esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### What Causes Diabetes in the Human Body?  
The common causes of diabetes are: insufficient production of the insulin (either absolutely or relative to the body's requirements), production of the defective insulin (which is uncommon in a human body), or the inability of cells to use insulin properly and efficiently leading to hyperglycemia. It can also occur due to the flaw in the insulin secretion or insulin action or both. There can be multiple causes for each diabetes type outlined earlier. The causes for each diabetes are summarized below:  
* T1 DM diabetes causes are not as well documented compared to T2 DM. Family history is a known risk factor for the T1 DM. Other risk factors can include having certain infections or diseases of the pancreas. T1 DM is primarily characterized as an autoimmune disease resulting in damage of the insulin-producing \(\beta\)-cells in the pancreas by T-cells (CD\({}^{4+}\) and CD\({}^{8+}\)), and macrophages penetrating the islets. Both genetic as well as environmental factors as yet unclear trigger autoimmune responses against \(\beta\)-cells and destroy them, thus significantly proliferating the disease in humans [4]. According to the latest studies, genetic factors are becoming more evident in causing T1 DM disease [5; 6; 7].
* T2 DM develops when the body becomes resistant to insulin or when the pancreas is unable to produce enough insulin. The main cause of this is as yet unknown, although genetics and environmental factors, such as being inactive, and overweight seem to be main causes for the T2 DM disease.
* GD can occur due to the significant hormonal changes during the pregnancy period, and blood sugar elevation in genetically predisposed individuals.  
The morbidity and mortality rates of diabetes mellitus have increased throughout the world in recent years. Diabetes has influenced 463 million people around the globe [8]. Furthermore, the World Health Organization (WHO) predicts that the number of patients can soar to 552 million approximately worldwide by 2030, which is approximately a 19.2% increase in the current patients [9].
## 2 Materials and Methods
### TEC Values
According to Equation (3), with \(d_{m}\) equal to this length of 2981 km along the meridian and Earth's radius \(R\) of 6371 km, this supposes an increment in latitude \(\Delta\varphi\) of 0.467894833 rad or 26.8\({}^{\circ}\) both north and south of the potential location of the epicenter. Taking into account the 2.5-degree spacing in the latitude of the TEC values in the GIM files, this represents some 10 cells north and south of the epicenter's latitude. Similarly, according to Equation (4), with \(d_{p}\) equal to this length of 2981 km along the 28\({}^{\circ}\) N parallel and Earth's radius \(R\) of 6371 km, this supposes an increment in longitude \(\Delta\lambda\) of0.529923675 rad or 30.4\({}^{\circ}\) both east and west of the potential location of the epicenter. Taking into account the 5-degree spacing in the latitude of the TEC values in the GIM files, this represents some 5 cells east and west of the epicenter's longitude. Please note that, for the purposes of better visualization, the cell grid size in Figure 2 has been enlarged so that it does not correspond to the set of cells of 2.5\({}^{\circ}\) size in the latitude and 5\({}^{\circ}\) size in the longitude, while the total extent of the affected area is represented fairly faithfully. In fact, the situation is a bit more complicated: as TEC variations in both spatial and temporal components need to be analyzed, the data structures will be 3D matrices, having in the different rows and columns the TEC values for the different latitudes and longitudes, respectively, and in the third component, the different values for different times (for example every 2 h) as taken from the GIM files. This idea is depicted in Figure 3, where, instead of using a 2D matrix, a 3D matrix with the geographical area of interest for different available times in the GIMs (from the most recent one back to several days before, as seen for the purpose of prediction).
