esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sloshing Simulation
### Pressure History of Sloshing Pressure  
To investigate the validity of the inhomogeneous multiphase flow model, the CFD analysis results were compared with the experimental results. The results were also compared for the conditions of the grid condition considered in the CFD model. The experimental results showed that the hydro pressure was generated at the lower part of the free surface at the 50% filling ratio, and the sloshing impact pressure was generated at the upper part. Each pressure history was examined in detail. When comparing the results of the hydro pressure history as shown in Figure 7, although some phase differences in pressure history are found due to experimental errors, the cycle of the sloshing pressure is well maintained to confirm that the proposed approach is appropriate for CFD modeling of the sloshing simulation. The phase difference generated at this time is thought to be from the error that occurs since the motion condition applied in the experiment does not exactly match the natural frequency of the model test calculated by Equation (10).  
And the sloshing impact pressure was observed on sensors located above the free surface at the 50% filling ratio as shown in Figure 8. Although uncertainty in the experiment resulted in some differences in C6 sensor positions, it was confirmed that the pressure history had similar behavior for certain impact pressures at C7 and C9. In particular, the  
Figure 7: Comparison of hydro pressure history under the free water surface between experiment and CFD analysis at C1 sensor (**a**) and C2 sensor (**b**).  
CFD models with a grid of 192 by120 were found to calculate better behavior of impact pressure than other models. High impact pressures are occurring repeatedly near the top corner of the model test. The results show that the proposed CFD model is appropriate for the sloshing pressure calculation by observing the impact pressure history of the C6, C7 and C9 sensors in Figure 8, which is thought to have the least experimental error. Since the analysis solution estimated the pressure peaks at steady state, the results did not completely match the experimental results at the reference points.  
Figure 8: Comparison of impact pressure history between experiment and CFD analysis: at C6 sensor (**a**); C7 sensor (**b**); C9 sensor (**c**).  
In this study, the velocity of induced maximum fluid impact loads calculated from the sloshing CFD analysis is calculated and transferred to the FSI (fluid structure interaction) analysis. Therefore, the CFD analysis determined when the maximum impact load occurred, and a pressure of approximately 9 kPa was calculated at the C7 sensor location as shown in Figure 9.
## 3 Results  
### Step 0: Identify Case Study and Analysis Accident Data  
#### 3.1.1 Case Study Description  
This step defines a hypothetical case of integrating UAS into an existing public safety system. The case study is based on a comprehensive literature review of existing public safety systems, UAS applications, surveillance, and situational awareness [27; 42; 43; 44; 45; 46; 47].  
Existing public safety systems consist of law enforcement, fire, rescue, and emergency medical services. They also include communication with external agencies that provide mission-relevant information.  
The primary purpose of this system is to monitor vulnerable areas within urban environments and patrol CCTV blind spots. The Operational Management Center (OMC) plays a central role in this system, as described in Figure 3. Within the OMC, the Operational Management System (OMS) monitors weather information and current situations in real time and handles communication with government agencies, including law enforcement agencies and fire departments. Based on the collected information, it issues mission directives to the Ground Control Station (GCS). The GCS, in turn, sets flight paths considering no-fly zones and undertakes control related to the UAVs. Following the GCS instructions, UAVs perform flights, collecting and transmitting information. In cases of crimes or specific situations where deterrence is necessary, warning broadcasts are conducted through speakers to address the issue.
