Miracle-dz/UniFAR-FTUs huggingface dataset:
Web3.0: A Review And Research Agenda

This paper examines existing Web3.0 research published between 2003 and 2022. We uncover seven research themes that have been featured in previous research, as well as the key phrases associated with each theme, using a topic modeling procedure called Latent Dirichlet Allocation (LDA). We emphasize the interaction of research themes that contribute to the understanding of a variety of solutions, applications, and use cases, such as metaverse and Non-fungible tokens (NFT). We conclude by proposing an exploratory agenda for future research based on pioneering research in the fields of Blockchain, decentralized networks, smart contracts, and algorithms.
PSO-OLSR: A Particle Swarm Optimization based Proactive Routing Protocol for UAV Networks

The application spectrum of Unmanned Aerial Vehicles (UAVs) has been expanding rapidly in recent years, beyond their intended original use in the military sector, to target many strategic civilian sectors such as agriculture, rescues, surveillance etc. In this paper, we focus on the application of UAVs in disaster scenarios to provide efficient and rapid alternative to the damaged communication infrastructure. In this context, a swarm of UAVs is deployed as a wireless mesh network to provide connectivity to users on the ground and ensure real-time communication between rescue teams. To this end, we proposed a new routing protocol, named Particle Swarm Optimization enabled OLSR (PSO-OLSR) to create and maintain connectivity within the mesh network. PSO-OLSR consists of three phases; the initialization phase where OLSR is used to compute routes that enable UAVs to operate collaboratively. Afterwards, due to UAVs’ high speeds, their dynamically changing topologies, and their three-dimensional mobility, a Particle Swarm Optimization (PSO) model is designed to maintain the UAVs connectivity within the deployed mesh network. PSO-OLSR’s performance has been evaluated through simulation and compared against the legacy OLSR. The obtained results show that PSO-OLSR outperforms OLSR in terms of packet delivery ratio and end-to-end delay.
On Three-Phase S-Parameters and its Application to Coupling Attenuation of Automotive High-Voltage Lines

The increasing demand for electric vehicles asks for high volume optimized drive trains reducing cost and weight. Ensuring electromagnetic compatibility (EMC) is of great importance and accomplished by adding a costly shield. In analogy with the well-established standard series IEC 62153-4-x where e.g. for shielded twisted-pair configurations the screening attenuation for common-mode (CM) excitation and coupling attenuation for differential-mode (DM) excitation is defined. For three-phase systems only screening attenuation can be measured by connecting all inner conductors in parallel and using the shield as a return path. A method quantifying coupling attenuation is missing. This paper fills that gap by adopting the mixed-mode (MM) concept for three-phase S-parameters. By applying the Fortescue-transformation S-parameter measurements are decomposed into three modes: zero phase sequence (Z), positive (+), and negative phase sequence (-). The theoretical relations are deduced first and verified practically by measuring two typical automotive high-voltage (HV) cables with the line injection method.
The Relative Consistency of the Axiom of Choice Mechanized Using Isabelle⁄zf

The proof of the relative consistency of the axiom of choice has been mechanized using Isabelle⁄ZF, building on a previous mechanization of the reflection theorem. The heavy reliance on metatheory in the original proof makes the formalization unusually long, and not entirely satisfactory: two parts of the proof do not fit together. It seems impossible to solve these problems without formalizing the metatheory. However, the present development follows a standard textbook, Kenneth Kunen's Set theory: an introduction to independence proofs , and could support the formalization of further material from that book. It also serves as an example of what to expect when deep mathematics is formalized.
PV-Powered Base Stations Equipped by UAVs in Urban Areas

Recently, the application of unmanned aerial vehicles (UAVs) to support the base stations in cellular telecommunication networks attracts attentions. UAV-assisted base stations can provide the extra users’ demand in extreme and/ or unpredictable situations such as Olympic Games to avoid extra cost of installing ground base stations. In this paper, a PV-battery power system is presented to supply UAV-assisted base stations in cellular telecommunication networks in urban areas to prevent environmental issues as well as to reduce the cost of fulfilling the energy demand. First, the energy consumption profile of the batteries of UAVs is estimated. Afterwards, the impact of the PV system sizing and battery capacity are studied based on sensitivity analysis.
Repetition-Based NOMA-HARQ with Adaptive Termination for URLLC

