esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System modeling
### Unsupervised learning methods
#### 4.2.3 Density-based clustering of application with noise (DBSCAN)  
DBSCAN presents a very unique clustering process. Different from many other cluster analysis methods including ART-2a, DBSCAN performs a cluster territory expansion process by examining the density and continuity of sample distribution. The entire clustering process is controlled by two parameters: neighbor number (\(k\)) and neighborhood radius (\(\varepsilon\)). Briefly, the clustering process starts with a randomly selected sample and a cluster is set up for this sample. Controlled by the two predefined parameters, \(k\) and \(\varepsilon\), the process will expand the territory of this cluster and examine if the cluster can be further expanded. If not, a new cluster will be generated. This searching process will continue until all the samples are clustered. DBSCAN presents a one-step clustering process based on a recursive procedure. The details can be found in the literature [84-86].  
Figure 7 shows a schematic illustration of the difference between the clustering principles of ART-2a and DBSCAN. DBSCAN is able to cluster the samples that have a continuous distribution into one group, while ART-2a and other methods such as K-means divide each group into a couple of small groups based on the sample similarity (distance). With this advantage, DBSCAN surpasses many clustering methods including ART-2a in clustering the data with continuous and irregular distributions.  
A comparison study of the clustering effects of DBSCAN and ART-2a on MS data [81, 87] indicates that a proper vigilance factor can produce a reasonable ART-2a clustering result, but an overly fine or \"crashed\" clustering result for an ART-2a with a high vigilance factor can be recovered by a post-processing strategy. DBSCAN seems to be more effective and robust in the post-processing step than conventional regrouping analysis [81].
## 2 The Comprehensive Vulnerability Rate Model
### Ice Accretion Failure Rate Model  
Calculating different types of failure rates is the primary step in risk assessment. Fault events based on icing weather are mainly mechanical faults and electrical faults, which consist of five events: no event \((Y_{1})\), distribution line disconnection \((Y_{2})\), inverted tower \((Y_{3})\), distribution line dancing \((Y_{4})\), and insulator flashover \((Y_{5})\). The fault rate in ice weather is conditional probability. The probability of different events is calculated according to parameters such as ice accretion condition, wind load, critical voltage of insulator, and actual running voltage combined with the difference between the parameters of the lines and the micro-topography and micro-meteorology [22]. The 66 kV overhead distribution line is taken as the research object in this paper. The probability of the icing events can be formulated as:  
\[P_{k}^{t}(Y_{i})\ =\ 1\ -\ \prod_{j=1}^{z}\left(1\ -\ P_{kj}^{t}(Y_{i})\right) \ i\ =\ 2,3,4,5 \tag{4}\]  
Set \(Z\) is the span of the distribution line, the probability of no fault event, line disconnection event, inverted tower event, line dancing event, and ice flashover event of the distribution line \(k\) is \(P_{k}^{t}(Y_{1})\), \(P_{k}^{t}(Y_{2})\), \(P_{k}^{t}(Y_{3})\), \(P_{k}^{t}(Y_{4})\), \(P_{k}^{t}(Y_{5})\), then \(P_{k}^{t}(Y_{1})\) can be calculated by the following formula:  
\[P_{k}^{t}(Y_{1})\ =\ 1\ -\ \sum_{a=2}^{5}P_{k}^{t}(Y_{i}) \tag{5}\]  
Finally, the definition of \"faults directly related to icing events\" is event \(X\), according to the full probability formula, the ice failure rate of the distribution line as follows:  
\[P_{k}^{t}(X)\ =\ \sum_{a=1}^{5}P(X|Y_{i})\cdot P_{k}^{t}(Y_{i}) \tag{6}\]  
These events are not only related to the ice accretion condition of the line, but also involve complex analysis processes such as mechanics, meteorology and aerodynamics. In the calculation, a large amount of measurement data, grid component design parameters, state parameters, etc. are required. At the same time, it is limited by the distribution network related monitoring system. Therefore, a simplified calculation based on the existing reference and actual conditions is used in this paper. See Appendix A for details.
