esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Prediction of Operator Users' Number Portability Based on Community Detection
## 3 Conclusions and the Following Work  
In this paper, we take the telecom operator user information as the basic data regarding the groundwork of information clustering, feature engineering, and information prediction. Here, we successively discuss the role of Word2 Vec and its improved method in the data preprocessing of this project and compare the prediction precision of different models on telecommunications users' number portability.  
It was found that using Word2 Vec to model user data for consecutive months can uncover more potentially useful information in an easier way. In terms of feature selection for user information, it was found that the embedded method is more suitable based on the time it takes to perform and considerations regarding score performance. Through the prediction accuracy comparison, it was found that the DNN model and the Light GBM model are significantly better than the traditional KNN, SVM, and RF models. Finally, in this paper, we applied the method of building a community network for the first time and adopted a network embedding method based on the matrix factorization framework for network analysis. This study integrates the regional attributes of telecommunications users with portability prediction. The experimental comparison shows that after introducing the user community network, the evaluation indicators of the DNN model and the Light GBM model improved. Thus, the behavior of user porting numbers is highly related to the community environment they live in. Operators can solve targeted problems and bring back potentially lost customers.  
This research provides a reference for telecommunications enterprises in the prediction of number portability. In the future, research can be conducted regarding user information feature selections and model parameter optimizations to further improve prediction accuracy.  
Conceptualization, R.C. and B.L.; methodology, R.C.; validation, R.C. and B.L.; data curation, R.C.; writing-original draft preparation, R.C.; writing-review and editing, R.C.; visualization, R.C.; supervision, B.L.; project administration, R.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Because it involves the real user data of the telecom companies and privacy issues, the data is unavailable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: Model performance comparison.
## 5 Results and Discussion
### Failure Mechanism--Numerical Result  
Figure 4 illustrates the pattern of column bulging at varying depths for two conditions: uncased (OSC) and encased (VESC and HRSC) columns. Figure 6a depicts the bulging failure mechanism of OSCs occurring at a distance of \(D\)-\(2D\) from the top of the column, similar to the experimental result of the OSC case (Figure 5a). The reason is the generation of hoop stresses near the column head. For VESCs, when \(Lr\) = \(L\), minimal or no deformation was observed, as shown in Figure 6b(i). For \(Lr\) = 0.75 L and \(Lr\) = 0.5 L, slight bulging was observed near the junction of the encased and uncased zones (Figure 6b(ii,iii)). When \(Lr\) = 0.25 \(L\), the failure was similar to that of the OSC, which was the same as that of the experimental failure mechanism (Figure 6b(iv)).  
Figure 6: _Cont_.  
For HRSCs, when the spacing between reinforcements was 100 mm throughout the column length, local bulging was noted (similar to that of OSCs), indicating large spacing between the reinforcements (Figure 6c(i)). To reduce this deformation, the spacing should be minimized. For reinforcement provided only for the top half and bottom half, minimized bulging was noticed at the intersection of the reinforced and unreinforced areas (Figure 6c(ii,iii)), similar to the result obtained from the experimental analysis.
## 5 Discussion
### Model Limitations and Future Directions
#### 5.1.3 Impact of Input Quality on Model Performance  
The resolution of the input images is critical to the accuracy of object detection. Too low a resolution can lead to a decrease in object detection accuracy in the YOLO model. Conversely, too high a resolution increases computational requirements and can lead to model overfitting [60]. The quality of the data, including their completeness, consistency, and freedom from errors, has a direct impact on the performance of the model. The original resolution of the input images is critical for efficiency, especially when distinguishing fine details such as roof breaklines or roof texture.
