esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Computer Aided Weld Defect Detection from Radiography Images
## 5 Defect Classification
### Feature Selection  
Sometimes, the number of features extracted is large, thus the dimension of feature vector is high, which lead to excessive computational burden. Feature selection as one of the mainstream approaches for processing high-dimensional data is essential to reduce the difficulty of classification task and computational costs. It tries to find an optimum subset of original features which can provide useful information. The work is too complicated for people, even impossible. There are a variety of methods for evaluating the quality of the extracted features. [PERSON] compared the performance of feature selection [46]. Many feature selection methods found the optimum subset by evaluation function which assessing the quality of feature subsets. [PERSON] used a simple feature selection approach based on the correlation coefficients between independent variable and the dependent variable to meet 7 \(\pm\) 2 rule of fuzzy expert system. The method reduced the dimension of features from 12 to 7 or 9 without too much loss of accuracy [35]. However, to some extent, using of feature subset reduced the accuracy. [PERSON] presented sequential forward floating selection (SFFS) as the substitution of principal component analysis (PCA) for weld detection. The algorithm performed the dimensionality reduction better than PCA, and improved the computational performance [47]. In the work of [PERSON], 148 texture features were extracted for each segmented region which is too many to be input of classifier. In order to reduce the computational time, they used a sequential forward selection (SFS) method which requires an objective function obtained from the Fisher discriminant to evaluate the performance of classifier with m feature. The method began with one feature (m = 1), then added one feature in each calculation, finally searched the features that maximize the objective function until the optimal n features were obtained [41]. [PERSON] used a sequential backward selection (SBS) method with classifier and compared the performance of classification using different feature sets (43 features and its subset with seven features) [29]. The results showed that the SBS saves about 80% feature computation. The accuracies of the artificial neural network (ANN) classifier using 7 features were almost as high as those using 43 features. However, the situation is not the same when using the support vector machine (SVM). The feature selection procedure favored the ANN but not SVM in classification perform.  
Many selection methods applied the classification performance (classification accuracy or error) of classifier as the evaluation function. In this situation, the effectiveness of feature selection method is related to the choice of classifier.
# Fast Sound Source Localization Based on SRP-PHAT Using Density Peaks Clustering
## 4 Obb-Srp-Phat
### Online Sound Source Localization  
During online localization, SRP-PHAT carries out a grid search scheme. Assuming that the azimuth range is \(1-N_{\theta}\), the elevation range is \(1-N_{\varphi}\), and the search step is 1, SRP-PHAT will calculate the SRP values of \(N_{\theta}\times N_{\varphi}\) grid points. As for the suggested ODB-SRP-PHAT, the SRP values only at the positions stored in the ODB need to be calculated using Equation (5). By comparing the calculated SRP values, the position corresponding to the maximum value is considered as the sound source position.  
The search workload of ODB-SRP-PHAT in the online localization stage is determined by the number of possible sound source positions, i.e., \(m\), in the ODB. Thus, compared with the SRP-PHAT algorithm, the online computational cost of ODB-SRP-PHAT is \(m\). The corresponding computational load ratio is \(R\):  
\[R=\frac{m}{N_{\theta}N_{\varphi}}\text{-}100\%. \tag{13}\]
# Dynamic Behavior Analysis of I-Shaped RC Beams under Combined Blast and Impact Loads
## 4 Numerical Results of I-Shaped RC Beams under Combined Loads
### Influences of Different Longitudinal Reinforcement Spacing  
By changing the longitudinal reinforcement spacing, the effect of different longitudinal reinforcement ratios on the damage behavior of the structure under combined load was studied. The explosion proportional distance was set to 0.32 m/kg\({}^{1/3}\) and the impact velocity to 6 m/s. The dynamic response and failure behavior of the structure were studied when the longitudinal reinforcement spacing of I-shaped RC beams was 40 m, 60 mm and 100 mm, respectively. Figure 13 shows the damage states of the beams under combined loads of different longitudinal reinforcement spacing. Figure 14 shows the time-history curves of the mid-span displacement of the beams under different longitudinal reinforcement spacing. Numerical results show that the damage area and mid-span displacement of the structure increase significantly with the increase in the longitudinal reinforcement spacing no matter under impact-blast load or blast-impact load. Due to the increase in the reinforcement ratio of the longitudinal reinforcement, the flexural bearing capacity of the structure was improved. Concrete damage in the tension zone was significantly reduced.  
