esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Proposed Method
### Improved Particle Swarm Optimization Algorithm
#### 2.1.2 Inertia Weight and Learning Factor  
The value of inertia weight \(\omega\) will affect the searching ability of particles, and the range of values is usually [0.2, 1.2]. Inertia weight can be divided into fixed weight and dynamic weight, and the dynamic inertia weight factor will change with the number of iterations, which can maintain the search ability, and the particles with dynamic weight have higher exploration ability.  
In the initial stage of the search, \(\omega\) is generally set to be larger, and the particles have the ability to have a larger global search area. With the increase in the number of iterations, it is necessary to optimize the local search and then reduce the value of \(\omega\). The common method is linear iterative decline:  
\[\omega_{l}=\omega_{\max}-\frac{(\omega_{\max}-\omega_{\min})t}{t_{\max}} \tag{9}\]  
Figure 1: Flowchart of fitness evaluation function calculation.  
where \(\omega_{t}\) is the value of the first iteration, \(\omega_{\max}\) and \(\omega_{\min}\) are the maximum and minimum inertia weight, and the value of \(\omega_{\max}\) and \(\omega_{\min}\) are 0.8 and 0.2, respectively.  
The learning factors \(c_{1}\) and \(c_{2}\) represent the weight of each particle, where \(c_{1}\) is the private learning weight, and \(c_{2}\) is the global social weight. The learning weight of the particle swarm is different at different stages in the process of optimization. When the value of \(c_{1}\) is large, the particles have a trend around their own optimal. When the value of \(c_{2}\) is large, the PSO is more like a global search method, and even more, it always tries to exploit new areas. In order to achieve a better optimization effect, this paper designs a way to define the learning factor, which varies with the number of iterations. At the beginning of the iteration, the value of \(c_{1}\) is larger, and the particle swarm can explore more areas on its own. With the increase of the number of iterations, the value of \(c_{1}\) becomes smaller, while the value of \(c_{2}\) increases, and the particle swarm learns more from the social global optimal. Through the comparison of several local optimal solutions, the global optimal solution will be found. The learning factor update is as follows:  
\[c_{1}=c_{1a}-0.01(c_{1a}-c_{1b})t \tag{10}\]  
\[c_{2}=c_{2a}-0.01(c_{2b}-c_{2a})t \tag{11}\]  
where \(c_{1}\)  
As can be seen from Figure 2, when the value of \(\gamma\) is less than 1, the brightness of the output image is brighter than that of the input image, and the transformation has the effect of brightness enhancement. When the value of \(\gamma\) is more than 1, the brightness of the output image is attenuated compared with the input image. When using gamma transform to realize image enhancement, it is generally desired to achieve a larger enhancement of luminance in the low-illuminance region and a small enhancement in the high-illuminance region. We randomly select a face in the Yale face dataset as the test image, and the enhancement effects of different \(\gamma\) are shown in Figure 3 below.
## 4 Performance Evaluation
### Experiments with Hot Spot Data Set (Data Set 2)  
We also perform experiments with the hot spot data set (Data Set 2 in Table 1) and the hot spot query set (Query Set 2 in Table 1). As described earlier, the second data set has hot spots. The experimental process is the same to that of the experiments using Data Set 1. Figure 12 shows the experimental results, i.e., ingestion throughput of Geomesa and the proposed method as nodes increase. As shown in the figure, the ingestion throughput of our proposed method scales up well as nodes increase while that of Geomesa does not increase well when the number of nodes is greater than 6. Also, the throughput of the proposed method is about 4.8 times higher than that of Geomesa.  
In Figure 13, the experimental results are shown. As shown in the figure, the range query throughput of the proposed method scales well while that of Geomesa does not. The throughput of the proposed method is about 1.7 times higher than that of Geomesa. Specifically, when the number of nodes is 9, the throughput of the proposed method is about 2.2 times higher.  
Figure 12: Ingestion throughput of Geomesa and the proposed method (Ingestion throughput: number of insertion operations per second).  
Figure 13: Range query throughput of Geomesa and the proposed method (Range query throughput: number of range queries per second).
