Miracle-dz/UniFAR-FTUs huggingface dataset:
Base on New Featue of UHV Converter Substation Control and Protection Study

This paper makes a study on the characteristics and differences of the secondary design of the South Henan converter station, which is a combined construction of the UHV converter station and the AC station, presents the solutions to the new problems in the secondary design of the converter station, and gives the relevant reference for the design of the combined construction of UHV converter substation.
RESULTS FOR THE SECOND QUARTER 2011 TANK 50 WAC SLURRY SAMPLE: CHEMICAL AND RADIONUCLIDE CONTAMINANT RESULTS

The Saltstone Facility is designed and permitted to immobilize and dispose of low-level radioactive and hazardous liquid waste (salt solution) remaining from the processing of radioactive material at the Savannah River Site. Low-level waste (LLW) streams from the Effluent Treatment Project (ETP), H-Canyon, the DDA (Deliquification, Dissolution, and Adjustment) process, and the decontaminated salt solution product from the Actinide Removal Process/Modular Caustic Side Solvent Extraction (CSSX) Unit (ARP/MCU) process are stored in Tank 50 until the LLW can be transferred to the Saltstone Facility for treatment and disposal. The LLW must meet the specified waste acceptance criteria (WAC) before it is processed into saltstone. The specific chemical and radionuclide contaminants and their respective WAC limits are listed in the current Saltstone WAC. Waste Solidification Engineering (WSE) requested that Savannah River National Laboratory (SRNL) perform quarterly analysis on saltstone samples. The concentrations of chemical and radionuclide contaminants are measured to ensure the saltstone produced during each quarter is in compliance with the current WAC. This report documents the concentrations of chemical and radionuclide contaminants for the 2011 Second Quarter samples collected from Tank 50 on April 4, 2011 and discusses those results in further detail than the previously issued results report.more » The following conclusions are drawn from the analytical results provided in this report: (1) The concentrations of the reported chemical and radioactive contaminants were less than their respective WAC targets or limits unless noted in this section. (2) The reported detection limit for {sup 59}Ni is above the requested limit from Reference 2 but below the established limit in Reference 3. (3) The reported detection limit for {sup 94}Nb is above the requested limit from Reference 2; however, it is below the established limits in Reference 3. (4) The reported concentration of {sup 242m}Am is above the target in Listed in Attachment 8.4 of the Saltstone WAC. (5) {sup 247}Cm and {sup 249}Cf are above the requested limits from Reference 2. However, they are below the limits established in Reference 3. (6) The reported detection limit for Norpar 13{sup 5} is greater than the limit from Table 4 and Attachment 8.2 of the WAC. (7) The reported detection limit for Isopar L is greater than the limit from Table 3 of the WAC. (8) Isopar L and Norpar 13 have limited solubility in aqueous solutions making it difficult to obtain consistent and reliable sub-samples. The values reported in this memo are the concentrations in the sub-sample as detected by the instrument; however, the results may not accurately represent the concentrations of the analytes in Tank 50. « less
Autonomous Docking of the USV using Deep Reinforcement Learning Combine with Observation Enhanced

Unmanned Surface Vessels (USVs) have great potential for military and civilian applications as an unmanned device at sea that can perform complex and dangerous tasks in place of humans. This paper is concerned with the problem of autonomous docking for USVs under a dynamic environment. An End-to-End deep reinforcement learning autonomous docking (EDRLAD) algorithm is proposed to overcome the problems caused by the complicated control law in the traditional analytic approach without any mediated perception. The proposed observation enhanced lead together to faster convergence and more robust autonomous docking, speed correction, and attitude adjustment which using RGB image from a forward USV image sensor and USV's status sensor as input. The proposed algorithm based on reinforcement learning shows significant advantages in terms of utility and scalability compared to analytical methods. Using the same EDRLAD algorithm, targets and constraints can be highly customized to meet a variety of special requirements. Extensive experiments have been carried out to prove the effectiveness and simplicity of the algorithm.
A Comprehensive Review on Wireless Technologies and their Issues with Underwater Communications

