esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment
## 4 System Data Life Cycle and Analysis  
In this section, the system data life cycle and analysis of the proposed Vulnerability Managements Centre (VMC) is described. VMC consists of four core modules: Knowledge Collector, Asset Collector, Vulnerability Collector, and Processing Module. The first three modules are responsible for data collection, integration, and filtering while the Processing Module enriches the data with the CVSS environmental results.  
All modules work independently [53], communicating asynchronously via a queue system. Consequently, the software is vertically scalable and the system is configured from the administrator panel. VMC also has two administrational modules, i.e., Scheduler--which controls the sequence and the time of data collection from particular sources and Task Monitor--to provide a preview of the current state of the system.  
The software has been prepared to operate in the cloud computing environment and is based on Docker container technology [54]. All data is stored in the form of documents in Elasticsearch [55] that enables its processing in full-text mode, while the Ribana tool [56] is used to analyze and present the results. The software was implemented in Python due to its flexibility and ability to process data on the server side efficiently. The whole project includes multi tenancy support allowing for comprehensive data separation between the documents. Additionally, in VMC there are two data life cycles: operational and historical. Due to this fact, an operating engineer is able to see all changes immediately. The developed VMC allows previewing data and historical calculations, facilitating, at the same time, the analysis of the events occurring in the system. The last step, visualization, is done by Ribana (Figure 1). In order to simplify the principle of the data flow and VMC architecture, the Vulnerability Management Center is presented in the form of separate modules.
## 3 Results
### Finite Size Correction Schemes to Energy for Charged Supercell  
As discussed in the Section 2.2, the defect formation volume follows thermodynamically from the defect formation energy (Equation (3)). Therefore, it is important to investigate any possible connection between the corrections to the defect formation energy of charged defects and their defect formation volumes.  
Charged supercell calculations in DFT require an artificial background charge density (opposite in sign to the net charge of the supercell) to ensure global charge neutrality and convergence of the total energy of the charged system within the supercell approach. The genesis of this background charge is related to the DFT convention of setting the average electrostatic potential over the entire volume of the supercell to zero in order to evaluate the electrostatic Coulomb energy [61]. We note that the same convention is typically applied to calculations of charged defects using ionic potentials. The total energy of charged supercells must account for the chemical potential of the electrons, \(q(\varepsilon_{v}+\varepsilon_{F})\) in Equation (4), and needs to be corrected for finite size errors associated with the periodic supercell approach [39; 52; 61; 62]. There are two specific effects. The first is the interaction between a charged defect and its periodically repeated charged images, adding a spurious electrostatic energy; this is referred to as the image charge correction, \(E_{corr}^{Image}\). The second is the electrostatic potential between the defect and bulk system that needs to be aligned in order to accurately define the reference energy (chemical potential) of electrons, added or removed in charged supercell calculations. This is termed as the potential alignment correction, \(q\Delta V\). We adopt the method by [PERSON] et al. [39] to correct for finite size errors as it has been shown to achieve good convergence [49] for defect formation energies with system size.  
In the next subsections, we systematically investigate the relation between these finite-size energy corrections and the charged defect relaxation volumes. Any change in total energy that depends on the volume of the simulation cell affects the computed forces, pressure/stress tensors, and relaxation volumes in DFT simulations. Therefore, both volume and stresses need to be corrected in the same manner as the total energy.
## 2 Materials and Methods
### Lesion Segmentation
#### 2.1.2 Supervised Methods  
Supervised methods need a training data set in order to fix the parameters of the classifier. Some of these segmentation methods are based on SVMs, Bayes classifier, decision trees (DTs) or artificial neural networks (ANN) [34].  
As supervised algorithms, two different methods were chosen. The first method has been chosen because it segments by employing the information provided by the deep features of a CNN. This fact has the advantage that a small training database is required and even a pre-trained CNN may be utilized. The second supervised segmentation is a fully convolutional neural network, which has been demonstrated to be effective in medical image segmentation. More specifically, SegNet has been chosen because it is state of the art in the field.  
