esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To clarify the behavior of the internal fatigue cracks on \(\beta\) titanium alloys, fatigue tests and fractography were performed on a Ti-22V-4 Al alloy, and the following conclusions were drawn:  
1. The \(S\)-\(N\) curves under negative stress ratios exhibit a step-wise shape with a fatigue limit of the surface fracture. However, under a positive stress ratio, the fatigue limit of the surface fracture becomes less remarkable, and the \(S\)-\(N\) curve exhibited an asymptote shape.
2. The concepts of major and minor facet(s) and the classification of the facet bonding patterns were established to differentiate the various characteristics of the multiple facets and to describe their distinct growth behaviors. Both the major and minor facet(s) exhibited a 45\({}^{\circ}\) facet angle to the fracture surfaces, indicating that they were presumably formed by slip dislocation.
3. The total facet area and major facet area are applied to the \(\Delta K_{ini}-N_{e}/\sqrt{area_{ini}}\) diagram and fatigue life prediction. The effect of stress ratios mentioned in conclusion 1 was diminished by substituting the range of the applied stress. In the internal fracture regime, when the major facet area was used, the \(\Delta K_{ini}-N_{e}/\sqrt{area_{ini}}\) diagram showed a better linear relationship. In addition, there was a lower deviation in the predicted fatigue life when the major facet area was used than when the total facet area was used. These results confirm the reliability of two-step facet growth behaviors of the internal fractures. In addition, the results of the surface cracks obtained by the total facet area and major facet(s) area were all reliable, suggesting that the surface fracture was easily propagated owing to its high propagation rate regardless of the microstructural effect.  
Facet formation life can be omitted in both the surface and internal fracture regimes. However, the crack initiation life can only be considered negligible in the internal fracture regime, and not in the surface fracture regime, which can be attributed to the larger exponential material constant, \(m\), of the internal fracture.  
Conceptualization and methodology, G.X. and T.N.; formal analysis, G.X.; investigation, G.X. and N.F.; resources, H.O.; writing--original draft preparation, G.X.; writing--review and editing, T.N., N.F., and H.O.; supervision, T.N. and K.T.; funding acquisition, T.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Japan Society for the Promotion of Science, Japan, grant number: Scientific Research (A, 18H03748).  
The authors express sincere appreciation to Daido Steel Co., Ltd. for providing the experimental material. This work was supported by f3 Engineering Education and Research Center, Faculty of Engineering, Hokkaido University. The authors are profoundly grateful to a graduate student at Hokkaido University, [PERSON], for his technical support to the experiment.  
The authors declare no conflict of interest.
# Impacts of Diverse Natural Products on Honey Bee Viral Loads and Health
## 3 Results
### Beta-Carotene  
Supplementation by beta-carotene led to no difference in VDV loads between beta-carotene-fed bees and the control (ANOVA \(n\) = 6, DF = 1, \(p\) = 0.6531) or DWV (\(n\) = 6, DF = 1, \(p\) = 0.6862). The levels of the honey bee symbiont \(S\). _alvi_ were lower in the treatment samples versus the controls (mean control = 2.6885 +- 0.5713 (SE), \(n\) = 10; mean beta-carotene = 0.4533 +- 0.7376 (SE), \(n\) = 6, \(p\) = 0.0311) (Figure 5). There was also no difference in the immune-related genes hypernoptaecin (\(n\) = 6, DF = 1, \(p\) = 0.8622), abaecin (\(n\) = 6, DF = 1, \(p\) = 0.3702), apidaecin (\(n\) = 6, DF = 1, \(p\) = 0.8347), and eater (\(n\) = 6, DF = 1, \(p\) = 0.5425) between bees supplemented with beta-carotene and the controls. Similarly, a suite of physiological genes did not show differences between treatments and controls: Cytochrome p450 (\(p\) = 0.1735), defensin (\(p\) = 0.8176), Pgrp-lc (\(p\) = 0.1353), Vg (\(p\) = 0.1391), Mrip1 (\(p\) = 0.7023), endochitinase (\(p\) = 0.5428), glucosidase (\(p\) = 0.6669), trehalose (\(p\) = 0.2995), and peritrophin (\(p\) = 0.5006).  
