esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical and Microstructural Studies of High Performance Concrete with Condensed Silica Fume
## 3 Results and Discussion
### Modulus of Elasticity  
The examination was conducted in accordance with ASTM C469/C469M-14 [52] to obtain the elastic modulus after 28 days. Differences in elastic modulus with CSF replacement are displayed in Figure 5.  
An increase in the HPC modulus of elasticity was noted with an increase in substitution of condensed silica fume up to 10%. Additional augmented CSF replacement resulted in an increase in the amount of extra cementitious gel with a progressive decrease in the ratio of calcium-silicate. Elastic moduli of HPC were greater than modulus of elasticity of the reference HPC for CSF substitution up to 20%. The amount of gel created depended on the calcium hydroxide amount formed from the hydrated cement that could be obtained by reaction with the CSF. Thus, the additional amount of CSF did not contribute to development of elastic modulus. CSF replacement above 10% appears to have an effect on elastic moduli. The average elastic modulus increases by 5% to 10% of the CSF volume content and then falls by 5% in the range of 10-25%. Characteristic of the results obtained in this work is a slight reduction in the modulus of elasticity with the increase in compressive strength for CSF content from 10% to 25%. [PERSON] [13] found that a compressive strength greater than 90 MPa was associated with a significant reduction in elastic modulus. This was explained by an increase in HPC porosity above a certain critical level of CSF.  
Figure 4: Differences in (**a**) tensile splitting strength; (**b**) ratio of split tensile strength to compressive with CSF replacement.
# Comparative Vulnerability Analysis of Thai and Non-Thai Mobile Banking Applications
## 2 Background and Related Work
### Related Work
[4] explored the use of automated security vulnerability detection tools, such as AUSERA, to assess the security of mobile banking applications. The results demonstrated the effectiveness of these tools in identifying a wide range of vulnerabilities stated in the OWASP Mobile Top 10. In the context of threat modelling and security analysis, [PERSON] and [PERSON] [17] provided a review of a threat modelling framework to detect and mitigate threats in mobile banking applications. The study highlighted the major threats to banking applications, including malware and serious crimes, and proposed strategies to address these threats. While the review was thorough, it did not provide empirical evidence or case studies to validate the proposed threat modelling framework. Overall, the existing research illustrates the importance of vulnerability analysis in mobile banking applications. The studies demonstrate that mobile banking applications across different regions and countries have vulnerabilities that need to be addressed. The reviewed studies have used various vulnerability analysis tools, such as AndroBugs, MobSF, and QARK, to identify vulnerabilities in mobile banking applications. However, the studies have some limitations, such as focusing on a specific region or a limited set of mobile banking applications. This research aims to contribute to the existing body of knowledge by conducting a comprehensive vulnerability analysis of mobile banking applications in Thailand, focusing on identifying vulnerabilities so that the owners and developers of these applications can be aware, and the security of these applications can be improved. Additionally, while the existing literature has addressed various aspects of mobile banking security, there remains a notable gap in comparative studies that specifically analyse vulnerabilities of mobile banking applications from across different Asian countries. This makes our work different from the existing work in such a way that most research tends to focus on individual applications or localised studies. This study aims to fill this gap by examining the vulnerabilities of mobile banking applications from Thailand and other Asian countries, thereby contributing to a deeper understanding of the security landscape in this rapidly evolving sector.
