esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Morphological Changes
#### 4.3.2 Upstream Morphological Changes  
The morphological changes upstream of the selected dams are presented schematically in a similar way to that used for the downstream areas; however, these plots do not include the tributaries of the main river. The morphological changes upstream of the selected dams showed similar trends, and the obtained results are summarized in Table 4, while the schematic plots are included in Appendix D. To identify the extent of the morphological effects upstream of the reservoir, two reaches were adopted: (1) the length of the channel until the influence of the reservoir was identified and (2) the length of the channel upstream of the previous one in which the effect of the reservoir vanished.  
The morphological changes in the reach upstream of the reservoir (identified by reach 1 in Table 4) are related to the increased water levels in the reservoir with the correlated increase in the channel width. The magnitude of the increase in the channel width varies among the study sites, but it is possible to emphasize that upstream changes in the channel width are 1 order of magnitude larger than those observed downstream of the dam.  
Upstream of the Urra I Dam, an increase in the channel width of 600% and 562% was observed along the Verde and Sinu Rivers, respectively--a situation that occurs in zones with high confinement and medium erodibility but that are affected by the presence of faults that increase instability. Upstream of the Betania Dam, the Yagura River has low confinement and high erodibility and presents an increase in the channel width of up to 456%. Contrastingly, the zone of the Magdalena River with medium confinement and medium erodibility exhibits an increase up to 167%. Upstream of the Salvajina Dam, the channel width increases up to 123% in zones with high confinement and medium erodibility. The sinuosity, however, remains almost constant for the cases of Salvajina and Betania Dams, while in the Urra I Dam, it presents high variability.  
Considering the results presented in Table 4 and the plots presented in Appendix D, it is possible to identify that the magnitude of the morphological changes upstream of the studied sites depends on the hydrological variability of the water levels in the reservoir and the geometrical settings of the reservoir vase, such as the confinement degree and the valley lateral slope. The geological characteristics of the forming materials seem to have an influence on the morphological changes as high erodibility generates an increase in the channel width. The coincidence of the channel course with fault chains modifies the behavior of certain zones from low to high erodibility. Due to the changing morphology, the zones identified as reach 1 in each study site should be considered in risk reduction activities because of the increased water levels and channel widths.
# Multi-Domain Communication Systems and Networks: A Tensor-Based Approach
## 2 Tensor System Model for Multi-Domain Communication Systems
### Examples of Practical Systems
The channel can subsequently be defined as an order 5 tensor \(\mathcal{H}\in\mathbb{C}^{L\times K\times U\times M\times K}\) such that \(\mathcal{H}_{:,i,u,:,k}=\mathrm{H}^{(k,i,u)}\). Thus, the system model from (7) can be equivalently written using the [PERSON] product as:\[\mathcal{Y}=\mathcal{H}*_{2}\mathcal{S}+\mathcal{Z}. \tag{8}\]  
Other examples of using tensors to represent multi-domain signals can be found in the literature. For example, a Direct-Sequence Code Division Multiple Access (DS-CDMA) system [11] uses an order 3 tensor for signal representation accounting for antenna, temporal and spreading domains. Furthermore, [12] represents the signals using order 5 tensors in a MIMO OFDM CDMA system with antenna, data streams, sub-carriers, time blocks, and chips as the domains. Multi-domain index modulation as advocated in [27], uses transmission over spatial, temporal, frequency, coding, as well as a hybrid combination of such domains to exploit all the indices of transmit resources. Thus tensors are an effective tool for multi-domain signal representation. Note that several such multi-domain communication systems can also be represented using a degenerate setup employing vectors and matrices. The signals corresponding to different domains can be concatenated to be represented as vectors, and the channel can be modeled as a matrix. Since multiple indices of signals are collapsed into a single index using such concatenations, it leads to a cluttered and not an intuitive system model. The ease of representation through the tensor framework becomes even more prominent as the number of domains required to be incorporated in the system model increases. Furthermore, since the tensor representation preserves the structure of the signals, it helps in developing signal processing schemes which harness the structural properties of the signals. In addition, there can be cases where signals or data are represented as tensors for storage efficiency using the Tensor Train (TT) format [28], thereby restricting the flexibility to reshape the tensors into a vector or matrix. We elaborate on such advantages of the tensor formulation in the next section. We present some signal processing techniques that make use of various tools from multi-linear algebra to simultaneously exploit all the domains that exist within a communication system.
