esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Tunnel Face Support Pressure in Layered Soft Ground
## 2 Numerical Modeling of the TBM Face Support Pressure
### Material Constitutive Model and Parameter Selection  
Several authors [21; 24; 30] have shown that the elastic-plastic finite element method is well-suited to predicting the collapse loads of geotechnical structures. They studied the face stability of tunnels in soils and soft rocks by using the finite element method in combination with the elastic-plastic Mohr-Coulomb constitutive model and obtained good results. Therefore, the soft soil studied in this paper is assumed to conform to the Mohr-Coulomb failure criterion. This constitutive model requires determining five main parameters: the expansion angle \(\psi\), elastic modulus E, friction angle \(\varphi\), cohesion force c, and Poisson's ratio \(\
u\). The elastic strain is determined by the elastic modulus E and Poisson's ratio \(\
u\). For the ultimate load analysis, the deformation before failure is not very important, so the elastic modulus E and Poisson's ratio \(\
u\) do not affect the support pressure \(\mathrm{P_{f}}\). Meanwhile, different dilatancy angles will produce different load-displacement curves and different failure mechanisms but have little influence on failure loads [21]. In addition, \(\mathrm{K_{0}}\) affects the magnitude of the displacement but does not affect the pressure at the time of failure [24]. Therefore, the effects of E, \(\
u\), \(\mathrm{K_{0}}\), and \(\psi\) are not considered in this paper. Their values are determined to be 100 MPa, 0.3, 0.5, and 0.01\({}^{\circ}\), respectively. The unit weight takes a constant (18 \(\mathrm{kN/m^{3}}\)) at all stages. The concrete tunnel lining is modeled as a linear  
Figure 1: Typical 3D finite element mesh used in the analyses.  
Figure 2: Stratification scheme of layered ground.  
elastic model, in which the elastic modulus of the lining is 20 GPa, Poisson's ratio is 0.2, the thickness is 20 cm, and the unit weight is 24 \(\text{kN}/\text{m}^{3}\). Other parameters are shown in Table 1.
## 2 Materials and Methods
### Brief Description of the Bench-Scale Plant  
The equipment used consists of a biomass gasification plant based on atmospheric pressure bubbling fluidized bed technology and built of AISI 304 stainless steel for high temperatures, as shown in Figure 1. The bench-scale plant can be divided into five large interconnected modules, which are the following: (i) the feed system of the agent or gasification agents; (ii) the solid feed system; (iii) the gasification reactor and electric oven; (iv) the gas purification system; and (v) the monitoring and control systems. Sand particles were used as bed material during the gasification experiments, with a mean size of 200 \(\upmu\)m.  
The gasifier consists of a cylindrical reactor with two bodies. The lower body, with an inner diameter of 50 mm and a height of 750 mm, is connected to the upper body, which has an inner diameter of 100 mm and a height of 250 mm, by a truncated conical intermediate section. The total height of the reactor is 1150 mm.  
The biomass to be treated is stored in a 1 L capacity hopper with a lid and is introduced into the gasifier through the lower part of it, through an endless screw designed to feed until 0.5 kg/h. The feed screw is attached to the lower body of the gasifier through flanges located at 3 different heights so that it is possible to feed the reactor at different heights. In our work, the feed was introduced at 0.5 m from the bottom of the cylindrical reactor.  
The plant was also equipped with a cyclone, condenser system, and a filter for removing particulate matter and condensing tar and other condensable gases from the producer gas, thereby ensuring cleaner gas output and protecting downstream equipment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & C (wt., \%) & 44.0 \\ \cline{2-3} Elemental analysis & H (wt., \%) & 7.0 \\ (dry basis) & N (wt., \%) & 1.1 \\ \cline{2-3}  & S (wt., \%) & ND \\ \hline \hline  & Moisture (wt., \%) & 6.6 \\ \cline{2-3} Proximate analysis & Volatile (wt., \%) & 60.8 \\ \cline{2-3} Fixed carbon (wt., \%) & 22.2 \\ \cline{2-3} Ashes (wt., \%) & 10.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main properties of olive cake used in this study. Data from [PERSON] et al. [3] and [PERSON] et al. [16].
