esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Radar Remote Sensing: Sentinel-1 Polarimetry  
Dual-polarisation SAR radar imagery can be used to distinguish between different types of reflection. A flat surface (as, e.g., a water body with no significant waves) causes a specular reflection, which means that the side-looking radar system hardly receives any return power at all. On a rough surface the reflection is dominated by Bragg-like scattering [43] and the incident horizontal polarisation is mainly reflected back as a horizontal polarised wave (HH). In contrast, volume scattering (e.g., in the firn pack) causes many random reflections within the volume and a part of the incident horizontally polarised wave is reflected in vertical polarisation (HV). The change in polarisation can therefore be used to distinguish between the scattering mechanism, that can subsequently be used for inferring material properties. For this study we are only using Sentinel-1 scenes in HH and HV polarisation for two different time periods, one representing frozen conditions and one representing summer, melt conditions.  
The Single Look Complex (SLC) S1 scenes (IW mode) are processed using GAMMA Software [44] with the following steps: (1) calibration for instrumental noise, (2) mosaicing, (3) multilooking for speckle reduction and (4) georeferencing the scenes to EPSG:3413. The resulting products has a ground pixel resolution of 50 \(\times\) 50 m. Topographic effects in the radar backscatter are corrected with a line-of-sight area normalization that transfers \(\sigma^{0}\) to \(\gamma^{0}\) using the ArcticDEM [45]. The details of polarimetric processing is presented in [5].  
We selected two periods for S1 acquisition, one close to the acquisition of our airborne data in April 2018 and one during the melt season.
###### Abstract  
Aim: The primary aim of this randomized clinical trial was to evaluate the percentage of attachments' debonding at the template's removal both in Invisalign(r) and Sparktm systems. The secondary aim was to define the percentage of patients who did not show attachments' debonding at the template's removal. Materials and methods: Eighty patients who needed an orthodontic treatment were included in the study and randomly assigned to a treatment to be performed with Sparktm or Invisalign(r) clear aligners system. The patients were equally divided into two groups: Spark group (\(n=40\)) and Invisalign group (\(n=40\)). At the template removal by the teeth surface after the attachment bonding procedure, in each patient of both groups, it was assessed if some attachment debonding occurred and the number of attachments detached. Results: The Spark group showed, in general, a lower frequency in debonding in comparison with the Invisalign group, as 87.5% of patients in the Spark group did not show any bonding failure versus 27.5% in those of the Invisalign group. Conclusions: At template removal, the Sparktm template showed less attachment debonding compared to the Invisalign(r) template. The Sparktm template can be considered more effective in attachments' transferring to the tooth surface than the Invisalign(r) one.  
clear aligners; clear aligner's therapy; attachments; Sparktm; Invisalign(r)
# Role of Protein Glycoslation in Host-Pathogen Interaction
## 5 Proteins of the Host Are Glycoslated by Pathogens to Enhance Virulence  
Pathogens are capable of glycoslating self-proteins and host proteins to enhance their virulence. Toxin A (TcdA) and toxin B (TcdB) are two predominant virulence factors of the bacillus _Clostridium difficile_, they can glycoslate the host Rho GTPases which otherwise cannot be glycosylated [107]. Both TcdA and TcdB contain a glucosyltransferase domain in N-terminal, conferring cytopathic and cytotoxic effects in intoxicated host cells [108; 109]. TcdA/B enters into host cells through endocytosis [27], selectively modifies the host Rho GTPases by mono-\(O\)-glucosylation, and subsequently cause inhibitoryeffects [109; 110]. This interaction is remarkable since Rho GTPase is the key member involved in various biological processes and signaling pathways [111]. The inactivation of Rho GTPase would block signal transmissions and regulatory functions, and resulting in cell morphological changes [112], apoptosis [113], phagocytosis dysregulation [114], pseudomembranous colitis and antibiotic-associated diarrhea in hosts [107].
## 3 Literature Review
### Understanding of Digital Capability  
#### 3.3.1 Definition of Digital Capability  
In recent years, digital transformation research has brought digital capability to the forefront of academic and industry discussions. Scholars have expanded the understanding of digital capability by examining how organizations perform in digital practices. However, its definition remains inconsistent, with research primarily focusing on three perspectives: resource value realization, digital technology application, and dynamic adaptability [49].  
