Radio Access Networks (RANs) are responsible for the majority of the energy consumption of cellular networks, and their energy consumption has been growing at an unsustainable rate. Virtualized RANs (vRANs) are an alternative to monolithic RANs, which can adapt to ever-evolving traffic patterns in an agile way. Additionally, virtualization allows multiple radio sites to share the same computing infrastructure, which can lower energy consumption. However, the transition to vRANs is a slow process and there are many open questions about how to best manage vRANs to meet the requirements of cellular operators. In particular, it is unclear whether vRANs can reduce energy consumption compared to monolithic RANs and under which conditions. Energy models are crucial to address this question and identify opportunities for reducing energy consumption in vRANs. In this paper, we present an overview of the energy-modelling approaches that can be applied to RANs and we review models of monolithic and virtualized RANs. Additionally, we characterize the energy consumption of RANs at the network, node, component, and functional levels. Lastly, we examine the techniques that can be used to improve the performance of vRANs and highlight the challenges, unanswered questions, and opportunities in this field, concerning energy consumption.

Rice’s characteristic function method of analysis for combinations of signals and noise in a memoryless nonlinear system is extended in this paper to greatly facilitate numerical methods of solutions. This improvement is made by describing the nonlinearity as a Fourier series instead of a Fourier transform, as in the original method, thereby producing expressions for calculating the desired output correlation functions as summations rather than double or triple improper integrals that must be estimated. Even with modern computing tools, it has been found that estimating the values of the former improper integrals can be problematic for SNR conditions of particular interest that motivate the use of the characteristic function method, producing unreliable results and prompting researchers to look elsewhere for a solution. The modified method presented in this paper makes this powerful analytical tool more accessible to researchers and engineers, particularly as new semiconductor devices are under development requiring noise analysis. The extended method retains the useful properties of the original, including applicability to one or more signals (sine waves) and to any noise distribution since its characteristic function always exists, particularly Gaussian or random-telegraph shot noise. An alternative fundamental formula for the characteristic function method is presented expressed in terms of the Fourier coefficients and a discrete parameterization of the general characteristic function. New equations are presented and some numerical results for an example nonlinearity with a sinusoid plus Gaussian noise. The new procedure is concisely summarized for application to other problems.

We propose and experimentally demonstrate the design of a compact source for DAS systems using a mini-EYDFA commonly used in CATV networks together with an integrated, low-phase-noise Direct Digital Synthesis (DDS) device that can generate readily programmable probe waveforms with a bandwidth of up to 1.4 GHz. The DDS module is synchronized with a NI-PXIe system for real-time acquisition of traces at a rate of up to 200MS/s and, thanks to the low phase noise DDS characteristics as well as high gain and stability of the EYDFA and jitter-suppressed acquisition of traces, the scheme enables measurements of representative vibration signatures with a bandwidth of up to 4 kHz at a distance of 9.71 km, with an SNR of ~24 dB without trace averaging, offering a performance near the Nyquist limit set by round-trip-time of trace acquisition. Analyzes of the spatial, temporal, and spectral responses of extracted vibrations confirm the distributed dynamic sensing capability of the technique. The proposed configuration enables the simplification of sources used in DAS systems, paving the way toward further miniaturization of the interrogation units and their scalable commercialization for wider use in several safety and integrity monitoring applications.

In the context of India, a country with a rich tapestry of regional sign languages, effectively recognizing and interpreting Indian Sign Language (ISL) presents a formidable challenge for individuals with hearing and speaking impairments. This system introduces an innovative method for ISL recognition by leveraging the YOLOv5s (You Only Look Once version 5) object detection framework. Complementing the YOLOv5s, the project integrates Microsoft Azure’s cognitive app service, specifically the computer vision capabilities, and utilizes Mesa, a Python agent development framework. This comprehensive approach aims to enhance the expression and communication of individuals with hearing and speaking impairments in a predominantly spoken language-oriented world.

This paper explores the application of Pumped-Storage Hydroelectricity (PSH) within an electricity market characterised by a substantial share of renewable and intermittent electricity production. The purpose of PSH is to enhance the alignment of supply with demand by storing energy at electricity surplus and releasing it during shortage. The Juktan Power Station, once the PSH facility in Sweden with a storage capacity of about 25 GWh and maximum generation of 335 MW, serves as a case study. An in-depth analysis of the properties of the Swedish electricity market and its influencing factors is conducted. Wind speed data is successfully used to increase accuracy of electricity price prediction. Furthermore, a model is developed to calculate potential profitability employing various operation methods. In conclusion, large-scale PSH provide significant benefit to the robustness of the electricity system with a clear increase in value beginning in 2021. However, concerns about grid tariff design, electricity market development and energy storage incentives means that investments are not obviously profitable.