Since many years, the life and environment in underwater especially the oceans are being explored, yet there are many parts of oceans remained unexplored due to the particularities of underwater environment. The increasing interest in underwater exploration has paved way to the development of different technologies for underwater communication. Underwater wireless communication plays a vital role in the study of marine life, water pollution, monitoring of natural calamities, coastal surveillance, and to monitor various phenomena in underwater environment. Hence, the underwater wireless communication has become a key area of research for exploring underwater environment for a variety of applications. The primary underwater wireless technology uses acoustic waves, electromagnetic waves in RF band, and optical waves as communication carriers to transmit data in an underwater environment. This paper provides a survey of these communication technologies discussing their physical characteristics, compatibility and technical issues, thus enabling to understand use of these technologies for underwater communications.
Multipath Routing in Transparent Spontaneous MANET

In this paper, we proposed novel research used to control congestion and collision in a dynamic network. In opportunistic networks, this approach captures the proposals for strategies of storage congestion control and transfer reliability. Regarding lesser congestion and collision, the performance of multipath protocol measures based on hop count and data sending receiving and loss. The three scenarios of routing are considered in this research. In the first condition, the expected performance of AOMDV is measured. The second one is estimated based on unassigned slots utilization to senders and the third shows the enhancement through the positive (Acknowledgement) ACK concept. In MANET, the protocol called multipath routing has the capability of load balancing in spontaneous networks properly by providing the possibility of the alternative path. The proposed scheduling techniques reduce bandwidth consumption and make it possible to handle congestion and collision, i.e., necessary for improving the routing performance in a dynamic, spontaneous system. The simulation results show that the performance of the proposed approach is more improved than the existing approaches. This paper also recognizes the storage congestion control requirements, and based on the number of message copies distributed, we categories these issues in the network.
Application of the Voltage Holding Prediction Model to floating and fixed shield vacuum interrupters

This work describes the experimental activity and the analyses carried out to investigate more in depth the application of the Voltage Holding Prediction Model (VHPM) to predict the Lightning Impulse Voltage (LIV) breakdown probability of medium-voltage Vacuum Interrupters (VI). A new experimental campaign on two different VI models produced by Siemens, with same rated voltage but different structural features, has been carried out at the high voltage laboratory of the Industrial Engineering Department of the Padua University by systematic application of LIV pulses (up to 345 kV) on several samples of the two VI models. The comparison between the measured and predicted breakdown probabilities of one VI model is the main topic of this work.
D-STATCOM d-q axis Current Reference Control Applying DDPG Algorithm in the Distribution System

The high penetration level of renewable energy in large-scale power systems could adversely affect power quality, such as voltage stability and harmonic pollution. This paper assesses the impacts of Distribution Static Compensator (D-STATCOM), one of the Flexible AC Transmission System (FACTS) devices, on power quality of 4.16kV-level distribution systems via transient and steady-state analysis. Carrier-based Pulse Width Modulation (PWM) control in D-STATCOM generates d-q axis current reference via the PID (Proportional-Integral-Differential) controller to control d-q axis current and voltage. A new control method, via the Deep Deterministic Policy Gradient (DDPG) algorithm-based reinforcement learning (RL), is studied to create a new d-q axis current reference applying to the voltage control, which can improve voltage stability and transient response and derive fast convergence of current and voltage at the D-STATCOM bus. The real-time simulations on an IEEE 13-bus system show that the proposed approach can better control the D-STATCOM than the conventional control methods for enhancing voltage stability and transient performance.
Application of Information Diffusion of Negative Emotion and Binomial Regression Method on Retweet Cascades during Covid-19

This study based on text mining method on Weibo to reveal the relation of negative emotion and information cascade to the coronavirus (Covid-19) pandemic. With 176934 Weibo posts collected with the keyword “novel coronavirus pneumonia” from January 23 to February 7 2020, negative binomial regression method is applied to construct an empirical model between negative emotion and the cascade. Regression results demonstrated that emotional intensity is crucial to information cascade and the intensity threshold exist. The research also proved that positive is more effective than negative to the viral diffusion.
An Automated Approach for Bangla Sentence Classification Using Supervised Algorithms

Natural Language Processing (NLP) is one of the greatest familiar field of Artificial Intelligence. Bangla text classification is become a significant problem due to the availability of textual documents. So, it's high time to solve this problem for better information organization and knowledge management. This study identifies the type of Bangla sentence from a human story: exclamatory or interrogative text document. Several machine learning approaches are applying to the dataset and identify the sentence from the text document. Regulating data is collected from various platforms such as story, Bengali's blog, conversation, etc. To get the output, we need to go through hard work as data cleaning, preprocessing, tokenized by the Countervector, etc. After processing data for the feeding model, we conduct various classification approaches to predict the sentence and provide high accuracy for deciding the result. Among all the approaches, Random Forest and XGBoost provide the highest accuracy of 96.39% in parallel. This study also provides the advantage of sentimental analysis by detecting the type of document in further implementation.
A Deep Learning Approach for Prediction of Stock Price Based on Neural Network Models: LSTM and GRU

