esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 WFDB Window-Based Evaluation
### SUIMSTAT Measured Statistical Metrics  
The summary statistics (SUMSTAT) metrics are the derived aggregate statistics from bxb, rxx, etc. using the comparable window concept that can be categorized mainly into three groups: true positives (TPs), false positives (FPs), and false negatives (FNs). The SUIMSTAT function counts each of these statistical parameter for every record to generate a report file [32; 51; 52; 85]. These parameters are defined as follows:  
**TP:** If the predicted sample falls within the 150 ms window.  
**FP:** If the predicted sample falls outside the 150 ms window.  
**FN:** If the predicted sample does not fall within the 150 ms window.  
Figure 3 illustrates the basic working of the window and how metrics like TP, FP, and FN are measured. We can also use other metrics to evaluate the detector based on TP, FP, and FN. These metrics are positive predictability (+P), accuracy (Acc), and the detection error rate (DER) [36; 87; 86]. Each of these evaluation metrics is defined as follows:  
**Positive predictability:** It quantifies the algorithm's ability to differentiate true beats and false beats, and it is defined as  
\[+\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}\times 100\% \tag{1}\]  
**Sensitivity:** It measures the algorithm's ability to detect true beats accurately, and it is defined as  
\[\mathrm{Se}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}\times 100\% \tag{2}\]  
**Accuracy:** It represents the overall correctness of the algorithm, and it is defined as  
\[\mathrm{Acc}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}+\mathrm{FN}}\times 100\% \tag{3}\]  
Figure 3: Calculation of Window-based Statistical Metrics.  
**Detection error rate (DER):** It helps evaluate the detection algorithm's performance. It is important to note that a lower DER indicates better performance. It is defined as  
\[\text{DER}=\frac{\text{FP}+\text{FN}}{\text{TP}}\times 100\% \tag{4}\]
## 1 Introduction
### State of the Art
The meaning of plausibility in the simulation context is very well described by the English term \"likely valid\". The entire process is shown in Figure 1, starting with the FE simulation and resulting in a classification result. Before a Machine Learning model is trained, a dataset must be created, which consists of the prepared simulations and a corresponding label. Labeling of the simulations should be performed by experienced simulation experts [17]. A parameter study of FE simulations is an effective method for obtaining an adequate number of simulations. After training and examination of the model, it can be employed for theclassification of new simulations. For this purpose, new simulations must undergo the same preparation process to be classified as \"plausible\" and \"non-plausible\". In the first step (data preparation), the method uses the projection of points onto a detector sphere to convert the simulation input and results into matrices. In contrast to the unordered simulation data, the matrices are uniformly sized and hence suitable as input for a Neural Network (NN). For conversion of the whole FE simulation into matrices, different point clouds (bearing, loads, mesh, and results) are necessary. The transformation is performed with a projection of nodes onto a detector sphere, which is subdivided into different areas similar to the longitudes and latitudes of a globe. The fields are called pixels and correspond to the respective matrix input. The number of pixels can be chosen independently (for example, 10,000 pixels results in a 100 \(\times\) 100 matrix). Detailed calculation of the detector sphere and node projection is explained in more depth in [16] and illustrated in Figure 2 with its main steps. After projection of the points, the detector sphere is turned into a matrix, similar to transforming a globe into a map. Since the inputs and results of the FE simulation are node-bound, they can be transferred into matrices, allowing the entire simulation to be uniformly transferable. Consequently, different matrices can be created for one simulation, which are arranged channel-wise (comparable to RGB images). The choice of matrices is dependent on the simulation and the objective pursued. These matrices allow for the training of a Machine Learning model that predicts the two classes \"plausible\" and \"non-plausible\". Within this contribution, the aim is to investigate whether a differentiated detection of the cause of the simulation errors is also achievable. For this purpose, it is necessary to examine which classification approach can fulfill the stated goal. Therefore, the methodical background for classification and Deep Learning is presented in the next section. Figure 1: Overview of the Convolutional Neural Network (CNN)-based plausibility check method with example matrices according to [15; 16]. Figure 2: Process steps of the projection method for generating the node matrix according to [15; 16].
# Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model
###### Abstract  
Rotating machines such as induction motors are crucial parts of most industrial systems. The prognostic health management of induction motor rotors plays an essential role in increasing electrical machine reliability and safety, especially in critical industrial sectors. This paper presents a new approach for rotating machine fault prognosis under broken rotor bar failure, which involves the modeling of the failure mechanism, the health indicator construction, and the remaining useful life prediction. This approach combines signal processing techniques, inherent metrics, and principal component analysis to monitor the induction motor. Time- and frequency-domains features allowing for tracking the degradation trend of motor critical components that are extracted from torque, stator current, and speed signals. The most meaningful features are selected using inherent metrics, while two health indicators representing the degradation process of the broken rotor bar are constructed by applying the principal component analysis. The estimation of the remaining useful life is then obtained using the degradation model. The performance of the prediction results is evaluated using several criteria of prediction accuracy. A set of synthetic data collected from a degraded Simulink model of the rotor through simulations is used to validate the proposed approach. Experimental results show that using the developed prognostic methodology is a powerful strategy to improve the prognostic of induction motor degradation.  
rotating machines; prognostic health management; broken rotor bar; health indicator; principal component analysis; remaining useful life
# Real-Time Exercise Mode Identification with an Inertial Measurement Unit for Smart Dumbbells
###### Abstract  
Exercise is good for health, quality of life, and maintenance of human muscles. Dumbbells are popular indoor exercise equipment with several benefits such as low cost, high flexibility in space and time, easy operation, and suitability for people of all ages. Facilitated by advances in the Internet of Things, smart dumbbells that provide automatic counting and motion monitoring functions have been developed. To perform these tasks, the key process is identification of exercise mode. This study proposes a method to identify essential muscle groups' (biceps, triceps, and deltoids) exercise modes of a dumbbell using an inertial measurement unit to provide three-axis angular velocities and accelerations. The motion angles were estimated from the axial acceleration and angular velocity. Phase diagrams and time plots of the axial angle, angular velocity, and acceleration were used to extract significant features of each exercise. Machine Learning and weighting functions were developed to combine these features into an identification index value for accurate identification and classification of the exercise modes. An algorithm was developed to verify the exercise mode identification. The results show that the proposed method and weighting function can successfully identify the six exercise modes. The identification algorithm was 99.5% accurate. The exercise mode identification of the dumbbell is confirmed.  
exercise identification; motion monitoring; smart dumbbell; machine learning exercise classification +
Footnote †: journal: Applied Sciences  
10 November 2021  
Subject: 5 December 2021  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Copyright:** 2021 by the authors. License MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results
### Predictors of Sleep Disturbances
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Predictors in**} & \multicolumn{2}{c}{**Unstandardized Coefficients**} & \multirow{2}{*}{\(t\)} & \multirow{2}{*}{\(p\)} & \multirow{2}{*}{**95\% CI for B**} \\ \cline{2-2}
