###### Abstract
Overall, the proposed antenna array shows excellent potential for use in earth exploration satellite and space research. This paper [6] describes the design of three types of dual-band microstrip patch antennas for wearable devices. The first is a conventional ground plane antenna, while the other two are based on two different types of electromagnetic band gap (EBG) structures. The study shows that the two EBG-based antennas improve bandwidth and gain compared to the conventional ground plane antenna. Furthermore, the authors have demonstrated that the mushroom-like EBG and the EBG with plus-shaped slots enhance the bandwidth and gain at both lower and higher resonance frequencies. Additionally, the study investigates the conventional ground plane antenna under various bending conditions. It evaluates its specific absorption rate (SAR) on the human body using tissue models, concluding that the proposed antenna reduces SAR effects on the human body, making it useful for various wearable applications. This paper presents the design and analysis of a compact dual-band microstrip patch antenna operating at 2.5 and 5.2 GHz. The proposed antenna is designed to enhance its performance with two different electromagnetic band gap (EBG) ground planes. Results indicate that EBG-inspired antennas perform better than those with a conventional ground plane. The proposed antenna was tested in various bending conditions and remained tuned regardless of the bending radii. The SAR analysis revealed that the SAR value of the conventional antenna is within the standard SAR limit prescribed by European standards in both conditions when the antenna is placed over the flat body phantom or wrapped around the rounded body parts. The authors concluded that the proposed antenna design could be useful for various applications, including personal communications gadgets, equipment for combat and rescue operations, and health devices without compromising the wearer's health. This paper [7] addresses the challenges Intrusion Detection Systems (IDS) face in the Internet of Things (IoT) context due to IoT data's high dimensionality and diversity. The authors propose a novel feature selection and extraction approach using two entropy-based methods, information gain (IG) and gain ratio (GR), and mathematical set theory (union and intersection) to extract the best features. The proposed approach is trained and tested on the IoT intrusion dataset 2020 (IoTID20) and NSL-KDD dataset using four machine learning algorithms. The results show that the proposed method outperforms state-of-the-art studies, achieving 99.98% classification accuracy. The authors suggest that this approach can be useful for self-protective IDSs against various cyber-attacks in the IoT ecosystem. The paper presents a feature selection approach for intrusion detection systems (IDS) using mathematical set theory to extract efficient subsets of features. The developed IDS scheme consists of three phases: data preprocessing, dimensionality reduction, feature selection, and model training and classification. The dimensionality reduction phase uses IG and GR filter-based approaches to rank features and develops a hybrid feature selection approach using intersection and union rules to optimize performance. The model training and classification phase applies five machine learning algorithms to classify generated subsets of traffic features into standard or intrusion classes for binary and multi-classification purposes. The results indicate that the ensemble-based hybrid approach provides optimum results by eliminating irrelevant features before training. This approach outperforms related works and unique machine learning algorithms in classification performance, as demonstrated by significant improvements in accuracy, precision, recall, f1-measure, and the ROC area. In future works, the proposed system will be deployed for IoT gateway devices to detect and classify cyber-attacks and intrusions within an IoT device network.