## Case Study
### Determination of Numerical Simulation Parameters  
We use the proposed XGBoost-NSGA-II-based back analysis method to inversely analyze the mechanical parameters of surrounding rock according to the displacement monitoring data of D3, D4 and D6 of tunnel Section K3 + 060, and obtain the mechanical parameters in Table 2. In Table 2, \(\rho\) is the density of the rock mass; \(E_{0}\) is [PERSON]'s modulus of the rock mass; \(v\) is [PERSON]'s ratio of the rock mass; \(E_{1}\) is the elastic modulus of a Hooke body in a Kelvin body; \(\eta_{1}\) is the viscosity coefficient of a Newtonian body in a Kelvin body; \(c_{0}\) is the cohesion of surrounding rock; \(\varphi\) is the internal friction angle of the surrounding rock; \(\lambda\) is the coefficient of confinement pressure; \(\sigma_{\rm f}\) and \(\sigma_{\rm s}\) are, respectively, the long-term strength and instantaneous strength of a Bingham body; \(E_{1}^{\prime}\) is the equivalent concrete elastic modulus of the shotcrete supported by the grillage steel frame; \(A\) is a dimensionless parameter; \(T\) is the time for the surrounding rock to yield.  
Figure 6: Tunnel model.  
Then, the obtained mechanical parameters are substituted into the model for calculation. In addition, the relative errors (REs) between the calculated values and the measured values of displacements are 0.136-4.654%, 0.313-4.737%, and 0.25-3.959% for the monitoring points D3, D4, and D6, respectively. The comparison between the calculated values and the measured values of D3, D4, and D6 displacements is shown in Figure 7. Combining with Figure 7 and the REs of the displacements of each measuring point, we can note that the accuracy of the surrounding rock mechanical parameters obtained by the proposed back analysis method can meet the actual engineering requirements.
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 9 Efficient Signature Verification in Post-Quantum VerITAS
### Proof System for the Lattice Hash
#### 9.3.5 Permutation Check Using Polynomial Roots in zk-SNARKs  
The goal is to prove all elements of \(v\in F^{m}\) are in \([0,b-1]\). This is achieved by leveraging the properties of polynomials and their roots.  
The input we get the following polynomials \(u\in\mathbb{F}^{<b}[X],v\in\mathbb{F}^{<m}[X],z\in\mathbb{F}^{<b+m}[X]\) and commitments is \(\text{com}_{u},\text{com}_{v},\text{com}_{z}\).  
The key equality to verify is as follows:  
\[\prod\
olimits_{\alpha\in\Omega_{b+m}}(X-z(\alpha))=\prod\
olimits_{\beta\in \Omega_{b}}(X-u(\beta))\prod\
olimits_{\gamma\in\Omega_{m}}(X-v(\gamma)) \tag{18}\]  
This equality holds if and only if \(z\) is a permutation of \(u\|v\). It works as \(\prod\limits_{\alpha\in\Omega_{b+m}}(X-z(\alpha))\) is a polynomial whose roots are the evaluations of \(z\) over the set \(\Omega_{b+m}\).  
\(\prod\limits_{\beta\in\Omega_{b}}(X-u(\beta))\) and \(\prod\limits_{\gamma\in\Omega_{m}}(X-v(\gamma))\) are the polynomials whose roots are the evaluations of u over \(\Omega_{b}\) and v over \(\Omega_{m}\), respectively. If the two sides of the equation are equal, it means that the multiset of roots is exactly the same as the multiset of roots of u and v combined [23, 24]. This implies that \(z(\Omega_{b+m})\) is a permutation of \(u(\Omega_{b})\|v(\Omega_{m})\).