**2017** & & & & & \\ \hline Intercept & 3.495 & 0.611 & 5.724 & \textless{0.001} & (2.293, 4.697) \\ Shift type (Fixed night) \({}^{\text{a}}\) & 0.989 & 0.198 & 4.994 & \textless{0.001} & (0.599, 1.379) \\ Shift type (Fixed early morning) \({}^{\text{a}}\) & 0.616 & 0.176 & 3.488 & 0.001 & (0.268, 0.963) \\ Shift type (Fixed daytime) \({}^{\text{a}}\) & \(-\)0.013 & 0.131 & \(-\)0.095 & 0.924 & (\(-\)0.271, 0.246) \\ Trunk flexion (Very Frequent) \({}^{\text{b}}\) & 0.423 & 0.166 & 2.556 & 0.011 & (0.097, 0.749) \\ Trunk flexion (Sometimes) \({}^{\text{b}}\) & 0.247 & 0.149 & 1.654 & 0.099 & (\(-\)0.047, 0.541) \\ Work seniority \({}^{\text{b}}\) & \(-\)0.015 & 0.005 & \(-\)2.732 & 0.007 & (\(-\)0.025, \(-\)0.004) \\ Horizontal trust & \(-\)0.524 & 0.156 & \(-\)3.352 & 0.001 & (\(-\)0.831, \(-\)0.216) \\ Social support & 0.336 & 0.131 & 2.557 & 0.011 & (0.077, 0.594) \\ Homo-efficacy & \(-\)0.175 & 0.083 & \(-\)2.118 & 0.035 & (\(-\)0.338, \(-\)0.012) \\ \hline \end{tabular}  
* ANOVA. \({}^{\text{b}}\) Tukey multiple comparisons test showed significant differences between fixed daytime shift types (\(p\) = 0.027). \({}^{\text{*}}\) Tukey multiple comparisons tests showed significant differences between fixed night and fixed daytime, fixed night and rotating, fixed early morning and fixed daytime, and fixed early morning and and fixed daytime, and fixed early morning and and rotating shift types (\(p\) = 0.001 in all comparisons). \({}^{\text{d}}\) ANOVA with Welch correction. \({}^{\text{e}}\) Games–Howell multiple comparisons tests showed significant differences between fixed night and fixed daytime (\(p\) \(<\) 0.001), fixed night and rotating (\(p\) \(<\) 0.001), fixed early morning and fixed daytime (\(p\) \(<\) 0.001), and fixed early morning and rotating shift types (\(p\) = 0.005). \({}^{\text{f}}\) Games–Howell multiple comparisons tests showed significant differences between fixed night and early morning (\(p\) = 0.001), fixed night and rotating (\(p\) \(<\) 0.001), fixed early morning and fixed daytime (\(p\) \(<\) 0.001), fixed early morning and fixed daytime (\(p\) \(<\) 0.001), fixed early morning and fixed daytime (\(p\) = 0.002) (There were no significant differences in fatigue perception between the two WaSaD shift types). \({}^{\text{g}}\) COPSQ scales where the higher value is unfavorable. \({}^{\text{*}}\)\(p\)\(<\) 0.05.  
\end{table} 
Table 7: Multiple linear regression model results for the dependent variable sleep disturbance in 2017 and 2019.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 5 Performance Evaluation
### Numerical Experiment Setup  
Every client was randomly assigned four separate stations (geographical coordinates) through which it cycles over time (relocating at fixed time intervals). To ensure the heterogeneous distribution of clients, these stations were grouped into an arbitrary number of clusters of random size and position, so that at the end of every relocation phase, all clients would produce a new \"patchy\" distribution (featuring areas of high density separated by empty or near-empty spaces in which no coverage is needed).  
N.B.: clients belonging to the same cluster at one point of the cycle may and often move on to then belong to different clusters after their movement is completed. The change in the nodes' locations can be seen in the screenshots shown in Figure 1 where we present four screenshots corresponding to a time series, each showing the swarm's self-reorganization after the clients have relocated, cf. Figure 2 for more details on the temporary negative impact of relocation on LQM.  
This is not intended to mimic the real movement of mobile clients in any of the suggested scenarios. Instead, the clients' heterogeneous distribution and periodic relocation behavior described above were designed to force the swarm to first adapt its structure to reflect the variable coverage needs, then reconfigure when the demand (proportional to local client population density) changes dramatically and near instantaneously.  
The use of an unrealistic deterministic cycle was deemed inconsequential since the drones have no memory and so every configuration is treated as an entirely new problem to solve (even if it is a repeat of a previous one).  
The aim was to demonstrate that the swarm cannot only \"grow\" the network towards demand hotspots at the time of first deployment, but subsequently adapt the topology when conditions change (by letting the swarm search out the new areas of demand, seemingly \"following\" the clients without knowledge of their final destination).  
Different intervals between relocations (800 or 400 time units) were used to evaluate the effect of higher perturbation frequency over performance (average and minimum LQM before, during, and after topology adaptation).  