## 4 Results and Analysis
### Quantification of Uncertainty in the DoD
The variation in the visibility of the change in elevation with the minLoD threshold in the DoD is shown in Figure 9. The original DoD with no elevation threshold is shown on the far left, and gradually the more conservative the DoD becomes, the larger the MinLoD becomes, as shown on the right. When the DEM is more uncertain, the minLoD threshold is higher, and more information is lost in the DoD for assessment of the significance during a change in elevation, and a more reliable DoD is obtained. Although UAV data may be obtained in mountainous regions and areas with dense vegetation, the DEM derived from dense point clouds has potential for uncertainty in the quantification process and for detecting change. The DoD with a minLoD threshold facilitates the detection of small changes in elevation which may be associated with errors and can reliably quantify topographic changes caused by subsidence. Figure 9: MinLoD threshold assessment of the significance for the variability in the DoD elevation: (**a**) minlod = 0.00 m; (**b**) minlod = 0.10 m; and (**c**) minlod = 0.20 m.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Instance**} & \multicolumn{3}{c}{**CII Heuristic (Phase 2)**} & \multicolumn{3}{c}{**CII Heuristic (Phase 3)**} & \multicolumn{3}{c}{**Other Heuristics**} \\  & \(V\) & **Opt\(\dagger\)** & \(C(BKS)\) & \(C(T)\) & \(Error_{CII}\) & **Time** & \(C(T)\) & \(Error_{CII}\) & **Time** & **RAM** & \# & \(C_{min}(T_{H})\) & \(Error_{T}\) & Time & Heuristic Id \\ \hline \hline plan397 & 7397 & yes & 23,260,728 & 27,709,175 & 19.1\% & 4.4 s & 25,075,678 & 7.8\% & 45.3 s & 1.1 MB & 11 & 23,605,219 & 1.5\% & 1.8 m & DPIO \\  & & & & & & & & & & & 23,389,341 & 0.6\% & 3.6 m & ESACO \\ \hline rfl1849 & 11,849 & yes & 923,288 & 1,103,854 & 19.6\% & 12.4 s & 994,606 & 7.7\% & 2.3 m & 1.4 MB & 11 & 933,093 & 1.1\% & 5.0 m & DPIO \\  & & & & & & & & & & & 930,338 & 0.8\% & 9.6 m & ESACO \\ \hline usa13509 & 13,509 & yes & 19,982,859 & 24,125,443 & 20.7\% & 16.2 s & 21,907,190 & 9.6\% & 2.8 m & 1.5 MB & 10 & 2217,458 & 10.2\% & 4.5 m & DPIO \\  & & & & & & & & & & & 20,195,089 & 1.1\% & 152.2 m & ESACO \\ \hline brdf14051 & 14,051 & yes & 469,385 & 552,658 & 17.7\% & 15.9 s & 506,668 & 7.9\% & 3.1 m & 1.5 MB & 11 & 474,788 & 1.1\% & 5.1 m & DPIO \\  & & & & & & & & & & & 474,087 & 1.0\% & 11.4 m & ESACO \\ \hline d15112 & 15,112 & yes & 1,573,084 & 1,847,377 & 17.4\% & 19.2 s & 1,705,664 & 8.4\% & 3.6 m & 1.6 MB & 11 & 1,588,563 & 1.0\% & 8.7 m & DPIO \\  & & & & & & & & & & 1,589,288 & 1.0\% & 12.9 m & ESACO \\ \hline d18512 & 18,512 & yes & 645,238 & 756,668 & 17.3\% & 28.1 s & 696,542 & 8.0\% & 5.8 m & 1.9 MB & 12 & 626,613 & 1.1\% & 8.3 m & DPIO \\  & & & & & & & & & & 63,154 & 1.2\% & 11.4 m & ESACO \\ \hline pla3810 & 33,810 & yes & 60,489,945 & 76,625,752 & 16.0\% & 1.6 m & 69,626,380 & 5.4\% & 25.7 m & 2.9 MB & 17 & 67,185,647 & 1.7\% & 21.0 m & DPIO \\ \hline pla8500 & 85,900 & yes & 142,382,641 & 167,355,049 & 17.5\% & 10.5 m & 149,546,776 & 5.0\% & 4.1 h & 6.5 MB & 27 & 144,334,707 & 1.4\% & 1.4 h & DPIO \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for **Art TSP** benchmarks.
## 2 AC Power Router and Its Network System  
An AC power routing network system can be established by connecting AC power routers and information terminals. Figure 2(a) shows a setup of an AC power router and an information terminal, and Figure 2(b) shows a photograph of the prototype AC power router and the information terminal [6,10]. Each component includes a power line communication (PLC) modem and microprocessor CPU with Linux OS. By using the PLC modem, these components communicate with each other by a connected common PL. The function of the power router is to facilitate the exchange of PLs, _i.e._, circuit switching. The information terminal plays the role of the controller for power routers, and communicates with power routers to share information related to power capacity, the quality of each source and the demands from loads. The ratings of the prototype power router are set at 100-200 \(\mathrm{V_{rms}}\), a continuous power operation of 1000 W and a maximum power of 1700 W. The power router includes circuit exchange switches. The switching devices are Si MOSFETs 2 SK3935 (450 V, 17 A). The prototype has four input ports and eight output ports. In this study, we use two input ports and two output ports during the experimental verification. The CPU in the power router must apply a rule to decide appropriate combinations of sources and loads according to the demand and quality of energy. There are several propositions for rule generation methods and routing protocols for achieving an energy on demand system with appropriate source-and-load matching [16,17]. They are the collaborative research results from the same project. These methods are suitable for the AC power routing system. Their optimization is not directly the aim of this study.  
