esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Deep Learning Model for EPVS Rating Estimation
#### 2.4.2 Deep Neural Network  
As illustrated in Figure 1, the proposed deep neural network for directly estimating the EPVS rating consists of five convolutional layers, an average pooling layer, two fully connected layers, and a classification softmax layer. The final layer, for classification, is composed of five nodes, representing the EPVS' five-point rating scale. The pre-processed image size is provided as an input for the network. Since EPVS have thin and tiny structures, average pooling over the max pooling layer is selected after several convolutions to maximally preserve the EPVS features.  
**Training:** The proposed network was trained in an image-by-image manner. We divided the 576 images from 96 subjects into 462 training images from 77 subjects and 114 test images from 19 subjects for training and testing purposes. We used cross-entropy loss function for loss function. Additionally, for another loss function, we trained our model with mean square error (MSE). Adam optimizer with the learning rate of 0.001 was used. The network was trained for 300 epochs. To increase the diversity of the data and for better generalization of the trained network, data augmentation was performed using 2D rotation and flipping (each axis was rotated \(-\)30 and 30 degrees). The proposed method was implemented on TensorFlow 1.14 using a PC equipped with four GPUs (NVIDIA Geforce RTX 2080 Ti).  
**Testing:** During the testing phase, six images per subject were fed into the model and we obtained six EPVS ratings (EPVS rating score at image level). Finally, the highest score among the six images was selected for the EPVS rating estimation.
## 2 Materials and Methods
### Description of Datasets
#### 2.2.2 MODIS Data  
Theoretically, SIF is directly related to fraction of absorbed photosynthetically active radiation (fPAR; Equation (1)). Thus, for improving the interpretation of SIF variation and anomalies, we used the MODIS Aqua fPAR product (MYD15A2H) version 6 [47; 48], which is an 8-day composite dataset with a spatial resolution of 500 m covering the same period as the OCO-2 SIF dataset. The fPAR calculation is based on the 3D radiative transfer equation (Look-Up-Table based approach [49]), which takes the \"best\" quality spectral information of red and near-infrared bands within the 8-day period as an input, and a backup algorithm uses the empirical relationship based on NDVI. In addition, we also used the standard MODIS 16-day composite NDVI product [50] with a spatial resolution of 250 m, taken from Terra (MOD13Q1) and Aqua (MYD13Q1) for comparison with SIF. These composite products are constructed using the \"best\" quality pixel (i.e., low clouds, low view angle, and highest NDVI) over the 16-day period of Terra and Aqua measurements [50]. The MODIS products were obtained from the NASA Land Processes Distributed Active Archive Center (LP DAAC, [[https://lpdaac.usgs.gov/products](https://lpdaac.usgs.gov/products)]([https://lpdaac.usgs.gov/products](https://lpdaac.usgs.gov/products))). Moreover, only good quality MODIS pixels (VI quality variable = 0, indicating good quality as per [50]) for fPAR and NDVI were chosen for subsequent analysis.
## 3 Results
### Windows Type and Length Selection
#### 3.1.3 NHR  
The NHR parameter is determined as a function of Autocorrelation (1-A)/A. Therefore, the analysis to compare the NHR values of the algorithm with Praat was not performed because it was already completed for the Autocorrelation parameter. However, the subjects' analysis was performed to understand how the algorithm behaves compared to the reference value.  
For the NHR, the Hanning window with six glottal cycles was also used since it is the value used for Autocorrelation.  
Figure 6 shows the scatter plot of the 90 NHR measurements over 10 control subjects (left-hand side), and 90 measurements for 10 pathological subjects (right-hand side). Each subject has nine NHR measurements for three vowels, and each vowel has three tones. These measurements were determined by the developed algorithm and by the reference software (Praat).  
Figure 5: Comparison of Autocorrelation values for the 90 control measurements (**left**-hand side) and 90 patient measurements (**right**-hand side).  
