Miracle-dz/UniFAR-FTUs huggingface dataset:
A review on the main characteristics and security vulnerabilities of the wireless communication technologies in the Industry 4.0 domain

Humanity's endless pursuit for higher efficiency and perfection has led to the development of many new technologies and to the introduction of innovations in different areas of our life. The industrial sector is logically amongst the largest benefactors of these new technologies and the latest wave of innovations in the sector is something that is generally referred to as the Fourth Industrial Revolution or Industry 4.0. The possibility for remote access to the manufacturing devices, systems and equipment and the possibility for their interconnection are among of the most significant innovations of Industry 4.0. While the cable connections and communication standards are still the dominating method for connectivity between the individual elements of the industrial and manufacturing machines, in the last decade, the trend is slowly changing. Many new solutions rely the wireless connection technologies and there are even examples of whole industrial systems and manufacturing complexes that are entirely relying on different wireless communication methods. The rapid technological development and the competition to introduce the most recent solutions and technologies in the manufacturing processes has many advantages and can be highly beneficial. Nevertheless, many new technologies and solutions are suffering from specific disadvantages, design flaws and security vulnerabilities, which can be used to cause damage and can lead to system inoperability and financial losses. In this paper we provide a review on the main characteristics of the wireless communication technologies for Industry 4.0 and we discuss some of the security vulnerabilities and issues in the sector. The Introduction section of the paper presents a short overview of each of the industrial revolutions and highlights the main technological differences between them. The second part of the paper is focused on the wireless standards and communication technologies, which are in use in the industrial sector. Our analysis continues with discussion on the security mechanisms for the wireless standards and with highlights on some of their main vulnerabilities and security issues. The paper is then concluded with discussion on the presented topic and with recommendations and advices for the implementation of the different wireless standards in the Industry 4.0 domain.
Learning a Partial Graph and the Nyström Extension for Spectral Clustering

We focus on partial graph learning for spectral clustering based on the Nyström method, which uses only a subset of the columns of a positive semi-definite matrix to approximate its eigenvectors. We propose an algorithm to learn only those graph edges that are required by the Nyström method instead of learning the entire graph. Assuming a smoothness data model, we learn a multi-component subgraph and the cluster affiliations of the remaining nodes in the graph to the nodes in the subgraph. We propose an efficient solver based on alternating minimization that solves for the node embeddings of the partial graph via the Nyström method and learns the partial graph in an alternating manner. Experiments performed on real datasets demonstrate that the proposed method outperforms the standard Nyström based spectral clustering technique that uses Gaussian similarity kernel to construct the partial graph in terms of both run time and clustering performance, and achieves clustering performance comparable to that achieved by learning a complete multicomponent graph and its node embeddings in approximately half its run time.
Multiple Beamforming Using Multi-Tone Signals for Microwave Power Transfer System

This article proposed a multiple beamforming scheme using a multi-tone for microwave power transfer (MPT). To simultaneously transmit power to multiple targets, it is necessary to form a multibeam. It can be formed by generating multi-tone signals with frequency offset in each channel of an array antenna. For <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> targets, <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-tone signals are generated, and the directions of the formed <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> beams are changed by adjusting the weights of the signals. Therefore, we introduced a multi-tone modulation scheme based on a matrix calculation to express the modulated multi-tone signal for an <inline-formula> <tex-math notation="LaTeX">$M \times N$ </tex-math></inline-formula> planar array antenna (PAA). Moreover, for comparison with conventional beamforming using a continuous-wave (CW) signal, the multi-tone signal was analyzed in the time domain according to the directions of the multibeam. For verification, a <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> 4 digital beamforming (DBF) system operating at 5.8 GHz capable of generating multi-tone signals in each channel was designed. Based on the relative received power, beam patterns were measured for two, three, and four beams. When 39 dBm of RF power was radiated from a transmitting antenna located 1 m from a receiver, an average RF power of 14.7 dBm was received for four beams. We confirmed that the DBF system using the proposed scheme can concurrently transmit power for up to four receivers.
Studies of Unmanned Aircraft Systems from the Perspective of Operational Use

Unmanned Aerial Vehicles (UAV) have been used for a long time to improve situational awareness for many parties. During last 30 years UAVs role has received more attention and interest in global perspective. There are many reasons how and why this has occurred. This paper highlights those issues; why this matter is important and considerable. When discussing about situational awareness and real time picture, we should remember that many parties need these issues when they are working. Especially decision makers and their assistants need to know what is happening in the field. For that reason, it is noteworthy to focus on one of the most important way how to accelerate making of situational awareness and real-time picture. This is one of the components how to do it and why we need it. Keywords—Public Safety, Real-time awareness, Real-time picture, Situational awareness, Unmanned Aerial System, Unmanned Aerial Vehicle.
Grid-Forming Technologies Enabling a Decarbonized Power System [Technology Leaders]

