esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Channels Comparison  
Some previous research found the epicenter of an impending earthquake may be far from the anomaly [41; 42; 43; 44; 45], so both the abnormal region and the region around the anomaly should be considered as the predicted region. The possibility that there is any upcoming earthquake in the predicted region is larger if the area of the predicted region is larger. PPVs with different distance thresholds in four channels are calculated and shown in Figure 5. There are a total of 619 anomalies from 2016 to 2020, including 155 in the IR1 channel, 161 in the IR2 channel, 132 in the IR3 channel, and 171 in the IR4 channel. PPVs vary obviously with the change of the distance threshold. The difference between the PPVs in any two channels is less than 0.08 for the same distance threshold. It means that there is not much difference between the PPVs of anomalies from different channels.  
Figure 6 shows the TPRs with different distance thresholds in four channels. The TPRs in the two long-wave infrared channels are similar. The TPR in the IR4 channel is the highest among all channels whatever the distance threshold is, while that in the IR1 channel is the lowest. The difference between the TPRs in the IR2 channel and the IR3 channel becomes large with the increase in the distance threshold. The long-wave infrared channels (IR1 and IR2) behave worse on PPV and TPR, although they were earlier used for earthquake prediction.  
The infrared anomalies of 358 earthquakes in the study area were analyzed statistically. As shown in Figure 7, the horizontal axis is a four-digit number whose four digits represent the state of four channels (IR1, IR2, IR3, IR4) successively, where 1 means there was an anomaly before the earthquake, and 0 means there was no anomaly before the earthquake. For example, 1111 means there were abnormalities in all four channels. The state of the anomalies in the two long-wave channels is similar. There were only 26 earthquakes with four-channel anomalies within 400 km around the epicenter, and 18 of them were within 200 km.  
As shown in Figure 8, the Gain in each channel varies slightly with the distance threshold. The probability gain cannot be improved by changing the distance threshold.  
Figure 5: Positive predictive values in different channels at different distance thresholds.  
Figure 6: True positive rates in different channels at different distance thresholds.  
The gains in the IR3 channel are highest among those in all four channels for the same distance threshold and only a little higher than that in other channels.
# An Interactive System Based on the IASP91 Earth Model for Earthquake Data Processing
## 5 Conclusions
To identify the seismic phases in the mobile array seismic data, the traveltimes of different phases propagating in the Earth's interior are simulated numerically using ray tracing techniques based on the IASP91 Earth velocity model, and the simulation results of the orthogonal traveltimes of each phase can match well with the travel schedules of seismic waves of the corresponding phases calculated by [PERSON], and the calculation speed can be guaranteed. After the correct identification of the seismic phases, the traveltime picking using the cross-correlation technique greatly improves the picking accuracy, and the cross-correlation data can clearly reflect the good and bad quality of the waveforms at each station, reducing the errors in the qualitative analysis, which works well in the cross-correlation evaluation system through the system's interactive picking work. 3. The system provides good performance and has its own unique features in both seismic phase identification and traveltime picking calculations. The deviation between the theoretical and actual arrival times of the seismic phases calculated from the numerical simulations is mainly due to the non-uniformity of the velocity structure within the Earth, both in the radial and lateral directions. It is this deviation that opens up the possibility of studying the complex structure of the Earth's interior. The integration of seismic data processing and interpretation methods is one of the challenging and practical fundamental studies in geophysics today. Some of the functions of this system need to be further supplemented and improved, and the data processing and interpretation work is not deep enough and needs to be certified by further practical processing and interpretation work. In addition, more in-depth issues need to be explored in future efforts, and the study of seismic attribute methods is an important tool for future fine stratigraphic studies. Therefore, it is hoped that more people will carry out research on new methods and techniques for seismic attribute processing and interpretation based on existing platforms. Conceptualization, W.G. and Y.W. ; methodology, W.G. ; software, Y.W. and S.Y. ; validation, Y.W. ; formal analysis, W.G. ; writing--original draft preparation, W.G. and S.Y. ; writing--review and editing, W.G. and Y.W. ; supervision, Y.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the Guangxi Natural Science Foundation Key Fund Project (2016 GXNSFDA380014). Not applicable. Data Availability StatementData available in a publicly accessible repository ([[https://earthquake.usgs.gov/](https://earthquake.usgs.gov/)]([https://earthquake.usgs.gov/](https://earthquake.usgs.gov/))). The authors declare no conflict of interest.
