esa-sceva/satcom-chunk-collection huggingface dataset:
# Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands
###### Abstract  
This paper assesses the potential of Synthetic Aperture Radar (SAR) in the C and L bands to penetrate into the canopy cover of wheat, maize and grasslands. For wheat and grasslands, the sensitivity of the C and L bands to in situ surface soil moisture (SSM) was first studied according to three levels of the Normalized Difference Vegetation Index (NDVI \(<\) 0.4, 0.4 \(<\) NDVI \(<\) 0.7, and NDVI \(>\) 0.7). Next, the temporal evolution of the SAR signal in the C and L bands was analyzed according to SSM and the NDVI. For wheat and grasslands, the results showed that the L-band in HH polarization penetrates the canopy even when the canopy is well-developed (NDVI \(>\) 0.7), whereas the penetration of the C-band into the canopy is limited for an NDVI \(<\) 0.7. For an NDVI less than 0.7, the sensitivity of the radar signal to SSM is approximately 0.27 dB/vol.% for the L-band in HH polarization and approximately 0.12 dB/vol.% for the C-band (in both VV and VH polarizations). For highly developed wheat and grassland cover (NDVI \(>\) 0.7), the sensitivity of the L-band in HH polarization to SSM is approximately 0.19 dB/vol.%, whereas as the C-band is insensitive to SSM. For maize, only the temporal evolution of the C-band according to SSM and the NDVI was studied because the swath of SAR images in the L-band did not cover the maize plots. The results showed that the C-band in VV polarization is able to penetrate the maize canopy even when the canopy is well developed (NDVI \(>\) 0.7) due to high-order scattering along the soil-vegetation pathway that contains a soil contribution. According to results obtained in this paper, the L-band would penetrate a well-developed maize cover since the penetration depth of the L-band is greater than that of the C-band.  
**Keywords:** ALOS-2; Sentinel-1; soil moisture; wheat; grassland; maize
###### Abstract  
The paper is aimed at an assessment of the importance of the coordinate system (Cartesian vs. cylindrical) assumed for simulations of free-round jets. The research is performed by applying the large eddy simulation method with spatial discretisation based on high-order compact difference schemes. The results obtained for natural and excited jets at three different turbulence intensity levels, \(Ti=0.01\%,0.1\%\) and \(1.0\%\), are compared. In the case of the natural jet, it is found that both instantaneous and time-averaged results are significantly dependent on the coordinate system only for the lowest \(Ti\). In this case, in the Cartesian coordinate system, the errors introduced by an azimuthal non-uniformity of the mesh seem to have a larger impact on the solutions than the disturbances generated at the nozzle exit. The azimuthal non-uniformity of the mesh also has a substantial influence on the results of the modelling of the excited jets. In this case, the excitation is introduced as time-varying forcing, with the frequency corresponding to half of the preferred mode frequency and the amplitude equal to \(5\%\) of the jet velocity. Such an excitation leads to the formation of the so-called side-jets being revealed as inclined streams of fluid ejected outside the main jet stream. Primary attention is paid to the mechanism of the formation of the side-jets, their number and location. The results obtained on Cartesian meshes show that for very low turbulence intensity levels (\(Ti=0.01\%\)), the number and direction of the side-jets are dependent on the non-uniform distribution of the mesh nodes along the azimuthal direction of the jet. On the other hand, when the cylindrical coordinate system is used, the number of the side-jets and their locations are random and dependent only on inlet parameters. It has been demonstrated that the mechanism of side-jet formation is the same in both coordinate systems; however, its random nature can only be predicted when the cylindrical coordinate system is used.  