###### Abstract  
In the present research work, image segmentation methods were studied to find internal parameters that provide an efficient identification of the regions of interest in Magnetic Resonance (MR) images used for the therapy planning of High-Intensity Focused Ultrasound (HIFU), a minimally invasive therapeutic method used for selective ablation of tissue. The involved image segmentation methods were threshold, level set and watershed segmentation algorithm with markers (WSAM), and they were applied to transverse and sagittal MR images obtained from an experimental setup of a murine experiment. A parametric study, involving segmentation tests with different values for the internal parameters, was carried out. The F-measure results from the parametric study were analyzed by region using [PERSON]'s ANOVA followed by post hoc Games-Howell test to determine the most appropriate method for region identification. In transverse images, the threshold method had the best performance for the air region with a F-measure median of 0.9802 (0.9743-0.9847, interquartile range IQR 0.0104), the WSAM for the tissue, gel-pad, transducer and water region with a F-measure median of 0.9224 (0.8718-0.9468, IQR 0.075), 0.9553 (0.9496-0.9606, IQR 0.011), 0.9416 (0.9330-0.9540, IQR 0.021) and 0.9769 (0.9741-0.9803, IQR 0.0062), respectively. In sagittal images, threshold method had the best performance for the air region with a F-measure median of 0.9680 (0.9589-0.9735, IQR 0.0146), the WSAM for the tissue and gel-pad regions with a F-measure median of 0.9241 (0.8870-0.9426, IQR 0.0556) and 0.9553 (0.9472-0.9625, IQR 0.0153), respectively, and the Geodesic Active Contours (GAC) method for the transducer and water regions with a F-measure median of 0.9323 (0.9221-0.9402, IQR 0.0181) and 0.9681 (0.9627-0.9715, IQR 0.0088), respectively. The present research work integrates preliminary results to generate more efficient procedures of image segmentation for treatment planning of the MRgHIFU therapy. Future work will address the search of an automatic segmentation process, regardless of the experimental setup.  
F-measure; ground truth; image segmentation; MRgHIFU; non-parametric statistics 2019 9 5296; doi:10.3390/appr9245296
## 2 Results and Discussion  
### Comparison of 1 DGC and GC\(\times\)GC  
In typical 1 DGC analysis, it is often difficult to achieve pure mass spectra for compounds in a co-elution peak, thus leading to unreliable results. With improved separation power and enhanced sensitivity, the GC\(\times\)GC technique is able to resolve and detect more volatile aroma compounds in a complex sample compared to conventional one-dimensional GC-MS [30]. A clear illustration demonstrating the employment of GC\(\times\)GC is presented in Figure 1. Both the chromatogram obtained by GC\(\times\)GC-TOF/MS using a 4 s modulation period and the total ion chromatography by 1 DGC are shown. As can be seen in the partial chromatograms obtained by 1 DGC and GC\(\times\)GC-QTOFMS, linalool (Peak 1, \({}^{1}\)\({}_{\text{R}}\) = 19.783 min, \({}^{2}\)\({}_{\text{R}}\) = 1.405 s) and 2-nonen-1-ol (Peak 2, \({}^{1}\)\({}_{\text{R}}\) = 19.849 min, \({}^{2}\)\({}_{\text{R}}\) = 1.447 s) were responsible for the two peaks detected between retention times of 19.380 min and 19.850 min on the HP-5 MS column. However, three other minor compounds in addition to these two peaks were further separated as they exhibited different polarities on the DB-17 MS column; these were linalool oxide (Peak 3, \({}^{1}\)\({}_{\text{R}}\) = 19.383 min, \({}^{2}\)\({}_{\text{R}}\) = 1.467 s), \(p\)-cymenene (Peak 4, \({}^{1}\)\({}_{\text{R}}\) = 19.450 min, \({}^{2}\)\({}_{\text{R}}\) = 1.627 s),and benzoic acid, methyl ester (Peak 5, \({}^{1}\)\({}_{R}\) = 19.716 min, \({}^{2}\)\({}_{R}\) = 2.017 s). The co-eluted compounds in the peak region were interfered with by dominating compounds and would usually be ignored due to their low concentrations. In summary, GC\(\times\)GC successfully resolved a total of 129 compounds, while only 45 compounds were separated in 1 DGC (Table S1). The results revealed the great advantages of GC\(\times\)GC analysis, which is suitable for the investigation of volatile compounds in complex samples.
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 2 Mega-Constellation Design Model
### Ground Tracks  
The position of the sub-satellite point on the Earth's surface can be obtained by the coordinate system transformation. The coordinate transformation sequence is as follows: the period reference frame \(\rightarrow\) earth-centered inertial frame (ECI) \(\rightarrow\) earth-centered, earth-fixed frame (ECEF) [39]\(\rightarrow\) spherical coordinate system [40]. The specific solution of the satellite's ground trajectory is as follows.  
