Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Humidity on Overhead Line Insulator based on Boundary Frequency Obtained from Leakage Current Analysis

Transmission line insulators are one of the most vital equipment in the arena of power system. Owing to atmospheric degradation, insulator gets contaminated. Contamination severity results in the increment of leakage current in presence of moisture. Ultimately it leads towards flashover. For averting flashover, assessment of leakage current is very important. For measuring leakage current, a test setup had been arranged at laboratory. Leakage current data has been investigated using FFT analysis. Here, a new parameter, ratio of boundary frequency to fundamental frequency of leakage current has been investigated extensively. This ratio is mainly affected by humidity as well as by the severity of contamination or pollution level. Analysis of this ratio will be helpful to assess the surface state of the insulator and ultimately flashover can be prevented.
Bitki Koruma Bülteni / Plant Protection Bulletin

Many insect pests infest cotton ( Gossypium hirsutum L.) crop, resulting in significant economic losses. The tarnished plant bugs ( Lygus species) greatly damage the cotton crop during all growth stages. This study determined Lygus species infesting cotton in Diyarbakır province, their distribution, and population dynamics during 2020 and 2021. Lygus species samples were collected from 244 fields in 7 districts of the province by using either D-vac or sweep net based on the phenological period of cotton. Population dynamics of the species were monitored weekly by using sweep net and D-vac from two fields situated in the Sur (Gencan village) and Çınar (Şükürlü village) districts of the province during both years. Two species including Lygus gemellatus (Herrich-Schäffer) and L. pratensis (Linnaeus) were identified during the study. Lygus gemellatus was the most common and abundant species observed
Review: A Speculative Divide-and-Conquer Optimization Method for Large Analog/Mixed-Signal Circuits

We review the speculative divide-and-conquer (SDnC) optimization method. SDnC optimization method is a highly effective for large analog/mixed-signal (AMS) circuits. As the size of circuits grows, previous optimization methods face difficulties due to an exponential increase in design complexity. To reduce exponential design complexity, SDnC decreases the evaluation and optimization levels from the large AMS circuit to smaller module units. This significant reduction in design and optimization complexity could be validated through comparisons with other methods, such as parameter sweep and PSO. Furthermore, a key aspect of SDnC is modeling intricate interactions between modules. This results in power evaluations that differ from actual simulation results by only an average of 3.4%, demonstrating the accuracy of the SDnC approach.
Iterative Closest Point of Object-based Point Clouds for Multi-View Extension

Importance of LiDAR-based vehicle detection techniques for ensuring safe driving is increasing. However, LiDAR data from a single vehicle may have occlusions caused by obstacles, presenting a challenge. Eliminating these occluded areas through point cloud registration obtained from two vehicles can enhance autonomous driving systems. However, matching every point for point cloud registration requires a significant computational process, making real-time processing challenging. This paper proposes a novel technique that tackles this issue by introducing the iterative closest point (ICP) of object-based point clouds, enabling fast registration of point clouds scanned by two vehicles. The proposed approach demonstrates the ability to reduce the number of points significantly, resulting in a faster registration process compared to the conventional ICP approach.
A 90-dB DR Discrete-Time Delta-Sigma Modulator for Audio Applications

This work presents a second-order discrete-time (DT) delta-sigma modulator (DSM) for audio applications. Adding multiple feedforward paths in the cascade of integrators with feedback (FF-CIFB) topology can significantly reduce the integrator output swing. Thus, the proposed DSM architecture can effectively mitigate the dc gain of operational transconductance amplifiers used in the integrator. The prototype DT-DSM is fabricated in 180-nm CMOS technology and occupies an active area of 0.54 mm2. At 3.2-MS/s, this prototype ADC consumes $851 \mu \mathrm{W}$ from a 1.8-V supply. With an oversampling ratio of 64, the signal bandwidth is 25 kHz. Measured SNDR and SFDR are 89.3 dB and 102.5 dB, respectively. The measured dynamic range is 90 dB. The equivalent Schreier figure of merit (FOMs) is 163.9 dB.
A Joint Intent Classification and Slot Filling Method Based on Knowledge-Distillation

With the increasing machine computing power, the deep learning model has a large number of parameters and is painful to deploy, research on knowledge distillation methods used to compress large models is receiving increasing attention. This paper proposes a new Knowledge-distillation (KD) approach that introduces contrast learning to enhance the generic representation of models and outstanding the differences between teacher and student. Furthermore, joint intent classification and word slot filling learning are also invoked to output the results of intent category and lexical sequence annotation, and a CRF module can optionally be added after the encoder. The experiment is based on the ATIS and SNIPS, and the results showed that our models achieved 98.84% and 99.14% on intent accuracy respectively. This study provides an effective solution for the intent recognition task, which can accelerate model inference and achieves an outstanding classification result.
Using Artificial Intelligence to Fight Clickbait in Romanian News Articles

