esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite Element Analysis of the Distribution Parameters of a Metal Dot Array in a SAW Gyroscope
## 3 Modeling and Simulation  
### Modeling  
The finite element method (FEM) is a common method for accurately simulating SAW gyroscopes [33], which is based on the acoustic wave equation considering the Coriolis force shown in Equation (6) and specific boundary conditions. The FEM is suitable for analyzing SAW characteristics in different structures because it is highly flexible [18; 34].  
\[\begin{cases}\rho\frac{\partial^{2}u_{i}}{\partial t^{2}}-C_{ijkl}\frac{ \partial^{2}u_{j}}{\partial x_{j}\partial x_{k}}+2\rho e_{ijk}\Omega_{j}\frac{ \partial u_{k}}{\partial t}+\rho(\Omega_{i}\Omega_{j}u_{j}-\Omega_{j}^{2}u_{i} )=0\\ T_{i3}=C_{ijkl}\frac{\partial u_{i}}{\partial x_{l}}=0\end{cases} \tag{6}\]  
where \(\rho\) is the density of the medium, \(C_{ijkl}\) is the elastic stiffness tensor, \(\varepsilon_{ijk}\) is the Levi-Civita symbol, \(u_{i}\) is a component of the elastic displacement vector, \(T_{ik}\) is the stress and \(i,j,k,l=1\), 2, 3. The first two terms on the left-hand side of Equation (6) are related to inertia and elasticity. The third and the fourth terms are due to the Coriolis force and the centrifugal force, respectively.  
The metal dot array in the SAW gyroscope is composed of many periodically arranged metal dots, and due to the periodicity of the structure, this paper models the single-period piezoelectric substrate and the metal dots distributed on it as the research object [35]. Meanwhile, the Rayleigh wave mainly concentrates within the range of 1-2 wavelengths on the surface of the piezoelectric substrate and attenuates rapidly along the depth [36]. Therefore, it is sufficient to set the model height to three wavelengths and then set a PML layer with a thickness of one wavelength to eliminate reflections [37]. Although previous research have shown that the thickness of the metal dot array increase, the SAW gyroscopic effect will be stronger, but we can only set the thickness of metal dot to 0.9 um due to the technological limitations. In addition, the metal dot is arranged in the center of the piezoelectric substrate for each period.  
To improve the authenticity and the accuracy of the simulation experiment, based on the solving object (Figure 3), through the finite element simulation software, we have established a single-period 3D reference model, as shown in Figure 4[38], and the material parameters used in the simulation are listed in Table 1. The boundary conditions of the model are as follows: (1) mechanical boundary conditions: the upper surface of the piezoelectric substrate is free, and the lower surface of the PML is fixed; (2) electrical boundary conditions: the upper surface of the piezoelectric substrate has zero charge or is grounded, and the lower surface of the PML has zero electric charge; and (3) the front and back sides, and the left and right sides of the piezoelectric substrate and PML have periodic boundary conditions [39].
# A Setting Optimization Ensemble for a Distributed Power Grid Protective Relay
## 2 Optimization Model for Relay Device Setting
### Objective Function and Constraints  
Line faults can be broadly categorized into two types based on fault distance: near faults, which occur in close proximity to the relay device, and far faults, situated at the far end of the line. To attain comprehensive protection, the objective is to prompt the relay to execute protection swiftly and effectively. This is achieved by using the total operating time of all primary protection devices as an objective function, responding to and addressing all line failures. While ensuring the coordination of each protection device, the objective function is minimized.  
