esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation Results
### Mesoscopic Analysis of the Similar Material
#### 4.4.3 Mesoscopic Evolution of the Similar Material  
Discrete element analysis allows for a more visual representation of the distribution and morphology of macroscopic cracks in the numerical specimen through its discrete fragment network (DFN). The cracks in the specimens are formed due to contact fractures between the particles. They are represented by coloured line segments. The blue colour indicates an earlier occurrence of the crack, while the red colour represents a later appearance of the crack.  
Figure 13: Number of contacts in the numerical specimen with different RFCAs: (**a**) total number of contacts; (**b**) average number of contacts.  
Figure 14 shows the macroscopic crack expansion in the numerical specimen with different RFCAs when the uniaxial compression reaches the failure stage and compares it with the specimens from laboratory UCTs. As evidenced by Figure 14, micro-cracks initially appear near the upper and lower loading plates, as well as in proximity to the coarse aggregate. As the axial stress increases, these micro-cracks extend along the edges of the coarse aggregate, ultimately coalescing to form macroscopic cracks.  
When \(H\) is small (\(H\leq 0.3\)), the percentage of fine aggregates is comparatively low. Thus, the propagation of micro-cracks between fine aggregates is readily impeded by the presence of coarse aggregate. Consequently, these micro-cracks tend to extend around the edges of the coarse aggregate. The direction of crack propagation undergoes a shift as it manoeuvres around the edge of the coarse aggregate, making it more conducive to the formation of macroscopic shear cracks. However, when \(H\) is relatively high (\(H>0.3\)), the expansion of microcracks between fine aggregates is less influenced by the coarse aggregate. The expansion tends to occur vertically along the edge of the fine aggregates, ultimately forming vertical macroscopic cracks. The macroscopic crack patterns and angles observed in the simulation are similar to those found in indoor experimental results.
###### Abstract
Hence, characterizing the channel quality using NDA-EVM is preferred. However, there is a dearth of studies applying NDA-EVM to adaptive modulation in the NLOS scenario. This can be attributed to two reasons. First, the current literature does not provide closed-form expression of the NDA-EVM in an NLOS scenario. Hence, modeling the channel of NLOS scenario by using the \(\eta-\mu\) distribution and providing the closed-form expression of the NDA-EVM under the \(\eta-\mu\) fading channel are the theoretic basis of NDA-EVM-AM. Second, due to the lack of the expression for the relationship between NDA-EVM and SER, the adaptation strategy based on NDA-EVM remains an open problem. Thus, providing the closed-form expression of SER-NDA-EVM is the key for the design of the adaptation strategy. Against this background, the primary contributions of this paper can be summarized as follows. (1) A closed-form expression is derived for the NDA-EVM of the multilevel quadrature amplitude modulation (MQAM) signal over \(\eta-\mu\) fading channels, and the relationship between NDA-EVM and SER is also formulated; (2) A NDA-EVM state machine is designed for adaptation strategy of NLOS scenario in WSN; (3) The theoretical outage capacity of NDA-EVM-AM in NLOS scenario is given. The paper is structured as follows. In Section 2, the system model of NDA-EVM-AM in NLOS scenario is presented. In Section 3, NDA-EVM as a channel metric in the NLOS scenario is formulated, the closed-form expression of SER in terms of NDA-EVM is also presented. In Section 4, the adaptation strategy based on NDA-EVM state machine is designed. In Section 5, the algorithmic complexity of NDA-EVM-AM is analyzed. In Section 6, the theoretical outage capacity of NDA-EVM-AM in NLOS scenario is presented, while, in Section 7, the numerical results obtained by simulations are shown by comparing the performance of NDA-EVM-AM. Finally, the conclusions are drawn in Section 8.
