esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasibility of Optical Flow Field Measurements of the Coolant in a Grinding Machine
## 4 Measurement Results
### Flow Field with Rotating Grinding Wheel
Increasing the flow rate also increases the pressure inside the nozzle, which can lead to possible expansion of the orifice. To substantiate the assumption, theoretical heights of the nozzle are calculated from Equation (3) for the respective measured velocities and are added to Table 4. Considering the increase of the theoretical outlet opening that matches the measured flow velocities, small geometric deviations of the nozzle in the range of \(0.175\,\mathrm{mm}\) to \(0.292\,\mathrm{mm}\) might cause the difference in theoretical and measured supply velocities. Even though this study uses a special kind of nozzle, a measurement of the inflow condition is recommended for an accurate determination of the velocity ratio \(R\). It should be noted that the velocity is evaluated at a distance of \(80\,\mathrm{mm}\) to \(110\,\mathrm{mm}\) instead of directly at the nozzle's outlet. Although the velocity decreases with distance, as shown in Figure 8d, this is a minor effect that can be neglected. A measurement limitation currently remains in the area of the first interaction of the MWF supply jet close to the grinding wheel (compare with the area of interaction as shown in Figure 1). The shape of the flow has changed in comparison to the free jet but is not yet torn apart into droplets. The flow is opaque and no flow structures are detectable. The two present flows in this area (deflected and entrained) are subsequently torn into droplets. The further the flow is apart from the grinding wheel, the more the flow separates into droplets. The detectability of the droplets and thus also the evaluability of the flow field increases steadily with the distance to the grinding wheel. Therefore, the obtained velocities before and after the interaction are plausible, and flow structures which are assignable to the flow fields and fluid dynamic phenomena like a shear layer are obtainable, whereas the flow velocity close to the grinding wheel is difficult to obtain with shadowgram imaging. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(Q\) **in L/min** & \(v_{\mathrm{sup}}\) **in m/s** & \(R=\frac{v_{\mathrm{sup}}}{v_{\mathrm{g}}}\) & \(v_{\mathrm{sup,theo}}\) **in m/s** & \(h_{\mathrm{theo}}\) **in mm** \\ \hline
16.0 & \(12.4\pm 1.3\) & \(0.496\pm 0.052\) & 14.78 & 1.075 \\
26.6 & \(19.1\pm 1.8\) & \(0.764\pm 0.072\) & 24.63 & 1.116 \\
37.2 & \(26\pm 2.8\) & \(1.04\pm 0.11\) & 34.48 & 1.192 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Measured flow velocities of the MWF supply flow \(v_{\mathrm{sup}}\) and velocity ratio \(R\) for the used volume flows \(Q\) and added theoretical nozzle heights \(h_{\mathrm{theo}}\) to discuss the deviation to the theoretical MWF supply flow velocities \(v_{\mathrm{sup,theo}}\). Figure 9: Snapshot of the shadowgram and average velocity field for the MWF flow as colour-coded arrows.
## 4 Dynamic Model Updating Examples
### Engineering Example: Dynamic Model Updating of an Intermediate Case
Only for these six modes (first, second, third, fourth, sixth, and tenth) paired before and after updating were most of the FRE values reduced from more than 20% to less than 10%. The MAC values of the corresponding modes were increased. The mode swapping was improved to a certain extent. Furthermore, the \(RMSE_{FRE}\) was significantly reduced from 22.68% to 6.12%. The \(RSME_{MAC}\) was reduced from 0.3709 to 0.2037. The \(CV\) was reduced from 0.1802 to 0.1605. Based on the proposed CMPES method, all the radial modes (excluding the local mode) within the range of four times of operating speed were auto screened out and updated well. The comparisons between the updated analytical and experimental mode shapes for these paired modes are shown in Figure 13.  
In this example, even though the discrepancies of the updated dynamic FE and experimental model were not absolutely eliminated, the improvement in updating results demonstrates the great potential of the proposed method for complex engineering problems.
