esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Settlement Patterns of Tunnel-Surrounding Rock Under Groundwater Conditions
## 1 Introduction
However, current research has certain limitations, primarily in the following aspects: Firstly, most studies focus on the mechanical effects of single factors on rock deformation, while research on tunnel deformation mechanisms under complex geological conditions involving groundwater-rock interactions remains insufficient [33]. Secondly, while numerical simulation technology has been widely applied in the stability analysis of tunnel rock, its results often require integration with field monitoring data to effectively validate model reliability [34]. Therefore, systematically studying the deformation patterns of tunnel-surrounding rock under groundwater influence, through the integration of field monitoring and numerical simulation, remains a key research focus in rock mechanics and engineering. Given the complexity of interactions between groundwater and surrounding rock stability during tunnel excavation, understanding the mechanisms by which groundwaterinfluences the stability of surrounding rock remains a significant challenge. Based on this, the current study focuses on the Tunnel No. 1 of the Total Canal of the Xiaolangdi North Bank Irrigation Project as the research focus. Using ground surface and vault settlement monitoring data, we investigate the settlement patterns of the rock mass after tunnel excavation. Furthermore, numerical simulations are used to analyze the mechanisms that groundwater uses to influence rock mass deformation. This research aims to provide theoretical support for rock mass stability analysis and reinforcement design in similar engineering contexts, offering scientific guidance for ensuring the safety and stability of tunnel constructions [35; 36].
# A Classification Method for Academic Resources Based on a Graph Attention Network
## 5 Experiment
### Experimental Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**SIG**} & \multicolumn{2}{c}{**Cora**} & \multicolumn{2}{c}{**Citeseer**} \\ \cline{2-7}
**Metrics (\%)** & **Micro-F1** & **Macro-F1** & **Micro-F1** & **Macro-F1** & **Micro-F1** & **Macro-F1** \\ \hline
**DeepWalk** & 60.91 & 53.66 & 57.75 & 54.12 & 41.63 & 35.70 \\
**LINE** & 65.28 & 56.52 & 62.55 & 61.23 & 54.49 & 49.34 \\
**SDNE** & 69.23 & 60.01 & 70.11 & 67.47 & 60.01 & 55.38 \\
**GCN** & 75.31 & 69.32 & 78.72 & 71.38 & 64.35 & 58.21 \\
**GAT** & 80.20 & 77.65 & 86.83 & 78.84 & 68.72 & 64.36 \\
**A-GAT** & 83.11 & 75.27 & 88.71 & 83.26 & 73.26 & 69.24 \\
**C-GAT** & 86.26 & 81.39 & 88.43 & 85.17 & 71.11 & 66.83 \\
**ACGAT** & 90.67 & 85.22 & 92.62 & 88.54 & 74.69 & 70.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Micro-F1 and Macro-F1 of various algorithms. From Table 3 and Figures 7-9, we can observe that the performance of the network representation learning method DeepWalk, which is based on a random walk, and the LINE method, which is based on the assumption of domain similarity, was not good for the three academic resource datasets, and they could not effectively classify different types of nodes. This indicates that more similar nodes have the same vector representations with a certain deviation by relying on simple empirical indicators, which cannot reflect the structural characteristics around the nodes well. In contrast, SDNE, which is based on deep learning, achieved better classification results than the traditional methods. SDNE performed better in the self-built scholar cooperation network, SIG, and the Cora citation network, but the node classification effect was still unsatisfactory due to the limited modeling ability. The GCN assigned the same weights to the domain nodes [35], and its classification effect was inferior to that of the GAT. By introducing the attention mechanism, the graph attention network calculated the weight of each node's neighbor nodes and learned to allocate different aggregation coefficients to obtain new features of nodes. The A-GAT, C-GAT, and ACGAT proposed in this paper are all based on the GAT combined with the edge information and the influence information. The ACGAT mines the resource association features and content attributes and integrates an attention mechanism to update the node features of academic resources. Figures 12-14 show that the ACGAT has better effectiveness than the existing classification methods.
