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Record W4387957618 · doi:10.23977/cpcs.2023.070111

Various errors and correction methods affecting the positioning accuracy of satellite navigation

2023· article· en· W4387957618 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueComputing Performance and Communication systems · 2023
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsnot available
Fundersnot available
KeywordsSatelliteComputer scienceRemote sensingEphemerisGeodesyGlobal Positioning SystemTroposphereMeteorologyGeologyTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

This paper will briefly introduce the main methods to reduce or eliminate various errors at present. The main body of the paper is to introduce and discuss the correction methods of satellite ephemeris error and satellite clock difference, ionospheric delay, multipath effect, antenna phase center eccentricity correction, relativistic effect, tide correction and tropospheric path delay error which affect the accuracy of satellite navigation and positioning. In addition to the non-dispertic medium, the degree of change of tropospheric medium with time is relatively drastic, and it is difficult to use a simple and unified mathematical model to determine, especially in the long relative positioning of the baseline, the tropospheric delay between the two stations often shows a weak spatial correlation. Therefore, it is of great significance to establish a real-time tropospheric delay model in a certain region to improve the accuracy of satellite 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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.297
Teacher spread0.276 · 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