esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir
## 3 Results
### Maps of Turbidity for Lake Dobczyce  
The water turbidity map for the lake (Figure 20) was developed from the satellite images and model (17). Long retention times in Lake Dobczyce (average 157 days) favor the sedimentation of suspended particles. Therefore, the water turbidity in the middle  
Figure 18: Chlorophyll concentrations in Lake Dobczyce, 21 October 2021 (imagery by Sentinel 2).  
of the lake, towards the dam, where there are low velocities (Figure 17), is lower than turbidity close to the place where the Raba river enters the lake (Figure 20). In the branches of the lake, where water exchange is low and retention times are longer, low turbidity may be attributed to good sedimentation conditions (Figure 20); turbidity is also low on the south-eastern shores of the lake, where the flow velocities are low.  
In October (Figure 21) and in May (Figure 20), turbidity decreased along the lake towards the dam. Moreover, in the northern branch, sedimentation contributed to a lower turbidity (Figure 21). The turbidity model showed a good fit with the measurements (Figure 21, table); however, at point 2 (Figure 21), the turbidity calculations were poor due to clouds obscuring the view.
# An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals
## 3 Multi-Scale Dictionary Derivation
### Pre-Processing
#### 3.2.3 Windowing and Normalization  
After averaging, all the averaged QRS complexes were resampled again to obtain \(Q^{\prime}\) signals with the same number of samples, \(L_{\max}=\max_{1\leq q\leq Q^{\prime}}L_{\max}^{q}=124\), using a resampling factor \(L_{\max}/L_{\max}^{q}\). Then, the resulting signals were windowed to ensure a smooth decay of the QRS complexes towards zero, and normalized by removing their mean and dividing by their standard deviation. The signals that were finally stored in the QRS complex database are  
\[\tilde{z}_{q}[n]=\frac{\tilde{z}_{q}[n]w[n]-\mu_{q}}{\sigma_{q}}, \tag{8}\]  
where \(\tilde{z}_{q}[n]\) are the averaged QRS complexes after the second resampling stage (i.e., after ensuring that their sample length is equal to \(L_{\max}\)), \(\mu_{q}\) and \(\sigma_{q}\) are their sample mean and standard deviations, respectively,  
Figure 3: Leftmost and rightmost samples of an original QRS complex (from patient 214 in the PTB database) and its resampled version with and without edge effects.  
\[\mu_{q} =\frac{1}{L_{\max}}\sum_{n=0}^{L_{\max}-1}z_{q}[n]w[n], \tag{9}\] \[\sigma_{q} =\sqrt{\frac{1}{L_{\max}-1}\sum_{n=0}^{L_{\max}-1}(z_{q}[n]w[n]-\mu _{q})^{2}}, \tag{10}\]  
and \(w[n]\) is the window used in this case, a window that follows the spectral shape of the raised cosine filter, widely used in digital communications [34], in the time domain: \(w[n]=w_{c}(nT_{s})\) for \(n=-(L_{\max}-1)/2,\ldots,-1,0,1,\ldots,(L_{\max}-1)/2\), with  
\[w_{c}(t)=\begin{cases}1,&|t|\leq(1-\alpha)T_{0};\\ \frac{1}{2}\left[1+\cos\left(\frac{\pi}{2\alpha T_{0}}|t-(1-\alpha)T_{0}| \right)\right],&(1-\alpha)T_{0}<|t|<(1+\alpha)T_{0},\end{cases} \tag{11}\]  
\(T_{0}=\frac{L_{\max}-1}{1+\alpha}\frac{T_{s}}{2}\), and \(\alpha\) denoting the roll-off factor that controls the decay of \(w_{c}(t)\) towards zero: for \(\alpha=0\), we have a rectangular window that abruptly goes to zero at \(\pm T_{0}\), whereas for \(\alpha=1\) the window is bell-shaped and starts decaying smoothly towards zero immediately after \(|t|>0\). This window, whose time-domain shape is shown in Figure 4 for several values of \(\alpha\), ensures that the central samples of the QRS complexes remain undistorted, while their amplitudes quickly decay towards zero at the borders. Throughout the paper, we have always used \(\alpha=0.25\).
