esa-sceva/satcom-chunk-collection huggingface dataset:
# Memory and Cache Contention Denial-of-Service Attack in Mobile Edge Devices
## 3 Memory and Cache Contention Denial-of-Service (DOS) Attack
### Our Attack and Threat Model
Thus, the generated requests from the L1 data caches put a strong bandwidth pressure to the L2 caches. In the L2 cache, because of the stride-prefetcher, it may not generate a large number of the L2 cache misses from the malicious code. However, the L2 cache prefetcher will consecutively generate the memory access requests to fetch the data in the malicious program into the L2 cache in advance. In this case, the effective main memory bandwidth for the normal programs is also reduced. In summary, our memory and cache contention DoS attack eventually leads to two adverse impacts on the normal programs:  
* a large number of repetitive access requests to the L2 cache incurred by the malicious code will consecutively prefetch the data of the malicious code into the L2 cache, evicting the data of the normal program from the L2 cache (i.e., higher L2 miss rate of the normal programs); and,
* a huge number of L2 prefetch requests that are caused by the malicious code will exhaust the effective main memory bandwidth, resulting in available memory bandwidth reduction and longer memory access latency of the normal program. Consequently, there can be two system-level adverse impacts from the attack. Firstly, the shared resource contention between the normal and malicious program will lead to performance degradation (i.e., denial-of-service) of the normal programs. Secondly, the malicious program will draw non-negligible power, while a longer execution time of the normal programs results in higher static energy consumption. It means that the adverse consequences of the memory and cache contention attack would lead to user dissatisfaction due to the shortened battery life in mobile edge devices as well as performance degradation. Please note that typical mobile device users are hard to detect whether the malicious code is running or not in the device, because it is executed in the background. In addition, the application that is used by the attacker to hide the malicious code pretends to be a normal application (e.g., a category of applications that are widely and frequently executed in mobile devices), which also makes a detection of our malicious code even more difficult. Figure 3: The cases where (**a**) the normal program is solely running and (**b**) both normal and malicious programs are running in the system.
## 4 Data and Estimation Results
### Unit Root Tests  
Table 2 presents the results of the unit root tests on EUA and WTI, including their price levels and return rates. First, the unit root cannot be rejected for the price levels of EUA and WTI. This result implies EUA and WTI price levels are non-stationary time series. Second, the presence of unit root is rejected in EUA and WTI return rates, which means they are stationary time series. Therefore, EUA and WTI return rates are employed for our subsequent analyses.
# A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
## 3 Results and Discussion
### Uncertainty in \(R\) Factor
#### 3.1.2 Bulk Modulus Uncertainty
In the low \(R\) system, the pressure increased and decreased a few times before reaching a new steady state due to wave reflection at the two pipe ends. This finding is in agreement with that for the high \(R\) system. In general, the sign of \(BMC\) was positive when the pressure was decreasing and was negative when the pressure was increasing. As a result, leak detectability in the system with \(R=0.49\) was worse for the case where the bulk modulus error could be properly corrected in the system with \(R=2.20\), but better for the case where the bulk modulus error was incorrectly adjusted in the system with \(R=2.20\) (Table 5). The same relationship between the sign of \(BMC\) and pressure was observed for a flow increase transient. In conclusion, Statefinder determines the sign of \(BMC\) based on wave propagation, rather than on whether the error in the input bulk modulus is positive or negative. In addition, it was found that the exact amount of required correction must be specified in order for Statefinder to make full correction to the bulk modulus. This can be shown with Figure 6. Two cases were tested, both with +10% bulk modulus error, but the allowable correction (\(BMER\)) was set to be exactly the amount needed (0.058) in one case and larger (0.2) in the other case. It can be seen that the \(DVB\) curve for the case with the larger \(BMER\) deviated from the other case and the baseline. The detection time was delayed for 4.8 min compared to the detection time of 25.5 min for the baseline case. Therefore, if the allowable correction is set too large, Statefinder will make an over-correction to the wave speed, resulting in degraded leak detectability. This is unlike the case with viscosity uncertainty, for which errors can be corrected properly as long as the maximum allowable correction is set greater than the amount of required correction. Therefore, it is clearly more important to have accurate bulk modulus information than accurate measurements of viscosity. A similar trend was observed with noisy data, as shown in Figure 7. The leak detection was delayed for cases with \(-10\%\) error in bulk modulus because Statefinder made an incorrect adjustment (i.e., incorrect sign) to the wave speed. The detection time was delayed by 4.9 and 7.1 min from the 33.1 and 61.7 min for the baseline case for the flow decrease and flow increase transients, respectively. The cases with +10% error in bulk modulus were less affected than the \(-10\%\) error case but not exactly overlapped with the baseline. The detection time was early by 1.0 and 4.6 min for the flow decrease and flow increase transients, respectively. This was because the \(BMC\) value was oscillating due to the presence of data noise; thus, exact correction could not be applied. Figure 5: \(BMC\) behaviour for flow decrease transient initiated at: (**a**) the downstream end; and (**b**) upstream end with perfect data.
