Improvement in the antenna gain is usually achieved at the expense of bandwidth and vice versa. This is where the realization of this enhancement can be made through compromising the antenna profile. In this work, we propose a new design of incorporating periodic metasurface array to enhance the bandwidth and gain while keeping the antenna to a low-profile scheme. The proposed antenna was simulated and fabricated in order to validate the results in the operating frequency range from 10 MHz to 43.5 GHz. Computer simulation technology (CST) microwave studio software was used to design and simulate the proposed antenna, while LPKF prototyping PCB machine was utilized to fabricate the antenna. Results showed that the antenna generated a gain and bandwidth of 14.2 dB and 2.13 GHz, respectively. Following the good agreement between the numerical and measurement results, it is believed that the proposed antenna can be potentially attractive for the application of satellite communications in Ku-band electromagnetic wave.

This paper presents the design, simulation, fabrication, and measurement of a circular spiral microstrip antenna embedded in distilled water bolus. The antenna is mainly designed to operate at 434 MHz ISM band to be used for a hyperthermia applicator. The performance of the antenna is compared to a conventional patch antenna also embedded in water bolus. The results show that spiral antenna has significant narrower radiation pattern and is capable to operate at multiple frequencies as well. Narrow beam of the antenna is desired to design a multiantenna arrangement with less coupling and a better focusing resolution which can be crucial for deep regional hyperthermia applications. The option of other frequencies is desirable to have a better control over the penetration depth versus focusing depending on the application region.

Recent developments in machine learning (ML), deep learning (DL), and statistical signal processing have led to substantial improvements in the accuracy of time series forecasting, particularly for environmental parameters such as temperature. The accuracy of air temperature prediction is not only vital for meteorological forecasting but also critically impacts agriculture, energy management, and environmental monitoring. In this study, a comprehensive modeling approach is proposed by incorporating both data-driven learning methods and classical signal processing techniques. Specifically, statistical models such as Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) are evaluated alongside modern neural network architectures, including Long Short-Term Memory (LSTM) networks and Transformer-based attention mechanisms. The implemented models utilize key atmospheric variables—humidity, pressure, and past temperature values—to predict ambient temperature for future time horizons, such as one week and six months ahead. The SARIMAX model, which is grounded in digital signal processing theory, is particularly examined for its ability to capture seasonality and trend components in structured data. Meanwhile, deep learning models excel in learning complex, nonlinear temporal dependencies. Experimental results show that while LSTM performs well in short-term predictions (mean absolute error (MAE): 2.27, mean squared error (MSE): 6.63), the attention-based Transformer model is superior in capturing the predictions in the long term (MAE: 2.99, MSE: 14.92). SARIMAX, on the other hand, demonstrates a reliable performance in the short term compared to LSTM. These findings provide valuable insights into the strengths and limitations of each modeling approach, guiding future efforts in temperature forecasting and time series analysis.

In this study, a numerical simulation was used to evaluate the insulation performance of polyethylene (PE) and polyvinyl chloride (PVC) under varied environmental and electrical conditions. Tests were conducted at temperatures of 22 °C and 55 °C, with current levels of 40 A and 60 A, examining key parameters such as electric field intensity, current density, and Joule heating. The results show that, under lower temperature and current conditions, PE demonstrates greater current capacity but suffers from increased Joule heating and energy loss. Conversely, PVC provides more stable insulation with lower energy dissipation. At higher temperatures and currents, PE experiences significant electrical stress and thermal loading, increasing the risk of overheating, while PVC maintains consistent performance. These findings offer valuable guidance for selecting optimal insulation materials in power distribution systems.

This paper presents the adequateness of a rectangular spiral shaped patch antenna and a Vivaldi antenna having narrow beam width to use for antenna arrangements in microwave hyperthermia applications. The results obtained from simplified leg model show the adequateness of the small size spiral antenna due to its capability to operate at multi frequencies and narrow beam width. In addition its small lateral size is promising to use it in multi antenna configuration system for microwave hyperthermia application for cancer treatment.