The formulated objective function is outlined as follows:  
\[\text{min}\underset{i=1}{\overset{m}{\sum}}T_{close}^{i}+\underset{j=1}{ \overset{n}{\sum}}T_{far}^{j} \tag{3}\]  
where  
\[T_{close}^{i}=\frac{\lambda\cdot TDS^{i}}{\left(\frac{I_{fault}}{PS^{i}\cdot C _{ration}^{i}}\right)^{v}-\mu} \tag{4}\]  
\[T_{far}^{j}=\frac{\lambda\cdot TDS^{j}}{\left(\frac{I_{fault}^{i}}{PS^{j}\cdot C _{ration}^{i}}\right)^{v}-\mu} \tag{5}\]  
The constraints are as follows:  
\[TDS_{min}^{i}\leq TDS^{i}\leq TDS_{max}^{i} \tag{6}\]  
where \(i\) is the fault label in the near area of the system. The value range is \(i\in[1,m]\cap\text{N}\). The upper and lower limits of \(TDS\) are 0.1 and 1.1, respectively, according to the IEEE standard [34].  
\[PS_{min}^{i}\leq PS^{j}\leq PS_{max}^{j} \tag{7}\]  
where \(j\) is the fault label in the far area of the system. The value range is \(j\in[1,n]\cap\text{N}\); The upper and lower limits of \(PS\) are 1.25 and 1.55, respectively, according to the IEEE standard [34].  
\[T_{close}^{min}\leq T_{close}^{i}\leq T_{close}^{max} \tag{8}\]  
\[T_{far}^{min}\leq T_{far}^{i}\leq T_{far}^{max} \tag{9}\]where \(i,j\) are the near and far fault labels in the system; the upper and lower limits of both are set as 0.05 and 1.00, respectively, by the IEEE standard [34].  
\[T_{backup}-T_{primary}\geq CTI_{min} \tag{10}\]  
where \(T_{backup}\), \(T_{primary}\) are, respectively, the time required for the backup protective relay and the main protective relay to operate. \(CTI_{min}\) is the least coordinated time interval. In this paper, the timing coordination mechanism is adopted, and 0.35 s is set for the electromagnetic relay, which is widely used at present.
# Spectrum-Based Logistic Regression Modeling for the Sea Bottom Soil Categorization
## 3 Datasets
### Spectral Data  
Obtaining spectral features from acoustic signals reflected from the sea bottom involved several stages, as follows:  
1. The time series were auto-correlated and transformed into the frequency domain using the discrete Fourier transform. This results in a two-sided power spectrum containing information on the frequency components of the signal;
2. The positive frequency range was selected to obtain the one-sided power spectrum;
3. The power values were multiplied by two (except for the first term) and normalized by the spectrum area. The normalization step is necessary to correct for differences in attenuation between sand and sandstone sites due to the depth differences;
4. To ensure that the spectra data are clean and accurate, a noise reduction step was performed by applying a frequency bandwidth filter to match the transmitted signal's frequency range of 2.75 kHz to 6.75 kHz. The resulting discrete spectra points were used as features in machine learning models to classify sand and sandstone sea bottoms.  
The spectral datasets (the training and test) were derived from the time series datasets used in the study, and hence included the same composition of sand and sandstone  
Figure 1: Enlarged signatures of the first arrival of the reflected signal: (**a**) sand, and (**b**) sandstone.  
reflections as the time series data. Examples for the stacked power spectra for the four sites appear in Figure 2. Each trace has 109 features corresponding to the discrete values of the spectrum. The training and test sets were used to train two logistic regression models that were then assessed for their accuracy in classifying sand and sandstone sea bottoms.  
The presented method of using spectral features for ML-based classification offers a practical approach, as demonstrated next, as the spectral data correlate to the physical properties of the sand and sandstone bottoms.
## 3 Results
### Effect of PPF on Performance of Machine-Made Sand Concrete
#### 3.3.3 Effect of PPF Content on the Permeability of Machine-Made Sand Concrete  
The effect of PPF content on the water and chloride permeability coefficients of the concrete is shown in Figure 10. It can be seen that with an increase in the PPF content, both the water permeability coefficient and chloride permeability coefficient decreased first and then increased. When the PPF content was 0.6 kg/m\({}^{3}\), the permeability coefficients were the lowest, and the decrease in water and chloride permeability coefficients was 46.5% and 51.1%, respectively, compared to that of the concrete with no PPF. Even when the PPF content was 0.9 kg/m\({}^{3}\), the two permeability coefficients were still lower than those of the concrete with no PPF. When the PPF content was 1.35 kg/m\({}^{3}\), the water permeability coefficient was higher than the that of the concrete with no PPF. This indicates that the PPF has an advantage on improving the impermeability of the concrete when the PPF content is \(\leq\)0.9 kg/m\({}^{3}\). This is due to the fact that on the one hand, in the alkaline environment of concrete, the adhesion between the PPF and the cement matrix was enhanced [27]. On the other hand, the PPF restricted the formation of microcracks caused by any shrinkage of the concrete, thereby reducing the transportation channels for water and chloride ions. In addition, a large number of evenly distributed PPF in the concrete could block the capillary pores inside the concrete [17].
