esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The Back Propagation Neural Network (BPNN) model is used in the training process to determine the number of neurons needed in the two hidden layers of a neural network to forecast the magnitude, on the Richter scale, of earthquakes in a region of the Philippine Sea [8]. With the use of a data register for the earthquakes in the area from 1990 to 2014, several series of BPNN models were built to forecast the magnitude of the earthquake and compare them to the actual data. From the obtained data, it was concluded that a number of 10 neurons per hidden layer is the ideal number for the forecasting model, since the forecasting errors of the BPNN model with 10 neurons in each hidden layer were very similar to those of the models that use more than 10 neurons per layer, which involves a longer time of computation for similar results. The results were compared with the actual values of the magnitudes of earthquakes that have already occurred, and the authors concluded that the BPNN model forecasts a reliable Richter scale earthquake magnitude result. Neural network models are used to solve the text categorization problem. One of the models is the Improved Back-Propagation Neural Network (IBPNN), proposed by [9], that, with a parallel computational process, speeds up the neural network training for text categorization. The BPNN algorithm uses a Sun Cluster with 34 nodes (processors). The parallel IBPNN is integrated with the Singular Value Decomposition (SVD) technique, wherein the neural network input is represented as a low dimensional feature vector. The validation is performed using different databases wherein the number of processors is modified from 1 to 32, which produces an improvement in the execution time without diminishing the categorized text accuracy. The results show that the parallel IBPNN and the SVD technique achieve a faster, more adaptive, and more reliable training process in the text categorization problem. Table 1 shows a summary of techniques commonly used in the training of time series models. According to Table 1, the data modeling process is not unique when evaluating several different configurations' performances to select one that best fits the variables to be considered along with the data themselves. Depending on the configuration, it will be necessary to define which process is more suitable for adjusting each parameter that defines it. Since there is no formal theory for selecting the best model, in the case of neural networks, it is common to find authors who base their selection on iterative processes in which the training parameters change. The training process is supposed to be automatized, and an experimental design wherein the most significant parameters are identified within the modeling structure is proposed, in such a way that parameters change during the iterative process for selection and comparison.
## 3 Results and Discussion
### Performance of Enhancement Ephemeris Monitoring
#### 3.2.3 Inter-Satellite Ranging  
The inter-satellite ranging data and downlink broadcast ephemeris messages of the BDS-3 medium-circular orbit C25 and C26 satellites from 2020 were selected for analysis.  
The C25 and C26 satellites are co-orbital satellites, which can obtain more ranging data in a certain period of time. The distance between the C25 satellite and the C26 satellite calculated by inter-satellite ranging and the ephemeris is shown in Figure 18. The statistical results and Gaussian fitting of the distance between the C25 satellite and the C26 satellite calculated by inter-satellite ranging and the ephemeris are shown in Figure 19.  
The statistical results of the distance between the C25 satellite and the C26 satellite calculated by inter-satellite ranging and the ephemeris over time are shown in Table 3. This analysis of the characteristics of the differences in the ephemeris are very close to the normal distribution.  
Figure 19: The statistical results and Gaussian fitting of the distance between the C25 satellite and the C26 satellite calculated by inter-satellite ranging and the ephemeris.  
Figure 18: The distance between the C25 satellite and the C26 satellite calculated by inter-satellite ranging and the ephemeris.  
The statistical results of the distance between the C25 satellite and the C26 satellite calculated by inter-satellite ranging and the ephemeris show that the monitoring data have long-term stability, and the monitoring data have an approximately Gaussian distribution.  
According to the previous simulation results, we can see that all three methods are valid, and the simulation results conform to the normal distribution. According to the system integrity risk requirements, the satellite alarm threshold can be designed according to the confidence intervals of the Gaussian distribution.