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 3 Retina Vein Occlusion
### Role of FA in Retinal Vein Occlusion  
FA is commonly used to assess the perfusion status in RVO as non-perfusion is a strong predictor for the development of neovascularization [51]. In most RVO cases, FA demonstrates normal retinal arterial and choroidal filling, delayed retinal venous filling, variable staining of retina veins, and possible areas of capillary non-perfusion and retinal vascular leakage that results in retinal edema. In the presence of significant vision loss, FA can help assess the visual prognosis by differentiating between macular edema, which may improve with treatment or macular ischemia where the visual prognosis is guarded [52]. In the acute stages of RVO, the role of FA in assessing peripheral non-perfusion is limited due to blockage from dense intra-retinal hemorrhages (Figure 4C--yellow arrows) [47].
# Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach
## 3 Results and Discussion
### Correlation Analysis between Influential Factors  
#### 3.3.1 Pearson's Correlation Coefficient  
Pearson's correlation coefficient is used to analyze the correlations of parameters, and the correlation analysis is performed on two parameters. The calculation formula is as follows:  
\[r=\frac{\sum_{i=1}^{n}(X_{i}\!-\!\overline{X})\big{(}Y_{i}\!-\!\overline{Y})}{ \sqrt{\sum_{i=1}^{n}(X_{i}\!-\!\overline{X})^{2}}\sqrt{\sum_{i=1}^{n}(Y_{i}\!- \!\overline{Y})}^{2}} \tag{6}\]  
Figure 4 illustrates the correlations between pitch angle of flight (PITCH) and other factors. The upper-right triangle of the matrix presented in Figure 4 presents the correlation coefficients corresponding to the row and column numbers; the greater the correlation coefficient, the stronger the correlation. The lower-left triangle corresponds to the scatter plot between parameters. For example, the correlation coefficient between instantaneous vertical velocity (IVV) and PITCH is 0.88.  
PITCH was found to be highly correlated with IVV, and it tended to be the most significant correlated factor, as the correlation coefficient is always large when pitch overrun occurs.
## 3 Results and Discussion  
### Cell Viability Analysis after ES at Each Voltage  
To use the electrospray system as a tool for drug delivery to cells, it is necessary to check how much cell damage is done by the high voltage of the process. Thus, we measured cell viability after treating ES at various voltages. Figure S2 shows the results of the WST-1 assay after ES at various concentrations (2, 4, 6, 8 and 10 kV). In the ES treatment group below 6 kV, no significant cytotoxicity was measured. However, the ES treatment group with 8 kV or more showed more red fluorescence in the live/dead assay and significantly lower viability in the WST-1 assay than the control. These results showed that ES above a specific high voltage caused significant damage to the cells, and up to 6 kV allowed the delivery of a drug using ES. Thus, we decided to use a voltage of 6 kV or less for the ES drug delivery system.
# Hybrid-Based Analysis Impact on Ransomware Detection for Android Systems
###### Abstract  
Android ransomware is one of the most threatening attacks that is increasing at an alarming rate. Ransomware attacks usually target Android users by either locking their devices or encrypting their data files and then requesting them to pay money to unlock the devices or recover the files back. Existing solutions for detecting ransomware mainly use static analysis. However, limited approaches apply dynamic analysis specifically for ransomware detection. Furthermore, the performance of these approaches is either poor or often fails in the presence of code obfuscation techniques or benign applications that use cryptography methods for their APIs usage. Additionally, most of them are unable to detect ransomware attacks at early stages. Therefore, this paper proposes a hybrid detection system that effectively utilizes both static and dynamic analyses to detect ransomware with high accuracy. For the static analysis, the proposed hybrid system considered more than 70 state-of-the-art antivirus engines. For the dynamic analysis, this research explored the existing dynamic tools and conducted an in-depth comparative study to find the proper tool to integrate it in detecting ransomware whenever needed. To evaluate the performance of the proposed hybrid system, we analyzed statically and dynamically over one hundred ransomware samples. These samples originated from 10 different ransomware families. The experiments' results revealed that static analysis achieved almost half of the detection accuracy--ranging around 40-55%, compared to the dynamic analysis, which reached a 100% accuracy rate. Moreover, this research reports some of the high API classes, methods, and permissions used in these ransomware apps. Finally, some case studies are highlighted, including failed running apps and crypto-ransomware patterns.  
Android; ransomware; hybrid analysis; detection; dynamic analysis; static analysis; accuracy; security; attacks; VirusTotal; MobSF +
Footnote †: journal: Applied Sciences
# Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs
## 2 Models and Definitions  
### Network Model  
Sensor nodes are assumed to be randomly distributed in a 3D underwater monitoring space, and the sink node is placed in the water. Owing to the existence of various underwater network models, this study also considers other assumptions to achieve network function. These assumptions are as follows:Each node has a unique ID number, and data are received successfully as soon as the information is passed to the sink node.  
