esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Bogies on the Wake Flow of a High-Speed Train
## 4 Method Validation  
### Grid Independence  
In this section, convergence tests are conducted to exclude the influence of the mesh resolution. For a comparison with the experimental data, model 1 is used as an example and three different mesh resolutions are investigated. All other computational details were explained in Section 2. The details of the three different meshes are shown in Table 1. The simulation results of the aerodynamic characteristics using these meshes are compared with those obtained from a wind tunnel test. The test model used in the wind tunnel experiments is a 1/8 th scaled model of the modified CRH380A train with a 3-car grouping, including the head car, middle car, and trailing car; the length of the model is 9.75 m. The experiment was conducted at the China Aerodynamics Research and Development Center (CARDC) in Sichuan. The free stream velocity is 60 m/s. The experimental setups in the wind-tunnel are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **First Thickness of Mesh** & **Refine Mesh Near** & **Total Number of** \\  & **Close to Train Surface** & **Accessory Parts (size)** & **Mesh Cells** \\ \hline FM & \(1.14\times 10^{-4}\) H & Yes (4.46 \(\times\)\(10^{-3}\) H) & 74 million \\ MM & \(2.29\times 10^{-4}\) H & Yes (8.93 \(\times\)\(10^{-3}\) H) & 54 million \\ CM & \(4.57\times 10^{-4}\) H & No & 37 million \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the three meshes (FM: fine mesh, MM: middle mesh, CM: coarse mesh).  
Figure 6: Wind tunnel test setup at CARDC, Sichuan.  
Aerodynamic coefficients are often used for a comparison of models with different scales. The drag coefficient \(C_{d}\) is defined in Equation (13); the results are summarized in Table 2.  
\[C_{d}=\frac{F_{d}}{0.5\rho V^{2}S}, \tag{13}\]  
where \(F_{d}\) is the drag force, \(\rho\) is the air density, and \(S\) is a reference area, in this case, the train's cross-sectional area (in this study, \(S=11.123\) m\({}^{2}\)). To compare the differences in the \(C_{d}\) of the three meshes intuitively, the train is divided into 156 slices along the train body and each slice is 0.5 m in length. The time-averaged drag coefficients of the slices are shown in Figure 7.  
The results in Table 2 show that the relative error between the FM and wind tunnel test result is only 2.55%, whereas it is 3.65% for the MM. The errors are relatively small, indicating that both meshes can be used. However, the relative error between the CM and the wind tunnel test result is larger at 9.08% and it is not recommended to use the CM. In addition, it can be clearly seen in Figure 7 that the differences in the drag coefficients between the FM and MM are small and their curves are nearly the same. The results of the CM exhibit large deviations from those of the other meshes in the bogies, inter-carriage gaps, and the nose of the trailing car. In general, the MM is suitable for the current simulations with high accuracy and efficiency and it is, therefore, used for the subsequent simulations.
## 4 Data Analyses, Discussion and Findings
### Prefabrication Scope Identification
As mentioned above, in some circumstances, the welding can be replaced by the flange coupling or the groove coupling. The level of feasibility of piping prefabrication in different types of projects is surveyed. It can be indicated in Figure 4c that MEP contractors have a positive attitude to the piping prefabrication in residential, office, and hotel projects as the average scores of the three types of project are higher than the others (the average scores of 3.73, 3.81, and 3.69). The level of standardization in residential, office, and hotel projects is higher. In most cases, these buildings in China are tall and consist of typical floors and standard units. Therefore, the pipes in these projects are appropriate to be mass produced in the fabrication store. The commercial buildings are uniquely shaped. Besides, various pipes in fire sprinkler systems, water supply systems, and air conditioning systems are all required in commercial buildings. Pipes in commercial buildings are numerous in size and diameter. The health project is one of the most complex public projects for its special function requirements in consulting rooms, operating room, and diagnostic laboratory. The medical buildings consist of some special MEP systems such as sewage system, medical gas system. The average scores of commercial buildings and health projects are 2.67 and 2.69 respectively. The characteristics of these two types of projects increase obstacles for piping prefabrication.
