esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Testing  
The experimental setup for the fluid-conveying pipe was composed of three parts: pipeline systems, PVDF piezoelectric film sensors for measurement, and the system for signal acquisition and processing, as shown in Figure 12. To minimize possible electromagnetic interference from outside environment, we installed a shield case to cover the PVDF piezoelectric film sensors.  
The experimental data, the voltage of PVDF piezoelectric film sensors, were filtered and amplified by a preamplifier circuit, as shown in Figure 13a. Spectrum analysis of the experimental data is shown in Figure 13b. The simulation data under the same flow velocity are shown in Figure 13c, and their spectrum analysis is shown in Figure 13d.  
From Figure 13, it can be seen that the curve patterns of the simulation results and experimental results matched well when the mean velocity of the internal fluid was 1.6 m/s. Moreover, the dominant vibration frequency of the experimental results was 11 Hz, which differed from the simulation results only in its magnitude. The possible reasons were as follows: (1) the preamplifier circuit amplified the measured voltage of the PVDF piezoelectric film sensor; (2) the boundary condition constraint was different, namely, the hoops in the experimental measurement for the displacement restraint capacity of the actual pipe was much smaller than fixed support in the simulation; (3) the amplitudes of the flow velocity pulsation and mechanical vibration were set too small in the simulation, making the turbulence simulation signal obvious; (4) the simulation results were obtained under ideal conditions, while the experimental results were measured under realistic conditions (environmental temperature, noise interference, etc.), and thus there will be differences. We will continue to research and analyze this in the future.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Vibration Amplitude** **(mm)**} & **Plane1 and PVDF1** & **Plane2 and PVDF2** & **Plane3 and PVDF3** \\ \cline{2-4}  & **Pressure and Voltage** & **Pressure and Voltage** & **Pressure and Voltage** \\ \hline
0 & 0.9207 & 0.9179 & 0.9075 \\
0.1 & 0.9317 & 0.9373 & 0.9012 \\
0.2 & 0.9282 & 0.9314 & 0.9063 \\
0.3 & 0.9369 & 0.9195 & 0.8982 \\
0.4 & 0.9305 & 0.9294 & 0.8874 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Correlation coefficient.  
Figure 12: Experimental setup: (**a**) panoramic view; (**b**) details of the pipe and the signal processing unit.
# Estimating RQD for Rock Masses Based on a Comprehensive Approach
## 2 The Introduced Approach for Estimating the Representative RQD Based on Class Ratio Analysis
### Step 4  
The presence of discontinuities of varying sizes and morphologies within the rock mass, along with other factors, leads to a significant size effect in some properties and parameters of the rock mass, including the RQD [35; 46]. A model size that is excessively small fails to adequately represent the overall quality of the rock mass, whereas a model size that is excessively large becomes inefficient and meaningless due to the rapid increase in internal fractures and their intricate intersections. Consequently, further investigation is necessary to ascertain the representative RQD of the rock mass based on the representative RQD samples of the series size model.  
By employing the methods introduced in Sections 2.1-2.3, we can determine the representative RQD samples for the chosen sizes, yielding class ratio coefficients:  
\[\rho^{\mathrm{L}}(D)=\frac{E_{D}^{-}}{E_{D+S}^{+}} \tag{16}\]  
\[\rho^{\mathrm{U}}(D)=\frac{E_{D}^{+}}{E_{D+S}^{+}} \tag{17}\]\[\rho^{\rm M}(D)=\frac{M_{D}}{M_{D+S}} \tag{18}\]  
where \(S\) is the increment in the model size, and \(\rho^{\rm L}(D)\), \(\rho^{\rm U}(D)\) and \(\rho^{\rm M}(D)\) are the class ratio coefficients of the LCL, the UCL and the mean of the RQD samples from the thresholds of the number of models for a given size \(D\).  
Finally, the \(r\)RQD is determined to be the representative RQD of the rock mass, defined as the specific value which, once \(D\) exceeds, the RQD(\(D\)) does not change significantly. In other words, when all three class ratio coefficients of RQD(\(D\)) (i.e., \(\rho^{\rm L}(D)\), \(\rho^{\rm U}(D)\) and \(\rho^{\rm M}(D)\)) and their subsequent class ratio coefficients all fall within the allowed range (0.98 to 1.02), the RQD(\(D\)) is determined as the \(r\)RQD for the rock mass.