Figure 3: One-way analysis of DWV and VDV by treatment. The control was fed sugar water only. Treatments were dissolved in sugar water as described in the Methods section. The diamond tips represent the 95% confidence intervals for the ANOVA, with the mean designated with the middle horizontal line across each diamond. Increased values indicate an increased viral load. Student’s \(t\)-test. Comparison circles shown means, where circles that overlap are not significantly different, whereas circles that do not overlap are significantly different.  
Figure 4: One-way analysis of DWV by treatment. Cup study with newly emerged bees, 10 replicate cups/treatment or control. The control is DWV-inoculated sugar-fed control. GAR was DWV-inoculated with garlic oil supplemented with sugar water at 1000 ppm (**a**) and 10,000 ppm (**b**). The Y-axis shows \(\Delta\)Cq. The diamond tips represent the 95% confidence intervals for the ANOVA, with the mean designated with the middle horizontal line across each diamond. Increased values indicate an increased DWV load. (**a**) Mean DWV-fed control = \(-4.2800\) + \(-2.3144\) (SE) (mean DWV- and garlic-fed = \(-7.8870\) + \(-2.3114\) (SE), \(p\) = \(0.2844\), \(n\) = 10). (**b**) Mean DWV-fed control = \(-6.6170\) + \(-2.4050\) (SE); (mean DWV- and garlic-fed = \(-9.3700\) + \(-2.4050\) (SE), \(p\) = \(0.4288\), \(n\) = 10).  
Figure 5: One-way analysis of a honey bee gut symbiont in bees fed beta-carotene vs. the control. Normalized Cq values of a cup study with 6 replicates/cup per treatment or 10 replicates/cup for the control. Treatments were supplemented with sugar water at 2000 ppm. A larger value indicates increased detection of the honey bee gut symbiont _S. alvi._ The diamond tips represent the 95% confidence intervals for the ANOVA, with the mean designated with the middle horizontal line across each diamond.
## 3 Results and Analysis
For studying soil, buildings, vegetation, and water bodies in the 390-730 nm interval, the GS fusion method was observed to have the best performance. It was followed by HGF, while PCA and HA were less effective. In the 730-1400 nm and 1400-2260 nm intervals, HGF was the best, followed by GS, HA, and PCA.
## 3 Results and Discussion
### FT-IR Spectra  
To analyze the active site of ht-pDA/ACF, we further test the surface functional groups of CF and ht-pDA/ACF by FTIR. The FTIR spectra of CF (Figure 7) shows two strong peaks at 3435 and 1640 cm\({}^{-1}\), which are attributed to the bending vibration of hydroxyl groups (-OH) and the -C=O stretching vibration of the carboxyl group (-COOH), respectively. The weak absorption bands at 2930 and 2850 cm\({}^{-1}\) are ascribed to the stretching vibration of the methyl and methylene groups (-CH\({}_{2}\), -CH\({}_{3}\)) of CF. the bands at 1384 and 1055 cm\({}^{-1}\) originated from -C-O-H asymmetric bending vibration and -C-O-C asymmetric vibration. However, two sharp peaks at 1502 and 1285 cm\({}^{-1}\) appear on the spectra of ht-pDA/ACF, which can be assigned to the indole structure and the stretching vibration of -C-N according to the previous literature [23]. Such new peaks for ht-pDA/ACF prove that the dopamine polymerized into pDA during the hydrothermal process and pDA had been successfully introduced to the surface of CF. Similarly, the curve of ao-pDA/ACF shows the peaks of -C-N and indole structure at 1460 cm\({}^{-1}\) and 1285 cm\({}^{-1}\). However, the peak intensity of ao-pDA/ACF decreases significantly compared to ht-pDA/ACF, implying that the  
Figure 5: The SEM images of carbon felt (**a**), ao-pDA/ACF (**b**) and ht-pDA/ACF (**c**,**d**).  
functional groups could be buried by the film structure of ao-pDA/ACF. The results show that ht-pDA/ACF could expose more defects and active sites that are favorable to H\({}_{2}\)O\({}_{2}\) production because of the spherical structure formed during the hydrothermal process.
