esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Blockchain Technology for Enhancing Digital Twins' Security and Transparency
## 3 Proposed Approach
### Implementation
#### 3.1.4 Stub Components  
To achieve the validation objectives designated for the middleware component within the system, multiple implementations of the underlying logic were developed to communicate with the digital twin and the data senders and receivers. Specifically, in this study, stubs were created in different programming languages, including Java 11 and C++ 11 with Python 3-bound code and are available in the paper repository. Although the task of stub development might appear dependent on the specific characteristics of each system's actor, consistent logic was applied across all implementations in order to guarantee coherence of the system as a whole.  
Thus, the implementation preserved core logic integrity across diverse programming environments, aligning the middleware's behavior consistently with the system's broader validation requirements. In particular, each stub component of the system presented a corresponding implementation of the hash storing and retrieving while implementing a system's consistent hashing procedure in order to guarantee the autonomy of the solution within the containing environment.  
To ensure consistent functionality across all system stubs, each component was designed to communicate autonomously with the blockchain subsystem, enabling seamless data exchange with the digital twin. This interaction was facilitated by the hashing validation system, which served as an intermediary component to authenticate communication within the architecture. The hashing validation system not only verified the legitimacy of exchanges but also ensured robust security by systematically rejecting any unauthorized communication. The final diagram examining a communication interaction between a sending system and the digital twin is represented in Figure 5, which describes the data flow during a canonical communication.  
In order to allow interoperability by blockchain operators, we decided to implement each of the component through the utilization of the Web3 functionalities. In fact, over its vast use in different blockchain applications, the Web3 library also presents multiple implementations in different programming languages and a large following in the blockchain community. Due to its flexibility, the Web3 library represents a suitable solution to provide a shared implementation procedure while adopting a common logic between stubs.  
Figure 4: Relationship, in terms of contract’s functions, established in the component, clarifying the role division enforced in the system.
## 4 Numerical Analysis and Discussion
### Parametric Study
#### 4.3.2 Effect of Seismic Intensity  
To study the effect of the ground motion intensity, 125 cm/s\({}^{2}\), 220 cm/s\({}^{2}\), 400 cm/s\({}^{2}\), and 620 cm/s\({}^{2}\) peak ground acceleration are considered, respectively. The vibration reduction ratios of the maximum displacement with different intensities are shown in Figure 11. The reduction ratio decreases with the increase of the ground motion intensity. The reduction effect of the transmission tower with PTMD in the longitudinal direction is greater than that of in the transverse direction. The reduction ratio of the transmission tower with the PTMD is affected insignificantly by the change of the ground motion intensity.
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 2 Geodetic and Satellites Input Data
### Gauging Data of Lake Viedma, O'Higgins and Chico  
The water levels data from lake Viedma and La Leona river were obtained from the Argentine National Undersecteriat of Water Resources ([[https://www.argentina.gob.ar/obras-publicas/infraestructura-y-politica-hidrica](https://www.argentina.gob.ar/obras-publicas/infraestructura-y-politica-hidrica)]([https://www.argentina.gob.ar/obras-publicas/infraestructura-y-politica-hidrica](https://www.argentina.gob.ar/obras-publicas/infraestructura-y-politica-hidrica)), accessed on 21 December 2021). Lake Viedma: daily scale reading values from January 2015 to 2021; the limnometric scale in La Leona river: daily values of scale from 2015 to 2021. La Leona river connects lake Viedma with Argentino lake. Daily gauge data series collected by the Chilean Water Cadaster (DGA) at the lakes O'Higgins and Chico were also used (www.dga.cl, accessed on 21 December 2021).
## 4 Aeroacoustic Results
### Scattered Acoustic Fields  
Once the direct acoustic fields on the observers and on the scattering body have been computed, the scattered acoustic field can be computed according to the Equation (16). The same acoustic mesh of the aircraft surface already illustrated in the previous paragraph and in Figure 11 has been adopted. The computational analyses required a parallel computing with 8217 GB of RAM and 107 cores for the highest frequency, that corresponds to a non-dimensional wave number, kL, of around 610.6, where k is the wave number and L is the characteristic dimension of the fuselage portion. This value was very close to the hardware limit of CIRA computational cluster, therefore it was unable to compute the scattered noise field for the 5 th BPF.  
