esa-sceva/satcom-chunk-collection huggingface dataset:
# Static and Moving Target Imaging Using Harmonic Radar
## 5 Conclusions  
This paper summarizes our results toward imaging of moving and stationary nonlinear targets using a harmonic radar. Our experimental results clearly illustrate the radar system's capability of detecting a nonlinear target at a standoff range of at least 7.9 m (26 feet). The combined linear/nonlinear radar is able to separate linear targets from nonlinear targets and track both classes of targets in range and Doppler. Linear targets can be separated from stationary clutter with a minimum detectable velocity of approximately 1 m/s, if data are collected at 48 Hz. The nonlinear data collected show no visible antenna coupling or nonlinear clutter; thus, the minimum detectable velocity for the non-linear radar is 0 m/s. The natural clutter rejection properties of harmonic radar allows for detecting non-moving targets.  
We also presented linear and harmonic synthetic aperture radar images collected from a moving aperture. We conclude from our results that harmonic radar data can be used to form high quality SAR images. These images show that a harmonic radar is able to ignore the strong linear response from a linear target, such as a corner reflector, and focus only on the nonlinear target.  
Since our purpose was primarily to validate the theory of harmonic radar, the choice of frequency bands was made based on the availability of components in our laboratory and cost considerations. Under operational scenarios, one must take into account issues of frequency allocations and licenses/unlicensed bands. However, we believe that our validated concepts can work in any frequency range used.  
We believe that our results provide the first published evidence of the use of harmonic radar for imaging nonlinear targets, both static and moving. Nonlinear radar is a viable technique for the detection of concealed electronic components containing semiconductors. Applications include detection of humans carrying cell phones buried under earthquake rubble and concealed eavesdropping devices carrying RF transmitters.  
This research was supported by U.S. Army Research Office (ARO) Contract # W911 NF-12-1-0305 through a subcontract from Delaware State University (DSU).  
All authors equally conceived and designed the experiments; K.A.G. performed the experiments; all authors analyzed the data; K.D.S. contributed the logistics and measurement facilities; [PERSON] wrote the first draft of the paper, and other authors contributed to its final form.  
This paper is dedicated to the memory of [PERSON] of the U.S. Army Research Laboratory whose extensive experience in radar technology was taken advantage of during extensive discussions on the research.  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Analysis of a Sound Signal for Quality Monitoring in Laser Microlap Welding
## 5 Conclusions  
In this study, analysis of the correlation between sound signals and joint strength in laser lap microwelding was conducted by dividing sound signals into eight sections. Results revealed that RMS and STD values of the first section of signals, and the STD-to-RMS ratio of the second section of signals in the welding process have promising potential in identifying low-strength joints. A 100% classification rate was obtained when normal and low-strength joints were monitored using an HMM-based classifier with selected features to verify the performance of the three selected features.  
B.-S.K. contributes to the conduction of experiments, signal processing, and writing. M.-C.L. contributes to the conceptualization, methodology, development, signal analysis, and writing. All authors have read and agreed to the published version of the manuscript.  
The conducted experiments in this study were supported by the Geniray Technology Corporation in New Taipei, Taiwan. This research also partially received financial supports from Ministry of Science and Technology in Taiwan. Grant number: [MOST 104-2221-E-005-019 -].  
The authors declare no conflict of interest.  
Figure 14: Classification index for each section: (**a**) RMS value; (**b**) STD; (**c**) STD-to-RMS ratio.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Features** & \multicolumn{3}{c}{**Observation Level Assignment**} \\ \hline \multirow{5}{*}{**Section 1**} & \multirow{3}{*}{RMS} & \(<\)0.09 & 1 \\ \cline{3-4}  & & 0.09–0.18 & 2 \\ \cline{3-4}  & & \(>\)0.18 & 3 \\ \cline{3-4}  & & \(<\)0.04 & 1 \\ \cline{3-4}  & & 0.04–0.08 & 2 \\ \cline{3-4}  & & 0.08–0.12 & 3 \\ \cline{3-4}  & & 0.12–0.16 & 4 \\ \cline{3-4}  & & 0.16–0.2 & 5 \\ \hline \multirow{2}{*}{**Section 2**} & \multirow{2}{*}{Ratio of RMS to STD} & \(<\)0.75 & 1 \\ \cline{3-4}  & & \(>\)0.75 & 2 \\ \hline \end{tabular}
\end{table}
Table 3: Selected features for hidden Markov model (HMM)-based classifier.
