esa-sceva/satcom-chunk-collection huggingface dataset:
# Pavement Distress Detection Using Three-Dimension Ground Penetrating Radar and Deep Learning
## 2 Proposed Approaches
### Proposed Localization Model  
#### 2.2.1 Distress Localization Dataset  
It is necessary to generate a dataset of 3D GPR horizontal images before building a deep learning model for anomalous waveform localization. The collected 3688 horizontal radar images were divided into training, validation, and test sets, corresponding to 2470, 618, and 600 images, respectively. This study then made block-level labels for each image. Labellmg software in the Python environment was used to label the anomalous waveform areas in an image, such as the example shown in Figure 5a. A bounding box indicated the location of an anomalous waveform area. Table 1 presents the total number of bounding boxes in the training, validation, and test sets.  
Figure 4: Radar waveform features of different pavement distress: longitudinal radar images of (**a**) crack, (**b**) repair, (**c**) voids, (**d**) poor interlayer bonding, and (**e**) mixture segregation; horizontal radar images of (**f**) crack, (**g**) repair, (**h**) voids, (**i**) poor interlayer bonding, and (**j**) mixture segregation.  
Figure 5: Data enhancement: (**a**) original images and (**b–d**) images of random data enhancement.  
In order to alleviate the overfitting and improve the accuracy of the location model, random data augmentation was performed on the dataset. In this study, random data enhancement had the following three main points:  
1. Randomly crop an image;
2. Randomly resize an image in terms of its length and width;
3. Randomly distort the color gamut of an image.  
The three data enhancement methods were performed simultaneously, such as the examples shown in Figure 5b-d. The randomly enhanced image was filled with zeros in the remaining positions.
# MQC-MB: Multiphoton Quantum Communication Using Multiple-Beam Concept in Free Space Optical Channel
## 4 The Implementation of MQC-MB
### Multiple Beam Quantum Communication Phase  
This phase explains the process of transmitting, processing, as well as receiving quantum signals over the FSO network.
## 5 Use Case
#### Step 2: Filtering Erroneous or Irrelevant GPS Readings
Considering this interpretation, the method used to identify the points of types PP and SP on the route of a vehicle is based on the detection of these groups and subsequent classification into two categories: scheduled stop points (PP) and singular points (SP). The technique used is a recursive one in which the K-means classification algorithm is applied in each step to a subset of readings of the Z-set. In the first iteration, the K-means algorithm is applied to the entire Z-set, using as the initial solution the set of scheduled stops (PP-set) that have been identified by their positions. The number of stops along the route is denominated N. Once the K-means algorithm is applied, it will converge on a solution with many clusters equal to N. In Figure 2, the orange markings represent the centroids of the clusters, and the yellow markings represent the stops along the route being studied. There are 28 clusters represented by their centroids and 28 scheduled stops. The positions of the majority of the centroids are close to the positions of the points representing scheduled stops. known to be of type PP, then the cluster is associated with a point of type SP that has been identified. Figure 5 presents a case of this type of cluster (G3) in a first execution. The method ends its iterations when all the clusters generated in the different levels of recursion are of type 1 or type 2. According to what has been discussed, the presence of a cluster without classifying implies a recursive invocation to the identification procedure. The unidentified cluster (type G3) is shown in Figure 5 and is made up of 8896 readings of the initial Z-set. Because of the first invocation, the method identifies a cluster with its centroid identified by tag NL14 located in the urban area and includes two points PP (points with tags 175031 and 175041) and a roundabout (Figure 6). Next, the method is applied recursively, restricted to the readings in this cluster, with the following initial parameters: form three clusters, and the initial approximations to their centroids are the points 175031,  
Figure 6: Close-up aerial view of the same cluster presented in Figure 5. Result of the first iteration of the method. Tags 175031 and 175041 are scheduled stop points, and tag NL14 is the centroid obtained in the first iteration of the method. The readings have been grouped in a single cluster because there is a roundabout between the two scheduled stop points where the vehicle stops. Figure 7: Result after applying a second iteration of the method to the cluster shown in Figures 5 and 6. The method obtains three clusters, two associated with the stops and another one associated with the singular point, which is a roundabout. As the scatter of the clusters is less than 15 m, the identification process is considered to be complete. 175041 and NL14. When executing the second iteration, three clusters are obtained, represented by centroids NL2 of type G1, NL3 of type G1 and NL14 of type G2, with all the readings at a distance of less than 15 m from the centroid representing its class (Figure 7).