## 3 Results
### Surface Characterization through Contact Angle Measurements  
A total of 5 Obourg Chalk, 7 Ciply Chalk, and 7 pure resin samples were tested for contact angle, using 5 fluids for a total of 467 measurements. The average values obtained from these experiments are given in Table 6. As the surface energy is influenced by the surface roughness, which itself depends on the observation scale, we estimated this parameter at two different scales through AFM and Laser Scanning Confocal microscopy (Table 6), except for calcite crystal and resin for which no AFM study has been conducted. Both techniques provide different values: the discrepancy could be due to the different resolution; indeed, the AFM is able to retrieve information at a very fine scale, highlighting roughness at a smaller scale. However, there is consistency between both chalks and for this reason, the results can be compared.  
When applying [PERSON]'s law (Equation (2)), we get the corrected contact angle values \(\theta_{\rm c}\) corresponding to each chalk for each tested fluid, which are subsequently introduced into Equation (3). The parameters of the Owens-Wendt model are plotted in Figure 14a, showing a good linear correlation. While the two chalks display a similar trend, the calcite has a much steeper slope. The surface energies inferred from these plots will be discussed in Section 4.2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Obourg Chalk**} & \multicolumn{3}{c}{**Ciply Chalk**} \\ \hline \multirow{2}{*}{**Pore Fluid**} & **Number of** & \multirow{2}{*}{**KIC (MPa-m\({}^{12}\))**} & **Standard** & \multirow{2}{*}{**Deviation** (MPa-m\({}^{12}\))**} & **Number of** & \multirow{2}{*}{**KIC (MPa-m\({}^{12}\))**} & **Standard** \\  & **Samples** & & & **(MPa-m\({}^{12}\))** & & **Deviation** (MPa-m\({}^{12}\)) \\ \hline Air (dry) & 7 & 0.0778 & 0.0086 & 3 & 0.0479 & 0.0142 \\ \hline Distilled water & 6 & 0.0509 & 0.0068 & 3 & 0.0287 & 0.0042 \\ \hline NaCl brine & 3 & 0.0379 & 0.0015 & – & – & – \\ \hline MgCl2 brine & 3 & 0.0425 & 0.0141 & – & – & – \\ \hline CaCl2 brine & 3 & 0.0458 & 0.0077 & – & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 5: Compilation of fracture toughness measurements for Obourg and Ciply Chalk samples saturated with different pore fluids.  
In order to test the influence of the salt type and ionic strength on the wettability of chalk, we measured the static contact angle NaCl and MgCl\({}_{2}\) brings with different salinities on two calcite crystals. These results shown in Figure 14b display a decreasing trend of contact angle versus ionic strength, which is comparable for both salts. It is therefore important to take the fluid composition into consideration for the kind of analyses that were conducted in the present work.
## 4 Analysis of Field Test Results
### Residual Computations  
SBDD residuals were computed for each satellite pair combination using Equation (7). The SBDD geometric range can be calculated using the relative vector, without the need for absolute position information for each antenna, as discussed in Section 2.1 For example, Figure 4 illustrates the SBDD residuals obtained for the pair of GPS PRN 1 and 3.  
Next, a high-pass filter was applied to the SBDD residuals to separate the noise and multipath components from the residuals of the training dataset. The filtered output was then subtracted from the residuals to obtain the LPF. Figure 5 shows the resulting plot, where the HPF values are a zero mean and have smaller magnitudes compared to the LPF, which has a non-zero mean.  
Figure 4: Short-baseline double difference residuals for PRN 1 and PRN 3 combination.  
Figure 3: Configuration of the NovAtel antenna used for data collection at Sejong University.  
To incorporate all the training parameters into machine learning, the ENU components and C/No of each satellite were recorded along with the corresponding residuals. The observed values of azimuth and elevation angles, which were converted from the ENU components, as well as C/No for both satellites of the PRN 1 and 3 dataset are shown in Figure 6.
