esa-sceva/satcom-chunk-collection huggingface dataset:
# Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses
### Vehicle Modeling and Simulation of Road Roughness
The product of the tire stiffness \(k_{t1}\) and \(k_{t2}\) and the axle response \(y_{1}(t)\) and \(y_{2}(t)\) appears in the unknown force. If the whole vehicle is studied, the tire stiffness \(k_{t1}\) and \(k_{t2}\) appear only in the equations of motion in the vertical direction of the axle, whereas there are unknown forces in the axle's vertical equations of motion, resulting in identification failures due to underdetermination of the equations. The road roughness is modeled according to the power spectral density (PSD) method of the ISO-8608 standard [31] and is assumed to be a zero-mean smooth Gaussian stochastic process. The road roughness can be generated as follows:  
\[r(x)=\sum_{i=1}^{N}\sqrt{2G(n_{i})\Delta n}\cos(2\pi n_{i}x+\theta_{i});\ G(n_{i})=G(n_{0}) \left(\frac{n_{i}}{n_{0}}\right)^{-2} \tag{3}\]  
where \(n_{i}=\left(\omega_{\text{max}}-\omega_{\text{min}}\right)/\Delta\omega\), \(G(n_{i})\) is the one-sided power spectral density at spatial frequency \(n_{i}\), \(G(n_{0})\) is the one-sided PSD for the reference spatial frequency \(n_{0}=0.1\) m\({}^{-1}\). \(x\) is the location of the road roughness. \(\omega_{\text{max}}\) and \(\omega_{\text{min}}\) are denoted as the maximum and minimum frequencies in the frequency spectrum. \(\Delta\omega\) is the interval of the frequency band; \(\Delta n\) is the average frequency increment determined by the total length of the profile. The value of \(G(n_{0})\) indicates the road roughness class. In this paper, we use the ISO-8608 road roughness classes A, B, and C. Roughness classes A to C indicate an increase in the roughness class. The roughness amplitude \(G(n_{0})\) increases with increasing roughness grade and the values taken at different grades are shown in Table 1. \(\theta_{i}\) is a random phase angle uniformly distributed from 0 to \(2\pi\).
# Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase and Load Reduction
## 3 Simulation Result
### Tower Load  
Figure 9 shows the simulation results for the tower load. More specifically, the load is the fore-aft bending moment at the tower bottom. The mean wind speed, turbulent intensity, control frequency, and the turbine spacing were 8 m/s, 0.075, 0.1 Hz, and 7 RD, respectively. Figure 9a,c shows the dynamic tower loads in the time domain for three wind turbines with the baseline controller and the proposed controller, respectively. As can be seen from the figures, the most upstream wind turbine, regardless of wind farm controllers, experiences the most tower load. In addition, it is shown that with the proposed wind farm controller, the dynamic tower load of the most upstream wind turbine has been substantially reduced. Figure 9b,d shows the loads expressed in DEL (damage equivalent load) to express the fatigue load by periodic loads in a constant value for comparison. The DEL is mathematically defined as  
\[\mathrm{D_{eq}}=\left(\frac{\sum\limits_{k}n_{k}S_{k}^{m}}{\mathrm{N_{eq}}} \right)^{\frac{1}{m}} \tag{8}\]  
In Equation (8), \(S_{k}\) is the stress of the tower load having the \(k\)th magnitude out of the total tower load during a specified evaluation (simulation) time (commonly 600 s), \(n_{k}\) is the number of cycle of \(S_{k}\) and \(\mathrm{N_{eq}}\) is the equivalent number of cycle of \(\mathrm{D_{eq}}\) which is commonly calculated from the evaluation (simulation) time multiplied by a frequency of 1 Hz. In addition, in Equation (8), m is a constant related to the slope of the SN (stress to cycles to failure) curve of a material. For a steel tower, the value of 3.5 is used as \(m\)[36].  
With the baseline wind farm controller, the most upstream wind turbine has the highest load, and it is about 417 kN m. However, with the proposed wind farm controller, the most upstream wind turbine is commanded to generate power slightly less than that the wind turbine can actually generate. Therefore, the most upstream wind turbine passes more wind to downstream wind turbines. As the result, the load of the most upstream wind turbine decreased to 347 kN m.  
