esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Evaluation  
### Shaking Table Test Specimens  
In the shaking table test, the masonry wall specimen had dimensions of 1390 mm \(\times\) 1551 mm \(\times\) 190 mm and weighed 700 kg, as shown in Figure 1. The clay brick dimensions were 190 mm \(\times\) 57 mm \(\times\) 90 mm and comprised 23 layers with 10 mm mortar joints. The compressive strengths of clay brick and mortar are 25 and 16 MPa, respectively. Three different types of masonry wall specimens were prepared, as illustrated in Figure 2. The first type, designated as MWN, consisted of non-retrofitted control specimens. The second type, MWP2, was retrofitted with a 2 mm thick layer of STPU. The third type, MWP5, consisted of specimens retrofitted with X-shaped laminated GFRP strips, 5 mm in width, followed by the application of a 2 mm thick STPU layer on top. The GFRP used for strengthening had a tensile strength of 900 MPa and an elastic modulus of 45 GPa. A total of three MWN specimens were fabricated, including one for preliminary testing, whereas two specimens were prepared for each of the retrofitted types, MWP2 and MWP5.
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 5 Proposed Methodology
### Routing Strategy
In order to ensure the normal use of all applications, all weight coefficient is set as \(\frac{1}{4}\left(w_{1}=w_{2}=w_{3}=w_{4}=\frac{1}{4}\right)\). Calculate the score \(s\left(\widetilde{h}_{ij}\right)\) of every LEO satellite and the dominance matrix is established on the dominance relationship. The update information of routing table is obtained.
## 5 Non-Beats and Custom R-Peak Validation Method  
The MIT-BIH arrhythmia database is classified into two main annotation categories: beats with actual R-peaks, which have 15 subtypes and consist of 109,494 beat annotations, and non-beats without QRS or R-peaks, which have 24 additional subtypes consisting of 3153 non-beat annotations including 42 beats of ventricular flutter from record 207 [94]. We refer to them as non-beats because they do not represent QRS-waves, making them irrelevant for R-peak detection as shown in Figure 7.  
Figure 6: Validation of the actual tolerance for the AAMI benchmark window of 0.15 s.  
Figure 7: Some of the Non-beats in MIT-BIH Arrhythmia Dataset. VT: Ventricular tachycardia, T: Ventricular trigeminy, N: Normal sinus rhythm, 1: Isolated QRS-like artifact, 1: Ventricular flutter, B: Ventricular bigeminy, NOD: Nodal (A-V junctional) rhythm, IVR: Idioventricular rhythm, AFIB: Atrial fibrillation, and SVTA: Supraventricular tachyarrhythmia.  
Validating a custom R-peak methodology involves predicting a combination of beats and non-beats. However, if the predicted samples are tested against reference annotations without using WFDB comparators and the method instead relies solely on window size adjustments through a self-developed R-peak validation methodology, problems may arise. In this case, the methodology would be unable to distinguish between beat and non-beat annotations, resulting in an increase in TPs, FPs, and FNs. WFDB-provided comparators address this issue by filtering out all non-beats and only validate the actual beat annotations, as shown in Figure 8.  
Here, the WFDB comparator's general working is shown to illustrate how it processes the actual beats of normal (N) and premature ventricular contraction (V) beats but excludes a non-beat ventricular tachycardia (VT) using the shutdown and resume time mechanism. Shutdown is the time when excluded parts consisting of non-beats start and resume is the time when these parts end. The literature, such as [45; 46; 47] and many others as given in Table 1, does not provide the details of the employed R-peak validation procedures. Although these studies provide statistical metrics, it is reasonable to assume that they used custom validation methods for testing R-peaks that raise a question about how they addressed the non-beat issue, as simply adjusting the window size is insufficient to ensure accurate statistical outcomes. In contrast, WFDB R-peak validation tools filter out non-beats, highlighting the importance of a standard R-peak validation method to tackle these issues effectively.
