esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In conclusion, our findings demonstrate that spatial eGenes associated with T2 DM GWAS signals are enriched in the lysosomal pathway. This highlights the autophagic termination process related to lysosomes as a potential pathogenic mechanism underlying T2 DM. Some eGenes, including NPC1, IDUA, HYAL3, GBA, and CTSH, which contribute to this enrichment, have the potential to contribute to lysosomal dysfunction-based pathogenic mechanisms in T2 DM. Although this study is based on T2 DM GWAS signals, the lysosomal pathway was inferred from the eGenes associated with genetic variation in normal individuals. Differential eGene expression between T2 DM patients and controls would confirm the target genetic risk factors for T2 DM susceptibility suggested in this study. To gain a deeper understanding of the pathology and genetic etiology of T2 DM, further experimental studies are required to elucidate the underlying mechanisms that link eGene-based lysosomal dysfunction to susceptibility to T2 DM.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app131810447/s1](https://www.mdpi.com/article/10.3390/app131810447/s1)]([https://www.mdpi.com/article/10.3390/app131810447/s1](https://www.mdpi.com/article/10.3390/app131810447/s1)), Table S1: Significant KEGG terms resulted from the enrichment analysis with eGenes of visceral adipose tissue; Table S2: Significant KEGG terms resulted from the enrichment analysis with eGenes of liver; Table S3: Significant KEGG terms resulted from the enrichment analysis with eGenes of skeletal muscle; Table S4: Significant KEGG terms resulted from the enrichment analysis with eGenes of pancreas; Table S5: Significant KEGG term resulted from the enrichment analysis with mapped genes of T2D GWAS signals.  
Y.K. and C.L. designed the study, interpreted the results, and wrote the manuscript. Y.K. conducted data analyses. [PERSON] supervised the study. All authors have read and agreed to the published version of the manuscript.  
Funding: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, grant number 2021R1A6A1A10044154.  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study because we used publicly available population-based secondary data and subjects could not be identified.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used in this study are publicly available in the GTExPortal (dbGaP Accession phs000424.v8.p2).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Quantitative Assessment Methods of Early Enamel Caries with Optical Coherence Tomography: A Review
## 5 Conclusions  
In conclusion, the common aim of the discussed studies was to investigate and improve the capabilities of OCT in monitoring the pathophysiological process of early enamel caries and remineralization. Researchers have proposed multiple quantitative indicators for the assessment of early enamel caries and have studied correlations with the results of multiple histological detection techniques. By far the most widely used quantitative indicators include lesion depth, integrated reflectivity and attenuation coefficients. The differences in quantitative results are mainly attributed to the use of different systems, different methods and different sample preparation. Among them, there is a high linear correlation between depths of enamel lesions determined by OCT and DM, as well as between integrated reflectivity calculated by OCT and mineral loss determined by DM. However, the assessment methods using these three quantitative indicators still have certain limitations and lack objectivity and accuracy. It is a remarkable fact that the scattering coefficient of tooth enamel is very sensitive to changes in mineral content and increases significantly with increasing mineral loss at the initial stages of the enamel caries process. Therefore, the early detection of enamel lesions using the scattering coefficient based on OCT is a potential research direction. Meanwhile, most studies on quantitative assessment of enamel caries have been performed in vitro due to the limitations of the probe, and in vivo studies are mainly focused on the buccal and incisal/oclusal surfaces of premolars and anterior teeth. With the development of intraoral probes [4; 86], the problem of device availability is being solved slowly. However, a very important issue is the lack of consensus on the method, and OCT has not been applied for the clinical diagnosis of early caries. Although OCT has made great progress as a noninvasive imaging method for quantitatively assessing early enamel caries, efforts to standardize rigorous methodology in future research are crucial for detection, diagnosis, and treatment guidance of early enamel lesions using OCT.  
Conceptualization, all authors; methodology, B.S., J.N., X.Z.; formal analysis, B.S., J.N.; investigation, B.S., J.N.; resources, all authors; data curation, B.S., J.N.; writing--original draft preparation, B.S., J.N.; writing--review and editing, all authors; visualization, B.S., J.N.; supervision, X.Z., X.D.; project administration, B.S., J.N.; funding acquisition, B.S., J.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Tianjin (grant number 19 JCYBJC16200) and the Science and Technology Program of Tianjin (grant number 20 YDTPJC01530).
