Electrical modeling of photovoltaic devices has important real-life applications including system design, power forecasting, and degradation analysis. In this context, the singlediode model is widely used to simulate the electrical behavior of photovoltaic cells, modules, and arrays under various operating conditions. A popular adaptation commonly used in the field is the De Soto et al. model, which updates the five parameters of this onediode model to the operating conditions, particularly for silicon technologies. Alternative methods also exist, but they have not been thoroughly evaluated to see if they can keep up with modern technologies. In this study, we use current-voltage curve measurements of commercial crystalline silicon and thin-film solar panels to evaluate the accuracy of twelve techniques found in the literature to adjust the single-diode model parameters to temperature and irradiance. We find that these approaches have overall comparable performances, but that the optimal choice among them should be based on the prevailing operating conditions and cell technology. Although the De Soto model can potentially be improved by accounting for the resistances' dependence on irradiance, it still offers a good tradeoff between simplicity and accuracy.

This paper presents the analysis and effects of frequency diversity on finite uniform circular array for generation of range dependent Orbital Angular Momentum (OAM) modes. The analysis reveals that the array factor expression in terms of Bessel's function for a circular array with finite no. of elements possess a phase error in the azimuthal domain at any transverse plane due to which the range dependency of OAM modes is affected. In this report a detailed analysis of the phase distribution on concentric circles for an exact array factor expression without considering any assumption or approximation has been carried out. Effect of changing parameters such as total number of elements and frequency increment value is analyzed and compared with the approximated array factor expression. Fourier analysis in the azimuthal domain reflects the presence of desired modes at a specific range for finite array cases. Generated phase and amplitude profile validate the occurrence of range dependent OAM modes at specified distance. Analysis of phase error for different frequency increment for finite no. of elements is also done. A prior knowledge of the available modes at a specified distance away from the antenna is crucial for improving the spectral efficiency of a communication system or radar.

Further progress in industrial electrochemical generators' designs utilising SOFC considerably depends on problem of obtaining fuel cell materials with predefined properties [1,2]. Due to this fact, considerable efforts of designers are directed on creation of optimum composition «anode-electrolyte-cathode», and on microconstruction of SOFC materials (selection of phase and element composition, defect structure formation, etc.). In the second problem nanocomposite systems having unique properties are of greatest interest [3]. Utilising of such materials as solid electrolyte may allow significantly improve cell's parameters, in particular, increase ionic conductivity [4,5]. However mechanisms leading to this effect were not determined singly. In this article the influence of nanoparticles ensemble with work of exit that different from the rest of the matrix on the change of electron density in SOFC materials have been examined theoretically. The calculations of concentration distribution of free carriers around single particle and for microparticles ensemble were fulfilled. In conclusion possible directions of forming of nanostructures conformably to SOFC materials discussed.

In this work, we extend the standard quasi-Helmholtz filters to high-order discretizations in the context of the boundary element method. This generalization allows preconditioning integral equations such as the electric field integral equation (EFIE) in the h-refinement regime while preserving the accuracy gain of high-order discretizations. The theoretical framework will be corroborated by numerical results that validate the effectiveness of the proposed strategy when stabilizing the refinement-dependent spectral behavior of the high-order discretized EFIE.

This paper considers cellular mmWave networks with full-frequency reuse where base stations (BSs) use multiuser analog beamforming to serve users (UEs) in transmission blocks of time, divided into slots. We propose a beam scheduling scheme that guarantees UEs' target rates, using the smallest possible number of slots at each BS in each transmission block. We formulate the beam scheduling task as a stochastic mixed-integer nonconvex time allocation problem, allocating time to UE-BS links, and provide a novel iterative algorithm that solves the problem. In each iteration there are three stages. Stage one optimizes the time fraction allocation for UE-BS links given an estimate of UE rates in each transmission block. Stage two allocates these time fractions to the RF chains at each BS using a sand-filling policy. Stage three schedules beams, and updates the estimate of UE rates in each block. Numerical results show that our proposed scheme significantly outperforms round-robin (RR) baseline schemes. We provide feasible UE scheduling in close to 100% of cases, compared to 44% for the RR scheme, and our median BS utilization is 68% lower than RR.

Internet of underwater things (IoUT) is increasingly gathering attention with the aim of monitoring sea life and deep ocean environment, underwater surveillance as well as maintenance of underwater installments. However, conventional IoUT devices, reliant on battery power, face limitations in lifespan and pose environmental hazards upon disposal. This paper introduces a sustainable approach for simultaneous information uplink from the IoUT devices and acoustic energy transfer (AET) to the devices via an autonomous underwater vehicle (AUV), potentially enabling them to operate indefinitely. To tackle the time-sensitivity, we adopt age of information (AoI), and Jain's fairness index. We develop two deep-reinforcement learning (DRL) algorithms, offering a high-complexity, high-performance frequency division duplex (FDD) solution and a low-complexity, medium-performance time division duplex (TDD) approach. The results elucidate that the proposed FDD and TDD solutions significantly reduce the average AoI and boost the harvested energy as well as data collection fairness compared to baseline approaches.

