esa-sceva/satcom-chunk-collection huggingface dataset:
# Fall Recognition Based on Time-Level Decision Fusion Classification
## 3 Fall Detection System
### Skeleton-Based Feature Extraction  
Detecting falls using a skeleton-based approach has the advantage of preventing the learning of irrelevant information such as the background, thus leading to exceptional generalization performance. Furthermore, skeleton-based methods offer the advantage of enabling individualized fall detection for multiple individuals within a single video. In this study, the pre-trained AlphaPose [30] and ViTPose [31] were used to obtain skeleton data, and their respective results were compared. AlphaPose uses a CNN-based architecture to generate skeleton data as its output, whereas ViTPose employs the encoder structure of ViT [32] as its backbone. CNNs are among the most effective approaches for extracting features from images. However, very recently, research has demonstrated better performance in image feature extraction using the transformer [34] architecture than that achieved with models based on CNNs. This trend has prompted the development of ViTPose, which leverages the ViT structure as a complementary solution to the CNN-based AlphaPose to enhance pose estimation. These models produce 17 key points as their outputs. We utilized 13 key points, excluding the eyes and ears from both sides as these have a relatively minor impact on fall detection results. We compared the experimental outcomes of AlphaPose and ViTPose using the selected key points. Figure 3 shows an example of the skeleton output obtained using AlphaPose v0.2.0 and ViTPose.
# Simplified Pushover Analysis for Rapid Assessment of Shear-Type Frames
## 2 Displacement Capacity of a 2D Frame  
### Simplified Analytical Model of a 2D Frame  
The proposed analytical pushover method is thought to be applied to buildings that can be represented through a 2D frame, which is often the case when studying the response of large building stocks on a territory. A 2D frame is treated as a series system of stories, which are in turn parallel systems of columns, which are in turn series systems of different resisting mechanisms. A mechanical idealization of a two-story 2D frame is depicted in Figure 1, where each column is shown as a series system of flexural (\(M\)) and shear capacity, of both column (\(V\)) and end joint (\(V_{j}\)). Despite the simplified representation, this (stick-)model provides a good compromise between accuracy and computational effort. It is especially applicable to buildings designed for gravity loads exhibiting a shear-type behavior, which implies that the damage is sustained by the columns only. The beams are assumed to remain elastic, representing the strong-beam/weak-column situation typically found in most existing buildings designed to obsolete codes.  
The global capacity of the 2D frame is obtained in terms of total base-shear and top-displacement. This is defined by a trilinear curve, where three objectively measured limit states (LSs) are identified: Damage Limit State (DLS), Life safety Limit State (LLS), and Collapse Limit State (CLS). These global LSs are determined at the attainment of the corresponding LSs at the story levels, which are in turn defined by the attainment of the corresponding LSs of the pertaining columns, which are in turn defined by the attainment of the corresponding LSs of the resisting mechanisms. This implies that global LSs are a direct consequence of local LSs attained at the sectional level. This criterion provides a consistent relationship between local and global LSs, thus avoiding resorting to different metrics for the latter, such as interstorey drifts.  
The following sections show the sequential steps of the simplified pushover method, from the definition of the sectional capacity to that of the columns, to that of the stories, and, finally, to that of the entire frame.
## 3 Results and Discussion
### Kinetic Parameters of the Combustion Process  
Figure 5 shows a graphical interpretation of the Coats-Redfern equation for all fuels that gave the best linear results. Most of the obtained correlation coefficients (\(R^{2}\)) were above 0.99 (Table 3), which indicates the reliability of the calculated profiles. The kinetic curves of B-1 and CB-1 fuels have an inflection that characterizes the change in the fuel combustion mechanism in the studied temperature range. This is confirmed by the change in the reaction order from 0.5 for the initial stage to 1 for the late combustion stage. Perhaps the change in the mechanism is due to the combustion of cellulose. Figure 5 shows the linearization of the Coates-Redfern method for individual fuels and one mixture, since the kinetic parameters do not depend on the size of the fuel particles. The data (Table 3) confirms this.  
The coal combustion takes place in one stage, with the reaction mechanism \(n=0.5\). Table 3 presents the main results of the kinetic analysis.  
