esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Analysis of the Shear Lag Effect in Tube Structural Systems of Tall Buildings
## 5 Parametric Analysis
### The Impact of Column Spacing on the Shear Lag Effect  
In this chapter, the impact of exterior column spacing in a tube structure on the shear lag effect will be analyzed. As already stated, the column dimension perpendicular to the horizontal load direction will stay the same throughout the analysis (c = 0.5 m). The column spacing will be varied five times with values of s = 2.0 m, s = 2.18 m, s = 2.4 m, s = 2.67 m and s = 3.0 m while the layout dimensions stay constant A/B = 24 m/24 m (number of columns is varied). The entire building height and the storey height are constant. The same seven storeys as in chapter 4 will be analyzed. It is very important to note that as the column spacing is changed, the referent node area for point load changes. That means that for different column spacing the horizontal force in the nodes where beams and columns meet is different. The results of the analysis are shown in Figure 9.  
Figure 8: Layout for the calculation of \(\beta\) coefficient.  
According to the results (Figure 9) the following can be concluded:  
1. All storeys bellow the 12 th storey are in the positive shear lag effect area (\(\beta<1\)). It is also clear that the column spacing for these storeys has no meaningful impact on the positive shear lag effect (the value of \(\beta\) for the 2 nd, 6 th and 12 th storey is not changing).
2. The transition from the positive to the negative shear lag effect area for all column spacing variants is at the 12 th storey (\(\beta=1\)), which is approximately at 33% of the building height measured from the ground.
3. All storeys above the 12 th storey are in the negative shear lag effect area (\(\beta>1\)). It is also clear that the column spacing for these storeys has some impact on the negative shear lag effect. As the building rises, so does the coefficient \(\beta\). Furthermore, the increase of coefficient \(\beta\) in one storey due to the variation of column spacing is more and more prominent as the building rises. While on the 18 th storey this increase is mild, on the 36 th storey the increase of coefficient \(\beta\) with the increase of column spacing is quite notable.  
It can be deduced that the column spacing has practically no impact on the positive shear lag effect, while in the case of negative shear lag effect, the increase in column spacing results in the increase of this effect with this phenomenon being more prominent as the building rises.
## 1 Introduction
A new variable selection method called bootstrap soft shrinkage algorithm (BOSS) was proposed, which was derived from the idea of weighted bootstrap sampling (WBS) and model population analysis (MPA) [20]. In BOSS, WBS is used to generate sub-models based on the weights, and MPA is used to analyze the sub-models and update the weights of the variables [21; 22]. [PERSON] et al. used the BOSS method with mid-infrared (MIR) spectroscopy to determine chlorantraninibre in abamectin, and obtained the highest coefficient of determination of cross-validation (\(R_{cv}^{2}\) = 0.9998) and coefficient of determination of the test set (\(R_{p}^{2}\) = 0.9989) [23]. [PERSON] et al. showed that BOSS can improve prediction performance and markedly reduce features, and had the best accuracy in calibration and prediction with the correction determination coefficient (\(R_{c}^{2}\)) of 0.9907, the root-mean-square error of calibration (RMSEC) of 0.4257 mg/kg, \(R_{p}^{2}\) of 0.9821, and the root-mean-square error of prediction (RMSEP) of 0.6461 mg/kg [24]. From the above research, the BOSS algorithm not only improves the prediction accuracy of the model but also effectively reduces the number of variables to speed up the calculation of the model. However, BOSS directly processes the original spectral data, which includes processing irrelevant noise information. Therefore, noise elimination steps must be added. In this study, we constructed a novel variable selecting method based on DWT and BOSS. GA [25] and iPLS [26; 27] were compared with the new proposed method, which are categorized as a random variable selection method and interval variable selection method, respectively [28; 29]. As classical methods, many studies selected