esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Analysis of Outrigger Systems for High-Rise Buildings with Different Geometric Shapes
## 3 Refined Modeling for Outrigger Systems
### Results and Discussion
Figure 11: Wind-induced top displacement (\(\mathrm{d_{roof}}\)) (on the **left**) and inter-story drift IDR (on the **right**), comparisons for refined building models with and without outriggers. Figure 12: Wind-induced core base bending moment comparisons for refined building models with and without outriggers. whereas outriggers are effective in increasing lateral stiffness. The results are also well aligned with previous studies by [PERSON] and [PERSON] [66], which demonstrated that outrigger and belt-truss systems in shear-wall framed structures produce a combined increase in lateral and torsional stiffness. This latter contribution is paramount in complex structural forms like twisted buildings, where torsional stiffness plays a fundamental role in resisting rotational deformation demands induced by lateral loads. In the simplified models, where the torsional stiffening effect imparted by the belt-trusses is not explicitly modeled, the overall effectiveness of the outrigger system in mitigating torsional response may be underestimated. In conclusion, the findings demonstrate that simplified models can predict the global structural behavior of regular or moderately irregular structures with reasonable approximation. However, it is crucial to contextualize the application of such simplified models to the preliminary design stage and for comparative purposes only. Their inherent limitations, in fact, preclude the accurate representation of crucial tall building characteristics, including the discrete distribution of mass, the application of realistic gravity loading, and the attenuated stiffness contribution of the belt-truss system. Furthermore, this level of idealization inherently prevents the direct verification of a critical EDP for structural damage assessment, namely the inter-story drift profile. Refinement of the modeling assumptions is essential for the accurate analysis of complex geometries like highly twisted buildings, where accurately capturing torsional response and the complete lateral system interaction is fundamental for reliable structural performance assessment and design.
# Notes on Common Misconceptions in Microplastics Removal from Water
## 3 Microplastics Removal from Water
### Filtration
Detailed studies on mechanisms of particle/biochar filter interaction have been performed recently [115,120]. Three types of interactions were introduced: \"stuck\", \"trapped\" and \"entangled\". The first is described as a sieve, \"microplastic particles are retained in the gaps between the filter particles which are smaller than the particle size\". The removal efficacy thus depends on the size of microplastic particles and filter particles (this mechanism is described for sand filter) [112,115]. The second is described for filters with highly porous structure, such as biochar or activated carbons. The microplastic particle enters slightly larger pores and remains there. The third is described for particles caught in microscopic holes on the surface of the filter material [115]. [PERSON] et al. report on possible Van der Waals forces and physical interactions between microplastic particles and cloth filter, which is made of synthetic fibres [121]. Interactions of microplastic particles with membrane filters are usually described as size exclusion, hydrophobic interaction, biofilm formation and electrostatic interaction. At size exclusion, the size of pores is the dominating parameter--larger contaminants simply do not flow through and membrane fouling can easily occur [119]. Another mechanism is the hydrophobic interaction--it is described as the noncovalent force forming clusters of non-polar species on the non-polar surface of the membrane [14]. Biofilm formation is described as the interaction between microplastic particles and aquatic species (phytoplankton, microalgae) [122; 123]. Electrostatic repulsion is defined as \"a result of interaction between the electrical double layers surrounding particles or droplet\" [124]. This phenomena can be influenced for removal efficiency and does not necessarily lead to membrane fouling--it can be controlled via pH of treated solution and adsorbed contaminants on the surface of microparticles [125]. Deeper discussions of such phenomena are beyond the scope of the paper.
# Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future
## 3 Deep Learning for Osteoarthritis and Prediction of Arthroplasty Implants
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Location** & **Image Used** & **Author. Year** & **CNN Used** & **Work** & **Dataset Size** & **Accuracy** & **AUC** \\ \hline Knee & X-ray & [PERSON] et al. 2018 & Siamese CNN & Classification & 5960 & 66.7\% & \\ \hline Knee & X-ray & [PERSON] et al. 2019 & DenseNet & Classification & 5042 & 75\% & 0.83 \\ \hline Knee & X-ray & [PERSON] et al. 2020 & SE-ResNet & Classification & 4366 & \begin{tabular}{c} 61.6\% \\ (with additional information) \\ \end{tabular} & \begin{tabular}{c} 0.75 \\ (with additional information) \\ \end{tabular} \\ \hline Knee & X-ray & [PERSON] et al. 2021 & Faster R-CNN & Classification & 2802 & 71.9\% & \\ \hline Hip & X-ray & [PERSON] et al. 2017 & VGG-16 & \
\begin{tabular}{c} Binary \\ classification \\ \end{tabular} & 420 & 92.8\% & \\ \hline Hip & X-ray & [PERSON] et al. 2020 & VGG-16 &
\begin{tabular}{c} Binary \\ classification \\ \end{tabular} & 434 & 90.2\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of diagnostic performance for classifying osteoarthritis. Figure 2: The figure shows how a trained convolutional neural network classifies total hip replacement implants of different designs in A, B and C. Figure obtained from a study performed by [PERSON] et al. [34]. level of accuracy. This may be due to the fact that a shoulder anteroposterior X-ray can show a wider range of positions than an anteroposterior radiograph of the knee or hip.
# TraceGuard: Fine-Tuning Pre-Trained Model by Using Stego Images to Trace Its User
## 3 Proposed Approach
### Robustness of TraceGuard Against Fingerprint Removal Attack  
To prevent the inventor of a pre-trained model from tracing a pirate model, the adversary may fine-tune or prune the fingerprinted pre-trained model to conceal the evidence of piracy. Additionally, the adversary can also follow a fingerprinted pre-trained model with a similar  
Figure 6: (**a**) ROC curve for evaluating binary classification of the independent model and fingerprinted model. (**b**) Robustness against pruning.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**SSL**} & \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**ImageNet**} & \multicolumn{4}{c}{**STL-10**} \\  & & **FP** & **NFP-MIN** & **FP-ACC (\%)** & **Base-ACC (\%)** & **FP** & **NFP-MIN** & **FP-ACC (\%)** & **Base-ACC (\%)** \\ \hline \multirow{3}{*}{SimCLR} & RN-18 & 0.1293 & 0.2031 & 85.36 & 86.81 & 0.0334 & 0.1666 & 67.81 & 67.76 \\  & RN-34 & 0.1378 & 0.2639 & 86.33 & 90.61 & 0.0686 & 0.1351 & 70.40 & 71.66 \\  & DN-121 & 0.2518 & 0.3187 & 91.22 & 91.86 & 0.2704 & 0.3367 & 72.86 & 72.36 \\ \hline \multirow{3}{*}{MoCo v2} & RN-18 & 0.0315 & 0.1542 & 84.52 & 81.69 & 0.0337 & 0.1000 & 61.85 & 59.93 \\  & RN-34 & 0.0143 & 0.1377 & 83.43 & 80.86 & 0.1119 & 0.2447 & 60.24 & 58.89 \\  & DN-121 & 0.0786 & 0.1612 & 84.72 & 86.58 & 0.0695 & 0.2009 & 62.75 & 63.05 \\ \hline \multirow{3}{*}{MAE} & ViT-B & 0.0012 & 0.1333 & 88.64 & 86.78 & 0.2000 & 0.3042 & 72.38 & 70.63 \\  & ViT-L & 0.1001 & 0.1667 & 79.05 & 84.56 & 0.1332 & 0.2618 & 60.08 & 67.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effectiveness of the proposed TraceGuard.  
function via a model stealing attack. Thus, evaluating robustness against fine-tuning, pruning, and model stealing is essential. In this experiment, the fingerprinted model is fine-tuned, pruned, and stolen to assess its robustness against a fingerprint removal attack.
# Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method
## 2 Methodology
### Extraction of Repeways
#### 2.3.2 Analyses Based on Principal Components  
According to Formula (7), the covariance matrix of \(n\) points in a 3D point cloud data set \(\{P_{\mathrm{i}}(x_{\mathrm{i}},y_{\mathrm{i}},z_{\mathrm{i}});\mathrm{i}=1, \ldots,n\}\) can be calculated as follows:  
\[C_{3\times 3}=\frac{1}{n}\sum_{\mathrm{i}=1}^{n}(p_{\mathrm{i}}-\hat{p})(p_{ \mathrm{i}}-\hat{p})^{\mathrm{T}} \tag{7}\]  
The plane equation would be as follows:  
\[x\cos\alpha+y\cos\beta+z\cos\gamma+p=0 \tag{8}\]  
\[ax+by+cz=d,a^{2}+b^{2}+c^{2}=1 \tag{9}\]  
In this case, \(\cos\alpha,\cos\beta,\cos\gamma\) represent the cosines of the normal vector direction at point \((x,y,z)\) on the plane, while \(|p|\) represents the distance from the origin to the plane. This is where Formula (8) is transformed into Formula (9), and the calculation of the plane equation is converted into the calculation of the four parameters \(a,b,c\), and \(d\). Data points \((x_{\mathrm{i}},y_{\mathrm{i}},z_{\mathrm{i}})\) are distanced from the plane as follows:  
\[d_{\mathrm{i}}=|ax_{\mathrm{i}}+by_{\mathrm{i}}+cz_{\mathrm{i}}-d| \tag{10}\]  
As a result, the problem of fitting the best plane to find the normal vector \(\bigl{(}\min\sum d_{\mathrm{i}}^{2}\bigr{)}\) becomes the problem of finding the extremum:  
\[f=\sum_{\mathrm{i}=0}^{n}d_{\mathrm{i}}^{2}-\lambda\Bigl{(}a^{2}+b^{2}+c^{2}- 1\Bigr{)} \tag{11}\]  
Formula (12) is obtained by calculating the partial derivative of \(f\) for the four unknown parameters \(a,b,c,d\), and bringing the parameter \(d\) into Formula (11).  
\[d_{\mathrm{i}}=|a(x_{\mathrm{i}}-\bar{x})+b(y_{\mathrm{i}}-\bar{y})+c(z_{ \mathrm{i}}-\bar{z})| \tag{12}\]  
\[\left(\begin{array}{ccc}\sum\Delta x_{\mathrm{i}}\Delta x_{\mathrm{i}}&\sum \Delta x_{\mathrm{i}}\Delta y_{\mathrm{i}}&\sum\Delta x_{\mathrm{i}}\Delta z_{ \mathrm{i}}\\ \sum\Delta x_{\mathrm{i}}\Delta y_{\mathrm{i}}&\sum\Delta y_{\mathrm{i}}\Delta y _{\mathrm{i}}&\sum\Delta y_{\mathrm{i}}\Delta z_{\mathrm{i}}\\ \sum\Delta x_{\mathrm{i}}\Delta z_{\mathrm{i}}&\sum\Delta y_{\mathrm{i}}\Delta z _{\mathrm{i}}&\sum\Delta z_{\mathrm{i}}\Delta z_{\mathrm{i}}\end{array} \right)\left(\begin{array}{c}a\\ b\\ c\end{array}\right)=\lambda\left(\begin{array}{c}a\\ b\\ c\end{array}\right) \tag{13}\]  
As a result of the above formula, the parameter \((a,b,c)^{\mathrm{T}}\) of the solution position can be viewed as an eigenvector, which corresponds to the eigenvalue \(\lambda\), while the coefficient matrix represents the equivalent formula for the covariance matrix. Using Formula (9) and the characteristic equation \(Ax=\lambda x\), Formula (14) can be derived.  
\[\lambda=\sum_{\mathrm{i}=1}^{n}(a\Delta x_{\mathrm{i}}+b\Delta y_{\mathrm{i}}+ c\Delta z_{\mathrm{i}})^{2}=\sum_{\mathrm{i}=1}^{n}d^{2} \tag{14}\]  
The best plane fitting requires \(\min\sum d_{\mathrm{i}}^{2}\), which is the eigenvector A corresponding to the smallest eigenvalue of the coefficient matrix \((a,b,c)^{\mathrm{T}}\) and also the solution vector corresponding to the smallest eigendecomposition of the covariance matrix (7).