## 3 Results  
The changes between M1 and M2 in anthropometrics, in-water force, dryland strength, performance and correspondent variables are presented in Table 1. An increase in anthropometrics, along with a T25 improvement, was seen after the 16 weeks of training. In contrast, no changes were observed in in-water force production, dryland strength, symmetry or even the rate of force transfer at the end of the macrocycle.  
Figure 2 presents the correlation matrix between T25 and the remaining variables in both testing moments. A moderate negative association was found between PpeakD or FpeakND and T25 in M1, while, in M2, a high negative association was observed for both variables. The IRD or IRND showed a negative and moderate association with T25 in M1, whereas, in M2, the association was just found for the IRND. The SyI in both in-water and dryland environments did not show any kind of significant association with T25 before or after the 16 weeks.  
Figure 3 presents the individual dot plots of the link between T25 and in-water SyI. Although no representative trend towards an optimal SyI was shown, the faster swimmers seemed to show SyI around 10-30%. In fact, more than 80% of the dots in M1 and M2 were under the 30% SyI cut-off value.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Variables** & **M1** & **M2** & **\(p\)** & **d** & \(\Delta\) \\ \hline Anthropometrics & & & & & \\ BM, kg & 48.14 \(\pm\) 8.63 & 48.16 \(\pm\) 7.48 & 0.959 & 0.00 & 0.34 \\ Height, m & 156.99 \(\pm\) 7.82 & 158.19 \(\pm\) 7.82 & \textless{}0.01 & 0.15 & 0.76 \\ AS, m & 157.18 \(\pm\) 9.56 & 158.36 \(\pm\) 9.81 & \textless{}0.01 & 0.12 & 0.74 \\ HSA D, cm2 & 102.26 \(\pm\) 11.90 & 106.76 \(\pm\) 12.74 & \textless{}0.01 & 0.37 & 4.14 \\ In-water & & & & & \\ FpeakD, N & 48.59 \(\pm\) 11.52 & 49.43 \(\pm\) 9.95 & 0.735 & 0.08 & 7.80 \\ FpeakND, N & 54.01 \(\pm\) 12.79 & 56.88 \(\pm\) 13.90 & 0.255 & 0.21 & 10.61 \\ SyIwater, \% & 18.02 \(\pm\) 15.21 & 18.11 \(\pm\) 12.51 & 0.982 & 0.01 & – \\ Dryland & & & & & \\ IRD, N & 90.68 \(\pm\) 20.03 & 94.82 \(\pm\) 21.04 & 0.344 & 0.20 & 2.22 \\ IRND, N & 72.91 \(\pm\) 13.70 & 74.49 \(\pm\) 14.97 & 0.462 & 0.11 & 0.96 \\ SyIland, \% & 23.78 \(\pm\) 14.34 & 24.97 \(\pm\) 20.12 & 0.758 & 0.07 & – \\ Performance and RFT & & & & & \\ T25, s & 17.51 \(\pm\) 1.40 & 16.80 \(\pm\) 1.22 & \textless{}0.01 & 0.54 & –4.32 \\ RFTD, \% & 55.46 \(\pm\) 15.50 & 54.12 \(\pm\) 13.79 & 0.698 & 0.09 & – \\ RFTND, \% & 74.66 \(\pm\) 14.13 & 76.38 \(\pm\) 10.14 & 0.606 & 0.14 & – \\ \hline \end{tabular}
\begin{tabular}{c c c c c} \hline M1, evaluation moment 1; M2, evaluation moment 2; BM, body mass; AS, arm span; HSA, hand surface area; Fpeak, mean peak force; SyI, symmetry index; IR, shoulder internal rotation strength; T25, 25 m time; RFT, rate of force transfer; D, dominant; and ND, non-dominant.  
