esa-sceva/satcom-chunk-collection huggingface dataset:
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 2 Materials and Methods
### Feature Extraction Based on PRPD Data
#### 2.3.1 Implementation of PRPD Feature Extraction  
According to the three discharge models shown in Figure 1 and the measurement device shown in Figure 5, PD data of 4000 IF cycles were collected in this paper. In order to improve the recognition-generalization ability of the neural network, we collected the discharge patterns of different discharge models at voltage levels from 4.5 kV to 6.5 kV, with a voltage interval of 0.5 kV.  
Since a single dataset contains less useful feature information, most of the data were concentrated around 0 V; this situation arose due to the use of the ADC to collect data, as, when no discharge data was present, the amplitude of the data collected by the ADC was around 0.1 V, as shown in Figure 6a,c,e. The features extracted from these datasets for pattern-recognition were limited, so we needed to filter out the useless data first. In order to filter out the data around the zero value, we chose the downsampling method to retain the useful information regarding the PD, achieved by uniformly dividing the 108,000 sampled data points into 360\({}^{\circ}\) phase intervals, with each phase interval containing 300 data points. We then extracted the maximum value from each phase interval using the fast-sorting algorithm and used the maximum value from each phase interval as the discharge amplitude through downsampling. The pre-processed PRPD maps have limited signal features and are easily interfered with by noise signals, making it difficult to recognize the PD patterns. To enhance the recognition of discharge characteristics, we chose to superimpose the continuously obtained PRPD maps. Through testing, we found that, for the type of tip discharge, the recognition effect achieved by superimposing 15 times and 20 times was nearly identical. Considering the running time and program complexity on the embedded side, in this paper, we continuously collected the signals for 15 cycles and then performed the same-phase superposition. The superposition of the PRPD map is shown in Figure 6b,d,f.
## 8 Technical Challenges for 6G Development
### Spectrum and Interference Administration  
Given the limited availability of spectrum resources and the issues associated with interference, the effective administration of 6G spectra is of paramount importance. This necessitates the development of strategies for sharing spectrum and the adoption of novel approaches to spectrum management. Optimal management of the spectrum is crucial for enhancing the efficiency of resource use and ensuring the optimization of QoS. Within the 6G framework, researchers are tasked with addressing the complexities of spectrum sharing and the governance of spectrum mechanisms across diverse networks that coordinate transmissions at the same frequency. Moreover, investigating ways to reduce interference, including established methods such as parallel interference cancellation and successive interference cancellation, is a key area of focus for those working in the 6G field [274].
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 3 Materials and Methods
### Bicubic Interpolation  
Applying bicubic interpolation to process the detection box images with low scores to improve the resolution of the target is of great significance for extracting target features. Bicubic interpolation uses the \(4\times 4\) pixel area where the target pixel is located for interpolation calculation. The interpolation basis function [17] is:  
\[W(x)=\begin{cases}(a+2)|x|^{3}-(a+3)|x|^{2}+1,&\text{if }|x|\leq 1\\ a|x|^{3}-5a|x|^{2}+8a|x|-4a,&\text{if }1<|x|<2\\ 0,&\text{otherwise}\end{cases} \tag{4}\]  
As [17], \(a\) is \(-0.5\). When (\(x\), \(y\)) is a point in the rectangular subdivision \([x_{j},x_{j+1}]\times[y_{k},y_{k+1}]\), the bicubic interpolation function is:  
\[g(x,y)=\sum_{l=-1}^{2}\sum_{m=-1}^{2}g(x_{j+l},y_{k+m})W(x-x_{j+l})W(y-y_{k+m}) \tag{5}\]
# A Circularly Polarized Millimeter Wave Radar for Wind Turbine Sensing
## 5 Radar Experiments  
### Radar Performance Test  
In Figure 12, the circularly polarized radar used to monitor wind turbine blades is composed of a structural shell, a RF board, and a baseband signal processing board. The latter two boards are connected by a flexible wire.  
Figure 11: Chirp and frame geometry of AWR2243.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Index Name** & **Value** \\ \hline
1 & Numbers of ADC samples N & 1024 \\
2 & Numbers of Doppler samples M & 128 \\
3 & Chirp slope k & 10 MHz/us \\
4 & Frequency modulation bandwidth B & 1.024 GHz \\
5 & Chirp period T\({}_{\rm c}\) & 240 us \\
6 & Frame period T\({}_{\rm f}\) & 35 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: RF waveform configuration.  
