bluuebunny/crossref_metadata_2025_split huggingface dataset:
Supercritical CO2 heat pumps for industrial decarbonization: a techno-economic analysis
Authors: Massulli Axel Riccardo , Mojtahed Ali , Ciancio Alessandro , Vespasiano Domiziana , Santoli Livio de 
Published: December 2024

Abstract
The hard to abate sectors produce a quarter of the annual global CO2 emissions. Their decarbonization is, therefore, pivotal in tackling climate change. The main strategies for decarbonizing the hard to abate sectors include synthetic fuels, hydrogen and electrification. The electrification and the direct use of hydrogen strategies would require drastic changes for the end users’ production process. Synthetic fuels, on the other hand, present approximately the same chemical characteristics of fossil fuels, hence, their utilization would not require substantial modifications for the end users. This strategy is, for that reason, the most promising. In low temperature industries, however, the direct electrification of the thermal energy needs could immediately be feasible by substituting natural gas boilers (NGB) with supercritical CO2 heat pumps (SCO2HP). In the present work, the cost-effectiveness of such a strategy is assessed. To do so different indicators such as the levelized cost of heat and the annual CO2 emissions have been calculated. The results show that SCO2HPs could represent a very interesting option in terms of CO2 emissions reduction (up to -67%), due to their intrinsic renewable energy share. From the economic point of view, however, this solution presents serious drawbacks. The cash flow of the investment is, in fact, negative despite very optimistic technical assumptions for the SCO2HP. Moreover, a sensitivity analysis has also been performed. From which, it emerges that the cost-effectiveness of such a solution is strongly dependent on the price of the energy vectors, mainly natural gas and electricity. Specifically, just for the cash flow to be greater than zero, a 30% reduction of the electricity price would be required. Symmetrically, a 30% increase in the natural gas price could catalyse a shift from the NGB to the SCO2HP.
Design and testing of an innovative electro-dynamic filter for gas turbine intake systems
Authors: Pinelli Michele , Piovan Mattia , Suman Alessio , Zanini Nicola , Minotti Stefano , Rossin Stefano 
Published: December 2024

Abstract
Gas turbines require filtered air to preserve the efficiency and the life of the system. Different degradation processes can occur depending on the size and composition of the contaminants. The compressor’s performance can be negatively impacted by fouling and erosion, which alter the shape and roughness of the blades. To limit the effects of the aforementioned phenomena, multistage filtration systems are commonly installed to reduce the contamination at the gas turbine inlet. Traditionally, particle separation is performed through mechanical filtration, which means that the particles are trapped inside cartridges of porous materials. This typology of filter guarantees a high filtration efficiency but on the other hand, generates remarkable pressure drop increasing over time. Inertial filters instead separate contaminants from the flow by utilizing the effect of inertial force acting on solid particles. In recent years, electrostatic filters have become widespread. The fibers of the filters are charged with an electrostatic field before installation and this charge is capable of separating and attracting metallic particles from the airflow. However, the electrostatic field’s effect diminishes with the exposure time with this type of filter. This work presents an innovative electro-dynamic filtration system for gas turbine application. The geometry and electrical features of the devices are designed to keep low-pressure drop and high capturing efficiency over time. To achieve these goals, a numerical campaign is performed using an in-house OpenFOAM solver, which takes into account the effect of the electrostatic force of the Lagrangian phase. After the definition of the optimal geometry, experimental tests are performed to validate the numerical results. Finally, a comprehensive laboratory-scale campaign is carried out to evaluate filtration efficiency under various environmental conditions, including different types of contaminants, concentrations, and exposure times.
Optimal configuration of adjustable capacity transformers considering OPF distribution
Authors: Hao Shuqing , Cai Yongxiang , Wang Zhuoyue , He Guanglu 
Published: December 2024

