esa-sceva/satcom-chunk-collection huggingface dataset:
# Uncertainty-Aware Time Series Anomaly Detection
## 2 Related Work
### Anomaly Detection Approaches  
With the rise of deep neural networks, various methods have been developed to approach anomaly detection [12; 13]. Unlike traditional machine learning approaches, deep neural networks excel in handling complexity and flexibility enabling them to scale to high dimensional data. Recurrent Neural Networks (RNN) are a type of neural network specifically designed for sequential data. Through a hidden state, they keep information over past states. Nevertheless, RNNs have a small memory and forget long-range dependencies. Moreover, they struggle with vanishing/exploding gradients. Long Short-Term Memory (LSTM) [9] was developed to overcome these problems making it valuable for anomaly detection in sequential data. LSTMs also found application in anomaly detection. The study in [14] uses an LSTM network to predict healthy electrocardiogram signals. A further variation of LSTMs has been developed by [15] where LSTM layers are stacked. It is supposed to be trained on normal data and uses multiple predictions in the future to find anomalies based on prediction error.  
Autoencoders [16] try to compress the information into a lower dimensional (latent) space, to extract the most relevant features. In [17], the authors use this approach to detect anomalies from satellite telemetry and artificial data, while in [18], the authors proposed _Donut_, an unsupervised anomaly detection algorithm based on Variational Autoencoders (VAE) [19] for anomaly detection. VAEs try to learn a distribution over the latent space through variational inference. Since they model the latent space as a distribution instead of point estimates, one can generate new data points through sampling. Finally, there are approaches that combine several presented techniques. The work presented in [20] shows a combination of an LSTM with an encoder and decoder for multi-sensor time series data. OmniAnomaly [21] uses a combination of GRU and VAE with Planar Normalizing Flow. Combinations of autoencoder and time series forecasting methods based on latent space were also introduced and successfully applied. For example, [22] uses an autoencoder to reconstruct data and additionally uses a feed-forward network to predict one value ahead in terms of timesteps. Similar to that, [23] uses an autoencoder to predict one step ahead in time, and one step in the past relative to the given input window.
## 2 Governing Equation and Numerical Method
### Turbulence Model  
For the cavitation flow analysis, a filter-based \(k\)-\(\varepsilon\) model was used as the turbulence model. In the \(k\)-\(\varepsilon\) model, \(k\) represents the kinetic energy due to turbulence, and \(\varepsilon\) represents the dissipation rate due to turbulence.  
\[\rho_{m}\frac{dk}{dt}=\frac{\partial}{\partial x_{j}}\left[\left(\mu_{m}+ \frac{\mu_{t}}{\sigma_{k}}\right)\frac{\partial k}{\partial x_{j}}\right]+P_{k} -\rho_{m}\varepsilon, \tag{18}\]  
\[\rho_{m}\frac{d\varepsilon}{dt}=\frac{\partial}{\partial x_{j}}\left[\left( \mu_{m}+\frac{\mu_{t}}{\sigma_{\varepsilon}}\right)\frac{\partial\varepsilon }{\partial x_{j}}\right]+c_{\varepsilon 1}P_{k}\frac{\varepsilon}{k}-c_{ \varepsilon 2}P_{k}\frac{\varepsilon^{2}}{k}, \tag{19}\]  
where \(P_{k}\) is the production of turbulence defined as \(P_{k}=\tau_{ij}(\partial u_{i}/\partial u_{j})\), and \(\sigma_{k}\), \(\sigma_{\varepsilon}\), \(\sigma_{\varepsilon 1}\), \(\sigma_{\varepsilon 2}\), and \(c_{\mu}\) are the empirical constants. In this paper, the constants are used as \(\sigma_{k}=1.0\), \(\sigma_{\varepsilon}=1.3\), \(\sigma_{\varepsilon 1}=1.44\), \(\sigma_{\varepsilon 2}=1.92\), and \(c_{\mu}=0.09\). This \(k\)-\(\varepsilon\) model is not suitable for an unsteady state due to excessive turbulent viscosity. To compensate for this, an improved turbulence model using a filtering method by [PERSON] was used. The grid size for filtering and the turbulent viscosity using it is expressed as follows [19]:  
\[\delta=(\Delta x\Delta y\Delta z), \tag{20}\]  
\[\mu_{t}=\rho_{m}c_{\mu}Mint\left[1,\frac{\Delta\cdot\varepsilon}{k^{2}}\right] \frac{k^{2}}{\varepsilon}. \tag{21}\]
# Uncertainty Theory-Based Resilience Analysis for LEO Satellite Communication Systems
## 2 Preliminaries
### Chance Measure  
The chance measure [15] combines probability and uncertain measures to solve problems with both aleatory and epistemic uncertainties. Its concepts, as used in this article, are as follows.  
