esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Validation (Simulated)
### Scenario and Attack Model  
The attack simulation scenario involved 50 discrete threat incidents over a 24 h period. The threats encompassed various typical IoT attacks such as distributed denial-of-service (DDoS), ransomware intrusions, insider threats, and simplified unauthorized access attempts. Each simulated attack scenario tested the system's ability to quickly detect, respond to, and mitigate threats using the adaptive security orchestration provided by the framework AI engine. For example, the simulated threat profile included events abstractly modeled on distributed denial-of-service (DDoS), ransomware, and unauthorized access scenarios. While the simulation did not model packet-level behaviors, it used randomized statistical inputs to represent threat types, occurrence timings, and system detection/response patterns. The AI-driven security orchestration responded to each threat instance by adjusting internal control policies, and performance metrics such as detection accuracy and response time were logged accordingly.  
This approach aligns with standard early-stage evaluation practices for integrated security frameworks. Prior works such as QBIoT and SD-PQBA adopted similar statistical modeling strategies in the absence of granular real-world data, focusing instead on validating architectural feasibility and theoretical performance.  
The use of exponential and uniform distributions to model threat behavior reflects practices used in security simulations where real-time attack data are unavailable. Prior studies such as QBIoT and SD-PQBA also adopted simplified modeling approaches to validate quantum-enhanced security architectures under constrained environments.  
Table 3 summarizes the representative attack scenarios used during simulation. Each threat type was modeled using randomized triggers and statistical profiles to emulate behavioral diversity while preserving computational efficiency. This modeling approach enabled the evaluation of key performance indicators, including detection accuracy, response time, and resource impact under varied but repeatable conditions. The use of abstracted threat events reflects a common approach in the early-stage validation of integrated security frameworks where protocol-level emulation is impractical.
## 2 Failure Recovery in SFC and Challenges  
This section is structured into three parts to define the scope of this study, discuss background technologies, and present a review of recent studies. SFC failure recovery consists of multiple sub-tasks such as the placement of backup SFs, deployment, and path setup, which are incorporated into different phases of the SFC creation process. The first part of this section describes the creation and management of SFC to define the scope of the proposed protection mechanism. The latter part of the section explains segment routing and convention failure recovery approaches that play a fundamental role in the proposed HP-SFC. The last part presents recent studies related to different aspects of failure recovery in SFC in the context of the proposed protection mechanism.
## 3 Results and Discussion
### Flow Field Distribution  
The jet flow in the impingement cavity is closely related to the heat transfer. Figure 6 is the streamlines and \(Nu\) distribution with and without pin fins on the lower surface at \(Re=2\times 10^{3}\), 14 impingement holes with a hole diameter of 7.2 mm.  
Impinging jet flow directly affects the heat transfer in the cavity, and the \(Nu\) distribution on the lower surface corresponds to the streamline. The heat transfer near the outlet holes is good, while the heat transfer in the vortex area is poor. The impingement cavity is inclined, and the upper and lower surfaces of the impingement cavity have an angle of 32\({}^{\circ}\) with respect to the horizontal plane. So, there is less symmetric with respect to the flow  
Figure 4: Schematic of the experimental system.  
direction, and the heat transfer at the below outlet is stronger than that at the above outlet. Comparing Figure 6a,b, the pin fins can make the heat transfer more uniform.
# A Study on the Channel Expansion VAE for Content-Based Image Retrieval
## 4 Experiment
### Experiment
#### 4.2.3 Regional Masking Effect  
The last layer could be not optimal for the descriptor of the image; thus, we visualized the activation map at each layer and selected a layer for the final representation. The activation map can be obtained by gradient calculation from the output. Figure 5 shows the activation map at each layer.  
As shown in Figure 5, the activation map capturing the flower region precisely appears in block 6. Hence, the activation maps of \"DW_Conv\" and \"ReLu\" in block 6 were average and used for binary masks to reduce an effect from the background.  
In Figure 6, the threshold of the mask takes the mean value of the activation map, and its masking increased the mAP, but the increment was small (2%). Furthermore, we applied a Gaussian filter, since most of the flowers were placed in the center of the image. The variance of the Gaussian filter was tested at one to four because the feature size was \(8\times 8\). When the variance size was two, precision was shown to be the best--46.1%. With the filter and mean mask, performance was extremely varied in accordance with the class of flower. Total mAP increased, but the prediction rate of some classes decreased. For instance, a portion of the oxeye daisy was hidden but the rose image still included the background part.
