esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Top-\(k\) Spatial Dataset Search Processing
## 3 Notations, System Model and Problem Description
### Problem Description  
Given a spatial data repository \(\mathcal{S}\) and an exemplar spatial dataset, the top-\(k\) spatial dataset search is described as Definition 1.  
**Definition 1** (Top-\(k\) Spatial Dataset Search).: _For an exemplar spatial dataset \(S_{\epsilon}\), the top-\(k\) spatial dataset search is to search the \(k\) most similar spatial datasets in \(\mathcal{S}\) to \(S_{\epsilon}\), which is denoted as \(Q=(\mathcal{S},S_{\epsilon},k)\). Assuming that \(R_{Q}\) is the search result of \(Q\), \(R_{Q}\) satisfies the following condition:_  
\[|R_{Q}|=k\wedge\forall S_{i}\in R_{Q},S_{i}\
otin R_{Q}\to sim(S_{\epsilon},S_{ i})>Sim(S_{\epsilon},S_{i}), \tag{1}\]_where \(sim(S_{\epsilon},S_{i})\) is the similarity between \(S_{\epsilon}\) and \(S_{i}\). The similarity calculation between datasets will be introduced in Section 4, and the larger the similarity between two spatial datasets, the more similar they are._  
The goal of this paper is to design an efficient top-\(k\) spatial dataset search processing that can obtain the top-\(k\) similar spatial datasets to a given exemplar dataset. We will design efficient optimization strategies to achieve this goal. The metrics for evaluating the search schemes' performance are presented below.  
* Search effectiveness evaluation. The evaluation metric for search effectiveness is the Mean Squared Error (MSE), which measures the distance between the search results and the exemplar dataset's data location points. This metric helps assess the effectiveness of the search results.
* Search efficiency evaluation. The evaluation metric for search efficiency is time cost, which reflects the duration from the start of a search to the return of search results. This metric helps assess how quickly the system can process searches and deliver the relevant results.
# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 4 Case Analysis
### Evaluation of Data Augmentation Capability of the DAE-GAN Model
#### 4.2.2 Evaluation of Model-Generated Sample Diversity and Distribution Similarity
After converting the 3D probability distribution plots into 2D grayscale images and applying a zero-removal process, the similarity between the probability distribution of the generated partial discharge samples and the real samples was assessed. This evaluation was done using the Multi-Scale Structural Similarity Index (MS-SSIM). Higher MS-SSIM values indicate greater similarity between the generated and real sample distributions. The  
Figure 9: Probability distribution of dimension reduction samples of local emission signals. specific MS-SSIM values calculated for each model are summarized in Table 4, providing a clear comparison of how closely each model's generated samples align with the actual data. As shown in Table 4, the MS-SSIM values for BEGAN and VAE are clearly lower than those of the other two models. The DAE-GAN model has the highest MS-SSIM value, indicating the highest fitting accuracy of the probability distribution for the generated partial discharge samples.
# The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins
###### Abstract  
Wind and photovoltaic (PV) power forecasting are crucial for improving the operational efficiency of power systems and building smart power systems. However, the uncertainty and instability of factors affecting renewable power generation pose challenges to power system operations. To address this, this paper proposes a digital twin-based method for predicting wind and PV power. By utilizing digital twin technology, this approach provides a highly realistic simulation environment that enables accurate monitoring, optimal control, and decision support for power system operations. Furthermore, a digital twin platform for the AI (Artificial Intelligence) Grid is established, allowing real-time monitoring, and ensuring the safe, reliable, and stable operation of the grid. Additionally, a deep learning-based model WPNet is developed to predict wind and PV power at specific future time points. Four datasets are constructed based on weather conditions and historical wind and PV power data from the Flanders and Wallonia regions. The prediction models presented in this paper demonstrate excellent performance on these datasets, achieving mean square error (MSE) values of 0.001399, 0.001833, 0.000704, and 0.002708; mean absolute error (MAE) values of 0.025164, 0.027854, 0.018592, and 0.033501; and root mean square error (RMSE) values of 0.037409, 0.042808, 0.026541, and 0.052042, respectively.  
