esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 2 Methods and Data
### Detection of Flood-Prone Area by Sentinel-1  
Sentinel-1A and Sentinel-1B, launched in April 2014 and April 2016, respectively, are the first of a series of Earth-imaging satellite constellations operated under the Copernicus program of the European Space Agency. The Sentinel-1 satellites collect data in four separate imaging modes: interferometric wide-swath (IW), strip map (SM), extra wide-swath (EW), and wave (WV), each with its own acquisition configurations [63]. The fact that radar beams cannot penetrate dense foliage is its fundamental disadvantage [64]. Sentinel-1 SAR data packages, which are freely available through the Sentinel Scientific Data Hub, can be used to identify backscatter signals from underwater areas (scihub.copernicus.eu, accessed on 6 May 2021). Sentinel-1 Level-1 ground range detected (GRD) data were projected onto the land using an Earth ellipsoid model (WGS84) in the current work since the specular reflection of C-band signals over flooded areas is substantially lower than over bare ground. Finally, using the SAR approach, Sentinel-1 SAR data were used to locate and map flooded areas [38; 65].
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 3 GNSS Error Analysis  
### Low-Cost GNSS in Urban Areas  
Low-cost multi-GNSS receiver manufacturers have recently announced the development and release of low-cost multi-frequency multi-GNSS receivers [37]. Accordingly, the analysis in this section considers a vehicular platform equipped with a multi-frequency multi-GNSS receiver capable of tracking both code and carrier phase of GNSS signals.  
Development of an extensive dense reference network in support of CDGNSS consumer vehicular positioning in urban areas, as suggested in [38], could be an expensive affair. PPP is a low-cost alternative to CDGNSS that requires only a sparse network of reference stations across the globe, but is not considered a viable option for urban GNSS positioning in this paper because the constant cycle slips and outages experienced in urban areas [6] make it difficult for PPP's float carrier phase ambiguity estimates to converge [7], in which case PPP degrades to code-phase positioning accuracy.  
While convergence of PPP carrier-phase ambiguities may be infeasible in urban areas, a partial PPP solution that exploits precise satellite orbits and clocks, as well as ionospheric and tropospheric corrections, can certainly improve the accuracy of code-phase-based GNSS position estimates. Since connected and automated vehicles will performe enjoy network connectivity, this paper assumes the availability of such GNSS corrections. Thus, the kind of GNSS errors assessed in this section lie between those corresponding to the two extremes of standard standalone code-phase positioning and PPP. This type of GNSS positioning, hereafter referred to as enhanced code-phase positioning, exploits both code and carrier phase or frequency tracking, but, as opposed to PPP, does not attempt to estimate a quasi-constant float carrier phase ambiguity, making it suitable for urban applications.
## 8 Conclusions  
In this paper, a WAMS-based optimal UFLS scheme is proposed. A procedure for online identification of the SFR model based on WAMS is also proposed. Then, the identified SFR model is used for developing a new UFLS framework. UFLS could be started much earlier unlike in traditional scheme, since based on the ROCOF the disturbance would have been estimated well in advance. Three objective functions were proposed for UFLS optimization model. The ICA which works well in solving the optimization problems is adopted for obtaining the optimal solutions. For our simulations all constraints were well accounted for in the decision-making process. The effects of time delay though not presented in detail in this paper are shown to have significant effect on UFLS. The conclusion of the findings in this work can be summarized as follows  
* The SFR model of interconnected power system can be realized accurately using WAMS technologies.
* The use of evolutionary computing methods can help the performance of adaptive UFLS techniques.
* Multi-stage UFLS technique has better performance in comparison with other techniques.
* The communication delay has noticeable effects in the performance of WAMS-based UFLS techniques that should be considered in practice.  
As research directions for future studies in this important topic, the practical issues related to WAMS can be considered. In this regard, the possible cyber-attack issues should be taken into account for future power systems. Such attacks during emergency events might result in undesired consequences such as blackouts. Furthermore, new UFLS techniques based on the dynamic model of power systems is required for improving the general stability of modern power systems. Moreover, the impact of inertia variations on practical UFLS techniques can be considered as a good research point in the topic of the paper.  
