esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hierarchical Decision Fusion Diagnosis Method for Rolling Bearings
## 1 Introduction
In addition, when fault data is updated, only relevant neural networks need to be updated, which enhances the maintainability of the method. The wavelet packet decomposition and signal reconstruction of the raw vibration signals of the rolling bearing is carried out, and the time-domain characteristics of the reconstructed signals at each node are extracted as the input signal of the decision fusion networks, which improves the accuracy of the diagnostic results. The decision fusion networks model of each layer includes many BPNNs. A time-domain feature corresponds to a BPNN, and the diagnostic results of BPNNs are fused by the majority voting method. The highest diagnostic rate of the hierarchical diagnosis method can be obtained without artificial selection of time-domain features. Finally, the proposed method is verified experimentally. The results show that the proposed method can realize rolling bearing fault detection, fault location isolation and fault degree estimation under different operating conditions. The structure of the paper is organized as follows: Section 2 introduces wavelet packet decomposition, BPNN and decision fusion based on the voting method, and proposes the rolling bearing hierarchical diagnosis method. Section 3 verifies the proposed method by experimental data. Section 4 provides the conclusion.
## 4 Conclusions  
To provide a durable concrete infrastructure in severe environmental conditions, a comprehensive experimental and analytical study was conducted on a bridge in the Tohoku region, Shinkesen Bridge. This study focused mainly on the bridge's early-age performance (i.e., thermal and shrinkage-induced cracks), and extension to long-term durability will be addressed in a future study. First and foremost, on a small scale (decimetres), validation of the multiscale integrated computational system was satisfactory and agreed well with the experimental results. Verification with a medium-scale experimental mock-up slab specimen was then carried out to further confirm the capability of the numerical model, which also presented consistency with the experimental results. In addition, to reduce the calculation effort for large-scale analysis, an element discretisation process was performed in the mock-up slab specimen to determine the appropriate element size. Preliminary parametric studies on different mixture designs suggested the validity of the proposed mix design for Shinkesen Bridge. Finally, after bridge construction was complete, the computational model was again verified with acceptable agreement with data from strain gauges embedded in the concrete deck slab. The conclusions achieved are summarised as follows.  
1. The multiscale thermodynamic integrated analysis was verified and validated from the laboratory scale on the order of decimetres up to the structural scale on the order of decametres, which adequately confirms its ability to assess the behaviour of actual structures.
2. By conducting the preliminary analyses before bridge construction, the superiority and inferiority of each mix proportion could be displayed to a great extent, which helped the engineers to be more decisive and confident when designing their mix proportions.
3. Through the success of the study, the multi-scale thermodynamic computational platform would be implemented for long-term performance study by tracing the behavior of concrete through the course of time to propose maintenance plan abiding with preventive maintenance strategy currently endorsed in Japanese civil engineering situation.  
This study was financially supported by the Council for Science, Technology and Innovation, \"Cross-ministerial Strategic Innovation Promotion Program (SIP), Infrastructure Maintenance, Renovation and Management\" through a grant by Japan Science and Technology Agency (JST).  
[PERSON] and [PERSON] conceived and designed the analytical and experimental scheme; [PERSON] and [PERSON] supervised over the analytical process, [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Open RAN--Radio Access Network Evolution, Benefits and Market Trends
## 4 Open Xhaul Transport Network--Requirements and Solutions
Among them, we can discern [40]: \(\bigcirc\) exploration of higher frequency bands, such as W-band (100 GHz) and D-band (150 GHz) enabling up to 100 Gbps radio links; \(\bigcirc\) expansion of capacity in traditional (lower) frequency bands; \(\bigcirc\) increasing spectral efficiency by application of higher order modulation formats and by utilization of LoS MIMO systems; \(\bigcirc\) introduction of new signal multiplexing mechanisms such as orbital angular momentum; aggregation of bands and carriers;introduction of higher class antennas enabling reuse of channels in a given geographical area by reducing the minimum angle between two links using the same channel.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 4 Model Architecture
### Traditional Classifier Experiments
#### 4.2.2 Multilayer Perceptron (MLP)  
With the help of the MLP, it was possible to minimize the system's expected and real output differences. For the current model, the MLP topology design included the following parameters:  
* activation = 'tanh'
* hidden_layer_sizes = 100
* activation ='relu', solver = 'adam'
* validation_fraction = 0.1
* learning_rate_init = 0.001
* max_iter = 200  
The hyperbolic tangent activation function was applied for all hidden layer neurons. For the output layer neurons, the linear ones were applied.
