esa-sceva/satcom-chunk-collection huggingface dataset:
### Geometry Reconstruction on the Full-Scale  
After obtaining the relative density distribution, the mesh size function \(f_{h}^{i}(x,y)\) could be reconstructed based on the mapping relationship between the relative density and the average aspect ratio of the struts. For the relative density distribution, the signed distance function, \(f_{g}^{i}(x,y)\), corresponding to the contour of the density, \(\rho_{i}\), could be calculated easily.  
The mesh size function \(f_{h}^{i}(x,y)\) could be obtained based on the signed distance function, \(f_{g}^{i}(x,y)\):  
\[f_{h}^{i}(x,y)=\triangle h\Big{[}1-H\Big{(}-f_{g}^{i}(x,y)\Big{)}\Big{]}+h_{1} \tag{16}\]  
where \(H(x)\) is the Heaviside function, \(\Delta h=h_{0}-h_{1}\) with \(h_{0}\) and \(h_{1}\) being given as  
\[\left\{\begin{array}{l}h_{0}=\alpha(\rho_{i-1})t\\ h_{1}=\alpha(\rho_{1})t\end{array}\right., \tag{17}\]  
where \(t\) is the width of the strut and \(\alpha\) is the average aspect ratio of the struts.  
After obtaining a series of signed distance functions, \(f_{h}^{i}(x,y)\), the final mesh size function, \(f_{h}(x,y)\), is calculated by  
\[f_{h}(x,y)=\min\Big{(}f_{h}^{1}(x,y),f_{h}^{2}(x,y),\cdots,f_{h}^{N_{\rho}}(x, y),f_{h}^{N_{\rho}+1}(x,y)\Big{)}, \tag{18}\]  
where \(f_{h}^{N_{\rho}+1}\) is the mesh size function corresponding to the contour of the relative density, \(\rho_{\text{max}}\).  
With the mesh size function, \(f_{h}(x,y)\), the skeleton of the design domain can be obtained [34; 35]. The signed distance function, \(f_{g}(x,y)\), of the Voronoi skeleton can be calculated by using the fast marching method [36]. The Voronoi cellular structure was reconstructed by cutting the level set function, \(\phi_{g}=f_{g}(x,y)+t/2\),  
\[\begin{cases}\phi_{g}>0&\Leftrightarrow\quad\text{solid material}\\ \phi_{g}=0&\Leftrightarrow\quad\text{material interface}\\ \phi_{g}<0&\Leftrightarrow\quad\text{void material}\end{cases}, \tag{19}\]  
where \(t\) is the width of the strut.
# Advances in Riblets Design
## 6 Crossflows and Riblet Surfaces
The authors indicated that increasing yaw angle would have a negligible effect on the lower values of \(s^{+}\) when compared to those with higher values. The study concluded that the decay in drag reduction was attributed to sloshing phenomena (the flow between ribbed geometries would move laterally between the rib geometries, which produced a motion normal to the wall). To explore the complexity of aircraft surfaces leading to crossflows on the wing or wing-body flow interactions, detailed studies were conducted to find optimum riblet families with adjusted geometrical variables. The optimum design of riblet geometries, in terms of direction, configuration, height and spacing, should consider the flow behavior over the solid surfaces. Infinite wings were investigated by considering the effect of yaw angle [53; 131]. [PERSON] et al. [53] carried out experimental studies with a 25\({}^{\circ}\) yaw angle. The authors indicated that the angle of attack and riblet streamwise location would affect the effectiveness of drag reduction. By changing the incidence angle between 0\({}^{\circ}\) to 6\({}^{\circ}\), they showed that the maximum drag reduction was continuously reduced from its maximum of about 8% at 0\({}^{\circ}\) to 1% at 6\({}^{\circ}\). The rapid fall in riblet effectiveness was recorded for increased angle of attack with significant yaw angles. [PERSON] et al. [131] numerically studied triangular geometries mounted on four different wings with variations of sweep angle between 0\({}^{\circ}\) and 45\({}^{\circ}\). The maximum drag reduction observed was 9.5% at 45\({}^{\circ}\) of yaw angle. The authors evaluated local streamlines behavior with the possibility of re-laminarization. They indicated that riblets have no effect on the direction of the streamlines for x/c < 0.3, while streamlines would be aligned with the direction of riblets for x/c > 0.3. Suppression of crossflows by riblets continued until the trailing edge region but rose for x/c > 0.99.