## 7 Discussion  
We studied the feasibility of CSEM monitoring of resistivity changes in the TU Delft campus reservoir, which involves a custom surface-to-borehole setup. This setup requires the drilling of a monitoring well for the installation of borehole receivers. The forward modelling results did not identify an optimal location for such a monitoring well. An optimized experimental design may help to find such a location. When assessing the location of a monitoring well, the relatively high fluid flow between wells resulting from energy production should be considered. This high fluid flow would cause a sharp edge in the changes toward the production well, which should ideally be monitored.  
The results of our study show--as can be observed in Figure 4--that the CSEM method is sensitive to resistivity changes inside the TU Delft geothermal reservoir model, regardless of their radius or magnitude. However, we found that changes of large radius and small magnitude provide the same difference as changes of a small radius and large magnitude, which introduces uncertainty into the interpretation of time-lapse CSEM data. As such, it may be necessary to combine CSEM data with other geophysical data; in particular, coupling it with sub-surface fluid flow models and local temperature and pressure measurements in the injection and monitoring wells is necessary to obtain better insights regarding the changes.  
Undesired effects pose significant challenges for CSEM monitoring. These effects distort the field response and can make it difficult to detect resistivity changes. While random recording noise and near-surface changes have a minor impact, survey repeatability errors lead to more severe effects. Close to the source, repeatability errors may generate a difference greater than the difference due to the resistivity change. The steel casing also has a severe impact, heavily damping the vertical electric field in its vicinity. To reduce the sensitivity of CSEM data to severe undesired effects, the data should be collected at least 200 m away from the source and 100 m from steel-cased boreholes. Our results suggest that the CSEM method can provide valuable information about the resistivity changes inside the TU Delft campus reservoir, despite the presence of undesired effects.
## 5 Experiments
### Experimental Results
#### 5.3.2 Ablation Study of Inversed Distance Attention  
We used the attention mechanism to calculate the correlation between target prediction regions and grids with monitoring stations. By multiplying the inversed distance feature as a modification term before the softmax layer, the performance of our model will be improved. In this section, we show an ablation analysis with the attention mechanism and the influence of the inversed distance feature. Here we introduce three models for comparison:  
* **Model without Attention mechanism:** We disabled all the attention structures in our model and used summation to aggregate the embedding vectors of all labeled grids.
* **Model without Inversed Distance Attention:** In Figure 0(b), we only disregard the inversed distance feature \(X_{Sn}^{ID}\) to calculate the attention weight, while the process and parameter settings are the same as our proposed method.
* **Model with Inversed Distance Attention:** This is our proposed model with inversed distance attention structure. We compare our prediction result with the two above models to show the advantage of the proposed inversed distance attention layer.  
The overall results for inversed distance attention are shown in Table 2. In the first and second rows, a simple attention layer can improve the performance by giving the high influential grids critical weights. Moreover, in the second and third rows, we prove that multiplying the inversed distance before the softmax layer can further decrease errors. Thus, the distances between unlabeled grids and labeled grids can significantly impact the finding of related reference grids with monitoring stations.
## 4 Results
### Cohort and Disciplinary Analyses of mHealth Publication Proportion Shifts  
To further substantiate the observed shift in research focus toward mobile health among mHTI participants, we conduct traditional comparative analyses of scholars' pre- versus post-training mHealth publication ratios across cohort years and disciplinary subgroups. We maintain the three-year windows before training (\([-3,0)\)) and after training (\((0,3]\)) as our observation periods. Using cohort years and research disciplines as subgroup indicators, we present changes in the proportion of mHealth publications among all publications for scholars in each subgroup. Positive changes in this proportion may suggest increased research interest in mHealth among participants.  