In order to manage electricity transmission and distribution it is now common practice for system operators to offer financial incentives that encourage large consumers to reduce energy usage during designated peak demand periods. For train operators on large rail networks it may be profitable-with selected individual journeys-to reduce energy usage during peak times and increase energy usage at other times rather than simply minimizing overall energy consumption. We will use classical methods of constrained optimization to find optimal driving strategies for a fleet of trains subject to limits on total energy consumption during specified intermediate time intervals but with no change to individual journey times. The proposed strategies can be used by a large rail organisation to reduce overall operating costs with only minimal disruption to existing schedules and with no changes to important departure and arrival times.

Digital prototypes of a ground-effect aircraft use an upper-surface trailing-section ducted fan to reduce both drag and rolling losses of trucks and railcars with exceptionally good lift-drag ratio (L/D) efficiencies for multimodal rail-highway clearances of 1-5 cm. Extending the multimodality to include flight over water would provide unprecedented seamless connectivity and passenger-oriented end-to-end transit with nonstop routing extending from inland to off-shore. The electric vehicles have a design constraint of a core ground-effect flight transit (GEFT) vehicle having a width of 2.5 meters to allow operation on railways and highways. The problem is the rapid decrease of L/D efficiency as clearances approach and exceed ~0.2 meters. This paper evaluates the impact of fences “skiing” on the water and wing extensions to increase L/D efficiency over water. The data identifies a water-corridor market entry position of extending rail-highway multimodality to rivers, river crossings, and calmer coast waters. The high L/D efficiency teaches toward electric vehicles. Solar panels would be particularly effective at locations with high solar irradiance.

Solar PV micro-inverters are typically designed to be operated in grid-connected mode. By interfacing a battery with a micro-inverter, it is possible to minimize the wide variations in power drawn from solar PV module. In this article a battery-integrated transformer-less solar PV micro-inverter is proposed which can integrate a battery having voltage lower than that of the PV module. Depending on the available solar irradiation and the battery's state of charge, it simultaneously regulates the battery current and the current drawn from the PV module. Although it is suitable for getting interfaced with a 220 V ac grid, it can operate in standalone mode as well in the absence of the grid. The proposed micro-inverter has inherent power decoupling capability, and the existence of double grounding ensures that there is no leakage current flow. In-depth analysis of the proposed scheme is carried out. An appropriate control algorithm is developed for operating the micro-inverter under different conditions. The feasibility of the proposed scheme is validated by carrying out detailed experimental studies on the prototype developed for the purpose.

Federated Learning (FL) is a decentralized machine learning approach that enables collaborative model training across distributed data sources while ensuring data privacy. Unlike traditional centralized approaches, FL allows multiple clients (e.g., mobile devices, IoT sensors, and hospitals) to train a global model without sharing their raw data. This survey provides a comprehensive overview of FL, discussing its fundamental concepts, architecture, algorithms, and optimization techniques. We highlight the diverse applications of FL across various industries, such as healthcare, finance, autonomous vehicles, and smart cities, showcasing its potential to address real-world challenges related to data privacy, security, and scalability. Additionally, we identify the key research challenges and open issues in FL, including communication overhead, data heterogeneity, adversarial attacks, and regulatory compliance. Finally, we discuss the future directions of FL, focusing on potential advancements in communication efficiency, privacypreserving techniques, and integration with emerging technologies such as edge computing and 5G networks. This survey aims to provide a thorough understanding of FL's current state and its promising future in shaping the landscape of distributed machine learning.

Transition metal dichalocogenide bilayers provide scaling opportunities for CMOS technology. They also lead to versatile vertical stacking that can achieve powerful tensor cores for advanced processors. The quantum wire uniformity of edge atomic positions becomes crucial to achieving tunable and controllable quantum conduction in angstrom-scale vertically integrated MOSFETs (VIMOSFETs). Advanced complex scalable VLSI/Versatile Logic memory integration (VLMI) hierarchies require strong quantization control and tuning. The photon-phonon complexities are relevant in petahertz digital switching.

The k-Nearest Neighbors (kNN) algorithm is a widely-utilized nonparametric and lazy learning technique commonly applied in classification tasks. Its simplicity and effectiveness make it an attractive option for various applications, including remote sensing. This project focuses on using kNN to classify the central pixel in a 3x3 neighborhood within a multi-spectral Landsat satellite image. Each pixel in the dataset is represented by four spectral bands, and the goal is to predict the class of the central pixel based on its neighbors' spectral values. This study provides an overview of the kNN algorithm, detailing its core concepts, operational principles, and applications specific to satellite imagery classification. We explore the influence of the parameter k, the choice of distance metric, and the impact of different weighting schemes on the algorithm's performance. By comparing kNN's effectiveness with traditional statistical methods, this project aims to demonstrate the potential of kNN in handling diverse remote sensing data types and enhancing pixel classification accuracy.

