Architecture for Efficient Local Content Insertion in ATSC 3.0 SFNs
Bibliographic record
Abstract
This paper presents a novel approach for seamless and efficient local content insertion in Single Frequency Network (SFN) networks. In practical deployments, the proposed architecture supports content replacement with services of varying bitrates, as long as they conform to the Physical Layer Pipes (PLP’s) configured capacity. This work proposes the introduction of a new entity, Stream Processor, close to the transmitter facilities, which supports dynamic local content management without altering the ATSC 3.0 Broadcast Gateway or central headend configuration. Moreover, this method ensures that synchronization, a key aspect of SFN networks, is maintained without duplicating or modifying signaling data. Finally, the presented technique has been tested with commercial ATSC 3.0 hardware and software components, demonstrating reliable reception, compatibility with standard receivers, and resilience under Studio-to-Transmitter Link Transport Protocol (STLTP) network stress conditions.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".