esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In addition, combining fusion techniques with advanced post-processing algorithms can improve the evaluation results. Fusion can help reduce the uncertainty of the results [33]. Currently, data fusion extends to a wide range of diverse applications [34]. [PERSON] et al. illustrated the implementation of the data fusion algorithm in advanced NDT techniques for the identification of defects in single lap composite adhesive joints [35]. [PERSON] et al. have performed terahertz and X-ray image fusion to improve the evaluation of damage of composite materials [36]. In addition, when NDE is applied in industry, there is always a desire to automate the evaluation of data to avoid human factor and increase the probability of detection [37]. Therefore, one of the objectives of this work is to develop the process of determination and extraction of features for different inspection results for their further fusion, as well as to create a fusion algorithm to develop a reliable NDT technique for the investigation of multi-layered structures with bonding defects. The aim of this work is to achieve reliable non-destructive evaluation (NDE) of adhesively bonded aerospace components by developing novel multidimensional data fusion techniques, which would combine the information obtained by ultrasonic and X-ray NDE methods. Separately, both NDE techniques have their advantages and limitations. For instance, ultrasonic NDE methods are sensitive to elastic properties and density and are good at detecting planar defects, such as lack of bonding/delamination; however, they may suffer from attenuation or diffraction. X-rays are very sensitive to changes in density and volumetric defects, but their performance on planar defects is limited. The integration of data obtained from pulse-echo immersion ultrasound testing and radiography holds immense potential for helping to improve the reliability of non-destructive evaluation. The objective of this investigation was to study and compare distinctive features obtained from single techniques, traditional ultrasonic pulse echo testing and radiography, and fused images, as well as to evaluate the suitability of different features and fusion techniques to improve the probability of defect detection. On the basis of the data fusion algorithms, this article presents feature-based image fusion of ultrasonic and radiography testing. For this purpose, single lap aluminum joints with brass inclusions were analyzed using ultrasound pulse echo and radiography techniques. Then, different features were extracted from the obtained data. Then, images of the features of both techniques were fused together. Different combinations of features and fusion algorithms were investigated considering the desire to automate data evaluation in the future. The discussion of the qualitative and quantitative analysis obtained is presented in the discussion section, followed by the conclusions.
## 2 Materials and Methods
### Design of the Experimental Protocol  
The protocol began with instrumentation of the participant using 36 reflective markers and five inertial measurement unit (IMU) sensors with EMG tracking, as shown in Figure 1. The instrumentation procedure aligned the Delsys(r) Trigno Avanti(r) IMUs, Delsys Inc., Natick, MA, USA with the longitudinal muscle fibers of the upper trapezius, middle trapezius, triceps brachii, quadriceps, and gastrocnemius muscles to record electromyographic (EMG) signals during protocol execution. Prior to sensor placement, the skin was prepared by shaving (if necessary), lightly abrading, and cleaning the area with alcohol wipes to reduce impedance. The Trigno Avanti(r) sensors feature built-in electrodes with a parallel bar configuration. All sensors were secured according to the manufacturer's guidelines to ensure stable and high-quality signal acquisition.  
After the on-site observation and audiovisual recording of truck drivers performing their daily professional activities, the following protocol, which is composed of three stages, was defined.  
* In the first stage, the participant starts the engine and checks the dashboard indicators. Subsequently, they adjust the rearview mirrors and the seat, emulating drivers' actions to ensure proper visibility and comfort. Next, the participant shifts the gear lever into the first gear and presses the clutch pedal to set the vehicle in motion. This phase simulates the driver's initial preparation.
* In the second stage, the participant shifts to second gear and accelerates. Since Ecuadorian roads are often winding, the protocol includes two turns. The first is a left  
Figure 1: Placement of reflective markers (red dots) and inertial sensors (green arrows) on participants.  
turn, and the second is a right turn. During these maneuvers, the participant checks the mirrors and returns the steering wheel to its neutral position before continuing straight ahead until the next turn.