Figure 1 shows a schematic diagram of the connection setup of the AC power routing network system with the power router. The AC power router exchanges the lines to conn  
Figure 2: Schematic diagram of the connection setup and a photograph of an AC power routing system: (**a**) setup of AC power router and information terminal; and (**b**) a photograph of prototype AC power router and information terminal.  
loads appropriately according to the gathered demand and supply information. The power distribution system is expected to have a mesh network structure when power routers are applied. Thus, when several routes are shut down, it is possible to deliver power by finding an alternate path in the network. In addition, it is also possible to prevent the mixing of powers from different sources by implementing a time-dependent exchange. Therefore, the source derivation of each electric power stream must be guaranteed. In general, a network structure reduces to two connections, _i.e._, a parallel connection and a series connection. Figure 3(a) and 3(b) shows examples of the parallel and series connections, respectively. In this study, in order to confirm the feasibility of the prospective power routers in a local network system, we verify the circuit switching operation by power routers depending on whether two power routers are connected in parallel or in series.
# A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
## 4 Experimental Results
### Driver Drowsiness Detection Results  
Considering the results, the CNN based on ResNet50V2 was the most optimal in this research; therefore, it is appropriate to perform driver drowsiness detection. In Figure 14, nine consecutive scene analyses are shown, being a sequence of the process with drowsiness. In the examples it is observed that the driver starts in a normal state (Figure 14a), then goes into a state of wakefulness (Figure 14b), closing his eyes with a time of approximately 230 ms (Figure 14c). Then, the driver goes to the drowsy state, which is longer than 300 ms, activating the visual alarm, as can be observed (Figure 14d-f). This is followed by a normal blink (Figure 14g-i). Specifically, in Figure 14h, it can be seen that the system detects the closed eyes with the Drowsy class, but the time is approximately 130 ms, which does not detect it as drowsiness.  
Figure 13: Visualized with Grad-CAM. (**a**) ROI extraction in 5 scenes. (**b**) Grad-CAM InceptionV3. (**c**) Grad-CAM VGG16. (**d**) Grad-CAM ResNet50V2.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{3}{c}{**Results in Training**} & \multicolumn{2}{c}{**Results in Test**} \\ \cline{2-5}  & **Training Time** & **File Size (KB)** & **Total Params** & **Response Time** \\ \hline InceptionV3 & 6.2 min \(\pm\) 3 s & 182,072 & 29,997,786 & 137.8 ms \\ VGG16 & 6.1 min \(\pm\) 12 s & 111,603 & 19,325,690 & 71.3 ms \\ ResNet50V2 & 6.2 min \(\pm\) 5 s & 476,612 & 56,335,802 & 106.5 ms \\ \hline \hline \end{tabular}
\end{table}
Table 5: CNN processing results.
# Modernising Receiver Operating Characteristic (ROC) Curves +
## 4 Results and Discussions
### Assessing Relative Performance of Devices  
[PERSON] [12] proposed using the intercept, \(c\), of each of his linearised ROC curves (Section 3.4.1 and Figure 2) as a better measure of device performance than the traditional _AUC_ (Appendix A.1). A comparison between Linacre \(c\) intercepts and _AUC_ for the five device types studied here for pregnancy testing is shown in Figure 7a (Pearson correlation coefficient \(R=0.73\)).  
In this work, we additionally first assessed the correlation between the [PERSON] device abilities \(\theta_{TPR}\) and \(\theta_{FPR}\) vs. _AUC_ (Section 3.4.2). Figure 7b,c shows that those correlation coefficients (0.95 and 0.92) are comparable with each other and that both are better than [PERSON]'s plots (Figure 7a). The degree of correlation between \(\theta_{TPR}\) and \(\theta_{FPR}\) is found to be 0.94 (as expected, the average of the correlations shown in Figure 7b,c) Finally, the new \(z_{ROC}\) plot intercepts C (Figure 7d) and correlates slightly better (0.77) than [PERSON]'s (Figure 7a) but less well than the other alternatives based on the Rasch parameters, \(\theta_{TPR}\) and \(\theta_{FPR}\). This poorer performance of the linearisation plots reflects limitations in extending linear  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**M**} & \multicolumn{2}{c}{**C**} \\
**Device Type** & **Experiment** & **Theory** & **Experiment** & **Theory** \\ \hline Device 1 & \(-0.50\) & \(-0.57\) & \(-2.61\) & \(-2.78\) \\ Device 2 & \(-0.50\) & \(-0.57\) & \(-3.02\) & \(-3.34\) \\ Device 3 & \(-0.50\) & \(-0.57\) & \(-2.58\) & \(-2.77\) \\ Device 4 & \(-0.50\) & \(-0.55\) & \(-1.79\) & \(-2.50\) \\ Device 5 & \(-0.50\) & \(-0.56\) & \(-1.84\) & \(-1.96\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental values of slope \(M\) and intercept \(C\) for linearised ROC curves in Figure 6.  
