Power factor correction (PFC) remains a critical requirement in medium-voltage (MV) distribution networks, particularly at the 33 kV level, where high reactive power demand and increased integration of distributed energy resources (DERs) lead to elevated losses, poor voltage regulation, and reduced overall efficiency. Conventional solutions, such as fixed or switched capacitor banks, provide cost-effective reactive power support but lack the flexibility to handle dynamic load variations and are prone to resonance issues. Similarly, advanced devices such as Static Synchronous Compensators (STATCOMs) deliver fast dynamic compensation but entail high investment costs when deployed as standalone solutions. In parallel, inverter-interfaced DERs have emerged as potential providers of reactive support, though their contribution is often underutilized due to fragmented control strategies. This paper proposes a novel hybrid STATCOM–Capacitor–DER framework for advanced power factor correction and enhancement of distribution efficiency in 33 kV networks. The framework employs optimally placed switched capacitor banks to provide economical bulk reactive power, a substation-level STATCOM for fast dynamic compensation and power factor trimming, and DER inverters configured with Volt-VAR droop control to deliver distributed support at feeder ends. A coordinated control hierarchy, supported by predictive Volt-VAR optimization, ensures synergy among the three elements. Simulation studies on a representative 33 kV feeder demonstrate significant improvements, including a correction of the power factor correction to ≥ 0.99, a reduction in feeder loss of up to 20% and improved voltage stability under variable load and renewable conditions. The proposed framework offers a scalable and practical way for utilities to achieve advanced PFC while using existing DER assets.
This article presents the design, implementation, and experimental validation of a compact triple-band microstrip antenna for multi-standard wireless communication and sensing applications. The proposed antenna operates at three resonant frequencies of 726 MHz, 1.8 GHz, and 2.4 GHz for Wireless Energy Sensor (WES), Long-Term Evolution (LTE), and Wireless Fidelity (Wi-Fi) systems, respectively. A compact geometry is optimized to generate multiple resonant modes within a single radiating structure with overall dimensions of 60×50×1.62mm3. Electromagnetic performance is analyzed using a full-wave simulation tool, evaluating input reflection coefficient (S11), radiation characteristics, gains, and impedance bandwidths. The simulation results show resonances across the three operating bands, with minimum S11 values of -20.85 dB, -41.89 dB and -23.31 dB, and corresponding bandwidths of 3.44%, 6.83% and 3.62%. Realized gains of 1.81 dBi, 2.38 dBi, and 2.44 dBi are achieved. Surface current analysis confirms the excitation of distinct resonant modes responsible for the multi-band operation. To validate the design, a prototype is fabricated on a low-cost FR-4 substrate, which is widely used in practical wireless device applications due to its affordability and ease of fabrication. The experimental characterization focuses primarily on measuring S11 and comparing it with the simulated results. Good agreement is observed between the measured and simulated data, demonstrating the reliability of the proposed design approach. The proposed design is suitable for integration into smart grid and IoT-based communication systems that require compact, multi-band operation with reliable performance and efficiency.
This paper presents a 3D multiphysics model of a 20 Torr argon microwave plasma torch in a WR−187 waveguide. Coupling plasma transport, Maxwell’s equations, compressible flow and heat transfer, the simulation solves the 2.45 GHz TE10 microwave input from 10 to 50 W using nine-reaction argon chemistry. At 50 W, the results show the maximum electron density ∼ 1018m−3 in an annular pattern near the waveguide-tube junction, gas temperatures up to ∼ 1000 K, and strong plasma shielding of the electric field. The self-consistent coupling of electromagnetic fields, ionization, and heating is aligned with prior experimental and computational work, validating the approach for reactor design.