## 3 DEM-FDM Coupled Model
### Model Verification  
According to the document \"Code for Soil Test of Railway Engineering TB10102-2010\" [31], a foundation coefficient \(K_{30}\) test was performed using the coupled model for verification. An FD model of a loading plate was established above the subgrade. According to the requirements in the aforementioned document, a preload of 0.4 MPa was first applied to the loading plate and then unloaded after the subgrade stabilized. Reloading was then performed. The load and displacement of the loading plate were recorded. The schematic diagram of the \(K_{30}\) test and the resulting load-displacement curve are presented in Figure 6.  
The load \(P_{s}\) at the reference settlement value \(S_{s}\) (1.25 mm) was obtained from the load-displacement curve in Figure 6b. The foundation coefficient \(K_{30}\) can be calculated as follows:  
\[K_{30}=\frac{P_{s}}{\pi r^{2}S_{s}}, \tag{12}\]  
where \(r\) represents the radius of the loading plate (0.15 m).  
The calculation results indicated that the foundation coefficient \(K_{30}\) of the coupled model was 200.26 MPa/m, which meets the requirements of the document \"Code for Design of High Speed Railway TB 10621-2014,\" [32], which requires that the foundation coefficient \(K_{30}\) of the surface layer of the subgrade should be no less than 190 MPa/m. This indicates the practicality of the coupled model in this paper.  
When the displacement of the loading plate is 1.25 mm, the resulting stress on the loading plate and force chains in the surface layer of the subgrade are presented in Figure 7. In Figure 7, the force chains represent contact between gravel grains. The radius of a force chain corresponds to the value of the associated contact force.  
As shown in Figure 7a, under the action of the loading plate, the strongest force chains in the surface layer of the subgrade are mainly distributed under the loading plate and have the shape of a rough truncated cone. The value of the force chain in contact with the loading plate is the largest and continues to attenuate as the chain spreads downward. It can be seen from Figure 7b that the coupled interface can transfer the supporting force of the subgrade onto the loading plate and change the stress state of the loading plate. It can be inferred that the node velocity, displacement, and load information can be stably transferred between the continuous domain and discrete domain through the coupled interface.  
Figure 6: Schematic diagram and simulation results of the \(K_{30}\) test.
## 4 Experimental Tests of the Coaxial Line  
A photograph of the manufactured coaxial line before installation in the vacuum chamber is shown in Figure 10. Measurements of the characteristics of the line without plasma filling were performed using an Agilent E5063A vector network analyzer (VNA) with an operating frequency band of 100 kHz-18 GHz. This analyzer has a time domain reflectometry (TDR) measurement function that allows one to synthesize pulses of various durations, and analyze the transmitted and reflected signals in the time domain. During tests the first and second ports of the analyzer were connected by pre-calibrated high-frequency cables to the input and output of the coaxial line, respectively.  
The results of measuring the transmitted signals when software synthesized pulses with a duration of 600 ps and 200 ps are applied to the line are shown in Figure 11. For 600 ps input pulse, waveform distortion was negligible; the increase in FWHM was no more than 5%. A 200 ps pulse propagating through the line became approximately 1.7 times wider, which is generally consistent with the results of numerical simulations. The measured amplitudes of the transmitted pulses turned out to be smaller than in the numerical simulations: 83% for a duration of 600 ps (95% in a simulation), and 54% for a duration of 200 ps (63% in a simulation). The difference is due to the fact that the model did not take into account: (i) ohmic losses in the conductors; (ii) additional dielectric elements of the structure, namely, Caprolon spokes supporting the inner conductor. Reflections and re-emission of waves on these elements lead to additional losses in the amplitude of the transmitted signal. Moreover, the used model did not take into account the design features of the connectors and possible imperfections of the contacts in them.  
Figure 9: Waveforms of the voltage at the input (**a**) and at the output (**b**) of the coaxial line, filled with the plasma of density \(n_{e}=7.7\times 10^{8}\) cm\({}^{-3}\), and corresponding frequency spectra (**c**). The voltage level at the input of the line is designated as 1.  
