esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Cracking Simulation and Cracking Damage Range Analysis
### Analysis of Tunnel Cracking Width  
Under the action of internal water pressure, the lining may produce cracks. When the cracks occur, the modified polydimethylsiloxane coating may be pressed into the cracks under the action of internal water pressure. Then, the modified polydimethylsiloxane coating is cut by the edge of the cracks so that the modified polydimethylsiloxane coating loses its waterproof effect. Therefore, the crack width of the composite lining structure is calculated and analyzed.  
Figure 11: The maximum principal stress of the surrounding rock at the buried depth of 100 m. (**a**) Without cohesive element. (**b**) With cohesive element.  
Figure 10: The maximum principal stress of the composite lining at the buried depth of 100 m. (**a**) Without cohesive element. (**b**) With cohesive element.  
Three hundred and thirty sets of conditions including different in situ stress and internal water pressure were set up for comparative analysis. Among them, the simulated maximum vertical in situ stress is 51.94 MPa, and the maximum horizontal in situ stress is 72.716 MPa. The distribution of horizontal stress and vertical stress is shown in Figure 12. The range of internal water pressure is 1 MPa\(\sim\)10 MPa, and the internal water pressure of 153 groups of conditions is set to 5 MPa.  
When the water pressure is within 5 MPa, the maximum crack width of lining structure under different in situ stress conditions is shown in Figure 13. It shows that the influence of in situ stress on the crack width of the lining is affected by both the buried depth of the tunnel and the horizontal stress. The maximum crack width shows an overall downward trend with the increase in buried depth, indicating that the greater the buried depth of the tunnel, the more stable the lining structure is, and the less prone to crack damage.  
The influence of horizontal stress on the maximum crack width of lining structure is shown in Figure 14. As shown, as the horizontal stress increases, the maximum crack width decreases firstly and then increases under the same buried depth. The horizontal  
Figure 12: The simulated range of in situ stress.  
Figure 13: Isoline cloud diagram of maximum cracking width of lining structure. (**a**) Overall graph. (**b**) Local enlarged graph.  
stress of the inflection point is similar to the vertical stress. In other words, when the lateral pressure coefficient is less than 1, the maximum crack width decreases with the increase in the lateral pressure coefficient; when the lateral pressure coefficient is greater than 1, the maximum crack width increases with the increase in the lateral pressure coefficient. Therefore, when the lateral pressure coefficient is smaller or larger, the shallower the buried depth of the tunnel is, the more likely it is to crack.
## 4 Design of Key Parts
### Design and Manufacture of the Cam and Shifting Fork  
The gearbox is equipped with a cam that is affixed to it and a transplanting arm that is hinged to a shifting fork. Through the unequal rotation of the transplanting arm relative to the gearbox, the cam and shifting fork form a cam-swing-follower mechanism. In Figure 12, it can be seen that the pusher rod reciprocates to accomplish the clamping and pushing, which are made possible through the matching of the cam and the shifting fork with the spring seat. Figure 13 depict the precise casting and machining processes used to manufacture the cams and shifting forks. The transplanting mechanism of rice pot seedlings has a seedling-picking stage, a seedling-transportation stage, a planting stage and a return stage, and the cam curve is designed according to the movement characteristics of each stage. The cam curve comprises a lifting stage, a return stage, a resting stage and an intermediate stage, and the transplanting mechanism passes through the return stage, intermediate stage, lifting stage, resting stage of the cam successively in a working cycle. When the transplanting mechanism is in the seedling-picking stage, the cam is in the return stage, and the fork swings to drive the seedling-pushing rod to retract. When the transplanting mechanism is in the seedling-transportation stage, the fork is in the intermediate stage of the cam, and the seedling-pushing rod is kept in a contracted state. When the transplanting mechanism is in the planting stage, the fork is in the lifting stage of the cam, and the seedling-pushing rod swings to drive the seedling-pushing rod to push out, and the rice seedling is pushed out. When the transplanting mechanism is in the return stage, the fork is in the resting stage of the cam.  
Figure 12: Cam and shifting fork design. (**a**) Cam structure; (**b**) Shifting fork; (**c**) Working principle.  
