esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Emission Characteristics
#### 3.3.4 Smoke Emissions  
Figure 10 shows the smoke emissions of all test fuels according to different engine loads. It can be clearly seen that smoke emissions of all fuels shows an obvious increasing trend with the engine load increasing from 30 to 90 Nm. At 60 Nm, the smoke emission of D100, D95E5, D90E10, and D85E15 is increased by 34.55%, 4.17%, 2.33%, and 0.00% compared with that at 30 Nm, respectively. At 90 Nm, the smoke emission of D100, D95E5, D90E10, and D85E15 is increased by 150.91%, 125.00%, 104.65%, and 148.00% compared with that at 30 Nm, respectively. The higher the load, the more smoke emission is mainly because the increase of load leads to more fuel being injected into the combustion chamber, reducing the air-fuel ratio and increasing the fuel rich areas [33; 34]. On the other hand,  
Figure 8: HC emissions for all test fuels according to various engine loads.  
Figure 9: NOx emissions for all test fuels according to various engine loads.  
with the addition of ethanol to diesel fuel, the smoke emission of all blended fuels shows a gradually decreasing trend compared with that of diesel fuel. At 30 Nm, the smoke emission of D95E5, D90E10, and D85E15 is decreased by 12.73%, 21.82%, and 54.55% compared with that of D100, respectively. At 60 Nm, the smoke emission of D95E5, D90E10, and D85E15 is decreased by 32.43%, 40.54%, and 66.22% compared with that of D100, respectively. At 90 Nm, the smoke emission of D95E5, D90E10, and D85E15 is decreased by 21.74%, 36.23%, and 55.07% compared with that of D100, respectively. This can be attributed to the fact that ethanol is a highly oxygenated fuel, which improves the problem of local hypoxia and promotes the complete combustion. In addition, the evaporation latent heat effect of ethanol and low cetane number will increase the ignition delay, which can provide enough time for fuel and air to form a uniform mixture [34]. Moreover, what is exciting is that the addition of bioethanol can not only reduce smoke emissions from the CRDI diesel engine, but also reduce NOx (see Table 4). In general, the NO and smoke emitted by diesel engines have a contradictory relationship (often called as trade-off relationship), that is, when the fuel is fully combusted, the higher temperature in the combustion chamber results in more NOx generation, and the carbon particles are also fully combusted and oxidized, resulting in less smoke generation. However, in this study, bioethanol not only has a high oxygen content to promote the full combustion of fuel, but also has a high latent heat to curb the rise of temperature in the combustion chamber.
# Optimization of Glass Edge Sealing Process Using Microwaves for Fabrication of Vacuum Glazing
## 2 Experimental Setup
### Variable Setting  
The process and response variables were set by conducting basic experiments on the glass edge sealing using a microwave heating chamber. Among the various process variables used for glass edge sealing, the heating rate for the glass and the holding time for pressing the glasses together were set as the process variables. If the heating rate is too high, the glass could be damaged because of thermal shock [11; 12; 13], and if the heating rate is too low, the fabrication process could take too long. The sealing temperature was set approximately equal to the transition temperature of the glass. The holding time was set as the duration after the maximum temperature was reached, if this is too short the glass could break.  
For glass edge sealing using microwaves, the thermal energy must be concentrated at the edges of the glass and a constant pressure must be maintained. The pressure was kept constant along all the edges owing to the design characteristics of the mechanical pressing device. Thus, in this research, three factors in three levels were identified in the basic experiment. The heating rate was set in the range of 6-8 \({}^{\circ}\)C/min, the sealing temperature was set in the range of 560-580 \({}^{\circ}\)C, and the holding time was set in the range of 30-50 min. Based on the response variable, it was determined in the basic experiment that the maximum step height corresponding to incomplete sealing was 0.93 mm. Therefore, considering the errors in the basic experiments, a nominal-is-best experiment was performed with a target step height of 0.98 mm in this study. Table 1 lists the conditions for the process and response variables.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Factors**} & \multicolumn{3}{c}{**Conditions**} \\ \hline \multirow{3}{*}{Process variables} & Heating rate (\({}^{\circ}\)C/min) & 6 & 7 & 8 \\  & Sealing temperature & & & \\  & (\({}^{\circ}\)C/min) & 560 & 570 & 580 \\ \cline{1-1}  & Holding time (min) & 30 & 40 & 50 \\ \cline{1-1} Response variable & Step height (mm) & & 0.98 & \\ \hline \end{tabular}
\end{table}
Table 1: Conditions for the process and response variables.  
