esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### TPCCNN-Based Fault Diagnosis  
This fault diagnostic model architecture includes data pre-processing, model pre-training, and model fine-tuning. Time series classification is an essential field in time series data mining. It has been widely used in different areas, such as medical science electrocardiogram for health diagnosis, identification of human activities, and computer science for speech recognition and machine fault detection. With the advent of deep learning, new methods were developed, especially convolutional neural network (CNN) models. Although it has drawn great interest in the past few decades, it is still challenging and inefficient due to the nature of its data: high dimensionality, large data volume, and constant updates. [PERSON] et al. [21] have reviewed several techniques to deal with time series classification, which can be categorized as model-based, distance-based, and feature-based. Most deep learning architectures are unable to directly process the raw input data of vibration for final defect classification and prediction. Further, the TPCCNN-based model is unable to deal with the raw data. In order to enable end-to-end computation for deep learning architectures, data preprocessing techniques play a crucial role in intelligent fault diagnosis [22; 23; 24].  
The original fault data of the source and target domain are obtained by the vibration sensor and presented as time-domain data. First, the fast Fourier transform (FFT) is applied to map the data into frequency domain, as shown in Equation (2). The frequency-domain signals have higher fault recognition accuracy than time-domain signals, and the frequency domain data is then normalized with the maximum and minimum values for normalization. That is, the data is scaled within the interval of \(0\leq X^{\prime}\leq 1\). The calculation method is shown in Equation (3).  
\[X_{k}=\sum_{n=0}^{N-1}X_{n}e^{-2\pi k\frac{N}{N}},k=0,\;1\dots,\;N-1. \tag{2}\]  
\[Xt\;=\frac{X-X_{min}}{X_{max}-X_{min}}\;\in[0,\;1] \tag{3}\]
## 5 Results  
The hypotheses investigated the claim that the changing media environment relates to the citizen type. Specifically, the study hypothesized that new media relate with specialists while traditional media (television, newspaper, and radio) relate with generalists. To test these hypotheses, the SDI was regressed on various demographics, political interest, and four medium variables. Table 1 summarizes the results of the ordinary least squares hierarchical regression.  
The first block included control variables. Among demographics, the young and males showed a higher level of SDI than their counterparts, indicating they tend to perceive a few issues to be particularly important to them and pay little attention to other issues. This analysis allowed us to assess the construct validity of the proposed measure. The construct validity is generally established either when the target measure correlates with what it is theoretically predicted to correlate with (_i.e._, convergent validity), or when it is uncorrelated with other measures with which it should not be associated (_i.e._, discriminant validity). The regression results added to both the convergent and discriminant validity of the SDI. First, general political interest related strongly and negatively with the SDI. This demonstrates the convergent validity of the SDI because the SDI is supposed to represent specialists whose general political interest is limited. Second, the SDI is independent of education, which corroborates the discriminant validity. This suggests that the SDI is neither a certain tautological measure nor statistical artifact but instead taps onto a unique dimension of individual characteristics.  
The regression results of the full model yielded support for H1, H2, H3, and H4. Not only did all four medium variables relate with the SDI, but there was a striking difference between online use and traditional media use in relation to the SDI. When people pay attention to online news, their SDI values also grow, indicating that their perceived issue importance varies significantly across issues. On the other hand, as people use traditional media such as television, newspaper, and radio, they distribute their perceived importance evenly among various issues. Although these results do not demonstrate causal relationships between the type of medium and the citizen type, the findings confirm the recent speculation that the Web use relates to the growth of specialists, while traditional broadcasts are associated with generalists [6, 8, 9].
## 2 BDS-3 SAIM Monitoring Method
### Current Monitoring Methods
#### 2.1.2 Pseudo-Range Monitoring  
Satellite load anomalies may cause abnormal jumps in the pseudo-range measurements received by the user receiver. Since the monitoring unit can adopt different frequency sources for the satellite's atomic clock, the pseudo-range measurement value of the satellite's autonomous monitoring usually has a linear drift. By increasing the corresponding linear compensation, the pseudo-range measurement value of the satellite's autonomous monitoring can fluctuate within a certain range around the mean value. If an abnormal jump is found in the pseudo-range of a certain navigation signal and exceeds the alarm threshold, the alarm information of the signal is given.  
Figure 1: Current SAIM on BDS-3 satellites.
## 3 Results and Discussion
### Evaluation of the Gamification Model in Production
#### 3.2.2 Analysis of the Gamified Model Based on the Engineers' Answers  
Analyzing the profiles of the 15 engineers who responded, 67 percent of the sample is made up of young engineers, between the ages of 26 and 35, and 35.3 percent of engineers are over the age of 56, indicating a diverse range of experience, as shown in Figure 10.  
