esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Data Processing for Variable-Flow Regulation  
Ranges of the water quality data and backwash frequency from the measurements at three total circulation rates in RAS are shown in Table 1. The variable-flow regulation was decided by the frequency of the circulating pump. The upshifting and downshifting of the circulating pump inverter as two indicators of the classifier were labeled as 1 (upshift) and \(-\)1 (downshift) in the dataset. In order to develop the variable-flow regulation models based on the machine learning methods, water quality indicators, current circulation flow rate, and current backwash frequency were used as input variables, and regulating data (upshift/downshift) for the next period (0.5 h) were used as output variables. Upshift/downshift data were labeled by manual marking. The marking principal was decided from the variable-flow experiments under three circulation rates in RAS. The binary classification models can be applied for variable-flow regulation strategy, and the current data for water quality indicators and backwash frequency can be used to determine the total circulation rates for the next period through the classification models.  
A total of 375 datasets were collected in the experiment, of which 280 were used as the training set and 95 as the test set. The training data were normalized after data pre-processing. The first step in developing the machine learning models was to simplify the explanatory variables by principal component analysis (PCA). PCA can reduce the complexity of the dataset and reveal hidden structures. The simplified principal components  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **55 m\({}^{3}\)/h** & **65 m\({}^{3}\)/h** & **75 m\({}^{3}\)/h** \\ \hline Temperature (\({}^{\circ}\)C) & 27.30–27.70 & 27.40–27.70 & 27.40–27.80 \\ pH & 6.90–7.50 & 6.91–7.50 & 6.91–7.49 \\ DO (mg/L) & 7.00–8.95 & 7.00–8.92 & 7.01–8.97 \\ Salinity (\%) & 30.50–30.90 & 30.50–30.90 & 30.50–30.90 \\ TAN (mg/L) & 0.18–2.68 & 0.20–1.36 & 0.19–1.13 \\ Nitrite nitrogen & 0.06–1.91 & 0.07–0.81 & 0.05–0.72 \\ TSS (mg/L) & 11.60–33.54 & 12.06–42.40 & 8.85–26.25 \\ Turbidity (NTU) & 3.52–8.98 & 3.63–13.68 & 2.83–8.87 \\ Backwash frequency & & & \\ (Times per 0.5 h) & 1–9 & 4–18 & 3–22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ranges of water quality indicators at three total circulation rate levels.  
can be used as valid indicators to develop models. Figure 4 illustrates that the simplified variables reduced the original dataset from nine dimensions (water quality indicators) to three dimensions and could reflect 99% of the information in the original independent variables. However, the key components extracted from the original data were compressed and mapped to another space, and the simplified variables were not directly related to the original data [32]. Hence, in the present study, PCA successfully provided the optimal reduced representation for the data. The new dataset could then be used to develop machine learning models to reduce the complexity of the computation process.
# A Survey on Adversarial Perturbations and Attacks on CAPTCHAs
## 4 Adversarial CAPTCHAs
### Usability of Adversarial CAPTCHA  
The usability of adversarial CAPTCHAs reflects user satisfaction in terms of avoiding an effect on the human perception of the image content. To date, most of the conducted studies have experimentally evaluated the usability aspect of the proposed adversarial CAPTCHA scheme. The results of our study have shown a generally high satisfaction level with the developed schemes. However, the sample size of some experiments, such as in [4; 17], was too small; this decreases the power of the study and rises the margin of error, which can render the study meaningless. Figure 3 shows the sample size in each study to date. In particular, the samples sizes in [6; 14] are not reported, while [16] used Amazon Mechanical Turk (MTurk), [[https://mturk.com](https://mturk.com)]([https://mturk.com](https://mturk.com)) (accessed on 22 December 2022). In addition, there was no usability study in [13].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Perturbation Techniques** & **Recommendation** & **Remarks** \\ \hline Optimization methods [39] & Not recommended & Too slow and easy to remove as demonstrated in [39] \\ \hline FGSM [42] & & Easy to remove, as demonstrated in [42] \\ \hline JSMA [5] & & Its robustness level is empirically evaluated in [5] and shows a sufficient resistance level. Also, no attack has been reported yet. \\ \hline IAN [3] & & Its robustness level is empirically evaluated in [3] and shows a sufficient resistance level. Also, no attack has been reported yet. \\ \hline EOT [14] & & Its robustness level is empirically evaluated in [14] and shows a sufficient resistance level. Also, no attack has been reported yet. \\ \hline CTC [6] & Recommended & Its robustness level is empirically evaluated in [6] and shows a sufficient resistance level. Also, no attack has been reported yet. \\ \hline SGTCS [16] & & Its robustness level is empirically evaluated in [16] and shows a sufficient resistance level. Also, no attack has been reported yet. \\ \hline GAN [14; 17] & & Its robustness level is empirically evaluated in [14; 17] and shows a sufficient resistance level. Also, no attack has been reported yet. \\ \hline Semantic Image Generator [18] & & Its robustness level is empirically evaluated in [18] and shows a sufficient resistance level. Also, no attack has been reported yet. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Recommended and not recommended perturbation techniques.
