esa-sceva/satcom-chunk-collection huggingface dataset:
# MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
## 4 Experimental Validation
### Comparative Experiments
#### 4.5.1 Single-Condition Comparison Experiment  
The results of the single-condition comparison experiment are detailed in Table 8. The results show that although SVM has certain advantages in training and testing time, its classification accuracy is much lower than that of the other deep learning models, indicating its limitations in extracting complex signal features. In contrast, CNN and LSTM demonstrate certain advantages in feature extraction and sequence modeling, but they are limited by the small sample size, preventing them from fully learning the data features, which leads to relatively lower classification accuracy. Additionally, their complex network structures result in increased computational overhead. Our proposed method, however, excels in both classification accuracy and computational efficiency. This can be attributed to two key factors: first, the LBP feature extraction method not only effectively captures critical texture features in the vibration signals but also reduces computational complexity by using uniform patterns, significantly accelerating the feature extraction process. Moreover, the lightweight design of MTC-GAN and its inherent data augmentation capability enable the method to achieve a high classification accuracy of 99.28% while also significantly reducing training and testing times to 182 s and 35 s, respectively.  
Figure 10: Final accuracy vs. initial accuracy under variable operating conditions.
# Single-Shot Coherent X-Ray Imaging Instrument at PAL-XFEL
## 3 Results
**(b)** The sum of the radial intensity (blue dotted line) for the diffraction pattern of the intact Ag nanosphere is fitted to the sum of the radial intensity of the ideal sphere of the same size expected from Equation (1) (black line). **(c)** The measured diffraction pattern after a time delay of 16 ps shows the morphological transformation and expansion of the nanoparticles by the IR laser. The low-density void is shown in the reconstructed image (the inset of **(c)**). The butterfly-shaped signal was measured after a 45-ps time delay **(d)**. As there is no speckle at all, this signal indicates the complete destruction of the nanosphere. The length of scalebar corresponds to 100 nm. same diameter ideal sphere calculated by Equation (1) (Figure 5(b)). This indicates that the measured sample is close to the ideal sphere of \(\sim\)112 nm diameter. The reconstructed image in the inset of Figure 5(a) intuitively displays the shape of the sample. The diffraction pattern in Figure 5(c) shows the shrinkage of the speckles size and the reduction of visibility at the 16-ps time delay. This diffraction pattern is a snapshot of the melting process of the Ag nanosphere. Heat transfer from electrons to the lattice excited by a femtosecond infrared laser leads to the melting and thermal expansion of the Ag nanosphere. The narrower pattern width, compared to the diffraction pattern in Figure 5(a), is the result of the thermal expansion of the Ag nanosphere. The distorted diffraction pattern in Figure 5(c) implies a deformation of the shape of the Ag nanosphere during the melting process, which is directly observable in the reconstructed image (the inset of Figure 5(c)). In the reconstructed image in the inset of Figure 5(c), the sample deformed and expanded during melting. At the same time, a low-density void formed in the center. After the 45-ps time delay, an anisotropic butterfly-shaped diffraction signal appeared (Figure 5(d)). As there was no observable speckle, it can be inferred that the Ag nanosphere was completely destructed. The process of the Ag nanoparticle melting is similar to the previous result [9].
# A QoS Classifier Based on Machine Learning for Next-Generation Optical Communication
## 2 Dataset Validation and Preprocessing  
The coupling of a laser beam in an optical fiber causes losses due mainly to its noncircularity and to the Fresnel reflections created at the injection face of the fiber. These reflections, moreover, disturb the proper functioning of the laser. The impact of these reflections on the stability of the optical source is easily avoided in a free-space assembly by using inclined optical surfaces, which deflect the beam but do not reduce losses. The constant reduction of the space requirement in the interrogators leads to using fiber components. In this case, it is possible to reduce the Fresnel reflections significantly by applying an antireflection coating to the injection face of the fiber.  
