esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Analysis and Experimental Verification
### Experimental Verification  
An experimental test system was developed to assess the validity of the developed model, as shown in Figures 14 and 15. The work was conducted on a CNC lathe, CAK3665, which is widely used for machining purposes. A three-phase AC motor was used to drive the spindle, which was connected to a belt system comprising three v-belts, a driver pulley, and a driven pulley. The experimental equipment and parameters are listed in Table 3.  
Figure 14: Experimental scheme.  
Figure 13: Amplitudes at \(2f_{r}\), \(f_{s}\), and \(f_{r}\) with changes in belt tension, \(P_{0}\).  
The tension can be adjusted by setting the belt with different centre distances. High-performance data-acquisition systems were used to measure the three-phase current when the lathe was idle. A high-speed data acquisition system, YMC9004, was connected to the current clamp output. The data accuracy for this acquisition system is 24 bits. To collect the harmonics of the current signal, the sampling rate was set to 100 kHz. Moreover, a high-performance measurement system was chosen to capture the dynamics of the current modulation during tuning, despite its significant attenuation by the long-drive train.  
A belt tension increase is observed when the belt pulley center distance is increased (315 mm-360 mm). In Figure 16, it is consistent with the simulation results, that the modulation characteristics of the current signals revealed that belt looseness can cause a high degree of modulation, reflected by the rich harmonic sidebands at the belt-passing frequency in the motor current spectrum.  
An integrated motor coupling model with transmission was presented herein. This model differs from the belt system model described by [PERSON] et al. [32,33] and includes the electromagnetic response of the motor. In this analysis process, the interference caused by incorrect belt tension and belt system resonance on the machining status monitoring was also analysed, unlike the previous literature [17,18]. Frequency shifts caused by belt sliding were not considered in this study; however, they can be used to monitor the belt wear and system overload.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Device**} & \multicolumn{1}{c}{**Parameters**} \\ \hline CNC model & CAK3665 \\ \hline Motor model & YVP112M-50-B5; 4 kW; 50 Hz; 2 Poles \\ \hline Centre distance setting & D = 315 mm, 330 mm, 345 mm, 360 mm \\ \hline Spindle speed & 850 rpm \\ \hline Current clamp model & Fluke Is400; 40 A; 5–10 KHz \\ \hline Data acquisition model & YMC9004 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in simulations with IM.  
Figure 15: Experimental facilities: (**a**) CNC lathe, (**b**) belt transmission system, and (**c**) current clamp installation position.
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 2 Common Methods for Detecting Underwater Cracks in the Surface of Concrete Dams
### Intelligent Monitoring Techniques
#### 2.2.2 Electromagnetic Methods  
Electromagnetic methods mainly distinguish cracks based on differences in the electromagnetic properties of the medium. At present, the commonly used electromagnetic method for dam underwater crack detection is the ground penetrating radar test (GPR) method [33]. GPR is a non-destructive detection technique that uses high-frequency electromagnetic waves to detect underground objects. This method detects cracks based on the difference in dielectric constant and conductivity between the underwater crack of the dam and the surrounding solid medium, resulting in a change in the waveform of the electromagnetic wave as it crosses the crack. However, this method is usually used to identify cracks in concrete structures above water due to the influence of the complex underwater environment and the fact that the detection effect depends on parameters such as the propagation medium, wave absorption, and reflection coefficient. Among them, [PERSON] et al. [33] used GPR to detect the cracks on the auxiliary buildings of the dam in the actual project. Besides GPR, electromagnetic methods include impedance tomography, the high-density resistivity method, transient current method, etc. However, these methods are rarely used for underwater dam concrete crack detection due to their high cost, limited survey depth, and the limitations of their own use conditions.
