esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### Encrypted Traffic Sample Collecting and Labeling  
If we need to train a encrypted traffic classification model, we need a labeled dataset \(L\) contains both samples \(X\) and the corresponding categories of each sample \(A\). Hence, \(L=\{(X,A)\}=\{(x_{1},a_{1}),(x_{2},a_{2}),\cdots,(x_{n},a_{n})\}\). It is easy to obtain \(X\), but hard to acquire the corresponding \(A\) at the same time with integrity, purity, authenticity, and credibility.  
The label and the sample should be acquired simultaneously because, in encrypted traffic communication, the label is the source of the sample (application generates the traffic). The integrity means that the samples in a category need to cover as many application scenarios as possible and be complete, while the total category of samples also needs to meet the objectives of the classifier, which is \(set(A)\subseteq C\). Purity means that there should be no other types of samples and interference traffic led by background traffic in a certain sample,\(\forall x_{p},x_{q}\in X^{\text{C}_{i}},a_{p}=a_{q}\). No category is not in \(C\), which is \(C\subseteq set(A)\); hence, \(set(A)=C\). Authenticity means that labeled samples meet the needs of classifiers through verification. Credibility means the process of sample collection and labeling must be credible.
## 1 Introduction
In addition, a hybrid DI scheme is proposed that is associated with various acoustic features, which can be used to indicate the fatigue crack propagation quantitatively by experimental validation. This is deemed as an improvement to characterize crack growth in current monitoring studies. Although it is a highly challenging assignment in perceiving small-scale fatigue cracks, it is also of vital importance for continuously monitoring the crack propagation [10]. The objective of this paper is to propose a technique to monitor the fatigue crack propagation in an SMA490 BW steel plate-like structure. Ultrasonic guided waves DIs calculated by various acoustic features are used to track the crack propagation. The features of crack characterization are firstly expressed by magnitude/amplitude-based and energy-based DIs extracted from time-frequency domain signals. Based on the DI weights of each actuator-sensor path, a hybrid fusion scheme is then proposed to fuse all the available DIs of each path. Subsequently, an experimental setup of an active sensor network with a sparse transducer configuration is used for actuating and sensing guided waves. Finally, the different synthetic fusion DI algorithms of the S0 guided waves mode are calculated to emphatically characterize the crack propagation and destruction level of an in-service metallic structure.
###### Abstract  
A battery management system (BMS) plays a pivotal role in providing optimal performance of lithium-ion batteries (LIBs). However, the eventual malfunction of the BMS may lead to safety hazards or reduce the remaining useful life of LIBs. Manganese hexacyanoferrate (MnHCF) was employed as the positive electrode material in a Li-ion half-cell and subjected to five cycles at high current densities (10 A \(\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text  
Nevertheless, the malfunction of the sensors on which the BMS relies, for instance, current sensors, may lead to hazardous consequences [7].  
In this study, we evaluate the structural and chemical effects of the improper application of a high current density to a positive electrode material. The material under investigation is sodium-rich manganese hexacyanoferrate (MnHCF), a member of the Prussian blue analogs (PBAs) family, which appear promising for large-scale energy storage applications because of their ease of synthesis, low-cost, and earth abundancy [8; 9]. PBAs have also been demonstrated to be robust host materials; therefore, the stability and lifetime of the batteries may be enhanced [10]. MnHCF features a high specific capacity based on the redox activity at two species, i.e., Mn and Fe, and displays a large local structural effect following the Mn(II) oxidation to the Jahn-Teller (JT) active Mn(III) (Mn-N equatorial bond lengths are contracted by 10%) [11; 12], which is, however, buffered in the long-range by the PBA open-framework. Indeed, MnHCF shows a non-cooperative JT effect and low volume change (2% only) during cycling [13].  