## 3 Results and Discussion
### Omega-6/Omega-3 Ratio in VOs  
The omega-6/omega-3 ratio is considered to be an indicator for comparing the nutritional value of VOs. In our research, the lowest omega-6/omega-3 ratios were found for linseed oil (0.28%), rapeseed oil (2.68%) and soybean oil (9.70%). These ratios were similar to the data from the literature [50; 61]. The high contents of \(\alpha\)-linolenic acid C18:3n3 as the main FAs in linseed resulted in a low omega-6/omega-3 ratio. Because of the richer linoleic acid C18:2n6c and poorer \(\alpha\)-linolenic acid C18:3n3 and docosahexaenoicenoic acid C22:6n3, maize oil, cottonseed oil, sunflower oil and safflower oil showed higher omega-6/omega-3 ratios of 103.93, 207.14, 442.86 and 530.35, respectively. The results presented for maize and cottonseed oils had slightly different ratios. Nevertheless, sunflower and safflower oils had great differences from those published by [PERSON] et al. [61], namely ratios of 131 and 253 for sunflower and safflower, respectively.
# SVDC: Synchronous Update with the Division and Combination of SRv6 Policy
## 5 Performance Evaluation
### Result and Analysis
#### 5.2.1 Number of Times for Policy Distribution
As shown in Figure 6, the coexisting time of new and legacy policies for SUDC is not affected by the size of the test set. The coexisting time of new and legacy policies for SUDC is the time lag between the time when the push-stop signal finally reaches a node and the time when it first reaches a node. As a result, this metric does not become larger as the size of the test set increases. The results indicate that the coexisting time in SUDC is independent of the quantity size of policies. The data implies that SUDC can effectively improve the problem of unsynchronized updates across routers and reduce the risk of network instability.
## 3 Preliminary Calibration Attempt and Selection of Parameters for Optimization
### Reaction Rate  
[PERSON] et al. [19] and [PERSON] et al. [20] apply the same reaction kinetics but with different rate constants. Reference [19] take \(k_{R,1}=0.2\), whereas Reference [20] use \(k_{R,1}=15\). The influence of this parameter is shown in the dashed blue line in Figure 2. This curve results from a simulation with \(k_{R,1}=0.2\), whereas the previously described simulations are performed with \(k_{R,1}=10\). Obviously, reducing this parameter renders the temperature curve more smooth and also slightly reduces the slope at the temperature jump. Further reducing the reaction-rate constant, however, prevents reaching the equilibrium temperature plateau. Thus, varying the reaction rate constant \(k_{R,1}\) is not sufficient to reproduce the shape of the temperature distributions. Therefore, we multiply the reaction rate by an additional term that becomes limiting at progressed conversion: \((1-X)^{k_{R,2}}\). For \(k_{R,2}=0\), the reaction kinetics correspond to the kinetics of Reference [19]. Simulation results for \(k_{R,2}=2\) are displayed in the solid blue lines in Figure 2. The combination of the two reaction constants, \(k_{R,1}\) and \(k_{R,2}\), generates both a plateau at equilibrium temperature and a flatter slope at the temperature jump. A physical interpretation of the newly introduced term is discussed in Section 5.
## 3 Results
### Stage 2
#### 3.2.2 Linearized State-Space Model  
Linearization in state variables is a technique used to approximate the solution of a nonlinear system by linearizing the model around a stable operating point. To linearize the system, it is assumed that all system variables can be expressed as a linear combination of state variables and their derivatives. Nonlinear variables are then replaced by their linear approximation around the stable operating point; see Equations (10)-(15).  
\[x_{1}=a \tag{10}\] \[\dot{x}_{1}(t)=\frac{da}{dt}\] (11) \[x_{2}(t)=b\] (12) \[\dot{x}_{2}(t)=\frac{db}{dt}\] (13) \[x_{3}(t)=c\] (14) \[\dot{x}_{3}(t)=\frac{dc}{dt} \tag{15}\]  
Linearization in state variables allows the non-linear system to be described as a linear system with constant coefficients, which facilitates the analysis and solution of the system using linear-algebra techniques. In addition, this technique is useful for the design of linear controllers for the nonlinear system.  
