esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Therefore, for building DFPs adjacent to existing underpass tunnels, a complex project that has not yet formed a mature management model, it is urgent to find an effective risk analysis model, conduct research on its dynamic coupled risk, analyze the law of risk evolution, and take efficient measures to achieve dynamic risk control. Several mathematical models are utilized to measure coupling risk. Among them, the coupling degree model refers to the use of the expert scoring method to obtain quantitative data, which has certain limitations. The interpretative structural modeling method is easily affected by the internal factors of the system, and the primary and secondary hierarchical relationship of the system cannot be clarified, while the algorithm of the nonlinear dynamic model is too complex [15]. Because the N-K model adopts mathematical and statistical methods by applying objective accident statistics to measure the coupled risk between factors and thereby avoiding the influence of subjective factors, it has been widely accepted by scholars [16]. However, research on the coupled risk of construction accidents in DFPs adjacent to existing underpass tunnels is still lacking. Based on a large number of existing underpass tunnels and DFP construction site monitoring data, association rule mining (ARM) can discover unknown strong correlations and rules implied in the large dataset (i.e., correlations between risk factors). In particular, the combination of risk factors with a low probability of occurrence but strong correlation can provide a basis for risk dynamic analysis and assessment. Consequently, this study introduces the N-K model and DBN into the field of construction risk assessment of DFPs adjacent to existing underpass tunnelsfor the first time and proposes a new coupled risk dynamic analysis model. The model constructs a dynamic Bayesian network structure that relies on the interrelationship of risk factors obtained by association rule mining and optimizes the dynamic Bayesian network structure using the N-K model to compensate for the shortcomings of Bayesian networks. The model can diagnose and predict the dynamic risk during the construction of DFPs adjacent to existing underpass tunnels, and the method's effectiveness and feasibility were verified by analyzing the underground space utilization project from Guangxi University Station to Guangxi Finance and Economics College of Nanning Rail Line 5 as an example.
# Bacteriotherapy in Breast Cancer
## 3 Anticancer Substances Released by Bacteria
### Bacteriocins in Cancer Therapy
#### 3.1.3. Laterosporulin 10  
Laterosporulin 10 (LS10) is a peptide of 5.6 kDa produced by a gram-positive bacterium. Brevibacillus sp. LS10 is a defensin-like peptide which has antibacterial activity against a wide range of gram-positive and gram-negative pathogens. The N-terminal sequencing of LS10 is thermostable, and therefore this peptide shows low similarity with the existing antimicrobial peptides. The anticancer activity of this bacteriocin was investigated on a variety human cancer cell lines in vivo. Lower doses of LS10 induce  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bacteriocins** & **Source** & **Breast Cancer Cell Line** &
\begin{tabular}{c} **Other Human Cancer Cells/Cell** \\ **Lines** \\ \end{tabular} & **Ref.** \\ \hline Bovicin HC5 & _Streptococcus bovis_ HC5 & MCF-7 & Liver, hepatocellular carcinoma (HepG) & [44,45] \\  & & & & Osteosarcoma (HOS), \\  & & & & \\  & & & MCF7, ZR75, BT549, & fibrosarcoma (MRC5, HS913T), \\  & & & & leiomyosarcoma (SKUT-1 cells), \\  & & & SKBR3 and T47D & lung cancer (A-549, PC-14, \\  & & & & REER-LC-AD), ovarian carcinoma, colon cancer (HCT116) \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The origin and biological activity of anticancer bacteriocins.  
apoptosis in cancerous cells, while higher doses of this bacteriocin cause necrotic death in the cancer cell. In addition, LS10 shows the highest activity against the breast cancer line MCF-7 cells [47].
