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Efficiency Analysis of Control Procedures for Operation Systems of Navigation Equipment

2025· article· W7131316228 on OpenAlexaff
Oleksiy Zuiev, Oleksandr Solomentsev, Maksym Zaliskyi, Onyedikachi Chioma Okoro

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicMathematical Control Systems and Analysis
Canadian institutionsRaytheon Technologies (Canada)
Fundersnot available
KeywordsControl (management)AviationState (computer science)Air traffic controlAir navigationCivil aviationNavigation system

Abstract

fetched live from OpenAlex

Navigation equipment in civil aviation is used to provide members of flight activity with information about the coordinates and location of the aircraft. The technical state of navigation equipment affects the risks of air navigation services and air traffic control. Under the influence of various random factors, degradation of the technical state may occur. Therefore, for timely detection of degradation, it is necessary to use monitoring and control procedures. This paper is devoted to the analysis of the effectiveness of navigation equipment control procedures under conditions when a changepoint in the trend of the diagnostic parameter may occur. The main attention is paid to determining analytical equations for the indicators of the control procedure in case of a changepoint and multi-alternativeness during decision-making. The results of the study can be used when designing and modernizing navigation equipment operation systems.

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)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.280
Teacher spread0.269 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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