esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Eight-State Continuous Variable Quantum Key Distribution by Applying Photon Subtraction
## 5 Conclusions  
This paper presents a method for enhancing the performance of eight-state CVQKD, by use of photon subtraction on the original eight-state CVQKD. Only using optical beam splitters and photon detectors, this operation can be well-applied in practice. The simulation results show that the eight-state CVQKD with photon subtraction significantly improved the transmission distance. In addition, it is very important to control the excess noise, in practice, since this noise has a significant impact on the performance of the proposed protocol. It is worth noting that the results also show that the optimal performance can be obtained by utilizing a one-photon subtraction operation.  
Figure 4: Performance of eight-state protocol with a photon subtraction operation. Here, \(\epsilon=0.001\) and \(\beta=0.95\). The orange line is for the original eight-state protocol and the other lines are for the photon subtraction with \(k\in\{1,2,3\}\), respectively.  
Figure 5: The relationship between the secret key rate of the system and different values of the excess noise \(\epsilon\) under the one-photon subtraction operation of the eight-state CVQKD.  
Author Contributions:[PERSON] gave a general idea of the study, designed the concept of the study, and provided relevant studies and literature. [PERSON] wrote the article and completed the derivation of formulae and simulation analysis. [PERSON] provided practical recommendations for this study. [PERSON] revised some of the content in the paper. All authors have read and approved the final manuscript. Funding: This work is supported by the National Natural Science Foundation of China (Grant No. 61379153, No. 61572529).  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61572529, 61871407, 61872390, 61801522), and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 18 KJB510045).  
The authors declare no conflict of interest.
## 1 Introduction
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}}  & **FOG** & **CIOUD** & **5G** \\ \hline
**Distributed deployment and low-access latency** & Fog nodes are expected to undergo a pervasive deployment. This reduces the access distance and, then, the resulting propagation delay & **Centralized deployment and global areas and, then, high-access latency** & **Cloud data centers typically serve large spatial areas and, then, incur high high-access temporal delay & **Real-time support for heterogeneous network** technologies & A number of ultra-short-short-/short-/long-range UWB/Wifi/3G-4G Cellular networks technologies are simultaneously supported and dynamically turned ON-OFF \\ \hline
**Low-density resource virtualization** & Clones of the served mobile devices may be allocated onto medium/small-sized Fog servers in real time. In order to reduce the resulting bootstrapping delay, light low-density virtualization technologies are employed & **Class of the served devices are quasi-statically deployed by resorting with “in-density virtualization of the computing resources of large servers** & **Dynamics provisioning, multiplexing, and isolation of the wireless bandwidth** & Wireless bandwidth is dynamically provided on-demand to the requiring Mobile devices. Dynamic and isolation of the wireless wired network to provide inter-device isolation and per-device bandwidth guarantee. \\ \hline
**Support for light delay-sensitive mobile applications** & Fog nodes may exploit low-latency WiFi links, in order to enable seamless light data/code offloading from Mobile devices & **Support for computing-intense delay-tolerant applications** & By exploiting the their huge computing resources, Cloud data centers may support computing-intensive delay-tolerant applications offloaded by remote devices & The simultaneous utilization of multiple transmission technologies allows increasing the total available wireless bandwidth \\ \hline
**Energy efficient** & Resource-limited mobile devices may save energy by exploiting nearby Fog nodes as computing clones & **Energy** & Both network and server infrastructures hosted by Cloud data centers are resource-rich, but energy hunger & **Utra-low access latency** & Sub-millisecond access delays are achieved by the synergistic exploitation of multiple network technologies \\ \hline \end{tabular}
\end{table} 
Table 1: The expected Fog-Cloud-5G synergic interplay for the support of future computing-intensive and delay-critical Mobile applications.
