@InProceedings{RodriguesSaRoMaRoFlMa:2023:ChLe,
               author = "Rodrigues, Italo Pinto and Santos, Bruno Lima dos and Rodrigues, 
                         Gabriel Alberto and Matos, Jo{\~a}o Gabriel dos Santos Dias Moura 
                         and Rodrigues, Thales Lessa and Florencio, Ualison Silva and 
                         Matos, Wellington Pereira de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Centro 
                         Universit{\'a}rio de Volta Redonda} and {Centro 
                         Universit{\'a}rio de Volta Redonda} and {Centro 
                         Universit{\'a}rio de Volta Redonda} and {Centro 
                         Universit{\'a}rio de Volta Redonda} and {Centro 
                         Universit{\'a}rio de Volta Redonda} and {Centro 
                         Universit{\'a}rio de Volta Redonda}",
                title = "Experience Report on the Prototyping of a Mobile System for 
                         Geomagnetic Storm Forecasting: Challenge-Based Learning",
                 year = "2023",
               editor = "Souza, Felipe Santos and Fabbro, Maria Tereza and Panad{\'e}s 
                         Filho, Waldemar",
         organization = "Workshop em Engenharia e Tecnologia Espaciais, 14. (WETE)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Challenge-Based Learning, DSCOVR, Geomagnetic Storm.",
             abstract = "This study, conducted as part of the NASA Space Apps Challenge, 
                         exemplifies Challenge-Based Learning, allowing students to develop 
                         innovation skills while addressing the significant risks of 
                         geomagnetic storms to global electronic infrastructures, which 
                         have potential economic impacts of \$2.6 trillion. Utilizing a 
                         Long Short-Term Memory (LSTM) neural network, we analyzed data 
                         from the DSCOVR satellite, navigating its limitations. Our 
                         methodology entailed developing a Solar Classification (SC) index 
                         from the Z component of the magnetic field (Bz) to address data 
                         inconsistencies. We trained the LSTM with 80% of the refined 
                         dataset and validated it with the remaining 20%, achieving a Root 
                         Mean Square Error (RMSE) of 0.8724%. This research, arising from a 
                         collaborative and competitive educational setting, highlights the 
                         effectiveness of team-based approaches in tackling complex 
                         scientific challenges and demonstrates the potential of AI in 
                         improving space weather forecasting and enhancing public 
                         preparedness readiness.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos",
      conference-year = "6-8 dez. 2023",
                 issn = "2177-3114",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGPDW34R/4AG9C52",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34R/4AG9C52",
           targetfile = "12 - [Oral] [EXTERNO] Italo Rodrigues.pdf",
                 type = "CSE",
        urlaccessdate = "29 jun. 2024"
}