esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Graphene-to-Phoneme Conversion Methods
## 1 Introduction
Statistical machine learning methods surpassed rule-based methods, alleviating some of the burden of technical expertise and improving model performance, but they required training and tuning to achieve optimal performance. Neural network models eliminated the dependency on explicit alignment and captured finer and more effective features, and it could generalize well to predict the correct pronunciations of unseen words, thus achieving high performance. The introduction of attention mechanisms addressed the issue of neural networks not being able to compute in parallel. Even with long data sequences, attention mechanisms could capture focal information without losing important details, reducing model parameters, speeding up computation, and improving model performance. Due to the similarities in vocabulary, spelling, writing systems, and phoneme inventories between low-resource and high-resource languages, multilingual MT techniques have been used to train large-scale multilingual grapheme-to-phoneme conversion systems. Besides discussion about the various deep-learning models, this paper also represents four learning strategies for G2P conversion. A pre-trained model is a kind of deep learning model that has been trained; the pre-trained model can be fine-tuned to adapt to other specific tasks. Transfer learning is the idea of applying features learned from one problem to a new, similar problem. Multi-task learning is a form of collaborative learning where multiple tasks are learned in parallel, and the results influence each other. Multimodal learning aims to integrate and process different types of data during the learning process. Although the majority of G2P converters are monolingual, multilingual G2P conversion is also a popular research direction since the grapheme-to-phoneme conversion is a subfield of linguistics. After talking about the learning strategies, this paper discussed recent approaches for multilingual G2P conversion. The above Figure 1 illustrates the overall structure of this paper. This paper will mainly discuss the various methods of G2P conversion and focus on deep learning-based G2P; then, the learning strategies and multilingual G2P will be covered; finally, this paper will talk about the dataset and the evaluation metrics.
# Prediction of Air Pollution Concentration Based on mRMR and Echo State Network
## 4 Results and Discussion
### Data Processing  
The optimal subset selection of original data was performed using mRMR. In this paper, the first 75% of the dataset is used for training set, and the last 25% is used for testing set. The optimal subset is selected based on training set, and each model parameter in the simulation is obtained only from the training set.  
Firstly, PM\({}_{2.5}\) time series is chosen as reference variable. Other 10-dimensional variables are used as comparison variables. The data was quantitatively analyzed using mRMR to obtain sorting results: PM\({}_{2.5}\), CO, WS, PM\({}_{10}\), H, WD, SO\({}_{2}\), NO\({}_{2}\), P, T, and O\({}_{3}\). According to the influence of different factors on PM\({}_{2.5}\) concentration, the correlation between different variables and PM\({}_{2.5}\) is shown in Figure 3.  
According to the mRMR method, the irrelevant and redundant variables in the original dataset are reduced. The selection result of obtained optimal subset is shown in Figure 4.  
According to the prediction results in Figure 4, when the predictor is with 5 dimensions, the prediction error is the smallest, namely 9.199. Therefore, the optimal subset is PM\({}_{2.5}\), CO, WS, PM\({}_{10}\), and H. The phase space reconstruction is then performed on the selected optimal subset.  
The delay time \(\tau\) and embedding dimension \(m\) calculated by C-C method [33] are shown in Table 1. The 5 variables in the optimal subset are expressed as bold in the table. As shown in Table 1, the delay time for the obtained optimal subset is [8,8,6,4,4], and the embedding dimension is [2,2,2,4,4] for PM\({}_{2.5}\), PM\({}_{10}\), CO, H and WS respectively.
## 2 Experimental Apparatus and Procedure
### Test Procedures  
In order to eliminate environmental noise, we set the DRS threshold to 40 dB, and the ultrasonic wave and AE signals were amplified by 40 dB to ensure a clear signal. In the process of ultrasonic velocity measurement, three ultrasonic wave signals considered of a group; each signal had a 1 s time interval, and the cycle time between groups were set to 30 s. Therefore, 1 s after the first emission by sensor No. 1 in UW-1, sensor No. 2 in UW-1 discharged another emission, and sensor No. 3 in UW-1 launched another signal 2 s after the emission by sensor No. 1; another group of three signals was emitted in the same way 30 s after the emission by sensor No. 3.  
The experimental and data processing procedures were as follows.  
