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Method for assessing interference immunity of special-purpose radio communication systems using artificial intelligence theory

2025· article· en· W4413956302 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.

fundA Canadian funder is recorded on the work.
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

VenueScienceRise · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Signal Processing Techniques
Canadian institutionsnot available
FundersFederation for the Humanities and Social Sciences
KeywordsInterference (communication)Computer scienceCommunication theoryArtificial intelligenceTelecommunicationsPsychologyCommunication

Abstract

fetched live from OpenAlex

The object of the research is special-purpose radio communication systems. Investigated problem: The experience of operations (combat operations) of recent years shows the growing role of information influence measures on the systems of collection, processing and transmission of special purpose information and decision-making officials. Classical approaches to armed conflict have proven incapable of achieving the objectives of military confrontation, even when possessing numerical superiority in conventional forces and means. This was demonstrated by the open military aggression of the Russian Federation during its full-scale invasion of Ukraine. One example of this is the use of group suppression of radio communication systems (RCS) by the enemy using electronic warfare (EW) tools, where two or more EW units suppress a single RCS receiver. Given the enemy’s frequency-energy, spatial, numerical, and technological superiority, maintaining reliable radio communication in electromagnetic warfare is only possible by seeking new technical solutions rather than combining classical technological approaches. This necessitates the implementation of various strategies to improve the convergence speed and accuracy of the main metaheuristic algorithms when processing heterogeneous data for evaluating the interference immunity state of special-purpose RCS. One way to enhance the processing speed of heterogeneous data for assessing the interference immunity state of special-purpose RCS using metaheuristic algorithms is to further improve them by integrating, comparing, and developing new procedures for their combined use. The main scientific results: The study proposed method for assessing interference immunity of special-purpose radio communication systems using artificial intelligence theory. The area of practical use of the research results: It is advisable to use the proposed scientific results when conducting research and development works on the creation of automated troop control systems, development of software for processing various types of data in special-purpose radio communication systems. Innovative technological product: new information technology for evaluation of noise immunity of special-purpose radio communication systems. Scope of the innovative technological product: software, special purpose systems, information and automated troop control 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.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.652
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.065
GPT teacher head0.385
Teacher spread0.320 · 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