esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 FEM Model
### Steel Reinforcement Modelling
#### 3.3.4 Modelling Strategy 3: Bar Discretization and Section Reduction  
The \"macro-morphology\" of the reinforcing bars is considered in the third modelling strategy. In this case, the measurements of the minimum diameter along each corroded bar developed during the experimental tests, are exploited to discretize the bar in pieces over its corroded length. Therefore, an equivalent section is considered for each piece identified. It should be noted that the diameter measurement of bars deformed by test loads, due to yielding or distortion, is omitted from the discretization process. The longitudinal bar is then modeled through connected in series truss bond-slip reinforcement elements, to which a reduced section diameter is assigned. It is remarked that through this strategy no direct reductions are applied on the constitutive laws of the reinforcements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Column** & **Bar** & **Mloss\%** & **fyuniform** & _exp.uniform_ & **fy,pitting** & _exp.pitting_ \\ \hline \multirow{4}{*}{B-25} & 1-2 & 25\% & 334 & 0.16\% & 261 & 0.12\% \\  & 2-3 & 27\% & 319 & 0.15\% & 240 & 0.11\% \\  & 3-4 & 26\% & 326 & 0.16\% & 250 & 0.12\% \\  & 4-1 & 18\% & 386 & 0.18\% & 333 & 0.16\% \\ \hline \multirow{4}{*}{B-30} & 1-2 & 32\% & 281 & 0.13\% & 188 & 0.09\% \\  & 2-3 & 18\% & 386 & 0.18\% & 333 & 0.16\% \\ \cline{1-1}  & 3-4 & 17\% & 393 & 0.19\% & 344 & 0.16\% \\ \cline{1-1}  & 4-1 & 31\% & 289 & 0.14\% & 198 & 0.09\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mechanical parameter reduction for modelling strategies 1 and 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Column** & **Bar** & **Lenght** & **Un-Corroded Weight** & **Corroded Weight** & **Mloss\%** \\ \hline \multirow{4}{*}{B-25} & 1-2 & 705 & 1114 & 840 & 25\% \\  & 2-3 & 698 & 1103 & 810 & 27\% \\  & 3-4 & 710 & 1122 & 835 & 26\% \\  & 4-1 & 710 & 1122 & 920 & 18\% \\ \hline \multirow{4}{*}{B-30} & 1-2 & 700 & 1106 & 750 & 32\% \\  & 2-3 & 696 & 1100 & 900 & 18\% \\ \cline{1-1}  & 3-4 & 700 & 1106 & 915 & 17\% \\ \cline{1-1}  & 4-1 & 696 & 1100 & 760 & 31\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Weight and loss mass value of the corroded steel rebars.
###### Abstract  
Compared with electromagnetic compatibility (EMC) testing in anechoic rooms, open-area EMC testing takes advantage of in situ and engine running status measurement but suffers from non-negligible external electromagnetic interference. This paper proposes a novel environmental interference suppression method (named the EMC environmental interference suppression algorithm (\(E^{2}ISA\))) that separates signals from backgrounds via image segmentation and recognizes the near-far site signal via a group of time-varying features based on the difference in the near-site EM radiative characteristic. We find that the proposed \(E^{2}ISA\) method, which combines the deep learning segmentation network with the classical recognition methods, is able to suppress environmental interference signals accurately. The experiment results show that the accuracy of \(E^{2}ISA\) reaches up to 95% in the face of VHF (Very High Frequency) EMC testing tasks.  
open-area EMC testing; near-far site; image segmentation; time-frequency spectrum Article  
Environmental Interference Suppression by Hybrid Segmentation Algorithm for Open-Area Electromagnetic Capability Testing.