This paper proposes a repetition-based low-latency non-orthogonal multiple access (NOMA)-hybrid automatic repeat request (HARQ) method with adaptive termination for ultra-reliable low latency communications (URLLC). To reduce transmission delay, the proposed method retransmits packets in short transmission time intervals in advance and terminates packet retransmissions adaptively according to acknowledgement (ACK) feedback from the receiver. In addition, to reduce the throughput loss due to unnecessary retransmitted packets sent in the time until ACK feedback, retransmitted packets of the URLLC user are non-orthogonally multiplexed in the same channel with packets of other users based on NOMA. Two non-orthogonal multiplexing methods are compared: superposition coding (SPC) and joint modulation (JM). As for the receiver structure, a successive interference canceller (SIC) and complexity reduced maximum likelihood detection (R-ML) are investigated. We confirm that the proposed method using JM and R-ML provides the best improvement in the transmission delay time versus throughput trade-off based on computer simulations.
Data Analysis and Modeling of Body Sensor Network in Healthcare Application

Data are now processed relatively in an efficient manner due to the development of machine learning techniques. Such strategies for knowledge extraction are frequently employed in a variety of contexts, including business, social media, voting, wagering, forecasting, and more. Healthcare in Body Sensor Network is one of these key fields where modelling and data analysis are extensively used. The data that is captured and processed in this network is used to track a person's everyday activities, check that the data is accurate, determine when a medical emergency is required, and more. There are sufficient studies based on such analysis; some offered their own methodology while others employed pre-defined techniques such as Machine Learning, Neural Networks, Deep Learning, and more. In order to analysis the sensor data, various methodologies that have been stated in some selected research articles are compared in this document. Both the analysis methods and the study's findings are very diverse and have many unique characteristics. The comparison study provides a comprehensible demonstration of these methods and features.
Analysis of Employee Surveillance System using Deep Learning Models

Achieving maximum productivity becomes difficult in the conditions when the corporate working arrangements change from office to home. Any type ofinattentiveness can cause companies to incur huge financial losses. So in order to increase work efficiency and lessen the errors and risks in work, there comes a need for a system that monitors an employee's performance. This research paper titled “Analysis of Deep Learning Models for Employee Surveillance System” proposes an idea that will help corporate employers to solve this issue by providing a system that has a feature of monitoring attentiveness of their remote employees. This system is developed using OpenCV for real-time video capturing and deep-learning algorithm CNN for image classification and haar-cascade for cascading. CNN algorithm has been used in order to classify 3000 images of various eye states in a more proficient manner than traditional ones. It alerts the employee by an alarm which starts triggering after a certain threshold and it also starts recording a video by the same camera which will act as an evidence of the employee's negligence to his work. In addition, an internet checker has been installed, which allows it to analyze the internet connectivity of employee machines. Additionally, a SMS API has been used to notify the company/employer of the status of inactivity of the user/employee using a message or notification. A variety of models are being tested to determine the accuracy of the system. The conclusion of analysis work has been derived in order to come up with a more efficient system.
Design of Robust LoS-MIMO Transmission in HAPS Feeder Link

In line of sight multiple input multiple output (LoS-MIMO) transmission in a feeder link, which involves communication between a ground station and a high altitude platform station (HAPS), the design of antenna arrays that ensure a stable and high channel capacity is a challenge because of the degradation of the channel capacity caused by changes in the distance and angle owing to the movement of the HAPS. In this study, we first evaluate the channel capacity characteristics of a basic uniform linear array (ULA) for LoS-MIMO transmission during movement. To compensate for this drawback, we propose a uniform cross array (UXA) configuration with two crossed ULAs as the antenna array and show that the LoS-MIMO transmission with the UXA configuration is robust against HAPS movement. The method in this paper is applicable to HAPS as well as LoS-MIMO transmission between two stations with changing positions. LoS-MIMO transmission using UXA exhibits excellent performance with the channel capacity approaching the theoretical upper bound.
Yoga Recommendation System for the Mental Well-Being of Students using Machine Learning