###### Abstract
However, initial stability of the RAI is based on the press-fit phenomenon [7; 17; 18]. Since patient-specific RAIs are based on cone beam computed tomography (CBCT), CAD, and 3D printing technology, unlimited modified RAI designs are available. According to previous studies, most RAI design options, aimed at increasing the initial stability of the RAI, are created to achieve initial mechanical fixation by varying the surface shape of the RAI root without additional drilling protocol [19; 20]. Finite element analysis (FEA) has become an effective method to evaluate peri-implant bone stresses and the relative micro-displacement between bone-to-implant interfaces under certain loading conditions [21]. However, most previous FEA studies on RAIs were limited in the analysis of the initial stability, which is one of the most important factors for the success of implant placement; they assumed the contact condition between the bone and implant was fully bonded and in a completely osseeo-integrated state [19; 22]. Thus, in this study, we aimed to evaluate the initial stability of a modified RAI design with additional mechanical fixation through anchorage at an additional drilling site at the root apex through comparison with several RAI designs that only modify the root surface shape without additional drilling protocol. To analyze the initial stability immediately after implant placement, the contact conditions between the bone and implant were set with a low friction coefficient (0.1 to 0.5), allowing for a sliding motion to mimic incomplete osseo-integrated conditions in the 3D FEA.
## 1 Introduction
### Contributions  
This study's topic is communication delay outlier detection for teleoperation of a remote vehicle using stochastic state estimation, and the contributions of this study are as follows:  
* The primary aim of this study was to assess outliers in communication delays during real-time teleoperation accurately. Since communication delays are influenced by various environmental factors such as signal quality, channel conditions, buffer status, and network load, distinguishing erroneous outliers in real time poses a significant challenge. Traditional outlier detection methods typically rely on predetermined rules based on empirical samples. However, our proposed approach employs safety-oriented criteria utilizing a coverage interval to evaluate acceptable delay thresholds dynamically. Consequently, we introduced stochastic criteria for promptly identifying communication delay outliers. Particularly, we demonstrated the efficacy of our outlier detection algorithm by showcasing enhanced performance through compensatory actions for detected outliers when employing a predictor-based framework approach;
* Moreover, this study relied on actual communication delay measurements achieved through GPS time synchronization. Unlike traditional methods that measure round-trip delay, we focused on estimating one-way communication delay outliers. To accomplish this, we synchronized the time between the vehicle and the control center using GPS and compared the synchronized times to gauge one-way communication delay. This approach offers practicality for various wireless communication applications and furnishes guidance on measuring one-way communication delay, applicable not only to vehicles but also to remote control scenarios involving robots, agricultural machinery, and embedded systems;
* The findings indicate that the proposed approach is versatile and suitable not only for stable communication environments but also for dynamic conditions. Particularly, it effectively handles unpredictable communication delays, such as those encountered when transmitting between continents or traversing shielded spaces where electromagnetic waves face obstacles. An important feature of the proposed method is its ability to detect communication anomalies based on emerging trends without prior knowledge of the communication delays in the specific area. Consequently, it offers the advantage of applicability in dynamic environments, with or without prior local knowledge, making it valuable for remote work scenarios.
# An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning
## 3 Materials and Methods
### Machine Learning Models
#### 3.2.3 Support Vector Regression (SVR)  
The main objective in most linear regression models is to minimize the sum of squared errors. However, if we are interested in error reduction to some acceptable range, we do not care about the error value. In such cases, the SVR helps us in defining the errors' acceptable range and finding a fitting line to the inputted data. SVR is an ideal choice for linear and non-linear regression curve fittings and forecasting. SVR is established on the various components of the support vector machine (SVM). Support vectors are marks in an n-dimensional feature space. They precisely disconnect the marks around the hyperplane that is nearer to the generated hyperplane. The resulting equation is  
\[y=wX+b \tag{4}\]  
Here, w represents weight, b represents the intercept at \(X=0\), and epsilon represents the tolerance margin.
# Glueless Multiple Input Multiple Output Dielectric Resonator Antenna with Improved Isolation
## 1 Introduction
MIMO performance parameters, such as total active reflection coefficient (TARC) and envelope correction coefficient (\(\rho\)), are calculated and measured, and they are found within an acceptable range for MIMO operation. The chosen frequency band is used in high-speed digital radio links in satellite communication and radio communication. The X-band has several applications for example telemetry, satellite tracking, military communication, etc.