# SMARTEN--A Sample-Based Approach towards Privacy-Friendly Data Refinement
## 5 Evaluation
### Lessons Learned  
The main innovation of SMARTEN is that, in contrast to state-of-the-art approaches, the characteristics of the phases of the data refinement process are fully addressed. By supporting different privacy metrics for data sampling in the data preparation phase, and different privacy filters for data provisioning in the data processing phase, any use case is supported, even those dealing with highly sensitive data, such as location-based services [99] or eHealth services [100]. Moreover, SMARTEN can also be extended by further privacy metrics and privacy filters to meet future challenges. This way, the privacy requirements of data producers can be fully satisfied and at the same time the quality requirements of data consumers can be satisfied. To the best of our knowledge, SMARTEN is the only approach that covers the entire data refinement process and thus enables privacy-aware and demand-oriented data provisioning.  
The results of our evaluation show that SMARTEN meets the requirements towards a privacy-by-design solution for the data refinement process. Furthermore, our sample-based approach has a positive impact on the work of the data standards and the involved overhead in terms of data throughput and memory consumption is reasonable.
# Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
## 2 Method
### BP Neural Network Inversion Algorithm  
The core purpose of the neural network inversion method is to gain accurate detection results by training a neural network model and gradually fitting the relationship between the input and output data during the training iterations [23; 24].  
\(b_{j}^{l}\) denotes the bias of the \(j^{th}\) neuron in the \(l^{th}\) layer, and \(a_{j}^{l}\) denotes the activation value of the \(j^{th}\) neuron in the \(l^{th}\) layer. We have:  
\[a_{j}^{l}=\sigma\Big{(}\sum_{k}\omega_{jk}^{l}a_{k}^{l-1}+b_{j}^{l}\Big{)}, \tag{19}\]  
where the summation is performed over all \(k\) neurons in the \((l-1)^{th}\)-layer and \(\omega^{l}\) is the weight matrix on the \(l\)-layer, where \(\sigma\) is expressed as:  
\[\sigma(\omega\cdot x+b)\equiv\frac{1}{1+\exp(-\omega x-b)} \tag{20}\]Thus, Equation (19) can be rewritten in matrix form as:  
\[a_{j}^{l}=\sigma\Big{(}\omega^{l}a^{l-1}+b^{l}\Big{)} \tag{21}\]  
The intermediate quantity \(z^{l}\) is often used in the calculation process to simplify the form of the calculation:  
\[z^{l}=\omega^{l}a^{l-1}+b^{l} \tag{22}\]  
\(z^{l}\) is the weighted input of the \(l\)-layer neuron.  
The intermediate quantity \(\delta_{j}^{l}\) is the error of \(j^{th}\) neuron on the \(l^{th}\) layer:  
\[\delta_{j}^{l}\equiv\frac{\partial C}{\partial z_{j}^{l}} \tag{23}\]  
The backward propagation operation from the output layer is called backpropagation, and \(\delta^{l}\) denotes the output layer error, which is obtained using Equations (19)-(23):  
\[\delta^{l}\equiv\frac{\partial C}{\partial a_{j}^{l}}\sigma^{\prime}\Big{(}z _{j}^{l}\Big{)} \tag{24}\]  
Rewriting Equation (24) in matrix form, we achieve:  
\[\delta^{L}=_{a}C\odot\sigma^{\prime}\Big{(}z^{L}\Big{)} \tag{25}\]  
\[\delta^{L}=\Big{(}a^{L}-y\Big{)}\odot\sigma^{\prime}\Big{(}z^{L}\Big{)} \tag{26}\]  
The rate of change of bias and weights in the substitution function is:  
\[\frac{\partial C}{\partial b_{j}^{l}}=\delta_{j}^{l} \tag{27}\]  
\[\frac{\partial C}{\partial\omega_{jk}^{l}}=a_{k}^{l-1}\delta_{j}^{l} \tag{28}\]
## 2 Theoretical Background
### Fractal Dimension  
A fractal is a geometric shape in which, when segmented, each section approximates the original shape on a smaller scale. The concept of fractal dimension corresponds to how a fractal fills a space while approaching smaller scales [31], as well as an indication of the self-similarity or repeatability of patterns in time domain signals [32]. Fractal dimensions range from 1.0 for a straight line to 2.0 for a plane, with several formulations to calculate this quantity, with the following being used in this paper.
