bluuebunny/crossref_metadata_2025 huggingface dataset:
PLC Honeypots: Enhancing Interaction-Level Assessment
Authors: Heluany Jessica B.
Published: October 2024

The motivation for this work arose when noticing that definitions of honeypots’ interaction level are mainly based on the information technology environment and do not reflect operational technology even if several honeypot projects approach this field. Within operational technology, programmable logic controllers (PLCs) have a main role, resulting in several honeypot researchers choosing to mimic this device at a certain interaction level. However, searching for an interaction level definition that approaches PLCs results in few studies. In this context, this work aims to explore how to adapt the information technology definition of the interaction level in order to encompass PLCs and their specific features. The method chosen to obtain inputs was a literature review where, in attempting to keep the connection with information technology, the features were based in terms of honey system, honey service, and honey token. The findings of this review provide a means to translate these terms when developing a PLC honeypot for a desired interaction level, resulting in a metrics proposal for low and high interaction. Summarizing the proposed metrics, the system of a PLC can be considered as the vendor specific firmware, its unique device banner, and a realistic network topology. For services, a PLC honeypot reflects the tasks performed by the real device, thus resulting in industrial communication protocols, network management protocols, appropriate response times, code-related interactions, dynamic input and output data processing, physical process simulation, and web interface. Lastly, a PLC honey token can be approached with the PLC program file, MIB file, and software license, among other elements. Based on these metrics, researchers can better evaluate how to design a programmable logic controller honeypot or select tools that match their target interaction level.
Task-Level Customized Pruning for Image Classification on Edge Devices
Authors: Wang Yanting , Li Feng , Zhang Han , Shi Bojie
Published: October 2024

Convolutional neural networks (CNNs) are widely utilized in image classification. Nevertheless, CNNs typically require substantial computational resources, posing challenges for deployment on resource-constrained edge devices and limiting the spread of AI-driven applications. While various pruning approaches have been proposed to mitigate this issue, they often overlook a critical fact that edge devices are typically tasked with handling only a subset of classes rather than the entire set. Moreover, the specific combinations of subcategories that each device must discern vary, highlighting the need for fine-grained task-specific adjustments. Unfortunately, these oversights result in pruned models that still contain unnecessary category redundancies, thereby impeding the potential for further model optimization and lightweight design. To bridge this gap, we propose a task-level customized pruning (TLCP) method via utilizing task-level information, i.e., class combination information relevant to edge devices. Specifically, TLCP first introduces channel control gates to assess the importance of each convolutional channel for individual classes. These class-level control gates are then aggregated through linear combinations, resulting in a pruned model customized to the specific tasks of edge devices. Experiments on various customized tasks demonstrate that TLCP can significantly reduce the number of parameters, by up to 33.9% on CIFAR-10 and 14.0% on CIFAR-100, compared to other baseline methods, while maintaining almost the same inference accuracy.
Atmospheric Gravity Wave Detection in Low-Light Images: A Transfer Learning Approach
Authors: Xiao Beimin , Hu Shensen , Ai Weihua , Li Yi
Published: October 2024

Atmospheric gravity waves, as a key fluctuation in the atmosphere, have a significant impact on climate change and weather processes. Traditional observation methods rely on manually identifying and analyzing gravity wave stripe features from satellite images, resulting in a limited number of gravity wave events for parameter analysis and excitation mechanism studies, which restricts further related research. In this study, we focus on the gravity wave events in the South China Sea region and utilize a one-year low-light satellite dataset processed with wavelet transform noise reduction and light pixel replacement. Furthermore, transfer learning is employed to adapt the Inception V3 model to the classification task of a small-sample dataset, performing the automatic identification of gravity waves in low-light images. By employing sliding window cutting and data enhancement techniques, we further expand the dataset and enhance the generalization ability of the model. We compare the results of transfer learning detection based on the Inception V3 model with the YOLO v10 model, showing that the results of the Inception V3 model are greatly superior to those of the YOLO v10 model. The accuracy on the test dataset is 88.2%.
Station-Keeping Control of Stratospheric Balloons Based on Simultaneous Optimistic Optimization in Dynamic Wind
Authors: Fan Yuanqiao , Deng Xiaolong , Yang Xixiang , Long Yuan , Bai Fangchao
Published: October 2024