The rapid expansion of Low Earth Orbit (LEO) satellite constellations for 6G Non-Terrestrial Networks demands reliable short-horizon channel prediction under highly dynamic propagation conditions. LEO channels evolve under accelerated orbital motion, severe Doppler curvature, atmospheric attenuation, and ionospheric scintillation, rendering quasi-static and linear prediction models inadequate for latency-sensitive broadband services. Existing learning-based approaches improve statistical forecasting but rarely enforce orbital-kinematic consistency, incorporate synchronised environmental metadata, or validate gains at the protocol level. This paper proposes a physics-constrained, protocol-integrated deep learning framework for short-horizon forecasting of Ku-band channels. The study contributes by formalising LEO channel evolution as a bounded-acceleration stochastic process, embedding Doppler-gradient regularisation to enforce second-order temporal consistency, constructing a synchronised multimodal benchmark dataset, and demonstrating direct translation of prediction accuracy into adaptive network performance gains. The proposed hybrid architecture integrates dilated causal convolution, bidirectional Long Short-Term Memory, attention-based feature fusion, and uncertainty-aware inference. Training and validation utilise a unified dataset comprising 420 hours of measured Ku-band ground-station data and 50,000 simulated satellite passes, enriched with orbital telemetry and environmental metadata at 10 Hz. At a 200 ms prediction horizon, the model achieves a Normalised Mean Squared Error of 0.112 and an R² of 0.89, representing an 18–32% improvement over Kalman, ARIMA, GRU, and Transformer baselines. Integration with adaptive modulation and handover mechanisms yields a 12.1% increase in spectral efficiency and a 70.3% reduction in handover failures. These findings establish physics-informed, environment-aware learning as a deployable and operationally impactful solution for resilient LEO broadband communications.
Business Email Compromise (BEC) has emerged as one of the most financially devastating and strategically sophisticated forms of cyber-enabled fraud, leveraging advanced social engineering techniques to circumvent conventional email security infrastructures. Existing detection mechanisms, predominantly rule-based or static in nature, exhibit limited adaptability to the dynamic, context-aware, and linguistically nuanced strategies employed by modern attackers. This study proposes an adversarially resilient hybrid detection framework that synergistically integrates Natural Language Processing (NLP), classical machine learning models (Support Vector Machines and Random Forest), and deep learning architectures, including Long Short-Term Memory (LSTM) networks and Bidirectional Encoder Representations from Transformers (BERT). To address the critical challenge of limited labeled BEC datasets, a controlled synthetic data augmentation strategy was implemented using a fine-tuned Generative Pre-Trained Transformer (GPT), enabling the generation of high-fidelity adversarial email samples. A comprehensive hybrid feature engineering approach was adopted to capture the multifaceted characteristics of BEC emails, encompassing linguistic, structural, metadata, stylometric, and contextual attributes. Model training and evaluation were conducted using stratified cross-validation, with performance assessed through accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Model interpretability was enhanced through SHapley Additive exPlanations (SHAP), providing transparent insights into feature contributions. Empirical results demonstrate that the LSTM model achieved superior performance, attaining an accuracy of 98.5%, significantly outperforming Random Forest (95.3%), Support Vector Machines (94.8%), and baseline rule-based approaches (85.4%). The proposed framework demonstrates strong potential for real-world deployment within enterprise email security ecosystems. Future work will focus on multilingual detection, real-time system integration, and large-scale validation within operational Security Operations Center (SOC) environments.
The design and development of terahertz (THz) reflectarray antenna is presented with a hybrid asteroid–stub–cross unit cell to achieve continuous and complete 360° reflection-phase coverage with minimal amplitude variation of -1.6 dB for an operating frequency of 1 THz. The proposed reflectarray consists of 417 elements arranged with a spacing of 0.5λ, demonstrating significant suppression of -120 dB between the co-polar and cross-polar components. This proposed reflectarray antenna demonstrates a peak gain of 25.3 dBi, a 3-dB beamwidth of 5.5°, a fractional gain bandwidth of 30.8%, a sidelobe level (SLL) of −19.5 dB, and a total radiation efficiency of 88%, validating accurate phase synthesis, high aperture efficiency and minimal phase-quantization error from 1 to 1.5 THz. In addition, the desired phase distribution across the aperture is generated by calculating the phase compensation for each element using MATLAB code. These results are then integrated into the design through a parametric scaling factor α, which is applied to the geometry of the unit cell. A pyramidal horn antenna designed for providing uniform illumination of the 417-element reflectarray, which is built on a low-loss quartz substrate with a gold conductive layer. Owing to its compact aperture, high directivity, and stable broadband behavior. From the results, the proposed design is a strong candidate for 6G THz wireless communication links.
