esa-sceva/satcom-chunk-collection huggingface dataset:
# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
## 6 Conclusions  
In SDN-oriented WAMS communication networks, this paper introduces a novel backup path algorithm, BPDM-GCN, which leverages the GCN enhanced DDPG method. This approach aims to tackle challenges such as ineffective application of existing backup path algorithms for fault link recovery across diverse network topologies and congestion in backup paths. The BPDM-GCN backup path algorithm outputs the comprehensive link weight in the network topology after iterative training based on the improved DDPG algorithm based on GCN. Subsequently, in order to reduce the phasor data transmission delay in the backup path, the comprehensive link weight is employed as the shortest path in order to implement the backup path method based on the incremental shortest path tree. The experimental results demonstrate that the backup path designed by this algorithm exhibits superior performance in terms of low data transmission delay and high failure link recovery success rate.  
The communication network of the wide-area measurement system is divided into two categories: single-link failures and multi-link failures. Given that the probability of large-scale multi-link failures is lower than that of single-link failures, this paper primarily addresses the issue of rapid recovery from single-link failures. In subsequent research, the problem of multi-link failure recovery in the communication network of the wide-area measurement system will be further investigated.  
Conceptualization, W.H. and H.Y.; software, W.H. and Y.L.; validation, W.H., H.Y., and X.H.; formal analysis, W.H. and R.C.; investigation, W.H. and H.Y.; data curation, W.H., Y.L., and X.H.; writing--original draft preparation, W.H. and H.Y.; writing--review and editing, H.Y., Y.L., and X.H.; supervision, R.C.; project administration, H.Y.; funding acquisition, W.H. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Major Science and Technology Programs in Henan Province (No. 241100210100), Henan Provincial Science and Technology Research Project (No.242102211068, No.232102210078), The Special Project for Research and Development in Key areas of Guangdong Province (No.2021 ZDZX1098), The China University Research Innovation Fund (No.2021 FNB3001, No.2022 IT020) and The Stabilization Support Program of Science, Technology and Innovation Commission of Shenzhen Municipality (No.20231128083944001).  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
[PERSON] was employed by the company Henan Jiuyu Tenglong Information Engineering Co., Ltd. The authors declare no conflicts of interest.
## 3 Maintenance Process Modeling Based on SCPN
### Maintenance State and Resource Expression
#### 3.2.2 Expression of Consumable Resources  
Consumable resources are expressed by consumable-resource places. As there are many types of consumable resources in the maintenance process, the model will be very complex if each consumable resource is expressed by an individual consumable-resource place. Therefore, token colors are introduced to express different kinds of consumable resources. In this way, only one consumable-resource place is needed in the maintenance model.  
The elements in the pre-set of consumable-resource places are transitions expressing the maintenance tasks of acquiring the corresponding consumable resources, and the elements in their post-set are transitions expressing maintenance tasks of consuming the corresponding consumable resources. Therefore, the intersection of the pre-set and the post-set of consumable-resource places is also empty.  
Let \(S_{c}=\{s_{c}\}\) be the set of consumable-resource places, their marking and capacity function can be expressed as  
\[\begin{array}{l}M(s_{c})=m_{c1}c_{c1}+m_{c2}c_{c2}+\cdots+m_{cn_{c}}c_{cn_{c}} \\ K(s_{c})=k_{c1}c_{c1}+k_{c2}c_{c2}+\cdots+k_{cn_{c}}c_{cn_{c}}\end{array} \tag{6}\]  
where \(c_{ci}\)\((i=1,2,\cdots,n_{c})\) is the \(i\)th token color expressing the \(i\)th type of consumable resources; \(m_{ci}\) and \(k_{ci}\) are both nonnegative integers, the former represents the number of the \(i\)th token color in the place \(s_{c}\), and the latter represents the allowable maximum number of the \(i\)th token color in the place \(s_{c}\); \(n_{c}\) is the number of token colors used to express consumable resources.
# Assessment of the Impact of Electric Vehicle Batteries in the Non-Linear Control of DC Microgrids
## 3 Methodology
### Integration of the Battery in Microgrid  
The battery model is now connected as a load in the proposed DC microgrid with non-linear control. The output current of the EV battery is depicted in Figure 6. This current load returns back in the connected microgrid model. There is also a look-up table that calculates the battery's internal resistance value, depending on the battery pack output voltage, which was obtained from [33]. Note that the microgrid has three input voltages sources, which are: PV (150 V), a storage battery (350 V), and a supercapacitor (136 V).  