# Optimization of Different Deployments and Resolution of Antenna Arrays in SWIPT
## 5 Conclusions  
In this paper, we compared three different deployments and two different resolution adjustments to optimize the harvesting power and information quality for the SWIPT system at the mm-Wave in a real environment. We applied SADDE to optimize the three different deployments of the antenna array. In the SWIPT system, we not only used BER for information quality, but we also used the energy harvester for harvesting power. The high-resolution adjustment can converge faster than the low resolution, in addition to achieving better a harvesting power ratio. In addition, three antenna arrays can achieve the goal for information quality in both resolutions, but the harvesting power ratio may decrease by approximately 25% due to the difficulty of adjustment for the low-resolution case. Numerical results show that the circular array has the largest harvesting power ratio for both the high- and low-resolution cases. Thus, the circular antenna array is the best deployment in this environment due to its uniform characteristic deployment. Although the deployment shape and environment may affect the results, our proposed multiple objective function was proven to be good for SWIPT optimization.  
Conceptualization, C.-C.C. and E.H.L.; methodology, C.-C.C. and Y.-T.C.; software, Y.-T.C. and W.C.; validation, H.-Y.W.; formal analysis, C.-C.C.; investigation, H.-Y.W.; resources, W.C.; data curation, H.-Y.W.; writing--original draft preparation, C.-C.C. and Y.-T.C.; writing--review and editing, H.-Y.W.; visualization, H.-Y.W.; supervision, E.H.L.; project administration, H.-Y.W.; funding acquisition, H.-Y.W. All authors have read and agreed to the published version of the manuscript.  
[Ministry of Science and Technology, Taiwan] [MOST 110-2221-E-032 -012 -].  
The authors declare no conflict of interest.
# Protein Kinases in Mediating Phage-Bacteria Interactions
## 3 Bacteria-Encoded STKs in Phage-Bacteria Interactions
### Staphylococcus aureus StpK: Altruistic Suicide via Phosphorylation [31]  
In _Staphylococcus_, infection by a temperate phage CNPx triggers the activation of StpK by the viral protein PacK, a P-loop NTPase domain protein. Activated StpK phosphorylates many essential host proteins involved in translation, DNA repair, and central metabolism, inducing rapid cell death. This altruistic suicide limits phage CNPx propagation, protecting neighboring populations of _Staphylococcus_ cells--a defense mechanism akin to abortive infection systems. Intriguingly, StpK (Stk2) seems to collaborate with a second conserved kinase Stk1 (or PknB) (linked to cell wall integrity, antimicrobial resistance [4; 46] and virulence [47] of _Staphylococcus_), forming a phosphorylation cascade reminiscent of eukaryotic antiviral pathways.
# Numerical Simulation Analysis of Wellbore Stability in Weak Interlayer of Igneous Rock
## 3 Numerical Simulation Model for Wellbore Stability
### Mohr-Coulomb Guidelines  
The wellbore collapse pressure represents the lower threshold of the safe density window. It indicates the point at which shear failure occurs in the wellbore rock, resulting in wellbore collapse. The Mohr-Coulomb criterion serves as the foundation for identifying instability in the wellbore.  
\[M(\sigma_{1}-\sigma_{3})-\sin\varphi\big{(}\sigma_{1}+\sigma_{3}-2\alpha p_{p} \big{)}-2C_{0}\cos\varphi=0 \tag{30}\]  
where \(M=\cos\varphi(\cos 2\varphi-\sin 2\varphi)\), \(\varphi\) is the internal friction angle; \(\sigma_{1}\) is the maximum principal stress; \(\sigma_{3}\) is the minimum principal stress; \(\alpha\) is the effective stress factor; \(P_{p}\) is the pore pressure; \(C_{0}\) is the cohesion.
# An Architecture for a Tri-Programming Model-Based Parallel Hybrid Testing Tool
## 2 Background
### Tri-Level Programming Model (OPENMP + CUDA + MPI)  
Combining several programming frameworks can enhance the abilities and efficacy of heterogeneous platforms. Nevertheless, such models necessitate extensive and robust programming to benefit from extensively parallel supercomputing systems. Integrating OpenMP, MPI, and CUDA may improve the performance and dependability of a model. Such frameworks can then be used to estimate the effects of climate change, locate oil wells, perform real-time imaging, conduct protein folding research, and train deep-learning systems. It can also be applied in numerous other domains, such as machine learning, Big Data systems, cloud computing, cybersecurity, simulation, software development, computer networking, and graphics. Therefore, integrating OpenMP, CUDA, and MPI can offer different advantages, such as better performance and parallelism and lower programming requirements, and benefit from GPU acceleration.  
This present study discusses a tri-level programming model (MPI + OpenMP + CUDA) as the benefits of combining these three frameworks are numerous and will not only enhance parallelism and performance but decrease programming effort and capitalise on GPU acceleration.  
