esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Outcomes
### Thermodynamics of the Adsorption Process  
Entropy (\(\Delta S^{\circ}\)) and change in free energy (\(\Delta G^{\circ}\)) in the dye-adsorbent system are main features of the process design of adsorption thermodynamics. The relationship of \(\Delta G^{\circ}\), \(\Delta H^{\circ}\) and \(\Delta S^{\circ}\) are presented in Equations (9)-(12) (Table 1). The thermodynamic parameters were presented in Table 3. The decrease in negative \(\Delta G^{\circ}\) value with rise in temperature specifies the spontaneity of the adsorption process. The positive values of \(\Delta S^{\circ}\) indicate the decrease in the randomness at the dye/NIFGS interface.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Initial**} & \multicolumn{3}{c}{**Pseudo-First Order**} & \multicolumn{3}{c}{**Pseudo-Second Order**} \\ \cline{2-7}  & \(q_{e}\) **(mg g\({}^{-1}\))** & \(K_{1}\) **(min\({}^{-1}\))** & \(R^{2}\) & \(q_{e}\) **(mg g\({}^{-1}\))** & \(K_{1}\) **(min\({}^{-1}\))** & \(R^{2}\) \\ \hline
50 & 3.22 & 0.054 & 0.99 & 19.12 & 48.77 & 0.97 \\ \hline
100 & 4.52 & 0.045 & 0.98 & 44.24 & 237.25 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adsorption kinetics model predicted parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dye Concentration**} & \multirow{2}{*}{**Temperature**} & \multicolumn{2}{c}{**Change in Free**} & \multicolumn{2}{c}{**Change in Entropy**} & \multicolumn{2}{c}{**Change in Enthalpy**} \\  & & \(\Delta G^{\circ}\) & \(\Delta S^{\circ}\) & \(\Delta H^{\circ}\) \\ \hline
**[ug mL\({}^{-1}\)]** & **[K]** & **[kJ/mol]** & **[J/mol K]** & **[kJ/mol]** \\ \hline \multirow{4}{*}{50} & 303 & \(-\)2.52 & & & \\ \cline{2-5}  & 313 & \(-\)2.65 & 23.28 & 9.91 \\ \cline{2-5}  & 323 & \(-\)2.77 & & & \\ \hline \multirow{4}{*}{100} & 303 & \(-\)3.26 & & & \\ \cline{2-5}  & 313 & \(-\)3.46 & 43.35 & 13.79 \\ \cline{1-1} \cline{2-5}  & 323 & \(-\)3.64 & & & \\ \hline \multirow{4}{*}{200} & 303 & \(-\)4.07 & & & \\ \cline{2-5}  & 313 & \(-\)4.29 & 63.28 & 17.82 \\ \cline{1-1} \cline{2-5}  & 323 & \(-\)4.53 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: CV-NIFGS thermodynamic factors.
## 1 Introduction
[PERSON]'s research [16] showed that the incident angle of stress wave had a great influence on the tunnel, and the dynamic response of the structure at oblique incidence was greater than that at vertical incidence. [PERSON] [17] used the finite element method of fluid dynamics to simulate the dynamic response and damage of the tunnel arch in the transverse direction, mainly due to high bending stress. When blasting occurs in a square or circular cross-section tunnel, the blasting stress wave takes on different forms, resulting in redistribution of stress. The FEM-DEM coupled numerical simulation method was used to effectively predict the multiple fractures of rock caused by the blasting loads; the crack initiation and propagation was also captured [18]. [PERSON] [19] analyzed the development of a fracture network in the blasting process through drilling and blasting methods, and the stability of the tunnel was affected by the breakage of the surrounding rock, especially the overbreak area. [PERSON] [20] used LS-DYNA software to analyze the ground side blasting angle and the buried depth of the tunnel; the results showed that the blasting stress waves had changed the stress state around the tunnel, resulting in damage. Based on the empirical calculation formula of blasting dynamic loads, [PERSON] [21] obtained the time history curve of the shock wave pressure on the tunnel structure, and pointed out that the engineering structure would not be damaged when the shock wave propagating from the blast to the tunnel structure was within a certain range. [PERSON] [22] used the [PERSON] method to solve the vibration differential equation using a numerical model, and the conclusion showed that the blasting center distance had a significant impact on the kinematic parameters of the suspension tunnel, i.e., the maximum displacement of the tunnel is approximately inversely proportional to the increase in the blasting center distance. Research on the dynamic response of rocks around tunnels has been carried out by numerous scholars [23; 24; 25], and numerous research results have been achieved using numerical simulation methods [26; 27; 28; 29]. However, few studies have been conducted on the dynamic stress distribution and fracture area of rock tunnels under different orientations of blasting disturbance. In this study, the finite difference numerical software AUTODYN was used to simulate the dynamic response of the blasting source at different locations around the tunnel, using a 1:1 tunnel numerical model. Gauge monitoring points at the surrounding rock of the tunnel were set to analyze the displacement change, stress change, and failure mode of the tunnel-surrounding rock in order to study the law of dynamic stress distribution and the fracture areas of rock tunnels, thereby providing theoretical support for the safety and stability of tunnels under dynamic impact loads such as blasting.