All authors have worked on this manuscript together and all authors have read and approved the final manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UFLS & under-frequency load-shedding \\ ICA & imperialist competitive algorithm \\ ICE & Inter-Empire Competition \\ PMU & phasor measurement units \\ GPS & global positioning system \\ SFR & system frequency response model \\ GDB & Governor Dead Band \\ ISE & integral of squared error \\ ITSE & integral of time multiplied by squared error \\ Obj & objective function \\ ROCOF & rate of change of frequency \\ WAMS & wide-area measurement system \\ TSO & transmission system operators \\ WAMPAC & wide-area monitoring, protection and control \\ \end{tabular}
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 8 Applications
### Pants Decomposition Curves  
A Reeb graph naturally gives rise to a collection of curves that can be used to decompose a surface into a pair of pants. A pair of pants is a genus zero surface with three boundary components. Beside surface segmentation [87], surface pants decomposition has found applications in mesh parametrization [88], surface matching [89], and surface classification and indexing [90]. The method for obtaining a pants decomposition from the Reeb graph is illustrated in Figure 22 and is described, as follows:  
1. Let \(R^{\prime}(M,f)\) be the deformation retract of \(R(M,f)\). This graph can be obtained by recursively deleting nodes with valency one from \(R(M,f)\) and the edge attached to them until no such indices exist. We exclude from this deletion the 1-valence nodes originating from the boundary of \(M\). We also delete all of the nodes with valency 2 and combine the two edges that meet at that node to form a single edge. This step is illustrated in step (3) Figure 23.
2. We select one interior point from each edge in \(R^{\prime}(M,f)\), provided that this edge does not have a node of valency one.
3. We use our Reeb algorithm to determine the curves on the surface that correspond to the points that we selected on the graph in the previous step.  
Figure 21: Examples of cut system curves on triangulated surfaces.  
The results of the previous algorithm were tested on triangulated meshes with various topological complexities. We show some examples in Figure 23.  
**Branch Curves.** A branch curve on a surface \(M\) is a simple closed curve that bounds a topological disk on \(M\). Such a curve is also called null homotopic. A branch curve is determined by a Reeb graph edge, which has a vertex of valence one. Note that cutting the surface along a branch curve increases the number of connected components of the surface. Cutting along such curves can be used for the segmentation of a genus zero surfaces. There are many Reeb graph-based segmentation algorithms in the literature for segmentation of genus zero surfaces, such as the surface obtained from a humanoid character. See, for instance, [54; 83; 84]. Because this type of curves is essentially utilized elsewhere in the literature, we simply list it here for completeness of our discussion. However, all of these methods lack the description of a method to extract the manifold data from the Reeb graph data.  
Figure 22: A Reeb graph can be used to segment a surface into a collection of topologically consistent patches. (1) The surface \(M\), with a scalar function \(f\), is given as an input. (2) The Reeb graph of \(R(M,f)\) is calculated. (3) The deformation retract graph \(R^{\prime}(M,f)\) of the graph \(R(M,f)\) is calculated. (4) For each edge in the graph \(R^{\prime}(M,f)\), we select a point. (5) We select the curves on the manifold \(M\) that corresponds to the points that were selected in the previous step.
## 2 Experimental Study
### The Development of a Time Scale Method  
The purpose of this section was to develop a time scale method for investigating how critical factors (e.g., tides, rainfall and vehicle vibration) affect the structural conditions of the underground road box-type structure. The time scales and details of the critical factors investigated in this study are listed in Table 2.  
Figure 10: Health condition monitoring system for the compound road box-type structure in the reclamation area.  
Figure 9: CWST01 stability analysis system for remote fiber optic sensors.  
* Effects of tide on crack width in the box-type structure (\(t_{tidc}\), time scale: Months)
* Critical time-dependent crack width can be defined when \(\frac{\partial S_{capall}}{\partial t_{tidc}}=0(\frac{\partial^{2}S_{capall}}{ \partial t_{tidc}^{2}}<0)\);
* Effects of rainfall on crack width in the box-type structure (\(t_{rainfall}\), time scale: Days)
* Critical time-dependent crack width can be defined when \(\frac{\partial S_{capall}}{\partial t_{rainfall}}=0(\frac{\partial^{2}S_{capall }}{\partial t_{rainfall}}<0)\).  