The rapid evolution of fifth-generation (5G) communication systems has intensified the need for high data rates, enhanced capacity, and ultra-reliable low-latency connectivity. Multiple-Input Multiple-Output (MIMO) technology plays a pivotal role in achieving these objectives through spatial diversity and multiplexing gains. However, the practical deployment of MIMO in 5G environments faces persistent challenges, including interference, channel fading, and hardware complexities. This study presents an intelligent performance analysis and optimization framework for 5G MIMO systems using machine learning (ML) techniques. A hybrid MIMO architecture was simulated in MATLAB under realistic millimeter-wave conditions, and the generated dataset was used to train and evaluate four ML models—Random Forest, Linear Regression, Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost). Performance evaluation was based on key metrics such as throughput and bit error rate (BER). The results revealed that the ensemble learning models, particularly XGBoost and Random Forest, outperformed conventional methods by achieving superior accuracy and minimal error margins. These findings highlight the effectiveness of machine learning-driven optimization to improve spectral efficiency, reduce latency, and improve overall reliability in 5G MIMO networks.
Malware remains a major cybersecurity concern, which demands effective techniques for accurate detection and classification. This study presents a novel framework that leverages binary image representations of malware to enhance classification performance. The process begins by transforming malware files from their hexadecimal form into binary data, which is then converted to grayscale images serving as input for deep learning models. The study also examines the distinctive visual characteristics of various malware families, revealing how structural patterns in binary images are correlated with classification outcomes. By examining the role of image processing and deep learning, the research provides valuable insight into the intersection of artificial intelligence and cybersecurity. The findings highlight the strength of CNNs for malware classification, while acknowledging the complementary potential of ResNet and Autoencoder-based approaches. As cyber threats become increasingly sophisticated, advancing detection methods is essential. This work demonstrates that combining deep learning with binary image analysis presents a promising approach to developing more resilient malware detection systems and enhanced protection for digital environments. Three architectures—Convolutional Neural Networks (CNN), Residual Networks (ResNet), and Autoencoders—are systematically evaluated using a dataset of 3,240 malware samples categorized into nine families. The dataset is carefully divided into training and testing sets, and all images are resized to maintain consistency between inputs. Among the evaluated models, CNN with image-scaling techniques shows a superior accuracy of 91%, outperforming the ResNet and Autoencoder models, which achieve accuracies of 86% and 85%, respectively.
Voice over Internet Protocol (VoIP) technology has transformed global communication by providing cost efficiency, scalability, and integration with digital platforms. Nevertheless, its widespread adoption has increased exposure to cyber threats, including vishing attacks, call spoofing, and data breaches. Vishing, a social engineering technique designed to manipulate users into disclosing confidential information, has become particularly prevalent, underscoring the need for intelligent and adaptive defense mechanisms. This study developed a Deep- Reinforcement Learning (DRL) framework to detect and mitigate VoIP threats. The framework integrates deep neural networks with reinforcement learning, enabling adaptive decisionmaking and proactive security responses. Development comprised objective formulation, data set preparation, feature engineering and iterative training using historical VoIP-to-cellular attack datasets and simulated datasets generated within a virtual testbed. The DRL model achieved 90.0% accuracy in historical data (precision 88.7%, recall 91.3%, F1-score 90.0%) and 89.24% accuracy in simulated data (precision 87.5%, recall 90.6%, F1-score 89.0%), with false-positive rates below 6% and an average detection latency of 2.1 seconds. Comparative benchmarking against Support Vector Machines, Random Forest, and Convolutional Neural Networks confirmed superior performance, highlighting the scalability, robustness, and practical significance of the framework in securing VoIP communications across evolving environments.
Wireless communication is one of the fastest evolving fields of communication engineering and has made interaction within and between nations a reality, with Nigeria not left out of the loop. The rapid evolution of wireless communication from 1G to 5G has revolutionized global connectivity, yet rising demands for data speed, ultra-low latency, and ubiquitous connectivity have revealed the limitations of existing systems. This paper gives a comprehensive review of 6G wireless communication from more than 35 works, predominantly from recent journals and conferences. The study explores the architecture, enabling technologies, application domains, and expected impact of 6G networks. Core enabling elements such as terahertz communication, artificial intelligence (AI), machine learning (ML), reconfigurable intelligent surfaces (RIS), and cell-free massive MIMO are discussed in detail. The paper also highlights the transformative potential of 6G in sectors including healthcare, education, transportation, agriculture, and smart cities. Despite its promise, 6G faces critical challenges related to infrastructure development, spectrum management, energy efficiency, cybersecurity, and regulatory frameworks. The review ended with a future direction to serve as a guide for future researchers that focuses on under-tapped aspects of 6G wireless technology: generative AI and Machine Learning Integration in 6G.
