esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dry Mechanical Connections
#### 3.5.2 Number and Disposition of Connecting Rebars  
This subsection examines the influence of reinforcement configuration and placement on the shear capacity of precast walls. Fixed parameters for this analysis include concrete compressive strength (fck) of 30 MPa and normalised axial force (v) of 0.2.  
Figure 11a shows the global trends for all section heights (hc = 1.5 m, 2.25 m, and 2.5 m) with different connection types (2\(\Phi\)25 (Figure 3b), 3\(\Phi\)25 (Figure 3c), 5\(\Phi\)25 (Figure 3d), and 5\(\Phi\)25* (Figure 3e)) with the fixed scale of 0 to 5000 kN consistent throughout this paper. Figure 11b-d present detailed magnified views for each specific section height.  
For the smallest section height (hc = 1.5 m), as shown in Figure 11b, increasing the number of rebars from two to five results in a significant enhancement in shear capacity from 400 kN to approximately 520 kN. The edge placement configuration (5\(\Phi\)25*) maintains this elevated shear capacity, showing minimal additional improvement over the in-between placement.  
At the intermediate section height (hc = 2.25 m), Figure 11c demonstrates a more pronounced increase in shear capacity across configurations, rising from 850 kN to 1050 kN. The transition from 3\(\Phi\)25 to 5\(\Phi\)25 shows the most substantial improvement, with the edge placement configuration (5\(\Phi\)25*) providing a slight additional enhancement.  
Figure 10: The influence of bar sizes (**a**) Global Trends for all Section Heights, (**b**) Magnified view forhc = 1.5 m, (**c**) Magnified view forhc = 2.25 m, (**d**) Magnified view forhc = 2.5 m.  
For the largest section height (hc = 2.5 m), Figure 11d reveals the most significant impact of reinforcement configuration, with shear capacity increasing from approximately 950 kN to 1500 kN. Notably, the edge placement configuration (5 dB25*) shows a marked improvement over the in-between placement, particularly at this larger section height.  
In summary, the data demonstrate that both the number of rebars and their placement significantly influence shear capacity, with the effect becoming more pronounced as section height increases. The edge placement configuration (5 dB25*) proves particularly effective at larger section heights, suggesting that the combination of increased reinforcement and strategic placement can substantially enhance the shear capacity of precast walls.
# Implicit Subspace Iteration to Improve the Stability Analysis in Grinding Processes
## 3 Results
### Application of the Method to Centerless Grinding Processes  
Firstly, the application of the subspace iteration method to centerless grinding processes is validated by comparing it to the work by [PERSON] et al. [23], where stability maps are calculated and validated in the frequency domain. Figure 6a shows the map obtained in the frequency domain and Figure 6b shows the one by the subspace iteration method. Good correlation between both can be noticed. The main difference is that the stability limit for frequency domain is equal to zero with negative unstable conditions, while subspace iteration is equal to 1 with unstable conditions over that value.  
Figure 5: Comparison of time for calculation of stability maps between semi-discretization and subspace iteration methods for cylindrical grinding with the application of continuous workpiece speed variation.  
Figure 6: Dynamic stability map for centerless grinding by (**a**) frequency domain method [21]; (**b**) subspace iteration method.  
In the case of centerless grinding processes, aside from the work done by [PERSON] et al. [19], there is no bibliography regarding the obtainment of stability maps with continuous workpiece speed variation. Then, the subspace iteration method is compared to the time domain simulation procedure by [PERSON] et al. [19] in terms of calculation accuracy and computational time consumption. Figure 7 shows the map obtained in [19] by the time simulation approach while Figure 8 shows the one obtained by the subspace iteration method. Good correlation can be noticed again, validating the accuracy of the proposed method.  
Calculation time for achieving this type of instability reduction ratio in centerless grinding with the application of continuous workpiece speed variation is represented in the Figure 9. In this case,  
Figure 8: Instability reduction ratio for centerless grinding by subspace iteration method.  