## 3 Results
### DPPH Method  
Our work shows that a delay after the irradiation treatment, prior to extraction, increases the extract's capacity of scavenging the DPPH free radical (Figure 1) from 18.5 \(\pm\) 1.88% (untreated control) to a range of 28.83 \(\pm\) 1.4-34.99 \(\pm\) 1.14% (irradiated) af  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Assay**} & \multicolumn{2}{c}{**Compared Variables**} & \multirow{2}{*}{**24 h**} & \multirow{2}{*}{**48 h**} & \multirow{2}{*}{**72 h**} \\  & **(Irradiation Doses)** & & & \\ \hline \multicolumn{5}{c}{**Statistical Significance of the Difference**} \\  & 0 Gy/200 Gy & 0.0001 & 0.0014 & 0.0532 \\ DPPH & 0 Gy/300 Gy & 0.0000 & 0.0000 & 0.0003 \\  & 0 Gy/400 Gy & 0.0000 & 0.0002 & 0.0007 \\  & 0 Gy/500 Gy & 0.0001 & 0.0003 & 0.0027 \\ \hline \multicolumn{5}{c}{**Statistical Significance of the Difference**} \\  & 0 Gy/200 Gy & 0.0000 & 0.0036 & 0.7250 \\ TPC & 0 Gy/300 Gy & 0.0000 & 0.0001 & 0.5286 \\  & 0 Gy/400 Gy & 0.0000 & 0.0086 & 0.0382 \\  & 0 Gy/500 Gy & 0.0000 & 0.0073 & 0.0006 \\ \hline \multicolumn{5}{c}{**TFC**} & 0 Gy/200 Gy & 0.0200 & 0.0006 & 0.6781 \\ TPC & 0 Gy/300 Gy & 0.0086 & 0.0254 & 0.0000 \\  & 0 Gy/400 Gy & 0.0010 & 0.0433 & 0.0204 \\  & 0 Gy/500 Gy & 0.1100 & 0.0879 & 0.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A one-way ANOVA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Assay**} & \multicolumn{2}{c}{**Compared Variables**} & \multirow{2}{*}{**24 h**} & \multirow{2}{*}{**48 h**} & \multirow{2}{*}{**72 h**} \\  & **(Irradiation Doses)** & & & \\ \hline \multicolumn{5}{c}{**Statistical Significance of the Difference**} \\  & 0 Gy/20 Gy & 0.0234 & 0.0012 & 0.0186 \\ DPPH & 0 Gy/50 Gy & 0.1220 & 0.0003 & 0.0719 \\  & 0 Gy/70 Gy & 0.6468 & 0.0001 & 0.0045 \\  & 0 Gy/100 Gy & 0.0720 & 0.0000 & 0.0004 \\ \hline \multicolumn{5}{c}{**Statistical Significance of the Difference**} \\  & 0 Gy/20 Gy & 0.0250 & 0.0929 & 0.8858 \\ TPC & 0 Gy/50 Gy & 0.1468 & 0.0183 & 0.2233 \\  & 0 Gy/70 Gy & 0.0002 & 0.6792 & 0.1678 \\  & 0 Gy/100 Gy & 0.0006 & 0.0001 & 0.0002 \\ \hline \multicolumn{5}{c}{**0 Gy/20 Gy**} \\  & 0 Gy/20 Gy & 0.2837 & 0.1741 & 0.5092 \\ TPC & 0 Gy/50 Gy & 0.0447 & 0.0162 & 0.0146 \\  & 0 Gy/70 Gy & 0.0304 & 0.0026 & 0.4435 \\  & 0 Gy/100 Gy & 0.0007 & 0.0029 & 0.0012 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A one-way ANOVA.  
ter 24 h, from 29.8 \(\pm\) 1.45% (untreated control) to a range of 38.06 \(\pm\) 1.6-45.48 \(\pm\) 2.45% (irradiated) after 48 h, and from 51.07 \(\pm\) 2.53% (untreated control) to a range of 54.90 \(\pm\) 1.94-60.19 \(\pm\) 0.6% (irradiated) after 72 h. Overall, this represents a maximum mean enhancement of 1.89 times of scavenging capacity, recorded after 24 h, at 300 Gy (Figure 1). The three tested response times shown the maximum increase at 300 Gy. Very low doses, in the range of 20 to 100 Gy, had negative or non-significant effect on the antioxidant activity (Figure 1).
# A Cross-Chain-Based Access Control Framework for Cloud Environment
## 4 The Framework's Functionality
### Requirements  
To achieve the framework security features, the following requirements must be met:  
The CSP deploys the AC node and configures the ABAC mechanism. This mechanism is defined by the request \"\(r\)\", which provides information about who (sub) wants to perform what (act) on which resource (obj), and enforces the policy \"p\", which generates a boolean decision (granting or denying access). Assuming that a doctor has attributes that fulfill an access policy rule enumerated in the laboratory list of policies, a matcher defines in which way this doctor is allowed to perform any action (read, write, update, etc.) on the specific requested resource in relation to the evaluated authorization policy.  
The cloud service consumer (laboratory) deploys policies and rules to decide actions and permissions for the objects it holds. The basic syntax of the used access policy is: \(p\) = sub, obj, act, eff, where \"\(p\)\" is policy, \"sub\" is subject, \"act\" refers to action, and \"eff\" to effect. The enforcement of these policies intends to verify that the sent attributes originating from the doctors match the rules specified by the laboratory in the list of policies deployed on its end.  
