esa-sceva/satcom-chunk-collection huggingface dataset:
# Regional Seismicity of the Northeastern Tibetan Plateau Revealed by Crustal Magnetic Anomalies
## 4 Methodology
### Distribution of Crustal Magnetic Anomalies  
The location of a magnetic anomaly can change due to the effects of inclined magnetization. This leads to challenges in determining the location, morphology, and distribution of a magnetic geological unit and, thus, can affect the interpretation of magnetic anomalies. Therefore, it is necessary to conduct reduction-to-the-pole (RTP) calculations for magnetic anomalies to convert inclined to perpendicular magnetization. The technique of differential RTP with a variable inclination angle was used to conduct RTP calculations for the region considered in this study [33]. Figure 5 shows the RTP magnetic anomaly diagrams of the total intensity \(\Delta F\) on the ground surface. A comparison between the magnetic anomaly map (Figure 4a) and RTP magnetic map (Figure 5) shows a shift in Figure 5 to the north relative to Figure 4a. The shapes of the magnetic anomalies are also slightly altered because the effects of the inclination and declination of the magnetic field were removed. In the following, we focus on the features of the magnetic anomalies after RTP.  
As shown in Figure 5, the magnetic anomalies with different strikes and intensities in the NETP and adjacent areas reflect diverse geological-structural features. The NETP is dominated by orogenic belts undergoing intense tectonic activity, which correspond to weak/negative magnetic anomalies, while the areas adjacent to the NETP are ancient mass with stable structures that have strong magnetic anomalies. The NETP is mainly characterized by NW-SE-trending magnetic anomalies and faults, corresponding to the  
Figure 5: Crustal magnetic anomalies with the total intensity \(\Delta F\) after reduction to the pole. For black lines, please see Figure 1. A comparison between the magnetic anomaly map (Figure 4a) and RTP magnetic map (Figure 5) shows a shift to the north.  
Tethyan-Himalayan tectonic domain. The Ordos Basin, which forms the eastern NETP, is dominated by NE-SW-trending magnetic anomalies, corresponding to the Pacific tectonic domain.
# LCA as a Tool for the Environmental Management of Car Tire Manufacturing
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Substance**} & \multicolumn{2}{c}{**Production**} & \multicolumn{2}{c}{**USE**} & \multicolumn{2}{c}{**End of Life**} \\ \cline{2-7}  & **Traditional Tire** & **Eco Tire** & **Traditional Tire** & **Eco Tire** & **Traditional Tire** & **Eco Tire** \\ \hline Sulphur oxide & \(1.33\times 10^{-2}\) & - & \(1.58\times 10^{1}\) & \(1.50\times 10^{1}\) & \(-4.07\times 10^{-3}\) & \(-3.60\times 10^{-3}\) \\ \hline Nitrogen dioxide & \(9.12\times 10^{-3}\) & \(1.41\times 10^{-2}\) & \(1.00\times 10^{1}\) & \(9.49\times 10^{0}\) & - & - \\ \hline Suspended particulate matter (SPM) & - & - & \(2.49\times 10^{0}\) & \(2.35\times 10^{0}\) & - & - \\ \hline Sulphur dioxide & \(2.00\times 10^{0}\) & \(1.58\times 10^{0}\) & \(5.86\times 10^{-3}\) & \(5.83\times 10^{-3}\) & - & - \\ \hline Fine particles, \(<2.5\) um & \(1.46\times 10^{0}\) & \(1.14\times 10^{0}\) & \(4.34\times 10^{-3}\) & \(4.34\times 10^{-3}\) & - & - \\ \hline Nitric oxide & \(1.31\times 10^{0}\) & \(1.01\times 10^{0}\) & \(1.16\times 10^{-2}\) & \(1.16\times 10^{-2}\) & \(-1.68\times 10^{-2}\) & \(-3.06\times 10^{-2}\) \\ \hline Particulate matter, \(>2.5\) um, and \(<10\) um & \(1.67\times 10^{-1}\) & \(1.31\times 10^{-1}\) & \(3.99\times 10^{-3}\) & \(3.99\times 10^{-3}\) & - & - \\ \hline Ammonia & \(2.56\times 10^{-3}\) & \(2.07\times 10^{-3}\) & \(1.49\times 10^{-6}\) & \(1.49\times 10^{-6}\) & \(5.99\times 10^{-6}\) & \(1.04\times 10^{-5}\) \\ \hline \end{tabular}
