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Record W2489997503 · doi:10.1109/tbc.2016.2590824

Multiple Service Configurations Based on Layered Division Multiplexing

2016· article· en· W2489997503 on OpenAlexaff
Jae-Young Lee, Sung-Ik Park, Sunhyoung Kwon, Bo-mi Lim, Heung Mook Kim, Jon Montalbán, Pablo Angueira, Liang Zhang, Wei Li, Yiyan Wu, Jeongchang Kim

Bibliographic record

VenueIEEE Transactions on Broadcasting · 2016
Typearticle
Languageen
FieldEngineering
TopicTelecommunications and Broadcasting Technologies
Canadian institutionsCommunications Research Centre Canada
FundersMinistry of Science, ICT and Future Planning
KeywordsMultiplexingComputer scienceDivision (mathematics)TelecommunicationsTime-division multiplexingService (business)Frequency-division multiplexingElectronic engineeringOrthogonal frequency-division multiplexingComputer networkElectrical engineeringEngineeringBusinessMathematicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this paper, we present multiple service configurations based on layered division multiplexing (LDM), which is adopted as a baseline technology of the Advanced Television Systems Committee 3.0 standard. Based on a two-layer LDM technology, a variety of multiple-physical layer pipe (PLP) configurations as well as physical layer framing is introduced depending on the choices of service scenario, time interleaving, and cell addressing. A performance analysis is provided when three different broadcasting services - robust audio, indoor/mobile, and high data rate services - are delivered through different PLPs when a number of broadcasting service scenarios is presented.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.029
GPT teacher head0.229
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2016
Admission routes1
Has abstractyes

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