Here, the applied protocol causes the formation of an additional spectral component compared to the previous study [12], as retrieved by multivariate curve resolution analysis with an alternating least squares algorithm (MCR--ALS). Although the loss in coulombic efficiency in the first cycle is drastic, the local structure analyzed by X-ray absorption fine structure (XAFS) spectroscopy is not significantly affected at the full state of charge and underlines the fair structural stability of the host, even after the applied electrochemical protocol.
## 2 Experiment Setup
### Deep Echo State Network (DeepESN) Performance Study  
DeepESN requires a large reservoir size (\(U\)) to adequately train the randomly initialized weights of its hidden layers. The network performance with different reservoir configurations has been investigated based on datasets from both simulations and practical applications [48; 49]. The general conclusion is that a higher \(U\) improves the prediction performance of DeepESN, while an excessively large \(U\) may lead to substantial computational resource consumption and overfitting. Thus, reservoir configuration should be studied for different applications.  
As a layered RNN architecture, DeepESN retains more information from previous inputs with a larger number of \(U\). When the UWOC signal is fed into the DeepESN, \(U\) determines the number of previous samples used for predicting and equalizing noise or distortion in subsequent samples. In accordance with traditional equalization methods (e.g., RLS), equalizing communication signals typically requires a few prior symbols as references. For digital-to-analogy conversion, each symbol is composed of several samples (\(\mathbf{N_{sys}}\)). Consequently, value changes in adjacent samples are attributed to either noise/distortion in the UWOC channel or variations in symbols. If the reservoir size of the DeepESN is equal to an integer (m = 1, 2, 3, etc.) multiplied by \(\mathbf{N_{sys}}\), the network is expected to achieve optimal prediction performance.  
\[U=m\mathbf{N_{sys}} \tag{4}\]  
where  
\[\mathbf{N_{sys}}=\frac{\mathit{Sampling\ Rate\ of\ ADC}}{\mathit{Symbol\ Rate}} \tag{5}\]  
Meanwhile, DeepESN will be at the risk of overfitting if \(m\) is too large.
# A Low-Profile Dual-Polarized Magneto-Electric Dipole Antenna for 5G Applications
## 2 Materials and Methods  
### Antenna Structure  
The 3D structure of the low profile broadband dual polarization MED antenna is shown in Figure 1a, which consisted of four parts: (1) horizontal electric dipoles composed of four-square metal patches; (2) magnetic dipoles consisting of four vertical metal structures; (3) two crossed \(\Gamma\)-type feeding structures located in the middle of the antenna; (4) a metal floor with reflective structure. Moreover, the bottoms of the \(\Gamma\)-type feeding structures were connected to the SMA connectors under the ground, and the rectangular slots were etched on the vertical metal wall to reduce the antenna profile. The specific structure is shown in Figure 1b-d.  
The center frequency of the proposed antenna was set at 3.8 GHz, and then the antenna was designed and optimized to realize a high gain and a broadband working bandwidth with the commercial software ANSYS High Frequency Structure Simulator (HFSS). The final optimized parameters in Figure 1 are presented in Table 1, and the optimized procedure is provided in Section 2.4. Furthermore, the metal thickness of the electric dipoles, feeding structures, and metal floor were 1 mm, and that of the magnetic dipole was 2 mm.
## 3 Results and Discussion
### Temperature Indices
#### 3.1.3 Number of Frost Days (Tmin < 0 \({}^{\circ}\)C)  
A decrease in the number of frost days is visible in both RCP scenarios (Figure 4). For the 2030 horizon, very similar results were obtained in most regions of Poland, with the decrease not exceeding 9% (7.5 days) for both scenarios. A slightly larger decrease, between 5% and 10%, is expected in the country's north. In the 2050 horizon, in RCP4.5, a 15% to 20% (15.3 days) decrease in the number of frost days is projected (except in the country's south).  
Figure 3: Spatial distribution of the percentage change in the number of hot days for 49 cities based on the results of statistical downscaling of the EURO-CORDEX model ensemble. **(a)** Scenario RCP 4.5 2030 **(b)** Scenario RCP 8.5 2030 **(c)** Scenario RCP 4.5 2050 **(d)** Scenario RCP 8.5 2050.  
northeastern regions). A slightly higher 20% to 25% (21.9 days) decrease for the RCP8.5 is also calculated.
