esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
## 3 Proposed Methodology
### Class Aggregation and Class Separability Strategy  
The accuracy of the classifier in machine learning is largely affected by the extracted features. Furthermore, the more obvious the difference between the features, the better the classification effect. Thus, to improve the feature extraction capability of the autoencoder, a class aggregation and class separability strategy are introduced to limit feature mining from the data level to achieve better classification results.  
Figure 3: The visual interpretation of hierarchical sparse strategy. (**a**) The schematic diagram of SSAE ordinary sparse strategy; (**b**) the schematic diagram of SSAE hierarchical sparse strategy.  
The class aggregation and class separability strategy mean that the data points of each class are as compact as possible, and the distance between classes is as far as possible. Assuming that the same type of data point is drawn on a two-dimensional plane in the form of scattered points, there is usually a circle with the smallest radius to live all the data points. At this time, the center of this circle can be regarded as the fitting center point \(\delta\) of this type of data, and the radius of this circle can be called the class radius \(r\) of this data type. According to this idea, assuming that there are \(N\) classes and the \(i\)-th class has \(n_{i}\) group data \(d_{i}\), the class fitting center point \(\delta_{i}\) and class radius \(r_{i}\) of the \(i\)-th class are defined as:  
\[\delta_{i}=\frac{1}{n_{i}}\sum_{k=1}^{n_{i}}d_{ik} \tag{9}\]  
\[r_{i}=\frac{1}{n_{i}}\sum_{k=1}^{n_{i}}\left\|d_{ik}-\delta_{i}\right\|^{2} \tag{10}\]  
At the same time, when there are \(N\) classes in total, the radius \(R_{c}\) between classes and distance \(D_{\text{c}}\) between classes is defined as:  
\[R_{c}=\frac{1}{N}\sum_{i=1}^{N}r_{i} \tag{11}\]  
\[D_{\text{c}}=\frac{2}{N(N-1)}\sum_{i=1}^{N-1}\sum_{j=l+1}^{N}\left\|\delta_{i }-\delta_{j}\right\|^{2} \tag{12}\]  
When plotting data points of different classes on the same plane, the larger the distance between the fitting center points of each class, the better the class separation. Meanwhile, the smaller the class radius of each data class, the better the class aggregation. When the data points are distributed under these two conditions, it can be considered that the feature extraction effect is enhanced, as shown in Figure 4.  
The loss functions \(E_{c}\) of class aggregation and class separability strategy are as follows:  
\[E_{c}=R_{c}-D_{c}+\gamma\|d\|^{2} \tag{13}\]  
where \(\gamma\|d\|^{2}\) is the regular term and \(\gamma\) is the regular term coefficient. In the optimization process, ensure that \(E_{c}\) takes the minimum value. In order to avoid negative optimization, the regular term can play a balancing role.
# A Method of Aerial Multi-Modal Image Registration
## 1 Introduction
A method based on neural networks needs large numbers of image samples. For application scenarios with low-visibility aerial images, such methods have certain limitations. Low visibility is the main reason for flight accidents during approach and landing, so virtual reality has come into being. In order to solve this problem, the current global technical means mainly include heads-up guidance systems, enhanced visual systems (EVSs), and synthetic visual systems (SVSs) [28; 29]. Among these, EVSs and SVSs are designed to provide pilots with enhanced views of the ground and air conditions by using advanced infrared thermal imaging and visible-light sensors, as well as cockpit displays. As the two most important photoelectric sensors, infrared thermal imaging and visible-light imaging sensors have different imaging mechanisms and application scenarios, and the information that they obtain is naturally complementary. Infrared thermal imaging obtains a target's temperature radiation intensity information, while visible-light imaging reflects the target's texture and contour information. By integrating infrared information and visible-light information into one image, we can achieve feature information complementation and reduce redundant information [30; 31; 32; 33], which has an important value and significance in the field of low-visibility monitoring. [PERSON] et al. [34] proposed a new main direction for feature points, which is called the contour angle direction (CAO), and they described an automatic infrared and visible-light image registration method called CAO-Coarse to Fine (CAO-C2F). CAO is based on the contour features of an image, and it is invariant to the image's viewpoint and scale differences. C2F is a feature-matching method for obtaining correct matching. Repeated experiments showed that the method could accurately propose feature points in low-visibility visible-light images, and the feature points had good geometric invariance, which is conducive to the accurate registration of infrared and visible-light images. However, contour extraction is the key to the algorithm. The canny edge detection algorithm was used to extract contours in the literature. Fixed high and low thresholds are very poorly adaptive for low-visibility aerial images. The selection of an automatic threshold depends on the proportion of image edge pixels in an image. The method based on virtual reality proposed in this paper can give an a priori proportion. The main contributions of our paper are:  
1. A virtual reality-based automatic threshold for Canny edge detection is proposed. According to the prior knowledge of the virtual image, the high and low threshold of Canny edge detection is automatically determined. 2. A registration method for extracting low visibility multi-modal aerial images based on virtual reality is proposed. The virtual image provides the matching region of interest, and gives the approximate position of the matching points of the image to be registered, so as to eliminate the wrong matching point pairs.
