esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Emergency Call Service for Disaster Management
###### Abstract  
Reliable and efficient transmission of emergency calls during a massive network failure is both an indispensable and challenging task. In this paper, we propose a novel fully 3 GPP and 5G compatible emergency call protocol named 5G StandalOne Service (5G-SOS). A 5G-SOS-enabled emergency service provides potential out-of-coverage victims' devices with a way to contact the 4G/5G core network through D2D multi-hop relaying protocol. The objective of 5G-SOS is to maintain this connection even when a large fraction of the network infrastructure is destroyed. 5G-SOS is a fully distributed protocol designed to generate zero additional control traffic and to adapt its parameters based on the local emergency call congestion. Therefore, devices behave as an ad-hoc network with the common purpose to ensure the best chances for emergency call transfer within a reasonable delay. A densely populated Traverse city of Michigan, USA, with a 15,000 population, is used to evaluate 5G-SOS under extreme emergency scenarios. The performance of 5G-SOS is shown to be significant when compared with existing protocols, namely, M-HELP and FINDER, in terms of transmission success rate, end-to-end latency, network traffic control, and energy management. 5G-SOS provides satisfactory performance (success rate of 50%) even when the number of simultaneous emergency calls is very high (5000 calls over 10 min). On average, 5G-SOS performs 24.9% better than M-HELP and 73.9% than FINDER in terms of success rate. Additionally, 5G-SOS reduces the average end-end latency of the emergency calls transfer by 20.8% compared to M-HELP and 61.7% compared to FINDER.  
adaptive; emergency call protocol; end-to-end latency; transmission success rate; 4G/5G emergency service; 3 GPP, D2D
# Characterization of Coffee Silver Skin as Potential Food-Safe Ingredient
## 3 Results  
### pH, \(a_{W}\), and Color of CSS  
pH measurements gave values of 5.34 \(\pm\) 0.018 (Table 2). No bibliographical references have been found to compare our results; however, other authors have reported pH levels of 4.9-5.6 for coffee beverages, and they considered these pH values suitable for the activity of biomolecules (e.g., polyphenols) [30]. As already mentioned, CSS has a coarse structure and dusty appearance which make it suitable for adding to food formulations and easily stored. Our values confirm this characteristic even if no reference is available to compare these values.  
Very low water activity values were found (0.319 \(\pm\) 0.00), which is important evidence for the low availability of water for degradation reactions in CSS [31]; consequently, its stability depends on the environmental humidity conditions in which the CSS is stored, which must be such as to prevent water absorption and provide a prolonged shelf life to CSS.  
Color, L*, a*, and b* parameters were like those reported in the literature [13]. The color mainly depends on the interaction of constitutive polyphenols and anthocyanins, which are involved in oxidation and polymerization reactions during the coffee roasting step [32]. Moreover, roasting processes cause degradation of proteins, and Maillard reactions occur, so melanoidins also contribute to the characteristic color of CSS.
## 3 Hazard Analysis Foundations and Key Terms of STPA and ISO26262
### Hazard Analysis Foundations of the HARA Process in ISO26262 and STPA  
First of all, both STPA and ISO26262 are based on the system engineering framework which views a system as an integrated whole. They both aim to embed safety into a system engineering process and to design safety into the system from the very beginning of a development process. Another thing in common is that both the STPA and HARA process are top-down processes. The HARA process of ISO26262 only focuses on hazards caused by component failures, while hazards in STPA may result from dysfunctional interactions among components, design flaws, human factors and external disturbance, beyond component failures. Activities in the HARA process are mainly divided into three parts as mentioned above. The most important difference between the HARA and STPA processes here is that the HARA process requires risk assessment according to the classification of hazardous events and determination of ASIL, while STPA does not intend to estimate risk. We list foundations of STPA and ISO26262 in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Foundations** & **ISO26262 HARA** & **STPA** \\ \hline \multirow{4}{*}{Characteristics} & Focus hazards on only component failures. & Have a broader scope of hazards causes. \\  & HARA and determination of ASIL are & View safety as a control problem. Use an \\  & used to determine SGs for the item. & hierarchical safety structure to describe a \\  & & system and explain why accidents occur. \\  & & Do not estimate risk. \\ \hline \multirow{3}{*}{Application Phase} & Applicable in the development stage a & Assumed to be used in any stages within \\  & system, with little known about the detail & the whole life cycle of a system, especially \\ \cline{1-1}  & design. & in the early stage of the \\ \hline \hline \end{tabular}
\end{table}
Table 1: The foundations of ISO26262 hazard analysis and risk assessment and systems theoretic process analysis (STPA).  