# Instance Segmentation Method of User Interface Component of Games
## 1 Introduction  
With the recent advancement of deep learning technology, various deep learning technologies are influencing game development. The GAN algorithm adopted in level generation has been applied on a pilot basis to a large number of dungeons and level generation [1; 2; 3]. Convolution neural network (CNN) or parameter-based reinforcement learning algorithms applied to nonplayable characters (NPC) are used to develop NPC that can learn user behavior patterns [4; 5; 6]. Learning algorithms for time-series data based on long-short term memory (LSTM) used in traditional user log analysis are applied to technologies such as user churn prediction [7; 8] or bot detection [9]. Natural language recognition technologies used in the voice interface provide users with a new interface experience [10].  
In order for deep learning technology to be used in the game field, a large amount of data must be secured. Game data can be accessed freely by a game company which developed a game, but it is not easily accessible from outside for researchers who use this data. Therefore, game-related researchers in the deep learning community attempted to get access to the relevant parameters through various indirect methods at the client level [11]. However, this indirect data acquisition process has been a major obstacle for deep learning researchers to conduct research in the game field. Because of this technical obstacle, datasets in the game field are smaller than datasets in the image processing field in terms of the size and number. Moreover, this has become the biggest obstacle for image-based algorithms to be applied to the game research field.  
In this paper, in order to address these issues, we propose a segmentation method that extracts only the meaningful UI information from the game play screen. We built a large amount of UIImage segmentation data required for machine learning by using the game engine and the published UI image dataset. After this, using this data, we designed a network that segmented only the UI components on the general game play screen and trained it. When the trained network was applied to games, it could detect the main UI components on the game screen at the level of approximately 80%. Our methodology demonstrates that UI component images can be effectively extracted from the game play screen without additional data labeling. These segmented images have the advantage of enabling meaningful game play analysis with only the game screen by facilitating additional semantic analysis later on.
# Space Object and Background Polarization Models and Detectability Analysis
## 3 Characteristics of the Sky Background Polarization Distribution
### Sky Background Polarization Scattering Model Complex Spherical Projection Representation
The comparison of this model with the simulation effect of the model using direct vertical projection is shown in Figures 5 and 7. It can be found that the model using complex spherical projection derived in this section can effectively correct the aberrations caused by the direct vertical projection method, and the percentage of the zenith region has been reduced to a certain extent.
## 3 Particle Filter Frame
### Bayesian Filter Algorithm  
To illustrate the PFF algorithm, the theoretical background of general Bayesian filter theory should be firstly introduced. For a non-linear stochastic system, we use the state equation and measurement equation to describe the dynamic state-space formulation, i.e.,  
\[\begin{array}{l}x_{k}=f_{k}(x_{k-1},n_{k-1})\\ y_{k}=h_{k}(x_{k},u_{k})\end{array} \tag{7}\]  
where \(x_{k}\) and \(y_{k}\) represent the state variable value and data measurement at \(k\), respectively. Symbols \(n_{k-1}\) and \(u_{k}\) denote the process noise representing uncertainty and measurement update noises, and \(f_{k}\) is state transition function. Symbol \(h_{k}\) is measurement function, which denotes the nonlinear relationship between the dynamic system states and the noisy measurements.  
Within a Bayesian framework, the recursive process (or posterior distribution estimation) is basically divided into two parts.  