The growing sophistication of cyberattacks has led to an urgent need for advanced techniques in malware detection and mitigation. Neural networks (NNs), with their powerful learning capabilities, have emerged as a promising tool for predicting malware propagation patterns in complex, dynamic environments. This research explores the application of neural network architectures, particularly deep learning models, to predict and analyze the spread of malware across network systems. By leveraging large datasets of network traffic, malware signatures, and attack vectors, the study employs supervised learning techniques to train NNs to identify key indicators of infection and transmission. Through experimentation with various NN models, including feedforward, recurrent, and convolutional networks, we assess their performance in predicting the speed, scope, and potential impact of malware outbreaks. The results demonstrate that neural networks can not only predict malware propagation more accurately than traditional methods but also adapt to evolving attack strategies, providing real-time insights for proactive defense mechanisms. This study highlights the potential of neural networks in enhancing cybersecurity measures and lays the foundation for developing intelligent, adaptive systems capable of combating future malware threats.

Understandability plays a significant role in the effective deployment of autonomous robots, as it ensures the robot clearly communicates its decisions, actions, and plans to the human it interacts with. To advance robot understandability, this work proposes a model to structure levels of explanation (LoE) and methods to minimize existing discrepancies between the human's and robot's state of mind. The applicability of the LoE model is demonstrated through two different scenarios, i.e. search and rescue operations and physical training robots. Two Markov process models are utilized to estimate existing discrepancies, a hidden Markov model and a partially observable Markov decision process model. Following these models, robot utterances are generated to minimize these discrepancies using the LoE model. The results show that both models successfully estimate existing discrepancies and can generate appropriate communicative actions for the robot, thereby enhancing its understandability.

With the growing need for accessible information across diverse linguistic backgrounds, text summarization in multilingual contexts has become increasingly essential. Text summarization is a crucial task in natural language processing, particularly in multilingual settings involving Indian languages. This paper presents our approach for the FIRE 2024 task, where we leverage large transformer-­based language models for summarizing Indian languages, addressing the linguistic diversity and frequent instances of code­mixing and script-­mixing unique to this context. Our methodology incorporates both extractive and abstractive summarization techniques, optimized for Indian languages through advanced fine­tuning of models like mT5, IndicBART, and BART. While prompt engineering has predominantly been applied to English tasks, we adapt it alongside fine­tuning to enhance summarization performance and computational efficiency. Our models achieved top results in five languages—Hindi, Gujarati, English, Tamil, and Bengali—and ranked second in Telugu. These results demonstrate substantial improvements in summarization accuracy, underscoring our approach’s efficacy in handling the complexities of Indian languages and advancing text processing in multilingual, mixed­-language environments.

This study presents an end-to-end deep learning pipeline for bleeding detection and classification in Wireless Capsule Endoscopy (WCE) imagery. ResNet-18 [1] is employed for classifying bleeding and non-bleeding frames, while UNet [2] performs precise segmentation of bleeding regions. For detection, YOLOv8 [3], implemented via the Ultralytics library, efficiently identifies bleeding areas. The dataset was adapted to the YOLO format, with a custom YAML file crafted for seamless model integration. This multi-model approach addresses critical challenges in WCE analysis, offering scalable and accurate AI-driven solutions for gastrointestinal bleeding diagnostics.

Low Earth Orbit (LEO) satellite networks serve as a cornerstone infrastructure for providing ubiquitous connectivity in areas where terrestrial infrastructure is unavailable. With the emergence of Direct-to-Cell (DTC) satellites, these networks can provide direct access to mobile phones and IoT devices without relying on terrestrial base stations, leading to a surge in massive connectivity demands for the serving satellite. To address this issue, group communication is an effective paradigm that enables simultaneous content delivery to multiple users and thus optimizes bandwidth reuse. Although extensive research has been conducted to improve group communication performance, securing this communication without compromising its inherent spectrum efficiency remains a critical challenge. To address this, we introduce StarCast, a secure group encryption scheme for LEO satellite networks. Our solution leverages ciphertext-policy attribute-based encryption (CP-ABE) to implement fine-grained access control by embedding access policies directly within the ciphertext. Unlike standard secure communication approaches that require dedicated per-user channels and significantly deplete limited satellite spectrum resources, StarCast maintains efficient spectrum reuse within user groups while ensuring that only authorized users can access transmitted data. Additionally, it significantly reduces the costly key management overhead associated with conventional encryption schemes.

This research delves into the advancements in embedded systems, with a focus on three pivotal areas: IoT Implementation, Real-time computing, and Energy consumption. Embedded systems are now the foundation of most current technology, networked systems, smart gadgets, or even straightforward automation systems. In this paper, the author discusses ways IoT integration has increased the range of applications in embedded systems and enhanced their interaction. It also explores new developments in real-time processing; here, the focus is on the parts played by fast computation and quick response to them, such as medical, automobile, and industrial control. Also, the research raises the issue of the growing relevance of energy efficiency. It focuses on energy-efficient notions, innovative approaches, and hardware solutions aimed at reducing power consumption and sufficient performance. Security issues, system issues in general, and system scalability are also presented, providing a perspective of the complexity of the field. The research results should be helpful for young researchers, developers, and other industry members to understand the current trends that should be continued and the gaps that need to be filled in the future.