## 5 Results
### Option Price for EGARCH Price Model
#### 5.4.1 EGARCH Price Model Calibration  
Similar to the GARCH method, the Maximum Likelihood Estimation (MLE) method was used to estimate the parameters of the EGARCH model by calibrating it to oil price data (Figure 7). The estimated parameters of the EGARCH model are presented in Table 2. Figure 12 contrasts the realized volatilities of Brent Crude Oil price data with the forecasted volatilities from the calibrated EGARCH model. In Figure 12, he EGARCH(1,1,1) price model, due to its short memory (1,1,1), does not perfectly reconstruct the data. However, it is important to clarify that the primary focus of this paper is not on forecasting or developing models that perfectly fit the data. Rather, our work is centered on \"Sequential Decision-Making\" in American Options, where these models are used to simulate future paths as inputs to different pricing methods. Thus, even though the EGARCH(1,1,1) model may not reconstruct the data with high accuracy, it still serves as a consistent input for all methods under comparison.
# The Prediction of the Performance of a Twisted Rudder
## 4 Results and Discussions
### Prediction and Correction of Twist Angles
The initial twisted rudder model was designed based on the predicted inflow angle distribution along the rudder span at \(\delta_{\rm R}\) = \(0^{\circ}\). However, it was found that the twist angles can be overpredicted when extracted from the leading edge lines projected onto the measurement plane shown in Figure 9. When the streamline distribution on the cross-sections in the span direction of the port rudder, as shown in Figure 11, was examined, it was found that the inflow angles computed at the initial prediction points should be corrected. At the selected span locations of \(z\)/\(s\) = 0.3, 0.5, 0.7, and 0.9, the streamlines passing through the initial measurement points are seen to be slightly biased toward the outboard direction of the port rudder which leads to a slight overestimation of the predicted initial inflow angles. The correction of the flow angle distribution was done by searching the streamline that had the stagnation point on the leading edge of the rudder. In this work, the maximum inflow angle was estimated to be about \(3^{\circ}\) smaller. Figure 12 compares the twist angles before and after correction along the rudder span direction. The symbols show the inflow angles calculated at each span position, and the lines are the results of the interpolation of those angles using polynomial curves. The intermediate twist angle distribution was extracted to validate the final corrected twist angles by applying them together to the numerical and experimental analyses. The corrected twist angle distribution had a range from \(0^{\circ}\) to about \(8.1^{\circ}\), while the intermediate twist angles were in a range from \(0^{\circ}\) to about \(9.2^{\circ}\). Figure 10: Numerically predicted inflow angle distribution on a transverse plane in front of the port flat rudder and comparison of inflow angles predicted and measured according to rudder angle: (**a**) inflow angle distribution on the measurement plane shown in Figure 9; (**b**) inflow angles in the span direction according to rudder angle.
## 3 Results and Discussion
### Soil Water Change in Slopes under Natural Evaporation Conditions
#### 3.3.2 Soil Water Dissipation Law of Bare Slopes under Natural Evaporation Conditions  
Under natural evaporation conditions, the dissipation of water content from bare slopes was observed at different depths over the course of 14 days, as depicted in Figure 13. It is evident that the soil water dissipation at a depth of 60 cm gradually increased after rainfall. However, the soil water dissipation at a depth of 100 cm remained almost unchanged for 14 days after the end of rainfall, with a maximum dissipation of only 0.2%. On the first day following the end of rainfall, the soil water dissipation of the slope with a slope ratio of 1:1.5 at depths of 20 cm, 40 cm, and 60 cm was 1.6%, 1.2%, and 0.8%, respectively. For the slope with a slope ratio of 1:1.75, the soil water dissipation at each depth was 2.0%, 1.2%, and 1.1%, respectively. Similarly, for the slope with a slope ratio of 1:2, the soil water dissipation at each depth was 1.7%, 1.1%, and 1.0%, respectively. After 14 days of natural evaporation, the soil water dissipation of the slope with a slope ratio of 1:1.5 at depths of 20 cm, 40 cm, and 60 cm was 4.7%, 3.8%, and 2.7%, respectively. For the slope with a slope ratio of 1:1.75, the soil water dissipation at each depth was 4.6%, 3.9%, and 2.8%, respectively. Similarly, for the slope with a slope ratio of 1:2, the soil water dissipation at each depth was 4.4%, 3.9%, and 2.8%, respectively. It is evident that the dissipation of soil water from the bare slope is most significant within the first day following the end of rainfall under natural evaporation conditions. However, over the next 14 days, the rate of soil water dissipation decreased. The overall trend of soil water dissipation within 14 days indicates a significant decrease in dissipation and dissipation rate with increasing soil depth. However, the slope ratio does not significantly affect the dissipation of soil water on bare slopes. After 14 days of natural evaporation, the trend of soil water dissipation with time is similar across different slope ratios. The maximum difference in soil water dissipation at depths of 20 cm, 40 cm, and 60 cm on bare slopes with varying slope ratios is 0.3%, 0.1%, and 0.1%, respectively. This indicates that the impact of slope ratio on the dynamic change in water content in bare slopes under natural evaporation is not significant. Instead, the depth of the slope soil and the duration of natural evaporation are the main factors influencing water dissipation in bare slopes.