Figure 6: Comparison of the NHR values for the 90 control measurements (**left**-hand side) and 90 patient measurements (**right**-hand side).  
Through Figure 6, it is possible to observe that the results obtained by the algorithm and the reference to the NHR are practically identical.  
In conclusion, this algorithm obtains values very close to the reference values and can be used to extract this parameter.
## 3 Integrity Monitoring Method of Space-Ground Integration
### Simulation Results and Sensitivity Analysis
#### 3.4.1. LEO Constellation Simulation  
The simulation parameters for the LEO constellation are shown in Table 8. This LEO configuration is given by referencing the LEO constellations that are under development, such as Centispace, China's National Satellite Internet System, and the American XONA System [31; 37; 38]. Figure 8 demonstrates the number of visible BDS-3 satellites (99.74% quantile) for ground users, and Figure 9 shows the number of visible satellites considering both the BDS-3 and LEO. Similarly, Figures 10 and 11 compare the position dilution of precision (PDOP) without and with the LEO constellation. Table 9 shows the number of LEO satellites that track the same GNSS satellite simultaneously. These results suggest that the LEO constellation in Table 8 can provide sufficient coverage for both ground users and GNSS satellites.
## 2 Key Steps in a FRB Search Pipeline
### Dedispersion
#### 2.2.4 Fourier Domain Dedispersion  
Typically, brute force dedispersion is performed on time-domain data, where the dynamic spectrum is shifted in time per frequency channel depending on the DM, to generate a new dynamic spectrum. This algorithm requires a large memory bandwidth for only a minimal number of computations. In the Fourier Domain, a time delay corresponds to a phase rotation. These phase rotations can be represented as dense matrices that can be efficiently solved on the GPU/CPU.  
Figure 4: An overview of the FDMT technique for dedispersion during FRB searches. FDMT maps the time-frequency plane (\(|\{t,f\}|\), **left**) to the plane (**right**) of time versus time-delay (a proxy for dispersion measure), \(A[t,\Delta t]\). The purple DM track (**left**) is transformed into the single purple pixel (**right**). Figure taken from [23].  
In Fourier Domain dedispersion, one can convert the dynamic spectrum \(I(t,\
u)\), where \(t\) is time and \(\
u\) is the observing frequency, to a Fourier spectrum where \(k\) defines the Fourier frequencies. This amounts to \(N_{\
u}\) (\(N_{\
u}\) is the number of frequency channels across the bandwidth) 1-D Fourier transforms \(I(t,\
u)\leftrightarrow\mathcal{I}(t,k)\) that should not be confused with a 2-D Fourier transform of the whole dynamic spectrum. Under this framework, the DM delays in the \(k\) space can be represented as phase rotations such that  
\[\mathcal{A}(k,\
u,\text{DM})=e^{-2\pi\mathrm{i}k\delta t(\
u,DM)}. \tag{12}\]  
Hence, the final dedispersion product is  
\[I(t,DM)=\sum_{0}^{N_{\
u}}\mathcal{F}^{-1}(\mathcal{A}(k)\ \mathcal{I}(t,k)), \tag{13}\]  
where \(\mathcal{F}\) is the inverse Fourier transform operator. Since the Fourier transform is linear, the inverse transform and the summation operations can be swapped, such that one has to perform the inverse transform only once, thus significantly improving the efficiency of the algorithm. Details of this algorithm are provided in [25].
# The Influence of Pillar Displacements on Geodetic Measurements
## 4 Conclusions  
If we want to determine the displacements of the control points located on the object, we need well stabilized reference points, which must be in the same position during the individual measurements. Since the points are usually stabilized in nature, they are constantly exposed to changing natural conditions. Apart from different movements of the ground, the positions of these points, when stabilized by measuring pillars, can be influenced by temperature loads due to solar radiation. These loads lead to a deflection of the measuring pillar, which moves the screw for forced centering, which is attached to the top of the pillar and to which we attach the tachymeter or prism. If we use such a pillar or point for measuring, our measurements may show an error in distance or horizontal direction due to the deflection of the pillar.  
