esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation of Prototypes with Edge Damping
### Introduction of the Adaptable Prototype  
All measurements in this paper are performed with the adaptable prototype shown in Figure 11 and described in earlier publications [20]. The adaptable prototype enables the exchange of different parts of the flat panel loudspeaker for comparison, e.g., different panels with the same boundary conditions. A subsequent section of the framed panel is exchanged with panels that have additional damping. To guarantee sufficient input force at higher frequencies, the chosen exciter Dayton Audio DAEX25 FHE-4 has an upper-frequency limit above 15 kHz, as measured in [10]. The influence of edge damping is evaluated with two additional variants. Both variants are depicted in Figure 11 in comparison to the simple construction without any damping applied. The option of bitumen inside the frame (BIF) has a layer of bitumen impregnated paper with a width of 40 mm width and a thickness of 2 mm directly attached to the panel. The HPL panel is glued to the frame. Because of the fixed boundary conditions, a similar standing wave pattern is expected. Furthermore, the additional mass of the bitumen impregnated paper increases the mass of the moving panel, which can cause a rise in the coincidence frequency \(f_{c}\). The second prototype featuring bitumen on the frame (BOF) has a damping layer between the frame and the panel, and the panel is decoupled. However, this construction might have similar fixed boundary conditions at lower frequencies, where the bending wavelength is large. If the bending wavelength decreases, these boundary conditions can be softened, and a different frequency-dependent wave pattern can occur. No additional mass is attached to the moving panel, and therefore, \(f_{c}\) should be equal to that of the raw undamped panel.  
Figure 10: Simulated frequency-dependent radiation of a panel with different damping loss factors applied. With increased damping, the off-axis radiation is minimized.
# An Interpretable XGBoost Framework for Predicting Oxide Glass Density
## 2 Materials and Methods
### Data Treatment  
This study focuses on oxide glasses, which we define as compositions containing a minimum oxygen molar fraction of 30%. Applying this criterion, all compositions with an oxygen content below this threshold were removed from the dataset. This filtering step reduced the total number of records to 84,860. Figure 1a shows the histogram of the oxygen molar fraction for the remaining compositions. The distribution is unimodal, with a distinct peak centered at approximately 60%. This confirms that the filtered dataset is dominated by conventional oxide glasses, as the vast majority of entries have an oxygen fraction in the 50-70% range.  
In the next step, density outliers were removed from the dataset. Specifically, all values falling outside the range defined by the 2.5 th and 99.75 th percentiles were eliminated. This procedure is based on the assumption that such extreme values are likely the result of significant measurement errors. After this filtering step, the dataset was reduced to 76,593 entries.  
A histogram of the final glass densities is presented in Figure 1b. The distribution shows a characteristic maximum in the range of 2.2-3.0 g/cm\({}^{3}\). However, it should be noted that the distribution is broad, with density values extending up to approximately 6 g/cm\({}^{3}\).
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 1 Introduction
The fire detection results were evaluated on a validation set and compared with the results of the contextual algorithm.
## 4 Results and Discussion
### Analysis Results and Geotechnical Classification
According to the French (Figure 5a) and Moroccan (Figure 5b) classifications, we can deduce that:  
* The carbonate rocks are mainly between Classes A and E according to the French standard and Classes 1A and 6D for the Moroccan standards. * The magmatic rocks are mainly divided between classes A and C for the French standard and classes 1A and 3C for the Moroccan standards. * The detritic and volcano-detritic rocks fall between classes B and E according to the French standard and classes 2B and 6D for the Moroccan standards. * There is a similarity between the two classifications, except that the denominations are particular at the level of the zones, and MDE is limited to 30% by Moroccan standards. The intrinsic characteristics of the materials analyzed (Table 4) and the classification results (Figure 5) show that the fields of use of these materials are very wide, with very satisfactory quality. The carbonate rocks represent a medium- to very high-quality material with a wide spectrum of use, especially for road aggregates and gravel used in the manufacture of any type of concrete, provided that a class higher than C or 3C is used according to the standard requirements. The magmatic rocks have superior quality and highly solicited geomechanical characters (L.A and MDE around 20%, and a density greater than 2.58 t/m\({}^{3}\)). These rocks can be considered noble materials whose use can go beyond simple road aggregates and be used as ballast for railway tracks as is the case for basalts (Class A or 1A) or rockfill used in port works in the case of rhylite (Class A or 1A). The detritic and volcano-detritic rocks represent a very high quality overall (except conglomerates), which can be used in several fields, especially in the road sector, and gravel is used in medium-quality concrete. Furthermore, any exploitation of a deposit must be preceded by additional tests to have a very precise idea about the geochemical and geomechanical quality of the existing rocks, for example:  
* Increase the number of samples to be analyzed over the entire surface of the deposit. * Determine the geochemical and petrographic composition of the deposit facies. * Carry out alkali-reaction tests for granites to verify the reactivity of these rocks with the binders.
# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 4 Architecture Overview
### Network Model  
An opportunistic network can be modeled as a graph G (\(N\), \(E\)), comprising a set of N vertices representing the nodes and a set of E edges representing the contacts between the nodes. Initially, each node has an initial random energy value. The energy level decreases when one of the following operations occurs: neighbor scanning, message transmission, or reception.  
We assumed the nodes did not recharge their batteries during the simulation. After the full discharge of the node battery, they did not take part in the network operations. The mobility relied on human interactions by using contact traces extracted from real experiments.
## 2 Information about Materials
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Modulus of** & **Unit Weight** & **Poisson Ratio** & \multirow{2}{*}{**References**} & **Shear** & **Volumetric** \\  & **Elasticity (E)** & **(t/m3)** & **(v)** & & **Modulus (G)** & **Modulus of** \\  & **(MPa)** & **(t/m3)** & **(v)** & **(MPa)** & **(Elasticity (K)** \\ \hline Ahlat Stone & 5000 & 2.45 & 0.20 & [42,45] & 2083 & 2778 \\ C25 Concrete & 30,000 & 2.50 & 0.20 & [73] & 12,500 & 16,667 \\ Bitlis Stone & 4006 & 2.30 & 0.22 & [53; 54; 55] & 1642 & 2385 \\ Küféki Stone & 20,000 & 2.24 & 0.15 & [46; 47; 48] & 8696 & 9524 \\ Diyarbakar Basalt & 30,727 & 2.60 & 0.14 & [49; 50; 51; 52] & 13,477 & 14,225 \\ Brick & 2985 & 2.10 & 0.25 & [57; 60] & 1194 & 1990 \\ Andesite Stone & 12,240 & 2.20 & 0.30 & [65; 66] & 4708 & 10,200 \\ Natural Stone & 8490 & 2.30 & 0.20 & [26] & 3538 & 4717 \\ Cut Stone & 4000 & 3.06 & 0.20 & [33] & 1667 & 2222 \\ Adobe & 880 & 2.04 & 0.20 & [48; 57] & 367 & 489 \\ Edremit Traverting & 14,480 & 2.01 & 0.30 & [67; 68] & 5569 & 12,067 \\ Süphan Basalt & 47,630 & 2.63 & 0.25 & [65; 69] & 19,052 & 31,753 \\ Karamürsel Od & 19,100 & 2.06 & 0.25 & [70; 72] & 7640 & 12,733 \\ Stone & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of selected material types. The modulus of elasticity measures the elastic deformation of the material under force. The modulus of elasticity indicates the stiffness of the material. The greater the modulus of elasticity, the material is more rigid, and the elastic strain is smaller. While the lowest periods were obtained for Subphan Basalt, which has the highest modulus of elasticity, the highest periods were obtained for adobe with the lowest modulus of elasticity.