## 4 TSN Network-Wide Scheduling--Approaches Overview, Challenges, and Future Directions
### Literature Analysis
#### 4.2.4 Expansion of the Scope of the Problem  
Traditionally, optimization of network configuration included only scheduling and routing. However, some of the more recent works included the topology design as well as the talker/listener placements in the optimization formulation, as can be seen from the third column of Tables A1 and A2 (T stands for topology and A for application (or task)). Few works so far include application placement optimization into the TSN scheduling approaches. However, with recent trends towards edge computing and TSN hardware support on commodity end-hosts (e.g., TAS-configurable hardware queues in the NIC) and virtualization (e.g., NFV and virtualized PLCs), flexible placement of TSN-enabled applications on the TSN-enabled edge-nodes is becoming a reality. However, to provide tight end-to-end timeliness guarantees and tight integration of end-host placement decisions, resource allocation in the end-hosts and their network stacks and a network-wide schedule configuration need to interact properly. On the other hand, a flexible placement of talkers and listeners on the TSN-enabled end-hosts adds another degree of freedom which could be exploited to increase the solution space of the scheduling problem and reduce the strictness of the requirement (i.e., placing HTS talker and listener closer in the network  
Figure 5: Evolution of optimization objectives over time.  
reduces the minimum required accumulated latency), and could lead to better robustness and less jitter by exploiting the topology design flexibility.
## 4 Evaluation over Use Cases
### ICT Gateway
#### 4.1.1 Context Establishment  
This phase involves establishing the security context by defining relevant security and privacy claims tailored to each layer of the ICT GW architecture. These claims are selected from a predefined set that aligns with industry standards and best practices, establishing the basis for security evaluation. A total of 11 claims were identified as critical to the system's secure operation. Additional details about them are given in [29].  
* C10: Changes in user authentication values are executed securely.
* C11: Sensitive parameters for secure association establishment are protected for integrity during communication.
* C17: Sensitive data in transit are encrypted with appropriate methods.
* C22: The system is resistant to Denial of Service (DoS) attacks.
* C23: Input data are validated to prevent injection vulnerabilities.  
Figure 6: Overview of the ICT GW.  
C45: All protocols and libraries used are up-to-date.
* C46: Authentication protocols utilize recommended algorithms for security.
* C47: Authenticated sessions expire and require re-authentication for enhanced security.
* C52: The system allows data subjects to delete their personal data permanently.
* C58: The system enforces a limit on consecutive failed login attempts.
* C72: Logs are protected against unauthorized removal.  
In addition to these claims, we defined TP specific to the ICT GW use case. These profiles specify the minimum acceptable risk levels across each security property--such as confidentiality, integrity, and availability--according to the ICT GW's role in the smart grid. The profiles were structured in YAML [40] format (first block of Listing 1, lines 2-8) to facilitate interpretation and integration with automated tools, ensuring that the methodology is adaptable and scalable for different implementations.  
1 Methodology:yaml
2 +-tolerance,profile
3 | +-confidentiality: table[int]
4 | +-integrity: table[int]
5 | +-availability: table[int]
6 | +-authorization: table[int]
7 | +-authentication: table[int]
8 | +-nonrepudiation: table[int]
9 +-components
10 | +-Component [name]*
11 | +-sensitivity: double
12 +-claims
13 | +-claim [name]*
14 | +-component: string
15 | -security_properties
16 | | +-security_property [name]
17 | +-reference?: string
18 | +-impact?: double
19 | +-tests'
20 | | +-test [name]*
21 | +-priority?: int
22 | +- vulnerabilities?