# Observer-Based Consensus Control for Heterogeneous Multi-Agent Systems with Output Saturations
## 3 Main Results
### Leaderless Case
Then, in the set \(\mathcal{M}\), the error dynamics in (15) can be rewritten as  
\[\dot{e}= \bar{A} \tag{19}\] \[\dot{e}= (A+BK)e.\]  
Since \((A_{i}+B_{i}K_{i})\) is Hurwitz, \(\forall i\in\mathcal{V}\), we have  
\[\lim_{t\to\infty}\lVert\epsilon_{i}\rVert=\lim_{t\to\infty}\lVert\hat{x}_{i}- \Pi_{i}w_{i}\rVert=\lim_{t\to\infty}\lVert C_{i}\hat{x}_{i}-C_{i}\Pi_{i}w_{i} \rVert=\lim_{t\to\infty}\lVert C_{i}\hat{x}_{i}-Rw_{i}\rVert=0. \tag{20}\]  
Moreover, \(\bar{\zeta}=0\) implies \(w_{i}=\bar{w}\), and, thus, from Condition 4 in Theorem 1, we have \(\lVert Rw_{i}\rVert_{\infty}=\lVert R\bar{w}\rVert_{\infty}<1\) in \(\mathcal{M}\). Therefore, there exists a finite time \(t_{1}\) such that \(\sigma(C_{i}\hat{x}_{i})=C_{i}\hat{x}_{i}\), \(\forall t\geq t_{1}\), which gives, for \(t\geq t_{1}\), \(\hat{z}_{i}=(\sigma(C_{i}x_{i})-\sigma(C_{i}\hat{x}_{i}))=(C_{i}x_{i}-C_{i} \hat{x}_{i})=\bar{C}_{i}\epsilon_{i}=0\) in \(\mathcal{M}\). Then, for \(t\geq t_{1}\), the observer error dynamics can be rewritten as  
\[\dot{e}=\bar{A}e=(\bar{A}-\beta P^{-1}\bar{C}^{T}\bar{C})e. \tag{21}\]  
Since \((\bar{A}-\beta P^{-1}\bar{C}^{T}\bar{C})\) is Hurwitz from Lemma 1, \(e\) converges to \(0\). In summary, we have shown that \(\dot{V}\leq 0\) and \(e=\bar{\zeta}=0\) is a unique, asymptotically stable equilibrium point in \(\mathcal{M}\). Therefore, according to Lemma 2, we have \(\lim_{t\to\infty}\lVert\epsilon_{i}\rVert=0\) and \(\lim_{t\to\infty}\lVert\hat{\zeta}_{i}\rVert=0\), \(\forall i\in\mathcal{V}\). Moreover, since \((A+BK)\) is Hurwitz, the regulation error dynamics is input-to-state stable. Then, from Lemma 3, we have \(\lim_{t\to\infty}\lVert\epsilon_{i}\rVert=0\). Finally, we can conclude that \((\bar{\zeta},e,\epsilon)\to(0,0,0)\) and \(\lim_{t\to\infty}\lVert C_{i}x_{i}-C_{j}x_{j}\rVert=0\), \(\forall i,j\in\mathcal{V}\), which completes the proof.
## 2 Materials and Methods
### Dataset  
Sentinel-1 radar data operating in the C band (~5.6 cm wavelength) was obtained in a joint venture between the European Space Agency (ESA) and the European Commission and we used the data to detect surface deformations. Sentinel-1 data, which can be used to visualize large areas with short revisit times (6 or 12 days) and capture images in all weather conditions day and night, is available free of charge from the ESA open access portal. For this study, 76 images in descending acquisition geometries and 74 images in ascending acquisition geometries, captured between May 2018 and October 2022, were used. SAR acquisition times were 03:09 UTC for the descending orbit and 15:01 UTC for the ascending orbit. Details of the SAR data are given in Table 1. In addition, the GACOS delay products corresponding to each SAR acquisition for the GACOS correction were downloaded by sending a request to the official GACOS website ([[http://www.gacos.net](http://www.gacos.net)]([http://www.gacos.net](http://www.gacos.net)), accessed on 13 February 2023) along with the study area location and SAR data-acquisition times.
## 3 Analysis of Experimental Results
### Influencing Factor of Normal Load (CNL)  
Figure 8 shows the effect of normal load (CNL) on the peak shear strength (\(\tau_{p}\)), peak shear displacement (\(u_{p}\)), post-peak modulus (S), and stress drop (\(\Delta\tau\)) of rock joints. As shown in Figure 8a, \(\tau_{p}\) of groups A, B, C, and D all increased linearly with the increase in CNL. Figure 8b shows that \(u_{p}\) of groups A, B, and C increases linearly with the increase in CNL, whereas \(u_{p}\) of group D increased first and then decreased with the increase in CNL. With the continuous increase in CNL, group D is prone to crack propagation due to the larger grain size, and the proportion of calcite with lower Mohs hardness increases, resulting in the decrease in \(u_{p}\).  