(1) First, UW-1 and UW-2 were set at each end of the rock samples with the same direction, and axial and circumferential strain gauges were placed around the sample to record deformation information. AE receivers were arranged at designed locations to monitor AE activity (Figure 3).  
(2) The loading and data recording systems were turned on at the same time. With increasing loading stress, all information on P-waves, S-waves, and AE activities were recorded. This stage continued until the rock broke.  
(3) After testing, data processing was conducted to analyze the ultrasonic velocity, AE activity, and deformation properties of the salt rock sample.  
Figure 2: Salt samples for testing. (**a**) Rock cutting machine; (**b**) standard cylinder samples with a diameter of 100 mm and a length of 200 mm.
# Kinematics and Statics of the Gough-Stewart Platform
## 2 Geometry of a Gough-Stewart Patform  
The Gough-Stewart platform is composed of a moving platform and a base platform, and they are connected by six identical scalable kinematic chains. Every kinematic chain consists of one spherical pair (S), one universal pair (U), and one prismatic pair (P) as the actuated joint. In this paper, six universal pairs are assembled on the moving platform, and six spherical pairs are connected on the base platform. Six kinematic subchains are distributed uniformly around the \(z\)-axis shown in Figure 1. The origin of the absolute coordinate system is set at the geometric center of the base platform, the \(z\)-axis is perpendicular to the base platform and points to moving platform, the \(x\)-axis is superposed with \(OA_{1}\), and the \(y\)-axis is obtained using the right-hand rule. Suppose the radius of the base platform is \(R\), and the radius of the moving platform is \(r\).
# Fault Location in Double Circuit Medium Power Distribution Networks Using an Impedance-Based Method
## 4 Simulation Results
### Numerical Results
#### 4.2.4 The Effect of Instruments Error  
The accuracy of fault location algorithms depends on different parameters, such as the performance of measurement transformers (CTs, PTs). In this part, according to [13] the instrument transformers were modelled and simulated in MATLAB software according to CT error protection code is 5P20 and PT error class is 0.1%, It is done. The accuracy of the proposed method in different fault locations was analyzed, and the results are shown in Table 5. The obtained results show that the accuracy of the proposed method was high.
_Applied_  
_sciences_  
An Exploration of the Three-Layer Model Including Stakeholders, Markets and Technologies for Assessments of Residential Smart Grids  
**[PERSON] 1,2**, **[PERSON] 3**, **[PERSON] 4**, **[PERSON] 1,*,5**, **[PERSON] 4**, **[PERSON] 5**, **[PERSON] 3**, **[PERSON] 6**, **[PERSON] 7** and **[PERSON] 7**  
1 Department of Design, Production and Management, Faculty of Engineering Technology, University of Wentre, P.O. Box 217, 7500 AE Enschede, The Netherlands; [EMAIL_ADDRESS] or [EMAIL_ADDRESS]  
2 Energy Technology Group at Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands  
3 Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria; [EMAIL_ADDRESS] (S.U.); [EMAIL_ADDRESS] (F.L.)  
4 Copernicus Institute of Sustainable Development, Utrecht University, P.O. Box 80.115, 3508 TC Utrecht, The Netherlands; [PERSON]W.v.S.); [EMAIL_ADDRESS] (W.S.)  
5 Van Hall Larenstein University of Applied Sciences, PO Box 1528, 8901 BV Leeuwarden, The Netherlands;[EMAIL_ADDRESS]  
6 Knowledge, Technology and Innovation, Wageningen University and Research, P.O. Box 8130, 6700 EW Wageningen, The Netherlands; [EMAIL_ADDRESS]  
7 Energy Technology Section, Department of Process and Energy, Delft University of Technology, Leeghwaterstraat 39, 2628 CB Delft, The Netherlands; [EMAIL_ADDRESS] (C.R.); avanwijk@xs4 all.nl (A.v.W.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +31-534897875  
**Abstract:** In this paper, a framework is presented for the evaluation of smart grid environment which is called the three-layer model. This three-layer model comprises three specific categories, or 'layers', namely, the stakeholder, market and technologies layers. Each layer is defined and explored herein, using an extensive literature study regarding their key elements, their descriptions and an overview of the findings from the literature. The assumption behind this study is that a solid understanding of each of the three layers and their interrelations will help in more effective assessment of residential smart grid pilots in order to better design products and services and deploy smart grid technologies in networks. Based on our review, we conclude that, in many studies, social factors associated with smart grid pilots, such as markets, social acceptance, and end-user and stakeholder demands, are most commonly defined as uncertainties and are therefore considered separately from the technical aspects of smart grids. As such, it is recommended that, in future assessments, the stakeholder and market layers should be combined with the technologies layer so as to enhance interaction between these three layers, and to be able to better evaluate residential smart energy systems in a multidisciplinary context.  
