bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detecting and tracking high-velocity plasmoids produced by a magnetized coaxial plasma gun in visible images
Authors: Li Zhaoxuan , Ye Yang , Kong Defeng , Tan Mingsheng , Zhong Fubin , Wang Mingyuan , Qu Chengming , Zhao Zhihao , Wu Yahao , Zhang Qiaofeng , Wang Chao , Huang Yanqing , Zhang Shoubiao 
Published: December 2024

The compact torus (CT) injection device, widely known as a magnetized coaxial plasma gun, creates self-contained magnetic field structures, known as plasmoids, which exhibit high densities and velocities. Owing to its remarkable energy density, the CT injection device holds immense potential for tokamak core fueling, rendering it promising for future fusion reactor applications. This paper presents a novel algorithm, comprising a segmentation module based on the UNet neural network and a tracking module leveraging the simple online and real-time tracking (SORT) algorithm, developed for detecting and tracking plasmoids in visible images. The algorithm is specifically designed for the recently manufactured CT injection system of the EAST tokamak, known as EAST-CTI [Kong et al., Plasma Sci. Technol. 25(6), 065601 (2023)]. Our analysis reveals the presence of multiple plasmoids within the plasma flow ejected by the EAST-CTI system. The UNet convolutional neural network successfully detects these plasmoids, achieving a dice coefficient of 0.813 on the test dataset, indicating high accuracy. Meanwhile, a modified version of the SORT algorithm successfully tracks these plasmoids, demonstrating robust performance without false tracking or identity assignment errors. Overall, the developed algorithm offers critical insights into the evolution characteristics of CTs and meets the requirements of the EAST-CTI system’s visible imaging diagnostics. This advancement creates a favorable environment for extensive data analysis using imaging data in future research endeavors.
Bibliometric synthesis of intelligent systems in agriculture
Authors: Smaali Samah , Bouanaka Chafia , Smaali Sahar 
Published: December 2024

Abstract
This study undertakes a bibliometric analysis of recent investigations centered on intelligent systems in agriculture, a combined quantitative and qualitative approach is adopted, which is based on Visualization of Similarities (VOS) method. We then map prominent areas of interest, evaluate international collaboration extents, and identify potential research gaps. Aligned with the United Nations’ vision, the Sustainable Development Goals (SDGs) place a spotlight on the interconnection of poverty eradication (SDG 1), zero hunger (SDG 2), and fostering innovation and infrastructure development (SDG 9). The United Nations recognizes the profound challenges posed by global food insecurity, malnutrition, and the increasing vulnerability of the food system to environmental changes. As populations grow and the demand for food surges, there is an imperative to innovate and transform our agricultural and food systems. Adopting advanced technologies, like precision agriculture, remote sensing, Internet of Things (IoT), artificial intelligence (AI), and robotics, offers a promising avenue to address these challenges. Yet, despite the profound implications of these technologies in transforming agriculture, there remains a notable need for comprehensive research in this domain. Enhancing agricultural productivity, reducing resource wastage, and championing sustainability are pressing concerns. This study undertakes a bibliometric analysis of recent investigations centered on intelligent systems in agriculture, sourcing data from the Scopus database. To comprehensively analyze this vast dataset, a combined quantitative and qualitative approach is adopted, which is based on the visualization of similarities (VOS) method. We then map prominent areas of interest, evaluate international collaboration extents, and identify potential research gaps. Our findings offer pivotal insights that can shape the trajectory of future research and development in the agricultural sector. The significance of our analysis extends beyond academic interest. The insights derived hold the potential to catalyze the growth of smart systems in agriculture, addressing global food security challenges in the process. Moreover, by championing sustainable agricultural practices via intelligent systems, our study underscores its alignment with SDGs, notably those aiming at ensuring zero hunger and promoting responsible consumption and production.
A note on the topological synchronization of unimodal maps
Authors: Gianfelice Michele 
Published: December 2024

