esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Topical User Modeling
### Topic Assignment Procedure
#### 3.2.3 Topic Assignment with Gibbs Sampling  
After preprocessing, we estimate the latent topic for each occurrence of a user using Gibbs sampling. First we assign random topics for each latent variable \(\mathbf{z}=\langle z_{j}^{(i)}\rangle\), where \(z_{j}^{(i)}\) indicates the latent topic of the user for the \(j\)-th comment in the \(i\)-th article. Subsequently, iterating each of indices \(i\) and \(j\), we choose the latent topic \(z_{j}^{(i)}\) by random sampling, regarding the distribution of topics in the remainder of the assignment after removing \(z_{j}^{(i)}\), denoted by \(\hat{\mathbf{z}}_{j}^{(i)}\). We also denote by \(\mathbf{z}_{-j}^{(i)}\) the partial assignment of \(\hat{\mathbf{z}}_{j}^{(i)}\) corresponding to the \(i\)-th article; i.e., the topic assignment for the user participating in comments in the \(i\)-th article except its \(j\)-th comment. The probability for sampling can be computed as follows:  
\[\begin{split} P(z_{j}^{(i)}=k|\hat{\mathbf{z}}_{j}^{(i)}\,, \mathcal{A})&=P(z_{j}^{(i)}=k|\hat{\mathbf{z}}_{j}^{(i)}\,,u_{j} ^{(i)})\\ &\propto P(z_{j}^{(i)}=k,u_{j}^{(i)}|\hat{\mathbf{z}}_{j}^{(i)}) \\ &\propto P(u_{j}^{(i)}|z_{j}^{(i)}=k,\hat{\mathbf{z}}_{j}^{(i)})P (z_{j}^{(i)}=k|\hat{\mathbf{z}}_{j}^{(i)})\\ &\propto P(u_{j}^{(i)}|z_{j}^{(i)}=k)P(z_{j}^{(i)}=k|\mathbf{z}_ {-j}^{(i)})\end{split} \tag{3}\]  
We define two matrices \(M\) and \(D\) where \(M_{k,x}\) is the number of assignments of topic \(\tau_{k}\) to user \(x\in\{1,\cdots,m\}\), and \(D_{i,k}\) is the number of assignments of topic \(\tau_{k}\) in article \(a_{i}\). Then the first factor  
Figure 1: Lifetime filter can moderate temporal biases caused by temporary users.  
\(P(u_{j}^{(i)}|z_{j}^{(i)}=k)\) can be computed from the \(k\)-th row of \(M\), and the second factor \(P(z_{j}^{(i)}=k|\mathbf{z}_{-j}^{(i)})\) can be computed from the \(i\)-th row of \(D\). Thus, we can compute this probability as follows:  
\[\begin{split} P(z_{j}^{(i)}=k|\hat{\mathbf{z}}_{j}^{(i)},\mathcal{ A})&\propto\frac{M_{k,x}}{\sum_{y=1}^{m}M_{k,y}}\frac{D_{i,k}}{\sum_{l=1}^ {K}D_{i,l}}\\ &\propto\frac{D_{i,k}M_{k,x}}{\sum_{y=1}^{m}M_{k,y}}\end{split} \tag{4}\]  
We also use Laplace smoothing for these matrices by adding \(\alpha\) (and \(\beta\)) for every entry of matrix \(D\) (and \(M\), respectively), in order to avoid dividing by zero. We used \(\alpha=1\) and \(\beta=0.1\) in this work.
