esa-sceva/satcom-chunk-collection huggingface dataset:
# SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams
## 3 SPinDP: Stream Purifier in Distributed Platform
### Plan Manager  
The third component, Plan Manager (Plan Manager is available at [[https://github.com/dke-knu/i2](https://github.com/dke-knu/i2) am/tree/master/i2 am-core/i2 am-plan-manager]([https://github.com/dke-knu/i2](https://github.com/dke-knu/i2) am/tree/master/i2 am-core/i2 am-plan-manager) (accessed on 1 December 2023)), and the fourth component, Shared Storage, both serve functions for metadata sharing and execution management among the components. SPinDP aims for a modular structure that separates each function. However, this structure can make each core component seem like a separate technology, so we need to provide an integral functionality that organically connects them to operate as a single platform. The Plan Manager and Shared Storage play an important role in enabling the use of such a platform that integrates the modularized functions into a unified form. By integrating these components, the SPinDP platform can prevent issues that frequently arise in existing purification methods due to frequent algorithm modifications, framework-specific settings, and code redistribution, resulting in input stream loss.  
The Plan Manager [28] is a UX/UI-based technology introduced to manage data transmission and integration between the main system, expansion system, and integrated interface. This technology supports the management of sampling and filtering processes between Data Stream Processing Engine and Purification Library through plans that define every stage of stream processing, as well as by monitoring the algorithm processing status. In SPinDP, a plan is divided into three stages: source (input), Topology (algorithm), and destination (output), with one source and one destination each, and multiple Topologies per single source.  
Figure 6 shows the overall structure of Plan Manager [28]. As shown in the figure, it consists of a command server for RPC communication and a handler layer for plan management. When a user performs operations such as creating, deleting, executing, or stopping a plan through the Web-based integrated interface, the information is stored in JSON format and transmitted to the command server of Plan Manager. The command server distinguishes the received JSON file by command type and calls the specified handler. Following this, the called handler performs the operation according to the input command. At this time, the input command andthe various information sent to each handler are stored in Shared Storage through the handler's DB Adapter for monitoring, purifying algorithm processing, and so on.
## 4 Parameter Sensitivity Analysis
### Effect of Poisson's Ratio on Wave Behaviour  
The Poisson's ratio is generally set as 0.3 for the isotropic material model. Because there is no reference range of [PERSON]'s ratio for the same species of wood, 0.35 is set for comparison in the isotropic material model. However, in the orthotropic model, three independent Poisson's ratios  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Density (kg/m\({}^{3}\))** & **Change Percentage** & **Wave Velocity (m/s)** & **Change Percentage** \\ \hline
950 & N/A & 4920 & N/A \\
1050 & 10.53\% & 4685 & \(-\)4.78\% \\
1150 & 21.05\% & 4428 & \(-\)10.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parametric study of density for isotropic material model.  
Figure 16: Wave propagation behaviours with different densities in isotropic material model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Density (kg/m\({}^{3}\))** & **Change Percentage** & **Wave Velocity (m/s)** & **Change Percentage** \\ \hline
950 & N/A & 5000 & N/A \\
1050 & 10.53\% & 4789 & \(-\)4.22\% \\
1150 & 21.05\% & 4616 & \(-\)7.68\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parametric study of density for orthotropic model.  
Figure 17: Wave propagation behaviours with different densities in orthotropic model.  
obtained from related literature are firstly defined for three directions, and then the Poisson's ratios obtained from experimental testing are used for wave to investigate the behaviour under different sets of Poisson's ratio. The same measurement points mentioned above are used to indicate the time history. Tables 8 and 9 give the analysis results in both isotopic and orthotropic models, and Figures 18 and 19 show the corresponding wave propagation behaviours. In the isotropic material model, it can be concluded that the longitudinal wave velocity did not change too much due to the variation of Poison's ratio. In the orthotropic model, it can be observed that the Poisson's ratio has obvious variation in the tangential direction that leads to the corresponding change in the longitudinal wave velocity, even though the Poisson's ratios in other two directions are quite similar. Therefore, it can be concluded that the longitudinal wave velocity is more sensitive to the Poisson's ratio in the orthotropic material model than the isotropic material model.
