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Record W2593139211 · doi:10.1109/ibcast.2017.7868140

Contribution to develop a generic hybrid technique of satellite system for RFI geolocation

2017· article· en· W2593139211 on OpenAlexaff
Abulasad Elgamoudi, Aamir Shahzad, René Landry

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsGeolocationComputer scienceCommunications satelliteSatelliteBroadcasting (networking)WirelessTransmission (telecommunications)TelecommunicationsRadio frequencyRemote sensingInterference (communication)Electromagnetic interferenceRadio spectrumReal-time computingComputer networkEngineeringGeography

Abstract

fetched live from OpenAlex

In the current information age, most of the World-systems are setup, and are communicating wirelessly with each others through the uses of wireless media. Wireless based communications have been accounted as more efficient, faster, cost less and reliable ways to exchange information between the remote stations which may locate in the distance ranges of meters, kilometers and, in any part of the World via satellite transmission. As the satellite communication SATCOM applications and services, e.g., broadcasting and cellular communications and others, are numerous, and rapidly increasing day by day; thus to overcome the required demands to connect the distance located earth stations, number of satellites are in-functional, which, increasing the immersive conjunctions in available radio frequency RF spectrum, generating the issues of radio frequency inference RFI. In this study, we take a step of assessing the interferences sources, using of hybrid geolocation technique, which could happen at the receiver's side, based on the existing most-prominent interference detection scenarios, in SATCOM. While interferences sources were located successfully and accuracy derived, further these signals were characterized, and classification process was performed to identify the belonging interferences type through employed of classification tree method.

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: none
Teacher disagreement score0.959
Threshold uncertainty score0.323

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 studies0.0000.000
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 head0.232
Teacher spread0.219 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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