After the engineers' evaluation of the model, a summary of the answers is presented in Table 3.  
Figure 10: Profile of the sample of construction engineers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Item** & **Mean** & **Variance** & **Std. Dev.** & **No.** & **Negative** & **Positive** & **Scale** \\ \hline
1 & 1.8 & 2.5 & 1.6 & 15 & obstructive & supportive & Pragmatic Quality \\ \hline
2 & 2.0 & 1.4 & 1.2 & 15 & complicated & easy & Pragmatic Quality \\ \hline
3 & 2.2 & 0.5 & 0.7 & 15 & inefficient & efficient & Pragmatic Quality \\ \hline
4 & 2.1 & 0.9 & 1.0 & 15 & confusing & clear & Pragmatic Quality \\ \hline
5 & 2.2 & 0.5 & 0.7 & 15 & boring & exciting & Hedonic Quality \\ \hline
6 & 2.6 & 0.4 & 0.6 & 15 & not interesting & interesting & Hedonic Quality \\ \hline
7 & 2.6 & 0.5 & 0.7 & 15 & conventional & inventive & Hedonic Quality \\ \hline
8 & 2.3 & 1.1 & 1.0 & 15 & usual & leading edge & Hedonic Quality \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the engineers’ answers to the UEQ-S.  
Figure 9: Promotion of the model (NPS) to the worker.  
Table 3 demonstrates that the model has positive characteristics, as measured by values greater than 1.8, in the engineers' opinion. Engineers rate pragmatic quality (efficiency, ease of use, dependability) lower than hedonic quality (originality, stimulation), indicating how open they are to gamification on the construction site. According to the evaluations for general quality above 2.0, they believe that the solution will motivate workers to achieve their goals, as shown in Figure 11.  
When the evaluated product's results are compared to the reference data, conclusions about the evaluated product's relative quality can be drawn. Table 4 summarizes these findings.  
Regarding NPS, all engineers would recommend the gamified model, which demonstrates that the model is in the Zone of Excellence (76-100) for engineers, as shown in Figure 12.  
Would you recommend this model to a feiend (engineer or technical builder)? 15 answers
# A Site-Specific Response Analysis: A Case Study in Hanoi, Vietnam
## 3 Site Response Analysis Methodology  
DEEPSOIL v7.0 [17] was used to perform equivalent linear and nonlinear 1-D site response analyses. The most widely used pressure-dependent hyperbolic, the Modified Kodner-Zelasko (MKZ) model [18], implemented in DEEPSOIL, was used. The dynamic curves proposed by [PERSON] [19] were adopted. The variables required to generate the nonlinear curves for each layer are the coefficient of lateral earth pressure (\(K_{0}\)), plasticity index (PI), number and frequency of cycles (\(N\)), loading frequency (\(f\)), and over consolidation ratio (OCR). Because of the unavailability of site-specific index properties, PI for the sand and clay was assumed as 0% and 15% [20; 21; 22; 23; 24; 25], respectively, whereas OCR was taken as 1. \(N\) and \(f\) were set to 10 and 1, respectively, as recommended by [PERSON] [19], [PERSON] et al. [22], and [PERSON] et al. [23]. \(K_{0}\) was calculated as 0.5 using the [PERSON] [26] equation based on the representative friction angle of 30 degrees, as specified in the laboratory investigation report. Small strain damping (\(D_{\rm min}\)) was also modeled using the functions of [PERSON] [19], which predicts generally larger damping values near the ground surface, whereas it keeps on decreasing along with the depth. Figure 4 shows the site response analysis flowchart in this study. A detailed discussion about soil profiles, input ground motions is presented in sections below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ground Type** & \(S\) & \(T_{\rm B}\) **(s)** & \(T_{\rm C}\) **(s)** & \(T_{\rm D}\) **(s)** \\ \hline A & 1.0 & 0.15 & 0.4 & 2.0 \\ B & 1.2 & 0.15 & 0.5 & 2.0 \\ C & 1.15 & 0.2 & 0.8 & 2.0 \\ D & 1.35 & 0.2 & 0.8 & 2.0 \\ E & 1.4 & 0.15 & 0.5 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of parameters describing elastic response spectra [14; 15].
## 2 Topologies in Smart Inverters
### Topologies under Wired Communication
#### 2.1.2 Data Rate  
Data rate indicates the speed at which data is transmitted and plays an important role in terms of taking quick actions. Data rate is more than 10 Mbps for a wide area network (WAN) and can be less than 10 Mbps for home or industrial application.
