esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Distributed Order Damage Creep Model of Rock Mass
### Establishment of the Distributed Order Damage Creep Combined Model of Rock Mass  
The creep model can be established by combining the mechanical elements. Using the method, various distributed order damage creep models of the rock mass could be built by combining the fractional order damper model and the distributed order damage damper model. Based on the integer-order Nishihara model, the distributed order damage creep model of the rock mass was established in this paper, as shown in Figure 9. During the establishment of the model, the former establishment model of the fractional order creep model was used to replace the integer-order damper of the Kelvin model in the integer-order Nishihara model, and the integer-order damper in the Bingham model with the fractional order Abel damper and the distributed order damage damper, respectively.  
According to the stress-strain relationships among the elements and the series-parallel combination relationships, when \(\sigma(t)=\sigma_{0}\), the distributed order damage Nishihara model equation of state could be expressed by Equation (66).  
\[\left\{\begin{array}{l}\sigma_{0}=E_{0}\varepsilon_{1},\ \sigma_{0}=E_{1} \eta_{10}^{a}D_{t}^{a}[\varepsilon_{2}]+E_{1}\varepsilon_{2}\\ \varepsilon_{3}=e^{\lambda t}L^{-1}\left\{\int_{a}^{b}\frac{\omega(\gamma)}{( \delta+\lambda)^{4}}d\gamma\frac{\sigma_{0}-\sigma_{3}}{s}\right\}\\ \varepsilon=\varepsilon_{1}+\varepsilon_{2}+\varepsilon_{3}\end{array}\right. \tag{66}\]  
Figure 8: Curves of the creep properties of the distributed order damper with various damage parameters.  
Figure 9: The distributed order damage creep model of the rock mass. (**a**) The integer-order Nishihara model, (**b**) The distributed order damage Nishihara model.  
To solve Equation (66), the creep equation of the distributed order damage Nishihara model could be obtained, as follows:  
\[\begin{array}{ll}\varepsilon(t)&=\frac{\sigma_{0}}{E_{0}}+\frac{\sigma_{0}}{E_ {0}}t^{\alpha}E_{\alpha,k+1}(-\frac{E_{1}}{E_{0}^{\alpha}}t^{\alpha})\\ &+\frac{\sigma_{0}-\sigma_{0}}{\pi}\int_{0}^{\infty}\frac{\sigma_{0}^{\mu}- \varepsilon^{-\tau}}{x+\lambda}\frac{1}{\ln^{\alpha}(x)+\pi^{2}}\int_{a}^{b} \omega(\gamma)x^{-\gamma}\sin(\tau\gamma)d\gamma dx\end{array} \tag{67}\]  
A calculation example: With \(a=0.1\), \(b=0.3\), \(\sigma_{0}=50\)MPa, \(\sigma_{\rm s}=38\)MPa, \(E_{0}=5\)GPa, \(E_{1}=15\)GPa, \(E_{1}=5\)GPa/h, \(\alpha=0.4\), and \(\omega(\gamma)=1\), the curves of the creep properties of the distributed order damage Nishihara model could be obtained under various damage parameters by substituting the above parameters into Equation (67). The curves are shown in Figure 10. It could be seen that the curves could better reflect the creep properties of the rock mass. Particularly, when \(\lambda>1\), the curves could show the creep properties at the stages of instantaneous deformation, deceleration deformation, and accelerating deformation. When \(\lambda\) was relatively small, the curves could reflect the creep properties at the stages of instantaneous deformation, deceleration deformation, and constant deformation.
# Experimental Study on the Effect of Wind Speed on Hail Impacts
## 4 Multivariate Regression Analysis of Hail Impact Forces
The test results are summarized in Table 6.  
