esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The kClusterHub Application
### Web Service
JSON request: {\"dataset\":\"data.csv\",  \"dataset-type\":\"personal\",  \"clusters\":\"10\",  \"columns\":[\"Age\",\"Salary\"],  \"algotith\":\"auto\",  \"apixey\":\"0a8366a07c0d8 fccx48 bab2e657f12d0\"  } JSON response: {\"metric\":[\"41.2\",\"12.1\",\"6.9\",\"5.5\",\"4.6\",\"4.7\",\"3.8\",\"3.2\",\"3.3\",\"2.7\"],  \"suggested-k\":\"3\"  } The following request concerns POST endpoint with No 4. It executes k-means on the \"sepal.length\", \"sepal.width\", \"petal.length\", and \"petal.width\" columns of the \"iris.csv\" dataset. The dataset is public, and the user is associated with the API key: 905a7d7f02b2512 fc3e02b1f6 ec52 ab. The k-means algorithm will discover three clusters. The response contains the cluster assignments for each instance, along with the corresponding attribute values and cluster identifiers. Of course, since all selected columns are numeric, k-means would be used even if the auto algorithm was selected. JSON request: { \"dataset\": \"iris.csv\", \"dataset-type\": \"public\",  \"clusters\": \"3\",  \"columns\": [\"sepal.length\", \"sepal.width\", \"petal.length\", \"petal.width\"],  \"algorithm\":\"k-means\"  \"apixey\": \"905a7d7f02b2512 fcfc3e02b1f6 ec52 ab\"  } JSON response: { \"items\": [ {\"sepal.length\": \"5.1\", \"sepal.width\": \"3.5\",  \"petal.length\": \"1.4\", \"petal.width\": \"0.2\",  \"cluster\": \"1\"},  {\"sepal.length\": \"4.9\", \"sepal.width\": \"3.0\",  \"petal.length\": \"1.4\", \"petal.width\": \"0.2\",  \"cluster\": \"2\"},  //... (remaining instances)  ] } The user interface of kClusterHub encompasses a dedicated web page, designed to guide users through the utilization of the eleven endpoints, all of which are accessible with the user's API key. On this informative web page, users are provided with detailed instructions on how to interact with these endpoints effectively. Moreover, the page serves as a valuable resource by showcasing examples of potential HTTP requests complemented by the corresponding responses they can expect to receive. This interface serves to enable users with the knowledge and practical insights required to utilize the full capabilities of kClusterHub's API.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Large-Scale Data Handling
#### 5.2.2 Existing Solutions  
Several papers discuss potential solutions to this problem. This paper summarizes five common big data handling mechanisms in the healthcare domain: ML mechanisms, agent-based mechanisms, heuristic and meta-heuristic-based mechanisms, cloud-based mechanisms, and hybrid mechanisms. Each offers high performance and scalability but may also present challenges, such as requiring significant training time and resources, being complex, lacking optimal solutions, raising privacy concerns, and lacking quality control and access to learning resources [99]. The first method involves using Hadoop to implement the MapReduce algorithm, which is well established for processing and generating large datasets. In healthcare, Hadoop has been effectively applied to analyze electronic health records (EHRs). For example, the Research Data Warehouse at the Mayo Clinic employs Hadoop to manage and analyze extensive datasets from clinical trials and patient records. By distributing computation across a cluster of machines, Hadoop's MapReduce facilitates the aggregation of patient data, complex queries, and statistical model generation. This method excels in environments where data are dispersed across multiple sources, enabling comprehensive analysis without requiring data centralization [100]. The second method utilizes Apache Spark, which provides in-memory processing through resilient distributed datasets (RDDs). This capability significantly accelerates data analysis compared to traditional disk-based methods. In genomic research, for instance, Spark's efficient execution of iterative algorithms has been pivotal. The Genomics England initiative illustrates Spark's application, where it analyzes whole-genome sequencing data to identify genetic variations associated with rare diseases. Spark's in-memory processing allows rapid analysis of large and complex datasets, such as those involving numerous genomic variants, thus speeding up discovery and improving diagnostic efficiency [101]. However, processing very large datasets may require substantial memory resources, making it important to compare Spark and Hadoop based on dataset size and computational needs [102]. The third method addresses the need for robust recognition models to handle the diverse, large-scale, and complex nature of healthcare data. Data mining techniques, such as clustering and classification algorithms, are employed to identify patterns and extract insights from extensive datasets. For example, the Diabetes Control and Complications Trial (DCCT) used data mining to predict the risk of complications based on historical patient data. Predictive models using data mining, such as decision trees and neural networks, analyze EHRs to forecast cardiovascular events and other health risks. These models help in developing targeted interventions and personalized treatment plans, thereby enhancing patient care and outcomes [103].