On-line press is infested with poor quality content that uses misleading headlines in order to increase web traffic and gain popularity and profit. This phenomenon is called clickbait and impacts the usability and, in some cases, even the security by exposing the user to malicious advertisement. This paper proposes an automated solution for detecting clickbait by using Natural Language Processing techniques to extract features from both the article title and the article content. The extracted features are used with a set of Machine Learning classifiers which are fine-tuned to improve the performance metrics. The resulting model shows good results and can be integrated in a news aggregator that selects only quality content for the user to read.
3D Object Recognition using Enhanced Slicing

The ability to recognize and classify 3D object models into discrete categories is becoming more and more important. It has applications in a wide variety of fields ranging from augmented reality to robotics and self-driving cars. In this paper, we present a novel approach to 3D Object Recognition that aims to transform 3D objects into 2D representations that preserve depth as well as possible. The transformed models are then fed into Vision Transformer-based neural networks that are able to classify them into 40 discrete categories. For the training, we use Princeton's ModelNet40 data set which contains 12, 311 pre-aligned models labeled into 40 categories. We believe our approach is important as it allows us to use recent breakthroughs in the task of 2D classification, such as Vision Transformers, for the task of 3D classification.
Simulation of Substation Electrical Main System Based on Internet Interactive Virtual Reality Technology

With the rapid growth of the power industry, simulating and analyzing the electrical main system of substations has become a necessary means. However, traditional electrical main system simulation methods have many problems that require the search for new technical means to solve. The application of emerging technologies such as artificial intelligence, cloud computing, and the Internet of Things can greatly improve the accuracy and reliability of electrical main system simulation analysis, ensuring the stable operation of the power system. This article studies the simulation of substation electrical main system based on Internet interactive virtual reality technology (VRT). Firstly, this article introduces the basic structure and working principle of the substation electrical main system. Then, this article proposes a simulation solution based on Internet interactive VRT to address the shortcomings of traditional electrical main system simulation. This solution adopts a combination of VRT, network technology, and interaction technology to achieve real-time interaction between users and the simulation system. Finally, this article verifies the effectiveness and feasibility of this scheme through experiments. The experimental results show that the calculation speed of the electrical system is 146m/s, with a maximum value of 176m/s. This scheme can improve the efficiency and accuracy of electrical main system simulation, providing strong support for the design and operation of substation electrical main system.
Experimental Analysis of a Time-Dependent FSO System under Summer Conditions in South Korea

In this study, the time-dependent performance of free-space optical (FSO) communication is investigated through a practical experiment. Employing an experimental setup spanning 400 m in an outdoor environment, we measure bit error rate (BER) utilizing BERT (bit error rate tester) automatically, while recording corresponding weather conditions. The experimental results demonstrate that BER performance shows better performance during period without sunlight, indicating a critical role of sunlight. Moreover, the impact of temperature and rainfall on FSO performance is observed. Our practical approach, conducted outdoors and using automated equipment, contribute to a deeper understanding of FSO communication under diverse weather conditions.
A Method on Neural Network Optimization Deployment Frameworks for Lightweight Target Devices

The development of artificial intelligence neural networks requires specialized knowledge in the industry. The process of creating and deploying neural networks is very difficult for software developers who lack knowledge. Therefore, there is a need for tools that can easily develop neural network applications in industries. In order to meet these needs, this paper proposes a deployment optimized for a target device by automatically generating a specification-based neural network. First, the user simply selects the desired system and neural network requirements. The proposed framework utilizes user specifications to generate the desired neural network. Next, template code is generated based on the generated neural network. Finally, the neural network is deployed to target devices. In this way, we present a method to build an artificial intelligence neural network application deployment easily and quickly.
Training CSI Feedback Model with Federated Learning in Massive Mimo Systems

The basic centralized learning method in Deep learning-based downlink channel state information (CSI) feedback requires a large dataset that needs to be collected either in advance with huge time and workforce costs or by distributed user equipment (UE) through wireless communication links producing huge communication overhead. Federated learning (FL)-based training can reduce the burden of CSI data collection, but the requirement of iterative model information transmission over wireless channels still leads to high communication overhead. To overcome this communication bottleneck, we develop a novel communication-efficient FL algorithm named FedDec, which only needs the local decoders to participate in model aggregation and allows each UE to upload only part of the decoder parameters by a client grouping method. Simulation results support the effectiveness of FL in CSI feedback and illustrate that FedDec can reduce the communication overhead by more than 50% compared with the widely-accepted FL algorithms, which require all model parameters to participate in model aggregation.
CNN and TOPSIS Based Wordle Development Study

