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In the field of paper conservation and archival research, identifying the raw materials of paper is important to elucidate its history and culture. As the most basic element of the raw materials for paper, fibers have not been sufficiently investigated. In this study, we propose a nondestructive method for estimating paper fibers from macro photographs ( $4000\times3000$ pixels) captured using a digital camera. The proposed method consists of background patch (500 pixels per side) detection (BPD), wherein background regions with no text are identified; patch fiber classification (PFC), wherein background patches obtained after BPD are analyzed for fiber classification; and paper fiber estimation (PFE), wherein macro images obtained after PFC are analyzed for fiber estimation. BPD and PFC are employed to perform patch-based classification on segmented macro images, which are reconstructed during PFE to obtain the final fiber estimation results. We performed experiments using 1337 macro images (64176 patches) to evaluate the fiber estimation accuracy for kozo, mitsumata, and gampi via three-fold cross-validation. The average fiber classification accuracy for patch images was observed to be 79.1%; accordingly, the average fiber estimation accuracy for macro images was 85.8%. Experimental results indicated that PFE can be realized in a nondestructive manner on macro images of paper captured using a digital camera.

In the era of Industry 4.0, digital transformation has spurred the swift advancement of technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction of remaining useful life plays a crucial role in these technologies as it extends power equipment’s safe operational duration and decreases the maintenance costs associated with unforeseen shutdowns. Also, the increased accessibility of data for monitoring system conditions has paved the way for the more immense adoption of machine learning models in prognostics and health management for power transformers. At the moment, with the ever-increasing demand for electricity, there is a corresponding increase in the degradation processes of power transformers. Transformers insulation system and more importantly, the paper insulation happens to be the principal part where the degradation is prominent. Therefore, an accurate prediction of the insulating paper condition through its degree of polymerization is required to guarantee the reliability of power transformers. In this regard, the predictions, reliability, and health monitoring of this power equipment can be actualized by modeling the degradation of transformer insulation paper through several machine learning frameworks. In this view, this review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.

Evaluating the long-term influence of academic papers in the early stage after publication is crucial for research management and decision-making. Although the total number of citations has been considered as a valid indicator to measure the academic influence of papers, there are still some bottleneck problems in predicting future citation counts. Firstly, it remains a challenging task to select the reasonable features due to the diversity of model features. Secondly, some prediction models are overly complex and their defects may cause prediction bias or even errors. Finally, the most important thing is that very few models possess long-term predictive ability. This paper proposes a prediction model that can provide an early evaluation of the long-term citations, or the long-term influence, of academic papers. The model uses features available immediately after publication or after a certain period of time. Taking academic papers in Information Science and Library Science as our experimental subject, we train the model using three machine learning algorithms and find that the artificial neural network performs satisfactorily. Our findings may offer guidance for research management and decision-making.

Recently, Polarization Depolarization current (PDC) measurement is widely accepted time domain spectroscopy-based method for assessing the insulation condition. Various performance parameters like Dissipation factor (%tan $\delta $ ), Paper Moisture (%pm), Dielectric Adsorption Ratio (DAR), Polarization index (PI) etc. can be estimated by analyzing the PDC data. During field measurement various factors influences the recorded PDC data. As per existing literature, presence of low frequency noise, effect of temperature variation and influence of residual charge are common during field measurement. These factors significantly affect recorded polarization current and hence estimated performance parameters. Hence, analysis using recorded polarization current data may provide misleading information regarding insulation condition. Under such practical situation where polarization current is affected by above mentioned factors that generally observed during field measurement, depolarization current should be used for analysis of insulation condition. The depolarization current does not influence by such external factors. The present work shows the importance of depolarization current where polarization current is influenced by external low frequency noise and residual charge. The analysis firstly applied on sample prepared in the laboratory and then on data collected from real life in-situ transformers. The results obtained from the analysis shows that the data obtained from depolarization current is more reliable.

Cardiotocography (CTG) is a clinical procedure that is used to track and gauge the severity of fetal distress. Although CTG is the most often used equipment to monitor and assess the health of the fetus, the high rate of false positive results due to visual interpretation significantly contributes to needless surgical delivery or delayed intervention. In this study, a novel approach is introduced where both printing CTG paper is digitized and a machine learning approach is employed to detect the abnormality in the digitized CTG signal. Image processing-based preprocessing steps are employed to make the printing of CTG paper more convenient to extract the CTG signal. Various signal-processing techniques are used to calibrate the extracted CTG signal. Then, Empirical Mode Decomposition (EMD) is used to decompose the CTG signal into its frequency components and instantaneous frequency and spectral entropy features are extracted. After feature normalization and feature selection with ReliefF algorithm, support vector machines (SVM) is used for the classification of the normal and abnormal classes. A novel dataset is used in the experimental works and various performance evaluation metrics are used for the evaluation of the achievement of the proposed method. 10-fold cross-validation-based experiments show that the proposed method is quite efficient in abnormality detection in printing CTG papers where an average accuracy score of around 90.0% is produced.

Flashover characteristics are closely related to the electrical field distribution on the surface of oil-paper insulation. In this study, the effects of electrical field and DC voltage components on the creepage flashover characteristics of oil-paper insulation under combined AC-DC voltage (CADV) are investigated using two electrode models. The results demonstrate that when the DC voltage component is large, the flashover voltage under the influence of strong parallel electric field component is approximately 1.2 times higher than that under the influence of strong normal electric field component. When the DC voltage component value increases under the influence of strong normal electric field component, the value of corresponding AC voltage component of the creepage flashover voltage exhibits a decreasing trend. It is believed that due to the strong normal electric field component, the electrons intensify the collision on the surface of the insulating pressboard. Under the influence of the DC voltage component and due to the presence of space charge on the insulating pressboard, the distribution shape of carbonized particles on the surface of the insulating pressboard after creepage flashover is different. Furthermore, the instantaneous energy release during creepage flashover under CADV is higher than that under AC voltage, and the greater the DC voltage component value, more severe is the damage to the fibers on the insulating pressboard surface.

In on-site measurement, as the transformer’s structure is often unknown, the application of frequency domain spectroscopy (FDS) is greatly limited in moisture assessment of transformer. This paper proposes a moisture assessment method for oil-immersed paper based on the dynamic characteristic frequency even for an unknown transformer structure. FDS tests are performed on the oil-paper insulation, corresponding oil-immersed paper, and mineral oil under different moisture conditions. The moisture criterion of oil-immersed paper and the influence of the structural parameters on the calculated frequency spectroscopy at different frequencies are discussed. It is concluded that the moisture criterion’s effectiveness, which indicates the sensitivity of assessed moisture content to the error of the tan $\delta $ , changes at different frequencies. Moreover, there are some frequencies at which tan $\delta $ is not influenced by structural parameters and is close to the measured value, which are regarded as “characteristic frequencies.” Based on the characteristic frequency, the moisture content of oil-immersed paper is assessed according to the frequency spectroscopy of oil-paper insulation without the structural parameters. The result shows that the assessing frequency changes under different moisture conditions, and the error of the assessed moisture content is less than 0.1%.

