esa-sceva/satcom-chunk-collection huggingface dataset:
Molecules_**2010**, _15_, 3744-3756; doi:10.3390/molecules15053744  
_molecules_  
**ISSN 1420-3049**  
www.mdpi.com/journal/molecules  
_Article_  
**Oak (_Quercus frainetto_ Ten.) Honeydew Honey--Approach to Screening of Volatile Organic Composition and Antioxidant Capacity (DPPH and FRAP Assay)**  
**[PERSON] \({}^{1\star}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) Faculty of Chemistry and Technology, University of Split, N. Tesle 10/V, 21000 Split, Croatia  
\({}^{2}\) Marko Marulic Polytechnic in Knin, P. Kresimira IV 30, 22300 Knin, Croatia  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 26 April 2010; in revised form: 17 May 2010 / Accepted: 24 May 2010 / Published: 25 May 2010_  
**Abstract:** Two samples of oak honeydew honey were investigated. Headspace solid-phase microextraction (HS-SPME) combined with GC and GC/MS enabled identification of the most volatile organic headspace compounds being dominated by terpen (mainly _cis_- and _trans_-linaloid oxides). The volatile and less-volatile organic composition of the samples was obtained by ultrasonic assisted extraction (USE) with two solvents (1:2 (v/v) pentane - diethyl ether mixture and dichloromethane) followed by GC and GC/MS analysis. Shikimic pathway derivatives are of particular interest with respect to the botanical origin of honey and the most abundant was phenylacetic acid (up to 16.4%). Antiradical activity (DPPH assay) of the honeydew samples was 4.5 and 5.1 mmol TEAC/kg. Ultrasonic solvent extracts showed several dozen times higher antiradical capacity in comparison to the honeydew. Antioxidant capacity (FRAP assay) of honeydew samples was 4.8 and 16.1 mmol Fe\({}^{2+}\)/kg, while the solvent mixture extracts showed antioxidant activity of 374.5 and 955.9 Fe\({}^{2+}\)/kg, respectively, and the dichloromethane extracts 127.3 and 101.5 mmol Fe\({}^{2+}\)/kg.  
**Keywords:**_Quercus frainetto_ Ten. honeydew honey; headspace solid-phase microextraction (HS-SPME); ultrasonic solvent extraction (USE); gas chromatography and mass spectrometry (GC and GC/MS); DPPH and FRAP assay
## 2 Materials and Methods
### A Method of Edge Line Restoration Based on Cutting Triangular Patches
#### 2.2.4 Three-Dimensional Model Reconstruction  
The reason for reconstructing a 3D model is that the number of vertices and triangular patches in the overall 3D model will be increased by cutting triangular patches. Therefore, it is necessary to reorganize the data in the 3D model.  
The reconstruction of a 3D model must return the 2D triangulation on the projection surface to 3D space, which only requires modification of the 3D coordinates. In our analysis, the original vertex was fitted into two planes according to the classification results in Section 2.2.2, and the new vertex was fitted to the manually selected feature line to complete the model reconstruction. The effect is shown in Figure 10.  
Figure 8: Triangle clipping method 2. (**a**) Triangle before cutting and (**b**) triangle after cutting.  
Figure 9: Triangle clipping method 3. (**a**) Triangle before cutting. (**b**) Triangle cut by an auxiliary line. (**c**) Triangle cut by an auxiliary line and a cutting line.  
The oblique photography 3D model requires a real texture map to enhance the realistic effect of the 3D model; thus, it also requires the texture coordinates corresponding to the new vertices. In this study, the texture coordinates of the new vertices were calculated by linear interpolation. The formula is as follows:  
\[\left\{\begin{aligned} & u_{JD}=u_{p1}+\left(u_{p2}-u_{p1}\right) \times d/D\\ & v_{JD}=v_{p1}+\left(v_{p2}-v_{p1}\right)\times d/D\end{aligned}\right. \tag{1}\]  
where \(u_{JD}\) and \(v_{JD}\) are the texture coordinates of the new vertex, \(u_{p1}\) and \(v_{p1}\), respectively; \(u_{p2}\) and \(v_{p2}\) are the texture coordinates corresponding to the vertex \(p1p2\) of the edge where the intersection is located; \(d\) is the distance from the intersection to the vertex \(p1\); and \(D\) is the edge length of the edge.