The satellite's mean anomaly at time t can be written as:  
\[M=t\cdot\sqrt{\frac{\mu}{a^{3}}} \tag{2}\]  
where \(\mu=3.986\times 10^{5}km^{3}/s^{2}\) is the Earth gravitational constant [41].  
According to the Kepler equation, the relationship between the eccentric anomaly \(E\) and the mean anomaly \(M\) is expressed as:  
\[M=E-esinE \tag{3}\]  
\(E\) can be obtained effectively by combining Equations (2) and (3), adopting the Newton iteration method. Then, we can calculate the true anomaly \(f\).  
\[f=2\text{arctan}\left(\tan\frac{E}{2}\cdot\sqrt{\frac{1+e}{1-e}}\;\right) \tag{4}\]  
Eventually, the satellite's position in the periodal frame is written as:  
\[\mathbf{r}=\frac{h^{2}}{\mu}\frac{1}{1+e\cos f}\left[\begin{array}{c}\cos f\\ \sin f\\ 0\end{array}\right] \tag{5}\]  
The classical Euler angle sequence \(\mathbf{M}_{3}[\omega]\cdot\mathbf{M}_{1}[i]\cdot\mathbf{M}_{3}[\Omega]\) can convert the satellite's position from the periodal frame to the ECI coordinate system. The transformation relationship is:  
\[\mathbf{r}_{ECI}=\left[\begin{array}{cc}\cos\omega\cdot\cos\Omega-\sin\omega \cdot\cos i\cdot\sin\Omega&\cos\omega\cdot\sin\Omega+\sin\omega\cdot\cos i \cdot\cos\Omega&\sin\omega\cdot\sin i\\ -\sin\omega\cdot\cos\Omega-\cos\omega\cdot\cos i\cdot\sin\Omega&-\sin\omega \cdot\sin\Omega+\cos\omega\cdot\cos i\cdot\cos\Omega&\cos\omega\cdot\sin i \\ \sin\mathrm{i}\cdot\sin\Omega&-\sin i\cdot\cos\Omega&\cos i\end{array}\right]^{- 1}\cdot\mathbf{r} \tag{6}\]  
The transformation from the ECI to the ECEF can be implemented by a rotation matrix \(\mathbf{M}_{3}[\theta]\cdot\mathbf{M}_{2}[0]\cdot\mathbf{M}_{1}[0]\), and \(\theta\) is given by \(\omega_{e}(t-t_{0})\). The transformation is written as:  
\[\mathbf{r}_{ECEF}=\left[\begin{array}{ccc}\cos\theta&\sin\theta&0\\ -\sin\theta&\cos\theta&0\\ 0&0&1\end{array}\right]\cdot\mathbf{r}_{ECI} \tag{7}\]The longitude and latitude \((\alpha,\delta)\) of the satellite's projection on the Earth's surface can be obtained by converting the ECEF coordinate system to the spherical coordinate.  
\[\delta=\arcsin\biggl{(}\frac{z_{ECEF}}{\|\mathbf{r}_{ECEF}\|}\biggr{)} \tag{8}\]  
\[\alpha=\left\{\begin{array}{cc}\arccos\Bigl{(}\frac{1}{\cos\delta}\frac{x_{ ICEF}}{\|\mathbf{r}_{ECEF}\|}\Bigr{)}&y_{ECEF}>0\\ -\arccos\Bigl{(}\frac{1}{\cos\delta}\frac{x_{ICEF}}{\|\mathbf{r}_{ECEF}\|}\Bigr{)}& y_{ECEF}\leq 0\end{array}\right. \tag{9}\]
## 2 Materials and Methods
### Analysis  
Statistical analysis included a data normality test (Kolmogorov-Smirnov test), a data uniformity test (Levene test), a one-way repeated measures analysis of variance (one-way ANOVA) across three PPE ensemble conditions, and an analysis of differences between the means of each treatment (Bonferroni post-hoc test). If normality and/or uniformity of the data could not be confirmed, a variance analysis using the Kruskal-Wallis test was used. A value of \(p\) = 0.05 was used as the limit of statistical significance.