1. Suppose the prior probability distribution \(p(x_{k-1}|y_{1:k-1})\) is known, the number of samples is \(N\) from the posterior distribution according to Equation (7). Considering the first-order Markov process, the approximation of the posterior distribution can be given by, \[p(x_{k}|y_{1:k-1})=\int p(x_{k}|x_{k-1})\,p(x_{k-1}|y_{1:k-1})dx_{k-1}\] (8)
2. The posterior distribution function estimation can be updated by the Bayes formula as follows, \[p(x_{k}|y_{1:k})=\frac{p(y_{k}|x_{k})\,p(x_{k}|y_{1:k-1})}{p(y_{k}|y_{1:k-1})}\] (9)where \(p(y_{k}|x_{k})\) is likelihood function at time \(k\), and \(p(y_{k}|y_{1:k-1})\) is the normalizing constant which can be calculated as follow,  
\[\begin{array}{l}p(x_{k}|y_{1:k-1})\\ =\int p(x_{k}|y_{1:k-1})\,p(y_{k}|x_{1:k-1})\,dx_{k}\end{array} \tag{10}\]
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 3 Results
### Visualization Example  
The convergence time was estimated using the scintillation-free data on 29 May 2016 at SNA0P, 1 September 2017 at SAOOP and 1 September 2017 at SJCU in Section 3.1.1. The relationship between standard and scintillation parameters on 13 September 2017 at SAOOP station has already been investigated by [PERSON] et al. [20], where the scintillation occurred during the first three hours. In Section 3.1.2, RMSE and GDOP changes of PPP positioning outputs before and after removing satellites with the strongest scintillation are presented. For the observation removal strategy, there are 10 combinations (7 for standard parameters and 3 for scintillation parameters) between parameters for each threshold type, as discussed in Section 3.2.2 as an example. Thus, in Section 3.1.3, PPP outputs of 4 out of 7 combinations of standard parameters in the observation removal strategy based on MT are displayed as an example. The results of other combinations are presented in the statistical results in Section 3.2. In Section 3.1.4, the RMSE changes of PPP outputs before and after weighting observations based on scintillation and standard parameters are shown.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 3 Case Studies of PDoS Attacks
### Bad Rabbit  
Bad Rabbit, a notable PDoS malware, was first detected in 2017, deploying a unique blend of ransomware and destructive attack tactics. It primarily infiltrates systems through a faux Adobe Flash installer on compromised websites, as a drive-by download. Upon execution, Bad Rabbit swiftly spreads across networks using a combination of hardcoded SMB credentials and the Mimikatz tool, which extracts login information from system memory.  
Technically, Bad Rabbit was engineered to encrypt system files and the MBR, rendering the affected machines unbootable. This dual mechanism of attack, targeting both data and essential boot processes, distinguishes it from typical ransomware. The MBR encryption means that even if victims had backups of their data, the system itself requires significant remediation efforts, often involving complex recovery processes like complete OS reinstalls.  
Moreover, Bad Rabbit stands out for its rapid propagation capability, echoing the behaviors of Petya/NotPetya, and its selective targeting of high-profile organizations and infrastructure. The malware's sophistication, including the use of legitimate tools for malicious purposes and its ability to cause widespread system disruptions, marks it as a formidable PDoS threat.
## 3 Results and Discussion
### Microhardness Measurements  
Figure 7 shows an optical microscopy image of the Al-Mg composite surface taken from the non-irradiated side of the sample (Figure 7a), as well as after ion irradiation (Figure 7b). A different color contrast was noticed for Al, Mg and interlayers corresponding to the Al\({}_{3}\)Mg\({}_{2}\) and Al\({}_{12}\)Mg\({}_{17}\) intermetallic phases. The dark color contrast on the irradiated composite surface was related to the Al\({}_{3}\)Mg\({}_{2}\) phase and was the result of light from the incandescent lamp of an optical microscope (Figure 7b). This indicated the relatively high light absorption capacity of the Al\({}_{3}\)Mg\({}_{2}\) phase surface with a cellular wall relief. The Al areas and the interlayer corresponding to the Al\({}_{12}\)Mg\({}_{17}\) phase had the same light contrast. The area corresponding to the Mg surface, with cones located on it, had an orange-like contrast. The difference in contrast of these areas did not depend on the level of their depth relative to each other; rather, they depended only on their surface morphology and the optical properties of these metals and intermetallic phases.  