* The third stage of the protocol replicates the necessary actions to bring the vehicle to a stop upon reaching the destination. In this stage, the participant gradually releases the accelerator, presses the brake pedal, shifts the gear lever to neutral or park, and turns the key, thus completing the stopping process. The protocol takes approximately 120 s. Figure 2 shows the set of stages in the protocol. The cabin remains static, with only the steering wheel, pedals, gear lever, and mirrors moving.
## 4 Result
### Distribution Characteristics of Geothermal Anomalies Detected by MODIS NLST  
Figure 9 illustrates the spatial variation in monthly average LST across the study area from 2003 to 2023. Although the distribution of temperature anomalies varies slightly across months, they remain primarily concentrated in the Yangbajing and Yangyi geothermal fields, where monthly average temperatures are significantly higher than those in the surrounding areas.  
Figure 8: (**a**) Geothermal anomaly results obtained through the combined analysis of daytime and nighttime LST data; (**b**) Boreholes emitting boiling geothermal water. Latitude: 30.06\({}^{\circ}\) N, Longitude: 90.48\({}^{\circ}\) E; (**c**) Boiling Spring. Latitude: 30.07\({}^{\circ}\)N, Longitude: 90.49\({}^{\circ}\)E; (**d**) Boiling spring, Cited from [58].  
Figure 10a presents the monthly average LST data over 252 months, revealing the general surface temperature distribution in the study area. The central region exhibits higher temperatures, while the surrounding areas show relatively lower values. The figure highlights four significant temperature anomaly zones: the Yangbajing Geothermal Field, the Yangingxue area, the southern part of Regaguo, and the Yangyi Geothermal Field, with areas of 17.08, 26.19, 3.02, and 4.99 km\({}^{2}\), respectively.  
Figure 10b displays the shape and orientation of the isotherms. The first three anomaly zones are arranged in a U-shape along the NE-SW direction, aligning with the orientation of regional fault structures.  
Figure 9: Spatial distribution of monthly mean nighttime land surface temperature (LST) in the Yangbajing–Yangyi Basin from 2003 to 2023, derived from MODIS data. (**a–l**) show the average nighttime LST for each month (January to December) across the 21-year period. For example, (**a**) depicts the mean LST for all Januarys during the study period. LST values are represented using a blue-to-red gradient, where blue indicates lower temperatures and red indicates higher temperatures. Units are in degrees Celsius.
# Gradient Agreement Hinders the Memorization of Noisy Labels
## 3 Proposed Method  
In this section, we introduce GAL, our proposed framework for noisy label learning, in detail. We utilize a pair of identical network to distinguish noisy gradients and prevent the memorization of noisy labels, thus upgrading the performance of the trained models. The overview of GAL is illustrated in Figure 2. We propose a triplet network structure (i.e., \(\theta_{A}\), \(\theta_{B}\) and \(\theta_{C}\) in Figure 2). To avoid divergence caused by different initialization, \(\theta_{A}\) and \(\theta_{B}\) are two identical nets, while \(\theta_{C}\) can be any classification network. At each mini-batch after warm-up period, training samples with same class distribution are fed into \(\theta_{A}\) and \(\theta_{B}\). To hinder the memorization of noisy labels, for every pair of corresponding parameters, we evaluate whether their gradient directions achieve agreement and only update agreed parameters. To further improve the performance and avoid the accumulated confirmation bias, at every epoch, the predictions of \(\theta_{A}\) and \(\theta_{B}\) on the training set are used as pseudo labels to supervise the training of \(\theta_{C}\). In general, GAL consists of three parts: class distribution agreement sampling, gradient agreement updating and pseudo-labels supervising. The three components will be introduced in order in the following subsections.
## 4 Experiment
### The Recommendation System Experiment  
#### 4.4.1 Different Recommendation Methods  
To test the effectiveness of the S-GAN method and the performance of three different recommendation methods, RippleNet, KGCN, and KGNN-LS, the original and enhanced datasets were used as the input of the recommendation system.  