The availability of the raw materials needed to produce fibre is decreasing as demand for fibre increases over time. In an effort, to preserve natural biodiversity and improve waste management, this research aims to substitute available raw materials for wood fibres. Empty fruit bunch (EFB) is regarded as waste in the oil palm industry and is abundantly available as a renewable energy source, particularly in Southeast Asian countries. The primary objective of this study is to investigate the utilisation of EFB as a substitute for insulating paper in transformers. The morphology, degree of polymerization, tensile strength, thermal characteristics, permittivity, conductivity, and electrical properties (breakdown field strength and lightning impulse performance) of EFB were measured and compared to the properties of Kraft insulating paper. Using FESEM at a magnification of $500\times $ , the morphological properties were examined. Based on FTIR measurement, the chemical bonding between Kraft and EFB samples varied slightly. Compared to EFB, Kraft-pulped insulating paper has a higher degree of polymerization and the ability to endure high temperatures. Furthermore, the breakdown field strength performance demonstrates that Kraft insulating paper is 20.30% higher than EFB insulating paper. In terms of tensile strength and lightning impulse, EFB insulating paper demonstrated comparable performance to conventional insulating paper. A new approach to the manufacturing of EFB insulating paper could result in improvements to the material’s insulation qualities.

This study aims to develop a prediction model for paper quality assessment to support technology-assisted peer review. The prediction technique is intended to reduce the review burden, which is becoming a critical issue in today’s paper submission process. However, most existing works on this topic were built by involving the reviewers’ comments, which is considered unfair and inapplicable for reducing the review burden. Therefore, our prediction method relies only on features extracted from the paper to address this issue. The method covers three tasks as follows: two are classification tasks and one is a regression task. The classification tasks predict the final review decision (accepted-rejected) and estimate the paper quality (good-poor), while a regression task predicts the review scores. Additionally, the classification and regression tasks are implemented using three main features i.e., citing sentence features developed based on the labeling scheme of citation functions, regular sentence features created by applying the label of citation functions to non-citation text, and reference-based features constructed by identifying the source of citations. Furthermore, the classification experiments on the dataset obtained from the International Conference on Learning Representations 2017–2020 showed that our methods are more effective in the good-poor task than the accepted-rejected task by demonstrating the best accuracy of 0.75 and 0.73, respectively. Moreover, we also reached a satisfactory recall of 0.99 using only the citing sentence features to obtain as many good papers as possible in the good-poor task. Our regression experiments indicate that the best result in predicting the average review score is higher than the individual review score by showing Root Mean Square Error (RMSE) of 1.34 and 1.71, respectively.

Reading newly-published papers in time is important for researchers since these papers provide the latest research findings. However, it is challenging to retrieve newly-published papers through common query-based search engines because the papers lacking sufficient citations and links are usually ranked too low in the search list. To this end, we design a time-aware joint model to infer users’ preference for the newly-published papers with the help of subsidiary relations of social and article linkages. The temporal preference of researchers for articles is jointly modeled with social and article relations by a group of matrices sharing common dimensions of researchers and articles. A joint multi-relational factorization algorithm is devised to approximate the latent factor matrices along with a temporal recommendation algorithm to predict the personalized new referential papers based on the factor matrices. The experimental results on real-world datasets show that the proposed model outperforms the state-of-the-art methods.

Robotic process automation (RPA) is automating various job processes in many offices. However, most existing RPA systems automate only processes that are performed on computers. It is highly desirable to develop physical RPA systems that incorporate real robots to automate physical job processes. When developing physical RPA systems, handling paper is particularly important because many physical office jobs involve handling paper documents. However, it is not easy for robots to handle paper because paper is fragile. To address this issue, we propose an electroadhesive paper gripper in this study. The use of electroadhesion enables a robot to handle paper in a simple, non-destructive, and quiet manner. We provide (1) a mechanism for lifting a sheet of paper without accurate control of robot hands, (2) a mechanism for a small gripper to hold a large sheet of paper without bending it, and (3) a method for quickly releasing a sheet of paper. Furthermore, we apply the proposed paper gripper to a document-sorting robot. The robot that we have developed automatically arranges a given stack of paper documents in a predetermined order (e.g., alphabetical or numerical). Experimental results show the validity of the paper gripper and the sorting robot.

The hydrophilic nature of cellulose compromises the dielectric properties of kraft paper insulation when exposed to water and moisture. The effort to address this setback is an ongoing research enquiry. In the present work, surface-modified rutile-titanium dioxide nanoparticles (rutile-TiO2 NPs) are reinforced into cellulose pulp to fabricate a nanocomposite kraft paper insulation. Surface modification of the nanofiller particles is intended to reduce the hydrophilicity of the resultant nanocomposite kraft paper insulation. The nanoparticles (rutile-TiO2 NPs) improve other dielectric properties of the insulation. The chemicals used for the surface modification of the rutile-TiO2 NPs were the alkyl ketene dimer (AKD) and alkenyl succinic anhydride (ASA). The nanofiller retention, dispersion and hydrophobic properties of the resultant reinforced kraft paper were experimentally analyzed. The results show that more than 50% of the nanofillers were retained within the reinforced kraft paper and translated to 1.5% by weight loading of the nanoparticles in the kraft paper. Scanning electron microscopy (SEM) images showed evenly distributed nanofillers, with some traces of agglomeration. The moisture absorption property of the kraft paper specimen modified with rutile-TiO2 NPs surface conditioned with 5 vol/vol% ASA improved by 74% compared to the control (reference) specimen. Water vapor transmission rate of the surface-modified nanoparticles reinforced kraft paper insulation decreased by 30%. Compared to the unfilled paper, the contact angle of water droplets on the surface of the reinforced kraft paper improved by 12%. Water absorption rate improved by being 4 times slower in paper specimens containing rutile-TiO2 NPs surface conditioned with 5 vol/vol% ASA. Dielectric dissipation factor measurement results showed that the specimen modified with rutile-TiO2 NPs surface conditioned with 5 vol/vol% ASA had 40% lower dielectric losses than the reference samples. This study, therefore, has successfully improved the hydrophobic properties of kraft paper by filling it with surface-modified rutile TiO2 using 5% ASA as the nanoparticle surfactant.

Nowadays many research articles are prefaced with research highlights to summarize the main findings of the paper. Highlights not only help researchers precisely and quickly identify the contributions of a paper, they also enhance the discoverability of the article via search engines. We aim to automatically construct research highlights given certain segments of a research paper. We use a pointer-generator network with coverage mechanism and a contextual embedding layer at the input that encodes the input tokens into SciBERT embeddings. We test our model on a benchmark dataset, CSPubSum, and also present MixSub, a new multi-disciplinary corpus of papers for automatic research highlight generation. For both CSPubSum and MixSub, we have observed that the proposed model achieves the best performance compared to related variants and other models proposed in the literature. On the CSPubSum dataset, our model achieves the best performance when the input is only the abstract of a paper as opposed to other segments of the paper. It produces ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 38.26, 14.26 and 35.51, respectively, METEOR score of 32.62, and BERTScore F1 of 86.65 which outperform all other baselines. On the new MixSub dataset, where only the abstract is the input, our proposed model (when trained on the whole training corpus without distinguishing between the subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31.78, 9.76 and 29.3, respectively, METEOR score of 24.00, and BERTScore F1 of 85.25.