# SPIN: A Blockchain-Based Framework for Sharing COVID-19 Pandemic Information across Nations
## 2 Design
### Fingerprint Authentication  
The SPIN framework employs fingerprint authentication to verify individuals holding COVID-19 certificates. Using fingerprint templates is an effective technique for authentication due to fingerprint uniqueness, as even identical twins have two different fingerprints. In addition, compared to other biometric-based techniques, the hardware used to scan fingerprints is relatively cheap and mostly available in the border security for most countries [24]. When individuals visit other countries, they are authenticated using their passport (ID) along with their fingerprint template fp to prevent fraudsters from impersonating others.  
To preserve the privacy of individuals' fingerprints and meet all the requirements described in the ISO/IEC 24745 biometric data protection standards [34], our framework uses cancelable biometrics to protect fingerprint templates, as described in [23]. The technique works by transforming the original fingerprint template in a way that it becomes practically infeasible to obtain the original template from the transformed one. If the transformed fingerprint template is compromised, a new template can be reissued and used subsequently. Note that the transformed data here are fingerprint templates and not the actual fingerprint images. Fingerprint templates represent unique features that have been extracted from actual fingerprints, which can be used for identification and authentication. Cancelable biometrics are shown to be effective in matching biometric information while preserving the privacy of the stored biometric templates [23].  
Fingerprint authentication involves two stages, the enrollment stage and the authentication stage. In the enrollment stage, the fingerprint template fp is captured from an individual, and the system then transforms it before storing it in the local database. Details of the transformation process are described in [23]. When an individual decides to travel, the individual's transformed fingerprint template T(fp) along with the key that is used for the transformation are sent to the traveler's destination country per the individual's request. The authentication stage takes place when individuals arrive at their destination country. At the border control of the destination country, the record that matches the individual's passport ID is retrieved from the local database, and the individual's transformed fingerprint templates T(fp) are matched against the ones that the country received. This puts an additional factor of authentication on top of authenticating individuals only by their passport IDs, which helps in preventing individuals' impersonation.
# The Summertime Diurnal Cycle of Precipitation Derived from IMERG
## 2 Materials and Methods
### Methodology
All times are converted from UTC to local solar time (LST) using the equation:  
\[\text{LST}=\text{UTC}+\frac{\lambda}{15^{\circ}\text{h}^{-1}} \tag{5}\]  
where longitude \(\lambda\) is in unit degrees, and UTC and LST are in unit hours. Times are referred to in LST only henceforth. The harmonic analysis is not conducted on grid boxes where: (1) latitudes are greater than \(60^{\circ}\text{N}/\text{S}\) due to the complexity of estimates over snow- and ice-covered surfaces [11], or (2) a rainfall quantity has not been calculated for more than 20% of the daily half hours (note that criterion 2 is only met for seasons other than summertime). These quality controls ensure that incomplete observations do not affect the results. Such regions are represented by blank pixels in the following global plots. In regions that are too arid or where the harmonic fitting provides a limited representation of the diurnal cycle, no statistically significant conclusions can be drawn for the diurnal cycle. Such regions are identified by the following criteria: (1) the diurnal mean accumulation is \(<\)0.275 mm; (2) \(R^{2}<0.4\); (3) the error on an amplitude or phase parameter of the harmonic function (\(A_{1}\), \(A_{2}\), \(T_{1}\), \(T_{2}\)) surpasses 50% or 3 h. These regions are highlighted by black dots in Figures 4-6 and 8, and are not commented upon henceforth. Figure 1: The summertime diurnal cycle of precipitation (**a**) occurrence, (**b**) intensity, and (**c**) accumulation for Cambodia and Vietnam (Latitude: 12\({}^{\circ}\)–14\({}^{\circ}\)N; Longitude: 106\({}^{\circ}\)–108\({}^{\circ}\)E). The black circles and corresponding error bars represent the IMERG-derived precipitation and random error for a specific daily half hour across the four-year period. Note that there are no error bars on the occurrence data as there is assumed to be zero error in the count of rainfall events. The red line represents the harmonic function (Equation (4)) fitted to the IMERG data, and the red shading represents the extents of the 95% confidence intervals on the fitted harmonic function. The green and blue lines represent the 24 h and 12 h harmonics from the fitted function, respectively, whilst the error bars on one of their peaks represent the error on the amplitude (vertical) and on the phase (horizontal). The locations of these amplitude and phase error bars are outlined by black dashed boxes. The magenta line represents the mean of the diurnal cycle \(f_{0}\), whilst the magenta shading represents the extents of the error on the mean.
