esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## 7 Robust Fault Detection Using LSTM-AE  
### Learning Temporal Information  
Deep learning models have continuously advanced over many years. For applications in time-series data research, such approaches as the recurrent neural network (RNN) introduced the idea of a time sequence within a neural network structure design that enables it to handle time-series data analysis [36]. The subsequent development of LSTM cells extended this capability to enable the learning of long-term dependencies through gates that control the learning process [37]. With the option to add or delete information from this cell state, LSTM could then solve the vanishing-gradient problem that occurs in the standard RNN [38]. The cell computes a hidden state, or output vector \(h_{t}\), of the LSTM cell, and then updates the cell state \(c_{t}\) based on the previous cell (\(c_{t-1}\), \(h_{t-1}\)) and the input sequence \(x_{t}\) at time step \(t\). The three gates of the input gate \(i_{t}\), forget gate \(v_{t}\), and output gate \(o_{t}\) are the key components for the LSTM in learning long-term relations through the selection of which information should be kept. These LSTM gates are represented in Equation (9) and illustrated in Figure 4. The \(c_{0}\) and \(h_{0}\) coefficients are initialized with the value of 0, and the operator \(\circ\) represents an element-wise product. The term \(\check{c}_{t}\) is the cell input activation vector, and \(\sigma\) and tanh are the sigmoid and hyperbolic tangent functions, respectively. The training objective is to optimize \(W\), \(R\), and the \(b\) parameter, where \(W\) and \(R\) are the input and recurrent cell weights, respectively, while \(b\) is the bias vector:  
\[\begin{array}{l}v_{t}=\sigma\left(W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\right)\\ i_{t}=\sigma\left(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}\right)\\ o_{t}=\sigma\left(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}\right)\\ \check{c}_{t}=\tanh\left(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}\right)\\ c_{t}=f_{t}\circ c_{t-1}+\dot{i}_{t}\circ\check{c}_{t}\\ h_{t}=o_{t}\circ\tanh\left(c_{t}\right)\end{array} \tag{9}\]
## 2 Underwater Wireless Sensor Network
### Acoustic Link and Its Constraints
#### 2.1.2 High Energy Consumption  
The use of acoustic as a communication medium in UWSNs consumes high energy compared to traditional WSNs using radio frequency as a communication medium [21; 22]. More precisely, the energy consumption in UWSNs for transmission is more than for receiving, and the energy consumption for receiving is much more than that for the idle mode.
# A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm
## 2 System Model and Problem Formulation  
In this framework, we consider the three matters of spectrum allocation issue as suggested in [15]: (a) possible interference to primary users; (b) collision with cognitive users; (c) system utility and the fairness of spectrum access. We build a graph-based mathematical model [16] to demonstrate the spectrum allocation problem, as shown in Figure 1, where \(PUi\) represents the primary user who has the authority using a certain licensed spectrum channel, and \(SUi\) denotes the cognitive user who can only access to'spectrum holes' in an opportunistic manner. Each user has coverage area with radius \(d_{p,m}\) or \(d_{n,m}\) on channel \(m\), where \(d_{p,m}\) indicates the protection radius of the primary user \(p\) on channel \(m\), whereas \(d_{n,m}\) represents the interference radius of the cognitive user \(n\) with access to channel \(m\). \(SUi\) cannot access the licensed band that overlays with \(SUi\) in some area to avoid interfering with primary user, meanwhile, any two cognitive users cannot use the same band if they overlap in some area to diminish collision with each other.  
Figure 1: Graph-based System Model.  
We define a graph \(G(V,E)\), where \(V\) is a set of vertices representing the cognitive users that compete for the licensed spectrum, \(E\) is a set of undirected edges between vertices denoting interference between any two vertices. For any two vertices \(n,k\in V\), an edge exists between \(n\) and \(k\) if \(c_{n,k,m}=1\). The existence of the edge depends on the interference constraint \(C\) (see Section 2.1).
## 4 Implementation and Evaluation of the Mobile Social Music Karaoke System
### Mobility and Availability  
In terms of user mobility and availability, the voice-based interface of the karaoke system is effective in several ways. The significant points are as follows: (i) Enhanced portability and accessibility: the voice-based interfaces, such as the speech recognition technique, liberate users from carrying physical input devices so that users can access the karaoke system at a wider distance due to the hands-free service when they are in motion, at a social gathering, or driving a vehicle. The mobility feature of the setup grants a dynamic and pleasant experience to users; (ii) User convenience: using the voice-based technique of the karaoke system, users can immediately find their favorite songs, control playback, and adjust settings just by giving voice commands. This service is more useful when they are in social places or groups to enjoy music with their smartphones; (iii) Real-time interaction and availability: real-time interaction with the karaoke system is possible due to the voice-based interface. Users can tune to their favorite songs, adjust the volume, and provide feedback without interfering with their activities.  
