esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Kolmogorov-Smirnov Non-Parametric Test  
The K-S test [33; 34] is a non-parametric test used to check equality and to relate two samples (two-sample K-S test). The two-sample K-S test is the most useful non-parametric method that compares two variable samples by checking differences in the maximum vertical distance between two empirical distribution functions. The previous studies reveal that the K-S test is highly applicable to hydro-meteorological studies [35] and to the evaluation of bias correction techniques [36]. Thus, this test is used to evaluate bias correction methods.  
Different datasets provide different cumulative distribution functions. Every cumulative distribution function starts with the lowest value and extends up to the highest value of that series. The K-S test estimates the statistic D (14), which is the maximum distance between the cumulative distribution function of the observed time series and the cumulative distribution function of the bias-corrected data series.  
Here, the null hypothesis, H\({}_{0}\): \(S_{N1}(x)=S_{N2}(x)\), i.e., two distribution functions are equal and the alternative hypothesis, H\({}_{1}\): \(S_{N1}(x)\
eq S_{N2}(x)\), i.e., two distribution functions are not equal. The comparison of different cumulative functions using the K-S test, i.e., \(S_{N1}(x)\) and \(S_{N2}(x)\) is given in (14):  
\[D=max_{-\infty<x<\infty}\;|S_{N1}(x)-S_{N2}(x)| \tag{14}\]  
In the K-S test, the calculated \(p\)-value represents the level of significance at which it should not reject the hypothesis that \(S_{N1}(x)\) and \(S_{N2}(x)\) have the same distribution. As \(n_{1}\), \(n_{2}{\rightarrow}\infty\), the \(p\)-value for this statistic is given in (15):  
\[p=Q\bigg{(}T\sqrt{\frac{n_{1}n_{2}}{n_{1}+n_{2}}}\bigg{)} \tag{15}\]  
where \(n_{1}\) and \(n_{2}\) are the number of sizes of two samples, and \(Q(z)=2\sum_{k=1}^{\infty}(-1)^{k-1}e^{-2k^{2}z^{2}}\).  
The K-S test is more sensitive to variations in both location and form of the empirical cumulative distribution functions of the two samples than the Mann-Whitney U test and Wilcoxon Signed Rank test [37].
# Wind-Based Parametric Design in the Changing Climate
## 3 Brownfield in Loudden Docks, Stockholm Case Study
### Parametric CFD Analysis
#### 3.4.3 FlowBrane No. 3  
The third FlowBrane shows a design of aerodynamic architecture, which only minimally interferes with the wind flow. The CFD analysis of the site without the new architectural interventions showed an accelerated flow between the two silos, selected for the placement of FlowBrane No. 3 in the westerly winds. Moreover, in the southerly gusts, the zone is in a turbulent wake of one of the silos (Figures 6 and 7).  
With the new intervention, the wind flow approaching from the west is, by the designed aerodynamic form, deflected in the horizontal as well as vertical direction. That results in an acceleration of the flow on the top of the shape and the lateral sides (Figure 12). The maximum wind suction on the designed membrane in the 5 m/s westerly winds is \(-\)20 m\({}^{2}\)/s\({}^{2}\) (\(-\)25 Pa), while the maximum wind pressure equals \(+\)17 m\({}^{2}\)/s\({}^{2}\) (\(+\)21.25 Pa). The drag coefficient \(c_{\rm d}\) depends on the reference area \(A\) of the object, drag force \(F_{\rm d}\), air density \(\rho\), and the mean wind velocity \(v_{m}\). In westerly winds, the calculated value of \(c_{\rm d}\) in Paraview equals 0.396, meaning the designed shape is very aerodynamic. FlowBrane No. 3 is even more streamlined in the southerly gusts with the drag coefficient \(c_{\rm d}\) 0.049. The maximum value of the wind suction on the membrane surface in the 9 m/s southerly gusts is \(-\)40 m\({}^{2}\)/s\({}^{2}\) (\(-\)50 Pa), the maximum wind pressure is \(+\)43 m\({}^{2}\)/s\({}^{2}\) (\(+\)53.75 Pa).  
