esa-sceva/satcom-chunk-collection huggingface dataset:
# A Stability Analysis of the Ancient Site of Liye Based on the Strength Reduction Method
## 3 Basic Properties of Earthen Soil
### Direct Shear Test  
Through field investigation in the ancient city of Liye, it was found that the water content is high due to the existence of long-term water storage ditches in the earthen site. In the direct shear test, considering the actual engineering problems to be solved, the soil sample was not completely saturated, but based on the water content of the trench soil sample and the water content continuing to increase after rain, so the water contents of 20%, 22%, 24% and 26% were taken for analysis, respectively. The test data can provide data support for the cracking of trench topsoil. To understand how the strength characteristics of the soil changed when underwater, we performed direct shear tests under different moisture content conditions that were based on the maximum dry density obtained from the test. The instruments used to test the samples were ZJ quadruple strain-controlled direct shear instruments. The obtained indexes are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Natural Moisture Content (\%)** & **Natural Wet Density (g/cm\({}^{3}\))** & **Natural Dry Density (g/cm\({}^{3}\))** \\ \hline
21.80 & 1.05 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic physical properties of the soil samples.  
Figure 3: Damage status. (**a**) Wall surface stripping; (**b**) bottom erosion; (**c**) longitudinal cracking of wall; (**d**) complete part of trench.  
Based on the results in Table 2, we created a broken line diagram of the strength change of the site soil. This is shown in Figure 4. We observed a downward trend in the cohesion of the site soil that corresponded with increases in water content. When the water content is 20%, 22%, 24%, or 26%, the decreasing trend of the friction angle slows and remains stable while the cohesion continues to decrease with additional water content [15]. Because the soil particles are connected by a liquid bridge, when the water content is within a certain range, cohesion results from the surface tension that is generated by the liquid bridge and the van der Waals force between ions in the soil [16]. In this case, the phenomenon of basically stable cohesion and a decreasing friction angle can be explained.
# On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
## 4 Analysis on Eavesdropping Probability
### Analysis of Fri-Jam Schemes  
Recall that we consider three Fri-Jam schemes: FJ-Reg, FJ-Ran and FJ-PC schemes. Thus, we categorize our analysis into the following cases.
## 4 Adaptive Fault-Tolerant Communication Scheme  
Figure 2 illustrates the AFTCS scheme. AFTCS consists of three parts: fault-related information collection, fault-tolerant priority queue management and channel resource reservation based on priority queue. Biosensors collect and send physiological data and some fault-related information to the control node. They also execute commands from the control node. The control node analyzes perceived fault-related information, dynamically changes fault-tolerant priority of the sensors and allocates channel resources to different sensors to reduce the effect of channel impairments. Obviously, the AFTCS scheme is entirely consistent with the asymmetric structure of BSN. In order to guarantee the priorities of critical sensors when the channel resource is scarce, the fault-tolerant priority will be dynamically adjusted. We use the similar resource reservation approach used in [11, 21] to address channel impairments. The reserved bandwidth is adjusted according to fault-tolerant priority.  
\begin{table}
\begin{tabular}{l l} \hline \(K_{min}\) & Minimum packets transmitted in each interval for high delay sensitivity sensors \\ \(BW_{required}\) & Bandwidth requirements \\ \(BW_{Scount}\) & Bandwidth reservations for non-polling packets \\ \(BW_{ScCuave}\) & Bandwidth reservations for fault-related information packets \\ \(BW_{Scdata}\) & Bandwidth reservations for sampled data packets \\ \(BW_{ScSpelling}\) & Bandwidth reservations for polling packets \\ \(Q_{reversed}\) & Reservation sensor queue \\ \(Q_{removed}\) & The queue containing all sensors removed from reservation \\ \(Q_{nonreversed}\) & New \(Q_{reversed}\) after re-reservation \\ \(Q_{nvernored}\) & New \(Q_{removed}\) after re-reservation \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Schematic diagram of AFTCS.
## 2 Analytical Models
### Direct Models
#### 2.1.1 Hodkinson Equation  
The Hodkinson model comes from the method developed by [PERSON] in [10]. Thus, in order to provide a proper explanation of the current method, [PERSON]'s model is now illustrated. It is worth highlighting that it was numerically implemented and applied to the available experimental data but the results are not shown in the following since it is very similar to the more general Hodkinson equation.  
