esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Material Characterization
#### 3.1.2 Fourier-Transform Infrared Spectroscopy (FTIR)  
Figure 2 displays the FTIR spectra of the Hap series (Hap-H1, Hap-H2, Hap-H3, and Hap-H3 750), FCF, and the Hap-FCF composite.  
For the Hap series samples, the FTIR spectra reveal bands associated with the carbonate (CO3\({}^{2-}\)) and phosphate (PO4\({}^{3-}\)) groups, characteristic of hydroxyapatite [31]. The  
Figure 2: FTIR spectrum analysis of the samples: Hap series, FCF, and Hap-FCF composite.  
bands linked to the \(\
u_{3}\) vibration mode, attributed to O-C-O bonds, are observed at 874 cm\({}^{-1}\). Additionally, a band at 1413 cm\({}^{-1}\) appears due to the C-O bond vibration in the \(\
u_{3}\) mode of carbonates (CO\({}_{3}\)\({}^{2-}\)). Bands corresponding to the \(\
u_{4}\) vibration mode of the O-P-O bond are evident at 563 and 601 cm\({}^{-1}\). The P-O bond's symmetric \(\
u_{1}\) and \(\
u_{3}\) vibration modes are detected at 960 cm\({}^{-1}\) and 1021 cm\({}^{-1}\), respectively, while the antisymmetric \(\
u_{3}\) degenerate mode is observed at 1090 cm\({}^{-1}\)[32; 33; 34; 35]. These findings provide strong reassurance of the successful synthesis of hydroxyapatite in all cases, validating our research.  
In the FCF spectrum, a peak at 3409 cm\({}^{-1}\) corresponds to the N-H stretching, a characteristic feature of the FCF structure, as reported by [PERSON] et al. [36]. In the Hap-FCF composite, the precise detection of the N-H bond band of FCF at 3641 cm\({}^{-1}\), alongside the characteristic bands of hydroxyapatite, further validates the presence of both components within the composite, demonstrating the precision of our research.
## 3 Results
### Mathematical Model for the Probability of Failures and Solutions  
Failure Probability Model: assuming that the occurrence of failures follows a Poisson process. The failure rate (lambda) is represented as the average number of failures occurring in a given period of time. The probability of observing \"k\" failures in a period of time \"t\" is expressed using the Poisson probability function:  
\[\text{P(k failure in t)}=(\text{e}^{\text{*}}(-\lambda)\ \lambda^{\text{*}} \text{k})/\text{k}! \tag{26}\]  
where:  
\(\lambda\) (lambda) is the failure rate (average number of failures per unit of time).  
Solution Effectiveness Probability Model: the effectiveness of solutions can be modeled as a conditional probability, i.e., the probability that a solution is effective given that a failure occurred. We will represent this probability as \(\text{P(E}\ \text{I}\ \text{F})\), where E is the effectiveness of the solution and F is the occurrence of a failure.  
General Effectiveness Probability Model: The overall probability that a solution is effective is obtained through the theorem of total probability. It is the probability of a failure occurring multiplied by the probability that the solution is effective given that a failure occurred:  
\[\text{P(General Effectiveness)}=\text{P(Failure)P(E}\ \ \text{I}\ \text{F}) \tag{27}\]  
During the 100-day trial as shown in Figure 16, we initially observe a peak because the model is practically new and may encounter various failures. As time passes, the probability of failures decreases and remains stable, as the system has adapted to the errors that arose in the assigned work area.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 5 Simulation Results and Analysis
### Results Analysis
#### 5.5.5 Latency and Complexity Performance  
We use inference time to characterize the latency and complexity of the model across various stages. Table 4 provides a comparative analysis of the inference time among different model schemes when processing the test set. As seen in the table, the Deep JSCC model exhibits the shortest delay due to its reliance solely on the CNN architecture; however, this also results in suboptimal image restoration quality. The latencies associated with the CCB and WITT models are similar because both employ comparable codec architectures. Nonetheless, the WITT model experiences longer delays due to the necessity of channel estimation through feedback. Consequently, our proposed CCB model preserves transmission quality without increasing model complexity.
