esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Machine Learning Models
#### 2.3.4 Model Evaluation Framework  
To assess the effectiveness of the evaluation methods, it is essential to establish evaluation metrics for the experimental results. The Jaccard index serves as a metric for evaluating the classification performance of the model from the perspective of the well logging sample dataset. It is defined as the size of the intersection between the predicted label set and the true label set divided by the size of their union. The Jaccard index for the \(k\)-th class can be calculated using the following equation:  
\[J_{k}=\frac{\left|Y_{k}\cap\hat{Y}_{k}\right|}{\left|Y_{k}\cup\hat{Y}_{k}\right|} \tag{12}\]  
where \(Y_{k}\) represents the true label set for the \(k\)-th class and \(\hat{Y}_{k}\) represents the corresponding predicted label set. A higher Jaccard index score indicates that the model's predicted label set is closer to the true label set, reflecting better lithology prediction performance. We let \(N\) denote the total number of lithology classes. The overall Jaccard index score of the model is represented by the average of the scores for each class:  
\[J=\frac{\sum_{k=1}^{N}J_{k}}{N} \tag{13}\]  
Well logging lithology discrimination is a multi-class classification problem, and the F1 score is a commonly used evaluation metric for such problems. The F1 score reflects the classification performance of each class, and the F1 score for the \(k\)-th class can be calculated using the following equation:  
\[F_{1}^{k}=\frac{2 TP_{k}}{2 TP_{k}+FP_{k}+FN_{k}} \tag{14}\]  
where \(TP_{k}\) represents the number of true positives for the \(k\)-th class, \(FP_{k}\) represents the number of false positives for the \(k\)-th class, and \(FN_{k}\) represents the number of false negatives for the \(k\)-th class. The overall F1 score of the model can be represented by the average of the scores for each class:  
\[F_{1}=\frac{\sum_{k=1}^{N}F_{1}^{k}}{N} \tag{15}\]  
Here, \(N\) still represents the total number of lithology classes. A higher F1 score for the model indicates a better lithology prediction performance.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 3 Background Concepts
### Privacy-Preserving and Cryptographic Techniques  
This section describes cryptographic techniques as well as privacy preservation mechanisms. The selection of techniques was based on ISO/IEC 20889:2018 [66] and on the techniques referred to throughout this paper, as a solution to the risks of re-identification in smart cities.  
**Zero-Knowledge Proof (ZKP)**[67] is an encryption technique that allows one to verify or prove (mathematically) that a statement about something or someone is real, without having to reveal details about that something or someone. An example of application would be to be able to prove that someone is over eighteen years old to access a site with adult content, without having to reveal identity or even the date of birth, thus guaranteeing anonymity.  
**Homomorphic Encryption** (HE) refers to a class of encryption methods devised by [PERSON], [PERSON], and [PERSON] as early as 1978 [68] and first constructed by [PERSON] in 2009 [69]. HE differs from typical encryption methods in that it allows computation to be performed directly in encrypted data without requiring access to a secret key. The result of such a calculation remains in encrypted form and can later be revealed by the owner of the secret key.  
**Multiparty computation** (MPC) is presented as a suitable option to offer the basic building block for building decentralized computers that preserve privacy. The purpose of MPC allows, as parts, the calculation to be a joint function of its private inputs [70]. This protocol must preserve some security properties: the accuracy of the outputs and the privacy of the inputs, even if some of the players are protected by active or passive equipment, without revealing more information about the output of the function itself.  
**Federated Learning**[71] helps in the formation of the machine learning algorithm and keeps the data at the device level. This means that FL allows each device to have its own private and local data. This technology will provide pervasive machine learning solutions as well as flexible, real-time managed data. The technique can be used for numerous tasks and contexts. It includes offline and online learning procedures for the algorithms. Depending on the operational context and data type, the algorithm will choose a suitable technique. Traditional methods, such as centralized machine learning, did not include these benefits and comprise a high risk for data protection and transfer of large files.
