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Record W7095771496

Prototype Galileo Receiver Development

2016· article· en· W7095771496 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsnot available
Fundersnot available
KeywordsBinary offset carrier modulationGalileo (satellite navigation)Field-programmable gate arrayGNSS applicationsSIGNAL (programming language)BasebandTransmission (telecommunications)Software
DOInot available

Abstract

fetched live from OpenAlex

Over the past few years the Galileo signal specification has been maturing. Of particular interest to receiver manufacturers is the Binary Offset Carrier (BOC) modulation format. In preparation for the transmission of the first Galileo signals from space, NovAtel has initiated development of their first prototype Galileo receiver, capable of tracking BOC signals. NovAtel is currently under contract with the Canadian Space Agency to develop an FPGA based hardware prototype of a Galileo BOC receiver. The receiver is based on NovAtel’s new L5, FPGA based precise positioning receiver. The receiver FPGA and software is configured to track the open access BOC signal that will be transmitted on the Galileo L1 frequency. To reduce hardware design costs for this pre-production development, the design will be demonstrated at the L5/E5a frequency. No commercial Galileo hardware simulators are currently available to test the receiver. NovAtel is therefore modifying an existing prototype L5 signal transmitter, developed by NovAtel for Zeta Associates of Fairfax, Virginia, to output a BOC(1,1) test signal. The current receiver/transmitter development effort will be discussed. An overview of the NovAtel FPGA based precise positioning receiver will be presented. The benefits of using the FPGA based receiver to verify the design will be presented, outlining how the receiver design team can handle changes in the signal specification.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.721
Threshold uncertainty score1.000

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)0.0010.001

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 head0.011
GPT teacher head0.189
Teacher spread0.178 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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