esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Model Selection
#### 3.6.3 Random Forest  
Random Forest (RF) is an ensemble supervised learning technique that can be used for classification and regression applications [57]. To understand how RF works, one must first understand how Decision Trees (DT) works. Decision Trees is another machine learning technique that is used for classification and regression. It works as a flowchart, splitting the dataset based on different features at different levels, to ultimately predict the label of a target sample [58].  
Random Forest implements multiple decision trees, where a random subset of features is dropped in each decision tree, thus minimizing the chance of overfitting to the training data. The class label of a sample is predicted by feeding that sample's feature values into each decision tree, obtaining each decision tree's predicted class label, and then taking the majority of votes to assign the output class label [59].  
An RF model's accuracy is based on multiple parameters, namely the number of decision trees, the best-split algorithm, and whether the tree has been pre-pruned or not. Pre-pruning refers to limiting the growth of the trees. This is conducted to further ensure that the model does not overfit. One way of conducting this is by limiting the maximum depth of the trees or by varying certain parameters during training.  
The _n_estimators_ parameter refers to the number of decision trees used in the random forest. The _criterion_ parameter specifies the method of choosing the best feature for splitting the data at each node. We experimented with the _gini_, _log_loss_, and _cross-entropy_ methods for _criterion_ and discovered that the _log_loss_ method results in a better performance. The _max_depth_ and _ccp_alphas_ parameters are used to pre-prune the trees. _max_depth_ refers to the maximum depth of each decision tree in the RF model, whereas _ccp_alphas_ is a constant that specifies which sub-trees are allowed in a singular tree based on their cost complexity. By limiting the depth and complexity of the trees, we can ensure better performance. Table 6 summarizes the different RF models resulting from parameter variations.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Varying Parameter** & **n\_estimators** & **criterion** & **max\_depth** & **ccp\_alphas** \\ \hline RF\_1 & n\_estimators & 1–20 & log\_loss & 6 & 0.1 \\ \hline RF\_2 & max\_depth & 4 & log\_loss & 2–14 & \(N/A\) \\ \hline RF\_3 & ccp\_alphas & 4 & log\_loss & \(N/A\) & 0.0–1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Random Forest models and their respective parameters.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Solver/C** & _C = 0.001_ & _C = 0.01_ & _C = 0.1_ \\ \hline lbfgs & LR\_M1 & LR\_M5 & LR\_M9 \\ \hline liblinear & LR\_M2 & LR\_M6 & LR\_M10 \\ \hline newton-cg & LR\_M3 & LR\_M7 & LR\_M11 \\ \hline newton-cholesky & LR\_M4 & LR\_M8 & LR\_M12 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Logistic Regression models and their parameters.
# Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain
## 4 Results and Discussions  
In this section, in order to demonstrate that the proposed approach is effective, many remote sensing images were tested using MATLAB 2016a. Four existing image enhancement methods were selected to compare with the proposed method, namely histogram equalization (HE) [29], exposure based sub-image histogram equalization (ESIHE) [15], feature-linking model for image enhancement (FLM) [30], and linking synaptic computation for image enhancement (LSCN) [31]. We used subjective and objective aspects to evaluate the image enhancement effects. In terms of the objective metrics, six evaluation metrics were applied, namely information entropy (H) [32], mean square error (MSE) [33], peak signal-to-noise ratio (PSNR) [34], gradient magnitude similarity deviation (GMSD) [35], mean absolute error (MAE) [36], and multi-scale structural similarity (MSSSIM) [37]. For the indicators of H, PSNR, and MSSSIM, larger values demonstrate a better enhancement of the image; for the other three metrics MSE, GMSD, and MAE, lower values demonstrate a better enhancement of the image. The data of the experimental results are shown in Tables 1-4. In this article, the decomposition level of NSCT is 3, and the directions in the scales from coarser to finer are 8, 16, 16. We used the 9-7 filter and dmaxflat7 as the NSPFB filter and NSDFB filter, respectively.  
Figure 4: The flow chart of the proposed method.
## 7 Conclusions  
The paper introduces a novel approach for electrical impedance imaging by combining the convolution graph neural network and LM connection method. This method leverages [PERSON]'s neural network convolution graph to effectively address non-linear issues. The numerical experiments conducted underscore the success of GN graph convolution, despite some constraints in depicting intricacies. Moreover, the proposed technique demonstrates promising viability in attending to practical non-linear inverse functions. In the forthcoming research, our objective is to refine our proposed method and devise new architectures that will augment the excellence of EIT reconstruction images and optimize processing speed.  