## 4 Results and Discussion
### Evaluation of UCS Tests
#### 4.2.3 Effect of Sawdust on Sulphate Resistance for 28 Days Curing  
Figure 8a-c shows the influence of SD effect on the soil resistance against exposure to sulphate after 28 days. In Figure 8 there is a clear trend of increasing in UCS of benchmark specimens with a range of 962 \(\leq\) UCS \(\leq\) 1407 kPa, 1194 \(\leq\) UCS \(\leq\) 1807 kPa and 1489 \(\leq\) UCS \(\leq\) 2159 kPa for 3%, 5% and 7% PC treated specimens when 3% to 15% SD was added to the specimens. The increasing trend in UCS of SD treated specimens was observed even after exposure to the MgSO4. For example, UCS of 3% PC treated samples increased from 145 to 520 kPa with increasing SD content from 3% to 15%. The same increasing trend from 649 to 1275 kPa and from 1105 to 1812 kPa was observed for 5% and 7% PC treated specimens respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SD Content**} & \multicolumn{2}{c}{**Unconfined Compressive Strength, UCS (kPa)**} \\ \cline{2-4}  & **3\% PC = 602** & **5\% PC = 702** & **7\% PC = 908** \\ \cline{2-4}  & **UCS (kPa)** & **UCS (kPa)** & **UCS (kPa)** \\ \hline
3\% & 710 & 715 & 1050 \\
5\% & 725 & 735.56 & 1248 \\
10\% & 738 & 850.71 & 1306 \\
15\% & 751 & 947.16 & 1391 \\ \hline \hline \end{tabular}
\end{table}
Table 6: UCS results before sulphate exposure in presence of SD (7 days curing).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**SD Content**} & \multicolumn{4}{c}{**Unconfined Compressive Strength, UCS (kPa)**} \\ \cline{2-5}  & **3\% PC = Vanished** & **5\% PC = 131.37** & **7\% PC = 204** \\ \cline{2-5}  & **UCS (kPa)** & **Improvement (\%)** & **UCS (kPa)** & **Improvement (\%)** \\ \hline
3\% & 150 & - & 223.37 & 70 & 480 & 135 \\
5\% & 284 & - & 336.29 & 156 & 685 & 235 \\
10\% & 396 & - & 454.25 & 245.8 & 720 & 252 \\
15\% & 401 & - & 485.96 & 270 & 810 & 297 \\ \hline \hline \end{tabular}
\end{table}
Table 7: UCS results after sulphate exposure in presence of SD (7 days curing).  
Figure 8: Effect of sawdust addition on USC peak values (28 days curing): **(a)** 3% PC; **(b)** 5% PC; **(c)** 7% PC.  
The effect of mixing cemented samples with different SD contents on the improvement of UCS for benchmark and exposed samples cured at 28 days is shown in Tables 8 and 9. As illustrated in Table 8, adding 3% to 15% SD increased the improvement of benchmark 3% PC treated specimens. As shown in Table 9, the improvement of 3% PC treated samples continued at an increasing rate of 21% to 333% after exposure to the MgSO4. However, the improvement was 124.56% \(\leq\)_IMP_\(\leq\) 341% and 170% \(\leq\)_IMP_\(\leq\) 343% for 5% and 7% PC treated specimens.  
