esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Semi-Automated Procedure to Estimate the NKH Model Parameters for Soils and Rocks  
### MATLAB Algorithm Estimating Shear Stress-Shear Strain Loop  
Figure 5 shows the algorithm of the program based on the ABAQUS calculation procedures [18]. For a given incremental shear strain \(\Delta\varepsilon_{k}\), the von-Mises yield criterion was initially checked with the original predicted elastic stress (\(\Delta\sigma^{\eta\tau}\)_k_) by the assumption of pure elastic strain. Then, the iteration process was applied to determine the correct incremental plastic strain (\(\Delta\varepsilon_{p}\)), incremental back stress (\(\Delta\alpha\)), and incremental total stress (\(\Delta\sigma\)) at a step.  
Figure 4: G/Gmax and damping curves in the NKH model for (**a**) silica sand, (**b**) mixture soil, and (**c**) Toyoura sand.  
In the iteration process, new plastic strain was estimated based on the flow rule [18], and the back-stress values either for the positive (\(\alpha_{p}\)) or negative (\(\alpha_{n}\)) phase could be calculated as follows [21]:  
\[\left\{\begin{array}{l}\alpha_{p}=\sum_{i=1}^{3}\left[\frac{2}{3}\frac{C_{i}}{ \gamma_{i}}+\left(\alpha_{in}-\frac{2}{3}\frac{C_{i}}{\gamma_{i}}\right)exp(- \gamma_{i}\Delta e^{p})\right]\ \ (positive\ phase)\\ \alpha_{n}=\sum_{i=1}^{3}\left[-\frac{2}{3}\frac{C_{i}}{\gamma_{i}}+\left( \alpha_{ip}+\frac{2}{3}\frac{C_{i}}{\gamma_{i}}\right)exp(\gamma_{i}\Delta e^{ p})\right]\ \ (negative\ phase)\end{array}\right. \tag{6}\]  
where \(\alpha_{ip}\) and \(\alpha_{in}\) are the maximum and minimum values of the back-stress component during positive and negative loading, respectively, and \(\Delta e^{p}\) is the incremental plastic strain at the step. The total shear stress could be calculated from the incremental elastic strain (\(\Delta e^{e}\)), which was calculated from the incremental strain and plastic strain at that step (\(\Delta e^{e}=\Delta\varepsilon_{k}-\Delta e^{p}\)), and the yield function, F, could be defined following Equation (2). The process of iteration finishes when F reaches the tolerated value [18].  
The procedure was implemented in MATLAB, which can estimate shear stress-strain loops with a much faster speed compared to ABAQUS. The results obtained from this procedure were then validated by comparing the stress, hysteretic loops, G/Gmax, and damping curves obtained in the ABAQUS simulation. Figure 6a,b show a comparison of the stresses obtained in ABAQUS and MATLAB with 10 cycles of sinusoidal shear strains, and the identical results between hysteretic loops obtained in both ABAQUS and MATLAB were typical for the silica and Toyoura sands. The shear modulus (G) and damping (D) were then estimated and compared with the ABAQUS results for the silica and Toyoura sands, as shown in Figure 6c. Similar results were also obtained for the mixture soil. The good matching of the results in both the G/Gmax and D curves proves that the algorithm could predict the soil nonlinear properties in a wide range of shear strain of 0.0001% to 1%.
# Surface-Wave Extraction Based on Morphological Diversity of Seismic Events
## 4 Examples
### Synthetic Examples
#### 4.1.2 Recovery of the Surface-Wave Dispersive Energy  
The proposed method is applied to the synthetic seismic data shown in Figure 5a to display the results of surface-wave extraction and the improvement of the surface-wave dispersive energy. Compared with the dispersive energy of the original seismic data shown in Figure 5b, the dispersive energy of the surface waves extracted from the data is more continuous in Figure 6. The energy at frequencies of 25-27 Hz and 28-33 Hz is separated into two parts corresponding to the second higher mode and the third higher mode, respectively, which means that \"mode kissing\" disappears. Additionally, the dispersive energy is close to the theoretical dispersion curves, which implies that surface waves can be effectively extracted using the proposed method.  
Figure 6: (**a**) The result of surface-wave extraction by the proposed method and (**b**) its image of dispersive energy in the f-v domain, where the white dotted lines represent the theoretical dispersion curves.  
