Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-referencing Reflection Sensor for Industrial Applications

Terahertz sensors have found their way into many industrial applications, though undertaking regular reference measurements could be inconvenient on some occasions. A sensor with a self-referencing (SR) mechanism (in a linear configuration) is designed and manufactured to tackle the challenge. The sensor head is used on Lithium Iron Phosphate (LFP) cathodes to report the thickness and refractive index of the material.
Transfer Learning-Enabled IoT System for Continuous Prediction of Vehicle CO2 Concentration

In this paper, we present the design, implementation, and deployment of an IoT-based system for machine learning (ML)-based real-time prediction of CO2 exhaust concentrations in road vehicles, by means of transfer learning. The system offers a scalable and cost-effective solution for monitoring and predicting CO2 exhaust concentrations from a vehicle fleet. Moreover, the system is specifically designed to be installed in a low-powered microcontroller unit (MCU) to collect and process data from a CO2 sensor, as well as to perform ML-based emission prediction without a sensor. To accomplish this, we have developed an artificial neural network (ANN) model able to generate training labels to further train subsequent ANN models via transfer learning mechanisms. The resulting models are sent to the IoT devices installed in the vehicle to perform the on-board predictions. Empirical experiments show very promising results in terms of high prediction accuracy and satisfactory transfer learning loop execution time.
Broadband mm-wave sealed-volume liquid bio-sensor exploiting tailored delocalization of modal fields in a micro-scale silicon waveguide

We demonstrate mm-wave sensing of a sealed volume of bio-liquid via a dielectric waveguide’s evanescent fields, which are enhanced by narrowing the guide core in order to reduce field confinement. This is exploited to investigate whether a culture of Gliboblastoma cells effects a measurable change in the composition of a nutrient solution. A small discernible decrease in absorption is observed, indicating potential for future safe and noninvasive mm-wave diagnostic tools
Induction motor bearing faults diagnosis based on Auto-Regressive spectral analysis

The purpose of this paper is to apply a diagnosis technique based on the Auto Regressive (AR) model called ROOT-AR with several improvements. Indeed, by choosing to apply the AR model to a limited frequency band chosen according to the type of searched faults, the number of samples is then reduced, which leads to the reduction of the calculation time. At the same time, by working on a limited frequency band, the order of the model is chosen instead of being estimated. This will offer a reliable model and thus reduce modeling errors. In this perspective, the ROOT-AR approach will be applied to the motor current analysis in the case of bearing faults diagnosis for an induction motor fed with and without inverter. This new approach will be used to solve the frequency resolution problem of the stator current analysis using conventional methods based on Fourier Transform. Finally, both approaches will be applied in the diagnosis of induction motor bearing faults through experimental tests.
Comparative genomics and proteomics analysis on Capsicum species reveals insights about the capsaicin biosynthesis

Capsaicin is the primary capsaicinoid compound responsible for the spiciness of chilli peppers. Several known and unknown genes synthesize capsaicin through various metabolic pathways, such as the phenylpropanoid or the L-valine metabolism pathways. We conducted comprehensive comparative genomics and proteomics analyses to identify genes and proteins associated with the capsaicin pathway in Capsicum chinense, Capsicum baccatum and the two C.annuum cultivars, CM334 and ECW. A BLAST search against the NCBI database identified 26 and 58 enzyme genes and proteins, respectively. These enzyme genes displayed distinct expression patterns, indicating diverse regulatory functions in capsaicin biosynthesis. Several enzymes were crucial for capsaicin accumulation, including 13 important genes. We identified unique proteins in C. chinense, with the highest pungency level, and others missing in C. annuum cultivar ECW, with the lowest pungency level. Our study increases our understanding of the genetics underlying the capsaicin biosynthesis pathway and influencing pungency variation in chilli pepper cultivars, further contributing to the broader knowledge of capsaicin production. Studying the functional properties of these enzyme genes is highly needed to explore their potential applications in biotechnology, food science, and medicine.
A Two-stage Prediction-based Beam Selection Algorithm in MmWave Massive MIMO Systems

