esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Preprocessing of Regular and Irregular Gridded Ocean Scalar Data
Data interpolation: Concerning the first dataset, interpolation in the vertical direction is necessary due to its nonuniform depth intervals. We utilize the spline interpolation method to interpolate the data in the depth range from 0 m to \(-\)2000 m with a 5 m interval, resulting in a total of 401 vertical layers. Similarly, horizontal interpolation is required for the second dataset. The cubic spline interpolation method is employed to generate regular latitude and longitude grids, and the horizontal grid size of each depth layer is 300 \(\times\) 300 after interpolation, containing a total of 90,000 data points. 2. Volume texture generation: Volume textures, also known as 3D textures, are generally used to store volume data, enabling color mapping during volume rendering. Volume textures are a logical extension of traditional 2D textures. Whereas a 2D texture is typically a picture that provides surface color information for a 3D model, a volume texture is composed of multiple 2D textures grouped together to describe three-dimensional spatial data. Volume textures are particularly well suited for shader programming as they can be efficiently accessed and manipulated in shaders, and their sizes are compact enough to conserve memory resources consumed during the visualization process. Therefore, in this study, we ultimately process the data into volume textures. To generate the volume texture, the data in each JSON file are first organized into a three-dimensional array with a shape of (\(N_{longitude}\), \(N_{latitude}\), \(N_{depth}\)), where \(N\) represents the number of data layers in each corresponding dimension. After mapping the data values to the range of 0 to 255, the data are subsequently converted into a RAW file since the RAW image format is one of the most commonly used volume texture file formats. The data mapping process is illustrated in Equation (2), where \(v\) represents the original data value, \(v^{\prime}\) represents the mapped data value, and \(v_{min}\) and \(v_{max}\) denote the minimum and maximum data values, respectively. \[v\prime=\frac{(v-v_{min})\cdot 255}{v_{max}-v_{min}} \tag{2}\]
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Plasma Irradiation
#### 2.7.1 Active Nitrogen N\({}_{2}^{*}\)  
[PERSON] et al. [98] investigated oxygen-plasma-irradiated MoS\({}_{2}\) mono-layers deposited on sapphires at 450 \({}^{\circ}\)C. Figure 22 shows Raman scattering of irradiated MoS\({}_{2}\) mono-layers. Raman A\({}_{1g}\) mode shifted 1.79 cm\({}^{-1}\) towards a higher wavenumber and E\({}_{2g}^{1}\) 1.11 cm\({}^{-1}\) towards a lower wavenumber after 3 min of N\({}_{2}\) plasma irradiation. The peak intensity ratio \(I_{A_{1g}}/I_{E_{2g}^{1}}\) and FWHM of the \(E_{2g}^{1}\) peak increased with plasma irradiation time. XPS investigations indicated that N-Mo bonds formed during the irradiation and the binding energies (calculated from Mo-3\(d^{5/2}\), Mo-3\(p^{3/2}\), and S-2\(p^{3/2}\) peaks) shifted towards lower binding energy after N\({}_{2}^{*}\) irradiation. The valence band maximum (VBM) reduced to 0.9 eV after 1 min of irradiation and 0.5 eV after 3 min of irradiation from 1.0 eV of pristine MoS\({}_{2}\) mono-layer.  
[PERSON] et al. [124] doped MoS\({}_{2}\) with nitrogen through a remote N\({}_{2}\) plasma surface treatment. Nitrogen covalently bonded to MoS\({}_{2}\) upon nitrogen plasma exposures and substituted chalcogen sulfur of MoS\({}_{2}\). The nitrogen doping converted MoS\({}_{2}\) to \(p\)-type and changed electrical properties. The nitrogen concentration in the doped MoS\({}_{2}\) was controlled through adjusting N\({}_{2}\) plasma exposure time. XPS measurements indicated that binging energies of Mo-3d\({}_{5/2}\) and S-2p\({}_{3/2}\) decreased with increasing exposure time.  
Mono-layer MoS\({}_{2}\) heterojunctions, such as intrinsic GaN/\(p\)-type MoS\({}_{2}\) heterojunction, were also irradiated by N\({}_{2}\) plasma under UHV conditions [98]. The values of VBM were reduced to 0.5 eV for N\({}_{2}^{*}\) irradiated MoS\({}_{2}\) layers.  