## 4 Discussion  
In this section, we further compare the dynamic response of the E-B beam and the Timoshenko beam, as well as the critical values of nonlinear stiffness. Figure 15 illustrates the dynamic responses of E-B and Timoshenko beams when the vehicle speed, viscous damping coefficient, and foundation nonlinear stiffness coefficient are all the same, with \(A_{0}=0.05\) m, and other parameters taken from Tables 1 and 2. From Figure 15, it can be observed that when all parameters are the same, the maximum deformation of the E-B beam is significantly higher than that of the Timoshenko beam. As the viscous damping coefficient increases, the gap between the deformations of the two types of beams begins to narrow. Moreover, the critical value of nonlinear stiffness corresponding to the E-B beam is much smaller than that of the Timoshenko beam. When the nonlinear stiffness exceeds the critical value, the position of the maximum displacement on the beam starts to noticeably deviate from the wheel position. When the nonlinear stiffness of both types of beams is either less than or greater than their respective critical values, the offset between the maximum displacement and the wheel position is almost the same for both beams,  
Figure 14: The influence of assorted viscous damping factors and nonlinear stiffness factors of the foundation on the pavement’s reaction is depicted at a vehicle velocity of \(v=60\) m/s. (**a**) \(c=3\times 10^{5}\) N\(\cdot\) s/m\({}^{2}\). (**b**) \(c=9\times 10^{5}\) N\(\cdot\) s/m\({}^{2}\). (**c**) \(c=3\times 10^{6}\) N\(\cdot\) s/m\({}^{2}\). (**d**) \(c=6\times 10^{6}\) N\(\cdot\) s/m\({}^{2}\).  
aside from the difference in maximum deformation. The greatest difference between the two beams occurs when the nonlinear stiffness is just between the critical thresholds of the two types of beams.  
This indicates that in pavement design, if the nonlinear stiffness is very small or very large, the dynamic behavior of the two types of beams will be relatively close, with the only difference being that the results calculated using the E-B beam are larger than those using the Timoshenko beam. In this case, the results obtained using the E-B beam are always on the safe side. However, if the nonlinear stiffness of the foundation is just between the critical values of the two types of beams, the difference in their dynamic behavior is the greatest. At this point, using the E-B beam does not guarantee the reliability of the calculated results.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 5 Industrial Application Perspective
### Smart Environment  
IoT-based innovative environments contain information about water quality, lowering levels of pollution in the air, lowering temperatures, preventing forest fires and landslides, tracking animals, monitoring snow levels, and early detection of earthquakes [13; 107]. This type of project calls for sensors with long battery lives and also takes coverage and range into account. However, they also require high QoS, large bandwidth, and efficient bypassing of interference. Also, the projects are often carried out by large-scale undertakings that can make higher expenses bearable. As a result, NB-IoT and LTE-M are more appropriate for this sort of application [108]. However, the administration of smart water grids can benefit from the deployment of LoRaWAN [109]. The LoRa technology is also suitable for long-distance communication. [PERSON] et al. [110] conducted an experimental study in Portugal and Brazil to evaluate the LoRa communication range and received signal strength indicator (RSSI) in urban and natural settings. The results showed that LoRa is robust and suitable in dense urban environments, with a possible 2.1 km distance connection.
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 5 Results
### Simulated Annealing  
Simulated annealing is another stochastic optimization method that is inspired from annealing in metallurgy. The algorithm starts with a random trial solution, \(\mathcal{G}\), whose fitness is determined by \(\mathcal{F}(\mathcal{G})\), defined by Equation (21), and a large initial temperature, \(T_{0}\). At each iteration, \(t\), the algorithm randomly generates a new solution, \(\mathcal{G}_{t+1}\), and its corresponding fitness, \(\mathcal{F}(\mathcal{G}_{t+1})\), is calculated. If the new solution is better than the current solution, it is automatically selected as the next solution. However, if the new solution is worse, the algorithm accepts it as the next solution based on a probability of acceptance function defined by Equation (24).  
\[P(\mathcal{G}_{t+1})=\frac{1}{1+\exp\!\left(\frac{\Delta}{\max(T)}\right)} \tag{24}\]  
where \(\Delta=\mathcal{F}(\mathcal{G}_{t+1})-\mathcal{F}(\mathcal{G}_{t})\) and \(T\) is the current temperature, which is systematically lowered by the algorithm at each iteration according to Equation (25).  
\[T=\frac{T_{0}}{\ln(\kappa)} \tag{25}\]  
where \(\kappa\) is the annealing parameter. Since the values of \(\Delta\) and \(T\) are positive, larger \(\Delta\) and a lower temperature leads to a smaller acceptance probability. The algorithm stores the best solution found so far and proceeds until the desired fitness is achieved or maximum number of iterations reached.  