The results (Table 3) show that the most suitable model of the combustion stage of fuels is the 0.5-order reaction. The activation energy for coal was 60.3 kJ/mol; for biomass at the third stage, it was 24.6 kJ/mol; and for the fuel mixture at the third stage, it was 42.5 kJ/mol. It can be assumed that coal dominates the oxidation of semi-coke at the third stage.  
The activation energy of coal has a higher value compared to biomass. This indicates that the reactivity of coal during combustion is lower than that of biomass. The addition of biomass to coal affects the decrease in activation energy, which indicates an increase in reactivity during the combustion process compared to coal. V  
Figure 5: Linear fitting at pre-peak in release and combustion kinetics of volatile matters for coal, biomass, and mixtures.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fuel** & **Particle Size, \(\mu\)m** & **Temperature Range, \({}^{\circ}\)C** & **\(E\), kJ mol\({}^{-1}\)** & \(n\)** & \(R^{2}\) \\ \hline C-1 & 100–200 & 277–738 & 60.3 & 0.5 & 0.9984 \\ \hline \multirow{2}{*}{B-1} & \multirow{2}{*}{100–200} & 202–352 & 36.8 & 0.5 & 0.9930 \\ \cline{3-5}  & & 357–547 & 24.6 & 1.0 & 0.9906 \\ \hline \multirow{2}{*}{CB-1} & \multirow{2}{*}{100–200} & 217–342 & 50.7 & 0.5 & 0.9947 \\ \cline{3-5}  & & 347–592 & 42.5 & 0.5 & 0.9942 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Kinetic characteristics of combustion of individual fuels and their mixtures.  
obtained the same effect of reducing the activation energy value during the co-combustion of woody biomass and bituminous coal.
## 6 Discussion
### Personnel--Dispatchers (Operators)  
In any event in the transmission system, the dispatchers are responsible for immediately intervening. Dispatchers are engineers who work 24/7 in rolling shifts to keep the system balanced, and they will be called to face difficult system events or even a blackout. Their work is extremely crucial, and therefore, they should be healthy, qualified, and well trained. As mentioned before, the EUR_2006 blackout happened during the night shift due to a mistake by the dispatchers. The human mistake is something that should worry us.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Island** & **BS Unit** & **NBS Unit** & **Critical Loads to be Restored** & **Cranking Path (Bus No.)** \\ \hline \multirow{3}{*}{Island 1} & \multirow{3}{*}{G14} & \multirow{3}{*}{G15} & \multirow{3}{*}{42} & 66\(\rightarrow\)41\(\rightarrow\)42\(\rightarrow\)67 \\ \cline{3-5}  & & & & 42\(\rightarrow\)52\(\rightarrow\)68 \\ \cline{3-5}  & & & & 52\(\rightarrow\)49\(\rightarrow\)46\(\rightarrow\)38\(\rightarrow\)31\(\rightarrow\)62 \\ \cline{3-5}  & & & & 38\(\rightarrow\)33\(\rightarrow\)32\(\rightarrow\)63 \\ \hline \multirow{3}{*}{Island 2} & \multirow{3}{*}{G1} & \multirow{3}{*}{G2} & \multirow{3}{*}{3} & 53\(\rightarrow\)2\(\rightarrow\)3\(\rightarrow\)44\(\rightarrow\)5\(\rightarrow\)6\(\rightarrow\)54 \\ \cline{3-5}  & & & & 2\(\rightarrow\)1\(\rightarrow\)30\(\rightarrow\)9\(\rightarrow\)36\(\rightarrow\)64 \\ \cline{3-5}  & & & & 36\(\rightarrow\)37\(\rightarrow\)65 \\ \hline \multirow{3}{*}{Island 3} & \multirow{3}{*}{G9} & \multirow{3}{*}{G8} & \multirow{3}{*}{28} & 61\(\rightarrow\)29\(\rightarrow\)28\(\rightarrow\)26\(\rightarrow\)25\(\rightarrow\)60 \\ \cline{3-5}  & & & & 55\(\rightarrow\)10\(\rightarrow\)13\(\rightarrow\)14\(\rightarrow\)15\(\rightarrow\)16\(\rightarrow\)19\(\rightarrow\)56 \\ \cline{3-5}  & & & & 19\(\rightarrow\)20\(\rightarrow\)57 \\ \cline{3-5}  & & & & 16\(\rightarrow\)24\(\rightarrow\)23\(\rightarrow\)22\(\rightarrow\)58 \\ \cline{3-5}  & & & & 23\(\rightarrow\)59 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Cranking paths from BS unit to NBS unit for the IEEE-68 bus system.  