Millimeter Wave (mmWave) is usually combined with large-scale multiple antenna technology to produce a large number of narrow beams for better coverage. In order to improve the network throughput, an optimal beam or beam pair is selected to achieve high data rate. However, it is challenging to perform beam selection in vehicular systems with dynamic blocking scenario due to its complex geometric environment and variable number of multipaths. In this paper, a two-stage prediction-based beam selection (TS-PBBS) algorithm is proposed to find the optimal beam accurately and quickly in blocking scenarios. A deep neural network (DNN) is constructed to perform the mapping between the performance of predesigned probing beams and narrow beams. Furthermore, a local search method is introduced to improve the beam selection accuracy. Simulation results reveal that the proposed algorithm is able to recognize the blockage in the environment and achieve high accuracy for beam selection with relatively low searching complexity. In addition, it also appears to have the capability of resisting channel estimation error because the deep learning model weakens the effects of noise, which provides an effective solution for practical deployment of mmWave massive MIMO systems.
Terahertz radar sensing for real-time monitoring of powder streams

In pharmaceutical manufacturing processes, the flow properties of powder streams moving in the manufacturing pipes directly impact the properties of the final drug product, and there is a need for real-time non-invasive monitoring of the powder flow properties with process analytical tools. In this study, we propose a frequency-modulated continuous wave (FMCW) Doppler radar system with a center frequency of 340 GHz to measure the flow properties of falling powder streams in a vertical transparent tube. We successfully measured the velocity profile and powder flow density variation along the height of the tube with a spatial resolution of about 5 mm. In conclusion, the terahertz FMCW Doppler radar system was shown to be highly promising for real-time sensing of flow properties of powder streams in the pharmaceutical manufacturing processes.
Methods of Studying the Process and Synergy of the Effectiveness of Digital Business Transformation

The formation of a new technological way of society and Industries 4.0 and 5.0 puts forward many tasks of the organizational and technical plan for the development of theoretical foundations, causes and sources, consequences and nature of the process of digital business transformation. The solution to this problem consists in the scientific substantiation of the process and consequences of digital transformation, the nature of business models, the synergy of the effectiveness of digital business transformation and the methodology of its measurement. To solve the problem, it is proposed to use a systematic approach to identifying the causes, factors and evolution of the stages of development of a new technological stage, developing an integrated model of global transformation of society and movement towards the new society and the neurosphere, the conceptual apparatus of digital transformation, identifying the sources, factors, nature and consequences of digital transformation.
Addressing Cybersecurity Challenges in 5G-enabled IoT and Critical Infrastructures: A Comprehensive Overview

Significant advancements in communication and processing technology over the past two decades have led to the emergence of new intelligent objects known as the "Internet of Things" (IoT). These "things" range from wearable technology, such as smartwatches, to infrastructures for transportation, energy, information, health care, and financial services. They are linked through an Internet-connected control system in order to provide services. The majority of modern industrial critical infrastructures (CIs) rely on Supervisory Regulate and Data Acquisition (SCADA) systems to observe, monitor and control the whole business cycle of operations and data. This article aims to overview and study ICT security in the 5G environment, with many outlines connecting security issues and critical infrastructure. Starting with Protecting 5G infrastructure from cyber risks, to SCADA-based IoT critical infrastructure, third the Security and Privacy Issues of Fog Computing Supported Internet of Things, an intrusion detection system (IDS), Best practices to avoid cyber threats, security recruitment, 5G network vulnerabilities. Finally, concluding remarks and future work are discussed.
Cooperative Multi-User Detection for Satellite IoT under Constrained ISLs