The width variation of cracks influenced by vehicle vibration, which is non-stationary in nature, were processed using short-time Fourier transform (STFT) analysis. STFT is a time-frequency localization method which can be defined as:  
\[F(t,f)=\int_{-\infty}^{\infty}x(u)g^{*}(u-t)e^{-2\pi fu}du \tag{5}\]  
where \(g(t)\) is the window function and \(x(t)\) is the vehicle vibration signal.  
By discretizing the STFT and sampling it at points (\(m\Delta t\), \(n/(N\Delta t)\)) in the time-frequency domain, we obtain:  
\[F(m,n)=\sum_{k=0}^{N-1}x(k\Delta t)g^{*}(k\Delta t-m\Delta t)e^{-2 nk\pi/N} \tag{6}\]  
where \(x(k)\) is the discrete form of the signal, \(\Delta t\) is the sampling interval and \(N\) represents the sampling points, \(m\), \(n=0,\ldots\)\(N-1\). A window function (\(g(t)\)) could exist to analyze the width variation of cracks influenced by vehicle vibration in the time and frequency domains simultaneously, and ultimately the effects of vehicle vibration on the tunnel.  
To quantify the correlation between temperature change and crack propagation, the Pearson correlation coefficient (\(\rho_{X,Y}\)) [19] was adopted:  
\[\rho_{X,Y}=\frac{\sum{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}}{\sqrt{\sum{(X _{i}-\overline{X})}^{2}(Y_{i}-\overline{Y})}^{2}} \tag{7}\]  
The value of \(\rho_{X,Y}\) is between \(-1\) and \(+1\). The larger \(|\rho_{X,Y}|\) is, the stronger the correlation between vectors \(X\) and \(Y\) becomes. Thus, the correlation between temperature change and width variation of cracks can be established.
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 4 xPON Activation Processes
### NG-PON2 Activation Process
While the ONU completes upstream transmission of SDU units whose fragmentation already began in the previous subset, it performs further fragmentation if necessary and transfers any unfragmented SDU units. State O6, Intermittent LODS state: The ONU can reach this state from the O5 state in the case of downstream synchronization loss. Upon entering this state, the unit turns on the timer. When wavelength channel protection (WLCP) is enabled, the TO3 timer is turned on. If the WLCP is turned off, the TO2 timer is turned on. If the downstream signal is restored before any of the two timers expire, the ONU returns to the O5 state. However, once the TO2 timer expires, the ONU moves to the initial state O1. If, on the other hand, the TO3 timer expires, the ONU moves to the O8 state (to be described; see below). State O7, Emergency Stop State: The ONU moves to this state if it receives the Disable Serial Number message with the \"Disable\" option on. In this case, it deactivates the laser. However,  
Figure 2: State diagram of ONU unit for NG-PON2 [28]. it keeps the downstream synchronization machine running and analyzes the PLOAM section of the downstream FS frames (however, at this point it is forbidden to pass any downstream data or send any upstream data). If the ONU receives the Disable Serial Number message with the \"enable\" option on, it returns to the O1 state. State O8, Downstream tuning state: In this state, the ONU tries to restore the transmission using the new TWDM channel while maintaining the configuration of the TC layer except for its burst profiles. In this state, the TO4 timer is used. When it expires, the ONU returns to the initial O1 state and discards the TC layer configuration. In the O8.1 substate, also known as Off-Sync, the ONU tunes its receiver and tries to synchronize with the downstream signal. As soon as it is synchronized, it moves to the O8.2 substate, known as Profile Learning. In this state, it analyzes the downstream framing sublayer (FS) frame and starts collecting information about the system, channel and burst profile. When enough information has been gathered, the ONU will evaluate the downstream wavelength of the channel. If this channel is suitable for activation, the ONU continues the activation process and moves to the next state. However, if it is not suitable, it searches for an alternative channel and returns to the O8.1 substate, retaining the system and channel information, but discarding the burst profile information. State O9, Upstream tuning state: As long as the ONU is in this state, it waits for a feedback from the OLT and performs a fine-tuning of its transmitter. Subsequently, it moves to the O5 state. In this state, the TO5 timer is started. If this timer expires, the ONU returns to the initial state.