EIT is undeniably an invaluable tool for diagnosing pulmonary issues in the future, particularly for patients in the ICU who require constant monitoring. The convenience and bedside applicability of EIT make it especially beneficial in this setting.  
Conceptualization, R.Z. and C.X.; methodology, R.Z.; software, R.Z. and W.M.; validation, R.Z.; investigation, Z.Z.; resources, Z.Z. and W.M.; data curation, R.Z.; writing--original draft preparation, R.Z.; writing--review and editing, R.Z.; visualization, R.Z.; supervision, C.X. and W.M.; project administration, C.X.; funding acquisition, R.Z., C.X., Z.Z. and W.M. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (61967004, 11901137, 11961011, 72061007, 62171147), the Guangxi Key Laboratory of Cryptography and Information Security (GCS201927), and the Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (YQ20113, YQ20114, YQ23015).  
Not applicable.  
The data that support the findings of this study will be available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest and the funders had no role in the design of the study.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **MSE** & **PSNR** & **SSIM** \\ \hline Ours & 1314.36 & 34.7755 & 0.8658 \\ HNN & 938.56 & 33.9552 & 0.8398 \\ Tikhonov & 1073.80 & 34.2260 & 0.8509 \\ TV & 1138.56 & 34.0933 & 0.8465 \\ \hline Ours & 1179.74 & 33.9058 & 0.8594 \\ HNN & 589.62 & 33.8748 & 0.84590 \\ Tikhonov & 1081.04 & 34.0121 & 0.8444 \\ TV & 1156.31 & 33.8860 & 0.8557 \\ \hline \end{tabular}
\end{table}
Table 2: Evaluation indexes of tank experiments.
# Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance Measures
## 2 Kohonen Neural Networks for Clustering Problem  
### Distance Measures  
In clustering problems, the key concept is the concept of distance metrics between objects. Metric is a function that determines the measure of the distance between objects in the metric space \(R_{p}\). Metric space is a set of points with a distance function \(d(x_{i},y_{i})\). The distance of order \(p\) between two points is determined by the Minkowski function (\(l_{p}\)-norm) [1; 43; 44]:  
\[d(x,y)=\left(\sum_{i=1}^{M}\lvert x_{i}-y_{i}\rvert^{p}\right)^{\frac{1}{p}}, \tag{2}\]  
where \(x\) and \(y\) are vectors of parameter values, \(M\) is the vector dimension. The parameter \(p\) is determined by the researcher; it can be used to progressively increase or decrease the weight of the \(i\)-th variable. Special cases of the Minkowski function depend on the \(p\) value. For \(p=2\), the function calculates the Euclidean distance between two points (\(l_{2}\)-norm):  
\[d(x,y)=\sqrt{\sum_{i=1}^{M}(x_{i}-y_{i})^{2}}. \tag{3}\]The squared Euclidean distance is often used:  
\[d(x,y)=\sum_{i=1}^{M}(x_{i}-y_{i})^{2}. \tag{4}\]  
For \(p=1\), the function calculates the Manhattan distance, also called rectangular (\(l_{1}\)-norm):  
\[d(x,y)=\sum_{i=1}^{M}|x_{i}-y_{i}|. \tag{5}\]  
For \(p=\infty\), the function calculates the Chebyshev distance, returning the largest value of the difference between the object parameters modulo:  
\[d(x,y)=\max|x_{i}-y_{i}|. \tag{6}\]  
In addition to the cases of the dependence of the distance function on the parameter \(p\), there are other methods for calculating distances, for example, the Mahalanobis distance [45]. Mahalanobis distance can be defined as a measure of dissimilarity (difference) between vectors from the same probability distribution with the covariance matrix \(C\):  
\[d(x,y)=\sqrt{\sum_{i=1}^{M}\left(x_{i}-y_{i}\right)^{T}\cdot C^{-1}\cdot(x_{i} -y_{i})}. \tag{7}\]  
If the covariance matrix is identity, then the distance becomes equal to Euclidean. The covariance matrix is defined as:  
\[C=\text{cov}(x,y)=\mu[(x-\mu(x))(y-\mu(y))], \tag{8}\]  
where \(\mu\) is expected value. In most cases, the literature presents problems with Euclidean or Manhattan metrics.  