## 7 Measurement Data Reports
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No** & **D** & **MA** & **MB** & **m** & **X** & **Y** & \(\delta\)**A** & \(\delta\)**B** \\  & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(\%)** & **(\%)** \\ \hline
1 & 23.05 & 22.90 & 23.90 & \(\sim\)2 [1.96] & 22.96 & 23.50 & 0.65\% & 3.69\% \\
2 & 22.37 & 22.33 & 18.99 & \(\sim\)2 [1.88] & 22.35 & 21.24 & 0.18\% & 9.88\% \\
3 & 15.15 & 15.06 & 14.65 & \(\sim\)2 [2.01] & 15.04 & 14.87 & 0.59\% & 3.30\% \\
4 & 14.12 & 13.95 & 12.99 & 2 & 13.97 & 14.10 & 1.20\% & 8.00\% \\
5 & 6.05 & 5.98 & 5.95 & \(\sim\)2 [1.95] & 5.97 & 5.96 & 1.16\% & 1.65\% \\
6 & 6.09 & 5.97 & 5.95 & \(\sim\)2 [2.06] & 5.98 & 5.96 & 1.97\% & 2.30\% \\
7 & 0.50 & 0.50 & 0.50 & 0.10 & 0.50 & 0.50 & 0\% & 0\% \\
8 & 0.30 & 0.30 & 0.30 & 0.10 & 0.30 & 0.30 & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Measurements for Method A and Method B. MA denotes a Method A survey, MB denotes a Method B survey, X denotes a Method A survey taken from a captured object picture, and Y denotes a Method B survey taken from a captured object picture (sensor data (4160 x 3120 px)). In the case of deviations in measurements, to resolve the problem of inconsistent data, the authors propose a triangulation method, which can be developed also on a smartphone. This method uses triangle properties, particularly the method that allows for calculating the height of a triangle based on its angles. Processing a few measurements allows for creating a triangle. On the basis of the azimuth to which the phone is directed and the precise distance of the measurement points, it is possible to calculate the angles and distance to the object. The following formula describes the triangulation method: \(d=l\frac{sin\text{\it{using}}}{\sin(a+\beta)}\), where \(d\) denotes the distance to the object (triangle height), and \(l\) denotes the distance between points where the measurements were taken. The obtained value of the distance to the detected object is transferred to the headquarters, where it is applied to the actual tactical or operational situational image and then resent to all subordinates. This common knowledge is the essence of the situational awareness required on the battlefield. The responsibility of each commander is raising the situational awareness of their subordinates by all means (Figures 12 and 13). As already shown, this article presented mechanisms that provide for the ability to realize such requirements. Figure 11: Diagram showing the concept of the Method A (**a**) and Method B (**b**) distance measurement techniques. Figure 12: Alternative extended mCOP recon view RARV measurement grid with calculated object’s parameters was supplemented by an distortion evaluator (bottom right part of the screen) utilizing a handheld’s accelerometer and gyro sensor data.
# Development of Lift Control System Algorithm and P-M-E Analysis in the Workplace
## 2 Control Network Development  
The lift system controller design operates with division zoning technique and a fuzzy control system for the efficient computing of fitting values \(\text{F}_{\text{p}}\) for a quicker lift hall-call. These fitting values computation is based on numerous lift performance conditions, such as the waiting time of the passengers, load capacity and distance between floor hall-calls. The controller implements a fuzzy system in traffic pattern recognition, while the division zoning scheme helps in tailoring the controller according to the patterns from the fuzzy system [23; 24; 37].  
