Regional Mapping of Small‐Scale Equatorial Ionospheric Irregularities Using Swarm Echo Satellite Measurements
Bibliographic record
Abstract
Abstract We propose a novel approach to produce regional maps of small‐scale scintillation‐causing irregularities using a single satellite. To construct the maps, we employ several ionospheric GPS indices, including total electron content, high‐resolution ROTI, and S4, calculated from the Swarm Echo GPS Attitude, Positioning, and Profiling Experiment Occultation (GAP‐O) receiver with its antenna pointed upward. GAP‐O's high‐sample‐rate observations enable irregularities as small as 320 m to be resolved. We present two case studies in which we compare the maps with in situ measurements of irregularities and simultaneous vertical TEC maps obtained from the ground. In situ measurements of net current onto the external surface of the Imaging and Rapid‐scanning Ion Mass Spectrometer sensor on board Swarm Echo were utilized to quantify plasma density fluctuations. Then, we apply the method to synthetic data to illustrate the efficacy of the method. Modeling results show that the irregularity maps can determine the horizontal geo‐locations of small‐scale irregularities, though with significant uncertainties in the cross‐track direction (east‐west). As Swarm Echo traverses different altitudes, these maps provide additional information on the altitudinal distribution of plasma fluctuations. This technique facilitates a better understanding of the morphology of scintillation‐causing irregularities, which are challenging to map from ground‐based receiver arrays alone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".