In this paper, the performance of dual-hop relay transmission in modern wireless communication systems is analyzed by considering two fundamental relaying techniques, namely, Amplify-and-Forward (AF) and Decode-and-Forward (DF). The propagation conditions on the source–relay (S-R) and relay–destination (R-D) links are modeled using the κ-μ statistical distribution, which effectively captures the fading characteristics in both line-of-sight (LoS) and non-line-of-sight (NLoS) environments. The analysis focuses on key performance metrics, including the outage probability (P_out) and average bit error probability (P_e), for Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK) modulation schemes, assuming transmission via a single relay without a direct S–D link. Closed-form expressions for the considered metrics are derived based on the κ-μ model and verified by numerical evaluation. In addition to classical analytical modeling, a Generative Artificial Intelligence (GenAI)-enabled workflow is incorporated as a supportive tool in order to aid in automated analysis, the interpretation of the results in the context of network management under varying channel and system parameters based on the P_out and P_e calculations with the aim to tackle the underlying complexity and cognitive load of infrastructure adaptation and re-configuration operations. The combined analytical and GenAI-assisted approach provides valuable insights for the optimization, design, and continuous evolution of robust relay-based architectures in next-generation wireless networks.

This paper investigates the performance of a maximal ratio combining (MRC) receiver with $L$ diversity branches operating over a $\mathrm{k}-\mu$ fading channel, which is a generalized fading model suitable for various wireless environments. An exact closed-form expression for the channel capacity (CC) is derived based on the signal-to-noise ratio (SNR) at the output of the MRC receiver. Furthermore, the impact of various link-level and system-level parameters on the channel capacity is analyzed through detailed graphical simulations. The presented results provide valuable insights into the design and optimization of wireless communication systems operating over $\mathbf{k}$ $\mu$ fading channels. Finally, we also introduce Large Language Model (LLM) based approach to electromagnetic radiation (EMR) estimation, leveraging the previously calculated values of channel capacity among other factors.

This paper presents the implementation of a solar-powered resonant vibratory conveyor driven by an electromagnetic actuator. The power supply system comprises a 470 W photovoltaic panel, a $24 \mathrm{V} / 65 \text{Ah}$ battery, an MPPT charge controller, a DC/DC boost converter, and a currentcontrolled power converter providing stable excitation to the actuator. The system is compact and compatible with standard electromagnetic actuators designed for $230 \mathrm{V}, 50 \text{Hz}$ AC mains. In contrast, actuators intended for 24 V (or 48 V) DC operation typically require higher current levels, resulting in an increase in their size and consequently their cost. The paper details the energy calculation methodology and presents experimental validation based on a developed prototype. The proposed system offers promising potential for applications involving automated dispensing of fine-grained materials, such as feed distribution in aquaculture (primarily in fishponds), avian reserves, and remote rural environments requiring precise and energyefficient material handling.