Stratospheric balloons serve as cost-effective platforms for wireless communication. However, these platforms encounter challenges stemming from their underactuation in the horizontal plane. Consequently, controllers must continually identify favorable wind conditions to optimize station-keeping performance while managing energy consumption. This study presents a receding horizon controller based on wind and balloon models. Two neural networks, PredRNN and ResNet, are utilized for short-term wind field forecast. Additionally, an online receding horizon controller, based on simultaneous optimistic optimization (SOO), is developed for action sequence planning and adapted to accommodate various constraints, which is especially suitable due to its gradient-free nature, high efficiency, and effectiveness in black-box function optimization. A reward function is formulated to balance power consumption and station-keeping performance. Simulations conducted across diverse positions and dates demonstrate the superior performance of the proposed method compared with traditional greedy and A* algorithms.
A UUV Cluster Route-Planning Method for Dynamic Target Search
Authors: Feng Jingxiang , Xu Weicheng , Dong Jingwei , Yao Yao , Hu Zhixing
Published: October 2024

Aiming to address the problem of regional dynamic target search under weak communication conditions, this paper proposes a UUV cluster search method based on cumulative probability optimization. First, by estimating the probability distribution of the initial target location, an initial probability map is established. Then, based on the Bayesian model and Markov decision model, the target probability distribution is periodically updated, and based on the cumulative detection probability optimal principle of the UUV cluster, the UUV cluster is guided to search the region with high detection probability preferentially. Finally, we implement the simulation experiment and compare with the random search method. The results verify that the proposed method has higher search efficiency in the cases of without prior information and with prior information.
Research on IP Node Port Openness Prediction Method Based on PSO-CatBoost
Authors: Liu Xiaoxuan , Yang Guozheng , Xie Yi , Yan Xuehu
Published: October 2024

The development of network measurement technologies has greatly increased the speed of network scans, but it also poses risks for the stability of the scanned networks. How to reduce probing traffic and enhance the effectiveness of probing has become a new research issue. In this paper, we utilize network measurement and machine learning techniques, leveraging public interfaces from network mapping platforms to construct a dataset with 44 feature dimensions. By combining the categorical boosting (CatBoost) model with the particle swarm optimization (PSO) algorithm for heuristic optimization, we propose a host port openness prediction model that integrates the PSO algorithm and the CatBoost model. Through comparisons with various machine learning models, the effectiveness of our proposed model was validated. Using this model in network scanning can save approximately 65% of bandwidth on average, effectively reducing the impact on the probed network.
The Impact of Gate Annealing on Leakage Current and Radio Frequency Efficiency in AlGaN/GaN High-Electron-Mobility Transistors
Authors: Kim Junhyung , Lee Gyejung , Cho Kyujun , Park Jong Yul , Min Byoung-Gue , Jeong Junhyung , Ji Hong-Gu , Chang Woojin , Lee Jong-Min , Kang Dong-Min
Published: October 2024

Gallium Nitride (GaN) high-electron mobility transistors (HEMTs) are highly promising for high-frequency and high-power applications due to their superior properties, such as a wide energy bandgap and high carrier density. The performance of GaN HEMTs is significantly influenced by the interfacial states of the AlGaN barrier, and gate annealing has emerged as a key process for reducing leakage currents and enhancing DC/RF characteristics. This research investigates the impact of gate annealing on AlGaN/GaN HEMTs, focusing on two main aspects: leakage current reduction and improvements in DC and RF efficiency. Through comprehensive electrical analysis, including DC and RF measurements, the effects of gate annealing were experimentally evaluated. The results show a significant reduction in gate leakage current and noticeable improvements in DC/RF performance for the devices that underwent gate annealing. The study confirms that the annealing process can effectively enhance device performance by modifying the material properties at the gate interface.
Development of a New Non-Destructive Analysis Method in Cultural Heritage with Artificial Intelligence
Authors: Bilici Genc Bengin , Bostanci Erkan , Eskici Bekir , Erten Hakan , Caglar Eryurt Berna , Acici Koray , Ketenoglu Didem , Asuroglu Tunc
Published: October 2024

Cultural assets are all movable and immovable assets that have been the subject of social life in historical periods, have unique scientific and cultural value, and are located above ground, underground or underwater. Today, the fact that most of the analyses conducted to understand the technologies of these assets require sampling and that non-destructive methods that allow analysis without taking samples are costly is a problem for cultural heritage workers. In this study, which was prepared to find solutions to national and international problems, it is aimed to develop a non-destructive, cost-minimizing and easy-to-use analysis method. Since this article aimed to develop methodology, the materials were prepared for preliminary research purposes. Therefore, it was limited to four primary colors. These four primary colors were red and yellow ochre, green earth, Egyptian blue and ultramarine blue. These pigments were used with different binders. The produced paints were photographed in natural and artificial light at different light intensities and brought to a 256 × 256 pixel size, and then trained on support vector machine, convolutional neural network, densely connected convolutional network, residual network 50 and visual geometry group 19 models. It was asked whether the trained VGG19 model could classify the paints used in archaeological and artistic works analyzed with instrumental methods in the literature with their real identities. As a result of the test, the model was able to classify paints in artworks from photographs non-destructively with a 99% success rate, similar to the result of the McNemar test.
A Novel Deep Learning Framework Enhanced by Hybrid Optimization Using Dung Beetle and Fick’s Law for Superior Pneumonia Detection
Authors: Sabaawi Abdulazeez M. , Koyuncu Hakan
Published: October 2024