2022
## 2 Physical Model and Mathematical Formulation
Using aforementioned assumptions, the governing flow equations are given below [28; 29; 43]:  
\[\frac{\partial\tilde{u}}{\partial\tilde{x}}+\frac{\partial\tilde{ \varphi}}{\partial\tilde{y}}=0,\] (1) \[\frac{\partial\tilde{u}}{\partial\tilde{t}}+\tilde{u}\frac{ \partial\tilde{u}}{\partial\tilde{x}}+\sigma\frac{\partial\tilde{u}}{\partial \tilde{y}}=\Big{(}\frac{\tilde{u}+\tilde{K}}{\tilde{\rho}}\Big{)}\frac{ \partial^{2}\tilde{u}}{\partial\tilde{y}^{2}}+\frac{\tilde{k}}{\tilde{\rho}} \frac{\partial\tilde{N}}{\partial\tilde{y}}-\frac{\sigma\tilde{B}^{2}(\tilde{ x})\tilde{u}}{\rho}-\frac{\tilde{v}}{\tilde{k}}\tilde{u}+\] \[\frac{1}{\tilde{\rho}}\Big{[}\tilde{\rho}\tilde{\beta}\tilde{ \xi}(\tilde{T}-\tilde{T}_{\infty})(1-\tilde{C}_{\infty})-\tilde{\xi}(\tilde{ C}-\tilde{C}_{f})(\tilde{\rho}_{p}-\tilde{\rho})-\tilde{\tau}^{*}\tilde{\xi}( \tilde{\rho}_{m}-\tilde{\rho})(\tilde{n}-\tilde{n}_{\infty})\Big{]},\] (2) \[\tilde{\eta}\Big{(}\frac{\partial\tilde{N}}{\partial\tilde{t}}+ \tilde{u}\frac{\partial\tilde{N}}{\partial\tilde{x}}+\sigma\frac{\partial \tilde{N}}{\partial\tilde{y}}\Big{)}=\tilde{\tau}\frac{\partial^{2}\tilde{N} }{\partial\tilde{y}^{2}}-K\Big{(}2\tilde{N}+\frac{\partial\tilde{u}}{\partial \tilde{y}}\Big{)},\] (3) \[\frac{\partial\tilde{T}}{\partial\tilde{t}}+\tilde{u}\frac{ \partial\tilde{T}}{\partial\tilde{x}}+\sigma\frac{\partial\tilde{T}}{\partial \tilde{y}}=\tilde{\alpha}\frac{\partial^{2}\tilde{T}}{\partial\tilde{y}^{2}}- \frac{\partial\tilde{\eta}}{\partial\tilde{y}}+\tau\Bigg{(}\tilde{D}  
After transformation above equations we get:  
\[(1+K)\frac{d^{3}f}{d\eta^{3}}+f\frac{d^{2}f}{d\eta^{2}}-\left(\frac{ df}{d\eta}\right)^{2}-\delta\left(\frac{\eta}{2}\frac{d^{2}f}{d\eta^{2}}+\frac{ df}{d\eta}\right)+K\frac{dg}{d\eta}-M\frac{df}{d\eta}-k_{p}\frac{df}{d\eta}\] \[\tilde{T}_{w}-\tilde{T}_{0}\bigg{(}\frac{\tilde{a}\tilde{x}}{2\tilde{v}(1- \tilde{\lambda}\tilde{t})^{2}}\bigg{)}=\tilde{T}_{\infty}.\]0 \[\tilde{T}_{w}-\tilde{T}_{0}\bigg{(}\frac{\tilde{a}\tilde{x}}{2\tilde{v}(1- \tilde{\lambda}\tilde{t})^{2}}\bigg{)}=\tilde{T}_{\infty}.\]1 (10) \[\tilde{T}_{w}-\tilde{T}_{0}\bigg{(}\frac{\tilde{a}\tilde{x}}{2\tilde{v}(1- \tilde{\lambda}\tilde{t})^{2}}\bigg{)}=\tilde{T}_{\infty}.\]2 (11) \[\tilde{T}_{w}-\tilde{T}_{0}\bigg{(}\frac{\tilde{a}\tilde{x}}{2\tilde{v}(1- \tilde{\lambda}\tilde{t})^{2}}\bigg{)}=\tilde{T}_{\infty}.\]3 (12) \[\tilde{T}_{w}-\tilde{T}_{0}\bigg{(}\frac{\tilde{a}\tilde{x}}{2\tilde{v}(1- \tilde{\lambda}\tilde{t})^{2}}\bigg{)}=\tilde{T}_{\infty}.\]4  
\[\left\{ \begin{array}{l}f(0)=f_{w},\ \frac{df(0)}{d\eta}=1+S_{f}\frac{d^{2}f(0)} {d\eta^{2}},\ g(0)=-m\frac{d^{2}f(0)}{d\eta^{2}},\ \theta(0)=1+S_{b}\frac{d\theta(0)}{d\eta},\\ \phi(0)=1+S_{\phi}\frac{d\theta(0)}{d\eta},\ \xi(0)=1-B,\ \ \ \ \text{at}\ \ \ \eta=0,\\ \frac{df(\infty)}{d\eta}\to 0,\ g(\infty)\to 0,\ \theta(\infty)\to 0,\ \phi(\infty)\to 0,\ \xi(\infty)= \to 0.\ \ \ \text{at}\ \ \ \eta\rightarrow\infty.\end{array} \right.