From the resource perspective, digital capability involves identifying and utilizing complementary resources effectively, though it overlooks drivers like organizational structures and processes [50]. From the technology perspective, digital capability highlights the skills and innovation needed to develop and manage scalable, flexible digital technologies, enabling value creation and collaborative innovation [51]. From the adaptability perspective, digital capability allows organizations to integrate and reconfigure resources to address uncertainty and seize opportunities in volatile environments, ensuring sustainable competitive advantages [52].  
This study defines digital capability as the ability to use digital technologies and tools to collect, process, and analyze data in order to support decision making, optimize processes, and drive innovation.
# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Stacking Ensemble Model Construction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Type** & **Class Name** & **Precision** & **Recall** & **F1 Score** & **Specificity** & **AUC** & **Support** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Proposed Model \\ (Type 1, Group 5) \\ \end{tabular} } & Low Severity & 0.9913 & 0.9955 & 0.9933 & 0.9517 & 0.9977 & 1594 (94\%) \\ \cline{2-8}  & High Severity & 0.9888 & 0.9861 & 0.9875 & 0.9369 & 0.9977 & 102 (6\%) \\ \cline{2-8}  & Average/total support & **0.9901** & **0.9908** & **0.9904** & **0.9443** & **0.9977** & 1696 (100\%) \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} Proposed Model \\ (Type 2, Group 5) \\ \end{tabular} } & Week & 0.9785 & 0.9873 & 0.9829 & 0.7456 & 0.9583 & 1438 (84.8\%) \\ \cline{2-8}  & Severe & 0.8889 & 0.8498 & 0.8690 & 0.9823 & 0.7941 & 156 (9.2\%) \\ \cline{2-8}  & Critical & 0.9317 & 0.9551 & 0.9434 & 0.7527 & 0.9839 & 102 (6.0\%) \\ \cline{2-8}  & Average/total support & **0.9330** & **0.9307** & **0.9318** & **0.8269** & **0.9121** & 1696 (100\%) \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Proposed Model \\ (Type 3, Group 5) \\ \end{tabular} } & Asymptomatic & 0.9723 & 0.9872 & 0.9804 & 0.7612 & 0.9044 & 1227 (72.3\%) \\ \cline{2-8}  & Mild & 0.7951 & 0.8030 & 0.7993 & 0.5724 & 0.7114 & 101 (6.0\%) \\ \cline{2-8}  & Moderate & 0.9223 & 0.9696 & 0.9473 & 0.7923 & 0.9046 & 156 (9.2\%) \\ \cline{2-8}  & Severe & 0.7144 & 0.8338 & 0.7598 & 0.9629 & 0.9169 & 27 (1.6\%) \\ \cline{2-8}  & Fatal & 0.8781 & 0.8824 & 0.8902 & 0.7823 & 0.9797 & 75 (4.4\%) \\ \cline{2-8}  & Average/total support & **0.8564** & **0.8952** & **0.8754** & **0.7742** & **0.8834** & 1696 (100\%) \\ \hline \end{tabular}
\end{table} 
Table 12: Performance results of clinical severity for Types 1, 2, and 3 using the proposed models. Overall, the oversampling technique contributed to the improvement in the F1 and AUC scores for all classifiers. For Type 1, the proposed model showed an increase in the F1 score of 3.08% when applying SMOTE and of 5.89% when applying ADASYN. Additionally,
## 4 Genetic and Ontogenetic Factors
### Intrspecific Variability  
Intraspecific variability exerts a considerable influence on the composition of thyme essential oil. A study on 103 Korean populations of _Thymus quinucostatus_ revealed different chemotypes characterized by dominant compounds in the essential oil, including thymol, geraniol, geranyl acetate, and linalool. The chemical polymorphism observed among thyme populations was associated with the genetic diversity of individuals [85]. Substantial variability was observed in the 'Standard Winter' cultivar of _Thymus vulgaris_, showing diverse chemical compositions of the essential oil and in phenolic acids (caffeic acid, rosmarinic acid and p-Coumaric acid) and flavonoids contents [86].  