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 5 General Discussion
### How Do Preferences Change When Listening in Noise?  
The other issue addressed concerns whether listeners' preferences change in noise. Apart from bandwidth modification in Expt. 2, all other features showed clear monotonic changes with noise level (Figures 2 and 5). In all cases, the modification direction was the one that promoted audibility in the region above 1 kHz. There is also some evidence that preferences become more consistent as noise level increases. Combined across the 32 preference distributions of the two experiments, distributional entropy is positively correlated (\(r=0.58,p<0.001\)) with intelligibility. The entropy of preference distributions for 6 of the 8 features showed a reduction from quiet to the most adverse SNR, suggesting that noise has a constraining effect on listener choice. Taken together, these lines of evidence suggest that a large component of listener preference in masked conditions is aimed at the maintenance of comprehension rather than expressing choices influenced by supra-intelligibility factors. One explanation might be that listeners need a degree of informational richness as the basis for adjusting speech in ways that address factors other than comprehensibility, and some of the necessary information may not survive energetic masking, or may be sacrificed in the interest of maintaining intelligibility. For example, pitch cues and related intonational information are likely to be less salient in noise following spectral modifications that transfer energy from the sub-1 kHz region.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 5 Discussion
### About the Backbone Network  
To improve the quality of features extracted by the backbone network, some researchers have tried to add an attention mechanism to the backbone network. [PERSON] et al. [51] combined the resnet-50 and the position attention module (PAM) to achieve high accuracy in the change detection of remote sensing images. [PERSON] et al. [52] introduced a new spatial attention mechanism into the Siamese network, which also improved the accuracy of the model for detecting changes in multispectral images. Our research drew on these previous studies, and added the ECA attention mechanism after the backbone network of YOLOX, which improved the detection accuracy of the model.  
To find the most suitable attention mechanism, we compared the results of five attention mechanisms (as shown in Table 1), which include three types of attention: spatial, channel, and mixed. From the results of the detection accuracy and speed, ECA has the best results. We found that channel domain attention is the friendliest to our model and dataset, and it improves accuracy without adding excessive inference time.  
Previous researchers used some lightweight networks to replace the backbone network in order to realize the lightweight of the model. For example, [PERSON] et al. [53] replaced the backbone network of the YOLO model with Mobilenet, which greatly reduced the amount of parameters and calculations, and realized the real-time detection of remote sensing images. This is a general way to improve the detection speed of the model. However, the Mobilenet constructed by depthwise separable convolution has low detection accuracy, so our research did not use this method.
###### Abstract  
Highways built on soft clay subgrade are more prone to subsidence due to the geotechnical characteristics of soft clay. Monitoring ground movements in this area is significant for understanding the deformation dynamics and reducing maintenance cost as well. In this paper, small baseline subset synthetic aperture radar interferometry (SBAS-InSAR) technique is exploited to obtain and investigate the time series ground surface deformation after the construction of a road embankment over soft clay settlement. Considering the important effect of temporal deformation models on the final accuracy of estimated deformation, both the linear velocity model and seasonal deformation model are utilized to conduct the comparative investigation of deformation time series. Two highways in Fuoshan, China--G1501 Guangzhou Belt Highway and Lungui Highway--were selected as the test area. Thirteen TerraSAR-X images acquired from October 2014 to November 2015 were analyzed. Comparative study based on two groups of analyses generated from the two models for both highways were conducted. Consequently, several feature points distributed near the two highways were analyzed in detail to understand the temporal evolution of the settlement. In order to evaluate the reliability of our measurements, the residual phase was analyzed to assess the modelling accuracy of the two models. In addition, leveling data were also used to validate the experimental results. Our measurements suggest that the seasonal model is more suitable for the test highways, with an accuracy of \(\pm 3\) mm with respect to the leveling results.  