Each node except for the sink node has abilities of communication and mobility.  
The nodes collect data cyclically. The sink receives information all the time.
## 4 Discussion
On the other hand, the application of biochar often increases the pH value significantly, as shown by [PERSON] et al. [48], and could hence decrease the mobility of the Cd. The mechanism for this is that biochar increases the adsorption capacity of cations by increasing the negative charge on the soil surface [49; 50; 51]. Also, biochar is a porous substrate with a high surface area for the absorption of heavy metals and the formation of a complex with soluble organic carbon which immobilizes heavy metals [52]. According to Table 4, soil containing treatment B5 had the highest amount of organic carbon (1.92%). Similar results were reported by other studies [53; 54]. Our findings show that the addition of biochar significantly reduced the concentration of Cd in the tissue of plants (Figure 5) as reported previously for maize by [PERSON] et al. [3]. The low concentration of metals in the plant was directly related to the loss of metal bioavailability in the soil [38]. The pH of soil with the B5 treatment increased by 1.03 in the soil over the control. This increase may be related to pH = 8.14 in biochar. [PERSON] et al. [54] stated that the high pH of biochar is due to the dominance of carbonates and organic anions over ash. Generally, data on Cd accumulation in roots, stems, and leaves shows that Cd accumulates in the roots more than other plant organs (Figure 5), a strategy that was also employed by quinoa seedlings regarding Cu toxicity [45]. Due to the toxicity of Cd to cytosol in its free form, plant cells likely try to fix it at the root by ways such as attaching it to the cell wall [55], storing it in vacuoles [56], and chelating it with phytochemicalatin [55], thus reducing its toxicity. The result will be a low transfer of Cd to the upper plant organs. As shown in Figure 6, increasing the level of biochar application decreased the cadmium concentration at all levels. Adding biochar to the soil will have a series of incremental consecutive consequences, including porosity, surface reactive sites, contact between Cd and biochar, the redox potential, and the removal efficiency of Cd [57] (Figure 7). The high CEC of biochar is directly related to its specific surface area and high porosity and therefore the CEC of soil can be increased (Table 4) [53]. The increase in negative charge due to the biochar oxidation is also a reason for the high biochar CEC [58].
# Unmanned Aerial Vehicle Routing Problems: A Literature Review
## 5 Challenges in UAV Routing  
What is noticeable is that there are some issues in the UAVRP that are unaddressed in the existing literature. Specifically, the authors have identified the following issues that are highly relevant. In outdoor route optimization for UAVs, we must deal with the following stochastic conditions, which have received limited attention in the current state. Stochastic conditions influencing the UAV routing can be identified under weather conditions [14; 90; 133], air traffic control [7; 14; 133; 134; 135], fuel consumption, and range [136; 137; 138; 139; 140; 141].  
These elements are not encountered in the general VRP or TSP and have some characteristics that can potentially greatly influence the solution strategy for the UAVRP. Ignoring the impact of weather will not provide more realistic solutions as flying with the wind could reduce energy consumption and cold temperatures may adversely affect battery performance [17]. As UAVs have fuel constraints,  
Figure 6: UAV routing in various wireless networks [130; 131; 132].  
Figure 7: UAV routing taxonomy.  
it may not be possible for a UAV to complete a mission before refueling at one of the depots [84]. Even though these challenges with regard to stochastic conditions and minimizing fuel consumption are studied for some years, they are different regarding the context of this paper as in UAVRP, it is different to typical stochastic VRPs where in UAVRP the routings are done in 3D environment with highly non-linear fuel consumption models and the stochastic behavior is not similar to typical stochastic VRPs. Weather makes the conditions time-dependent as changes in the weather conditions impact the fuel consumption and the flying dynamics of the UAVs. There are various approaches to address the time dependency in routing and, in UAV routing, more focus should be put on the sources of uncertainty and time dependency than on the particular manner in which this stochasticity and time dependencies are addressed.