Electric Power Systems are undergoing rapid transformations toward those having increasing proportions of renewable generation (such as wind and solar), significant amounts of energy storage, advanced grid infrastructures, and a myriad of end-use electrification technologies. Driven by drastic cost reductions and deployment incentives, the solar contribution to U.S. electricity generation has increased from less than 0.1% in 2010 to 3.3% in 2020. Wind generation has seen similar trends in cost reduction and accounted for 8.4% of U.S. electricity generation in 2020. [See Frequently Asked Questions (FAQs) - U.S. Energy Information Administration (EIA), “What is U.S. electricity generation by energy source? ,” eia.gov/tools/faqs/. ] To achieve the Biden administration’s clean energy goals, hundreds of gigawatts more of wind and solar will need to come online in the next 15 years. Similar transformations are happening at a global scale, and countries have laid out pathways to achieve a net-zero energy sector by 2050 to tackle climate change. (See 2 Net Zero by 2050: A Roadmap for the Global Energy Sector, https://www.iea.org/events/net-zero-by-2050-a-roadmap-for-the-global-energy-system.)
Unsupervised Classification of Wind Speed Directions Based on Functional Discriminative Latent Mixture Model

Classification of wind speed pattern is useful in several fields, such as studying the long-term effects on the environment and estimating wind energy potential. Nowadays, analyzing high-dimensional functional data is an essential topic of modern statistics and learning systems which are widely used in different applications. While there have been numerous studies on modeling and clustering wind speed data using classical methods, functional methods are limited. The classical multivariate classification methods of wind speed data naturally depend on a finite number of characteristics that do not include all the available information in the data domain. As a result of these limitations, in this work, we focus on classifying wind speed curves using a functional mixture model-based clustering. This approach has powerful and effective classification tools. In the present research, the functional discriminative mixture (FDM) model has been adapted and applied to cluster wind speed curves in the Taiz region over the last four decades. The parameters of the FDM model have been estimated using the functional Fisher EM-based algorithm. The proposed method has been successfully performed and demonstrated meaningful results for wind speed profiles and their temporal variations. The current findings show that the proposed functional method is the best-suited model to cluster functional wind speed data at different aspects. Compared to traditional multivariate approaches, functional results provide with additional insights for understanding the dynamic behavior of wind speed curves.
Automatic construction and meshing of multiscale image-based human airway models for simulations of aerosol delivery

The author developed a computational framework for the study of the correlation between airway morphology and aerosol deposition based on a population of human subjects. The major improvement on the previous framework, which consists of a geometric airway model, a computational fluid dynamics (CFD) model, and a particle tracking algorithm, lies in automatic geometry construction and mesh generation of airways, which is essential for a population-based study. The new geometric model overcomes the shortcomings of both centerline (CL)-based cylindrical models, which are based on the skeleton and average branch diameters of airways called onedimensional (1-D) trees, and computed tomography (CT)-based models. CL-based models are efficient in terms of preand post-processing, but fail to represent trifurcations and local morphology. In contrast, in spite of the accuracy of CT-based models, it is time-consuming to build these models manually, and non-trivial to match 1-D trees and three-dimensional (3-D) geometry. The new model, also known as a hybrid CL-CT-based model, is able to construct a physiologically-consistent laryngeal geometry, represent trifurcations, fit cylindrical branches to CT data, and create the optimal CFD mesh in an automatic fashion. The hybrid airway geometries constructed for 8 healthy and 16 severe asthmatic (SA) subjects agreed well with their CT-based counterparts. Furthermore, the prediction of aerosol deposition in a healthy subject by the hybrid model agreed well with that by the CT-based model. To demonstrate the potential application of the hybrid model to investigating the correlation between
Application of Convolutional Neural Network for Classification of Consumer Preference from Hybrid EEG and FNIRS Signals

