esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Data Reduction and Processing  
Captured marker coordinates were imported into Kwon3D Motion Analysis Suite (Version 5, Visol Inc., Seoul, Korea) for processing and analysis. Imported position data were upsampled to 1000 Hz. A 4 th-order zero-phase lag Butterworth low-pass filter was used in filtering the marker coordinates. The residual plot method outlined by [PERSON] [32] (pp. 42-50) was used in assessing the frequency contents of the marker trajectory data, and markers were eventually clustered into three groups, each with different cutoff frequencies (8, 15, and 30 Hz) [2].  
Secondary points (including joint centers) were defined and computed from the filtered marker coordinates and measured anthropometric parameters [2]. The Tylkowski-Andriacchi hybrid method was used in locating the hip joint centers, while the mid-point method was used in locating the shoulder joint centers [4; 33]. The mid-trunk point was defined as the centroid of the xiphoid process and T12 (i.e., the spinous process of the 12 th thoracic vertebra). The mid-hand point was defined as the centroid of the hand centers. The HL and the SL were defined by the vectors drawn from the left joint center to the right for the hips and shoulders, respectively. The UL was defined as the line drawn from the mid-trunk point to the mid-hand point, whereas the LL (club) was defined as the line drawn from the mid-hand point to the clubhead point.
## 3 Simulations and Results
### Pandemic Intervention Policies  
PIPs are all the actions of individuals that aim to control the pandemic spread. Indeed, policy makers use multiple types of PIPS, such as lockdowns [81] and artificial job separations [25]. For airborne pandemics, mask-wearing PIP is considered efficient and was used for the COVID-19 pandemic [82] and the influenza pandemic [83] to name a few. However, in practice, masks differ in their effectiveness and the portion of the population that wears them [81; 82; 84]. Hence, we define the mask-wearing PIP using two parameters \(\eta\in[0,1]\) and \(\kappa\in[0,1]\) that stand for the portion of the population that wears a mask and the portion of the pathogen particles filtered by the mask, respectively. The reduction of the mask's effectiveness over time is neglected due to the short-horizon duration of the simulation [84].  
In addition, air ventilation is also considered an effective PIP for airborne pandemics [44; 48; 85]. Unlike other PIPs, air ventilation is highly affected by the room's topology in directed and undirected ways. For example, the location of a window, alongside its size and the outdoor air temperature can alter the pandemic spread. Thus, we defined a simplified version of air ventilation, assuming a centralized air ventilation system that removes a portion \(\chi\) of the pathogen particles from the air in the room in a uniform way and occurs in a discreet manner every \(\zeta\) time steps. This definition is identical to mixing the air in the room with fresh air at a proportional portion in a uniformly distributed way.  
Figure 4: The \(SEI\) over time for each type of room, shown as mean \(\pm\) standard deviation. The results are computed as a result of 100 realisations for each room and 5 rooms for each room type (\(n=500\)).  
Figure 5a-h present the model sensitivity for both the mask-wearing and air-ventilation PIPs, divided into the four room types. For the mask-wearing PIP, the x-axis shows the rate of the average quality of mask-wearing in reducing infection rate, and the y-axis shows the portion of the population that wears masks. Similarly, for the air-ventilation PIP, the x-axis shows the portion of the pathogen particles that are removed in each ventilation \(\chi\), and the y-axis shows the rate in minutes that the air-ventilation occurs \(\zeta\). The heat maps present the average portion of exposed individuals out of the population after 90 min which is computed from 500 realisations (100 realisations for each room, 5 rooms for each room type). For each realisation, the infected individual is chosen randomly as are the individuals that wear masks.
# New Four Points Initialization for Digital Image Correlation in Metal-Sheet Strain Measurements
## 2 Theoretical Bases
### Normalized Cross Correlation (NCC)  
Points matching, which consists of locating a point (photographed from different positions) in the left photograph and in the right image, is necessary to carry out the triangulation process. 2D-DIC is used to correlate a given set of points in the two stereo views of the reference configuration and match these points along the sequence of images. The correlated image points that are set are used to reconstruct and track the 3D position of the material points of the ROI (region of interest) through time [6]. In the literature, there are several works that report using 2D-DIC to accomplish this process, because this technique is so meaningful for 3D point reconstruction based on 2D images. The first advantage is that cross correlation is equally simple to calculate. Once a coincidence for a patch in a typical position inside an image is achieved, the Fourier methods can be used to calculate the correlation in a fast way. The second advantage is that cross correlation is independent from translations and invariant in the scaling dominance of the intensity. Equation (5) presents the definition of a normalized cross correlation:  
\[NCC=\frac{\sum_{(i,j)\in S}[f(u,v)-\overline{f}][g(u,v)-\overline{g}]}{\sqrt {\sum_{(i,j)\in S}[f(u,v)-\overline{f}]^{2}\sum_{(i,j)\in S}[g(u,v)-\overline{ g}]^{2}}} \tag{5}\]  
Here, \(f\) and \(g\) are each the grayscale functions of the windows of the current image at a specific location (\(x\), \(y\)). The functions \(\overline{f}\) and \(\overline{g}\) correspond to the gray scale mean of the reference image and the current subset.
