esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 MGTO for Solving Engineering-Optimization Problems
### Three-Bar-Truss Design  
The three-bar-truss-design problem [61] is a typical engineering problem which can be utilized to evaluate the performance of algorithms. The main intention is to minimize the weight, which is subject to deflection, stress and buckling constraints on each of the truss members, by adjusting the cross-sectional areas \(A_{1}\), \(A_{2}\) and \(A_{3}\). It is illustrated in Figure 9.  
The problem considers a non-linear function with three constraints and two decision variables. The mathematical model is given as follows:  
Consider:  
\[\overset{\rightarrow}{x}=[x_{1}\ x_{2}]=[A_{1}\ A_{2}] \tag{71}\]  
Minimize:  
\[f(\overset{\rightarrow}{x})=\left(2\sqrt{2}x_{1}+x_{2}\right)*l \tag{72}\]  
Constraints:  
\[g_{1}(\overset{\rightarrow}{x})=\frac{\sqrt{2}x_{1}+x_{2}}{\sqrt{2}x_{1}^{2} +2x_{1}x_{2}}P-\sigma\leq 0 \tag{73}\]  
\[g_{2}(\overset{\rightarrow}{x})=\frac{x_{2}}{\sqrt{2}x_{1}^{2}+2x_{1}x_{2}}P- \sigma\leq 0 \tag{74}\]  
\[g_{3}(\overset{\rightarrow}{x})=\frac{1}{\sqrt{2}x_{2}+x_{1}}P-\sigma\leq 0 \tag{75}\]  
Variable range:  
\[0\leq x_{1},x_{2}\leq 1 \tag{76}\]  
where we have the following:  
\[l=100\ \text{cm},\ P=2 KN/\text{cm}^{2},\ \sigma=2 KN/\text{cm}^{2} \tag{77}\]  
The results of the three-bar-truss-design problem are shown in Table 11, and it can be seen that the MGTO has significant superiority compared with other optimization  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Optimum Variables**} & **Optimum** \\ \cline{2-5}  & **D** & **D** & **N** & **Weight** \\ \hline MGTO & 0.05000 & 0.3744 & 8.5465 & **0.0099** \\ AO & 0.0502439 & 0.35262 & 10.5425 & 0.011165 \\ HHO & 0.051796393 & 0.359305355 & 11.138859 & 0.012665443 \\ SSA & 0.051207 & 0.345215 & 12.004032 & 0.0126763 \\ WOA & 0.051207 & 0.345215 & 12.004032 & 0.0126763 \\ GWO & 0.05169 & 0.356737 & 11.28885 & 0.012666 \\ PSO & 0.051728 & 0.357644 & 11.244543 & 0.0126747 \\ HS & 0.051154 & 0.349871 & 12.076432 & 0.0126706 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The comparative optimization results of the compression/tension-spring problem.  
Figure 9: Three-bar-truss-design problem.  
algorithms in solving this problem. The proposed model can obtain the lowest optimum weight when \(x\) is equal to 0.7884 and 0.4081.
## 4 Results and Discussion
### Multi-Objective Optimization Results: Efficiency vs. Sustainability Trade-Off  
The MOOM-OEP was solved with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) using the following hyper-parameters:population size = 100, crossover probability = 0.9, mutation probability = 0.1, and a termination criterion of 200 generations. With these settings, the optimization proceeded as follows: the objectives were defined (S8) and the objective functions formulated (S9); NSGA-II then initialized the population (S10), performed non-dominated sorting to rank solutions (S11), computed crowding distances to preserve diversity (S12), applied simulated-binary crossover and polynomial mutation (S13), and iterated until convergence, ultimately producing the Pareto-optimal front evaluated in stage S14 (Figure 10).
# A Comprehensive Survey of In-Band Control in SDN: Challenges and Opportunities
## 4 A Survey of In-Band SDN Control in SDN
### Network Bootstrapping
[91] define an in-band control protocol with emphasis in hybrid networks (composed of both SDN and non-SDN) and also heterogeneous (both wired and wireless) devices. They evaluate their design with Waxman and Barabasi-Albert network topologies. **[PERSON]** et al. [92] propose in-band control plane called P4 IBN for P4 networks with network bootstrapping, status monitoring, topology discovery, and fast failover. Among the challenges it manages to overcome is the fact that unlike OpenFlow switches, P4 switches are passive. The controller has to actively search for new P4 switches and initiate the control channel. Finally, they have been able to modify the restriction that a P4 switch can only have one control port to communicate with the controller. The evaluation was carried out using emulation and a Linux testbed. The results of the experiments indicate that the in-band control system proposed effectively carries out network bootstrapping and accomplishes rapid failover, even being able to handle multiple simultaneous failures.