variables to improve prediction ability. [PERSON], H monitored yeast concentrations of _Saccharomyces cerevisiae_ cultivations with NIRS and compared the results with different variable selection methods. The GA model was built on fewer data points than that based on full spectra, which ranges from 1557 to 71 points, with \(R_{p}^{2}\) ranging from 0.9777 to 0.9806 [30]. [PERSON] rice amylose determination using NIRS with the iPLS method. The full spectrum was split into 10, 20, 25, and 50 intervals, and the optimal model was obtained for the Savitzky-Golay filter (\(R_{p}^{2}\) = 0.92 and RMSEP = 2.133), which was better than the full-spectrum PLS model [7]. [PERSON] et al. used different regression methods such as PLS, iPLS, and SiPLS with multiple pretreatment methods. The Al\({}_{2}\)O\({}_{3}\) models obtained using the iPLS algorithm had \(R_{p}^{2}\) values of 0.8273 to 0.9196 [31]. PLS, which can improve the prediction ability by selecting informative variables or eliminating uninformative variables, was used to build a prediction model in this paper [32; 33; 34]. DWT and BOSS were combined as a new variable selection method. After previous variable selection, DWT-BOSS-PLS, GA-PLS, and iPLS models were established, which are based on the NIRS data and moisture content. By comparison, three variable selection methods are discussed to choose the best one.
## 4 Calculation Results and Experimental Validation
### Experimental Validation of Isolation Effect
In contrast, the maximum acceleration-transmission ratios of the isolated object with optimized devices are 48.8%, 24.2%, 35.6%, and 21.2%. This indicates that the optimized devices exhibit better horizontal seismic isolation performance. Finally, the isolation effect of the three-dimensional seismic isolation bearings was validated using the vertical EL-Centro wave and the horizontal Tianjin wave. The acceleration-time curves are shown in Figures 15 and 16. Figure 11: Acceleration–time curve under the horizontal harmonic load with a frequency of 0.5 Hz. Figure 10: Acceleration–transmission ratio versus time curves under the vertical impact load. Figure 14: Acceleration–time curve under the horizontal harmonic load with a frequency of 10 Hz. Figure 12: Acceleration–time curve under the horizontal harmonic load with a frequency of 1 Hz. Figure 13: Acceleration–time curve under the horizontal harmonic load with a frequency of 5 Hz. From Figures 15 and 16, it can be seen that the maximum acceleration-transmission ratios for the isolated object under the vertical EL-Centro wave and the horizontal Tianjin wave are, respectively, 52.1% and 58.2%, indicating that the three-dimensional seismic isolation bearing exhibits a good isolation effect. According to Fourier series theory, random loads can be decomposed into a linear superposition of several harmonic loads, which cover loads ranging from 0 Hz to 20 Hz. The isolation bearing used in this example optimally considers a total of twenty sets of harmonic loads ranging from frequencies of 1 Hz to 20 Hz, with the optimization goal being to minimize the maximum acceleration-transmission ratio at the top of the structure under independent action from these twenty sets of loads. Therefore, it demonstrates good performance against random seismic loads.
# Deep Representation of EEG Signals Using Spatio-Spectral Feature Images
## 3 Methods
### Neural Network Architecture, Training, and Test  
A CNN architecture (Figure 3), consisting of five convolutional layers (CNV), two fully connected layers (FC), and one output layer with three output units, was used to solve the proposed ternary classification problem. The number of input channels in this architecture is six, which corresponds to the number of frequency bands in the input SSFI arrays. Each convolutional layer consists of a bank of filters of size \(3\times 3\). The first four convolutional layers are followed by a \(2\times 2\) max-pool layer, batch normalization, and dropout (0.3) layer. As shown in Figure 3, the first layer (CNV1) has 32 filters and the remaining CNV layers have 16. The activation function used for the output layer is softmax, whereas for the hidden layers, it is a rectified linear unit (ReLu) with \(l_{2}=0.01\) regularization parameter. Dropout and L2 regularization are used to reduce the risk of over-fitting.  