## 4 Protocol Description
### Basics  
A BEACON is periodically broadcast by each node (sensor and gateway) throughout its network lifetime and is used for:  
1. Neighbor discovery
2. The estimation of link quality between neighboring nodes
3. The update of gradients throughout the network  
It contains the:  
1. Source node identifier
2. The monotonically-increasing sequence number for the detection of expired links and the prevention of routing loops
3. The gradient towards the gateway  
During network initialization, each sensor node has a gradient value of infinity. The gateway then updates its gradient to be zero and sends this information together with its current sequence number, in its next BEACON. Upon receiving a non-infinite value of the gradient in a BEACON from a neighboring node \(v_{j}\), the sensor node \(v_{i}\) updates its gradient (according to the selected metric) if the following conditions are satisfied: (i) the estimated link quality between \(v_{i}\) and \(v_{j}\) is above a pre-defined threshold T; and (ii) the gradient of \(v_{j}\) is smaller than the gradient of \(v_{i}\).  
When a sensor node \(v_{i}\) has a DATA packet to forward to the gateway, the packet is broadcast into the wireless medium together with the gradient of \(v_{i}\). A neighboring node \(v_{j}\) that receives the DATA packet from \(v_{i}\) will send an acknowledgment(ACK) and help to forward the packet only if the gradient of \(v_{j}\) is smaller than that of \(v_{i}\). The process is repeated along each hop until the gateway receives the DATA packet. It is thus possible for the gateway node to receive multiple copies of the DATA packet.
# Congestion Evaluation of Pedestrians in Metro Stations Based on Normal-Cloud Theory
## 2 Literature Review
### Measures to Track Passenger Crowding  
Previous studies divided the crowding status into five or six levels to characterize the degree of crowding in the station, with considering the LOS (Level of Service) and the passenger density of the facilities such as channel, stairway, and platform [26; 27; 28]. For instance, a related study by [PERSON] et al. [26] set up the crowding assessment of the light rail transit by dividing the LOS of the station platform into five categories. Their results provided a reference for the safety assessment of the facilities. [PERSON] et al. [27] classified the LOS criteria of the rail transit channels in Beijing. It was found that the criteria values were lower than that in the HCM 2010. Similarly, [PERSON] et al. [28] assessed right-turning vehicle-pedestrian conflicts at intersections, their results provided that the frequency and severity of conflict can be represented by SSMs (surrogate safety measures).  
To our knowledge, there are few studies focusing on the grading criteria of the crowding degree in the metro station. There are no uniform criteria for grading of the crowding levels, while it was mainly referred to as the passenger standing density. In some countries [29; 30; 31; 32], such as Australia, The Netherlands, Chile, USA, etc., the standards for the passenger standing density were divided into two categories: the comfortable criterion with a standing density of three passengers per square meter and the crowding criterion with a standing density of six passengers per square meter. For example, in the US, the Transit Capacity and Quality of Service Manual (TCQSM) [33] provides guidance and reference for measuring passenger crowding. It contains official definitions of station capacity, and pedestrian levels of service (LOS) [27]. In China, there have been three different criteria for the passenger standing density [20; 21; 34; 35; 36].  
The literature review indicated that previous studies focused on passenger crowding were undertaken at a microcosmic level. Most crowding identification studies rely on ridership data but do not explain much information about the uncertainty and fuzziness nature of the passenger distribution in the metro station. Furthermore, little is known about the grade of crowding that characterizes the congestion degree of the channel, stairway, and platform facilities in the urban rail transit system. Hence, this study developed a NC model to examine simultaneously the passenger crowding status associated with the forward normal cloud generator and the grade of crowding. A forward normal cloud generator was used to determine the digital eigenvalues of the template cloud and the candidate cloud as well as the GPC was calculated according to the principle of the maximum membership degree.