## 3 Results and Discussion
### Ratio of the Upper Kerf to Lower Kerf  
Table 6 presents the results of the variance analysis conducted on the ratio of the upper kerf to the lower kerf. The analysis indicated that the linear parameters of the laser power (LP) and the laser cutting speed (CS) were significant factors. Furthermore, the quadratic term of the cutting speed (CS\({}^{2}\)) and the focal plane position (FPP\({}^{2}\)) were also determined to have a significant effect. The final regression equation for the ratio of the upper kerf to the lower kerf, which considers the significant parameters and coded values, is given with Equation (4).  
\[\begin{split}(\text{Ratio})^{0.87}=+13.76-0.30\times\text{LP}-0.89 \times\text{CS}+0.12\times\text{FPP}+0.03\times\text{LP}\times\text{CS}-0.06 \times\text{CS}^{2}-0.30\times\text{FPP}^{2}\end{split} \tag{4}\]  
Figure 9 displays the response surfaces for the ratio of the upper kerf to the lower kerf. As shown in Figure 9a, the focal plane position (FPP) has an effect on this response in the middle position at its maximum value. Moreover, both Figure 9a and 9b demonstrate that the minimum ratio is observed at the lowest laser power. It is worth noting that the linear effect of the laser power changes, which can be attributed to the interaction effect of the laser power and the focal plane position.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source of Variation** & **Degree of Freedom** & **Sum of Squares** & **Mean Squares** & **F Value** & **\(p\) Value** \\ \hline Model & 3.87 & 6 & 0.65 & 3.75 & 0.0840 \\ A-LP & 1.04 & 1 & 1.04 & 6.03 & 0.0575 \\ B-CS & 1.38 & 1 & 1.38 & 8.04 & 0.0365 \\ C-FPP & 0.16 & 1 & 0.16 & 0.93 & 0.3787 \\ AB & 0.79 & 1 & 0.79 & 4.59 & 0.0851 \\ B\({}^{2}\) & 1.51 & 1 & 1.51 & 8.76 & 0.0315 \\ C\({}^{2}\) & 2.06 & 1 & 2.06 & 12.00 & 0.0180 \\ Residual & 0.86 & 5 & 0.17 & & \\ Lack of Fit & 0.55 & 3 & 0.18 & 1.21 & 0.4831 \\ Pure Error & 0.31 & 2 & 0.15 & & \\ Total & 4.73 & 11 & & & \\ \hline R-Sq = \% 81.84 & & & R-Sq (adj) = \% 60.04 & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Revised analysis of the variance of the ratio of the top kerf to the bottom kerf.  
Figure 9: (**a**) Contour plot for the focal plane position and the laser power, and (**b**) surface plot for the focal plane position and the laser power (numbers in the figure correspond to the ratio in that point, red color: more ratio and blue color: less ratio).
