esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Integration of Multi-Domain MEC Environments
### Business Implications
#### 4.1.2 Service Level Agreements (SLAs)  
A third party requiring the deployment of an MEC Application may require an MEC system service with a main provider negotiating a specific SLA. For implementing such an MEC system service, the MEC provider facing the third party could require leverage on some other MEC providers according to the any of the coordination models explained above. This first provider, then, may manage to seek the collaboration of other providers in order to meet the expected end-to-end QoS agreed in the SLA with the third party for its application. The relation among the rest of actors in this business chain should be transparent to the third party (i.e., it is not aware of the federation of multi-domain edge environments) and must meet the overall QoS objectives that guarantee parameters such as capacity and performance of the resources, but also some other constraints and restrictions like geographical location. Then, automatic aggregation of SLAs is required.  
Each provider in each administrative domain should have its own internal SLA evaluation capabilities, including interfaces with SLA aggregation components that automatize the multi-domain aggregation process. These SLA management components will be in charge of providing mechanisms to get an agreement, to store all the gathered SLA, and to inform both the components handling the multi-domain federation about the SLA fulfilment and the billing system for possible penalties in case the SLAs are not met.
###### Abstract  
SPME-GC-MS and PTR-ToF-MS techniques were applied to describe the content of volatile flavor compounds in a craft beer before and after adding spirulina. The obtained results showed that the volatile profile of the two beer samples differed. Furthermore, to chemically characterize biomass spirulina, a derivatization reaction followed by GC-MS analysis was performed, highlighting a high content of molecules belonging to different chemical classes, such as sugars, fatty acids and carboxylic acids. A spectrophotometric analysis of total polyphenols and tannins, investigation into the scavenging activity towards DPPH and ABTS radicals and confocal microscopy of brewer's yeast cells were carried out. Moreover, the cytoprotective and antioxidant properties towards the oxidative damage induced by tert-butyl hydroperoxide (tBOOH) in human H69 cholangiocarcinomaytes were investigated. Finally, the modulation of Nrf2 signaling under oxidative stress conditions was also evaluated. Both samples of beer were shown to contain similar levels of total polyphenols and tannins, with slightly increased levels in that containing spirulina 0.25% \(w/v\). Moreover, the beers were found to be endowed with radical scavenging properties towards both DPPH and ABTS radicals, albeit with a weak contribution of spirulina; however, a higher riboflavin content was detected in spirulina-treated yeast cells. Conversely, the addition of spirulina (0.25% \(w/v\)) appeared to improve the cytoprotective properties of beer towards tBOOH-induced oxidative damage in H69 cells and reduce intracellular oxidative stress. Accordingly, the cytosolic Nrf2 expression was found to be increased.  
SPME-GC-MS; PTR-ToF-MS; chemical analyses; radical scavenger activity; oxidative stress; cytoprotective properties; Nrf2
## 1 Introduction
### Pool Fire Scenario Definition  
The conditions under which fuel spillages occur are determined by defining metocean- and operation-related parameters. Metocean-related parameters include sea state, wind direction, and wind speed. Sea state affects the motion of ships and offshore installations and is a major consideration in the design phase. The wind condition significantly influences fire behaviour and thus needs to be defined. The metocean-related parameters can be determined utilising available data for the route through which or the site in which the target structure is operational. Alternatively, they can be measured at the location of interest.  
Operation-related parameters include the fire type, spilled fuel type, spill rate, spillage duration, spillage location, and substrate type. Various fire types which occur on ships and offshore installations, such as pool fires and jet fires, pose different kinds of hazards, and thus clarifying the fire type to be analysed is essential. Spilled fuel, spill rate, spillage duration, and substrate type can be determined by considering the design purpose and apparatus of the target structure. Spillage location should be considered in structural response analyses under thermal loads, as the magnitude of the thermal load acting on the structure may differ substantially by location.  