Dispatchers working in energy control centers are strained due to both the increased responsibility and pressure they experience during their work. Fatigue can affect an operator not taking the right decision (and often without being particularly stressed by the event) [91]. The worst nuclear accident in Europe in Chernobyl happened during the night shift. Over time, people working in rolling shifts for many years develop various diseases which in turn affect their performance and how well they manage situations [92; 93].  
For this reason, dispatchers should be physically capable of doing such work and their physical and mental health must be regularly checked. They should not work too many hours and should rest satisfactorily. Finally, they should not work for many years in the same job position, and they must be replaced by younger and well-trained employees.
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 3 Link Quality Based Routing Metrics for Tactical MANETs
### RSS Metric  
The advantage of using RSS metric is that it can be measured directly, and possibly averaged over time. It reflects relative link quality. Many commercial transceivers have in-build sensor specifically for continuous monitoring and reporting RSS values to upper protocol layers as RSS indicator (RSSI) reports. RSSI mapping can be obtained analytically or using empirical techniques. The analytical approach assumes a mathematical model to estimate the received power level for given channel conditions. Empirical calculation translates RSS into perceived distance. In tactical scenarios, hand-held and man-pack soldier equipment normally operates close to the ground, so ground influence cannot be neglected for more realistic range estimation. For instance, we can assume a 2-ray ground reflection model having a LOS and a multipath component. Since fast fading effects can be suppressed by physical layer techniques such as channel coding and frequency hopping spread spectrum (FHSS) [23], we assume that fast multipath fading will not cause loss of connectivity, and therefore, will not trigger topology changes.  
The receiver sensitivity corresponds to the minimum acceptable RSS because the actual transmission range of nodes is environment-specific and often unknown [24]. Specifically, the node \(i\) and \(j\) are connected if both of them experiences RSS above a certain threshold \(X_{th}\) (in dBm), and they are disconnected otherwise. The connectivity is then defined as a random variable:  
\[\mathbb{C}_{ij}=\left\{\begin{array}{l}1,\,min\text{RSS}=\frac{\mathbb{P}_{t} \,\,\mathbb{G}_{t}\,\,\mathbb{G}_{r}\,\,\mathbb{H}^{2}\,\,\mathbb{H}^{2}}{ \mathbb{R}^{4}\,\,\mathbb{L}}\geq X_{th}\,\,[\text{dB}m]\\ 0,\,\,\text{otherwise}\end{array}\right. \tag{2}\]  
where \(H\) is the transmitter and receiver antenna height, \(G\) is the antenna gain, \(L\) represents propagation and system losses, and \(R\) is the distance between nodes. However, RSS values are known to be sensitive to environmental factors such as noise and interference. Therefore, RSS alone is insufficient to be used as a metric for accurately characterizing quality of the wireless link [25]. It is well known that the link connectivity ultimately depends on SINR. The reason why to consider RSS in addition to SINR is that RSS is easier to measure, so it can be used for fast routing decisions in highly dynamic propagation environments with highly mobile nodes. Moreover, SINR is usually estimated over longer time periods where some packets could have been received correctly despite the low value of SINR [26].
# Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
## 4 Experimental Results and Discussion
### Experimental Results
#### 4.2.3 Case III (Severe Weather Day)  
Case III covers a day in which the rainfall starts at 2:00 and continues until the end of the day. TW-DNN shows the least MAPE among the models, as shown in Figure 12, and significant gaps between TW-DNN and others can be observed around 4:00 and 17:00. The phenomena are better explained in the speed heatmap.  
The real data show some speed slow-down for the periods of 02:00-05:00 and 15:00-19:00. TW-DNN predicts the congestion due to the rainfall, especially at dawn and rush hours. However, CNN-only, \(k\)-nn, and ARIMA cannot predict it because they do not consider weather factors at all.