## 4 Kinetostatic Model of _n_-4R Compliant Parallel Pointing Mechanism
### Kinetostatic Model of 4-4R Compliant Parallel Pointing Mechanism  
When the forces \(\mathbf{F}_{1}\) and \(\mathbf{F}_{2}\) act on the mobile platform simultaneously, displacement \(\mathbf{U}_{4-4R}\) at the center of the mobile platform can be regarded as the superposition of the displacement \(\mathbf{U}_{1}\) and \(\mathbf{U}_{2}\) at the center of the mobile platform under the separate action of force \(\mathbf{F}_{1}\) and \(\mathbf{F}_{2}\). Therefore, the total displacement of mobile platform is defined as:  
\[\mathbf{U}_{4-4R}=\mathbf{U}_{1}+\mathbf{U}_{2} \tag{13}\]  
According to Equations (11) and (13), one can obtain:  
\[\mathbf{U}_{4-4R}=\begin{bmatrix}\mathbf{C}_{TOF_{1}}&\mathbf{C}_{TOF_{2}}\end{bmatrix} \begin{bmatrix}\mathbf{F}_{1}\\ \mathbf{F}_{2}\end{bmatrix} \tag{14}\]  
where \(\mathbf{C}_{TOF_{2}}\) is the mapping matrix between force \(\mathbf{F}_{2}\) and displacement \(\mathbf{U}_{2}\) of mobile platform center.  
Due to the symmetry of the structure, the mapping relationship between the force \(\mathbf{F}_{2}\) and the displacement \(\mathbf{U}_{2}\) of the mobile platform center can be obtained by rotation transformation of relative elements in Equation (8). We define a new adjoint transformation matrix as:  
\[\mathbf{A}\mathbf{d}_{\pi/2}=\begin{bmatrix}\mathbf{R}_{y,\pi/2}&0\\ 0&\mathbf{R}_{y,\pi/2}\end{bmatrix} \tag{15}\]  
where \(\mathbf{R}_{y,\pi/2}\) is the rotation matrix, representing 90\({}^{\circ}\) rotation around the \(y\)-axis of global coordinate frame \(\mathbf{O}xyz\).  
The coordinate transformation of related elements in Equation (8) yields:  
\[\left\{\begin{array}{l}(\mathbf{K}_{OO})_{F_{2}}=\mathbf{A}\mathbf{d}_{\pi/2}\Big{[}( \mathbf{K}_{OO})_{F_{1}}\Big{]}(\mathbf{A}\mathbf{d}_{\pi/2})^{\mathrm{T}}\\ \mathbf{K}_{F_{2}F_{2}}=\mathbf{K}_{F_{1}F_{1}}\\ \mathbf{K}_{OF_{2}}=\mathbf{A}\mathbf{A}\mathbf{d}_{\pi/2}\big{[}\mathbf{K}_{OF_{1}}\big{]}\\ \mathbf{K}_{F_{2}O}=\big{[}\mathbf{K}_{F_{1}O}\big{]}(\mathbf{A}\mathbf{d}_{\pi/2})^{\mathrm{T} }\end{array}\right. \tag{16}\]  
By rearranging Equations (12) and (16), one can obtain:  
\[\mathbf{C}_{TOF_{2}}=[\mathbf{A}\mathbf{d}_{\pi/2}]^{-\mathrm{T}}\Big{[}-\Big{(}(\mathbf{K}_ {OO})_{F_{1}}-\mathbf{K}_{OF_{1}}\mathbf{K}_{F_{1}F_{1}}^{-1}\mathbf{K}_{F_{1}O}\Big{)}^{- 1}\Big{(}\mathbf{K}_{OF_{1}}\mathbf{K}_{F_{1}F_{1}}^{-1}\Big{)}\Big{]}\]  
The above equation can be further simplified as:  
\[\mathbf{C}_{TOF_{2}}=[\mathbf{A}\mathbf{d}_{\pi/2}]^{-\mathrm{T}}\big{[}\mathbf{C}_{TOF_{1}} \big{]} \tag{17}\]  
So far, the kinetostatic model of the 4-4R compliant parallel pointing mechanism has been established.
# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 4 Simulations and Results
### YOLO Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Box**} & \multirow{2}{*}{**Box Recall**} & \multirow{2}{*}{**mAP 50**} & \multirow{2}{*}{**mAP 50-95**} \\  & & **Precision** & & & \\ \hline \multirow{4}{*}{YOLOv5 Nano} & Combined & 0.8332 & 0.7086 & 0.7550 & 0.4430 \\  & Crack & 0.8723 & 0.8165 & 0.8495 & 0.5060 \\  & Hole & 0.8455 & 0.6261 & 0.6653 & 0.3375 \\  & Erosion & 0.7817 & 0.6833 & 0.7501 & 0.4855 \\ \hline \multirow{4}{*}{YOLOv5 Medium} & Combined & 0.8753 & 0.7566 & 0.8058 & 0.5004 \\  & Crack & 0.8931 & 0.8604 & 0.8954 & 0.5693 \\  & Hole & 0.8804 & 0.6674 & 0.7281 & 0.4027 \\  & Erosion & 0.8523 & 0.7420 & 0.7938 & 0.5291 \\ \hline \multirow{4}{*}{YOLOv5 X-Large *} & Combined & 0.8504 & 0.7336 & 0.7637 & 0.4579 \\  & Crack & 0.9044 & 0.8370 & 0.8754 & 0.5456 \\  & Hole & 0.7824 & 0.6289 & 0.6377 & 0.3087 \\  & Erosion & 0.8645 & 0.7350 & 0.7781 & 0.5194 \\ \hline \multirow{4}{*}{YOLOv8 Nano} & Combined & 0.8574 & 0.6755 & 0.7564 & 0.4436 \\  & Crack & 0.8601 & 0.7876 & 0.8373 & 0.5097 \\  & Hole & 0.9229 & 0.5943 & 0.7095 & 0.3710 \\  & Erosion & 0.7892 & 0.6445 & 0.7225 & 0.4502 \\ \hline \multirow{4}{*}{YOLOv8 Medium} & Combined & 0.8811 & 0.7667 & 0.8233 & 0.5194 \\  & Crack & 0.9307 & 0.8581 & 0.9048 & 0.5872 \\  & Hole & 0.8952 & 0.6827 & 0.7430 & 0.4122 \\  & Erosion & 0.8174 & 0.7592 & 0.8222 & 0.5589 \\ \hline \multirow{4}{*}{YOLOv8 X-Large *} & Combined & 0.8900 & 0.7526 & 0.8110 & 0.5176 \\  & Crack & 0.9254 & 0.8644 & 0.9052 & 0.5960 \\  & Hole & 0.8814 & 0.6486 & 0.7108 & 0.3907 \\  & Erosion & 0.8631 & 0.7448 & 0.8170 & 0.5660 \\ \hline \multirow{4}{*}{**YOLOv9 C**} & Combined & 0.8850 & 0.7920 & **0.8490** & **0.5390** \\  & Crack & 0.9250 & 0.8790 & 0.9180 & 0.5980 \\  & Hole & 0.8610 & 0.6940 & 0.7570 & 0.4230 \\  & Erosion & 0.8700 & 0.8020 & 0.8710 & 0.5980 \\ \hline \multirow{4}{*}{YOLOv9 E} & Combined & 0.8910 & 0.7830 & 0.8450 & 0.5380 \\  & Crack & 0.9330 & 0.8740 & 0.9180 & 0.6030 \\  & Hole & 0.8800 & 0.6770 & 0.7510 & 0.4130 \\  & Erosion & 0.8590 & 0.7980 & 0.8650 & 0.5990 \\ \hline \hline \end{tabular}  
* X-Large indicates extra large size.  
\end{table} 
Table 5: The performance outcomes of the YOLO models trained on the dataset. When comparing the performance of each YOLO architecture on the test set, there is a common trend between the three classes, hole, erosion, and crack. Each YOLO version excels in the performance of crack localization with the lowest reported mAP50 obtained by YOLOv8 Nano at 0.8373 and the highest with YOLOv9 C and E achieving 0.9180. However, all models suffered performance loss in the localization and classification of hole damage in the dataset. YOLOv9c achieved the highest in the hole class with a mAP50 of 76% while YOLOv5 extra-large had the lowest performance of 64%. Reflected in Table 5, the lowest performing class for each YOLO model was hole damage.
# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 5 Works Based on Bug Reports
### Information Retrieval Approaches for Deduplication and Triage
Topic modeling is used to define bug-related models, and fuzzy logic is used to learn the relationship between developers and bug-related models, enabling the mapping between developers and bugs. Experimental results on the Eclipse, Mozilla, and NetBeans datasets showed accuracy rates of 0.903, 0.887, and 0.851, respectively, for the TM-FBT method. [PERSON] et al. introduced CTEDB [79], a method that utilizes information retrieval (IR) techniques to detect duplicated bug reports. This approach begins by extracting terminology from bug reports using Word2 Vec and TextRank. Then, it computes semantic similarity using Word2 Vec and SBERT. Finally, it employs DeBERTaV3 to process the extracted terminology from bug reports and calculate the confidence score for duplicate detection. Some works focus on data reduction for bugs before performing triage, which involves converting bugs into important features stored in a bug repository. This approach has the advantage of reducing the dimensionality of bugs, reducing redundancy, and improving the quality of the bug dataset. [PERSON] et al. surveyed the literature on bug triage based on data reduction and described the methodological framework, including preprocessing, vector model construction, data reduction, feature extraction, and triage modules.
In navigation practice, due to the lack of methods of scientific mooring area detection, the ships usually choose a larger anchorage circle radius to ensure their anchoring safety, which objectively results in a waste of anchorage space resource. Additionally, some space in the anchorage could be considered as mooring area. However, due to the lack of detection capacity, those anchorage space resources are wasted. Therefore, determining a suitable dropping anchor position and maintaining the mooring safety in operations are major challenges that intelligent and MASS systems need to undertake at different development stages. Intelligent mooring area detection is a strategy and technology that relies on various vessel sensors, fuses multi-source data, adopts relevant detection technologies to detect positions qualified for anchor operations, and conducts safety monitoring of the mooring position. Moreover, as for the development of intelligent vessels, mooring area detection is one of the key technologies related to their mooring safety and the utilization rate of anchorages space. More importantly, the mechanism of mooring area detection must be revealed, thus filling in the lacuna in mooring area detection, and promoting the development of related theories of intelligent vessels. The Monte-Carlo stochastic simulation originated in 1946 and was named after physicists [PERSON] and [PERSON], using random sampling to simulate neutron chain reactions on computers [13]. The simulation was conducted using simple and repeated sampling, meaning it was less affected by the limitation of conditions. It enjoys characteristics of the simple method and easy programming, and provides a relatively accurate answer. The Monte-Carlo stochastic simulation is widely used in research on Structural Safety [14], risk assessing workers' unsafe behavior [15], working space analyses of Robotic Arms [16], efficient long-term extreme analysis [17], helicopter maritime search and rescue response plans [18] and the dynamic process simulation of ship pilotage risk [19]. The above engineering problems share the same scientific principle with the mooring area detection problem, and act as a significant reference.
# Deformation Analysis of Large-Scale Rock Slopes Considering the Effect of Microseismic Events
## 3 Engineering Background
### Failure Characteristics of the Left Bank Slope after Excavation  
The excavation of the left bank slope began on 22 September 2013, and the excavation elevation reached 600 m on 3 June 2016. Some sprayed concrete cracked at the peripheries of the entrances to the drainage tunnel PSL2 and the curtain grouting tunnel WML2 at an elevation of 654 m on 13 December 2014, as shown in Figure 4a,b. A mortar strip was added to the cracks on 18 December 2014, and ruptures and mal-position occurred at the mortar strips on 23 December 2014. Moreover, a crack formed at the slope of the downstream spandrel groove along fault F17 on 25 December 2014. As shown in Figure 4c, the expansion width is approximately 1 cm at the top and approximately 0.3 cm at the toe. Therefore, the slope excavation was suspended immediately on 29 December 2014. Related supporting measures were completed on February 2015 [28]. After approximately one year, the excavation of the slope restarted on 11 February 2016. Some similar sprayed concrete cracked at the peripheries of the entrances to WML1 at an elevation of 605 m on 25 May 2016, as shown in Figure 4d.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rock Mass** & **Elasticity** & **Compressive** & **Poisson’s** & \multicolumn{2}{c}{**Shear Strength**} & **Density** \\
