esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Datasets and Methods
### Random Forest
#### 3.2.4 Classification-Neural Networks  
This section trains the features extracted in Section 3.2.1 on a standard ANN and a hybrid ANN. Classification is also performed using a CNN on the NUS I and II datasets. Table 7 highlights the steps followed for the neural networks.  
Artificial Neural Network(ANN)  
ANNs are comprised of neurons or nodes arranged in layers, starting from the input layer followed by some hidden layers and ending at an output layer, all of which are interconnected with each node assigned an initial weight. After each step or epoch of the training phase, ANN is capable of learning hidden patterns in the data (in this case, patterns distinguishing various sign gestures) and adjust its weights accordingly to generalize better on unseen data.  
ANN is applied on the ASL finger-spelling dataset for leave-one-out-accuracies discussed in Section 4.4 with a corresponding architecture having four fully-connected or Dense layers of 64 neurons each and ReLU activation alongside he-uniform [30] initialization and a softmax classification layer with 24 neurons.
## 3 Results and Discussion
### Question 7 Distribution of Respondents  
Question 7 begins a set of questions (Questions 7 to 10) asking respondents about whether they would trust computer analysis under various conditions. Respondent answers to these questions indicate methods that the autonomous software development community can utilize to validate their systems and how well received they would be in the planetary science community. Question 7 asks respondents whether they would trust computer data analysis if the researcher was able to access or evaluate the source code. This could be achieved, for example, via open-sourcing autonomous control software (and exposing the source code to community review) or making it available for review and analysis by the researcher's team (possibly under a non-disclosure agreement, if it was considered proprietary or export controlled).  
Of the respondents, 74% indicated some form of agreement (between agree-no preference and strongly agree), 18% indicated no preference and 7% indicated some form of disagreement (between disagree-no preference and strongly disagree). Note that the percentages do not add up to 100% due to  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Strongly Agree** & **Agree** & **No Preference** & **Disagree** & **Strongly Disagree** & **No Response** \\ \hline
86 & 28 & 92 & 8 & 12 & 1 & 2 & 0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Responses to the statement “I use/would use a computer to identify trends in data”.  
Figure 6: Graph of responses to the statement “I use/would use a computer to identify trends in data”.  
rounding and one survey where an invalid (an indication of a response between two categories) response was provided. This data is presented in Table 6 and Figure 7. Anecdotally, some respondents indicated a level of concern as to whether they personally could or would be able to find someone with the requisite skills to perform a source code evaluation. It is unclear how this factored into the response in the few instances where this sentiment was voiced.
# The Current Development of Structural Health Monitoring for Bridges: A Review
## 5 Damage Identification Methods
### Finite Element Model Updating Methods
#### 5.2.1 Gaussian Process-Based FEMU  
The Gaussian process (GP) is a random process in statistics. The conceptual basis of GP begins with a reference to a simple multivariate Gaussian distribution, and marginalization and conditioning are its two fundamental operations. GP is a powerful model that can directly model functions to generate non-parametric models [134]. Therefore, it has the unique advantage of efficiently quantifying a variety of uncertainties.  
[PERSON] et al. [135] proposed a new probabilistic framework for structural performance evaluation, using the first-order reliability method (FORM) and GP to consider various sources of uncertainty. In this framework, the GP surrogate model was replaced by a finite element model with an associated discrepancy function. They also proposed a modular Bayesian approach (MBA) for placing such GP models. The feasibility of the proposed frame was verified on a laboratory box girder bridge, and the results showed that the proposed method can accurately detect the decrease in structural performance and the increase in failure probability. [PERSON] et al. [136] used a GP metamodel to replace the 3D finite element model of the bridge for updating. They first studied the thermal effect mechanism of the bridge deck and established the relationship between the longitudinal boundary stiffness (LBS) and the structural temperature. The GP metamodel was then used to map the relationship between LBS and longitudinal displacement. The analytical value of the longitudinal displacement of the proposed method was compared with the measured results of the Jiangyin Suspension Bridge. The results showed that the identified LBS had sufficient accuracy, and the analytical value of the longitudinal displacement was in good agreement with the actual value. [PERSON] et al. [137] also used a GP model to replace FEM in a study of model updating methods based on influence lines. In a case study of a long-span suspension bridge, this substitution improved the efficiency of the iterative optimization of boundary condition estimates.
