esa-sceva/satcom-chunk-collection huggingface dataset:
# Vectorized Numerical Algorithms to Solve Internal Problems of Computational Fluid Dynamics
## 1 Introduction
It is shown that the convergence rates are greatly enhanced by using multigrid methods with a conventional scalar processor [14]. The structure of numerical algorithms and their implementation on vector computers are widely discussed. There are many examples of vectorized numerical algorithms that enable improving performance of code running on modern CPUs. These examples include adaptive quadrature codes process [15], solution of linear algebraic equations [16; 17], integration of ordinary differential equations [18], and convergence acceleration techniques [19]. Applications of various vectorized algorithms for solving CFD problems are reported in [20; 21; 22; 23; 24]. Modern GPUs provide additional possibilities for acceleration of computer codes, but their use requires algorithm adaptation to the memory structure [25]. The Godunov method is widely used for solving systems of unsteady equations of gas dynamics. In this method, at each iteration of calculations, the Riemann problem is solved on each face of each cell of the computational mesh to determine the fluxes through these faces. Due to the fact that the dimensions of the computational meshes used for calculations amount to tens and hundreds of millions of cells, the effective use of numerical methods with Riemann solvers requires the use of supercomputers and the use of various approaches regarding parallelization of calculations. The most low-level approach used to increase supercomputer application performance is code vectorization. Numerical methods based on solving the Riemann problem on the decay of an arbitrary discontinuity are demanding on computing resources. The instruction sets of modern programming languages have a number of features that allow vectorization to be applied to the software context of the Riemann solver, which leads to significant speedup of the solver. Using an example of an exact Riemann solver, a practical approach to vectorizing program code for solving CFD problems, including simple linear sections and also nested loops, is considered. The exact Riemann solver has a compact implementation and accommodates a number of features of the software context that require their own techniques when performing vectorization. Examples are provided and implementation of the developed computational algorithms is discussed.
## Introduction  
Electroglottography (EGG) has been widely used in the clinic and in voice research. Its main quantitative parameters include: contact quotient, open quotient, and F0 extraction; [PERSON] 1 described four distinct electroglottographic signal shapes, which are used during voice assessment. Many publications refer to the knee at the beginning of the closing phase or during the opening phase. However, little has been said about quantifying these inflection events (IE). This study is built on previously published research 2. With the goal of improving the automated performance of the ATool, we ran a statistical analysis including larger database and different threshold settings.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline  & & \multicolumn{4}{c}{**GAW Type**} \\ \cline{3-6}  & & **D** & **EP** & **N** & **PD** \\ \hline \multirow{4}{*}{**Predicted Type**} & **D** & 56.1\% & 25.6\% & 30.6\% & 23.9\% \\  & **EP** & 30.0\% & 45.8\% & 13.5\% & 0.0\% \\ \cline{1-1}  & **N** & 0.0\% & 28.6\% & 54.2\% & 1.96\% \\ \cline{1-1}  & **PD** & 13.9\% & 0.0\% & 1.7\% & 74.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix regarding logistic regression based automatic classification of glottal area waveform (GAW) types. GAW types are shown in columns; predicted types are shown in rows. The diplophonic type (D), the extrapulsed type (EP), the normophonic type (N), and the random phase differences type (PD) are shown.
# Extraction and Synthesis of Silicon Nanoparticles (SiNPs) from Sugarcane Bagsase Ash: A Mini-Review
## 1 Introduction
### Sugarcane Bagasse in South Africa  
The sugarcane, shown in Figure 2, is an economical and strategic crop for the Mpumalanga and Kwazulu Natal, where the production is located, it is comprised of a substantial percentage of field crop gross farming income across the two provinces. To date, about 22,949 registered sugarcane growers annually produce an average of 20 million tons of sugarcane from 14 million supply areas, ballooning from southern Kwazulu-Natal to the Mpumalanga Lowveld [8].  
