Miracle-dz/UniFAR-FTUs huggingface dataset:
LDPC-Coded Modulation Performance Analysis and System Design

In this work, the low-density parity-check (LDPC)-coded modulation scheme which has been selected for inclusion in the next generation of International Telecommunication Union (ITU) broadband standard is investigated. The multi-level mapping of this scheme offers excellent performance along with a straightforward modulation which is separable into simple one-dimensional schemes. A subset of modulated bit positions are protected by the LDPC code, while the remaining positions benefit from improved Euclidean distance through assignment to distinct constellation regions. To date, there has been a lack of analytical treatment of the scheme. This is problematic given the number of tunable scheme and code parameters, necessitating time-consuming Monte Carlo simulation. This motivates the novel analytical work of this article, which derives overall error-rate and threshold performances through considering separately the effects of the channel on the coded and uncoded bits. First, new closed-form expressions are derived for the hard-decision performance of the bit mapping of the multi-level modulation scheme, for both coded and uncoded bits. Extrinsic information transfer (EXIT) analysis is then applied to the coded bits in the scheme. The derived theoretical performance of the uncoded bit positions is used in combination with the derived EXIT threshold to provide for the first time for this modulation scheme a method to evaluate analytically whether a designed system will offer capacity-approaching performance. Following this, an approximation to the capacity curve for the coded modulation scheme is produced, again based on the derived analytical performance. Finally, through the relationship between mutual information and error rate performance for error control codes, the derived analytical expressions are used to produce a semi-analytical finite-length performance predictor, whereby error rate results for a given code on the binary input additive white Gaussian noise (AWGN) channel can be mapped to the coded modulation scheme under consideration for any coded system parameters.
The viscosity of nematic liquid crystals

A simultaneous examination of data on the dynamic, kinematic, and rotational viscosities of nematic liquid crystals (NLC) belonging to 320 chemical classes has been carried out for the first time. The methods for the measurement of the viscosities of the NLC have been subjected to a comparative analysis and the limits of applicability of different procedures for the description of their temperature variation are discussed. The tables present data permitting the calculation with satisfactory accuracy of the viscosities of the majority of mesogenic substances at any temperature in the nematic range. The influence of the molecular structure of the NLC on the viscosities and on their temperature variation has been analysed and information is presented about the viscosities of the Soviet and foreign liquid crystal materials used in practice. The bibliography includes 158 references.
Effects of buffer and BSF contact layer on ZnMnO/CIGSSe heterojunction solar cell

This paper proposes necessary steps for improving the performance of a stable and highly efficient Mndoped ZnO/CIGSSe based buffer and absorber layer solar cells. Modification in the conventional (Zn:B/Zn(O,S,OH)/ZnMgO /CIGSSe/Mo(S,Se)/Mo) structure is done by substituting the ZnMnO buffer layer and ZnSe2 Back Surface Field (BSF) contact layer. The tuning of donor and acceptor concentration has been explored. Furthermore, the tuning of the thickness of donor concentration has also been tried. Computer simulation has been carried on to evaluate the parameters like short-circuit current density, efficiency, open-circuit voltage, and external quantum efficiency (EQE) at $N_{d}=1x10^{20}cm^{-3}$ of ZnMnO with 50nm thickness and $N_{a}=7x10^{16}cm^{-3}$CIGSSe with $2\mu m$ thickness. The numerical simulation has been carried out on AFORS-HET $ver. 2.5$ simulator with 500nm monochromatic light, with $2.8x10^{17}cm^{2}s^{-1}$ flux photon and 10nm spectral width.
Hydrofluoroether Impurities—Chemical Detection Using a Deep Learning Laser Speckle Contrast Evolving Spiking Neural Network

Hydrofluoroether (HFE) impurities detection is an issue related to detecting chemical contamination within a high volume manufacturing (HVM) chiller caused by a rapid emulated environmental attack. The aftereffects of the cycle emulated attack may eventually create a micro-crack in the heat-exchanger. This event eventually causes the contamination of HFE due to multiple chemical interactions. This study proposes a new classification methodology to detect HFE chemical impurities using induction by a 532nm laser. The purpose of the laser induction is to leverage its laser speckle contrast attributes and amplify its detection. One of the reasons for choosing the 532nm laser spectrum is its highest quantum efficiency. Once amplification of detection is achieved, the detector tends to be in a highly sensitive mode due to low marginality to differentiate between two different conditions. This mode is prone to be stochastic. Thus, a new form of architecture known as Deep Learning Laser Speckle Contrast Evolving Spiking Neural Network (DL-LSC-ESNN) is proposed. The architecture utilizes speckle contrast domain conversion, dimensional additivity of the receptive neuron of evolving spiking neural network (ESNN), followed by the strength of Convolution Neural Network (CNN) feature extraction (FE) capability. Ultimately, the evolving and adaptive ability of ESNN is assimilated and integrated seamlessly. CNN acted to extract only an important spike train, and the result is its essences of important spikes or spike feature maps. The spike feature maps are then fed into the ESNN neuron repository, which either assimilates or creates a new neuron repository. The proposed methods show significant improvement in the accuracy of detection against multiple baseline state of the art CNN architecture and ultimately demonstrated its capability to detect real-time contamination of HFE thus improved the detection rate significantly for the HVM environment.
Optimization of Piezoresistive Motion Detection for Ambient NEMS Applications