## 3 Results and Discussion
### DRH and CRH
[50] pointed out that the presence of metal oxides (i.e., Fe2O3, TiO2) in the aerosol's particles can lead to a CRH increase of up to 32% more compared to a pure saline aerosol because such oxides can act as crystallization nuclei driving heterogeneous nucleation, which results in crystallization at higher RH values. Furthermore, the presence of organic material can also increase the aerosol's CRH [51]. Indeed, the results presented in Section 3.3 reveal that, for MR1, 16.6 \(\pm\) 126.% of the deposition comprises the carbonaceous fraction (WINSOM + BC), while the remaining 36.5 \(\pm\) 27.3% is attributed to an unknown fraction (UF_NSDD) of likely crustal origin as indicated from ED-u-XRF measurements. These percentages exhibit a decrease in MR2 (15.8 \(\pm\) 4.4% and 25.8 \(\pm\) 15.3%) and MR3 (16.9 \(\pm\) 5.1% and 15.1 \(\pm\) 8.5%), consistent with the observed CRH measurements. These results underline that, in Italy, due to the temporal behavior of the atmospheric RH, the deposits on insulators can be hydrated or not in different conditions in the three MRs. These results are in agreement with the literature findings of previous works on Italy. For example, [PERSON] et al. [19] reported the wintertime and summertime behavior of the aerosol's DRH and CRH; they found that the seasonal behavior of sulfate and nitrate fraction affected both DRH and CRH, which were lower in winter (DRH range: 51.6 \(\pm\) 0.7%-58.5 \(\pm\) 0.7% RH; CRH range: 48.1 \(\pm\) 0.5%-44.3 \(\pm\) 0.6% RH) when nitrates were the most abundant ionic species with respect to summer (DRH range; 68.3 \(\pm\) 1.0%-72.8 \(\pm\) 1.0% RH; CRH range: 63.0 \(\pm\) 1.2%-59.3 \(\pm\) 1.3% RH) when the water-soluble inorganic fraction was mainly constituted by sulfates. [PERSON] et al. [13] investigated the aerosol deliquescence and crystallization of samples mainly consisting of nitrates, sulfates, and ammonium, where nitrates were approximately five times greater than sulfates; they found DRH values in agreement with those presented in this paper for MR3 (characterized by nitrates > sulfates) with a DRH region between 56.9 \(\pm\) 1.0% and 64.5 \(\pm\) 1.0% RH while the CRH region was between 49.9 \(\pm\) 0.6% and 45.3 \(\pm\) 0.7%, which differs by 19.2%-19.3% from the CRH values obtained in the present study because of the presence in the samples of organic acids, which can increase the aerosol's CRH [51]. Finally, [PERSON] et al. [12] modeled the DRH of the aerosol using E-AIM and ISORROPIA. They found the highest values of DRH for the sampling collected during the cooler period (from March to April 2010) in which the main components were the nitrates; the lowest values of DRH were found for sampling collected during the warmer period (form June to July 2010) in which the main components were the sulfates.
# Review on the Recent Numerical Studies of Liquid Atomization
## 3 Interface Capturing
### Application Cases
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Mathematical Model** & **Key Parameters** & **Grid Size** & **Application Scenario** \\ \hline
[28] & VOF, LES & Gas–liquid velocity ratio, turbulence intensity & \textasci{-}10 million cells & Primary breakup of airblasted liquid sheets in combustion systems \\ \hline
[29] & VOF, Level Set, Ghost Fluid Method & Weber number, Reynolds number, evaporation rate & 4.23 billion cells & Atomization and evaporation in turbulent liquid jet flows \\ \hline
[30] & Two-phase CFD with Eulerian–Lagrangian coupling & Gas pressure (1–10 MPa), droplet Sauter mean diameter & \textasci{-}1 million cells & High-pressure gas atomization for metal powder production \\ \hline
[31] & LES with VOF & Turbulence models, interfacial tension & \textasci{-}5 million cells & Liquid jet atomization in multiphase flows \\ \hline
[32] & Entropy-stable conservative Level Set & Surface tension, viscosity, density ratio & Not reported & Primary atomization in fuel injectors \\ \hline
[33] & VOF with adaptive mesh refinement & Air–liquid momentum flux ratio, film thickness & \textasci{-}3 million cells & Prefilming air-assisted atomizers in aerospace engines \\ \hline
[34] & Experimental and CFD (ANSYS Fluent) & Injection pressure, nozzle geometry & \textasci{-}500,000 cells & Laser-processed fuel injection holes in automotive systems \\ \hline
[35] & VOF with shear–stress transport (SST) models & Cough-induced shear flow, droplet size distribution & \textasci{-}2 million cells & Biofluid atomization in respiratory systems \\ \hline
[36] & High-resolution VOF and DNS & Turbulent kinetic energy, breakup length scales & 6 billion cells & Primary atomization of airblasted liquid sheets in industrial burners \\ \hline
[37] & Experimental/PIV and LES & Ligament formation time, droplet velocity & Not reported & Prefilming airblast atomization in gas turbines \\ \hline
[38] & CLSVOF (Coupled Level Set/VOF) & Crossflow velocity, jet-to-crossflow momentum ratio & \textasci{-}100 million cells & Liquid jet atomization in subsonic crossflow (e.g., scramjet combustors) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of commonly used mathematical modeling methods.