## 3 Methods
### Data Screening
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & \(\overline{T}_{\text{out}}\) & \(\overline{\text{RH}}\) & \(\Sigma E_{\text{sol}}\) & \(\Sigma R^{*}\) & \(\overline{WS}\) & \(\overline{S}_{c}^{*}\) & \(\overline{T}_{\text{in}}\) & \(\overline{\text{acr}}^{*}\) \\ \hline Outdoor air temp., \(\overline{T}_{\text{out}}\) [\({}^{\circ}\)C] & 1.0000 & — & — & — & — & — & — & — \\ (daily mean) & \(-\)0.2522  & 1.0000 & — & — & — & — & — & — \\ Outdoor air rel.htm, RFI [\%] & \(-\)0.2522  & 1.0000 & — & — & — & — & — & — \\ (daily mean) & \(\Sigma E_{\text{sol}}\) [M]/m\({}^{2}\)  & 0.4237  & \(-\)0.6649  & 1.0000 & — & — & — & — & — \\ Solar energy, \(\Sigma E_{\text{sol}}\) [M]/m\({}^{2}\)  & 0.4237  & \(-\)0.6649  & 1.0000 & — & — & — & — & — \\ (daily total) & \(-\)0.1708  & 0.6256  & \(-\)0.5326  & 1.0000 & — & — & — & — & — \\ (daily total) & \({}^{a}\) & \(-\)0.1336  & 0.0462 & \(-\)0.0545 & 0.3614  & 1.0000 & — & — & — & — \\ Wind speed, \(\overline{WS}\) [m/s] & \(-\)0.1336  & 0.046 & 0.0191 & \(-\)0.0074 & 0.0172 & \(-\)0.0075 & 1.0000 & — & — \\ (daily mean) & \({}^{b}\) & \(-\)0.047 & 0.0191 & \(-\)0.0074 & 0.0172 & \(-\)0.0075 & 1.0000 & — & — \\ Indoor air temp., \(\overline{T}_{\text{in}}\) [\({}^{\circ}\)C] & 0.8821  & \(-\)0.2232  & 0.4255  & \(-\)0.1500  & \(-\)0.1400  & 0.0200 & 1.0000 & — \\ (daily mean) & \({}^{c}\) & & & & & & & \\ \hline \multirow{4}{*}{**Statistic**} & Mean & 15.0276 & 75.1532 & 13.0961 & 0.7314 & 2.4513 & 27.4841 & 20.5263 & 1.0824 \\  & SD & 3.5051 & 11.9659 & 7.7576 & 1.0321 & 1.1503 & 4.5551 & 2.8012 & 0.6757 \\  & Skew & 0.611 & \(-\)0.454 & 0.571 & 1.233 & 0.536 & 0.198 & 0.394 & 0.006 \\  & Kurtosis & \(-\)0.247 & \(-\)0.291 & \(-\)0.724 & 0.308 & \(-\)0.346 & \(-\)0.231 & \(-\)0.719 & \(-\)0.758 \\ \hline \hline \end{tabular} _(a) \(\Sigma R^{*}=\ln(1+\Sigma R)\), with \(\Sigma R\) being the daily total precipitation; (b) \(\overline{S}_{c}^{*}=\overline{S}_{c}\times 10^{-1}\); (c) classroom occupancy periods only; (d) \(\overline{\text{acr}}^{*}=\ln(\overline{\text{acr}})\); (*) \(p<0.01\)._  
\end{table} 
Table 3: Pearson product moment correlations, means, standard deviations (SD), skew, and kurtosis of sample data.
## 4 Numerical Results
### Influence of System Parameters
#### 4.1.2 Effect of the Optimization Span and Node Mobility  
The optimization span \(F\) was defined in the proposed model to trade the frequency of the optimization events and the mobility speed of the sensor nodes, the sink node or both. Recall that a higher mobility speed can be simulated by reducing the correlation coefficient \(\rho\) between the energy consumptions in neighbor frames. The higher the mobility speed, the smaller must be the optimization span. The effects of these two parameters are analyzed simultaneously in this subsection. We consider \(F=5\), \(F=10\) and \(F=20\) to complement the results already presented for \(F=1\). We consider \(\rho=0.98\) to represent a network with moderate-to-high mobility nodes and \(\rho\approx 1\) to represent a fixed network (see Figure 4 and the related comments). In the former case, to keep consistency with previous results, we have used the same single realization of the consumptions throughout the \(KF\) frames. In the latter case, we have coined another realization of such consumptions with \(\rho=0.99999\) and reused it. Equal initial energies \(\mathbf{s}_{1}(1)=S_{\max}\times\mathbf{1}\), with \(S_{\max}=10\), \(B_{\min}=0.001\), \(B_{\max}=1\), and no recharge were assumed. Without loss of generality, we have chosen \((w_{1},w_{2})=(1,0)\), \(N=10\) and \(KF=400\) so that the effects of interest are clearly seen in the corresponding graphs.  
Figure 8 shows the results obtained by considering different optimization spans and correlation coefficients. Comparing the results for \(\rho=0.98\), one can conclude that an increase in the optimization span produces an expected performance degradation, i.e., smaller lifetime improvements. This is due to the fact that past information on residual energies produces estimated consumptions progressively less correlated with the current ones as \(F\) increases. Comparing these results with the graph (**a**) of Figure 6, in which the death moment of the optimized network occurred around \(t=t_{\rm d}=375\), we have now death instants around \(t_{\rm d}=370\) for \(F=5\), \(t_{\rm d}=350\) for \(F=10\) and \(t_{\rm d}=300\) for \(F=20\).  
Figure 7: Activity levels assigned by the proposed optimization method respective to the situations considered in Figure 6, i.e., \((w_{1},w_{2})=(1,0)\) (**a**); \((w_{1},w_{2})=(0,1)\) (**b**); and \((w_{1},w_{2})=(1,2)\) (**c**).  
