Design Procedures and Field Test Results of a Distributed-Translator Network, and a Case Study for an Application of Distributed-Transmission
Bibliographic record
Abstract
This paper presents the implementation procedures and field test results of a Distributed-Transmission Network (also referred to as "DTx network" or "DTxN" throughout the paper) consisting of three coherent translators. As will be explained later in the text, a network of coherent translators, which is called "distributed translator network", is one of the three methods of implementing a DTxN. The performance of such distributed translator network was tested in a strong static and dynamic multipath environment. The target area of the distributed-translator network under consideration was selected to be a small part of the coverage area of a distant single transmitter. This provided the possibility of taking the reception quality of the distance transmitter as a reference, and evaluating the reception quality of distributed-translator network in its target area. Two types of ATSC receivers, a new prototype and an older generation one, were used for this study. This in turn made it possible to compare the performance of the two receivers under tough conditions, and to investigate the impact of DTxN on the older generation receiver. As an application of the Distributed-Transmission Network, the possibility of changing a number of low-power (LP) existing DTV assignments into a DTxN was also investigated in a case study. The existing LP assignments, the candidates for changing into DTxN, were all part of a provincial network that is broadcasting the same program on different channels across the province of Ontario-Canada. Using DTxN can improve the quality of service of the LP assignments and reduce the spectrum congestion within the existing allotment plan
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".