esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for Learning and Rapid Implementation of Real-Time Global Illumination Methods
## 4 Demonstrative Methods of Real-Time Global Illumination  
The framework contains a set of real-time global illumination techniques for demonstration purposes. We have implemented a set of illumination effects for four classes of methods that produce real-time global illumination. More precisely, we chose as lighting effects:  
* Diffuse indirect illumination;
* Glossy reflections;
* Subsurface scattering and translucency;
* Direct illumination of area light sources. These effects have been implemented for global illumination classes that contain:  
* Techniques based on RSMs;
* Techniques based on discrete ordinate methods (DOMs);
* Techniques based on voxel volume;
* Techniques based on screen space information. The field of real-time global illumination is complex and the description of the implemented techniques goes beyond the scope of this article. Below, we only mention the techniques used to implement the real-time illumination effects. From the class of the RSM-based methods, we implemented diffuse indirect illumination according to the sampling technique proposed by [PERSON] and [PERSON] [6] and both glossy reflections and refraction according to the approach that uses 2D ray casts in RSM proposed by [PERSON] et al. [46]. For the 2D ray cast process on images, we used the approach proposed by [PERSON] and [PERSON] [47]. This class is the only one that cannot produce direct illumination for area light sources. From the class of DOM-based methods, we implemented all the illumination effects based on the method proposed by [PERSON] and [PERSON] [7] that uses a light propagation volume (LPV). Moreover, from the class of voxel-based techniques, we implemented all the effects based on a single approach, more precisely the one proposed by [PERSON] et al. [7]. From the class of techniques that use the information on the screen, we implemented diffuse indirect illumination, illumination, following the method proposed by [PERSON] et al. [4] and the glossy reflections together with the translucency, based on the one proposed by [PERSON] et al. [5]. Direct illumination of area light sources is obtained with these methods. The visual results of the four lighting effects in the same scene can be seen in Figure 5. As previously mentioned, the RSM-based methods cannot produce direct illumination for area light sources, so the light source has been excluded. The visual results are not identical due to the significant differences between the implemented approaches. In addition, the framework provides a set of parameters that can be changed interactively by the user through the GUI and for which the visual results are modified in real time. This set of parameters are presented in Table 1. The DOM- and voxel-based techniques have a set of generic parameters for the properties of the 3D volumes they use.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 4 Analysis and Validation of Results  
Several aspects of the simulation are verified either by calculating the result based on the protocol behavior or by rerunning the tests in a virtualized network environment.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 4 Implementation and Results
