esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCSE was implemented to extract damage features, and finite element analysis software was then used to generate training samples, which included stiffness reductions to achieve various damage patterns. A neural network model was constructed and trained using entropy values for these damage patterns. An experiment was conducted on a seven-story steel benchmark structure to validate the performance of the proposed system. Additionally, a confusion matrix was established to evaluate the performance of the proposed system. The results obtained for a scaled-down benchmark structure indicated that 89.8% of the floors were accurately classified, and 90% of the practical damaged floors were correctly diagnosed. The performance evaluation demonstrated that the proposed SHM system exhibited increased damage location accuracy.  
structural health monitoring; artificial neural network; multi-scale cross-sample entropy Article
## 3 Discussion
### Phenotyping in Terms of THC and CBD Composition
[PERSON] and colleagues (2018) found that, despite the similarity in CBD contents, not all equally high-CBD cannabis extracts produced the same effects. They stated that, as cannabinoids profiling of diverse medical cannabis plants are different, analyzing the effects of specific cannabis compositions for pharmacological-based research is critical [69]. Therefore, it seems likely in the future that both THC and CBD content may be of wide relevance for further development within the pharmaceutical industry [68]. This is supported by a previous study reported that the combination of the psychoactive cannabis A9-THC with other non-psychotropic cannabinoids such as CBD demonstrated a higher activity than THC alone [70; 71]. Additionally, previous findings showed that all three major products--food, fiber, and medicine--were extracted from the same crop of the accessions from Darchula district in the northwest of Nepal [72]. In addition, some Iranian cannabis populations were evaluated using wood and fiber anatomy and stem biometry characteristics [73]. They suggested that both populations of Bsh-01 and Zah-01 are significant candidates in terms of fiber anatomy, fiber length, and stem biometry and can be considered for textile and paper industries, while in this study, the aforesaid populations were defined as Type I and, despite the prevalence of THC, also have eligible fiber anatomy characteristics. Our research showed that populations of Nhv-01, Ban-01, Sir-01, Rmhz-01, Ark-01, and Sam-01 are defined as Type III. However, in the earlier study, some populations are expected to be putative high-potential fiber populations, indicating that both populations Ban-01 and Nhv-01 have strong fiber characteristics such as a higher average of bast and woody cores and, alongside populations of Rmhz-01 and Ark-01, can be considered an option for breeding programs towards producing fiber [73]. The results of this study contribute important pre-breeding information for cannabis breeders to improve breeding programs utilizing this collection. Although the uses of these populations cannot be predicted with certainty, as accessions high in THC or high in CBD are Type I and III, respectively, they are not necessarily \"hemp\" or \"medicinal marijuana\" in the classic sense; however, according to previous study using genomic data, Iranian populations were located in two distinct marijuana and hemp clusters [60]. Therefore,populations of this collection assumed to offer a range of measurable health benefits in the pharmaceutical, dietary supplement industries, dual-proposal (seeds and fiber), and even renewable and sustainable feedstock for the production of biofuels [5; 74; 75; 76; 77]. Further studies are needed to evaluate the potential of these populations. Additionally, fiber anatomy is required to assess for fiber production purposes.
## 2 Materials and Methods  
The lack of information on malfunctioning conditions in real-world machines increases the need for modeling and simulation, whether numerically or in a lab setting. On the one hand, the FEM is a dependable method for carrying out numerical modeling. Furthermore, disregarding important factors, e.g., uncertainties, lead to imprecise outcomes since in real-world application of any condition monitoring task, there is a wide spectrum of uncertain sources, such as varying wind speed, different humidity, and discrepant temperature.  
A data set is required to train the designed network and assess the effectiveness of the learning process when using classification algorithms. Unbalanced and bowed rotor systems should be modeled in ABAQUS/CAE first, and then the modeled systems should be run under various circumstances, such as changing the physical properties and operational conditions. The feature extraction phase is performed utilizing WTS to prepare inputs for the LSTM and SVM models. Additionally, the original time series data are applied in the classification using LSTM and the feature extraction material before applying the SVM to represent the effectiveness of the applied feature extraction method, i.e., WTS in the classification of unbalanced and bowed rotor systems.  
