esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Optimization of Cascade Blade Profile Based on Reinforcement Learning
## 3 Numerical Method
### Governing Equations, Turbulence, and Transition Model
[PERSON] and [PERSON] [43] investigated the experimental data of transition on a flat plate with pressure gradients and proposed the model to compute the location of transition from the flow solution by using empirical relations related to external parameters. The AGS model dictates that transition starts at a momentum thickness Reynolds number:  
\[Re_{\Theta s}=163+e^{F(\lambda_{\Theta})-\frac{F(\lambda_{\Theta})}{6.91}Tu}. \tag{8}\]  
The dimensionless pressure gradient is defined as:  
\[\lambda_{\Theta}=\frac{\Theta^{2}}{\
u}\frac{dU_{e}}{ds}, \tag{9}\]  
where \(U_{e}\) is the velocity at the edge of boundary layer, \(S\) is the streamwise distance from the leading edge, and \(\Theta\) denotes the momentum thickness in the laminar region. For the SA model, the free stream turbulence level \(Tu\) is computed based on the upstream eddy viscosity and assuming a turbulent length scale of about 10% of the reference length scale:\[Tu=86\sqrt{\frac{v_{t}}{UL_{ref}}}. \tag{10}\]  
The function \(F(\lambda_{\Theta})\) depends on the pressure gradient:  
\[F(\lambda_{\Theta})=\left\{ \begin{array}{l}6.91+12.75\lambda_{\Theta}+63.64 \lambda_{\Theta}^{2},\lambda_{\Theta}<0\\ 6.91+2.48\lambda_{\Theta}-12.27\lambda_{\Theta}^{2},\lambda_{\Theta}\geq 0\end{array}  \right.. \tag{11}\]  
Therefore, according to these relations, transition is promoted in the adverse pressure gradient, whereas it is retarded in the favorable pressure gradient. The AGS model proves sufficiently accurate [44; 45; 46] and is used in predictions of transition in turbomachinery [47; 48; 49; 50].
## 4 Potential IIoT Applications
### Transportation Applications  
NTNs can ensure the safety and effective management of the transportation infrastructure and realize the Internet of Vehicles (IoV) by expanding the use of networking vehicles, sensors, and controls [81]. Specifically, the smarter use of road and rail transportation can be enabled and important applications can be supported, e.g., signaling and routing and alerts for road and weather conditions. The global positioning system (GPS) though satellites can determine the positioning of each vehicle, whereas HAPs represent a feasible solution for data acquisition in IoV, especially in rural areas. Besides the above, the monitoring of vehicular traffic via autonomous UAVs employing cameras or sensors is also viable. By taking advantage of NTNs, the operation of commercial autonomous shipping can be enabled along with future commercial marine vessels, smart ports, and cargo logistics [82].
## 2 Materials and Methods
### Experimental Design  
#### 2.7.1 Data Sources  
The raw OHLC price data utilized in this study were obtained through the _Yahoo! Finance_ API via the Python package _vfinance_. These data comprise historical prices for the financial instruments under investigation. Additionally, technical indicators were derived using the _TALIB_ library, which provides an extensive collection of technical analysis tools in Python. All datasets used in this study are publicly available at [42], ensuring the full replicability of the experimental results.  
The analysis focuses on six financial instruments with varying return profiles, complemented by an additional instrument representing a risk-free asset with a constant return of 1, serving as a stable, inflation-adjusted instrument. The six financial instruments examined are as follows:  
1. *GSPC (S&P 500): This index tracks the performance of 500 large-cap companies listed on U.S. stock exchanges. It is widely regarded as a benchmark for the overall health of the U.S. economy and is dominated by blue-chip companies across various sectors, including technology, healthcare, and financial services.
2. *GDAXI (DAX 30): The DAX 30 is composed of 30 large-cap companies listed on the Frankfurt Stock Exchange. It is a key indicator of the German economy and represents major industries, such as automotive, industrial goods, and chemicals, making it a key player in European markets.