## 2 Materials and Methods
### Numerical Methods
Here, the velocity profile is the definition given for the distribution of velocities in the axial direction over the cross section of the circular pipe or along the acoustic paths of a UFM. For different numbers of tested UFMs, the total cell number was in the range of 5-15 million after verifying the grid-independence. The finite volume solver in Fluent was used to calculate the time-averaged Navier-Stokes equations and the k-epsilon closure equations. The realizable k-epsilon turbulence model in combination with the wall treatment of SWF was finally employed in our simulations because it provided the best agreement with the experimental data obtained from a single tested UFM and better prediction for flow separation into the recesses after comparison with other commonly used turbulence models. Furthermore, the second-order upwind discretization scheme and SIMPLE algorithm were adopted. Figure 4: Schematic of the grid: (**a**) a single UFM; (**b**) detail profile of the recess. According to the manufacturer's instructions (Emerson Daniel-3400 DN250 and 150), the mean axial velocity measured by the UFMs can be calculated from  
\[U_{\text{UFM}}=w_{\text{A}}\times U_{\text{A}}+w_{\text{B}}\times U_{\text{B}}+w_ {\text{C}}\times U_{\text{C}}+w_{\text{D}}\times U_{\text{D}}, \tag{1}\]  
where \(w\) is the weight associated with chordal velocity, subscripts A to D indicate the path A to D, respectively, and thus \(U_{\text{A}}\) is the velocity measured along path A, etc. As for Daniel 3400, the weight coefficients are 0.1382 along paths A and D (\(w_{\text{A}}=w_{\text{D}}=0.1382\)) and 0.3618 along paths B and C (\(w_{\text{B}}=w_{\text{C}}=0.3618\)). Similarly, the space-averaged numerical velocity on a path was first obtained by extracting the velocity profile on that path from the numerical velocity field, which was then used to calculate the numerical mean axial flow velocity with the help of the same weight coefficients mentioned above. For both the experimental measurement and numerical simulation, the relative measurement error \(E\) can be expressed as  
\[E=\frac{Q-Q_{\text{in}}}{Q_{\text{in}}}\times 100\%, \tag{2}\]  
where \(Q\) can be calculated either from the UFM measured or from the UFM attained by post-processing the simulated results, and the corresponding \(Q_{\text{in}}\) is either the standard flowrate generated by a flow-standard facility or the inlet flowrate given in advance as a boundary condition.
## 3 Results and Discussion
gloeosporoides_ conidia was notably reduced in the treated samples. The detailed inoculation results presented in Table 2 show that the lesion size reduced from 13.50 to 11.00 nm as the Ag particle size increased from 92 nm to 1.2 \(\upmu\)m. In other words, the results are consistent with those of the plate fungistatic and spore germination tests, which showed that the Ag sample with a particle size of 1.2 \(\upmu\)m had particularly good fungistatic properties and a superior suppression ability of _C. gloeosporoides_. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{**Lesion Size (mm)**} \\ \cline{2-3}  & **Wound Made** & **Non-Wound Made** \\ \hline Control (Glass) & 15.56 \(\pm\) 0.76 \({}^{\text{a}}\) & 13.10 \(\pm\) 0.91 \({}^{\text{a}}\) \\
92 nm & 13.50 \(\pm\) 0.66 \({}^{\text{b}}\) & 7.00 \(\pm\) 1.95 \({}^{\text{b}}\) \\
570 nm & 11.75 \(\pm\) 0.69 \({}^{\text{c}}\) & 6.80 \(\pm\) 1.42 \({}^{\text{b}}\) \\
1.2 \(\upmu\)m & 11.00 \(\pm\) 0.66 \({}^{\text{c}}\) & 3.30 \(\pm\) 1.15 \({}^{\text{c}}\) \\ \hline \hline \end{tabular}  
* significant differences at \(p=0.05\) (ANOVA, post-hoc Tukey-HSD test).  