## 3 Procedure, Data and Methodology
### Construction of Lexical Situation Type System
#### 3.1.3 Consistency Check  
The annotation task was completed by four annotators who had received specialized training in linguistics. Subsequently, we conducted a multi-level cross-validation using [PERSON]'s Kappa consistency test [43]. The Kappa coefficient is a statistical measure used to assess consistency, evaluating the ratio of observed agreement among annotators to the agreement expected by chance. The coefficient ranges from \(-1\) to \(1\), with values closer to \(1\) indicating higher agreement between annotators. We established a threshold of 0.75; if the Kappa value from the test exceeded this threshold, it demonstrated that the annotated data had passed the consistency verification.  
The consistency verification was conducted at three levels:  
(1) Intra-annotator consistency: For the 20 repeated verbs, we performed intra-annotator consistency checks by comparing each annotator's initial and subsequent annotations. This was performed to evaluate the stability of the judgments formed by each annotator through intuition and introspection.  
(2) Inter-annotator consistency: We conducted inter-annotator consistency checks across the four annotators to measure the degree of agreement among them in performing the annotation task.  
(3) Cross-validation with existing resources: After finalizing the annotations results, we performed consistency checks between our annotations and those of [PERSON] [12]. By cross-validating with pre-annotated linguistic resources, we ensured the scientific rigor of the annotation results.  
Only the annotations that passed the consistency checks were used in the hierarchical clustering experiments. These tests effectively improve the methods used by previous researchers in annotating data, to some extent reducing subjectivity in situation type classification.
## 4 Results and Analysis  
### Influence of Roadway Height-Width Ratio  
#### 4.1.1 Plastic Damage Zone of Roadway Sidewall  
The distribution of plastic areas in the rock masses of roadways with various roadway height-width ratios \(h/w\) is illustrated in Figure 7. As shown in the figure, as the \(h/w\) ratio increases, the distribution height and width range of the tensile and shear plastic rock around the roadway sidewall are increasing. When the \(h/w\) is small, the tensile plastic zone is a circular arc. When the \(h/w\) is large, the tensile plastic zone is also arc-shaped, but it is approximately V-shaped, while the shear plastic zone appears as a circular arc. Importantly, the increase in the height-width ratio of the roadway results in a more significant change in the area of the plastic failure zone of the roadway compared to the roof and floor. This observation highlights the notable impact of the roadway's height-width ratio on the stability of the roadway sidewall. Therefore, the rational design of the \(h/w\) of a roadway should be carried out in the design stage, before construction.  
The range of change of the tensile and shear plastic areas of rock masses of different height-width ratios is illustrated in Figure 8. The width of the tensile and shear plastic area increases linearly as the roadway's \(h/w\) becomes larger. Under different roadways' \(h/w\), especially when the roadway height-width ratio is small, the area of the shear plastic zone of a roadway rock mass is generally greater than that of the tensile plastic area. For example, when \(h/w\) = 0.25, the depth of the shear plastic zone is about three times that of the tensile plastic zone. When \(h/w\) = 2.00, the width of the shear plastic area is about 1.45 times that of the tensile plastic area. From the perspective of the forming process of the plastic area of surrounding rock, a sidewall is damaged first by tensile stress and then by shear damage, but the final roadway failure is mainly a shear failure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Roadway Height-Width Ratio \(h/w\)** & **Lateral Pressure Coefficient \(\lambda\)** \\ \hline Scheme 1 & 0.25, 0.50, 0.75, 1.0, 1.25, 1.50, 1.75, 2.00 & 0.5 \\ Scheme 2 & 1.0 & 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scheme design of simulation.  
Figure 7: Distribution of tensile and shear plastic zones in roadway sidewall under various height-width ratio \(h/w\) (green is tensile and red is shear): (**a**) \(h/w\) = 0.25; (**b**) \(h/w\) = 0.50; (**c**) \(h/w\) = 0.75; (**d**) \(h/w\) = 1.00; (**e**) \(h/w\) = 1.25; (**f**) \(h/w\) = 1.50; (**g**) \(h/w\) = 1.75; (**h**) \(h/w\) = 2.00.