Microhardness measurements were carried out on the irradiated side of the Al-Mg sample. The microhardness value for Al was found to be 55 \(\pm\) 10 HV, and it was 100 \(\pm\) 20 HV for Mg, 460 \(\pm\) 50 HV for the Al\({}_{3}\)Mg\({}_{2}\) intermetallic phase and about 200 HV for the Al\({}_{12}\)Mg\({}_{17}\) phase. The microhardness values for the non-irradiated side of the Al-Mg composite were found to be as follows: 50 \(\pm\) 10 HV for Al, 100 \(\pm\) 20 HV for Mg, 440 \(\pm\) 30 HV for the Al\({}_{3}\)Mg\({}_{2}\) intermetallic phase and about 200 HV for the Al\({}_{12}\)Mg\({}_{17}\) phase. The imprints of the diamond pyramid obtained during the microhardness measurement are visible in Figure 7. The obtained microhardness values for Al and Mg, as well as for the Al\({}_{12}\)Mg\({}_{17}\) intermetallic phase on the non-irradiated side of the composite, were similar to the previously reported data. At the same time, the microhardness for the Al\({}_{3}\)Mg\({}_{2}\) phase, 460 HV on average, exceeded the known data for the microhardness of this phase (about 300 HV). However, the microhardness of the Al\({}_{3}\)Mg\({}_{2}\) phase had the highest value compared with the Al\({}_{12}\)Mg\({}_{17}\) phase, Al and Mg. It can also be seen that irradiation with argon ions and the corresponding change in the morphology on the surfaces of Al, Mg and intermetallic phases did not result in a microhardness change. This correlated with the results in [53], where no effect of Al irradiation with argon ions on microhardness was noticed.  
Figure 7: Optical image of the Al-Mg nanostructured composite surface before (**a**) and after (**b**) ion irradiation.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 3 Linear Programming Problems
### Solving LPs
#### 3.1.1 Simplex Method  
The simplex algorithm first needs a reformulation to be done to the LP in Equation (1), to put it in the _standard form_, before it can be applied. In optimization solvers, this is done automatically at a preprocessing stage, provided you provide the problem to the solver in the form in Equation (1). Basically the standard form of any LP requires representing all inequality constraints in the form of inequality constraints by the introduction of nonnegative _slack_ and _excess_ variables. Any unrestricted variable \(x_{i}\) gets replaced by two variables such that \(x_{i}=x_{i}^{{}^{\prime}}-x_{i}^{{}^{\prime\prime}}\) and \(x_{i}^{{}^{\prime}}\geq 0\), \(x_{i}^{{}^{\prime\prime}}\geq 0\). A simplex _tableau_ is first constructed [24], for an optimization problem in Equation (2), the initial tableau is given by Table 1.  
\[max\;\;z=60x_{1}+30x_{2}+20x_{3}\] (2a) s.t. \[x_{1}+6x_{2}+\;x_{3}\leq 48 \tag{2b}\] \[4x_{1}+2x_{2}+1.5x_{3}\leq 20\] (2c) \[2x_{1}+1.5x_{2}+0.5x_{3}\leq 8\] (2d) \[x_{2}\leq 5;x_{1},x_{2},x_{3}\geq 0 \tag{2e}\]  
Then the simplex algorithm needs a _basic feasible solution_ (**bfs**) to start from, in which all the variables in the standard form LP are non-negative. In case the LP has only (\(\leq\)) constraints, slack variables become the set of _basic variables_ and are assigned initial values equal to the right hand side values of the corresponding constraints as in Table 1. The rest of the decision variables are initially in the set of _non-basic variables_, i.e., take the values of zero. If there are any (\(\geq\)) constraints, then two of the methods that can be used to find a starting **bfs**, are the _Big-M_ method and the _Two Phase Simplex_ method [23; 24; 25].  
The simplex algorithm utilizes the Gauss-Jordan algorithm which originally solves a system of linear equations. Since we have many solutions for the system of linear equations for an LP formulation in the standard form, the simplex uses the Gauss-Jordan algorithm to move across \(\mathbf{bfss}\) that are adjacent to each other as long as the objective function value (\(z\) in the tableau) decreases. In each iteration, elementary row operations (eros) are performed to obtain the improving \(\mathbf{bfss}\). If in a given iteration, the coefficients of all non-basic variables in the objective function row in the tableau (row 0 usually) are negative (assuming we are considering a minimization problem), then current \(\mathbf{bfss}\) is optimal. If any variables in row 0 have negative coefficients, the simplex algorithm chooses the variable with the most negative coefficient in row 0 to enter the basis (this variable is called the entering variable) and (eros) make it a basic variable. The variable that leaves the basis to become a non-basic variable and the new value of the entering variable are decided by a ratio test [24].