(1) **RippleNet**[22] is a recommendation system that integrates a knowledge graph as additional information. This method draws on the propagation of the ripple, which takes the items that the user is interested in as the starting point and goes through multiple layers of diffusion on the knowledge graph to extract the user's features.  
(2) **KGCN**[4] is a recommendation system that combines a knowledge graph and a graph convolutional neural network. Through multi-layer convolution, the semantic information in the knowledge graph is automatically captured and the connection between entities is mined to achieve more accurate and diverse recommendations.  
(3) **KGNN-LS**[30] is a knowledge graph recommendation system based on a graph neural network. The recommender system uses a label propagation algorithm (LPA) for prediction and recommendation. At the same time, a regularization term is introduced to prevent overfitting.
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 2 Theoretical Background
### Challenges in Mitigating RFI
#### 2.4.5 Radio Communication Test Sets  
Radio test sets, such as the CMA 180, are comprehensive testing tools [12] designed for aviation communication systems. These devices assess the performance of air-to-ground radios and their resilience to interference, ensuring the clarity and reliability of voice and data transmissions.
## 1 Introduction
We introduce compressed sensing to the frequency-wavenumber domain, which reduce data load for transmission and memory storage, as well as maintaining the image quality and stability compared with that in the temporal domain. This paper is stated as follows: Section 2 introduced the modified Stolt's f-k method and compressed sensing theory in the wavenumber domain at first. Then, beamforming in the frequency-wavenumber domain and compressed sensing are combined to assemble the novel framework: compressive beamformer in the wavenumber domain. In Section 3, simulated data and experimental acquisitions are used in the new framework to compare with DAS and compressive beamformer in the temporal domain. In Section 4, the influence of compounding angles on image qualities of different beamformers are discussed. Moreover, memory occupation, computational complexity and telemedicine imaging speed are discussed. Section 5 provides a conclusion of the novel compressive wavenumber domain beamforming framework.
# Study on the Damage Process and Numerical Simulation of Tunnel Excavation in Water-Rich Soft Rock
## 5 Validation of the Simulation Method Based on Dual Softening Effect
### Simulation Results  
The results of the numerical model considering excavation unloading and water softening are compared and analyzed with the results of the fluid-solid coupling in Figures 16-18, which can show the differences and connections between the different algorithms. From Figure 16, it can be seen that, after excavation, the results of both models show a settlement zone above the tunnel arch shoulder and an uplift zone below the tunnel arch base. The uplift is also affected by the Mohr-Coulomb criterion used in the model, which is not discussed here. The results after considering the dual softening show a larger settlement area in the upper part of the tunnel, with a maximum vault settlement of 58.3 mm, which is 23% larger than the result without softening, while the uplift zone in the lower part of the arch is smaller. In the contrast, the non-softening result shows a smaller settlement area in the upper part of the tunnel than that of the softening model, with a maximum vault settlement of 47.5 mm, while the uplift zone in the lower part of the tunnel is larger than the result of softening model. As can be seen from Figure 17, the lateral deformation trend of both models is the same after excavation, and the lateral deformation of the surrounding rock is mostly concentrated at the arch waist on both sides of the tunnel. The horizontal convergence of the non-softening model is 34.0 mm, while the horizontal convergence of the model with dual softening is 40.8 mm, which is 20% larger. Figure 18 illustrates that the plastic zone development trend of both