A Novel Multi-Dimensional Spectrum Estimation Technique using Antenna Array Displacement
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
Conventionally, in multi-dimensional spectral estimation techniques, each data snapshot in space is captured simultaneously. All antenna elements or sensors being used to collect data are sampled at the same time. By doing so, the size of the antenna array is proportional to the area of interest in space. The antenna array is prohibitively huge if the area that we want to cover is large. In this paper, in order to reduce the number of antenna elements in use, we propose a novel multi-dimensional spectrum estimation technique based on displacing small antenna arrays along predefined paths. It includes a data measurement technique which sequentially collects data samples within each snapshot in space according to a predefined order, and a spectral estimation technique which is based on the Discrete Fourier Transform (DFT) of the collected data. The key idea is to create a large synthetic antenna aperture by displacing a small antenna array along a predefined trajectory. Impinging waves are assumed uniform plane waves. The performance of the proposed technique is evaluated by simulation. The applications of the proposed technique include synthetic aperture radar, radar image processing and sonar 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 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 | 0.000 |
| 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