The densely deployment of satellites enables the realization of direct-to-satellite Internet-of-things system with tremendous terminals through multi-satellite cooperation. Multi-user detection (MUD) based on cooperative satellite network can dramatically increase the detection performance. However, it is chained by the limited number of inter-satellite link (ISL) bandwidth resources. To cope with the stringent constraints on ISLs, we propose a novel auxiliary node (AN)-aided factor graph and the corresponding multi-user detection (MUD) algorithm named auxiliary node-cooperative message passing algorithm (AN-CMPA). Simulation results show that our proposed algorithm achieves only 0.5dB loss with 75% decreased information cost under effectively designed information filter criterion.
PAPR Reduction Using Particle Swarm Optimisation Algorithm in OFDM-IM Systems

Index modulated orthogonal frequency division multiplexing (OFDM-IM) is a transmission method that expands the spatial modulation principle to OFDM subcarriers. In OFDM-IM, bits are transmitted using index modulation (IM); this provides better error performance and is more effective in the high signal-to-noise ratio (SNR) region. However, the high peak-to-average power ratio (PAPR) poses a significant challenge in OFDM-IM systems. The high PAPR can degrade system performance by introducing signal distortion, particularly when power amplifiers operate in a nonlinear regime. In this paper, we propose the combination of particle swarm optimisation (PSO) algorithm with partial transmit sequence (PTS) method, which is the one of the PAPR reduction method, to obtain an efficient PAPR reduction method. This paper includes a simulation study of this approach, in which graphical results are obtained in 10000 iterations. The computer simulation results demonstrate that the PSO-PTS used in the OFDM-IM system significantly reduces the PAPR value compared to the original system and the conventional PTS method. Simulation results obtained for different subcarrier numbers (N) and modulation index parameters (M) show the variation between the PAPR threshold $\mathrm{(PAPR}_{0})$ and complementary cumulative distribution function (CCDF).
A 680 pJ / task Processor for Sensor Network Applications in 130 nm CMOS

Networks of ultra-low-power nodes capable of sensing, computation, and wireless communication have applications in medicine, science, industrial automation, and security. Over the past few years, deployments of wireless sensor networks (WSNs) have utilized nodes based on off-the-shelf general purpose microcontrollers. Reducing power consumption requires the development of Systemon-chip (SoC) implementations that must provide both energy efficiency and adequate performance to meet the demands of the long deployment lifetimes and bursts of computation that characterize WSN applications. This work takes a holistic approach and, thus, studies all layers of the design space, from the applications and architecture, to process technology and circuits. This paper introduces the emerging application space of wireless sensor networks and describes the motivation and need for a custom system architecture. The proposed design fully embraces the accelerator-based computing paradigm, including acceleration for the network layer (routing) and application layer (data filtering). Moreover, the architecture can disable the accelerators via VDD-gating to minimize leakage current during the long idle times common to WSN applications. We have implemented a system architecture for wireless sensor network nodes in 130nm CMOS. It operates at 550 mV and 12.5 MHz. Our system uses 100x less power when idle than a traditional microcontroller, and 10-600x less energy when active.
Effective Mitigation Strategies for Social Engineering Attacks in Mobile Money Services: A Case Study in Kenya

This background paper explores the effectiveness of mitigation strategies for social engineering attacks in Mobile Money (MM) services. Social Engineering Attacks (SEA) exploit human psychology, making them difficult to prevent with technical safeguards. Although MM has brought the poor into the financial mainstream, the rapid adoption of this technology poses security risks to users, particularly in sub-Saharan Africa, where SEA are common. The study reviews literature on SEA and identifies vishing, smishing, and phishing as the most common types of attacks. The research question focuses on assessing mitigation strategies for SEA in MM services. The study aims to provide insights for financial institutions and Mobile Money Operators (MMO) to develop effective strategies to prevent fraud. It also contributes to the body of knowledge on MM fraud and helps improve the security and reliability of MM systems.
Cloud-Based PMUs for Real-Time Power System Monitoring: Theoretical and Experimental Analysis