Furthermore, we compare the proposed method with other methods of surface-wave extraction to test the superiority of the proposed method. High-resolution LRT is applied to the original data and a 2D window is used to select and extract surface waves in the f-v domain. Figure 7 shows that the surface waves are mainly extracted but \"mode kissing\" is not changed. The f-k filtering method is also used to extract surface waves. The result of the surface-wave extraction consists of residual reflections in Figure 8a and \"mode kissing\" is reduced in Figure 8b. However, there is also a risk of mode misidentification owing to the discontinuous dispersive energy shown in Figure 8b.  
Figure 7: (**a**) The result of surface-wave separation by a two-dimensional (2D) window of the f-v domain and (**b**) its image of dispersive energy in the f-v domain.
## 3 Methodology
### Difference-Based Approach: Change-Enhanced Segmentation
#### 3.2.1 Image Differencing  
First, standard transformations and normalizations, denoted by \(\mathcal{T}\), are applied to both images. Then, a single difference image, \(\mathbf{X}_{\text{diff}}\), is created by computing the element-wise subtraction of the pre-disaster image tensor from the post-disaster image tensor.  
\[\mathbf{X}_{\text{diff}}=\mathcal{T}(\mathbf{X}_{\text{post}})-\mathcal{T}( \mathbf{X}_{\text{pre}}) \tag{4}\]
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 2 Theoretical Background
### Bayesian Networks  
Let \(X\) be a _discrete random variable_ that takes values over a finite set \(\mathcal{X}\). We denote an \(n\)-dimensional random vector by \(\mathbf{X}=(X_{1},\ldots,X_{n})\) where each component \(X_{i}\) is a random variable over \(\mathcal{X}_{i}\). We denote the elements of \(\mathcal{X}_{i}\) by \(x_{i1},\ldots,x_{ir_{i}}\), where \(r_{i}\) is the number of values \(X_{i}\) can take.  
A _Bayesian network_ (BN) is a probabilistic graphical model which encodes conditional relationships among variables. It is composed by a _directed acyclic graph_ (DAG) defined as \(G=(V,E)\), where the vertices \(V\) coincide with a set of random variables \(\mathbf{X}=(X_{1},\ldots,X_{n})\), also known as _nodes_, and the _edges_\(E\) with their conditional dependencies. Variables are independent of all its non-descendant nodes given its parents. A node \(X_{i}\) contains a local probability distribution, encoding the probabilities of every possible configuration of node \(X_{i}\) given its set of parents \(\Pi_{X_{i}}\),  
\[P(X_{i}=x_{ik}|\Pi_{X_{i}}=w_{ij}), \tag{1}\]  
where \(x_{ik}\in\mathcal{X}_{i}\) is the \(k\)-th possible value from the domain of \(X_{i}\) and \(w_{ij}\) the \(j\)-th configuration of \(\Pi_{X_{i}}\). The set of conditional probabilities associated with each node denotes the BN _parameters_.  
The joint probability distribution of the network is composed by several local probability distributions associated with each variable, as  
\[P(X_{1},...,X_{n})=\prod_{i=1}^{n}P(X_{i}|\Pi_{X_{i}}), \tag{2}\]  
and can be used to compute the probability of an evidence set.  
Learning the structure of a BN [21] can be summarized as finding the DAG which better fits a training dataset. The goodness of fit of a network is measured using a _scoring function_. If the scoring function is _decomposable_ over the network structure then _local score-based algorithms_ can be employed, turning the DAG search extremely efficient [22; 23; 24]. A known decomposable scoring criterion is the _log-likelihood_ (LL) [25]. Network parameters are computed by using the observed frequencies of each configuration.
## 5 Discussion  
### Analysis of the Results of the LUCI in the Su-Xi-Chang Region  
Due to the global development of urban agglomerations, more attention is being drawn to research on land use conflict in urban areas [7; 10]. Because the field of land use conflict research is still developing and improving, studies are more concerned about the identification and measurement of the conflicts [9; 18; 21]. However, they often neglect the relationship between the conflicts and the level of urbanization. Therefore, our research constructs an effective spatial measurement model of land use conflict based on the landscape risk assessment method and tries to implement the urban spatial attribute index so as to validate the identified conflict and its spatial feature. Consequently, it provides a good reference point for coordinating the sustainable use of regional land resources. Similar to previous research results on spatio-temporal changes of coordinated land use allocation and the landscape pattern in urban agglomerations [8; 46; 53], our model supports the hypothesis that land use conflict is stronger in urban areas with high land use tension and rapid change in the frequency of land use. The results also imply that our model is accurate and applicable to a certain extent. Since land use conflict is quantified by the landscape risk index, we adopt the sampling method of 2500 \(\times\) 2500 m and combine the requirements of a landscape scale in order to introduce innovative perspectives of spatial statistical analysis of model calculation results. The analysis results not only focus on the degree and spatial distribution of land use conflict, but also demonstrate the spatial dynamic characteristics of land use conflict in more detail through hot spot analysis.  