## 2 Materials and Methods
### Simulation and Load Cell Verification  
We estimated the forces acting on straps 1 and 2 by analyzing a video recording, via an RGB camera, of a healthy participant using the passive gait assist system. We initially obtained the force profile of the elastic cord during the gait cycle by measuring its change in length. This was carried out by marking the two ends of the elastic cord for each frame  
Figure 1: Passive gait assist system. (**a**) Overall design. (**b**) Top view of the spring-bar system (**c**) Specialized shoe shown with the pulling force of the elastic cord (red line). When the force is at a low angle (top), tension resides in strap 1 (blue line) and slack is present in strap 2 (green line). The opposite tension and slack relationship appears when the force angle is high (bottom). (**d**) The passive gait assist system during the gait cycle.  
and then scaling the pixel lengths to mm lengths by normalizing them against the known free length of the elastic cord (Figure 2a). We then applied simple geometry (Figure 2b) to estimate the forces acting on each strap when varying the lengths of straps 1 and 2. The equations for estimating the tension in straps 1 and 2:  
\[T_{1}=T_{e}\cdot sin\theta_{2}/sin\left(\theta_{1}+\theta_{2}\right) \tag{1}\]  
\[T_{2}=T_{e}\cdot\sin\theta_{1}/sin\left(\theta_{1}+\theta_{2}\right) \tag{2}\]  
In order to verify that our simulations indeed reflect the actual forces, we attached load cells (HUMMA, HAIAM ENG., Seoul, Republic of Korea) to the end of the elastic cord and straps 1 and 2.
# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 1 Introduction  
Public Safety Systems (PSSs) have gained a growing interest owing to events such as natural disasters and terrorist attacks that pose a significant threat to human society. Within PSSs, the problem of evacuation planning is an issue of major practical importance, as the efficient and effective guidance of the crowds can contribute and improve evacuees' survivability, and save human lives [1]. During the evacuation process, the evacuees face the problem of incomplete information regarding the surrounding environment, e.g., a bottleneck in evacuation routes, which further hampers the decision-making process and deteriorates their fast and accurate evacuation-route selection [2]. Moreover, a disaster area dynamically changes over the time due to the evolving catastrophic event. Consequently, human behavior becomes unpredictable due to the imposed uncertainty by the public safety system [3]. Thus, evacuation planning becomes an even more complicated process, especially if a large number of coevacuees [4] and available evacuation routes are included within humans' decision map [5].  
In this paper, we address the aforementioned challenges and, particularly, propose an autonomous evacuation process based on the principles of reinforcement learning and game theory to support distributed and efficient evacuation planning. Humans/evacuees act as rational entities in the publicsafety system who are able to sense changes in the disaster area via the limited provided information, and make optimal evacuation decisions about themselves.
# A Semi-Explicit Multi-Step Method for Solving Incompressible Navier-Stokes Equations
## 4 Summary and Conclusions  
This paper has shown how an efficient semi-explicit strategy can be derived in the framework of the stabilized Galerkin finite element methods. A methodology based on a combination of the Runge-Kutta time integration scheme with the fractional step technique was presented. It was shown that introducing an approximation for the pressure at the intermediate steps of the Runge-Kutta method leads to a scheme where the implicit step necessary for computing the pressure is performed only once.  