For 28 days curing, the samples for 3% cement without sawdust vanished while exposed to sulphate, and the samples with higher sawdust generally showed better performance in terms of resistance against sulphate. Once the cement percentage increased, we could see better results with respect to sulphate exposure resistance.
## 3 Controller Design  
To achieve the previous control objectives, the system error vector is defined as \(E(t)\in\mathbb{R}^{2\times 1}\).  
\[E(t)=X-X_{d}, \tag{18}\]  
where \(X_{d}=\left[\begin{array}{cc}x_{1d}&0\end{array}\right]^{T}\) and \(x_{1d}\) are the target air gaps of the maglev vehicle.  
Taking the time derivative of Equation (18), we can obtain the following relationship:  
\[\dot{E}=\dot{X}-\dot{X}_{d} \tag{19}\]  
The following condition must be met if the system is to be stable at the target air-gap position.  
\[\underset{t\rightarrow\infty}{\lim}E(t)=0 \tag{20}\]  
To facilitate subsequent controller design, actuator saturation is not considered. Taking into account the actual working conditions of a maglev train, the following reasonable assumption is given:  
**Assumption 1**.: _There exist matrix \(P\) and positive definite matrices \(Q\), \(J\), and \(K_{2}\) that satisfy the following Riccati equation:_  
\[(A_{1}-BK_{2})^{T}P+P(A_{1}-BK_{2})+PA_{2}J^{-1}A_{2}^{T}P=-Q-J \tag{21}\]  
On the basis of the desired state in Equation (18) and the system in Equation (17), the ideal response of the system is given by the following reference model:  
\[\dot{X}_{d}=\phi_{m}X_{d}+A_{2}X_{d}(t-\tau)+\varphi_{m}\sigma, \tag{22}\]  
where \(\sigma\in\mathbb{R}^{2\times 1}\) is a bounded reference signal; \(\phi_{m}\), \(\varphi_{m}\in\mathbb{R}^{2\times 2}\) and their equations are as follows:  
\[\phi_{m}=A_{1}+B(K_{1}-K_{2}),\ \varphi_{m}=BK_{3}, \tag{23}\]  
where \(K_{1}\), \(K_{2}\), and \(K_{3}\in\mathbb{R}^{1\times 2}\), and they are optional.  
From Equation (23), matrices \(\phi_{m}-A_{1}+BK_{2}\) and \(\varphi_{m}\) are a linear combination of matrix \(B\). If matrix \(B\) is linearly independent of matrix \(\varphi_{m}\), which can avoid redundancy in inputs, matrices \(K_{1}\) and \(K_{3}\) are as follows:  
\[K_{1}=(B^{T}B)^{-1}B^{T}(\phi_{m}-A_{1}+BK_{2})=(\varphi_{m}^{T}B)^{-1}\varphi _{m}^{T}(\phi_{m}-A_{1}+BK_{2})\]  
\[K_{3}=(B^{T}B)^{-1}B^{T}\varphi_{m}=(\varphi_{m}^{T}B)^{-1}\varphi_{m}^{T} \varphi_{m}\]  
For the sake of improving system adaptability and robustness, an adaptive sliding-mode controller is proposed as follows:  
\[u=\frac{4}{\mu_{0}A_{m}N_{m}^{2}}\Big{[}K_{2}X-K_{1}X_{d}-K_{3}\sigma+\hat{ \eta}\text{sgn}(B^{T}PE)\Big{]}, \tag{24}\]where \(\text{sgn}(\cdot)\) is the signum functional; \(\hat{\eta}\) is the adjustable estimate of \(\eta\) and it was designed as follows:  
\[\hat{\dot{\eta}}=\xi\big{\|}E^{T}PB\big{\|}, \tag{25}\]  
where \(\xi\) is the adaptive factor and \(\xi\in R^{+}\), which was used to adjust the estimated speed of \(\hat{\eta}\).  
Choosing suitable adaptive gain can effectively reduce the chattering phenomenon and remove the requirement of knowing the bound of uncertainties.