# Advancements in Gaze Coordinate Prediction Using Deep Learning: A Novel Ensemble Loss Approach
## 3 Materials and Methods
### Dataset  
As the demand for improving gaze tracking performance through deep-learning-based approaches, particularly in the context of appearance-based methods, has been on the rise, large-scale datasets have been constructed. These datasets encompass various environments, ranging from controlled experimental settings to unconstrained outdoor scenarios. In this paper, to verify the versatility of the ensemble loss in terms of accuracy enhancement, we trained our model using the MPIIFaceGaze dataset [6], which was collected in an unconstrained experimental environment. We evaluated the accuracy of the predicted gaze coordinates using the Euclidean distance metric. This dataset is one of the most commonly used datasets for appearance-based gaze tracking methods and consists of cropped face, left eye, right eye images, grid information, and head pose information from facial datasets, tailored for our research. MPIIGaze is a publicly available dataset that provides a total of 213,659 images collected over several months from 15 participants for the gaze estimation task. It offers appearance-based data such as face images, eye images, and head pose, along with 2D gaze positions in pixel coordinates for each participant. The dataset comprises images captured under various backgrounds, lighting conditions, and natural head movements during data acquisition. Utilizing this dataset, we aimed to train a model that is robust in gaze tracking across diverse environments. In this paper, we employed a leave-one-out cross-validation approach, training the model using data from 14 out of the 15 participants and evaluating it on the data from the remaining participant. This process was repeated 15 times to perform leave-one-out cross-validation.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Previous Study** & **Used Method** & **Performance (MAE)** \\ \hline [PERSON] et al. [21] & uses ResNet18, remove blinking data & GazeCapture: 3.05 cm \\ [PERSON] et al. [6] & uses VGG, propose MPIIGaze dataset & MPIIGaze: 13.9\({}^{\circ}\), Eyediap: 10.5\({}^{\circ}\) \\ FAZE [9] & few shot calibration & MPIIGaze 3.42\({}^{\circ}\) \\ iTracker [7] & CNN-based model, uses images to predict x, y & GazeCapture (mobile phone): 1.71 cm, GazeCapture \\  & coordinates, propose GazeCapture dataset & (tablet FC): 2.53 cm \\ AFF-Net [23] & CNN-based model, state of the art performance in & GazeCapture (mobile phone): 1.62 cm, GazeCapture \\  & MPIIFaceGaze & (tablet FC): 2.30 cm, MPIIFaceGaze: 3.9 cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of related research with comparative performance.
## 2 Basic Theoretical Parameters of the FE-SEA Method  
### Modal Density  
In the mixed FE-SEA model, the three basic parameters required by the model are modal density, the internal loss factor, and the coupling loss factor between different subsystems. Within the research frequency range, the modal density is one of the important parameters of the subsystem, which represents the energy-storage capacity of the subsystem.