# A Data-Driven Intermittent Online Coverage Path Planning Method for AUV-Based Bathymetric Mapping
## 3 Proposed Method  
The method proposed in this paper can be considered as a combination of area-based approach and cell-based approach. The basic trajectory is composed of waypoints and lines that connect them, like area-based approach, so the coverage area will not be limited by computing resources. In the meantime, the workspace is divided into cells, so that the measured data and sensor attributes can be properly processed to assist online path planning.
## 3 Orthogonal Simplex Chebyshev-Laguerre Cubature Kalman Filter  
The key of the Bayesian filter is how to compute Gaussian weighted integrals whose integrands are a product of a nonlinear function and a Gaussian density function that are present in (5)-(7). Considering the standard Gaussian distribution \(N(\mathbf{x};0,I)\), the integral can be expressed as follows:  
\[I(f)=\int_{\mathbb{R}^{n_{x}}}f(\mathbf{x})N(\mathbf{x};0,\mathbf{I})d\mathbf{x}=(2\pi)^{-\frac {n_{x}}{2}}\int_{\mathbb{R}^{n_{x}}}f(\mathbf{x})e^{-\frac{1}{2}\mathbf{x}^{T}\mathbf{x}}d \mathbf{x}, \tag{12}\]  
where \(f(\mathbf{x})\) is an arbitrary nonlinear function.  
Now, we transform the integral from the Cartesian coordinate to the spherical-radial coordinate. Assuming that \(\mathbf{x}=\mathbf{r}\mathbf{y}\) with \(\mathbf{y}^{T}\mathbf{y}=1\) and \(r=\sqrt{\mathbf{x}^{T}\mathbf{x}}\), Equation (12) can be rewritten in the spherical-radial coordinate system as follows [18]:  
\[I(f)=(2\pi)^{-\frac{n_{x}}{2}}\int_{0}^{\infty}\int_{U_{\mathbf{x}_{x}}}f(\mathbf{r} \mathbf{y})\mathbf{r}^{n_{x}-1}e^{-\frac{2}{\sigma}}d\sigma(\mathbf{y})dr, \tag{13}\]where direction vector \(y\) and radius \(r\) are the corresponding parameters in the spherical-radial coordinate [18]. \(U_{n_{x}}=\left\{\mathbf{y}\in\mathbb{R}^{n_{x}}\big{|}\mathbf{y}^{T}\mathbf{y}=1\right\}\) is the spherical surface and \(d\sigma(\cdot)\) is the spherical surface measure. As a consequence, two kinds of integrals are given by the following:  
\[S(r)=\int_{U_{n_{x}}}f(r\mathbf{y})d\sigma(\mathbf{y}), \tag{14}\]  
\[G_{s}=(2\pi)^{-\frac{n_{x}}{2}}\int_{0}^{\infty}S(r)r^{n_{x}-1}\mathbf{e}^{-\frac{ r^{2}}{2}}dr, \tag{15}\]
## 5 Discussion  
### Accuracy and Real-Time of Scan Matching Algorithm  
It can be observed in the single-frame experiment that the parameter setting of the registration algorithm directly affects the accuracy and speed of registration. This paper also sets some parameters taking into account these two aspects. The parameter selection in this paper gives priority to accuracy, followed by speed. However, if an embedded system with limited computing power is used, this will not be a good choice. This experiment uses a 16-line LiDAR, and the number of laser points is not very large. It can be observed in the final registration result map that the registration speed is fast. The registration time of the NDT algorithm is more than 100 ms, and other registration algorithms are less than 100 ms. Scan-to-map has higher accuracy than scan-to-scan, but the registration time also increases accordingly. Real-time registration can be obtained by properly adjusting the filtering parameters.