Figure 6: \(z_{ROC}\) curves for the five devices used for pregnancy testing (**a**) experiment (uncertainty coverage factors, \(k=2\)) and (**b**) theory. (The dotted lines indicate linear regressions to each curve, as expressed by the equations \(y\) versus \(x\)).  
regression over the full concentration span to match traditional ROC practice (Section 4.3), rather than only up to \(SL\), in accordance with the theory (Appendix A.3).  
In summary, the new linearised ROC curves adopting the Rasch model are better correlated with _AUC_ than the Linacre [12] approach of using the intercepts, but both linearisation methods (with the results shown in Figure 7a,d, respectively) perform less well than our new Rasch ability method (results shown in Figure 7b,c). This new method--evaluating device ability in terms of \(\theta_{TPR}\) and \(\theta_{FPR}\) from separate Rasch regressions for each axis of ROC curves--performs significantly better and is therefore recommended to replace traditional methods of evaluating device ability.
# A Channel Allocation Mechanism for Cellular Networks
## 5 Simulation Analyses
### Simulation Case Design and Performance Metrics
#### 5.1.2 Case 2: Traffic Congestion  
This study designed a traffic accident to simulate the MS communication behavior and handover events in the situation of traffic congestion. This study observed the number of channels used in a cellular system during traffic congestion occurrence and ease. The setting of road environments is the same as Case 1. In Case 2, the parameters of road information and communication behaviors were assumed as these parameters in Case 1. However, the traffic flow was assumed as 5000 vehicles per hour in Case 2 for the estimation of traffic congestion. The desired speed of each vehicle was assumed between 85 and 120 km/h when the status of traffic condition is free flow. The total simulation time is 1.5 h. This study simulated a traffic accident that impacts two lanes on the road, and the mileage is between 5.05 and 5.35 km from simulation time from the 15 th min to the 45 th min. When vehicles go through this road segment, the average speed of car is dropped to 4-6 km/h. Until simulation time to the 45 th min, the traffic accident is removed and the traffic conditions returns to a free flow.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 5 Results and Discussion
### Phase I: Maze Exploration
#### 5.1.3 Estimated Energy Cost  
_5.2. Phase II: Signal Routing and Victim Extraction_  
To study the impact of changing the number of agents on the final solution in Dijkstra-based Phase II (the \"rescue\" part in SAR), the three performance metrics variables were generated--the total cost, the number of hops and the number of steps, in all cases as function of the number of agents. These metrics are shown in Figure 19. Here, number of steps implies the number of iterations of the algorithm execution to find a solution, while number of hops refers the number of logical hops in the relay network formed.  
Figure 18: Performance of iAA algorithm as a function of variable maze size and maze complexity (cf. Table 3), with a constant search group of 100 agents. The plot in (**a**) shows the (weakly supra-linear) growth in complexity while (**b**) compares performance on the basis of the number of steps.  
For the steps count, one can intuitively assume that a small number of agents requires more steps to achieve the task, which slowly decreases as the number of agents increases and converges to a certain value. The reason is that fewer agents will need more time to form the shortest path, as the probability of it having more voids and going through the proposed search with the stopping algorithm is higher. The number of agents does not strongly affect the time taken to reach the target, as it depends on what path agents will follow. For the total cost, it decreases slightly when increasing the number of agents. By observing the simulation examples figure, one can deduce that the path looks almost the same; however, for the larger agents' population, the dependence is more smooth. Furthermore, the total cost when penetrable walls are considered is much less than for impenetrable walls. The number of steps is also lower for penetrable walls, but not by the same factor as the cost difference.  