## 2 Results and Discussion
### Influence of Contact Time on the Adsorption Process: Kinetic and Mass Transfer Studies
Dimensionless numbers in fluid mechanics and mass transfer were also calculated to determine external and internal mass transfer parameters such as the solid-liquid external mass transfer coefficient (\(\rm k_{SL}\)) and effective diffusivity of the enzyme inside the pores of the support (\(\rm D_{eff. }\)), respectively [41; 43; 45]. According to Table 2, the Reynolds number, a dimensionless number that describes the ratio between inertial and viscous forces, was 7692.4, thus showing that the adsorption process was conducted in a turbulent flow  
Figure 3: Representative scheme of the mechanism of lipase adsorption on PSty-DVB beads. Step 1 is the film diffusion (boundary layer) and Step 2 is the internal diffusion (inside the pores of the support). This illustration was adapted with permission from Elsevier, Ref. [34], ON: 5425270722857. (Re \(>\) 4000) [46]. Under such conditions, a drastic decrease on the boundary layer thickness is expected (see Figure 3), thus indicating that the internal mass transfer is the limiting-step on the adsorption process. The Biot number, a dimensionless that gives the ratio between internal and external mass transfer resistances [47; 48; 42], was also determined to elucidate the controlling step on the proposed adsorption system. According to Table 2, the Biot number was 105.2, which confirms that the proposed protocol for immobilization of lipase on PSty-DVB beads was clearly controlled by the internal diffusion [47; 48]. This result is in accordance with SEM analysis that shows the preferential adsorption of the enzyme on the internal support surface that corresponds to nearly 95% of all its specific surface area. The catalytic performance of the biocatalyst (immobilized lipase) was also determined on the hydrolysis of olive oil emulsion and compared with soluble lipase. According to the results, the prepared biocatalyst exhibited a hydrolytic activity of 157.3 \(\pm\) 12.3 U\(\cdot\)g\({}^{-1}\). The immobilization process drastically reduced the specific activity, ratio between hydrolytic activity and maximum immobilized protein amount--q\({}_{\text{e}}\)[49], of the enzyme from 560 U\(\cdot\)mg\({}_{\text{protein}}\)\({}^{-1}\) (soluble lipase) to 5.05 U\(\cdot\)mg\({}_{\text{protein}}\)\({}^{-1}\) (immobilized lipase). These results may be mainly due to the low solubility of oil molecules in the aqueous media and/or the restricted access of drops of substrate to the internal microenvironment of the heterogeneous biocatalyst, suggesting strong diffusive effects using olive oil emulsion, which is also a highly viscous reaction mixture. Moreover, the active center of the lipase is oriented towards the support surface without any spacer arm (in contraposition to the use of supports coated with acyl groups such as octyl or octadecyl) [9]. The objective of this study was the preparation of a heterogeneous biocatalyst to catalyze the production of several monoesters
## 1 Introduction
[75; 76; 77]--have been carried out to increase the bandwidth and miniaturization. The bow-tie antenna, one of the microstrip antenna types, has also been studied in many different applications. It is well known that the bow-tie antenna designs are application-specific, and the slots are added to increase the bandwidth. Therefore, two parameters need to be well specified for better performance: the location and size of the slot. One of the contributions of this study is that the slot dimensions are chosen especially for the required bandwidth and gain. Graphene-based antenna studies have been carried out intensively, especially for the last five years [33; 35; 78; 79; 80; 81; 82; 83; 84; 85; 86]. However, using graphene as an antenna is not always an easy task, and the production techniques also play an essential role in this manner. Another contribution of this study is that a standard and cheap 3D printer is used to produce the antennas. In addition, the study results can be used as a practical reference of the effect of the slot size on the antenna performance and can be considered the novelty of this study. Finally, the graphene-based patch antenna's gain is better than the copper antennas when used as the radiating element.
# Segmentation of Infant Brain Using Nonnegative Matrix Factorization
## 2 Materials and Methods
### Extracting the Brain
#### 2.3.2 GGMRF Inhomogeneity Reduction Model  
The accuracy of the current techniques of infant brain extraction from DWI are significantly hindered by the intensity inhomogeneity. This could be handled by compensating for the intensity inhomogeneity with low frequency. In the proposed brain extraction method, the generalized Gauss-Markov random field (GGMRF) model is used to reduce this inhomogeneity [37], which is applied after DWI data are preprocessed using DTIprep software. In this step, the residual image inconsistencies are removed (smoothed) by compensating for the 3D pair-wise spatial homogeneous interactions along the DWI gray levels. Particularly, the values of the gray levels \(q\in\mathbf{Q}\) are treated as samples of a 3D GGMRF model [37] with measurements of the voxel 26-connected neighborhood (neighbors to the pixel from all directions, edges, and corners). The continuity of the \(q\) values for each of the brain DWI scans is modified by applying the gradient descent algorithm [38] to search for the closest minimum of the following equation:  
\[\widehat{q}_{s}=\arg\min_{\widehat{q}_{s}}\left[\left|q_{s}-\widetilde{q}_{s} \right|^{\alpha}+\rho^{\alpha}\lambda^{\beta}\sum_{r\in\mathbf{v}_{s}}\eta_{s, r}|\widetilde{q}_{s}-q_{r}|^{\beta}\right], \tag{1}\]  
Figure 2: (**a–d**) Procedure of extracting the brain starting from original volume, (**e**) the final estimated LCDG density for each of the two classes: brain vs. non-brain, (**f**) the discriminant threshold \(\tau\) calculation method.  