# Design and Implementation of a Multi-Band Active Radar Calibrator for SAR
## 3 Results
### Measurement of the ARC
#### 3.3.3 Gain over Frequency Bandwidth  
This measurement allowed us to evaluate the working frequency bandwidth of ARC and the relative gain flatness. The inferior gain flatness influences the target image quality of the ARC. Many measures were taken to improve the specifications of the ARC. In Figure 13, the gain flatness for the four bands is shown. It can be seen from Figure 13 that the manufactured ARC has gain flatness values of \(\pm\)0.5 dB (L-band), \(\pm\)0.2 dB (C-band), \(\pm\)0.5 dB (X-band), and \(\pm\)1.5 dB (Ka-band) over the available bandwidth of 200 MHz for the L-band, 240 MHz for the C-band, 1200 MHz for the X-band, and 1200 MHz for the Ka-band, respectively.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 3 Evolution of V2X Technologies
The V2X must have a higher reliability for message exchange between the vehicles. The reliability can be maintained by using consistent communication links to reduce risks, such as vehicular crashes. Some of the advanced V2X applications are vehicle platooning, advanced driving, extended sensors, and remote driving. These applications not only improve road safety, but they also improve traffic control and serve the infotainment needs of passengers. Table 2 shows advanced use cases and their QoS requirements based on the reliability of transmitted messages, variable packet sizes, delay requirements, and transmission range. On the other hand, several complex control systems are addressed more commonly, such as safety-critical systems or Cooperative Adaptive-Cruise Control (CACC), which need to be examined independently. Such systems require stringent specifications in the communication network for transmission latency and reliability. Reliable communication is especially challenging for vehicle applications because of the constantly evolving nature of the wireless channel, which results in a constantly outdated channel estimation. Moreover, Inter-Channel Interference (ICI) becomes a bottleneck because of the high Doppler change in V2X scenarios.
## 3 Materials and Methods
### Datasets  
For the aims of the current study, radar Sentinel-1 and optical Sentinel-2 images were downloaded through the EO Browser of the Sentinel Hub service [15]. The characteristics of these images are shown in Tables 1 and 2.  
In particular, a radar Sentinel-1 image in Interferometric Wide Swath (IW), acquired on 12 May 2020 was processed. In addition, a Sentinel-2 image with processing level L2A (orthorectified BOA) and limited cloud-coverage acquired on 13 May 2020 was used. Figure 3 visualizes the Sentinel-1 and -2 images as well as other ready products available from the Sentinel Hub service. It should be mentioned that all these images were downloaded in high-resolution format (which corresponds to 10-m spatial resolution per pixel) and in a 32-bit tiff image format. Figure 3a,b display the orthorectified Sentinel-1 VH and VV gamma 0 respectively, while Figure 3c shows the Sentinel-1 SAR urban visualization product. The SAR urban product is an RGB pseudo-composite, whereas the combination VV-VH-VV is used for the RGB composite. The Radar Vegetation Index (RVI) is displayed in Figure 3d. Figure 3e-i refer to products and bands of the optical Sentinel sensor as follow: Figure 3e,f show the Sentinel-2, BOA Band 5 (red-edge) and band 8 (near-infrared) respectively. The Normalized Difference Vegetation Index (NDVI) can be observed in Figure 3g. Figure 3h shows the Sentinel-2 Scene Classification product, which is developed under the Sen2 Cor project to support an atmospheric correction module.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Sentinel-1**} \\ \hline Parameter & Value \\ \hline Minimum Ground Swath Width & 250 km \\ Incidence Angle Range & 29.1\({}^{\circ}\)-46.0\({}^{\circ}\) \\ Number of Sub-swath & 3 \\ Azimuth Steering Angle & \(\pm\)0.6\({}^{\circ}\) \\ Azimuth Resolution & 20 m \\ Ground Range Resolution & 5 m \\ Polarisation Options & Single (HH or VV) or \\  & Dual (HH + HV or VV + VH) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the Sentinel-1 (Interferometric Wide Swath (IW)), sensor.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Sentinel-2**} \\ \hline Parameter & Value \\ \hline Instrument principle & Pushbroom \\ Repeat cycle (days) & 5 (at the Equator) \\ Swath width (km) & 290 \\ Spectral bands & 13 \\ Spatial resolution (metres) & 10, 20, 60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the Sentinel-2 sensor.  
