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Trap plays an important role in the charge transportation of three-layer oil-paper and the investigation of trap parameters at different degradation degree is of great significance for further research of the partial discharge (PD) mechanism. This article considers the turn-to-turn structure of on-board traction transformer. A defect model of three-layer oil-paper insulation was designed for PD test. The PD degradation samples are selected from the inception of PD to the breakdown of PD to study the characteristics of surface potential decay on degradation samples by using isothermal surface potential decay method. Given that, the trap distribution at different degradation stages was calculated and its influence on PD characteristics was investigated. The result shows that AC fluctuation voltages caused by inter-harmonic has a great influence on PD distribution, surface charge distribution and trap distribution. With the increase of degradation, the decay rate of surface potential increases and the decay rate of AC fluctuation voltages is larger than that pure AC. And also, the number of shallow trap with AC fluctuation voltages is larger than that with pure AC. The rate of free charge increases with the number of shallow trap increasing. More free electrons involves in the process of PD, accelerating the aging rate of three-layer oil paper samples. Therefore, it can be concluded that surface charge and trap distribution are helpful to clarify PD mechanism.

An electrical tree discharge model of multilayer insulation paper was prepared, and a voltage of 20 kV was used for electrical tree cultivation and initiation tests. Based on the microscopic image information and partial discharge (PD) information during the aging of the electrical tree, the development of electrical tree discharges between layers of paper comprising epoxy resin-impregnated paper insulation was studied. The results show that, by analysing PD information such as the discharge volume phase spectrum and the discharge repetition rate phase spectrum, the development of the electrical tree branch progresses in four stages: initial, growth, stagnation, and bursting. During the growth and stagnation stages, the main discharge paths of the electrical tree are mainly formed, and there are many filamentous fibres on the path edge. During the burst stage, more branches will be formed on the electrical tree, which will cause the greatest damage to the insulation. With the extension of the electrical tree path, the discharge magnitude and discharge times also increase. There are specific discharge patterns in different growth stages of the electrical tree, and the number of discharge events in each positive half-cycle is significantly greater than that in a negative half-cycle. Combining the microscopic image information and PD information during the aging of such an electrical tree, a theoretical basis for evaluating insulation state by PD monitoring is realised. The long-chain molecules of cellulose and epoxy resin break bonds and oxidise, which decompose into a large number of free radicals, monosaccharides, and other low-molecular-weight products.

The low frequency section of frequency domain spectroscopy (FDS) of oil-paper insu- lation system can effectively reflect the ageing state and moisture of the oil-immersed paper. Meanwhile, steady-state insulation resistance is the one of indexes for the condition of transformer. In actual measurement, these two both are time-consuming and poor accessibility, thereby restraining their field applications. Aim at this problem, this paper analyses the polarization process of the oil-paper insulation in transformer, and adopts a low frequency equivalent circuit model for the oil-paper insulation to characterize the polarization process at low frequency. The simulated annealing particle swarm optimization (SAPSO) algorithm is proposed to identify the parameters in equivalent circuit, which are affected by change of insulation state. Compared with the experimental results, the errors of the steady-state insulation resistance and the tan δ below 10-3 Hz, which are calculated by the identified parameters, are less than 2.5%. This method can avoid the influence from external environment and reduce the measurement time. The time and frequency domain parameters calculated can provide reference for the ageing assessment of oil-immersed paper.

Kevlar pulp treated with low-temperature plasma and nano cellulose fibrils (NCFs) have been introduced as reinforcements in the preparation for composite insulating paper with low relative permittivity, dielectric loss, good mechanical properties, and thermal stability. More polar groups of Kevlar fibers could be exposed after plasma treatment. Results showed that the relative permittivity of the composite paper containing 20% Kevlar pulp, 10% NCF, and 70% softwood pulp is decreased by 29.6% and the dielectric loss is decreased by 43.2% at 50 Hz, 25°, compared with the conventional paper formed by pure softwood pulp. The change was due to the introduction of Kevlar pulp with intrinsic low dielectric constant and loss, which decreases the whole polarizability of the paper. The mechanical properties of the composite paper became worse after introducing 20% Kevlar pulp. About 10% NCF can work as a mechanical reinforcement to compensate for the tensile and Yang's modulus loss. Scanning electron microscopy results showed that NCF could fill the void defects and improve the interface bonding of the fibers to enhance the paper mechanical properties. The composite paper also had better insulating and thermal properties than the conventional paper. It has been concluded that the Kevlar pulp/NCF/softwood pulp composite paper has the potential to replace the pure softwood paper for the application of high-voltage ac transformer insulation.

Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library to be contaminated with a surprisingly large number (0.3-2%) of malicious PDF documents (over 55% were crawled from the IPs of US-universities). We developed a two layered detection framework aimed at enhancing the detection of malicious PDF documents, Sec-Lib, which offers a security solution for large digital libraries. Sec-Lib includes a deterministic layer for detecting known malware, and a machine learning based layer for detecting unknown malware. Our evaluation showed that scholarly digital libraries can detect 96.9% of malware with Sec-Lib, while minimizing the number of PDF-files requiring labeling, and thus reducing the manual inspection efforts of security-experts by 98%.

When the inner insulation of oil-impregnated paper bushing breaks down in service, gases decomposed by oil and paper may cause an explosion. The explosion of bushing occurs from time to time because of the lack of an effective monitoring method. In order to provide an effective online monitoring method for the bushing to avoid explosion, this paper presents a method to monitor the PD defect in bushing by measuring the inner pressure of bushing. In this paper, the relationship of variation between characteristic gases content and inner pressure is studied, the effect of inner temperature on inner pressure is analyzed and the method for reducing this effect is also presented, and the monitoring method is proved to be useful by PD tests on a real bushing. The results show that the inner pressure increases obviously due to the generation of characteristic gases by internal discharge in oil-impregnated paper bushing, and the pressure increases with the deterioration of the discharge; although the inner pressure has a great influence on the inner pressure, the influence can be reduced or eliminated greatly by the method of common-mode rejection. According to the results, the internal discharge defect of the oil-impregnated paper bushing can be detected by monitoring the pressure, and the bushing explosion can be avoided.

The aging of the transformer oil-paper insulation distributes spatially, which results in changes in paper resistivity in different regions. This paper establishes an iterative inversion algorithm using the finite element method to calculate the oil-immersed paper resistivity in different regions of a transformer. This algorithm sets the transformer dielectric loss factor tan δ at low frequency, the dielectric parameters of the transformer oil as inputs and the oil-immersed paper resistivity as output. The resistivity obtained from inversion can be used as a reference to access the insulation state of the oil-paper insulation. This paper aims at achieving the nondestructive detection of the partial state of the oil-paper insulation.

Failures of bushing caused by corrosive sulfur has gradually appeared in recent years. Apart from copper and sulfur, certain amount of aluminum was also found both in oil and paper after disassembling. The corrosion mechanism of the aluminum foil and its effect on oil-paper insulation were unknown. In this study, series of sulfur corrosion experiments were taken to investigate the sulfur corrosion of aluminum foil. The results showed the aluminum foil could certainly be corroded in oil containing corrosive sulfur (DBDS). Thermal products of insulting paper triggered the corrosion by removing the alumina film on the surface. Then, the contact corrosion occurred between aluminum and DBDS. In addition, the mechanisms of corrosion products generation both in oil and on paper were studied. The corrosion products in oil was DBDS-Al(3,4) and that on paper was RCOOAln. It caused the decrease of electrical properties of oil-paper. Particularly, the dielectric loss of oil and paper were dramatically enhanced.

In order to understand the aging process of paper used in high voltage transformers, transformer grade Kraft paper samples were aged in a fan oven in order to access the full range of DP values from 1100 (new) to ~200 (end of life). The absence of significant oxidation was verified by infrared spectroscopy and the mechanical and dielectric properties were assessed as a function of DP. Whilst the dielectric properties (in the absence of water) were unaffected by aging, the tensile strength was reduced. This confirms studies in the literature which show that most transformer breakdowns occur through mechanical failure of the paper and crucially, provides a mechanism of providing paper samples of known DP for subsequent exposure to oil flow.

The use of vegetable oil (natural ester) in electrical devices like power transformers is increasing due to their high biodegradability and better safety. The lifespan of power trans- formers is mainly defined by cellulose insulation condition, which usually works together with dielectric oil as electrical insulation and also as mechanical winding protector and compactor. That is why the aim and results of this research shows us not only the dielectric parameters evolution, but also the relationship between the mechanical factors and the moisture content of thermal accelerated ageing processes, with commercial vegetable oil, of Kraft paper and Diamond Dotted Paper (DDP). These are two of the most common insulating materials in electric power transformers. In addition, the new tests have been done by a different method of paper ageing analysis. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement Nº 823969 - BIOTRAFO.