###### Abstract  
A novel sulfated xylogalactan-rich fraction (JSP for _J. adhaerens_ Sulfated Polysaccharide) was extracted from the red Tunisian seaweed _Jania adhaerens_. JSP was purified using an alcoholic precipitation process and characterized by Attenuated Total Reflectance-Fourier-transform infrared spectroscopy (ATR-FTIR), high-pressure size exclusion chromatography (HPSEC) with a multi-angle laser light scattering (MALLS), gas chromatography coupled to mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR, 1D and 2D). JSP was then evaluated regarding its physicochemical and rheological properties. Results showed that JSP was mainly composed of an agar-like xylogalactan sharing the general characteristics of corallinans. The structure of JSP was mainly composed of agaran disaccharide repeating units (\(\rightarrow\)3)-\(\beta\)-p-Gal-(1,4)-\(\alpha\)-\(\iota\)-Galp-(1\(\rightarrow\))\({}_{\text{n}}\) and (\(\rightarrow\)3)-\(\beta\)-p-Galp-(1,4)-3,6-\(\alpha\)-\(\iota\)-AnGalp-(1\(\rightarrow\))\({}_{\text{n}}\), mainly substituted on \(O\)-6 of (1,3)-\(\beta\)-p-Galp residues by \(\beta\)-xylosyl side chains, and less with sulfate or methoxy groups. (1,4)-\(\alpha\)-\(\iota\)-Gal\(p\) residues were also substituted by methoxy and/or sulfate groups in the \(O\)-2 and \(O\)-3 positions. Mass-average and number-average molecular masses (M\({}_{\text{w}}\)) and (M\({}_{\text{n}}\)), intrinsic viscosity ([\(\eta\)]) and hydrodynamic radius (R\({}_{\text{h}}\)) for JSP were, respectively, 8.0 \(\times\) 10\({}^{5}\) g/mol, 1.0 \(\times\) 10\({}^{5}\) g/mol, 76 mL/g and 16.8 nm, showing a flexible random coil conformation in solution. The critical overlap concentration C\({}^{\star}\) of JSP was evaluated at 7.5 g/L using the Williamson model. In the semi-diluted regime, JSP solutions displayed a shear-thinning behavior with a great viscoelasticity character influenced by temperature and monovalent salts. The flow characteristics of JSP were described by the Ostwald model.  