Figure 6: Liquid penetrant examination of the cross-sections of sealed edges. (**a**) Cross-section of unsealed edge; and (**b**) cross-section of sealed edge.
# Ergonomic Sports Mouthguards: A Narrative Literature Review and Future Perspectives
## 2 Narrative Literature Review Search Strategy
### Characteristics and Mechanisms of Protection  
The mechanism of protection provided by these devices in reducing orofacial traumas involves absorbing and dissipating the energy from a received impact, thereby preventing it from directly affecting the oral structures. They function somewhat like a cushion, acting as shock absorbers and reducing the severity of trauma to the surrounding structures [8; 20; 26]. However, for these devices to effectively fulfill their purpose, they rely on various factors including material type, design, correct manufacturing process, appropriate thickness, and sufficient retention [8; 22; 28].  
Ensuring adequate retention of a mouthguard is a crucial step in determining its effectiveness. This is because the device should remain securely in position during sports activities to ensure it effectively fulfills its function in the event of an impact. Moreover, this characteristic can boost athletes' willingness to use the protective device, as it provides comfort without compromising their performance [29].  
Another important parameter to consider in the characteristics of a mouthguard is its thickness. It should be sufficient to absorb and dissipate impact energy, providing adequate protection without compromising the athlete's breathing, performance, and comfort. Neglecting these factors can lead to non-compliance with wearing the device [22; 28]. While a thicker material tends to absorb more energy [9; 28] it will also be more difficult to be used. The mouthguard's thickness needs to fall within the range of 3 to 4 mm to ensure proper impact absorption and comfort [14; 22].  
In addition to the aforementioned points, a constant analysis of occlusion on the mouthguard's surface is of utmost importance. Using mouthguards with inadequate occlusal adjustments can lead to mandibular fractures upon significant impact or even the development of temporomandibular joint arthritis due to prolonged use. Additionally, wear on occlusal surfaces can occur during use, making periodic checks by a dentist necessary for occlusal adjustments [30].  
Several materials have been used for intraoral sports mouthguards, such as latex rubber, vinyl resins, acrylic resins, and acrylic resins combined with silicone. The optimum material for sports mouthguards should exhibit satisfactory mechanical properties with intermediate hardness and adequate energy absorption capacity, derived from good elasticity and compressive behavior. They should also be biocompatible and comfortable, to allow proper breathing and speech [31]. In this context, ethylene vinyl acetate (EVA) has been considered the most suitable material for mouthguard fabrication due to its satisfactory mechanical properties and ease of manipulation [14; 31].
# Examining the Amount of Particulate Matter (PM) Emissions in Urban Areas
## 3 Measurement Results
### Measuring Points at Primary Schools  
For primary schools, we expect an increased intensity of vehicle traffic in the morning hours, which is caused by the transportation of pupils to schools. We set the duration of the survey from 7:00 a.m. to 10:00 a.m. During our measurement, we also monitored the number of vehicle stops near the schools.  
Figure 6 shows similar trends to the other measurements. Particulate matter emissions concentration increases directly corresponding with the number of vehicles that pass the measuring point. When the monitoring vehicle stopped at schools, only 16% of the total number of 335 vehicles that passed stopped. This means that the air pollution in this section is not caused by the transportation of children to schools; instead, air pollution is attributed to general traffic activity. When monitoring the air quality, we found that the air quality in the monitored section reached a good level despite the high number of vehicles in the morning rush hour.  
