esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison with Other Numerical Solutions and Experimental Results  
In Figure 12, the fatigue propagation of the initial crack \(b_{0}/a_{0}=1\), \(b_{0}/D_{0}=0.05\) and \(1\) is compared with numerical solutions by [PERSON] [11; 12]. The curves in the present results are similar for all cases. However, some discrepancy between the present result and [PERSON] can be seen, especially for the initial crack \(b_{0}/a_{0}=1\), \(b_{0}/D_{0}=0.05\). It should be pointed out that in the above comparison, the deviation is mainly due to the difference in the crack growth method adopted and the idealized crack front geometry. A two-parameter elliptical-arc shape with fixed center is assumed only by employing the Paris-Erdogan law ordinarily by Carpinteri [11; 12]. The two-parameter shape assumption method mentioned above can simplify the fatigue calculations, but it is also clear that better predications should be obtained if the shape restraint can be reduced, such as those generated by the present method. Moreover, the crack growth circles, which are tangent to the new crack front as well as to the current crack front, can accurately represent the real path of the fatigue crack and thus yield more accurate results. In addition, the better mesh refinement demonstrated in this paper also leads to improved prediction accuracy.  
Figure 11: Ratio of crack growth along the vertical central line and toward the horizontal surface.  
Figures 13 and 14 compare the crack propagation result with the experimental data deduced from [PERSON]. [PERSON] [19]. It is shown that the present results agree well with the experimental data. The experimental result deviates abnormally around the relative crack depth of \(b_{n}/D_{0}=0.4\) in Figure 14. The maximum discrepancies are approximately 12%. The deviation of the two solutions are acceptable since as the fracture begins to happen in the experimental method approach, the relative crack depth \(b_{n}/D_{0}=0.4\). It is confirmed that the present method could provide more accurate results.  
Figure 12: Crack propagation patterns compared with numerical solutions.  
Figure 13: Relationship of crack propagation with depth and chord length compared with experimental data.
## 5 Discussion
### Impacts of Landscape Pattern Changes on the Ecosystem in Xiong'an  
Changes in landscape patterns significantly influence the composition, structure, functioning, and biogeochemical processes of regional ecosystems, ultimately leading to fluctuations in ESV [25]. The relationship between landscape patterns and ESV exhibits distinct regional characteristics. For instance, in the Yanbian area, [PERSON] et al. [67] observed a strong positive correlation between ESV and AI and CONTAG values, but a negative correlation with the PD, LSI, and SHDI. Conversely, [PERSON] et al. [68] reported that ESVin karst regions of northwest Guangxi was negatively associated with the DIVISION and SHDI. In contrast, [PERSON] et al. [69] found that in the southern Hangzhou Bay area, greater land use diversity, fragmentation, and landscape heterogeneity contributed to higher overall ESV.  
In Xiong'an (2014-2022), cultivated land remained the dominant matrix type. However, rapid urbanization and large-scale afforestation accelerated the conversion of other land use types into construction land and coniferous forests, increasing landscape fragmentation and shape complexity. Spatiotemporal analysis revealed that the ED, NP, SHDI, and DIVISION were positively correlated with ESV, whereas the CONTAG, AI, and AREA_MN exhibited negative correlations. Notably, ESV in Xiong'an increased alongside rising landscape fragmentation.  
Following the establishment of Xiong'an New Area in 2017, the shift from traditional urban sprawl to an ecologically prioritized development model significantly enhanced regional sustainability. The \"Millennium Forest Project\" played a pivotal role in improving ecological quality, particularly in southeastern Xiongxian County--a finding consistent with [PERSON] et al.'s [70] assessment of ESV gains (2014-2020). While conventional urban expansion typically degrades ecological quality [71], Xiong'an's afforestation of unused and cultivated lands established a robust green infrastructure system, substantially boosting regional ESV [72].