Figure 13: Processing and manufacturing of cams and shifting forks. (**a**) Cam mold; (**b**) Shifting-fork mold; (**c**) Casting the cam; (**d**) Casting the shifting fork; (**e**) Cam processing; (**f**) Shifting-fork processing.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 5 Results and Discussions
### Comparative Methods
#### 5.3.4 Comparative Analysis of M5 Forecasting Dataset Based on K-Fold  
The evaluation's Figure 6(a) demonstrates the UDAF model's exceptional performance in demand forecasting, outperforming NARX with the lowest MAE of 3.57.  
Figure 6(b) further showcases the UDAF model's effectiveness in demand forecasting, surpassing NARX by 43.27% with an MSE of 3.5 for a K-fold 10.  
When it comes to demand forecasting, the UDAF model, as seen in Figure 6(c), excels in comparison to NARX. It achieves an improvement of 24.59% and has an RMSE of 1.87 for a K-fold 10, surpassing previous techniques.  
Figure 6(b) shows the UDAF model's effectiveness in demand forecasting, surpassing NARX by 43.27% with an MSE of 3.5 for a K-fold 10.  
When it comes to demand forecasting, the UDAF model, as seen in Figure 6(c), excels in comparison to NARX. It achieves an improvement of 24.59% and has an RMSE of 1.87 for a K-fold 10, surpassing previous techniques.  
Figure 6(a) shows the UDAF model's effectiveness in demand forecasting, surpassing NARX by 43.27% with an MSE of 3.5 for a K-fold 10.  
When it comes to demand forecasting, the UDAF model, as seen in Figure 6(c), excels in comparison to NARX. It achieves an improvement of 24.59% and has an RMSE of 1.87 for a K-fold 10, surpassing previous techniques.  
Figure 7: Comparative analysis of M5 forecasting dataset based on K-fold: (**a**) MAE, (**b**) MSE (**c**) RMSE, (**d**) RMSSE, and (**e**) R-squared.  
Furthermore, Figure 7d demonstrates the UDAF model's proficiency in demand forecasting through RMSSE, outperforming NARX by 3.12% with a 0.031 RMSSE for a K-fold 10.  
Regarding demand forecasting, Figure 7e showcases the superior performance of the UDAF model compared to NARX with an R-squared of 0.90 for a K-fold 10, which is 2.22% improved over NARX.
## 1 Introduction
This led to a better prediction accuracy of critical shear stress states. The triaxiality \(T\) and Lode Angle \(\theta\) are dimensionless and can be expressed as a function of the three principal stresses. To predict the ductility of each material, the calibration of the damage models has to be performed, which includes the execution of a series of different experimental tests devised to induce as many varied multiaxial stress states as possible in the material. Usually, these comprise tensile tests on smooth or notched specimens, torsion, compression, plane strain tests, as well as more complex multiaxial tensile-shear or tensile-torsion tests. Because of this, in addition to a uniaxial universal testing machine (UTM), the use of torsion or in general biaxial equipment is required, which requires a more complex experimental setup. This makes the calibration procedure of damage models non-trivial, and not suitable for small or even medium-sized companies interested in structural design, which in many cases do not have dedicated facilities. Therefore, the application of damage models is often restricted to the scientific community, without any solid contribution on an industrial scale. One goal of this work is to establish that it is possible to calibrate ductile damage models just using simple specimen geometries and a conventional uniaxial testing machine to run the tests. This would ease the testing phase and should promote the use of damage models by design companies. Two recent unconventional multiaxial specimen geometries were considered, which are capable of generating mixed tensile-shear stress states while loaded uniaxially. The first geometry was proposed by [PERSON] [32], while the second is proposed by [PERSON] [33]. Thanks to these specimens and proper fixtures, several stress states covering the range of low triaxiality can be reproduced, replacing torsion or mixed tensile-torsion specimens. To assess the performance of these geometries, three damage models were calibrated, and the ductility of a 17-4 PH steel and a of Ti6 Al4V titanium alloy was estimated. In the first part of this paper, an essential background on damage models is introduced, followed by a description of the setup and methodology used for the experiments. Along with the experimental activities, finite element analyses and digital image correlation postprocessing is used to evaluate the local values of stress and strain states at the critical points, which are not directly measurable quantities, and that are needed both for a ductility assessment and for the calibration procedures. Subsequently, the experimental and numerical results are presented, along with the outcome of the models' calibration, and the effectiveness of the approach is discussed thoroughly.
