esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 3 Data Used and Adopted Model of the Pseudorange  
RINEX (Receiver Independent Exchange Format) 3.x [13] observation data was used for all receivers except Topcon, for which the provided RINEX 2.x files are converted to RINEX 3.x with the utility _gfzrnx_ of the German Research Centre for Geosciences (GFZ) [14]. As to the orbits, satellite clocks and other ancillary data (e.g., quality flags), broadcast ephemeris in RINEX format downloaded from the MGEX FTP servers was used [1]. Several codes are available in the carriers at several frequencies, for the different GNSS constellations. The well-known \"iono-free\" linear combination of dual frequency code observations was used in order to remove first order ionospheric delay [9]. Table 6 summarizes the GNSS specific frequency bands and observation codes. The GPS broadcast clock model refers to iono-free combination of precise codes, i.e., C1W-C2W. We decided to analyze the combination C1C-C2W because about half of the stations (more precisely all the Leica and Trimble stations) do not track C1W. The error introduced using C1C in place of C1W depends on GPS satellite and varies between about \(-1\) and \(1\) m. This means about \(\pm 3\) nanoseconds which is within the accepted tolerance. For GLONASS, the k factor varies from \(-7\) to \(+6\) according to the satellite [15]: such information is provided in the header of the observation RINEX file. For Galileo the I/NAV combination of codes and the related navigation message is used [11]. The model of the pseudorange \(p(t)\) is described in Equations (2)-(6). In Equation (2) the effect of the Differential Code Bias (DCB) as listed in files provided by CODE has been ignored. For GPS the DCBs are up \(\pm 2\) nanoseconds which is within our error budget. For GLONASS the DCBs are station-dependent and the estimates are provided only for a small subset of stations, for the other constellations no DCBs are provided ([[ftp://igs.bkg.bund.de/IGS/products/mgex/](ftp://igs.bkg.bund.de/IGS/products/mgex/)]([ftp://igs.bkg.bund.de/IGS/products/mgex/](ftp://igs.bkg.bund.de/IGS/products/mgex/))). Table 7 contains the explanation of the variables. The matrix of partial derivatives is shown in Equation (7), where seven GNSS are considered: GPS, GLONASS, Galileo, BeiDou, QZSS, NAVIC and GAGAN. The term \(|\rho|\) indicates the geometric range between satellite and receiver. In Equation (8) \(y\) is defined as the column vector of observed minus computed: each element of \(y\) is the difference between iono-free linear combination of pseudoranges and the right side of Equation (2). The number of elements of \(y\) is equal to the number of satellites in view. The column vector of the solution, denoted as \(x\), is the solution of the normal equations expressed by Equation (8). The vector \(x\) consists of three coordinates, \(n\) terms \(TSC_{X}+dT_{Rec}\), where n is the number of GNSS's considered, and one TZD (Irpospheric Zenith Delay). The explicit form of the
# A Web-Based Application for Smart City Data Analysis and Visualization
## 4 Experimental Protocol: System Implementation
### Dataset Management and Description
#### 4.1.5 Electric Vehicle Charging Stations  
EV infrastructure sensors collect operational and usage metrics that support sustainability goals:  
* Charging session duration tracks the time each vehicle remains connected, which helps estimate availability and user behavior.
* Energy consumption (kWh) records energy transferred during each session, informing capacity planning and infrastructure scaling.
# Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
## 4 Solver by Alternating Direction Method of Multipliers (ADMM)  
### The Proposed Model Solution Based on the Alternating Direction Method of Multipliers  
To resolve the HTV-Lp model defined by Equation (3), the ADMM framework [33,39] is used. The complex problem was transformed into several simple decoupled sub-problems using variable substitution. The split variables can be defined as \(\mathbf{Z}_{1}=\mathbf{K}_{h}*\mathbf{F},\mathbf{Z}_{2}=\mathbf{K}_{v}*\mathbf{F},\mathbf{Z}_{3}=\mathbf{K}_{h }*\mathbf{K}_{h}*\mathbf{F},\mathbf{Z}_{4}=\mathbf{K}_{v}*\mathbf{K}_{v}*\mathbf{F},\mathbf{Z}_{5}=\mathbf{K}_{ h}*\mathbf{K}_{v}*\mathbf{F}\). The original problem is transformed into the following equations:  
\[\left\{\begin{array}{l}J=\frac{1}{2}\|\mathbf{H*F-G}\|_{2}^{2}+u_{1}\|\mathbf{Z}_{1} \|_{p_{1}}^{p_{1}}+u_{2}\|\mathbf{Z}_{2}\|_{p_{2}}^{p_{2}}+u_{3}\|\mathbf{Z}_{3}\|_{p_ {3}}^{p_{3}}+u_{4}\|\mathbf{Z}_{4}\|_{p_{4}}^{p_{4}}+u_{5}\|\mathbf{Z}_{5}\|_{p_{5}}^{ p_{5}}\\ \mathbf{Z}_{1}=\mathbf{K}_{1}*\mathbf{F}\\ \mathbf{Z}_{2}=\mathbf{K}_{2}*\mathbf{F}\\ \mathbf{Z}_{3}=\mathbf{K}_{3}*\mathbf{F}\\ \mathbf{Z}_{4}=\mathbf{K}_{4}*\mathbf{F}\\ \mathbf{Z}_{5}=\mathbf{K}_{5}*\mathbf{F},\end{array}\right. \tag{4}\]  
where \(\mathbf{K}_{1}=\mathbf{K}_{h},\mathbf{K}_{2}=\mathbf{K}_{v},\mathbf{K}_{3}=\mathbf{K}_{h}*\mathbf{K}_{h}, \mathbf{K}_{4}=\mathbf{K}_{v}*\mathbf{K}_{v},\mathbf{K}_{5}=\mathbf{K}_{h}*\mathbf{K}_{v}\).  
According to the ADMM principle, dual variables \(\mathbf{\bar{Z}}_{i}(i=1,2,\cdots,5)\) are introduced. The problem shown in Equation (4) can be converted into an unconstrained extended Lagrange function:  
\[J=\frac{1}{2}\|\mathbf{H*F-G}\|_{2}^{2}+\sum\limits_{i=1}^{5}\left(u_{i}\|\mathbf{Z}_ {i}\|_{p_{i}}^{p_{i}}+\frac{\beta_{i}}{2}\|\mathbf{Z}_{i}-\mathbf{K}_{i}*\mathbf{F}\|_{2}^ {2}-\left\langle\beta_{i}\mathbf{\bar{Z}}_{i},\mathbf{Z}_{i}-\mathbf{K}_{i}*\mathbf{F}\right\rangle \right), \tag{5}\]  
where \(\left\langle\mathbf{X},\mathbf{Y}\right\rangle\) represents the inner product of \(\mathbf{X}\) and \(\mathbf{Y}\), and \(\beta_{i}\) is the penalty factor of the quadratic penalty function.  
Figure 1: Contour maps of different norms. (**a**) L1-norm, (**b**) L2-norm, (**c**) Lp-pseudonorm (\(0\leq p\leq 1\)).
# A Cluster-Based Optimal Computation Offloading Decision Mechanism Using RL in the IIoT Field
## 3 Problem Definition and Proposed Algorithm
\(\prec_{d}(t)\): the task created by device \(d\) at time slot \(t\), \(\forall\ d\in D\)  
\(\prec\)\(q_{d}(t),\ q_{m}(t),\ q_{j}(t)\): the load of device \(d\), serving MECS \(m\), and serving D2D device \(j\) at time slot \(t\), \(\forall d,j\in D\), \(\forall m\in M\)  
\(\prec\)\(l_{d}(t),\ l_{m}(t),\ l_{j}(t)\): the location of device \(d\), serving MECS \(m\), and serving D2D device \(j\) at time slot \(t\), \(\forall d,j\in D\), \(\forall m\in M\)  
_Action_: \(a^{i}(t)\in\{0,1,2\}\), being the offloading strategy of a task at time \(t\) in cluster \(i\). _Reward (Penalty)_: \(R\big{(}s^{i}(t),a^{i}(t)\big{)}=1/\{(1-\alpha)\cdot E_{nor}(t)+\alpha\cdot L _{nor}(t)\}\), where \(\alpha\) is the weighting factor between 0 and 1. \(E_{nor}(t)\) indicates a normalized value of the total of computing and transmission energy consumed by the device when executing the task in a time slot \(t\). \(L_{nor}(t)\) denotes the normalized value of the execution delay of the task from the time slot \(t\) where it occurs until the job execution is completed. According to the above mentioned MDP, we update the Q-value as follows:  
\[Q\Big{(}s^{i}(t),a^{i}(t)\Big{)}\leftarrow(1-\delta)\cdot Q\Big{(}s^{i}(t-1),a^{i }(t-1)\Big{)}+\delta\cdot\Big{(}R\Big{(}s^{i}(t),a^{i}(t)\Big{)}\Big{)} \tag{6}\]  
where \(\delta\) is the learning rate. Our proposed algorithm does not consider multi-hop transmissions.