# Improved Neural Networks Based on Mutual Information via Information Geometry
## 2 Materials and Methods
### Regression  
In machine learning, regression is a set of statistical processes for estimating the relationships among the variables. In this approach, the accuracy is the error rate of regression substituted by 1.  
We design neural networks as regression functions to solve the regression problems in the Forest Fires dataset. For the dataset, 50% of the training samples are used for learning. In these samples, the percent of the labeled training samples \(\alpha\) ranges from 10% to 50%. In the remainder of the dataset, 10% of the samples are randomly selected as test data. The constant \(\lambda\) is set to -0.1.  
The test accuracy on the Forest Fires dataset for NN, DBN, AMSA, MI-NN, and MI-DBN (\"MI-\"denotes the proposed methods) can be seen in Figure 5. The network structures used in this experiment are 13-15-1 and 13-15-17-1, which correspond to shallow networks (i.e., NN and MI-NN) and deep networks (i.e., DBN, AMSA, and MI-DBN), respectively. The activation function is sigmoid, and the output function is linear.  
Figure 4: Improvements over the previous methods. (**a**) Iris; (**b**) Poker Hand.  
We can see that the proposed algorithm outperforms the previous methods on the dataset successfully. This finding means that the proposed algorithm can be applied to the problem of regression. In addition, the proposed algorithm improves the traditional algorithms constantly, when the percent of labeled training samples changes. To study the correlation between the performance and \(\alpha\), we illustrate the difference between the two methods with varied \(\alpha\) in Figure 6. According to Figure 6, there is a positive correlation between the improvement brought by the proposed method and the value of \(\alpha\). The reason is that more labeled data leads the proposed method to absorb more useful information for its classification. In contrast, this aspect does not hold true for the other algorithms.  
Figure 5: The tests of NN, MI-NN, DBN, AMSA, and MI-DBN with Forest Fires. The accuracies are plotted on the horizontal axis. (**a**) \(\alpha=10\%\); (**b**) \(\alpha=30\%\); (**c**) \(\alpha=50\%\).
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
## 3 Results
### Petrophysical Analysis  
Key results of the petrophysical testing are presented here as descriptive statistics for specimens (Table 1). The smallest and largest observed densities were 2608 and 2748 kg/m\({}^{3}\), respectively, with gneiss yielding on average slightly higher densities (mean value of 2732 kg/m\({}^{3}\) vs. 2622 kg/m\({}^{3}\) for PGR). The variance of the density values within rock types was small. Porosities ranged from 0.27% to 0.75%, with the mean values for VGN and PGR being practically identical.  
Resistivities and relative permittivity values of the VGN specimens were significantly higher than those of the PGR. P-wave velocities of VGN were slightly lower than PGR on average (mean values of 5592 and 5800 m/s, respectively), while the observed ranges were very close to each other. The S-wave velocities of VGN were slightly lower than PGR on average (mean values of 3161 and 3263 m/s, respectively), while VGN showed a much higher variance. [PERSON]'s modulus shows a similarly high variance in VGN compared to PGR, but on average the stiffness of the rock types is similar (mean values of 69.0 and 70.4 GPa for VGN and PGR, respectively).  
The measured values were compared to previous data from Olkiluoto (results prior to 2009 summarised in [PERSON] et al. [32]) when possible and found to be generally in good agreement. It must be noted that the sample sizes (\(N=10\) for both rock types) are small. More comprehensive petrophysical testing and analysis of specimens from the study area can be found in Kiuru [6].  
Figure 7: Example of the visualized results of modelling in a 2-D section of a model with 75% pegmatoid granite and 25% gneiss. Coloured fractures compose connected clusters. Size of the image is 10 m \(\times\) 10 m.
# Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques
## 2 System Model
### Capacity and Bit Error Rate  
We assume that each SU belongs to a certain class \(c\in C\) and transmits the signal with power \(\bar{P}\). Let \(\rho_{k}\) be the interference experienced by an SU over the \(k\)th channel, and \(\sigma_{k}^{2}\) is the noise content. Then, the achievable data rate \(a_{k}\) of a \(u\)th SU over the \(k\)th channel can be computed using Equation (1) as follows [45]:  
\[a_{k}^{u}=\mu_{k}log(1+K\frac{\bar{P}_{k}^{u}}{\rho_{k}^{u}+\sigma_{k}^{2}}) \tag{1}\]  
where \(K=-1.5/log(5*b_{k}^{u})\) and \(b_{k}^{u}\) is the BER of a \(u\)th SU over the \(k\)th channel. It is proven to be a good model for the modulation scheme, such as MQAMwith a constellation size greater than or equal to four [46].
## 2 Methodology
### Clustering Method Based on Dimensionality Reduction
#### 2.1.1 Dimensionality Reduction Method Based on SVD  
Singular value decomposition (SVD) is an important matrix decomposition in linear algebra, it is based on the generalization of arbitrary matrices [31]. Singular value decomposition is similar in some respects to eigenvector-based diagonalization of symmetric or Hermite matrices [32].  
Given a matrix \(A\) of size \(\text{m}\times\text{m}\), it is decomposed diagonally, such as in Equation (1):  
\[A=U\Lambda U_{-1} \tag{1}\]  
In this equation, each column of \(U\) is an eigenvector, and the elements on the \(\Lambda\) diagonal are the eigenvalues arranged from large to small. If \(U\) is recorded as \(U=\left(\overset{\rightarrow}{u}_{1},\overset{\rightarrow}{u}_{2},\ldots, \overset{\rightarrow}{u}_{m}\right)\), then the results are calculated as shown in Equation (2):  
\[\begin{split} AU=A\left(\overset{\rightarrow}{u}_{1},\overset{ \rightarrow}{u}_{2},&\ldots,\overset{\rightarrow}{u}_{m}\right)= \left(\lambda_{1}\overset{\rightarrow}{u}_{1},\lambda_{2}\overset{ \rightarrow}{u}_{2},\ldots,\lambda_{m}\overset{\rightarrow}{u}_{m}\right)\\ &=\left(\overset{\rightarrow}{u}_{1},\overset{\rightarrow}{u}_{2 },\ldots,\overset{\rightarrow}{u}_{m}\right)\left[\begin{matrix}\lambda_{1} &\cdots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&\lambda_{m}\end{matrix}\right]\\ &\Rightarrow AU=U\Lambda\Rightarrow A=U\Lambda U_{-1}\end{split} \tag{2}\]  
when matrix \(A\) is a symmetric matrix, and it is decomposed symmetrically and diagonally, as shown in Equation (3):  
\[A=Q\Lambda Q^{T} \tag{3}\]  
In this equation, each column of \(Q\) is a mutually orthogonal eigenvector and is a unit vector, and the elements on the \(\Lambda\) diagonal are eigenvalues arranged from large to small.  
When the matrix \(Q\) is written as \(Q=\left(\overset{\rightarrow}{q}_{1},\overset{\rightarrow}{q}_{2},\ldots, \overset{\rightarrow}{q}_{m}\right)\), then the matrix \(A\) can also be written as shown in Equation (4):  
\[A=\lambda_{1}\overset{\rightarrow}{q}_{1}\overset{\rightarrow}{q}_{1}^{T}+ \lambda_{2}\overset{\rightarrow}{q}_{2}^{T}\overset{\rightarrow}{q}_{2}^{T}+ \ldots\lambda_{m}\overset{\rightarrow}{q}_{m}^{T} \tag{4}\]  
The goal of the singular value decomposition method is to find a mapping matrix, and then multiply it with the original data \(A\), to achieve the effect of dimensionality reduction, which is suitable for the massive residential electricity numerical data collected by smart meters. Dimensionality reduction of data through singular value decomposition not only reduces the data dimension and accelerates model training, but it also effectively filters noise data and improves model generalization.
