esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Crack Propagation in Reinforced Concrete Elements
## 3 Crack Propagation Model (CPM)  
### Model Setup  
To implement the features listed in the previous section, the rheological model described in [6; 7] was extended. The basic model consists of two uniaxial bars (concrete and reinforcing steel), which are connected with springs at a defined distance (Figure 2). Here, the springs represent the bond between the steel bar and the concrete bar. The extended crack propagation model (CPM) allows \"free crack formation\" of the concrete tensile strength scattering along the bar or component axis (cf. models in [3; 4]). The scattering of the concrete tensile strength is randomly generated according to a given distribution (e.g., normal distribution, logarithmic normal distribution).  
In contrast to the rheological model in [6; 7], the specified model length \(l\) of the CPM corresponds to the entire length of the simulated component. The crack formation is recorded via stiffness reduction of the corresponding concrete element \((EA)_{\text{c,l}}=0\) (Figure 3).  
Figure 2: Crack propagation model (CPM).  
Since a crack can only emerge between two nodes, i.e., in the middle of a concrete bar element (Figure 3), the crack width is calculated by using the slip difference of the left and right nodes, taking into account the steel bar strains. Considering that the strain values in the remaining \"concrete stumps\" (between concrete nodes \(\text{KC}_{\text{i}}\) and \(\text{KC}_{\text{i+1}}\)) equal zero and the steel strain values in the steel bar element i (between steel nodes \(\text{KS}_{\text{i}}\) and \(\text{KS}_{\text{i+1}}\)) correspond to \(\varepsilon_{\text{s,i}}=(u_{\text{KS,i+1}}-u_{\text{KS,i}})/l_{\text{i}}\), the calculated crack width can be simplified as the difference between the translation \(u\) of the concrete nodes \(\text{KC}_{\text{i+1}}\) and \(\text{KC}_{\text{i}}\):  
\[\begin{array}{c}w_{\text{cal,i}}\,=\,s_{\text{i}}\,-\,s_{\text{i+1}}+ \varepsilon_{\text{s,i}}\cdot l_{\text{i}}\\ =(u_{\text{KS,i}}-u_{\text{KC,i}})-(u_{\text{KS,i+1}}-u_{\text{KC,i+1}})+ \varepsilon_{\text{s,i}}\cdot l_{\text{i}}\\ =u_{\text{KC,i+1}}-u_{\text{KC,i}}\end{array} \tag{1}\]  
where \(s_{\text{i}}\) is the slip at node i; \(u_{\text{KS,i}}\) is the translation of the steel bar at node i; \(u_{\text{KC,i}}\) is the translation of the concrete bar at node i; \(\varepsilon_{\text{s,i}}\) is the strain of the steel bar i; and \(l_{\text{i}}\) is the length of element i.  
Using such simplified linear displacement methods, the element size should be selected carefully to obtain reasonable results. In the CPM, the error of the determined crack widths is usually less than 3%, with 6 to 8 elements between two cracks. Therefore, the element length \(l_{\text{e}}\) is generally selected with regard to the expected crack spacing \(s_{\text{r}}\), which is assumed over a reasonable range for the investigated specimen (e.g., considering the reinforcement ratio, rebar diameter, and bond-slip relationship).
## 3 Proposed ATMALS-GSK Algorithm
### Atmals-Gsk
#### 3.2.3 Adaptive \(K_{f}\)  
The knowledge factor (\(K_{f}\)) in the GSK algorithm governs the intensity with which individuals move toward their knowledge sources during the learning process. Serving as a scaling factor, \(K_{f}\) directly influences the magnitude of updates in both the junior and senior phases. With its range defined over (0, \(\infty\)), different values of \(K_{f}\) induce diverse search behaviors. Smaller values (e.g., 0.1-0.4) encourage fine-grained, cautious movements, favoring local refinement and stability--particularly beneficial in complex or noisy problem spaces. Moderate settings (around 0.5-1) offer a balanced trade-off, promoting steady convergence without excessive risk, which is often ideal for general-purpose optimization tasks. On the other hand, higher values of \(K_{f}\) (e.g., 2, 5, or even 10+) induce more aggressive movements toward knowledge sources, enabling faster exploration and improving the algorithm's chances of escaping local optima early in the search. However, such large steps can lead to instability or overshooting if not regulated.  