Summary: the number of terminals was kept constant. The upper size of the swarm is bounded. Data collection was conducted over 3200 time units with the relocation of terminals happening after either 400 or 800 time units. Monte Carlo simulation is used to model the execution of the decentralized algorithm. Simple link quality metrics (LQM) are used as a first-order approximation for performance measurements. The simulation was implemented in native JAVA without specific libraries, on a Windows 10 laptop.
## 3 Proposed ML Method
### Wear-State Temporal Models  
When analysing the horizontal vibration signal from the bearing's external race, a degradation trend can be identified in the signal amplitude values in the discrete-time domain. The degradation trend can be identified as having a non-linear increase in amplitude as the bearing failure stage is approached. In this study, five temporal wear-state classes were used to characterise the RUL of the seven bearings under test.  
Two different wear-state models, one _linear_ and one _non-linear_, were considered and tested to determine the optimum scale to determine the RUL of the components accurately and robustly. The linear wear-state model divides the data into five equidistant temporal classes, as illustrated by Equation (5), where each temporal class represents a 20% portion of the bearing's overall lifetime. In contrast, the non-linear wear-state approach uses five classes that are strategically spaced to add greater granularity or compression to the class boundaries towards the latter stages of the bearing's life. This is achieved as follows: the first 63% of the bearing's lifetime is allocated to class 1 (healthy), the second class consists of the 86% of the bearing's lifetime and so on as presented in Equation (6).  
\[\alpha_{i}=\left(\frac{1}{5}\right)\times i \tag{5}\]  
\[\beta_{i}=1-e^{-i} \tag{6}\]  
where \(\alpha_{i}\) and \(\beta_{i}\) define the linear and non-linear temporal class boundaries, respectively, and the index \(i=\{1,2,3,4\}\) corresponds to those class numbers. Note: \(\alpha_{5}=\beta_{5}=1\) as the boundaries are normalised with respect to time.  
Figure 5 shows a graphical representation of the linear and non-linear wear-state class boundaries.
# Automated Design and Parametric Modeling of Excavator Buckets
## 2 Literature Review
### Designing Buckets for Excavators  
The design of the discussed buckets is a long process, which is implemented in many stages. Each stage has its own specific characteristics that can be presented in the following sequence [7]:  
PRELIMINARY PHASE  
* Preliminary design--CAD software SolidWorks 2022 is used to model the parts, sub-assemblies, and assemblies of the designed bucket model;
* Simulation inspections--the buckets are inspected with the Finite Element Method;
* Three-dimensional model adjustments--depending on the results obtained during the previous stage, the necessary structural changes are made to the parts, sub-assemblies, and assemblies of the bucket;
* Production of the preliminary prototypes--a real prototype of the bucket is produced;
* Conducting prototype tests--prototype tests are carried out in real conditions.  
DESIGN PHASE
* Adjustments to 3D models and drawings--based on the results from the testing stage, the necessary design changes are made to the parts, sub-assemblies, and assemblies of the bucket;
* Simulation inspections--reverification of the bucket using the Finite Element Method;
* Production of the design prototypes--a real prototype of the bucket is produced;
* Conducting prototype tests--prototype tests are carried out in real conditions;
* Design freeze--the design process of the product is finalized.  
INDUSTRIALIZATION PHASE
* Final modeling--3D models of the parts, sub-assemblies, and assemblies of the bucket are finalized and drawings and specifications are developed;
* Engineering Pilot--mass production technologies are used to manufacture the bucket;
* Testing and Certification--the produced buckets are tested and certified.  
PRODUCT LAUNCH
* Start of production--serial production of the bucket begins.  
This publication will only consider 3D modeling of products or modification of already created 3D files by using the capabilities of modern CAD systems. Emphasis will be placed on the capabilities of parametric modeling of buckets and on the automated creation of design documentation (drawings of parts, drawings of assemblies, and specifications). The CAD system used is SolidWorks 2022 [3].  
Regardless of the type of CAD system used, the principles of modeling are the same. Popular CAD systems for modeling and simulations in 3D space are Autodesk Inventor [8], Solid Edge [9], Creo [10], CATIA [11], Altair Inspire [12], and others.  