## 2 The Proposed Method
### Walsh Transform Versus Fourier Transform  
Unlike the Fourier transform, the Walsh transform is operated by real number additions and subtractions. Walsh functions are formed in rectangular waveforms of \(\pm 1\) that are piecewise constant. This characteristic makes the calculations much less complicated, as opposed to complex number handling in FFT [29; 30]. Moreover, all Walsh functions are arranged by a certain order and in constant formations (Figure 1). Their shapes are invariable regardless of the selected order number \(k\) for a given random signal. As a result, the stability of the integral result is guaranteed. On the contrary, the Fourier transform aims to convert a signal into a trigonometric series whose frequencies, amplitudes and corresponding resolution depend on the sample number \(N\) used in FFT. In addition, in accordance with the Nyquist-Shannon sampling theorem [31], the Nyquist folding frequency \(fc\) in FFT is determined by the time interval \(\Delta t\). Aliasing of components higher than \(fc\) into the spectral zone of \(f<fc\) is inevitable. Hence, selections of parameters would significantly influence the integral results [17].
# An Assessment Scheme for Road Network Capacity under Demand Uncertainty
## 3 Sensitivity-Analysis-Based Approximation Method
### Linear Approximation of the Reaction Function  
The proposed model with demand uncertainty can be resolved by shifting to a single-level programming, where the implicit relationship is expressed by a first-order Taylor expansion, as illustrated below:  
\[v_{a}(\mu q)\approx v_{a}(\mu^{*}q)+\left[\left.\frac{\partial v_{a}(\mu q)}{ \partial\mu}\right|_{\mu=\mu^{*}}\right](\mu-\mu^{*})+\sum_{i\in I}\sum_{j\in I }\left[\left.\frac{\partial v_{a}(\mu^{*}q)}{\partial q}\right|_{q=q_{m}} \right]\cdot\epsilon_{q}, \tag{14}\]  
where \(\mu^{*}\) is the max demand multiplier with the nominal demand for the capacity model, and \(q_{m}\) is the demand equilibrium solution of the capacity model under the nominal demand value, which is the product of \(\mu^{*}\) and the nominal demand. \(\epsilon_{q}\) is the perturbation of disruptions to travel demand in the network.  
Considering the optimal solution of the overall network capacity model with nominal demand, the reserve capacity under demand uncertainty can be estimated by utilizing the derivatives with regard to both the demand multiplier \(\mu\) and travel demand \(\mathbf{q}\). The sensitivity analysis approach is described in Appendix A and Section 3.2. As a result, the capacity with uncertain demand can be approximated to the following single-level programming.  
\[max\ \mu, \tag{15}\]  
\[s.t.\ v_{a}(\mu q)\approx v_{a}(\mu^{*}q)+\
abla_{\mu}v_{a}(\mu^{*}q)(\mu-\mu^ {*}) \tag{16}\]  
\[+\
abla_{q}v_{a}(\mu^{*}q_{m})\cdot\epsilon_{q}\leq C_{a},\forall a\in A,\]  
The procedure of repeated volume-loading experimental are described as Figure 5.  
Figure 5: Flowchart of sensitivity-based approximate algorithm.
## 1 Introduction
For noise removal from high resolution digital surface models (DSMs), deep learning methods are also applied, e.g., to sensitive filtering of raw surface elevation data or to effectively increase the automation of the segmentation process [29; 30]. The CSF also handles the classification of TLS data relatively well [31]. Consequently, the accuracy of the quantitative estimation of the resource based on LiDAR data depends on the uncertainty range, which, in turn, is connected to survey errors and classification errors. The first group (survey errors) includes the range of accuracy of the measuring devices (scanner rangefinder) and positioning devices (GPS/GNSS, total station, etc.) as well as the accuracy of point cloud integration and georeferencing. While the first group includes among its input parameters the DoD for estimating the range of the measurement uncertainty, it does usually omit the accuracy of the ground point classification. The aim and novelty of this study is therefore to answer the question as to what extent the ground point classification method affects the estimation of the quantitative parameters of mineral resources. For this purpose, an analysis was carried out on three collections of PC data sets that were obtained by utilizing one type of laser scanner at different time periods in a single area (area of interest (AoI)) whose surface was subject to significant changes during the survey's three-year period. The classification of the ground points in this data was performed manually whilst using the CSF algorithm to obtain three sets of data to compare the range of measurement uncertainty.
