esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Network Model and Underwater Acoustic Propagation
### Network model  
We first consider a sparsely deployed UWA-SNs: an underwater moored monitoring system in this paper. Underwater moored monitoring systems provide unprecedented abilities and opportunities to monitor changes in oceans and atmosphere by collecting real-time sensed data throughout the entire water column over large temporal scales [18]. These real time oceanographic data enable us to better understand the oceans, and to solve world problems such as natural disaster prediction and global warming.  
As shown in Figure 1, a typical moored oceanic monitoring system is composed of an anchor, a mooring line, and a floating buoy (surface sink node) with satellite, radio frequency (RF) or cell phone technology to transmit data to shore in real-time [19]. The sensors are attached to the mooring line and they may be physical, hydrographic, bio-optical, or chemical sensors which can measure temperature, conductivity, salinity, pressure, current speed etc. The periodic sensed data must be transmitted from different depths in the water column to the surface buoy.  
Though these sensors can be wired together using special mooring cable, the wired system has some disadvantages, such as cable breakage, too expensive to be widely used, etc. [19]. It is cost-effective and robust alternatives to use wireless acoustic communication in such mooring systems. In that case, these sensors form a linear acoustic sensor network.  
There are mainly two types of applications of UWA-SNs: event-driven and periodic sensing, which lead to different traffic patterns [17]. In this paper, we focus on periodic sensing networks in which the nodes constantly sense the environment and report their findings to an end node. The moored monitoring system is working in periodic sensing. To evaluate the lifetime of the networks, we divide the time into rounds as it has been done in most previous research [7]. For simplicity, we assume each node precisely generates one data packet in each round.
# Internet Traffic Prediction with Distributed Multi-Agent Learning
## 3 Dataset and Problem  
In this paper, we use an open real-world dataset [41] to validate the proposed distributed multi-agent learning approach and compare it with the centralized learning baseline. The dataset contains the Internet traffic data collected in a university campus backbone network located in the State University of Ceara for half a year from 16 January 2019 to 15 July 2019, with a sampling time period of one hour. The mean, maximum, and minimum bandwidth usage values are recorded. Figure 1 shows the Internet traffic data in the first three weeks of the data collection time period. For clarity, the mean, maximum, and minimum values are plotted in three subfigures. Both a weekly pattern and a daily pattern on weekdays are observed from Figure 1. The stationarity of this specific Internet traffic time series has also been validated with the Dickey-Fuller test, and the rationality of using the mean, maximum, and minimum usage values as input has also been justified with the cointegration test [15].  
Following previous studies [15], the Internet traffic prediction problem is modeled as a supervised learning problem, in which the mean traffic value in the future is the short-term prediction target and the historical traffic data (containing the mean, maximum, and minimum usage values) in the historical time window are the input features. We consider the single-step prediction problem in this study, in which the mean traffic value in the next hour is chosen as the prediction target. To compare with existing centralized learning results, we choose 24 h as the historical time window size and leave the exploration for multi-step predictions as well as the influence of varying historical time window sizes in our future studies.  
In this study, we take a step further and consider Internet traffic prediction using supervised learning and distributed multi-agent learning approach. The problem is to design an effective cooperation and aggregation process in which the historical traffic data are used by different agents as the base prediction model input and a final result is aggregated from the predictions of different agents as the output. Different from the centralized learning approach in which only one prediction model is trained in a central server, the learning approach for the base prediction models is distributed among different agents with their own facilities.
## 2 The Evolution from Cloud Computing to MEC
### Fog Computing
#### 2.2.1 Architecture  
The three-tier architecture shown in Figure 3 is the most widely utilized one in fog computing [33]. The tiers are presented as follows:  
* Tier 1--IoT Devices: This level mainly comprises user equipment such as smartphones, intelligent cars, etc. We call these devices Terminal Nodes (TNs). TNs might have features such as a Global Positioning System (GPS).
* Tier 2--Fog (middle layer): There is a special plane in this architecture called the fog computing plane. In this plane, there are devices such as routers, switches, and Access Points (APs). These devices not only exchange data but they can also share their storage space and computing power.
* Tier 3--Cloud: This faraway layer is equipped with extensive storage capacity and powerful computing resources that can handle a lot of information and perform complex tasks.  
Figure 2: Basic Architecture of Fog Computing.