InSAR; soft clay; time series; deformation; seasonal model +
Footnote †: journal: Applied Soft. 2019, 9, 2038; doi:10.3390/appr9102038
## 5 Discussion of the Australian HSR Risks
### The Benefits of Implementing HSR in Australia
P2 emphasised that such a transition would have a substantial impact, potentially making one of Australia's most significant reductions in carbon emissions from the airline industry. Hence, the benefits of HSR in Australia are significant. The primary data aligns with the theory established in the literature review. This shift would transform conventional practices like fly-in-fly-out and drive-in-drive-out to a streamlined and comfortable train-in-train-out commuting system. This transition could significantly impact workforce mobility and regional development. The literature review found that the Sydney to Newcastle HSR alignment would have a worse BCR than the Sydney to Melbourne alignment. It found this to be a significant risk. However, the interviews showed that this held true only if HSR were to be considered a transport project. P1, P2, P4, and P3, all of whom positioned HSR as a Regional Economic Development (RED) project rather than just a transportation initiative. P1 mentioned that in this feasibility report, the HSRA is pivoting from thinking about travel time and comparisons with airlines and \"_think about what it actually does for the regional economy_\". P4 further attested to that saying, think of \"_it being an economic development project_\". This important benefit needs to be captured in HSRA's upcoming feasibility study to ensure approval, as this would capture the RED benefits. Ensuring HSRA's feasibility study positions this alignment as a viable option for HSR in Australia is important. P2 further supported this approach, stressing that positioning HSR as an economic development initiative is essential to its success. The word cloud analysis, as shown in Figure 9, presents interviewees discussing the benefits of high-speed rail and reveals key themes and priorities emphasised by the interviewees. The most frequently mentioned words, displayed in larger font sizes, such as \"people\", \"high\", \"speed\", \"rail\", \"land\", \"Sydney\", and \"Newcastle\" stand out. However, these were expected as the interviews specifically focused on the Sydney to Newcastle alignment. Additionally, words like \"construct\", \"corridor\", \"cities\", \"connect\", \"plan\", \"opportunities\", and \"emissions\" reflect the broader considerations of infrastructure development, economic opportunities, and environmental benefits. They highlight the significance of connectivity, speed, and impact on people and land, and the importance of linking major cities and regions. This visualisation offers a clear and immediate understanding of the main themes and priorities identified by the interviewees regarding high-speed rail.
## 3 Results and Discussions
### Optimum s/p Ratio  
In the current research database, optimum S/p (S/p (opt)) ratios were found to range from 7.5 to 18. Figure 10 shows the relationship between the optimum s/p ratio and brittleness indices. For the considered brittleness indices, the specific energy tended to decrease as the index decreased. The results indicated that B3 had a good linear correlation with the optimum s/p ratio.  
Although the optimum s/p ratio could not be accurately determined with the prediction model, by revealing the upper and lower boundaries, it is possible to roughly determine the optimum s/p ratio for the wide range of rock strength. The relationships, including the upper and lower boundaries of the optimum s/p ratio, are useful information in the preliminary stage of TBM design. It is possible to estimate the number of cutters on the cutter head, cutter arrangement, main specifications (i.e., thrust, torque, and power), and construction period if the optimum s/p ratio can be determined.
# System Design and Experimental Study of a Four-Roll Bending Machine
## 3 Design
### Critical Component Simulation  
Due to the maximum load borne by the lower roller guide mechanism during operation, it is necessary to conduct static stress, static displacement, and static strain analyses on the lower guide seat and connecting shaft. The guide seat primarily withstands pressure from the roller assembly and reaction forces from the connecting shaft, while the connecting shaft mainly bears support forces from the rod end bearings and counterforces from the guide seat. Both components are manufactured from Q235 steel with a yield stress of \(2.206\times 10^{8}\)\(\mathrm{N}/\mathrm{m}^{2}\). In this study, SolidWorks Simulation was used for static analysis. Loads were applied to the guide seat and connection shaft based on actual conditions. Approximately 1000 N load was applied to the guide seat, and approximately 900 N load was applied to the connection shaft. The results indicate: For the guide seat, the maximum stress of \(7.118\times 10^{3}\)\(\mathrm{N}/\mathrm{m}^{2}\) occurs at the junction with the connecting shaft and upper sleeve. Static displacement analysis reveals a maximum displacement of \(5.859\times 10^{-7}\) mm, while static strain analysis shows a peak strain of \(1.968\times 10^{-8}\) at the same connection points. For the connecting shaft, the maximum stress of \(1.455\times 10^{3}\)\(\mathrm{N}/\mathrm{m}^{2}\) is observed at the upper and lower fixed ends. Static displacement analysis identifies a maximum displacement of \(1.874\times 10^{-7}\) mm, and static strain analysis indicates a peak strain of \(5.822\times 10^{-9}\) at the fixed ends near the guide seat connection. Based on these static stress, displacement, and strain results, the designed lower roller guide mechanism demonstrates sufficient strength and stiffness under rolling conditions, meeting the design requirements. Detailed analysis diagrams are presented in Figures 9 and 10.  