The primary rationale behind the selection of LSTM and SVM for the classification phase stems from their distinct mechanisms in establishing relationships between independent and dependent variables. Specifically, LSTM has been engineered to discern temporal dependencies within sequential and time-series data, whereas SVM predominantly excels in identifying non-linear dependencies within the data set. Consequently, the observed outcomes ensuing from the application of these methodologies demonstrate that the employed feature extraction procedure, herein referred to as WTS (wavelet transform and statistical features), exhibits considerable efficacy in capturing dependencies in rotor systems signals, encompassing both temporal dynamics and non-linear characteristics. Figure 1 reveals the workflow of the present research.
## 2 Numerical Modeling of Ropes and Large Floating Structures Based on the ANCF
### Elastic Internal Forces and Stiffness Matrix
\[\kappa\simeq\left|\frac{d^{2}\mathbf{r}}{dx^{2}}\right|=\left|\mathbf{r}^{ \prime}\right| \tag{18}\]  
It is important to note that this formula applies solely to the case of minor longitudinal deformations and does not make assumptions about lateral deformations. According to the principle of virtual work, the formula for the axial force induced by changes in axial strain energy is as follows:  
\[ \begin{array}{rl}\delta U_{E}&=\frac{\partial U_{E}}{\partial \mathbf{q}}\delta\mathbf{q}=\frac{1}{2}J_{0}^{\int_{0}}\,EA\frac{\partial \varepsilon^{2}}{\partial\mathbf{q}}dx\cdot\delta\mathbf{q}\\ &=EA\int_{0}^{\int_{0}}\varepsilon\frac{\partial\varepsilon}{\partial \mathbf{q}}dx\cdot\delta\mathbf{q}=\left(\mathbf{F}_{\text{int}}^{E}\right)^{ T}\delta\mathbf{q}\end{array}  \tag{19}\]  
where \(\mathbf{F}_{\text{int}}^{E}\) represents the axial force in the element. The axial tangent stiffness matrix \(\mathbf{K}_{\text{tan}}^{E}\) is determined according to the principle of virtual work through derivation with respect to \(\mathbf{F}_{\text{int}}^{E}\). \[ \begin{array}{rl}\delta\mathbf{F}_{\text{int}}^{E}&=\frac{ \partial\mathbf{F}_{\text{int}}^{E}}{\partial\mathbf{q}^{T}}\cdot\delta \mathbf{q}=EA\int_{0}^{\int_{0}}\frac{\partial}{\partial\mathbf{q}^{T}} \bigg{(}\varepsilon\frac{\partial\varepsilon}{\partial\mathbf{q}}\bigg{)}dx \cdot\delta\mathbf{q}\\ &=EA\int_{0}^{\int_{0}}\Bigg{[}\bigg{(}\frac{\partial\varepsilon}{\partial \mathbf{q}}\bigg{)}^{T}\frac{\partial\varepsilon}{\partial\mathbf{q}}+ \varepsilon\frac{\partial^{2}\varepsilon}{\partial\mathbf{q}^{T}\partial \mathbf{q}}\bigg{]}dx\cdot\delta\mathbf{q}=\mathbf{K}_{\text{tan}}^{E}\delta \mathbf{q}\end{array}  \tag{20}\]  
Similarly, the bending force \(\mathbf{F}_{\text{int}}^{Q}\), caused by the change in bending strain energy, along with the corresponding bending tangent stiffness matrix \(\mathbf{K}_{\text{tan}}^{Q}\), can be derived. \[\mathbf{F}_{\text{int}}^{Q}=EI\int_{0}^{\int_{0}}\kappa\frac{\partial\kappa}{ \partial\mathbf{q}^{T}}dx \tag{21}\]  
\[\mathbf{K}_{\text{tan}}^{Q}=EI\int_{0}^{\int_{0}}\Bigg{[}\bigg{(}\frac{ \partial\kappa}{\partial\mathbf{q}}\bigg{)}^{T}\frac{\partial\kappa}{\partial \mathbf{q}}+\kappa\frac{\partial^{2}\kappa}{\partial\mathbf{q}^{T}\partial \mathbf{q}}\bigg{]}dx \tag{22}\]  
Here, the elastic forces and stiffness matrices for each element are obtained. The elastic internal forces and stiffness matrices for the elements are presented as follows. \[\mathbf{F}_{\text{int}}=\mathbf{F}_{E}+\mathbf{F}_{Q}=\sum_{\varepsilon}\left( \mathbf{F}_{T}^{\varepsilon}+\mathbf{F}_{Q}^{\varepsilon}\right) \tag{23}\]  
\[\mathbf{K}_{\text{tan}}=\mathbf{K}_{E}+\mathbf{K}_{Q}=\sum_{\varepsilon}\left( \mathbf{K}_{T}^{\varepsilon}+\mathbf{K}_{Q}^{\varepsilon}\right) \tag{24}\]