## 3 Numerical Modelling
### Fluid Conditions  
The fluid flow was assumed to be an unsteady, viscous and incompressible flow with constant fluid properties. The flow over the gate was modelled by the unsteady Reynolds-averaged Navier-Stokes (URANS) equations, including the mass and momentum conservation equations. Since URANS equations have been proposed in a large number of publications, they are not repeated in this study [15]. URANS equations are usually solved with the assistance of a turbulence model in practice. The realizable \(k\)-\(\varepsilon\) turbulence model has been demonstrated by many studies to give good performance in modelling flow with adverse pressure gradients and high-speed flows. A turbulence model including the kinetic energy and its specific dissipation rate transport equations was employed to close the URANS equations. The coupling between the pressure and the velocity was solved by the pressure-implicit with splitting of operators (PISO) method. The spatial discretization of the convective terms and the diffusion terms were carried out by second order upwind scheme and second order centered scheme, respectively. The second-order implicit scheme was adopted in the time domain. The free surface was tracked by Volume of Fluid (VOF) [16]. As for the boundary conditions in the numerical simulation, the upstream boundary was set to the pressure inlet and given different upstream water depths. The gate shaft of the emergency gate was also set as a pressure inlet with standard atmospheric pressure. The service heights were adjustable and the downstream boundary was set to pressure outlet with the standard atmospheric pressure. The simulated upstream water depths were 49, 65, 85 and 100 m, respectively. The service gate heights were set to 1.0 m and 4.0 m. The time step was 0.001 s and the total simulation time was over 360 s.
###### Abstract  
In Visible Light Communication (VLC) Systems, data are transmitted by modulating light from an illumination source, that could be an ordinary lamp or light-emitting diodes (LEDs). Photovoltaic cells based on massive heterojunctions of semiconductor polymers have focused the attention of researchers due to several potential advantages over their inorganic counterparts, such as simplicity, low cost and the ability to process large area devices even on flexible substrates. In this paper, we use commercial LEDs in transmission and organic photodetectors (OPD) based on poly(3-hexylthiophene) (P3 HT) and a phenyl-C61-butyric acid methyl ester (PCBM) blend used as active layer in reception. We have fabricated and characterized the I-V curve and the Bit Error Rate (BER) response of the OPD using low cost processing techniques and we have used an Atmel 8-bit microcontroller in order to control the electronics to transmit and modulate the signal. Finally, in this work, we have developed and characterized organic photodetectors in a low cost visible light communications system capable of transmitting an image file in real-time, as a proof of concept that is cost effective, since the whole system was implemented using low cost components. You can find more information in the supplementary materials.  
visible light communications; organic photodetector; P3 HT:PCBM; IEEE 802.15.7
# Target Detection Technology of Mechanical, Electrical, and Plumbing Components Based on CV
###### Abstract  
Mechanical, electrical, and plumbing (MEP) systems are vital in construction engineering as their installation quality significantly impacts project success. Traditional inspection methods often fail to ensure compliance with building information models (BIMs), leading to safety hazards due to deviations during construction. Spurred by these concerns, this paper introduces a novel BIM-based pipeline construction comparison system that relies on computer vision technology. The developed system uses deep learning algorithms for real-time data collection to enhance monitoring efficiency and accuracy, as well as advanced object detection algorithms to compare real-time construction images with BIMs. The proposed architecture addresses the limitations of existing techniques in handling MEP complexities, and through an automatic comparison and verification process, it detects deviations promptly, ensuring adherence to design specifications. This study innovatively integrates real-time data collection, deep learning algorithms, and an automated BIM comparison mechanism to enhance the accuracy, efficiency, and automation of pipeline installation monitoring, addressing the limitations of existing inspection methods.  
deep learning; edge detection; pipeline construction; matching +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-000000-000000-0  
Faster R-CNN [4], have significantly improved detection accuracy and efficiency. However, current methods suffer from dealing with the complexity of MEP components and fail to meet the requirements of practical engineering applications. Primary concerns include insufficient real-time monitoring capabilities, difficulty in comprehensively capturing dynamic changes during the pipe installation process, inadequate accuracy of monitoring data, inability to reflect the actual installation state of the pipes accurately, and the complex and cumbersome nature of the comparison and analysis work between the monitoring results and the BIM, due to the lack of an automated comparison and verification mechanism.  