## 4 Numerical Simulation Model
### Irrelevance Verification  
To ensure the accuracy of the simulation calculation results, the consistency of the calculation is confirmed by comparing the number of grids and time steps. The jet temperature data at a fixed depth and distance in the water tank are shown in Table 3 under the convergence residuals of the same order of magnitude, with grid numbers of 650,000, 780,000, and 970,000. It can be observed from Table 3 that increasing the number of grid points further has no effect on the calculation results; therefore, the grid setting conditions are the same as a grid size of 970,000. Similarly, comparing time steps set at 0.01 s, 0.02 s, and 0.05 s for velocity size of jets at fixed depth and distance positions is also shown in Table 4. The time step clearly affects stability of the calculation results; when set to 0.01 s and 0.02 s, there is only a small difference between results. Thus, for computational resource saving purposes, it is decided to fixate on a time step setting of 0.02 s. In the initial state of the jet, only the velocity in the \(X\)-axis direction exists, and the velocity is 0.1 m/s. When the time step is 0.02 s, the basic mesh size is set to 0.01 m, satisfying the CFL conditions.  
Figure 3: Schematic diagram of the experimental tank and simulation computational domain grid.
## 2 Results and Discussion
### TEM Analysis of the L-Zr-S-1400-8 and L-Zr-N-1000-P-H2 Samples  
We studied HRTEM samples containing ZrC\({}_{0.58}\) and ZrC\({}_{0.94}\) prepared from the L-Zr-N and L-Zr-S copolymers in RF plasma and tubular furnace. The L-Zr-S-1400-8 sample shows a crystalline ZrO\({}_{2}\) monoclinic phase (ESI Figure S14) together with the ZrC cubic phase. (Figure 10).  
Figure 9: X-ray photoelectron spectra of the C\({}_{18}\) lines of the composites containing zirconium-carbide prepared in RF plasma.  
The HAADF image and elemental maps show the distribution of Zr and O to be very similar. This may be partly attributed to the presence of ZrO\({}_{2}\) and partly to the ZrC\({}_{0.58}\) phase embedded in a carbon matrix containing a homogeneous oxidized surface. The zirconium-rich ZrC\({}_{0.58}\) phase surface may contain some amount of oxygen, as oxycarbide species were detected by XPS as well.  
A cubic ZrC\({}_{0.94}\) (Fm3m) main phase was identified in L-Zr-N-1000-P-H\({}_{2}\) sample by the selected area electron diffraction (SAED) pattern (Figure 11b). Oxygen mainly appears in the smallest particles, which might be attributed to the oxidation of the carbon surface.  
The plasma-treated L-Zr-N-1000-P-H\({}_{2}\) sample mainly contains ZrC\({}_{0.94}\) (Figure 5), which may be attributed to the reaction of zirconium dioxide particles passing through the plasma flame, evaporating, and reacting with carbon. The appropriate residence time for particles in the hottest zone of the plasma varied depending on the particle sizes and the actual thermal trajectory of the individual particles.  
Figure 10: Images (**a**,**c**), SAED pattern identifying the presence of cubic ZrC (**b**) and HAADF image (**d**) of L-Zr S-1400-8 sample, together with the elemental distribution maps for zirconium (yellow), oxygen (red) and carbon (blue).