The controlling node examines the doctor's \"subject\" access permission to the laboratory cloud resources \"object\" by evaluating the rules expressed by the laboratory in the access policy. Subject attributes include name, ID, role, affiliation to an organization or department, etc. Object attributes involve data creation date, resource owner, file name, criticality level, etc. Environmental attributes encompass elements such as access time, data location, used devices, etc. Access permissions are granted to doctors with attributes matching the subject, object, and environment conditions defined in the laboratory access policy.  
Before making a decision, semantic reasoning is integrated with ABAC using an ontology according to the context in which the framework is adopted (healthcare, education, banking, etc.). The goal of using semantics in our proposed framework is to build knowledge about the attributes received in relation to CSCs access policies while enforcing a fine-grained access control that secures cloud data and resources.
## 4 Data Modeling
### Prediction Model Algorithm  
Several different classification models were tested for bid risk prediction of the collected data. In this study, risk prediction modeling was performed using artificial neural networks (ANNs), support vector machines (SVM), k-nearest neighbors (KNN), and Naive Bayes (NB) classification algorithms. The description of each classification model is as follows:  
* ANNs: ANNs is a model that computes output values of artificial neurons by performing learning and assigning weights (connection strength of neurons) from a large number of input data. Input neurons are activated through sensors that sense the environment, and other neurons are activated through the weighted connections of previous active neurons [30]. One of the characteristics of ANNs is that they allow the development of sophisticated models through hidden layers.
* SVM: SVM is an algorithm for determining the optimal separating hyperplane that causes minimal generalization error [31]. SVM covers both linear and nonlinear classifiers [32]. For nonlinear classification, the given data needs to be mapped to the high dimensional feature space, and the kernel function is used to perform this efficiently.
* KNN: k-nearest neighbor classification is also called lazy learner. ANNs or SVMs first develop a model from the collected data and then apply it to the data to be classified, while KNN develops the model if data needs to be classified. The KNN algorithm classifies new objects according to the outcome of the closet object or the outcomes of several closest objects in the feature space of the training set [32]. Although one nearest data similar to the target data can be used for the classification, it is generally classified using k-nearest data.
* NB: The Naive Bayes classifier considerably simplifies learning by assuming that the features are independent in each given class [33]. The Bayes classification is an algorithm that classifies groups with high posterior probability based on the knowledge of the prior probability and likelihood probability of each group using Bayes theorem.
# Parametric Optimization Study of Novel Winglets for Transonic Aircraft Wings
## 5 Optimization Studies
### ANOVA of Winglet Design Using TGRA Coupled with PCA  
Significant input process parameters are investigated using the ANOVA in the winglet design of the transonic wing. The sum of the squared deviations of the present work is measured by separating the variability of the weighted grey relational grade with a reduction in error. This method shows the major contributing responses in the transonic wing design and how the input process parameters affect the responses to achieve optimum results.  
The results of ANOVA for the weighted grey relational grade are listed in Table 17. It shows that the sweep angle is the most dominant process parameter affecting the multiple responses due to its highest percentage contribution among the process parameters, followed by twist angle, cant angle, and taper ratio, respectively. It may be noted that the minimum error percentage on the responses is observed. The appearance of the minimum error percentage indicates that the effects of all the input process parameters involved in different weightage are mostly involved to obtain the optimum output responses of the design [48,49].  
Figure 16: Means for weighted grey relational grade.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Symbol** & **Design Parameters** & **Level 1** & **Level 2** & **Level 3** & **Level 4** & **Max-Min** & **Rank** \\ \hline A & Cant angle of the first tip & 0.3003 & 0.3371 & 0.3890 & 0.6100 & 0.3096 & 1 \\ B & Taper ratio & 0.4022 & 0.4355 & 0.4335 & 0.3653 & 0.0702 & 4 \\ C & Sweep angle & 0.3848 & 0.4605 & 0.4001 & 0.3911 & 0.0757 & 3 \\ D & Tip Twist & 0.4299 & 0.4304 & 0.3282 & 0.4480 & 0.1198 & 2 \\ \multicolumn{8}{c}{Average Weighted Grey Relational Grade = 0.4091} \\ \hline \hline \end{tabular}
\end{table}
Table 16: Response table for weighted grey relational grade (WGRG).  
