esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Mechanism
### Viscosity Control  
During the EOR process, high mobility of the displacing fluid often results in viscous fingering, which leads to a poor sweep efficiency and conformance [13]. Thus, it is necessary to control the mobility of the injected fluid to achieve a better sweep efficiency for higher oil recovery. Polymer flooding has been successfully used as a viscosity control agent for increasing the sweep efficiency [180; 181]. Nevertheless, in a reservoir with harsh conditions (high temperature, pressure, salinity, etc.), the polymer solution often becomes degraded, which will decrease its viscosity and sweep efficiency [182; 183]. NPs have been proposed to enhance the viscosity of the polymers in the injection fluid because they improve the thermal stability of the polymer solution and prevent degradation.  
Thickening of the solution occurs due to the ability of NPs to form a network structure via hydrogen bonding [184], which will directly affect the fluid shear stress [15]. [PERSON] et al. [184] showed that viscosity enhancement by silica NPs in a water solution is relatively low due to the high polarity of water. However, in the polymer solution, NPs seem to be able to enhance pseudoplasticity behavior considerably, even at a low shear rate [83; 185]. Moreover, they prevent premature degradation of the polymer in high salinity conditions by preventing polymers from interacting with ions and can be substituted for polymers to attract cations [83].  
[PERSON] et al. [83] investigated the effect of the dispersion of silica NP on polymer flooding at different levels of fluid salinity. As shown in Figure 6, by adding silica NP in the polymer  
Figure 5: Wedge-shaped nanoparticle structure and forces (**A**) and wedge contact pressure (**B**). Reproduced with permission from reference. [149]. Copyright 2011 American Chemical Society.  
solution, degradation can be significantly reduced. The trapped oil can be efficiently mobilized by increasing the concentration of NPs. A comparison of different injection fluids shows that silica nanoparticle-polymer-flood has the highest oil recovery compared to water flood and conventional polymer flood [186].  
Nanoclay has been shown to significantly increase the viscosity of the nanoclay-polymer solution and improve the stability of the mixture at a high temperature and salinity [187]. [PERSON] et al. [188] reported a 5-6% higher recovery rate by adding nanoclay to a polymer solution. Several metal oxides such as TiO\({}_{2}\)[91] and FeO [189] have also been shown to improve the sweep efficiency during EOR.
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 4 Simulation Results
### Cooperative Control and Performance Metrics  
In order to avoid collisions, we further extend the cooperative controller (18) as:  
\[u_{i}(t)=\sum_{j=0}^{n}a_{ij}(\alpha((x_{j}(t_{s}^{j})-d_{j})-(x_{i}(t_{s}^{j})- d_{i}))+\beta(v_{j}(t_{s}^{j})-v_{i}(t_{s}^{j}))) \tag{35}\]  
where \(d_{i}\) and \(d_{j}\) are the given safety distances for the \(i\)-th and \(j\)-th robots, respectively. The controller can effectively coordinate multiple robots and hold formation. The parameters of the proposed decision-control approach can be found in Table 2. The position of signal source is [15 m, 15 m]. Moreover, the safety distances are:  
\[d=\begin{bmatrix}0&1&0\\ 0&0&1\end{bmatrix}^{T}\]  
and \(d_{0}=[1/3,1/3]\). The initial velocities of robots are:  
\[v=\begin{bmatrix}0.1&0.1&0.1\\ 0.1&0.1&0.1\end{bmatrix}^{T}\]  
In order to evaluate the proposed decision-control approach, we use two performance metrics: one is the communication frequency, while the other is the localization error.  
The communication frequency is calculated by:  
\[fre_{i}~{}=~{}\frac{Event-Triggered~{}Number}{Total~{}Sampling~{}Number}\times 100\% \tag{36}\]  
where \(fre_{i}\) denotes the communication frequency of the \(i\)-th robot. \"_Event-Triggered Number_\" refers to the communication times of the \(i\)-th robot. Note that if the event-triggered rule (16) is violated, a new control input needs to be calculated; otherwise, the previous control input is unchanged. \"_Total Sampling Number_\" stands for the total sampling times in a run. Hence, \(fre_{i}\) is a quantitative evaluation metric that is used to evaluate communication burden.  