Control measurements show whether the object has been displaced. Due to temperature related deflections of the pillar, which can lead to considerable measurement errors, we may come to a wrong conclusion. We could conclude that the displacement of the object did not take place although it did, or we could come to the opposite conclusion and find that a displacement took place although the object remained still.  
In this paper, we have shown that if such a displacement occurs at the orientation point, it can be minimized with a sufficient distance between the survey and orientation points. If such a displacement occurs at the survey or control point, it cannot be eliminated by geometrically dividing the points. However, we can try to minimize its effect by measuring under conditions that do not lead to a temperature variation in the pillar body and temperature induced displacements.  
This work was achieved in collaboration among all authors. Conceptualization--R.M., B.K. and T.A.; Formal analysis--R.M. and D.Z.; Investigation--R.M.; methodology--R.M., T.A. and B.K.; Project administration--B.K.; Supervision--D.Z.; Visualization--R.M. and T.A.; Writing, original draft--R.M., B.K., D.Z. and T.A. All authors reviewed and provided their comments on the manuscript. All authors have read and agreed to the published version of the manuscript. This research was funded by the Slovenian Research Agency (research core funding No. P2-0227 Geoinformation infrastructure and sustainable spatial development of Slovenia, J2-8170 Nonlinear dynamics of spatial frame structures with enforced kinematic compatibility for advanced industrial applications and J2-924 Multi-parametric dynamic modelling of layered strongly inhomogeneous elastic structures.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Numerical Phase-Field Model Validation for Dissolution of Minerals
###### Abstract  
Modelling of a mineral dissolution front propagation is of interest in a wide range of scientific and engineering fields. The dissolution of minerals often involves complex physico-chemical processes at the solid-liquid interface (at nano-scale), which at the micro-to-meso-scale can be simplified to the problem of continuously moving boundaries. In this work, we studied the diffusion-controlled congruent dissolution of minerals from a meso-scale phase transition perspective. The dynamic evolution of the solid-liquid interface, during the dissolution process, is numerically simulated by employing the Finite Element Method (FEM) and using the phase-field (PF) approach, the latter implemented in the open-source Multiphysics Object Oriented Simulation Environment (MOOSE). The parameterization of the PF numerical approach is discussed in detail and validated against the experimental results for a congruent dissolution case of NaCl (taken from literature) as well as on analytical models for simple geometries. In addition, the effect of the shape of a dissolving mineral particle was analysed, thus demonstrating that the PF approach is suitable for simulating the mesoscopic morphological evolution of arbitrary geometries. Finally, the comparison of the PF method with experimental results demonstrated the importance of the dissolution rate mechanisms, which can be controlled by the interface reaction rate or by the diffusive transport mechanism.  
mineral dissolution; numerical simulation; phase-field (PF) method; moving boundary problem; reaction rate; diffusive transport +
Footnote †: journal: Applied Sci. 2021, 11, 2464. [[https://doi.org/10.3390/app11062464](https://doi.org/10.3390/app11062464)]([https://doi.org/10.3390/app11062464](https://doi.org/10.3390/app11062464))
# Effects of Interference and Mitigation Using Notch Filter for the DVB-S2 Standard
## 4 Proposed Solution and Efficiency Analysis
### Bit Error Rate (BER)  
Another important criterion for performance measurement is the bit error rate (BER). For every modulation technique, there is a different maximum BER limit before the signal can no longer be reproduced. For example, based on a simulation result, by using a QPSK modulation with a code ratio of 1/4, an image will not be received if the BER is 0.000126 and the SNR is \(-\)2.743 dB. The image will partially or completely be reproduced when the BER is \(4.94\times 10^{-5}\) and the SNR is \(-\)2.713 dB.  