Underwater acoustic backscatter communication, which allows an underwater sensor node (USN) to communicate with the surface sink node (SN) by reflecting the acoustic signal from the SN, is a promising solution for enabling the Internet of Underwater Things. However, underwater backscattering is vulnerable to jamming attacks, especially from a gliding autonomous underwater vehicle (AUV) that can inject jamming signal to the underwater acoustic channel accordingly. This paper considers the attack-defense interactions between the AUV jammer and the SN defender in their transmit power allocations of jamming signals and acoustic signals over multiple USNs. We formulate the strategic power allocation problem for the SN and AUV as an asymmetric anti-jamming Colonel Blotto (CB) game model with a finite budget of total transmit powers. In particular, the competitive interactions between the SN and the AUV is modeled as the competition of two players in the game for limited resource budgets over the battlefield set. We analyze the mixedstrategy Nash equilibrium to the game, and obtain the closedform performance bound of the defense power allocation strategy. Numerical results show the superiority of the proposed CB game based multi-USN secure transmission scheme as compared to the benchmarks in terms of the expected sum-utility.

The J-A formulation has been proposed as a way to model the electromagnetic behavior of superconducting devices in finite element simulations. It is based on the current density, J, and the magnetic vector potential, A. It has been found that, to ensure better stability of simulations results, the shape functions of J should be discontinuous Lagrange of constant order, whereas for A second order continuous Lagrange functions should be used. However, applying second order shape functions increase the computational complexity of the simulation, both of degrees of freedom and computation time. In this paper, an approach to solve this issue is proposed. The finite element domain is divided into two parts, superconducting and non-superconducting. For the superconducting parts, the shape function of A is second order continuous Lagrange to ensure numerical stability. For the non-superconducting parts, a first order continuous Lagrange is used for A. To couple the two types of domains, Dirichlet Boundary Conditions are used. Three case scenarios are investigated and their results are compared to those of the common J-A formulation. Full agreement is obtained.

The stochastic calibration of low-cost and consumer grade inertial sensors has recently become very important due to their wide-spread utilization in a multitude of mass-market applications like smartphone and drone navigation. The reason behind this is because if accurate stochastic modeling about the inertial sensor noise is obtained, then the estimation quality of the navigation solution may improve significantly. Generally, the mainstream methods for stochastic calibration consider only a single signal, collected under static conditions, to infer that knowledge. However, it has been observed that even though the stochastic model structure that characterizes each (static) calibration signal remains the same, its parameter values vary from one replicate to another. Even though techniques have been recently proposed to address this in a statistically efficient way, a very important factor has been neglected, namely the influence of outliers on the estimation process. In this paper, a robust multi-signal framework for the stochastic modeling of inertial sensor errors is proposed, which contains two layers of robustness: one that reduces the influence of outliers in each observed signal (data corruption) and one that safeguards the estimation process from the collection of calibration signal replicates with notably different stochastic behaviour compared to the majority (sample contamination). Furthermore, two estimators are defined from this framework, with each encompassing either one or both layers of robustness, and their efficiency in different data contamination scenarios is assessed in a simulation setting. Finally, real data collected from a consumer-grade MEMS-based device are used within a navigation simulator to evaluate the relationship between the quality of the stochastic models obtained by the two robust estimators in different data collection scenarios and the navigation solution stability.

Inspired by the auditory systems of small animals such as spiders, the tachinid fly, Ormia ochracea, and mosquitoes, a novel low-noise, flow-sensing capacitive MEMS microphone capable of sensing acoustic particle velocity is introduced. Unlike virtually all conventional microphones that have a diaphragm for sensing sound pressure, this design consists of a thin, porous, movable structure that is intended to be driven by viscous forces due to the sound-induced flow. This viscous force then rotates the movable structure around a middle central hinge and creates a change in capacitance due to a relative motion between neighboring beams on the movable and fixed electrodes which creates an electronic signal through a charge amplifier circuit. The whole structure is made of one layer of silicon using a Silicon-on-Insulator (SOI) wafer using photolithography technology consisting of moving and fixed electrodes with a thickness of 5µm. The movable part has dimensions of 0.7mm×1.2mm and is placed above a cavity inside the bulk silicon that facilitates the flow of sound particles. Because this microphone responds to flow (a vector) rather than pressure (a scalar), this design has an output that depends on the sound propagation direction and can cancel out sound from unwanted directions. Experimental results show that it has very promising capabilities for achieving the next generation of MEMS microphones that respond to acoustic flow rather than pressure and can be integrated with other sound sensors for consumer electronics and hearing aids.

