bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Influence of Vortex Formation on the Electrical Characteristics of Argon Plasma in a Rotating Gliding Arc Discharge
Authors: Mohan Pathak Ram , Rao Lakshminarayana 
Published: June 2024

Abstract
Rotating Gliding Arc (RGA) discharge exhibits the characteristics of non-thermal plasma states when operated at atmospheric pressure with argon (Ar), and this study elucidates its properties. Experimental findings from an alternating current RGA plasma generator reveal a strong dependence of current density, which increases with higher discharge current and gas flow rates (Q). This research investigates the impact of swirling on the electrical properties of argon (Ar) plasma within RGA. The study focuses on two distinct flow conditions: TRANSFLOW at 5 SLPM and HIGH-TURBFLOW at 50 SLPM. The examination incorporates two different swirl hole diameters, namely 1.6 mm and 1 mm, to analyze their influence on the electrical characteristics of the system. Based on these flow regimes different types of discharge i.e., glow and spark are get formed and their characteristics are studied well. High-speed imaging and electrical measurements were employed to acquire insights into the depth of the physics associated with the Ar plasma. Compared to 1.6 mm, V

t
is higher for 1 mm for both the Q, i.e., for TRANS-FLOW at 5 SLPM and for HIGH TURB-FLOW at 50 SLPM. Finding shows that V

t
increases from 1.2 ms−1 to 22 ms−1 for 1.6 mm and it increases from 2.5 ms−1 to 40 ms−1 for 1 mm. V

t
nearly increased twice time when swirl hole diameter decreased from 1.6 mm to 1 mm. The average electric field (E) increases from 8 Vmm−1 to 15 Vmm−1 for 1.6 mm and from 11.8 Vmm−1 to 24 Vmm−1 for 1 mm. The linear fit R² ranges from 0.78 to 0.90.
Recent advances in circulating current suppression strategies for modular multilevel converters
Authors: Wang Zichen 
Published: June 2024

Abstract
Due to the fluctuation in power generation from renewable energy sources, multiple power electronic devices are required to ensure the stable operation of the electrical grid. Among these, the inverter, as a crucial power electronic device, is extensively employed in emerging technologies like flexible DC transmission. The modular multilevel converter (MMC) is currently widely used in various fields such as flexible DC transmission and wind or solar distributed power regulation systems. However, during the start-up and shutdown processes, the inter-phase circulating currents generated in the submodules can reduce the MMC’s dynamic performance, safety, and stability. This paper provides an overview of the current research status of circulating current suppression in modular multilevel converters. Firstly, it introduces the principles of commonly used Proportional-Integral (PI) control and Quasi-Proportional Resonance (QPR) control. Secondly, it discusses the drawbacks of these two control methods and elaborates on several improvement approaches. Building upon this, it analyzes a novel control method that combines PI control with QPR control. Finally, it summarizes different circulating current suppression methods and offers prospects for future research directions.
Early warning and managing system for regulating high-traffic periods in subway stations
Authors: Tang Yidan 
Published: June 2024

Abstract
Addressing the persistent issue of passenger overflow during peak hours in subway stations, this paper introduces an innovative adaptive early warning system. The system utilizes real-time data analytics and advanced predictive algorithms to monitor passenger volumes dynamically and generate timely alerts when traffic becomes excessive. These alerts are specifically designed to provide station staff with vital information that enables them to respond promptly. As a result, the normal operation of the subway station can be ensured, along with the safety of the passengers. The effectiveness and accuracy of this proposed system are demonstrated through a series of case studies. These studies confirm the system’s capability to enhance the management of passenger flow significantly. This research has the potential to revolutionize contemporary practices in subway station management and contribute to safer, more efficient public transportation systems.
Advancements in GaN HEMT structures and applications: A comprehensive overview
Authors: Li Zijian 
Published: June 2024