In our study, we use five different types of distance measures to evaluate which one is preferable in our case.
# Profile Control Using Fly Ash Three-Phase Foam Assisted by Microspheres with an Adhesive Coating
## 2 Experimental Study
### Materials and Preparation
#### 2.1.2 Three-Phase Foam  
The compositions of the prepared three-phase foam contain sodium dodecyl sulfate (SDS), modified starch gel system, and consolidation agent (including fly ash and MCL, with a mass ratio of 1:4). The modified starch gel is the grafted copolymer of \(\alpha\)-starch and acrylamide monomer, which has been successfully used to generate high tensile foam [44; 45; 46]. Fly ash with an average size smaller than 15 \(\upmu\)m is provided by HX oil field company, China. The major components of the fly ash are SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\), accounting for over 90 wt.% on the whole.
# Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods
## Abbreviations  
ASIC Application Specific Integrated Circuits  
BMS Battery Management System  
CAVs Connected and Autonomous Vehicles  
CSV Comma Separated Value  
CV Cross Validation  
DL Deep Learning  
DNNs Deep Neural Networks  
DTW Dynamic Time Warping  
EV Electric Vehicle  
FGPA Field-programmable gate array  
FMI Functional Mock-up Interface  
FMU Functional Mock-up Units  
FSD Full Self-Driving  
GPS Global Positioning System  
GPU Graphics Processing Unit  
GRU  
Gated recurrent unit  
MAPE Mean Absolute Percentage Error  
MCU Microcontroller  
ML Machine Learning  
MSE Mean Squared Error  
NEDC New European Driving Cycle  
OBD On Board Diagnostics  
OCV Open circuit voltage  
RDC Real Driving Cycle  
RMSE Root Mean Squared Error  
RNN Recurrent Neural Network  
RRA Redundancy reduction algorithm  
SARIMA Seasonal Autoregressive Integrated Moving Average  
SDC Standard Driving Cycle  
SOC State of Charge  
TA-LSTM Temporal Attention Long Short-Term Memory  
TFLOPS  
Tera floating point operations per second  
V2I Vehicle to Infrastructure  
**Variables**  
\(a\) Angle of the driving surface, rad  
\(\eta_{g}\)  
Gearbox efficiency  
\(\eta_{m}\)  
Efficiency of power consumed (motoring mode)  
\(\eta_{r}\)  
Efficiency of power generated (regenerative braking mode, W  
\(\alpha_{c}\)  
Linear temperature coefficient of the capacity C, K\({}^{-1}\)  
\(\mu_{rr}\)  
Coefficient of rolling resistance  
\(\rho\)  
Density of the air  
\(A\)  
Frontal area of the vehicle, m\({}^{2}\)  
\(a\)  
Acceleration of the vehicle, m/s\({}^{2}\)  
C  
Battery capacity, Ah  
C\({}_{d}\)  
Aerodynamic drag coefficient  
\(E_{R0}\)  
Open-circuit voltage of the battery, V  
\(F_{t}\)  
Traction force of the vehicle, N  
\(F_{ad}\)  
Aerodynamic drag, N
## 2 Materials and Methods
### Design of Tracking Differentiators
#### 2.3.2 Designing a Three-Block Tracking Differentiator
This procedure allows us to accept the maximum possible value of \(p_{1}\). However, in general, this does not provide an absolute minimum of the tracking error estimate (30). If necessary, this figure can be improved by decreasing \(p_{1}\) and increasing \(l_{1}\). As a result, the tracking differentiator (18) with the corrective influence (21) will be realized as a closed-loop system  
\[ \begin{array}{l}\dot{z}_{1}=z_{2},\dot{z}_{2}=z_{3},\\ \dot{z}_{3}=-p_{3}\sigma(l_{3}z_{3})=-p_{3}\sigma(l_{3}(z_{3}+p_{2}\sigma(l_{ 2}(z_{2}+p_{1}\sigma(l_{1}(z_{1}-\chi_{1}))))))).\end{array}  \tag{32}\]  
The dynamic smoothing algorithm (32) along with the gains setting (31) has two universal features. First, it can be used for all wheeled robots whose dynamic model is flat and represented in the form of (2). The peculiarities and constraints of a particular vehicle will be considered when the corresponding constants are substituted into the Equation (31). The values of the corresponding gains \(p_{i},l_{i}\) are calculated at the preparatory stage. They do not depend on the features of the external signal \(\chi_{1}(t)\) and remain unchanged. At different nominal values of (4), the signals \(z_{i}(t)\) will automatically approximate the external signals and their derivatives in the neighborhood of special points in different ways. In the steady-state mode, in the admissible cases, their forms will be almost identical. The results of the corresponding numerical simulations are presented in Section 3. Second, by changing