esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Mud pulse telemetry (MPT) systems are a promising approach to transmitting downhole data to the ground. During transmission, the amplitudes of pressure waves decay exponentially with distance, and the channel is often frequency-selective due to reflection and multipath effect. To address these issues, this work proposes a fractional Fourier transform (FrFT)-based channel estimation and equalization method. Leveraging the energy aggregation of linear frequency-modulated signals in the fractional Fourier domain, the time delay and attenuation parameters of the multipath channel can be estimated accurately. Furthermore, a fractional Fourier domain equalizer is proposed to pre-filter the frequency-selective fading channel using fractionally spaced decision feedback equalization. The effectiveness of the proposed method is evaluated through a simulation analysis and field experiments. The simulation results demonstrate that this method can significantly reduce multipath effects, effectively control the impact of noise, and facilitate subsequent demodulation. The field experiment results indicate that the demodulation of real data achieves advanced data rate communication (over 12 bit/s) and a low bit error rate (below 0.5%), which meets engineering requirements in a 3000 m drilling system.  
fractional Fourier transform; mud pulse telemetry; channel estimation; channel equalization; signal recovery +
Footnote †: journal: Marine Science and Engineering
## 4 Conclusions  
We have demonstrated here a new conceptual diagnostic strategy for MS lesion detection. Our newly developed application, which is based on a combination of four detection algorithms, will improve the diagnosing of MS by greatly reducing the number of images relevant for manual scanning by the diagnosing doctors, saving precious time and energy.  
We tested our tool on 4202 images from three MS patients. Only 38 images of the 4202 were considered relevant for diagnosis using expert guidelines for manual screening. Our application was extremely successful, with 99.86% correct rejection of unnecessary images, 0.14% false alarm, 84.2% hits out of all relevant images with lesions, and 15.8% miss. Thus, overall workload was automatically reduced by 99.2% (98.9 \(\pm\) 0.4% per patient), theoretically saving most of the time and efforts invested by the diagnosing physician. These results suggest that our application can serve as a reliable preprocessing tool for relieving workload in MS diagnosis and monitoring.  
It is important to emphasize that the statistical results depend on the quality of the MRI machine, type of the MS and the numbers of patients.  
In addition, the application is easy to use by the graphical user interface, which can be helpful for the radiologist to make his/her MS diagnosis report.  
Similar applications may be developed in future for improving MR-based diagnostics of other diseases.  
Figure 10: Results: (**a**) patient #1; (**b**) patient #2; (**c**) patient #3; (**d**) summary of the results for all patients.  
**Author Contributions:** [PERSON] envisioned the project, provided guidance and supervision to [PERSON] and [PERSON]. [PERSON] provided guidance and funding. [PERSON] and [PERSON] performed simulations with the support of [PERSON], [PERSON]. [PERSON] and [PERSON] performed statistical analysis with the support of [PERSON]. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] wrote the manuscript text and all authors reviewed the manuscript. [PERSON], [PERSON] and [PERSON] made the figures, [PERSON] and [PERSON] wrote the reply letter.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Inherent Optical Properties (IOPs)
### IOPs for a Size Distribution of Particles
Thus, by combining the absorption and scattering cross sections, and the moments of the scattering phase matrix elements, one obtains the following IOPs for the mixture (subscript \(m\) stands for mixture):  
\[\Delta\tau_{m} = \Delta z\sum_{i}n_{i}\gamma_{n,i}=\Delta z\sum_{i}\gamma_{i}= \gamma_{m}\Delta z \tag{61}\] \[\alpha_{m} = \frac{\beta_{m}}{\gamma_{m}}=\frac{\sum_{i}\beta_{n,i}n_{i}}{\sum _{i}\gamma_{n,i}n_{i}}=\frac{\sum_{i}\beta_{i}}{\sum_{i}\gamma_{i}}=\frac{\sum _{i}\alpha_{i}\gamma_{n,i}f_{i}}{\sum_{i}\gamma_{n,i}f_{i}}\] (62) \[\chi_{m,\ell} = \frac{\sum_{i}\beta_{n,i}n_{i}\chi_{i,\ell}}{\sum_{i}\beta_{n,i} n_{i}}=N\frac{\sum_{i}\beta_{n,i}f_{i}\chi_{i,\ell}}{\beta_{m}}=\frac{\sum_{i}f_{i} \alpha_{i}\gamma_{n,i}\chi_{i,\ell}}{\sum_{i}f_{i}\beta_{n,i}} \tag{63}\]  
where \(\Delta\tau_{m}=\) layer optical depth; \(\beta_{m}=\) total scattering coefficient; \(\gamma_{m}=\) total extinction coefficient; \(\varpi_{m}=\) single-scattering albedo; and \(\chi_{m,\ell}=\) scattering phase function expansion coefficient for the particle mixture. A mixing rule similar to Equation (63) may be used for each element of the scattering phase matrix.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 3 The Modulation Recognition Method Based on Deep Learning
### Application of the Recurrent Neural Network in Modulation Recognition
#### 3.2.2 Modulation Recognition Based on Recurrent Neural Network
First, the author uses the convolution layer to map the time-domain sequence of the signal to a high-dimensional space, then uses the transformer encoder to complete the feature extraction of the signal, and finally uses the fully connected layer to complete the signal classification. Experiments show that the model can complete the classification of 10 modulated signals well. This paper summarizes the last 5 years of RNN-based modulation identification methods in Table 2. Since it is easier for the RNN to extract the information from the sequence signal, inputting the IQ sequence of the signal into the RNN for identification is more conducive to improving the identification accuracy.