The fuzzy system used in this work is divided into three main parts: (i) fuzzification; (ii) fuzzy inference; and (iii) defuzzification. The fuzzifier helps to classify the traffic patterns by converting the signal into a set of fuzzy variables. The signal is translated into five linguistic terms, which are namely very large (VL), large (L), medium (M), small (S) and very small (VS). The traffic patterns are received in terms of the number of passengers going in different directions, which is namely upwards (UP) or downwards (DN), and also in the case of steady state traffic. The priority is given to the direction with higher traffic, i.e., if UP = VL and DN < VL (L, M, S, VS), F\({}_{\text{P}}\) = High Priority is thus assigned to UP.  
Once the traffic direction is identified, the input information is passed to the fuzzy inference engine along with extra linguistic variables, such as (a) waiting time (WT); (b) space availability in lifts (SA); and (iii) distance between the elevators and distance of hall-calls floors and destination floors (Dist). The inference engine serves as the fuzzy decision block, which calculates the entire F\({}_{\text{P}}\) to set the priority for the number of lifts (N) based on a rule sets (F\({}_{\text{P}}\) = High, Medium or Low) as shown in Table 1.  
This means that for a smaller loading, closer proximity of lifts, shorter waiting times and higher number of passengers waiting, the priority is assigned to a lift with highest combined F\({}_{\text{P}}\). These details are sent to the defuzzifier, which generates a single output based on the total priority assigned. This is conducted on the number of lifts (N) and the traffic mode is subsequently set.  
The defuzzification process employs the center of gravity method, which assigns the priority according to the total fitting values for 1 to N lifts. The lift with the highest total priority fitting value is assigned as the main preference and the information is passed as a single real traffic output. This output value serves as control instructions for responding to lifts according to the traffic data and hall-call assignment.  
In this section, we will look at the development of the lift control algorithm and the programming languages used.
# Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest-Ordinary Kriging Model
## 2 Materials and Methods
### Eda  
Before preprocessing the 40 sampled data points, EDA was performed. Its primary purpose was to understand how the collected heavy metal data were distributed within the study area by identifying patterns in the dataset, detecting outliers, and verifying assumptions. The process involved the use of various visualization methods, such as histograms, scatter plots, and Q-Q plots, to investigate the frequency distribution of data, correlations between auxiliary variables, and normality. Basic statistical measures were used to calculate the minimum, maximum, mean, median, skewness, and standard deviation of the data to quantify the central tendency and variance of the data. To normalize the data and stabilize the variance, a log transformation was applied in cases of high variability. This enhanced the reliability and efficiency of the subsequent analyses.
# Networked Roadside Perception Units for Autonomous Driving
## 3 Design and Implementation of the RSPU System
### System Design of AutoC2X
#### 3.1.1 Autoware  
ROS-based Autoware [31; 32] is discussed in this subsection. As a middleware (open-source) framework, ROS [36] is extensively used to develop robot applications. This distributed computing platform involves topics and nodes. The nodes represent the processing module of tasks, and the nodes use topics to communicate with each other. Moreover, ROS comes up with a robust tool such as Rosbag to record and replay messages in topics. The developer can consider using Rosbag to record sensor data within the real environment before making improvements in the algorithm by using data without hardware. Furthermore, ROS is inclusive of a 3D visualization tool called RViz, that efficaciously presents the tasks' status.  
In Figure 2, it can be seen that a 3D map denotes a common digital infrastructure to operate an autonomous vehicle, particularly across urban areas. Autoware entails the use of two kinds of beforehand recorded 3D maps: Point cloud map data and vector map data. The former is used for scan matching to enable localization, whereas the latter derives the lane data. In Autoware, sensing primarily involves using 360-degree LiDAR cameras and scanners. From the LiDAR scanners, point cloud data is used for detecting surrounding objects and localization. This localization algorithm utilizes scan matching between the point cloud data sourced from LiDAR scanners and 3D point cloud maps. Generally, the normal distributions transform-based algorithm [37] is employed for the purpose of localization. Object detection is premised on point cloud data's clustering from the LiDAR scanners, concerning with the Euclidean cluster extraction algorithm [38] is used in Autoware. Thereafter, Autoware calculates the distance between the detected objects and the own vehicle (in the following referred to as ego-vehicle). While Autoware delivers software packages for planning, decision, and prediction; however, these are currently beyond the scope of this work. Figure 2 illustrates that a CAN controller is used to perform the actuation of autonomous vehicles is with a view to manipulating steering, accelerating, and braking.