# Plant-Derived Phytobiotics as Emerging Alternatives to Antibiotics Against Foodborne Pathogens
## 3 Mechanisms of Action of Phytobiotics Against Foodborne Pathogens
### Disruption of Bacterial Cell Wall Integrity, Membrane Structure, and Biofilm Formation
Selected plant-derived compounds, particularly essential oils and their phytochemical constituents, have been shown to inhibit biofilm formation by blocking intercellular communication or disrupting the synthesis of extracellular substances such as exopolysaccharides and structural proteins [132; 133]. Another relevant mechanism is the inhibition of extracellular polymer production, particularly exopolysaccharides (EPS), which are essential for biofilm adhesion and structural stability. Inhibition of EPS biosynthesis weakens biofilm integrity and increases susceptibility to external stressors [134]. Phytobiotics may also prevent bacterial adhesion to surfaces by interacting with adhesins or by altering physicochemical properties of bacterial cells, such as hydrophobicity and aggregation capacity [134; 135]. Alterations in cell wall structure and function impair the initial contact between bacterial cells and surfaces, thereby inhibiting early biofilm development. A critical aspect of phytobiotic activity is the modulation of gene expression associated with adhesion, EPS production, and resistance to environmental stressors [136; 137]. By targeting regulatory pathways, phytobiotics can influence the transition of bacteria from the planktonic (free-living) to the sessile state necessary for biofilm maturation [136; 137]. Phytobiotics influence not only the development and stability of mature biofilms but also the very early stages of their formation, such as the initial adhesion of bacterial cells to surfaces. This initial adhesion phase is critical for subsequent biofilm maturation and the persistence of infection. It has been documented that plant-derived compounds can disrupt the function of bacterial adhesin proteins, which play a key role in the attachment of bacteria to substrates [138]. The structural similarity of certain phytobiotics to signaling molecules, such as acyl-homoserine lactones, enables competitive inhibition of these molecules, thereby impeding biofilm formation [133]. For example, an extract from _Adiantum philippense_ significantly reduced exopolysaccharide production and disrupted biofilm architecture, as confirmed by both in vitro and in silico analyses [138]. This disruption of biofilm architecture is closely linked to interference with bacterial communication systems. Quorum sensing is crucial during the initial stages of biofilm development, as it coordinates adhesion and biofilm formation. Berry fruit extracts and wood-derived terpen have been shown to reduce quorum sensing activity, resulting in diminished bacterial adhesion and inhibition of biofilm formation [139]. In addition to their effects on membranes and biofilm formation, phytobiotics also interfere with bacterial resistance mechanisms.
# Cleaning of Phototrophic Biofilms in a Show Cave: The Case of Tesoro Cave, Spain
## 3 Results and Discussion
### Composition of biofilms in Different Cave Sectors
#### 3.1.2 Sector T2  
This sector is characterized by the location of an elevator and the staircase in the center, connecting with the entrance, and with halls and galleries at both sides connecting with sectors T1 and T3. Therefore, this sector was anthropically modified and showed high environmental changes with respect to the rest of the cave.  
In Sala de Marco Craso, there was a phototrophic colonization occupying large extensions. The light microscope as well as the scanning electron microscope study revealed that the phototrophic community was almost exclusively formed by _M. israelensis_. The substratum was heavily corroded, and on it had been deposited masses of the algae.  