\end{table} 
Table 5: Results of grouping and weighting of environmental damage from respiratory inorganics at individual stages of the traditional and ecological tire life cycle [unit. Pt]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Substance**} & \multicolumn{2}{c}{**Production**} & \multicolumn{2}{c}{**USE**} & \multicolumn{2}{c}{**End of Life**} \\ \cline{2-7}  & **Traditional Tire** & **Eco Tire** & **Traditional Tire** & **Eco Tire** & **Traditional Tire** & **Eco Tire** \\ \hline Nitrogen dioxide & \(1.76\times 10^{-3}\) & \(2.72\times 10^{-3}\) & \(1.93\times 10^{0}\) & \(1.83\times 10^{0}\) & - & - \\ \hline Sulphur oxide & \(7.61\times 10^{-4}\) & - & \(9.05\times 10^{-1}\) & \(8.57\times 10^{-1}\) & \(-2.32\times 10^{-4}\) & \(-2.06\times 10^{-4}\) \\ \hline Nitric oxide & \(2.52\times 10^{-1}\) & \(1.94\times 10^{-1}\) & \(2.26\times 10^{-3}\) & \(2.23\times 10^{-3}\) & \(-3.24\times 10^{-3}\) & \(-5.90\times 10^{-3}\) \\ \hline Sulphur dioxide & \(1.14\times 10^{-1}\) & \(9.00\times 10^{-2}\) & \(3.43\times 10^{-4}\) & \(3.33\times 10^{-4}\) & - & - \\ \hline Ammonia & \(1.41\times 10^{-3}\) & \(1.14\times 10^{-3}\) & \(8.23\times 10^{-7}\) & \(8.19\times 10^{-7}\) & \(3.29\times 10^{-6}\) & \(5.71\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of grouping and weighting of environmental damage from acidification and eutrophication at individual stages of the traditional and ecological tire life cycle [unit: Pt].
## 1 Introduction
Bulk rheology can be determined if the diameter of the microsphere exceeds the mesh size of the gel structure [22]. Particle tracking can also give information about the pore size of a gel network by using microspheres of different diameters. In the mucin network, microspheres of 500 nm diameter were entrapped, whereas smaller particles diffused freely through porcine respiratory mucus and human airway mucus [16; 17]. Coating of the microspheres with polyethylenglycol was found to be advantageous to minimize mucoadhesion [14]. [PERSON] et al. reviewed several studies of PTM of mucus, revealing mucus as a viscoelastic gel with low-viscosity fluid, between the entangled mucins [22]. The rheology of human laryngeal mucus has not been investigated yet, to the best of our knowledge. A detailed analysis will enable the design of an artificial mucus with natural viscoelastic properties. This will shed light on investigations regarding the impact of mucus on the oscillatory behavior of the vocal folds, phonation and the treatment of voice disorders. Our hypotheses for this study are:  
1. Human laryngeal mucus from the vocal folds is viscoelastic and reveals gel characteristics, as mucus of another origin. 2. Variations of the viscoelasticity of the investigated mucus samples can be correlated with the demographic data. 3. Bulk rheology of human laryngeal mucus can be assessed by PTM with microspheres of appropriate diameter. The measurement results gained by PTM are comparable with OSR, the second applied measurement technique.
## 3 Computational Model
### Realizable \(\mathbf{k}-\varepsilon\) Model  
In the present study, the realizable \(\mathbf{k}-\varepsilon\) model in the mixture form is utilized for computing the turbulence fields. The choice of turbulence model is very critical in any CFD simulation. The realizable \(\mathbf{k}-\varepsilon\) model is can provide outstanding performance in many complicated flows. Additionally, this turbulent modeling is characterized over the other turbulence models as it has a modified equation for calculating turbulent viscosity and a new transport equation for the dissipation rate \(\varepsilon\)[28; 29].  
