esa-sceva/satcom-chunk-collection huggingface dataset:
# Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series
## 2 Supervised Dynamic Correlated Topic Model
### Parameter Estimation
#### 2.2.2 Selecting Initial Values of the Model Parameters  
We use the SA technique, one of the top methods for non-derivative-based random search optimization, to estimate the model parameters \(\mathbf{\eta}\), \(\mathbf{\Phi}\), and \(\mathbf{\Sigma}\) in the sDCTM framework. To ensure that the optimization works effectively, it is important to select appropriate initial values. We describe the steps to select initial values for \(\mathbf{\eta}\), \(\mathbf{\Phi}\), and \(\mathbf{\Sigma}\) below.  
**Step 1.** We select an initial search space within \(\mathbb{R}^{n}\), where \(n\) represents the number of variables involved in the estimation (for instance, \(n=C\times f\) for \(\mathbf{\eta}\) and \(n=J\times J\) for \(\mathbf{\Phi}\) and \(\mathbf{\Sigma}\)). The initial search space is chosen based on our belief of where the estimates are likely to be found. Then, we divide this search space into \(2^{n}\) smaller subspaces.  
**Step 2.** For \(\mathbf{\Phi}\) and \(\mathbf{\Sigma}\), we evaluate the optimization function at the midpoint within each of the subspaces and choose the subspace with the highest (or lowest) value of the function based on the optimization problem as our new search space. In cases where multiple subspaces optimize the function, we consider each of them in the next step for furtherexploration. In the sDCTM framework, for \(\mathbf{\eta}\), we consider the training accuracy as our optimization (maximization) function, as \(\mathbf{\eta}\) plays a crucial role in predicting the response variable. For model parameters \(\mathbf{\Phi}\) and \(\mathbf{\Sigma}\), we consider the lower bound on the log likelihood as the optimization (maximization) function.  
**Step 3.** We repeat steps 1 and 2 until no significant improvement in the optimization function value is seen.  
**Step 4.** If a single subspace is chosen as a search space to select the initial values for optimization, we choose our starting values from this subspace and estimate the parameters using the SA technique. If there are multiple subspaces selected as the initial search space, we choose the initial values from each of these subspaces and run the SA to obtain the parameter estimates. We choose the final parameter estimates from the search space yielding the highest (or lowest) value of the function based on the optimization problem. The sDCTM model is implemented using a standalone code in Fortran 77 [30]. The code for model estimation is posted on [[https://github.com/NamithaVionaPais/sDTCM](https://github.com/NamithaVionaPais/sDTCM)]([https://github.com/NamithaVionaPais/sDTCM](https://github.com/NamithaVionaPais/sDTCM)) (accessed on 10 June 2024).
## 1 Introduction
Kinect depth cameras transmit near-infrared rays to measure the movements of an object. This non-wearable device efficiently records data and can provide the precision of time series data accurately. Joint positions and angle trajectories are also detected using Kinect. Then, the joint rotational data are used to find out kinematic metrics such as range of motion, mean error, and so on. A wide range of rehabilitation robots help to move different body parts of patients to regain their everyday lives. Three rehabilitation robots assist clinical or elderly patients: entire body, upper limb, and lower limb rehabilitation robots [6]. Even in the case of stroke patients, rehabilitation robots can perform the exercises needed by applying a specific force to cause movement of the patient's body parts. According to the latest research, the number of elderly and disabled people is growing daily. Various algorithms are used in this rehabilitation field to ensure proper classification, prediction, and treatment strategies [7]. Support vector machines (SVMs) and random forest (RF) algorithms help by learning human behavior. Flexible sensors can identify various postures of the upper body with the help of different algorithms [5,8]. However, most of the ML models on rehabilitation assessment mainly work to develop a much more accurate and improved model by applying complex algorithms. ML models not only identify and classify patients' movements but also predict patients' rehabilitation as well as recovery status. Furthermore, most of the research has been conducted on upper limbs as well as vision-based gesture detection. However, in this study, we shed light on a Kinect sensor-based automated rehabilitation model.
