esa-sceva/satcom-chunk-collection huggingface dataset:
# An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications
## 4 Deep Learning Methods
### Datasets and Performance Metrics
High PA values are obtained by methods that are negative conservative that for cloud/cloud-free classification, it means that the masks tend to be cloud-free conservative, while for high UA values they tend to be positive conservative (cloud conservative). F1 takes into account both precision and recall, its a good index when both metrics have the same weight. It is worth noting that the quantitative assessment of the accuracy of methods are highly dependent on the reference datasets, as was highlighted in studies carried out in [95] and in CMIX [117]. In CMIX is also shown that the accuracy achieved by reference algorithms for cloud masking proves to be quite satisfactory although variable across all datasets. In CMIX, 10 algorithms, including traditional thresholding, Machine Learning and FCN-based methods for Sentinel-2 and Landsat 8 images (including Fmask, Sen2 core, MAJA, S2 cloudless, CD-FCNN), were compared on five cloud reference datasets. The BOA of the methods across the datasets ranged between \(80.0\pm 5.3\%\) and \(89.0\pm 2.4\%\) for Sentinel-2 and between \(79.8\pm 7.1\%\) and \(97.6\pm 0.8\%\) for Landsat 8, with an improvement \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Metric** & **Formula** & **Description** \\ \hline \multirow{3}{*}{Overall accuracy (OA)} & & Proportion of TP and TN detected out of all the predictions made by the model. \\  & & Index of the model’s ability to predict both classes. \\ \hline \multirow{3}{*}{Ballanced OA} & & Proportion of TP \\  & & detected out of all positive predictions made by the model. \\ \hline \multirow{3}{*}{Precision (UA)} & & Proportion of TP that have been detected out of all actual positives. \\  & & & \\ \hline \multirow{3}{*}{Recall (PA)} & & Proportion of TP that have been detected out of all actual positives. \\ \cline{2-3}  & \(2\times\)\(Precision\times\)\(Recall\) & Harmonic mean of recall and precision. \\ \hline \multirow{3}{*}{Intersection of Union (IoU) or Jaccard Index} & & Widely used for semantic segmentation, measurements the overlap between predicted and actual positive. \\ \cline{2-3}  & \(TP+TP+FN\) & Proportion of actual positives missed by the model out of all actual positives. \\ \hline \multirow{3}{*}{Omission Error} & & Proportion of negative instances incorrectly identified as positive by the model (overdetection). \\  & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of evaluation metrics. in the algorithm OA from +1.5% to 7.4% when more challenging cases were omitted from the datasets.
# Multi-Lens Array Full-Field X-ray Microscopy
## 1 Introduction  
This article introduces a method for hard X-ray full-field microscopy at laboratory sources. Throughout this paper, full-field microscopy is understood as imaging the sample plane to the image plane using an objective [1]. Magnified shadow projection is another technique, strictly to be distinguished from full-field microscopy. The availability of laboratory sources, with cone-beam configurations and small coherence lengths, is advantageous for full-field microscopy when compared to using synchrotron sources with their narrow beam profile. The key disadvantage of laboratory sources is the comparably low photon flux, in many cases leading to long exposure times in the range of hours [2]. This can be inacceptable with respect to throughput or when samples change over time. The multi full-field microscopy technique proposed here can drastically reduce the exposure time by making use of the cone-beam characteristics of lab sources. The basic idea is making use of the otherwise lost photon flux at large angles by using a special objective.  
In X-ray full-field microscopy, sample illumination is crucial. Each sample point has to be illuminated under different angles to obtain a high-resolution image [3]. At the same time, only rays passing the objective will contribute to the image. At this point, a distinction between classical X-ray sources with large X-ray spots and micro focus tubes with their micro focus is helpful. Having a large source spot (e.g., of a rotating anode source), the sample could be placed directly behind the exit window of the source for proper illumination. The large source spot will illuminate the whole sample under many different angles. Using a micro-focus source requires additional illumination optics between source and sample to provide proper illumination. In both cases, the bottleneck is the _NA_ of the objective [4]. Accordingly, [PERSON] introduces three possibilities to increase the flux on the detector: increasing the source brightness, enlarging the limiting etendue (i.e., the _NA_ of the objective), and/or enlarging the efficiency of all optical elements which are used.  