The emergence of modern cloud computing has brought a revolution in various fields by providing scalable and readily available computational resources, enabling efficient data processing. These advancements have significantly enhanced the connectivity between local and remote systems, facilitating streamlined data transmission processes. Furthermore, the adoption of Software as a Service models has allowed the hosting of programs and applications in the cloud, offering accessibility, scalability, and cost savings. These advancements have not gone unnoticed in the power system domain. Applications such as monitoring, and protection can take advantage of the improved telecommunication technology. Phasor Measurement Units (PMUs) play a vital role in these applications by synchronizing quantities obtained from instrument transformers and computed quantities using precise time stamps. The objective of this paper is to conduct a comprehensive analysis of the pros and cons associated with a cloud-based PMU (CB-PMU) that integrates the PMU Synchrophasor estimation directly into the cloud infrastructure. This approach has received limited attention in existing literature, underscoring the necessity for this in-depth study. The analysis in this paper is both theoretical and experimental. The results allow for a complete assessment of the applicability and the limits of the CB-PMU.
Independent and Interdependent Self-construal on Communication Styles among the Malaysian Young Adults

Understanding the relationship between self-construal and communication style is important because it allows for a better comprehension of how cultural values and individual perspectives shape communication behaviors. This leads to a more effective cross-cultural interactions and improved mutual understanding. Therefore, this study was conducted to determine the relationship between self-construal and communication styles among public university students in Klang Valley. A total of 380 students have participated in this study with a self-administered questionnaire was used in collecting the data. The results revealed that the majority of the respondents had moderate level independent self-construal and interdependent self-construal meanwhile majority of the respondents had high level of communication styles. Independent self-construal was correlated with friendly, impression leaving, relaxed, argumentative, attentive, animated, dramatic, open, and dominant, meanwhile, interdependent self-construal was correlated with friendly, impression leaving, argumentative, attentive, animated, open and dominant. Moreover, the regression analysis showed that interdependent self-construal was the strongest predictor in all types of communications. From this study, self-construal has a significant impact on communication styles, influencing how individuals express themselves, interpret messages, and engage in interpersonal interactions within their cultural context. This knowledge can help minimize miscommunication, enhance collaboration, and promote successful interpersonal relationships in diverse cultural contexts.
Classification of Patients with Dementia and Parkinson’s with Support Vector Machine

Clinical findings and magnetic resonance images are used to diagnose dementia and Parkinson’s, which are neurodegenerative diseases. Studies are carried out for prediction of neurodegenerative diseases and/or for the efficient treatment process. Although there are studies that address and classify neurodegenerative diseases such as dementia and Parkinson’s individually with healthy control groups to accelerate the diagnosis stage, there are no studies that include two or more diseases at the same time. We aimed to make an original contribution to the literature by considering dementia and Parkinson’s diseases together. In our study, where we classify dementia and Parkinson’s on the differences in brain volume and brain thickness, we used the voxel-based morphometry technique for the volumetric measurement of shrinkage and the support vector machine (SVM) algorithm for the estimation of the diseased/healthy state. In our study, we used data from 3082 participants, 1141 with Parkinson’s disease, 1145 with dementia, and 796 with healthy controls. Datasets were obtained from NFID and PPMI. Atlases in SPM12 and CAT12 libraries in the MATLAB program were used for brain-volume measurements, cerebral cortex thickness, and area calculations. Obtained volume measurements were classified by the support vector machine algorithm and 10-fold cross-validation method was used for validation. As a result of the study, the mean accuracy rate of 93.51% and the mean AUC-ROC value of 97.59% were obtained. These findings show that although there are two diseases and a healthy group, it gives better results than some studies in the literature with dementia and control group or Parkinson’s and control group. As a result, the SVM algorithm is in parallel with the studies conducted with dementia and control group or Parkinson’s and control group, and it has been proven that it can be an effective method in a database containing both disease and control data.
Remote Sensing-Based Model for Estimating Groundwater Pumping: Case Study of Irrigated Perimeter of the Medjerda Lower Valley, Tunisia