AI Grid; digital twin; renewable energy; electric load forecasting
## 4 Example of Application: Performance Simulations using the EIPS framework
### Performance Simulations Analysis Using EIPS Framework  
After fixing all the inputs and settings (as described in Section 4.1 and in Table 1) needed for performance simulations, we run the EIPS framework for all three MW system configurations in an end-to-end manner. The output of these simulations is then used to analyse the performance of these instrument configurations; this analysis is described in the following Sections 4.3.1-4.3.3.
###### Abstract  
In order to solve the problem that the measurement noise covariance may be unknown or change with time in actual multi-target tracking, this paper brings the variational Bayesian approximation method into the trajectory probability hypothesis density (TPHD) filter and proposes a variational Bayesian TPHD (VB-TPHD) filter to obtain measurement noise covariance adaptively. By modeling the unknown covariance as the random matrix that obeys the inverse gamma distribution, VB-TPHD filter minimizes the Kullback-Leibler divergence (KLD) and estimates the sequence of multi-trajectory states with noise covariance matrices simultaneously. We propose the Gaussian mixture VB-TPHD (AGM-VB-TPHD) filter under adaptive newborn intensity for linear Gaussian models and also give the extended Kalman (AEK-VB-TPHD) filter and unscented Kalman (AUK-VB-TPHD) filter in nonlinear Gaussian models. The simulation results prove the effectiveness of the idea that the VB-TPHD filter can form robust and stable trajectory filtering while learning adaptive measurement noise statistics. Compared with the tag-VB-PHD filter, the estimated error of the VB-TPHD filter is greatly reduced, and the estimation of the trajectory number is more accurate.  
trajectory PHD filter; variational Bayesian approximation; noise covariance matrix; inverse Gamma distribution; estimation of trajectory  
10390/app1213638
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 3 Methods and Materials
### Experiment One Application--MAUI Chat
The client is now part of the group, sends the first secure message to the server, and waits for a response. The message exchange is as follows. The client generates a plaintext message of type **string** which is then converted to type **bytes** via our **convert** class. As indicated above, type **bytes** is then encrypted by the MLS library **protect** function. The ciphertext output of the **protect** function is in type **bytes** and then it is converted to type **char*** to be transmitted over the wireless ad-hoc network. The receiver converts the encrypted message and converts it from type **char*** to type **bytes**, then calls the MLS library **unprotect** function to decrypt the message. Once the message is decrypted, it is converted from type **bytes** to type **string** in order for the message to be displayed. This bidirectional exchange of secure messages repeats until the server or client sends a message with the bound sign \"#\" to close the TCP connection. The client MAUI chat functionality has two sections: sending and receiving. The sending section has the following sequence: input a message (data type string), convert the message (data type string to bytes), encrypt the message with MLS, convert the encrypted message (data type bytes to char*), send the encrypted message over TCP (data type char*). The receive section commences after a message is sent. This section has the following sequence: receive the encrypted message, convert the message (data type char* to bytes), decrypt the MLS message, convert the message (data type bytes to string), and finally display string. Between these two sections, the while loop checks if the message contains \"#\" sign. If the \"#\" sign is present, then the while loop stops and the program exits, else the while loop continues and prompts the user to input another message. The server MAUI chat functionality has two sections: receiving and sending. The receive section has the following sequence: receive the encrypted message, convert the message (data type char* to bytes), decrypt the MLS message, convert the message (data type bytes to string), and finally display string. The receive section commences after a message is received. The sending section has the following sequence: input a message (data type string), convert the message (data type string to bytes), encrypt the message with MLS, convert the encrypted message (data type bytes to char*), send the encrypted message over TCP (data type char*). Between these two sections, the while loop checks if the message contains \"#\" sign. If the \"#\" sign is present, then the while loop stops and the program exits, else the while loop continues and waits for another message.