models is roughly the same, and the maximum equivalent plastic strain area both appear in the arch waist position of the tunnel, but the maximum equivalent plastic strain and plastic area considering the dual softening are much larger than the non-softening results, which is mainly caused by two factors. First, due to the physicochemical effect of groundwater on the surrounding rock, the shear strength of the surrounding rock is reduced, making its bearing capacity lower; second, due to the presence of groundwater, the effective stress of the surrounding rock is reduced. According to the Mohr-Coulomb criterion, the stress state of the surrounding rock is prone to reach the ultimate equilibrium state and destabilize. In the computational model, the unloading effect and softening of water immersion after excavation make some of the mechanical parameters of the surrounding rock element, such as elastic modulus, internal friction angle, cohesion, etc., have different degrees of deterioration. The specific degree of damage is expressed as the damage factor calculated by Equations (36)-(38). The mechanical parameters of the surrounding rock under softening damage are calculated using damage factors, which show the damage deterioration of rock parameters. The deterioration of the elastic modulus of rock elements causes the deformation to be larger than that of the undamaged result, and the damage deterioration
# Development of an Autonomous Underwater Helicopter with High Maneuverability
## 3 Dynamic Analysis of the AUH
### Analysis of Motion in the Horizontal Plane
Both of them will cause horizontal thrust \(F_{h}\) and fluid resistance \(f_{p}\) to produce moments \(M_{Fh}\) and \(M_{fp}\) that will cause the AUH to pitch. After pitching, due to the distance between the center of gravity and the center of buoyancy, the restoring moment will be generated, and the \(M_{fp}\) will change with the appearance of the angle of attack \(\alpha\). The motion description of the pitch axis is shown in Equation (8). \[-GR_{BG}\sin\theta+M_{fp}+M_{fp}-M_{fq}=J_{q}\frac{d^{2}\theta}{dt^{2}} \tag{8}\]where \(G\) is gravity force. \(R_{BG}\) is the distance of the center of gravity and the center of buoyancy. \(\theta\) is the pitch angle. \(J_{q}\) is the AUH's rotating inertia of pitch axis. \(M_{fq}\) is the hydrodynamic resistance moment caused by the pitch. It can be approximately calculated by Equation (9) [19]:  
\[M_{fq}=\int_{0}^{R}\rho C_{d}\frac{d\theta}{dt}\sqrt{R-x^{2}}xdx=\frac{1}{3} \rho C_{d}R^{\frac{3}{2}}\frac{d\theta}{dt} \tag{9}\]  
where \(\rho\) is the water density. \(C_{d}\) is the resistance coefficient. \(R\) is the radius of AUH. Bringing Equation (9) into Equation (8):  
\[\left\{ \begin{array}{c}A\sin\theta+D+E\dot{\theta}=H\ddot{\theta}\\ A=-GR_{BG}\\ D=M_{\rm{F}p}+M_{fp}\\ E=-\frac{1}{3}\rho C_{d}R^{\frac{3}{2}}\\ H=J_{q}\end{array} \right. \tag{10}\]  
To simplify the calculation, only consider the case where \(\theta\) is small, and simplify \(\sin\theta\) to \(\theta\):  
\[H\ddot{\theta}-E\dot{\theta}-D=A\theta \tag{11}\]  
\[\theta=\frac{D}{2A\xi}\Big{[}e^{t(E-K)/2H}(E+K)-e^{t(E+K)/2H}(E-K)\Big{]}- \frac{D}{A} \tag{12}\]  
It can be seen from Equation (12) that since \(E<0\), when \(K>0\), the pitch motion is overdamped, and when \(K<0\), the pitch motion is underdamped. In addition a stable pitch angle will eventually be maintained. Due to the existence of inertia, when there is a pitch angle, there is generally an angle of attack \(\alpha\). Unlike traditional torpedo submarines, pitch and attack angles are harmful to AUH. \(f_{p}\) is directly related to \(\alpha\). The flat disc shape causes the AUH to have greater fluid resistance in the presence of an angle of attack, and the non-zero pitch angle will prevent the AUH from moving in the plane. Therefore, a set of active recovery moments is necessary for high maneuverability. At the same time, for constant-depth stable hovering, the driving force in the z-direction is essential. A set of vertical propellers can be added in addition, which can meet the requirements of fixed depth hovering and pitch recovery moment in the z-direction.