As important parameters to characterize the shear instability intensity of rock joints, \(S\) of groups A, B, C, and D shows an exponential increase with the increase in CNL, as shown in Figure 8c, and \(\Delta\tau\) of groups A, B, C, and D increases first with the increase in CNL and then tends to be stable or decreases, as shown in Figure 8d. \(\Delta\tau\) tends to be stable or decreases with the increase in CNL because the grains are crushed under high shear and normal loads, and the shear failure behavior of rock joints changes from brittle shear failure to ductile failure.
## 1 Introduction  
Cavitation is a phenomenon occurring in a liquid that undergoes rapidly changes in pressure. At first a bubble, or cavity, nucleates and growth over a nucleation site. After growing up to maximum value, the cavity collapses generating shock waves [1].  
When the pressure field in the liquid is isotropic, the cavity preserves its symmetry and the collapse has spherical symmetry [2, 3, 4]. However, when an anisotropic pressure field drives the collapse, the cavity does not preserve its symmetry and folds in a specific direction generating a high-velocity flow known as jet [1, 5]. The anisotropic pressure field is generated by the so-called anisotropic driver, which also defines the jet folding direction [6]. The most common anisotropic drivers are rigid or free surface, gravity, presence of neighbor bubbles or a combination of the above. Another mechanism that makes a cavity folds during the collapse is the interaction with a shock waves that folds the cavity in the shock direction. This situation is known as shock-induced collapse, while the pressure driven collapse is called Rayleigh collapse [7, 8].  
From an engineering point of view, the high-speed jet generated in the Rayleigh collapse by the presence of a nearby solid surface is the scenario most addressed in the literature [8, 9]. In fact, the high-speed flow can hit the surface causing erosion and eventually loss of material. This phenomenon, known as cavitation erosion, is undesirable in industrial, military and power station equipment such as pump impellers, high-speed propellers and turbine blades [10, 11, 12].  
Thanks to its Lagrangian nature, Smoothed Particle Hydrodynamics (SPH) can handle problems with large deformations better than mesh-based technique [13] and, for this reason, it is particularly suited for studying cavitation. [PERSON] et al. [14] developed a Smoothed Particle Hydrodynamics axisymmetric solver to simulate a shock-induced collapse of an empty cavity and the erosion process of the nearby surface. [PERSON] et al. [15] used a SPH-ALE method to study a gas filled cylindrical cavity Rayleigh collapse far and near a surface. [PERSON] and [PERSON] [16] used SPH to simulate an oscillating gas filled cylindrical cavity under a variable isotropic pressure field. [PERSON] and [PERSON] [17] developed a SPH model to study the Rayleigh collapse of a gas filled cylindrical cavity that takes in account the heat diffusion at the gas-liquid interface. Among these studies, only [PERSON] et al. and [PERSON] et al. take in account anisotropic collapses. [PERSON] et al. only for the shock-induced collapse case, while [PERSON] at al only for cavity detached from the surface. Despite its importance in practical applications, non-symmetrical Rayleigh collapse of surface attached cavities, to the best of our knowledge, has never been investigated with the SPH method. Moreover, cavities commonly nucleate on surfaces rather than away from it [18; 19; 20]. This work, therefore, develops a SPH model for a non-symmetrical Rayleigh collapse of a cylindrical cavity which is considered the greatest causes of erosion and material loss [8; 9].
## 3 The Proposed Algorithm and Its Applications
### Theoretical Analysis and Performance of FRSVssMP  
The proposed FRSVssMP employs an initial sparsity level estimation approach to establish initialization for iterations part firstly. Then, it integrates strategies of regularization and variable step-size adaptive, and further filters reconstruction results.  
The regularization is employed to ensure the correctness of the identified support set. It implements initial selection of atoms. Therefore, the accuracy of the selected atoms is significantly improved. The fixed step size is improved by variable adaptive step size for high efficiency. Two stages of variant step sizes leveraged in FRSVssMP can achieve higher reconstruction accuracy with fewer iterations. Furthermore, the proposed filtering mechanism is adopted to exclude the incorrect supports. It removes atoms that make the least contributions to the reconstruction. Utilizing filtering iteratively, we can further confirm signal sparsity level and improve accuracy. In conclusion, with the initial sparsity level estimation approach and such mechanisms, FRSVssMP shows superior reconstruction accuracy over other methods with less computational complexity.
