Performance Analysis of a Portable Low-Cost SDR-Based Ionosonde
Bibliographic record
Abstract
This work presents a software-defined radio ionosonde (ISDR) developed at the Abdus Salam International Centre for Theoretical Physics (Italy) and the Institute of Radio Astronomy (Ukraine) and installed at the Ukrainian Antarctic Station in 2017. For the first time, the results of the long-term data comparison of the ISDR with the conventional ionosonde IPS-42 produced by KEL Aerospace are presented and discussed. The matching of the ionograms obtained during the whole year of 2021, as well as a comparison of the critical frequencies and virtual heights of F, E, and Es layers manually scaled from the ionograms showed that the ISDR has a similar level of performance to IPS-42. At the same time, the ISDR is a more versatile instrument that supports a bistatic operation, provides Doppler measurements and polarization information, and has a significantly lower cost and transmission power. Different configurations of the ISDR are considered. The basic configuration allows for using the ISDR as a conventional vertical ionospheric sounder. An enhanced configuration of the ISDR allows for oblique sounding, as well as polarization information that enables the O- and X-propagation modes of the ionospheric signal to be distinguished. The enhanced passive version of the ISDR was successfully tested onboard the research vessel “Noosfera” on distances up to 1,400 km from the transmitting ISDR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".