esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment
## 1 Introduction
Further, the developed VMC operates on normalized data, which renders the system independent of either the specific vulnerability scanner or asset management solution. A novel contribution of this work consists in performing automatic calculations of the environmental component of CVSS score vector by combining the data obtained from vulnerability scanner with the data retrieved from the inventory database. To the best of the authors' knowledge, such an approach has not been presented yet in the publicly available literature. An additional novel aspect of the present paper unfolds in the context of data life cycles presented in [11], which pertains to Smart Data and consists in retrieving knowledge from \"the mass of initially unstructured data\" [12] collected by VMCs, i.e., the results of vulnerability scanning, vulnerability related classification of data stemming from several publicly accessible databases and integration with information on organization's assets within the organization-specific context. In this contribution, the initially completely unrelated data, gathered by VMC is structured, normalized and filtered to bring in value and novel knowledge relevant specifically to vulnerability management. This article is organized as follows:  
* Background--section describes foundations of this research, introduces the problems, processes and trades off that are present in vulnerability management research. * Related Work--section presents other work related to the present topic. It is a brief description of work related vulnerability management. * System Data Life Cycle and Analysis--section presents data life cycle and analysis of the proposed framework. * VMC Implementation and Experiment Design--section shows an experiment design for conducted research. * Results--section starts the discussion about the results illustrating the advantages of the proposed VMC system, shows a summary of the presented work. * Conclusions--section gives the summary, starts critical discussion about the presented solution, and introduces fields for further research.
## 2 Materials and Methods
### Critical Velocity  
For further research into smoke movement characteristics and their impact on personal evacuation, longitudinal ventilation velocity must be taken into consideration. In practical fire scenarios, longitudinal ventilation velocity has a significant influence on the diffusion of fire smoke. However, natural wind in tunnels exhibits frequent fluctuations with constantly changing wind direction and velocity. Notably, mechanical ventilation measures implemented within the tunnel were not accounted for in this calculation.  
According to the existing research, Equation (1) is chosen to calculate critical velocity ([PERSON] _et al._, 2018).  
When \(\dot{Q}/\rho_{a}C_{p}T_{a}\sqrt{g}H^{5/2}\leq 0.15(H/W)^{-1/4}\),  
\[u/\sqrt{gH}=0.81\left(\dot{Q}/\rho_{a}C_{p}T_{a}\sqrt{g}H^{5/2}\right)^{1/5}(H /W)^{1/2}e^{-\iota_{a}/\text{h}\text{s}\text{s}H},\] (1a) When \[\dot{Q}/\rho_{a}C_{p}T_{a}\sqrt{g}H^{5/2}>0.15(H/W)^{-1/4},\] \[u/\sqrt{gH}=0.43e^{-\iota_{a}/\text{h}\text{s}H} \tag{1b}\]  
where \(\rho_{a}\) is the ambient density; \(C_{p}\) is the heat capacity; \(g\) is gravitational acceleration, \(H\) is the tunnel height; \(L_{b}\) is the back-layering length, where \(L_{b}=0\) defines critical velocity; \(T_{\alpha}\) is the ambient gas temperature; \(u\) is the longitudinal velocity; and W is the tunnel width.  
Figure 1: Tunnel fire model.  
As shown in Equation (1), ambient density, ambient gas temperature, and heat capacity change as altitude increase. As a result, the maximum critical velocity of 3.56 m/s is chosen to design the simulations.  
Based on the existing tunnel operating period data [31; 32], the control wind velocity of tunnel longitudinal ventilation varies from 2.0 m/s (the maximum natural longitudinal ventilation velocity) to 8.0 m/s (the maximum control longitudinal ventilation velocity). When a fire occurs, the tunnel wind velocity should be a little greater than the critical wind velocity. According to the national code, the radius of the curvature of simulations was chosen as the minimum turning radii at different speed [33].  
Existing studies [6; 7; 15; 34; 35] show that the curvature of a tunnel will affect the critical velocity of a tunnel fire. Therefore, 4 m/s was selected as the critical velocity in this paper. Subsequent simulation results also show that tunnel curvature has an effect on the critical velocity.  