**Type** & **Modulus** & **Strength** & **Ratio** & \multicolumn{2}{c}{**\(\Phi\)/(\({}^{\circ}\))**} & **c/(MPa)** & **(g/cm\({}^{3}\))** \\ \hline II & 15 & 100 & 0.23 & 36 & 1.4 & 2.80 \\ III1 & 12 & 70 & 0.24 & 36 & 1.1 & 2.68 \\ III2 & 11 & 55 & 0.26 & 35 & 0.75 & 2.60 \\ IV & 5 & 35 & 0.32 & 33 & 0.5 & 2.50 \\ f110 & 0.6 & 20 & 0.35 & 28 & 0.15 & 2.22 \\ f108 & 0.3 & 16 & 0.35 & 27 & 0.15 & 2.20 \\ F17 & 1 & 23 & 0.34 & 28 & 0.15 & 2.13 \\ C3-1 & 0.71 & 22 & 0.35 & 28 & 0.04 & 2.05 \\ C3 & 0.4 & 18 & 0.34 & 28 & 0.1 & 2.13 \\ LS3319 & 0.25 & 15 & 0.33 & 26 & 0.1 & 2.11 \\ LS337 & 0.2 & 18 & 0.35 & 25 & 0.05 & 2.03 \\ LS3318 & 0.3 & 10 & 0.35 & 26 & 0.1 & 2.14 \\ LS331 & 1.3 & 23 & 0.35 & 27 & 0.06 & 2.15 \\ Overburden & 2 & 30 & 0.22 & 32 & 1.4 & 2.30 \\ layer & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mechanical parameters in the numerical calculation [25].  
Figure 3: Geological cross-section of the left bank slope along the longitudinal axis of the arch dam after excavation to an elevation of 628 m [26] (Copyright © 2017, Springer Nature).
## 2 Materials and Methods
### Proposed Method for Collecting and Processing Information for IRI Calculation
#### 2.3.4 Data Processing  
For obtaining the IRI, there are various procedures or simulations; as far as this research is concerned, it is proposed to use two methods: (1) to obtain the IRI through of the quarter car model considering the longitudinal profile with a spacing between the level equal to 1 cm; and (2) the use of statistical techniques and correlations using a re-creation of the low-cost instrument called MERLIN, which considers 1-m spacing between dimensions.  
For the series of spaced data every 1 cm, it is almost mandatory to filter the sequence of data points that make up the profile. The use of ProVAL software is considered for filtering and analysis of the longitudinal profile obtained. Filtering corresponds to a mathematical procedure that transforms a series of data into a new data series. A simple filter commonly used in longitudinal profile analysis is the moving average filter, which replaces each point in the profile with a new point obtained considering an average of adjacent points, producing a smoothing effect [3]. The value of the moving average low-pass (MALP) short wave filter proposed in this research is 8.3 m. Finally, the analysis is run, and the value of IRI is achieved.  
On the other hand, when defining a profile with 1-m spacing between dimensions, the simulation proceeds to consider the MERLIN method. To do this, you must calculate the difference between the actual dimension measured \"n\" and the average of dimensions \"n - 1\" and \"n + 1\", which define the \"average string\". Calculated differences must be recorded in a separate column to make a histogram that reveals the number of times a certain deformation value is presented. To do this, a tolerance of 1 mm is taken into consideration, or 3 decimal places are used. Once the histogram is created, the class range must be obtained, multiplied by a factor of 5 to transform it into millimeter units. This factor corresponds to the length of each box that has the MERLIN method form template that must be filled with the deformations when using the MERLIN equipment. This is how the class range \"D\" is obtained, which, before being entered into the correlation equation established for this class 3 equipment, 10% of the data located at the ends of the histogram are eliminated. That is if the measurement corresponds to 200 m, it is the first and last 10 data points that must be deleted in order to give greater preponderance to the deformations that are repeated more times; below is the correlation equation for the calculation of IRI.  
\[\textit{For IRI }<2.4,\textit{ IRI }=0.0485*D\left(\text{m/km}\right) \tag{1}\]  
The equation shown corresponds to the correlation used for the calculation of the IRI, considering the use of MERLIN equipment for new or semi-new paths. Finally, the value for the IRI is obtained.