## 3 Results and Discussion
The peaks of O\({}_{\text{I}}\), O\({}_{\text{II}}\), and O\({}_{\text{III}}\) are associated with the oxygen ions in the lattice surrounded by Ga, In, and Zn atoms, O\({}_{\text{V}}\) and oxygen in hydroxide (O\({}_{\text{OH}}\)), respectively [11; 21]. Thus, the relative amount of O\({}_{\text{V}}\) existing in the a-IGZO film can be calculated by the proportion of the peak area O\({}_{\text{V}}\) to the whole area O 1s (O\({}_{\text{whole}}\)). As shown in Figure 5a,b, it can be seen that the area ratio of O\({}_{\text{II}}\)/O\({}_{\text{whole}}\) is clearly reduced from 35% to 25% for the undoped a-IGZO film and a-IGZO: 20% P\({}_{\text{N/O}}\) film, indicating that the O\({}_{\text{V}}\) is suppressed by N-doping. In contrast, compared with the a-IGZO: 20% P\({}_{\text{N/O}}\) film, the O\({}_{\text{V}}\) rises to 31% in a-IGZO: 40% P\({}_{\text{N/O}}\) film as shown in Figure 5c, suggesting that the extra O\({}_{\text{V}}\) is generated when excess nitrogen atoms are incorporated into the a-IGZO film. This result agrees with previous reports that heavy N-doping in the a-IGZO film could suppress the bonding of O and Ga because of the facilitated formation of N-Ga bonds, which could result in the increase of O\({}_{\text{V}}\) within the a-IGZO film. Besides, as shown in Figure 5d, the N 1s spectrum of the a-IGZO: 20% P\({}_{\text{N/O}}\) film is fitted by two energy bonds centered at 395.7 eV and 397.3 eV corresponding to the Ga Auger and N-Ga bonds [22], respectively. Thus, the XPS analysis reveals that the enhanced reliability of the a-IGZO TFT with moderate P\({}_{\text{N/O}}\) is determined by passivating the O\({}_{\text{V}}\) at the a-IGZO/SiO\({}_{2}\) interface. Figure 4: Evolution of the transfer curves against negative bias stress (NBS) time under white light illumination for the a-IGZO TFTs fabricated using P\({}_{\text{N/O}}\) of (**a**) 0%, (**b**) 20%, and (**c**) 40%.
# Choose the feature that maximizes the evaluation function J()
### Preliminaries of LR, RF and SVM
#### 2.1.2 Random Forest  
Random forests correspond to a modification of bagging that builds a large collection of de-correlated trees, \(T_{b}\), and then averages them. Bagging, also known as bootstrap aggregation, is a technique for reducing the variance of an estimated prediction function. Bagging seems to work especially well for high-variance, low-bias procedures, such as trees. The essential idea in bagging is to average many noisy but approximately unbiased models, \(\{T_{b}\}_{1}^{N}\), and hence reduce the variance. Trees are ideal candidates for bagging, since they can capture complex interaction structures in the data and, if grown sufficiently deep, have a relatively low bias. Since trees are notoriously noisy, they benefit greatly from the averaging. Moreover, since each tree generated in bagging is identically distributed (i.d.), the expectation of an average of N such trees is the same as the expectation of any one of them. To make the prediction at any point, one uses Equation (6), and to make the classification \(\overbrace{C}_{b}\), Equation (7) is used:  
\[\overbrace{f}^{N}_{r\,f}(x)=\frac{1}{N}\sum_{b=1}^{N}\,T_{b}(x), \tag{6}\]  
\[\overbrace{C}^{N}_{r\,f}(x)=\text{majority vote}\,\,\{\overbrace{C}_{b}(x) \}_{1}^{N} \tag{7}\]  
The random forest model classification is capable of self-learning, and it is based on various classifiers \(\overbrace{C}_{b}\) for decision making. The model is used for prediction and classification tasks, as shown in Figure 3[24; 25].  
Figure 3: Example of the random forest model, the green dots indicate the main options.  
Figure 2: Example of the logistic regression model, the colored dots indicate the upper and lower limits.  
The random forests are based on a collection of decision trees, and each tree is composed of one root node, branches, internal nodes, and leaf nodes. The learning process is complex, since the optimal quantity of decision trees must be obtained and, at the same time, the optimal structure of each decision tree must be attained.