In the proposed ATMALS-GSK framework, \(K_{f}\) is adaptively tuned within a practical range of [0.2, 0.8] with a discretized level of 0.1, allowing the algorithm to adjust its aggressiveness over time. To support this adaptivity, the same Gaussian-based probability function used in Equations (3)-(7) is applied to \(K_{f}\) as well, enabling intelligent learning from historical performance and dynamically steering the search behavior. This adaptive mechanism ensures that \(K_{f}\) evolves with the problem landscape, contributing to a robust balance between effective exploration and reliable convergence.
## 4 The Floor Impact Noise Tests of SBR Latex-Modified Mortar
### Heavy-Weight Floor Impact Noise
#### 4.2.2 Impact Ball  
The major test on the impact ball excitation was conducted using the field measurement method of KS F 2810-2, similar to the test method for heavy-weight impact noise. The results of heavyweight impact noise using the impact ball are shown in Figure 14. As seen in Figure 14, the results of heavy-weight floor impact noise through impact ball excitation are similar across all mock-ups in the frequency range of 125-1000 Hz. This indicates consistent performance in noise reduction within this frequency band for the different mock-ups tested.  
Similarly to the results obtained with the bang machine excitation, the Tg \(-16\)\({}^{\circ}\)C SBR latex-mixed mock-up exhibited a higher reduction performance by about 7 dB at the 63 Hz frequency band, which is crucial for controlling heavy-weight floor impact noise compared to the other mock-ups.  
According to the test results on light-weight floor impact noise, it was found that mortar with high strength is more effective in reducing light-weight impact noise. Conversely, for heavy-weight floor impact noise, mortar with low strength enhances the reduction effect. To comprehensively analyze the reduction performance of floor impact noise, it was observed that the Tg \(-16\)\({}^{\circ}\)C SBR latex-mixed mortar showed a reduction effect of about 2-5 dB for light-weight impact noise and about 7-10 dB for heavy-weight impact noise.
## 3 Test Process and Failure Characteristics
### Analysis of Failure Characteristics  
By analyzing the strain data obtained in the test and observing the failure mode of the specimen, the failure characteristics of the specimen were analyzed as follows:  
(1) At the end of the beam of the RACFCST frame, bending cracks first appear, and then cross diagonal cracks evolve obliquely at 1/7-1/6 of the beam span and gradually develop into critical diagonal cracks. Finally, the RAC protective layer in the shear pressure area is lifted, forming a more  
Figure 6: The failure modes. (**a**) The failure process of the beam; (**b**) The ultimate failure mode of the frame.  
obvious bending shear plastic hinge. The strain at the bottom of the column reaches the yield strain, but the steel tube has no bulging and the plastic hinge is not obvious. In this test, the failure mode of beam results from bending shear failure, which meets the seismic design requirements of \"strong shear weak bending\". In the test of the RACFCST frame, the plastic hinge appears first in the beam and then in the column, which illustrates that the failure mechanism of the RACFCST frame structure results from beam hinge failure, which meets the seismic design requirements of \"strong column and weak beam\".  
(2) If the yields of the bottom steel tube and the end longitudinal bar are taken as the yield marks of the RACFCST column and beam respectively, according to the measured data of the strain at the bottom of the steel tube and the end of the longitudinal bar, we know the sequence of the plastic hinges of the frame, and the sequence of the plastic hinges in the specimen is shown in Figure 7. It can be seen from this figure that under the action of push-pull, the sequence of plastic hinge generation is from the beam end to column bottom, which indicates that the full failure of the RRAC beam delays the appearance of the plastic hinge at the column bottom, and that this kind of new structure can meet the energy dissipation mechanism of the beam hinge.  
