esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Relevant Basic Theories
#### 2.2.3 Ridge Regression  
Ridge Regression (RR) is an extension of linear regression. The advantage of ridge regression lies in its unbiased estimation, which tends more towards shrinking some coefficients towards zero. This characteristic makes it suitable for dealing with multicollinearity and overfitting problems [35]. In traditional linear regression, a high correlation among independent variables leads to increased variance in parameter estimates. This results in model instability. Ridge regression addresses these issues by adding a regularization term to the loss function, effectively mitigating these problems. The loss function of ridge regression can be expressed as follows:  
\[\begin{split}\mathcal{L}&=\sum\limits_{i=1}^{n} {(y_{i}-\hat{y_{i}})^{2}+\lambda\sum\limits_{j=1}^{p}{\beta_{j}^{2}}}\\ &=\sum\limits_{i=1}^{n}{(y_{i}-\hat{y_{i}})^{2}+\lambda\|\hat{p} \|^{2}}\end{split} \tag{15}\]In the formula, \(\mathcal{L}\) denotes the loss function, \(y_{i}\) refers to the actual value of the ith observation, \(\hat{y_{i}}\) indicates the predicted value of the ith observation, \(\beta_{j}\) represents the regression coefficient for the jth feature, \(p\) stands for the number of features, \(\lambda\) is the regularization parameter in ridge regression, which governs the effect of the regularization term on the model, and \(\lambda\|\beta\|^{2}\) signifies the L2 norm of regularization.  
In ridge regression, our goal is to minimize the loss function. The regularization term \(\lambda\|\beta\|^{2}\) acts as a penalty on the regression coefficients, constraining their magnitudes and thus reducing model complexity. The choice of the parameter \(\lambda\) is crucial, as it determines the strength of the regularization term. A larger \(\lambda\) results in smaller regression coefficients, thereby lowering model complexity and reducing the risk of overfitting. Ridge regression is typically solved using a variant of the Ordinary Least Squares (OLS) method, known as Ridge Estimation, which has a closed-form solution:  
\[\hat{\beta}=(X^{T}X+\lambda I)^{-1}X^{T}y \tag{16}\]  
In the equation, \(X\) represents the design matrix, containing all the independent variables, \(y\) is the vector of the target variable, and \(I\) is the identity matrix.
## 3 Results and Discussion
### Durability to Wear: Bolme Test
\begin{table}
\begin{tabular}{c c} \hline \hline
**System** & **Density (g/cm\({}^{3}\))** \\ \hline CEM I & 2.51 \(\pm\) 0.0066 \\ AAS-N & 2.49 \(\pm\) 0.0021 \\ AAS-WG & 2.52 \(\pm\) 0.0021 \\ HS-3\% & 2.56 \(\pm\) 0.0072 \\ HS-5\% & 2.54 \(\pm\) 0.0051 \\ HFA-3\% & 2.64 \(\pm\) 0.0025 \\ HFA-5\% & 2.64 \(\pm\) 0.0069 \\ HWT-5\% & 2.56 \(\pm\) 0.0075 \\ HPT-5\% & 2.51 \(\pm\) 0.0012 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Density of all systems evaluated at 28 days. Figure 5: **(a)** Height loss in different mortars studied after wear test; **(b)** lost volume after the wear test (Böhme test) in all systems evaluated. It has previously been stated that the abrasion resistance of mortars improves with increasing strength (flexural or compression) and compactivity [31], and fly ash and slag can enhance the abrasion resistance of concretes when they replace fine aggregates [32; 33]. To understand the performance of hybrid mortars, the relationship between their compression strength and lost volume is shown in Figure 6. The linear relationship previously found by other authors for traditional mortars [31] is found for hybrid materials, highlighting the importance of mechanical strength on abrasion resistance. It can be appreciated how HS-5% presents less abrasion for almost the same strength than CEM I. Moreover, the performance of HPT-5%, which presents very low abrasion with very low strength, is worth mentioning. The Na\({}_{2}\)O content of its composition (Table 1) might provide a very hard gel that reduces the compression strength of the material due to its brittleness (Figure 4) and is able to withstand abrasion in a very noticeable way (Figures 5b and 6). To complete this study, each sample was analyzed after the Bohme test with a profilometer to find a correlation with the rest of the properties determined. In Figure 7, it is possible to observe the different wear tracks that each evaluated system (CEM I, HS-5%, HFA-5%, HPT-5% and HWT-5%) presents. By using this technique, we were not able to obtain clear information regarding the different mechanisms that took place during the wear process. Almost flat surfaces can be observed, typical of an abrasion process taking place on all the surfaces. Any effect at the microstructural level cannot be inferred, and only the detachment of sand particles can clearly be appreciated. Moreover, voids left by detached sand particles seem to be refilled with the debris formed during the abrasion process in all studied materials. Figure 6: Effect of compression strength of hybrid mortars on the lost volume (mm\({}^{3}\)) after the Böhme test.
