Hydrogen is one of the main pillars in the transition to renewable energy and can be used in particular for buffering and storing energy. Electrolyzers are needed to produce sustainable, green hydrogen. Today, these electrolyzers are mainly manufactured by hand. An electrolyzer plant consists of two main com-ponents, the stack in which the actual electrolysis takes place and the balance of plant that ensures the operation of the stack. Different electrolysis technologies have essential similarities in the balance of plant so that automation can achieve particular optimization potentials at this point. The use of automation technologies such as industrial robots is intended to bring this production to series maturity. For this reason, large-scale electrolysis plants are analyzed with regard to their design, the combination of different elec-trolysis technologies and the connection technology used. A significant propor-tion of the necessary assembly steps are cable, hose and tube connections, which automation technology has not yet been able to assemble in a process-safe and economical manner. In this paper, the process of tube connection using tube nuts is explained in more detail and a design system for a robotic tool is presented. A robot tool is called an end-effector because it is used at the end of the robot kinematics. This end-effector is designed for use on industrial robots and is intended to make series production more economical. For this purpose, the necessary information pro-cessing, material flows and energy transformations are investigated and their in-terrelationships are presented. The goal is to evaluate a suitable physical operat-ing principle for bolting hydrogen tube nuts.

The use of technology has permeated all aspects of modern society, including education. Within pedagogy, computer- assisted language learning (CALL) has gained popularity as a result of technological advancements. The potential for combining this sub-field of L2 learning and algorithm-driven approaches to monitor in-class performance has remained unexplored. This study draws on PageRank (PR) and social network analysis (SNA) to monitor student interactions in an SLA environ- ment. This study compares the effectiveness of PageRank and eigenvector centrality in reflecting student in-class performance using data from an L2 Spanish lecture. The results suggest that PageRank outperforms other metrics at abstracting in-class performance, showing 91% of score uniqueness against the 33% obtained by eigenvector centrality.

The likelihood of cyberattacks that pose a threat to the safety of wireless sensor networks (WSNs) and Android devices increases with their use. A comprehensive literature review on how to protect WSNs and Android devices from cyberattacks is presented in this paper. The survey centers around the utilization of AI and different methods to recognize and forestall malware, as well as the utilization of encryption and other safety efforts to safeguard against unapproved access. According to the findings, although there is no one solution that can completely secure WSNs and Android devices, employing a proactive strategy and a variety of security measures can significantly lower the risk of cyberattacks.

Optical parametric amplification (OPA) represents a powerful solution to achieve broadband amplification in wavelength ranges beyond the scope of conventional gain media, for generating high-power optical pulses, optical microcombs, entangled photon pairs and a wide range of other applications. Here, we demonstrate optical parametric amplifiers based on silicon nitride (Si3N4) waveguides integrated with two-dimensional (2D) layered graphene oxide (GO) films. We achieve precise control over the thickness, length, and position of the GO films using a transfer-free, layer-by-layer coating method combined with accurate window opening in the chip cladding using photolithography. Detailed OPA measurements with a pulsed pump for the fabricated devices with different GO film thicknesses and lengths show a maximum parametric gain of ~24.0 dB, representing a ~12.2 dB improvement relative to the device without GO. We perform a theoretical analysis of the device performance, achieving good agreement with experiment and showing that there is substantial room for further improvement. This work demonstrates a new way of achieving high photonic integrated OPA performance by incorporating 2D materials.

With the recent advancement of software development methodologies and frameworks, service meshes has been to rapidly gain the lime light. In this paper, we provide an introduction to service meshes and discuss their key use cases in modern cloud-native architectures. We also explore the challenges associated with performance analysis of service meshes and present a survey of recent research in this area. To address these challenges, we propose the use of soft computing techniques for performance analysis of service meshes. Our study includes a comparative analysis of different service mesh platforms and their performance under varying workloads. Overall, this paper provides insights into the benefits and limitations of service meshes and highlights the importance of performance analysis in ensuring the reliability and scalability of micro-services based applications.

In Bangladesh, access to clean water is a major problem for many areas because of its location, lack of freshwater resources, and growing population. In this paper, we explore sustainable methods for producing clean water that may help in resolving Bangladesh's problems with water shortage. We conduct a thorough analysis of several technologies and procedures that are appropriate for Bangladesh. We assess these approaches based on their cost-effectiveness, scalability, environmental impact, and sustainability. According to our findings, these technologies can be useful for alleviating Bangladesh's water shortage, particularly in rural regions where access to clean water is scarce. We also stress the value of community knowledge and participation in the effective application of these technologies.

Digital Delta Sigma modulators (DDSM) are used in applications that require a reduction of the wordlength of a digital signal. In the presence of non-idealities, the quantization error of the DDSM can interact with nonlinearities further along the signal chain and generate spurious tones in addition to excess noise.