## 1 Introduction
The key advantage of this method is that the measurement using the Fizeau interferometer can be carried out without significant investments in time and/or capital to assess the out-of-flatness and pitch deviations of the planar scale grating accurately compared with other measurement techniques. Nevertheless, further experimental demonstration as well as the uncertainty analysis of the proposed method have not been conducted yet, and they remain to be addressed. In this paper, as the second step of the research, experimental verification of the feasibility of the improved method [12] has been conducted as well as the uncertainty analysis when considering the possible error factors influencing the measurement uncertainties, such as the form error of the reference surface in a Fizeau interferometer or the inclination error of a scale grating in measurement. Theoretical equations are derived to evaluate the influence of the error factors on the final uncertainties in the measured \(Z\)-directional out-of-flatness as well as the \(X\)- and \(Y\)-directional pitch deviations of a scale grating. Moreover, when considering the drawbacks of the traditional uncertainty matrix approach [13], which is intellectually satisfying but difficult to handle or specify the surface form in a straightforward way, a method aiming to evaluate the uncertainty by using one of the simplest but most commonly used peak-to-valley (P-V) deviation of the surface form is proposed. Experiments are also conducted to demonstrate the feasibility of the proposed method.
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 4 Experimental Results
### Crossband Filtering--Length Preservation
#### 4.5.2 Optimal CD (\(L_{\text{bb}}=14\))  
The obtained results are presented in Figures 6a-c and are summarized in Table 2. Interestingly, it is observed that for all measures, the optimal gain is achieved when \(L_{\text{cb}}=2\). In this particular setup, the length preservation method outperforms the length extension method in terms of FWSegSNR and CD. However, regarding PESQ, the length extension method provides better and competitive performance. These findings emphasize the significance of considering the specific setup and context when evaluating the performance of different dereverberation methods, as their effectiveness may vary depending on the chosen measures and objectives.
## 4 Numerical Modeling: Justification of the Model of Fiber-Reinforced Concrete
### Determination of Concrete Stress-Strain Diagram under Uniaxial Tension Simulation  
The approach of constructing the diagram of the extreme deformation of concrete under uniaxial tension is used based on the results of concrete test under bending. It consists of selecting the parameters of the model of plastic behavior of concrete based on the results of a numerical experiment.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Material** & \(E\)**, **GPa** & \(v\) & \(f_{cm}\)**, **MPa** & \(f_{cm}\)**, **MPa** & \(\psi\) & \(\alpha_{f}\) & \(\mathbb{C}\) & \(K_{c}\) \\ \hline Non-reinforced B15 concrete strength class & 24 & 0.2 & 11 & 0.75 & 30 & 1.16 & 0.1 & 0.667 \\ Non-reinforced B25 concrete strength class & 30 & 0.2 & 22 & 1.05 & 30 & 1.16 & 0.1 & 0.667 \\ Fiber-reinforced concrete with the fiber amount of 3 kg/m\({}^{3}\) & 30 & 0.2 & 24 & 1.05 & 30 & 1.16 & 0.1 & 0.667 \\ Fiber-reinforced concrete with the fiber amount of 7 kg/m\({}^{3}\) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the model of plastic behavior of concrete with damage accumulation.  
Figure 4: Tensile damage factor versus CMOD according to CMOD test data.  
The sequence of selection of damaged concrete plasticity model parameters is shown in Figure 5 as flow chart.  
The selection of the model parameters sequence includes:  
1. Approximate the parameters of the model of plastic behavior of concrete with fracture, which are determined taking into account the data published in scientific papers [23] and experimental data in representation of stress-strain and stress-displacements diagrams of a specific specimen of concrete or fiber-reinforced concrete.
2. Create the finite-elements model of concrete beams using three-point bending to build the virtual experiment.
3. If the results of the virtual experiment differ up to 10% (the peak value of the load at which the crack originates must differ up to 5%) from the experimental laboratory data, then proceed to Step 4. If the condition is not met, the model parameters are corrected and Step 3 of the virtual experiment is repeated.
