esa-sceva/satcom-chunk-collection huggingface dataset:
# Impacts of Integration of Wind Farms on Power System Transient Stability
###### Abstract  
To compensate for the ever-growing energy gap, renewable resources have undergone fast expansions worldwide in recent years, but they also result in some challenges for power system operation such as the static security and transient stability issues. In particular, as wind power generation accounts for a large share of these renewable energy and reduces the inertia of a power network, the transient stability of power systems with high-level wind generation is decreased and has attracted wide attention recently. Effectively analyzing and evaluating the impact of wind generation on power transient stability is indispensable to improve power system operation security level. In this paper, a Doubly Fed Induction Generator with a two-lumped mass wind turbine model is presented firstly to analyze impacts of wind power generation on power system transient stability. Although the influence of wind power generation on transient stability has been comprehensively studied, many other key factors such as the locations of wind farms and the wind speed driving the wind turbine are also investigated in detail. Furthermore, how to improve the transient stability by installing capacitors is also demonstrated in the paper. The IEEE 14-bus system is used to conduct these investigations by using the Power System Analysis Tool, and the time domain simulation results show that: (1) By increasing the capacity of wind farms, the system instability increases; (2) The wind farm location and wind speed can affect power system transient stability; (3) Installing capacitors will effectively improve system transient stability.  
doubly fed induction generator; wind turbine with two-lumped mass; transient stability; power system
# Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation
## 3 Methodology of the Multi-Spacecraft Tracking  
The method proposed in this paper can be divided into three portions, i.e., the uncertainty propagation, the data association and the orbit estimation, which are detailed in Sections 3.1-3.3, respectively. Then, the overall procedure of the proposed approach is presented in Section 3.4.
## 2 Analytical Model of Mesh Stiffness of the Internal Gear Pair
### Calculation of Slice Mesh Stiffness
#### 2.2.2 Contact Stiffness  
The contact stiffness can be obtained by Hertz contact theory. The internal and external gear teeth meshing with each other are regarded as two convex and concave cylinders with Hertzian contact and has little relation to the contact position, load, and shape. The error of mesh stiffness obtained was less than 0.5% [23; 24]. Therefore, it can be calculated as a fixed value, and the specific formula is:  
\[k_{h}=\frac{\pi EdB}{4(1-v^{2})} \tag{7}\]
## 3 Material and Methods  
### Description of the Aeromagnetic Data  
This study made use of geophysical data acquired during a comprehensive survey of Morocco, conducted by Canadian Fugro Airborne Surveys Corporation for MMETSD between October 1998 and May 1999 [28]. The survey was an integral component of the National Geological Mapping Project, encompassing the entire Anti-Atlas region.  
The latter refers to a comprehensive initiative with two primary objectives: first, to enhance searching for minerals in the Anti-Atlas area, which stands out as one of the most important metallogenic regions in Morocco, and second, to consolidate geoscientific databases. The study made use of a high-resolution data collection generated from an aerial geophysical survey on the part of providing accurate structural and geology cartography of the research region. Specifically, this research focuses on aeromagnetic data obtained from a rectangular region measuring 40 km by 69 km surrounding the BAEI (Figure 1). The data were captured using an optically pumped cesium vapor magnetometer that operates using pumping, featuring a level of sensitivity of 0.01 nT, and a data collection frequency of 10 samples per second was applied.  
For the airborne magnetic survey, the N30\({}^{\circ}\) E flying path was utilized, set apart at intervals of 500 m. The magnetic sensor's height above the terrain was set to approximately 30 m, and the tie lines were perpendicular with N120\({}^{\circ}\) E direction, a spacing of 4000 m. These parameters allowed for the collection of a substantial amount of magnetic field data, covering a total distance of 5700 km. As an advantage of these survey settings, a high-resolution aeromagnetic survey of the research region was effectively conducted. The preliminary handling of the unprocessed data involved standard levelling, noise reduction, diurnal adjustment, and other essential steps.  