the dimensionality of the tracking differentiator blocks, it is possible to automatically generate smoothed trajectories with defined constraints in phase spaces of different dimensionalities. For example, the system (32) at \(\forall z_{i}\in R\) generates an achievable trajectory for a single-channel control plant (e.g., a single-link manipulator), and at \(\forall z_{i}\in R^{3}\) it generates a spatial trajectory for a UAV [25]. From a computational viewpoint, the dynamic smoothing algorithm (32) is a sigmoid computation with three integration operations. Their implementation is not difficult using any software. The counting time of the algorithm is negligible and does not lead to lag in real-time operation when vector signals \(z_{1}(t)\approx\chi_{1}(t)\), \(z_{2}(t)\approx\dot{\chi}_{1}(t)\), \(z_{3}(t)\approx\ddot{\chi}_{1}(t)\), and \(w(t)\approx\ddot{\chi}_{1}(t)\) are input to the tracking system of the mobile robot as needed.
# Radiometric Identification of Signals by Matched Whitening Transform
## 2 Framework for Radiometric Identification
### Development of a Whitening Measure
All three similarity measures behave monotonically with correlation with the Kullback-Leibler measure virtually coincident with Forstner-Moonen. (**a**) Comparison of Forstner-Moonen, CMD and Kullback-Leibler distance metrics, (**b**) The Forstner-Moonen distance is lower when data are transformed by matched whitening transfom. Every time a whitened block is closer to its own true class as measured by the Forstner-Moonen distance, a 1 is recorded. The rule outputs are then fused in the following way,  
\[C(\mathbf{X}_{i})=mode\Bigg{\{}\sum_{i=1}^{p}h_{1}(\mathbf{X}_{i}),\sum_{i=1}^{p }h_{2}(\mathbf{X}_{i}),\ldots,\sum_{i=1}^{p}h_{m}(\mathbf{X}_{i})\Bigg{\}} \tag{6}\]  
where \(p\) is the number of blocks. The _mode_ function is the number that occurs most often in the set, i.e., \(h_{j}(\mathbf{X}_{i})\) is the number of times \(\mathbf{X}_{i}\) is voted to belong to \(c_{j}\). The unknown measurement \(\mathbf{X}_{i}\) is classified to the class receiving the most votes. This process is pictured in Figure 2. This is an example of \"hard\" voting. The alternative is \"soft\" voting where the frequency of assignments to classes are retained. The computational complexity of the algorithm consists of the whitening matrix, whitening transform and eigenvalue decomposition. If \(X\in\mathcal{R}^{d\times M}\), where \(d\) is the number of variables and \(M\) is the number of measurements, the complexities of the whitening transform is \(\mathcal{O}(d^{2}M+d^{3})\), whitening transform is \(\mathcal{O}(d^{2}M)\) and eigendecomposition is \(\mathcal{O}(d^{3})\). With IQ signal representation, \(d=2\) and it's constant throughout. Therefore, each of the complexities above ultimately reducing the overall complexity to \(\mathcal{O}(M)\). i.e., linear with the number of measurements.
## 7 Joint Virtual Debugging and Running Results  
(1) Execute the PLC program: The connection to the actual PLC equipment is established through the \"Connect PLC\" function block of the IO Server, which enables synchronization between the variables in the PLC program and the software.  
(2) Start the IO Server software: Before establishing a system connection, verify that the IP address and port number in the IO Server's settings match those configured for PQ Factory's connection to the PLC. This step ensures accurate communication. After confirming that there are no discrepancies, launch the engineering application files to establish real-time data acquisition and transmission between PQ Factory and the PLC. Next, initiate the virtual debugging process to simulate the actual operating environment and evaluate the system's performance.  
(3) Operate the digital twin production line: Once the connection between the PLC, IO Server, and PQ Factory is established, proceed with the commissioning and operation of the digital twin production line. Utilize the signal debugging panel to verify device actions by setting or resetting the signals to confirm their functionality. When the PLC connection status in PQ Factory indicates that the system is ready, you may initiate the operation of the digital twin production line. Figure 13 illustrates the joint virtual commissioning status and schematic diagram of the digital twin smart production line for the automotive hub.  