## 1 Introduction
A large number of on-site explosive tests and numerical simulations have only focused on traditional full-length bonded anchor bolts and anchor cables; although this support and reinforcement method can improve the resistance of surrounding rock for underground engineering structures to a certain extent, the blast resistance results are not ideal. Therefore, as a mainstream direction for the future development of blast-resistant anchor bolts, it is necessary and urgent to study and improve the blast resistance of new types of anchor bolts used for reinforcing caverns. Some scholars have focused on the development of new types of anchor bolts for blast-resistant and impact ground pressure aspects, with new energy-absorbing large deformation anchor bolts as the main research and development direction [25]. Among them, the constant-resistance and large-deformation (CRLD) anchor bolt developed by the team led by academician [PERSON] [26] is the most representative, which is composed of a cone, rod, casing, and tray, and uses friction between the cone and the casing to provide working resistance and absorb the deformation energy of the surrounding rock. However, in various engineering applications, it has been found that the CRLD anchor bolt has high constant resistance and large deformation capacity and has been well applied in the large deformation problems of soft rock [27; 28; 29; 30; 31; 32; 33; 34; 35; 36]; but it still has limitations. The dynamic load propagates in the form of stress waves, which causes the structural vibration of the engineering structure during the propagation process. After the internal elastic-viscous segment of the anchor bolt consumes energy during motion, it can only rely on the elastic deformation rebound of the rod material itself, and it no longer has deformation recovery capacity after exceeding the deformation limit of the rod material. Therefore, when the instantaneous deformation is too large, the outer anchor head of the CRLD anchor bolt cannot continue to adhere to the wall of the cavern during rebound, resulting in a significant reduction in its continued anchoring effect. To address these issues, this paper has independently developed a type of energy-absorbing anchor bolt with partial deformation recovery (RDEA) and conducted relevant static loading performance tests in the early stage, obtaining good results. Based on this, this paper continues to carry out field comparative tests on the blast resistance performance of the cavern reinforced by RDEA bolts. Comparative studies were conducted on the overall damage and dynamic response of caverns reinforced with ordinary steel-reinforced bolts and those reinforced with RDEA bolts under completely enclosed explosive conditions. It was found that RDEA bolts can effectively absorb explosion energy, provide effective anchoring force for anchoring engineering, and exhibit good partial deformation recovery capacity. This has important practical significance for improving the resistance level of underground engineering.
## 3 Haar Wavelet Method and Laplace Haar Wavelet Method
### Haar Wavelet Method (HWM)  
We summarize the Haar wavelet approach in the following steps.  
**Step 1**: After we determine the level of resolution \(J\) to approximate \(u(x,t)\), we assume  
\[\frac{\partial}{\partial t}u(x,t)\approx\sum_{i=1}^{2^{J+1}}\sum_{j=1}^{2^{J+ 1}}a_{ij}h_{i}(x)h_{j}(t), \tag{9}\]  
where \(\{a_{ij}\}\) are to be determined. Using the initial condition in (8) and integrating with respect to \(t\) over \([0,t]\), one can write  
\[u(x,t)\approx\sum_{i=1}^{2^{J+1}}\sum_{j=1}^{2^{J+1}}a_{ij}h_{i}(x)p_{j}(t).\]  
