esa-sceva/satcom-chunk-collection huggingface dataset:
# Heart Disease Prediction Using Concatenated Hybrid Ensemble Classifiers
## 3 Proposed Model
### Hyperparameter Settings and Optimization  
To enhance the reproducibility of our results, we present comprehensive details on the best hyperparameter settings for each base classifier incorporated into the proposed Concatenated Hybrid Ensemble Classifier:  
Support Vector Machine (SVM):* C: The regularization parameter managing the compromise between minimizing classification errors and optimizing the margin, with values equal to 0.1.
* gamma: The kernel coefficient in the SVM decision boundary that establishes the weight of a single training example, with values equal to'scale'. Decision Tree:
* max_depth: The maximum depth of the decision tree, limiting the complexity of the tree, with values equal to 5.
* min_samples_split: The minimum number of samples required to split an internal node in the tree, with values equal to 10.  
K-Nearest Neighbors (KNN):
* n_neighbors: The number of nearest neighbors considered for classification, with values equal to 7.
* weights: 'Distance' (weights inversely proportional to distance) and 'Uniform' (equal weights) are the alternatives for the weight function used in prediction, with values equal to 'Uniform'.  
Logistic Regression:
* C: The regularization parameter managing the trade-off between avoiding overfitting and maximizing likelihood, with values equal to one.
* penalty: The type of regularization penalty applied, with options '11' (L1 regularization) and '12' (L2 regularization), with values equal to '12'. AdaBoost:
* learning_rate: A hyperparameter that scales the contribution of each weak learner. Smaller values may prevent overfitting, with values equal to 0.1.
* n_estimators: The number of weak learners (base classifiers) to combine in the ensemble, with values equal to 50.
* Naive Bayes:
* var_smoothing: A smoothing parameter that adds a small value to the variances of features. This helps stabilize the computation of conditional probabilities and prevents issues with zero variances, with values equal to \(1\times 10^{-9}\).  
We employed GridSearchCV, a function from the scikit-learn library, for hyperparameter optimization in fundamental classifiers: SVM, Decision Tree, KNN, Logistic Regression, AdaBoost, and Naive Bayes. Each classifier underwent a five-fold cross-validation methodology, exploring a grid of hyperparameters to identify the optimal settings.
## 3 Results and Discussion
### Dynamic Elastic Modulus  
The calculated damage parameters at different FT cycles and exposure conditions are listed in Table 4. The effects of different FT cycles on the damage parameter of water- and sulfate-exposed samples are shown in Figure 6. For RAC, specimens subjected to water-exposed FT cycles showed a much more severe elastic modulus deterioration at all studied FT cycles when compared to the sulfate-exposed ones. For water- and sulfate-exposed specimens, the loss in elastic modulus (damage parameters) reached their maximum values (24.6% and 21.5%, respectively) after being subjected to 180 FT cycles. For water-exposed specimens subjected to 120 or fewer FT cycles, the damage parameter increasingly grew with FT cycles. However, when subjected to 120 or more cycles, the growth of the damage parameter was in a decreasing manner. Compared to the water-exposed specimens, the increase of the damage parameter of sulfate-exposed ones displayed a contrary tendency.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen** & **FT Cycles** & **D** & \(f_{e}\) **(MPa)** & \(f_{t}\) **(MPa)** & \(G_{F}\) **(J-mm\({}^{-2}\))** \\ \hline
0 (W0/S0) & 0 & 0 & 59.5 & 4.0 & 300.9 \\ W60 & 60 & 0.068 & 56.2 & 3.5 & 437.1 \\ W90 & 90 & 0.109 & 51.1 & 3.2 & 326.7 \\ W120 & 120 & 0.173 & 47.5 & 3.0 & 342.5 \\ W150 & 150 & 0.211 & 44.3 & 2.9 & 309.2 \\ W180 & 180 & 0.246 & 42.4 & 2.6 & 274.1 \\ \hline S60 & 60 & 0.048 & 57.8 & 3.6 & 417.9 \\ S90 & 90 & 0.073 & 53.5 & 3.3 & 386.1 \\ S120 & 120 & 0.092 & 51.2 & 3.1 & 322.2 \\ S150 & 150 & 0.15 & 49.8 & 3.0 & 252.1 \\ S180 & 180 & 0.215 & 46.1 & 2.6 & 254.1 \\ \hline \hline \end{tabular} Note: \(D\) is damage parameter; \(f_{c}\), \(f_{t}\) and \(G_{F}\) are compressive strength, splitting tensile strength and fracture energy, respectively.  