Pneumonia is an inflammation of lung tissue caused by various infectious microorganisms and noninfectious factors. It affects people of all ages, but vulnerable age groups are more susceptible. Imaging techniques, such as chest X-rays (CXRs), are crucial in early detection and prompt action. CXRs for this condition are characterized by radiopaque appearances or sometimes a consolidation in the affected part of the lung caused by inflammatory secretions that replace the air in the infected alveoli. Accurate early detection of pneumonia is essential to avoid its potentially fatal consequences, particularly in children and the elderly. This paper proposes an enhanced framework based on convolutional neural network (CNN) architecture, specifically utilizing a transfer-learning-based architecture (MobileNet V1), which has outperformed recent models. The proposed framework is improved using a hybrid method combining the operation of two optimization algorithms: the dung beetle optimizer (DBO), which enhances exploration by mimicking dung beetles’ navigational strategies, and Fick’s law algorithm (FLA), which improves exploitation by guiding solutions toward optimal areas. This hybrid optimization effectively balances exploration and exploitation, significantly enhancing model performance. The model was trained on 7750 chest X-ray images. The framework can distinguish between healthy and pneumonia, achieving an accuracy of 98.19 ± 0.94% and a sensitivity of 98 ± 0.99%. The results are promising, indicating that this new framework could be used for the early detection of pneumonia with a low cost and high accuracy, especially in remote areas that lack expertise in radiology, thus reducing the mortality rate caused by pneumonia.
Research on Asymmetrical Operation of Multilevel Converter-Type Solid-State Transformers Based on High-Frequency Link Interconnection
Authors: You Yanfei , Yu Minli , Fan Xintao , Qi Lei , Teng Jiaxun
Published: October 2024

The large size of the sub-module (SM) capacitor is a typical problem in traditional modular multilevel converter-type solid-state transformers (MMC-SSTs). The MMC-SST based on high-frequency link interconnection is an effective solution for achieving lightweight capacitance. This structure can help to eliminate the symmetric SM fluctuating power, thereby reducing the SM capacitance. In a three-phase interconnected MMC-SST with low capacitance, potential risks may arise during transient processes, especially in cases of three-phase voltage asymmetry, such as large fluctuations in the SM voltage and unstable DC bus voltage. Aiming to solve this problem, this article re-analyzes the internal power characteristics of the MMC-SST under asymmetric operation and re-derives the SM capacitance constraint suitable for different degrees of three-phase voltage asymmetry. The new SM capacitance constraint enhances the asymmetric voltage ride-through capability of the MMC-SST. The new capacitance constraint is higher than that in symmetric operation, but it still has significant advantages in capacitance compared with the traditional MMC-SST.
The Research of Intra-Pulse Modulated Signal Recognition of Radar Emitter under Few-Shot Learning Condition Based on Multimodal Fusion
Authors: Liu Yunhao , Han Sicun , Guo Chengjun , Chen Jiangyan , Zhao Qing
Published: October 2024

Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach’s effectiveness. Particularly, under a 5−way5−shot setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model’s ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments.
RS-Net: Hyperspectral Image Land Cover Classification Based on Spectral Imager Combined with Random Forest Algorithm
Authors: Li Xuyang , Fan Xiangsuo , Li Qi , Zhao Xueqiang
Published: October 2024

Recursive neural networks and transformers have recently become dominant in hyperspectral (HS) image classification due to their ability to capture long-range dependencies in spectral sequences. Despite the success of these sequential architectures, mainstream deep learning methods primarily handle two-dimensional structured data. However, challenges such as the curse of dimensionality, spectral variability, and confounding factors in hyperspectral remote sensing images limit their effectiveness, especially in remote sensing applications. To address this issue, this paper proposes a novel land cover classification algorithm that integrates random forests with a spectral transformer network structure (RS-Net). Firstly, this paper presents a combination of the Gramian Angular Field (GASF) and Gramian Angular Difference Field (GADF) algorithms, which effectively maps the multidimensional time series constructed for each pixel onto two-dimensional image features, enabling precise extraction and recognition in the backend network algorithms and improving the classification accuracy of land cover types. Secondly, to capture the relationships between features at different scales, this paper proposes a SpectralFormer network architecture using the Context and Structure Encoding (CASE) module to effectively learn dependencies between channels. This architecture enhances important features and suppresses unimportant ones, thereby addressing the semantic gap and improving the recognition capability of land cover features. Finally, the final prediction results are determined by a voting mechanism from the Random Forest algorithm, which synthesizes predictions from multiple decision trees to enhance classification stability and accuracy. To better compare the performance of RS-Net, this paper conducted extensive experiments on three benchmark HS datasets obtained from satellite and airborne imagers, comparing various classic neural network models. Surprisingly, the RS-Net algorithm achieves high performance and efficiency, offering a new and effective tool for land cover classification.
Optimal Integration of Renewable Energy, Energy Storage, and Indonesia’s Super Grid
Authors: Amiruddin Ahmad , Dargaville Roger , Gawler Ross
Published: October 2024