## 3 Numerical Verification  
### VBI Design of Simply Supported Bridges  
Bridge and vehicle parameters in finite element simulations are derived from [26]. The bridge's total length is 25 m and is divided into 25 elements of 1 m each. The damage of the bridge is simulated by reducing the element stiffness EI, and the degree of damage is noted as 5. The time step for the VBI analysis is established at 0.001 s. Additionally, the vehicle speed is fixed at 5 m/s, with the parameters of the vehicle and bridge system being detailed in Table 1. The VBI is executed using a self-developed Abaqus program. The first three natural frequencies of the bridge are identified as 2.97, 11.80, and 25.99 Hz, respectively. Figure 2 shows the analytical and FEM acceleration response of the bridge mid-span and front axle contact point response.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{Vehicle body} & Mass (kg) & \(M_{\text{br}}=1000\) \\  & Mass moment of inertia (kg.m\({}^{2}\)) & \(J_{\text{br}}=500\) \\  & Stiffness (kN/m) & \(K_{s1}=K_{s2}=500\) \\  & Damping coefficient (kN\(\cdot\)s/m) & \(C_{s1}=C_{s2}=1\) \\  & Axle distance to gravity center(m) & \(d_{1}=0.9\), \(d_{2}=1.1\) \\  & Vehicle speed (m/s) & \(v=5\) \\  & Mass (kg) & \(m_{f}=m_{r}=100\) \\  & Wheel stiffness (kN/m) & \(K_{w1}=K_{w2}=1000\) \\  & Wheel damping (Kn\(\cdot\)s/m) & \(C_{w1}=C_{w2}=1\) \\ \hline \multirow{3}{*}{Bridge} & Bridge Span (m) & \(L=25\) \\  & Modulus of elasticity (GPa) & \(E=27.5\) \\ \cline{1-1}  & Cross-sectional moment of inertia (m\({}^{4}\)) & \(I=0.12\) \\ \cline{1-1}  & Mass per unit length (kg/m) & m = 2400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle and bridge parameters.  
Figure 2: The dynamic response of the vehicle–bridge system: (**a**) bridge mid-span acceleration; (**b**) front axle contact point acceleration.  
A total of nine damage scenarios were considered, as shown in Table 2. In Cases 1-4, the damage was assumed to be located at the 13 th element at mid-span, with varying degrees of severity: 5%,10%, 20%, and 30%. In Case 5, the speed was reduced from 5 m/s to 2 m/s and the speed was increased from 5 m/s to 8 m/s in Case 6 to study the impact of vehicle speed further. And in Case 7, we investigated the effect of multiple damages and the damage severity in Elements 7, 13, and 18 was 30%. And in Cases 8 and 9, we investigate the effect of bridge span size on damage identification, and damage cases for two- and three-span girders are arranged.
# An Integrated Adaptive Kalman Filter for High-Speed UAVs
## 3 Verification and Analysis
#### 3.1.2 Scene Design  
In order to verify the performance of the proposed strong adaptive filtering method, it is necessary to simulate the high-dynamic environment of the UAV. So we designed two kinds of scenes that are representative.  
1. **Scene 1.** Anomaly of SINS is simulated. During 250 s to 300 s, the drift of gyroscope is set at 20\({}^{\circ}\)/h, and the random walk is set at 5\({}^{\circ}\)/ \(\sqrt{\mathrm{h}}\); The bias of accelerometer is set at 40 mg, and the random walk is set at 2 mg/ \(\sqrt{\mathrm{h}}\).
2. **Scene 2.** Anomaly of GNSS is simulated. During 100 s to 150 s, the error of horizontal position is set at 40 m, and the vertical one is 50 m; the error of horizontal velocity is set at 0.5 m/s, and the vertical one is 2 m/s. The hopping point appears during 230 s to 232 s, where latitude data steps with 0.008\({}^{\circ}\).  
The accuracy and anti-interference ability of SHAKF, H\({}_{\infty}\) filter, STF and SAKF is compared by calculating the root-mean-square (RMS) of the filtering output.  
\[RMS=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}{\left({{\hat{\mathbf{X}}}_{i}-{\mathbf{X} }_{i}}\right)^{2}}}} \tag{23}\]  
Here, \({\mathbf{X}}_{i}\) represents the ith true value of navigation, \({\hat{\mathbf{X}}}_{i}\) represents the ith filtering results, and \(n\) represents the times of filtering iteration.
