esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Truncated Group Plan under a Weibull Distribution based on Neutrosophic Statistics
## 3 Illustrative Example  
Suppose that a manufacturer wants to provide the mean life assurance for his product, and he claims that the true mean life of the product is \(\mu_{N}=500\) h. The quality inspector decides to check whether the manufacture's claim on the lifetime of the product is true or not and, therefore, specifies the experiment time as \(t_{N0}=500\) h. Hence, the experiment termination ratio is calculated as \(a=1.0\). The failure probability corresponding to the mean ratio \(\mu_{N}/\mu_{N0}=4\) is considered as AQL and the same at mean ratio 1 is taken as LQL. The consumer risk is assumed to be \(\beta=0.25\). The shape parameter of the Weibull distribution is specified as \(\delta_{N}=0.9\). Suppose the quality inspector wants to implement the proposed GASP under neutrosophic statistics, and he decides to allocate \(r_{N}=6\) items to each tester. Therefore, in order to execute the proposed plan for the above-specified conditions, we obtain the optimal parameters \(g_{N}=[8,10]\) and \(c_{N}=[3,4]\) from Table 3. The input values (or specified values) and the optimal values determined for those input values are reported in Table 5 for easy identification.  
This shows that the number of groups for the inspection lies between 8 and 10. Suppose the quality inspector chooses eight groups. Then, implementation procedure of the proposed plan is as follows.  
A random sample of 48 items is chosen from the submitted lot, and 6 sample items are distributed to 8 groups. The sample items are included in the life test, and the test is conducted up to the specified time 500 h. The submitted lot is accepted if there are, at most, 3 sample items that failed before the time 500 h in each of all 8 groups. Otherwise, the lot is rejected.
## 5 Models for the Effective SNR
### Ideal Model for the Effective SNR  
The maximum effective SNR that would be achievable in an ideal scenario would be equal to the sum of the SNR of all the individual CCs, \(\gamma_{\text{CA}}^{\text{ideal}}=\sum_{n=1}^{N}\gamma_{n}\). This model characterises the maximum possible diversity gain across the \(N\) available CCs that could hypothetically be achieved in an ideal diversity propagation scenario and can, thus, be used to derive theoretical upper bounds on the performance of CA. This model has been commonly considered in the existing literature (e.g., see equation (10) in [32] and equations (1)-(3) in [33]).  
**Proposition 1**.: _Ideal SNR model for CA with homogeneous SNR_  
_The PDF and CDF of the instantaneous effective SNR under an ideal CA scenario with homogeneous SNR are given, respectively, by_  
\[f_{\gamma_{\text{CA}}}(x) =\frac{1}{(N-1)!}\frac{x^{N-1}}{\overline{\gamma}^{N}}\exp\left( -\frac{x}{\overline{\gamma}}\right) \tag{3}\] \[F_{\gamma_{\text{CA}}}(x) =\frac{\gamma(N,x/\overline{\gamma})}{\Gamma(N)} \tag{4}\]  
_where \(\Gamma(\cdot)\) and \(\gamma(\cdot,\cdot)\) in (4) represent the gamma function (equation (8.310.1) in [34]) and the lower incomplete gamma function (equation (8.350.1) in [34]), respectively._  
Proof.: The effective SNR in this case is calculated as the sum of \(N\) i.i.d. exponential random variables, therefore it follows an Erlang distribution (i.e., a gamma distribution with integer shape parameter). Refer to chapter 12 in [35] for the PDF; the CDF follows from the direct integration of the PDF with the help of equation (3.351.1) in [34], noting that \((N-1)!=\Gamma(N)\).  
