English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A systematic study concerning the discretization of the angle of incidence in surrogate models obtained with support vector regression (SVR) is presented. The problem addressed in this work arises from the dependence of the reflection coefficients on the angle of incidence. While the direct coefficients are usually stable with the angle of incidence, this is not the case with the cross-coefficients, which translates this behavior to the crosspolar component of the radiation pattern. Then, correctly assessing this influence and minimizing radiation pattern distortion allow training SVR surrogate models per angle of incidence without penalizing accuracy in the prediction of the far field. The results shown in this work are directly relevant to improving the computational performance of SVRs applied to reflectarray design since they allow reducing the dimensionality of the models by generating surrogate models per angle of incidence instead of including the angles of incidence as input variables. In addition, it highlights the importance of a proper discretization of the angles of incidence for a correct prediction of the crosspolar pattern for its subsequent optimization, especially for advanced space applications with tight crosspolar requirements.

electronics

Systematic Study of the Influence of the Angle of Incidence Discretization in Reflectarray Analysis to Improve Support Vector Regression Surrogate Models