The paper manufacturing process is a large-scale process in which the control performance is required to be optimal in the machine direction (MD) and cross direction (CD). This work focuses on CD control, which is more complicated than MD control. Uncertainties in the process arise from different sources, so there is a requirement for better modeling techniques. The control of paper profile properties, namely basis weight, moisture, and caliper, is done with the help of actuators that are distributed spatially. Basis weight is taken as the profile parameter in this work. The work aims to improve robust performance and simplify the control design problem. Here, $\mu $ synthesis methods are used for controller design, as this method provides guaranteed robust performance. To simplify the design problem two-dimensional modeling is explained explicitly. The conventional methods of $\mu $ analysis are extended to spatial coordinates. The performance of the $\mu $ synthesis designed method is compared with the open loop shaping method. Control performance is judged in terms of sensitivity, stability margin, $\sigma $ performance, and robust performance. The effectiveness of the developed $\mu $ synthesis designed method is demonstrated using the industrial example from the pulp and paper industry.

Recently reconfigurable intelligent surface (RIS) has attracted great attention because it can create a smart wireless environment. Hence it can enhance the capacity and coverage of the wireless network significantly. A thorough review of RISs has been presented in this paper focusing on the hardware aspect of the RIS. Beyond-5G/6G communication will have a smart propagation environment, where RIS can be used for such communications. RIS consists of various small unit cells. The unit cells should have some tunning mechanism to reflect or transmit the incoming waves in the desired direction. It is possible to tune the impedance of the unit cells using PIN diodes, varactor didoes, microelectromechanical (MEMS), thermal, and other ways. In this paper, the background of RIS has been discussed where RIS will play a significant role in beyond-5G/6G communications. We have also added the theoretical background of RIS and motivations to writing this paper. After that several published papers in the literature have been presented so that the readers can get an overall idea about the RIS and its hardware. Hence, this paper will be very useful for practitioner engineers and researchers. RISs have been presented in various tables and various parameters have been presented. We have discussed challenges and solutions for the hardware of the RIS design. We have also discussed potential research and research gap that can be explored in the future. Lastly, we have added a conclusion for this review paper. In our manuscript, we have added 154 references. There are various kinds of RIS available in the literature. We have added different types of RISs in this manuscript. The magnitude and phase of the reflection and transmission coefficients are the main parameters of any kind of RIS.

Personalized PageRank is a variant of PageRank, widely developed for citation recommendation. However, the personalized PageRank that works with a vast amount and rich scholarly data still results in information overload. Sometimes, junior scholars still need help to arrange queries quickly because of limited domain knowledge. Senior researchers need reference papers regarding a similar topic they intend to search for and related topics as a new insight. In this research, scientific citation recommendation aims to find the most influential papers with similar and related topics. Related topic papers in serendipitous perspectives are reference papers that are novel, diversified and unexpected to a user. The unexpectedness of recommended papers can be papers with different topics to queries but still relevant. To accomplish these challenges, we propose a framework of scientific citation recommendation with serendipitous perspectives. The framework includes feature extraction of an academic citation network, selection of multi-topic communities, and ranking papers in the selected multi-topic communities by modified PageRank. Papers in the chosen communities tend to link to similar and related papers. Modified PageRank is an extension of personalized PageRank, which works on multi-topic communities and manuscript queries. The experiments reveal that the proposed models outperform some models of personalized PageRank and some models of Content-Based Filtering. The multi-topic communities-based models work more effectively than the baselines if they run in a large dataset since the topic communities become more cohesive.

With the accelerating development of science and technology, the academic papers being published in various fields are increasing rapidly. Academic papers specially in science and technology fields are a crucial media for researchers who develop new technologies by identifying knowledge regarding the latest technological trends and conduct derivative studies in science and technology. Therefore, the continual collection of extensive academic papers, structuring of metadata, and construction of databases are significant tasks. However, research on automatic metadata extraction from Korean papers is not being actively conducted currently owing to insufficient Korean training data. We automatically constructed the largest labeled corpus in South Korea to date from 315,320 PDF papers belonging to 503 Korean academic journals and this labeled corpus can be used for training the models of automatic extraction for 12 metadata types from PDF papers. This labeled corpus is available at https://doi.org/10.23057/48. Moreover, we developed inspection process and guidelines for the automatically constructed data and performed a full inspection of the validation and testing data. The reliability of the inspected data was verified through the inter-annotator agreement measurement. Using our corpus, we trained and evaluated the BERT based transfer learning model to verify its reliability. Furthermore, we proposed new training methods that can improve the metadata extraction performance of Korean papers, and through these methods, we developed KorSciBERT-ME-J and KorSciBERT-ME-J+C models. The KorSciBERT-ME-J showed the highest performance with an F1 score of 99.36%, as well as robust performance in automatic metadata extraction from Korean academic papers in various formats.

In forensic document analysis, the authenticity of a document must be properly checked in the context of suspected forgery. Hyperspectral Imaging (HSI) is a non-invasive way of detecting fraudulent papers in a multipage document. The occurrence of a forged paper in a multi-page document may have a substantial difference from rest of the papers in its age, type, color, texture, and so on. Each pixel in an HSI data can be used as the material fingerprint for the spatial point it corresponds to. Hence, hyperspectral data of paper samples made of the same substance have similar characteristics and can be grouped into a single cluster. Similarly, paper samples made of different substances have different spectral properties. This paper relies on this heuristic and proposes a tensor based clustering framework for hyperspectral paper data, with an application to detect the forged papers in multi-page documents. Information embedded in the hyperspectral patches of the papers to be clustered is arranged into individual lateral slices of a third-order tensor in this framework. Further, this work employs the self-expressiveness property of submodules and an objective function is formulated to extract self-expressive representation tensor with low multirank and f-diagonal structure. Objective function of the proposed method incorporates $l_{\frac {1}{2}}$ -induced Tensor Nuclear Norm (TNN) and $l_{\frac {1}{2}}$ regularization to impart better low rankness and f-diagonal structure to the representation tensor. Experimental results of the proposed method were compared to the state-of-the-art subspace clustering approaches. The results demonstrate improved performance of the proposed method over the existing clustering algorithms.

Currency recognition has always been a troublesome task for blind and visually impaired people (BVIP). The problem is more severe in developing countries such as India, where there is still a lack of robust currency recognition systems. BVIP primarily relies on size variations and patterns such as intaglio printings for recognizing the underlying currency denominations. Most of the current Indian legal tenders resemble in size, thus making the identification process more strenuous. Also, the engraved patterns are not as distinctive as BVIP standards, and they fade over time. For an automated paper currency recognition system, issues such as folded or partial views, uneven illumination, and background clutter make it non-trivial and challenging. This paper ventures to present an end-to-end and robust framework for assisting BVIP in recognizing the Indian paper currency denomination. This paper presents a lightweight network, IPCRNet, useful in a resource-constrained environment such as low/medium level smartphones. The proposed network is based on Dense connection, Multi-Dilation, and Depth-wise separable convolution layers. Additionally, we congregated one of the most diversified Indian paper currency image dataset with more than 50,000 images belonging to almost all denominations in circulation. A customized and publically available android application, “Roshni-Currency recognizer”, has also been introduced. The experimental results on multiple datasets demonstrate the superiority of the proposed model. IPCRNet improves the classification accuracy by more than 2% on the proposed dataset compared to the state-of-the-art networks.