## 3 Performance Analysis
### EDP  
When the power constraint in the information transmission process is satisfied, namely, the set \(\Lambda\) is met, we set the probability of \(R_{m}\) transmitting covert information as \(\theta\).  
Theorem 3.1: _For given \(\lambda\) and \(\theta\), the EDP can be given by_  
\[\mu=P_{fa}P(H_{0})+P_{md}P(H_{1}), \tag{33}\]  
_where \(P(H_{0})=1-P(H_{1})\) and \(P(H_{1})=\theta P(\Lambda|\Gamma)\)._Furthermore,  
\[\mu=\left\{\begin{array}{ccc}1-\varepsilon&\lambda<{\sigma_{5}}^{2}\\ (1-\varepsilon)FA(\lambda)&{\sigma_{5}}^{2}\leq\lambda\leq\beta\\ \varepsilon MD(\lambda)+(1-\varepsilon)FA(\lambda)&\beta\leq\lambda\leq \alpha\\ \varepsilon MD(\lambda)&\alpha\leq\lambda\leq\gamma\\ \varepsilon&\lambda>\gamma\end{array}\right., \tag{34}\]  
where  
\[\varepsilon=\theta\frac{\left[1-\left(1-\exp\left(-\frac{\gamma_{th}\sigma_{ U}l^{2}}{P_{M}-(\gamma_{th}+1)P_{C}}\right)\right)^{N}\right]}{\left[1-\left(1-\exp \left(-\frac{\gamma_{th}\sigma_{U}l^{2}}{P_{M}}\right)\right)^{N}\right]}. \tag{35}\]  
Proof.: See Appendix B.  
Obviously, the EDP given above can be minimized by optimizing \(\lambda\). From the Equation (34), according to its functional properties, it is evident that the optimal solution of \(\lambda\) to minimize the EDP belongs to the interval \([\beta,\alpha]\), which has been solved by numerical simulation in Section 4.
# DLP Light-Curing 3D Printing Combustible Lighting Shell and Performance Study
## 3 Results and Discussion
### Compatibility of Combustible Lighting Shell  
Differential scanning calorimetry (DSC) is one of the most important characterization tools to study the compatibility of solid fillers [21]. The shells were mixed with sodium nitrate and magnesium powder, respectively, and DSC tests were performed after curing. The DSC curves before and after the addition of NaNO\({}_{3}\), Mg, and NaNO\({}_{3}\) + Mg to the light-cured resin are shown in Figure 11.  
When evaluating the compatibility of a combustible lighting shell, according to GJB 5383.11-2005, it is considered to possess good compatibility when the peak temperature change amounts to \(\Delta\)Tp \(\leq\) 2 \({}^{\circ}\)C, and poor compatibility when \(\Delta\)Tp \(\geq\) 5 \({}^{\circ}\)C. From Figure 11, the first exothermic peak of NaNO\({}_{3}\) is 310.5 \({}^{\circ}\)C, and the exothermic peak of NaNO\({}_{3}\) mixed with resin is 311.333 \({}^{\circ}\)C--that is, the decomposition peak of NaNO\({}_{3}\) is shifted back by 0.833 \({}^{\circ}\)C after adding resin. The first exothermic peak of Mg is 610.181 \({}^{\circ}\)C, the exothermic peak of Mg mixed with resin is 611.459 \({}^{\circ}\)C, and the decomposition peak of Mg is shifted back by 1.278 \({}^{\circ}\)C after adding resin. The first exothermic peak of NaNO\({}_{3}\) + Mg is 365.5 \({}^{\circ}\)C, the exothermic peak of NaNO\({}_{3}\) + Mg mixed with resin is 367.333 \({}^{\circ}\)C, and the decomposition peak of NaNO\({}_{3}\) + Mg after adding resin is shifted back by 1.833 \({}^{\circ}\)C. \(\Delta\)Tp values were less than 2 \({}^{\circ}\)C, so NaNO\({}_{3}\) and Mg were more compatible with the light-curing resin.  