Figure 12: Radar system physical picture.  
Figure 13: Photograph of mmWave radar performance test.  
Figure 14 shows the test results. The nearest and the farthest end of the guide rail is 2.9 m and 5.1 m with a measured distance of 2.92 m and 5.11 m, the corresponding speeds are 2.75 m/s and \(-\)2.5 m/s in forward and reverse direction, respectively. Angle is also calibrated to \(0^{\circ}\), the measured maximum and minimum angle are 0.125\({}^{\circ}\) and 0.125\({}^{\circ}\), with a tolerance less than 0.125\({}^{\circ}\). The results show that the radar measured distance, velocity, and angle distribution is consistent with the actual movement of the target on the rail, indicating that the radar achieves a high degree of accuracy in terms of performance and can be used for field clearance detection.
## 1 Introduction  
There has been increasing interest recently in evaluating the elasticity of different tissues using elastography techniques. The ability to quantify changes in tissue stiffness using elasticity imaging techniques improves the diagnosis of diseases at early stages [1]. Ultrasound elastography is relatively inexpensive, provides real time measurements of tissue stiffness [2], and is currently being used in clinical practice for assessing the liver, breasts, prostates, thyroids, and the musculoskeletal system [3].  
It has been reported that ultrasound shear wave elastography (SWE) is a reproducible imaging method for assessing tissue stiffness [4]. There are three approaches to perform SWE, Transient elastography (TE), Two-dimensional SWE (2D SWE) and point SWE (pSWE) [5]. pSWE is an elasticity estimation technique that produces a shear wave by applying acoustic radiation force impulse (ARFI) in a small region of interest (about 1 cm\({}^{3}\)). This technique stimulates the tissue to provide a quantitative stiffness metric that corresponds to tissue elasticity [6]. Several studies have recently been conducted to investigate the variability of pSWE measurements [7; 8; 9; 10; 11; 12], in which a variation in measurements has been reported [7; 13; 14; 15; 16; 17].  
In vivo and in vitro studies have been undertaken to investigate the effect of measurement depth measured by pSWE on many applications [18; 19; 20; 16]. Discrepancies in pSWE measurements have been reported due to differences in a body organ, used probes, or sample sizes that have been examined. However, these discrepancies might provide inaccurate findings and thus affect the reliability of diagnosis. Therefore, phantom studies could be ideal for evaluating the performance of US elastography in a known depth.  
Attenuation of the ARFI and tracking waves can be considered as a source of variability in pSWE measurements. In addition, the attenuation of tissue could dampen the signals of the ultrasound as increasing the depth limits the accurate measurement of deeper tissue [5].  
To the authors' knowledge, only a few studies have assessed the effect of depth on SWE measurement using commercially available phantoms. As a result, we aimed to assess the reliability and accuracy of pSWE measurements at different depths in an elasticity phantom.
## 5 Swarm-Based Solution
### Routing Problem  
The original ACO method has been updated since it must minimize both time and complexity for the suggested algorithms to be acceptable for real-time applications:  
Any real-time data packets should be routed via the relay nodes until they reach the selected sink. Each source node looks at its neighbors and unicasts the packet to the best one using the ACO algorithm probability. The packet is then routed via this neighbor, who has chosen the best relay node among its neighbors until it reaches the selected sink. This means a route has been identified from the source node carrying crucial data to the designated sink, and the data packet arrives simultaneously. This reduces the ACO method's time and complexity. It is decided during packet transmission which path the real-time data packet will take.  
Two steps make up the suggested solution. If a particular source node in the first phase has to communicate real-time data to the selected sink through the routing process, it sends a forward ant across neighboring nodes, which serves as a relay node until the selected sink receives the data. Before choosing the relay nodes, each node must determine which of its neighbors is allowed to take part in the routing operations. The neighbor nodes that can achieve the necessary speed are the ones that are qualified to be regarded as candidate relay nodes and participate in the routing process. The neighbor node is qualified if it can transmit the data packet to the selected sink in a period of time that is less than or equal to the packet's remaining deadline. On the other side, we will cooperate with all neighbor nodes if no neighbor node meets such a requirement.  
