esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Nanoindentation Test Results of Asphalt Mixture with Different Gradations
## 2 Experiment
### Test Method  
The mechanical properties of the specimens were tested using the NanoTest TM(Micro Materials, Wrexham, NorthEast Wales, UK). The maximum load was determined as 3 mN for the nanoindentation asphalt mixture specimens and 0.12 mN for the mastic samples, with a loading/unloading rate of 0.01 mN/s and a creep satiation time of 200 s. The load-depth curves were obtained from the nanoindentation test, and the Oliver-Pharr model was used to calculate the materials' elastic modulus and hardness-depth curves [24]. Figure 4 shows the area of the aggregate-asphalt mastic interface in a typical asphalt mixture specimen under optical microscopy. It should be noted that the asphalt mastic phase tends to appear white under the microscope, while the aggregate phase is gray-black and may contain reflective crystalline compounds. Black lines in Figure 4 distinguish them.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Test** & **SBS Modified Asphalt** \\ \hline Penetration(25 \({}^{\circ}\)C, 100 g, 5 s)/0.1 mm & 55 \\ Softening point/\({}^{\circ}\)C & 87.7 \\ Ductility(10 \({}^{\circ}\)C, 5 cm/min)/cm & 41 \\ Mass change/\% & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of SBS modified asphalt.  
Figure 3: Nanoindentation asphalt mastic specimens.  
Figure 2: Preparation of nanoindentation specimens.  
For each nanoindentation sample, 16 measurement points (\(4\times 4\) matrix, measurement point interval 80 \(\upmu\)m) were arranged in a clear and flat area. Some micrographs of the test area and the distribution of measurement points are shown in Figure 5.  
The FEI Inspect F50 (FEI, Hillsboro, OR, USA)field emission SEM was used to observe the external morphology of the asphalt mastic with different RFA. In addition, since all asphalt mastic is inorganic and non-conductive, the sample surfaces were gold-plated before the electron microscope scanning was performed.
# Adaptive Clustering via Symmetric Nonnegative Matrix Factorization of the Similarity Matrix
## 5 The Clustering Algorithm for Problem \(\mathcal{P}_{kvar}\)  
For problem \(\mathcal{P}_{kvar}\), the dimension \(k\) of the clustering is not a priori assigned, but an interval \(\mathcal{J}_{k}=[k_{\min},k_{\max}]\) is only given where \(k\) has to be looked for, according to some performance criterium. The problem can be tackled by applying the algorithm designed for problem \(\mathcal{P}_{kfix}\) with different values of \(k\) in the interval, and then by choosing the one which gives the better result. The strategy we propose in the following algorithm Kvar selects the consecutive integers \(k=k_{\min},\ldots,k_{\max}\). For each \(k\), the triple \(\boldsymbol{\sigma}^{(k)}\) of scale parameters is constructed using formula (10), given in the next section, and function Kfix is applied. The array \(\boldsymbol{m}\) of the optimal DB values and the array \(M\) of the corresponding clusterings \(\Pi\) are returned. The indices DB** and CL, described in Section 3, help in choosing the best \(k\) and the corresponding clustering. Moreover, if a proper value of \(k_{\max}\) is not assigned, plots of DB** and CL can indicate when the computation may be stopped.  
```
functionKvar\(\left(k_{\min},k_{\max}\right)\)  initialize \(\boldsymbol{m}\) and \(M\); for\(k=k_{\min}\)to\(k_{\max}\)  compute triple \(\boldsymbol{\sigma}^{(k)}\); \((M,\;m)=\texttt{Kfix}\left(k,\boldsymbol{\sigma}^{(k)},q\right)\); endfor; return\((M,\;m)\)
```  
From (10) we see that the same \(\sigma\) appears repeatedly for different values of \(k\). Hence, it is not worthwhile to construct each time the matrix \(A(\sigma)\). Whenever possible, it is better to construct and store beforehand the matrices \(A(\sigma)\) for all the different scale parameters \(\sigma\) that are expected to be used.  
