esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Development of the Predictive Models
These components account for the maximum amount of variation in the data and are chosen to be orthogonal to previous components. PCA has various applications, such as data visualization, data compression, feature selection, noise reduction, and clustering. Overall, PCA is a powerful technique for reducing dataset dimensionality while retaining important features. By transforming large and complex datasets into linearly uncorrelated variables, PCA simplifies the analysis process and helps researchers draw meaningful insights from the data. In summary, principal component analysis (PCA) offers the significant advantage of reducing dataset dimensionality while preserving essential information, benefiting machine learning and data analysis tasks that often involve numerous variables. This reduction aids in visualization and analysis, enabling more efficient training of machine learning algorithms. PCA has been particularly influential in earphone design research. However, PCA has limitations, including the assumption of normal distribution and linear correlation in data, which may not hold true in practice. Moreover, interpreting principal components can be challenging, especially with many variables, as seen in earphone design problems. All in all, principal component analysis (PCA) is an effective method in data analysis and machine learning for simplifying intricate datasets and discovering underlying patterns. In a specific computation, the first four components accounted for 82.78% of the explanatory power, leading to their use in modelling. Consequently, a formulation was established to connect these components with the eight design parameters. In essence, the formulation reveals that the eight design parameters can be condensed into four components, which reduces storage and computational demands. With the utilization of these four components, a regression model can be created. It is important to note that the PCA-based model outputs z-scores and additional conversion is needed for comparison studies. However, z-scores are adequate for performance estimation in practice since only a relative benchmark is needed as long as component values can be generated from the existing dataset. \[ \begin{bmatrix}Z-\text{score of THD}\\ Z-\text{score of Power of Output}\\ Z-\text{score of Range of Frequency Response}\\ Z-\text{score of SNR}\\ Z-\text{score of Impedance of the Speaker}\\ Z-\text{score of Headroom}\end{bmatrix} = \begin{bmatrix}0.334&-0.362&-0.356&0\\ 0.332&-0.182&0.270&-0.526\\ 0.276&0&0&0\\ 0.288&0&0&0\\ 0.270&0&0&0\\ 0.295&0&0&0\end{bmatrix} \times \begin{bmatrix}\text{Component 1}\\ -1.716\times 10^{-6}\\ 0.338\times 10^{-6}\\ -8.618\times 10^{-7}\\ 0.600\times 10^{-6}\\ 0.666\times 10^{-6}\end{bmatrix} \]Overall, it can be seen that along with the training dataset, both traditional and principal component analysis-based regression formulations were deduced. Performance analysis is conducted in the next section.
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 3 Methods
### Training WFCNN
#### 3.2.2 Training Algorithm  
WFCNN is used end-to-end, with a stochastic gradient descent algorithm [50] used as the training algorithm with the following steps:  
1. The hyperparameters in the training process are determined and the parameters of the model are initialized.
2. The selected image-label pairs are input into the model as training data.
3. The model carries out a forward calculation on the current training data.
4. Equation (2) is used to calculate the loss function of the real probability distribution and the predicted probability distribution.
5. The random gradient descent algorithm is used to update the parameters of the model and complete the training process.
6. Steps (3), (4), and (5) are repeated until the loss function is less than the specified expected value.
# Flavonoids as Promising Multitarget Agents in Alzheimer's Disease Therapy
## 2 Activities of Flavonoids from Natural Sources
### Inhibition of Acetylcholinesteraseerase (AChE)
#### 2.4.4 Flavonols  
Quercetin (**1**) exhibits an IC\({}_{50}\) value of 19.80 uM [55; 56] and 3-methoxyquercetin (**149**), isolated from _Agrimonia pilosa_ Ledeb., an IC\({}_{50}\) value of 37.90 uM [55; 56], being weaker inhibitors of AChE than the flavone luteolin (IC\({}_{50}\) = 9.27 uM) [76]. Galangin (3,5,7-trihydroxyflavone) (**150**) [107], tamarixetin (3,3',5,7-tetrahydro-4-methoxyflavone) (**151**), isolated from the buds of _Cleistocalyx operculatus_ [86], kaempfer (**76**) [86], morin (**77**) [87] and myricetin (**78**) [88], exhibit IC\({}_{50}\) values of 19.10, 22.30, 30.40, 210.00, and 157.11 uM, respectively, suggesting that there is a decrease in the AChE inhibition when the number of hydroxy groups on ring B increases.  
