esa-sceva/satcom-chunk-collection huggingface dataset:
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 6 Conclusions  
Designing efficient and robust communications is indispensable for 5G and 6G networks, where many requirements, such as low latency, high reliability, low complexity, and flexibility, must be fulfilled. These requirements can be fulfilled by using the fountain codes [32]. This paper proposes using a modified version of the fountain codes (i.e., LT codes) over the Y-network. Then, we optimize the LT codes' parameters to maximize the decoding success probability (DSP). Furthermore, we propose an efficient encoding scheme using a memory-based strategy to enhance the performance of the Y-network. Finally, simulation results show that the proposed scheme consistently outperforms traditional LT code schemes with respect to the DSP, BER, complexity, and delay. In fact, the proposed scheme framework can be efficiently adapted to any work with other encoding processes in the literature. With this intention, we adapt our proposed scheme to one of the state-of-the-art encoding processes, decreasing ripple size distribution (DRSD), and compare the results. Likewise, the proposed scheme outperforms the DRSD in terms of the DSP and BER. For instance, for a code length \(k=256\) and a number of transmitted symbols \(n=328\), and when all the three channels in the Y-network are perfect, our results show that the proposed scheme achieves significant improvements compared with regular LT codes as well as DRSD codes.
# Influence of the Radial Gap on the External Gear Pump Performance
## 4 CFD Results and Verification
### Verification of the CFD Results with Manufacturer Data  
The experimental data are available from the manufacturer data sheet [54] of the studied pump, which can be used to verify the CFD results. It is in graphical and tabular form--Figure 9. As can be seen from the figure, the flow rate changed linearly with the rotation frequency, and for the considered pump displacement volume, at a pressure of 10.5 MPa, two specific values were given--19 L/min at 1000 RPM and 27.6 L/min at 1450 RPM. From these two values, some discrete values of the line \(q_{Tech}(n)\) were obtained using linear extrapolation and filled in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **CFD** & \multicolumn{3}{c}{**Experiment**} & \multicolumn{3}{c}{**Tech Data**} & **CFD/Exp** & **CFD/Tech** & **Exp/Tech** \\ \hline \(n\) & \(\Delta p\) & \(q\) & \(n_{exp}\) & \(\Delta p_{exp}\) & \(q_{exp}\) & \(q_{Tech}\) & \(P_{m}\) & \(\Delta q_{exp}\) & \(\Delta q_{Tech}\) & \(\Delta q_{ET}\) \\
**RPM** & **MPa** & **L/min** & **min\({}^{-1}\)** & **MPa** & **L/min** & **L/min** & **kW** & **\%** & **\%** & **\%** \\ \hline
500 & 10.5 & 9.08 & 498 & 1  
As can be seen from Table 1, the percentage difference \(\Delta q_{Tech}\) between the CFD simulation results and the technical data at 1450 RPM was \(-1.37\%\). While the experimental verification was for the range of 500-1450 RPM, as our test bench had a maximum of 1500 RPM, the verification with the manufacturer's data confirmed the reliability of the CFD model results over the entire pump operating range (500-3500 RPM). Additionally, in Table 1, the percentage difference \(\Delta q_{ET}\) between the experimental and technical data for \(q\) was calculated. The values did not exceed \(-6.5\%\), which indicates that the characteristics of the investigated pump corresponded to those given in the technical documentation, and the technical data can be successfully used for verification.
## 4 Results and Discussion  
The experiments conducted to test this hypothesis were based on feeding the data of extracted features to the sequential feed-forward artificial neural network. The limitation of this method is the manual selection of feature sets for model training, which can be automated with the help of various feature selection techniques and variations in the neural network models. Based on the results, accuracy levels were tested across different feature sets from the PCG dataset.
# Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things +
## 7 Conclusions  
In this paper, we put forward a novel scheme named FAF-LSTM to improve the anomaly-detection accuracy of the FloT. In FAF-LSTM, the input data are processed and divided into multi-flows to train the model according to the time correlation among the features. Based on this, FAF-LSTM fully utilizes the time correlation in the FloT traffic in the aspect of the feature level. Then, in feature-attended federated learning, the parameter aggregation optimization strategy is proposed based on the correlation of flow entropy calculation, and federated learning architecture is applied to realize cooperative training at multiple detection nodes. We conduct simulations to evaluate FAF-LSTM based on the UNSW-NB15 dataset. The simulation results show that our proposed scheme can fundamentally improve the anomaly detection performance in FloT and outperform the benchmark schemes by up to 334.36% in aspect of the detection accuracy. For future work, the performance of FAF-LSTM can be verified across different datasets and environments. And other models, such as CNN or SVM, can also be combined with federated learning and compared with FAF-LSTM.  
Conceptualization, Y.W., R.Z. and Y.L.; methodology, Y.L. and R.Z.; software, P.Z.; validation, P.Z., R.Z. and Y.L.; formal analysis, Y.L.; investigation, Y.L. and Y.W; resources, Y.L.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.W. and R.Z.; visualization, Y.L.; supervision, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by by Innovation Ability Construction Project for Sichuan Provincial Engineering Research Center of Communication Technology for Intelligent IoT (2303-510109-04-03-318020), the Municipal Government of Quzhou under Grant No. 2022D027 and No. 2023D003.  
Figure 5: ROC of Node I to Node IV.
## 6 Recent Developments and Innovative Approaches
### Advances in Visible-Light-Responsive Photocatalysts  
One of the major research directions is the development of photocatalysts capable of utilizing visible light, which constitutes a much larger portion of the solar spectrum than UV. Strategies include the following:  
* Band gap engineering through doping (e.g., nitrogen, sulfur, carbon) to extend light absorption.
* Heterojunction formation to promote charge separation and extend absorption range (e.g., TiO\({}_{2}\)/g-C\({}_{3}\)N\({}_{4}\), BiVO\({}_{4}\)/CeO\({}_{2}\)).  
Such materials enable the use of solar energy, reducing operational costs and supporting decentralized treatment systems.  
[PERSON] et al. [95] investigated the impact of nitrogen-doped graphene quantum dots (N-GQDs) on the photocatalytic performance of TiO\({}_{2}\). The incorporation of N-GQDs significantly increased light absorption, narrowed the band gap, and improved charge transfer within the TiO\({}_{2}\) matrix. This enhancement led to a notable improvement in the degradation efficiency of organic pollutants under visible-light irradiation, demonstrating the effectiveness of nitrogen doping in extending the light absorption range of TiO\({}_{2}\)-based photocatalysts. Some researchers developed novel heterojunctions combining sulfur-doped titanium dioxide (S-TiO\({}_{2}\)) nanoparticles with graphitic carbon nitride (g-C\({}_{3}\)N\({}_{4}\)) sheets. The S-TiO\({}_{2}\) (5%)/g-C\({}_{3}\)N\({}_{4}\) catalyst exhibited the highest activity for the photocatalytic degradation of methylene blue (MB) dye under visible-light irradiation [165]. The enhanced performance was attributed to improved separation and transfer of photogenerated charge carriers. The photocatalyst demonstrated stability and reusability over multiple cycles, indicating its potential for wastewater remediation applications.  
Figure 13a illustrates the main strategies for developing visible-light-responsive photocatalysts aimed at enhancing solar energy utilization in water treatment and the material-level innovations that underpin recent progress in visible-light-responsive photocatalysts. It offers a clear representation of the structural and functional modifications involved, aiding the reader in understanding the relationships between design strategies and enhanced photocatalytic behavior under solar illumination.  
Figure 12: Summary of recent innovations in heterogeneous photocatalysis for sustainable water treatment. The figure highlights key strategies such as band gap engineering, heterojunction formation, and plasmonic enhancement aimed at improving visible-light responsiveness, charge carrier separation, and overall photocatalytic efficiency under solar irradiation.