Typical objectives for X-ray full-field microscopy at photon energies in the range above several keV are, for example, multilayer Laue lenses (MLLs) [5], Fresnel zone plates [6], orcompound refractive lenses (CRLs) [7; 8; 9; 10; 11]. Fresnel zone plates have clear advantages for X-ray energies below about 15 keV. The state-of-the-art optics for multi-keV X-ray energy applications are CRLs [12]. Although MLLs have a potentially better performance than CRLs at high X-ray energies [12], today, they do not reach the relatively large field of views (FoVs) of 100 \(\upmu\)m \(\times\) 100 \(\upmu\)m of CRLs, on which this project concentrates.
###### Abstract  
In this paper, a new WHT-LWT-GFDM waveform obtained by combining Walsh-Hadamard Transform (WHT), Lifting Wavelet Transform (LWT), and Generalized Frequency Division Multiplexing (GFDM) is presented for use in next-generation wireless communication systems. The proposed approach meets the requirement of 5 th-generation (5G) and beyond communication schemes in terms of low latency, low peak-to-average-power ratio (PAPR), and low bit-error rate (BER). To verify the performance of the presented waveform, PAPR and BER simulation results were obtained in additive white Gaussian noise (AWGN) and flat Rayleigh fading channels, and the performance of the proposed system was compared with conventional Orthogonal Frequency Division Multiplexing (OFDM), GFDM, and Walsh-Hadamard transform-based GFDM (WHT-GFDM). Simulation results show that the proposed waveform achieves the best BER and PAPR performances and it provides considerable performance gains over the conventional waveforms.  
waveform design; beyond 5G; GFDM; lifting wavelet transform; WHT-GFDM; OFDM 2021 21831. 10.3390/s21051831 1
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 2 Materials and Methods
### Data and Processing
#### 2.2.4 Resampling to Coarser Grids  
As shown in Table 2, the GLDAS data have the coarsest grid. For the sake of consistency of spatial resolution, SMAP and GPM data products were resampled to a spatial resolution matching GLDAS. For this purpose, the average resampling method was performed [81]. Figure 4 demonstrates examples of SMAP and GPM data products before and after the resampling.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Study Region** & **Earthquake Name** & **Pre-Event SMAP Data** & **Event Date** & **Post-Event SMAP Data** \\ \hline Croatia & Petrinja, Mw 6.4 & 29 December 2020 & 29 December 2020 & 30 December 2020 \\ Greece & Samos, Mw 7 & 30 October 2020 & 30 October 2020 & 2 November 2020 \\ Turkey & Elazig, Mw 6.7 & 23 January 2020 & 24 January 2020 & 26 January 2020 \\ Indonesia & Palu, Mw 7.5 & 27 September 2018 & 28 September 2018 & 30 September 2018 \\ Mexico & Puebla, Mw 7.1 & 17 September 2017 & 19 September 2017 & 20 September 2017 \\ New Zealand & Kaikoura, Mw 6.4 & 12 November 2016 & 13 November 2016 & 15 November 2016 \\ Italy & Accumoli, Mw 6.2 & 23 August 2016 & 24 August 2016 & 24 August 2016 \\ Ecuador & Muisne, Mw 7.8 & 16 April 2016 & 16 April 2016 & 18 April 2016 \\ Taiwan & Meinong, Mw 6.4 & 5 February 2016 & 5 February 2016 & 6 February 2016 \\ Chile & Illapel, Mw 7.8 & 16 September 2015 & 16 September 2015 & 19 September 2015 \\ Nepal & Gorkha, Mw 8.3 & 23 April 2015 & 25 April 2015 & 26 April 2015 \\ \hline \end{tabular}
\end{table}
Table 3: Available pre- and post-event SMAP data (SMAP event window).  
Figure 3: Soil moisture change of Croatia region reported by (**a**) SMAP (\(\Delta SM_{SMAP}\)) and (**b**) GLDAS (\(\Delta SM_{GLDAS}\) ), and (**c**) GPM accumulated precipitation of Croatia region in the SMAP event window. White grids are locations where SMAP soil moisture data are not available (e.g., due to mountain, water, etc.).
