esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Dispersion of Lamb Wave  
Considering that the bulkhead structure of an orbiting spacecraft is a thin metal plate, due to its large curvature, it is approximated as a metal flat plate in this paper to simplify the research. In thin metal plates, ultrasonic waves propagate in the form of Lamb waves. Lamb waves have less attenuation than body waves, which is beneficial to the location of leaks. However, the propagation velocity of Lamb wave varies with wave frequency, which is called the dispersion characteristic of Lamb waves. Frequency dispersion complicates the characteristics of ultrasonic waves, and it is difficult to directly determine the speed of waves to complete the beamforming algorithm. As a consequence, studying the dispersion characteristics of Lamb waves is critical. There are two modes of waves--symmetric and anti-symmetric--that independently propagate in plates. By equipping a numerical method, equations known as the Rayleigh-Lamb frequency relations for both symmetric and anti-symmetric waves expressed as Equations (5) and (6) can be solved [17].  
\[\text{Symmetric mode}:\frac{\tan(qh)}{q}+\frac{4k^{2}p\tan(ph)}{\left(q^{2}-k^ {2}\right)^{2}}=0, \tag{5}\]  
\[\text{Anti-symmetric mode}:\ q\tan(qh)+\frac{\left(q^{2}-k^{2}\right)^{2} \tan(ph)}{4k^{2}p}=0, \tag{6}\]  
where \(k\) represents the wave number, \(\omega\) is the angular frequency, and \(d\) is the thickness of the plate.  
When the thickness of a plate and the frequency of the signals are given, a set of numerical solutions, denoted by \(k_{a}\) and \(k_{s}\), are acquired by solving the Rayleigh-Lamb equations.  
represent the wave numbers of anti-symmetric and symmetric waves, respectively. This phenomenon proves that multiple Lamb wave modes exist in the plate, and each mode has its own phase velocities and group velocities. The number of the numerical solutions increases with the frequency of the signals and the thickness of the plates, which means that the number of wave modes increases. Usually, symmetric modes are represented by \(\mathrm{S_{0}}\), \(\mathrm{S_{1}}\),..., \(\mathrm{S_{n}}\), and anti-symmetric modes are represented by \(\mathrm{A_{0}}\), \(\mathrm{A_{1}}\),..., \(\mathrm{A_{n}}\). In each mode, the corresponding phase velocity also varies with the frequency, and the phenomenon is called the frequency dispersion. Dispersion curves consist of a set of curves can be drawn to depict these characteristics. From the dispersion curves, the theoretical values of phase speeds at any particular frequency-thickness product of any specific mode can be obtained.
## 3 Results and Discussion
### Flocculation Performance  
The effect of any flocculant on the flocculation process is influenced by the dosages, with the flocculation efficiency decreasing at lower or higher dosages than the optimum dose. The flocculation decolorization rate can be used to measure the flocculation decolorization performance of polymer samples in dye wastewater. In this study, we determined the effect of the flocculant dosage on the decolorization rate using the hybrid flocculant and the composite flocculant, as shown in Figure 12.  
Figure 12 illustrates the decolorization rates of disperse-violet-dye wastewater with a concentration of 100 mg/L using both the hybrid flocculant and the composite flocculant. As depicted in the figure, an increase in dosage led to higher decolorization rates for both types of flocculants. However, a consistently superior performance was observed with the hybrid flocculant compared to the composite one. When dosed at 400 mg/L, a remarkable decolorization rate of 99% was achieved by the hybrid flocculant for disperse-violet-dye wastewater treatment. Disperse violet is a compound with the structure of anthraquinone,  
Figure 11: TGA curves of PAC-PDMDAAC hybrid flocculant (**a**) and PAC-PDMDAAC composite flocculant (**b**).  
