esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In the field of biomedical signal processing, accurately finding brain activity from electroencephalography (EEG) signals is the focus of much research. In the brain-computer interface (BCI) applications based on event-related potentials (ERP), fast and efficient feature extraction and classification of EEG signals to understand human intention are the current research hotspots in this field [1]. For the real-world brain-computer interface, it is necessary to extract the ERP waveform from the EEG signal obtained in a single trial [2]. This is not an easy task, since the ERP is usually submerged in noise. Although the commonly used superposition and averaging method can remove part of the random noise, it requires EEG data from multiple trials to be superimposed in order to obtain the results, so the response speed of the entire BCI system cannot be guaranteed, and it cannot be directly applied to real-world BCITherefore, it is necessary to use the feature extraction method to extract the classification features from the EEG signal to identify the ERP waveform [3].
## 3 Mobile Network Security Evolution and the Evolution of Threat Landscape
### Fifth-Generation Security Architecture
#### 3.3.1 Authentication Framework  
In 5G networks, the Authentication and Key Agreement (AKA) protocol serves as the cornerstone for secure communication. It has undergone substantial revisions compared to its 4G counterparts to address emerging security challenges: (1) Home network-based authentication: The authentication in 5G is primarily orchestrated by the home network rather than the serving network, a shift that eliminates several risks associated with rogue base stations. (2) SUPI (Subscription Permanent Identifier): A key feature introduced in 5G AKA is the use of SUPI. SUPI allows the home network to securely identify and authenticate a user, thereby creating an additional layer of security. (3) Extensibility: The AKA protocol in 5G is designed to be extensible, providing a framework that can adapt to future security requirements without necessitating a complete architectural overhaul [60; 61].
## 3 Results and Discussion
### Structure and Morphology by Means of XRD, HRTEM, and Raman Analyses
#### 3.1.3 Raman Investigation
Therefore, a relationship has not been systematically investigated for MoS\({}_{2}\) particles grown on oxide particles as a support (in situ method). As a matter of fact, in the case of highly dispersed supported MoS\({}_{2}\), the morphology (i.e., effect of particle roughness and curvature) and surface properties of the support (i.e., preferential growth of peculiar crystal faces and surface defects) may affect the shape and positions of the MoS\({}_{2}\) Raman bands, thus meaning that the A\({}_{\text{1g}}\) and E\({}^{\text{1}}\)\({}_{\text{2g}}\) peak positions cannot be taken as a direct reference of MoS\({}_{2}\) staking level. However, by reasonably applying the established relationships [50] to our MoS\({}_{2}\)/TiO\({}_{2}\) nanosheets, an average stacking order of 2 \(\pm\) 1 layers per particle could be estimated for samples coming from the in situ method, whereas a higher stacking number of 3-4 for samples obtained by the ex situ method can be found, in agreement with XRD and HRTEM analyses.
## Appendix A
### Radiation Testing of the Avalanche Photodiode Array and Readout IC  
One of the design methods used in MISTIC Winds to simultaneously provide high-quality hyperspectral IR sounding and imaging from a miniature spacecraft is to employ cooled mid-wave IR (MWIR) focal plane arrays operating at a relatively high temperature. The APDIS Avalanche Photodiode (APD)-Mode HgCdTe FPA, developed by DRS [31], and shown in Figure 11, has been used effectively in operational airborne hyperspectral imaging applications by BAE Systems over the last several years. This FPA employs an HgCdTe APD array that raises detector signal to the point where simple-and compact pre-amplifiers within the small (25 micrometers) unit cells of the FPA manage the signal effectively, even for low radiance channels. However, neither the HgCdTe avalanche photo-diode array nor the APDIS ROIC has been used in space.  