Abstract
Compared with silicon, GaN has advantages such as high electron mobility large band gap and high breakdown field strength. Therefore, GaN High Electron Mobility Transistors (HEMT) is resistant to high voltage and high temperature, and GaN HEMT performs well in high-frequency and high-power fields. Enhancement mode GaN HEMT is more suitable for most applications than depletion mode GaN HEMT due to enhancement mode GaN HEMT’s normally-off characteristic. This paper introduces cascode structure GaN HEMT and p-GaN gate structure GaN HEMT, as well as two improved structures, monolithically integrated Si-GaN cascoded FET and Hybrid Drain embedded Gate Injection Transistor, then compares the four structures and analyzes the advantages and disadvantages. Because of the unique advantages of GaN HEMT, many industries that require the use of high-frequency devices or high-power devices are very interested in the application research of GaN HEMT. This paper mainly introduces the research progress of GaN HEMT’s application in the three fields of radio frequency amplifiers, charger circuits, and LiDAR drivers, and the paper summarizes the advantages and current challenges of GaN HEMT, as well as the future of GaN HEMT’s development and application.
Accelerating convolutional neural networks: Exploring FPGA-based architectures and challenges
Authors: Ye Haobo 
Published: June 2024

Abstract
This article provides an in-depth exploration of the principles of Convolutional Neural Network (CNN) and Field Programmable Gate Array (FPGA), focusing on the reasons that make FPGAs well-suited for accelerating CNN algorithms. The discussion begins with an overview of CNN and FPGA fundamentals, highlighting the potential advantages of utilizing FPGAs in accelerating CNN computations. Next, the article introduces four distinct FPGA-based CNN accelerator designs, each presenting its unique creative architecture. These designs showcase a range of characteristics, including reconfigurability, parameterizable, and energy efficiency. The article delves into a detailed analysis of each design, elucidating their innovative aspects and potential benefits in CNN acceleration. By thoroughly understanding the proposed design approaches, the author emphasizes the challenges that arise when implementing FPGA-based CNN accelerators. These challenges encompass aspects such as algorithm mapping, hardware resource utilization, and achieving a balance between flexibility and efficiency. Additionally, the article sheds light on the future prospects of FPGA-based CNN accelerators, exploring potential advancements and research directions in this domain.
Optimizing performance: A comprehensive study of 4-bit absolute value detector circuit using static CMOS technology
Authors: Fu Xiaqing 
Published: June 2024

Abstract
In the realm of computing advancements, the utilization of numerical comparators has become increasingly pervasive. This scholarly paper delves into the intricacies of a 4-bit absolute value detector circuit, employing a foundation rooted in static CMOS. The comprehensive exploration encompasses both circuit determination and optimization methodologies. The experimental phase entails the derivation of the simplest absolute value converter circuit through the synthesis of a logic expression derived from a truth table. Subsequently, the minimum critical path delay is computed using the path effort method. The final phase involves voltage optimization, wherein, at a voltage of 1V, the minimum critical path delay registers at 64.6, accompanied by an energy consumption of 154.2. Notably, through meticulous optimization at 0.776V, the minimum critical path delay increases to 94.2, while concurrently reducing energy consumption to 92.9. This optimization results in a 50% delay increase and an almost 40% reduction in energy consumption. The implications of this voltage optimization extend to circuits aspiring for diminished delay or consumption.
Solar-powered vibration sensing based on TENG
Authors: Zhu Tianchen , Wu Yonghui , Zheng Haiwu , Wang Zifa 
Published: June 2024

Abstract
In recent years, the triboelectric nanogenerator (TENG) has become an emerging sensing element with simple fabrication and low-cost advantages. The contact separation design of the TENG dual plates has a natural oscillator structure, which responds well to mechanical vibration. The voltage value changes generated by the TENG during vibration can serve as a signal source for sensing. In this paper, a new type of vibration sensing system is designed based on TENG, which uses the capacitor’s voltage from TENG to characterize the relative vibration intensity. It can realize the functions of vibration exceeding threshold alarm, real-time data transmission, LabVIEW terminal display, and data saving. At the same time, the solar energy is combined with the battery to realize the all-weather self-power supply of the vibration sensing system. This terminal will have a wide range of applications in vibration scenarios and hopefully promote embedded technology development.
Optimization of the delay and energy of a 4-bit Absolute-value Detector
Authors: Han Yuyin 
Published: June 2024