# Optical Realization of Wave-Based Analog Computing with Metamaterials
## 2 Computational Metamaterials
### [PERSON]'s Function Approach
#### 2.2.1 Diffraction Gratings  
Diffraction gratings have been widely used in processing optical signals with integration or differentiation operations, most of them are based on Fano resonance or guided-mode resonance in which the reflection or transmission coefficient can approximate the transfer function in the vicinity of the resonance. In [131], [PERSON] et al. experimentally demonstrate that a guided-mode resonant grating with period TiO\({}_{2}\) slab waveguide on a quartz substrate can perform spatial differentiation with an obliquely incident beam. As shown in Figure 4a, the transverse profile of the incident wave \(P_{inc}(x,y)\) is formed by zeroth order diffraction to the transmitted profile of \(p_{tr}(x,y)\). Figure 4b is the scanning electron microscopy image of the designed diffraction grating. A similar strategy is to adopt high-contrast grating, as illustrated in Figure 4c [132]. By observing the amplitude and phase (indicated by red dot and blue line in Figure 4d) of the spatial spectral transfer function evolving with different incident angle, it is found that the transfer function has a phase variation of \(\pi\) around a certain angle \(\theta_{0}\). Within slight deviation of \(\theta_{0}\), the transfer function satisfies first-order differentiation. In a recent work, [PERSON] et al. [134] propose a novel dielectric subwavelength grating by employing high quality suspended Si\({}_{3}\)N\({}_{4}\) films, where the first- and second-order spatial differentiation can be implemented at oblique and normal incidence, respectively. The even guided mode is excited with normal incident wavefront, and change dramatically to the odd guided mode as increasing the incidence angle. In addition to these dielectric gratings, metallic gratings are also investigated and experimentally demonstrated to realize analogue spatial differentiators. It is well-known that when a metallic grating is illuminated by an incident wave, at certain incident angle, the surface plasmon resonance will be excited. [PERSON] et al. [133] found that by adjusting the geometric parameters of the subwavelength gratings, in the vicinity of surface plasmon resonance, the transfer function can be tailored to a linear function which satisfies the requirements of first-order differentiation.
## 1 Introduction
Otherwise, which is the case in almost all real-life engineering problems, we need to deal with simplicial (or even hybrid) meshes and use an appropriate Galerkin-type discretization. Generating this mesh with the corresponding data structure and collecting (assembling) the system of equations may be rather time-consuming. For the details of this procedure, we refer to [13], Section 8. In the case of linear problems like the Laplacian equation, this can take even longer compared to the solution of the system of equations. Even for nonlinear problems, including iterative solvers, the assembling may take a significant portion of the computational time [14]. Here, we consider the case of the Laplacian operator. Accordingly, our aim in the present work is to develop an ANN that generates the finite difference discretization matrix of the Laplacian and meets the following requirements:  
1. It also works on nonuniform and nonrectangular grids;
2. It is very quick and uses only neighboring relations between the grid points;
3. It delivers an accurate discretization of the Laplacian. Note that the numerical approximation of the Laplacian operator has a long history and a number of efficient approximations were elaborated. It has a central role not only in classical diffusion problems but also in several computationally intensive mathematical models, such as equations for wave propagation, Navier-Stokes equations or free-surface wave equations [15]. Summarized, the main motivation of the present work is to utilize the tool of the ANNs for the accurate discretization of the Laplacian operator. We perform this in the framework of a finite difference approximation. Our contribution here is to provide the coefficients for nonregular meshes. Instead of a pointwise optimization procedure, these coefficients will be computed by an ANN, which we train in advance. The setup of our contribution is the following. After a short review, we discuss the principle of our algorithm. Then, the two main components, the procedure for generating learning data and the construction of the neural network, will be explained. Finally, in a series of numerical experiments, an experimental analysis will be carried out to test the efficiency of our algorithm. Note in advance that the methodology we develop here can be easily extended for a number of differential operators, where a computationally efficient finite difference approximation is needed on a nonuniform spatial mesh.
# Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database
## 2 Methods
### Smart Classification Program
#### 2.1.1 Problem Formulation  
We formulate the verification of protein annotation as a binary classification problem: whether or not peer-reviewed biomedical publications indicate experimental evidence of the target protein. If such publications exist, we classify the annotation as verified; otherwise, we classify the annotation as to-be-verified.