# Pressure Transient Model of Water-Hydraulic Pipelines with Cavitation
## 3 Simulation Methods
### Method of Characteristics  
The continuity equation (Equation (1)) should be solved together with the momentum equation (Equation (2)) since they are partial differential forms about the two unknown parameters \(p\) and \(q\). However, via the MOC, they can be transformed into ordinary differential equations (Equations (8) and (9)) along the characteristic lines \(C^{+}\) and \(C^{-}\).  
\[C^{+}:\left\{\begin{array}{l}\frac{\rho c_{0}}{\pi r_{0}^{2}}\frac{dq}{dt} +\frac{dp}{dt}+c_{0}F(q)=0\\ \frac{dx}{dt}=c_{0}\end{array}\right. \tag{8}\]  
\[C^{-}:\left\{\begin{array}{l}\frac{\rho c_{0}}{\pi r_{0}^{2}}\frac{dq}{dt} -\frac{dp}{dt}+c_{0}F(q)=0\\ \frac{dx}{dt}=-c_{0}\end{array}\right. \tag{9}\]  
As shown in Figure 2, the pressure and flow rate values at points \(A\) (\(p_{A}\) and \(q_{A}\)) and \(B\) (\(p_{B}\) and \(q_{B}\)) are known. Integrating Equations (8) and (9) along the characteristic lines \(C^{+}\) and \(C^{-}\), the following Equation (10) is obtained to further derive the pressure and flow rate at point \(P\) (\(p_{P}\) and \(q_{P}\)).  
\[\left\{\begin{array}{l}\frac{\rho}{\pi r_{0}^{2}}q_{P}+\frac{1}{c_{0}}p_{P} =\frac{\rho}{\pi r_{0}^{2}}q_{A}+\frac{1}{c_{0}}p_{A}-\frac{\Delta x}{c_{0}}F (q_{A})\\ \frac{\rho}{\pi r_{0}^{2}}q_{P}-\frac{1}{c_{0}}p_{P}=\frac{\rho}{\pi r_{0}^{2} }q_{B}-\frac{1}{c_{0}}p_{B}-\frac{\Delta x}{c_{0}}F(q_{B})\end{array}\right.. \tag{10}\]Then, from Equation (10), the values of \(p_{P}\) and \(q_{P}\) can be obtained by  
\[\left\{\begin{array}{l}p_{P}=\dfrac{c_{0}}{2}C_{L}-C_{R}\\ q_{P}=\dfrac{\pi r_{0}^{2}}{2\rho}C_{L}+C_{R}\end{array}\right., \tag{11}\]  
where  
\[\left\{\begin{array}{l}C_{L}=\dfrac{\rho}{A}q_{A}+\dfrac{1}{c_{0}}p_{A}- \dfrac{\Delta x}{c_{0}}F(q_{A})\\ C_{R}=\dfrac{\rho}{A}q_{B}-\dfrac{1}{c_{0}}p_{B}-\dfrac{\Delta x}{c_{0}}F(q_{ B})\end{array}\right.. \tag{12}\]  
Details of the MOC are given by [PERSON] et al. [29] and [PERSON] et al. [3]. Incorporated with Equation (4), this method determines the time at which cavitation first arises in respective elements and the volume of cavitation. Furthermore, the method also determines whether cavitation has already collapsed at each time step \(\Delta t=\Delta x/c_{0}\) and the time at which cavitation occurs again.
# A Network-Based Method to Analyze EMI Events of On-Board Signaling System in Railway
## 4 Case Study
### Paths in the Model  
Although the explanation of the nodes can be found in Table 3, the reason for the events is still not investigated in detail. Here, we try to give the basic information and the attributions of the nodes and edges.  
