Power Spectral Characteristics of In‐Situ Irregularities and Topside GPS Signal Intensity at Low Latitudes Using High‐Sample‐Rate Swarm Echo (e‐POP) Measurements
Bibliographic record
Abstract
Abstract Ionospheric density structures at low latitudes range in size from thousands of kilometers down to a few meters. Radio frequency (RF) signals, such as those from global navigation satellite systems, that propagate through irregularities suffer from rapid fluctuations in phase and intensity, known as scintillations. In this study, we use the high‐sample‐rate measurements of the Swarm Echo (CASSIOPE/e‐POP) satellite's GPS Occultation (GAP‐O) receiver taken after its antenna was re‐oriented to vertical‐pointing, simultaneously with e‐POP Ion Mass Spectrometer surface current observations as a proxy for plasma density, to obtain the spectral characteristics of GPS signal intensity and in‐situ irregularities at altitudes from 350 to 1,280 km. We show that the power spectra of both measurements can generally be characterized by a power law. In the case of density irregularities, the spectral index with the highest occurrence rate is around 1.7, which is consistent with previous studies. Also, all the power spectra of GPS signal intensity in this study show a single spectral index near 2. Moreover, roll‐off frequencies estimated in this work range from 0.4 to 2.5 Hz, which is significantly higher than Fresnel frequencies calculated from ground GPS receivers at low latitudes (between 0.2 and 0.45 Hz). Part of this increase is due to the 8 km/s orbital velocity of Swarm Echo near perigee. Another key difference is that variations in the GPS signals in this study are dominated by the topside ionosphere, whereas GPS signals received from ground are affected mostly by the relatively dense F‐region plasma in the 250–350 km altitudinal range.
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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".