# Using a Deep Quantum Neural Network to Enhance the Fidelity of Quantum Convolutional Codes
## 2 Basic Concepts
### Quantum Convolutional Codes  
Quantum convolutional codes use the stabilizer formalism which was first proposed by [PERSON] [20; 21]. The error syndrome is defined by the Pauli matrix.  
\[\mathbf{X}=\left[\begin{array}{cc}0&1\\ 1&0\end{array}\right],\mathbf{Y}=\left[\begin{array}{cc}0&-i\\ \mathrm{i}&0\end{array}\right],\mathbf{Z}=\left[\begin{array}{cc}1&0\\ 0&-1\end{array}\right] \tag{1}\]  
Pauli matrix \(\mathbf{X}\) refers to bit flipping, \(\mathbf{Z}\) refers to phase flipping and \(\mathbf{Y}\) refers to both. The code subspace \(\mathcal{C}\) of any stabilizer code is defined as the largest subspace stabilized by an Abelian group \(S\) acting on the N physical qubits of the code. In practice, \(S\) is a subgroup of the multiplicative Pauli group \(G_{N}=\mathrm{sp}\left\{I,X,Y,Z\right\}^{\otimes N}\)[22]. The code subspace \(\mathcal{C}\) can be defined by a set of independent generators \(\left\{M_{i}\right\}\) of \(S\):  
\[\forall i,|\psi\rangle=M_{i}|\psi\rangle,|\psi\rangle\in\mathcal{C} \tag{2}\]  
An \([n,k,m]\) stabilizer code is possible to encode k qubits of quantum information in n qubits and correct at most (m \(-\) 1)/2 errors. Similarly, the stabilizer group \(\mathcal{S}\) for an \([n,k,m]\)-convolutional code can be given by:  
\[S=\mathrm{sp}\Big{\{}M_{j,i}=I^{\otimes j\times n}\otimes M_{0,i},1\leq i\leq n -k,0\leq j\Big{\}} \tag{3}\]where \(M_{0,i}\in G_{n+m}\). Above, the \(M_{j,i}\)s are required to be independent and to commute with each other. The encoding circuit maps the to-be-protected qubits \(c_{j,i}\) onto the code subspace, written as follows:  
\[\left|c_{0,1},\ldots,c_{q-1,k}\right\rangle\rightarrow\left(\prod_{i,j}\frac{ 1+M_{j,i}}{\sqrt{2}}\right)\prod_{r,s}\bar{X}_{s,r}^{c_{i,r}}\left|0,\ldots,0\right\rangle \tag{4}\]  
for \(c_{s,r}\in\{0,1\},0<i\leq n-k,0\leq j<q+\lambda,0\leq s<q\) and \(1\leq r\leq k\). The \(\bar{X}_{i}\) operator is the encoded Pauli operator \(\mathsf{X}\). This operation can be decomposed in two steps. The first one, \(\prod_{r,s}\bar{X}_{s,r}^{c_{i,r}}\) applies the different flip operators depending on the value of the to-be-protected qubits in the computational basis. The second projects this state onto the code subspace.  
Take the [5,1,2] convolutional code, for example [23]. The generator matrix of the stabilizer group can be written as:  
\[M=\left(\begin{array}{ccccc|cccc}0&1&1&0&0\\ 0&0&1&1&0\\ 0&0&0&1&1\\ D&0&0&0&1\end{array}\right|\begin{array}{ccccc|cccc}1&0&0&1&0\\ 0&1&0&0&1\\ D&0&1&0&0\\ 0&D&0&1&0\end{array}\right) \tag{5}\]  
From the generator matrix, it is easy to obtain the encoding circuit as Figure 1[23].  
To estimate the error at the receiver, the quantum Viterbi algorithm was introduced. The algorithm examines the syndromes block by block and updates a list of error candidates, among which one of them coincides with the most likely error. Through the algorithm, the most likely error can be selected and then be corrected.
# Study on the Effect of Anchor Cable Prestress Loss on Foundation Stability
## 3 Numerical Simulation
### Analysis of Numerical Simulation Results
#### 3.4.5 Supporting Axial Force  
Under different prestress loss rates, the maximum axial force of the support under various working conditions is shown in Figure 11. In the process of foundation pit excavation, the maximum axial force of the support increases with the increase in the excavation depth, especially when the excavation is near the interface between the soil layer and the rock layer (step 5), the maximum axial force increases greatly. Under each working condition, the maximum axial force increases gradually with the increase in the prestress loss rate, and the total growth range of the maximum axial force before and after excavation decreases gradually with the increase in the prestress loss rate.  
Figure 10: Bending moment of the retaining pile under different prestress loss rates.