This paper develops a BIM-based pipeline construction comparison system that monitors the pipe installation process in real time and automatically compares and analyzes it with the BIM to ensure its accuracy and compliance. The proposed solution integrates real-time data collection and deep learning algorithms for high-precision monitoring and automatic verification of the pipe installation status. The core objective of this study is to enhance the level of automation of pipeline installation monitoring, reduce human interference, and improve monitoring efficiency and accuracy.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 6 Performance Evaluation  
In this section, the performance of the proposed scheme is analyzed in terms of computation cost, communication cost and energy consumption, where the cost mainly depends on the authentication and key agreement phases. This section also compares the proposed scheme with related representative schemes (i.e., [PERSON] et al. [2], [PERSON] et al. [13], [PERSON] et al. [17], [PERSON] et al. [18], and [PERSON] et al. [20]) to evaluate the performance of our scheme.
## 4 Discussion
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Source**} & \multicolumn{2}{c}{**Absorbed Dose (nGyh\({}^{-1}\))**} & \multicolumn{2}{c}{**Annual Effective Dose (mSv/y)**} \\ \cline{3-6}  & & **Average** & **Range** & **Average** & **Range** \\ \hline The study & Current study & 47.00 & 40.00–58.00 & 0.173 & 0.124–0.222 \\ \hline POLAND & [2] & 47.40 & 18.80–86.00 & - & - \\ \hline North Europe & [2] & 54.40 & 4.00–580.00 & - & - \\ \hline West Europe & [2] & 47.75 & 2.00–209.00 & - & - \\ \hline East Europe & [2] & 59.68 & 4.00–245.00 & - & - \\ \hline South Europe & [2] & 47.00 & 17.00–150 & - & - \\ \hline Czech Republic & [57] & 65.60 & 6.00–245.00 & 0.575 & 0.053–2.148 \\ \hline Switzerland & [58] & 68.00 & 6.00–368.00 & 0.950 & 0.745–1.052 \\ \hline Novi Sad, Republic of Serbia & [50] & 78.20 & 70.9–83.80 & 0.096 & 0.087–0.103 \\ \hline Parks in Lithuania & [51] & 24.51 & 21.52–28.65 & 0.052 & 0.050–0.061 \\ \hline Republic of Srpska & [47] & 69.00 & 9–118 & 0.0848 & 0.011–0.1440 \\ (Bosnia and Herzegovina) & [2] & 58.00 & 1–1300 & 0.07 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of current results of the average absorbed dose and annual effective dose with data available in the literature. Pollution in the area of parks is also the result of the impact of nearby roads with high traffic [29; 67; 68]. Pollution of the soil environment in protected areas may also be caused by intensive agricultural activity resulting from inadequate fertilization or the use of plant protection products [38; 69; 70; 71; 72; 73]. Within National Parks, in situ activities, such as environmental overexploitation and tourism, also exert anthropogenic pressure.