# Identifying the Initial Corrosion Fatigue Failure Based on Dropping Electrochemical Potential
## 1 Introduction
### Aim and Interest of This Study  
Up to now there are no data available upon the correlation of frequency, electrochemical behavior, and initial failure during corrosion fatigue to predict early failure from monitoring the combined data within the system. Therefore, this study investigates the relationship between crack initiation and failure during corrosion fatigue experiments, focusing on the correlation between the onset of failure (appointed by a drop in frequency)and changes in electrochemical potential. By examining how these factors are linked, this research aims to identify crack initiation as a significant cause of failure. The findings highlight the critical role that electrochemical potential shifts and frequency variations play in the early stages of crack development, offering valuable insights into the mechanisms behind material degradation and the estimation of failure.  
The importance of the findings lies in the development and successful implementation of an algorithm that predicts early failure during corrosion fatigue in duplex stainless-steel specimens exposed to a highly corrosive geothermal environment. By leveraging real-time electrochemical potential measurements from a custom test setup [19; 20], the algorithm demonstrates a strong correlation between potential curve behavior and failure onset, achieving a predictive accuracy of 93%. This innovative approach combines tailored data analysis techniques, such as splitting time series and applying regression, to establish a practical, efficient tool for anticipating material failure in highly corrosive and mechanically stressed conditions, offering significant implications for geothermal energy and CCS industries.
# Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion
## 2 Using Genetic Algorithm to Optimize Spatiotemporal Correlation Forecast Model
### Multi-Channel and Multi-Location Attention Mechanism
#### 2.2.1 Attention Module of Channel Dimension  
The main idea of CAM is to perform a global pooling operation in the channel dimension, obtain the weights through the shared network output, and then add them as the final attention vector. The specific structure of CAM is shown in Figure 6, and the main process can be expressed by Equation (8). In this paper, the dimension of \(F\) is \(7\times 4\), and the feature map of size \(4\times 55\) is obtained after the convolution pooling operation. \(\mathrm{AvgPool(F)}\) and \(\mathrm{MaxPool(F)}\) represent the average pooling feature and max pooling feature operations,  
Figure 4: BiLSTM structure diagram.  
Figure 5: CBAM network structure diagram.  
respectively, and \(\sigma\) is the sigmoid function. After the operations of \(\mathrm{F}_{\mathrm{avg}}^{\mathrm{c}}\) and \(\mathrm{F}_{\mathrm{max}}^{\mathrm{c}}\), two corresponding feature maps are generated. The shared network consists of a Multi-layer Perceptron (MLP), which has a hidden layer, and W0 and W1 are the two weights of the MLP. After applying the shared network to each feature map, the output feature vectors are summed and subjected to the sigmoid activation function to obtain the channel attention feature Mc(F). After Mc(F) is multiplied by the input feature map F, the feature map F\({}^{\prime}\) with different weights on each channel is obtained.  