Figure 11: DSC curves of the compatibility of each component of the shell ((a) NaNO\({}_{3}\), NaNO\({}_{3}\) + resin; (b) Mg, Mg + resin; (c) NaNO\({}_{3}\) +Mg, NaNO\({}_{3}\) +Mg + resin).
## 4 Proposed Frequency-Offset Estimation Method
### Pilot Subset Selection  
A close look at (35) and (36) reveals that the system parameters such as \(N_{b}\), \(N_{g}\) and \(N\) are predetermined, whereas non-zero valued \(\varepsilon\) and \(\xi\) have to be estimated. Therefore, the first term in (35) is determined by variables \(N_{p}\) and \(L_{p}\), whereas the first term in (36) depends on variables \(M_{p}\) and \(L_{p}\). By using non-uniformly- and non-symmetrically-distributed CPs, the first two terms are left as non-zero constants, even as SNR grows, which makes the CFO and SFO estimates tightly related to one another. One possible way to separate these two estimation tasks is to group the CP subcarriers.  
Therefore, the quantities \(L_{p}\), \(N_{p}\) and \(M_{p}\) are important design parameters that must be selected appropriately. In order to minimize (35) and (36), one CP subset is chosen to enable \(L_{p}\) to be minimized, as well as to enforce \(N_{p}\) and \(M_{p}\) to be maximized. To achieve this goal, a subset \(\mathcal{S}_{\hat{p}}\) is selected to meet the following criteria:  
\[\hat{p}=\underset{1\leq p\leq N_{c}}{\text{arg min}}\left\{\frac{L_{p}^{2}}{N_ {p}^{2}}\right\} \tag{37}\]  
and:  
\[\hat{p}=\underset{1\leq p\leq N_{c}}{\text{arg min}}\left\{\frac{L_{p}^{2}}{M_ {p}^{2}}\right\} \tag{38}\]  
where \(N_{c}\) is the sum of the number of combinations choosing \(m\) subcarriers from \(N_{f}\) subcarriers given by \(N_{c}=\sum_{m=1}^{N_{f}}N_{f}C_{m}\).  
As a consequence, the proposed LISE scheme is in the same form as (32) and (33), replacing the subset index \(p\) with \(\hat{p}\) and putting \(L_{\hat{p}}=0\) thanks to the use of the optimal subset selected from (37) and (38). Assuming the channel is constant over \(N_{b}\) OFDM symbols, the weighted version of the proposed LSE can be obtained similarly to (18) and (19):  
\[\hat{\varepsilon}=\frac{1}{\pi\beta N_{b}\sum_{k\in\mathcal{S}_{\hat{p}}}|\hat {H}_{l}(k)|^{2}}\sum_{k\in\mathcal{S}_{\hat{p}}}|\hat{H}_{l}(k)|^{2}\angle \hat{Y}_{l}(k) \tag{39}\]and:  
\[\xi=\frac{1}{\pi\beta N_{b}\sum_{k\in\mathcal{S}_{p}}k^{2}|\bar{H}_{l}(k)|^{2}} \sum_{k\in\mathcal{S}_{p}}k|\bar{H}_{l}(k)|^{2}\zeta\bar{Y}_{l}(k). \tag{40}\]
# Low Ionosphere under Influence of Strong Solar Radiation: Diagnostics and Modeling
## 2 Methods  
### VLF Technique for Monitoring Low Altitude Ionosphere  
At ionosphere altitudes from 50 km up to 90 km (D-region), measurements mainly rely on EM wave propagation techniques [26]. In our study, synchronal monitoring of phase and amplitude of VLF narrow band radio signal data by the receivers during SFs occurrences were used to obtain daytime ionospheric parameters. For this reason, the perturbation of the VLF phase and amplitude was estimated as a difference between values of the perturbed signal induced by solar X-ray flare and signal in the normal condition.  
The analysis of VLF data was carried out simultaneously with the examination of the correlative solar X-ray fluxes collected from GOES satellite (see e.g., Figure S1). The intensity of X-ray irradiance was recorded by GOES satellite by X-ray sensors in the band 0.1-0.8 nm. We based our study on investigation of series of stronger flares, i.e., flares of C-, M-, and X-classes. For our study, the effects of A- and B-class flares on VLF signals are rarely recorded and were non-essential.  