This article adds more information to the problem of natural ester use as an electroinsulating fluid in cellulose paper-oil systems, e.g., for use in power transformers. An accelerated thermal aging (120, 140 and 160 °C) of samples consisting of cellulose paper (transformerboard), a treated natural ester (filtered, basic Al2O3, 0.53% DBPC), a copper plate and moisture was studied. Research was especially focused on the impact of moisture on the aging of the above system. Samples with three different oil moisture levels (80, 143 and 305 ppm water content) were subjected to thermal aging to display the change in a selection of properties (tanδ, acid number, water content). For a study of the polarization effects, broadband dielectric spectroscopy (BDS) was performed to observe the complex permittivity and conductivity dependencies on the temperature and frequency (±30 °C, 0.01-107 Hz). Havriliak-Negami (H-N) diagrams were used to further understand the results. The degradation products during thermal aging were recognized by Fourier transform infrared (FT-IR) spectroscopy. Hydrolysis and hydrogenation occurred in the insulating system with a natural ester aged by elevated temperatures, which is in contrast to CO2 formation in the case of electric aging. The results showed an increase in the acid number, dissipation factor, and number of hydrolysis and hydrogenation products. Furthermore, changes in polarization were observed as a consequence of the thermal aging of paper-natural ester insulation system with the presence of copper and moisture.

© 2013 IEEE. With the rapid growth of scientific publications, it is hard for researchers to acquire appropriate papers that meet their expectations. Recommendation system for scientific articles is an essential technology to overcome this problem. In this paper, we propose a novel low-rank and sparse matrix factorization-based paper recommendation (LSMFPRec) method for authors. The proposed method seamlessly combines low-rank and sparse matrix factorization method with fine-grained paper and author affinity matrixes that are extracted from heterogeneous scientific network. Thus, it can effectively alleviate the sparsity and cold start problems that exist in traditional matrix factorization based collaborative filtering methods. Moreover, LSMFPRec can significantly reduce the error propagated from intermediate outputs. In addition, the proposed method essentially captures the low-rank and sparse characteristics that exist in scientific rating activities; therefore, it can generate more reasonable predicted ratings for influential and uninfluential papers. The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike data sets.

Oil-paper insulation will inevitably endure impulse voltage with a superimposed dc voltage (superimposed voltage) because of introduced lightning or switching, which can induce charge accumulation and trigger flashover during the operation of a converter transformer. Temperature rise is also a nonnegligible factor that can significantly influence the performances of oil-paper insulation. This paper aims to reveal the effects of ambient temperature and superimposed voltage on surface charge and flashover behaviors in an oil-paper insulation system. Surface potential decay (SPD) experiments were performed under superimposed voltage with various polarity and amplitude combinations and at the temperature ranging from 20 °C to 80 °C. The results show that the initial surface potential and decay rate increase as the amplitude of the impulse voltage increases at 20 °C. The initial surface potential declines, and the process of SPD is accelerated at higher temperatures because of the increased concentration of shallow traps. Subsequently, flashover tests were conducted under superimposed voltage. It is found that flashover occurs after applying superimposed voltages even with a rather low dc voltage. In addition, the dc voltage component is primarily responsible for flashover. Weibull results also indicate that it is prone to trigger flashover at higher temperatures, because thermal excitation helps more carriers to escape from deep traps and participate in the process of flashover. Accordingly, the insulation characteristics of oil-paper system can be significantly influenced by superimposed voltage and temperature, which should draw great concern among the power grid.

With the rapid growth of scientific publications, research paper recommendation which suggests relevant research papers to users can bring great benefits to researchers. In this paper, we focus on the problem of recommending conference papers to the conference attendees. While most of the related existing methods depend on the content-based filtering, we propose a unified conference paper recommendation method named $CPRec$ , which exploits both the contents and the authorship information of the papers. In particular, besides the contents, we exploit the relationships between a user and the authors of a paper for recommendation. In our method, we extract several features for a user-paper pair from the citation network, the coauthor network, and the contents, respectively. In addition, we derive a user’s pairwise preference towards the conference papers from the user’s bookmarked papers in each conference. Furthermore, we employ a pairwise learning to rank model which exploits the pairwise user preference to learn a function that predicts a user’s preference towards a paper based on the extracted features. We conduct a recommendation performance evaluation using real-world data and the experimental results demonstrate the effectiveness of our proposed method.

The exchange of academic marks between HEIs (Higher Education Institutions) is mandatory in every student mobility programs (i.e. the EU Erasmus Program) but that process remains to present date with insufficient technological support and the absence of a comprehensive reference model that allows the integration of potential technological solutions for the exchange of academic data with existing Academic Information Systems seems to limit greatly the possibility of adopting solutions of this type referred to in the existing literature. This work addresses this issue, conducting an initial bibliographic review aimed at the identification of the fundamental requirements of such an architecture as well as explores some of the technologies that are showing potential for usage in the safe exchange of academic results between partner HEIs, with particular interest in blockchain technology applied in an educational context. info:eu-repo/semantics/publishedVersion

