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Dynamic nonlinear equations (DNEs) are essential for modeling complex systems in various fields due to their ability to capture real-world phenomena. However, the solution of DNEs presents significant challenges, especially in industrial settings where periodic noise often compromises solution fidelity. To tackle this challenge, we propose a novel approach called Periodic Noise Suppression Neural Dynamic (PNSND), which leverages the gradient descent approach and incorporates velocity compensation to overcome the limitations of the traditional Gradient Neural Dynamic (GND) model.

The development of metaverses and virtual worlds on various platforms, including mobile devices, has led to the growth of applications in virtual reality (VR) and augmented reality (AR) in recent years. This application growth is paralleled by a growth of interest in analyzing and understanding AR/VR applications from security and performance standpoints. Despite this growing interest, benchmark datasets are lacking to facilitate this research pursuit.

The presented DataSet is dedicated to a W-band (92-96 GHz) focal plane array (FPA) for 50 dBi-reflector antennas intended for the next-generation point-to-point communication links with antenna mast sway compensation. The FPA comprises seven metal waveguide elements arranged in a hexagonal lattice, ensuring the cross-over level of the corresponding reflector beams above -6 dB.

Herein, we constructed a finite element human eyeball model, encompassing a biophysically detailed retinal layer, optic nerve segment, and its surrounding tissues, as well as extraocular electrodes.A multi-conductivity 3D human eye model was constructed based on the AC/DC module in COMSOL Multiphysics. This model encompassed essential eye structures, such as the cornea, atria, lens, vitreous body (VB), retinal, choroid, sclera.