Figure 21: EDS measured stoichiometry, showing an initial value of \(S/Mo=2.01\pm 0.07\), as expected for CVD-grown MoS\({}_{2}\) where a small amount of sulfur vacancy (\(\sim\)0.5%) can be present after growth [79]. Reprinted with permission from Reference [79]. Copyright © 2016 American Chemical Society.
## 4 Results and Discussion
### Aerodynamic Characteristics  
Figure 6 shows the average value of the lift-to-drag ratio (\(L/D\)) with the minimum and maximum values in each case. The Continuous and Burst cases show the \(L/D\) value superior to the PA-OFF cases except for the Burst case at \(\alpha=4\). This result indicates that the flow control using PA helps the improvement in the aerodynamic characteristics even at lower angles of attack than the stall angle.  
Figure 7 shows the lift coefficient (\(C_{\rm L}\)) and drag coefficient (\(C_{\rm D}\)) in each case. The \(C_{\rm L}\) of the Continuous case was higher than the PA-OFF case in all cases, while \(C_{\rm L}\) of the Burst case at \(\alpha=4\) and \(8\) was lower than PA-OFF. The \(C_{\rm D}\) values of Continuous and Burst cases were more stable than the PA-OFF case at any angle of attack and control method, and the fluctuation was slight. At the post-stall angles of attack, \(L/D\) improved by a \(C_{\rm L}\) increase and a \(C_{\rm D}\) decrease [23]. However, at \(\alpha=8\), which is a pre-stall angle of attack, \(C_{\rm L}\) of the Burst case was lower than the PA-OFF case. On the other hand, the \(C_{\rm D}\) of the Burst case was lower than the PA-OFF case, and as a result, \(L/D\) was higher than the PA-OFF case. In other words, at the pre-stall angle of attack, the reduction of \(C_{\rm D}\) contributes more to the improvement of \(L/D\) than the improvement of \(C_{\rm L}\).  
Figure 6: Lift-to-drag ratio (\(L/D\)) versus angle of attack (\(\alpha\)).  
Figure 8 shows the breakdown of \(C_{\text{D}}\): viscous drag (\(C_{\text{D}_{\text{v}}}\)) and pressure drag (\(C_{\text{D}_{\text{p}}}\)) in each case. Although there was no significant change in \(C_{\text{D}_{\text{v}}}\) by using PA, \(C_{\text{D}_{\text{p}}}\) decreased in both pre-stall and post-stall angles. In particular, the reduction in \(C_{\text{D}_{\text{p}}}\) was more remarkable in the Burst cases than in the Continuous cases. The \(C_{\text{D}_{\text{p}}}\) reduction effect of the Burst cases is discussed in detail in the following section (Section 4.3).
# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 4 Trustworthy, Security and Privacy in Crowdsourcing Internet of Things
### Trustworthy in Crowd-IoT
The authors focused on two aspects: (1) Trust-aware data quality estimation; and (2) Task valuation optimization with team connectivity. For the first aspect, they first proposed a trust-aware quality estimation for crowdsourcing in social IoT (termed as TAQ-Crowd). Figure 14 shows the TAQ-Crowd model. There are two major components in TAQ-Crowd. The first component is the TAQ model, an unsupervised probabilistic graphical model to estimate sensing quality. The model utilizes trustworthy relationships to increase the quality estimation. The second component is a computationally efficient mechanism that approximates the maximal overall task valuation and ensures communication connectivity for collaborated task execution. The authors designed a task assignment algorithm termed as CS-Selection. Concerning cost constraints from route planning, a variation of the Traveling Salesman Problem (TSP) algorithm is used to produce a routing network for team communications. The proposed TAQ-Crowd framework is evaluated through simulations. The results showed the proposed TAQ-Crowd model can achieve good performance and gives near-optimality results for the task valuation. [PERSON] [51] considered both trust and privacy-preserving in their solution. They proposed a novel architecture of edge-crowd integration for cooperative trust and privacy preservation in social IoT. In their approach, crowdsources such as mini-edge servers are used to form an edge-crowdsourcing network. The network model, which consists of Stations (BSs) and the Access Points (APs), is shown in Figure 15. Their model contains multiple query manager systems (Main Query Manager (MQM), Crowd Query Manager (CQM), and Individual Query Manager (IQM)). For privacy, data flow, device confidence and authorization, and device authentication are included. For trust, their approach uses crowd-resources to maintain trust-based on-demand trust relaying system. The authors presented a case study for preventing fake news in S-IoT by using the proposed computational paradigm for trust and privacy preservation through edge-crowdsourcing. Figure 14: Crowdsourcing in social IoT (TAQ-Crowd) [50].