Accordingly, each of the cells is modeled, and then each current will be added together.  
In this case, the assumed \(SOC\) is 50%. Figure 6a shows that the output current is almost constant. It is observed in Figure 6b that the current oscillates between 31.90 A and 32.05 A, which are very close values from the reference value of 32 A.  
Three different values of the output current \(I_{Load}\) were evaluated to assess the impact of the microgrid's output voltage: 12 A, 16 A, and 32 A. These values are selected, since they correspond to the typical currents for charging an EV in Level 1.  
Figure 7 illustrates the output voltage, where the expected final value is 400 V. In all cases, it is observed that there are a few fluctuations of the output before reaching the final value of 400 V. Moreover, note that, with an increase of \(I_{Load}\), the deviation from the reference value increases. This is expected since the microgrid's stability is affected by the fluctuation, in this case of the Load.  
The output voltage stabilizes quickly (less than 0.2 s). The voltage deviations are 0.63%, 0.88%, and 0.93%, for \(I_{Load}=12\) A, 16 A, and 32 A, respectively. For all of the cases, the deviations are minimal, and none of them exceed 1%. Therefore, it can be concluded that the non-linear control works satisfactorily in a DC microgrid when considering the charging of an EV.  
Figure 6: Output current of the EV battery with: (**a**) Plot that shows almost constant current; (**b**) Zoomed-in plot revealing a small current oscillation.
## 4 Simulations Results and Discussions
### Velocity Distribution  
For the sudden change of the nozzle geometry as shown in Figure 4, the high-speed flow passes through the nozzle, which would lead to the rapid fluid changes, and forms velocity vortexes in the direction of the flow. Figure 8 is the vector field of the velocity distribution at the outlet of the nozzle, to the knowledge that it consists of the first and the second shear zone. Figure 8a illustrates the first shear zone, the interaction between the shear layer of the jet and the inner surface of the nozzle exciting the jet shear layer and forms the high energy velocity vortices at the throat of the nozzle; Figure 8b illustrates the second shear zone, when the high-speed jet interacts with the relatively stationary liquid, the formation of the vortexes is caused. Figure 9 is the streamline of the velocity distributions, where the vortexes can be seen clearly in the first shear zone, and much bigger vortexes in the second shear zone, and the stationary vortex adjacent to the hit plate.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**i** & **1** & **2** & **3** & **4** \\ \hline \(f_{e}(i)\) & 2894 & 5992 & 11,960 & 17,880 \\ \(f_{s}(i)\) & 2934 & 4961 & 9942 & 14,900 \\ \(\text{E}a(i)\) & 1.3\% & 17.2\% & 16.88\% & 16.7\% \\ \hline \end{tabular}
\end{table}
Table 3: Frequency comparison between the numerical simulation and experiment of the test.
# Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
## 2 Methodology, Materials and Methods
### Classifier Models
#### 2.5.7 Neural Network Classifier (NN)  
A neural network consists of a series of units (neurons) arranged in layers. This arrangement converts an input vector into some output. To do so, each neuron takes its inputs and calculates the output by applying a usually nonlinear function (the activation function), to later pass the output to the next layer. Generally, neural networks are defined as feed-forward: a unit feeds its output to all the units on the next layer, but there is no feedback to the previous layer. Signals are weighted when fed the input of a unit neuron. The weights are tuned in the training phase of the classifier.  
Neural networks are considered to be good classifiers due to their inherent features, such as adaptive learning, robustness, self-organization and generalization capability.
## 1 Introduction
In contrast, the proposed antenna broadens the CP bandwidth by incorporating symmetrical parasitic elements beneath the magnetoelectric dipole arms to introduce an additional CP operating frequency. Moreover, the grid slots etched on the ground plane are solely utilized to generate radiation nulls and couple energy to the magnetoelectric dipole without being directly excited, thereby avoiding significant back lobe radiation. This paper presents an antenna utilizing two orthogonally placed metallic ME dipoles and a phase delay line to achieve CP radiation characteristics. An inverted L-shaped metallic parasitic element is introduced to create an additional CP operating frequency, thereby expanding the antenna's CP bandwidth. Leveraging the inherent characteristics of the crossed ME dipoles, a radiation null is naturally introduced in the lower stopband. Subsequently, etching grid slots in the metallic ground plane introduces a radiation null in the upper stopband. To further enhance the suppression level in the upper stopband, a pair of open-circuited stubs are loaded on the microstrip feed line, introducing a second radiation null in this band. Ultimately, the proposed antenna achieves a broadband axial ratio (AR) bandwidth (31.9%) and out-of-band suppression levels of 11.5 dB and 26.8 dB on the lower and upper sides of the passband, respectively. The manuscript is organized as follows: Section 2 presents the antenna design methodology and working principles. Section 3 details the implementation process, including parametric studies and performance comparison. Finally, Section 4 summarizes the key findings and discusses potential applications.
# Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation
## 2 Background  
### Clustering-Based Segmentation  
Clustering-based segmentation is a powerful technique for dividing data into distinct groups based on similarities within the data points. This method is particularly useful for analyzing complex signals, such as those generated by mechanical components, where identifying specific frequency bands containing relevant information is crucial for fault diagnosis. The process involves grouping data points that share similar characteristics, such as frequency, amplitude, or other relevant features. These clusters represent distinct segments within the signal, each potentially holding unique information about the system's health.  
Clustering algorithms, like K-means, hierarchical clustering, or density-based clustering, are employed to group data points based on distance or similarity metrics. The choice of algorithm depends on the specific dataset and desired outcome. Once the clusters are formed, each segment can be analyzed independently, allowing for more targeted signal processing and the extraction of meaningful information related to faults or other system anomalies. Clustering-based segmentation, therefore, provides a valuable tool for isolating relevant frequency bands, enhancing signal quality, and ultimately improving the accuracy and efficiency of condition monitoring and fault diagnosis in various applications.
###### Abstract  
Recent progress in Type-II strained layer superlattice (SLS) material systems has offered viable alternatives towards achieving large format, small-pitch, and low-cost focal plane arrays for different military and commercial applications. For focal plane array fabrication, in order to address difficulties associated with mesa-isolation etching or the complex surface treatment/ passivation process, planar structures have been considered. In this work, a comparative study on the recent progress on the planar SLS photodetector using ion-implantation for device isolation is presented. The devices presented here are _nBtn_ and _pBtn_ heterostructure InAs/InAsSb SLS photodetectors, where Zn and Si were chosen as the ion implants, respectively. The electrical and optical performance of the planar devices were compared to each other and with associated mesa-etched fabricated devices, to give a deeper view of the device performance.  
ion implantation; quantum efficiency; ion implantation energy; planar photodetectors; strained layer superlattice +
Footnote †: journal: Applied Physics Letters
# The Method of Production Scheduling with Uncertainties Using the Ants Colony Optimisation
## 3 Basics on Ant Colony Optimization
### Main Transition Rule  
Each ant follows the pseudo--random--proportional rule taking the next step. The rule determines whether the ant is focused on exploration (random path selection) or exploitation (determinism) moving from point r to point s (Equation (8)).  
If an ant is focused on exploration, it does not react to the pheromone trace in its environment. This makes it more likely that the ant will pass over to an area that may be more attractive. If an ant is focused on exploitation, it only goes where it senses the pheromone trail, which makes the paths from the anthill to the food more abundant in pheromone.  
\[S=\left\{\begin{array}{cc}\arg\max\limits_{u\in N_{k}(r)}\{\ \left[\tau(r,u) \right]\cdot\left[\eta(r,u)\right]^{\beta}\right\},&\text{for}\ q\leq q_{0}\\ p_{k}(r,s),&\text{for}\ q>q_{0},\end{array}\right. \tag{8}\]  
where: \(q_{0}\)--a parameter, \(q_{0}\in\left\langle 0,1\right\rangle,\ q\)--a random number from \(\left\langle 0,1\right\rangle,\ \tau\)--size of the pheromone trace on the edge u, between points \(r\) and \(s,\ \eta=\frac{1}{\delta}\)--reciprocal of the distance \(\delta(r,u)\) representing heuristics, \(\beta\)--the parameter of the relative importance between the pheromone trace and the reciprocal of the distance, \(N_{k}(r)\)--the set of those points that ant \(k\) (located at point \(r\)) has not yet visited, \(p_{k}(r,s)\)--a random variable selected according to the formula:  
\[p_{k}(r,s)=\left\{\begin{array}{cc}\frac{\left[\tau(r)\right]\cdot\left[\eta (r,s)\right]^{\beta}}{\sum\limits_{u\in N_{k}(r)}\left[\tau(r,s)\right]\cdot \left[\eta(r,s)\right]^{\beta}},&\text{for}\ s\in N_{k}(r)\\ 0,&\text{for}\ s\
otin N_{k}(r)\end{array}\right. \tag{9}\]  
If parameter \(q\leq q_{0}\), the ant is driven by the desire to exploit already discovered areas. The most attractive point \(s\) for an ant is the one to which the distance from \(r\) is the shortest, and the pheromone value on the path from \(r\) to \(s\) is the highest.  
