esa-sceva/satcom-chunk-collection huggingface dataset:
# Polarization Transmission of Visible Light in Inhomogeneous Sea Fog Particle Environment
## 2 Establishment of an Inhomogeneous Particle Scattering Model
### Relative Humidity Formula  
Given the hygroscopicity of sea fog particles, relative humidity becomes another factor which affects the equivalent refractive index of inhomogeneous particles. The relative humidity is defined as the ratio of water content to particle content in the medium environment. It can be calculated using the following formula [7]:  
\[m^{\prime}=n_{w}+(n_{s}-n_{w})[\frac{r_{s}}{r(a_{w})}]^{3} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mixing ratio** & 0.1 & 0.3 & 0.4 & 0.5 & 0.7 & 0.9 & 0.99 \\ \hline
**Equivalent refractive index** & 1.492+ & 1.37+ & 1.345+ & 1.421+ & 1.494+ & 1.44+ & 1.49+ \\  & i0.00214 & i0.00034 & i0.000178 & i0.00024 & i0.000236 & i0.00154 & i0.00138 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equivalent refractive index corresponding to different mixing ratios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wavelength/nm** & 450 & 532 & 671 \\ \hline
**Equivalent refractive index** & \(1.358+i0.000268\) & \(1.405+i0.00167\) & \(1.455+i0.001699\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Equivalent refractive index at different wavelengths.  
Figure 2: Relative differences under different mixing ratios.  
\[r(a_{w})=r_{s}[1+\rho\frac{m_{w}}{m_{s}}(a_{w})]^{\frac{1}{3}} \tag{6}\]  
\[a_{w}=RH\cdot\exp(\frac{-0.001056}{r_{a}}) \tag{7}\]  
where \(n_{w}\) is the refractive index of water particles, \(n_{s}\) is the refractive index of sea salt particles, \(\rho\) is the concentration, \(m_{w}/m_{s}\) is the mass ratio of water particles to sea salt particles in the inhomogeneous sea fog particles, \(RH\) is the relative humidity, \(r_{s}\) is the radius of the sea salt particles, and \(r_{a}\) is the radius of the inhomogeneous sea fog particles, \(r(a_{w})\) can be solved from Equations (6) and (7) iteratively.  
The relationship between relative humidity and refractive index can be obtained by combining Equations (5)-(7):  
\[m^{\prime}=n_{w}+(n_{s}-n_{w})(\frac{1}{1+\rho\cdot\frac{m_{w}}{m_{s}}\cdot RH \cdot\exp(\frac{-0.001056}{r_{a}})}) \tag{8}\]  
In an environment with high relative humidity (e.g., 99%), the particles of pure NaCl solution are spherical and homogeneous. As the relative humidity drops below 85%, the particle shape and refractive index changes because of deliquescence. Moreover, the particles are spherical and inhomogeneous at this time. When the relative humidity is lower than 45%, NaCl begins to crystallize, and the particles become nonspherical [7]. Therefore, we selected the relative humidity range: 45-85%, and a step size of 10% to study the inhomogeneous sea fog particles. The equivalent refractive index of the inhomogeneous particles is calculated according to Equation (8). The results are shown in Table 3.
## 5 Conclusions  
This paper presents investigations on the impact of IMS/NGN network and traffic source parameters on \(E(CSD)\) and \(E(CDD)\) times. These times belong to the set of call processing performance parameters, which proper values must be ensured in the network to achieve user satisfaction with the provided services. In the performed research, our analytical traffic model of a multidomain IMS/NGN network based on the MPLS technology in the transport stratum was used. The model allows the calculation of \(E(CSD)\) and \(E(CDD)\) times for all types of successful call scenarios with respect to an extensive set of input variables regarding the service and transport stratum.  
During the investigations, particular input variables in domain 1 were modified, and the effect of these modifications on all \(E(CSD)\) and \(E(CDD)\) times was examined. The obtained results can be used to determine changes in all output parameters when any combinations of input variables in domains 1 and/or 2 are modified. The performed experiments also allowed indicating the network and traffic source parameters with a significant influence on \(E(CSD)\) and \(E(CSD)\), which should be definitively taken into account during IMS/NGN network analysis and design. They include the intensities of registration requests as well as intra- and inter-operator call set-up requests; the processing times of CSCF servers, RACF C1 and RACF C2; the mean response time of RACF A1, RACF A2 and MPLS domains as well as link lengths and bandwidths.  