## 2 Materials and Methods
### Measurement
#### 3.2.4 MSCR Test  
The experimental results of the MSCR tests conducted on pre-cut SBS-modified asphalt with different shredded particle sizes are shown in Figure 13.  
Figure 12: Temperature sweep tests of modified asphalt with different shredded particle sizes. (**a**) Complex modulus, (**b**) phase angle.  
In Figure 13a, it can be observed that the creep recovery rates of SBS-modified asphalt samples prepared by mixing with different shredded particle sizes were all greater than those of the conventional-process asphalt samples. This indicates that the pre-cut SBS-modified asphalt had a stronger elastic recovery capability. Among the three different shredded particle sizes, the rates of the 30-18 mesh and 100-30 mesh samples were greater than those of the 18-10 mesh samples. The test showed that SBS-modified asphalt containing fibrous morphology had a stronger elastic deformation recovery capability, which was attributed to the easier intertwining of fibrous SBS-modified asphalt, which enhanced the elastic deformation recovery capability of the modified asphalt.  
In Figure 13b, it can be seen that the non-recoverable creep stiffness of the SBS-modified asphalt prepared by mixing with different shredded particle sizes was smaller than that of the conventional-process asphalt. This indicates that the pre-cut SBS-modified asphalt had a stronger resistance to deformation. Among the three different shredded particle sizes, the non-recoverable creep stiffness of the 18-10 mesh was the smallest, followed by the 100-30 mesh, and that of the 30-18 mesh was the largest. The test showed that SBS-modified asphalt with obvious granular morphology had a stronger resistance to deformation. This is because the advantages of the network structure formed by granular structures of SBS are reflected in terms of strength. When the SBS morphology is a combination of fibrous and granular shapes, the larger the width of the fibrous structure and the diameter of the granular structure, the stronger the network structure strength and the stronger the resistance to deformation.
## 3 Results and Discussion
### Technical Parameters
#### 3.6.2 Tensile Strength  
The results of tensile testing, i.e., tensile strength [kN/m] and elongation at maximum load [%], of PETE fabric and PETE-Cu\({}^{(0)}\) composites, determined in longitudinal and horizontal modes, are listed in Table 5.  
The results presented opposite tendencies regarding as well elongation determined for PETE and PETE-Cu(0) in longitudinal and horizontal modes. Thus, ca 10% increase of tensile strength in longitudinal mode for PETE-Cu\({}^{(0)}\) in comparison with starting PETE fibre (7.8-7.6 vs 7.0 [kN/m]) is accompanied by corresponding ca 3% decrease in horizontal mode (6.0 vs 6.2 [kN/m]), respectively. At the same time, the differences of tensile strengths, measured for PETE-Cu\({}^{(0)}\)-1 and PETE-Cu\({}^{(0)}\)-2 in both modes are negligible.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**PETE**} & \multicolumn{3}{c}{**PETE-Cu\({}^{(0)}\)**} \\ \cline{3-5}  & & **PETE-Cu\({}^{(0)}\)-1** & **PETE-Cu\({}^{(0)}\)-2** \\ \hline Average air permeability & 100 Pa & 65.5 \(\pm\) 1.5 & 54.5 \(\pm\) 1.0 & 51.8 \(\pm\) 1.0 \\
[mm/s], pressure decrease: & 200 Pa & 147.0 \(\pm\) 4.0 & 123.0 \(\pm\) 2.0 & 117.0 \(\pm\) 2.0 \\ \hline \hline \end{tabular} The results have been measured in triplicate and presented as a mean value with. \(\pm\)deviation approximately 3%.  
\end{table}
Table 4: The air permeability of the unmodified PETE fabrics and PETE-Cu\({}^{(0)}\) composites, according to the EN ISO 9237:1998 [115].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**PETE**} & \multicolumn{3}{c}{**PETE-Cu\({}^{(0)}\)**} \\ \cline{2-7}  & \multicolumn{3}{c}{**PETE-Cu\({}^{(0)}\)-1**} & \multicolumn{3}{c}{**PETE-Cu\({}^{(0)}\)-2**} \\ \cline{2-7}  & **Longitudinal** & **Horizontal** & **Longitudinal** & **Horizontal** & **Longitudinal** & **Horizontal** \\ \hline Tensile strength & & & & & & \\
[kN/m] & & & & & & & \\ \hline Elongation at & & & & & & \\ maximum load [%] & & & & & & \\ \hline \hline \end{tabular} The results have been measured in triplicate and presented as a mean value.  
