Miracle-dz/UniFAR-FTUs huggingface dataset:
Albedo Changes Drive 4.9 to 9.4C Global Warming by 2400

This study ties increasing climate feedbacks to projected warming consistent with temperatures when Earth last had this much CO2 in the air. The relationship between CO2 and temperature in a Vostok ice core is used to extrapolate temperature effects of today’s CO2 levels. The results suggest long-run equilibrium global surface temperatures (GSTs) 5 degrees Celsius warmer than immediately "pre-industrial" (1880). The relationship derived holds well for warmer conditions 4 and 14 million years ago (Mya). Adding CH4 data from Vostok yields 8.5 degrees Celsius warming due to today’s CO2 and CH4 levels. Long-run climate sensitivity to doubled CO2, given Earth’s current ice state, is estimated to be 8.2 degrees Celsius: 1.8 directly from CO2 and 6.4 from albedo effects. Based on the Vostok equation using CO2 only, holding change in GST to 2 degrees Celsius requires 318 ppm CO2. This means Earth’s remaining carbon budget is estimated to be negative 313 billion tonnes. Meeting this target will require very large-scale CO2 removal. Lagged warming of 4.0 degrees Celsius (or 7.4 when CH4 is included), starting from today’s 1.1 degree Celsius change in GST, comes mostly from albedo changes. Their effects are estimated here for ice, snow, sulfates, and cloud cover. This study estimates magnitudes for sulfates and for future snow changes. Magnitudes for ice, cloud cover, and past snow changes are drawn from the literature. Albedo changes, plus their water vapor multiplier, caused an estimated 39 percent of observed GST warming over 1975-2016. Estimated warming effects on GST by water vapor; ocean heat; and net natural carbon emissions (from permafrost, etc.), all drawn from the literature, are included in projections alongside ice, snow, sulfates, and clouds. Six scenarios embody these effects. Projected change in GSTs on land by 2400 range from 2.4 to 9.4 degrees Celsius. Phasing out fossil fuels by 2050 yields 7.1. Ending fossil fuel use immediately yields 4.9, similar to the 5.1 inferred from paleoclimate studies for current CO2 levels. Fossil fuel phase-out by 2050 coupled with removing 71 percent of CO2 emitted to date yields 2.4 degrees Celsius. At the other extreme, postponing peak fossil fuel use to 2035 yields 9.4 degrees Celsius GST, with more warming after 2400.
Research on congestion pricing to improve connected autonomous vehicles penetration rate

Connected autonomous vehicles (CAVs) are an important development direction of future traffic, and they play a positive role in reducing pollution, easing congestion and improving driving safety. Therefore, it is very necessary to take measures to encourage the popularization of CAVs. This paper studies the impact of congestion pricing on users' choice of travel mode, establishes a bi-level programming model, and discusses how to induce people to choose a more environmentally friendly travel mode and a more reasonable travel path through economic means in the presence of a hybrid traffic network of CAVs and human-driven vehicles (HDVs). The interaction between CAVs and HDVs is considered in the lower-level model, and the mode selection of individual traveler is described by using variational inequality (VI). The upper-level model constructs a mathematical programming model with the goal of minimizing the total travel time of the system. Diagonalization algorithm and genetic algorithm are used to solve the numerical example. The results show the influence and improvement of congestion pricing on the index.
RF Reliability of Power RF N-LDMOS Devices under Pulsed Accelerated Aging Life Test

This paper experimental results by an innovative reliability bench for pulse RF life testing in radar application of device life under pulse conditions. And material clarifications for the phenomenon of failure. Results of accelerated aging stress are displayed with different temperatures (3000 hours at 150°C and 1O°C). Based on RF parameter shifts (Gain, Pout, Drain Efficiency: DE and Psat), the reliability of different tests was compared. To explain the effects of these theses according to the decomposition data, the prevailing physical acoustics concerned were studied and validated using the Silvaco-Atlas simulator. This supports finding a relationship between shifts of electrical parameters for radio frequencies to failed physical phenomena caused by impact ionization. The deterioration of N-LDMOS behavior is related to the hot carriers (traps) and trapped electrons, which leads to the accumulation of negative charge in the Si/SiO2 interface. At low temperature, interface states are created more, due to the maximum target impact ionization rate at the gate edge region. Finally, an analysis of the reliability of radio frequency behavior was discussed.
Grid-Forming Converters: Control Approaches, Grid-Synchronization, and Future Trends—A Review