Abstract
In this note we complete the analysis carried on in Caby et al (2023 Nonlinearity
36 3603–21) about the topological synchronisation of unimodal maps of the interval coupled in a master–slave configuration, by answering to the questions raised in that Paper. Namely, we compute the weak limits of the invariant measure of the coupled system as the coupling strength

k
∈

(
0
,
1
)

tends to 0 and to 1 and discuss the uniqueness of the invariant measure of its random dynamical system counterpart, proving that the convergence of the associated Markov chain to its unique stationary measure is geometric.
Kalman filter time scale algorithm based on noise characteristics of optically pumped cesium clock
Authors: Song Hui-Jie , Dong Shao-Wu , Zhang Yu , Wang Xiang , Guo Dong , An Wei , Qi Yi , Zhang Shougang 
Published: December 2024

Abstract
The time scale algorithm plays an important role in time keeping. Currently, the time scale algorithm primarily adopts the predictability weighting algorithm for microwave clocks. Due to the different working principles and performance of optically pumped small cesium clocks compared with microwave clocks, a new time scale algorithm must be developed to adapt to optically pumped clocks. Based on the noise characteristics of the optically pumped clocks, the state model is established and the time scale is calculated using the Kalman filtering algorithm. The state model of the optically pumped clocks simultaneously considers the effects of white frequency modulation, flicker frequency modulation, and random walk frequency modulation. The proposed algorithm models the flicker frequency modulation for filtering estimation, which effectively improves the stability of the time scale. Here, the flicker frequency modulation is modeled through the linear combination of Markov processes, and the impact of different numbers of Markov processes on the time scale stability is analyzed. Compared with the predictability weighted algorithm, the results obtained by the proposed algorithm demonstrate that the frequency stability of the Kalman filtering time scale based on the noise characteristics was better than that of the predictability algorithm. Taking rapid Coordinated Universal Time (UTCr) as a reference, the frequency stabilities of the time scale based on three optically pumped clocks obtained by the proposed algorithm were 1.14 × 10−14 for 15 days and 5.50 × 10−15 for 30 days.
The production of biogenic carbon and green hydrogen through biomethane pyrolysis: the environmental benefits for the metallurgy sector
Authors: Negro Viviana , Riorda Alessio , Chiaramonti David 
Published: December 2024

Abstract
The iron and steel industry is responsible for around 4% of androgenic CO2 emissions in Europe and 9% worldwide. This is due to the massive use of carbon coke in metallurgy, which is not only linked to energy purposes, but also to the chemical process of iron ore refining. The steel sector urgently needs to find alternative solutions to improve environmental sustainability, with the transition to a low-carbon scenario representing a major challenge. The pyrolysis of methane is gaining more and more attention for the production of hydrogen, as this alternative process to hydrogen production does not generate any CO2 emissions, but provides a solid carbon product that can be re-used in an industrial symbiosis. If biomethane is used, the pathway is even carbon-negative, and generates a net GHG reduction compliant with the EU ETS (Emission Trading Scheme). However, given the high demand for inlet streams in the EU steel sector, the biomethane currently available is not able to meet the entire hydrogen demand assuming that the steel is produced using the DRI-EAF (Direct Reduction Iron-Electric Arc Furnace) route. Only when looking at the European projections for biomethane production in 2050, it is expected that the demand for steel hydrogen can be totally met, in particular by using 48% of the available biomethane, allowing up to 6 million tons of green hydrogen to be produced, and a significant reduction in net CO2 emissions. Finally, the aim of this work is to assess the energy consumption and environmental benefits resulting from the production of green hydrogen and biogenic carbon in Europe for DRI-EAF, compared to the benchmark market (DRI-EAF fed with natural gas) and the electrolysis alternative.
The use of Approximate Entropy analysis for flow pattern identification in radial compressors to detect instable operating conditions
Authors: Carrattieri Lorenzo , Cravero Carlo , Marsano Davide , Valenti Emiliano 
Published: December 2024