\end{table}
Table 4: Mechanical properties of notched beams subjected to freeze–thaw cycles.  
Figure 5: Changes of solution-filled pore volume of water-exposed (W) and sulfate-exposed (S) RAC samples at different freeze–thaw cycles.  
The more severe deterioration in the elastic modulus was also seen in the specimen exposed to deicing salt solution, which showed a difference in liquid pressure caused by the water and salt solution [21; 24]. Moreover, the study conducted by [PERSON] et al. [21] showed that the sodium chloride in solution tends to decrease the ice formation, and, as a result, the maximum liquid pressure decreases. Here, sodium sulfate may have similar effects on pore pressure with respect to the deicing salt.
# Characterization and Joint Control Study of Pneumatic Artificial Muscles
## 5 Experimental Results and Analysis  
### Analysis of Dynamic Characteristics Experimental Results  
The experimental plots of dynamic characteristics are shown in Figures 22-29, where Figures 22-25 show the variation of pneumatic artificial muscle length with time, and Figures 26-29 show the variation of internal pressure of pneumatic artificial muscle with time. The plot lines with black dots in the figures are the experimental data curves, and the smooth curves are the simulation curves.  
Figure 21: Physical diagram of the pair-pulling experiment.  
Figure 22: Pneumatic artificial muscle length change graph under a load of 1.5 kg and a pressure of 0.3 MPa.  
Figure 23: Pneumatic artificial muscle length change graph under a load of 1.5 kg and a pressure of 0.5 MPa.  
Figure 24: Pneumatic artificial muscle length change graph under a load of 4 kg and a pressure of 0.3 MPa.  
Figure 25: Pneumatic artificial muscle length change graph under a load of 4 kg and a pressure of 0.5 MPa.  
Figure 28: Internal pressure change of pneumatic artificial muscle under a load of 4 kg and a pressure of 0.3 MPa.  
Figure 26: Internal pressure change of pneumatic artificial muscle under a load of 1.5 kg and a pressure of 0.3 MPa.  
Figure 27: Internal pressure change of pneumatic artificial muscle under a load of 1.5 kg and a pressure of 0.5 MPa.  
From Figures 22-25, it can be seen that at 0.3 MPa, there is a big difference between the theoretical model and the actual model, and at 0.5 MPa, the difference between the actual model and the theoretical model is very small. With an increase in pressure inside the pneumatic muscle, the difference between the actual and the theoretical is smaller and smaller. The main reason may be that the elasticity of rubber and the influence of friction between the woven mesh and rubber should be considered when establishing the static model of the pneumatic muscle. Because friction and elasticity will hinder the movement of the pneumatic muscle, the actual change length is smaller than the theoretical change length. In addition, the lower the air pressure, the larger the gap, which indicates that the lower the air pressure, the greater the relative influence of elasticity and friction may also be.  
From Figures 26-29 above, it is easy to see that the actual internal pneumatic muscle pressure change relationship with time is in good agreement with the theoretical model. When the load is 4 kg, the actual curve of pneumatic muscle is under 0.3 MPa, and 0.5 MPa conditions have a slight separation from the theoretical curve, which is due to the friction between the woven mesh and rubber consuming energy, which will slow down the speed of the pneumatic muscle movement, thus slowing down the decline of the internal pressure of the pneumatic muscle. Moreover, the theoretical model does not consider the two factors of elasticity and friction, so there will be some deviation.
# Channel Characteristics and Link Adaption for Visible Light Communication in an Industrial Scenario
## 2 Simulation Setup
### DCO-OFDM System Setup  
In DCO-OFDM, the binary information is first mapped as the M-order quadrature amplitude modulation (QAM). Then, \(N_{p}\) pilots are inserted for channel state information estimation and equalization at the receiver. To ensure that the output of the inverse fast Fourier transforms (IFFT) is real-valued, Hermitian symmetry is added to the signal [24]. After the N-point IFFT operation where the output is denoted by \(x_{s}[N]\), a cyclic prefix with length \(N_{CP}\), greater than or equal to the delay spread of the channel (L), is appended to the beginning of each DCO-OFDM frame and parallel streams are converted to serial. Then, the modulated signal is direct-current (DC)-biased for the constraint of IM/DD and transmitted via LED. The time-domain-received signal at photodetector (PD) is [24]:  
\[y(t)=x_{s}(t)*h_{VLC}(t)+v_{n}(t), \tag{1}\]  
where \(v_{n}(t)\) is the additive White Gaussian noise (AWGN) with zero means and \(\sigma_{N}^{2}=N_{0}W\) variance, where \(N_{0}\) is the noise power spectral density (PSD) and W denotes the system bandwidth; * denotes the convolution operation. \(h_{VLC}(t)\) is the optical channel response obtained by the simulation (the nonlinear effect of LED is ignored here).  