## 2 Engineering Overview and Research Background
### Engineering Geological Characteristics  
The elevation of the horizontal section of the main inclined shaft in Yuxi Coal Mine is +320, and the deepest distance is 582 m from the surface. Therefore, the original stress of the wellbore failure section itself is larger. The lithology of the main inclined shaft is mainly composed of mudstone and sandy mudstone, with a small amount of medium-fine sandstone, siltstone and limestone. The local surrounding rock shows obvious mechanical properties of soft rock. The main aquifers in the mine field mainly include karst fissure aquifers in the Middle Ordovician system, Taiyuan Formation aquifers in the upper Carboniferous system, Shanxi Formation aquifers in the lower Permian system, sandstone fissure aquifers in the upper Permian system, bedrock weathering zone aquifers, and Quaternary loose sand and gravel aquifers.  
The space of the direct water-filled aquifer in the roof of the No. 3 coal seam is dominated by sandstone fractures, which belong to the aquifer of the lower Permian Shanxi Formation. This layer is a clastic rock fractured aquifer, and there is no surface spring outflow in the mine field. The aquifer is mainly composed of medium-fine sandstone: thickness 1.51-15.02 m, average 8.97 m. In the drilling process, the maximum consumption is 0.242 m\({}^{3}\)/h, the minimum is 0.026 m\({}^{3}\)/h, generally, 0.161 m\({}^{3}\)/h. The water level has no obvious change, the unit water inflow is 0.001-0.00281 L/s\(\cdot\)m, the permeability coefficient is 0.0073\(\cdot\)0.0105 m/d, which is a weak water-rich aquifer. The water quality is Cl-K + Na or Cl-HCO\({}^{3}\)-K + Na. The safety of mining under pressure in the No. 3 coal seam is evaluated. The water inrush coefficient of Ordovician limestone karst water in the pressure area is 0.025\(\cdot\)0.047 mpa/m. Normal sections will not cause Ordovician limestone water inrush, and there is water inrush in structural water diversion sections. Water prevention (isolation) measures shall be taken during excavation in the structural water diversion section.  
Figure 2: Geographical location of the mine.
# Numerical Study on the Pressure Relief Characteristics of a Large-Diameter Borehole
## 4 Results
### Effects of the Hole Depth on the Pressure Relief Effect
#### 4.2.3 Reasonable Hole Depth Analysis  
By analyzing the evolution law of stress and strain energy density at different hole depths, the model of Figure 10 was established. As shown in Figure 10a, when \(L_{\text{h}}\leq L_{\text{P}}\), the peak stress and energy did not move to the deep area, and the peak stress and energy did not decrease. The peak stress and high energy were transmitted to the free surface, and a new free surface was formed at the end of the borehole, which was closer to the peak stress and the high energy than the roadway side. The transmission path of the peak stress and high energy to the free surface was shortened, and the impedance capacity of the surrounding rock before the peak was reduced, which can induce impact. As shown in Figure 10b, when \(L_{\text{P}}<L_{\text{h}}\leq 2.5L_{\text{P}}\), the stress and energy peaks extended to the deep area and decreased with the increase in the hole depth, so that the stress and energy of the borehole coal body decreased, reducing the danger of impact on the roadway side and forming a protective body.  