A general velocity group (the longitudinal ventilation velocity is 2 m/s; tests 1, 3, 5, 7, 9, 11, 13, 15, and 17) and a critical velocity group (the longitudinal ventilation velocity is 4 m/s; tests 2, 4, 6, 8, 10, 12, 14, 16, and 18) were set to study fire smoke movement characteristics under natural longitudinal ventilation velocity and control longitudinal ventilation velocity in the fire scenario. Except longitudinal ventilation, no other mechanical ventilation measures were taken into account in the simulation. All the tests are shown in Table 1.
# Improved Q Network Auto-Scaling in Microservice Architecture
## 2 Literature Review: Related Work
### Container Computing  
Over the past few years, various middleware frameworks of containers have been proposed in attempts to reduce the difference between the modern high computing needs with simultaneous interest in multiple applications and the hardware capabilities by IoT node/end user [21]. For example, small VMs supported in Spring and NodeJS, Python runtime environments, are designed to allow for programming and code mobility of sensors and other IoT devices [22; 23; 24; 25]. The efficiency of applications depends heavily on resource virtualization technologies that limit hardware flexibility and depend on code [26]. As a result, there has recently been increased interest in lightweight virtualization solutions such as docker containers [27]. Although virtualized applications/services can be implemented more effectively with respect to hypervisor or VM-based virtualization technology, there is a need for lower overhead [28].  
Containers can be defined as a collection of processes that are separated from the rest of the system encapsulating the associated dependencies [29]. Containers do not require a complete guest operating system (OS), so they are much lighter than VMs [30]. For example, a container can boot faster than a VM, in just a few seconds, and involves a resource set of less than 2 GB, thus allowing it to scale to meet requirements as necessary [27].  
Lightweight virtualization is highly related to microservice networks [31]. This is because it allows for the fast creation and booting of virtualized instances, the possibility of having many applications simultaneously running on the same host, and separation between instances running on the same host while reducing overhead costs. Further, container-based services do not imply strong dependence on a given platform, programming language, or specific application domain, so they have the flexibility to be developed once and deployed everywhere.  
Research related to the implementation of smart containers, dockers, Apache, and Kubernetes is currently being actively conducted. For example, studies have proposed fuzzy enhancement learning strategies for microservice allocation [32]. Research has also been conducted to suggest cluster intelligence-based strategies that can contribute to scheduling in big data applications [33]. In both cases, the proposals are limited to the scheduling of smart containers in cloud computing, and no proposals have been presentedfor the scheduling of smart containers in intelligent auto-scaling. Therefore, previous studies have not been able to propose an essential solution for building an intelligent microphone service.
# Demosaicing by Differentiable Deep Restoration
## 3 The Proposed Method
### Differentiable Deep Restoration
#### 3.3.2 Bases Generation Network with U-Net Structure  
Most deep learning based demosaicing methods use a CNN structure extracting features at the original input resolution to directly regress the color image. In contrast, we adopt a U-net structure to generate our basis maps from the preprocessed data \(D\) as,  
\[B=f(D;\Theta). \tag{13}\]  
Here, \(f\) indicates the non-linear convolutional neural network and \(\Theta\) are the network parameters. By extracting features on multi-resolution, the U-net structure can easily achieve large receptive field without consuming too much computation and GPU memory.  
We adopt a U-Net [43] of fully convolutional network [44] with four different resolutions to extract features and generate the full-resolution basis maps, as illustrated in Figure 1. In this U-Net structure, we employ additional skip connections to fuse the features with the same resolution from the encoder to the decoder. At each resolution, the encoder consists of 2 stacked ResBlocks. A ResBlock is a residual block with two sequential \(3\times 3\) convolution layers from [45]. Each convolution layer has replicated padding ahead it and is followed by a batch normalization layer [46].
## 4 Experimental Study
### Quantitative Analysis  
We conducted a quantitative evaluation based on the Mean MIoU across the following three methods: (1) Panoplane360 alone, (2) Panoplane360 combined with a single execution of SAM, and (3) the proposed method. The results for the horizontal planes (H-plane) and vertical planes (V-plane) are presented in Table 2.  