This paper proposes a miniaturized conformal band stop frequency selective surface for electromagnetic shielding in the X band from 8 to 12 GHz. The structure is realized on a flexible polyimide substrate, which allows seamless integration on curved platforms. The unit cell occupies an area of 5 mm × 5 mm and incorporates a meandered square loop combined with an inverted I dipole to achieve an electrical length of nearly 0.13 λ0 at the 10 GHz resonance. The surface provides a peak attenuation of 47 dB at 10 GHz with a fractional bandwidth of 40 percent, which fully covers the X band. The symmetric topology yields stable characteristics in both TE and TM modes and retains performance up to an incident angle of 80◦. The key novelty lies in the combination of miniaturization and conformal wideband shielding achieved through the hybrid resonant geometry, which enables compact scalable and angle stable operation. These features make the proposed surface suitable for high performance X band shielding in platforms that require both reduced size and robust frequency response.
The sixth generation (6G) of wireless communication networks have their vision to achieve such high levels of data rates, ultra-low latencies, massive connectivity and better energy efficiency unfathomable and capable of serving a wide variety of applications such as holographic communications, immersive extended reality (XR) and intelligent Internet of Everything (IoE). These ambitious goals require tremendous improvements in the technologies of the physical layer, especially modulation schemes that can maximize the use of the spectrum and with minimal costs in energy consumption. The present review is an overview of newly proposed spectrum and energy-efficient modulation schemes for 6G systems. Some conventional modulation types, such as quadrature amplitude modulation (QAM) and phase-shift keying (PSK), are severely challenged due to their inability to satisfy the high requirements of 6G. Newly introduced modulations, such as Orbital Angular Momentum (OAM)-based, Index Modulation (IM), Media-Based Modulation (MBM), Time-Frequency Packing (TFP), and Non-Orthogonal Multiple Access (NOMA)-based modulations are comprehensively studied on the basis of spectral efficiency, energy consumption, complexity, robustness, and compatibility with high-frequency band including terahertz and millimeter waves. In addition, hardware implementation issues, performance complexity trade-offs, and possible integration with sophisticated coding and waveform designs are addressed in the review.
Wireless charging of biosensors represents a significant advancement in biomedical devices, especially in wearable and implanted health monitoring systems. This approach uses magnetic resonator coupling to transmit energy without physical connections, hence enhancing patient comfort and mobility, unlike conventional batteries. This paper discusses a transmitting coil powered by a class-E RF power amplifier operating at a frequency of 5 MHz, which energizes two parallel receiving coils positioned 2 × 10−3 meters apart and located 2 × 10−2 meters away from the transmitter. This arrangement establishes a loosely coupled inductive link. The proposed wireless power transfer (WPT) system is designed for low impedance biosensors affixed to the human skin surface or implanted beneath the skin for biosignal detection, hence eliminating the need for resonant capacitors on the receiving end. The simulation results demonstrate that the use of parallel coils on the receiving side of a wireless power transfer system markedly enhances link efficiency compared to a single receiving coil. The proposed system achieved a maximum link efficiency of 14.5% at a load resistance of 7.5Ω, exceeding the efficiency of 11.25% achieved by the single receiving coil configuration under identical load conditions. The proposed system is appropriate for low impedance loads, particularly biosensors. This is corroborated by the simulation results. The greatest DC power extracted from the DC source is 2.85W, which is markedly low in relation to the operational efficacy of such systems. It is feasible to improve output power and inductive link efficiency by reducing the distance between transmitting and receiving coils.