If parameter \(q>q_{0},\), the ant is driven by the desire to discover new areas--exploration. In this case point \(s\) is a random point from all available points connected to point \(r\). Each ant exploring a new area learns about it, which, if useful, passes on to other ants by means of the pheromone left behind. Any available point can be chosen, not only the best one.  
Appropriate selection of parameter \(q_{0}\) results in the quality improvement of solutions generated by the algorithm. By lowering parameter \(q_{0}\), ants may start to pay too much attention to explore new areas. Already discovered routes leading to the target are quickly forgotten by other ants.  
On the other hand, when parameter \(q_{0}\) is overestimated, it is likely that ants focus on the suboptimal solution. There is not enough ants to explore new areas in search of a new, perhaps better solution.
# Anti-Jamming Power Control Algorithm for Wireless Communication Systems Based on MPC
## 3 Design of the MPC Controller
\right] \tag{24}\]  
Upon substituting Equation (21) into Equation (23), the result is:  
\[J=\left(\mathbf{M}\widetilde{\mathbf{x}}_{k}+\mathbf{P}\widetilde{\mathbf{U} }_{k}\right)^{\mathrm{T}}\overline{\mathbf{Q}}(\mathbf{M}\widetilde{\mathbf{x }}_{k}+\mathbf{P}\widetilde{\mathbf{U}}_{k})+\widetilde{\mathbf{U}}_{k}^{ \mathrm{T}}\overline{\mathbf{R}}\widetilde{\mathbf{U}}_{k} \tag{25}\]  
Subsequent calculations yield further results:  
\[J=\widetilde{\mathbf{x}}_{k}^{\mathrm{T}}\mathbf{M}^{\mathrm{T}}\overline{ \mathbf{Q}}\mathbf{M}\widetilde{\mathbf{x}}_{k}+2\widetilde{\mathbf{x}}_{k}^{ \mathrm{T}}\mathbf{M}^{\mathrm{T}}\overline{\mathbf{Q}}\mathbf{P}\widetilde{ \mathbf{U}}_{k}+\widetilde{\mathbf{U}}_{k}^{\mathrm{T}}\mathbf{P}^{\mathrm{T}} \overline{\mathbf{Q}}\mathbf{P}\widetilde{\mathbf{U}}_{k}+\widetilde{\mathbf{U} }_{k}^{\mathrm{T}}\overline{\mathbf{R}}\widetilde{\mathbf{U}}_{k} \tag{26}\]  
Let \(\mathbf{G}=\mathbf{M}^{\mathrm{T}}\overline{\mathbf{Q}}\mathbf{M}\), \(\mathbf{E}=\mathbf{M}^{\mathrm{T}}\overline{\mathbf{Q}}\mathbf{P}\), \(\mathbf{H}=\mathbf{P}^{\mathrm{T}}\overline{\mathbf{Q}}\mathbf{P}\):  
\[J=\widetilde{\mathbf{x}}_{k}^{\mathrm{T}}\mathbf{G}\widetilde{\mathbf{x}}_{k} +2\widetilde{\mathbf{x}}_{k}^{\mathrm{T}}\mathbf{E}\widetilde{\mathbf{U}}_{k} +\widetilde{\mathbf{U}}_{k}^{\mathrm{T}}\mathbf{H}\widetilde{\mathbf{U}}_{k} \tag{27}\]The variable \(\widetilde{\mathbf{x}}_{\mathbf{k}}^{\mathrm{T}}\widetilde{\mathbf{G}}\widetilde{ \mathbf{x}}_{\mathbf{k}}\) is determined by the initial state and does not affect the optimization outcome. Therefore, the final quadratic performance index is:  
\[J=2\widetilde{x}_{k}^{\mathrm{T}}\mathbf{E}\widetilde{\mathbf{U}}_{k}+ \widetilde{\mathbf{U}}_{k}^{\mathrm{T}}\mathbf{H}\widetilde{\mathbf{U}}_{k} \tag{28}\]  
At this juncture, through the derivation, the control problem has been transformed into a standard quadratic programming form, which can be solved using the Quadprog function in MATLAB 2023a. The resulting control sequence \(U(k)^{*}\) represents the optimal transmission power. In practical control, due to inherent system uncertainties and inevitable measurement errors, only the first element of the optimal control sequence within the prediction horizon is applied to the system. From Equation (28), the predicted transmission power \(\widetilde{U}(k)\), \(\widetilde{U}(k+i)\), \(\widetilde{U}(k+N)\) can be determined by taking its first element. Since variable substitution was previously performed for ease of system analysis, the output of the current power controller for the wireless communication system can be obtained from Equations (8) and (10) as follows:  
\[U(k)^{*}=u_{1}(k)+P_{\mathrm{J}}=\widetilde{U}(1)+P_{\mathrm{J}}+u_{\mathrm{r}} \tag{29}\]  
\[u_{\mathrm{min}}<U(k)^{*}<u_{\mathrm{max}} \tag{30}\]  
where \(u_{\mathrm{min}}\) and \(u_{\mathrm{max}}\) are the lower and upper power limits, respectively.