In the course of experimental testing, the influence of circular antennas with a diameter of 50-100 cm, installed into the line for RF inductively coupled plasma generation on the characteristics of transmitted pulses, was checked. The measurements did not reveal any noticeable changes in the shape and amplitude of the transmitted pulses when two antennas were installed in central uniform section of the line.  
Thus, the experimental results showed that the manufactured line ensured the propagation of subnanosecond signals with shape distortions close to the simulation results. The amplitude of the transmitted signal was at least 50% of the incident one for 200 ps pulses, and at least 80% for 600 ps pulses. The change in the amplitude and distortions in the shape of pulses longer than 1 ns were even smaller.
## 5 Results and Discussion
### Parameter Setting of the Optimization Method  
Parameters in the PSO algorithm are set as follows, population size \(N=100\), maximum number of iterations \(T=100\). It is more appropriate for individual learning factor \(c_{1}\) and social learning factor \(c_{2}\) to be 2 [29]. As shown in Figure 5, the range of the horizontal coordinate \(x\) of the three B-spline curves from the side span to the mid tower ranges from 0 to 350 m, 0 to 350 m and 0 to 428 m respectively. The range of the vertical coordinate \(y\) is determined by Equation (4). In the main span, the amplification factor \(\beta\) of the cable pre-tension force feasible region is 0.4. Because an auxiliary pier and a non-cable zone girder section are set in the side span, a wider feasible region of cable pre-tension force is established, and the amplification factor \(\beta\) is 0.5. The maximum particle velocity is set to 10% of the value range to prevent algorithm divergence. The maximum velocity of the particle at time \(t\) is (35,600). The inertia weight decreases from \(\omega_{\text{max}}=0.9\) to \(\omega_{\text{min}}=0.4\). The weight vector in the objective function is (5,5).  
According to Chinese bridge design specification [32; 33], there are 6 lanes in total, and the lane after considering the reduction factor is 46.68 kN/m uniformly distributed load + 1600 kN concentrated force. There are many loading ways for live loads on the deck, and different loading positions will affect the allocation ratio of the deck's dead load between the crossing cables. In order to maximize the constraint stiffness of the crossing cables, the optimization process only considers the loading way with the most unfavorable  
Figure 7: Flow chart for evaluation of the cable pre-tension forces.  
structural stress. The mid tower of multi-tower cable-stayed bridge lacks the longitudinal constraint provided by backstays. When the live load only acts on one main span and the adjacent main span is not loaded, the deformation of the structure is the most significant. The arrangement of dead load and dead load + live load (the most unfavorable load case) is shown in Figure 8.
# TPSQLi: Test Prioritization for SQL Injection Vulnerability Detection in Web Applications
## 5 Evaluation
### Weights for Test
#### 5.3.3 Discussion of Effectiveness
The increase in \(FPM\) values and positive IR percentages confirm that TPSQLi is superior to ART4 SQLi, particularly in handling test execution time and precision. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Target** & **Method** & **FPM** & **IR** & **Average IR** \\ \hline \multirow{2}{*}{DVWA (SQL-blind)} & ART4 SQLi [9] & 1.0028 & \multirow{2}{*}{3.37\%} \\  & TPSQLi & 1.0366 & \\ \hline \multirow{2}{*}{DVWA (SQL)} & ART4 SQLi [9] & 1.0057 & \multirow{2}{*}{0.00\%} \\  & TPSQLi & 1.0057 & \\ \hline \multirow{2}{*}{R1} & ART4 SQLi [9] & 1.0004 & \multirow{2}{*}{19.95\%} \\  & TPSQLi & 1.2000 & \\ \hline \multirow{2}{*}{R2} & ART4 SQLi [9] & 1.0018 & \multirow{2}{*}{2.17\%} \\  & TPSQLi & 1.0236 & \\ \hline \multirow{2}{*}{R3} & ART4 SQLi [9] & 1.0004 & \multirow{2}{*}{16.62\%} \\  & TPSQLi & 1.1667 & \\ \hline \multirow{2}{*}{R4} & ART4 SQLi [9] & 1.0004 & \multirow{2}{*}{0.00\%} \\  & TPSQLi & 1.0004 & \\ \hline \multirow{2}{*}{R5} & ART4 SQLi [9] & 1.0009 & \multirow{2}{*}{0.71\%} \\  & TPSQLi & 1.0080 & \\ \hline \multirow{2}{*}{R6} & ART4 SQLi [9] & 1.0031 & \multirow{2}{*}{1.09\%} \\  & TPSQLi & 1.0140 & \\ \hline \multirow{2}{*}{R7} & ART4 SQLi [9] & 1.0018 & \multirow{2}{*}{1.70\%} \\  & TPSQLi & 1.0189 & \\ \hline \multirow{2}{*}{R8} & ART4 SQLi [9] & 1.0030 & \multirow{2}{*}{0.92\%} \\  & TPSQLi & 1.0123 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of TPSQLi and ART4 SQLi Based on False Positive Measure (\(FPM\)) and Improved Rate (\(IR\)) Across Various Test Targets.
# Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method
## 3 Theoretical Method
### Determining the Optimal SVD Decomposition Order  
An excessively high choice of the SVD order can result in the inclusion of noise information in the filtered signal, thus hindering the desired denoising effect. Conversely, when the selected singular order is excessively low, it may inadvertently filter out valuable components as noise. In this study, we individually reconstruct each singular value of the spectral signal into a one-dimensional signal. We then employ the frequency domain to compare and analyze each singular component, facilitating the determination of the optimal order for SVD-based denoising while preserving the essential components.  
The singular values of the signal \(Y\) are obtained through Equations (2)-(4) mentioned earlier. During each iteration \(\Delta\), only one of the \(\lambda_{i}\) is preserved, while the remaining singular values are set to zero. This process generates a collection of singular value vectors \(\Delta_{i}^{\prime}=\text{diag}(0,\cdots,\lambda_{i},\cdots,0)\), along with the singular value matrix and Hankel matrix, which can be represented in the following form:  
\[\sum_{i}^{\prime}=\begin{bmatrix}\Delta_{i}^{\prime}&0\\ 0&0\end{bmatrix} \tag{5}\]  
\[H_{i}^{\prime}=U\sum_{i}^{\prime}V^{T} \tag{6}\]  
Each singular value \(\lambda_{i}\) in \(\Delta_{i}^{\prime}\) is transformed into a matrix of singular values. The matrix is then inverted using singular value decomposition, resulting in the transformation matrix \(H_{i}^{\prime}\), as depicted in Equations (5) and (6) provided earlier. Subsequently, the Hankel matrix is reduced to yield the component signals \(Y_{i}\), which correspond to the original signal \(Y\). These component signals are utilized to reconstruct the spectral signal.  
We apply the FFT to each of the r component signals. Within each signal, we identify the frequency value corresponding to the maximum amplitude. These frequency values are then arranged in descending order, based on the singular values of the original signal. The primary frequency difference spectrum is derived through the application of first-order lagged difference processing. To accommodate specific situations, we establish a predetermined threshold range for differences. Singular values that precede the first difference value surpassing this threshold are preserved in the primary frequency difference spectrum, while the remaining singular values are nullified.
## 8 Results
### Frequency Dependent Separation of Seagrasses  
Frequency separation of habitats is a key field of research, aiming to introduce multi-spectral satellite approaches to seafloor mapping. In this work, multi-spectral backscatter data came from the  
Figure 9: Average angular response curves (ARCs) per Habitat type, as derived by the MBES backscatter data.  