## 4 EMOR Protocol Design for WSN in NAN
### Probe Message Forwarding Mechanism in Data Collection Scope
Figure 3: Probe message receiving and forwarding mechanism in EMOR. (**a**) \(V\)s receives probe message from \(V\)r; (**b**) \(V\)s forwards the received probe message to working neighbor \(V_{t}\); (**c**) \(V_{t}\) has only one neighbor \(V\)s to forward the probe message; (**d**) \(V\)s has no neighbor. ```
1. Mobile sink broadcasting SID and data collection duration to all nodes within \(R_{l}\)
2. Sensor nodes within \(R_{l}\) receive the data collection message
3. Select neighbor which is optimal forwarder
4. Calculate the working-sleeping cycle and status transition frequencies
5. Estimate the distance between sensor node and mobile sink using RSSI
6. Calculate EOH based on distance estimation \(d_{(V_{s},V_{min})}\)
7. Determine the optimal forwarders based on the following criteria:
8. When an intermediate working node \(V_{s}\) receives the mobile sink message
9. if (\(V_{s}\) has not forwarded the probe message to any of its neighbors), then
10. Add \(V_{s}\) source ID in the forwarders ID list and calculate total number of forwarders (TNF)
11. Compute \(EOH_{V_{s}}-TNF\)
12. if (\(V_{s}\) has working neighbors), then
13. Check EOH of all of its working neighboring nodes
14. if \((EOH_{NE}\approx EOH_{V_{s}}-TNF)\), then
15. Select the neighbor as an optimal forwarder
16. end if
17. endif
18. elseif (\(V_{s}\) has already forwarded probe message to neighbor node \(V_{t}\) but receives it again), then
19. Explore the working neighbors other than \(V_{t}\) that have not forwarded the message yet
20. Remove the recently added source IDs of \(V_{s}\), \(V_{t}\) from forwarders ID list
21. Add \(V_{s}\) source ID in the forwarders ID list and calculate total number of forwarders (TNF)
22. Compute \(EOH_{V_{s}}-TNF\)
23. if (\(V_{s}\) has working neighbors other than \(V_{t}\)), then
24. Check EOH of working neighboring nodes other than \(V_{t}\)
25. if \((EOH_{NE}\approx EOH_{V_{s}}-TNF)\), then
26. Select the neighbor as an optimal forwarder
27. end if
28. end if
29. end if
30. if (all \(V_{s}\) neighbors are sleeping), then
31. Stop forwarding the probe message
32. Wait for the next data collection by sink node. 33. end if
```  
**Algorithm 1**Initialization and Probe Message Forwarding Mechanism in EMOR
# Modeling of Vessel Traffic Flow for Waterway Design-Port of Swinoujscie Case Study
## 3 Results
### Ship Spatial Distribution  
Figure 4 shows the fitted functions of the spatial distribution of ship positions for three groups of ships. The graphs show that most ships navigated on the starboard side of the fairway, which is the standard behaviour for ships.  
For the subsequent section of the route, the spatial distribution of ship position relative to the centreline of the traffic route was determined. Figure 5 shows the mean and Figure 5 the standard deviation of vessel position from cross-section N1 to N9. For the gates numbered N4 to N6, the traffic flow outside the marked and dredged channel was considered. The graphs show that the oil product tankers navigated closer to the centreline of the fairway than general cargo vessels. The mean and standard deviation had lower values. Like the vessels on the regular line (which allowed for following the same defined route), the passenger vessels travelled the closest to the centreline of the defined route. The traffic flow parameters for all groups of vessels decreased relatively by the same value when approaching the port entrance (leg N9). The difference in the traffic flow parameters of passenger vessels resulted from the character of the traffic. The vessels mostly joined the traffic at gates N3 and N4 and steer as close as possible to the marked channel.  
Figure 4: (**a**) Spatial distribution of ship hull position relative to track axis at cross-section N5 for three ship types, outside the marked fairway for inbound and (**b**) outbound ships.  
Figure 3: (**a**) Spatial distribution of ship’s position relative to track axis at leg N4, incoming traffic; (**b**) scatterplot of ships straight versus distance from centre.