## 4 Experiments and Results
### Analysis Results for the Urban Area  
#### 4.3.1 Interpolation of Spectro-Temporal Profiles  
Figure 11 shows two examples of original and interpolated STPs for the urban environment. Similar to the suburban environment, in this case, the STPs also show higher energy values at low frequency bands (below 500 Hz), due to the presence of RTN. As can be seen in the left-most graph of Figure 11a, a significant increase of the measured energy was observed at 8:00 and 14:00. In this case, it was mainly caused by an increase in various types of ANEs during these hours. After data interpolation, this increase in sound levels was smoothed, as can be observed in the right-most graph of Figure 11a. Figure 11b shows the result of interpolating a missing data period of an STP (left-most graph) due to not having enough RTN frames (below \(\gamma=40\%\)) as a result of an episode of persistent birdsong at 14:00.
## 1 Introduction
### Directional Effects  
Since the reflectance of surfaces depends on the observation and illumination directions [6], particular attention has to be payed to the acquisition geometry. Directional effects are especially important in (nearly) specular reflections, but also in the case of shadow or volume effects.  
The MultiSpectral Instrument (MSI) is composed of two focal planes covering the VNIR and the SWIR channels, respectively, each one having an array of 12 detectors. Due to the shifted positioning of the detectors along the track direction on the focal planes, angular differences between the two alternating odd and even clusters of detectors are induced in the measurements. The parallax base/height (B/H) ratio ranges between 0.022 and 0.059. A similar issue occurs between the VNIR and SWIR detectors, resulting in an inter-band B/H which is less than 0.01 for the VNIR channels and less than 0.018 for the SWIR.  
The values of the solar and sensor angles on a 5 km grid are provided in the L1C product metadata. We leverage this information in the band reconstruction algorithms that will be used in this work.
# Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics
## 3 Background
### Pattern Unlock  
Pattern unlock was introduced by Google in 2008 as a graphic-based screen unlock scheme for Android devices. Instead of entering a PIN or typing a password, the user draws a pattern by connecting dots in a 3 \(\times\) 3 grid. Figure 1 shows the pattern unlock grid with the dots labeled from 1 to 9. A pattern is represented as a sequence of connected dots, such as the pattern shown in Figure 1 is \(1\to 5\to 9\to 8\to 7\).  
A pattern has to follow the following rules to be valid: (1) It has to connect at least 4 dots; (2) each dot can only be connected once; (3) a pattern always connects the first unconnected dot along its path; (4) a pattern can go through a previously connected dot to connect an unconnected dot. Based on these rules, it has been shown that the total number of valid patterns is 389,112 [6; 16], which is less than the number of possible passwords for a four-character, case-insensitive alphabetic password. This indicates that pattern unlock is relatively weak in security compared with other schemes, such as the PIN and password, in terms of pattern space.
## 4 Model Implementation and Validation
### Arima Modeling of SIC
Additionally, the forecasted SIC values for the year 2021 align well with the reanalysis data, signifying that the proposed ARIMA model is capable of generating reliable predictions of SIC. Furthermore, the quarterly means of the estimated and forecasted SIC values are illustrated in Figure 10:1 The red lines denote the reanalysis data, while the blue curves represent the SIC values from the ARIMA model. The red shaded areas represent the 95% confidence intervals of forecasts. The plotting of the quarterly means of SIC values allows for easier discernment of the changing trends of the SIC across the quarters. Throughout all quarters, the estimated SIC values align with the reanalysis data, effectively capturing the declining trend of SIC values. The largest discrepancy occurs in the October to December quarter (OND) of the year 2021. Nonetheless, the reanalysis SIC remains within the confines of the 95% confidence intervals. The distributions of quarterly means of SIC values across the various sub-regions are depicted in Figure 10:2 It is observed that the SICs in sub-region No. 1 are notably lower than in other sub-regions, reaching close to zero from October to December. Conversely, higher SICs are observed in the other sub-regions from January to March and April to June. Additionally, a discernible downward trend in SICs is evident from July to September. The forecasting of the SIC values reveals that the fluctuations are less pronounced than in the reanalysis data, yet the trends are properly captured. Figure 10:3 The estimated and forecasted SIC values from the ARIMA model in sub-region No. 2. Figure 10:4 The quarterly means of reanalysis and forecast SICs in sub-region No. 2. (**a**) January, February, and March (JFM). (**b**) April, May, and June (AMJ). (**c**) July, August, and September (JAS). (**d**) October, November, and December (OND).