Figure 9 gives the path of the EMI event in case A. As we discussed above, \(v_{2}\) is the disturbance from the pantograph arcing and \(v_{14}\) is the virtual coupling node of radiation. \(v_{17}\) is the antenna of the LTE-R and \(v_{26}\) is the function part. Then, the three edges are \(e_{2.14}\), \(e_{14.17}\), and \(e_{17.26}\) correspondingly. In other words, for radiation coupling represented by \(v_{14}\), the pantograph arcing in \(v_{2}\) will be received at \(v_{17}\) and the influence on \(v_{26}\) can be reflected in an index of its function. It should be noted that not all of the attributions of the nodes and edges have specific values. Some of them indicate the deterministic  
Figure 8: The case paths of the EMI events in EN model.  
value for existence. In path A, \(A_{e2\text{-}14}\)\(A_{v14}\), and \(A_{e17\text{-}26}\) are assigned with the deterministic value of 1. It indicates the path that happened in this scenario. On the contrary, \(A_{v2}\), \(A_{e14\text{-}17}\), and \(A_{v26}\) will be represented by measurement and simulation. In this case, \(A_{v17}\) is the measurement results from the receiving antenna, and \(A_{v26}\) can be considered as a result output module with a specific index by simulation.  
Figure 10 gives the path of the EMI event in case B. \(v_{5}\) is the disturbance from power lines in the auxiliary power and \(v_{13}\) is the virtual coupling node of cable coupling. \(v_{16}\) is the cable between the DMI device and the kernel \(v_{20}\) in OTCS. Then, the three edges are \(e_{5\text{-}13}\), \(e_{13\text{-}16}\), and \(e_{16\text{-}20}\) correspondingly. The path has the following scene meaning, for cable coupling represented by \(v_{13}\), the disturbance in power line in \(v_{5}\) will be received by \(v_{16}\) and the influence on \(v_{20}\) can be reflected on the device \(v_{15}\). If the signal cannot be received within a certain time, the DMI \(v_{15}\) will display a \"communication interruption error\". At this time, the \(v_{20}\) would give the emergency brake control order to the EMU leading to temporary parking. In this path, \(A_{e5\text{-}13}\), \(A_{e16\text{-}20}\), and \(A_{v13}\) are assigned with the deterministic value of 1. It indicates the path that happened in this scenario. \(A_{v5}\), \(A_{v16}\), and \(A_{e13\text{-}16}\) will be represented by simulation and measurement. \(A_{v20}\) is the signal results at the cable terminal.
# A Study on Analyzing Travel Characteristics of Micro Electric Vehicles by Using GPS Data
## 1 Introduction
The delivery service utilized micro-EVs for food and postal freight delivery. Additionally, the public service offered micro-EVs as a means of transportation for public affairs in rural areas. In this study, GPS data were used to analyze the travel and driving characteristics of micro-EVs, as well as the roads they traveled, in order to identify the most suitable service type for promoting the use of micro-EVs. GPS data collected from all regions where micro-EVs were used, covering a period of 10 months from January 2021 to October 2021, was utilized. The scope of the study will be discussed in detail in the Section 3. The proceeding sections of this study include the following contents. In the Section 2, the existing literature related to micro-EVs and methodologies was examined. Moreover, the key aspects to be referenced in this study were assessed and the strengths of the studies were identified. The Section 3 introduces the analytical methods used in this study. The Section 4 explains the findings based on the analysis methods introduced in the Section 3. Finally, the Section 5 summarizes the research content, derives implications, and outlines future research directions.
