esa-sceva/satcom-chunk-collection huggingface dataset:
# HEAD Access Control Metamodel: Distinct Design, Advanced Features, and New Opportunities
###### Abstract  
Access control (AC) policies are a set of rules administering decisions in systems and they are increasingly used for implementing flexible and adaptive systems to control access in today's internet services, networks, security systems, and others. The emergence of the current generation of networking environments, with digital transformation, such as the internet of things (IoT), fog computing, cloud computing, etc., with their different applications, bring out new trends, concepts, and challenges to integrate more advanced and intelligent systems in critical and heterogeneous structures. This fact, in addition to the COVID-19 pandemic, has prompted a greater need than ever for AC due to widespread telework and the need to access resources and data related to critical domains such as government, healthcare, industry, and others, and any successful cyber or physical attack can disrupt operations or even decline critical services to society. Moreover, various declarations have announced that the world of AC is changing fast, and the pandemic made AC feel more essential than in the past. To minimize security risks of any unauthorized access to physical and logical systems, before and during the pandemic, several AC approaches are proposed to find a common specification for security policy where AC is implemented in various dynamic and heterogeneous computing environments. Unfortunately, the proposed AC models and metamodels have limited features and are insufficient to meet the current access control requirements. In this context, we have developed a Hierarchical, Extensible, Advanced, and Dynamic (HEAD) AC metamodel with substantial features that is able to encompass the heterogeneity of AC models, overcome the existing limitations of the proposed AC metamodels, and follow the various technology progressions. In this paper, we explain the distinct design of the HEAD metamodel, starting from the metamodel development phase and reaching to the policy enforcement phase. We describe the remaining steps and how they can be employed to develop more advanced features in order to open new opportunities and answer the various challenges of technology progressions and the impact of the pandemic in the domain. As a result, we present a novel approach in five main phases: metamodel development, deriving models, generating policies, policy analysis and assessment, and policy enforcement. This approach can be employed to assist security experts and system administrators to design secure systems that comply with the organizational security policies that are related to access control.  
access control; metamodel; heterogeneous systems; security and privacy; IoT; industry 4.0; COVID-19; digital transformation +
Footnote †: journal: Cybersecur. Priv.
## 2 Methodology
### Determine Crash Risk Factors Using Fault Tree Analysis (FTA)  
Crash causes are intricately linked to three primary transportation factors: driver behavior, the vehicle, and the environment. While statistics reveal that over 70% of crashes stem from driver behavior, it is the complex interplay among these factors that can either induce driver errors or intensify the impact of a crash. Fault-Tree Analysis (FTA), recognized as a root cause analysis method for safety and risk management applications, is used to determine the causes of system failure and their probable reasons [6; 11; 19]. This approach aims to diagrammatically represent the failure chain of events. If the causes are identified, then corrective actions can help ensure that the problem does not reoccur. Figure 2 shows the FTA developed in this study. In the case of this study, the top event is a crash on a mountainous highway, with causes associated with driver behavior, the vehicle, and the environment. Intermediate events linked to driver behavior include factors such as speeding, fatigue, or low visibility. The bottom events pertain to design parameters. These design parameters are risk factors that are associated with road safety. The design parameters require standardization since they have different units and risk impacts. A normalized risk intensity value (\(RIN\)) is computed, ranging from 1 (indicating the lowest risk intensity) to 10 (representing the highest risk intensity). Based on the value of the design factor (\(DF_{i}\)), a corresponding risk intensity is assigned using the scale outlined in Table 1 based on [20; 21]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. **} & \multirow{2}{*}{ \begin{tabular}{c} **Design Risk Factors** \\ (DF\({}_{i}\)) \\ \end{tabular}  } & \multirow{2}{*}{**Risk Factor Ranges**} & \multirow{2}{*}{ \begin{tabular}{c} **Risk** \\ **(RIN)** \\ **(L-10)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Distributing** \\ **Inference** \\ **(L-10)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Receiving** \\ **Inference** \\ **(L-10)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Relative** \\ **Inference** \\ **(L-10)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Net Effect** \\ **(R\({}_{i}\))** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Weight** \\ **(\(w_{i}\))** \\ \end{tabular}  } \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Tangent length} & \multicolumn{6}{c}{Between reverse curves} \\  & & \(\begin{array}{c}{}_{L}\!I\!<\!2\,\mathrm{V}\\ \!\Sigma\!\leq\!L\!\
## 2 Materials and Methods  
### Choice of Adsorbent Materials  
Two well-known adsorbent materials, slaked lime and 13X zeolite, were employed to validate the pilot dry fixed-bed FGD system in mild conditions.  