Table 5 shows a worsening of the hourly air quality in the vicinity of elementary schools. However, this pollution is high enough to represent deteriorated air quality. Based on our results, we assumed that the air quality deterioration was because of the increased passage of vehicles through the measuring point.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Interval** & **PM\({}_{2.5}\) [ug/m\({}^{3}\)]** & **PM\({}_{10}\) [ug/m\({}^
## 7 Overview of Algorithms for Energy Optimization UAV-Based Cellular Networks
### Swarm Intelligence-Based Algorithms
However, the path selection made by the ants coming from behind, depending on the amount of pheromone, shortens the total time to trip for food [135]. These algorithms employ a single agent that traces a trajectory while moving through the search space to determine the global optimal solution. In the process, a better solution is accepted while a solution that is not so good may be accepted with a specific probability. Hence, in the following paragraphs, we briefly discuss two common trajectory-based methods: simulated annealing and variable neighborhood search algorithm. * Simulated Annealing Algorithm (SA): SA is a meta-heuristic algorithm developed by Kirkpatrick et al. in 1983 to solve optimization problems. The SA method is based on the analogy between the annealing process in physical systems that minimizes the energy state of the solids and the solution process in combinatorial optimization problems [136]. The SA algorithm starts with an initial solution and a relatively high-temperature value to avoid being trapped by the local minimum. At each iteration, the algorithm produces the next solution within the local neighborhood and the temperature decreases according to specific rules. A new solution that represents the energy level of the system and improves the objective function is always accepted. On the other hand, a workaround proposal that allows for an increase in the temperature of the system or allows a certain degree of divergence/deterioration from the objective function in the system is also accepted. The algorithm is conducted with a new solution if the new solution is accepted and with an existing solution if the new solution is rejected. These processes continue until the termination criteria (number of iterations, the smallest temperature value, etc.) are met. * Variable Neighborhood Search Algorithm (VNS): The VNS meta-heuristic was developed by Pierre Hansen and Nenad Mladenovic in 1997 [137]. The VNS method, which has been continuously developed since its inception and has applications in numerous fields, is a single solution-based, static/dynamic objective function, based on various neighborhood structures (meta-heuristics other than VNS use a single neighborhood structure). Based on the systematic modification of neighborhood structures used in the search, VNS is a simple and effective meta-heuristic aimed at solving combinatorial optimization problems. Since the local minimum in any neighborhood may not be valid for other neighborhoods, the use of the multiple neighborhood structures is advantageous because it enables the best solutions in different regions of the search space to be obtained. In addition, these neighborhood structures are systematically changed during the search process. Thus, by providing diversification in the search space, the disadvantage of being stuck in the local optimum can be overcome. VNS offers significant advantages over other algorithms due to its simple structure, integration with different solution techniques and it requires few parameters.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 3 Trust Establishment and Trust-Based Monitor Selection
### Trust Establishment and Link Duration Estimation
#### 3.1.1 Inter-UAV Trust Establishment  
When focusing on inter-UAV trust, we generally distinguish between two metrics: direct trust and indirect trust. Direct trust can be defined as the local knowledge-based evaluation of the direct interactions among UAVs, whereas indirect trust is the evaluation of the direct interactions between two UAVs based on the opinions of other UAVs about the honesty of the two participant UAVs (also referred to as recommendation-based evaluation). Since direct trust is more relevant than indirect trust when the number of interactions (\(\#int\)) increases, our UAV-to-UAV trust levels are adapted using the following factor: \(\frac{1}{\#int+1}\); this way, if we have more interactions, we assign a higher weight to direct trust than to indirect trust, and vice versa. In addition, UAVs may also rely on Ground Stations (GSs) if they are available in their communication range. In this case, the opinion of the GS is directly considered instead of the UAV local evaluation. In FANETs, GSs are generally represented by the (Air Traffic Controllers). Equation (2) shows how the inter-UAV trust is adjusted.  
\[Trust(i,j)=\begin{cases}\left[\left(1-\frac{1}{\#int+1}\right)\cdot DT(i,j) \right]+\left[\frac{1}{\#int+1}\cdot IT(i,j)\right]&if\ No\_GS\\ GSE(GS,j)&Else\end{cases} \tag{2}\]  
\(DT(i,j)\) and \(IT(i,j)\) are the direct and indirect trust evaluation, respectively, calculated by a UAV \(i\) concerning another UAV \(j\). The computation details of \(DT(i,j)\) and \(IT(i,j)\) are provided in the following sections.