## 4 Discussion
### Characterization of the Heat-Affected Zone  
Studies on thermal effects caused by femtosecond irradiation are mostly theoretical, and only a few experimental works are available on understanding the effect of USP irradiation on the HAZ [4,39]. Previous works have estimated the width of the HAZ when irradiating aluminum and titanium alloy with femtosecond pulses to be less than 2 \(\upmu\)m [40, 41, 42]. However, it should be noted that those results, which are not in agreement with the present work, were obtained with different experimental conditions, such as higher pulse energy, the use of different materials, and different pulse durations. When analyzing the SEM and STEM-in-SEM micrographs of Figure 4d-f, it was observed that the multilayers are preserved until \(<\)100 nm of the cut edge, showing that the heat dissipation was confined within the optical penetration depth, common in low ablation fluence regimes [10]. The HAZ for all conditions tested was smaller than 100 nm, indicating no heat accumulation caused by multiple pulses. Additionally, using TEM, it was possible to observe distinct regions between the multilayers, with the mixing that happened on the region affected by laser irradiation and the oxide layer. Furthermore, the EELS results in Figure 6 show an oxide phase rich in Al and O, while Ni does not appear to be part of the oxide itself but rather in the form of Ni-Al inclusions. The shape of the Ni-containing regions protruding along the Al layers hints towards melting of aluminum, which means that a temperature of 660 \({}^{\circ}\)C was exceeded here. Strong variations in the composition of the mixed Ni-Al phases indicates rapid cooling without much time for homogenization [43,44]. Since these ablation mechanisms have been shown to have a significant mobilization effect on liquified matter, the not yet solid Ni-layers provide a foothold for the stronger affected Al layers with a buffering region of a combined Ni-Al melt formation within a few tens of nm. This intermixed superficial layer represents the main part of the HAZ within the original sample, which also plays the critical role in ignition of the self-propagating joining reaction. Although heat is released by the mixing of Ni and Al, the mixing volume is too small to provide sufficient energy to ignite a self-propagating reaction.
# Representing and Inferring Massive Network Traffic Condition: A Case Study in Nashville, Tennessee
## 1 Introduction
[17] applied clustering algorithms to obtain labels automatically from the data and presented the results of clustering analysis using both point-based and area-based features, highlighting the superiority of the area-based approach in producing meaningful clusters. [PERSON] et al. [18] proposed an improved fuzzy c-means clustering method to classify urban traffic states with real world traffic flow data. [PERSON] et al. [19] utilized dynamic time warping (DTW) k-means clustering to classify lane-changing risk profiles into several categories. In the traffic domain, clustering methods are mostly used to categorize time series over different locations.
# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 2 Proposed System
### Motion Detection
The general thresholds obtained experimentally for different lighting conditions are shown in Table 1.  
This method recognizes general movement patterns, even in low illumination and low contrast, as shown in Figure 2, namely, semitransparent smoke regions, as well as detects isolated pixels that are considered as noise since they are produced by the shadows, video compression, or camera quantification. It also has a low computational cost. However, several factors do not allow a complete removal of residual noise without using a post-processing stage. To this end, we use morphological and median filtering as the post-processing method. In this research, the morphological erosion operation was used. The mathematical formulation applied to the \(M(x,y)\) image for erosion is given as:  
\[M\Theta E=\{(x^{\prime},y^{\prime})(x^{\prime}+i,y^{\prime}+j)\in P_{M}(i,j), \forall(i,j)\in P_{E}\} \tag{9}\]  
where moving image \(M(x,y)\) is to be filtered; \(E\) is the reference structure or structural element, where the structural element of the diamond was used for this research; \(\Theta\) is the erosion symbol; and \(P_{M}\) and \(P_{E}\) are the pixels sets with value 1 of \(M(x,y)\) and \(E\), respectively. The median filter, with a window of \(7\times 7\), was used to remove the remaining isolated pixels that are considered as noise. In addition, the criterion for selecting candidate regions of movement for further analysis is that they must contain at least the same number of pixels as the block used in the training of classifiers, i.e., \(22\times 22\) pixels. These dimensions were chosen because they have a good statistical consistency and are less prone to noise from other textures captured on a larger block. The approved regions are obtained using a bounding box algorithm, which contains a binary region in the smallest possible rectangle avoiding the noise related to the shape of smoke, such as holes and omitted parts. Figure 2 shows the results of the detected motion regions, marked with white boxes in some of the videos used, which have different variations in lighting and contrasts, translucent smoke and smoke camouflaged by the background. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Movement Threshold** & **Minimum** & **Maximum** \\ \hline \(U\) & 3.77 & 10.11 \\ \(U_{F}\) & 7.03 & 21.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thresholds obtained for movement detection. Figure 2: Result of motion detection, where white boxes indicate regions with motion.
# Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks
## 5 Performance Evaluation
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Scheme** & **Demodulation** & **Decoding** \\ \hline FDMA-based & \(N_{modsym}\times N_{constel}\times N_{DRUI}\) & \(\left\{\begin{array}{l}L\times N_{mc}\times\log_{2}(N_{mc})\\ +0.5\times L\times(\log_{2}L+1)(\log_{2}L+2)\\ \end{array}\right\}\times N_{DRUI}\) \\ (w/ SIC) & \(N_{modsym}\times N_{constel}\times N_{UE}\) & \(\left\{\begin{array}{l}L\times N_{mc}\times\log_{2}(N_{mc})\\ +0.5\times L\times(\log_{2}L+1)(\log_{2}L+2)\\ \end{array}\right\}\times N_{UE}\) \\ TTMMA & \(\left(N-1\right)\times 2I_{max}\times M\times\) & N/A \\ \cline{2-3}  & \(\left(\sum\limits_{i=1}^{d}\left(\begin{array}{l}d\\ i\end{array}\right)(i-1)+2^{d}\right)\) & \multicolumn{1}{c}{N/A} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computational complexity of the FDMA-based discovery and trellis tone modulation multiple-access (TTMMA). Figure 10: Average number of discovered UEs according to the number of multiple-access UEs in TDL-A channel with only applying SIC to FDMA-based scheme. messages from all DRUs. At least 6 decoding trials are taken in the FDMA-based scheme without SIC. If the scheme runs with SIC, then multiple decoding trials can be carried out for a single DRU. One may repeat the polar code decoding up to the maximum number of times. The minimum number of these trials is 6 since there are 6 orthogonal DRUs, and in our simulation setting the maximum number is 14, the maximum possible number of UEs. The corresponding minimal and maximal complexities are given in Table 4. On the other hand, TTMMA runs with a fixed complexity which is 22% lower than that of the FDMA-based scheme without SIC and the minimal complexity of the SIC scheme.
# Modeling Undrained Shear Strength of Sensitive Alluvial Soft Clay Using Machine Learning Approach
## 4 Conclusions  
This study presents ML predictive modeling of the alluvial sensitive soft clay soil undrained shear strength, an essential parameter, and intensively investigated topic in geotechnical engineering. The creation of an accurate machine learning model facilities to achieve a significant cost reduction in field testing. In this regard, data is essential in machine learning challenges in order to build an accurate and reliable prediction model. One aspect of this investigation was the collection of extra data comprising many important features of the soft clay soil. Collecting realistic and rich datasets along with the selection of the appropriate algorithm leads to substantial improvement of prediction accuracy. The dataset that was utilized to forecast soil undrained shear strength has eight input variables, namely the moisture content, specific gravity, void ratio, dry unit weight, liquid limit, plasticity and consistency indexes, and Pocket penetration shear. Several ML approaches were trained including linear regression, regression trees, Gaussian process regression, the ensemble of trees, and SVR. Among those, the fine Gaussian SVR accurately exhibited the best fit model. Many evaluation metrics of the model prediction performance were computed including MSE, RMSE, and R\({}^{2}\) metrics. The latter value for the investigated soft clay dataset was evaluated to be 0.96 for the testing dataset. The attained results confirm the model reliability to accurately forecast the sensitive alluvial soft clay vane-test undrained sheer strength. This accurate prediction of ML model has a great positive impact in the geotechnical and structural engineering field saving hard work, high costs, and long time.  