A tri-level programming architecture comprising MPI, OpenMP, and CUDA would facilitate parallelism for different hardware systems. It would also facilitate parallelism at numerous stages that comprise intra-node (OpenMP), inter-node (MPI), and graphics processing units (CUDA). Its benefits would include scalability, robust performance due to MPI, error management using OpenMP, portability, atomics, tasking extensions, and accelerated computing. Moreover, CUDA offers robust programming, superior capabilities, optimisation, and thread synchronisation. Therefore, combining these frameworks to create a tri-level programming architecture would yield better performance and energy requirements than similar architectures. More specifically, as it is based on several parallelism levels, the software can regulate and scale resources based on the hardware's dynamics to yield better efficiency, performance, and robustness.  
Nevertheless, this combination may produce run-time errors that occur due to unique reasons and that behave differently. For instance, a tri-level programming model could become over-synchronised, move redundant data, and be inefficient and complex.
## 3 FEM Simulation and Data Collection
### Model Assessment  
The normalized data were employed in this study. Moreover, two well-known statistical criteria of coefficient of determination (R2) and root mean square error (RMSE) were specified to calculate the error rate of the predicted network results. Equations (1) and (2) formulate these indices:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{s}\left(Y_{i_{\textit{predicted}}}-Y_{i_{ \textit{observed}}}\right)^{2}}{\sum_{i=1}^{s}\left(Y_{i_{\textit{observed}}}- \overline{Y}_{\textit{observed}}\right)^{2}} \tag{1}\]  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left[\left(Y_{i_{\textit{observed}} }-Y_{i_{\textit{predicted}}}\right)\right]^{2}} \tag{2}\]  
where \(Y_{i\textit{observed}}\), and \(Y_{i\textit{predicted}}\) are the real and estimated values of a bearing capacity, respectively. Also, the term N indicates the number of instances and \(\overline{Y}_{\text{observed}}\) denotes the average of capacities.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ground Conditions** & **Foundation Diameter (B) (m)** & **(Upper Layer Thickness/Foundation Width) or h/B** \\ \hline Soil layer S\({}_{1}\) on soil layer & & \\ (S\({}_{2}\), S\({}_{3}\), S\({}_{4}\), S\({}_{5}\), S\({}_{6}\), S\({}_{7}\), S\({}_{8}\)) & 1.0 & 0, 0.2, 0.4, 0.8, 1.0, \\ Soil layer S\({}_{8}\) on soil layer & & \\ (S\({}_{1}\), S\({}_{2}\), S\({}_{3}\), S\({}_{4}\), S\({}_{5}\), S\({}_{6}\), S\({}_{7}\)) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: FEM plan used for the input layers’ preparation.  
Figure 7: A schematic view of the finite element modeling employed for the current study
# Investigation on Interference Test for Wells Connected by a Large Fracture
## 4 Verification of the Interference Test Analysis Model for Wells Connected by a Large Fracture  
The previously proposed model was verified against interference test data from [PERSON] et al. [36]. Due to the fracturing of the horizontal well, [PERSON] et al. [36] found that the 3H well was connected to the 4H and 6H wells. Although they are horizontal wells and 3H and 4H wells are vertically connected, it is not difficult to understand that our model still applies. It can be seen that the fitting results are very good (Figure 4), and this proves the correctness of the proposed model. The fitting parameters for the 4H well are  
\[\frac{q\mu Br}{2 Khb}=9.584\times 10^{4},\ \frac{r^{2}}{\eta}=1.440\times 10^{3} \tag{40}\]  
where \(r\) is the distance between wells along the fracture (\(m\)). For the 6H well, the fitting parameters are  
\[\frac{q\mu Br}{2 Khb}=1.186\times 10^{5},\ \frac{r^{2}}{\eta}=4.500\times 10^{3} \tag{41}\]  
It should be noted that \(h\) in the above Equations (40) and (41) is not the thickness of the formation, but the width of the inter-well fracture communication. Since the production rate \(q\), distance between wells \(r\), viscosity \(\mu\), and volumetric ratio of fluid \(B\) are not given, and \(h\) is unknown, the permeability \(K\) and the pressure conductivity coefficient \(\eta\) cannot be further determined.  
Figure 4: Fitting results for interference test data of [PERSON] et al. [36]. **(a)** 4H well. **(b)** 6H well.
