esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Constraint Handling Mechanisms in Nature Inspired Algorithms  
As said before, Nature Inspired Techniques have been applied frequently in literature to obtain eco-driving from detailed simulation models. However, the methods applied are unconstrained optimization procedures so that they do not fit in the eco-driving problem subject to intermediate time restrictions.  
A solution can be found in the developments of the research field that investigates ways to incorporate the constraint-handling mechanisms into these algorithms [72]. These techniques are meant to solve the Constrained Numerical Optimisation Problem (CNOP) [73].  
Several constraint-handling techniques have been proposed in the literature to be incorporated in Nature Inspired algorithms.  
One of the most common techniques are the penalty functions. Penalty functions consist in including in the fitness function the non-compliance with the constraints multiplied by a penalty factor [74]. By this way, solutions with better fitness are solutions closer to be feasible. This is a straightforward way to incorporate constraints. However, penalty factors require a careful tuning that increases the number of control parameters of the algorithms.  
Another technique is the separation of objective function and constraints. Thus, if the solution is feasible, its fitness value is equal to the objective function value. Otherwise, its fitness function is calculated based on the constrain violation [75]. The idea is that feasible solutions always present better fitness values and move the population to the feasible space.  
Another popular approach is the feasibility rules. In [76], the application of the binary tournament selection combined with three feasibility criteria in genetic algorithms was proposed. These feasibility rules are:  
* The individual with the best value in the objective function is selected when two feasible solutions are compared.
* The feasible individual is selected when a feasible solution is compared with an infeasible solution.  
* The solution with the lowest constraint violation is selected when two infeasible solutions are compared.  
In [77; 78; 79], the feasibility rules are adapted for their application to other algorithms.  
The use of decoders to solve CNOPs was proposed in [80; 81]. Decoders map the feasible region of the search space in an easier-to-sample space were the algorithm can perform better. Decoders are one of the most competitive techniques to handle constraints. However, their implementation is complex and requires a high computational cost to transform the search space.
# A Compact Dual-Band Microstrip Patch Antenna for C- and X- and Ku-Band Applications +
## 2 Design of Antenna
### Step 2 Antenna  
In the second step, the full ground configuration of the proposed antenna is converted into a partial ground configuration while the other parameters of the antenna remain the same. The length of the ground and the design of the second-step antenna is shown in Figure 1b.
## 3 Results and Discussion
### Analysis of Features
#### 3.3.3 Correlation Analysis
In sum, the obtained findings do not deviate from practical understanding considering proposed continuous predictors; they further emphasise the problem of dealing with EoL (End of Life) issues in industrial manufacturing and service industrial machinery [49]. However, novel conclusions are obtained regarding features not commonly analysed in practice and will be discussed in the following. Figure 10: [PERSON]’s correlation heatmap (\({}^{\star}\)\(p\) < 0.05; \({}^{\star\star}\)\(p\) < 0.01; \({}^{\star\star}\)\(p\) < 0.001; blue indicating positive correlation and red indicating negative correlation).
# Incipient Fault Feature Enhancement of Rolling Bearings Based on CEEMDAN and MCKD
###### Abstract
How to remove noise from the redundant signal to highlight the fault characteristics is the key to achieving bearing fault diagnosis because the vibration signal has the characteristics of low amplitude, weak shock component, and the amplitude may change significantly in a short period of time when an incipient fault occurs in rolling bearings. Fault feature enhancement methods can filter out the noise in the signal by means of filtering, decomposition reconstruction, deconvolution, etc., highlighting the weak shock components in the signal and extracting the fault features in the signal. Despite the fact that the MCKD algorithm can reveal continuous impulses hidden by loud noise, it is challenging to meet the requirements for fault feature extraction. As a result, the signal can be processed by combining the CEEMDAN and MCKD algorithms, first using CEEMDAN to reduce noise in the contained signal and then using the MCKD algorithm to enhance features, which can obtain more failure shock components, increase SNR, decrease noise interference, and extract fault features. According to the analysis above, the issue is that it is difficult to extract the tiny defect features of rolling bearings, enhance the components of mild shock, and lessen the impact of noise on fault detection. This paper proposes a strategy for improving incipient fault features based on CEEMDAN-MCKD. Prior to reconstructing the fault signal using the intrinsic mode function (IMF) components chosen based on kurtosis and correlation coefficient selection criteria, the rolling bearing vibration signal is first initially noise reduced using the CEEMDAN algorithm. Afterward, the MCKD algorithm is used to enhance the weak shock components in the reconstructed signal and find the envelope to extract the fault feature frequency to realize feature extraction.