The aerodynamic form of FlowBrane No. 3 influences the reduction of surface wind pressure, positive or negative, which can lead to lighter-structured envelopes.  
The further shape optimization could focus on making the west facade of the auditorium even more fluid to eliminate the wind acceleration on the top of the designed shape.
# Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design
## 4 Proportional-Integral-Derivative Parameter Design
### Controller Parameter Decision
#### 4.3.2 Evaluation of the Prescribed Robust Stability  
Non-obtained controller parameters are interpolated, where using the tuning rule proposed in Section 4.3.1, the controller parameters are calculated on the pair of the normalization parameters for the controlled plant: \(\tau_{0}\in\{0.3,0.31,\cdots,1.7\}\) and \(\tau_{a}\in\{0.01,0.011,\cdots,1.0\}\), where \(M_{\rm s}^{d}\) is varied in \(\{1.4,\cdots,2.0\}\). The calculated controller parameters for \(M_{\rm s}^{d}=1.4\), merged into the preliminary calculated parameters shown in Figure 2, are shown in Figure 3. The calculated parameters using the proposed tuning rule are sufficiently close to the optimized parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(M_{\rm s}^{d}\) & **1.4** & **1.6** & **1.8** & **2.0** \\ \hline \(a_{00}\) & \(0.2085\) & \(0.2718\) & \(0.2999\) & \(0.3672\) \\ \(a_{01}\) & \(-0.6075\) & \(-0.8871\) & \(-0.6490\) & \(-1.4148\) \\ \(a_{10}\) & \(0.4445\) & \(0.5897\) & \(0.7267\) & \(0.7914\) \\ \(a_{11}\) & \(-0.3597\) & \(-0.3261\) & \(-0.7568\) & \(-0.1116\) \\ \(a_{20}\) & \(-1.0048\) & \(-1.0010\) & \(-0.9840\) & \(-1.0107\) \\ \(a_{21}\) & \(2.4219\) & \(2.5022\) & \(2.1738\) & \(2.7688\) \\ \hline \(b_{00}\) & \(0.2175\) & \(0.1208\) & \(0.1676\) & \(0.1793\) \\ \(b_{01}\) & \(1.0142\) & \(1.4350\) & \(0.5152\) & \(0.5668\) \\ \(b_{10}\) & \(1.3058\) & \(1.5359\) & \(1.4478\) & \(1.3845\) \\ \(b_{11}\) & \(-4.3025\) & \(-4.9006\) & \(-1.6551\) & \(-1.4977\) \\ \(b_{20}\) & \(-0.7838\) & \(-0.8310\) & \(-0.6531\) & \(-0.4397\) \\ \(b_{21}\) & \(3.7862\) & \(4.0734\) & \(0.9992\) & \(0.8169\) \\ \(b_{30}\) & \(0.2250\) & \(0.2067\) & \(0.1519\) & \(0.0589\) \\ \(b_{31}\) & \(-1.0977\) & \(-1.1117\) & \(-0.2245\) & \(-0.1967\) \\ \hline \(c_{00}\) & \(-0.0031\) & \(0.0139\) & \(0.0152\) & \(0.0314\) \\ \(c_{01}\) & \(0.0802\) & \(0.1103\) & \(0.0765\) & \(0.1761\) \\ \(c_{10}\) & \(0.4456\) & \(0.3783\) & \(0.3607\) & \(0.3006\) \\ \(c_{11}\) & \(0.3391\) & \(0.0800\) & \(-0.0139\) & \(-0.3791\) \\ \(c_{20}\) & \(-0.0467\) & \(-0.0296\) & \(-0.0374\) & \(-0.0100\) \\ \(c_{21}\) & \(-0.1076\) & \(-0.0107\) & \(0.0186\) & \(0.2333\) \\ \hline \end{tabular}
\end{table}
Table 2: Coefficient parameters in Equations (26)–(35) in regulator design.  
Figure 3: Relationship between approximated controller parameters in servo design.  
Next, \(M_{\rm s}\) values calculated using the calculated controller parameters in the servo and regulation optimization are shown in Figures 4 and 5, respectively. The minimum and maximum calculated \(M_{\rm s}\) values are summarized in Table 3. Since the errors between \(M_{\rm s}^{\rm d}\) and \(M_{\rm s}\) are within \(\pm\)5%, the proposed tuning rule has sufficient precision.  