## 2 Background Study
### Dataset
#### 2.2.1 Utd-Mhad  
The members at embedded systems and signal processing (ESSP) Laboratory of the University of Texas at Dallas captured the UTD-MHAD skeleton dataset by using Microsoft Kinect camera. They extracted twenty skeleton joint coordinates of human body along \(X\), \(Y\), and \(Z\) axes as shown in Figure 4. Figure 4a shows the representation of the twenty skeleton joints with the corresponding names: _Head'_, _'Shoulder Center'_, _'Spine'_, _'Hip Center'_, _'Shoulder Left'_, _'Elbow Left'_, _'Wrist Left'_, _'Hand Left'_, _'Shoulder Right'_, _'Elbow Right'_, _'Wrist Right'_, _'Hand Right'_, _'Hip Left'_, _'Knee Left'_, _'Ankle Left'_, _'Foot Left'_, _'Hip Right'_, _'Knee Right'_, _'Ankle Right'_, _'Foot Right'_. For better understanding and analysis, we order the joints from \((x_{1},\ y_{1},z_{1})\) to \((x_{20},\ y_{20},\ z_{20})\) as shown in Figure 4b. UTD-MHAD contains a total of 27 actions dataset which is accomplished by 8 persons (4 females and 4 males). Each person performs each action 4 times. There are three corrupted data removed and a total of 861 sequences are kept for the experiments. The names of the action classes are as follows: _'SwipeLeft'_, _'SwipeRight'_, _'Wave'_, _'Clap'_, _'Throw'_, _'ArmCross'_, _'BasketballShoot'_, _'DrawX'_, _'DrawCircle(CLW)'_,  
Figure 2: DenseNet (**a**) dense and (**b**) transition blocks.  
_'DrawCircle(counter CLW)', 'DrawTriangle', 'Bowling', 'Boxing', 'BaseballSwing', 'TennisSwing', 'ArmCurl', 'TennisServe', 'Push', 'Knock', 'Catch', 'PickLlpandThrow', 'Jog', 'Walk', 'SitToStand', 'StandToSit', 'Lunge', 'Squat'_. Most of actions in this dataset are captured by hands. Three actions such as 'Jog', 'Walk', 'Lunge' is performed by the leg, and only two actions '_'SitToStand'_ and '_'StandToSit'_ are done by the full body. We consider the dataset obtained by the first 5 subjects for training and 3 subjects for testing.  
Figure 4: Human skeleton joints views; (**a**) twenty joints names and (**b**) corresponding joints numbering.  
Figure 3: ResNet residual blocks (**a**) without and (**b**) with \(1\times 1\) convolution block.
## 4 FPGA-Based Hardware and Software Architecture Design
### Pixel Point Display Module  
The calculated star point coordinates pix_x, pix_y, and pix_gray are cached through registers to obtain mem_x, mem_y, and mem_gray. The number of registers can be configured according to the number of star points to be displayed in the predicted star map. Eighty registers are used for caching in this project. In the display module, the pixels are scanned one by one in a display plane with a resolution of \(1920\times 1080\) by control of line synchronization signals and field synchronization signals. The disp_x and disp_y signals are compared with mem_x and mem_y, respectively, and an enable signal is output when the signal levels are the same. When the enable signal is valid, pre_red, pre_green, and pre_blue are replaced with mem_gray, which in turn completes the replacement of the gray value of the pixel point. A complete star map can be obtained through the above process. During the display of the dynamic star map, the line synchronization signal and field synchronization signal will continuously scan the pixel points one by one to achieve the effect of the dynamic star map.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**State Transfer Number** & **State Transition Condition** \\ \hline
1 & init\_calib\_complete = 1 \\
2 & rd\_ddr3\_req == 1\^{}b1 \\
3 & m\_axi\_array\&\&\_rma\_axi\_arrival \(=\) 1 \\
4 & m\_axi\_ready \&\&\_m\_axi\_valid \&\&\_m\_axi\_rlast \&\&\&\_ \\
4 & (m\_axi\_resp == 2\^{}b00) \&\&\_(\&\_(\)m\_axi\_rid == AXI\_ID) \\
5 & wr\_ddr3\_req == 1\^{}b1 \\
6 & m\_axi\_array\&\&\_rma\_axi\_arrival \&\&\_1\&\_ \\
7 & m\_axi\  
The grayscale distribution of the star-point diffuse spots conforms to a two-dimensional Gaussian distribution. In a Gaussian distribution, about 90% of the energy is distributed in the interval \((\mu-2\sigma,\mu+2\sigma)\). At the same time, considering the way binary data is processed, a \(3\times 3\) Gaussian filter template with weight assignment, as shown in Figure 8, is used in the project.  