As shown in Figure 10c, when the \(L_{\text{h}}>2.5L_{\text{P}}\), the peak stress and energy were unchanged, and the enhancement of the pressure relief and energy release effect due to the hole depth reached its limit. Increasing the hole depth only leads the extension of the  
Figure 10: Analysis model of drilling length. (a) \(L_{\text{h}}\leq L_{\text{P}}\). (b) \(L_{\text{P}}<L_{\text{h}}\leq 2.5L_{\text{P}}\). (c) \(L_{\text{h}}>2.5L_{\text{P}}\).  
pressure relief and energy release area, and the stress readjustment time of the roadway after drilling is prolonged with the increase in the hole depth, which increases the time for the roadway to enter a steady creep and the deformation of the roadway, which is not conducive to the long-term stability of the roadway [30]. At the same time, for gas mines, therefore, the reasonable drilling range is determined as follows: \(L_{\text{P}}<L_{\text{h}}\leq 2.5L_{\text{P}}\).
## 3 Related Work
### Neural Network for GDOP and WDGOP Approximation
#### 3.1.2 Neural Network for WDGOP Approximation  
Similarly, neural network can also be applied to obtain WGDOP values. The author of this paper are devoted to using the novel Rprop-based architectures to simplify WGDOP calculation [27]. The original four types of GDOP mapping of traditional BPNN is extended to WGDOP based on Rprop and two new mapping architectures are proposed. Instead of calculating the inverse matrix, we collect the elements of related matrix and the desired WGDOP value to train the Rprop neural network in practical applications. Finally, the subset with minimum WGDOP is used to estimate the MS location. From simulation results, the proposed architectures always give a better accuracy, compared with the other architectures for WGDOP approximation. Compared with BPNN, the proposed Rprop-based architecture for Type 6 provides faster convergence on a solution and reduce the number of training iterations [27]. However, the drawback in the neural network-based WGDOP algorithms is the need of a long duration training period with several input-output patterns.
## 5 Conclusions  
The CIID-YOLO method introduced in this study significantly enhances indoor human detection accuracy by addressing challenges faced by traditional YOLOv8 models in indoor settings. By incorporating specific enhancements like spatial pyramid pooling with effective partial self-attention, a generalized separated and enhancement aggregation network, and global spatial and channel reconstruction convolution, this model offers a balanced solution for low-light indoor environments and multi-scale human detection.  
Moreover, a specialized dataset, the HCIE dataset, was curated for training and testing the indoor human detection model. Experimental results indicate that CIID-YOLO, with a model size of 22.8 MB, 11.24M parameters, and 27.8G FLOPs, achieves an mAP50 of 86.76% and an mAP50-95 of 64.19%, showcasing a 2.67% enhancement over the original algorithm on the HCIE dataset and superior detection efficiency compared to existing models.  
CIID-YOLO represents a significant advancement in real-time indoor human detection, with potential applications in various object detection domains like industrial production and smart buildings. Future enhancements could include integrating a low-light image enhancement network for clearer input images, exploring 3D visual data for occlusion handling and background reconstruction, optimizing parameter quantity and computational demands for improved real-time performance, and mitigating camera angle impacts on detection tasks for enhanced human detection capabilities.  
Conceptualization, Y.X. and Y.F.; methodology, Y.F.; software, Y.X.; validation, Y.X. and Y.F.; formal analysis, Y.F.; investigation, Y.X.; resources, Y.F.; data curation, Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, Y.X. and Y.F.; visualization, Y.X.; supervision, Y.F.; project administration, Y.F.; funding acquisition, Y.X. and Y.F. All authors have read and agreed to the published version of the manuscript.  
This study was funded by Project No. 7430125 of the Cross Research Support Program at Huazhong University of Science and Technology, which is named Development and Application Research of a Big Data Platform for Active Health Monitoring of Elderly People at Home Based on Artificial Intelligence. General Facility Dimension Layout Detection and Evaluation Method in Research and Application of Key Technical Standards in Ergonomics for Aging-Friendly Community, a Key Special Project of the National Quality Infrastructure System. Funding Number: 2022 YFF0607004-01.  
Not applicable.  
