esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Extracellular Vesicles Characterization  
EVs were purified from conditioned medium of HVEC cell line. Results for the characterization of EV samples are shown in Figure 2. Purity of EV samples with respect to exogenous single and aggregated protein contaminants and titration was determined by CONAN assay. The assay, based on a 3 nM dispersion of 15 nm gold nanoparticles (AuNPs), grades the purity of the preparation by color change. Change is visible by eye and can be quantified through the aggregation index (AI) which is inversely proportional to the preparation purity. AI is defined as the ratio between the localized surface plasmon resonance (LSPR) absorbance at 519 nm and 650 nm, and is determined by conventional UV-vis spectrometry. The determined AI for the used preparation resulted below 25% of the AI of the dispersed AuNPs (Figure 2A), corresponding to a contaminant content \(<\)10 ng/\(\rm\mu L\) (according to the calibration reported in Ref. [9]), that is a high purity preparation.  
Figure 2B shows the western blot analysis of the EV samples. The conventional EV markers Alix, HSP70, CD63, and annexin V were tested, and all of them were found to be expressed in the EV population and in the parent cells. GM130 (a Golgi-related protein) was used as a negative control, that is considered to be not expressed in EVs, and it confirms the absence of cell debris. Due to the intrinsic problem related to the quantification of protein content in EVs, the western blot must be interpreted in a qualitative way. The only purpose is to check the presence of typical EV markers in the EV preparations and ensure their presence in the parent cells.  
The morphological characterization of the EVs was carried out by means of AFM in dried ambient conditions. A representative image is displayed in Figure 2C. The nanometer flatness of the background confirms absence of exogenous contaminants [32]. Figure 2D reports the bar chart of EV size distribution, showing EVs with a size ranging from 30 nm to 160 nm, with a peak at the size of 65-75 nm.  
Figure 2E,F show the EV samples titration results, obtained conducting the experiments. POPC liposomes are used as synthetic, convenient mimic of EVs to create the calibration line used for EV titration. Figure 2E reports the UV-vis spectra of the POPC liposome standards used to create the calibration line reported in Figure 2F. Each standard is prepared by adding a fixed amount of AuNPs to liposome solutions at decreasing total titers. The redshift of the AuNP spectra is directly proportional to liposome molar concentration. Therefore, the lower is the liposome molar concentration, the greater is the redshift and the lower is the AI. The same applies to pure EV samples with unknown concentration. The red dot represents the calibration intercepts of the EV preparations used for the experiments, which resulted in a final molar concentration around 10 nM.
## 3 Results
### Laboratory-Scale Fermentation
Furthermore, under aerobic conditions, the inoculant produced acetic acid, ethyl ester associated with floral notes in cacao beans spontaneously fermented for 72 h.  
The inoculation under anaerobic conditions of _Limosilactobacillus fermentum_ yielded butanal, 3-methyl with a chocolate aroma and acetaldehyde with fruity notes in cacao beans spontaneously fermented for 48 h. In cacao beans spontaneously fermented for 72 h, the inoculant produced 1 butanol-3 methyl associated with a malty aroma under aerobic conditions, but generated ethanol 2-nitro with an undescribed aroma under anaerobic incubation. In the inoculation of _A. ghanensis_ under aerobic conditions, volatiles associated with floral, malty, green, waxy and sour aromas, including benzene ethanol, benzene acetaldehyde, pentadecane and acetic acid, were generated in cacao beans spontaneously fermented for 72 h and 96 h. In contrast, under anaerobic conditions, _A. ghanensis_ inoculation produced 1-butanol-3 methyl (malty aroma), benzene ethanol (floral aroma) and acetic acid, 2-phenylethyl ester (undescribed aroma) in the same cacao beans. The inoculation of _A. pasteurianus_ under aerobic conditions on cacao beans spontaneously fermented for 0 and 24 h was characterized by the production of compounds linked to floral aromas (acetophenone, benzene ethanol), chocolate notes (butanal, 3-methyl), fruity aromas (acetaldehyde), almond aromas (benzaldehyde) and sour notes (acetic acid). Nevertheless, the isolate only yielded benzaldehyde with an almond aroma under anaerobic conditions. At the end of the fermentation, _A. pasteurianus_ inoculation generated a variety of compounds with undescribed aromas in both aerobic and anaerobic incubation. _A. syzygii_, under aerobic as well as anaerobic conditions, only generated benzaldehyde, associated with almond notes in fine-flavor cacao beans spontaneously fermented for 48 h.  
