esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Fineness and Dosage of Fly Ash on the Fracture Properties and Strength of Concrete
## 4 Results and Discussion
### Compressive Strength
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mixture no.**} & \multicolumn{2}{c}{**Compressive Strength (MPa)**} & \multicolumn{2}{c}{**Fracture Energy * (N/m)**} \\ \cline{2-5}  & **14 Days** & **56 Days** & **14 Days** & **56 Days** & **Increment N/m (\%)** \\ \hline R0 & 62.1 \(\pm\) 0.29 & 70.3 \(\pm\) 0.20 & 97.1 \(\pm\) 2.21 & 110.3 \(\pm\) 2.51 & 13.2 (13.6) \\ F1R1 & 57.3 \(\pm\) 0.33 & 69.8 \(\pm\) 0.31 & 84.3 \(\pm\) 1.69 & 109.4 \(\pm\) 2.44 & 25.1 (29.8) \\ F1R2 & 56.0 \(\pm\) 0.34 & 68.7 \(\pm\) 0.36 & 78.5 \(\pm\) 1.88 & 103.5 \(\pm\) 2.10 & 25.0 (31.8) \\ F2R1 & 61.9 \(\pm\) 0.29 & 74.2 \(\pm\) 0.25 & 95.7 \(\pm\) 2.46 & 115.8 \(\pm\) 1.98 & 20.1 (21.0) \\ F2R2 & 59.2 \(\pm\) 0.19 & 73.1 \(\pm\) 0.35 & 88.5 \(\pm\) 2.21 & 114.9 \(\pm\) 2.56 & 26.4 (29.8) \\ F3R1 & 63.5 \(\pm\) 0.35 & 75.8 \(\pm\) 0.29 & 95.8 \(\pm\) 3.10 & 121.4 \(\pm\) 2.88 & 25.6 (26.7) \\ F3R2 & 61.5 \(\pm\) 0.29 & 74.6 \(\pm\) 0.23 & 98.1 \(\pm\) 2.66 & 123.5 \(\pm\) 3.11 & 25.4 (25.9) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fracture energy of concrete. between 50 and 80 MPa. This demonstrates the unique effects of using finer fly ash on the enhancement of the fracture toughness of high-strength concrete.
# Depth Image Completion through Iterative Low-Pass Filtering
## 3 Experimental Results
### Denoising Ability  
Figure 5c depicts the creation of a spherical depth image designed to assess the denoising capability of the Depth Image Completion Process (DICP). The dimensions of the depth image were 2.6 \(\times\) 1.2 m at a distance of 2 m, with a matrix size of 540 \(\times\) 960 pixels. Deliber  
Figure 4: (**a**) Cylinder RGB Image. (**b**) Cylinder depth Image. (**c**) Defect area accuracy and precision analysis.  
ately introducing high noise, distributed within the range of a 65 mm standard deviation, we systematically increased the continuous defected area along the \(x\)-axis. Subsequently, the DICP was executed to evaluate its performance.  
We also conducted a comparative analysis between the Depth Image Completion Process (DICP) and polynomial surface fitting methods [41, 42]. The polynomial surface fitting method considers available inlier data, iteratively seeking the best-fitting surface  
Figure 5: Analysis of a spherical depth image with a 65 mm standard deviation of noise. (**a**) Accuracy error for the sphere depth image with and without noise. (**b**) Precision for the sphere depth image with and without noise. (**c**) Point cloud of a spherical depth image with a 51% defected area and a 65 mm standard deviation of noise.  
to fill the defected area. Figure 5a,b presents the accuracy error and precision error of the DICP in the presence of added high-interruption noise.  
The results indicate that the introduced noise did not significantly impact the completion result, as evidenced by the calculated accuracy error. Furthermore, the precision error remained relatively stable even when the defected area exceeded 30%. Notably, the DICP outperformed polynomial surface fitting in the 0-42.77% defected area range, suggesting that noise had a more pronounced effect on the polynomial surface fitting recovery process.  
Figure 6 illustrates the accuracy error and precision error resulting from noise simulation. The added noise was sourced from a Kinect for Xbox One (Kinect 2.0), measured with a standard deviation of 6.5 mm at a distance of 5 m. The precision results demonstrate that our method surpassed polynomial surface fitting in the 0-46.88% defected area range. These simulations affirm that the Iterative Low-Pass Filter (ILF) can effectively recover spherical surfaces within a standard deviation of 6.5 mm and a 65 mm range of noise. This capability positions ILF as a versatile solution for addressing noisy depth images captured by commercial-grade RGB-D cameras.