## 3 Dataset and Feature Description
### Dataset Description
#### 3.1.3 Preprocessing  
Facial emotion images generally suffer from various extraneous noises, as they are captured in different environmental conditions. These affect the feature extraction process. A suitable preprocessing technique was required to overcome this problem. Hence, Viola Jones [42] was applied to focus attention on the important region within the whole image. The important region or point of interest was mainly concerned with the areas containing facial expressions such as the lips, eyebrows, nose, eyes, etc. The Viola Jones algorithm returns the coordinates of the bounding box that contains the regions of interest. To make the method more robust and comparable with real-world scenario, the technique was assessed with various image dimensions. In the present work, facial images of three different dimensions that include \(32\times 32,\ 48\times 48\) and \(64\times 64\) were considered for the evaluation purpose. After preprocessing, the facial images were resized to their corresponding resolutions. Then, the images were used for the feature extraction. Finally, the extracted features were fed to the proposed feature selection algorithm. The use of three image sizes took into account the variation in image quality in practical applications for emotion recognition.
## 3 Analysis of Calculation Results
### Comparison of Total Dissipation under Different Backflow Clearances  
In order to study the extent to which hydraulic performance degradation is caused by backflow clearance, the distribution of \(P_{\text{total}}\) for each hydraulic component was calculated under different flow rates (\(R_{\text{b}}\) = 2 mm) as shown in Figure 7. Since the \(P_{\text{total}}\) of the inlet passage and front guide vane is so small, the hydraulic performance of the full tubular pump is mainly determined by \(P_{\text{total}}\) of the impeller, backflow clearance, rear guide vane, and outlet passage. The \(P_{\text{total}}\) of backflow clearance and rear guide vane decrease with the increase of flow rate. But the \(P_{\text{total}}\) of the impeller and outlet passage fluctuate with the increase of flow rate, and the valley value of the impeller and outlet passage can be found in 1.0\(Q_{\text{des}}\) and 1.1\(Q_{\text{des}}\), respectively.  
Figure 8 shows the \(P_{\text{total}}\) of the impeller, rear guide vane and outlet passage under different backflow clearance radii. The \(P_{\text{total}}\) of the rear guide vane decreases with the increase of \(R_{\text{b}}\) and the maximum declines are 11.1%, 13.6% and 9.2% under 0.8\(Q_{\text{des}}\), 1.0\(Q_{\text{des}}\), and 1.2\(Q_{\text{des}}\), respectively. The \(P_{\text{total}}\) of the outlet passage also decreases with the increase of \(R_{\text{b}}\) under 0.8\(Q_{\text{des}}\) and 1.0\(Q_{\text{des}}\), and the maximum declines are 22.4% and 26.6%, respectively. However, the \(P_{\text{total}}\) of the outlet passage increases with the increase of \(R_{\text{b}}\), and the maximum increase is 7.4%. In addition, the \(P_{\text{total}}\) of the impeller increases with the increase of \(R_{\text{b}}\) under 0.8\(Q_{\text{des}}\), and the maximum rise is 36.3%. However, the \(P_{\text{total}}\) of the impeller decreases first and then rises with the increase of \(R_{\text{b}}\) under 1.0\(Q_{\text{des}}\) and 1.2\(Q_{\text{des}}\). Figure 9 shows the \(P_{\text{total}}\) of different backflow clearance under three flow rates. The \(P_{\text{total}}\) of backflow  
Figure 6: Comparison of calculated (**a**) head, (**b**) shaft power and (**c**) efficiency under different \(R_{\text{b}}\).  
clearance almost remains stable with the increase of \(R_{\rm b}\) under 0.8\(Q_{\rm des}\). However, the \(P_{\rm total}\) of backflow clearance decreases with the increase of \(R_{\rm b}\) under 1.0\(Q_{\rm des}\) and 1.2\(Q_{\rm des}\), and the maximum decreases are 3.9% and 7.2%.  
Figure 8: Total dissipation of hydraulic component under different \(R_{\rm b}\) at (a) 0.8\(Q_{\rm des}\), (b) 1.0\(Q_{\rm des}\), and (c) 1.2\(Q_{\rm des}\).  
Figure 7: Comparison of total dissipation for each hydraulic component under different \(Q\)/\(Q_{\rm des}\) (\(R_{\rm b}\) = 2 mm).  
Figure 9: Total dissipation of different backflow clearances.