Abstract
The configuration of adjustable capacity transformers will be affected by photovoltaic (PV) penetration. In this paper, we propose a method for optimizing the configuration of adjustable capacity transformers by analyzing the parameters and topology of the low-voltage distribution network, which considers both the operational safety of the grid and economic objectives under optimal power flow (OPF) distribution. We construct the objectives and constraints of the OPF-solving model in different PV penetration scenarios for two types of node topologies and utilize second-order cone (SOC) relaxation to solve the results of OPF distribution. In order to calculate the long-term power loss and comprehensively assess the transformer replacement cost, we analyze the low-voltage side load curves of the adjustable capacity transformers based on the OPF solutions. We design an optimization scheme for the capacity configuration of transformers under different PV penetration rates. Simulation results show that the proposed scheme can effectively reduce power loss and transformer operating costs.
Research on sliding mode active disturbance rejection based on permanent magnet synchronous motor
Authors: Mu Poxuan , Yao Xiaodong 
Published: December 2024

Abstract
The motor is more and more common in our daily life, because the permanent magnet synchronous motor has many advantages such as high efficiency, simple structure and reliability. For the motor speed response time is long, overshoot and load instability. The motor speed loop controller is designed by combining the mathematical model of a permanent magnet synchronous motor with sliding mode control theory and nonlinear active disturbance rejection control theory. By combining with ADRC, this method has the characteristics of small overshoot, small buffeting, very fast response and strong anti-load disturbance ability. The feasibility of the sliding mode active disturbance rejection control algorithm is verified by Matlab/Simulink simulation experiments.
Short-term prediction of offshore wind power based on CNN and LSTM
Authors: Liu Jingang , Wang Jianbao , Yu Lei , Zhang Feng , Li Yu 
Published: December 2024

Abstract
This paper presents an innovative short-term wind power forecasting model based on deep learning, aimed at improving the economic benefits and system stability of wind power projects. The model combines a CNN, known for efficient feature extraction, with an LSTM network, recognized for its exceptional time series processing ability. Additionally, an attention mechanism is applied to probabilistically assign weights, enhancing key feature influence and further improving wind direction prediction accuracy. First, the LSTM predicts the gas properties of the unit state, followed by a convolutional model with attention mechanisms to forecast wind power. Experimental results demonstrate that this model significantly reduces prediction errors and enhances accuracy. Specifically: 1) A comprehensive deep learning model is proposed, integrating CNN and LSTM advantages, with an attention mechanism to enhance performance. 2) Key issues in wind power forecasting are effectively addressed, improving accuracy and stability. 3) Both real and simulated data demonstrate the model’s exceptional performance. 4) Experimental results confirm that the model surpasses traditional methods with lower forecast errors and higher accuracy. Future work will focus on refining the model to handle complex environments and overcoming technical challenges in practical applications.
Mining cap light based on EEG control
Authors: Chen Zhuo 
Published: December 2024

Abstract
A new type of lamp holder based on brain-computer interface control is proposed, which makes the application of new technology in underground mining intelligent, including eye potential and myoelectricity generated when you blink. The extraction of brain wave shapes with different frequencies can reflect the state of the human brain. The current brain wave frequency mainly studied is between 0-100 Hz. The concrete method is to extract the original brainwave data by Fourier transform and wavelet transform. A double-threshold blink detection algorithm is designed, which can effectively avoid pseudo-blink interference and extract the concentration. And the function verification is carried out. The specific implementation method is to use the TAGM module to collect and preprocess brainwaves and transmit them to the Arduino controller through the Bluetooth module to generate control instructions. The results show that the scheme is effective.
Preparation and pressure-bearing performance of polymer-encapsulated optical fiber for high lateral pressure sensing
Authors: Huang Tingyu , Zhu Pingyu , Li Jianlin , Xiong Danli , Wang Mingxing 
Published: December 2024