## 4 Results
### Precision of the Navigation Solutions
#### 4.4.1 Tests Results Using GPS and Galileo Signals at Nominal Power Levels  
The 2D precision results are presented in Table 19, and the 3D precision results are provided in Table 20. The receiver successfully processed 7 Galileo E1b/c, 7 Galileo E5a, 7 GPS L1 C/A, and 4 GPS L5 signals. The navigation solutions were obtained using up to 11 observations. The satellites broadcasting the unhealthy flag were not used for the computation of the PVT.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measure** & **Formula** & **Confidence Region Probability** \\ \hline
2D 2 DRMS & \(2\sqrt{\sigma_{E}^{2}+\sigma_{N}^{2}}\) & 95\% \\
2D DRMS & \(\sqrt{\sigma_{E}^{2}+\sigma_{N}^{2}}\) & 65\% \\
2D CEP & \(0.62\
u_{N}+0.56\sigma_{E}\) (accurate if \(\frac{\
u_{N}}{\sigma_{E}}>0.3\)) & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 17: Most common 2D precision measures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measure** & **Formula** & **Confidence Region Probability** \\ \hline
3D 99\% SAS & \(1.122(\sigma_{E}+\sigma_{N}+\sigma_{U})\) & 99\% \\
3D 90\% SAS & \(0.833(\sigma_{E}+\sigma_{N}+\sigma_{U})\) & 90\% \\
3D MRSE & \(\sqrt{\sigma_{E}^{2}+\sigma_{N}^{2}+\sigma_{U}^{2}}\) & 61\% \\
3D SEP & \(0.51(\sigma_{E}+\sigma_{N}+\sigma_{U})\) & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 18: Most common 3D precision measures.  
Significantly, 2D precision measurements demonstrate greater accuracy compared to their 3D counterparts, as expected due to the superior GNSS accuracy in the horizontal plane when contrasted with the vertical plane. This variation is influenced by the angle between the line of sight to various satellites and the Earth's surface.
## 3 Results and Discussion
### Four-Port MIMO Antenna
These results underscore the directional characteristics of the MIMO structure in the E- and H-plane, highlighting its ability to focus energy efficiently in specific directions.
# An Insightful Overview of the Wiener Filter for System Identification
## 8 Conclusions  
The regularization process is a critical step in any ill-posed problem, especially in noisy environments. This important step is also required in conjunction with the well-known Wiener filter, to avoid an ill-conditioned matrix inversion, as well as to control and improve its performance in low SNR conditions. In this paper, the focus was on several regularization techniques of the Wiener filter.  
As shown in Section 4, the classical \(\ell_{2}\)-norm (or Tikhonov) regularization performs reasonably well only in good or moderate SNR environments. Nevertheless, for sparse systems, the overall performance of the Wiener filter can be improved by using a regularization technique based on the \(\ell_{1}\) norm. The iterative solution proposed in this paper outperforms the \(\ell_{2}\)-norm regularization, especially in challenging conditions, i.e., for low SNRs and/or when using less accurate statistical estimates.  
The KPD-based approach is also suitable when only a small quantity of data is available for the estimation of the statistics, as supported in Section 5. The resulting iterative KPD-based Wiener filter can lead to significantly better results, as compared to the conventional Wiener filter for the identification of low-rank systems. This KPD-based version combines the solutions provided by two shorter filters, while the conventional  
Figure 11: Misalignment of the conventional Wiener filter with \(\ell_{2}\)-norm regularization using \(\delta=10^{-8}\) and the Wiener filter with QEP regularization using the linearizations from (54) and (55) (namely QEP 1) and (57) and (58) (namely QEP 2), for different values of \(N\) and (**a**) \(\mathrm{SNR}=30\) dB, (**b**) \(\mathrm{SNR}=20\) dB, (**c**) \(\mathrm{SNR}=10\) dB, and (**d**) \(\mathrm{SNR}=0\) dB.  