The inoculation of _B. amyloliquefaciens_ under aerobic conditions yielded benzaldehyde with an almond aroma in cacao beans spontaneously fermented for 48 h. Similarly, the inoculation of the bacteria in cacao beans spontaneously fermented for 96 h yielded notable concentrations of ethylphenyl acetate (floral aroma), ethanol (alcoholic) and benzophenone (herbal aroma) under aerobic incubation. Similarly, inoculated cacao beans under anaerobic conditions also showed increased levels of ethylphenyl acetate and ethanol. Cacao beans spontaneously fermented for 96 h and inoculated with _B. subtilis_ under aerobic growth conditions showed increased levels of compounds with floral, almond and sour notes, such as benzaldehyde, benzeneacetic acid, ethyl ester and acetic acid. Moreover, the inoculant under aerobic incubation yielded 3-Penten-2-ol (green aroma) and caffeine (undescribed aroma).
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 5 Network Performance Comparison
### Routing Protocols  
This subsection provides an overview and comparison of two established routing protocols for achieving dynamic routing: OSPF and EIGRP. It also explains the rationale behind choosing OSPF for adoption.  
OSPF vs. EIGRP  
The OSPF protocol analyzes the speed, cost, and path congestion, while the Enhanced Interior Gateway Routing Protocol (EIGRP) combines the features of link state routing. The OSPF protocol uses [PERSON]'s shortest path algorithm and relies only on bandwidth to calculate the cost of a specific link, whereas the EIGRP uses the Diffusing Update Algorithm (DUAL), which incorporates bandwidth and delay in a composite metric calculation with a complex formula [47]. For our network models, we chose to use the OSPF protocol for the dynamic routing and neighbor relationship. This decision was based on the availability of open-source protocols that can be used across different platforms and facilitates result comparison.
# Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems
## 4 Finite Blocklength Analysis  
Going beyond the asymptotic setting discussed in Section 3, we complement our study by delving into a more practical scenario that closely relates to mMTC applications, where packets transmitted by users can be in the order of few hundreds bits. To capture this aspect we leverage tools of finite-length information theory, and focus in particular on the normal approximation [31; 32] to characterize the rates achievable by \(\mathscr{F}_{\text{a}}\) and \(\mathscr{F}_{\text{b}}\).
## 3 Numerical Implementation of Phase Field Fracture Model Based on Finite Elements
### Finite Element Discretization  
Within the finite element framework, the displacement field is first discretized. Under small-strain assumptions, the strain tensor is derived from displacement gradients  
\[\varepsilon=\frac{1}{2}(\
abla u+\
abla u^{T}) \tag{31}\]  
The continuum domain undergoes spatial discretization into finite elements, interconnected via nodes [33, 34]. The displacement field is approximated through shape function interpolation. For \(n\) nodes, the discretized displacement field is  
\[u=\sum_{l=1}^{n}N_{l}^{u}u_{l} \tag{32}\]  
In this formula, \(N_{l}^{u}\) denotes the displacement shape function for node \(l\); \(u_{l}\) represents the nodal displacement vector.  
For two-dimensional quadrilateral elements, the bilinear shape function matrix \(N^{u}\) is expressed as  
\[N_{I}^{u}=\begin{bmatrix}N_{1}\ 0\ N_{n}\ 0\\ 0\ N_{1}\ 0\ N_{n}\end{bmatrix} \tag{33}\]  
In this formula, \(N_{i}\) (\(i=1,\,2,\,\ldots\,n\)) are bilinear shape functions, and for a quadrilateral element, the expression in the local coordinate system (\(\zeta,\eta\)) are given as  
\[N_{i}=\frac{1}{4}(1+\zeta_{i}\zeta_{i})(1+\eta_{i}\eta) \tag{34}\]  
In this formula, \(\zeta_{i}\) and \(\eta_{i}\) are local coordinates of node \(i\).  
Substituting Equation (32) into Equation (31) yields the strain-displacement relation  
\[\varepsilon=\sum_{I=1}^{n}B_{I}^{u}u_{I} \tag{35}\]  
In this formula, \(B_{I}^{u}\) is the strain-displacement matrix, derived from the spatial derivatives of \(N_{I}^{u}\).  
Similarly, the phase field \(\phi\) is similarly discretized via nodal interpolation  
\[\phi=\sum_{I=1}^{n}N_{I}^{\phi}\phi_{I} \tag{36}\]  
In this formula, \(N_{I}^{f}\) is the phase field shape function of node \(I\), and \(f_{I}\) is the phase field value of node \(I\).  