The quick action of voice-based commands greatly enhances the effectiveness of the presented karaoke system by improving mobility and availability.
## 4 Experiments and Performance Evaluation  
In this section, we present the overall experimental procedure and results of the proposed model, including performance comparisons with mainstream lightweight baseline models, analysis of experimental outcomes, and evaluation of deployment on real devices.
## 2 Model Assumptions
### Energy Consumption Model  
To quantify the energy consumption, this paper refers to the underwater acoustic energy consumption model given in [55]. We assume that the minimum power for one node to receive a data packet is \(P_{0}\). Then the minimum transmission power needs to be \(P_{0}A(l)\). \(A(l)\) is the attenuation function, which is presented by:  
\[A(l)=l^{k}d \tag{1}\]  
where \(l\) is the distance between transmitter node and receiver node and \(k\) is the energy spreading factor (1 for cylindrical, 2 for spherical, 1.5 in general), and  
\[a=10^{a(f)/10} \tag{2}\]  
is decided by the absorption coefficient, which is presented by:  
\[\alpha(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4100+f^{2}}+2.75\times 10^{ -4}f^{2}+0.003 \tag{3}\]  
where \(f\) is carrier frequency in kHz. Then we can define the energy consumption for sending and receiving:  
\[E_{t}(l)=T_{t}P_{0}A(l) \tag{4}\]  
\[E_{r}=T_{r}P_{0} \tag{5}\]  
where \(E_{t}\) (\(l\)) and \(E_{r}\) are energy consumption for transmitting and receiving, respectively. \(T_{t}\) and \(T_{r}\) are the time duration for a node to transmit and receive one data packet, respectively. The time duration can be calculated by the data packet length and the data transmission rate.
## 3 Mathematical Concept and Modeling
### Baseline Methods
#### 3.2.1 Cnn  
In [30], the author proposed a CNN architecture designed for automatic modulation classification. The architecture comprises four convolutional layers followed by two fully connected layers. ReLU is used as the activation function for all hidden layers, while the output layer features ten neurons with a SoftMax activation function to provide the modulation classification results.
# Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model
## 5 Case Study
### MVMD Decomposition  
Next, the relevant weather features and PV power time series are combined for MVMD to obtain frequency-aligned multivariate intrinsic modes. Considering that when the number of decompositions exceeds seven, the IMF central frequencies show too many similarities, and having too many decompositions increases the computational burden on the model, this paper sets the number of MVMD \(K\) to seven. The multivariate mode diagram and FFT frequency diagram are shown in Figures 3 and 4.  
According to Figure 3, PV power, total cloud cover, irradiance, relative humidity, and surface temperature are input as five channels into MVMD for decomposition. Each channel consists of seven IMF features. Unlike VMD, each channel's IMF is a feature decomposition chart of multidimensional data in both time and spatial dimensions. IMF1 is the primary decomposition component of each channel, retaining the main feature information. IMF2 to IMF7 in each channel represent different frequency components, with their frequencies progressively decreasing. They represent lower-frequency components or long-term variations over different time scales.  
As seen in Figures 3 and 4, the same IMF index across different feature channels has a consistent frequency distribution. This indicates that because MVMD can perform multivariate decomposition, the central frequencies of IMF1 to IMF7 obtained from the decomposition of PV power and meteorological factor channels are the same, achieving time-frequency synchronized analysis. This ensures that the prediction model is built at the same central frequency. Therefore, the same IMF index in each channel represents a distinct time scale, providing the model with rich information across different time scales and improving the prediction accuracy for various PV power IMFs.  
Figure 3: IMF chart after MVMD.
## 1 Introduction
Furthermore, the vision errors may be proportional to the length of the light-of-sight as the vision system is inherently angling system [12]. To ensure the accuracy of the vision system, the received GPS signals are utilized to calibrate the vision measurements, so as to reduce the error of the vision system. Then, the calibrated vision measurements are utilized to expand the GPS measurement equation in order to improve the performance integrity monitoring. To the best of our knowledge, studies on vision-aided RAIM in the approach and landing phase are quite rare. This paper aims to improve the RAIM performance with the computer vision system in the low GPS visibility condition. In addition, the method in this paper contains some similarity with the vision navigation, while as mentioned by [PERSON] _et al._[13], most emphasis in the vision navigation is on positioning where the satellites is unavailable. Actually, there are only a few researches on the vision/GPS integration. For example, [PERSON] _et al._ proposed an integrated navigation system that improves the performance of vision-based navigation by integrating the limited GPS measurements in a low GPS visibility condition [14]. Some other applications of the video-based navigation method can be found in [15]. However, they paid comparatively little attention on utilizing the vision system to augment RAIM. The remainder of this paper is organized as follows. The detail of the proposed VA-RAIM approach is presented in Section 2. Section 3 is the experiments to demonstrate the practical utility of our approach. Finally, the conclusions are shown in Section 4.