Second, the forward ant chooses the next hop based on probability as a relay node at each neighbor node. The likelihood of choosing a neighbor to serve as the next-hop relay node, which is specified by Equation (65), is computed using a number of variables, including the relaying speed, environmental effect metric, energy consumption load metric, connection quality, along with the pheromone value as follows:  
\[p_{xy}^{k}(t)=\frac{\left[\tau_{xy}^{2}(t)\right]^{\phi_{1}}\left[\eta_{xy}^{2 }(t)\right]^{\phi_{2}}\left[\lambda_{xy}^{2}(t)\right]^{\phi_{3}}\left[\beta_{ xy}^{2}(t)\right]^{\phi_{4}}\left[\gamma_{xy}^{2}(t)\right]^{\phi_{5}}\left[ \delta_{xy}^{2}\right]^{\phi_{6}}}{\sum\limits_{l\in FCHN_{x}}\left[\tau_{xl}^ {2}(t)\right]^{\phi_{1}}\left[\eta_{xl}^{2}(t)\right]^{\phi_{2}}\left[ \lambda_{xl}^{2}(t)\right]^{\phi_{3}}\left[\beta_{xl}^{2}(t)\right]^{\phi_{4} }\left[\gamma_{xl}^{2}(t)\right]^{\phi_{5}}\left[\delta_{xy}^{2}\right]^{\phi_ {6}}}} \tag{66}\]  
where \(\tau_{xy}^{2}(t)\) is the pheromone value of the link (\(x\), \(y\)) at the time \(t\), \(\eta_{xy}^{2}(t)\), \(\lambda_{xy}^{2}(t)\), \(\beta_{xy}^{2}(t)\), \(\gamma_{xy}^{2}(t)\), and \(\delta_{xy}^{2}(t)\) are the heuristic information; \(\phi_{1}\), \(\phi_{2}\), \(\phi_{3}\), \(\phi_{4}\), \(\phi_{5}\), and \(\phi_{6}\) are the control factors that control the pheromone value and the heuristic information parameters, respectively.
# Development of a Modular Software Architecture for Underwater Vehicles Using Systems Engineering
## 3 Modular Software Architecture Definition  
Modularity means that the design is based on a set of independent blocks, called modules, whose structure and behavior follow certain rules. The modular design allows developing scalable systems and makes components of the system to be reusable in a collaborative development [75]. For Pionero500, this modularity has been addressed hardware-wise in [54]. To introduce the corresponding software architecture, a customized unified modeling language (UML) deployment diagram is presented in Figure 4 to establish its relationship with physical systems.  
Physical modules are represented as UML nodes, and are differentiated in the architecture according to its dependability. Some modules are required for the architecture to work; others can be removed or added, including original equipment manufacturer (OEM) modules that are usually closed software platforms; custom devices are developed from scratch. An example of required modules are those associated with communication tasks and data processing, such as topside computer, Ethernet switch, multiplexer units, and the National Instruments (NI) CompactRIO (cRIO) vehicle's computer. Modules with software that has been developed or modified by the team are distinguished from the rest by having UML components inside. Finally, this structure considers other components that can be added in the future, which gives advantages in terms of modularity and scalability.  
Components that are hosted in NI MyRIO, computer, and NI cRIO have been developed in LabVIEW. Real-time capabilities of the RIO-based devices are leveraged to improve the prioritized engineering specifications, such as control system's time response. Gigabit Ethernet compatibility was also established as a requirement for communication devices on the main link between topside and subsea devices, such as computer, Ethernet switch, multiplexers, and NI cRIO. The NI cRIO serves as the main processing unit of the vehicle and is equipped with a communication module that allows it to exchange data with every instrument on board: altimeter, AHRS, CTD, and USBL modem, regardless of the physical standard. For example, while the CTD uses RS-232, the AHRS uses RS-422. It also communicates with every box's SmartCard in a master-slave scheme, using an RS-422 full-duplex bus.  
Each SmartCard processing unit is a 32-bit microcontroller. One of the key characteristics taken into account for microcontroller platform selection was developing an environment and language supporting OOP. In this particular implementation, Atmel(r) SAM3X series complied with this specification. Arduino(tm) provided a simplified framework for implementing functionality while reducing development time by using third-party libraries provided by device manufacturers and developers community.
