Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Review of Optimal Sizing Approaches for Battery Storage Technology

As the world undergoes a transformation towards utilizing renewable energy sources, there is a growing preference for continuous, cost-effective power supply throughout the day. The primary drivers for this shift are the environmental pollution associated with non-renewable resources and the volatility in their costs. The expansion of renewable energy sources has resulted in an increased incorporation of energy from renewable sources into the electrical grid. However, since renewable energy generation is intermittent, the efficient and effective performance of the electric utility system necessitates the Battery Energy Storage Systems (BESS) integration. Typically, batteries are employed to store electrical energy. Moreover, to manage power imbalances and voltage fluctuations at the distribution level, it is crucial to identify the optimal size of energy storage devices. Therefore, this paper explores a comprehensive review of Battery Storage systems for energy in the perspective of the latest technologies, optimum sizing of energy storage devices, and their associated advantages.
Controller Approaches - A Comparative Study for Processes with Dead Time

Controlling the dead time processes such as reboiler or furnace temperature control of distillation column for product purity,mass balance and energy, is difficult as the true process model becomes non linear with high order.Measurements can add noise as random components.Finding response of such systems is practically difficult.Model based Smith Predictor and Internal Model Control have been accepted as alternative for classical PID (Proportional Integral Derivative) controllers for dead time processes.Modern computer algorithms are being evaluated for such applications.Experiment on assumed transfer functions for such processes is carried with simulating different control strategies.
Research on Intrusion Detection Based on Markov Chain Monte Carlo Method

He rapid development of the Internet has brought about exponential growth in the amount of data in the network. With the growth of data volume in the network and the increasing dependence of humans on the Internet, network intrusion has become increasingly frequent, posing a serious threat to network security. In view of this, network intrusion detection has gradually become an important research topic in the field of network security. Due to the increasing maturity of deep learning technology, a large amount of intrusion detection research is currently based on deep learning technology. Deep learning technology can certainly improve the accuracy of intrusion detection, but it requires a large amount of data and computing resources and can easily cause overfitting when the data volume is insufficient, thereby reducing the recognition rate of unknown intrusions. This paper proposes an intrusion detection algorithm based on Markov chain Monte Carlo method, which can complete network intrusion detection tasks with low resource consumption. Compared with the intrusion detection algorithm based on basic Extreme learning machine, this method has better detection performance.
A Service-differentiated Virtual Network Function Migration Method

In the network function virtualization (NFV) environment, virtual network function (VNF) migration can effectively deal with the failure of virtual network functions, but there is also a problem that the failure restoring ratio is not high due to insufficient substrate network resources. This paper proposes a service-differentiated virtual network function migration method. First, network services are differentiated into high-delay-sensitive service (HDS) and low-delay-sensitive service (LDS) according to the delay sensitivity of network services, and failed VNFs are perceived and sorted in real time. Second, a topology-aware VNF migration algorithm proposed in this paper is used to select available server nodes for the migration of failed VNFs. Finally, the failed VNFs of HDS that fail to be successfully migrated are restored by a service-differentiated VNF restoring mechanism proposed in this paper. Experimental results show that the VNF migration method proposed in this paper can improve the failure restoring ratio, effectively reduce the impact of server node failure on the revenue of service providers.
Knowledge Bases, Cooperation Networks, and Influential Contributions of Channel Modeling for Terahertz Communications

The terahertz communication in the band 0.1-10 THz is well recognized as one of the supporting paradigms for the next generation of wireless communication. The channel modeling for THz communications has attracted lots of attention in the literature, which produces complex knowledge relationships. Since technical contributions and potential applications have been widely reported in the field, it is necessary to make clear the underlying knowledge development paths and directions. In this work, we use the co-citation analysis and co-occurrence analysis of CiteSpace to recognize the knowledge bases and cooperation networks behind the literature of the channel modeling for THz communications as well as the influential contributions in the field. Technical knowledge bases such as THz channel modeling and ray tracing as well as application knowledge bases such as nano communication and smart rail mobility are detected. Both author and institution cooperation networks worldwide are recognized, and the top 18 institutions and top 11 contributions are also recognized based on the number of citations, which is helpful to establish cooperative relationships in channel modeling for THz communications.
Development of Intrusion Detection Models for IoT Networks Utilizing CICIoT2023 Dataset