3. *IXIC (NASDAQ Composite): The NASDAQ Composite tracks a broad set of stocks listed on the NASDAQ, with a significant focus on technology and growth-orientedcompanies. It includes large-cap tech giants as well as smaller, innovative companies, making it a leading index in global technology markets.
4. \({}^{\lambda}\)RUT (Russell, 2000): This index represents small-cap companies primarily from the U.S. It is widely used as a benchmark for the performance of smaller, growth-oriented businesses and often reflects the health of the domestic economy in comparison to larger multinational firms.
5. \({}^{\lambda}\)N225 (Nikkei, 225): The Nikkei 225 represents the Japanese stock market and tracks 225 large-cap companies listed on the Tokyo Stock Exchange. The index is a key measure of the economic performance of Japan, with significant representation from manufacturing, technology, and financial sectors.
6. \({}^{\lambda}\)FTSE (FTSE 100): The FTSE 100 index tracks the top 100 companies listed on the London Stock Exchange by market capitalization. It is heavily weighted toward multinational companies in sectors such as energy, financial services, and consumer goods, making it a key indicator of both U.K. and global economic conditions.  
These instruments provide diverse representations of global markets, offering a broad perspective for evaluating portfolio performance across varying economic environments.
# Instrumental Uncertainty of Conductance Transducers for
## 3 Metrological Characterization Methods
### Thermal Influence  
The characterization of the thermal influence on conductance transducers is considered a relevant condition to be studied, namely in the temperature interval ranging from 10 \({}^{\circ}\)C up to 40 \({}^{\circ}\)C, since maritime reduced-scale models are usually tested in large experimental indoor facilities where the air temperature can easily reach these extreme values between winter and summer seasons.  
The proposed characterization method is supported in the use of a climatic chamber, where a conductance sensor can be installed, having half-immersion position at the water level. Due to the evaporation phenomenon, the water level can be significantly reduced, namely, if long-term thermal testing is made. In order to maintain a constant water level in the testing box of the conductance transducer, a water reservoir prototype was designed and produced, being composed by two separated compartments, being the water circulation between the compartments assured by a system of a propeller connected to an electrical engine, as seen in Figure 5.  
The established hydraulic circuit originates different, but nearly stationary, water levels in each compartment. One of them is used for the conductance transducer immersion, while the remaining one contains the propeller and the electrical engine.  
In this study, due to the volume restriction inside the climatic chamber (Aralab, model Fitolima 300, with known thermal stability and uniformity), this assembly was applied to the thermal testing of a conductance transducer (id. 57.12) with a measurement interval of \(\pm\)140 mm, connected to the studied off-the-shelf power source, regulated for a nominal input voltage of 15 V (DC). Water and air temperature measurements were obtained from two resistance thermometers connected to a Wheatstone bridge (ASL, model F250) and installed, respectively, in the water compartment of the conductance transducer and in the climatic chamber, as shown in Figure 6.  
Figure 5: Water reservoir for conductance transducer thermal testing: (**a**) schematic representation; (**b**) developed prototype.  
The thermal cycle setup in the climatic chamber was composed by the following air temperature sequence: 10 \({}^{\circ}\)C; 25 \({}^{\circ}\)C; 40 \({}^{\circ}\)C; 25 \({}^{\circ}\)C; 10 \({}^{\circ}\)C; 25 \({}^{\circ}\)C; 40 \({}^{\circ}\)C. Each of the mentioned testing steps had a duration of 20 h with a transition ramp of four hours between steps; therefore, the total duration of the thermal testing corresponded to 172 h. For these temperatures, input and output voltages were measured at conductance transducer terminals (using a digital multimeter HP, model 3457A) having a sampling rate for all the measured quantities of two minutes between consecutive measurements.
