esa-sceva/satcom-chunk-collection huggingface dataset:
# Accuracy, Time, and Comfort of Different Intraoral Scanners: An In Vivo Comparison Study
## 3 Results  
No systematic error was found between the repeated measurements. The method errors and reliability coefficients for the linear measurements ranged from 0.999 for the upper first interimolar width to 0.478 for the upper inter canine width, respectively. Descriptive statistics for linear measurements and the results of statistical analysis are shown in Table 2. \begin{table}
\begin{tabular}{l} \hline Q1 “Comfort”: How would you rate the comfort during intraoral scanning? \\ \hline Q2 “Stress”: How would you rate your stress during intraoral scanning? \\ \hline Q3 “Pain”: How would you rate pain during intraoral scanning? \\ \hline Q4 “Mouth dryness”: How would you rate your mouth dryness during intraoral scanning? \\ \hline Q5 “Gag reflex”: How would you rate the gag reflex during the intraoral scanning? \\ \hline Q6 “Experience”: How would you rate the experience with the intraoral scanning? \\ \hline Q7 “Duration”: How would you rate the duration of the intraoral scanning procedure? \\ \hline Q8 “Technology”: How would you rate the technology of the intraoral scanning procedure? \\ \hline Q9 “Communication”: How would you rate the communication of the intraoral scanning procedure? \\ \hline Q10 “General satisfaction”: How would you rate the general satisfaction of the intraoral scanning procedure? \\ \hline Q: question. \\ \hline \end{tabular}
\end{table} 
Table 1: Satisfaction survey question list. The statistical comparison between digital casts made with the three IOSs revealed few significant differences between the three analyzed impression techniques. The region in which the most significant differences were appreciable was the upper central incisor region (f-ratio of 4.186 for Mesiodistal 1.1 and f-ratio of 4.222 for mesiodistal 2.1, \(p\)-value < 0.05), while intercanine width Upper 13-23 showed the smallest f-ratio (0.226) when compared to the other results, followed by Mesiodistal 14 (f-ratio of 0.433). Table 3 shows the results of the satisfaction survey carried out to analyze the experience with the iTero Element and iTero Lumina IOSs and the different perceptions in terms of duration and comfort. According to the questionnaire, patients had a more comfortable experience with iTero Lumina when compared to iTero Element. The most significant differences between iTero Lumina and iTero Element reported by patients were more comfort (\(p\) < 0.01), less pain (\(p\) < 0.01), decreased duration (\(p\) < 0.001), and a better technology visualization of the impression (\(p\) < 0.01). Another aspect that patients appreciated was the immediate and realistic visualization of their upper and lower dental impression on the scanner screen, showing a general
# APTM: A Model for Pervasive Traceability of Agrochemicals
## 5 Implementation and Evaluation
### Evaluation Aspects
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline  & **Question** & **Answer** & **Comments** \\ \hline
16 & Do you find it interesting to have an artificial intelligence system that can predict future demands to buy pesticides? & Yes & This model features machine learning modules to assist the farm in predicting the volume of agrochemicals that will be needed. \\ \hline
17 & Do you consider it important to register with the inspection and control entities of returned empty packages? & Yes & The model foresees the connection with a public regulatory entity, thus the model is flexible as it allows supply chain data to be shared with more than one blockchain consortium. \\ \hline Do you consider a pesticide use record important? Which could compliance? & Yes & The model foresees the presentation in the visualization layer of the record of transactions in blockchains, in addition to presenting a scoring system via gamification for performed operations. \\ \hline The registration of pesticide use, being a history of environmental compliance, could it be exchanged for carbon credits? & Not applicable at the moment. & The farm does not currently carry out transactions with carbon credits. The model can predict transaction records to audit operational processes. \\ \hline A scoring system (similar to a mileage system) could record the amount of packages required. Consider this system of points important for future exchanges for carbon credits? & Yes & The data provided by the practical application of the model can help its implementation in a self-sustainable way. The model is economically viable for the farm. \\ \hline
21 & Is the farm interested in testing new technologies with means to control, monitor, record the use of agrochemicals? & Yes & The question is very broad and allows even accepting answers as a test of technologies for soil samples. In spite of that, we indicate in this work about future works the possibility of field tests. \\ \hline
22 & Could the registration of the correct movement of pesticides be rever reverted to tax incentives with the control bodies? & Yes & Data logging in a public blockchain consortium may in the future be used by regular agencies to issue tax incentives. The model offers this future possibility. \\ \hline Could the record of the correct movement of pesticides be reversed in discounts together with supplies? & Yes & The transaction record used in the private blockchain consortium can be used in a future mileage system in the reverse supply chain. \\ \hline \end{tabular}
\end{table} 
Table 8: _Cont._Positive responses indicate that the gap identified initially exists and needs to be addressed by the proposed model. It allows us to see the usefulness of the proposed model, the integration of technologies with all the players involved. We also realize the possibility of future utility, including for needs not yet demanded at the time, such as the registration of transactions for exchange for carbon credits, among others.