The intelligent production line system for automobile wheel hubs developed in this study and based on digital twin technology enables real-time monitoring and ensures seamless data synchronization between the physical and virtual environments. The IO Server acts as a vital conduit for information exchange. Figure 14 illustrates the synchronized operation of the physical production line and its digital twin counterpart within the automotive hub intelligent digital twin system, clearly demonstrating the system's effectiveness in enhancing production processes.  
This integrated digital twin system significantly enhances the efficiency and accuracy of production monitoring, providing robust technical support along with practical examples to advance intelligent manufacturing. Furthermore, it fosters digital transformation and improves intelligence within the automotive wheel manufacturing sector.  
Figure 13: Joint virtual tuning attempt of automobile wheel hub digital twin intelligent production line.
## Materials and Methods
### Experiments
#### 2.4.6 Adsorption Kinetic Studies  
The kinetic study was conducted at 25, 40, and 70 \({}^{\circ}\)C by mixing 50 mL of \(3\times 10^{-5}\) M CV solution with 10 mg of each adsorbent at 30 rpm, followed by aliquots sampling at 1, 2, 4, 7, 10, 15, 20, 30, and 60 min. Tests were carried out at media pH (6.5-7.5). Samples were then centrifuged at 1550 rpm for 30 min (Precision Scientific CO Ref. 10-T-5, Chicago, IL, USA), passed through a 0.45 \(\upmu\)m filter,followed by absorbance measurements at 590 nm using a UV/VIS spectrophotometer (UV5, Mettler Toledo LabX(r) ready, Lagacher Greifensee, Switzerland). A negative control was carried out with no CV and subjected to the same experimental conditions. The adsorption data were adjusted to the pseudo-first (Laggergren), pseudo-second, and pseudo-third orders, and the Avrami, Bangham, Esquivel, and Weber-Morris models. The pseudo first-order model (Equation (10)) postulates that the speed of occupation of the adsorption sites is related to the number of vacant sites as follows [33]:  
\[Q_{t}=Q_{e}*\left(1-e^{-kt}\right) \tag{10}\]  
where, \(Q_{e}\) is the amount of adsorbate removed at equilibrium (mg/g), \(Q_{t}\) corresponds to the amount of adsorbate removed (mg/g) at time \(t\) (min), and \(K\) is the pseudo-first order equilibrium constant (1/min). In contrast, the pseudo-second order adsorption kinetics assumes that the occupation speed of the adsorption sites is proportional to the square of unoccupied active adsorption sites as follows:  
\[Q_{t}=Q_{e}\left[1-\left(\frac{1}{1+k*t}\right)\right] \tag{11}\]  
where, \(K\) corresponds to the pseudo-second order adsorption constant (mg/g/min) [34]. Likewise, the pseudo-third order adsorption model (Equation (12)) is expressed as:  
\[Q_{t}=Q_{e}\left[1-\left(\frac{1}{\left(1+2k*t\right)^{0.5}}\right)\right] \tag{12}\]  
where, \(K\) corresponds to the pseudo-third order adsorption constant [35]. The Elovich model is expressed by:  
\[Q_{t}=\beta*\ln(\alpha\beta t) \tag{13}\]  
where, \(Q_{t}\) corresponds to the quantity of dye adsorbed at time \(t\) (mg/g), \(\alpha\) is a constant related to chemisorption rate and \(\beta\) is a constant which depicts the extent of surface coverage. The Avrami kinetic model (Equation (14)) has been applied in reaction and crystallization kinetics [36]. This model is expressed as follows:  
\[Q_{t}=Q_{e}*\left[\left(1-e^{-kt}\right)\right]^{n} \tag{14}\]  
where, \(K\) and \(n\) correspond to the Avrami constant and the exponent of time related to the change in mechanism of adsorption, respectively. Further, the Esquivel model (Equation (15)) was expressed by:  
\[Q_{t}=Q_{e}\left[\left(\frac{t}{t+k}\right)\right] \tag{15}\]  
where, \(K\) corresponds to the Esquivel constant. The non-linear fitting was accomplished using the Statgraphics(r) Centurion XVI software using the Marquardt's Percent error function [37]. It is used to analyze the fitting degrees of isotherms and kinetic models with experimental data.