Figure 1: A 2D sketch of the experimentally tested stepped labyrinth seal. The main geometrical parameters are highlighted.  
[PERSON] derived his model directly from [PERSON]'s one with an empirical value for the flow coefficient. However, he added the carry-over factor (\(\mu\)), since the shape of a straight-through labyrinth seal allows a considerable percentage of kinetic energy to be carried from one cavity into the next one. Hence, Equation (1) becomes:  
\[\dot{m}=\mu\cdot K\cdot A\cdot\frac{P_{0,in}}{\sqrt{RT_{0,in}}}\cdot\sqrt{\frac {1-(\frac{P_{h,N}}{P_{0,in}})^{2}}{N-\ln\frac{P_{h,N}}{P_{0,in}}}} \tag{2}\]  
In addition, [PERSON] stated that, actually, the flow coefficient can be directly related to the percentage of kinetic energy carried through each tooth and hence, to \(\mu\) itself. Thus, \(K\) is assumed to be constant (\(K=0.65\)) and the variation of leakage is accounted for by varying only \(\mu\). [PERSON] obtained some empirical curves for labyrinths with a different number of teeth, showing that the kinetic energy carried over rises as the spacing between the teeth or the clearance gap increases. Starting from [PERSON]'s remarks, [PERSON] performed a series of dedicated experimental tests in order to retrieve a semi-empirical formula of the carry-over factor, defined as:  
\[\mu=\sqrt{\frac{1}{1-\left(\frac{N-1}{N}\right)\cdot\left(\frac{c/t}{c/t+0.02 }\right)}} \tag{3}\]  
[PERSON] developed this equation assuming that the jet downstream each tooth expands conically inside the cavity at a small angle from the fin tip [11]. The carry-over is clearly a function of the clearance-to-pitch ratio (\(c/t\)) and, for a fixed number of sealing points \(N\), it increases as \(c/t\) increases. However, for a very large value of clearance, \(\mu\) does not become infinity, but a constant asymptotic value is reached at approximately \(\sqrt{N}\).
## 4 Model and Control of PZT  
Actuators, such as the PZT, are widely employed in ultra-precision motion systems because of their distinctive advantages over other driving techniques; for example, their infinite displacement resolution, high stiffness, high actuating force and low heat generation. However, the actuators usually exhibit nonlinear hysteresis behaviors in response to electric fields due to the ferroelectric properties of their materials. Therefore, the voltage-driven strategy based on hysteresis compensation is prevalent in the control of piezoelectric actuators. The improvement of the linearity of the piezoelectric actuator can be realized by implementing model-based compensation strategies.
## 2 Materials and Methods
### Grid Sensitivity Analysis (GSA)  
Grid sensitivity analysis (GSA) was performed for the mixing chambers with obstacles, mainly to assess the reliability and accuracy of the present model. The two main parameters chosen for GSA were the blood volume fraction exiting the outlet boundary of the mixing chamber (which demonstrated the reliability and accuracy of fluid dynamics with level set interface module) and the temperature of the outlet nozzle (which demonstrated the reliability and accuracy of the heat transfer module). Three different grid sizes were considered using the concept of grid refinement factor (\(r\)) defined as  
\[r=h_{coarse}/h_{fine} \tag{10}\]  
where \(h_{coarse}\) was the mesh or grid size before refinement while \(h_{fine}\) was the mesh or grid size after refinement. In practice, \(r\) should be greater than 1.3 [47], and the chosen average grid sizes of 2 \(\times\) 10\({}^{-4}\), 1 \(\times\) 10\({}^{-4}\) and 3 \(\times\) 10\({}^{-5}\) m gave \(r\) values of 2.0 (=0.02/0.01) and 3.3 (=0.01/0.003). The blood volume fractions exiting the outlet boundary of the mixing chamber and the temperature of the outlet nozzle are shown in Figure 3. Variations in the obtained results for significantly different grid sizes (i.e., \(r\) = 2.0 and 3.3) were not significant, so the grid or mesh convergence of the present model was ensured.  
