esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Seismic Response of Shield Tunnel Structures with the Preload Loss of Bolts
###### Abstract  
Shield tunnels can experience preload loss in their connecting bolts during the operational phase, leading to changes in tunnel structure stiffness, which, in turn, affect the seismic performance of shield tunnels. A refined three-dimensional model of shield tunnel was established using the finite element method to study the impact of preload loss in connecting bolts on the seismic dynamic response of shield tunnels. An artificial viscoelastic boundary was used to simulate the propagation of seismic waves from an infinitely distant field. This study investigated the effects of different levels of preload loss on the seismic response of shield tunnels. In addition, the Arias intensity, which can reflect the degree of seismic impact on structures, was used to analyse the extent of damage to the tunnel. The conclusions drawn from the study are as follows: As the level of preload loss increases, the tightness of the segments during the static phase gradually deteriorates, and the maximum joint opening during the seismic loading phase continues to increase. Post-earthquake non-recoverable ellipticity and radial deformation progressively increase with an increase to preload loss level. Overall tunnel damage becomes more significant with the degree of preload loss increases depending on the Arias intensity. Preload loss leads to a decrease in the overall structural stiffness and an increase in longitudinal relative displacement. In conclusion, preload loss also affects structural failure mode and seismic performance. These research findings are of reference value for enhancing the seismic performance of shield tunnel structures and ensuring engineering safety.  
shield tunnel; seismic response; preload loss; segment joint +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### Geometry Model and Boundary Conditions  
PDMS assisted thermal reflow is a novel approach to fabricate microstructures with different morphologies and layouts [10; 11; 12]. The process is presented in Figure 1a. Firstly, the PDMS (SYLGARD 184, Dow Corning, Midland, USA, 10:1 \(wt/wt\) in our case) solution is poured on the substrate at room temperature to cover the microstructures prepared by home-made DMD (Digital Micromirror Device)-based maskless lithography. The PDMS solution will flow to level because of low surface energy. Then, the thermal reflow process is conducted on the hotplate (Lab Tech EH35A plus) with temperature set at 180 \({}^{\circ}\)C for 150 s. In this process, the shapes of microstructures will be reshaped or smoothed by the surface tension. Finally, the sample is cooled down at ambient temperature and the PDMS film is separated from the formed tilted cylinder microlens array.  
According to the thermal reflow process, a two-dimensional geometry model is established as shown in Figure 1b. It adopts the cross-section of a part of the practical reflow system. The top boundary is set as pressure outlet. The side boundaries are set as adiabatic wall, for each microstructure has nearly the same reflow environment, which means that there will be hardly any heat transfer between the adjacent microstructures. For the bottom boundary, an isothermal and wetted wall is imposed on it.
## 4 Discussion
In angiographic or post-mortem studies, equal-sized bilateral VAs were found in 6-26% of patients [30]. Consistent with previous studies, in our study, the incidence of left VAD was higher than the right side [30; 47; 48; 49]. Although VAD was considered clinically irrelevant in the past, recent studies have demonstrated the association between the VAD and posterior circulation ischemic stroke [28; 29]. VAD causes basilar artery bending by promoting an asymmetric flow pattern at the vertebosilar junction [50], which subsequently increases the risk of developing posterior circulation ischemic stroke [48; 51]. The anomalous origin of VA is more common on the left side than the right side [3]. Usually, such anomalous origin is from the arch of aorta with an incidence rate of 2.4-5.8% [52; 53]. VA originating from the right common carotid artery is rare and was reported only in a few cases with and without the persistence of brachycephalic trunk [3; 4; 5]. In the present study, we identified right VA arising from the right CCA. During embryonic development, the persistence of the 3 rd-6 th intersegmental artery would result in the origination of VA from the CCA [5]. The strengths of the present study were the larger sample size, the sample being representative of patients from different parts of Oman, the CTA technique, and the focus on the Oman population. Knowledge of baseline data of VA variations reported in the present study is of tremendous importance for the preoperative planning of endovascular interventions and to avoid inadvertent arterial injuries in neck surgeries. Furthermore, it may be advised that patients undergo a radiologic evaluation prior to any invasive procedure or neck surgeries to prevent accidental injuries of the VA.