[PERSON] et al. [87] investigated the chemodiversity of 85 populations of _Thymus vulgaris_ in Spain, ultimately revealing five distinct profiles based on the essential oil composition as shown presented in Figure 3: camphane skeleton, oxygenated sesquiterpene fraction, 1,8-cineole-rich with camphor and borneol, camphor and terpinen-4-ol, and linalool chemotype. A similar study was conducted on 21 populations of _Thymus zygis_ L., leading to the distinction of seven main chemotypes [88]. High chemical variability in essential oil content was observed among seven populations of _Thymus caramanicus_ in Iran, classifying the populations into three chemotypes (carvacrol, carvacrol/thymol, thymol/carvacrol) [89]. This variation can be an indicator of the importance of genetic diversity in the chemodiversity of thyme. In fact, the variability in essential oil composition can pose challenges in standardizing raw materials, but it also provides opportunities for breeding and the development of valuable components.  
Figure 3: Average percentage (%) of the main components in the essential oil of _Thymus vulgaris_ among the defined groups from the Cluster Analysis. Different letters within the same component indicate a significant difference between the group clusters, as determined by the [PERSON]’s HSD test (\(p<0.05\)) (After [PERSON] et al., 2020) [87].
# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 1 Introduction
It is elaborated that the deep learning model, with a suitable set of parameters, can be implemented on a well-equipped remote server, thus facilitating rich learning results by use of sophisticated algorithms trained on large, growing and diversifying performance data. * Keeping the resource-constrained user-devices free from the computational load by making the prediction data available to the user premises in the form of _metric:value_, and _parameter:values_ pairs. This data can be directly used for choosing values of communication parameters, meeting the constraint for metrics under consideration. * Desirably accurate estimation of performance metrics without having to deal with the optimization problems which are intrinsically NP-hard. * A flexible and evolving system that can adapt to the circumstantial and even design changes that may occur over time. This adaptiveness can facilitate a sustainable system in contrast to the most client-side approaches where learning is based on the missing value prediction in the output matrix rather than the input communication parameters. The rest of this paper is organized as follows: a literature survey is carried out in Section 2. Description of data and deep learning models used are discussed in Section 3. Prediction results are presented in Section 4. Section 5 concludes the paper.
## 1 Introduction
Bulk-fill composites represent a significant advancement in restorative dentistry, enabling the placement of increments up to 4 mm in thickness without compromising the degree of conversion (DC) [12; 13; 14; 15; 16; 17; 18], polymerization shrinkage, or marginal adaptation [11]. This improvement is possible due to the incorporation of larger fillers [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] and a reduction in color pigments [23], which enhances the depth of cure by increasing translucency. However, earlier versions of bulk-fill composites exhibited a grayish shade due to their higher translucency. Newer materials have addressed this issue by incorporating more efficient germanium-based photoinitiators [24] or utilizing different polymerization mechanisms, such as reversible addition-fragmentation chain transfer polymerization, which is less dependent on translucency [25; 26; 27]. Furthermore, there are studies demonstrating that bulk-fill composites can achieve a bottom-to-top hardness ratio exceeding 80% when cured at depths up to 4 mm using LED LCUs, indicating sufficient DoC [19; 20; 21; 22; 23; 24; 28; 29; 30], but others do not support these results [10; 11; 31; 32; 33; 34]. Despite the proven advantages of polywave LED LCUs in curing conventional composites containing additional photoinitiators [12; 17], their efficacy for bulk-fill composites remains insufficiently studied. Given that the performance of bulk-fill composites is heavily influenced by the effectiveness of different LCUs, it is crucial to explore their impact on depth-dependent microhardness for optimizing clinical outcomes and enhancing the longevity of restorations. Thus, this study aimed to determine the depth of cure (DoC) of various bulk-fill composites by evaluating the bottom-to-top Vickers microhardness ratio using different LCUs, including monowave LED, polywave LED, and halogen LCUs. Additionally, this study compared the microhardness ratios of each bulk-fill composite at a depth of 4 mm across different LCUs to identify potential variations in curing efficacy. The null hypotheses tested were the following: (1) there are no significant differences in the Vickers Hardness Number (VHN) ratios for each bulk-fill composite tested with each LCU, and (2) all LCUs can effectively polymerize each bulk-fill composite up to a depth of 4 mm.