The intrinsic propagation losses in AR fibers are due to diffusion at the interfaces, leaks, and absorption of the material. However, the losses due to scattering are considered negligible for this type of fiber, given the small amount of surface area seen by the light propagating, compared with photonic band-gap fibers.  
This section shows the dataset validation. The total dataset features are extracted from Optisystem version 19. The features are Q-factor, SNR, and BER. The data response has four different groups carrying two different quality of services (video and audio) at different data rates--1.25 Gbps and 2.5 Gbps. These groups are assigned with variable weight fixed right shift (VWFRS) code that applies to a spectral amplitude coding optical code division multiple access (SCA-OCDMA) system using two detections schemes: direct detection (DD) and single photodiode (SPD). These are groups are:  
1. Group A/D for video service at 1.25 Gbps and odd weight FRS code with SPD scheme.
2. Group B/C for audio service at 1.25 Gbps and even weight FRS code with SPD scheme.
3. Group A/D for video service at 2.5 Gbps and odd weight FRS code with DD scheme.
4. Group B/C for audio service at 2.5 Gbps and even weight FRS code with DD scheme.  
The total number of unique groups is four, where the groups are {SPD: A/D, SPD: B/C, DD: A/D, DD: B/C}. The number of data points is equiprobable for all such classes. The sample data taken are validated through hypothesis testing. The rest of the workflow procedures are data normalization, data visualization, and multicollinearity detection.
# Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone
## 4 Results
### Validation of Dynamap: Sites 10 and 12
For instance, it is clear from our basic definition of the variations in Equation (14), \(\delta^{(\tau)}(t,s)\), that a small error in the reference level, \(L_{\text{eq}}^{(1h)}(t_{\text{ref}},s)\), may result in a shift of the total equivalent level prediction by quite an amount. This discrepancy can have two origins: one may be a poor estimation of the traffic flow at site \(s\) at time \(t_{\text{ref}}\) by the traffic model, and a second, an inaccurate determination of the noise contributions from the different groups at site \(s\). In order to elucidate these issues further, one can calculate the deviations of the predictions from the measured ones, for both the equivalent hourly noise levels, \(L_{\text{eq}}^{(1h)}(t_{\text{ref}},s)\), and the logarithm of hourly traffic flows, \(\log F^{(1h)}(t_{\text{ref}},s)\), at time \(t_{\text{ref}}\) at different sites \(s\). These are defined, respectively, as,  
\[\varepsilon_{\text{Leq}}(t_{\text{ref}},s) = \frac{L_{\text{eq}}^{(1h)}(t_{\text{ref}},s)_{\text{calc}}-L_{ \text{eq}}^{(1h)}(t_{\text{ref}},s)_{\text{meas}}}{L_{\text{eq}}^{(1h)}(t_{ \text{ref}},s)_{\text{meas}}}, \tag{15}\] \[\varepsilon_{\text{Fhw}}(t_{\text{ref}},s) = \frac{\log F^{(1h)}(t_{\text{ref}},s)_{\text{calc}}-\log F^{(1h)} (t_{\text{ref}},s)_{\text{meas}}}{\log F^{(1h)}(t_{\text{ref}},s)_{\text{meas}}}. \tag{16}\]  
The above values for \(\varepsilon_{\text{Leq},\text{Fhw}}(t_{\text{ref}},s)\) can be averaged over all sites \(s\) belonging to the same group \(g_{i}\), and, in addition, an average can be performed over different hours of the day. In the latter case, we will denote the resulting mean values as \(\left\langle\varepsilon_{\text{Leq}}\right\rangle_{X}\), where the subscript \(X\) indicates the type of average employed. A preliminary analysis of correlations between values of \(\varepsilon_{\text{Leq}}(t,s)\) and \(\varepsilon_{\text{Fhw}}(t,s)\) have been performed [86], from which empirical 'corrections', denoted with the subscript \(C\), have been obtained in order to improve the performance of Dynamap. The results are shown in Figure 9b, for both Sites 10 and 12, suggesting that some improvement can be obtained in this way. Figure 8: Comparison of traffic flow measurements (black lines) with the model predictions (red lines) for: (a) Site 10 (\(g_{1}\)), (b) Site 12 (\(g_{2}\)) [86]. (With permission from Elsevier). Despite the presence of significant deviations from the measured values still observed in Figure 9, the analysis extended to other available sites, belonging to groups (\(g_{1}-g_{6}\)), yields a more optimistic picture. This can be appreciated from the results reported in Table 6. Quite illustrative are the mean group values reported in Table 7, yielding mean deviations below 3 dB in most cases. Again, the largest discrepancies occur for \(g_{1}\), as expected.
# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
###### Abstract  
There is a pressing need across various applications for efficiently compressing point clouds. While the Moving Picture Experts Group introduced the geometry-based point cloud compression (G-PCC) standard, its attribute compression scheme falls short of eliminating signal frequency-domain redundancy. This paper proposes a texture-guided graph transform optimization scheme for point cloud attribute compression. We formulate the attribute transform coding task as a graph optimization problem, considering both the decorrelation capability of the graph transform and the sparsity of the optimized graph within a tailored joint optimization framework. First, the point cloud is reorganized and segmented into local clusters using a Hilbert-based scheme, enhancing spatial correlation preservation. Second, the inter-cluster attribute prediction and intra-cluster prediction are conducted on local clusters to remove spatial redundancy and extract texture priors. Third, the underlying graph structure in each cluster is constructed in a joint rate-distortion-sparsity optimization process, guided by geometry structure and texture priors to achieve optimal coding performance. Finally, point cloud attributes are efficiently compressed with the optimized graph transform. Experimental results show the proposed scheme outperforms the state of the art with significant BD-BR gains, surpassing G-PCC by 31.02%, 30.71%, and 32.14% in BD-BR gains for Y, U, and V components, respectively. Subjective evaluation of the attribute reconstruction quality further validates the superiority of our scheme.  
point cloud attribute compression; graph transform; texture-guided graph optimization
## 2 Background and Related Work
The authors of [19] suggested a residual densely connected network (Densely-ResNet) to identify attacks. It was put together with the help of surviving core modules, each comprising several Conv-GRU subnets connected by wide links. Where three virtual layers (fog, edge, and cloud) are connected, the security system can detect external threats using the (ToN-IoT) database for test evaluation. The authors of [20] proposed a detailed analysis of feature sets for network assaults with relevance and predictive power. Three feature-selection procedures were used to find and rank data characteristics: chi-squared, correlation, and information gain. The characteristics were input into two machine learning classifiers to measure the accuracy of their assault detection: random forests, and deep feedforward. [PERSON], in [21], proposed a new test architecture for the ToN-IoT dataset, which can be used to assess AI-based security solutions. The NSX vCloud NFV platform was utilized to assist software-defined network installation, network function virtualization, and service orchestration to create dynamic test networks that allow interaction between fog, edge, and cloud layers. Four intrusion-detection techniques based on machine learning were used to validate the dataset: gradient boosting machine, deep neural networks, naive Bayes, and random forests. The authors of [22] proposed the Edge-IIoT dataset as an experimental resource, which uses federated deep learning and centralized intrusion detection with standard assessment criteria. Three studies were conducted using binary, 6-class, and 15-class categorization to study the traffic predictability and detection effectiveness of various cyber-attacks and threat models. Four classifiers were used to achieve this: RF, SVM, kNN, and DNN. Table 1 summarizes previous studies. Research into architectures that improve the accuracy and performance of CNNs has been an active area of study for some time. This research has resulted in different types of CNN models, including LeNet, AlexNET, VGGNet [23], GoogleNet/Inception, ResNet, ZFNet, MobileNets, and DenseNet [23; 24]. In this paper, we compare two CNN models--the Inception Time and DenseNet models--and evaluate them using three databases to detect cyber-attacks.
# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
## 4 Backup Path Design Method Based on BPDM-GCN
### Theoretical and Practical Correlation of GCN and DDPG Fusion Mechanism
#### 4.3.3 Deep Integration Between BPDM-GCN and SDN Architecture  
In the SDN control plane, the specific association of BPDM-GCN is as follows [21]:  
(1) Data plane awareness: The SDN controller collects link status data (latency, bandwidth usage) in real time via the OpenFlow protocol and builds a dynamic topology map \(G(V,E)\).  
(2) Control plane reasoning: BPDM-GCN receives topology data and uses the pre-trained DDPG-GCN model to output the optimal link weight matrix \(W\).  
(3) Application level decision: Generate the primary path and backup path based on the \(W\) increment shortest path tree algorithm and send them to the switch through the flow table entry. The comparative analysis of BPDM-GCN and traditional methods is shown in Table 1.  
In the BPDM-GCN algorithm, GCN is used to compute link weights. The calculation of GCN mainly revolves around the adjacency matrix and the node feature matrix. When computing each layer of GCN, as in formula \(H^{(l+1)}=\sigma(\overset{\sim}{D}-\frac{1}{2}\overset{\sim}{A}\overset{\sim} {D}-\frac{1}{2}H^{(l)}W^{(l)})\), it involves matrix multiplication operations, and its computational complexity is roughly related to the number of edges \(E\), with each layer being approximately \(O(E)\). Due to the usually small number of GCN layers, the total computational complexity of the GCN part  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Topology Adaptability** & \begin{tabular}{c} **Computational** \\ **Complexity** \\ \end{tabular} & \begin{tabular}{c} **Dynamic Optimization** \\ **Capabilities** \\ \end{tabular} \\ \hline DDPG-FCN & \begin{tabular}{c} Low (relies on fixed input \\ dimensions) \\ \end{tabular} & \(O(N^{2})\) & Optimize only local links \\ \hline HFPR-A & \begin{tabular}{c} Medium (manual tuning \\ required) \\ \end{tabular} & \(O(E\log V)\) & None (static path) \\ \hline BPDM-GCN & \
\begin{tabular}{c} High (supports graphs of \\ any size) \\ \end{tabular} & \(O(E+V)\) &
\begin{tabular}{c} Global \\ collaborative optimization \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of BPDM-GCN and traditional methods.  
can be approximated as \(O(E)\). In addition, the complexity of constructing the shortest path tree in the algorithm (using [PERSON]'s algorithm) in sparse graphs (actual power system communication networks are mostly sparse graphs) is approximately \(O(E)\), and the complexity of other auxiliary operations is relatively small and can be ignored. Considering these factors, the computational complexity of the BPDM-GCN method is finally \(O(E+V)\).
# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
## 2 Materials and Methods
### Classification of Environmental Noise  
To properly reduce the noise in a noisy environment, different noise reduction algorithms have to be applied to each noise. For that, the current noise characteristics need to be extracted according to the hearing aid's environment and distinctly classified into various noise categories.  
The noise classification result using CNNs was applied for speech enhancement. CNNs are a kind of deep learning techniques and are widely used for image classification while maintaining the spatial information of the image. Convolutional and pooling layers are added between the input and output layers of CNNs, for excellent performance in processing data composed of multi-dimensional arrays, such as color images [23]. The feature map of the input data is produced by moving a convolution filter in the convolution layer, and the values obtained from the final feature maps are then extracted to reduce the computational complexity and improve the accuracy of the pooling layer [30].  
In order to improve the classification rate of the environmental noise, a spectrogram image of the noise was used to transform sound signals in the time frequency-domain into image signals. In addition, a sharpening mask and median filter were applied to improve the noise classification rate [31; 32]. The environmental noise used the same dataset as the experiment outlined in this paper. The classification results using CNNs were better than the conventional noise classification algorithms of hearing aids. This improved noise classification can contribute to the enhancement of speech for hearing aids [23].