## 2 Related Work
This approach focuses only on protecting the SDN environment against the SYN flooding attack. However, our approach provides the capability of detecting a family of saturation attacks, including the SYN flooding attack. [PERSON] and [PERSON] [21] proposed the FlowIDS framework to detect simple mail transfer protocol (SMTP) flooding attacks. They utilized the decision tree (DT) and deep learning (DL) algorithms to detect malicious SMTP traffic. After being trained to identify benign SMTP traffic, the deep learning algorithm and the decision tree classifier were used in detecting any traffic used to launch the flooding attack. [PERSON] et al. [22] introduced the ATLANTIC framework to detect DDoS attacks against the SDN environment. The proposed system uses entropy analysis to detect the deviations of the SDN network traffic to discover the suspicious traffic flows. It also uses a K-means unsupervised model to cluster similar traffic flows. Therefore, an SVM classifier is employed to label flows as malicious or normal. The detection performance of the proposed system is relatively low; it obtained 88.7% accuracy and 82.3% precision, which prevents such systems from being adopted in real life. [PERSON] et al. [23] proposed the FL-Guard detection and defense system against DDoS attacks in the SDN environment. FL-Guard uses the SVM classifier to detect saturation attacks. However, FL-Guard cannot detect major saturation attacks, such as UDP flooding attacks or unknown saturation attacks. [PERSON] et al. [24] proposed a detection method for domain name service (DNS) and network time protocol (NTP) reflection amplification attacks. This work aims to detect two attacks by using an SVM classifier. In a prior work [9; 10; 11], we focused on different challenges of building machine-learning-based saturation attacks detection methods and presented solutions to these challenges, such as identifying the proper time window for OpenFlow traffic analysis. Most of the related work uses periodic detection methods, which means that the detection method starts with a pre-determined time window, without providing a justification for this time window. We also investigated the K-NN, NB, and SVM classifiers for detecting the saturation attack in both physical and simulated SDN setups. The results show that the detection performance of these classifiers are heavily impacted by the time window of OpenFlow traffic. In this paper, we extend our work by investigating and evaluating the robustness of supervised and semi-supervised classifiers in detecting the unknown DoS saturation attacks in SDN networks. We also propose an enhanced detection method that can detect both known and unknown saturation attacks in SDN by combining supervised and semi-supervised classifiers.
# CNN-Based Model for Landslide Susceptibility Assessment from Multispectral Data
## 4 Implementation and Evaluation of the Method: A Case Study in Boyaca, Colombia
### Features and Labels
Following this, the importance of each attribute was evaluated, obtaining the results shown in Figure 7.  
As illustrated in Figure 7, the most contributing attribute in the classifier is slope, followed by DEM as expected from the correlation analysis. The bands and spectral indices contribute to a lesser extent, with a similar contribution. In this regard, the attributes whose correlation (in absolute value) is equal to or greater than 0.2 were selected, i.e., B2, B3, B4, NDVI, NDMI, MSI, BSI, ALT, SF, NBRI, NDGI, DEM and slope. Figure 5: Correlations between bands (B2–B8, B8A, B11, B12) and the susceptibility label (SUSC). Figure 6: Correlations between bands (B2–B8, B8A, B11, B12), 12 spectral indices (see the list in Table 1), DEM, slope and the susceptibility label (SUSC). Figure 7: Feature importance calculated for 10 bands (B2–B8, B8A, B11, B12), 12 spectral indices, DEM and slope in a landslide susceptibility classification model. In conclusion of the above, the binary susceptibility value obtained using the heuristic method is used as the label for the training and evaluation of the proposed model, while the selected features include three bands from the original image, eight spectral indices, DEM and slope, and the problem is posed as a binary classification problem.