In Figure 15, the comparison among the BEM scattered field (left side) and the free-field (right side), computed on the microphones hemisphere, is reported in terms of SPL, for the Clean case and at the 2 nd BPF. In Figure 16, the same comparison is illustrated for the BLI case. The comparisons show that the presence of the aircraft geometry modify the noise directivity pattern, especially in the middle region of the hemisphere, thus increasing the acoustic levels propagated under the engine.  
For a better comparison among the two cases at all frequencies, the acoustic directivity along X-axis, in terms of SPL, are reported in Figure 17. Free-field (label DIRECT) and scattered field (label BEM), with (label BLI) and without (label CLEAN) engine boundary layer ingestion, are presented in Figure 17. As already mentioned, the contribution of the 5 th BPF at 3333 Hz has been neglected due to the computational effort, being higher than the capability of the CPU cluster. However, a scattering effect similar to that observed in the lower range of frequencies would be reasonably expected also at the highest one. For all frequencies, the scattered noise field has higher sound pressure levels than the direct field in the middle of the hemisphere. For the lowest frequency, the BLI case has lower sound pressure levels than the clean one, while for the higher frequencies, an opposite behavior appears as already highlighted at source level in the previous paragraph.  
Figure 14: Comparison of direct acoustic field between the cases without (**a**) and with (**b**) boundary layer ingestion, along the Y axis.  
Figure 16: Scattered noise (BEM, **left**) results for frequency 2000 Hz, BLI case, compared to Direct noise field (**right**).  
Figure 17: _Cont._  
Figure 15: Scattered noise (BEM, **left**) results for frequency 2000 Hz, Clean case, compared to Direct noise field (**right**).
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Mycotoxins and Food Safety
#### 2.5.5 Patulin  
One common mycotoxin in fruit products is patulin, which is mostly present in apples and other fruit-based goods. According to recent research, PAT is frequently found in fruit products. Over the past four years, the PAT levels in meals within the European Union (EU) have mostly stayed below the regulation limits, despite the substance's ubiquitous prevalence. Nevertheless, a few products tested in the Czech Republic showed levels of 122 ug/L in apple juice, 231 ug/L in pears, and 56 ug/L in mixed fruit juices beyond these limits. In many European countries, thorough tests have been carried out for the presence of patulin. In a percentage greater than 20%, this mycotoxin was detected in the samples, indicating that the public are regularly exposed to low dosages of this mycotoxin [130]. Once tested as a possible cold remedy, patulin is a low-molecular-weight lactone hemiacetal with broad-spectrum antibacterial properties. Although it might not cause cancer, patulin is thought to be mutagenic. Additionally, negative effects on the developing fetus have been observed in animal testing [131]. Although classified as a non-carcinogen, patulin has been associated over the past few decades with adverse neurological, gastrointestinal, and immunological effects, primarily causing damage to the liver and kidneys [132].
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 3 Methodology
### Design of the Conv-for-Sum Module
#### 3.3.1 Two Different Convolution Kernels  
To fully extract features from different sub-spaces in CSI, we adopt two convolution layers with different convolution kernels for feature extraction. The convolution layer with a larger \(3\times 3\) convolution kernel is responsible for extracting the feature information ofthe mixed channel, while the convolution layer, with a smaller \(1\times 1\) convolution kernel, is responsible for extracting the local antenna features.
## 6 Discussion  
### The Fras of Basalts in the Raohe Subduction-Accretion Complex  
Previous geochronological studies on mafic-ultramafic components within the RSAC suggested Late Triassic to Middle Jurassic emplacement ages (166-222 Ma). However, our new zircon U-Pb dating yields a significantly younger Early Cretaceous age of 117.5 \(\pm\) 2.1 Ma for the pillow basalts (Figure 1). The selected zircons exhibit distinct oscillatory zoning, confirming their magmatic origin. Furthermore, the Th/U ratios are greater than 0.1, and strict data screening was performed to ensure the credibility of the results.  