Based on the results of the \(F\)-test (\(p\leq 0.001<0.05\)), the regression equation is statistically significant overall, demonstrating a linear relationship between hail impact force and the variables of wind speed, hail diameter, and hail velocity. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Event** & **Square sum** & **Degrees of freedom** & **Mean square** & **F** & **Significance** \\ \hline Regression & 19.892.879 & 3 & 6630.960 & 176.368 & \(\leq\)0.001 \\ Residual & 451.169 & 12 & 37.597 & & \\ Aggregate & 20.344.048 & 15 & & & \\ \hline \end{tabular}
\end{table} 
Table 6: F-test. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Event**} & **Regression** & **Standardized** & \multirow{2}{*}{**\(t\)**} & \multirow{2}{*}{**Significance**} & \multicolumn{2}{c}{**Covariance Statistics**} \\ \cline{3-4}  & **Coefficient** & **Coefficient Beta** & & & **Tolerance** & **VIF** \\ \hline Constant & \(-262.402\) & — & \(-13.240\) & \(\leq\)0.001 & — & — \\ Hail diameter & 6375.502 & 0.506 & 10.187 & \(\leq\)0.001 & 0.750 & 1.334 \\ Wind speed & 2.321 & 0.344 & 8.007 & \(\leq\)0.001 & 1.000 & 1.000 \\ Hail velocity & 9.487 & 0.564 & 11.368 & \(\leq\)0.001 & 0.750 & 1.334 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Determination of regression coefficient (dependent variable: force/N). The overall reliability of the model has been confirmed through rigorous validation. The fitted regression formula was subsequently applied to predict the peak hail impact forces. A goodness-of-fit plot comparing predicted and observed values is presented in Figure 12. As illustrated in Figure 12, the model exhibits strong agreement between predictions and experimental data, demonstrating its robustness. Therefore, the use of this regression model to estimate the peak structural loads induced by hail impacts is reasonably justified for engineering applications.
## 2 Background
### Energy Harvesting
Electromagnetic energy harvesting is a technology that uses an antenna to collect radio waves floating on the ground and convert them into electrical energy [32]. Currently, energy harvesting based on micro-energy sources has low industrialization and commercialization compared with energy harvesting based on macro-energy sources, because the power and voltage of the produced energy are low. However, energy harvesting based on micro-energy sources has the potential to solve the inconsistency of energy production from solar and wind power caused by weather conditions.
## 5 Use Case
### New Bipartite Circulant Topology Generation Using Cartesian and Tensor Products  
The problem of searching for new topologies for NoCs and supercomputers can also be taken another look at: while supercomputers are already capable of containing tens of thousands of computing nodes [49], chips have recently also reached such sizes that they can accommodate a huge number of computing cores [50; 51]. At the same time, finding the optimal circulants for hundreds of nodes is already a rather difficult task [49]. There is a significant number of works that offer various methods for generating new topologies. These are both hierarchical topologies [52; 53; 54] and topologies obtained by the graph product [55] (a special case is optimal circulant product [52]), etc. However, as shown earlier, it is not enough to propose a new topology and evaluate its characteristics. We still need to develop the effective routing algorithms, as well as methods for dealing with deadlocks. Therefore, we propose to consider this problem from the reverse: one can choose a basic subgraph without cycles and then, based on it, use the methods of Cartesian and tensor products to generate new topologies. Thus, in such graphs, it can be guaranteed that the method of dealing with deadlocks, described in Section 5.1, can be applied.  
**Example 4**.: _According to Figure 3, the bipartite circulant graph \(C_{8,4}\) can be generated from the graph \(K_{1,4}\)._  
**Example 5**.: _Based on tensor product in Example 2 in Section 4, the union of the graphs in Figure 7 provides \(C_{18,9}\) shown in Figure 9._  
Figure 8: Deadlock-free routing in \(C_{8,4}\) circulant.
## 4 Simulation Results and Security Evaluation
### Chosen/Known Plain Image Attack  
According to [PERSON] et al. [41], several image encryption algorithms, highly regarded for their remarkable statistical performance, have succumbed to vulnerabilities arising from the exploitation of chosen/known plain image attacks. In these attacks, different encryption keys can be employed to decrypt the cipher image of another encrypted image. The failure of the secret key used for the Pepper image to successfully decrypt the encrypted Lena image is illustrated in Figure 19.  
Figure 17: Row and column floating frequency and means for plain Lena image.  
To enhance the proposed encryption scheme's resilience against cryptanalysis attacks, several measures have been implemented. In order to create a greater challenge for attackers who attempt brute-force attacks, the key size has been increased. K1 and K2 together have a total length of approximately \(2^{65,536}\) key spaces, which makes them extremely robust against cryptanalysis attacks. Additionally, the scheme incorporates multiple rounds and utilizes various techniques, such as permutation, substitution, and diffusion, to enhance its robustness. Multiple chaotic maps have been incorporated into the key generation process, iteration numbers have been increased, and initial map conditions have been modified to make it even more complicated. Furthermore, the pixel scrambling process has been enhanced through the utilization of diverse techniques such as row-column scrambling and block-level scrambling. These collective measures contribute to fortifying the encryption scheme and bolstering its resistance against cryptanalysis attacks, ensuring heightened security for the encrypted data.  