Segmentation from Feature Images of a CNN  
A CNN possesses convolutional layers that provide a wide information about global and local features of an image. Hence, the deepest convolutional layers contain information of the global, abstract and conceptual features, whereas the lower convolutional layers give information about the local structure, which is relevant for the segmentation process [35]. Likewise, convolutional layers can be used to obtain the image features [36].  
We used a VGG-16 pre-trained with ImageNet database. A set of images from the fourth convolutional layer of VGG-16 network was extracted. These images were normalized and filtered by applying a Gaussian filter with standard deviation equal to 2, before being added. Finally, a threshold using [PERSON]'s method and morphological operations (dilation and hole filling) was applied to obtain the final segmentation result. A scheme of this segmentation algorithm is presented in Figure 2.  
Semantic Segmentation with SegNet Deep Neural Network  
Semantic Segmentation allows us to identify an object in an image by classifying each pixel into a labeled class, which is called pixel-wise labeling. As is described by [PERSON] et al. [37], SegNet is a Fully Convolutional Network (FCN) architecture whose encoder is topologically similar to the convolutional layers from VGG-16, but without its fully connected layers. The convolutions are performed with a filter bank. The last layer of the decoder works as a soft-max classifier, which allows us to obtain the predicted segmentation labels for each pixel as output, where each label is associated with an existing class. SegNet admits as input a map of features or an image.  
Figure 1: Selected areas of dermoscopic images for calculating the histograms. (**a**) Original image; (**b**) Selected region inside the lesion; (**c**) Selected region in the healthy skin.
## 2 Background and Motivation
### Typical SFC Environments
#### 2.2.2 Residential and Consumer Services  
Because NFV/SDN allows for the provisioning of highly specialised solutions to meet customers' quality of service requirements, using the concept of service function chaining, service providers can steer residential traffic such as parental control and VoIP-related services. The idea of follow-the-user service deployment in residential environments is an important use case that is achieved using SFC implementations.  
Network operators make use of vCPEs to easily create a chain of services that meet user requirements [44]. Users in these environments are more likely to make use of web-based applications that use HTTP as the de facto protocol [43]. One of the requirements for such deployments is to create a service chain that prioritises security [45]. A typical example is a service chain which follows the order: firewall \(>\) IDS \(>\) proxy. Security of users is a priority and an important requirement, especially in this scenario.
## 4 Discussions
### Comparative Analysis of E3R Model
Most of the models examined considered the vulnerabilities inherent within systems as part of their risk analysis, with some making decision support recommendations for building more resilient infrastructure at critical nodes. A few studies considered the mission value that assets provided, but this was not scoped within the context of mission assurance, and the process of defining and prioritizing wider organizational objectives was not defined. No models in the literature specifically implemented methods to deal with the features of complex adaptive systems, although some did have the capacity to take feedback loops in their risk analysis to inform risk change over time. No existimg models addressed the social dimensions of risk or were built to be adaptive to emergent phenomena. This highlights the strength of the new proposed E3R model, where each module is designed with dynamic feedback loops in mind to be responsive to emergent events and information. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Social** & & & \begin{tabular}{c} **Dynamic** \\ **Adaptive** \\ **Systems** \\ \end{tabular} & \
\begin{tabular}{c} **Feedback** \\ **Loops** \\ \end{tabular} &
\begin{tabular}{c} **Asset** \\ **Mission** \\ **Value** \\ \end{tabular} \\ \hline E3R Model & X & X & X & X & X & X \\ ISO 31000:2018 Process Clause 6 [93; 94] & - & - & X & - & X & - \\ Bayesian risk model [95] & - & - & X & - & - & - \\ Fuzzy Inference Model [96] & - & - & - & - & X & - \\ Time-To-Compromise Model [97] & - & - & - & - & X & - \\ Three-phase assessment [98] & - & - & - & - & - & X \\ CRAM Framework [99] & - & - & - & - & - & - \\ MaCRA Model [100] & - & - & X & - & - & X \\ CSCCRA model [101] & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cyber risk model comparison, where ‘X’ indicates inclusion. The model is build to include the cyber, physical, and social aspects of risk modeling, and prioritizes systemic resilience in the context of mission assurance priorities. Based on the current scope of cyber risk modeling literature, this is a novel approach to building resilient cyber architecture.