The motion of ships and offshore installations induces substrate angles that vary over time. However, in this study, the inclination angle of the substrate was assumed to be constant to consider the most critical spillage scenario and analyse the spillage in a conservative manner. This assumption enabled the consideration of the maximum spreading extent of spilled fuel, as variation in the substrate angle may prevent the fuel from spreading as widely as it would under a constant substrate inclination angle. Furthermore, as this study aimed to characterise fuel spillage on inclined, impermeable substrates, only pool fires and spillages occurring in a sufficiently large compartment that does not physically confine the spilled fuel were considered. Moreover, the wind conditions were assumed to be calm.  
Figure 2: Research procedure for fire consequence analysis considering the spreading extent of spilled flammable liquid in inclined conditions.
## 3 Results and Discussion:
### Statistical Analyses
#### 3.2.1 Z-Score Method  
The Z-score method uses the mean and standard deviation. The procedure is based on the following rule: if X follows a normal distribution N (\(\mu\), \(\sigma^{2}\)), then Z follows the standard normal distribution, N (0, 1). Generally, absolute values of Z-score above 3 are defined as outliers. The maximum Z-score depends on the sample size [31]. [PERSON] showed that a Z-score below 3 is acceptable for a sample size equal to or greater than 10. For a sample size below 10, smaller values of the Z-score are obtained. According to [PERSON]'s studies, the maximum Z-score for a sample size of 5 is 1.79. In this work, a sample size of 6 was used, so the maximum Z-score was established at 2.  
Figure 8: Compressive strength results (blue: control sample; green: ZnO as an additive; red: ZnO as a cement substitute).  
Figure 7: Specimens tested: (**a**) in the testing machine; (**b**) specimen after breakdown.  
The Z-score value is determined using Equation (2):  
\[Z_{i}=\frac{|x_{i}-\overline{x}|}{sd} \tag{2}\]  
where:  
\(x_{i}\): experimental value of each specimen;  
\(\overline{x}\): mean of the sample;  
\(sd\): standard deviation of the sample.  
The results obtained using the Z-score method are shown in Table 5. The experimental data have no outliers as the maximum Z-score is below 2 (\(maxZ\) = 1.91).
# Light-Matter Complex Interactions in Stereolithographies
## 2 Resins and Additive Manufacturing
### Materials  
Several situations have been observed since 1984 [1]:  
* The photopolymer must not dissolve in the resin that gave birth to it, which requires multifunctional monomers (see Figure 4);
* The polymer is generally denser than the initial monomer, which leads to shrinkage (as in foundry); and if the object was not supported, it falls (Stokes' law) to the bottom of the reactor by being deformed;
* A complex deformation linked to the manufacturing process (anisotropy of local tensions and global deformation);
* An obligation of post-treatment with the risk of an ageing likely to lead to the destruction in a few days of the realized object.  
Figure 3: Radical mechanism of chain polymerization (A corresponds to the initiator producing free radicals R. reacting with the monomer M under light (hv) or thermal (\(\Delta\)) initiation).  
Figure 2: Typical polymerization kinetic curve under continuous irradiation. Phase I: very slow kinetics related to the consumption of the inhibitor(s); phase II: radical polymerization before reaching the gel point; phase III: cross-linking of the multifunctional monomer links.  
On this broad basis, the materials are commercially available and are summarized in Table 1.  
Table 1 should be seen as a wealth of choices of resins that can be made by the experimenter or obtained ready-made. Thus, only a few references are presented there. Industrial 3D machine manufacturers offer the opportunity to purchase their own resin that broadly meets the user's specifications [7; 8]. They provide the expertise and industry knowledge necessary to accurately assess specific customer requirements and recommend/sell the most appropriate 3D printing products. The links between the process and materials processed in the same company give the company an unparalleled ability to provide a \"complete value chain\". This raises the difficult question of choice, which depends on the nature of the objects to be manufactured [9].  
For example, Merck [10] proposes photochemical initiators such as Irgacure (2-Hydroxy-4\({}^{\prime}\)-(2-hydroxyethoxy)-2-methylpropiophenone) or ketone compounds of the same family, [PERSON] et al. [11] benzophenone, and Sigma-Aldrich [12] thermal initiators such as AIBN (2,2\({}^{\prime}\)-Azobis(2-methylpropionitrile)) and benzoyl peroxide [13].