## 3 Results
Site KFS shows the least divergence at the four-day scale (\(R\) = 0.88), while site KZU shows the least divergence at timescales of 32 days (\(R\) = 0.81) and 64 days (\(R\) = 0.84). K4B shows the least divergence to mid-tropospheric concentrations at the four-day to the 512-day scale, with lowest \(R\) values occurring at the four-day (\(R\) = 0.66), eight-day (\(R\) = 0.84), and 128-day (\(R\) = 0.83) scales. Figure 3: Relative entropy of (**a**) AIRS time series with wavelet decomposed versions of EC time series; (**b**) EC time series with wavelet decomposed versions of AIRS time series; (**c**) wavelet decomposed versions of AIRS with wavelet decomposed versions of EC; and (**d**) wavelet decomposed versions of EC with wavelet decomposed versions of AIRS. Lower relative entropy corresponds to less divergence or greater similarity between time series. When assessing how close mid-tropospheric concentrations are to original surface EC measurements (Figure 3b), the KFS site stands out as being similar to mid-tropospheric CO\({}_{2}\) at all but the four-day timescale. The least divergence between mid-tropospheric CO\({}_{2}\) and KFS is seen at the two-day (\(R\) = 0.14) and eight-day (\(R\) = 0.44) scales, and the 16- to 512-day scales also have low \(R\) values. The lowest \(R\) values at the KZU site are seen at timescales of 16 days (\(R\) = 1.48) and 64 days (\(R\) = 1.22). The timescales of least divergence for the K4B site occur at 128 days (\(R\) = 0.52) and 256 days (\(R\) = 0.68), though \(R\) is also low at the 32- and 512-day scales. To evaluate \(R\) between wavelet decomposed versions of both AIRS and EC time series (Figure 3c, d), the distances from pdfs of one decomposed series at a particular timescale to the pdfs of the other series at the same timescale are considered. When the distance from the pdf of the decomposed EC time series to the pdf of the decomposed AIRS time series is measured (Figure 3c), KFS shows the greatest similarity or least divergence from the 16-day (\(R\) = 0.20) to the 512-day (\(R\) = 0.10) scales; KZU shows the greatest similarity or least divergence from the 32-day (\(R\) = 0.04) to the 512-day (\(R\) = 0.003) scales; and K4B shows the greatest similarity or least divergence from the 4-day (\(R\) = 0.10) to the 512-day (\(R\) = 0.04) scales. This pattern of timescales is reflected for all sites in Figure 3d, where the distance from the pdf of the decomposed AIRS time series to the pdf of the decomposed EC time series is measured. In general, all values of \(R\) are lower when comparing series that are both decomposed.
## 1 Introduction
Dependency trees convey rich structural information that is proven helpful for extracting relations among entities in text. Therefore, in this paper, dependency relations are used as the prior knowledge to supervise the model to learn metonymies. Soft structural constraints based on dependency parse trees are imposed on state-of-the-art pre-trained language representations such as BERT. This work builds on a rich line of recent efforts on relation extraction models and graph convolutional networks (GCN). Differing from the previous works that either try to directly use dependency parse trees or concatenate the transition vector based on dependency parse trees, this work employs attention-guided GCN(AGCN) to integrate information. Additionally, the outputs of the BERT layer are passed to attention-guided GCN to extract the syntactic information through dependency parsing. Then both syntactic and semantic information will be fed into the attention-guided GCN integration component that learns how to selectively attend to the relevant structures valid for MR. Therefore, the model takes advantage of the relevant dependency ancestors and efficiently removes the noise from irrelevant chunks. Finally, sentence-level representations, as well as the outputs of attention-guided GCN, are combined as the input to a multi-layer neural network for classification. Figure 1: A metonymy example on the geographical parsing problem. According to linguistic valency in English grammar, the meaning of metonymic entity “Ma” is mainly decided by the root of the sentences, i.e., the verb “acquired”. Dependency relations are the basis of understanding metonymy. Considering entity awareness and dependency information both benefit MR, this paper presents EBAGCN (Entity BERT with Attention-guided GCN) to leverage entity constraints from sequence-based pre-trained language representations and soft dependency constraints from dependency trees at the same time. As a result, the proposed approach is superior in capturing the semantics of the sentence and the long-distance dependency relations, which better benefits MR. To summarize, the main contributions of this paper are as follows:  
* propose a novel method for metonymy resolution relying on recent advances on pre-trained language representations that integrate entity knowledge and significantly improve the accuracy. * incorporate attention-guided GCN to MR with hard/soft dependency constraints, which imposes the pre-trained language representations with prior syntactic knowledge. * experiments on two benchmark datasets show that the proposed model EBAGCN is significantly superior to previous works and improves the BERT-based baseline systems.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 4 Applications of LLMs in Maritime Operations
### Decision Support Systems  
In complex maritime operations, timely and informed decision-making is crucial. LLMs can enhance decision support systems by processing large volumes of data and providing actionable insights [126].  
A practical example of using LLMs in maritime operations is real-time weather data analysis. For instance, an LLM can process data from AIS systems, weather forecasts, and port reports to identify potential hazards, such as storms or collisions in congested routes. Another example is the automated generation of failure reports, which streamlines communication between the crew and onshore support teams, accelerating response times and minimizing risks.