## 3 Results and Discussion
### Inflection Point on the Drying Curve and the Rate of the Drying Process  
Figures 4 and 5 show that as the initial moisture content of the material increases, an inflection point appears on the curve in Figures 4a and 5a, in contrast to the curve in Figures 4b and 5b. Formally, this means that at the inflection point, the second derivative of the moisture function with respect to time is zero. Let \(t^{*}\) be the abscissa of the inflection point; \(P\)--the right side of relation (22a). Then from the equation \(\frac{d^{2}P}{dt^{2}}=0\), we find after transformations:  
\[t^{*}=\tau\ln\frac{C_{water}^{(w.b.)}}{1-C_{water}^{(w.b.)}} \tag{22b}\]  
In the problem under consideration \(t^{*}\geq 0\), \(\tau>0\). The physical meaning of the problem corresponds to such values \(C_{water}^{(w.b.)}\), for which \(\frac{C_{water}^{(w.b.)}}{1-C_{water}^{(w.b.)}}\geq 1\), i.e., \(0.5\leq C_{water}^{(w.b.)}<1\). For example, if \(C_{water}^{(w.b.)}=0.85\) and \(\tau=300\) min, then \(t^{*}=520.4\) min.  
_The question then arises:_ What features of the drying process is simulated by the inflection point?  
_Answer:_ If an inflection point exists (22b), then the drying process rate increases up to the inflection point \(0<t<t^{*}\). At a point \(t=t^{*}\), the rate of the drying process reaches an extreme. If \(t>t^{*}\), then the rate of the drying process decreases. If \(T^{*}=0\), then the drying rate only decreases from start to finish of this technological process.  
Figure 6 illustrates the noted features of the drying process for material with high initial moisture content.  
The initial moisture content \(C_{water,start}^{(w.b.)}\) of the material and the parameter \(\tau\) are equal to 0.85 and 300 min, respectively. Point 1 is the inflection point on the curve simulating the dependence of on time. At point 3, as noted above, \(\frac{d^{2}P}{dt^{2}}=0,\ t=t^{*}520.4\) min. At point 2, the drying process rate is extreme at the same value of \(t\).  
Experimental data that confirm the existence of the above features of the drying process can be found in the literature, for example, in the graphs in Figure 2c from [20]. However, it was not possible to find a theoretical justification for these features, including analogs of relation (22b).  
Figure 5: Normalized moisture content (w.b. (**a**) and d.b. (**b**) depending on time at the initial moisture content (w.b.) by Figure 4. The ratio \(C_{water}^{(d.b.)}/C_{water,start}^{(d.b.)}\) is independent of the initial moisture content (22).
# A Blockchain Network Admission Control Mechanism Using Anonymous Identity-Based Cryptography
## 3 Proposed Mechanism
### Generation of User Pseudonyms and Private Key Groups
#### 3.3.3 Generation of User Communication Identifiers and Private Key  
The communication identifiers are the user's actual anonymous identity used for authentication with validating nodes, and can also be regarded as the access credential for this blockchain network.  
\(VKGC_{p_{k-1}}\) generates a communication identifier \(ID_{B}=H(ID_{A}||salt||timestamp)\) for the user and records the correspondence between \(ID_{B}-ID_{A}\). Then, \(VKGC_{p_{k-1}}\) calculates \(m_{1}=H_{1}(ID_{B}||hid,N)+kv_{p_{k-1}}\) in a finite field and produces the communication private key component \(m_{2}=[m_{1}]^{-1}P_{1}\), which is then sent to the user.  
The user uses the pseudonym \(ID_{A}\) and private key \(ds_{A}\) obtained from VKGC to verify identity validity with CKGC. The user then selects a secret random number \(s_{2}\in[1,N-1]\) and sends \(m_{2}s_{2}=s_{2}\cdot[m_{1}]^{-1}P_{1}\) to CKGC. Each CKGC calculates the private key component \(kc_{i}\cdot m_{2}s_{2}=kc_{i}\cdot s_{2}\cdot[m_{1}]^{-1}P_{1}\) and sends it to the user. The user then multiplies by \({s_{2}}^{-1}\) to obtain multiple communication private key components \(ds_{Bi}=kc_{i}\cdot[m_{1}]^{-1}P_{1},i=1,2,\ldots,n\).  