During ionospheric disturbances, a standard numerical procedure for obtaining ionospheric daytime parameters and electron densities was used. It was based on a comparison of the recorded changes of amplitude and phase with the corresponding values obtained in simulations by the LWPC numerical software package [27; 28] and [PERSON]'s two-component exponential model, as presented in [26; 29].
## 1 Introduction
Nowadays, first order control network points are created in accordance with the EUPOS standard for national CORS; the following assumptions were made: existing EPN and IGS stations have been incorporated into the network of reference stations and the mean distance between stations is 70 km. Moreover, the locations of the reference stations were chosen to ensure convenient conditions for GNSS satellite observations and only precise dual-frequency GNSS receivers have been used. Typically, the estimation of the accuracy of the network horizontal and vertical points are specified with respect to an appropriate national geodetic datum [24]. The tasks involving constructing control network points will most often need to know the position's relationship to the realization of the reference frame [25]. Constructing control networks is a huge challenge that individual countries and research centers are working on. Previously, the horizontal geodetic control networks were established using triangulation and traversing or using photogrammetry methods, while the vertical geodetic control networks were established using geometric leveling. For example, we know the standards and specifications for geodetic control networks from the United States of America [26]. Today, Global Navigation Satellite Systems and GNSS methods are generally used to establish both horizontal and vertical control network points. In this process, some tasks must be resolved, including planning of observation [27], and precise point positioning and calculation in the aspect of the solution of the fixed points [28, 29, 30].
## 6 Discussion  
### Processing Time  
As said before, we tested our parallel configuration on Raspberry Pi 3B using sequences B and C. To analyze the impact of the number of features computed by the VO method on its final runtime performance, two values for it were used, set by the features per bucket parameter.  
Starting to look at the RandomSample() method, we can see that this was the least heavy block of RANSAC. It took, on average, about 9.80% of RANSAC's total time for the serial configuration. Using the parallel configuration, we were able to reduce its processing time by 10 times. The GetInliers() method had more weight in RANSAC than the previous one. On average, it took about 18.3% of its total processing time for the serial configuration. It is also visible that the increase in the number of features computed reveals the impact on the performance of this method. This was expected since this method iterated through all the matches in each RANSAC iteration and calculate the Sampson distance to each one. However, for the parallel configuration, this factor did not have the same impact. As the method execution was distributed by the 12 QPUs of Raspberry Pi's GPU, the processing requirements were distributed when increasing the density of features. This led to an increase of gain when increasing the feature density for this method. In fact, using two features per bucket led to an average gain of approximately 1.53 times, and setting this parameter to three resulted in 1.80 times.  
By analysis of the EssentialMatrix() method, it is visible that this was the most expensive method of RANSAC. For the serial configuration, it occupied, on average, 72.0% of RANSAC's processing time. This is due to the complexity of SVD calculations and the fact that it is an iterative method. Besides this, the CPU optimization was revealed to be efficient since we obtained an average gain of approximately 2.64 times.  
Figure 10: (**a,c**) Original version of Libviso2 and (**b,d**) FAST-FUSION motion estimation with and without the orientation correction running on a Raspberri Pi 3B.  
Looking for the final results, we achieved a global average gain of 2.35 times on RANSAC comparing the serial and the parallel configurations. This had a direct impact on the global process runtime performance. Using two features per bucket allowed us to obtain for sequence B an average of 3.28 frames per second and for sequence C 3.75 frames per second. Although for RANSAC the gains were equally good, using three features per bucket resulted in a slower process. For sequence B the result was 2.48 frames per second and for sequence C 3.02 frames per second.  
Summing up, we were able to optimize a VO method using heterogeneous computing techniques using a low cost and low power GPU. Using the default level of features per bucket of the original version of Libviso2, which is 2, we achieved embedded real-time performance.
## 3 GLCM-Based Image Texture Features  
### Brief Introduction of GLCM  
The texture is formed by the repeated appearance of the gray distribution in spatial positions. Thus, there will be a certain gray relationship between two pixels separated by a certain distance in the image space, which is the spatial correlation characteristic of gray in the image. In image recognition, the GLCM is a commonly used method for image texture analysis [47; 49]. By studying the spatial correlation characteristics of gray levels in the image, the GLCM can be used to accurately describe image texture features. The GLCM can be obtained by counting the co-occurrence frequency of gray levels of a pair of pixels with a certain positional relationship in the two-dimensional (2D) space of a gray image.  