This research investigates the design and performance analysis of a miniaturized, dual-band terahertz (THz) metamaterial sensor engineered for material sensing applications. Operating within the 0.1–1 THz spectral window, the sensor exhibits distinct resonances at 0.5 THz and 0.9 THz, with near-perfect absorptivity levels ranging from 95.5% to 99.9% and 99.4% to 99.9%, respectively. The unit cell incorporates an outer square loop electromagnetically coupled with an inner loop integrated with dual T-shaped stubs, achieving dual resonant modes through mutual inductive and capacitive interactions. The proposed sensor achieves substantial miniaturization, with a footprint of just 0.147 λ₀ × 0.147 λ₀ (λ₀ corresponding to the lowest operating frequency), facilitating dense integration in THz systems. Sensitivity analysis, performed by varying the refractive index of an analyte layer, reveals a resonance shift sensitivity of 0.083 THz/RIU at 0.5 THz and 0.152 THz/RIU at 0.9 THz, indicating strong electromagnetic field confinement and high refractive index contrast sensitivity. The influence of analyte thickness on the spectral response is also evaluated, showing minimal frequency drift, validating the sensor's robustness. The unit cell's symmetric design ensures polarization insensitivity, offering consistent response under both transverse electric (TE) and transverse magnetic (TM) excitations. Furthermore, the sensor demonstrates angular stability up to 60° for oblique incidence, ensuring reliable operation across diverse illumination conditions. The combination of deep subwavelength miniaturization, dual-frequency response, near-unity absorption, and good electromagnetic stability positions the proposed sensor as a highly promising candidate for next-generation THz sensing platforms targeting biochemical, dielectric, and hazardous material analysis.
This study examines the impact of atmospheric variables on the propagation of digital terrestrial television signals from Ogun State Television, located in Abeokuta, Ogun State, Nigeria, with a focus on the relationship between weather parameters and surface radio refractivity. Data on received signal strength and meteorological factors such as temperature, relative humidity, and atmospheric pressure were collected over 12 months. The analysis revealed significant seasonal variations, with higher relative humidity and surface refractivity observed during the rainy season (April to October) compared to the dry season (November to February). The study found that the lowest signal strength occurred in June, probably due to increased rainfall, while the highest signal strength was recorded in February. The results indicate that surface radio refractivity is influenced by climatic conditions, with higher values during the rainy season, affecting the performance of UHF signal propagation. These findings provide valuable information for radio engineers in the design and optimization of microwave communication systems in regions with similar climatic conditions.
Several remote user authentication schemes have been proposed using external memory and smart cards. Smart card-based schemes have user mobility and deployment problems, and those based on external memory have a tamper resistance problem. Although Cherbal and Benchetioui’s scheme solved remote authentication problems using Elliptical Curve Cryptography and 2-Factor authentication, they still have not addressed key distribution, perfect forward secrecy, and session hijacking issues. This research proposes an enhanced multi-factor authentication and access control scheme based on the S13 quantum key distribution protocol, using a client file in an external memory. The proposed scheme combines the powers of an enhanced quantum key distribution, a lightweight tamper-resistance client file, and biometrics to overcome these limitations. The security of the proposed scheme was verified, validated, and evaluated against the reviewed scheme using various tools. The AVISPA simulation results showed that the proposed scheme is validated, secure against session hijacking, has a secure key distribution policy, and ensures perfect forward secrecy. The security functionalities analysis shows that the proposed scheme has the highest security index of 13 and is the only scheme that used the tamper-resistance client file in an external memory for the first time. The performance evaluation results showed that the proposed scheme is more efficient than the reviewed scheme with a computation cost of 0.83125s and 0.25902s and a percentage improvement of 48.48% and 73.98% for the respective user and server and a more efficient communication costs of 2112 bits than the reviewed scheme with a percentage improvement of 16.46%.
