esa-sceva/satcom-chunk-collection huggingface dataset:
# Quaternion Processing Techniques for Color Synthesized NDT Thermography
## 3 Proposed Approach  
### Foundation of the Proposed Methodology  
The proposed methodology consists of two stages (Figure 1). The first stage appliesa novel method called Thermographic Colorization by Computational Synthesis (TCCS), whose main purpose is to extract a large amount of relevant information from NDT thermographic inspections and synthesize it in a single-color video sequence. The hue and saturation characteristics of color images offer greater information synthesis capacity than conventional grayscale images, which capture only information about the intensity levels. The proposed TCCS method synthesizes in only three channels the information contained in a series of thermographic sequences obtained from different polynomial approaches, using dimensionality reduction techniques, so that synthesized information may be represented through color scale images. The second stage of the proposed methodology applies quaternionic algebra operations to the produced color images to optimize the visualization and characterization of the detected defects.  
The motivation to develop the TCCS method arises from a limitation detected in TSR and polynomial fitting techniques. These processing techniques approximate the original thermographic signal using a low-degree polynomial, generally between 6 and 8 [23; 28], and consider that this unique polynomial represents the best approximation for the thermal evolution of all the observed elements, both defects and internal structure of the material and healthy areas. However, it was verified in previous studies that the degree of the polynomial approximation that produces the highest detection levels is not always the same, and the optimal fitting degree does not guarantee the visualization of all the detected defects [18; 21; 29]. Furthermore, it was shown in [21] that different degrees of polynomial approximation enable the detection of different defects.
# BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning
## 2 Data and Methods
### DCB Product Stability Analysis  
To analyze the stability of the DCB products provided by CAS, we collected the data for one month from CAS. The time series of BDS-3 DCB products provided by CAS in one month are shown in Figure 1. The difference between the day of year (DOY) 182 TGD parameters and DCB products are shown in Figure 2.  
Except for C33, the DCB daily solution values of all satellites are approximately \(-\)30-30 ns, and most satellites have stable DCB values over the month, with standard deviation (STD) values smaller than 0.5 ns. However, some satellites showed jumps; for example, the DCB of satellite C32 suddenly increased by approximately 3 ns after DOY 201, and the C42 satellite also showed noticeable hops on DOY 205 and 206; these hops may be  
Figure 1: Time series of CAS DCP products for one month.  
Figure 2: Difference between TGD parameters and DCB products.  
related to the satellite working health status during that month. We found that the monthly stability of \(DCB_{C2I-C6I}\) was not as good as the remaining three types of DCB products, with the average STD of the other three types of DCB products being approximately 0.3 ns while the average STD of \(DCB_{C2I-C6I}\) was approximately 0.5 ns. In addition, the difference between TGD and DCB for most satellites was less than 2 ns.
## 1 Introduction
It has also been extensively used to evaluate damage caused by loading in concrete specimens, with particular emphasis on flexural damage in RC and fiber-reinforced concrete beams [92; 111; 112; 113; 114], as well as compression-induced damage in standard concrete specimens [90; 93; 115; 116; 117; 118]. Research has further extended to examining the behavior of full-scale RC structural members, such as beams [3; 119; 8; 120] and joints [120; 121; 122]. Moreover, the EMI method has proven effective for the early detection of FRP debonding in RC members [98; 123]. In this study, the EMI-based PZT-enabled technique is used. The integration of advanced monitoring technologies in retrofitting applications offers valuable insights into the performance and longevity of the retrofit system. Despite extensive research into bond-slip behavior between concrete and various reinforcement materials [124; 125], no similar studies have evaluated the bond performance of U-shaped jackets by monitoring the interfacial zone between the original RC core and the jacket. Therefore, this study aims to experimentally investigate the cohesion between construction materials and assess the effectiveness of U-shaped jackets composed of cement-based mortar, mild steel bars, and open stirrus for retrofitting shear-damaged RC beams. Moreover, the structural integrity of these retrofitted beams is evaluated through data analysis from the embedded and the externally epoxy-bonded PZT transducers. Through the proposed SHM implementation, any degradation at the interface or slippage of the retrofitting jacket can be promptly detected, restraining further damage development and potential failure of the structure. This way, severe and catastrophic causalities can be prevented and avoided, leading to targeted and sufficient recovery actions in real-life structures. Moreover, the proposed technique can provide useful insight into the structural integrity of the monitored structure or infrastructure, permitting the stakeholders to make critical decisions during an event.
# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 3 Evolving Computing Paradigms and Related Concepts
### Edge Computing  
Despite the fact that edge computing has been mentioned in the literature before cloud computing, its significance grew dramatically with the introduction of IoT and the ensuing demands. Edge computing places cloud computing's services close to the end-user, distinguished by a rapid processing and response time [26]. Edge computing denotes technologies that enable computation to be accomplished at the network's edge, upon downstream data for cloud services, and upstream data for IoT services [69]. It unfolds the cloud's network by extending the computation, storage, and resources to the edge of the network, close to the data source, with the resolve to accomplish critical needs of real-time servicing, application intelligence, security, and privacy, along with the network's requirements for low latency and high bandwidth [13; 27].  
The significant aspects of cloud computing are its ability to grasp the big picture, process vast amounts of data, perform in-depth analyses, process data in non-real-time, and determine business decisions. Being centralized, entire data must be transferred to the cloud with underlying risks of data loss and data leakage, as security and privacy cannot be ensured, and sensitive information is at threat of disclosure [27]. Edge computing is an extension to cloud computing, which considerably minimizes the volume of data transmitted across nodes, lowering transmission costs and the network bandwidth usage. It leads to utilization and computing efficiency along with energy consumption. Edge computing can be more effective in small-scale, intelligent, real-time analyses. It eliminates the risk associated with the network transmission, ensuring data security. If data become compromised, it impacts only local data. The edge computing architecture is federated, wherein edge devices are positioned between the cloud and terminal devices to tender cloud services to the network's edge [24; 70]. As edge computing shifts service provisioning from the cloud to the edge, it favors IoT application demands, enabling IoT devices to be more scalable and energy-efficient [71]. The cloud-edge alliance involves a typical three-layer model distinguished as the terminal (sensors, cameras, and smartphones), edge (base stations, access points, routers, switches, and gateways), and cloud [27]. In terms of device types, communication protocols, and services, edge computing can be implemented in various ways [9; 13; 26].
## 3 Experiments and Results
### Ablation Experiment  
To validate the effectiveness of our proposed method and determine the contribution of each component to the results, we designed four ablation experiments on five subjects: (1) 1 DCNN+BiLSTM, (2) 1 DCNN+BiRNN, (3) local 1 DCNN+BiRAU, and (4) global 1 DCNN+BiRAU. In experiments (1) and (2), the 1D CNN comprised both the local 1D CNN and global 1D CNN. We examined the individual contributions of the local 1D CNN, the global 1D CNN, and the BiRAU to the model's performance. The results of the ablation experiments are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & **Sbj1** & **Sbj2** & **Sbj3** & **Sbj4** & **Sbj5** & **Mean** \\ \hline
1 DCNN + BiLSTM & 99.02\% & 95.06\% & 51.48\% & 96.75\% & 93.75\% & 87.21\% \\
1 DCNN + BiRNN & 96.87\% & 97.38\% & 97.43\% & 94.56\% & 97.20\% & 96.69\% \\ BiRAU & 51.19\% & 50.15\% & 49.61\% & 50.83\% & 51.19\% & 50.59\% \\ Local 1D CNN + BiRAU & 90.91\% & 99.55\% & 95.92\% & 99.53\% & 97.03\% & 96.59\% \\ Global 1D CNN + BiRAU & 99.43\% & 96.98\% & 97.89\% & 97.83\% & 97.81\% & 97.99\% \\
**1 DCNN-BiRAU** & **99.72\%** & **99.61\%** & **99.15\%** & **99.77\%** & **99.16\%** & **99.48\%** \\ \hline \end{tabular}
\end{table}
Table 1: The accuracies of the ablation experiments.  
Clearly, both the local 1D CNN and global 1D CNN play a role in the feature extraction of EEG information. In particular, after using the SegPool Strategy, the local 1D CNN can focus more on the feature information extraction of each task itself, and the global 1D CNN can help better learn different tasks and differentiate features between tasks. Moreover, the BiRAU also exhibits better classification ability and effect than the BiLSTM and BiRNN.
## 2 Related Studies
### IntDCT-Based Reversible Audio Watermarking  
In our previous work [5], we proposed a reversible watermarking scheme based on the integer DCT coefficients expansion for tampering detection.  
Audio tampering detection using reversible data hiding offers several advantages over other methods. First, by embedding a content-based hash within the audio, any modification to the audio, no matter how subtle, will cause the hash to mismatch during extraction. This enables the precise detection of tampering. This technique also allows for the detailed localization of tampered segments within the audio, pinpointing the exact areas that have been altered. Since the watermarking is reversible, the original audio can be perfectly recovered, which ensures that the audio quality remains unaltered and avoids any potential degradation caused by tampering detection techniques. Moreover, the techniques used in reversible data hiding are computationally efficient, allowing for real-time or near-real-time processing of audio files.  