The next step after linearization in state variables is to replace these variables in differential equations. This allows us to obtain a set of linear differential equations in the state variables, which can be solved to obtain the temporal evolution of the state variables. The advantage of having linear differential equations is that their solution is simpler and more efficient than those of nonlinear differential equations.  
Once you have the linear differential equations in the state variables, you can use control and optimization techniques to design control strategies and improve system performance; see Equations (16)-(18).  
\[\dot{x}_{1}(t)=k1(Xd1-x_{1}) \tag{16}\] \[\dot{x}_{2}(t)=k2(Xd2-x_{2})-k1(Xd1-x_{1})\] (17) \[\dot{x}_{3}(t)=k3(Xd3-x_{3})-k2(Xd2-x_{2}) \tag{18}\]  
The linearized model is obtained with the state variables defined to carry out the next procedure.
## 4 Attack Surface Analysis
### Radio Access Control
#### 4.1.1 Pre-Shared Key Based Systems
However, a survey involving nearly 400 participants [43] reveals vulnerabilities among users of the WPA2 protocol. The findings indicate that a significant portion, approximately 60%, of the examined users possess Wi-Fi passwords with 12 characters or less, and a notable 61% never change their passwords. This situation poses potential security risks and underscores the need for heightened awareness and proactive measures to address these issues. In addressing the limitations of WPA/WPA2, WPA3 introduced the Dragonfly handshake, also known as Simultaneous Authentication of Equals (SAE) [44]. This cryptographic protocol improves security by establishing a secure connection between a user device and a Wi-Fi access point. SAE is designed to thwart password-based attacks and man-in-the-middle attacks by using a zero-knowledge proof mechanism. This means that intercepted communication does not yield useful information to compromise the connection. However, a downgrade attack exploits security vulnerabilities by coercing a system to revert to a less secure version of a protocol or encryption standard. In the realm of Wi-Fi security, a downgrade attack targets the transition between WPA3 and older protocols such as WPA2 or WPA. Attackers manipulate communication between a device and an access point to induce the use of an older, less secure protocol, thus exploiting known vulnerabilities to gain unauthorized network access.
## 2 Results and Discussions
### Static Magneto-Optical Functions
Figure 1: Magneto-optical functions, \(\Delta\beta\) and \(\Delta\delta\), of an Fe\({}_{26}\)Gd\({}_{74}\) alloy (**a**) at the Gd \(N_{4,5}\) resonance around a photon energy of \(E=150\) eV as well as (**b**) at the Fe \(M_{2,3}\) resonance around a photon energy of \(E=55\) eV. The dispersive part, \(\Delta\delta\), is retrieved by the Kramers–Kronig relation and \(\theta_{F}\) is calculated according to Equation (3). Figure 2: (**a**) Scheme of a Faraday setup using single color FEL radiation. A linearly polarized XUV pulse is transmitted through the sample and the Faraday rotation is detected by a Rabinovitch polarimeter. (**b**) Scheme of an MCD setup using HHG radiation. The multiple emission peaks of the radiation are circularly polarized by a reflective phase-shifter, transmitted through the magnetic sample and detected by a spectrometer. In both setups, we realized a pump-probe geometry to measure optical driven ultrafast magnetization dynamics. Schematic depiction of (**c**) an out-of-plane magnetized FeGd alloy and of an in-plane magnetized (**d**) Co and (**e**) CoPt film.