## 3 Optimization Problem Formulation  
### Objective Function Terms  
This study aims to minimize one of the reliability indicators defined in Equations (1)-(3) by optimally allocating limited investment funds among predefined improvement measures. Given that DSOs have financial constraints when planning investment strategies to enhance grid reliability, the optimization problem is formulated as follows:  
\[\min\left\{\sum_{i\in S}x_{i}SAIDI_{i}\right\}, \tag{4}\]  
\[\min\left\{\sum_{i\in S}x_{i}ENS_{i}\right\} \tag{5}\]  
\[\min\left\{\sum_{i\in S}x_{i}COST_{i}\right\} \tag{6}\]  
where \(x_{i}\) is a binary decision variable indicating whether the investment option \(i\) is selected (\(x_{i}=1\)) or not (\(x_{i}=0\)); \(SAIDI_{i}\), \(ENS_{i}\), and \(COST_{i}\) represent the reduction in \(SAIDI\), \(ENS\), and \(COST\) achieved by implementing investment option \(i\); and \(S\) is the set of available investment options.  
The objective is to minimize each of the Equations (4)-(6) separately, subject to the constraint:  
\[\sum_{i\in S}x_{i}C_{i}\leq C_{\max}, \tag{7}\]  
where \(C_{i}\) is the cost of implementing investment option \(i\), and \(C_{max}\) is the maximum available investment budget.  
Individual minimization of (4)-(6) allows the DSO to clearly understand the trade-offs between different reliability aspects and select the most suitable investment strategy based on specific priorities. Additionally, this method avoids potential biases that could arise from normalizing or weighting the objectives, ensuring a more objective evaluation of each reliability criterion.  
This optimization problem belongs to the class of MILP problems, where all decision variables are binary, meaning they can take only the values 0 or 1. A variable set to 1 indicates that a given component has been replaced with a new one or that a new power line has been built. MILP has been widely used in reliability studies for distribution networks due to its ability to handle discrete decision-making and linear constraints effectively. In [11; 15; 32], the application of MILP in improving distribution network reliability demonstrates effectiveness in addressing complex reliability optimization problems.
# Mobility-Multihoming Duality
## 3 Identifier Locator Network Protocol (ILNP)
### ILNP Control Plane  
When node \(X\) locally changes the state of its ILCC, relevant information needs to be communicated to the remote correspondent node (CN), \(Y\). This is so that \(Y\) becomes aware of the new connectivity of \(X\) and can update its own ILCC. For a transport flow that is in progress, the NID stays fixed for the duration of that flow. However, the L64 values can be changed. When \(X\) has new L64 values, it notifies these to \(Y\) with a simple control plane protocol using a _Locator Update (LUI)_ message [40]. An example of such a handshake for a Mobile Node (MN) and a Correspondent Node (CN) is shown in Figure 2. This is a simple, efficient messaging system with low latency--a single round trip time (RTT) for an update.  
A similar exchange would occur for multihomed nodes: the handshake allows multiple L64 values to be communicated in a single LU message.  
Additionally, the dynamic name bindings in ILNP allow for NID-L64 bindings to be independently managed. So, there is, effectively, a network layer soft handover as a node moves or changes connectivity. This means that packet loss should be minimised, as packets 'in-flight' have the opportunity to be delivered (and acknowledged) before NID-L64 bindings are removed.