Ca-based materials, namely, slaked lime (Ca(OH)2), quicklime (CaO), limestone (CaCO3), and calcium sulfide (CaS), are widely used as adsorbents in industrial FGD processes [15], despite some limitations. For instance, CaO requires hydration with water vapor to enhance its desulfurization capacity [15]. This preliminary treatment increases the operational costs of CaO and may lead to adsorbent inhomogeneity. CaCO3 and CaS, on the other hand, emit pollutants and toxic gases, namely, CO2 and S2, respectively. In contrast, Ca(OH)2 was chosen over the other industrial standards due to its strong basicity, which enables high reactivity vs. SO2, especially in humid environments. Furthermore, the reaction of Ca(OH)2 with SO2 does not produce pollutant co-products, as evidenced in Equation (1) [15]:  
\[\text{Ca(OH)}_{2}+\text{SO}_{2}+1/2\text{O}_{2}\rightarrow\text{CaSO}_{4}+ \text{H}_{2}\text{O} \tag{1}\]  
Despite the advantages of Ca(OH)2, including its low cost and abundance of raw material sources, several challenges remain to be addressed [15]. They include poor regeneration performances and lower sulfur uptake capacity than adsorbents with larger specific surface areas and better catalytic properties. These limitations have prompted exploration of innovative materials like transition metal oxide desulfurizers, activated carbons, mesoporous silica, metal-organic frameworks, and zeolites [16]. Among them, zeolites exhibit properties particularly well-suited to FGD applications [15], including high surface areas, good thermal stability, and promising regeneration potential [17].  
Zeolite 13X (FAU-type topology) has already been proposed as an adsorbent for SO2 due to its favorable catalytic properties, high surface area, and microporous structure. In 2017, [PERSON] et al. [18] demonstrated that zeolite 13X, calcined at 500 \({}^{\circ}\)C in dry conditions, exhibits remarkable SO2 adsorption characteristics. In 2018, [PERSON] et al. [19] conducted a study on zeolite 13X, examining its adsorption capabilities for SO2 and H2S at high concentrations (>10,000 ppm) at a temperature of 150 \({}^{\circ}\)C and 15 vol % of water vapor. Moreover, 13X displayed good regeneration capabilities, which might be improved by the addition of reducing gases during the treatment [18]. The development of efficient regeneration processes would enable the economic and practical feasibility for real-world applications of more expensive and less plentiful materials. Indeed, the recycling of the spent sorbent for several sorption/desorption cycles offers significant benefits, including waste reduction and lower byproduct generation in dry FGD systems [17].
## 5 Benchmark Testing and Performance Analysis of MRSO
### Comparative Study of MRSO and RSO Using Standard Benchmarks
#### 5.2.3 Fixed-Dimension Multimodal Functions (F17-F23)  
Fixed-dimension multimodal functions, characterized by multiple local optima, are crucial for testing an algorithm's ability to avoid getting trapped in local solutions while searching for the global optimum. Based on the results presented in Table 1 (and further detailed in Table A3 in Appendix A), the MRSO outperforms the RSO in five out of the seven fixed-dimension multimodal functions, as evidenced by lower average values in these cases. For example, in F19, the MRSO achieves an average of \(-\)3.856, significantly better than RSO's \(-\)3.426, demonstrating superior performance in navigating complex landscapes. Similarly, the MRSO outperforms the RSO in F20 with an average of \(-\)2.775 compared to the RSO's \(-\)1.723, and in F22, the MRSO performs better with an average of \(-\)3.719 against the RSO's \(-\)1.035, indicating its improved exploratory capacity. The MRSO also shows better results in F21 and F23, further confirming its strength in solving fixed-dimension multimodal functions. In contrast, the RSO slightly outperforms the MRSO in F17, with an average of 3.990 \(\times\) 10\({}^{-1}\) compared to the MRSO's 4.076 \(\times\) 10\({}^{-1}\), showcasing the RSO's advantage in this case. Overall, the MRSO demonstrates improved performance across most fixed-dimension multimodal functions.  