Now, observing the graph (**d**) of Figure 8, which considers fixed sensor nodes, it can be noticed that the lifetime does not change with \(F\). Consequently, a few optimization events during the network operation would suffice to account for the variabilities in the energy consumption with respect to the one predicted by the optimization due to causes other than mobility, as for instance unused activity levels, sensor node state changes, different task-dependent processing burdens and the like.
## 2 Antenna Design
### Performance of the CP Element  
The proposed element's reflection coefficient and AR have been displayed in Section 2.2. The simulated gain of the element is illustrated in Figure 7; it is stable across  
Figure 6: Simulated AR for different parameters of vertical metallic walls: (**a**) spacing between the metal walls and the patches along the \(x\)-axis direction D1; (**b**) spacing between the metal walls and the patches along the \(y\)-axis direction D2; (**c**) spacing between the metal walls along the \(x\)-axis direction D3; (**d**) spacing between the metal walls along the \(y\)-axis direction D4; (**e**) lengths of the metal walls along the \(x\)-axis direction L3; (**f**) lengths of the metal walls along the \(y\)-axis direction L4.  
the frequencies. In the frequency range of 23 to 33.5 GHz, the gain fluctuates between 5.7 and 8.3 dBic, and the element's 3-dB gain bandwidth exceeds 37.1%. Figure 8 depicts the simulated normalized radiation patterns of the element at three separate frequency points. It can be observed that the cross-polarization level of the antenna is under \(-11\) dB, and its front-to-back ratio exceeds 17 dB. In addition, with the increase in frequency, the cross-polarization performance decreases slightly, which is caused by the introduction of the vertical metal walls.
# Balance of Antioxidants vs. Oxidants in Perinatal Asphyxia
## 4 Animal Models of Neonatal Hypoxia  
The most commonly employed model for neonatal hypoxic/ischemic injury in animals is the model established by [PERSON] [54]. Although initially developed for rats [54], this model has also been applied to other rodents [55; 56; 57]. In this model, after blocking one carotid artery, the piglets are exposed to a period of hypoxia, reproducing the injury of stroke of the middle cerebral artery, which is rare in preterm neonates [58]. Other methods for inducing brain injury from hypoxia/ischemia in piglets include the combination of hypoxia and hypotension-induced ischemia [59], cardiac arrest followed by cardiopulmonary resuscitation [60], and bilateral common carotid artery occlusion in conjunction with hypoxia [61].  
In previous studies, it was shown that six hours after the asphyxic event, post-asphyxic hypothermia significantly reduced the formation of protein carbonyls in the parietal cortex and the striatum of newborn piglets [62; 63]. On the other hand, when comparing hypothermic versus morphometric piglets 48 h after the hypoxic insult, no significant changes in cerebral protein carbonyl levels were found [64]. This evidence indicated that the role of oxidative stress in brain damage was crucial during the initial hours after the hypoxic event,which then normalized over time. Nonetheless, newborn piglets exposed to hypoxemia followed by 29 h of hypothermia had higher levels of carbonylated proteins compared to those that remained normothermic. Similar findings were also recorded in the cerebral white matter of newborn piglets subjected to cardiac arrest followed by hypothermia of limited duration [65]. Additionally, significantly lower levels of antioxidant enzymes like SOD, GPx, or catalase activities were found in the cerebral tissue of newborn rats exposed to asphyxia [66]. Of note, there was a reduction in antioxidant enzyme activities in newborn rats that were asphyxiated, both in normoxic and hypoxic conditions; however, the activities of these enzymes were lower in asphyxiated rats compared to controls [66].  
Following an ischemic event, mild hypothermia delayed the utilization of SOD, GPx, and GSH in the brain tissue [67]. In newborn rats exposed to hypoxemia/ischemia followed by hypothermia, a notable increase in antioxidant protection was noted, characterized by higher levels of SOD and GPx expression [68]. Hypothermia resulted in an increase in the expression of SOD-1 in the neocortex and the caudate putamen, although the expression of GPx-1 increased only in the caudate putamen but not in the neocortex [69].  
Finally, the expression of DNA-repair genes (DNA glycosylages OGG1, NEIL1, and NEIL3) was reduced in hypothermic pigs compared to controls or resuscitated pigs [70]. Decreased expression of these genes was primarily detected in the hippocampus but also included the cortex, cerebellum, and liver [70].
