esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Scaling Laws of Impact Crater Parameters
Therefore, the logarithmic form of Equation (8) can be obtained as follows:  
\[\log\!\left(\frac{H}{l}\right)=\log\!\left(K\right)+\mu\log\!\left(\frac{u \rho_{t}^{\frac{1}{2}}}{Y^{\frac{1}{2}}}\right)+\
u\log\!\left(\frac{\rho_{p}}{ \rho_{t}}\right) \tag{9}\]Following the above process, similar logarithm equations for the dimensionless diameter and volume can be obtained as below:  
\[\log\left(\frac{D}{d}\right)=\log(K)+\mu\log\left(\frac{u\rho_{t}^{\frac{1}{2}}}{ Y^{\frac{1}{2}}}\right)+\
u\log\left(\frac{\rho_{p}}{\rho_{t}}\right) \tag{10}\]  
\[\log\left(\frac{V}{ld^{2}}\right)=\log(K)+\mu\log\left(\frac{u\rho_{t}^{\frac{1} {2}}}{Y^{\frac{1}{2}}}\right)+\
u\log\left(\frac{\rho_{p}}{\rho_{t}}\right) \tag{11}\]  
By using the multiple regression analysis on Equations (9)-(11), the predicted regression equations for the impact crater parameters are achieved as follows:  
\[\frac{H}{l}=0.36\left(\frac{u\rho_{t}^{\frac{1}{2}}}{Y^{\frac{1}{2}}}\right)^{ 0.562}\left(\frac{\rho_{p}}{\rho_{t}}\right)^{0.537} \tag{12}\]  
\[\frac{D}{d}=1.82\left(\frac{u\rho_{t}^{\frac{1}{2}}}{Y^{\frac{1}{2}}}\right)^{ 0.898}\left(\frac{\rho_{p}}{\rho_{t}}\right)^{0.439} \tag{13}\]  
\[\frac{V}{ld^{2}}=0.18\left(\frac{u\rho_{t}^{\frac{1}{2}}}{Y^{\frac{1}{2}}} \right)^{2.626}\left(\frac{\rho_{p}}{\rho_{t}}\right)^{1.313}=0.18\left(\frac {\rho_{p}u^{2}}{Y}\right)^{1.313} \tag{14}\]  
The coefficients of determination (\(R\)-squared) of Equations (12)-(14) are 0.975, 0.955, and 0.978, respectively, and the standard estimation errors are 2.6%, 3.6%, and 7.3%, respectively. All the coefficients of determination are higher than 0.95, while all the standard estimation errors are lower than 7.5%, indicating that the regression equations have good reliability with small error for the estimation of the crater parameters. Figure 12 illustrates the comparison between the predicted values of the regression equations and the experimental results. The dashed lines with slope of 1 through the coordinate origin represent the predicted values of the regression equations for the dimensionless crater parameters. Each experimental result corresponding to the predicted one is distributed along the dashed line of estimation, which verifies the accuracy of the regression equations on predicting the crater parameters. The exponents of Equations (12) and (13) are compared to study the dependence relationships of the crater parameters on the impact parameters. The crater depth has almost the same dependence on the impact velocity and the \(\rho_{\text{p}}/\rho_{\text{t}}\) ratio. The crater diameter is more dependent on the impact velocity than the \(\rho_{\text{p}}/\rho_{\text{t}}\) ratio. Compared to the crater diameter, the crater depth is less affected by the impact velocity and more affected by the \(\rho_{\text{p}}/\rho_{\text{t}}\) ratio. As presented in Equation (14), the crater volume is dominated by the impact kinetic energy.