Abstract
High pressure resin transfer molding technology is used for the mass production of lightweight composite parts. The external pressure exerted has an impact on the actual pressure value of the preform in the mold cavity, which affects product quality. A polymer-encapsulated optical fiber pressure sensor is proposed to measure the lateral pressure in a closed and severely confined mold cavity. The quartz sand particles were mixed with water-borne polyurethane in a semi-cured state, and then made into a thin ribbon, and single-mode bare optical fiber was embedded between the two layers for overall curing and molding to obtain an ultra-thin polymer optical fiber transverse pressure sensor. Several sensor samples with two specifications of length×width×thickness of 150 mm×1.3 mm×0.45 mm and 150 mm×4.0 mm×1.0 mm were prepared, and both samples were placed simultaneously in the middle layer of 30 layers of uniaxial non-crimped glass fiber, namely 15 layers, for pressure experiments. The experimental results show that the optical loss of polymer-encapsulated optical fibers is proportional to pressure, and optical fiber samples with a width of 4 mm can withstand lateral pressures up to 40 MPa, providing an important monitoring method for pressure measurement in high-pressure resin transfer molding chambers.
Analysis of the impact of IGS station spatial distribution on GNSS data processing
Authors: Xiong Hongwei , Ai Qingsong 
Published: December 2024

Abstract
The selection of IGS stations has been a hot topic in GPS control network processing in recent years. But so far, the problem has not been resolved. In order to improve the accuracy of GNSS control networks and get some conclusions for engineering applications. Thus, the impacts of the spatial distribution and number of the introduced IGS stations are analyzed with GAMIT software. The results show that the GAMIT software can obtain ideal results in both processing short baselines and long baselines. The root mean square error has been significantly improved after IGS stations are introduced. The spatial distribution of IGS stations has a significant impact on the GNSS control network solution. The unreasonable distribution of stations can cause errors of more than 10 mm in three-dimensional components and baseline length. Meanwhile, the accuracy of the solutions can be improved to a certain mm with the reasonable 4~8 IGS stations evenly distributed around the measured station.
A design of a data acquisition unit for an annular fuel rod gap detection system
Authors: Zhang Yanzhen , Liu Chong , Wu Longxiong , Li Kaijia 
Published: December 2024

Abstract
A design scheme of the data acquisition unit for the annular fuel rod gap detection system is proposed in response to the particularity and importance of the annular fuel rod in application. The working principle and hardware composition of the data acquisition unit are introduced. Functional module design, software design, and the test of the data acquisition unit are achieved. A dual CPU approach is adopted, one for parameter settings and control and the other for data collection to ensure that the data are not lost. The experimental results show that the proposed data acquisition unit has good adaptive ability, high stability, complete and accurate data recording. All performance indicators can meet the system detection requirements.
An evaluation method for multi-energy complementary configuration of urban energy consumption terminals
Authors: Gao Yuqun , Dong Hong , Hu Liujun , Gao Yanna , Zeng Fanhong , Lei Yi , Xu Yunxia 
Published: December 2024

Abstract
The paper established a quality evaluation method for the quality of multi-energy complementary configurations of urban energy consumption terminals. Firstly, an index System Based on unit area evaluation indicators is established based on economic cost, energy consumption, and pollutant emission. Secondly, a calculation method for the comprehensive evaluation index based on the entropy weight method is devised. The weight can be modified considering expert preferences. Finally, the scientific nature of the method is proved through the actual evaluation of three multi-energy complementary systems. The conclusions showed that distributed PV power generation has the most prominent contributions to reducing fuel costs, improving environmental performance, and improving primary energy utilization. Distributed gas co-generation can also improve the comprehensive benefit index of multi-energy systems.
A probability prediction model for flood disasters based on Multi-layer Perceptron
Authors: Wang Yiquan , Zhang Jialin , Chang Yuhan 
Published: December 2024

Abstract
Flood disasters are characterized by high frequency, severe destructive power, and extensive impact. The prediction of flood disasters holds great significance. This paper proposes a flood disaster prediction model based on a multi-layer perceptron (MLP). Firstly, the model employs the Spearman correlation coefficient and random forest feature importance algorithm to identify the most influential feature indicators. Subsequently, an MLP neural network is established, trained, and optimized. Experimental findings demonstrate that the model accurately forecasts the likelihood of flood disasters through sample processing, achieving a coefficient of determination of approximately 85.27%.
A proximal bundle algorithm for solving generalized variational inequalities with inexact data
Authors: Zhang Siqi , Huang Ming , Feng Yongxiu , Lin Sida , Zhang Yajing 
Published: December 2024

