Scientific Objectives and Mission Design of Ionospheric Anomaly Monitoring by Magnetometer And Plasma-Probe (IAMMAP) for a Sounding Rocket in Low-Altitude Ionosphere
Bibliographic record
Abstract
Sounding rockets are cost-effective and rapidly deployable tools for directly exploring the ionosphere and microgravity environments. These rockets achieve their target altitudes quickly and are equipped with various scientific instruments to collect real-time data. Perigee Aerospace plans its inaugural test launch in the first half of 2024, followed by a second performance test launch in January 2025. The second launch, scheduled off the coast of Jeju Island, aims to reach an altitude of approximately 150 km with a payload of 30 kg, conducting various experiments in the suborbital region. Particularly in mid-latitude regions, the ionosphere sporadically exhibits increased electron densities in the sporadic E layers and magnetic fluctuations caused by the equatorial electrojet. To measure these phenomena, the sounding rocket version of ionospheric anomaly monitoring by magnetometer and plasma-probe (IAMMAP), currently under development at the KAIST Satellite Research Center, will be onboard. This study focuses on enhancing our understanding of the mid-latitude ionosphere and designing observable missions for the forthcoming performance tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".