# Attention-Based Active Learning Framework for Segmentation of Breast Cancer in Mammograms
## 3 Method
### Overall Structure  
The existing segmentation models based on deep learning generally require a large number of labeled images for training at substantial cost. Thus, it is crucial to achieve as high an accuracy as possible for segmentation with few labeled samples. To achieve this goal, we propose the overall framework as shown in Figure 1, which consists of three steps, i.e., the basic segmentation model, attention-based sampling and active-learning-based labelling.  
During Step A, a labeled set of breast cancer images are first used to train the basic segmentation model, which obtains distinguished feature maps via multi-scale feature fusion and enhancement. In Step B, uncertainty sampling is first adopted to classify good and bad segmentation results for breast cancer in mammograms. Then, a novel attention mechanism is built to calculate weights for samples of the unlabeled breast cancer set. During Step C, unlabeled samples with higher weights, implying that they are more informative for learning, are selected to be manually labeled by professional medical experts. All these samples can then be further used to retrain the breast cancer segmentation model, thus boosting performance in an iterative manner.  
On the basis of structure design, we propose a loss function to involve the traditional IoU loss \(Loss_{I}\) and binary cross-entropy loss \(Loss_{B}\), i.e., \(Loss=Loss_{I}+Loss_{B}\). Specifically, IoU loss \(Loss_{I}\) can be calculated via  
\[Loss_{I}=1-\frac{\sum_{i\in I}p_{i}\widehat{p}_{i}}{\sum_{i\in I}p_{i}+\widehat {p}_{i}-p_{i}\widehat{p}_{i}}, \tag{1}\]  
where \(I\) refers to the input breast cancer image, \(i\) represents each pixel in the image, and \(\widehat{p}_{i}\) and \(p_{i}\) represent the predicted and true labeled values for each pixel, respectively. Moreover, the binary cross-entropy loss \(Loss_{B}\) can be calculated via  
\[Loss_{B}=-\sum_{i\in I}p_{i}\log(\widehat{p}_{i})+(1-p_{i})\log(1-\widehat{p} _{i}), \tag{2}\]  
Note that \(Loss_{I}\) and \(Loss_{B}\) are calculated in each iteration stage to achieve convergence of the training process.
## 4 Simulation Results
### Analysis on Average Blocking Probability  
Figure 9 shows the effect of the average blocking probability \(\overline{p}\) on the average system throughput. The performance of the proposed algorithm outperforms the RDR-PA method and the uniform method. The performance curves of the four methods follow the same trend. The average system throughput is an approximate negative linear function of \(\overline{p}\), which is consistent with Equation (5). Compared with the RDR-PA method, the performance improvement ratio increases as \(\overline{p}\) increases and the improvement ratio is at least 56.4%.  
Figure 8: The average ratio of satisfied terminals versus the average required data rate of terminals.  
Figure 7: The average system throughput versus the average required data rate of terminals.  
As shown in Figure 10, we studied the effect of \(\overline{p}\) on the average ratio of satisfied terminals. When \(\overline{p}\) is fixed, the proposed algorithm outperforms the RDR-PA method and the uniform method. The performance curves of the RDR-PA method and the uniform method gradually decrease as \(\overline{p}\) increases, which is similar to a linear decreasing relationship. Compared with the RDR-PA method and the uniform method, when \(\overline{p}\) is less than 0.5, the performance degradation of the proposed algorithm is slower. When \(\overline{p}\) is greater than 0.5, the performance degradation of our method is faster. This is because the resource utilization efficiency of the proposed algorithm is higher compared to the other methods. More specifically, when \(\overline{R}\) is fixed, the average achievable data rate is higher than \(\overline{R}\) in general if \(\overline{p}\) is low. Therefore, the average ratio of satisfied terminals is high and the performance degradation occurs slower. If \(\overline{p}\) is high, the quality of communication channels is worse and the performance degradation occurs faster. Compared with the RDR-PA method, the performance improvement ratio increases as \(\overline{p}\) increases. When \(\overline{p}\) is 0, the performance improvement ratio is 16.9%. When \(\overline{p}\) is 0.1, the improvement ratio is 39.1%. The simulation results indicate that the proposed algorithm has a better QoS guarantee under different blocking situations.