In addition, monthly Sentinel-1 and Sentinel-2 ready products (RVI and NDVI) were used for over a year (May 2019-May 2020) so as to investigate temporal changes of vegetation growth with these indices.  
Furthermore, OpenStreetMap data were retrieved through the QGIS open-access software [40]. In our case study area, various vector data are available for download, as shown in Figure 3i. From these vector polygons, areas characterized as vegetated and non-vegetated were identified and compared with available images from the Google Earth platform.
# Protection of Buried Pipelines from High-Speed Train Vibrations with Expanded Polystyrene Geofoam
## 1 Introduction
In the case of a strike-slip fault rupture, geofoam blocks decrease the axial tensile strain of a non-pressurized pipeline from 4.16% to 0.75% in the case of a crossing angle equal to 135\({}^{\circ}\)[21]. As previously mentioned, several studies have investigated the protection of underground pipes or culverts with the use of EPS geofoam [22; 23]. Nevertheless, there is a lack of studies in the international literature focusing on the impact of railway traffic loads on buried pipelines. Thus, aiming to contribute to this research field, the present paper investigates the dynamic response of pipelines crossing vertically under HSR lines, thus being exposed to HST-induced vibrations as well as their protection with EPS blocks. Initially, the adopted numerical approach was compared with pre-existing experimented data. In the sequence, several types of pipes have been examined. Furthermore, the impact of the geofoam layer thickness, placed on the top of the pipeline, on the reduction in vibrations level has been investigated for three typical HST passing velocities. Apart from the level of vibrations, the reduction in the pipeline vertical diameter change, i.e., pipe ovalization, has also been examined as this can be detrimental for its integrity and safe operation. The findings of the conducted parametric study can be useful in engineering practice for the protection of buried pipelines, as well as other underground lifelines, crossing under or near continuously expanding HSR lines.
## 3 Analysis of Accuracy and Performance
### Analysis of Accuracy
Although the attenuation trends of VLF waves are approximately the same during the day and at night, the attenuation during the day is significantly greater than that at night. The maximum difference in phase between the algorithm proposed in this paper and the traditional FDTD algorithm is 9\({}^{\circ}\) during the day and 13\({}^{\circ}\) at night. The composition of the ionosphere changes the most at night, mainly because the sun sets. Without the source of electrical radiation, the ionosphere at D and E levels (as shown in the above figure) stops ionizing, but the F region (especially F2) remains completely ionized.
## 5 Conclusions and Future Work  
Implementations of BIM technology can provide visualization and integrated information models for FM. However, the logic relationships between MEP components are often ignored not only during the modeling process but also in the widely adopted IFC standard. Considering that manual generation of the logic chain is time and labor consuming, this study proposes an approach to generate the logic chain for MEP systems from BIM with identification rules.  