# An Experimental Study on Repairing of Reinforced Concrete Beams Having Damaged Longitudinal Bars
## 2 Materials and Methods
### Repairing Details and Procedures
#### 2.4.1 The Beams Repaired with the SNSM Technique (Group B)  
In this group, two beam specimens were repaired using the SNSM technique as shown in Figure 4a. Two side grooves, one in each side of every beam, were drilled in the concrete cover with a 25 mm depth and 25 mm width, and 2000 mm along the length of the beam. The grooves were made at the same level of the existing lower reinforcement layer. Steel rebars were placed in the grooves of one beam while the other beam was repaired with GFRP rebars. The grooves were cleaned, and a fiber-reinforced mortar was applied to fill the grooves and embed the rebars. A detailed step-by-step application procedure is illustrated in Figure 4b, which outlines the essential stages involved in the SNSM method, including surface preparation, groove formation, and reinforcement installation.  
Figure 4: (**a**) A schematic illustration of the beam repaired with the SNSM technique in Group B (dimensions in mm); (**b**) step-by-step application procedure in the SNSM technique.  
2.4.2. The Beams Repaired with the NSM Technique (Group C)  
In this group, two beam specimens were repaired using the NSM technique, as shown in Figure 5a. Two grooves were drilled in the concrete cover below the beam. GFRP rebars were placed in the grooves by using a fiber-reinforced mortar. The second beam specimen in this group was wrapped with U-shaped CFRP sheets at each side of the beam, while the first beam was left without side supports. The application procedure for the NSM method closely parallels that of the SNSM method. Both methods involve identical essential stages, including surface preparation, groove formation, and reinforcement installation. A detailed step-by-step application procedure of NSM technique is illustrated in Figure 5b.
## 5 Results and Discussion
### Voxel-Based Classification Results
#### 5.3.1 Lung Cancer Pathology Phenotyping
The confusion matrices of sample runs are outlined in Figure 12. The confusion matrices and the reported performance metrics show comparable performance across the three datasets. Moreover, the learning curves show minor differences between the training and validation curves, ruling out the possibility of overfitting.
## 4 Results
### Evaluation Metrics  
We adopted the metrics accuracy, precision, recall, and F1-score to evaluate the classification models' performances. \(TP\) represents the true positives, \(TN\) represents the true negatives, \(FP\) represents the false positives, and \(FN\) represents the false negatives. According to Equation (3), accuracy is the ratio of correctly predicted observations to the total observations, and it represents how well the model performs across all classes. This value grows with a higher level of accuracy. In Equation (4), precision is defined as the ratio of correctly predicted positive observations to the total number of positively predicted observations. By measuring this metric, we can determine how accurate the model is at classifying samples as positive. In Equation (5), recall is calculated by dividing the number of positives correctly classified as positive by the total number of observations in the class. Observations that were correctly classified as positive indications that the model can detect them. Observations are detected more often when the recall is higher. Lastly, the F1-score in Equation (6) refers to the average precision and recall. Precision and recall must be 100% to reach the maximum value of 1. Overall, the F1-score is a way to compare two models that predict the same variable.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{3}\]  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
\[F1=2\cdot\frac{Recall\cdot Precision}{Recall\ +Precision} \tag{6}\]
# Numerical Investigation of the Dynamics of 'Hot Spots' as Models of Dissipative Rogue Waves
## 2 Effect of Background Gain/Loss
### Linear Background Gain/Loss  
We shall first consider the simplest case where the medium possesses only a linear background loss or gain \(\beta\), i.e., the variable linear gain and cubic gain functions vanish. When Equation (1) experiences a small loss (\(\beta<0\)) globally, a rogue wave can still exist, but the maximum is reached at a later time as compared to the evolution governed by the NLSE (Equation (3)). The maximum attained by the rogue wave is also lowered, e.g., 1.7 for special choice of parameters as compared to 3.0 of the Peregrine breather of the NLSE [2; 3] (Figure 1a). Conversely, a small gain (\(\beta>0\)) will trigger a pulsating mode with growing peaks. As compared to the Peregrine breather, the wave reaches the peak amplitude before \(t=0\) and attains an amplitude greater than 4.1 (Figure 1b). Due to the presence of the gain, the spatially localized pulse does not decay back to the constant background plane wave, but instead evolves into multiple peaks with increasing amplitude.
# Active Noise Control over Space: A Subspace Method for Performance Analysis
## 4 Subspace Method
### Noise Control in the Subspace  
In the subspace, matching the secondary sound field coefficients to the projected primary noise field coefficients, the optimal solution of the secondary noise field coefficients can be written by  
\[\mathbf{\gamma}=-\mathrm{Proj}_{\mathbf{O}}\mathbf{\beta}. \tag{29}\]  
The projection from the primary noise field into the loudspeaker subspace \(\mathrm{Proj}_{\mathbf{O}}\mathbf{\beta}\) can be calculated by (27).  