# HVdc Circuit Breakers: Prospects and Challenges
## 4 HVdc Circuit Breaker Topologies
### Hybrid HVdc Circuit Breakers  
This type of circuit CB opens new prospects for fast switching and extinguishing the arc. They employ solid-state devices and mechanical switches for switching operations, as suggested by its name, hybrid dc breakers. The hybrid circuit breaker theory is suggested to overcome the shortcomings of solid-state and electro-mechanical breakers. The use of electronic components that are sensitive to short-circuit faults makes the hybrid DCCB expensive. The steady-state reliability of hybrid DCCB is fairly high, but the failure rate is much greater than that of a similar voltage rating of ac breaker. The structure of a hybrid dc breaker is very complicated as it contains electronic devices and mechanical components that lead to a high failure rate, and the electronic components are also comparatively fragile [100]. However, the breaker's failure to timely isolates the fault can be harmful to both human beings and the protected component. Hybrid DCCB continues to attract a lot of attention, as recently witnessed in [101]. Moreover, Alstom [102] and ABB [80] have established hybrid dc circuit breaker prototypes with significant enhancement in recent research [103; 104; 105]. There are many available topologies of hybrid DCCB at various development stages, but only two attract more attention. Figure 7 shows the basic idea of hybrid DCCB [94; 95]. The operation of hybrid CB is similar to that of SSCB but with the only difference that the hybrid breaker has additional sets of solid-state switches connected in parallel. Compared to other breakers, hybrid CB has low losses, and because of the solid-state switches, it is faster and reliable.
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
## 6 Performance Evaluation
### Performance Results
#### 6.3.2 Energy-Efficiency  
The energy-efficiency performance of GAFOR and existing schemes is shown in Figure 12. The \(x\)-coordinate illustrates network size while average energy consumed per round in joules is represented on the \(y\)-coordinate. The less energy that is used, the better the energy-efficiency performance of that scheme. We save more energy in proposed schemes at lower \(FTR\). It is observed that, as the \(FTR\) increases, the relative gain in the energy-efficiency decreases as evident in Table 5 and Figure 12.  
GAFOM average energy-efficiency is similar as compared to CCEF and CCEF-CR, while DEF performs better since it does not employ re-clustering, which saves energy. There is re-clustering and optimization cost associated with the proposed scheme that results in a decrease in energy saving. However, GAFOR has comparable energy efficiency in comparison to CCEF and CCEF-RC.
###### Abstract  
Attempts are made in this paper to improve the quality of the stitching between adjacent arc-profiles in the stitching linear-scan method for the roundness measurement of a cylinder in a small dimension. The data in the edge region of an arc-profile, which could be influenced by the pressure angle of the measurement probe of a linear-scan stylus profiler, are eliminated in the stitching process to improve the quality of stitching. The effectiveness of the elimination of the edge region of an arc-profile is evaluated by employing the cross-correlation coefficient of two adjacent arc-profiles as an evaluation index. Furthermore, a modification is made to the experimental setup to reduce the misalignment of a workpiece along its axial direction with respect to the scanning probe. Experiments are carried out by using the modified setup to demonstrate the feasibility of the stitching linear-scan method for the roundness measurement of a small cylinder, which is difficult to measure by the conventional rotary-scan method.  
improved stitching linear-scan method; small cylinders; precision measurement; roundness; diameter
## 3 Background
### EdgeSimPy
It stands out due to its extensive capabilities despite being more tailored for edge scenarios. Task scheduling and maintenance activities are absent from SimEdgeIntel despite its scalability and support for several features. Although _CloudSim_ and _IoTSim_ perform \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Simulator** & **Task Scheduling** & **Service Migration** & **Maintenance Operation** & **Mobility Support** &
\begin{tabular}{c} **Network Flow Scheduling** \\ \end{tabular} \\ \hline SimEdgeIntel [22] & No & Yes & No & Yes & Yes \\ CloudSim [23] & Yes & No & No & Yes & Yes \\ IOTSim [24] & Yes & No & No & Yes & Yes \\ EdgeSimPy [3] & Yes & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of built-in features supported by simulation tools. well in cloud and IoT contexts, their flexibility is limited by the lack of support for service migration and maintenance. In general, the most reliable and flexible tool for intricate edge simulations is EdgeSimPy.