Many expectations placed on students by society have made stress a part of their academic lives. Youth are susceptible to the issues brought on by academic stress since they are going through a phase of transitions in both aspects i.e personal and social. Academic stress has been shown to lower academic achievement and lower motivation toward academics. Therefore, it becomes crucial to develop appropriate and effective intervention options. In recent times, due to COVID, the utilization of online health blogs and sites recommending health, exercise, and yoga has been significantly increased. The blog will provide solution to a problem and then provide precautions to common people but they lack the dynamics to suggest yoga that can be done any person or a personalized yoga by considering their health condition and not a static article. This research work intends to develop an AI model to predict the possible practices a student can do to alleviate their problem by considering their BPM, blood pressure (both systole and diastole), sleep time and some questions related to stress. The proposed stress prediction model has achieved an accuracy of 94.4% and the yoga pose recommendation system has achieved an accuracy of 97.3%.
A Modular and Extended Positioning Robotic System for Percutaneous Puncture Surgery

Percutaneous puncture is a minimally invasive surgery that is widely used in surgical fields such as biopsy and interventional therapy. Through biopsy, cancer can be detected earlier, which facilitates radical treatment and improves the prognosis of cancer patients. During the traditional puncture operation, the doctor manually performs the puncture operation, especially in some special locations and special circum-stances, the operation become more difficult, and there are higher requirements for the doctor's experience and skills. The targeted positioning robot for percutaneous puncture surgery can provide precise positioning and stable needle guidance for surgery, improve the success rate of surgery, and reduce the risk of radiation exposure for doctors. In this paper, by decom-posing the puncture action, a bed-based modular percutaneous targeted positioning robot system is designed, which includes a positioning mechanism, an posture-adjusting mechanism based on a remote center-of-motion (RCM) mechanism, a depth-feed mechanism, and simulation analysis of the workspace of the surgical robot and the angle of puncture needle insertion is carried out. Finally, the rationality of the designed surgical robot and the feasibility of the scheme are verified by simulation experiments.
Fog Enabled Cyber-Physical Attack detection using Ensemble Machine Learning

A cyber-physical system (CPS) is a system that can make decisions independently; this system is controlled or monitored by a computer-based algorithm. In CPS, devices are susceptible points for attackers, which makes the systems vulnerable to attacks. CPS can remotely control the network sensors, actuators and gates, and other physical devices. As a result, these systems are also used in critical infrastructures such as water distribution systems (WDS), power grids, and other similar systems. WDS is the large infrastructure that can deliver an adequate amount of water to industry, home, and agriculture areas. However, in recent years several cyber-attacks have been increased over WDS. In this paper, we proposed, that fog computing and fuzzy logic-based methods (Intuitionistic Fuzzy System) for feature selection and voting classifier are applied over the selected features to detect anomalies over WDS. The proposed system is tested with the (WADI) Water Distribution dataset, and the results are compared the state-of-the-art methods.
Nanowire grid polarizers for mid- and long-wavelength infrared applications

