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Record W2327031016 · doi:10.1109/intlec.2014.6972226

Indoor distributed antenna systems and NEC class 2

2014· article· en· W2327031016 on OpenAlexaff
Rahul Baliga

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsAlpha Technologies (Canada)
Fundersnot available
KeywordsAntenna (radio)Electrical engineeringNode (physics)InstallationDistributed antenna systemPower (physics)Computer scienceTelecommunicationsElectronic circuitPower budgetEngineeringComputer network

Abstract

fetched live from OpenAlex

An indoor distributed antenna system (DAS) is a network of small antennas that prevents isolated spots of poor coverage inside densely populated indoor spaces. In addition to an antenna, a DAS node can include equipment such as amplifiers, remote radio heads, signal converters and power supplies that propagate, process or control the communications signals transmitted and received through the DAS node. Providing power to these remote nodes can be a challenge. This paper describes two options for powering the remote nodes - either locally at the nodes or from a centralized location at the head end. The local power approach has issues with access to AC power, space allocation and aesthetics of equipment cabinets as well as ongoing maintenance of batteries. On the other hand, deploying a centralized power solution incurs cabling costs to the individual remote sites. To be compliant with local and national standards, armored cable or conduits may be necessary; both require the assistance and cost of a licensed electrician. The paper then describes an alternative centralized power solution, using NEC Class 2 circuits to power the remote nodes. NEC class 2 circuits provide the added advantage of running standard composite fiber cables, reducing the capital expenditure incurred with deploying and installing conduits or armoured cabled. In addition, the assistance of a certified electrical personnel is not required thereby eliminating the associated costs. The pros and cons of this line powered technique are provided. Finally, the paper concludes with a summary of the requirements for NEC class 2 circuit compliance.

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 score0.022
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.008

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 head0.006
GPT teacher head0.176
Teacher spread0.170 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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