## 3 Technology Used for Diagnosis
### Fast Fourier Transform  
Consider a set of \(N\) points signals that change with time that can be defined as \(x(n)\). Discrete Fourier Transform corresponding to \(x(n)\) is shown in Equation (3), and the inverse Discrete Fourier Transform is as shown in Equation (4).  
\[X(k)=\sum_{n=0}^{N-1}x(n)e^{-j\frac{2\pi}{N}nk},0\leq k\leq N-1 \tag{3}\]  
\[x(n)=\frac{1}{N}\sum_{k=0}^{N-1}X(k)e^{j\frac{2\pi}{N}nk},0\leq n\leq N-1 \tag{4}\]  
where \(n\) or \(k\) is the sample serial number, \(N\) is the total sample numbers.  
According to Equations (3) and (4), the concept of Fourier transform is to fit the original signal through multiple sets of waves, so as to obtain the frequency components. This paper will document fast Fourier transform on the motor's current and record the value of energy on characteristic frequency of broken rotor bar, so as to establish training samples.
## 3 Results
### Selection of Input Parameters Taking into Account the Variability of Parameters  
To account for variability (Section 2.4.3), the 16 parameters with the highest ratio of the variability coefficient obtained for each input parameter in the stroke patient group to the variability coefficient obtained in the reference group (\(CV\_M3_{i}\)) were selected. The parameters were chosen for both the dominant and non-dominant upper limb (Table 6).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Dominant Limb**} & \multicolumn{2}{c}{**Non-Dominant Limb**} \\ \cline{2-5}  & **Selected Parameters** & \(CV\_M3_{i}\) & **Selected Parameters** & \(CV\_M3_{i}\) \\ \hline
1 & P11 & 3.59 & P16 & 2.12 \\
2 & P12 & 1.92 & P7 & 1.50 \\
3 & P21 & 1.30 & P11 & 1.33 \\
4 & P23 & 1.30 & P23 & 1.18 \\
5 & P22 & 1.19 & P21 & 1.18 \\
6 & P24 & 1.19 & P19 & 1.15 \\
7 & P19 & 1.16 & P17 & 1.15 \\
8 & P17 & 1.16 & P9 & 1.14 \\
9 & P30 & 1.13 & P22 & 1.13 \\
10 & P15 & 1.09 & P24 & 1.13 \\
11 & P29 & 1.03 & P2 & 1.05 \\
12 & P18 & 1.02 & P12 & 1.00 \\
13 & P20 & 1.02 & P18 & 1.00 \\
14 & P1 & 0.88 & P20 & 1.00 \\
15 & P28 & 0.84 & P29 & 0.83 \\
16 & P26 & 0.84 & P30 & 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Variability values for the parameters selected according to method M3 for the stroke patient group.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Dominant Limb**} & \multicolumn{2}{c}{**Non-Dominant Limb**} \\ \cline{2-5}  & **Selected Parameters** & **Differences in Mean Values [\({}^{\circ}\)]** & **Selected Parameters** & **Differences in Mean Values [\({}^{\circ}\)]** \\ \hline
1 & P11 & 38.18 & P12 & 25.96 \\
2 & P12 & 25.38 & P9 & 13.30 \\
3 & P9 & 20.10 & P8 & 13.24 \\
4 & P10 & 19.93 & P5 & 13.03 \\
5 & P4 & 16.07 & P4 & 12.58 \\
6 & P8 & 9.70 & P7 & 11.67 \\
7 & P7 & 9.51 & P11 & 10.02 \\
8 & P14 & 6.67 & P6 & 9.27 \\
9 & P24 & 5.97 & P16 & 7.82 \\
10 & P5 & 5.85 & P3 & 7.05 \\
11 & P15 & 5.74 & P24 & 5.64 \\
12 & P3 & 5.53 & P20 & 5.57 \\
13 & P20 & 5.19 & P15 & 4.69 \\
14 & P23 & 5.08 & P23 & 4.44 \\
15 & P19 & 4.36 & P14 & 4.44 \\
16 & P16 & 3.30 & P19 & 4.42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Differences in mean values of parameters selected according to method M2.