In the last decade, the concept of grid-forming (GFM) converters has been introduced for microgrids and islanded power systems. Recently, the concept has been proposed for use in wider interconnected transmission networks, and several control structures have thus been developed, giving rise to discussions about the expected behaviour of such converters. In this paper, an overview of control schemes for GFM converters is provided. By identifying the main subsystems in respect to their functionalities, a generalized control structure is derived and different solutions for each of the main subsystems composing the controller are analyzed and compared. Subsequently, several selected open issues and challenges regarding GFM converters, i. e. angle stability, fault ride-through (FRT) capabilities, and transition from islanded to grid connected mode are discussed. Perspectives on challenges and future trends are lastly shared.
Promising Passive Candidates for detecting Single Inverter and Multi-inverter Unintentional island Formation at Different Locations in a Power Distribution Network

Large grid integration of distributed power generation (DG) sources presents few protection challenges like the formation of a sustained unintentional island which is hazardous to both equipment and personnel. Here potential passive islanding detection candidates are obtained by suitably ranking 40 different passive parameters out of which 36 are extracted from a basic point of common coupling (PCC) voltage, inverter DG output current, phase angle, active and reactive power outputs of inverter DG source and remaining 4 are harmonic based voltage and current parameters. These 40 parameters are ranked for their performance under different islanding and non-islanding events on a standard IEEE 13 bus distribution system by employing a suitable performance ranking technique for three different scenarios. MATLAB/SIMULINK results show that for single inverter-single PCC and multi-inverter multi-PCC unintentional island scenarios different passive parameters among 40 have shown comparatively best performance under various conditions. This paper most crucially gives a set of best passive candidates that can promise detection of unintentional islands especially for the challenging scenario of multi-inverter islands formation at different DG locations (PCC points) in a given power distribution network.
Gait Parameters Analysis Based on Leg-and-shoe-mounted IMU and Deep Learning

We propose a wearable system and a chain of methods for estimating stride lengths from inertial measurement units (IMU). The data are first processed by a Long Short-Term Memory (LSTM)-based method to determine the timing of step events. The raw IMU data and extracted features are also fed to LSTM to construct a regression model for learning stride lengths. Experimental results show that the proposed step event detector can reach −0.015 s to 0.02 s accuracy, and the proposed precision of stride-length estimator achieves 3.8 cm for both the mechanical model and LSTM model with extracted features.
A Tool to Design DC Inductor Using Core Geometry Method

A DC Inductor is a crucial element in many power electronic circuits. The conventional procedure to design a DC Inductor is iterative hence difficult and time-consuming. Also, the design requires the background knowledge of Magnetics, Material Science, Heat Transfer, and Fundamental of physics. This paper proposes a new mathematical tool that eliminates the need for expertise in these subjects. The tool based on Core Geometry coefficient is devised in a pervasive software like Microsoft Excel to design DC inductors and analyze their performance. It simplifies the time-consuming calculations, which enhances the efficiency and optimizes the realization of the DC inductor. It also provides a better understanding of the DC inductor's operations and performance trade-offs, which accelerates the design of power converters.
Machine Vision Based Segmentation of the Goldplate Area from a Flexible Printed Circuit Board

Inspection of flexible printed circuit board (FPCB) is a crucial process in the manufacture industry. Besides, edge detection and extraction of the region of interest (ROI) is always a main issue. An effective segmentation method helps to reduce the influence of the irrelevant factors, thus largely augment the performance of defect inspection in the following process. In this paper, a method of segmentation of the gold finger area of FPCB was proposed to solve the problem of adhesion between gold finger and cover film. First, a Gaussian filter was applied to the grayscale image. Second, a blurry edge image was obtained by the residual image between the Gaussian image and the other after an erosion operation. Third, a binary image was created by using an adaptive threshold method, and finally, with the application of histogram statistic, the rectangle area of gold finger was extracted. Experimental results show that the proposed method accurately locate the gold finger area for our FPCB images. In addition, it takes 2.654s to extract the ROI with the adaptive threshold and 1.442s without it. By feeding it to a classifier, a total time of 3.903s was spent to analyze the possible defects, which means the algorithm is suitable for automated optical inspection (AOI) for FPCBs.
Local Quality Pattern (LQP): A Quality Aware Local Statistical Feature Descriptor for No-Reference Image Quality Assessment

Assessment of the quality of the no-reference images in real-life applications is of immense importance. The no-reference image quality assessment (NR-IQA) algorithms are designed to measure quantitatively and accurately the perpetual quality of an image without any prior knowledge about its reference. In this work, we have proposed a novel NR-IQA metric to replicate the human visual system (HVS) for the perception of image distortion using the structural details and luminance information of the image. We propose a local descriptor that extracts the perceptual structural features of the image with highlighted reflectance to represent the variation of illuminance in the distorted probe image. We name this quality-aware descriptor as Local Quality Pattern (LQP). The extracted features are trained with support vector regression (SVR) to model the mapping of the feature vectors with the quality measure. Finally, the metric obtained is named Local Quality Pattern Score (LQPS). Extensive experiments conducted on publicly available datasets namely, LIVE, MLIVE and CSIQ databases to show that the proposed LQPS metric outperforms the compared NR-IQA methods with a prominent margin,
Tracking Attention of Social Media Event by Hidden Markov Model–Cases from Sina Weibo