## 4 Results and Analysis
### Influence of Roadway Height-Width Ratio
#### 4.1.2 Horizontal Displacement of Roadway Sidewall  
The relation between the height-width ratio \(h/w\) of a roadway and the maximum horizontal displacement of the roadway's sidewall is illustrated in Figure 9. As the roadway's height-width ratio \(h/w\) increases, the horizontal displacement of the roadway sidewall increases linearly. For each unit increase in the roadway's \(h/w\), the maximum horizontal displacement of the roadway sidewall increases by about 11.33 mm. For example, when \(h/w=0.25\), the deformation of the sidewall is small, only 1.99 mm. When \(h/w=2.00\), the deformation of the sidewall increases to 22.21 mm. It can be seen that the effect of the height-width ratio \(h/w\) of a roadway on the displacement of the rock mass of the roadway is obvious. Therefore, we should provide timely and effective support for a roadway with significant damage and deformation in the field.
## 3 Results and Discussion
### Antifungal Activity  
The _Helianthus_ essential oils were screened for antifungal activity against three potentially pathogenic fungi, _Aspergillus niger_, _Candida albicans_, and _Cryptococcus neoformans_, as shown in Table 2. The most susceptible fungus was _C. neoformans_. Both _H. annuus_ \"Chianti\" and _H. strumosus_ essential oils showed minimum inhibitory concentration (MIC) values of 78 \(\upmu\)g/mL. It is tempting to suggest that the major component, \(\alpha\)-pinene, is responsible for the observed anti-_Cryptococcus_ activity; all three _Helianthus_ essential oils have around 50% \(\alpha\)-pinene. Furthermore, \(\alpha\)-pinene has shown antifungal activity against _C. neoformans_ with an MIC around 70 \(\upmu\)g/mL [38,39]. In addition, \(\alpha\)-pinene-rich (46.1% \(\alpha\)-pinene) commercial _Myrtis communis_ essential oil showed a similar antifungal activity against _C. neoformans_ (MIC = 78 \(\upmu\)g/mL) [26]. Conversely, commercial _Cupressus semperviens_ essential oil, with 49.7% \(\alpha\)-pinene was less active against _C. neoformans_ (MIC = 313 \(\upmu\)g/mL) [26]. There may be synergistic or antagonistic effects of \(\alpha\)-pinene with minor components. Limonene [39,40] and \(\beta\)-pinene [39], have also shown antifungal activity against _C. neoformans_; camphene, however, was inactive [41]. Although we do not know which of the enantiomers is present in the _Helianthus_ essential oils, we have screened both (+)- and (-)-\(\alpha\)-pinene, (+)- and (-)-limonene, and (-)-\(\beta\)-pinene against the three fungal strains, as shown in Table 2. Consistent with previous investigations, (-)-\(\beta\)-pinene and (+)-limonene both showed activity against _C. neoformans_ with MIC values of 39 and 78 \(\upmu\)g/mL. Furthermore, both enantiomers of \(\alpha\)-pinene were active against _C. neoformans_; MIC = 20 and 39 \(\upmu\)g/mL for (+)- and (-)-\(\alpha\)-pinene, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{3}{c}{**Fungal Species**} \\ \cline{2-4}  & _Aspergillus niger_ & _Candida albicans_ & _Cryptococcus neoformans_ \\ \hline _H. annuus_ \"Chianti\" & 625 & 625 & 78 \\ _H. annuus_ \"Mammoth\" & 625 & 625 & 156 \\ _H. strumosus_ & 625 & 1250 & 78 \\ (+)-\(\alpha\)-Pinene & 625 & 313 & 20 \\ (–)-\(\alpha\)-Pinene & 156 & 625 & 39 \\ (–)-\(\beta\)-Pinene & 156 & 625 & 39 \\ (+)-Limonene & 1250 & 625 & 78 \\ (–)-Limonene & 2500 & 1250 & 313 \\ Amphotericin B & 0.78 & 0.78 & 1.56 \\ \hline \hline \end{tabular}  
* Each MIC determination was carried out in triplicate.  