In recent years, there has been a predominant use of Artificial Neural Networks (ANN) to solve real-world problems. One such conundrum is the prediction of stock prices since they are convoluted, sporadic, and dynamic. This study focuses on the use of RNN, the Recurrent Neural Networks to identify and forecast trends that stock prices will follow using based on historical data. The LSTM and GRU models are considered for developing prediction models of stock prices. These models do not have the vanishing gradient problem, unlike traditional RNN. Both models are compared and analyzed to find a model that would predict satisfactory prices with maximum accuracy which can help a trader to make efficient decisions.
Design of Compact CPW-Fed Microstrip Slot Antenna for S-band Applications

In this paper, a compact coplanar waveguide (CPW)-fed rectangular slot patch antenna is presented. This antenna consists of two rectangular slots and two C-shaped rings on the main radiator to achieve wider bandwidth and better gain at 2.45 GHz for wireless power transmission (WPT) applications. The rectangular slot and C-shaped ring on the radiator, which can enhance the bore-sight gain, resulting in the better gain of the patch. The measured impedance bandwidth nearly the simulated results is about 480 MHz (from 2.18 to 2.66 GHz) (S 11< -10 dB) and measured peak gain about 1.62 dBi.
A Case Study of ROS Software Re-usability for Dexterous In-Hand Manipulation

Real world objects handling requires a complete robot and sensor system that must be managed by an adequate software architecture. This paper presents the lessons learned in the successful integration of a software architecture based on ROS within the European project HANDLE. It focuses on re-usability and adaptability of several packages of the ROS community for the purpose of real world in-hand manipulation. Solutions proposed can be transferred to other arm and hand systems.
Artificial Intelligence - Enabled IoT for Health and Wellbeing Monitoring

The recent advancement of the Internet of Things (IoT) paves the way for the ubiquitous connection of things, which enables these things to collect and exchange data, and thus made a huge difference in the way we live. Healthcare can be considered as one such major domain, which reaps the benefits of this, where the latest movement in IoT is to bridge disparate technologies to enable new insights, by connecting physical objects, to help smart and intelligent decision-making. As the amount of IoT sensing devices in healthcare increase, it also makes doubt of proper analysis of this large volume of data, where recent advancements in Machine Learning (ML) and other associated Artificial Intelligence (AI) algorithms have made it possible to collect, analyze, and interpret an unprecedented amount of loT sensory information for effective health and wellbeing monitoring. Accumulating raw data in an efficient way and processing that data for better insights will enable a new era in healthcare including the prediction of chronic diseases in early-stage, and personalized and physiological health monitoring systems. In this regard, this paper aims to synthesize the literature mainly on AI-enabled IoT systems for health and wellbeing monitoring, highlighting current research and applications in practice.
EfficientNet-based Convolutional Neural Networks for Malware Classification

Malicious software is posing a great challenge to Cybersecurity by causing huge damage to enterprises and users. Traditional signature-based techniques have found it challenging to detect malware as it has changed and evolved over time. Machine learning techniques have become popular for malware detection but they are based on heuristic feature engineering, which is expensive and requires domain expertise. In the recent years, deep learning, are being actively investigated for this crucial malware detection application to achieve robust solutions with higher accuracy. To assist in achieving this goal, the EfficientNet-B1 model pretrained on the ImageNet dataset with the last layer adapted to attain malware family classification is presented. The malware samples to be inputted are represented as byteplot grayscale images. The experimental results on a dataset comprising of 10,868 samples from 9 different malware families showed that the proposed approach can be effectively used to classify malware families. Classification accuracy of 98.57% on the held-out test set was obtained by the proposed model which outperformed the other pretrained deep learning models.
Bridging the Gaps: A Case Study on the Implementation of Educational Technologies in High Schools