# An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques
## 3 IoT Sensor Data Processing
### Data Aggregation  
The data aggregation method is referred to as the method that collects and communicates information in a summary form. This can be used for statistical analysis. In IoT, heterogeneous data are collected from various nodes. Sending data separately from each node leads to high energy consumption, and needs a high bandwidth across the network, which reduces the lifetime of the network. Data aggregation techniques prevent these kinds of problems by summarizing data, which reduces the excessive transfer of data, increases the network's lifetime, and reduces network traffic. Data aggregation in the Internet of Things (IoT) helps to decrease the number of transmissions between objects. This lengthens the lifetime of the network and decreases energy consumption [36]. It also reduces network traffic.  
The data aggregation methods in IoT are classified into the following:  
1. [label=()]
2. Tree-based approach [37; 38; 39; 40; 41; 42; 43]--This approach deploys the nodes in the network in the form of a tree. Here, the hierarchical and intermediate nodes perform the aggregation. The aggregated data are then transferred to the root node. The tree-based approach is suitable for solving energy consumption and lifetime of network problems;
3. Cluster-based approach [44; 45; 46; 47; 48]--The entire network is organized as clusters. Each cluster will contain several sensor nodes with one node as the cluster head, which performs the data aggregation. This method aims to carry out the effective implementation of energy aggregation in large networks. This helps to reduce the energy consumption of nodes with limited energy in huge networks. Therefore, it results in a reduced overhead bandwidth due to the transfer of a limited number of packets. In the case of static networks where the cluster structure does not shift for a long time, cluster-based techniques are successful
4. Centralized [49; 50]--All sensor nodes in this system send the data to a header node, which is a strong node with all the other nodes. The header node is responsible for aggregating the data and sending out a single packet.  
The authors in [51] addressed the issue of data uncertainty in IoT sensor data. Mainly, the data aggregation focused on device to device communication. The proposed technique involves the reconstruction of subspace-based data sampling. Next, the low-rank approximation is performed to identify the dominant subspace. Further, the robust dominant subspace is utilized for reliable sensor data, in an entirely supervised manner. The proposed method exhibits improvements in terms of accuracy and efficiency as regards removing the uncertainties and data aggregation of sensor data from the experimentation results.
## 5 Results and Discussion
### MDDA with TC Integration Scenario
The specific satellite positions and the visualization of this selection process can be observed in Figure 20 and Figure 21, respectively. The proposed MDDA considered both CNR and El to select the optimal satellite geometry on the horizon during the final epoch. In addition, the average of the dilution of precision (DOP) was computed for the whole trajectory to support the findings and insights regarding the optimal satellite geometry. The results of these analyses are summarized in Table 5.  
The graphical representation in Figure 22 provides clear evidence that the MDDA consistently maintained the most favorable geometry throughout the trajectory, as observed in the horizontal dilution of precision (HDOP). The MDDA demonstrated a consistent pattern of superiority, with a lower average HDOP value of 1.28, compared to CNR, with an average HDOP of 1.35, and EL, with an average HDOP of 1.54. This indicated that the MDDA consistently offered superior precision and geometry throughout the trajectory. Figure 21: Carrier-to-noise ratio (CNR) of the selected and eliminated satellites. Figure 12:0 Geometry of the selected satellite. Finally, a simulated outage was conducted to assess the performance of the TC algorithm implemented in this study. The outage simulations varied in duration, ranging from 10 to 30 s, and were performed with different numbers of available satellites, from 3 down to 1. The results of these simulations are shown in Figure 12:1 In comparison, the LC algorithm's performance deteriorated as the number of available satellites decreased. When fewer than four satellites were available, the LC algorithm was unable to update the solution using GPS measurements, as it depended on previously known GPS position and velocity information. Without GPS updates, the LC approach gradually drifted over time, requiring a new update to correct the error and restore accurate positioning. On the other hand, the performance of the TC integration method remained robust, even during satellite outages, showcasing its capability to maintain accurate positioning with fewer satellites.