However, the loads of two downstream wind turbines with the baseline controller, which were 260 kN m and 226 kN m, increased to 285 kN m and 236 kN m with the proposed controller. Although the loads of the two downstream wind turbines increase, they are small compared to the load of the most upstream wind turbine. Therefore, the largest load with the proposed controller becomes lower than the largest load with the baseline controller. The load reduction with the proposedcontroller was not targeted in the proposed wind farm control but it can be automatically achieved by reducing the power generation of the most upstream wind turbine. As the result, the proposed wind farm controller is found to levelize the load distribution of wind turbines to some extent in the wind farm.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Ministatellites  
Alike medium/large satellites, existing SAR minisatellites have been launched one-at-a-time despite their volume and mass footprints reduced from medium/large satellites. Actually, minisatellites were just at the verge of enabling a multiple satellite launch onboard a single launch vehicle, which was realized in smaller SAR satellites later.
# Fatigue Crack Growth from Notches:
###### Abstract  
A numerical approach based on plastic crack tip opening displacement (CTOD) was followed to study fatigue crack growth (FCG) from notches. The identification of fundamental mechanisms was made considering notched and unnotched models, with and without contact of crack flanks. Different parameters were studied, namely, notch radius, crack length, stress state, and material. The notch increases the plastic CTOD, and therefore fatigue crack growth rate, da/dN, as expected. The reduction of notch radius increases da/dN but reduces the notch affected zone. Ahead of the notch affected zone, da/dN increases linearly with crack growth, with a rate that increases linearly with the plastic CTOD. The crack closure phenomenon has a dramatic effect under plane stress conditions but a limited effect on plane strain conditions. In the former case, the contact of crack flanks reduces substantially the effect of notch radius and the size of the notch affected zone. These trends are associated with the increase of crack closure level with notch radius. The material does not affect the global trends, but the reduction of yield stress increases the level of plastic deformation and therefore da/dN. The effect of material, and also of stress state, is mainly associated with crack closure.  
fatigue crack growth; notch; notch radius; CTOD; crack closure +
Footnote †: journal: Applied sciences
## 1 Introduction
Various industries benefit from this method, including medicine, cosmetics, agriculture, thermal energy storage, polymers, textiles, and self-healing materials [14; 15; 16; 17; 18]. The most commonly used polymers for nanoencapsulation include polymethyl methacrylate (PMMA), poly-caprolactone (PCL), and poly-lactic-co-glycolic acid (PLGA), the structures of which are illustrated in Figure 1 [19; 20; 21]. PMMA, PCL, and PLGA polymers were chosen for this study because they are biocompatible, non-toxic, and widely used in drug delivery applications [22; 23; 24]. PMMA is a thermoplastic polyester with high chemical hydrolysis resistance and high drug permeability [25; 26]. It is a well-established polymer for medical applications, including drug delivery systems, lenses, dental prosthetics, and tissue engineering scaffolds [27; 28]. PCL is an attractive polyester for drug delivery systems because of its slow degradation, high permeability to many drugs, and ease of functionalization [24]. PLGA is a versatile polyester, as various combinations of lactic and glycolic acid provide molecules with different properties that affect nanoparticle size, encapsulation efficiency, and release ability [29]. PMMA, PCL, and PLGA polymers are approved by the U.S. Food and Drug Administration (FDA) for biomedical applications and have been extensively studied in the development of carrier systems for anticancer drugs [22; 23; 24; 30]. This theoretical study aims to characterize the QSPR molecular properties of QPhNO\({}_{2}\), PMMA, PCL, and PLGA molecules and focuses on the molecular docking simulations to identify and visualize the most likely interactions between QPhNO\({}_{2}\) and these polymers in order to identify factors influencing suitable drug release. To achieve this, we will first describe the QSPR properties, vibrational spectra, and thermodynamic characteristics derived from the geometry optimization in the density functional theory (DFT) framework, as well as the physical and quantum-chemical properties of the QPhNO\({}_{2}\) anticancer molecule and the PMMA, PCL, and PLGA polymers. This information will help determine the appropriate nanoparticle type, structure, and release profile. Finally, we will discuss the interaction outcomes using molecular docking analysis for the drug-polymer pair.