In a given loudspeaker setup, substituting (24) into (9), the representation of secondary noise field coefficients can be rewritten by  
\[\mathbf{\gamma}=\mathbf{O}\mathbf{\kappa}\mathbf{d}, \tag{30}\]  
where \(\mathbf{d}=\{d_{1},\ldots,d_{L}\}^{T}\), \(\mathbf{\kappa}=\{\kappa^{(1)},\ldots,\kappa^{(L)}\}\).  
Substituting (27) and (30) into (29), the final equation to design the driving signal can be written as  
\[\mathbf{\kappa}\mathbf{d}=-\mathbf{y}. \tag{31}\]The loudspeaker driving signals can be calculated by solving the system of linear equations described by (31). The number of principal components specifies whether the linear system (31) can be solved exactly.  
* Case 1: \(B=L\) When we reserve all the information in the PCA, (31) has only one unique solution. In that case, the driving signals can be represented by \[\mathbf{d}=-(\mathbf{\kappa})^{-1}\mathbf{y}.\] (32)
* Case 2: \(B<L\) When we only use the largest components to generate the subspaces, instead of solving the over-determined system in (11), Equation (31) solves an under-determined system. In that case, the driving signals \(\mathbf{d}\) can be derived by \[\mathbf{d}=-(\mathbf{\kappa})^{\dagger}\mathbf{y},\] (33) where \((\mathbf{\kappa})^{\dagger}\) is the pseudoinverse of the secondary-path coefficients in the subspace, with the dimension of \(L\times B\).  
In all cases, loudspeaker driving signals \(\mathbf{d}\) are designed by the secondary-path information in the subspace \(\mathbf{\kappa}\) and the primary noise field coefficients in the subspace \(\mathbf{y}\), as shown in (32) and (33). Therefore, the method is here called the'subspace method'.
## 1 Introduction
Two track segments are selected and validated as mutual instantaneous baselines for each other. There are various feature-extraction methods for analyzing the field monitoring data acquired by the fiber optic monitoring system, including sophisticated ones using machine learning approaches [43; 44], but it is hard to tell which one is suitable for debonding detection. For the purpose of easy implementation and fast calculation in real applications, six statistical methods in the time domain, Fourier transform [45; 46] in the frequency domain, Hilbert-Huang Transform (HHT) [47] in the time-frequency domain, are adopted for features extraction in this paper, and it is followed by the proposal of a new indicator combining multiple debonding-sensitive features. The features include clearance factor, crest factor, impulse factor, kurtosis factor, shape factor, and peak acceleration in the time domain, amplitude and frequency in the frequency domain, and local peak and energy in a frequency band and their maximums in the time-frequency domain. In addition, correlation assurance criterion (CAC) values are calculated in the time-frequency domain and they are additional features in this domain. The equation of CAC is the same as that of modal assurance criterion (MAC), and the term CAC is used herein for avoiding the confusion with the MAC commonly in modal analysis. The feature values acquired in situations with and without interlayer debonding are compared for identifying some significant features capable of obviously differentiating debonding state from the normal state. On this basis, a new indicator that combines the significant features is developed. The weight of each feature in this indicator is obtained by calculating the feature similarity between the two track segments in the early stage of monitoring. Euclidean distance between the indicators of the two track segments in the current monitoring stage is employed for quantitatively assessing the deviation between one segment with debonding and the other segment in a normal state. To the authors' knowledge, the study on this type of interlayer-debonding monitoring scheme and its application to the debonding identification of an in-service HSR track with the assistance of the new indicator is rarely reported. The rest of the paper is organized as follows. Section 2 presents the monitoring scheme with the design of mutual instantaneous baselines and trackside sensor deployment near debonding-prone areas for facilitating debonding detection and describes the high-sensitivity fiber optic system for the implementation of the monitoring scheme. It is followed by the description of feature extraction methods and the development of a new indicator combining multiple debonding-sensitive features in Section 3. The application of the methods is provided in Section 4, in which all features are compared, and the new indicator is applied to the identification and assessment of interlayer debonding. The conclusion of this work is given in Section 5.
## 2 Materials and Methods
### Feature Extraction  
Sines and cosines of the wind direction are used instead of the wind direction in degrees:  
\[\begin{array}{l}\text{Wind direction sin}=\text{sin(Wind direction)}\\ \text{Wind direction cos}=\text{cos(Wind direction)}\end{array} \tag{1}\]  
The next step is extracting the values of month and hour as the separate features instead of date and time. In addition, based on the hypothesis about how the power-wind speed curve changes according to seasons, a new categorical feature, \"season\", was added to the original data (winter, spring, summer, and autumn). One-hot encoding was applied after that, so the season feature is transformed into four binary features.  