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 3 OBFNs with TTD Architectures  
TTD architectures have the advantages of no beam squint and a large delay tuning range [8; 20; 41]. As addressed above, OBFNs with TTD architectures can be based on fiber-optic or free-space beamforming systems. Meanwhile, TTD architectures with integrated photonic circuits have also been proposed and demonstrated with the advancement of photonic integration technology, as shown in Figure 4[17; 18; 23; 24; 42; 43; 44; 45; 46; 47; 48; 49; 50]. It is also shown in this figure that the 1D and 2D OBFN architectures have been proposed since the 1990s. Up to now, TTD architectures mainly include fiber dispersion delay architectures, microring resonator (MRR) group delay architectures, Mach-Zehnder interferometer (MZI) delay architectures, photonic crystal (PC) delay architectures and time delay selection architectures, which will be elaborated in subsequent sections.
## 4 Performance Analysis
### Advantages of the Proposed Algorithm  
The advantages of NC-Euler-WESPRIT algorithm are summarized as follows:  
1. The proposed NC-Euler-WESPRIT algorithm has more available degrees of freedom than original two-step positioning method, SDF method and can distinguish more targets.
2. The proposed NC-Euler-WESPRIT algorithm significantly reduces the computational complexity compared with SDF method and NC-ESPRIT algorithm.
3. By weighting the received data of each observation station, the proposed NC-Euler-WESPRIT algorithm has higher positioning precision than original two-step positioning method and SDF method.
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### Access Models for IoT-over-Satellite
#### 3.3.3 Case III: Scheduled Access with Packet Repetition  
In case IIIIoT devices are assumed to transmit \(L\) number copies of data packets randomly within the scheduled satellite availability periods. Transmission repetition increases the chances of transmission success by creating packet redundancy.
# Cross-Coupled Contouring Control of Multi-DOF Robotic Manipulator
## 4 Simulation Verification and Discussion
### Linear Motion Contour Tracking  
In the first example, a linear motion of the end-effector, which is defined from point A (0.5, 0.0) to point B (0.2, 1.0) with a constant orientation of \(\pi/3\) for time duration of 4 s, is tracked for the 3-DOF robotic manipulator. To form a smooth motion in each joint, the 5 th order polynomial trajectories for positions at the end-effector level are designed, and the inverse kinematics analysis is conducted to get positions and velocities of the joints [20].  
In all simulations for linear contour motions, the following control gains are selected based on Equation (15) and through the trial-and-error method.  
\[\left\{\begin{array}{l}K_{p}=diag\left(3000,\ 3000,\ 3000\right)\\ K_{d}=diag\left(2000,\ 2000,\ 2000\right)\end{array}\right.\ \ \text{and}\ \left\{\begin{array}{l}K_{cp}= \left[750,\ 750,\ 750\right]^{T}\\ K_{cd}=\left[500,\ 500,\ 500\right]^{T}\end{array}\right. \tag{30}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Link \(i\)** & **Mass \(m_{i}\) (kg)** & **Length \(I_{i}\) (m)** & **Mass Center \(r_{i}\) (m)** & **Inertia \(I_{i}\) (kg\(\cdot\)m\({}^{2}\))** \\ \hline
1 & 1.0 & 0.50 & 0.25 & 0.10 \\
2 & 1.0 & 0.50 & 0.25 & 0.10 \\
3 & 0.5 & 0.30 & 0.15 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of a 3-DOF robotic manipulator.  
Figure 2: Scheme of a 3-DOF robotic manipulator.  
Figure 3 shows the simulation results for tracking errors, controlled torques, and tracking errors and contour errors in the end-effector under PD control and CC-PD control, respectively. From Figure 3, one can see that the overall trajectory tracking and contour tracking performances in the task space, as shown in Figure 3c, are better controlled by the proposed CC-PD control than those controlled by the PD control, although the required control torques under these two control laws are very similar, see Figure 3b, and the overall tracking errors in the joint space under PD control are slightly smaller than those under CC-PD control, especially for joints 2 and 3, see Figure 3a. It demonstrates the effectiveness of the cross-coupled control action in the CC-PD control law for contour tracking performance improvement at the end-effector level.  
Figure 3: Tracking performance comparison under PD and CC-PD controllers. (**a**) Tracking errors for PD control and CC-PD control; (**b**) Controlled torques for PD control and CC-PD control; (**c**) Tracking and contour errors of the end-effector for PD control and CC-PD control.  
Figure 5: Tracking control results for a vertical line under PD and CC-PD control; (**a**) Tracking errors in joint space for PD control and CC-PD control; (**b**) Controlled torques for PD control and CC-PD control; (**c**) Tracking and contour errors of the end-effector for PD control and CC-PD control.  
Figure 6: Comparison of contour errors for vertical line tracking under different CC gains.