## 3 Results and Discussion
### Overall Heat Transfer Performance for Different Flow Channels
#### 3.2.1 Heat Transfer and Flow Performance with Different Structural Parameters  
Figure 5 shows the relationship between the convective heat transfer coefficient (_h_) and pressure drop (\(\Delta P\)) with the inlet velocity (\(u_{in}\)) corresponding to the circular-arc-shaped flow channels at different inlet heights (\(F_{h}\)) and circular radii (\(R\)), in which \(F_{h}\) = 9.5, 10.5 and 11.5 mm, \(R\) = 200 mm, 250 mm and 300 mm, \(L_{f}\) = 60 mm, \(S_{f}\) = 4.0 mm and \(t_{f}\) = 0.4 mm.  
It can be seen from the figure that for different structure parameters of flow channels, with the head on the wind speed increases, \(h\) and \(\Delta P\) increase, and the growth of the heat transfer coefficient is close to the linear growth trend. At the same time, the pressure drop is a parabolic growth pattern of growth. In the figure, #4 and #3 have the maximum and minimum convective heat transfer coefficients, while #1 and #7 have the maximum and minimum pressure drops, respectively. Through the comparison of #4, #5 and #6, it can be found that, at the same circle radius, the convective heat transfer performance gradually increases with the decrease in the inlet height; that is to say, a smaller inlet height will improve the convective heat transfer performance. In addition, it can be seen that the heat transfer coefficient curves under different structural parameters are close to each other. This shows that the influence of changing the inlet height and the radius of the circle on the heat transfer performance is relatively small.
# A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM
###### Abstract
By applying VMD denoising to the sample dataset, noise impact is effectively reduced, enhancing the effectiveness of neural network training. Secondly, conventional methods for recognizing grinding wheel wear states often exhibit low accuracy in distinguishing different wear conditions. To address this limitation, the paper innovatively adopts the comprehensive neural network structure known as AO-CNN-LSTM, thereby improving the accuracy of wear state recognition. This paper bridges the research gap by addressing shortcomings in noise processing and neural network structures for grinding wheel wear state recognition. It provides a more reliable and efficient method for identifying grinding wheel wear states in industrial production.
# Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning
## 3 Proposed Continuous Sinusoidal Model  
This section will introduce our developed sinusoidal model, a framework to address a speech analysis-synthesis system derived from continuous parameters.  
Based on a sinusoidal model, an analysis/synthesis system is characterized in terms of the amplitudes, frequencies, and phases of the component sine waves to synthesize high-quality speech. The scope of this scenario is designed to model the voiced speech as a sum of quasi-harmonics with instantaneous phases and fundamental frequency (F0) [27]. Conventionally, the F0 contour is discontinuous at voiced-unvoiced (V-UV) boundaries because F0 is not classified in unvoiced sounds. This can cause some issues in statistical modeling, which involves building separate models for voiced and unvoiced frames of speech. On the other hand, the continuous F0 does not need modeling around V-UV and UV-V transitions. In this study, the V/UV decision can be left up to the maximum voiced frequency feature. Moreover, recent work has demonstrated that a neural vocoder (such as the WaveNet model) yields state-of-the-art performance and gives good-sounding speech. However, it requires a large quantity of data, computation power, and it needs to be repeated sequentially (one sample at a time), making it difficult to train for real-time implementation, especially in embedded environments. Therefore, a continuous sinusoidal model (CSM) is built to tackle the limitations of discontinuity in the speech parameters and the complexity of neural vocoders. Unlike the standard source-filter vocoders, CSM uses harmonic features to simplify and enhance the synthesizing phase prior to reconstruction. Figure 1 shows the main components of the developed sinusoidal model.
## 4 Numerical Modeling with Anisotropic Slate
### Numerical of the Brazilian Tests  
#### 4.2.1 Modelling Procedure  
The CZM mentioned previously was implemented in the FCEM framework of Abaqu s/Explicit software, the 3D computational model for the disc specimen was established first, the model characterizes weak plane spacing from top to bottom mainly of 9.1-12.5 mm by partitioning the model into different blocks under the design bedding angle. Second, the elastic solid element (C3D4) was employed in the model to simulate the mechanical behavior of the slate, and the bedding planes were simplified, and were described by the inserted cohesive elements of an in-house computer program using MATLAB code to insert cohesive elements (COH3D6) into the initial finite element model, and a typical 3D computational model with the bedding angle of 0\({}^{\circ}\) as indicated in Figure 11.  