In this era, research and development are becoming a continuous and accelerating process because technology changes rapidly with a short lifecycle. As a result, various methodologies are being developed to monitor these rapidly changing research trends; In particular, clustering method-related studies in science and technology documents are being developed with a variety of approaches. However, previous studies on document clustering methods focus on a specific field or language but do not take into consideration certain important pieces of information in science and technology documents. Therefore, this study proposes an embedding methodology that uses important content from scientific and technical documents. We took into consideration the importance of information containing core structures in science and technology documents and proposed a clustering methodology that analyzes structured and unstructured data, such as textual information, author information, and citation information. The proposed method combines both textual and structural data from the paper, using a method that focuses on screening important information by sections in science and technology documents. Then, Girvan-Newman clustering and Louvain clustering models are applied to generate embedding vectors and show evaluation results through the clustering indices. As a practical example, we applied the proposed methodology using paper data from the field of hydrogen cell vehicles. The results of this study will be effective in identifying gaps in technology for new technological development, identifying technology trends, and presenting directional information for future technology development.

Music generation using deep learning has received considerable attention in recent years. Researchers have developed various generative models capable of imitating musical conventions, comprehending the musical corpora, and generating new samples based on the learning outcome. Although the samples generated by these models are persuasive, they often lack musical structure and creativity. For instance, a vanilla end-to-end approach, which deals with all levels of music representation at once, does not offer human-level control and interaction during the learning process, leading to constrained results. Indeed, music creation is a recurrent process that follows some principles by a musician, where various musical features are reused or adapted. On the other hand, a musical piece adheres to a musical style, breaking down into precise concepts of timbre style, performance style, composition style, and the coherency between these aspects. Here, we study and analyze the current advances in music generation using deep learning models through different criteria. We discuss the shortcomings and limitations of these models regarding interactivity and adaptability. Finally, we draw the potential future research direction addressing multi-agent systems and reinforcement learning algorithms to alleviate these shortcomings and limitations.

This research proposes an inkjet printed dual-band dual-sense circularly polarized antenna using CPW-feeding on PET substrate. The antenna is designed and optimized using ANSYS HFSS, which operates at 4.01 GHz - 5.05 GHz (22.96%) and 6.23 GHz - 7.58 GHz (19.55%) with a return loss of $0.44\lambda $ x $0.52\lambda $ x $0.002\lambda $ , where $\lambda $ is the free-space wavelength at the lowest circular polarization frequency. Measurement of the fabricated version shows good agreement with the simulated version. To the best of author’s knowledge, this proposed design is the first circularly polarized antenna integrating flexibility with dual-band and dual-sensing characteristics.

Mineral oil has traditionally been used as a cooling fluid in power transformers, but its low biodegradability and low fire point have motivated the search of alternatives. In this work, six different dielectric fluids have been studied, including four vegetable liquids, from sunflower, rapeseed, soybean, and palm, one synthetic ester and a mineral oil used for comparison. These oils were subjected to an accelerated thermal ageing in glass vessels at 150°C for four weeks (672 hours) in presence of Kraft insulating paper. Different oils parameters were measured during the ageing, i.e. breakdown voltage, dielectric dissipation factor, permittivity, DC resistivity, density, kinematic viscosity, flash and fire points, interfacial tension, acidity, and dissolved gases; additionally, the degree of polymerisation (DP) of the paper was measured. Results showed that the changes of the natural esters properties, except for the palm oil, were similar along the ageing time. Palm oil results were similar to those of the mineral oil, whereas synthetic ester showed a behaviour similar to natural esters. The kraft paper degradation was higher in the mineral oil, followed by the synthetic ester and the palm oil. No significant differences were found in the ageing with the natural esters.

Discovering promising research themes in a scientific domain by evaluating semantic information extracted from bibliometric databases represents a challenging task for Natural Language Processing (NLP). While existing NLP methods generally characterize the research topics using unique key terms, we take a step further by more accurately modeling the research themes as finite sets of key terms. The proposed approach involves two stages: identifying the research themes from paper metadata using LDA topic modeling; and, evaluation of research theme trends by employing a version of the Mann-Kendall test that is able to cope with multivariate time series of term occurrences. The results obtained by applying this general methodology to Information Security domain confirm its viability.

The citation intent extraction and classification has long been studied as it is a good measure of relevancy. Different approaches have classified the citations into different classes; including weak and strong, positive and negative, important and unimportant. Others have gone further from binary classification to multi-classes, including extension, use, background, or comparison. Researchers have utilized various elements of the information, including both meta and contents of the paper. The actual context of any referred article lies within the citation context where a paper is referred. Various attempts have been made to study the citation context to capture the citation intent, but very few have encoded the words to their contextual representations. For automated classification, we need to train deep learning models, which take the citation context as input and provides the reason for citing a paper. Deep neural models work on numeric data, and therefore, we must convert the text information to its numeric representation. Natural languages are much complex than computer languages. Computer languages have a pre-defined fixed syntax where each word has a unique meaning. In contrast, every word in natural language may have a different meaning and may well be understood by understanding the position, previous discussion, and neighboring words. The extra information provides the context of a word within a sentence. We have, therefore, used contextual word representation, which is trained through deep neural networks. Deep models require massive data for generalizing the model, however, the existing state-of-the-art datasets don’t provide much information for the training models to get generalized. Therefore, we have developed our own scholarly dataset, Citation Context Dataset with Intent (C2D-I), an extension of the C2D dataset. We used a transformers based model for capturing the contextual representation of words. Our proposed method outperformed the existing benchmark methods with F1 score of 89%.

Tracking the trends and taking early steps accordingly is important in academia, as well as in other domains such as technology and finance. In this work, we focus on the problem of predicting the trending research topics from a collection of academic papers. Previous efforts model the problem in different ways and mostly apply classical approaches such as correlation analysis and clustering. There are also several recent neural model based solutions, however they rely on feature vectors and additional information for the trend prediction. In this work, given a collection of publications within the observation time window, we predict whether the use of a keyword will increase, decrease or be steady for the future time window (prediction window). As the solution, we propose a family of deep neural architectures that focus on generating summary representations for paper collections under the query keyword. Due to the sequence based nature of the data, Long Short-Term Memory (LSTM) module plays a core role, but it is combined with different layers in a novel way. The first group of proposed neural architectures consider each paper as a sequence of keywords and use word embeddings to construct paper collection representations. In this group, the proposed architectures differ from each other in the way year based and overall summary representations are constructed. In the second group, each paper is directly represented as a vector and the use of different paper embedding techniques are explored. The analyses of the models are performed on a variety of paper collections belonging to different academic venues, obtained from Microsoft Academic Graph data set. The experiments conducted against baseline methods show that proposed deep neural based models achieve higher trend prediction performance than the baseline models on the overall. Among the proposed models, paper embedding based models provide better results for most of the cases.

The next-generation wireless technology that can fulfill such a demand, namely the fifth-generation (5G) technology, should provide 1000 times larger capacity. Moreover, sixth-generation (6G) communication, which represents a significant upgrade from the fifth-generation (5G) network and is anticipated to operate from 100 GHz to 3 THz band, will be required in the years after 2030 due to newly developed data-hungry applications and the greatly expanded wireless network. To meet the ever-growing demands of wireless carriers, an efficient wireless access method that can improve wireless area throughput without expanding bandwidth or cell size is required. Radio Frequency (RF) Orbital Angular Momentum vortex waves (which is now on referred to as OAM waves) to address the concerns mentioned above have attracted much attention in recent years. Due to their orthogonality, different OAM waves of different modes can be multiplexed in the same frequency channel, which can greatly increase the channel capacity. Using the orthogonal modes, a new type of multiple access scheme known as Mode Domain Multiple Access (MDMA) can be used by multiple users using the same frequency channel without additional resources such as frequency and time. As a result, the channel capacity for the next generation wireless communication systems can be enhanced as well as the overall spectrum efficiency can be improved. This review paper begins with an overview of the next generation communication such as 5G communication technology and beyond. This paper first briefly discusses the theory of OAM waves and several methods to generate OAM waves. Various different designs have also been analyzed for their ability to generate OAM waves and discussion on several restrictions and solutions to resolve. Open concerns and development trends are discussed for possible future RF OAM antenna upgrades. This study also proposes that for next generation wireless communication employing OAM, the typically used Uniform Circular Array (UCA) could be paired with the Multiple-Input-Multiple-Output (MIMO) system to improve performance in dense or urban areas for multiusers. In addition, the purity of OAM-modes needs to be considered for efficient utilization of the OAM system for future communications at the radio domain.