## 1 Introduction
There are considerable research results in the study of WMA's high- and low-temperature performance and aging mechanism after UV aging, but little has been reported on the moisture damage characterization and evaluation of WMA after UV aging. At the same time, it is important to characterize and evaluate the moisture-induced damage of the adhesion of WMA during UV aging conditions. At present, the evaluation methods for water damage of the asphalt mixture include the Marshall water immersion, freeze-thaw split, indirect tensile strength, and Hamburg wheel tracking tests. However, the correlation between the above methods and field observations is not good, which cannot suitably reflect the mechanism of water damage as reported. From the perspective of the mechanism, the attenuation of adhesive force between asphalt and aggregate surface is the main cause of moisture damage. Cohesive failure or adhesive failure at the asphalt-aggregate interface is the main cause of moisture-induced damage. In recent years, many researchers have tried to use the surface free energy (SFE) theory to find a direct quantitative characterization method for moisture damage of asphalt mixtures [24]. [PERSON] systematically described the asphalt SFE test method based on Wilhelmy plate method and the column wicking technique from the aspects of test solution selection, contact angle data validity verification, and SFE calculation [25]. In terms of raw materials, the adhesion work produced by the larger Lewis acidity and the smaller van der Waals force is beneficial to the improvement of the interfacial adhesion performance of the asphalt-aggregate system [26]. According to the surface energy of different matrix asphalts, the obtained clay cohesive work with its pull-off strength was compared. It was found that the cohesive work had a good correlation with the two parametersof pull-off strength [27]. It is believed that the SFE method can be used to characterize the water damage resistance of an asphalt mixture [28; 29]. In summary, researchers have done much research and achieved many results on the water damage of warm asphalt. However, the surface energy theory has rarely been used to study the adhesion properties of WMA after UV aging. The purpose of this study is to investigate the effect of a warming mix agent on the bonding performance of WMA under UV aging based on the SFE theory. WMA binder was prepared by adding Evotherm M1 and Sasobit to pure asphalt. The self-developed UV aging box and rotating film oven were used to age the uniform asphalt and two kinds of asphalt binders. The asphalt samples were aged by pressure aging vessels. The contact angle of pure asphalt and two kinds of WMA binders with distilled water, glycerine, and formamide during UV aging was measured using the solid drop method. Using the SFE theory, the SFE and its composition, bonding work, stripping work, and compatibility of asphalt mixture before and after UV aging were obtained.
## 4 Implementation of MIMO-VLC Using OptiSystem  
To evaluate the performance of the presented MIMO-VLC system, we utilize the optical system simulation software OptiSystem 21.0. As explained above, the main contribution of this study is to provide a comparative analysis of SISO-VLC and MIMO-VLC without and with the influence of ambient light with different line coding techniques.
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 4 Results and Discussion  
This section presents a detailed evaluation of the experimental results obtained for three visible-wavelength laser diodes--650 nm (red), 532 nm (green), and 405 nm (violet)--tested under identical electrical and environmental conditions. Performance was assessed based on three primary metrics: optical power output, quantum efficiency (QE), and modulation frequency response.
## 5 Conclusions  
This work demonstrates the feasibility of using ML algorithms as an alternative to classical techniques, used in wireless communication systems, to estimate and equalize the vehicular communication channel. A channel estimation and equalization algorithm based on extreme learning machines was designed and tested over two different channel system configurations: overtake and intersection scenarios.  
