esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Quasi-Classical Dielectric Relaxation Functions  
In Section 2.9, quasi-classical Formulas (108a) and (108b), which were generalized at the fundamental frequency (\(\omega\)), were obtained to calculate the components of the complex dielectric constant of a crystal. Accordingly, by limiting (108) to zero approximation  
\[ \begin{split}\varepsilon^{(\omega)}=\varepsilon(\omega;\mathrm{T}) =\varepsilon_{\infty}\frac{1}{1-\Gamma^{(\omega)}}=\varepsilon_{\infty}\Biggl{(} \frac{1-\Gamma_{1}^{(\omega)}-\mathrm{i}\Gamma_{2}^{(\omega)}}{\left(1-\Gamma_{1 }^{(\omega)}\right)^{2}+\left(\Gamma_{2}^{(\omega)}\right)^{2}}\Biggr{)}, \end{split}  \tag{116}\]  
and separating the real and imaginary components, (108a) and (108b) are transformed:  
\[\mathrm{Re}\left[\hat{\varepsilon}(\omega;\mathrm{T})\right]=\varepsilon_{ \infty}\frac{1-\Gamma_{1}^{(\omega)}(\mathrm{T})}{\left(1-\Gamma_{1}^{( \omega)}(\mathrm{T})\right)^{2}+\left(\Gamma_{2}^{(\omega)}(\mathrm{T})\right)^{2}}, \mathrm{Im}\left[\hat{\varepsilon}(\omega;\mathrm{T})\right]=\varepsilon_{ \infty}\frac{\Gamma_{2}^{(\omega)}(\mathrm{T})}{\left(1-\Gamma_{1}^{(\omega)}( \mathrm{T})\right)^{2}+\left(\Gamma_{2}^{(\omega)}(\mathrm{T})\right)^{2}}. \tag{117}\]  
Here, \(\varepsilon_{\infty}\) denotes the high-frequency dielectric permittivity determined within the range of optic frequencies and characterizes the effects of induction polarization (when the relaxation time is calculated (in theory) and measured (in experiments) in the diapason \(T\approx\left(10^{-12}-10^{-10}\right)\) s). The expressions of (117) are, in a certain manner, formulated relations between quasi-classical relaxation parameters interpreted as the dimensionless functions (100) of variables \(\alpha_{1}=\frac{T_{\mathrm{D}}}{T_{\mathrm{M}}}\) and \(\alpha_{2}=\omega T_{\mathrm{M}}\). For a detailed study of the properties of parameters \(\Gamma_{1}^{(\omega)}(\mathrm{T})\) _and_\(\mathrm{T}\) _\(\Gamma_{2}^{(\omega)}(\mathrm{T})\)_, depending on the specified temperature range, the following expressions are taken.
# The Spec-Radiation Method for Layered Fluid Media
## 3 Results
### Example with a Three-Layer Model: Perfect Impedance Match  
Another academic example is the perfect impedance matching of the individual layers to each other. With a perfect impedance match, no reflections occur at an interface layer. However, this requires that the condition:  
\[c_{1}\rho_{1}=c_{2}\rho_{2} \tag{24}\]  
be fulfilled.  
We present here the three-layer model (see Figure 5) again to demonstrate that the method presented here also included this special case. The simulation space had the same geometric dimensions as in the previous Section 3.1. The step sizes in the \(x\)-, \(y\)-, and \(z\)-directions remained as in the previous Section 3.1, and the same number of reflections and transmissions were calculated as depicted in Figure 5. The medium up to the first interface was air with a speed of sound of \(c_{1}=343\) m/s and a density of \(\rho_{1}=1.2041\) kg/m\({}^{3}\). The second medium had a speed of sound \(c_{2}=2c_{1}\) and a density of \(\rho_{2}=\frac{\rho_{1}}{2}\). The third medium had a speed of sound \(c_{3}=\frac{c_{1}}{2}\) and a density of \(\rho_{3}=2\rho_{1}\). Thus, the conditions  
Figure 8: Particle deflection in a cross-section of the calculated volume of Figure 7 at time 0.4 ms with an enlarged section to show with vectors that here, there are longitudinal waves propagating.  
for the three media for perfect impedance matching were fulfilled. The same excitation signal as in Figure 6b was used, and the transmitter also had the same size and position as in Figure 6a, with the difference that the transmitter was now tilted by \(10^{\circ}\). As a result of the calculation and the superposition of all components, Figure 9 was obtained as a cross-section of the volume for the maximum sound pressure in dB related to the maximum in the entire volume.  