The process of generating user communication identifiers and private keys is illustrated in Figure 3.
## 4 Results
### Dynamic Feature Monitoring of Glacier Movement Based on Sentinel-1
#### 4.2.2 Coseismic  
Two scenes of Sentinel-1A images (26 August 2022 and 7 September 2022) covering the earthquake were selected to produce the coseismic glacier deformation map for the Luding earthquake based on POT stacking. Figure 6 shows the POT daily average rate results for the two scenes of images before and after the Luding earthquake. The glaciers with more drastic coseismic velocity changes were Hailuogou Glacier Nos 1-3 on the eastern side of Mt. Gongga. The Mozigou Glacier and its trailing mountains also showed obvious deformation, while the glaciers on the west side of the ridge line were relatively less affected by the earthquake. The glacier flow velocity on the eastern slope of Mt. Gongga is mostly along the track direction, and the maximum value for the movement along the track direction is approximately 1.5 m/d, the velocity of the movement against the track direction is approximately \(-0.9\) m/d, and the overall movement is along the track direction. The most obvious flow velocity is that of Hailuogou Glacier No. 1, where the deformation at its end in the ice waterfall area from 3700 m to 4980 m above sea level is the most significant, and the flow velocity of the glacier surface is approximately 1 m/d.  
The accuracy of the monitoring results was affected by the presence of some null values in the processing results from the Sentinel-1A images. Therefore, the differential interferometry results from the LT-1 satellite were compared and analyzed. Influenced by the SAR observation angle and the geometric relationships of the topographic relief, most of the western side of the ridgeline showed subsidence, while the eastern side of the glaciers mostly showed uplift (movement to the east), and the end of the glaciers was where the most drastic changes occurred. There were several areas with obvious deformation signals on the surface of the Mt. Gongga glaciers, which were suspected to be glacial mudflows and landslides. This reflects the advantages of using L-band SAR data in the study of glacier movement velocity.  
Figure 5: Geomorphologic map of Hailuogou Glacier No. 1 on the eastern slope of Mt. Gongga, southeastern Tibetan Plateau (background image: © Google Earth, 5 October 2020). The blue dotted line indicates the boundary between Hailuogou Glacier No. 1 and the Mozigou Glacier. The solid border indicates the three fast-moving tributaries of Hailuogou Glacier No. 1.
## 11 Responses for the Incremental-N
### Step-Type Reference and Output Disturbance  
The results are presented for the system subjected to step-type reference and disturbance inputs. A simulation time of 3500 s was used for all cases. The step input for the output flow rate (\(Q_{s}\)) occurs at 710 s, and for the output temperature (\(T_{s}\)), it occurs at 1200 s. The disturbance for \(Q_{s}\) has an amplitude of 0.5 (m3%) and occurs at 1800 s. For \(T_{s}\), the disturbance has an amplitude of 5 (\({}^{\circ}\)C) and occurs at 2800 s.  
Figure 9 presents the output flow rate and temperature responses using the traditional discrete-state model (incremental-0). In both cases, it can be seen that the system fails to achieve zero steady-state error. From Figures 10-14, the responses obtained using incremental-1 through incremental-5 are shown. It is demonstrated that all of them achieve zero steady-state error in response to step-type references and disturbances (at the output). The difference among the incrementals lies in the response speed: As the incremental number increases, the response becomes slower. To better understand this behavior, Table 5 presents the IAE and ISE values for the output flow rate and temperature for all incrementals.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method for Obtaining \(Q\) and \(R\) Matrices** & \(J(\theta)\) & **RMSE (\(Q_{s}\))** & **RMSE (\(T_{s}\))** \\ \hline Without Global ESC & 1.6660 & 1.1355 & 6.1335 \\ With Global ESC & 1.0691 & 0.8373 & 6.0641 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Value of \(J(\theta)\) and RMSE of \(Q_{s}\) and \(T_{s}\) using the matrix values obtained without Global ESC and with Global ESC.  