## 5 Background Noise Elimination Method
### The Principle of ALO-VMD Method  
#### 5.1.1 VMD Method  
VMD is an adaptive signal decomposition method, which can adaptively decompose signals into a series of components with a separate property. The essence of VMD is to effectively solve the constrained variational problems, as described below.  
\[\left\{\begin{array}{ll}\min\limits_{\{u_{k}\},\{\omega_{k}\}}\left\{\sum \limits_{k}\|\partial_{t}[(\delta(t)+\frac{j}{nt})\cdot u_{k}(t)]e^{-j\omega_ {k}}\|_{2}^{2}\right\}\\ \text{s.t.}&\sum\limits_{k}u_{k}=f\end{array}\right. \tag{20}\]  
where \(u_{k}\) is the \(k\)-th signal component, and \(\omega_{k}\) is the corresponding center-frequency of the \(k\)-th signal component.  
By adding Lagrangian multiplier \(\lambda\) and penalty factor \(\hat{a}\) into Equation (20), the constrained variational problem is rewritten as Equation (21).  
\[\begin{array}{ll}L(\{u_{k}\},\ \{\omega_{k}\},\ \lambda)=&\hat{a}\sum \limits_{k}\|\partial_{t}[(\delta(t)+\frac{j}{nt})\cdot u_{k}(t)]e^{-j\omega_ {k}}\|_{2}^{2}\\ &+\|f(t)-\sum\limits_{k}u_{k}(t)\|_{2}^{2}\\ &+\left(\lambda(t),f(t)-\sum\limits_{k}u_{k}(t)\right)\end{array} \tag{21}\]  
Equation (21) can be processed by the alternate direction method of multipliers (ADMM) [24]. Subsequently, the signal components \(\hat{\mu}_{k}\) and its corresponding center-frequencies \(\omega_{k}\) are updated by Equations (22) and (23).  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{f}(\omega)\sum\limits_{i<k}\hat{u}_{i}^{n +1}(\omega)-\sum\limits_{i>k}\hat{u}_{i}^{n}(\omega)+\frac{\lambda^{n}(\omega )}{2}}{1+2\hat{\alpha}(\omega  
After each updating, the Lagrangian multiplier \(\lambda\) is also replaced by Equation (24).  
\[\hat{\lambda}^{n+1}(\omega)=\hat{\lambda}^{n}(\omega)+\tau(\hat{f}(\omega)-\sum_ {k}a_{i}^{n+1}(\omega)) \tag{24}\]  
The above process is updated repeatedly until the stop conditions are met, as shown in the equation below.  
\[\sum_{k}\frac{\|\hat{u}_{k}^{n+1}-\hat{u}_{k}^{n}\|_{2}^{2}}{\|\hat{u}_{k}^{n} \|_{2}^{2}}<\varepsilon, \tag{25}\]  
where \(\tau\) denotes the noise-tolerance and the tolerance of convergence criterion \(\varepsilon\) is usually set as \(10^{-6}\). For more details on VMD [35].