Our future work will include extensions of the analytical traffic model. Our aim is to take into account more details regarding the structure and operation of MPLS core networks, which affect, for example, the probability of transport resource unavailability in these networks. We are also planning to include modeling of user (voice) traffic in MPLS transport networks and apply segment routing [28; 29] for transport resource optimization. Another planned research pursuit is to use the experience gained from the experiments described in this paper to create a set of analytical traffic models enabling the analysis of IMS/NGN service stratum resource virtualization, which is mentioned in Section 3. In such a solution, there exist several instances of the same types of IMS servers working in parallel, where the number of active instances depends on the network load, which results from, among others, the values of request intensities. Increasing the number of active IMS server instances will reduce the \(E(CSD)\) and \(E(CDD)\) times for the same values of request intensities.  
Conceptualization, S.K. and M.S.; methodology, S.K. and M.S.; software, M.S.; validation, S.K. and M.S.; formal analysis, S.K.; investigation, S.K. and M.S.; data curation, S.K. and M.S.; writing--original draft preparation, M.S.; writing--review and editing, S.K.; visualization, M.S.; supervision, S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
## 3 Results and Discussion
### EPS Box Strength Study
#### 3.2.2 Bottom strength test  
Load versus displacement curves obtained from the bottom load test are presented in Figure 8c, and the averaged breaking loads are shown in Figure 8d with error bars. The breaking load and corresponding deformation are displayed in the last two columns of Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **Sim22 (\%)** & **Sim23 (\%)** & **Sim23X (\%)** \\ \hline Mid 1 & 10.3 & 3.4 & 1.6 \\ Mid 2 & 8.60 & 3.1 & 1.7 \\ Mid 3 & 11.7 & 4.6 & 1.5 \\ Botm 1 & 11.9 & 9.3 & 2.1 \\ Botm 2 & 14.9 & 7.1 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Plastic strain values of EPS boxes from simulation results.  
Figure 9: Equivalent stress across the body and plastic strain distribution, from left to right: Sim22, Sim23, Sim23X. Unit: MPa and mm/mm.  
Non-intuitively, the bottom strength of EPS-B23 varied \(-\)10.3% compared to EPS-B22. The cause could be worse fusion in this batch of EPS boxes with a density of 23 kg/m\({}^{3}\). Nonetheless, EPS-B23 was 22.6% higher in maximum breaking load than EPS-B23X. Boxes with higher maximum load also break at larger deformation, and there was no plateau region when testing bottom strength. The failure pattern in the bottom strength test was different from that in the stacking strength test; the boxes failed promptly once they reached their elastic limits without experiencing plasticity.
# Uncertainty Quantification Approach on Numerical Simulation for Supersonic Jets Performance
## 1 Introduction
The LHS method is widely used because it ensures a better coverage of the design space [14; 15; 16; 17] and a faster convergence with respect to the basic Monte Carlo method. The surrogate model was generated through the Gaussian Process (also known as Kriging) approach which uses a Gaussian correlation function with parameters that are selected by Maximum Likelihood Estimation (MLE); this correlation function results in a response surface that is C\({}^{\infty}\) continuous [14]. In a first application, an optimized nozzle geometry with air flowing at a supersonic Mach number was considered. The commercial software ANSYS Fluent(r) was used to generate a series of CFD simulations to identify the influence of the nozzle discharge pressure on the convergent-divergent operating condition, flow structure and performance. A perturbation range of the exit static pressure was chosen with respect to the critical gas-dynamic condition, where the normal shock wave is positioned at the nozzle exit section (referred to the r2 condition in classical literature). The choice of this particular condition is related to the potential performance drop that will occur if the shock wave moves into the divergent part of the nozzle. After a preliminary sensitivity analysis, a full UQ approach was developed to quantify the effects of the input variable distribution (ambient static pressure) on the nozzle performance. A second application considers the flow of natural gas in a convergent-divergent nozzle to understand the effects of the gas chemical composition on the expansion nozzle performance. The gas composition variation can occur on a daily or seasonal base, according to the geographical area and pipeline used [18], and it is a serious problem for several industrial applications, where large quantitiesof natural gas are treated and energy consumption is an issue [19; 20]. The same investigation approach was applied as for the first case and the effectiveness of the UQ analysis is confirmed.