Figure 7: Instability reduction ratio for centerless grinding by time simulation method [19].  
the time-domain simulations are independent of the variation frequency, since the simulation is done during all the workpiece revolutions of the whole grinding cycle. The time required for achieving the time-domain \(11\times 11\) map of Figure 7 is 63,525 s, while the time for achieving the subspace iteration map of Figure 8 is 2541 s, being 25 times faster.
## 6 Conclusions  
UWCGES is one of the most feasible solutions for large-scale offshore energy storage. Though facing many challenges, its potential has driven significant recent attention. Technical challenge considerations include: effective ballast anchoring, structural stability under complex and cyclic loads, safety under the impacts of falling objects and gas escape, vortex-induced vibration under different ocean current conditions, local scouring near the foundation, low-cost gas transportation support technologies, as well as the biorfouling of marine organisms. In terms of the environment, the potential hazards of underwater gas storage accumulators are to destroy the habitat of marine organisms and the ecological balance of seabed microorganisms. However, the underwater system may also have a series of positive impacts on the marine ecological environment with proper protection and regulations. In terms of economy, UWCGES is affected by many factors, such as application mode, technical scheme, energy storage scale, etc. There remains a great uncertainty in the return on investment, and there are significant differences between individual projects. In terms of policy, there should be a distinction between onshore and offshore energy storage. Offshore energy storage policy is lacking globally and is subsequently hampering development.  
Despite various challenges, it is likely that the strength of energy demand and clean resource demand at large should help to advance the development of marine renewable energy technology. UWCGES is a highly feasible offshore energy storage solution. It can make up for the shortages in traditional energy storage and supply measures in the far and deep sea. With strategic research and development investment, UWCGES can become a critical building block in the next generation of clean energy infrastructure that our planet will depend on.  
Conceptualization, H.W., Z.W. and W.X.; methodology, H.W., Z.W., C.L., D.S.-K.T., R.C. and W.X.; formal analysis, H.W., Z.W. and C.L.; investigation, H.W., Z.W. and C.L.; resources, Z.W., P.L., D.S.-K.T., R.C. and W.X.; writing-original draft preparation, H.W., Z.W. and C.L.; writing--review and editing, H.C., D.S.-K.T., R.C. and W.X.; visualization, H.W., Z.W. and C.L.; supervision, W.X.; funding acquisition, Z.W. and W.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51905066 and 52075065.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 7: An integrated application mode of UWCGES.
# Hub Airport End-Around Taxiway Construction Planning Development: A Review
## 3 End-Around Taxiways (EATs) around the World  
### End-Around Taxiway (EAT) Layouts  
There were formerly more end-around taxiways in Europe and the United States, which were mostly constructed in the early 20 th century. Frankfurt Airport, Amsterdam, and Atlanta Airport are typical representatives of EATs. However, with advancements in communication and navigation technologies, many airports started adopting alternative measures, such as runway status lights and end liaison channels, to perform bypass functions to reduce the occurrence of runway incursions. The construction of EATs has come to a standstill in these countries.  
As the aviation business continued to grow, the utilization of more parallel runways has led to increasingly frequent movements between runways. Without EATs, conflict hotspots persist within parallel or intersecting runway systems. Lighting, navigation, and other facilities can only provide limited reductions.  
In recent years, Asian countries have entered a new stage of development and expansion; many Asian countries have constructed EATs and included even more in their airport master plans.  
Figure 3: Example calculation of EAT length for DTW (Detroit).  
Figure 4 shows the 15 EAT airports that are currently in use worldwide. As shown in Figure 5, the layouts of the EATs in these airports vary from one another. Similar runway configurations have led to the development of similar EATs, such as DFW and SHA, which can be used as a reference for airport planners. The layout mainly refers to the \"shape\" of the EATs and the distance from EATs to the runway threshold or end. Different layouts result in different traffic lengths and traffic times.  
Figure 5: _Cont_.  
Figure 5: End-around taxiway layouts. (The red lines indicate EATs).  