Figure 9: Guide seat analysis: (**a**) Static Stress Analysis; (**b**) Static Displacement Analysis; (**c**) Static Strain Analysis.
## 5 Performance Analysis and Simulation
### Comparison of the Performance Simulation
#### 5.3.2 Comparison of Communication Overhead
However, compared with the shortest path algorithm, RPBMP and EPUSBRF, the safety periods of our proposed NRSPS and NRDPS are still greatly improved. The average total safety period of the RDPS is as high as 4087, but the average total communication overhead is as high as 2292.6, which means that it causes a lot of communication overhead while achieving high safety period. Therefore, we can choose the most suitable solution according to different requirements of different scenarios. For example, whether to use single PN or dual PNs, or whether to change the PN. **Experiment 8:** The impact of \(M\) on the communication overhead of the proposed schemes  
In order to further study the impact of \(M\) on the communication overheads of the proposed schemes in the case of replacing the PN, we compare the communication overheads of RSPS and RDPS. The average communication overhead is obtained by repeating the experiment 100 times, as shown in Figure 17. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Overhead 1** & **Overhead 2** & **Total Overhead** & **Total Safety Period** & **SC Ratio** \\ \hline \multicolumn{2}{c}{**Shortest path**} & 0 & 20.93 & 20.93 & 20.93 & 1 \\ \hline RPBMP & 30 & 335.76 & 365.76 & 45.68 & 0.12 \\ \hline EPUSBRF & 731.26 & 87.05 & 818.31 & 35.68 & 0.04 \\ \hline \multirow{3}{*}{Proposed} & NRSPS & 0 & 43.18 & 43.18 & 127.21 & 0.34 \\ \cline{2-6}  & RSPS & 0 & 959.62 & 959.62 & 3070 & 3.20 \\ \cline{2-6}  & NRDPS & 0 & 323.04 & 323.04 & 969 & 3.00 \\ \cline{2-6}  & RDPS & 0 & 2292.60 & 2292.60 & 4087 & 1.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the performance. Figure 17: Comparison of communication overhead of RSPS and RDPS with different \(M\). From Figure 17, we can see that the communication overhead of our proposed RDPS is higher than that of the RSPS, due to the actual use of dual PNs, and as \(M\) increases, the communication overheads of the RSPS scheme and the RDPS scheme increase accordingly. Although Figure 14 shows that the average safety period of the two schemes increases with \(M\), the communication overheads are also increased greatly. Considering the balance between security and network performance, it is necessary to select the most suitable solution according to different requirements of different scenarios.
## 3 Problem  
Cubic systems can approximate many nonlinear effects (e.g., due to the power amplifier or quantization effects), and we find that more complex nonlinear characteristics can be replaced by equivalent lower-order nonlinear characteristics in general, and cubic characteristics in particular [21]. Indeed, Figure 1 describes the block diagram of the nonlinear system identification problem which is represented by a Volterra series with only a small number of kernels. The system, which we propose to identify in this work, is a cubic nonlinearity that has the form  
\[\left\{\begin{array}{l}y_{0}(k)=\sum\limits_{i=0}^{q}h(i,i,i)x^{3}(k-i)\\ \\ y(k)=y_{0}(k)+n_{G}(k)\end{array}\right. \tag{10}\]  
where \(\{y_{0}(k)\}\) is the cubic system output, and \(\{y(k)\}\) is the observation output, which is contaminated by a zero-mean additive Gaussian noise \(\{n_{G}(k)\}\), assumed independent of \(\{y_{0}(k)\}\), excluding the effect of the noise where \(n\)th-order cumulants are superior of 2 (i.e., \(C_{n,y_{0}}(\tau_{1},\tau_{2},\ldots,\tau_{n})=C_{n,y}(\tau_{1},\tau_{2},\ldots,\tau_{n})\)) as indicated above. The input random signal, \(\{x(k)\}\), of the model is the unobservable zero mean, independent identically distributed (i.i.d) stationary, ergodic and non-Gaussian with known distribution statistics \(\gamma_{3,x}=E[x^{3}(k)]\
eq 0\), \(\gamma_{6,x}=E[x^{6}(k)]\
eq 0\), \(\gamma_{9,x}=E[x^{9}(k)]\
eq 0\), \(\gamma_{12,x}=E[x^{12}(k)]\
eq 0\) and \(\gamma_{15,x}=E[x^{15}(k)]\
eq 0\). Unknown cubic kernels include of \(\{h(i,i,i)\ \forall\ i=1,\ldots,q\}\), and \(\sum\limits_{i}|h(i,i,i)|<\infty\), which suggest that the system is stable causal implying bounded input-output.  