Figure 5 shows the calculation schematic of the GLCM. Given a pixel pair with gray level \(i\) and \(j\), the co-occurrence frequency \(P(i,j,d,\theta)\) of the pixel pairs can be defined as follows:  
\[P(i,j,d,\theta)=\Gamma\{[(x,y),(x+\Delta x,y+\Delta y)]|f(x,y)=i,f(x+\Delta x,y +\Delta y)=j\}, \tag{18}\]  
where \(\Gamma\) denotes the total number of pixel pairs satisfying the conditions, \(i,j=0,1,2,\cdots,N-1\) is the gray level, \((x,y)\) are the coordinates of the pixel with gray level \(i\), \((x+\Delta x,y+\Delta y)\) are the coordinates of the pixel with gray level \(j\), \(\Delta x\) and \(\Delta y\) are the position offsets in the horizontal and vertical directions, respectively, \(d=\sqrt{\Delta x^{2}+\Delta y^{2}}\) is the generation step, and \(\theta\) is the direction of generation.  
The matrix composed of frequency \(P(i,j,d,\theta)\) with specified values of \(d\) and \(\theta\) is the GLCM, an \(N\times N\) square matrix. Four GLCMs with \(\theta=\{0^{\circ},45^{\circ},90^{\circ},135^{\circ}\}\) are generally used for texture analysis.  
The generation step \(d\) is determined by the horizontal and vertical offsets \((\Delta x,\Delta y)\). Therefore, the GLCMs of an image can change with respect to \((\Delta x,\Delta y)\). The value of \((\Delta x,\Delta y)\) should be selected according to the characteristics of the periodic distribution of texture. For thinner and more slowly changing texture images, lower values should be taken, and higher values should be chosen for images of textures that change quickly.  
Figure 4: Peak SNR (PSNR) values obtained by different methods (i.e., STFT, PWVD, WT, and the proposed STFA-PD method) at different SNR levels.
# Influence of Fracture Geometric Characteristics on Fractured Rock Slope Stability
## 5 Conclusions  
The mean stability of the fractured rock slope in the presence of varying fracture geometric characteristics is studied. The study stresses the fact that to better evaluate the slope stability, differences in rock mass structure should be carefully considered, including the variability of the fracture trace length, dip, and spacing. The following conclusions are drawn based on this study:  
1. The results indicate that the proposed DFN-DEC model based on the MATLAB platform is adequate, robust, and can generate more freedom and realistic fracture networks. The Monte Carlo simulation method is appropriate to estimate the slope stability uncertainty caused by randomness in the geometry of fractures.
2. Although the stability of slopes can be determined by deterministic analysis, the result of one-time realization is highly uncertain due to the random fractures. By means of probabilistic analysis (i.e., multiple realizations), we can obtain a more accurate stability assessment result of a fractured rock slope by analyzing the obtained \(\mu_{Fs}\), \(\sigma_{Fs}\), and COV.
3. The maximum changes in \(\mu_{Fs}\) are 0.66, 0.52, and 0.82 when \(\mu_{T_{2}}\), \(\mu_{D_{2}}\), and \(\mu_{S_{2}}\) increase in their corresponding interval, while the changes of \(\mu_{Fs}\) are only 0.31, 0.27, and 0.54 as \(\mu_{T_{1}}\), \(\mu_{D_{1}}\), and \(\mu_{S_{1}}\) increase. The result indicates that geometric characteristics related to inclined fractures have a much greater influence on \(\mu_{Fs}\) than those related to horizontal fractures. Further, the mean stability of the slope decreases with the increase in \(\mu_{T_{1}}\), \(\mu_{T_{2}}\), and \(\mu_{D_{1}}\), increases gradually with the increase in \(\mu_{D_{2}}\), \(\mu_{S_{1}}\), and \(\mu_{S_{2}}\).
4. These conclusions can be well applied in engineering practice, e.g., preliminary evaluation of the slope stability according to the statistics of fracture geometric characteristics.