Time-Frequency Analysis and Type Identification of High-Density Communication Countermeasure Electronic Signals
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.
Bibliographic record
Abstract
With the development of communication technology, the electronic signals of communication countermeasures become more and more complex. Facing the increasingly complicated and changeable environment of such signals, the existing cluster analysis algorithms cannot identify the type of communication countermeasure electronic signals ideally. Therefore, this paper carries out the time-frequency analysis and type identification of high-density communication countermeasure electronic signals. The signals were analyzed in both time and frequency domains to extract the time frequency features of the signals more accurately. In this way, the authors captured the variation of non-stationary communication electronic signals in both time and frequency domains. Next, the pulse repetition interval (PRI) transform, a typical pulse repetition frequency (PRF) type identification algorithm, was modified, and applied to the type identification of communication electronic signals, followed by an illustration of the type identification of high-density communication countermeasure electronic signals. After that, several experiments were carried out to compare the absolute errors and relative errors of different time-frequency analysis methods, and contrast the effects of the original and modified PRI transforms, revealing the effectiveness of the proposed approach.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it