Figure 14: Test image set rotated by 30\({}^{\circ}\) angle.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measures**} & \multicolumn{4}{c}{**DTG (n = 17)**} & \multicolumn{4}{c}{**NDG (n = 17)**} & \multicolumn{2}{c}{\(p\)**-Value**} \\ \cline{2-10}  & \begin{tabular}{c} **Pre-Mean** \\ **(SD)** \\ \end{tabular} & \begin{tabular}{c} **Post-Mean** \\ **(SD)** \\ \end{tabular} & \begin{tabular}{c} **Difference** \\ **Mean (SD)** \\ \end{tabular} & \begin{tabular}{c} **\(p\)-** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Pre-Mean (SD)** \\ \end{tabular} & \begin{tabular}{c} **Post-Mean** \\ **(SD)** \\ \end{tabular} & \begin{tabular}{c} **Difference** \\ **Mean (SD)** \\ \end{tabular} & \
\begin{tabular}{c} **\(p\)-** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **Between** \\ **Group** \\ \end{tabular} \\ \hline Toe Touch Test & 2.19 \(\pm\) 9.00 & 7.15 \(\pm\) 8.88 & 4.96 \(\pm\) 4.70 & \textless{}0.001 & \(-\)0.977 \(\pm\) 12.46 & 3.99 \(\pm\) 11.16 & 4.97 \(\pm\) 4.09 & \textless{}0.001 & 0.997 \\ \hline \multirow{2}{*}{Y-Balance} & Lt & 89.23 \(\pm\) 11.71 & 95.41 \(\pm\) 11.20 & 6.19 \(\pm\) 4.43 & \textless{}0.001 & 93.96 \(\pm\) 7.69 & 98.53 \(\pm\) 8.69 & 4.57 \(\pm\) 3.42 & 0.003 & 0.349 \\ \cline{2-10}  & Rt & 89.93 \(\pm\) 13.36 & 96.80 \(\pm\) 11.04 & 6.87 \(\pm\) 4.68 & \textless{}0.001 & 92.44 \(\pm\) 8.26 & 99.05 \(\pm\) 9.58 & 6.61 \(\pm\) 5.86 & \textless{}0.001 & 0.890 \\ \hline \multirow{2}{*}{SLR} & Lt & 60.88 \(\pm\) 13.02 & 72.47 \(\pm\) 11.51 & 11.59 \(\pm\) 5.22 & 0.000 & 66.77 \(\pm\) 14.89 & 72.65 \(\pm\) 13.24 & 5.88 \(\pm\) 10.04 & 0.028 & 0.048 \\ \cline{2-10}  & Rt & 59.71 \(\pm\) 16.44 & 69.12 \(\pm\) 13.14 & 9.41 \(\pm\) 6.59 & \textless{}0.001 & 64.71 \(\pm\) 12.05 & 70.88 \(\pm\) 14.28 & 6.18 \(\pm\) 14.09 & 0.09 & 0.400 \\ \hline \multirow{2}{*}{ROM} & Lt & 14.82 \(\pm\) 4.57 & 16.12 \(\pm\) 3.35 & 1.29 \(\pm\) 3.69 & 0.167 & 14.12 \(\pm\) 4.05 & 15.47 \(\pm\) 3.79 & 1.35 \(\pm\) 4.66 & 0.249 & 0.968 \\ \cline{2-10}  & Rt & 15.06 \(\pm\) 4.56 & 16.53 \(\pm\) 3.94 & 1.47 \(\pm\) 4.16 & 0.164 & 14.49 \(\pm\) 2.81 & 17.24 \(\pm\) 4.67 & 2.65 \(\pm\) 3.90 & 0.013 & 0.401 \\ \hline \end{tabular}
\end{table} 
Table 3: Changes in outcome measures and the difference between pre- and post-intervention (0 to 4 weeks).