Figure 15a shows the evolution of the mean and standard deviation of fitness function over generations for all the 3 algorithms simulated and averaged over 20 times. The constant parameters used for PSO and SA are presented in Table 2. It can be clearly seen that the optimal solution found using simulated annealing lies in the infeasible region as the fitness function is positive. Both the evolutionary algorithm and particle swarm optimization were able to find a feasible solution, but the performance of the EA is better than PSO, as shown in Figure 15b, making it the better choice for this problem. Our observations show PSO stagnates prematurely, while EAs are more effective in continuing to innovate over generations.
# Photocatalytic Activity of TiNbC-Modified TiO2 during Hydrogen Evolution and CO2 Reduction
## 3 Results and Discussion  
### X-ray Diffraction  
X-ray diffraction patterns of the original MXenes and the titanium dioxide used to produce the heterostructures, as well as the XRDs of the synthesized heterostructures, are shown in Figure 2. The X-ray diffraction patterns of titanium dioxide contained characteristic peaks of rutile and anatase. From the ratio of the intensities of the characteristic reflexes of their widths, it can be concluded that the used titanium dioxide consists of 86% of anatase, with a crystallite size of 18 nm, and 16% of rutile, with a crystallite size of 24 nm. X-ray diffraction patterns of the heterostructures show characteristic peaks of rutile and anatase, as well as the main peak of MXene. At the same time, there are no peaks of compounds such as titanium carbonate or niobium oxide or carbonate, from which we can conclude that the chosen strategy of obtaining the heterostructures does not lead to a change in the structure of MXene itself.  
Figure 1: Experimental setup with the photocatalytic reactor (**a**) and emission spectra of the light source with a wavelength of 380 nm (**b**).
## 2 Materials and Methods
### Chlorophyll-a, Caroenoid, and Total Protein Content Analysis  
The analysis of chlorophyll-a (Chl-a) and carotenoid content followed the ethanol-thermal method with modifications [25]. The collected samples were added to 1 mL of 95% ethanol and heated in a water bath at 75 \({}^{\circ}\)C for 10 min after mixing well. After sufficient extraction, the samples were centrifuged again at 13,500\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C, and the supernatant was collected. The absorbance of the supernatant was measured at 665, 649, and 470 nm. Chlorophyll-a and carotenoid contents were calculated using Equations (3) and (4) [26; 27], respectively.  
\[C_{a}=13.95A_{665}-6.88A_{649} \tag{3}\]  
\[C_{x\cdot c}=\frac{1000A_{470}+811.74A_{665}-2851.30A_{649}}{245} \tag{4}\]  
where \(C_{a}\) is chlorophyll-a content;  
\(C_{x\cdot c}\) is carotenoid content;  
\(A_{665}\), \(A_{649}\), and \(A_{470}\), are the optical densities of the extracts at wavelengths 665, 649, and 470 nm, respectively.  
The total protein content was analyzed using the Bradford method with modifications [28]. Pigment-removed cells (samples used for Chl-a extraction) were mixed with 0.5 mL of 0.5 mM NaOH and heated in a water bath at 70 \({}^{\circ}\)C for 10 min. The treated cells were centrifuged at 10,000\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C. The supernatant was collected and mixed with 1 mL of Bradford reagent (Wako Chemical, Tokyo, Japan) for 10 min, and the optical density of the mixed solution at 595 nm was measured. Total protein content was calculated using a standard curve measured before the experiment.
## 2 Data and Methodology
### Data Analysis from Sampling and Response Designs
#### 2.4.1 Accuracy Assessment per VI, Ensemble VIs, and Type of Disturbance  
The sampling and response design for the accuracy assessment of the CCDC disturbance detection in 2019 was based on the Area Two [41] and Collect Earth Online ([[https://collect.earth/](https://collect.earth/)]([https://collect.earth/](https://collect.earth/)); accessed on 30 June 2021) tools, respectively. These applications provide the necessary tools to comply with the recommended practices and international guidelines for assessing map accuracy. Using these approaches, we estimated the optimal number of samples and randomly located them in the study area (sampling design). In addition, we built a reference dataset for these sample locations (response design) to support the validation of the results. The sampling design considered only the native vegetation areas in 2018. Land cover changes observed until 2018 and reported by the INPE's program of _Cerrado_ monitoring were masked.  