Figure 4: Relationships among the obtained \(M_{\rm s}\), \(\tau_{0}\), and \(\tau_{a}\) in servo design.  
Figure 5: Relationships among the obtained \(M_{\rm s}\), \(\tau_{0}\), and \(\tau_{a}\) in regulation design.
## 4 Experimental Results
### Experimental Results for Skin Lesion Detection Using Various CNNs
Consequently, as part of our future endeavors, we plan to delve into more comprehensive and advanced image-processing techniques to augment the performance of the diverse CNNs. Additionally, we provided some predictions made by the CNNs during the testing phase for both the ISIC and DermIS databases in Tables 3 and 4. We selected three images (Im1--Image 1, Im2--Image 2, Im3--Image 3) from each database with the real label (NM--Non-Melanoma and M--Melanoma) and tracked the result predicted by the architectures. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CNN** & **Database** & **Time Required** & **Validation Accuracy** \\ \multirow{2}{*}{**ResNet-101**} & DermIS & 11 min 47 s & 84.50\% \\  & ISIC & 40 min 30 s & 77.00\% \\ \hline \multirow{2}{*}{**Xception**} & DermIS & 16 min 38 s & 77.00\% \\  & ISIC & 42 min 47 s & 73.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Im1**} & \multicolumn{3}{c}{**Im2**} & \multicolumn{3}{c}{**Im3**} \\ \cline{3-11}
**Image** & & & & & & & & & & & \\ \hline \multicolumn{11}{c}{**Ground Truth**} & \multicolumn{3}{c}{**NM**} & \multicolumn{3}{c}{**M**} & \multicolumn{3}{c}{**NM**} \\ \hline
**CNN** & \(w_{i}^{M}\) & \(w_{i}^{NM}\) & \(D_{i}\) & \(d_{i}^{M}\) & \(d_{i}^{NM}\) & \(D_{i}\) & \(d_{i}^{M}\) & \(d_{i}^{NM}\) & \(D_{i}\) & \(d_{i}^{M}\) & \(d_{i}^{NM}\) \\
**CNN1** & 0.8543 & 0.8599 & 1 & 1 & 0 & 0 & 0 & 1 & 1 & 1 & 0 \\
**CNN2** & 0.7897 & 0.7945 & 0 & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction of the used CNNs in the testing phase on the DermIS database. Figure 18: Confusion matrices for considered CNNs. (**A**–**H**): DermIS database, (**I**–**P**): ISIC database.
# ML-Based Pain Recognition Model Using Mixup Data Augmentation
###### Abstract  
Machine learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. This is beneficial, especially in pain recognition, where patient communication may be limited. However, ML models often face challenges such as memorization and sensitivity to adversarial examples. Regularization techniques like mixup, which trains models on convex combinations of data pairs, address these issues by enhancing model generalization. While mixup has proven effective in image, speech, and text datasets, its application to time-series signals like electrodermal activity (EDA) is less explored. This research uses ML for pain recognition with EDA signals from the BioVid Heat Pain Database to distinguish pain by applying mixup regularization to manually extracted EDA features and using a support vector machine (SVM) for classification. The results show that this approach achieves an average accuracy of 75.87% using leave-one-subject-out cross-validation (LOSOCV) compared to 74.61% without mixup. This demonstrates mixup's efficacy in improving ML model accuracy for pain recognition from EDA signals. This study highlights the potential of mixup in ML as a promising approach to enhance pain assessment in healthcare.  
machine learning; pain recognition; support vector machine; mixup; electrodermal activity 2024
## 3 Results
### Cluster Analysis  
In order to clarify the relationships among the accessions studied, a cluster analysis was performed to show the average distribution of each compound in the accessions (Figure 1). As far as the measured indicators, two main categories were concerned. The first category (Cluster I) mainly included parameters such as capsaicinoids and a variety of fatty acids, indicating that there was a positive correlation between the concentrations of capsaicinoids and fatty acids. The second category (Cluster II) contained metabolites such as capsaicin, \(\beta\)-carotene, zeaxanthin, and ascorbic acid. Carotenoids, such as \(\beta\)-carotene, zeaxanthin, etc., were clustered together due to the common carotenoid biosynthetic pathway.  
