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Record W4315648755 · doi:10.3390/atmos14010159

Performance Analysis of a Portable Low-Cost SDR-Based Ionosonde

2023· article· en· W4315648755 on OpenAlexaff
Anton Kashcheyev, Oleksandr Bogomaz, A. A. Sopin, Bogdan Gavrylyuk, A. V. Zalizovski

Bibliographic record

VenueAtmosphere · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of New Brunswick
FundersNational Academy of Sciences of Ukraine
KeywordsIonosondeRemote sensingIonosphereRadio SciencePolarization (electrochemistry)Computer sciencePhysicsGeologyMeteorologyGeophysics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.007
GPT teacher head0.223
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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