Recently, an anagram game called Wordle has become popular. To better understand the game’s current status and provide constructive suggestions for its next development, this paper examines both the number of results classification and the word difficulty of the game based on relevant public data on the Internet. To begin with, the distribution of seven attempts (from 1 to X, where X indicates greater than or equal to 7, i.e., failed challenge) as the output, and finally obtains a model that can predict the percentage distribution of attempts for any five-word word. The average deviation of the model is 6.0450%. Taking EERIE as an example, after extracting the features and inputting them into the network, we obtained its percentages of attempts from 1 to X as follows: 4%, 7%,11%,14%, 18%, 21%, and 25%, respectively. ton order to build a classification model based on word difficulty, the 12 attributes of words were subjectively classified into two categories: memorability, and complexity. The TOPSIS method was adopted to find the weights of the 12 indicators. The scores of word difficulty were calculated separately from the two dimensions (i.e., memorability and complexity). Further, based on the distribution of words in the two-dimensional plane, the words were divided into four categories according to their difficulty: O, C, M, and H, using their mean values as the boundary.
Minimizing The Global Waiting Time of Swarm UAV Network for Efficient Battery Charging in Persistent Monitoring Scenario

Persistent monitoring is important in indoor farming to maintain a safe environment for plant growth. Typically, various static sensors are employed to ensure continuous surveillance within indoor farming. However, these static sensors are susceptible to damage, and repairing them can be time-consuming. Such repairs can disrupt the real-time monitoring process. In this study, we employ Crazyflie swarmUAVs to substitute for malfunctioning static sensors while they undergo repairs or replacements. The primary objective of this research is to develop Ant Colony Optimization (ACO) and Cloud-Based Drone Navigation (CBDN) algorithms to minimize the global waiting time of the swarm UAV network. Simulation results showed that the proposed modified algorithm successfully allocates the Crazyflie UAVs to specific task areas and enables efficient battery charging, thereby minimizing the overall travel time.
Compact Three-layer Band-pass Filter Based on SIW-technology

The results of electrodynamic analysis and a band pass filter synthesis are presented in the article. The filer is developed using SIW technology, and a three-layer filter is made by bending the original filter three times. The main advantage is the reduction in the weight and size of the filters while maintaining the same resonant characteristics and bandwidth. Both filters configurations, their characteristics and dimensions are shown. The computer simulation was carried out using the CST Microwave Studio software package.
Optimization of Reinforcement Learning-Based Backoff Indicator for 5G NR Random Access Procedure

This paper proposes a reinforcement learning approach to improve the performance of the random access procedure in the massive machine type communication (mMTC) environment, as defined by 3GPP, to accommodate an increasing number of user equipment (UEs). The existing random access process results in channel congestion when UEs that have failed to transmit due to a fixed backoff indicator choose a subsequent random access opportunity (RAO) slot. To address this, we introduce an agent that automatically adjusts the backoff indicator based on the state of the network environment. As a result of simulations using Q-learning and DDPG, DDPG achieves a higher success rate and reward, but the backoff indicator continually increases. In contrast, Q-learning exhibits relatively lower performance, yet the stability of the backoff indicator is maintained. From these findings, it is evident that the integration of reinforcement learning to optimize the random access procedure enhances performance. However, it necessitates careful algorithm selection and precise tuning of the reward mechanism.
Systematic Error Compensation in a Unary Segment of a 10-bit Current-Steering DAC

Experimental results for a segmented digital-to-analog converter (DAC) are presented showing an integral nonlinearity (INL) reduction with help of switching schemes. Based on previously published simulation results, four switching schemes have been selected for the experiment. A 10-bit segmented current-steering DAC has been designed in X-FAB 180-nm SOI technology. The designed Multi-Scheme Unary Decoder allows implementing the selected switching schemes that control the same array of elements. According to the simulation results, one of the gradient-optimized switching schemes has promised the INL reduction but this has not been confirmed in the experiment. Therefore, corrections of the approach for processing the simulation results are derived. Among switching schemes, the Reshuffled Diagonal Rotated Walk (RDRW) switching scheme confirms the best INL reduction. Based on the experimental results, systematic error profiles have been reconstructed and their parameters have been calculated. Obtained data have shown that the systematic error is joint, anisotropic and rotated. The linear component is about 10 times larger than the quadratic one. The contribution of the 3rd order component is negligible. Recommendations for a function-level simulation of switching schemes are given.
PERCEPTION OF THE JAPANESE WORD-MEDIAL SINGLETON/GEMINATE CONTRAST BY KELANTAN MALAY SPEAKERS