## 4 Results and Discussion
### Effect of Nanoparticle Volume Fraction on Cutting Tool Heat Transfer Characteristics  
Figure 6 depicts the variation in maximum temperature on increasing the volume fraction of dispersed hybrid nanoparticles. The volume fraction varied from 2 to 8%, and the maximum temperature of the system was noted. It can be seen from Figure 4 that the maximum temperature for all hybrid nanocoolants at a lower volume fraction is almost equal. However, Cu-Al\({}_{2}\)O\({}_{3}\) hybrid nanocoolants showed a lower temperature when compared to other variants. Furthermore, it can be noticed that the temperature of the system declines linearly with an addition in volume fraction. This is in accordance with the idea that nanoparticles increase the rate of heat transfer, thus resulting in better cooling. At higher volume fractions of 6% to 8%, the temperature of the drilling system is significantly lower when Cu-Al\({}_{2}\)O\({}_{3}\) hybrid nanoparticles are dispersed in mineral oil. Hence, it can be concluded that meta-metal-oxide hybrid nanoparticles are more efficient in heat transfer compared with metal-oxide-metal-oxide nanoparticles.  
Figure 7 shown above represents the trend in convective heat transfer coefficient with the addition of hybrid nanoparticles in the volume fraction. It can be inferred that SWCNT-Al\({}_{2}\)O\({}_{3}\) and MWCNT-Al\({}_{2}\)O\({}_{3}\) nanoparticles show a greater wall heat transfer coefficient compared with Cu and TiO\({}_{2}\) alternatives. This can be validated on the basis of the fact that SWCNT and MWCNT nanoparticles have a higher thermal conductivity value compared with Cu and TiO\({}_{2}\). Hence, a higher thermal conductivity of SWCNT and MWCNT results in a greater net thermal conductivity of the mixture.  
The above Figure 8 shows the trends in Nusselt number with change in the volume fraction of dispersed nanoparticles. The above relation shows that the Nusselt number decreases with an increase in the volume fraction of hybrid nanoparticles. Furthermore, it can be seen that Cu-Al\({}_{2}\)O\({}_{3}\) nanoparticles dispersed in mineral oil show the highest value of Nusselt number at all volume fractions. Hence, it can be inferred that Cu-Al\({}_{2}\)O\({}_{3}\) nanocoolants show enhanced heat transfer by convection as opposed to conduction.  
Figure 6: Variation of maximum drill bit temperature with different volume fractions of dispersed nanoparticles.
## 3 Results and Discussion
### Effect of the Key Operating Conditions  
#### 3.4.1 Effect of Flowrate on Purity  
In the real case, the adsorbent and adsorption step times of the PSA process are the most significant variables that affect the purity of biomethane, as explained above. However, it is difficult to change these variables at the commercial scale. Nevertheless, we can take into account the flowrate of biogas, which can also control the purity of biomethane. The changes in the purity according to variation of flowrate are shown in Figure 13. The results show that we can increase the purity by up to 97.92% with the same dimensions of the adsorption bed by reducing the flowrate by 60%.  
Figure 12: The mole fraction of CO\({}_{2}\) at the cyclic steady state with Aspen Adsorption.  
Figure 13: Effect of flowrate on biomethane purity.
## 2 Numerical Simulation
### Boundary Conditions and Mesh  
To evaluate the vibration stability of nearby structures in the event of a hydrogen gas explosion in a shallow storage tank (Figure 9), the bottom boundary of the analysis domain was fixed (U\({}_{\rm x}\) = U\({}_{\rm y}\) = U\({}_{\rm z}\) = U\({}_{\rm Rx}\) = U\({}_{\rm Ry}\) = U\({}_{\rm Rx}\) = 0). Additionally, fixed boundaries were applied in the x-direction at the front face of the domain, and roller conditions were applied in the y- and z-directions (U\({}_{\rm x}\) = U\({}_{\rm Ry}\) = U\({}_{\rm Rx}\) = 0). On the right side of the domain, fixed boundaries were applied in the z-direction, whereas roller conditions were applied in the x- and y-directions (U\({}_{\rm z}\) = U\({}_{\rm Rx}\) = U\({}_{\rm Ry}\) = 0). The left side of the domain has fixed boundaries in the z-direction, and the rear face has fixed boundaries in the x-direction. Three-dimensional 8-node reduced integration elements (C3D8R) were used for the protective concrete structure and the ground, whereas the reinforcing steel bars in the protective concrete structure were discretized using B31 elements. The explosive charge and surrounding atmosphere were represented by grids of the EC3D8E element type, which are Eulerian-continuum-based reduced-integral elements with eight nodes. In this study, a mesh-independent test was conducted to determine the optimal element size to ensure consistency of the simulation results. Multiple analyses were performed for various grid sizes, and the optimal grid size that yielded the convergent analysis results was determined. Consequently, it was confirmed that the grid size near the explosion point should be at least 5 cm to obtain stable analytical results.