# HS-SPME Combined with GC-MS/O to Analyze the Flavor of Strong Aroma Baijiu _Daqu_
## 3 Results
### Analysis of Daqu at Different Maturity Stages by HS-SPME Combined with GC-MS
_Daqu_ used in production is made from wheat, rich in aldehydes and ketones [24]. Therefore, the type and content of aldehydes and ketones in S-0D are relatively high, which might be related to the high content of aldehydes and ketones in grain raw materials. Ten species of aldehydes decreased significantly after the early fermentation (about S-4D) from Table 1. With the progress of fermentation, aldehydes and ketones were generated by non-enzymatic chemical reactions. Acid compounds are the primary metabolites and the premise substances for the synthesis of other compounds. The changes in the fermentation process are mainly reflected in the changes in the relative content. After the early fermentation (about S-4D), the range of acid compounds reached the maximum (11.086%) and then decreased slowly. Alcohols and phenols are primary metabolites, showing the same trend of change. The content of alcohols and phenols first increased and then also reduced during _Daqu_ ripening. Furan compounds have furan heterocyclic structure and strong activity. With the fermentation, the kinds of furan compounds increased significantly, but the overall content of furan compounds decreased. Eight furan compounds were identified in _Daqu_ after storage (S-3M). Furtryl alcohol content in S-3M increased, which showed coffee aroma, malt aroma, and baking aroma. Terpenes with isoprene as a structural unit multiple and their oxygen-containing derivatives widely exist in plants [25]. Therefore, the species (9 species) and content (27.399%) of _Daqu_ (S-0D) were higher, and the species and content decreased significantly after early fermentation. PCA analysis was performed based on data obtained from area percentage at different maturity stages _Daqu_ in Table 1 to reveal the distribution of samples and detect outliers. PCA score plots of varying maturity stages _Daqu_ were shown in Figure 3B. The first principal component (PC1) accounted for 42.8% of the variance, and the second principal component (PC2) accounted for 29.4%. Two components (PC1 and PC2) explained 72.2% of the variance. Different maturity stages _Daqu_ were separated in the PCA score plots. The variables have no outliers and are within the 95% confidence interval. According to Figure 3B, the S-0D sample was significantly different from the others, and the S-4D and S-6D fermentation samples showed similarities. In addition, S-10D, S-22D, and S-3M have a certain similarity, but S-3M can be appropriately distinguished from the other two samples.
## 3 Materials and Methods
### Soil and Water Assessment Tool (SWAT) Model  
The SWAT semi-distributed hydrological model is developed under the collaboration of the U.S. Department of Agriculture and the Texas A&M University [19]. Based on a web-based popularity comparison of modelling tools in water, soil, and waste applications conducted by [PERSON] et al. [20], the SWAT model is selected as the most popular model. This is because the model can be executed at different time-scales (i.e., sub-daily, daily, monthly, and annual), with extensive hydrology and pollutant transport applications. More information of the SWAT model is available in on-line documentation [21; 22].  
The model has been successfully applied in several basins of Malaysia; i.e., the Johor River Basin [23; 24], Bernam River Basin [25], Bukit Merah Reservoir Catchment [26], and Hulu Langat Basin [27]. To date, the only SWAT modelling of the KRB is conducted by [PERSON] et al. [28] to study the climate change impact on water resources. However, most of the Malaysian SWAT studies validated the model using monthly streamflow data, so the SWAT capability in daily streamflow simulation is less understood. Therefore, this study also intended to understand its reliability in a daily streamflow simulation of the KRB. In this study, we used SWAT version 2012 (Revision 635) with the ArcSWAT interface version 2012.10_2.16.
# Plant Synthetic Promoters
## 2 Sections of Plant Promoters
### Plant Enhancers  
Enhancers can regulate gene expression over large distances, independent of orientation [122; 123]. Although the plant enhancers stimulate target gene expression over a distance of up to 140 kb, they could also be found within up to 400 bp from TSS (Figure 1) [124; 125; 126; 127; 128]. Moreover, differences between enhancers and promoters are often fuzzy, and their roles are interchangeable, as some promoters (epromoters) could function as enhancers [129].  
Enhancer activation requires the binding of an initial set of _trans_-factors which recognize their cognate cis-elements on DNA wrapped in nucleosomes [130; 131; 132]. Following this, the signal-dependent _trans_-factors interact with cis-elements, these being more degenerate in enhancers than promoters. These support the protein-protein interactions and strengthen the cooperativity of their binding to DNA [133; 134; 135; 136; 132]. Together with _trans_-factors, various coactivators such as the mediator complex, lysine acetyltransferases p300, and CREB-binding protein (CBP) are recruited to maintain the nucleosome-free chromatin state (Figure 1) [137; 138; 139; 140; 141]. As a result, the plant active enhancers pea PetE and maize b1 are enriched in H3/H4 ac and H3K9/K14 ac, respectively [142; 127]; however, elevated H3K27 ac only acts as a marker of active enhancers in animals, not in plants [143; 144]. Another epigenetic modification typical of active enhancers is the monomethyllation of H3K4 (H3K4 me1) [145]. It is possible that H3K4 me1 is required for nucleosome removal, and H3K27 ac for the production of the short (\(\sim\)200-nt) transcripts known as enhancer RNA (eRNA) [145; 146]. Nascent eRNA in active enhancers recruits repressive histone and DNA modifiers, as polycomb repressive complex 2 (PRC2) and DNA methyltransferase 1 (DNMT1) to further protect the open DNA state from their suppressive influence [147; 148].  