Figure 8: The spatial patterns of hot spots and cold spots in (**a**) 1990, (**b**) 2000, (**c**) 2010, and (**d**) 2018.
# Saturation Based Nonlinear FOPD Motion Control Algorithm Design for Autonomous Underwater Vehicle
## 3 Fractional Calculus  
### Fractional-Order Derivative  
Fractional calculus is an extension of traditional calculus. Until now, there is still no unified definition of fractional calculus. Grunwald-Letnikov, Riemann-Liouville, and Caputo definitions are used extensively in related studies. Here, we primarily introduce the Riemann-Liouville and Caputo definitions, which have been frequently applied in solving engineering and computing problems [17].  
Definition 1: [28] The \(\alpha\) order Riemann-Liouville derivative of a function \(f(t)\in C^{n+1}([t_{0},+\infty]\,,\,\mathrm{R})\) is  
\[{}_{t_{0}}^{L}D_{t}^{\alpha}f(t)=\frac{1}{\Gamma(n-\alpha)}\left(\frac{d}{dt} \right)^{n}\int_{t_{0}}^{t}\frac{f(\tau)}{(t-\tau)^{\alpha+1-n}}d\tau,\]  
where the positive integer \(n\) satisfies \(n-1<\alpha\leq n\), \(\Gamma(\cdot)\) is the Gamma function defined in Appendix A, and \(t_{0},\,\,t\) are the lower and upper limits of the operator, respectively.  
Definition 2: [28] The \(\alpha\) order Caputo derivative of a function \(f(t)\in\mathbb{C}^{n+1}([t_{0},+\infty]\,,\,\mathrm{R})\) is defined as  
\[{}_{t_{0}}^{C}D_{t}^{\alpha}f(t)=\frac{1}{\Gamma(n-\alpha)}\int_{t_{0}}^{t} \frac{f^{(n)}(\tau)}{(t-\tau)^{\alpha+1-n}}d\tau,\]  
where the positive integer \(n\) satisfies \(n-1<\alpha\leq n\).  
Remark 1: _There are some differences between Riemann-Liouville and Caputo definitions, especially in terms of their initialization [29; 30; 31]. However, with null initial conditions, the Laplace transforms the Riemann-Liouville and Caputo derivatives are_  
\[\mathcal{L}\left\{{}_{t_{0}}^{L,C}D_{t}^{\alpha}f(t);s\right\}=s^{\alpha}F(s).\]  
That means that fractional-order systems with a steady (null) initial condition described by the Riemann-Liouville and Caputo derivative exhibit a physically coherent response for controlled systems [28]. Due to this uniform characteristic, a unified fractional-order operator \(\mathcal{D}^{\alpha}\) is used instead of \({}_{t_{0}}^{L}D_{t}^{\alpha}\) or \({}_{t_{0}}^{C}D_{t}^{\alpha}\) throughout the remainder of this paper.  
The Mittag-Leffler function is a generalization of exponential function and is an important component in the solution of fractional differential equations. A two-parameter Mittag-Leffler function can be expressed as [17]:  
\[E_{a,b}(x)=\sum_{k=0}^{\infty}\frac{x^{k}}{\Gamma(ak+b)}, \tag{3}\]  
where \(a>0,\,\,b>0\) and \(x\in\mathrm{C}\). The Laplace transform of a two-parameter Mittag-Leffler function is:  
\[\mathcal{L}\left\{t^{b-1}E_{a,b}(-\lambda t^{a})\right\}=\frac{s^{a-b}}{s^{a} +\lambda},(\mathrm{Re}(s)>|\lambda|^{\frac{1}{2}}), \tag{4}\]  
where \(t\geq 0\), and \(\mathrm{Re}(s)\) is the real part of \(s\).
# Dual-Band Multiple-Element MIMO Antenna System for Next-Generation Smartphones
## 6 MIMO Characteristics  
In order to evaluate the MIMO characteristics, the key performance parameters are evaluated. The parameters included are: ECC, MEG, and CC.  
ECC is the measure of how well the antennas are correlated and well isolated. For practical applications, an ECC value of 0.5 or less is recommended [13]. Figure 11a,b shows the simulated and measured ECC characteristics of the proposed MIMO antenna system. The ECC is calculated using the far-field radiation characteristics described in [14,15]. From the figure, it can be seen that the simulated ECC is below 0.035 and 0.03 at 3.5 GHz and 5.4 GHz, respectively, while the measured ECC is well below 0.09 and 0.05 at 3.5 GHz and 5.4 GHz, respectively. This indicates minimal interference among radiating elements in the MIMO system.  