A stratified random sample design with equal sample allocation on each class (disturbed and non-disturbed) was adopted in the work. The stratification was based on the classes (disturbed by fire, clearing, and non-disturbed) and on the inspection of ancillary maps. There are two main purposes for stratification. First, the strata are important to report accuracy per class. Second, the stratification ensures a sufficient representation of each class, even that occupying a small proportion of the area (e.g., disturbance class). The sample unit is pixel. We choose an equal number of samples per strata to make possible the comparison of accuracy per type of disturbance. We selected 588 samples for the spatial accuracy assessment and area estimation of disturbance in 2019. In addition to the visual inspection of the Landsat time series, we used other satellite data with more detailed spatial resolution than the Landsat instruments for the identification of disturbed and non-disturbed areas. Examples of these satellites include the MultiSpectral Instrument (MSI)/Sentinel-2 images having some bands with 10-m spatial resolution, and the PlanetScope satellite constellation with 3-m spatial resolution data.  
From the 588 allocated samples, we selected 294 non-disturbed sites and 294 disturbed sites. We divided also the set of 294 disturbed areas into two groups having an equal number of samples: one group affected by fire and another one affected by clearing. The cross-tabulation of the class label (disturbed and non-disturbed) assigned from the CCDC results against the reference dataset generated the error matrix. The error matrix was the basis for quantification of the overall accuracy, user's accuracy (and its complementary measure, commission error), and producer's accuracy (and its complementary measure, omission error) estimates. Based on these estimates, we identified the performance of each VI and the ensemble VIs for overall detection of disturbance and specific detection of savanna-cleared and fire-affected areas.
## 5 Results
### Correlation between PAR and NDVI  
There was a linear relationship (R\({}^{2}\) = 0.77, N = 133) between the fraction of canopy-absorbed PAR, \(fPAR_{c}\), and NDVI, for the Abisko forest from the start of the growing season until before the leaf discoloration period (DOY 135-249) (Figure 11). The linear relationship deteriorated during the leaf discoloration and defoliation period (DOY 250-280), when NDVI values were generally decreasing. Similar results are also obtained for the relationship between the canopy-intercepted PAR, \(fIPAR\), and NDVI. \(fPAR_{c}\) was consistently 5% lower than \(fIPAR\) throughout the snow free period (\(fPAR_{c}\) = 0.95 \(\cdot fIPAR\); R\({}^{2}\) = 1.00, N = 133). The fraction of ground absorbed PAR, \(fAPAR_{g}\), showed a negative relationship with NDVI, which reflects that the ground vegetation absorbed less and less PAR during the course of the NDVI increase, since an increasing amount of PAR was absorbed by the forest canopy before reaching the ground. This PAR competition lasted until the tree leaf discoloration and defoliation, evidenced by the NDVI increase during DOY 259-282 (Figure 6, Abisko forest). This short term recovery is attributed to the ground vegetation. There was no linear relationship between the total PAR absorbed by the ecosystem, \(fAPAR_{e}\), and NDVI. The variability of \(fAPAR_{e}\) during the snow free period (standard deviation, std = 0.004) is much smaller than the variability of other PAR fractions (std = 0.10 - 0.11).  
Figure 11: Relationships between fractions of PAR and NDVI in the Abisko forest during the snow free season (DOY 135–280). Red circles denote measurements for DOY 135–249, black diamonds for DOY 250–280.
# Recent Progress on Aberration Compensation and Coherent Noise Suppression in Digital Holography
## 2 Basic Principle of DHM
Finally, the intensity distribution \(I\) and the phase distribution \(\varphi\) of the reconstructed image are described as follows:  
\[I(x_{i},y_{i})=\text{Re}[U(x_{i},y_{i})]^{2}+\text{Im}[U(x_{i},x_{i})]^{2}, \tag{9}\]  
\[\phi(x_{i},y_{i})=\text{arctan}\frac{\text{Im}[U(x_{i},y_{i})]}{\text{Re}[U(x_{ i},y_{i})]}, \tag{10}\]  
where Im[.] and Re[.] denote the imaginary and real parts, respectively. This realizes the reconstruction in the DH pre-amplification configuration and obtains the quantitative intensity and phase information.