## 3 Model Development
### Dynamic State Model Development
#### 3.3.3 Closed Loop Control Scheme and Controller Tuning
During the test, the controller has to be set into manual mode available on the faceplate, the output is stepped up or down with a step size equal to a percentage of the output range depending on the expected variation due to the disturbance. To obtain an accurate test result, several runs were performed for each controller with varying percentages, and the mean value was then inserted to estimate the required parameters. The dead time refers to the sum of transportation lag, the sample or instrument lag, and the lag due to the behavior of higher-order processes. The dead-time control element was inserted separately within the controller loop to account for the delay between measuring the change in variable and the final control element signal. Temperature measurement lags were within the 30-60 s range, thus a one-minute dead-time element was inserted into the loop to account for the dynamic lags present in the controllers [45]. The tuned control scheme would then allow the process to operate in a dynamic and switchable manner, moving from one set point to another with ease. The controllers and tuned-up parameters are summarized in Table 5.
# Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
## 3 Method
### Vibration System Simulator
#### 3.1.1 FIR Filter  
Adaptive FIR filters are used to construct the simulator. As shown in Figure 4, in order to realize the FIR filter, we need to obtain the input signal \(x(n)\) and the desired input and output signals \(d(n)\), \(x(n)\) and \(d(n)\) as vectors:  
\[x(n)=[x(n)x(n-1)\cdots x(n-N+1)]^{T} \tag{4}\]  
\[d(n)=[d(n)d(n-1)\cdots d(n-N+1)]^{T} \tag{5}\]  
where \(N\) is the length of the filter. The input signal \(x(n)\) of a given filter is known, and the output signal \(y(n)\) of the filter is expressed as follows:  
\[y(n)=\omega^{T}(n)x(n) \tag{6}\]  
where \(n\) indicates the serial number of the discrete-time data, and \(\omega(n)\) is the vector of the filter value.  
Figure 3: Description of the vibration system simulator. The FIR filter-identified signal is shown in blue, while the input–output vibration signal is shown in red.  
The output signal \(y(n)\) is the estimation of the desired signal \(d(n)\). The error \(e(n)\) between the output signal \(y(n)\) and the desired signal \(d(n)\) is defined as follows:  
\[e(n)=d(n)-y(n) \tag{7}\]  
By using the least-mean-square (LMS) algorithm [37], we estimate the filter weight \(\omega(n)\) to minimize the error \(e(n)\):  
\[\omega(n+1)=\omega(n)-2\mu e(n)x(n) \tag{8}\]  
where \(\mu\) is the step size.  
If the appropriate input signal \(x(n)\) and the desired output signal \(d(n)\) are given, it is easy to estimate the filter weight \(\omega(n)\). In this paper, both the input signal and the expected output signal are experimental data.
## 4 Experimentation and Analysis of Results
### Experimental Data  
This study conducted gesture recognition experiments in two distinct environments: an office and a conference room. Figure 7 illustrates the experimental setup and equipment  
\begin{table}
\begin{tabular}{c c c c} \hline
**Environment** & **Strategy** & **Optimal Validation Accuracy** & **Convergence Epoch** & **Train/Test Gap** \\ \hline Conference Room & Without Early Stop & 91.60\% & 95 & +6.4\% \\
**Conference Room** & **With Early Stop** & **94.26\%** & **88** & **+4.2\%** \\ Office & Without Early Stop & 89.73\% & 97 & +8.7\% \\
**Office** & **With Early Stop** & **93.95\%** & **93** & **+3.2\%** \\ \hline \end{tabular}
\end{table}
Table 4: Inhibition effect of regularization strategies on overfitting.  
Figure 6: WiFi gesture recognition system demonstration.  
configuration. We recruited ten participants (five male and five female) aged 23-28 years for the experiments. The experimental protocol was designed as follows: (1) Each gesture was performed for a duration of 5 s; (2) Rest periods were implemented before and after each gesture trial; (3) Each participant executed six different gestures; and (4) Every gesture was repeated 20 times. This protocol yielded a total of 2400 samples for analysis. The dataset was divided using a stratified random split: 80% (1920 samples) for training, 10% (240 samples) for validation, and 10% (240 samples) for testing. This partition ensures that the distribution of gesture categories and environmental conditions is preserved across subsets, enabling robust model evaluation.
# Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review
###### Abstract  
This paper provides an overview of latest progress on the novel advanced digital signal processing (DSP) techniques for long-haul mode division multiplexing (MDM) systems with high capacity. Space-division multiplexing (SDM) techniques have been developed for a period to increase the capacity of optical communication system by at least one order of magnitude through MDM techniques using few-mode fibers (FMTs) or multi-core multiplexing (MCM) using multi-core fibers (MCFs). The signals in MDM links are mainly impaired by the linear and nonlinear effects in FMTs, making DSP techniques become necessary to undo these impairments. In this paper, we not only review the advanced multiple-input multiple-output (MIMO) DSP techniques for compensating linear impairments in FMTs, but also enclose the state of the art of novel DSP techniques to deal with nonlinear effects. Firstly, we introduce the MIMO schemes for equalizing modal crosstalk and modal dispersion. Then, we focus on the fast tracking of time-varying (TV) channels in FMF links through frequency-domain (FD) recursive least square (RLS) algorithm. Besides, we also cover the mainstream DSP solutions for mode-dependent loss (MDL) and several possible methods to compensate nonlinearity in FMF. Moreover, artificial intelligence (AI) technologies are also discussed for its high nonlinearity tolerance and may bring a revolution in MDM systems on the process of channel equalization, link monitoring, etc. In the end, a brief conclusion and perspective will be provided.  
optical fiber communication; digital signal processing; multi-input multi-output; mode-division multiplexing; least mean squares; frequency-domain equalization; recursive least squares; time-varying channel; mode-dependent loss; nonlinearity compensation; machine learning; neural network +
Footnote †: journal: Applied Sciences  
**Keywords:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Model Modification of the Soil-Water Characteristic Curve of Unsaturated Weak Expansive Soil
## 6 Correction of the Soil-Water Characteristic Curve by the Mercury Intrusion Test
### SWCC Three-Parameter Model Fitting
#### 6.1.1 Fitting Analysis of the Three-Parameter Model by Test Method  
The soil-water characteristic curve, obtained by the pressure plate method (0-0.5 MPa) and the filter paper method (0-40 MPa), was fitted and analyzed using the three-parameter model shown in Equation (9), while the fitting information of each parameter under different dry densities was obtained, as shown in Table 3.  
The three-parameter logarithmic model has a fitting coefficient (R\({}^{2}\)) that ranges between 0.974 and 0.996. Notably, the fitting parameters are significantly affected by dry density and are strongly correlated, indicating that the soil-water characteristic curve model has excellent applicability to the expansive soil in the H  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Initial Dry Density**} & \multicolumn{6}{c}{**F-X Model**} & \multicolumn{3}{c}{**V-G Model**} & \multicolumn{3}{c}{**Gardner**} \\ \cline{2-9}  & \(\theta_{s}\) & \(C_{r}\)/kPa & **a** & **n** & **m** & **R\({}^{2}\)** & **R\({}^{2}\)** & **R\({}^{2}\)** \\ \hline
1.4 & 48.33 & 9900 & 26.75 & 0.872 & 0.686 & 0.980 & 0.988 & 0.  
In order to analyze the influence of soil dry density on the soil-water characteristic curve, the linear/nonlinear fitting analysis of dry density was carried out on the fitting parameters \(\alpha\), \(\beta\), and \(\gamma\). The results are shown in Figure 7.  
The relationships between \(\alpha\), \(\beta\), and \(\gamma\) and the three-parameter model of the filter paper method are as follows:  
\[\alpha=11.936+27.572\rho_{d} \tag{10}\]  
\[\beta=-2.406-1.206\rho_{d} \tag{11}\]  
\[\gamma=1.034\times 10^{-8}\cdot e^{13.56\rho_{d}} \tag{12}\]  
where \(\rho_{d}\) is the initial dry density of the soil.  
Equations (10)-(12) were substituted into Equation (9), and a mathematical model of the soil-water characteristic curve containing only the initial dry density \(\rho_{d}\) of the expansive soil was obtained. Equation (13), which can predict and calculate the suction of the soil matrix under the condition of known soil volume moisture content and dry density, can be used for other characteristic analyses of rock and soil.  
\[\theta_{1}=11.936+27.572\rho_{d}-(2.406+1.206\rho_{d})ln(\psi_{1}+1.034\times 1 0^{-8}e^{13.56\rho_{d}}) \tag{13}\]  
where \(\theta_{1}\) is the volumetric moisture content of the filter paper method (%), and \(\psi_{1}\) is matric suction measured for the filter paper method test (kPa).