## 2 Types of Flexible Wearable Antennas
### Patch Antennas  
The patch antenna generates enhanced directivity and provides a unidirectional radiation pattern. It is important to determine the advantages of applications that call for focused communication because satellite communication, radar systems, and point-to-point communication are all examples of such applications. It is necessary to identify situations in which the application requires directed energy. Examples of such situations include microwave imaging, global positioning systems (GPS), and wireless local area networks (WLANs). Both patch antennas and monopole antennas radiate in their own unique ways. Patch antennas are better suited for applications that require high directivity and precision due to their unidirectional emission pattern.
# Transmission Performance Analysis of RV Reducers Influenced by Profile Modification and Load
## 3 Effects of Modification Coefficients and Load
### Tooth Profile Modification Method
The second aspect is the mechanical performance, including the maximum normal contact force and maximum friction on the cycloid profile, which is obtained using the improved contact force formula proposed herein. The third aspect is the change in contact ratio and transmission efficiency.
# Seismic Assessment and Strengthening of a Load-Bearing Masonry Structure Considering SSI Effects
###### Abstract  
This article examines the seismic assessment and strengthening of a traditional load-bearing masonry structure subjected to strong motion data, with particular emphasis on the effects of soil-structure interaction (SSI). The case study is the Archaeological Museum of Lemnos (AML)--a three-storey building with a composite load-bearing system of timber-framed stone masonry. Over time, the structure has undergone irreversible modifications, primarily involving reinforced concrete (RC) interventions. The building's seismic performance was evaluated using two finite element models developed in the SAP2000 software (v. 25.3.00). The first model simulates the original structure, strengthened by grout injections, while the second represents the current condition of the structural system following RC additions. Soil-structure interaction was also investigated, given that the local soil is classified as Category D according to Eurocode 8 (EC8). Each model was analyzed under two different support conditions: fixed-base and SSI-inclusive. A suite of appropriate accelerograms was applied to both models, in compliance with Eurocode 8 using the SeismoMatch software, and linear time-history analyses were conducted. The results underscore the significant impact of SSI on the increase of peak tensile stress and interstorey drift ratios (IDRs), and highlight the influence of different strengthening techniques on the seismic response of historic load-bearing masonry structures.  
timber-reinforced masonry; soil-structure interaction (SSI); linear time history analysis; strengthening interventions Article
## 4 Results
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cases** & **MODMSA** & **SPEA2** & **NSGA-II** & **MOEA/D** & **I-MOMA** & **D-MOMA** \\ \hline Case400\_100 & **0.7605** & 0.1634 & 0.1597 & 0.0304 & 0.1722 & 0.1794 \\ Case400\_200 & **0.7163** & 0.1722 & 0.1529 & 0.0299 & 0.1735 & 0.1709 \\ Case400\_300 & **0.6751** & 0.1573 & 0.1292 & 0.0322 & 0.1798 & 0.1570 \\ Case400\_400 & **0.6271** & 0.1317 & 0.1255 & 0.0261 & 0.1708 & 0.1387 \\ \hline Case600\_150 & **0.6528** & 0.0540 & 0.0445 & 0.0120 & 0.0595 & 0.0695 \\ Case600\_300 & **0.5690** & 0.0796 & 0.0297 & 0.0066 & 0.0955 & 0.0667 \\ Case600\_450 & **0.5381** & 0.0590 & 0.0422 & 0.0145 & 0.0922 & 0.0811 \\ Case600\_600 & **0.4932** & 0.0686 & 0.0371 & 0.0120 & 0.0872 & 0.0759 \\ \hline Case800\_200 & **0.5382** & 0.0261 & 0.0188 & 0.0042 & 0.0472 & 0.0452 \\ Case800\_400 & **0.4725** & 0.0311 & 0.0262 & 0.0087 & 0.0500 & 0.0448 \\ Case800\_600 & **0.4381** & 0.0265 & 0.0285 & 0.0090 & 0.0436 & 0.0382 \\ Case800\_800 & **0.4331** & 0.0312 & 0.0241 & 0.0076 & 0.0390 & 0.0356 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The HVs of all multi-objective scheduling algorithms in different test cases.