# Exhaustive Variant Interaction Analysis Using Multifactor Dimensionality Reduction
One of the potential strategies to overcome these major limitations is a two-stage approach, where the first step involves reducing the dimensionality of the problem. This reduction focuses on decreasing the number of variant interactions that will be analyzed and, in most cases, involves filtering the data. This filter can be done, among other methods, based on data mining techniques, such as the ones used in Relief [13], where a proximity measure is calculated between individuals based on genome-wide similarity, or using data-integration techniques, which involve selecting only the genomic variants that are relevant to the study based on previous biological information, such as protein-protein interaction databases like ChEMBL [14] or BioGRID [15]. After reducing the size of the problem, non-exhaustive explorations can be performed using statistical methods and artificial intelligence. In this direction, multiple approaches have been tested and have identified pairwise combinations that show associations with complex diseases in reduced datasets. Some examples are random forest [16], Bayesian networks [17], computational evolution systems [18], and ant colony optimization [19]. Importantly, among these methods, multifactor dimensionality reduction (MDR), which is a supervised classification approach based on contingency tables, has been previously used to approach the analyses of gene-gene interactions [20] and variant-variant interactions. However, the complexity of the analysis surrounding the study of interactions has limited the inspection and the use of this methodology to the analysis of reduced datasets [21]. Since this method performs the analysis of the interactions as independent events--loading only two variants at a time and, thus, avoiding any memory bottlenecks, making it ideal for parallelizing the processing of big datasets--in this study we tackle the pairwise interaction problem at a genome-wide level. To that extent, in this work, we have developed a containerized framework using high-performance computing (HPC) technologies and applied MDR to discover relevant variant interactions in an efficient parallel environment. This method has been tested to find associations with type 2 diabetes (T2D) in a subset from the Northwestern University NUGene cohort [22].
## 3 Discrete Time Transfer Matrix Method
Substituting \(z=0\), \(\mathbf{E}(0,t_{i})=\mathbf{I}_{14\times 14}\), \(\widetilde{\mathfrak{n}}_{r}(z,t_{i})=0_{14\times 1}\) into Equation (34) gives:  
\[\widetilde{\mathfrak{n}}(0,t_{i})=\mathbf{V}^{-1}\boldsymbol{\varphi}(0,t_{i}) \tag{35}\]  
when \(z=0\), substituting Equation (35) into (34), the solution of the FSI fourteen-equation model is  
\[\boldsymbol{\varphi}(z,t_{i})=\mathbf{V}\mathbf{E}(z,t_{i})\mathbf{V}^{-1} \boldsymbol{\varphi}(0,t_{i}) \tag{36}\]  
Define the field transfer matrix expression of the pipe as:  
\[\mathbf{U}(z,t_{i})=\mathbf{V}\mathbf{E}(z,t_{i})\mathbf{V}^{-1} \tag{37}\]  
Thus, the time domain solution of the FSI fourteen-equation model is:  
\[\boldsymbol{\varphi}(z,t_{i})=\mathbf{U}(z,t_{i})\boldsymbol{\varphi}(0,t_{i}) \tag{38}\]  
For the single straight pipe or curved pipe with the length L, there will be seven relations at both ends of the pipe, respectively:  
\[\left\{\begin{array}{l}[\mathbf{D}_{0}]_{\gamma\times 14}\big{[}\boldsymbol{ \varphi}_{0t_{i}}\big{]}_{14\times 1}=[\mathbf{Q}_{0}]_{\gamma\times 1}\\ [\mathbf{D}_{L}]_{\gamma\times 14}\big{[}\boldsymbol{\varphi}_{Lt_{i}}\big  
The relation of the variables vector at the initial end of the series pipe can be expressed as:  
\[\varphi(0,t_{i})=\mathbf{D}^{*-1}(t_{i})\mathbf{Q}(t_{i}) \tag{42}\]  
where  
\[\mathbf{D}^{*}(t_{i})= \tag{43}\]  
Hence, the variables vector of \(\mathbf{\Phi}(z,t)\) at any position of the pipe system can be obtained by \(\varphi(0,t_{i})\) and \(\mathbf{U}_{\text{all}}(t_{i})\).
## 2 The Proposed Method
### Developed Algorithms
#### 2.2.2 Genetic Algorithm  
The Genetic Algorithm (GA) is an evolutionary intelligent algorithm based on heuristic search algorithms. The GA was inspired by the theory of biological evolution, and it simulates the processes of natural selection mechanisms to find the optimal solutions [27]. The GA aims to achieve the evolution of the population via mutation, crossover, and natural selection. A solution produced by a GA is calleda chromosome, which is made up of genes. Moreover, the set of chromosomes represents a population. In order to measure the suitability of the solution produced, the quality of these chromosomes is computed via a fitness function. Therefore, the chromosomes have a high chance of remaining in the next generation that has a higher fitness. Subsequently, to produce a new generation, some chromosomes in the population, called the parents, generate offspring through crossover operation, so that the parents' chromosomes exchange some of their genes. Moreover, the genes of the new generation may be changed by mutation operation after crossover. It should be noted that crossover rate and mutation rate value control the number of intersected and mutated chromosomes [28].  