23 | +-vulnerability [name]*
24 +-vulnerabilities
25 +-vulnerability [name]*
26 +-reference: string
27 +-impact: double
28 +-tests
29 +-test [name]*
30 +-priority?: int ```
**Algorithm 1** YAML-based file syntax for system description.
## 1 Introduction
Conventional modeling methods for MMC valve electric fields suffer from long modeling times and low accuracy. With the rapid development of information technology, data derivation technology, as an important data processing technique, has been widely applied in various industries, especially in the era of big data. Its core principle is to generate a large amount of derived data from a small amount of raw data in a specific manner, thereby providing richer and more comprehensive data support for the analysis and modeling of complex systems [14; 15; 16; 17]. This paper innovatively introduces data derivation technology into the electric field modeling of the MMC valve, aiming to derive the parameter matrix for MMC valve electric field modeling by combining a small number of MMC system parameters with the design process and principles of the MMC valve. This method also digitizes the modeling parameters and process of the converter valve, thereby providing an efficient and accurate new approach for the optimized design and performance evaluation of converter valves. In this paper, a high-speed modeling method for the MMC valve is proposed to overcome these drawbacks. The data derivation is used to generate multiple sets of parameters from a limited number of system input parameters (three to four). These derived parameters are then utilized to establish the electric field analysis model for the MMC valve. Firstly, the relationship between the MMC valve electric field calculation model parameters and the MMC-HVDC transmission system parameters was studied. Based on this, the data derivation system for the electric field calculation model parameters of all components of the MMC valve was established. A modeling parameters database based on empirical knowledge is also used in the calculation process of the data derivation system. Empirical knowledge is used to constrain and correct the parameters. The output model parameter matrix includes the geometric parameters, position parameters, and electrical parameters of the components. Based on the output matrix, the electric field calculation model of the converter valve can be quickly generated. Furthermore, to improve the accuracy of the calculations and reduce computation time, a discrete modeling method that combines mesh optimization was proposed. In the process of discretized electric field modeling, a mesh division influence factor based on the accuracy of electric field calculation is proposed. By adjusting the mesh division influence factor during the electric field calculation, the accuracy of the electric field calculation can be optimized rapidly. Finally, the effectiveness and practicality of the high-speed modeling method for the MMC valve are verified through comprehensive case studies conducted on the \(\pm\)320 kV onshore MMC-HVDC valve. By comparing the modeling times, calculation error rate, and calculation times, it was confirmed that the method described in this paper has significant advantages.
## 3 Expansions of the IG Modes by the HG and LG Modes at Small Values of \(p\)
### Cases with the Characteristic Polynomial of Degree 2  
Below, without derivation, we give explicit analytical expressions for those IG modes, for which the characteristic polynomial reduces to a quadratic polynomial and the respective roots \(\lambda_{q}(p,\varepsilon)\) can be expressed in square radicals.  
It is worth noting that if the initial definition \(\varepsilon=2\sigma^{2}\) is dropped, the parameter \(\varepsilon\) (ellipticity) can be considered to be a negative number (as observed by [PERSON] [18]). Therefore, in addition to the limiting cases of IG modes with \(\varepsilon=0\) and \(\varepsilon=\infty\), we also consider a case wherein \(\varepsilon=-\infty\).  
In Table 1, the following designations are used: \(\mathbf{HG}_{n,m}(\mathbf{r})\) and \(\mathbf{LG}_{p,l}(\mathbf{r})\) are the normalized Hermite-Gaussian and Laguerre-Gaussian modes (6), and \(\mathbf{IG}_{p,q}^{(\varepsilon,\varrho)}(\mathbf{r},\varepsilon)\) are the normalized Ince-Gaussian modes (13).  