Figure 19: Chosen/know plain image attack.  
Figure 18: Row and column floating frequency and means for encrypted Lena image.
# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 2 Related Work and Motivation
### Depth Estimation Using Deep Learning  
In order to develop depth maps, three types of learning methodologies are followed: supervised, semi-supervised, or unsupervised. When features are learned with all available datasets, this is known as supervised learning. Depth sensing in supervised learning is based on a single or monocular view. Datasets used in supervised learning are single RGB images with respective depth maps. Semi-supervised machine learning is a hybrid of supervised and unsupervised learning techniques. It employs a small amount of labeled data and a large amount of unlabeled data, providing the advantages of both unsupervised and supervised learning. Primarily, this technique relies on virtual data and certain auxiliary information. Similarly, in unsupervised learning, no ground truth dataset is used. The dataset fed in the network is prepared directly from epipolar geometry constraints. In certain cases, a stereo camera system is used to create a dataset before feeding it into the network ([PERSON] et al., 2018). Different types of learning techniques can be seen in Figure 3.  
Figure 3: Types of deep neural networks based on learning. (**a**) Supervised learning, (**b**) semi-supervised and unsupervised learning.  
Our proposed method is based on supervised learning because it has many advantages. The significant advantage is that the model learns a greater number of features from the ground truth dataset, and they create very detailed depth maps. The model can be quickly evaluated, both qualitatively and quantitively, as the datasets provide test samples to elaborate the performance. We will briefly explain the methods by which depth maps are generated using different techniques.
## 3 Datasets and Methodology
### Datasets
#### 3.1.2 TEC Dataset  
For the TEC dataset, we used the International GNSS Service (IGS) Analysis Centre dataset, which contains final global ionospheric TEC maps, extracted from averaging four different available models (CODE-University of Bern in Switzerland, ESA-ESOC Darmstadt Germany, JPL-Jet Propulsion Lab in Pasadena USA, and UPC-University Polytechnic  
Figure 1: A plot of all the earthquake events with magnitudes larger than Mw 6.8 spread on a global map, taken from our earthquakes dataset. The radius and the color of each earthquake are consistent to the magnitude–yellow corresponds to earthquakes in the range of Mw 6.8–7, orange corresponds to earthquakes in the range of Mw 7–7.9, and red for earthquakes \(\geq\)Mw 7.9.  
Catalonia in Barcelona, Spain) [6]. The IGS dataset also includes rapid global TEC maps in the cylindrical projection along with root mean square error (RMSE) values and rapid TEC maps in polar projection ([[https://spdf.gsfc.nasa.gov/pub/data/gps/tec15](https://spdf.gsfc.nasa.gov/pub/data/gps/tec15) min_igs/]([https://spdf.gsfc.nasa.gov/pub/data/gps/tec15](https://spdf.gsfc.nasa.gov/pub/data/gps/tec15) min_igs/), accessed on 10 January 2020). The TEC maps (Figure 2) are produced with a resolution of 15 min, \(5^{\circ}\) and \(2.5^{\circ}\), in time, longitude, and latitude, respectively. This results in 96 maps per day with dimensions of \(73\times 71\) for each map.