Figure 12: Effect of different flocculants’ dosages in disperse-violet-dye wastewater on decolorization rate.  
which can only be evenly dispersed in water, so any treatment only needs to neutralize a small amount of negative charge on the surface of the dye, without the need to precipitate the dye from the water. Therefore, hybrid flocculants with long chain structures and high molecular weights are more effective in the treatment of disperse violet. This highlights that the synergistic effects between its inorganic and organic components were fully utilized by this hybrid flocculant, resulting in a better binding-bridging capacity than that exhibited by the composite counterpart.
## 2 Mechanical Model
### Structure-Soil-Structure Interaction Model
It is worth remarking that the paper does not refer to a specific GSI model, but aims to investigate the role of the soil characteristics below the external protection device, simply scaling the stiffness of the soil. Although the application of the GSI also modifies the damping of the soil, in this paper, this aspect is not taken into account. As can be observed in Equation (12), the stiffness and the damping depend on the ratio \(d/a\), where \(d\) is the distance between the two foundation platforms and \(2a\) is the dimension of such platforms (see Figure 2). Finally, it is important to highlight the assumptions used to build the model, which may limit the investigation of actual structural systems. Specifically:  
* The interaction between the foundation platforms and the soil, as well as between the two structures through the soil, is studied by modeling the soil with linear Kelvin-Voigt devices. Although commonly used in SSSI problems, this assumption considers the soil as functioning in both tension and compression. * The adopted SSSI model assumes equal dimensions for the foundation platforms, a hypothesis that may be difficult to prove in practical applications. However, since this paper presents a preliminary parametric study based on the use of simplified mechanical models, its aim is to offer general insights into this approach rather than specific design guidelines. * Using a low-dimensional mechanical model that captures only the primary dynamics of actual structures provides preliminary insights into the effectiveness of this protection method. For the application to real structures, a more comprehensive analysis should be performed. * The inerter device is assumed to be connected to an infinitely stiff soil and to the vibrating mass. This assumption would require the use of a deep foundation to connect the inerter to the bedrock, ensuring that one of its terminals directly experiences the earthquake acceleration.
# Numerical Modeling, Analysis, and Optimization of RFID Tags Functioning at Low Frequencies
## 3 Software Program Used for Determining Possible Structures
### Influence of the Dielectric Material and Thickness on the Gain Distribution and Resonance Frequency  
Dielectrics play an important role in the structures functioning at higher frequencies. Therefore, a study regarding their influence on the tag operation is crucial. Four different dielectrics were considered, as can be seen in Table 4, with different relative permittivity values varying from 2.2 to 10.2. For this study, we considered three different types of dielectrics from Rogers Corporation with different relative permittivity values and FR4 epoxy due to its wide range of use and the fact that it has a low cost. We considered the Rogers dielectrics due to their availability in our laboratory.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dielectric Type** & \(\varepsilon_{r}\) & **Loss Tangent** \\ \hline Rogers 5880 & 2.2 & 0.0009 \\ FR4 Epoxy & 4.4 & 0.02 \\ Rogers 3006 & 6.15 & 0.0025 \\ Rogers 3010 & 10.2 & 0.0035 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dielectrics used for the study.  
Figure 16: Gain representation of exterior diameters of 13 mm, 14 mm, and 15 mm of the octagonal spiral-shaped structures: (**a**) gain of the octagonal-shaped coil structures with \(D_{e}=13\) mm and 14 mm; (**b**) gain of the octagonal-shaped coil structures with \(D_{e}=15\) mm.  
It can be stated that at low frequencies, the dielectrics do not considerably influence the gain of the structures, as can be observed in Figure 17; therefore, a dielectric with a lower price, like FR4 epoxy, was sufficient to achieve our goal.  
As in previous representations, in the graph in Figure 14, the structures are named using their exterior diameters, number of turns, distance between the turns, and thickness of the turn. For example, for a structure with \(D_{e}\) = 14 mm, \(N\) = 7 turns, \(s\) = 0.1 mm, and \(w\) = 0.1 mm, the name is 14 7 01 01.  