## 2 Related Work
### Heterogeneous Federated Learning  
Using Federated learning we need to address heterogeneity in several dimensions: communication, models, statistics, and devices.  
* It can be difficult to coexist with Networks with variable bandwidth, latency, and reliability. These are difficult to manage and correct. For instance, protocols such as TCP/IP are compatible with changing the reliability of the communication, but not so elastic in terms of infrastructural changes such as IP or handover [17].
* Different models, machine-learning architectures, data formats, and data dimensions can create difficulties in aggregation. For instance, two distinct layouts of neural networks can generate different results with the same data. In this case, the results need to be evaluated and analyzed with a specific algorithm [18].
* Non-uniform data distributions and misaligned statistics can affect the quality of the overall model. For instance, dealing with heterogeneous data from different sources can be difficult and needs to be adapted with specific algorithms and preparations, which otherwise can lead to over-fit and under-fit problems in the models.
* Limitations in computing power, memory, and communication capabilities between devices can slow the learning process [2]. For instance, distributing the analysis between low-powered end devices such as smartphones or low-end IoT devices and high-end processing servers could slow down the high-end devices.  
Despite these challenges, FL offers opportunities to exploit data diversity and improve model robustness. Techniques such as data compression, differential federated learning, and model adaptation can mitigate heterogeneity issues. As described by Section 1 FL in heterogeneous environments is an active research field with the potential to advance distributed and adaptive artificial intelligence in complex contexts. We argue that, to the best of our knowledge, the integration of heterogeneous environments and cloud-native infrastructures such as Kubernetes or Docker is a novel approach.
## 2 Materials and Methods
### General Synthesis of 9,10-aryl-2-(3-(trifluoromethylomethyl)phenyl)anthracene (**1-3**)
#### 2.2.1 Data for 9,10-diphenyl-2-(3-(trifluoromethylomethyl)phenyl)anthracene (**1**)  
Pale yellow solid (0.24 g, 48%). \({}^{1}\)H NMR (CDCl\({}_{3}\)): 8 7.89 (d, \(J\) = 1.2 Hz, 1H), 7.80 (d, \(J\) = 8.2 Hz, 1H), 7.77 (br s, 1H), 7.72 (d, \(J\) = 7.2 Hz, 1H), 7.70 (d, \(J\) = 4.8 Hz, 1H), 7.68 (d, \(J\) = 7.8 Hz, 1H), 7.61 (m, 4H), 7.56 (m, 4H), 7.51 (m, 5H), 7.34 (q, \(J\) = 8.0 Hz, 2H). \({}^{13}\)C NMR (CDCl\({}_{3}\)): 8 141.92, 138.80, 138.66, 137.73, 137.14, 135.85, 131.29, 131.27, 130.96, 130.55, 130.39, 130.22, 129.84, 129.25, 129.13, 128.53, 128.49, 128.08, 127.71, 127.62, 127.03, 127.01, 125.45, 125.33, 125.31, 125.05, 124.48, 124.05, 124.01, 123.92, 123.88, 122.75, 77.20 (CF\({}_{3}\)). \({}^{19}\)F NMR (CDCl\({}_{3}\)): 8-62.7. Anal. Calcd. for C\({}_{33}\)H\({}_{21}\)F\({}_{3}\): C, 83.53; H, 4.46. Found: C, 82.99; H, 4.13.  
2.2 Data for 9,10-di(1,1\({}^{\prime}\)-biphenyl)-4-yl)-2-(3-(trifluoromethylomethyl)phenyl)anthracene (**2**)  
Pale yellow solid (0.30 g, 44%). \({}^{1}\)H NMR (CDCl\({}_{3}\)): 8 7.99 (d, \(J\) = 1.2 Hz, 1H), 7.91 (d, \(J\) = 8.0 Hz, 1H), 7.86 (d, \(J\) = 7.6 Hz, 4H), 7.84 (m, 1H), 7.80 (m, 4H), 7.88 (d, \(J\) = 1.2 Hz, 2H), 7.71 (d, \(J\) = 8.0 Hz, 1H), 7.61 (m, 4H), 7.58 (m, 1H), 7.52 (m, 6H), 7.42 (m, 2H), 7.39 (m, 2H). \({}^{13}\)C NMR (CDCl\({}_{3}\)): 8 141.92, 140.78, 140.74, 140.44, 137.77, 137.65, 137.43, 136.89, 136.03, 131.79, 131.75, 131.29, 130.97, 130.62, 130.49, 130.31, 129.91, 129.29, 129.22, 128.93, 128.15, 127.51, 127.21, 127.19, 127.16, 127.09, 127.07, 125.46, 125.44, 125.06, 124.63, 124.10, 124.06, 123.96, 123.92, 122.75, 77.20 (CF\({}_{3}\)). \({}^{19}\)F NMR (CDCl\({}_{3}\)): 8-62.6. Anal. Calcd. for C\({}_{45}\)H\({}_{29}\)F\({}_{3}\): C, 86.24; H, 4.66. Found: C, 85.78; H, 4.20.
