esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Arc Faults in Transformer Windings via Transient Signal Analysis
## 5 Conclusions  
At the initial phase, the faults that occur in the transformer windings are in the form of minor arcs and become continuous strong arcs, causing a rigid short circuit. The transients that occur during an arc ignition provide important information for the early detection of transformer winding faults. In this study, an attempt is made to predict an arc fault in transformer windings by examining the voltage and current signals measured at transformer terminals through simulations carried out in Matlab and Ansys. These signals are transformed into the frequency domain to distinguish the arc-generated frequencies. In the study, it has been shown that a fault arc can be predicted online by analyzing current and voltage at transformer terminals without the need for the transformer to be out of operation. To detect the fault point, the arc fault is considered at five equally spaced points on the same winding, and it has been observed that as the arc fault moves away from the terminals to the end of the winding, the amplitude and high-frequency component decrease and almost remain constant toward the end of the winding. It has also been shown that the arc-generated frequencies change as the fault point changes. For the arc fault at the point close to the transformer terminal, the arc-generated frequency of the first largest amplitude is 3550 Hz, while it is 2200 Hz for the arc fault at the far end of the winding. To prove that this application is valid for another transformer with different sizes and ratings, additional work is needed. Such a comprehensive study has been targeted as a subject of future research.  
Methodology, F.A. and M.S.M.; Software, F.A. and M.S.M.; Writing--original draft, F.A. and M.S.M.; Writing--review & editing, F.A. and M.S.M. All authors have read and agreed to the published version of the manuscript.  
This study was supported by Scientific and Technological Research Council of Turkey (TUBITAK) under the Grant Number 123E658. The authors thank TUBITAK for their support. The APC of this study was funded by the authors.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Fault Condition** & **1 st Largest Amplitude** & **2 nd Largest Amplitude** \\  & **Frequency (Hz)** & **Frequency (Hz)** \\ \hline Case I & 3550 & 4950 \\ \hline Case II & 2750 & 3700 \\ \hline Case III & 2500 & 3500 \\ \hline Case IV & 2350 & 3250 \\ \hline Case V & 2200 & 3100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Arc generated frequencies of the largest amplitude for several fault conditions.
# Evaluating the Degradation of WE43 for Implant Applications: Optical and Mechanical Insights
###### Abstract  
In the context of an ageing society, advancements in medicine and biomedical technology are becoming increasingly important. A major goal is to minimise the number of surgical operations. Magnesium alloys are gaining attention due to their degradable properties, good biocompatibility, and osteoconductivity. However, for implants made from this material to be usable, a precise understanding of the degradation rate and a correspondingly adapted design must be available. This work focuses on constructing a suitable experimental chamber for degradation analysis, as well as investigating the impact of sample positioning on degradation using two different geometries of WE43 alloy for potential use as osteosynthesis implants. Optical and mechanical tests were carried out on these geometries. The tests revealed that the sample positioning in the experimental chamber affects degradation, with the central position yielding the most suitable results for future applications. In addition, mechanical tests demonstrated reduced mechanical properties in the degradation layer. This work provides an initial basis for further investigations into the use of the WE43 alloy as an osteosynthesis implant and supports the numerical calculation of degradation.  
WE43; degradation; experimental chamber; optical measurements; mechanical properties; degradation layer Article
## 4 Results and Discussion
### Results in FEV Operations  
The array used in input to the script was \(T_{o}=-280:1:280\) Nm. The procedure described in Section 3.1 was carried out for each vehicle speed, whereby the respective optimal operating point was selected after excluding those outside the boundaries of Figure 5 and Table 5. The results of Figures 12 and 13 are related to the best powertrain operating points resulting in the highest global efficiency for each vehicle speed.  
Figure 12: (a) Best global efficiency in FEV operation; (b) battery power in the optimal FEV operating points.  
Figure 13: Electric machines optimal FEV operation, indicated by “X”: (a) MG I functioning points; (b) MG O functioning points.  
Figure 12a shows that the global efficiency in FEV operation is averagely higher than the one achievable in power-split operation, thus the FEV driving is specifically suggested for low-medium speed. As stated in Section 4.1, exploiting the only battery power for propelling the vehicle in steady-state driving at higher speeds would imply a reduced range, thus it should be avoided. Over 145 km/h the FEV driving cannot be realized because the required rotational speed of MG O would overcome its maximum value. Figure 13 shows that the optimal FEV operation in steady-state driving involves the exploitation of MG I except for two limited vehicle ranges from 5 to 15 km/h and from 50 to 60 km/h. From 135 to 145 it is advisable to operate with both electric machines acting as motors.
# Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems
## 5 Evaluation  
### SDFG Random Generator  
To evaluate the advantages of our proposed algorithm, we designed an SDFG random generator. The SDFG generator can randomly generate SDFGs that satisfy the consistency requirement, and then convert the SDFGs to equivalent HSDFGs using the algorithm in the [3]. In the conversion algorithm, how to choose the optimal _unfolding factor_[31] is not a problem we consider, so we only consider the case where the unfolding factor is 1, i.e., only the HSDFG consisting of all instances under a unit period is considered.  
The main modules of our designed SDFG random generator are given in Figure 6, along with the relationships between the modules. The number of randomly generated nodes and directed edges obey a uniform distribution, and only the maximum values of the number of nodes and directed edges need to be set at the APIs. The number of nodes and directed edges are checked to ensure that both satisfy the boundedness of the directed graph. The key function implemented by the randomly generated directed graph module is to randomly construct the adjacency matrix and then do a connectivity test on it. Randomly generated port rates are used to make the SDFG satisfy the consistency requirement; randomly generated initial caches are used to resolve the deadlock situation where the SDFG has a directed loop. The parameters of the SDFG random generator that we use in this evaluation are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **SDFG** \\ \hline Number of SDFG & 958 \\ Number of nodes & \(U(1,50)\) \\ Number of edges & \(U(1,80)\) \\ Port rate & \(U(1,3)\) \\ Execution time & \(U(1,3)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation settings for SDFG random generator.  
Figure 6: SDFG random generator process.
## 7 Discussion
The parameters given in Table 3 were used for the analysis, where additionally, the probability of depassivation was set as \(P_{dep}=0.6\) and the probabilities of damage given depassivation occurred, \(P\) (\(D_{crack}\mid D_{dep}\)), varied from 0.5 to 0.9. Furthermore, the analysis was performed for the unit cost of measurement, i.e., \(C_{meas}=1\). The results are presented in Figure 4, where one can easily notice the decrease of the minimum cost of reactive repair, \(C_{rep,r,min}\), with the increase of the probability of damage, \(P\) (\(D_{crack}\mid D_{dep}\)). However, the decrease is more intensive than in Figure 3, thus emphasizing the high influence of the probabilities of damage once the depassivation occurred, \(P\) (\(D_{crack}\mid D_{dep}\)), to the cost effectiveness of measurement. \begin{table}
\begin{tabular}{c c c} \hline \hline \(P_{dep}\) & \(a_{0}\) & \(a_{1}\) \\ \hline
0.20 & 2.14 & 8.23 \\
0.40 & 1.78 & 4.10 \\
0.60 & 1.59 & 2.74 \\
0.80 & 1.52 & 2.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Obtained constants \(a_{0}\) and \(a_{1}\) in Equations (10) and (11), for different probabilities of depassivation, \(P_{dep}\).
###### Abstract  
This paper verifies the applicability of multiple Global Navigation Satellite Systems (GNSSs) and side lobe signal utilization in Space Service Volume (SSV), especially for Geostationary Earth Orbit (GEO) missions over the Korean region. Unlike the ground or terrestrial systems, various constraints of space exploration in SSV cause a problem when estimating position using GNSS. This is mainly due to the limit of GNSS signal availability where its dominant variables include altitude, side lobe issues, as well as longitude because of different constellations of several GNSS. The numerical simulation shows the effectiveness of additional side lobe signals from multi-GNSS. In addition, the effect of non-MEO satellites' signals in SSV for different longitudes is presented.  
GNSS; Space Service Volume (SSV); Geostationary Orbit (GEO); side lobe signal Article GNSS Signal Availability Analysis in SSV for Geostationary  
1  
H.-H.S.  
Analysis in SSV for Geostationary Satellites Utilizing multi-GNSS with First Side Lobe Signal over the Korean Region. _Remote Sens._**2021**, 13, 3852. [[https://doi.org/10.3390/rs13193852](https://doi.org/10.3390/rs13193852)]([https://doi.org/10.3390/rs13193852](https://doi.org/10.3390/rs13193852))
## 4 Anomaly Detection Method Based on DeepESN
### Deep Echo State Network  
A DeepESN model is shown in Figure 4. The DeepESN [57] is an echo state network model with more reservoir layers.  