Abstract
This research paper is dedicated to the design and optimization of a 4-bit Absolute-value Detector, with a specific focus on minimizing the delay and energy consumption inherent to the device. The primary function of this detector is to compare the absolute value of a 3-bit input, which ranges between −7 and +7, against a predetermined threshold value. Subsequently, the detector outputs a binary signal, either “1” or “0”, signifying whether the absolute magnitude of the input surpasses the set threshold. Based on the principle of operation, the detector is divided into three parts: a carry look-ahead adder, a 2-1 multiplexer and a comparator. After building the basic model, the critical path of this detector can be found. Therefore, the minimum delay and the energy can be obtained, as the delay equals to 82.1311 and the energy is 132.556. Then, by changing the V

DD
and size of the stage in this path, the energy can be optimized when delay becomes 1.5 times the minimum delay which equals to 123.2. As a result, both changing the two factors can make the energy become 24.95 and the energy reduction is 81%.
Research on conveyor belt deviation detection method based on machine vision
Authors: Yang Jie , Li Zhanyin , Gao Lu , Zhang Hancheng , Wang Jiatao , Wang Zhen 
Published: June 2024

Abstract
This study aims to identify the conveyor belt deviation. It presents a machine vision-based detection approach that uses the coordinates of the crossing point between the conveyor belt centerline and the laser line to determine whether the deviation fault occurs. In order to avoid the influence of the defects of the traditional Canny operator, an improved Canny edge detection algorithm combining hybrid filter and maximum inter-class variance method (OTSU) is used. Then the Hough transform is used to detect the straight line of the edge detected image and extract the laser centerline with the centerline extraction algorithm; finally, the Shi-Tomasi operator is used to detect the corners to get the intersection of the edge line and the laser line. The slope and center coordinates of the conveyor belt edges are calculated to determine whether the conveyor belt has run-off faults and calculate the offset amount. The results show that the proposed method can accurately determine the conveyor belt deviation and calculate the deviation amount.
Enhancing water level system based on Fuzzy PID control
Authors: Jiang Kuanyu 
Published: June 2024

Abstract
In specific fields such as industrial manufacturing, the water level control of containers such as water tanks is an important step. To enhance the liquid level control with its efficiency and stability, a fuzzy PID liquid level control system integrates the principles of PID control theory and fuzzy control theory, and Simulink simulation program is used to compare it with traditional PID control in this paper. The liquid level control system will adjust the water flow speed according to the current situation in the tank by using PID control, such as the liquid level height, the rate of change of the height, so that the liquid level height can reach the desired value in the shortest time. The fuzzy control will adjust the output result of the former more precisely, making the whole system more stable and reliable. The results demonstrate that, within the context of a standard water tank system, the response time of the fuzzy PID controller is more rapid compared to the traditional PID controller, with a reduction in response time from 117 seconds to 98 seconds, an improvement of more than 10 percent, but it is consistent with the latter in terms of regulation time, overshoot and steady-state error.
DeepJDOT: Deep joint distribution optimal transport in facial recognition
Authors: Cai Yuchen , Wen Chenglong , Chen Haoyuan , Liu Yijia , Peng Haofei 
Published: June 2024

Abstract
This paper explores the application of deep learning in domain adaptation, with a particular focus on using the Deep Joint Distribution Optimal Transportation (DeepJDOT) method for face recognition. We test DeepJDOT on a series of visual domain adaptation tasks and find that it exhibits high accuracy and competitive advantage, especially under smaller image sizes, compared to recent state-of-the-art competitors. This research not only highlights the potential of deep learning in domain adaptation but also underscores DeepJDOT as an effective approach for improving the performance of applications like face recognition. Furthermore, we introduce a domain adaptation-tailored loss function based on optimal transport to further enhance model performance. This study provides valuable insights and methods for research in visual domain adaptation and face recognition.
The influence of photovoltaic panel spacing on levelized cost of electricity
Authors: Li Taiqi 
Published: June 2024