# Research on the Dynamic Characteristics Analysis and Power Control Method of Heat Pipe Reactors
## 3 Analysis of Dynamic Characteristics of HPR
### The Disturbance of Reactivity  
At \(t=100\) s, a reactivity step disturbance of \(-10\) PCM is introduced to obtain the relative power and temperature response of the reactor. The response curves of the HPR under the reactivity disturbance are shown in Figure 8.  
As can be seen from Figure 8, within about 100 s after the introduction of negative reactivity, the reactor power decreases rapidly and the heat generated by the fuel elements decreases simultaneously, causing the fuel and monolith temperatures to decrease fast as well. The decrease in fuel and monolith temperature introduces positive reactivity, which makes the decreasing trend in reactor power slow down gradually. At the same time, the energy output to SCO\({}_{2}\) decreases, making its outlet temperature lower and taking away less heat. After the fuel and monolith temperatures drop to 986 K and 977 K, respectively, the reactivity fed back to the core from the fuel and monolith is sufficient to compensate for the introduced reactivity disturbance, and then the reactor power gradually picks up. After a small oscillation, the reactor power, fuel temperature, monolith temperature, and SCO\({}_{2}\) heat exchanger outlet temperature all re-stabilize at a new level, and the system gradually enters a new steady state after about 1000 s. The new steady state values of nuclear power and each temperature have decreased compared to the initial state.  
Figure 8: The response of HPR to the reactivity disturbance: (**a**) the response of relative power; (**b**) the response of temperature.  
Figure 7: Simulink model of the HPR.
## 3 B-Mode Ultrasound Velocimetry Based on Optical Flow
### Pyramidal L-K Algorithm  
The constraints of the L-K algorithm are small motion speed, constraint brightness, and consistent region, which are stronger assumptions and not easily satisfied in many applications. When particles in water are moving fast, the assumptions above do not hold, and computational errors will arise, which would gradually accumulate, making subsequent calculation more deviant and eventually failing to obtain valid and reliable optical flow values.  
Because fast motion speed will generate large errors of the algorithm, it is desirable to reduce the motion speed of particles in the ultrasound images to meet the algorithm requirement [16]. An intuitive way to achieve this is to shrink the size of the images. Suppose that when an image's size is \(400\times 400\), a pixel's flow velocity is 16 pixels per frame; then when the image is downscaled to \(200\times 200\), the pixel's flow velocity is 8 pixels per frame. If ultrasound images do not satisfy the motion speed assumption, L-K algorithm can still be applied after downscaling the images several times.  
In this way, a multi-scale representation of images, image pyramid, can be used in the L-K optical flow algorithm, which generates pyramid images of the ultrasound images with different sizes, to solve the problem mentioned above layer by layer. The basic approach is to obtain the UIV image sequence, \(I(x,y,t)\), and downsample it separately until it reaches the specified level. In practice, a pyramid of three or four layers is usually sufficient to meet the requirements of efficiency and accuracy. Figure 5 shows the pyramid images.  
Figure 5: Pyramid images schematic.  
The Pyramid L-K algorithm is an iterative calculation in layers, which continuously improves the resolution of the results. The optical flow value calculated from the L layer image is used as the initial value of the optical flow of the L-1 layer. Then the exact value of the optical flow of the L-1 layer is calculated, which is used as the initial value of the optical flow value of the L-2 layer. This process continues iteratively until the exact value of the optical flow of the original image is calculated.
## 3 Simulation Model of the Drive and Transmission Mechanism
### Torque Characteristics  
For the creation of the torque characteristics as a graphical representation of the dependence of the engine torque \(M_{e}\) on the engine revolutions \(n_{e}\) at full fuel supply, it was necessary to define the input values of the IVECO F1 CE0489C engine model. For this purpose, a general engine block was chosen with the parameterization according to the engine revolution vectors and its torques obtained from the manufacturer data. In the simulation model, the same values as in the analytical method were applied. The values of idle revolutions, revolution limiter, engine inertia, initial conditions, etc., were also defined.  
The engine block contains one mechanical rotary port F connected to the engine crankshaft and a second mechanical rotary port B connected to the engine block (Figure 7). To determine the torque characteristics of the engine, it was necessary to connect an ideal torque sensor to the mechanical rotary port B. This block represents a device that converts the values of the variable passing through the sensor into a control signal proportional to the torque with a specific proportionality coefficient.  
However, the output of this measurement is the dependence of the engine torque \(M_{e}\) at simulation time \(t_{s}\). To convert this dependence to the dependence of the engine torque  
Figure 10: Values of the gearbox mechanical efficiency determined numerically.  
Figure 9: Power values at the input and output shaft of the gearbox.  
\(M_{e}\) on the velocity \(v\), it was necessary to send the values from the torque sensor and the tachometer obtained in Simulink to the Matlab workspace and to process the obtained signals to create the required characteristics shown in Figure 11.  