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 2 Related Works and Background
### Weight-Based Enhancements to DV-Hop
#### 2.1.1 Basic DV-HOP Algorithm  
In this section, we detail the process of localization using DV-Hop in WSNs. DV-Hop has demonstrated its ability to precisely determine the locations of sensor devices in both IoT applications and WSNs. The algorithm estimates the locations of these devices based on the relative distances between nearby anchor nodes. This solution enables precise positioning, making it the preferred choice for localization tasks in the aforementioned contexts. The basic DV-Hop algorithm consists of three main stages: (i) disseminating information; (ii) determining the average hop size distance; and (iii) estimating the position. These stages collectively contribute to estimation of the location of devices within a network. In the first stage of the DV-Hop localization process, the anchor node (denoted as \(A_{i}\)) broadcasts a hello packet to initialize the network. As the hello packet is relayed through the network, the hop count value, which is initially 0, increases; each hop count reflects the number of traversed nodes. When a node, either an anchor or a target/unknown node (unknown nodes are denoted as \(U\)), receives the hello packet for the first time, it saves the location of the sending anchor node \(A_{i}\) and initializes \(H_{i;u}\) as the hop count value collected in the packet. This hop count value is computed as the minimum number of hops required for node \(U\) to reach anchor node \(A_{i}\). When node \(U\) receives a hello packet, it alters its \(H_{i;u}\). If the received packet has a lower hop count than the existing \(H_{i;u}\) value, then node \(U\) updates its \(H_{i;u}\) with the new lower hop count and keeps the packet for further processing; otherwise, node \(U\) ignores this new higher \(H_{i;u}\) value. Throughout this stage, all sensor nodes diligently gather and maintain the hop count and send information to every anchor \(A_{i}\). This meticulous recordkeeping ensures that accurate hop count data are available for other localization steps, facilitating the precise estimation of node locations within the network.
# Modeling the Formation of Urea-Water Sprays from an Air-Assisted Nozzle
###### Abstract  
Ammonia preparation from urea-water solutions is a key feature to ensure an effective reduction of nitrogen oxides in selective catalytic reduction (SCR) systems. Thereby, air-assisted nozzles provide fine sprays, which enhance ammonia homogenization. In the present study, a methodology was developed to model the spray formation by means of computational fluid dynamics (CFD) for this type of atomizer. Experimental validation data was generated in an optically accessible hot gas test bench using a shadowgraphy setup providing droplet velocities and size distributions at designated positions inside the duct. An adaption of the turbulence model was performed in order to correct the dispersion of the turbulent gas jet. The spray modeling in the near nozzle region is based on an experimentally determined droplet spectrum in combination with the WAVE breakup model. This methodology was applied due to the fact that the emerging two-phase flow will immediately disintegrate into a fine spray downstream the nozzle exit, which is also known from cavitating diesel nozzles. The suitability of this approach was validated against the radial velocity and droplet size distributions at the first measurement position downstream the nozzle. In addition, the simulation results serve as a basis for the investigation of turbulent dispersion phenomena and evaporation inside the spray.  
CFD; droplet kinematics; gas jet; injection processes; selective catalytic reduction; shadowgraphy; spray characteristics; turbulent dispersion Article
# Advanced Unified Earthquake Catalog for North East India
###### Abstract  
Northeast India is one of the world's most seismically active regions. The event data included in this research for the period 1737-2012 is mostly obtained from worldwide database agencies such as ISC, NEIC, and GCMT. Historical seismicity is collected from published and unpublished documents and some earthquake events are collected from the Indian Meteorological Department Bulletins. As the M\({}_{\rm w}\) scale is developed and validated in the southern California region and overestimates the smaller magnitude earthquakes, therefore, recent literature suggested an improved version of the seismic moment magnitude scale (M\({}_{\rm wg}\)) applicable for the entire globe considering both long- and short-period frequency-spectra using modern instrumental data. To update the earthquake catalog of Northeast India, we prepared empirical relationships between different magnitudes to M\({}_{\rm wg}\) using robust statistical General Orthogonal Regression. A procedure is also suggested for converting different earthquake sizes towards seismic moment scale. The Magnitude of Completeness (M\({}_{\rm c}\)) and the Gutenberg-Richter (GR) recurrence parameter values for the declustered homogenized catalog in four time periods, namely 1737-1963, 1964-1990, 1964-2000, and 1964-2012, have been computed. Our analysis suggests that the use of the M\({}_{\rm wg}\) scale improves seismicity parameters 'b' up to 30%, 'a' up to 17%, and M\({}_{\rm c}\)' up to 18% for the Northeast India region. A complete unified earthquake catalog in terms of advanced seismic moment magnitude scale could help understand seismicity and earthquake engineering studies of the region.  