Corallinales; sulfated xylogalactan; _Jania adhaerens_; polysaccharide; rheology +
Footnote †: journal: Applied Sciences
# Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network +
## 1 Introduction
As it can be adopted for other tasks, such as surveillance, the basis of the system is usually already installed, and it can be managed by other applications after deployment. However, those who advocate sensor-based methods, such as the authors of [2], claim that video cameras are considerably more expensive than sensors and produce a large volume of data that may not be easily disseminated through a wireless network. On the contrary, a review of the literature, [3], revealed that image-based parking reservation detection policies can be deployed using extant surveillance cameras that are already attached to a primary monitoring system. Thus, it appears that image-based systems are a valid choice, especially for very large or outdoor parking areas where the installation of hundreds of sensors is impracticable. In consideration of this, many vision-based parking space management systems have been discussed and developed recently. Frequently, researchers turn to solve the issue with deep learning since it can extract features and learn to classify automatically. Deep networks, such as convolutional neural networks (CNNs), are robust to lighting variations and diverse circumstances. Nevertheless, without adjustment, a standard CNN cannot operate for all possible conditions. Thus, to create a robust parking space location system, we should take many difficulties into account, such as lighting variations, varying vehicle sizes, illuminance, perspective distortion, inter-object occlusion, camera resolution, and unfavorable weather. Most previous efforts were focused on resolving these difficulties. However, this task cannot be simply generalized and indeed the adaptation of a specific solution to an unknown parking lot is not easy. In this paper, to address the aforementioned issues, we introduce a CNN structure that uses dilated convolutional neural networks to indicate parking space occupancy status. The PKLot dataset [4], which is publicly accessible, was used to evaluate the performance of our model. The use of images of different scenarios and parking areas allowed us to test the generalizability of our approach. We trained our model on one subset of images and tested it on a completely different one. Several approaches already exist, such as that investigated in the study reported in [5], mAlexNet, where this type of generalization property was tested experimentally; however, we show that our method outperforms them. Most of the related studies compared their approach with AlexNet [6], which has demonstrated a high-level performance on this task. Thus, we analyzed our approach in comparison with AlexNet, as well as with other well-known deep learning structures. The remainder of the paper is organized as follows. Section 2 reviews the related work. The features of the dataset we used in our experiments are described in Section 3. In Section 4, we provide a detailed explanation of our classification method. Our experiments and the obtained results are described in Section 5. Finally, Section 6 concludes the paper.
# A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
## 6 Conclusions  
The developed method allows for the embedding of a fragile watermark into a selected vertex attribute, assuming that the attribute is accessible. In all scenarios considered, the method operated as anticipated--any alteration to the model failed to establish authenticity.  
Depending on the number of least significant bits allocated for watermark embedding and the chosen attribute, the introduced changes may be either visible or imperceptible. As demonstrated (Figure 6), replacing the entire mantissa of a floating-point number leads to noticeable model deformations, whereas modifying the eight LSBs remains below the threshold of perceptible distortion. In contrast, for texture coordinates, altering more than 12 bits results in visible artefacts (Figures 9-11). However, the visibility of these artefacts depends on the model's geometry and the distribution of its vertices.  
It is recommended that no more than 10 LSBs be used for attributes such as normal vectors and texture coordinates and no more than 8 LSBs for position attributes. The optimal choice is to employ the LSB.  
The study also examined whether the watermarking time depends on the number of bits used. It was found that the duration of both the embedding and verification processes is independent of the number of bits utilised. Therefore, it is preferable to modify a greater  
Figure 12: Interference level comparison. [PERSON] 2017 [21], [PERSON] 2024 [22], [PERSON] 2020 [28], [PERSON] 2022 [30], [PERSON] 2025 [40].  
number of vertices with minimal changes rather than apply more intrusive modifications to a smaller subset, which would lead to greater distortion.  
The proposed method is designed for glb files. It can also be applied to other file formats that support the storage of 3D mesh data in binary form (e.g., fbx, ply, blend, etc.). The watermark is embedded in the LSBs. There is no guarantee that the LSBs will remain intact during binary-to-text or text-to-binary conversion. Therefore, text-based (ASCII) formats (e.g., obj) are not suitable for our method.  