## 8 Fixed and Adaptive Wave Domain Equalization
### Wave-Domain Adaptive Filtering  
An adaptive solution to the computation of RRE filters is desirable since the acoustic transfer paths may change, for instance due to people entering the room or due to temperature changes. As an example, the consequences of varying the room temperature on RRE using static filters are illustrated in [198]. A wide variety of adaptation algorithms have been developed in the past. Since RRE is an inverse problem, the class of filtered-x algorithms is well suited. The filters may be computed adaptively with the multichannel filtered-x recursive least-squares algorithm (X-RLS) [199]. However, in the context of multichannel RRE, an adaptive solution has three fundamental issues: (1) ill-conditioning; (2) non-uniqueness; and (3) numerical complexity. The first problem is related to the spatio-temporal correlation of typical loudspeaker signals, the second to the underlying optimization problem, and the third to the size of typical MIMO systems following the wave-theoretical view. A solution to the third problem--which also augments the other two issues--has been proposed by wave domain adaptive filtering (WDAF) [10,200]. Here the underlying MIMO system is decoupled by a set of spatio-temporal transforms, as illustrated in Figure 10.  
Figure 9: Application of the Helmholtz integral equation (HIE) to room compensation and spatial sampling of the loudspeaker and microphone contour.  
The transforms \(\mathcal{T}_{1}\) through \(\mathcal{T}_{3}\) are motivated by the physical background of the room equalization problem and its solution using orthogonal expansions, as outlined in the previous section. In terms of the underlying multichannel problem, this can be achieved by diagonalization of the MIMO systems using a generalized singular value decomposition (GSVD). This approach is known as eigenspace adaptive filtering (EAF) [193]. As a consequence, the adaptation problem is reduced to the adaptation of the main diagonal elements of the MIMO room equalization filter \(\underline{\underline{\mathbf{\zeta}}}\) in the transformed domain. In this way, the computational complexity is lowered significantly and the non-uniqueness problem is improved. However, EAF requires that the transfer paths from the loudspeakers to the microphones are known, which contradicts the idea of an adaptive computation of the equalization filters. Using analytic transformations which are based upon the free-field solutions of the wave equation, an approximate diagonalization of the MIMO system has been achieved [10; 200].  
The original approach focused on adapting only the diagonal paths in the transformed domain. In [201], this was extended towards a flexible adaptation framework also considering off-diagonal paths. The full adaptation of all paths in the transformed domain is investigated in [202]. Invertible transformations for WDAF have been introduced in [203], while a subband approach to WDAF has been published in [204]. Furthermore, strategies for the use of irregularly-spaced loudspeaker arrays have been proposed in [205].
## 3 Results
### Relationship Between the Seven VIs and AGB at Four Different Stages
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Growth** & **Model** & **Vegetation** & **Model** & **R\({}_{\rm cal}\)\({}^{2}\)** & **RMSEC** & **R\({}_{\rm val}\)\({}^{2}\)** & **RMSEV** & **RPD** \\ \hline \multirow{8}{*}{**Seeding**} & 1 & Clred edge & Y = 102.97e4.02X & 0.75 & 516.02 & 0.60 & 667.45 & 1.49 \\  & 2 & Clgreen & Y = 23.79X1.75 & 0.46 & 765.90 & 0.43 & 754.32 & 1.32 \\  & 3 & NDVI & Y = 0.15e10.88x & 0.44 & 782.97 & 0.35 & 808.95 & 1.23 \\  & 4 & GNDVI & Y = 66244.38X2 & \({}^{2}\)-71070.06X + 19149.09 & 0.70 & 567.58 & 0.62 & 634.92 & 1.57 \\  & 5 & RVVI & Y = 140.90X2 & 532.25X + 4694.94 & 0.66 & 603.71 & 0.60 & 645.25 & 1.54 \\  & 6 & DVI & Y = 103.302X2 & \({}^{2}\)-3315.29X + 430.26 & 0.38 & 820.85 & 0.48 & 719.00 & 1.38 \\  & 7 & SAVI & Y = 9193.38X2 & \({}^{2}\)-6090.45X + 1306.71 & 0.42 & 794.13 & 0.51 & 698.61 & 1.42 \\ \hline \multirow{8}{*}{**Bolting**} & 8 & Clred edge & Y = 9197.71X2.35 & 0.90 & 548.12 & 0.95 & 563.53 & 3.36 \\  & 9 & Clgreen & Y = 28.60X2 & \({}^{2}\)-144.13X + 510.72 & 0.60 & 1116.29 & 0.68 & 1200.59 & 1.58 \\  & 10 & NDVI & Y = 0.00E14.48x & 0.74 & 909.41 & 0.83 & 931.60 & 2.03 \\  & 11 & GNDVI & Y = 0.00E29.10x & 0.76 & 860.64 & 0.85 & 917.05 & 2.06 \\  & 12 & RVI & Y = 414.29X2 & \({}^{2}\)-3126.00X + 6200.30 & 0.75 & 880.71 & 0.84 & 935.99 & 2.02 \\  & 13 & DVI & Y = 5\(-\)544.29X2 & \({}^{2}\)-31010.60X \(-\) 1593.67 & 0.65 & 1045.51 & 0.64 & 1116.31 & 1.70 \\  & 14 & SAVI & Y = 19512.53X2 & \({}^{2}\)-1249.74X + 2389.74 & 0.68 & 1000.69 & 0.71 & 1039.45 & 1.82 \\ \hline \multirow{8}{*}{**Flowering**} & 15 & Clred edge & Y = 15030.54X2 & \({}^{4}\)-4155.57X + 617.51 & 0.51 & 1930.70 & 0.73 & 1383.12 & 1.94 \\  & 16 & Clgreen & Y = 40.19X2 & \({}^{2}\)-4165.32X2 & \({}^{2}\)-513.96 & 0.46 & 2035.52 & 0.69 & 1506.24 & 1.78 \\  & 17 & NDVI & Y = 38617.39X2 & \({}^{2}\)-3298.07X + 10536.23 & 0.07 & 2670.14 & 0.22 & 2313.93 & 1.16 \\  & 18 & GNDVI & Y = 8088.29X2 & \({}^{2}\)-6126.29X + 15637.80 & 0.09 & 2647.08 & 0.21 & 2331.43 & 1.15 \\  & 19 & RVI & Y = 665.18X2 & \({}^{2}\)-205.16X2 & \({}^{2}\)-1605.16X & 0.06 & 2689.12 & 0.20 & 2393.91 & 1.14 \\  & 20 & DVI & Y = \(-\)3202.02X2 & \({}^{2}\) + 49340.35X - 8223.08 & 0.61 & 1725.98 & 0.65 & 1562.56 & 1.72 \\  & 21 & SAVI & Y = 82431.02X2 & \({}^{2}\)-104058.89X \(-\)25617.79 & 0.44 & 2080.93 & 0.54 & 1794.47 & 1.50 \\ \hline \multirow{8}{*}{**Pod stage**} & 22 & Clred edge & Y = 19903.34X2 & \({}^{2}\)-12952.22X + 30.53 & 0.60 & 2885.85 & 0.57 & 2789.80 & 1.55 \\  & 23 & Clgreen & Y = 3694.02X & \({}^{2}\)-3109.04X + 9343.05 & 0.63 & 2757.06 & 0.46 & 3128.60 & 1.39 \\ \cline{1-1}  & 24 & NDVI & Y = 158.148X & \({}^{2}\)-705.18X & \({}^{2}\)-705.18X & 0.67 & 2627.16 & 0.52 & 2690.42 & 1.46 \\ \cline{1-1}  & 25 & GNDVI & Y = 30047.05X2 & \({}^{2}\)-25579.54X + 54925.75 & 0.68 & 2577.58 & 0.61 & 2768.50 & 1.57 \\ \cline{1-1}  & 26 & RVI & Y = 929.40X & \({}^{2}\)-2566.60 & 0.68 & 2570.50 & 0.61 & 2826.18 & 1.53 \\ \cline{1-1}  & 27 & DVI & Y = \(-\)26309.222X & \({}^
# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 3 Extraction Model of Visual Region of Interest
### Algorithm Improvement
#### 3.2.2 Edge Structure Feature Extraction  
Edge is the most important feature of the image. Currently, there are many kinds of edge detection methods, such as [PERSON] operator detection method, [PERSON] operator detection method, [PERSON] operator detection method and so on. However, because of the noise, the Canny method is not easy to be disturbed, or to detect the real weak edges. The advantage is that the two different thresholds are used to detect strong edges and weak edges. Weak edges are included in the output image when the weak edges and strong edges are connected. In this paper, we use Canny edge detection algorithm to extract the edge structure features of the image. Steps are as follows:  
1. We use Gauss filter to smooth image. Canny method firstly uses two-dimensional Gauss's function to smooth the image: \[G(x,y)=\frac{1}{2\pi\sigma^{2}}\exp\left|-\frac{x^{2}+y^{2}}{2\sigma^{2}}\right|\] (7)Its gradient vector is \(\
abla G=\left[\begin{array}{c}\partial G/\partial x\\ \partial G/\partial y\end{array}\right]\). Where \(\sigma\) is distributed parameter of Gauss filter.