# Protection against Shiga Toxins
## 2 Compounds that Protect Cells against Stx
### The Glucose Analogues 2-Deoxy-D-glucose and 2-Fluoro-2-deoxy-D-glucose
Similar to 2 DG, FDG was found to deplete calcium from the ER and to inhibit release of the A\({}_{1}\)-moiety from the holotoxin [48]. However, since the concentrations for FDG and 2 DG used were different, it is difficult to conclude whether the effect on ER calcium levels and the efficiency in blocking StxA\({}_{1}\) release are similar for these drugs. However, we have found that FDG also inhibits Stx1 transport from the Golgi to ER [48], which did not seem to be the case for 2 DG [99]. This additional block in the Stx transport might explain why FDG is more efficient than 2 DG in protecting cells against Shiga toxin and Stx2 following short-term incubation. Furthermore, since the long-term preincubation (24 h) with FDG, but not 2 DG, also leads to reduced Stx1 binding, it makes FDG a promising candidate drug for STEC infections. However, in vivo studies are required to test whether FDG could potentially be used for treatment. The first challenge is to reach high enough concentrations of FDG in the Stx-targeted cells. When [18F]FDG is used in the clinics for PET, the main limiting factor for the concentration of [18F]FDG used in patients is the allowed maximal radiation dose. This is not the problem when using stable nonradioactive FDG. There are no clinical studies describing maximal FDG doses that could be safely achieved in human plasma/tissues, but there are several clinical studies that have analyzed the safety of 2 DG when used in combination with chemotherapy or radiation. Based on the study by [PERSON] et al. [146] the recommended daily dose of 2 DG in combination with docetaxel was 63 mg/kg, which resulted in a median maximum plasma 2 DG concentration of 0.7 mM, and caused tolerable  
Figure 6: Effect of FDG and 2 DG on total levels of Gb3 and its precursors. Cells were treated with or without 1 mM FDG or 10 mM 2 DG for 4 h or 24 h, and lipids were analyzed by mass spectrometry in whole-cell lysates. The total amount of lipid was normalized to the total amount of protein in each sample (protein content was measured by BCA assay). The graph shows the levels of Cer, GlcCer, LacCer, and Gb3 in treated cells compared to control samples; the error bars show the deviation from the mean of two biological samples. For a detailed method description and the raw data see [48; 99]. adverse effects. The tolerable concentration and adverse effects of FDG might differ from those shown for 2 DG and have to be assessed in the future, but if the achievable concentration is similar to that shown for 2 DG (0.7 mM), it is then in the same range as the concentration (1 mM) shown to protect cells against Stx in vitro [48].
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 3 Classifier Models
### Linear Discriminant Analysis  
LDA is a classifier with a linear decision boundary that is generated by adjusting class-conditional densities to the data using [PERSON] theorem. The model adjusts the estimated density curve for each class assuming that all classes share the same covariance matrix [14].  
LDA aims to find a linear combination of features that separates two or more classes. In a two-class problem, LDA determines the optimal direction that maximizes the distance between the means of the two classes while minimizing the variance within each class. Consider a dataset with \(N\) samples, each with \(n\) features, denoted as  
\[\mathbf{X}=[\mathbf{x}_{1}  
The goal of LDA is to determine the direction \(\mathbf{w}\) that maximizes the separation between classes. Specifically, LDA maximizes the ratio of the between-class to within-class scatter. This ratio can be expressed as follows:  
\[J(\mathbf{w})=\frac{\mathbf{w}^{T}\mathbf{S}_{b}\mathbf{w}}{\mathbf{w}^{T} \mathbf{S}_{w}\mathbf{w}}.\]  
Maximizing this ratio ensures that the distance between the class means is large, while keeping the variance within each class small. The optimization problem leads to the following generalized eigenvalue problem [14]:  
\[\mathbf{S}_{w}^{-1}\mathbf{S}_{b}\mathbf{w}=\lambda\mathbf{w}.\]  
The solution \(\mathbf{w}\) is the eigenvector corresponding to the largest eigenvalue \(\lambda\), which provides the direction that maximizes the class separability. Once the optimal projection vector \(\mathbf{w}\) is obtained, the data points are projected onto \(\mathbf{w}\), i.e., \(z=\mathbf{w}^{T}\mathbf{x}\). LDA makes predictions by classifying a data point \(\mathbf{x}\) into one of two classes based on the position of its projection \(z\) relative to a threshold. The threshold was computed as the midpoint between the projected means of the two classes.  