4. Create the finite-elements model of the concrete specimen under uniaxial tension to build the virtual experiment.
5. Process the obtained data of numerical modeling at Step 4 and present them using the axes \"axial stress\" and \"tensile rupture crack opening.\"
## 5 Results and Discussion
### Current Density Distribution Analysis  
Current density distribution is pivotal in understanding the skin and proximity effects within conductors. This subsection presents both normalized current distribution line charts and color-coded current density maps for single-segment, three-segment, and five-segment 2D models at 50 Hz. These visualizations illustrate how segmentation and mesh density influence current distribution across the conductor's cross-section.
# How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
## 2 Energy Requirements of 5G Networks
Following this progress in terms of satisfying the capacity demand of each device density class raises challenges on MNOs in terms of keeping 5G RAN deployment and operation as energy efficient as possible. These challenges can be precisely expressed in changes of EE metrics for specific device density classes of 5G networks. In our preliminary work, the impact of the increasing number of active users in 5G networks on network EE metrics was analyzed [11]. However, in mobile access networks, the increasing trend of changes in user densities is not the same as the increasing trend of changes in data volumes (DVs). Therefore, analyses dedicated to the explanation of how the expected increasing trend of DVs during the 2020s impacts EE of 5G RAN have not been analyzed yet. Satisfying this increasing trend of DVs can be performed in 5G RANs through the exploitation of different small and macro BSs implementation and operation scenarios. They can differ in the number of used BSs and corresponding ECs, which ultimately influence on 5G network EE. The EE in the 5G RAN can be validated through recently standardized metrics which enable consistent evaluation of RAN EE in different device density classes. Hence, the impact of the increased DVs on EE metrics of 5G RANs in versatile device density classes was analyzed in this work for the first time. The analyses give an explanation of how the ascending trend of DVs impacts distinct EE metrics. Moreover, the analyses for the first time clarify the impact of increased DVs on instant power and yearly energy consumption of the 5G RAN.
# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 2 Materials and Methods
### Site Feature Learning
The site feature vector needs to calculate the cosine similarity with the phenotypic information of the site, so we use the mean and variance of the phenotypic information of all subjects in a site to represent the phenotypic information of the site. The way to calculate cosine similarity is to first arrange the mean and variance of phenotypic information of all sites into mean vector and variance vector, respectively, according to the order of sites, and then some dimensional features of all site feature vectors are formed into a vector according to the same site order. The form of the vector is the same as that of the mean vector and variance vector, and the cosine similarity can be calculated with the mean vector and variance vector, respectively. Finally, some dimensions with high cosine similarity are selected to form a new site feature vector. Figure 3: Site feature learning. The arrows in the figure represent the sequence of steps in the site feature learning process. The specific process Is to first calculate the mean and variance of the phenotypic information of each site, and then arrange the mean and variance of each site according to the sequence of a site, form the mean vector and variance vector, and standardize. The final vector form is N \(\times\) 1, where N is the number of sites. The feature vectors of all image sites are arranged in the same order to form a site feature matrix of the form N \(\times\) F, where N is the number of sites and F is the feature dimension. A one-dimensional vector (form N \(\times\) 1) is selected along the dimensionality of the site in the site feature matrix, and the cosine