###### Abstract  
In this paper, the finite-difference time-domain (FDTD) method is derived for electromagnetic simulations in media described by the time-fractional (TF) constitutive relations. TF Maxwell's equations are derived based on these constitutive relations and the Grunwald-Letnikov definition of a fractional derivative. Then the FDTD algorithm, which includes memory effects and energy dissipation of the considered media, is introduced. Finally, one-dimensional signal propagation in such electromagnetic media is considered. The proposed FDTD method is derived based on a discrete approximation of the Grunwald-Letnikov definition of the fractional derivative and evaluated in a code. The stability condition is derived for the proposed FDTD method based on a numerical-dispersion relation. The obtained numerical results are compared with the outcomes of reference frequency-domain simulations, proving the accuracy of the proposed approach. However, high spatial resolution is required in order to obtain accurate results. The developed FDTD method is, unfortunately, computation and memory demanding when compared to the ordinary FDTD algorithm.  
finite-difference time-domain; fractional calculus; Grunwald-Letnikov derivative; Maxwell's equations; stability limit 2023
## 4 Animals as Mobile Biological Sensors for Harsh Terrains
### Proposed System Structure
#### 4.1.2 Sample HPD scenario  
The followings are the steps of the location detection process in the software:  
1. Firstly, the reading interval is adjusted according to the number of animals used. This interval shows data flow frequency for each animal. In the sample application, it has been taken as 10 minutes.
2. One of the animals with an injected sensor in advance is selected, using Animal ID. In this example, B-002, \"bear\" has been selected.
3. The \"Absence Ratio\" is determined. This value changes with the animal type. Fast, frequently moving animals should have smaller reading interval and \"absence ratio\". In this example, absence ratio for the \"bear\" is 30 minutes.
4. \"Noise level\" is a variable used to recognize the human voice. If the animal has audio sensors, these are used to decompose between the different volumes of human voice. \"Pessimistic\" selection in this field is for the detection of very low level human voices in the critical territories. However, it was not used in this example, since signal tracking method was used for location detection.
5. The location grid in the software shows the tracking periods and coordinates of the selected animal. A different color indicates no signal for a 30 minutes period taken as the absence ratio. If the signal is picked up again, the distance information in the table is checked. A value of  
Figure 4: Detection software screenshot.  
between 0 and 10 meters (these ratios can be defined as parameters) clearly demonstrates that the animal stayed in a place from where no signal could be recorded in the period of not less than the absence ratio.
6. Security forces determine and check the location of first signal loss (considered as entrance point for hiding place) and where the signals re-establish themselves.  
Some predictions can be made depending on the animal used, a bear in this example. Because of the size of the bear the cavity can be considered correspondingly large. It is important to select parameters according to the size and features of animals, as well as regional features, and therefore cooperation with a zoologist with the required knowledge is essential.
## 4 Results
### Experiments
Figure 6: Graph of (**a**) PHQ-V predictions and (**b**) GAD-V predictions (right) compared with values calculated from the PHQ-9 and GAD-7 assessments. Multimodal  
One of the most surprising results was that combining different modalities did not improve the model scores compared with using text alone. Previous research [34; 38] has shown success with multimodal approaches, so similar results here may have been expected. Due to the surprising nature of that finding, several further experiments were run in an attempt to extract value from multimodal features. These experiments included:  
* Removing the transformer blocks and using simple concatenated embedding followed by the prediction head. * Using an outer product to combine features as suggested by [7]. * Adding dropout at the concatenation layer as suggested by [42]. * Using multiple pre-trained audio network variants including versions of wav2 vec, wav2 vec 2.0, and Hubert. * Weighting modalities with different learning rates and loss scales. None of the above approaches provided additional value when combining modalities. One possible explanation for this discrepancy is the difference in datasets used. The dataset introduced in this research used questions adapted from the PHQ-9, which has been shown to be highly effective at detecting depression [43]. It is possible that the highly specific nature of these questions increases the value from text to the point where audio provides no additional value. That said, it is also possible that multimodal solutions are by nature difficult [38; 42] and require finely crafted solutions that have not yet been found for this dataset. Further research is planned to investigate proposed solutions from [42], as well as using deep spectrum features instead of Hubert, as in [41]. One interesting note is that adding audio and video did increase the capacity of the network. The training loss of the multimodal model dropped quicker and stayed lower compared to text alone. Unfortunately, this added capacity does not appear to have generalized to the validation losses which remained nearly identical with or without the extra features. This agrees with findings [38] that adding extra modalities may make networks more prone to overfit. While the above approaches attempted to mitigate that overfitting, additional research is planned that will investigate other approaches to the problem.