2. We calculate the magnitude and direction of the gradient, and suppress the amplitude of the gradient.
3. We perform a dual threshold method to detect and connect edges. \(E(x,\,y)\) is the final edge structure map. The edge structure characteristic is calculated as: \[E(c,s)=|E(c)\Theta E(s)|\] (8)  
The above feature maps are combined to get the edge structure salient map. The combined formula is as:  
\[\overline{E}=\underset{c=2}{\overset{4}{\oplus}}\underset{s=c+3}{\overset{c+ 4}{\oplus}}N(E(c,s)) \tag{9}\]  
In the end, the final image salient region is obtained by linear addition of 5 salient maps, and the formula is shown below:  
\[S=\frac{1}{5}(N(\overline{I})+N(\overline{C})+N(\overline{O})+N(\overline{T} )+N(\overline{E})) \tag{10}\]  
where, \(I\), \(C\) and \(O\) represent intensity, color and orientation feature respectively.
## List of Nomenclature  
\begin{tabular}{l l l l} FCHEV & fuel cell hybrid electric vehicle & \(E_{a}\) & activation energy \\ EMS & energy management strategy & \(\theta\) & temperature in Celsius \\ ECMS & equivalent consumption minimization strategy & \(Ah\) & Ah-throughput \\ PEMFC & proton exchange membrane fuel cell & \(z\) & power law factor \\ LSTM & long short-term memory & \(\alpha\) & SOC-related coefficient \\ _SOC_ & state of charge & \(\beta\) & SOC-related coefficient \\ _F\({}_{t}\)_ & traction force & \(I_{c}\) & charge/discharge rate \\ \(f\) & rolling resistance coefficient & \(P_{mot}\) & motor power \\ \(m\) & mass of FCHEV & \(T_{mot}\) & motor torque \\ _\(\alpha\)_ & angle of road slope & \(\omega_{mot}\) & motor speed \\ \(A\) & frontal area & \(\eta_{mot}\) & motor efficiency \\ _C\({}_{D}\)_ & drag coefficient & \(\hat{m}_{fc}\) & fuel cell instantaneous hydrogen consumption \\ _\(\delta\)_ & vehicle rotational mass conversion coefficient & \(P_{dem}\) & power demand \\ \(v\) & velocity & B & pre-exponential factor \\ \(g\) & gravitational constant & \(\eta_{mot}\) & battery hydrogen consumption \\ _P\({}_{req}\)_ & powertrain power demand & \(\lambda(t)\) & covariate \\ _P\({}_{fc}\)_ & fuel cell stock output power & \(I_{bat}\) & current \\ _P\({}_{bat}\)_ & output power of battery & \(s(t)\) & equivalence factor \\ _\(\eta_{fc\_dc}\)_ & unidirectional DC/DC converter efficiency & \(R\) & internal resistance \\ _\(\eta_{bat\_dc}\)_ & bidirectional DC/DC converter efficiency & \(V_{dec}\) & degradation loss voltage \\ _V\({}_{fc\_cell}\)_ & single fuel cell output voltage & \(V_{act}\) & activation loss \\ _E\({}_{ner}\)_ & theoretical thermodynamic electric potential & \(V_{olm}\) & ohmic loss \\ _E\({}_{o}\)_ & reference potential per unit of activity & \(V_{con}\) & concentration loss \\ _P\({}_{aux}\)_ & other component power loss & \(F\) & Faraday constant \\ _\(\hat{m}_{H_{2}}\)_ & fuel cell hydrogen consumption rate & \(R_{0}\) & gas molar constant \\ _M\({}_{H_{2}}\)_ & hydrogen molar mass & \(P\) & total pressure in pile \\ _LHV_ & hydrogen low heating value & \(P_{H_{2}}\) & partial pressure of hydrogen \\ _V\({}_{cell}\)_ & voltage degradation of single fuel cell & \(P_{O_{2}}\) & partial pressure of oxygen \\ _\(\hat{m}_{bat}(t)\)_ & battery instantaneous consumption rate & \(P_{H_{2}O}\) & partial pressure of water vapor \\ _P\({}_{bat}\)_ & battery output power & \(P_{fc}\) & fuel cell stack output power \\ _m\({}_{oav}(t)\)_ & instantaneous total hydrogen consumption rate & \(N\) & number of single cells \\ _U\({}_{acc}\)_ & open-circuit voltage & \(I_{fc}\) & single cell current \\ _m\({}_{fc}(t)\)_ & fuel cell instantaneous hydrogen & \(P_{fc}\) & fuel cell system net output power \\  & consumption rate & \(P_{ac}\) & air compressor power loss \\ _SOC\({}_{int}\)_ & initial value of SOC & \(\mathcal{Q}_{loss}\) & battery capacity loss \\ _Q\({}_{bat}\)_ & nominal battery capacity & \(\mathcal{Q}_{loss}\) & \(\mathcal{Q}_{loss}\) \\ \end{tabular}