# Calculation Method for Settlement Deformation of Existing Tunnel Induced by Underpass Construction
## 6 Engineering Optimization Recommendations
### Optimization Recommendations  
The diameter of the newly constructed tunnel exerts the most significant influence on the existing tunnel. When the burial depth of the newly constructed tunnel is within 1D of the existing tunnel (D representing the diameter of the existing tunnel), it is evident that the extant tunnel has been more significantly impacted by the excavation of the newly constructed tunnel. Consequently, when designing the construction of the underpass, excavation should be avoided within 1D of the existing tunnel. For large diameter tunnels, it is recommended to adopt the construction method of step-by-step excavation and segmental support, and to closely monitor the existing tunnel during the construction process. The magnitude of the stratum loss rate exerts a substantial influence on the settlement of the existing tunnel. Consequently, during the construction period, measures such as grouting and reinforcing the soil surrounding the existing tunnel [31] should be considered, accompanied by stringent control of the travelling attitude. In addition, the use of shield machines has been shown to minimize the impact of stratum disturbance. Furthermore, the implementation of ring support reinforcement has been proposed as a means to ensure the safety of the existing tunnel during the construction of the new tunnel. The depth to which the new tunnel is buried also has a certain impact on the existing tunnel, and it is necessary to strictly grasp the clear distance between the new and old tunnels. It is recommended that, in the case of shallow tunnels, a smaller excavation step be used, and grouting reinforcement measures be taken in time, with a view to reducing any impact on the existing tunnel.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & \(S(x_{k})\) & \(x_{k}^{*}\) & \(S_{k}^{*}\) \\ \hline New Tunnel Depth & \(\frac{0.15x}{-0.15x+5.18}\) & 12 & 0.53 \\ Diameter of new tunnels & \(\frac{0.03x^{2}+0.41x}{0.15x^{2}+0.41x-0.59}\) & 4 & 1.87 \\ Stratigraphic loss rate & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensitivity results for each parameter.
# Prediction and Control of Broadband Noise Associated with Advanced Air Mobility--A Review
## 2 Sources/Types of BBN  
Broadband noise arises from unsteady, nondeterministic blade loading, with sources attributed to turbulent disturbances from flow-surface interactions. As depicted in Figure 2, noise sources are classified into three main categories: inflow turbulence ingestion noise (TIN), blade self-noise, and blade-wake interaction noise (BWI). Among these, blade self-noise is the predominant source and is further subdivided into five distinct boundary layer turbulence mechanisms, detailed in subsequent sections. AAM has a complex configuration that leads to noise interactions, such as blade-airframe interaction, fuselage-wake interaction, and rotor-rotor interaction. However, it is unnecessary to define these inter  
Figure 1: Rotor noise variation by tip Mach number [5].  
actions separately, as their mechanisms are fundamentally associated with the primary noise sources mentioned above. Thus, the dominant source of BBN in AAM remains blade self-noise, while TIN and BWI are becoming increasingly significant and cannot be overlooked, especially given the structured wake generation and the operation of AAM vehicles in urban environments.
## 2 Materials and Methods
### Analytical Methods  
The pH was checked with a pH meter and the oxygen content was measured with an oxygen meter. N-NO\({}_{3}\)\({}^{-}\) and P-PO\({}_{4}\)\({}^{3-}\) concentrations were measured by spectrophotometric methods. The Kjeldahl nitrogen content was measured by titration. TOC was determined with a Multi N/C 2100 analyser and volatile fatty acids were measured with a Buchi K-355. BOD\({}_{5}\) was determined using the manometric method and COD was determined using spectrophotometric methods (HACH DR4000). Phosphorus deficiencies were compensated by supplying an adequate amount of KH\({}_{2}\)PO\({}_{4}\) to obtain a N:P = 16:1 ratio.  