In terms of standard deviation, the MRSO exhibits more consistent results in four out of the seven functions, further demonstrating its reliability. For instance, in F19, the MRSO has a much lower standard deviation (1.807 \(\times\) 10\({}^{-3}\)) compared to the RSO's 2.942 \(\times\) 10\({}^{-1}\), indicating better stability across multiple runs. Similarly, in F20, the MRSO shows a lower standard deviation (3.885 \(\times\) 10\({}^{-1}\)) than the RSO (3.772 \(\times\) 10\({}^{-1}\)), highlighting its consistency in producing reliable results. The MRSO also demonstrates improved stability in F21, with a higher standard deviation (2.536) compared to the RSO's 3.728 \(\times\) 10\({}^{-1}\). Although the MRSO performs less consistently in some cases, such as F22 where it has a higher standard deviation (2.774) than the RSO (6.255 \(\times\) 10\({}^{-1}\)), the MRSO overall provides more stable outcomes across most of the fixed-dimension multimodal functions.
## 4 Experimental Test Results  
### Results of Slump, Air Content and Density  
Figure 4 shows the test results of concrete properties at the fresh state such as slump, air content. The mixtures slumps ranged between 140-155 mm and are depicted in Figure 4a. It was explained by [PERSON] [5] that, primarily because of higher water content, concrete mix with NCA resulted in bigger slump, comparing to the EMV mixture, and this usually results in slump loss. In this study, however, the slump loss was adjusted by the superplasticizer.  
The air contents values of the mixtures ranged between 3.7-5.8% depicted in Figure 4b. Air content of the control mix was 3.9%, whereas that of the modified EMV mixtures ranged from 5.0 to 5.8%, except for the ERA2 mix. It was noted in a previous study [28] that higher air content in the modified EMV mixes may be a result of entrapped air because of its rough mixture and smaller mortar amount. Nonetheless, it appears that the ERA2 mix with a smaller admixture amount, compared to the ERA1 mix, results in the lowest air content value.  
Figure 5 shows the density variation of each mixture at the fresh state and hardened states, compared to the CNC mixture. Figure 5 shows that as concrete hardened, its density showed little gain. Figure 5 indicates that the relative densities at the hardened state of the EMV mixtures (excluding 92.8% of ERA2) ranged from 95.6-97.6, compared to the CNC mix. The density of the ERA2 mix dropped about 3% more in both the fresh and hardened states. It is suspected that specimens of the ERA2 were inappropriately made.  
Figure 4: Test values of slump and air content by different mix design methods.  
Figure 5: Test values of density by different mix design methods.
# Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors
## 3 Theoretical Basis
### Fault-Related Frequency Components for Detecting Faults in Induction Motors and Gearboxes
In this sense, it is possible to perform fault detection in gearboxes by means of MCSA because the gearbox is usually driven by an electrical motor such as an induction motor; thus, it has been demonstrated that gearbox faults may negatively affect the stator current consumption of the induction motor. Specifically, faults in gearboxes lead to modulations of the stator current spectra as a function of the rotational gear speed in which the fault is present; gearbox failure is commonly associated with discrete faults such as chipped or cracked teeth, although little research has been focused on the analysis of gradual faults, such as the uniform wear of the gearing teeth [38]. Regardless of the cases under study, the identification of gearbox faults can be performed by analyzing specific fault-related frequency components, particularly the frequency components described in Equation (6) [39; 40]:  
\[f_{fg}=\Big{|}f_{s}\pm kf_{fp}\Big{|} \tag{7}\]  
where \(f_{fg}\) refers to the fault-related frequency component produced by the faulty gear, \(f_{s}\) is the supply frequency, \(f_{fp}\) is the fundamental frequency of the fault profile related to pin or wheel rotation frequencies within a one-stage gearbox and \(k\) is an integer value that can take values of \(k=1,\ 2,\ 3\).