## 2 Explanation Methods
### Lore  
LOcal Rule Explanation (LORE) method was introduced by [PERSON] et al. [15]. This method is similar to the Anchor method in using rules to explain the decision of ML models. However, it differs from Anchor by generating perturbations of x by implementing a genetic algorithm (instead of linear regression) and training a decision tree (instead of linear regression) using two fitness functions.  
LORE first uses a genetic algorithm to produce a local interpretable prediction based on the perturbations. Then, it generates a coherent explanation from the concept of the local explainable predictor, which includes decision rules set and counterfactual rules set. The decision rules set contains the rules that explain why a decision was taken, whereas the counterfactual rules set specifies fluctuations in the instance's properties that would result in a contrasting conclusion. LORE defines an explanation \(e\) as a joint object:  
\[e=\langle r=p\ \rightarrow\ y,\ \Phi\rangle \tag{7}\]  
The first part \(r=p\ \rightarrow\ y\) is a decision rule that describes the cause of the decision result \(y=c(x)\), while the second part, \(\Phi\), defines the collection of counterfactual rules, i.e., the smallest number of fluctuations in \(x\) that could cause the predictor's select to be reversed.  
In LORE, the perturbation \(x^{\prime}\) of instance \(x\) is composed of two sets: \(D_{=}=\left\{x^{\prime}\mid f(x)=f(x^{\prime})\right\}\), which represents the decision rules, and which represents the decision rules, and \(D_{\
eq}=\left\{x^{\prime}\mid f(x)\
eq f(x^{\prime})\right\}\), which represents the counterfactual rules. LORE adopts a genetic algorithm to generate \(x^{\prime}\in\ D_{=}\cup D_{\
eq}\) when maximizing the fitness functions below:  
\[fitness_{=}^{x}(x^{\prime})=I_{f(x)=f(x^{\prime})}+\left(1-d\big{(}x,x^{\prime }\big{)}\right)-I_{x=x^{\prime}} \tag{8}\]  
\[fitness_{\
eq}^{x}\big{(}x^{\prime})=I_{f(x)\
eq f(x^{\prime})}+\left(1-d\big{(} x,x^{\prime}\big{)}\right)-I_{x=x^{\prime}}, \tag{9}\]  
where \(I_{true}=1\), and \(I_{false}=0\). The first function seeks to \(x^{\prime}\) similar to \(x\) (term \(1-d(x,x^{\prime})\)), but not identical (term \(I_{x=x^{\prime}}\)) for which the used learning model \(f\) produces the similar outcome as \(x\) (term \(I_{f(x)=f(x^{\prime})}\)). The second fitness function leads to generating \(x^{\prime}\) similar to \(x\), but identical, so that \(f\) returns a contrasting decision.  
The important term in the listed above fitness functions is the distance \(d(x,\ x^{\prime})\). For the various types of features, the sum of a simple matching coefficient is weighted to be appropriate for the categorical features, while the normalized Euclidean distance is used for the continuous features.