When considering the dielectric thickness, it was discovered that it influences the maximum gain of the structures but not in a linear manner; therefore, it is very hard to predict their operation. The influence of three different dielectric thicknesses, namely 0.51 mm, 1.27 mm, and 1.52 mm, is presented in Figure 18.  
Figure 19 presents the influence of changing the dielectric thickness of a structure with a dielectric thickness of 1.52 mm for which the circuit elements were considered for it to resonate at 13.56 MHz. It can be observed that the decreases in the dielectric thickness to 1.27 mm and 0.51 mm led to shifts in the resonant frequency.  
Figure 17: Peak gain for square spiral coil structures with exterior diameters of 13 mm, 14 mm, and 15 mm.  
Figure 18: The influence of the dielectric thickness on the peak gain of square coil structures.
## 3 Results
### Stability Study  
As depicted in Figure 10, checking for muiprocin-based NEG stability was conducted relative to specific evaluated parameters including physical inspection, pH, viscosity, spreadability, and in vitro release behavior. The study was performed by comparing stored samples with fresh NEG formulation following storage for 3 months at variant conditions. Obviously, the formulation showed no change in color, odor, or homogeneity, and no phase separation was detected during the entire period of storage. Additionally, non-significant variations were detected in all evaluated parameters for muiprocin-based NEG formulation following 3 months of storage at conditions of 4 \({}^{\circ}\)C and 25 \({}^{\circ}\)C when compared with freshly prepared NEG preparation (\(p<0.05\)). This study highlights the formulation's stability and validates NEG as a powerful drug carrier.  
Figure 9: FTIR spectra of pure muiprocin, blank NEG, and muiprocin-loaded NEG.
## 3 Research, Results, and Discussion
### Seismic Collapse Performance of Steel Ordinary Moment Frames
Median \(SDR_{i}\) values for W0- and W51-OMFs are included in this figure. For the 15-story steel OMFs, the larger elastic \(SDR_{i}\) values are obtained in the upper stories of the structure. This is because the effect of higher modes. As seen in the previous section, the W51-OMF has larger \(SDR_{i}\) in the upper stories than W0-OMF because of the increase in member sections in the lower stories when wind loads are taken into consideration. The dispersion of \(SDR_{i}\) in the upper stories for the W51-OMF is larger than that for the W0-OMF. The larger dispersion of \(SDR_{i}\) in the upper stories for the W51-OMF may be attributed to the effect of larger lateral drifts in the upper stories and more significant contributions of higher modes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Design** \\ **Type** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Stories** \\ \end{tabular} &
\begin{tabular}{c} **Structural** \\ **Height (m)** \\ \end{tabular} & \(V_{des}\)/W & \(V_{max}\)/W & \(\Omega\) & \(\mu_{T}\) & \(\beta_{RTR}\) & \(\hat{S}_{CT}\) **(g)** \\ \hline \multirow{4}{*}{W0-OMFs} & 9 & 36.3 & 0.030 & 0.060 & 2.02 & 3.13 & 0.406 & 0.132 \\ \cline{2-8}  & 12 & 48.2 & 0.024 & 0.050 & 2.11 & 2.35 & 0.445 & 0.088 \\ \cline{2-8}  & 15 & 60.0 & 0.022 & 0.045 & 2.07 & 2.09 & 0.461 & 0.091 \\ \cline{2-8}  & 18 & 71.9 & 0.022 & 0.044 & 1.99 & 2.11 & 0.484 & 0.104 \\ \hline \multirow{4}{*}{W44-OMFs} & 9 & 36.3 & 0.030 & 0.060 & 2.02 & 3.13 & 0.406 & 0.132 \\ \cline{2-8}  & 12 & 48.2 & 0.024 & 0.050 & 2.11 & 2.53 & 0.459 & 0.087 \\ \cline{2-8}  & 15 & 60.0 & 0.022 & 0.046 & 2.08 & 2.72 & 0.481 & 0.086 \\ \cline{2-8}  & 18 & 71.9 & 0.022 & 0.047 & 2.14 & 2.63 & 0.484 & 0.101 \\ \hline \multirow{4}{*}{W51-OMFs} & 9 & 36.3 & 0.030 & 0.059 & 1.98 & 3.75 & 0.416 & 0.121 \\ \cline{2-8}  & 12 & 48.2 & 0.024 & 0.049 & 2.05 & 3.76 & 0.478 & 0.084 \\ \cline{1-1} \cline{2-8}  & 15 & 60.0 & 0.022 & 0.046 & 2.08 & 3.12 & 0.497 & 0.083 \\ \cline{1-1} \cline{2-8}  & 18 & 71.9 & 0.022 & 0.048 & 2.16 & 3.04 & 0.571 & 0.106 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters from pushover and IDA results.