# Combined Optimization of Maintenance Works and Crews in Railway Networks
## 3 Methodological Framework and Problem Formulation
### Parameters  
* \(G_{al}\)--frequencies per T scheduling period of activities \(a\in RA\) on link \(l\in L\), where \(G_{al}=0\) if the activity \(a\in RA\) is not relevant to the link \(l\in L\);
* \(TW_{pl}\)--total workload (in hours) for a long-term work/project \(p\in PA\) on link \(l\in L\);\(I_{pl}\)--0-1 values depending on whether a project \(p\in PA\) will be performed on the link \(l\in L\) during the scheduling period \(t\in T\);
* \(D_{kpl}\)--the duration of a project \(p\in PA\) on link \(l\in L\) using option \(k\in K\), with \[D_{kpl}=ceil\bigg{(}\frac{TWpl}{7\cdot maxl}\bigg{)}\text{ if }k\ =\ 1,\] (1) \[D_{kpl}=ceil\bigg{(}\frac{TWpl}{48}\bigg{)}\ \ \text{if }k\ =\ 2,\text{ and}\] (2) \[D_{kpl}=ceil\bigg{(}\frac{TWpl}{7\cdot 24}\bigg{)}\ \ \text{if }k\ =\ 3\] (3)
* \(LSTpl\)--the earliest possible starting time of project \(p\in PA\) on link \(l\in L\);
* \(USTpl\)--the latest possible starting time of project \(p\in PA\) on link \(l\in L\) ;
* \(F_{al}\)--\(F_{al}=ceil(T/Gal)\) is the planning cycle for each routine work \(a\in RA\) at each different link \(l\in L\);
* \(C_{ll}\)--cost for possession of link \(l\in L\) at time \(t\in T\);
* \(CCos_{kl}\)--cancellation cost on link \(l\in L\) using option \(k\in K\);
* \(MCos_{kpl}\)--penalty cost for resource utilization if option \(k\in K\) is chosen for performing project \(p\in PA\) on link \(l\in L\).  
Contrary to the base model, our enhanced model searches and makes decisions for the work crews. For the execution of the maintenance tasks \(a\in A\) by the work crew \(e\in E\), their availability for link \(l\in L\) at every time \(t\in T\) is predefined by the parameter \(Z_{ealt}\). Moreover, for moving from one link to another, a distance \(W_{lq}\) is defined between the two links \(l\in L\) and the work crew links \(q\in LL\). In the case of two identical links, \(W_{lq}\) will take a value of 0 since no movement distance is measured. Otherwise, it will take the value of 1, which corresponds to the kilometric distance defined and is added to the objective function. The added parameters to our enhanced model are described below:  
* \(Z_{ealt}\)--binary, indicates whether a work crew \(e\in E\) can serve a link \(l\in L\) at time \(t\in T\), or not;
* \(W_{lq}\)--distance between two links.  
To be able to distinguish which tasks will be executed, at which time, on which link, and whether the link is used for a task, we define the following binary decision variables that will be used in the problem formulation.
## 4 Experiment and Analysis
### Experimental Detail  
We deploy the LDE-Net network on a Linux server with a hardware environment, which consists of an Nvidia V100 GPU with 24 GB memory and 72 GB RAM. The software environment includes Cuda version 11.0, Cudnn version 8.74, TensorFlow version 1.15, and Python version 3.8.  
In the model training process, we set the batch size to 8 and used an Adam optimizer with an initial learning rate of 0.01. We trained the model for 100 epochs, with a learning rate reduction of 5% after each epoch. The number of nearest neighbor points searched by KNN was set to 16. A fixed number of points (40,960 points) were sampled from each training point cloud and fed into the network for training. Multiple non-repetitive samplings were performed to ensure that the network could learn the features of the original point cloud.  
During the testing phase, we sampled a fixed number of points from each point cloud as input for inference. To ensure that all points in each point cloud were inferred, we performed non-repetitive sampling multiple times until all points were inferred to complete the point cloud test. In order to evaluate the performance of the model, we used the following evaluation methods for comprehensive evaluation: overall accuracy (OA), mean class accuracy (mAcc), and mean intersection over union accuracy (mIoU). These can be defined as follows:  
\[\text{OA}=\frac{\sum_{i=0}^{n-1}p_{ii}}{\sum_{i=0}^{n-1}\sum_{j=0}^{n-1}p_{ij}} \tag{12}\]  
\[\text{IoU}=\frac{p_{ii}}{\sum_{j=0}^{n-1}p_{ij}+\sum_{j=0}^{n-1}p_{ji}-p_{ii}} \tag{13}\]  
\[\text{mIoU}=\frac{1}{n}\sum_{i=0}^{n-1}\frac{p_{ii}}{\sum_{j=0}^{n-1}p_{ij}+ \sum_{j=0}^{n-1}p_{ii}-p_{ii}} \tag{14}\]  
where \(p_{ij}\) represents the number of points with ground truth label \(i\) and predicted label \(j\) in the point clouds and \(n\) represents the number of semantic classes.