# A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC
## 2 Background
### Building Information Modeling  
BIM is defined as the concept supporting the overall facilities' life-cycle. Figure 2 shows the building model for design, construction, operation, modification, and conceptual design. The operation phase includes facility management, maintenance, and repair. Numerous studies dealt with design and construction, but less effort was put into the operation phase.  
Several stakeholders are involved during the operation phase, which is similar to design, planning, and construction processes. These stakeholders have to continuously exchange building information back and forth interactively. The conservative way is to utilize software for discipline-specific tasks and exchange 2D plans and documents with third party parties; this approach is called Little BIM. Conversely, Big BIM means sharing digital building models between parties instead of 2D plans and documents. The building model may be exchanged via an open standard or proprietary data formats; this is called open or closed BIM, respectively [4]. Proprietary data formats may limit the choice of software products or exclude stakeholders that do not have access to the required software. Hence, an open standard is preferable during operations phase.  
Figure 1: The as-damaged BIM model as a central point for involved domains during the operation phase. Pictograms have been taken from the Noun Project [6; 7; 8; 9; 10; 11; 12].  
Central to the entire BIM concept are the Industry Foundation Classes (IFC). This standard defines an open data format to exchange building data. IFC has been designed to cover geometric and semantic data within the AEC sector [4]; hence, it includes several extensions, for example, for products, processes, and materials. Leading proprietary software, for example, Autodesk Revit [14], Archicad [15], as well as open source software, such as xBIM [16] and Java IFC Toolbox [17], are able to handle IFC files. BIM was originally focused on building construction and not infrastructure; thus, bridge models could not be properly exchanged using the IFC. This led to the extension of BIM and the IFC standard to bridges and roads [18]. Further extensions focus on tunnels, airports, ports, and harbors. Although, there is the prospect of exchanging bridge data between the different stakeholders with the help of IFC, 78% of the bridges were built before 1990. For bridges pre-dating that time, no BIM model exists, which is a prerequisite for the application of DIM. A number of studies have been conducted in the domain of 3D model generation of existing buildings via point clouds, widely known as scan-to-BIM [19; 20; 21]. In accordance with such as-built models, damage information could be added to extend the use of BIM for inspection and assessment.
## 3 Results and Discussion
### Connection Force  
The connection force obtained by the press-out experiment and ultrasonic measurement is shown in Figure 13. The results show that the ultrasonic measurement error is within 16%. However, the measurement error of the No. 10 uniform cylinder reached 29.29%. The reason for the relatively large error is the serious interface wear during the press-fit process (see Figure 14). Coil chips on this part indicate severe wear at the interface. The stress-distribution map shows several wide and deep furrows on the mating interface, which aggravates the non-uniformity of stress distribution. Therefore, interface wear will significantly affect the contact state and change the static friction coefficient of the interface. In order to verify the above analysis, the static friction coefficient of this component was calculated as 0.1147, which is less than the average static friction coefficient of 0.1483. Therefore, the reliability of the calculation results should be analyzed according to the wear condition of the interface.