Sina Weibo has significantly impact on the information diffusion processes in many real-world social events. A large number of active users on Sina Weibo not only push the opinion diffusion, but also increase the influence abilities of events which conversely attracted much attentions of users to follow them. How to effectively track the event attention of users is one of the most important channels to get the public opinions. In order to predict the event attention more accurately, motivated by observations of social events’ influence concerning with users and microblogs, we quantify the user popularity from the four dimensions: the user activity, the user behavior, the user authenticity and the user infection ability. And the non-collinearity of these four dimensions is tested to ensure the comprehensiveness and non-redundancy of the evaluation. Then, combining with the logic framework of Hidden Markov Model, we propose an algorithm to predict the Weibo event attention by using the user popularity. Meanwhile, in order to better detect the performance of the prediction algorithm, we integrate the static and dynamic information of microblog content to directly quantify the current Weibo event attention as a benchmark, and the performance of four prediction algorithms (including our algorithm) is tested with six real data sets which are chosen from the popular events in China from 2019 to 2020. Through comparison, we find that the user popularity can be used to predict the event attention, and the Hidden Markov Model prediction method by using the user popularity shows good prediction performance.
Numerical Analysis of the Thermal Impact of Ceramic Materials in Double-Sided Cooled Power Modules

This article focuses on the thermal analysis of direct bonded copper (DBC) substrates embedded in SiC-based multi-chip power modules. Due to the development of the innovative double-sided cooled technology, such substrates turn out to play a relevant and growing role in defining the PMs thermal, electrical, and mechanical performances. The study is aimed to compare typical ceramic materials adopted for the DBC manufacturing process; the comparison is carried out by means of exceptionally accurate nonlinear FEM 3-D simulations performed in the COMSOL Multiphysics environment. The influence of dissipated power and boundary conditions is thoroughly investigated by accounting for the temperature dependence of the thermal conductivities.
Computer Programming Classroom Platform

It is widely accepted that a crucial part of learning is practicing. This is also true for computer programming courses. Students develop advanced skills and understand what they have learned better through practice. The more they practice, the more skillful they become. However, with hundreds of students in a class, an instructor may not be able to check the outcomes of student practice thoroughly nor give sufficient and timely feedback on practice to each student. An easily accessible learning platform plays an important role in dealing with this issue.Nowadays, since the internet can be accessed by anyone, a web application as a learning platform was expected to be able to play this role. Therefore, we developed a web application. It was intended for any student in any computer programming course: it was used by students in a programming course in King Mongkut’s Institute of Technology Ladkrabang, Thailand, since 2017. No prior programming knowledge was required for students to use the application. The programming course had 10 chapters and one examination. In each chapter, students were required to code five programs on a PC in the programming laboratory and to submit the source files by uploading them via the web interface. The outputs of the submitted programs from all students were collected in a few seconds rather than minutes. If a submitted program was correct, proper scores were assigned. If not, the student’s wrong source code was displayed on the application page together with test cases both of sample and students, and the student could examine effortlessly the incorrect part of his or her source code. Later, the student could modify the original source code and resubmit it, until the student has arrived at the correct code. This strategy was less stressful on students, because they could keep working on assignments at their own time and pace, until they were successful. Throughout the course with 789 students in the first semester of academic year 2020, the web application platform handled over 70,000 submissions of source code, beyond the capability of a human instructor. This large number of submissions indicated that the students had more time, opportunity and will to edit their source code thoroughly with this platform. Moreover, the students seemed to enjoy their attempts to overcome the programming challenge with this platform.
Indoor Geomagnetic Positioning Using the Enhanced Genetic Algorithm-Based Extreme Learning Machine

Indoor positioning using the geomagnetic field has become a popular technique because of the infrastructure-free characteristic and the ubiquitous magnetic signals in indoor environments. Geomagnetic intensity will generate fluctuations due to the factors such as device heterogeneity, dates, and electronic facilities. Stable geomagnetic data are essential for high-precision positioning. To find a robust and low time-consuming geomagnetic positioning model (GPM), the extreme learning machine (ELM) optimized by the enhanced genetic algorithm (EGA) is constructed. The EGA with three optimization strategies is designed to find the best initial parameters solutions for the ELM. Magnetic patterns collected by different participants using different mobile phones on different dates are extracted characteristics and divided into segments for ELM training. Extensive experiments are conducted to evaluate the proposed model’s performance. The experimental results demonstrate that the EGA-based ELM model can achieve meter-level location accuracy and has good robustness under different testing conditions. The model construction is much faster than that of the popular learning algorithms, such as the convolutional neural networks (CNNs) and backpropagation (BP) networks.
Applicability of RINA in IoT communication for acceptable latency and resiliency against device authentication attacks