### Vulnerability Risk Scoring Calculation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Port** & **Service** & **I** & **L** & **M** & **D** & **T** & **R** & \(R_{\text{{normalized}}}\) & **Risk Level** \\ \hline
21 & FTP & 4 & 3 & 3 & 3 & 3 & 165.47 & 3.31 & Medium \\
22 & SSH & 5 & 4 & 4 & 4 & 3 & 435.86 & 8.72 & Extremely High \\
23 & Telnet & 5 & 5 & 2 & 2 & 182.85 & 3.66 & Medium \\
25 & SMTP & 3 & 3 & 3 & 3 & 117.17 & 2.34 & Low \\
53 & DNS & 4 & 3 & 4 & 4 & 3 & 243.01 & 4.86 & Medium \\
80 & HTTP & 4 & 4 & 3 & 3 & 3 & 227.07 & 4.54 & Medium \\
111 & RPCbind & 5 & 4 & 2 & 4 & 4 & 286.10 & 5.72 & Medium \\
139 & NetBIOS & 5 & 5 & 3 & 2 & 3 & 316.14 & 6.32 & High \\
445 & Microsoft-DS & 5 & 5 & 3 & 2 & 3 & 316.14 & 6.32 & High \\
512 & exec & 4 & 4 & 2 & 3 & 3 & 164.17 & 3.28 & Medium \\
513 & login & 4 & 4 & 2 & 3 & 3 & 164.17 & 3.28 & Medium \\
514 & shell & 5 & 5 & 2 & 2 & 2 & 182.85 & 3.66 & Medium \\
1099 & rmrigeistry & 4 & 4 & 3 & 4 & 4 & 302.76 & 6.06 & High \\
1524 & ingreslock & 3 & 2 & 4 & 3 & 3 & 94.42 & 1.89 & Low \\
2049 & NFS & 5 & 4 & 3 & 3 & 3 & 296.79 & 5.94 & Medium \\
2121 & ccpproxy-ftp & 4 & 3 & 3 & 3 & 3 & 165.47 & 3.31 & Medium \\
3306 & MySQL & 5 & 4 & 4 & 4 & 3 & 435.86 & 8.72 & Extremely High \\
5432 & PostgreSQL & 5 & 3 & 4 & 4 & 3 & 317.63 & 6.35 & High \\
5900 & VNC & 5 & 4 & 2 & 2 & 3 & 178.81 & 3.58 & Medium \\
6000 & X11 & 3 & 3 & 3 & 3 & 3 & 117.17 & 2.34 & Low \\
6667 & IRC & 2 & 3 & 4 & 3 & 3 & 90.67 & 1.81 & Low \\
8009 & AJP13 & 4 & 4 & 3 & 3 & 3 & 227.07 & 4.54 & Medium \\
8180 & Unknown & 4 & 3 & 1 & 4 & 4 & 91.62 & 1.83 & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Port vulnerability risk assessment. analyzed the vulnerabilities categorized as \"medium\", \"high\", and \"extremely high\" risk levels, which also validates the effectiveness of the risk assessment method we proposed from the side.
## Appendix A  
The kernels of the functions defined in Equation (4) are summarized in the following:  
\[F_{i,j}(u,x,z)=\frac{U_{i,j}(u,x,z)V_{i}(u,x,z)-U_{i,j}^{\infty}(u,x,z)V_{i}^{ \infty}(u,x,z)}{u} \tag{11a}\] \[U_{i,j}(u,x,z)=R_{2}(u)\Big{[}jus\text{gn}(z_{i}(x,z))s_{i,j}+R_{2}(u)c_{i,j}-v ^{2}\Big{]}\] (11b) \[V_{i}(u,x,z)=\frac{e^{-jux_{i}(x,z)-R_{2}(u)|z_{i}(x,z)|}}{R_{2}(u)}\] (11c) \[U_{i,j}^{\infty}(u,x,z)=|u|\big{[}jus\text{gn}(z_{i}(x,z))s_{i,j}+|u|c_{i,j} \big{]}\] (11d) \[V_{i}^{\infty}(u,x,z)=\frac{e^{-jux_{i}(x,z)-|uz_{i}(x,z)|}}{|u|}\] (11e) \[T_{i,j}(u,x,z)=jR_{2}(u)\text{sgn}(z_{i}(x,z))s_{i,j}+uc_{i,j}\] (11f) \[G_{i,j}^{(1,1)}(u)=v^{2}A_{i,j}(u)+u^{2}B_{i,j}(u)\] (11g) \[G_{j}^{(1,2)}(u)=uv\big{[}B_{0,j}(u)-A_{0,j}(u)\big{]}\] (11h) \[G_{i}^{(2,1)}(u)=uv[B_{i,0}(u)-A_{i,0}(u)]\] (11i) \[G^{(2,2)}(u)=u^{2}A_{0,0}(u)+v^{2}B_{0,0}(u)\] (11j) \[A_{i,j}(u)=c_{i}c_{j}\frac{k_{2}^{2}}{R_{2}(u)(u^{2}+v^{2})}C_{H}(u)\] (11k) \[B_{i,j}(u)=-\Big{[}s_{i}s_{j}\frac{u^{2}+v^{2}}{u^{2}R_{2}(u)}+c_{i}c_{j}\frac{R _{2}(u)}{u^{2}+v^{2}}+js_{i,j}\frac{1}{u}\Big{]}C_{E}(u)\] (11l) \[C_{H}(u)=[R_{1}(u)-R_{2}(u)\;t(u)]/[R_{1}(u)+R_{2}(u)\;t(u)]\] (11m) \[C_{E}(u)=[\varepsilon_{1}R_{2}(u)-\varepsilon_{2}R_{1}(u)\;t(u)]/[\varepsilon_{1} R_{2}(u)+\varepsilon_{2}R_{1}(u)\;t(u)]\] (11n) \[t(u)=\tanh(R_{1}(u)\;d) \tag{11o}\]\[S_{i}(u)=uc_{i}+jR_{2}(u)s_{i}\] (A1p) \[W_{i}(u,x,z)=e^{-ju(x-\overline{x}_{i})-R_{2}(u)(z+\overline{z}_{i}-2d)}\] (A1q) \[R_{l}(u)=\sqrt{u^{2}+v^{2}-k_{l}^{2}}\] (A1r) where \[i,j\in\{1,2,\ldots L\}\], \[l\in\{1,2\}\], \[c_{i,j}=\cos\bigl{(}\varphi_{i}-\varphi_{j}\bigr{)}\], \[s_{ij}=\sin\bigl{(}\varphi_{i}-\varphi_{j}\bigr{)}\], \[c_{i}=\cos\varphi_{i}\], \[s_{i}=\sin\varphi_{i}\], \[c_{0}=1\] and \[s_{0}=0\]
## 4 Proposed Scheme
### Election of CH  
#### 4.3.1 Input Parameters  
In this paper, we used a fuzzy logic algorithm to choose CH, and the trustworthiness of the selected cluster head nodes was mainly judged by three indicators: energy indicator, distance indicator, and node density. The energy indicator ensures that the elected nodes can support the data transmission task of CH, and the distance indicator ensures that the elected CH nodes remain upstream in the cluster and can try to ensure the performance of the UWSNs in terms of packet transmission, as well as reducing energy consumption. The node densities enable better transmission of packet data towards the CH during data transmission.  
The formula for calculating node density is shown in Equation (3):  
Figure 1: Proposed scheme framework.  
\[V_{ki}=\frac{(Neighbor_{iM})}{\left(Num_{M_{i}}\right)}, \tag{3}\]  
where \(Neighbor_{iM}\) is the number of neighboring nodes of UW-sensor \(n_{i}\) in layer \(M\). \(Neighbor_{iM}\) satisfies two conditions: the first is that the neighboring nodes belong to the same layer \(M\) as node \(n_{i}\), and the second is that the distance between the neighboring node and \(n_{i}\) is less than \(0.9R_{max}\). \(Num_{M_{i}}\) is the number of nodes in layer \(M\).  
The energy metrics are given by Equation (4), where \(E_{res}^{i}\) is the residual energy of the UW-sensor \(n_{i}\), and \(E_{initia}^{i}\) is the initial energy of the UW-sensor \(n_{i}\), which is the same for all UW-sensors.  
\[e_{i}=\frac{\left(E_{res}^{i}\right)}{\left(E_{initia}^{i}\right)}, \tag{4}\]  
\[d_{i}=\frac{1-d_{iB}}{d_{max}}, \tag{5}\]  
where \(d_{iB}\) is the distance from the UW-sensor \(n_{i}\) to the BN, and \(d_{max}\) is the maximum distance of the network among all nodes to the surface BN. The larger the node of the distance indicator \(d_{i}\), the closer to the core area in the node network.
## 2 Fire as a Threat  
### Fire Dynamics  
In fires, the most important parameter characterizing the fire behavior is the heat release rate (HRR) [32]:  
\[\dot{Q}_{\mathrm{c}}=\Lambda_{\mathrm{f}}\dot{m_{\mathrm{f}}}\cdot\chi\cdot \Delta H_{\mathrm{c}}\ (\mathrm{W}) \tag{1}\]  
where \(\Lambda_{f}\ (\mathrm{m}^{2})\) is the fuel surface area, \(\dot{m_{\mathrm{f}}}\) (kg/m\({}^{2}\) s) is the fuel surface mass flux, \(\chi\) is the burning efficiency (\(\leq\)1) and \(\Delta H_{\mathrm{c}}\ (\mathrm{J}/\mathrm{kg})\) is the heat of combustion for the fuel involved. With the exception of solid phase oxidation of char coal, the combustion takes place in the gas phase (flame) where the products released from the fuel surface react with oxygen entrained in the flame zone.  