# Nonlinear Solid-Fluid Coupled Seismic Response Analysis of Layered Liquefiable Deposit
###### Abstract  
A seismic response analysis of layered, liquefiable sites plays an important role in the seismic design of both aboveground and underground structures. This study presents a detailed dynamic site response analysis procedure with advanced nonlinear soil constitutive models for non-liquefiable and liquefiable soils in the OpenSees computational platform. The stress ratio controlled, bounding surface plasticity constitutive model, PM4 Sand, is used to simulate the nonlinear response of the liquefiable soil layers subjected to two seismic ground motions with different characteristics. The nonlinear hysteretic behavior of the non-liquefiable soil under earthquake excitations is captured by the Pressure Independent Multi Yield kinematic plasticity model with a von Mises multi-yield surface. The soil elements are modelled utilizing the solid-fluid fully coupled plane-strain \(\mathbf{u}\)-\(\mathbf{p}\) elements. The seismic response of the layered liquefiable site in terms of the development of excess pore water pressure, acceleration, ground surface settlement, and stress-strain and effective stress path time histories under two representative earthquake excitations are investigated in this study. The numerical results indicate that both the characteristics of ground motions and the site profile have a significant influence on the dynamic response of the layered liquefiable site. Under the same intensity of ground motion, the loose sand layer with a 35% relative density is more prone to liquefaction and contractive deformation, which causes irreversible residual deformation and vertical settlement. The saturated soil layer can effectively filter the high-frequency components and amplify the low-frequency components of ground motions. Moreover, the liquified site produces a 40% post-earthquake consolidation settlement after the excess pore pressure dissipation.  
liquefiable soil; solid-fluid fully coupled analysis; numerical simulation; seismic analysis; constitutive models +
Footnote †: journal: Applied Sciences
## 4 Discussion
At the same time, a broken field in the south makes it less resistant to disasters, which may lead to a decrease of CLP in the future. The 500-m MODIS data can give very accurate surface information. Moreover, the cultivated land in Jilin Province is large and concentrated, so there are less mixed pixels in the 500-m MODIS data. Therefore, it is reasonable to use MODIS to study cultivated land in Jilin. The GPP was selected as the indicator of CLP because, compared with NDVI, the estimated model is combined with water, temperature, and photosynthetic radiation effective coefficient indicators, which are the conditions inherent in the productivity of cultivated land. The method combining the Theil-Sen median slope analysis and the MK test is superior to linear regression analysis, as it can not only obtain trend information but also quantify the amplitude of the trend, and furthermore, it has an early warning effect on preventing the degradation of productivity and appropriately reducing cultivated land-use intensity. In addition, the time series data do not need to obey specific distributions, strong error avoidance, and significance level tests. The sustainability, shown by the H value, only indicates whether the future changes are consistent with the past or not. The direction of change is still unclear. Therefore, the future CLP trend was quantified by superimposing the trend analysis results and the Hurst exponent. The result of coupling these tests is that it reflects the impact of past trends on the future more scientifically. Based on the GPP time series of Jilin Province from 2000 to 2018, the trend in spatial and temporal variation, the amplitude of change and the sustainability of future changes of CLP were studied. However, different crop types may exhibit different inter-annual variations. The main crop types in Jilin are rice, corn, soybean, sorghum, etc. Therefore, more detailed work is needed to explore the effects of different crop types on GPP temporal and spatial changes. Based on studying the spatial and temporal changes of GPP for many years, combined with multi-source data such as soil type, high-standard farmland construction data, and other social and climate data, we could explore the driving force analysis of the trend, and provide effective regulation for providing regional productivity  
Figure 10: (**a**) Description of precipitation in Jilin Province; (**b**) Description of temperature in Jilin Province. degradation policy. While the changes in GPP are affected by climate change, human interference, and national or local land and agricultural policies, the drivers associated with the CLP should be explored further. These explorations will be helpful to fully understand the regional CLP, and local governments should adopt policies or projects that promote regional CLP.
## 4 Simulation and Discussion
### Reconstruction Performance versus the Sparsity Level
#### 4.3.2 Reconstruction Time  
Figure (a)a,b show reconstruction time versus the sparsity level \(K\) for synthetic and real signals. The greedy algorithms in the first category were omitted since they took significantly longer time. The horizontal axis denotes the different \(K\) drawn from [45; 60], and the vertical axis indicates the corresponding reconstruction time (in seconds). The initial step size selected according to the initial estimation approach may not work well in greedy algorithms with the fixed step size (such as SAMP). To make the comparison fair, we must evaluate reconstruction time with the similar success rates achieved by those algorithms. After a series of experiments, to ensure the similar success rates obtained by FRSVssMP, the step size was set to 5 for SAMP, and we also employed the appropriate parameters for CBMP.  
It can be observed that, both for synthetic and real signals, compared with SAMP and CBMP, FRSVssMP performs better in terms of reconstruction time, which reveals that FRSVssMP is more efficient with the same sparsity level. This is due to the fact that, FRSVssMP selects a more appropriate initial step size by the initial sparsity level estimation approach and employs two stages of sparsity variant step sizes mechanism. Moreover, as sparsity level becomes larger, the reconstruction time increases. However, FRSVssMP shows better robustness as the amount of time increased for it is less compared to those for other algorithms, which means it works well for complex scenarios.  