# Methodology to Assess Sustainable Mobility in LATAM Cities
## 2 Methodology
### Evaluation of Mobility-Related Performance Indicators
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Pillar** & **Category** & & **Performance Indicators (PIs)** & **Units** & **KPI\({}_{i}\)** & **KPI\({}_{i}\)\({}^{*}\)** & **KPI\({}_{i}\)\({}^{*}\)** & **S\({}_{i}\) (\%)** \\ \hline \multirow{9}{*}{Physical Infrastructure} & 1. & Length of the road network \({}^{*}\) & km/10\({}^{6}\) hab & 41.2 & 2.84 & 58.6 & 31.2 \\  & 2. & Length of lanes for public transport & km/ha & 0.00 & NR & NR & NR \\  & 3. & Length of rails transport & km/ha & N/D & NR & NR & NR \\  & 4. & Length of the roads for bicycles & km/10\({}^{6}\) hab & 0.47 & 0.16 & 7.8 & 4.1 \\  & 5. & City blocks with pedestrian accessibility & \% & 55.24 & 0 & 100 & 55.2 \\  & 6. & City blocks with wheelchair ramps & \% & 2.58 & 0 & 100 & 2.6 \\  & 7. & Semaphored intersections with defined pedestrian crossings & \% & 5.00 & NR & NR & NR \\  & 8. & Roads in good condition & \% & 10.00 & 0 & 100 & 0.00 \\ \hline \multirow{9}{*}{Economic scope} & 9. & Transportation affordability (Percentage of household expenditure on transport) & \% & 19 & 2.8 & 25 & 27 \\  & 10. & Gross domestic product per capita & USD/hab & 32,000 & 6000 & 70,000 & 40.7 \\  & 11. & Public transport fee & USD/trip & 0.55 & 0.20 & 2.80 & 87.7 \\ \hline \multirow{9}{*}{Energy Supply} & 12. & Number of gas stations & stations/10\({}^{6}\) hab & 1.2 & 0.1 & 4.3 & 27.0 \\  & 13. & Number of EV charging stations & km/10\({}^{6}\) hab & 0.09 & 0 & 5.5 & 1.5 \\ \hline \multirow{9}{*}{Vehicles for the mobility of people and goods} & 14. & Private vehicles per habitant \({}^{*}\) & veh/hab & 0.18 & 0.05 & 0.70 & 79.7 \\  & 15. & Equivalent public transport vehicles & veh/10\({}^{6}\) hab & 1232.89 & NR & NR & NR \\  & 16. & Percentage of motorcycles & \% & N/D & NR & NR & NR \\  & 17. & Percentage of +10-year-old vehicles \({}^{*}\) & \% & 67 & 0 & 100 & 33 \\  & 18. & Percentage of hybrid or electric vehicles & \% & <0.01 & 0 & 17 & 0.00 \\  & 19. & Percentage of taxicabs & \% & N/D & NR & NR & NR \\ \hline \multirow{9}{*}{City distribution} & 20. & Urban density \({}^{*}\) & [hab/ha] & 48.89 & 13.7 & 286 & 87.08 \\  & 21. & Percentage of urban area with accessibility (5-min walk) to public transport stations & [\%] & 16.40 & 0 & 100 & 0.00 \\ \cline{1-1}  & 22. & Number of commer- & [1/10\({}^{6}\) hab ] & 1.2 & NR & NR & NR \\ \hline \end{tabular}
\end{table} 
Table 3: Performance indicators, lower and upper benchmarks for key performance indicators (_KPI\({}_{i}\)_\({}^{*}\) and _KPI\({}_{i}\)\({}^{*}\)_), and normalized _KPI\({}_{i}\)_(S\({}_{i}\)). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Pillar** & **Category** & **Performance Indicators (PIs)** & **Units** & \(\textit{KPI}_{i}\) & \(\textit{KPI}_{i}\)* & \(\textit{KPI}_{i}\)* & \(\textit{S}_{i}\) (\%) \\ \hline \multirow{9}{*}{Mobility patterns} & \multirow{2}{*}{23. Existence of regulatory mobility organisms} & \multirow{2}{*}{[0/1]} & \multirow{2}{*}{1} & \multirow{2}{*}{0} & \multirow{2}{*}{1} & \multirow{2}{*}{100} \\  & & & & & & & \\  & &