# Calibration of Rotary Encoders Using a Shift-Angle Method
## 3 Experiments
### Uncertainty Evaluation
The setup error uncertainty \(u(U)\) is caused by the noncentrality of the autocollimator beam on the polygon face and the eccentricity of the polygon setup. (8) Setup error of the polygon, \(U\). The misalignment error of the polygon is 0.05\". The distribution assumes a rectangular probability. \[u=0.13^{\prime\prime}/\sqrt{6}=0.0531^{\prime\prime}\]1 The relative uncertainty is estimated as 10% with 50 degrees of freedom. The values of the two sensitivity coefficients \(\partial\alpha/\partial\varepsilon\) and \(\partial\alpha/\partial U\) are 1. As shown in Table 2, the pitch angle deviations are calculated using \(n*n\) measured values. According to the reference paper [22], the sensitivity coefficient can be expressed as follows: \[u=0.13^{\prime\prime}/\sqrt{6}=0.0531^{\prime\prime}\]2where \(n\) is the number of faces of the polygon. In this case, \(n\) is 24 and \(\partial\alpha/\partial\beta\) is 0.0401. In Table 3, the combined standard uncertainty of the shift-angle method, the root sum square of all uncertainty contributions, is 0.07\". The effective degree of freedom \(\
u_{\rm eff}\) of the combined standard uncertainty is 13. Thus, the appropriate coverage factor can be set to \(k\) = 2.31 from the t-distribution of the aforementioned effective degree-of-freedom value. The expanded uncertainty for a confidence level of 95% in \(U_{\rm shift}\) is given as follows:  
\[u=0.13^{\prime\prime}/\sqrt{6}=0.0531^{\prime\prime}\]3  
For self-calibration, two uncertainty sources should be considered: the resolution of the SelfA rotary encoder and repeatability of self-calibration. The first is described above. The self-calibration results, ranging from \(1^{\circ}\) to \(360^{\circ}\), are computed as the average of the six measurement results. The maximum standard deviation of all angle errors is 0.01\". The standard uncertainty is 0.01\"/\(\sqrt{6}\) = 0.0041\". The combined standard uncertainty, the root sum square of the two uncertainty contributions, is 0.01\". The effective degree of freedom \(\
u_{\rm eff}\) of the combined standard uncertainty is 53. Thus, the appropriate coverage factor can be set to \(k\) = 2.01 from the t-distribution of the aforementioned effective degree-of-freedom value. The expanded uncertainty for a confidence level of 95% is 0.02. The reliability of measurements by the shift-angle method is calculated as the \(E\)n number, given as follows:  
\[u=0.13^{\prime\prime}/\sqrt{6}=0.0531^{\prime\prime}\]4  
where \(\alpha\) (\(\theta\)) is the measured results of the shift-angle method with uncertainty \(U_{\rm shift}\), and \(\mu\) is the self-calibration results with uncertainty \(U_{\rm self}\). Figure 12 shows the difference in calibration results between self-calibration and the shift-angle method. The maximum \(E_{\rm n}\) number is 0.69, which is less than 1. The \(E_{\rm n}\) number means that the shift-angle method is considered reliable.
# Moving Surface Boundary-Layer Control on the Wake of Flow around a Square Cylinder
## 2 Numerical Calculation Model
### Numerical Model and Solution Setting
The left side was a velocity inlet with a uniform velocity \(U_{\infty}\) of 0.054 m/s; the right side was a pressure outlet, where the relative pressure was set to 0; and the upper and lower sides were set as symmetry boundaries. In addition, the square cylinder and the rotating circular cylinder surfaces were set as no-slip walls, and the interfaces between Zone 1 and Zone 2 were set as slip boundaries. The rotation of the circular cylinder and the surrounding mesh was realized through a dynamic mesh technique. The numerical simulations were carried out at \(\text{Re}=200\), and the laminar model was employed. The finite volume method (FVM) was used to discrete the flow field around a square cylinder. The SIMPLE algorithm was used for the calculation of the coupling between the pressure and the velocity fields. The format of the pressure interpolation was selected to be 'Standard'. The second-order upwind scheme was utilized for the momentum discretization because of its stability and veracity. The time steps were changed according to the numerical simulation conditions. At the beginning, the dimensionless time step was set to 0.05. When the flow became fully developed and the vortex shedding occurred in the wake of the square cylinder, the MSBC became activated; then, the dimensionless time step was automatically reduced to 0.01.
# The Fast Detection and Identification Algorithm of Optical Fiber Intrusion Signals
## 2 Design of Fast Detection and Identification Algorithm for Optical Fiber Intrusion Signals
### Single-Channel Detection Algorithm  
As shown in Figure 2, the original signals are summed adopting the dislocation processing method, and the data is accumulated at a time interval of 4 ms. The total length of the optical cable is 0.72 km. We divide the cable into 45 parts with equal length. Accordingly, the number of the columns is set 45 in our subsequent processing and the length of each column is 16 m. All columns are divided into four groups and the sum value is filtered when the cumulative time reaches 4 ms. As shown in the Figure 3, the number of columns to be filtered in one millisecond is 1/4 of the total columns, and the same processing is performed on the Q-channel data at the same time.  