\end{table}
Table 2: Antifungal activities (minimum inhibitory concentration (MIC), \(\upmu\)g/mL) of _Helianthus_ essential oils and major components \({}^{\text{a}}\).
## 4 Branch-and-Price-and-Cut Algorithm
### Enhancement Strategies
#### 4.8.1 Strategies for Accelerating the Column Generation Method  
One major drawback of column generation is the need for repetitive applications of an algorithm to solve the strongly NP-hard pricing subproblems. This inherent computational complexity can significantly impact the efficiency of the column generation method. In this paper, we attempt to address this challenge by introducing the following strategies aimed at enhancing the performance of the column generation method.  
_(1) Heuristic label setting algorithm._ Due to its exact nature, the label setting algorithm can be time consuming when dealing with realistic-sized instances. Given that any column with a negative reduced cost can lead to a better objective value of the RLMP, it does not need to solve the subproblem to optimality via an exact approach in every iteration. In light of this, we can heuristically solve the pricing subproblems to reduce the number of calls to the exact label setting algorithm. The exact algorithm is only activated in cases where the heuristic is unable to identify columns with negative reduced cost. This paper presents a heuristic variant of the exact label setting algorithm by ignoring dominance rules: \(V_{i}^{1}\leq V_{i}^{2}\). This relaxed dominance rule results in a considerably greater number of dominations, enabling the elimination of a much larger number of labels during the extension process. As a result, it is possible to rapidly identify columns with negative reduced cost.  
_(2) Decremental state-space relaxation._ A powerful approach to accelerating the label setting algorithm is the decremental state-space relaxation technique, introduced by [PERSON] et al. [38] and [PERSON] and [PERSON] [39]. This technique has been extensively applied to improve the performance of branch-and-price algorithms for a variety of routing  
Figure 6: Schematic illustration of the arc flow branching.  
problems [40; 41; 42]. Initially, the pricing subproblem is solved in a relaxed manner, where the elementary visit constraints are temporarily eliminated, and nodes can be visited repeatedly. If the relaxed subproblem yields an optimal solution with no repeated node visits, that solution is also optimal for the original subproblem. If some nodes are visited more than once, they are included in a set, represented as \(\Theta\). The label setting algorithm is then re-applied to solve a more constrained pricing subproblem, where the elementary visit constraints are enforced only on the nodes in the set \(\Theta\). This iterative process continues, with the set \(\Theta\) incrementally expanding to include additional nodes that violate the constraints. The iterations terminate once the solution to the relaxed subproblem fully satisfies the elementary constraints, ensuring optimality for the original subproblem. By focusing computational effort on enforcing elementary visit constraints only for problematic nodes, this approach significantly improves the efficiency of solving the subproblem.
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 3 Advances in Physical Modeling Method
### 1-g Modeling
#### 3.2.3 Load-Penetration Behavior and Soil Flow Dynamics in Sandy Soil  
To explore the load-penetration behavior and the soil flow dynamics in sandy soil surrounding the cutting edge of a circular open caisson, [65] a series of 1-g model studies were conducted; the test setup is shown in Figure 6. A square tank with a height and width of 350 and 450 mm, respectively, was used to carry out the test in order to minimize boundary effects and provide an accurate depiction of the soil-structure interaction. Different caisson models with radii ratios of 0.615, 0.737 and 0.783, cutting-edge widths of 25, 12.5 and 16.5 mm, respectively, and various tapered angles of 30, 45, 60, 75 and 90\({}^{\circ}\) were used. These tests considered both full and half open caissions, varying the tapered angles of the cutting edge. Utilizing image-based deformation measurement techniques, the study evaluated the soil flow mechanism and concluded that during sinking, both slope angle and blade foot depth significantly affect the resistance of the foot blade and the flow behavior of the soil adjacent to the blade foot.  