Abstract — The Segment Anything Model (SAM) is a state-of-the-art deep learning architecture for image seg- mentation that allows users to segment arbitrary objects in images using points, or bounding boxes. SAM uses a novel two-stage approach that first encodes the input image into a high-dimensional embedding, and then generates object masks using the embedding and the input prompt. This ap- proach allows SAM to produce high-quality segmentation even for objects that were not seen during training, and to handle complex input prompts with multiple objects and attributes. We evaluate SAM on the open source Chest X- ray dataset known as Japanese Society of Radiological Technology (JSRT) dataset and another dataset obtained from Emory Hospital, Atlanta, Georgia,USA and show that it achieves state-of-the-art performance in terms of Stability Score and Jaccard Index including the perfor- mance across different demographic data such as age and gender. Overall, SAM provides a powerful tool for image segmentation, enabling efficient and accurate object detec- tion and segmentation for a wide range of applications. It also represents a significant advance in the field of image segmentation and opens up new opportunities for interactive and multi-modal image analysis.

Voltage controlled magnetic components, which consist in dynamically controllable inductances and transformers, are a promising yet understudied technology of growing interest. In fact, these components offer circuit designers an additional degree of freedom to achieve multi-objective optimization with improved Pareto fronts. This article provides a review of some technologies that can be used to create controlled magnetics, including emerging technologies with high potential. Furthermore, a list of possible applications is proposed, where these components can provide a significant advantage in terms of efficiency, size reduction, or controllability. Special emphasis is laid on a use case: a 20 kW multiport DC/DC converter in which power flow control is achieved using voltage-controlled inductors based on partially saturable magnetic cores.

This paper delves into the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) as they pertain to national security, with a particular emphasis on their integration into military applications. Through an in-depth analysis of the United States’ progress in assimilating AI technologies into the battle space, the paper seeks to reinvigorate enthusiasm about the potential of AI in safeguarding national interests. Key aspects of this paper include the necessity of protecting AI systems from adversaries, as well as the inherent challenges and risks that come with integrating them into military operations. The paper concludes by underscoring the strategic significance of AI in the realm of national security and outlines a set of strategic imperatives for military leaders and policymakers to ensure success in this critical domain.

In this paper, we propose a novel control strategy for the optimal scheduling of an energy community constituted by prosumers and equipped with unidirectional vehicle-to-grid (V1G) and vehicle-to-building (V2B) capabilities. In particular, V2B services are provided by long-term parked electric vehicles (EVs), used as temporary storage systems by prosumers, who in turn offer the V1G service to EVs provisionally plugged into charging stations. To tackle the stochastic nature of the framework, we assume that EVs communicate their parking and recharging time distribution to prosumers, allowing them to improve the energy allocation process. Acting as selfish agents, prosumers and EVs interact in a rolling horizon control framework with the aim of achieving an agreement on their operating strategies. The resulting control problem is formulated as a generalized Nash equilibrium problem, addressed through the variational inequality theory, and solved in a distributed fashion leveraging on the accelerated distributed augmented Lagrangian method, showing sufficient conditions for guaranteeing convergence. The proposed model predictive control approach is validated through numerical simulations under realistic scenarios. This preprint has been accepted for publication in IEEE Transactions on Control Systems Technology. How to cite: N. Mignoni, R. Carli and M. Dotoli, “Distributed Noncooperative MPC for Energy Scheduling of Charging and Trading Electric Vehicles in Energy Communities,” in IEEE Transactions on Control Systems Technology.

The development of sustainable and resilient networking and communication technologies, artificial intelligence, dispersed computing, and beyond 5G are expected to play a crucial role in the realization of Industry 5.0. More specifically, antennas are recognized as a vital technology to support the continuously growing demand for 5G/6G connectivity and data rates. In the development of wireless communication systems, an effective antenna design is critical. In this work, we present the design and modeling of a hexagonal microstrip antenna for using in ultra-wide band applications such as 5G mobile networks. The suggested antenna has a frequency range of 3.3 GHz to 4.5 GHz, dimensions of 20 mm × 14 mm, and is built on a FR-4 substrate, with a relative permittivity of 4.3, and of 1.6 mm in height. It is expected that this antenna will operate in two notched bands with resonance frequencies of 3.3 GHz and 4.5 GHz and a voltage standing wave ratio (VSWR) of less than 2. The proposed antenna, which has been expanded to be a multiple-input multiple-output (MIMO) antenna, was created and modeled using CST Microwave Studio. The comparison against the recent works is given to show the efficiency of the proposed design.

This paper introduces a new analytical framework to evaluate the capacity of intelligent reconfigurable surface (IRS)-aided wireless networks in the presence of a direct link (DL). The obtained analysis is used to characterize the signal-to-noise ratio (SNR) at the user equipment (UE) while using adaptive power and rate transmission. In particular, we consider the channel inversion with a fixed rate, optimum power and rate adaptation, and the truncated channel inversion with a fixed rate. The obtained expressions are derived in a unified closed-form. All the single-hop channel gains are modeled as independent and identically distributed Nakagami-m fading channels. Consequently, the channels’ gains at the receiver become independent and non-identically distributed. The moment generating function (MGF) is used to derive an accurate approximation of the probability density and cumulative distribution functions of the instantaneous SNR, which are used to evaluate the channel capacity at low and high SNRs to quantify the achievable multiplexing gain. The obtained analytical and simulation results indicated that a strong DL may significantly enhance the channel capacity gain obtained using the IRS. In particular scenarios, the capacity improved by about 30% for a large number of IRS elements when the DL Nakagami fading parameter m is increased from 2 to 6.