Figure 9: Response of \(Q_{s}\) and \(T_{s}\) to step-type references and output disturbances for incremental-0.  
Figure 11: Response of \(Q_{s}\) and \(T_{s}\) to step-type references and output disturbances for incremental-2.  
Figure 12: Response of \(Q_{s}\) and \(T_{s}\) to step-type references and output disturbances for incremental-3.  
Figure 10: Response of \(Q_{s}\) and \(T_{s}\) to step-type references and output disturbances for incremental-1.
## 1 Introduction
[24] proposed the Hierarchical Clustering Algorithm (HCA), which incorporates fast random clustering and scheduling algorithms to handle channel access and intra-cluster information transmission. [PERSON] et al. [25] proposed the MSCA clustering algorithm based on mobility and stability, which considers vehicle mobility, relative positions, and link lifetime estimation, which performs well in terms of average cluster-head lifetime and number of cluster heads. [PERSON] et al. [26] proposed the Unified RSU Deployment Algorithm (URDA) based on linear programming to maximize the benefits of RSU deployment. When investigating the factors influencing RSU deployment, some scholars have considered traffic flow density [27; 28], while others have considered communication factors such as transmission delay [29; 30]. However, considering that there is relatively little research on permeability of CAVs in heterogeneous traffic flows when studying RSU deployment, [PERSON] et al. [31] addressed the path flow reconstruction in Connected Vehicle (CV) environments while considering CAV permeability, while [PERSON] et al. [32] considered the CAV permeability in heterogeneous traffic flows to deploy RSUs with the aim of improving traffic prediction efficiency. Based on the premise of clustering heterogeneous traffic flows on highways, this paper proposes a D-LEACH algorithm, an improved LEACH-clustering routing algorithm based on the DBSCAN clustering algorithm, to verify the effectiveness of the algorithm by analyzing network performance indicators, such as system network connectivity and energy consumption, and to find the optimal RSU deployment interval and communication radius for different permeabilities of CAVs, thus analyzing a relatively efficient and reasonable RSU deployment scheme for different permeabilities of CAVs. This paper presents two innovations: (1) The clustering part and the cluster-head election part of the LEACH algorithm are improved, and the D-LEACH algorithm is proposed, which adopts the DBSCAN density-clustering algorithm for clustering and selects the vehicle node closest to the RSU as the cluster head. (2) The influence of CAV permeability on the RSU deployment interval in heterogeneous traffic flows on highways is analyzed, and a reasonable and efficient RSU deployment interval scheme under different permeabilities is given. The rest of this paper is structured as follows: Section 2 describes the clustering problem of highway vehicles and related work; Section 3 describes and improves the vehicle clustering algorithm; Section 4 carries out simulation verification; and Section 5 summarizes and proposes future research prospects.