# Versatile Video Coding-Post Processing Feature Fusion:
## 4 Experimental Evaluations and Discussion
### Ablation Studies
Table 10 outlines LD configuration and QP 22. Both results are for the D class, which is the toughest to improve due to its low-resolution frames. Even so, our proposed solution outperforms the methods without a QP map-based solution. Figure 12 illustrates a visual quality comparison of the proposed method with 12 feature extraction blocks for RA and LD scenarios at QP 42, using the RitualDance and Cactus sequences. Notably, the results show that increasing the number of feature extraction blocks to 16 yields PSNR gains of 0.38 dB, 0.15 dB, and 0.09 dB for the RA scenario and 0.01 dB, 0.02 dB, and 0.54 dB for the LD scenario, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sequences**} & \multicolumn{3}{c}{**PSNR (12 Block)**} & \multicolumn{3}{c}{**PSNR (16 Block)**} \\ \cline{2-7}  & **Y** & **U** & **V** & **Y** & **U** & **V** \\ \hline MarketPlace & 30.82 & 38.69 & 39.06 & 30.83 & 38.73 & 39.14 \\ \hline RitualDance & 31.57 & 40.20 & 39.42 & 31.58 & 40.13 & 39.45 \\ \hline Cactus & 29.38 & 36.33 & 36.83 & 29.39 & 36.32 & 36.87 \\ \hline BasketballDrive & 30.62 & 38.19 & 37.11 & 30.63 & 38.22 & 37.18 \\ \hline BQTerrace & 28.73 & 37.72 & 40.22 & 28.72 & 37.68 & 40.27 \\ \hline BasketballDrill & 29.08 & 35.01 & 34.55 & 29.09 & 34.90 & 34.55 \\ \hline BQMall & 28.06 & 36.40 & 36.84 & 28.06 & 36.39 & 36.85 \\ \hline PartyScene & 24.35 & 33.29 & 33.66 & 24.35 & 33.21 & 33.60 \\ \hline RaceHorses & 26.89 & 33.20 & 34.58 & 26.89 & 33.24 & 34.61 \\ \hline Average & 28.83 & 36.56 & 36.92 & 28.84 & 36.54 & 36.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: PSNR comparison of proposed method with 12 and 16 feature extraction blocks in LD configuration. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sequences**} & \multicolumn{3}{c}{**PSNR (12 Block)**} & \multicolumn{3}{c}{**PSNR (16 Block)**} \\ \cline{2-7}  & **Y** & **U** & **V** & **Y** & **U** & **V** \\ \hline MarketPlace & 30.82 & 38.69 & 39.06 & 30.83 & 38.73 & 39.14 \\ \hline RitualDance & 31.57 & 40.20 & 39.42 & 31.58 & 40.13 & 39.45 \\ \hline Cactus & 29.38 & 36.33 & 36.83 & 29.39 & 36.32 & 36.87 \\ \hline BasketballDrive & 30.62 & 38.19 & 37.11 & 30.63 & 38.22 & 37.18 \\ \hline BQTerrace & 28.73 & 37.72 & 40.22 & 28.72 & 37.68 & 40.27 \\ \hline BasketballDrill & 29.08 & 35.01 & 34.55 & 29.09 & 34.90 & 34.55 \\ \hline BQMall & 28.06 & 36.40 & 36.84 & 28.06 & 36.39 & 36.85 \\ \hline PartyScene & 24.35 & 33.29 & 33.66 & 24.35 & 33.21 & 33.60 \\ \hline RaceHorses & 26.89 & 33.20 & 34.58 & 26.89 & 33.24 & 34.61 \\ \hline Average & 28.83 & 36.56 & 36.92 & 28.84 & 36.54 & 36.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: PSNR comparison of proposed method with 12 and 16 feature extraction blocks in LD configuration.
# Review on Structural Health Evaluation with Acoustic Emission
## Appendix A Limiting Frequency of Rayleigh Wave Propagation  
Wave motion of Rayleigh waves decreases rapidly as the depth increases [20; 21]. When the thickness of a plate, h, becomes smaller than three to four times the wavelength, wave propagation gradually shifts to Lamb wave modes. [PERSON] [217] examined by experiment and by finite element analysis the limiting frequency of the Rayleigh wave propagation. Since this is of practical importance, some of his results are reproduced in Figure 11. He used a large steel plate (25.4-mm thick) in modeling Rayleigh wave propagation from a pencil-lead break. Wavelet transform spectrogram of calculated signal is shown in Figure 11 after 381-mm travel on the same side. Corresponding spectrogram for experimentally detected signal is given in Figure 11. Both spectrograms show the strongest peak at the arrival time of Rayleigh waves (130 \(\upmu\)s) and signal intensity diminishes below 250 kHz, shifting to Lamb wave S\({}_{0}\) and A\({}_{0}\) modes. His results of the low frequency limit of Rayleigh wave propagation, \(f_{\mathrm{L}}\), (in MHz) can be described by \(f_{\mathrm{L}}\) = 8.2/h, as shown in Figure 11. For a given thickness, h (in mm), Rayleigh waves exist only at frequencies above \(f_{\mathrm{L}}\). At h = 100 mm, this \(f_{\mathrm{L}}\) is 82 kHz. This was the case for [PERSON]'s attenuation study discussed in Section 2.2.
# Virtual Belt Algorithm for the Management of Isolated Autonomous Intersection
## 5 Initial Idea of Virtual Belt Algorithm
### Discussion of The Initial Idea  
Consider that \(G_{v}\) and \(G_{v}^{\prime}\) (as shown in Figure 9) are placed on an intersection with two through lanes. The start and end points of these VPs can be used to represent the departures and destinations of several approaching CAVs. The size of the VG is bigger than these CAVs. It is required that these CAVs should follow the trajectories of \(G_{v}\) and \(G_{v}^{\prime}\) to their destinations one by one without collision.  