multi-source swath-sonar systems used. The SSS system had a dual-frequency capacity producing instantaneously co-registered maps at 100 kHz and 400 kHz. This offered the option to explore the level of information revealed with each frequency. Figure 10 shows such an example in _Area A_ in Figure 1, where _P. oceanica_, _C. nodosa_ and sandy beds laying in proximity are ensonified using 100 kHz and 400 kHz frequencies. Those two frequencies are further combined to produce a false-color composite, with green and blue channels corresponding to 100 kHz and 400 kHz respectively. Comparably higher backscatter at 100 kHz is shown in green shades in the pseudo-color image, higher 400 kHz intensities in blue and similar intensities in grey. The maximum benefit of using both frequencies is that the _C. nodosa_ beds are remarkably better separated. This is because the slightly submerged rhizomes of _C. nodosa_ exhibits a much higher contrast at 100 kHz than at 400 kHz, as lower-frequency signals penetrate some centimeters in the sediments [71; 72]. This intensity difference between the color channels highlights the respective areas in the composite image. Conversely, soft sediments exhibit higher backscatter strength at 400 kHz than at 100 kHz, because of increased volume scatter (due to penetration) in the latter, thus producing blue shades in the respective regions. Finally, _Posidonia oceanica_, being a strong acoustic reflector with an isotropic scattering behavior, produces similar backscatter intensities in both frequencies, leading to grey shades in the pseudo-color image.  
SSS backscatter mosaics cannot be directly compared to MBES because of the lack of accurate co-registration. Moreover, the different pulse geometries and lengths between the two systems affect the volume scattering components of the seabed and thus a false-color image would not truly reflect the seabed response just on different frequencies but also to other transmit parameters. Nonetheless, each one of the three available backscatter mosaics exhibits different levels of separation between the various bottom types, and so they all worth to be included in a robust seafloor classification scheme, as it was realized in this work. Figure 11 shows how different habitat types _P. oceanica_ meadows, _C. nodosa_ beds and Sandbanks seem when mapped using the different transmit frequencies and swath mapping sonars.  
Figure 10: A dual frequency SSS record (_Area A_ in Figure 1) with _P. oceanica_, _C. nodosa_ and sand beds. (**a**) The 100 kHz record, (**b**) the 400 kHz record and (**c**) a pseudo-color image with green and blue channels corresponding to 100 kHz and 400 kHz respectively.
## 4 Experimental Results
### Model Analysis
#### 4.6.3 T-SNE Visualization  
To further validate the ability of the proposed method to synthesize unseen visual features, we visualize the distribution of unseen visual features synthesized by SDGZSL and ESTD-GZSL (ours) in Figure 6. We choose 20 unseen classes from the CUB dataset that have enough classes to show the class-wise comparison. Clearly, our proposed method generates more discriminative and robust visual representations. This will have a great positive impact on the disentanglement module.
# Multimodal Magnetic-Plasmonic Nanoparticles for Biomedical Applications
## 2 Synthetic Methods
### Coprecipitation Techniques  
Chemical coprecipitation is a common method of synthesis for magnetic nanoparticles, and involves mixing chosen metal precursors in a specific ratio, then using a base, such as NH\({}_{4}\)OH, to precipitate out the nanoparticles. Ordinarily, coprecipitation is carried out in aqueous media, which lends itself to biomedical applications--phase transfer reactions are not usually necessary, and the stabilising agent and coating material can be easily chosen to fit a specific purpose. A common approach involves precipitating magnetic nanoparticles and stabilising them in situ, then further functionalisation with another outer layer on the nanoparticles that can facilitate either the attachment or targeting of another species.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 5 Case Study and Result
### Comparison and Analysis of Dynamic Reconfiguration Results
These figures illustrate the voltage profiles of all nodes over 24 time periods using a 3D surface plot, where the color gradient reflects the voltage magnitudes. The horizontal axes represent node indices and time periods, while the vertical axis indicates the corresponding voltage values. As illustrated in Figure 11, the dynamic reconstruction method proposed in this study results in an overall increase in node voltage compared to the other two schemes. Voltage fluctuations are smaller, and the voltage levels are higher. Specifically, the lowest voltage has increased from 0.928 p.u. before reconstruction  
Figure 8: Network power loss at each time period under different schemes. This article introduces a random difference mutation strategy and a binomial crossover strategy into the iteration process of the EO algorithm to enhance the diversity of the candidate set in the entire equilibrium pool. The improved algorithm exhibits fast optimization and ensures population diversity throughout the process. While the standard EO algorithm tends to converge to local optima within the same number of iterations, the improved EO algorithm overcomes these limitations, enabling faster convergence toward the global optimal solution. In conclusion, the enhanced EO algorithm offers significant advantages in terms of optimization efficiency and solution quality.