The contractor performed the gridding of the magnetic data using the minimal curvature technique, yielding a standardized square grid with a grid spacing of 250 m (half of the line interval, regardless of the data sampling rate) [29]. Aeromagnetic maps of Bou Azzer Inlier Reduced to the north pole and the Residual Aeromagnetic map of Bou Azzer Inlier (Figure 2), computed by [28], were referenced using the southern Moroccan Lambert metric coordinate projection. The residual magnetic field performed by [28] shows a very agitated magnetic relief and a certain heterogeneity with the presence of several anomalies (Figure 2A), the observed class ranging from \(-900\) nT to 1800 nT.
# Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks
## 3 System Model
### Computation Model  
The task completion time when the task is offloaded from PM \(m\) to node \(k\) at time \(t\) can be expressed as  
\[T_{m}^{k}(t)\in\{T_{m}^{0}(t),T_{m}^{1}(t),...,T_{m}^{M}(t)\}, \tag{3}\]  
Figure 3: Task offloading queue model.  
where \(T_{m}^{k}(t)\) includes task transmission time and execution time, which can be computed by  
\[T_{m}^{k}(t)=\begin{cases}\frac{D_{m}(t)}{B_{m,k}}+\frac{A_{m}(t)}{f_{k}}&m\
eq k,\\ \frac{A_{m}(t)}{f_{k}}&m=k,\end{cases} \tag{4}\]  
where \(m=k\) represents that the task is executed locally, that is, there is no transmission delay, while \(m\
eq k\) indicates that the task is offloaded to other node. \(D_{m}(t)\in\{D_{1}(t),D_{2}(t),...,D_{M}(t)\}\) denotes the size of data transmitted by PM \(m\) at time \(t\), which is uniformly distributed and independent.  
Similarly, the energy consumption is denoted by  
\[E_{m}^{k}(t)\in\{E_{m}^{0}(t),E_{m}^{1}(t),...,E_{m}^{M}(t)\}, \tag{5}\]  
where \(E_{m}^{k}(t)\) represents the energy consumption of PM \(m\) when offloading the task to queue \(k\) at time \(t\). Then, we obtain  
\[E_{m}^{k}(t)=\begin{cases}p_{tr}\cdot\frac{D_{m}(t)}{B_{m,k}}+p_{i}\cdot\frac {A_{m}(t)}{f_{k}}&m\
eq k,\\ p_{c}\cdot\frac{A_{m}(t)}{f_{k}}&m=k,\end{cases} \tag{6}\]  
where \(m=k\) indicates that the task is executed locally, and \(m\
eq k\) means that the task is offloaded. \(P_{tr}\) represents the transmission power of a vehicle, \(p_{i}\) represents the idle power of a vehicle, and \(p_{c}\) represents the computation power when the task is executed locally.
## 4 Antenna Array
### Normal Array
#### Array with 180\({}^{\circ}\) Rotation of the Antenna Elements  
In this configuration, a 180\({}^{\circ}\) sequential rotation of the array elements that involves the rotation of antennas 2 and 4 is performed, as it can be seen in Figure 11. This approach involves the out-of-phase feeding of the rotated elements. This feeding arrangement can lead to a suppression or reduction of the cross-polarized fields due to the phase inversion, while maintaining the same co-polarization pattern [20]. It can be noted in Figure 11 that, despite the 180\({}^{\circ}\) rotation of the array elements 2 and 4, the same geometrical slots as the previous configuration are involved in order to excite each polarization.
## 4 Case Study
### Abnormal Uplift Pressure Applied in the FEM Model  
To analyze the safety state of the concrete arch dam in the worst case, the measured uplift pressure, under a high water level, is selected as the load applied in the FEM model of the arch dam. Therefore, the recent high reservoir water level of 876.51 m on 20 October 2020, is selected. The measured uplift pressure of the 3# cross section and vertical section is shown in Figure 10, and the other measured uplift pressure is approaching normal.  