# Proteomic Landscape of the Mature Roots in a Rubber-Producing Grass _Taraxacum Kok-saghyz_
## 3 Discussion
To our best knowledge, it is the first visual proteome based on the combination of 2-DE gel and high throughput proteomics methods, and the results may help us to deepen our understanding of the roles of protein isoforms in the mature TKS roots. Large-Scale Shotgun Proteomics Landscape Revealed both Mevalonate Acid (MVA) and Methylerythritol Phosphate (MEP) Pathways are Important for Natural Rubber Biosynthesis (NRB) in the Mature TKS Roots  
Natural rubber is an isoprenoid polymer that is synthesized on the rubber particles in many latex-producing plants [16]. Although more than 2500 plants can biosynthesize natural rubber [31], the _Para_ rubber tree _H. brasiliensis_ is the only plant commercially cultivated to produce natural rubber for industry [2]. Rubber latex is a kind of specialized cytoplasm of laticifer cells in the bark phloem of the rubber tree, and natural rubber, as an elastomer with physical and chemical properties, cannot be fully matched by synthetic rubber [31]. Annotation of the rubber tree genome revealed that although 22 MEP genes can be identified, only two DXS genes (_DXS7_ and _DXS10_) show substantial and preferential expression in the rubber latex; on the other hand, at least one gene for the identified 18 enzymes in MVA pathway shows latex-biased abundant expression [10]. Proteomics analysis also supported that MVA pathway is more important than MEP pathway for NRB in the latex of the _Para_ rubber tree [2,24,25,29]. Similar results were observed in the recently assembled TKS genome, which contains 102 rubber biosynthesis-related genes. Among them, 40 genes in the MVA pathway and 23 genes in the MEP pathway were identified, respectively. Comparison of gene expression level proved that, for each process in the MVA pathway in TKS, at least one enzyme shows a predominant gene expression level in latex and roots, but most genes in the MEP pathway have a medium or low expression level in the TKS latex [16]. These results indicated that it is the MVA pathway rather than MEP pathway that is involved in the NBR in the rubber latex of both the rubber tree _H. brasiliensis_ and the rubber grass TKS,However, our comprehensive proteomics data showed that many proteins involved in both MVA (22 unique proteins) and MEP (unique 13 proteins) pathways have been positively identified from the mature TKS roots (Figure 7; Table S6). Among the identified 13 MEP-pathway proteins, two unique proteins named DXS 3 (spot 257) and DXS 8 (spots 199 and 204) are visible on the 2-DE gel (Figure 1), and their relative abundance is very high (Table S1). These proteomic data suggest that, by contrast with that in the inner bark of the _Para_ rubber tree [10], both the MEP pathway and the MVA pathway might be the main source of IPP, and MVA pathway is also crucial for rubber biosynthesis in the mature TKS roots.
## 5 Evaluation, Results and Discussion
### Disturbed Radiation  
A series of simulations is performed to analyze different modes of operation with a solar radiation of 500 kW/ m\({}^{2}\) considered as low radiation, which starts from 7:00 a.m. and ends around 7:00 p.m., as shown in Figure 13b.  
The first simulation test with a perturbed radiation is for Mode 0; according to Figure 19a, the collector reaches an approximate temperature of 85 \({}^{\circ}\)C, and the reference temperature \(T_{S(ref)}\) is kept constant at 68 \({}^{\circ}\)C. On the other hand, Mode 5, as shown in Figure 19b, reaches a collector temperature of over 80 \({}^{\circ}\)C with a reference temperature \(T_{S(ref)}\) of 75 \({}^{\circ}\)C required. The variable mode reaches a collector temperature of 85 \({}^{\circ}\)C, and the reference varies at two different temperatures, starting with 68 \({}^{\circ}\)C; after 7.5 h, it reaches 70 \({}^{\circ}\)C; passing 4.5 h, the reference temperature drops from 70-68 \({}^{\circ}\)C; during 8.7 h, it says constant until the day is over. This is the behavior of variable mode, as shown in Figure 19c.  
Figure 19: _Cont._  
Figure 18: Comparison of the generator temperature of the three modes of operation with a solar radiation of 900 kW/m\({}^{2}\).  