Enhancers communicate with promoters mostly through looping interactions, engaging nuclear matrix components such as lamins, to bring the two gene elements into close proximity (Figure 1) [127]. Although the relationship between these interactions and gene activity is unclear, several models suggest that the pivotal role in creating linking contact is played by mediator/RNA PoIII, polycomb repressive complex, or trans-factor dimerization. Moreover, low-affinity interactions or trans-factors, transcriptional co-activators, and other proteins can create protein aggregates, promoting and reinforcing enhancer-promoter association [149; 150; 151].  
Therefore, the activation of enhancer and the maintenance of its active state is a coordinated, multistep process, precisely regulated through the recruitment of particular proteins that are sensitive to molecular signals, modifying their performance. Thus, the activity of mediator subunits, affecting the functions of the entire enhancer is regulated by crucial plant phytohormones. Among them are jasmonic acid (JA) regulating MED25, ABA controlling the MED18 or general plant defense processes affecting MED21, MED19a and CKD8 activity [34].
## 4 Graph-Based Classification with Human Engagement
### Human-in-the-Loop Classification
#### 4.2.4 Implementation Details  
For training the DeepWalk model with our malware-feature graph, we set the walk length (L), the number of paths per each node (P), the window size (\(w\)), and the dimensionality of the latent space (z) as 80, 10, 10, and 32, respectively. These values have been obtained via an extensive grid search. Stochastic gradient descent with a learning rate of 0.025 was used for model training. Hierarchical softmax [9], which was used in other embedding models, such as word2 vec [28], was also employed. We tried several values for \(\theta\) within \(\{1.2,1.5,2,3\}\).
## 2 Numerical Modelling Using Plaxis 2D
### Material Input Parameters  
#### 2.3.1 Soil Material Input Parameters  
There are three main soil materials that are used in this study. One is the backfill soil, which is assumed to be a granular or gravelly material. The constitutive model used to simulate the behavior of the soil backfill is the hardening soil (HS). The HS model is an advanced model for the simulation of soil behavior [23]. The HS model is an elastoplastic type of hyperbolic model that accounts for the stress-dependency of stiffness moduli, which results in accurate behavior of the granular soil during the loading stage. In the HS model, the soil stiffness is defined more accurately by using three different stiffness parameters: (1) secant stiffness in standard drained triaxial test, \(E_{50}^{ref}\); (2) tangent stiffness for primary oedometer loading, \(E_{oat}^{ref}\); and (3) unloading and reloading stiffness, \(E_{w}^{ref}\), which is by default equal to \(3\times E_{50}^{ref}\)[23]. Several related studies also used the HS model to simulate the backfill soil including [PERSON] et al. [19], [PERSON] et al. [15,21], and [PERSON] et al. [25, 26, 27]. In this study, the parameters used for the backfill soil were taken from the study of Ardah  
Figure 6: Plaxis 2D numerical model configuration for IB (Case 3).  
et al. [15]. Yet, the advanced parameters are obtained by default in Plaxis 2D. Consequently, the parameters of the soil materials used in this study are summarized in Table 1.  