For investigation of the hypothesis of using power values from the previous time step to enhance accuracy by accounting for the turbine's inertial properties, this feature was added.  
As a result, the full set of features is as follows:  
* Wind speed, m/s;
* Wind direction sin;
* Wind direction cos;
* Month;  
Figure 7: Flow chart of the approach.  
\begin{tabular}{r l} \(\bullet\) & Hour; \\ \(\bullet\) & Winter (0/1); \\ \(\bullet\) & Spring (0/1); \\ \(\bullet\) & Summer (0/1); \\ \(\bullet\) & Autumn (0/1); \\ \(\bullet\) & Theoretical power (kW); \\ \(\bullet\) & Previous value of real power (kW); \\ \(\bullet\) & Real power (kW) as a target. \\ \end{tabular}  
Figure 8 shows the Pearson correlation coefficients for the numerical features.  
Standardization of the data was provided according to the following equation:  
\[F^{\prime}=(\text{F}-\text{mean}(F))/\text{std}(F) \tag{2}\]  
where \(F\) is the values of a feature. It was applied to all features except the binary features and the target. Standardization of the dataset makes the distribution law closer to normal, which has a positive effect on the model training process.
# Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study
## 2 Materials and Methods  
### Methodology  
Figure 1 shows an overview of the ear-image-capturing and ear-image-preprocessing procedures. The ear images of ten subjects (six males and four females, age: 21-24 years, and nationality: Japanese) were captured using the telephoto lens camera of an Apple iPhone 12 Pro (Apple Inc., Cupertino, CA, USA) with a 2\(\times\) zoom. The telephoto lens was selected for its distortion-minimizing ability compared with a wide-angle lens. Thus, it can preserve the structural integrity of the ear features [33; 34]. The ear images were captured during daylight hours to ensure optimal illumination and clarity. To obtain the features of ear images under both illuminated and non-illuminated conditions, we captured the images in these two settings.  
During image capturing, each subject was made to sit on an adjustable-height chair with a swiveling seat. The height of the chair was adjusted such that the subject's ear aligned with the camera lens at a standardized ear position, maintaining a fixed distance of 50 cm between the ear and lens. Under these conditions, the subjects performed continuous 180\({}^{\circ}\) counterclockwise rotations for 10 s, allowing for the ear images to be captured from multiple perspectives. Additionally, the images were captured from two other vertical positions: the high and low positions, where the camera lens was elevated and lowered by 30 cm above and below the ear position, respectively (Figure 1; data collection). Overall,  
Figure 1: Schematics of the ear-image-capturing and ear-image-preprocessing methods.  
three sets of ear images were captured from three different vertical positions, i.e., the High, Ear, and Low positions.  
The raw images contained extraneous elements, such as hair, facial features, and other background components. To ensure that the dataset exclusively contained ear-related features, we manually cropped all the images along the auricle contour using ImageJ (version 1.53t) [35]. Thereafter, the cropped ear images were overlaid onto a square white noise background to standardize the dataset. Finally, all the images were resized to 96 \(\times\) 96 pixels to ensure image size uniformity.
# Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
## 1 Introduction
On the other hand, it takes account of certain empirical factors, and these research results lack universality as they are mostly based on the horizontal or vertical state of expanded anchor cables. These theoretical calculation formulas are convenient for practical engineering applications, but it is easy to introduce large errors when the engineering scheme used for comparison is not closely replicated. For deep and large foundation pits in particular, design errors in the ultimate pullout capacity of expanded anchor cables can lead to serious consequences due to their provision of a larger bearing capacity than other components. Therefore, it is necessary to further study the computation of the ultimate pullout capacity of expanded anchor cables and clarify their bearing mechanism in practical applications. This paper aimed to present an effective approach to evaluate the ultimate pullout capacity of an expanded anchor cable. The logarithmic spiral failure model is widely used to determine the support pressure of a shield tunnel face. In the present study, this model was applied for the first time to model the failure of the soil region at the front surface of an anchorage segment. Based on the upper-bound theorem of limit analysis, the ultimate end resistance of the expanded anchor cable was derived, and the optimal upper-bound solution of the ultimate end resistance was obtained through the particle swarm optimization algorithm. The failure mechanism of the anchorage side surface was assumed to satisfy the slippage model, and the ultimate lateral resistance was also derived based on the upper-bound theorem of limit analysis. Then, the upper-bound solution of the ultimate pullout capacity was obtained by summing the ultimate end resistance and the ultimate lateral resistance, and this calculation was verified through a comparison with a three-dimensional numerical simulation. Finally, the influences of the anchor cable's key parameters on the ultimate end resistance, ultimate lateral resistance, and total ultimate pullout capacity were explored in detail. The upper-bound solution of the ultimate pullout capacity of an expanded anchor cable presented in this paper is expected to provide a useful reference for practical engineering applications.