Applying the deep learning paradigm [58] to the ESN model, it is possible to exploit multiple hidden states (one for each layer) related to different degrees of abstraction of the processed inputs. All of the reservoir layers are connected sequentially, meaning that the input of a deep reservoir layer is the hidden state of the previous one. All of the hidden states computed by all of the reservoirs are concatenated and passed to the readout for computing the prediction.  
The DeepESN can be formalized by reusing the equations from Section 3.1.2, while the reservoir layers of the DeepESN can be formalized as follows:  
\[\mathbf{h}^{l}(t)=tanh(\mathbf{W}_{x}^{l}\mathbf{h}^{l-1}(t)+\mathbf{W}_{h}^{l} \mathbf{h}^{l}(t-1)+\mathbf{b}_{h}^{l}) \tag{10}\]  
where \(l\) is the number of layers, \(\mathbf{h}^{0}(t)=\mathbf{x}(t)\), and the hidden state of layer zero is the input time series. The readout maps the concatenation of the collection of all \(\{\mathbf{h}^{l}(t)|\,l\in[1,L]\}\) into the output prediction. It can be formalized as follows:  
\[\mathbf{o}(t)=\mathbf{W}_{o}\Big{[}\mathbf{h}^{1}(t),\cdots,\mathbf{h}^{L}(t )\Big{]}+\mathbf{b}_{o} \tag{11}\]  
where \(\mathbf{W}_{o}\in\mathbb{R}^{N_{b}\times L\cdot N_{h}}\), and the square parentheses represent the concatenation of the hidden state vectors in a unique vector with size \(L\cdot N_{h}\). The ridge regression formula is the same as (3), where the only change is in terms of size, i.e., \(\mathbf{H}\in\mathbb{R}^{N_{a}\times L\cdot N_{h}}\).
## 2 Protocol Description
### Definition of the Sensorization, Computing, and Communication Configuration for ADL Monitoring  
Following the selection of ADLs, the protocol requires the definition of the optimal configuration of sensors, data acquisition methods, and required computational and communication equipment (the flowchart for this phase is shown in Figure 1). This includes the type of sensors (e.g., depth sensors, audio sensors, and wearable devices), their placement (considering the details of the space where the functional assessment will take place), and the data they will collect (including required data throughput and storage requirements).  
The configuration should capture the detailed information on the functional parameters, environmental interactions, and physiological parameters during the performance of ADLs. A key goal is to ensure that the data collected are robust, reliable, and capable of supporting the objective assessment of functional capacity. Coordination between the clinical and engineering teams is essential in this task, as clinics will need to adapt and modify the proposed activities based on the characteristics and functions of the selected sensors together with the engineers, as well as their placement.  
Special attention should be devoted to communication and storage specifications, as they will be crucial to avoid problems during the data acquisition efforts.
## 2 Feature Selection Using Radial Chebyshev Moment Invariants  
### Pre-Processing of Candidate Image  
In the pre-processing phase, the potential flame pixels are segmented from candidate images. Most of the approaches on flame pixels detection are based on color information and the relationship between the different components. In general, the colors of a flame region are between red and yellow. As a result, the RGB color space is widely used. However, the methods based on RGB color space are easily affected by flame color-like objects, such as the setting sun, red flags and so on, increasing the false alarm rate.  
As is commonly known, the flame region is generally brighter than the neighborhood in the observed scene. This visual property can be used to detect the existence of flame pixels. YCbCr color space has the ability to separate the luminance from the chrominance, which is more effective than other color spaces such as RGB [31; 32]. Meanwhile, most of the image acquisition devices provide output in RGB color space. According to the ITU-R BT.601 standards for standard-definition television, YCbCr color space can be derived from RGB color space as follows [33].  