The Internet of Things (IoT) is a rapidly growing technology that enables devices to communicate and exchange data with minimal human intervention. However, this growth increases the volume of sensitive data, making it more vulnerable to security attacks. DDoS is a perilous form of attack that targets IoT networks frequently. Intrusion detection systems (IDSs) are a solution for protecting IoT devices by monitoring network activities and detecting real-time threats and attacks. However, implementing IDS in IoT networks presents several challenges, including power and memory constraints imposed on IoT devices and implementation datasets requiring greater comprehensiveness to accurately define the features of IoT networks. Thus, this study developed intrusion detection models using lightweight ML algorithms, such as decision tree, k-nearest neighbors, random forest, and Naïve Bayes, to identify network DDoS attacks. The latest dataset, CICIoT2023, which includes multiple attacks unavailable in previous IoT datasets, was utilized. We evaluated the model’s performances using accuracy, false positive rate, F1-score, recall, precision, and training and testing time usage. The results show that the random forest and decision tree models outperformed other detection models with 100% accuracy. Regarding time usage, the decision tree model outperformed other models, which could classify 2,926,588 instances in 1 second.
Evaluation of Surface Electromyography Parameters as Possible Biomarkers for the Assessment of Sarcopenia

Sarcopenia is a condition characterized by a decline in muscle mass and strength. The current non-invasive techniques for evaluating sarcopenia have limitations in terms of portability, cost, and expertise required. To overcome these limitations, this study proposes the use of surface electromyography (sEMG) as a diagnostic tool. This research aims to evaluate the performance of different biomarkers obtained from the sEMG signal for sarcopenia assessment. Nine sarcopenic and nine healthy volunteers performed thirty-second sit-to-stand consecutive movements. A bipolar sEMG signal was recorded simultaneously from the rectus femoris and biceps femoris muscles. Muscle activations were segmented and eleven sEMG features were extracted regarding temporal, spectral, complexity, and shape characteristics of the signals. Biceps femoris showed more differences between groups than the rectus femoris muscle. In biceps femoris, nine out of eleven parameters were statistically significant. From these nine significant features, a discriminant analysis was conducted and concluded that the median frequency and sample entropy were the biomarkers with the highest performance for diagnosing sarcopenia.
Integrated PV-Based Boost-Cuk Converter For EV Charging Station Applications

Electric Vehicles (EVs) deliver ecologically friendly transportation, however, the electric vehicle industry has relatively little growth on an annual basis due to a lack of EV charging stations, a lengthy charging process, and grid uncertainty during high usage times. In this study, a quick charging, high gain DC to DC converter and a network combined solar PV-founded electric vehicle charging station application control algorithm are proposed. Fast accusing positions, chiefly super-fast charging stations puts a strain on the electrical infrastructure due to potential excess during top hours, abrupt control outages, and voltage sags. By combining the boost-Cuk converters, a unique DC-to-DC converter that combines the impressive advantages of a large conversion range, reduced voltage stress, and great efficiency is devised to overcome the inherent intermittency associated with PV. The Ant Lion Optimized PI Controller is consequently utilized to extract the best values of frequency and voltage PI gains and to control the proposed hybrid converters. With an ideal accuracy of 93 %, the simulation conducted to measure the presentation of the planned EV charging system consuming MATLAB has proven that the methodology's better performance is delivered.
A Discrete-time Linearization Feedback Control for Nonlinear Continuous-time Systems

This paper proposes a linearization feedback method resilient to structural alterations arising from discretization, thereby elevating controller efficacy during the discretization process. The proposed method employs the least-squares method to derive the feedback law in a straightforward manner. Extensive numerical simulations validate that the proposed method exhibits comparable performance to the continuous-time-defined method in terms of response and error. Moreover, the study confirms that adhering to a specific discretization method improves controller performance. Consequently, the proposed method is deemed effective for both discretization and control of continuous-time systems.
A Multilingual Text to Speech Engine Hindi-English: Hinglish

Being a diverse country, India has a plethora of local & regional languages which sum up-to 16000. For languages like English, a leading language & considered as the lingua-franca in many regions & professional contexts such as science, law, IT applications and services have become more and more prevalent globally. However, nearly five percent of Indian population can communicate in English due to relatively lower literacy rates. Need for a constructive solution as a basis for developing services in local language has been realized. A recent survey reveals that over 528 million native speakers use Hindi or precisely Hinglish in various communication platforms like WhatsApp, Facebook, Instagram etc. As of now, systems being developed on cutting edge technology still lag behind for diverse Indian languages. Through this paper, the authors have proposed a Text-To-Speech application in Hinglish which would be a tremendous effort to overcome the communication barrier and make information exchange meaningful and fun at the same time in India. The main motive behind this solution is to uplift the marginalized section of Indian society. Also, the authors tried to address the solution of the application performance & natural speech output quality.
Medicinal Plants Attribute Detection by Deep learning Image Processing Techniques