At the receiver, the captured optical signals are converted to parallel streams and the cyclic prefix is removed. Then, the N points fast Fourier transform (FFT) is applied to the output. The least-square channel-estimated method is applied to estimate the VLC channel state information, and channel equalization is used to equalize the effect of multipath channels. The specific TX and RX modules can be observed in [24; 25]. The simulated parameters in this paper are listed in the Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Parameter** & **Value** \\ \hline \multirow{3}{*}{Specifications of scenario} & Dimensions & \(30\times 10\times 8\)  m\({}^{3}\) \\  & Number of sources & 21 \\  & Average height of machine & 2 m \\ \hline \multirow{3}{*}{Coating materials of objects} & Walls, ceiling, and floor & Plaster [18] \\  & Manufacturing equipment & Cu \\  & Assembly lines, forklifts & Al, Fe \\ \hline \multirow{3}{*}{Scatter fraction} & Walls, Ceiling, Floor & 0.8 \\  & Manufacturing equipment & 0.2 [11] \\  & Other objects & 0.5 [11] \\ \hline \multirow{3}{*}{TX} & Model of LED & Lambert \\  & Optical power of each LED & 2 W \\ \cline{1-1}  & Relative minimum Intensity & \(10^{-3}\)[18] \\ \hline \multirow{2}{*}{Channel} & Channel & Delay resolution & 0.2 ns [15] \\ \hline \multirow{2}{*}{RX} & Type of receiver & Detector Polar \\  & Radial size & 10 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of simulation setup.
## 4 Simulation Results
### Results and Discussion  
#### 4.4.1 Channel Estimation Performance  
Figure 2 shows the NMSE as a function of the pilot SNR in both low- and high-IPI regimes. In the low-IPI case at low PSNR, the performance of the proposed variant and the DDIPIC algorithm are comparable, while at higher values of PSNR, the proposed variant outperforms the DDIPIC algorithm, achieving much lower NMSE. On the other hand, in the high-IPI regime, the P-IPIC algorithm achieves an NMSE 1-2 dB lower than the DDIPIC, except for the case of PSNR = 35 dB, where the NSME performances become comparable. We attribute this performance gain to the fact that during the _search phase_, possible paths are detected with the goal of reducing the residue energy, and thus to achieve high accuracy in channel matrix estimate. After that, the good channel estimate obtained in the _search phase_ is used to refine the estimates and declare false alarms. The performance of the threshold method in [10] is also presented in both cases as a baseline for comparison, and it can be noticed they are drastically degraded due to the presence of IPI, since it assumes integer DDs.
## 1 Introduction
These results are qualified as linear dispersion distortions of the UWB EMP shape in a line filled with an isotropic (unmagnetized) plasma, which were also studied analytically and by the methods of numerical FDTD simulation [21]. In this paper, we present the first results of studying experimentally the propagation of a subnanosecond UWB EMP in a GCL filled with quasiuniform magnetized plasma in a wide range of electron densities \(N_{\mathrm{e}}\) (plasma frequencies, \(f_{\mathrm{p}}\)). To the best of our knowledge, this experimental configuration was implemented for the first time. Previously, the propagation of relatively long-duration (several hundred ns) EMPs in a magnetized plasma was experimentally studied under conditions where the pulse excited low-frequency whistler waves only with frequencies below the electron cyclotron frequency, \(f<f_{\mathrm{c}}\)[22]. Here, the EMP duration was chosen to be so short that it allowed us to simulate a wideband response of the magnetized plasma in all physically meaningful frequency (\(f\)) ranges, both above \(f_{\mathrm{p}}\) and below and around \(f_{\mathrm{c}}\). Moreover, the absence of refraction divergence and, correspondingly, the quasiunidimensional propagation of the EMP in a magnetized plasma inside the GCL allows one to obtain additional experimental information about the balance of the energy injected into the plasma in the form of an EMP in terms of the functions (or coefficients) of transmission, reflection, and absorption [23]. When complemented with the theory of coaxial waveguides filled with magnetized plasma, which is developed for quasi-monochromatic signals [24], this approach can be efficient for interpretation of the results of natural experiments and prediction of linear and nonlinear effects during propagation of EMPs from various sources including pulsed radiators and lightning discharges.