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 6 Conclusions  
This paper provides a comparative study to evaluate the classification performance of the MTS and to suggest the appropriate thresholding method to be utilized in MTS methodology between four common thresholding methods namely the Type-I-Type-II error method, the Probabilistic Thresholding Method, ROC curve method and the Box-Cox transformation method. To the best of the authors' knowledge, no comparison works have been conducted to evaluate the effectiveness of those common thresholding methods towards MTS classification performances on several datasets. The outcome of this study could provide an initial insight on a general thresholding method that is suitable across several case data. The result found that none of the four thresholding methods outperformed one over the others in (if it is not for all) most of the datasets. It could also be found that the effective use of the four thresholding methods to produce promising classification performances is dataset dependant. Hence, further studies to investigate the cause of these dependency behaviours and their relationships are urged. In addition, the study also found an unpromising predictive ability of the MTS in classifying several datasets of the study. Improving the significant variable selection process of the MTS using several alternative approaches was suggested. PSO-based thresholding studies could also be considered as another thresholding alternative in improving the MTS classification problem. Nevertheless, from the study, the Type-I-Type-II error method seems favourable due to its lower algorithm complexity as compared to the other three thresholding methods. It is also recommended to evaluate the computational time complexities of these algorithms in the future to further support the findings.  
The lead author of this paper is [PERSON]. Additional support was provided by W.Z.A.W.M., N.H., M.Y.A., H.Y., K.R.J. and H.H.A.T. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Collaborative Research Grant Project (Project Grant No. Q.K130000.2456.08G27) under the Collaborative Research Grant Program (Program Grant No. Q.K130000.2456.08G38).  
Not applicable.  
Data Availability StatementData are available from KEEL repository.  
The authors would like to express sincere gratitude to Research Management Centre (RMC) from Universiti Teknologi Malaysia, Universiti Malaysia Perlis, Universiti Malaysia Pahang, Universiti Tenaga Nasional and Ministry of Higher Education (MoHE), Malaysia.  
The authors declare no conflict of interest.
## 1 Introduction  
### _Deep Neural Networks_  
Deep neural networks (DNNs) are powerful computational models designed to capture complex, nonlinear, or hidden relationships between inputs and outputs through layered architectures composed of various types, such as convolutional, normalization, pooling, dense, and long short-term memory (LSTM) layers [1; 2]. DNNs require less human intervention and are more flexible than traditional machine learning methods, which often demand extensive expertise in tasks like feature extraction and engineering. Each layer refines the mapping between input and output data, but greater precision increases the model complexity.  
The DNN detection efficiency comes with trade-offs, including the increased computational demand, significant energy consumption, extended execution time, and substantial memory usage. Deploying DNN models from graphical processing unit (GPU) or central processing unit (CPU) development environments to resource-constrained devices presents a significant challenge. In medical applications, a common workaround involves offloading computations to fog or cloud computing environments, either on local networks (fog) or remote servers (cloud). However, traditional cloud computing approaches, where large streams of computational tasks are processed remotely, can introduce unacceptable delays in real-time scenarios due to network congestion and latency issues [3]. Moreover, both cloud and fog computing require sending medical data for processing, which is associated with increased privacy and security risks [4].
# A Real Case of Implementation of the Future 5G City
## 3 Future 5G City Use Cases
### The 5G Project Use Cases
#### 3.2.7 Structural Monitoring of Buildings and Infrastructures  
Another objective is the implementation of a monitoring service for public/private buildings and civil infrastructures, which can also be applied to cultural assets. In particular, the most significant structural parameters must be fully and immediately visible, thus reporting any anomalies and critical situations even in emergency conditions (e.g., earthquakes). The experimentation scenarios will betwo, one based on the use of sensors installed into the buildings and the other one on the use of drones connected to the 5G network. The use of drones allows the rapid, efficient, and precise collection of very-high-resolution frames that, after being processed through the use of neural networks, can be used to determine the current state of the health of buildings. Data can be compared to previously collected data to determine any damage.
## 5 Results
### Experimental Performance Measurement  
Two experimental performance measurements were used in this paper.  
1. Average isomorphism time The running time of each algorithm in this paper contains two parts: the time of filtering and the time of verification. The time of filtering includes the time of searching and pruning in the induced subgraph space. Since the algorithms use the same method to search the induced subgraph space and the time of pruning is negligible relative to the time of searching, the filtering time was almost the same. Therefore, we adopted the verification time as the performance measurement. The verification time is the time of subgraph isomorphism testing. During the algorithms' running process, the performance of the computer dynamically changes. Therefore, the average isomorphism time of five experiments was used as a criterion for evaluation. The average isomorphism time is represented by the symbol \(\overline{T}_{iso}\).
2. The times of calling the recursive function Compared with the isomorphism time, the times of calling the recursive function were more stable and could better reflect the performance of the algorithms. As long as the data graph and query position were given, the times of calling recursive functions were the same for each algorithm and would not change when the PC hardware and software environment change.  