It is emphasized that the same cohesive elements inserted in the model were used to simulate failure or fracture; different mechanical properties were considered to address either the behavior of the bedding plane or the intrinsic performance of the slate matrix. The elastic parameters required for the matrix are [PERSON]'s modulus \(E\) and Poisson's ratio \(\
u\), which are given by \(E\) = 5.2 GPa and \(\
u\) = 0.2. Since there was not a perfect approach to directly obtain the model parameters of the cohesive elements from the conventional experiments of TI slate, we used the trial and error method to fit the experimental data and the macroscopic response of the specimens to obtain the parameters in the present work. Using the above-mentioned process, the constitutive parameters of the cohesive elements were inserted in the weak plane and the internal matrix of the slate. All of the parameters used in the CZM simulations are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cohesive Elements** & **Matrix** & **Bedding Planes** \\ \hline \(E_{n}\) (N/mm\({}^{3}\)) & 1150 & 950 \\ \(E_{s}\) (N/mm\({}^{3}\)) & 1350 & 1150 \\ \(T_{mmx}\) (MPa) & 2.0 & 1.0 \\ \(T_{smx}\) (MPa) & 4.0 & 1.5 \\ \(G_{IC}\) (N/mm) & 0.015 & 0.008 \\ \(G_{IIC}\) (N/mm) & 0.15 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The input parameters of the slate specimens in CZM simulations.  
Figure 11: 3D computational model of carbonaceous slate under bedding angle of 0\({}^{\circ}\), (**a**) model setup, (**b**) embedded cohesive elements for the matrix, and (**c**) embedded cohesive elements for the bedding planes.
# A Review of the Performance of Infilled RC Structures in Recent Earthquakes
## 2 Seismic Behaviour of Infilled RC Structures: Definition of Damage Type
### Representative Damage Type Observed in RC Buildings Due to Earthquakes
#### 2.1.4 Damage Type 4: Damage Associated with the Inadequate Shear Capacity of the Structural Joints  
The beam-column joints can be subjected to a significant stiffness reduction due to the insufficient shear strength, poor and inadequate reinforcement anchorage. Both types of failure are related to the deficient use of transverse reinforcement and the poor detailing of the reinforcement anchorage through the joint. The collapse or extensive damage observed in buildings' structures due to the absence of good beam-column joints is widespread during an earthquake event. For example, Figure 7 presents beam-column joints with poor detailing that resulted in the building's collapse during the L'Aquila earthquake in 2009.
## 4 Results and Discussion
### Axial Porosity Distribution  
To thoroughly analyze the axial porosity distribution across a range of different particle size ratios within a stack, the particle accumulation model shown in Figure 1 was subdivided into 100 circular segments, each with unique circular voids in the axial direction. These segments were then integrated to determine the corresponding cross-sectional area [17]. The following equation was used to calculate axial porosity, providing us with a comprehensive understanding of the distribution of porosity within the particle stack:  
\[\varepsilon(Z)=\frac{A_{fluid}(Z)}{A_{dir}}=\frac{A_{fluid}(Z)}{2\pi R}. \tag{2}\]  
Figure 5 shows the normalized axial porosity distribution averaged over each cross section, demonstrating the oscillations caused by the presence of the bottom wall. It is important to note that this effect is negligible for tall cylinders but can result in inconsistent results when considering averages for shallow columns. To ensure uniform axial porosity throughout the bed, only the middle portion (\(0\leq\text{m}\leq 25\)) of the filling section from the DEM simulation was exported to the CFD domain. The axial porosity distribution fluctuates around 0.45 under different particle size ratios, reflecting the randomness of the stacking structure generated by DEM.  
Figure 5: _Cont._