For Modes 0 and 5, the cooling capacities are shown in Figure 20. For Mode 0, a capacity of 3 kW is needed. For Mode 5, its cooling capacity is 5 kw. The variable mode is shown in Figure 21; the modes operate from 0-2, as shown in Figure 21a. The power varies according to the solar radiation, as shown in Figure 21b.  
The mass flow of the generator and evaporator for Modes 0 and 5 is shown in Table 6 and Figure 22. For variable mode, different powers and mass flows are visualized in Table 7; Figure 21c shows the behavior of the evaporator and generator flow for variable mode, which is perceived to have the same behavior and operating times, but with different flows.  
Finally, a comparison is made for the three modes of use with a solar radiation of 500 kW/m\({}^{2}\) according to Figure 23. In variable mode, it is obtained with a use time of 11.5 h. Mode 0 (economical) maintain a time of use of 11.93 h at the lowest cooling power of 3 kW. Mode 5 (greater power) is maintained with a use time of 9.5 h; with the maximum cooling power of 5 kW, the use time is the lowest compared with the two previous modes.  
Figure 21: Behavior of the variable mode of use of the cooling system defined by the end user for a radiation of 500 kW/m\({}^{2}\). (**a**) Variable mode used; (**b**) kW of the variable mode; (**c**) mass flow for the generator and evaporator.  
Figure 20: Cooling capacity for Modes 0 and 5.
## 4 Design of the Proposed Structures
### Small Bit Compensation Structure (SBCS)  
The conventional method to achieve a 16 dB attenuator is to cascade two 8 dB conventional \(\pi\)-type attenuators. However, there are many parasitic parameters in the \(\pi\)-type structure, and the parasitic effect will cause errors in the high frequency attenuation and additional phase shift. Cascading two \(\pi\)-type attenuators will accumulate these errors and deteriorate matching performance. The matching performance of the conventional T-type structure deteriorates with the increase of attenuation, which will lead to the deterioration of the performance of the whole 6-bit DSA.  
Figure 3a shows the simplified T-type structure, which has the dual capability of compensating attenuation and minimizing insertion loss. Notably, the attenuation values of both the T-type and \(\pi\)-type structures diminish with increasing frequency. In contrast, the attenuation value of the simplified T-type structure escalates with frequency, enabling a small bit compensation through its utilization. Specifically, a modified \(\pi\)-type and T-type attenuator is employed to achieve most of the attenuation firstly, and a simplified T-type attenuator is then inserted between them to compensate for the high-frequency attenuation and further improve the matching performance, as shown in Figure 7.  
Figure 6: Simulated (**a**) relative attenuation and (**b**) additional phase shift using SICS for different \(Ls\) values.  
Figure 8 shows the comparison of simulation results for these two structures, where, the dashed line represents the simulation results of the conventional structure, and the solid line represents the simulation results of the small bit compensation structure proposed in this paper. The simulation results show that the implementation of SBCS enhances the high-frequency attenuation value of a 16 dB attenuator by 1.2 dB, significantly broadening the operating bandwidth of the attenuator. Furthermore, it substantially improves the return loss.  
The SBCS offers an effective design strategy for the creation of attenuators with large attenuation value.