The interest in developing and testing dielectric nanofluids for electrotechnical purposes has increased significantly in the last ten years. A number of works have been published reporting dielectric and thermal properties for these liquids that enhance those of conventional insulating fluids. Some authors propose that the application of dielectric nanofluids to transformer insulation could improve the reliability of transformers and open new possibilities for transformer design. However, this will not be feasible until thorough studies on the interaction of these materials with other elements of the transformer, specially the solid insulation, are carried out. In this work, the effect of the presence of nanoparticles on the dielectric response of oil and oil-paper insulation is analysed in laboratory conditions. Specimens have been prepared to emulate the insulation system of transformers and the dielectric response in the frequency domain of these specimens has been characterized. The dependence of nanoparticle concentration on the dielectric response has been analysed. Proposing physical mechanisms that explain the experimental results observed over the oil and the paper samples. Some conclusions are extracted in this work that may be of interest if nanofluids are considered as liquid insulation for transformers.

In HVDC converter transformers, the potential DC component in electric stresses imposed on valve windings would unfavorably cause space charge accumulation inside oil-paper insulation. Insulation degradation and aging may have great effects on space charge behaviors in oil-paper insulation. This paper focuses on the effect of the low molecular weight (LMA) and the moisture on space charge phenomenon in oil impregnated-paper insulation. Pulsed electro-acoustic (PEA) technique was used in this study to measure space charge behaviors in 400 $\mu$ m thick, dry and moist single-layer paper sample impregnated in mineral oil prepared with different amounts of formic acid. The measurements were carried out at $30 ^ { \circ } \mathrm { C }$ under a field strength of 20 kV/mm. It was found that LMA greatly strengthens the homo charge accumulation. The combination of LMA and moisture results in even more active space charge accumulations. In dry samples, the existence of LMA causes slower charge decaying rate. However, the introduction of the moisture accelerates the charge dissipation.

In this paper, we study the sum rate maximization for successive zero-forcing dirty-paper coding (SZFDPC) with per-antenna power constraint (PAPC). Although SZFDPC is a low-complexity alternative to the optimal dirty paper coding (DPC), efficient algorithms to compute its sum rate are still open problems especially under practical PAPC. The existing solution to the considered problem is computationally inefficient due to employing high-complexity interior-point method. In this study, we propose two new low-complexity approaches to this important problem. More specifically, the first algorithm achieves the optimal solution by transforming the original problem in the broadcast channel into an equivalent problem in the multiple access channel, then the resulting problem is solved by alternating optimization together with successive convex approximation. We also derive a suboptimal solution based on machine learning to which simple linear regressions are applicable. The approaches are analyzed and validated extensively to demonstrate their superiors over the existing approach., Comment: 5 pages, 4 figures

International audience; As part of the Historic Photographic Paper Classification Challenge, a multitude of approaches to quantifying paper texture similarity have been developed. These approaches have yielded encouraging results when applied to very controlled datasets containing photomicrographs of familiar specimens. In this paper, we report on the k-nearest neighbors classification performance of two multiscale analysis-based texture similarity approaches when applied to a much larger reference collection of silver gelatin photographic papers. The clusters for this data set were derived from a visual sorting experiment conducted by art conservators and paper experts later extended through crowd-sourcing. The results show that these texture similarity approaches, when combined with a simple k-nearest neighbors classification algorithm, yield workable performances with accuracy of up to 69%. We discuss this outcome in the context of available data and the cross-validation procedure used, then provide suggestions for improvement.

This paper focuses on the potential and practice of alternative forms of assessment to the conventional written exam. Readers will be interested in issues such as why would a staff member want to replace the written exam and how can I be sure that the replacement is equally effective at capturing student attainment. The paper summarises a trial at the University of Sheffield where the written exam was replaced by a computer quiz which was marked automatically.

International audience; The design of a dipole antenna printed on a paper substrate is presented in this paper. The antenna which integrates a compact balun is devoted for dual-band 2.4/5 GHz WLAN applications. The antenna is based on a double-side printed multilayer paper substrate and is fed with a coaxial cable for the testing. The simulated results of the whole structure are also presented in detail and compared with the measured performances.

To improve the accuracy of user implicit rating prediction, we combine the traditional latent factor model (LFM) and bidirectional gated recurrent unit neural network (BiGRU) model to propose a hybrid model that deeply mines the latent semantics in the unstructured content of the text and generates a more accurate rating matrix. First, we utilize the user’s historical behavior (favorites records) to build a user rating matrix and decompose the matrix to obtain the latent factor vectors of users and literature. We also apply the BERT model for word embedding of the research papers to obtain the sequence of word vectors. Then, we apply the BiGRU with the user attention mechanism to mine the research paper textual content and to generate the new literature latent feature vectors that are used to replace the original literature latent factor vectors decomposed from the rating matrix. Finally, a new rating matrix is generated to obtain users’ ratings of noninteractive research papers and to generate the recommendation list according to the user latent factor vector. We design experiments on the real datasets and verify that the research paper recommendation model is superior to traditional recommendation models in terms of precision, recall, F1-value, coverage, popularity and diversity.