Neuromarketing research has emerged as an innovative and creative method to understand consumers' minds and improve marketing effectiveness. Neuromarketing uses physiological and neural signals to provide insight into customers' motives and preferences, developing improved marketing materials, pricing, and other marketing areas. Brain scanning, which monitors neural activity, and physiological tracking, which detects eye movement, heart rate, and skin conductivity, are the most often used measurement methods. A combination of electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) is utilized in this study to examine how different snack items alter the preferences of consumers. A deep learning technique is applied to identify the preferences of the consumers. Thus, the convolutional neural network (CNN) method is utilized.
Optimization of Survivability of a Mission Critical System in the Presence of Hardware Trojans

The paper presents a novel redundancy-based technique to optimize the survivability of a mission-critical system when its operational components can have hardware Trojans which are activated at run-time. A computationally efficient technique is presented to find the optimal set of redundant components under a given cost budget of the system designer. Numerous numerical results are presented which provide important insights and show the performance advantages of our proposed technique.
Collaborative Route Management to Mitigate Congestion in Multi-Domain Networks Using SDN

In software-defined networking (SDN), a controller monitors switches and directs them to process packets, thereby making network management easier. In this paper, we consider an SDN-based network with multiple connected domains, where each domain contains a set of switches supervised by a controller. Moreover, flows can have different priorities. Then, we propose a multi-domain collaborative route management (MCRM) scheme to schedule routing paths of flows to improve network performance. Each controller adaptively adjusts the paths of some flows in the domain based on their priorities to avoid congestion. However, when congestion occurs, but no paths can be adjusted, the controller borrows links from another domain. To build such cross-domain paths, two controllers exchange information through the XPub/XSub framework. This link borrowing mechanism results in heterogeneous flows, where a link carries the interior flows in its domain and the link-borrowing flows from exterior domains. Then, MCRM handles heterogeneous flows on the congested links by adopting OpenFlow meter and group tables. Simulation results show that MCRM can efficiently increase network throughput and decrease packet loss.
Side Information Effect on Semi-Blind Channel Identification for MIMO-OFDM Communications Systems

This paper investigates the impact of different priors, related to communications channels, on semi-blind channel estimation performance for Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) communications systems. For an estimator-independent study, the Cramér-Rao Bound (CRB) is considered to analyze the performance limits and to quantify the channel estimation gain obtained by properly exploiting side information about the propagation channel. The analysis is carried out by considering three cases: (i) no modeling while estimating the channel coefficients in the frequency domain (for each frequency bin); (ii) a finite memory linear time invariant channel model while estimating the channel taps in the time domain; and finally (iii) a specular channel model while estimating the propagation channel parameters, i.e. the fading, the delay and the angle of arrival of each path. In the latter case, the impact of a model misspecification (caused for example by array calibration errors) on the channel estimation performance is investigated by using the misspecified CRB (MCRB).
Analysis and Exploratory of Lecture Preparation Process to Improve the Conformance using Process Mining

The lecture preparation process marks the start of lectures in the new semester. Telkom University has utilized online-based information technology, Integrated Academic Information System (i-Gracias). One of the applications available on i-Gracias is Outcome-Based Education (OBE). The significant increase in I-Gracias users indeed produces many data records in event logs. This data has become an opportunity to evaluate and improve the lecture preparation process already running. Data processing is needed to gain knowledge from the event log, one of which is process mining. This study starts from the discovery stage to conformance checking. Before the process mining is carried out, pre-processing of the data will be carried out to produce good quality logs and define the case id, activity, and timestamp. Existing event logs will be processed using ProM tools using the Heuristic Miner Algorithm to model the process and achieve the best fitness, precision, generalization, and simplicity values. The Heuristic Miner algorithm was adopted in this study because of its capability to manage event logs with noise and display the primary behavior of existing business processes. The conformance checking result shows a fitness value of 0.98, precision's value of 0.29, generalization's value of 0.51, and simplicity's value of 0.45 for the lecture preparation process. It means the process model has a good representation of the data event log. This study hopes to add new insight to improve the lecture preparation process at Telkom University.
Decentralized Design of Fast Iterative Receivers for Massive MIMO with Spatial Non-Stationarities