## 3 DSGSD and BSZ  
Evidence of the Luco dei Marsi DSGSD are different kinds of geomorphic features distributed over an area of about 4 km\({}^{2}\), with a NNW-SSE-oriented elongation axis (Figure 5). Trenches, plurimetric in length and width, are distributed both along the slope edge (Figure 6a) and the internal sectors, where they are often associated with grabens or downhill- and uphill-facing scarps (Figure 6b,c). In the same areas, roughly corresponding to the fold axial zone, complex features such as grabens and ridge-top depressions were also observed, as well as bulging and landslides at the toe of the carbonate slope [38]. The activity of the DSGSD during the last 30 years, of the order of a few mm/yr, was acknowledged by means of analysis of DInSAR data and time-series elaboration [39].  
Figure 5: Inventory map of geomorphological features ascribable to the Luco dei Marsi DSGSD and location of the BSZ outcrops along the southern edge, modified after [PERSON] et al. [39]. Structural and tectonic elements from Figure 2 are reported. Quaternary deposits: (1) landslides; (2) residual clay deposits; (3) talus slope deposits; (4) eluvial-coluvial deposits; (5) alluvial and lacustrine deposits of the Fucino Basin. G1–G4 = gravitational grabens.  
As a singular feature in the geological framework of the central Apennines, a basal shear zone (BSZ) was observed along a deep valley incision in the southern, lower sector of the slope (Figure 5). With a maximum observable thickness of about 5 m and a low dip angle towards the Fucino Basin (Figure 6d), the BSZ is made by a cataclastic rock mass composed of intensely-jointed, centimetric blocks (\(GSI\) = 20) with calcite-filled veins and a fine matrix. A tectonic origin of the cataclastic zones has been discarded since (i) the anticline is a wide structure formed in the hanging-wall of a buried main thrust without secondary features such as plays, back-thrusts, or out-of-sequence features; (ii) the gently dipping attitudes of the BSZ deny a link with the high-angle longitudinal normal faults or the transverse transtensional faults; (iii) no evidence of low-angle normal faults was observed at the meso-ac macro-scale.
# Forecasting of Power Demands Using Deep Learning
## 1 Introduction
In this paper, we studied the performance of deep learning models such as convolution neural network (CNN) and recurrent neural network (RNN) for electricity demand forecasting. Unlike previous works [28] that include year and month index to electricity consumption, we use only the daily demand as an input to our predictive models. We designed different deep learning models such as convolution neural network (CNN), recurrent neural network (RNN), and a hybrid model that combines both CNN and RNN. We applied these models to the data provided by the Korea Power Exchange. We built an independent model for each feature namely facility capacity, supply capacity, and power consumption. The experimental results showed that the CNN model outperforms the other two models significantly for the three features forecasting.
# Simulation of a Quadrotor under Linear Active Disturbance Rejection
###### Abstract  
The quadrotor aircraft has the characteristics of simple structure, high attitude maintenance performance and strong maneuverability, and is widely used in air surveillance, post\(-\)disaster search and rescue, target tracking and military industry. In this paper, a robust control scheme based on linear active disturbance rejection is proposed to solve the problem that the quadrotor is susceptible to various disturbances during the take\(-\)off process of non\(-\)horizontal planes and strong disturbances. Linear Active Disturbance Rejection Control (LADRC) is a product of a tracking differentiator (TD), a linear extended state observer (LESO) and an error feedback control law (PD) and is a control technique for estimating compensation for uncertainty. Radial Basis Function Neural Networks (RBFNN) is a well\(-\)performing forward network with best approximation, simple training, fast learning convergence and the ability to overcome local minima problems. Combined with the advantages and disadvantages of LADRC, Adaptive Control and Neural Network, the coupling force between each channel, gust crosswind disturbance and additional resistance of offshore platform jitter in the flight state of the quadrotor are optimized. In the control, the RBF neural network is designed, the nonlinear control signal is wirelessly approximated and the uncertain disturbance to the quadrotor is identified online. Finally, the real\(-\)time estimation and compensation are performed by LESO to realize the full\(-\)attitude take\(-\)off of the quadrotor. In addition, this paper uses adaptive control to optimize the parameters of LADRC to reduce the problem of many LADRC parameters and difficulty to integrate. Finally, the robust control system mentioned in this paper is simulated and verified, and the simulation results show that the control scheme has the advantages of simple parameter adjustment and stronger robustness.  