## 1 Introduction
These reflectance ratios have been shown to track the changes in fluorescence, photochemical efficiency and carbon assimilation rates for healthy and stressed plants due to high co-variation of red-edge reflectance/absorption, pigment concentration, and leaf physiology [31; 47; 48; 49; 50; 51; 52; 53]. Numerous studies had shown PRI is sensitive to water stress [33; 49; 54; 55; 56], but no studies have illustrated that PRI was sensitive enough to track recovery after drought stress, at least to the best of our knowledge. Few studies so far have either exploited the applicability of the reflectance ratios based on the red-edge region of the spectrum for detecting the photosynthetic response to water stress [31; 48; 49]. Only the study by [PERSON] et al. [31] monitored photosynthetic response to water stress and recovery with only a four-day-long experiment. Along with the increasing occurrence of drought, it is important to find an easy, non-destructive and efficient method to monitor both the effect of drought on plant physiological and functional variability and the subsequent recovery of the photosynthetic machinery. This is particularly important for regions confronted with progressively more extreme summer droughts such as the Mediterranean basin. Based on the previous studies, PRI and reflectance ratio based on the red-edge region of the spectrum (R690/R630) are thus promising methods to test such photosynthetic responses. In this study, we conducted a two-month long greenhouse experiment using the typical Mediterranean species \(Q\). _ilex_ by exposing the plants to nine different progressive drought treatments through controlling the SWC, and then re-watering for five weeks to explore the levels of plant recovery. We simultaneously measured foliar reflectance to obtain PRI and R690/R630, PAM ChIP to get maximum (\(\rm F_{V}/F_{M}\)) and actual (Yield) photochemical efficiency of photosystem II (\(\rm\Phi\)PSII), and \(\rm CO_{2}\) assimilation rates (\(A\)). We hypothesized that both PRI and R690/R630 would be sensitive to the progressively increasing drought, and could therefore be used to monitor photosynthetic dynamics. We also hypothesized that both PRI and R690/R630 would detect the photosynthetic recovery after experiencing different levels of drought stresses.
## 6 Discussion
### The Overall Performance of the Methods  
The overall results obtained in Table 4 can be evaluated based on the properties of the selected benchmark functions. GWO outperforms other algorithms by successfully obtaining the global optimum in 13 out of 15 cases, followed by ABC which outperforms others in 12 out of 15 cases. The third and the fourth best algorithms are FFA and CSA with 9 and 6 respectively out of 15 successful convergence functions, while both BA and FPA solve only 4 of the 15 problems. In terms of accuracy, results suggest that GWO is the best performing approach in all three cases. However, in terms of the required computation time, ABC is superior to the others with an average computation time of 0.2369 s, followed by GWO with an average of 0.3136 s. Despite its simplicity, FFA is limited by its tendency to get trapped at local minima, mainly because of its relatively weak global search capability with complex and multimodal problems. That being said, FFA provides excellent solutions in most optimization problems in this work. In addition, BA shows a poor performance in this study, mainly due to some insufficiencies at exploration and the required high number of function evaluations. Yang [93] BA has been effectively used to handle many challenging GO issues such as multimodal objective problems. Moreover, FPA, known to be a highly efficient method with low-dimensional problems, becomes problematic with high-dimensional problems. Finally, the CS algorithm shows a good performance on number of benchmark tested function. However, the algorithm becomes inefficient when objective function evaluation represents a significant computational cost such as CFD problems. In conclusion, although this study shows GWO and ABC have the highest convergence rates with acceptable computational costs, there is, as yet, no universal algorithm capable of obtaining the global solution for all kind of problems. More research is needed to be carried out to solve computationally-expensive black-box problems. Table 4 presents the overall performance of the tested algorithms under different levels of complexity and dimensionality in order to examine their efficiency and robustness.