The experimental results demonstrated that integrating SAM enhanced segmentation performance relative to Panoplane360 alone, with the proposed iterative SAM execution and filtering algorithms yielding the highest performance. Specifically, Panoplane360 alone achieved a value of 0.504 for the H-plane and a value of 0.519 for the V-plane. When SAM was applied only once, the performance improved to 0.546 for H-plane and 0.571 for V-plane. However, the proposed method achieved the highest scores of 0.642 for the H-plane and 0.606 for the V-plane.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Panoplane360**} & \multicolumn{2}{c}{**SAM+Pano**} & \multicolumn{1}{c}{**Ours**} \\  & & & **(No Algorithm)** & **(Proposed)** \\ \hline \multirow{4}{*}{H\_plane} & Site B & 0.563 & 0.657 & 0.733 \\  & Site J & 0.572 & 0.607 & 0.665 \\  & Site K & 0.377 & 0.375 & 0.527 \\ \cline{2-5}  & Total & 0.504 & 0.546 & 0.642 \\ \hline \multirow{4}{*}{V\_plane} & Site B & 0.538 & 0.616 & 0.650 \\  & Site J & 0.512 & 0.556 & 0.616 \\ \cline{1-1}  & Site K & 0.508 & 0.542 & 0.552 \\ \cline{1-1} \cline{2-5}  & Total & 0.519 & 0.571 & 0.606 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison of Mean MIoU for the plane segmentation mask.  
These quantitative results indicate that incorporating SAM-based object segmentation enhances the segmentation quality of Panoplane360, and that iterative SAM execution with filtering algorithms further improves exactness, ensuring that the labels are more accurately assigned within object boundaries.
# Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHAs Generation
## 3 Methodology
### Evaluation
#### 3.3.2. Usability Evaluation  
An experiment was conducted to evaluate the usability of the Arabic handwriting CAPTCHA system.  
* Experimental Set-Up: The experimental set-up of the experiment was as follows:
* Participants: To enhance participant engagement, reduce respondent burden, and increase the likelihood of completing the entire experiment, the experiment was divided into two equal-length sections, accessible via separate links. Each link contained an identical number of images and a balanced distribution of meaningful and meaningless words. Link 1 had 132 participants out of a total of 294, and Link 2 had 162 participants.
* Design: An online test containing 33 pages with two links was developed. There were 30 CAPTCHA questions in each link. 30 distinct CAPTCHA images were chosen at random from the test dataset. There were 15 meaningful words in the CAPTCHA images and 15 meaningless words. There was a text field for participants to enter their answer next to each CAPTCHA image.
* System: The usability testing system consisted of an online website, created using Figma and Weavely forms. It was responsively designed for both desktop and mobile use.
* Experimental Procedure:
* The Procedure of the usability experiment:
* 1. The initial page gathered demographic information such as age, gender, proficiency in Arabic, and experience in CAPTCHA.
* 2. The task and method of interaction were explained on the second page of a set of instructions.
* Participants were presented with an example CAPTCHA image and detailed step-by-step instructions on how to complete it.
* 4. Each page displayed one CAPTCHA sample for participants to solve.
* 5. A progress bar tracked completion across all pages.
* 6. Participants could not advance pages without solving the CAPTCHA correctly.
* 7. The system did not provide feedback on answer correctness or hints.
* 8. Upon completion, a final page contained a thank-you page.
* Usability Evaluation Metrics:  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Recognition Category** & **Description** \\ \hline Completely & All characters recognized correctly \\ \hline Partially & Some characters recognized correctly \\ \hline Incorrectly & All characters recognized incorrectly \\ \hline Not & No characters recognized \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition Categories.  
Usability was evaluated based on the following:  
1. Time spent solving CAPTCHAs: The average time taken to complete a CAPTCHA (in seconds), from the 'Start' button click to the 'Submit' button click. Quicker average solution times indicated a better user experience.
2. Accuracy in solving CAPTCHAs: Accuracy referred to the percentage of CAPTCHAs that participants were able to solve accurately. A higher accuracy rate indicated improved usability.