Quadrotor; LADRC; adaptive control; RBF neural network; robustness
# Assessment of Two Streamline Curvature Correction Methods for an Elliptic Blending Turbulence Model
## 2 Turbulence Modeling
In the SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-CC1 model, all coefficients are the same as those in the original SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model; however, in the SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-CC2 model, some coefficients need to be re-tuned to ensure the model works well for canonical flows. Based on our tests of three simple flows, i.e., the 2D fully developed turbulent channel flow, 2D flat plate turbulent boundary layer flow, and 2D asymmetry diffuser flow [16], it was found that similar results to the original SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model can be obtained by modifying only two coefficients. Specifically, the value of \(\sigma_{d}\) in the cross-diffusion term (the \(C_{D}\) term in the \(\omega\)-equation) changes from 0.52 to 0.45, and the value of \(C_{\eta}\) in the definition of the turbulent length scale changes from 79 to 88. All computations were performed on the ANSYS Fluent (version 17.0) platform. In all test cases, the original SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model and the SST \(k\)-\(\omega\) model with rotation and curvature correction [3] (denoted as the SST \(k\)-\(\omega\)-CC model) were used for comparison. The original SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model and the two new models proposed in the present work were implemented using the user-defined function (UDF) functionality, while the SST \(k\)-\(\omega\)-CC model is an in-built model and could be used directly. A steady-state solver with a pressure-based coupled algorithm was chosen to solve the transport equations, where the second-order upwind scheme was used for spatial discretization, a second-order scheme was used for pressure interpolation, and the least squares cell-based method was used for the evaluation of gradients and derivatives. At solid walls, the no-slip condition was applied. Specifically, for the original SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model and the two proposed models, \(u_{i}=0,k=0,\)\(\varphi=0,\)\(\alpha=0,\) and \(\omega_{w}=3\
u/\left(\beta_{0}y_{1}^{2}\right)\)[16]. In the SST \(k\)-\(\omega\)-CC model, an automatic near-wall treatment method was used [18]. The inlet and outlet were considered problem-dependent and will be stated for each case later. Based on experience, the first grid point adjacent to the wall should match the condition of \(y+<1\), to ensure grid-insensitive solutions. In addition, to reasonably limit the number of meshes and save computing resources, a successive ratio mesh was used in the vertical direction of the wall.
## 4 Simulation
### Influence of SNR
#### 4.2.3 Image Results of Different Methods  
To more clearly compare the differences in sound source imaging visualization between different methods, a working condition with sound source frequency of 500 Hz and SNR of 20 dB is used as an example to show the comparison results. The actual values of randomly generated sound source amplitudes are listed in Table 4.  
The source identification cloud maps of the different methods are shown in Figure 9, with RMSE and RMSEM marked at the top of the figures. The actual source locations are marked with red boxes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Source** & **Sound Pressure Level (dB)** \\ \hline Source A & 88.8 \\ Source B & 92.3 \\ Source C & 90.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The actual sound pressure levels of the three sound sources.  
Figure 9: Sound source identification results of \(f=500\) Hz, SNR = 20 dB. (**a**) SBL; (**b**) OMP; (**c**) FISTA; (**d**) off-grid-GL\({}_{1}\); (**e**) RPCA-SBL; (**f**) VG-SBL. The different colors in the figure correspond to the colorbar on the right side of the figure, representing different amplitudes.  
As seen in Figure 9, SBL can relatively accurately identify the locations of the three sound sources. However, due to the basis mismatch error, there is a certain deviation between the identified and actual source locations, and pseudo-sources appear near the sources. Due to the limitation of the resolution, OMP has a large identification deviation for Source A and Source B, which are closely distributed, and there are several fake sources. The main lobe of FISTA is wide and the resolution is low, so obtaining accurate source locations is challenging. Off-grid-\(GL_{1}\) cannot accurately identify Source A or Source B either, and the identified source locations deviate severely from the actual locations. RPCA-SBL improves the amplitude accuracy based on SBL, but is ineffective in improving location accuracy. The VG-SBL method can identify the locations of the three sources relatively accurately, jumping out of the location constraints of the initial meshes without sidelobe contamination and with relatively minimal location and amplitude errors.
# Panchromatic and Multispectral Image Fusion Combining GHS, NSST, and PCA
## 3 Evaluation Metrics
### Evaluation Index without Reference  
The evaluation mode without references fuses the original MS and PAN images directly. There is no actual reference image to evaluate the fusion results in this evaluation mode, so the method is evaluated using a comprehensive evaluation index without a reference. This method uses the spatial information of the PAN image to evaluate the spatial distortion index (\(D_{s}\)) of the fused image and the spectral information of the MS image to evaluate the spectral distortion index (\(D_{\lambda}\)) of the fusion influence, while the hybrid quality with no reference (HQNR) is calculated based on both images [23]. The smaller the values of \(D_{s}\) and \(D_{\lambda}\), the smaller the spatial and spectral distortion of the fused image, with the best value being 0. The larger the value of HQNR, the higher the overall evaluation of the fused image, with the best value being 0. This evaluation index can evaluate the performance of different fusion methods without references to real images.
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 2 Materials and Methods
### Methods
#### 2.2.4 Algorithm Training and Validation  
As described in Section 2.2.2, the GPS tracks of the water lines at tidal reference levels from Langnes and Hillesey were used to refine the backscatter percentile thresholds (Table 2).  