## 3 Methodology
```
0:\(m_{0}\) (Starting model), \(G\) (Augmented kernel), \(d^{\text{CG}}\) (Data Vector), \(n\) (Iterations). 0:\(Gm=d^{\text{CG}}\)
1:\(d_{0}\gets d^{\text{CG}}\)
2:\(r_{0}\gets G^{T}d^{\text{CG}}\)
3:\(p_{0}\gets r_{0}\)
4:\(t_{0}\gets Gp_{0}\)
5:while\(k<n\) or stop criteria is satisfied do
6:\(\alpha_{k}\leftarrow\|r_{k-1}\|^{2}/\|t_{k-1}\|^{2}\)
7:\(m_{k}\gets m_{k-1}+\alpha_{k}p_{k-1}\)
8:\(d_{k}\gets d_{k-1}-\alpha_{k}t_{k-1}\)
9:\(r_{k}\gets G^{T}d_{k}\)
10:\(\beta_{k}\leftarrow\|r_{k}\|^{2}/\|r_{k-1}\|^{2}\)
11:\(p_{k}\gets r_{k}+\beta_{k}p_{k-1}\)
12:\(t_{k}\gets Gp_{k}\)
13:\(k\gets k+1\)
14:endwhile
15:return\(m_{k}\) (Final model)
```  
**Algorithm 1** CGLS algorithm to iteratively solve the problem \(Gm=d^{\text{CG}}\)
# IoT-Based Smart Surveillance System for High-Security Areas
## 1 Introduction
### Contributions of this Study
#### 1.2.6 Line of Sight  
When monitoring suspected objects using CCTV, the LoS and workload of the security framework are strained because extra memory is required to process the images and a huge bandwidth is required during the transmission of video-related packets to the system. Additional costs are also required to store the backup data. Passive InfraRed (PIR) also senses LoS because it works at an angle of 35\({}^{\circ}\). These issues can be resolved via SS-HSA using GMS.
## 5 Case Study
### Architecture and Implementation of the System  
Based on the principle proposed, this system comprise three roles: User Equipment (UE), Base Station (BS) and Resolve Center (RC). Their relationship is depicted in Figure 9. The UE is carried by the underwater user node. Whenever UE has requirements for localization, communication, or timing service, it will establish a connection with BS. If the requirement is communication, the messages of UE will be processed and transmitted by BS under the control of the network protocol. And if the localization requirement is received, the surrounded BS will measure the arrival time of the signal, also known as TOA. These measurements will be used for the fusion in RC to resolve a position result for the user node. In other words, the functionality of RC may be assigned to a specific central BS or a shore-based center, according to the design of the system.  
Based on the system architecture, a representative implementation of the proposed system is schematically illustrated in Figure 10. Now we detail the configuration of each  
Figure 8: Network management framework with a core module to perform cross-layer network optimization.  
Figure 9: The BSs receive signals sent by UEs, and RC will resolve the UEs’s position. Meanwhile, BSs can send user messages or request to RC via BS, and RC distributes information needed (CPNT service information, control and measurement order and etc.) via BSs. Besides, test and telemetry messages can be transmitted between BSs when needed.  
component. As illustrated, BS can be deployed in two configurations: surface buoy or submerged buoy. The surface buoy configuration, unlike its submerged counterpart, integrates two additional devices: a GNSS receiver and a radio network interface. The GNSS receiver provides the position of the base station in real time, while the radio network module is dedicated to exchange messages between BS and shore-based center. However, in the submerged configuration, the GNSS receiver and radio network module are omitted due to the ineffective penetration of radio signals through water. UE integrate a pressure sensor in its configuration.This sensor provides depth measurements, allowing RC to transform the 3-dimensional positioning problem into 2-dimensional, thus simplifying the resolution process.  
Given the above implementation, we will now provide the results of experiments conducted for the system in the following.
# Numerical Simulation Analysis of Concrete-Filled Circular CFRP-Steel Middle Long Columns
## 2 Axial Compression Test  
### The Test Sample  
The steel tubes used in the samples are Q235 circular cross-section steel tubes with an inner diameter of 160 mm and outer diameters of 165 mm, 167 mm, and 168.5 mm. The cast-in-place concrete column is a cylinder with a diameter of 160 mm. The lengths of the columns are 500 mm, 1000 mm, and 1333 mm. A cover plate is added at the upper and lower ends, with a square cross-section of side length 385 mm and thickness 10 mm. The plate is also made of Q235 steel [15]. Strength grades of concrete: C40, C50, and C60 [16]. Number of CFRP layers: 0, 1, 2, and 3. The specific parameters of the samples are shown in Table 1. The experimental program included 10 groups of column specimens with different configurations, with each group comprising 3 identical specimens for parallel testing to minimize experimental errors.
