esa-sceva/satcom-chunk-collection huggingface dataset:
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 4 Methodology
### Aspects of the Development of Selected Models  
After selecting and engineering the relevant features, XGBoost, LTC, LSTM, and Hybrid models were developed. All four models were refined through Bayesian hyperparameter and architecture tuning to minimize mean squared error (MSE) loss (see Section 4.3 for details). During hyperparameter search and training, each model used a different set of lags (see Table 4).  
LTC as well as LSTM layers were wrapped by additional layers of a fully connected neural network. For the LTC model, the fully connected layer with a tanh activation function, accompanied by a dropout rate of 20% was used. In both models, the output layer is composed of a single neuron. The final LTC and LSTM architectures are presented in Figures 5 and 6. The best results for the XGBoost model were achieved using a Bayesian optimizer, which aimed to minimize the Mean Squared Error (MSE) by identifying optimal parameters within the following ranges: number of estimators [100, 500], learning rate [0.01, 0.3], maximum depth [3, 10], and minimum child weight [0.1, 10]. The resulting best parameters were: learning rate: 0.055, maximum depth: 4, minimum child weight: 1.522, and number of estimators: 2  
XGBoost and Hybrid models were trained using K-Fold cross-validation, a technique that contributed to improved model performance. This method enhances the robustness of training by reducing the likelihood of overfitting to specific subsets of the data. As a result, models trained with K-Fold cross-validation are better equipped to generalize to unseen data, leading to more reliable and accurate predictions.  
Statistical models RWD, ETS, and ARIMAX were trained without a Bayesian optimization step, while ARIMAX passed a multicollinearity check. The _predict_wind_ parameter showed the highest variance-inflation factor and was therefore removed prior to model fitting. Also, to improve the forecast, in all models, the lagged values method (using up to three previous steps) was used.  
Finally, in the Hybrid model (see Figure 7), to combine XGBoost and LTC, a multi-layer perceptron regressor (MLP regressor) was chosen. The MLP Regressor is used to predict wind-generated power values based on several input features, such as predicted outputs from XGBoost and LTC models, along with the predicted wind speed and wind gust.  
Figure 6: LSTM model architecture.  
Figure 7: Hybrid model architecture.  
The MLP network has three hidden layers, with the data features for this model selected manually by testing the accuracy with different feature combinations. The number of neurons and other hyperparameters was chosen using Bayesian optimization.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 2 Low-Voltage Distribution Network Topology
### Communication Logical Topology of the LVPLC  
Using the tree-like physical topology structure as a basis, we must build a logical topology for our communications to improve network stability and robustness. The LVPLC logical topology is composed of a CCo, a repeater, and a terminal. However, the stations are switched in and out as required, and changes occur in both operating and channel states, directly leading to logical topology changes for the LVPLC network. An improved artificial cobweb topology in the LVPLC network is used to improve stability performance.
# Augmented MIMO: Body-Mounted Antennas for Tiny Wearable Devices
## 6 Discussion
### Practical Textile-Based Implementation  
The Augmented MIMO clothing was prototyped using a commercially available 2.8 mm thick coaxial cable with acceptable flexibility for application in clothing. However, considering more realistic use cases where cables are frequently bent, twisted, and stretched, the durability of the cable is a critical issue. In addition, washability is another important factor for daily use.  
From this point of view, the implementation of the system by integrating textile-based signal transmission media into a flexible textile, but not by cables attached outside the clothing, will be explored in future work. Related works found in the literature include coaxial cables [20], microstriplines [21], metamaterial transmission lines [22], and 2-D waveguides [23]. The integration of commercially available ultra-thin coaxial cables into flexible textiles is also an option, as such an implementation with machine washability has been reported [24]. In particular, the development of practical structures that achieve lower losses \(L_{\text{cable}}\) with higher flexibility and durability will be of great interest. The magnitude of loss that can be acceptable is also an essential issue, and is related to the discussion in Section 6.1.