The incoming heat flux to the surface of the fuel consists of the heat flux from engulfing flames and hot gases, \(\dot{Q_{\mathrm{F}}}\) (W/m\({}^{2}\)), and any radiant heat flux from external heat sources, \(\dot{Q_{\mathrm{E}}}\) (W/m\({}^{2}\)). The incoming heat flux is balanced by the inwards and outwards surface heat losses, \(\dot{Q_{\mathrm{L}}}\) (W/m\({}^{2}\)), and the enthalpy required for pyrolyzing the fuel, \(L_{\mathrm{V}}\) (J/kg), at the respective fuel surface mass flux, \(\dot{m_{\mathrm{f}}}\):  
\[\dot{Q_{\mathrm{F}}}+\dot{Q_{\mathrm{E}}}=\dot{Q_{\mathrm{L}}}+\dot{m_{ \mathrm{f}}}\cdot L_{\mathrm{V}}\ \mathrm{\left(W/m^{2}\right)} \tag{2}\]  
Solving Equation (2) for the mass flux gives:  
\[\dot{m_{\mathrm{f}}}=\frac{\dot{Q_{\mathrm{F}}}+\dot{Q_{\mathrm{E}}}-\dot{Q_{ \mathrm{L}}}}{L_{\mathrm{V}}}\ \mathrm{\left(kg/m^{2}\ s\right)} \tag{3}\]  
Fire spread is generally caused by direct flame contact (convection), heat radiation, and by airborne firebrands and glowing embers. Virgin fuel needs to be heated to temperatures well above 100 \({}^{\circ}\)C to be involved in the combustion. The term \(L_{\mathrm{V}}\) therefore includes heating the fuel to 100 \({}^{\circ}\)C, drying it at about 100 \({}^{\circ}\)C, heating it to temperatures typical for the onset of pyrolysis, pyrolysing it, and further heating the pyrolysis products to the ignition temperature.  
Since the heat of vaporization for water is very high (\(\Delta H_{\mathrm{vap}}\) = 2.444 MJ/kg), higher water content will increase the latent heat of evaporation/pyrolysis considerably and significantly reduce the mass flux of combustibles, thus significantly reduce the HRR and the rate of fire spread. Further, vice versa, the dryer the combustibles, the faster and more intense the combustion and fire development becomes.  
It is therefore apparent that fires in dry homes will develop faster and represent a greater threat to people (and neighbor homes if windy) than a similar start fire when the fuel contains more moisture. The same holds for dry wildland fuel.
## 2 The Seismic Reliability Analysis Process for the Substation System
### System Functionality Definition  
System functionalities are usually defined according to the specific requirements of engineers, users, or regulatory agencies [27; 28]. For example, a water or gas supply system can be expressed as the amount of water or gas supplied in the service area per unit of time, and a road and bridge system can be expressed as the traffic capacity in the target area [29]. Similarly, the functionality of a substation system can be defined as the number of lines that can normally output power after an earthquake. In a severe earthquake, the functions of a simple system may be completely suspended, but due to system redundancy, the functions of a complex infrastructure system may only be partially damaged rather than completely lost [30]. At the same time, the quantification of system functionalities is extremely important for post-earthquake seismic resilience assessments and recovery decisions.  
The number of remaining normal output power lines can be used as a measure of the post-earthquake functionality \(F_{s}\) of the substation system. The values of \(F_{s}\) range from 0 to \(N-1/N\) in increments of \(1/N\). For example, \(F_{s}=5/N\) means that five of the \(N\) lines are in normal operation, and \(F_{s}=9/N\) means that nine of the \(N\) lines are in normal operation. Substations are always designed with redundancy in mind. In this case, assume that two fully operational transformers are sufficient to maintain power. This assumption is reasonable because, for economic reasons, a transformer will not operate at full load under normal operating conditions (usually less than 75%). Therefore, Equation (1) can measure the post-earthquake functionality of the substation system.  
\[F_{s}=\text{min }(2\,N_{\text{line-in}}/N,5N_{\text{transformer}}/N,N_{\text{ line-out}}/N) \tag{1}\]  
where \(N_{\text{line-in}}\), \(N_{\text{transformer}}\), and \(N_{\text{line-out}}\) are the number of normal line-in units, transformer units, and line-out units after the earthquake, respectively. \(N\) is the total number of line-out units.