Electrical readout of nanomechanical motion in ambient pressure and temperature imposes an important challenge for emerging applications of nanoelectromechanical systems (NEMS). Here, we optimize a metallic piezoresistive motion transducer for NEMS resonators in air. The nanomechanical motion of the NEMS resonator serves as a signal down-mixer and enables the detection of the motional signal by a low-frequency circuit. A balanced circuit in the detection loop reduces some of the unwanted background and allows for detection without significant losses. We explore the detection parameter space and use an optimized parameter set to detect the fundamental, second and third harmonic resonances of a NEMS doubly-clamped beam resonator. Our simple circuit model agrees with experimental observations and points the way for further optimization.
Decision Making and Local Trajectory Planning for Autonomous Driving in Off-road Environment

Different from urban environment with high-precision map, off-road environment lacks rich priori road structure information and precise location information, which is a huge challenge for autonomous driving. To solve these problems, this paper proposes a real-time decision-making and local trajectory planning method based on a predefined global reference line. According to the reference line, a cluster of parallel offset curves with corresponding obstacle and deviation costs are generated. Then, an appropriate parallel line with minimum cost is selected as the preliminary decision, and the target position with a matched speed on this line is choosed for local planning. Finally, the lateral and longitudinal trajectories planning is carried out with the initial constraint and target constraint of the quintic polynomials. The proposed method is verified in a 3.6 km bumpy off-road environment, including straight road, curves, continuous U-turn and other driving conditions. The maximum autonomous driving speed of an small-medium-sized platform can reach 30 km/h under the condition of stable and smooth driving.
VisualMail+ as a Dialogue-oriented Forum: An Empirical Case about Cooperation and Learning

This study experimentally tested whether cooperation among participants increases when they use a forum application with added functionalities derived from the language-action and situated-action perspectives compared with participating in a forum without such features. We used two versions of a visual-based forum application that included functionalities to visualize the threads of an argumentation. The first one is called VisualMail, and the situated language-action enriched-forum is called VisualMail+. A total of 84 college students participated in the study and were randomly assigned to one of 20 different groups. Subsequently, these groups were randomly assigned to use VisualMail or VisualMail+. The results indicate that using VisualMail+ significantly increases cooperation among participants compared with VisualMail, with a large effect size (d = 1.01).
Study of Security Flaws in the Linux Kernel by Fuzzing

An exceptional feature of the development of modern operating systems based on the Linux kernel is their leading use in cloud technologies, mobile devices and the Internet of things, which is accompanied by the emergence of more and more security threats at the kernel level. In order to improve the security of existing and future Linux distributions, it is necessary to analyze the existing approaches and tools for automated vulnerability detection and to conduct experimental security testing of some current versions of the kernel. The research is based on fuzzing - a software testing technique, which consists in the automated detection of implementation errors by sending deliberately incorrect data to the input of the fuzzer and analyzing the program's response at its output. Using the Syzkaller software tool, which implements a code coverage approach, vulnerabilities of the Linux kernel level were identified in stable versions used in modern distributions. The direction of this research is relevant and requires further development in order to detect zero-day vulnerabilities in new versions of the kernel, which is an important and necessary link in increasing the security of the Linux operating system family.
Cybersecurity of Smart Electric Vehicle Charging: A Power Grid Perspective

With the roll-out of electric vehicles (EVs), the automobile industry is transitioning away from conventional gasoline-fueled vehicles. As a result, the EV charging demand is continuously growing and to meet this growing demand, various types of electric vehicle charging stations (EVCSs) are being deployed for commercial and residential use. This nexus of EVs, EVCSs, and power grids creates complex cyber-physical interdependencies that can be maliciously exploited to damage each of these components. This paper describes and analyzes cyber vulnerabilities that arise at this nexus and points to the current and emerging gaps in the security of the EV charging ecosystem. These vulnerabilities must be addressed as the number of EVs continue to grow worldwide and their impact on the power grid becomes more viable. The purpose of this paper is to list and characterize all backdoors that can be exploited to seriously harm either EV and EVCS equipments, or power grid, or both. The presented issues and challenges intend to ignite research efforts on cybersecurity of smart EV charging and enhancing power grid resiliency against such demand-side cyberattacks in general.
EnergyBudgets: Integrating Physical Energy Measurement Devices into Systems Software