## 2 Cooperative SCARA System
### Controllers
#### 2.4.1 Nonlinear Model Predictive Controller
 \tag{33}\]  
where  
\(\mathrm{f}(\mathrm{x}(\mathrm{k}),\mathrm{u}(\mathrm{k})):\) System's dynamics. \(\mathrm{g}(\mathrm{x}(\mathrm{k}),\mathrm{u}(\mathrm{k}))\), \(\mathrm{h}(\mathrm{x}(\mathrm{k}),\mathrm{u}(\mathrm{k}))\): Inequality and equality constraints, respectively. Therefore, the complete cost function \(\mathrm{J}\) that the NLMPC seeks to minimize is  
\[ \begin{array}{c}\mathrm{J}(\mathrm{x}(\mathrm{u}),\mathrm{u}) =(\mathrm{x}(\mathrm{N})\ \ -\mathrm{x}_{\mathrm{ref}}(\mathrm{N}))^{\mathrm{T}}\,\mathrm{P}( \mathrm{x}(\mathrm{N})-\mathrm{x}_{\mathrm{ref}}(\mathrm{N}))+\ldots\\ \sum\limits_{\mathrm{K}=0}^{\mathrm{N}-1}(\mathrm{x}(\mathrm{k})\ \ -\mathrm{x}_{ \mathrm{ref}}(\mathrm{k}))^{\mathrm{T}}\,\mathrm{Q}(\mathrm{x}(\mathrm{k})- \mathrm{x}_{\mathrm{ref}}(\mathrm{k}))+\mathrm{u}(\mathrm{k})^{\mathrm{T}}\, \mathrm{Ru}(\mathrm{k})\end{array}  \tag{34}\]  
NLMPC offers several advantages, such as its ability to handle nonlinear dynamics and adjust to changes in the environment or the system state. Continuous optimization ensures that control actions are always optimal and to date, reducing the accumulated error and improving the trajectory tracking accuracy. It is ideal for applications where precision and adaptability are crucial, such as cooperative robotics and complex object manipulation, to ensure that trajectories are safe and efficient, even in the presence of uncertainties and disturbances.
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
## 4 Optimization Methods
### PolySCIP  
The strength of exact algorithms is the guarantee of reaching the global optimum, but the related computational cost can prevent their usage for large size NP-hard problems. Examples of classical exact algorithms are branch and bound, branch and cut, or A*. There exist also commercial exact solvers such as IBM CPLEX [37] and AMPL [59] but to our knowledge, these algorithms and solvers are only able to solve single objective optimization problems. This limitation led to research for multi-objective exact solvers and in 2016, PolySCIP was proposed [58].  
PolySCIP employs a \"Lifted Weight Space Approach\" [58]. This approach first optimizes the objectives lexicographically. The weighted (single objective) optimization problem from the first phase is optimized by using positive weight vectors. This guides the algorithm in exploring the Pareto front in the problem space. If the new non-dominated solution is found, the old solution (the one that has been dominated) is discarded and the process continues until all non-dominated solutions are found. As a result, the outcome is a Pareto front instead of just one solution. The method was proven mathematically in finding all global optima by the authors.
# Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
## 5 Conclusions  
Various machine-learning techniques have been used for customer churn in CRM. This study proposes a customer-churn prediction system based on a stacking ensemble of machine learning, which consists of XGB in level 1; LR, DT, and NBC in level 2; and soft voting. Feature construction is used to expand the feature space and discover latent information from implicit features. The proposed feature construction through feature grouping improves the prediction accuracy compared to the original customer-churn dataset. The result of proposed system showed the best accuracies of 96.12% and 98.09% for the old and new datasets respectively compared to other prediction systems. This proposed system can determine important factors affecting customer purchasing behavior in the telecommunications industry.  
Conceptualization, T.X.; Data curation, T.X.; Formal analysis, T.X.; Funding acquisition, K.K.; Methodology, T.X.; Project administration, K.K.; Resources, K.K.; Software, T.X.;  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-3}  & **ODS** & **NDS** \\ \hline LR & 0.9463 & 0.9585 \\ DT & 0.9493 & 0.9560 \\ NBC & 0.9471 & 0.9535 \\ \hline (ODS: Original dataset, NDS: New dataset). & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy of stacking model with ODS and NDS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-3}  & **ODS** & **NDS** \\ \hline Proposed model & 0.9612 & 0.9809 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Soft voting results for different datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Work**} & **Model** & **Accuracy** \\ \hline [PERSON] [14] & Hybrid genetic programming approach & 0.9140 \\ [PERSON] [15] & AdaBoost & 0.9639 \\ [PERSON] [16] & Hybrid swarm intelligent neural network model & 0.9630 \\ This study & Ensemble system & 0.9809 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison with other works.  