Presently, the growers are represented by the Southern African Cane Growers Association (SACGA) and the South African Farmers Development Association (SAFDA). Thus, there are approximately 21,581 small-scale growers, of whom about 12,019 delivered cane in the 2018/2019 season, with the production at 9.33% of the total crop. This is composed of 116 consolidated units, including co-operatives, trusts, and projects, with the make-up of 7536 individual beneficiaries. There are 1368 large-scale growers, including 345 black  
Figure 1: Flow diagram of sugarcane bagasse ash [6; 7].  
emerging farmers, and they produce 81.17% of total sugarcane. Milling companies with their own sugar estates produce 9.17% of the crop [9].  
Advanced studies have been ongoing to investigate the various ways for the production of silica from sugarcane bagasse using simple processing routes. The traditional routes moderately utilized for agricultural wastes to silica include synthetic routes, such as acid treatment, calcination, partial burning, leaching, enzymatic treatment, pyrolysis, hydrolysis, and sol-gel. Traditionally, silica is produced by reaction of sodium carbonate powder and quartz sand at higher temperatures to form sodium silicate, which will then react with sulfuric acid to precipitate silica [10].  
This method is considered hazardous to the ecosystem since most of its by-products (sodium sulphate, carbon dioxide, and lots of wastewater) are detrimental to the environment. In this review, the production of silicon nanoparticles from nano-silica is explained in detail, emphasizing the importance of turning waste into wealth. Interestingly, biomass is in abundance and serves as the future source of renewable energy reservoirs [11].
## 2 Supervisory Control and Data Acquisition Systems
### Justification for 3D Visualization in the Lerna Basin System  
The Lerna Basin, a critical water source for Mexico City, presents a complex and geographically dispersed infrastructure that requires advanced monitoring and controlcapabilities [16]. Traditional 2D SCADA systems are insufficient to address the challenges of this system, which includes the following:  
* Geographical Complexity: The basin spans multiple states, with numerous wells, pipelines, and pumping stations [4].
* Real-Time Monitoring: The need for real-time data on water levels, pressure, and flow rates to ensure efficient operation [13].
* Emergency Response: The ability to quickly identify and respond to issues such as leaks, pump failures, or contamination [19].  
By integrating 3D visualization into the SCADA system, operators can gain a comprehensive understanding of the Lerma Basin's infrastructure, enabling more effective monitoring, control, and decision-making [16]. This approach not only improves operational efficiency but also provides a scalable solution for future expansion and contingency planning [18].
## 3 Results
### Landslide Susceptibility Maps  
The LSIs were calculated as described in Section 2.5 based on the FRs presented in Appendices A and B and afterwards sorted into five susceptibility classes by Natural Jenks classification. Figure 6a,b present various LSIs calculated for the model based on \"Lososina\" and based on \"Grodek\", respectively. Similarly, Figure 6c,d present the susceptibility classes (SC) for the model based on \"Lososina\" and that based on \"Grodek\", respectively.  
Figure 5: Importance of landslide conditioning factors in landslide susceptibility mapping (LSM) based on (**a**) “Lososina” and (**b**) “Grodek”.  
The results from the \"Lososina\" model show that the very low, low, moderate, high and very high landslide susceptibility zones of the LSM cover 5%, 10%, 28%, 31% and 26% of the investigated study area, respectively (Figure 7a). The \"Grodek\" model results show that the very low, low, moderate, high and very high landslide susceptibility zones of the LSM cover 7%, 20%, 35%, 28% and 10% of the investigated study area, respectively (Figure 7b). From these indices, the same quantity can be observed for very high and high susceptible classes. Some relatively high differences can be observed within the low susceptibility class.  
Figure 6: Landslide Susceptible Index (LSI) and landslide susceptibility zones for the (**a**,**c**) “Lososina”-based model and (**b**,**d**) “Grodek”-based model.