Excessive energy consumption is a critical problem for mobile computing systems due to their limited battery capacity. Software developers aim to improve energy efficiency by monitoring and profiling the energy consumption of their systems in order to discover and resolve energy hot-spots. However, energy measurement is often tedious since it involves a hardware setup as well as software integration. To support accurate but also convenient energy measurements, we propose the inclusion of external energy measurement devices into existing performance profiling subsystems. This approach allows the energy-consumption analysis of applications that run on the system under test (SUT) using the same tools as used for other performance metrics. To enable low-overhead self-monitoring, we propose a modular analysis approach, EnergyBudgets, which bridges external energy measurement hardware to the Linux perf subsystem. The evaluation of our implementation shows that energy budgets accurately measure the energy consumed by different workloads and allow for an overhead-reduction on the SUT by 20% to 51% in comparison to regular timers, while still guaranteeing the same level of precision.
Real time Design of PID Controller for Process control system

In this paper, usefulness of PID type controller in real time application of process control plant is presented. Further, various existing techniques for tuning of PID controller has been discussed. Finally, design of PID controller has been carried out using Good gain method for liquid level control systems and it is shown that Good gain method is better than Ziegler Nichols approach of tuning.
Simulation of the south-eastern Polish subpopulations behaviour

Using the large database of STR (Short Tandem Repeat) DNA results of south-eastern Polish population were validated with regard to the correctness of data by testing the interdependence of loci and the Hardy-Weinberg hypothesis. It turned that the data was correct so that this topic was given up. Furthermore, dividing this population according to the place of birth, we evaluated the FST, Nei’s distance value and other measures used in Phylogenetics, of the subpopulations chosen in that way. phylogenetic trees for this supopulation were obtained and the focus was made validation of hierarchical classifications. We evaluate classifications with the Dunn’s index, and compared the inter classifications using the cophenetic correlation of classifications. After comparing and validating the classification methods the behaviour of these distances was simulated in the following generations. We gathered information about migration (emigration as well as immigration) rate in the considered subpopulation. Therefore changes of phenotype frequencies could be simulated in the these generations based on some reasonable assumptions. On the basis of these simulated frequencies the future distance arrays were computed. If the maximum of this distance is smaller than the assumed level, we state that differences between the subpopulations vanish. We focus on the number of generations till all the differences vanish. It is worthwhile mentionary that the migrations in south-eastern Poland are significant, thus that number is relatively small.
NYC CS4All: An Early Look at Teacher Implementation in One Districtwide Initiative

Computer science for all initiatives have broadened the participation of students enrolled in elective computer science (CS) courses and introduced compulsory CS instruction in many areas of the United States. However, there is a shortage of K-12 teachers with the background, preparation, and experience necessary to teach CS. To build capacity to deliver this instruction, districts must provide teacher preparation that includes not only CS content, but also high-quality pedagogical approaches that will meet the needs of all students enrolled in a wide variety of school settings. In this paper, we explore teacher outcomes across multiple CS professional development opportunities, in one large urban district. The teacher outcomes were measured via a survey administered between eight months and two years after teachers received training to implement CS. Though our findings are from a single district, we believe these findings are relevant to other settings and provide useful information about the outcomes of teacher professional development for CS education, as well as supports and barriers to implementing CS, in a large, urban school system. The results offer insight into professional development quality, teacher confidence, the ability of teachers to implement CS in their classrooms, and supports and barriers to offering CS instruction (even in a district where CS education is a priority). They also shed light on how supports and barriers differ in schools serving students with high economic needs and lower academic performance compared with schools serving students with lower economic needs and higher academic performance. These differences underscore the importance of considering economic need and academic performance (in addition to race and gender) when developing and executing CS for all initiatives.
Adaptive Control of a Ball and Beam System

Ball and beam balancing systems are commonly found in control systems laboratories because they are mechanically simple while presenting the challenge of controlling a nonlinear system. The main ideas of this paper are to model the ball and beam system and to control it using adaptive control. The model was formed by separating the system into two parts, and this was then controlled using a cascading control scheme. For the inner loop control, the motor was controlled using simple adaptive control (SAC) and compared to proportional derivative (PD) control. In the outer loop with the ball and beam system, ball position was controlled using PD control and was compared to linear quadratic regulator (LQR) control. Experimental results show that though PD control performed better when only controlling the motor, SAC performed better in the cascading control scheme when paired with either PD or LQR as outer loop control. When comparing PD and LQR, the results show that PD performs better.
Feasibility Study of Predicting Semiconductor Thin Film Thickness Based on 1D Convolutional Neural Network

