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The gridded retarding ion drift sensor for the petitSat cubeSat mission.
Davidson, R L; Oborn, B; Robertson, E F; Noel, S; Earle, G D; Green, J; Kramer, J.
  • Davidson RL; Electrical and Computer Engineering, Utah State University, Logan, Utah 84322, USA.
  • Oborn B; Electrical and Computer Engineering, Utah State University, Logan, Utah 84322, USA.
  • Robertson EF; Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA.
  • Noel S; Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA.
  • Earle GD; Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA.
  • Green J; Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA.
  • Kramer J; Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA.
Rev Sci Instrum ; 91(6): 064502, 2020 Jun 01.
Article en En | MEDLINE | ID: mdl-32611012
ABSTRACT
The Gridded Retarding Ion Drift Sensor (GRIDS) is a small sensor that will fly on the 6 U petitSat CubeSat. It is designed to measure the three-dimensional plasma drift velocity vector in the Earth's ionosphere. The GRIDS also supplies information about the ion temperature, ion density, and the ratio of light to heavy ions present in the ionospheric plasma. It utilizes well-proven techniques that have been successfully validated by similar instruments on larger satellite missions while meeting CubeSat-compatible requirements for low mass, size, and power consumption. GRIDS performs the functions of a Retarding Potential Analyzer (RPA) and an Ion Drift Meter (IDM) by combining the features of both types of instruments in a single package. The sensor alternates RPA and IDM measurements to produce the full set of measurement parameters listed above. On the petitSat mission, GRIDS will help identify and characterize a phenomenon known as plasma blobs (or enhancements).

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2020 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2020 Tipo del documento: Article