Considering glycosylated flavonoids, several glycosylated quercetin (**1**) molecules were isolated from _Gingko biloba_ namely 3-O-(B-D-glucopyranosyl)quercetin (**152**), 3-O-(\(\alpha\)-D-glucopyranosyl)quercetin (**153**), 3-O-[\(\alpha\)-L-rhamnopyranosyl-(1\(\rightarrow\)\(\rightarrow
## 3 Results and Discussion
### Baseline Performance Characteristics
#### 3.1.1 Key Operating Parameters  
Gasification process consisted of a series of thermo-chemical reactions under oxygen-lean condition; hence, oxygen content and temperature posed a crucial influence on the reaction products. A test matrix of ER and temperature was set for carrying out gasification experiments of eucalyptus wood chips as reference bases. There are three equivalent ratio (ER = 0.2, 0.3, and 0.4) and three experimental temperature (700, 800, and 900 \({}^{\circ}\)C) as the experimental conditions in this study.  
Figure 3: The hot model of DFB gasification system.  
The effect of ER on CO and H\({}_{2}\) contents in the syngas was addressed in the following. According to Le Chatelier's Principle, with more air supplying to the gasifier, whole gasification would tend to oxidation that enhances char combustion to generate CO\({}_{2}\) and suppresses other contents such as H\({}_{2}\), CO, and CH\({}_{4}\) of the product gas. As a result, there was more CO\({}_{2}\) while less H\({}_{2}\) and CO when ER was increased. In contrast, it was shown that CO and H\({}_{2}\) increased as ER decreased. The trends are consistent with previous research [2; 28; 29].  
Temperature is another important parameter in gasification. Higher temperature promotes some endothermic reactions, like Boudouard reaction (C + CO\({}_{2}\)\(\rightarrow\) 2 CO) and water-gas reaction (C + H\({}_{2}\)O \(\rightarrow\) CO + H\({}_{2}\), C + 2H\({}_{2}\)O \(\rightarrow\) CO\({}_{2}\) + 2H\({}_{2}\)), to produce more CO and H\({}_{2}\). When the temperature ascends, CO and H\({}_{2}\) increase, which is consistent with the results of previous studies [30; 31]. On the other hand, CO/CO\({}_{2}\) ratio will be higher as temperature increases. When temperature is higher than 830 \({}^{\circ}\)C, the Boudouard reaction will have more effect than the water gas reaction [32]. The development of two-stage gasification which consists of two tandem reactors has been reported in the literature. The raw syngas generated in the first stage was further gasified or cracked again in the second stage, under relatively higher temperature than that in the first stage. This technique increased the syngas yield and H\({}_{2}\) content, which was affected by the temperature and indirect gasification technology [33; 34].
## 4 Numerical Example
### Bridge System Reliability Estimation of the Single Failure Criterion
#### 4.2.3 Failure Criterion III
For comparison, the system failure probability based on the other three copulas were also calculated, which were \(P_{f_{\rm III}-\rm Clayton}=5.702\times 10^{-6}\), \(P_{f_{\rm III}-\rm Gumbel}=1.575\times 10^{-6}\), and \(P_{f_{\rm III}-\rm Frank}=1.893\times 10^{-12}\).
## 3 System Design Results  
The integrated system in this paper is based on a 500 km orbit. The system has two imaging bands, the visible light band is 450-900 nm, covering visible light and near-infrared bands; the long-wave infrared band is 7700-10,500 nm. The pupil diameter of the system is 500 mm, and the system requires that the ground resolution in the visible light band is better than 1 m, the ground resolution in the long-wave infrared band is better than 10 m, and the ground width is greater than 8 km. According to the above system indicators, the design parameters of the two subsystems can be calculated separately, and the actual design of the two subsystems can be carried out.
# Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network
## 2 Surface Defect of Copper Strip  
### Classifications and Characteristics of Surface Defects  
Surface defects of copper strip may occur in different process stages, such as cold rolling, annealing and cleaning. Multiple sets of high-speed cameras are usually installed at the end of the cleaning line to photograph the surface of the copper strip continuously, and the image acquisition process is shown in Figure 1. The surface defects must be accurately classified, identified and counted firstly during the practical production. The targeted defect control measures can then be formulated to improve the surface quality and product performance.  