In a two-dimensional problem, the phase field shape function matrix is given as  
\[N_{I}^{\phi}=[N_{1}N_{2}\ldots N_{n}] \tag{37}\]  
The phase field gradient is expressed as  
\[\
abla\phi=\sum_{I=1}^{n}B_{I}^{\phi}\phi_{I} \tag{38}\]  
In this formula, \(B_{I}^{\phi}\) is the derivative matrix of the phase field shape function with respect to the coordinate axes.
## 2 RF Communication Devices
### Satellites  
Satellites are becoming more and more important parts of telecommunications. Currently, the most important satellite communication tool is the Global Navigation Satellite System (GNSS), which provides very accurate information about an object's position in space. There are many GNSS providers, the most notable are GPS from the USA, Glonass from Russia, Galileo from Europe, and Beidou from China. Drones are using GNSS services for almost every activity that they do: calculating their position in space, controlling their guiding of motors, calculating their distance from the remote control and home point, and many other functions [38; 39]. The military use communication satellites to control drones remotely beyond their line of sight [40; 41], enabling real time first person capabilities. In the last few years, Internet satellite constellations have been designed and deployed, with the goal to provide a real global Internet connection.
## 2 Materials and Methods
### AFA Test  
The composition of NBG is presented in Table 1. F-NBG was prepared by mixing pastes A and B at a ratio of 1:1. P-NBG was prepared by mixing the BG-based powder and F-NBG at a ratio of 6:10. The NBG samples (\(n=4\) each) were inserted into a polytetrafluoroethylene (PTFE) mold (internal diameter 2 mm, length 5 mm; one side sealed with 1 mm of gutta percha); immersed in 5 mL of SBF or 1-, 5-, or 10-g/L BSA-SBF; then stored at 37 \({}^{\circ}\)C for 7 days, refreshing the media every 2 days. After aging the NBG samples, scanning electron microscopy (SEM) images, elemental composition information, and Raman spectra were obtained from the surfaces of the NBG samples. To obtain SEM images and elemental composition information, the samples were dried at room temperature and coated with a thin layer of gold using an ion sputtering device (IC-50; Shimadzu Corp., Kyoto, Japan). A representative area (100.7 \(\times\) 75.5 \(\upmu\)m) of each sample was then analyzed using an SEM equipped with wavelength-dispersive X-ray spectroscopy (EPMA1610; Shimadzu Corp.). The accelerating voltage was set at 15 kV. Raman spectra were obtained using a micro-Raman spectroscopic (NRS-3100; JASCO, Tokyo, Japan) together with a microscope set at 100\(\times\) magnification. A laser beam with an excitation wavelength of 532 nm and laser power of 7.4 mW was used. The charge-coupled device (CCD) detector was cooled down to a temperature of \(-\)50 \({}^{\circ}\)C. The measurements were carried out at 3 randomly selected points for each sample. Raman spectra of the as-prepared NBG samples were also measured as negative controls.
## 2 Materials and Methods  
### Study Sample  
The study sample size was determined using G*Power (version 3.1; Heinrich-Heine University, Dusseldorf, Germany). Briefly, sample size calculation entailed two independent sample \(t\)-tests, assuming a two-tailed distribution, an effect size of 0.5 (indicating a medium effect), a significance level of 0.05, and a power of 0.8. The calculation indicated that a minimum of 63 patients would be necessary for a comparative analysis between the ASO and NASO groups. This retrospective study was conducted between 1 September 2016 and 1 September 2021. Among 562 patients diagnosed with skeletal Class II malocclusion, 67 (ASO: 31, NASO: 36) aged 19-40 years, who visited the Department of Orthodontics at Ajou University Dental Hospital, were selected based on the study's inclusion and exclusion criteria. Pre- and post-treatment lateral cephalometric radiographs were obtained and analyzed. Notably, patients with an ANB greater than 7.33\({}^{\circ}\) were excluded to avoid including cases that would require orthognathic surgery or more complex interventions, as suggested in a recent study by [PERSON] et al. [9], where patients with ANB \(\geq\) 7.33\({}^{\circ}\) showed unsuccessful skeletal normalization after ASO. The decision to undergo anterior segmental osteotomy (ASO) or non-surgical orthodontic camouflage treatment (NASO) was made based on thorough consultation. Patients were provided with comprehensive explanations regarding the risks, benefits, and costs of both treatment options. The final group allocation was determined according to the patients' informed preferences after understanding all relevant information.  
The inclusion criteria were as follows:  
A diagnosis of skeletal class II malocclusion (3\({}^{\circ}\) < ANB < 7.33\({}^{\circ}\));  
Adults aged \(\geq\) 18 years;  
Patients who underwent orthodontic treatment;  
Those who underwent extraction of the premolars;  
Those seeking treatment for lip protrusion.  