# Effect of Bentonite Content and Hydration Time on Mechanical Properties of Sand-Bentonite Mixture
## 3 Results of Calcium Bentonite-Sand Mixture
### Comparisons between Sodium Bentonite and Calcium Bentonite  
Two types of bentonites (i.e., sodium bentonite and calcium bentonite) were mixed with sand and compared by consolidated drained shear tests. Table 2 lists the maximum deviator stress (i.e., peak stress) at various bentonite contents and confining pressure conditions. It should be noted that the hydration time was kept constant (i.e., 1 h) for this comparison. It can be found that the content of bentonite has a significant effect on the shear strength of the sand-bentonite mixture. Both for sodium bentonite and calcium bentonite, the maximum deviator stress decreased with the increase in bentonite contents. The maximum deviator stress of the calcium bentonite-sand mixture was higher than that of the sodium bentonite-sand mixture.  
Figure 4: Deviator stress–strain behavior of sand–bentonite mixture with various hydration times: (**a**) bentonite content 5%; (**b**) bentonite content 10%.  
Figure 5: The tangent modulus of sand–bentonite mixture with various bentonite contents under different hydration time: (**a**) hydration time 24 h; (**b**) hydration time 40 h; (**c**) hydration time 48 h.  
Figure 6: The nonlinear behavior of tangent modulus of sand–bentonite mixture at various bentonite contents and stress states: (**a**) confining stress 300 kPa, hydration time: 48 h; (**b**) confining stress 400 kPa, hydration time: 48 h.  
The hydration rate of calcium bentonite is higher than that of sodium bentonite. In this study, due to the difference in hydration rate, the hydration degree of calcium bentonite is higher than that of sodium bentonite in a short time. This verifies the test results wherein the shear strength of the calcium bentonite mixed specimen was noted to be greater than that of the corresponding sodium bentonite mixed specimen.  
Figure 7 shows the working mechanism of sand-bentonite mixture. The hydrated bentonite filled the skeleton between sand particles. The particle contact transformed to sand-mud mode instead of the direct contact of sand-sand, accordingly. The occlusion degree between sand particles decreased due to the lubrication of the bentonite, which leads to a decrease in the shear strength of sand specimens macroscopically. While the experimental test results show that the shear strength of the sand-bentonite mixture remained more than 90% of that of the pure sand specimen, which indicates that the strength loss was not persistent as bentonite increased. It is suggested that the bentonite played the roles of filling and partial reinforcement during the process of expansion and gelation. From this perspective, the impact that bentonite has on the strength of the sand-bentonite mixture was the result of the coupling effect of the hydration, expansion and gelation of bentonite. It is also proof that bentonite is a good choice for sand to improve the water retention and compactness properties under the premise of less loss of mechanical strength.
# Using Imaging Techniques to Analyze the Microstructure of
## 4 X-ray Computed Tomography (CT Scan)
### Principle and Method  
CT scanning uses a narrow X-ray beam that circles one slice of the sample at a certain depth by slowly rotating the sample 360\({}^{\circ}\) and taking a series of 2D images from different angles, as depicted in Figure 4a. These 2D images are used to create a full 2D image of the sample cross-section, which shows a part of the internal structure of the sample at that depth. This process is repeated to produce many slices throughout the whole sample [49]. These scans can be stacked one on top of the other to create a 3D reconstructed image of the sample as shown in Figure 4b.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Imaging Technique** & **Scanning Electron Microscope** \\ \hline Sample Size & A destructive technique AC cores are cut into small and thin slices with a depth up to 10 mm and a length up to 50 mm Figure 2 \\ \hline Sample Penetration & Can penetrate up to 3 micrometres into the sample surface based on the electron beam energy \\ \hline Type of Produced images & 2D (Direct) and 3D (3D images are formed by the image processing software of the attached computer) \\ \hline Source & Electron beam \\ \hline Imaging Tools & SEM or ESEM; attached computer Image processing software \\ \hline Image Resolution & up to 1 nm (0.001 microns) \\ \hline Produced Data & Coarse and fine aggregate gradation, size and distribution Air void volumes Cracks and changes due to F–T cycles \\ \hline \end{tabular}
\end{table}
Table 2: Scanning Electron Microscope (SEM).  
Figure 4: Schematic of an X-ray CT scanning device: (**a**) device components; (**b**) reconstructed 3D image of an asphalt sample.  