Specifically, our analysis is still based on 12,838 articles with well-identified topics. For each scholar, we calculated the percentage of mHealth publications relative to their total publications during both the pre- and post-training three-year periods. For each subgroup, we then report the mean values of these percentages, together with the validated number of scholars (scholars who have identified publications in both \([-3,0)\) and \((0,3]\) time windows).  
Tables 5 and 6 reveal a widespread increase in mHealth publication proportions following training across multiple cohorts and disciplines. Cohort-level analysis demonstrates consistent positive growth in mean proportions for cohorts with complete data (2015-2019). For the 2021 and 2022 cohorts, we observed more modest changes. This attenuated effect likely reflects incomplete data collection, as our dataset extends only through 2023--insufficient to capture the full 3-year post-training window for these recent cohorts. Disciplinary comparisons indicate substantial increases in engineering, medical, and psychological sciences, with public health showing more modest gains and other disciplines displaying merely negligible effects. However, the number of scholars in other disciplines is limited, which could lead to random fluctuations for this subgroup.  
Figure 11: Disciplinary-based migration patterns of mHTI scholars toward mHealth topics in semantic space, with time window \([-3,0)\) vs \((0,3]\). The arrows indicate migration direction.
## 2 Related Works
### Supervised Methods  
The availability of a training dataset with labeled cases for both normal and anomalous classes is assumed by supervised techniques. The training and testing datasets must then be chosen to perform cross-validation. The training dataset is modeled using a supervised learning technique. Creating a prediction model for normal vs. anomalous classes is a common strategy in these situations. The model is then assessed using the testing dataset.  
The representative supervised method is a classification-based anomaly detection algorithm. A classifier is trained by using the labels in the training dataset so that it can distinguish between normal and abnormal data. Once this classifier is trained, it can accurately distinguish between normal data and abnormal data when facing new data.  
Many algorithms exist for supervised anomaly detection in health monitoring [26], equipment failure detection, system state monitoring, and other industrial applications [27; 28; 29; 30].  
A detailed description of these methods can be found in [22]. The merits of supervised methods include the fact that they are (1) supervised, which means they are easy to use, and (2) robust to different data types. However, their shortcomings are obvious. Namely, (1) labeled data are difficult to obtain or require large costs to obtain, especially in industrial and commercial applications; also, (2) the abnormal result finally obtained by the supervised algorithm is binary, and the degree of abnormality cannot be further compared.
# Faster RCNN Target Detection Algorithm Integrating CBAM and FPN
## 2 Related Works
The CF-RCNN algorithm proposed in this paper is based on group convolution, which significantly reduces the weight of the feature extraction network. The CBAM attention mechanism is introduced to fully consider local features. The FPN structure is integrated to improve the detection ability of small-sized targets and truncated targets. The CF-RCNN algorithm is superior to many of the most advanced methods. The specific advantages and disadvantages are compared in Table 1.  
The following are the primary contributions of this work:  
* The ResNet-50 network model is optimized, and a VS-ResNet network model with stronger expression ability is proposed, which improves the classification accuracy in the target recognition process. * The improved model of non-maximum suppression is applied to solve the problem of eliminating prediction frame errors in the process of classification and regression. * A CF-RCNN algorithm is proposed and implemented for small-sized targets and truncated target detection. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Detection Method** & **Advantage** & **Disadvantage** \\ \hline GrabCut algorithm & The proposed algorithm is not country-specific and can be used to detect LPs in complex environments & This method is not extended to motorcycle license localization \\ \hline DF-SSD algorithm & The algorithm has a fast processing speed and a compact model & This algorithm is not applicable to large objects and objects without specific relationships \\ \hline Scalable and Efficient Object Detection & This method uses fewer parameters and has a wide range of resource limits & The compound scaling method mentioned is complex and difficult to operate \\ \hline Multi-Head Attention Detection & This method is suitable for small target detection in complex environments & There are obvious false detection and missing detection phenomena \\ \hline SNIP & This method is effective in target recognition and detection under extreme scale changes & This method does not have universal generalization and the mAP value is low \\ \hline CF-RCNN & This method has a strong detection ability for small-sized blocked or truncated targets, and has strong stability in complex environments & It is easy to cause the problem of multi-frame prediction of a single target complex environments \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of advantages and disadvantages of CF-RCNN and advanced algorithms.