Let \((\Gamma,\mathcal{L},\mathcal{M})\) be a **uncertainty space** and \((\Omega,\mathcal{A},\Pr)\) be a **probability space**. Then the \((\Gamma,\mathcal{L},\mathcal{M})\times(\Omega,\mathcal{A},\Pr)\) is called a **chance space**.  
**Definition 5** (Liu [14] and Liu [15]).: _An **uncertain random variable** is a function \(\zeta\) from a chance space \((\Gamma,\mathcal{L},\mathcal{M})\times(\Omega,\mathcal{A},\Pr)\) to the set of real numbers such that \(\{\xi\in B\}\) is an event in \(\mathcal{L}\times\mathcal{A}\) for any Borel set \(B\) of real numbers._  
**Definition 6** (Liu [14] and Liu [15]).: _Let \(\xi\) be an uncertain random variable on the chance space \((\Gamma,\mathcal{L},\mathcal{M})\times(\Omega,\mathcal{A},\Pr)\), and let \(B\) be a Borel set of real numbers. Then \(\{\xi\in B\}\) is an uncertain random event with **chance measure**_  
\[\mathrm{Ch}\{\xi\in B\}=\int\limits_{0}^{1}\Pr\{\omega\in\Omega\mid\mathcal{M }\{\gamma\in\Gamma\mid\xi(\gamma,\omega)\in B\}\geq x\}dx. \tag{5}\]  
**Definition 7** (Liu [14] and Liu [15]).: _Let \(\xi\) be an uncertain random variable. Then, its **chance distribution** is defined by \(\Phi(x)=\mathrm{Ch}\{\xi\leq x\}\) for any real number \(x\)._
## 2 Methodology
### Model Scaling of Full-Scale OCT
In this scenario, the propeller may experience decreased overall performance because of the limited quantity of kinetic energy in the fluid. The upper limit of 2.450 m/s (\(U<\) 2.450 m/s) indicates that the propeller operates in faster-flowing water conditions. In this regime, the propeller experiences higher flow velocities, potentially leading to increased thrust and better performance. According to the scale-down procedure, the Reynolds number (\(Re\)) is given as \(2\times 10^{5}<Re<7\times 10^{6}\) in this study. BEM theory is a well-known technique used to analyze the performance of turbines. In this study, a unique BEM algorithm developed in the MATLAB (2023b) environment was used to consider the specific flow conditions and rotor characteristics. This unique BEM algorithm had a good capacity to make an estimation of various parameters, such as differential torques, thrusts, and power, for the turbines under investigation. To validate the predictions obtained from the BEM algorithm and conduct proof-of-concept testing, a small-scale three-bladed horizontal underwater turbine was constructed. Utilizing a small-scale turbine is a cost-effective approach compared to full-scale testing. This small-scale turbine served as a prototype for testing and numerical model verification. For the experimental setup, an electrical system was required to meet the necessary requirements and measure the generated power. OCT generates power based on local forces, primarily the lift force acting on the rotor blades. In addition, to prevent any structural damage to a full-scale prototype, the undesirable forces acting on the carriage were estimated and analyzed. To estimate the nominal generated power at different carriage speeds and rotor blade rotational speeds, the following method was employed. The carriage speed was used to mimic the motion of the water current, which contains kinetic energy. The tip speed ratio was adjusted to achieve optimal power production at a constant carriage speed. This adjustment was accomplished by varying the rotational speed of the rotor blades. Prior to the design process, several considerations were made. It was crucial to define the expectation for the entire system and determine the best approach for assembling the system with the required components. Due to the limited carriage speed at the UNO towing tank, the DC machine must reach steady-state rotational speeds before functioning as a generator. Therefore, electricity was supplied to the motor using a power supply to achieve the desired speed. Additionally, each circuit element within the system had to be carefully selected and sized to ensure appropriate operation. By incorporating these considerations and implementing the described methodology, this study aims to develop an appropriate design and gather the necessary data for further analysis and evaluation of the OCT's performance.
# Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar
## 5 Conclusions  
The problem of the target three-dimensional localization for the 2-D radar is considered in this paper. RISs are introduced to realize the target three-dimensional localization. In the wide scanning beam of the 2-D radar in the elevation dimension, one or more RISs are cleverly distributed to construct additional auxiliary receiving channels. With the additional measurement information obtained by the RIS-assisted auxiliary channels, the 2-D radar system can realize the target three-dimensional localization with high target localization accuracy.  
For the proposed RIS-assisted 2-D radar system, the target three-dimensional localization is transformed into a least squares problem with constraints. It is proved that the optimization problem is convex, and the convex optimization method is used to solve it, which has high computational efficiency and stability. In addition, the theoretical CRLB is  
Figure 8: Simulated GDOP versus the target location with RISs site errors.  
Figure 9: Difference of GDOP with the site error as 5 m and 1 m.  
derived to measure the target localization accuracy for the proposed RIS-assisted target localization problem. The effectiveness of the proposed method in practical application is verified by simulation results, and the influence of the number and the geometry conditions of RISs on the localization accuracy is discussed, which provides a useful reference for the future 2-D radar system design and optimization.  
Compared with traditional 2-D radar localization methods, the RIS-assisted method proposed in this paper can not only effectively reduce the cost but also reduce the time delay of processing information, which will effectively improve the target three-dimensional localization accuracy.  
Conceptualization, Z.L. and S.Z.; methodology, Z.L.; validation, B.X. and J.A.; formal analysis, Z.L. and B.X.; writing--original draft preparation, Z.L. and B.X.; writing--review and editing, S.Z.; supervision, Z.L.; funding acquisition, Z.L. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61801233 and Grant 61801445 and in part by the Science and Technology Foundation of State Key Laboratory under Grant JKW202209.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Long-Tail Instance Segmentation Based on Memory Bank and Confidence Calibration
## 4 Experiments  
### Dataset and Experiment Details  
LVIS is a large vocabulary lexical-level labeling dataset for object detection and instance segmentation. It contains 164,000 images and 2 million high-quality instance segmentations for over 1000 classes of objects. Based on the number of images included in each category, these categories were divided into three broad categories: tail classes (1-10 images per class), common classes (11-100 images per class), and frequent classes (>100 images per class). The dataset was not collected using unknown category labels, but by collecting images and then labeling them according to the natural distribution of the targets in the images. In contrast to automated machine annotations, a large number of manual annotations can enable the effective identification of naturally occurring long-tail distributions in the dataset images.  
In this paper, we use the LVIS v0.5 training set for training and the LVIS v0.5 validation set for testing. In this dataset, we target tail classes and common classes with a number of images \(\leq\) 30 samples, and we have a total of 706 classes in our target repository. The images were resized during training so that their shorter and longer edges were 800 and 1333 pixels, respectively. The training platform uses 8 Titan Vs with an initial learning rate of 0.02, using a momentum SGD gradient descent method with a weight decay of 0.0001, and the model is trained for 90,000 iterations, decaying at 60 k and 80 k iterations. Two-hundred and fifty-six regions are used by RPN for training. The memory bank in this paper has a fixed maximum size as a constraint on the amount of memory occupied, which is set to 60 to balance memory and performance, and 20 samples are added to the target memory bank class for each batch.  
In this paper, we choose a 128 \(\times\) 128 mask size and a 300-dimensional DCT mask vector as the default mask representation. In Table 1, we compare the \(l_{1}\) loss and smooth \(l_{1}\) loss of \(\lambda\) masks with different weights. The results show that \(l_{1}\) and smooth \(l_{1}\) have similar performance and the DCT-Mask is robust to the corresponding weights. In this paper, the best combination of \(l_{1}\) loss and \(\lambda\) mask = 0.007 is selected. The value of \(a_{c}\) is adjusted in training with reference to Equation (5) by controlling the dependence of \(a_{c}\) on \(N_{c}\). According to the experiments, when \(\lambda\) = 0.6, we can achieve better training results and do not consume too many computational resources.  