The module caches \(3\times 3\) templates by calling two RAM-base Shift Registers to cache pixel rows in series. The cached data is calculated by the Gaussian filtering formula, as shown in Equation (18). The data flow diagram for Gaussian filtering is shown in Figure 9.  
\[g(x_{0},y_{0})=\sum_{j=y_{0}-1}^{j=y_{0}+1}\sum_{i=x_{0}-1}^{i=x_{0}+1}\frac{f \big{(}x_{i},y_{j}\big{)}\cdot a_{ij}}{16} \tag{18}\]  
where \(g(x_{0},y_{0})\) is the gray value of the pixel point after filtering, and \(a_{ij}\) is the weight assigned by the template.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
###### Abstract  
The accurate prediction of small-scale three-dimensional wind fields is of great practical significance for aviation safety, wind power generation, and related fields. This study proposes a novel method for predicting small-scale three-dimensional wind fields by combining the mesoscale Weather Research and Forecasting (WRF) model with Computational Fluid Dynamics (CFD). The method consists of three components: the WRF module, the hybrid neural network prediction module, and the CFD module. First, mesoscale meteorological fields are simulated using the WRF module to establish a historical inflow boundary dataset for the CFD domain. Next, deep separable convolutions are incorporated, and convolutional long short-term memory (ConvLSTM) is combined with a deep separable convolution-gated recurrent unit (DSConvGRU) to construct a hybrid neural network prediction module named ConvLSTM-DSConvGRU. This module is employed for predicting inflow boundary data. Finally, the predicted inflow boundary conditions drive the CFD module to predict small-scale three-dimensional wind fields. The effectiveness of the WRF and CFD downscaling coupling method was validated using observed data from meteorological stations within the simulated domain, along with statistical indicators of errors. Additionally, a comparative evaluation was conducted between the proposed hybrid network model and the four commonly used spatiotemporal prediction models to assess its prediction performance. The results demonstrate that our proposed wind field prediction method achieves accurate simulation and short-term prediction of small-scale three-dimensional wind fields, and the hybrid network model exhibits comprehensive advantages in terms of model complexity and prediction accuracy.  
WRF; CFD; three-dimensional wind field; spatiotemporal prediction +
Footnote †: journal: _Applied_  
_sciences_
## 3 Results and Discussions  
### Experimental Characterisation and HER Performance  
In this study, we successfully synthesised PCzPO and PNoFPO via Schiff-base condensation. The polymers were poorly soluble in any solvent because of their large molecular weight. The polymers could only be dispersed in an aqueous solution as a suspension. We examined the photophysical properties of both polymers, using UV-vis absorption and photoelectron spectroscopy. The two polymers showed strong absorption bands around 330-337 nm, as summarised in Table 1. The onset of UV-vis absorption of PNoFPO was blue-shifted (Figure S1), compared to that of PCzPO, due to the electronegative fluorine substitution at the ortho sites of the donor group [27; 28], bringing about a larger optical gap (3.03 eV in Table 1). Higher planarity of PCzPO with more extended \(\pi\) conjugation and more propensity to stack together led to a narrower optical gap (2.74 eV in Table 1).  
Next, we added the polymers into an aqueous mixture and tested the hydrogen evolution performance. Ar was chosen as bubbling gas because there was no hydrogen evolution using N\({}_{2}\) atmosphere. It showed that the hydrogen evolution rate of PCzPO was almost five times that of PNoFPO. This could be explained by the lower optical gap of PCzPO, resulting in more opportunities to absorb light energy for HER. Other factors, such as solubility [29; 30] and reaction mechanism [17] might also affect HER performance. In the proceeding sections, we specifically analyse their frontier molecular orbitals and explore HER mechanisms using DFT calculations.
## 2 Methodology  
Traffic data change quickly; hence, static spectral clustering algorithms are unable to deal with data where the characteristics of link speeds to be clustered change over time. We are interested in developing a methodology based on evolutionary spectral clustering to dynamically partition a graph-based traffic network into connected and homogeneous clusters. First, we define the network of links and how to convert it into a graph of links. Next, we convert the graph of links into a graph of snakes, and then we compute the similarity matrix between snakes in order to apply clustering algorithms.  
Next, we briefly explain the spectral clustering algorithm. We also introduce the evolutionary spectral clustering framework through normalized cuts and finally we give the efficient evolutionary spectral clustering algorithm in order to minimize the time and space complexities. Figure 1 shows the data flow chart (DFC) that describes our methodological steps, and Glossary shows a summary of notation symbols used in the paper.