Several important advancements in the integration of energy storage into microgrids have fueled a lot of research and development over the last ten years to achieve the global decarbonization goal by 2050. The effective integration of the energy storage system in the microgrid is essential to ensure a safe, reliable, and resilient operation. Nevertheless, the utilization of energy storage in microgrids brings several issues, including poor power quality and intermittence characteristics. To address these concerns, appropriate energy storage controllers and optimization schemes are required to manage and optimize the power efficiently and securely. Although various research works have been performed and published over the years, the analytical assessment of energy storage controllers and optimization schemes integration into microgrids has not been carried out yet. Thus, this paper presents a comprehensive analytical evaluation of energy storage controllers and optimization schemes in Microgrid by recognizing and evaluating the highly influential 110 manuscripts using the Scopus database within the year 2010-2021. The analytical analysis emphasizes the current research trends, keyword evaluation, research classification, country analysis, authorship, and research collaboration. The paper also discusses and compares 24 controllers and 21 optimization schemes in the highly cited 110 manuscripts. Besides, critical discussion and assessment are conducted over 15 emerging subject areas. The constructive analysis identifies the existing limitations and research gaps in the selected 110 papers. By analyzing the existing issues, this manuscript provides several guidelines and suggestions for future improvement. This survey will help to deepen the development concepts to achieve improved power quality, economic prosperity, energy savings, and increased efficiency towards sustainable operation and management in the microgrid.

Temperature compensation occupies a significant function in the insulation condition evaluation of converter transformer bushings based on the Frequency Domain Spectroscopy (FDS), which could influence the accuracy of its insulation diagnosis. In order to accurately obtain the variation law of FDS at different temperature, a Resin Impregnated Paper (RIP) insulation bushing for converter transformer was designed by the equivalence of maximum electric field, coaxial cylinder structure and RIP materials. The increment law of dielectric parameters both dissipation factor and capacitance at wideband frequency (1mHz ~ 10kHz) was measured for a quality RIP insulation samples at the temperature from 30°C to 100°C. 1) Under the uniform temperature, it was found that the value of dissipation factor was rising with the frequency from 10 Hz to 0.001Hz, while the tendency was reversed at the frequency from 100Hz to10kHz. 2) The minimum value of tan $\delta $ from 0.001Hz to 10kHz could be driven to higher frequency moving (10Hz to 10kHz) by the action of temperature rising 30°C to 100°C.3) The effect of temperature on the lower frequency 0.001Hz ~ 1Hz tan $\delta $ of RIP insulation closely conformed to the index law of Arrhenius equation, while it was in accordance with the linear decrease at higher frequency 1kHz ~ 10kHz. The effect of temperature on dissipation factor could be eliminated by curve segment shifting based the experiment equation, which it at 0.001Hz dissipation factor could be eliminated by curve shifting based the index equation originated Arrhenius equation. The method is beneficial to eliminate the influence of temperature on the dielectric spectroscopy test results, and then to eliminate the influence of temperature on the dielectric spectroscopy diagnosis of RIP bushings.

Tree-shaped Nanosheet FETS (NSFET) is the most dependable way to scale down the gate lengths deep. This paper investigates the 12nm gate length (LG) n-type Tree-shaped NSFET with the gate having a stack of high- $k$ dielectric (HfO2) and SiO2 using different spacer materials, which can be done using TCAD simulations. The Tree-shaped NFET device with ${\mathrm {T}}_{\mathrm {(NS)}} =5$ nm, ${\mathrm {W}}_{\mathrm {(NS)}} =25$ nm, ${\mathrm {W}}_{\mathrm {IB}} =5$ nm, and ${\mathrm {H}}_{\mathrm {IB}} =25$ nm has high on-current ( $I_{ON}$ ) and low off-current ( $I_{OFF}$ ). The 3D device with single- $k$ and dual-k spacers are compared and its DC characteristics are shown. It is noted that the dual- $k$ device achieves the maximum $I_{ON}/I_{OFF}$ ratio, which is $10^{9}$ , compared to $10^{7}$ because the fringing fields with spacer dielectric lengthen the effective gate length. Additionally, the impact of work function, interbridge height, width, gate lengths, and temperature, along with the device’s analog/RF and DC metrics, is also investigated in this paper. Even at 12 nm LG, the proposed device exhibits good electrical properties with DIBL =23 mV/V and SS =62 mV/dec and switching ratio ( $I_{ON}/I_{OFF}) = 10^{9}$ . The device’s performance confirms that Moore’s law holds even for lower technology nodes, allowing for further scalability.

When the electro-magnetic rail-gun is fired, the transient strong magnetic field environment in the bore will produce induced current in the radio fuze circuit and affect the normal operation of the fuze. Therefore, reasonable shielding and protective measures need to be taken to ensure the reliable operation of the fuze control circuit. Based on Faraday’s law of electro-magnetic induction, a comprehensive electro-magnetic protection method for radio fuze suitable for electro-magnetic rail-gun is proposed in this paper. This method utilizes a high permeability material shell for magnetic shielding to decrease the magnetic induction intensity of the fuze circuit. Additionally, the eddy current effect of high conductivity metal material is utilized to slow down the change rate of magnetic induction intensity of the fuze circuit. The circuit area of the fuze is minimized through reasonable circuit design, and the layout of the circuit board is optimized to reduce the dot multiplication of the circuit loop area vector and the magnetic field direction. Through the simulation analysis of the finite element model of radio fuze in strong magnetic field, the effectiveness of the above electro-magnetic protection method was proved. The research results of this paper can provide reference for the design of radio fuze of electro-magnetic rail-gun.

In the network structure analysis, we explore an underestimated key metric, the Relative Size of Largest Connected Component (RSLCC) and demonstrate its importance in post-disaster network connectivity assessment. RSLCC was first investigated in the study of complex network structures but remains largely unexplored in terms of analysis within a specific application domain such as scenarios in transportation networks, wireless networks, communication networks, power networks, etc. Through the research presented in this paper, we not only prove that this metric is underestimated, but also design 7 methods to predict the value of this metric, with a Deep Neural Network (DNN) prediction accuracy of more than 99%. This study focuses on the assessment and analysis of post-disaster network connectivity, by exploring how the RSLCC, a key metric of network connectivity, can be used to efficiently predict and assess network connectivity in a disaster scenario, specifically, the approximate network connectivity value can be predicted simply by knowing the number of connected edges in the pre-disaster network and the number of connected edges in the post-disaster network. To achieve this, firstly, a sufficiently large-scale 100,000 datasets containing the values of attributes related to the network structure is prepared. Secondly, based on the preprocessing of the data, principal component analysis and variance contribution analysis are carried out, and the metric with the highest contribution to the principal component is approximated as the network connectivity. The next step is the prediction process, Network Disruption Degree (NDD) is chosen as the independent variable. since it is best to choose an extremely simple metric as the independent variable for prediction, rather than all network structure-related metrics, this paper demonstrates that it is possible to get satisfactory prediction results with this metric. It is found that NDD prediction methods have the highest prediction accuracy but take the longest run time and require training data of a sufficiently large size. If the prediction is done in small-size data, then Random Forest Regression (RFR) is proven to have the highest prediction accuracy. Although the network connectivity metric proposed in this paper is only an approximation, it provides good directions for simplifying the network connectivity analysis and the use of this metric for the study of practical modelling problems is also highly interpretable.