The localization error (\(LE\)) is computed by:  
\[LE_{i}~{}~{}=~{}~{}\|p_{i}^{\prime}(i,t)-r(t)\| \tag{37}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Sampling time & 0.001 s \\ Noise variance \(R\) & 5, 8 \\ Total run time & 20 s for two cases \\ Communication distance & 5 m \\ The number of robots \(n\) & 3 \\ The velocity range of robots & [\(-3\) m/s, \(3\) m/s] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the simulation environment.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline \(\alpha\) & 17 \\ \(\beta\) & 22 \\ \(\gamma\) & 0.1 \\ \(\lambda\) & 0.001 \\ \(N\) & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of the proposed decision-control approach.
## 2 Literature Review
### Theory of Inventive Problem Solving  
The Theory of Inventive Problem Solving (TRIZ), developed by [PERSON] and his team, is a highly effective innovation methodology intended to help designers analyze and resolve conflicts and contradictions in design and to optimize design solutions. The core of TRIZ lies in identifying and eliminating technological contradictions and improving design outcomes using innovative approaches [44].  
TRIZ has been successfully combined with AHP, QFD, ergonomics, and other methods to achieve notable results. For example, [PERSON]'s team integrated the AHP/FBS/TRIZ methodological framework to overcome the innovation bottleneck in orchard management machine design [45]; [PERSON]'s group improved the human-computer interaction and operational safety of a micro-tillage machine by integrating TRIZ and ergonomics [46]; and [PERSON] et al. enhanced the efficiency of a rosebud picking machine using an integrated AHP/QFD/TRIZ design method [47].  
Although TRIZ offers strong advantages in resolving technical conflicts, it primarily focuses on technical aspects and tends to overlook emotional needs and user experience. This limitation may result in a disconnect between the design solution and user expectations. Moreover, the theoretical nature of TRIZ may hinder designers from effectively translating its principles into practical solutions.  
In this study, through the Dual-Path Optimization Model, AHP and TRIZ are effectively combined to align demand priorities with design elements, enabling the reconciliation of users' emotional needs with technical requirements. AHP is used to determine the prioritization of user needs, while TRIZ addresses technical contradictions with innovative solutions, ensuring that the final design is both user-centered and technically feasible.  
The integration of QFD further strengthens the role of TRIZ in resolving design conflicts. Within QFD, user requirements are translated into design attributes and structured through the \"House of Quality.\" TRIZ is then applied to identify and resolve potential conflicts among these attributes, thereby enhancing the functionality and coherence of the design.  
Additionally, TRIZ contributes to functional optimization through technological innovation, while Kansei Engineering ensures that the design emotionally resonates with users. The synergy between these approaches facilitates a balanced integration of function and emotion.  
Therefore, the role of TRIZ in the Dual-Path Optimization Model is essential. By working in conjunction with AHP, QFD, and Kansei Engineering, TRIZ not only resolves design contradictions but also supports the harmonization of functional and emotional needs, ultimately enhancing user experience and design innovation.
## 2 Numerical Methodology
### Turbulence Models
#### 2.3.1 Large Eddy Simulation (LES)  
In Large Eddy Simulation (LES), an essential aspect is the representation of sub-grid scale stresses. This study employed two different models, namely WALE and Smagorinsky models, to address this task. During LES simulations, turbulent eddy viscosity \(v_{t}\) is introduced as an additional viscosity to capture the characteristics of turbulence. The value of this eddy viscosity is determined by the chosen model [19].  
\[v_{t}=C_{x}^{2}\Delta x^{2}\overline{\overline{\omega}} \tag{6}\]  
Here, \(C_{x}\) represents a constant specific to the chosen LES model, \(\Delta x\) refers to the spacing between lattice points, and \(\overline{\overline{\omega}}\) corresponds to the LES model operator. In the lattice Boltzmann model, the viscosity is connected to the relaxation time \(\tau\). This relationship governs the rate at which the distribution function approaches equilibrium. By appropriately adjusting the relaxation time, the desired viscosity can be achieved, influencing the flow behaviour in the simulation.  