# Intelligent Reception of Frequency Hopping Signals Based on CVDP
## 3 Algorithm Simulation and Performance Analysis
### Performance of Frequency Hopping Intelligent Reception  
The CVDP network is applied to the frequency hopping reception system shown in Figure 1 to realize the intelligent reception when the frequency hopping sequence is unknown. A 100,000-bit (0,1) sequence is sent randomly to test the reception performance of the frequency hopping system separately under BPSK (Binary Phase-Shift Keying) modulation and QPSK (Quadrature Phase Shift Keying) modulation. The experiment was conducted in seven environments, namely multi-tone interference environment, single-band interference environment, multi-band interference environment, swept frequency interference environment, mixed interference environment, frequency flat Rayleigh fading channel, and frequency selective Rayleigh fading channel. The BER curves of the CVDP-based frequency hopping intelligent receiver system and the conventional receiver system are shown in Figures 8 and 9.  
Figure 8: The BER curve in five types of the interference environment (**a**) The modulation type is BPSK, (**b**) The modulation type is QPSK.  
Figure 7: Average test time bar graph.  
Conventional reception refers to an ideal reception scenario in which a receiver is aware of the frequency hopping sequence transmitted from the transmitter. It can be seen from Figures 8 and 9 that the intelligent reception system performs well under both BPSK modulation and QPSK modulation. The intelligent reception system exhibits remarkable reception performance in five different interference environments, and its BER is only slightly different from that of the conventional reception system when the SIR is low. Despite severe signal distortion caused by multipath fading and Doppler shift in two fading channel environments, the CVDP network ensures accurate hopping frequency estimation and enables the intelligent receiver system to attain the performance of a conventional receiver system.  
When the transmitter of a frequency hopping system makes intelligent decisions according to an electromagnetic interference environment, the hopping sequence is no longer fixed [2]. Consequently, a conventional receiver is unable to predict the hopping sequence, and its BER may increase up to 50% or more, resulting in communication un normally. However, an intelligent reception system based on CVDP for hopping signals can achieve optimal reception performance when the hopping period and signal bandwidth are known, and it has better adaptability and interference immunity.
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### More Recent Development of NNs for Navigation System Designs
#### 4.3.5 Convolutional Neural Network (CNN) with GRU  
The ELM has garnered considerable interest and attention in recent years from the scientific community because of its lightning-fast learning rate and exceptional generalization capabilities. As a result, the ELM is used and the RBFNN is contrasted with it. The simulation results demonstrate that by implementing the suggested strategy, the integrated navigation system's accuracy is greatly increased, and in terms of both real-time capability and refinement power, the ELM gives the best performance.  
Figure 19: Structure of GRU-aiding INS/GNSS integrated navigation: (**a**) training mode; (**b**) predicting mode.  
Figure 18: The structure of GRU.  
Four one-dimensional CNNs [115; 116; 117; 118; 119; 120] are used in the studied INS/GPS neural network (GI-NN) architecture to project IMU inputs into a high-logarithmic space for improved IMU feature extraction from sensor noise. To help the INS during GPS outages, a crucial hybrid fusion approach on the basis of GRU for load calculation reduction is devised. The GNSS/INS simultaneously incorporates the CNN and GRU. By using GI-NN to extract the IMU's feature space from the sensor, it is possible to simplify calculations and boost measurement accuracy. Between navigational data and INS output, deep learning is primarily utilized to learn the velocity, attitude-specific force, and angular velocity. Additionally, to accurately calculate the INS error data and vehicle dynamic status, the GI-NN advanced fusion technique takes into account both the historical INS data and the current INS error. At the same time, the onboard computer saves the GPS and INS information to update the GI-NN model. When GPS signals are not available, the updated GI-NN model can forecast the GPS increment to maintain a reasonable level of navigational accuracy. In contrast with other NNs, GI-NN can boost measurement accuracy with a low computational load by extracting IMU spatial properties from sensor noise.
# A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance
## 5 Discussion and Conclusions  
In this paper, we propose a multi-path semantic segmentation network with convolutional attention guidance (dubbed MCAG). It has a multi-path architecture and feature guidance, which forces the model to focus on the object boundaries and details. It also explores multi-scale convolutional features through spatial attention, and captures both local and global contexts in spatial and channel dimensions in an adaptive manner.  