similarity is calculated by means of the mean and variance vectors of the phenotypic information. The cosine similarity is calculated as shown in Equation (3), where \(A\) and \(B\) represent two vectors. According to the cosine similarity, the similarity order of phenotypic information of each site can be obtained. In cases where the mean and variance vectors of multiple phenotypic information are ordered differently, we use a voting mechanism to determine the unique ordering. The higher the similarity ranking and the stronger the phenotypic information correlation, the more likely it is to reflect the site heterogeneity. Finally, the threshold is defined according to the top percentage of similarity ranking, and the features within the threshold range are selected to form a new site feature vector. Phenotypic information we selected included sex, age, full scale IQ, verbal IQ, and operational IQ. Through experiments, the features with the top 30% cosine similarity are selected as site features. \[similarity=\cos(\theta)=\frac{\sum\limits_{i=1}^{n}A_{i}\times B_{i}}{\sqrt{ \sum\limits_{i=1}^{n}\left(A_{i}\right)^{2}}\times\sqrt{\sum\limits_{i=1}^{n} \left(B_{i}\right)^{2}}} \tag{3}\]
## 3 Results
### Simulation Analysis  
#### 3.2.1 Water Nutrients  
Water nutrients, particularly WTN and WTP, serve as valuable indicators that reflect lake ecological processes within lake water ecosystems. Variations in WTP and WTN concentrations can significantly influence lake nutrient levels. The WTN and WTP simulations were analyzed, as depicted in Figure 3. Both the WTN and WTP concentrations exhibited a noticeable increasing trend over the past 40 years. WTN levels rose from 0.135 mg L\({}^{-1}\) during the 1980s to 0.245 mg L\({}^{-1}\) during the 2010s, with an increasing trend of 0.0369 mg L  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value Set for Diatom Algae** & **Value Set for Green Algae** \\ \hline Optimal temperature & 18 \({}^{\circ}\)C & 20 \({}^{\circ}\)C \\ Photorespiration coefficient & 0.05 d\({}^{-1}\) & 0.08 d\({}^{-1}\) \\ Mortality coefficient & 0.001 g (g d)\({}^{-1}\) & 0.001 g (g d)\({}^{-1}\) \\ Sedimentation rate & 0.015 m d\({}^{-1}\) & 0.005 m d\({}^{-1}\) \\ Saturation intensity & 300 Ly d\({}^{-1}\) & 110 Ly d\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The physiological parameters of phytoplankton set in the model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Simulated Relative Error (\%)** \\ \hline SD & 12 \\ WTP & 17 \\ WTN & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulated relative errors compared to observations.  
per decade. Similarly, WTP concentrations increased from 0.0321 mg L\({}^{-1}\) during the 1980s to 0.0395 mg L\({}^{-1}\) during the 2010s with an increasing trend of 0.00245 mg L\({}^{-1}\) per decade. The regression coefficients \(R^{2}\) for WTN and WTP were found to be 0.99 and 0.97, respectively. Despite Namco Lake on the Tibetan Plateau being characterized as an oligotrophic alpine lake [55; 56], it demonstrated an improving trend in terms of its nutritional condition. However, it is worth noting that the lake remains in a state of lower nutrient levels.
# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 2 The Proposed Method  
In this section, firstly, we describe the basic idea of our proposed SSD-SAR-BS, where only the speckle noise model and speckled SAR images are needed. Then, we introduce the MSDNet to achieve despeckling and utilize dense residual blocks to enhance the network performance. Lastly, we propose the dropout-based ensemble for testing. The overall flowchart of our proposed SSD-SAR-BS is presented in Figure 2.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 3 Noise Model  
In this study, we considered the impact of extreme weather conditions on WSN systems, as illustrated in Figure 1, by building attenuation models of wireless signal transmissions. In addition, we investigated the effect of noise due to internal factors by considering the effect of thermal noise on the transmission process.  
Figure 1: Low-energy adaptive clustering hierarchy (LEACH)–based WSN system being affected by the environment and external and internal events.