\[\frac{\partial}{\partial x_{i}}(\rho\rm ku_{i})=\frac{\partial}{\partial x_{i }}\left[\left(\mu+\frac{\mu_{t}}{\sigma_{k}}\right)\frac{\partial k}{\partial x _{i}}\right]+G_{b}+G_{k}-\rho\varepsilon-Y_{M}+S_{k} \tag{6}\]  
\[\frac{\partial}{\partial x_{i}}(\rho\varepsilon\rm u_{i})=\frac{\partial}{ \partial x_{i}}\left[\left(\mu+\frac{\mu_{t}}{\sigma_{\varepsilon}}\right) \frac{\partial\varepsilon}{\partial x_{i}}\right]+\rho C_{1}S\varepsilon- \rho C_{2}\frac{\varepsilon^{2}}{k+\sqrt{\upsilon\varepsilon}}+C_{1\varepsilon }\frac{\varepsilon}{k}C_{3\varepsilon}G_{b}+S_{\varepsilon} \tag{7}\]  
In the mixture turbulence model, the same turbulence field is given for all phases. This means that all phase properties are replaced with mixture properties in the equations. \(\rm G_{k}\) is the turbulent kinetic energy generation, due to the mean velocity gradients, while \(\rm G_{b}\) is the turbulence kinetic energy generation due to buoyancy. \(S_{k}\) and \(S_{\varepsilon}\) are user-defined source terms. The constants in these equations are [28; 29].  
\[C_{1}=\max\left[0.43,\ \frac{\eta}{\eta+5}\right],\ \eta=S_{k},\ S\sqrt{2S_{i} \rm S_{ij}} \tag{8}\]  
In turbulence modeling, \(\mu_{t}=\rho\ C_{\mu}\frac{k^{2}}{\varepsilon}\), \(\mu_{\rm eff}=\mu+\mu_{t}\).  
The favorable features of the considered turbulence model, as discussed earlier, is \(C_{\mu}\), which does not have a fixed value and can be correlated as [28; 29; 30].  
\[C_{\mu}=\frac{1}{A_{0}+A_{s}\frac{kU^{\prime}}{\varepsilon}},\ U^{*}=\sqrt{S_{ ij}S_{ij}+\hat{\Omega}_{ij}\ \hat{\Omega}_{ij}},\ \ \hat{\Omega}_{ij}=\ \overline{\Omega}_{ij}-\epsilon_{ijk}\omega_{k} \tag{9}\]  
Here, \(\overline{\Omega}_{ij}\) is the rotation tensor rate with angular velocity \(\omega_{k}\) and \(A_{0}=4.04\), \(A_{s}=\sqrt{6}\cos\varphi\), \(\varphi=\frac{1}{3}\cos^{-1}\left(\sqrt{6}W\right)\), \(W=\frac{S_{ij}S_{ij}S_{ii}}{S^{3}}\), \(\overline{S}=\sqrt{S_{ij}S_{ij}}\), \(S_{ij}=\frac{1}{2}\left(\frac{\partial U_{i}}{\partial x_{j}}+\frac{\partial U _{i}}{\partial x_{i}}\right)\). In the current study, \(C_{1\varepsilon}=1.44\), \(C_{2}=1.9\), \(\sigma_{k}=1.0\), and \(\sigma_{\varepsilon}=1.2\).
## 2 The Evolution from Cloud Computing to MEC
### Cloud Computing
#### 2.1.1 Deployment Models  
**Public cloud:** Public clouds provide services such as server access and storage capacity through the Internet. Operated by third-party companies, these clouds oversee and control all aspects of hardware, software, and substructure [20]. Users can avail themselves of these services through easily accessible accounts, and payment is typically based on the usage of the public cloud assets.  
**Private cloud:** A private cloud is a cloud computing infrastructure dedicated solely to a single organization. It may be administered either by the organization itself or by a third-party service provider. In this setup, the resources within the cloud are restricted to the customers affiliated with the owning organization, thereby heightening the security and privacy of the stored information [21].  
**Hybrid cloud:** Hybrid cloud is a combination of both the above-mentioned types of clouds. Hybrid cloud deployment is implemented when a corporation simultaneously employs both public and private clouds, deploying distinct segments of interconnected infrastructure, platforms, and applications across both domains. This strategy allows the establishment to access the security and management attributes of private clouds where required, such as storing and processing sensitive data. Concurrently, it capitalizes on the advantages of a public cloud environment for activities such as inter-organizational data sharing and managing capacity overflows [22].
## 3 Materials and Methods
### Methodology  
Since the resolution of GPM datasets are \(0.1^{\circ}\times 0.1^{\circ}\), and the resolution of TRMM and the CGDPA datasets are \(0.25^{\circ}\times 0.25^{\circ}\) resolution, for convenience of analysis, we used the bilinear interpolation method to resample the GPM dataset to \(0.25^{\circ}\times 0.25^{\circ}\) with the same spatial resolution [24], as shown in Figure 2.  