## 4 SBAS InSAR Results and Precision Validation
### Precision Validation of SBAS Data  
PS-InSAR, as another time-series deformation monitoring method, was used by us to validate the precision of the results. We set the threshold for adjacent distance within the same range to 2.5 m, and ultimately screened 232 pairs of adjacent points for cross-validation analysis; the analysis results are shown in Figure 6. The horizontal axis is the processing results of PS-InSAR technology and the vertical axis is the processing results of SBAS-InSAR technology; the correlation coefficient R is 0.72, indicating a good validation result. Considering the differences in candidate point selections between the two methods and the limitations of InSAR technology accuracy, the small deviation between the two results is satisfactory. Therefore, we can consider that the deformation rate results obtained by the two methods are similar in most regions.  
Figure 4: SBAS results on the Wuhan Yangtze River Bridge.  
Figure 5: Statistics of the deformation rate in the study area.  
Figure 6: Results obtained from cross-validation of 232 pairs of adjacent PS and SBAS data.
## Materials and Methods
### Electrochemical Characterization  
The working electrode was prepared by mixing the active material with carbon black and PTFE, which acts as a binder in the ratio of 80:10:10. The mixture was then dispersed in ethanol to form a slurry. The slurry was pasted on a nickel foam (1 cm \(\times\) 1 cm) and dried at 60 \({}^{\circ}\)C for 12 h. The nickel foam was then subjected to a mechanical pressure of 5 MPa for good attachment between the current collector and material. The electrochemical characterizations of the composites were conducted using an Autolab (M101) potentiostat with a three-electrode configuration in 1 M Na\({}_{2}\)SO\({}_{4}\). Nickel foam, pasted with active materials, was used as a working electrode (WE), platinum wire served as a counter electrode (CE), and silver/silver chloride (Ag/AgCl) served as a reference electrode (RE). Cyclic voltammetry (CV) was performed at a potential range of 0-1 V with various scan rates, i.e., 25, 50, 100, 150, and 200 mV/s, whereas galvanostatic charge-discharge (GCD) was evaluated at different current densities of 0.5, 1.0, 1.5, 2.0, and 2.5 A/g. Electrochemical impedance spectroscopy (EIS) was carried out in the frequency range of 0.1 Hz to 10 kHz at an open circuit potential (OCP) with alternating current (AC) potential at an amplitude of 5 mV. The charge transfer resistance (\(R_{\text{ct}}\)) and equivalent series resistance (ESR) of the composites were determined from the diameter of the semicircle and the intercept of the semicircle at the Z\({}^{\prime}\) axis at the high frequency region of the Nyquist plot, respectively. The cyclic stability test of the composites was performed via CV at a scan rate of 100 mV/s for 5000 cycles.
# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 1 Introduced
Generally speaking, there are two kinds of noises in CIS images: 1. random noise--this kind of noise is generally caused by many factors [5], such as thermal noise, Poisson noise, flicker noise, shot noise, etc; 2. fixed pattern noise--as analyzed above, this type of noise is generally caused by the mismatch between pixels. It has the feature that the noise remains constant between frames under constant working conditions and within a certain period of time. FPN noise mainly presents regular stripes, and the human eye is very sensitive to the regular stripes, so the impact of fixed pattern noise is far greater than random noise [6]. For the processing of random noise, the method of multi frame stack and average is generally used, which can effectively reduce the fluctuation amplitude of random noise after multi frame stack and average. Under certain conditions, the fixed pattern noise is almost stable between different frames. Therefore, the intensity and shape of the noise remain the same after multi frame stacking and averaging. The conclusion is that the Inhomogeneity of CIS is mainly caused by FPN. After CDS processing, the Inhomogeneity is mainly caused by CFPN. Figure 1: A 3T circuit structure of CMOS Image Sensor (CIS) single pixel. (**a**) A 3T active pixel structure. (**b**) A 3T structure equivalent circuit.
## 4 Open Issues and Challenges
### Trajectory Planning of the UAV
#### 4.3.2 Dynamic Path Planning  
The static path planning algorithm works well when the unknown nodes are assumed to be uniformly deployed. However, it may not be a better solution when the nodes are deployed disarray. Due to the disarray deployment, it spends a lot of localization time and moves long path if the system use the static path planning algorithms. Some researchers concentrate on the work of dynamic case to fully utilize the distribution information and minimize the energy consumption and path length. In dynamic case, none of the mobile path is defined in advance. All of the trajectories are determined based on the real-time information.  