The estimation of groundwater exploitation in arid and semi-arid countries such as Tunisia remains a challenging issue. In the Medjerda lower basin, water resources are overexploited due to Climate Change, anthropogenic pressures, and the intensification of irrigated areas. To address this, estimating spatially distributed crop water consumption is crucial for promoting efficient water management. This study aims to estimate volumes of groundwater pumping for irrigation in a citrus orchard area of the Medjerda Lower Valley basin using earth observation data as input of the SAtellite Monitoring of Irrigation (SAMIR) model based on the FAO-56 dual crop coefficient water balance model. The SAMIR model's simulated irrigation volumes were compared with the actual volumes of water that were supplied by farmers. The results showed significant difference between the simulated required volumes of $\boldsymbol{1252074 \ \mathrm{m}^{3}}$ and the declared amount of $663 \ 882 \ \mathrm{m}^{3}$, indicating an estimated groundwater irrigation pumping of about $239 \ 813 \ \mathrm{m}^{3}/\text{year}$. These findings confirm the feasibility of earth observation data for estimating irrigation groundwater pumping in water-scarce areas, and that they can be useful for decision-makers.
Hyper Parameter Optimized NIDS via Machine Learning in IoT Ecosystem

Today’s dynamic and hyperactive world is confined to a ball size connectivity only via network and internet. People knowing or unknowingly risk their privacy at a huge rate in the network. Undoubtedly, this is the demand of the time to be digitized and technical. As the population increases, the threat to security increases as well. To detect cyber-attacks and malicious threats to the network, network intrusion detection system is a very prominent concept to deal with them. There is no standard protocol to build up NIDS from scratch as it varies accordingly. Machine learning based NIDS paved a way towards autonomous working of this system keeping in mind that the systems can’t be compared, not even on basic entities or architecture of the system. The paper introduces the recent work and circumstances of the NIDS though the authors didn’t consider the working technique of all the works while implementing. Secondly, a proposed solution for the detection and its methodology has been described in detail while testing it on the UGR ‘16 dataset for further evaluation and benchmarking the durability and efficiency of the model.
Uplink/Downlink Reciprocity in VLC Channels and Its Application to Rate Analysis of SLIPT Systems

In visible light communication (VLC) systems, the uplink and downlink channel gains depend on environmental geometry. This geometric nature induces a relationship between the uplink and downlink VLC channel gains. In this paper, the reciprocity relation between the uplink and downlink VLC channel gains are analyzed. It is shown that the reciprocity relationship can be accurately modeled as affine or polynomial. This reciprocity analysis can be a useful tool that enables several VLC system analysis and design. As an interesting application, using this channel reciprocity and model of the solar cell, the relationship between the downlink rate, harvested power and uplink rate are obtained for a solar cell based VLC-SLIPT system. This enables the formulation of the uplink-downlink rate region for a solar cell-based SLIPT system. The rate region gives the set of all simultaneously achievable rates in the uplink and downlink. The rate analysis, enabled by the reciprocity result, gives the trade-off between downlink and uplink rates that is essential in the system design of solar cell based SLIPT receivers.
Exact calculation of matrix entries in space-time Galerkin time domain boundary element method for the scalar wave equation

The contribution of this work is to propose an exact calculation for the interaction elements appearing in space-time time domain boundary element methods based on a dimensionality reduction procedure. Similar methods have been applied in the frequency domain, while in the time domain only numerical or semi-analytical approaches have been proposed. The calculation presented here replaces a four dimensional interaction integral with a sum of lower dimensional integrals of a new effective Green function that can be evaluated in closed form. In this paper the method proposed is applied to scalar basis an testing function on flat supports but it can be adapted to the electromagnetic case. The results can be used as a benchmark to evaluate the precision and stability of existing numerical methods. It allows to distinguish instabilities inherent to the method and those that appear as a consequence of limited quadrature precision.