## 2 Network Model and Optimal Cluster Number Calculation
### Optimal Number of Clusters
 \tag{15}\]  
The distances between some CHs and BS in the model may be larger than \(d_{0}\), so it is necessary to simultaneously refer to the free space model and the multipath fading channel model when considering the energy consumption between clusters. Then the value range of the diameter \(M\) is greater than \(2d_{0}\). According to Equation (5):  
\[f(d_{toBS})=\left\{ \begin{array}{l}e\varepsilon_{fs}d_{toBS}^{2},\quad d_{toBS }<d_{0}\\ e\varepsilon_{mp}d_{toBS}^{4},\quad d_{toBS}\geq d_{0}\end{array} \right. \tag{16}\]Then the expectation of \(f(d_{toBS})\) is calculated:  
\[ \begin{split}& E[f(d_{toBS})]=E[f(\rho,\theta)]\\ &=\frac{4}{\pi M^{2}}\int\limits_{0}^{2\pi}\int\limits_{0}^{\frac {M}{2}}f(\rho,\theta)\rho\,d\rho d\theta\\ &=\frac{16\epsilon}{\pi M^{2}}\cdot\frac{\pi}{2}\left[\int \limits_{0}^{d_{0}}\varepsilon_{fs}\rho^{3}\,d\rho+\int\limits_{d_{0}}^{\frac {M}{2}}\varepsilon_{mp}\rho^{5}\,d\rho\right]\\ &=\frac{2\epsilon_{fs}d_{0}^{4}}{M^{2}}+\frac{\epsilon_{mp}M^{4}} {48}-\frac{4\epsilon_{mp}d_{0}^{6}}{3M^{2}}\\ \end{split}  \tag{17}\]  
\[\text{Let}\;\;A=\frac{2\epsilon_{fs}d_{0}^{4}}{M^{2}}+\frac{\epsilon_{mp}M^{ 4}}{48}-\frac{4\epsilon_{mp}d_{0}^{6}}{3M^{2}} \tag{18}\]  
\[\text{So}\;\;E[f(d_{toBS})]=A\cdot e \tag{19}\]  
Thus, the average energy consumed by a cluster in one round is  
\[E_{cluster}=E_{CH}+(\frac{n}{k}-1)E_{non-CH}\approx E_{CH}+\frac{n}{k}E_{non- CH} \tag{20}\]  
The energy consumed by all of the clusters in the region in one round is  
\[ \begin{split}& E_{SUM}=kE_{cluster}=kE_{CH}+nE_{non-CH}\;=\;ne(E_{ elec}+E_{DA})+kAe+neE_{elec}+ne\epsilon_{fs}\cdot\frac{2 kR^{4}}{M^{2}}\\ &\quad=ne(2E_{elec}+E_{DA})+kAe+\frac{M^{2}}{8k}\cdot ne\epsilon_ {fs}\end{split}  \tag{21}\]  
Deriving for \(E_{SUM}\), let \(\frac{dE_{SUM}}{dk}=0\), so we can obtain the optimal number of clusters  
\[k=\sqrt{\frac{M^{2}n\epsilon_{fs}}{8A}} \tag{22}\]  
Figure 3: Network structure model diagram (16 clusters).
## 4 Results and Discussion
### Influence Analysis of Pipeline Parameters
#### 4.3.4 Pipeline Material  
Figure 17a-c show the vertical deformation, horizontal deformation, stress and axial force of the pipeline when the pipeline material is iron, concrete, and PVC. The physical parameters of the pipeline material in the model are shown in Table 3.  
Figure 17 indicates that the maximum vertical deformation of pipeline under three working conditions were 15.2, 17.3, and 17.7 mm, the maximum horizontal deformation of the pipeline was 3.73, 4.14, and 4.38 mm, and the maximum pipeline stress was 1.11, 83.2, and 0.69 kPa, respectively, and the maximum axial force of the pipeline was 54.8, 4.15, and 0.034 kN, respectively. The vertical deformation and horizontal deformation of PVC pipe were the largest, and the vertical deformation and horizontal deformation of iron pipe were small, but the stress and internal force value of iron pipe was large. Generally speaking, the greater the stiffness of the pipeline, the stronger the ability to resist deformation and the smaller the deformation. However, the stress and internal force of the pipeline were large, which may lead to brittle failure when reaching the stress limit state. The smaller the stiffness of the pipeline, the stronger the ability to coordinate deformation with the surrounding rock and soil mass. Therefore, the settlement of the flexible pipeline was greater, and the pipeline would be damaged when the deformation reached the limit.  