(3) In the test, there is no vertical or diagonal crack at either end of the beam of the RACFCST frame, as shown in Figure 8. The reason is that the I-shaped extended corbel is arranged in this local area, and the corbel is welded with the stiffening ring outside the steel tube as a whole, which increases the strength and rigidity of the beam end and can effectively transfer the bending moment and shear force to the RACFCST frame column.  
Figure 8: The core area of the joint.  
Figure 7: The sequence of the plastic hinges of the test frame under (**a**) positive loading and (**b**) negative loading.  
(4) The RACFCST frame node area adopts the connection mode of the external stiffening ring. According to the test phenomenon, the core area of the node remains intact (see Figure 8). It complies with the seismic design requirements of \"strong node, weak component\".
## 2 System Description
### Evaluation Metrics  
The evaluation of the model was made based on different metrics to quantify the quality of the predictions of the detection system. We considered the following metrics:  
* Accuracy: In terms of a classifier, accuracy is defined as the probability of correctly predicting a class [14].
* Equal error rate: The equal error rate (EER) is the value where the proportion of false positives (FAR) is equal to the proportion of false negatives (FRR). This sets the detection sensitivity to where the number of errors produced is minimized [15; 16].
* Precision: Precision measures the fraction of examples classified as positive that are truly positive [17].
* Recall: Recall measures the fraction of positive examples that are correctly labeled [17].
## 2 Results and Discussion
### Validation
#### 2.2.2 Linearity, Limits of Detection (LODs) and Quantification (LOQs)  
The calibration curves for heptane, a-pinene, toluene, 2-methylpyrazine, 3-heptanone, heptanal, 2-octanone, 1-heptanol, benzaldehyde and 1-octanol are shown in Table 1. For this purpose, spiked samples of different concentrations (10, 25, 50, 100, 250 500, 1000, 5000, 10000, 25000 ng g\({}^{-1}\)) were analyzed. LOQ values ranged from 1.00-5.00 ng g\({}^{-1}\), while LOD values ranged from 0.33 to 1.67 ng g\({}^{-1}\). The upper limit of the calibration curves ranged between 2500 and 25000 ng g\({}^{-1}\), depending on the analyte. To the best of our knowledge, there are no mentioned LOQ and LOD values in the literature for the HS-SPME-GC-MS methods about nut drinks and nut extracts [5; 11; 25]. As it can be concluded, the developed HS-SPME method has satisfactory linearity range, as well as low LOD and LOQ values.  
Figure 5: Effect of sample volume on hexanal, 4-methyl-3-penten-2-one, heptane, benzaldehyde and 1-octen-3-ol.
## 2 Mathematical Model and Numerical Method
### Structure Motion Equations  
Considering that the cylinders are elastically mounted and free to oscillate in the transverse condition only, the corresponding motion of a single cylinder is governed by the following non-dimensional equation:  
\[\ddot{Y}+\frac{4\pi\zeta}{U_{I}}\dot{Y}+\frac{4\pi^{2}}{U_{I}^{2}}Y=\frac{2C_ {y}}{\pi m^{*}}, \tag{5}\]  
where \(Y\), \(\dot{Y}=\frac{dY}{dt}\), and \(\ddot{Y}=\frac{\dot{d}^{2}Y}{dt^{2}}\) are the cylinder displacement, velocity, and acceleration. \(C_{y}\) in Equation (5) represents the transverse force coefficient, defined as:  
\[C_{y}=\frac{2F_{y}}{\rho DU_{\infty}^{2}}, \tag{6}\]  
with \(F_{y}\) being the transverse force component, obtained from the solution of the N-S equations.  