## 3 Methodology
### Collaborative Management of Meso-Macro Information
#### 3.2.2 Data Integration  
In the process of data integration within the multi-scale information management framework, its usability is a crucial aspect that demands comprehensive assessment. Regarding data accuracy and reliability, while the unified standard system generally ensures proper matching and calculation of data like a building plot ratio, challenges from complex geometries and terrains persist, necessitating continuous algorithm refinement. The SuperMap platform exhibits relatively high processing efficiency with its advanced engine and techniques, yet it must adapt to the ever-increasing data volumes of growing projects. User experience is a mixed bag; the visual interface and controls are helpful but the learning curve for non-technical users remains steep, especially when it comes to code-based operations. The method shows good scalability and adaptability across various project types and sizes, yet it needs to keep evolving to accommodate new technologies and diverse data requirements. Overall, although the current data integration approach has its merits, improvements in handling complex data scenarios, enhancing user-friendliness, and maintaining adaptability are essential for its long-term viability and effectiveness in multi-scale information management.  
Similar to the micro-meso data path, some government units require control data that needs to be processed before it can be obtained. After importing BIM and GIS data into the platform, based on a unified data classification and coding standard system, the powerful data processing functions of the platform such as spatial analysis, spatial statistics, and terrain analysis are used to integrate the BIM data with the surrounding environmental data. Then, according to the spatial control data indicators required by the government agency, the model data are analyzed from a spatial geographic perspective. The data are then extracted, calculated, and translated through program code writing. This allows government units to obtain the required control data, such as land use red line, building density, plot ratio, etc. This facilitates regional management and leadership in making the best planning decisions and achieving coordinated control of data information from mesoscale to macroscale. The integrated logical architecture of each data control index is shown in Figure 13.  
Figure 13: Data information integration logical architecture.  
For example, integrating building plot ratio data information in the SuperMap platform. After importing BIM and GIS data into the SuperMap platform, add four controls, namely the SuperMap Legend Control, SuperMap Control, SuperWorkspace Control, and SuperWorkspace Manager Control, to the Visual Studio ToolBox. Then, use C# language to write corresponding code through SuperMap Objects.NET to integrate and calculate the plot ratio data in the model, and finally obtain the plot ratio data information for construction units and government units to view and process. The main part of the code for integrating plot ratio data is shown in Figure 14.
# SATCOM Earth Station Arrays Anti-Jamming Based on MVDR Algorithm
## 4 Simulation Analysis
### Jamming Suppression Performance in Actual Satellite Communication
In this experiment, the amplified jamming power after the antenna is much greater than the noise power, resulting in a small contribution of the noise power to the output SINR of the MVDR algorithm and the single antenna. Figure 9: Output SINR versus EIRP of jamming. Therefore, the output SINR of the array based on the MVDR algorithm with diagonal loading is approximately equal to that of the single antenna at the above angles, resulting in poor jamming suppression performance of the MVDR algorithm with diagonal loading. The CBF algorithm can be understood as a special case of the MVDR algorithm with all weight values equal.
## 3 Experimental Analysis
### Sensitivity Analysis and Model Stability  
For the further analysis of the proposed method, we applied three evaluation indices for each type of fault detection, namely precision, recall, and f1-score. Precision is the ratio of correctly predicted positive observations to the total predicted observations, recall is the ratio of correctly predicted positive observations to all the observations in the actual class, and the f-measure is the weighted average of precision and recall [49]. The precision, recall, and f-measure for each bearing class \(c\) are defined as follows:  
\[\begin{split}&\text{{Precision }}(c)=\frac{\text{{True }Positive}}{\text{{True }Positive}+\text{{False }Positive}}\\ &\text{{Recall }}(c)=\frac{\text{{True }Positive}}{\text{{True }Positive}+\text{{False }Negative}}\\ &\text{{f-measure }}=2\frac{\text{{Precision }Recall}}{\text{{Precision }}+\text{{Recall}}}\end{split} \tag{5}\]  
Table 4 shows the sensitivity analysis results of the proposed model. It shows the values for precision, recall, and f1-score obtained when we evaluated the performance of the proposed model using those measures. The training, testing, and validation accuracy are highlighted in Table 5.  
In order to check the model stability, we performed each experiment three times. The proposed model was found to be stable. The tolerance was found to be \(\pm\)0.02.
# Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey
## 2 Control of Autonomous Vehicle
### Lateral Control of Autonomous Vehicles
#### 2.1.1 Lateral Model-Based Control Techniques
A novel approach utilizing a fractional-order PID control algorithm based on data-driven control was proposed to enhance tracking precision in autonomous driving [32]. The algorithm's parameters were optimized using the particle swarm optimization (PSO) algorithm. Comparative analysis against PID control and linear quadratic regulator (LQR) was performed using MATLAB/CarSim. Experimental results showed that the fractional-order PID control effectively reduces tracking error caused by path curvature changes while maintaining comfort, stability, and safety. In [33], a control algorithm based on a lateral dynamic model was introduced for path tracking in autonomous vehicles. To enhance stability at high speeds, an improved model predictive control (MPC) controller was proposed. By combining the steady-state response and MPC, the lateral motion of the vehicle can be controlled smoothly, ensuring accurate path tracking at high speeds. Simulation results obtained using MATLAB were presented to validate the effectiveness of this approach. An efficient model predictive control (MPC) approach for lateral control (LC) in autonomous vehicles was presented in [34]. To address computational time limitations, a proposed approximate explicit model predictive control (AEMPC) scheme utilized pre-computed control gains and eliminated the need for online quadratic programming. The suitability of AEMPC for real-time implementation on automotive electronic control units (ECUs) was emphasized, and the usefulness of AEMPC in lateral control was demonstrated through computational experiments. A lateral control for autonomous vehicles aiming to compare the performance of three controllers (PID, Stanley, and sliding mode control) across multiple environments was investigated in [35]. By evaluating their performance in seven environments, the study identified the controllers that excel in each specific setting. These findings enable the design of an efficient controller suitable for diverse environments. Table 2 summarizes the lateral model-based control techniques existing in the literature. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Papers** & **Publication** & **Control Techniques** & **Vehicle Model/Vehicle** & **Output/Primary** & **Validation** & **Advantages** & **Disadvantages** \\  & **Year** & **Type** & **Baseline** & **Objective** & & & & \\ \hline \hline
[23] & 2023 & SAGE in conjunction with & The model of the vehicle & The model of the vehicle & - & Chattering is completely removed. & - & - & - \\ distributed observer & & tuned dynamics, including the conditions of the & Path-defining & Simulation & – & Chen isn't differentiable & - & Requires bounded disturbances. \\  & & and gain scheduling & & external disturbances & & - & Stability is robust. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Conf.
# Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory
## 2 Trip Distribution Characteristics of Suburban Tourism Activities  
Tourism activity refers to the process that tourists take time and space for tours and sightseeing. Tourism activities could be influenced by many factors such as the number, distribution, trip distance, and attraction of scenic spots. Normally, tourists attracted by multiple scenic spots will organize multiple trips and activities in a certain order. Thus, tourism activities within a city's suburbs could have many kinds of modes (Figure 1).  
The primary characteristics of tourism activities are listed below:  
1. The multiple trips characteristic. Tourists usually like to visit multiple scenic spots when they travel in a tourism city. Each visit to a scenic spot is recorded as a trip. Thus, tourism activity could involve multiple trips. Note that the number of trips is generally limited due to the factors such as travel time and tourists' willingness.
2. The sequential characteristic. The travel paths of tourism activities are not randomly distributed but follow certain rules. For example, when tourists visit different scenic spots of a tourism city, they generally travel in a certain order. Specifically, they always travel starting from a departure point (e.g., a hotel or transportation hub), then arrive at various scenic spots, and finally end at an accommodation place or transportation hub. This indicates that the trips involved in tourism activity are sequential trips that can be linked as a trip chain.
3. The trip distance characteristic. Activities between transportation hubs and scenic spots reflect the trip distance of tourists [23]. Trip distance between departure points and scenic spots, or between scenic spots, is a very important factor that can affect the time, costs, and experiences of tourism activities. It can be regarded as a traveling obstacle since long-distance travel is time-consuming, costly, and tiring. Thus, the scenic spots near the urban areas generally could attract more tourists.