## 3 Methodology
### Data Preprocessing
#### 3.1.3 Unsharp Masking  
In digital image processing, unsharp masking is a popular image improvement technique. By drawing attention to borders and limits, it seeks to improve the appearance of sharpness and fine details in an image [50; 51]. This process can be divided into three steps. Firstly, it applies a low-pass filter to the original image to capture the low-frequency components, resulting in a blurred image. Let the original image be \(I_{\text{input}}\) and \(f\) be a low-pass filter. Now, the blurred image, \(I_{\text{blurred}}\), can be obtained using Equation (6).  
\[I_{\text{blurred}}=I_{\text{input}}*f \tag{6}\]  
In the second stage, the blurred image is subtracted from the original image to obtain the mask, which contains only high-frequency components. The resulting \(I_{\text{mask}}\) is the element-wise subtraction of pixel values from the original image and the blurred image. Equation (7) presents the mathematical illustration of this stage.  
\[I_{\text{mask}}=\sum_{i=1}^{x}\sum_{j=1}^{y}I_{\text{input}}(i,j)-I_{\text{ blurred}}(i,j) \tag{7}\]  
Finally, the mask is amplified and added to the original image. Let the amplification value be denoted with \(k\), which is 1 for this research. Now the final unsharp mask image is retrieved, performing the operations mentioned in Equations (8) and (9).  
\[I_{\text{mask}}=I_{\text{mask}}\times k \tag{8}\]  
\[I_{\text{output}}=\sum_{i=1}^{x}\sum_{j=1}^{y}I_{\text{mask}}(i,j)+I_{\text{ input}}(i,j) \tag{9}\]
## 3 Validation of CFD Simulation Method
### NASA Coaxial Rotor in Hover  
In this section, the experimental model of the NASA coaxial rotor is taken as an example to validate the accuracy of numerical simulation in the hovering flight. The detailed parameters of the NASA coaxial rotor are shown in Table 1.  
The four different tests in the experimental data were studied in this paper. The upper and lower rotor rotation speed is \(\Omega=3100\) RPM, the collective pitch angles of the upper and lower rotors \(\theta=3^{\circ},~{}6^{\circ},9^{\circ},12^{\circ}\), respectively. The total thrust coefficients \(C_{T}\) and torque coefficients \(C_{M}\) of the upper and lower rotor system in each flight are calculated, respectively, and compared with the experimental data. The calculation results are shown in Figure 6:  
Figure 6 shows that the calculated values are in good agreement with the experimental date, and the error is within 1%. This confirms that the CFD simulation method and grid method proposed in this paper are suitable for the simulation of the interference flow field of the coaxial rotor hovering flight, and can achieve high calculation accuracy.
## 3 Materials and Methods  
### Paleoclimate Proxies  
Cycloststratigraphic investigations impose rigorous requirements on stratigraphic sections: sedimentary success must exhibit continuity and integrity while avoiding fault-prone zones to minimize noise interference from stratigraphic hiatuses or tectonic disruptions [22]. When selecting paleoclimate proxies, two critical criteria govern the decision-making process: (1) sensitivity to climatic oscillations and fidelity in recording cyclic signals, and (2) sampling resolution sufficient to resolve target orbital periodicities [23]. Comparative analysis of multiple high-resolution proxies demonstrates that natural gamma-ray (GR) logging data exhibit superior signal-to-noise ratios and exceptional stability. The GR technique quantifies the gamma radiation intensity from sedimentary deposits, effectively discriminating lithological variations and decoding paleoenvironmental shifts. This method has become the preferred proxy for Milankovitch cycle extraction, widely employed in time-series analysis due to its robust stratigraphic continuity and orbital-scale resolution [24,25].  
The Enping Depression primarily receives sediments from the paleo-Pearl River drainage system and adjacent tectonic uplifts, with a sustained supply of terrigenous elastic materials dominating the depositional regime. Given the relatively stable provenance, the variations in grain size and compositional maturity of sedimentary deposits predominantly reflect paleoclimate fluctuations, thereby providing high-fidelity geological archives for reconstructing regional climate evolution. This study selects natural gamma-ray (GR) logging data from Well EP17 in the Enping Depression as the primary dataset. Strategically located in the central basin area, distal from the fault zones, Well EP17 penetrates continuous delta-front successions characterized by mudstone-siltstone lithologies, representing a stable depositional environment ideal for cycloststratigraphic analysis. The 0.125 m sampling interval satisfies the Nyquist sampling theorem, ensuring adequate resolution for detecting Milankovitch-band signals [26].