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 2 Power Quality Issues of WPP Integration
### Voltage Issues of WPP Integration  
Voltage variations due to WPPs are affected by many factors, such as wind variations, the tower shadow effect, grid strength, i.e., short-circuit capacity ratio, type of WTGs, number of operating turbines, and switching period [24]. The voltage variation is directly dependent on real and reactive power variations. Variations in wind speed cause voltage excursions during the continuous operation of WTGs. This results in the fluctuation of active and reactive power output [25] and is a concern in fixed-speed WTGs because of the 3P effect. The frequency at which the effects of tower shadow and wind shear is observed will reduce the mechanical torque [25]. However, variable-speed WTGs can change the rotor speed which in turn mitigates this effect. Moreover, switching the WTGs vary the voltage due to the variations of the output power when the generator responds to cut-in or cut-out speeds [26]. According to Ref. [27], the voltage variation can be classified as short duration and long duration voltage variation.
## 2 Materials and Methods
### Experimental Data
#### 2.2.1 Hyperspectral Data  
The hyperspectral imaging system was responsible for acquiring hyperspectral data from the corn leaves, consisting of a hyperspectral camera (Specim Fx10e), a three-axis stage, a constant light source, and a computer and control software, as shown in Figure 2. The whole spectral system was in a closed dark box to reduce the influence of environmental light sources. During the experiments, the hyperspectral images were acquired using the Fx10e hyperspectral camera from Specim, Finland, with a spectral band range of 400-1000 nm, containing 224 bands, a spectral resolution of 5.5 nm, and a spatial pixel count of 1024 px. The camera was mounted on a three-axis platform to obtain stable and accurate hyperspectral data. The acquisition software was the Lumo Recorder 2020 software provided by the manufacturer. In order to acquire clear images, the hyperspectral camera focal length, the distance between the lens and the halogen lamp and between the halogen lamp and the measured object, the scanning speed, and the exposure time needed to be adjusted. After several pre-experiments, the distance between the lens and the halogen lamp was 25 cm, the distance between the halogen lamp and the object to be measured was 30 cm, the scanning speed was 20 mm/s, and the exposure time was 20 ms.  
Figure 1: The samples after drying treatment.  
In order to reduce the impact of camera dark current and light intensity variations on the hyperspectral data [19], the acquired data were corrected for black and white to achieve reflectance correction, and the correction equation is:  
\[R=\frac{I_{\text{raw}}-I_{dark}}{I_{white}-I_{dark}} \tag{1}\]  
where \(R\) is the corrected relative reflectance image, \(I_{raw}\) is the sample image, \(I_{dark}\) is the all-black image, and \(I_{white}\) is the white image obtained from a standard white plate with 99% reflectance.
