Impact of Plasma Sheath on Rocket-based E-region Ion Measurements
Bibliographic record
Abstract
We model the particle velocity distribution functions around the entrance window of the Suprathermal Ion Imager (SII). The SII sensor was mounted on a 1 m boom carried by the scientific payload of NASA rocket 36.234 as part of Joule II mission to investigate Joule heating in the E-region ionosphere. The rocket flew above Northern Alaska on 19 January 2007. The payload was spin-stabilized with a period of 1.6 s, giving an apparent rotation of the ion flow velocity in the frame of reference of the payload. The SII sensor is an electrostatic analyzer that measures two dimensional slices of the distribution of the kinetic energies and arrival-angles of low energy ions. The study is concerned with the interpretation of data obtained from the SII sensor. For this purpose, we numerically investigate ram velocity effects on ions velocity distributions in the vicinity of the SII sensor aperture at an altitudes of approximately 150 km. The electrostatic sheath profiles surrounding the SII sensor, boom and payload are calculated numerically with the PIC code PTetra. It is observed that the direction of the ion flow velocity modifies the plasma sheath potential profile. This in turn impacts the velocity distributions of NO+ and $\mathrm{O}_{2}^{+}$ ions at the aperture of the particle sensor. The velocity distribution functions at the sensor aperture are calculated by using test-particle modeling. These particle distribution functions are then used to inject particles in the sensor, and calculate the fluxes on the sensor microchannel plate (MCP), from which comparisons with the measurements can be made.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".