Finally, the image will be received without any errors when the SNR is at least \(-\)2.658 dB [35]. The value of this metric is compared before and after filtering the signal in the presence of a CW interference. The bit errors are caused mostly by noise and interference which distort the original signal. Figure 27 shows a simulation of the effect of the notch filtering on the BER of the QPSK transmission under the CWI. Without any interference, the BER drops dramatically as the Energy per Bit over the Spectral Noise Density (Eb/N0) ratio increases. This is to be expected since a signal with lower AWGN noise will cause fewer errors.  
In contrast, the CWI causes a huge amount of bit errors and the received SNR cannot exceed 6 dB. When the Eb/N0 ratio is 12 dB, the BER with CWI cannot exceed the \(10^{-2}\) value. After notch filtering, a significant improvement is observed, the system can continue its d  
Figure 27: BER performance of QPSK with JSR = \(-\)6 dB.  
Figure 26: Notch filter performance on SNR.  
degradation on the SNR. The post-filtering curve is much closer to the theoretical minimum curve of the QPSK BER, especially for lower Eb/N0 ratios. At the ratio of 12 dB, the BER of filtered signal excels the curve without filtering by approximately 7 dB.
## 2 Materials and Methods
### New Method of Controlled Disaggregation  
The objective of the present study is to compare how compaction affects the particle size distribution of the MU after shaking, in this case using compacted Wyoming bentonite specimens. However, the purpose is not only to develop a method applicable to this kind of clay but to any soil. For this reason, the protocol is based on basic equipment in geotechnical and materials laboratories, such as the mechanical shaker included in the sand equivalent test UNE-EN 933-8:2012+A1:2015/1M [22]. The procedure for the new disaggregation method is explained below.  
Duplicate samples of height 17 mm and diameter 49.5 mm, with a water content of 10%, were prepared with target dry density values of 1.40 g/cm\({}^{3}\), 1.60 g/cm\({}^{3}\), and 1.80 g/cm\({}^{3}\). The compliance of the target density was checked by accurately determining the weight and dimensions of each specimen once prepared. Similarly, part of the excess material was used to check its moisture content by oven drying at 105 \(\pm\) 5 \({}^{\circ}\)C.  
Then, each cylindrical specimen (Figure 1a) was cut radially in successive subdivisions into 8 portions (Figure 1b). These portions were placed inside a plastic test tube presenting standardized dimensions of 430 mm in height and 32 mm in inner diameter [22] and closed with a rubber stopper. Next, the test tube containing the sample was coupled to the mechanical shaker (Figure 1c) and put into operation to start the agitation process. According to the sand equivalent test standard, the device used exerted a horizontal, periodic, and sinusoidal rectilinear motion of 200 \(\pm\) 10 mm amplitude with a period of 1/3 s. To know the gradual evolution in the size of the MU, the process was interrupted after 5 s, 10 s, 20 s, 30 s, 1 min, 2 min, 5 min, and 10 min. For those specimens in which relatively large pieces were still observed, additional shaking times were applied, up to 50 min. At each shaking time, the material was extracted from the test tube and passed through sieves with apertures (in mm) of 10, 5, 2, 1.25, 0.63, 0.40, 0.16, and 0.080 (Figure 1d). Once the weights of each fraction were recorded, their particle size distribution was determined to analyze the change in the size of the MU as the shaking time increased and thus determine the disaggregation pattern.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mineralogical Composition** & **wt.\%** \\ \hline Smectite & 87.6 \\ Plagioclase & 4.2 \\ Quartz & 4.1 \\ K-feldspar & 1.8 \\ Ratile & 0.9 \\ Calcite & 0.6 \\ Pyrite & 0.6 \\ Illite & 0.1 \\ \hline
**Other Properties** & **eq/kg** \\ \hline Cation Exchange Capacity & 0.84 \\ Na\({}^{+}\) (eq/kg) & 0.58 \\ Ca\({}^{2+}\) (eq/kg) & 0.25 \\ Mg\({}^{2+}\) (eq/kg) & 0.08 \\ K\({}^{+}\) (eq/kg) & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the tested bentonite [15].
