Low-permittivity microwave dielectric ceramics are essential for high-frequency communication and radar systems, as they minimize signal delay and interference, thereby enabling compact and high-performance devices. In this study, LiAl_5−xNi_xO_8−0.5x (x = 0.1–0.5) ceramics were synthesized via a solid-state reaction method to investigate the effects of Ni²⁺ substitution on crystal structure, microstructure, and dielectric properties. X-ray diffraction and Rietveld refinement reveal a phase transition from the P4₃32 to the Fd$\stackrel{-}{3}$m spinel structure at x ≈ 0.3, accompanied by a systematic increase in the lattice parameter (7.909–7.975 Å), attributed to the larger ionic radius of Ni²⁺ compared to Al³⁺. SEM analysis confirms dense microstructures with relative densities exceeding 95% and grain size increases from less than 1 μm at x = 0.1 to approximately 2 μm at x = 0.5. Dielectric measurements show a decrease in permittivity (ε_r) from 8.24 to 7.77 and in quality factor (Q × f) from 34,605 GHz to 20,529 GHz with increasing Ni content, while the temperature coefficient of the resonant frequency (τ_f) shifts negatively from −44.8 to −69.1 ppm/°C. Impedance spectroscopy indicates increased conduction losses and reduced activation energy with higher Ni²⁺ concentrations.

To address the insufficient retrieval accuracy of temperature and humidity profiles from ground-based microwave radiometers, this study develops an improved retrieval method based on intelligent optimization and attention mechanisms under clear-sky conditions. The research first constructs a brightness temperature dataset by performing forward modeling of ERA5 reanalysis data for the Haikou region using the MonoRTM model. Subsequently, five common machine learning algorithms—Ridge Regression, XGBoost, Multilayer Perceptron (MLP), Random Forest, and Support Vector Machine (SVM)—are employed to reconstruct atmospheric temperature and humidity profiles. A systematic evaluation of the retrieval results reveals distinct performance characteristics across the five machine learning models. For temperature retrieval, the Wolf Pack Algorithm (WPA) is introduced to optimize SVM parameters, resulting in a WPA-SVM model that reduces the overall Root Mean Square Error (RMSE) by 13.2% compared to conventional SVM. For humidity retrieval, an innovative MLP model integrated with an attention mechanism is proposed. By incorporating adaptive weighting and oversampling strategies, this model significantly improves retrieval accuracy at high altitudes, achieving a 15.1% reduction in overall RMSE compared to traditional MLP. The hybrid retrieval framework developed in this study integrates swarm intelligence and machine learning, under clear-sky conditions, providing a reliable technical pathway for high-precision and high-robustness atmospheric parameter retrieval. These findings not only hold significant value for advancing high-accuracy atmospheric parameter remote sensing systems, but also establish a foundation for precisely retrieving atmospheric refractivity profiles to resolve atmospheric ducts, thereby carrying important implications for marine wireless communications.

Microwave dielectric ceramics are indispensable in modern communication technologies, playing a pivotal role in components such as filters, oscillators, and antennas. Among these materials, ZnAl₂O₄ ceramics have garnered attention for their excellent quality factor (Q × f) and low dielectric constant (ε_r). However, their high sintering temperature (~1650 °C) limits practical applications. This study investigates ZnAl_2-x(Zn_0.5Si_0.5)_xO₄ (ZAZS) (x = 0.1–0.9) ceramics, where [Zn_0.5Si_0.5]³⁺ substitutes Al³⁺, to reduce sintering temperature while maintaining high-performance microwave dielectric properties. ZAZS ceramics were synthesized via the solid-state reaction method and characterized for their structural, morphological, and dielectric properties. X-ray diffraction analysis confirmed the formation of a single-phase solid solution up to x = 0.8, with minor secondary phases appearing at x = 0.9. The substitution increased lattice parameters and enhanced material densification, as observed through SEM and relative density calculations. Microwave dielectric measurements showed that ZAZS ceramics achieved a maximum Q × f of 20,200 GHz and a τ_f value reduced to −62 ppm/°C at x = 0.8, while ε_r decreased from 7.90 to 6.98. Bond-valence calculations reveal that the reduction of the average Al/Zn/Si–O bond valence weakens octahedral rigidity, systematically tuning τ_f toward zero. These results demonstrate that ZAZS ceramics, with a reduced sintering temperature of 1400 °C, exhibit excellent potential for application in low-loss microwave devices.

Existing metasurface-based radar countermeasures commonly manipulate echoes from metasurface-covered regions, whereas protecting exposed scattering centers that cannot accommodate metasurfaces remains challenging. This paper presents a multi-region time-coding metasurface (MRTCM) architecture for coherent target cancellation and deceptive jamming. The proposed system uses multiple independently controlled metasurface regions distributed over available platform surfaces. Each region generates zeroth-order and higher-order harmonic components through periodic phase modulation, and these components coherently combine with echoes from uncovered areas. The zeroth-order component is invariant under cyclic shifts of the coding sequence, enabling stable cancellation during multi-pulse coherent processing without locking radar-pulse arrival to the start of the coding cycle. Higher-order harmonics can shift the apparent range of the metasurface response and support cancellation at range-separated cells, but require accurate timing and synchronization. Numerical simulations demonstrate more than 40 dB suppression in high-resolution range profiles and validate the cancellation behavior under moving-target detection processing. The MRTCM architecture therefore provides a theoretically grounded framework for electromagnetic protection when full metasurface coverage is impractical.