It can be seen, from Figure 10, that the measured uplift pressure of each osmometer at the bottom of the 3# section has exceeded the empirical value. The measured P6, P7, and P8 points show apparent abnormal phenomena, and the other measured points conform to the empirical uplift pressure distribution law. According to all uplift pressure points, abnormal uplift pressure mainly occurs in the riverbed. The uplift pressure of other dam sections meets the empirical. In the FEM simulation, the larger value of measured uplift pressure and experienced uplift pressure will be selected as uplift pressure load. The measured uplift pressure is adopted for the dam section in the riverbed because it is greater than the empirical uplift pressure. Based on similar considerations, the empirical uplift pressure is adopted for the dam sections on both banks.  
Based on the anomaly identification and analysis results of uplift pressure, the application method and software proposed in Chapter III are adopted. The measured uplift pressure value is adopted in the middle dam section of the riverbed. Finally, the design and measured value and distribution of uplift pressure in the dam foundation are shown in Figure 11. Under the measured uplift pressure, the uplift pressure after the grouting curtain is obviously increased. Thus, this application method can better reflect the spatial distribution and abnormal position of uplift pressure.  
Figure 10: Measured uplift pressure under the high reservoir water level.  
Figure 9: Non-destructive testing result of dam foundation.
## 4 Current Security Challenges in Maritime Networks
* In the maritime industry, several network types are used for the transmission of data collected and processed by networked information systems. These technologies include SHIPNET, SAFENET, C3I system, RICE 10, SHIP system 2000, Smart Ship, and TSCE. Several security vulnerabilities exist in these technologies, as the design and configuration of communication links between IT networks neglect to consider authentication and encryption methods, leading to potentially vulnerable and outdated systems being available on the Internet [41].
## 3 Fault Diagnosis
### FD Methods  
#### 3.1.1 Physics Model-Based Method  
Physics model-based methods construct working processes of MSAE through building mathematical models based on the failure mechanisms. The core of analytical model-based technologies is the mathematical descriptions (modeling), and model analysis of process dynamics and main characteristics [70; 71]. The model-based FD implementation process includes two parts: (I) Generating residual signal, the difference between model estimate values and measurements parameters; (II) Analyzing and estimating the residuals and making decisions.  
Typical methods based on physical models for FD are bond graphs [72; 73; 74], parity space [75], parameter estimation [76] and state observers [77; 78; 79]. Those residuals are evaluated to realize the identification of abnormal conditions in the monitoring process. These methods compare the available measured values with the prior information, and the differences (residuals) between the measured variables and estimated values are used as the fault indicators.  
Many model-based FD methods have been developed and applied to mechanical systems for fault identification. However, owing to the complexity of MSAE, there are many problems for building an accurate mathematical model to maintain robustness and fault sensitivity. Therefore, the model-based FD system has some restrictions in the practical application of marine engineering.
# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 2 Methods
### Canonical Data Identification
#### 2.1.2 Sensitive Attribute Division Based on Cluster Analysis  
Equation (3) only provides the initial sensitivity of attributes, which is insufficient for accurate classification. Therefore, we employ clustering techniques to further divide the sensitivity set into 2 groups: a suspicious sensitive attribute set and a sensitive attribute set.  
Cluster analysis is an unsupervised learning method commonly used to group objects in a dataset into distinct clusters based on their similarity. As a result, data points within the same cluster exhibit high intra-cluster similarity, while those in different clusters exhibit significant inter-cluster dissimilarity [13].  
Clustering algorithms are generally categorized into 4 major types: partitioning clustering, hierarchical clustering, density-based clustering, and grid-based clustering [14]. The choice of clustering algorithms typically depends on the nature of the data, the purpose of clustering, and the specific application scenario. Through research, it was found that partition clustering and hierarchical clustering were more suitable for our method.  
* Partition clustering. This divides the samples in a dataset into distinct groups, each represented by a prototype, such as the center or core sample of the category. The main representative algorithms include K-means [15], k-medoids [16], and so on.
* Hierarchical clustering. Unlike partition-based methods, hierarchical clustering does not require the number of clusters to be specified in advance. Instead, it builds a nested tree-like structure based on similarity or distance measures between samples to construct a hierarchy of clusters. The main representative algorithms include AGNES [17] and BIRCH [18].  