## 4 Experimental Results and Analysis
### Error Analysis  
The error correction capability increased with \(n\), but the larger the \(n\), the higher the probability of random bit error. Based on the raw bit error probability \(p\), parity bits, and message code \(cpar=2\cdot t\cdot m+|m(x)|\), the sector with correctable error \((P_{secErr})\) might increase and is given as:  
\[P_{secErr}=1-\sum_{i=t+1}^{cpar}\binom{cpar}{i}\cdot p^{i}\cdot(1-p)^{(cpar)-i} \tag{7}\]  
Figure 6 plots the bit error vs. sector error for different BCH codes. We can also see that the \(P_{secErr}\) decreased as \(p\) decreased. Furthermore, there was a slight increase in \(P_{secErr}\) when compared against different \(m\). This was due to the increase in the probability of error within bigger sector sizes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **GPGPU** & **CPU** \\ \hline Platform & \begin{tabular}{c} George GTX 760. 1152 cores \\ 1.033 GHz \\ \end{tabular} & \begin{tabular}{c} Intel Xeon i7 \\ 3.7 GHz \\ \end{tabular} \\ Clock Freq. & \
\begin{tabular}{c} 1.033 GHz \\ \end{tabular} &
\begin{tabular}{c} 3.7 GHz \\ \end{tabular} \\ Memory & GDDR5(2 GB), 6 Gbps & DDR2 (32 GB), 102.4 Gbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental setup.  
Figure 6: Raw bit error vs. sector error.
## 5 Discussion
The main question will be regarding how much confidence there can be that the images captured are correctly classified and sorted in the respective label for the re-training process. Humans should be involved in the training process of the DL algorithm. Therefore, image classification is only implemented in the final software. Meanwhile, a different approach was studied by [PERSON] et al. [49], where the classifier will be shared between both onboard and ground learning. The study was interesting, but that will eventuallybecome a burden to the communication load between the satellite and GS, where most of the CubeSat failures involving communication occur. In summary, although this study tested only three CNN networks, other existing models have not been initialized yet with the wildfire dataset, such as Inception, DeeperGoogLeNet, DenseNet, and U-Net. [PERSON] et al. [50] presented a study showing that image processing can have these three tasks: image classification/recognition, object detection and location, and image segmentation. An extension study is also recommended to further analyze the classified images from the satellite on the ground by implementing a CNN network like U-Net for image segmentation. Remote-sensing technology via satellite has an important role to play in reducing wildfire impacts. A CNN fusion of onboard image classification and on-the-ground image segmentation can truly assist the authorities in handling those catastrophes. [PERSON] and [PERSON] [51] reviewed that remote sensing techniques in fire ecology are related to fire-risk mapping, fuel mapping, active fire detection, burned area estimates, burn severity assessment, and post-fire vegetation recovery monitoring. Consistent with this study, the CNN image classification was implemented onboard a CubeSat to detect active fires. Moreover, the mapping and assessment should be conducted on the ground, utilizing CNN image segmentation. Overall, it can create an artificial intelligence ecosystem to overcome the natural disaster consequences, particularly in regard to global wildfires.
## 6 Performance Evaluation
### Discussion  
In this section, we have evaluated the performance of our proposed protocol, LBRP, for the multi-hop large volume content transmission, and verified the accuracy of our analytical model for the calculation of the probability distribution and moments of the back-off timer delay. Even a simple scenario as the one considered in this performance evaluation is enough to highlight the benefit brought about by accounting for link lifetime besides the hop length in the construction of the multi-hop chain of BVs. The key point is that we shift the emphasis of the performance metric on the overall content transmission time, which is a key performance indicator closer to user experience and hence valuable to assess the overall acceptability of the protocol. While a single path made of less hops is the furthest possible next BV searched for, path breakdowns are then more frequent due to vehicle mobility. Hence, the importance of accounting explicitly for the expected link lifetime when setting up the link chain of the path. This is achieved by LBRP only using information known to vehicles or carried via the same signaling message that is used to elect the chain of BVs. No beacons or pre-acquired maps or infrastructure nodes (RSUs) are required to support LBRP, which make it suited for an easy deployment, provided vehicles are equipped with DSRC devices (or any equivalent communication interface that enables direct proximity communication in the range of a few hundred meters, e.g., millimeter waves, VLC, LTE D2D).  
Note that the LBRP has one parameter \(\epsilon\) that balances the importance on the back-off timer delay between the link transmission distance and link lifetime. Given the deterministic network topology, we can find a balance between these two metrics, which cannot easily be achieved in a dynamic network. In the simulator, we set the value of \(\epsilon\) as 0.5 in advance for both protocol evaluation and model verification. This is not necessarily an optimal value for each network topology; nevertheless, we can see that LBRP outperforms the other three selected ones. As future work, we will continue to find the optimal strategy for \(\epsilon\) in each possible network topology, including more road networks.