## 2 Theoretical Infrastructure
### An Overview of Crosstalk Reduction Method
This approach not only mitigates coupling but also maintains impedance matching, making it particularly effective for high-speed transmission systems. The significance of this method lies in its ability to enhance signal integrity in high-frequency transmission systems while maintaining structural simplicity. Unlike conventional shielding techniques, which often introduce additional complexity and material constraints, the capacitor-based approach provides a more efficient means of crosstalk mitigation by establishing a controlled return current path. This not only reduces electromagnetic interference but also stabilizes signal propagation without necessitating major modifications to the transmission line structure. Additionally, its seamless integration into existing UCPW configurations enhances its practical applicability, making it a scalable and cost-effective solution for improving signal integrity in high-density electronic systems.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## Appendix A  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Land Cover Type** & **Coefficient** & **Correlation Coefficient (\(R^{2}\))** \\ \hline Evergreen Needleleaf Forest & 157 & 0.50 \\ Evergreen Broadleaf Forest & 176.4 & 0.64 \\ Deciduous Needleleaf Forest & 93.1 & 0.53 \\ Deciduous Broadleaf Forest & 106.4 & 0.53 \\ Mixed Forest & 111.7 & 0.45 \\ Closed Shrublands & 110.5 & 0.49 \\ Open Shrublands & 151.3 & 0.34 \\ Woody Savannas & 133.3 & 0.42 \\ Savannas & 89.7 & 0.30 \\ Grasslands & 78.9 & 0.36 \\ Permanent Wetlands & 167.8 & 0.59 \\ Croplands & 77.9 & 0.25 \\ Urban and Built-up & 130.7 & 0.45 \\ Cropland/Natural Vegetation Mosaics & 125.4 & 0.33 \\ Permanent Snow and Ice & 271.2 & 0.39 \\ Barren & 85.6 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Regression coefficient between a vegetation index (MODIS normalized vegetation index (NDVI)) and a biomass simulated by the vegetation integrative simulator for trace gases (VISIT) terrestrial biosphere model [28] in each land cover type of the MODIS land cover type product (MCD12Q1) to estimate biomass. Biomass = a \(*\) ln (NDVI) was used as a regression (where \(a\) is the regression coefficient). The fire emission estimation process was reported in detail in previous study [44].
# HCL Control Strategy for an Adaptive Roadway Lighting Distribution
## 2 Materials and Methods
### Establish the Freeform Surface Formula for a Roadway Lighting Lens
#### 2.4.2 Freeform Surface Refractive Lens Area  
After the LED light is refracted into the lens through the incident ray's surface, it travels according to its optical path and increases its output angle after travelling through the freeform surface refraction lens area. The coordinates of each \(C_{F}(x_{F},y_{F})\) are obtained after calculation. The light travels through the incident lens area and passes the \(C_{c}(x_{c},y_{c})\) point. The ray equation and slope relationship \(n_{3}\) after refraction are as follows:  
\[y-y_{c}=m_{3}(x-x_{c}) \tag{19}\]  
\[m_{3}=\tan(90-\beta) \tag{20}\]  
Based on [PERSON]'s law, when discussing the geometric relation of \(\theta_{3}\) and \(\theta_{4}\) in Figure 7, where \(\gamma\) is the included angle between ray axis and refracting ray from freeform surface, the calculation formula of \(\theta_{3}\) is as shown in (22):  
\[\theta_{4}=\theta_{3}+(\gamma-\beta) \tag{21}\]  
\[\theta_{3}=\cot^{-1}[\frac{n_{2}-n_{3}\cos(\gamma-\beta)}{n_{3}\sin-(\gamma- \beta)}] \tag{22}\]  
Figure 7: Mathematical model of a roadway lighting lens.  
In addition, we establish the tangent equation of the light that travels through the freeform surface and that passes point \(C_{F}(x_{F},y_{F})\) as in (23). The tangent slope of the freeform surface \(m_{4}\), as shown in (24), is obtained by using characteristics of refraction.  