Eight classifications of defects, namely line mark (LM), black spot (BS), concave-convex pit (CP), edge crack (EC), hole (Ho), insect spot (IS), peeling (Pe), and smudge (Sm), were determined after long-term site tracking, sampling analysis, and technical exchanges in the copper strip production line. The morphology of the eight classifications of surface defects is shown in Figure 1, and the specific feature of these classifications of surface defects is presented in Table 1. The table reveals that the shapes and textures amongst the defects are not completely consistent, which is beneficial for defect recognition. However, some similarities are found between different defects; for example, EC and Ho show blocky features, which increases the difficulty of accurate defect recognition.
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## 4 Discussion
### Pixel-Wise Reproducibility of Orthomosaics  
The pixel-wise correlation of identically computed RGB orthomosaics leads to a quantitative measurement of reproducibility. This is a necessary addition to assess the photogrammetic processing of images, especially considering the \"black box\" nature of non-open-source software like Metashape. With the pixel-wise approach, deviations between computations are assigned to certain spatial regions of the orthomosaic. The mesh and DSM as orthorectification surfaces in the forest time series showed similar amounts of reproducible pixels (DSM: 69%, mesh: 74%). However, the mesh is considered superior since it leads to a better reproducibility in canopy areas. The calculation of the mesh is also less time consuming than the computation of the DSM. Both digital surfaces failed to reproduce fine structures like single tree branches or forest gaps. This can be problematic, since these structures are most likely the ones researchers aim to observe with UAS imagery [11; 16; 43].  
The results also suggest that non-reproducibility can be tracked down to uncertainties in the initial step of the photogrammetic process, the feature identification and feature matching of the individual images [31]. These uncertainties increase with the presence of fine structures in the images since they are prone to move even under light wind conditions. It is therefore more likely that their position changes in consecutive images. In particular, [PERSON] et al. (2020) [44] recently demonstrated that acquiring UAV data for forest, grasslands and crop environments in low-light conditions such as low Solar Elevation Angle or high cloud cover causes problems in matching characteristics in the image alignment process.  
Although this study declares these areas as not reproducible, the structures are still apparent in the orthomosaics. Image analysis methods (e.g., an object-based classification) of these areas might still lead to viable results and consistent geometries. This should be investigated in subsequent studies.
## 2 Methodology
### Machine Learning Models
#### 2.3.1 Linear Models  
1. Linear regression assumes a linear relationship between the input features and the oil recovery factor. It seeks to find the best-fitting straight line (or hyperplane in higher di  
Figure 4: Dataset after adding noise.  
mensions) that minimizes the sum of squared errors between the predicted and actual values. This model serves as a baseline for comparison with more complex algorithms. 2. Polynomial regression is an algorithm that addresses the nonlinear relationships in the data. This allows the model to fit a curved line (or surface) to the data, potentially improving the accuracy when the relationship between the parameters and oil recovery is not strictly linear. A polynomial regression of degree n is modeled as follows: \[y=\beta_{0}+\sum_{j=1}^{k}\beta_{j}x_{j}+\sum_{j=1}^{k}\sum_{i=j}^{k}\beta_{ji}x _{i}x_{j}+\sum_{j=1}^{k}\beta_{ji}x_{j}^{2}+\ldots,\] (2) where \(y\) is the target variable (the oil recovery factor), \(x_{1},\ x_{2},\ \ldots,\ x_{k}\) are the independent variables (input parameters), \(\beta_{1},\beta_{j},\ \beta_{ji},\ \beta_{ji},\ \ldots\) are the coefficients (weights) of the input parameters, and \(\beta_{0}\) is the intercept term.  
Specifically, PolynomialFeatures with a degree of 5 was implemented to transform the original feature set by introducing new features, such as \(x^{2},x^{3}\), and up to \(x^{5}\) for each independent variable. This transformation of the feature space enabled the application of a linear regression model to the expanded set of features.  
To enhance the model's accuracy and ensure optimal performance, grid search cross-validation was employed. This technique involves systematically searching through a specified hyperparameter space--in this case, the degree of the polynomial features--and evaluating the model's performance using cross-validation.