# Two-Dimensional Profile Simulation Model for Five-Axis CNC Machining with Bull-Nose Cutter
## 2 Model of Surface Topography with a Bull-Nose Cutter
### Calculation of Scallops  
Each scallop can be considered an envelope of moving annulus. The parametric equation of an annulus in the tool coordinate system \(CS_{T}\) can be expressed in the form below:  
\[C(\alpha,\beta)=\begin{bmatrix}x(\alpha,\beta)\\ y(\alpha,\beta)\\ z(\alpha,\beta)\end{bmatrix}=\begin{bmatrix}[(R-r)+r\cdot\sin\alpha]\cdot\cos \beta\\ [(R-r)+r\cdot\sin\alpha]\cdot\sin\beta\\ r\cdot(1-\cos\alpha)\end{bmatrix}. \tag{1}\]  
In five-axis CNC machining, the transformation matrix of the tool coordinate system \(CS_{T}\) to the workpiece coordinate system \(CS_{W}\) is:  
\[M_{T-W}=\begin{bmatrix}X_{T-W}&Y_{T-W}&Z_{T-W}&O_{T-W}\\ 0&0&0&1\end{bmatrix}, \tag{2}\]  
Figure 1: Model of a bull-nose cutter.  
Figure 2: Surface topography of the workpiece varies with tool orientation. (**a**) The tilt angle at any \(CC\) point is \(0^{\circ}\). (**b**) The tilt angle at any \(CC\) point is \(30^{\circ}\).  
where \(X_{T-W}\), \(Y_{T-W}\), and \(Z_{T-W}\) represent the axes of the tool coordinate system \(CS_{T}\) in the workpiece coordinate system \(CS_{W}\), and \(O_{T-W}\) represents the cutter location (CL) in the workpiece coordinate system \(CS_{W}\). In the workpiece coordinate system \(CS_{W}\), the parametric equation of the annulus is:  
\[\begin{bmatrix}x_{W}(\alpha,\beta)\\ y_{W}(\alpha,\beta)\\ z_{W}(\alpha,\beta)\\ 1\end{bmatrix}=M_{T-W}\cdot\begin{bmatrix}x(\alpha,\beta)\\ y(\alpha,\beta)\\ z(\alpha,\beta)\\ 1\end{bmatrix}. \tag{3}\]  
The cutter and the workpiece are tangent at the \(CC\) point, so the scallop is tangent to the surface of the workpiece. For an arbitrary scallop, the approximate surface of the scallop at the \(CC\) point in the local coordinate system \(\{CC,T_{\max}^{\mathrm{C}},T_{\min}^{\mathrm{C}},N\}\) can be expressed in the form below:  
\[z=\frac{1}{2}\Big{(}\kappa_{1}x^{2}+\kappa_{2}y^{2}\Big{)}+o\Big{(}x^{2}+y^{2 }\Big{)}, \tag{4}\]  
where \(\kappa_{1}\) and \(\kappa_{2}\) are the principal curvatures of the bull-nose cutter at the \(CC\) point. This equation is the second-order approximation of the annulus surface at the \(CC\) point, and it is actually the equation of the so-called oscillating paraboloid [33]. This equation describes the geometric properties of the bull-nose cutter at the \(CC\) point. It is used in tool motion planning, tool orientation optimization, and other problems in CNC machining.
## 1 Introduction
While most researchers work on the performance of the linked systems like the engine and the fuel pump [1; 15; 16], there is a lack of investigation in isolating the faults from the EPS, which have propagating effects into other aircraft systems. In this paper, an 'Adaptive Neuro-Fuzzy Inference System (ANFIS) monitor' is developed in MATLAB R2019a to detect the presence of faults in the EPS, within a customizable time period, defined by the user. A hybrid combination of ANFIS monitor with causal reasoning is applied to isolate the root cause of faults found at the Line Replaceable Units (LRUs) connecting the EPS with other aircraft systems. With this objective, in Section 2 of this paper, the digital twin for an aircraft EPS is modeled in MATLAB Simulink R2019a along with its connections to the aircraft engine, the Environmental Control System (ECS), and the fuel system through LRUs as simple as valves and pumps. This is followed by exercising this digital twin under healthy and faulty conditions. Section 3 discusses the steps involved in developing the diagnostic reasoning for EPS using ANFIS and presents the ANFIS monitor that is developed for the detectability of faults in the EPS. Section 4 presents two approaches to processing the results generated by ANFIS: (i) the crisp boundary approach and (ii) the fuzzy boundary approach. These two methods are compared based on their misclassification rate (the ratio of the number of misclassified simulation runs to the total number of simulation runs). Causal reasoning is combined with the better method to isolate the faults and their root causes affecting the aircraft system LRUs. Section 5 provides the conclusion of this paper.