## 3 Results and Discussions
### Artificial Neural Network (ANN)  
Thermoluminescence (TL) analysis requires precise modelling of dose-response relationships to better understand the behavior of any material exposed to different radiation doses. This study presents the implementation of an artificial neural network (ANN) model to predict the thermoluminescence glow curves of a plagioclase mineral based on the input of different and increasing dose values. The proposed model is implemented by using the neural network toolbox of MATLAB 2024b for data preprocessing, model training, and evaluation.
## 1 Introduction
Some studies have estimated aerosol information content from simulated GOSAT measurements, indicating that hyperspectral measurements can support part of the aerosol parameters retrieval independently [28]. The spectrum in the O\({}_{2}\)-A absorption band could also be used in aerosol height retrieval [29]. However, when considering CO\({}_{2}\) retrieval together with aerosols, the aerosol model should be simplified. A Gamma distribution aerosol model with the three parameters shows positive evidence of the correcting aerosol effect when the 550 nm AOD is lower than 0.3 [24]. In the TanSat IAPCAS retrieval algorithm, we assume that the wavenumber dependence of aerosol extinctions with a bimodal lognormal distribution is quadratic in the retrieval of the coefficients [30,31]. Questions regarding the amount of aerosol parameter information that can be extracted and used for correction, as well as the determination of which aerosol parameters are most suitable for corrections, have been largely overlooked. The purpose of this study is to optimize the aerosol model used in the CO\({}_{2}\) retrieval algorithm to minimize aerosol-induced XCO\({}_{2}\) retrieval errors, using TanSat as an example and applying an information content estimation and error analysis based on the optimal estimation theory. The a posteriori errors of XCO\({}_{2}\) can be derived without any real retrieval process and can be used as the criteria for choosing an optimal aerosol model from simulated TanSat measurements. In Section 2, the theoretical basis of the optimal estimation theory and the method used to quantify the information content and retrieval errors of CO\({}_{2}\) and aerosols are described. The numerical simulation of the TanSat spectrum and the assumptions necessary for the analysis are introduced in Section 3. In Section 4, we focus on estimating the information of the aerosol model in CO\({}_{2}\) retrieval, including the degree of freedom for signal (DFS) and interference in XCO\({}_{2}\). Changes in the COinformation and the impact of aerosols on XCO\({}_{2}\) retrieval errors for different aerosol models are evaluated in Section 5. Based on these analyses, the aerosol property combination groups appropriate for the synchronous retrieval of CO\({}_{2}\) are selected, and the aerosol-induced XCO\({}_{2}\) errors are minimized, while large amounts of CO\({}_{2}\) information remain available. The discussions about several limits and more scenarios of this study are shown in Section 7. Finally, the concluding section summarizes the new approach and explores the possible applications of this approach to future CO\({}_{2}\) retrievals.
## 2 Materials and Methods
### Characterization  
The crystal structure of the Fenton iron-peat catalyst was characterized using an X-ray diffractometer (XRD) (Rigaku SmartLab 9 kW, Rigaku, Osaka, Japan) with Cu K\(\alpha\) radiation. The surface morphology of samples was captured using a scanning electron microscope (SEM, Genimi500, Zelss, Germany). Raman spectra were acquired with a Horiba Lab RAM HR Evolution spectrometer (HORIBA Jobin Yvon, Kyoto, Japan). The N\({}_{2}\) adsorption-desorption isotherms at \(-\)196 \({}^{\circ}\)C were measured using an Autosorb-IQ3 automatic gas adsorption analyzer (Quantachrome Instrument, Boynton Beach, FL, USA). The specific surface area was determined by the multipoint Brunauer-Emmett-Teller (BET) method. At the same time, the single-point pore volume was estimated from the adsorption quantity at a relative pressure of 0.99 Pa. X-ray photoelectron spectroscopy (XPS) analysis was performed using a Thermo ESCALAB 250 instrument with an Al K\(\alpha\) radiation source (Thermo Fisher Scientific, Massachusetts, USA). The chemical oxygen demand (COD) removal efficiency of secondary effluent was evaluated using a COD analyzer (DR1010, Hach, Loveland, CO, USA). X-ray fluorescence (XRF) spectroscopy was employed to analyze the elemental composition of the raw Fenton iron sludge (ZSX PRIMUS II, Rigaku, Japan). Additionally, reactive oxygen species (ROS) in the system were qualitatively analyzed using an electron paramagnetic resonance (EPR) spectrometer (JES-FA200, JEOL, Tokyo, Japan). COD measurements were conducted with a rapid digestion instrument (DRB 200, Hach, USA) and a spectrophotometer (DR900, Hach, USA).