# Removal of Cu(II) from Aqueous Solutions Using Amine-Doped Polyacrylonitrile Fibers
## 3 Results and Discussion
### Adsorption Kinetics  
The kinetics of the Cu(II) adsorption on the surface-modified PAN fibers were analyzed using pseudo-first- and pseudo-second-order kinetic models. Figure 4 depicts the plot of the experimental data for the initial Cu(II) solution concentration of 100 mg/L. The calculated kinetic parameters are summarized in Table 1. The experimental data fitted the pseudo-second-order kinetic model better than the pseudo-first-order one. These results indicated that the adsorption rate of Cu(II) on the surface-modified PAN fibers depended on the adsorbate concentration and the number of active sites of the adsorbent [24]. Previous studies have also revealed that the adsorption of Cu(II) on amine-modified PAN fibers followed the pseudo-second-order kinetic model [2; 8; 25].  
Figure 4: Pseudo-first-order and pseudo-second-order kinetic models (1 st and 2 nd, respectively) for adsorption of Cu(II) ions onto surface-modified polyacrylamide fibers. Here, q and t denote the adsorption capacity and time, respectively.  
Figure 3: Fourier-transform infrared spectroscopy (FTIR) spectra of (**a**) unmodified PAN fiber and PAN fiber with different concentration of DETA: (**b**) 40% DETA, (**c**) 55% DETA, (**d**) 70% DETA, and (**e**) 85% DETA.
## 1 Introduction  
The need for new 3D orthotropic damage models is primarily due to their use in simulating 3D connections in composite structures [1; 2; 3]. To predict the behavior of exterior profiles in hybrid beams and novel carbon-reinforced fiber aluminum laminates, a number of 2D orthotropic damage models have occasionally been used in conjunction with shell elements [4; 5]. Other authors, however, discovered limitations in the accuracy of numerically simulating 3D GFRP connections using the classical 2D formulation with continuum shell elements [6], related to either pilling, shear-out, or bearing due to the incorrect stress distribution in the thickness in shell formulation for non-linear analysis.  
Since the start of the 2000s [7], a more comprehensive 3D orthotropic damage formulation has been sought after due to the engineering industry's rapid adoption of composite structures. The need for structural design has also grown among structural engineers in the field of composites [8]. In all circumstances, the problem must be described in 3D, and the accuracy of the computed stress level relies on whether the problem is being examined at the micro, meso, or macro scale [9].  
For the structural designer, applying a complete 3D orthotropic damage model still poses significant issues such as (i) full 3D formulation with explicit material parameters and (ii) simple and clear guidelines for direct application. The scientific community mostly uses these damage orthotropic damage models to confirm experimental campaigns and adopted material properties, therefore these two factors are crucial for the structural designer [10].  
Unfortunately, academic researchers are not using composite orthotropic damage models without a current experimental campaign to arrive at better design solutions, despite the fact that this is a fundamental requirement for engineering in the composites industry given the absence of structural codes for these materials.  
There are two main goals for this project. The first is to analyze a novel 3D orthotropic damage model's numerical effectiveness using the Hashin failure criterion, using classical MTL damage progression [11] with energy regularization [12]. Experimental data are compared and used to validate this orthotropic damage model. The second study looks at the new ITE-UMAT algorithm, which speeds up the convergence of the UMAT subroutine when the secant matrix rather than the traditional tangent matrix is employed in the predictor-corrector. The algorithm is compared with fully implicit and explicit numerical results.  
According to the authors' best knowledge, the use of a 3D orthotropic damage model with residual stresses using viscous regularization and the secant matrix for the predictor-corrector algorithm has not been fully studied and validated. This work plans to fill this gap in knowledge.
## 5 Decentralized Decision-Making
### Game Theory and Decentralized Decision-Making  
Game theory is a mathematical framework which was developed in order to comprehend interactive decision-making operations among rational agents, and this can be especially applied in decentralized decision-making in distributed systems. What makes game theory relevant to decentralized decision-making is its ability to model and predict node behaviors, in a system where no single authority dictates the participants' actions. As highlighted previously, nodes in a decentralized network must make decisions that keep a balance between personal incentives and the collective good of that network. This is where game theory comes in, helping in comprehending these dynamics by examining various scenarios, or \"games\", that showcase how rational nodes are likely to behave under different kinds of scenarios. Some prominent examples of these scenarios are the Nash equilibrium and the prisoner's dilemma [58].  