## 4 Results  
### Selection of Feature Indicators  
Based on the XGBoost algorithm, we randomly selected 80% of the data for training, and the other 20% of the data were utilized for performance testing. The parameters of this model were optimized by utilizing the grid search method based on the performance. Taking 2017, for example, the parameters were set as shown in Figure 2. And the mean of the parameters was as follows:  
eta: the parameter for learning rate (also known as the step size). n_estimators: the number of decision trees (iterations). max_depth: the maximum depth of each tree, controlling its complexity and preventing overfitting. reg_alpha: the weight of L1 regularization. reg_lambda: the weight of L2 regularization. min_child_weight: the minimum sum of sample weights required at a child node during tree growth. learning_rate: the rate controlling the weight update magnitude for each tree. colsample_bytree: the fraction of features used for training each tree. As a result, the accuracy, precision, recall, and F1 score for experiments in 5 years (2013-2017) are all over 0.75, which indicates the validity and feasibility of XGBoost in selecting the feature indicators of world-class high-tech zones. Then, the results for selecting feature indicators for excellent parks show significant differences in weights among the evaluation indicators across different years, among the over 200 evaluation indicators. Over 80% of the indicators have small weights (<0.01), while there are 20-30 indicators that have a significant contribution to distinguishing excellent parks from other parks. Taking the year 2017 as an example, approximately 20 indicators  
Figure 1: The identification of feature indicators of world-class high-tech zones based on XGBoost. Figure 2: The parameter set in XGBoost model for the year 2017 in this study. have contribution weights greater than 0.01 (see Figure 3). Among them, indicators, such as park value added, number of graduate employees, number of valid patents, government funding for scientific and technological activities, and number of patent transfers and licenses, have higher weights. Five indicators contribute over 70% towards distinguishing excellent parks from other parks, indicating that they are significant indicators of the advantages of excellent parks. Based on the above analysis, this article selected the top 20 indicators ranked by weight from 2013 to 2017 as the feature indicators for excellent parks in each year (Table 4).
## 4 Implementation Details
### Parallelization
#### 4.2.3 Distributed Memory Parallelization  
A disadvantage of empirical Gramian matrices is the quadratically growing memory requirements with respect to the state-space (and parameter-space) dimension, since for an \(N\) dimensional system, a (dense) empirical Gramian of dimension \(N\times N\) is computed. To combat this shortcoming, a specific property of the empirical cross Gramian can be exploited: The columns of the empirical cross Gramian, and thus the empirical joint Gramian, may be computed separately,  
\[\widehat{W}_{X} =[\widehat{w}_{X}^{1},\ldots,\widehat{w}_{X}^{N}],\] \[\widehat{w}_{X}^{j} =\frac{1}{|S_{u}|}|S_{x}|M}\sum_{k=1}^{|S_{u}|}\sum_{l=1}^{|S_{x}|} \sum_{m=1}^{M}\frac{1}{c_{k}d_{l}}\int_{0}^{\infty}\psi^{klmj}(t)\,\mathrm{d}t \in\mathbb{R}^{N\times 1},\] \[\psi_{i}^{klmj} =(x_{i}^{km}(t)-\bar{x}_{i}^{km})(y_{m}^{j}(t)-y_{m}^{lj})\in \mathbb{R},\]  
hence this **distributed empirical cross Gramian**[72] (Section 4.2) is computable in parallel and communication-free on a distributed memory computer system, or sequentially in a memory-economical manner as a **low-rank empirical cross Gramian**[73] on a unified memory computer system. This column-wise computability translates also to the empirical joint Gramian and the non-symmetric variants of the empirical cross and joint Gramian.  
Based on this partitioning, a low-rank representation can be obtained in a memory-bound or compute-bound setting together with the hierarchical approximate proper orthogonal decomposition (HAPOD) [72]. This POD variant allows to directly compute a Galerkin projection from an arbitrary column-wise partitioning of the empirical cross Gramian.
## 5 Experimental Validation  
The purpose of the experiment was to validate the performance of the derived contact force equations, as well as verify the feasibility of the developed vision-based vehicle weight identification method. The experiment was conducted on two 2-axle passenger cars and two 3-axle trucks. As shown in Figure 17, the experimental devices include the portable wireless axle weigh pad, SLR camera, and tire pressure detector. For convenience, the tires are represented as the right front (RF), left front (LF), right middle (RM), left middle (LM), right rear (RR), and left rear (RR) according to their position on the vehicle. The process is described below:  
1. Actual weight measurement: the portable wireless axle weigh pad was used to measure the stationary axle weight and gross weight of the vehicle. It should be noted that the vehicles were all controlled by the same driver to eliminate the influence of the driver's weight.
2. Actual inflation pressure measurement: since most trucks do not have a TIPS, the actual inflation pressure of each truck tire was measured by the pressure detector, which was also used to calibrate the pressure identified using the computer vision method mentioned in Section 4.
3. Image acquisition: the tested vehicles traveled on the road at a speed of approximately 15 km/h. The camera was set up on the roadside 5-7 m away from the vehicle and the optical axis was configured as perpendicular to the vehicle traveling direction. Tire images with a resolution of 6000 \(\times\) 4000 pixels were captured.