For a new point \(\mathbf{x}\), the classification rule is  
\[y=\begin{cases}0,&\text{if}\quad\mathbf{w}^{T}\mathbf{x}<t,\\ 1,&\text{if}\quad\mathbf{w}^{T}\mathbf{x}\geq t,\end{cases}\]  
where \(t\) is the threshold often chosen as the midpoint of the projected class mean.
## 3 Computation of the principal components
### Centralized approach
#### 3.2.1 Scalability analysis  
Highest network load:The centralized estimation of the covariance matrix from a set of \(t\) vectors of observations requires \(t\) operations of type D as defined in Section 2.1.3. The root node supports the highest network load, which is therefore in \(O(tp)\). Once the eigenvector decomposition is performed at the base station, the root node then transmits the principal components to the sensor nodes, which requires \(qp\) operations of type \(F\). Overall, the communication cost amounts to \(O(tp+qp)\).  
Computational and memory costs:From Equation (10), the computational cost related to the covariance matrix at the base station is \(O(tp^{2})\). The memory cost for storing the matrix of observations and the covariances is \(O(tp+p^{2})\) or \(O(p^{2})\) if recursive updates are relied on. As far as the estimation of the principal components is concerned, the cost of standard eigendecomposition algorithm is \(O(p^{3})\) in terms of computation and \(O(p^{2})\) in terms of memory [21].
# Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders
## 2 Materials and Methods
### XAI Visualization Techniques  
Grad-CAM [27] and attention rollout [28] techniques were applied to investigate which areas in a scalp image were focused on by the trained deep learning model for classifying scalp hair disorders. These visual explanation techniques provide the basis for decision-making of the trained model and help users to receive information about the problem region in the image.  
Grad-CAM is one of the techniques to explain the classification decision of any CNN-based network including ResNet and EfficientNet without structural changes or retraining [27]. It provides a localization map highlighting important parts in the image to predict the class. The gradients of the classification score concerning the last convolutional feature maps are calculated. These gradient values are globally averaged over each feature map dimension to obtain weights. These weights are linearly combined with the final feature maps, and then ReLU is applied to the linear combination maps. Finally, the heatmap is generated and the high-impact areas for the classification are emphasized.  
Visualizing attention weights is useful for interpreting a model's decision. Attention rollout is one of the ways to compute an attention map when the model includes multi-head self-attention and residual connections [28]. To acquire an approximate attention map from the output token to the input image, the identities of input tokens are linearly combined through all layers based on attention weights. The attention weights of ViT-B/16 over all heads were averaged and the weight matrices in all layers were multiplied, recursively. The acquired attention map demonstrated the image parts that are semantically related for classification.
## 4 Discussion
### Modeled Phenology  
Differences in RMSD and correlation coefficient between the modeled and observed phenology have been found in comparison with those between the corresponding remotely sensed phenology and ground observation. However, there is no evident pattern of whether the RMSD and correlation coefficient would increase or decrease, which suggests that the discrepancy between modeled and remotely sensed phenology may either add to or offset the discrepancy between remotely sensed phenology and ground observations. Overall, the magnitude of differences in the RMSD is small (usually less than 1 day for the onset, and less than 5 days for the offset). Thus, the RMSD between modeled and observed phenology is still on the same order as the RMSD between the phenology used for parameterization and observed on the ground. In other words, the bias in remotely sensed phenology is generally maintained by the modeled phenology. The magnitude of changes in correlation varies in a relatively wide range (Table 3 _vs._ Table 5 and Table 4 _vs._ Table 6), because the correlation is not considered in the cost function of the genetic algorithm. In most cases, correlation became lower suggesting the capability of capturing the inter-annual variability is weakened after the modeling process. Even though correlation became higher in some cases, it does not necessarily indicate that the capability of capturing the inter-annual variability has been improved. If the modeling period were extended, the correlation might further change. This highlights the fact that there is still a need to evaluate whether the phenological models and the optimized parameters can properly capture the changes in phenology in response to the changing climate, even if the RMSD is minimized. One possible solution is to maximize the correlation between modeled phenology and the reference and minimize the RMSD during the optimization process, which requires developing a cost function that incorporates both metrics.