The results of the ablation experiments show that the role of each module is indispensable and that convolutional attention provides powerful global feature extraction capabilities with low computational complexity. In the traditional convolution model, the details of small objects and the features of boundaries are easily lost during multi-layer convolutional extraction, and they are difficult to recover. So, the multiple paths are particularly important for the feature extraction of details and boundaries, which can be reflected in Table 4 showing the semantic segmentation results of small objects, thanks to the extraction ability of local features of our multi-path approach. Finally, a good fusion mechanism is also necessary. This paper does not use the traditional simple fusion mechanism; a PFM can better fuse global and local information. The experimental results indicate that MCAG surpasses the performance of state-of-the-art Transformer-based methods to a certain extent with a lower parameter count. The study suggests that CNN-based approaches can still outperform Transformer-based methods. It is hoped that this paper will encourage researchers to further explore the potential of CNNs.  
Conceptualization, C.F.; Investigation, C.F.; Methodology, C.F.; Project administration, S.H. and Y.Z.; Resources, Y.Z.; Software, C.F.; Supervision, S.H. and Y.Z.; Validation, C.F.; Visualization, C.F.; Writing--original draft, C.F.; Writing--review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript. This research received no funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the privacy. The authors declare no conflicts of interest.
## 7 Feature Extraction Approaches
### Texture-Based Approaches
The theoretical evaluation involved demonstrating the orthogonality properties of the transform, whereas the empirical evaluation considered its performance under various challenging conditions. For the classification step, they adopted the k-nearest neighbors (KNN) algorithm, using the Euclidean distance metric for similarity assessment. [PERSON] et al. (2020) [77] proposed a contactless palmprint identification and recognition technique integrating hierarchical multi-scale complete local binary patterns (HMS-CLBPs)for scale-invariant texture encoding with weighted sparse representation-based classification (WSRC) for pattern matching. Local texture descriptors are first extracted over multi-scale domains to capture fine and coarse palmprint texture. The descriptors are weighted, vectorized, and augmented to construct a highly overcomplete dictionary of training sets. Following the creation of the dictionary, it is utilized to find a sparse representation of the test sample, yielding the sparse coefficients corresponding to each dictionary atom. Palmprint image recognition is then performed by computing the reconstruction residuals between the test sample and its synthesized approximation under each class-specific sub-dictionary. [PERSON] et al. (2022) [78] introduced a feature extraction approach called multiresolution analysis. First, they applied the discrete wavelet transform (DWT) to an original palmprint image. This application facilitated the creation of multiple image representations, known as sub-bands, each with different resolutions. In addition, they utilized the local texture descriptor known as binarized statistical image features (BSIF) and applied it not only to the original image but also to the sub-bands produced by the DWT at lower resolutions. The resulting histograms from each of these levels were then merged to form a final feature vector. Table 6 provides a summary of the studies discussed in this subsection, presenting the feature extraction methods used, the datasets employed, the experimental protocols, and the key findings.
## 1 Introduction  
As a kind of important mechanized mining equipment, a shearer is usually used to cut and load coal in modern fruitful and efficient coal mining, which directly impacts on the automation level of the entire mechanized mining face [1; 2; 3]. The reliability of a shearer decides whether the coal can be mined with high efficiency and low risk. Therefore, effective and accurate evaluation of the health condition of a shearer is an important way to reduce safety accidents and enhance coal mine production, and it has become a challenging and significant research subject.  
Due to the poor working conditions of coal mining, it is rather difficult to acquire the health condition of a shearer depending only on the subjective consciousness of an operator. In the real mining condition, some key index parameters are very important and have a strong relationship with the shearer health condition. However, the relationship is highly nonlinear in nature so that it is hard to develop a comprehensive mathematic model. To deal with this kind of problem, the commonly used methods are fuzzy theory and neural networks [4; 5; 6]. A fuzzy neural network (FNN) is the combination of fuzzy logic and a neural network, and possesses the advantages of processing vague information and good learning abilities. It can also handle imprecise information through linguistic expressions. For several decades, FNN has attracted much attention and has been applied in many domains [7; 8].  
However, the parameters of FNN have great influences on its performance. The common learning algorithm for FNN is the gradient descent method (GD), which may lead to being easy to trap into a local minimum point and poor ability on a global search [9; 10]. In addition, the performance of GD training a FNN depends on the initial settings for system parameters, and the method has to derive and formulate different mathematical expressions to adapt the corresponding networks topologies. Based on the past works on artificial intelligent optimization algorithms, this paper tries to tackle this problem.  
Through the survey mentioned above, we propose an integrated approach based on a fuzzy neural network and improved particle swarm optimization (IPSO) to solve the problem of shearer health condition evaluation. The remainder of this paper is as follows: Section 2 reviews some related works based on the literature. The improved fuzzy neural network model is proposed in Section 3. The experiment example is provided to verify the proposed method in Section 4. In Section 5, a field application example is presented to indicate the actual application effect of proposed model in predicting the shearer health condition. Our conclusions and future work are summarized in Section 6.