Figure 2: Steps of implementation process of failure mode and effect analysis (FMEA).
## 3 Analysis of the Influencing Factors in Topology Optimization
### Geometric Model Simplification  
In theoretical TO research, hexahedral meshes have been extensively employed due to their high computational accuracy and numerical stability. However, in practical engineering applications, to reduce the complexity of mesh generation for complex geometries, certain geometric features that affect mesh generation often need to be removed or simplified. For instance, small chamfers can be removed, or concave regions can be filled before meshing with hexahedral units, or more flexible tetrahedral meshes can be used directly. To investigate the specific influence of different factors on the TO results under hexahedral mesh conditions, this study utilizes the original model of the gooseneck chain by removing and filling chamfered and concave regions, as illustrated in Figure 1.  
The choice of the mesh size substantially affects the accuracy and efficiency of the FEA. When the mesh size is too large, the accuracy of the computational results may be compromised. Conversely, a mesh size that is too small could significantly increase the computational time and lead to excessively high computational costs. Therefore, choosing an appropriate mesh size is crucial to ensure computational accuracy while controlling costs. For this purpose, a mesh sensitivity analysis is performed on the simplified model (model after feature removal and filling), the results of which are demonstrated in Figure 2. From Figure 2a, it can be seen that when the hexahedral mesh size approaches 1.75 mm, the maximum stress value gradually converges to roughly 800 MPa. Similarly, Figure 2b demonstrates that when the tetrahedral mesh size approaches 4 mm, the maximum stress value converges to approximately 851 MPa. The stress discrepancies between hexahedral and tetrahedral meshes arise from the combined effects of shape function accuracy, material anisotropy, singularity handling mechanisms, and convergence efficiency.
## 3 Results and Analysis of Prefabricated Lightweight CFST Framework-Composite Slab Structure
### Energy Dissipation Capacity  
For the purposes of this study, the equivalent viscous damping coefficient, \(h_{e}\), and the accumulative dissipated energy, \(E_{i}\), were employed to indicate the energy dissipation capacity of the specimens. Figure 20 showed variations in the equivalent viscous damping coefficient with the displacement angle of the tested specimens. Figure 21 showed the variations in accumulative dissipated energy with a displacement angle.  
Figures 20 and 21 revealed several things.  
The equivalent viscous damping coefficients of all the specimens, except for specimen FM-SS, decreased rapidly at displacement angles smaller than 0.5%, decreased gradually at displacement angles from 0.5% to 2.0%, and exhibited an approximately flat, straight line at displacement angles greater than 2.0% until the failure of the specimens. The accumulative dissipated energies of specimens FM-S, FM-SW, and FM-SD were clearly greater than the accumulative dissipated energy of specimen FM. This was because sliding between the panels resulted in friction, and the friction resulted in energy dissipation, which contributed to a higher dissipated energy of the structure. This indicates that the sandwich slab and the framework acted synergistically by contributing to the greater energy dissipation capacity of the structure.  
The variation patterns of the equivalent viscous damping coefficients of specimens FM-SW and FM-SD were the same as the pattern of specimen FM-S, and the accumulative dissipated energy of specimen FM-S was slightly greater than the accumulative dissipated energies of specimens FM-SW and FM-SD. This indicates that an opening to a sandwich slab had a degree of impact on the energy-dissipation capacity of the structure. Additionally, the energy-dissipation capacity of the specimen with a window opening was slightly greater than that of a specimen with a door opening.  
Figure 21: Variations in accumulative dissipated energy with a displacement angle of the tested specimens.  
Figure 20: Variations in the equivalent viscous damping coefficient with a displacement angle of the tested specimens.  
The equivalent viscous damping coefficient of specimen FM-SS gradually increased at displacement angles smaller than 0.5%, remained at a stable level at displacement angles between 0.5% to 2.0%, and exhibited a decreasing trend at displacement angles greater than 2.0%. At displacement angles greater than 0.5%, the equivalent viscous damping coefficient and dissipated energy of specimen FM-SS were significantly greater than those of other specimens, which indicates that the concealed steel-truss slab and the lightweight CFST framework acted together to form a good energy dissipation system.
