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\begin{document}
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\title{m-NLP inference models using simulation and regression techniques}



\author[1,1,1]{Guangdong Liu}%
\author[2,2,2]{Sigvald Marholm}%
\author[3,3,3]{Anders Eklund}%
\author[4,4,4]{Lasse Boy Novock Clausen}%
\author[1,1,1]{Richard Marchand}%
\affil[1]{University of Alberta}%
\affil[2]{University Of Oslo}%
\affil[3]{SINTEF Industry}%
\affil[4]{University of Oslo}%


\vspace{-1em}



  
  \date{May 08, 2026}


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\begin{abstract}
Current inference techniques for processing multi-needle Langmuir Probe
(m-NLP) data are often based on adaptations of the Orbital
Motion-Limited (OML) theory which relies on several simplifying
assumptions. Some of these assumptions, however, are typically not well
satisfied in actual experimental conditions, thus leading to
uncontrolled uncertainties in inferred plasma parameters. In order to
remedy this difficulty, three-dimensional kinetic particle in cell
simulations are used to construct a synthetic data set, which is used to
compare and assess different m-NLP inference techniques. Using a
synthetic data set, regression-based models capable of inferring
electron density and satellite potentials from 4-tuples of currents
collected with fixed-bias needle probes similar to those on the NorSat-1
satellite, are trained and validated. The regression techniques
presented show promising results for plasma density inferences with RMS
relative errors less than 20 \%, and satellite potential inferences with
RMS errors less than 0.2 V for potentials ranging from -6 V to -1 V. The
new inference approaches presented are applied to NorSat-1 data, and
compared with existing state-of-the-art inference techniques.%
\end{abstract}%



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