Another prominent parameter of any MIMO system is MEG, which is used to calculate gain in a multipath environment. The MEG of the proposed MIMO system is calculated using the equation described in [16, 17, 18]. Table 1 shows the calculated MEG at both resonant frequencies of 3.5 GHz and 5.4 GHz. The MEG shows satisfactory values of less than 1 dB at both resonances for all radiating elements.  
The distribution of channel resources is a crucial phenomenon as communication technology progresses. Because more radiating components are used on the transmitter and receiver sides of the MIMO systems, the channel capacity is significantly increased compared to a single-input single-output (SISO) system. Figure 12 shows the calculated ergodic CC of the proposed MIMO system, which is in the range of 37 to 40 bps/Hz. The CC has been calculated using an SNR value of 20 dB. The calculated CC shows a maximum value of 40 bps/Hz, which is close to the ideal range of 44 bps/Hz.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline
**Frequency (GHz)** & **MEG 1** & **MEG 2** & **MEG 3** & **MEG 4** & **MEG 5** & **MEG 6** & **MEG 7** & **MEG 8** \\ \hline
3.5 & \(-\)3.25 & \(-\)3.21 & \(-\)3.9 & \(-\)3.74 & \(-\)3.11 & \(-\)2.98 & \(-\)3.09 & \(-\)2.8 \\ \hline
5.4 & \(-\)2.96 & \(-\)3.06 & \(-\)3.16 & \(-\)3.46 & \(-\)3.66 & \(-\)3.17 & \(-\)3.32 & \(-\)3.35 \\ \hline \end{tabular}
\end{table}
Table 1: Calculated MEG of the proposed MIMO antenna.  
Figure 11: (a) Simulated and (b) measured ECC of the proposed MIMO antenna.  
Table 2 shows the performance comparison of the proposed MIMO antenna with other published literature. The comparison is made using the antenna dimensions and resonance bandwidths of other performance parameters. Through the comparison table, it can be seen that the proposed MIMO system offers high CC and low ECC compared to the designs presented in [5,7,8,10,11,12,19].
## 3 Materials and Methods  
### Background for Ship Dataset  
The dataset used strongly influences the accuracy and validity of DLRI studies. The number of total samples, the variety of the classes, the balance among the classes, the quality, the originality, the validity, the foreground-background separation format, and the accessibility of the datasets are the key specifications required for these studies [40].  
This research is built explicitly for the DLRI process of ships from optical satellite images. The main features of the optical satellite images are the number of spectral bands, the spatial resolution, the incident angle of the sensor, and the radiometric resolution, which are provided with the original satellite image and metadata file. On the other hand, deep learning studies require the collection of a large and diverse dataset from satellite images for target recognition/identification; a process that is time-consuming and expensive. When using archive data, it is not always possible to obtain satellite images from the inshore and offshore regions with enough data to represent the ship types. Beyond being ship-specific, the requirement for an extensive dataset is valid for most geospatial applications where objects need to be automatically extracted from high-resolution and very high-resolution optical satellite images. However, such comprehensive datasets are not generally freely available. Nevertheless, some open-source datasets can be used in DLRI studieswith some constraints. Detailed information about the most popular open-source ship datasets is provided in Table 1.  
However, to process DLRI in an optical satellite image, the dataset must be suitable and include the required number of images for the training, validation, and testing of all DLRI stages defined in this research. Other requirements of an effective dataset are given below.  
* High definition images collected by optical satellites,
* Images with spatial resolution information to ensample a real scenario,
* Image metafile of an existing ship with location and class information,
* Defined recognition and identification levels of each ship in the dataset,
* Images from the inshore and offshore sea,
* Images with rural and urban coasts,
* Images from different locations around the world,
* Some images with clouds and wave clutter to sample a noisy background.  
When we focused on the above-indicated requirements, we concluded that the existing datasets were not eligible for this research.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 6 Nanoparticle Toughening Method
### Hybrid Epoxy Nanocomposite  
In recent years, hybrid epoxy nanocomposites have emerged as a research focus in the polymer materials field due to their unique synergistic reinforcement mechanisms. By introducing nanoparticles (e.g., nano-SiO\({}_{2}\), graphene oxide) into conventional modification systems (such as liquid rubber/epoxy resin or thermoplastic resin/epoxy resin), multi-scale reinforcement networks can be constructed, where the synergistic interaction between these components overcomes the limitations of single-modification mechanisms [104; 105].
# Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data
## 6 Discussion
Specifically, we computed the correlation between the EEG power spectra in a frequency range of 0.5 Hz to 32.5 Hz at a given 1 s time segment from one channel (e.g., F3) and the EEG powers at the same frequency range at the same time segment but from another channel (e.g., C3), resulting in one correlation coefficient per time segment. Our algorithm identified artifacts in the frequency domain (after multitaper spectrogram analysis), and the expert annotated artifacts in the time domain. Small misalignments can occur. Nonetheless, we showed that our method can effectively identify and label outlying segments and amalgamate them into artifactual regions that reflect expert annotation. Depending on their needs, users can choose to ignore individual outlying segments or very short, isolated artifactual regions. Our paper has limitations. First, we were unable to thoroughly evaluate our method's performance using additional expert-annotated examples. Secondly, our method may perform poorly if all channels experience artifactual regions simultaneously because of a common disturbance; then, the correlations between channels in those regions may remain nearly as high as correlations in non-artifactual regions. Any method would likely perform poorly in situations like that. However, we believe that such situations are rare.
# Vacuum Ultraviolet Single-Photon Positionization of Amino Acids
###### Abstract  
In this study, ultraviolet (UV) laser desorption and vacuum UV single-photon (VUV SP) postionization were performed to ionize and successfully analyze 20 common amino acids. The analytical merit and efficiency of the ionization was compared with those of conventional UV matrix-assisted laser desorption ionization (UV-MALDI). A VUV light source (118 nm) was generated from the ninth harmonic of a Q-switched Nd:YAG laser, and the photon number was determined to be larger than \(10^{12}\) for each laser pulse in the ionization region. In general, the detection sensitivity of VUV-SP-positionization was 10-100 times higher than that of conventional UV-MALDI. In particular, the ion signal from VUV-SP-positionization was considerably larger than that from UV-MALDI for analytes with low proton affinity such as glycine. However, some fragmentation of intact ions was observed in VUV-SP-positionization. Quantitative analysis performed using a glycine/histidine mixture and tryptophan/phenylalanine mixture revealed that the dynamic range of VUV-SP-positionization was one order of magnitude larger than that of UV-MALDI, indicating that VUV-SP-positionization is suitable for the quantitative analysis of amino acids.  
photoionization; mass spectrometry; MALDI, VUV; postionization; photoionization; amino acid +
Footnote †: journal: Applied Sciences
## 1 Introduction
To date, the existing research tools have not been capable of accurately measuring the constantly changing affect and exercise intensity (heart rates) at the same time, particularly for a large group of people in the exercise field. However, with the recent development of the Internet of things (IoT), several wearable devices have been introduced [20]. Of these devices, the smartwatch enables us to send, respond, and collect questionnaires and measure the heart rate required for identifying exercise intensity and automatically store all the data in the cloud. Smartwatches generally use photoplethysmographic (PPG) signals to measure heart rate [21]. Prior studies verified the accuracy and reliability of heart rate measured with smartwatches in varying conditions (e.g., rest, walking, cycling, dancing) [22]. In addition, it is possible to control multiple smartwatches simultaneously, transmit and store data automatically via an application developed to suit the purpose. Thus, wearable devices and mobile apps have made field research possible beyond existing research tools. Zumba fitness is one of the representative dance-based sports that have recently dominated the group exercise market. Zumba fitness is a workout program that blends energetic Latin music with basic dance steps in a fitness program that encourages participants to enjoy aerobic exercise and have fun, as indicated by its official motto, \"Ditch the workout. Join the party\" [23]. The exercise intensity of Zumba is known to be 66 \(\pm\) 10.5% of the average VO2 max, consuming 369 \(\pm\) 108 Kcal for 40 min [24], which meets the American College of Sports Medicine (ACSM)'s recommended dose to maintain a healthy weight [25]. Research evidence suggests that Zumba effectively improves aerobic fitness [26] and weight loss [27]. In addition, participation in Zumba fitness enhances positive affect. In a recent study, [PERSON] et al. [28] investigated the affective changes during Zumba fitness as a function of exercise intensity. They found that 45-min low-intensity and moderate-intensity Zumba programs both affected improving effect during exercise, with greater positive affect observed in moderate intensity relative to low intensity. Furthermore, Zumba fitness improves quality of life [29] and alleviate depressive symptoms [30]. Therefore, this study uses a smartwatch in a real-life exercise setting to investigate the effect of a dance-based aerobic group class on the affective experiences of participants with different fitness levels.