## 2 Related Work
### Extracting and Sharing CTI Information  
Recent research works make extensive use of machine-learning approaches to extract CTI information, which has various advantages, such as the identification of indicators of compromise and automatic categorization of CTI reports with domain-specific tags (e.g., finance, government). Various methods have been used, such as analysis of raw log data [15], analysis of social media data based on convolutional neural networks [16], analysis of conversations (e.g., forum data) based on support vector machines [17; 18], use of natural language processing models [19], analysis of semantic and contextual information collected from varying sources [20], mining of CTI reports to automatically learn the semantics of malicious campaigns [21], and leveraging entropy and mutual information [22], from structured or unstructured sources [23; 24]. A common characteristic of these works is that they rely on artificial intelligence and that the analysis of the CTI information is typically organized (e.g., in CTI reports), which is then shared among cybersecurity stakeholders.  
Regarding the sharing of CTI information, [PERSON] et al. [25], in a recent review, showed that various sharing models are adopted, covering peer-to-peer, peer-to-repository, and hybrid aspects. Considering the importance of actionable CTI and the value of various dimensions (e.g., timeliness of sharing CTI, trust establishment of CTI sharing, stakeholder reputation, data interoperability, privacy, and anonymity), the use of automated tools (e.g., through established CTI repositories) plays a major role. One of the most known and widely used tools is the MISP Threat Sharing platform [26], which is an open-source solution that supports utilities and documentation for effective CTI by sharing indicators of compromise. Other tools, such as SecurityKG [27], are also used for automated CTI gathering and management, supporting higher-level concepts (e.g., adversary tactics, techniques, and procedures). Moreover, organizations often use technologies such as STIX [12] and TAXII [28] to describe and share CTI information in a secure and automated manner.
###### Abstract
Next, as a basic process for traffic engineering or security, research on measurement and monitoring has tried to improve the accuracy of the process while simultaneously reducing the load of the controller [18; 19; 20; 21; 22]. Moreover, because of the vulnerability of the SDN controller itself (i.e., one point of failure) some studies consider new possible attack scenarios (e.g., crash and data compromise attacks [23], host location hijacking attacks [24], or control plane saturation attacks [25]) and design a proper defense for each. However, these studies do not provide a comprehensive system for monitoring and preventing internal faults, anomalies and outside attacks. Thus, the synthetic system research providing an agile and efficient defense is necessary. Our goal in this study is to develop readily available and stable SDNs that take adept actions against both fault and anomaly traffic, quickly detect anomalies by means of packet inspection, generate automatic flow rules, and directly apply them on switches. Our proposed architecture helps to develop a comprehensive solution for fault and abnormal detection and prevention, minimize latency, and increase network availability and efficiency under any circumstances. Moreover, our architecture adopts proper SDN rule management methods (i.e., proactive and reactive) based on flow types (i.e., mice and elephant flows). To achieve these goals, we propose a robust and agile defense (RAD) system against both fault and anomaly traffic by utilizing SDN technologies. Our RAD system consists of three main modules: a _traffic analyzer_, _a traffic engineer_, and a _rule manager_. The traffic analyzer monitors the traffic using sFlow-RT [26], a real-time network measurement tool, and Snort IDS [27], a signature-based intrusion detection system, to detect elephant flows and attacks, respectively. If an anomaly is detected, the traffic engineer module generates new routing paths based on the network status (i.e., network utilization or delay). The newly determined routing paths enable the rule manager to generate flow rules for the data plane on network devices. These steps are automatically and circularly performed according to the outbreak of anomaly events. The contributions of this study are as follows:  
* We design the RAD system having fast detection and defense against faults and attacks as well as generating reactive flow rules for elephant flows by considering network status. * We evaluate the RAD system with respect to robustness, agility, and efficiency through experiments with various scenarios by employing each module. The remainder of this paper is organized as follows. Section 2 reviews previous studies on SDN in relation to fault tolerance and attack defense. Section 3 explains our proposed system architecture (RAD) with the three modules. Section 4 discusses the experimental results of the RAD system. Section 5 provides concluding remarks and suggestions for future research.
## 4 Results and Discussions
### Comparative Analysis  
The efficacy of the planned research was evaluated against existing studies.  