\end{table} 
Table 2: Efficacy of different Ag particle samples for anthracnose treatment of mango. Figure 8: Relative expression of LAC1, PKS, THR, SCD, Ecg and PEL genes following treatment with different Ag samples. SR: internal reference 18 s rRNA; LAC1: lactose gene; PKS: polyketide synthase gene; THR: tetra-HN reductase cystalone gene; SCD: cystalone dehydratase gene; Ecg: exo-glucocanase gene; and PEL: pectate lyase gene. Figure 7: Morphologies of _C. gloeosporoides_ following treatment with different Ag samples. Figure 10 presents SEM images showing the morphologies of the _C. gloeosporoides_ conidia following contact with the untreated control sample and the sample with an Ag particle size of 1.2 \(\upmu\)m. The _C. gloeosporoides_ conidia of the untreated sample had an oval cell-like appearance with a smooth surface. By contrast, the conidia surfaces for the treated sample contained small irregular fragments, indicating that exposure to the Ag particles resulted in damage to the cell surface and hence inhibited the invasion and growth of the _C. gloeosporoides_ spores during incubation. Figure 10: SEM micrographs of _C. gloeosporoides_ treated with different Ag particle samples: (**a**) glass (control) and (**b**) 1.2 \(\upmu\)m.  
Figure 9: Effects of Ag particle size on anthracose of mango: (**a**) control, (**b**) 92 nm, (**c**) 570 nm and (**d**) 1.2 \(\upmu\)m.
## 7 Optimization Techniques for Demand-Side Resources in Active Distribution Networks
### Defining Objectives and Constraints of Optimization  
The foundation of any optimization technique lies in the clear definition of objectives. There are diverse objectives associated with DSR optimization, such as cost minimization, demand response, grid stability, and environmental sustainability. There is the necessity to prioritize objectives based on specific use cases and stakeholder goals. For instance, utilities may focus on cost reduction, while policymakers emphasize carbon emission reduction. Optimizing DSRs in active distribution networks necessitates a thorough understanding of constraints. Constraints can arise from technical, regulatory, or operational aspects. Different constraints exist that need consideration, including network capacity, voltage limits, DSR availability, and regulatory requirements. Recognizing and modeling these constraints is vital to ensure the feasibility of optimization solutions.  
To translate objectives and constraints into actionable optimization strategies, mathematical models are indispensable. The mathematical modeling approaches commonly used in DSR optimization include linear programming, mixed-integer linear programming, and non-linear optimization [129]. In many cases, DSR optimization involves conflictingobjectives. Multi-objective optimization techniques are explored to strike a balance between these competing goals [130]. Methods such as Pareto-based optimization, weighted sum methods, and evolutionary algorithms are essential in handling multiple objectives. Managing constraints effectively is essential to ensure that optimization solutions adhere to real-world limitations. Therefore, it is necessary to investigate various constraint-handling techniques, including penalty functions, barrier methods, and feasibility-based approaches. This investigation, however, focuses further on how these methods address different types of constraints, ensuring that optimization solutions are both optimal and feasible. Emerging technologies such as artificial intelligence and machine learning are increasingly being applied to DSR optimization [31]. In the context of optimizing DSRs within active distribution networks, it is important to explore the role of these technologies in enhancing objective definition and constraint management. The subsections below outline and investigate the frequently employed optimization methods employed in DSRs.
# Study on Microseismic Monitoring of Landslide Induced by Blasting Caving
## 4 Microseismic Monitoring Analysis of Landslide
### Principal Component Plane Ellipticity Analysis
\begin{cases}\sqrt{\frac{\pi^{2}-c^{2}}{a^{2}}}&c<a\\ \sqrt{\frac{c^{2}-a^{2}}{a^{2}}}&c>a\end{cases} \]  
Based on the concept of moment of inertia, we can assume each point has a mass of \(m=1\), calculate the moments of inertia of these points about the \(x\) and \(y\) axes, and then assume a total mass of \(M=\sum\limits_{i=1}^{n}m_{i}=nm=n\). By applying the equivalent moment of inertia method, the equivalent radius of rotation \(r1=\sqrt{\frac{\sum\limits_{i=1}^{n}m_{i}r_{i}^{2}}{M}}=\sqrt{\frac{\sum \limits_{i=1}^{n}r_{i}^{2}}{n}}\) is derived, as illustrated in Figure 18.  