## 4 Discussion
### Energy Level Analysis of the UAV  
According to the model, the UAV as edge computing server and relay node assumes important functions. The UAV is at four energy levels due to the green energy charging and fixed battery energy storage. This section analyzes the effects of the four energy levels on system utility and offloading decisions. The reliability index is the same as the Task 2 parameter, and the energy level weight is initialized as above. Figure 7 depicts the simulation analysis.  
Figure 6: Optimized offloading decision.  
Figure 7: Analysis of the four energy levels. (**a**) Effect of energy level on system utility function (**b**) Effect of energy level on offloading level.  
In Figure 7a, when the task is small, that is, the computing task is processed at the terminal, and no UAV intervention is present, almost no gap exists in the system utility function. As the computing task increases, if the energy level is efficient, then the function is also efficient. Thus, the high energy level UAV has low energy costs and can provide better resource optimization. The effect of energy level on the utility function when the UAV acts as a relay node is less than such an effect when the UAV acts as an edge server. The UAV as a relay node can reduce the burden of energy and optimize system resource allocation.  
In Figure 7b, when the UAV is at a high energy level, more tasks will be offloaded on the UAV. When the UAV is at a low energy level, the task will abandon the UAV offloading when it is smaller and select second-level offloading. The UAV energy level will affect the decision-making of the terminal. The tasks are more willing to offload on the UAV at high energy levels. Conversely, offloading the task on the UAV at low energy levels will result in small gains.
## 6 Conclusions  
A three-dimensional stratum-structure model was established for the two-line subway tunnel excavation under the existing Beijing S1 line medium-low-speed Maglev track project. Combined with the measured results of the project site, the following conclusions can be stated:  
1. According to the particularity of the Maglev track structure, the main deformation control point of this project is the differential deformation of the magnetic levitation track. Considering the layout conditions of the field measuring points, the deformation difference between the Maglev rail panels can be monitored by measuring the rail joint and the dislocation of the F-shaped steel. The actual monitoring process data prove that the method is feasible.
2. The maximum deformation value of the pier adjacent to the tunnel crossing position is \(-1.73\) mm and the maximum vertical deformation value of the magnetic levitation rail is \(-1.56\) mm. It can be seen that the settlement deformation of the pier will be attenuated after passing through the pier, bearing, bridge, and track beam to the rail. Because the bridge is a continuous beam, the deformation curve of the rail is relatively flat, and there is no obvious differential settlement between the rails.  
Figure 21: Track seam monitoring method.  
Figure 22: F-shaped steel deviation monitoring method (H: differential height).  
3. The measured maximum vertical deformation of the Maglev rail is \(-1.25\) mm, which is slightly smaller than the numerical calculation. Due to the slow deformation curve, no obvious dislocation or differential deformation were found in the field rail joint and F-shaped steel during the monitoring process. The numerical simulation results of the existing magnetic levitation rail were basically consistent with the final vertical deformation trend of the field-measured data. Numerical simulation prediction combined with field measurement can better reflect the deformation trend of the existing structure. 4. In the project of the double-line tunnel crossing under the existing Maglev track viaduct, the deformation of the existing structure can be effectively controlled by using deep-hole grouting to reinforce the surrounding soil of the tunnel in advance. During the crossing process, jack lifting is used on the upper structure of the bridge as an emergency measure. This study can provide a reference for similar projects.  
Conceptualization, H.P.; methodology, H.P. and Z.W.; software, Z.W. and X.P.; validation, X.X. and Z.L.; investigation, H.P. and X.X.; resources, H.P.; data curation, Z.W. and X.P.; writing--original draft preparation, X.X.; writing--review and editing, Z.W. and X.P.; visualization, H.P.; project administration, H.P. and Z.L.; funding acquisition, H.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Science and Technology Research and Development Program Project of China National Railway Group Co.] grant number [N2020G009].  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Abrasion Loss and Worn Surface Micromorphology
By comparison, it is found that there were obvious wear scars and debris on the surface of the smooth and Bionic Type 1. Although the surface of Bionic Type 1 had more wear scars and debris, the grinding marks were narrow in length, short in length and small in size. However, on the smooth worn surface, the wear scars were thicker, longer and larger as compared with Bionic Type 1. Therefore, the smooth surface suffered much more severe abrasive wear as compared with the Bionic Type 1 surface. Figure 7: Temporal evolution of wear rates. The wear morphology of different bionic geometry surfaces is shown in Figure 9. The convex hull structured surface of Bionic Type 1 showed a higher degree of worn severity. A large volume of debris and wide wear scars (Figure 9a) were distributed. However, as shown in Figure 9b,c, only minor wear scars and debris were observed on the worn surfaces of Bionic Type 2 and Bionic Type 3. Especially for surface of Bionic Type 3, only a mild wear phenomenon was observed. There were coarse wear scars and debris distributed on the wear surface of Bionic Type 4 (Figure 8d). Therefore, among bionic non-smooth surfaces, Bionic Type 3 showed the best anti-abrasive wear performance. Figure 8: Worm micromorphology of different test samples.
