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Record W2885617765 · doi:10.1109/lgrs.2018.2856110

Measurement of the Ionospheric Reflection Height of an HF Wave in Vertical Incidence With a Resolution of Minutes

2018· article· en· W2885617765 on OpenAlexaboutno aff
Leonardo A. Aguero Guzman, E. Ovalle, R. Reeves

Bibliographic record

VenueIEEE Geoscience and Remote Sensing Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
FundersUniversidad de Chile
KeywordsIonosondeField-programmable gate arraySoftware-defined radioHigh frequencySoftwareIonosphereComputer scienceReflection (computer programming)SIGNAL (programming language)Remote sensingRadarRadio waveGeologyPhysicsTelecommunicationsEmbedded systemGeophysics

Abstract

fetched live from OpenAlex

In this letter, we present a prototype of an RF signal receiver operating in the HF band, whose design considers the use of software-defined radio signal processing technology, based on field-programmable gate arrays (FPGA) reconfigurable hardware and the use of gnuradio open software. The purpose of this letter is to improve the measurement rate at a fixed frequency of the reflection height, which is now obtained with a rate of 15 min using the IPS-42 ionosonde. The proposed method uses a pulse generated by the Canadian Advanced Digital Ionosonde as the transmitted signal. For the receiving section, the FPGA-based “Universal Software Radio Peripheral 1” was directly connected to a PC, where the return signals were analyzed by gnuradio. The measurements are taken with 1-min cadence, approximately, and are validated by comparing them with 15-min measurements taken with a colocated IPS-42 ionosonde. The acquisition rate of order 1 every minute is of interest in the study of a number of physical processes, i.e., traveling ionospheric disturbance, disturbances generated by seismic events, meteorological processes, and so on.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2018
Admission routes1
Has abstractyes

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