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 4 Microencapsulation of Polyphenols
### Encapsulation Carriers
#### 4.2.3 Lipids  
Lipid-based carriers, such as lecithin, play a pivotal role in encapsulating hydrophobic compounds such as polyphenols. One of the primary advantages of lipid encapsulation is the improvement in solubility, protection from oxidative degradation, and enhancement of bioavailability [163; 164]. For instance, lecithin, a natural phospholipid, is commonly used in emulsification-based encapsulation techniques. It is notable for its ability to form stable bilayer structures, similar to cell membranes, which can encapsulate polyphenols within their hydrophobic core. This not only protects polyphenols from degradation but also facilitates their absorption in the body by mimicking natural biological transport mechanisms. Consequently, lecithin-based systems have been applied in various food products, such as functional beverages and supplements, to deliver polyphenols with improved stability and bioavailability [165; 166]. Furthermore, lipid-based encapsulation allows for the formation of liposomes and nano-emulsions, which are highly effective at encapsulating polyphenols [167]. These systems offer an additional benefit by providing sustained release profiles, where the encapsulated polyphenols are gradually released into the digestive system, ensuring a longer-lasting bioactive effect [168; 169].
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
## 2 Materials and Methods
### Data Preprocessing and Correction
#### 2.1.2 Data Correction Method of Swarm-C Accelerometer  
This study will refer to the GRACE-A satellite accelerometer's calibration method when calibrating the Swarm-C satellite's accelerometer [25; 33; 34; 35; 26]. Slightly different from previous research, the main content of Swarm-C accelerometer correction is the data error caused by the temperature change of the accelerometer carrier. Given this feature, this section will add a linear temperature correction and compare the final correction results with ESA's.  
For each point of the satellite orbit, the calibration equation is defined as shown in Equation (1) [25; 26], where \(a_{ACC}^{UNCAL}\) represents the uncalibrated accelerometer data, \(Q\) represents a linear temperature factor, \(T(t+F)\) represents a temperature signal with a time shift of \(F\), \(B\) represents the coefficient of deviation, \(S\) represents the scale coefficient,  
Figure 2: Comparison before and after preprocessing and correcting Swarm-C satellite data. (**a**) Peak signal processing; (**b**) EDAC fault signal processing.  
\(G\) represents the trend coefficient, \(t\) and \(t_{0}\) represent the elapsed time and the start time, respectively, and \(\varepsilon\) represents noise.  
\[a_{NG}^{GPS}=B+S\cdot a_{ACC}^{UNCAL}+Q\cdot T(t+F)+G\cdot(t-t_{0})+\varepsilon \tag{1}\]  
In this paper, the non-conservative force acceleration data derived from GPS are used as the calibration standard, which is projected in the direction of the calibrated accelerometer data component to obtain the projected non-conservative acceleration \(a_{NG}^{GPS}\). Then, combined with Equation (1) to find the calibration parameters \(\hat{B}\), \(\hat{S}\), \(\hat{Q}\), \(\hat{G}\), through the generalized least square method, the estimated value of the linear calibration parameters is expressed by adding a pointed cap to the variable. After obtaining the estimated value of the variable, the corrected acceleration value \(a_{ACC}^{CAL}\) can be calculated by Equation (2). Figure 3 compares the accelerometer data of the Swarm-C satellite before and after correction.  
\[a_{ACC}^{CAL}=\hat{B}+\hat{S}\cdot a_{ACC}^{UNCAL}+\hat{Q}\cdot T(t+F)+\hat{G} \cdot(t-t_{0}) \tag{2}\]  
As shown in Figure 3, the gray curve is the original accelerometer signal. After correcting the method in this paper, the accelerometer data becomes smooth, as shown by the blue curve. The red curve is the \(\hat{Q}\cdot T\) temperature term in Equation (2). The figure shows the influence of the temperature term on acceleration. The amplitude of the temperature term accounts for more than 50% of the amplitude of the corrected data curve. It proves that the accelerometer data error caused by temperature is relatively large for Swarm-C satellites. Therefore, it is necessary to perform linear temperature correction on the accelerometer data.