As shown in Figure 6, the length of EAT is closely correlated with the distance from the EAT to the runway threshold or end, which refers to the distance between the end or threshold and the portion of EAT that is perpendicular to the extended runway centerline. This distance has a significant impact on the total length of the taxiway.  
The distance depends largely on the design, aircraft type, and obstacle clearance criteria. The taxi time and taxi efficiency are closely related to this distance. Therefore, it is an important factor to consider in the design process. As shown in Figure 5, the minimum runway end distance from EATs can be less than 400 m, which is generally used for taxiing behind the runway flow. The EAT at MIA has the shortest runway end distance of only 350 m.  
The distance from the runway end varies, but the trend between this distance and the EAT length is consistent. In EAT design, this distance is dictated primarily by aircraft size and limitations on surfaces and usage patterns, making it a core consideration in EAT layouts.
# Waste Silt as Filler in Hot Mix Asphalt: A Laboratory Characterization
## 3 Methods
### Phase I  
In brief, the rheological tests included amplitude sweep (AS), frequency sweep (FS) and multiple stress creep recovery (MSCR) tests.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **Unit** & **Value** & **Standard** \\ \hline Penetration @ 25 \({}^{\circ}\)C & dmm & 53 & EN 1426 \\ Softening point & \({}^{\circ}\)C & 50 & EN 1427 \\ Dynamic viscosity @ 60 \({}^{\circ}\)C & Pa.s & 327 & EN 13702 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rheological properties of neat bitumen.  
Figure 1: Untreated (**left**) and thermally treated (**right**) silts.  
The rheological studies were carried out on binder mastics that were prepared based on the mix design of the HMA (5.5% of bitumen and 7% of filler) [24]. Accordingly, three different mastics (44% bitumen and 56% filler) were prepared: the control mastic (M-C), the mastic with normal silt (M-SN) and the one with calcined silt (M-SC).  
An Anton Paar MCR302 Dynamic Shear Rheometer using 8 mm parallel plate settings (2 mm gap) was used to study the rheological behavior of the produced mastics. An amplitude sweep (AS) test was performed to determine the linear viscoelastic (LVE) range of the mastics (EN 1477). The samples were tested at a constant frequency of 1.59 Hz at 10 \({}^{\circ}\)C.  
A frequency sweep (FS) test was then applied considering the lowest linear viscoelastic limit obtained using the AS test on the three mastics. The FS test allows for the evaluation of the mastics' behavior and strength under various loading conditions. The samples were tested at different temperatures of 10, 20, 30, 40, 50 and 60 \({}^{\circ}\)C for frequencies ranging between 0.01 and 10 Hz. For each mixture, the complex shear modulus (G*) and phase angle (8) were calculated.  
The permanent deformation of each mastic was determined using the multiple stress creep recovery (MSCR) test (ASTM D7405). The MSCR tests were performed at 60 \({}^{\circ}\)C on mastic samples loaded at constant creep stress for 1 s followed by a zero-stress recovery of 9 s. The samples underwent shear creep loading and recovery at two stress levels, namely, 0.1 kPa and 3.2 kPa. Two cycles, each having 20 runs at 0.1 kPa and 10 runs at 3.2 kPa, were performed on the mastics. The non-recoverable creep compliance (Jnr) and the percent recovery after the latest ten runs at 0.1 and the ten runs at 3.2 kPa were assessed. The Jnr values were determined as the ratio between the average non-recoverable strain for 10 creep and recovery cycles and the applied stress for those cycles. On the other hand, the percent recovery was evaluated by considering the recoverable strain, as the ratio between the average recoverable strain after 10 cycles and the applied stress.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### Pre-Processing Techniques
#### 3.2.2 Standardization  
Z-score standardization or standardization is a method to standardize a set of data based on its global mean distance relative to all samples to balance the analysis. It begins with computing the global mean value for each parameter in the dataset. Then, it replaces all initial values with the differences between the global mean and the initial values. In this study, we simplified the dataset distribution according to Z-score to balance the analysis based on the global mean value of the dataset. It was applied directly after the windowed averaging technique. We used the standardization implemented by the sklearn library [18]. The method was implemented based on the z-score calculation [19].