We apply partition clustering and hierarchical clustering to perform binary classification on the attribute sensitivity \(H_{\text{S}}(X)\). This process generates a set of sensitive and suspicious sensitive attributes for each data table, forming the basis for subsequent in-depth association relationship mining across system attributes.
## 5 Conclusions  
In this paper, we propose a novel spectrum-aware clustering algorithm based on reinforcement learning to minimize network energy consumption and enhance channel sensing in cognitive radio sensor networks. We first modelled the network energy consumptions in terms of cooperative channel sensing, and inter-cluster and intra-cluster data communication energy consumptions, and then show that network energy consumptions can be minimized by determining an optimal number of clusters that balances energy consumption for inter-cluster and intra-cluster communications. The problem of nodes to choose their respective optimal clusters is formulated as a Markov decision problem and the results obtained show that the algorithm is capable of adapting to a dynamic environment and converging to an optimal solution.  
We also showed that pairwise constraints can be implemented in spectrum-aware clustering to improve primary user detection. Also the energy cost and local decision accuracy have a significant influence on determining the optimal cluster. We further showed through simulations the performance improvement of our approach over groupwise constraint-based algorithms in terms of energy efficiency, channels sensing performance and computational complexity, which are vital to resource constrained devices such as CR-WSN.
## 3 Construction of Local and Global Approximations  
NMM is based on partition of unity (PU), thus the global approximation is obtained by the weighted average of the local approximations of all the physical patches, reading  
\[\phi^{*}=\sum_{ij}w_{i}a_{ij}L_{ij} \tag{1}\]  
where \(w_{i}\) is the weight function of the \(i\)th mathematical patch, and the subscript \(i\) represents the \(i\)th mathematical patch. \(j\) stands for the \(j\)th physical patch cut out of the \(i\)th mathematical patch, \(L_{ij}\) is the local approximation function, \(a_{ij}\) are called generalized degrees of freedom of physical patch \(j\).  
The weight function \(w_{i}\) can be any function which just needs to satisfy the PU condition \(\sum w_{i}=1\) and the property of compact support.  
The local approximation function can be any function which can express the properties of solution on the physical patch. The polynomial bases are usually chosen, since the polynomial bases are easy and simple to approximate the real solution.  
In this study, for convenience, if the weight function is hat function  
\[w_{i}=\frac{1}{2}(\frac{|x-x_{c}|-|x-x_{b}|}{|x_{c}-x_{b}|}-\frac{|x-x_{b}|-| x-x_{a}|}{|x_{b}-x_{a}|}) \tag{2}\]  
where \(x_{a}\) and \(x_{c}\) are the left and right boundary of \(i\)th MP, respectively. \(x_{b}\) is the position of the star point.  
The weight function has the \(\delta\) property. In Figure 2, the weight function only depends on the mathematical patch, regardless of whether there is discontinuity or not in it.  
Figure 2: Local approximation to a discontinuous function \(\varphi(x)\).
## 4 Conclusions  
The present study focused on the prospecting of bioactive compounds present in the microalgae _I. galbana_ by SFE for potential use in food, pharmaceuticals, and cosmetics. Several variables were studied including extraction yield, total carotenoids content and recovery, total phenols, antioxidant activity (TEAC method), and fatty acid content and profile. In general, the addition of ethanol as co-solvent significantly increased the efficiency of SFE extraction for all variables, and to a lesser extent, TEAC as antioxidant measurement. Another parameter to enhance the bioactive compound extraction from _I. galbana_ was pressure, although it was not a significant variable except in the TEAC method. Particularly, linoleic acid (C18:2) followed by linolenic acid (C18:3) was highlighted in the profile that was improved under higher pressure, thus indicating that as pressure increased, solubility of triglycerides containing the more unsaturated fatty acids increased at higher densities. Concurrently, desirability function that aided in the operational condition closer to the optimum was found in Run 7 (40 MPa, 50 \({}^{\circ}\)C, and 8% co-solvent, desirability predicted 0.79). To better explore the potential of _I. galbana_, new and improved supercritical extraction with a higher percentage of ethanol could be developed in spite of it could increase the operational costs in the process. In conclusion, this  
Figure 6: Surface of the desirability function in terms of pressure, temperature and maximum co-solvent (8% ethanol, v/v) obtained for maximizing extraction yield (Y), total carotenoids content (TCC) and recovery (TC recovery), phenol content (TPC), TEAC method, and FAMEs content from _I. galbana_.  