The aim of using CAD systems is to increase the efficiency of engineering work by reducing design time and the possibility of errors. This can only be achieved through automation of product design. Another effect is to reduce the volume of design documentation.
###### Abstract  
To investigate the impact of crack inclination angle on the tensile strength and characteristics of splitting failure in rock, split tests were conducted on fine-grained sandstone with pre-existing cracks under different crack inclination angle conditions. Acoustic emission and digital image techniques were continuously monitored during the tests. The evolution of absorbed energy during the loading and failure processes was calculated and discussed, aiming to elucidate the interconnections among the maximum absorbed energy, the pre-existing crack inclination angle, the tensile strength, and the acoustic emission energy of the rock, which can provide a reference for the design and construction of tunnels or mines in rock formations with pre-existing cracks. The experimental findings indicate the following: (1) The tensile strength and failure displacement decrease first and then increase with the increase in the pre-existing crack inclination angle, demonstrating an approximate \"V\"-shaped alteration. (2) The failure modes of specimens with pre-existing cracks can be classified into three types: tensile failure along the center of the disk, tensile failure along the tip of the pre-existing crack, and tensile-shear composite failure along the tip of the pre-existing crack. (3) The crack inclination angle exerts a substantial influence on the evolution curve of energy absorption. The maximum energy absorption of the specimens first increases and then decreases with the increase in the crack inclination angle. Moreover, a corresponding nonlinear relationship is observed between the maximum energy absorption and the tensile strength, as well as the AE energy.  
splitting failure; pre-existing cracks; tensile strength; failure mode; absorbed energy +
Footnote †: journal: Applied Sciences
# Performance Evaluation of Ground AR Anchor with WebXR Device API
## 4 Results and Discussion
### WebXR with Checked Pattern
The total number of pixels in each image was 4,446,720 from the experiment. In weak reflection ground, the number of pixels in the intersection image are 57,513, 69,283, and 78,231 for 1, 2, and 3, respectively. The number of pixels in the union image are 62,113, 77,936, and 88,145, and match rates of 92.6%, 88.9%, and 88.8% were obtained. In strong reflection ground, the number of pixels in the intersection image are 56,826, 62,567, and 73,255 for 1, 2, and 3, respectively. The number of pixels in the combined image are 61,346, 90,720, and 90,963, and match rates of 92.6%, 69.0%, and 80.5% were obtained. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Direction**} & \multicolumn{1}{c}{**Specified Rate Resolution**} & \multicolumn{1}{c}{**Intersection Pixels**} & \multicolumn{1}{c}{**Union Pixels**} & \multicolumn{1}{c}{**Match Rate (\%)**} \\ \hline \multirow{3}{*}{WR} & Front (1) & 4,446,720 & 57,513 & 62,113 & 92.6 \\  & Left (2) & 4,446,720 & 69,283 & 77,936 & 88.9 \\  & Right (3) & 4,446,720 & 78,231 & 88,145 & 88.8 \\ \hline \multirow{3}{*}{SR} & Front (1) & 4,446,720 & 56,826 & 61,346 & 92.6 \\  & Left (2) & 4,446,720 & 62,567 & 90,720 & 69.0 \\ \cline{1-1}  & Right (3) & 4,446,720 & 73,255 & 90,963 & 80.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Match rate in WebXR. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Direction** & **Front (1)** & **Left (2)** & **Right (3)** \\ \hline \multirow{2}{*}{GT-WR} & Mean of Difference image & 0.1926 & 0.7154 & 0.9611 \\  & Normalization & 0.0756\% & 0.2805\% & 0.3770\% \\ \hline \multirow{2}{*}{GT-SR} & Mean of Difference image & 0.2833 & 1.3664 & 1.1958 \\  & Normalization & 0.1110\% & 0.5358\% & 0.4689\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Match rate of difference images in WebXR with checkered pattern. Figure 9: Intersection and union image to determine the intersection over union.