The coefficients \(a,b,c,d\) here are positive, monotonically increasing functions of \(\varepsilon\), as shown below:  
\[a=\varepsilon+\sqrt{\varepsilon^{2}+1},\;\;b=\varepsilon-1+\sqrt{\left( \varepsilon-1\right)^{2}+3},\;c=\varepsilon+1+\sqrt{\left(\varepsilon+1 \right)^{2}+3},\;d=(\varepsilon+\sqrt{\varepsilon^{2}+9})/3. \tag{16}\]  
We should note a similarity among the IG modes with equal indices \(p\) and parity flags \((e,o)\). For instance, to obtain mode \(\mathbf{IG}_{4,4}^{o}(\mathbf{r},\varepsilon)\) from mode \(\mathbf{IG}_{4,2}^{o}(\mathbf{r},\varepsilon)\), it is sufficient to swap the superposition coefficients and change the sign of one of them. It turns out that a similar property is valid in a more general case. Namely, if the whole set of IG modes except one is known for fixed values \(p\) and \((e,o)\), this unknown mode can be obtained by manipulating the expansion coefficients of the modes from the set. When the characteristic polynomial is quadratic, there are only two modes and the \"manipulation\" is just a swapping of the coefficients with a sign change. For more complicated cases, manipulations are also more complicated. However, they are incomparably simpler than obtaining the dependence of the superposition coefficients on the ellipticity parameter.
## 4 Non-Parametric Methods
### Artificial Neural Networks  
Artificial Neural Networks (ANNs) model the way the biological brain solves problems to enable computational systems to have the same capacity to learn [48]. They consist of several interconnected artificial neurons that are categorised into 3 main layers: the input, hidden and output layers. The ability of neural networks to detect patterns in the input/output relationships of given data sets makes them ideal contenders for anomaly detection systems. However, their inability to handle unseen data is a major limitation for these kinds of applications so ANNs are seldom used in isolation [49].  
An adaptive neural swarm approach was proposed for anomaly detection by the author in [50]. The system uses a decentralised collaborative approach which utilises agent-based swarm intelligence. Modular neural networks are formed autonomously by the swarm agents and the reinforced learning approach is used in the training process to facilitate an unsupervised learning environment. The proposed scheme is theoretically capable of monitoring the application environment and identifying possible anomalies in real-time. Simulation results were promising, but more research is required to explore the feasibility in resource constrained devices.  
An interesting research focus in the application of neural networks in anomaly detection is on the training process. A variation of the online sequential extreme learning machine (OS-ELM) was proposed for the training of single layer feed-forward neural networks (SLFN) by the author in [51]. The proposed scheme uses an embedded fixed-point implementation which has overcome the stability issues inherent with fixed-point implementations. The scheme is ideal for resource constrained devices because the underlying algorithm has a low computational overhead. When compared to state of the art techniques, it was found to have comparable results.  
Another related application scenario is the deployment of IWSNs for the specific task of detecting anomalies. In this case, the anomalies are usually caused by equipment malfunction but as already mentioned, there is little difference between the effects faulty equipment and an attack. An architecture for industrial condition monitoring that is based on a dynamic neural network was proposed by the authors in [52]. The proposed scheme was designed to work in noisy, dynamic non-linear systems and was tested on the cooling system of a poly-phase induction motor. It was able to produce a high detection accuracy when tested using unseen anomalous data.
# Impacts of Arc Length and ECOM Solar Radiation Pressure Models on BDS-3 Orbit Prediction
###### Abstract
Therefore, this article will concentrate on the impact of the ECOM SRP model on BDS-3 satellite orbit prediction. The BDS MEO satellites enter the eclipse season twice a year, each time lasting 8-15 days [20]. If this study does not consider the eclipse season and only investigates satellite orbit-prediction accuracies in the non-eclipse season, all orbit prediction starting times are outside the eclipse season. Hence, orbit prediction times are extended to 8-15 days, which would significantly reduce the prediction accuracy of the satellite orbit. Therefore, it is worthy to study the prediction accuracy of BDS-3 satellite orbits during the eclipse season. The China Satellite Navigation Office (CSNO) released BDS-3 satellite metadata in 2019, announcing that the BDS-3 MEO/IGSO satellites adopt continuous yaw steering (CYS) attitude modes during eclipse seasons [18]. The accuracy of current GNSS satellite orbits is significantly degraded during eclipse seasons, particularly for long-arc solutions and orbit predictions [21]. [PERSON] et al. (2019) pointed out that the reason for such degradations is primarily due to the ignorance of thermally imbalanced forces [22]. [PERSON] et al. (2022) also found that orbit degradations result from the unaccounted non-conservative forces, e.g., thermal radiation during Earth's shadow transitions [23]. The above studies are all based on the precise orbit determination of the BDS-3 satellites, and the study of the orbit prediction accuracy of the BDS-3 satellites during the eclipse season is essential as well. This contribution mainly studies the impact of the arc length and ECOM SRP models on BDS-3 orbit prediction. The article is organized as follows. We first introduce the orbit-prediction method and the experimental data in Section 2. Afterward, the final precise ephemeris and SLR data were used to evaluate BDS-3's orbit prediction accuracy by using different arc lengths in Sections 3.1 and 3.2, and the orbit prediction accuracy of the BDS-3 satellites during and outside of eclipse seasons is provided in Section 3.3. Additionally, we analyze the impact of different ECOM SRP models on the predicted orbit accuracy of the BDS-3 satellites in Section 3.4. Finally, discussions and conclusions are provided in Sections 4 and 5.