An improved version of an SS-ELM channel estimator and equalizer was introduced for vehicular communications considering the 802.11p amendment. The proposed SS-ELM scheme outperformed the extreme learning machine (ELM) and the fully complex extreme learning machine (C-ELM) algorithms for the evaluated scenarios. Furthermore, the proposed SS-ELM scheme performed similarly compared to other traditional techniques.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Algorithm** & **Time [ms]** \\ \hline LS & 0.0269 \(\pm\) 0.0095 \\ STA & 13.5 \(\pm\) 0.344 \\ CDP & 18.4 \(\pm\) 0.471 \\ ELM & 40.5 \(\pm\) 5.1 \\ C-ELM [13] & 127 \(\pm\) 9.74 \\ SS-ELM & 658 \(\pm\) 10.4 \\ Parallel SS-ELM & 167 \(\pm\) 2.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Algorithm execution times.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Model** \\ \hline Central Processing Unit (CPU) & Intel i5 10400F \\   Random Access Memory (RAM) & 2.9 GHz–4.1 GHz \\   Graphics Processing Unit (GPU) & 16 GB 2133 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computational system specifications.  
The proposed scheme had a better performance for lower \(E_{b}/N_{0}\) values compared to STA and CDP for the first and second system configurations, respectively, considering that \(E_{b}/N_{0}\) values in wireless environments are usually lower than 20 dB.  
We analyzed the impact that \(\delta\) has on the equalization process, and concluded that there is still room for improvement when considering rougher channel configurations than the ones shown in this work. As the value of \(\delta\) increases, the algorithm requires a longer execution time, but a larger \(\delta\) enhances the performance of the algorithm, it becomes suitable for optimization.  
Finally, the execution time of the proposed model was simulated on a general-purpose computer with an interpreted language such as Matlab, for a fair comparison with the other models. Even though the SS-ELM scheme requires the largest execution time among the evaluated techniques, it still falls within the latency window specified by the standard.  
Conceptualization, E.S., C.A.A.-M. and D.Z.-B.; methodology, E.S. and D.Z.-B.; software, E.S. and D.Z.-B.; formal analysis, E.S.; investigation, E.S.; writing--original draft preparation, D.Z.-B., C.A.A.-M., S.B., E.S. and I.S.; writing--review and editing, D.Z.-B., C.A.A.-M., S.B., E.S. and I.S.; supervision, C.A.A.-M.; project administration, C.A.A.-M.; funding acquisition, C.A.A.-M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Vicerrectoria de Investigacion y Desarrollo (VID) de la Universidad de Chile Proyecto ENL 01/20.  
The authors declare no conflict of interest.
# Approximation Method for Stress-Strain Using Metamodel Parameter Updating
## 2 Pre-Processing of the Constitutive Model
### CFT Method for Constitutive Model of the True Stress-Strain Curve  
As shown in Figure 4, the constitutive model properties initialized by the ASTM E646 standard did not correctly represent the actual physical phenomenon. This error can result in an error in the FE simulation and the load of the inverse method. Therefore, individual curve fitting of the constitutive model properties is required. The result of modifying a once-initialized parameter using MATLAB CFT is shown in Figure 5. Notably, the parameter initialization method reduced the difference between the experimental data and the constitutive model properties. This finding reveals that the constitutive model properties using the power and exponential functions cannot represent the actual physical phenomena. Furthermore, it can be observed that the combination function with several parameters exhibited accurate nonlinear characteristics.  
Figure 4: Constitutive model properties initialization using the ASTM E646 standard.  
Figure 5: Constitutive model properties initialization using ASTM E646 and MATLAB CFT.  
The difference between the experimental data and the constitutive model can be observed qualitatively in Figures 4 and 5; RMSE is used to compare the differences quantitatively. Figures 4 and 5 show the actual strain assuming zero yield strain. This method was later used to apply to material cards for FE simulations. The results of the quantitative comparison and analysis of the two datasets are shown in Figure 6. The X-axis and Y-axis in Figure 6 represent the constitutive model number and the RMSE value (between the constitutive model properties and experimental data), respectively. The solid red line indicates the RMSE value of the once-initialized constitutive model, and the dotted line indicates the RMSE value of the constitutive model to which the MATLAB CFT was applied. It may be observed that, except for Models 1 and 2 with no parameters, the proposed method using MATLAB CFT obtained an RMSE reduction effect for every constitutive model. This can be seen as a process of giving an initial value of a parameter rather than a specific result. Since the target of curve fitting is the engineering stress-strain, it is difficult to discuss the accuracy of Figure 6. The parameter initialization of the pretreatment step plays a role in determining the curve characteristics represented by the constitutive model. The accuracy of the configuration model can later be increased in the inverse method. However, if the typical method (Figure 4) is to be used, both the characteristics and accuracy of the curve should be determined with the inverse method.