The times of calling recursive function are further explained below. In the MUIS query process, when the isomorphism testing was performed, the recursive function is called to determine whether the vertex pairs can join the current partial mapping. A better performing algorithm has a good pruning strategy at every stage to avoid some isomorphism testing or avoid calling recursive functions, so in general, the total number of times to call the recursive function is less. Figure 10 is an example of calling the recursive function and backtracking, where each line represents a calling of the recursive function. Assuming that the vertex set of the candidate induced subgraph is \(\{v_{1},v_{2},v_{3},v_{4}\}\), it can be seen from the figure that Algorithm b utilizes the pruning strategy to avoid the isomorphism testing between the vertex \(v_{1}\) and the vertex \(v_{5}\); thus, the times of calling recursive function were less than those of Algorithm a. The times of the calling recursive function are denoted as \(CT\).
# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 1 Introduction
### Computational Methods for Tumor T-Cell Antigen Prediction  
In recent years, a multitude of methodologies has been devised for the prediction of immunogenicity in tumor antigens (Table 1). They can be conditionally defined into two groups, namely methods that rely on classical machine learning prediction algorithms and methods that implement a combination of other algorithms into complex biological pipelines.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 6 Methodological and Technical Details
### \(\alpha\)-Cut Derivation Using P-NLP  
To transition from linguistic fuzziness to quantifiable intervals, we employed Parametric Nonlinear Programming (P-NLP) to compute the lower and upper bounds of the membership functions at various \(\alpha\)-levels. This enabled us to systematically extract the \(\alpha\)-cut intervals for each fuzzy performance index (e.g., waiting time, service cost), with \(\alpha\) ranging from 0 (maximum uncertainty) to 1 (complete certainty).
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 5 Settings for Performance Assessment
### Performance Metrics
#### 5.1.2 Overhead  
The routing overhead of COOP, NCA(MPR), CACA, DyTE, RGoV, and CAEL is presented against different network densities in Figure 9 with nodes deployed in a \(2500\,\mathrm{m}\times 2000\,\mathrm{m}\) topological region. As the number of nodes rose, so did the routing burden. As seen in Figure 9, the CAEL's network routing load is 16% less than that of RGoV and 50% less than the rest of the other mentioned protocols, especially in dense networks, because CAEL's adoption of a dependable route resulted in a reduction in route disconnections. The reduced chances of route failures result in a decrease in the commencement of route  
Figure 8: Packet delivery ratio against various number of nodes.  
rediscovery and maintenance procedures, and therefore CAEL's reliable approach has a lower network routing burden than other aforementioned protocols.
## 2 Materials and Methods
### Data Analysis Model  
There are several options for data analysis; it all depends on the needs and resources of the organization. In the first segment are the BI. These analysis models are characterized by offering the extraction, transformation, and loading (ETL) of the data [32]. This process guarantees the quality of the data with which one works, carrying out an exhaustive cleaning of the information [33]. The transformation consists of passing the data to the format in which it is working, in addition to the generation of new identifiers if it is the case. The load consists of uploading the data already transformed to a data warehouse, from where it will be consumed by the On-Line Analytical Processing (OLAP) cubes [34]. In a BI, the use of data mining algorithms is common; data mining allows obtaining knowledge. This knowledge is presented to the interested parties, through dashboards that facilitate decision-making [35].  
Another segment of data analysis that currently has a great penetration in its use is Big Data. Its ability to process large volumes of data, and its independence to work with structured and unstructured data, makes organizations begin to use it to detect trends in their customers [36]. Big Data is generally part of an architecture that includes the sources that are integrated into the analysis, communication, and exploitation of knowledge. In these analytics architectures, Big Data is integrated as a framework for data processing, through a cluster model [37]. The cluster contains several nodes, where one acts as a master and two or more function as slaves. The most used frameworks are designed under Apache, which generates an open-source environment.  
It is important to point out that, for the application of a data analysis model, the tools to be used must go through the identification of the organization's needs. For this work a Big Data architecture is used, the necessary requirements analysis, the authors present it in previous works [38; 39]. This section establishes the necessary infrastructure for the implementation of the framework that is developed in Apache Hadoop [40]. The scalability of Hadoop allows the analysis to be carried out with the data of the students in a face-to-face education model and contrast it with the data of the new modality by which the students are studying. In this way, it is possible to obtain a guide based on the results of the data analysis to generate a hybrid education modality that guarantees learning [41].