A selection operation is employed to maintain the solution with the highest fitness. After several generations, the chromosome value will converge at the optimal solution for the problem. The procedure of the GA is given in the following steps [29]:  
1. Randomly generate the initial population.
2. Evaluate the fitness value of each chromosome according to a fitness function.
3. Perform the genetic operations including selection, crossover, mutations.
4. The iteration is terminated if the terminating condition is met. Otherwise, return to step two.
## 4 Results and Discussion  
### Mineral Compositions and Lithofacies Classification of the Shales  
XRD analyses of the Dalong Formation shales from well ED-2 returned extremely high contents of brittle minerals (averaging 85.5%). Brittle minerals mainly include quartz, feldspar, calcite, dolomite, and pyrite (Table 1, Figure 3). Quartz contents were the highest (36.4-87.2%, averaging 62.2%), followed by carbonates (0-17.9% calcite, averaging 5.1%; 0-17.9% dolomite, averaging 5.1%), clay minerals (4.7-36.4%, averaging 13.7%), plagioclase (4.7-36.4%, averaging 13.7%), and pyrite (0.8-6.6%, averaging 3.0%). The samples also contained minor amounts of hematite and potassium feldspar (Figure 3).  
According to the shale lithofacies classification scheme, based on a ternary diagram with siliceous (quartz + feldspar), clay, and carbonate (calcite + dolomite) mineral endmembers [16], the Dalong Formation shales in well ED-2 include siliceous rocks (S), carbonate-rich siliceous shales (S-1), siliceous shales (S-2), and clay-rich siliceous shales (S-3), all of which are siliceous shales (Figure 4). Siliceous shales are mainly deposited in inter-platform basin environments. The siliceous shale from well ED-2 is black or gray-black and relatively hard. Small high-angle fractures havedeveloped and filled with calcite (Figure 5).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sample** & **Depth** & **TOC** & **\(\mathbf{R_{o}}\)** & **Kerogen** & \multicolumn{6}{c}{**Mineral Contents (\%)**} \\ \cline{6-10}
**No.** & **(m)** & **(wt.\%)** & **(\%)** & **Type** & **Quartz** & **Feldspar** & **Clay Minerals** & **Carbonates** & **Pyrite** \\ \hline P
## 5 Case Studies
### Hysteresis Control with PID Correction for Temperature Control
#### 5.1.1 Proposed Objective Function  
Note that the main objective of the considered example is to provide a lower error between the reference and the current temperature. An ISE indicator is proposed to minimize the control loop error in this case. On the other hand, the hysteresis controller commonly uses the electronic relay. Therefore, the number of switches should be minimized to ensure long-term operation of the system without maintenance. If the ISE indicator is minimized, the number of changes in the relay state increases the overall value of the objective function as a punishment. The objective function for the considered hysteresis control with PID correction for temperature control is as follows:  
\[F_{\text{heater}}=\sum_{k=0}^{N}\left(y^{\text{ref}}-y(k)\right)^{2}\cdot T_{ s}+\alpha\sum_{k=0}^{N}\Delta u(k) \tag{39}\]  
with  
\[\Delta u(k)=\left\{\begin{array}{ll}1,&\text{if }u(k-1)=0\;\;\&\;\;u(k)=1\\ 0,&\text{otherwise}\end{array}\right.\]  
where \(N\) is the number of samples per experiment; \(y^{\text{ref}}\) and \(y\) are reference and current temperature, respectively; \(T_{\text{s}}\) is the sampling period; \(\alpha\) is a weighting factor; \(u\) is a state of the hysteresis controller; and \(\Delta u\) is used to count the rising edges of the control signal.
## 1 Introduction
### Intelligent Terminal Measurement Time Synchronization  
Due to the different delays in the links of transmission, communication, reception, storage, and application caused by the measurement data obtained by multi-type intelligent terminals, their direct use may result in information incompatibility, which will negatively impact the observability of faults. By examining the correlation coefficients of various types of measurement data and their sampling time using the signal correlation theory, the base sampling time of the measurement system is established, allowing the real-time measurement data of the intelligent terminal to be synchronized at a specific time section. The Pearson correlation coefficient, which measures the time synchronization relationship of different measurement signals, is:  
\[\rho(t_{1},t_{2}-\delta)=\frac{C(t_{1},t_{2}-\delta)}{\sqrt{C(t_{1},t_{1})\cdot C (t_{2}-\delta,t_{2}-\delta)}} \tag{1}\]  
where \(C\) is the covariance; \(t_{1}\) is the measurement data without the time scale, and the representative device of this type of intelligent terminal is FTU; \(t_{2}\) is the measurement data with time scale, \(\delta\) is the measurement time series, and the representative device of this type is \(\upmu\)PMU.  