## 2 Novel Technologies of 6G for Smart Grid Applications  
This section introduces the novel technologies of futuristic 6G communication for various smart grid applications. The future smart city and smart grid applications require a huge number of connected devices, as well as the performance of reliable, flexible, and time-critical operations [10; 73]. The 6G-adopted new technologies that are required to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodologies** & **Limitations** & **Ref.** \\ \hline IoT-Fog architectures for smart cities & Focus on security and latency & [66] \\ DL based security for SG networks & Focus on security & [67] \\ Cyberattacks in SG components & Focus on cyberattacks and security & [68] \\ The role of 5G/6G in SC & Unexplored major 6G-enabled SG applications & [2] \\ Research trends of SG & Unexplored 6G-SG applications & [24; 69] \\ Vulnerability assessment of SG & Security-centric SG applications & [70] \\
6G-based SG applications & Latency and security-centric SG applications & [71] \\
6G incorporated ACDs & SG applications were not deeply studied & [72] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Methodologies and limitations of some potential previous survey papers on smart grids (SG) and smart cities (SC).  
enhance the KPIs and fulfill the requirements of smart grid communication networks are also discussed [10; 73; 74].  
In this study, we consider massive connectivity, secured communication, and the time-critical monitoring and operations. Thus, various novel technologies will be introduced in 6G communication to address the specific requirements and challenges of smart grid systems. The novel technologies of 6G, such as WPT and EH, mMIMO, a green IoT, NOMA, mmWave, terahertz (THz), AI-based ultra-dense networks (UDNs), intelligent reflecting surface (IRS), device-to-device (D2D) communication, beamforming, and AI-based secured edge/cloud computing to provide significant performance improvement in case of the considered KPIs for smart grid system are reviewed [1; 77; 75; 76; 77; 78]. Figure 3 illustrates the novel technologies regarding 6G wireless communication.
## 4 IRS-Aided Wireless Network Performance and Deployment
### IRS as a Wireless Network Signal Reflector
#### 4.1.3 Throughput Performance of IRS  
Consider a link between an arbitrary source and its intended destination which is blocked by a temporary or permanent obstacle. The link is therefore established using an IRS with _N_elements. The channel gain between the source and destination is given by \(h_{s,dR}\), the source and IRS is given by \(h_{s,IRS}\in\mathbb{C}^{N}\) and similarly the channel gain between the IRS and destination is given by \(h_{IRS,d}\in\mathbb{C}^{N}\)then as shown in [69] the achievable rate is given by:  
\[R=\log_{2}\!\left(1+\frac{P_{TX}\big{(}\sqrt{\beta_{sd}}+N\alpha\sqrt{\beta_{ IRS}}\big{)}}{\sigma^{2}}\right), \tag{1}\]  
where \(P_{TX}\) is transmit power, \(\beta_{sd}=\left|h_{sd}\right|^{2}\), \(\beta_{IRS}=\frac{1}{N}\sum_{n=1}^{N}\left[h_{s,IRS}\right]_{n}\left[h_{IRS,d} \right]_{n}\), \(\alpha\in(0,1]\) is amplitude of reflection associated with the IRS and \(\sigma^{2}\) is noise variance. Furthermore, let us assume that the LoS and NLoS propagation follows as articulated in [69], then Figure 5 shows the achievable rate against minimum vertical separation between the IRS source and the destination. As shown in Figure 5 the throughput decreases with an increase in separation as the path loss increases with the distance. Moreover, the throughput increases with an increase in the number of elements as expected. The interesting insight here is that the decrease with distance is more pronounced when \(N\) is small. This implies that perhaps an adaptive selection of \(N\) which depends on the distance between source and destination should be employed. As a matter of fact, such distance dependent adaptations are common features of current wireless networking standards. In particular, distance dependent adaptive rate selection is part of most 802.11x standards. Therefore, it is plausible to explore distance dependent activation of \(N\) to realize a similar rate adaptation technique for IRS empowered future wireless networks.