## 5 Problem Formulation: Mathematical Model and Algorithm
```
1:Input:\(G\), \(src\), \(dest\), \(N\)
2:Output:\(\hat{S}\)
3:\(N\): Total number of requested layers
4:\(\hat{S}\rightarrow\phi\) // To store \(N\) paths for \(N\) layers from \(src\) to \(dest\)
5:\(P_{src\to dest}\) = 0 // To save a path from \(src\) to \(dest\)
6:while\(P_{src\to dest}\) == NULL do
7:\(P_{src\to dest}\) = Dijkstra\((src,dest,G)\)
8:if\(P_{src\to dest}\) FOUND then
9: Check Shortest Path Edges Capacity
10:if\(P_{src\to dest}\) edge(s) are \(overUtilized\)then
11: FIND overUtilized edge of \(P_{src\to dest}\)
12: REMOVE overUtilized edge(s) of \(P_{src\to dest}\) from \(G\)
13:\(P_{src\to dest}\) == NULL
14:else
15:\(\hat{S_{(l)}}\gets P_{src\to dest}\)
16: REMOVE edges of \(P_{src\to dest}\) from \(G\)
17: UPDATE path counter value \(l++\)
18: UPDATE \(G\)
19: LOG Request Accepted
20:else
21: LOG Request Rejected
22:RETURN\(P_{src\to dest}\)
```  
**Algorithm 1**_Dynamic Multipath Finder (DMPF) Core._
# Reliable Semantic Communication System Enabled by Knowledge Graph
## 2 Related Work
### Performance Metrics
#### 2.2.3 Semantic Similarity Score  
The semantic similarity score converts text into vectors by using the BERT model [39]. It evaluates the semantic similarity between sentences by comparing the degree of similarity between vectors. For the transmitted sentence's vector \(\mathbf{v}(s)\) and the received sentence's vector \(\mathbf{v}(\hat{s})\), the semantic similarity score can be expressed as  
\[sim_{\text{v}}(s,\hat{s})=\frac{\mathbf{v}(s)\cdot\left(\mathbf{v}(\hat{s})\right)^{T} }{\parallel\mathbf{v}(s)\parallel\cdot\parallel\mathbf{v}(\hat{s})\parallel} \tag{4}\]  
All the performance metrics introduced above take values between 0 and 1. A higher score given by the performance metrics means that the received text's semantic is closer to the transmitted text's semantic; 0 means semantically irrelevant; 1 means semantically consistent.
# Agricultural Entrepreneurship in the European Union: Contributions for a Sustainable Development
## 5 Results for Cross-Section Regressions
### Stressing the Cobb-Douglas Model Adequacy for Agriculture  
It is important to highlight that the variables selection took into account the original Cobb-Douglas model, the literature review carried out before and to avoid problems of multicollinearity. The Cobb-Douglas model, with proper adjustments, was considered in analyses for the agricultural sector by several authors, namely for efficiency surveys with data envelopment analysis [99] or through stochastic frontier [100].  
Specifically for agriculture in the European Union and with the most diverse approaches (including efficiency analysis), several studies considered the Cobb-Douglas model from the theory of production and taking into account different databases with micro- or macroeconomic statistical information. For example, [PERSON] et al. [101] explored data related to production factors and output from 80 Greek pig farms through the Cobb-Douglas developments. These authors highlighted the relevance of the results obtained with the Cobb-Douglas production function and the adequacy of this approach for the agricultural sector. The relevance of the model in terms of economic and agronomic dimensions was also stressed by [PERSON] and [PERSON] [102]. [PERSON] et al. [103] considered microeconomic data from the Italian Farm Accountancy Data Network and used variables such as area and labour as inputs. [PERSON] et al. [104] used financial data from 56 Spanish farming-marketing cooperatives to analyse the externalities from sustainability on agricultural productivity, considering as a base the Cobb-Douglas model. [PERSON] [1] considered the Cobb-Douglas developments to analyse the common agricultural policy impacts on the dynamics of the Portuguese agricultural sector. [PERSON] et al. [105] analysed the technical efficiency from some Polish broiler production farms, taking into account as a base the Cobb-Douglas model and considering as inputs, for example, several costs, labour and fixed capital. In fact, in these models, namely when regression approaches are considered, it is important to limit the number of variables to avoid statistical problems, specifically multicollinearity. Typically, inputs which are taken into account are those such as labour and capital (or proxies for it) and other variables for extended versions.