Figure 3: Grid sensitivity analysis (GSA) for: (**a**) blood volume fraction exiting the outlet boundary of the mixing chamber; and (**b**) temperature of the outlet nozzle for chosen average grid sizes of 2 \(\times\) 10\({}^{-4}\), 1 \(\times\) 10\({}^{-4}\) and 3 \(\times\) 10\({}^{-5}\) m.  
Figure 2: Seamless mesh employed for two mixing chambers with different configurations.
# A Review of the Influence of Steel Furnace Slag Type on the Properties of Cementitious Composites
## 5 Requirements for SFS Characterization
### Free MgO Content  
There is no commonly applied MgO equivalent to the ethylene glycol test for CaO, so free MgO can be difficult to estimate. Some selective extraction methods have been proposed, such as with potassium dichromate [287], ammonium nitrate [288], or ethylene glycol with iodine and ethanol [289; 290], while other studies proposed solid-state \({}^{25}\)Mg nuclear magnetic resonance spectroscopy [291] or petrographic methods [292], but their data are limited. However, the free MgO content is very important to estimate, because the MgO reaction with water is a longer-term reaction than CaO, resulting in the potential for unexpected failures at later ages. If the free MgO content is high enough, then quantitative XRD could provide a reasonable estimate [267; 268]. Alternatively, a method proposed by [PERSON] and [PERSON] [9] uses TGA to estimate the Mg(OH)\({}_{2}\) content in an SFS sample that was exposed to autoclave conditions to convert all free MgO to Mg(OH)\({}_{2}\). Microwave irradiation has also been proposed to rapidly convert all free MgO to Mg(OH)\({}_{2}\)[293]. From the Mg(OH)\({}_{2}\) content, the initial free MgO content can be estimated by stoichiometric conversion. For the BOF 1, EAF 1, and LMF 1 samples in Table 7, the free MgO contents were estimated by this method to be 2.2%, 0.2%, and 0.3%, respectively [9].  
One complication with assessing free MgO content is that the SFS can contain a solid solution of MgO and FeO (i.e., magnesiowistite), making the exact quantification of free MgO difficult [294].
# Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences
## 2 Approach
### Driver Fatigue Recognition Features
#### 2.3.1. EAR/MAR: An Outstanding Feature for Eyes/Mouth State Recognition  
In [33], a conceptual model is proposed for real-time eye blink detection using facial landmarks. However, the detection performance might be limited by the following facts: (1) it is evident that EAR (eye aspect ratio) fluctuates greatly when wearing glasses and (2) it is challenging to deal with photo pixels. The lower the number of pixels, the faster the processing, but also a certain resolution is required to improve the recognition.  
As mentioned above, the proposed method in this paper is different from the traditional method of calculating blink image processing. Using the EAR/MAR by TCDCN is a more concise solution regardless of whether the driver wears glasses or not. No matter whether the driver wears glasses or not, the scheme can accurately identify the eye feature points and it is a very simple calculation based on the distance ratio between eye feature points to identify driver fatigue.  
Figure 4: Facial landmarks with glasses.  
Figure 3: Facial landmarks with gender.  
In Figure 5a,b, \(P_{2}\), \(P_{3}\), \(P_{5}\), and \(P_{6}\) key points are the value of longitudinal coordinate; \(P_{1}\), \(P_{4}\) key points are the value of horizontal coordinate. The value of EAR/MAR can be calculated according to key points.  
\[\text{EAR {or} MAR}=\frac{\|P_{2}-P_{6}\|+\|P_{3}-P_{5}\|}{2\|P_{1}-P_{4}\|} \tag{3}\]  
As shown in Figure 5c, the two diagrams correspond to the change of the MAR curve in yawning state and the change of the ear curve in the one eye closing process, respectively. The ordinate values on the figure are MAR and EAR values corresponding to different times. The EAR value drops rapidly to close to zero, and then rises rapidly, representing that the driver blinks once. When the eyes of the driver are open, the EAR is over 0.2, or when the EAR is less than 0.2, it means that the eyes of the driver closed due to dozing [34]. When the driver yawns, the wider the mouth opens, the greater the MAR value, which determines that the driver yawns. According to the experimental curve, when the driver's mouth yawns, the MAR value is more than 0.60. Therefore, the values of EAR/MAR can be adopted to detect whether the driver is fatigue driving.
# Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification
## 2 Related Work and Background
### Locality Preserving Projection  
LPP builds a graph to obtain the connections among all HSI pixels and then intends to find an optimal projection \(\mathbf{A}\) to map the original HSI data into a low-dimensional space, while maintaining the local connection relationships [15; 16; 17; 18; 19]. Its objective function can be mathematically represented as  
\[\min\sum_{i,j}\|\mathbf{y}_{i}-\mathbf{y}_{j}\|^{2}\mathbf{W}_{ij} \tag{1}\]  
and the weight \(\mathbf{W}_{ij}\) is defined as  
\[\mathbf{W}_{ij}=\begin{cases}e^{-\frac{\|\mathbf{x}_{i}-\mathbf{x}_{j}\|^{2}} {2\sigma^{2}}},&\text{if}\quad\mathbf{x}_{i}\in\mathrm{N}(\mathbf{x}_{j}) \quad\text{or}\quad\mathbf{x}_{j}\in\mathrm{N}(\mathbf{x}_{i})\\ 0,&\text{otherwise}\end{cases} \tag{2}\]  
where \(\sigma\) denotes the scale and \(\mathrm{N}(\mathbf{x}_{i})\) stands for the neighbors of \(\mathbf{x}_{i}\). Equation (2) measures the similarity or the distance between pixels \(\mathbf{x}_{i}\) and \(\mathbf{x}_{i}\). Under the similarity, the objective in Equation (1) would lead to a heavy penalty if \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\) are mapped to be far away from each other; thus minimizing it guarantees that a small distance between \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\) tends to force a small distance between \(\mathbf{y}_{i}\) and \(\mathbf{y}_{j}\). Given a constraint \(\mathbf{Y}\mathbf{D}\mathbf{Y}=\mathbf{I}\), which means removing the arbitrary scaling factor, the aforementioned optimization problem can be further formulated as  
\[\begin{array}{l}\operatorname*{arg\,min}\,tr(\mathbf{A}^{T}\mathbf{X} \mathbf{L}\mathbf{X}^{T}\mathbf{A})\\ s.t.\quad\mathbf{A}^{T}\mathbf{X}\mathbf{D}\mathbf{X}^{T}\mathbf{A}=\mathbf{I} \end{array} \tag{3}\]  
where \(\mathbf{I}\) is an identity matrix, \(\mathbf{D}\) is a diagonal matrix with diagonal entries \(\mathbf{D}_{ii}=\sum\limits_{j}\mathbf{W}_{ij}\), and \(\mathbf{L}=\mathbf{D}-\mathbf{W}\) is the graph Laplacian matrix. The problem in Equation (3) can be solved by the following generalized eigenvalue formulation:  
\[\mathbf{X}\mathbf{L}\mathbf{X}^{T}\mathbf{a}=\lambda\mathbf{X}\mathbf{D} \mathbf{X}^{T}\mathbf{a} \tag{4}\]  
where \(\lambda\) denotes the eigenvalue and eigenvectors corresponding to the \(d\) smallest eigenvalues of Equation (4) from the projection matrix \(\mathbf{A}=[\mathbf{a}_{1},\mathbf{a}_{2},\cdots,\mathbf{a}_{d}]\).  
In brief, LPP aims to obtain a projection to perform an HSI dimensionality reduction while preserving the neighborhood structure of the data. Besides the standard LPP mentioned above, some considerations such as sparsity, tensor, and orthogonality can be utilized additionally to solve the projection [17; 18; 19; 29].
