@Article{GonzálezGonMenDomRos:2014:NoFlAn,
               author = "Gonz{\'a}lez, Arian Ojeda and Gonzalez, Walter Dem{\'e}trio and 
                         Mendes, Odim and Domingues, Margarete Oliveira and Rosa, Reinaldo 
                         Roberto",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Nonlinear fluctuation analysis for a set of 41 magnetic clouds 
                         measured by the Advanced Composition Explorer (ACE) spacecraft",
              journal = "Nonlinear Processes in Geophysics",
                 year = "2014",
               volume = "21",
               number = "5",
                pages = "1059--1073",
             abstract = "The statistical distribution of values in the signal and the 
                         autocorrelations (interpreted as the memory or persistence) 
                         between values are attributes of a time series. The 
                         autocorrelation function values are positive in a time series with 
                         persistence, while they are negative in a time series with 
                         anti-persistence. The persistence of values with respect to each 
                         other can be strong, weak, or nonexistent. A strong correlation 
                         implies a {"} memory{"} of previous values in the time series. The 
                         long-range persistence in time series could be studied using 
                         semivariograms, rescaled range, detrended fluctuation analysis and 
                         Fourier spectral analysis, respectively. In this work, persistence 
                         analysis is to study interplanetary magnetic field (IMF) time 
                         series.We use data from the IMF components with a time resolution 
                         of 16 s. Time intervals corresponding to distinct processes around 
                         41 magnetic clouds (MCs) in the period between March 1998 and 
                         December 2003 were selected. In this exploratory study, the 
                         purpose of this selection is to deal with the cases presenting the 
                         three periods: plasma sheath, MC, and post-MC. We calculated one 
                         exponent of persistence (e.g., ±, ², Hu, Ha) over the previous 
                         three time intervals. The persistence exponent values increased 
                         inside cloud regions, and it was possible to select the following 
                         threshold values:(±(j )i = 1.392, hHa(j )i = 0.327, and hHu(j 
                         )i=0.875. These values are useful as another test to evaluate the 
                         quality of the identification. If the cloud is well structured, 
                         then the persistence exponent values exceed thresholds. In 80.5% 
                         of the cases studied, these tools were able to separate the region 
                         of the cloud from neighboring regions. The Hausdorff exponent (Ha) 
                         provides the best results.",
                  doi = "10.5194/npg-21-1059-2014",
                  url = "http://dx.doi.org/10.5194/npg-21-1059-2014",
                 issn = "1023-5809",
                label = "scopus 2014-11 OjedaGonz{\'a}lezGonMenDomRos:2014:NoFlAn",
             language = "en",
        urlaccessdate = "28 abr. 2024"
}