Another shortcut that immediately comes to mind is to exploit the already computed NMF factorization of an \(A(\sigma)\) for a given dimension \(k\) to obtain the NMF factorization of the same matrix for a larger \(k\), instead of computing it from scratch as suggested in Kvar. In [5], these updating algorithms are proposed. We have tried them in our experimentation and have found that, in our clustering context, the improvements on the computational cost are negligible in the face of a more complex code. Hence we do not recommend them.
## 3 Theoretical Analysis
### Range Characteristic between MBS and EGT  
Because the revolution speed of Moon's centroid (ranging from about 960 m/s to 1100 m/s) is much faster than its maximum rotation speed (about 4.6 m/s) in the equatorial region, the Moon's revolution plays a major role in slant range compared with its rotation. Because the maximum rotation speed (about 465 m/s) of Earth in its equatorial region is not identical to Moon's revolution, and the rotation orientation of Earth as well as the Moon's revolution orientation are all from west to east, as observed from the north celestial pole, the delay time from MBS to corresponding EGT will not be equal to delay time from the corresponding EGT to MBS. Take January 2001 as an example: the moon will pass the descending node, perigee, ascending node, and apogee in sequence, and the sub-point of different MBS, as well as the location of the EGT with different look angles, may vary during periodic motion, as shown in Table 1.  
In this study, three sample MBS's locations, (30\({}^{\circ}\) W, 3 \({}^{\circ}\) S), (0\({}^{\circ}\), 0\({}^{\circ}\)), and (30\({}^{\circ}\) E, 30\({}^{\circ}\) N), appear to exhibit a centrosymmetric distribution because the Moon is locked by Earth tide, and the zero-longitude line of the Moon is almost facing the Earth. Here, the EGTs are assumed to be the intersection points of the MBS's antenna bore-sight on the Earth's surface. The antenna has been steered into the zero-Doppler plane, and the look angle is left-looking. As displayed in Table 1, the EGTs' latitude changes are greater than the corresponding longitude changes when the Moon reaches apogee and perigee, indicating that the intersection line of the zero-Doppler plane on Earth's surface is almost parallel to the longitude line when the Moon is at apogee and perigee. However, an angle exists because of the angle between the Moon's rotation axis and revolution orbit when the Moon is at either the ascending node or the descending node. The influence of MBS's location on the sub-point, and the influence of the look angle on the intersection points suggest that the MBS cannot be deemed to be consistent with the Moon's centroid, although the Moon's radius is far smaller than the two planets' average distance, if a more realistic scenario had been presented. The diversity of MBS's location and look angle have different numerical effects on the dual-delay of transmission and the maximum difference; therefore, because of the discrepancy caused by the delay time between uplink and downlink, using MBS and EGT, the results will no longer be constant with the changing of the synthetic aperture time and the look angle, as shown in Tables 2 and 3, suggesting that it is necessary to calculate the two delay time separately for the improvement of accuracy, the discrepancy when synthetic aperture time is 150 s is larger than when the time is 80 s if other parameters are the same, mainly because with an increase in the synthetic aperture time, the fringe angle, i.e., the angle from sampling points at fringe to the zero-Doppler plane, also increases, thereby
# Retention of the Antibiotic Cofruoxime onto Agricultural and Forest Soils
## 3 Results and Discussion
### Adsorption of CFX and Modeling
#### 3.2.2 Fitting to Adsorption Models
[35] also found high correlations among these soil properties and values of te Freundlich's \(K_{F}\) coefficient corresponding to adsorption of various types of antibiotics onto soils. As previously indicated, these results show the importance of pH in the adsorption processes, mainly due to affecting the charge of both antibiotics and soil components, as well as the possible intervention of cationic bridges. The values of Freundlich's \(n\) parameter (related to the reactivity and