Published datasets contain overlooked younger ages aligning with our results. For instance, samples 14]H-42-02/14]H-42-04 (206 Pb/238U: 1733 \(\pm\) 22 Ma, 2285 \(\pm\) 48 Ma) and RH03-1-02/RH03-1-03 (207 Pb/235U: \(<\)172 \(\pm\) 1 Ma), reported by [21] and [41] respectively, were either statistically reconciled or excluded despite their temporal proximity to our Early Cretaceous constraints (Table 4 and Figure 6). Field observations and microscopic analyses confirm that the studied basalts retain primary magmatic textures without metamorphic overprinting or deformational fabrics, validating the reliability of our geochronological interpretations. These findings necessitate revising the basalts' chronology of the RSAC into three periods: The Early Cretaceous, Middle Jurassic, and Late Triassic.  
In addition, Late Triassic basalts are dominated by komatitite-melilitite assemblages exhibiting distinctive spinifex textures (indicative of rapid quenching). These rocks feature high-Mg lavas (MgO = 20-25 wt%) and vesicular amygdales (5 vol.%) filled with carbonate/chlorite [15; 21]. In contrast, Middle Jurassic basalts are characterized by well-developed pillow structures (20-50 cm diameter) and magnesium andesite interlayers, containing zoned clinopyroxene and apatite-ilmenite microlites within an intergranular matrix [29; 41]. These petrographic characteristics distinctly differ from those of the Early Cretaceous basalts described in this study, further supporting the existence of three discrete basalt episodes in the region.  
Figure 6: Histogram distribution of zircon U-Pb ages for basalts from Raohe Subduction–Accretion Complex: cross-validation of 206 Pb/238U and 206 Pb/237U chronometers [21; 41].
# Analysis and Assessment of a Brushless DC Outrunner Motor for Agriculture Drones Using JMAG
## 2 Propulsion System Design in Agriculture Multi-Rotor UAVs  
This section provides the specific characteristics of agricultural applications in UAVs. The iterative nature of agricultural drone propulsion system design makes it costly and time-consuming. The extensive experimental tests require specialized equipment and rigorous safety measures. Numerical methods can be included in the design process to reduce the number of experimental tests conducted. The propulsion system consists of the battery technology, the electronic speed controller, the BLDC motor, and the propeller. The mass distribution in commercial agriculture drones is shown in Figure 1; the propulsion system takes up to 24% of the nominal take-off weight. Compared to other large-sized drone applications, agriculture UAVs focus on structure integrity and payload, sacrificing autonomy. The most important design requirements are divided into four categories and introduced in Table 1.  
In order to find an efficient propeller-motor combination with an appropriate specific energy source while fitting into the desired wheelbase of the vehicle, the maximum propeller diameter is calculated.  
A maximum D1 distance of 265 cm for transportability and a minimum D3 distance of 5 cm were set for a hexarotor with the configuration shown in Figure 2. Equation (1) expresses the maximum allowable propeller diameter in terms of D3 and D1, resulting in a 33.4 inch maximum diameter.  
\[MaxD_{propeller}=\frac{D1-2D3}{3} \tag{1}\]  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Design Requirements**} \\ \hline
**Propeller** & **Motor** & **Battery** & **Structure** \\ \hline Diameter & KV & Energy density & Take-off weight \\ Pitch & Power & Weight & Maximum dimensions \\ Efficiency & Weight & C-rate & Thrust–weight ratio \\ - & Efficiency & Cells & Weight distribution \\ - & Nominal voltage & - & - \\ \hline \multicolumn{3}{c}{Overall efficiency} & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic UAV design requirements for agriculture applications.  
Figure 1: Mass distribution in commercial agriculture UAVs.  
Figure 2: Propeller and overall skeleton size characterization.