Although the research was restricted to 1-g model tests, which may not adequately capture the complexities of field conditions and the study only examined sandy soils, leaving the behavior in other soil types unexplored. However, the results offer important insights into the mechanics of caisson penetration and can be helpful in the design and construction of circular open caissons in sandy environments. The study discovered that  
Figure 5: Shows experimental setup after [64].  
the resistance provided by the soil to the cutting edge and the soil flow surrounding it varied continuously during the sinking process.
# PRANAS: A Process Analytics System Based on Process Warehouse and Cube for Supply Chain Management
## 4 Applications to Process Analytics
### Decision Point Analysis  
In this subsection, we introduce a hybrid approach of process-oriented and data-oriented analytics. Decision mining, also known as decision point analysis, is an interesting approach that can potentially take a full advantage of the PRANAS. The authors of [21; 22] have introduced similar techniques, in which a separation between control flow and data flow was distinguished. This characteristic particularly suites the PRANAS, since it can clearly separate the event log that is represented by the control-flow perspective as well as by the data-flow perspective. Suppose decision points in the event log are provided, and in each decision point the process splits in different branches. These branches are assigned as a dimension of the process cube. For example, in Figure 3, the events generated by companies of different sizes were taken to be analyzed. If a manager is interested in analyzing the branches of a specific decision point, the rolling-up operation can be used to extract the corresponding data. Figure 7a shows the discovered process model with decision points and data annotations obtained with the ProM plugin presented in [22]. Moreover, Figure 7b depicts a decision tree classifier from the decision point of company size obtained by rolling up the first two branches of the process, and then a decision tree was generated. With the result of the decision tree classifier, the effects of specific attributes on the path taken by the running process can be analyzed, which can help managers predict performance measures such as completion time or costs.
## 3 Results and Discussion
### Electrochemical Characterization of the TPU electrodes  
The electrochemical characterization of the TPU electrodes revealed clearly worse performance when compared to PLA. On the one hand, the CV tests did not show any well-defined oxidation or reduction peaks (Figure 4). On the other hand, the EIS analyses showed relatively large values of charge and mass transfer resistances. The resistance associated with the electrodes is in the order of 100 times higher than that modeled for PLA electrodes. Moreover, the data fitting of the EIS experiment showed that PLA electrodes presented a pure capacitor in the place that TPU electrodes revealed a constant phase element modeling the double layer as an imperfect capacitor (Figure 5). Diffusion resistance is also higher, in the order of 100 times, except for the 0.6 electrode, and there is a higher diffusion time constant (Table 2). On the other hand, the resistance associated with the electrode and the current collector (R\({}_{\text{s}}\)) have similar values in both materials, which seems to associate the low performance of the TPU to the double-layer phenomena and the charge transfer resistance (R\({}_{\text{cl}}\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & NP & 0.2 & 0.6 & 1 & 1.4 & TH \\ \hline Rs (\(\Omega\)) & 14.22 & 6.009 & 7.291 & 5.821 & 7.637 & 4.169 \\ C (F) & 3.07\(\cdot 10^{-5}\) & 1.42\(\cdot 10^{-5}\) & 2.88\(\cdot 10^{-5}\) & 2.65\(\cdot 10^{-5}\) & 6.68\(\cdot 10^{-5}\) & 3.5\(\cdot 10^{-5}\) \\ Rct (\(\Omega\)) & 647.9 & 713.7 & 563.3 & 687.4 & 369 & 1450 \\ Rd1 (\(\Omega\)) & 3.08\(\cdot 10^{2}\) & 2.81\(\cdot 10^{2}\) & 1.80\(\cdot 10^{2}\) & 2.06\(\cdot 10^{2}\) & 1.77\(\cdot 10^{2}\) & 5.27\(\cdot 10^{2}\) \\ td1 (s) & 5.959 & 7.802 & 6.78 & 9.678 & 9.56 & 7.376 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrical equivalent circuit data for PLA electrodes.  
Figure 3: Electrochemical impedance results for the PLA electrode. (**A**) EIS and (**B**) electric equivalent circuit.