heterogeneity of the active sites of the sorbent) [36; 37] were highly variable for the different soils. The highest oscillations were observed in crop soils (between 0.011 and 1.655 in corn soils, between 0.21 and 1.589 in granite vineyard soils, and between 0.685 and 1.378 in vineyard soils on schist and slate), while the variability was lower, and the values were relatively high, in forest soils (from 0.978 to 1.323). A good fit to the Freundlich model indicates that, at low adsorbate concentrations, the amount adsorbed clearly increases with a rise in the concentration of the sorbate in solution [37]. When the values of the \(n\) parameter are between 0 and 1, they indicate that the adsorption is carried out in a heterogeneous matrix, where the highest energy sites are occupied first. Being closer to zero, \(n\) values indicate a higher degree of heterogeneity in the adsorption surface [36; 38]. When the values of the \(n\) parameter are \(>\)1, it is considered indicative of deviation from the Freundlich equation, due to factors, such as the possible existence of irreversible reactions, as precipitation [39], or the presence of cooperative adsorption [36]. When \(n\) = 1, the Freundlich equation would be equivalent to a linear equation. In the present study, values of \(n<\) 1 are obtained in 57% of the soils (Table 1). This parameter is positively and significantly correlated with the soil organic matter content (r = 0.44 and \(p<0.05\)), indicating that CFX adsorption would be stronger in soils with more organic matter. This could explain the high adsorption of the antibiotic shown by forest soils, despite their low pH, since their high organic matter contents create adsorption sites by dissociating certain functional groups, such as carboxylic, even at pH \(<\) 5 [33].
## 2 Experimental Details  
### Glass Fabrication  
Table 1 shows the mole fractions of chemical constituents of V2O5-ZnO-BaO-B2O3-M(PO3)n (where M = Al, Mg, Na, n = 3, 2, 1, respectively, mol%) based glasses. The glasses were prepared by the melt-quenching method. Reagent grade chemicals of V2O5 (99.9%), ZnO (99.9%), BaO (99.99%), B2O3(99.9%), and M(PO3)n (99.9%) were used as raw materials to fabricate the glass ingots. The batch chemicals (about 50 g) were weighed using a microbalance and then crushed using a ball mill with zirconia balls for 30 min to get a homogeneous mixture. Then, the well-mixed powder was transferred into an alumina crucible and kept in an electric furnace for the melting process. For the preparation of glasses, a two-step melting process was applied, i.e., in the 1 st step, the glass composition was melted at 550 \({}^{\circ}\)C for 30 min, and in 2 nd step, the temperature was raised to 700 \({}^{\circ}\)C and the glass composition was then heated under N2 atmosphere for 30 min (see Figure 1). Then, the melt was poured onto a preheated brass mold and transferred to another electric furnace for the annealing process to remove internal stress. The glass ingots were annealed at 280 \({}^{\circ}\)C for 3 h. The glass ingots were used to examine the feasibility for laser sealing through physical, thermal, and structural properties.
# Artificial Intelligence in Virtual Telemedicine Triage:
## 4 Results  
### Characteristics of Participants  
In total, 6000 patient data presenting symptoms of respiratory infections were included for training the artificial intelligence algorithms. Of these patients, 52% were female and 48% were male. The mean age of the participants was 38 years, with an age range of 18 to 85 years; see Figure 10.  
Figure 10: Testing of telemedicine virtual assistant on patients in the doctor’s office.  
In terms of the symptoms reported by the participants, the most common were fever (present in 72% of patients), cough (in 65% of patients), and fatigue (in 54% of patients). Other less common symptoms included sore throat (in 33% of patients), nasal congestion (in 28% of patients), and shortness of breath (in 15% of patients).  
In terms of diagnosis, 40% of participants were found to have COVID-19, 35% had a common cold, and 25% had allergic rhinitis. Notably, most patients with COVID-19 had diarrhea and vomiting, while most patients with the common cold had nasal congestion and sore throat. Patients with allergic rhinitis presented mainly with nasal congestion and sneezing.