With a global energy system in rapid transformation from fossil fuels, Green Hydrogen is one of few solutions to hard-to-abate emissions within industry. While most hydrogen projects are in a planning phase, the Ovako hydrogen facility in Hofors, with a scrap-based Electric Arc Furnace process, was inaugurated in September 2023. This project studies wider system benefits of the electrolyser such as power grid support, oxygen byproduct, providing hydrogen to external actors, and district heating. This is analysed both with current capacity and in regards to possible future development. Replacing fossil fuel with hydrogen produced by an atmospheric alkaline electrolyser is an indirect electrification with the potential to decrease Green House Gas emissions. Industry-wide electrification increases the demand for electricity, affecting all existing users. Therefore, system benefits and sector couplings such as enabling ancillary services to the grid, producing low-marginal cost hydrogen for hydrogen-powered trucks, and using waste heat for district heating, are important to ascertain system-wide improvements.

CMOS-based computing promises drastic improvement in performance in the cryogenic environment. The field of CMOS cryogenic environment-based computing holds the promise of delivering remarkable enhancements in both performance and power consumption. This is important to enable Quantum computing that holds great opportunity to achieve order of magnitude improvements in performance over traditional CMOS computing. Among the pivotal components enabling this advancement, Static Random-Access Memory (SRAM) stands as an indispensable element characterized by its superior performance and density. This contribution aims to develop a framework for examining the influence of leakage current, parasitic effects of bit-line and word-line, and voltage on the size and performance of the SRAM array under cryogenic temperatures. To accomplish this, we conducted simulations on the SRAM array, varying the number of rows and columns. For a comprehensive analysis, we further investigated the maximum attainable array size, extending our study to 10 K, utilizing three distinct cell types. Our findings substantiate the affirmative impact of cryogenic temperatures on both array size and performance. Additionally, we elucidate the influence of transistor threshold voltage engineering on the optimization of the SRAM array. A detailed spice simulation using measured data in the cryogenic environment using 5 nm FinFET is used. This work contributes a significant step forward in understanding and leveraging cryogenic technology.

This article proposes a fluid mechanics-inspired perspective to explore galactic dynamics and spacetime curvature. It suggests that spacetime can be considered as a fabric under tension, influencing the trajectory of galaxies analogously to particles in a viscous fluid. The presented visualization artistically reflects this concept, showing galaxies moving over a concave surface akin to a convex lens, moving away from the center and towards the edges, akin to the behavior of viscous fluid droplets on a slanted surface. This model offers an opportunity to rethink cosmological theories and could provide new avenues for interpreting astronomical observations.

Women's empowerment is a well-known and intriguing subject. Every business and institution should place a priority on the health and privacy of women because they are the foundation of society. The women must be given access to facilities that will improve sanitation and menstrual hygiene for this reason. In order to make women's pads conveniently accessible at their workplaces or other public locations, many vending machines have been explored and designed specifically for this reason. Our goal is to create a prototype that can dispense napkins depending on female students' IDs stored in a CSV (Comma-Separated Values) file and then update and receive their data from the cloud, as well as remotely check the system's health using IoT.

Silicon (Si) has a high theoretical gravimetric capacity and can replace the graphite in the negative electrode of lithium-ion batteries. Among numerous Si electrode geometries, the Si microwire (SiMW) geometry shows a promising performance compared to Si wafers and particles and alleviates the mechanical instability during lithiation/delithiation process due to high volume expansion/contraction. In this paper, a single-wire model (SWM) is proposed for lithium-ion battery half-cell composed of SiMWs as the positive electrode and lithium metal as the negative/reference electrode. In the proposed SWM, an array of SiMWs is modeled by only one equivalent micro-scale wire. The SWM is the simplest model that incorporates the electrochemical behavior with a minimal computational effort suitable for real-time applications. Despite its simplicity, it is equivalent to a one-dimensional (1D) model of conventional graphite-based lithium-ion batteries. Both numerical and experimental results on a 2.5 mA SiMW half-cell sample, are provided to verify of the proposed SWM.