Figure 5: Reconstruction success rates vs. the sparsity level (**a**) Synthetic signals; (**b**) Real signals.  
Figure 6: Reconstruction time vs. the sparsity level. (**a**) Synthetic signals; (**b**) Real signals.
## 3 Results  
Figure 1 shows the percentage depth dose (_PDD_) curves from 4, 6, 10, and 20 MV photon beams in a 10 \(\times\) 10 cm field. Table 1 presents the characteristics of the percentage depth dose curves.  
Figure 1: The percentage depth doses from 4, 6, 10, and 20 MV photon beams in a 10 \(\times\) 10 cm field.  
Table 2 presents the main dosimetry parameters for the photon beams studied; including: \(S_{w,air}\), \(TPR_{20,10}\), and \(\%dd\) (10)\(x\). The \(k_{Q,Q_{o}}\) values for the ion chambers used in this study are presented in Table 3. \(TPR_{20,10}\) estimation from a \(PDD\) is less accurate than a direct measurement. Therefore, in this study, we used \(TPR_{20,10}\) values obtained from direct measurements.  
Table 4 presents the \(k_{pol}\), \(k\), and \(k_{TP}\) correction factors. A maximum variability of 0.2% was found in the kpol values using the DIN 6800-2 protocol, while the variability was 0.38% using TRS-398.  
Table 5 presents the absorbed dose conversion factors of TRS-398, TG-51, and DIN 6800-2. Excluding the FC65-G reading at 6 MV, the absorbed dose ratios TRS-398/TG-51, TRS-398/DIN 6800-2, and TG-51/DIN 6800-2 varied from 0.01% to 1.04%, 0.1% to 0.88%, and 0.1% to 0.7%, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**IAEA TRS-398** & **4 MV** & **6 MV** & **10 MV** & **20 MV** \\ \hline R100 (mm) & 11.10 & 13.20 & 23.20 & 33.00 \\ D100 (\%) & 62.60 & 67.09 & 73.34 & 80.90 \\ D200 (\%) & 33.58 & 38.85 & 46.39 & 54.50 \\ TPR 20/10 & 0.628 & 0.677 & 0.745 & 0.797 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of percentage depth dose curves for 4, 6, 10, and 20 MV photon beams.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**MV**} & \multicolumn{3}{c}{\(TPR_{20,10}\)} & \multirow{2}{*}{\(\%dd\) (10)\(x\).} & \multirow{2}{*}{\(S_{w,air}\)} \\ \cline{2-2} \cline{4-5}  & **Measurements** & **From PDD** & & \\ \hline
4 & 0.619 & 0.628 & 62.66 & 1.127 \\
6 & 0.675 & 0.677 & 67.27 & 1.119 \\
10 & 0.738 & 0.745 & 72.60 & 1.106 \\
20 & 0.793 & 0.797 & 82.50 & 1.084 \\ \hline \end{tabular}
\end{table}
Table 2: The main dosimetry parameters for the photon beams studied, including: \(S_{w,air}\), \(TPR_{20,10}\) and \(\%dd\) (10)\(x\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Chamber**} & **Beam Energy** & \multicolumn{3}{c}{**kQ**} \\ \cline{2-5}  & **(MV)** & **TRS-398** & **TG-51** & **DIN 6800-2** \\ \hline \multirow{4}{*}{NACP-02} & 4 & 0.9991 & 1.0003 & 0.996 \\  & 10 & 0.9665 & 0.9633 & 0.9607 \\  & 6 & 0.9991 & 1.0032 & 0.9960 \\  & 20 & 0.9665 & 0.9633 & 0.9607 \\  & 4 & 0.9996 & 0.9991 & 0.9981 \\  & 10 & 0.9854 & 0.9836 & 0.9815 \\ CC15 & 6 & 0.9953 & 0.9913 & 0.9933 \\  & 20 & 0.9981 & 0.9656 & 0.9622 \\  & 4 & 0.9981 & 0.9991 & 0.9971 \\ FC65-G & 10 & 0.9854 & 0.9914 & 0.9815 \\  & 6 & 0.9953 & 0.9913 & 0.9925 \\  & 20 & 0.9685 & 0.9656 & 0.9638 \\ \hline \end{tabular}
\end{table}
Table 3: \(k_{Q,Q0}\) values for the ion chambers used in this study.