Figure 5: DCT network.  
The metric \(\text{AP}_{\text{bbox}}\) was used to comprehensively evaluate each method. It represents the accuracy of the instance segmentation frame, and the subscripts \"r\", \"c\", and \"f\" represent the tail, common, and frequent categories, respectively; \(\text{AP}_{\text{m}}\) is the mask accuracy.
_Article_  
**Influence of Weld Parameters on the Fatigue Life of Deck-Rib Welding Details in Orthotropic Steel Decks Based on the Improved Stress Integration Approach**  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 2, [PERSON] 2 and [PERSON] 3**  
Received: 17 August 2019; Accepted: 13 September 2019; Published: 18 September 2019  
**Abstract: Fatigue cracks in orthotropic steel decks (OSDs) have been a serious problem of steel bridges for a long time. The structural stress approach is an important approach for fatigue life evaluation of welded structures. Firstly, two parameters and the mesh sensitivity of the stress-based integration equivalent structural stress approach (stress integration approach for short) are analyzed in this paper. Then, the applicability of the master S-N curve is verified based on experimental data of the deck-rib welding details in OSDs. Finally, the multi-scale finite element model (FEM) of Jiangyin Bridge is established, and the bridge fatigue life calculation steps based on the stress integration approach are given. The influence of the slope of the master S-N curve at high cycles on the bridge fatigue life is discussed. Further, the weld parameter influences on the bridge fatigue life are analyzed, as including the following: (1) The determination of the influence of the weld size changes caused by weld manufacturing errors on the bridge fatigue life; (2) the proposal of a new grinding treatment type, and the analysis of influence of the grinding radius on fatigue life; and (3) a comparison of the fatigue life of the deck-rib welding details under 80% partial penetration and 100% full penetration. The results show that the structural stress calculated by the stress integration approach does not change significantly with the parameters of the isolation body width \(w\) and the distance \(\delta\) between the crack propagation surface and the reference surface. To simplify the calculation, \(\delta\) is set as 0, and \(w\) can be set as the mesh size along the weld length direction. The mesh size of the stress integration approach is recommended as 0.25 times the deck thickness. The slope of the master S-N curve at high cycles significantly affects the bridge fatigue life, and a slope of 5 is reasonable. The weld parameter studies for the deck-rib welding details in the OSD of Jiangyin Bridge show that the change of weld size caused by manufacturing errors can obviously affect the bridge fatigue life, and the fatigue life of five different weld types varies from 51 years to 113 years. The new grinding treatment type, without weakening the deck, is beneficial to improving the bridge fatigue life. The fatigue life increases by approximately 5% with an increase of the grinding radius of 2 mm. The fatigue life of 80% partial penetration is slightly higher than that of 100% full penetration.**  
**Keywords: steel bridge; deck-rib; fatigue evaluation; equivalent structural stress; master S-N curve; weld size; grinding treatment; penetration rate**
# A Deep Learning-Based Mapping Model for Three-Dimensional Propeller RANS and LES Flow Fields
## 3 Method  
The problem of mapping the propeller RANS flow field to the LES flow field can be analogized to image mapping and regression problems. The goal of this work is to transform RANS data points into LES data points using a data-driven deep learning model. To this end, we first convert all point data in each sample set (generally recorded in a single data file) of RANS or LES into an image and then construct a mapping model based on the deep learning model for the purpose of establishing a mapping between the two different flow fields. Next, the mapping model is trained on the RANS sample images and LES images. After that, with the LES image obtained from the corresponding RANS image by the mapping model, a regression module is employed to predict the LES velocity information at a particular position in the flow field from the obtained LES image. Therefore, the above process can be represented as follows:  
\[\left(V\_axial_{p},\ V\_tangential_{p},V\_radial_{p}\right)=F(f(I),x,y,z) \tag{1}\]  
where \(V\_axial_{p},\ V\_tangential_{p},V\_radial_{p}\) are the predicted axial, tangential, and radial velocity components at the point \(p\) with spatial coordinates \((x,y,z)\) in the LES field, respectively, \(f\) is the mapping model between the RANS image and the corresponding LES image, \(I\) is the obtained LES image, and \(F\) is the regression module used for regression from the LES image to the LES velocity information.