A priori estimates, as well as numerical tests have shown that the time accuracy of the resulting scheme reduces to the second order; however, the value of the error for a given time step is several orders of magnitude smaller than the one arising when applying fractional step to second-order implicit schemes. Thus, for the problems where critical time step estimates due to CFL (Courant-Friedrichs-Lewy) criteria are favorable, the method proposed here defines a very advantageous option in comparison with the implicit schemes.  
The overall computational cost of the resulting method is governed by the cost of a single solution of the pressure Poisson equation, which is an equation of inhomogeneous Laplace type. Thus, an important step to make the proposed method even more efficient is to devise a scheme for minimizing the cost of solving [PERSON]'s equation, which can be done at the level of the linear solver following algebraic multi-grid or deflation techniques [32].  
Another observation is that even though implicit time integration schemes are preferred in the literature against explicit ones, the latter are in a better position attending to the present hardware technology. This is oriented toward the usage of parallel computers and general purpose graphic processor units (GPGPU) [33]. Thus, the cost associated with the first part may be considerable reduced.  
This work was supported under the auspices of the COMETADproject of the National RTDPlan (Ref. MAT2014-60435-C2-1-R) by the Ministerio de Economia y Competitividad of Spain.  
The methodology has been developed by both authors. [PERSON] implemented the multi-step method. [PERSON] implemented the pressure correction. The accuracy analysis has been done by [PERSON], while computational efficiency estimation has been done by [PERSON].  
The authors declare no conflict of interest.  
Figure 6: Computational time of different solution steps. Semi-explicit vs. implicit method. Mesh of 160,000 nodes.
## 3 Results and Discussion
### Effect of Holding Time under Compression Pressure
The formation of granules increases the bulk density of the block; however, the elongated pores formed around them by granulation are thought to have also increased the porosity of the block. Figure 8 shows SEM images of the carbon blocks fabricated with different holding times under compression pressure. The size and shape of granules and pores differed depending on the holding time; they were the smallest at a holding time of 1 min. There was no significant difference in the size of both granules and pores between the holding times of 2 and 3 min. Pores were situated around granules in elongated shapes. Some pores were located within granules, and their size was 20 \(\upmu\)m or less. As the pressure holding times increased, granules grew in size, with pores connecting to each other and thus forming elongated shapes around the granules. The observed increase in the size of the granules is attributed to the pressure applied to kneaded particles during compression molding, causing them to connect and coalesce. Concurrently, some pores protruded out of the kneaded particles, thereby surrounding the granules, while others that failed to escape ended up within the granules. As the size of the granules increased, the distance between granules became smaller, leading to the connection of surrounding pores. The large, elongated pores observed in Figures 7 and 8 are considered to be the products of this mechanism. The degradation in both mechanical and electrical properties observed at holding times of 2 and 3 min can be attributed to these variations in the size and shape of granules and pores. Indeed, when subjected to compression pressure during molding, granules tended to connect and coalesce, leading to the formation of large pores around them. These large pores were expected to serve as a channel facilitating the release of volatiles from the binder pitch during carbonization. This mechanism was considered to contribute to a larger volume reduction in carbon blocks after carbonization. Figure 7: OM images of carbon blocks fabricated with different holding times under compression pressure at a magnification of 100\(\times\). Figure 8: SEM images of carbon blocks fabricated with different holding times under compression pressure at magnifications of 1000\(\times\) and 5000\(\times\).