## 2 Case Studies
### Annotations for Electronic Scholarly Editions
By using ORE to represent complex scholarly annotation bodies, we hope to leverage existing tools such as LORE [15], a graphical authoring, publishing and visualization tool that we developed for authoring ORE Resource Maps. In Figure 2, both of the target resources are transcripts rendered as HTML, however, scholars may also annotate other digital surrogates of the same version of the work (including transcripts) in alternate formats such as plain text, TEI XML or scanned page images. Each target transcript is associated with a FRBR Manifestation via custom ontology properties, which identify the bibliographic entities for the versions of the work under study. These properties can be traversed by SPARQL queries to retrieve annotations across all digital surrogates of the same FRBR Manifestation. A URI identifying the person who created the annotation, as well as timestamps for when the annotation was created or modified are recorded as properties of the Annotation (see Figure 3). Also associated with each creator URI is their FOAF name property, which is used for display purposes. The OA model allows metadata such as _dterms:creator_ and _timestamps_ (associated with _dcterms:created_ and _oa:annotated_) to be specified for each Body and Target resource as well as the Annotation object. This means that the author of an annotation Body can be different to the author of the Annotation object, allowing scholars to create Annotations in which the Body comprises content created by other scholars. An example of this type of reuse is the creation of Annotations that use selected notes from an existing electronic scholarly edition as commentary. Figure 3 shows an OA model instance associating a textual note originally created by [PERSON] for a digital Scholarly Edition of [PERSON]'s Jerilderie Letter [16]. By attaching _dcterms:creator_ and _dcterms:created_ to the body resource, the original scholar who created the note and the date it was created can be clearly specified. The person who has created the new Annotation ([PERSON]) is  
Figure 2: oa: Annotation describing Textual Variation. attributed, since they discovered and reused the original content, and may have attached tags or other metadata to the Annotation to aid discovery or classification. Figure 4 above illustrates how collaborators can generate a discussion around a particular piece of text by replying to existing annotations. This example only shows a single reply, however it is possible to have a chain of replies in which each previous _Reply_ is the _Target_ of the next _Reply_.
# Universal Behavior of the Image Resolution for Different Scanning Trajectories
## 4 Conclusions  
This study attempted to establish a correlation between image resolution and scanning parameters by investigating the characteristics of different scanning trajectories, such as pixel size, fill factor, scan time, frequency, and frequency ratio. Our findings suggested that the pixel size and the frequency ratio \(N_{P}\) of the scanning trajectory have a straightforward relationship. More specifically, \(N_{P}\) and the pixel size formed by the scanning trajectory investigated in this work are inversely related. This relationship holds true across all scanning trajectories investigated in this work and is universal. The research produced a theory to explain the previously neglected correlation between scanning parameters and pixel size, which determines image resolution. The proposed theory can serve as a foundation for the correlation between image resolution and a number of variables, including scan time, fundamental frequency, and frequency ratio \(N_{P}\). The work also provides a novel method for estimating the pixel size a priori, giving the user/operator more control over how to modify the scanning parameters for a desired resolution. Image reconstructions were also performed for all scanning trajectories. Our results indicated that SL is the preferred trajectory because it produced the best image quality with high signal-to-noise and low error when compared to other trajectories. Our findings also suggest that a good scanning trajectory for producing high-quality images is one that can produce a highly regular pattern and small pixel size. The findings from this study can be utilized to improve the effectiveness and quality of scanning images in a variety of real-world scenarios, such as topological mapping, heat mapping, and water mapping for LiDAR-based area surveillance as well as MPI and AFM imaging for biological and biomedical applications.  
[PERSON] performed simulations, data analysis, and manuscript preparation. [PERSON]-[PERSON] assisted in the theory development and image reconstruction. [PERSON] and [PERSON] secured funding and conceptualized, designed, and directed the project, wrote codes, supervised, and wrote--review and editing. All authors have read and agreed to the published version of the manuscript. This work was supported by Nazarbayev University under the Faculty Development Competitive Research Grant Program (FDCRGP), Grant No. 11022021 FD2924 (TDD) and 021220 FD4451 (TTP).  
Data Availability Statement: Data is available upon request.