Since individual differences in the brain folding structures can result in participants producing different EEG distributions [4], we took a subject-specific approach and trained the proposed CNN architecture on each of the 25 participants separately, which resulted in 25 trained CNN models. In creating the training and test sets for each subject, we took into consideration the existing overlap between consecutive EEG segments, as it can potentially lead to information leak from the training to the test stage. For each subject, the EEG segments corresponding to the first 100 auditory tasks (first 100 trials) were used for training, whereas the EEG segments extracted from the remaining trials (i.e., 44) were used for evaluating the trained CNN models. This serial split of training and testing is used by taking into consideration the role of time in the design of the experiments in the PhyAAt dataset and to replicate a scenario where a model is trained on past data and evaluated in real-time. We used the classification accuracy to quantify the performance of  
Figure 3: CNN architecture used for the ternary listening–writing–reading classification task. It consists of five convolution layers followed by two fully connected layers.  
the resulting CNN models. The categorical cross-entropy was chosen as a loss function during training [39] and the Adam (Adaptive Moment Estimation) method was used during optimization.  
In order to understand differences of brain activity for the same task in different individuals, we carried out an ISD analysis. In ISD analysis, a trained model on one subject is tested on all the other subjects. For consistency of comparative results, each trained model is also tested on the data from the same subject, including training and test data (i.e., SSFI from all 144 trials).
# Limit Equilibrium Analysis of Landfill Instability Based on Actual Failure Surface
## 2 Theoretical Model and Analysis Method
### Analysis Method
#### 2.4.3 Three-Wedge Model with [PERSON]  
The traditional three-wedge analysis method is shown in Figure 4a. The interfaces of active wedge and passive wedge, and passive wedge and block wedge, are perpendicular to the horizontal plane.  
Limit equilibrium analysis of the passive wedge is carried out, and the balance of vertical and horizontal forces is taken into account, which can be expressed as:  
Equilibrium of forces in Y direction \(\sum F_{Y}=0\) gives  
\[W_{P}+E_{VP1}=U_{P}+N_{P}^{\prime}+E_{VP2}, \tag{22}\]  
\[E_{VP1}=\frac{C_{1}+E_{HP1}\cdot tan\phi_{sw}}{FS_{V}}, \tag{23}\]  
\[E_{VP2}=\frac{C_{2}+E_{HP2}\cdot tan\phi_{sw}}{FS_{V}}, \tag{24}\]  
Equilibrium of forces in X direction \(\sum F_{X}=0\) gives  
\[E_{HP1}=F_{P}+E_{HP2}, \tag{25}\]  
\[F_{P}=\frac{C_{P}+N_{P}^{\prime}\cdot tan\phi_{P}}{FS_{P}}, \tag{26}\]  
Assume that \(n_{1 sw}=C_{1}/FS_{V}\), \(n_{2 sw}=C_{2}/FS_{V}\), \(m_{sw}=tan\phi_{sw}/FS_{V}\); then, the relationship between the horizontal component of two interfaces can be expressed as  
\[E_{HP1}-E_{HP2}=\frac{(W_{P}-U_{P}+n_{1 sw}-n_{2 sw})\cdot\frac{tan\phi_{P}}{FS _{P}}+\frac{C_{P}}{FS_{P}}}{1-m_{sw}\cdot\frac{tan\phi_{P}}{FS_{P}}}, \tag{27}\]  
According to the force balance state of the interceptor wedge, Equilibrium of forces in Y direction \(\sum F_{Y}=0\) gives  
\[W_{B}+E_{VB}+F_{B}\cdot sin\theta=(U_{B}+N_{B}^{\prime})\cdot cos\theta, \tag{28}\]  
\[E_{VB}=\frac{C_{2}+E_{HB}\cdot tan\phi_{sw}}{FS_{V}}, \tag{29}\]  
\[F_{B}=\frac{C_{B}+N_{P}^{\prime}\cdot tan\delta_{B}}{FS_{B}}, \tag{30}\]  
Equilibrium of forces in X direction \(\sum F_{X}=0\) gives  
\[E_{HB}=F_{B}\cdot cos\theta+(U_{B}+N_{B}^{\prime})\cdot sin\theta, \tag{31}\]  
Figure 4: (**a**) The traditional three-wedge force analysis model. (**b**) The three-wedge force analysis model based on the actual failure surface. Subscript B represents the block wedge.  