# Research on a Comprehensive Evaluation Method of Train Anti-Slip System Performance
## 4 Construction of a Comprehensive Test Platform for Anti-Slip Evaluation
### Software Design
\[F_{fw}=K_{1x}\cdot\left(x_{f}-x_{w}\right)+C_{1x}\cdot\left(\dot{x_{f}}-\dot{ x_{w}}\right) \tag{22}\]  
\[F_{cf}=K_{2x}\cdot\left(x_{c}-x_{f}\right)+C_{2x}\cdot\left(\dot{x_{c}}-\dot{ x_{f}}\right) \tag{23}\]  
\[F_{coupler}=K_{c}\cdot\left(x_{c1}-x_{c2}\right)+C_{c}\cdot\left(\dot{x_{c1}}- \dot{x_{c2}}\right) \tag{24}\]  
where \(K_{1x}\) is the longitudinal positioning stiffness between single wheel and frame, \(C_{1x}\) is the longitudinal damping between single wheel and frame, \(K_{2x}\) is the longitudinal positioning stiffness between single wheel and frame, \(C_{2x}\) is the longitudinal damping between single wheel and frame, \(K_{c}\) is the longitudinal positioning stiffness between vehicles, and \(C_{c}\) is the longitudinal damping between vehicles. In the semi-physical simulation of anti-slip control, the accuracy of establishing the wheel-rail adhesion model is crucial for the construction of the test platform. This article selects the adhesion calculation model proposed by [PERSON] [12] as the simulation model. This model can reflect the relationship between adhesion coefficient and axle load, vehicle speed, and slip rate, and is suitable for the calculation of adhesion coefficient under unstable conditions in the slipping area, i.e., slipping conditions. Both efficiency and accuracy are guaranteed, and the calculation method is shown in Equation (25) below. \[\mu=\frac{2f_{0}\big{[}(1-A)e^{-Bw}+A\big{]}}{\pi}\Bigg{[}\frac{\frac{2\chi \pi a^{2}b}{3Q\mu}s}{1+\left(\frac{2\chi\pi a^{2}b}{3Q\mu}s\right)}+arctan \left(\frac{2X\pi a^{2}b}{3Q\mu}s\right)\Bigg{]} \tag{25}\]  
In the formula above, \(\mu\) is the adhesion coefficient; \(f_{0}\) is the maximum friction coefficient of the wheel rail; \(Q\) is the axle load; \(A\), \(B\) are the friction coefficient adjustment parameters, and the values of \(A\) and \(B\) can be determined from Table 9; \(w\) is the relative sliding speed; \(a\) is the length of the longitudinal half axis in the elliptical contact area between the wheel and rail; \(b\) is the length of the transverse half axis in the elliptical contact area between the wheel and rail; \(X\) is the wheel rail contact shear stiffness; \(s\) is the slip rate. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Parameter** & **Dry Rail** & **Wet Rail** & **0.08** & **0.07** & **0.06** & **0.05** \\  & & & **Maximum** & **Maximum** & **Maximum** & **Maximum** \\  & & & **Adhesion** & **Adhesion** & **Adhesion** & **Adhesion** \\ \hline \(A\) & 0.30 & 0.20 & 0.20 & 0.20 & 0.20 & 0.20 \\ \(B\) & 0.60 & 0.60 & 0.10 & 0.10 & 0.10 & 0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Parameter values of adhesion model under different working conditions.
# Laplacian Eigenmaps Dimensionality Reduction Based on Clustering-Adjusted Similarity
## 4 Results
### Robustness Analysis  
Since our method combines clustering with dimensionality reduction, the performance of LE-CAS seems heavily depends on whether the clustering method used in previous steps. To clarify the robustness of our method, we employed two different clustering methods, \(k\)-means and kernel \(k\)-means, as the initial clustering method in the course of LE-CAS. The performance of these methods are shown as Figure 7.  
From Figure 7, we can illustrate that the performances of two clustering methods are merely close to each other on ten datasets and each method performs better than LE method, which indicates that the performance of our LE-CAS cannot be influenced much by the clustering method used in initial steps.
## 3 Results
### The Three-Dimensional Velocity and Its Accuracy Deduced by BDS and GPS Observations, Respectively  
The accuracy of estimated velocity fields for both BDS and GPS position time series is displayed in Table 3. The average accuracy of the BDS velocity field are 1.2, 1.5, and 4.1 mm/year on the north, east and up components, respectively, while they are 0.3, 0.3, and 0.7 mm/year for the GPS velocity field, which are remarkably smaller, indicating high accuracy of the GPS velocity field.  