**Proposition 2**.: _Ideal SNR model for CA with heterogeneous SNR_  
_The PDF and CDF of the instantaneous effective SNR under an ideal CA scenario with heterogeneous SNR are given, respectively, by_  
\[f_{\gamma_{\text{CA}}}(x) =\sum_{n=1}^{N}\frac{\Omega_{n}}{\overline{\gamma}_{n}}\exp\left( -\frac{x}{\overline{\gamma}_{n}}\right) \tag{5}\] \[F_{\gamma_{\text{CA}}}(x) =1-\sum_{n=1}^{N}\Omega_{n}\exp\left(-\frac{x}{\overline{\gamma} _{n}}\right) \tag{6}\]  
_where_  
\[\Omega_{n}=\prod_{\begin{subarray}{c}j=1\\ j\
eq n\end{subarray}}^{N}\frac{1}{1-\overline{\gamma}_{j}/\overline{\gamma}_ {n}}. \tag{7}\]  
Proof.: The effective SNR in this case is calculated as the sum of \(N\) i.n.i.d. exponential random variables, therefore it follows a generalised Erlang (hypoexponential) distribution. Refer to equation (7) in [36] for the PDF; the CDF readily follows from the direct integration of the PDF, noting that \(\sum_{n=1}^{N}\Omega_{n}=1\).
## 1 Introduction
[19] presented interesting results, showing positive effect of Lithovit fertilisation on cotton productivity and [PERSON] et al. [20] on potato yield, but there is no research indicating the influence of Lithovit fertilisation on strawberry performance. Lithovit is CO\({}_{2}\) foliar fertilizer that contains calcium carbonate and therefore accelerates photosynthesis. Since aroma is one of the most valued characteristics of the fruit, volatile flavour compounds play a key role in determining consumer acceptance of strawberries. The volatile profile of strawberries consists of more than 360 constituents, including esters, aldehydes, ketones, alcohols, terpenes, furanones and sulphur compounds [21], and it is considered one of the most complex fruit flavours. Many of these compounds are produced in trace amounts that most analytical instruments cannot measure, but humans can detect them with their senses [22]. The diversity of aroma compounds leads to a harmonious taste, but fewer than 20 of them contribute significantly to strawberry flavour [21]. Furanones are considered to be the dominant aroma compounds in strawberries [21], resulting in the typical example-like, sweet and fruity aroma. Esters comprise 90% of total volatile compounds in ripe strawberries and represent the largest and one of the most important groups contributing to the strawberry aroma profile [21], providing sweet fruity notes [23]. The second largest group of important volatile fruit components are terpenoids, which account for more than 20% of total volatile components in some strawberry varieties. Terpenoids such as linalool [21], meridolol and terpineol, which provide spicy and citrus notes, tend to be particularly variety-specific. Other equally important compounds that contribute to strawberry flavour are hexenal, trans-2-hexenal, and cis-3-hexenal, which give strawberries unpleasant green and unripe notes [21]. Primary and secondary metabolites, especially sugars, organic acids and phenolics play an important role in plant existence and human wellbeing. Strawberries have a remarkable nutritional quality, correlated especially to its high levels of vitamin C and phenolic components [24]. Fertilisation has also been shown to affect the phenolic content of strawberry [25]. Since very little work has been done to investigate the influence of specific fertilisation practices on strawberry phenolic, aroma and other quality parameters, especially in relation to yield, the aim of this study was to evaluate the influence of different nitrogen and calcium fertilisation on yield and the content of selected volatile and phenolic compounds contributing to strawberry quality.