# CL-TAD: A Contrastive-Learning-Based Method for Time Series Anomaly Detection
## 3 The Proposed Method for Time Series Anomaly Detection: CL-TAD
### Problem Definition  
A time series data can be expressed in a sequence \(X=(x_{0},x_{1},\ \ldots,x_{i},\ \ldots,x_{T-1})\) of length \(T\), where \(x_{i}\in R^{d}\) is the \(d\)-dimensional data point at time step \(i\). If \(d=1\), this indicates that \(X\) is univariate, whereas if \(d>1\), this implies that \(X\) is multivariate. The time series anomaly detection task aims to produce a new output sequence \(Y=(y_{0},y_{1},\ \ldots,y_{i},\ \ldots,y_{T-1})\), where \(y_{i}\in\{0,1\}\) indicates whether the corresponding input value \(x_{i}\) is an anomaly event. This means that we are concerned with point anomalies in time series data.  
Similarly to many other deep-learning-based methods, we create a training dataset comprising subsequence samples \(W\) of length \(L\) from the time series data \(X\). This is achieved by sliding a window of size \(L\) over \(X\) with a step size of \(1\), as depicted in Figure 5. The generated sequence samples are denoted by \(W=(W_{0},W_{1},\ \ldots,W_{L-T})\), where \(L\) is the length of samples and \(T\) is the length of the input time series data.
# Development of Automatic Crack Growth Simulation Program Based on Finite Element Analysis
## 2 Development of AI-FEM
### Crack Tip Meshing Criteria
#### 2.2.2 FE Model  
Figure 3a shows the FE model and boundary conditions of a plate with a crack. Considering the symmetry of geometry, a 1/4 symmetric model was used, and symmetry conditions were applied to each symmetrical plane. As a loading condition, a tensile distribution load of 1 MPa in the \(Z\)-axis direction was considered. Except for the case of performing sensitivity analysis on element types, the element type was considered as a 20-node brick element with reduced integration (C3D20R in Abaqus library). The middle node of the wedge element was located at the center position, and the overlapping state of the crack tip nodes was considered as a basic element characteristic condition.  
Figure 3b shows the detailed shape of the crack tip mesh and the shape variables constituting the crack tip mesh. \(x\) means the shorter of the crack depth (\(a\)) and the residual length (\(b\)), and \(r\) means the contour radius of the spider-wedge mesh. \(r\) and \(x\) are used as dimensionless variables as \(r/x\) and the effect of the contour radius on the SIF is considered. Also, since \(r/x\) is the ratio of the relative contour size to the crack size, the size of the contour mesh can be defined in the plate if \(r/x\) is in between zero and one. \(N\) and \(M\) are the numbers of elements in the circumferential direction inside the contour and radial direction of the contour, respectively. \(m\) is defined as \(r/M\) and represents the element size in the radial direction inside the contour. \(AR\) is defined as \(d/m\) and represents the ratio of the total  
\begin{table}
\begin{tabular}{c c c} \hline \hline _w/t_ & _alt_ & _alc_ \\ \hline
3.15 & 0.3 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Shape variables of plates with semi-elliptical surface crack.  
Figure 2: Structural schematics considered in crack tip meshing strategies: (**a**) A flat plate with a surface crack; (**b**) Shape parameters of semi-elliptical surface crack.  
length of the crack tip to the radial size of the element. As \(AR\) increases, the element density in the direction of the crack angle decreases relatively, and it is used as a non-dimensional variable to examine the sensitivity analyses of the SIF.