## 3 Results and Discussion
### Research Limitations  
In this study, all of the OH-PAHs evaluated in a previous study [30] were not analyzed. In future studies, hydroxylated benzo[c]phenanthrene should be analyzed. High molecular weight PAHs in the atmosphere are mainly distributed in the particle phase; however, low molecular weight PAHs are mainly distributed in the gas phase [56]. Therefore, monitoring of atmospheric PAHs and OH-PAHs should involve both the gas and particle phases. Additionally, atmospheric environmental conditions, such as wind direction, humidity, ozone, temperature, and sunlight, are important in the secondary formation of OH-PAHs from PAHs in the atmosphere. To assess the health risks of environmental pollutants via  
Figure 7: Daily relative binding potentials of atmospheric PAHs and OH-PAHs to the hER in April, August, and November 2017, and February and April 2018 in Kanazawa.  
inhalation, uptake factors are also important, in addition to their toxicities. Therefore, the uptake factors of PAHs and OH-PAHs, and especially differences among isomers, should be analyzed in future studies.
## 3 Results and Discussion
### MD Simulation of CH\({}_{4}\) Desorption and Diffusion in Coalbed
#### 3.2.2 Diffusivity of CH\({}_{4}\)  
MD simulations were performed to investigate the diffusivity of CH\({}_{4}\) both in the presence and absence of water. The diffusion coefficients of CH\({}_{4}\) under different pressures and in models with varying pore sizes are shown in Figure 5.  
The most obvious observation from Figure 5 is that, with H\({}_{2}\)O present, the diffusion of CH\({}_{4}\) is significantly impeded. If mixed with H\({}_{2}\)O, the diffusion coefficients of CH\({}_{4}\) are reduced to only one fourth to one half compared with the cases when only CH\({}_{4}\) is considered. The reason is that H\({}_{2}\)O molecules, which can form a hydrogen-bond network, can partially trap CH\({}_{4}\) molecules to reduce their mobility. This effect, sometimes known as the WBE, can be observed in coalbed extraction processes [38]. Residual fracturing fluid can be present at the entrance of micropores in the coalbed and act like seals to prevent methane from leaving those pores.  
The results also suggest that the pressure does not have a strong influence on the diffusivity of CH\({}_{4}\), while the diffusion coefficient of CH\({}_{4}\) is higher in larger pores. In small pores, such as in the 20 A  
Figure 5: Diffusion coefficients of CH\({}_{4}\) in the coalbed models with varying pore sizes. Solid and dashed lines show the cases where CH\({}_{4}\) alone or a mixture of CH\({}_{4}\) and H\({}_{2}\)O is adsorbed in the cell, respectively. Blue, red, and black lines represent the results generated from models with 100 Å, 60 Å, and 20 Å pore sizes, respectively.  
case, the rugged surface and small crevices within the pore may trap CH4 molecules and hinder the movement of CH4.
## 4 Algorithms
### [PERSON]'s Fixed Point Theorem  
Recall that a _lattice_ is a partially ordered set, \(L\), with the property that any two elements, \(x,y\), of \(L\) have a greatest lower bound \(x\wedge y\) and a least upper bound \(x\lor y\). A lattice, \(L\), is _complete_ if any subset, \(X\), of \(L\) has a greatest lower bound \(\bigwedge X\) and a least upper bound \(\bigvee X\). Function \(f:L\to L^{\prime}\) from lattice \(L\) to lattice \(L^{\prime}\) is _monotone_ if \(x\leq y\) implies \(f(x)\leq f(y)\) for any elements, \(x,y\), of \(L\).  
**Theorem 4** ([PERSON]'s fixed point theorem [10]).: _Let \(L\) be a complete lattice and \(f:L\to L\) be a monotone function on \(L\). Then, set \(L_{f}=\{x\in L:f(x)=x)\}\) of fixed points of \(f\) is a nonempty, complete lattice on the restricted partial order._  
If lattice \(L\) is finite in Theorem 4, there is a straightforward algorithm to find the least and greatest fixed points. Let zero be the smallest element in lattice \(L\). Therefore, \(0\leq f(0)\) and from monotonity \(0\leq f(0)\leq f(f(0))\leq f(f(f(0)))\leq\ldots\). Since the lattice is finite, there exists an \(i\), where \(f^{i}(0)=f^{i+1}(0)\). Therefore, \(f^{i}(0)\) is a fixed point.  