Equation (5) is solved by transforming into the following forms:  
\[\dot{Y}=V, \tag{7a}\] \[\dot{V}+\frac{4\pi\zeta}{U_{I}}V+\frac{4\pi^{2}}{U_{I}^{2}}Y=\frac{2C_{y}}{\pi m ^{*}}. \tag{7b}\]The above equations are discretized as follows:  
\[\frac{Y^{n+1}-Y^{n}}{\Delta t}=\frac{1}{2}\Big{(}V^{n+1}+V^{n}\Big{)}, \tag{8a}\] \[\frac{V^{n+1}-V^{n}}{\Delta t}+\frac{4\pi\zeta}{U_{r}}V^{n+1}+\frac{4\pi^{2}}{U_{ r}^{2}}y^{n+1}=\frac{2C_{y}^{n+1}}{\pi m^{*}}. \tag{8b}\]  
Solving Equations (8a) and (8b), \(\dot{Y}^{n+1}\), \(\dot{Y}^{n+1}\), and \(Y^{n+1}\) can be found and they will be used as the boundary condition of the N-S equations.  
The loose coupling (LC) algorithm of FSI requires low computational cost as it requires the solution of equations of fluid and structure only once at each physical time step. The FSI-LC algorithm is always applicable to high \(m^{*}\)[30; 31] and so the algorithm for FSI-LC presented by [PERSON] et al. [32] is adopted in this paper.
## Abbreviations  
\begin{tabular}{l l} Latin symbols & \\ \(A^{*}\) & dimensionless coefficient \\ \(a\) & coefficient \\ \(a_{\text{mix}}\) & coefficient, the size of which depends on the molar fraction of the lighter component \\  & in the mixture \\ \(c_{1}\) & fuel-vapour molar concentration \\ \(c_{2}\) & oxidizer's molar concentration \\ \(c_{10}\) & fuel-vapor molar concentration of the drop surface (at the moment of ignition) \\ \(c_{p}^{gas}\) & specific heat capacity of vapor \\ \end{tabular}  
\begin{tabular}{l l} \(c_{p}^{Jiq}\) & specific heat capacity of liquid fuel \\ \(c_{v}\) & constant-volume specific heat \\ \(d_{0}\) & initial drop diameter \\ \(E\) & activation energy \\ \(H\) & enthalpy \\ \(K\) & dynamic modulus (Maxwell) \\ \(k\) & coefficient \\ \(k_{0}\) & temperature independent rate constant \\ \(L\) & latent heat of vaporization \\ \(I\) & average distance between collisions for a molecule \\ \(Le\) & Lewis number \\ \(M_{1}\), \(M_{2}\) & molecular weights of the components in a gaseous mixture \\ \(Nu\) & Nusselt number \\ \(p_{1}\) & partial pressure of the fuel-vapor \\ \(p_{2}\) & partial pressure of the oxidizer \\ \(Pr\) & Prandtl criterion \\ \(r\) & radius \\ \(R\) & universal gas constant \\ \(r_{0}\) & initial drop radius \\ \(r_{1}\) & distance to the zone, where chemical reaction begins \\ \(r_{c}\) & radius of a flame zone \\ \(t\) & time \\ \(T\) & temperature \\ \(T_{10}\) & temperature at which 10\% of the oil fraction evaporates \\ \(T_{50}\) & temperature at which 50\% of the oil fraction evaporates \\ \(T_{70}\) & temperature at which 70\% of the oil fraction evaporates \\ \(T_{cr}\) & critical temperature \\ \(T\): & temperature (K) \\ \(T_{0}\) & droplet temperature at initial time moment \\ \(T_{1}\) & temperature of the drop surface at the moment of ignition \\ \(T_{\infty}\) & ambient temperature \\ \(T_{Iij}\) & temperature of a liquid \\ \(Tb\) & boiling temperature \\ \(W\) & rate of a chemical reaction \\ \(\varpi\) & average molecular speed \\ \(x\) & number of carbon atoms in fuel molecule \\ \(x_{M(1,2,3...i)}\) & molar fractions of the components of a gaseous mixture \\ \(x_{Mpol}\) & sum of the mole fractions of the polar components in a mixture \\ \(y\) & number of hydrogen atoms in fuel molecule \\ \(Y\) & dimensionless constant \\ Greek symbols \\ \(\theta_{0}\) & temperature tensions between droplet surface and environment summative coefficient \\ \(\Lambda\) & dimensionless coefficient \\ \(\Lambda^{\rm mix}\) & thermal conductivity of gaseous mixture \\ \(\lambda^{\rm gas}\) & thermal conductivity of fuel-vapor \\ \(\mu\) & dynamic viscosity \\ \(\rho\) & density \\ \(\rho_{15}^{15}\) & relative density of a liquid (at \(T=15\) K) \\ \(\tau_{ign}\) & ignition delay time \\ \(\varphi\) & stoichiometric coefficient \\ \(\Phi\) & sum of the parts of atoms, groups, and bonds \\ DF & diesel fuel \\ RME & rapeseed methyl ester \\ \end{tabular}