## 2 Cooperative SCARA System
### Kinematic and Dynamic Analyses of the SCARA Robot
#### 2.1.2 Inverse Kinematics  
Inverse kinematics focuses on determining the specific angles and distances of a robot's joints such that its end effector is positioned and oriented at a specific location in a three-dimensional space [46; 47; 48]. This process was approached using a geometric method along with trigonometric functions.  
Given the configuration of the SCARA robot, the first two joints were responsible for generating movements in the XY plane. Therefore, the robot was projected onto this Cartesian plane to simplify the inverse kinematics analysis, as shown in Figure 4. In this context, using the cosine theorem, angle \(\theta_{2}\) can be calculated as expressed in Equation (11), which is fundamental for positioning the robot's end effector at the desired location.  
\[\cos(\theta_{2})=\frac{\mathrm{P_{x}}^{2}+\mathrm{p_{y}}^{2}-\mathrm{a_{1}}^{2 }-\ \mathrm{a_{2}}^{2}}{2\mathrm{a_{1}}\mathrm{a_{2}}} \tag{5}\]  
\[\theta_{2}=\arctan\left(\frac{\sqrt{1-\cos(\theta_{2})^{2}}}{\cos(\theta_{2} )}\right) \tag{6}\]  
To determine \(\theta_{1}\), we begin with Equation (7), which establishes the relationship between the geometric angles of the first two joints.  
\[\theta_{1}=\beta-\alpha \tag{7}\]  
Next, \(\theta_{1}\) is expressed as a function of the end effector coordinates and robot parameters in Equation (8).  
\[\theta_{1}=\arctan\left(\frac{\mathrm{P_{y}}}{\mathrm{P_{x}}}\right)-\arctan \left(\frac{\mathrm{a}_{2}\sin(\theta_{2})}{\mathrm{a}_{1}+\mathrm{a}_{2}\mathrm {cos}(\theta_{2})}\right) \tag{8}\]  
The prismatic joint \(\mathrm{d}_{3}\) of the robot represents a linear movement along the \(z\)-axis, which is defined as the difference between the height of the robot's base, the desired position in z, and the height of the end effector, as shown in Equation (9).  
\[\mathrm{d}_{3}=\mathrm{d}_{1}-\mathrm{p}_{2}-\mathrm{d}_{4} \tag{9}\]  
Figure 4: SCARA robot projected onto the Cartesian plane, where \(\mathrm{p_{x}}\): position of the end effector on the \(x\)-axis; \(\mathrm{p_{y}}\): position of the end effector on the \(y\)-axis; and \(\mathrm{p_{z}}\): position of the end effector on the \(z\)-axis.  
The fourth joint \(\theta_{4}\) determines the orientation of the robot's end effector, facilitating its precise alignment with respect to the specific target or task to be performed, as shown in Equations (10) and (11).  
\[\alpha=\theta_{1}+\theta_{2}-\theta_{4} \tag{10}\]  
\[\theta_{4}=\tan\biggl{(}\frac{\mathrm{P_{y}}}{\mathrm{P_{x}}}\biggr{)}+\theta_{ 1}+\theta_{2} \tag{11}\]
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Prediction Intervals Formulation and Evaluation Metrics  
To ascertain the uncertainty in point forecasts, it is necessary to provide the prediction intervals (PIs) so as to quantify these uncertainties [31]. This section gives the formulation of the PIs and various metrics for evaluating the performance of estimated PIs:
## 2 Materials and Methods
### Vulnerability Analysis of Coastal Flooding
#### 2.2.9 Sea-Level Factor (Fsl)  
Through this factor, the relative sea-level change is analyzed using historical data recorded by tide gauges located near the study area. The data were sourced from the REDMAR dataset, which, as in the previous case, is available through Ports of State (Figure 5). The primary purpose of this dataset is to continuously measure, record, analyze, and store sea-level data in ports, with real-time data access being one of its key features. In this instance, data were obtained from the tide gauges in Villagarcia 2, Marin, and Vigo 2, resulting in different time series for each of them. Data for Villagarcia 2 and Vigo 2 cover the period from 1992 to 2021, while data for Marin spans the period from 2010 to 2021. Additionally, data from the Cascais tide gauge, located in Portugal, were acquired through the PSMSL (Permanent Service for Mean Sea Level) website (psmsl.org), where international tide gauge data are archived. Historical data for this particular tide gauge extend from 1882 to 1993.  