It follows that the integral term can be estimated by  
\[\int_{0}^{1}\int_{0}^{1}k(x,t,y,\tau)u(y,\tau)dyd\tau\approx\sum_{i=1}^{2^{J+1 }}\sum_{j=1}^{2^{J+1}}a_{ij}\int_{0}^{1}\int_{0}^{1}k(x,t,y,\tau)h_{i}(y)p_{j} (\tau)dyd\tau\]  
**Step 2**: Now, to estimate the term \(D_{t}^{\alpha}u(x,t)\), we plug Equation (9) into the definition of \(D_{t}^{\alpha}u(x,t)\) to get  
\[D_{t}^{\alpha}u(x,t) =\frac{1}{\Gamma(1-\alpha)}\int_{0}^{t}\frac{\partial}{\partial t }u(x,t)(t-\tau)^{-\alpha}d\tau\] \[\approx\frac{1}{\Gamma(1-\alpha)}\sum_{i=1}^{2^{J+1}}\sum_{j=1}^{2 ^{J+1}}a_{ij}h_{i}(x)\int_{0}^{t}h_{j}(\tau)(t-\tau)^{-\alpha}d\tau\]  
**Step 3**: Use the results of step (1) and (2) to obtain the equation  
\[\frac{1}{\Gamma(1-\alpha)}\sum_{i=1}^{2^{J+1}}\sum_{j=1}^{2^{J+1}}a_{ij}h_{i} (x)\int_{0}^{t}h_{j}(\tau)(t-\tau)^{-\alpha}d\tau=f(x,t)+\sum_{i=1}^{2^{J+1}} \sum_{j=1}^{2^{J+1}}a_{ij}\int_{0}^{1}\int_{0}^{1}k(x,t,y,\tau)h_{i}(y)p_{j}( \tau)dyd\tau\]  
Use the equally distance nodes \(x_{m}=\frac{m-0.5}{2^{J+1}}\) and \(t_{n}=\frac{n-0.5}{2^{J+1}}\) to construct the system:  
\[\frac{1}{\Gamma(1-\alpha)}\sum_{i=1}^{2^{J+1}}\sum_{j=1}^{2^{J+1} }a_{ij}h_{i}(x_{m})\int_{0}^{t_{n}}h_{j}(\tau)(t_{n}-\tau)^{-\alpha}d\tau =f(x_{m},t_{n})\] \[+\sum_{i=1}^{2^{J+1}}\sum_{j=1}^{2^{J+1}}a_{ij}\int_{0}^{1}\int_ {0}^{1}k(x_{m},t_{n},y,\tau)h_{i}(y)p_{j}(\tau)dyd\tau\]  
for \(m,n=1,2,3,...,2^{J+1}\).  
The wavelet coefficients \(a_{ij}\) are obtained by solving the above the \(2^{J+1}\times 2^{J+1}\) system of equations.
## 5 Assessment of Energy Consumption of the Two Considered Pump Systems
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(Q_{exp}\) \% & **10** & **20** & **30** & **40** & **50** & **60** & **70** & **80** & **90** & **100** \\ \hline \(Q_{exp}\)**, \(\mathbf{m^{3}/h}\)** & 2.40 & 4.80 & 7.20 & 9.60 & 12.0 & 14.4 & 16.8 & 19.2 & 21.6 & 24.0 \\ \hline System with one pump & nmotor & 0.431 & 0.507 & 0.580 & 0.651 & 0.723 & 0.783 & 0.838 & 0.874 & 0.879 & 0.834 \\ (FC-IM) & nConv. & 0.939 & 0.944 & 0.950 & 0.955 & 0.960 & 0.963 & 0.964 & 0.961 & 0.957 & 0.952 \\ Two parallel pumps & nmotor1 & 0.699 & 0.734 & 0.778 & 0.816 & 0.840 & 0.831 & 0.818 & 0.826 & 0.819 & 0.792 \\ (FC-IM1) + IM2 & nmotor2 & - & - & - & - & - & - & 0.796 & 0.797 & 0.796 & 0.792 \\  & nConv.1 & 0.431 & 0.507 & 0.580 & 0.651 & 0.723 & 0.783 & 0.838 & 0.874 & 0.879 & 0.834 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of efficiency calculation for motors and frequency converters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Operation Point** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline \(n\), \% & 90 & 50 & 90 & 50 & 25 & 50 & 25 \\ \(T\), \% & 100 & 100 & 50 & 50 & 100 & 25 & 25 \\ nmotor IM-1.5 kW & 0.853 & 0.728 & 0.865 & 0.819 & 0.533 & 0.822 & 0.772 \\ nmotor IM-750 W & 0.830 & 0.768 & 0.809 & 0.749 & 0.645 & 0.653 & 0.523 \\ nconv. FC-1.5 kW & 0.965 & 0.947 & 0.952 & 0.925 & 0.905 & 0.888 & 0.784 \\ nconv. FC-750 W & 0.937 & 0.906 & 0.898 & 0.849 & 0.857 & 0.771 & 0.686 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Efficiency data for motors and FC at standard points, in accordance with IEC 60034-30-2.  
consumption is higher in comparison with a single-pump unit, in the rare operation modes when the two pumps operate together. However, significant energy savings can be achieved in operation modes when one of the pumps of the multi-pump system can provide the required flowrate. As a result, in the case of the multi-pump system (two parallel pumps), the energy consumption during the working cycle is reduced by 29.8% in comparison with the single-pump system.