## 5 Temporal Acoustic Scattering  
In addition to the previous spatial scattering results, a temporal acoustic scattering comparison is also here presented for evaluating the presence of time dispersion within the above-collected FDTD data. Figure 5 thus shows several wavelet transforms of scattered impulse responses corresponding to a flat panel, a traditional sound diffuser and the ZEQ. A fully modelled \(N=5\) Quadratic Residue Diffuser (QRD) of dimensions [x, y, z](35, 35, 28) cm and design frequency \(f_{0}=500\) Hz is here presented instead of a QRM due to the difficulty in modelling the latter structure in the FDTD solver. One must note that the QRM and QRD possess different scattering characteristics over the frequency range considered, only matching around 2 kHz.  
Figure 5a shows the time-frequency information of the scattered impulse response captured at the top of a flat surface, at \(z=1\) m. As expected, only a single hard reflection is obtained in the impulse response, covering the entirety of the frequency range of interest. A secondary reflection with much less intensity can also be identified, generated from the edge diffraction of the panel coming back to the receiver a couple of milliseconds after the first major reflection. This is supported by the inset displaying the time series of the reconstructed signal by inverse wavelet transform. Additionally, a late reflection with small amplitude can be observed at the end of the time window which may be due to spurious reflections not entirely absorbed by the surrounding Engquist-Majda absorbing boundary conditions.  
The scattering obtained with the QRD via FDTD is illustrated in Figure 5b, where a strong temporal dispersion can be distinguished by the spreading of the scattered waves through time. In such figure, one can see a major reflection shortly followed by a similarly strong one, after which a series of multiple reflections appear with varying frequency content and continuously less energy. This provides with a good illustration of the scattering generated by the frequency-dependent behaviour of a sound diffuser. The blue dots represent the absence of frequency content in very narrow time periods. These are caused by wave interference due to the wavelength delay generated by the phase-grating diffuser, providing with a time-frequency dispersion pattern.  
Ultimately, Figure 5c displays the scattered impulse response of the ZEQ obtained through RLC circuit filtering. It can be observed that a strong temporal dispersion is also obtained, with a similar pattern than the QRD. Even if the surface of the ZEQ is flat, it reproduces a similar frequency-dependent behaviour than a QRD--although, as mentioned  
Figure 5: Wavelet transforms of scattered impulse responses corresponding to (a) a flat panel via 3D FDTD, (b) the QRD via 3D PSTD, and (c) the ZEQ via 3D FDTD. Insets represent the reconstructed inverse wavelet transform of the original impulse responses.  
previously, both temporal dispersions cannot be strictly compared one to the other. However, the scattering from the QRD serves as a good reference to observe the added temporal dispersion of the ZEQ.
## 5 Results and Discussion
### Final Discussion  
From the knowledge of the study area, its morphology, and the connotation of the soil typologies, it emerges that the UHI detection model provides high accuracy in the localization of UHIs. In fact, the determined hotspots cover urban areas characterized mainly by impermeable materials and with high building density. On the contrary, the cold spots cover urban areas rich in permeable green surfaces with high vegetative vigor. A further benefit of the model is constituted by the assessment of the reliability of the UHIs detected.  
The majority of the detected UHI areas have high reliability and, in comparisons made with the model in [29], almost all the UHIs with high reliability are covered by UHIs detected executing the model [29]. The main limitation of the model is constituted by the low resolution of the satellite LST data, equal to 30 m \(\times\) 30 m, which affects the precision of the localization of UHIs at an urban scale. To obtain a higher resolution, it would be necessary to integrate the satellite data with on-the-ground measurements of the LST; however, acquiring on-the-ground measurements at high spatial resolution would require high costs and acquisition times.
## 2 Theory Background
### Empirical Models
#### 2.3.1. Hareland Model  
In 1994, [PERSON] et al. [10] proposed an ROP prediction model considering various drill-bit feature parameters. The model is suitable for all types of drag bits, including natural diamond bits (NOB) and polycrystalline diamond compact (PDC) bits.  