The recent advancements in visible light communication (VLC) will pave the path for high precision, cost-effective, power-efficient, and secure indoor communication networks. The characteristics of VLC make them a potential candidate for the 6th generation of cellular standards, specifically for applications related to short distance and high throughput communication scenarios. However, indoor VLC faces some challenges such as line-of-sight (LoS) link blockages due to obstacles, and uneven power distribution due to unguided non-Line of Sight (NLoS) reflections from the wall which limits its large scale deployment in dense scenarios. In this paper, we have proposed an intelligent reflecting surface (IRS)-aided indoor VLC system with an optimal irradiance angle guided toward the user subject, which can facilitate optimal orientation of the IRS element in order to maximize the received power at the user. Further, we also derive the closed-form expression for the obtained optimal irradiance angle, essentially a cosine of the angle of irradiance for an element of IRS based on the location of IRS and the receiver’s (users) position. In the designing of the irradiance angle, we considered all 4 vertical walls of the room for placing the IRS. In addition, for a given receiver, we have proposed an algorithm to find the optimal orientation of an element of IRS that will produce the maximum power at the receiver. The proposed algorithm returns the rotation angles which will result in the optimal orientation of the element with the time complexity of O(1).

A document by Jifan Liang . Click on the document to view its contents.

In this paper, a practical precoding method for the downlink of filter bank multicarrier-based (FBMC-based) massive multiple-input multiple-output (MIMO) is developed. The proposed method includes a two-stage precoder consisting of a fractionally spaced prefilter (FSP) per subcarrier for flattening/equalizing the channel across the subcarrier band, followed by a conventional precoder whose goal is to concentrate the signals of different users at their spatial locations. This way, each user receives only the intended information. We take note of the fact that channel reciprocity may not hold perfectly in practical scenarios due to the mismatch of radio chains in uplink and downlink. Additionally, channel state information (CSI) at the base station may not be perfectly known. These can lead to detrimental effects on the downlink precoder performance. We theoretically analyze the performance of the proposed precoder in the presence of imperfect CSI and channel reciprocity calibration errors. This leads to an effective method for compensating for these effects. Our study includes both cases of co-located (i.e., cell-based) and cell-free massive MIMO. Finally, we numerically evaluate the performance of the proposed precoder. Our results show that the proposed precoder leads to an excellent performance when benchmarked against orthogonal frequency division multiplexing (OFDM).

Given the increasing complexity in the modern semiconductor integrated circuit manufacturing process, a variety of defects may occur in each process step, which would eventually lead to loss in wafer yield. The complication and heterogeneity of defect morphologies add great challenges for in root cause analysis. In addition, because the defect data of scanning electron microscope(SEM) images is not easy to obtain, there is no public large data set, which brings difficulties to the training of the algorithm. In this study, we introduce a novel UNet architecture that integrates deep residual networks and an attention mechanism for the segmentation of wafer SEM images. The proposed methodology adopts an encoder-decoder structure, and adds an intermediate attention module (IAM) to enhance features using residual attention mask blocks (RAMBs). To validate the efficacy of the proposed RA-UNet model, a real dataset of defect SEM images in a foundry was manually collected and labeled. The results demonstrate that the proposed model achieves an Intersection over Union (IoU) of 71.11%, providing empirical evidence for the effectiveness of the segmentation approach in the analysis of wafer defect SEM images.

In interconnected electricity systems dominated by intermittent renewable electricity generation, resource adequacy assessments need to consider cross-zonal and intertemporal dependencies. This calls for a decision-making mechanism that determines a fair and unique allocation of inevitable demand curtailment across space and time. Otherwise, traditional resource adequacy indicators like the loss of load expectation (LOLE) or the expected energy not supplied (EENS) become arbitrary on a national level. Against this background, we develop a conceptual framework for evaluating the spatial and temporal fairness of demand curtailment allocation. We then introduce two model formulations – a linearized approach and a post-processing approach – that allow to implement a demand curtailment allocation mechanism into any dispatch optimization model. Simulation results from a European-wide case study show that on an overall system level, both approaches substantially increase the spatial fairness of the demand curtailment allocation while only insignificantly increasing the total EENS across all zones. However, only the linearized approach achieves a more fair temporal allocation across different time steps. The implications in an individual zone, e.g. Germany, are stark, which is illustrated by the LOLE varying between 12 and 116 h/a as well as the EENS between 94.9 and 293.2 GWh/a depending on the applied configuration.