Tight assembly of stator windings with no insulation paper damage is a manufacturing challenge. We evaluate different sets of parts according to the following parameters: magnet wire thickness, stator slot smoothness, length of the straight magnet wire after the slot end, and type and amount of insulation cap at the end of the slot. These parameters have discrete values with small differences between them. The damage criterion is the decrease of the insulation paper breakdown voltage after assembly/disassembly of parts, assembled in a small set of designed experiments. Parameter values, i.e., levels at individual experiments are set by an orthogonal experiment matrix. Repetition of each experiment provides statistical significance. Data analysis shows that the additive model alone is not sufficient due to the high correlation of the parameters’ influences. We extend the model to include interparameter influences, which we model by adding a virtual parameter. The extended additive model generates parameter values that do not degrade the insulation paper breakdown voltage within the manufacturing process. These values are verified by repetitions of the control experiment.

Usability and the use of automated static analysis tools in the software development process have been an evolving subject of research in the last decades. Several studies shed light on issues like high false positive rates and low comprehensibility, which hinder tool adoption for even software engineers. Yet, the tools' perceived usefulness and ease of use play a much larger role when it comes to untrained software developers, as is usually the case in scientific software development. In this paper, we outline a multi-stage interview study to learn more about how scientists come to accept and use static analysis tools.

Machine learning (ML) components are increasingly adopted in many automated systems. Their ability to learn and work with novel input/incomplete knowledge and their generalization capabilities make them highly desirable solutions for complex problems. This has motivated the inclusion of ML techniques/components in products for many industrial domains including automotive systems. Such systems are safety-critical systems since their failure may cause death or injury to humans. Therefore, their safety must be ensured before they are used in their operational environment. However, existing safety standards and Verification and Validation (V&V) techniques do not properly address the special characteristics of ML-based components such as non-determinism, non-transparency, instability. This position paper presents the authors' view on the safety of automotive systems incorporating ML-based components, and it is intended to motivate and sketch a research agenda for extending a safety standard, namely ISO 26262, to address challenges posed by incorporating ML-based components in automotive systems.

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. On one hand, QML models can be useful in both of these tasks. Convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoencoder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.

For nearly four decades 2-furfural (2-FAL) in transformer oil has been used as a chemical marker to indicate the ageing state of transformer paper insulation. Nevertheless, it has always been a challenge to define an acceptable limit for 2-FAL due to issues related to non-uniform temperature distribution in transformers, stability of 2-FAL in oil and its partitioning between liquid and solid insulation. Therefore, transformer users try to combine the laboratory findings, the operational experience and post-mortem analyses together for predicting ageing state of paper insulation through 2-FAL content in oil. Due to increase in the use of ester liquids in transformers, there is a need to investigate the ability to use 2-FAL as a paper ageing indicator for ester based insulation systems. Laboratory ageing studies have shown that 2-FAL is indeed generated in ester based insulation systems. However, there is a lack of understanding related to the partitioning of 2-FAL in ester based insulation systems. This paper studies the partitioning of 2-FAL in synthetic ester based insulation systems and compares to that in mineral oil based insulation systems with the focus on the effect of paper type and water content in paper. Under the investigated conditions the synthetic ester had higher partitioning coefficients (ratio between the 2-FAL content in oil and paper) than the mineral oil. Partitioning coefficients in both oil types decreased with the increase of the water content in paper. However, change in paper type did not significantly affect the partitioning coefficients in this study.

Proceedings of the 10th International Conference on computer supported education (CSEDU): Funchal, Madeira, Portugal: March 15, 2018, This paper examines the preferences of students regarding computer-based versus paper-based assessment, in an introductory computer programming course. Two groups of students were surveyed about their preference between paper-based and computer-based tests and respective rationale. All students had already been assessed: one group using two paper-based tests and the other group using two computer-based tests. Both groups expressed an overwhelming preference for computer-based tests independently of their previous programming experience. We conclude that, from the students’ point of view, computer-based tests should be the used over paper-based ones for introductory programming courses. This adds to existing literature about computer-based testing of programming skills.

