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The energy dissipation in collisionless plasmas as the solar wind is not yet fully understood. The intermittent nature of magnetic structures appears to be a fundamental part of the energy cascade. Understanding energy transfer and dissipation in the solar wind requires an accurate description of its intermittency. Upcoming multi-spacecraft missions will provide new insight on this matter. However, the use of multi-point data requires developing new data analysis techniques as well as cross-validating these techniques. In this study, we address the latter and explore the intermittency in a 3D simulation of anisotropic plasma turbulence using two approaches. We implement the standard single-spacecraft partial variance increments technique as well as a multi-point partial variance increments technique. We contrast these two techniques and explore their dependence on the angle between the spacecraft-configuration travel direction and the background magnetic field. 

Unlike the vast majority of astrophysical plasmas, the solar wind is accessible to spacecraft, which for decades have carried in-situ instruments for directly measuring its particles and fields. Though such measurements provide precise and detailed information, a single spacecraft on its own cannot disentangle spatial and temporal fluctuations. Even a modest constellation of in-situ spacecraft, though capable of characterizing fluctuations at one or more scales, cannot fully determine the plasma’s 3-D structure. We describe here a concept for a new mission, the Magnetic Topology Reconstruction Explorer (MagneToRE), that would comprise a large constellation of in-situ spacecraft and would, for the first time, enable 3-D maps to be reconstructed of the solar wind’s dynamic magnetic structure. Each of these nanosatellites would be based on the CubeSat form-factor and carry a compact fluxgate magnetometer. A larger spacecraft would deploy these smaller ones and also serve as their telemetry link to the ground and as a host for ancillary scientific instruments. Such an ambitious mission would be feasible under typical funding constraints thanks to advances in the miniaturization of spacecraft and instruments and breakthroughs in data science and machine learning.