Both Japanese and Kelantan Malay (KM) use consonant length contrastively, though they differ in terms of word position, i.e., Japanese permits the contrast in word-medial position, while KM restricts such a contrast to word-initial position only. It is thus of theoretical interest to examine the extent to which speakers of these languages process familiar consonant length contrasts (i.e., short/singleton vs long/geminate) in unfamiliar word position. To this end, we examined the perception of Japanese consonant length by KM participants who had no experience with Japanese. The KM and control Japanese participants responded to 200 trials via an AXB discrimination task. The overall mean discrimination accuracy was 84% and 99% for the KM and Japanese groups, respectively. While there was a clear between-group difference in discrimination accuracy, compared to participants from other first language backgrounds, it appears that the KM group benefitted from their experience with L1 consonant length.
Propriedades energéticas da madeira e carvão de Corymbia e Eucalyptus em diferentes condições de secagem - DOI:10.5039/agraria.v10i3a3601

Humidity control of wood for carbonization is essential for charcoal production. The objective was to evaluate drying and the influence of moisture of Eucalyptus urophylla and Corymbia citriodora wood on carbonization of the material. Drying and net calorific value of E. urophylla and C. citriodora logs were followed for 90 days. These samples were carbonized after 30, 60 and 90 days of drying as well as without moisture and had gravimetric yield, ash, volatile matter, fixed carbon and calorific value were evaluated. The logs of C. citriodora showed lower moisture and higher net calorific value throughout the period, reaching less than 30% moisture, recommended for carbonization. Considering the carbonization parameters evaluated, the wood moisture affected only the gravimetric yield on a wet basis.
Live Cell Imaging by Single-Shot Common-Path Wide Field-of-View Reflective Digital Holographic Microscope

Quantitative phase imaging by digital holographic microscopy (DHM) is a nondestructive and label-free technique that has been playing an indispensable role in the fields of science, technology, and biomedical imaging. The technique is competent in imaging and analyzing label-free living cells and investigating reflective surfaces. Herein, we introduce a new configuration of a wide field-of-view single-shot common-path off-axis reflective DHM for the quantitative phase imaging of biological cells that leverages several advantages, including being less-vibration sensitive to external perturbations due to its common-path configuration, also being compact in size, simple in optical design, highly stable, and cost-effective. A detailed description of the proposed DHM system, including its optical design, working principle, and capability for phase imaging, is presented. The applications of the proposed system are demonstrated through quantitative phase imaging results obtained from the reflective surface (USAF resolution test target) as well as transparent samples (living plant cells). The proposed system could find its applications in the investigation of several biological specimens and the optical metrology of micro-surfaces.
Modeling and Mining Ubiquitous Social Media: International Workshops MSM 2011, Boston, MA, USA, October 9, 2011, and MUSE 2011, Athens, Greece

This book constitutes the joint thoroughly refereed post-proceedings of the Second International Workshop on Modeling Social Media, MSM 2011, held in Boston, MA, USA, in October 2011, and the Second International Workshop on Mining Ubiquitous and Social Environments, MUSE 2011, held in Athens, Greece, in September 2011. The 9 full papers included in the book are revised and significantly extended versions of papers submitted to the workshops. They cover a wide range of topics organized in three main themes: communities and networks in ubiquitous social media; mining approaches; and issues of user modeling, privacy and security.
Lifestyle Factors in Myopic Spanish Children

Background: Childhood myopia represents a global concern with increasing prevalence in recent decades. Lifestyle factors significantly impact myopia. Aim: To evaluate lifestyle factors in myopic children from a metropolitan area in Europe. Methods: This was a descriptive study including myopic subjects aged 4–18 years. Patient demographic and clinical data were collected, including cycloplegic refraction in spherical equivalent refraction (SER) and axial length (AL). In addition, a questionnaire on lifestyle factors was conducted between September 2022 and April 2023. Results: A total of 321 myopic children were included, aged 10.72 ± 3.05 years, of whom 51.4% were boys, with SER −2.25 ± 1.9 D and AL 24.54 ± 0.98 mm. The mean age of myopia onset was 7.69 ± 3.05 years. A total of 59.8% had family history of myopia. Those children who had <2 h/day of screen time (on weekdays) presented SER −2 ± 1.91 D, compared to those who had >2 h/day, SER: −2.50 ±1.88 D (p = 0.009). Children who spent <2 h/day doing near work after school were less myopic compared to those who spent >2 h/day (SER: −1.75 ± 1.83 vs. SER: −2.75 ± 1.82, respectively, p = 0.03). However, no significant association was observed between SER and AL and time spent outdoors nor between SER and AL and academic performance (p > 0.05). Conclusions: Screen time and near-work time appear to be lifestyle factors related to myopia.
A Review on Fog Removal with Its Techniques and Types

Now days, Images play essentialrole in real world, images defines not only the changes in environment and also use for traffic analysis. But images are captured in open environment due to the bed whether or atmosphere images are not too good. This paper review on the different haze removal processes to eliminate the haze in the captured hazy images to recover a better and improved quality haze free images [1].