## 6 Experiments
### Experiment Results
#### 6.2.2 Analysis of Policy Pruning Strategy  
To illustrate the effectiveness of the extended policy pruning strategy, we list the max number of policies in the policy pool for different horizons. Figure 3 shows a comparison of the original ICE and CEMC. Note that the original ICE maintains a larger policy pool than CEMC. For problems with approximatively \(\hat{V}(\varphi^{t})\), ICE expands nodes back and forth to construct a tree carefully. This will  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**(d)**} \\ \hline \multicolumn{6}{c}{**Box Pushing**} \\ \hline \multirow{3}{*}{2} & ICE & 30.73 & 0.01 & 32.39 & 17.60 & \(-\)0.01 \\  & CEMC & 0 & 0.54 & 1.81 & 17.60 & \(-\)0.01 \\  & ICE & 492.58 & 21.76 & 540.00 & 66.08 & \(-\)0.61 \\  & CEMC & 0 & 0.82 & 2.36 & 66.08 & \(-\)0.61 \\  & ICE & 2974.12 & 371.83 & 3253.22 & 98.59 & - \\  & CEMC & 0 & 3.24 & 5.98 & 98.59 & - \\  & ICE & * & * & * & * & * \\  & CEMC & 0 & 21.49 & 30.79 & 107.72 & - \\ \hline \hline \multicolumn{6}{c}{**(e)**} \\ \hline \multicolumn{6}{c}{**Mars Rovers**} \\ \hline \multirow{3}{*}{2} & ICE & 1171.76 & 0.17 & 1179.05 & 5.80 & \(-\)3.17 \\  & CEMC & 0 & 3.81 & 10.93 & 5.80 & \(-\)3.17 \\  & ICE & - & - & - & - & - \\  & CEMC & 0 & 7.50 & 14.74 & 9.38 & - \\  & ICE & - & - & - & - & - \\
4 & CEMC & 0 & 102.74 & 122.97 & 10.18 & - \\ \hline \multicolumn{6}{c}{**(f)**} \\ \hline \multicolumn{6}{c}{**Broadcast Channel**} \\ \hline \multirow{3}{*}{3} & ICE & \textless{}0.01 & \textless{}0.01 & 0.28 & 2 & 0.85 \\  & CEMC & 0 & \textless{}0.01 & 0.27 & 2 & 0.85 \\  & ICE & \textless{}0.01 & 0.01 & 0.28 & 2.99 & 1.13 \\  & CEMC & 0 & 0.03 & 0.29 & 2.99 & 1.13 \\  & ICE & 0.13 & \textless{}0.01 & 0.42 & 3.89 & 1.47 \\  & CEMC & 0 & 0.21 & 0.30 & 3.89 & 1.47 \\  & ICE & 2.77 & 0.01 & 3.09 & 4.79 & 1.76 \\
5 & CEMC & 0 & 0.23 & 0.32 & 4.79 & 1.76 \\  & ICE & * & * & * & * & * \\
10 & CEMC & 0 & 0.38 & 0.46 & 9.29 & - \\  & ICE & * & * & * & * & * \\
20 & CEMC & 0 & 2.06 & 2.63 & 18.31 & - \\  & ICE & * & * & * & * & * \\
30 & CEMC & 0 & 219.22 & 220 & 27.42 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._significantly increase the search time. We use Equation (14) to prune the policy pool, and the promising nodes are left.
## 3 Results
### Influence of Phenology on the Carbon Cycle
#### 3.3.2 Influence of Phenology on NPP  
Figure 10 displays the spatial distribution of correlation coefficients, highlighting the significance of the relationships among SOS, EOS, and LOS with NPP. Figure 10a,c,e display how the correlations among SOS, EOS, and LOS relate to NPP spatially, while Figure 10b,d,f display the significance test results for these correlations.  
From Figure 10a,b, it can be seen that the correlation coefficients of SOS and NPP in Hangzhou forests were mostly negative, covering 77.53% of the area. These negative correlations were more uniformly distributed, with 30.12% having coefficients between \(-\)0.2 and \(-\)0.4, and 18.63% having coefficients less than \(-\)0.4. In contrast, 22.47% of the area had positive coefficients, primarily in the northern region of Lin'an, where only 0.93% of the region showed a correlation coefficient greater than 0.4. Similar to the effect of SOS on GPP, the effects of SOS and NPP are mainly negatively correlated in most forests, suggesting that an earlier SOS positively influences NPP  
From Figure 10c,d, the correlation coefficients between EOS and NPP in Hangzhou forests were mostly positive, with only 14.32% showing negative correlations. The positive correlations accounted for 85.68%, widely distributed across the region. Among these, 32.99% had correlation coefficients between 0.2 and 0.4, and 28.05% had coefficients greater than 0.4. The majority of Hangzhou forests showed a positive relationship between EOS and NPP, suggesting that a delayed EOS has a beneficial impact on the increase in NPP. This effect is similar to that of SOS on GPP.  
As can be seen from Figure 10e,f, it is observed that only 11.14% of Hangzhou forests showed negative correlations between LOS and NPP, with 8.61% having coefficients between \(-\)0.2 and 0. The remaining 88.86% showed positive correlations and were widely distributed across the area. Of this, 32.67% had coefficients between 0.2 and 0.4, and 35.19% had coefficients greater than 0.4. The vast majority of Hangzhou forests show a positive relationship between LOS and NPP, suggesting that the lengthening of the growing season positively influences NPP. This effect is similar to that of LOS on GPP.