\[\begin{array}{ll}\mathrm{Mc(F)}&=\sigma(\mathrm{MLP(AvgPool(F))+MLP(MaxPool(F)))}\\ &=\sigma\Big{(}\mathrm{W1}\Big{(}\mathrm{W0(F}_{\mathrm{avg}}^{\mathrm{c}}) \Big{)}+\mathrm{W1}(\mathrm{W0(F}_{\mathrm{max}}^{\mathrm{c}}))\Big{)}\end{array} \tag{8}\]
## 2 Materials and Methods
### Films Characterization
#### 2.4.5 Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR)  
ATR-FTIR spectra were collected by using a Bruker Analitik IFS 66 FTIR spectrometer (Ettlingen, Germany) equipped with a Golden Gate Single Reflection Diamond ATR accessory. Films (\(1\times 1\) mm\({}^{2}\)) were directly placed on the ATR crystal area. Spectra were recorded in the absorbance mode from 4000 to 600 cm\({}^{-1}\), using 64 scans and 4 cm\({}^{-1}\) resolution, and corrected against the background spectrum of air. Two spectra replicates were obtained for each sample.
## 2 Related Work
### End-to-End Network Slicing
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Work** & **Core Slicing** & **RAN Slicing** & \begin{tabular}{l} **Transport** \\ **Slicing** \\ \end{tabular} & \begin{tabular}{l} **Ochestration \&** \\ **Automation** \\ \end{tabular} & \begin{tabular}{l} **Resource Monitoring** \\ **Resource** \\ **Management** \\ \end{tabular} & \
\begin{tabular}{l} **Intelligent** \\ **Resource** \\ **Networking** \\ \end{tabular} &
\begin{tabular}{l} **Intent-Based** \\ **Mangement** \\ \end{tabular} \\ \hline
[1] & \begin{tabular}{l} JUU NFV \\ management \\ framework \\ \end{tabular} & \
\begin{tabular}{l} FlexRAN \\ Controller \\ \end{tabular} & No &
\begin{tabular}{l} JOX, JUU \\ Framework \\ \end{tabular} & No & No & No & Yes \\
[6] & Yes & Yes & Yes &
\begin{tabular}{l} Huawei \\ Orchestrator \\ \end{tabular} & No & No & No & No & No \\
[23] & Dedicated & FlexRAN \\ Core Network & Controller & No & No &
\begin{tabular}{l} Monitioring \\ using ELK \\ \end{tabular} & No & No & No & Yes \\
[24] & Yes & Yes & No &
\begin{tabular}{l} GSM MANO \\ No \\ \end{tabular} & No & No & No & No \\
[26] & No & Yes & No & No & No & No & No \\
[27] & No & Yes & No & Yes & Yes &
\begin{tabular}{l} RL \& heuristic \\ based algorithm \\ \end{tabular} & No & No \\
[28] & No & Yes & No & No & Yes &
\begin{tabular}{l} Intelligent \\ Networking \\ \end{tabular} & No & No \\
[30] & No & Yes & & \
\begin{tabular}{l} Yes \\ \end{tabular} &
\begin{tabular}{l} Netflight \\ RT algorithm \\ \end{tabular} & No & No \\
[35] & No & \begin{tabular}{l} Resources \\ Scheduling \\ \end{tabular} & No & No & No & \begin{tabular}{l} DL \& ML \\ algorithm \\ \end{tabular} & No & No \\ Our Work & \begin{tabular}{l} GSM \\ Controller \\ \end{tabular} & No & \
\begin{tabular}{l} GSM, FlexRAN \\ \& IBN \\ \end{tabular} &
\begin{tabular}{l} Openstack \& \\ GAN model \\ \end{tabular} & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of proposed work with existing methods.