To mitigate this risk, total-dose radiation tolerance testing for the APDIS FPA was performed under the MISTiC Winds IIP by the Infrared Radiation Effects Lab (IRREL), based in Albuquerque, NM in August of 2015. Total-dose tolerance was evaluated by characterizing the changes in the FPA characteristics introduced by 68 MeV proton irradiation provided by an accelerator on the campus of the University of California at Davis. FPA radiometric characteristics (responsivity, broadband noise, and 1/f noise) and Readout IC current and voltage characteristics were evaluated prior to irradiation, and then following a series of six escalating radiation dose increments, while holding the FPA at its planned operating temperature (below 90 K), while at the bias voltages planned for operation. Table 11 shows some representative results from this test.  
The key results of this total-dose testing are the following:  
ROIC characteristics were essentially unchanged up to 70 krad accumulated dose-the highest used in this test,  
Detector dark current and broadband noise increased with dose-but at a rate acceptable for the projected four-to-five-year mission-life for a MISTiC instrument. The FPA noise remained below the instrument flow-down requirement through a dose of 20 krads (Si), and  
Only modest increases in 1/f noise were observed, and these only at relatively high APD gains (higher than planned for MISTiC operation), and at higher proton doses (above 20 krads).  
Specifically, array-average dark current is shown, both prior to radiation, and following the accumulated dose shown in the table. The proton radiation total-dose flow-down requirement for dark current (5 fA/diode) was only exceeded at a total dose between 15 and 25 krads. This level of total-dose tolerance meets the anticipated mission requirements for MISTiC Winds. Additionally, a large fraction of the total-dose damage introduced into cryogenically operated HgCdTe photodiodes anneals out when the diodes returned to room temperature.
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Cargo Port Drayage
#### 4.1.2 Context  
Port drayage refers to the movement of containers between a port terminal and an inland distribution point or rail terminal. A typical drayage assignment involves either delivering an export container to a marine terminal or picking up an import container that has arrived by ship [46]. According to the Intermodal Association of North America (IANA), there are more than 60 million drayage movements each year in North America.
# A Study on the Effects of Vertical Mass Irregularity on Seismic Behavior of BRBFs and CBFs
## 2 Buildings Studied  
### Design of the Buildings  
In the HAZUS-MH MR5 guidelines [59], buildings with more than eight stories are referred to as high-rise buildings. To assess and compare the seismic behavior of lateral systems, eight 10- and 15-story steel-frame buildings--four regular and four irregular--with CBF and BRBF were chosen to be modeled. Three-dimensional models were designed in accordance with the ACI318-05 [60] in ETABS software. The structures were designed in ETABS using ST37 steel with a yield stress (F\({}_{\text{y}}\)) of 2400 kg/cm\({}^{2}\) and an ultimate stress (F\({}_{\text{u}}\)) of 3700 kg/cm\({}^{2}\). The dead and live loads of these buildings were 450 (kg/m\({}^{2}\)) and 200 (kg/m\({}^{2}\)), respectively. According to the design code [23], when the mass of a building floor exceeds 1.5 times of the mass of the adjacent floors, the building has mass irregularity. On the other hand, there is no agreement about the most effective and critical position of an irregular floor [61]. In the present study, floors with mass irregularities were chosen to be the second and sixth floors in the 10-story building and the second and 12 th floors in the 15-story building. Figure 1 shows the position of floors with mass irregularities.  
Figure 1: Mas irregularity positions in the simulated buildings.
# Predicting the Mine Friction Coefficient Using the GSCV-RF Hybrid Approach
## 2 Predictive Model Construction
### GSCV Optimization Algorithm  
GSCV (grid search cross-validation) is an algorithm comprised of a grid search and cross-validation that enables automatic parameter tuning in order to identify the ideal combination of parameters, which frequently optimizes the process, in conjunction with other algorithms [22]. As the prediction results of RF are significantly affected by the values of the hyperparameters, the GSCV algorithm is introduced and combined with RF to circumvent this drawback. GSCV is then used to optimize RF by determining the optimal input parameters so as to build a GSCV-RF prediction model for predicting \(\alpha\). The GSCV optimization algorithm is as follows:  
1. Set the range of each hyperparameter and set range of RF's hyperparameter (growth condition) as an example: \[\begin{array}{l}x_{1}\in[1,n]\\ x_{2}\in[1,m]\\ x_{3}\in[1,z]\end{array}\] (9) where \(n\) is the upper limit of the value of the hyperparameter \(x_{1}\); \(m\) is the upper limit of the value of the the hyperparameter \(x_{2}\); and \(z\) is the upper limit of the value of the hyperparameter \(x_{3}\).