Figure 16: Vertical deformation of pipeline with different section shapes.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Pipeline Materials** & **Density (kg/m3)** & **Elastic Modulus (GPa)** & **Poisson’s Ratio** \\ \hline Iron & 7900 & 200 & 0.3 \\ Concrete & 2500 & 25 & 0.2 \\ PVC & 1500 & 2 & 0.35 \\ \hline \end{tabular}
\end{table}
Table 3: Influencing factors and parameter values of pipeline dynamic response.  
Figure 17: Vertical deformation, horizontal deformation, stress and axial force of pipeline with different pipeline materials: (**a**) Iron; (**b**) Concrete; (**c**) PVC.  
The settlement of the top of pipeline 1 above the central axis of the tunnel is shown in Figure 18. The maximum settlement of PVC pipe was 15.78 mm, the maximum settlement of concrete pipe was 14.89 mm, and the maximum settlement of iron pipe was 13.17 mm. Therefore, more attention should be paid to the settlement of PVC pipeline caused by tunnel excavation, the stress and internal force of iron pipe in the aviation process, and the segment cracking caused by settlement deformation of concrete pipe.
# Power Management Strategy for an Autonomous DC Microgrid
## 6 Conclusions  
In this paper, a specific power management strategy was proposed for an autonomous DC microgrid using a DG as a backup source and a SC as compensatory power to compensate the DG's sluggish behavior. To overcome its self-discharging property, the SC was regularly recharged by the other sources with a higher priority than the electrochemical battery bank. In particular, once the DG starts outputting power, the SC is recharged before recharging the electrochemical battery bank. SC energy is thus always kept above a certain level to supply the whole system during the start-up of the DG. The proposed strategy was experimentally proven to be able to maintain the power balance and consider the slow start-up that is characteristic of the DG, the self-discharge of the SC, dynamic load management, and the economic operating mode of the DG.  
In addition, an economic analysis method was presented for determining the DG operation cost. The experimental results showed that the duty-cycle operating mode can reduce the energy cost for both the DG itself and the whole microgrid, in comparison with the load-following mode studied in the previous research.  
Further energy cost savings can be expected through adoption of an appropriate duty-cycle time of the DG. The choice can be made using either optimization methods or simply in an empirical method. To clearly formulate an optimization problem, the PV and load powers forecasting for the day ahead, as well as for intraday, should be integrated. Future work will focus on the optimization problem.  
All the authors contributed equally to this research work. This research received no external funding. In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments). The authors declare no conflict of interest.
## 2 System Model
### Parameter Estimation Based on Carrier Phase
The differential value of the carrier phase measurement is represented as \(\mathbf{x}=[d\[PERSON]_{rcv}(0),d\[PERSON]_{rcv}(T_{0}),d\[PERSON]_{rcv}(T_{1}),\cdots,d\[PERSON]_{rcv}( (N-1)T_{0})]^{T}\). The variable \(\mathbf{\theta}=[M,\gamma]\) is calculated through MLE and the cost function is established as follows:  
\[J(\mathbf{\theta})=\sum\limits_{n=0}^{N-1}\left[d\[PERSON]_{rcv}(nT_{0})-M\sin(\omega nT _{0}+\gamma)\right]^{2}=\sum\limits_{n=0}^{N-1}\left[d\[PERSON]_{rcv}(nT_{0})-M \cos(\gamma)\sin(\omega nT_{0})-M\sin(\gamma)\cos(\omega nT_{0})\right]^{2} \tag{17}\]  
Based on MLE, \(\hat{\gamma}\sim N(\varphi+\varphi_{0},\sigma_{\gamma}^{2})\) and \(\hat{M}\sim N(-2r\sin(\omega T_{0})\cos\theta,\sigma_{M}^{2})\) can be obtained [18]:  