**Keywords:** smart grids; electricity market; flexibility; stakeholders; end-users; renewable energy; products and services
# Risk Causal Analysis of Traffic-Intensive Waters Based on Infectious Disease Dynamics
## 2 Methodology
### Screening of Risk Factors in Traffic-Intensive Waters
#### 2.2.2 Risk Factors Affecting Navigation System Safety in Traffic-Intensive Waters  
The establishment of the risk indicator system of ship navigation is helpful in better analyzing the relationship between various risk factors. It is also important to support screening of the key risk factors. The \"4M\" theory of the accident causation theory indicates that the causes of water traffic accidents can be grouped into four categories: man, machine, media, and management [42]. Based on that, the risks of ship navigation in this research are also divided into four categories considering the characteristics in traffic-intensive waters, which are human, environment, ship, and management. The four categories are composed of the following factors with reference to related literature, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Factors** & **Reference** \\ \hline \hline Human & Office duties, office skills & [PERSON] et al. [43], [PERSON] et al. [44], [PERSON] et al. [45], [PERSON] et al. [46], [PERSON] and [PERSON] [47], [PERSON] et al. [48], [PERSON] and [PERSON] [49], [PERSON] and [PERSON] [50], [PERSON] and [PERSON] [51], [PERSON] and [PERSON] [52], [PERSON] et al. [53], [PERSON] et al. [54], [PERSON] et al. [55]. \\ \hline \hline Environment & Meteorology, hydrology, navigation channel, and traffic flow & [PERSON] et al. [43], [PERSON] and [PERSON] [47], [PERSON] et al. [55], [PERSON] [56]. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The risk factors of ship navigation in traffic-intensive waters.  
According to Table 1, the human factors include onboard officer duties and officer skills. The environmental factors include meteorology, hydrology, navigation channel, and traffic flow. Among them, meteorological factors include Beaufort wind scale of greater than or equal to 6 and visibility; traffic flow factors include speed, traffic flow density, ship front and back spacing, and flow rate; hydrological factors include wave class, fluid state, and tide; the channel factors include route complexity and channel water depth. The ship factors include ship age and ship navigation performance. The management factors include normative level of navigation regulations, VTS (vessel traffic service) coordination degree, early warning, and emergency reliability. Finally, the risk index system of ship navigation in traffic-intensive waters is established, as shown in Figure 2.
## 2 Member Discrete Element Method (MDEM)
### Particle Motion Equations and Internal Forces
#### 2.1.2 Particle Internal Forces  
From the discretization procedure of a three-dimension steel frame in Figure 1, it can be found that the establishment of MDEM model is different from that of the conventional DEM model. In the MDEM, the radius of the particles at both ends (\(R_{B}\)) of all the members is first given, and then the radii of the particles in the members (\(R_{A}\)) are determined via equally dividing the remaining lengths of these members. The internal forces arising at the contact points (e.g., Point C) between connected particles (e.g., Particles A and B) need be first calculated, and then they are exerted reversely to these particles (e.g., Particles A and B), thereby acquiring internal forces of an arbitrary particle in Figure 1. The internal force and moment increment at a contact point can be obtained by contact constitutive model, which are  
\[\left\{\begin{array}{c}\Delta f(n){=}\ k\Delta u(n)\\ \Delta m(n)=\mathbf{k}_{\theta}\Delta\theta^{\prime}(n)\end{array}\right., \tag{6}\]  
where \(\Delta f\) and \(\Delta m\) are the incremental internal force and moment of inertia arising at a contact point, respectively; \(\mathbf{k}\) and \(\mathbf{k}_{\theta}\) indicate translational and rotational contact stiffness coefficients, respectively; and \(\Delta\mathbf{u}\) and \(\Delta\theta^{\prime}\) denote pure translational and rotational displacements of a contact point in a time step \(\Delta t\) respectively, which are in the local coordinate system.  