# Theoretical Description of Carbon Felt Electrical Properties Affected by Compression
## 6 Conclusions  
(1) CF material has widespread requests in different applications, including heating elements and diverse electrochemical cells.  
(2) Electrical (electronic) conductivity of CF plays a crucial role for electrons to reach the surface of the electrodes and to participate in reactions. Likewise, electrical resistivity (the inverse of electrical conductivity) plays a significant role in generating heat and causing electrical losses, thus diminishing the efficiency of electrochemical equipment.  
(3) Owing to the significant importance of CF resistivity and conductivity for different electrochemical applications, a special comprehensive investigation was carried out aiming to find the magnitudes of these parameters.  
(4) It was observed that electrical resistivity was diminished during felt compression, having a non-linear relation versus volume decrease like the negative exponential function. In the initial stage of volume decrease, the resistance drops quickly. However, after 80-60% of its initial value, additional compression has a negligible effect on conductivity.  
(5) Electrical conductivity moderately depends on liquid permittivity properties. Dielectric constant (\(\varepsilon\)), among other liquid parameters, obviously has the main role influencing the quantity of interconnection between carbon filaments and electrodes. The increase of \(\varepsilon\) causes a slight improvement in CF conductivity.  
(6) The theoretical approach based on the stochastic distributions of filaments lengths and their stochastic dispositions inside the felt frame was developed. The conductivity for each current felt thickness was estimated by an integral which summarizes the conductivities of all filaments that this time are connecting both of the cell electrodes.  
(7) It could be assumed that the type of lengths and angles distribution inside a frame plays a significant role in defining CF conductivity and should be investigated in future research work.  
Conceptualization--M.A.; experiments and methodology--S.L.; validation--M.A. and S.L.; writing--original draft preparation, review and editing--M.A.  
This research received no external funding.  
The authors would wish to express their deep gratitude to [PERSON], [PERSON] and Engineer [PERSON] for their help in comprehensively realizing this research and discussing the obtained results, as well for [PERSON] for his significant efforts in improving the text of the article.  
The authors declare no conflict of interest.
## 3 The Control Strategy of FMSS for Maximum Consumption of DG
### Constraints of Distribution Network Operation Including Flexible Multi-State Switch
#### 3.2.2 System Operating Constraints  
The system operating constraints mainly include the system operating voltage level constraints, branch capacity constraints and the system power flow constraints. The system operating voltage horizontal constraint and branch capacity constraint are inequality constraints, as shown in (16) and (17) respectively. The system power flow constraint is an equation constraint, as shown in (18) and (20).  
\[\left(U_{n}^{\min}\right)^{2}\leq(U_{t,n})^{2}\leq(U_{n}^{\max})^{2} \tag{16}\]  
\[0\leq(I_{t,nm})^{2}\leq(I_{mm}^{\max})^{2} \tag{17}\]  
\[\left\{\begin{array}{l}\sum\limits_{l\in\psi_{i}}P_{t,nl}=\sum\limits_{m\in \psi_{i}}\left[P_{t,mn}-R_{mm}(I_{t,mn})^{2}\right]+P_{t,n}\\ \sum\limits_{l\in\psi_{i}}Q_{t,nl}=\sum\limits_{m\in\psi_{i}}\left[Q_{t,mm}-X _{mm}(I_{t,mm})^{2}\right]+Q_{t,n}\\ \end{array}\right. \tag{18}\]  
\[\left\{\begin{array}{l}P_{t,n}=P_{t,n}^{\rm DG}+P_{t,n}^{\rm FMSS}-P_{t,n}^{ \rm LOAD}\\ Q_{t,n}=Q_{t,n}^{\rm DG}+Q_{t,n}^{\rm FMSS}-Q_{t,n}^{\rm LOAD}\\ \end{array}\right. \tag{19}\]  
\[(I_{t,mm})^{2}=\frac{(P_{t,mm})^{2}+(Q_{t,mm})^{2}}{(U_{t,n})^{2}} \tag{20}\]  
where \(t\) is the network number, \(n\) is the number of nodes, \(U_{t,n}\) is the voltage of \(n\) nodes in the network \(t\). In this paper, in addition to the above system constraints, the output range of DG should also be considered. The constraints are shown in (21).  
\[0\leq P_{DC,i}\leq P_{DC,i\max} \tag{21}\]
## 4 Integration of RDF and LPG in SKATEBOARD  
Within the framework of SKATEBOARD, the harmonious integration of two fundamental data models, Resource Description Framework (RDF) and Labelled Property Graph (LPG), serves as a cornerstone for our approach. This integration empowered us to leverage the respective strengths of these data formats, significantly expanding the framework's scope and versatility. This section delves into how SKATEBOARD combines and harnesses these two data models to enrich knowledge and enhance system adaptability.