## 3 Results and Discussion  
### HF Structure in Obtained Composites  
The structure of the HF taken from obtained composites (Figure 2) is significantly different compared to that shown in Figure 1. First of all, the fibers in the obtained composites possess a significantly reduced length and diameter (even 10 times their length). This is due to two reasons. Firstly, the lengths of the fibers were shortened by the use of a plasticising system containing elements that induced intense mixing and shearing, as well as retracting elements. These elements divided the fibers into smaller particles, including single macrofibrils and, as a result, a fine fraction with high dispersion in the polymer matrix was obtained. Secondly, some of the fibers were additionally shortened in the granulation process.  
The fibers in the CF2.5 composite (Figure 2A) have a larger diameter and a more uniform structure compared to the fibers in the other composites. Here and there, a single fiber bundle can be seen. As the composite fiber content increases, their structure changes. At the highest percentage filling, there are a few coarse fibers with intact structure and the dominant fraction is composed of single fiber bundles or individual macrofibrils (Figure 2D).  
The change in the fiber structure in individual composites is related to the changing processing parameters, which in turn are a derivative of the increasing fiber content in the plasticised polymer mass. As the content of HF in the composite increases, all the measured parameters of the extrusion process, i.e., M, E, T and P also increase (Table 1). The increase in these parameters, which are indirectly related to the increase in shear forces, result in changes in the structure of the fibers as shown in Figure 2. Thus, the structure of the obtained fibers may have a significant impact on the mechanical properties of the irradiated PCL/HF composites, which are discussed in Section 3.3.  
Figure 2: HF structure after extraction from PCL/HF composites ((**A**) CF2.5; (**B**) CF5; (**C**) CF10; (**D**) CF20).
###### Abstract  
To improve the reconstruction image spatial resolutions of ultrasonic guided wave ray tomography, a sparse model, based on the differences between the inspected and original slowness of the ultrasonic guided waves propagating in the plate-like or pipe-like materials, is first proposed in this paper. Unlike the conventional ultrasonic guided wave tomography whose reconstruction image resolutions are limited by an underdetermined linear model, analyses show that our new model, although it is also underdetermined, can give the optimal solution of the reconstruction image when the constraints on the sparsity of the slowness difference distribution are valid. The reason for the validation of the sparse constraints on the corroisons of the materials is explained. Based on our new model, a least absolute shrinkage and selection operator (LASSO) approach to do the thickness change mapping of a structure health monitoring (SHM) application is then formulated. Analyses also show that the visible artifacts can be avoided using our method, and the spatial resolutions of reconstruction image by our approach can further be improved by increasing the number of grids in the calculation. The approach is validated by experimental work on an aluminum plate. It is also shown that compared to the conventional ray tomography, the presented method can achieve a relatively high spatial resolution, with good suppression of artifacts.  
ultrasonic guided wave; LASSO; thickness mapping; material corroisons Article  
A Sparse Model of Guided Wave Tomography for Corrosion Mapping in Structure Health Monitoring Applications  
[PERSON] & [PERSON] 2,*
# A Fractional-Order Creep Model of Water-Immersed Coal
## 3 Evolution of Creep Performance
### Evolution of Creep Rate  
#### 3.2.1 Steady Creep Rate  
In order to analyze the influence of the water-immersion time and stress level on the creep properties of the coal samples, the creep rates were calculated in this subsection. In order to obtain the steady-state creep rate of the coal samples during the constant velocity creep stage, stress levels of 5 MPa, 7 MPa, 9 MPa, and 11 MPa were selected during the graded loading processes. The steady-state creep rates of the coal samples were calculated for different immersion times and a three-dimensional scatter plot was drawn. The X-axis represents the immersion time, the Y-axis means the stress, and the Z-axis is the creep rate. The results are shown in Figure 9a. From Figure 9a, it can be observed that the loading stress and immersion time have certain effects on the creep rate. To investigate this effect, three-dimensional scatter plots are projected onto the Y-Z plane and X-Z plane, respectively.  
From Figure 9b, it is not difficult to see that for coal samples A, B, C, and D with different immersion times, the creep rate of coal is positively correlated with the stress level. The higher the stress level, the faster the creep rate of coal. This indicates that applying stress can accelerate the creep of coal under the condition that the stress does not exceed the ultimate strength of the coal rock. In addition, it can be seen from Figure 9c that at the same stress level, the creep rate increases with a longer immersion time. This is mainly due to the increase in microcracks within the coal body with the increasing immersion time. When the immersion time is less than 30 days, the immersion time has a significant effect on the creep rate. After it passes 30 days, the effect of immersion time on the creep rate begins to decrease. Therefore, the creep rate increases with both the promoted stress level and immersion time.  