To evaluate and compare the remote sensing satellite dataset against the ground reference dataset, the following indicators were computed: the coefficient of variation (CV), correlation coefficient (R), relative bias (RB) and root mean square error (RMSE). The calculation formulas are shown in Table 2. The CV is used to measure the change of daily precipitation with respect to its daily mean. R evaluates the degree of linear correlation between two datasets. RB describes the systematic deviation of satellite dataset: positive values indicate that the rainfall is overestimated, while negative values indicate that the rainfall is underestimated. RMSE measures the average absolute error of the GPM/TRMM. The smaller the RMSE value, the closer the precipitation measured by the satellite is to the measured value [24].
## 3 Blockchain Technologies for Maritime Applications/Use Cases
### Blockchain for Maritime Supply Chain
#### 3.3.2 Walmart's Blockchain  
Walmart also integrated blockchain technology into its supply chain, achieving significant success. In collaboration with IBM in 2017, Walmart developed a blockchain-based supply chain using Hyperledger to combat food contamination issues. For example, in the case of pork in China and mangoes in the Americas, Walmart's blockchain system reduced the time required to trace the origin of mangoes from seven days to just 2.2 s [66]. Furthermore, the transparency offered by the blockchain-enabled supply chain system enhanced consumer trust in the products.  
Similar to the TradeLens project, Walmart implemented blockchain technology in its supply chains with IBM as a partner, but the outcomes were markedly different. Walmart adopted blockchain to address the specific challenge of food contamination, a problem that was difficult to resolve through traditional systems but for which blockchain provided an effective solution. Walmart established clear goals and demonstrated the utility of blockchain in supply chain management through rigorous testing. Additionally, Walmart encouraged its suppliers to adopt the blockchain system, and because the system was operated under Walmart's leadership, the adoption process was straightforward. Walmart's centralized management of the blockchain ensured consistent application of rules and procedures, leading to efficient operations, cost savings, and enhanced trustworthiness of the supply chain [67]. This approach enabled Walmart to realize direct value from the adoption of blockchain technology.  
In contrast, TradeLens was developed to streamline data management in maritime trade. However, we faced significant technical, legal, and operational challenges to force participation from a diverse group of stakeholders (shipping companies, ports, customs, shippers, etc.) into a single platform. The goals of TradeLens were not clearly defined, and it failed to engage all relevant stakeholders. Although TradeLens was a project led by two major companies, Maersk and IBM, other significant shipping lines and ports either did not adopt the system or chose not to collaborate due to competitive concerns [68]. As a result, the platform's network effects were limited, reducing the system's overall efficiency. TradeLens' reliance on industry-wide cooperation, without enforcing such cooperation or establishing clear rules and procedures, led to conflicts among stakeholders. Ultimately, despite its ambition to develop a widely used platform for maritime trade, the project lacked a clear revenue model, which hindered stakeholder participation and led to the termination of the platform.
## 4 Experiment Setup and Results
### Traceability
We calculated the computation cost of the proposed BESA scheme with state-of-the-art schemes to evaluate the scheme overrun of a real-time processor. Computational cost is the execution time per time step during simulation. To estimate this time, we executed the scheme in a simulation, and measured the execution time and determined the average execution time per time step on a real-time target. It is clearly shown in Figure 9 that the computation cost of message verification of the proposed solution is lower than that of the  
Figure 8: Detection rate of malicious miners. Figure 7: Probability of corrected data blocks. existing schemes of CPPA [8], ATAAP [22], and EAAP [9]. Figure 10 shows the comparison of BESA communication cost with that of existing schemes. A graphic representation of the comparison results in Figure 10 is provided. In comparison to the three other approaches--CPPA [8], ATAAP [22], and EAAP [9]--the BESA solution has a lower communication cost. Figure 11 depicts the reputation of a malicious miner candidate as seen through the eyes of a well-behaved vehicle in three scenarios: the traditional Hyperledger Fabric scheme, the traditional subjective logic scheme, and our BESA scheme. In the standard Hyperledger Fabric scheme, because there is no reputation element, vehicles are unable to identify the malicious vehicles, and thus the vehicle's evaluation of malicious candidates increases. The traditional subjective logic scheme and our BESA scheme are both based on the reputation values of vehicles; thus, we note that opinions from other well-behaved vehicles decrease the reputation of malicious vehicles in both schemes. Reputation values are below the reputation value of 0.50. It is also clear that the MWSL is more efficient  
Figure 10: Comparison of BESA communication cost with that of existing schemes. Figure 9: Comparison of BESA computation cost of message verification with that of existing schemes. because it is based on different weights. As result, our BESA scheme has a more precise reputation calculation than the TSL, which leads to a more secure leader selection process.