## 3 Method
### Model Architecture
#### 3.3.4 Encoder  
The encoder comprises \(N_{en}\) encoder layers, each containing TSA, SSA, and FFN. TSA and SSA extract temporal and spatial dependencies and correlations while FNN conducts further spatial-temporal feature learning.  
The TSA performs linear transformation on \(X^{t}\) to generate the query \(Q_{TSA}\in\mathbb{R}^{T_{H}\times N_{h}\times d_{model}}\), key \(K_{TSA}\in\mathbb{R}^{T_{H}\times N_{h}\times d_{model}}\), and \(V_{TSA}\in\mathbb{R}^{T_{H}\times N_{h}\times d_{model}}\). Then, a one-hidden-layerMLP projects \(TE=\{TE^{1},TE^{2},\dots,TE^{T_{H}}\}\in\mathbb{R}^{T_{H}\times N_{\text{I}_{x}} \times d_{model}}\) to three weighted matrices \(W_{Q}^{TSA}\in\mathbb{R}^{T_{H}\times N_{\text{I}_{x}}\times d_{model}}\), \(W_{K}^{TSA}\in\mathbb{R}^{T_{H}\times N_{\text{I}_{x}}\times d_{model}}\), and \(W_{V}^{TSA}\in\mathbb{R}^{T_{H}\times N_{\text{I}_{x}}\times d_{model}}\), which respectively guide the MHA computation on \(Q_{TSA}\), \(K_{TSA}\) and \(V_{TSA}\):  
\[MultiHead(Q_{TSA},K_{TSA},V_{TSA})=Concat(head_{1},head_{2},\dots,head_{N_{h}} )W^{0}, \tag{9}\]  
\[head_{i}=Softmax\Bigg{(}\frac{Q_{TSA}W_{Q}^{TSA}(K_{TSA}W_{K}^{ TSA})^{\top}}{\sqrt{d_{K}}}\Bigg{)}V_{TSA}W_{V}^{TSA}. \tag{10}\]  
Similarly, the SSA transforms the output of TSA into \(Q_{SSA}\in\mathbb{R}^{T_{H}\times N_{\text{I}_{x}}\times d_{model}}\), \(K_{SSA}\in\mathbb{R}^{T_{H}\times N_{\text{I}_{x}}\times d_{model}}\), and \(V_{SSA}\in\mathbb{R}^{T_{H}\times N_{\text{I}_{x}}\times d_{model}}\) and transforms \(STE=\{SE^{1},SE^{2},\dots,SE^{T_{H}}\}\in\mathbb{R}^{T_{H}\times N_{\text{I}_ {x}}\times d_{model}}\) into \(W_{Q}^{SSA}\in\mathbb{R}^{T_{H}\times N_{\text{I}_{x}}\times d_{model}}\), \(W_{K}^{SSA}\in\mathbb{R}^{T_{H}\times N_{\text{I}_{x}}\times d_{model}}\), and \(W_{V}^{SSA}\in\mathbb{R}^{T_{H}\times N_{\text{I}_{x}}\times d_{model}}\) to perform MHA computation. After TSA and SSA computation, the encoder applies an FNN to convert the output sequence into the encoding sequence \(X_{en}\).