# Hydrophobic Fluorinated Porous Organic Frameworks for Enhanced Adsorption of Nerve Agents
## 3 Results and Discussion  
### Structure and Morphology  
FT-IR spectroscopy was used to confirm the successful preparation of C-1 and C-1-F. As shown in Figure 3, the characteristic peaks of the aldehyde and amino groups at 1700 and 3300-3500 cm\({}^{-1}\), respectively, disappeared, indicating that both materials were successfully synthesized.  
Moreover, XRD studies on the crystallinity of the synthesized materials (Figure 4) showed that no characteristic diffraction peaks were observed between 4-40\({}^{\circ}\), indicating that both C-1 and C-1-F were amorphous materials. SEM measurements (Figure 5) also revealed that C-1 and C-1-F had quite different morphologies. C-1 had a hollow network structure, while C-1-F formed irregular hollowed-out spheres consisting of 100-200 nm elliptic particles.  
Figure 4: XRD diffraction patterns of C-1 and C-1-F.  
Figure 3: FT–IR spectra of C-1 and C-1-F.  
The obvious hysteresis loop observed on the N\({}_{2}\) adsorption isotherms at 77 K indicated that the existence of mesopores in both materials (Figure 6a,b) with a Brunauer-Emmett-Teller (BET) surface area of 344 and 434 m\({}^{2}\cdot\)g\({}^{-1}\) for C-1 and C-1-F, respectively. Based on further density functional theory studies (DFT), the pore size was also widely distributed around 1.1 and 1.4 nm, respectively (Figure 6c,d).  
Figure 5: SEM pictures of (**a**) C-1 and (**b**) C-1-F.  
Figure 6: N\({}_{2}\) adsorption isotherms of (**a**) C-1 and (**b**) C-1-F at 77 K. Pore size distribution curve of (**c**) C-1 and (**d**) C-1-F. Solid and open symbols denote adsorption and desorption, respectively.
## 2 Materials and Methods
### Dynamics and Thermal Behavior of PEM Fuel Cell
1D modeling of a PEM fuel cell simplifies the complex multidimensional processes into a single spatial dimension, typically across the cell's thickness. It considers key components such as flow channels, gas diffusion layers (GDLs), catalyst layers, and the proton exchange membrane (PEM) and models phenomena like species transport, charge conservation, electrochemical reactions, heat transfer, and water management. Simplifications like steady-state operation and ideal gas behavior reduce computational complexity, making it effective for analyzing polarization curves, thermal and water management, and overall fuel cell performance. While 1D models are computationally efficient and suitable for parametric studies, they lack the ability to capture multidimensional effects like flow misdistribution  
Figure 8: Hydrogen fuel consumption of PEM fuel cell. and localized phenomena. In Equation (5), total heat \(Q_{n}\) is expressed by multiplying the fuel cell thermal capacity and the temperature change. \[Q_{n}=Q_{g}-h\ A(T\ -T_{a})=C_{FC}\frac{dT}{dt} \tag{5}\]  
\[Q_{g}=\frac{\Delta h_{H2 OJ}}{zF}N_{fc}I-V_{FC}I-\big{(}m_{H2O}c_{p,H2O}+m_{H2}c _{p,H2}+m_{O2}c_{p,O2}\big{)}(T_{FC}-T_{a}) \tag{6}\]  
where \(Q_{g}\) represents the energy generated by the fuel cell during a chemical reaction. The internal enthalpy of water \(\Delta h_{H2 OJ}\) equals 285.84 kJ/mol at 25 \({}^{\circ}\)C temperature under 1 bar pressure in liquid form. \(V_{FC}\) is the fuel cell voltage and \(V_{FC}=(V_{FC}-V_{loss})N_{FC}\). \(C_{FC}\) is the molar heat capacity, \(T_{a}\) is the ambient temperature, \(A\) is the heat convection area, \(m_{H2O}\) is the water vapor mass, \(m_{O2}\) is the oxygen mass, and \(c_{p,H2O}\), \(c_{p,H2}\), \(c_{p,O2}\) are the heat capacities of water vapor, hydrogen, and oxygen, respectively.