The cubic system identification is more complex and we need even more information to calculate the cumulants of the generated output signal. The main objective of this investigation is to identify the kernels of the cubic nonlinear system in (10) by using the blind approach based on fifth-order cumulants of the system's measured output, \(C_{5,y}(\tau_{1},\tau_{2},\tau_{3},\tau_{4})\), and some knowledge of the properties of the input random signal \(\{x(k)\}\).
## 4 Experimental Results
### Results for the Jasper Ridge Dataset  
The spectral resolution of the Jasper Ridge dataset is 9.46 nm and it has a total of 512 \(\times\) 614 pixels. Each pixel was recorded on 224 spectral bands in the wavelength range of 0.38 \(\upmu\)m to 2.5 \(\upmu\)m. Although the HSI is too complex, it is impossible to obtain its basic facts. Therefore, a widely used region was selected for the experiment. This region had a sub-region size of 100 \(\times\) 100 pixels and encompassed 198 spectral bands. The research scenario consisted of four endmembers: \"Road\", \"Soil\", \"Water\" and \"Tree\".  
Table 4 shows the estimates of abundance and endmembers for the Jasper Ridge dataset, and Figure 5 shows the abundance map for this dataset. Figure 6 shows the endmember extraction results. The FCLSU and gtvMBO strictly adhered to the ASC constraints, resulting in poor estimation in terms of the endmembers and abundances. While it retained the details, the GLNMF did not capture the global feature information well, presumably due to the complexity of the ground feature distribution. It can be seen from Figure 5 that GraphL and gtvMBO performed well at distinguishing narrow roads due to their use of a graph structure and that non-local similarity played an important role in the narrow pixel information provided in the abundance map. However, it can be seen that there was a lot of noise in the abundances obtained by GraphL. In addition, the DAEU performed well. EGU-pw, which has a two-stream architecture, achieved excellent results by refactoring the HSI. Compared with the former, the EACNN pays more attention to the capture of feature information and network guidance, and thus, achieved excellent Mean SAD and RMSE values.  
Figure 4: The abundance result for the synthetic data estimated by the proposed methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Methods**} & **FCLSU** & **GLNMF** & **GraphL** & **gtvMBO** & **DAEU** & **EGU-pw** & **EACNN** \\ \hline \multirow{4}{*}{SAD} & Material 1 & 0.0275 & 0.1133 & 0.0172 & 0.0438 & 0.0072 & **0.0061** & 0.0062 \\  & Material 2 & 0.0270 & 0.0738 & 0.0213 & 0.0308 & 0.0069 & 0.0056 & **0.0021** \\  & Material 3 & 0.0066 & 0.0227 & 0.0039 & 0.0055 & 0.0032 & 0.0017 & **0.0016** \\  & Material 4 & 0.0132 & 0.0436 & 0.0115 & 0.0191 & 0.0037 & **0.0021** & 0.0022 \\  & Material 5 & 0.0072 & 0.0213 & 0.0056 & 0.0061 & 0.0033 & 0.0040 & **0.0029** \\ \hline \multicolumn{1}{c}{Mean SAD} & 0.0163 & 0.0549 & 0.0119 & 0.0210 & 0.0049 & 0.0039 & **0.0030** \\ \hline \multicolumn{1}{c}{RMSE} & 0.0461 & 0.1322 & 0.0355 & 0.0535 & 0.0329 & 0.0206 & **0.0197** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results for the synthetic data. The best one is shown in bold.  
Figure 5: The abundance results of the Jasper Ridge dataset estimated using the proposed method.