The approach consists of three steps which focus on the geometric information of MEP models, identification rules for logic inferences and the data structure of MEP components, respectively. The assessment of IFC-based spatial relationships is mainly based on 3D solid models or triangular facet models. The identification rules of MEP systems contain typical prior knowledge and user-defined logic rules. After determining the connection table, classification rules, and the terminal component, the graph data structure can be generated to represent the relationships between MEP components. Finally, the logic is generated by performing traversal searching for each connected pair of components with an appropriate identification rule.  
The feasibility and accuracy of the approach were tested in an MEP model of an actual project. The results indicated that the approach was capable of generating logic chains for different subsystems and the accuracy of generating both connection tables and logic model exceeded 80% in 15 subsystems. Some problems, such as mismatching, occurred during the tests while they were discussed to be solved by adding connections or logic information manually.  
Figure 14: Results of spatial connection judgement within an MEP subsystem.  
Future works will focus on improving the accuracy of the approach and exploring efficient control methods of energy conservation based on the MEP logic chain and related control theories of MEP systems. Based on the experiences gained from this application, more service-oriented functions can be developed for FM depending on a thorough exploration and a comprehensive application of BIM and related information technologies.  
Conceptualization: S.-W.L. and Z.-Z.H.; data processing: Y.-Q.X.; funding acquisition: Z.-Z.H.; methodology: Y.-Q.X.; software: Y.-Q.X.; supervision: [PERSON]-[PERSON] and [PERSON]-[PERSON]; validation: Y.-Q.X. and Z.-Z.H.; visualization: Y.-Q.X.; writing--original draft: Y.-Q.X.; writing--review and editing: [PERSON]-Z.H. and [PERSON].  
This research is supported by the National Key R&D Program of China (no. 2017 YFC0704200), the National Natural Science Foundation of China (no. 51778336 and no. 51478249), and the Tsinghua University-Glodon Joint Research Centre for Building Information Model (RCBIM).  
The authors also thank [PERSON] (Carnegie Mellon University) and [PERSON] (Stanford University) for their valuable comments and contributions.  
The authors declare no conflict of interest.
## 5 Results and Discussion  
The present iterative algorithm has been applied to two-dimensional and three-dimensional finite element models. Such an approach becomes advantageous when the size of the Local Model is very small with respect to the Global Model.  
Mesh convergence studies have been conducted on all the finite element models adopted. Furthermore, the elastic-plastic model considered in the present work has already been compared to other models that can be found in literature works; in particular, stresses and strains are in good accordance (the percentage difference is about 10%) with those achieved with more sophisticated elastic-plastic models, as already demonstrated in [8].  
The iterative algorithm was first implemented and assessed for the two-dimensional model, which is not very demanding from a computational point of view, in order to find an optimum value for parameter \(\
u_{n}\). Once \(\
u_{n}\) has been identified, iterative Global/Local three-dimensional analyses are conducted.  
The convergence criterion adopted in the following analyses is based on the Euclidean norm of the displacements, prescribing that the maximum between the Euclidean norms of the displacements in the horizontal, vertical, and axial directions (corresponding respectively to x, y, and z axes) be lower than a threshold value \(\varepsilon\):  
\[\phi=max\{\|\mathbf{u}_{x}\|_{2},\|\mathbf{u}_{y}\|_{2},\|\mathbf{u}_{z}\|_{2}\ \}<\varepsilon \tag{15}\]  
where \(\varepsilon\) has been chosen to be equal to \(10^{-4}\).
## 2 Materials and Methods
### Explainable AI (XAI)
#### 2.8.2 Local Interpretable Model-Agnostic Explanations (LIME)  
LIME is a methodology that provides localized explanations by approximating the behavior of a complex model around a specific prediction [43]. It works by generating perturbed versions of the original data point with slight modifications to feature values and then training a simple, interpretable model (such as linear regression) on these modified data. This simple model serves as a local approximation of the complex model's behavior in close proximity to the prediction. Any ML model can apply LIME, and its computational efficiency makes it suitable for real-time analysis. However, LIME's explanations are valid only within the local region around the specific prediction and may not generalize well to the entire model, as it assumes local linearity, which might not always hold true.