A novel ultrawideband circularly polarized (CP) phased array antenna was developed by combining the low-profile design of a stripline-to-slotline balun with the wide scanning characteristics of a tightly coupled dipole array (TCDA). This antenna boasts a low voltage standing wave ratio (VSWR) and low axial ratio (AR). To address the common-mode resonance issue, a circular slotline cavity was implemented as a four-stage series fan-shaped cavity. This design effectively extends the length of the short-circuit stub, enhancing the short-circuit impedance value and expanding the scanning bandwidth from 3:1 to 6.4:1. Notably, a rotating wide-angle impedance matching (WAIM) layer was introduced for the CP TCDA, marking the first time this approach has been employed. This innovation not only increased the number of matching network stages for achieving a low VSWR but also enhanced the consistency of the main polarization, reducing the cross-polarization ratio to achieve a low AR. The resulting 8 × 8 CP phased array antenna demonstrated exceptional performance, operating over a remarkable 6:1 bandwidth (2–12 GHz). It satisfies the criteria of Active VSWR<2.5 and AR<2.7 dB within a ±60° scanning range.

The collection of timely and sustainable sensing data presents significant challenges for Internet of Things (IoT) systems deployed across remote and expansive geographical areas. Focusing on the space-air-ground integrated networks (SAGIN), this work investigates the age of information (AoI) minimization problem of sensing data, where the wireless power transfer (WPT) is employed to address energy constraints of sensing nodes (SNs) in persistent monitoring scenarios. To address the key challenges of hierarchical multi-hop architecture, temporal coupling between wireless energy supply and data uploading, dynamic topology of low-earth-orbit (LEO) satellite network, and time-varying channel states, we propose an AoI optimization scheme that employs alternating long-short time slots to decouple the system AoI optimization into two independent sub-problems: air-ground and air-space link AoI optimization. The maximum AoI utility scheduling (MAUS) algorithm is designed to minimize the AoI value of the air-ground link by jointly optimizing the network clustering, the unmanned aerial vehicle (UAV) flight trajectory, and the allocation strategy for SNs’ charging time and data uploading time. The matching game-based network resource allocation (MGNRA) algorithm with proportional fairness-based bandwidth allocation is designed to minimize the UAV data queue backlog, thereby reducing the system long-term average AoI. Simulation experiments demonstrate that our proposed scheme has significant advantages in reducing the long-term average AoI value and maintaining operational stability compared with benchmark schemes.

In this paper we establish a comparison approach to study stabilization of stochastic differential equations driven by \begin{document}$ G $\end{document}-Brownian motion with delayed (\begin{document}$ G $\end{document}-SDDEs for short) feedback control. This theory also extends to a general range of moment order and brings more choices of \begin{document}$ p $\end{document}. Finally, a simple example is proposed to demonstrate the applications of our theory.