study provides functional information to optimize extraction of bioactive compounds from _I. galbana_ using SFE because of its rich content of bioactive compounds and antioxidant activity with increasing demand in food, pharmaceuticals, and cosmetics market.  
Conceptualization, M.C.R.-D; data curation, F.S. and E.M.; formal analysis, M.C.R.-D. and E.M.; funding acquisition, M.C.R.-D. and P.C.; investigation, M.C.R.-D., F.S., and E.M.; methodology, M.C.R.-D. and P.C.; project administration, M.C.R.-D. and P.C.; resources, G.R.-C.; software, M.C.R.-D. and P.C.; writing--original draft, M.C.R.-D.; writing--review and editing, P.C.. All authors have read and agreed to the published version of the manuscript.  
This research was financed by several projects with public funds of Chile, grants FONDECYT-11170017, PAI-79160037 and FONDECQUIP EQM-160073 (CONICYT) and MINEDUC-UA project, code ANT-1855.  
The research group \"LAMICBA\" at University of Antofagasta, Chile, thanks \"Microalgas Oleas de Mexico S.A.\" for providing microalgal samples.  
There is no conflict of interests.
## 4 Experiments
### Feature Reduction  
The experiments presented in the previous section involve local-maximum filtering, which ensures that at most 6.25% of matrix elements are non-zeros. In this section, however, the results related to even higher levels of sparsity are considered. The number of zeroed elements in the encoding is increased, but exactly the same decoders, trained in Section 4.1, are used to generate the results presented below.  
Figures 4-6 include the results of sparse matrices decoding for MF4, AF4 and AF5 experiments respectively. Each table includes the following.  
* The original encoding errors (for comparison).
* The result achieved with each matrix being greedily reduced to 5 highest values, and all the other elements being replaced by zeros. The description of there results consists of an absolute encoding error and a relative increase (compared to the first column).
* The results of an experiment similar to the previous point, but with 3 points instead of 5.  
Figure 4: The pretrained decoder from the MF4 model can be used either with the original data without a specific limit of non-zero elements in the encoding, or with modified encodings, where each matrix contains up to 3 or 5 non-zero elements. The plot presents decoding errors for images showing individual digits and for the whole test set.  
As we can conclude from Figures 4-6, experiment AF4 seems to be most sensitive to additional thresholding, which is particularly evident in the case of encoding digits 2, 3, and 5. However, the other experiments, namely, MF4 and AF5, behave in quite a similar way, giving slightly above 20% greater average loss when 3 points per matrix are used, and only a few percent in the case of 5 points.  
Figure 5: The pretrained decoder from AF4 used for decoding of both the original and the highly sparse data, as in Figure 4.  
Figure 6: The pretrained decoder from AF5 used for decoding of both the original and the highly sparse data, as in Figure 4.  
The most remarkable phenomenon related to experiment AF5 is the sensitivity of digit 4 to sparse autoencoding. The autoencoder error increased by 5% for 5 points and above 50% for 3 points. This leads to the conclusion that digit 4 consists of a greater number of visual feature occurrences than any other digit, and omitting some of these features generates a significant error.  
The most important conclusion is that further sparsity enforcement is generally acceptable, unless the features are too specific (AF4 case) and the reduction is too great (3 points case). With 5 points per matrix, both MF4 (error increase: up to 4%, 3% in average) and AF5 (error increase: up to 5%, only 1% in average) models yielded acceptable results. This means that the whole \(28\times 28\) digit can be compressed into 20 points (in the case of MF4) or 25 points (AF5), with encoding errors presented in Figures 4 and 6.