###### Abstract
[19] used fractal dimension (FD) analysis, observed in other studies, finding that it is efficient to evaluate bone quality in the mandible and trabecular architecture (structural patterns), correlating it with mandibular cortical width (MCW), verifying if there were differences in FD and MCW in panoramic radiographs. Most studies to date were performed mainly on adult patients, and there are practically no studies on pediatric patients with OI receiving bisphosphonate treatment where it was checked if there were changes in the cortical bone. Recently, studies have been published on child and adolescent patients with OI treated with bisphosphonates, such as [PERSON] et al. in 2015 and [PERSON] et al. in 2021, which highlight changes in the mandibular cortex and cortical bone after treatment with bisphosphonates [1; 9; 22; 23; 24; 25]. The present research work was based on the analysis of dental panoramic indices and fractal dimensions to evaluate the trabecular and cortical bone in panoramic records of child patients with OI, in comparison with a control group of healthy children, and to analyze the influence of treatment with bisphosphonates. In addition, the present study aims to corroborate with an easily accessible program whether visible changes in bone mineral density are observed in panoramic radiographs routinely used in dental practice.
## 5 Platform Testing and Validation
### CLAP Instance
#### 5.3.2 Sensor 2: Jammer Detector (JDet)  
The JDet (IP address 192.168.66.170) is a sensor of the CLAP that identifies anomalies on the radiofrequency spectrum where the sensor is located. The signal is received by physical device(s) and is transformed into an alert that is then shared with the XL-SIEM agent. A plugin has been developed to allow communications of the JDet and the CLAP. The jamming events are sent to a specific IP address using a predefined format, which will trigger an alert in CLAP. JDet generates an alert when a jammer generates noise in the area covered by the sensor.
## 4 Simulations
### Disturbance Rejection  
Consider the controller \(G_{c}=K_{p}+K_{d}s\) with the parameters of Table 1, and a ramp disturbance of magnitude \(M=3.14\times 10^{-3}\) emerging at \(t=20\) s. Then, the simulation is performed, and Figure 9 presents the disturbance \(x_{d}(t)\) and the force error \(e\) from the simulation. Note that the disturbance \(x_{d}(t)\) produces a force error of \(e_{ss}\approx 0.74\) [N]. This error corresponds with that estimated theoretically using \(M=0.00314\) and \(K_{p}=0.004211\), i.e., \(e_{ss}\approx\frac{0.00314}{0.004211}\approx 0.74\).  
Considering the design curves of Figure 6, if we want to reduce the error \(e_{s}s\), we need to increment the gain \(K_{p}\). Then, increasing the gain \(K_{p}\) to \(K_{p_{a}}=K_{p}+0.002\) and \(K_{p_{b}}=K_{p}+0.004\), the simulation of the force control system using these gains is performed, and the resulting error is shown in Figure 9. One can observe that \(e_{ss}\) is reduced when the value of \(K_{p}\) is increased.  
Time-Varying Disturbances  
Considering a sinusoidal disturbance \(x_{d}(t)=0.01\sin(0.628t)\) emerging at \(t=20\) s, and the controller \(G_{c}=K_{p}+K_{d}s+\frac{K_{i}}{s}\) with the gains in Table 1, the simulation of the force control system is performed. The resulting force error \(e\) is presented in Figure 10 for different values of control magnitude. One can observe that the error decreases when the gains of magnitude of \(G_{c}(s)\) increase, as expected from Equation (6).  
Figure 10: Force error \(e_{ss}\) in presence of sinusoidal disturbance \(x_{d}(t)=0.01\sin(0.628t)\) and disturbance \(x_{d}(t)\).  
Figure 9: Force error \(e_{ss}\) in presence of ramp disturbance and disturbance \(x_{d}(t)\).
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Hierarchical Classification Trees Construction by Decision Tree Algorithms
#### 3.6.2 Multi-Type Classification Feature Sets  
In this study, more than 40 features, including spectral features, topographic features, texture features and shape features, were conducted for constructing classification trees and automatically classifying for each mapping unit, which are shown in Table 2. Some satellite-derived indices were also involved in classification feature sets, including the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI, [42]), and the Normalized Difference Built-Up Index (NDBI, [43]). All of selected features were calculated in the eCognition 8.7 platform. For spectral features, the mean value and the standard deviation of each band of multi-temporal satellite images were involved.