## 2 Materials and Methods
### Proposed Aggressiveness Detection Models
#### 2.1.4 Word Embedding Approach  
This approach seeks to represent the features vector using the Word Embedding technique. In a similar way as the TF-IDF approach, this is implemented to have a basis for comparison for the rest of the approaches that include Lexicons. Word Embedding is an approach of distribution semantics that represents words of a phrase as real number vectors. This representation has useful grouping properties, as it groups semantically and syntactically similar words. For example, it is expected that the words \"dolphin\" and \"seal\" are found to be close, but \"Paris\" and \"dolphin\" are not, since there is not a strong relationship between them. Therefore, the words are represented as real value vectors, where each value captures a dimension of the meaning of the word. This means that semantically similar words have similar vectors. In other words, each dimension of the  
Figure 4: TF-IDF_Lexicon approach feature vector.  
Figure 3: TF-IDF_Lexicon approach process.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Anger** & **Anticipation** & **Disgust** & **Fear** & **Joy** & **Sadness** & **Surprise** & **Trust** & **BW/NW** & **BW** \\ \hline
56 & 64 & 85 & 64 & 92 & 38 & 68 & 46 & 0.333 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Example feature vector Lexicon approach.  
vectors represents a meaning, and the numerical value in each dimension captures the proximity of the association of the word to said meaning. In [35] was showed the power of Word Embedding. In their work, they establish this tool as being highly effective in different Natural Language Processing tasks, while presenting a neural network architecture that many of the current approaches are based upon.  
Firstly, just as in the previous approaches, a preprocessing of the test is done, filtering special characters (stress marks, punctuation, signs, among others), as well as eliminating stopwords and making a lemmatization of each word depending on the definitive values of the defined hyperparameters. Then, the representation of the feature vector of each text is done using the sum of the Word Embedding vectors of each word present in the phrase. In this way, a vector is obtained that represents the entire text. It is worth stating that, after the sum is made, a standardization of the resulting vector is made. Figure 5 shows, as an example, a vectorial representation of the phrase \"me gustan los gatos\" (I like cats), without standardizing it.  
A pretrained Word Embedding model is used to obtain the features vector from the phrases. This was implemented with FastText and Skipgram [36] and was trained with 1.4 billion words, using the Spanish Billion Word Corpus [37]. Each vector has 300 dimensions; therefore, each text will be represented with a 300-size vector. This vector is received as input for the classification algorithms implemented.
## 2 Applications of ANNs to Renewable Energies
### Applications of ANNs for Wave Prediction
Research using the DNN [124] and RBF [133] has also been found. * Structure of the ANN: most of the studies analyzed use simple structures with a single hidden layer. Research has also been found that uses two hidden layers or even the research of [124], which uses three. The number of neurons in the hidden layer is usually in the order of 10, reaching 300 neurons in the research of [125]. * Amount of data: in most research, the volume of data is in the order of hundreds or thousands. Normally, a major part of the data is used for training, with the remainder applied to testing. The percentage of studies that make use of data for validation is 5%. * I/O configuration: temperature, wind speed, wind direction, and historical wave data are normally used as inputs. Outputs predict wave heights from one hour to 24 h in advance. * Activation function: the activation function is specified in 15 out of the 20 research. In the hidden layer, linear functions are used, with tansig and logsig being the most commonly used, while in the output layer, linear functions of the purelin and sigmoid types are adopted. As a summary of all the previous sections, in the case of renewable energies, the predominant structure chosen is the multilayer perceptron structure with one or two hidden layers, because it may act as a universal function approximator. In addition, together with the backpropagation algorithm, it is able to learn any type of continuous function between a set of input and output variables. Figure 5 details the most common inputs and outputs used by ANNs in wave height prediction and the operating scheme.