## 3 Results and Discussion
### Verification and Validation of Computational Modeling of Buckling
#### 3.1.2 Verification of Elastoplastic Buckling Model Under Combined Loading on Stiffened Plates

Table 4: Results of \(\sigma_{uX}\) and \(\sigma_{uY}\) obtained from the present study and from [PERSON] and [PERSON] [24]. Figure 10: Stiffened plate: (**a**) geometry and (**b**) boundary conditions of the proposed model, with the blue symbols representing the displacement constraints according to the axis direction, the red arrows representing the biaxial compressive loading, and the red lines on the plate representing the applied lateral pressure. \(P_{u}\) through FEM model. Moreover, the \(\sigma_{cr}\) of this stiffened plate using different solution methodologies (EBPlate (a) and (b), FEM using ABAQUS version 6.7, and the EN1993-1-5 A.2 standard [26]) is also presented in [PERSON] et al. [25], allowing a verification of the proposed elastic buckling model. The studied stiffened plate has material properties of \(E=210\) GPa, \(\
u=0.3\), and \(\sigma_{y}=355\) MPa, with dimensions \(a=1800\) mm, \(b=1800\) mm, \(t_{p}=12\) mm, \(t_{s}=10\) mm, and \(h_{s}=100\) mm, being submitted to a compressive uniaxial loading, as depicted in Figure 11.  
This computational model achieved the \(\sigma_{cr}\) and \(P_{u}\) through the mesh convergence test, shown in Table 5.  
The results obtained are compared with the reference results in Table 6.  
The present study obtained \(\sigma_{cr}=277.79\) N/mm\({}^{2}\), showing a small difference when compared to other methods. The maximum difference was 4.22%, which was observed when compared to the EN1993-1-5 A.2 methodology, although with a lower value, providing safety. The smallest difference was obtained in relation to the EBPlate (a) method, with a difference of 0.64% higher. Regarding the finite element method, the difference \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methodology** & \(\sigma_{cr}\) **(MPa)** & **Difference (\%)** & \(P_{u}\) (kN) & **Difference (\%)** \\ \hline Present study & 277.76 & - & 4566.01 & - \\ EBPlate (a) & 276.00 & 0.64 & - & - \\ EBPlate (b) & 289.00 & \(-\)3.89 & - & - \\ ABAQUS & 268.00 & 3.64 & 4424.00 & 3.21 \\ EN1993-1-5 A.2 [26] & 290.00 & \(-\)4.22 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of \(\sigma_{cr}\) and \(P_{u}\) results between the present study and the referenced literature. Figure 11: Modeling of plate with two longitudinal stiffeners, with the blue symbols representing the displacement constraints according to the axis direction and the red arrows representing the uniaxial compressive loading. between the result obtained in this study and the reference using ABAQUS (version 6.7) was 3.64% higher. According to the ultimate strength, the present study results in \(P_{u}=4566.01\) kN, 3.11% higher than \(P_{u}=4424.00\) kN obtained from FEM by [PERSON] et al. [25]. Overall, the results demonstrate good consistency across the different approaches.
# ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles
## 4 Discussion

Table 6: An example of mission parameters and MBES power consumption for a survey mission in the Bari Canyions area shown in Figure 6.  