# Theory and Design of Blink Jamming
## 2 Analysis of Formation Jamming
### The Two Target Problem  
In the context of a monopulse radar conducting an angle measurement, targets are \"unresolved if an angle measurement cannot be made on each target without significant error due to the presence of others\" [6]. To resolve two or more targets they must be sufficiently separated in coordinates including: angle, range, or Doppler, to make interference with each other negligible. The intent of this section is to study what occurs when a monopulse radar encounters two unresolved target and its effect on angle tracking as shown in Figure 3.  
The two target problem assumes the tracking radar is using either a passive monopulse receiver which is being guided by an off-board illuminator, providing only angular information, or in the monostatic case the targets are in the same range and Doppler cells. Assuming the returns from the two sources are superimposed we can explore this mathematically at true angular displacements \(\theta_{a}\) and \(\theta_{b}\). The true difference signals for the two targets are:  
\[d_{a}=k_{m}\theta_{a}s_{a} \tag{6}\]  
\[d_{b}=k_{m}\theta_{b}s_{b} \tag{7}\]  
with an indicated angle of:  
\[\theta_{i}=\frac{1}{k_{m}}\frac{d}{s}=\frac{1}{k_{m}}\frac{d_{a}+d_{b}}{s_{a} +s_{b}} \tag{8}\]  
Substituting in (6) and (7), we then get a more useful solution:  
\[\theta_{i}=\frac{\theta_{a}s_{a}+\theta_{b}s_{b}}{s_{a}+s_{b}}. \tag{9}\]  
The new equation effectively describes the relation of the indicated angle to the weighted sum of the two sources.
## 1 Introduction
The main contributions of this article are listed below:  
* A new application for the FOFP-iPID controller is proposed for the LFC strategy. * The SFs of the controllers are adjusted employing the ALO algorithm. * The proposed FOFP-iPID controller is evaluated on an interconnected two-area power system in which the physical constraints are taken into account for challenging realization. * The performance of the suggested controller is evaluated by comparing the results with other controllers, such as FOiPD and FOPID controllers for the same plant [17]. * Sensitivity analysis is carried out to show the robustness of the FOFP-iPID controller under a wide range of parameter variations and LPs. The rest of the sections of this article are organized as follows: The investigated power system is modeled in Section 2; Controller structure is elaborated in Section 3; the objective function and its optimization are stated in Section 4; simulation results are offered in Section 5; finally, the conclusion of the article is presented in Section 6.
## 3 Results and Discussion
### Synthesis of GOPBG with Different Branching Densities
The G1 and G2 PBG arborescent substrates were synthesized and purified by the same method used for the G0 samples except that, in all cases, the deprotection level of the arborescent substrates used was \(\approx\)30% and a 1:1.1 molar ratio of side chains (PBG-NH\({}_{2}\)) to CO\({}_{2}\)H groups was used to maximize the grafting yield and the coupling efficiency (Table S3, SM). The molecular weight of the polymers increased with the generation number, while a low dispersity (D \(\leq 1.09\)) was maintained. The branching functionality also increased over successive generations, as more coupling sites were available after each grafting cycle. Sample G1 PBG\({}_{15}\) showed the lowest G\({}_{y}\) and C\({}_{e}\), which is attributed to the difficulty for the coupling sites to react in the compact and dense G0 PBG\({}_{15}\) substrate because of increased crowding.
# A Tunable Microstrip-to-Waveguide Transition for Emergency Satellite Communication Systems
## 2 Transition Design
Meanwhile, the length of the metal probes can change the suppression frequency at the receiving frequency band, as shown in Figure 3d,e. It is clear that increasing the probe length can lower the suppression frequency. A change in \(m1\) or \(m2\) would lead to a mismatch in the transmitting frequency band, while a change in \(m3\) would slightly influence the passband characteristics. As a result, we can design a suitable \(m1\), \(m2\), and \(m3\) to enhance the out-of-band suppression and then optimize the three patches and WG2 to enhance the match from 14 to 14.5 GHz.