The improvement in the responses of the transonic design at the optimal conditions is verified after obtaining the optimal level of the winglet design parameters of the transonic design. Table 18 compares the results of the confirmation experiments using the optimal winglet design parameters (A4, B2, C2, D2) obtained by the proposed methods and those of the initial design parameters (A1, B1, C1, D1). From Table 15, \(\mathrm{C_{L}/C_{D}}\) increases from 0.9214 to 0.9509, \(\mathrm{C_{M}}\) decreases from 0.1042 to 0.1022, and \(\mathrm{(C_{L}/C_{D})/\left|\right.C_{M}\left|\right.}\) increases from 5.6556 to 6.3320. Accordingly, these confirmation tests reveal that the proposed optimum method for solving the optimal combinations of the design parameters in this work improves the \(\mathrm{C_{L}/C_{D}}\), \(\mathrm{C_{M}}\), and \(\mathrm{(C_{L}/C_{D})/\left|\right.C_{M}\left|\right.}\) of the transonic wing. The optimized dependent design parameter, \(\mathrm{(C_{L}/C_{D})/\left|\right.C_{M}\left|\right.}\), is examined with an improvement of 20.771% compared to other models such as NASA CRM and blended models for the design parameters viz. the first tip cant angle of 75\({}^{\circ}\), taper
# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
###### Abstract  
Hyperspectral remote sensing image classification has been widely employed for numerous applications, such as environmental monitoring, agriculture, and mineralogy. During such classification, the number of training samples in each class often varies significantly. This imbalance in the dataset is often not identified because most classifiers are designed under a balanced dataset assumption, which can distort the minority classes or even treat them as noise. This may lead to biased and inaccurate classification results. This issue can be alleviated by applying preprocessing techniques that enable a uniform distribution of the imbalanced data for further classification. However, it is difficult to add new natural features to a training model by artificial combination of samples by using existing preprocessing techniques. For minority classes with sparse samples, the addition of sufficient natural features can effectively alleviate bias and improve the generalization. For such an imbalanced problem, semi-supervised learning is a creative solution that utilizes the rich natural features of unlabeled data, which can be collected at a low cost in the remote sensing classification. In this paper, we propose a novel semi-supervised learning-based preprocessing solution called NearPseudo. In NearPseudo, pseudo-labels are created by the initialization classifier and added to minority classes with the corresponding unlabeled samples. Simultaneously, to increase reliability and reduce the misclassification cost of pseudo-labels, we created a feedback mechanism based on a consistency check to effectively select the unlabeled data and its pseudo-labels. Experiments were conducted on a state-of-the-art representative hyperspectral dataset to verify the proposed method. The experimental results demonstrate that NearPseudo can achieve better classification accuracy than other common processing methods. Furthermore, it can be flexibly applied to most typical classifiers to improve their classification accuracy. With the intervention of NearPseudo, the accuracy of random forest, \(k\)-nearest neighbors, logistic regression, and classification and regression tree increased by 1.8%, 4.0%, 6.4%, and 3.7%, respectively. This study addresses research a gap to solve the imbalanced data-based limitations in hyperspectral image classification.  
hyperspectral image; classification; imbalanced data; semi-supervised learning; nearest neighbors; pseudo-label +
Footnote †: journal:
## 1 Introduction
In this context, the use of simplified physics-based models is made herein, to capture the general nonlinear dynamical behavior of the structure at an acceptable time-computational cost (e.g., to support real-time applications), without resorting to complex FE models that would have required the introduction of surrogate modelling with a consequent increase in uncertainty. Specifically, the well-known Bayesian inversion framework (Section 2) has been applied to the calibration of nonlinear hysteretic systems. This approach clearly defines the errors and considers the uncertainties present in the model explicitly, providing the full multivariate distribution of the calibrated parameters and insights into a variety of quantities of interest (e.g., expected values, maximum a posteriori estimates). The uncertainty quantification (UQ) framework developed in [6] is first applied to a numerical benchmark and then on a case study, whose data came from a building monitored within the Italian Seismic Observatory of Structures (OSS) [26] (Section 3). The calibration of the latter is conducted exploring different parametric and non-parametric degrading models, quantifying the uncertainties that arise from each of them using the same discrepancy function. Finally, the effect of different levels of degradation on the inference of model parameters is also investigated in Section 3 and discussed in Section 4. Conclusions are then drawn in Section 5.
## 3 Results
### Morphological and Biochemical Characterization  
QH23 and QH24 strains are rod-shaped, Gram-negative bacteria that occur singly or in pairs. Both bacteria on agar plates form circular, smooth, white-cream colonies. They are moderately halophilic, tolerate up to 2.5 M NaCl (15%), and have optimal growth at 28 \({}^{\circ}\)C; their maximum growth temperature is <45 \({}^{\circ}\)C (Table 1). The two strains can be distinguished by different biochemical characteristics (Table 2), but are similar regarding catalase, oxidase, capacity to reduce NO\({}_{3}\)- to NO\({}_{2}\)- and capacity to assimilate glucose and maltose. M21 and M23 are Gram-positive bacteria. Their cells are rod-shaped and grow singly or in a chain. Both bacteria form leather, circular, irregularly bordered, white-cream colonies. They tolerate up to 2.0 M NaCl and are able to growth at up to 50 \({}^{\circ}\)C (Table 1). M21 and M23 are catalase- and oxidase-positive and reduce NO\({}_{3}\)- to NO\({}_{2}\)- but not reduce NO\({}_{2}\)-. Urease activity, indole production, and glucose fermentation are negative for both strains. They can be distinguished by arginine dihydrolase (ADH)--positive for M23--and hydrolysis of gelatin, which is positive for M21 (Table 2).  