From the obtained data, the change in mean sea level was calculated for each tide gauge in millimeters, and these values were entered into and represented in a Geographic  
Figure 6: (**A**) Map of significant wave heights in the study area (**left**) and its corresponding vulnerability map (**right**). Vulnerability intervals: 3.5–4 m, 4–5 m, 5–6 m, 6–7 m, and \(>\)7 m. (**B**) Map of sea-level change around the study area (**left**) and its corresponding vulnerability map (**right**). Vulnerability intervals in mm/year: 1.100–2.050, 2.051–3.000, 3.001–3.850, 3.851–4.910, and 4.91–5.860. (**C**) Map of extreme tidal range around the study area (**left**) and its corresponding vulnerability map (**right**). Vulnerability intervals in meters: 4.385–4.400, 4.401–4.420, 4.421–4.430, 4.431–4.450, and 4.451–4.478.  
Information System (GIS). Interpolation was employed to generate a map depicting the average sea-level rise in the vicinity of the study area (Figure 6B). By reclassifying the acquired intervals, the following values were assigned: 1.1-2.05 (value 1), 2.05-3 (value 2), 3-3.96 (value 3), 3.85-4.91 (value 4), and 4.91-5.86 (value 5).
## 1 Introduction
### Motivation and Contributions  
Recently, a wide range of research studies has been carried out to investigate covert communication in relay networks. In many of these works, covert communication is achieved with the help of channel uncertainty or jammer assistance. However, they are commonly based on separate terrestrial networks. Moreover, for multi-relay networks, relay selection is considered an effective technique to achieve spatial diversity gain, which is hardly involved in the related work. As far as we know, the performance analysis of covert communication in ISMTRN combined with relay selection has not been reported, which is innovative enough to investigate.  
The above observations motivate us to investigate the covert performance of the ISMTRN with relay selection. Specifically, the key novelty and main contributions of this paper are summarized below:  
1. We first pioneer the study of covert communication in the ISMTRN with the partial relay selection scheme, where the selected relay opportunistically transmits covert information;
2. Furthermore, considering the actual signal transmission situation, the statistical characteristics of the channels are given;
3. On this basis, the closed-form expression of error detection probability (EDP) under this covert communication network is deduced to obtain more in-depth insights. Besides, the average covert communication rate (ACR) is given to measure the covert performance of this system;
4. Finally, the numerical simulation results are given to analyze covert performance considerably. Moreover, observation results are summarized in detail.  
The subsequent composition of this article is as follows. Section 2 consists of the system description, transmission model and problem formulation, while the performance of the considered system is investigated in Section 3. Moreover, numerical simulation results are given in Section 4. Finally, the whole article is discussed in Section 5.
## 4 Network Creation and Energy-Efficient Clustering
### Extraction of Higher-Order Statistical Features  
**Skewness [27]**: \"It is a symmetry measure or the lack of symmetry exactly. A data set or distribution is symmetric only if it is similar to the left and right of the centre point\". The mathematical expression of skewness \(SF_{1}\) is given in Equation (11).  
\[SF_{1}=\frac{\sum_{i=1}^{k}\left(Y_{i}-\mu\right)^{3}/k}{L^{3}} \tag{11}\]  
In Equation (11), \(Y_{i}=Y_{1}\), \(Y_{2}\),..., \(Y_{k}\), \(\mu\) indicates the mean value, \(L\) denotes the standard deviation and \(k\) refers to the number of data points. Moreover, \(L\) is calculated with \(k\) present in the denominator rather than \(k-1\) while computing the skewness. Further, the skewness value is near zero for any symmetric data, and zero for the skewness for a normal distribution.  