## 3 Results & Discussion
### Morphological Observation  
From having the greatest reduction of elongation at break, degree of crystallinity, thickness of long period, and intermediate layer, shear-treated HDPE at 100/s was selected to be a representative of the shear-treated samples for comparison of morphology with VPE. Figure 8 shows the TEM images of VPE and shear-treated HDPE at 100/s thin film at different magnification levels. At low magnification (\(\times\)10,000, Figure 8a), the morphology of the crystalline region was similar between VPE and 100/s. It is worth noting that a difference can be obviously detected in the high-magnification TEM observation (\(\times\)40,000, Figure 8b). The stacking of lamellar was detected in both samples; however, the lamellar orientation was different. In case of VPE, a non-oriented lamellar structure was detected which was a general form of PE without any treatment. On the other hand, shear treatment affected the generation of the orientation of lamellar structure in shear-treated HDPE in parallel to the shear direction. These changes of lamellar orientation also affected the decreased elongation at break of the shear-treated HDPE at 100/s as compared to VPE.  
AFM was also used for the characterization of morphological observations. Figure 9 shows AFM phase images of VPE (Figure 9a) and the shear-treated HDPE at 100/s (Figure 9b). The bright area indicates the crystalline phase; however, the dark area defines the amorphous region. In a similar trend with TEM images (Figure 8), it was found that the non-oriented crystalline phase was detected in the VPE. However, the orientation of the crystalline region obviously occurred in shear-treated HDPE at 100/s. In addition, the generation of lamellar orientation was in parallel to the shear direction.  
Figure 7: Relationship between elongation at break and (a) thickness of core-amorphous layer; (b) thickness of intermediate layer.  
From these morphological observations by TEM and AFM, it can be confirmed that crystalline orientation was detected in the shear-treated HDPE which was affected from shear treatment. In addition, the creation of a crystalline orientation in the shear-treated samples was also related to the changes of elongation at break of HDPE.
# Nanosized Ni/SBA-15 Catalysts for CO\({}_{2}\) Reforming of CH\({}_{4}\)
## 1 Introduction
The Mg- and Sc-doped catalysts exhibited stable performance during a 50 h test, whereas the undoped Co-Ni/S15 was stable for only 20 h. The authors found that La addition had almost no effect on the activity of Co-Ni/S15. [PERSON] et al. [21] reported a noticeable positive outcome on the overall enactment of Ni/SiO\({}_{2}\) when adding Li. It was concluded that the effect of a certain promoter/dopant is dependent on the textural properties, support material, electronic state, and reaction conditions. [PERSON] et al. [12] reported different effects of introducing Ce into the hexagonal structure of S15 for promoting Ni/S15. The variation of the Ce effect was attributed to the Ce/Si ratio. The best catalytic stability and activity was observed at a Ce/Si molar ratio of 0.04, around 80% CH\({}_{4}\) conversion at 700 \({}^{\circ}\)C for 40 h. [PERSON] et al. [11] examined the effects of MgO addition to Ni/S15, whereby MgO addition enriched catalytic enactment and carbon resistance of Ni/S15. In this study, various nickel and cobalt catalysts supported on S15 are evaluated in the CO\({}_{2}\) reforming of methane. Ce and Zn oxides are used as support dopants. The effect of the catalyst/support and promoters/dopants combination was studied to optimize the catalyst performance and conversion. Different characterization techniques were employed to probe the effect of adding cobalt and promoters on the catalyst structure and textural properties. The prepared catalysts were assessed in CO\({}_{2}\) reforming of methane in a fixed bed reactor, over the course of 8 h, to examine the effect of the addition of cobalt and dopants.
# Adsorption of Methyl Orange on a Novel
## 2 Materials and Methods
### Theoretical Simulations  
The adsorption data at equilibrium were fitted with a Langmuir-type isotherm [39]:  
\[\mathrm{q_{e}}=\frac{\mathrm{q_{max}\cdot C_{e}\cdot K_{L}}}{1+\mathrm{K_{L}\cdot C _{e}}} \tag{3}\]  
where \(\mathrm{q_{e}}\) represents the equilibrium dye concentration onto the absorbents (mg g\({}^{-1}\)), \(\mathrm{C_{e}}\) is the dye concentration in the solution at equilibrium (mg L\({}^{-1}\)), \(\mathrm{q_{max}}\) is the adsorbents' monolayer adsorption capacity (mg L\({}^{-1}\)), and \(\mathrm{K_{L}}\) is the Langmuir adsorption constant (L mg\({}^{-1}\)). The corresponding linear form of Equation (3) is:  
\[\frac{\mathrm{q_{e}}}{\mathrm{C_{e}}}=\frac{1}{\mathrm{d_{max}\cdot K_{L}}}+ \frac{\mathrm{C_{e}}}{\mathrm{q_{max}}} \tag{4}\]  
Based on Equation (4), the linear regression between \(\frac{\mathrm{q_{e}}}{\mathrm{C_{e}}}\) and \(\mathrm{C_{e}}\) was analyzed, and the variables \(\mathrm{q_{max}}\) and \(\mathrm{K_{L}}\) were estimated.  
