esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
**Nomenclature**  
\(\zeta\)  
Damping ratio  
\(\omega_{0}\)  
Undamped natural angular frequency  
\(\theta\)  
Deflection of fluid surface  
\(s\)  
Horizontal path of transport motion  
\(t\)  
Time  
\(x\)  
Horizontal coordinate  
\(\dot{x}\)  
Time derivative of horizontal coordinate  
\(\ddot{x}\)  
Second time derivative of horizontal coordinate  
\(y\)  
Vertical coordinate  
\(s_{\text{stroke}}\)  
Container's conveyed stroke in one cycle  
\(b\)  
Ratio of motion time to cycle time  
\(T_{\text{cycle}}\)  
Cycle time  
\(T\)  
Kinetic energy  
\(n\)  
Operating speed  
\(\mathcal{G}\)  
Container's geometry  
\(U\)  
Potential energy  
\(\boldsymbol{g}\)  
Vector of generalized coordinates  
\(\boldsymbol{Q}\)  
Vector of forces acting on the generalized coordinates  
\(\eta\)  
Measured fluid surface's vertical displacement  
\(\ddot{\eta}\)  
Approximated fluid surface's vertical displacement  
\(\eta_{\text{eff}}\)  
Effective value of \(\eta\)  
\(\Delta\eta\)  
Difference of effective value  
\(w\)  
Fluid volume's width  
\(d\)  
Damping constant  
\(F\)  
Actuating force  
\(g\)  
Gravitational acceleration  
\(l_{\text{P}}\)  
Pendulum's length  
\(m_{\text{P}}\)  
Pendulum's mass  
\(m_{\text{M}}\)  
Linear drive's mass  
\(L\)  
Lagrangian  
\(D\)  
Dissipative Energy  
\(a\)  
Motion's profile acceleration  
\(p\)  
Model parameter describing the acceleration's magnification  
\(i\)  
Index for stating the discrete horizontal coordinates  
\(j\)  
Index for stating the discrete time  
\(k\)  
Index for stating the experiment's repetition  
\(u\)  
Index for stating the operating speed  
\(v\)  
Index for stating the container's geometry  
\(o\)  
Index for stating an item of the generalized vector  
\(l\)  
Number of discretization points for horizontal coordinate  
\(J\)  
Number of captured frames per measurement  
\(l_{\text{b}}\)  
Number of captured frames per measurement during motion phase  
\(K\)  
Number of repetitions  
FR Frame rate  
\(SEOE\)  
Standard error of estimate  
\(\text{SOSM}\)  
Single-operating-speed model  
\(\text{MOSM}\)  
Multiple-operating-speed model
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 2 Data and Methodology
### Data Coverage and Processing
Once all interferograms have been generated and corrected for topographic, tropospheric, orbital, and phase unwrapping errors, time-series inversion is performed to estimate cumulative subsidence during the acquisition periods [22]. Parameters are estimated using the least squares method for each pixel. Then, a temporal filter is applied using Gaussian Process Regression. The velocity of the subsidence is then estimated using linear models for all good coherence pixels. The map of LS rate has been generated from the estimated vertical displacement using the least squares method. To estimate the subsidence rate, a linear model has been applied, which assumes that there is no acceleration or deceleration in the vertical displacement across the island. This linear assumption simplifies the analysis by presuming a constant rate of subsidence over time. Although this assumption may not be accurate for every pixel, our detailed inspection of Continuous Operating Reference Stations (CORSs) and findings from previous studies suggested that, in most cases, the LS rate in Java is indeed constant. This consistency in the LS rate across the island supports the validity of the linear model for generating the LS rate map. However, it is important to note that it is not applicable where the subsidence rate is not linear, and this should be considered in further detailed analyses. The result has also been verified with shallow earthquake data in the same period of the data using the USGS earthquake database. Close inspection has been performed on all interferograms within co-seismic events and has identified the location of the earthquake epicenter. While several LOS displacements due to earthquakes have been detected, none of them coincide with the LS area. During the SAR observation period, one notable earthquake occurred in Cianjur on 21 November 2022, with a magnitude of 5.6. The source was located at 6.836\({}^{\circ}\) S 106.997\({}^{\circ}\) E at a focal depth of approximately 10 km. The close inspection of several interferograms in this location found that a deformation pattern related to this earthquake was clearly detected. However, the distance from the LS area to the epicenter is about 30 km, making the seismic deformation related to this particular earthquake negligible at the LS location.