## 5 Conclusions and Future Works  
The paper proposes an optimal siting and timing methodology for primary substations to expand existing distribution networks. The proposed approach relies on three main features: a GIS for capturing, elaborating, and displaying spatial input data; a PSO algorithm to locate and timing the new primary substations; a Voronoi diagram-based approach to find the primary substation service areas and loading. Thanks to Unareti, the method has been tested on Milano and Rozzano by carrying out several simulations, progressively increasing the number of new primary substations. The results confirm the proposed approach's effectiveness, giving valuable suggestions on the optimal siting, timing, and the number of new PSs. As expected, the more the number of PSs, the lower the fitness value, but it is worth noting that the first PSs installed guarantee a substantial reduction which progressively decreases with the following PSs settled. As already mentioned, it is worth noticing that recent projects for significant urban development could change the location of the new PSs, especially in the areas of Porta Romana and Milano Innovation District. The estimates of energy needs have been ongoing at the time of modeling development and simulations, and, therefore, its impact is not included in the simulation results. However, the model is highly flexible so that the input can be easily adjusted to run additional simulations. Several are the potential improvements to the proposed approach. Firstly, the objective function could be sophisticated by including CAPEX and OPEX costs or considering others objectives, such as reliability. Secondly, additional constraints related to the MV and LV networks could be added. Lastly, the input data could be better investigated and improved, including uncertainties related to the load demand and the regulatory framework.  
This research received no external funding.  
Not applicable.  
The authors gratefully acknowledge the contributions of [PERSON].  
The authors declare no conflict of interest.  
Figure 30: Trend of the fitness concerning the number of new PSs.
## Appendix A Framework for Physital Cultural Heritage Tourism Application Development
### Applying the Framework to the Cham Culture AR App
Training and Education:
* Create training materials for museum staff to guide visitors in using the app and understanding its cultural content. Stage 5: Solution Development Technical Specifications:
* Use blockchain standards (e.g., ERC-721, etc.) to mint NFTs representing Cham cultural elements. * Develop AR animations with high fidelity to ensure accurate representation of Cham traditions. Iterative Development:
* Conduct iterative testing with educators, students, and museum visitors to refine the app's usability and cultural content. Marketing Strategy:
* Highlight the app's role in cultural preservation and education through partnerships with museums and educational institutions. Stage 6: Testing and Refinement Functional Testing:
* Validate the accuracy and responsiveness of QR code triggers and AR animations. * Test NFT functionalities, ensuring seamless integration with digital wallets. User Feedback:
* Conduct surveys and interviews with diverse user groups, focusing on teachers, students, and tourists. * Address feedback from older users about technological adoption concerns by simplifying the app interface. Compliance Check:
* Ensure that the app adheres to cultural preservation standards and aligns with the goals of the Cham Museum and other stakeholders. Stage 7: Deployment and Support Launch Strategy:
* Provide technical support during the app's launch phase, ensuring smooth adoption by museum staff and visitors. * Train staff to explain the app's features and cultural significance to visitors. NFT Placement and Accessibility:
* Offer digital collectibles, such as Cham artifacts or dance sequences, as NFTs through the app. * Ensure these NFTs are accessible to users through integrated wallets and museum kiosks. Compatibility and Maintenance:
* Regularly update the app to include new features, such as additional Cham dances or artifacts, and maintain compatibility with new AR technologies. Stage 8: Evaluation and Iteration Performance Metrics:
* Track app engagement through metrics such as QR code scans, NFT redemptions, and user interaction times. * Assess educational impact through feedback from teachers and students. Long-Term Improvements:
* Expand the app's reach by including content from other Cham-related sites or artifacts. * Use user feedback to refine cultural content and improve usability for diverse age groups. By applying the integrated framework, the Cham Culture AR app can evolve into a comprehensive cultural preservation tool that bridges digital and physical experiences. The inclusion of NFTs and blockchain ensures authenticity and provides lasting value for users. Gamified elements and interactive storytelling enhance engagement, making the app a model for using AR in the preservation and sharing of endangered cultural traditions. This approach not only preserves the Cham heritage but also fosters global appreciation and sustainable cultural tourism.