Abstract
This study uses a statistical analysis on given pressure signals to identify the surge condition in radial compressors, providing insight into their dynamics and performance. Specifically, the investigation focuses on a two-stage centrifugal compressor used in refrigeration applications. Through Computational Fluid Dynamics (CFD) Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations, static pressure time-series are collected to cover four distinct operational points of the machine, ranging from a stable condition to an unstable one. To assess the stability of the compressor operation, the analysis introduces the Approximate Entropy (ApEn) as a statistical parameter to measure the regularity and predictability of the time-series signals. The results show that the proposed methodology is effective in pattern recognition, able to identify sources of instabilities, and to distinguish between different operating conditions. This approach enhances the comprehension of the operational characteristics of centrifugal compressors and sets the stage for improved monitoring and diagnostic methodologies. ApEn can therefore be a useful index to analyse time-series from sensors easy to install in real compressor configurations. The proposed methodology is included in a set of models, based on machine learning techniques, previously developed by the authors, to identify compressor instability during operation.
Research on trajectory tracking of wheeled mobile robots using fuzzy PID based on TD3
Authors: Wang Kai , Deng Sanpeng , Zhang Xiangling , Cao Yucong , Zhang Shiping 
Published: December 2024

Abstract
This study addresses the issues and limitations of trajectory tracking for Non-holonomic wheeled mobile robot (NWMR) under traditional PID control, including low accuracy, high response delay, poor robustness, and stability concerns. We propose a strategy that combines the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with fuzzy PID control to optimize PID parameter tuning for more precise robot trajectory tracking. Fuzzy logic is introduced to adjust the PID controller parameters, making them more adaptive and robust. The bidirectional long short-term memory (BiLSTM) network enhances the time series processing capability of the Actor-Critic network, improving the system’s state representation and prediction abilities. A curiosity-driven exploration method is employed to increase policy diversity and avoid premature convergence. Simulation experiments using the ROS Noetic and Gazebo platforms demonstrate that this method significantly outperforms traditional PID and TD3 algorithms in terms of trajectory alignment, training time, accuracy, and stability.
Modeling and end vibration suppression of space manipulators based on structural flexibility
Authors: Liu Qingyu , Ding Yongkang , Wang Yuqing , Zhang Hongkang , Yang Han 
Published: December 2024

Abstract
With the rising demand for more flexibility and larger workspaces in collaborative robots, rigid-flexible coupling robotic systems have garnered increasing attention. This study focuses on trajectory tracking and vibration suppression for RFCRSs operating in 3D space. The nonlinear coupling between angular joint positions and flexible structural displacement leads to complex vibration behaviors. To address this issue, the assumed mode method is employed to curve-fit the first-order modal shape of the flexible arm, resulting in a shape function. This approach simplifies the nonlinear infinite-dimensional dynamics of RFCRSs into a fourth-order dynamical system. The reference trajectory is planned using the polynomial programming method based on the established kinetic model. The reference trajectory is tracked with a model reference adaptive control (MRAC) method to reduce the robot end vibration. Finally, the effectiveness of the robot model reduction method and the associated control strategy is validated through a comparison with PD control and MRAC, which does not account for arm flexibility.
Design of optical loss suppression for ultrathin and high-pressure resistant sensing fiber
Authors: Chen Dejie , Zhu Pingyu , Huang Tingyu , Xiong Danli , Li Jianlin , Wang Mingxing 
Published: December 2024

Abstract
High pressure resin transfer molding (HP-RTM) is a widely used method for molding fiber reinforced composites. It uses the fluidity of the resin to infiltrate the fiber material and cure it into a whole. The fiber optic sensors have the characteristics of small size and high sensing accuracy, and are often buried inside the fiber material for in-situ condition monitoring of the resin transfer molding (RTM) process. HP-RTM process requires higher leak tightness, and its molding molds are usually high-precision leak-tight molds with high clamping force on mold edges. In this paper, a fiber optic package process is proposed to prepare an ultra-thin and ultra-high-pressure resistant fiber optic encapsulation element using a composite of two materials, quartz sand and waterborne polyurethane (WPU). The relationship between the optical power and pressure of the fiber optic under this element package was also studied. The experimental results demonstrate that this package structure has excellent compressive strength and can protect the fiber optic from passing directly through the molded contact surface, which improves the application range of fiber optic sensors.
Optimization of energy storage based on floating charge lithium iron phosphate battery
Authors: Gu Guoxing , Ren Junlong , Wang Jiandong , Meng Xudong , Han Xu , Hu Liang 
Published: December 2024