substituting Equations (29)-(31) into Equation (28) gives  
\[E_{HB}=\frac{(W_{B}+n_{2 sw})\cdot\left(sin\theta+cos\theta\cdot\frac{tan\theta _{B}}{FS_{B}}\right)+\frac{C_{B}}{FS_{B}}-U_{B}\cdot\frac{tan\theta_{B}}{FS_{B}}} {(cos\theta+sin\theta\cdot m_{sw})-(sin\theta+cos\theta\cdot m_{sw})\cdot \frac{tan\theta_{B}}{FS_{B}}}, \tag{32}\]  
The force balance analysis of the active wedge is the same as that of the two-wedge model  
\[E_{HA}=\frac{(W_{A}-n_{1 sw})\cdot\left(sin\beta-cos\beta\cdot\frac{tan\delta_{ A}}{FS_{A}}\right)+U_{A}\cdot\frac{tan\delta_{A}}{FS_{A}}-\frac{C_{A}}{FS_{A}}}{ cos\beta+sin\beta\cdot m_{sw}+(sin\beta-cos\beta\cdot m_{sw})\cdot \frac{tan\delta_{A}}{FS_{A}}}, \tag{33}\]  
On the condition of \(E_{HP1}=E_{HA}\) and \(E_{HP2}=E_{HB}\), substituting Equations (32) and (33) into Equation (27) gives the solution of FS.
## 3 Simulation Results  
Monte-Carlo simulations are carried out in order to assess the performance of the LMMSE algorithm in estimating the true total DC from a set of measurements available at RSs and testing its sensitivity to modeling errors. It is assumed that five RSs are distributed along a railway track of 400 km, _i.e._, separated by 80 km. In [9], the same number of RSs are distributed over a railway track of 300 km and considered to be separated by a distance less than the decorrelation distance. In the following, three cases will be considered: the case where the true total DC is simulated and modeled according to a correlation function that is a single exponential function as in Equation (5), or the sum of multiple exponential functions with different correlation distance as in Equation (3) or the sum of multiple exponential functions with the same correlation distance.
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
###### Abstract  
Underwater sensor nodes are usually deployed by ships, aircraft, etc., in random drops, and there is current movement in the underwater environment, which results in an uneven distribution of sensor nodes and thus, different energy consumption in each area of the network. In addition, the underwater sensor network also has a \"hot zone\" problem. To address the uneven energy consumption of the network caused by the above problem, the non-uniform clustering algorithm for energy equalization is put forward. Considering the residual energy, density and coverage redundancy of nodes, this algorithm selects the cluster heads and makes them more reasonably distributed. Additionally, according to the selected cluster heads, the size of each cluster is designed to equalize the energy consumption of the network during multi-hop routing. In this process, the residual energy of cluster heads and the mobility of nodes are considered, and real-time maintenance is performed for each cluster. The simulation results demonstrate that the proposed algorithm is effective in prolonging the network lifetime and balancing the network energy consumption; moreover, the network coverage maintenance is better than that of other algorithms.  
energy equalization; non-uniform cluster; underwater sensor networks +
Footnote †: journal: Sensors
# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
## 2 Materials and Methods
### The Late Fusion Branch
#### 2.4.2 The Architecture of the Late Fusion Branch  
The late fusion branch combines different types of data after extracting features from the network. In this branch, the background and the foreground (leaves) are input into their respective CNN models to concatenate two fully connected layers together. This is a typical late fusion method, as shown in Figure 5.  