# A Framework for Symmetric-Quality S3D Video Streaming Services
## 4 Results
### Test Results and Discussions
Compared with HEVC, the average _PSNR_ improved by 2.62 dB, and it improved by 1.81 dB compared with Cross-View SHVC. A difference of over 1 dB in _PSNR_ indicates a visually noticeable difference in quality. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**Over HEVC**} & \multicolumn{3}{c}{**Over Cross-View SHVC**} \\ \cline{3-6}  & & **BDBR (\%)** & **BDSNR (dB)** & **BDBR (\%)** & **BDSNR (dB)** \\ \hline \multirow{6}{*}{Nutjob} & Part 1 & \(-\)63.36 & 3.17 & \(-\)54.89 & 2.44 \\  & Part 2 & \(-\)54.08 & 2.07 & \(-\)43.82 & 1.52 \\  & Part 3 & \(-\)38.32 & 1.79 & \(-\)26.33 & 1.21 \\  & Part 4 & \(-\)51.68 & 2.00 & \(-\)42.32 & 1.64 \\  & Average & \(-\)51.86 & 2.26 & \(-\)41.84 & 1.70 \\ \hline \multirow{6}{*}{Sweet Rollercoaster} & Part 1 & \(-\)80.85 & 3.53 & \(-\)66.28 & 2.89 \\  & Part 2 & \(-\)53.17 & 1.74 & \(-\)35.58 & 1.20 \\  & Part 3 & \(-\)47.53 & 2.43 & \(-\)27.81 & 1.02 \\  & Part 4 & \(-\)51.19 & 1.75 & \(-\)39.30 & 1.39 \\  & Average & \(-\)58.19 & 2.36 & \(-\)42.24 & 1.63 \\ \hline \multirow{6}{*}{Secret of the Universe} & Part 1 & \(-\)65.46 & 3.25 & \(-\)51.68 & 2.03 \\  & Part 2 & \(-\)49.79 & 2.46 & \(-\)37.08 & 1.87 \\ \cline{1-1}  & Part 3 & \(-\)56.69 & 2.52 & \(-\)43.81 & 1.93 \\ \cline{1-1}  & Part 4 & \(-\)81.27 & 4.75 & \(-\)72.26 & 2.61 \\ \cline{1-1}  & Average & \(-\)63.30 & 3.25 & \(-\)51.21 & 2.11 \\ \hline \multicolumn{2}{c}{Total Average} & \(-\)57.78 & 2.62 & \(-\)45.10 & 1.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BDBR and BDSNR of the proposed method (excludes base layer bitrate). Table 3 presents the comparison of BDBR and BDSNR performance for all encoded videos. This table illustrates how the proposed structure performs when encoding left and right videos at UHD resolution, compared with standard codecs such as HEVC and Cross-View SHVC. In other words, it reflects the performance of 3D video encoding. Across the entire sequence, the proposed structure achieved an average bitrate reduction of 10.53% compared with the HEVC standard and 15.24% compared with Cross-View SHVC. Additionally, in terms of quality improvement at the same bitrate, the proposed method showed a 1.04 dB improvement over HEVC and a 1.76 dB improvement over Cross-View SHVC.
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 2 Related Work
### Standardization Activities
#### 2.1.4 Etsi Nfv Mano  
The ETSI NFV MANO technical committee is working on the standardization of management and orchestration frameworks required for the provisioning of Virtual Network Functions (VNFs) and Network Services (NSs). The MANO committee focuses on the definition of a reference architecture to overcome the challenges of the virtualization paradigm by covering operational and management aspects, such as service life-cycle workflows, information elements and interfaces. The final release has been available since 2014 [12], and the correspondent architecture is shown in Figure 4. This reference architecture allows network operators to apply a smooth transition to the new management paradigm, where legacy services and functionalities are gradually augmented with SDN and NFV capabilities. The ETSI NFV MANO architecture is composed of four main functional blocks:  
* Operations Support System/Business Support System (OSS/BSS):
* Responsible for the control of software applications used to support back-office activities;
* Coordination of business operations, such as billing and customer services.
* NFV Orchestrator (NFVO):
* Responsible for the registration of new VNFs and NSs;
* Life-cycle management (including instantiation, scale-out/in, termination).
* VNF Manager (VNFM):
* Life-cycle management of VNF instances;
* Coordination of the main configurations between the NFV Infrastructure (NFVI) and the Elemental Management System (EMS).
* Virtualized Infrastructure Manager (VIM):
* Controlling and managing the NFVI compute, storage and network resources;
* Collection and forwarding of performance measurements and events;  
Figure 4: The NFV MANO architectural framework with reference points.  
The proposed fog computing framework in this article follows the ETSI NFV MANO reference architecture, extending it with additional software components. Our architectural elements share concepts with the MANO architecture; however, the characteristics of fog computing solutions lead us to a different approach, since ETSI NFV MANO has been conceived of to be applied in data centers, while in fog environments, each fog node must be able to manage and control its infrastructure. Management and orchestration are still open issues in the fog computing domain due to the dynamic behavior and distributed management of the network, which makes the investigation of using ETSI NFV MANO standards as the base for a fog-based MANO framework implementation necessary.