4. Results and evaluation: the proposed CV-based method was used to calculate the tire contact force in each tire. The absolute error was used to evaluate the accuracy of the weight estimates \[e_{AE}=\frac{|W_{I}-W_{A}|}{W_{A}}\times 100\%\] (14) where \(W_{I}\) is the identified weight; and \(W_{A}\) is the actual weight.  
Figure 16: Inflation pressure identification.
# Left Ventricular Non-Compaction Cardiomyopathy-Still More Questions than Answers
## 8 Prognosis  
Given the fact that there is no uniform definition of LVNC and that there are different criteria for diagnosis, the prognosis of the course of the disease is difficult, if not impossible. The situation is additionally complicated by the fact that the studies available in the literature concern various cohorts of patients diagnosed with the use of various criteria.  
For the reasons mentioned above, the mortality of patients diagnosed with LVNC in follow-up could not be unequivocally assessed. In a meta-analysis of 1822 patients performed by [PERSON], the event rate for cardiovascular mortality was 1.92 (95% CI, 1.54-2.30) per 100 person-years during 2.9 years of follow-up [77]. In comparison, the event rate in a general population was 0.08 and 0.41 per 100 person-years for cardiovascular and all-cause mortality. The risk of cardiovascular mortality was similar to patients with dilatative cardiomyopathy. The extent of trabeculation had no significant impact on mortality, but the worse survival was noted in patients with impaired left ventricular function.  
In the MESA (Multi-Ethnic Study of Atherosclerosis) 2742 patients with LVNC were followed for 9.5 years [78]. The diagnosis was made with CMR using Petersen criteria. The authors analyzed the end-systolic volume index (ESVi), end-diastolic volume index (EDVi), and EF during follow-up depending on the extent of trabeculation. No clinically relevant changes in EDVi, ESVi, or EF were found irrespectively from the extent of trabeculation.  
A benign course of the disease also in symptomatic patients was found by [PERSON] and colleagues who followed 45 patients with LVNC for more than 10 years. A large number of patients were symptomatic at presentation: 62% reported dyspnea, 91% had abnormal ECG, and 66% impaired left ventricular function [79]. Freedom from death or transplantation was 97% after 46 months despite a highly symptomatic cohort.  
The retrospective analysis of 339 patients with LVNC performed by the Mayo group showed reduced survival of patients with LVNC compared to that expected in the US population over 6.3 years [80]. LVNC was defined using [PERSON], [PERSON] (echocardiography), and [PERSON] (CMR) criteria. The authors found higher mortality in patients with LV EF \(<\) 50% and the noncompaction zone extending from the apex to mid or basal segments of the left ventricle as compared to the general US population (\(p<\) 0.001). The differences were not found in patients with localized apical trabeculation and with preserved LV EF. The survival in this group of patients was similar to the general US population.  
Many other parameters were found to correlate negatively with the prognosis as left atrial size, sustained ventricular arrhythmias, atrial fibrillation, or left bundle branch block. There are no generally accepted parameters allowing for long-term prognosis in patients with LVNC.
## 3 Materials and Methods
### EEG Data and Preprocessing  
The dataset consists of the WM EEG dataset: 36 healthy subjects (mean age is 18 with an age range of 16-26) performing a mental arithmetic task [27]. The dataset used in this manuscript is a publicly available dataset in the PhysioNet public repository ([[https://physionet.org/content/eegmat/1.0.0/](https://physionet.org/content/eegmat/1.0.0/)]([https://physionet.org/content/eegmat/1.0.0/](https://physionet.org/content/eegmat/1.0.0/)), accessed on 17 December 2018). Out of 36 subjects, 9 are male and 27 are female. Researchers consider mental arithmetic tasks as standard experimental protocols to estimate cognitive load. According to [28], a 15 min serial subtraction is a psychological stress. Therefore, assessing the functional connectivity for mental arithmetic tasks provides insight into underlying brain mechanisms with increasing WM load. The experimental design is as follows:  
* The subjects were asked to sit on the reclined armchair in a dark, soundproof chamber.
* A resting-state EEG was recorded for 3 min.
* Later, the subjects performed arithmetic tasks without finger movements or speaking mentally.
* The task involved performing a serial subtraction with four-digit and two-digit numbers, with a minuend and a subtrahend, respectively (ex: 3141 and 42). The participants had to perform the subtraction in mind.