\[v_{total}=v_{o}+v_{t}=\frac{2\tau_{o}-1}{6}+\frac{\tau_{t}}{3} \tag{7}\]  
The relaxation time \(\tau\) can be decomposed into molecular and turbulent components, analogous to the separation of the viscosity term. This allows for distinguishing the contributions of molecular viscosity \(v_{o}\) and turbulent effects. The total relaxation time encompasses both these aspects, as expressed by the following equation:  
\[\tau_{total}=3v_{total}+0.5 \tag{8}\]  
By applying Equation (8), we arrive at:  
\[\tau_{total}=3(v_{o}+C_{x}^{2}\Delta x^{2}\overline{\overline{\omega}}+0.5) \tag{9}\]  
Derived the operator \(\overline{\overline{\omega}}\) exclusively from the shear stress tensor for Smagorinsky as follows:  
\[\overline{\overline{\omega}}=\sum_{i,j}S_{ij}S_{ij}\]  
The WALE model considers the shear stress tensor, including the rotation tensor. The operator \(\overline{\overline{\omega}}\) is defined as follows:  
\[\overline{\overline{\omega}}=\frac{\left(C_{ij}^{d}C_{ij}^{d}\right)^{\frac{ 3}{2}}}{\left(S_{ij}S_{ij}\right)^{\frac{5}{2}}+\left(C_{ij}^{d}C_{ij}^{d} \right)^{\frac{5}{2}}}\]  
The difference between WALE and Smagorinsky, LES model constant was given the value of \(C_{x}\), 0.325 and 0.12, respectively, in Equation (6).
## 6 Conclusions  
This paper develops a novel MRFCO-ZTS approach with the motivation of strengthening the security of inter-satellite communication and enhancing resource efficiency by optimizing a novel well-defined cost function of ZTS. The implemented MRFCO-ZTS approach efficiently authenticates the identity of satellites before they can connect with each other, thus achieving secure data exchange between them. Also, the MRFCO-ZTS approach minimizes the attack surface and avoids illegitimate access to the satellite network. By continuously monitoring every communication link, the MRFCO-ZTS model increases the network's resilience against both ground-based and space-based risks by classifying users as honest or malicious users. By performing continuous authentication within the satellite network, the MRFCO-ZTS approach minimizes the requirements of ground station contribution which effectively decreases latency. In addition, the MRFCO-ZTS approach improves the overall reliability and efficiency of satellite communication by minimizing the cost function of ZTS based on attack, risk, and computational delay. The simulation testing results proved that the MRFCO-ZTS model gives better performance with an improvement of security level, efficiency, and minimization of latency when compared to existing research works.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attack Type** & \begin{tabular}{c} **Detection Rate** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **False Negative** \\ **Rate (\%)** \\ \end{tabular} & \begin{tabular}{c} **Avg Detection** \\ **Delay (ms)** \\ \end{tabular} & **Remarks** \\ \hline Baseline & & & & High \\ Random & 97.4 & 2.6 & 1.3 & sensitivity \\ Attack & & & & maintained \\ \hline Mimicry & & & & Delay \\ Behavior & 89.5 & 10.5 & 2.4 & \
\begin{tabular}{c} Delay \\ increases; cost \\ function less \\ discriminative \\ \end{tabular} \\ Attack & & & & MRFCO \\ \hline Distributed & 91.8 & 8.2 & 2.1 &
\begin{tabular}{c} MRFCO \\ adaptation \\ helps isolate \\ spatial patterns \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 13: Simulation performance of robustness to adaptive attacks.  
**Author Contributions:** Conceptualization, H.X. and S.V.; methodology, H.X. and S.V.; writing--original draft preparation, H.X. and S.V.; writing--review and editing, H.X. and S.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Due to the involvement of our research data in another study, we will not provide details regarding where data supporting the reported results can be found.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# An Accurate Probabilistic Model for TVWS Identification
## 4 Proposed Probabilistic Model
### Adaptive Threshold  
To obtain the adaptive \(T\), two signals (i.e., the noise \(W(f)\) and the composite signal \(X(f)\)) are sensed in parallel in a specific point. From the saved data, the statistical components like mean values and standard deviations are obtained and the pdf are plotted, as shown in Figure 10a. If a \(T\) with a \(P_{fa}\) of 3% is considered, then \(z_{W}=1.88\), as shown Figure 10b.  