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Modelling Techniques
#### 3.3.4 Transformer-Based and Attention Models  
Transformers: Transformer-based architectures have gained prominence in multimodal educational modelling due to their capacity to capture long-range dependencies across modalities and time. In [94], a Siamese neural framework incorporates Transformers alongside LSTM and GRU models for classroom activity detection, enhancing representation learning. Similarly, ref. [2] leverages Bidirectional Encoder Representations from Transformers (BERTs) to predict collaborative problem-solving skills based on student communications. In the context of engagement detection, Transformers are integrated with TCN and CNN in a hybrid approach to improve performance in multimodal environments [72]. For physical education contexts, ref. [42] introduces a Transformer-based structure (TEMS) to extract temporal features from skeletal data, while ref. [15] proposes HRformer, a dual-stream Transformer framework that unifies skeletal and video inputs through self-attention and cross-attention mechanisms for effective behaviour recognition.  
Attention mechanisms: these models have been increasingly employed to enhance multimodal modelling by enabling models to prioritise salient features and manage noisy inputs effectively. In [69], an attention-optimised DNN is proposed for predicting teaching quality, demonstrating improved accuracy through the integration of complex network theory and multimodal data sources such as academic performance, behavioural cues, and psychological attributes. Similarly, ref. [15] utilises both self-attention and cross-attention within the HRformer framework to align skeletal and video modalities, facilitating robust behaviour recognition in physical education contexts through enhanced multimodal fusion strategies.
# A Quantum Approach for Exploring the Numerical Results of the Heat Equation
## 2 Methodology
### Converting Heat Equations to Quantum Form
#### 2.2.1 Initialization of Quantum States  
Let us represent the temperature distribution at time level \(t\) at point \(x\) using a quantum state on qubit \(i\), where \(i\) corresponds to the index of the point \(x_{i}=i\Delta x\), and \(\Delta x\) is the spatial step. The initial temperature distribution in the system can be encoded into qubit states. For example, if we have \(N\) points in the system, we can use \(N\) qubits, where each qubit represents one point, and the initial state is determined by their values.  
Let us denote the initial temperature distribution as \(T_{i}^{0}\), where \(T_{i}^{0}\) represents the temperature at point \(x_{i}=i\Delta x\) at time \(t=0\). We can encode this initial distribution into the quantum states as follows:  
\[\begin{array}{l}If\ T_{i}^{0}=1:|\ 1\rangle i,\\ If\ T_{i}^{0}=0:|\ 0\rangle i.\end{array}\]  
where \(|\ 1\rangle i\) represents that the temperature at point \(x_{i}=i\Delta x\) is \(1\) and \(|\ 0\rangle i\) represents that the temperature at point \(x_{i}=i\Delta x\) is \(0\).  
Therefore, the initial quantum state representing the temperature distribution at time level \(t\) can be written as the tensor product of the individual qubit states:  
\[|\ T(0)\rangle=|\ T_{1}^{0}\rangle\otimes|\ T_{2}^{0}\rangle\otimes\ldots \otimes|\ T_{N}^{0}\rangle, \tag{5}\]  
\(|\ T(0)\rangle\) is the initial quantum state representing the temperature distribution at time level \(t=0\), and \(|\ T_{i}^{0}\rangle\) is the quantum state representing the temperature at point \(x_{i}=i\Delta x\) at time \(t=0\).  
To encode the \(x\) co-ordinate in this example, qubits are used, where each qubit represents a specific position \(x_{i}\) on the spatial axis. Thus, the qubit \(|\ T(x)\rangle\) encodes information about the initial temperature at point \(x_{i}\) based on the function \(T(x)\). Each qubit contains information about the temperature at the corresponding point \(x_{i}\), which allows us to represent the temperature distribution in the system in the form of a quantum state.