\[ROP=\frac{a_{c}}{RPM^{b}WOB^{c}}*\frac{14.14N_{c}RPM}{D_{B}}A_{v} \tag{3}\]  
\[A_{v}=\cos\theta*\sin\theta\left[\begin{array}{c}(\frac{d_{v}}{\Sigma})^{2} \cos^{-1}(1-\frac{2P_{c}}{\cos\theta*d_{c}})-\\ \left(\frac{d_{v}P_{c}}{\cos\theta}-\frac{P_{c}^{2}}{(\cos\theta)^{2}}\right) ^{0.5}\left(\frac{d_{v}P_{c}}{2\cos\theta}\right)\end{array}\right] \tag{4}\]  
\[P_{c}=\frac{2W_{mech}}{\pi d_{c}\sigma_{c}} \tag{5}\]  
where \(N_{c}\) is the number of cutting teeth; \(A_{v}\) is the rock-breaking area of a single cutting tooth, in \({}^{2}\); \(\alpha\) is the side rake angle, \({}^{\circ}\); \(\theta\) is the back rake angle, \({}^{\circ}\); \(d_{c}\) is the diameter of the cutting tooth, in; \(\sigma_{c}\) is the uniaxial compressive strength, psi; \(W_{f}\) is the wear degree of the bit; \(a_{c},b,c\) are the correction coefficients of the cutting-tooth structure.  
This model considers many factors in the characteristics of the bit, and it can theoretically optimize the bit structure. However, the relatively detailed structural parameters of the drill bit increase the quality requirements of the field data, which limits the application and promotion of this method.
## 5 Experimental Verification
### Analysis of Results  
The CE test results are presented in Figure 16. The CE between wheel 1 and wheel 2 is still analyzed as an example. All of the experimental CEs are greater than those that were simulated CEs. The maximum experimental CE absolute values are 0.012 rad: s\({}^{-1}\), 0.01 rad: s\({}^{-1}\), 0.017 rad: s\({}^{-1}\), and 0.015 rad: s\({}^{-1}\) for CM, IR, DM, and CM mode switching, respectively. Although the experimental and simulated values are different, the CE change trends are consistent. In particular, large CE fluctuations occur as the MMS tests start. These gradually decay until they approach zero. Obviously, the proposed controller plays a role in MMS and the CEs are well controlled. These results prove that the proposed control method is effective.  
The maximum and average acceleration rates are the main values used to evaluate longitudinal and lateral motion trends. Therefore, the results also focus on these two indices. Table 3 shows the longitudinal and lateral acceleration test results. As with the CEs, the experimental maximum absolute acceleration rates all exceed their simulated values, but are all in an acceptable range. The average absolute values are all quite small at approximately 9 to 12% of the maximum absolute value in most tests. This indicates that the changes in longitudinal and lateral FC directions are not notable. This shows that the proposed methodology can effectively guarantee FC stability during MMS.  
Figure 17 shows the centroid trajectory under distribution control and coupling control when the FC is moved in a fixed posture. Under the coupling control, the trajectory of the FC is more consistent with the expected trajectory. For the CM and DM process, the trajectory deviates early under distribution control. As the displacement increases, the trajectory deviation becomes more serious. In the coupling control, the trajectory does not shift slightly until the FC moves a large distance. In the IR process, the trajectory under distribution control is farther away from the desired center than coupling control. During the SM process, the trajectory deviates most from the expected path under distribution control. These results indicate that, under coupled control, the MMS effect is better, and the mode switching accuracy is higher than the distribution control.  
Figure 13: Control panel for FC motion mode switching: (**a**) control panel; (**b**) configuration of flexible chassis used for test.  
Figure 16: Angular velocity coupling errors of motion mode switching: (**a**) cross motion; (**b**) in-place rotation; (**c**) diagonal motion; (**d**) steering motion.  
Figure 14: Flexible chassis mode switching on hard pavement: (**a**) cross motion; (**b**) in-place rotation; (**c**) diagonal motion; (**d**) steering motion.  
Figure 15: Schematic diagram of flexible chassis trajectory measurement: (**a**) method of measurement; (**b**) example of trajectory.
## 5 Evaluation
### Experimental Results
#### 5.2.3 Effectiveness of Man-Machine Collaboration  
In this experiment, for the 130 malware postponed according to inter-class closeness and intra-class closeness with \(\theta=1.5\), two cybersecurity experts examined the sorted-out malware with ambiguity for this experiment. Here, the experts classified the malware through the following two stages: First, they simply referred to the visualized box plots introduced in Section 4.2.3, in order to reduce time and effort. Second, if they were not able to judge based on the visualization results, they manually inspected the given malware in detail. The second step will be a time-consuming job but will provide 100% accuracy of classification.  
Table 4 shows the results. For comparison, we included the results of the KNN classification with and without applying inter- and intra-class closeness with \(\theta=1.5\). Two human experts classified (with the visualized interface and without manual inspection) 49 and 73 samples out of postponed 130 samples, respectively. Then, they selected the remaining 81 and 57 samples for manual inspection, respectively. After manual inspection, all the selected postponed samples were classified correctly and were able to classify 1858 and 1846 samples appropriately, with 95.7% and 95.1% accuracy, respectively.