* Then, 23-channel EEG data were collected from the subjects while performing the task according to the 10-20 system (see Figure 2a). The sampling frequency was 500 Hz. The performance of each subject was noted for reference.
* EEG data were recorded for 4 min, and the subject could perform as many subtractions as possible in the given time (see Figure 2b).
* Each EEG recording contained 180 s of resting-state and 60 s of mental-arithmetic-task data.  
Figure 1: Methodology overview.
## Materials and Methods
### EO and Data Processing  
For wet-snow detection from space, we used Sentinel-1A and -1B EO data in interferometric wide (IW) swath mode. IW mode is the main acquisition mode over land and satisfies the majority of service  
Figure 3: (a) demo suitcase with the applied low-cost LEA-M8T GPS receivers (u-blox, Thalwil, Switzerland), u-blox patch antennas and a small microcontroller and data storage; (b) examples of GPS antennas of GPS\({}_{1}\) mounted at a pole and of GPS\({}_{2}\) laid out on the ground before snowfall; (c) example of a SnowSense station including a mast system, a self-supplied power management (solar panels on top), a processing unit (including battery pack), and a communication module installed near Millertown, Newfoundland, Canada. GPS\({}_{1}\) and the communication antenna are mounted at the top of the mast and GPS\({}_{2}\) is covered by snow.  
requirements. It acquires data with a 250 km swath at 5 m by 20 m spatial resolution [27]. The satellite data is automatically downloaded from Copernicus Data Hub ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/))), and pre-processed using ESA Sentinel Toolbox for geometry and terrain corrections and it is spatially resampled on a 100 m by 100 m grid [28]. Afterwards, the data is radiometrically calibrated and corrected with regard to the elevation angle [29]. The wet-snow mapping is performed as proposed by [PERSON] and [PERSON] [30] with a fixed threshold of -3 dB from a reference scene. Here, we are using a predefined reference data set with averaged standard backscatter values for different satellite geometries according to [PERSON] et al. [29].  
The results are binary wet snow maps with a resolution of 1 km by 1 km, containing three classes: wet snow, no information (which includes dry snow or snow free conditions), and no data. The maps were used in a further step for the assimilation process (see Section 2.6) and as additional visualization product, i.e., provided for the demo users in a password protected online portal (see Figure 4).
## 4 Control System Design
### Nonlinear Dynamic Inverse Controller  
The attitude control structure diagram of system is shown in Figure 4. The mathematical model of the attitude system of UAV is described as the following affine nonlinear form:  
\[\left\{\begin{array}{l}\dot{x}=f(x)+g(x)u+d(t)\\ y=h(x)\end{array}\right. \tag{47}\]  
where the nonlinear coupling torque \(f(x)\) and the manipulated matrix \(g(x)\) can be expressed as:  
\[f(x)=\begin{bmatrix}\frac{I_{\chi}-I_{2}}{I_{\chi}}\dot{\theta}\dot{\psi}- \frac{I_{\Gamma}\Omega_{\chi}}{I_{\chi}}\dot{\theta}-\frac{k_{\dot{\phi}}\dot{ \phi}}{I_{\chi}}\\ \frac{I_{\chi}-I_{\chi}}{I_{\chi}}\dot{\theta}\dot{\psi}-\frac{I_{\Gamma}\Omega _{\chi}}{I_{\chi}}\dot  
The control input variable is \(u=\begin{bmatrix}u_{2}&u_{3}&u_{4}\end{bmatrix}^{T}\). The total disturbance of the system is defined as the sum of the inaccurate part of the model and the external disturbance, \(d(t)=\Delta f(x)+\Delta g(x)u+w(t)\). The pseudo control variable \(\hat{v}\) is introduced in the design of the inverse controller, and the structure of \(\hat{v}\) is given in Formula (20). According to the previous research content, the output parts of the command filter \(v_{d}\), the linear controller \(v_{pd}\) and the fuzzy neural network controller \(v_{fun}\) have been obtained. Substituting it into (17) gives \(\hat{v}=\breve{x}_{c}+k_{p}\dot{e}+k_{d}\ddot{e}-\hat{W}^{T}L_{3}\), so the control law of the nonlinear dynamic inverse is  
\[u=g^{-1}(x)[\breve{x}_{c}+k_{p}\dot{e}+k_{d}\ddot{e}-\hat{W}^{T}L_{3}-f(x)-d(t )]. \tag{49}\]