The identification of medicinal plant species leads to many benefits to wide range of stakeholders such as, forestry services, taxonomists, doctors, and pharmaceutical labs, organizations that protect endangered species, the government, and the general public. Important plant species are disappearing around the world on a daily basis. Steps include plant identification, restoration, and protection in order to control the phenomenon. Typically, leaves are used to identify plants. Convolutional Neural Network which is the widely used architecture for image base classification motivated us to creating automated methods for classifying various plant species and identify medicinal attributes of the plants. In this paper, using computer vision and machine learning approaches, a fully automated front end based software system has been designed and described for achieving this. Each leaf's length, width, perimeter, area, number of vertices, color, perimeter, and hull area were all just a few of the many features that were taken out. Then, from these properties, a number of derived features were calculated. The native people are expected to get more knowledge about medicinal leaves with the use of this work for the automatic recognition of medicinal leaves. Additionally, it helps taxonomists in the development of more effective methods for identifying species, which will greatly help in the preservation of endangered species. The utilization of various plant traits ranges from the most basic, such as leaf color and form, to the most complicated, such as tissue and cell structure are applied. The results obtained with these analytics gives 98% accuracy in 100 epochs.
A Behavioral Model for Single Leader: Leader-Follower Drone Swarm in Leader’s Failure

The single-leader, leader-follower formation control algorithm is one of the most frequently used formation control approaches in the domain to control drone swarms because of its simple design and overall operational simplicity. However, the major concern faced by such algorithms is the single point of failure problem. This research suggests a distributed leader election approach by incorporating capability-aware measurement to solve the single point of failure problem. The proposed algorithm has three timely bounded phases, and it elects a leader who has the highest capability to be the leader concerning the situation. The performance of the proposed algorithm is assessed using the scenario-based simulations conducted on the SITL simulation environment and showcased the expected behavior from the drone swam in different stages of the simulations.
Attention-based Feature Weight Fusion Network for Scene Text Detection

Due to the diversity of scene texts and background interference, designing an effective and accurate text detector remains challenging. To address this issue, we propose a scene text detection network named as CWDNet, that combines attention mechanisms and weight branches. Firstly, the Coordinate Attention (CA) mechanism is introduced into the residual blocks of the ResNet network to enhance feature extraction. Secondly, a weight branch fusion module is proposed to dynamically adjust the significance of features at different scales. Finally, we conducted experiments on benchmark datasets of ICDAR2015, MSRA-TD500, and ICDAR2017-MLT, the CWDNet achieves F-measure of 85.8%, 84.6%, and 76.3% respectively, demonstrating strong robustness and competitiveness.
Evaluation and Comparison of Ten Machine Learning Classification Models Based on the Mobile Users Experience

Machine learning prediction is a statistical prediction model based on computer algorithms, which has been very popular with the development of AI. It is the most commonly used tool in big data prediction and data mining. To establish an effective machine learning model and get better prediction accuracy, we must choose a suitable algorithm based on actual problems and then fully improve the model. This article lists ten commonly used machine learning models and train and fit these classification models in Python based on the data set provided by the 2022 MathorCup Big Data Competition. Meanwhile, by establishing feature engineering and choosing the best hyperparameter combination, we got the best performance of the model’s prediction. The result shows that among the ten Machine learning models, the CatBoost Classifier had the highest accuracy 0.7849, while the XGBoost Classifier had the highest macro-F1 score 0.4898; after hyperparameter tuning, the best macro-F1 score of XGBoost Classifier could attain 0.5216.
Effect of Eccentric Tooth Tip Arc on Cogging Torque of PM Machines

This paper proposes an eccentric tooth tip arc on a stator for a reduction in the cogging torque of permanent magnet synchronous machines (PMSM). The proposed technique is applicable for both inner rotor and outer rotor PM machines. The variation in the stator tooth geometry and a corresponding change in the tooth tip arc of the eccentric tooth is derived mathematically. Two different PM synchronous motors, i.e. 12-slot/10-pole inner rotor and 12-slot/14-pole outer rotor PMSM are taken for this study to analyze the effect of the proposed method. A detailed finite element analysis (FEA) is performed on both the machines, and their cogging torque is analyzed. It is found that the cogging torque reduces with an increase in the eccentricity of the stator tooth tip arc.
Pioneering Frontiers in Natural Language Processing: Exploring Promising Areas and Emerging Applications

NLP, short for Natural Language Processing, refers to a domain of research that centers on the interaction between computers and human language. Its objective is to devise algorithms and models that empower machines to comprehend, interpret, and produce human language in a coherent and meaningful manner. NLP is of great importance due to its ability to enable effective communication, facilitate information retrieval and extraction, analyse sentiment and opinions, break down language barriers, summarize text, recognize named entities, extract structured information, generate human-like text, process voice commands, and support cognitive computing. As NLP continues to advance, it holds immense potential for transforming various industries and enhancing human-machine interactions across numerous domains. This study emphasizes the significance of NLP in narrowing the divide between human language and machine comprehension, resulting in a broad spectrum of applications and advantages.
Discovering Breast Cancer Comorbidity: A Network Analysis - Community Detection Approach