# May the Extensive Farming System of Small Ruminants Be Smart?
## 3 Results
### Wearable Sensors
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Device** & **Position** & **Application** & **Technology** & **Recorded parameters** & **References** \\ \hline Camera & \begin{tabular}{c} fixed in the stable or in \\ the field \\ \end{tabular} & handheld or fixed camera & Optical image & Posture & [67] \\ \hline Camera coupled & \begin{tabular}{c} mounted on remotely \\ with remote \\ sensing techniques \\ \end{tabular} & fixed camera & Optical, infrared & Behaviour, growth, supervision, body & [30,42] \\ \hline GPS devices coupled & \begin{tabular}{c} mounted on remotely \\ with remote \\ sensing techniques \\ \end{tabular} & \begin{tabular}{c} Collar with GPS device; \\ controlled camera \\ \end{tabular} & \begin{tabular}{c} Satellite network; LiDAR \\ or multispectral camera; \\ handheld or fixed camera \\ \end{tabular} & \begin{tabular}{c} Behaviour, supervision, \\ ultrawideband real-time \\ tracking system \\ (UWB RTLS) \\ \end{tabular} & \begin{tabular}{c} [43,64,68,70,71] \\ \end{tabular} \\ \hline Wireless (WSN) & \begin{tabular}{c} sensor nodes with data \\ storage and \\ processing capabilities \\ \end{tabular} & sensor nodes with data storage and processing capabilities & and processing capabilities & \
\begin{tabular}{c} Transceiver, sensors, \\ microcontrollers, and \\ energy sources \\ \end{tabular} &
\begin{tabular}{c} monitoring grazing \\ livestock, and their \\ activity in real time \\ using Wi-Fi \\ \end{tabular} & [16,46,59,72] \\ \hline \end{tabular}
\end{table} 
Table 2: Main non-wearable sensors and network technologies. portable devices are among the most widely used systems in precision animal husbandry. These systems have small features and can be placed on animals to provide relevant information on each animal continuously and in real time. Wearable devices are also called \"portable\" as they are non-invasive sensors that can be put on and taken off without the need for veterinary assistance or specialized equipment or tools. However, a small percentage of portable devices, such as subcutaneous implants, are not considered wearable sensors [14]. For the optimal operation of these devices, a strategic position in the breeding environment is necessary. The choice of sensor or device to be used and its position depend on the technology, the type of breeding, the environmental conditions, and the characteristics of the animals [56; 59; 61]. Recently, there has been much research on sensors used to detect behaviours such as position changes, temperature changes, resting, grazing, or rumination in small ruminants [50; 52; 64; 65].
# Smart Grid Testing Management Platform (SGTMP) +
## 4 SGTMP Deployment Scenario--SG Component Stress Testing
### SG Topology  
The physical test topology is as follows (Figure 11):  
* Several energy sources generating electricity for the SG system. These can include photovoltaic panels, wind turbines, or power plants using non-renewable energy sources. Each of these energy sources can be created at full scale or virtualized, providing only a simulated energy production profile for other parts of the system. For example, this whole layer can be emulated through cheap hardware devices such as Arduino, as described in our previous work in [PERSON] and [PERSON] [22], providing the benefits of a quick set-up for educational needs.
* Electric distribution lines that collect and transmit energy generated to other parts of the tested network. Depending on the system setup, transmission losses can be simulated or observed in this step.
* Several houses in one or more neighborhoods connected to the electric distribution lines. Each home must have an SM; some can optionally have photovoltaic panels mounted on their roofs. The energy generated by them can be used inside the house or sold back to the grid.
* Smart meter data concentrators (SMDCs) attached to the endpoints at each neighborhood. Their amount depends on the specific data collection requirements of the energy distributor.
* The SG main server, collecting data from SMDC to run aggregated analytics on the data and respond appropriately.  
The logical test topology extends the physical one by adding an SGTMP monitoring node to each device or replaces a physical device with a simulated device controlled directly by SGTMP. For hardware devices which require inputs from other devices, these inputs can be either simulated or  
Figure 11: Logical test topology.  
created by various generators controlled by an SGTMP node. A single device can be tested in isolation, where the rest of the environment is fully simulated. Using a similar principle, only certain parts of the SG topology can be simulated to remain as close as possible to real-world deployments when using hardware. Using the above-described topology, it is possible to perform different tests on each device or at a whole SG level, either per test or at once.
# Erasure Codes for Cold Data in Distributed Storage Systems
## 2 Related Works
### The Array Codes  
Array codes use an exclusive OR operation for code verification, and they have a higher encoding speed and decoding speed. Not all array codes are MDS codes. Only the array codes whose code length and minimum distance reach the singleton boundary belong to MDS codes. The MDS array codes can achieve the highest storage efficiency, which means the storage redundancy is the lowest. The array erasure code can be divided into horizontal coding and vertical coding according to the distribution form of the data chunk and the parity chunk.  