**Statement 4**.: _The above fixed point \(a=f^{i}(0)\) is the least of all fixed points of \(f\)._  
Proof.: Let \(x\) be an arbitrary fixed point of \(f\). Since \(f\) is monotone, \(0\leq x\Rightarrow f(0)\leq f(x)=x\) and \(f^{j}(0)\leq f^{j}(x)=x\) for every \(j\geq 1\). We get that \(a=f^{i}(0)\leq x\).  
Similarly, we can start with the greatest element one. From \(1\geq f(1)\geq f(f(1))\geq f(f(f(1)))\ldots\), we see that there is a \(j\), such that \(f^{j}(1)=f^{j+1}(1)\).  
This \(f^{j}(1)\) is the greatest of all fixed points of \(f\).
# On-the-Fly Machine Learning for Improving Image Resolution in Tomography
## 3 Method
### Reconstruction
The matrix \(\mathbf{A}^{\text{(high)}}\) similarly defines the projection of the high-resolution volume \(\mathbf{x}^{\text{high}}\) at magnification \(\alpha_{\text{high}}\). To reconstruct \(\mathbf{x}^{\text{high}}\), we subtract the reprojection of the masked reconstruction \(\mathbf{M}\,\widetilde{\mathbf{x}}^{\text{low}}\) from the acquired high-resolution projection data  
\[\hat{\mathbf{p}}=\mathbf{p}^{\text{high}}-\mathbf{A}^{\text{(low}\to\text{ high})}\mathbf{M}\,\widetilde{\mathbf{x}}^{\text{low}}. \tag{11}\]  
Next, we apply the FDK algorithm to the processed projection data to obtain the reconstruction  
\[\widetilde{\mathbf{x}}^{\text{high}}=\mathbf{FDK}\,(\hat{\mathbf{p}})\in \mathbb{R}^{N_{\text{high}}^{3}}. \tag{12}\]  
In a conventional FDK reconstruction, the outside contributions of \(\Omega\setminus\Omega_{\text{high}}\) to the projection data \(\mathbf{p}^{\text{high}}\) cause truncation artifacts. Because we have approximately removed these contributions, we can apply the FDK algorithm to obtain a high-resolution reconstruction of the region of interest. Note that similar subtraction methods have been proposed before to remove truncation artifacts in region-of-interest tomography [25; 27; 28]. Some artifacts may occur on the border of the region of interest, which may be due to the specifics of the interpolation kernel that is used in the forward projection \(\mathbf{A}^{\text{(low}\to\text{high)}}\) or due to discontinuity at the image borders enhanced by the FDK filter kernel. Water cylinder extrapolation or truncation robust FBP methods like [29] may alleviate these artifacts. These artifacts are not a problem in practice, as the affected border voxels may be ignored.
## 2 Prediction of Penetration Depth Using the Finite Cavity Pressure Method
### Finite Cavity Pressure Method  
The finite cavity pressure method is an application method for predicting the penetration depth of a projectile. The main difference between the analytical method using cavity pressure and the finite cavity pressure method is that the deformable projectile is employed and the pressure is applied to each element of the projectile in the finite cavity pressure method. The pressure normal to the surface of the projectile acts as a reaction pressure to penetration into the target. The projectile was modeled as a solid element such as hexahedral elements. As a result, the deformation and kinetics of the projectile can be calculated. The pressure was assumed to be applied to each node of the element, as shown in Figure 3 and applied in the finite element analysis as a boundary condition.  
\[\frac{\sigma_{r}}{Y}=A+B(\sqrt{\frac{\rho_{0}}{Y}})(\overset{\rightarrow}{V} \cdot\overset{\rightarrow}{n})+C(\sqrt{\frac{\rho_{0}}{Y}})^{2}(\overset{ \rightarrow}{V}\cdot\overset{\rightarrow}{n})^{2} \tag{2}\]  
Figure 1: Expansion of the cavity pressure at the projectile tip.  
Figure 2: Distribution of the cavity pressure at the projectile tip.  
Equation (2) presents the pressure at each node where \(\overset{\rightarrow}{V}\) is the nodal velocity, \(\overset{\rightarrow}{n}\) is the unit normal vector of the corresponding element. \(Y_{0}\) is the yield stress of the material, and \(\rho_{0}\) is the density of the projectile material. Coefficients such as A, B, and C are defined from the cavity pressure method. In this work, the target was the aluminum plate. The cavity pressure of the previous work [17] was employed.  