# Co-Production of Hydrogen and Methanol Using Fuel Mix Systems: Technical and Economic Assessment
## 4 Conclusions  
This study involves the development of two process models for the simultaneous production of methanol and H2. Case 1 used the coal and biomass-based gasification process for the production of two clean fuels, namely methanol and hydrogen. On the other hand, the case 2 design integrates both the gasification and reforming techniques while using three different feedstocks, namely coal, biomass and natural gas, for the co-production of fuels, i.e., H2 and methanol. By conducting the technical and economic analysis, it has been found that case 2 outperforms the case 1 design in terms of process efficiencies and economics. The methanol and hydrogen production rates from case 1 and case 2 are calculated as 29.97 kg/s and 4.22 kg/s, and 37.91 kg/s and 3.87 kg/s, respectively. The comparative analysis between the two cases reveals that case 2 offers 3.5% higher process efficiency. Moreover, the analysis on the process economics showed that the fuel production/selling prices in case 2 are 27.8% less compared to the case 1 design. It has been analysed from the results that the proposed integration of reforming technology with the gasification process has a potential to improve the overall process performance. Moreover,  
Figure 7: Comparison of capital and operational cost.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Units** & **Case 1** & **Case2** \\ \hline Selling Fuel Price (H2+CH3 OH) & \(\epsilon\)/MT & 443.74 & 411.30 \\ \hline Selling Price of Methanol & \(\epsilon\)/MT & 412.26 & 371.06 \\ \hline Selling Price of H2 & \(\epsilon\)/MT & 31.49 & 40.24 \\ \hline \end{tabular}
\end{table}
Table 3: Economic analysis results for case 1 and case 2.  
retrofitting existing processes is also possible to incorporate the proposed design along with an efficient heat exchanger network that can improve the overall process economics of existing processes.  
Conceptualization, U.A.; methodology, U.A. and U.Z.; Software, U.A.; Formal analysis, H.A.; Investigation, N.A. ([PERSON]); Writing--original draft preparation, U.A.; writing--review and editing, U.Z., A.G.A.J., N.A. ([PERSON]), S.A.O., N.A. ([PERSON]) and H.A.; visualization, A.G.A.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Deanship of Research Oversight and Coordination (DROC) at King Fahd University of Petroleum & Minerals (KFUPM), grant number SB201019 and The APC was funded by DROC.  
The authors acknowledge the support provided by the Deanship of Research Oversight and Coordination (DROC) at King Fahd University of Petroleum & Minerals (KFUPM) for funding this work through Project No. SB201019.  
The authors declare no conflict of interest.
# Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
## 3 Materials and Methods
### Datasets
#### 3.1.1 WISDM Dataset  
The WISDM dataset [26] consists of tri-axial accelerometer data samples from 36 volunteer subjects while performing a specific set of activities. These subjects carried an Android phone in their front pants' pocket and were asked to walk, jog, ascend stairs, descend stairs, sit, and stand for specific periods of time. In all cases, the accelerometer data were collected every 50 ms, so there were 20 samples per second. We can see a detailed description of the dataset in Table 2.
## 3 Results
### Validation  
The total motion of material from the main deposition zone further into the Selanacka river valley is also depicted. The final results were compared with deposited material heights measured using ERT (Electric Resistivity Tomography) profiles. According to geophysical ERT investigations, the highest depths of the deposits are about 20 m. As the main profile, we use the same profile position in both the RAMMS and ERT investigations.  
Comparison profiles are shown in Figure 11 as well as the final depths in the deposition zone of the SPH models. In both models of final depths, the eroded mass is about 50,000 m\({}^{3}\), which can be concluded from field research, including estimated depths averaging about 10 m. Hence, this amount of material, although not huge in comparison to the total mass, shows an influence in changing depths in the deposition zone in both models.  
The maximum velocity registered is about 45 m/s in SPH, while in RAMMS, it is about 33 m/s. Since there are no field measurements during the process, these results are assumed.  
The cross-section shows different thicknesses according to the position of the ERT5 profile. For the ERT5 profile, the thickness is shown relative to the lower surface, i.e., the depth of the channel predicted by geophysical investigations (Figure 11). For the models obtained using RAMMS and SPH, the thickness is defined based on the input surface of the DTM. All models predict greater material deposition on the right side of the flow  
Figure 10: **(a,b)** Model for calculating achieved maximum flow velocities; **(c)** model of the material entrainment rate (er); **(d)** model for simulating material movement and final deposition depths.  
deposition compared to the ERT5 cross-section. The SPH model predicts greater thicknesses throughout the area but is more representative in the sense that the majority of the material is deposited within the deposition zone. In contrast, the RAMMS model predicts a large amount of overflow material, as seen in the final model (Figure 9c), where the amount of overflow material is significantly greater than the deposited material.  