## 4 Testbed Development  
This section presents the testbed developed to evaluate the proposed NTN architectures. Aiming to provide a clear understanding of the system, we firstly characterize the performance achieved by the selected mega-constellation connectivity. This will help to quantify and discuss the results obtained from this work. Then, we detail the technical implementation of the testbed, delving into hardware and software domains and offering a critical perspective on some technological limitations that must be considered during testing.  
Regarding mega-constellation connectivity, the current market is predominantly dominated by Starlink, as well as by OneWeb. These two initiatives are the only ones that offer commercial services. After reviewing the end-user service accessibility and subscription plans of both options, we opted for Starlink due to its end-user-centric business model, which facilitated equipment acquisition and deployment.  
The continuous evolution of the Starlink system (e.g., frequent launches of satellites or upgrades of user terminals) leads to a constantly changing performance. This performance is also influenced by external factors, such as satellite congestion at a given time, geographical location, or weather conditions [42]. Although Starlink's service performance has been widely studied in the literature, the interest in conducting our own service characterization lies in achieving a better comprehension of our specific deployment and under our particular conditions.
## 5 Discussion
The frequency \(f_{2}\) has roughly the same value for each of the three points. Overall, it can be stated that the best solution obtained by this approach is solution P\({}_{3}\). In terms of computational costs, the entire process based on GPR takes about 327 h. If the same optimization process were based on FEAs, it would take about 150 h. Therefore, using the surrogate model might seem inefficient; however, the following points should be considered. With a surrogate model, the optimization problem can be run multiple times, considering different populations or iterations, with each objective function call taking about 7 s. This flexibility is not possible with FEAs, as each FEA run takes about 147 s. Additionally, the surrogate model can be applied to other design problems using the same device. Specifically, the surrogate model is a field model that provides the temperature profile given the source. If a different target temperature or temperature profile is required, another optimization problem can be solved, and the same GPR model remains valid. In this case, only the optimization cost needs to be considered, which takes approximately 7 h to solve and obtain the optimal solutions. The deep learning approach is divided into two problems solved in cascade. The first problem is the optimal design problem, which returns the optimal parameters of coil 2, given a prescribed temperature profile. The trained DNN used for solving this problem shows some discrepancies between true and predicted values, as shown in Figure 8, probably because of the difficulty in identifying parameters of coil 2; the reason could be that the problem is less sensitive to those parameters. However, after solving the optimal design problem, the values that need to be included with the deep learning approach are the two frequencies \(f_{1 ad}\) and \(f_{2 ad}\), the latter being the output of the DNN, while \(f_{1 ad}\) was found after a manual search among DB2 solutions. These two optimal frequencies are close to the frequencies found by the multi-objective approach for points P\({}_{2}\) and P\({}_{3}\), which show a rather flat temperature profile. The second problem is the optimal control problem, which is solved using a DNN trained using database DB3, based on \(f_{1\text{od}}\) and \(f_{2\text{od}}\). The optimal control problem returns the optimal currents I\({}_{\text{loc}}\) and I\({}_{\text{2 oc}}\) of the two coils. The values so found (\(I_{1\text{oc}}\) = 255 A and \(I_{2\text{oc}}\) = 996 A) are close to those found by the multi-objective approach for point P\({}_{3}\) (\(I_{1}\) = 288 A and \(I_{2}\) = 1134 A). The relevant temperature profile is all inside the range of \(\pm\)5% of 1100 \({}^{\circ}\)C (target temperature). In terms of computational time, it is worth noting that the multi-objective approach and the deep learning approach take approximately the same time (327 vs. 330 h) to obtain the optimal solutions.