# A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem
## 2 Selective Dynamic Sampling Approach
### Selecting \(\mu\) Values  
The appropriate selection of the variable \(\mu\) is critical to select the average samples or other kind of samples (border or safe samples [42]). Variable \(\mu\) is computed under the following consideration: the target ANN outputs (\(t_{j}\)) are usually codified in zero and one values [43]. For example, for a two-class problem (Class A and Class B), the desired ANN outputs are codified as \((1,0)\) and \((0,1)\) for Classes A and B, respectively. These values are the target ANN outputs (\(t_{j}\)), i.e., the desired final values emitted by the ANN after training. In accordance with this understanding, the expected \(\mu\) values are:  
* \(\mu\approx\mathbf{1.0}\) for safe samples. It is expected that ANN classifies with a high accuracy level, i.e., it is expected that the real ANN outputs for all neurons (\(z_{j}\)) will be values close to (1, 0) and (0, 1) for Classes A and B, respectively. Whether we apply Equation (2), the expected value is 1.0, at which the \(\gamma\) function has its maximum value.
* \(\mu\approx\mathbf{0.0}\) for border samples. It is expected that the classifier misclassifies. The expected ANN outputs for all neurons are values close to (0.5, 0.5), then the \(\Delta\) is approximately 0.0, at which the \(\gamma\) function has its maximum value for these samples.
* \(\mu\approx\mathbf{0.5}\) for average samples. It is expected that ANN classifies correctly, but with less accuracy. In addition, the average samples are between safe (\(\mu\approx 1.0\)) and border (\(\mu\approx 0.0\)) samples.  
The recommended \(\mu\) values to select the average samples are those around 0.5. An independent validation set to find the most appropriate \(\mu\) value is proposed to avoid any bias in the testing process.  
For this independent validation, a minimal subset from the training data is used. Firstly, the ten-fold cross-validation for each dataset is applied (Section 5.1); next, only 10% of samples are randomly taken from each training fold (TF\({}^{10}\)), then TF\({}^{10}\) is split into two disjoints folds of the same size (TF\({}^{5}_{train}\) and TF\({}^{5}_{test}\), respectively). Next, the proposed method (SDSA) is applied over the TF\({}^{5}_{train}\) and TF\({}^{5}_{test}\) to find the best \(\mu\) value. The tested values for \(\mu\) were 0.25, 0.375, 0.5, 0.625 and 0.75. Finally, the \(\mu\) value, for which the best Area Under the Curve (AUC) [44] rank was obtained, is chosen by SDSA on TF\({}^{10}\).  
Note that this independent validation does not imply an important computational cost, because it only uses 10% of the training data to find the most appropriate \(\mu\) value. This independent validation unbiased the performance on the testing data process, due to the test data not being used.
# Differential Evolution: A Survey and Analysis
## 3 Parameter Control  
As suggested in the previous section, the DE algorithm is a simple and effective optimization algorithm for problems from the real world when its control parameters are properly set [8; 37; 38]. In this section, we review the most current approaches for improving DE. First, the DE algorithm applies certain control parameters to the system implementation. The accomplishment of DE is influenced by the value of parameters, such as the crossover and mutation rate. Although some studies have recommended certain values for these parameters, their effect on performance is complex and their exact values are unclear. In particular, there is a wide variety of different recommended values that are appropriate for different problems [39; 40; 41].  
The mutation rate \"\(F\)\", crossover rate \"\(C_{r}\)\" and population dimension \"\(Np\)\" maintain balance between exploration and exploitation [6]. Exploration is associated with finding new solutions, and exploitation is associated with searching for new, suitable solutions; the two processes are linked in the evolutionary search [42; 43]. Therefore, the mutation and crossover rates influence the convergence rate and the effectiveness of the search space [44].  
However, specifying suitable values for these rates is not easy [45]. Three types of strategies are used to set these parameter controls: deterministic parameter control (sometimes called random), self-adaptive parameter control and adaptive parameter control [21; 46; 47; 48]. Adaptive and self-adaptive parameter control [16; 17; 18; 19; 20; 21; 22; 23; 24; 25] have recently been proposed to dynamically alter the control parameters without requiring the user's prior knowledge or information about the problem behaviors throughout the search process [49; 50; 51; 52; 53]. In the following sections, the self-adaptive parameter, the adaptive parameter, and hybrid control strategies are discussed.