\end{table}
Table 1: Changes in anthropometrics, in-water force, dryland strength, performance and correspondent variables between M1 and M2.
## 5 Results and Discussion
### Structural Optimization Design
#### 5.2.3 Saddle Optimization Results  
Table 13 presents the static characteristics of the saddle after optimization. The mass before and after optimization was 545 kg and 471 kg (a 13.6% reduction), respectively. For a load of 10,000 N, the maximum deformation before and after optimization was 6.28 and 6.1 \(\upmu\)m, respectively, showing a decrease of 2.9%. The first-, second-, and third-order natural frequencies before optimization were 248, 274, and 277 Hz, respectively. After optimization, they were 269, 297, and 304 Hz, increasing by 8.5%, 8.4%, and 9.7%, respectively. The body was then reanalyzed using modal and structural analysis and checked for improvements.
# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 5 CNN-Based Approaches for Fruit Quality Control Tasks
The results show that VGGNet achieves the best accuracy for both datasets (96.49% and 89.12%), and GoogLeNet was the most computational efficient architecture by requiring much less training time and less memory. [PERSON] et al. [80] applied Residual Network (ResNet) and ResNeXt, to detect the internal mechanical damage of blueberries using data of hyperspectral transmittance. Four ML algorithms were used in the comparison experiments--Sequential Minimum Optimization (SMO), Random Forest (RF), Linear Regression (LR), and Multilayer Perceptron (MLP). They plotted the Precision-Recall and ROC curves to observe the performance of the classifier. The two CNN models reach better performance in classification than traditional ML methods. The fine-tuned ResNet/ResNeXt achieves F1-score and average accuracy of 0.8952/0.8905 and 0.8844/0.8784, respectively, and the classifiers SMO/RF/LR/Bagging/MLP obtained 0.8082/0.7314/0.7606/0.7113/0.7827 and 0.8268/0.7796/0.7529/0.7339/0.7971, respectively. [PERSON] [81] proposes a novel CNN architecture designed for the fine-grained classification of banana's ripening stages. The image data were 17,312 images of bananas in different stages of ripening, taken in the standard RGB of \(3200\times 2400\) pixels and stored in PNG format. The overall accuracy resulting from the proposed CNN is 95.6%, which is better than the Gabor + SVM (85.2%), Wavelet + SVM (86.5%), Wavelet + Gabor + SVM (88.2%), and Combined features + SVM (89.2%) methods. [PERSON] et al. [82] introduce a framework which combines a stacked sparse auto-encoder (SSAE) with a CNN, called the CNN-SSAE system, for detecting surface defects and internal defects of cucumbers in a hyperspectral imaging (HSI) system. CNN is used for self-learning of local image features, which are used for classification by SSAE. Images were obtained from a hyperspectral imaging system performed with two conveyor speeds of 85 and 165 mm/s. Their testing results showed that by using spectral features, they achieve accuracies of 85.6% and 78.3% on average at the conveyor speeds. They also showed that by combining spectral and spatial features, the accuracies improved to 91.1% and 88.6% at two speeds. [PERSON] et al. [83] used a 5-layer CNN for the recognition of lesions on the skin of melon and fruits. The dataset is acquired in real-time using an infrared video sensor. They use a system and methodsfor image transformation of apple skin lesion to simulate orientation and alteration of light in orchards. The results show accuracy and a recovery rate of up to 97.5% and 98.5% respectively. The proposed method is compared with LeNet-5, k-Nearest-Neighbor (kNN), Boosted-LeNet-4 (B-LeNet-4) and multi-layer neural network with 3 layers (MNN).