High contrast wire grid polarizers on silicon suitable for mid-wavelength infrared (MWIR) and long-wavelength infrared (LWIR) applications have been developed using wafer-scale aluminum nanowire patterning capabilities. The 144 nm pitch MWIR polarizer typically transmits better than 95% of the passing polarization state from 3.5-5.5 microns while maintaining a contrast ratio of better than 37dB. Between 7 and 15 microns, the broadband LWIR polarizer typically transmits between 55 and 90% of the passing state and has a contrast ratio better than 40 dB. A narrowband 10.6 micron polarizer shows about 85% transmission in the passing state and a contrast ratio of 45 dB. Transmission and reflection measurements were made using various FTIR spectrometers and compared to RCWA modeling of the wire grid polarizer (WGP) performance on antireflection-coated wafers. Laser Damage Threshold (LDT) testing was performed using a continuous wave CO2 laser for the broadband LWIR product and showed a damage threshold of 110 kW/cm2 in the blocking state and 10 kW/cm2 in the passing state. The MWIR LDT testing used an OPO operating at 4 microns with 7 ns pulses and showed LDT of 650 W/cm2 in the blocking state and better than 14 kW/cm2 in the passing state
LiDAR aided Wireless Networks - Beam Prediction for 5G

5G New Radio (NR) mmWave operates with narrow beams. Beam-based connections require careful management of beams to ensure a reliable connection, specially when the user has mobility. 5G NR defines beam management procedures to achieve this, at the expense of periodic reporting with increased overheads and resource usage. Concurrently, recent interest in sensing for assisting wireless systems provides an opportunity to extract situational awareness information which can aid in proactive decisions for the network. In this work, we utilize an infrastructure-mounted light detection and ranging (LiDAR) sensor system simultaneously operating with the wireless system to predict future beam decisions. A recurrent neural network (RNN) based learning model is proposed for the beam prediction, employing tracking information of users facilitated by the LiDARs and beam sequence information from the wireless system. Furthermore, a method for predictive beam management with increased periodicity of the reporting mechanism and aperiodic reporting is analyzed. The results for the considered scenario reveal 86.8% of the resources can be saved compared to the conventional beam reporting procedure, while achieving an 88.7% accuracy for optimal beam decisions.
Chaotic time series prediction using mean-field theory for support vector machine

This paper presents a novel method for predicting chaotic time series which is based on the support vector machines approach and it uses the mean-field theory for developing an easy and efficient learning procedure for the support vector machine. The proposed method approximates the distribution of the support vector machine parameters to a Gaussian process and uses the mean-field theory to estimate these parameters easily and select the weights of the mixture of kernels used in the support vector machine estimation more accurately and faster than traditional quadratic programming-based algorithms. Finally, relationships between the embedding dimension and the predicting performance of this method are discussed and the Mackey–Glass equation is applied to test this method. The stimulations show that the mean-field theory for support vector machine can predict chaotic time series accurately and even if the embedding dimension is unknown, the predicted results are still satisfactory. This result implies that the mean-field theory for support vector machine is a good tool for studying chaotic time series.
Efficient Interpolation Filters for Chroma Motion Compensation in Video Coding

Interpolation filters for motion compensation play an important role in video coding. Until recently, interpolation filters for luma motion compensation (MC) draw most of the attention while interpolation filters for chroma motion compen-sation are less noticeable. This paper comprehensively analyzes different types of interpolation filters and presents 6-tap DCT based chroma interpolation filters for chroma MC. The proposed interpolation filters are designed considering multiple factors, comprising the characteristics of the chroma component, the frequency response of interpolation filter and the expenses of computational complexity. The experimental results show the proposed method can achieve 0.05%, 1.23% and 1.25% BD-rate reductions on average for Y, Cb and Cr components, respectively, with only 2% decoding time increasing under Random Access configuration, compared with ECM-4.0, which is a reference software developed by JVET, targeting at future video coding technologies beyond VVC. Owing to the good trade-off between coding performance and complexity, the proposed method has been adopted in ECM.
Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable

Abstract We consider a model in which one observes multiple indicators and multiple causes of a single latent variable. In terms of the multivariate regression of the indicators on the causes, the model implies restrictions of two types: (i) the regression coefficient matrix has rank one, (ii) the residual variance-covariance matrix satisfies a factor analysis model with one common factor. The first type of restriction is familiar to econometricians and the second to psychometricians. We derive the maximum-likelihood estimators and their asymptotic variance-covariance matrix. Two alternative “limited information” estimators are also considered and compared with the maximum-likelihood estimators in terms of efficiency.
Robust Q-learning for Fast And Optimal Flying Base Station Placement Aided By Digital Twin For Emergency Use