Typical CT devices used for medical applications can generate an image with a resolution of 1-2 mm for large samples (decimetre- to metre-size objects). While advanced CT systems in special institutions, such as the Canadian Light Source in Saskatchewan, Canada, can provide a resolution of 100-200 \(\upmu\)m for smaller-size samples (centimetre- to decimetre-size objects) [50]. CT scanners use X-rays to reveal the internal structure of a material based on its components' densities. X-ray systems produce ionizing radiation and need to be completed and operated by a radiology technologist.
## 1 Introduction
[PERSON] [20] conducted research on the difference between the horizontal and vertical propagation of underwater acoustic channels and proposed a method for analyzing the throughput performance based on this differentiation. However, this research failed to acknowledge that the discrepancy between horizontal and vertical propagation is caused by the channel arrival structure and did not thoroughly analyze the channel structure and physical layer communication. On the other hand, [PERSON] [21] and [PERSON] [22] utilized the signal-to-noise ratio to assess the successful reception of physical layer underwater acoustic communication signals and designed an MAC protocol. Nevertheless, in an underwater acoustic environment, channel characteristics such as the signal Doppler shift and multipath expansion structure significantly impact the transmission status of the physical layer. Currently, the propagation characteristics of ocean channels are not considered in underwater acoustic networks. This can lead to inadequate descriptions of interference conflicts, ultimately affecting the performance analysis and protocol design of underwater acoustic networks. Building upon the aforementioned research, this paper initially analyses the influence of underwater acoustic channels on the bit error rate and packet loss rate of underwater acoustic communication. It develops a decoding conflict model that adheres to the transmission laws of underwater acoustic channels, thus accurately modeling conflicts in underwater acoustic networks. Subsequently, by taking into account physical layer characteristics such as dynamic channels and multimode transmission, the transmission of the network is described using the decoding conflict model. By integrating the Markov decision process, an analysis model for achieving high-precision performance in multimode underwater acoustic networks is formulated. Additionally, a performance analysis method for multimode underwater acoustic networks is proposed. These efforts lay a theoretical foundation for underwater acoustic network design and subsequent protocol research. The rest of the paper is organized as follows. The system model is introduced in Section 2. In Section 3, the Markov decision process modeling of throughput performance limits of multimode underwater acoustic networks is presented. Additionally, a solution method based on dynamic programming and greedy algorithms is provided. Simulation results are presented and discussed in Section 4 and conclusions are drawn in Section 5.
## 4 Vibrational Spectroscopic Techniques for Non-Invasive Food Authentication
### NIR Spectroscopy for Non-Invasive Food Authentication
PLS-DA models for milk powder adulteration with melamine showed RMSECV < 0.5%, [66; 67], and it has also been used to identify protein adulteration in yogurt [68]. NIR reflectance spectra within the wavelength range of 1100 to 2498 nm provide a straightforward method for detecting 10% pulp wash in orange juice and sugar-acid mixtures, achieving an accuracy of 90% [69]. Additionally,visible and NIR reflectance spectroscopy has been utilized to detect the adulteration of strawberry and raspberry pureses with apples [70]. The transmission mode of NIR spectroscopy is highly effective for analyzing both the external and internal properties of a sample, as the transmitted light provides information about its internal composition. This method is one of the simplest and most versatile sampling techniques, applicable to solid, liquid, and gaseous samples. Transmission mode has been employed to assess the authenticity and detect adulteration in a variety of substances, including oils [71] and juices [72]. Additionally, transmission mode has been utilized to differentiate white wine samples based on their varietal origin [73] and predict the fatty acid composition of beef and chicken [63; 74]. The interactance mode of NIR spectroscopy offers a balanced approach between reflectance and transmission modes, making it particularly effective for obtaining internal information from samples, such as fruits and vegetables, where transmission measurements may be difficult [75]. In this mode, the light source and detector are placed close to each other, ensuring that specular reflection does not directly enter the detector. The light travels through the outer layer of the sample, where part of it is reflected from the surface, and the rest is either scattered by internal structures or captured by the detector. This setup results in a higher signal-to-noise ratio, improving measurement accuracy [76]. Transflectance mode in NIR spectroscopy integrates features of both transmission and reflectance, making it ideal for analyzing thin or transparent samples. Although it is not as commonly used as the other modes, transflectance proves effective for assessing liquid samples, particularly when paired with optical bundle probes. A variety of studies have explored the use of NIR transflectance spectroscopy for evaluating the authenticity and quality of honey [77; 78; 79]. Beyond honey, NIR spectroscopy has been applied to evaluate the quality and authenticity of various food products, including oils [80], cow milk [81], and meats [82]. Additionally, it has been utilized for the geographical classification of wines [83]. However, it must be pointed that water-rich matrices (e.g., fruits) required spectral preprocessing to mitigate scattering effects [45].