Keeping the stress as a constant, the strain slowly increases with time, which is also known as the steady creep deformation stage. From the above analysis, it can be seen that loading stress and immersion time are the main factors affecting the coal creep rate. The steady creep rate shows an increasing trend with the increase in immersion time and loading stress, and their relationship is shown in Figure 9.  
The relationship of viscoelastic strain versus stress and time, where the viscosity coefficient satisfies  
\[\dot{\varepsilon}^{pe}=\frac{\sigma}{\eta_{1}} \tag{6}\]  
Considering the evolution of the viscosity coefficient according to the surface fitting of the data of Figure 9, it results in  
\[\eta_{1}^{*}=\eta_{1}\exp(k_{1}\sigma+k_{2}T) \tag{7}\]  
The fitted values of \(\eta_{1}\), \(k_{1}\) and \(k_{2}\) are 0.0005946, 0.00978 and 0.00419, respectively. The fitted surface is plotted in Figure 9a, where its squared residual error \(R^{2}=0.88\). Despite a certain range of differences existing between the measured data and the fitted result due to the discreteness in the coal samples, the fitted surface is capable of covering the tendency as a whole.
## 5 Progress of Data Transmission Protocols for Wireless Communication in Solar PV Monitoring Systems  
In this section, a comprehensive review of the data transmission protocols for solar PV monitoring systems is presented, emphasizing their implementations, design, specification, results, and limitations.
# Porous Electrodeposited Cu as a Potential Electrode for Electrochemical Reduction Reactions of CO2
## 2 Materials and Methods
### CO\({}_{2}\)R Experiment  
Three sets of porous Cu samples from A, B and C sample groups, electrodeposited with 3 A/cm\({}^{2}\) for 40 s, were chosen as working electrodes for CO\({}_{2}\)RR tests. The electrochemical CO\({}_{2}\)RR process was conducted using a 3-electrode H-cell setup, consisting of a working electrode (WE: Cu deposit samples), a counter electrode (CE: platinum foil), and a reference electrode (RE: Ag/AgCl). The cathodic and anodic compartments were separated by Nafion 117 membrane. Potassium bicarbonate (KHCO\({}_{3}\)) with concentration and volume of 0.1 M and 40 cc was used as the electrolyte. A schematic picture of the setup is illustrated in Figure 1. Before each test session, high purity (99.9%) CO\({}_{2}\) was purged into the cell for 60 min. Once the pH level of the electrolyte changed from 8.2 to 6.8, the test session was initiated. CO\({}_{2}\) was purged continuously during the test, and the cell was applied with a voltage of \(-\)1.3 V vs. Ag/AgCl.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} 20, 40, 60 \\ 2, 3, 3.5 \\ 1.5M H\({}_{2}\)SO\({}_{4}\) + 0.4M \\ CuSO\({}_{4}\) + 0.05M HCl \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrodeposition parameters employed for fabrication of the porous Cu electrodes.  
In addition to the porous Cu samples, Cu foils were also tested in the CO\({}_{2}\)RR process for a comparison purpose. The surface of the Cu foil samples was polished by 400 grit silicon carbide paper prior to the tests.
## Abstract  
Vegetation belongs to the components of the Earth surface, which are most extensively studied using historic and present satellite records. Recently, these records exceeded a 30-year time span composed of preprocessed fortnightly observations (1981-2011). The existence of monotonic changes and trend shifts present in such records has previously been demonstrated. However, information on timing and type of such trend shifts was lacking at global scale. In this work, we detected major shifts in vegetation activity trends and their associated type (either interruptions or reversals) and timing. It appeared that the biospheric trend shifts have, over time, increased in frequency, confirming recent findings of increased turnover rates in vegetated areas. Signs of greening-to-browning reversals around the millennium transition were found in many regions (Patagonia, the Sahel, northern Kazakhstan, among others), as well as negative interruptions--\"setbacks\"--in greening trends (southern Africa, India, Asia Minor, among others). A minority (26%) of all significant trends appeared monotonic.  
**Keywords:**GIMMS v3G; vegetation activity; trend shifts; greening-to-browning reversal