Although this work focuses on authenticating 3D assets in GLB format using fragile watermarking, the proposed method can be applied in various areas. One possibility is integration with Digital Rights Management (DRM) systems to help protect 3D models from unauthorised use or redistribution. It could also be useful in 3D supply chain applications, where authentication is needed to ensure that assets remain intact as they move through stages of design, production, and distribution. Due to its low computational cost, the method could be implemented during the resource loading phase in computer games or virtual reality environments to verify asset authenticity in real time. Additionally, it may be incorporated into IoT-connected devices that process or display 3D content, providing lightweight integrity checks across distributed platforms.
## 1 Introduction
With chaos phase modulation techniques, a synchronized copy of the chaotic symbol signal is generated at the receiver side and the demodulation is performed by exploiting this replica in different methods to recover the transmitted data, which is known as a coherent communication system [37]. Compared to the nonherent systems, such as differential chaos shift keying, chaos phase modulation does not have the problem of low data transmission rate, in which half of bit energy is used to transmit the reference signal [38; 39]. Because it modulates the phases of the carrier wave with chaos sequence, which can provide a huge number of modulation keyings and code resources, it can also overcome the problem of periodicity and binary values caused by conventional PN sequences. Furthermore, the signals of this type have nice orthogonality, providing high distinguishability between symbols, which can potentially facilitate the demodulation task at the receiver end. This paper innovatively processes the demodulation of chaos phase modulation signals as a target recognition problem, so it can be solved using either matched filter or machine learning methods. For the purpose of low bit error rate, a partial least square (PLS) regression-based demodulation framework is conceived. PLS algorithm is initially a standard tool for processing a wide spectrum of chemical data problems. Its success in chemometrics resulted in a lot of applications in other scientific areas including bioinformatics [40], food research [41], medicine [42], pharmacology [43], social sciences [44], etc. Recently, this regression method has been successfully used in hyperspectral image recognition applications in the biometric field [45; 46], which effectively improve the test accuracy by modeling the relations between training and prediction matrices. The use of PLS regression in chaos phase modulation communications allows us to weigh the received discrete signal through statistical analyses, further raising the accuracy of the system. The proposed demodulation scheme is implemented with a multi-core center processing unit (CPU). The evaluation experiments of this paper are conducted using simulation datasets, within which the time variation, multi-path effect, propagation loss and random noise are considered as distortions. In order to obtain unbiased results, two reference algorithms are implemented with different optimization forms. The results demonstrate that the proposed method achieves the best accuracy performance with high temporal efficiency within the experimental protocol of this paper. The remainder of this paper is organized as follows: Section 2 describes the chaos phase modulation scheme; Sections 3 and 4 present the proposed demodulation method and its parallel implementation, respectively; Section 5 analyzes the evaluation experiment results; finally, a conclusion is given in Section 6.
## 2 Automatic Segmentation Using U-Net
### U-Net Training Phase

Table 1: Time spent in ICA segmentation (232 image slices). Figure 7: Prediction results by U-Net: (**a**) ICAs; (**b**) optic nerves. Here, _TP_ represents cases where U-Net correctly predicted ICA or optic nerve locations, while, _FP_ represents cases where what U-Net predicted as true was actually false. Recall was defined in our study as shown in Equation (5):  
\[\text{Recall}=\frac{\textit{TP}}{\textit{TP}+\textit{FN}}. \tag{5}\]  
_FN_ represents cases where what U-Net predicted as false was actually true. Overall, a precision and recall close to 1 indicated that the U-Net prediction result was similar to that for the ground-truth images. The segmentation similarity of ICAs and optic nerves using U-Net is shown in Figure 8. As shown, the Jaccard similarity for the optic nerves was relatively high in comparison with that for the ICAs, indicating that the U-Net-based learning produced relatively accurate automatic segmentation for the optic nerves. This was because the distribution of optic nerve in the CTA images was narrower than that of ICAs. Although a different trend was observed for precision and recall, indicating that while overall performance was satisfactory, based on precision and recall results, trade-offs between precision and recall existed, as can be seen in Figure 9.  
