On the Role of Reflectarrays for Interplanetary Links
Bibliographic record
Abstract
Interplanetary links (IPL) serve as crucial enablers for space exploration, facilitating secure and adaptable space missions. An integrated IPL with inter-satellite communication (IP-ISL) establishes a unified deep space network, expanding coverage and reducing atmospheric losses. In order to evaluate the performance of both realistic and cost efficient IP-ISL scenario, the challenges, including irregularities in charged density, hardware impairments, and hidden celestial body brightness are analyzed with a reflectarray-based IP-ISL between Earth and Moon orbiters. In this regard, free-space channel is modeled with the charge density irregularities that disturb the angle of arrival. A discretize solution for noise temperature analyze in celestial body brightness is given with average beam occupancy. Lastly, the impact of aggregated phase noise, in-phase and quadrature imbalance and non-linear amplifier are modeled with generalized stochastic hardware impairments. As a solution, the ideal and non-ideal compensation of angle of arrival with the perfect knowledge of it is analyzed. It is observed that$10^{-8}$order severe hardware impairments with intense solar plasma density drops an ideal system’s spectral efficiency (SE) from$\sim 38~\textrm {(bit/s)/Hz}$down to$0~\textrm {(bit/s)/Hz}$. An ideal full angle of arrival fluctuation recovery with full steering range achieves$\sim 20~\textrm {(bit/s)/Hz}$gain and a limited beamsteering with a numerical reflectarray design achieves at least$\sim 1~\textrm {(bit/s)/Hz}$gain in severe hardware impairment cases. As a result, we assess the feasibility of an integrated IP-ISL system design to establish a unified deep space network, expanding coverage and reducing atmospheric losses between the Moon and Earth in terms of spectral efficiency, addressing real-life deep space communication challenges.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".