Up to the first interface at \(z\) = 0.1 m, the tilted transmitter could be detected well. Likewise, the near-field could be identified well. A reflection from the interface was, as expected, not recognizable. After the first interface, the speed of sound in the medium was twice as high as in the first medium, which can also be recognized very well by the propagation angle. Here, as it was to be expected according to [PERSON]'s law of refraction, it was approximately twice as large as the angle of incidence. However, no reflection can be seen even up to the second interface at \(z\) = 0.2 m, from any interface. The sound passed from Medium 2 to Medium 3 without reflection. Here, due to the fact that the speed of sound was only a quarter compared to Medium 2, the angle of propagation was also only about a quarter of what is present in Medium 2.  
With this special case, we once again proved the functionality of the spec-radiation method.
## 4 Results and Discussion
### V\({}_{s30}\) Structure and Site Classification  
V\({}_{s30}\) was calculated from the shear-wave velocity models and the additional SPT surveys. A map of V\({}_{s30}\) variation, along with site classification, is provided for each of the three cities (Figures 7-9).  
Figure 8: V\({}_{\rm s30}\) map and soil classification in the Ain-Defla city [21].  
Figure 7: V\({}_{\rm s30}\) map and soil classification in the El-Attaf city [21].  
In El-Attaf city, the V\({}_{\text{S}30}\) values vary between 290 and 460 m/s (Figure 7). In most of the city, the soil is classified as very dense (C), except for a small area in the center where the soil is classified as stiff (D). The increase in velocity towards the west is caused by the presence of shallow Jurassic limestones, which octop about 1 km west of the city in the Temoulga Massif (Figure 2). In the southern part of the city, the slight increase in V\({}_{\text{S}30}\) is related to the thinning of the Quaternary alluvium and the predominance of the Miocene stiff formations [21].  
In the Ain-Defla city, the V\({}_{\text{S}30}\) values vary between 250 and 550 m/s (Figure 8). The soil is classified as very dense and soft rock (C) in most of the city. The velocity gradually decreases towards the northwest and the soils become stiff (D). The variation in V\({}_{\text{S}30}\) at Ain-Defla is mainly controlled by the ratio of Quaternary alluvium to Jurassic limestone in the first 30 m. In the south, where the city backs onto the Doui Massif, the ancient Quaternary alluvium forms a thin layer and the upper 30 m of the soil is dominated by Jurassic limestone. Moving northwest, the Quaternary deposits begin to be thicker and dominate the top 30 m of the soil column, and thus, V\({}_{\text{S}30}\) values decrease and the soils are classified as stiff. In the El-Abadia city, the V\({}_{\text{S}30}\) values range from 340 and 530 m/s (Figure 9). In most of the city, soils are classified as very dense and soft rock (C). The high V\({}_{\text{S}30}\) values are related to the presence of Pliocene conglomerates and sandstones at shallow depths. The shear-wave velocity values decrease towards the south where the Quaternary alluvium is thicker [16]. The lowest Vs values are observed around the southern part of the Boukalli River in the city, where the upper layer is composed of present alluvium.
## 5 Security Analysis and Discussion
### Security Properties Discussion  
We compared three existing works of key management in terms of some security properties of the whole method. Table 1 shows the comparison among the three protocols based on ECC, certificateless degree, and blockchain technology.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & [5] & [11] & [19] & **This Article** \\ \hline Key escrow resilience & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) \\ Security of key distribution & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Perfect forward/backward security & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Resist private key disclosure & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ Resist temporary information disclosure & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ Resist an MITM Attack & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Resist an eavesdropping attack & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison on security.  
As shown in Table 1, the methods of Refs. [11; 19] require an extra computational overhead for bilinear pairing or blockchain. Although this article also uses blockchain to record the key distribution process of KGC, it does not add computational overhead or reduce authentication efficiency during the certification phase. Furthermore, Ref. [11] and this article are based on a certificateless approach; thus, they provide key escrow resilience whereby the KGC only has to provide half of a private key to the user. Refs. [5; 11; 19] use a private key to complete the encryption process or temporary information to a generate session key, so are unable to resist the security threat caused by key disclosure.
# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 4 Results and Discussion
### Analysis of SVM
The focus of this research work is to detect victims in fire scenarios from their scream; therefore, we use'scream' as one class and all the other classes combined as another class'rest' to generate the ROC curve. The dotted blue line in the plot represents 'perfect chance'. In other words, if the ROC curve follows the diagonal blue line, it has a 50-50% chance of getting detected as either class ('scream' or'rest'). The ROC curve represented by yellow line in Figure 7a looks closer to the top left corner indicating good performance with an Area Under Curve (AUC) of 0.86. Figure 7b represents a plot of SVM accuracies over the number of iterations. The accuracy keeps increasing with the increase in epochs. It can also be seen that after around 280 epochs, all the accuracies become saturated with a value close to 96%. From the above discussion, we can conclude that the SVM classifier is doing an excellent task in detecting streams in a burning site. Lastly, Figure 8 shows the most contributing features of this classification process. Figure was obtained by using the permutation importance of the features. After the model has been fitted, the permutation importance is calculated from the intercept and coefficient values of the predictors (features) [62]. The higher the value of importance, the most contribution the feature makes on the classification. The purpose of this importance is to verify and select the contributing features to reduce computation. The following figure was obtained using'sklearn.inspection' library. It can be observed that all the features are contributing to classification; however, MFCC coefficient 4, MFCC coefficient 8, MFCC coefficient 6, and RMSE stand out with the highest importance. Figure 8 clearly indicates that the specific choice of features can provide high accuracy classification rates for all  
Figure 6: Confusion Matrix for the SVM classifier. Figure 7: (**a**) ROC curve of scream with OvR; (**b**) accuracy plot over iteration. targeted sounds for this specific application, likely leading to a computationally more efficient solution.
# Integration of LTE 230 and LTE 1800 in Power Wireless Private Networks
## 1 Introduction
Each has advantages and disadvantages, so the two systems are better used simultaneously. This can improve the reliability and coverage according to the current research. However, this would increase the cost since two independent core networks are needed, thus requiring a significant amount of hardware. Therefore, understanding how to integrate these two private networks effectively is a challenge. In this paper, we discuss the core network and access network integration of LTE 1800 and LTE 230 and analyze the capacity of the integration system. For the core network, we discuss the integrated core network structure to combine the same unit in the two private networks. For the access network, we propose a multinetwork integration controller to help a device access the optimal cell. In Section 2, we briefly describe the high-level design of the PWPN and compare LTE 1800 and LTE 230. In Section 3, we propose the high-level design for core network integration. In Section 4, we propose the design for access network integration. Section 5 presents the conclusions.
## 2 Materials and Methods
### Signal Processing  
#### 2.3.1 Reference Bone Model: Flat Bone Plate  
Details of the multi-focus signal processing steps have been described previously [19]. From the delay and beamformed (DAS) Hilbert-transformed envelope signal, the amplitudes [\(H_{\text{F}}(F_{z})\) and \(H_{\text{B}}(F_{z})\)] and pulse travel times [\(TOF_{\text{F}}(F_{z})\) and \(TOF_{\text{B}}(F_{z})\)] of the signals reflected from the front- and backsides of the plate were tracked for each beam focus position \(F_{z}\). The time-of-flight difference between front- and backside reflections was defined as \(\Delta TOF=TOF_{\text{B}}(F_{z})-TOF_{\text{F}}(F_{z})\). Spline interpolation was used to estimate \(H_{\text{F}}(F_{z})\) and \(H_{\text{B}}(F_{z})\) at an \(F_{z}\) increment of 0.1 mm. The interpolated data, and the front- and backside focus positions \(F_{F}\) and \(F_{B}\), respectively, were retrieved from the peak positions of \(H_{\text{F}}(F_{z})\) and \(H_{\text{B}}(F_{z})\), and \(\Delta F_{z}\) (i.e., the shift needed to focus either on the front- or backside of the plate, and to estimate the time delay between front- and backside reflections \(\Delta TOF\)). \(Ct.Th\) and \(Ct.v_{11}\) were estimated using Equation (3) in [19] with sound velocity in water \(v_{H20}\):  