## 4 Discussion  
The relationship between specific surface, pore type, and degradation rate was described by [PERSON] et al. [43]. According to their research, not the size of the nanoparticles but rather the correlation with the specific surface and porosity of the structure is crucial in the degradation process of silica structures. Therefore, on one hand, it can be assumed that for mSiO\({}_{2}\) after GO, which is characterized by pores, a larger volume will be more suitable for drug delivery systems. However, the size of the nanoflakes and structures formed during degradation should also be taken into account. From previous reports on the interaction of nanoparticles with cells, it can be concluded that 600 nm particles are worse endocytosed by cells than spherical nanoparticles (size distribution from 80 to 150 nm) [47]. This is also confirmed by other reports; e.g., [PERSON] et al. show that well-ordered mesoporous silica nanoparticles with a size of \(\approx\)110 nm have been high-efficiently internalized into mouse fibroblast cells [48]. In addition, the shape can be important in these processes. It has been proven that longitudinal nanoparticles (longer dimension \(\approx\)110-150 nm) penetrate cells better than longitudinal nanoparticles with smaller dimensions or spheres [49; 50]. Therefore, it can be assumed that mSiO\({}_{2}\) after GO can exhibit better potential use as drug delivery systems due to both a larger pore volume and smaller flake size. It can also be assumed that the newly formed structures during degradation will not penetrate the cells as easily as nanoflakes. However, the flakes should be subjected to further investigation.
## 3 Results

Table 3: _Cont._Figure 5: Rotation—Tesca and VM qualitative and quantitative stress display in PDL for 0.6 N (**A**), 1.2 N (**B**), and 2.4 N (**C**), and 0-, 4-, and 8-mm periodontal breakdowns. For tipping (Figure 6, Table 5), qualitatively, the main stress areas were in the cervical third for all simulations, with an extension of the red-orange stress areas after 4 mm of loss. In intact periodontitis, up to 1.2 N loads, no visible ischemic and resorptive risks were visible, while, for 2.4 N, stress exceeding three times the MHP was observed. Reduced periodontitis under a 0.6 N load displayed a moderate stress increase, found only in the cervical third (up to three times more than the MHP for 8 mm of loss). 1.2 N displayed an extremely small stress increase in the middle third but higher stress exceedances of MHP in the cervical third (3-5.5 times higher, after 4 mm of loss). The 2.4 N load displayed higher overruns of MHP in the middle and cervical thirds, and moderated exceedances apically. Thus, after 4 mm of loss, loads higher than 1.2 N should be carefully considered. For tipping (Figure 6, Table 5), qualitatively, the main stress areas were in the cervical third for all simulations, with an extension of the red-orange stress areas after 4 mm of loss. In intact periodontitis, up to 1.2 N loads, no visible ischemic and resorptive risks were visible, while, for 2.4 N, stress exceeding three times the MHP was observed. Reduced periodontitis under a 0.6 N load displayed a moderate stress increase, found only in the cervical third (up to three times more than the MHP for 8 mm of loss). 1. Figure 6: Tipping–Tresea and VM qualitative and quantitative stress display in PDL for 0.6 N (**A**), 1.2 N (**B**), and 2.4 N (**C**), and 0-, 4-, and 8-mm periodontal breakdown. For translation (Figure 7, Table 6), qualitatively, the main stress areas were in the PDL cervical third, with a visible increase in red-orange areas after 4 mm of bone loss. Quantitatively, in intact periodontitis, 0.6 N was safely applied, while the other two loads produced, on the cervical third, stress that exceeded the MHP (1.2 N--by two times, 2.4 N--by 4.3 times). In reduced periodontitis, a moderate progressive stress increase (after 4 mm loss) was seen in the cervical third for 0.6 N. 1.2 N displayed a higher stress exceedance of the MHP, limited to the cervical third (3-8.4 times) and a smaller one in the middle third (up to a stress doubling at 8 mm loss). 2.4 N displayed stress in the cervical third, which was 6-16.8 times higher than the MHP, as well as a moderate stress increase in the middle and apical (mild) thirds after 4 mm of loss. Thus, a load of more than 1.2 N could significantly increase the ischemic and resorptive risks for more than 4 mm bone loss levels. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multicolumn{2}{c}{**Resorption (mm)**} & & **0** & **1** & **2** & **3** & **4** & **