The leaf temperature and background temperature are independent information, so it is more appropriate to extract leaf temperature and background temperature features separately. This also explains the results presented in Table 3.  
Figure 4: Result of foreground and background segmentation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**CNN Architecture** & \begin{tabular}{c} **Early Fusion with** \\ **Softmax** \\ \end{tabular} & \
\begin{tabular}{c} **Late Fusion with** \\ **Softmax** \\ \end{tabular} &
\begin{tabular}{c} **MLP with** \\ **TLS\({}_{\text{ef}}\) + TLS\({}_{\text{lf}}\)** \\ \end{tabular} \\ \hline VGG16 & 36.59\% & 75.61\% & 100.0\% \\ ResNet34 & 51.22\% & 73.17\% & 87.80\% \\ DenseNet & 34.15\% & 75.61\% & 85.37\% \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of the accuracy of the three CNN architectures.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Gray-Level Co-Occurrence Matrix (GLCM) Analysis
#### 3.4.2 Contrast  
Contrast analysis assesses the differences in pixel intensities between a pixel and its neighboring pixels in a cipher image [54]. A high contrast value between neighboring pixels indicates a strong encryption algorithm that is effective at hiding the plaintext image's features. Furthermore, a high degree of contrast between neighboring pixels indicates greater randomness in the cipher image. The contrast can be calculated as follows:  
\[Contrast=\sum_{i,j}|i-j|^{2}p(i,j), \tag{21}\]
## 7 Comparative Analysis
### Answers to Questions  
In this work, we applied the PL approach in SDP and computed ROC-AUC scores for basic as well as paired learners. We selected two NASA datasets and nine versions of the Jureczko prop defect datasets from public online data repositories. We answer the questions raised in Section 1 by referring to the results posted in previous sections.  
**RQ1**-_How significantly are the concept-drift points present in defect data used for SDP?_  
The software-defect data presented in Table 4 comprises both dependent and independent variables of categorical software metrics data collected from various software projects. A defect-prediction model predicts the value of a dependent variable by analyzing the data of the independent variables. The presence of CD in data may lead to worse predictions and therefore may downgrade the model's performance. Paired learners of NB and RF were used for detecting the presence of CD in defect data. The experimental results presented in Table 8 show that the PL-based approach has significantly detected more drift points compared to a similar approach [13]. To cope with CD, the PL-based method described in Section 4 updates the stable learner of the paired learner; refer to Algorithm 1 and Figure 17 for details.  
**RQ2**-_How would the proposed approach for CD adaptation in SDP impact the performance of the defect prediction model?_  
The ROC-AUC measure was used as a performance-evaluation parameter in our current study. The performances of the base learner and its corresponding paired learner on the NASA and Jureczko datasets are presented in Table 6 and in Sections 6.1 and 6.2. Running the Wilcoxon test on accuracy scores of NB and PL-NB resulted in a rejection of the null hypothesis, with a corresponding \(p\)-value of 0.007, whereas that of RF and PL-RF also rejected the null hypothesis, with a corresponding \(p\)-value of 0.027. The hypothesis test results are presented in Table 10.
## 5 Result Analysis
### The Velocity Vectors
#### 5.5.1 Minimum Air Flow Velocity  
In the case of the minimum air flow velocity, the velocity vectors predictions can be seen in Figures 22-24 for the \(x\), \(y\) and \(z\) planes, respectively.  
It is possible to observe in Figure 22b,d for \(x_{adm}=0.463\) and \(x_{adm}=0.716\) that near the inlet air flow vent the air collides with the plates at the extremity of the server racks. Thus, it becomes noticeable that the air flow gains speed at the end of each row.  