The selected \(T\) is common for both normal pdf, i.e., signal and noise. By using Equations (11), (12), (14), and (15), we obtain the value for \(z_{X}\), whereas \(P_{d}\) is calculated using Equation (16). The relation between \(P_{fa}\) and \(P_{d}\) is observed in Figure 10c, which is called the Receiver Operating Characteristics (ROC) curve [33]. Note that \(P_{fa}\) for this \(T\) is located in the lower part of the axis, so the area of interest for this study is reduced to the light-blue coloured area in the plot. By zooming the area of interest, the maximum values for \(P_{fa}\), re-named as \(P_{famax}\), and the maximum values for \(P_{d}\), re-named as \(P_{dmax}\), are observed in Figure 10d. Let us consider that \(P_{dmax}\) becomes the new highest percentage of signals that may be detected, so the value switchs from 56.36% to 99.99% in the Y-axis of the ROC plot.  
When we apply Equations (15) and (16), the proposed adaptive \(T\) gives 98.57% of detection probability whereas with \(M=5\) dB [25] it is 86.94%. Finally, for 7 dB [26] and 10 dB [34], the detection probability stands at 79.54% or less.
# Efficiency of Algorithms for Computing Influence and Information Spreading on Social Networks
## 3 Case Study Results
An important observation to make is that spreading stops at the target node, even in the CC model which otherwise allows breakthrough infections. By the path combination formula (Equation (3)), the combined probability of two paths such that one is a prefix of the other is simply the probability of the shorter path. It follows that any path which visits the target node more than once will not increase the final probability. This is visible in Figure 3 where the probability of spreading to node 3 in the CC model is noticeably smaller than to nodes 9 or 11, even though the neighbourhood of node 3 contains much more paths. This behaviour is logical when we recall that our model calculates the probability of influence spreading from one node to another; that is to say, we are not interested in the probability of being influenced more than once. Figure 4: Comparison of SC and CC models using out-centrality values calculated in the Dutch Student network (see Figure 6) with \(L_{\text{max}}\rightarrow\infty\), \(\lambda=1\), \(T\rightarrow\infty\), and \(w_{(u,v)}=0.5\) for all \((u,v)\in E\). The out-centrality values are normalised and expressed as a percentage of all nodes. \(C(s)^{(out)}\) is defined for all \(s\in V\) in Equation (5). Figure 3: Comparison of SC and CC models using spreading probabilities calculated in the Dutch Student network (see Figure 6 where node 1 is highlighted) with \(\lambda=1\), \(T\rightarrow\infty\), and \(w_{(u,v)}=0.5\) for all \((u,v)\in E\). The graph depicts, for each node \(t\), the probability of spreading from node 1 to node \(t\). Two different path lengths are used, \(L_{\text{max}}=5\) and \(L_{\text{max}}=10\). The CC model considers a set of paths that is a superset of the paths that the SC model considers. This is because all self-avoiding (SC) paths are also part of the CC model. For this reason, out-centrality values are always at least as large in the CC model (see Figure 4). Differences in out-centrality values reflect the network structure in the local neighbourhood of each node. For example, in Figure 4, node 10 has a larger CC out-centrality than node 24, while node 24 has a larger SC out-centrality. This is related to the higher edge-density in the neighbourhood of 10, which allows for relatively more CC paths than SC paths. Recall from Equation (6) that betweenness is defined as the ratio of change in cohesion to the original cohesion when a node is removed from the network. In other words, betweenness corresponds to the relative importance of a node in the network. Because of this, some nodes become larger betweenness values in the SC model than in the CC model, even though the underlying centrality values are always smaller in the SC model. For example, in Figure 5, node 23 is a clear example of this. From Figure 6, we see that node 23, while being an important mediator in the network, is not part of many cycles, which decreases its relative importance in the CC model.
# Two-Phase Deep Learning-Based EDoS Detection System
## 6 Result Analysis
### Quality of Service  
To prove the effectiveness of the proposed EDoS Detection system, we compare our model to other existing works with the same environmental setup. Firstly, to the best of our knowledge, based on a review of the relevant works [13; 15], the most recent and state-of-the-art research applying machine learning to tackle an EDoS attack and detect multiple types of EDoS attacks are used in our work. Hence, to prove the detection accuracy of our proposed model, we compare our approach with the two approaches (support vector machine (SVM) and neural network (NN)) in [13]. Our approach is proposed to eliminate the disadvantage of calculation and delay time of the RNN-based method. Thus, to prove the advantages of calculation time, the response of the system, and the resource usage, we compare our proposed approach with the proposed model in [15], which uses GRU, another variant form of RNN but with a longer sequential input (100) than the sequential input we use for the LSTM model. Four metrics, i.e., accuracy (AC), detection rate (R), F1-score (F1), and false alarm rate are calculated to evaluate the anomaly detection capacity of our proposed mechanism. The formula of the four metrics are expressed as follows:  
Figure 7: Experimental topology.  