If a CAV is required to follow the trajectory of a VG to through the intersection, the CAV is called to be **transferred** by the VG and the VG is called **assigned** with the CAV as well. When a CAV arrives at the destination, it should move away from the intersection in constant speed to avoid affecting the \"transfer service\" of the intersection.  
The VGs used for the transfer of CAVs should be selected carefully. An inappropriate choice may lead to collisions among CAVs. For instance, if the VGs demonstrated in Figure 8 are used for CAV transfer, the approaching CAVs may collide with each other. If a VG and its CVGs are used for the transfer of CAVs simultaneously, collisions among these CAVs may occur. A natural idea is that when a VG is used to transfer a CAV, its CVGs should not be used. Thus, the collisions among CAVs can be avoided at the considered intersection.  
The starting point of the initial idea is that several VBs are placed on the considered intersection. Each approaching CAV is assigned with a VG before the conflict points in the intersection. Thus, the approaching CAVs can follow the trajectories of the selected VGs and pass the intersection without collisions. Two requirements should be satisfied to ensure the safety of intersection management: First, the VGs which are assigned with CAVs should not collide with each other at the intersection. Second, the CAVs should be able to follow the planned trajectories with small tracking error.  
Figure 12: Collision checking of three VBs.
# Design and Experimental Study of a Down-Drive Piezoelectric High-Frequency Fatigue Testing Machine
## 5 Conclusions  
In response to the need for a piezoelectric fatigue tester capable of generating small alternating loads, a down-drive piezoelectric high-frequency fatigue testing machine prototype was developed with a load range of 0.1 N to 100 N. Firstly, the structural composition and working principle of the device were analysed, and a spring-damping system model was established to analyse the effects of mass block mass and spring stiffness on the intrinsic frequency of the fatigue testing machine. Next, the experimental platform was set up, and tensile fatigue tests were performed on steel specimens in the shape of a dog bone. The test results show that the amplitude of the stress acting on the specimen increases gradually with the increase in the driving voltage, the increase in the specimen stiffness, the increase in the preset load, and the increase in the fast mass of the counterweight mass. Through the load change curves during the fatigue test, it was found that both the static and dynamic loads acting on the specimen decayed after 500 s. The designed piezoelectric fatigue testing machine is experimentally verified to be suitable for fatigue testing with small dynamic loads and high loading accuracy. It provides a technical reference for subsequent scholars to study the piezoelectric fatigue testing machine in the range of less than 100 N, which has great application value in the field of fatigue testing technology for reliability and durability determination of microelectromechanical devices and bionic devices products.  
Methodology, Z.Y.; writing--original draft preparation, S.Z.; formal analysis, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant number 51875234.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
## 5 Conclusions  
The aim of this study was to evaluate the correlation between three soil water content investigation techniques in an effort to understand if satellite data obtained from remote sensing has a high potential for use in the management of water resources and in precision irrigation, as well as no-vegetation conditions. The results showed that indexes utilized from satellite data correlate in different cases with proximal sensing methods. Even if the method cannot be considered fully satisfactory, the high correlation between the MCARI2 and the in-field sensor network suggests possible applications in the condition of bare soils. This opens the opportunity to monitor soils after sowing to guarantee the success of the seed emergence. To fully exploit the potential of this approach, its association with proximal investigation methods is still recommended. These aspects will be investigated in future studies. In view of using digital systems to make living conditions simpler and affordable for the population, this paper has contributed towards exploring satellite technology to facilitate the process of planning precision irrigation and to reduce the costs of investment in tools and equipment for future farming generations.  
Conceptualization, E.R., S.B., C.B., R.P. and A.S.; Data curation, E.R.; Formal analysis, E.R.; Methodology, S.B.; Supervision, C.B. and A.S.; Validation, R.P.; Writing--original draft, E.R., S.B. and A.S.; Writing--review & editing, S.B., C.B., R.P. and A.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Italian Minister of Agriculture, Food Sovereignty and Forests (MASAF) as part of the project \"AGRIDIGIT\" subproject \"AGROFILIERE\" DM n. 36503 of 20/12/2018.  
Ethical review and approval were waived for this study because they were not applicable.  
The authors wish to thank [PERSON], [PERSON], and [PERSON] for their assistance in performing field operations and for their professionalism and availability.  
The authors declare no conflict of interest.