As the brightest region in the scene, the luminance of a flame region should be greater than the mean of luminance of all the pixels. On the other hand, the Chrominance Blue should be less than the values of luminance and Chrominance Red. In our case studies, the rules to detect flame pixels are proposed as follows:  
\[\begin{split} I(x,y)=\left\{\begin{array}{l}1,\,if\left\{ \begin{array}{l}Y(x,y)>1.2Y_{mean}\\ Y(x,y)>Cb(x,y)\\ Cr(x,y)>Cb(x,y)\end{array}\right.\\ 0,\,otherwise\end{array}\right.\\ Y_{mean}=\frac{1}{5}\sum\limits_{i=1}^{S}Y(x_{i},y_{i})\end{split} \tag{1}\]  
where \(I(x,y)\) is a binary image representing the existence of flame in a pixel location \((x,y)\) by 1 and non-existence by 0, \(Y_{mean}\) is the mean of luminance of all the pixels, \(S\) is the total number of all the pixels in a frame image.  
Furthermore, it is noted that the shape feature of a flame is composed of external contour and internal region. The former is the local feature, containing finer details of the shape. The latter is the global feature, containing general features of the shape. However, as region-based descriptors,image moments generally just use all the pixels within a shape. In order to take advantage of local features, the Canny edge detector [34] is used to extract the potential flame contour from pre-processed images \(I(x,y)\). Finally, the edge images \(I_{e}(x,y)\) are also generated, which contribute to obtaining the boundary-based descriptors.
# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 4 Conclusions and Remarks
Future research could focus on developing diverse forms of satisfaction and dissatisfaction functions to better represent individual characteristics and preferences, thereby enhancing the accuracy of offer evaluations. 4. The adaptation and learning in real time. Negotiation agents often struggle to adapt to the changing preferences and behaviors of their counterparts in real time. The authors propose a methodology based on Bayesian approaches and Unscented Particle Filtering (UPF) for opponent learning in real-time automated negotiation. This approach enables negotiation agents to dynamically estimate their counterparts' preference profiles and adjust their negotiation strategies accordingly. Future research could explore further enhancements to this methodology, such as leveraging natural language processing techniques for more accurate initial approximations of opponents' preferences. 5. The optimization in Energy Markets. Negotiation frameworks applied in energy markets face challenges such as maximizing utility for both trading parties while ensuring speed in reaching agreements. The authors propose various optimization algorithms, such as Multi-Objective Optimization and Particle Swarm Optimization (PSO) variants, to address these challenges. Future research could focus on analyzing the optimality and convergence of these algorithms further, as well as exploring new prospects for their application in energy markets. Overall, the authors advocate for the integration of advanced AI techniques, such as FL, Bayesian approaches, and optimization algorithms, into negotiation frameworks to overcome existing limitations and improve negotiation outcomes in various domains. Generally, by integrating flexible DR, RTP mechanisms, bi-level optimization approaches, and intelligent negotiation methods, prosumers, and the grid can optimize energy transaction negotiations, maximize economic benefits, and enhance the efficiency and reliability of the energy system.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 2 System Overview
### Description
#### 2.1.1 Rank Metric Mapping  
In the multi-carrier transmission system, the signals can be \"naturally\" represented in a matrix system, where each column is used to generate an OFDM symbol. Let us define a vector \(\mathcal{B}\) of elements in the finite field \(\mathbb{F}_{q}^{N}\), \(\mathcal{B}=(b_{1},b_{2},\ldots,b_{n})\) the signal to be sent. \(\mathbb{F}_{q}^{N}\) can be considered as a vector over the space \(\mathbb{F}_{q}\). Let us call \(\mathcal{A}\) a base of \(\mathbb{F}_{q}^{N}\) over \(\mathbb{F}_{q}\). We can represent vector \(\mathcal{B}\) as a matrix \(B\) with entries in the finite field \(\mathbb{F}_{q}\) by decomposing the elements \(b_{i}\) of \(\mathcal{B}\) following the base \(\mathcal{A}\).  
\[B=\left[\begin{bmatrix}b_{11}\\ b_{21}\\ \vdots\\ b_{N1}\end{bmatrix}\begin{bmatrix}b_{12}\\ b_{22}\\ \vdots\\ b_{N2}\end{bmatrix}\cdots\begin{bmatrix}b_{1n}\\ b_{2n}\\ \vdots\\ b_{Nn}\end{bmatrix}\right] \tag{1}\]  
Figure 1: The physical layer overview.  