Abstract
The efficiency and economic viability of photovoltaic (PV) systems are key determinants of solar energy adoption and diffusion. In order to investigate the correlation between PV panel spacing and levelised cost of electricity (LCOE), the existing methods for optimizing PV design are first investigated and summarized. Numerical methods are then chosen to simulate the construction of a PV farm in Brisbane, and modelling simulations are carried out with PVsyst and SAM to derive the Shading loss and LCOE for different panel spacing. It is found that closer panel spacing may increase the number of panels per unit area, but also cause shading loss during peak hours. Conversely, wider spacing can reduce potential shading effects and increase the efficiency of individual panels, but it requires more land or installation infrastructure. The analysis reveals a non-linear relationship between panel spacing and LCOE, with the optimal spacing range providing the lowest LCOE. This study highlights the need for site-specific spacing optimization to achieve cost-effective PV deployments.
Advances in the Large Area Picosecond Photo-Detector (LAPPDTM): 8" × 8" MCP-PMT with Capacitively Coupled Readout
Authors: Shin S. , Aviles M. , Clarke S. , Cwik S. , Foley M. , Hamel C. , Lyashenko A. , Mensah D. , Minot M. , Popecki M. , Stochaj M. 
Published: June 2024

Abstract
We present advances made in the Large Area Picosecond Photodetector (LAPPD), an 8" × 8" microchannel plate photomultiplier tube (MCP-PMT), since pilot production was initiated at Incom, Inc. in 2018. The Gen-I LAPPD utilizes a stripline anode for direct charge readout. The novel Gen-II LAPPD employs an internal resistive thin-film which capacitively couples to a customizable external signal readout board, streamlining production and increasing customer flexibility. The Gen-II LAPPD, with an active area of 373 cm2, is capable of high single photoelectron (PE) gain of ∼107, low dark rates (∼1 kHz/cm2), single PE (SPE) timing resolution of ∼65 ps, and 𝒪(mm) position resolution. Coupled with a UV-grade fused silica window, the LAPPD features a high quantum efficiency (QE) bialkali photocathode of >30% at 365 nm with spectral response down to ∼165 nm. The LAPPD is an excellent candidate for electromagnetic calorimeter (ECAL) timing layers, photon-based neutrino detectors, high energy collider experiments, medical imaging systems, and nuclear non-proliferation applications.
Offline filter of data with abnormal high voltage at BESIII drift chamber
Authors: Zhang Ze-Heng , Ma Qiu-Mei , Wu Ling-Hui , Wang Liang-Liang , Zou Jia-Heng , Fang Wen-Xing , Liu Huai-Min , Sun Sheng-Sen , Dong Liao-Yuan , Dong Ming-Yi , Lu Ze-Hui , Rong Shao-Shi , Li Xu , Zhang Yao 
Published: June 2024

Abstract
Stable operation of the detector is essential for high
quality data taking in high energy physics experiments. But it is
not easy to keep the detector always running stably, especially
during data taking period in an environment with high beam-induced
background. In the BESIII experiment, serious beam-related
background can cause instability of the high voltages in the drift
chamber, the innermost sub-detector. This could result in the
decrease of gain and wrong dE/dx measurements. The
relationship between the dE/dx measurement and the
change in high voltage has been studied. To guarantee the data
quality for physics study, an offline event filter algorithm has
been developed to remove the data with abnormal high voltages of the
drift chamber. After applying the event filter on the data set with
serious high voltage problem, the events with wrong
dE/dx measurement are removed effectively.
GaN-based W-band receiver chip development for fusion plasma diagnostics
Authors: Li Xiaoliang , Chen Pin-Jung , Chen Ying , Hu Robert , Lin Chin-Chi , Yang Chin-Hsin , Yu Hai , Qiu Shasha , Domier Calvin , Yu Guanying , Zhu Yilun , Luhmann Neville 
Published: June 2024