This paper examines the optimal integration of renewable energy (RE) sources, energy storage technologies, and linking Indonesia’s islands with a high-capacity transmission “super grid”, utilizing the PLEXOS 10 R.02 simulation tool to achieve the country’s goal of 100% RE by 2060. Through detailed scenario analysis, the research demonstrates that by 2050, Indonesia could be on track to meet this target, with 62% of its energy generated from RE sources. Solar PV could play a dominant role, contributing 363 GW, or 72.3% of the total installed capacity out of over 500 GW. The study highlights that lithium-ion batteries, particularly with 4 h of storage, were identified as the most suitable energy storage option across various scenarios, supporting over 1000 GWh of storage capacity. The introduction of a super grid is shown to reduce the average energy generation cost to around USD 91/MWh from the current USD 98/MWh. These findings underscore the potential of a strategic combination of RE, optimized energy storage, and grid enhancements to significantly lower costs and enhance energy security, offering valuable insights for policymakers and stakeholders for Indonesia’s transition to a sustainable energy future.
The Economic Efficiency of Micro Biogas Plants: A Sustainable Energy Solution in Slovenia—Case Study
Authors: Vindiš Peter , Pažek Karmen , Prišenk Jernej , Rozman Črtomir
Published: October 2024

This paper presents a simulation model for determining the most suitable type of microbiogas plant for small local communities on Slovenian farms, focusing on the efficient processing of organic waste. This model uses various input parameters, including different types and quantities of slurry and corn silage. Four different scenarios were developed to represent potential plant sizes, each evaluated using key economic indicators: net present value, breakthrough price, and internal rate of return. A scenario sensitivity analysis was conducted to assess the effects of changes in investment costs, fluctuations in energy prices, and the addition of corn silage to the anaerobic digestion process. The results highlight significant differences in economic viability across the scenarios, with some demonstrating positive financial outcomes and shorter payback periods and others indicating potential financial risks and longer recovery times under certain conditions. The analysis suggests that smaller micro biogas plants may struggle to achieve profitability without optimizing input ratios or reducing costs, whereas larger plants show more favorable economic indicators, provided certain conditions are met. Furthermore, the economic efficiency improves when adding maize silage to the fermentor mixture.
Identification of Sub-Synchronous Oscillation Mode Based on HO-VMD and SVD-Regularized TLS-Prony Methods
Authors: Chen Yuzhe , Wu Feng , Shi Linjun , Li Yang , Qi Peng , Guo Xu
Published: October 2024

To reduce errors in sub-synchronous oscillation (SSO) modal identification and improve the accuracy and noise resistance of the traditional Prony algorithm, this paper focuses on SSOs caused by the integration of doubly fed induction generators (DFIGs) with series compensation into the grid. A novel SSO modal identification method based on the hippopotamus optimization–variational mode decomposition (HO-VMD) and singular value decomposition–regularized total least squares–Prony (SVD-RTLS-Prony) algorithms is proposed. First, the energy ratio function is used for real-time monitoring of the system to identify oscillation signals. Then, to address the limitations of the VMD algorithm, the HO algorithm’s excellent optimization capabilities were utilized to improve the VMD algorithm, leading to preliminary denoising. Finally, the SVD-RTLS-improved Prony algorithm was employed to further suppress noise interference and extract oscillation characteristics, allowing for the accurate identification of SSO modes. The performance of the proposed method was evaluated using theoretical and practical models on the Matlab and PSCAD simulation platforms. The results indicate that the algorithms effectively perform denoising and accurately identify the characteristics of SSO signals, confirming its effectiveness, accuracy, superiority, and robustness against interference.
Active Support Pre-Synchronization Control and Stability Analysis Based on the Third-Order Model of Synchronous Machine
Authors: Liu Chuang , Zhi Yuemei , Su Zhida , Yang Zedong , Yin Limin , Man Jiawei , Yang Yuze
Published: October 2024