In Figure 6, we present the trend free time series at station SXTY for both BDS and GPS results, which is located in Shanxi Province of China. There are notable seasonal signals that emerged in the trend free time series at Station SXTY. The dispersion of the BDS result is larger than GPS. For the GPS result, the estimated annual amplitudes are 5.1, 2.4, and 4.5 mm on the north, east and up components, respectively. For the BDS results, the corresponding annual amplitudes are 6.1, 0.5, and 6.2 mm. As the annual amplitudes are remarkable on the north and vertical components, the annual signals are visible in both BDS and GPS position time series with similar amplitudes estimated. While it is unremarkable on the east component, estimable in GPS results and invisible in BDS results, thus resulting in different estimation of amplitudes. To summarize the feature of seasonal signals at all 29 stations, for GPS results, 15 stations have significant annual variations on the horizontal components for GPS results, and 13 stations possess clear annual variations on the vertical component for GPS results. These annual signals can also be visible and well estimated for BDS results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **North (mm/year)** & **East (mm/year)** & **Up (mm/year)** \\ \hline BDS & 1.2 & 1.5 & 4.1 \\ GPS & 0.3 & 0.3 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average uncertainty of three-dimensional velocity field for BDS and GPS data.  
Figure 6: The trend free position time series of BDS and GPS results at station SXTY located in Shanxi Province of China.  
The velocity fields aligned to the ITRF2014 reference frame for BDS, GPS and BDS/GPS combined results, are presented in Figure 7. It is negligible for the differences between GPS results and BDS/GPS combined results. However, they are remarkable for the differences between GPS velocity field and BDS velocity field, with maximum horizontal differences reaching up to 1.6 mm/year, which is similar with the estimated accuracy of BDS velocity field. The values of vertical differences are less than the vertical accuracy of BDS velocity field at 27 stations, and the direction of vertical differences are also randomly distributed, thus indicating that it is insufficient to derive precise vertical velocity fields with BDS results in CMONOC.
# Reconstruction Optimization Algorithm of 3D Temperature Distribution Based on Tucker Decomposition
## 4 Reconstruction Parameters Optimization of Tucker Decomposition
### Effect of Measuring Point Number  
The analysis of the reconstruction optimization algorithm shows that the temperature measurement information is the key to establishing a prior dataset and the temperature distribution to be reconstructed. The number of measuring points directly affects the accuracy of temperature field reconstruction. The goal of this paper is to establish a new method to reconstruct the temperature distribution quickly and accurately by using a small amount of temperature measurement data. Therefore, it is important to explore the influence of the number of measuring points on the initial reconstruction accuracy of the algorithm.  
Therefore, combined with the optimization results of the core tensor dimension of 9 \(\times\) 4 \(\times\) 5 \(\times\) 3 for reconstruction calculation, the error variation of temperature distribution  
Figure 7: Optimized effects of core tensor dimension.  
reconstruction calculation with a different number of measuring points (10-25) is analyzed. Set the analysis experiment steps as follows.  
1. Using different numbers of measuring points (10-25), the measuring points are randomly selected to construct a measurement matrix, and the temperature measured is extracted from the test condition data;
2. The feature vector is obtained by the Tucker decomposition of the empirical dataset. The optimized core tensor and temperature measurement are used to reconstruct the temperature distribution, and the reconstruction error is calculated;
3. Repeat the first two steps 1000 times to obtain a general conclusion.  
Following the above steps, the temperature distribution reconstruction results for the five test conditions are shown in Figure 8.  
It can be seen from Figure 8 that in the five test conditions, as the number of measuring points increases, the reconstruction error gradually decreases. When the number of measuring points is 13, the reconstruction errors of the five working conditions are all less than 1%. When the number of measuring points exceeds 18, the change in the reconstruction error is no longer obvious. Comprehensively considering the reconstruction accuracy and the number of sensors, 13 temperature measurement points are used for reconstruction calculation in the following three-dimensional temperature distribution reconstruction research work.