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Jaswant

Publications and source records attributed to Jaswant.

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Traversable Wormhole De-singularization: Almost $\eta$-Ricci-Yamabe Solitons in Static Spherically Symmetric Imperfect Fluid Spacetimes

In this paper, we investigate the almost $\eta$-Ricci-Yamabe soliton as a fundamental geometric regulator for a static, spherically symmetric black hole coupled to an imperfect fluid. We have shown that the scaling parameter $\omega(r)$ is governed by thermodynamic friction along the radial vector field, and the geometric coupling with the Hawking temperature: $\alpha(r_H) S_{tt} = 2\pi T_H$ at the horizon. We also derive the Poisson equation along the gradient vector field of the soliton and prove that the flow's kinematic expansion is explicitly dependent on the fluid's equation of state $\rho = \gamma \sigma$. Diverging from traditional methodologies that assume a geometric shape function apriori, we analytically proved the geometric flow endogenously transitions the black hole geometry into a traversable wormhole throat by regularizing of temporal coordinate and satisfying spatial flare-out condition. This transition occurs when fluid enters the dark energy era at $\gamma = -1$ and violates the Null Energy Condition $\rho + \sigma < 0$, with the soliton strictly dominating the local curvature gradient $\omega^{\prime}(r_H) > f^{\prime\prime}(r_H)$, to keep the throat open. Moreover, by smoothly attenuating at spatial infinity, the soliton preserves the exact cosmological spacetime. Finally, through tensorial perturbation analysis, we demonstrate that the geometric flow introduces a localized dissipative mechanism, that the perturbation evolution reduces to damped wave equation, imposing geometric drag on the manifold.

gr-qc

Drone-Based Antenna Measurement System with Optimized Positioning and ASPIRE-Based NF-FF Transformation

Unmanned Aerial Vehicle (UAV)-based antenna measurement systems provide a flexible and cost-effective alternative to conventional antenna test ranges for characterizing large and installed antennas. However, their accuracy depends on precise UAV positioning and efficient flight-time utilization, both of which are strongly influenced by the selection of drone assemblies, including the airframe, flight controller, propulsion system, positioning modules, and onboard instrumentation. This paper presents a comprehensive study of UAV-based antenna measurements with emphasis on improving positioning accuracy and optimizing flight endurance through systematic drone assembly selection. The acquired near-field measurement data are susceptible to positioning errors, amplitude and phase inconsistencies, and irregular sampling, which degrade the reconstructed far-field pattern. To address these challenges, the recorded near-field data are processed using the Adaptive Sparse Inverse Radiation Estimation (ASPIRE) algorithm. ASPIRE compensates for positioning inaccuracies and reconstructs the far-field pattern from irregularly sampled near-field data using sparse signal recovery, enabling accurate Near-Field to Far-Field (NF-FF) transformation. At 6.7125 GHz, ASPIRE achieves a residual of 1.94% and a beamwidth error of 0.4 degrees relative to a conventional facility measurement while using only 24% of the 17,298-element RWG mesh as active support. The results demonstrate that the combination of optimized drone assembly selection and ASPIRE-based NF-FF transformation significantly improves the accuracy of UAV-based antenna measurements and produces far-field patterns that closely agree with conventional antenna test range measurements.

eess.SP