## 2 Materials and Methods
### Monitoring of Thermal Environment Inside Enclosed Vertical-Type Compositing Facilities  
This experiment was conducted over 8 days in a separate space measuring approximately 60 m\({}^{2}\) (5 m \(\times\) 12 m) under constant temperature conditions of 25 \({}^{\circ}\)C. The experimental reactor designed to monitor the internal thermal environment of the enclosed vertical-type compositing facility was fabricated at a scale of 1/100 of the actual size, with a height of 1000 mm and a diameter of 400 mm. The reactor's internal shaft was designed with 7 attached stirring blades, with the rotation rate set at 1 rotation per hour, and the air supply rate was set at 150 L m\({}^{-3}\cdot\)min\({}^{-1}\). Pig manure separated into solids and liquids using a decanter was used as the substrate, and the 125 L reactor was filled nearly to capacity with this manure.  
Temperature sensors were installed to precisely measure the temperature changes that occurred during the compositing process based on height and position. K-type thermocouples were used for temperature measurement, and the recorded temperature data were logged using a data logger (GL840, Graphtec, Tokyo, Japan). The sensors were divided into 4 layers: the bottom (B) layer located 100 mm above the reactor's base, the middle bottom (MB) layer at 350 mm, the middle top (MT) layer at 600 mm, and the top (T) layer at 900 mm. To accurately measure temperature changes at different depths, a total of 5 points from the center to the outer edge of the reactor were selected for the temperature measurement. Each layer had 5 temperature measurement points, making a total of 20 points in the entire reactor where temperature measurements were taken (Figure 2).  
To analyze the spatial distribution of temperature and compare it with the validation data of the temperature model, a contour map of temperature distribution was created using Surfer 22.0 software. The temperature values used in this process were based on the average temperatures measured from the four reactors. Table 1 presents the results of the analysis of the characteristics of the sewage used in the experiment, including pH, EC (electrical conductivity), elemental analysis (C, N, S, H), total solids (TS), and volatile solids (VS).  
Figure 1: Enclosed vertical-type compositing facility for monitoring the thermal environment: (**a**) field-scale; (**b**) pilot-scale reactors for experiment.
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 3 Damping Extraction Methods
### Frequency-Domain Damping Extraction Methods
#### 3.1.1 Peak-Picking  
From Equation (2), the frequency response of an MDOF vibrating system represented in [35] can be expressed as  
\[H(\omega)=\sum_{j=1}^{N}\frac{1/m_{j}}{-\omega^{2}+i2\zeta_{j}\omega_{n_{j}} \omega+\omega_{n_{j}}^{2}}. \tag{6}\]  
As shown in Equation (6), the frequency response of an MDOF system, which can be found by the Fourier transform of the impact response, can be expressed as the summation of \(j\) SDOF systems, where each has a peak at the damped frequency \(\omega=\omega_{d}=\sqrt{1-\zeta^{2}}\omega_{n}\). The peak-picking (PP) method, represented in [36], presumes that each significant peak illustrates one mode, and the system behaves similarly to SDOF systems close to these peaks. Therefore, given the first significant peak at \(\omega=\omega_{p_{1}}\), one is able to fit a curve on \(k\) samples on either side of the peak by using Linear Least Square (LLS) on the following equation:  
\[\begin{bmatrix}H(\omega_{p_{1}-k})&i2\omega_{p_{1}-k}H(\omega_{p_{1}-k})&-1\\ \vdots&\vdots&\vdots\\ H(\omega_{p_{1}})&i2\omega_{p_{1}}H(\omega_{p_{1}})&-1\\ \vdots&\vdots&\vdots\\ H(\omega_{p_{1}+k})&i2\omega_{p_{1}+k}H(\omega_{p_{1}+k})&-1\\ \end{bmatrix}\begin{bmatrix}\omega_{n_{1}}^{2}\\ \zeta_{1}\omega_{n_{1}}\\ 1/m_{1}\\ \end{bmatrix}=\begin{bmatrix}\omega_{p_{1}-k}^{2}H(\omega_{p_{1}-k})\\ \vdots\\ \omega_{p_{1}}^{2}H(\omega_{p_{1}})\\ \vdots\\ \omega_{p_{1}+k}^{2}H(\omega_{p_{1}+k})\\ \end{bmatrix}. \tag{7}\]
# Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis
## 4 Experiments
### Discussion of AP-GCN  
Due to the two modules of learnable neighbor selection mechanisms, and vertex- and edge-based convolution operation, the proposed AP-GCN outperforms the classical approaches. Compared with classical neighbor selection methods such as farthest point sampling or random sampling according to a certain radius, the proposed neighbor selection method can learn much more information. In detail, the proposed method fully considers the relationship between each point and can construct a graph structure that can  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Input** & \multicolumn{2}{c}{**Class mIoU**} & \multicolumn{2}{c}{**Instance mIoU**} \\ \hline PointNet & 2k & & 80.4 & & 83.7 & \\ DGCNN & 2k & & 82.3 & & 85.1 & \\ Kd-Net & 4k & & 77.4 & & 82.3 & \\ Ours & 2k & & **83.6** & & **85.4** & \\ SO-Net & 1k + norm & & 80.8 & & & 84.6 & \\ PointNet++ & 2k + norm & & 81.9 & & & 85.1 & \\ \hline  & **Input** & **Aero** & **Bag** & **Cap** & **Car** & **Chair** & **Guitar** & **Knife** \\ \hline PointNet & 2k & 83.4 & 78.7 & 82.5 & 74.9 & 89.6 & **91.5** & 85.9 \\ DGCNN & 2k & **84.2** & 83.7 & 84.4 & 77.1 & 90.9 & **91.5** & 87.3 \\ Kd-Net & 4k & 80.1 & 74.6 & 74.3 & 70.3 & 88.6 & 90.2 & 87.2 \\ Ours & 2k & 83.4 & **84.7** & **87.8** & **79.3** & **91.0** & 91.0 & **88.1** \\ SO-Net & 1k + norm & 81.9 & 83.5 & 84.8 & 78.1 & 90.8 & 90.1 & 83.6 \\ PointNet++ & 2k + norm & 83.5 & 81.0 & 87.2 & 77.5 & 90.7 & 91.1 & 87.3 \\ \hline  & **Input** & **Lamp** & **Laptop** & **Motor** & **Mug** & **Pistol** & **Rocket** & **Table** \\ \hline PointNet & 2k & 80.8 & 95.3 & 65.2 & 93.0 & 81.2 & 57.9 & 80.6 \\ DGCNN & 2k & 82.9 & **96.0** & 67.8 & 93.3 & 82.6 & 59.7 & 82.0 \\ Kd-Net & 4k & 81.0 & 94.9 & 57.4 & 86.7 & 78.1 & 51.8 & 80.3 \\ Ours & 2k & **84.1** & 95.8 & **73.7** & 94.1 & **83.0** & **60.4** & **83.3** \\ SO-Net & 1k + norm & 82.3 & 95.2 & 69.3 & **94.2** & 80.0 & 51.6 & 82.6 \\ PointNet++ & 2k + norm & 83.3 & 95.8 & 70.2 & 93.5 & 82.7 & 59.7 & 82.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Shape part segmentation IoU results (%) on the ShapeNet dataset. The best results are marked in bold.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Neighborhood Selection Method**} & \multicolumn{1}{c}{**OA (\%)**} \\ \hline k-NN & 91.7 \\ Ball query & 92.0 \\ Ours & 92.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification results on ModelNet40 with different neighbor selection methods.  
reflect the actual relationship more accurately based on affinity measurements. On the other hand, the proposed convolution operation based on graph vertices and edges captures much more structure information. As an important part of the graph, the edge in a graph contains important structure information between the vertexes in the graph. The full use of edges and vertices can better contribute to multi-scale structural information extraction.
# Pyrophyllite: An Economic Mineral for Different Industrial Applications
## 3 Pyrophyllite's Industrial Applications
### Pyrophyllite as Filler
#### 3.3.1 Paper Industry  
Papermakers seek to reduce costs and increase product quality through the use of fillers. The benefits of using fillers vary according to the product to be made. The most important characteristics of the final paper product are smoothness, brightness, opacity, print quality, dimensional stability, and low total cost. There are different types of fillers used in the manufacture of paper and can be either natural or synthetic. Natural fillers include ground limestone (ground chalk), kaolin, talc, and pyrophyllite, while synthetic fillers include precipitated calcium carbonate (PCC), precipitated aluminum silicate, titanium dioxide, and gypsum. Finely ground pyrophyllite is used as a filler in the paper industry because it is cheap and has excellent properties that improve the product quality, such as chemical inertness, softness, high reflectance, a strong particle shape, and hydrophobicity [3; 72; 73]. For this grade, the pyrophyllite should have high alumina content, low silica content, and a high degree of whiteness (Table 5).