# Designing and Implementing Weather Generators as Web Services
## 3 Design and Implementation  
The earlier sections described characteristics of weather generation models, business process of the models, user experiences and related technologies. In order to design efficient web services for serving user requirement, understanding of the business process and the behavior of the models is essential [28]. Thereafter, we designed system requirements and implement web service architecture in order to facilitate weather generators' accessibility as well as mechanism to share identical weather realizations without repeating stochastic generation process.  
The design constraints of weather generator web service are global accessibility through resource identification, standardize interface and service interoperability.  
1. Global accessibility through resource identification: All resources, historical climatological data, weather realization, weather generator models and data transformation in the system should be defined in unique form in order to enable interaction over network.
2. Interface Standardization: Climatic service interoperability is an essential requirement of this development. Interfaces of retrieving historical climatological data and weather realizations should be in standard format in order that users need not spend time on learning various interfaces. Historical weather data are provided in compliance with SOS interface. Weather realizations are provided in compliance with both ICASA standards and O & M format.
3. Program interoperability: Weather generator programs that are written in different programing languages should be able to register to the web service.
4. Classification of user role: Currently, the role of users are blended together. It means that users who want to aquire weather realization also need to manage the weather generator program. Classification of user role will separate users from service providers and from weather generator programmers.  
We developed a web service framework for providing weather generator models based on Representational state transfer (RESTful) principle [29] by utilizing Jersey libraries toolkit [30]. The architecture consists of several components, as shown in Figure 4. Each component is wired to one another to provide sufficient and interoperable services. The architecture diagram is divided into three main parts: Client, Web Service Server and Data source. The main development in this research is Web Service Server part. Details of each part and component are described as follows. The system has been deployed on Amazon Web Service (AWS), a cloud computing service [31], for scalability and future migration purposes.
# Conjugate Heat Transfer Advancements and Applications in Aerospace Engine Technology
## 3 Case Study of Conjugate Heat Transfer
### Computational Details  
A brief description of the numerical technique is as follows:  
1. The spatial discretization is based on the finite volume cell vertex method for hybrid unstructured grids in the fluid domain.
2. The Roe's approximate method is used for the up-winding of convective fluxes.
3. A slope limiter based on the Unstructured mesh Monotone Upstream-centered Schemes for Conservation Laws (U-MUSCL) reconstruction of the solution inside elements provides second-order discretization to maintain monotonicity.
4. Turbulent viscous flows are represented by the conventional mass-averaged system of Navier-Stokes equations. Eddy viscosity is determined according to the k-\(\omega\) Shear Stress Transport (SST) two-equation model.
5. The spatial discretization method for solid domains adopts the finite element method.
6. The steady thermal solver is adopted to heat conduction in the solid domain.
7. The PETSC library is employed to iteratively solve both in the fluid and solid domains.  
The methodology for addressing the conjugate heat transfer challenge begins with the segmentation of both the fluid and solid realms into discrete elements. To achieve this, we harness the capabilities of ANSYS ICEM 2021 R2 grid generation software, meticulously delineating the fluid and solid regions to ensure a precise representation of their respective physical attributes. In the fluid domain, a refined tetrahedral mesh is strategically deployed to encapsulate the central flow dynamics, complemented by structured prismatic elements along the solid boundaries. This configuration enhances the resolution within the viscous boundary layer, thereby augmenting the fidelity of the computational fluid dynamics (CFD) simulation. In contrast, the solid domain is uniformly meshed with tetrahedrons, extending throughout the entirety of the model to capture the intricate temperature gradients and thermal responses inherent to the material. The su-F-TFTB are succinctly encapsulated in Figure 8, providing a visual blueprint of the procedural workflow.  
Figure 7: A schematic of the interface grid [43].  
The solver employs a multi-block parallelization approach mirrored after that of the underlying CFD software, which markedly diminishes the aggregate computational burden. Leveraging the standard MPI (Message Passing Interface) protocol, the parallel solver ensures exceptional versatility across diverse computational platforms.