# Polarization Effect on the Performance of On-Chip Wireless Optical Point-to-Point Links
## 2 Design of the Dielectric Vivaldi Antenna
### Direct-Mode Coupling Analysis  
Although the modes that propagate in the strip and in the slot waveguides are, in principle, quite different (Gaussian- and non-Gaussian-like, respectively), the direct coupling of power between the two modes is possible, provided that their components  
Figure 1: Scheme of the dielectric Vivaldi antenna directly coupled to a strip waveguide. The antenna is composed of silicon nitride and, to investigate its radiation properties, it is embedded into a homogenous medium with refractive index n\({}_{\text{SiO2}}=1.445\).  
overlap efficiently [48]. The overlap integral between the two modes can be calculated by the following rigorous formula [48]:  
\[\eta=\left(\frac{\text{Re}\big{\{}\int\textbf{E}_{1}\times\textbf{H}_{2}^{*}\text {-}\text{d}\textbf{s}\big{\}}}{\int\textbf{E}_{1}\times\textbf{H}_{1}^{*}\text {-}\text{d}\textbf{s}}\right)\left(\frac{\text{Re}\big{\{}\int\textbf{E}_{2} \times\textbf{H}_{1}^{*}\text{-}\text{d}\textbf{s}\big{\}}}{\int\textbf{E}_{2} \times\textbf{H}_{2}^{*}\text{-}\text{d}\textbf{s}}\right) \tag{3}\]  
where \(\textbf{E}_{1}\) and \(\textbf{E}_{2}\) are the electric field components in the strip and in the slot waveguides, respectively, and \(\textbf{H}_{1}^{*}\) and \(\textbf{H}_{2}^{*}\) are the complex conjugates of the corresponding magnetic field components.  
In order to analyze the direct coupling mechanism between the strip waveguide and the slot one, the electric and magnetic field components were calculated at the two sections denoted in Figure 1 as A\({}_{1}\) and A\({}_{2}\) by the Discrete Fourier Transform (DFT) of the time domain fields obtained via 3D-FDTD simulation of the whole structure. Sections A\({}_{1}\) and A\({}_{2}\) in Figure 1 correspond, respectively, to the strip and to the slot waveguides. The overlap integral of Equation (3) was calculated, either for the TE or for the TM fundamental modes, considering the electric and magnetic field distributions at these two sections.  
Figure 2 shows the overlap integral that was calculated according to Equation (3) and as a function of the slot gap g for the TE (solid curves) and the TM (dashed curves) fundamental modes of the strip and of the slot waveguides. The results for the three chosen configurations are reported: width w = 600 nm (configuration A--blue curves), w = 900 nm (configuration B--red curves), and w = 1100 nm (configuration C--yellow curves). As it can be seen in Figure 2, the overlap integral is always above 0.9, thus allowing an efficient coupling of the optical signal from the strip to the Vivaldi antenna. Once the strip mode is coupled to the slot one, it propagates along the Vivaldi antenna being radiated in the surrounding medium.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Challenges of IoMT
#### 3.2.2 Interoperability  
The extent of usage is limited by the heterogeneity of devices and data from various sources, mostly due to inter-operator variation. The difficulty of data interchange between several IoMT systems with disparate capabilities makes interoperability challenging [51]. Therefore, the creation of standardized interfaces is essential, especially for programmers that facilitate cross-organizational communication.
# Efficient Regression Testing of Software Product Lines by Reducing Redundant Test Executions
## 5 Evaluation
### Results and Discussion
#### 5.3.2 RQ2  
Does the Reduction of Test Executions Resulting from CPLTE Affect the Fault Detection Effect?  
To answer RQ2, we compared the number of faults detected by the single-test-single-execution, the exhaustive execution and CPLTE. Table 5 presents the total number of faults injected (column 2) and the total number of faults detected (columns 3-5) for each test execution method. Because the RTS methods presented in Section 5.1.2 selected all the test cases that expose faults under the Controlled Regression Testing assumption (i.e., their fault detection effects were the same), they do not affect the result of this comparison.  
