MétaCan
Menu
Back to cohort
Record W2900863526 · doi:10.1002/navi.250

GPS IIR-M L1 Transmit Power Redistribution: Analysis of GNSS Receiver and High-Gain Antenna Data

2018· article· en· W2900863526 on OpenAlexafffund
Steffen Thoelert, André Hauschild, Peter Steigenberger, Richard B. Langley, Felix Antreich

Bibliographic record

VenueNAVIGATION Journal of the Institute of Navigation · 2018
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGNSS applicationsIntermodulationGlobal Positioning SystemEffective radiated powerComputer scienceRepeater (horology)Geodetic datumAntenna (radio)Transmitter power outputTelecommunicationsRemote sensingElectronic engineeringGeodesyEngineeringTransmitterBandwidth (computing)GeographyAmplifier

Abstract

fetched live from OpenAlex

All seven operational GPS Block IIR-M satellites experienced short maintenance periods in February 2017. It was later identified that the satellites' transmit power of different L1 signal components had been changed during this maintenance. The total radiated power of the satellites remains constant, but the powers of the C/A-code and the P(Y)-code signals have increased while the powers of the M-code and the intermodulation product have reduced. Further examination reveals a more efficient use of the total available power on the spacecraft as more power is available for the three navigation signals on L1. Observations from geodetic GNSS receivers were analyzed to demonstrate the effect of the change in L1 signal power distribution on the measured C/N0 of the C/A-code and P(Y)-code for different receiver types. High-gain antenna data collected before and after the maintenance periods are used to analyze improved utilization of the available power through the reduction of the losses caused by the intermodulation product. © 2018 Institute of Navigation

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)0.001
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 score0.373
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.255
Teacher spread0.237 · 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

Citations21
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueNAVIGATION Journal of the Institute of NavigationSame topicGNSS positioning and interferenceFrench-language works237,207