## 4 Simulation and Application
### Simulation with an Artificial Signal
While the estimation errors, as shown in Figure 8, are capable of revealing the difference, the estimation errors of GA and PSO, as shown in the bottom-right figure, are quite large (MSE of GA PSO are 0.26777 and 0.39747, respectively)
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
Principal component analysis transforms the original measured variable into new non-correlated components. Based on the correlation matrix (Figures 2 and 3), two principal components were identified, explaining 79.5% total variability, 56.51% variation for PC1, and 26.06% for PC2. PC1 was positively correlated with ascorbic acid content and mass gain but negatively with pH value and browning index. The influence of the ascorbic acid content on the susceptibility of various apple cultivars to browning is indicated, among others, by [PERSON] et al. [35]. In turn, PC2 was positively correlated with hardness and soluble solids content (Figure 2). VI of apple cubes of different cultivars contributes to the increase in ascorbic acid content, decrease in the browning index, pH value, soluble solids content and firmness (Figure 3). The PCA scores plots indicate that the cultivars with favorable characteristics of apple cubes after the impregnation process in terms of vitamin C content, hardness, and browning index are Cortland, Shampion, and Ligol (Figure 3).  
Figure 2: PCA loading plot.
## 2 Materials and Methods
### Model Training  
The current more popular gradient descent algorithms include the following: Adam [32], stochastic gradient descent (SGD), and AdaGrad [33]. To reduce the training time of each time step, the SGD algorithm is introduced earlier. However, the SGD algorithm is subject to local saddle points in some cases due to its drawbacks. Therefore, momentum is introduced to accelerate the convergence of the model in the correct direction and reduce the possibility that the model falls into the local optimum. The AdaGrad algorithm can dynamically adjust the learning rate during training. When the number of parameter updates is long, the learning rate decreases, while when the number of parameter updates is small, the learning rate increases. Adam uses the first-order moment estimation and the second-order moment estimation of the gradient to dynamically adjust the learning rate of each parameter. Each iteration learning rate has a certain range, which makes the parameter update relatively stable. SGD is the most commonly used method to optimize the CNN network [34]. In this study, SGD with a batch size of 32 is used. The initial learning rate is 0.1, and the exponential learning rate decay is 10 times in every 10 epochs, the maximum number of early stop epochs to 100 by default. The learning rate \(\eta\) decayed as follows:  
\[\eta=\eta_{0}*0.1^{N/10} \tag{11}\]  
where \(\eta_{0}\) is the initial learning rate, and \(N\) is the number of epochs.  
Overfitting is a common phenomenon in machine learning. It is manifested as the model is trained with higher accuracy on the training set and lower accuracy on the testing set. The noises and individual differences in the ECG signals are the main factors leading to the overfitting of the model. To prevent overfitting, regularization techniques in the proposed model are used. During the proposed model training process, the L2 regularization parameters of each convolutional layer of the MCNN module were added to the loss function. Therefore, our loss function can be defined as follows:  
\[L(x)=-\frac{1}{\left|D\right|}\sum_{i=1}^{\left|D\right|}\log\hat{y}^{(i)}+ \lambda\left\|W\right\|^{2} \tag{12}\]  
where \(\left|D\right|\) is the size of the training set, \(\hat{y}^{(i)}\) is calculated using Equation (4), \(\lambda\) is the penalty factor, and \(W\) is the weight parameter in the proposed model.  
Another regularization method is the dropout technique [35]. The dropout technique effectively alleviates the overfitting of the model by stopping certain neurons during the training process. L2 regularization is achieved by modifying the loss function to reduce overfitting, while dropout is achieved by modifying the network. For the ECG signal recording whose length is less than the average ECG recording length, the information between the segments is redundant. To alleviate this situation, the dropout technology between MCNN and BLSTM is added, and set the \(p\)-value to 0.4.  