## 4 Results
### Reduction of Torsion Effect for the Structures
#### 4.1.2 Results for Case Study 2  
The nonlinear Equations (47) and (48) undergo the optimization process, and the solution is presented by system (49), for which \(\theta_{1}~{}=~{}-1.307e^{-7}\):  
\[A~{}=~{}0+(0.064\,\sin 2\alpha_{1})+(-0.123\sin 2\alpha_{2})+(0.091\sin 2 \alpha_{3}) \tag{47}\] \[+(-0.448\sin 2\alpha_{4})+(-0.161\sin 2\alpha_{5})+(-0.819\sin 2 \alpha_{6})\] \[+(-0.448\sin 2\alpha_{7})+(-0.312\sin 2\alpha_{8});\]  
\[B~{}=~{}-0.50958+ (0.064\cos 2\alpha_{1})+(-0.123\cos 2\alpha_{2})+(0.091\cos 2 \alpha_{3}) \tag{48}\] \[+(-0.448\cos 2\alpha_{4})+(-0.161\cos 2\alpha_{5})+(-0.819\cos 2 \alpha_{6})\] \[+(-0.448\cos 2\alpha_{7})+(-0.312\cos 2\alpha_{8});\]  
\[\alpha_{1}~{}=~{}0.571\,\,\text{rad};~{}\alpha_{2}~{}=~{}0.532\,\,\text{rad}; ~{}\alpha_{3}~{}=~{}-0.622\,\,\text{rad};~{}\alpha_{4}~{}=~{}0.449\,\,\text{ rad}; \tag{49}\] \[\alpha_{5}~{}=~{}-0.349\,\,\text{rad};~{}\alpha_{6}~{}=~{}-0.349\, \,\text{rad};~{}\alpha_{7}~{}=~{}0.524\,\,\text{rad};~{}\alpha_{8}~{}=~{}-0.449 \,\,\text{rad}.\]  
The results of the optimization and modal analysis are presented in Table 8 and Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**Period**} & \multirow{2}{*}{**UX**} & \multirow{2}{*}{**UY**} & \multirow{2}{*}{**RZ**} & \multirow{2}{*}{**Sum UX**} & \multirow{2}{*}{**Sum UY**} & \multirow{2}{*}{**Sum RZ**} \\  & **(s)** & & & & & & & \\ \hline
1 & 0.433 & 0.0002 & 0.775 & 0.0001 & 0.0002 & 0.775 & 0.0001 \\
2 & 0.416 & 0.7689 & 0.0002 & 0.0002 & 0.7691 & 0.7753 & 0.0003 \\
3 & 0.249 & 0.0002 & 0.0001 & 0.8307 & 0.7693 & 0.7753 & 0.831 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ratios of the modal participating mass after the reducing of the torsion.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**Period**} & \multirow{2}{*}{**UX**} & \multirow{2}{*}{**UY**} & \multirow{2}{*}{**RZ**} & \multirow{2}{*}{**Sum UX**} & \multirow{2}{*}{**Sum UY**} & \multirow{2}{*}{**Sum RZ**} \\  & **(s)** & & & & & & & \\ \hline
1 & 0.414 & 0.0104 & 0.7384 & 0.0266 & 0.0104 & 0.7384 & 0.0266 \\
2 & 0.397 & 0.7443 & 0.0124 & 0.0015 & 0.7547 & 0.7508 & 0.0282 \\
3 & 0.3 & 0.0033 & 0.021 & 0.7799 & 0.7581 & 0.7718 & 0.8081 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ratios of the modal participating mass after the reducing of the torsion.  
Figure 9: The 1 st mode shape for \(T_{1}\) = 0.433 s: (**a**) plane view, and (**b**) 3D view.