## 1 Introduction
[PERSON] and [PERSON] [20] introduced a theoretical ANN model to predict the shear strength of fiber reinforced polymer (FRP) reinforced concrete members. The developed comparisons between the developed ANN model and experimental data indicated that the ANN model resulted in better accuracy than other existing design equations. [PERSON] et al. [21] presented an ANN model to predict the strength of FRP confined concrete. Extensive test data were considered to define the variables of the proposed equations. The proposed ANN model was adapted for the FRP confined concrete design, and guaranteed improved accuracy. [PERSON] et al. [22] used an ANN model to predict the punching shear strength of slab-column connections based on 244 test data. Two simplified punching shear equations were developed in the study. [PERSON] and [PERSON] [23] predicted the seismic damage state of reinforced concrete buildings based on ANN investigation using multilayer feedforward perceptron networks. The ANN model can be used to reliably approach the seismic damage state of buildings. [PERSON] et al. [24] proposed an autoencoder-based framework that can support deep ANN. To verify the accuracy and efficiency of the proposed framework, experimental studies on steel frame structures were conducted. [PERSON] et al. [25] introduced an ANN to estimate the structural performance of asphalt pavements. Several significant input parameters were considered in the analysis. The authors trained three different ANNs to analyze datasets. [PERSON] and [PERSON] [26] provided an ANN model with the objective of predicting the consumption of natural gas during asphalt production. [PERSON] et al. [27] used linear regression and artificial neural network modeling to predict engine performance. It was reported that the use of ANN was more accurate than the use of linear regression modeling. In addition to the various studies mentioned above, various ANN models have been proposed to predict the tensile and compressive strength of concrete [28; 29; 30; 31; 32]. The proposed models considered various input data and showed good precision and accuracy compared with the experimental results. Therefore, the applicability of ANN models can be verified based on diverse studies. Many previous studies on resilient materials have been conducted based on experiments. However, to the best of our knowledge, the behavior of resilient material using ANN modeling has not been studied. In this paper, a prediction of the relation between the deflection response and the dynamic stiffness of the resilient materials was investigated using ANN models. [PERSON] et al. [10] proposed an empirical equation to predict the responses between long-term deflection and dynamic stiffness. The proposed equation can predict the trend of the responses. However, since only four variables were considered in the equation, there was a limit to the accuracy. Therefore, in this paper, mathematical approaches based on the universal approximation theorem were conducted to predict the dynamic stiffness of resilient materials. Three different ANN models with seven variables were proposed, and the results were compared.
## 4 Simulation Analysis  
In order to verify the effectiveness of the dynamic balance correction method in this paper, the rotor dynamic balance correction simulations are carried out at different speeds. The simulation parameters are shown in Table 1. The initial unbalance parameters of the rotor system are as follows. The unbalanced mass \(m_{e}\) is 100 g, the distance to the centroid \(l_{e}\) is 30 mm, the phase is 30 degrees and the eccentricity \(e\) is 20 mm.  
The method presented in this paper only requires trial weight and counterweight calculation to be performed at a constant speed. After applying the dynamic balance correction method to the rotor, the vibration displacement in the rigid rotor region will be reduced. Therefore, in the simulation analysis, the dynamic balance effects at two speeds of 1800 r/min and 4200 r/min are presented to verify the correctness of the counterweight calculation.  
Figure 8 shows the rotor displacement of the \(x\) direction at 1800 r/min. Figure 9 shows the rotor displacement of the \(x\) direction at 4200 r/min.  
As can be seen from Figures 8 and 9, due to the existence of unbalanced mass, centrifugal force is generated when the rotor rotates, causing vibration displacement at the same frequency as the rotational speed. The vibration displacement is sinusoidal. Dynamic balance correction is applied at 0.5 s; the rotor vibration displacement decreases rapidly and the decrease ratio reaches more than 90%.  
In order to fit the actual situation more accurately, the finite stud holes on the counterweight plate are simulated. Suppose that there are sixteen stud holes on the counterweight plate and the angle between the two adjacent stud holes is 22.5 degrees. The simulated counterweight mass is taken as an integer in grams.  
In order to better observe the correction effect of the two dynamic balances, the axis track before the dynamic balance correction and the axis track after the two dynamic balance corrections are placed on a graph for comparison, as shown in Figure 10.  
It can be seen from the figure that the vibration displacement of the rotor decreases after a dynamic balance correction. But due to the limitations of the counterweight plate and the counterweight mass, the accuracy requirements cannot be met. After two dynamic balance corrections, the vibration displacement of the rotor is significantly reduced.  