## 2 Overview of SG Operations for Smart Manufacturing
### Distribution Side Management through SGs  
The centralized power generation and one-way power distribution of conventional electrical grids cause problems of excessive energy loss and uneven distribution. The SG makes the controlled electrical network more automated and more effective by installing smart measures and monitoring equipment so as to achieve more efficient, reliable, and environmentally friendly electricity distribution. Recent instances on the distribution side management of electrical grids through SGs are reviewed as follows. [PERSON] et al. [10] proposed a cyclic neural network with stacked gated cyclic units for single-variable and multi-variable actual situations to effectively control and manage the grid. For smart power control and saving, [PERSON] et al. [11] proposed a multivariate neural network model for demand forecasting and electricity price estimation, and showed that their overall accuracy was higher than other univariate forecasting methods. To effectively control electricity demand, [PERSON] et al. [12] installed smart meters to provide measurement, communication, control, display, and synchronization functions, and established a smart energy network that can be effectively managed. To effectively reduce energy demand and carbon emissions, [PERSON] and [PERSON] [13] adopted the control and management of smart meters in the SG based on detailed electricity consumption and price information to plan the energy use. [PERSON] et al. [14] established an energy-saving and reliable SG by installing smart meters in the power system, analyzing the actual power consumption data, and introducing probabilistic load forecasting to better control the uncertainty and volatility of future demand. [PERSON] et al. [15] considered a complex SG system integrated with solar power and renewable energy and provided an AI solution for the stability analysis and control of solar power generation.
# Uncovering Vegetation Changes in the Urban-Rural Interface through Semi-Automatic Methods
## 2 Materials and Methods
### Land Cover  
The land cover map used in this work is the Simplified Land Cover Map (_Carta de Ocupacao do Solo Simplificada_--COSsim) made by DGT using data from 2018. This map was drawn up to be a simplified land cover map that allows its use as a reference in the years between the elaborations of the COS.  
Contrary to COS, COSim is in raster format with spatial resolution of 10 m since it is made upon Sentinel-2 images. The nomenclature, at its most detailed level, has 13 land cover classes. We reclassify these classes in a binary mask representing pixels 'with fuel' (1) or 'without fuel' (0) materials (Table 1). To improve our model predictive capacity, the land cover classes 'without fuel' material were not used, namely: artificial land, agriculture, spontaneous grassland, cork oak and holm oak, bare soil, wetlands and water.  
The decision for using Cork oak (_Quercus suber_ L.) and Holm oak (_Quercus rotundifolia_) class as without fuel material was taken because [37] shows that the total area burned in this type of land cover is small when compared to other types of vegetation. According to the report, the burned areas of Cork oak and Holm oak have low expression when compared to the total amount of burned area in most of the years analysed, except for the years 2003 and 2004, when the rates of burnt area in these types of vegetation were 11.21% and 10.38% respectively.
# Black Ice Classification with Hyperspectral Imaging and Deep Learning
## 4 Methodology  
Table 1 is a detailed model description with the filter's dimensions. To implement the model, the data cube was divided into 3D overlapping patches. During classification, the hyperspectral image data cube was divided into small 3D patches. According to the HybridSN [26] model, the truth labels are decided by the center label's pixels. From X, which is the modified input after dimension reduction, the 3D neighboring patches are P \(\in\) R. The neighboring patches, S \(\times\) S \(\times\) B, were created after implementing traditional Principal Component Analysis (PCA) where S is the window size and B is the reduced number of bands after performing PCA.  
Principal component analysis or PCA uses orthogonal transformation to convert observations of correlated variables into another set of values that have linearly uncorrelated variables, known as principal components. First, the data are standardized so that each spectral band's mean is 0 and standard deviation is 1. After standardizing, the covariance matrix is calculated. This covariance matrix is a square matrix that captures how each feature varies with every other feature. In simple words, the matrix captures the covariance between the bands. After these steps, we calculated the eigenvector and eigenvalues to determine the magnitude and the direction of new space, which helped us to select the informative bands for each dataset. The main reason behind using PCA was to extract the bands containing the majority of the information, thereby aiding in dimension reduction. It helps to reduce the work of dealing with many uninformative bands, potentially improves the model's accuracy and efficiency, and helps eliminate noise.