In order to compare the performance of U-Net-based learning in this study, we applied the network structures of V-Net [28] and SegNet [29]. Only the neural network structure was changed but all training conditions were identical. The V-Net has a 3D convolution layers. V-Net has a characteristic of using 3-D volume directly as input/output because of using 3D convolution layer. However, U-Net performance is better than V-Net performance because the interval of patient data in this study is not the same. SegNet is different from U-Net. SegNet has pooling indices instead of concatenation. SegNet  
Figure 8: U-Net network based Jaccard similarity (20 CTA image slices). Figure 9: U-Net network based precision and recall (20 CTA image slices). (**a**) optical nerves precision and recall; (**b**) internal carotid arteries precision and recall. trains faster than U-Net because there was no concatenation but the learning performance was lower than that of U-Net. The total learning time was 576 min and 281 min for V-Net and SegNet, respectively. Comparing the results of U-Net learning with those of V-Net and SegNet revealed that U-Net exhibited the best performance as shown in Figures 8-12.
# A New Flow Control Method of Slat-Grid Channel-Coupled Configuration on High-Lift Device
## 2 Flow Control Optimization Strategy for Leading-Edge Slot of the Airfoil 30P30N Based on Grid Fin
### Aerodynamic Optimization Model of Grid-Slat Composite Configuration  
The DOE module in the Isght-integrated platform is adopted, and the numerical optimization of control design parameters is used to replace the optimization of grid-slat configuration geometry. It is a structured research method used to systematically study the relationship between the independent variable and the dependent variable. This method studies the effect of one or more input parameters (factors) on multiple output results (responses) by arranging experiments scientifically and reasonably and replacing comprehensive tests with partial tests. The seven steps of the DOE experiment design are target determination, process analysis, factor screening, fast approach, factorial experiment, regression experiment, and robust design. The optimized design variables are parameters in the parameterized expression of grid-slat curve configuration \((x_{1},x_{2},y_{2},y_{3},k_{3},x_{4},x_{5},y_{5},y_{6},k_{6})\). The objective of optimization is to obtain the maximum lift-drag ratio K. The calculation formula is as follows:  
\[C_{l}=\frac{L}{0.5\rho U_{\infty}^{2}c} \tag{1}\]  
\[C_{d}=\frac{D}{0.5\rho U_{\infty}^{2}c} \tag{2}\]  
\[K=C_{l}/C_{d} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Design Variable** & \(x_{1}\) **(mm)** & \(x_{2}\) **(mm)** & \(y_{2}\) **(mm)** & \(y_{3}\) **(mm)** & \(k_{3}\) **(\({}^{\circ}\))** \\ \hline value range & 11 \(\pm\) 30\% & 9 \(\pm\) 15\% & 11 \(\pm\) 15\% & 0–6 & 40 \(\pm\) 15\% \\ \hline
**Design Variable** & \(x_{4}\) **(mm)** & \(x_{5}\) **(mm)** & \(y_{5}\) **(mm)** & \(y_{6}\) **(mm)** & \(k_{6}\) **(\({}^{\circ}\))** \\ \hline value range & 16 \(\pm\) 30\% & 9 \(\pm\) 15\% & 11 \(\pm\) 15\% & 0–6 & 35 \(\pm\) 15\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design variable and value range.  
Figure 1: Grid-Slat configuration parameterization diagram.  
As shown in Figure 2, the optimization process of grid-slat configuration is as follows:  
1. The optimization module assigns values to ten design variables for the first time according to the constraints, and Catia establishes a two-dimensional parametric model of grid-slat configuration according to the assignment;
2. The pointwise divides the structural grid and output of the case file for Fluent calculation;
3. The flow field of the grid-slat configuration is calculated at an angle of attack of \(16.3^{\circ}\), and outputs the corresponding lift coefficient \(C_{l}\) and drag coefficient \(C_{d}\);
4. The Calculator module feeds back the lift-drag ratio K into the optimization platform according to the formula;
5. The DOE algorithm reassigns design variables according to the calculation results, and then enters the next round of optimization calculations.