## 5 Conclusions  
This paper presents a case study on the mechanical fault diagnosis of HVCBs. The proposed model accurately diagnoses faults without requiring a large amount of data. The GWO-gcForest model outperforms traditional algorithms such as gcForest, GWO-RF, RF, PSO-SVM, and SVM in the case of small and imbalanced samples. The effectiveness of the fault diagnosis algorithms is demonstrated through cases. This provides a valuable method for the field of fault diagnosis.  
A novel model for diagnosing faults in HVCBs on the basis of GWO-gcForest is introduced. The GWO algorithm is used for optimization of the critical parameters of the gcForest model, effectively mitigating random fluctuations in the model's output and enhancing its generalization ability. By leveraging the power of the GWO algorithm, the performance and reliability of the gcForest model are significantly improved.  
Experimental results provide evidence that the proposed approach surpasses gcForest, GWO-RF, and other models mentioned in this study with regard to accuracy. Even when the data sample size is small, the proposed model maintains a high level of accuracy. In addition, it also has good performance under unbalanced sample data, which proves the efficacy and superiority of the method.  
The model provides a feasible solution for fault identification of HVCBs, offering promising application prospects. However, this study was conducted in the laboratory under ideal experimental conditions. Improving the practical application of the project for actual operation in the field is a problem that must be solved in our future work.  
Conceptualization, M.Q.; methodology, Z.X. and Y.W.; software, G.S.; validation, J.W. and Y.G.; formal analysis, Z.Z.; investigation, Z.X.; writing--original draft preparation, Z.X. and J.Y.; writing--review and editing, J.Y.; project administration, J.W. and Y.G.; funding acquisition, J.Y. and Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China grant number [No. 2022 YFB2403700].  
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 and ethical.  
The authors declare no conflicts of interest.  
Figure 11: Confusion radar maps of four methods: (**a**) GWO-gcForest, (**b**) gcForest, (**c**) GWO-RF, and (**d**) PSO-SVM.
## 2 Materials and Methods
### Wavelets Based on the Second Derivative of the Logistic Function  
Let a mother wavelet \(\psi_{2}(x)\) (see Figure 1) be the second derivative of the logistic function \(u(x)=\frac{1}{1+e^{-x}}\). Since \(u^{\prime}(x)=-u(u-1)\), then by (6) or directly we get  
\[u^{\prime\prime}(x)=u(1-u)(1-2u), \tag{10}\]  
and by (10) it follows that the wavelet has the following exact form  
\[\psi_{2}(x)=\frac{1}{1+e^{-x}}\Big{(}1-\frac{1}{1+e^{-x}}\Big{)}\Big{(}1-\frac {2}{1+e^{-x}}\Big{)}=\frac{e^{-2x}-e^{-x}}{(1+e^{-x})^{3}}. \tag{11}\]  
By changing the variable \(u=\frac{1}{1+\exp(-x)},u^{\prime}(x)=u(1-u)\) in the following three integrals we calculate  
\[\int_{-\infty}^{\infty}\psi_{2}(x)dx=\int_{0}^{1}(1-2u)du=0,\] \[\int_{-\infty}^{\infty}|\psi_{2}(x)|dx=\int_{0}^{1}|1-2u|du=\frac {1}{2},\] \[\int_{-\infty}^{\infty}(\psi_{2}(x))^{2}dx=\int_{0}^{1}u(1-u)(1-2 u)^{2}du=\frac{1}{30}, \tag{12}\]which proves that \(\psi_{2}(x)\in L^{1}(\mathbb{R})\cap L^{2}(\mathbb{R})\) (the intersection of the space of integrable functions and the space of square integrable functions defined on the set of real numbers \(\mathbb{R}\)). In fact \(\psi_{2}(x)\in S(\mathbb{R})\) (the space of rapidly decreasing functions on \(\mathbb{R}\)). We will discuss this in the next section.  