When traditional grid-forming converters directly participate in the grid-connected operation of the power grid, due to the lack of a pre-synchronization control system, the voltage amplitude and initial phase on both sides of the grid-connected point will deviate, resulting in voltage and current distortion during grid-connected mode. An active support phase-locked loop free pre-synchronization control strategy based on the third-order model of a synchronous generator is proposed to address the grid-connected problem of the grid-forming converter mentioned above. First, a model of active support control with frequency integral feedback at small signal levels was constructed. The root locus method was employed to examine how system parameters affect the stability of the active support control system. Second, by adding phase pre-synchronization controllers and amplitude pre-synchronization controllers to the active frequency loop and excitation voltage loop of the third-order model, it was ensured that the frequency, phase, and voltage amplitude of the unit are consistent with the power grid, achieving a fast and smooth grid-connected mode of the unit. Finally, by using a DC source to simulate all types of new energy power generation equipment, the active support pre-synchronization control system based on the three-order model of synchronous generator is built in the MATLAB/Simulink simulation environment, and the accuracy and effectiveness of the control strategy in this paper is verified.
Combined Wind Turbine Protection System
Authors: Kaverin Vladimir , Nurmaganbetova Gulim , Em Gennadiy , Issenov Sultanbek , Tatkeyeva Galina , Maussymbayeva Aliya
Published: October 2024

The increasing deployment of wind turbines in technologically advanced nations underscores the need to enhance their reliability, extend their operational lifespan, and minimize failures. The current protection devices for wind turbine components do not sufficiently shield them from various external factors that degrade performance. This study addresses the environmental and technical challenges that disrupt wind turbine operations and reviews existing research and technical solutions for protecting individual components, supported by experimental findings. Using a decomposition method followed by the integration of protection components, we propose a combined protection system designed to improve the overall resilience of wind turbines. The proposed system aims to reduce incidents, extend service life, and increase reliability, addressing a critical gap in wind energy technology and contributing to its continued development and efficiency.
Importance of Clay Swelling on the Efficacy of Cyclic Steam Stimulation in the East Moldabek Formation in Kazakhstan
Authors: Zholdybayeva Assel , Syzdykov Askar , Pourafshary Peyman , Ismailova Jamilyam , Delikesheva Dinara
Published: October 2024

Both steam and hot water flooding of high-viscosity oils in the presence of swelling clays are difficult methods for producing oil efficiently because of potential formation permeability reduction. This paper pertains to heavy oil recovery from the East Moldabek formation where the oil API gravity is about 22 and is inundated with swelling clays. To achieve this, we used the IntersectTM reservoir simulator to compare oil recovery economics using both hot water and steam injection as a function of steam cycle duration, temperature, and steam dryness. We also studied clay swelling in the East Moldabek formation where clay poses a significant challenge due to its impact on permeability reduction. In this research, we developed an equation based on experimental data to establish a relationship between water mineralization and permeability in the East Moldabek formation. The equation provides valuable insight on how to mitigate clay swelling which is crucial for enhancing oil recovery efficiency—especially in sandstone reservoirs. Our modeling studies provide the recovery efficiencies for salinities of the hot water EOR versus cyclic steam EOR methods in a formation containing swelling clays. Specifically, the reduction in formation permeability as a function of the distilled water fraction is the controlling parameter in hot water or steam flooding—when the formation water mixture becomes less saline, oil recovery decreases. Our research shows that clay swelling can significantly impact cyclic steam stimulation outcomes, potentially reducing its effectiveness, while hot water flooding may offer a more cost-effective and operationally feasible solution in formations where clay swelling is a concern. Economic analysis reveals the potential for achieving an optimal favorable condition for hot water injection. Therefore, this paper provides a guideline on how to conduct thermal oil recovery for heavy oils in fields with high clay content such as the East Moldabek deposit.
The Problem of Power Variations in Wind Turbines Operating under Variable Wind Speeds over Time and the Need for Wind Energy Storage Systems
Authors: Chioncel Cristian Paul , Spunei Elisabeta , Tirian Gelu-Ovidiu
Published: October 2024

One of the most important and efficient sources of green electricity is catching air currents through wind turbine technology. Wind power plants are located in areas where the energy potential of the wind is high but it varies. The time variation of the wind generates fluctuations in the power produced by the wind farms that is injected into the grid. This elevates, depending on the intensity, problems of network stability and the need for balancing energy, thus raising both technical and cost issues. The present paper analyzes the behavior of a wind turbine (WT) over time in varying wind speed conditions, highlighting that without automation algorithms, a WT is far from the operation at the maximum power point (MPP). However, even when it is brought to operate at MPP, there are still significant variations in the power injected into the network. These power variations can be compensated if the wind system has energy storage facilities for the captured wind. All of these assumptions are analyzed using improved mathematical models and processed in simulations, with experimental data used as input from a wind turbine with an installed power of 2.5 [MW] in operation on the Romanian Black Sea coastal area. Consequently, the paper demonstrates that during an operation in the optimal area, from an energy perspective, the wind turbine’s maximum power point requires a storage system for the captured wind energy.
Unconventional Fracture Networks Simulation and Shale Gas Production Prediction by Integration of Petrophysics, Geomechanics and Fracture Characterization
Authors: Huang Wensong , Wang Ping , Hui Gang , Kong Xiangwen , Jia Yuepeng , Huang Lei , Bai Yufei , Pi Zhiyang , Li Ye , Yao Fuyu , Bao Penghu , Zhang Yujie
Published: October 2024