## 5 Conclusions  
In conclusion, for defect detection in underwater plates, a non-contact method using laser ultrasonics is applied. The dispersion curves and attenuation relations of both A\({}_{0}\) and S\({}_{0}\) modes are calculated for mode selection. The specific S\({}_{0}\) mode, with low dispersion and attenuation, is generated by pulsed laser, laterally irradiating the side of the plate symmetrically. The A\({}_{0}\) mode is efficiently restrained, not only because of the symmetric lateral generation, but also because of the high attenuation caused by the surrounding water. The results show that the lateral laser source can generate non-dispersive S\({}_{0}\) mode efficiently in underwater plate, and this specific S\({}_{0}\) mode at 1 MHz\(\cdot\)mm has  
Figure 9: B-scan images of underwater plate with multiple defects: (**a**) two notches; (**b**) three notches.  
good potential for defect detection and localization in underwater plates. The successful identification of multiple defects has been experimentally verified in a plate with multiple defects within a short spatial interval. This method could serve as an alternative approach for evaluating and quantifying the integrity of underwater plates.  
Conceptualization, Z.S.; Funding acquisition, C.N. and Z.S.; Investigation, Q.X.; Writing--original draft, Q.X.; Writing--review & editing, C.N. and Z.S.  
This research was funded by National Natural Science Foundation of China, grant number 61627802, National Natural Science Foundation of China, grant number 61405093, Natural Science Foundation of Jiangsu Province, grant number, BK20140771 and Fundamental Research Funds for the Central Universities, grant number 30916014112-001.  
The authors declare no conflict of interest.
## 5 Conclusions  
In conclusion, the AKF-ALS algorithm introduced in this paper represents a significant advancement in the field of real-time visual object tracking. By integrating the autocovariance least squares estimation method with an adaptive Kalman filter, we have demonstrated a robust approach to tracking that compensates for system model mismatches and invalid offline calibration, which are critical in dynamic environments. The computational complexity analysis indicates that AKF-ALS is efficient enough for real-time applications, striking an optimal balance between computational load and tracking accuracy. Our experimental results have shown that the AKF-ALS algorithm exhibits superior performance over traditional Kalman filter methods that rely on fixed noise statistics. The algorithm's capability to adapt to changing noise covariances in real-time ensures greater accuracy in tracking the centroid of a moving object under various motion patterns, including projectile motion, free-falling with bounces, and linear back-and-forth motion. Experimental results on the complex PETS09-S2L1 dataset from the MOT-15 challenge demonstrate that the AKF-ALS VOT algorithm is capable of tracking pedestrians with occlusion where traditional methods fail. The algorithm's dynamic adaptation to changing noise covariances in real-time significantly enhances accuracy and reliability in tracking various motion patterns. Overall, the AKF-ALS algorithm's ability to iteratively refine noise covariances and adjust to abrupt motion changes makes it an excellent candidate for autonomous vehicles or mobile robotics.  
Future work should focus on expanding the validation of the AKF-ALS algorithm across a broader range of applications, including those with more complex and non-linear object dynamics. Additionally, further research could explore the integration of this algorithm with deep learning techniques to enhance tracking performance in highly cluttered and dynamic visual scenes.  
Conceptualization, J.L. and X.X.; methodology, J.L. and B.J.; software, J.L. and Z.J.; validation, J.L. and B.J.; writing--original draft preparation, J.L. and X.X. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by the National Natural Science Foundation of China under grant 61803034, 61973035, R&D Program of Beijing Municipal Education Commission (KM202311417006), and the National Natural Science Foundation of China Basic Science Center Program under Grant 62088101.