Finally, the default parameter settings of the model are shown in Table 2, in which all parameters are the optimal values selected after the experiment.
## 2 Outlook for Smart Grid Integration
### Technical Gap for VSG  
Most converter-based renewable energy generation has almost zero inertia, as photovoltaic (PV) has no spinning components, and the kinetic energy of wind turbine (WT) blades is often minimized by the control strategy of a back-to-back converter. The energy is intermittent, non-dispatchable, and needs adequate balancing resources to maintain safe operation against transients, fluctuations, and contingencies. As a result, the key problem of high-penetration renewable energy generation is not only energy source substitution, but also providing grid service in both steady and transient states. Although the conventional architecture of traditional power systems is not designed for distributed and decentralized generation, synchronous generators do not have this problem. As a result, even though there are challenges regarding the integration of decentralized renewable energy sources, their operation theories and architecture also have advantages over traditional power systems. At the same time, the advanced knowledge system that governs the current power grid can be applied to the new renewable energy sources. For example, the VSG control inherits the advantages of both flexible converters and the properties of synchronous machines. Thus, regarding the existing knowledge of traditional system operations, the present technical gap for the VSG algorithm is ensuring that its converters contribute a similar grid service as that of the synchronous machines, as shown in Figure 2.  
Figure 1: Traditional power system operation on a time-scale.
# Laser Processing of Intraocular Lenses
## 3 Results
### Nanosecond Laser Ablation of Acrylic IOLs  
We conducted experiments involving the creation of ablated spots or craters on the surface of acrylic IOLs using the nanosecond laser with different exposure times and pulse energy at 7.2 mJ. This allows us to observe how the duration of exposure affects the dimensions and characteristics of the resulting craters. The SEM images of the ablated spots are shown in Figure 7.  
For the 30 s exposure, the crater dimensions are measured as 630 \(\times\) 440 \(\upmu\)m with resolidified debris around the edges (Figure 7a). With an increase in exposure time to 1 min, the crater dimensions expand to 880 \(\times\) 650 \(\upmu\)m (Figure 7b). Notably, in the case of the 1 min exposure, we observe a dome-like feature above the crater, which indicates a micro-explosion inside the bulk or beneath the surface of the IOL. This suggests that prolonged exposure to the nanosecond laser results in significant material disruption within the IOL. Subsequently, for the 6 min exposure, the crater dimensions further increase to 1030 \(\times\) 740 \(\upmu\)m, and the ablation results in a well-formed crater but the ablation depth exceeds the IOL thickness, which is \(\sim\)500 \(\upmu\)m, penetrating the back side of the IOL (Figure 7c). The elliptical pattern observed as evidenced by the dimensions of the ablated spots indicates the spatial characteristics of the corresponding laser beam during the ablation process. This variation in crater dimensions and characteristics highlights the sensitivity of the nanosecond laser ablation process to exposure time, where despite being slow and relatively controllable in the regime of a several-minute processing duration, with longer exposures it can lead to more extensive material removal and potential structural changes within the IOL.  
Comparing these results with those obtained with the femtosecond laser reveals interesting insights. The femtosecond laser ablation process typically yields finer and more precise ablation patterns due to its ultrafast pulse duration, high peak power, and suppressed thermal effects [32; 33]. In contrast, nanosecond laser ablation tends to result in larger and less controlled ablation areas, as observed in these experiments. Additionally, the presence of an explosion-like patterning of material under the surface, as seen in the 1 min exposure, highlights a distinct effect of nanosecond laser ablation, attributed to increased thermal effects, not typically observed with femtosecond lasers.