The 75 pepper accessions were divided into two major groups and distinct subgroups according to the column. In the interspecific peppers, _C. chinense_ (S22, S23, S24, S26), _C. frutscens_ (S19, S20, S61), C. baccatum (S58), and some of the _C. annuum_ (S02, S18, S25, S31, S32, S46, S50, S59, S67) were clustered into one large group (Cluster III). Among them, all the _C. chinense_ (S22, S23, S24, S26) were grouped alone into one small sub-branch. The remainder of the _C. annuum_ accessions were grouped into a second (Cluster IV), large _C. annuum_-specific group. It could be seen that the relationship between _C. chinense_, _C. frutscens_, and _C. bacatum_ was close, whereas the classification of _C. annuum_ was differentiated.  
Figure 1: Cluster heat map of quality-related indicators in the 75 pepper germplasm resources.
# Numerical Solutions to the Variational Problems by [PERSON]'s Path-Finding Algorithm
## 5 Ray Tracing for the Gradient-Index Waveguide
From symmetry, the problem is two-dimensional. The optical path will lie on a plane perpendicular to the xz-plane. If we place the x-axis on this plane, the optical path will lie on the xy-plane and can be described as the functions y = y(x) and z = 0. The analytic solution of the ray path can be obtained by solving Equation (18), which is given by  
\[\mathrm{y(x)}=-\frac{\alpha}{4\beta^{2}}\mathrm{x}^{2}+\mathrm{atan}\,\theta _{0}. \tag{21}\]  
Here, \(\beta=\mathrm{n}_{0}\mathrm{cos}\,\theta_{0}\), and \(\theta_{0}\) is the incident angle of the ray at the origin of the coordinate system relative to the x-axis. To simulate the ray path using [PERSON]'s algorithm, we can simply use the method described in the previous section. The change is only in the definition of wave speed. From the definition of refractive index, the wave speed can be written as \(\mathrm{c}/\mathrm{n}(\mathrm{y})=\mathrm{c}/\sqrt{\mathrm{n}_{0}^{2}-\alpha \mathrm{y}}\). Thus, the wave speed of vertex i at the coordinate y\({}_{\mathrm{i}}\) can be written as  
\[\frac{\partial\mathrm{F}}{\partial y}-\frac{\mathrm{d}}{\mathrm{d}x}\bigg{(} \frac{\partial\mathrm{F}}{\partial y^{\prime}}\bigg{)}=\sqrt{1+{y^{\prime}}^{ 2}+{z^{\prime}}^{2}}\frac{\partial\mathrm{n}}{\partial y}-\frac{\mathrm{d}}{ \mathrm{d}x}\left(\frac{\mathrm{ny^{\prime}}}{\sqrt{1+{y^{\prime}}^{2}+{z^{ \prime}}^{2}}}\right)=0, \tag{15}\]0  
The weight of each edge can be defined from Equation (12). The ray path can be finally traced by executing [PERSON]'s algorithm. Figure 7 shows the ray paths through the GRIN medium with the axial index of refraction profile described by Equation (20) with various values of \(\alpha\). The dashed lines are  
the paths obtained from [PERSON]'s algorithm, while the solid lines are the exact solutions from Equation (21). We perform the simulation on the Moser lattice lying on the xy-plane. In all simulations, we use the 40 \(\times\) 10-unit square lattice (\(\Delta\mathrm{x}=\Delta\mathrm{y}=1\)) with 30 sub-lattices on each plaquette side. The light speed c is set to 1 for convenience. The starting and destination points are the lower left vertex at the coordinate (0,0) and the lower right vertex at the coordinate (0,40), respectively. We define the simulation step length \(\Delta\mathrm{s}\) as the average edge length \(<\)s\({}_{\mathrm{ii}}\)\(>\) along the shortest path. The percentage errors E of the area under the curves are calculated to indicate the discrepancy between the simulation and the exact results. For the results shown in Figure 7, \(\Delta\mathrm{s}\) = 0.927, 0.857, 0.867, and 0.815, and E = 0.274, 0.357, -0.252, and 0.399 for \(\alpha\) = 0.070, 0.080, 0.090, 0.100, respectively. The simulation results agree well with the theoretical predictions.