## 3 Results and Discussion
### Adsorbent Characterisation
#### 3.1.5 Surface Function Analysis by Boehm's Method  
The results of the Boehm titration method reported in Table 3 indicate that the synthesised activated carbon has a slightly basic character. These results are confirmed by the pH\({}_{\text{PZC}}\) value obtained. The treatment with KOH considerably increases the amount of basic groups and decreases the amount of acidic groups, which is in line with the previous findings from FTIR analysis that show the decrease in band intensities of oxygenated groups with an acidic character. The same results were reported by [PERSON] et al. [42], who studied the activation of coconut shells by KOH.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 3 Wi-Fi 6 Overview
### Novel Features
#### 3.1.1 Orthogonal Frequency Division Multiple Access (OFDMA)  
With OFDM, transmissions are spread over several subcarriers, thus requiring the entire spectrum. In dense networks, many stations compete for medium access and increase the collision probability, which reduces the throughput. To overcome this issue, Wi-Fi 6 utilizes OFDMA. With OFDMA, a transmission requires only parts of the spectrum so  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Benefits** \\ \hline MU OFDMA & Higher throughput \\  & Overhead reduction \\  & High spectral efficiency \\ Longer OFDM symbol & Higher efficiency for indoors \\  & Robustness for outdoors \\ Spatial reuse & Spectral efficiency \\  & Increased capacity \\  & Higher throughput \\  & Reliability \\ TWT & Lower latency \\  & Power saving \\  & Reduced jitter \\ MU MIMO & Up to 8\(\times\) capacity increase in uplink \\  & Up to 2\(\times\) capacity increase in downlink \\
1024-QAM & 25\% higher data rate \\ \hline \hline \end{tabular}
\end{table}
Table 5: Innovations in Wi-Fi 6.  
multiple transmissions can happen in parallel. This considerably reduces contention and overheads on the MAC layer. Thereby, it reduces latency and improves throughput in dense networks.  
Although OFDMA has already been used by Long Term Evolution (LTE) networks for downlink multi-user transmission, Wi-Fi 6 supports uplink and downlink transmissions in multi-user mode. However, Wi-Fi 6 hardware also supports OFDM to maintain backward compatibility with the 802.11a/g/n/ac standards. Details of OFDMA in Wi-Fi 6 are explained in Section 4.
## 2 Related Works
A natural question arises: Can associated user posting behavior information on social media platforms (e.g., Twitter) improve the performance of fake news detection? To this end, noting the power of user behavioral features, this study focused on leveraging joint learning of news content and the user posting behavioral clues on Twitter for fake news detection. We hypothesize that news content and the user posting behavior attributes contain complementary information that needs to be encoded and captured simultaneously for fake news detection. Furthermore, the majority of existing studies modeled text input using representations extracted from pre-trained context-free embedding models, such as GloVe, to train fake news detection models. A key limitation of this process is that it assigns fixed vector values to each word in a given sequence regardless of its context. Progress in the realm of natural language processing has centered on deep contextualized models that leverage the deeper long-term semantical contextual relationships between words and sentences in a given corpus. In this work, we leveraged the power of context-aware embedding models, such as BERT, to encode the news text. Generally speaking, the textual news content is initially encoded using BERT to extract deep contextual representations, followed by a deep learning architecture. Next, user features are processed by stacking BiGRU layers. These two components are concatenated to combine the features extracted from news articles and their associated user behavior clues into a single feature vector. This combined feature vector is then fed into a classification layer with a single neuron, activated using a sigmoid function.
## 2 Jamming Attack Models
### Jamming Attack Models from Academia
#### 2.2.3 Models of [PERSON] _et al._[12]  
[PERSON] _et al._[12] have also suggested four jamming attack models, described below:  
* _Interrupt Jammer_ is a variation of _Reactive Jammer_ in the sense that instead of listening to the channel constantly, it gets activated by means of a hardware interrupt when a preamble and start of frame delimiter (SFD) are detected from a received frame.
* _Activity Jammer_ is yet another variation of _Interrupt Jammer_ (in fact, that of a _Reactive Jammer_) meant for encrypted packets where detection of the SFD is other-wise not possible.
* _Scan Jammer_ is similar to the _Sweep Jammer_. Instead of detecting a packet in a single channel, it searches out all possible channels for a packet during a defined period of time, and having succeeded, it then attacks the channel.
* _Pulse Jammer is akin_ to the _Constant Jammer_ in the sense that it sends small packets constantly to jam a channel.