Compared with RS codes, array codes are completely based on an XOR operation, which is a hot topic in erasure code research. The purpose of the array codes is to store the original data and redundancy in a two-dimensional (or multidimensional) array [20]. Compared with the traditional RS codes, only the XOR operation is used in the array code, which is easy to implement. At the same time, the processes of coding, updating, and reconstruction are relatively simple, making the application the most widely used. The array codes are divided into horizontal parity and vertical parity. Horizontal parity array codes refer to array code methods in which the redundancy is independent of the data strips and is stored separately in redundant strips [21]. The parity data are stored separately in parity disks, and the original data are stored in the remaining disks. There is very good scalability in the entire disk array using this construction. Parity data in disks are used continuously in successive write operations, which always cause disk hotspots concentrated by continuous write operations. The tolerance of horizontal array codes is generally not very large. For example, the codes with two tolerances include EVENODD code [22], RDP code [23; 24], Liberation code [25], and so on. Our team has conducted much related research already [26]. There is currently less research involving more than two tolerances, such as the STAR [27] code.  
Some chunks store both original data and parity data in vertical parity array codes, which is different from that in horizontal parity array codes. The computational complexity overhead is distributed to each disk because of the simple geometric structure in vertical parity array codes. Unlike horizontal parity array codes, the bottlenecks caused by continuous write operations have been solved naturally due to its structural balance. However, the uniformity also leads to the strong dependence of disks on each other in vertical parity array codes, which leads to poor scalability [28].
## 2 Related Works
### Overall Workflow of Decision Map  
Given a classifier \(f\), a _decision boundary_ is a surface in \(\mathbb{R}^{n}\) that separates high-dimensional data points \(\mathbf{x}\in\mathbb{R}^{n}\) into regions, also called _decision zones_. All points in a given zone are assigned the same label \(y\in C\) by the model \(f\). Decision zones are separated by _decision boundaries_, which are hypersurfaces embedded in \(\mathbb{R}^{n}\), where the classifier \(f\) changes the output. Understanding how the high-dimensional space is partitioned into such decision zones and how data samples in a training or test set are distributed across the zones effectively helps understand how a classifier behaves [20; 21]. For example, seeing how labeled samples distribute close to decision boundaries can help categorize misclassification problems, and seeing how unlabeled samples (whose class is to be predicted by \(f\)) spread across decision zones can help understand how well \(f\) can handle a given data distribution.  
Decision-map algorithms are techniques that aim to create 2D representations of the decision zones and boundaries of any classifier \(f\)[20; 21; 22]. Their general workflow is as follows (see also Figure 1):  
1. Train a classifier \(f\) on a dataset \(D_{t}\). This is the classifier whose decision map we next want to visualize.
2. Construct a direct projection \(P\) and inverse projection \(P^{-1}\) using a dataset \(D^{\prime}\).
3. Project \(D^{\prime}\) to create a 2D scatter plot \(P(D^{\prime})\).
4. Sample the extent of \(P(D^{\prime})\) on a uniform pixel grid \(I\).
5. Backproject all pixels \(\mathbf{y}\in I\) to the data space using \(P^{-1}\).
6. Use \(f\) to predict the labels of the backprojected points \(P^{-1}(\mathbf{y})\).
7. Color \(I\) according to the predicted labels \(f(P^{-1}(\mathbf{y}))\).  
Figure 1: General workflow of decision-map techniques (see Section 2.2).  
In the above, \(D^{\prime}\) can be, at a minimum, a subset of the training set \(D_{t}\). However, if more samples of the target domain are available, such as in the form of test data \(D_{T}\) or even unlabeled data, these can be added to \(D^{\prime}\). By doing this, the constructed decision map can better sample the actual data distribution of the investigated phenomenon and thus the classifier's behavior. In this paper, we set \(D^{\prime}=D_{t}\) following earlier examples of this workflow [22].  
We next describe three specific techniques that implement the above workflow and introduce some of their key features.
# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 4 Proposed Research Framework
### Inducers Employed  
To provide a wider range for observations in the research, several classifiers were used with different modes of operation. All inducers were capable of operating on both continuous and discrete attributes. They included Naive Bayes (NB), Bayes Network (BNet), J48, k-Nearest Neighbours (kNN), and Random Forest (RndF), which were all implemented in the popular WEKA environment and used with default settings [40].  
Naive Bayes and the Bayes Network are known as probabilistic classification models, because they use the [PERSON] probability theory [12] to present the relationships between attributes and class labels. NB classifiers are based on the assumption of the conditional independence of variables. In the case of BNet, this assumption is considered in a more flexible way, so it makes it possible to capture more general representations of conditional independence among attributes by using simple schematic, graphical representations.  
J48 is a decision tree classifier. It is an implementation of the C4.5 algorithm [41] in the WEKA workbench. A decision tree learning algorithm approximates a target concept using a tree representation, where each internal node corresponds to an attribute, and each terminal node (called a leaf) corresponds to a class label. The root node is at the top, and the leaves are at the bottom of a tree. Reading a tree from root to leaves, a decision is proposed for a considered object. In this way, it is possible to see the reasons for reaching a given decision. Decision trees are considered not only good classifiers but also as a popular form of knowledge representation. For the Random Forest classifiers, each constituent decision tree is induced from a bootstrap sample of the training data [42].  
The k-Nearest Neighbour inducer belongs to instance-based learning approaches [43], which are based on the calculation of similarity (distance) between instances. A parameter \(k\) refers to the number of nearest neighbours to be included in the voting process during classification, so each test object has assigned a decision based on the voting of \(k\)-nearest neighbours of the considered object. This type of classifier is categorised as a lazy learning algorithm, where the learning process is postponed until the classification point. In the case of decision trees, the learning process is connected with model creation.
## 2 Methodology
### Granger Causality  
Since the Gaussian assumption is constant variance, this method can merely capture linear and symmetric dependence in time and between series. Therefore, we employed the Vine-COPAR model based Granger causality, which determines interdependencies among multiple time series, to deal with high-order causality. The Vine-COPAR(\(p\))-based Granger causality was then built in the following form:  
\[lnY_{kt}=\beta_{k0}+\sum\
olimits_{i=1}^{s}\beta_{ki}lnY_{1t-i}+\sum \
olimits_{j=1}^{q}\beta_{kj}lnY_{2t-j}+\sum\
olimits_{l=1}^{n}\beta_{kl}lnY_{ 3t-l}+\ \varepsilon_{kt}. \tag{10}\]  
where \(\beta_{k0}\) (for \(k=1,\,2,\,3\)) is the interception term. \(\beta_{ki},\ \ \beta_{kj}\) and \(\beta_{kl}\) are the estimated coefficient of the lagged variables. \(s,\,q\) and \(n\) are the optimal serial lag lengths and \(\varepsilon_{kt}\) refers to random disturbance terms.  
The log-likelihood ratio (LR) tests of unrestricted (L\({}_{\text{u}}\)) and restrictions (L\({}_{\text{r}}\)) models were imposed with the null hypothesis of no Granger causality \(H_{0}:\beta_{ki,1}=\beta_{kj,2}\ldots=\beta_{kl,3}=0\). Then, we employed the maximum likelihood method to maximize the Vine-COPAR models to obtain the estimated parameters, which was better than a two-step estimation.
## 3 Proposed Approach
### Image Preprocessing  
CT images exhibit variations in size and pixel intensity and may be affected by unwanted artifacts such as noise. These artifacts can lead to irregular segmentation results in certain images and compromise the overall model performance. To address these issues, standard image preprocessing techniques were employed before segmentation. To enhance the network's feature representation capabilities and minimize the impact of redundant noise, skull removal preprocessing was performed on the CT images. This process, illustrated in Figure 3, involves selecting the rough skull outline utilizing OpenCV knowledge, followed by the elimination of extraneous features outside the outline. Finally, the remaining white pixels are converted to black pixels, completing the skull removal operation. This preprocessing approach effectively eliminates the most irrelevant features and improves segmentation accuracy.