## 3 Results
### Dielectric Inset Waveguide
Usually, Equation (19) is solved numerically, e.g., using the finite element method (FEM). For this simple model, a semi-analytical solution exists for the reflection coefficient \(r\). Therefore, errors due to spatial discretization can be neglected. Equation (19) is transformed into a stochastic problem by recasting all geometrical and Debye material model parameters, as well as the frequency, to RVs. In the nominal configuration, the  
Figure 5: View of the waveguide in the \(xz\) plane. The input and output ports are shown in black, the PEC (perfect electric conductors) walls in dark gray, the vacuum-filled area in white, and the dielectric material in light gray. parameter values are \(\tilde{f}=6\,\mathrm{GHz}\), \(\tilde{w}=30\,\mathrm{mm}\), \(\tilde{h}=3\,\mathrm{mm}\), \(\tilde{\ell}=7\,\mathrm{mm}\), \(\tilde{d}=1\,\mathrm{mm}\), \(\tilde{\varepsilon}_{\mathrm{s},1}=2\), \(\tilde{\varepsilon}_{\mathrm{s},2}=2.2\), \(\tilde{\varepsilon}_{\infty}=1\), \(\tilde{\mu}_{\mathrm{s},1}=2\), \(\tilde{\mu}_{\mathrm{s},2}=3\), \(\tilde{\mu}_{\infty}=1\), \(\tilde{\tau}_{\mathrm{c},1}=1\), \(\tilde{\tau}_{\mathrm{e},2}=1.1\), \(\tilde{\tau}_{\mathrm{\mu},1}=1\), and \(\tilde{\tau}_{\mathrm{\mu},2}=2\). Each parameter is now assumed to follow a truncated normal distribution, such that \(Y_{n}\sim\mathcal{TN}\left(g_{n},\left(0.01\,g_{n}\right)^{2},0.95\,\tilde{g} _{n},1.05\,\tilde{g}_{n}\right)\), \(n=1,\ldots,15\), i.e., the standard deviation of the Gaussian distribution is equal to 1% of the nominal parameter value and a maximum deviation of \(\pm 5\%\) from the nominal parameter value is imposed. The comparison between the dimension-adaptive Leja interpolation and the degree-adaptive LAR-gPC for both error metrics in Equations (14) and (15) is presented in Figure 6. Similarly to the results obtained in Sections 3.3 and 3.5, the Leja interpolation method was significantly superior to the LAR-gPC approximation. For both error metrics, the difference was typically one order of magnitude, with an increasing tendency for larger simulation budgets.
# Diagnosis of the Permian Fengcheng Formation in the Mahu Sag, Junggar Basin, China
## 2 Geological Setting and Samples  
The northeastern part of the Junggar Basin is close to the Qingridi Mountains and Kelamei Mountains, the southern part is adjacent to the Tianshan Mountains, and the northwest/western parts are the Delun Mountains, Halaalate Mountains, and Zaire Mountains, which are nearly triangular, with a sharp southeast and a wide northwest (Figure 1a). The Junggar Basin, an important part of the Central Asian Orogenic Belt [21], belongs to the superimposed basin [28; 29], whose Late Devonian/Early Carboniferous contribution was rift basins, while the Late Carboniferous/Permian period formed foreland basins, and the Triassic/Paleogene periods formed intracontinental depression basins [27]. From the Early Paleozoic to the end of the Late Paleozoic Carboniferous period, the Junggar Basin had a long-term multi-island oceanic paleogeographic pattern with discrete island arcs and ocean basins arranged alternately [28], and with the evolution of the Paleo-Asian Ocean Basin, the marine area gradually diminished [28]. When the Jiamuhe Formation was deposited in the Early Permian period, some areas in the basin evolved into residual marine facies [30], until the Early Permian Fengcheng Formation. During this period, the Fengcheng, Wuerhe, and Xiazijie areas in the Mahu sag evolved into salinized lake basin facies after the closure of the residual sea [25].  