# An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform
## 1 Introduction
In this paper, an optimal resonant frequency band feature extraction method based on EWT is proposed. First, the Fourier spectrum of vibration signals is segmented by a parameterless clustering scale-space histogram to obtain all the empirical modes. What's more, in order to improve the accuracy and robustness of the method to select the center frequency in the face of different intensity noise, the correlation coefficient is used as the adaptive regulatory factor to the ASN. Finally, by effectively combining the adjacent modes with the central mode, the optimal resonant frequency bandwidth is established for envelope spectrum analysis. The remainder of this paper is organized as follows: In Section 2, the basic theory of the EWT and scale space representation is reviewed. In Section 3, the specific implementation process of the AEWT method in repetitive transients is given. The simulation signals are studied using the proposed method in Section 4, and the actual application performance of the proposed method is shown in Section 5. Finally, the discussion and some conclusions are presented in Section 6.
## 4 Results
### Statistical Results  
We tested the above simulations under each condition several times to get the statistical results. In particular, for the large-scale formation problem, we set a different sensing radius to get the impact of this parameter. As shown in Table 1, the success rates with seven agents in the environment with obstacles and 31 agents in an open environment are both 100%. The sensing radius of those situations is all set to 30 m. For the large-scale test, we set this value to 30 m, 50 m, 70 m and 100 m. The corresponding success rates are 0%, 10%, 50% and 100%, respectively. The sensing radius obviously affects the results of large-scale simulations.
# Modes Control of Lamb Wave in Plates Using Meander-Line Electromagnetic Acoustic Transducers
## 3 Mode Control of Lamb Waves  
### Fundamentals of Meander-Line EMAT  
Previous studies revealed that there are three coupling mechanisms that contribute to excite the guided wave in a metal plate: The Lorentz force mechanism, magnetostriction mechanism, and magnetization force mechanism, respectively. The Lorentz force mechanism arises in all conducting materials, while the magnetostriction mechanism and magnetization force mechanism appear only in ferromagnetic material [18; 19; 20]. In this work, we investigate the EMATs based on the Lorentz force mechanism to generate single-mode Lamb waves. Figure 1 shows a classical configuration of a meander-line EMAT, which consists of a permanent magnet, a coil, and a specimen. The magnet is used to provide a bias magnetic field, and the coil is used to carry the dynamic current. When a pulsed alternating current feeds to the meander-line coil, then an eddy current (\(J\)) will be induced in the skin depth of the specimen. The interaction between the bias magnetic field (\(B\)) and the eddy current (\(J\)) produces a Lorentz force (\(F\)) in the specimen, as shown in Equation (22):  
\[F=J\times B \tag{22}\]  
When the particle in the skin depth vibrates with the Lorentz force, then the ultrasonic waves are generated.  
Figure 1: Schematic diagram of a meander-line coil electromagnetic acoustic transducer (EMAT).  
The acoustic field Equation (1) can be divided into two-dimensional wave motion equations:  
\[(\lambda+\mu)\frac{\partial}{\partial x}(\frac{\partial u}{\partial x}+\frac{ \partial v}{\partial y})+\mu(\frac{\partial^{2}u}{\partial x^{2}}+\frac{ \partial^{2}u}{\partial y^{2}})+F_{x}=\rho\frac{\partial^{2}u}{\partial t^{2}} \tag{23}\]  
\[(\lambda+\mu)\frac{\partial}{\partial y}(\frac{\partial u}{\partial x}+\frac{ \partial v}{\partial y})+\mu(\frac{\partial^{2}v}{\partial x^{2}}+\frac{ \partial^{2}v}{\partial y^{2}})+F_{y}=\rho\frac{\partial^{2}v}{\partial t^{2}} \tag{24}\]  
where \(u\) and \(v\) are displacements in \(x\) and \(y\) directions; \(F_{x}\) and \(F_{y}\) are Lorentz forces in \(x\) and \(y\) directions.  