**Kurtosis [27]**: \"It is a measure that identifies whether the data are light-tailed or heavy-tailed and related to the normal distribution\". Datasets with less kurtosis [27] tends to provide a lack of outliers or lower tails. Moreover, a dataset with larger kurtosis tends to provide outliers, or heavy tails. The mathematical formula of kurtosis \(SF_{2}\) for univariate data such as \(Y_{1}\), \(Y_{2}\),..., \(Y_{k}\), is expressed in Equation (12).  
\[SF_{2}=\frac{\sum_{i=1}^{k}\left(Y_{i}-\overline{Y}\right)^{4}/k}{L^{4}} \tag{12}\]  
The standard deviation is calculated by the \(k\) value present in the denominator rather than \(k-1\) while computing the kurtosis.  
**Entropy feature**[28]: Entropy is computed as in Equation (13), where \(u\) and \(v\) are the coordinates of the co-occurrence matrix.  
\[SF_{3}=Ent=-\sum_{u}\sum_{v}g(u,v)\log(g(u,v)) \tag{13}\]  
**Second Moment**[28]: \"It is a measure of the uniformity of data\" and it is computed as in Equation (14).  
\[SF_{4}=SM=\sum_{u}\sum_{v}\left\{g(u,v)\right\}^{2} \tag{14}\]  
**Percentile**[29]: It provides an idea of 'how the data values are spread over the interval from the smallest value to the largest value'. About \(Q\) percent of data values come under \(Q^{th}\) percentile, and around 100- \(Q\) percentage of data values exceed the \(P^{th}\) percentile. The percentile features are denoted by \(SF_{5}\). The higher-order statistical features are indicated as \(Fe_{hist}\), and is given in Equation (15).  
\[Fe_{hist}=SF_{1}+SF_{2}+SF_{3}+SF_{4}+SF_{5} \tag{15}\]  
Accordingly, the extracted statistical and higher-order statistical features are summed up as \(Fe(Fe=Fe_{st}+Fe_{lst})\). These features are given as input to optimized DCNN for classification.
# Clinical Applications of In Vivo and Ex Vivo Confocal Microscopy
## 3 Ex Vivo Confocal Laser Scanning Microscopy
### Conclusions  
Ex vivo CLSM offers a real-time examination of the excised tissue in reflectance and fluorescence modes, enabling also the application of fluorescent-labelled antibodies. Reflectance and fluorescence ex vivo CLSM, along with H&E-like digital staining images, show to be promising in different clinical fields, especially skin pathology.  
In conclusion, ex vivo CLSM is a novel imaging diagnostic tool enabling the analysis of tissue sample of a diameter of 2 cm. Thin sections of surgical samples are not required for the examination. Currently, the main clinical application is represented by skin tumor margin assessment in BCC and promising results have been obtained in the analysis of prostate cancer. Additionally, ex vivo CLSM features of cancers such as SCC, LM and others as well as skin inflammatory disorders have been preliminary explored, thus representing potential fields of application in the future.  
However, the use of ex vivo CLSM is limited by the high cost of the tool as well as by the learning curve for the image readers. However, ex vivo CLSM has proven to be a reliable and reproducible technique as compared to histopathology, especially in dermatologic and urologic applications.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
Explainable artificial intelligence (XAI) models with Shapley additive explanation (SHAP) values allows multidimensional representation of movement performance interpreted on both global and local levels in terms understandable to human intuition. We aimed to evaluate the swimming performance (World Aquatics points) predictability of a combination of demographic, training, anthropometric, and biomechanical variables (inputs) through XAI. Forty-seven swimmers (16 males), after completing a training questionnaire (background and duration) and anthropometric assessment, performed, in a randomised order, a 25 m front crawl and three countermovement jumps, at maximal intensity. The predicted World Aquatics points (516 \(\pm\) 159; mean \(\pm\) SD) were highly correlated (r\({}^{2}\) = 0.93) with the 529 \(\pm\) 158 actual values. The duration of swimming training was the most important variable (95_SHAP), followed by the countermovement jump impulse (37_SHAP), both with a positive effect on performance. In contrast, a higher percentage of fat mass (21_SHAP) corresponded to lower World Aquatics points. Impulse, when interpreted together with dryland training duration and stroke rate, shows the positive effects of upper and lower limb power on swimming performance. Height should be interpreted together with arm span when exploring positive effects of anthropometric traits on swimming performance. The XAI modelling highlights the usefulness of specific training, technical and physical testing, and anthropometric factors for monitoring swimmers.  
performance prediction; anthropometry; deep learning; competitive swimming Article