\[y-y_{F}=m_{4}(x-x_{F}) \tag{23}\]  
\[m_{4}=-\tan(\beta-\theta_{3}) \tag{24}\]  
We solve the simultaneous equations of (19) and (23) by using an iterative method to obtain the coordinates of the control points of the freeform surface refractive lens area, and the points are connected to form the curved surface of the lens:  
\[x=\frac{m_{4}x_{F}^{(0)}-y_{F}^{(0)}-m_{3}x_{c}+y_{c}}{m_{4}-m_{3}}=x_{F}^{(1)} \tag{25}\]  
\[y=m_{4}(\frac{m_{4}x_{F}^{(0)}-y_{F}^{(0)}-m_{1}x_{c}+y_{c}}{m_{4}-m_{3}}-x_{F} ^{(0)})+y_{F}^{(0)}=y_{F}^{(1)} \tag{26}\]
## 4 Discussion
### Adjustments to the School Food Environment  
A number of studies (\(n=8\)) facilitated interventions that mainly focused on or had elements involving adjustments to the school's food environment. Despite varied approaches, all of these aimed to reshape the school food environment to encourage healthier behaviours, yet the consistency and depth of implementation varied greatly. Rearranging food placement to make healthier options easier to choose showed potential, but its impact was limited by mixed messages and a lack of readily available healthy choices [79]. The cause of this shortfall is unclear, but it could be due to budget, supply chain issues, or school policies which limited the healthy food options made available. Furthermore, some students found it difficult to prioritise the available healthy foods when there were still unhealthy options to choose from [79]. Other studies took a more compressive approach and included adjustments to the schools' food environment, such as healthier canteen menu options, alongside educational learning opportunities [91, 92, 94]. The results of the initiatives indicated improvements in health metrics such as cholesterol, blood pressure, and obesity levels among participants, along with notable increases in healthy eating behaviours and nutritional understanding [91, 92]. Another study took an approach that focused on improving the canteen atmosphere and fostering better interactions with staff, alongside increasing the availability of dairy, vegetables, and fruits [81]. This consequently lead the majority of participating schools to achieve their goals of increased healthy food consumption. An intervention targeting sugar-sweetened beverage consumption in disadvantaged Australian schools [67] achieved only modest reductions, underscoring the difficulty of driving behavioural change in such contexts. In contrast, the \"Thirsty? Choose Water!\" campaign effectively reduced sugary drink intake and boosted water consumption and the use of reusable water bottles, through the provision of chilled water stations and targeted education [65]. One explanation for the effectiveness of these two beverage-focused studies, could be the duration, with the more successful one taking place over the course of one year, compared to 6 months. Collectively, these findings highlight the potential of school-based interventions while emphasising the need for well-funded, multifaceted strategies and extended durations to ensure lasting impact and sustained behavioural change. However, environmental changes, such as menu modifications or infrastructure improvements (e.g., chilled water stations) can carry substantial upfront and maintenance costs. Without adequate budgeting and long-term funding models, such changes may not be sustainable. Policymakers should prioritise interventions with demonstrated cost-effectiveness and feasibility in varying economic contexts.
# A New Under-Sampling Method to Face Class Overlap and Imbalance
## 2 Resampling Algorithms to Face Class Imbalance
### Neighborhood-Based Algorithms  
Most \(k\)NN-based under-sampling algorithms are inherited from the literature on prototype selection. These techniques exploit the analysis of the feature space, either by removing instances far from the decision boundary of two classes to reduce redundancy (condensing or thinning) or removing those close to the boundary for generalization (editing, cleaning, or filtering) [32]. [PERSON]'s editing [33] and [PERSON]'s condensing [22] are the most representative examples of these families of prototype selection methods.  
[PERSON]'s editing (ENN) removes all instances that are misclassified by the \(k\)NN rule. The [PERSON]'s condensing (CNN) algorithm uses the concept of consistent subset to eliminate the majority class instances that are sufficiently far away from the decision boundary because these are considered to be irrelevant for learning. Analogously, the Tomek links (TL) [23; 34] have also been employed to remove the majority class instances since, if two instances form a Tomek link, then either one of these is noise or both instances are borderline.  
The one-sided selection (OSS) technique developed by [PERSON] and [PERSON] [35] eliminates only the majority class instances that are redundant or noisy (i.e., those that are bordering the minority class). The concept of Tomek links is used to discover the border instances, whereas the [PERSON]'s condensing algorithm is applied to discover the redundant cases (i.e., those that are far enough from the decision boundary).  
[PERSON] [24] proposed the neighborhood cleaning rule (NCL) algorithm, which is quite similar to the OSS technique. With NCL, the majority class instances whose class label differs from the class of at least two of their three nearest neighbors are removed by means of [PERSON]'s editing [33]. In addition, this algorithm also discards the neighbors that belong to the majority class if a minority class instance is misclassified by its three nearest neighbors.