Abstract
This paper proposes a proximal bundle algorithm based on the proximal point method for solving generalized variational inequalities with inexact data. First, the explanation of subgradient values and function inexactness is given. The algorithm’s fundamental steps are then presented. Ultimately, the algorithm’s convergence is confirmed under specific circumstances.
Relations between general sombor index and other general indices
Authors: Shi Zhanfei 
Published: December 2024

Abstract
The Sombor Index is a topological index in graph theory used to measure the complexity of a graph, particularly in describing the connectivity and structural diversity of a graph. We utilize the Sombor index as a bridge to explore the relationships between the Sombor index and other indices (General Zeroth-Order Randić Index, General Randić Index, General Sum-connectivity Index, General Redefined First Zagreb Index) in a more general multiparameter setting, thereby establishing new upper and lower bounds. Through a detailed analysis of data visualized from the Erdős-Rényi model, we’ve pinpointed limitations in our estimation methods. Nevertheless, the majority of our estimates are praiseworthy.
Modeling and analysis transmission and crosstalk of through-silicon via
Authors: Pan Jingjing , Wang Weimin , Wu Yongle , Liu Yuanan 
Published: December 2024

Abstract
Based on through-silicon-vias (TSVs) technology, a circuit model of ground-signal-signal-ground (GSSG)-type TSVs structure has been proposed. The lumped parameters are derived based on the size of the structure considering the adjacent structure. The results of the equivalent-circuit model match well with the results of the electro-magnetic simulation. The model is also placed on a two-layer PCB board for simulation. Signal integrity, near-end and far-end crosstalk are researched in this model. Finally, based on GSSG-type TSVs, the transmission performance and crosstalk behaviors are analyzed with different structure parameters.
Performance evaluation of Hard-Isolated GPONs using regular perturbation methods
Authors: Zeng Ying , Kang Zhongmiao , Shi Zhan , Zhang Peiming 
Published: December 2024

Abstract
In this study, we conduct a detailed performance evaluation of hard-isolated Gigabit Passive Optical Networks (GPONs) using Regular Perturbation (RP) methods. The Nonlinear Schrodinger Equation (NLSE) is utilized to model signal propagation in optical fibers, capturing the effects of nonlinearities and dispersion. By applying RP techniques, we derive approximate solutions to the NLSE, offering a more manageable approach for performance analysis in highly nonlinear regimes. Key performance metrics such as Normalized Squared Deviation (NSD), Bit-Error Rate (BER), and Achievable Information Rates (AIR) are analyzed to validate the accuracy and efficiency of the RP models. Simulation results demonstrate that the RP method provides good accuracy in modeling nonlinear effects, particularly at lower input power levels. Additionally, the study investigates the impact of hard isolation on mitigating nonlinearities, showing significant benefits in maintaining signal integrity and reducing BER. The findings highlight the potential of RP methods as robust tools for performance evaluation and optimization in advanced optical communication systems.
Deep learning-based Doppler-spread target detection with attention mechanism
Authors: Bu Linsheng , Chang Wanyu , Chen Defeng , Fu Tuo 
Published: December 2024

Abstract
In radar systems, Doppler spread issues commonly arise in space small target detections when integration is adopted during a long coherent processing interval (CPI). Even with precise compensation for translational effects, phase variations introduced by changes in target observation attitudes (TOA) can still cause the target’s echo energy to spread across multiple Doppler cells. As the TOA undergoes several periods over a CPI, the echo energy disperses into equidistant Doppler cells within a range cell in the range-Doppler (RD) map, which poses challenges for traditional methods and results in degraded detection performance. Leveraging the convolutional neural network’s ability to autonomously extract data features, we propose a neural network based on the Doppler auto-correlation attention mechanism (DAAM) to enhance detection accuracy and precisely estimate target positions by exploiting the sparse characteristics of the target Doppler spectrum. Training and testing are conducted using an RD map dataset, and experimental results demonstrate the network’s advanced performance in both target detection and position estimation.
Satellite imagery reveals widespread coconut plantations on Pacific atolls
Authors: Burnett Michael W , French Rory , Jones Breonna , Fischer Alexander , Holland Alexandra , Roybal Irina , White Timothy , Steibl Sebastian , Anderegg Leander D L , Young Hillary , Holmes Nick D , Wegmann Alexander 
Published: December 2024