Holographic-type communication (HTC) is on the verge of revolutionizing current communication paradigms. It will seamlessly connect people from distant locations through a fully immersive experience that will engage all five senses: sight, hearing, touch, smell, and taste. However, the practical realization and widespread adoption of HTC impose significant demands on current networks and end-user devices. While recent studies have primarily focused on discussing the HTC challenges and potential research directions, this paper takes a further step by evaluating the capabilities of wireless networks to meet HTC requirements. Specifically, it highlights the limitations of the fifth-generation (5G) networks by identifying HTC-related Key Performance Indicators (KPIs) and explores the potential of the sixth-generation (6G) networks. Moreover, it not only proposes potential research approaches for HTC enhancement but also analyzes their impact on the challenges in HTC implementation. Finally, the paper questions the ubiquitous potential of 6G, suggesting that a coordinated approach leveraging artificial intelligence (AI) for jointly optimizing user sites and communication networks is the most promising strategy, rather than solely relying on the capabilities of specific network generations.

This paper presents a systematic review of K-12 cybersecurity education literature from around the world. 24 academic papers dated from 2013–2023 were eligible for inclusion in the literature established within the research protocol. An additional 19 gray literature sources comprised the total. A range of recurring common topics deemed as aspects of cybersecurity behavior or practice were identified. A variety of cybersecurity competencies and skills are needed for K-12 students to apply their knowledge. As may be expected to be the case with interdisciplinary fields, studies are inherently unclear in the use of their terminology, and this is compounded in this field due to the pervasive nature of cybersecurity, relevant and important to every person using a digital device. Almost all the studies within the data focused on secondary school settings and it appears the primary school years are largely ignored. This review suggests that most aspects of cybersecurity are not being systematically taught at K-12 around the world.

Deep learning has revolutionized object detection, with YOLO (You Only Look Once) leading in real-time accuracy. However, detecting moving objects in visual streams presents distinct challenges. This paper proposes a refined YOLOv8 object detection model, emphasizing motion-specific detections in varied visual contexts. Through tailored preprocessing and architectural adjustments, we heighten the model’s sensitivity to object movements. Rigorous testing against KITTI, LASIESTA, PESMOD, and MOCS benchmark datasets revealed that the modified YOLOv8 outperforms the state-of-the-art detection models, especially in environments with significant movement. Specifically, our model achieved an accuracy of 90%, a mean Average Precision (mAP) of 90%, and maintained a processing speed of 30 frames per second (FPS), with an Intersection over Union (IoU) score of 80%. This paper offers a detailed insight into object trajectories, proving invaluable in areas like security, traffic management, and film analysis where motion understanding is critical. As the importance of dynamic scene interpretation grows in artificial intelligence and computer vision, the proposed enhanced YOLOv8 detection model highlights the potential of specialized object detection and underscores the significance of our findings in the evolving field of object detection.

The Virtual Power Plant (VPP) serves as the primary form of participation for distributed power generation resources in electricity markets. Throughout this market engagement, uncertainties stemming from renewable energy sources and market prices present significant revenue risks for VPPs. Addressing this challenge, the paper proposes a VPP bidding risk management model centered on dominance constraints. In contrast to conventional methods reliant on risk metrics like Conditional Value at Risk, the proposed model offers improved control over downside and tail risks while reducing optimization iterations to just one, thus substantially reducing computational cost associated with risk management. The study also contrasts the efficacy and computational costs of first-order and second-order dominance models. For instance, in a three-scenario benchmark, the first-order dominance model exhibits a 16.7% lower overall risk level compared to the second-order model. Similarly, in a nine-scenario benchmark, the computational time of the first-order model increases by 0.96% due to the introduction of binary variables, and this increase scales with the number of benchmark scenarios. The paper additionally incorporates mean deviation of profits and expected excess returns to develop a bi-objective optimization model. This model not only fulfills risk control requisites but also shapes return distributions to align more closely with VPP decision-makers’ profit preferences. The case study is based on a VPP comprising conventional thermal units, wind turbines, energy storage stations, and flexible loads.

The radio frequency spectrum serves as a fundamental resource for wireless communication, encompassing various frequency bands allocated for diverse services and applications. Dynamic spectrum management (DSM) is essential to optimise the utilisation of this limited and valuable natural resource, with the aim of improving performance and adapting to changing wireless communication demands. Traditional static spectrum allocation methods have shown inefficiencies, leading to spectrum scarcity and under-utilisation. To address these challenges, the integration of blockchain and Cognitive Radio (CR) technologies has emerged as a promising approach. Blockchain, with its decentralised and secure attributes, can improve transparency and trust in spectrum allocation processes, while CR enables intelligent spectrum sensing and allocation to maximise utilisation. However, this promising approach comes with its own critical challenges, especially when dealing with the 6th Generation (6G) mobile communication. These challenges are related to the fact that the blockchain ecosystem needs to be interoperable and scalable enough to be compatible with the 6G high-demand and substantial resources. Specifically, integrating blockchain with CR requires efficient interoperability techniques where blockchain can easily and effectively interact with the CR platforms as well as radio spectrum environments. Furthermore, the spectrum management system over 6G networks needs to be designed in a way where massive 6G resources can be accommodated and managed without having any service performance degradation. This paper introduces a novel radio spectrum management model in 6G networks, named as BSM-6G, which integrates blockchain technology with CR where interoperability is preserved and scalability is maximised. Specifically, the proposed BSM-6G model merges blockchain’s transparent record keeping with CR’s intelligent spectrum management capabilities. To overcome the interoperability issue, BSM-6G provides an interoperable blockchain Oracle approach which facilitates the real-time interaction among the blockchain platform, the CR, and any data sources off-chain. This paper details all the technical and procedural challenges when implementing the proposed interoperability Oracle approach. To address the scalability challenge, BSM-6G utilizes the Proof-of-History (PoH) consensus protocol to align with the requirements of DSM in advanced networks like Beyond 5th Generation (B5G) and 6G. Evaluation results indicate that BSM-6G offers viable and less complex blockchain Oracle integration architecture measured by the technical implementation of BSM-6G, as well as low interoperability cost measured by transaction response time and transaction fee cost. Compared to state-of-the-art spectrum-based blockchain systems, BSM-6G shows a high scalable DSM-based blockchain in 6G networks measured by transactions per second (TPS).

Digitalization has begun as a transformative force within the energy sector, reforming traditional practices and paving the way for enhanced operational efficiency and sustainability. Enabled by key technologies such as smart meters, digitalization embodies a paradigm shift in energy management. Nonetheless, it is crucial to recognize that these enabling technologies are only the catalysts and not the end goal. This paper presents a comprehensive overview of digital services and products in the energy sector, with a specific focus on emerging technologies like AI and Connected Data Spaces. The objective of this review paper is to assess the maturity and adoption levels of these digital solutions, seeking to draw insights into the factors influencing their varying levels of success. This maturity and adoption assessment was carried out by applying a Fuzzy logic approach which allowed us to compensate for the lack of detailed information in current literature. By analyzing the reasons behind high maturity-low adoption and vice-versa, this study seeks to cast light on the dynamics shaping the digital transformation of the energy sector.