The key point of the DEM is the determination of contact stiffness coefficients (i.e., \(\mathbf{k}\) and \(\mathbf{k}_{\theta}\)) in Equation (6). The coefficients are obtained mainly from experiences or experiments for the conventional DEM, while the MDEM gives the formulas of contact stiffness coefficients. Prof. [PERSON] team [22] supplemented rotational springs into contact constitutive model (Figure 1), which make that the force state of each contact element (i.e., equivalent beam AB in Figure 1) is the same as that of the spatial beam element with three translational and three rotational degrees of freedoms in the FEM.  
According to the strain energy equivalent principle and simple beam theory, the formulas of contact stiffness coefficients for three-dimension member structures can be expressed as [24]  
\[\mathbf{k}=\left[\begin{array}{cc}EA/l&&\\ &12 EI_{y}/l^{3}\\ &&12 EI_{z}/l^{3}\end{array}\right]\qquad\mathbf{k}_{\theta}=\left[\begin{array}{ cc}GI_{x}/l&&\\ &EI_{y}/l&\\ &&EI_{z}/l\end{array}\right]\, \tag{7}\]  
where \(l\) is the distance between the two particle centers; \(A\), \(E\) and \(G\) denote the area, elastic modulus and modulus of shearing of each member, respectively; and \(I_{x}\), \(I_{y}\) and \(I_{z}\) are the cross sectional moments of inertia of a member about the \(x\), \(y\) and \(z\) axes, respectively.
# Classification of Road Surfaces Based on CNN Architecture and Tire Acoustical Signals
## 4 Data Processing  
The measured noise signal must be converted into a proper image form because a CNN was utilized as a classifier in this study. A 1D noise signal is transformed into 2D time-frequency data using a signal processing technique. The 2D data are then mapped to a predefined color map to generate an image. Here, CWT was selected for the 1D to 2D transformation. CWT has a good resolution in both the time and frequency domains, which is essential for revealing the complex properties of TPIN. The better the generated image reflects the characteristics of noise, the better the CNN is likely to extract these characteristics. This is directly related to the good classification performance of the CNN. Images from the CWT were analyzed to determine a set of CWT parameters, which are time, frequency and magnitude of CWT and are independent of the neural network. For this pre-analysis, the length of the data for each case was limited to 6 s for fairness.  
Figure 9 shows scaled colormap images for magnitude of CWT for 3 s data of each test sample. The color bar in right side of image shows scale for sound pressure in each image in \"pa\" unit. The color bar is presented by 128 integers from 0 to 127. Sound pressure is rescaled by 128 integers. For production of input data of CNN, time signal of each sample is truncated into 0.05 s with 80% overlap. CWT for the truncated signal of 0.005 s is performed and a thousand numbers of scaled images in \"pa\" unit per each test sample are generated as listed in Table 1. Since road surface is random during driving, 11,040 images are randomly arranged for the input of CNN. These images are normalized within range \([0,1]\) and transformed to RGB images. This RGB image is composed of 3 layers and is resized to 224 \(\times\) 224 \(\times\) 3 pixels. By comparing snow and asphalt roads in the frequency range of 20-600 Hz, the overall noise level difference appears, as indicated in the frequency spectrum analysis. The TPIN property of the asphalt road was well reflected in the 800-1000 Hz frequency range. The CWT data were scaled and mapped into an RGB color map. The final procedure is image resizing. The dimensions of the final image were 224 \(\times\) 224 \(\times\) 3 pixels in the order of height, width, and channel. An example of the final image is shown in Figure 10. In the case of asphalt, the effect of road texture is emphasized. The component of the low-frequency region was well visualized in the snowy road case.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Front Wheel** & **Tire-A** & **Tire-B** & **Rear Wheel** & **Tire-A** & **Tire-B** \\ \hline Asphalt & 4718 & 6322 & Asphalt & 4718 & 6322 \\ Snow & 11,157 & 7785 & Snow & 11,157 & 7785 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total number of generated images for each test case.  
Figure 8: Power spectral density of TPIN signals measured by microphones.