Supervision, K.K.; Validation, Y.M.; Visualization, T.X. and Y.M.; Writing--original draft, T.X.; Writing--review & editing, Y.M. and K.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository. The data presented in this study are openly available in ([[https://www.kaggle.com/becksddf/churn-in-telecoms-dataset](https://www.kaggle.com/becksddf/churn-in-telecoms-dataset)]([https://www.kaggle.com/becksddf/churn-in-telecoms-dataset](https://www.kaggle.com/becksddf/churn-in-telecoms-dataset)), accessed on 21 May 2021) website download. This is a dataset in the book \"Discovering Knowledge in Data\" by [PERSON] [17].  
The authors declare no conflict of interest.
# Modeling Open Channel Flows of a Viscous Fluid: Critical Transition and Apparent Bottom
## 4 Comparison of Surface Heights between NSM and SWM
### Case \(F_{n}=1.75\)  
The last case analyzed here is \(F_{n}=1.75\). The results shown in Figure 5a-c are not much different from those obtained for \(F_{n}=1.25\) but the distance between the bore and the obstacle, in the SWM, is smaller and the height of the bore, higher. This is due to the fact that, for a given flow rate, the higher the Froude number upstream a hydraulic jump, the lower it is downstream. At the obstacle, the motion becomes trans-critical and downstream the equilibrium depth is reached earlier. During the transient simulation, a bore departing from the obstacle travels upstream and stops at a certain distance from it.  
The last case, depicted in Figure 5d is different, the motion is completely super-critical and no hydraulic jump is observed. The free-surface \(Z_{s}\) is parallel to the bottom upstream and downstream and follows the obstacle shape showing the maximum height at the top of the obstacle. This is in agreement with the theoretical critical curve displayed in Figure 1, in which we can see that for \(F_{n}=1.75\) and \(\alpha=0.2\) there are no hydraulic jumps. The NS model results are similar to those obtained for \(F_{n}=1.25\) in terms of \(Z_{s}\) at least. The free-surface profile has the typical super-critical shape, but looking at Figure 6d, we realize that the results deviate significantly from the SWM.  
The free-surface predicted in the NSM, is much lower and wider as compared to the SWM. It looks like the NSM \"sees\" the obstacle wider and lower than what it actually is. The maximum of \(Z_{s}\) does not correspond to the maximum of the obstacle. This was true in the previous case as well, but here it is more evident.
## 3 Results
### Helicity-Dependent Domain Wall Motion (HD-DWM)
#### 3.2.1 Helicity-Dependent Domain Wall Motion with Inverse Faraday Effect  
Our initial configuration contains a down-up DW. In this case, we are not interested in the nucleation of inverted domains so we need to be within the range of \(F\) and \(B_{MO}\) that does not lead to switching. As an example, Figure 7 shows the final position of the DW for \(F~{}=~{}0.3\,\mathrm{J}/\mathrm{m}^{2}\) and \(B_{MO}^{0}~{}=~{}10\,\mathrm{T}\) and for different laser positions and polarizations after 100 pulses. The initial state is also shown for visual comparison. Note that those values of \(F\) and \(B_{MO}\) are in the no-switching range (see Figure 3a), but close enough to have a significant DW displacement.  
Micromagnet simulations lead to similar results (Figure 7) as the experimental observations of Figure 1 in [11]. Indeed, the HD-DWM is achieved when a magneto-optical field \(\overset{\rightarrow}{B}_{MO}\) pointing in the opposite direction than the local magnetization under the laser beam is produced by the proper helicity (Figure 7). In order to emphasize the importance of the laser helicity (i.e., the \(\overset{\rightarrow}{B}_{MO}\) direction), the effect of laser pulses with linear polarization was also evaluated and shown in the bottom snapshots of Figure 7. When the laser pulse is applied, there is a local temperature increase, and the DW motion is favored towards the hotter region (located at the center of the laser beam) by the reduction of the DW energy [32; 38]. In the case of linear polarization (bottom graphs in Figure 7), the DWM caused by thermal gradients is negligible as compared to the one produced by effective field \(\overset{\rightarrow}{B}_{MO}\) due to the circular polarization (see top and central in Figure 7a,b). Therefore, such effective field \(\overset{\rightarrow}{B}_{MO}\) due to the IFE is the main responsible for the DWM for both combinations of the relative laser-DW position and laser helicities (see Figure 7). We conclude that the IFE provides a valid explanation for the HD-DWM observations and that the main cause of the DW displacement originates the presence of the magneto-optical induced effective field \(\overrightarrow{B}_{MO}\), and not by the thermal gradients (note that laser pulses with linear polarization do not produce an observable DW displacement).