Abstract
Millimeter-wave diagnostics have proven effective on various
magnetic fusion devices worldwide, yet the formidable challenges
posed by the harsh environments of future burning plasma devices,
characterized by extreme temperatures, pressures, and radiation
levels, remain a significant hurdle. To address these challenges,
the utilization of wide bandgap Gallium Nitride (GaN)-based
millimeter-wave diagnostics is a most promising solution for fusion
reactor safety monitoring and control. A noteworthy W-band GaN-based
system-on-chip receiver has been the demonstrated by employing HRL
T3 40 nm GaN technology. This receiver chip, compactly designed
with dimensions of 3 × 5 mm2, incorporates essential
components such as the 75–110 GHz RF Low-Noise Amplifier (LNA),
mixer, Intermediate Frequency (IF) amplifier, and Local Oscillator
(LO) chain. This receiver chip will be packaged as a millimeter-wave
receiver module and applied on the DIII-D National Fusion Facility,
for fusion plasma edge shape monitoring for operational safety and
dangerous disruption prediction. The laboratory measurement results
have demonstrated suitable performance. This advancement is pivotal
for accurate analysis of plasma behavior in the extreme conditions
of burning plasma devices, driving progress in fusion research and
technology.
Implied threats of the Red Sea crisis to global maritime transport: amplified carbon emissions and possible carbon pricing dysfunction
Authors: Peng He , Wang Meng , An Chunjiang 
Published: June 2024

Abstract
Recent military acts in the Red Sea and Gulf of Aden are forcing merchant ships to reroute, thereby driving up international shipping rates, prolonging delivery dates, and causing additional greenhouse gas emissions. Utilizing the European Union (EU) Monitoring, Reporting, and Verification emissions database and real time Automatic Identification System data, this study conducted frequency analysis and causative investigation on container ships circumnavigating the Cape of Good Hope. The findings indicate that the current policy framework under the EU Emissions Trading System (EU-ETS) poses a higher risk of carbon leakage, particularly for medium and small-sized container ships, thereby undermining the effectiveness of the nascent EU maritime carbon pricing. If the crisis continues, combined with anticipated tighter emission regulations, this risk is expected to escalate. International maritime policy administrators should make timely adjustments in response to the chain reactions caused by war, enhancing the robustness of cross-regional carbon pricing.
Climate, weather, and child health: quantifying health co-benefits
Authors: Dasgupta Shouro , Robinson Elizabeth J Z 
Published: June 2024

Abstract
Climate change affects human health negatively in a number of complex ways, and children are particularly vulnerable. Quantifying the negative impacts of climate change on health, and identifying locations where children are at greater risk, can aid evidence-based policy making. We combine high-resolution climatic data with a dataset on infant and child mortality, wasting, and stunting, from more than a hundred countries, to estimate the effects of both gradual and acute climate change, focusing on drought and heatwaves, to plausibly attribute changing child health outcomes to historical climate change. Our results suggest a non-linear relationship between temperature and children’s health, adverse effects of increases in acute events, and a strong regional heterogeneity in these impacts. Our findings also highlight the importance of poverty reduction. Greater wealth is associated with better child health outcomes, and partially mitigates the negative impacts of climate change on child health. Finally, using updated warming scenarios, our projections show that there are substantial health co-benefits from achieving low emissions scenarios.
Leveraging machine learning to understand opposition to environmental tax increases across countries and over time
Authors: Brehm Johannes , Gruhl Henri 
Published: June 2024

Abstract
Taxes targeting fuel, road usage, or carbon emissions for environmental protection often face public opposition. Can widely accessible machine learning methods aid in predicting and understanding opposition to environmental taxes? This study uses the random forest algorithm to predict opposition to increased environmental taxes based on 41 theoretically relevant respondent characteristics. Drawing on nationally representative surveys, we predict individual tax opposition across 28 countries in 2010 and 2020 (N = 70 710). Personal values and environmental evaluations tend to be more influential than demographics in predicting tax opposition, with key variables differing between countries and over time. A lack of commitment to pro-environmental behavior is the most important predictor in emerging economies. Conversely, concerns about environmental issues and prioritization of jobs and prices are influential in high-income countries, gaining prominence over the previous decade. Policymakers can leverage these insights to tailor communication of environmental tax increases in different contexts, emphasizing, for instance, job creation.