## 2 Materials and Methods
### Spectral Resampling  
Next, calibration and validation pixels (originated from the Fenix hyperspectral image) were spectrally resampled according to each sensor band center and FWHM and saved as a new (resampled) database (see DB3 in Figure 1). The resampling process was executed using Spectral Python, which is a Python library for processing spectral image data ([[http://www.spectralpython.net](http://www.spectralpython.net)]([http://www.spectralpython.net](http://www.spectralpython.net))). In order to better illustrate the resampling process, Figure 3 shows an average spectrum (average of all clean and oil pixels in the DB3) per sensor. The spectra of the hyperspectral sensors are plotted continuously, while the spectra from the multispectral sensors are plotted segmentally, where each segment starts and ends with black dots, which represent the spectral band centers. For better visualization, the range of the values displayed on the axes in Figure 3 are sensor-specific (not identical for all sensors). The absorption features at approximately 2200 and 2300 nm stem from the presence of clay minerals and CaCO\({}_{3}\), respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Band Name** & **Min (nm)** & **Max (nm)** & **Band Center (nm)** & **FWHM (nm)** \\ \hline SWIR-1 & 1196.25 & 1228.75 & 1212.5 & 32.5 \\ SWIR-2 & 1261.25 & 1293.75 & 1277.5 & 32.5 \\ SWIR-3 & 1682.5 & 1707.5 & 1695 & 25 \\ SWIR-4 & 1732.5 & 1757.5 & 1745 & 25 \\ SWIR-5 & 2247.5 & 2282.5 & 2265 & 35 \\ SWIR-6 & 2317.5 & 2352.5 & 2335 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the theoretical Self sensor. FWHM: full width half maximum.
## 3 Results and Discussion  
### Results  
The concentration of NaCl in the aqueous solution could not be increased because NaCl reacts with the electrode with the voltage applied. The deterioration of the electrode during the experiment was minimized by setting the electrolyte concentration as low as possible (0.0001 mol/L), but when the electrolyte concentration was 0.001 mol/L, the electrode discolored and SPP resonance could not be detected. In addition, the experiment was carried out with an aqueous solution of sodium dihydrogeninghug a concentration of 0.1 mol/L, but the electrode was similarly deteriorated and SPP resonance could not be observed.  
Figure 2a,b show \(R_{p}/R_{s}\) spectra at three incidence angles \(\theta\) within the quartz prism. The resonance dip of the SPP in Ag was observed and its energy was blue-shifted as \(\theta\) was increased, expected from the resonance (intersection) between the SPP dispersion curve and the prism light line as a function of \(k_{\parallel}\), light wavenumber parallel to the Ag surface [12]. The resonance energy was red-shifted when the dielectric medium in contact with Ag surface was replaced from air to water, as expected from the surface plasmon energy \(\omega=\frac{\omega_{p}}{\sqrt{n^{2}+1}}\) as \(k_{\parallel}\rightarrow\infty\), where \(\omega_{p}\) is the bulk plasma frequency and \(n\) is the refractive index of the medium.  