# Application of Neural Networks to Explore Manufacturing Sales Prediction
## 4 Results and Discussion
### Pearson's Correlation  
To improve BPN prediction accuracy the data was pre-processing used Minitab V16 to identify significant correlations, as shown in Table 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{2}{c}{**X6 Net Entry Rate**} & \multicolumn{1}{p{113.8 pt}}{**X8 Coincident**} & \multicolumn{1}{p{113.8 pt}}{**X11**} & \multicolumn{1}{p{113.8 pt}}{**X12 Customs**} & \multicolumn{1}{p{113.8 pt}}{**X27**} & \multicolumn{1}{p{113.8 pt}}{**X29 General**} & \multicolumn{1}{p{113.8 pt}}{**X31 The Total**} & \multicolumn{1}{p{113.8 pt}}{**Sales**} \\ \multicolumn{2}{c}{**Manufacturing Industries and Service Index**} & \multicolumn{1}{p{113.8 pt}}{**Includes Tables**} & \multicolumn{1}{p{113.8 pt}}{**Manufacturing Sales (SNTD Billion)**} & \multicolumn{1}{p{113.8 pt}}{**Export Values**} & \multicolumn{1}{p{113.8 pt}}{**Business**} & \multicolumn{1}{p{113.8 pt}}{**Price Index**} & \multicolumn{1}{p{113.8 pt}}{**Value of Trade**} & \multicolumn{1}{p{113.8 pt}}{**Amount**} \\ \multicolumn{2}{c}{**Service Index**} & \multicolumn{1}{p{113.8 pt}}{**Includes (\%)**} & \multicolumn{1}{p{113.8 pt}}{**Nonline Index**} & \multicolumn{1}{p{113.8 pt}}{**Nonline Index**} & \multicolumn{1}{p{113.8 pt}}{**Nonline Index**} & \multicolumn{1}{p{113.8 pt}}{**Nonline Index**} & \multicolumn{1}{p{113.8 pt}}{**Nonline Index**} & \multicolumn{1}{p{113.8 pt}}{**Nonline Index**} \\ \hline
1 & 0.12 & 110.72 & 97.3 & 723.28 & 1,160,273 & 98.99 & 679,550 & 67,953,351 \\
2 & \(-\)0.34 & 111.24 & 81.84 & 569.69 & 984,024 & 98.43 & 522,532  
+++ ==WARNING: Truncated because of repetitions==
& 820,464,625 \\  
++  
Pearson's correlation was considered significant (i.e., \(\rho<0.05\)) for 12 items: X6, X8, X9, X11, X12, X13, X24, X25, X26, X27, X29, and X30. To improve prediction accuracy, it was recommended to use the correlation coefficient with moderate correlation and low correlation indicator items, with \(p\) values selected from the significance below 0.001 as an indicator item. The values with negative correlations were not included. The scenario studied contains 120 entries of actual data released by the government [46].  
Thus, we included X6, X8, X11, X12, X27, X29, and X31 (7 items), as shown in Table 3.
## 2 Methods
### Parallel Programming  
Both the flux integrals (Equation (5)) in the DG method and the function borrowing (Equation (6)) in WENO limiters put a requirement on each cell to access its neighbors in \(\mathcal{O}(1)\) time, which is not supported by commonly used mesh formats. For this reason, we do not partition the input mesh directly using the METIS_PartMeshDual(...) function as in traditional finite element methods, but convert the mesh to its dual graph by the METIS_MeshToDual(...) function, and then partition the graph using the METIS_PartGraphKway(...) function. The METIS_MeshToDual(...) function stores the cell adjacency information in dynamically allocated arrays pointed by raw pointers, which should then be the released exactly once by some caller in the call stack. To avoid memory bugs, we suggest to wrap such raw pointers into some smart pointers, such as those provided by the standard library of modern C++ [35].  
Once we obtain the partitioning, each part of the mesh should then be load by a process, which holds and updates local data sequentially and shares data on inter-part boundaries with neighbors when necessary. This is a typical scenario of the distributed memory parallelization, which achieves acceleration by solving relatively equal-sized subproblems simultaneously on multiple cores. Compared with this, the shared memory parallelization, which provides a global memory address space shared by multiple threads, is generally easier to program but less scalable. The price we paid for scalability is the explicit management of message passing for sharing data between processes. Thanks to the publicly available implementations of the MPI standard, such as MPICH ([[https://www.mpich.org](https://www.mpich.org)]([https://www.mpich.org](https://www.mpich.org))) and Open-MPI ([[https://www.open-mpi.org](https://www.open-mpi.org)]([https://www.open-mpi.org](https://www.open-mpi.org))), the code for doing this is much simpler than it used to be. To improve readability and maintainability of our code, we wrap these communication operations in functions names as ShareSomething(...), which share the same code structure:  
1. For each destination, put the data to be sent into a sending buffer and register a request of sending by calling the MPI_Isend(...) function.
2. For each source, allocate a receiving buffer for the data to be received and register a request of receiving by calling the MPI_Irecv(...) function.
3. Performed other computations that can be conducted without communications.
4. Block the process until all its requests complete by calling the the MPI_Waitall(...) function.  
The third step is optional but may help to improve parallel efficiency, since it allows computations to overlap with communications.