Recently, the increased requirement of data processing and reduced data latency has driven the demand for 3D stacks for high-performance semiconductors. The process of stacking hundreds of layers of semiconductor thin film is essential. However, there is a problem that the uniformity of thickness is degraded due to the defect of the semiconductor thin film. This causes deformation of the device structure and is a significant factor in the performance degradation of the semiconductor. To prevent this, it is important to measure the thickness of the thin film quickly and accurately. Optical spectrum analysis is widely used for measuring the thickness of semiconductor thin films. In this paper, we proposed a method to measure the thickness of semiconductor thin films by learning the reflectance spectrum according to the thickness and wavelength of four-layer thin films using a modified model of the well-known deep learning structure (VGG-16 Network). Our semiconductor thin film thickness prediction method performance of about 0.589 MAE was shown. Experimental results show that the proposed model can be a baseline method for predicting the thickness of semiconductor thin films.
Novel L2-Discriminant Locality Preserving Projection Integrated with Adaboost and Its Application to Fault Diagnosis

Nowadays, the safety of industrial production processes has been paid more and more attention. Fault diagnosis methods based on data-driven techniques have been widely discussed in recent years. For handling the high dimensional data generated by large and complex systems, reducing the data dimension to extract fault information has been widely studied. However, the performance of traditional dimension reduction methods is limited due to the high complexity and integration of process data. As a result, the accuracy of fault diagnosis is unacceptable. In order to handle this limitation, a novel fault diagnosis model integrating L2-Discriminant Locality Preserving Projection with AdaBoost is proposed in this paper. Discriminant Locality Preserving Projection (DLPP) as a kind of manifold learning methods is good at extracting information from high-dimensional data but its performance is subject to the singular matrix decomposition problem. So, the L2 regularization term is incorporated into the objective function of DLPP to solve the singular matrix decomposition problem. After extracting fault information using the proposed L2-DLPP method, AdaBoost is adopted to classify faults. In order to verify the performance of the proposed fault diagnosis methodology, a case study using the Tennessee Eastman process (TEP) is carried out. The effectiveness of the proposed fault diagnosis methodology is confirmed by simulation results.
Model Computations on Sequestration of Carbon in Managed Forests and Wood Products under Changing Climatic Conditions in Finland

Abstract The aim of this study was to assess the effects of forest management on carbon sequestration in forests and wood products by using a gap-type forest model interfaced with a wood product model. The assessment is based on total carbon sequestration, i.e. the amount of carbon left in vegetation, litter, soil organic matter and products when the flows of carbon back to the atmosphere have been subtracted. Thirty mixed-species stands, representing medium fertility sites in southern Finland, were included in each simulation for 300 years under current climatic conditions and predicted conditions of changing climate. The average total balance for the first 100 years was higher in the unmanaged system than in the managed system, but for the second and third 100-year periods the results were clearly opposite. Differences in the total balance between the treatments were larger during the first 100 years than over the whole 300-year period. Under conditions of changing climate, differences in carbon sequestration between management options were more pronounced than under current climatic conditions. Under current climatic conditions with the 100-year time frame, the ratio between the total annual balance and annual gross production was 0·208–0·289. Over the whole 300 years, however, efficiency was much lower, 0·088–0·121. Under changing climatic conditions, efficiency was also lower, 0·182–0·252 and 0·081–0·096, respectively. Different management alternatives clearly produced different amounts of timber for the production process; under conditions of changing climate, timber production was substantially enhanced. However, total carbon storages at the end of the simulation varied less than timber production. In the managed system, the flow back into the atmosphere was largest from litter, 41–51% of the total outflow, the flow from vegetation was 23–28%, from soil organic matter 22–25%, emissions from products 1–7%, and emissions from landfills 0–3%. If emissions due to the use of machinery in timber harvesting and transportation were included, they made up only 0·03–0·33% of the total outflow.
Automated system for monitoring the ammonia concentration in the air of agricultural complexes

Today, Russian agriculture is developing quite rapidly, especially in the livestock and poultry industries. Despite the stable saturation of food products with these sectors of the consumer market, there are a number of problems directly related to the development of these areas, for example, unsatisfactory indoor and outdoor air quality, which affects animals, birds and people accordingly.In modern production, almost every technological process uses some kind of control system. A huge number of different systems are developed and used in agricultural industries. In this paper, we consider a control system for small poultry farms. In the developed system, it is planned to realize the ability to control the ammonia content and display the current concentration readings, to implement data transfer to a remote user. The possibility of integration into existing monitoring and control systems is being considered, in addition, the system should be easy to maintain, convenient and, importantly, cheap.