The proficient application of multistage fracturing methods enhances the status of the Duvernay shale formation as a highly esteemed shale reservoir on a global scale. Nevertheless, the challenge is in accurately characterizing unconventional fracture behavior and predicting shale productivity due to the complex distributions of natural fractures, pre-existing faults, and reservoir heterogeneity. The present study puts forth a Geo-Engineering approach to comprehensively investigate the Duvernay shale reservoir in the vicinity of Crooked Lake. To begin with, on the basis of the experimental results and well-logging interpretations, a high-quality petrophysical and geomechanical model is constructed. Subsequently, the establishment of an unconventional fracture model (UFM) takes into account the heterogeneity of the reservoir and the interactions between hydraulic fractures and pre-existing natural fractures/faults and is further validated by 18,040 microseismic events. Finally, the analysis of well productivity is conducted by numerical simulations, revealing that the agreement between the simulated and observed production magnitudes exceeds 89%. This paper will guide the efficient development of increasingly important unconventional shale resources.
A Comparative Analysis of Machine Learning Techniques for Predicting the Performance of Microchannel Gas Coolers in CO2 Automotive Air-Conditioning Systems
Authors: Ishaque Shehryar , Ullah Naveed , Kim Man-Hoe
Published: October 2024

The automotive industry is increasingly focused on developing more energy-efficient and eco-friendly air-conditioning systems. In this context, CO2 microchannel gas coolers (MCGCs) have emerged as promising alternatives due to their low global warming potential (GWP) and environmental benefits. This paper explores the application of machine learning (ML) algorithms to predict the thermohydraulic performance of MCGCs in automotive air-conditioning systems. Using data generated from an experimentally validated numerical model, this study compares various ML techniques, including both linear and nonlinear regression models, to forecast key performance metrics such as refrigerant outlet temperature, pressure drop, and heat transfer rate. Spearman’s correlation was employed to develop performance maps, whereas the R2 and MSE metrics were used to evaluate the models’ predictive accuracy. The linear models gave around 70% forecasting accuracy for pressure drop across the gas cooler and 97% accuracy for refrigerant outlet temperature, whereas the nonlinear models achieved more accurate predictions, with an accuracy ranging from 71% to 99%. This implies that nonlinear regression generally performs better than linear regression models in assessing the overall thermohydraulic performance of microchannel gas coolers. This research brings forth new ideas on how ML methods can be applied to enhance efficiency and effectiveness in gas coolers, contributing to the development of more eco-friendly automotive air-conditioning systems.
Experimental Implementation of Reinforcement Learning Applied to Maximise Energy from a Wave Energy Converter
Authors: Pierart Fabian G. , Campos Pedro G. , Basoalto Cristian E. , Rohten Jaime , Davey Thomas
Published: October 2024

Wave energy has the potential to provide a sustainable solution for global energy demands, particularly in coastal regions. This study explores the use of reinforcement learning (RL), specifically the Q-learning algorithm, to optimise the energy extraction capabilities of a wave energy converter (WEC) using a single-body point absorber with resistive control. Experimental validation demonstrated that Q-learning effectively optimises the power take-off (PTO) damping coefficient, leading to an energy output that closely aligns with theoretical predictions. The stability observed after approximately 40 episodes highlights the capability of Q-learning for real-time optimisation, even under irregular wave conditions. The results also showed an improvement in efficiency of 12% for the theoretical case and 11.3% for the experimental case from the initial to the optimised state, underscoring the effectiveness of the RL strategy. The simplicity of the resistive control strategy makes it a viable solution for practical engineering applications, reducing the complexity and cost of deployment. This study provides a significant step towards bridging the gap between the theoretical modelling and experimental implementation of RL-based WEC systems, contributing to the advancement of sustainable ocean energy technologies.
Interactive Cycles between Energy Education and Energy Preferences: A Literature Review on Empirical Evidence
Authors: Hu Jin-Li , Yang Po-Sheng
Published: October 2024