This study combines qualitative and quantitative analysis of interviews with teachers and technology staff at a high school in the Midwest in order to understand the organizational factors that affect the integration of educational technologies into the education system. Analysis of the interview transcriptions led to three major conclusions. The first is that educational technologies are often unreliable and difficult to learn, which discourages many teachers from prolonged use. The second is that the Technology Director’s position within the organization bridges previously loosely coupled departments and creates new pathways for communication and collaboration. Finally, the findings suggest that technology trailblazers in the school can influence their peers’ technology use more effectively than formal trainings, especially with the Technology Director’s help. This study contributes to the current understanding of education reform and pedagogical change by integrating existing theories and observing how they manifest in the real world. IMPLEMENTATION OF EDUCATIONAL TECHNOLOGIES 4
Examining the Effects of Social Stories™ on Challenging Behavior and Prosocial Skills in Young Children: A Systematic Review and Meta-Analysis

Social stories are a commonly used intervention practice in early childhood special education. Recent systematic reviews have documented the evidence base for social stories, but findings are mixed. We examined the efficacy of social stories for young children (i.e., 3–5 years) with challenging behavior across 12 single-case studies, which included 30 participants. The What Works Clearinghouse standards for single-case research design were used to evaluate the rigor of studies that included social stories as a primary intervention. For studies meeting standards, we synthesized findings on the efficacy of social stories using meta-analysis techniques and a parametric effect size measure, the log response ratio. Trends in participants’ response to treatment were also explored. Results indicate variability in rigor and efficacy for the use of social stories as an isolated intervention and in combination with other intervention approaches. Additional studies that investigate the efficacy of social stories as a primary intervention are warranted.
Fast Frequency Regulation Support by LED Street Lighting Control

The replacement of conventional power plants with Distributed Energy Resources (DERs) is reducing total system inertia. As a consequence, frequency deviations induced from power supply-demand imbalances increase with DERs penetrations. In the future years, grid services such as frequency regulation support will be provided by means of distributed generators, prosumers and controllable loads, in order to ensure the power system reliability. LED lamps of road lighting systems represent an interesting case of controllable loads since they are widespread in smart cities and can be regulated by means of local or remote control signals. This paper investigates the possibility to regulate the power consumption of lighting street systems to provide frequency support. Power Hardware-in-the-Loop (PHIL) simulations with an actual LED street lamp have been carried out in order to validate the proposed control strategy. Test results and final conclusions are provided.
An Awareness to Multisystem Inflammatory Syndrome (MIS-C) using Social Networks

The study about the health conditions on patients post-Covid-health is exhibiting alarming statistics. The children under the age of eighteen constituted the only 8.5% of the total affected population. A month into the pandemic, a novel multi-system inflammatory syndrome in children (MIS-C) emerged. MIS-C is found to be serious and even life-threatening. This demands introducing awareness and knowledge among the parents regarding the clinical signs and symptoms of MIS-C. In this paper, we identify the need and method to spread awareness of post-COVID-19 health care to society through social media. In this paper, we identify the need and method to spread awareness of MIS-C to parental society through social media.
Top-Down analysis based performance failure bucketing for Pre-Silicon simulation

Integration of high-end features in core microarchitecture of CPU plays a key role in meeting performance expectations in the industry. However, it also brings challenges to the Pre-Silicon Performance Validation team in uncovering deep-seated performance issues within the core. Performance bucketing of hardware failures is a difficult task due to the absence of an error signature, unlike its functional counterpart. This poses a challenge in terms of man-hours required to do the precise categorization of failures over a huge database of tests. The proposed solution is based on a Top-Down Microarchitecture Analysis (TMA) approach, which can predict a core pipeline unit responsible for maximum IPC difference between RTL and reference model in Pre-Silicon. It uses Performance-Monitors (PMONs) available in core hardware, whose accuracy is validated by dedicated teams in Pre-Silicon and Post-Silicon, and when used in TMA can help the Performance Validation team to triage the failures quickly and accurately.
Development of Smart Height Measuring Scale

The system proposed here is a smart height measuring system. A smart height measuring system has been developed in this project. There are several methods used to measure human height. In this research, we develop a human height measurement scheme. In our scheme, we calculate the height with an Ultrasonic sensor. Two round-looking parts in the ultrasonic sensor are called the ultrasonic speakers. When a 40 kHz sound wave collides with a solid surface, it bounces back (echoes). The echo is detected by the other. We may estimate the distances between the sensor and the item by measuring the overall time it takes to complete this operation. It measures the height of a human more accurately (2 cm to 13 feet). We have an extra feature comparing to the existing market products and that is Mobile Application. We can easily take the measurements using the app.