# Impact and Stability of mandibular Setback after Intraoral Vertical Ramus Osteotomy
## 4 Discussion
### Skeletal and Dental Stability  
The evaluation of dental and skeletal stability has a major contribution and allows the surgeon to predict the final occlusion. In this study, skeletal stability was measured by assessing the SNB angle and the distance between the Co and B points. The mean distance between the Co and B points at T2 was 104.89 mm compared to 106.15 mm at T1; the changes from T1 to T2 were not statistically significant (\(p=0.07\)), representing skeletal stability with a mean relapse of 1.6 mm backward movement that is considered stable [16]. The results of our present study are consistent with other findings in the common literature that describe the backward displacement of the mandible after 1-year follow-up [16; 17; 18; 19]. A possible cause of backward displacement could be the gravity of the mandible, the soft tissues attached to it and muscle pull, as described by [PERSON] et al., who reviewed skeletal changes in 41 patients after IVRO and found that the mean movement of the mandible was 0.1 mm backward [17]. The backward displacement of the mandible was also reported by [PERSON] et al. [16], with a mean backward displacement of about 0.7 mm after 1-year follow-up corresponding with findings by [PERSON] et al. and [PERSON] et al. that showed posterior movement after IVRO [18; 19].  
At T2, the mean SNB angle was 79.99 (SD = 4.39), while at T1, the mean SNB was 81.71, with a significant difference (\(p<0.01\)). This finding also indicates the backward movement of the B point and possible clockwise rotation of the mandible that could relate to the position of the condyle after the release of IMF fixation as part of the \"sag\" phenomenon that was described by [PERSON] et al. [4].  
Dental stability was evaluated for a period of 8 months after the surgery, from T1 to T2. The changes regarding the overbite (\(p=0.19\)) and overjet (\(p=0.46\)) were measured in during this period and were not found to be statistically significant; furthermore, the mean relapse was less than 2 mm (OB = 0.17 mm, OJ \(=-0.06\) mm).
# Progress in the Knowledge, Application and Influence of Extremely Low Frequency Signals
## 3 Results and Discussion
### Community Analysis
In the Figure 12d the 10 keywords that appear the most in the documents of this community are represented, establishing the same trend as in the previous community, of equal size. The word that is presented twice is \"electromagnetic spectrum\" and the rest appear only once. All the works of this community are developed by the same authors between 2017 and 2018, they are not very referenced and most of their references are carried out by other articles of the same community. The most important document is in nodes [108], and reflects on the existence of these signals, their behavior and the implications of waves with frequencies lower than one Hertz, which have so far been little studied. These waves are extremely long and relevant because they carry the information radiated by distant cosmic objects even larger than the Earth and which oscillate with frequencies lower than one cycle per second, and their interaction with matter (e.g., dust, gas, and particles) could create stationary structures in the Universe.
## 2 Characterization of Spatial Variability and Slope Stability Assessment Methods
### Point Safety Factor Method  
The point safety factor (PSF) method facilitates rapid assessment of overall slope stability through computation of local safety factors at multiple points within the slope mass [27; 28]. Unlike the strength reduction method, which requires iterative calculations, this approach eliminates the need for repeated numerical trials, substantially enhancing the computational efficiency for large-scale, complex grid models. The local safety factor, denoted as \(k\), is mathematically defined as follows:  
\[k=\frac{\sigma_{n}\tan\phi^{\prime}+c^{\prime}}{\tau} \tag{16}\]  
where \(\sigma_{n}\) denotes the normal stress (kPa), \(\tau\) denotes the shear stress (kPa), \(\phi^{\prime}\) denotes the equivalent internal friction angle (\({}^{\circ}\)), and \(c^{\prime}\) denotes the equivalent cohesion (kPa). For a material point under a three-dimensional stress state (\(\sigma_{1}\), \(\sigma_{2}\), \(\sigma_{3}\)), with the direction cosines (\(l\), \(m\), \(n\)) defining the normal to the potential slip surface relative to the principal stress directions, the normal stress \(\sigma_{n}\) and shear stress \(\tau\) are given by the following:  
\[\sigma_{n}=l^{2}\sigma_{1}+m^{2}\sigma_{2}+n^{2}\sigma_{3} \tag{17}\]  
\[\tau=\sqrt{l2\sigma_{1}^{2}+m^{2}\sigma_{2}^{2}+n^{2}\sigma_{3}^{2}-\sigma_{ n}^{2}} \tag{18}\]  
A schematic diagram illustrating the PSF method is presented in Figure 1. Given the typical stress characteristics in slope engineering and noting that the direction of the intermediate principal stress \(\sigma_{2}\) is approximately orthogonal to the potential slip surface's normal vector, it is reasonable to set \(m\approx 0\). Under this condition, the direction cosines \(l\) and \(n\) can be derived from the following expressions:  
\[l=\sqrt{1-n^{2}} \tag{19}\]  
\[n=\pm\sqrt{\frac{\sigma_{1}\tan\phi^{\prime}+c^{\prime}}{(\sigma_{1}+\sigma_ {3})\tan\phi^{\prime}+2c^{\prime}}} \tag{20}\]  
Figure 1: Schematic diagram of the point safety factor method. The blue, green, and red arrows represent the major principal stress directions \(\sigma_{1}\), \(\sigma_{2}\) and \(\sigma_{3}\), respectively. The gray plane denotes the potential failure surface.  
