions based on physical assumptions and mathematical expressions. The second type does so based on past data, applying linear regression algorithms along with polynomial and wavelet functions. The project investigates the mechanism of small-scale changes in near- Earth space, and the interaction between the charged particles of the solar wind and Earth’s magnetosphere. We have developed a decision tree-based machine learning model that has the capability to make predictions about various physical parameters of the Earth’s magnetosphere. The training data set is provided by the Cluster II mission from ESA, courtesy of Dr. Elena Kornberg. The Cluster II is a space mission of the European Space Agency (ESA) with NASA collaboration, comprising four satellites flying in a tetrahedral formation while collecting the most detailed data yet on small- scale changes in near-Earth space, and on the interaction between the charged particles of the solar wind and Earth’s magnetosphere for a continuous period of two solar cycles (22 years). We also investigate the role of neutral dynamics in the evolution of ring current and the terrestrial magnetosphere as a whole. We study the role of the geocoronal density distribution in the ring current loss by incorporating different geocoronal models with the Hot Electron and Ion Drift Integrator (HEIDI) model coupled with the Space Weather Modeling Framework (SWMF). This approach provides insight into the role of neutral constituents of Earth’s exosphere in the overall magnetosphere dynamics.","abstract_has_math":false,"creators":["Huang, Yu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Ilie, Raluca"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-11-26T20:49:32Z","date_published":"2019-11-26T20:49:32Z","updated_at":"2026-07-22T22:24:45Z","subjects":["Space Science, Machine Learning, Decision Tree, Magnetosphere, Ring Current, Geocorona"],"languages":["en"],"rights":["Copyright 2019 Yu Huang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105832","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ilie, Raluca"]},{"key":"dc:creator","label":"Author","values":["Huang, Yu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-11-26T20:49:32Z","2021-11-27T10:15:34Z","2019-07-16","2019-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Space Science, Machine Learning, Decision Tree, Magnetosphere, Ring Current, Geocorona"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Yu Huang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105832"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["There are generally two types of models to simulate space science parameters: physics-based models and statistics-based models. The first type of model makes predictions based on physical assumptions and mathematical expressions. The second type does so based on past data, applying linear regression algorithms along with polynomial and wavelet functions. The project investigates the mechanism of small-scale changes in near- Earth space, and the interaction between the charged particles of the solar wind and Earth’s magnetosphere. We have developed a decision tree-based machine learning model that has the capability to make predictions about various physical parameters of the Earth’s magnetosphere. The training data set is provided by the Cluster II mission from ESA, courtesy of Dr. Elena Kornberg. The Cluster II is a space mission of the European Space Agency (ESA) with NASA collaboration, comprising four satellites flying in a tetrahedral formation while collecting the most detailed data yet on small- scale changes in near-Earth space, and on the interaction between the charged particles of the solar wind and Earth’s magnetosphere for a continuous period of two solar cycles (22 years). We also investigate the role of neutral dynamics in the evolution of ring current and the terrestrial magnetosphere as a whole. We study the role of the geocoronal density distribution in the ring current loss by incorporating different geocoronal models with the Hot Electron and Ion Drift Integrator (HEIDI) model coupled with the Space Weather Modeling Framework (SWMF). This approach provides insight into the role of neutral constituents of Earth’s exosphere in the overall magnetosphere dynamics.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-08-01","The student, Yu Huang, accepted the attached license on 2019-07-16 at 11:38.","The student, Yu Huang, submitted this Thesis for approval on 2019-07-16 at 11:44.","This Thesis was approved for publication on 2019-07-16 at 14:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14344 on 2019-11-26 at 13:06:05","Made available in DSpace on 2019-11-26T20:49:32Z (GMT). No. of bitstreams: 2 HUANG-THESIS-2019.pdf: 9153876 bytes, checksum: 7345d252a92d29c9611234a892795ef4 (MD5) LICENSE.txt: 4205 bytes, checksum: 7159b6a7bce879437639089203855e76 (MD5) Previous issue date: 2019-07-16","Embargo set by: Seth Robbins for item 112977 Lift date: 2021-11-26T20:49:41Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 112977 on 2021-11-27T10:15:34Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Decision tree application to satellite measurement and analysis of exospheric neutral densities"]}]}],"canonical_facts":{"dc:contributor":["Ilie, Raluca"],"dc:creator":["Huang, Yu"],"dc:date":["2019-11-26T20:49:32Z","2021-11-27T10:15:34Z","2019-07-16","2019-08"],"dc:description":["There are generally two types of models to simulate space science parameters: physics-based models and statistics-based models. The first type of model makes predictions based on physical assumptions and mathematical expressions. The second type does so based on past data, applying linear regression algorithms along with polynomial and wavelet functions. The project investigates the mechanism of small-scale changes in near- Earth space, and the interaction between the charged particles of the solar wind and Earth’s magnetosphere. We have developed a decision tree-based machine learning model that has the capability to make predictions about various physical parameters of the Earth’s magnetosphere. The training data set is provided by the Cluster II mission from ESA, courtesy of Dr. Elena Kornberg. The Cluster II is a space mission of the European Space Agency (ESA) with NASA collaboration, comprising four satellites flying in a tetrahedral formation while collecting the most detailed data yet on small- scale changes in near-Earth space, and on the interaction between the charged particles of the solar wind and Earth’s magnetosphere for a continuous period of two solar cycles (22 years). We also investigate the role of neutral dynamics in the evolution of ring current and the terrestrial magnetosphere as a whole. We study the role of the geocoronal density distribution in the ring current loss by incorporating different geocoronal models with the Hot Electron and Ion Drift Integrator (HEIDI) model coupled with the Space Weather Modeling Framework (SWMF). This approach provides insight into the role of neutral constituents of Earth’s exosphere in the overall magnetosphere dynamics.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-08-01","The student, Yu Huang, accepted the attached license on 2019-07-16 at 11:38.","The student, Yu Huang, submitted this Thesis for approval on 2019-07-16 at 11:44.","This Thesis was approved for publication on 2019-07-16 at 14:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14344 on 2019-11-26 at 13:06:05","Made available in DSpace on 2019-11-26T20:49:32Z (GMT). No. of bitstreams: 2 HUANG-THESIS-2019.pdf: 9153876 bytes, checksum: 7345d252a92d29c9611234a892795ef4 (MD5) LICENSE.txt: 4205 bytes, checksum: 7159b6a7bce879437639089203855e76 (MD5) Previous issue date: 2019-07-16","Embargo set by: Seth Robbins for item 112977 Lift date: 2021-11-26T20:49:41Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 112977 on 2021-11-27T10:15:34Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/105832"],"dc:language":["en"],"dc:rights":["Copyright 2019 Yu Huang"],"dc:subject":["Space Science, Machine Learning, Decision Tree, Magnetosphere, Ring Current, Geocorona"],"dc:title":["Decision tree application to satellite measurement and analysis of exospheric neutral densities"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:45Z"}