Let \(x_{1}\ldots x_{N}\) be an audio signal within a frame, where \(N\) is a fixed frame length. Then, these values are transformed from the time domain to the DCT domain with integer DCT. Let \(X_{1}\ldots X_{N}\) be DCT coefficients. When one bit of payload \(B\) is embedded to the \(n\)-th DCT coefficient \(X_{n}\), the expansion technique is applied, such as  
\[X_{n}^{\prime}=2X_{n}+B. \tag{1}\]  
To embed the payload, we modify the audio signal's higher-order integer DCT coefficients (\(X_{n}\), where \(N/2+1\leq n\leq N\)). This region is chosen because the human auditory system is less sensitive to changes in high-frequency components [28]. By modifying these coefficients, we can embed approximately 0.5 bits per sample.  
However, this approach has a potential vulnerability. Since the modifications are concentrated in the higher frequency range, a distinct boundary between the modified and unmodified coefficients can emerge. Attackers can exploit this boundary to detect the presence of hidden data through spectral analysis.  
To illustrate this, Figure 1 compares the original and stego signals of an audio file. The horizontal axis represents the frame index, and the vertical axis represents the frequency. The plot reveals that when high-frequency coefficients are modified for embedding, a clear boundary becomes visible between the modified and unmodified regions. This boundary can serve as a strong indicator of the presence of hidden data.  
Figure 1: _Cont._
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
###### Abstract  
Although various unmanned aerial vehicle (UAV)-assisted routing protocols have been proposed for vehicular ad hoc networks, few studies have investigated load balancing algorithms to accommodate future traffic growth and deal with complex dynamic network environments simultaneously. In particular, owing to the extended coverage and clear line-of-sight relay link on a UAV relay node (URN), the possibility of a bottleneck link is high. To prevent problems caused by traffic congestion, we propose Q-learning based load balancing routing (Q-LBR) through a combination of three key techniques, namely, a low-overhead technique for estimating the network load through the queue status obtained from each ground vehicular node by the URN, a load balancing scheme based on Q-learning and a reward control function for rapid convergence of Q-learning. Through diverse simulations, we demonstrate that Q-LBR improves the packet delivery ratio, network utilization and latency by more than 8, 28 and 30%, respectively, compared to the existing protocol.  
MANET; VANET; UAV relay; load balancing; routing; Q learning +
Footnote †: journal:  
_Article_
## 3 Description of the Vibration Test on the Dampers  
This section provides precise information on how the signals used for health monitoring the dampers were obtained. With this information the vibration test can be replicated. Vibration tests consisted of induced vibrations, \(x(t)\), conducted on each instrumented I-section by means of the piezoelectric transducer (PZT) actuator attached to the center of the web, and measuring the signal response, \(y(t)\), through the PZT sensor adhered on the opposite side of the I-section web as shown in Figure 1. It also shows a general scheme of a building with dampers installed in it and a particular WPD damper with several I-sections instrumented with PZT transducers connected to the electronic equipment for online and real-time inspection (Structural Health Monitoring of the Dampers). The I-sections were excited with random white noise generated by CoCo80 equipment  
Figure 1: (**a**) Instrumented I-sections in a particular Web Plastifying Damper (WPD) damper; (**b**) I-section instrumented with two piezoelectric transducers (PZT), one acting as actuator (input signal) and one acting as sensor (output signal); (**c**) Connection of the PZT transducers to the electronic system; (**d**) Installation of the dampers in the building.  
Figure 2: Deformation of an I-section segment of a WPD.  
(CRYSTAL Instruments, Santa Clara, CA, USA) and an EPA104 piezoelectric amplifier (Piezo Systems, Woburn, MA, USA) that provided a gain of 20. The level of deformation induced by the vibration tests in the web of the damper was very low: strains were less than 2 mm/mm. Each vibration test was carried out four times with different white noise trials. The duration of each test was 20 s and the sampling rate of signals was 65,536 Hz. The response signal of the I-sections was recorded by two PULSE 3560-B Bruel & Kjaer systems (Naerum, Denmark). Figure 3 shows a typical response signal obtained from the vibration tests. All electronic devices were properly connected to the grounding system; connections were made so that the measurement system would achieve a good performance against the electrical and environmental noises. In this way, a robust measurement system was obtained, with controlled noise conditions and high quality vibration signals. Analysis of the output signal shows that it is affected by the level of damage in the damper, as shown in the following sections.