# Semantic Image Segmentation Using Scant Pixel Annotations
## 4 Evaluation of the SSPA Approach
### Evaluation Metrics  
When an image is classified using a semantic segmentation model, each pixel in the image is assigned two values (model confidence, pixel label). Recall from Section 3 that the min-max normalization of the raw model confidence prediction values of all pixels in each image is performed to normalize them to \([0...1]\). We use the confidence prediction values (\(p_{i}\)) to compute the entropy of an image. A low entropy value indicates low uncertainty about the pixel labels assigned by the model. Entropy \(S\) is given by Equation (1) as follows:  
\[S=-\sum_{i=0}^{n-1}p_{i}(log_{b}p_{i}) \tag{1}\]Following the standard practice [7, 52], intersection over union (IoU) and Dice similarity score are used to evaluate the segmentation performance. As given in Equation (2), IoU computes the area of overlapping between the predicted model and the \(GT\) label divided by the area of union between the predicted label and the \(GT\) label. Similarly, the Dice score is calculated as twice the overlap between the predicted label and the \(GT\) label, divided by the sum of the number of pixels in both labels as given in Equation (3). Both the IoU and Dice score are positively correlated. However, the IoU metric tends to penalize single instances of incorrect classification more than the Dice score.  
\[IoU=\frac{TP}{TP+FP+FN} \tag{2}\]  
\[Dice=\frac{2 TP}{2 TP+FP+FN} \tag{3}\]
# Positioning Using IRIDIUM Satellite Signals of Opportunity in Weak Signal Environment
## 4 Positioning Using Iridium Satellite SOPs in Weak Signal Environment
\right]+\epsilon \tag{17}\]  
where \(\mathbf{d}\) denotes the delta range residual between the Doppler-shift observation vector \(\dot{\mathbf{p}}=\left[\dot{\rho}_{1}\;\dot{\rho}_{2}\cdots\dot{\rho}_{k}\right]^ {T}\) and estimation vector \(\hat{\mathbf{\rho}}=\left[\hat{\rho}_{1}\;\hat{\rho}_{2}\cdots\hat{\rho}_{k}\right] ^{T}\). \(\mathbf{u}_{0}=[\mathbf{r}_{u0}^{T}\;f_{u_{0}}]\) is the prior information of the state of receiver. \(E_{3\times 3}\) is the unit matrix, \(\Delta\mathbf{u}=\left[\Delta\mathbf{r}_{u_{x}}\Delta\mathbf{r}_{u_{y}}\Delta\mathbf{r}_{u_{z} }\Delta f_{\mathbf{u}}\right]^{T}\) is the vector update of receiver state, and \(\epsilon\) denotes the measurement errors and any approximation errors from the linearization. Generally, the number of the Iridium satellite that can be seen simultaneously in weak signal environment is less than four in a low latitude area. When the number of visible satellites is insufficient, a multi-epoch positioning method can be used for static receiver [19]. The receiver continuously collects signals in a period of time, and the data is centralized processing, which means the receiver position is calculated with many Doppler-shift measurements of different time of different satellites.
## 4 Manipulability Maximization and Constraints Satisfaction
### Joint Position Constraints
#### 4.2.2 Self-Collision Avoidance  
To construct the weighting matrix for self-collision avoidance \(W_{Col}\), an exponential function of the distance between two collision points is used as the criterion function [16]:  
\[H_{Col}(\mathbf{q})=\rho e^{-c_{1}d(\mathbf{q})}d(\mathbf{q})^{-c_{2}},\]  
where \(\rho>0\) controls the amplitude of \(H_{Col}(\mathbf{q})\), and \(c_{1},c_{2}>0\) control the rate of decay. This function has a maximum value when the distance \(d\) between two links is zero, and exponentially decreases as this distance increases. The distance between two collision points is defined as \(d(\mathbf{q})=\|\mathbf{p_{11}}-\mathbf{p_{12}}\|_{2}\), where \(\mathbf{p_{11}}\) and \(\mathbf{p_{12}}\) represent the position vectors, referred to a common frame, of the collision points on two nonadjacent links. \(p_{11}\) and \(p_{12}\) can be calculated from the configuration vector \(\mathbf{q}\) through forward kinematics.  