Figure 6: The survey areas in the South Adriatic sea. The Bari and Monopoli Canyons are shown in the two polygon areas. In each of the two areas zoom areas are highlighted for further accurate surveys. The mission described in Table 6 consists into three MBES survey actions. In the first action a square area with the side 5 Km long is surveyed at a 200 m of distance from the seabed. The data acquisition is performed through parallel transects where 20% of each transect overlap each other. According to the MBES specifications [69] the swath width is about 200 m, the rate of data acquisition is 60 MB per minute and the average MBES instant power consumption is about 40 W. The execution time of the survey is about 10.5 h considering an average AUV speed equals to 2 Km. Within this context the total amount of MBES acquired data is equal to 38 GB, while the total MBES power consumption is equal 0.42 KW. Considering the thruster power consumption equals to 400 W, 4.2 KW are needed by the AUV for navigating the whole survey area, for a total power consumption equals to 4.62 KW. The Bari Canyons area is about 22 Km from the Bari harbour, corresponding to about 2 days of AUV navigation in glider mode with an horizontal speed equal to 0.45 Km/h. After the large area survey, the AUV should reach the sea surface and transfer the compressed acquired data to the USV. The USV should then transfer a simplified version of data to the RMCC. Moreover, if the sea state allowed for the vehicle docking, the AUV batteries should be recharged by the USV. Table 6 shows the mission parameters for the two smaller areas Zoom 1 and Zoom 2, where the acquired data should be 8 GB and 7 GB respectively, and the power needed for performing the two survey actions is 0.97 KW and 0.88 KW respectively.
## 3 Proposed Model and its Phases
### Multi-Hoping Network Transmission  
We extend our proposed model by introducing the multi-hoping technique during transmission and achieving network scalability. The multi-hop proposed model utilizes a distance-based intermediate CHs selection process and follows the threshold level of received signal strength indication (RSSI) of intermediate CHs. Initially, all CHs advertise their current location and RSSI information within a certain transmission range set by the network administrator. Remote CHs receive the following advertisements and compute the multi-hoping criteria (MHC). The MHC's value is directly proportional to RSSI and inversely proportional to the mutual distance of intermediate CHs toward BS. The following equation computes the value of MHC:  
\[MHC=\frac{RSSI_{ICHs}}{Dis_{ICHs-BS}} \tag{19}\]  
where \(RSSI_{ICHs}\) is RSSI value intermediate CHs and \(Dis_{ICHs-BS}\) is a mutual distance of intermediate CHs and BS. Along with multi-hoping assistance, the threshold-based actual data transmission of sensor nodes saves significant energy resources.
## 3 Legal Challenges Posed by Mega-Constellations to Existing International Space Law Regime
### Principle of Non-Appropriation  
#### 4.2.1 Legal Interpretation and Application of Non-Appropriation Principle  
The Outer Space Treaty forbids national appropriation in its second article. This demonstrates that the utilization of space for national purposes is not acceptable. This even persisted in the early United Nations talks on space exploration [8; 94]. This principle was also reiterated in 1963, in a draft precursor to the binding Outer Space Treaty.  
States are effectively prohibited from lawfully appropriating outer space, including void space and celestial bodies, as a form of alien territory that is ready for annexation and occupation [95]. From a historical standpoint, a state could lawfully expand its territory by unilateral sovereign claims. However, tensions between space states would undoubtedly arise from such a rush to claim territory on, for example, the moon. Therefore, it was preferable to agree that no state could or would be able to directly and permanently possess space, including orbits and celestial bodies. Beyond this comprehension, the article's intricacies are somewhat unclear, particularly with regard to how it should be applied to specific tasks and how it relates to current and upcoming space missions. It enumerates the ways in which national appropriation can be accomplished, including claims of sovereignty, use, occupation, and any other method, concluding that space is not subject to national appropriation. Stated differently, there is no legal or acceptable way to justify the national appropriation of space. At the end of the list, \"by any other means\" is used to indicate that the article is not all-inclusive and to close any potential gaps. Therefore, it is used cautiously to emphasize that no state may claim sovereignty over space and that no state may ever bring it under its sovereign jurisdiction. Consequently, based on this reasoning, a state cannot appropriate space or regions of space, such as spots on celestial bodies, specific trajectories, or orbits, since any action taken would not be considered authorized appropriation.