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 3 Results
### Regional Enhancement Effect  
After obtaining the precise light components, the algorithm presented in this paper performs distinct enhancements on both high- and low-light regions, as illustrated in Figure 8.  
The image on the left showcases the original state before enhancement, while the image on the right demonstrates the improvements after the algorithm's application. The grayscale histogram illustrates that the grayscale peak in the low-light area decreases and spans a broader range after enhancement by the algorithm in this paper. Meanwhile, the high-light area retains its original histogram distribution characteristics and accentuates the finer details within the image.
###### Abstract  
Since its publication by ISO in 1993, the GUM document has become the model to follow for the estimation of measurement uncertainty in the industry. However, the document itself recognizes its limitations in justifying the level of coverage. The calibration of volumetric meters for the custody transfer of liquid products can be affected by these limitations. The objective of this work is to calculate the calibration uncertainty of such meters by applying the Monte Carlo method of Supplement 1 of the GUM document (GUM-S1). In this way, the use of the classic GUM method is avoided. Therefore, the adaptive implementation of the Monte Carlo method (AMCM) has been applied to optimize the calibration uncertainty of a positive displacement meter against a standard tank in an ISO 17025 accredited volume laboratory. These meters are used for custody transfer in liquid hydrocarbon logistics where any reduction in the uncertainty estimation may have an important economic impact. Several innovations are proposed when applying AMCM; regulation of AMCM convergence by applying the Student's t-distribution, validation of repeatability and filtering of outliers by performing 50 iterations of the AMCM, and characterization of the measurand's Probability Density Function (_PDF_) which results in a Flatten-Gaussian. This work proves that the GUM method does not assign the correct _PDF_. The uncertainty estimation is reduced by 7.1% compared to the GUM method.  
Monte Carlo; custody transfer; flow meter; meter factor; uncertainty +
Footnote †: journal: Applied sciences
# A Communication Framework for Image Transmission through LPWAN Technology
###### Abstract  
Analyzing the conditions of use and selecting which technology is more efficient to apply is required when transmitting information through wireless networks.The Internet of Things (IoT) has gained traction in industry and academia as a paradigm in which information and communication technologies merge to deliver unique solutions by detecting, actuating, calculating, and sharing massive volumes of data via embedded systems. In this scenario, Low-Power Wide-Area Networks (LPWAN) appear to be an attractive solution for node connectivity. Typical IoT solutions demand flexible restrictions for wireless communication networks in terms of data rates and latency in exchange for having larger communication ranges and low energy consumption. Nonetheless, as the amount of data and data speeds demanded for particular applications increase, such as image transmissions, IoT network connectivity deteriorates. This paper proposes a communication architecture for image transmission across LPWAN networks utilizing LoRa modulation. The framework combines image processing techniques (classification, compressive sensing (CS), and reconstruction) with an investigation of LoRa modulation parameters using a Software-Defined Radio (SDR) environment. The results show that is possible to communicate an image of \(128\times 128\) pixels with four packets and one frequency channel in 2.51 s.  
Keywords:Internet of Things (IoT); low-power wide-area networks (LPWAN); LoRa; compressive sensing; software-defined radio (SDR); image transmission +
Footnote †: journal:
# Dynamic Response Analysis of Soil around Curve Section Tunnel under Train Vibration Load
## 5 Analysis of Computational Results
### Analysis of Vertical Deformation Response
#### 5.3.2 Vertical Deformation at Different Velocities  
The vertical deformation variations after a single load at different speeds while maintaining a curve radius of 350 m are shown in Figures 23 and 24. When the train vibration load acts on the tunnel, rapid vertical deformation occurs in the soil mass, followed by fluctuation around the peak vertical deformation and a subsequent rebound trend. After the rebound, the vertical deformation in all working conditions rises upwards and then stabilizes back to the initial level. From the perspective of fluctuation after reaching the maximum vertical deformation at each node, higher speeds result in smaller amplitude and shorter duration of fluctuations, and the deformation rebounds earlier.  
