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@ARTICLE{Salazar:909650,
      author       = {Salazar, Luigui and Heuraux, Stéphane and Sabot, Roland
                      and Krämer-Flecken, Andreas},
      title        = {{E}xtraction of quasi-coherent modes based on reflectometry
                      data},
      journal      = {Plasma physics and controlled fusion},
      volume       = {64},
      number       = {10},
      issn         = {0032-1028},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {FZJ-2022-03318},
      pages        = {104007 -},
      year         = {2022},
      abstract     = {The identification of turbulence sources would drive to a
                      deeper understanding of confinement dynamics in tokamak
                      plasmas. Turbulence results from a mixture of instabilities
                      corresponding to sources at different timescales and spatial
                      scales. Using poloidal correlation reflectometry and
                      multi-pin Langmuir probe, it was shown in the T-10 and the
                      Tokamak Experiment for Technology Oriented Research (TEXTOR)
                      tokamaks that the reflectometry frequency spectrum is the
                      superposition of several components: broadband component,
                      quasi-coherent (QC) modes and low-frequency components. The
                      relevance of QC modes is associated with their link with the
                      trapped electron mode instability. This link was exhibited
                      in the transition from the linear ohmic confinement (LOC) to
                      the saturated ohmic confinement (SOC) regime. A method is
                      presented in this paper to extract the QC mode component
                      from the reflectometry data, enabling its separation from
                      the broadband component and the study of its time evolution.
                      It is a first step toward the discrimination of turbulence
                      sources. The central idea explores a way to combine the
                      approach of signal processing and machine learning. The
                      continuous wavelet transform on the basis of complex Morlet
                      wavelet has proved to be efficient in providing a
                      decomposition of a signal at different scales over time for
                      fluctuation tackling; clustering techniques, such as the
                      mini-batch K-means, are able to tackle clusters at different
                      scales. The method was applied to Tore Supra and TEXTOR
                      reflectometry data. In Tore Supra, the amplitude of the
                      extracted QC mode component decreases during the LOC–SOC
                      transition. In TEXTOR, the amplitude of the coherent spectra
                      of the extracted QC mode component is similar to the
                      experimental coherent spectra obtained through correlation
                      reflectometry. The developed method permits the extraction
                      of components, preserving their physical and statistical
                      properties.},
      cin          = {IEK-4},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-4-20101013},
      pnm          = {134 - Plasma-Wand-Wechselwirkung (POF4-134)},
      pid          = {G:(DE-HGF)POF4-134},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000850358100001},
      doi          = {10.1088/1361-6587/ac828a},
      url          = {https://juser.fz-juelich.de/record/909650},
}