Figure 1: Taxonomical positions of the isolate strains based on the 165 rDNA sequence. Phylogenetic trees, obtained by the neighbor-joining method, show the relationships of _Halomonas_ sp. QH23 and _Halomonas_ sp. QH24 (**A**), and of _Bacillus_ sp. M23 and _Bacillus_ sp. M21 (**B**), with the related bacterial species. Bootstrap values \(\geq\) 50 are reported at the branch points. T indicates the type strains. The access number of the relative strains is indicated.
## 4 Case Study
### Non-Hierarchical Privacy Policies  
An identified benefit from the deployment of the hierarchy approach, mentioned at Section 3.3, regarded the length reduction of the derived privacy policy documents, apart from ensuring compliance with the underlying legal and regulatory framework. Within the context of the case study and without applying the hierarchy scheme, AVT electronic would comprise of additional three lines and 100 ASCII characters, as depicted in Table 6 below. However insignificant this reduction may seem in one document, when applied to all respective documents, from electronic services, service providers, and ministerial departments, it is expected to produce more impressive results in terms of absolute numbers and workload.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Line** & **Privacy Policy Content** \\ \hline D.1 & \textless{Privacy\_Policy}\textgreater{} \\ D.2 & \textless{Policy\_ID=}^{\text{\text{\textminus}}}1038^{\textgreater{}} \\ D.3 & \textless{Service\_Provider}\textgreater{} General Secretary of Information Systems (GSIS) \\ D.4 & \textless{/Service\_Provider}\textgreater{} \\ D.5 & \textless{Electronic\_Service} Annual Vehicle Tax \textless{/Electronic\_Service}\textgreater{} \\ D.6 & \textless{Description}\textgreater{} Privacy Policy for Annual Vehicle Tax Electronic \\ D.7 & Service \textless{/Description}\textgreater{} \\ D.8 & \textless{/Policy\_ID}\textgreater{} \\ D.9 & \textless{Data}\textgreater{} \\ D.10 & \textless{Personal\_Identifiers}\textgreater{} \\ D.11 & \textless{Identifier\_ID=}^{\text{\textminus}}26^{\textgreater{}} National Taxation Identifier (AFM) \\ D.12 & \textless{Processed=}”Confidential”\textgreater{} Identification \textless{/Processed}\textgreater{} \\ D.13 & \textless{Storage=}”Yes” Retention=”90”\textgreater{} \textless{/Storage}\textgreater{} \\ D.14 & \textless{Transmitted=}”Yes”\textless{/Transmitted}\textgreater{} \\ D.15 & \textless{/Identifier\_ID}\textgreater{} \\ D.16 & \textless{/Personal\_Identifiers}\textgreater{} \\ D.17 & \textless{Personal\_Data}\textgreater{} \\ D.18 & \textless{Data\_ID=}^{\text{\textminus}}873^{\textgreater{}} License Plate \\ D.19 & \textless{Processed=}\"Confidential”\textgreater{} Identification \textless{/Processed}\textgreater{} \\ D.20 & \textless{Storage=}”Yes” Retention=”90”\textgreater{} \textless{/Storage}\textgreater{} \\ D.21 & \textless{Transmitted=}\"Yes\"\textless{/Transmitted}\textgreater{} \\ D.22 & \textless{/Data\_ID}\textgreater{} \\ D.23 & \textless{Transmitted=}\"Yes\"\textless{/Transmitted}\textgreater{} \\ D.24 & \textless{Data\_ID=}^{\text{\textminus}}32^{\textgreater{}} First and Last Name \\ D.25 & \textless{Processed=}”Confidential”\textgreater{} Identification \textless{/Processed}\textgreater{} \\ D.26 & \textless{Storage=}”No” Retention=”0”\textgreater{} \textless{/Storage}\textgreater{} \\ D.27 & \textless{/Data\_ID}\textgreater{} \\ \hline \hline \end{tabular}
\end{table}
Table 6: AVT Service Non-Hierarchical Privacy Policy.