2. To obtain a hyperparameter combination, set each hyperparameter individually. Assuming that each hyperparameter step is 1, \(n\times m\times z\) hyperparameter combinations \((x_{n},x_{m},x_{z})\) are created. Hence, each hyperparameter combination represents one of the growth conditions of RF.
3. To avoid the chance of outcomes owing to dataset partitioning, the dataset is divided into \(K\) mutually exclusive subsets of the same size, \(d_{1}\), \(d_{2}\), \(\cdots\), \(d_{k}\), and each subset is utilized as a separate validation set once, and the remaining \(K\)-1 subsets are used to produce \(K\) new datasets.
4. Each hyperparameter combination \((x_{n},x_{m},x_{z})\) is trained once on each of the \(K\) new datasets, the goodness-of-fit \(R_{1}^{2},R_{2}^{2},\cdots,R_{k}^{2}\) under each dataset is produced, and the output of the hyperparameter combination is the mean value \(\overline{R_{(x_{n},x_{m},x_{z})}^{2}}\) of the corresponding goodness-of-fit for each dataset. \[\overline{R_{(x_{n},x_{m},x_{z})}^{2}}=\frac{1}{k}\sum_{i=1}^{k}R_{i}^{2}\] (10)
5. Repeat step 4 for each combination of hyperparameters in order to identify the optimal output as an input parameter for the algorithm combined with GSCV. The following is an expression of the optimal output: \[\max_{x_{n},x_{m},x_{z}}[\overline{R_{(x_{n},x_{m},x_{z})}^{2}}]\] (11)
## 5 Study on Fracture Development  
### Development of Cracks  
In the crack resistance test stage, the change of the crack development of the frame beam specimen with the loading is shown in Figures 13-16. For the frame beam specimens KJ-1 and KJ-2, when loaded to \(P_{\mathrm{cr}}=5\) kN (0.05 \(P_{\mathrm{u}}\)), the first crack appears in the specimen and the crack width is 0.01 mm. When \(P=20\) kN (0.13 \(P_{\mathrm{u}}\)), several bending cracks appear on the side near the column and the crack direction is perpendicular to the beam axis. When \(P=40\)-60 kN (0.26-0.40 \(P_{\mathrm{u}}\)), the number of cracks increases. The height and width of cracks increase, while the cracks on the left and right sides are approximately symmetrically distributed. When \(P=80\)-100 kN (0.53-0.67 \(P_{\mathrm{u}}\)), the cracks near the loading end incline to the loading point, the number and width of cracks increase further, and secondary oblique cracks appear locally. When \(P=120\) kN (0.8\(P_{\mathrm{u}}\)), the new cracks are connected with the previous shear oblique cracks and the crack width increases significantly. The obvious difference between specimens with the ECC protective layer (KJ-E1 and KJ-E2) and comparison specimens (KJ-1 and KJ-2) is that the cracks within the range of the ECC protective layer are more intensive and compact, and the spacing of the cracks is significantly smaller than that of the comparison specimens (KJ-1 and KJ-2). The ECC protective layer did not detach from the RC during the loading stage of the beam. It shows that the interface construction of the ECC protective layer in Section 2 achieves a reliable connection between the ECC protective layer and the bonding surface of RC, which plays a greater degree of crack resistance in the negative moment zone of RC frame beams.  
Figure 13: Crack development of the specimen KJ-1.  
Figure 16: Crack development of the specimen KJ-E2.  
Figure 14: Crack development of the specimen KJ-2.  
Figure 15: Crack development of the specimen KJ-E1.  