Despite the extensive use of a centralized approach to design receivers for massive multiple-input multiple-output (M-MIMO) base stations, their actual implementation is a major challenge due to several bottlenecks imposed by the large number of antennas. One way to deal with this problem is by fully decentralizing the classical zero-forcing (ZF) receiver across multiple processing nodes based on the gradient descent (GD) method. In this paper, we first explicitly relate this decentralized receiver to a distributed version of the Kaczmarz algorithm and to the use of the successive interference cancellation (SIC) philosophy. In addition, we propose three methods to further accelerate the initial convergence of this decentralized receiver by exploring the connection with the Kaczmarz algorithm: 1) a new Bayesian distributed receiver, which iteratively estimates and eliminates noise; 2) a more practical method for choosing a relaxation parameter on an iteration basis; and 3) extension of it to tree-based architectures. The discussion also considers spatial non-stationarities that arise when the antenna arrays are extremely large (XL-MIMO). With our methods, we are able to improve the performance of the decentralized GD receiver for both spatially stationary and non-stationary channels, but mainly the non-stationary performance can still be improved. Future research directions are provided with the aim of further improving the effectiveness of receivers based on the so-called principle of successive residual cancellation (SRC).
Security Analysis of a Key Exchange Protocol under Dolev-Yao Threat Model Using Tamarin Prover

In recent days, security and privacy is becoming a challenge due to the rapid development of technology. In 2021, Khan et al. proposed an authentication and key agreement framework for smart grid network and claimed that the proposed protocol provides security against all well-known attacks. However, in this paper, we present the analysis and shows that the protocol proposed by Khan et al has failed to protect the secrecy of the shared session key between the user and service provider. An adversary can derive the session key (online) by intercepting the communicated messages under the Dolev-Yao threat model. We simulated Khan et al.'s protocol for formal security verification using Tamarin Prover and found a trace for deriving the temporary key. It is used to encrypt the login request that includes the user's secret credentials. Hence, it also fails to preserve the privacy of the user's credentials, and therefore any adversary can impersonate the user. As a result, the protocol proposed by Khan et al. is not suitable for practical applications.
Throughput Improvement in Vehicular Communication by Using Low Density Parity Check (LDPC) Code

The vehicular community is moving towards a whole new paradigm with the advancement of new technology. People rely on the technology included in the vehicles undoubtedly. As a result, Road conditions are becoming more dynamic and complex as cars having autopilot or autonomous vehicles are evolving faster than before. Vehicular communication plays a vital role to improve traffic and passenger safety and provide entertainment services. Traffic safety will be compromised if the performance of vehicular communication degrades with the growth of technology enabled traffic. The performance of vehicular communication depends on bit error rate (BER) and throughput. A good communication system can be defined by having high throughput while maintaining low BER. Multiple Input Multiple Output (MIMO) has the potential to provide high throughput and Low Density Parity Check (LDPC) Code has the potential to provide low BER. Therefore, we propose a PHY layer model containing both LDPC and MIMO in the Wireless Access for Vehicular Environments (WAVE) standard supported by Dedicated Short-Range Communication (DSRC). Our experimental results show that our proposed approach can achieve more than 3 times better throughput while keeping the BER lower compared to the existing approach.
Utilizing Time-of-Flight LIDARs For Spatial Audio Processing

Methods for sensing acoustic displacement of a surface using optical means have included high speed video imagery, laser Doppler vibrometry, and sound-induced optical intensity. Methods for measuring sound from different points in space with a microphone array allow us to localize a source and focus audio recovery at specific points in space. When we combine these two concepts, we could theoretically have an array of optical sensors on a single device to perform single device spatial audio processing. Rotating LIDARs have become prevalent for use in rapid volumetric mapping and have the potential for acoustic recovery, allowing for simultaneous visual and acoustic mapping with one device. An ideal rotating LIDAR has the potential to simultaneously sense acoustic energy at numerous locations throughout an acoustic scene and may enable localization through processing the synthetically-induced array across the environment. We show the parameters that a rotating LIDAR would require for acoustic source localization. We also localize an acoustic source using a high precision light distance sensor.
Predicting the Outcome of Soccer Matches Using Machine Learning and Statistical Analysis

The prediction of future soccer match outcomes has been a challenging task for data scientist for years. Researchers used different features to represent soccer teams performance and players skills. These features are then used to create predictive models using machine learning algorithms. This paper presents a new hybrid approach to predict the outcome of future soccer matches. Our hybrid approach combines machine learning and statistical models to predict future match outcomes. The paper analyzes the hidden patterns within a training dataset that has the results of 205,182 soccer match outcomes, played between 2000/2001 and 2016/2017 seasons. Using feature engineering techniques, the paper explores individual leagues and teams statistics, discusses the impact of playing at home or away on winning the match, compares the effectiveness of using only recent match outcomes data versus all matches in the training set, and evaluates the prediction accuracy when creating separate models for each league versus a single model for all leagues. This paper presents two different hybrid models to predict soccer match outcomes. Our best model achieved 46.6% prediction accuracy of the test set at a ranked probability score of 0.2176.