## 3 Conceptual Model: Procurement Order Quantities Regulation for Shortage Scenarios
### MRP Planning and Shortage Impacts  
The logic of an MRP system starts from the master production plan that determines the quantity to be produced since it considers orders and sales forecasts. The list of materials contains all the information related to the articles and the composition of the finished products. The purchase and production orders are given by the materials requirements planning (MRP), for which the replenishment times and the availability of the necessary materials are needed, allowing for inventory control. In turn, purchasing and production also feed the system with information on the receipt of orders, which helps determine availability in the near future. MRP systems allow the production system to anticipate strategies for different supply shortage scenarios to determine the steps to follow to achieve a pre-defined goal set.  
The procurement system in consideration can be generated as a closed-loop regulated system as shown in Figure 3 in which target service levels and costs are defined. Based on them, the MRP system is executed and its result is implemented operatively in the procurement process. In the implementation of the planned procurement process according to the output of the MRP, the process presents disturbances such as procurement shortages. As a result, the actual values are measured and are different in comparison to the expected indicators. Therefore, in each cycle, optimization needs to be performed. In the case of the developed model, the improvement is made on the procurement order quantity. Based on the simulation of scenarios, the different options can be assessed thanks to the previously defined target system of indicators. Finally, the procurement order quantity is then adjusted to meet target service levels and costs considering the deviations between plan and actual indicator values and based on the simulated scenarios.  
Figure 2: Target parameter system (own elaboration).
# Analysis of Load Transfer and the Law of Deformation within a Pile-Supported Reinforced Embankment
## 2 Model Tests  
### Model Test Setup and Measurement System  
This setup is suitable for simulating three-dimensional embankment model, which is helpful to deepen the understanding of load-transfer mechanism and deformation characteristics of pile-supported reinforced embankment. In addition, the model test setup adopts a separate structure, and the horizontal reinforcement is fixed at the connection between the upper box and the lower box, which is conducive to enhancing the lateral restraint of the reinforcement and exerting the membrane effect of the reinforcement.  
The model test setup consists of an upper box, a lower box, and four model piles. The details of this setup are shown in Figure 2. The size of the setup is 1000 mm \(\times\) 1000 mm \(\times\) 1700 mm (length \(\times\) width \(\times\) height). The model pile is 450 mm tall overall. The pile cap is 250 mm in length by 250 mm in breadth.  
Figure 1: The flowchart of the research methodology.  
Figure 2: Layouts of test setup (unit: mm), (**a**) Side view; (**b**) Top view. (h is the embankment height; a is the width of the pile cap; s is the pile spacing.).  
The measurement System consists of single-point displacement meters and soil pressure cells. The layout of the components is also shown in Figure 2. d1 and d2 are single-point displacement meters used to measure the pile-subsoil relative displacement. d1 and d2 are situated at the mid-span of the subsoil between four piles and two piles, respectively. P1-14 are soil pressure cells, which are diaphragm strain-gauged type. P1 and P2 are, respectively, located at the pile cap's center and the corner. P3 is situated in the center of the subsoil surface between two piles. Between four piles, in the middle of the subsoil surface, is P4. P5-14 is distributed along the central axis of the embankment. All the soil pressure cells have been calibrated before use.