\[\hat{\gamma}=\arctan\frac{\sum\limits_{n=0}^{N-1}d\[PERSON]_{rcv}(nT_{0})cos(n \omega T_{0})}{\sum\limits_{n=0}^{N-1}d\[PERSON]_{rcv}(nT_{0})sin(n\omega T_{0})} \tag{18}\]  
\[\hat{M}=-\frac{2}{N}\sqrt{\left[\sum\limits_{n=0}^{N-1}d\[PERSON]_{rcv}(nT_{0})cos( n\omega T_{0})\right]^{2}+\left[\sum\limits_{n=0}^{N-1}d\[PERSON]_{rcv}(nT_{0})sin(n \omega T_{0})\right]^{2}} \tag{19}\]  
\[\sigma_{\gamma}^{2}=\frac{N\sigma_{\Phi}^{2}}{2\left[\left[\sum\limits_{n=0} ^{N-1}d\Phi_{rcv}(nT_{0})cos(n\omega T_{0})\right]^{2}+\left[\sum\limits_{n=0} ^{N-1}d\Phi_{rcv}(nT_{0})sin(n\omega T_{0})\right]^{2}\right]} \tag{20}\]  
\[\sigma_{M}^{2}=\frac{2\sigma_{\Phi}^{2}}{N} \tag{21}\]  
Without considering multipath interference, for navigation signals from satellite \(i\) and satellite \(j\), CPSD can be used to obtain the corresponding \(\gamma\) and \(M\) of two sets of signals, satisfying \(\hat{\gamma}_{i}\sim N(\varphi_{i}+\varphi_{0},\sigma_{\gamma,i}^{2})\hat{ \gamma}_{i}\sim N(\varphi_{i}+\varphi_{0},\sigma_{\gamma,j}^{2})\hat{M}_{i} \sim N(-2r\sin(\omega T_{0})\cos\theta_{i},\sigma_{M,j}^{2})\) and \(\hat{M}_{j}\sim N(-2r\sin(\omega T_{0})\cos\theta_{j},\sigma_{M,j}^{2})\). Furthermore, \(d\hat{\gamma}_{ij}\sim N(d\varphi_{ij},\sigma_{\gamma,i}^{2}+\sigma_{\gamma,j} ^{2})\), standardized as:  
\[\frac{d\hat{\gamma}_{ij}-d\varphi_{ij}}{\sqrt{\sigma_{\gamma,i}^{2}+\sigma_{ \gamma,j}^{2}}}\sim N(0,1) \tag{22}\]  
\[\frac{\hat{M}_{i}+2r\sin(\omega T_{0})\cos\theta_{i}}{\sigma_{M,i}}\sim N(0,1) \tag{23}\]  
\[\frac{\hat{M}_{j}+2r\sin(\omega T_{0})\cos\theta_{j}}{\sigma_{M,j}}\sim N(0,1) \tag{24}\]  
The above is the analysis result when the navigation signal is real. When there is a spoofing signal, the above variables will not conform to the standard normal distribution.
## 2 Materials and Methods
### Finite Element Numerical Modeling
Then, the values of the contact pressure at the point of the shaft-hub interface placed in the symmetry plane of the hub \(z=0\) (point B in Figure 3) obtained from each one of the considered meshes were compared with the theoretical value given by Equation (1), as shown in Figure 4. Notice that point B was placed far away from the local stress concentration appearing at the hub edges, point A in Figure 3 and, consequently, the obtained value from numerical simulation should be similar to the one given by theoretical expressions and, hence, the precision of the numerical results can be estimated, and the numerical simulation results will be validated. As result, a mesh with 4736 elements and 4958 nodes is suitable for simulations (Figure 5) with an estimated error, \(\varepsilon\), lower than 3%, as shown in Figure 4. For the selected mesh, the size of the elements at the contact zone was about 0.625 \(\times\) 0.625 mm as shown in Figure 5. For the sake of clarity, the shaft-hub interface where contact was imposed was drawn and shown (black line) in the detailed view of Figure 5. In a similar way, the boundary conditions were drawn and are shown (red lines) in the mesh shown in Figure 2.0 Thus, on the one hand, nodes placed on the symmetry axis \(z=0\) were fixed in the radial direction (\(
# Study on Lubrication Performance of Journal Bearing with Multiple Texture Distributions
## 2 Simulation Model  
### Geometry of the Journal Bearing  
Figure 1 shows the schematic of a journal bearing. The shaft rotates inside the bearing with a constant angular velocity \(\omega\). Points \(O_{1}\) and \(O_{2}\) correspond to the centers of the bearing and the shaft. The radii of the bearing and the shaft are \(R\) and \(r\), respectively. \(\psi\) is the angle between the load direction and the centerline \(O_{1}O_{2}\), and \(e\) is the eccentricity. The radial clearance is \(c=R-r\) and the eccentric ratio is \(\varepsilon=e/c\).  