\[Ct.Th\ =\ \frac{\Delta F_{z}}{0.5\frac{Ct.v_{11}}{v_{H_{2}0}}.\bigg{(}1\ -\frac{Ct.v_{11}}{v_{H_{2}0}}^{2}\bigg{)}.\bigg{(}1\ -\cos\Bigl{(}k_{\text{\emph{eff}}}\theta\Bigr{)}\bigg{)}-\frac{Ct.v_{11}}{v_{ H_{2}0}}}, \tag{2}\]  
where \(\theta\) is the semi-aperture angle of the transmitting and receiving beams, and \(k_{\text{\emph{eff}}}\) is an effective aperture contributing to the beam focusing on the backside of the plate. The effective aperture accounts for the increased conversion of compressional waves into shear waves with increasing inclination angles and the absence of compressional wave transmission into the bone tissue for inclination angles larger than the critical angle \(\theta_{\text{\emph{crit}}}\)[19]:  
\[\theta_{\text{\emph{crit}}}\ =\ \sin^{-1}\biggl{(}\frac{v_{H_{2}O}}{Ct.v_{11}} \biggr{)}. \tag{3}\]  
In contrast to our previous study [19], we have used an aperture size of 64 elements and adjusted the factor to estimate the effective aperture \(k_{\text{\emph{eff}}}\) from 0.1 to 0.122:  
\[k_{\text{\emph{eff}}}\ =\ \biggl{(}\begin{array}{cc}1&\text{if }\theta\ <\ \theta_{\text{\emph{crit}}}\ -\ 10^{\circ}\\ 0.122\cdot\Delta\theta&\text{if }\theta\ >\ \theta_{\text{\emph{crit}}}\ -\ 10^{\circ}\end{array}\biggr{)}. \tag{4}\]  
More details on the estimation for \(k_{\text{\emph{eff}}}\) can be found in Appendix A.
## 2 Proposed Circuit Topology and Operation Analysis
### LC Resonant Converter  
Since the self-excited driving voltages were directly obtained from the resonant circuit, the switching frequency of the power MOSFETs was, of course, synchronous with the resonant voltage waveform, thus making the switching frequency and the resonant frequency identical. The voltage gain \(M_{vr}\) of the resonant circuit is defined as the ratio of the magnitude of \(T_{1}\)'s primary voltage to the magnitude of the input voltage, and can be expressed as:  
\[M_{vr}=\frac{V_{p}}{V_{AB}}=\frac{1}{\sqrt{1+\left[Q\left(\frac{\omega_{s}}{ \omega_{r}}-\frac{\omega_{r}}{\omega_{s}}\right)\right]^{2}}} \tag{5}\]  
\[Q=\frac{\sqrt{L_{r}/C_{r}}}{R_{p}} \tag{6}\]  
where \(Q\) is the quality factor of the resonant circuit, \(\omega_{s}\) designates the switching angular frequency, \(\omega_{r}\) is the resonant angular frequency, and \(R_{p}\) represents the equivalent load resistance between the primary winding. At steady-state operation, \(R_{p}\) can be expressed as [22]:  
\[R_{p}=\frac{8n^{2}R_{LED}}{\pi^{2}} \tag{7}\]  
Figure 2: Schematic waveforms of the self-excited mechanism.  
where \(R_{LED}\) represents the equivalent impedance of the LED string. Figure 3 depicts the relationship between voltage gain \(M_{vr}\) and frequency ratio \(f_{s}/f_{r}\) based on Equations (5) and (6). It can be seen that the circuit exhibits capacitive characteristics when the switching frequency is smaller than the resonant frequency. The power switches will possess the feature of ZCS when\(f_{s}/f_{r}<1\). On the contrary, the circuit appears inductive when the switching frequency is greater than the resonant frequency. The power switches will possess ZVS when\(f_{s}/f_{r}>1\). As soon as the switching frequency and the resonant frequency are equal, the circuit possesses a resistive feature and the power switches can operate at both ZCS and ZVS. Figure 4 shows the ideally schematic waveforms of the input voltage of the resonant circuit \(v_{AB}\) and the resonant current \(i_{r}\) when \(f_{s}=f_{r}\), i.e., that the voltage switches to zero the moment the resonant current reaches zero. This makes the power switch undergo no extra switching loss.