The Mahu sag is located in the northwest part of the Central sag of the Junggar Basin in an NE trend. It is a secondary structural unit in the basin [28], with a length of about 100 km, a width of about 50 km, and an area of about 5000 square kilometers [30]. The tectonic unit dips gently to the southeast as a whole [31] and is one of the sags with the richest oil and gas content in the basin [32; 33]. The Mahu sag is adjacent to the Kebai and Wuxia large fault zones in the northwest [34], the Yingxi sag in the east, the Dabasong-Xiayan uplift in the southeast, and the Zhongguai uplift in the southwest [25; 35] (Figure 1a), and it was developed on the pre-Carboniferous folded basement [27]. The Lower Permian Fengcheng Formation is the main source of rock formation in the Mahu sag [36], with a maximum formation thickness of 1500 m [26]. It is the oldest alkali lacustrine source rock discovered in the world so far [37]. It experienced three periods of hydrocarbon generation peaks: Late Permian, Late Triassic, and Late Cretaceous [29], and the generated oil and gas were accumulated in the Fengcheng Formation and its overlying Wuerhe Formation and Baikouquan Formation [38]. According to the rock combination, from bottom to top, the Fengcheng Formation can be divided into three sections, including Feng 1, Feng 2, and Feng 3. The Feng 1 Member mainly develops volcanic rocks, the Feng 2 Member develops mudstone and carbonate rocks, which are rich in reedmergnerite, and the Feng 3 Member mainly develops terrigenous detrital rocks (Figure 1b,c).
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 6 Applications and Outcomes from Spaceborne LiDAR Data
### Climate Change and Cryospheric Monitoring  
Monitoring ice sheets and glaciers can provide insight into the rise in sea levels. Spaceborne LiDAR altimetry provides direct measurements of ice surface elevation and changes over time. These measurements are used to infer mass balance and to monitor the dynamics of ice caps and glaciers. Recent studies using LiDAR for snow and ice measurements underscore its ability to capture even small changes in surface elevation, which are useful climate models for assessing the impact of global warming on polar regions. NASA's ICESat (2003-2009) and ICESat-2 (2018-present) missions have been instrumental in this regard.  
Based on ICESat and/or ICESat-2, several studies were published. For example, Ref. [160] shows that between October 2018 and April 2021, satellite data from ICESat-2 and CryoSat-2 revealed a decrease in the depth and thickness of the Arctic sea ice snow. The mean snow depth in April decreased by approximately 2.50 cm and the ice thickness decreased by approximately 0.28 m, leading to a 12.5% loss in ice volume. Multiyear ice showed significant thinning, with a 16.1% reduction in thickness by 2021, while first-year ice remained relatively unchanged. Using climatology-based snow depth resulted in thicker ice estimates. The study in [161] focuses on understanding the mass loss of Earth's ice sheets and the climate processes responsible for it. Using satellite laser altimetry data from NASA's ICESat and ICESat-2 satellites, the study estimates changes in grounded and floating ice mass for the Greenland and Antarctic ice sheets from 2003 to 2019. According to the study, Greenland lost \(200\pm 12\) gigatons per year, while Antarctica lost 103 gigatons per year, with \(118\pm 24\) gigatons per year from grounded ice and a small net gain of \(15\pm 65\) gigatons per year from ice shelves. Together, these losses contributed approximately 14 mm to the global rise in sea level during the 16-year period (8.9 mm from Greenland and 5.2 mm from Antarctica).
## 4 Results & Discussion  
The 2 EVRP-DF model in this study was solved using the GUROBI solver (a MILP solver), coded in the AMPL mathematical programming language (AMPL/GUROBI). The experiment was conducted on a PC with an Intel(R) Core(TM) i5 processor at 3.60 GHz, 8 Gb RAM, and running on a Windows 10 64-bit operating system platform. The computational experiments aimed to demonstrate the improvement benefit of considering 2 EVRP-DF and the effectiveness of the proposed SA in resolving 2 EVRP-DF. The following subsections describe the test instances, parameter settings, and computational results.