This work presents an experimental study of second mode positive streamers propagating along mineral oilpaper interfaces. A point-plane arrangement immersed in mineral oil with the paper inclined 60 degrees to the plane electrode is used to create the liquid-solid interface. Kraft paper and a kraft fibril paper, made from cellulosic micro and nano fibrils, with higher density and lower surface roughness are used as the solid materials. High speed shadowgraphy and charge recordings are used to compare the propagation of second mode positive streamers along the mineral oil-kraft paper and mineral oil-kraft fibril paper. Streamers creeping along the mineral oil-kraft paper interface propagate mainly into the liquid, with one or two main filaments. In comparison, the streamers propagating along the kraft fibril paper show a strong reduction of the branching; these streamers consist of a single filament that propagates exactly on the solid surface. Streamers along the kraft fibril paper also have longer propagation time than for the case with kraft paper. Mutual electrostatic shielding between filaments is observed for the streamers creeping on the kraft paper. An electrostatic analysis of the influence of permittivity, density and surface roughness of the solid in the electrical properties of the streamer filaments is also performed.

Microfluidic engineering technology has been widely used for implementing lab-on-chip (LOC) type point of care (POC) devices since its origins in the 1990s. LOC devices offer some distinct advantages such as the possibility of reducing the quantity of valuable samples or reagents needed and the shortening of the detection times, both of which result from their compact structure [1]. Paper-based microfluidic devices, which are regarded as a low-cost alternative to conventional POC diagnostics tools, have also been popularly studied in the last few decades, and a huge number of advantages have been explored, such as low-cost, mass production, disposability, being equipment free etc. [2] However, some disadvantages or limitations are also apparent, such as issues with the control of flow rate, multiplexed detection on the same device footprint and further reduction of size [2]. As a result, 3D microfluidic paper analytical devices, as an alternative solution, have been proposed in recent years, which enable distribution of fluids in both lateral and vertical directions.

In the era of emerging Smart Built Environments (SBEs), a smart house, unlike regular houses with static "components", consists of numerous interconnected and often actuated devices, capable of executing tasks independent of user supervision. Living in such a SBE, where for example, the furniture can rearrange itself, and the doors open and close of their own volition, may be difficult and unpredictable. Furthermore, cyber-security attacks and intrusion could allow attackers to assume control of the SBE, damage its components and to potentially harm its inhabitants. Such novel characteristics of SBEs present developers with several unique challenges with regards to implementing the needed safety and security measures and protocols that go along with them. With such environments, therefore, there is a need for a system that is capable of monitoring user activities in real-time, identifying the safety and security hazards to users in their immediate local context, warning users of these hazards, and perhaps even taking preventative and mitigative action against the hazards that it identified. In this paper, we survey some of these challenges and explore the design and implementation of a system designed around the safety and security of SBE inhabitants. We propose an approach to modeling SBE safety that combines the three laws of robotics and the swarm behavior model. We also present a preliminary prototype and discuss a case study.

Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.

In this paper the experimental results of a compact low-power rectenna in paper substrate, designed to operate in the Wi-Fi band, are presented. The complete prototype, based on an annular slot antenna and a single-diode rectifier, features a weight of 1.5 grams and shows an RF-to-dc conversion efficiency in the design band of about 40 % for a -10 dBm available input power, of about 28 % at -15 dBm, and in the range [10, 22] % at -20 dBm, corresponding to an output DC voltage in the order of 320, 240 and 60 mV respectively. Additionally, the rectenna features an efficiency higher then 7 % in the whole band 1.8-2.7 dBm for a power density estimated around 3 μW/cm2.

We consider the problem of communicating over a channel that randomly "tears" the message block into small pieces of different sizes and shuffles them. For the binary torn-paper channel with block length $n$ and pieces of length ${\rm Geometric}(p_n)$, we characterize the capacity as $C = e^{-\alpha}$, where $\alpha = \lim_{n\to\infty} p_n \log n$. Our results show that the case of ${\rm Geometric}(p_n)$-length fragments and the case of deterministic length-$(1/p_n)$ fragments are qualitatively different and, surprisingly, the capacity of the former is larger. Intuitively, this is due to the fact that, in the random fragments case, large fragments are sometimes observed, which boosts the capacity.

For transformers in service, aging markers from cellulose in oil such as furans, water, methanol and acids are measured for diagnostic purposes. The content of these indicators, measured in oil, may therefore vary with load, temperature, solid and liquid insulation aging, as well as marker stability and evaporative loss. Correction factors estimating the level of the aging marker to a reference temperature would make the diagnostic more relevant. In this study, correction factors for temperature differences have been estimated for different aging markers in insulation systems impregnated with either mineral oil or synthetic ester. In the case of mineral oil based systems, we observe correction factors for water, methanol and furans in line with previously reported findings. On the other hand, for the synthetic ester system, novel correction factors for water and methanol are suggested while for furans, no temperature dependence was observed.

We report a very simple and fast method to develop a fluidic system on Parylene C coated paper, based on fabrication of surface-tension-confined channels. As described here, the proposed method can be applied to fast fabrication of reusable and cheap analytical devices, as an alternative to disposable fluidic devices on paper. © 2017 IEEE.