Abstract
Efforts to mitigate tropical deforestation overlook coconut palm (Cocos nucifera L.) plantations on atolls—low island ecosystems that represent the most common landforms in the Pacific basin. Coconut palms have a deep history in the Pacific and were planted extensively over the last two centuries to meet the surging demand for coconut oil exports. But despite wide interest in the global footprint of palm crops, the distribution of coconut palms on Pacific atolls has remained unknown. We applied a supervised machine learning classifier to satellite imagery to produce 2 m resolution vegetation maps of 235 of 266 Pacific atolls. Despite the abandonment of many plantations in recent decades, we find that coconut palms surpass native broadleaf trees in terms of canopy area: coconut presently covers 58.3% of the mapped atolls’ total forested area and 24.1% of their total land area. 51.2% of these coconut canopies occur in monocultures indicative of plantation agriculture and drastic ecological changes. Even among atolls with climates equally suitable for coconut palms, coconut canopy coverage is 32.1 percentage points greater on those that historically exported coconut products, demonstrating the significant and persistent effects of plantations on forest compositions in the tropical Pacific. Coconut palms are most dominant on large, wet islands, reflecting their high rates of water use and thus their potential to deplete critical groundwater resources. The spread of coconut plantations also came at the expense of native vegetation critical for wildlife habitat, nutrient cycling, and soil formation. The severe environmental impacts of coconut plantations urge ecosystem management in a region uniquely exposed to climate change.
Utilization of natural and chemical activation corncob as adsorbent for continuous purification of used cooking oil using adsorption column
Authors: Haryanto B , Tambun R , Alexander V , Winarta J , Tarigan G I B B , Siadari J T C 
Published: December 2024

Abstract
Corncob waste can be utilized for various purposes. The purpose of this research is to determine corncob adsorption ability and adsorption kinetics model of purified used cooking oil using natural and activated corncobs which is effective and economical method due to its low cost, simple, and regenerative. The research method used in this research is experimental. This study consisted of three stages, namely the manufacture adsorbent of natural and activated corncobs, purification, determination turbidy of used cooking oil. The natural corncob adsorbent turbidity is 13.2 NTU and the activated corncob adsorbent turbidity is 10.8 NTU. Percentage removal efficiency of natural corncob adsorbent is 97.238% and activated corncob adsorbent is 97.741%. It is shown from the pseudo-second-order graph that a chemical interaction occurs, the value of the coefficient of determination (R2) obtained is 1, using natural and activated corncob adsorbent.
Effect of watering interval on growth and production of 2 maize (Zea mays L.) varieties in drought stressed media treated with chitosan
Authors: Lubis K , Satria R 
Published: December 2024

Abstract
Maize is an important food source after rice, but its production is often hampered by drought stress. This study aimed to assess the effect of watering intervals on two maize varieties by applying chitosan from black soldier fly (Hermetia illucens) cocoon shell waste. The study used a Randomized Block Design (RBD) with two treatment factors. The first factor was watering interval consisting of five levels: daily watering, no watering for 4, 8, 12, and 16 days. The second factor was maize variety, Bonanza F1 and Bisi-18. The results showed that the watering interval had a significant effect on plant height, number of leaves, stem diameter at 7 weeks of age, cob weight, 100 seed weight, root volume and length, and time of female flower emergence. The treatment of Bonanza F1 and Bisi-18 varieties also had a significant effect on plant height at 7 weeks of planting, stem diameter at 6-7 weeks of planting, root volume, root length, female flowering time, cob weight and length. In addition, the interaction between watering interval and maize varieties significantly affected plant height, 100 seed weight, cob length, root volume, root length, and time to female flower emergence.