\end{table}
Table 5: The results of tensile strength test of the unmodified PETE fabrics and PETE-Cu\({}^{(0)}\) composites, in accordance with the EN ISO 10319:2015 [116].  
Similarly, the elongation at maximum load increases ca 6% from 375% for PETE to 396%-403% for PETE-Cu\({}^{(0)}\) fabrics in longitudinal mode and decreases ca 5% from 421% for PETE to 405%-413% for PETE-Cu\({}^{(0)}\) fabrics in horizontal mode, respectively. At the same time, the differences in the elongations at maximum load, measured for PETE-Cu\({}^{(0)}\)-1 and PETE-Cu\({}^{(0)}\)-2 are negligible (lay in error limit).
## 5 Method Verification  
### Model and Parameters  
To optimize the blasting parameters and verify the simulation and analysis method for rock blasting fragmentation, a three-dimensional numerical model was established based on the actual parameters of the blasting test site, as shown in Figure 22. The model dimensions are 9.0 m \(\times\) 6.0 m \(\times\) 16.0 m. It is divided into fine elements, with a minimum size of 5 cm \(\times\) 5 cm and a maximum size of 6.5 cm \(\times\) 6.5 cm. The model consists of 2,094,777 nodes and 2,010,504 elements. To ensure proper transmission of the blasting stress wave and improve the fluid-structure coupling effect, some elements of the rock material and ALE fluid material are overlapped. To mitigate the impact of reflected stress waves from the artificial cut-off boundary, the top and front surfaces are designated as free boundaries, while the other surfaces are treated with non-reflective boundaries.  
To simulate the macroscopic rock cracks induced by blasting, the *MAT_ADD_EROSION keyword is employed to remove elements that exceed the damage threshold. According to the relevant reference [16], the damage threshold is defined as the point at which the tensile stress reaches 3.5 MPa or the tensile strain exceeds 0.1. Mesh elements that meet these criteria are then deleted.  
The explosive material is modeled using the *MAT_HIGH_EXPLOSIVE_BURN keyword. The relationship between pressure, volume, and energy of the detonation products during the explosive detonation process is simulated using the JWL equation. Detailed parameters for the explosives are provided in Table 2.  
In this study, the RHT model is selected to simulate the behavior of rock material. Based on the actual conditions at the test site, the parameter values for the rock RHT material are provided in Table 5.  
Air is used as the medium for the decoupled charge and as the filling material in the fluid-structure coupling region. It functions as the material for the fluid elements, enabling material exchange between the explosive and rock materials. During the modeling process, fluid elements and air units are connected to improve the accuracy of the numerical simulation. The material model for air is defined using the *MAT_NULL keyword, and its equation of state is specified by the *EOS_LINEAR_POLYNOMIAL keyword. The stemming material was defined with the MAT_SOIL_AND_FORM keyword.
## 3 Results and Discussion
### Topic 2: Healthcare Delivery
#### 3.3.3 Preventative Healthcare: Organisation-Led Initiatives
Similar findings have been reported whereby miners who perceived that their company was not committed to employee mental health had higher levels of distress [13]. For resource-sector organisations that have delivered and evaluated mental health-related interventions to employees, the results have been positive. [PERSON] et al. [31] evaluated an intervention for mine workers at two sites called the MATES in Mining (MIM) program, a peer-support program involving general awareness training (all employees), specialist training (to a smaller number of volunteers), and applied suicide intervention skills training (for individuals in a health and safety role at the workplace). Six and eighteen months after the intervention, employees were more confident and likely to seek both professional and non-professional support for mental health, were less likely to agree that they would be treated differently by friends/colleagues if they knew they had a mental illness, and were less likely to think they would be treated poorly in the workplace [31]. While mental-health-related outcomes are important to the individual (employee) it may also be a sustainable investment for the organisation, with poor mental health suggested as being the leading cause of absenteeism, poor work performance, and employee turnover [40]. [PERSON] et al. [41] sampled data from 1456 coal miners which was used to estimate lost productivity in the Australian mining industry due to psychological distress at $154 million per year [41]. In addition, higher psychosocial risk has been seen in mining employees who reported lower job satisfaction and work commitment and higher turnover intention [42].
# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 1 Introduction
There is a lack of algorithms specifically designed for detecting ship targets in complex backgrounds such as intense sunlight reflection, and the robustness of existing algorithms is generally limited. Under strong sunlight, the phenomenon of glare on the sea surface is often severe. After infrared imaging, there may be areas of high brightness or bright bands in the sea clutter, making it difficult to distinguish targets from the background clutter. Additionally, the disturbance caused by sea wind and the fluctuation of sea waves results in irregular patches of various sizes and grayscale values in infrared images. If there are cloud formations in the sky, the image may contain dark areas or dark bands. Moreover, due to the diverse reflective properties of ships and the unknown polarity of imaged targets, targets often exhibit dark polarity in the presence of glare. Furthermore, factors like islands and waves in the background further increase the challenge of correctly detecting infrared ship targets in such complex maritime environments. In this paper, the research focuses on a complex sea surface scene consisting of light and dark bands, as well as island and reef backgrounds on a bright sea. The target of interest is a dark polar patch of a specific size on an imaged ship. Traditional infrared ship target detection methods typically rely on grayscale and gradient analysis to identify differences between the target and the background, enabling target detection. However, in the scenario described in this paper, there are often interference regions that resemble the target, leading to a high false alarm rate with these conventional methods. To address this challenge, this paper proposes a dual-channel threshold segmentation method based on local low-gray region detection and geometric feature analysis. Figure 1 illustrates the overall algorithm flowchart. Experimental results demonstrate that the proposed algorithm effectively detects dark polar ship targets in complex sea surface environments. Figure 1: Algorithm flowchart.
# Reverse Engineering and Topology Optimization for Weight-Reduction of a Bell-Crank
## 4 Bell Crank Optimization  
### Optimization Problem Statement  
The optimization algorithms are based on published work [18; 19; 29]. Topology optimization for a continuum structure can be regarded as a material distribution problem, where the target is to find an optimized material distribution within the design domain, for the minimum compliance (or maximum global stiffness) according to Equation (3). Since \(Stiffness\propto\frac{1}{\text{{Conpliance}}}\), minimizing the compliance will maximize the stiffness.  
In this study, the bell crank was discretized into finite elements and the Abaqus Topology Optimization Module (ATOM) was used to search for a minimum compliance design to minimize strain energy or compliance, based on the boundary conditions and forces being applied to the bell crank. The bell crank was discretized into finite elements. Assuming a constant \(E_{e}\) for each element, and using Equations (4) to (7), the discrete form can be expressed as:  
\[\text{Minimize}:\ \mathbf{C}=\{\mathbf{F}\}^{\mathbf{T}}\{\mathbf{u}\}\text{Subject to }[\mathbf{K} \cdot\mathbf{E}(\mathbf{x}_{e})]\{\mathbf{u}\}=\{\mathbf{F}\}\]  
where \(\mathbf{E}(\mathbf{x}_{e})\in\mathbf{E}_{\mathbf{a}\mathbf{d}}\), \(\mathbf{K}=\sum\limits_{e=1}^{N}\mathbf{K}_{e}(\mathbf{E}(\mathbf{x}_{e}))\), summing \(e=1,\ldots,\mathbf{N}\) elements. The objective function for this study was to minimize the overall displacements \(\{\mathbf{u}\}\), and a global measure of displacements is the strain energy, thereby minimizing the strain energy according to Equation (9).  