###### Abstract  
The objective of this study is to present a novel fragility analysis method that combines principal component analysis (PCA) and the K-means clustering algorithm for a probability assessment of seismic damage in high-pier bridges undergoing pulse-like ground motions. Firstly, the method uses the correlation coefficient and the condition number as judgment indices to eliminate those seismic intensity measures (_IMs_) with weak correlation and multicollinearity from all 29 of the initial candidate seismic _IMs_, the optimal combination of _IMs_ that satisfies the requirements for the PCA method is determined. Secondly, the method utilizes PCA to reduce the dimensionality of the optimal combination of _IMs_ to obtain the principal components, after which the K-means algorithm is applied to classify the original group of selected pulse-like ground motions into four classes. Thirdly, a 3D finite element model of the exemplary high-pier bridge is developed via OpenSees, while incremental nonlinear dynamic time-history analyses are conducted to record the maximum cross-section curvatures of high piers under the influence of various categories of ground motions. Finally, based on the analytical procedures used in the increment dynamic analysis (IDA) method, this study develops and compares the fragility curves for the various classes of pulse-like ground motions. The results indicate the necessity of utilizing the PCA and K-means approach for classifying pulse-like ground motions in the seismic fragility analysis of high-pier bridges. This approach also significantly improves the precision and accuracy of damage probability analysis.  
high-pier bridge; principal component analysis; K-means clustering algorithm; seismic fragility analysis; pulse-like ground motions Article
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 3 Improved Security AODV Routing Protocol
### Elliptic Curve Cryptosystem
#### 3.1.1 Addition Rule of Elliptic Curve  
For any two points \(P(x_{1},y_{1})\) and \(Q(x_{2},y_{2})\) on the elliptic curve, there is a third point \(R(x_{3},y_{3})\ =\ P\ +\ Q\) also on the elliptic curve.  
When \(P(x_{1},y_{1})\
eq Q(x_{2},y_{2})\),  
\[P(x_{1},y_{1})+Q(x_{2},y_{2})=R(x_{3},y_{3}) \tag{2}\]  
\[\left\{\begin{array}{l}x_{3}=\lambda^{2}-x_{1}-x_{2}\\ y_{3}=\lambda(x_{1}-x_{3})-y_{1}\\ \lambda=(y_{2}-y_{1})/(x_{2}-x_{1})\end{array}\right. \tag{3}\]  
When \(P(x_{1},y_{1})=Q(x_{2},y_{2})\),  
\[P(x_{1},y_{1})+Q(x_{2},y_{2})=2P(x_{1},y_{1})=R(x_{3},y_{3}) \tag{4}\]  
\[\left\{\begin{array}{l}x_{3}=\lambda^{2}-2x_{1}\\ y_{3}=\lambda(x_{1}-x_{3})-y_{1}\\ \lambda=\big{(}3x_{1}^{2}+a\big{)}/2y_{1}\end{array}\right. \tag{5}\]  
Among them, \(a\) is the first-order coefficient in the elliptic curve equation.
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 3 Traditional Feature-Based Mosaic Process for SAR Images
### Homography Matrix Compensation
 \tag{13}\]  
First, \(\mathbf{H}\) was calculated after keypoint detection and matching in downsampled overlapping areas. Then, the projection of the original-size images was achieved based on \(\mathbf{H}_{0}\). This method not only projected correctly and preserved the original information of the images, but also decreased the keypoint detection and matching times to make the image mosaic process faster. However, although the efficiency of keypoint detection and matching was improved, the overlapping areas could not be downsampled indefinitely. According to Equation (13), if there was a computational error in \(c_{x}\) or \(c_{y}\), it would be magnified \(n\) times in \(\mathbf{H}_{0}\), which affected the image projection accuracy. Therefore, a tradeoff between the efficiency and accuracy of image mosaics should be determined according to the specific mosaic requirements, which will be discussed in Section 5.
# High Optical Feedback Tolerance of a Detuned DBR Laser for 10-Gbps Isolator-Free Operation
## 3 Results
### Modulation Performance under Feedback
#### 3.2.2 BER Property and Eye Diagram  
After evaluating the SNR, the BER and eye diagram were examined for the cases of back-to-back (BHB) and 20 km standard mode single-mode fiber (SMF) transmission. Laser 1 was biased at the red-detuned region of 50 mA for the BER test. The modulation signal was still 10.3125 Gbps PRBS NRZ. According to the requirement of the XG(S)-PON application, the ER should be set to at least 6 dB. The influence of ER on modulation and feedback was first evaluated from the eye diagram using two values, 6 dB and 10 dB, respectively. As seen in Figure 6, the eye diagram for the ERs of 6 dB and 10 dB are both clear without feedback. The distinctions are that the 10 dB case has an overshoot, and its logical zero bit is much closer to the zero level than that of the 6 dB case. Under a strong feedback level of  
Figure 5: The SNR of the two lasers under \(-15\) dB feedback at a fixed ER of 2 dB.  