In blockchain technologies, the use of game theory is fundamental when designing consensus algorithms that strengthen the network's security and promote the participants' cooperation. A prime example of how game theory contributes to blockchain is Bitcoin's proof-of-work system. Here, game theory explains why it is much more beneficial for rational miners to follow protocol rules. This is because not following the typical consensus rules of a blockchain system is much less profitable for them than honest contribution (through the process of mining). Additionally, game theory demonstrates how trust can be achieved through mechanisms and incentives that promote cooperation between the participants as the most logical path. Thus, game theory highlights the concept of trustlessness in decentralized distributed systems [59].
## 5 Results and Discussion
### Data Distribution and Model Grounding  
The fidelity of the simulation is critical for the validity of the evaluation. Figure 3 illustrates the distributions of the generated multimodal data (TCT, HRV, and TLX) for each proficiency state. The distinct overlapping distributions for Novice, Intermediate, and Expert closely mirror the empirical benchmarks established in the surgical literature ([PERSON] et al., 2025) [24]. For instance, TCT shows a clear rightward shift and increased variance for novices, while HRV shifts rightward (indicating lower stress) with increased expertise. This demonstrates that the synthetic data successfully capture the nuanced statistical signatures of real-world proficiency, providing a valid foundation for benchmarking the models.  
Figure 3: Distributions of the generated multimodal data for each proficiency state.  
To further elaborate on the characteristics of the multimodal data, Figure 4 provides a detailed view of the density distribution of each physiological (HRV), behavioral (TCT), and subjective (NASA-TLX) metric within each proficiency state (Novice, Intermediate, Expert). These violin plots, unlike simple box plots, illustrate the full shape of the data distribution, including peaks and spread. This allows for a more nuanced understanding of how these multimodal inputs differentiate (or exhibit overlap between) proficiency levels, thereby confirming the statistical properties and distinctiveness of the generated data for each state.
## 1 Introduction  
### Background and Motivation  
In this current era, Wi-Fi and various peer-to-peer (P2P) networks are integral to our daily lives. Safeguarding such networks and their sensitive data from various cyberattacks is paramount. Sybil attacks have emerged as a prominent concern ([PERSON] et al. [1]), targeting and exploiting the vulnerabilities of distributed and P2P networks, thus infiltrating and compromising networks. Thus, these attacks pose serious risks to applications ranging from military operations to social networks and everyday activities like connected commuting [2, 3, 4, 5]. Given increasing sophistication and prevalence of Sybil attacks, they pose a significant threat to the integrity and security of many distributed systems, like online voting systems, social media platforms, and even blockchain networks, among others. In all these cases, the attacker creates and controls multiple fake nodes or identities within a network, with aims of compromising user privacy, manipulating voting outcomes, and undermining the consensus mechanisms of the system. The consequences for a network that becomes a victim of a Sybil attack is more than just immediate compromise: empowering the attackers to manipulate the network through fake identities, thus identifying newer vulnerabilities [1]. This study's comprehensive literature review focused on such growing challenges posed by Sybil attacks and prevention strategies, with the aim of contributing a novel machine learning technique to detect Sybil attacks [6].  
So, the primary motivations for continued research in Sybil node detection and attack prevention are preserving the integrity, reliability, and trustworthiness of distributed systems. Sybil attack resistance is a challenging goal that often requires significant compromises in terms of complexity, performance, and cost [7]. The development of effective detection techniques for the constantly changing configurations and dynamics in social networks is an important but not an easy task. The aim is to protect the core functionalities of the system and ensure that all are legitimate nodes but still preserving the users' anonymity. Otherwise, Sybil attacks may have severe social and economic consequences. Sybil attackers can influence a voting process to favor their interests, leading to an unfair distribution of rewards or gaining control over a network. They can also track user behavior, spread misinformation, and influence public opinion [8; 9]. Effective detection and prevention techniques can help safeguard user privacy and protect against identity theft. Novel detection algorithms or methods of detecting Sybil nodes are based on network topology, patterns of behavior, and some other factors depending on specific characteristics or cases. There are also techniques to prevent the creation and propagation of Sybil nodes, such as reputation systems, cryptographic techniques, and game-theoretic approaches [10].