By observing Figure 23a-c, it is expected that air recirculation will occur in some racks. This is due to the difficulty that air has in entering them, since the injected inlet air collides with the plates at the extremity of the server racks as mentioned above. In Figure 23b, it is predicted that the air flow will follow the desirable path. In this plan, it is possible to understand the behavior of the air flow in most of the server racks present in the data center room. It can be seen in the hot aisles that the prediction of the flow direction of the air exiting the server racks follows the desirable path to the extraction zone, with the exception of the air adjacent to the ends of the aisles, which tends to enter the exit of the server racks.  
By observing Figure 24a,b, it is predicted that, in the mid-height of the inlet air flow vent area, more pronounced values of air velocity can be noticed. In the inlet air flow vent area, vortices and small air recirculation are expected to occur due to the amount of movement blown in by the air vents. Air is still expected to be diverted from the desirable path towards the cold aisles as it collides with the plates at the extremity of the server racks and with the containment doors. Due to deviations in the trajectory, the air increases its velocity at the beginning of the cold aisles, gradually losing it as it moves towards the center of the cold aisle. It is also predicted that the floor and ceiling of the data center room will experience slower air flow velocities.  
Figure 21: The representation of three-dimensional geometry of velocity vectors for: (**a**) maximum air flow velocity; and (**b**) minimum air flow velocity.  
Figure 23: The results of the dimensionless velocity vectors for the minimum air flow velocity in the \(x\)–\(z\) plane represented through the planes: (**a**) \(y_{adm}=0.100\); (**b**) \(y_{adm}=0.485\); and (**c**) \(y_{adm}=0.900\).  
Figure 22: The results of the dimensionless velocity vectors for the minimum air flow velocity in the \(y\)–\(z\) plane represented through the planes: (**a**) \(x_{adm}=0.042\); (**b**) \(x_{adm}=0.116\); (**c**) \(x_{adm}=0.526\); (**d**) \(x_{adm}=0.589\); (**e**) \(x_{adm}=0.779\); and (**f**) \(x_{adm}=0.842\).
## 5 Numerical Experiments
### Results of Search Strategy before Obtaining a Feasible Solution

Table 6: Results of the \(t\)-test to evaluate the difference time [s] until a feasible solution was obtained between the search methods 1A-S and 1C-S.  
Figure 8: Relationship between time until a feasible solution is firstly obtained and the number of obtained feasible solutions. (**a**) Search method 1A-S (search for all neighborhood solutions of the Or-opt and the CROSS-exchange). (**b**) Search method 1C-S (search for normal order insertion of the Or-opt and normal order exchange of the CROSS-exchange). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1A-S**} & \multicolumn{2}{c}{**1C-S**} & \\
**No.** & **Avg. [min]** & **SD** & **Avg. [min]** & **SD** & **_p_** \\ \hline \(1\) & 243.76 & 5.72 & 241.63 & 4.51 & 0.041 \\ \(2\) & 238.16 & 5.08 & 236.61 & 4.34 & 0.104 \\ \(3\) & 228.47 & 4.36 & 228.31 & 6.42 & 0.886 \\ \(4\) & 242.56 & 4.92 & 242.15 & 4.54 & 0.668 \\ \(5\) & 249.06 & 4.64 & 249.59 & 4.38 & 0.558 \\ \(6\) & 270.81 & 6.42 & 269.55 & 7.13 & 0.464 \\ \(7\) & 238.52 & 6.27 & 236.17 & 6.96 & 0.163 \\ \(8\) & 213.15 & 3.39 & 213.14 & 4.00 & 0.985 \\ \(9\) & 241.64 & 6.19 & 239.44 & 5.25 & 0.061 \\ \(10\) & 243.28 & 4.62 & 239.80 & 5.14 & 0.001 \\ \hline Avg. & 240.94 & 5.16 & 239.64 & 5.27 & 0.393 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the \(t\)-test to evaluate the difference of the average objective function values [min] between the search methods 1A-S and 1C-S.