Since a binary modulation will be used, \(q=2\). By using an RC defined on \(\mathbb{F}_{q}^{N}\), a code vector can be expressed as follows:  
\[v=x\times G=(v_{1},v_{2},\ldots,v_{n}) \tag{2}\]  
where \(x\) is the message to sent and \(G\) is the generator matrix. Thus, \(v\) can be represented as a matrix in the finite field \(\mathbb{F}_{2}\) as follows:  
\[V=\begin{pmatrix}v_{1,1}&v_{1,2}&\cdots&v_{1,n}\\ v_{2,1}&v_{2,2}&\cdots&v_{2,n}\\ \vdots&\vdots&\vdots&\vdots\\ v_{N,1}&v_{N,2}&\cdots&v_{N,n}\end{pmatrix} \tag{3}\]  
The elements of the matrix \(V\) are then transposed into a serial format in order to prepare them for the convolutional encoding. A Binary Phase Shift Keying (BPSK) is applied to ensure transmission of the coded sequence with an effective bandwidth.
## 2 Interactive BM Segmentation Based on Supervised Learning Models
### Supervised Classification of Pathological and Healthy Tissues
#### 2.2.1 BM Delineation Using SVM  
SVM is a classification algorithm based on kernel methods [17; 38] mapping the input patters into a high dimensional feature space. Classes which are non-linearly separable in the original space can be linearly separated in the higher dimensional feature space.  
Let \(\{(x_{i},y_{i})\}\) be a supervised training set of elements for a two-class classification problem, with \(x_{i}\in X\subseteq R^{n}\) and \(y_{i}\in\{-1,1\}\). Considering the case of linearly separable data, the solution to the classification problem consists of the construction of the decision function:  
\[f(x)=sgn(g(x))with \tag{1}\]  
\[g(x)=w^{t}x+b \tag{2}\]  
that can correctly classify an input pattern \(x\) not necessarily belonging to the training set.  
SVM classifier defines the hyperplane that causes the largest separation between the decision function values for the \"borderline\" examples from the two classes. This hyperplane can be found by minimising the cost function, as follow:  
\[J(W)=\frac{1}{2}\|W\|^{2}\quad\text{subject to} \tag{3}\]  
\[W^{T}X_{i}+b\geq+1\text{ }for\text{ }y_{i}=+1 \tag{4}\]  
\[\text{or }W^{T}X_{i}+b\leq-1\text{ }for\text{ }y_{i}=-1 \tag{5}\]  
The extension to the nonlinear classification is based on the function \(g^{\prime}=W^{T}\varphi(X)+b\), in which the non-linear operator \(\varphi(.)\) hyperparameters.  
In this case, the SVM cost function to be minimized is  
\[J(W,\xi)=\frac{1}{2}\|W\|^{2}+C\sum_{i=1}^{l}\xi_{i} \tag{6}\]  
\[y_{i}\Big{(}w^{t}\varphi(X_{i})+b\Big{)}\geq+1-\xi_{i}\text{ with }\quad\xi_{i}\geq 0,\ i=1,\ 2,\ \ldots,\ l \tag{7}\]  
[PERSON] [18] proposed a new formulation of SVM by adding an LS term in the original formulation of the cost function. This modification significantly reduces computational complexity.  
The trained SVM classifier receives in input multidimensional patterns, in the form of vectors of measured features and assigns labels to corresponding T1c MR elements. Multidimensional input patterns are composed of T1c voxel intensities and corresponding textural and contextual features extracted from the MR scan. The literature proposes different sets of features related to the supervised classification of MRI data. Features are selected in function of the MRI channels used, and the classifiers adopted [3; 10]. Based on our previous works, in addition to image intensities from the T1c MR scan, we consider features describing neighbour relationships and texture [39]. The feature set adopted is described in the next section. As detailed in Section 3, several strategies were conceived to conduct the learning stage. Results obtained were analysed, and the resulting optimal configuration has been used in the comparison analysis.
# On the Performance of LDPC-Coded Large Intelligent Antenna System
## 3 System and Signal Model for the Receivers
### Linear Feedforward Non-Iterative FDE Receivers
#### 3.1.3 Maximum Ratio Combining  
It is proposed to use sub-optimal equalization techniques that do not require channel matrices' inversions and are thus easier to implement. Maximum ratio detection has the best performance of all the combining schemes because the set of possible combining vectors is not limited. Less complicated receivers are often used in massive MIMO systems, being also well suited for LIS systems. Undoubtedly, the ones constructed using the MRC [21] are the most well liked.  
Using the MRC algorithm, \(F_{k}\) becomes:  
\[F_{k}=H_{k}^{H} \tag{8}\]