# Authentication by Keystroke Dynamics: The Influence of Typing Language
## 5 Conclusions  
This study presented the Bilingual Keystroke Dynamics Dataset (BKSD), a dataset designed to test whether keystroke dynamics are affected by language. BKSD is divided into two components: password and phrase, with a total of 50 and 35 users, respectively. Each user has approximately 400 attempts in English, and 400 attempts in Arabic.  
The results show that using different languages (Arabic and English) prevents authentication systems from accurately identifying users, despite the letter patterns typed being identical, i.e., the finger movements of the subject were the same. EER values deteriorated to an average of 0.486% when enrolling in English and testing on Arabic, and 0.475% when enrolling in Arabic and testing on English. Previous work reported similar results for the Chinese and English languages. This work suffers from a number of limitations. As keystroke dynamics is used to authenticate users, authentication becomes susceptible to emotions or tiredness, and other human factors, as these factors all affect keystroke dynamics. In addition, using keystroke dynamics in an authentication system places an extra energy load on those systems. Finally, a keystroke dynamics authentication system's performance may differ when hardware is changed.  
For future work, a number of interesting directions present themselves. We plan to study the effect of frequent \(n\)-grams on keystroke dynamics in depth. The location of frequent \(n\)-grams on the keyboard might also come into play, where a cluster of letters may have a faster typing time. Another direction is the effect of languages and language families on keystroke dynamics, which necessitates the collection of data in the same and different language families. Finally, what effect does the letter layout on keyboards have on keyboard dynamics? In Arabic, for example, there are two major keyboard layouts that require further exploration.  
This work has been funded by King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Project number (RSPD2023R857).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The Bilingual Keystroke Dynamics Dataset (BKSD) is freely available at [[https://github.com/ntwaiiry/BKSD](https://github.com/ntwaiiry/BKSD)]([https://github.com/ntwaiiry/BKSD](https://github.com/ntwaiiry/BKSD)) (accessed on 17 October 2023).  
Special thanks to [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for overseeing the collection of the raw keystroke data from participants, and to [PERSON] for the fruitful discussions.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BKSD & Bilingual Keystroke Dynamics Dataset \\ AdaB & AdaBoost \\ DT & Decision tree \\ RF & Random forest \\ FRR & False rejection rate \\ FAR & False acceptance rate \\ EER & Equal error rate \\ ZM-FAR & Zero-miss false alarm rate \\ \end{tabular}
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & & & & **Criteria for Waste Landfilling Acceptance**[26] \\ \hline  & & & & & **Other Than** \\  & & & & & **Neutral and** & **Hazardous** \\  & & & & & **Hazardous** \\ \hline PL & Unit & WM1 & WM2 & WM3 & & \\ Arsenic, As & mg/kg & \(0.42\) & \(0.39\) & \(0.42\) & \(0.5\) & 2 & 25 \\ Barium, Ba & mg/kg & \(0.040\) & \(0.020\) & \(<\)0.01 & 20 & 100 & 300 \\ Cadmium, Cd & mg/kg & \(<\)0.01 & \(<\)0.01 & \(<\)0.01 & 0.04 & 1 & 5 \\ Chromium, Cr & mg/kg & \(0.24\) & \(0.19\) & \(0.13\) & \(0.5\) & 10 & 70 \\ Copper, Cu & mg/kg & \(2.54\) & \(1.59\) & \(0.99\) & 2 & 50 & 100 \\ Mercury, Hg & mg/kg & \(0.003\) & \(0.003\) & \(0.009\) & 0.01 & 0.2 & 2 \\ Molybdenum, Mo & mg/kg & \(<\)0.20 & \(0.22\) & \(0.3\) & 0.5 & 10 & 30 \\ Nickel, Ni & mg/kg & \(0.49\) & \(0.58\) & \(0.46\) & 0.4 & 10 & 40 \\ Lead, Pb & mg/kg & \(4.08\) & \(2.03\) & \(0.92\) & 0.5 & 10 & 50 \\ Antimony, Sb & mg/kg & \(<\)0.20 & \(<\)0.20 & \(0.21\) & 0.06 & 0.7 & 5 \\ Antimony, Sb & mg/kg & \(0.24\) & \(<\)0.20 & \(<\)0.20 & 0.1 & 0.6 & 0.7 & 5 \\ Selenium, Se & mg/kg & \(0.24\) & \(<\)0.20 & \(<\)0.20 & 0.1 & 0.5 & 7 \\ Zinc, Zn & mg/kg & \(0.13\) & \(0.050\) & \(0.040\) & 4 & 50 & 200 \\ Chlorides, Cl\({}^{-}\) & mg/kg & \(17.0\) & \(15\) & \(13.8\) & \(800\) & 15,000 & 25,000 \\ Fluorides, F\({}^{-}\) & mg/kg & \(28.4\) & \(20.6\) & \(13.1\) & \(10\) & 150 & 500 \\ Sulphates, SO\({}_{4}\)\({}^{2-}\) & mg/kg & \(117\) & \(45.3\) & \(175\) & \(1000\) & 20,000 & 50,000 \\ Disolved organic carbon, DCC & mg/kg & \(633\) & \(965\) & \(1000\) & \(500\) & 800 & 1000 \\ Total dissolved solids (TDS) & mg/kg & \(72\),\(760\) & \(70\),\(000\) & \(67\),\(540\) & \(4000\) & 60,000 & 100,000 \\ Phenolic index & mg/kg & \(0.870\) & \(1.35\) & \(0.620\) & 1 & - & - \\ \hline \end{tabular}
\end{table} 
Table 5: Leachability of selected contaminants from geopolymer mortar, expressed as mg/kg dry weight compared with maximum acceptable waste disposal rates.
## 4 Field Data Processing
### Post-Processing for Virtual Shot Gathers  
Although preprocessing the raw field data before cross-correlation could effectively enhance the surface waves, the surface waves on the virtual shot gather were still concealed by the strong transverse axis energy. Determining how to eliminate the strong transverse axis energy, improve the S/N of the virtual shot gather, and further enhance the surface waves is of great significance for subsequent inversion and imaging.  
[PERSON] et al. [34] proposed several methods to improve the fidelity of noise cross-correlation based on the discrete orthogonal S transform, which showed that time-frequency denoising of correlograms (i.e., virtual shot gathers) can alleviate this problem. The noise or signal removal algorithm based on SS-CWT proposed by [PERSON] et al. [25] can also be applied to the virtual shot gather after cross-correlation to denoise correlograms and improve the S/N. Figure 7 shows the virtual shot gather (Figure 7a) and its dispersion curve image (Figure 7b) after denoising the correlogram of Figure 5a using the noise or signal removal algorithm based on SS-CWT. Comparing Figure 7a with Figure 5a, the strong transverse axis energy on the correlogram was effectively eliminated, and the surface waves were highlighted. The dispersion curve in Figure 7b (red part) is more concentrated and continuous than that in Figure 5b.  
The above research showed that the noise or signal removal algorithm based on SS-CWT could effectively enhance the surface waves after pre  
Figure 6: (**a**) Virtual shot gather formed by cross-correlation of preprocessed separated noises and (**b**) the corresponding dispersion curve image.  
Figure 7: (**a**) Virtual shot gather after denoising the correlogram of Figure 5a and (**b**) the corresponding dispersion curve image.  
data. At the same time, the method could also denoise the correlogram to further enhance the surface waves. Therefore, for the passive seismic datasets acquired in Sichuan, we first preprocessed the raw field data, then used the separated noise records to perform cross-correlation calculations to form the virtual shot gathers, and finally post-processed the correlograms, thus obtaining the final virtual shot gather, shown in Figure 8a, and its corresponding dispersion curve image (Figure 8b).  
Comparing Figure 8 with Figures 5-7, we observed that the strong transverse axis energy in the virtual shot gather (Figure 8a) was eliminated, and the surface waves were richer and clearer; the information of the low-frequency part in the dispersion map shown in Figure 8b is prominent, and the dispersion curve is clear and continuous.