The sensitivity of state-of-the-art supervised classification models is compromised by contamination-prone biomedical datasets, which are vulnerable to the presence of missing or erroneous labels (i.e., inliers). From codon frequencies, electrocardiogram signals, biomarkers, morphological features, and patient questionnaires, we attempted to cover a wide range of typical biomedical databases exposed to the risk of inlier contamination. In some very niche fields, such as image recognition, missing labels have received a lot of attention, but in Life Sciences, where outliers are almost systematically filtered, inliers have remained orphans. Our study introduced a pragmatic and innovative methodology that consists of upcycling one-class semi-supervised anomaly detection models for filtering potential inliers in training datasets. By validating this methodology and benchmarking five one-class semi-supervised models and two ensemble methods were benchmarked against various traditional classifiers on 6 databases with 10 different contamination levels and 10 random samplings, achieving an average Matthews correlation coefficient of 78±17% in validation, whereas 22 supervised classifiers achieved an average score of 81±9% trained with the complete and uncontaminated trainset. Therefore, by filtering the training set with an isolation forest, the average resilience to inliers of 22 tested Machine Learning models increased from 69±11% to 95±1%, including neural networks and gradient-boosting methods. Taken together, our study showcased the efficacy of our versatile approach in enhancing the resilience of Machine Learnig models and highlighted the importance of accurately addressing the inliers challenge in the domains of Life Sciences.

The increasing proliferation of internet-connected devices and the widespread reliance on wireless communication in daily activities are limiting the capabilities of the radio frequency (RF) spectrum to meet data traffic demands. The rise in connectivity requirements to realize sixth generation (6G) networks, coupled with the growth in utilization of artificial intelligence and its associated transmission of massive amounts of data, are contributing to the need to leverage the optical spectrum to complement the already congested RF bands. In general, optical wireless connectivity encompasses a range of solutions that can support data transfer in various outdoor and indoor use cases, benefiting from advantages in terms of operation within a broad unlicensed spectrum, minimization of RF interference, and inherent security. On the other hand, optical wireless communication (OWC) systems might experience a decline in performance as a result of factors like atmospheric conditions and link blockage. This requires customizing system designs according to the considered use cases and investigating new solutions for realizing optical wireless connectivity. In this article, we present an overview of OWC, encompassing both its conceptual frameworks and the scientific and technological advancements that hold potential for shaping its future in 6G networks and beyond.

We report enhanced nonlinear optics in integrated nanophotonic chips through the use of integrated with 2D graphene oxide (GO) films. We investigate nanophotonic platforms including silicon, silicon nitride and high index doped silica. Due to the high Kerr nonlinearity of GO films and low nonlinear absorption we observe significant enhancement of thirdorder nonlinear processes. In particular, in silicon we observe an increase in both the Kerr nonlinearity and nonlinear figure of merit of up to 20 times. These results show the strong capability of GO films for improving the nonlinear optical performance of integrated photonic devices.

The k-anonymity problem introduced by Samarati and Sweeney in 1998, guarantees that it is impossible to distinguish user data from at least (k − 1) others in the same database. The methods used to achieve k-anonymity result in an information loss as the data in the database is modified, making it less accurate through a process of generalization or micro-aggregation of the stored data. Mauger et al. proposed a O(n²)-time sequential algorithm that gives good results while minimizing the information loss using their designed metrics. However, their solution is very time-consuming and therefore not suitable for large-scale databases. In this paper, we tackle this problem using parallelism. We propose three coarse-grained parallel algorithms to solve the k-anonymity problem. The first is the straightforward algorithm that runs in O(n ²/p) execution time with O(n) communication rounds, where n is the number of lines in the database and p is the number of processors. The second runs in O(n² /p²) execution time with O²(p) communication rounds. The third runs in O(n² /plog2(p)) execution time with O(np/ (log2(p))²) communications rounds. For the latter two algorithms, we introduce the concept of data reorganization to minimize the information loss when data are partitioned. Experimental results show that for a database of size n = 10^6 , p = 2^7 , and k = 10^2 , second, and third parallel algorithms are respectively 1127.59× and 41.13× faster than the sequential algorithm while achieving anonymity with 4.03% and 2.62% information loss.