# Dynamic Responses of Asphalt Concrete Waterproofing Layer in Ballastless Track
## 5 Numerical Model
### Model Verification  
To verify the reliability of the model, the calculated results are compared with the field test data. A dynamic performance test system was set in the ACWL test section on the Zhengzhou-Xuzhou high-speed railway at 2016. The sensors to monitor the acceleration and dynamic displacement were embedded in the ACWL and the buried position was below the rail. The monitor sections were selected  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Track Components** & **Modulus (MPa)** & **Poisson Ratio** & **Density (kg/m\({}^{3}\))** \\ \hline Rail & 210000 & 0.3 & 7800 \\ Track slab & 36000 & 0.2 & 2500 \\ SCC & 32500 & 0.2 & 2500 \\ Base plate & 32500 & 0.2 & 2500 \\ ACWL & Table 3 (20 \({}^{\circ}\)C) & 0.3 & 2500 \\ Surface layer of roadbed & 200 & 0.3 & 2000 \\ Bottom layer of roadbed & 140 & 0.25 & 1800 \\ Subgrade & 110 & 0.25 & 1700 \\ Subsoil & 80 & 0.25 & 1600 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material parameters of the model.  
Figure 12: Finite element model of China Railway Track System (CRTS) III ballasteless track structure with ACWL (**a**) Overall view; (**b**) Side view.  
at the center line and the joint of the base plate, respectively. The measured data showed that the vertical displacement was about 0.01-0.5 mm, and the vertical acceleration is about 1.6-3.2 m/s\({}^{2}\)[27]. The related parameters in the model is adjusted according to the structural conditions of the test section on the Zhengzhou-Xuzhou high-speed railway. The dynamic responses of ACWL below the center line of the base plate are calculated by the model when the train speed is 350 km/h, as shown in Figure 13. It can be observed from Figure 13a,b that the peaks of acceleration and displacement are basically within the range of test data, which proves the reliability of the model. It is indicated that the dynamic loading effect of the two wheelsets under a single bogie is superimposed in the ACWL because the curves in Figure 13 have two peaks instead of four peaks.
# Modeling Wicking Fabric Inhibition Effect on Frost Heave
## 5 Results and Discussions
### Reliability of the Proposed Model
Compared to the NWF model, the WWF model showed a more significant decrease in the water content over time. This phenomenon occurs because the water in the WWF model is sucked out by the WF. A comparison of the measured frost heave strain during the frost heave test with the simulated results is shown in Figure 8. It appears that the simulation results agree well with the experiment results. It should be noted that the WWF model produced a significantly lower frost heave rate and ratio than the NWF model at the same freezing time. This is due to the corresponding decrease in the water content in the WWF model since the WF has significant drainage effects [10; 14]. In addition, from the comparison of the experimental data and simulation results, it is shown that the model proposed in this study accurately describes the inhibition effect of WF during freezing. Figure 9 shows the change in the distribution for the degree of saturation (\(S\)) within the WWF and NWF models with time in the freezing process. Here, \(d\) represents the distance from the bottom of the soil as the origin, with upwards as positive. The saturation of the WWF and NWF models gradually decreased as the temperature of the cold end decreased. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**WWF**} & \multicolumn{2}{c}{**NWF**} \\ \cline{2-5}  & **Simulated** & **Tested** & **Simulated** & **Tested** \\ \hline \(U_{0}\) (mm/h) & 1.724 & 1.882 & 1.587 & 1.562 \\ \(U_{h}\) (mm/h) & 0.157 & 0.156 & 0.276 & 0.295 \\ \(l_{0}\) (mm) & 172.4 & 188.2 & 158.7 & 156.2 \\ \(\zeta\) (\%) & 6.28 & 6.26 & 11.04 & 11.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Frost heave test and simulation results. Figure 8: Comparison between numerical and experimental results of frost heave strain for frost heave tests of Tomakomai soil. Compared to the NWF model, the saturation around the WF was significantly reduced in the WWF model. In addition, the comparison between the WWF and NWF models revealed that the saturation in the WWF model was significantly lower than the saturation in the NWF model. Based on the saturation changes observed in the WWF model and the NWF model during the freezing process, it was found that the WF can significantly reduce the saturation during the freezing process, thereby inhibiting the development of frost heave. Furthermore, the saturation on the upper side of the WF reduced more dramatically than on the underside, and [PERSON] et al. (2015) also came to a similar conclusion in their experiment. Figure 9 illustrates that the water absorption effect of the WF in Figure 7 and the frost heave inhibition effect of the WF in Figure 8 can be rationally explained based on the simulation results with the proposed model.