## Materials and Methods
### Burned Area Mapping Method
#### 2.3.4 Back-Propagation Neural Network  
Back-propagation neural networks have been widely used for the classification of multispectral remote sensing data [58] and confirmed to be valid for smoke identification in the process of fire monitoring [31]. A BPNN can capture the nonlinear relationships between variables in complex systems [59] with low computation memory and time. In this study, a BPNN technique was utilized to learn the spectral differences between the burned area and unburned types from training samples that are generated from the optimal indicators with high distinguishing capacity for the burned area. The BPNN framework includes the input layer, hidden layer and output layer, as visualized in Figure 4. Connections are built between the neurons of neighboring layers while no connections exist between the neurons of one common layer [58]. The input layer presents the feature information of each type to the network, and the data is computed by the hyperbolic tangent sigmoid transfer function in the hidden layer, after which the outputs of the hidden layer are computed by the linear transfer function in the output layer. The computation formulas of the activation function in the hidden layer (Equation (4)) and the activation function in the output layer (Equation (5)) are shown as follows, respectively:  
\[a_{1i}=f_{1}(\sum_{j=1}^{r}\omega_{1 ij}\cdot p_{j}+b_{1i}) \tag{4}\]  
\[a_{2k}=f_{2}(\sum_{i=1}^{s_{1}}\omega_{2 ki}\cdot a_{1i}+b_{2k}) \tag{5}\]  
In these formulas, \(j\), \(i\) and \(k\) index the neurons of the input, hidden and output layers respectively, and \(r\) and \(s_{1}\) are the number of neurons in the hidden and output layers respectively. The \(p_{j}\) are the feature vectors of the input layer. Also, \(\omega_{1 ij}\) and \(\omega_{2 ki}\) represent the connection weights of hidden neuron \(i\) to input neuron \(j\) and output neuron \(k\) to hidden neuron \(i\), respectively; \(b_{1i}\) and \(b_{2k}\) represent the bias of hidden neuron \(i\) and output neuron \(k\), respectively; and \(a_{1i}\) and \(a_{2k}\) are the outputs of hidden neuron \(i\) and output neuron \(k\) respectively. Function\(f_{1}\) is the hyperbolic tangent sigmoid transfer function in the hidden layer and \(f_{2}\) is the linear transfer function in the output layer. The data information spreads forward from the input layer and moves through the hidden layer to the output layer while the error information propagates in reverse to update the weights and biases of neurons between layers based on the back-propagation algorithm [59; 60]. The weights and biases are updated until the value of mean squared error is minimized, which demonstrates that the actual
## 4 Sparse Signal Transmission Framework
### Problem Formulation
information acquisition rate (IAR) as follows, where \(threshold\)denotes the application's need for signal recovery error. For example, in the practical application of structural health monitoring (SHM), the reconstruction error should be less than 30%, so as to satisfy the engineering requirement [35], which means \(threshold=0.3\). \[IAR=\left\{ \begin{array}{cc}1&0<\varepsilon<threshold\\ 0&\varepsilon>threshold\end{array} \right. \tag{11}\]
## 5 Simulation Result Analysis and Discussion  
### Simulation Details  
The given power system model (Figure 7) is simulated using MATLAB/Simulink software to evaluate the performance of the proposed control scheme. The PV array is used to deliver the maximum value of 100 kW at 1000 W/m\({}^{2}\) irradiance; a boost converter is used to increase the PV voltage to 500 V DC; the I-V and P-V characteristics of SPV system are shown in Figure 8; the switching duty cycle is optimized by the MPPT controller under normal conditions; and the VSC converts 500 V DC to 260 V alternating current (AC) and retains the unity power factor, and is connected with the single machine-infinite bus (SMIB) system via step-up transformer. To validate the performance of the proposed LVRT control scheme for the grid connected PV system, the German E.ON grid code (GCC), shown in Figure 3, is taken as a reference.  
Figure 6: The proposed inverter control scheme of reactive power injection.
## 4 Results and Discussion
### Data Preprocess with Statistical Filter  
The raw signal of sliding bearing is contaminated with intensive background noises due to the characteristic of the abnormal state in the early stage, and it is difficult to ensure the diagnostic efficiency and accuracy. Although deep learning has a strong ability to extract useful information through multiple non-linear transformations and approximate complex non-linear functions with little error [25], it is necessary to filter the signal with the statistical filter according to the quality of the practical signal. The whole process can be described as Figure 8.  
As shown in Figure 8, the filter process can be divided into 3 parts. Firstly, the raw signals, including normal signal and one of abnormal signal, are inputted for envelop analysis that is good for the frequency domain transform with FFT. Secondly, the spectrum is divided equally into \(M\) parts for statistical filter. In this section, in order to vividly express the experimental results of this filter process, the value of \(M\) and SDI in the contact rubbing (C) and unbalance (U) are 1536 and 1.2, respectively; the value of \(M\) and SDI in the Oil whirl (O) are 1536 and 0.9, respectively. Moreover, the total number of the data is 24,576 (about 0.24576 s) as an example, and the length of each part is 16 since it is the power  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rotating Speed** & **Number** & **State** & **Description** \\ \hline \multirow{6}{*}{5400(RPM)} & \multirow{6}{*}{1,000,000} & Normal (N) & / \\  & & Contact rubbing (C) & Bolt contact the rotation axis; \\  & & Oil whirl (O) & Rotation frequency is greater than the natural frequency; \\  & & Static unbalance (SU) & One flange is loaded with 1 hammer with masses of 10 g \\  & & Dynamic unbalance (DU) & Two flanges are loaded with 2 hammers with masses of 10 g and angle is 180\({}^{\circ}\); \\ \hline \hline \end{tabular}
\end{table}
Table 1: Introduction of faults in sliding bear.  