Table 5 shows a comparison and contrast between the proposed method and current works, displaying the results of the analysis based on accuracy. The proposed method allowed the attainment of a greater level of accuracy, which was 99.05%, in comparison to the approaches that were previously used. This was owing to the consideration of system parameter optimization using 2E-HRIO, side channel attack detection using ADSCNN, authentication using CHMAC-EPQH, and link failure prediction using RDBSCAN, meaning the proposed methodology was able to achieve a higher level of performance. On the other hand, the currently available technologies, such as CNN, Forest, ResNet50, and EfficientNet-B7, achieved lower levels of accuracy. In general, the proposed methodology was successful in providing an effective continuous-variable quantum key distribution, together with parameter optimization and detection of side attacks.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Techniques** & **SNR (dB)** & **BER** \\ \hline Proposed \(R^{2}LMS\) & 53.658 & 0.00713 \\ LMS & 48.599 & 0.00955 \\ NLMS & 46.53661447 & 0.01116 \\ RLS & 44.76138179 & 0.01630 \\ KF & 42.95629554 & 0.01947 \\ \hline \hline \end{tabular}
\end{table}
Table 4: SNR and BER analysis.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Techniques** & **Approach** & **Clustering Time (s)** & **Jaccard Index** \\ \hline Proposed RDBSCAN & 0.005994 & 0.543541 \\ DBSCAN & 0.0060 & 0.53861 \\ KMC & 0.01100 & 0.533327 \\ AP & 0.02248 & 0.431435 \\ GMM & 0.50199 & 0.424038 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Clustering time and Jaccard index analysis.
## 5 Sampling and Analytical Methods  
The chalcopyrite and cassiterite samples used in this study were taken from fresh rock cores and ores in drills ZK0403 and ZK2204 and tunnels. The collected samples were ground into optically thin sections for mineralogical observation, and representative points were selected to carry out electron microprobe analysis (EMPA) and finally LA-ICP-MS in situ trace element analysis.  
On the basis of detailed microscopic observation, the major and trace element fractions of cassiterite were determined. The electron microprobe analysis (EMPA) of cassiterite was completed at the Key Laboratory of Mineralization and Resource Evaluation, Institute of Mineral Resources, Chinese Academy of Geological Sciences, and the testing instrument was a JEOL JXA-iHP200F field emission electron microprobe (Figure 4). The accelerating voltage was 15 kV, the beam current was 20 nA, the electron beam spot diameter was 5 \(\upmu\)m, and the tested elements included F, Si, Mg, Zr, Ca, Fe, Hf, Sn, Sr, and Th, using natural minerals or the national standards of synthetic metal oxides as the standard sets. The data were uniformly corrected by ZAF.  
Based on the results of the electron microprobe analysis (EMPA), LA-ICP-MS in situ trace element analysis was carried out on chalcopyrite and cassiterite. A total of 5 samples were collected in this work, including 2 samples of cassiterite with 32 points tested and 13 samples of chalcopyrite with 21 points tested. The specific sampling location and mineral combination characteristics are shown in Table 1. In this paper, we focus on testing early chalcopyrite and cassiterite connected with gauge minerals. As far as possible, samples with clean surfaces and free of fissures and inclusions were selected for testing and analysis. The in situ LA-ICP-MS analysis of chalcopyrite and cassiterite was carried out at the National Research Center for Geoanalysis (CAGS), using a Thermo Element II single-receiver, high-resolution magnetic sector ICP-MS connected to a New Wave UP 213. The denuded material was transported by helium. The operating conditions were a 40 \(\upmu\)m wide spot beam, 10 Hz frequency, and a laser flow of 10-30 J/cm\({}^{2}\) with an energy of 0.176 mJ. In the analysis process, first, 15 s of background values was collected, followed by 45 s of sample stripping, and finally 15 s of cleaning the system. The specific analysis process and technique refer to [23]. The calibration of trace elements was carried out using MASS1, NIST610, and NIST612 as the joint external standards and KL2G was used as a monitoring standard sample; the analytical precision was 0.1 \(\times\) 10\({}^{-6}\). GJ-1 and AY-4 were used as standard samples for cassiterite trace element correction. Compared with the electron microprobe analysis, the LA-ICP-MS analysis was characterized by a lower detection limit and higher accuracy.  
Figure 4: Electron probe BES of minerals in the Shuangjianzishan silver polymetallic deposit, Inner Mongolia. (**a**) Cassiterite is parceled by sphalerite and pyrite; (**b**) cassiterite is associated with quartz. Cst—assiterite; Sp—sphalerite; Py—pyrite.