Figure 11: Damage states of beams under combined loads of different beam depths.  
Figure 12: Time-history curves of mid-span displacement of beams under different beam depths.  
This shows that adding longitudinal reinforcement can significantly improve the ability of the structure to endure combined loads.
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 3 System
### Moving Target  
In the experiment, we prepared a sample moving target as a reference for visual servoing to verify the performance quantitatively. For convenience of fast feature detection in the image processing, we attached four LED lights on a plate to serve as the moving target, as shown in Figure 3a. We define the coordinates of each LED as \((0,0,0)\), \((0.21,0,0)\), \((0,0.15,0)\), \((0.21,0.15,0)\) from points 1-4 respectively. For moving the target plate, we used a Barrett WAM 7-DoF robot manipulator, as shown in Figure 3b, and moved it with different speeds and accelerations [40].
## 3 CWT-SSSM Feature-Based Damage Index
### Damage Index (DI)  
The occurrence of damage inevitably leads to a reduction in the local stiffness of the structure, which in turn leads to singularities (differential discontinuities) in the mode shapes at the location of the damage. CWT is known as a signal microscope that can detect singularities in the mode shapes. It has been demonstrated above in the frequency domain that \(SSSM^{i}\) contains the \(i\)th-order strain modal, and, in particular, it contains strain mode shapes that are sensitive to local damage. Therefore, it is possible to locate the damage location based on the abrupt change in the spatial location of the CWT coefficients of \(SSSM^{i}\).  
In this paper, CWT-\(SSSM^{i}\) is defined as \(CS^{i}(b,s)\). A strain response can be separated into multiple-order strain components of IMF\({}^{r}\) using EWT, thus the corresponding IMF\({}^{r}\) also has multiple orders. Different orders of IMF\({}^{r}\) have different sensitivities to the same damage, and for multi-damage identification, it may be difficult to accurately identify all the damage locations by using only the one order \(CS(b,s)\). For this purpose, the data fusion technique of averaging is used to integrate all orders of \(CS(b,s)\), and the overall CWT-\(SSSM\) is defined as:  
\[\overline{CS}(b,s)= \frac{1}{n}\sum_{i=1}^{n}\text{normalize }(CS^{i}(b,s)) \tag{18}\]  
where normalize \((CS^{i}(b,s))\) represents the maximum normalization of \(CS^{i}(b,s)\), which can be expressed as: normalize \((CS^{i}(b,s))=CS^{i}(b,s)/\text{max}(|CS^{i}(b,s)|)\), and the superscript \(i\) represents the order. \(\overline{CS}(b,s)\) fuses damage information of all the orders but \(\overline{CS}(b,s)\) characterizes the occurrence of damage with abrupt peaks in the 3D plot, while the smaller peaks due to noise could confuse the user's judgment. For this reason, this paper combines statistical theory to construct the damage identification index \(SSSM_{DI}\):  
\[SSSM_{DI}=\begin{cases}T(b),&\text{if }T(b)\geq SSSM^{TH}_{DI}\\ 0,&\text{if }T(b)\leq SSSM^{TH}_{DI}\end{cases} \tag{19}\]where,  
\[T(b)=\sum_{s=1}^{s_{\text{max}}}\overline{CS}(b,s) \tag{20}\]  
\[SSSM_{DI}^{TH}=\mu+3\sigma \tag{21}\]  
In the above formula, \(s_{\text{max}}\) represents the maximum scale. \(SSSM_{DI}^{TH}\) is the damage threshold defined and based on the three-sigma rule in statistics, \(\mu\) and \(\sigma\) are the mean and standard deviation of \(T(b)\), respectively. The probability of a normal distribution with values distributed at \((\mu-3\sigma,\mu+3\sigma)\) is 99.73%. For damage detection, if the value of the \(SSSM_{DI}\) index of a measurement point exceeds the range of \((\mu-3\sigma,\mu+3\sigma)\), the structural health monitoring system has a 99.7% certainty that the damage occurs at this measurement point. Hence, for damage assessment, the location of the damage can be located based on the peak of \(SSSM_{DI}\).