Figure 8: Detailed information for filter process.  
exponent of 2. Thirdly, the filter process is performed as shown in Figure 8 and the result is described in Figure 9. It can be seen that the noise in the raw signal can be removed though the statistical filter and the filtered signal can be treated as the input data for the stepwise intelligent fault diagnosis based on SAE to perform fault diagnosis with high precision and efficiency.
## 2 Bernoulli-Euler Beam with Variable Cross-Section
### Boundary Condition and Solution  
For the beam with free ends, the moment and shear force at both ends are zero:  
\[\begin{cases}Y_{J}^{\prime\prime}(0)=0,\ Y_{1}^{\prime\prime\prime}(0)=0\\ Y_{N}^{\prime}(L)=0,\ Y_{N}^{\prime\prime}(L)=0\end{cases} \tag{8}\]  
Substituting Equation (8) into Equation (4), it can be obtained that  
\[B_{1}=D_{1},\quad A_{1}=C_{1} \tag{9}\] \[\Lambda\cdot K_{N}=0\]  
where \(\Lambda=\begin{bmatrix}-sin\beta_{N}l_{N}&-cos\beta_{N}l_{N}&sinh\beta_{N}l_ {N}&cosh\beta_{N}l_{N}\\ -cos\beta_{N}l_{N}&sin\beta_{N}l_{N}&cosh\beta_{N}l_{N}&sinh\beta_{N}l_{N}\\ \end{bmatrix}\).  
Substituting Equation (7) into Equation (9), the boundary equation is obtained:  
\[\begin{bmatrix}\Gamma_{11}+\Gamma_{13}&\Gamma_{12}+\Gamma_{14}\\ \Gamma_{21}+\Gamma_{23}&\Gamma_{22}+\Gamma_{24}\\ \end{bmatrix}\begin{bmatrix}A_{1}\\ B_{1}\\ \end{bmatrix}=0 \tag{10}\]  
where \(\Gamma=\Lambda\cdot Z\).  
The determinant of the coefficient matrix of Equation (10) must be zeros for all non-trivial solutions. Thus, it can be written as  
\[\begin{vmatrix}\Gamma_{11}+\Gamma_{13}&\Gamma_{12}+\Gamma_{14}\\ \Gamma_{21}+\Gamma_{23}&\Gamma_{22}+\Gamma_{24}\\ \end{vmatrix}=0 \tag{11}\]  
The root of Equation (11) is the natural frequency of the crossbeam with variable cross-section. Then, substituting the natural frequency into Equation (10), the mode shape coefficients \(A_{1}\) and \(B_{1}\) of the first segment beam can be solved. By using the transfer relation given in Equation (6), the mode shape coefficient of the whole beam with variable cross-section can be obtained. However, in order to facilitate the modal analysis of the crossbeam, the mass normalization of the mode shape function should be carried out. The modal mass of the beam with variable cross-section is  
\[m=\sum_{i=1}^{N}\int_{0}^{l_{i}}(\rho A)_{i}Y_{i}^{2}(x)dx \tag{12}\]  
The modal shape function of the beam with variable cross-section after the mass normalization is as follows:  
\[Y_{i}(x)=\frac{1}{\sqrt{m}}(A_{i}sin\beta_{i}X_{i}+B_{i}cos\beta_{i}X_{i}+C_{ i}sinh\beta_{i}X_{i}+D_{i}cosh\beta_{i}X_{i}) \tag{13}\]  
As can be seen from the derivation process, the advantage of the analytical method based on the Euler beam theory and transfer matrix method is that it has strong programmability and adaptability for various problems. In actual application, it should be noted that this analytical method is not accurate enough for higher-order modes, due to Bernoulli-Euler beam theory ignoring the influence of shear deformation and the moment of inertia. Fortunately, modal analysis of machine tool crossbeams mainly focuses on the low-order modes, and it is suitable for this.