Abstract
Lithium iron phosphate batteries are often used as power supplies, power batteries and energy storage batteries for electronic equipment, and their charge and discharge cycle characteristics have been studied extensively. However, when it is used as a new battery power source in a substation, the battery pack must be hung on the charger in a fully charged state for a long time and run for float charging. In this operating mode, the choice of the float billing voltage of the lithium iron phosphate battery, the size of the float charging existing, and features of unexpected discharge and battery life under lasting float charging still need to be studied. For that reason, a simulation model of the lithium battery power storage space system was developed, and the control criteria were adapted to make the particular curve constant with the examination information. Based on the real control logic of the energy storage inverter, a simulation model of the power storage inverter is built. With simulation screening and comparison with the actual outside characteristics of the inverter, the control parameters of the simulation model are continuously optimized to make them regular with the outside attributes of the actual inverter. The actual battery access point substation operating mode data is collected and sorted out, an appropriate equivalence on the battery system is performed, and an equivalence model in MATLAB/SIMULINK and PSCAD/EMTDC is built, which forms a complete simulation model together with the battery system model[1].
Adaptive frequency modulation optimization algorithm for supporting power grid of large-scale energy storage power station based on small samples
Authors: Yao Gang , Jiang Zhengtao , Lu Shuai , Xie Huan , Wu Chunyan , Li Haoran 
Published: December 2024

Abstract
Addressing frequency instability issues stemming from scarce modulation resources and limited willingness to modulate in large-scale energy storage power station grids, this paper introduces a small-sample-based adaptive optimization algorithm for frequency modulation. Leveraging cross-correlation coefficients of small sample nodes, it analyzes and extracts frequency modulation characteristics, optimizing existing fluctuations to enhance adaptive control efficiency. Experimental outcomes demonstrate its effectiveness in adaptively optimizing frequency modulation, yielding low modulation and initial disturbances across varying scenarios, thereby showcasing robust adaptive optimization performance.
Fast L1 compact difference methods for the time-fractional Burgers-Fisher equation
Authors: Chen Ke , Cao Yanhua , Yang Xiaozhong 
Published: December 2024

Abstract
In this paper, we provide a fast L1 compact difference scheme (FL1CDS) to solve the non-homogeneous time-fractional generalized Burger-Fisher (TFGBF) equations. Our approach involves using a fast L1 equation to approach the time-fractional derivative and CDS to approximate the spatial derivatives. We analyze the existence and uniqueness of solutions, as well as the convergence and stability of the FL1CDS. Numerical experiments demonstrate that our scheme, under strong regularity assumptions, converges to 0(τ2-α + h
4). This makes the FL1CDS an efficient method for solving the non-homogeneous TFGBF equations.
A balanced sample generation method for semi-supervised carotid ultrasound image classification via pseudo-label distribution and model confidence
Authors: Yu Wenjie , Gan Zhenyu , Zhou Ran 
Published: December 2024

Abstract
The rupture of carotid plaques dramatically increases the risk of stroke, making it essential to classify these plaques to promptly identify high-risk cases and assist in patient management. Deep learning-based plaque classification methods need large labeled datasets, which are often challenging to acquire. Semi-supervised learning offers a solution by enabling deep learning networks to perform well with fewer labeled images. However, this method filters pseudo-labels based on thresholds, resulting in imbalanced quantities and cognitive bias in the network. To tackle this issue, we present a Balanced Sample Generation based Pseudo-label algorithm (BSGP) for semi-supervised carotid ultrasound image classification. BSGP generates new samples based on pseudo-label distribution and model confidence in each category, reducing cognitive bias from imbalanced pseudo-label quantities. Evaluated on 1,270 ultrasound carotid plaque images, our BSGP outperformed other semi-supervised methods (FixMatch, FlexMatch, AdaMatch, FreeMatch) when using 10%, 30%, and 50% labeled data. This illustrates the efficacy of our method in leveraging unlabeled images for model training, potentially advancing the use of deep learning in ultrasound-based plaque classification and supporting clinical diagnoses.
Insects biodiversity on beras basah mangrove ecosystem in Langkat, Northern Sumatera
Authors: Siregar A Z , Basyuni M , Priawandiputra W , Sagala R A 
Published: December 2024