This approach not only enhances long-term data accessibility and integration with the MI platform but also streamlines cross-comparisons and future data-mining efforts. * **Secure and Accessible Data Management:** Robust security measures protect sensitive data while facilitating controlled access, promote effective collaboration between academic and industrial partners, and encourage widespread utilization of collected data. This review provides a comprehensive overview of the design, implementation, and role of the FC-BENTEN in PEFC research. First, we detail the development of standardized experimental protocols that ensure the reproducibility of synchrotron X-ray measurements. Next, we introduce a metadata framework that underpins data interoperability and long-term usability, thereby extending the relevance of these datasets beyond their initial experimental contexts. This review discusses the data management infrastructure and illustrates how it addresses the complexities inherent in handling large-scale synchrotron X-ray data. Practical examples show how FC-BENTEN's streamlined data integration and standardized workflows significantly enhance the quality and interpretability of PEFC material analysis. Finally, this review identifies ongoing challenges and proposes future directions. By continuously improving metadata standards, integrating with evolving analytical platforms, and adhering to the FAIR principles, FC-BENTEN sets the stage for a broader and more efficient utilization of PEFC data. This holistic approach paves the way for future innovations in PEFC technologies and contributes to the larger goals of sustainable energy and advanced material research. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement Technique** & **Beamlines** & **Instruments** & **Information Obtained** \\ \hline XAFS & BL14B2, BL36 XU &
\begin{tabular}{c} 25-element \\ \end{tabular} & Local structure, \\  & Degree of alloying \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Synchrotron X-ray measurement techniques and their applications in catalyst analysis for PEFCs at SPring-8. This table overviews key measurement techniques, their corresponding beamlines, instruments, and specific information yielded. XAFS fluorescence detection, using a 25-element Ge detector, enables nondestructive analysis of local structure, valence states, and alloying degrees. HAXPES utilizes a high-resolution photoelectron analyzer to examine materials’ electronic states, offering insights into chemical shifts and the d-band center. The d-band center, calculated from the integration of the valence band density of states, serves as a critical descriptor of catalytic activity and often correlates with activity trends in volcano plots. XRD uses multi-axis diffractometers to investigate crystal structures and alloying behaviors. PDF applies multi-detector setups to characterize amorphous structures and particle sizes. SAXS features an extended vacuum pathway between sample and detector to minimize X-ray absorption and scattering by air, enabling precise analysis of nanoscale structural characteristics.
## 4 Wind Loading and Vibration Mitigation
### Considered Load Cases
#### 4.2.1 Turbulent Wind Loading  
Here, we focus on the excitation typically arising in the Atmospheric Boundary Layer (ABL). Fluctuations in the wind field occur naturally for the CAARC-B, with available provisions describing the target state. This is characterized by the (streamwise, time-averaged) mean velocity profile along the height, accompanied by the turbulence intensity, as well as the (integral) turbulence length scale for this velocity component at the top of the structure. To achieve this state, we numerical modeled the inlet by synthetic turbulence, a specific type of generated condition (using the WindGen generator [42] based on the theory by [43]). We provide our assessment, additionally including the Power Spectral Density (PSD) for the streamwise velocity component. As the velocity is directly linked to the surface pressure, the along-wind excitation force will have a similar spectrum.  
Figure 6: Definition of the wind loading: direction and simulation domain.  
This scenario is thus characterized by a broad-band excitation, mostly in a streamwise (i.e., along-wind) direction.  
The load mechanism is typically dominated by the oncoming flow, this being the gustness approaching the ABL. It is acceptable to assume that the deformation state of the structure will not have a strong influence on the flow field itself. Consequently, we recorded the wind forces by CFD and applied it on the structural model, as typically carried out in Computational Structural Dynamics (CSD). Following the approach defined as One-Way Coupling (OWC), forces are transferred to the structure, but the resulting deformation state is not updated in CFD.
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 5 Experiments
### Experiments with Online Rendering and Tracking
The ATE increases slightly across most datasets, and the tracking percentage sees a slight reduction. For instance, in the _CBDBuilding_ sequence, the ATE increases from 0.048 m to 0.057 m, while tracking robustness drops from 93.5% to 89.2%. In several cases, such as _Garden_ and _RetailStreet_, the impact is \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**AKAZE**} & \multicolumn{2}{c}{**ORB**} \\  & **ATE (m)** & **Trck (\%)** & **ATE (m)** & **Trck (\%)** \\ \hline Garden & 0.031 & 84.1 & 0.055 & 79.5 \\ RetailStreet & 0.037 & 92.8 & 0.068 & 90.4 \\ CBDBuilding & 0.057 & 89.2 & 0.167 & 71.5 \\ HITGraffitiWall & 0.061 & 92.1 & 0.186 & 73.2 \\ HKULandmark & 0.071 & 81.1 & 0.167 & 72.9 \\ Drive & 0.077 & 90.9 & 0.199 & 69.7 \\ HKUCulturalCenter & 0.102 & 88.4 & 0.217 & 71.8 \\ RedSculpture & 0.122 & 88.3 & 0.298 & 64.2 \\ SYSU & 0.115 & 77.5 & 0.281 & 62.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tracking results of the online rendering and tracking approach, without using IMU data. negligible, demonstrating the strong standalone capability of the vision-based matching with AKAZE features. In contrast, the ORB detector shows a greater dependence on the IMU's motion prior. Without it, both accuracy and robustness decline significantly. The ATE for ORB more than doubles in some sequences, such as _HITGraffitiWall_ (from 0.069 m to 0.186 m) and _RedSculpture_ (from 0.210 m to 0.298 m). Similarly, tracking rates fall more sharply, with the _Drive_ sequence dropping from 89.2% to 69.7%. This analysis indicates that while the IMU provides a clear benefit to the overall system, its influence is most pronounced when using the ORB detector. As ORB is the less discriminative feature descriptor in our pipeline, it relies more heavily on the predictive power of the IMU to constrain the search space for feature matching. Ultimately, this experiment confirms that the IMU is a valuable component for enhancing robustness but is not strictly necessary to obtain good results, especially when a strong feature detector like AKAZE is employed. Although not explicitly presented here for brevity, similar findings were observed for the offline tracking methods, as they rely on the same fundamental pose estimation process.