# Acceleration of a Production-Level Unstructured Grid Finite Volume CFD Code on GPU
## 2 Unstructured Grid Finite Volume Method (FVM) CFD Solver  
### Cell-Centered FVM Scheme  
The governing equation solved by FlowStar is the Reynolds Averaged Navier Stokes (RANS) equation, discretized by a cell-centered finite volume method for unstructured grids,  
\[\frac{\partial}{\partial t}\int_{\Omega}\tilde{W}d\Omega+\oint_{\partial\Omega} \left(\vec{F}_{c}-\vec{F}_{v}\right)dS=\vec{Q} \tag{1}\]  
where \(\Omega\) defines the control volume, bounded by the closed surface \(\partial\Omega\), \(\tilde{W}\) is the vector of conservative variables, \(\vec{F}_{c}\) is the vector of convective fluxes, and \(\vec{F}_{v}\) is the vector of viscous fluxes. \(\vec{Q}\) is the source term. For cell-centred schemes, the control volumes are identical with the grid cells and the flow variables are associated with the centres of the grid cells. If considering a particular volume \(\Omega_{I}\), the following expression will be obtained after space discretization:  
\[\frac{d\tilde{W}_{I}}{\partial t}=-\frac{1}{\Omega_{I}}\left[\sum_{m=1}^{N_{F} }\left(\vec{F}_{c}-\vec{F}_{v}\right)_{m}\Delta S_{m}-\left(\vec{Q}\Omega \right)_{I}\right] \tag{2}\]  
where \(I\) is the index of the control volume, \(N_{F}\) denotes the number of the faces of the control volume \(\Omega_{I}\), and \(\Delta S_{m}\) is the area of \(m^{th}\) face.  
After solving the right-hand side of Equation (2), the final residual for every cell will be obtained. Then, the solving was advanced through a temporal discretization method, approximating the final solution:  
\[A\Delta\tilde{W}^{n}=-\vec{R}_{I}^{n} \tag{3}\]  
where \(A\) is the system matrix of left-hand side, \(n\) denotes to the number of iteration, and \(\Delta\tilde{W}^{n}\) is the variable's update for all cells.
## 1 Introduction
### Computed Tomography in Comparison with Destructive Testing Methods  
There are few efficient and reliable testing methods available for inspecting PM parts at early production stages (for example at the green state of pressing) due to the relatively weak adhesion between the particles. Until recently, destructive methods were used as the ultimate quality assurance of near net-shape production. Traditionally, sample parts are collected at the end of the manufacturing process and, following a pre-inspection preparation, taken into the laboratory for inspection. The preparation involves traditional metallography processes of sample cutting and polishing [12] that requires a dedicated workshop facility. The sampling is only a small percentage, (1-2%), of the full production run and these samples are of course destroyed during the inspection [8]. Additionally, the information is limited to the particular cross-section of material under inspection, which is a limitation as there may be defects elsewhere in the original part. Consequently, such destructive testing methods are not capable of providing accurate and reliable results for full coverage of parts, i.e., volumetric information from parts in-line with manufacturing.  
Compared to destructive testing, computed tomography offers a valid and reliable alternative for in line quality control that is much quicker and simpler to implement. In CT the part is placed between the X-ray source and the detector and rotated through 360 degrees in two to four thousand inspection steps. The photons of the X-ray beam pass through the specimen and are captured by the detector. The number of captured photons, and therefore image quality, largely depends on the density of the material, the thickness of the parts under inspection and the energy of the X-rays. The captured data in the form of a 2D projection image are then forwarded to an image processing and AutomatedDefect Recognition unit where the contrast of the different features is algorithmically enhanced and the flawed image compared against a database of flawless images. The processing time by this method can be as little as a few seconds to a few minutes, providing the capability for mass production scanning. Additionally with X-rays it is possible to extend the inspection volume to 100% of the part allowing recognition of the full range of defect types.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 4 Control Theory Progress for UAVs
\begin{table}
\begin{tabular}{c p{227.6 pt}} \hline \hline
**Year** & **Development History** \\ \hline
2015 & Collision avoidance strategies covered in the article include sensing, tracking, and collision avoidance [44]. \\ \hline
2016 & Focuses on surveying articles published from 2000 to 2015 that discuss the civil applications of UAVs [45]. \\ \hline
2017 & Open-source flight controllers that are specifically designed for research purposes [46]. \\ \hline
2018 & Cellular communication for UAVs. The research aims to bridge the existing gap between the current state of 3 GPP regulations and the need for further investigation in this field [47]. \\ \hline