## 3 Results and Discussion
### DPPH Radical Scavenging Assay  
Honey is a good source of natural antioxidants, which protect against oxidizing agents' effects on food preservation and human health, lowering the risk of heart disease, cancer, immune system deterioration, cataracts, and various inflammatory processes [51]. The overall hydrogen/electron donating activity of dietary antioxidant supplements, like honey, can be obtained through the DPPH radical scavenging effect. A common technique for assessing antioxidant activity is scavenging the stable DPPH radical method. The differences in antioxidant activity between the tested samples are most likely due to the floral source of honey. However, it is well known that plant species and a variety of other factors in plants influence phytochemical composition, including the presence of antioxidant-active molecules, and [52; 53; 54; 21; 43] may be responsible. The radical scavenging activity (RSA) results were presented in Figure 3, as DPPH inhibition (%) compared to the blank control. In general, the DPPH inhibition of the tested honey samples ranged between 3.4-66.4%. The Tallh sample demonstrated the highest inhibition (66.4%), followed by Sidr honey (32.6%), then Athel (13.9%) and Langnese (11.6%) honey. Spring flower honey has the lowest inhibition. The RSA of these samples very much correlated with the total phenolic content (TPC) (Figure 2) and phenolic compounds (Table 2). The antioxidant activity of these honey samples supported earlier studies that indicated phenolic chemicals are powerful antioxidants [55; 56].  
Talh honey had a higher percentage inhibition than the Malaysian honey (tualang) inhibition (59.89%), and Indian (57.5%) and Algerian honey samples (44.55%), again indicating that it has the highest antioxidant potential [57]. Serbian polyfloral honey' RSA ranged between 1.31-25.61% [58]. Vitamins, even in small amounts, do not contribute to a sample's antioxidant activity. Furthermore, many studies found no link between vitamin content and antioxidant activity [59].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Honey Sample** & **Vitamin C (mg/100 g)** \\ \hline Tallh & 1.4 \(\pm\) 0.11 \({}^{\text{a}}\) \\ Athel & 0.32 \(\pm\) 0.05 \({}^{\text{a}}\) \\ Spring flower & 0.25 \(\pm\) 0.11 \({}^{\text{a}}\) \\ Sidr & 2.59 \(\pm\) 1.03 \({}^{\text{a}}\) \\ Langnese & 1.35 \(\pm\) 0.31 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} Means sharing the same superscript (a) in the column are not significantly different from each other (\(p>0.05\)).  
\end{table}
Table 3: Analysis of vitamin C in honey samples.
# Resolving Gas Bubbles Ascending in Liquid Metal from Low-SNR Neutron Radiography Images
## 1 Introduction
Recently, dynamic mode decomposition (DMD) has been applied to the output of the MHD bubble chain flow simulation to study both large-scale flow structures and bubble wake flow in the bubble reference frame [55]. It has been demonstrated that DMD is a viable tool for an in-depth analysis of the complex dynamics mentioned above, and it was shown that there exists a very complex interplay of bubble wake flow and large-scale flow modes with a wide range of spatial and temporal scales. It has become clear that specialized and rather advanced image processing methods and tools are required to extract physically meaningful data from data sets acquired via dynamic neutron and/or X-ray imaging [1; 8; 43]. This is mainly due to the low signal-to-noise ratio (SNR) associated with imaging thick (>20-30 mm) layers of liquid metal at frame rates \(\gtrsim\)100 frames per second (FPS) and the need to resolve many often closely packed interacting objects. High frame rates are a requirement to enable capturing fast bubbles, drops and particles flowing in liquid metal and to avoid motion blur [1; 8; 43]. Meanwhile, neutron flux that can be used in experiments is limited by both the utilized neutron source and the rapid activation of model liquid metals such as gallium. Here we describe and demonstrate our new image processing methodology developed over the course of our dynamic neutron imaging experiments with bubble flow in liquid metal. We show that the implemented code is robust and can operate reliably at very low SNR in the presence of image artefacts. It is also shown that this version clearly outperforms our previous solution by resolving/avoiding known issues. In addition to demonstrating performance for dynamic neutron imaging data sets from our measurements with a rectangular gallium vessel with bubble chain flow, we have also performed direct experimental validation of the code by imaging a reference spherical body, both stationary and in motion, and have quantified the shape detection errors.
### Observational Data  
Observational data refers to information collected directly through instrument measurement and can be further categorized into remote sensing data and in situ observation data, depending on whether instruments observe ocean parameters from a distance or directly within the water column [11]. This section presents an overview of both categories, focusing on commonly used data sources, resolutions, and their applications in ocean studies.