Figure 1: The whole flow of the algorithm.  
Figure 3: Summation of every 4 ms in I-channel.  
Figure 2: Flow of single–channel detection.  
High-pass filtering is performed on the summation results expressed as follows:  
\[y(n)=d_{1}\cdot x(n)+d_{2}\cdot x(n-1)-c_{2}\cdot y(n-1) \tag{1}\]  
where \(y(n)\) is the current filtering result, \(x(n)\) is the current summation result, \(x(n-1)\) represents the summation result of the previous time unit, \(y(n-1)\) represents the filtering result of the last time unit. In addition, \(d_{1}=0.9313\), \(d_{2}=-0.9313\), \(c_{2}=0.8626\).  
Then the filtered data is normalized as follow  
\[z(n)\cong\frac{y(n)}{\sum\limits_{n=2}^{1024}\left(|x(n)-x(n-1)|\right)} \tag{2}\]  
Then the comparison between the normalized data and the threshold is performed.  
The selection process of the identification data is shown in Figure 4. The norm summation of 128 normalized data points of the I-channel and Q-channel are calculated respectively. Then the two results are compared, and the data with larger norm summation is selected as the identification data.
# Interfacial Shear Properties of Red Clay and Polyurethane with Different Densities
## 3 Results and Discussion
### Effect of Normal Stress on Shear-Shear Displacement Curve  
Figure 5 show the shear stress-strain curves of the composite interface under different normal stresses at polyurethane densities of 0.5 g/cm\({}^{3}\) and 0.8 g/cm\({}^{3}\). The graphs reveal that, under a constant polyurethane density, the shear stress-shear displacement curve follows a similar trend as the normal stress increases, both exhibiting shear softening characteristics. As the normal stress increases, the horizontal displacement at shear failure, peak shear stress, and residual stress all gradually increase. Additionally, the slope of the stress rise stage also becomes steeper, Similar patterns were also observed in the experimental studies by [PERSON] and [PERSON] on polyurethane-stabilized expansive soils and sandy soils [23; 25].  
The peak value of the curve is considered the shear strength of the polyurethane-red clay interface. Figure 6 illustrates that, at polyurethane densities of 0.2 g/cm\({}^{3}\), 0.5 g/cm\({}^{3}\), and 0.8 g/cm\({}^{3}\), as the normal stress increases from 100 kPa to 400 kPa, the shear strength of the interface increases by 133%, 142%, and 171%, respectively. Linear fitting was performed on the relationship between the interfacial shear strength of the composite and normal stress using the Mohr-Coulomb law. The coefficients of determination (R\({}^{2}\)) for polyurethane densities of 0.2 g/cm\({}^{3}\), 0.5 g/cm\({}^{3}\), and 0.8 g/cm\({}^{3}\) were 0.9253, 0.9996, and 0.9998, respectively, indicating excellent fitting results. This demonstrates that, for a given polyurethane density, the shear strength of the interface increases significantly with normal stress.  
Figure 4: Relationship between shear strength and density of polyurethane.