To validate the PSF method, we conducted a comparative analysis of safety factor (Fs) results and those obtained with the conventional shear strength reduction (SSR) approach (Figure 2). When the rock mass strength is largely unreduced (Figure 2a), low Fs zones develop only locally. After the strength is reduced to Fs \(\approx\) 1.0 (Figure 2b), the PSF-derived low Fs band coincides with both the maximum shear strain zone predicted by the SSR analysis (Figure 2c) and the corresponding plastic zone (Figure 2d). The overall factor of safety for Figure 2b, computed with the SSR, is Fs = 0.97--essentially identical to the minimum Fs \(\approx\) 1.0 on the PSF map--demonstrating the consistency of the two methods at the limit equilibrium stage.
## 3 Numerical Models and Parameter Selection
### Material Parameters of ALE  
#### 3.3.1 Explosives Material  
The keyword MAT-HIGH-EXPLOSIVE-BUEN joint JWL equations of state are used to describe the explosive reaction process [36]. The main parameters are explosive density \(\rho\) = 1090 kg/m\({}^{3}\), detonation velocity is 3500 m/s, blast wave pressure \(P_{C-J}\) = 3.34 Gpa, and initial specific internal energy \(E_{0}\) = 4.192 Gpa.
# Experimental and Modeling Study for the Solar-Driven CO2 Electrochemical Reduction to CO
## 2 Materials and Methods
### Numerical Model  
#### 2.3.1 EC Model  
The numerical model for the batch cell reactor consists of a 2D isothermal model. The system is simulated using COMSOL(r) Multiphysics 6.3 (Stockholm, Sweden), focusing on capturing the essential physical phenomena across different domains. A schematic overview of the model is reported; for more details about the governing equations, the reader is invited to view the previous work [21], also given in the Supporting Information section.  
**Electrolyte Domain.** Ionic transport is governed by a combination of diffusion, migration, and convection of chemical species, influenced by electric fields and concentration gradients (the Nernst-Planck equation). Equilibrium chemical reactions, such as carbonate-bicarbonate dynamics and water ionization, are implemented to account for the chemical environment. Lastly, charge conservation and electroneutrality ensure that the ionic fluxes and species concentrations are physically consistent throughout the domain.  
**Membrane Domain.** The Nafion 117 membrane selectively transports protons from the anode to the cathode. The model considers the fixed negative charges of the membrane and their impact on ion migration via Donnan potentials, which govern the proton concentration gradients at the membrane boundaries. Proton flux and membrane conductivity are essential parameters in this domain.  
**Electrode Boundaries.** The electrode boundaries are where the electrochemical reactions occur. At the cathode, CO\({}_{2}\)RR to CO competes with the hydrogen evolution reaction (HER). The anode supports the oxygen evolution reaction (OER), modeled as non-limiting. The kinetics of the electrochemical reactions are governed by the Butler-Volmer equation, which relates the reaction rate (or partial current density) to the overpotential and local species concentrations. For each reaction, the overpotential is the driving force and is defined as the difference between the electrode potential, the equilibrium potential for the reaction, and the electrolyte potential. The Butler-Volmer equations incorporate the following key parameters:  
* Exchange current density: Describes the reaction rate at equilibrium (zero overpotential).