## 3 Results and Discussion
### Simulations for Hierarchical Tree Structured Network
As proved for chain structure, \(n=1,1,6\) for AVG, proposed method and EBDKF respectively. Therefore, by substituting in Equation (39) for upper bound, \(N_{proposed}=(k-1)\), \(N_{AVG}=(4)*(K-1)\), \(N_{EBDKF}=(18)*(K-1)\). Therefore, expected lifetime of the nodes using diffusion technique for tree structure is lesser than that for chain structure. Figure 4.3 Bit level error performance comparison among the proposed statistical and the AVG information fusion method in a tree-based IoT platform when (**a**) BER = 0.005 and (**b**) BER = 0.05.
## 1 Introduction
To reveal the damage mechanism of fatigue crack growth, cluster analysis is introduced. Cluster analysis is a mathematical algorithm that divides a dataset into several clusters according to the similarity of features. This method is quite suitable for the AE analysis of structures that exhibit different modes of failure, partitioning the AE signals into clusters that correspond to the related damage mechanisms. Cluster analysis has been effectively applied to damage pattern recognition in anisotropic materials [36; 37]. For damage pattern recognition in fatigue crack growth in metals, studies have indicated that the cluster algorithm can identify different damage modes and evaluate the stress-strain status at the crack tip [38; 39]. Therefore, to fill in the research gaps concerning the AE behaviors of G20 Mn5 QT cast steel and related butt welds during fatigue crack growth, AE signals generated during fatigue crack growth tests employing compact tensile (CT) specimens were collected and investigated. Moreover, to distinguish AE sources, the AE signals were first classified into different clusters via the K-means clustering algorithm, and next, the time-frequency characteristics in each cluster were analyzed and compared to the micrographs of fracture surfaces. By analyzing the performances of different clusters during the crack growth process, the relationship between the AE and the fatigue crack mechanism was established for both G20 Mn5 QT cast steel and the related butt welds.
# Seismic Response of Aeolian Sand High Embankment Slopes in Shaking Table Tests
## 2 Shaking Table Tests
### Rationality of the Earthquake Excitations  
Seismic response laws can be influenced by the value change between the predominant frequency of earthquake excitations and natural frequency of embankment models. More concretely, when conducting shaking table tests, whether or not the predominant frequencies of earthquake excitations are larger than natural frequencies of embankment models may result in a difference of acceleration magnification of test points (becoming greater or smaller). In addition, when the predominant frequency of earthquake excitations and natural frequency of embankment models are close to each other, a resonance phenomenon will happen, which may cause the exaggeration of the acceleration response of models. Therefore, to obtain reliable seismic response results, a constant size relation between the predominant frequency of earthquake excitations and natural frequency of embankment models should be ensured, and the resonance phenomenon should be avoided. Therefore, it is necessary to test the rationality of the chosen earthquake excitations. So in this study, under the loading condition of WN8, the white noise spectral analyses of three models were conducted, and the analysis results of test points (A6-A7; A13-14; A20-A21) are shown in Figure 8, which shows that the natural frequency of three models is 17.5 Hz (1/1.5), 16.6 Hz (1/1.2), and 16.6 Hz (1/0.8),  
\begin{table}
\begin{tabular}{c c c} \hline
**Serial Number** & **Seismic Wave** & **Peak Acceleration/gal(cm/s\({}^{2}\))** \\ \hline
1 & WN1 & 30 \\  
N9 & 30 \\
26 & ES & 800 \\
27 & L8 & 800 \\
28 & WN10 & 30 \\
29 & E9 & 900 \\
30 & L9 & 900 \\
31 & WN11 & 30 \\
32 & E10 & 1000 \\
33 & L10 & 1000 \\
34 & WN12 & 30 \\
35 & SI (4 Hz) & 1200 \\
36 & WN13 & 30 \\
37 & SI (4 Hz) & 1500 \\ \hline \end{tabular}
\end{table}
Table 2: Loading scheme of the shaking table tests.  
respectively. Then, according to Fourier spectrum of El-Centro motions and Lan Zhou motions, which is shown in Figure 9, it can be seen that the predominant frequency of these two motions (over 50 Hz) always greater than the natural frequency of three models. Accordingly, the influence of predominant frequency of these two motions was removed, and the material of models and the microcosmic shear failure process during tests were the only factors that influenced seismic response laws. In addition, the maximum input peak acceleration of sinusoidal waves is greater than that of other excitations in this shaking table system, which makes the disruption of models easier, and the resonance phenomenon will not happen under sinusoidal waves due to their low frequency. Therefore, sinusoidal waves (4 Hz) were adopted at the stage of the disruption of models. Generally, the rationality of earthquake excitations and the reliability of seismic response laws were proven according to the above discussion.  
Figure 8: White noise spectral analysis of three models.