## 2 Methodology
### Data Pre-Processing Module
#### 2.1.1 Data Cleaning  
The historical data need to be smoothed because there are many missing values and outliers in the original raw data. If these outliers are included in the training data, the accuracy performance of the load predictions would be lower. One additional issue is the normalizing of data. So, it cannot predict the load correctly if the load is higher than previous data. To filter and smooth the raw data, a local regression filtering technique is used. It can be classified into four types: (1) the locally weighted scatterplot smoothing (lowess) regression which uses the method of linear regression analysis, (2) the locally estimated scatterplot smoothing (loess) regression which uses the method of polynomial square regression analysis, (3) robust lowess local regression, and (4) robust loess local regression, which are the robust functions that can be used to get rid of outlier values. If outliers are present in the dataset, load demand fluctuations can cause a reduction in forecasting accuracy. Therefore, a robust lowess/loess (rlowess, rloess) procedure is used to overcome the problem of distorted values of electricity data [37,38]. The loess local regression filtering technique is applied for cleaning data in this experimentation.  
The filtering technique fits a local regression function to the data within a chosen neighborhood of data points. A chosen neighborhood is specified by the percentage of data points which is known as a smoothing parameter (0 < smooth \(\leq\) 1). The larger the smoothing parameter, the smoother the graphed function. For calculating smoothed values, this filtering technique specifies a weight for every data point in the selected window by using the regression weight function, as shown in the following equation. Once the  
Figure 3: Variation in peak load based on time of the day (hourly) from 2019 to 2021.  
Figure 2: Variation in peak load based on day of week from 2019 to 2021.  
regression function values are calculated with flexible weights and polynomial degrees, the rloess fit is complete.  
\[w_{i}=\Bigg{(}1-\bigg{|}\frac{x-x_{i}}{d(x)}\bigg{|}^{3}\Bigg{)}^{3}. \tag{1}\]  
Equation (1) indicates that \(x\) is the predictor value associated with the response value to be smoothed, \(w_{i}\) is the regression weight of points \(i\), \(x_{i}\) is the nearest neighbors of \(x\) as defined by the selected window, and \(d(x)\) is the distance along the abscissa from \(x\) to the most distant predictor value within the selected window. The data at each period are separated into seven groups based on the day of the week. Therefore, data are cleaned \(48\times 7\) times for each period of each day using the rloess function. For example, Monday at 11 am in 2019 and 2021 is smoothed using the filtering technique which is shown in Figure 4. Once the data are filtered, the MiniMax Scaler function, as proposed in the models, is used for normalizing.
## 3 Results and Discussion  
### Moisture Content  
The results of the water content analyses for the white bread, steamed bun, and a donut with the addition of dragon fruit purece were 40.09% to 40.90%, 28.95 to 34.95%, and 26.65 to 29.65%, respectively, as presented in Figure 1a. The result revealed increasing the red dragon fruit purece in the formulation significantly affected the sample's moisture content. The moisture content of the control sample without red dragon fruit purece was 40.02% for white bread, 27.89% for steamed buns, and 26.05% for donuts, respectively. Meanwhile, the presence of the red dragon fruit purece increased the moisture content of the white bread (0.3% to 2.1%), steamed buns (1.75% to 18.21%), and donuts (2.30% to 13.81%) compared to their respective controls. The three bakery products containing red dragon fruit presented a higher moisture content due to the fiber compound in dragon fruit, which can absorb water and have a high water-binding capacity [21; 22].  
Moisture content is influenced by components that act as water binders in the product, such as starch, protein, and fiber. These components will interact with each other through  
Figure 1: The (**a**) moisture content and (**b**) specific volume of bakery products. Mean values with the same letters are not significantly different at \(p<0.05\).  
their hydroxyl groups to form a complex gel matrix that can trap both free water and weakly bound water so that it can reduce the evaporation that occurs during the baking, steaming, and frying processes [23]. Fiber can trap water, making it difficult to evaporate, even during high-temperature processing treatment. In addition, the high-protein content in wheat flour enhances water-binding capability through their hydroxyl groups. In the steamed buns-making process, the steaming stage allows water vapor to interact with the buns. Meanwhile, the red dragon fruit fiber binds and retains the water droplets, penetrating the fruit buns during steaming. The higher the amount of red dragon fruit used, the greater the moisture content in the product [24]. The initial moisture content of the ingredients also influences the moisture content of red dragon fruit bakery products. The moisture content of red dragon fruit flesh is around 82.5-83% [25]. The more significant the proportion of red dragon fruit flesh used, the higher the water content of the final bakery product.