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Calculating Path Loss for Stratospheric Channels During High Solar Activity

2023· article· en· W4391584177 on OpenAlexaff
Bensu Elmacı Gökmen, Funda Akleman, Güneş Karabulut Kurt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPath lossAttenuationIonosphereSatelliteAerospace engineeringChannel (broadcasting)Effects of high altitude on humansRemote sensingEnvironmental scienceCommunications satelliteMeteorologyComputer sciencePhysicsTelecommunicationsWirelessGeologyGeophysicsEngineeringOptics

Abstract

fetched live from OpenAlex

Beginning with the development of 6th generation (6G) networks, it is widely accepted that high-speed and wide coverage communications, which have been targeted for many years, will be achieved through the development and deployment of High Altitude Platform Station (HAPS) systems. In this study, we investigate the effect of high solar activity on HAPS to Low Earth Orbit (LEO) satellite channel models. Considering the effects of high solar activity on the attenuation of electromagnetic waves propagating through the ionosphere of HAPS-LEO satellite channels, it is expected that these effects may be non-negligible. This study presents comparative results of path loss analyses for high and low solar activity using the same model. A mathematical expression for the loss through the ionosphere is written using the Appleton-Hartree model. The effects of electron density and collision frequency on path loss are analyzed. Comprehensive numerical results are provided for specified frequency bands.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.232
Teacher spread0.223 · 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 designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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