Figure 23: Vertical deformation of the tunnel base after single loading at different speeds.  
The peak vertical deformation decreases with an increase in the depth distance from the tunnel base, and higher speeds lead to larger vertical deformation variations in the soil at the tunnel bottom. Additionally, as the train speed increases, the peak vertical deformation in the soil at the tunnel bottom also increases. The maximum vertical deformation peak differs by 0.077 mm under different speeds. Beyond 80 km/h, not only does the vertical load on the track increase, but the ballast bed also needs to provide centripetal force to maintain the balance of the train body. Therefore, it experiences greater horizontal loads. The increased train load leads to increased soil displacement. Therefore, it is important to pay attention to the speed of the train during cornering to prevent excessive tunnel settlement caused by high speeds. It is recommended to maintain a speed of 60-80 km/h for subway trains when passing through tunnels with smaller curve radii.
## 2 Materials and Methods
### Response Surface Methodology  
The Response Surface Methodology (RSM) is a set of mathematical and statistical techniques designed for the modeling and analysis of problems where multiple variables influence a response variable, with the primary goal being to optimize this response [31; 32]. Minitab, a statistical software program, provides robust tools for the performance of the RSM. The first step in the RSM is to design experiments that yield sufficient data to model the response surface, using designs such as central composite designs (CCD) and Box-Behnken designs (BBD). Once the data from these experiments are collected, the next step is to fit a polynomial model, typically a second-order (quadratic) model, to the data. This model can be expressed as in Equation (1):  
\[y=\beta_{0}+\sum_{i=1}^{k}\beta_{i}x_{i}+\sum_{i=1}^{k}\beta_{ii}{x_{i}}^{2}+ \sum_{i<j}^{k}\beta_{ij}x_{i}x_{j}+\epsilon \tag{1}\]  
where \(y\) is the response variable, \(x_{i}\) and \(x_{i}\) are the input variables, \(\beta_{0}\) is the intercept, \(\beta_{i}\) is the linear coefficient, \(\beta_{ii}\) is the quadratic coefficient, \(\beta_{ij}\) is the interaction coefficient, and \(\epsilon\) is the error term. Minitab uses regression analysis to estimate the coefficients \(\beta\) of this polynomial model, which includes performing an analysis of variance (ANOVA) to determine the significance of the model and individual predictors, as well as a lack-of-fit test to check the model's fit to the data. After fitting the model, Minitab generates response surface plots and contour plots to visualize the relationships between the input factors and the response. These plots aid in understanding how changes in the input variables affect the response and help to identify optimal conditions. The final step is to find the optimal settings of the input variables that maximize or minimize the response. Minitab's optimization tool uses the fitted model to identify these settings, often by solving the optimization problem using Equation (2):  
\[Maximize\text{ or }Minimize:f(x)=\beta_{0}+\sum_{i=1}^{k}\beta_{i}x_{i}+\sum _{i=1}^{k}\beta_{ii}{x_{i}}^{2}+\sum_{i<j}^{k}\beta_{ij}x_{i}x_{j} \tag{2}\]
## 2 System Model  
We consider the downlink of an OFDM-based MU-MIMO system, where a BS equipped with \(N_{tx}\) antennas transmits to \(N_{u}\) users. Each user is assumed to have \(N_{rx}\) antennas each, wherein that transmission is composed by grouping the subcarriers into \(N_{f}\)-sized subblocks, with only \(N_{gf}\) of them active at any given moment, as seen in Figure 1.  
As an example, if we have a scenario with \(N_{f}\) = 8 and \(N_{gf}\) = 6, then only six out of each group of eight subcarriers are selected, and only the selected ones are activated to convey modulated symbols through their respective indices. This pattern of activation of subcarriers is also combined with the pattern of activation of the spatial domain which means that not all antennas will be transmitting in an active subcarrier. In fact, denoting  
Figure 1: Transmitter and receiver structure [25].  
as the total number of streams assigned to the antennas on each subcarrier, then only \(N_{a}\) of the position will contain modulated symbols which will correspond to active antennas.