where the original values of the gray levels and their corresponding expectations are denoted respectively by \(q_{s}\) and \(\widehat{q}_{s}\) at the observed location \(s=(x,y,z)\). The GGMRF potential and the 26-neighborhood voxel set are denoted respectively by \(\eta_{s,r}\) and \(v_{s}\). In addition, \(\lambda\) and \(\rho\) are scaling factors. The level smoothness is controlled by the parameter \(\beta\in[1.01,2.0]\). For example, having \(\beta=2\) yields to smoothness, while \(\beta=1.01\) yields to relatively abrupt edges. The prior distribution is determined by the parameter \(\alpha\in\{1,2\}\), where \(\alpha=2\) is used for Gaussian and \(\alpha=1\) is used for Laplace. See Appendix A.2 for more details about this preprocessing step, yielding a final mask of the brain, which is used to find the final extracted brain (Figure 2).
## 2 Results and Discussion
### Adsorption
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \(a\) & \multicolumn{2}{c}{**Pseudo First Order Model**} & \multicolumn{2}{c}{**Pseudo Second Order Model**} \\ \cline{2-7}  & \(\mathbf{k_{t}\,(\text{min}^{-1})}\) & \(\mathbf{q_{e\,\text{calc}}}\) (mg/g) & \(\mathbf{R^{2}}\) & \(\mathbf{k_{2}\,(\text{g/mg}\cdot\text{min})}\) & \(\mathbf{q_{e\,\text{calc}}}\) (mg/g) & \(\mathbf{R^{2}}\) \\ \hline Amberlite & & & & & \\ \hline XAD2 & 2.74 & 0.80 & 0.8544 & 0.025 & 1.98 & 0.9998 \\ XAD4 & 2.25 & 1.54 & 0.9376 & 0.011 & 2.18 & 0.9929 \\ XAD7 HP & 22.5 & 0.49 & 0.9716 & 0.190 & 1.98 & 0.9998 \\ XAD16 HP & 29.0 & 0.89 & 0.9826 & 0.118 & 2.33 & 0.9997 \\ XAD761 & 17.9 & 0.70 & 0.9612 & 0.103 & 2.29 & 0.9997 \\ XAD1180 & 22.8 & 0.47 & 0.9597 & 0.197 & 2.18 & 0.9999 \\ \hline Diaion & & & & & & \\ \hline HP20 & 24.5 & 0.56 & 0.9614 & 0.170 & 2.19 & 0.9999 \\ HP2 MG & 26.8 & 0.38 & 0.9716 & 0.281 & 1.93 & 0.9999 \\ \hline Sepabeads & & & & & & \\ \hline SP70 & 16.2 & 0.52 & 0.8779 & 0.139 & 2.43 & 0.9998 \\ SP207 & 22.3 & 0.65 & 0.9855 & 0.139 & 2.57 & 0.9998 \\ SP700 & 26.1 & 0.46 & 0.9681 & 0.240 & 2.60 & 0.9999 \\ SP825 & 28.6 & 0.55 & 0.9558 & 0.220 & 2.39 & 0.9999 \\ SP850 & 20.1 & 0.72 & 0.9630 & 0.112 & 2.45 & 0.9998 \\ \hline \hline \end{tabular}
\begin{tabular}{l c c c c c} \hline \hline \(b\) & \multicolumn{2}{c}{**Pseudo First Order Model**} & \multicolumn{2}{c}{**Pseudo Second Order Model**} \\ \cline{2-7}  & \(\mathbf{k_{t}\,(\text{min}^{-1})}\) & \(\mathbf{q_{e\,\text{calc}}}\) (mg/g) & \(\mathbf{R^{2}}\) & \(\mathbf{k_{2}\,(\text{g/mg}\cdot\text{min})}\) & \(\mathbf{q_{e\,\text{calc}}}\) (mg/g) & \(\mathbf{R^{2}}\) \\ \hline Amberlite & & & & & \\ \hline XAD2 & 5.54 & 3.81 & 0.9475 & 9.59 & 7.91 & 0.9998 \\ XAD4 & 9.11 & 7.97 & 0.9916 & 2.42 & 8.51 & 0.9983 \\ XAD7 HP & 30.3 & 1.49 & 0.9169 & 105 & 6.90 & 0.9996 \\ XAD16 HP & 21.2 & 2.77 & 0.9478 & 29.3 & 8.26 & 0.9997 \\ XAD761 & 8.63 & 1.41 & 0.5970 & 40.1 & 7.90 & 0.9992 \\ XAD1180 & 13.5 & 1.80 & 0.9828 & 38.7 & 8.04 & 0.9996 \\ \hline Diaion & & & & & & \\ \hline HP20 & 15.7 & 1.33 & 0.7960 & 50.9 & 8.09 & 0.9999 \\ HP2 MG & 15.7 & 0.80 & 0.7926 & 105 & 6.90 & 0.9999 \\ \hline Sepabeads & & & & & & \\ \hline SP70 & 17.1 & 1.23 & 0.6610 & 62.5 & 8.94 & 0.9999 \\ SP207 & 43.8 & 2.65 & 0.9722 & 61.1 & 9.05 & 0.9999 \\ SP700 & 36.0 & 1.84 & 0.9579 & 60.9 & 9.06 & 0.9999 \\ SP825 & 28.4 & 1.32 & 0.9453 & 125 & 8.92 & 0.9999 \\ SP850 & 19.4 & 1.80 & 0.8165 & 40.6 & 9.06 & 0.9999 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Regression coefficients for pseudo first and pseudo second order models for the adsorption of **(a)** total phenolics (as mg gallic acid equivalents/g resin) and **(b)** ABTS radical scavenging compounds (as mg Trolox equivalents/g resin) present in winery wastes.
# Additive Manufacturing of PH 13-8 Mo Family: A Review
## 2 PBF-LB of PH 13-8 Mo Family
### Material Properties
#### 2.3.2 Impact Toughness
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Reference** & \multirow{2}{*}{**Process**} & \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Direction**} & \multirow{2}{*}{**Notched Impact**} & \multirow{2}{*}{\begin{tabular}{c} **Unnotched Impact** \\ **Toughness (J)** \\ \end{tabular} } \\  & & & & & & \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} EOS Stainless Steel CX \\ \end{tabular} } & \multirow{8}{*}{[PERSON] et al. [29]} & \multirow{8}{*}{PBF-LB} & As-built & - & 83.8 & - \\  & & & ST + 900 \({}^{\circ}\)C for 1 h & - & 73.5 & - \\  & & & A + 530 \({}^{\circ}\)C for 3 h & - & 5.5 & - \\  & & & ST: 900 \({}^{\circ}\)C for 1 h & A: 530 \({}^{\circ}\)C for 3 h & - & 5.3 & - \\ \cline{2-8}  & & & As-built & - & 57.7 \(\pm\) 2.5 & - \\  & & & ST: 900 \({}^{\circ}\)C for 1 h & - & 66.3 \(\pm\) 3.2 & - \\  & & & A- 530 \({}^{\circ}\)C for 3 h & - & 10.3 \(\pm\) 0.6 & - \\  & & & ST: 900 \({}^{\circ}\)C for 1 h & ST: 900 \({}^{\circ}\)C for 1 h & - & 8.7 \(\pm\) 0.6 & - \\ \cline{2-8}  & & & As-built & Vertical & 128 & - \\  & & & A-built & Horizontal & 139 & - \\  & & & ST: 850 \({}^{\circ}\)C for 3 h & - & Vertical & 22 & - \\  & & & 30 min & Horizontal & 29 & - \\ \hline \multirow{8}{*}{
\begin{tabular}{c} [PERSON] et al. \\ [50] \\ \end{tabular} } & \multirow{8}{*}{PBF-LB} & \multirow{8}{*}{ST: 850 \({}^{\circ}\)C for 30 min} & A: 525 \({}^{\circ}\)C for 4 h & Vertical & 6.22 \(\pm\) 0.53 & - \\  & & (TruPrint 1000) & 30 min & Horizontal & 11.17 \(\pm\) 0.68 & - \\  & & PBF-LB & ST: 850 \({}^{\circ}\)C for 4 h & Vertical & 18.70 \(\pm\) 2.16 & - \\  & & (GOS M200) & 30 min & Horizontal & 22.06 \(\pm\) 2.54 & - \\ \cline{2-8}  & & & ST: 850 \({}^{\circ}\)C for 30 min & A: 525 \({}^{\circ}\)C for 4 h & - & 9.8 \(\pm\) 1.1 & - \\ \cline{2-8}  & & & ST: 850 \({}^{\circ}\)C for 30 min & - & 11.4 \(\pm\) 1.2 & 156 \(\pm\) 26 \\ \cline{2-8}  & & & ST: 850 \({}^{\circ}\)C for 4 h & - & - & -240 \\ \cline{2-8}  & & & ST: 850 \({}^{\circ}\)C for 4 h & - & - & -210 \\ \cline{2-8}  & & & PRF-LB & 30 min in & A: 525 \({}^{\circ}\)C for 4 h & - & - & -175 \\ \cline{2-8}  & & & & ST: 850 \({}^{\circ}\)C for 4 h & - & - & -175 \\ \hline \hline \end{tabular}  