Figure 8: Rotor displacement at 1800r/min.  
Figure 9: Rotor displacement at 4200r/min.
## 3 Methodology
### Pedestrian Dangerous Crossing Intention Prediction Module  
#### 3.4.1 Feature-Fusion LSTM  
As shown in Figure 7a, a naive way to combine the \(\mathbf{Pose}_{t}^{i}\), \(\mathbf{Inter}_{t}^{i}\), \(\mathbf{Observe}_{t}^{i}\), and \(\mathbf{Velocity}_{t}^{i}\) features is to concatenate them in the feature level and feed them to the corresponding LSTM unit as the network's input data. However, the dimension of the \(\mathbf{Inter}_{t}^{i}\), \(\mathbf{Observe}_{t}^{i}\), and \(\mathbf{Velocity}_{t}^{i}\) features are very low intrinsically, while the \(\mathbf{Pose}_{t}^{i}\) features often have relatively higher dimensions. As a result of this inconsistency, concatenating these two types of features in the input stage of the network would degrade the overall performance. In [45], different body parts were fed into the Part-Aware LSTM separately and then assembled inside the LSTM unit. Inspired by this work, we propose to fuse the (\(\mathbf{Inter}_{t}^{i}\), \(\mathbf{Observe}_{t}^{i}\), \(\mathbf{Velocity}_{t}^{i}\)) and (\(\mathbf{Pose}_{t}^{i}\)) features inside the feature-fusion LSTM unit, rather than simply concatenating them at the input level, as shown in Figure 7b.  
Since the dimensionalities of the \(\textbf{Inter}_{t}^{i}\), \(\textbf{Observe}_{t}^{i}\), and \(\textbf{Velocity}_{t}^{i}\) features were low, they could be spliced in the network input stage to obtain the \(\textbf{(Inter}_{t}^{i}\), \(\textbf{Observe}_{t}^{i}\), \(\textbf{Velocity}_{t}^{i})\) vectors, which were combined with the six vectors \(\textbf{F}_{p}^{1}\), \(\textbf{F}_{p}^{2}\), \(\textbf{F}_{p}^{3}\), \(\textbf{F}_{p}^{4}\), \(\textbf{F}_{p}^{5}\) and \(\textbf{F}_{p}^{6}\) of \(\textbf{Pose}_{t}^{i}\), respectively, by Equations (2), (3), (5), (6), and (4) to obtain the corresponding input gate values \((i_{t},i_{t}^{1},\ldots,i_{t}^{6})\), forget gate values \((f_{t},f_{t}^{1},\ldots,f_{t}^{6})\), output gates \((o_{t},o_{t}^{1},\ldots,o_{t}^{6})\), cell state values \((c_{t},c_{t}^{1},\ldots,c_{t}^{6})\), and cell state update values \((c_{t}^{\prime})\).  
Then, the pedestrian crossing intention hidden state \(H_{t}\) was obtained:  
\[H_{t}= \tan\text{h}(c_{t}^{\prime\prime})\odot o_{t}^{\prime\prime} \tag{23}\] \[c_{t}^{\prime\prime}= c_{t}+c_{t}^{1}+c_{t}^{2}+c_{t}^{3}\] (24) \[+c_{t}^{4}+c_{t}^{5}+c_{t}^{6}\]  
\[o_{t}^{\prime\prime}=\sigma\left(\begin{array}{c}\textbf{Pose}_{t}^{i}\\ \textbf{Inter}_{t}^{i}\\ \textbf{Observe}_{t}^{i}\\ \textbf{Velocity}_{t}^{i}\\ h_{t-1}\end{array}\right)+\textbf{B}\end{array}\right) \tag{25}\]  
where **W** denotes the weight matrices, and **B** denotes the bias vector.  
The intermediate hidden state vector \(\begin{bmatrix}\wedge^{1}&\wedge^{2}&\wedge^{1}&\wedge^{1}\\ \wedge^{2}&H_{t},H_{t},\cdots,H_{t}\end{bmatrix}\) was obtained after the feature-fusion LSTM layer, where l is the number of hidden units (set to 100 in our network).