Distributed smart and connected community applications of the present and future will continuously sense perishable data on which they make decisions. We say the data are perishable when they represent information sensed at a past point in time, possibly at a distant part of the distributed application, and for which the ground truth could have since changed. This paper discusses actions the sensing source, the network, and the controller can take to make the best possible decisions given that we know only a delayed version of the truth. Some of these actions to preserve freshness of data are based on known real-time techniques such as Time Sensitive Networking. To these we add Information Theory applied on a message-by-message basis and cross-layer coordination between content and network handling. Both the unexpectedness (surprisal) and timeliness (freshness) properties of perishable information can be encoded using a Shannon information entropy framework. Examples of smart community systems of the future for which these concepts are relevant are given.

In this work, a large area, low cost and flexible polymer/paper-based double touch mode capacitive pressure sensor is demonstrated. Garage fabrication processes are used which only require cutting, taping and assembly of aluminum (Al) coated polyimide (PI) foil, PI tape and double-sided scotch tape. The presented pressure sensor operates in different pressure regions i.e. normal (0 to 7.5 kPa), transition (7.5 to 14.24 kPa), linear (14.24 to 54.9 kPa) and saturation (above 54.9 kPa). The advantages of the demonstrated double touch mode capacitive pressure sensors are low temperature drift, long linear range, high pressure sensitivity, precise pressure measurement and large die area. The linear output along with a high sensitivity range (14.24 to 54.9 kPa pressure range) of the sensor are utilized to wirelessly control the movement of a robotic arm with precise rotation and tilt movement capabilities.

In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems. Choosing the algorithm and hyperparameter setting correctly can promote the overall performance greatly, but users often fail to do so due to the absence of knowledge. How to help users to effectively and quickly select the suitable algorithm and hyperparameter settings for the given task instance is an important research topic nowadays, which is known as the CASH problem. In this paper, we design the Auto-Model approach, which makes full use of known information in the related research paper and introduces hyperparameter optimization techniques, to solve the CASH problem effectively. Auto-Model tremendously reduces the cost of algorithm implementations and hyperparameter configuration space, and thus capable of dealing with the CASH problem efficiently and easily. To demonstrate the benefit of Auto-Model, we compare it with classical Auto-Weka approach. The experimental results show that our proposed approach can provide superior results and achieves better performance in a short time., 12 pages, 3 figures

This paper considers a cell-free massive MIMO system with multiple-antenna access points and single-antenna users. The APs use conjugate beamforming to beamform the data to all users in the network. Total energy efficiency maximization is investigated. This optimization problem is nonconvex and thus difficult to solve. Existing solutions are based on second-order optimization methods in connection with convex approximations. These methods have been shown to perform very well but their complexity does not scale favorably with the network size. To tackle this issue, in this paper we propose to use a first-order method for nonconvex programming to our energy efficiency problem. Compared to the second-order methods, the proposed method achieves the same performance, while its run time is much faster. Thus, it is considered as a feasible solution for resource allocation in cell-free massive MIMO systems.

International audience; This paper presents a miniaturized microstrip antenna on an air-filled paper substrate. The presence of air in the substrate accounts for low dielectric losses and a high dielectric thickness, resulting in a high radiation efficiency and a large bandwidth. In addition, the edge of the antenna is exploited to feed the antenna with a coplanar line that can be connected to a 50 coaxial probe. This original feeding technique permits the reduction of the frequency of resonance as the air gap increases. The antenna is further miniaturized with the insertion of a slot in the ground plane. As a result, a high degree of miniaturization can be obtained along with a high efficiency. The influence of the air gap and of the slot on the antenna's performances is investigated. Two prototypes with dimensions of 0.29 x 0.29 x 0.065 radiating at 2.45 GHz are fabricated with copper tape and silver ink and the antennas' performances are experimentally validated: a gain of 4.5 dBi and a front-to-back ratio of 9.5 dB are measured, corresponding to a radiation efficiency of 70%. Index Terms-Paper substrate, microstrip antenna, air gap, air-filled, miniaturization, light weight antenna, low cost.

This study utilizes citation analysis and automated topic analysis of papers published in International Conference on Agile Software Development (XP) from 2002 to 2018. We collected data from Scopus database, finding 789 XP papers. We performed topic and trend analysis with R/RStudio utilizing the text mining approach, and used MS Excel for the quantitative analysis of the data. The results show that the first five years of XP conference cover nearly 40% of papers published until now and almost 62% of the XP papers are cited at least once. Mining of XP conference paper titles and abstracts result in these hot research topics: “Coordination”, “Technical Debt”, “Teamwork”, “Startups” and “Agile Practices”, thus strongly focusing on practical issues. The results also highlight the most influential researchers and institutions. The approach applied in this study can be extended to other software engineering venues and applied to large-scale studies.

