Integrated direct RF sampling front-end for VHF avionics systems
Bibliographic record
Abstract
For a market sector where size, weight, power and cost (SWaP-C) optimization is crucial, specifically with the proliferation of unmanned aerial systems, this paper proposes the minimization of SWaP-C requirements through a Direct RF Sampling (DRFS) approach. This work focuses on the integration of avionics systems operating at the VHF band, i.e. VOR, ILS (LOC and GS) and aeronautical communications (voice, ACARS, VDL, etc.). The scope of this work is to present a feasibility study of the proposed integrated avionics. Several factors must be taken into account: First, the selection of a best sampling frequency is a key point in the design of the system. Two approaches to sampling frequency selection are considered: 1) static, whose aim is to digitize and lock the frequency bands fully; and 2) dynamic, where only the occupied channels are sampled without aliasing. The second important factor to be considered in addition to sampling frequency refers to the maximum dynamic range of the system. The dynamic range of the Analog to Digital Converter (ADC) has to be large enough to receive without distortion the most and the least powerful signals at the antenna. Finally, this work studies the digital down-converter (DDC) architecture to be hardware implemented in a Field Programmable Gate Array (FPGA), and quantitatively analyzes the resources required for its implementation.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".