## 2 Materials and Methods
### Excess Spectra  
To calculate the excess spectrum of a mixture, the spectrum of the corresponding ideal (in a spectroscopic sense) solution is computed by adding the pure component spectra weighed by their mole fractions. This ideal spectrum is then subtracted from the experimentally measured mixture spectrum. A detailed description can be found in [16; 17]. The effects of excess mixing volume were compensated using density data determined by a vibrating tube densitometer (DMA 5000, Anton Paar GmbH, Graz, Austria). The densities were converted to excess volumes and fitted by a third-order Redlich-Kister equation for further use in the calculation of the excess spectra. The resulting difference spectrum from the real and ideal spectra shows signatures that are characteristic of certain molecular interactions. For instance, peak shifts manifest as S-shaped features. Moreover, in the IR excess spectrum, net positive or net negative signatures represent changes in the IR-activity resembling changes in the dipole moment of a certain bond. On the other hand, in the Raman excess spectrum, such signatures relate to changes in the polarizability.
# An AMR-Based Liquid Film Simulation with Surfactant Transport Using PLIC-HF Method
## 2 Governing Equations  
### Fluid Dynamic Equations  
The isothermal Navier-Stokes equation is derived from the compressible Navier-Stokes equation [20]. Under isothermal conditions, the time evolution of temperature \(T\) is not solved and \(T\) is considered to be a constant. When the Mach number is \(Ma=\frac{U}{c_{s}}\ll 1\), the density change is \(\frac{\delta\rho}{\rho_{0}}\ll 1\). \(U\) is the typical velocity, \(\rho_{0}\) is the constant density, and \(c_{s}\) is the sound speed. Assuming the flow is weakly compressible, in the computation of a single-phase flow, the density \(\rho\) is considered constant, and in the computation of a two-phase flow, the density \(\rho\) is calculated from the volume fraction. In this case, the density compressibility can be ignored.  
Additionally, when \(Ma=\frac{\Omega}{c_{s}}\ll 1\), the fluid velocity is much slower than the speed of sound, and propagation by sound waves becomes dominant, so the material derivative of pressure \(p\) can be approximated as the Eulerian time derivative of pressure \(p\). When this approximation is adopted, it has been reported that the results of the benchmark tests approach incompressibility [27]. This approaches was adopted in the present research.  
With the assumptions about the isothermal state and low Mach number, the governing equations of weakly compressible flows can be shown as  
\[\frac{\partial\mathbf{u}}{\partial t}+(\mathbf{u}\cdot\
abla)\mathbf{u}=-\frac{1}{\rho} \
abla p+\frac{1}{\rho}\
abla\cdot\mathbf{\tau}+\frac{1}{\rho}\mathbf{F}, \tag{1}\]  
\[\frac{\partial p}{\partial t}+(\mathbf{u}\cdot\
abla)p=-\rho c_{s}^{2}\
abla\cdot \mathbf{u}. \tag{2}\]  
where \(\mathbf{F}\) represents the external forces, which include the surface tension and gravity in the present research. \(\mathbf{\tau}\) is the viscous stress tensor.  
The speed of sound \(c_{s}\) is set using the typical velocity and initial Mach number as  
\[c_{s}=U/Ma \tag{3}\]  
where \(U\) is the typical velocity. \(c_{s}\) was given a large value in the present research to ensure that the low Mach number assumption was applicable during the simulation.  
An evolving pressure projection method was included in the current research to dampen the acoustic waves in weakly compressible fluid flows [30].
# Blockchain-Based Licensed Spectrum Fair Distribution Method towards 6G-Envisioned Communications
## 3 BEAST System Model  
A more attractive and effective mechanism for the 6G licensed spectrum distribution application scenario is proposed in this section, that is, BEAST. As an emerging distributed ledger technology, blockchain and a smart contract can be a quick and cost-effective alternative for fair and secure licensed spectrum distribution. In the following, we will describe the BEAST system composition and working principle.