The ultimate goal of energy education is to cultivate citizens with energy literacy, which in turn influences the energy preferences of the general public. Various aspects, such as teaching, practice, publicity, and participation, all profoundly impact the formation of energy literacy. This study reviews the role of energy education in educational policy-making, the operation of educational systems, the design of innovative energy industry environments, and public participation. Through a systematic review, this study integrates empirical research across various contexts and environments. The relevant topics of empirical research include ‘energy education’, ‘energy literacy’, ‘energy preferences’, ‘energy education policy’, ‘operation of energy education systems’, ‘creation of a renewable energy industry environment’, and ‘public participation’. These studies indicate that energy education can enhance participants’ awareness of energy through knowledge transfer, enabling them to adopt more effective energy solutions and cultivate citizens with energy literacy. Energy education not only shapes the public’s energy literacy but also further influences energy preferences, which in turn can have profound effects on social interactions, market outcomes, and political and social systems. Finally, from the perspectives of ‘educational shaping’ and ‘cultural shaping’, the research explores the impact of energy education on the energy environment and people’s values. The findings reveal that society gradually forms a consensus on energy through long-term interactions, establishing a unique energy culture that subsequently influences the direction and implementation of national energy policies. There exist interactive cycles between energy education and energy policy: energy education influences public energy preferences, while energy culture, in turn, affects policy formulation.
Effects of Water Injection in Diesel Engine Emission Treatment System—A Review in the Light of EURO 7
Authors: Szőllősi Dániel , Kiss Péter
Published: October 2024

Water in the engine/combustion chamber is not a novel phenomenon. Even humidity has a major effect on internal combustion engine emissions and can thus be considered the first invisibly present emission technology. With modern techniques, the problematic aspects of water, such as corrosion and lubrication issues, seem to disappear, and the benefits of water’s effect in combustion may also be enhanced in the context of EURO 7. The current study examines the literature on the effects of water on diesel combustion in chronological sequence, focusing on changes over the last three decades. Then it analyzes and re-evaluates the water effect in the current technology and the forthcoming Euro 7 regulatory context, comparing the conclusions with current automotive applications and mobility trends, in order to show the possible benefits and prospective research avenues in this sector. Techniques introducing water to combustion could be a major approach in terms of the EURO 7 retrofit mandate, as well as a feasible technique for concurrent nitrogen oxides and particulate reduction.
Effect of Trail Condition, Slope, and Direction of Extraction on Forwarding Performance: Insights from a Controlled Comparative Study
Authors: Marcu Marina Viorela , Borz Stelian Alexandru
Published: October 2024

The performance of timber harvesting equipment is important for local operational planning and for making decisions on the way in which the forests should be opened up. However, there are many options used to extract timber, and there is a high variability in their performance. Forwarding is commonly used and became an attractive option for low-access forests. A controlled experiment was set up in this study to see how the configuration of the trails, characterized in terms of slope and surface condition, and the extraction direction (uphill or downhill) may affect the performance of forwarding operations. GNSS (Global Navigation Satellite System) data were collected at a rate of 5 s for five replications of moving empty downhill and uphill, respectively, on a dirt trail, measuring 250 m in length and having a slope of about 11%. The same experiment was run with the machine loaded at full capacity, then four replications with the machine loaded and unloaded moving downhill and uphill, respectively, were performed on a forest road resembling a rocky trail, which measured 390 in length and had a slope of about 4%. GNSS data were used to extract the moving speed for all the tested conditions with the aim to compute the cycle time, and the payload volume estimate was used to estimate efficiency, and productivity, depending on extraction distance in a range of 50 to 1000 m. For the first trail, statistical comparison tests indicated significant differences in the speed of uphill and downhill movement, for both empty and loaded conditions, whereas for the second trail, there were no significant differences in speed. In addition, on the second trail, the sustained speed was almost double. These were reflected sharply in the cycle time, efficiency, and productivity, depending on extraction distance and trail condition. These findings are important for decision making on local operational planning and forest opening up.
The Impact of Different Thinning Regimes on the Growth Dynamics of Pure Norway Spruce Stands in Lithuania
Authors: Šilinskas Benas , Linkevičius Edgaras , Aleinikovas Marius , Beniušienė Lina , Škėma Mindaugas
Published: October 2024

Forest growth under changing environmental conditions has been a major concern worldwide. The estimations of forests’ growth trends may provide significant insight on changing the value of Norway spruce (Picea abies (L.) H. Karst.) forests. The purpose of this study was to estimate the growth dynamics of thinned and un-thinned spruce stands in Lithuania by assessing their standing volume, gross volume yield, and stand density. For this purpose, two pure Norway spruce sites, established in 1990 and 1992, were selected. The thinning treatment scheme for each trial, comprised of five distinct stand density variants and eight different yield parameters, were analysed for each plot. Control plots exhibited the smallest growth in diameter, whereas stands that had been thinned once at a young age displayed the largest diameter increment (peak—1.4 cm/year). An increasing trend in tree stem volume was registered in all thinning treatment plots. The greatest competition index was found in the control plots, indicating an exceptionally high competition (peak—4.6–5.1) between trees for growth space. The competition index was four times smaller (2.0) in the moderately thinned stand. Intensifying natural mortality rate and signs of growth stabilization in the control plots (un-thinned) cannot guarantee them the positions of the highest productivity and accumulated stand volume in the future compared with thinned spruce stands.
Dynamic Changes in Flavonoids’ Accumulation Pattern in Tilia miqueliana Flowers at Different Developmental Stages Based on Widely Targeted Metabolomic Analysis
Authors: Bao Wenqin , Shen Yongbao , Verdonk Julian C.
Published: October 2024