# Impact Analysis and Optimization of Key Material Parameters of Embedded Water-Stop in Tunnels
## 2 Optimization of Key Parameters for Water-Stop Materials
### Finite Element Model  
According to the adhesive force test of pure rubber and concrete test blocks, the normal adhesive force, tangential adhesive force, cohesive force parameters and so on were measured. In order to analyze the influence of water-stop hardness and the bonding force between the water-stop and concrete on its stress state, a finite element model of an embedded water-stop is established using the finite element software ABAQUS/Standard 2020. The embedded rubber water-stop is simulated using the Mooney-Rivlin model, with dimensions as shown in Figure 2. The concrete damage plasticity model is used to simulate the tunnel lining, considering the grade of the concrete as C30, the modulus of elastic \(E_{C}\) as 30 GPa, and the Poisson's ratio \(\upsilon_{c}\) is 0.2. The concrete damage plasticity model in ABAQUS is used to simulate the failure of the lining. In the damage plasticity parameters, the eccentricity \(e_{f}\), the ratio of biaxial to uniaxial compressive strength \(f_{\text{B}0}/f_{\text{C}0}\), and the coefficient \(k\) are set to 0.1, 1.16, and 0.667, respectively. Based on the implicit solution of ABAQUS, the viscosity parameter \(\mu_{c}\) needs to be set to 0.005. The dilation angle \(\psi\) is set to 30\({}^{\circ}\). The stress-strain curves of concrete are determined according to the Code for the design of concrete structures [25]. The dimensions are 300 mm in width and 400 mm in height.  
According to the specifications, the hardness of the rubber water-stop in the tunnel joint is 60 \(\pm\) 5, and the hardness of rubber in this paper is Shore hardness (HA). In this simulation, the middle value of 60 is taken for calculation. Based on experience, the value of \(C_{01}/C_{10}\) is generally taken as 0.25 for better fitting. The calculated values are \(C_{10}=0.483\) MPa and \(C_{01}=0.121\) MPa.  
The contact between the water-stop and the concrete is set as a normal hard contact, and the tangential direction uses the penalty function method with a friction coefficient of  
Figure 1: Waterproofing mechanism of water-stop. (**a**) extrusion sealing. (**b**) bonding water plugging.  
Figure 2: Water-stop dimensions (mm).  
0.3. Cohesive contact is also considered, with a normal stiffness of 3860, tangential stiffness of 1510, normal cohesive force of 0.386 MPa, and tangential cohesive force of 0.151 MPa. The plastic displacement is 0.0009 mm. Self-contact is set for the holes in the water-stop, with a friction coefficient of 0.8. It is assumed that the construction quality of the water-stop is great, and the finite element model is shown in Figure 3.  
For the deformation of the lining, a reference point is coupled with the concrete in the model, and deformations in various directions are applied to the reference point to simulate the settlement, elongation, and compression of the lining. The maximum limits for deformation, tension and compression can be taken to 30 mm, 20 mm, and 10 mm, respectively.