The pseudo-first- and pseudo-second-order rate-laws were used to fit the kinetic data [34; 37]:  
\[\frac{\mathrm{d_{q}}_{t}}{\mathrm{dt}}=\mathrm{k_{1}\cdot\left(q_{e}-q_{t} \right)} \tag{5}\]  
\[\frac{\mathrm{d_{q}}_{t}}{\mathrm{dt}}=\mathrm{k_{2}\cdot\left(q_{e}-q_{t} \right)^{2}} \tag{6}\]  
The corresponding linear forms of Equations (5) and (6) are:  
\[\ln\left(q_{e}-q_{t}\right)=\ln q_{e}-k_{1}\cdot t \tag{7}\]  
\[\frac{1}{q_{t}}=\frac{1}{k_{2}\cdot q_{e}^{2}}+\frac{t}{q_{e}} \tag{8}\]  
where \(q_{t}\) is the amount of dye adsorbed onto adsorbents (mg g\({}^{-1}\)); \(t\) is time (min); and \(k_{1}\) (min\({}^{-1}\)) and \(k_{2}\) (g mg\({}^{-1}\) min\({}^{-1}\)) are the first- and second-order adsorption rate coefficients of substance onto adsorbents, respectively. Based on Equations (7) and (8), the linear regression between \(\ln\left(q_{e}-q_{t}\right)\) and \(\frac{1}{q_{t}}\) and \(t\) was analyzed, and the variables \(q_{e}\) and \(K_{1}\) and \(K_{2}\) were estimated, separately.  
The comparison of the theoretical simulations with the experimental data was conducted using the Nash and Sutcliff efficiency index (E) [40]:  
\[\mathrm{E}=1-\frac{\sum_{i=1}^{n}\left(Y_{i}-S_{i}\right)^{2}}{\sum_{i=1}^{n} \left(Y_{i}-\overline{Y}\right)^{2}} \tag{9}\]  
where n is the number of samples; \(\overline{Y}\) is the average of the measured values; and \(S_{i}\) and \(Y_{i}\) are the simulated values by the model and measured data, respectively.
## 3 Results and Discussion
### Limitations  
The simulation results have not yet been validated through in situ measurements on the actual _Fuchslochtvelle_. Specifically, we have not measured the wave height or the velocities on the wave module, which are used as convergence criteria and for the grid independence study. As a result, we cannot confidently assert that the simulation accurately reflects real-world conditions. Relying on subjective judgment or visual estimation would not provide a sufficient basis for validation. The geometry used in the simulations is a simplified version of the real-world setup. Although this simplification was necessary for computational feasibility, it may not fully capture all the intricacies of the actual environment. For example, the outflow basin, which changes its topology over time, was modeled using a basic measuring method that cannot accurately represent the exact terrain. However, the rocks in the outflow basin move slightly over time due to the constant flow forces. Moreover, the inflow basin was not explicitly modeled; instead, a constant velocity was applied to a simplified rectangular inlet, potentially leading to deviations between simulated and observed flow characteristics. Similarly, the wave module was not precisely modeled but rather approximated in its shape. Furthermore, the area beneath the wave module was closed off in the simulation; however, in reality, this area is open. This could have an effect on the tumble forming the wave and influencing the wave height. These simplifications may introduce uncertainties that could affect the accuracy and applicability of the simulation results. It is important to address this in future simulations for not neglecting potential influences on the overall flow behavior and wave characteristics.
## 3 Results and Discussion  
This section highlights the results obtained in the seismic microzonation map for a fixed-jacket platform in the Malay Basin. The three objectives in this study are presented in  
Figure 6: Sample of the spectral acceleration for D4 under far-field seismic events.  
three main phases--developing a seismic microzonation map, developing a map of the soil amplification factor, and proposing a designed horizontal response spectral acceleration and site coefficient for the study area. Details for each phase are presented in Sections 3.1-3. Please note that the labeling of results was based on the site and soil type at that site. For soil type D, there were four sites, known as D1, D2, D3, and D4. As for soil type E, there were seven sites, known as E1, E2, E3, E4, E5, E6, and E7. As an example, D1 refers to the first site for soil type D.