## 2 Materials and Methods
### Statistical Analysis  
All the data were analyzed using GraphPad Prism (version 9.0.0) (GraphPad Software, Boston, MA, USA). The normality of the data distribution and residuals was evaluated using D'Agostino-Pearson tests. For the content of bioactive molecules and the evaluation of antioxidant activity, the data were analyzed using one-way analysis of variance (ANOVA). For the growth inhibition assay, the data were analyzed using a two-way analysis of variance (two-way ANOVA), which included the effects of treatment, time, and their interaction. Additionally, the data concerning vitality and anti-inflammatory activity were analyzed using two-way ANOVA. Post hoc pairwise comparisons were performed using [PERSON]'s test. The data are reported as the mean \(\pm\) standard deviation, and differences were considered statistically significant at \(p\leq 0.05\).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Gene 1** & **Nucleotide Sequence** & **Accession Number** & **Reference** \\ \hline IL-1\(\beta\) & F: TCATG
# Real-Time HIFU Treatment Monitoring Using Pulse Inversion Ultrasonic Imaging
###### Abstract  
Real-time monitoring of high-intensity focused ultrasound (HIFU) surgery is essential for safe and accurate treatment. However, ultrasound imaging is difficult to use for treatment monitoring during HIFU surgery because of the high intensity of the HIFU echoes that are received by an imaging transducer. Here, we propose a real-time HIFU treatment monitoring method based on pulse inversion of imaging ultrasound; an imaging transducer fires ultrasound twice in \(0^{\circ}\) and \(180^{\circ}\) phases for one scanline while HIFUs of the same phase are transmitted in synchronization with the ultrasound transmission for imaging. By doing so, HIFU interferences can be eliminated after subtracting the two sets of the signals received by the imaging transducer. This function was implemented in a commercial research ultrasound scanner, and its performance was evaluated using the excised bovine liver. The experimental results demonstrated that the proposed method allowed ultrasound images to clearly show the echogenicity change induced by HIFU in the excised bovine liver. Additionally, it was confirmed that the moving velocity of the organs in the abdomen due to respiration does not affect the performance of the proposed method. Based on the experimental results, we believe that the proposed method can be used for real-time HIFU surgery monitoring that is a pivotal function for maximized treatment efficacy.  
Keywords:high-intensity focused ultrasound; HIFU surgery; HIFU treatment guidance; pulse inversion; ultrasound harmonics; HIFU interference cancellation; organ motion +
Footnote †: journal: Applied Sciences
## 3 Numerical Simulation
### Turbulence Model  
For the purposes of CFD analysis, the type of flow had to be defined. There are no conditions for the occurrence of laminar flow for air flow around longitudinal finned tubes due to their complex geometry and high value of Reynolds number. Therefore, the standard \(k\)-\(\varepsilon\) turbulence model was adopted in the simulations. The kinetic energy of the turbulence and dissipation factor were computed on the basis of the transport Equations (7) and (8).  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{\partial x_{i }}=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{k }}\bigg{)}\frac{\partial k}{\partial x_{j}}\Bigg{]}+G_{k}+G_{b}-\rho\varepsilon -Y_{M}+S_{k} \tag{7}\]  
Figure 8: Mesh validation results based on case c5: (1) polynomial approximation of wind force for different mesh quality (m1–m8).  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial(\rho eu_{i})}{\partial x _{i}}=\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{\mu_{t}}{\sigma_{k}} \right)\frac{\partial\varepsilon}{\partial x_{j}}\right]+C_{1\epsilon}\frac{ \varepsilon}{k}(G_{k}+C_{3\epsilon}G_{b})-C_{2\epsilon}\rho\frac{\epsilon^{2} }{k}+S_{\epsilon} \tag{8}\]  
where: \(G_{k}\) is the increase in the kinetic energy of turbulence caused by the gradient of average velocities, \(G_{b}\) stands for the energy generated by the phenomenon of buoyancy, \(Y_{m}\) is the energy associated with the fluid compressibility. \(C_{1\epsilon}\), \(C_{2\epsilon}\), and \(C_{3\epsilon}\) are constants of the model, whereas \(S_{k}\), \(S_{\epsilon}\) are, respectively, the turbulent Prandtl numbers. The turbulence specificity is determined using the parameters given by Equations (9)-(11), which are: intensity \(I\), length scale \(l\), and turbulent velocity \(\mu_{t}\).  
\[I=0.16\cdot Re^{-0.125} \tag{9}\]  
\[I=0.07\cdot D_{H} \tag{10}\]  
\[\mu_{t}=\rho\cdot C_{H}\cdot k^{2}\cdot\varepsilon^{-1} \tag{11}\]  
where: \(Re\) is the Reynolds number and \(D_{H}\) is the relevant hydraulic diameter. The model constants were accepted using default values: \(C_{1\epsilon}=1.44\), \(C_{2\epsilon}=1.92\), \(C_{H}=0.09\), \(\sigma_{k}=1.0\), and \(\sigma_{\epsilon}=1.3\). These values have been determined from experimental studies for fundamental turbulent flows [27] and are recommended by ANSYS [28].