IoT is revolutionizing the consumer world these days. IoT being an element of ubiquitous networking gathered much interest among industry and academia. Its limitless capability of integrating the cyber world with the physical world made it one of the dominant technologies of the day. But heterogeneous nature of IoT along with its authentication and identity management in large-scale environments present a potential threat to its security. Many new technologies like SDN and Blockchain improved their security but with some limitations. In this context, we present an IoT model based on a secure network architecture called recursive internetworking architecture (RINA) which is fully programmable. The proposed work discusses porting of an IoT-enabled CPS model on to RINA network, verifies the acceptability of latency in remote control and data acquisition according to the IEEE standards, and presented a secure authentication that can be achieved by the RINA password authentication mechanism. Results demonstrate the acceptability of latency as per IEEE standards and resiliency against device authentication threats.
Tunable Random Number Generators Implemented by Spin-Orbit Torque Driven Stochastic Switching of a Nanomagnet for Probabilistic Spin Logic

Probabilistic spin logic that relies on tunable random number generators (tRNGs) has attracted great interest in unconventional computing paradigm. In this work, we propose an effective tRNG based on spin-orbit torque induced stochastic switching of a nanomagnet with perpendicular magnetic anisotropy. The switching probability is dependent on both the applied in-plane magnetic fields and the currents, and is fitted well with the sigmoidal function. The result provides an alternative building block for unconventional computing systems.
The Conceptual Model of Digital Transformation for Small and Medium Enterprises in Thailand

In today’s market, the survival or extinction of companies depend on their adaptability to the new digital environment. Digital transformation is known as a strategy that intends to achieve greater business outcomes. Previously, the project called “Northern Digital Economy” was launched which aimed to promote the utilization of technology in small and medium enterprises (SMEs) in the northern region of Thailand. Therefore, the conceptual model of digital transformation for Thai SMEs has been created and developed within the project. The digital transformation conceptual model’s list of elements in three categories and five states are presented throughout this paper.
An Energy-Efficient Periodic Resource Model for Bounded Delay-Tolerant Real-Time Systems

In the past few years, the energy consumption of real-time systems has become an essential topic of research, especially with the rise of the internet of things. In hard real-time systems, sensitivity to timing is the focal feature of system behaviors. Real-time systems must consider time constraints (mainly deadlines) to deliver the proper results. Therefore, typical real-time systems are inefficient in resource reservation, where resource supply is always higher than the workload demand and thus consumes more power than required. In firm and soft real-time systems, some deadline violations can be tolerated in a specific time interval. The worst-case execution time can be delayed to abound range. Therefore, instead of over-provisioning resources when considering the demand cannot be higher than supply, we propose a new algorithm that can efficiently estimate the resource reservation and reduce power consumption. The proposed algorithm is theoretically studied, and results show that while still satisfying the delay requirements of control systems, significant energy savings (up to 78.5%) could be achieved.
Design & Realization of X-Band Wide-Bandwidth Coaxial Cavity Resonator Band-Pass Filter for Spacecraft Checkout Applications

This paper presents X-Band wide bandwidth coaxial cavity band pass filter for spacecraft checkout applications. Mostly coaxial cavity filter is used for designing narrow bandwidth filter, less than 1 percent bandwidth but in this paper an attempt has been made to design and develop a compact size filter with more than 1 percent bandwidth. The main advantage of this designed filter is its wide bandwidth and excellent harmonic rejection performance. The realized filter has 1.1 dB insertion loss at center frequency of 8.2 GHz, better than 18 dB return loss over a passband of 7.96 GHz to 8.44 GHz and 60 dB rejection at 720 MHz away from center frequency. Achieved results of proposed filter are in good agreement with the design specifications.
On the Application of Internet of Things in Smart Agriculture

Being applied in smart agriculture, the technologies of "Internet of Things" (IoT) have given a guarantee to the development of modernized agriculture. Having expounded the connotations of IoT and smart agriculture, this paper will summarize the technologies that are related to the IoT -based Smart Agriculture System and analyze the applications of IoT technologies in smart agriculture. The IoT technologies are applied to various fields, such as agricultural irrigation, pest control, Application in food safety traceability, agricultural circulation, and so on. Being applicable in various fields of smart agriculture, IoT is a promising technology and has become the mainstream in the agricultural development.