Assuming the radial clearance \(c\) of the journal bearing is much smaller than its radius (\(c/R\ \sim\ 10-3\)), the local clearance distribution for an untextured bearing can be approximated as [15]:  
\[h_{0}=c(1+\varepsilon\cos\psi), \tag{1}\]
## 5 AGEI Application to a Case Study
### Configuration C  
Configuration C is a conventional improvement solution included in this study for a broader comparative analysis, aimed at gaining a deeper understanding of the applications and potential of the new efficiency comparison tool introduced in this paper.  
In this configuration, the original plant is upgraded by replacing the 48 electric boilers and the LPG boiler, previously used for domestic hot water heating, with a heat pump. The new machine is electrically powered and has a yearly average COP value of 5. The bottled water supply system and the indoor climate control, managed by the ATU, remain unchanged from Configuration A. Configuration C is shown in Figure 7, while detailed characteristics are provided in Table 15.  
Calculation of the monthly average AGEI\({}_{\text{c}}\) value is carried out by applying (16), as shown below. It is worth noting that the result is the same for each month.  
\[AGEI_{\text{C}}=\frac{n}{m}\sum_{j=1}^{m}\sum_{i=1}^{u_{j}}\frac{E_{ij}}{f_{i}} =\frac{3}{3}\cdot\left(\frac{5}{2.13}+\frac{0.5}{1}+\frac{3.5}{2.13}\right)=4.49\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Useful Effect** & \multirow{2}{*}{**Jan**} & **Feb** & **Mar** & **Apr** & **May** & **Jun** & **Jul** & **Aug** & **Sep** & **Oct** & **Nov** & **Dec** & **Avg** \\
**Indicator** & 0.85 & 0.89 & 0.92 & 0.91 & 0.98 & 1.46 & 1.51 & 1.49 & 1.56 & 1.41 & 1.13 & 0.97 & 1.17 \\
**AW\({}_{\text{G}}\)**\({}_{\text{E}}\)** & 1.86 & 1.81 & 1.89 & 1.89 & 2.05 & 2.36 & 2.47 & 2.44 & 2.38 & 2.20 & 2.07 & 1.91 & 2.11 \\
**AW\({}_{\text{G}}\)**\({}_{\text{COP}}\)** & 2.37 & 2.37 & 2.42 & 2.42 & 2.60 & 3.08 & 3.14 & 3.12 & 3.11 & 2.94 & 2.70 & 2.49 & 2.73 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Monthly and yearly average values regarding the useful effect indicators of the Integrated AWG.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Indicator** & **Jan** & **Feb** & **Mar** & **Apr** & **May** & **Jun** & **Jul** & **Aug** & **Sep** & **Oct** & **Nov** & **Dec** & **Avg** \\ \hline \hline \(\text{AGEI}_{\text{B}}\) (primary) & 4.69 & 4.68 & 4.76 & 4.75 & 4.95 & 5.54 & 5.64 & 5.61 & 5.61 & 5.37 & 5.07 & 4.83 & 5.13 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Calculation of Configuration B AGEI average monthly values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equipment** & **Energetic Input, _en_** & \
\begin{tabular}{c} **Useful Effect** \\ **Indicator** \\ \end{tabular} &
\begin{tabular}{c} **Primary Energy** \\ **Factors, \(f_{i}\)** \\ \end{tabular} \\ \hline Heat pump & Electricity & Average COP = 5 & 2.13 \\ \hline Bottled water supply & Primary & WET = 0.5 & 1 \\ \hline ATU & Electricity & Average EER = 3.5 & 2.13 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Configuration C: plant energy characteristic summary.
