Contribution Link Delivery with STLTP in Challenging Network Conditions
Bibliographic record
Abstract
Since the introduction of the first Digital TV (DTV) systems, the issue of video data transport degradation has been of paramount importance. The literature presents various methods and experiments for evaluating video quality at the receiving end, using either subjective or objective metrics. Most studies have focused on the MPEG2 Transport Stream (TS, as it is used by the majority of current DTV standards. However, ATSC 3.0 introduced the new STLTP protocol, which is entirely IP-based for transporting data between studios and transmitters. Despite this, there has bee limited research into the evaluation STLTP under packet loss conditions and its impact on objective video quality at the receiver. This work takes the first steps towards assessing the performance limits of STLTP by introducing distortions, such as packet loss, within an ATSC 3.0 chain in a controlled environment. Statistics on Packet Error Rates (PER) and recovery levels are collected, and metrics such as PSNR and SSIM are used to assess the robustness of STLTP to lossy network conditions. The results show that while STLTP’s recovery performance is near perfect with 1.5% packet loss, whereas the error-free transmission is obtained only achieved up to the 1.0% packet loss.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".