###### Abstract  
Endocrowns are primarily recommended in a molar region with a standardized preparation design. The aim of the study was to evaluate the effect of different occlusal preparation depths, pulp chamber-radicular extension, and all-ceramic materials on the fracture resistance of premolar endocrowns. Ninety human premolar teeth were root canal treated, randomly divided into three main groups according to all-ceramic material used for fabrication as Lithium Disilicate (LD) ceramic, Polymer infiltrated ceramic (PIC) and High translucency zirconia (HTZ). They were further subdivided into three subgroups (n = 10) according to preparation design of 2 mm occlusal reduction, 4.5 mm occlusal reduction and 4.5 mm occlusal reduction with 2 mm radicalular extension. The endocrowns from respective restorative materials were fabricated, surface conditioned, and cemented with self-adhesive resin cement. All samples were thermocyclted for 5000 cycles and subjected to compressive static load at 45\({}^{\circ}\) angluation with the cross-head speed of 0.5 mm/minute until the fracture. The mean fracture resistance of LD ceramic at 2 mm, 4.5 mm thickness and radicular extension was 62.55 MPa, 45.80 MPa, 74.27 MPa respectively. The corresponding values for the PIC and HTZ ceramics were 26.30 MPa, 21.65 MPa, 25.66 MPa and 23.47 MPa, 27.30 MPa, 37.29 MPa respectively. The LD ceramic and greater extension inside the pulp chamber had higher fracture resistance.  
endocrowns; fracture resistance; lithium disilicate; high translucency zirconia; polymer infiltrated ceramic
## 4 Deep Learning Methods for HDR Reconstruction
### Diffusion Models for HDR Reconstruction
#### 4.4.2 Diffusion Models for Multi-Frame  
The DiffHDR [19] architecture addresses generating HDR images from multiple LDR exposures, focusing on mitigating ghost artifacts resulting from scenes with large motion and saturation. HDR deghosting is treated as a conditional generative modeling task by adopting Diffusion Models, including a Feature Condition Generator (FCG) and a Domain Feature Align module, as shown in Figure 10. Furthermore, to mitigate the semantic confusion caused by saturation in LDR images, the authors introduced a Sliding-window Noise Estimator to smoothly sample noise in a patch-based approach. This method allows the model to focus on local contextual information, avoiding artifact introduction from ignoring correlation among adjacent patches.  
[PERSON] et al. [169] proposed the Low-Frequency Aware Diffusion Model (LF-Diff) approach to recover HDR images without ghosting artifacts from multiple LDR images at different exposures with significant motion. The LF-Diff architecture consists of an alignment module, a model extracting low-frequency information, and a refinement module combining this information with the output of a denoising UNet, which is a key component in DMs. The main distinction of LF-Diff lies in its usage of low-frequency information to guide HDR reconstruction.
# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 2 Smart Ships and Navigation Technology
### Navigation Technology
#### 2.2.2 Communication Technology  
Maritime radio first appeared on board ships at the beginning of the 20 th century and in the early days was mainly used for transmitting and receiving telegrams from passengers. Subsequently, the International Convention for the Safety of Life at Sea (the SOLAS Convention) was introduced, and maritime radio communications were developed in the fields of both applied technology and communication procedures, providing an effective technical means for the exchange of information and communication between ship and  
Figure 4: The flow of ship perception.  
shore and between ships. In the 1970s, telex, telephone, and facsimile communications were gradually applied to ship communications. Narrowband direct printing telegraphy (NBDP) and radiotelephone (RT) technology were applied to terrestrial communications systems in the 1970s and 1980s. Satellite communication systems have also been used occasionally. By the end of the 1980s, satellite systems became more widely used and had a larger share of applications in ship-to-shore communications [12]. Amendments to the GMDSS on radio-communications were adopted in 1998 and fully implemented in 1992 to improve the safety of ships in the field of radio-communications [29]. At present, the information exchange between ship and shore includes not only the ship's report and some instruction telegrams, but also image, voice and various other data about the ship. The information is digitized and transmitted, laying a certain foundation for the development of intelligent navigation in smart ships. Interference in communication networks between ships may lead to data distortion. [PERSON] et al. (2019) proposed a reliability modeling method for ship communication networks based on the Apriori algorithm, constructed a transmission link model for ship communication networks and adjusted the reliability of the forwarding nodes of the links in ship communication networks through a fuzzy PID neural network control model, which improved the accuracy of data transmission and greatly improved the quality of communication [30]. [PERSON] et al. (2013) used the parameters of a communication system for ship navigation to a predetermined port terminal, utilizing electromagnetic beams to keep the ship within a marked trajectory boundary line, improving the accuracy of ship navigation [31]. [PERSON] et al. (2024) proposed the Stackelberg Q-learning-based multi-hop cooperative routing algorithm (SQMCR) that balances packet forwarding benefits and energy costs. They also formulated collaborative communication strategies to ensure the reliability and efficiency of communication and further improve the performance of the routing algorithm [32].  
The communication architecture of a smart ship is shown in Figure 5:
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 2 Aerial Urban Vehicle Configurations and Concepts
### Hybrid
#### 2.3.3 Tilt-Wing
During the cruise mode, if the aircraft loses any wing rotating actuator, it cannot land vertically and the controller can use the elevators and ailerons to stabilize the aircraft and it could land like a fixed-wing HTOL aircraft. Hence, the aircraft should be able to land with little risk for the safety of the passengers. However, it should be noted that many tilt-wing designs may incorporate lifting rotors that have larger radii than the landing gear length. In which case, horizontal landings will result in unavoidable damage to the propulsion system. In both situations, the aircraft can land safely, and passengers are safe [87]. The fifth issue pertains to the aircraft's center of gravity when the wings rotate from a 90-degrees pitch angle to 0 during the transition phase. This may affect the aircraft's stability, but the controller is capable of managing this situation [85; 86]. The worst situation occurs when one side wing rotating actuator and some propulsion units sustain damage in VTOL or transition mode. In this case, the controller cannot handle the situation, causing the aircraft to lose its stability and crash. However, in cruise mode, the control surfaces may assist in stabilizing the vehicle and switching into gliding mode until it lands like a typical airplane [87].
## 2 Methodology and Network Design
### Protocol Stack Perspective  
Figure 1 shows the standard protocol stack followed in this study to verify the requirements in Table 1, in which the layers marked in light red are the data to be measured in the communication between the IXL and the object controller.  
In the physical layer, it is necessary to capture parameters that indicate the quality of communication at the radio level to see the correlation with the data in the network and application layers. The parameters for the analysis are RSRP, RSRQ and SINR.  
In the network layer, the latency and jitter are the essential parameters [12] since they determine the successful communication rhythm required by the application layer. In the signalling systems, some timeouts are defined to acknowledge the updated and recent information about the progress of the railway operation. This means that if the communication system is not fast enough, the railway application information may be overdue at the application layer, implying a problem in the system's availability.