Assume that the FTU measurement upload rate is \(f_{S}\), and the \(\upmu\)PMU data upload rate is \(f_{\mu}\). In a given time \(T\), there are \(n_{s}\)=\(T\):\(f_{S}\) sets of FTU measurement data:  
\[z_{F}=[z_{F,1},z_{F,2},\cdots,z_{F,n_{s}}] \tag{2}\]  
In the same way, there are \(n_{\mu}=T\):\(f_{\mu}\) sets of \(\upmu\)PMU measurement data:  
\[z_{\mu}=[z_{\mu,1},z_{\mu,2},\cdots,z_{\mu,n_{\mu}}] \tag{3}\]  
Then, calculate the Pearson correlation coefficient of each element in \(z_{F}\) and \(z_{\mu}\), arrange the correlation coefficient \(\rho\) according to the time series, take the \(\upmu\)PMU sampling time corresponding to the maximum value as the reference time of the multi-intelligent terminal measurement system, and calculate the FTU measurement and this time delay error \(\varepsilon_{t}\).  
\[\varepsilon_{t}=kt_{d,F} \tag{4}\]  
where \(k\) is the rate of change of the measurand, \(t_{d,F}\) is the deviation between the base time and the state estimation calculation time, which can be considered to obey the following probability density function.  
\[f_{t}(t_{d,F})=\frac{1}{\sigma_{t}\sqrt{2\pi}}\mathrm{e}^{-(t_{d}-t)^{2}/2 \sigma_{t}^{2}} \tag{5}\]  
The symbol \(\sigma_{t}\) is the standard deviation of the probability density function.
## 5 Social Big Data Analysis Applications
### Frequent Trajectory Mining from Social Media Data
#### 5.1.2 RoI Detection  
The RoI detection algorithm aims at defining the Regions-of-Interest by clustering the geotagged items assigned to the different PoIs. In fact, the geotagged items can be transformed into geographical points (i.e., pairs of \(\langle\)latitude, longitude \(\rangle\)), which can be aggregated through clustering. Specifically, an adapted version of DBSCAN [27] is used for RoI mining with an automatic estimation of the parameters required by the algorithm.  
The DBSCAN algorithm needs two key parameters: _eps_, the radius of a neighborhood with respect to some point; _minPts_, the minimum number of points required to form a cluster. These two parameters can be calculated using the following procedure as defined in [27]:  
1. Calculate the plot of the k-nearest-neighbor distances (\(k\)-dist), computed for each point, and sorted by descending order [28]. As suggested in [27], for bi-dimensional data, \(k\) can be set to 4.
2. Choose a _threshold point_ on \(k\)-dist plot for separating noise points (i.e., all points with a higher \(k\)-dist value than threshold) from points that are assigned to some clusters (i.e., all points with a lower k-dist value than threshold). The threshold point is calculated by estimating the noise percentage in the data (_noisePerc_).
3. The \(k\)-dist value of the threshold point is used as _eps_ value. Concerning _minPts_, it can be set as \(k+1\)[28].  
Since DBSCAN calculates one or more clusters on a set of points, we select the points that belong to the largest cluster, and starting from them we return the convex polygon that encloses these points (i.e., an RoI).
# A Novel Cloud Approach for Connected Vehicles
## 3 GeoVCDN Approach
### Centralized Context Cloud Architecture  
In the field of intelligent transport systems (ITSs), the data's relevance is determined by its location and is limited in time. Considering this, the context cloud paradigm gives the possibility to filter the data based on their locations (district, town, harbor, ski resort, etc.). Furthermore, the context cloud is hierarchical (e.g., a building belongs to a district that belongs to a town). According to the literature, the most efficient approach for naming in information-centric networking is the hierarchical way. In our architecture, the cloud is composed of a static part (RSUs and C-ITS central) and a dynamic part (the vehicles). We consider a vehicle as being a part of the cloud when it is linked, directly or indirectly, to an RSU over the G5 channel. Note that data management within the cloud is out of the scope of this study.  
Figure 2 describes the message exchanges between each actor of the architecture.  
* Step 1: Sensors send their data to the C-ITS central
* Step 2: The C-ITS central generates the metadata
* Step 3: The metadata are distributed over RSUs
* Step 4/6: Vehicles broadcast their interests over the CAMs
* Step 5: RSUs broadcast the metadata after receiving interest from vehicles (Step 4)
* Step 7: Vehicles broadcast the metadata after receiving interest from vehicles (Step 6)