## 3 Results
### Plantar Pressure Distribution
#### 3.2.1 Mean Force
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**FT**} & \multicolumn{1}{c}{**OT**} & \multicolumn{1}{c}{**UT**} & \multicolumn{1}{c}{**US**} & \multicolumn{1}{c}{**DS**} \\ \hline \multirow{2}{*}{FCU (\%)} & RBH & 8.409 \(\pm\) 2.740 & 29.071 \(\pm\) 5.621 \({}^{a}\) & 26.895 \(\pm\) 4.717 \({}^{b}\) & 16.121 \(\pm\) 2.769 \({}^{c,f,h}\) & 12.420 \(\pm\) 3.306 \({}^{s,i}\) \\ \cline{2-7}  & RHH & 8.799 \(\pm\) 2.972 & 13.683 \(\pm\) 3.264 \({}^{*}\) & 21.170 \(\pm\) 5.269 \({}^{b,c,a}\) & 19.116 \(\pm\) 5.968 \({}^{c}\) & 15.091 \(\pm\) 3.633 \({}^{d,i,*}\) \\ \hline \multirow{2}{*}{ECRL (\%)} & RBH & 17.623 \(\pm\) 2.807 & 23.183 \(\pm\) 2.933 & 34.829 \(\pm\) 7.675 \({}^{b,c}\) & 21.695 \(\pm\) 3.958 \({}^{h}\) & 23.896 \(\pm\) 5.974 \({}^{i}\) \\ \cline{2-7}  & RHH & 14.953 \(\pm\) 3.509 & 17.561 \(\pm\) 4.033 \({}^{*}\) & 24.497 \(\pm\) 4.853 \({}^{b,c,*}\) & 12.406 \(\pm\) 2.821 \({}^{f,h,*}\) & 14.660 \(\pm\) 2.752 \({}^{i,*}\) \\ \hline \multirow{2}{*}{RF (\%)} & RBH & 28.967 \(\pm\) 4.206 & 26.415 \(\pm\) 5.326 & 31.130 \(\pm\) 5.052 & 29.491 \(\pm\) 4.814 & 29.857 \(\pm\) 5.155 \\ \cline{2-7}  & RHH & 26.952 \(\pm\) 4.609 & 24.884 \(\pm\) 3.810 & 28.467 \(\pm\) 4.598 \({}^{*}\) & 29.428 \(\pm\) 4.697 & 27.829 \(\pm\) 4.869 \\ \hline \multirow{2}{*}{TA (\%)} & RBH & 30.443 \(\pm\) 4.751 & 28.850 \(\pm\) 4.110 & 31.029 \(\pm\) 4.608 \({}^{c}\) & 30.140 \(\pm\) 3.213 & 25.517 \(\pm\) 4.875 \\ \cline{2-7}  & RHH & 30.192 \(\pm\) 5.781 & 33.297 \(\pm\) 3.940 \({}^{*}\) & 26.305 \(\pm\) 3.534 \({}^{*}\) & 31.241 \(\pm\) 4.758 & 28.139 \(\pm\) 4.304 \\ \hline \multirow{2}{*}{BF (\%)} & RBH & 28.617 \(\pm\) 4.294 & 34.536 \(\pm\) 6.540 & 36.481 \(\pm\) 6.319 & 32.305 \(\pm\) 6.728 & 31.757 \(\pm\) 5.475 \\ \cline{2-7}  & RHH & 28.652 \(\pm\) 5.050 & 30.987 \(\pm\) 4.391 \({}^{*}\) & 33.187 \(\pm\) 5.468 \({}^{*}\) & 34.165 \(\pm\) 7.822 & 32.298 \(\pm\) 5.478 \\ \hline \multirow{2}{*}{GM (\%)} & RBH & 35.648 \(\pm\) 2.815 & 32.132 \(\pm\) 3.209 & 33.725 \(\pm\) 5.644 & 40.592 \(\pm\) 4.022 \({}^{f,h}\) & 37.728 \(\pm\) 5.690 \\ \cline{2-7}  & RHH & 37.910 \(\pm\) 3.300 & 31.647 \(\pm\) 3.710 \({}^{a}\) & 32.277 \(\pm\) 4.495 & 38.458 \(\pm\) 3.788 \({}^{f,h}\) & 37.122 \(\pm\) 6.184 \\ \hline \hline \end{tabular}   
* Notes: (M \(\pm\) SD); \({}^{a}\): Significant difference in muscle activity between the FT and OT (\(p<\) 0.05 or \(p<\) 0.01); \({}^{b}\): Significant difference in muscle activity between the FT and UT (\(p<\) 0.05 or \(p<\) 0.01); \({}^{c}\): Significant difference in muscle activity between the FT and DS (\(p<\) 0.05 or \(p<\) 0.01); \({}^{d}\): Significant difference in muscle activity between the FT and DS (\(p<\) 0.05 or \(p<\) 0.01); \({}^{e}\): Significant difference in muscle activity between the OT and UT (\(p<\) 0.05 or \(p<\) 0
###### Abstract  
Urban expansion is considered as one of the most important problems in several developing countries. Bangkok Metropolitan Region (BMR) is the urbanized and agglomerated area of Bangkok Metropolis (BM) and its vicinity, which confronts the expansion problem from the center of the city. Landsat images of 1988, 1993, 1998, 2003, 2008, and 2011 were used to detect the land use and land cover (LULC) changes. The demographic and economic data together with corresponding maps were used to determine the driving factors for land conversions. This study applied Cellular Automata-Markov Chain (CA-MC) and Multi-Layer Perceptron-Markov Chain (MLP-MC) to model LULC and urban expansions. The performance of the CA-MC and MLP-MC yielded more than 90% overall accuracy to predict the LULC, especially the MLP-MC method. Further, the annual population and economic growth rates were considered to produce the land demand for the LULC in 2014 and 2035 using the statistical extrapolation and system dynamics (SD). It was evident that the simulated map in 2014 resulting from the SD yielded the highest accuracy. Therefore, this study applied the SD method to generate the land demand for simulating LULC in 2035. The outcome showed that urban occupied the land around a half of the BMR.  