Abstract
Mangroves are an ecosystem with a unique function in the living environment. Mangroves are a typical coastal ecosystem determined by tides. Mangrove forests are unique ecosystems because of their tidal cycles. The vegetation consists of mangroves were Rhizopora stylosa, R. mucronata, R apiculata. The data collection techniques used nisbi (relative) methods sampling used three tools (Yellow Sticky Traps /YST, Blue Pan Trap/BPT, Sweep Net/SN) since January until February 2024. The data used in this research are primary data and secondary data obtained directly from the local community. The results showed that the insects caught were 9 orders, 32 families, 42 genera and 1518 individuals. Based on research conducted in the mangrove forest in Beras Basah Village, Pangkalan Susu District recorded 9 species of pests, namely: bagworms (Acanthopsyche sp), pagoda bagworm (Pagodiella sp.), termite pests (Prorhinotermes sp.), krama/tree climbing crab (Episesarma sp), nacerdes bettles (Xanthochroa sp), aphid (Prociphilus tessellatus), cobwebs, barnacles (Balanus amphitrite), leatherleaf slug (Vaginula bleekeri). The abiotic factors that influences are the temperature and humidity in the mangrove.
Paclobutrazol application effects on green leaf index, morphology, and productivity of maize (Zea mays)
Authors: Syafrizal Y , Wahyuningsih G I , Ulfah F , Pratama A B , Adileksana C 
Published: December 2024

Abstract
Indonesia’s growing demand for maize has necessitated the development of strategies to enhance crop yields. Paclobutrazol (PBZ), a growth regulator, presents a promising solution for improving plant growth and achieving crop production sustainability in maize. This study investigated the impact of various concentrations of PBZ on maize growth and yield. The study employed a single-factor randomized complete block design (RCBD), incorporating four treatments (0 ppm, 200 ppm, 400 ppm, and 600 ppm) and six replications. Data analysis employed Analysis of Variance (ANOVA) at the 5% significance level and followed by regression analysis. Results revealed significant effects of PBZ on agronomic and productivity variables in maize. Regression analysis demonstrated a linear correlation between PBZ and various parameters, including stem diameter, leaf area, leaf area index, plant dry weight, cob weight, and cob length. Furthermore, we identified a polynomial relationship between PBZ concentrations and both the green leaf index and corn-husk dry weight, with the optimal concentrations of PBZ are 567.5 ppm and 410.83 ppm, respectively. Additionally, various concentrations of PBZ showed a tendency for higher productivity at PBZ concentrations of 400 and 600 ppm. Therefore, 400 ppm became the lowest effective concentration of paclobutrazol for optimizing maize growth and yield.
The change in phytochemical content affected by harvesting stages and storage duration of “Gajah” Ginger (Zingiber officinale var officinarum)
Authors: Ketaren B R , Hidayat I Y , Nusa M I , Novita A , Julia H 
Published: December 2024

Abstract
“Gajah” ginger (Zingiber officinale) is a biopharmaceutical plant one of Horticultural Plant group, which is the largest producer, exporter, and harvest area in Indonesia. Chemical constituent in ginger was affected by harvesting ages and sensitive due to storage duration. Therefore, the objective of this research was to investigate Fresh ‘Gajah’ ginger rhizomes were harvested at 5, 7 and 9 months and were stored at 0, 1 and 2 months (room temperature). The treatments were examined to found the effects of harvesting stages and storage durations on postharvest chemical qualities of ginger rhizomes. The experiment showed significant interaction effects between harvesting stages and storage durations for Protein, 9 months harvest age is 50% higher protein content from 5 and 7 months but for carbohydrate were at 5 months of harvest, 41% higher than the 9 months harvest time of 38% and at 7 months harvest of 19%. and vitamin C the highest content was found at 5month harvest age, for storage duration the highest content of protein and vitamic C were found at 1 month followed by the highest content of carbohydrate was found in control (0) month of storage durations.