The equivalent rotational radius is used as the major and minor axes of the ellipse to calculate the ellipticity e. Based on the transformed scatter coordinates in Table 3, the length of the major axis is:  
\[a=\sqrt{\frac{x_{1}^{2}+x_{2}^{2}+\ldots+x_{n}^{2}}{n}}=\sqrt{\frac{\left(-10. 8765\right)^{2}+\left(-0.59293\right)^{2}+\ldots+54.13869^{2}}{54}}=\sqrt{ \frac{155103.8}{54}}=2872.292\]  
Figure 17: The distribution of spatial points. Figure 18: Schematic diagram of equivalent solution method. Similarly, the minor axis can be calculated as:  
\[c=\sqrt{\frac{y_{1}^{2}+y_{2}^{2}+\ldots+y_{n}^{2}}{n}}=\sqrt{\frac{9.95176^{2}+3. 109964^{2}+\ldots+\left(-33.6491\right)^{2}}{54}}=\sqrt{\frac{5207.096}{54}}=96.4277 \\ \text{so},e=\sqrt{\frac{a^{2}-c^{2}}{a^{2}}}=\sqrt{\frac{2926.5-98.5 }{2926.5}}=0.9663\]  
This indicates that the microseismic event cluster exhibits a high degree of clustering tendency, and in this case, the principal component plane corresponds to the slip plane. We know that there are tiny fractures, namely microseismic events, before a macroscopic landslide occurs in a mountain. These microseismic events occur earlier than the landslide, sometimes just a few minutes, several hours, several days or even longer before the landslide. Therefore, we can monitor the microseismic events before the landslide and analyze the occurrence of the potential sliding surface formed by the microseismic events according to the above method. We can also analyze the aggregation degree of these microseismic events on this potential sliding surface. If the microseismic events can form a distinct sliding surface and the aggregation degree is very high, it means that the possibility of a landslide is very high, thereby providing early warnings for landslides. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Event Number**} & \multicolumn{2}{c}{**Coordinate**} & \multicolumn{2}{c}{**Event Number**} & \multicolumn{2}{c}{**Coordinate**} \\  & **X/m** & **Y/m** & **Event Number** & **X/m** & **Y/m** \\ \hline  
13868\) & \(-33.6491\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coordinate values of spatial points after dimensionality reduction transformation.
## 2 Materials and Methods  
The workflow for the determination of the collocated GNSS CORS/InSAR CRs that was followed in the case of CyCLOPS infrastructure can be summarized in the following steps:  
1. Meetings with the competent authorities for the identification of candidate locations;
2. Initial assessment of the candidate location (visual inspection of site suitability in terms of geological background and natural or man-made obstructions);
3. Terrestrial and Airborne Surveying of the candidate location and acquisition of a 24 h sample dataset of GNSS observations.
4. Analysis of GNSS dataset (e.g., multipath, satellite availability) along with UAV and SAR image processing;
5. Suitability Analysis and Determination of candidate GNSS and CR locations;
6. Validation of the Suitability Analysis results and finalization of site selection;
7. Acquisition of official permits (i.e., lease) from the competent authorities.  
The suitability analysis (step e) proposed in this research was carried out in the following stages:  
1. Determination of Criteria and Constraints
2. Multi-Criteria Decision Analysis (MCDA)
3. Analytic Hierarchy Process (AHP) Analysis
4. Weighted Overlay (WO) Analysis
5. 3D Visibility Analysis  
The determination of the criteria and constraints that are involved in the proposed methodology was based on the guidelines of the responsible institutions for GNSS (i.e., IGS, UNAVCO, EPN) and the latest scientific literature on CR installation. Consequently, the criteria were categorized and classified based on the importance of one over another, using MCDA. The latter includes the following two subprocesses: (a) the AHP analysis, which was used to derive ratio scales from the criteria, and (b) the WO analysis. The number of candidate locations was then substantially reduced by applying constraints based on significant parameters, such as the underlying geology, the area ownership (state land), and others. The result of the analysis is an extracted raster layer of the most suitable locations, categorized according to the degree to which they conformed to the specified criteria. Moreover, a 3D visibility analysis is also included in the process to finalize the selection of the most suitable locations. Hence, Line of Sight (LOS) and the distance between the two CRs were checked along with the inter-visibility and distance between the GNSS antenna and CRs (for the mitigation of unwanted multipath in GNSS observations). Finally, the average incidence angle for ascending and descending passes were also considered to account for the baseplate elevation angle of the CR. The flowchart of the semi-automatic suitability analysis procedure for the determination of CRs' final locations is illustrated in Figure 2. Each step is analysed further in the following sections.