This paper studies a use case for quickly connecting users with an aerial bass station (BS) in emergency by leveraging digital twin (DT) and robust reinforcement learning (RL). Scattered communities of users are assigned a limited number of channels, and the flying BS is autonomously and optimally placed according to predefined criteria. Q-learning, a common type of RL, is employed as a solution to optimization of BS placement. Two optimization objectives are considered to maximize the per-user data rate in the worst condition and minimize the total BS transmitted power, respectively. To overcome resource limitations of the aerial BS, the RL training with many iterations is done in the DT virtual space connected to the physical space via an aerial BS. In particular, a practically sound max-min technique is proposed to handle the measurement and prediction uncertainties. It is shown that, even if the models used in DT are imperfect and measurements are inaccurate, nearly-optimal results can be obtained in DT. Compared to RL training 100% in the physical space, a huge number of BS moves can be avoided and a significant amount of time and energy can be saved. The assessment results suggest that model and measurement errors, especially when applying DT, should be seriously considered, and the robust optimization technique has potential to handle the uncertainties.
Effect of Variable Ionic Mobility on Ambipolar Diffusion

Recent measurements of the mobility of atomic rare gas ions in their parent gases have shown that the mobility falls with increasing field strength, becoming inversely proportional to the square root of the field at high fields. This paper presents the solution to the ambipolar diffusion problem for cylindrical geometry and with the observed field dependence of ionic mobility. Results for ${\mathrm{He}}^{+}$ ions in He indicate that ambipolar diffusion in a low-pressure positive column may be reduced by a factor of two or more from that predicted by using constant mobility. The theory is also applicable to ${\mathrm{Ne}}^{+}$ ions in Ne and to ${\mathrm{A}}^{+}$ ions in A.
High Precision Voltage Reference for ADC: Review and Design Methodology

High resolution analog-to-digital converters (ADCs) impose stringent accuracy requirements for the voltage reference, in terms of low temperature coefficient (TC), low noise, absolute accuracy and high driving capability. This paper reviews state-of-the-art circuit design techniques to implement a high precision ADC reference and the methodology to optimize the parameter metrics.
Harmonic Polynomials Associated With Reflection Groups

Abstract We extend Maxwell’s representation of harmonic polynomials to $h$ -harmonics associated to a reflection invariant weight function ${{h}_{k}}$ . Let ${{\mathcal{D}}_{i}},\,1\,\le \,i\,\le \,d$ , be Dunkl’s operators associated with a reflection group. For any homogeneous polynomial $P$ of degree $n$ ,we prove the polynomial ${{\left| x \right|}^{2\gamma +d-2+2n}}P\left( \mathcal{D} \right)\left\{ 1/{{\left| x \right|}^{2\gamma +d-2}} \right\}$ is a $h$ -harmonic polynomial of degree $n$ , where $\gamma \,=\,\sum \,ki$ and $\mathcal{D}\,=\,\left( {{\mathcal{D}}_{1}},\ldots ,{{\mathcal{D}}_{d}} \right)$ . The construction yields a basis for $h$ -harmonics. We also discuss self-adjoint operators acting on the space of $h$ -harmonics.
Design and implementation of an improved linear quadratic regulation control for oxygen content in a coke furnace

In view of the performance deterioration of conventional model predictive control strategies under model/plant mismatches, this study first proposes an improved linear quadratic regulation control (LQR) strategy and then tests it on the oxygen content in an industrial coke furnace. The control system design consists of two steps. Based on the direct use of the measured input and output variables and the formulation of output error, a state space model is first developed. Then, by incorporating the dynamics of process states into the cost function of the controller design, a new LQR is proposed to obtain an improved performance for closed-loop control systems. Comparisons with conventional state space model predictive control are also given in terms of both regulatory and servo performance. The case study on the oxygen content in the coke furnace shows that the proposed improves control performance under model/plant mismatches.