# Design of Wireless Sensor Network for Agricultural Greenhouse Based on Improved Zigbee Protocol
## 3 Experimental Design and Results
### Experimental Indicators  
The indicators to measure the performance of the Zigbee routing protocol are the end-to-average delay and packet delivery rate. This research will use the following four indicators for the comparative analysis of the original ZigBee routing protocol and EMP-ZBR routing protocol:  
1. Packet delivery rate: This indicates the ratio of the number of data packets successfully reaching the destination node to the number of data packets sent by the source node. The larger the packet delivery rate, the better the reliability of the transmitted data [26]. Equation (8) is as follows: \[\begin{array}{c}\text{ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
that the performance of the two protocols under different topological conditions can be studied and the final simulation results obtained are more authentic. Table 1 shows the simulation parameters.
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 5 Point-of-Care Clinical Diagnostics in HF
### Cardiac Troponins  
Besides BNP, cardiac troponin is an important biomarker for cardiovascular events, in particular, cardiac necrosis. The current guidelines for HF recommend the evaluation of cardiac troponin in patients with HFpEF as well as HFrEF. The evaluation of cTn levels is intended to be used for the prognosis prediction in HF. More than 20 years ago, the first rapid tests for the fast detection of cardiac troponin were developed (2018). Since then, several more tests have been established to facilitate their use as bedside tests, such as Spectral Cardiac STATus Troponin I Rapid Test (Spectrum Diagnostics, Cairo, Egypt) (2018), Nadal troponin I cassette ([PERSON], Regensburg, Germany), Hexagon Troponin (Human Diagnostics, Wiesbaden, Germany), Troponitest+ (All Diag, Strasbourg, France), and Amicheck-Trop (Zephir Biomedicals, Goa, India) (2018). Nowadays, there are more advanced bedside tests for the detection of cardiac biomarkers available that enable the simultaneous analysis of more than one protein of interest. For instance, H-FABP and cTnI Combo Rapid Test Cassette by BioZEK B.V. (Apeldoorn, the Netherlands) combines the detection of heart-type fatty acid protein (h-FABP), an MI and ischemia marker, with troponin I in a single test cassette. Although several companies provide cTNI assays, Roche Diagnostics (Indianapolis, IN, USA) has the monopoly for cTNT assays [170].