## 3 Numerical Results
### Performance in the AWGN Channel  
Figures 6-11 show the error performance of CPM-GFDM and compare it with that of QAM-GFDM or PSK-GFDM for an AWGN channel. The error performance for a binary system is shown in Figures 6 and 7 when using matched filter receiver and zero forcing receiver, respectively. It is observed that with matched filter receiver, there are several values of \(h\) for which the proposed system outperforms the conventional GFDM, with \(h=1/2\) giving the best performance. Table 4 identifies those values of \(h\) that outperform conventional GFDM. However, when using zero forcing receiver, there is no value of \(h\) that beats the conventional GFDM, the closest being \(h=3/5,\ 5/8,\ 9/16\).  
Figure 8: BER performance of CPM-GFDM vs. QAM-GFDM with \(J=4\) for matched filter receiver over AWGN channels.  
Figure 7: BER performance of binary CPM-GFDM vs. BPSK-GFDM for zero forcing receiver over AWGN channels.  
Figure 10: BER performance of CPM-GFDM vs. QAM-GFDM with \(J=8\) for matched filter receiver over AWGN channels.  
Figure 9: BER performance of CPM-GFDM vs. QAM-GFDM with \(J=4\) for zero forcing receiver over AWGN channels.  
Finally, the error performance when using three bits at a time (i.e., \(J=8\)) is shown in Figures 10 and 11. It is observed that, in this case, out of 23 values of \(h\) there are 17 values that give a performance better than QAM-GFDM when using a matched filter receiver. The best performance is observed in the case when \(h=5/16\) and \(h=3/10\). Moreover, as Figure 11 shows, the proposed system outperforms the conventional GFDM for 18 values of \(h\), with the best performance observed when \(h=5/16,\ 3/10\).
## 2 Materials and Methods
### Description of Datasets
#### 2.2.3 Corine Land Cover Data  
To study the SIF variation across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest), we used the most recent Corine Land Cover (CLC) 2018, Version 20b2 provided by the European Environmental Agency (EEA) (Figure S2). The CLC 2018 uses Sentinel-2 and Landsat-8 imageries for its construction, thereby achieving a minimum mapping width (resolution) of \(100\times 100\) m (CLC2018 Technical Guidelines, 2017; [51]). Agricultural land comprising arable lands, pastures, and heterogeneous agricultural area covered most of the parts of drought area (53%) and non-drought area (57%), followed by coniferous forest of about 37% in the drought area (Table S3).
## 3 Results and Discussions
### Effect of the Leakage Direction
#### 3.3.2 Diffusion Velocity  
In order to compare the influence of leak direction on the diffusion velocity, the amount of time for iso-surfaces of 4% and 1% mole fraction in each stage of diffusion for Model 1-Model 5 is listed in Table 3. In the first phase, the time required for 4% mole fraction hydrogen diffusion in Model 4 and Model 5 was 100 s and 5 s, respectively, which was a shorter time than for Model 1, Model 2, and Model 3. The time required for the whole process of 1% mole fraction hydrogen diffusion for Model 4 and Model 5 was 20 s, which was shorter than for Model 1, Model 2, and Model 3. From the results of the velocity vectors shown in Figures 3 and 7, it can be seen that the diffusion velocity at the ceiling was about 0.15 m/s for Model 1-Model 5. The leak direction had little effect on the horizontal diffusion velocity near the ceiling, but it significantly affected the velocity rising to the ceiling.  
Figure 10: Contours of mole concentration of hydrogen for Model 1–Model 5 after the envelope of 4% mole fraction (up) and 1% mole fraction (down) of hydrogen filling the whole ceiling.