# The Effect of Different Socket Types on Implant Therapy While Using Flapless Ridge Preservation
## 4 Discussion
### Factors Associated with Socket Class  
Aetiologies of extraction can be divided into periodontal and non-periodontal diseases. The total treatment time was found to be longer in extraction sockets associated with periodontitis in this study. It may be because periodontitis results in hard and soft tissue destruction, such as seen in Class III and Class IV sockets. Additionally, the post-extraction healing in periodontitis affected sockets is slow, complicated, and unpredictable [39]. Bone density is usually less in the maxilla than in the mandible; therefore, it takes longer for high-quality bone regeneration in the maxilla. However, ridge preservation or augmentation still has positive effects in periodontally compromised sockets, as compared to natural healing [26; 34]. Class II, III, and IV sockets had at least one bone wall with resorption; however, soft tissue walls could provide space for bone grafting in flapless ridge preservation [40]. Additionally, there was no significant difference in bone density distribution and implant insertion torque among the four-socket classes. Total treatment time is longer for maxillary implants than for mandibular implants. In addition, our study showed that a longer treatment time is associated with periodontal destruction of the socket. Increased bone destruction and, especially, greater soft-tissue recession in these sockets would decrease space maintenance and the potential for bone regeneration. However, there was no significant difference in the total treatment time among the different socket types in the mandible in the present study.
## 6 Application of Algorithm to Various Scenarios for Fault Identification: Case Studies
### Variation of Fault Incidence Angle  
The nature of fault transients also depends on the fault incidence angle which might affect the performance of the protection scheme. Hence, the performance of the algorithm was tested with various incidence angles of faulty events which include \(0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), \(90^{\circ}\), \(120^{\circ}\) and \(150^{\circ}\) for all types of investigated faults. The fault index magnitude associated with all the phases during the incidence of an AG fault at node 671 is provided in Table 5. It can be observed that the fault index has a higher value compared to the threshold (\(2\times 10^{8}\)) corresponding to phase-A and a low value corresponding to phases-B & C for all fault incidence angles. Hence, the AG fault was effectively detected with different fault incidence angles. The algorithm also works efficiently for all types of faults. Hence, it is established that the proposed algorithm is effective for the identification of different types of faults with different fault incidence angles.
## 5 Results and Discussion
### Classification Performance
Finally, this work showed that fruit-based quality detection works are highly diverse. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Albinism**} & \multicolumn{3}{c}{**Fungus-Rust**} & \multicolumn{3}{c}{**Over-Ripeness**} & \multicolumn{3}{c}{**Peduncle**} \\ \cline{2-13}  & **TPR** & **FPR** & **ACC** & **TPR** & **FPR** & **ACC** & **TPR** & **FPR** & **ACC** & **TPR** & **FPR** & **ACC** \\ \hline DS + ANN & 0.03 & 0.25 & 0.90 & 0.06 & 0.75 & 0.87 & 0.26 & 0.44 & 0.61 & 0.10 & 0.75 & 0.86 \\ \hline SM + ANN & 0.01 & 0.81 & 0.97 & 0.12 & 0.81 & 0.86 & 0.26 & 0.56 & 0.67 & 0.06 & 0.75 & 0.88 \\ \hline Faster R-CNN & \(\approx\)0 & \(\approx\)0 & 0.98 & 0.02 & 0.10 & 0.92 & 0.05 & 0.05 & 0.75 & 0.00 & 0.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: True positive rate or recall (TPR), false positive rate (FPS), and accuracy (ACC) classification performance metric for the three methodologies used, such as a descriptive statistical coupled with an ANN (DS+ANN), statistical modeling coupled with an ANN (SM+ANN), and Faster R-CNN.