## 5 Research Questions
Additionally, they have been applied to the European railway bridge network for safety and maintenance [203], assist in accident risk assessment in nuclear facilities [204; 205], and have been implemented in the construction industry for worker safety training [108]. They have also been used for risk analysis and reducing failure probability in complex work systems [206]. Other methods that can be considered subcategories of neural networks for workplace safety and risk assessment include HAZOP, which has been applied in the petrochemical industry and improves safety, RNA, which identifies risks in aerospace engineering [207], and FNN, which determines damage in engineering analysis models [208]. The PSA method, combined with neural networks, is more effective and efficient in safety and risk management in nuclear power plants [209; 210]. It has been applied to reactor safety concerning human error [211] and enhances safety in nuclear plants with aging equipment [212]. What application fields are addressed by the four risk analysis techniques? According to the literature review and the database created, the main fields where the four techniques are applied are the industrial sector with 134 articles, the transportation sector with 53 articles, the construction sector with 19 articles, and the mining sector with 14 articles. Additionally, a large category called \"all fields\", containing 71 articles, includes models from the four techniques, with authors claiming that the analyzed model can be applied across all professional sectors in matters of safety and risk. There are also seven other sectors (health, natural disasters, computers, fuels, high technology, environment, and chemistry) with one to five articles each. It should be clarified that the 71 articles categorized as \"all fields\" are not unclassified articles but mostly mathematical models proposed for occupational health and safety. Authors claim that some existing techniques, with minor modifications, have broad applicability. Additionally, case studies are included, whose resolution, with minor adjustments, renders them usable in other sectors, as the authors highlight. To better answer this research question, each sector with the most bibliographic references should be examined individually.
###### Abstract  
In this paper, a distributed Bayesian filter design was studied for nonlinear dynamics and measurement mapping based on Kullback-Leibler divergence. In a distributed structure, the nonlinear filter becomes a challenging problem, since each sensor cannot access the global measurement likelihood function over the whole network, and some sensors have weak observability of the state. To solve the problem in a sensor network, the distributed Bayesian filter problem was converted into an optimization problem by maximizing a posterior method. The global cost function over the whole network was decomposed into the sum of the local cost function, where the local cost function can be solved by each sensor. With the help of the Kullback-Leibler divergence, the global estimate was approximated in each sensor by communicating with its neighbors. Based on the proposed distributed Bayesian filter structure, a distributed cubature Kalman filter (DCKF) was proposed. Finally, a cooperative space object tracking problem was studied for illustration. The simulation results demonstrated that the proposed algorithm can solve the issues of varying communication topology and weak observability of some sensors.  
Keywords:cooperative space object tracking; distributed sensor network; distributed estimation; cubature Kalman filter; Kullback-Leibler divergence; consensus +
Footnote †: journal:
## 3 Main Framework
### Graph Neural Network  
In order to detect bridge nodes in the network more accurately, the model we build must have the ability to extract the deep topological information from the complex network. Therefore, based on the principle of graph representation learning, we propose a deep neural network model BND-GCN (bridge node detection-GCN) for detecting bridge nodes.  
As shown in Figure 2, we first superimpose two GCN layers as the main part of the model; with the graph structures and feature vectors they can learn representation vectors of nodes. Each GCN layer has propagation rules defined as follows:  
\[H^{i+1}=\sigma(AH^{i}W^{i}+b^{i}) \tag{6}\]  
where \(H^{i}\) is the representations of nodes at the \(i\)th GCN layer and \(W^{i}\) and \(b^{i}\) are its trainable parameters. \(A\) is the symmetric normalized Laplacian of the network and \(\sigma\) denotes the nonlinear activation function.  