homogenous earthquake catalog; North East region; general orthogonal regression; seismogenic zones +
Footnote †: journal: Physics Letters B  
1
Footnote 1: School of Computing and IT, Manipal University Jaipur, Jaipur 303007, India  
2
Footnote 2: Computer Science Department, Universidad Catolica del Norte, Antofagasta 1249004, Chile  
3
Footnote 3: Earthquake Engineering Department, Indian Institute of Technology Roorkee, Roorkee 247667, India  
4
Footnote 4: Correspondence: [EMAIL_ADDRESS]
## 2 Background  
### Exponentially Weighted Moving Average Model  
In 1956, a single ES model was first proposed [15], which was designed to forecast demands in inventory control systems. In the single EWMA model, it is assumed that time series data, \(Y_{1}\), \(Y_{2}\),..., \(Y_{t}\), have no trend or seasonal pattern, and the level factor at time \(t\), \(\hat{Y}_{t}\), is given by  
\[\hat{Y}_{t}=\alpha Y_{t}+(1-\alpha)\hat{Y}_{t-1}, \tag{1}\]  
where \(\hat{Y}_{1}=Y_{1}\) and \(\alpha\) is a smoothing parameter between 0 and 1. Equation (1) can be rewritten as follows:  
\[\hat{Y}_{t}=\sum_{i=0}^{t}\alpha(1-\alpha)^{i}Y_{t-i} \tag{2}\]Thus, the single ES model can be considered a weighted average of the current and all past observations. Because the multiplier of the past observation is (1-\(\alpha\)), which is smaller than one for any \(\alpha\) values, it gives more weight to the current observation than past observations. Finally, the forecast for \(s\) times ahead from the time \(t\) can be determined as follows:  
\[Y^{*}_{t+s}=\hat{Y}_{t}. \tag{3}\]  
As shown in Equation (3), single ES forecasts the future observations as a constant value. Hence, this method might not yield satisfactory prediction performance in many real situations because the time series data are dynamically changed over time. To accommodate a time-varying pattern with a linear trend, a double ES model was proposed [17]. The double ES model assumes that the time series, \(Y_{1}\), \(Y_{2}\),..., \(Y_{t}\), exhibit a linear trend with no seasonal pattern. In this model, the level factor and trend factor at time \(t\), denoted as \(\hat{Y}_{t}\), and \(\Delta\hat{Y}_{t}\), respectively, can be calculated by following equations:  
\[\hat{Y}_{t}=\alpha Y_{t}+(1-\alpha)\Big{(}\hat{Y}_{t-1}+\Delta\hat{Y}_{t-1} \Big{)}, \tag{4}\]  
\[\Delta\hat{Y}_{t}=\beta\Big{(}\hat{Y}_{t}-\hat{Y}_{t-1}\Big{)}+(1-\beta)\Delta \hat{Y}_{t-1}, \tag{5}\]  
where \(\hat{Y}_{1}=Y_{1}\), and \(\Delta\hat{Y}_{2}=Y_{2}-Y_{1}\), and \(0<\alpha<1\), \(0<\beta<1\). The parameter \(\alpha\) is a smoothing parameter for the level factor, and the parameter \(\beta\) is for a trend factor. The forecast for \(s\) times further from time \(t\) is given by  
\[Y^{*}_{t+s}=\hat{Y}_{t}+s\times\Delta\hat{Y}_{t}. \tag{6}\]  
This double ES model has successfully worked for forecasting the time series that has a constant trend, however, it does not properly work in a time series having a time-varying pattern of varying trends or seasonal variations. To consider the seasonal variations, [PERSON] [18] proposed the triple ES model to forecast the time series with both a linear trend and seasonality. Unlike to all the aforementioned methods, the triple ES method additionally estimates the parameters of a period term and seasonal factors with the historical data. This m is composed of three factors: the level factor, trend factor, and seasonality factor at time \(t\), denoted as \(\hat{Y}_{t}\), \(\Delta\hat{Y}_{t}\), and \(SN_{t}\), respectively.