Accuracy is the proportion of correct detections over the number of total flows:  
\[AC=\frac{TP+TN}{TP+TN+FP+FN} \tag{16}\]  
Detection Rate is the proportion of number of detected abnormal flows to the number of all abnormal flows:  
\[R=\frac{TP}{TP+FN} \tag{17}\]  
False Alarm Rate is the ratio of abnormal flows falsely classified as normal flows:  
\[FAR=\frac{FP}{FP+TN} \tag{18}\]  
F1-score is the weighted average of P and R:  
\[F1=\frac{2\times P\times R}{P+R} \tag{19}\]  
Here, \(P=\frac{TP}{TP+FP}\) is the ratio of correct abnormal flows between total detected abnormal flows.  
TP, FP, TN, and FN indicate True Positive, False Positive, True Negative, False Negative, respectively.
# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
## 3 Result and Discussion  
We analyze the suggested public road fragment QoS before comparing it to several versions of the global routing protocols (CAR [10], GSR [20]) and assisted routing protocols (EIGRP [21], SADV [12]). To further understand the benefits of the suggested model, we define the VANET-ACO as a modified VANET, in which the tracking route choices are studied for the terminal-per-terminal vehicles network rather than the ST-per-DT.
# Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework +
## 5 Applications
### UUV Simulation
#### 5.1.3 Simulation Results  
The success rate of UUV can be evaluated in various situations; however, in this case study, we evaluated this rate according to the various detection ranges of the warship. To demonstrate the effectiveness of the proposed multi-fidelity framework, we compared the developed multi-fidelity model with the existing UUV simulation model (i.e., the high-fidelity model) and the low-fidelity model. The low-fidelity model is a simulation model in which the maneuver model is replaced with the low-fidelity maneuver model. Figure 15 represents the success rate and execution time of the UUV simulation models with different fidelities. To achieve a standard error within 0.031 and a confidence level of 95%, the UUV success rate and the execution time were evaluated using 1,000 time-independent repeating simulations. As shown in Figure 15, the execution time of the multi-fidelity model is lower than that of the high-fidelity model while the success rate of the multi-fidelity model is almost the same as that of the high-fidelity model. On the other hand, the execution time of the low-fidelity model is much lower than that of the high-fidelity model and multi-fidelity model while its success rate is very different from the others. Based on the simulation speedup defined in Section 4.4, applying the proposed framework increases the speed about 1.25 times without a significant loss in the accuracy of the success rate and without modifying the simulation engine and the other existing models.  
Figure 14: The overall process of the multi-fidelity modeling framework: UUV tracking simulation as an example of dynamic systems.
## 3 Results and Discussion
### TPAD Results  
As noted above, the conventional TPAD technology had the least environmental impacts overall, and these were distributed fairly evenly among the climate change (39%), human heath (25%), and resources (31%) categories (Figure 3). The scores for each of the damage categories associated with the individual TPAD unit processes (dewatering, TPAD, and transportation) are displayed in Figure 4E. The TPAD unit process, which utilizes a thermophilic digester that is heated via the combustion of biogas and electric mixers and pumps, contributes the most to the climate change damage category, whereas transportation is the greatest contributor to the remaining categories. The environmental impacts associated with transportation are substantial for TPAD because the final dewatering step in the overall process produces a biosolids product containing 22% of solids, whereas the other Class A treatment processes generate a final product containing \(\geq\)60% of solids.  
TPAD's environmental impacts were analyzed further by examining the sources of the impacts, as shown in Figure 5E. The utilities/fuel category accounts for the majority of the environmental impacts. This includes the electricity used in mechanical dewatering and in TPAD for mixing and pumping, as well as the fuel used in transportation. The small environmental impact associated with materials is a result of the polymer used in mechanical dewatering.