# Multi-Source HRRP Target Fusion Recognition Based on Time-Step Correlation
## 3 Experimental Results and Analysis  
### Introduction to the Dataset  
Due to the fact that ship targets are mostly non cooperative targets, it is difficult to establish an HRRP database of targets based on measured ship data. In this paper, we use CAD3D software to build 10 kinds of a 1:1 ship target model and import them into the CST electromagnetic simulation software. The structural parameters of the 10 ship targets are shown in Table 1[32]. The CST simulation parameters are set as follows: the azimuth angle is 0\({}^{\circ}\)\(\sim\)360\({}^{\circ}\), the pitch angle is 90\({}^{\circ}\), and the angle step size is 1\({}^{\circ}\). The center frequency of the radar is 3 GHz, with a bandwidth of 150 MHz. The polarization methods include vertical polarization and horizontal polarization, and the frequency sampling points are 360. The software defaults to the optimal grid analysis size and selects the ray tracing algorithm for the solution. Finally, HRRP data for 360 azimuth angles of 10 types of ship targets are simulated.  
It is well known that the lack of training samples will lead to overfitting of the model. The HRRP data simulated by CST is far from enough and needs to be expanded. In this paper, according to the signal-to-clutter ratio of 10 dB, the original data is expanded by adding clutter that conforms to the K-distribution in three stages. At each signal-to-noise ratio, the data is expanded three times. The HRRP of 10 types of ship targets under the condition of a 10 dB signal-to-noise ratio is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline
**Ship Number** & **Length (m)** & **Width (m)** \\ \hline
1 & 182.8 & 24.1 \\
2 & 153.8 & 20.4 \\
3 & 162.9 & 21.4 \\
4 & 99.6 & 15.2 \\
5 & 332.8 & 76.4 \\
6 & 337.2 & 77.2 \\
7 & 17.1 & 3.6 \\
8 & 143.4 & 15.2 \\
9 & 17.6 & 4.5 \\
10 & 16.3 & 4.7 \\ \hline \end{tabular}
\end{table}
Table 1: Structural parameters of 10 types of ship targets.  
hidden layers are set to 128. The optimizer selected the Stochastic Gradient Descent (SGD) method with a batch size of 32. The preprocessed sliding window has a length of \(d\) = 48 and a translation distance of \(l\) = 24. The number of layers in the feature extraction network is set to 8.
## 3 Data Acquisition and Processing
### Selecting Performance Degradation Index  
#### 3.2.1 Feature of Time-Frequency Domain  
Because the statistical index of the vibration signal contains rich information regarding rolling bearings' running state, it can be used to assess performance degradation trends of rolling bearings or diagnose bearing faults. The running state of rolling bearings is effectively described by multiple time-frequency domain indexes of the whole life-cycle, then PCA is used to extract the first principal component from features of the time-frequency domain. Figure 6a shows performance degradation indexes from time-frequency domain features in three working conditions.  
Figure 5: Test Rig. (**a**) Overview of PRONOSTIA; (**b**) normal and degraded bearing.  
Figure 6: Different performance degradation index. (**a**) Time–frequency domain; (**b**) wavelet packet space; (**c**) EEMD space.
# Analytical Solution and Experimental Validation of the Electromagnetic Field in an IPT System
## 4 Electric and Magnetic Field in the IPT System  
In this section, the analytical solutions for the electric and magnetic field, induced both by the primary and secondary coil, are deduced, and the numerical integration method is used to acquire numerical results.