# Neither Denied nor Exposed: Fixing WebRTC Privacy Leaks
## 1 Introduction
As a result, anyone who has access to this rogue or compromised server is able to obtain the private and the public IP of the user, which is a piece of personal information that may lead to the identification, say, at minimum by means of geolocation, of the end-user. WebRTC technology exists in all modern web browsers, so any security and privacy issues would affect virtually any end-user browsing the web and this is the reason why proposing an effective solution is important. In order to avoid risking their endpoint security, most security-savvy users prefer to disable any WebRTC services. This of course is a temporary and certainly incommodious solution, as real-time communication is an integral part of most messaging platforms, including Messenger and Google Hangouts. Some users even employ a Virtual Private Network (VPN) solution or connect via The Onion Routing (Tor) [7] or the Invisible Internet Project (I2P) [8] anonymity networks, but this may come at the expense of experiencing low quality real-time communication. Furthermore, as detailed in Section 4, the aforementioned solutions are not always effective. A more reasonable solution would inform the user whether the visited website is trying to use the browser's WebRTC capabilities [9], rather than completely disabling it for all websites. Any website that is not related to web communication between users, for instance a news webpage, should probably not try to make any WebRTC requests and thus the user could safely select to block it. To solve the aforementioned vulnerability following a user-awareness approach, we implemented and propose two novel solutions: (a) a browser extension and (b) an intermediate trusted gateway, both able to examine a webpage before it is loaded on the browser and inform the user about any WebRTC actions that the webpage is about to execute. We scrupulously evaluate the proposed schemes in terms of performance and show that the additional delay is most of the times negligible, even for websites with an above average size. The rest of the paper is structured as follows. The next section briefly covers the way STUN and TURN mechanisms work, while Section 3 provides background details on the WebRTC framework. Section 4 provides details on the privacy issue that affects WebRTC by revealing peers' private and public IP addresses. A discussion about how the specific vulnerability can be addressed using two different kinds of solutions is provided in Section 5. Section 6 focuses on implementation issues, while Section 7 presents our results. Section 8 addresses the related work. Finally, the last section concludes and provides some pointers for future work.
# P System Design for Integer Factorization
## 2 Foundation
### Integer Factorization  
Integer decomposition, as a classical mathematical problem, has applications in mathematics, cryptography, and computer science. Finding efficient algorithms has been the goal of research pursuits. Examples are the GNFS, Fermat method, and Shor algorithm.  
Let the greatest common factor of the integers \(m\) and \(n\) be \(\gcd(m,n)\). Evidently, \(\gcd(m,n)\) is a factor of \(n\). For a given positive integer \(N\), the number of integers that are mutually prime with \(N\) and less than \(N\) is written as \(\varphi(N)\) (called the Euler function). We can obtain  
Theorem 1: **([PERSON]'s theorem).**_Suppose 0 < a < N, a and N are mutually prime, then: \(a^{\varphi(N)}\equiv 1(mod\ N)\). i.e., \(a^{\varphi(N)}\%\ N=1\)._  
Let \(f(x)=a^{x}\bmod N\). By Theorem 1 we have: \(f(\varphi(N))\equiv 1\ (\bmod N)\). If there exists an even number \(r\) such that \(f(r)=1\), then we have  
\[(a^{\frac{r}{2}})^{2}-1\equiv 0\bmod N, \tag{2}\]  
i.e.,  
\[(a^{\frac{r}{2}}+1)\ (a^{\frac{r}{2}}-1)\equiv\ 0\bmod N, \tag{3}\]  
when \(a^{\frac{r}{2}}\
eq 1\), \(\gcd(a^{\frac{r}{2}}-1,N)\) and \(\gcd(a^{\frac{r}{2}}+1,N)\) cannot both be 1, from which a factorization of \(N\) can be obtained. From this we can achieve  
Theorem 2: _Suppose 0 < a < N, a and N are relatively prime, \(f(x)=a^{x}\bmod N\). If there is an even number \(r>0\) such that \(f(r)\equiv 1\ (mod\ N)\), then nontrivial factors of N can be obtained by computing \(gcd(a^{\frac{r}{2}}+1,\ N)\) and \(gcd(a^{\frac{r}{2}}-1,\ N)\)._