The exclusion criteria encompassed:  
Patients with congenital dental anomalies;  
Those with congenital conditions such as craniosynostosis and cleft palate;  
Those with systemic diseases affecting bone metabolism;  
Those with severe facial asymmetry (chin displacement < 4 mm relative to the facial midline);  
Those who discontinued orthodontic treatment;  
Cases in which posterior TADs were used for further anterior teeth and lip retraction after ASO.  
This study received approval from and adhered to the guidelines of the Institutional Review Board of Ajou University. The IRB Committee waived the requirement for obtaining patient consent from both institutions. The demographic data are described in Table 1.
## 5 Description of the Laboratory Test and the Numerical Simulations
### Laboratory Tests  
The two dapped-end beams were tested according to the static scheme shown in Figure 10a,b. The bearing capacity of the damaged and reinforced end was intended to be similar to the sound one. Therefore, failure was expected on one of the two sides, more probably on the damaged one if some resisting mechanism of the jacketing system was overestimated.  
When one of the two ends collapses, if the other side is not broken, the static scheme can be changed to scheme 2, as shown in Figure 10c,d to load until collapse the other end.  
The test was performed in displacement control, imposing a displacement rate of one millimeter per minute.  
Tests of the Materials Used to Build the Beams  
Experimental tests were carried out on the materials used for the construction of the two dapped-end beams. The following specimens were tested:  
* 6 concrete cubes (150 mm side) were taken from the batch used for the casting of the flange of the two dapped-end beams (1 st casting phase).
* 6 concrete cubes (150 mm side) were taken from the batch used for the casting of the web of the beams (2 nd casting phase).
* **Steel**: 3 reinforcement bars of 8 mm diameter and 3 bars of 10 mm diameter.  
Tests on concrete cubes provided an average cubic concrete strength of 29.0 MPa for flanges and 29.6 MPa for webs; being the flange completely tensed in the test, its slightly lower compressive strength is not a problem. The results of the tests on rebars are shown in Figure 11; highly ductile behavior is observed, with an average yield stress of 530 MPa and ultimate strain greater than 10%. These mean material characteristics are used in nonlinear numerical models.  
Figure 10: Static schemes of the dapped-end beams during the test. (**a**) Scheme 1. (**b**) Real configuration of Scheme 1. (**c**) Scheme 2. (**d**) Real configuration of Scheme 2.
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
## 3 The Proposed Method  
### Initial DOA Estimation  
Preliminary DOA estimates can be computed using the coarray-based SS-MUSIC algorithm. This approach addresses the inherent signal coherence artifacts induced by the covariance augmentation process in coarray domain transformation. That is,  
\[\mathbf{\bar{R}}_{y}=\frac{1}{U}\sum_{i=1}^{U}\mathbf{Z}_{i}^{T}\mathbf{\bar{ y}}\mathbf{\bar{y}}^{H}\mathbf{Z}_{i}, \tag{9}\]  
where \(U=(L+1)/2\) and \(\mathbf{Z}_{i}=[\mathbf{e}_{i},\mathbf{e}_{i+1},\cdots,\mathbf{e}_{i+U-1}]\). Here, \(\mathbf{e}_{i}\) denotes the \(i\)th column of the \(L\times L\) identity matrix. \(\mathbf{Z}_{i}^{T}\) denotes the transpose of the matrix \(\mathbf{Z}_{i}\).  
Initial DOA estimation is carried out via the MUSIC algorithm, where the covariance matrix \(\mathbf{\bar{R}}_{y}\) undergoes eigendecomposition to obtain  
\[\mathbf{\bar{R}}_{y}=\mathbf{U}_{s}\mathbf{\Lambda}_{s}\mathbf{U}_{s}^{H}+ \mathbf{U}_{n}\mathbf{\Lambda}_{n}\mathbf{U}_{n}^{H}, \tag{10}\]  
where \(\mathbf{\Lambda}_{s}=\mathrm{diag}[\lambda_{1},\lambda_{2},\cdots,\lambda_{K}]\) is a diagonal matrix containing the signal eigenvalues, with the corresponding eigenvalues forming the columns of \(\mathbf{U}_{s}\). Similarly, \(\mathbf{\Lambda}_{n}=\mathrm{diag}[\lambda_{K+1},\)\(\lambda_{K+2},\cdots,\lambda_{U}]\) is the diagonal noise eigenvalue matrix, and \(\mathbf{U}_{n}\) is the noise eigenvector matrix.Detection of the spectral peaks enables estimation of the \(K\) on-grid DOAs \(\mathbf{\hat{\theta}}=[\tilde{\theta}_{1},\cdots,\tilde{\theta}_{K}]\), that is,  
\[\mathbf{\hat{\theta}}=\underset{\theta_{n}\in\mathbf{\hat{\theta}}}{\arg\max} \frac{1}{\mathbf{a}(\theta_{n})^{H}\mathbf{U}_{n}\mathbf{U}_{n}^{H}\mathbf{a}( \theta_{n})}, \tag{11}\]  
where the angular set \(\mathbf{\hat{\theta}}=[\tilde{\theta}_{1},\tilde{\theta}_{2},\ldots,\tilde{ \theta}_{Q}]\) is constructed through uniform discretization of the potential DOA space \([-90^{\circ},90^{\circ}]\) with a grid interval. Here, \(Q\) denotes the grid number and \(Q>>K\).
# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 7 Experimentation Results
### What Is the Best PPFS Technique?  
It is necessary to show that the results of the PPFS approaches are equivalent. To do this, the non-parametric statistical test \"Friedman\" was employed with a significance level of 0.01. This test is conducted with the best classification accuracy obtained by SVM for all the datasets after performing PPFS. The test does not adopt a normal distribution assumption [48].  
It is worth knowing that the Friedman test [48] is different from the Friedman index used in PP [40]. The Friedman index is defined in Section 4, and Friedman test shows the existence of a significant statistical difference between the methods.  
The Friedman test provides two main results, the \(p\)-value and the effect size. If the obtained \(p\)-value is less than the significance level (0.01), then the statistical hypothesis (that the PPFS methods are equivalent) is rejected. The effect size has a value ranging  
Figure 6: Performance rate (represented in the y axis) of each PPFS method using each classifier for all the datasets.  
between 0 (corresponding to no relationship) and 1 (stating an ideal relationship). The Friedman test resulted in \(\chi^{2}(2)=6.68\) and \(p\_value=0.0204>0.01\), with an effect size \(W=0.172\). According to the \(p\_value\), the hypothesis is not rejected. Moreover, the effect size is small. Consequently, there is no statistical difference between the PPFS methods. The obtained PPFS results are equivalent.  
Figure 7 displays the boxplot of the accuracy obtained by SVM after using the PPFS methods for all the datasets. The y-axis represents the minimum, first quartile, median, third quartile, and maximum value of the accuracy values. As indicated, the four PPFS have a similar median (the line inside the box) equal to 83.3 for GAF, TF, and TK, 83.5 for GAK. As the \(p\_value=0.0204>0.01\), the dissimilarity between the median values are not statistically significant. This confirms that the PPFS techniques are equivalent and that their results are statistically significant. Furthermore, the mean values of the four PPFS do not show a significant difference. The lowest value was provided by GAF (82.0), whereas the highest mean was obtained by GAK (84.2). TF yielded 82.6 and TK 83.3.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 5 Applications and Prospects in Civil SHM
### Strategy for Selection of Piezoresistive Sensor in Civil SHM  
Aiming for the requirement of deformation monitoring and damage detection of civil structures, one of the primary targets of this review is to provide appropriate strategies for the selection and improvement of flexible piezoresistive strain sensors. Note that the strain level of civil structures is usually below 1% under normal loading and ambient situations, and large strain or cracks will appear because of long-term plastic deformation and extreme loadings. Therefore, the flexible piezoresistive sensor should have not only enough sensitivity and linearity to measure small strains (\(\varepsilon<\) 1%) but also a wide strain range and long-term durability to sense large strains and damages. In addition, the selection principle should be comprehensive by considering different aspects such as sensitivity, feasibility and detection performance [22]. To name a few, some strategies are concluded as follows:  
First, carbonaceous materials are preferable to be chosen as conductive fillers since they have better piezoresistive properties and lower production costs than metallic materials. Integration of different carbonaceous materials might be able to produce better performance. Second, the sensors proposed in references [138; 144] are promising examples for civil SHM because of their balanced performances of relatively high GF, large sensing range and good linearity. The outstanding compatibility of multiple performances is attributed to not only the integration of complementary conductive fillers but also the reinforcement of the substrate. Besides, their feasibility for scale-up fabrication and long-term durability is suggested for further investigation. After that, the sensing performances can be improved via the methods presented in Chapter 4, such as utilizing hybrid reinforced materials or micro-structures. By contrast, a new form of graphene material, namely, the pure GAF, has potential because of its easy fabrication and long-term stability. However, the sensitivity of the pure GAF in a small strain range (\(\varepsilon<\) 1%) should be further enhanced.