## 3 Case Study on Huangguan Island  
Smart city management systems are now popular in the world of managing cities; they can increase the capabilities of local government with the help of ICT. On Huangguan Island, the SVSMS is composed of two databases; one is the physical information database, which includes the environment data and infrastructure data; the other is the human information database, including personal data collected from all smart services, such as mobile devices, transportation cards, personal identification systems, and cameras monitoring human activities on the island.  
Huangguan Island is an isolated island in Fujian Province, China. There are no original residents there. The island was developed by the Longshen Group in 2016. The island is located close to Dongbi Island, where it can take people one hour to get to Fuqing City, which belongs to the Fuzhou New District, as demonstrated in Figure 1. Huangguan Island is located near the Fuzhou New District, and belongs to the Fuzhou-Putian-Ningde Metropolitan Area. It is based within the 0.5-hour travel circle of Fuqing, and the 1-hour travel circle of Fuzhou. The capacity of visiting services is fifteen log houses for 60 persons or 15 families, and the main hotel for 100 guests. In total, there are 20 staff members for all of the office work on the island. Thus, it is a small database for management.  
Figure 1: Location of Huangguan Island.  
There are two systems proposed in the Huangguan SVSMS: the smart services system and the infrastructure monitoring system, as indicated in Figure 2. Therefore, the smart service system is used for the human information database containing personal information and data, while the infrastructure monitoring system is used for the physical information database containing the environment data and the infrastructure data. In this research, we focus on the understanding of the ways to deal with personal information collected by the SSS for the improvement of visiting management, which is an integrated system with all of the smart services on Huangguan Island.  
Hereafter, we explain how the SVSMS is composed of different smart services, how the personal information is collected by those systems, and how that data can be provided in order to support the integrated services on the island.
# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 3 Results
### BER Performance Comparison among NNMS, SNNMS, and New-SNNMS  
Comparisons are made between the decoding performance of the proposed new-SNNMS decoding network and that of NNMS and NMS. Bellow, a 22-layer neural network is utilized for the comparison.  
As shown in Figure 9, it can be observed that the BER performance of the new shared-parameter NNMS algorithm outperforms the SNNMS, NNMS, and NMS algorithms. In  
Figure 8: BER performance versus the number of network layers (SNR = 10).  
Figure 7: BER performance versus the number of network layers (new-SNNMS).  
the case of the 22-layer neural network (i.e., 10 iterations) with a BER of \(~{}10^{-3}\), the BER performance of new-SNNMS performs up to 0.3 dB better compared with the SNNMS.  
The new-SNNMS decoding algorithm exhibits better performance, it can achieve the same BER performance with fewer neural network layers. This implies that the decoding process exhibits lower latency and computational complexity. Therefore, it is suitable for decoding large amounts of power data transmitted on UAVs.
# Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
## 2 Backgrounds
### Graph Structure Learning  
Despite their powerful capability in learning discriminative embeddings for graph structured data, GNNs tend to suffer from the noisy or incomplete graph structures commonly found in real-world datasets, which can hinder their performance [30]. With the message-passing scheme, noise within the graph is propagated to further layers of nodes in the graph and amplified through the aggregation process. Furthermore, an incomplete or incorrect graph structure can prevent the GNN model's ability to discover critical structural connections among nodes, impeding its capacity to gather useful information and generate optimal node representations. Prior research has demonstrated that even small alterations to the graph structure can significantly impact the learning capabilities of GNNs [31; 32].  
The graph structure learning (GSL) method was introduced to address such limitations by aiming to unveil latent graph structures within data. Metric-based graph structure learning approaches measure node similarities by calculating the inner product [33] or cosine similarity [34] of their attributes and construct the adjacency matrix accordingly. Neural-based approaches seek to design deep neural network models to learn the adjacency matrix as a representation of the graph topology. Among them, MTGNN [4] is a representative framework that employs two Fully Connected Neural Networks to embed node representations and model the adjacency matrix, and then identifies the top-k values in each row as neighbors. Another approach involves parameterizing the graph structure under the assumption that the adjacency matrix is sampled from a specific distribution, such as the Bernoulli distribution. Therefore, the values in the adjacency matrix can be sampled from the distribution, which is parameterized by a learnable parameter denoted as \(\theta\)[35].