# Decision Support Application for Energy Consumption Forecasting
## 2 Material and Methods
### Forecasting Methods
#### 2.1.1 Ann  
ANN are widely used to solve many forecasting problems and are the most common methods for energy consumption forecasting. The main reason for their success is that the provided solution to model complex nonlinear relationships by these models is much better than the solutions of the other traditional linear models [26]. Several studies have been published using this method to predict the energy consumption for different terms and different situations of consumption. This method is inspired by the human brain and its number of neurons with high interconnectivity. ANNs are several combined nodes or neurons, divided into different levels and interconnected by numeric weights. They resemble the human brain in two fundamental points: the knowledge being acquired from the surrounding environment, through a learning process; and the network's nodes being interconnected by weights (synaptic weights), used to store the knowledge. Each neuron executes a simple operation, the weighted sum of its input connections, which originates the exit signal that is sent to the other neurons. The network learns by tuning the connection weights to produce the desired output [27]. Based on many correct examples ANNs can change their connection weights until they generate outputs that are coincident with the correct values. This way, the ANN can extract basic rules from data [13].
# All-Fiber Hyperparametric Generation Based on a Monolithic Fiber Fabry-Perot Microresonator
## 3 Hyperparametric Generation
The simulated spectrum is shown in Figure 4. The 30-dB bandwidth is less than 100 nm, which is much narrower than the experimental Raman-assisted hyperparametric generation. Raman-induced spectral broadening for Kerr combs in microresonators has been demonstrated in several recent works [16; 21; 24]; this results from the complex interplay between Kerr, Raman, and all-order dispersion.
## 3 Results: Optimization Workflow
### Executable Behavior  
The executable behavior of the optimization workflow triggers the solution of the MDAO problem in an external tool. The executable behavior of the optimization workflow is modeled in an activity diagram (Figure 14). In the get section, necessary parameters such as the flow rate cycle are read out from the MBSE system model and provided to the calculate section. The upper and lower variation bounds of the design variables are loaded collectively in the _Boundaries_ action. These limits can be based on empirical values or physical limits. Furthermore, the parameter values of the requirements (\(B2 max\), \(D2 max\), \(PsiThmax\), \(PsiThmin\), \(Nmax\)) are loaded, and thus the constraints of the optimization are parameterized. In this paper, a genetic algorithm is used for optimization. Therefore, a hyperparameter of the population size is loaded. The two weights, \(WVPump\) and \(WEel\), are used to select the technical variant with the best tradeoff between installation space and electrical energy consumption from the resulting pareto front. The numerical values of the weights are defined in the requirements (cf. Figure 6).  
The calculate section consists of an _optimize_ and a _select_ action. _Optimize_ calculates a matrix of non-dominating technical variants (pareto front) by solving the optimization problem. The _optimize_ action is implemented in an external tool reusing the predefined models in the internal behavior description. This corresponds to the implementation process in Figure 2. The _select_ action weights the technical variants on the pareto front to determine  
Figure 13: Activity diagram for the constraint calculation.  
the best tradeoff. In the _set_ section, the design variable values of the selected tradeoff are transferred to the MBSE system model to be checked for requirement satisfaction.  
In the MBSE system model, the _optimize_ action exhibits black box behavior, which is resolved with the help of the workflow architecture (Figure 10) and internal behavior (Figures 11 and 12).  
Changes in the requirement values (maximum diameter, flow rate cycle, etc.) can directly be considered in the optimization execution. For this purpose, the values linked to the requirements are automatically transferred to the _optimize_ action by the corresponding actions (_D2 max_, _FlowRateCycle_, etc.) in Figure 14. Likewise, the variation limits of the design variables can be changed via the defined interfaces. This reduces the reconfiguration effort of the MDAO problem.  
New requirements, other objectives, and design variables, as well as new calculation steps, represent changes to the internal behavior of the optimization workflow. These changes must be specified in the internal behavior by the systems engineer and, on this basis, reimplemented in new executable behavior.