* Charge-transfer coefficients: Reflect the fraction of the overpotential driving the anodic or cathodic reactions.
* Concentration dependencies: Influence the kinetics, particularly for CO\({}_{2}\)RR, where the surface CO\({}_{2}\) concentration significantly affects the reaction rates.  
The FE of each product is calculated using the partial current densities from the Butler-Volmer equation. This approach allows the model to quantify the selectivity of the cathode toward different products under varying operational conditions, such as the applied voltage or mass transport limitations
## 3 Roasting Process
### Hot Air Oven
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Roasting Method** & **Nut Sample** & **General Setting** & **Advantages** & **Disadvantages** & **References** \\ \hline \multirow{4}{*}{Hot air oven} & Almonds & 129.5 \({}^{\circ}\)C to 154.5 \({}^{\circ}\)C & & Time consuming & [26; 31] \\  & & for 30 to 10 min & & Energy intensive & \\ \cline{2-5}  & Pistachio & 90 to 190 \({}^{\circ}\)C and & & & Low production efficiency & \\  & kernels & times from 5 to & & & Uneven roasting & [31] \\  & & 65 min & & & \\ \hline \multirow{3}{*}{Infrared roasting} & Peanuts & 250 and 450 W for 10 to 30 min & Higher efficiency & & [32] \\  & & & Uniform heat distribution & & \\ \cline{2-5}  & Hazelnuts & 135 \({}^{\circ}\)C for 45 min and & Reduced pollutant & & \\ \cline{2-5}  & & 195 \({}^{\circ}\)C for 27 min & emissions & & [33] \\ \hline \multirow{2}{*}{Radio frequency roasting} & \multirow{2}{*}{Cashew nuts} & 120 \({}^{\circ}\)C and 130 \({}^{\circ}\)C for 30 min & Create hybrid systems & & [34] \\ \hline \multirow{3}{*}{Microwave roasting} & \multirow{2}{*}{Almonds} & 231 W, 608 W, and & & & Inadequate to trigger & \\  & & 773 W for & & Maillard reaction & [35; 36] \\ \cline{1-1}  & & different times & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main roasting processes applied to nuts, including their respective advantages and disadvantages. flavor and aroma compounds at lower temperatures compared to traditional roasting processes [41]. In recent years, innovative technologies such as infrared heating, dielectric radio frequency, and microwave processing have emerged as promising methods to optimize the roasting process. In particular, infrared roasting offers several advantages, including a reduced processing time, uniform heat distribution, minimal quality loss, compact equipment footprint, and significant energy savings [27,28].
# Numerical Algorithm for Dynamic Impedance of Bridge Pile-Group Foundation and Its Validation
## 4 Conclusions  
Existing research methods for the dynamic impedance functions of bridge pile-group foundations are too complex and specialized to be generalized in practical projects. Therefore, based on the viscous-spring artificial boundary theory and the finite element method, a project-oriented numerical algorithm for the impedance of bridge pile-group foundations is proposed in this paper, through which the dynamic impedance function is calculated and obtained according to its definition.  
The calculation results are compared with those obtained by the methods presented in existing literature, as well as those from a field experiment. The comparisons show that the results obtained by the numerical algorithm are consistent with those obtained by existing methods (error of maximum stiffness item is less than 5%) and the field experiment, and its accuracy can meet the needs of engineering, showing the effectiveness of the proposed numerical algorithm.  
The study in this paper may be beneficial for simplifying the modeling of the complex train-bridge-foundation-soil coupled system, and provide an efficient and simple means of further studying the influence of pile-soil interaction on the seismic response of a coupled train-bridge system.  
Figure 11: Dynamic flexibility obtained by the proposed algorithm and the field experiment.  
**Author Contributions:** Methodology, H.Q. and X.D.; software, C.W.; validation, C.W., H.Q. and Y.W.; writing--original draft preparation, C.W.; writing--review and editing, H.Q. and Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Fundamental Research Fund for Beijing University of Civil Engineering and Architecture (Grant No. X20033), the Beijing Municipal Natural Science Foundation (grant No. 8204058), and the Program for Scientific Research of Beijing Education Commission (grant No. KM202110016013).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.