The heterogeneity in the causes and responses to pain in patients makes neuralgia, a condition defined by persistent severe nerve pain, a challenging treatment problem. However, inconsistent therapeutic results and long patient suffering are common results of traditional therapy procedures that depend on generic methodologies. This research presents a technological framework that combines data mining and transcranial focused ultrasound (tFUS) to improve strategies for the treatment of neuralgia, with the aim of overcoming these limitations. The first step of the proposed system is to gather multimodal datasets that have been preprocessed using normalization, noise reduction, and feature extraction methods. These data sets include patient-reported pain ratings, clinical history, and brain imaging (fMRI, EEG). Next, data mining algorithms such as clustering and classification are used to find patterns of brain activity and pain attributes. Dimensionality reduction methods such as variational autoencoders (VAEs) make complex associations easier to observe and understand. Optimal tFUS parameters frequency, intensity, and focal depth are predicted for individual patients using machine learning models (MLM), such as gradient-boosted decision trees (GBDT) and Random Forests (RF). Based on the biomarkers detected, these predictions direct the deployment of tFUS procedures to a specific area of the brain. During treatment, real-time neural feedback systems track patients’ reactions, allowing adaptive alterations to boost effectiveness. Incorporating post-treatment results into an iterative feedback loop allows the continued improvement of prediction models for future sessions. An increase in pain reduction measures was observed compared to traditional techniques, greater neuroplasticity and fewer side effects when the framework was evaluated on data sets from patients with neuralgia. The proposed method achieves neuroplasticity by 97.86% and 97.14%, side effects of 34.61% and 37.83%, pain reduction of 98.64% and 96.36%, effectiveness and patient safety of 97.04% and 98.67%.
In the realm of digital signal processing, speech enhancement plays a crucial role in applications such as teleconferencing, voice recognition, and biometric systems. Noise and distortions significantly affect speech quality, necessitating advanced enhancement techniques. This paper proposes an optimized deep convolutional neural network (CNN)-based speech enhancement method, integrating signal subspace searching and the minimum error and time-spectral estimator (METS). The model is trained and evaluated using the LJ Speech Dataset, augmented with various noise conditions. Experimental results demonstrate that the proposed method achieves a PESQ of 3.7, STOI of 0.92, and SNR improvement of 12.3 dB, outperforming traditional and deep learning-based methods such as Spectral Subtraction, Wiener Filtering, MMSE, SEGAN, and DCRN. The integration of METS refines the spectral estimation, while CNN effectively reconstructs speech features, leading to better intelligibility and reduced spectral distortion. Future research will focus on real-time processing and adaptive noise handling, ensuring robust speech enhancement for diverse applications.
Strong security solutions are becoming crucial, especially in academic and institutional networks, due to the increasing frequency and sophistication of cyberattacks. This research is the implementation of the Snort Network Intrusion Detection System (NIDS) on the Local Area Network (LAN) segment. Its main goal is to create a system that can monitor, identify and notify network managers of any security risks within the LAN. The study entails setting up Snort on a server connected to the LAN network for the identification of malicious activity and other intrusion attempts. The use of the Snort tool to improve network security is demonstrated when properly configured. The results showed the Snort-ids system recording 106 TCP, 0 UDP, and 271 ICMP alerts. So, Snort can assist safeguarding the LAN’s network architecture from both internal and external threats through the offer of real-time monitoring and alarms. The results of the system evaluation showed a false positive rate of 13.23% and a false negative rate of 86.7%.
6G network is an innovative concept of connectivity, which offers unparalleled speeds, ultralow latency, and extensive device connectivity that surpass the capabilities of the current 5G networks. However, challenges such as network congestion and security threats pose significant hurdles to ensuring reliable and stable network performance. A novel Integrated Digital twin and self-healing mechanisms for 6G networks (IDEA-6G) approach has been proposed for addressing these challenges and for the security and performance of the 6G network. The proposed method leverages the Digital Twin (DT) sub-layer to bridge the physical and digital worlds, enabling real-time synchronization and monitoring of network assets. Meticulous feature extraction using Term Frequency - Inverse Document Frequency (TF-IDF) techniques and the innovative Generative Adversarial Networks and Long Short-Term Memory (GAN-LSTM) model have helped the approach in the enhancement of security monitoring capabilities and efficient detection of cyber-attacks within virtual models. Additionally, Deep Neural Networks (DNNs) facilitate informed decision-making for effective self-healing actions in response to identified threats. The effectiveness of the proposed IDEA-6G is compared with the existing B5GEMINI, DTFV, and DITEN techniques. Results of the proposed IDEA-6G technique indicate superior performance prediction accuracy, detection rates, load balancing, and service delay reduction. The detection of the proposed IDEA-6G technique is 17.55%, 27.54%, and 7.38% higher than the existing B5GEMINI, DTFV, and DITEN techniques respectively.