urban land use; urbanization; urban expansion; cellular automata; markov chain; multi-layer perceptron; system dynamic; Bangkok Metropolitan Region +
Footnote †: journal: Journal of the Royal Society B: Biological and Environmental Science
# Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
## 1 Introduction
However, due to the large number of parameters required in stacking neural networks to solve complex problems, previous algorithms have limitations such as algorithmic simplicity or a larger parameter count, impacting the timely control of controllers. To address this issue, this paper proposes vibration control with reinforcement learning based on a multi-reward lightweight network. Specifically, it is a deep residual shrinkage lightweight method based on deep reinforcement learning with multi-reward prioritized experience replay (DRSL-MPER). We introduce a multi-reward mechanism to assist the TD3 algorithm in finding control policies close to optimal. At the same time, we also introduce a priority experience playback mechanism to assign priority to the data, thereby accelerating the convergence speed of the neural network and improving the data utilization efficiency. In addition, we also introduce a deep residual shrinkage network to achieve adaptive denoising and lightweightness of the neural network. The algorithm proposed in this paper has two significant and practical advantages. Firstly, it employs a lightweight network, which not only enhances the computational efficiency and reduces the energy consumption but also facilitates deployment and maintenance in real-world scenarios, while supporting rapid iterative updates of the system. Secondly, the algorithm demonstrates excellent control performance and good generalization capabilities, which can significantly improve the performance and lifespan of equipment, enhance the reliability and safety of the system, and increase its adaptability and flexibility. The rest of this paper is organized as follows: Firstly, the vibration control problem is described. Secondly, our lightweight method of multi-reward priority experience playback based on deep reinforcement learning is proposed. Then, the vibration controller designed using this method is verified through experiments. Finally, we draw our conclusions.
## 2 Simulation Method
### Deletion Vacuum Simulation  
In order to compare the temperature profiles from the vacuum simulations and the equilibrium simulation, a virtual vacuum simulation using a standard approach is performed. In such simulations, the periodic boundary conditions are applied only in \(x\) and \(y\), while the vacuum condition near the liquid surfaces is achieved by deleting the atoms that cross the left- and right-side vapor boundaries in an outward direction (thus, this method is regarded as the deletion method throughout this paper). Since there are no incoming atoms from the vapor boundary side, the mass flux crossing the vapor boundary consists only of the spontaneously evaporated atoms as shown in Figure 3. In addition, the vapor and liquid boundaries of the left- and right-side interfaces move along with the depleting liquid film, and the boundary positions are functionals of time [32]:  
\[z\big{(}N_{sp}(t)\big{)}=z_{0}\pm\frac{\big{(}N_{0}-N_{sp}(t)\big{)}m}{2S_{xy }p_{l}} \tag{7}\]  
where \(z\) is the boundary position, \(N_{sp}(t)\) is the number of evaporated atoms over time \(t\), and \(N_{0}\) is the initial number of atoms. The left side interface boundaries move in a positive \(z\) direction, while the right-side interface boundaries move in a negative \(z\) direction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(z_{0,low}\), Å & \(z_{0,app}\), Å & \(\delta_{low}\)Å & \(\delta_{upr}\)Å & \(d\),Å \\ \hline \(-36.63\) & \(36.64\) & \(4.17\) & \(4.18\) & \(9.11\) \\ \hline \end{tabular}
\end{table}
Table 1: The values of the fitted transition region parameters for left- and right-side interfaces.  
\begin{table}
\begin{tabular}{c c c} \hline  & \(z^{\star}\) & \(z\), Å \\ \hline Left vapor boundary & \(3.0\) & \(-64.0\) \\ \hline Left liquid boundary & \(-0.9\) & \(-27.5\) \\ \hline Right liquid boundary & \(-0.9\) & \(27.5\) \\ \hline Right vapor boundary & \(3.0\) & \(64.0\) \\ \hline \end{tabular}
\end{table}
Table 2: The normalized and absolute simulation boundary positions listed in Figure 1.