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 5 Methods for Congestion Learning  
In this section, we discuss the algorithms that have been evaluated for congestion prediction, exploiting the metrics that were identified in Section 4. The methods that have been considered include both supervised and unsupervised learning techniques. Although the main goal is the development of an unsupervised approach, the supervised solutions will be mainly evaluated for comparison purposes (they are appropriate choices for our benchmark). It is worth mentioning that instead of targeting a continuous congestion prediction, we aim to classify the congestion according to several discrete levels. In particular, we have addressed binary classification, and multi-level congestion classification. The analyzed methods take as input samples of the defined metrics and group them in clusters that correspond to a predicted congestion level. In the case of unsupervised learning, once the classification  
Figure 7: Summary of correlation of all metrics with congestion for all the scenarios analyzed, for fixed distance and data rates of 30 m and 30 Mbps. \(\zeta\) is used for the congestion metric itself.  
is obtained, the labels are assigned based on the average delay level of each cluster, so that longer delay values would correspond to higher congestion.  
For the unsupervised learning algorithms we have mainly focused on those with low complexity for a two-fold reason. First, practical ML solutions for congestion-control algorithms should be simple, since it takes place in short temporal intervals and over devices with varying capabilities. In addition, after confirming that there exists a relationship between congestion at the access network and transport layer parameters, our objective is to assess the feasibility of applying ML solutions to improve congestion-control techniques. The use of more complex or specific ML algorithms and configurations are left for our future work.
## 3 Results
### Curve of Breakthrough Modeling
#### 3.6.2 The Adams-Bohart Model Application  
The sorption model of Adams-Bohart was applied to the investigational data as a way to define the primary breakthrough curve's portion. This method concentrates on the assessment of variables that describe the properties, for instance, the sorption's maximum capacity (\(N_{0}\)) and kinetic const. (\(k_{AB}\)) by the Adams-Bohart model. The \(N_{0}\) and \(k_{AB}\) values were determined from Equation 6, as shown in Table 4. The \(k_{AB}\) value decreased in accordance with the increase in the bed depth, as well as influent concentration of phenol. However, it increased as the rate of flow increased. On the other hand, the \(N_{0}\) value in all circumstances showed no substantial change. Moreover, the model provided a high \(R^{2}>0.975\). A comparison between predicted curves against their corresponding experimental ones was conducted as presented in Figure 10. There was clearly considerable consistency between the values (experimental vs. predicted), thereby indicating that the model of Adams-Bohart was valid for concentrations up to 0.5, above which big contradictions could be observed among the plotted curves (experimental-predicted) regarding the adsorption of phenol in the RHAC column. Although the model of [PERSON] deliv  
Figure 9: Experimental curves of breakthrough versus the predicted, attained at various (**a**) concentrations of phenol inlet, (**b**) phenol rates of flow, and (**c**) bed depths according to the model of Yoon–Nelson.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(C_{0}\) & \(Q\) & \(Z\) & \(k_{YN}\) & \(\tau\) & \(R^{2}\) & \(\tau_{cap}\) & **APE** \\ (mg-L\({}^{-1}\)) & (mL-min\({}^{-1}\)) & (cm) & (min\({}^{-1}\)) & (min) & & **(min)** & **(\%)** \\ \hline
1000 & 5.0 & 8.50 & 0.077 & 126.59 & 0.977 & 124 & 12.64 \\
1000 & 7.5 & 8.50 & 0.069 & 97.81 & 0.992 & 100 & 4.89 \\
1000 & 10.0 & 8.50 & 0.095 & 69.39 & 0.980 & 65 & 12.99 \\
400 & 10.0 & 8.50 & 0.142 & 170.58 & 0.988 & 173 & 9.51 \\
2000 & 10.0 & 8.50 & 0.239 & 46.67 & 0.992 & 45 & 4.73 \\
1000 & 10.0 & 12.75 & 0.070 & 137.86 & 0.994 & 138 & 4.09 \\
1000 & 10.0 & 15.30 & 0.077 & 170.21 & 0.989 & 165 & 11.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The Yoon–Nelson model parameters at dissimilar conditions.  
ers a basic and comprehensive methodology for running and assessing sorption-column experiments, it encounters a limited validity to the level of the employed conditions [48].