## 2 Application of Thermography in Pig Breeding
### Oestrus Detection  
Thermography is highly suitable for detecting changes in the surface temperature of the vulvar area (Figure 1) in gills and sows during the heat period, because it is not covered in bristles. In addition, the changes can be easily measured by the operator, when the individual is placed inside an insemination cage and has its backside facing the passageway. A few studies on oestrus detection [6; 7; 8] have shown that the peak vulvar temperature reaches values just above 36 \({}^{\circ}\)C in the pre-ovulatory phase, while in gills, it is lower by about half a degree. Subsequently, 12 h before ovulation, these values decrease to 34.6 \({}^{\circ}\)C and 33.9 \({}^{\circ}\)C, respectively. Like previous authors, they have confirmed the changes in vulvar temperature associated with oestrus, having found values slightly higher than those indicated above and having noticed the absence of any relationship between vulvar temperature and rectal or buttock surface temperature [9]. Recently, [PERSON] et al. have shown that the simultaneous application of thermographic measurement and ultrasonic sensors, used to quantify the time sows remain standing, tends to improve the success of artificial insemination interventions [10].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Application** & & **Area of Interest** & **References** \\ \hline \multirow{3}{*}{Enhance production} & Oestrus Detection & Vulvar, Body, Eye & [6; 7; 8; 9; 10] \\  & Neonatal mortality reduction & Dorsal, Body, Eye & [11; 12; 13; 14; 15; 16] \\  & Growing pig efficiency & Dorsal, Eye, Body & [17; 18; 19] \\  & Meat quality prediction & Dorsal, Body, Eye & [20; 21; 22; 23; 24; 25] \\ \hline \multirow{3}{*}{Enhance animal welfare} & Fever or diseases early detection & Eye, Ear, Body & [26; 27; 28; 29; 30] \\  & Environmental stress assessment & Dorsal, Eye, Ear & [29; 31] \\ \cline{1-1}  & Interaction or restraint stress assessment & Dorsal, Eye, Ear & [32] \\ \cline{1-1}  & Assessing the stress related to pre-slaughter procedures & Eye, Ear, Body & [33; 34; 35] \\ \hline \hline \end{tabular}
\end{table}
Table 1: An overview of the thermography technique applications in pig farming.  
Figure 1: Infrared image of a sow’s vulvar area shows a temperature of 39.07 \({}^{\circ}\)C at point A.  
Altogether, it seems clear that thermography is suitable for detecting changes in vulvar temperature at different stages of the ovarian cycle and may be an applicable technique, not only in detecting oestrus, but also in monitoring its lowering toward the ovulation phase. At present, the cost of thermographic sensors is still high, but it is expected to drop over the coming years, thus making their permanent placement possible inside sow farms, in order to identify the best moment to carry out inseminations.
## 7 Security Considerations for Proposed Mobility Schemes
### Securing MN's Data Packets  
When the MN first time sends a data packet to its CN, it encrypts the data packet with K\({}_{\text{c}}\) and forwards it to its SN. The SN then forwards it to its MSP, which in turn forwards MN's packet to the GW. The GW then decrypts the message by K\({}_{\text{c}}\) of the source MN and forwards the data packet to the destination MSP, using the Communication Key (K\({}_{\text{c}}\)) of the destination MN. Also, at the same time, the GW creates a session key, i.e., K\({}_{\text{s}}\), for the communication between source MN and destination MN. Next the GW informs the source MSP about the destination MSP's address. Also it sends the session key to source and destination MNs by encrypting the session key with their respective K\({}_{\text{c}}\). When the source MN sends a data packet to destination MN, the data packet can be encrypted using the session key as shown below [25]:  
\(\text{MN}_{\text{source}}\)\(\rightarrow\)\(\text{MN}_{\text{destination}}\): \(\{\text{data}\}_{\text{$<K_{\text{s}}>$}}\), MAC\(\{\) K\({}_{\text{s}}\), C\(\}\)\(\{\)data\(\}_{\text{$<K_{\text{s}}>$}\}\)  
Hence using K\({}_{\text{s}}\), both MNs can communicate securely.