## 5 Discussion
### Limitations and Potential Improvements  
According to the news released by China's Central Weather Bureau, from July 17 to 23, 2021, Henan Province suffered from severe flooding because of the historically rare and extraordinarily heavy rainfall [24]. Especially the heaviest rainfall occurred in Zhengzhou city on 20 July, and the one-hour rainfall in Zhengzhou reached 201.9 mm between 16:00 and 17:00 on that day, breaking the extreme historical value of hourly rainfall in mainland China. In this experiment, the GF-3 image acquisition time on 20 July was 22:28, which failed to monitor the maximum change in water level in time. The surface water level appeared to fall back, which in turn had some influence on the extraction of water from the inundation area and the road damage statistics in this experiment and might make the experimental results lower than the actual damage situation.  
This paper draws on the threshold segmentation algorithm to extract the area of water bodies that is too coarse, and the classification accuracy still needs to be improved. In the future, we will introduce topographic features, texture features, etc., and use deep learning and other methods to improve the extraction accuracy of water bodies and obtain more accurate boundary information for inundated water bodies. At the same time, land use data is introduced to estimate the inundation of different land types, such as arable land, forest land, and residential land, to achieve rapid monitoring and post-disaster assessment of flooding.
# Cross-Layer Protocol Based on Directional Reception in Underwater Acoustic Wireless Sensor Networks
## 4 Proposed CLIC Protocol
### Overview of CLIC  
The process of the CLIC protocol is described in Algorithm 1. From Algorithm 1, the main steps of the CLIC algorithm are as follows.  
Step 1: Initialize the parameters, e.g., \(Buffer_{max}\), \(range\), initial energy, and coordinates of nodes.  
Figure 4: Interference determination scheme for DMAC.  
Figure 5: Overview of DMAC.  
Step 2: Broadcast a beacon to obtain neighbor information., e.g., \(Buff_{now}\), \(Nei\), \(MeanP\) and coordinates.  
Step 3: Calculate the priority of the neighbor using (7).  
Step 4: Select the highest priority neighbor as relay.  
Step 5: Calculate the priority of CT using (17).  
Step 6: Send CT at the beginning of the CT slot.  
Step 7: If xCT is received, update the neighbor information.  
Step 8: Compare the priority of CT and xCT.  
Step 9: The nodes with high CT priority send DATA at the beginning of the DATA slot.  
Step 10: Nodes with low CT priority determine whether their own data transmissions and those with high CT priority interfere with each other.  
Step 11: Back off if it interferes with data transmission.  
Step 12: Send DATA if it does not interfere with data transmission at the beginning of the DATA slot.  
```
1:Initialize network;
2:Broadcast beacon;
3:Get \(E_{res}\), \(Buff_{now}\), \(Buff_{max}\), \(Inter\), \(MeanP\) and location of Neighbor;
4:Begin
5:// Before sending data
6:\(calPriority()\);
7:\(getRelay()\);//Obtain the best relay using (7)
8:\(calPriCT()\);//Obtain CT priority using (17)
9:\(sendCT()\);//At the beginning of CT slot
10:If received xCT then
11:\(updateNet()\);
12:\(compa_{T}()\);
13:If(\(p\_CT\geq p\_xCT\)) then
14:\(sendData()\);//At the beginning of DATA slot
15:\(\mathbf{Else}\)If (no interference with each other) then
16:\(sendData()\);
17:\(\mathbf{Else}\)
18:\(backoff()\);
19:\(\mathbf{End}\)If
20:\(\mathbf{End}\)If
21:End
```  
**Algorithm 1** CLIC Algorithm  
It can be seen from (7) that a node can maximize priority, which will be chosen as the best relay, meaning that the node does not need to solve a complex optimization problem to find the global optimal path. On the contrary, it sequentially finds the local optimal solution. Therefore, the CLIC has low complexity, which is proportional to the number of the neighbor nodes.
# Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder
## 4 Results and Discussion
### Subpicture Parallel Algorithms Analysis
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**NP** & **Layout** & \multicolumn{4}{c}{**Speed-Up**} \\ \hline  & & **QP = 22** & **QP = 27** & **QP = 32** & **QP = 37** \\ \hline \multicolumn{6}{c}{**FourPeople (1280 \(\times\) 720)**} \\ \hline
2P & \(1\times 2\) & 1.80 & 1.77 & 1.82 & 1.83 \\  & \(2\times 1\) & 1.90 & 1.84 & 1.88 & 1.98 \\
4P & \(1\times 4\) & 3.25 & 3.25 & 3.42 & 3.39 \\  & \(2\times 2\) & 3.32 & 3.25 & 3.42 & 3.42 \\  & \(4\times 1\) & 3.73 & 3.64 & 3.81 & 3.76 \\
6P & \(1\times 6\) & 4.47 & 4.47 & 4.86 & 4.98 \\  & \(2\times 3\) & 4.68 & 4.64 & 4.84 & 5.06 \\  & \(3\times 2\) & 5.08 & 4.99 & 5.07 & 5.13 \\  & \(6\times 1\) & 4.90 & 4.91 & 4.97 & 4.99 \\
8P & \(1\times 8\) & 4.64 & 4.44 & 4.79 & 5.09 \\  & \(2\times 4\) & 6.23 & 6.11 & 6.36 & 6.67 \\  & \(4\times 2\) & 6.57 & 6.32 & 6.59 & 6.71 \\  & \(8\times 1\) & 6.28 & 6.21 & 6.35 & 6.42 \\
9P & \(1\times 9\) & 4.51 & 4.63 & 4.51 & 5.02 \\  & \(3\times 3\) & 6.84 & 6.73 & 7.12 & 7.28 \\  & \(9\times 1\) & 6.32 & 6.29 & 6.51 & 6.50 \\
10P & \(1\times 10\) & 5.80 & 5.87 & 5.58 & 5.64 \\  & \(2\times 5\) & 6.53 & 6.50 & 6.67 & 6.88 \\  & \(5\times 2\) & 7.95 & 7.84 & 7.99 & 8.28 \\  & \(10\times 1\) & 8.63 & 8.48 & 8.68 & 8.94 \\ \hline \multicolumn{6}{c}{**PartyScene (832 \(\times\) 480)**} \\ \hline
2P & \(1\times 2\) & 1.88 & 1.95 & 1.87 & 1.88 \\  & \(2\times 1\) & 1.76 & 1.78 & 1.73 & 1.75 \\
4P & \(1\times 4\) & 3.55 & 3.49 & 3.27 & 3.54 \\  & \(2\times 2\) & 3.33 & 3.29 & 3.33 & 3.40 \\  & \(4\times 1\) & 2.94 & 2.85 & 2.82 & 2.94 \\
6P & \(1\times 6\) & 3.57 & 3.48 & 3.32 & 3.48 \\  & \(2\times 3\) & 4.34 & 4.25 & 4.27 & 4.29 \\  & \(3\times 2\) & 4.45 & 4.44 & 4.42 & 4.41 \\  & \(6\times 1\) & 3.86 & 3.71 & 3.65 & 3.64 \\
8P & \(1\times 8\) & 6.80 & 6.73 & 6.20 & 6.81 \\  & \(2\times 4\) & 6.22 & 6.19 & 5.98 & 6.21 \\  & \(4\times 2\) & 5.34 & 5.34 & 5.26 & 5.36 \\  & \(8\times 1\) & 5.64 & 5.32 & 5.30 & 5.40 \\
9P & \(1\times 9\) & N/A & N/A & N/A & N/A \\  & \(3\times 3\) & 5.74 & 5.66 & 5.25 & 5.73 \\  & \(9\times 1\) & 5.63 & 5.42 & 5.31 & 5.44 \\
10P & \(1\times 10\) & N/A & N/A & N/A & N/A \\  & \(2\times 5\) & 6.25 & 6.21 & 5.65 & 6.15 \\  & \(5\times 2\) & 7.09 & 6.85 & 6.74 & 6.76 \\  & \(10\times 1\) & 5.56 & 5.47 & 5.35 & 5.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Speed-up evolution for low resolution video sequences for the tile-based parallel algorithm. formed by the division of a frame into 1 column by \(N\) rows (\(1\times N\) layouts), always have the worst R/D performance.
# Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
## 2 Materials and Methods
### Data Preprocessing  
#### 2.3.1 Remote-Sensing Image Data Preprocessing  
The remote-sensing image data downloaded in this paper are of Level-1C, and the Level-1C remote-sensing images are atmospherically corrected by Sen2 Cor (V2.9), the Sentinel-2 image processing software provided by ESA, to produce Level-2A level remote-sensing image data. Furthermore, Sen2 Cor does not process band 10 because it is a convolutional cloud band; as a result, the final result consists of 12 bands of data in addition to band 10. The final experimental data are obtained by resampling the processed Level-2A remotely sensed image data using nearest neighbor interpolation, after which modified normalized difference water index (MNDWI) is used to extract the watershed. The remote-sensing image data preprocessing process is shown in Figure 2.
## 5 Tomographic Detection of Carotid Plaque and Artificial Intelligence
### Artificial Intelligence for Overcoming Technical Issues  
[PERSON] et al. developed a CT image restoration software, MatConvNet software (ver. 1.0) based on deep learning technique incorporating a noise and artifact reduction filter using a deep convolutional neural network able to obtain the reduction of the image noise by 20% in coronary CTA [129].  
Even better results were obtained by [PERSON] et al., who evaluated a DL reconstruction model that showed an upgrade in image quality by 62% [130].  
Interestingly, a recent study used CNNs to automatically detect and localize carotid plaques in cone beam computed tomography [131]. The authors used a U-Net neural network architecture to implement a neural network able to segment the image and detect calcification instances.  
Visualization of the vascular system on a routine CT scan is difficult as vessels have similar radiodensities to adjacent soft tissues. Considering the contraindications and related risks, one of the future directions is to avoid contrast agent use. In this regard, DL models were adopted in order to perform a vascular high-resolution CTA without the use of a contrast agent, with promising results [132], but only a few studies have focused on carotid disease.  
[PERSON] et al. adopted a DL-based software (MethinksLVO, Methinks Software S.L. Barcelona, Spain) to detect LVO in CT images, and they demonstrated a significant improvement in the sensitivity and specificity of LVO diagnosis [133].  
Artificial intelligence application in CT scans for carotid plaque detection has the potential to revolutionize the diagnosis and treatment of carotid artery disease. However, the models applied in the current studies present both advantages and disadvantages. AI algorithms can detect even small plaques that might not be visible to the human eye, enabling earlier detection and treatment of carotid artery disease. In addition, AI algorithms can help standardize the detection of carotid plaque across different imaging centers and clinicians. However, AI algorithms depend on high-quality data for accurate detection. If the quality of the CT scan images is poor, it can lead to false positive or false negative results. Finally, there are ethical concerns surrounding the use of AI in healthcare, including issues of data privacy, bias, and accountability, which are still debated.
# Optimized Design of Flexible Quick-Change System Based on Genetic Algorithm and Monte Carlo Method
## 1 Introduction
[18] proposed a genetic algorithm-based automatic search method for the initial structure of a double liquid lens zoom optical system. In terms of fixture error analysis, [PERSON] et al. [19] adopted a high-precision end gear plate to achieve high repetitive positioning accuracy and studied a new type of positioner fixture with high repetitive positioning accuracy and high rigidity. [PERSON] [20] carried out an overall error analysis of the process for the zero point clamping system but did not point out the magnitude of the zero point clamping system error relative to other errors. [PERSON] et al. [21] proposed an error compensation method based on a two-stage strategy of a pre-compensation stage and an exact compensation stage in order to achieve precise positioning. [PERSON] et al. [22] utilized a genetic algorithm to optimize positioner placement, allowing for the minimization of part positioning errors during machining operations. [PERSON] et al. [23] developed a comprehensive equivalent fixture error model that can compensate for datum errors, machine path errors, and workpiece deformation during machining through general fixture layouts. [PERSON] et al. [24] constructed a comprehensive position prediction model that can be used to predict the position error of large, thin-walled box-shaped workpieces in a fixture type. Less research has been performed on the combination of numerous zero point positioning systems for large structural sections; the majority of earlier studies have focused on the best design of individual zero point positioning systems. The article studies the layout of zero point positioning units, the dimensional tolerances of foundation plates and fixture plates, frequent lifting and changing positions, and gravity, which are the main factors affecting repetitive positioning accuracy, in order to design a high-quality flexible quick-change process system. The goal of this study is to create a high-precision, flexible quick-change system for many species of large, thin-walled parts that can achieve the interchanging and connecting of numerous processing flows with just one clamp.