# Smart Helmet and Insole Sensors for Near Fall Incidence Recognition during Descent of Stairs
## 2 Materials and Methods
### Stair Descent Simulation of Construction Workers
#### 2.2.2 Experiment Procedure  
According to the methods described in previous studies [26; 27], fall detection analyses during the descent of stairs were performed and the experimental protocol simulated a natural descent and near fall-simulated descent. Figure 5 shows a subject performing the experiment protocol with our developed helmet sensor (Smart Helmet) and insole sensor worn inside the given workers' shoes (Smart Shoes). The helmet was designed with a three-axis accelerometer to detect head movements and the insole was made with a capacitive sensor to detect plantar pressure changes.  
While wearing both of the sensors, each subject first stood still for 5 seconds on one leg (on the right leg first, then the left) for the calibration of the insole sensor. This step was repeated three times and the average value was used as the baseline for analysis.  
A total of three experimental (E) conditions were performed by each subject in random order: (1) naturally descending 10 stairs (E1), (2) naturally descending the stairs wearing a backpack weighing 15 kg (E2), and (3) descending the stairs with the backpack on while simulating a near fall (E3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject** & **Sex** & **Age** & **Height (cm)** & **Weight (kg)** & **Shoe Size (mm)** \\ \hline
1 & M & 26 & 181 & 73 & 280 \\
2 & M & 24 & 181 & 93 & 280 \\
3 & M & 27 & 176 & 84 & 280 \\
4 & M & 24 & 175 & 74 & 270 \\
5 & M & 23 & 182 & 83 & 280 \\
6 & M & 25 & 172 & 79 & 270 \\
7 & M & 32 & 182 & 92 & 270 \\
8 & M & 24 & 173 & 76 & 250 \\
9 & M & 24 & 170 & 60 & 265 \\
10 & M & 26 & 172 & 69 & 260 \\ AVG & & 25.5 & 176.4 & 78.3 & 270.5 \\ SD & & 2.59 & 4.70 & 10.18 & 10.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Subject characteristics.  
Figure 5: Experimental protocol, (1) Calibrated baseline, (2) Natural descent (E1), (3) Weighted descent (E2), (4) Near fall-simulated descent (E3).  
For E1 and E2, subjects walked down 10 stairs at a comfortable speed. For E2 and E3, the same weighted backpack was worn on the back and the shoulder straps were adjusted to fit each subject tightly; the bag was not allowed to hang more than two inches below the waistline. The lowest part of the backpack was on L4-L5 (lumbar) spinal segment level of each subject. The difference between E3 and E2 is that E3 moves the center of the body to the right as soon as the right foot hits the surface of the stairs. For E3 (near fall simulation), subjects were asked to step down the stairs on their dominant foot (right side for all 10 subjects) making the line of gravity fall outside the base of support (BOS) to cause instability.  
Stepping down on the dominant side was done so that the subjects would be able to avoid actual falling by taking compensatory actions, as shown below. Each experimental condition was practiced several times for familiarization before the study.
# Analytical Model to Calculate Radial Forces in Permanent-Magnet Synchronous Machines
## 2 Sources of Magnetic Forces in PMSMs
### Magnetic Pressures on the Rotor  
The magnetic pressure on the rotor surface oscillates due to the variation of the flux density that crosses this surface. This variation can be caused by both the magnetic flux created by the winding of the stator and the stator slots.
## 4 Results and Discussion
### Comparison with the State of Art Network
The recognition performance of YOLOv4 is also less than HieD's. The limited number of samples for navy classes negatively affects the recognition level classification. Lastly, the identification performance of HieD is better except for the precision metric. The reason for that is the high number of false positive-2 in HieD, originating from the recognition stage. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Stage/Metric**} & \multicolumn{4}{c}{**Proposed HieD**} & \multicolumn{4}{c}{**YOLO v4**} \\ \cline{2-9}  & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline Detection & 98.59\% & 99.81\% & 98.53\% & 99.17\% & 98.59\% & 99.44\% & 98.89\% & 99.17\% \\ Localization & **87.76\%** & **89.52\%** & **97.69\%** & **93.43\%** & 76.35\% & 86.41\% & 86.77\% & 86.59\% \\ Recognition & **69.44\%** & **70.91\%** & **77.38\%** & **74.00\%** & 60.72\% & 68.72\% & 69.01\% & 68.87\% \\ Identification & **44.27\%** & **59.86\%** & 54.49\% & **57.05\%** & 43.05\% & 45.77\% & **73.03\%** & 56.28\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The performance metrics of HieD and YOLOv4.