Figure 3 shows electromodulation spectra synchronized with \(f\) at \(\theta=\)70.3\({}^{\circ}\) for frequencies \(f\) of 222, 333, and 444 Hz, with the applied voltages from 0.4 to 2.0 V. Compared with the reflection spectrum at \(\theta=\) 70.3\({}^{\circ}\) in Figure 2b, it is clear that the \(E\) field modulation signal is an energy shift of the SPP resonance dip, because the latter takes the form of  
Figure 2: Reflection spectra of Ag in contact with (**a**) air and (**b**) water. The incidence angle \(\theta\) in the prism was calculated as \(\theta=45^{\circ}+\sin^{-1}[\sin(\theta_{a}-45^{\circ})/1.47]\) from the incidence angle \(\theta_{a}\) in air [12] with the quartz refractive index 1.47. (For example, \(\theta=\) 70.3\({}^{\circ}\) when \(\theta_{a}=\) 84\({}^{\circ}\)).  
the first derivative of the former with respect to photon energy. The modulation signal synchronized with \(2f\) was smaller by more than one order of magnitude, so that the signal was dominated by the Pockels effect.
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 1 Introduction
### Use of Monopole Antennas  
[PERSON] used a long vertically mounted monopole antenna in almost all of his transmitters and receivers. According to the [PERSON]'s law of transmission, the maximum working telegraphic distance varied as the square of the height of the antenna if the transmitting antenna and the receiving antenna have the same height. That is, if \(H\) is the height of the antenna and \(D\) the maximum signaling distance, then we have \(H\propto\sqrt{D}\). As is explained in the rest of the article, the height of an antenna top plays a critical role in determining the transmission efficiency. Wireless power transfer is practically impossible without an antenna.  
In the sections that follow, our theoretical explanations will be largely based on the theory of Spoof Surface Plasmonic Polaritons [23; 24; 25; 26]. Spoof surface plasmons, also known as spoof surface plasmon polaritons, are surface electromagnetic waves in RF/microwave frequencies that propagate along an interface between two media of sign-changing permittivities [19; 20; 21; 22; 27]. Plasma is known to be a medium of unbound and charged particles with zero net charge, whilst surface electromagnetic waves do not necessarily contain any charge. Under a high-voltage stress, these charges will move on the interface that supports the propagation of a surface electromagnetic wave, contributing an electrical current that can do work. So far, there has not been any investigation into the high voltage effects on a surface electromagnetic wave or the link between the altitude-induced atmospheric electrostatic field and the transmission efficiency. In this work, these issues are experimentally explored. The following is a list of innovations of this work:  
1. A high-voltage surface wave can be wirelessly transmitted along a curved or highly bent interface between a low-loss non-polar dielectric medium and a high-loss polar dielectric medium.
2. According to our findings, it is possible to deliver a wireless energy to multiple loads, if and only if each of the loads is connected to a vertically mounted monopole antenna which reaches a higher altitude.
3. Our experimental results reveal that the salinity level can enhance the transmission efficiency to a significant extent.
4. The correlation between the antenna height and the maximum transmission range is largely consistent with the [PERSON]'s law of transmission.
## 3 Results and Discussion
### Effect of Carbonization Pressure on Composition and Microstructure of CGBS  
#### 3.2.1 XRD  
The XRD patterns of CGBS at different carbonization pressures are shown in Figure 10. As shown in Figure 10, due to carbonization, the diffraction peak of calcium carbonate crystals (CaCO\({}_{3}\), 26 \(\approx\) 29.5\({}^{\circ}\)) appears in the diffraction pattern of CGBS and as the carbonization pressure increases. The diffraction peak of calcium carbonate crystals increases first and then remains basically unchanged. When the carbonization pressure is 0.7 MPa, the diffraction peak of calcium carbonate crystals is the strongest. After further increasing the carbonization pressure, the diffraction peak of calcium carbonate tends to remain unchanged. This may be because when the carbonization pressure is too high, the carbonization reaction intensifies, and a dense calcium carbonate shell is formed on the surface of the particles. This hinders the penetration of CO\({}_{2}\) into the interior of the particles, slowing down the carbonization reaction rate [11].  
Figure 8: Schematic diagram of CGBS hysteresis loop.  
Figure 9: Relationship between hysteresis loop area and carbonization pressure.