The effective velocity of the element was defined as \(\overset{\rightarrow}{V}\cdot\overset{\rightarrow}{n}\), which means the dot product of the element normal direction and the velocity vector of the node. For example, if the angle between the normal direction of the element and the velocity of the projectile is perpendicular, the effective velocity is zero. Consequently, no pressure is applied to the element. In this work, the finite cavity pressure method was employed in the ABAQUS/Explicit using the user load subroutine VDLOAD. User subroutine VDLOAD defines the magnitude of the distributed load as a function of position, velocity, and time. The calculating procedures are as follows: First, the velocity and the normal direction of the projectile were calculated. Next, the corresponding cavity pressure (\(\sigma_{\text{r}}\)) was calculated. Finally, the deformation of the projectile was calculated.  
In the full simulation model, the projectile and the target should be modeled with very fine mesh with appropriate contact conditions and material properties. However, in the finite cavity pressure method, only the projectile was modeled with solid elements. The material property of the target and the contact between the target and the projectile was not considered. The interaction between the target and the projectile was the cavity pressure distribution with respect to only the cavity speed.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Tool Comparison
#### 5.2.10 Dvc  
DVC is a Version Control System for ML Projects [68]. The tool is capable of tracking models, datasets (including large dataset 10-100 Gb vs. 2 Gb limit on Github), and pipelines. It is cloud- and storage-agnostic, i.e., the datasets can be stored, accessed, and versioned locally or on some cloud platform. It is git compatible and can track experiments but does not offer a dashboard. It works by creating the file '.dvc' in the project root, where metainformation such as storage location and format of the data is stored. For unstructured data, the changes are tracked as new versions by themselves, which requires high storage capacity. It is a very lightweight tool which offers great features with the minimum effort required for integrating it into any GitHub workflow.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Integrated Topologies
#### 3.3.3 Two-Switch Forward with Integrated Buck Converter  
In [18], the heritage design properties of DC-DC converters for satellites were shown to align with the International Satellite Electronics Design Standards [115]. According to the study, the following design characteristics must be met by a DC-DC converter designed for satellite EPSs:  
* Non-Leg Structure: The converter should have a conservative design in which a fault at a node should not interrupt the functioning of the entire system, thus ensuring an  
Figure 17: (a) Classification of integrated topologies of DC-DC converters for satellite applications. (b) Three-port modified half-bridge converter.  
extended life for the satellite. A non-leg structure was proposed in [115] as a potential solution in this context.
* PWM Controlled: To align with the strict EMI and EMC regulations in space and considering the variations in passive components in space, pulse-frequency modulation control is not recommended for DC-DC converters for satellites. Thus, PWM control is the proposed method of control [115].  
To create a DC-DC converter architecture that complies with the aforementioned requirements, the authors of [18] developed the topology shown in Figure 18b. A buck converter and a forward converter are combined, thus eliminating a diode and a switch. As a result, the topology's cost, mass, and volume decrease. Furthermore, ZVS is obtained for a few switches, increasing the efficiency of the converters. The above-mentioned integrated topologies are compared in terms of their switching frequency, power, and component count in Table 3.
# Optimal CNN-Hopfield Network for Pattern Recognition Based on a Genetic Algorithm +
## 2 General Description of the Method  
In this section, an overview of the architecture of the proposed method in its two phases--the training phase and the inference phase--is presented. Then, an architecture based on multiple parallel HNNs, which replaces the fully connected layer of the CNN, is discussed, and the strategy for distributing the patterns among a set of HNNs in the parallel architecture is analyzed.  
Feature Extraction  
As shown in Figure 2, a pretrained CNN model is used for feature extraction. These features come from the last layer before the dense (fully connected) layers.  
During the training phase, the architecture uses a pretrained CNN classification model to extract feature maps from an input image. The process consists of:  
1. Extraction of the class-specific feature set: a set of all features of an image.  
Figure 1: Ratio of the number of FC parameters to the total number of parameters in some well-known CNN models.  
Averaging of the pixels' gray level from the class-specific feature set.
* Conversion of the training patterns into binary patterns and distribution over \(K\) parallel Hopfield networks.  
In the inference phase, the same pretrained CNN is used to extract the feature maps from the input image. Then, the binary pattern of the input image is sent to all HNNs.The correct network among all of the networks (which converges to a state that is very close to the input state) is selected based on the comparison of the states of the networks with the input pattern and the number of changes that each one has produced. Then, the network that has produced the fewest changes in the input pattern is selected.