At the stage of the crack resistance test, the final failure of the frame beam specimen is shown in Figure 17. The variation of cracks in KJ-E1 and KJ-E2 specimens with the ECC protective layer with loading is the same as that of specimens KJ-1 and KJ-2. The cracks in the range of the ECC protective layer are denser, and the crack spacing is much smaller than that of the RC specimens. However, the crack width is significantly reduced, and the ECC protective layer and internal components have good working performance [38]. When the ECC protective layer is 2/3 beam height (KJ-E2), the maximum crack width outside the protection range is not more than 0.2 mm, which can also achieve a better protection effect.
# Ohmic Heating in the Food Industry: Developments in Concepts and Applications during 2013-2020
## 10 Effect of Ohmic Heating on Inactivation of Enzymes in Food
[109] used the ohmic heating at several temperatures to study its effect on inhibition and analysis of the kinetics parameters of alkaline phosphatase, pectin methylesterase and peroxidase in milk, fruit juices, and vegetables compared with the conventional method (indirect heating). The kinetics parameters were changed while inhibition mechanisms remained constant. The changes in the kinetics parameters were relatively few for pectin methylesterase and alkaline phosphatase, while significant changes and instability of peroxidase occurred due to the effect of ohmic heating on carrot juice [109]. [PERSON] [82] studied the effect of ohmic heating on the methyl esterase pectin (PME) and polyacaltokronase (PG) in fresh tomato juice at pH (3.9 and 4.4). The results observed a great variation in the inactivity of the pectin methyl esterase activity at pH 3.9 due to the interactions of enzymes with the electric field, while the PG enzyme showed the least variation with a higher rate of inhibition. The results also found that when pH increased to 4.4, PG was inhibited quickly using ohmic heating.
## 2 Development of the 7-DOF Human Model
### Derivation of Model Equations of Motion
The coordinates of the ends of the spring damper are derived by setting the hip joint as the reference point. For instance, the derivation of deflection of the spring-damper at the thigh segment is described in Figure 3a. The position of the hip joint before and after deformation is denoted as \(C_{31}^{*}(x_{h}^{*},z_{h}^{*})\) and \(C_{31}(x_{h},z_{h})\); \(B_{31}^{*}\) and \(B_{32}^{*}\) are the position of the end of the first and second spring-damper in the undeformed configuration and \(B_{31}\) and \(B_{32}\) in the deformed configuration attached to the thigh; \(A_{31}^{*}\) and \(A_{32}^{*}\), are the fixed ends of the spring-damper; \(\Delta_{31}^{*}\) and \(\Delta_{32}^{*}\), are the unstretched length of the first and second spring-damper. The asterisk * represent the undeformed state for each of these points. Considering the first spring damper in Figure 3a, \(\delta_{31}\) is the parallel spring extension from \(B_{31}^{*}A_{31}^{*}\) to \(B_{31}^{*}A_{31}^{*}\) with respect to the undeformed configuration, where x-z coordinates for points, \(A_{31}^{*}(x_{31}^{*},z_{31}^{*})\) and \(B_{31}(x_{31},z_{31})\) are derived as  
\[KE=\frac{1}{2}\overset{5}{\underset{i=1}{\sum}}m_{i}\Big{(}\dot{x}_{i}^{2}+ \dot{z}_{i}^{2}\Big{)}+\frac{1}{2}\overset{5}{\underset{i=1}{\sum}}j_{i}\dot{ \theta}_{i}^{2}, \tag{2}\]3  
\[KE=\frac{1}{2}\overset{5}{\underset{i=1}{\sum}}m_{i}\Big{(}\dot{x}_{i}^{2}+ \dot{z}_{i}^{2}\Big{)}+\frac{1}{2}\overset{5}{\underset{i=1}{\sum}}j_{i}\dot{ \theta}_{i}^{2}, \tag{2}\]4  