Table 5 shows that CPLTE detected more faults than the single-test-single-execution for all the subject product families. This result is caused by the difference in code coverage resulting from the two methods. The single-test-single-execution executes test cases only on a single product whereas CPLTE executes test cases on one or more products. For this reason, in the case of the test cases that cover different parts of source code depending on the product to apply, CPLTE has a higher code coverage than the single-test-single-execution and so CPLTE can detect faults that could not be detected by the single-test-single-execution. On the other hand, CPLTE showed the same fault detection effect as the exhaustive execution, which means that all the faults detected by the exhaustive execution were detected by CPLTE. This indicates that, under the Controlled Regression Testing assumption, CPLTE effectively avoids repeating equivalent test executions without losing fault detection effect.  
Table 5 also shows that, for the product families of the Prevayler, MobileRSSReader and Lampiro product lines, the fault detection effect of CPLTE is slightly better than that of the single-test-single-execution. This indicates that test executions on these product families contain high proportions of equivalent test executions. Despite the small amount of benefit in fault detection in such cases, applying CPLTE to such product families is still preferable. This is because, as presented in Table 4, CPLTE requires only a small number of additional test executions (i.e., on top of the number of test executions from the single-test-single-execution which is equal to the number of test cases) that can be executed within a few seconds and detects faults that the single-test-single-execution may miss.
## 4 Results and Discussion
### Moment and Yield Surface Function of Steel H Piles
#### 4.4.2 Yield Surface Function Value of Steel Piles  
Since the steel H piles are under axial load as well as bi-axial bending, it is necessary to consider the P-M-M interaction on the pile cross-section. Therefore, a yield surface function \(\varphi\) is defined in this paper to characterize the internal force response of the steel H piles, as shown in Equations (5) and (6) below, based on the strength interaction equation from the literature [31; 32]. When \(\varphi\geq 1\), the steel pile reaches a full-section yielding and a plastic hinge is formed.  
\[\varphi\big{(}N,\ M_{x},M_{y}\big{)}=\left(\frac{M_{x}}{M_{pcx}}\right)^{\alpha }+\left(\frac{M_{y}}{M_{pcy}}\right)^{\alpha} \tag{5}\]  
\[\alpha=1.6-\frac{N/N_{p}}{2\ln\big{(}N/N_{p}\big{)}} \tag{6}\]  
where \(M_{x}\) represents bending moment about the strong-axis, \(M_{y}\) represents bending moment about the weak-axis, \(M_{pcx}\) represents the plastic flexural strength about the strong-axis under axial force \(N\), \(M_{pcy}\) represents the plastic flexural strength about the weak-axis under axial force \(N\), \(N\) represents the axial force, \(N_{p}\) represents the axial compressive strength without considering bending moment and \(\alpha\) represents the influence parameter of the axial force.  
From the time-history analyses, it was found that the maximum bending moment was always located at the top. Therefore, only the \(\phi\) value at the pile top is discussed in this section, and the maximum \(\phi\) value at the pile top from time-history analyses on all SIAB models with varying skew angles and backfill compactness are shown in Figure 20. As shown in Figure 20, \(\phi<1\) for all cases, meaning that no plastic-hinge was formed at the pile top during the nonlinear time-history analyses. It is clear from the figure that under non-pulse type ground motions, the \(\phi\) value decreases with the backfill compactness, indicating that when the backfill is densely compacted, it decreases the normal stress in steel piles. For densely compacted backfill, the \(\phi\) value increases with the skew angle, but it is the opposite trend for loosely compacted backfill.  
Figure 19: Maximum moment of pile top.  
Under pulse type ground motions, the \(\phi\) value still decreases with the backfill compactness, but it consistently increases with the skew angle for all types of backfill. For the densely compacted backfill, the \(\phi\) value with a 45\({}^{\circ}\) skew angle can be 4.8 times the value with no skew. In addition, when there is no skew, the \(\phi\) values are smaller under pulse-type ground motions than non-pulse type ground motions, but as the skew angle increases, the \(\phi\) values under pulse-type ground motions increases so sharply that they are about 2 times the value under non-pulse type ground motions, when the skew angle is 45\({}^{\circ}\) for all types of backfill. Therefore, the near-fault effects significantly increase the normal stress in steel piles, and amplify the influence of the skew angle.