The Fourier transform of \(\psi_{2}(x)\) is as follows [24]:  
\[\hat{\psi}_{2}(\tilde{\xi})=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty}\psi_{ 2}(x)e^{-i\tilde{\xi}x}dx=\sqrt{\frac{\pi}{2}}\frac{i\xi^{2}}{\sinh(\pi\xi)}. \tag{13}\]  
It is well known (see Daubechies [25]) that a mother wavelet \(\psi(x)\in L^{1}(\mathbb{R})\cap L^{2}(\mathbb{R})\) should satisfy the following admissibility condition  
\[2\pi\int_{-\infty}^{\infty}|\xi|^{-1}|\hat{\psi}(\tilde{\xi})|^{2}d\tilde{\xi }<\infty. \tag{14}\]  
We will show that for \(\psi_{2}(x)\) the condition (14) is satisfied and even the integral can be expressed in a closed form in terms of the Riemann zeta function. Namely, using (13) and the following formula from Dwight's Tables [26] (item no 860.519):  
\[\int_{0}^{\infty}\frac{x^{p}}{(\sinh(ax))^{2}}dx=\frac{\Gamma(p+1)}{2^{p-1}a^ {p+1}}\xi(p),\quad a>0,\ p>1, \tag{15}\]  
we have  
\[2\pi\int_{-\infty}^{\infty}|\tilde{\xi}|^{-1}|\hat{\psi}_{2}(\tilde{\xi})|^{2 }d\tilde{\xi}=\pi^{2}\int_{-\infty}^{\infty}\frac{|\tilde{\xi}|^{3}}{(\sinh( \pi\xi))^{2}}d\tilde{\xi}=\frac{3\zeta(3)}{\pi^{2}}. \tag{16}\]  
As usual, we generate from \(\psi_{2}\) a doubly-indexed family of wavelets by dilating and translating,  
\[\psi_{2}^{ab}(x)=\frac{1}{\sqrt{a}}\psi_{2}\Big{(}\frac{x-b}{a}\Big{)},\]  
where \(a,b\in\mathbb{R},\ a>0\).
# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 3 Model Introduction
### Lstm  
To address the gradient issues encountered in RNN networks, this paper incorporates the Long Short-Term Memory (LSTM) model. The fundamental unit of the LSTM model comprises one or multiple Cells alongside three adaptive gates: _In-put_, _Forget_, and _Out-put_ (illustrated in Figure 5). These gates facilitate the retention and regulation of information within the network. The gates employ a sigmoid activation function for decision-making, whereas the Input and Cell states typically transform the tanh function [28; 29; 30].  
As depicted in Figure 5, given an input sequence represented as \(X(X_{1},X_{2},\ldots,X_{t})\) and a corresponding sequence of hidden layer states denoted as \(h(h_{1},h_{2},\ldots h_{t})\), the computational formulation is expressed as follows:  
\[i_{t}=sigmoid(W_{xi}X_{t}+W_{hi}h_{t-1})\]  
\[f_{t}=sigmoid(W_{xf}X_{t}+W_{hf}h_{t-1})\]  
\[C_{t}=i_{c}*sigmoid(W_{xc}X_{t}+W_{hc}X_{ht-1})+C_{t-1}*f_{t}\]  
\[o_{t}=sigmoid(W_{x0}X_{t}+W_{co}C_{t}+W_{ho}X_{t-1})\]  
\[h_{t}=o_{t}*sigmoid(C_{t})\]  
While \(*\) represents the Hadamard product, where \(i_{t}\), \(f_{t}\), and \(o_{t}\) represent outputs from various gates. Additionally, \(C_{t}\) denotes the cell state vector, \(h_{t-1}\) signifies the output information of the hidden layer unit from the preceding time step, and \(h_{t}\) represents the updated state of the memory cell. \(W_{hi},W_{xi},W_{hf},W_{xf},W_{xc},W_{hc},W_{x0},W_{ho},\)and \(W_{co}\) are the corresponding weights, and sigmoid is activation function, respectively.  
Figure 4: Input dimension of CNN.  
Figure 5: LSTM network structure.
## 3 Results and Discussion
### Influence of Pulse Frequency  
Figure 5 shows the photographs of holes machined with different values of the frequency, and with the increase of the frequency stray corrosion is clearly reduced. Double-layer charging is present in high-frequency pulsed ECM. Because the charging time is constant, the effective machining time decreases as the pulse frequency is increased. This decreases the material removal rate, which explains why the diameter decreases at a higher pulse frequency. The result concurs with the report that the localization of ECM can be improved by using ultrashort pulse [21]. Consequently, to achieve higher machining accuracy and better processing quality, we select 100 kHz as the frequency.