\(-12\) dB, the SNRs were 7.32 dB and 8.16 dB, respectively, for 6 dB ER and 10 dB ER cases. In the following BER evaluation, a high ER of 10 dB was selected for the transmission test.  
The BER curve and eye diagram of the laser for the cases of BIB and 20 km transmission are shown, respectively, in Figures 7 and 8. Without feedback, the minimum receiver sensitivities were \(-18.06\) dBm. In the BtB configuration, an error-free operation under \(-15\) dB feedback has been successfully demonstrated with a power penalty of \(<\)3 dB at 1 \(\times\) 10\({}^{-9}\). The BER degradation was relatively minor before that feedback level. When the feedback level increased to \(-12\) dB, a BER floor appeared at 1 \(\times\) 10\({}^{-8}\). The BER floor continued to rise with the increase in the feedback level. Under the maximum system feedback level of \(-9\) dB, the BER still met the FEC limit of 1 \(\times\) 10\({}^{-3}\) required by access networks. With the increase of the feedback level, the eye diagrams deteriorated gradually but were still wide open. For the case of 20 km transmission, an error-free transmission under \(-20\) dB optical feedback was reached with a power penalty of around 5 dB at 1 \(\times\) 10\({}^{-9}\). A BER floor of 1 \(\times\) 10\({}^{-7}\) was observed at \(-15\) dB feedback. The laser could still meet the FEC limit at \(-12\) dB feedback but failed at a higher feedback level (data not shown). In the worst case, there was a large amount of noise at the intersection of the rising and falling edges, as well as the center of the unit intervals, which led to the failure to reach the FEC limit.  
Figure 6: Eye diagrams of the directly modulated DBR Laser 1 for a fixed bias current of 50 mA with an ER of: (**a**) 6 dB without feedback; (**b**) 6 dB with \(-12\) dB feedback; (**c**) 10 dB without feedback; (**d**) 10 dB with \(-12\) dB feedback.  
Figure 7: The BER curves under different feedback levels (**a**) in BtB configuration and (**b**) in 20 km transmission configuration.
# Outlier Detection and Prediction in Evolving Communities
## 2 Related Work
### Community Detection
#### 2.1.1 Static Methods  
We first consider static community detection methods that ignore the time dimension. Following the categorization in [7], we start with early fusion methods, which are more popular. In [8], the authors proposed an efficient iterative algorithm for finding communities in attributed multi-graphs where nodes are connected by multiple types of edges. They first ranked each of the different types of edges based on their importance and assigned a corresponding weight, then the proposed CAMIR algorithm was assigned the number of communities as input and used to perform clustering based on k-means. Similarly, in [9] a graph augmentation method was performed prior to graph partitioning (and community detection) in order to conclude with a unified framework based on both the structure and attribute connections of the nodes. The new augmented graph preserves the structural connectivity while adding new edges between nodes with the same attributes. Afterwards, a random walk algorithm is used to optimize a distance measure based on the new graph. In the same direction, CESNA [10] is a linear-runtime community detection algorithm that outperforms its competitors in terms of accuracy and scalability and characterizes each community using relevant attributes in their nodes. The main drawback of this algorithm is that the number of communities needs to be assigned or calculated a priori. In [11], the authors merged structure and content into a multiplex network, then performed community detection on this network based on modularity. They underlined the importance of overlapping communities because of multiple relations between two nodes in a network.  
Our method considers this fact as well; real-world communities require overlap between them, as social actors are often members of more than one group.  
In [12], the fusion between structure and content was performed simultaneously. The authors proposed the metric of attribute-aware modularity, which they attempted to maximize at each step of their Louvain-type algorithms. In this approach, after an initial clustering, when a node is a candidate for exclusion from its community, it is only allowed to move if the modularity is increased.  
Finally, a late fusion algorithm for community detection was proposed in [13]. First, on a lower level, communities are detected based on either structure or content; later, they are fused using a weighted co-association matrix-based algorithm.