# Handling Skewed Data: A Comparison of Two Popular Methods
## 1 Introduction
If a link function is convenient in the sense that the inverse-linked linear model of explanatory variables adheres to the support for the expected value for that outcome, it does not further need to stabilize variance or produce normality; this is because the fitting process maximizes the likelihood for the choice of the probability distribution for \(Y\), and that choice is not limited to normality [16]. Alternatively, the transformations used on data are often undefined on the boundary of the sample space, like the log transformation with a zero-valued count or a proportion. Generalized linear models are now pervasive in much of applied statistics and are valuable in environmetrics, where we meet non-normal data frequently, as counts or skewed frequency distributions [17]. Lastly, it is worth mentioning that the two methods discussed here are not the only methods available to handle skewed data. Many nonparametric tests can be used, though their use requires the researcher to re-parameterize or reformat the null and alternative hypotheses. For example, The Wilcoxon-Mann-Whitney (WMW) test is an alternative to a \(t\)-test. Yet, the two have quite different hypotheses; whereas \(t\)-test compares population means under the assumption of normality, the WMW test compares medians, regardless of the underlying distribution of the outcome; the WMW test can also be thought of as comparing distributions transformed to the rank-order scale [18]. Althoughthe WMW and other tests are valid alternatives to the two-sample \(t\)-test, we will not consider them further here. In this work, the two-group \(t\)-test on log-transformed measures and the generalized linear model (GLM) on the un-transformed measures are compared. Through simulation, we study skewed data from three different sampling distributions to test the difference between two-group means.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 5 Results and Analysis  
It is difficult for an access control system to present the results directly in the form of data, as there are no real-world datasets available that provide the baseline of granting or denying access for various roles based on their access requests. Hence, a series of experiments that simulate real risk scenarios that may occur within the MC Children's Hospital will be conducted to evaluate the effectiveness of the proposed risk-aware RBAC and ABAC model. The following procedure was followed to carry out the experiments:  
* Define potential identities of attackers in the database, both internal and external.
* Initialize the simulated risk scenario in the IntelliJ IDEA environment.
* Manipulate independent variables (environmental factors, operational factors, background factors, etc.).
* Monitor access control decisions and record results in real time.
* Calculate decision accuracy, precision, and recall based on the obtained data.
* Repeat the process for multiple iterations to ensure reliable results.
## 2 Materials and Methods
### Elemental Profile Determinations  
#### 2.3.1 Sample Digestion for Elemental Profile Determinations  
For the sample mineralization before the ICP-MS analysis, a microwave digester (Speedware ENTRY by Berghof\({}^{\circledR}\), Eningen, Germany) was used. Thus, 0.3 g of honey was inserted in a PTFE digestion vessel, and a mixture of 4 mL of 60% (\(v/v\)) HNO\({}_{3}\) and 1 mL of 30% (\(v/v\)) H\({}_{2}\)O\({}_{2}\) was added afterwards. The followed temperature ramp was set from room temperature to 120 \({}^{\circ}\)C (2 min) and held for 5 min, followed by an increase from 120 \({}^{\circ}\)C to 135 \({}^{\circ}\)C (2 min), then maintained for 5 min. The temperature was increased from 135 \({}^{\circ}\)C to 160 \({}^{\circ}\)C in 2 min and held for another 12 min, and as final step, from 160 \({}^{\circ}\)C to 75 \({}^{\circ}\)C in 1 min, where it was kept for other 10 min. A cooling stage (30 min) was carried out to 23 \({}^{\circ}\)C, and the resultant mineralized samples were transferred to a 50 mL vial, to which ultrapure water was added. For the blank solutions as well as for the certified reference material (NIST SRM 1515), the preparation procedure was conducted in the same manner. For the quality control assurance, all the samples were prepared in duplicate.