## 3 Creating an Optimal Spectrogram  
The product obtained from the radar output is a time-dependent voltage. Short-time Fourier transform (STFT) is performed to extract the instantaneous Doppler frequency \(f_{d}(t)\), which is directly proportional to the object velocity \(v(t)\). STFT is used to analyze how the Doppler frequency of a non-stationary signal (i.e., the radar's output voltage) changes over time. The STFT of a signal is calculated by sliding a window of length W over the signal and calculating the discrete Fourier transform (DFT) of the windowed data. The window has an overlap of length L over the previous window. For example, Figure 1(a) shows a non-stationary signal, chirp, in which a cosine signal frequency linearly increases. Figure 1(b) illustrates the signal distribution to the windows and the overlap between them. Figure 1(c) shows the DFT of each windowed segment; here, it can already be seen how the Doppler frequency content changes over time. We used Matlab to perform the STFT calculations and generate different graphs.  
The final spectrogram is the same as Figure 1(c) but taking a different view. The intensity of the detected signal is represented by color. Blue represents low-level intensities, while red is for higher IF signal strength. The choice of the window length on which the DFT will be performed is highly important in creating an optimal spectrogram. The window should be long enough to achieve the resolution required to identify the different micro-Doppler frequency shifts, but not so long as to enable following the temporal behavior of the instantaneous frequency. For example, if the selected window is too short when obtaining the spectrogram for the chirp signal, we sample a tiny part of the signal at a time, corresponding to an approximately square signal. Therefore, we obtain a sequence of sinc signals in the spectrogram, as shown in Figure 2(a). If the selected window is too long, we sample a wide number of frequencies at a time, thus obtaining several frequencies in each window and a non-continuous graph as the spectrogram result, as shown in Figure 2(c). Finally, Figure 2(b) shows when the window selection is suitable, and the result of the spectrogram is continuous and smooth.
# One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments +
## 2 Distributed GEP Algorithm and Its Extensional Applications
### Extensional Applications of Distributed GEP  
Many statistical tools in machine learning fields can be formulated as special instances of GEP in (1) with the symmetric-definite matrix pair \((A,B)\). We briefly give three instances below. They all fit for the proposed Algorithm 1 when in the distributed framework:  
* PCA (Principal Component Analysis): It is the most widely used statistical technique for data analysis and dimensionality reduction. It involves the eigenvalue problem, which is reduced from GEP with a symmetric \(A\) and \(B=I\).
* FDA (Fisher's Discriminant Analysis [31]): It is desired to maximize the between-class variance \(S_{B}\) and minimize the within-class variance \(S_{W}\). Therefore, the instances of every class become close to one another, and the classes become far away from each other. It is a direct instace of GEP with \(A=S_{B}\) and \(B=S_{W}\).
* CCA (Canonical Correlation Analysis ): Given two random vectors \(X\) and \(Y\), let \(\Sigma_{XX}\) and \(\Sigma_{YY}\) be the covariance matrices of \(X\) and \(Y\), respectively. \(\Sigma_{XY}\) is the cross-covariance matrix between \(X\) and \(Y\). The canonical vectors \(w_{X}\) and \(w_{\gamma}\) can be obtained by solving GEP with \(A=\begin{pmatrix}0&\Sigma_{XY}\\ \Sigma_{XY}&0\end{pmatrix}\), \(B=\begin{pmatrix}\Sigma_{XX}&0\\ 0&\Sigma_{YY}\end{pmatrix}\), \(w=\begin{pmatrix}w_{X}&0\\ 0&w_{Y}\end{pmatrix}\).  
Figure 2: The classical one-shot communication process of distributed GEP.  
* PLS (Partial Least Squares [32]): It is a common method for dimension reduction. Derived from CCA with \(X\) and \(Y\), it is also an instance of GEP with \(A=\begin{pmatrix}0&\Sigma_{XY}\\ \Sigma_{XY}&0\end{pmatrix}\), \(B=I\), \(w=\begin{pmatrix}w_{X}&0\\ 0&w_{Y}\end{pmatrix}\).