A large number of investigations have been done on dynamic path planning. Due to the dynamic characteristics, one of the central issues is whether to consider obstacles. Thus, the existing dynamic path planning algorithms can be classified into two categories, with and without obstacle.  
The authors in [78] proposed a movement mechanism, mobile beacon-assisted localization (MBAL). It is a low computational complexity algorithm among movement path planning algorithms. However, the obstacle case is not considered. In [79], the authors proposed two algorithms, Breadth-First (BRF) and Backtracking Greedy (BTG) algorithms, based on which all nodes could be localized. However, it requires very high real-time condition. We provide a summarizing and comparison in Table 7.
## 2 Materials and Methods
### Median Modified Wiener Filter (MMWF) Modeling  
The MMWF is based on the Wiener filter. To minimize the average error by applying the median during Wiener filtering, it is modeled as follows [15]:  
\[b_{MMWF(x,y)}=\overline{\mu}+\frac{\sigma^{2}-\
u^{2}}{\sigma^{2}}\Big{[}a(i,j)- \overline{\mu}\Big{]} \tag{1}\]  
where, \((i,\ j)\) represents the coordinates of the median mask, \(\overline{\mu}\) is the median filter value of the local window, \(\sigma^{2}\) is the variance of the current slide window, and \(\
u^{2}\) is the variance of the noise variance. Based on Equation (1), we modeled the kernel size of the MMWF as \(3\times 3\), \(5\times 5\), \(7\times 7\), \(9\times 9\), and \(11\times 11\) using MATLAB and applied them to the standard images with Gaussian noise.
## 3 Results
### Comparison with the Actual Product  
Most features are perceived differently in the real product (Figure 10) than with the technologies used. Only \"Shape\" and \"Mode of Use\" present a low percentage (<25%) with no difference in perception. Differences in features such as dimensions, weight, shine, texture, reliability, or quality should be highlighted, with more than 50% of cases with a different perception, while in one case it was over 80% (dimensions with VR). On the contrary, \"Shape\" and \"Mode of Use\" are the features with the least differences detected between the real product and the technologies.  
For some features, differences between AR and VR are observed. For example, the perception of \"Materials\" and \"Appeal\" seems more similar to the real one through VR than through AR technology. In contrast, in the case of the perception of quality, the proportion of VR users who indicate a different perception is higher than for AR users. In any case, it should be noted that the difference with AR users seems to be mainly due to those VR users who believe that the quality of the real product is below that of the previous perception. Significant differences were detected using crosstabs in the perception of the real product quality, with respect to that previously perceived through the display technologies, depending on whether AR or VR had been used (Pearson Chi-Square \(\chi^{2}\) (2, N = 77) = 6.596; \(p\) = 0.037). In the case of color, VR users also appreciate more differences, this time mostly in the sense that the actual product has a darker color.  
Figure 8: Mean ratings for the perception of each feature with each technology against mean importance of their perception. The level of importance of each feature perception is represented on the X axis. For each feature, the ratings of the technologies are arranged on the Y axis. The initials of the technologies with a gray background indicate significant differences detected between technologies, according to the Friedman test for related samples. W: Weight; DT: Details; C: Color; R: Reliability; F: Finishes; M: Materials; U: Mode of use; A: Appeal; Q: Quality; DS: Dimensions and shape.  
Figure 9: Mean ratings for each perception and technology by type of product. Significant differences are marked with a red ellipse.  
On analyzing the results by product (Figure 11), it is remarkable that, in the case of the sideboard, no difference was detected in the perception of shape between the real product and that perceived through the technologies. The most notable difference between the two types of technology is the perception of \"Shine\" for the gooseneck lamp; while VR users detect a very high percentage of differences, AR users barely identify them. Regarding differences between the products, the perception of the materials stands out, with more differences detected, by both AR and VR users, for the gooseneck lamp, while, in contrast, many more differences were found in the case of the sideboard for \"Color\". Furthermore, in this case, the VR users mostly perceive the real product as darker, while the AR users perceive it as darker and lighter.