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
## 5 A QKD Protocol Based on a Shared Collaboration among Three Parties
### Considerations for Complexity and Number of Resources in the Procedure
Such formula goes into the continuous dominion for large \(N\), but for small \(N\) is discrete. Figure 8 shows in color \(\overline{P}_{E_{Total}}\) in agreement with the color bar inside on the left. Note such bar refers to the exponent \(c\) for \(\overline{P}_{E_{Total}}=10^{-c}\). Then, blue corresponds to lowest \(\overline{P}_{E_{Total}}\) values and red for the highest ones. A horizontal dashed black line shows the typical case where both cases are equally selected. As it could be expected, the case \(f\to 0\) gives the lower advantage for [PERSON]. Despite this, such an election is not the best because [PERSON] could change her strategy by learning from the public sharing of basis. Thus, if as an extreme fact [PERSON] knows [PERSON] always selects the basis \(H\), then the new conditional probability will become one because we add the sure knowledge of the basis by [PERSON], thus getting a sure outcome. If \(f_{D}=0.5\), it does not add new knowledge, thus the values of \(\overline{P}_{E_{Total}}\) become as those on the dashed red line in Figure 8. In any case, the probability \(\overline{P}_{E_{Total}}\) never exceeds \(0.013\) for \(N\geq 2\). As a reference, we are included the coloured bar at the  
Figure 8: \(\overline{P}_{E_{Total}}\) values for keys of small block length as function of \(f_{D}\) ([PERSON]’s proportion for choosing \(D\)) and their length \(N\). Values are coloured as \(c\) in \(\overline{P}_{E_{Total}}=10^{-c}\). Red dashed line corresponds to the typical case \(f_{D}=0.5\) when [PERSON] selects randomly the basis. bottom (using the same color pattern) which refers to the success probability for [PERSON] using the BB84 protocol: \(0.5^{N}\). It shows an outstanding advantage for the current procedure.
# IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving
## 3 The Proposed Approach
### Pre-Processing Step 2: Intra-Frame Correction
#### 3.4.3 Rectification by Registering Two Distorted Point Clouds  
In this approach, we note that intra-frame correction is in fact quite related to inter-frame registration. As the task of intra-frame correction is to estimate the lidar motion between 0 ms and 100 ms, inter-frame registration tries to estimate the motion between 100 ms and 200 ms. Both frame \(a\) (corresponding to the lidar date generated between 0 ms and 100 ms) and frame \(b\) (generated between 100 ms and 200 ms) are distorted point clouds. It is reasonable to assume that the degree of distortion of these two frames are quite similar. We could thus directly register these two frames without any intra-frame correction first, and then use the registration result to correct each frame.  
Let the relative transformation obtained from registering two distorted point clouds be \(\triangle\,\widetilde{T}_{\rm k,k-1}^{\rm B}\). We assumed that \(\triangle\,\widetilde{T}_{\rm k,k-1}^{\rm B}\) can approximate the lidar motion between time \(\left[t_{\rm k}^{\rm L},t_{\rm k-1}^{\rm L}\right]\) and linearly interpolate \(exp\left(\triangle\,\widetilde{\xi}_{\rm k,k-1}^{\wedge}\right)\) to rectify the raw distorted points of \(f_{\rm k}^{\rm L}\), as in Equation (15).  
\[\mathbf{\hat{X}}_{\rm(i,b,k)}^{\rm B}=\exp\left(\triangle\,\widetilde{\xi}_{\rm k, k-1}\cdot\frac{i}{S_{b}}\right)^{\wedge}\cdot\mathbf{X}_{\rm(i,b,k-j)}^{\rm B}\,. \tag{15}\]  
The undistorted frame after rectification is denoted as \(\mathbf{\hat{f}}_{\rm k}^{\rm L}\). In Section 4, we do experiments to compare these three approaches.
## 3 Materials and Methods  
### Samples  
A total of six sumac samples were analyzed. Samples 1 to 4 were collected in the territory of Licodia Eubea Municipality (37\({}^{\circ}\)09\({}^{\prime}\) N, 14\({}^{\circ}\)42\({}^{\prime}\) E), Sicily region (Italy), at an altitude of about 600 m above sea level from wild plants growing on soils belonging to the association 'Regosols on sandy and conglomeratic rocks [41], the climate of this area, according to the Koppen and Geiger classification [42], is defined as 'Csa, Hot-summer Mediterranean Climate' with an average annual rainfall of 575 mm and an average annual temperature of 16.1 \({}^{\circ}\)C. Sample 1 consists of drupes harvested fresh in July, the most appropriate period as far as the ripening stage is concerned; Sample 2 were harvested at the same time but subsequently dried in a vacuum stove at the temperature of 40 \({}^{\circ}\)C. Sample 3 and 4 were collected in October (overripe stage), with the difference that also in this case Sample 4 was subjected to the same drying process previously reported.  
