Modeling measurements of ionospheric density structures using the polarization of high‐frequency waves detected by the Radio Receiver Instrument on the enhanced Polar Outflow Probe
Bibliographic record
Abstract
The Cascade SmallSat and Ionospheric Polar Explorer (CASSIOPE) satellite is to be launched in late 2012. On board this satellite will be a suite of eight scientific instruments composing the enhanced Polar Outflow Probe (ePOP). The Radio Receiver Instrument (RRI) on ePOP will be used to receive high‐frequency (HF) (10–18 MHz) transmissions from ground transmitters such as the Super Dual Auroral Radar Network (SuperDARN) array. Modeling of the characteristics of the HF signal received at ePOP for various ionospheric conditions has been undertaken in preparation for this RRI‐SuperDARN experiment. The effect of ionospheric electron density enhancements and depletions on signal parameters such as polarization and mode delay difference has been modeled. It has been found that at HF the polarization state of the received signal is highly sensitive to regions of locally enhanced or depleted electron density in the ionosphere. In particular, analysis of the orientation angle of the received signal, which changes because of Faraday rotation as the spacecraft passes over a ground transmitter, will allow detection of small‐scale electron density structures (on the order of tens of kilometers) with electron densities as little as 10% different from background values. Because of the sensitivity of the polarization of HF transionospheric waves, the signatures of these small‐scale and relatively weak ionospheric density structures will be apparent. Larger and denser structures will also be detectable from both the polarization state and other signal parameters, such as signal delay. The modeling demonstrates that detailed analysis of the signal parameters received at the ePOP satellite will allow determination of the location, size, and density of structures in the ionosphere.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".