Over the past decade, an abundance of research has been conducted in the area of agricultural technology and innovations. The Internet of Things (IoT) has demonstrated its ability to connect numerous agricultural equipment, sensors, and specialists, boosting agricultural procedures in off-the-grid regions. Agriculture has experienced considerable improvements in production, cost reductions, service accessibility, and operational efficiency. With an emphasis on security, developments and trends in the sector, and technological implementation, this research paper offers an up-to-date analysis of existing and projected IoT applications in agriculture. In this article, enabling technologies, agricultural applications based on cutting-edge machine learning models, and services are all examined in relation to the development of IoT deployment in tackling diverse agricultural concerns. In the IoT-based agriculture system, potential challenges and limitations are also addressed. In its conclusion, this research provides an extensive review of the various aspects of IoT in agriculture, with the goal of empowering future researchers enthusiastic to make contributions to and advancement in their quest for a more in-depth comprehension of this field of study. A total of 96 papers were chosen for the selection from 2018 to 2023, and each was categorized using predetermined standards. The research’s results have been thoroughly examined, providing an overview of IoT in agriculture.

This paper proposes a parallel implementation of the Bron-Kerbosch algorithm, which finds all maximal cliques in large, complex graphs using CPU and thread-level parallelism and distributed memory with multiple cores. With the growing size and complexity of modern graphs, sequential algorithms can become impractical, making parallel computation a promising solution for reducing duration and enhancing scalability. Decomposing the graph into smaller components and distributing the workload across multiple threads using task parallelism is the proposed method. To optimise the algorithm’s efficacy across processes, load-balancing techniques are also investigated. The modified Bron-Kerbosch algorithm is implemented and evaluated on a variety of large graphs, exhibiting significant runtime and scalability enhancements over the sequential version. The conclusion of the paper discusses the prospective applications of the parallel Bron-Kerbosch algorithm in a variety of domains, including social network analysis, bio informatics, and network security. Overall, this research contributes to the expanding body of work on parallel computing and graph analysis by emphasising the advantages of CPU and thread-level parallelism for efficiently solving complex computational problems.

This paper explores the synergistic role of Distributed Resources (DR), including Distributed Generation (DG) and Battery Energy Storage Systems (BESS), in enhancing modern power systems’ sustainability, reliability, and flexibility. It addresses the gap in concurrent distribution network reconfiguration and DR allocation, especially under the variability of renewable energy. The study aims to minimize energy costs, losses, and voltage deviations by integrating wind and solar PV-type DGs with BESS. A novel multi-objective function and an improved bi-directional coevolutionary (I-BiCo) Algorithm are employed to find the optimal RES and BESS placement and sizing, showing marked improvements over existing methods. Furthermore, statistical comparisons using hypervolume, objective function values (diversity), and near-global solutions (convergence) underscore the proposed algorithm’s superiority over existing MOEAs. The final non-dominated solution, obtained through fuzzy set theory, highlights simulation results that minimize power loss, achieve substantial energy savings, and smooth demand, particularly with the integration of BESS devices. Moreover, optimal network reconfiguration (ONR) is a key strategy for balancing load demand. Simulation results affirm that minimizing bi-objective and tri-objective functions, coupled with optimal feeder reconfiguration, significantly reduces power loss and enhances voltage profiles, approaching unity across all buses. The proposed ONR formulation, in conjunction with DGs and BESS, maximizes the overall performance of power distribution networks. Furthermore, the paper addresses various time-dependent constraints of BESS, DG, and ONR, formulating and efficiently solving these constraints by integrating different constraint-handling techniques with the proposed multi-objective evolutionary algorithm. The study contributes to academic discourse and provides practical insights for designing more efficient and sustainable power systems in the face of evolving energy landscapes.

Radar echo image sequence prediction is a spatial-temporal forecasting problem, which is one of the main challenges in precipitation nowcasting. In response to issues such as poor extraction of spatiotemporal features and blurred prediction of image sequence in previous models, this paper proposes a spatial-temporal dual discriminator precipitation nowcasting method using SNGAN with InceptionGRU (STD-SNGAN-InceptionGRU). The design of temporal and spatial dual discriminators constrains the generator’s prediction samples, effectively enhancing the spatiotemporal prediction capability of the generator through adversarial training. The generator utilizes multiscale convolution (Inception) and multiscale gate recurrent unit (InceptionGRU) to enhance its ability to extract spatiotemporal features, thereby improving its learning capability for the evolution of radar echo sequences. The comparative experimental results demonstrate that the proposed STD-SNGAN-InceptionGRU model outperforms other algorithms in terms of critical success index (CSI) and probability of detection (POD) at different echo intensities, especially in areas with long time series and high echo intensity. Furthermore, through ablation experiments, this paper proves that the temporal discriminator, spatial discriminator, and InceptionGRU all effectively improve the model’s prediction capability for radar echo images.

Energy hubs (EHs) enable all types of energy customers to participate in demand response programs (DRPs), such as inelastic loads, by combining electricity, heat, natural gas, and other types of energy. Integrated demand response (IDR) is the result of this new vision. From a global warming perspective, environmental emissions are a significant issue to be considered. Furthermore, hydrogen has been recognized as an attractive fuel for decarbonizing sectors that contribute to global warming. Thus, this paper provides a solution to global environmental problems through the utilization of renewable energy sources (RESs) and green hydrogen. In addition, electric vehicles (EVs) are expected to contribute significantly to this scenario due to their rapid expansion. Considering the uncertainty of electricity prices, this paper focuses on coordinating EV parking with hydrogen storage systems (HSS) and IDR with the aim of increasing flexibility, where a robust optimization (RO) method has been implemented to solve the problem. The results demonstrate that in the case of a deterministic solution to the problem and where uncertainty is at the highest level, the proposed scheme reduces the total operating costs by 13.89% and 8.67%, respectively. This indicates that the proposed scheme could avoid overinvestment and cost-effectively achieve the given carbon emission target.

This paper presents a comprehensive review of the advancements and challenges in scalable modular antenna arrays for 5G Massive MIMO (Multiple Input Multiple Output) networks, a pivotal technology for the next-generation wireless communication. Tracing the evolution of wireless technologies from 1G to 4G, the paper contextualizes the paradigm shift brought by 5G, characterized by enhanced spectral efficiency, massive device connectivity, and higher frequency bands. Focusing on Massive MIMO, the paper explores its role in augmenting network capacity and signal quality via advanced techniques like beamforming and spatial multiplexing. It delves into the intricacies of designing and implementing scalable modular antenna arrays, essential for the flexibility and optimization of rapidly evolving 5G networks. The review also covers mathematical modeling, advantages of Massive MIMO, and the challenges in integration, performance under diverse conditions, and cost-complexity balance. Concluding with insights into the commercialization trajectory of MIMO technology and its integration into modern telecommunications, the paper highlights the ongoing research directions and future potential of scalable modular antenna arrays in meeting the ambitious demands of 5G and beyond.