2019 & The deployment of UAVs within cellular networks [48]. UAV applications, highlighting their benefits, potential challenges, major tradeoffs, and the mathematical tools involved [49]. The utilization of UAVs in the context of 5G/B5G wireless communication [50]. The integration of UAVs in millimeter-wave (mm-wave) communication [51]. \\ \hline
2020 & The applications of UAVs and the associated challenges, regulations, and future research aspects related to future research in the field of UAV technology [52]. The exploration of software-defined network (SDN) and network function virtualization (NFV) technologies [53]. Study on UAVs from three distinct perspectives: swarms, sensors, and communications [54]. Explore various application scenarios of multi-UAV systems and operating multi-UAV systems and offers insights into addressing these challenges [55,56]. \\ \hline
2021 & A comprehensive survey of green UAV communications [57]. The development of UAV prototypes and discusses experimental demonstrations that showcase their capabilities [58]. Deep learning tools for detecting vehicles using aerial images captured by UAVs [59]. \\ \hline
2022 & The study centers around optimization algorithms, such as chicken swarm optimization clustering, bee optimization algorithm, and genetic algorithm [60]. Surveys different task assignment algorithms and examines their main ideas, benefits, drawbacks, and operational features [61]. \\ \hline Current year & In the current year, this study aims to serve as a valuable resource for the research community. It provides researchers with guidelines and motivations to contribute to the advancement of UAV technology. Nonlinear control techniques are essential for UAVs operating in smart cities due to their ability to handle complex dynamics, enhance stability and robustness, optimize control performance, deal with nonlinear sensors and actuators, enable trajectory planning and obstacle avoidance, as well as facilitate fault detection and fault tolerant control. These techniques contribute to the safe, efficient, and reliable operation of UAVs in the urban environment. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Research developments.
## 3 Results
### Single Lap Shear  
The resultant peak lap shear stresses for each of the specimens are recorded in Table 1. The average lap shear stress for the samples without IPA content was 21.18 MPa, which is consistent with what has been previously observed [54]. The samples containing IPA experienced a considerable reduction in strength. The average lap shear stresses for the samples containing 2, 4, and 6 wt.% were 12.62 (\(-\)40.42%), 8.92 (\(-\)57.88%), and 6.07 MPa (\(-\)71.35%), respectively. These results show that even the presence of a small quantity of trapped solvent in the two-part epoxy adhesive considerably influenced its adhesion properties to the point where catastrophic failure could occur.  
The load-displacement curves of the specimens #1 in Table 1 for each sample type are shown in Figure 4a. The presence of IPA not only lowered the maximum load, but it also decreased the displacement required for failure to occur. This displacement ranged from 3.26 mm for the sample without IPA content down to 0.88 mm for the sample containing 6 wt.% IPA. As seen in Figure 4b, all the sample types experienced the same failure mechanism where the samples failed at the adhesive-substrate interface. No visual differences on the failure surfaces between each sample type were observed, which accentuates the need for a non-destructive evaluation method for this type of material.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample Type/Sample**} & \multicolumn{3}{c}{**Peak Lap Shear Stress (MPa)**} & \multirow{2}{*}{**Std. Dev. (MPa)**} & \multirow{2}{*}{**Avg. Stress (MPa)**} \\ \cline{2-2} \cline{5-6}  & **1** & **2** & **3** & **4** & **Std. Dev. (MPa)** & **Avg. Stress (MPa)** \\ \hline
0 wt.\% IPA & 21.37 & 17.63 & 25.55 & - & 3.46 & 21.18 \\ \hline
2 wt.\% IPA & 13.02 & 15.29 & 11.21 & 10.96 & 2.00 & 12.62 (\(-\)40.42\%) \\ \hline
4 wt.\% IPA & 8.81 & 6.96 & 10.73 & 8.18 & 1.68 & 8.92 (\(-\)57.88\%) \\ \hline
6 wt.\% IPA & 6.65 & 6.91 & 5.16 & 5.56 & 0.84 & 6.07 (\(-\)71.34\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Maximum lap shear stress of adhesive samples containing 0, 2, 4, and 6 wt.% IPA.  
Figure 4: (a) Load–displacement curves of the specimens #1 in Table 2 for each sample type; (b) failure surfaces for the specimens represented in the load–displacement curves in Figure 4a.
## 3 Results  
### Study Selection  
The initial search of the electronic databases generated 645 records. Subsequently, 261 duplicates were identified and removed, leaving 384 unique records for abstract screening. Following the process, 69 articles were excluded based on abstracts, which resulted in 27 articles remaining for full-text evaluation. Three articles were excluded due to the unavailability of full-text versions, and one was removed because of failure to meet predefined inclusion criteria. This culminated in a final selection of 23 articles for both qualitative and quantitative analyses.