In an ongoing discussion comparing the security properties of electronic and paper voting, decreased privacy is often presented as an argument against remote Internet voting. We contribute to this discussion by presenting a side-channel attack against the physical environment of traditional paper-based elections. More precisely, we build a device based on an Arduino development board and cheap electret microphones, capable of triangulating the locations of marks made on wooden tables with high precision. In the best configuration, we are able to determine the correct cell having dimensions 4×5 cm with more than 90% accuracy. This will allow breaching privacy of ballot sheet designs that rely on the voter marking her choice(s) between a potentially high number of candidates printed on one large sheet. We complement our attack with a study on various aspects of deployment of facial recognition. This gives rise to the setup where the attacker installs cameras in the polling stations, aiming at automated detection of people leaving the voting booths. Combining the two approaches, we will have a completely automated (and hence relatively well scalable) attack against the privacy of paper-based voting.

Field-Programmable Gate Arrays (FPGAs) are becoming increasingly available via commercial cloud providers, which currently allocate devices on a per-user basis. As the underlying hardware is often underutilized, several proposals for multi-tenant use of FPGA resources have been brought forth, along with some initial work on security attacks in this setting. Simultaneously, high-end FPGAs are being produced with 2.5D integration of multiple distinct dies, called Super Logic Regions (SLRs), onto the same chip. Although one might expect that physical separation of logic onto separate dies could prevent multi-tenant attacks, this paper demonstrates for the first time that cross-SLR information leaks based on sensing voltage changes within the FPGA chip are possible, without physical access to or modification of the boards. The cross-SLR covert channel is characterized analytically and experimentally on five Xilinx Virtex UltraScale+ FPGAs, both locally and on the Amazon and Huawei clouds. Several configurations of the source transmitters and the sink receivers are tested, including their locations, types, and sizes. The power-based channel is shown to have a bandwidth upwards of 4.6 Mbps and accuracy of over 97.6%. Consequently, as physical separation of tenants onto separate dies (SLRs) is an insufficient countermeasure against information leaks, hardware-level architectural improvements are necessary to make secure multi-tenant FPGAs on shared clouds a reality.

Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation.

In this paper we propose a statistical approach for the optimization of the phased codes used in radar. By considering each code as a realization of a random variable we show that we can build good codes if the distribution of the random variable is suitably chosen. We then consider sequences generated by the skew tent map and we show that for some values of the bifurcation parameter the invariant probability density coincides with the desired one. To confirm this idea we compute the maximal peak of the ambiguity function outside zero relative to the bifurcation parameter of the skew tent map.

This paper reviews the results of recent researches aimed to extend the standard-cell based digital design flow to analog building blocks, so that to enhance scalability, reconfigurability and portability across technology nodes and to reduce design effort, time-to-market and costs. In this framework, the application of the proposed fully digital design approach to a wake up oscillator and to a Digital-to-Analog Converter, which are two building blocks widely employed in IoT sensor nodes, is illustrated in detail.

We consider private information retrieval (PIR) for distributed storage systems with noncolluding nodes where data is stored using a non maximum distance separable (MDS) linear code. Recently, it was shown that when data is stored using certain non-MDS codes, the MDS-PIR capacity can be achieved, and is indeed the capacity of the system. In this paper, for storage codes not belonging to this class, we present a heuristic algorithm for their decomposition into punctured subcodes and a PIR protocol based on these punctured subcodes. The code decomposition is guided by the generalized Hamming weights of the storage code. We show that the proposed PIR protocol can achieve a larger PIR rate than that of all existing PIR protocols.

This paper presents the design of a novel rectenna based on a tapered annular slot and a single-diode rectifier, optimized to operate in the Wi-Fi band and in presence of low input power (Pavs=-15 dBm). In order to obtain a compact layout a double-layer architecture has been considered, according to which the interior metal surface of the annular slot is employed as ground plane for the rectifying circuit placed on the other side of the substrate. As a result, a rectenna with an active area of only 40 × 33 mm2 and an efficiency included between 26.5 and 28% has been obtained. The antenna has been fabricated in paper substrate and tested in the anechoic chamber. Then, the rectifier has been designed and optimized within the Advanced Design System suite. Finally, the rectenna performance has been discussed and compared to the State of the Art (SoA).

A general dynamic resource allocation scheme is introduced, which iteratively reconfigures an elastic optical network according to traffic dynamism and service diversity. As an application example, the scheme is employed for impairment-aware power-efficient resource allocation in a short-haul metro elastic optical network to demonstrate a typical 5% power plus service penalty for compensating bandwidth limitations compared with the corresponding limitless counterpart.