## 4 Multihop D2D Broadcasting
### Broadcast Management within the UE
In that case, since the information included in the messages is not the same, since e.g., originators' coordinates are different, the latter will be considered as different broadcasts by the Trickle instances running in the UEs' applications, and will be broadcast independently. As a result, multiple, independent broadcasts related to the same event will traverse the network, with a corresponding increase of the traffic load. On the other hand, the applications running at the UEs can easily be endowed with the necessary intelligence to associate two (or more) messages, possibly with a different payload, to the same event: for example, if the distance between the originators' is below a threshold, the message type is the same, and the reception times are again within a predefined window (which may be computed based on the Trickle window). In this case, merging can occur, i.e., the various broadcasts messages are associated to the same Trickle instance, thus being perceived as duplicates of the same broadcast process. Note that MDB can also accommodate messages generated by entities other than UEs, e.g., nodes located in the Internet, the LTE core network, or--possibly--Mobile-edge Computing servers running applications on behalf of the UEs. In that case, the network can select one (or more) proxy originator UE(s) and send them the message from the serving eNB(s) using downlink transmissions. The receiving UE(s) can, in turn, initiate the broadcasting procedure as described above.
###### Abstract  
This paper investigates the performance of a spacecraft equipped with a diffractive sail in a heliocentric mission scenario that requires phasing along a prescribed elliptical orbit. The diffractive sail represents an evolution of the more traditional reflective solar sail, which converts solar radiation pressure into thrust using a large reflective surface typically coated with a thin metallic film. In contrast, the diffractive sail proposed by [PERSON] leverages the properties of an advanced metamaterial-based film to generate a net transverse thrust even when the sail is Sun-facing, i.e., in a configuration that can be passively maintained by a suitably designed spacecraft. Specifically, this study considers a sail membrane covered with a set of electro-optically controlled diffractive panels. These panels employ a (controlled) binary metamaterial arrayed grating to steer the direction of photons exiting the diffractive film. This control technique has recently been applied to achieve a circle-to-circle interplanetary transfer using a Sun-facing diffractive sail. In this work, an optimal control law is employed to execute a rapid phasing maneuver along an elliptical heliocentric orbit with specified characteristics, such as those of Earth and Mercury. The analysis also includes a limiting case involving a circular heliocentric orbit. For this latter scenario, a simplified and elegant control law is proposed based on a linearized form of the equations of motion to describe the heliocentric dynamics of the diffractive sail-based spacecraft during the phasing maneuver.  
solar sail; diffractive film; binary metamaterial; orbital phasing; trajectory optimization; approximate spacecraft dynamics
## 4 Experiment Verification
### Security Aspect  
#### 4.3.1 Randomness and Unpredictability of Encryption Key  
Dynamic encryption key must satisfy randomness and unpredictability. The method proposed in this paper is to generate hash values based on sensor data history that measure the environment and use them as seed values for generate encryption keys. This method satisfies randomness because it generates different hash values in an environment where measured sensor data change even slightly. It also satisfies unpredictability because it requires knowing or reproducing the history of all sensor data in Devicesensor and DeviceGateway to know the next encryption key generated.
## 3 D2 WaVe: Design Rationale and System Architecture
### Video Frames Authentication Scheme
#### 3.3.4 Watermark Verification  
This stage aims to verify the authenticity of video data that may be used for malicious behavior analysis and vehicle forensics. A data user can extract watermarks from video frames and then verify that the watermarked frames were created by the actual owner. The FIAE-GAN-based decoder ensures that watermarked features can be captured effectively in the extraction stage; therefore, watermark \(W_{out}\) can be rebuilt blindly without needing any key. Given a watermarked frame \(F_{i}\), the FIAE-GAN-based decoder can reconstruct its watermark \(W_{out}=DW\_extract(F^{{}^{\prime}}_{i})\). We assume that the data user uses security communication channels to receive \(W_{in}\) from the data owner. Finally, the authenticity of video data can be verified by comparing the similarity of \(W_{in}\) and \(W_{out}\).  
Figure 5: The illustration of video data authentication.