Wiener filter operates with a single filter of much longer length. Consequently, the KPD-based approach fits very well for the identification of long-length impulse responses.  
Finally, the QEP regularization technique presented in Section 6 improves the performance of the Wiener filter (as compared to the \(\ell_{2}\)-norm regularization), especially in noisy environments (i.e., low SNRs), but also when using less accurate estimates of the statistics. Experimental results have supported the main theoretical developments and indicated the robust performance of these regularization techniques.  
Conceptualization, J.B.; validation, L.-M.D.; methodology, C.P.; formal analysis, S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant from the Romanian Ministry of Education and Research, CNCS-UEFISCDI, Project Number PN-III-P1-1.1-PD-2019-0340, within PNCDI III.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# User Pairing for Delay-Limited NOMA-Based Satellite Networks with Deep Reinforcement Learning
## 3 Effective Capacity and Power Allocation
### Power Allocation Strategy
Then, inserting (4), (5), (16)-(17) into (15) along with ([32], Equation (7.813.1)), we obtain the result as  
\[C_{c}^{\rm N}(\theta_{c}) =\frac{1}{\varphi_{c}\ln 2}\ln\left(\alpha_{c}\sum_{k=0}^{\infty} \sum_{m=0}^{\infty}\frac{\left(1-\alpha_{c}^{p}\right)^{k}\Phi_{c}^{m+k}\gamma ^{m+k}G_{\rm max}\Gamma(m+\varphi_{c})\Gamma(k-\varphi_{c})}{\beta_{c}^{k+m+1 }k!m!\Gamma(m_{c})\Gamma(-\varphi_{c})\Gamma(\varphi_{c})}\right.\] \[\times\left.G_{2,2}^{1,2}\biggl{[}-\frac{\delta_{c}}{\beta_{c}} \biggl{]}-k-m,1-m_{c}\right]\int_{R_{cn}}^{R_{c,f}}\left(\frac{1(ay)}{2 ay}+ \frac{J_{3}(ay)}{a^{3}y^{3}}\right)^{2}\frac{2y}{R_{cf}^{2}-R_{cn}^{2}}dy \Biggr{)}. \tag{18}\]  
By further defining \(\Psi_{c}\) to denote the integration part of (18) and, with the help of Equation (8.442.2) in [32], we obtain  
\[\Psi_{c}=\sum_{n=0}^{\infty}\Theta(n)\frac{(-1)^{n}a^{2n}\Bigl{(}R_{cf}^{2n+2} -R_{cn}^{2n+2}\Bigr{)}}{n!4^{n}(2n+2)\Bigl{(}R_{cf}^{2}-R_{cn}^{2}\Bigr{)}}, \tag{19}\]  
where  
\[\Theta(n)=\frac{F(-n,-1-n;2;1)}{16\Gamma(2+n)}+\frac{F(-n,-1-n;4;1)}{8\Gamma( 4)\Gamma(2+n)}+\frac{F(-n,-3-n;4;1)}{32\Gamma(4)\Gamma(4+n)}, \tag{20}\]  
with \(F(a,b;c;d)\) being the hypergeometric function ([32], Equation (9.100)). Finally, substituting (19) and (20) into (18), the desired result for the expression of \(C_{c}^{\rm N}(\theta_{c})\) can be obtained. Similarly, the effective capacity of User \(t\) can be given by  
\[C_{t}^{\rm N}(\theta_{c})=\frac{1}{\psi_{t}{\rm ln}2}\ln\biggl{(}\int_{R_{\rm ta }}^{R_{t_{f}}}\int_{0}^{\infty}\left(1+\gamma_{t}^{\rm N}\right)^{\psi_{t}}\!\! \!f_{\left[g_{\left[R\right]}2}(x)f_{d_{t}}(y)dxdy\right). \tag{21}\]  
By substituting (4) and (7) into (21) and following with the similar steps as those in the derivation of (12), the effective capacity expression of User \(t\) can be derived as  
\[C_{t}^{\rm N}(\theta_{t})= \frac{1}{\varphi_{t}\ln 2}\ln\left[\sum_{k=0}^{\infty}\frac{\alpha_{t} \Gamma(\varphi_{t}+k)\left(\alpha_{t}^{p}\hat{\tau}\Phi_{t}\right)}{k!\Gamma( \varphi_{t})\Gamma(m_{t})\right]\beta_{t}^{k+1}}C_{2,2}^{1,2}\biggl{(}\frac{- \delta_{t}}{\beta_{t}}\left| \begin{matrix}-k,1-m_{t}\\ 0,0\end{matrix} \right.\biggr{)}\] \[\times\ \sum_{n=0}^{\infty}\Theta(n)\frac{(-1)^{n}a^{2n}\left(R_{cf}^{2 n+2}-R_{cn}^{2n+2}\right)}{n!4^{n}(2n+2)\left(R_{cf}^{2}-R_{cn}^{2}\right)} \biggr{]}. \tag{22}\]  