### Research Contribution
Unlike many existing methods that face scalability issues or depend on unrealistic assumptions, such as ideal communication conditions, the proposed framework is designed to handle practical challenges, including communication delays and significant demand disturbances. * The paper also stands out for its focus on sustainability, incorporating pollution costs, such as CO\({}_{2}\), SO\({}_{2}\), and NOx, into the optimization framework. This alignment with global sustainability goals distinguishes the proposed method from many recent studies that overlook the environmental impact of power system operations. By addressing these concerns, the framework ensures that the AGC system not only meets technical and economic requirements but also supports environmentally conscious power generation. Compared to recent state-of-the-art research, the proposed method achieves system-wide optimization by integrating multiple objectives, effectively handling nonlinear system dynamics, and demonstrating exceptional adaptability to planned and unplanned disturbances, such as line outages and cyber-physical attacks. It consistently outperforms existing methods in terms of stability, cost-efficiency, and environmental impact. In conclusion, the hybrid NLMPC framework with data-driven integration represents a significant advancement in AGC for multimachine power systems. Its ability to maintain system stability, minimize operational costs, and reduce environmental impact establishes it as a leading approach for modern power system management, addressing both contemporary challenges and future needs.
# Compact Fuzzy Systems Based on Boolean Relations
## 2 Framework
### Extension of Boolean Tables to Kleene Tables
#### 2.5.1 Regularity Conditions  
According to [58; 59], regularity conditions allow handling ambiguity, from a ternary logic view. The regularity conditions indicate that if the ambiguity at the input increases, then the ambiguity at the output also increases.  
**Definition 4** ([58; 59]).: _Considering \(E=\{0,u,1\}\) a \(n\)-variable in ternary logic, then, a function \(f\) mapped from \(E^{n}\) to \(E\) such that \(X\in E^{n}\) assigns \(f(X)\in E\). Considering Figure 1 a relationship  
\begin{table}
\begin{tabular}{c c c} \hline \(a_{k,j}\) & \(f_{k,m}\) & \(\widehat{A}\) \\ \hline
0 & 0 & 0 \\ \(u\) & 0 & 0 \\
1 & 0 & 0 \\
0 & \(u\) & \(\bar{A}_{j}\) \\ \(u\) & \(u\) & \(A_{j}\wedge\bar{A}_{j}\) \\
1 & \(u\) & \(A_{j}\) \\
0 & 1 & \(\bar{A}_{j}\) \\ \(u\) & 1 & 1 \\
1 & 1 & \(A_{j}\) \\ \hline \end{tabular}
\end{table}
Table 4: Function values for \(\widehat{A}\).  
of ambiguity \(\preceq\) corresponds to:_  
\[0\preceq u,\ \ \ \ \ 1\preceq u,\ \ \ \ \ i\preceq i, \tag{22}\]  
The above means that 0 and 1 are less ambiguous than \(u\), if \(i\preceq j\), then \(i\) is more ambiguous than \(j\). In addition, for different elements \(X=(x_{1},\ldots,x_{n})\) and \(Y=(y_{1},\ldots,y_{n})\) of \(E^{n}\), then \(X\) is less ambiguous than \(Y\) if \(x_{i}\preceq y_{i}\) for \(i=1,2,\ldots,n\), namely \(X\preceq Y\).  
Considering that 0 and 1 are defined states and \(u\) is not state defined as 0 or 1. Then the partial order \(\preceq\) describe ambiguity [59].  
**Definition 5** ([58; 59]).: _A function \(f\) is regular if it meets the condition of ambiguity monotonicity given by:_  
\[f(A)\preceq f(B)\text{ when }A\preceq B\ \ \ \ \ (A,B\in E^{n}). \tag{23}\]  
This condition implies that if \(A\) is equal to or less ambiguous than \(B\), then, \(f(A)\) is equal to or less ambiguous than \(f(B)\). For example, with \((x_{1},x_{2})\) from Figure 2; for combinations \((1,1)\) and \((u,1)\), it is that \((1,1)\preceq(u,1)\); therefore, \(f(1,1)\preceq f(u,1)\), in this way, if \(f(1,1)=1\) implies that \(f(u,1)\) can be \(1\) or \(u\) in order to \(f(x_{1},x_{2})\) be a regular function.
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 4 Smart Mobility Services and Applications
### Traffic Flow Optimization
This integration and the possibility of real-time access to information and instant communication with drivers will be reflected in terms of safety, less accidents, greater efficiency in driving and planning routes and reducing congestion, as well as the emission of gases.