This article presents a threat detection system for Unmanned Aerial Vehicles (UAVs) designed to enhance operational security in high-risk environments. The system employs Frequency Modulated Continuous Wave (FMCW) radar, optimized to detect UAVs with a radar cross section (RCS) as small as 0.01 m², exemplified by models such as the DJI Phantom 3 and Phantom 4. Operating at 2.45 GHz, the radar achieves a maximum detection range of 300 meters and is implemented on the USRP B210 Software Defined Radio (SDR) platform, with real-time signal processing enabled through GNU Radio and integrated GPU resources. The transmission module includes a 100-watt Power Amplifier (PA) and dual-parabolic unidirectional antennas, while the receiver employs a Band-Pass Filter (BPF) and a Low Noise Amplifier (LNA) with a gain of 20 dB to improve sensitivity to reflected signals. The USRP B210 handles signal acquisition and processing, applying two-dimensional Fast Fourier Transform (FFT) techniques to extract doppler shift information. The processed data is visualized as a range-doppler map, facilitating effective target tracking. The system's modular architecture and parallel processing capability enable precise and efficient detection of low-RCS UAVs, even in cluttered environments. This work demonstrates a practical, cost-effective radar solution for security-sensitive applications, offering advantages in performance, adaptability, and real-time capability over traditional radar systems.

Several recent studies have found that measuring the dielectric permittivity of soil can be used to determine the level of environmental pollution. However, there is limited research on the measurement of dielectric properties in soil contaminated with biodiesel-diesel blends from Thailand. This paper presents to monitor the dielectric properties of soil contaminated with biodiesel-diesel blends. Specifically, we use the commercial grade diesel B7 to contaminate a sample of sand soil. We also study the measurement of dielectric properties in contaminated soil with the diesel B7 using a dual electrode plate-based radio frequency (RF) heating system. This allows us to observe the behavior of the contaminated soil before and after RF heating treatment. The experimental result showed that the dielectric properties of uncontaminated and contaminated soil were different. In addition, the RF heating system utilizing the electric field intensities of 450 kV/m resulted in the dielectric properties of the contaminated soil becoming similar to those of uncontaminated soil. These findings suggest that using RF heating on contaminated soil samples improve the air in the pore space compared to unheated contaminated soil. This approach may be effective for the treatment of soil in Thailand using an RF heating system with dual electrode plates.

The quality of rice is decreased when contaminated with fungi. Aspergillus species are the most frequently found in rice. This research proposes using a dielectric heating method for fungal inactivation in rice grains by radio frequency (RF) energy. In order to understand the interaction between the fungi contaminating rice and electrical energy, dielectric properties comparison between Aspergillus sp. BP17 and rice powder were measured using an open-ended coaxial probe with a vector network analyzer (VNA) to develop dielectric heating equipment. The effect of RF energy on the dielectric heating system (9 kW, 40.68 MHz) is investigated based on different electric field intensities (150, 190, 225, 300, and 450 kV/m) with different temperatures (70, 80, 90, 100, and 110 °C). The growth of fungi contaminating rice was determined using a direct count method and reported as a percentage of inactivation. The result showed that the fungal inactivation of 100% was obtained at the electric field intensity value ≥ 225 kV/m at the lowest temperature of 90 °C. The combination of temperature and electric field intensity significantly (p ≤ 0.05) increased the percentage of fungal inactivation in rice grains. The optimal conditions of dielectric heating are suitable for fungal inactivation in rice industries. These results indicate that the proposed dielectric heating system is useful for inactivation of Aspergillus species.

This study investigates the potential of Plasma-Activated Water (PAW) technology for the production of nitrate fertilizer, focusing on the dielectric properties of plasma-treated water, such as the dielectric constant and dielectric loss factor. The research aims to elucidate the impact of plasma treatment and varying water flow rates on these properties. An experimental approach was employed wherein ion-free water was subjected to plasma treatment at different flow rates (0.5, 1.0, and 2.0 L/min) and durations (1, 2, and 3 h). The results reveal a marked enhancement in the dielectric properties of the water following plasma treatment, with the most significant improvements observed at a flow rate of 0.5 L per minute and a treatment duration of 3 h, and dielectric efficiencies of 97.82%, 97.21%, and 96.61% achieved at flow rates of 0.5, 1.0, and 2.0 L/min, respectively. These findings demonstrate that PAW technology enhances the efficiency of nitrate fertilizer production by optimizing energy storage and reducing energy losses. The study underscores the potential of PAW as a sustainable, environmentally benign alternative to conventional chemical fertilizers, contributing to more efficient and sustainable agricultural practices.

The integration of autonomous vehicles in industrial settings necessitates advanced positioning and navigation systems to ensure operational safety and efficiency. This study rigorously evaluates the application of Ultra-Wideband (UWB) technology in autonomous industrial trucks and compares its effectiveness with conventional systems such as Light Detection and Ranging (LiDAR), Global Positioning System (GPS), and cameras. Through comprehensive experiments conducted in a real factory environment, this study meticulously assesses the accuracy and reliability of UWB technology across various reference distances and under diverse environmental conditions. The findings reveal that UWB technology consistently achieves positioning accuracy within 0.2 cm 99% of the time, significantly surpassing the 10 cm and 5 cm accuracies of GPS and LiDAR, respectively. The exceptional performance of UWB, especially in environments afflicted by high metallic interference and non-line-of-sight conditions—where GPS and LiDAR’s efficacy decreased by 40% and 25%, respectively—highlights its potential to revolutionize the operational capabilities of autonomous trucks in industrial applications. This study underscores the robustness of UWB in maintaining high accuracy even in adverse conditions and illustrates its low power consumption and efficiency in multi-user scenarios without signal interference. This study not only confirms the superior capabilities of UWB technology but also contributes to the broader field of autonomous vehicle technology by highlighting the practical benefits and integration potential of UWB systems in complex and dynamic environments.