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Record W2951679364 · doi:10.1109/jlt.2019.2921865

A Multi-Antenna GNSS-Over-Fiber System for High Accuracy Three-Dimensional Baseline Measurement

2019· article· en· W2951679364 on OpenAlexaff
Xin Jiang, Xiangchuan Wang, Angran Zhao, Jianping Yao, Shilong Pan

Bibliographic record

VenueJournal of Lightwave Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of Ottawa
FundersNational Natural Science Foundation of China
KeywordsBaseline (sea)GNSS applicationsSatellite systemComputer scienceElectronic engineeringAntenna (radio)Accuracy and precisionGlobal Positioning SystemPhotonicsGroup delay and phase delayOpticsEngineeringTelecommunicationsPhysicsBandwidth (computing)

Abstract

fetched live from OpenAlex

A novel multi-antenna global navigation satellite system that uses fibers to transmit signals from multiple remote antennas to a local station with real-time microwave-photonics-based fiber length monitoring is proposed for high accuracy three-dimensional (3D) baseline measurement. In the proposed approach, microwave-photonics-based fiber length monitoring is employed to obtain the delay difference between the different GNSS channels. With the obtained delay difference information, we use the carrier-phase single-difference (SD) algorithm to calculate the 3D baseline, which is able to improve the vertical precision of the 3D baseline measurement as compared with the use of the carrier-phase double-difference (DD) algorithm. Experimental results show that the 3D baseline measurement precision using the SD algorithm is within 2 mm and the vertical positioning precision is improved by over three times compared with the approach using the conventional DD algorithm.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.019
GPT teacher head0.232
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2019
Admission routes1
Has abstractyes

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