Method for assessing interference immunity of special-purpose radio communication systems using artificial intelligence theory
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".