## 3 Results and Discussion
### Stability Tests
A review of the study data shows that the cosmetic without added plant powders (HC_0) had a TSI value of 1.95 after one week of storage at 38 \({}^{\circ}\)C. The TSI rose to 2.95 after two weeks. The highest TSI values were recorded for the conditioner containing burdock powder at a concentration of 0.5% (HC_1): TSI\({}_{7\text{ days}}\) = 2.4 and TSI\({}_{14\text{ days}}\) = 3.2. Nevertheless, these TSI values are still low, indicating that the emulsion has a relatively high stability, and the differences are not significant. Based on the literature on the subject, plant-derived raw materials can impact the stability of the cosmetic form in a variety of ways. For example, [PERSON] et al. [49] conducted a study to evaluate the application of dogwood fruit extract in cosmetic emulsions stored at 40 \({}^{\circ}\)C for a period of 14 days. The TSI values measured with a Turbiscan device were at the level of approx. 2.4 after seven days of storage and approx. 3.3 after 14 days of storage. They were thus close to the values obtained in the present study. Figure 2: Dependence of transmittance (T) and back scattering (BS) on sample height for a prototype HC_0; dark blue line—evaluation of the preparation immediately after application into tubes (time “0”), green line—stability after 7 days, red line—stability after 14 days. Moreover, [PERSON] et al. [46] studied the effects of adding ground orange peel (_Citrus aurantium dulcis_ Peel Powder) and oat bran (Avena Sativa Bran) to cosmetic emulsions on their properties. Based on the measurements of samples stored at 40 \({}^{\circ}\)C, carried out for 60 days, the TSI values varied around and above 8.
# Bearing Fault Diagnosis Approach under Data Quality Issues
## 5 Conclusions  
In this paper, we studied the fractal characteristics of bearing vibration signals from CWRU data set using DFA method. Our results showed that vibration signal from faulted bearing had higher scaling exponent (\(\alpha_{1}\)) at the short scale compared to normal bearing. Based on the estimated scaling exponent, we proposed a novel method to detect bearing faults and classify their severity. The proposed method can predict faulted bearing with an accuracy rate of 100% using high sampling rate vibration data. We believe that the scaling exponent (\(\alpha_{1}\)) can be adopted as a novel classifier of different bearing faults. One of the limitations of the proposed method is low sampling rate of vibration data. It seems that the proposed method is not suitable for typical data acquisition systems at low sampling rate.  
It was shown that the proposed method is resilient against the common data quality issues, induced noise and data loss, in the real-measurement environment. It seems that the relative error in scaling exponent remained below 10% in the case of acceptable SNR and data loss levels. In the future, we intend to apply the proposed diagnostic approach on the other benchmark vibration data sets to test the capability of this approach in identifying the bearing faults under different operating conditions.  
Conceptualization, L.S. and A.A.; methodology, L.S. and A.A.; software, L.S.; validation, L.S. and A.A.; formal analysis, L.S. and A.A.; investigation, L.S. and A.A.; resources, A.A.; data curation, L.S.; writing--original draft preparation, L.S. and A.A.; writing--review and editing, L.S. and A.A.; visualization, L.S. and A.A.; supervision, L.S.; project administration, A.A.; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the deanship of scientific research at the German Jordanian University.  
The authors declare no conflict of interest.
## 7 Conclusions
\begin{tabular}{l l l l}
**Definition** & **Abbreviation** & **Definition** & **Abbreviation** \\ Added random noise & ARN & Linear discriminant analysis & LDA \\ Auto-associative neural network & AANN & Mahalanobis-squared distance & MSD \\ Autoregressive model & ARM & Mean absolute error & MAE \\ Back-propagation & BP & Mean-squared error & MSE \\ Back-propagation neural network & BP-NN & Modal assurance criterion & MAC \\ Camage classification rates & DCR & Modified ensemble empirical mode & MEEMD \\  & decomposition & & \\ Complete ensemble empirical mode & CEEMD & Naïve bayes & NB \\ decomposition & & & \\ Contribution damage localization methods & CDLM & Nonlinear time-history analysis & NTHA \\ Convolutional neural network & CNN & Particle swarm optimization & PSO \\ Coupled simulated annealing & CSA & Piecewise aggregate approximation & PAA \\ Cross-correlation coefficient & CC & Poly-reference least-square complex & p-LSCF \\  & & frequency domain & \\ Damage-sensitive energy feature & DEF & Power spectral densities & PSD \\ Decision tree & DT & Principal component analysis & PCA \\ Deep convolutional neural network & DCNN & Principal component pursuit & PCP \\ Deep neural network & DNN & Probabilistic neural network & PNN \\ Differential evolution algorithm & DEA & Quadratic discriminant analysis & QDA \\ Electromechanical impedance & EMI & Radial basis function neural network & RBFNN \\ Extended Kalman filter & EKF & Random projection & RP \\ Factor analysis & FA & Recursive algorithm autoregressive-moving & RARMX \\  & & average with the exogenous inputs & \\ Fast Fourier transform & FFT & Residual error & RE \\ Feed-forward multilayered perceptron & FFMLP & Scaled conjugate gradient algorithm & SCG \\ Fourier transform & FT & Self-organizing maps & SOM \\ Frequency-domain decomposition & FDD & Signal statistical indicators & SSaI \\ Frequency response functions & FRF & Singular value decomposition & SVD \\ Gaussian radial basis function & GRBF & Statistical features & SF \\ Gaussian white noise & GWN & Stiffness reduction factor & SRF \\ Generative adversarial network & GAN & Stochastic subspace identification & SSI \\ Gradient descent momentum & GDM & Support vector data description & SVDD \\ Harmony memory & HM & Support vector machine & SVM \\ Harmony search algorithm & HSA & Teager-Huang transform & THT \\ High-pass filter & HPF & Trainable look-up tables & LUT \\ Hilbert-Huang transform & HHT & Transmissibility function analysis & TFA \\ Imperial competitive algorithm & ICA & Unsupervised image transformation model & UITM \\ Improved reduction system & IRS & Wavelet package relative energy & WPRE \\ Kernel principal component analysis & KPCA & Wavelet packet decomposition & WPD \\ Least-square support vector machine & LS-SVM & Wavelet packet transform & WPT \\ Levenberg-Marquardt back-propagation & LMBP & Wavelet-based autoregressive model & WARM \\  & & & \\ \end{tabular}
## 3 System Model
Hence, an efficient learning model is crucial to identify intrusions across normal samples. Learning Model: An FL model contains a single server and multiple sensors, in which the server refers to the remote cloud server and the sensors refer to the IIoT network. The main objective of FL is to enable a range of knowledge to be gained from multiple IoT networks by utilising the parameters of the learning model instead of directly storing and processing all data of the distributed sensors on a central server. In an FL model, the edge of the IIoT network independently trains the local datasets generated from the heterogeneous IIoT application with the deep learning model. The federated model then provides the local learning model with parameters as the input to the global server through the edge network. The remote server provides a global shared model with the updated parameters in the FL. To model intrusion detection for an IIoT network, FTL is employed to establish communication across the IoT networks and obtain the different intrusion patterns of the local training data distributed across different silos and samples. Moreover, to address the issue of learning from a small number of attack behaviours in the IIoT network, we modelled FTL for RPL-IIoT intrusion detection. A CNN model is adopted as the FTL's local and global learning model with two different transfer scenarios: parameter transfer and relational knowledge transfer. Finally, the improved CNN model detects intrusions in heterogeneous IIoT networks.
# Dynamic Dark Channel Prior Dehazing with Polarization
## 2 Theoretical Analysis
### Polarization-Based Dark Channel Prior Dehazing Method  
According to the polarization detection theory in Section 2.2, the total light intensity received by the imaging system can be decomposed into two mutually orthogonal polarization components [6], which are denoted as \(I_{\parallel}\) and \(I_{\perp}\). The intensity of total light can be written as:  
\[I=I_{\parallel}+I_{\perp}, \tag{12}\]  
where \(I_{\parallel}\) and \(I_{\perp}\) represent a set of mutually orthogonal components.  
On the basis of the above theory, the atmospheric scattering physical model in Equation (1) is further given by:  
\[I_{\parallel}(x)+I_{\perp}(x)=(J_{\parallel}(x)+J_{\perp}(x))t(x)+(A_{ \parallel}+A_{\perp})(1-t(x)). \tag{13}\]  
The orthogonal polarization components are derived from the same scene and are independent of each other. Based on Equation (13), the characteristic information of each polarization channel can be further extracted by the following:  
\[I_{i}(x)=J_{i}(x)t(x)+A_{i}(1-t(x)), \tag{14}\]where \(i\) is orthogonal polarization channels.  
Based on the theoretical analysis, the images of the original scene are collected in the polarization channels of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\) and are individually dehazed by the dark channel prior. The processed images retain the polarization information, which is further combined with the polarization theory to detect different targets.