Low-Profile Artificial Grid Dielectric Resonator Antenna Arrays for mm-Wave Applications
Bibliographic record
Abstract
Wideband artificial grid dielectric resonator antenna (GDRA) arrays at 32 GHz for mm-wave applications are presented. The antenna array comprised a GDRA layer and a substrate-integrated waveguide feeding layer. The GDRA array layer is built by embedding small rectangular metal grid structures in low-permittivity dielectric polymethyl methacrylate (PMMA) using deep X-ray lithography (DXRL) and electroforming. The rectangular metallic inclusions significantly increase the effective permittivity of the base material up to 17 by creating high electric flux density regions inside. Low-loss substrate-integrated waveguide (SIW) feeding with longitudinal slots is utilized to excite the GDRA array layer. A 200 μm-thin perforated layer of PMMA is applied between the rectangular grid structures and the SIW feedlines to avoid shorting the metal inclusions to the excitation slots while improving broadband energy coupling to the GDRA layer. The size of the single GDRA array element is only 2.7 mm × 2.7 mm × 0.5 mm (0.29λo×0.29λo×0.05λo). Four-element (1×4) and eight-element (1 × 8) GDRA arrays have been fabricated and measured. A measured impedance bandwidth of 6 GHz with a broadside peak gain of 12 dBi and 76% measured radiation efficiency is obtained at 32 GHz for the 1 × 8 GDRA array.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".