Then, the sum effective capacity of the considered system can be given as \(C^{\rm N}=C_{c}^{\rm N}(\theta_{c})+C_{t}^{\rm N}(\theta_{t})\).
## 3 Results and Analysis
### NBAR Time Series Analysis
However, for the MODIS predicted values, especially in the forest, under the influence of mixed pixels, the predicted values are significantly higher than the measured values; there are large differences in small fluctuations. The HJ-1 NBAR predicted time series matches the measured NBAR values and reflects the seasonal changes in vegetation properties better; however, it is just an approximation of the MODIS NBAR predicted time series. In general, the predicted time series NBAR can represent the variation of interest area with a more reliable value compared with MODIS NBAR predicted data. The recursive estimation method is robust and reliable. We used RMSE, MAPE, AAD, and R to evaluate the results, as shown in Table 6. The accuracy test values indicate that the HJ-1 predictions are closer to the real measured values than the MODIS predictions. Figure 12 shows scatter plots of the HJ-1 predicted and actual observations. In the scatter plots, the points are distributed on both sides of the \(y=x\) line. For the MODIS predicted values, the MAPE values are within 25% and the \(R\) value is 0.75. For the HJ-1 predicted values, the MAPE values are within 20%, the \(R\) value is 0.91, and the HJ-1 predicted value is highly correlated with the actual observed value. The HJ-1 predicted values match the measured data very well. Thus, the proposed HJ-1 inversion data and time series methods perform well for NBAR recursive estimation. From these results, we found that (1) the fusion data had advantages of high spatial and temporal resolution, and matched HJ-1 data well; (2) the HJ-1 NBAR data adjusted by BRDF model were close to MOD43 data, and can reflect the annual variation characteristics of surface vegetation growth well; (3) the HJ-1 data had high-resolution features compared with the MOD43 NBAR data, reflecting the regional scale reflectivity spatial changes more accurately, and making related research and analysis more sophisticated; (4) compared to
## 4 Discussion
### Muscle Coordination  
The results revealed synergistic functioning of muscles, some of which were shared between different grasps, as follows:  
1. During power grasps, there was coordination between the wrist flexors and extensors. In particular, wrist flexors and ulnar deviators (spot 1) and wrist extensors and radial deviators (spot 7) worked together as synergistic muscles [32] to keep the wrist in a stable position. Finger flexors (spot 3) demonstrated coordination with the finger extensors (spot 5) and thumb muscles (spot 4).
2. Precision grasps involved coordination between the wrist and finger flexors (spots 1, 2, and 3), as well as the wrist extensors, finger extensors, and thumb muscles (spots 4, 5, 6, and 7).
3. The FCR (spot 2) primarily worked alone during power grasps with low activity levels. During precision grasps, the FCR collaborated with the finger flexors (spot 3) to contribute to thumb abduction movements.