Tilia miqueliana is an endemic species belonging to the genus Tilia L. (Tiliaceae) in China, which is known for its fragrant flowers and nectar, but the dynamic changes in metabolites during its growth and development are still unclear. In this study, the metabolic profiles from T. miqueliana flowers at three developmental stages were detected by performing an ultra-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (UPLC-ESI-MS/MS)-based widely targeted metabolomic analysis. A total of 1138 metabolites were detected, with 288 Differentially Accumulated Metabolites (DAMs) determined, flavonoids accounting for the largest proportion. The trend analysis showed that DAMs present seven distinctive patterns, and subclass 5 obtained the largest amount with continuously increased relative content during flower development. The Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation and enrichment analysis of DAMs showed different overlap and variability in metabolic pathways, indicating different directions of flavonoids’ metabolic flux in the three developmental stages. A correlation network analysis further revealed five core metabolites that played essential roles in flavonoid biosynthesis. This research provides comprehensive insights into the exploitation and utilization of T. miqueliana as well as a scientific basis for phylogenetic studies of the genus Tilia.
Preliminary Insights on Moisture Content Measurement in Square Timbers Using GPR Signals and 1D-CNN Models
Authors: Guo Jiaxing , Xu Huadong , Zhong Yan , Yu Kuanjie
Published: October 2024

Accurately measuring the moisture content (MC) of square timber is crucial for ensuring the quality and performance of wood products in wood processing. Traditional MC detection methods have certain limitations. Therefore, this study developed a one-dimensional convolutional neural network (1D-CNN) model based on the first 8 nanoseconds of ground-penetrating radar (GPR) signals to predict the MC of square timber. The study found that the mixed-species model exhibited effective predictive performance (R2 = 0.9864, RMSE = 0.0393) across the tree species red spruce, Dahurian larch, European white birch, and Manchurian ash (MC range 0%–133.1%), while single-species models showed even higher accuracy (R2 ≥ 0.9876, RMSE ≤ 0.0358). Additionally, the 1D-CNN model outperformed other algorithms in automatically capturing complex patterns in GPR full-waveform amplitude data. Moreover, the algorithms based on full-waveform amplitude data demonstrated significant advantages in detecting wood MC compared to those based on a traditional time–frequency feature parameter. These results indicate that the 1D-CNN model can be used to optimize the drying process and detect the MC of load-bearing timber in construction and bridge engineering. Future work will focus on expanding the dataset, further optimizing the algorithm, and validating the models in industrial applications to enhance their reliability and applicability.
Advances in Wood Anatomy: Cutting-Edge Techniques for Identifying Wood and Analyzing Its Structural Modifications
Authors: Balzano Angela , Merela Maks , de Micco Veronica
Published: October 2024

Wood, a natural lignocellulosic polymer, plays several important roles in trees, including water conduction, structural support, and nutrient storage [...]
Towards Ensemble Feature Selection for Lightweight Intrusion Detection in Resource-Constrained IoT Devices
Authors: Fatima Mahawish , Rehman Osama , Rahman Ibrahim M. H. , Ajmal Aisha , Park Simon Jigwan
Published: October 2024

The emergence of smart technologies and the wide adoption of the Internet of Things (IoT) have revolutionized various sectors, yet they have also introduced significant security challenges due to the extensive attack surface they present. In recent years, many efforts have been made to minimize the attack surface. However, most IoT devices are resource-constrained with limited processing power, memory storage, and energy sources. Such devices lack the sufficient means for running existing resource-hungry security solutions, which in turn makes it challenging to secure IoT networks from sophisticated attacks. Feature Selection (FS) approaches in Machine Learning enabled Intrusion Detection Systems (IDS) have gained considerable attention in recent years for having the potential to detect sophisticated cyber-attacks while adhering to the resource limitations issues in IoT networks. Apropos of that, several researchers proposed FS-enabled IDS for IoT networks with a focus on lightweight security solutions. This work presents a comprehensive study discussing FS-enabled lightweight IDS tailored for resource-constrained IoT devices, with a special focus on the emerging Ensemble Feature Selection (EFS) techniques, portraying a new direction for the research community to inspect. The research aims to pave the way for the effective design of futuristic FS/EFS-enabled lightweight IDS for IoT networks, addressing the critical need for robust security measures in the face of resource limitations.