In 2020, breast cancer was the most common type of new cancer cases and caused the death of 0.69 million people worldwide. As it is famous and lethal, this paper aims to discover the comorbidity of breast cancer using community detection algorithms, namely Belief, Girvan-Newman, and Spinglass to prevent further impacts. Four centrality algorithms were used to get the comorbidity. As a result, the Wang algorithm with a threshold = 0.5 is chosen to build the network. The significant comorbidities agreed by all three algorithms are the disease of mental health, gastrointestinal system disease, cardiovascular system disease, nervous system disease, lung disease, and lower respiratory tract disease. There are also four other comorbidities agreed by the two algorithms and eight comorbidities discovered by only the Spinglass algorithm. From the list of 12 breast cancer comorbidity diseases by the Danish National Patient Register (DNPR), all of the comorbidities’ causes led to our list of comorbidities, which is the rationale for the excellent result of this research.
IP and IoT-Based Waterside Surveillance for Early Floods Alarming System

Throughout this work, an apparatus that uses machine learning, image manufacturing, or Connectivity to track riverbed water levels is given. By using the processing of images, wherein boundary detection technology is used to analyze both pictures acquired using a television camera, such an approach is designed to determine the level of a shoreline and the level of the stream. The stream's water grade plus the shoreline rating are compared to calculate the flood intensity rating. The storm's intensity was then uploaded to an Internet of Things system. Once the water's intensity exceeded a specific critical level, a notice was delivered to the public via the popular social networking site Telegram. Within the aforementioned architecture gadget, a Banana Pi 3 B variant is employed as both a controlling device and a camera using the Banana Pi 5MP cameras package. Ubidots is an Internet of Things system that is employed, and it allows for user notification. Our work's major contributions focus on integrating image processing using Internet of things clouds to create an advanced flood tracking device in response to warming temperatures by identifying the flooding extent and warning individuals about the flooding harshness condition. According to experimental findings, combining machine vision using machine learning for image analysis using the Internet of Things Cloud infrastructure is a realistic strategy. In order to determine the liquid level versus bank straight, the paper compares the creepy-edge detection approach and reaching the point technique. To determine whether this device might function in the real world, it was additionally tested in both lab (indoor) as well as outdoor environments.
Evaluation of CMIP5 models on sea surface salinity in the Indian Ocean

Prior to future climate assessment of the 5th Coupled Model Intercomparison (CMIP5) experiments, how well CMIP5 models simulates present climate should be examined. Sea surface salinity (sss) play important role in ocean stratification and indirectly affects air sea interaction. However, few studies have been carried out to evaluate sss in CMIP5 models. In this study, performance of CMIP5 models in simulating sss in Indian Ocean was examined with respect to the observation. Our results showed that multi model ensemble (MME) mean of CMIP5 models displayed annual and seasonal salinity bias in three regions i.e. Western Indian Ocean (WIO), Bay of Bengal (BOB) and Southeastern Indian Ocean (SEIO). CMIP5 models overestimate sss in BOB about 1.5 psu and underestimated sss in WIO and SEIO about 0.4 psu. Biases in WIO and BOB were mainly attributed to bias in precipitation. CMIP5 models overestimated (underestimated) precipitation in WIO (BOB) with greater bias found during Boreal summer to winter. Meanwhile, advection process was responsible for negative SSS bias in SEIO.
An Empirical Study of Lightweight JavaScript Engines

Lightweight JavaScript engines have seen a con-siderable rise in recent years driven by the wide adoption of JavaScript programming for resource-constrained scenar-ios like edge computing and the Internet of Things. These resource-constrained scenarios present unique challenges for the lightweight JavaScript engines in terms of performance and memory usage. However, there is still a lack of comprehensive research that evaluates their reliability, especially concerning their support for modern ECMAScript standards and resilience. In this study, we present, to the best of our knowledge, the first empirical analysis of lightweight JavaScript engines in four three domains: conformance to ECMAScript standards, performance evaluation, and resilience evaluation. We designed and implemented a software prototype called Jasmin, which we used to assess the four most widely adopted and representative lightweight JavaScript engines: QuickJS, JerryScript, Duktape, and MuJS. Our empirical results yielded valuable findings and insights, such as: 1) we proposed 3 root causes contributing to incompatibility with ECMAScript standards; and 2) we identified 3 issues related to executing obfuscated JavaScript code. 3) we investigated the performance of these lightweight JavaScript engines. We believe that our study will serve as a useful reference for both JavaScript developers seeking optimal engine choices and for engine developers seeking to improve their products.