## 7 Networks
### Synchronization
#### 7.1.3 Impulsive Synchronization  
In the case of two coupled attractors, Zheng, Nian, and Liu [135] presented a theorem on impulsive synchronization using synchronization impulses with varying impulse intervals. As an example and application of the theorem, they derived sufficient conditions for the synchronization of two chaotic Lozi maps via impulse control.  
Letting \(w_{n}^{T}=(x_{n},y_{n})\), they rewrote (88) in the following matrix form:  
\[w_{n+1}=Aw_{n}+\Phi(w_{n}), \tag{101}\]  
where  
\[A=\left(\begin{array}{cc}0&b\\ 1&0\end{array}\right),\qquad\Phi(w_{n})=\left(\begin{array}{cc}1-a|x_{n}|\\ 0\end{array}\right).\]  
In the impulsive synchronization configuration, the master system is provided by (101) and the slave system is provided by the following impulsive equation:  
\[\left\{\begin{array}{l}\hat{w}_{n+1}=A\hat{w}_{n}+\Phi(\hat{w}_{n}),\quad n \
eq n_{i},\\ \Delta\hat{w}_{n_{i}}=\hat{w}_{n_{i}^{+}}-\hat{w}_{n_{i}^{-}}=-Be_{n_{i}^{-}}= -Be_{n_{i}},\quad n=n_{i},\quad i=1,2,\ldots\end{array}\right. \tag{102}\]  
where \(B\) is a \(2\times 2\) synchronization matrix and \(e_{n}=w_{n}-\hat{w}_{n}=(x_{n}-\hat{x}_{n},y_{n}-\hat{y}_{n})\) is the synchronization error.  
They proved the following theorem to guarantee that the impulsive synchronization of the Lozi maps is asymptotically stable.  
**Theorem 4** ([135]).: _Let \(d\) be the largest eigenvalue of \((I+B^{T})(I+B)\), where \(B\) is a \(2\times 2\) synchronization matrix and \(I\) denotes the \(2\times 2\) identity matrix. We assume that the impulse are equidistant from each other and are separated by an integer \(\Delta\) if there exists an \(r>1\) such that_  
\[0\leq ln(1+a)\leq-\frac{1}{2\Delta}ln(rd); \tag{103}\]  
_Then, the impulsive synchronization of the two Lozi maps provided in (102) is asymptotically stable._  
In addition, they carried out a number of numerical simulations to illustrate this theorem.  
Zhang and Jiang [136] generalized the impulsive synchronization method and illustrated it using Henon and Lozi maps.
## 3 Results and Discussion
### Effect of Stirring  
Given that the AUTOCLAVE ENGINEERS reactor is equipped with a stirring system, the effect of stirring on the DRs reached by the hydrothermal treatment under subcritical conditions was evaluated. For this purpose, two tests were conducted, one without stirring (P1) and another with a gentle stirring of 60 rpm (P2). The rest of the operating conditions remained unchanged at 300 \({}^{\circ}\)C, 130 bar, 35 min reaction time, 300 ml\({}_{\rm solv}\)/g\({}_{\rm comp}\) solvent/sample ratio, 5 wt.% IPA dilution as solvent, and 0.1 M KOH as catalyst.  
Figure 4 shows a comparison between the resin decomposition rates (DRs) achieved with and without stirring. A slightly higher DR (51%) could be observed when stirring was used with respect to the DR reached (49%) without the stirring of the reaction medium, even if it was not a significant difference.  
Similar results have been obtained by other authors. In their study, [PERSON] et al. (2008) [PERSON] et al. (2008) claim that, although the use of mechanical stirring would improve the matter transfer and therefore the performance of the hydrothermal treatment itself, the agitators might also affect the mechanical properties of the recovered fibers. In order to improve matter transfer while allowing a reduction in the reaction temperature, they proposed to operate under continuous flow conditions, using 5 mL/min of 1-propanol with 0.02 M KOH. Under these conditions, they registered DRs of 93% at 300 \({}^{\circ}\)C and 98% at 350 \({}^{\circ}\)C.  
Therefore, the small increment in the removal of resin that has been observed when mechanical stirring is used can hardly justify the additional complexity involved in the employment of reactors operating at high pressure and temperature while equipped with a stirring system (a growing complexity when the process is scaled up). Furthermore, the negative effect that stirring may have on the mechanical properties of the fibers should also be borne in mind.