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## Appendix D Scalable Update for BSFDA Fast  
For brevity, we denote the covariance of \(\zeta_{i}\) as \(S\), i.e., \(\zeta_{i}\sim\mathcal{N}(0,S)\). \(S\) is diagonal and \(S_{kk}=\zeta_{k}^{2}\beta_{k}^{-1}\). The variational update formulas are as follows:  
\[\Sigma_{\theta_{i}} \leftarrow\mathbb{E}_{\mathcal{Q}_{/\theta_{i}}}\Big{[}\Phi_{i} \Phi_{i}^{\top}\sigma^{-2}+S^{-1}\Big{]}^{-1} \tag{113}\] \[\mu_{\theta_{i}} \leftarrow\mathbb{E}_{\mathcal{Q}_{/\theta_{i}}}\Big{[}\Big{(}( \gamma_{i}-Z\Phi_{i})\Phi_{i}^{\top}\sigma^{-2}+Z_{i}WS^{-1}\Big{)}\Big{]} \Sigma_{\theta_{i}}\] (114) \[\Sigma_{Z_{i}} \leftarrow\mathbb{E}_{\mathcal{Q}_{/Z_{i}}}\big{[}WS^{-1}W^{\top}+ I\big{]}^{-1}\] (115) \[\mu_{Z_{i}} \leftarrow\mathbb{E}_{\mathcal{Q}_{/Z_{i}}}\Big{[}\theta_{i}S^{-1} W^{\top}\Big{]}\Sigma_{Z_{i}}\] (116) \[a_{\xi_{k}} \gets a_{0}+\frac{P}{2}\] (117) \[b_{\xi_{k}} \leftarrow\mathbb{E}_{\mathcal{Q}_{/\xi_{k}}}\bigg{[}b_{0}+\frac{ 1}{2}\sum_{i}(\theta_{ik}-Z_{i}W_{k})^{2}\beta_{k}\bigg{]}\] (118) \[\Sigma_{W_{k}} \leftarrow\mathbb{E}_{\mathcal{Q}_{/W_{k}}}\bigg{[}\zeta_{k}^{-2} \beta_{k}\sum_{i}Z_{i}^{T}Z_{i}+\beta_{k}\operatorname{diag}(\alpha)\bigg{]}^{-1}\] (119) \[\mu_{W_{k}} \leftarrow\mathbb{E}_{\mathcal{Q}_{/W_{k}}}\bigg{[}\zeta_{k}^{-2} \beta_{k}\sum_{i}(\theta_{ik}Z_{i})\bigg{]}\Sigma_{W_{k}}\] (120) \[a_{\beta_{k}} \gets a_{0}+\frac{1+K+P}{2}\] (121) \[b_{\beta_{k}} \leftarrow\mathbb{E}_{\mathcal{Q}_{/\beta_{k}}}\bigg{[}b_{0}+\frac {1}{2}\bigg{[}\hat{Z}_{k}^{2}\eta+\sum_{j}(W_{jk}^{2}a_{j})+\sum_{i}(\theta_{ik }-Z_{i}W_{k})^{2}\zeta_{k}^{-2}\bigg{]}\bigg{]}\] (122) \[\Sigma_{Z} \leftarrow\mathbb{E}_{\mathcal{Q}_{/Z}}\bigg{[}\sigma^{-2}\sum_{i }(\Phi_{i}\Phi_{i}^{\top})+\eta\operatorname{diag}(\beta)\bigg{]}^{-1}\] (123) \[\mu_{Z} \leftarrow\mathbb{E}_{\mathcal{Q}_{/Z}}\bigg{[}\sigma^{-2}\sum_{i }\Bigl{[}(Y_{i}-\theta_{i}\Phi_{i})\Phi_{i}^{\top}\Bigr{]}\bigg{]}\Sigma_{Z}\] (124) \[a_{\sigma^{-2}} \gets a_{0}+\frac{1}{2}\sum_{i}N_{i}\] (125) \[b_{\sigma^{-2}} \leftarrow\mathbb{E}_{\mathcal{Q}_{/\sigma}}\bigg{[}b_{0}+\frac{ 1}{2}\sum_{i}||Y_{i}-(Z+\theta_{i})\Phi_{i}||_{2}^{2}\bigg{]} \tag{126}\]Notably, the columns of \(W\) become conditionally independent of the introduction of the slack variable \(\theta\), akin to the strategy described in [18; 28]. Then, the surrogate posterior of \(W\) factorizes over the columns, thereby requiring the calculation of the covariance of each column separately, instead of the entire \(W\) at once. Thus, the computational complexity is significantly reduced. This factorization is introduced on top of the existing factorizations; thus, the low-dimensional optimization strategy of BSFDA also applies to BSFDAFast.