Furthermore, the second and third soil materials used in this study are the foundation soil and the retained original soil, which are modelled using linear elastic (LE). Both are assumed to have stiff properties in order to eliminate the influence of foundation deformation on the behavior of the bridge models. In addition, the influence of the pore water pressure is not considered in this analysis. Thus, all soil materials are modelled with drained properties. Literature reveals that the shear strength of the actual soil-structure interface is generally less than the surrounding soil [22; 25; 26; 27; 28]. Studies conducted by [PERSON] and [PERSON] [29] and [PERSON] et al. [30] on geogrid reinforcements were able to replicate the results of the experiment using a strength reduction factor of 0.67. In this study, the interface coefficient at the backfill soil and reinforcements, as well as at the wall facing elements, is computed as \(R_{inter}=\tan\delta/\tan\varphi\)[27; 31], where \(\delta=2/3\cdot\varphi\) and \(\varphi\) is the friction angle of the soil material. The interface coefficient, \(R_{inter}\), for the soil foundation and the retained original soil is assumed to be rigid and assigned a value of 1 (see Table 1). The fourth soil material used in the present study is the granular soil inside the gravel bags, which is only used in Case 1A-C models. The granular soil inside the gravel bags are assumed to have the same parameters as the adjacent backfill soil to avoid complexity of the results. Considering both materials are compacted and placed in a similar manner, the parameters are reasonably expected to be similar.
## 3 Results and Discussion
### Scanning Electron Microscopy Analysis  
The presence and uniform distribution of the carbon nanofillers disposed in the HDPE matrix have been disclosed by applying the scanning electron microscopy instrumental technique. SEM images of the pristine GNPs and MWCNTs verified their high-quality morphological parameters (see Figures S1 and S2). Two types of nanocomposite sample pretreatment, including cross-sectional breaking under liquid nitrogen or acid-etching procedure [20], were tested because of the specifics of the crystalline spherulite morphology of HDPE hindering the readily disclosure of the carbon nanoparticles. Distinguishable graphene flakes popping out from the composite matrix (Figure 7a) or laying down over its cross-sectional surface (Figure 7c) were observed by SEM imaging using a secondary electron detector. The scans have been performed right after the cutting of the samples made in liquid nitrogen in order to avoid the impending polymer relaxation that would not allow the visualization of the carbon nanofillers. A heap of well-dispersed carbon nanotubes can be seen on the surface of the etched monofiler nanocomposite sample 4 wt%MWCNT/HDPE (Figure 7b), explaining its already-established excellent nanomechanical and thermal properties. A comparison between that SEM micrograph and the SEM image of the neat polymer, which has not experienced acid etching (Figure 7d), discloses the adequacy of this type of sample pretreatment, leading to the removal of the upper spherulite layer and uncovering of the carbon nanotubes in the composite structure (see Figure 7b).  
Figure 7: SEM micrographs of the following specimens: 2 wt%GNP2 wt%MWCNT/HDPE (**a**), 4 wt%MWCNT/HDPE (**b**), 4 wt%GNP/HDPE (**c**), and the pure HDPE as a reference (**d**).
# Product Model Derivation from Feature Model and Formal Specification
## 3 Product Derivation Based on Feature Model and Specification Approach
### Feature Model  
The feature model uses a tree-like hierarchical structure to describe the relationship between features, reflecting the commonalities and variability of features. In order to illustrate the feature model, the Tourism Management System (TMS) is given as an example, which is used in the following sections. The main function of TMS is to customize personalized travel plans according to user needs and complete necessary transactions. The feature model is shown in Figure 1.  
The root node, in this case, is called _Tourism Management_, which can be decomposed into multiple sub-features. A _mandatory_ relationship dictates that children are necessary. For example, each product must have _login_ and _registration_ functions. An _optional_ relationship means that a child node can be selected or unselected. For example, the _Reserve_for_Tour_ and _Tour_Info_ can be included in any product. An _alternative_ relationship represents that one of the sub-features must be selected. For example, _UpdateProfile_ and _Query_User_ cannot appear in the same product. The _Or_ relationship representation can contain one or more sub-features. For example, _Reserve_for_flight_, _Reserve_for_Bus_, _Reserve_for_Hotel_, can be included in one or more products simultaneously. The three Boolean logic expressions at the bottom of the figure indicate the constraints between features. For example, the logic expression \(UpdateFile\Rightarrow\
eg Reserve\) for  
Figure 1: The feature model of Tourism Management.  
_Flight_\(\cap\
eg ReservoirHotel\) means that if \(UpdateFlight\) is selected, then \(ReserveforFlight\), \(ReserveforBus\) and \(ReserveforHotel\) cannot be selected.