Fixed Satellite Services (FSS) used to be alone at the C-Band spectrum in most countries. Since the deployment of 5G in many countries (i.e., 3.3–3.6 GHz), FSS is not the exclusive system in the C-Band anymore. In order to minimize the detrimental interference for the FSS to allowable levels, regional exclusion zones of maximum radiated power in 5G base stations (BS) are proposed and evaluated. In this paper, a measurement campaign has been carried out, and an analysis of the interference has been studied. A filtering model, namely Filter to Remove Broadband Interference 5G (FIREBRING), is proposed and analyzed concerning the carrier-to-noise ratio (C/N). Moreover, this paper focuses on the evaluation of the 5G interference into the FSS. The proposed solution deployed an Low-Noise Block (LNB) with a band frequency of 3.7 to 4.2 GHz to test the satellite down-conversion signal at the receiver. The paper offered a complete analysis of the 5G signal, taking into account the implications of out-of-band emissions, potentially LNB saturation into FSS receiver, and the repercussions of the deployment of the 5G BS active antenna systems. With the LNB and down-converter in place, it can be found that the signal interference between 1.450 GHz and 1.550 GHz, is nearly 18 dB.

The SARS-CoV-2 virus is responsible for the rapid global spread of the COVID-19 disease. As a result, it is critical to understand and collect primary data on the virus, infection epidemiology, and treatment. Despite the speed with which the virus was detected, studies of its cell biology and architecture at the ultrastructural level are still in their infancy. Therefore, we investigated and analyzed the viral morphometry of SARS-CoV-2 to extract important key points of the virus’s characteristics. Then, we proposed a prediction model to identify the real virus levels based on the optimization of a full recurrent neural network (RNN) using transmission electron microscopy (TEM) images. Consequently, identification of virus levels depends on the size of the morphometry of the area (width, height, circularity, roundness, aspect ratio, and solidity). The results of our model were an error score of training network performance 3.216 × 10⁻¹¹ at 639 epoch, regression of −1.6 × 10⁻⁹, momentum gain (Mu) 1 × 10⁻⁹, and gradient value of 9.6852 × 10⁻⁸, which represent a network with a high ability to predict virus levels. The fully automated system enables virologists to take a high-accuracy approach to virus diagnosis, prevention of mutations, and life cycle and improvement of diagnostic reagents and drugs, adding a point of view to the advancement of medical virology.

5G is the next-generation mobile communication technology expected to deliver more excellent data rates than Long Term Evolution (LTE). It offers ultra-low latency and ultra-high dependability, allowing revolutionary services across sectors. 5G mm-Wave base stations may emit harmful radiofrequency electromagnetic fields (RF-EMF). However, RF-EMF concern raises health and safety. From our research, the RF-EMF prediction model lacks papers or publications. This study uses IEEE and ICNIRP standards for assessment and exposure limits. The measuring campaign analyses one sector of a 5G base station (5G-BS) operation on 29.5 GHz in Cyberjaya, Malaysia. This study proposes two prediction models. The first model predicts signal beam RF-EMF, and the second predicts base station RF-EMF. Each model contains three neural network techniques to forecast RF-EMF values: Approximate-RBFNN, Exact-RBFNN, and GRNN. The results are analysed and compared with the measured data, observing which algorithm is more accurate by calculating the RMSE of each algorithm. As a result, it can observe that the Exact-RBFNN algorithm is the best algorithm to predict the RF-EMF because it shows a good agreement with the measured value. Besides that, in 1-min duration, the difference between predicted and measured values reached 0.2 less channels. However, in 6-min and 30-min, it can observe more accurate results since the differences between values reached 0.1in the situations. In addition, the ICNIRP standard was used and compared with the result and validation value of the algorithms.

Rain attenuation poses a significant challenge for high-throughput communication systems. In response, this paper introduces an artificial intelligence (AI) model designed for predicting and mitigating rain-induced impairments in high-throughput satellite (HTS) to land channels. The model is based on three AI algorithms developed using 3D antenna design to characterize, analyze, and mitigate rain-induced attenuation, optimizing channel quality specifically in the United Arab Emirates (UAE). The study evaluates various parameters, including rain-specific attenuation, effective slant path through rain, rain-induced attenuation, signal carrier-to-noise ratio, and symbol error rate, for five conventional modulation schemes: Quadrature Phase-Shift Keying (QPSK), 8-Phase Shift Keying (8-PSK), 16-Quadrature Amplitude Modulation (16-QAM), 32-QAM, and 64-QAM. Additionally, the paper introduces a new database detailing rain-induced attenuation in HTS channels in the UAE at different frequencies using measured rainfall intensities. The paper concludes by proposing a smart antenna design with a frequency diversity technique for fade mitigation. Results indicate that rain-induced attenuation varies significantly based on rainfall rate and frequency. Specifically, at 25 GHz and a rainfall rate of 100 mm/h, the rain-induced attenuation can reach as high as 15 dB, resulting in a significant decline in signal quality and link performance. The proposed AI model demonstrates the ability to intelligently predict rain-induced attenuation and channel quality for various rainfall rates and frequencies. This information can be valuable for optimizing satellite link design and operation, ultimately enhancing the reliability and quality of satellite communications. The proposed two-slot smart antenna design utilizes frequency diversity to effectively mitigate rain attenuation, contributing to the overall improvement of link reliability and quality.