Sunlight effects and denoising schemes
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
In this chapter, the effect of solar irradiance and the impact of noise from other external sources are investigated for a vehicle-to-vehicle (V2V) visible light communication (VLC) system with regard to signal-to-noise ratio (SNR), bit error rate (BER), and data rate. Then, we present two schemes to combat the effect of the ambient light on the V2V-VLC system. Firstly, we present the differential receiver as an efficient denoising scheme that cancels out the common mode (ambient) noise. This differential receiver exploits multiple wavelengths. Then, we propose a machine learning-based adaptive k-Nearest Neighbor (kNN) scheme to dynamically minimize the sunlight noise and maximize SNR by controlling both the transmitter irradiance and the receiver field of view (FoV). The second approach, combined with the extended Kalman filter approach proposed in Chapter 3 is very promising.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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