4. The clustering characteristic. Activity places of tourists in tourism cities mainly include transportation hubs, scenic spots (especially scenic spots close to each other), and accommodation places. Transportation hubs are distributing centers of tourists, scenic spots (tourist attractions) are the destinations of tourism activities, and accommodation places are the locations where tourists have rest, respectively. What they have in common is that they are the nodal areas that can attract and cluster a large number of tourists.
# Simulation of Partial Discharge Induced EM Waves Using FDTD Method--A Parametric Study +
###### Abstract  
This paper reports the results of a parametric study on the characteristics of electromagnetic (EM) waves propagated due to surface- and cavity-type partial discharges (PD) in materials using the finite-difference time domain (FDTD) method. First, the EM waves emitted by such discharges in material samples were measured using a broadband aperture antenna. The measurements showed that the frequency range of the measured signals lay within the ultra-high frequency (UHF) range, suggesting that by carefully choosing the UHF antenna characteristics and its location it might be possible to apply this method to characterize the PD-emitted waves; and hence, to potentially use it to detect and monitor PD defects. In this context, the FDTD simulations were used here to simulate the experimental set-up and examine the propagation characteristics of EM waves emitted by such discharges under uniform and non-uniform test electrode configurations. Using an approximation of the exciting PD current pulses, the electromagnetic field components and the voltage signals captured on a simulated monopole sensor were computed in the time domain at various locations. To explore the limits of the application of the UHF method for detecting these PD types, a parametric study was carried out to clarify how the captured signals are influenced by the PD intensity, the frequency content of the exciting PD pulse, the type of insulation material, the dimensions and the position of the UHF antenna. One of the challenges that needs further investigation is the accurate simulation of the actual PD current pulse produced by such discharges, and hence its frequency content, as there is limited or no measured data available. The results showed that while the amplitude of the captured EM signals increase with the PD intensity, no appreciable signal is detected when the PD pulse width is higher than about 4 ns, which may not occur often in unbounded air insulated systems. Equally important is the location and orientation of the UHF sensor--the results showed improved sensitivity when the sensor is vertically polarized and placed in close proximity in the lateral direction with reference to the discharge path.  
Keywords:partial discharge; surface discharge; UHF sensor; FDTD simulation; cavity discharge +
Footnote †: This paper is an extended version of our paper published in 2018 IEEE International Conference on High-Voltage Engineering and Applications (ICIVE 2018), Athens, Greece, 10-13 September 2018, doi:10.1109/ICHVE.2018.8642074.
## 4 Surrogate-Based Optimization (SBO)
### Genetic Algorithm  
GA is a survival strategy of the fittest based on the theory of biological evolution and is often utilized in the optimization of practical engineering problems [54; 55]. Its optimization process mainly includes selection, crossover, and mutation operations [56]. Three maximization problems are included in this study's SBO process. The first is constructing a parameter combination in the KM to determine the maximum likelihood function (MLE) value to obtain the best model fit. The second is to determine new infilled points according to the criterion of maximizing RMSE and add them to KM to improve the global prediction accuracy. The third is determining optimal heat transfer performance on the final KM. In GA, chromosomes represent parameters in the optimization problem, and fitness is the corresponding objective function. The GA code is compiled based on the book [57] in this paper. The basic GA steps are as follows:  
1. Generate the initial population: Randomly generate chromosomes (individuals), with each chromosome using binary coding to represent different parameters of the problem, and evaluate the fitness of each chromosome.
2. Selection: Mainly performed through tournament selection, selecting chromosomes with higher fitness for subsequent operations.
3. Crossover: Select two chromosomes to perform a two-point crossover operation to generate a new chromosome.
4. Mutation: Randomly transform some chromosome genes to introduce genetic diversity.
5. Selection and update: Update the population according to fitness, eliminate chromosomes with low fitness, and retain chromosomes with high fitness into the next generation.
6. Termination condition: Repeat the above process until the set number of generations is reached and the optimal generation is obtained.  
We execute these three optimization problems using the GA process mentioned above. The entire process gradually approaches the optimal solution through multiple iterations without calculating the differential of the objective function. It is suitable for complex objective functions or challenging to calculate the differential directly. The GA code is executed through MATLAB(r) to search for the optimal solution. The GA's setting parameters are as follows: The population size and number of generations are set to 100 and 200, respectively. The crossover and mutation probabilities are set to 0.6 and 0.1, respectively.