In other biofilms from Sala de Marco Craso, several species of cyanobacteria were observed: _Synechocystis_ sp. and _Nostoc_ cf. _adapticum_ (Figure 6A,B) and the microalgae _Pseudococcomyxa simplex_, _Chorella_ sp. and sporocysts of _M. israelensis_ (Figure 6C). Sporocysts are cells within which spores are formed. The identification of a species of _Nostoc_ is interesting, since it indicated that there is nitrogen fixation from the air in the cave by cyanobacteria, which would enrich the niche and provide this element for the growth of non-nitrogen-fixing heterotrophic microorganisms. The violet color of Figure 6A is due to the pigment phycocyanin that produces _Nostoc_ and that adheres to membranes.  
Figure 5: Scanning electron images of biofilms. (**A**) Cells of _Myrmecia israelensis_. (**B**) Filaments of _Phormidium_ sp. Scale bar: 5 μm.  
Figure 6: (**A,B**) _Nostoc_ cf. _adapticum_. (**C**) Sporocysts of _Myrmecia israelensis_. Scale bar: 10 μm in (**A,B**), and 5 μm in (**C**).
## 5 MIMO Extension
### Maximal Singular Value Estimation Using Gerschgorin Theorem  
Estimate the greatest singular value \(\bar{\sigma}(A)\) of the matrix \(A\in\mathbb{C}^{n\times m}\) using the Gerschgorin theorem. According to the Gershgorin Circle Theorem [35], every eigenvalue \(\lambda\) of a matrix \(A=[a_{ij}]\in\mathbb{C}^{n\times n}\) lies within at least one disk \(D(a_{ii},R_{i})\), where \(R_{i}=\sum_{j\
eq i}|a_{ij}|\). Consider the matrix \(A^{*}A\), where \(A^{*}\) is the complex conjugate transpose of \(A\) since this matrix is positive semi-definite, all its eigenvalues are real. Thus, by definition \(\bar{\sigma}(A)=\sqrt{\lambda_{\max}(A^{*}A)}\), where \(\lambda_{\max}\) is the largest eigenvalue of \(A^{*}A\).  
[PERSON]'s theorem provides an estimate on the location of the eigenvalues, so one can apply it to \(A^{*}A\). Let \(a_{ij}\) be an element of \(A^{*}A\) and let the quantity \(R_{i}\) be \(R_{i}=\sum_{j\
eq i}|a_{ij}|\) sum of all non-diagonal elements in a row. A circle with center \(a_{ii}\) and radius \(R_{i}\) is called a Gerschgorin circle \(D_{i}\). According to [PERSON]'s theorem, the eigenvalues of \(A^{*}A\) lie in one of the Gerschgorin circles. Since all the eigenvalues of \(A^{*}A\) are real, the estimate of \(\bar{\sigma}(A)\) is the largest point where a Gerschgorin circle intersects the real line.  
Let \(A\in\mathbb{C}^{n\times m}\) be a matrix. For the largest singular value \(\bar{\sigma}\) of \(A\) the following holds:  
\[\bar{\sigma}(A)\leq\sqrt{\max\{D_{i}\cap\mathbb{R},i=1\ldots rank(A^{*}A)\}}= \sqrt{\max\{R_{i}+a_{ii},i=1\ldots rank(A^{*}A)\}} \tag{33}\]  
where \(D_{i}\) is the \(i\)th Gerschgorin circle of \(A^{*}A\), \(R_{i}\) is the radius of the \(i\)th Gerschgorin circle and \(a_{ii}\) is the \(i\)th diagonal element of \(A^{*}A\).  
The inequality in (33) follows directly from the Gerschgorin theorem. The equality is true because the diagonal elements of \(A^{*}A\) are positive reals, so the center points of the Gerschgorin circles lie on the real line, and the greatest value where one of the Gerschgorin circles intersects the real line is \(\max\{R_{i}+a_{ii},i=1\ldots rank(A^{*}A)\}\) It also means that  
Figure 5: Basic geometric objects on Poincaré disk model of hyperbolic geometry.  
\[\sigma(A)\leq\sqrt{\max\left\{\sum_{j=1}^{m}|a_{ij}|,i=1\ldots m\right\}} \tag{34}\]  
where \(a_{ij}\) is an element of \(A^{*}A\).