The elements of the gradient of \(H_{Col}(\mathbf{q})\) are given by  
\[\frac{\partial\mathbf{H_{Col}}(\mathbf{q})}{\partial\mathbf{q}}=\frac{\partial H_{Col}( \mathbf{q})}{\partial d}\frac{\partial\mathbf{d}}{\partial\mathbf{q}}, \tag{19}\]  
where each of the partial derivatives is given by  
\[\frac{\partial H_{Col}(\mathbf{q})}{\partial d}=-\rho e^{-c_{1}d}d^{-c_{2  
a moving link and a fixed one, respectively. Each element \(\partial H(q)/\partial q_{i}\) tends toward zero as the distance between two evaluated collision points increases, and tends towards infinity as the distance gets closer to zero. Because \(d=0\) is not admissible, i.e., the two links are in contact, the chosen points must contemplate a threshold.  
Multiple pairs of potential collision points might exist in a robotic system. Let \(N_{c}\) be the number of potential collision point pairs that are evaluated. A self-collision matrix \(W_{Col_{j}}\) is constructed for each distance \(d_{j}\), with \(j=1,2,\ldots,N_{c}\). The weighting matrix that includes the contribution of each evaluated pair is finally obtained by  
\[W_{Col}=\prod_{j=1}^{N_{c}}W_{Col_{j}}. \tag{23}\]  
With this combination, the \(i\)th diagonal element of the matrix \(W_{Col}\) penalizes the movement of the \(i\)th joint. In contrast with the combination of collision weighting matrices proposed in [16], the combination (23) guarantees that the joints are stopped if they attempt to decrease any of the collision distances \(d_{j}\) to a value of zero. Since some potential collisions are taken care of by the joint limits, the number of points considered in (23) is small, and therefore the computation cost can be relieved.
## 3 Results and Discussion
### Effects of O\({}_{3}\) Treatments on Organic Content Degradation  
The effects of O\({}_{3}\) in pathogen removal are influenced by the material characteristics, like pH, water temperature, free chlorine, moisture, and the organic load of the materials [56; 57]. To assess the effects of O\({}_{3}\) treatments on the organic matter of dairy manure, we evaluated moisture content and organic matter during O\({}_{3}\) treatment. Results showed that the increase in O\({}_{3}\) concentrations resulted in decreased organic matter contents in dairy manure (Figure 6). The results suggest that increased ozone exposure reduces the organic matter in manure. During treatment, O\({}_{3}\) may firstly be consumed by the dissolved organic carbon of organic material, which results in high chemical oxygen demand (COD). This increased COD demand can lower pathogen reduction during ozone treatment [52]. Another study reported that aqueous O\({}_{3}\) can directly react with the dissolved organic compounds and generate radical species such as a hydroxyl radical (\(\bullet\)OH) that have higher oxidative potential (2.83 volts) than O\({}_{3}\), and ozone treatment can influence available organic carbon in the feedstock [79]. The increased oxidative potential may be due to the fact that conversion of O\({}_{3}\) to aqueous O\({}_{3}\) resulted in a low concentration of O\({}_{3}\) in the materials [61].  
Figure 6: The change in moisture content and organic matter of dairy manure during O\({}_{3}\) treatment (**left** side figures showed moisture content and organic matter in raw manure; **right** side figures showed moisture and organic matter in autoclaved manure, and black dots indicate outliers).
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 3 The Proposed IQA Metric  
Our proposed NR IQA metric for blurred images aims to be applied without advance training using distorted/undistorted images. It can be regarded as an improvement of SSIM from FR to NR by using re-blur theory, thus we call it NSSIM. In this section, we describe the detail of NSSIM in four parts. Firstly, we revisit the FR structural similarity index (SSIM), and then introduce _re-blur_, which gives the twice-blurred image. The following part describes feature extraction, where we define image blurriness \(\mathbf{d}\) from image grayscale histogram distribution. Finally, we compare luminance, contrast, structure and blurriness between the original image and its re-blurred image to land a quality score, i.e., our NSSIM.