The parameters of the meander-line coil are calculated by  
\[2L=\frac{C_{p}}{f} \tag{25}\]  
where \(C_{p}\) is the phase velocity,\(f\) is the ultrasonic frequency, and \(L\) is the wavelength of the Lamb wave; the \(C_{p}\) and \(f\) are selected according to the dispersion curves in Figure 2. When the thickness of the specimen and the excitation frequency are given, the wavelength of the Lamb wave is determined by Equation (25). The relation between the spacing interval of adjacent wires and the wavelength is described by  
\[L=2l(2N+1) \tag{26}\]  
where \(N\) is the integer (0, 1, 2...... ), and \(l\) is the spacing interval of adjacent wires.
# A Clutter Loss Model for Satellite Communication Systems
## 4 Reflection and Diffraction Loss
### Electromagnetic Properties of Ground and Building Materials  
The electromagnetic properties vary considerably from material to material, and they also typically depend on the wave frequency \(f\). The values of the relative dielectric permittivity \(\varepsilon_{r}\) (\(\varepsilon_{r}=\varepsilon/\varepsilon_{0}\)), the relative magnetic permeability \(\mu_{r}\) (\(\mu_{r}=\mu\) /\(\mu_{0}\) ) and the conductivity \(\sigma\) for several materials are provided in Recommendation ITU-R P.2040-1 [14].  
Different materials are considered in this work for the cities of London and Mel-bourne: the reflections induced by the buildings are calculated both for the incidence from air into a structure consisting of a mix of concrete and bricks (common walls), and for the incidence from air into a glass slab (windows). The structures of these two types of interfaces are sketched in Figure 5. The interface between air and asphalt is considered to account for ground reflections, with the electromagnetic parameters of the dry asphalt being retrieved from [15] at microwave frequencies (\(\sigma=0.005\) S/m, \(\varepsilon_{r}=4.5-j0.05\)).
## 2 Experimental Plan and Method  
### Experimental Plan  
This study was conducted to obtain empirical baseline data to support the development of performance-based application standards for fiestop system approval in Korea. In particular, it focuses on addressing a limitation of the current approval system, which requires a fixed volume of firestop material regardless of any reduction in opening or penetrant size. The aim is to quantitatively analyze how fire-resistance performance changes when the volume of firestop material is adjusted according to the cross-sectional areas of both the opening and the penetrant.  
The experiment was conducted separately for metallic and nonmetallic pipes, based on their thermal characteristics and structural continuity. Each condition was defined by combinations of opening and penetrant areas, calculated using the actual measured outer diameters. Although nominal sizes were categorized such as 50-100 mm, and 75-125 mm, all analysis and comparisons were based on actual cross-sectional area measurements derived from real dimensions.  
Metallic pipes maintain structural integrity even at high temperatures and serve as direct paths for heat transfer. In contrast, nonmetallic pipes tend to soften or disintegrate beyond a certain temperature depending on the material, losing their structural function. Therefore, it was anticipated that the two pipe types would exhibit different fire-resistance behavior under identical firestop conditions. To verify this, five different opening-penetrantcombinations were established for each pipe type; the detailed configurations are presented in Tables 1 and 2.  
The firestop material was applied uniformly under all conditions with a fixed thickness of 7.8 mm and a width of 40 mm. The applied area was incrementally adjusted by varying the length of material along the opening's perimeter. For the largest-opening condition, firestop material covered approximately 95% of the perimeter; this coverage was progressively reduced to 94%, 93%, 92%, and 91% depending on the experimental configuration. This approach enabled a comparative analysis of how reductions in firestop-material volume affect fire-resistance performance. This method was selected to simplify experimental variables, while also reflecting common field practices where shorter application lengths are typically used for smaller openings. Figure 1 shows the physical arrangement of the specimens, including the penetration opening, sleeve, pipe, firestop application, and insulation layout for both pipe types.