The progressive retirement of conventional power plants is decreasing the total system inertia and making the frequency control more delicate. In this context, voltage-sensitive loads will have a more pronounced impact on frequency. Therefore, there is an interest in understanding and quantifying how voltage dynamics impact the frequency response. For this purpose, a linearization of the system and the analysis of the obtained transfer functions are in focus. The paper aims to complement the traditional eigenvalue analysis, capture a system’s response to large disturbances, and estimate the frequency overshoots. The study also shows that controllers affecting the load voltage, such as Power System Stabilizers (PSSs), can impact frequency response during a large disturbance. The impact of PSS on system responses and its transfer functions is carried out by applying a general control configuration. Both conventional PSS1A and multi-band PSS designs are analyzed and compared. Thereby, the paper explicitly interlinks a PSS design, and its parameters with properties of the system’s frequency response reflected in the system transfer function. Illustrating enhancements facilitated by the presented approach, the explicitly derived PSS designs demonstrate the potential to reduce frequency overshoot. As an example, this reduction is achieved by improving a common mode damping or changing the position of the dominant zero of the closed-loop system.

In this paper, we present a 6.78 MHz Pulsed Wireless Power Transfer (PWPT) system specifically designed for powering the electronics placed on a suction roll of a paper mill machinery, thus enabling rotating equipment monitoring. The system utilizes a pair of different resonant coils to maintain a stable coupling coefficient for a fraction of the roll rotation period while guaranteeing compactness and ease of installation. Designed using both Commercial-off-the-Shelf (COTS) and custom components, the developed WPT system achieves a DC-DC efficiency of 62% in the specific working conditions. The transferred power is used to supply an Internet-of-Things (IoT) node that continuously measures and transmits data about the operation status. A fully operational system for battery and angular position monitoring has been developed and tested in a laboratory environment. This research presents a comprehensive analysis of the system and explores the interaction of different coils to maintain a stable coupling coefficient in dynamic applications. The same approach can be employed for rotating machinery with similar geometrical constraints or more general dynamic applications characterized by periodic movements and limited space for the coupling system.

In the process of frequent start-stop actions of the slewing platform in existing electric shovels, there is a substantial waste of kinetic energy. Furthermore, the installed power of the drive motor is often configured according to the maximum load, which makes it frequently operate in a low-efficiency region further exacerbating energy losses. To address these issues, this paper proposes an electro-hydraulic active-passive hybrid drive system for the shovel’s slewing system. The aim of this system is to efficiently store and utilize the kinetic energy generated by the shovel’s slewing system, reduce the installed power of the system’s drive motor, and consequently enhance the overall energy efficiency. To achieve high-efficiency energy recovery during the swing process of the slewing system, the proposed system introduces an additional hydraulic pump/motor and hydraulic accumulator. The pump/motor is situated at the original drive motor’s drive shaft. It serves to recover kinetic energy during the deceleration of the shovel’s slewing system and assists the motor in driving the load during the acceleration. This approach not only allows efficient storage of kinetic energy but also significantly reduces the installed power of the drive motor. The paper begins by analyzing the operational principles of the proposed electric-hydraulic active-passive hybrid drive slewing system of electric shovel. Subsequently, a test platform was constructed using a 36-ton excavator to preliminarily validate the proposed system feasibility. Experimental results indicate that, compared with the original system, the peck power and decrease energy consumption of the swing motor can be reduced by 28% and 49% respectively. Ultimately, an electromechanical-hydraulic coupling model was established for the electric-hydraulic active-passive hybrid drive electric shovel’s slewing system. Simulation results reveal that, compared with the original system, the peak power and energy consumption of the slewing motor can be reduced by 300 kW and 59%, respectively. The proposed system offers a significant improvement in the efficient storage of kinetic energy in large inertia slewing platforms.

Fuzzy PID control is a control method with good adaptability and stability in complex environments. It is used to achieve precise regulation and stable control of the system. In this paper, a fish scale evolution GSOM is proposed to improve the control method of fuzzy PID. Firstly, the fish scale regulation system is established and the differential evolution theory is introduced to realize the evolutionary upgrading of the system. Secondly, the GSOM module is introduced. The system is optimized by self-organized mapping neural network to achieve dynamic regulation of polymorphic inputs. Meanwhile, the fuzzy rule base and parameter regulation mechanism in fuzzy PID control are dynamically optimized. Improve the performance of the control system. Finally, the control method of improved fuzzy PID for fish scale evolution GSOM is simulated using MATLAB. The simulation experiments also compare several traditional PID control methods. The comparison indexes include stability, robustness, control accuracy and feedback output effect. The results show that the method in this paper is more stable and has fewer iterations when facing the dynamic input environment. The tracking error and control output of the controller system are significantly improved. It has good feedback output effect, solves the saturation problem and has higher control accuracy.

This paper proposes a stretchable-substrate-impregnated fabric for deployable high-gain antenna for small satellites. Constellations of small satellites offer worldwide coverage of wireless communication and earth observation; high-gain antennas are required to realize high-speed communication and high-resolution radar. This paper aims to develop a CubeSat onboard high-gain reflectarray antenna that can be folded compactly and deployed in space. The novelty of this paper is the stretchable-substrate-impregnated fabric, which can realize low stowage volume with high fabrication tolerance and low resistivity with single-piece reflectarray to avoid gain degradation. The origami-folding-pattern compatible reflection elements can avoid creases on the elements, avoiding gain reduction after folding. A $17\times 17$ -element reflectarray using the stretchable-substrate-impregnated fabric and the origami-folding-pattern compatible reflection elements is designed, fabricated, and characterized. The folded volume of the reflectarray is 27 mm in height and 58 mm in diameter, and the measured fabrication tolerance of the reflection element was $20 ~\mu \text{m}$ at most. Electrical antenna measurements confirmed low resistivity of 1.20 $\text{m}\Omega /\Box $ and a high realized gain of 23.6 dBi, comparable with conventional flexible printed circuit reflectarray antennas. The simulated and measured antenna efficiency is 25.3% and 25.1%, respectively. The simulated and measured 1-dB gain down bandwidth is 1.6% and 1.5%, respectively.

To ensure the reliability of autonomous driving, the system must be capable of potential hazard identification and appropriate response to prevent accidents. This involves the prediction of possible developments in traffic situations and an evaluation of the potential danger of future scenarios. Precise Collision Risk Assessment (CRA) faces complex challenges due to uncertainties inherent in vehicle and road environmental conditions. This paper introduces a new CRA approach, the Multi-Dimensional Uncertainties-CRA (MDU-CRA), which integrates uncertainties related to driver behavior, sensor perception, motion prediction models, and road infrastructure into a comprehensive risk evaluation framework. The estimation of vehicle state is initiated using Extended Kalman Filtering (EKF) to capture uncertainties in sensor perception. Concurrently, a probabilistic motion prediction model based on Gaussian distributions has been developed, which considers the uncertainty in driver behavior. Subsequently, the uncertainty of the road structure is modeled using a truncated Gaussian distribution. Finally, collision risk is quantified as the future probability of collision through heuristic Monte Carlo (MC) sampling. This paper presents the results of two experiments Firstly, our proposed method is demonstrated to outperform the reference neural network-based method in terms of short-term motion prediction accuracy. Secondly, two driving scenarios are extracted and reconstructed from the Next Generation Simulation (NGSIM) dataset for validation and evaluation, i.e., an active lane-change scenario and an emergency braking scenario. In the domain of collision risk assessment, our approach consistently outperforms other evaluation methods. It exhibits the capability to perceive collision risks 2 to 5 seconds in advance, significantly reducing the probability of imminent collision incidents.