* A: Aging, ST: Solution Treatment, URQ-HIP: Uniform Rapid Quenching–Hot Isostatic Pressing.  
\end{table} 
Table 4: Impact toughness of as-built and heat-treated PBF-LB/CX and Corrax from the literature.
## 4 Conclusions  
This study examined the application of a powertrain, i.e., CVT, to improve the driving distance on a single charge of an EV. The vehicle model of Company B's EV was implemented using the CRUISE and FluxMotor software. Then, the CVT was applied to the validated model to compare the driving distance per charge, composite electric economy, and acceleration/climbing performance in the HWFET and FTP-75 driving cycle modes. The following conclusions were drawn in this study:  
1. Based on the experimental data provided by the ANL, the reliability of the EV model developed in this study was verified by comparing changes in the SOC, voltage, and current during vehicle driving. A CVT-applied model was developed by applying a CVT based on the EV analytic model to verify the reliability.
2. In the analysis of the shift pattern, based on CVT of the electric vehicle, the operating point of the electric motor was expected to change by the combination of the CVT and single-reducer shift ratio and was optimized when \(n=3.5\).
3. The energy efficiency of the electric vehicle with CVT improved by approximately 5.11% when continuously variable transmission was applied through the combined driving simulation. The improvement was greater in highway driving than in the urban driving modes.
4. When the CVT was applied, the maximum driving distance on a single electric charge increased by approximately 7 km. Thus, it was expected that the driving performance of an EV with continuously variable transmission could be improved by downsizing the electric motor to reduce manufacturing costs. We will continue to conduct research on this matter in the future.  
Conceptualization, J.L.; methodology, J.L. and W.K.; project administration, J.L.; supervision, J.L.; validation, J.L.; investigation, W.K., D.K., D.L. and I.M.; data curation, W.K.; formal analysis, W.K.; writing--original draft preparation, W.K.; writing--review and editing, W.K., D.K., D.L., I.M. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethical review and approval are not applicable for this study not involving humans or animals.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
This research was supported by Soongsil University, Republic of Korea.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Time-Concentration (s)**} \\ \hline \multicolumn{1}{c}{**One Speed Reducer**} & \multicolumn{1}{c}{**CVT Applied**} & \multicolumn{1}{c}{**Reduction Ratio**} \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{**(\%)**} \\ \hline
10\% & 8.58 & 7.42 & 15.52 \\
20\% & 12.48 & 10.85 & 13.06 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of reduction degree according to speed section when slope is applied.