## 4 Current Approaches to Measure Resilience in Smart ICI
### Indicator-Based Approach
#### 4.1.1 The ANL Method  
A notable example of an indicator-based methodology for assessing resilience is the resilience index (\(RI\)) developed by Argonne National Laboratory. This approach utilizes uniform and consistent data, initially gathered through a revised report from the US Department of Homeland Security (DHS) Enhanced Critical Infrastructure Protection (ECIP) plan [36]. The resilience measurement index (RMI) comprises three primary categories for determining overall resilience: robustness, resourcefulness, and recovery. The methodology is structured across five hierarchical levels, with indicators generated at the lowest level.  
Resilience assessment involves inputting indicator values at level five and aggregating them up to the first level. The RI then evaluates the resilience level of critical infrastructures, ranking and prioritizing those with limited support to improve overall resilience. Equation (1) presents the fundamental mathematical model, which encompasses the sum of the three main components: robustness, resourcefulness, and recovery:  
\[RI=\sum_{i=1}^{3}e_{i}*V_{i} \tag{1}\]  
Here, \(RI\) represents the relative resilience index (ranging from 0 to 100); \(e_{i}\) is the scaling constant (weight; a number between 0 and 1) that indicates the relative importance of component \(i(i=1,2,3)\) of resilience; and \(V_{i}\) is the index value of component \(i\) of resilience (i.e., robustness, resourcefulness, and recovery). The index value of component  
Figure 2: Review Methodology [38].  
\(i\) is the aggregated value from level 1. However, in level 2, each component possesses sub-components. For instance, robustness contains three sub-components (redundancy, prevention, and maintaining key functions) in level 2, each with an index value representing the overall robustness value.  
A similar structure is observed in level 3, wherein each sub-component is further divided into additional information layers. For example, redundancy in level 2 is a primary element of robustness (level 1) and comprises eight subcategories (electric power, natural gas, telecommunications, information technology, water, wastewater, transportation, and critical products). These subcategories are recognized in level 3 and are further divided into distinct components. In this case, the electric power category (level 4) consists of four subcategories (on-site backup generation, uninterrupted power system, internal generation, and connections). To scale level 4 components, such as electrical power connections, raw data (level 5) is collected from the facility's responses to personal questions, resulting in a cumulative value for electric connections.
## 4 Proposed Approach
### Algorithm Selection
The algorithm usually stops after a certain number of iterations, or if the solutions start to become worse than the one already reached, as there is no backtracking [70]. The resulting algorithm, termed _greedy decision tree_, is used for feature selection using a greedy approach and is illustrated in Algorithm 1. ```
1:Input:\(F\) (Set of all features)
2:Output: A decision tree constructed using a subset of features
3:\(S\) is the set of selected features
4:\(f\) is a single feature
5:S \(\leftarrow\) {Initially S is empty}
6:while average of precision, F1-score, accuracy, and false alarm rate on S do not decrease OR\(F\
eq\{\}\)do
7: Construct \(|F|\) decision trees with features in \(S\) and each feature in \(F\)
8: Evaluate individual cost of each feature \(f\) individually using squared error \(\mid f\in F\)
9: Select the feature \(f\) from \(F\) with the minimum cost
10:\(S\gets S\cup\{f\}\)
11: Remove \(f\) from \(F\)
12:endwhile
13:return S
14: Construct a decision tree with \(S\)
15:return the constructed decision tree
```  
**Algorithm 1** Greedy Decision Tree (F).
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
## 4 Experimental Verification
### Experimental Test 1
#### 4.1.4 Visual Analysis of the Proposed Method  
The confusion matrix can directly reflect the diagnosis results of the intelligent fault diagnosis method, so the confusion matrix is selected to analyze the HSDAE method further. It is the confusion matrix diagram of the 20 th trial of the HSDAE method on data set A, as shown in Figure 11. It can be seen from the figure that, for the data of ten types of failures, the diagnostic accuracy of 99.27% can be achieved on the training set, and diagnostic accuracy of 99.1% can be achieved on the testing set. In the training set, categories 2, 4, 5, 6 and 9 all achieve 100% diagnostic accuracy, and the remaining categories have a small amount of misclassification. In the testing set, categories 1, 4, 6 and 9 achieved 100% diagnostic accuracy; except for categories 0 and 3, the number of misclassifications was very small. In conclusion, the diagnosis results show that the proposed method can achieve accurate fault diagnosis of rolling bearings.