In Figure 11, it can be seen that the graphical representation of the dependence of the IVECO FICE0489C engine torque values on the vehicle speed obtained numerically already automatically takes into account the predefined parameters of all other components of the vehicle's driving and transmission mechanism. In this case, these values differ from the characteristics obtained analytically, primarily due to the consideration of the mechanical efficiencies of all components of the driving and transmission mechanism, adhesion force, wheel slip, speed and power transmission ratio of the hydrodynamic converter and others.
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 2 Materials and Methods
### Methodology
#### 2.2.4 Performance Indicators  
In addition to producing forecasts, it is important to assess the predictive capacity of the proposed models [15; 44], to ensure their accuracy and practical utility [45]. Traditionally, forecast performance evaluation is based on accuracy [46].  
In this study, the following metrics were used to evaluate model accuracy: Mean Percentage Error (MPE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Among the available metrics, MAPE is the most employed [47]. Generally, the most accurate model is selected as the one exhibiting the lowest error metrics, since its predictions provide estimates with minimal bias (small errors) and are close to actual values. These three metrics are defined as follows.  
MPE calculates the error at each period as the ratio of the error in that period to the actual value of the corresponding period, followed by the average of these ratios.  
\[MPE=\frac{1}{n}\sum_{i=1}^{n}\frac{(y_{t}-\hat{y}_{t})}{y_{t}}\]  
RMSE is obtained as the square root of the mean squared error.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{t}-\hat{y}_{t})^{2}}\]  
Figure 4: Architecture of the Neural Network Model.  
Figure 5: Flowchart of the NNAR Model Architecture.  
MAPE measures the mean absolute percentage error, indicating how far the predicted values deviate from the actual data. It also reveals whether the chosen model tends to overestimate or underestimate the forecasts.  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{(y_{t}-\hat{y}_{t})}{y_{t}}\right|\]  
To assess the statistical significance of differences in predictive performance among the evaluated models, the Diebold-Mariano (DM) test [48; 49; 50] was used. This test compares model accuracy and determines if observed differences are statistically significant based on forecast errors.
## 4 Experimental Results  
This section presents a comprehensive evaluation of the proposed TSN scheduling algorithms. We validate their performance through simulations using the OMNeT++ platform and through experiments on a real-world hardware testbed, using the demanding in-vehicle network scenario as a representative use case for real-time systems. The evaluations focus on key metrics such as end-to-end latency, jitter, schedulability, and GCL computation time.
# Influence of Stray Current on Fastening System Components in Urban Railway Tracks
## 3 Materials and Methods  
### Sample Description and Preparation  
Laboratory tests were performed on four different types of fastening systems that are characteristic of the tram track infrastructure in Zagreb: ZG 3/2, PPE, DEPP, and ZG 21-CTT. ZG 3/2 and PPE are direct fastening systems, where the rails are laid on elastic rail pads and steel plates and fastened using clips and anchor bolts (Figures 2 and 3). Anchor bolts were used to anchor the fastening system to a concrete base. In the PPE fastening system, an elastomer pad was placed between the steel plate and the leveling layer, whereas in the ZG 3/2 fastening system, the steel plate was in direct contact with the leveling layer. In the ZG 3/2 fastening system, the SKL-2 clip was placed on a steel rib, whereas in the PPE fastening system, the SKL-1 clip was placed on a plastic rib.  
In the DEPP and Zagreb 21-CTT fastening systems, the rails are laid on the rail pad and steel plate but are fastened to the steel plate using clips and T-bolts (Figures 4 and 5). The anchor bolts are dislocated and used only to anchor the fastening system to the concrete base. In the Zagreb 21-CTT fastener, the underside of the steel plate is vulcanized. The bores for the anchor bolts on the steel plate are also vulcanized such that the anchor bolts do not directly contact the steel plate (Figure 5a).  
Figure 4: Fastening system DEPP: (**a**) cross section and (**b**) sample for testing.  
Figure 3: Fastening system PPE: (**a**) cross section and (**b**) sample for testing.  
Figure 2: Fastening system ZG 3/2: (**a**) cross section and (**b**) sample for testing.  
First, concrete bases with dimensions of 60 \(\times\) 30 \(\times\) 25 cm were fabricated for the samples. The leveling layers were created as in situ, with the rails adjusted primarily by direction and height. For the test samples, the steel base plates were raised approximately 5 cm above the concrete block, corresponding to the height of the leveling layer on the tracks. For the PPE, DEPP, and Zagreb 21-CTT fastening systems, the leveling layers of the concrete reinforced with synthetic microfibers were cast at the middle of the height of the elastomeric pads, whereas for the ZG 3/2 fastening system, the leveling layer was cast at half the height of the steel plate because there was no elastomer pad under the steel plate.