Sample 5 and 6 were purchased as fruit dry powders on the internet (sumac spice), Sample 5 coming from the Mediterranean area without NaCl addition, and Sample 6 from Iran and with the addition of NaCl as a preservative.
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
## 4 The FFT-ICP Scan-Matching Frame-Work  
Although FFT-based scan-matching is robust to noises and does not need any initial pose, the computation cost and accuracy are difficult to satisfy at the same time. In this section, based on our previous work in [47] and [48], a new FFT-ICP-based scan-matching frame-work is introduced, which could be faster, more robust, and more appropriate for low-cost LRF sensors.  
Firstly, after transferring the scan data into image data for further processing, the missing data features \(f_{m1}\) and \(f_{m2}\) are extracted. If the missing data features are obvious enough, for example, \(\|f_{m1}\|>Th1\) and \(\|f_{m2}\|>Th1\), where \(Th1\) is a threshold indicating the total missing angle of a scan, the missing data features are matched at the first place to determine a rough rotation angle of the two scans (Section 2.C). If the matching error is small enough, the rotation angle estimated by this step could be an effective initial guess for further processing.  
Secondly, if the FFT-based scan-matching method proposed in Section 3.3 has taken place for extracting the rotation and translation parameters \((R_{1},T_{1})\), it could be denoted that the step of the FFT-based method for estimating rotation angle could be bypassed, as the first step already has a good guess for rotation angle through the missing feature matching.  
Then, we take the transformation \((R_{1},T_{1})\) to the scene scan data and apply the ICP precise matching described in the following paragraph.  
According to [26], ICP calculates the transformation through iterations. In each iteration step, the algorithm selects the closest points as correspondences and calculates the transformation for minimizing the equation.  
\[\sum_{i=1}^{n}\|M_{i}-(RS_{i}+T)\|^{2} \tag{26}\]  
where \(R\) is the matrix composed of rotation parameters, \(T\) is the matrix composed of translation parameters, \(n\) is the number of corresponding points, \(M_{i}\) is the model point set and \(S_{i}\) is the scene point set.  
Two factors need to be considered. One is the selection of the closest points, the other is the calculation of transformation in each ICP iteration. The time complexity of ICP is dominated by the time for selecting the closest points, and some enhancements have been proposed, such as the  
Figure 4: Fast Fourier Transform (FFT)-based scan-matching.  
commonly used K-d tree. The most used method that is available to calculate the transformation in each iteration is Singular Value Decomposition (SVD). We give a brief introduction of SVD in the following.  
Introducing the centroids of the points to simplify (26):  
\[\overline{M}=\frac{1}{n}\sum\limits_{i=1}^{n}{{M_{i}}},\ \overline{S}=\frac{1}{n}\sum \limits_{i=1}^{n}{{S_{i}}} \tag{27}\]  
\[
## 3 Methodology
### Generating Explanations  
Once the models are trained, the white-box explainer model \(g_{\phi}\) naturally provides interpretable explanations for the predictions made by the black-box model \(f_{\theta}\). For linear models, the feature importance scores can be directly derived from the model coefficients, providing a global understanding of feature relevance across the entire dataset.  
For a specific instance, we generate feature importance scores based on the trained white-box model and the feature values. The importance score of feature \(j\) for instance \(i\) is calculated as follows:  
\[\text{Importance}(j,i)=\phi_{j}\cdot\text{MAD}_{j} \tag{21}\]  
where \(\phi_{j}\) is the coefficient of feature \(j\) in the trained white-box model, and \(\text{MAD}_{j}\) is the mean absolute deviation of feature \(j\) across the dataset, calculated as follows:  
\[\text{MAD}_{j}=\frac{1}{N}\sum_{i=1}^{N}|x_{ij}-\bar{x}_{j}| \tag{22}\]  
where \(x_{ij}\) is the value of feature \(j\) for instance \(i\), and \(\bar{x}_{j}\) is the mean value of feature \(j\) across all instances.  
The mean absolute deviation (MAD) serves as a scaling factor to normalize feature importance scores, ensuring that the scores are comparable across different features and datasets. By incorporating the MAD in the importance score calculation, we account for the variability and scale of the features, providing a more reliable and interpretable measure of feature importance. The MAD is chosen as a scaling factor because it is robust to outliers and provides a measure of feature variability that is interpretable across different scales. This scaling ensures that features with larger ranges do not dominate the importance scores solely due to their magnitude, allowing for fair comparison across heterogeneous features.