The GPS tracks from all sites combined were transformed into a 10 m raster image. Using the time stamp of each pixel, the tidal table, and the reference levels from [12], each pixel was then attributed the corresponding percentage of atmospheric exposure class from Table 3. In the case of narrow intertidal zones where different reference tidal level tracks were collocated in the same pixel, the ground-truth pixel was attributed to the class with the highest percentage of atmospheric exposure. These classified pixels were then used as ground-truth data in order to build a confusion matrix to validate the final result.
## 4 Algorithm
### Encoding Scheme  
A two-part coding scheme is adopted to effectively represent a solution for the JSSP-VME-MT problem. This scheme encompasses the sequencing of jobs and the scheduling of maintenance activities.  
1. Permutation of Jobs: A job sequence is represented as a permutation of the jobs for each machine. Each job is denoted by a unique identifier and arranged in the sequence in which it will be processed on a particular machine. The length of this permutation is equal to the number of jobs. For example, if we have five jobs, a possible permutation could be [2; 5; 1; 3; 4] indicating that job 2 is processed first, followed by job 5, and so on.
2. Maintenance Array: The scheduling of maintenance activities is represented through a binary array of length equal to the number of jobs minus one. Each position in the array corresponds to the index of the interval between the respective and the next job in the job sequence. A '1' in array position N indicates that a maintenance activity is scheduled in the interval after the corresponding processing Nth job in the job sequence and before processing the subsequent job. In contrast, a '0' indicates no maintenance activity is scheduled after the Nth job. For example, for the job sequence [2; 5; 1; 3; 4], a possible maintenance array could be [0, 1, 0, 0], indicating that a maintenance activity is scheduled after job 5 and before job 1. This representation ensures the feasibility of solutions concerning two problem constraints. By definition, the array is of length equal to the number of jobs minus one, indicating possible maintenance activities between jobs. Therefore, it inherently constrains scheduling maintenance activities before the initial job or after the final one, as these positions are not represented in the array and prevent consecutive maintenance activities by mandating that at least one job must be processed between any two maintenance activities.  
This representation scheme provides a flexible and intuitive way to denote job sequences and maintenance schedules. It allows easy optimization manipulation of the solution elements while preserving the problem constraints.
# Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic Correspondences
## 2 Materials and Methods
### Stochastic Cascaded Regression  
Two points are manually selected as the initial location for the proposed method. The two points are the central locations of the LV and LA, and because they are selected on axial, sagittal, and coronal views by using ITKSNAP software [29], these central locations are relatively accurate initial locations. Moreover, while this initial location provides only scale and translation information, shape-deformation information must be determined using the segmentation program. The flow chart of the proposed method is shown in Figure 3.  
Stochastic optimization was introduced by [PERSON] [34]. The approximation of the true gradient direction is realized by computing the gradient direction using only a small subset of randomly chosen samples in every iteration. In this manner, the computational cost per iteration is significantly reduced, while the convergence properties remain comparable to those obtained with deterministic gradient descent. The gradient direction is calculated in each iteration. Although the original cascaded regression method used all appearance sets owing to the small number of landmarks, the number of appearances can be too large in the proposed method because this framework is performed in 3D volume. To reduce computational cost and memory usage, we adopted a stochastic method by selecting a subset of the appearance set randomly. We denote the number of subsets in the appearance set as \(3n_{s}\). This subset usage can substantially reduce the cost of ridge regression.  
The ground-truth shape parameters \(\mathbf{p}_{k}^{*}\in\mathbf{R}^{n_{sh}}\) are defined in each training image \(I_{k},k=0,...,n_{K}-1\). After the ground-truth shape parameters \(\mathbf{p}_{k}^{*}\) are calculated in each training image, a set of \(n_{Y}\) perturbed shape parameters \(\mathbf{p}_{k,j}(t),j=0,...,n_{Y}-1\) are generated for \(t=0\) where \(t\) is the iteration number. This perturbation must capture the statistics of the heart-segmentation initialization process. SDM model of cascaded regression was used for the segmentation [11].  
Generalized ridge regression (GRR) can be applied if the number of predictors \(p\) exceeds the sample size \(n\)[35]. Furthermore, GRR can apply different sparsity parameters to different groups of predictor variables. The present study used GRR as the ridge regression method because this method can implement additional selectiveness in the proposed framework. The work of removing features can be performed by increasing the sparsity parameter in the GRR framework. The procedure of GRRis explained conceptually in the following. After input data of the design matrix are rescaled by using the inverse square root of sparsity parameters, ridge regression is performed in the sparsity parameter of 1. The result vector is applied with rescaling again by using the inverse square root of the same scale sparsity parameters.