4. Generally, thumb muscles (spot 4) coordinated with finger extensors (spot 5).
5. Finger extensors (spot 5) were consistently required with similar activation levels, independently of the grasp performed.  
However, apart from these synergistic behaviors, there were specific and significant differences between grasps in specific spots (Tables 2 and 3), which may help to discriminate between grasps, providing more accurate information about the person's intention to perform a particular movement. This is key to improving the control of actual prostheses and robotic devices used in rehabilitation. For example, to distinguish between cylindrical and intermediate power-precision grasps, there was a difference in spot 3 (finger flexors). In the case of the cylindrical grasp, it showed more coordination with spot 1 and required more muscle activity. Another example is distinguishing between precision grasps, which relied on spot 6 (wrist extensors and radial deviators). During the three-finger pad-to-pad pinch, spot 6 exhibited similar values, but only with wrist and finger extensors. However, during the lumbrical grasp, it also appeared more coordinated with the finger flexors, and, during the lateral pinch, with the thumb muscles. Additionally, the three-finger pad-to-pad pinch required a higher activity level than the lumbrical grasp and the lateral pinch.  
There were some pairs of grasps where there were no significant differences among any spots. However, different coordination between spots did appear, which could allow for distinguishing between them. For example, between the two-finger pad-to-pad pinch and the lumbrical grasp, the finger flexors seemed more prone to coordinating with the wrist flexors during the two-finger pad-to-pad pinch, whereas, during the lumbrical grasp, apart from coordinating with the wrist flexors, they also showed similar values to the thumb muscles and wrist and finger extensors.
## 1 Introduction
In particular, in shear band analysis, [PERSON] and co-workers [26; 27] used DIC to quantify the persistent shear bands and kinematic properties comprised of rotational and volumetric behaviors, as well as vortex structures of sand specimen undergoing plane strain deformation [28]. However, these works were carried out under two-dimensional stress conditions, which disregard the out-of-plane translational or rotational behaviors of the sand. To elicit the 3D displacement field, a stereovision-based system of DIC analysis, namely, 3D-DIC, has been proposed and used in the characterization of material response [29; 30]. However, no three-dimensional local strain or kinematics (translation, rotation, expansion/compaction, etc.) were revealed under triaxial compression conditions. This was due to the basic characteristic of DIC as an approach that performs non-intrusive sampling (i.e., boundary measurements only), thus, the specimen's internal deformation gradient, which is necessary for quantifying the motions of a 3D object, is consequently challenging to assess. Moreover, the selection of an appropriate coordinate system is another challenge. Ideally, the chosen coordinate system should align with the geometry of the studied object, so that deformation can faithfully represent how strain changed the object's geometry. Since the triaxial sand specimen is usually constituted in a cylindrical shape, the 3D cylindrical coordinate system is assumed suitable for the characterization of micro- and meso-scale kinematics observed on the boundary of a deforming sand specimen under triaxial loading conditions. In this paper, we provide the complete set of first-order 3D kinematic operators under cylindrical coordinates that are consistent with specimen geometry, and suitable to characterize micro- and meso-scale kinematics comprised of compressional, extensional, rotational, and shearing behaviors, for a series of sand triaxial compression tests. In Section 2, we give a brief introduction regarding the laboratory tests completed and coupled with the 3D DIC technique. In Section 3, we introduce 3D kinematic operators under cylindrical coordinates, as well as the use of these to characterize the local deformations. In Section 4, we provide the experimental design of kinematic analyses and the corresponding results. Section 5 presents the paper's findings and analyses of local kinematic phenomena observed and quantified on a series of sand specimens subjected to triaxial loading conditions. All supporting data and models needed to reproduce this paper are available at [[https://dataverse.tdl.org/dataverse/SGL](https://dataverse.tdl.org/dataverse/SGL)]([https://dataverse.tdl.org/dataverse/SGL](https://dataverse.tdl.org/dataverse/SGL)), accessed on 1 August 2022.