## 3 Results and Discussion
### Bearing and Deformation Characteristics of Gangue Slurry
#### 3.1.1 Change of Compression Rate
Based on the analysis of the particle size grading of gangue in Figure 1, it can be concluded that the greater the fluidity of the slurry, the higher the proportion of fine-grained gangue, the greater the deformation degree of the slurry after compression, and the poorer the deformation bearing capacity of the compacted body. * Creep loading Figure 8 shows the compression rate-time curve of gangue slurry under creep loading conditions. As shown in Figure 8, it can be seen that:  
1. The compression rate of the slurry after creep loading does not exceed 50%. Under different loading schemes, the compression rate of the slurry increases with the increase in fluidity, and with the increase of \(x\), the compression rate of the slurry with the same fluidity also increases. Throughout the entire loading process, the compression rate changes with time in three stages: rapid deformation, slow deformation, and stable deformation. The rapid deformation stage mainly occurs during the first loading pro cess. When entering the subsequent loading stage, the rate of increase in compression rate significantly decreases. 2. As \(x\) increases, the grouping differences in compression rates between slurries with different fluidities gradually weaken over time. When \(x=1\), the compression rates of C1-C2 and C4-C5 remain significantly different, and C1-C2 does not reach a stable stage when loaded to 20 MPa. When \(x=2\) and 3, the difference in compression rates between C1-C2 and C4-C5 gradually decreases. When \(x=4\), the difference in compression rates between C1-C2 and C4-C5 further decreases, and compression rates of C1-C5 have reached a stable stage when loaded to 20 Mpa. Under different loading schemes, the extreme variation of the compression rate of C3 is relatively small, and the trend of variation over time is relatively stable. This indicates that the gangue slurry has good bearing performance and deformation resistance when the fluidity is 240 mm. 3. Compared with instantaneous loading, the compression deformation range of slurry under creep loading conditions is smaller and more stable. The compacted body after loading has a larger spatial support height, stronger deformation resistance, and bearing capacity.
## 2 Optical Coherent Detection Technique
### Heart Rate Detection with Self-Mixing Optical Homodyne Technique  
The optical homodyne detection technique for \(HR\) detection makes use of self-mixing of the same type of frequency signal, as shown in Figure 3. The laser diode signal with frequency \(f_{0}\) is split in two orthogonal arms of a Michelson interferometer by a 3 dB beam splitter. The frequency of the optical signal remains the same \((f_{0})\) in both orthogonal arms. In this case, the local oscillator signal is provided by a fixed mirror M, which reflects back the same optical signal, whose frequency is \(f_{0}\). The modulated optical signal is the optical signal that is reflected from the skin surface of the human test subject. Both the local oscillator and modulated signals are obtained in two orthogonal arms of the Michelson interferometer. Optical signals travel in the two orthogonal arms and are combined at the same 3 dB splitter. In this case, both optical signals travel the same path distance in the two arms of the interferometer.  
Figure 3: Self-mixing homodyne detection for heartbeat measurement.  
Thus, the total path difference in the two orthogonal arms becomes zero. The zero-path difference in both orthogonal arms leads to zero phase difference between two optical signals. Therefore, the phase of the carrier signal with frequency \(f_{0}\pm f_{p}\) is matched to the local oscillator signal with frequency \(f_{0}\) and the phase of both the beams are locked [29]. The Modulated bio signal is given by,  
\[E_{s}=A_{s}Exp\ [-j[2\pi(f_{0}\pm f_{p})t+\Phi_{s}]] \tag{11}\]  
The local oscillator signal is given by,  
\[E_{LO}=A_{LO}Exp\ [-j[2\pi f_{0}t+\Phi_{LO}]], \tag{12}\]  
The output power of the photodetector is,  
\[P(t)=P_{s}+P_{LO}+2\sqrt{P_{s}P_{LO}}\cos(2\pi f_{\rm{IF}}t+\Phi_{s}-\Phi_{LO}), \tag{13}\]  
The intermediate frequency signal is given by \(f_{IF}\), which is the difference of the carrier signal and the local oscillator signal frequency.  
\[f_{IF}=(f_{0}\pm f_{p})-f_{0}, \tag{14}\]  
The beating of the two optical signals provides the \(HR\) frequency (\(f_{p}\)) at the photo detector, which is equal to \(f_{IF}\), i.e.,  
\[f_{IF}=f_{p}, \tag{15}\]  
The overall phase shift observed at the detector is the difference of phase of modulating bio signal (\(\Phi_{m}\)) and phase of input optical signal (\(\Phi_{s}\)). It is given as,  
\[\Phi=\Phi_{m}-\Phi_{s} \tag{16}\]  
The phase difference between both orthogonal beams is zero and constant inside the interferometer.
# Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
## 4 Multi-Channel Fault Fusion Diagnosis
### Analysis of Experimental Results  
In this experiment, 500 sets of experimental data were selected, and each set of data consisted of 1024 points. In addition, the experimental results showed that the accuracy of each sensor improved after the DS fusion theory. However, when two sensors were fused with each other, the accuracy was not as high as the accuracy of three sensors fused at the same time. In addition, the simultaneous fusion of three sensors improved the accuracy by 0.05%, 0.31%, and 0.58% over the fusion of sensors 1 and 2, sensors 1 and 3, and sensors  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Before Fusion** & \multicolumn{3}{c}{**After Fusion**} \\ \hline
**Single Sensor** & **Accuracy** & **Fusion Method** & **Accuracy** \\ \hline Sensor 1 & 96.43\% & Sensors 1 and 2 & 99.67\% \\ \hline Sensor 2 & 93.97\% & Sensors 1 and 3 & 99.93\% \\ \hline Sensor 3 & 93.28\% & Sensors 2 and 3 & 99.40\% \\ \hline \multicolumn{3}{c}{All sensors} & 99.99\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy comparison before and after multi-channel fusion.  
Figure 20: Before and after multi-channel sensor fusion. (**a**) sensor 1 and sensor 2 fusion; (**b**) sensor 1 and sensor 3 fusion; (**c**) sensor 2 and sensor 3 fusion; (**d**) 3 sensors fusion.  
2 and 3, respectively. The experiments showed that the use of the DS fusion method can compensate for the disadvantage of incomplete data collection by a single sensor and can improve the gear teeth fault diagnosis results. All in all, the fusion algorithm is important for the field of fault diagnosis.  
According to Figure 21, the diagnostic accuracy of the multiple-channel single sensors before fusion was higher than that of the single-channel, by 3.01%, 16.7%, and 5.17%, respectively. It can be proved that the multi-channel for the comprehensive data acquisition can improve the diagnostic accuracy. In order to improve the diagnostic accuracy of the gear fault identification, the DS evidence theory method was used in order to fuse three sensors of gearbox gear teeth, as shown in Figure 22. Both single-channel fault diagnosis and multi-channel diagnosis accuracy were improved and the accuracy of the multi-channel diagnosis after fusion was also mostly higher than the accuracy of single-channel diagnosis after fusion when the fusion theory was used.  
Figure 21: Accuracy comparison of sensor single-channel and sensor multi-channel before fusion.  
Figure 22: Accuracy comparison of sensor single-channel and sensor multi-channel after fusion.
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 2 Dimensionality Reduction
### Feature Selection Algorithms
#### 2.3.5 Gravitational Search  
Gravity Search (GS) is a metaheuristic optimization algorithm drawn from the principles of gravity and mass interactions [49]. It was initially proposed by [PERSON], [PERSON], and [PERSON] in 2009 [50]. The algorithm employs the principles of Newtonian gravity, where \"every massive particle in the universe attracts other massive one with a force that is directly proportional to the product of their masses and inversely proportional to the square of the distance between them\" [51, 52].  
The key steps involved in GS are the following [49, 52]:  
* Step 1: A set of candidate solutions (particles) is randomly generated.
* Step 2: The mass of each particle is calculated based on its fitness value.  
* Step 3: The gravitational force between each pair of particles is computed using a formula based on [PERSON]'s law of gravity, which takes into account the masses of both the acting and receiving particles, as well as the distance separating them.
* Step 4: The acceleration of each particle is determined and adjusted according to the gravitational force and mass of all interacting particles, and subsequently, their velocity is updated based on the calculated acceleration. The velocity update may also incorporate a random element to simulate stochastic behavior and prevent convergence to local minima.
* Step 5: The positions of the particles are adjusted according to their velocities. This simulates their movement towards areas of higher mass concentration, which are better solutions.
* Step 6: Steps 2-5 are repeated until a stopping criterion is reached, such as reaching a maximum number of iterations or attaining a satisfactory solution.