After propagation through the multi-layer graph neural network, we can obtain a low-dimensional node representation that aggregates the topological information of the complex network. At the end of the neural network, we add three fully connected layers to process the node representation and give the final output as a classifier. The activation function between fully connected layers is LeakyReLu, and the activation function of the output layer is Sigmoid. Finally, we choose the cross-entropy function as the loss function of the model. According to the classifier output and the known ground-truth labels of the nodes, the loss function can calculate the loss of the model and then adjust the relevant parameters of the neural network layer through backpropagation. In addition, we adopt the dropedge [31] strategy on GCN layers and apply the dropout technique on fully connected layers to prevent the model from overfitting. The skip connection technique is also used in our model.  
On the other hand, we also try to replace GCN layers in the model with GraphSAGE and GAT layers. Accordingly, we modified some model parameters to adapt to different network layer structures. Experiments show that the variants of BND-GCN, which we named BND-GraphSAGE and BND-GAT, also have good results on the bridge node detection task.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Type** & **Feature** & **Description** \\ \hline \multirow{2}{*}{Local} & Degree & Measure the number of the neighbors of a node. \\  & LocalRank & Aggregate the information contained in the fourth-order neighbors of each node. \\  & Clustering coefficient & Describe the degree of interconnection between the neighbors of a node. \\ \hline \multirow{2}{*}{Global} & PageRank & Measure the importance of a particular webpage relative to other webpages. \\  & Betweenness & Measure the degree of interaction between the node and other nodes based on the shortest path. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of the selected features.
# Automatic Evaluation of the Robotic Production Process for an Aircraft Jet Engine Casing
## 3 Solution of the Problem
### Identification of Essential Process Parameters
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Parameter** & **Designation** & **Units** & **Measurement** & **Devices** \\ \hline \multirow{4}{*}{Calibration parameters} & TCP calibration parameters & Kx, Ky, Kz, Px, Py, Z & [mm] & Pre-process & \begin{tabular}{c} ABB IRC5 \\ Keyence CV-S200M \\ \end{tabular} \\ \cline{2-6}  & Welding trajectory compensation & C1,C2,C3 & [mm] & Pre-process & \begin{tabular}{c} ABB IRC5 \\ Keyence CV-S200M \\ \end{tabular} \\ \hline \multirow{4}{*}{Welding process parameters} & Welding trajectory compensation & U & [V] & In-process & \begin{tabular}{c} Fronius \\ MagicWave 5000 \\ \end{tabular} \\ \cline{2-6}  & Welding current parameters & I & [A] & In-process & \begin{tabular}{c} Fronius \\ MagicWave 5000 \\ \end{tabular} \\ \cline{2-6}  & Shielding gas flow rate & A1, A2, A3 & [L/min] & In-process & \
\begin{tabular}{c} ABB IRC5 \\ Bronkhorst \\ Mass-Stream \\ \end{tabular} \\ \hline \multirow{4}{*}{Motion parameters} & Travel speed & v & [mm/s] & In-process & ABB IRC5 \\ \cline{2-6}  & Motion acceleration & a & [mm/s\({}^{2}\)] & In-process & ABB IRC5 \\ \cline{1-1} \cline{2-6}  & \(Z\)-axis position compensation & W, S & [mm] & In-process &
\begin{tabular}{c} ABB IRC5 \\ WeldGuide III \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Defined input parameters of the process. Material burn through largely occurred when the limit value of the gap between the edges of joined materials was exceeded (Figure 9a). This could be caused by material stress release due to temperature increase during welding. Foreign material inclusion in the weld seam could come from the welding electrode itself if, due to disturbances in the arc welding process, it was melted into the welding pool of the base metal (Figure 9b). Incomplete weld penetration was one of the main root causes for reworking the weld types discussed here. The defect could appear at different weld lengths (Figure 9c)  
Weld defects were detected after the process by quality control and, in some cases, by the operator, if the defect was noticeable. Non-conformities characterized by missing weld penetration were only detected during visual inspection using borescopes, FPI, or X-ray inspection.