A miniature, low‐power scientific fluxgate magnetometer: A stepping‐stone to cube‐satellite constellation missions
Bibliographic record
Abstract
Abstract Difficulty in making low noise magnetic measurements is a significant challenge to the use of cube‐satellite (CubeSat) platforms for scientific constellation class missions to study the magnetosphere. Sufficient resolution is required to resolve three‐dimensional spatiotemporal structures of the magnetic field variations accompanying both waves and current systems of the nonuniform plasmas controlling dynamic magnetosphere‐ionosphere coupling. This paper describes the design, validation, and test of a flight‐ready, miniature, low‐mass, low‐power, and low‐magnetic noise boom‐mounted fluxgate magnetometer for CubeSat applications. The miniature instrument achieves a magnetic noise floor of 150–200 pT/√Hz at 1 Hz, consumes 400 mW of power, has a mass of 121 g (sensor and boom), stows on the hull, and deploys on a 60 cm boom from a three‐unit CubeSat reducing the noise from the onboard reaction wheel to less than 1.5 nT at the sensor. The instrument's capabilities will be demonstrated and validated in space in late 2016 following the launch of the University of Alberta Ex‐Alta 1 CubeSat, part of the QB50 constellation mission. We illustrate the potential scientific returns and utility of using a CubeSats carrying such fluxgate magnetometers to constitute a magnetospheric constellation using example data from the low‐Earth orbit European Space Agency Swarm mission. Swarm data reveal significant changes in the spatiotemporal characteristics of the magnetic fields in the coupled magnetosphere‐ionosphere system, even when the spacecraft are separated by only approximately 10 s along track and approximately 1.4° in longitude.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".