# A Speed Tracking Method for Autonomous Driving via ADRC with Extended State Observer
## 6 Conclusions  
This paper proposes an extended state observer (ESO)-based active disturbance rejection control (ADRC) for the speed tracking of an autonomous vehicle. The ADRC controller provides an autonomous vehicle with the ability to compensate for unknown internal and external disturbances. Internal disturbances are due to the simplification of nonlinear features of vehicle components while modeling, and external disturbances come from the changing environment and road conditions, such as wind speed and the road incline. The extended state observer ESO is used to estimate the total disturbances, which are corrected for by a feedback loop algorithm. A simple vehicle longitudinal dynamics model including a mean value engine model (MVEM) is implemented to obtain the parameters in ADRC and design a feedforward controller (MFC) to enhance the controller's performance. An actuator switching module is included in the control architecture to ensure controller stability and to avoid frequent switching between actuators.  
The ADRC-MFC controller using CarSim vehicle model simulations was validated, as was the controller using on-road experiments. The results of the simulations using a WLTP speed profile showed the effectiveness and the adaptiveness of the controller. The simulation results show that the controller adapts to various conditions with a mean absolute percentage error of 1.26 and is able to improve the fuel consumption by 3.6% through changing the accelerator pedal depth and positive rate. The Tianjin University autonomous vehicle, with data from the HD-Map, was used to road-test the controller on the campus autonomous driving test field. Our ADRC-MFC controller ensures steady speed tracking at low and moderate speeds under different road conditions, and the sample points with a tracking error within \(\pm\)0.5 km/h account for 98.02% of the total sample points.  
With the proposed controller, the speed of an autonomous vehicle can be controlled well without an accurate model. Moreover, the parameters of ADRC controller can be estimated according to the vehicle model, which creates a lesser workload of calibration. Future researchers can develop a self-learning algorithm that makes the model more accurate based on this control frame.  
All the Authors contributed significantly to this current work. [PERSON] conceptualized the idea. [PERSON] and [PERSON] implemented the methodology. [PERSON] wrote the paper. [PERSON] and [PERSON] supervised the work, revised the paper and helped in the acquisition of the funds.  
We express our sincere thanks to the State key laboratory of engines, Tianjin University for provided us with experiment platform and funds.  
The authors declare no conflict of interest.
## 4 Satellite Self-Organizing Network Simulation Platform Based on OMNeT++
### Function Domain
#### 4.3.1 Initial Network Formation  
Based on the high-speed wireless self-organizing network clustering method from reference [18], this paper implements a satellite self-organizing network clustering process, which introduces a ground control node for clustering. Satellites send network access requests and their information (mainly location information) to the ground node, which completes the division of the cluster structure and the allocation of cluster heads and then sends back the cluster allocation information for each node separately. The initial network formation process is deployed at the network layer module of the ground node.  
The specific network formation process is as follows:  
1. The joining satellite node sends a network access request message to the ground node;
2. Based on the location information of the joining node and current network cluster structure, the ground node performs an initial cluster allocation for the new joining node. After the allocation is completed, the ground node sends a message to the joining node carrying the assigned node ID, cluster information, whether it is a cluster head, time slots, and other information;
3. The joining node takes corresponding actions based on its assigned status. If it is assigned as a member node, it sends a message to the cluster head node, informing its own ID information and time slot. If it is assigned as a cluster head, it broadcasts a \"hello\" message to its neighbors to announce its presence, while also listening for messages from neighboring cluster heads and new joining member nodes in its cluster to establish a local connection table.
## 5 Implementation of the Adaptive SMC and Detailed Circuital Simulations  
The power system described in Figure 2 was implemented in the power electronics simulator PSIM using the electrical parameters calculated in the previous section. The adaptive sliding-mode controller can be implemented following Algorithm 2 with analog or digital circuits. The following subsections discuss both implementations.