The chlorophyll-a content was measured in accordance with the Polish Standard PN-ISO 10260:1992 [8]. The size of algal cells was determined under a microscope (NIKON Eclipse E200) integrated with a colour digital camera (DS-Fi1) and software (NIS Basic Research). The concentration of biomass was determined from the number of cells counted in a Thoma chamber and as dry matter in accordance with the Polish Standard PN-78/C-04541 [9].  
Algae were cultured at photosynthetically active radiation (PAR) of 80 \(\pm\) 5 umol photons m\({}^{2}\)/s, in a climatic chamber with the light/dark period of 12/12 h and at temperature 29 \(\pm\) 0.5 \({}^{\circ}\)C during the day and 25 \(\pm\) 0.5 \({}^{\circ}\)C at night. All the measurements were conducted in triplicate and the data are presented as arithmetic means calculated from three measurements. The relative standard deviation (RSD) was between 5 and 10%.
# Discoloration Mechanisms of Natural Rubber and Its Control
## 5 Discoloration Mechanism of Natural Rubber
### Non-Enzymatic Browning
#### 5.2.2 Lipid Oxidation  
The important lipids involved in oxidation are the unsaturated fatty acid moieties, oleic, linoleic, and linoleic. The oxidation rate of these fatty acids increases with the degree of unsaturation. The mechanism of lipid oxidation is illustrated in Figure 5[45].  
To summarize, discoloration is a prevalent color reaction in NR because of the interaction of phenolic compounds, oxygen, and enzymes, such as PPOs[45]. The browning reaction has two types of reaction in NR. The Enzymatic reaction is when monophonic compounds react with oxygen and PPO, which can produce color polymer or pigments. The other is a non-enzymatic reaction, such as the Maillard reaction, where carbonyl reacts with amino compounds, followed by the dehydration, fragmentation, and aldol condensation reactions which originate the yellow-color product. Furthermore, lipid oxidation also causes the polymerization of unsaturated fatty acids into yellow products, which aggravates the discoloration of NR.  
Figure 4: The possible reaction between glucose and CBS produces brownish compounds [49].  
Figure 5: Lipid oxidation mechanisms.
# MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications
## 4 Experiments and Analysis
### Experiment Analysis
#### 4.4.2 Representative Cases
Difficult Category: Complex Backgrounds and Intricate Trajectories  
In challenging scenarios with complex backgrounds and diverse, dynamically moving targets such as pedestrians, bicycles, and vehicles, detecting objects is highly demanding, as shown in Figure 7. Point cloud data can be contaminated by noise, leading to inaccurate detections. Traditional methods like PointPillars often struggle with distinguishing between background and target features, resulting in false positives and missed detections. As illustrated in Figure 7b, traditional approaches may misclassify traffic signs, curved lane dividers, tall flower beds, and multi-track pedestrians as vehicles. Moreover, a fast-moving red car and a partially occluded blue car (due to backlighting and partial visibility) are not detected, highlighting the limitations of these methods in handling background complexity and incomplete imagery. In contrast, MS3D effectively detects the fast-moving red car and the severely occluded blue car while minimizing false positives, as shown in Figure 7c. Leveraging SecondFPN's multi-scale feature extraction and fusion capabilities, MS3D significantly enhances point cloud feature representation, enabling precise focus on critical features and accurate de  
Figure 6: Detection performance comparison in multi-object overlap and occlusion scenarios. tection of partially visible targets. The incorporation of 2D convolutions improves the model's interpretation of 2D projections from point cloud data, thereby enhancing detection performance for severely occluded and dynamically moving objects. Additionally, the Adam optimizer's efficient parameter update strategy facilitates rapid convergence and high accuracy in complex scenes. Collectively, these advancements markedly improve MS3D's performance in challenging and intricate environments. Despite advancements, the detection of pedestrians on multiple trajectories, especially in occluded scenarios such as individuals propelling bicycles, continues to pose challenges in terms of missed detections. While MS3D has enhanced detection performance through multi-scale feature fusion, the single fusion strategy struggles to adequately extract crucial information to address occlusion challenges, particularly in cases like cyclists. Additionally, the limitations of 2D Convolutional Neural Networks in boundary and detail processing, compounded by the small size of pedestrian targets, sparsity of point clouds, and motion artifacts caused by complex trajectories, all contribute to the complexity of feature extraction and recognition, resulting in persistent issues of missed detections. Subsequent research endeavors will be directed towards the refinement of motion distortion mitigation strategies and the identification and compensation of motion artifacts to bolster the detection precision for pedestrians in multi-track walking scenarios. Figure 7: Detection performance comparison in complex background scenarios.