We have analyzed the performance for two different maze types, M1 and M2. As we have seen earlier in the paper, both size (area) and complexity of the maze play role in the performance (for Phase I). We can say, based on Figure 19, that both of these parameters are relevant in the performance of Dijkstra-based reverse routing in Phase II, as well. we can further say that the dependence is not one of simple linearity or even smoothness, as evidenced by less monotonous behavior of the Number of Steps and Number of Hops variables for maze M3 (it being more complex) than for the maze M1.
## 3 Energy-Efficient Dynamic Routing Algorithm
### Fuel Consumption Model  
The vehicle's instantaneous fuel consumption is calculated using the VT-CPFM model. The VT-CPFM is a two-regime model and models the fuel consumption as a second-order polynomial function of the vehicle's power. The second-order model prevents the occurrence of the bang-bang control when applying the model. Furthermore, a higher-order model cannot be calibrated using publicly available data. Consequently, a second-order model achieves a good trade-off between model accuracy and applicability. In this study, only VT-CPFM-1 is utilized given that VT-CPFM-2 requires engine gear data for model calibration and implementation, which is usually not available. Hence, The VT-CPFM refers to the VT-CPFM-1 model. The VT-CPFM model utilizes the vehicle's instantaneous power as an input variable. The model parameters can be easily calibrated using publicly available fuel economy data (e.g., Environmental Protection Agency [EPA]-published city and highway gas mileage). Thus, the calibration of the model's parameters does not require gathering any vehicle-specific field data. The VT-CPFM model equation is:  
\[F(t)=\begin{cases}a_{0}+a_{1}\cdot P(t)+a_{2}\cdot P(t)^{2},&\text{if }P(t)\geq 0\\ a_{0},&\text{otherwise}\end{cases} \tag{1}\]  
where \(F(t)\) is the fuel consumption of a vehicle at time \(t\), \(a_{0}\), \(a_{1}\), \(a_{2}\) are the model parameters, \(P(t)\) is the power at time \(t\). The vehicle's power is calculated using the following equation:  
\[P(t)=\left(m\cdot a(t)+m\cdot g\cdot\frac{C_{r}}{1000}(\ c_{1}\cdot v(t)+c_{2} )+0.5\cdot\rho_{air}\cdot A_{f}\cdot C_{d}\cdot v(t)^{2}+m\cdot g\cdot\theta \right)\cdot v(t) \tag{2}\]  
where \(v(t)\) is the speed of the vehicle, \(a(t)\) is the acceleration of the vehicle, \(m\) is the mass of the vehicle, \(g\) is the gravitational acceleration, \(\theta\) is the grade of the road segment, \(C_{r}\), \(c_{1}\), and \(c_{2}\) are the rolling resistance parameters (depend on road surface type, road condition, and tire type), \(\rho_{air}\) is the air density, \(A_{f}\) is the frontal area of the vehicle, and \(C_{d}\) is the aerodynamic drag coefficient of the vehicle.
# Using the \(\beta\)/\(\alpha\) Ratio to Enhance Odor-Induced EEG Emotion Recognition
## 3 Results
### Subjective Evaluation
#### 3.2.2 Valence Dimension  
In the valence dimension (Figure 2B), participants who inhaled SEO or BEO demonstrated higher average valence scores (5.9 \(\pm\) 1.45 and 6.22 \(\pm\) 2.11, respectively). Similar to the arousal dimension, there is considerable variation among participants; however, only nine individuals (six females) categorized SEO in the low valence zone (<5), and ten participants (three females) categorized BEO in the same low valence zone (<5). Additionally, a paired \(t\)-test was conducted on the valence ratings following the inhalation of sandalwood essential oil (SEO) and bergamot essential oil (BEO) across all participants (\(\alpha\) = 0.05). The results showed no significant difference between the two conditions (\(p\) = 0.3924; see Supplementary Table S1 for detailed rating results). Therefore, based on these subjective assessments, we conclude that inhaling either SEO or BEO elicits similar valence emotions. The distribution of SEO ratings was relatively narrow for both genders, with females reporting a slightly lower mean (5.64 \(\pm\) 1.45) compared to males (6.23 \(\pm\) 1.41). Conversely, BEO ratings by females were more narrowly distributed with a higher mean (6.64 \(\pm\) 1.77), while males exhibited a broader distribution with a lower mean (5.68 \(\pm\) 2.42).  
In conclusion, both essential oils lead to similar emotional experiences in the valence dimension, with no significant difference, so the valence dimension will not be further analyzed in this study.