Bibliographic record
Abstract
Estimation of the sample covariance matrix is a challenge in array processing, particularly with large-aperture arrays operating in dynamic environments affected by fast maneuvering interferers and background noise. Minimizing the impact of time-dependent variations in the underlying signal statistics requires short observation intervals, thereby reducing the number of snapshots available for covariance estimation. Distinguishing between true variations in the received signal statistics and artifacts introduced by insufficient samples is still an open field of research. Recent developments in random matrix theory (RMT) have provided mathematical foundations to understand the behavior of sample eigenvalues and eigenvectors, and how they deviate from their population counterparts due to the lack of snapshot support. Similarly, expressions have been obtained to describe the “distance” between an initial eigenspace (spanned by p eigenvectors), relative to a subsequent eigenspace (spanned by q eigenvectors). In this paper, simulations corresponding to a horizontal array operating in a realistic environment are used to investigate RMT-based metrics that quantify time-dependent eigenspace stability. This research develops mathematically justifiable methods for proper data segmentation into intervals that exhibit local stationarity, providing data-driven higher bounds for the number of snapshots available for the computation of sample covariance matrices.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".