\[KE=\frac{1}{2}\overset{5}{\underset{i=1}{\sum}}m_{i}\Big{(}\dot{x}_{i}^{2}+ \dot{z}_{i}^{2}\Big{)}+\frac{1}{2}\overset{5}{\underset{i=1}{\sum}}j_{i}\dot{ \theta}_{i}^{2}, \tag{2}\]5  
\[KE=\frac{1}{2}\overset{5}{\underset{i=1}{\sum}}m_{i}\Big{(}\dot{x}_{i}^{2}+ \dot{z}_{i}^{2}\Big{)}+\frac{1}{2}\overset{5}{\underset{i=1}{\sum}}j_{i}\dot{ \theta}_{i}^{2}, \tag{2}\]6  
and by rotating the thigh through \(\theta_{3}^{*}\), we can see that \(\delta_{31}\) is the vertical axis (\(z\)-axis) displacement of the spring. Clockwise rotation transformation is applied to \(B_{31}A_{31}^{*}\) in the form  
\[ \begin{bmatrix}x_{31}-x_{31}^{*}\\ z_{31}-z_{31}^{*}\end{bmatrix} ^{\prime}= \begin{bmatrix}\cos\theta_{3}^{*}& \sin\theta_{3}^{*}\\ -\sin\theta_{3}^{*}&\cos\theta_{3}^{*}\end{bmatrix}\begin{bmatrix}x_{31}-x_{3 1}^{*}\\ z_{31}-z_{31}^{*}\end{bmatrix}  \tag{12}\]  
Figure 2: The deformed and undeformed configurations of the 7-DOF model during vibrations. which changed to \(d_{22}\) and \(\Delta_{22}^{*}\), respectively. Substituting the expressions in Equations (3), (4), and (7) and expressions for \(\delta_{11}\), \(\delta_{12}\), \(\delta_{21}\), \(\delta_{22}\) and \(\delta_{51}\), into the energy equations in (2), (5), and (6), we solve [PERSON]'s equation in (1) and obtained non-linear equations of motion with respect to each generalized coordinate, \(q_{k}\). The expressions for the deflection terms \(\delta_{21}\), \(\delta_{22}\), \(\delta_{32}\), and \(\delta_{51}\) are provided in Appendix A.
## 5 Circularly Polarized MIMO Antenna Design Approaches
The LHCP wave is generated by the clockwise rotation of surface currents on the T-shape antenna radiator and rectangular-shaped GP at different phase angles. In both E- and H-planes, the isolation between the LHCP and RHCP patterns is \(>\)15 dB, demonstrating that the developed antenna possesses strong cross-polarization discrimination. In [91], a modified square patch serves as the radiating structure. The truncated corner approach is used to obtain the CP, and an additional open-circited stub on the GP is used to increase the CP bandwidth. Two orthogonal modes with the same amplitude are produced by utilizing the hook-shaped open-circited stub and truncated corner patch. The various circularly polarized MIMO antenna designs and the different antenna parameters along with diversity performance are compared in Table 3. The different methods used to obtain CP characteristics and AR bandwidth are presented in the table.
# Improving Particle Swarm Optimization Analysis Using Differential Models
## 4 Differential Evolution PSO  
This section will mainly discuss the selection method of the step size \(h\) and verify the computational efficiency of several differential evolution PSO algorithms proposed above through numerical simulation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Order of Local Truncation Error** \\ \hline Euler method & \\ \(y_{n+1}=y_{n}+hf(x_{n},\,y_{n})\) & 1 st order \\ \hline Implicit Euler method & 1 st order \\ \(y_{n+1}=y_{n}+hf(x_{n+1},\,y_{n+1})\) & 2 nd order \\ \hline Trapezoidal Euler method & \\ \(y_{n+1}=y_{n}+\frac{h}{2}[f(x_{n+1},\,y_{n+1})+f(x_{n},\,y_{n})]\) & 2 nd order \\ \hline Improved Euler method & \\ \(\left\{\begin{array}{ll}y_{p}=y_{n}+hf(x_{n},\,y_{n})&2 nd order\\ y_{c}=y_{n}+hf(x_{n+1},y_{p})&\\ y_{n+1}=\frac{1}{2}(y_{p}+y_{c})&\\ \end{array}\right.\) & 2 nd order \\ \hline Second-order Runge–Kutta method & \\ \(\left\{\begin{array}{ll}y_{n+1}=y_{n}+hK_{2}&2 nd order\\ K_{1}=f(x_{n},\,y_{n})&\\ K_{2}=f\Big{(}x_{n+\frac{1}{2}},y_{n}+\frac{1}{2}K_{1}\Big{)}&\\ \end{array}\right.