## 5 Simulation and Analysis
### INCM Beamforming Performance
#### 5.3.1 Beampattern  
The following Figures 5 and 6 present the simulated beampattern of various algorithms under an input SNR of 20 dB and an SIR of \(-3\) dB, respectively. The black dotted lines in the figures represent the actual DOA of the DME signals, and the pink dotted line represents the actual DOA of the OFDM signal. The arrows in Figure 6 indicate magnified views of specific areas. Figure 5 illustrates the beampattern of the proposed algorithm based on 50 Monte Carlo trials, and Figure 6 contrasts the beampattern between the proposed and other algorithms. These simulations demonstrate that the proposed algorithm can accurately align its main lobe with the OFDM signal. In contrast, the main lobes of the INCM-subspace and INCM-volume algorithms do not precisely align with the direction of the OFDM signal. Furthermore, the proposed algorithm forms deeper nulls in the directions of the two DME signals compared to the other two algorithms.  
Figure 4: RMSE versus SNR curve.  
Figure 5: Beampattern diagram.
# Microstructural Characterization of the Mn Lepidolite Distribution in Dark Red Clay Soils
## 1 Introduction
Therefore, the presence of manganese is expected to influence the color of the clay particles, determining the final shade of the soil. The experimental setup supposes a complex characterization of the mineral composition of the soil samples, achieved by combining X-ray diffraction (XRD) with mineralogical optical microscopy (MOM). XRD evidences the characteristic peaks for each mineral in the soil samples by their matching with powder diffraction files (PDF) [28]. Mineralogical optical microscopy examines the soil samples under cross-polarized light, with each mineral having a specific color generated by the birefringence behavior. This allows for the monitoring of the size and shape of each mineral particle [29; 30]. The microstructural aspects can be visualized by scanning electron microscopy (SEM) at higher magnification, keeping a proper resolution and detail sharpness and bringing a strong correlation with the constituent element's distribution through EDX spectroscopy [31; 32]. Clay soils often contain very small particles, including submicron and nanostructural fractions [33]. Atomic force microscopy (AFM) can visualize such nano-features through advanced topographical probing. We aim to employ all these investigation techniques on the collected soil samples to reveal their mineralogical composition and their distribution. Manganese lepidolite is believed to be the primary factor responsible for the unusual dark red color of the targeted soil. Potential industrial applications of the investigated soil samples might be identified through the current findings, establishing pathways for research continuation. A secondary objective is to assess the pollutant potential of these soils under conditions of advanced erosion. It is expected to generate significant amounts of particulate matter like PM1 and PM2.5. These are highly hazardous to human health and are known to cause respiratory diseases such as asthma, silicosis, and cancer [34; 35; 36]. On the other hand, quartz particles are almost omnipresent in forest soils, with particles fitting the PM10 range, which is less harmful than the finest particles [37; 38].
## 5 Result Analysis and Discussion
### Input Data Analysis  
As mentioned in the previous section, the advanced distribution network, combined with its interconnected microgrids, utilizes decentralized power sources like wind turbines and photovoltaic systems to produce electricity. Figures 10-12 further depict the patterns of solar radiation, ambient temperature, wind speed, and load consumption at different load factor intervals within the network. These visuals provide a comprehensive overview of the dynamic factors influencing power generation and consumption in the system.  
Figure 10: Variations in solar radiation corresponding to load fluctuations at each step.  
Figure 9: Schematic of IEEE 118-bus case study involving microgrids and dynamic switches.