Conceptualization, M.B.D.E. and M.S.; Data curation, M.B.D.E.; Formal analysis, M.B.D.E. and M.S.; Investigation, M.F.A. and M.S.; Methodology, M.B.D.E., M.F.A. and M.S.; Project administration, M.F.A.; Software, M.S.; Validation, M.F.A.; Writing--original draft, M.S.; Writing--review and editing, M.F.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 7: Soft clay USS plotted against sample number of the measured, predicted, and tested dataset.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the Deanship of Scientific Research, Qassim University for funding the publication of this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization
## 2 System Model and Proposed Technique
### Proposed Localization Technique
#### 2.4.1 Updating \(\mathbf{L}\) for fixed \(\mathbf{\Phi^{(u)}}\)  
In the first sub-problem, (13) is rewritten as  
\[\min_{L}(\rho_{1}+\rho_{1}-2\rho_{3}), \tag{14}\]  
where  
\[\rho_{1}=\sum_{u=1}^{R}\sum_{i<j}\Phi^{(u)^{\gamma}}v_{ij}d_{ij}^{(u)^{2}}, \tag{15}\]  
\[\rho_{2}=\sum_{u=1}^{R}\sum_{i<j}\Phi^{(u)^{\gamma}}v_{ij}d_{ij}^{2}(\mathbf{L})^{( u)}, \tag{16}\]  
and  
\[\rho_{3}=\sum_{u=1}^{R}\sum_{i<j}\Phi^{(u)^{\gamma}}v_{ij}d_{ij}^{(u)}d_{ij}( \mathbf{L})^{(u)}. \tag{17}\]  
Optimization of this sub-problem is achieved by a majorization approach. It is clear from (14) that the first term \(\rho_{1}\) is dependent on fixed weights \(v_{ij}\) and constant estimated distance \(\tilde{d}_{ij}\), thus ignored from the optimization process. The second term in (14) is the sum of weighted squared distances, which is written as  
\[\rho_{2}=\mathit{Tr}(\mathbf{L}^{T}(v_{ij}\Phi^{(u)^{\gamma}})\mathbf{L})=\mathit{Tr} (\mathbf{L}^{T}\mathbf{\Theta}\mathbf{L}), \tag{18}\]  
where \(\mathbf{\Theta}\in\mathbb{R}^{((m+n)\times(m+n))}\) with elements  
\[\Theta_{ij}=\begin{cases}-\sum_{u=1}^{R}\Phi^{(u)^{\gamma}}v_{ij},&\text{if $i \
eq j$,}\\ \\ \sum_{j=1,j\
eq i}^{m+n}\sum_{u=1}^{R}\Phi^{(u)^{\gamma}}v_{ij},&\text{if $i=j$.} \end{cases} \tag{19}\]  
The last term in (14) is the sum of weighted distances for all the observations, by Cauchy-Shwartz inequality \(\rho_{3}\) is approximated as  
\[\rho_{3}=\sum_{i<j}\left(\sum_{u=1}^{R}\Phi^{(u)^{\gamma}}v_{ij}\tilde{d}_{ij }^{(u)}\right)d_{ij}(\mathbf{L})\leq\mathit{Tr}(\mathbf{L}^{T}\mathbf{\mathcal{B}}\mathbf{ \mathcal{Z}}), \tag{20}\]  
where \(\mathbf{\mathcal{Z}}\) are the estimated points from previous iteration and the elements of \  
By setting (24) to zero, we get  
\[\hat{\mathbf{L}}=\mathbf{\Theta}^{+}\mathbf{\mathcal{B}}\mathbf{\mathcal{Z}}, \tag{25}\]  
where \(\mathbf{\Theta}^{+}\) represents the Moore-Penrose inverse of \(\mathbf{\Theta}\). There are no missing elements in matrix \(\mathbf{\Theta}\) therefore, (25) is simplified as  
\[\hat{\mathbf{L}}=\frac{1}{(m+n)\sum_{u=1}^{R}\Phi^{(u)^{\gamma}}}\mathbf{\mathcal{B}} \mathbf{\mathcal{Z}}. \tag{26}\]  
The estimated locations in (26) can be refined using any linear transformation method with the help of anchors. Here we used Helmert transformation [31] for transformation from local coordinates to global coordinates, i.e.,  
\[\tilde{\mathbf{L}}=\kappa\Omega^{T}(\hat{\mathbf{L}})+\Psi, \tag{27}\]  
where \(\kappa\), \(\Omega\) and \(\Psi\) are the scaling, rotation, and translation factors for coordinates transformation, respectively.