\) & 2 nd order \\ \hline Third-order Runge–Kutta method & \\ \(\left\{\begin{array}{ll}y_{n+1}=y_{n}+\frac{h}{2}(K_{1}+4K_{2}+K_{3})&3 rd order\\ K_{1}=f(x_{n},y_{n})&\\ K_{2}=f\Big{(}x_{n+\frac{1}{2}},y_{n}+\frac{h}{2}K_{1}\Big{)}&\\ K_{3}=f(x_{n+1},y_{n}+h(2K_{2}-K_{1})&\\ \end{array}\right.\) & \\ \hline Fourth-order Runge–Kutta method & \\ \(\left\{\begin{array}{ll}y_{n+1}=y_{n}+\frac{h}{2}(K_{1}+2K_{2}+2K_{3}+K_{4})&4 th order\\ K_{1}=f(x_{n},y_{n})&\\ K_{2}=f\Big{(}x_{n+\frac{1}{2}},y_{n}+\frac{h}{2}K_{1}\Big{)}&\\ K_{3}=f\Big{(}x_{n+\frac{1}{2}},y_{n}+\frac{h}{2}K_{2}\Big{)}&\\ K_{4}=f(x_{n+1},y_{n}+hK_{3})&\\ \hline \end{tabular} & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table}
Table 1: Orders of local truncation errors for different numerical algorithms.
## 5 Experimental Design
### Analysis of Variance Results
#### 5.3.1 ANOVA Results under Different Imbalance Ratios
To further explore the effects of these parameters on recall under different imbalance ratios, we conducted an analysis of variance to obtain the \(p\)-values for the main effects and interaction effects of learning rate, dropout rate 1, dropout rate 2, and kernel size on the accuracy of minority class samples. Due to the substantial differences in the \(p\)-values, we standardized each \(p\)-value using the following formula:  
\[\text{Normalized Value}=\frac{x-\text{Minimum}}{\text{Maximum}-\text{Minimum}} \tag{20}\]  
This standardization process ensures that the effects of different parameters at various imbalance ratios can be compared on the same scale, thereby guaranteeing fairness and accuracy in the data analysis. By employing this method, we were able to illustrate and compare the substantial impact of various parameters on recall. As can be seen from Figure 6, the impact of different parameters on the model performance varied across different imbalance ratios. In highly imbalanced datasets, LR, KS, and their interaction effects significantly influenced the model. In moderately imbalanced datasets, the effects of LR and KS were more pronounced. In balanced datasets, the interaction effects of LR and KS (LR \(\times\) KS) and the interaction effects of DR 1 and KS (DR 1 \(\times\) KS) significantly impacted model performance. Based on these findings, it is possible to more precisely select and adjust the parameters of CNN models to improve their performance on imbalanced data. In summary, LR and KS significantly impacted the overall model performance across all imbalanced datasets, especially in highly imbalanced datasets. The interaction between these two parameters also significantly influences the model's performance. In balanced datasets, LR and KS still significantly affect the model performance. However, their effects are less pronounced than those for highly imbalanced datasets, whereas minor parameters such as DR 1 and DR 2 have a minor impact on model performance. In subsequent optimizations, attention should be focused on LR and KS, and their optimal combinations should be explored to enhance the performance of CNN models on imbalanced datasets. Figure 6: Effect of KS on recall in datasets with different imbalance ratios from the CIFAR-10 dataset.
## 3 Experiments and Analysis
### Experimental Setup
#### 3.2.3 Experimental Parameters  
The model uses Adaptive Moment Estimation (Adam) as the overall optimization and cross-entropy as the loss function. The initial learning rate is set to 0.01, the epochs of the training model is set to 10, and the batch size is set to 2260.