The differences in the estimation of deposition depth and calibrated parameters are linked to the distinct solution algorithms on which these two programs are based. The calibration of the turbulence parameter is the same for both flows, while the differences arise in the calibration of the friction parameter. In the case of Selanac, the importance of two-phase flow modeling becomes evident. By using the two-phase SPH model, the deposited material was contained within the deposition zone, unlike in the RAMMS model, where it was not possible to prevent the material from spreading significantly beyond the deposition zone.
# Study on the Seismic Effect of the Pebble Soil Site in the Zhongwei Basin
## 4 Results
### The Result of Variation in the Pebble Thickness
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Soil Name** & **Layer Depth/m** & **Soil Thickness/m** & \(\overline{Vs}/m\cdot s^{-1}\) & \(\rho/t\cdot m^{-3}\) & **Soil Type Number** \\ \hline \multirow{6}{*}{Model 2–1} & Silty clay & 0 & 0 & 0 & 0 & \\  & Pebble & 5 & 5 & 250 & 2.05 & 2 \\  & Pebble & 15 & 10 & 360 & 2.10 & 3 \\  & Pebble & 30 & 15 & 450 & 2.15 & 4 \\  & Sticky sand & 50 & 15 & 400 & 2.20 & 5 \\  & Sticky sand & 65 & 15 & 580 & 2.22 & 5 \\  & Sticky sand & 75 & 15 & 700 & 2.30 & 5 \\ \hline \multirow{6}{*}{Model 2–2} & Silty clay & 1 & 1 & 185 & 1.90 & 1 \\  & Pebble & 6 & 5 & 280 & 2.05 & 2 \\  & Pebble & 16 & 10 & 360 & 2.10 & 3 \\  & Pebble & 31 & 15 & 480 & 2.15 & 4 \\  & Sticky sand & 50 & 15 & 410 & 2.20 & 5 \\  & Sticky sand & 65 & 15 & 590 & 2.22 & 5 \\  & Sticky sand & 75 & 14 & 700 & 2.30 & 5 \\ \hline \multirow{6}{*}{Model 2–3} & Silty clay & 2 & 2 & 188 & 1.90 & 1 \\  & Pebble & 7 & 5 & 280 & 2.05 & 2 \\  & Pebble & 17 & 10 & 360 & 2.10 & 3 \\  & Pebble & 32 & 15 & 480 & 2.15 & 4 \\  & Sticky sand & 50 & 15 & 420 & 2.20 & 5 \\  & Sticky sand & 65 & 15 & 600 & 2.22 & 5 \\  & Sticky sand & 75 & 13 & 700 & 2.30 & 5 \\ \hline \multirow{6}{*}{Model 2–4} & Silty clay & 3 & 3 & 190 & 1.90 & 1 \\  & Pebble & 8 & 5 & 280 & 2.05 & 2 \\  & Pebble & 18 & 10 & 360 & 2.10 & 3 \\  & Pebble & 33 & 15 & 480 & 2.15 & 4 \\  & Sticky sand & 50 & 15 & 430 & 2.20 & 5 \\  & Sticky sand & 65 & 15 & 610 & 2.22 & 5 \\  & Sticky sand & 75 & 12 & 700 & 2.30 & 5 \\ \hline \multirow{6}{*}{Model 2–5} & Silty clay & 4 & 4 & 193 & 1.90 & 1 \\  & Pebble & 9 & 5 & 280 & 2.05 & 2 \\  & Pebble & 19 & 10 & 360 & 2.10 & 3 \\  & Pebble & 34 & 15 & 480 & 2.15 & 4 \\  & Sticky sand & 50 & 15 & 440 & 2.20 & 5 \\  & Sticky sand & 65 & 15 & 620 & 2.22 & 5 \\  & Sticky sand & 75 & 11 & 700 & 2.30 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Models and soil mechanical parameters. range of 0.2-0.6 s, the acceleration response spectrum value of the site with a 30 m pebble layer thickness (Model 1-2) was significantly smaller than that of the other site models, and the response spectrum curve showed a \"trough\" shape. The site with a pebble layer thickness of 30 m was the site type with the widest distribution and largest area in the Zhongwei Basin. Therefore, such sites have a certain \"weak isolation\" function in the middle/high-frequency band of 0.2-0.6 s.  
Figure 5: Amplification ratios of ground motion for the different thicknesses of pebble layer under four seismic inputs. Figure 6: Spectral accelerations for the different thicknesses of pebble layer under four seismic inputs.