## 3 Experimental Results and Discussion
We analyze the energy spectra of accelerated electrons in relation to GHz range spectra of EMP emission from metallic films with micrometer range thickness and we report here that the total number of electrons escaping at the back surface of the target, as well as the AEME, increases with the ratio \(\rho/Z\). The experimental data on the AEME as a function of target thickness \(d\), atomic number \(Z\), and electrical resistivity of the target material \(\rho\) are summarized in Figure 5d) (bottom plot). We note a linear increase in AEME with the inverse of the target thickness \(d\) and the 0.46 power of the ratio \(\rho/Z\). Figure 6c presents total EMP energy (further named EMPA) as a function of thickness d, atomic number Z, and electrical resistivity \(\rho\) of the target, while the energy generated in the GHz domain has been evaluated from the spectra presented in Figure 6b by the area under the curve. Figure 6c (top plot) indicates that EMPA increases linearly with the 1/d for Ag targets of different thicknesses. Figure 6c (bottom plot) demonstrates that, for targets of different materials and different thicknesses, EMPA depends linearly on 1/d and the 0.46 power of the ratio \(\rho/Z\). We should mention that each EMP-represented data point corresponds to the represented electron energy spectra, both obtained from the same single-shot laser pulse. EMP errors, estimated from a few pulses performed in similar experimental conditions, are around 5% for each experimental point presented here, except the Ag 5 um point, where the errors were a few times higher. Figure 6: Experimental data on the EMP radiation. (**a**) Antenna signal obtained under EMP generated from a 4 μm Ar target, (**b**) EMP spectra from different targets. and (**c**) on EMP amplitude (EMPA) as a function of (top plot) \(d\) and (bottom plot) \(d\), \(Z\), and \(\rho\). Previous studies have investigated correlations between target accumulated charges following the laser pulse and the generated EMP. Different reports have suggested the charge does not have linear dependence on the beam energy [34], but a saturation-like increase tendency on the laser power density [33], which is an idea confirmed both experimentally ([35], and references therein) and theoretically [34,36]. Even so, there are still reports [33] on the quasi-linear dependence between the EMP and the target charge. Based on our experimental results regarding the dependence of the AEME (Figure 5d) and the EMPA (Figure 6c) on film thickness d and the ratio \(\rho/Z\), we infer here a correlation between the AEME and the EMPA. In what follows, we propose a simplified analytical model for the EMP emission process supporting the correlation between EMPA and AEME.
# Quality Analysis of Bonded Joints in the Renovation of Plastic Automotive Parts
## 3 Results
### Assessment of the Tensile Strength Test of Bonded Joints after Curing  
The measured tensile strength test values for the different types of adhesives, when three different thicknesses were used, are described in Table 5, and a graphical comparison of the tensile strength test is shown in Figure 6.  
The graphical representation of the shear strength showed that the ChS EPOXY 1200 adhesive achieved the highest shear strength values, the DINITROL 860 adhesive had a lower value, and the MEGABOND 2000 adhesive achieved the lowest strength value. When the effect of adhesive thickness on the shear strength of the joint is evaluated, it is found that the highest strength values are achieved by adhesives with thicknesses of 0.33 mm, except for DINITROL 860, where the highest strength was achieved with an adhesive thickness of 0.25 mm. The characteristic fracture surface appearances for different types and thicknesses of adhesives are shown in Table 6. The failure modes of the tested bonded joints were analysed according to the standard [70].  
Dinitrol adhesive is based on a different material base than the other adhesives (Megabond, ChS Epoxy (Marstol-Domsel DmbH, Zulpich, Germany), which affects the strength of the bonded joints at all three thicknesses. Dinitrol is a flexible PU adhesive, its advantages are mainly in the dynamic stressing of joints. Corrosion tests did not significantly reduce the strength of the joint.  
Figure 6: Comparison of shear strength after curing for tensile test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Adhesive** & **Adhesive Thickness** & **Load at Failure** \\  & **[mm]** & **[N]** \\ \hline Megabond 2000 & 0.1 & 382 \\ Megabond 2000 & 0.25 & 318 \\  & 0.33 & 503 \\ \hline ChS EPOXY 1200 & 0.1 & 656 \\  & 0.25 & 635 \\  & 0.33 & 663 \\ \hline DINITROL 860 & 0.1 & 460 \\  & 0.25 & 597 \\  & 0.33 & 543 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Measured loads at joint failure-tensile test.  
For all specimens bonded with the same type of adhesive of a given thickness, adhesive failure of the bonded joint was observed. In the case of Dinitrol adhesive, 100% adhesive failure was observed. Table 6 shows the types of failure of bonded joints. It is possible to observe areas with typical adhesive failure (Dinitrol 860) and areas with cohesive failure (adhesive MEGABOND 2000, thickness 0.1 and 0.2 mm, adhesive ChS EPOXY 1200, thickness 0.2 and 0.3 mm). For bonded joints with adhesive failure, there was low adhesion between the adhesive and the material, it is possible to recommend for further experimental work a higher quality of pre-treatment of the surface of the contact areas.