\(\{\mathbf{F}\}^{\mathbf{T}}\) represents the forces applied, \(\mathbf{[K]}\) is the global stiffness matrix, which depends on the stiffness of the individual elements \(\mathbf{E}_{e}\), whereas \(\mathbf{E}_{\mathbf{a}\mathbf{d}}\) is a set of admissible stiffness tensors for design problems. The entire design optimization process is summarized in Figure 5.  
Figure 5: The design optimization process.
## 1 Introduction
### Analysis of Demagnetization Fault Identified Signal
#### 2.3.2 Effect of Demagnetization Severity on \(Sl_{\text{m-2}}\)  
The waveforms of \(Sl_{\text{m-2}}\) of the employed DDPMSM under Mode 7 with 100% demagnetization and Mode 8 with 50% demagnetization, are studied and presented in Figure 3.  
As shown in Figure 3, the waveforms of \(Sl_{\text{m-2}}\) are the same for Mode 7 with 100% demagnetization and Mode 8 with 50% demagnetization. Therefore, Mode 7 and Mode 8 are identified to be different fault modes. It can be noticed that, the waveforms of these two fault modes are sometimes the same and sometimes different, causing confusion when identifying them. This is due to the amplitude of \(Sl_{\text{m-2}}\) changing with the variation in the demagnetization severity, resulting in the waveforms for different fault modes with the same waveform trend sometimes being the same and sometimes being different. To solve the difficulty of demagnetization fault mode identification caused by demagnetization severity, \(Sl_{\text{m}}\) is normalized through (7).  
\[s_{\text{norm}}=\frac{s}{|s_{\text{max}}|}, \tag{7}\]  
where \(S_{\text{norm}}\) is the normalized value. \(S\) is the actual value. \(S_{\text{max}}\) is the maximum value of one electrical cycle signal.  
The normalized \(Sl_{\text{m-2}}\) under six different demagnetization fault modes, including Mode 6 with 100% demagnetization, Mode 6 with 50% demagnetization, Mode 7 with  
Figure 3: The waveforms of \(Sl_{\text{m-2}}\) under Mode 7 with 100% demagnetization and Mode 8 with 50% demagnetization.  
Figure 2: The waveforms of \(Sl_{\text{m-2}}\). (**a**) Mode 1 to 6. (**b**) Modes 6, 7, and 8.  
100% demagnetization, Mode 7 with 50% demagnetization, Mode 8 with 100% demagnetization, and Mode 8 with 50% demagnetization, are shown in Figure 4.  
As shown in Figure 4, the waveforms of normalized \(SI_{\text{m}}\) are the same for these six different demagnetization fault modes. Thus, Mode 6, Mode 7, and Mode 8 are determined to be the same fault mode. Confusion caused by different demagnetization severities in the determination of the demagnetization fault modes can be eliminated by normalization. However, \(SI_{\text{m}}\) cannot discriminate between Mode 6, Mode 7, and Mode 8. To identify Mode 6, Mode 7, and Mode 8, the first auxiliary identification signal is applied.
# Performance Analysis of a 3D Wireless Massively Parallel Computer
## 3 Simulation Results
### Link Utilisation Analysis
#### 3.2.2 Overall Link Wait Time  
OLWT is measured on the ABC and a 5D network (Figures 13 and 14, respectively). Like OLBT, the decrease in OLWT because of the increase in data rate is observed for most data points in both figures. The slope of decrease in OLWT is slightly steeper for the ABC compared with a 5D network.  
OLWT is an estimate of how long nodes have been waiting to access the links. Since the simulated nodes are multi-tasking, they can yield control to another task when they are waiting for link access. This makes OLWT a less crucial factor for measuring the performance of the proposed network. However, it still has importance, especially at the final stages of the simulation, when most of the nodes have only one task to finish. In this situation they have no other active task to switch to while waiting for link access. Such a wait has a direct impact on the overall execution time of the complete set of tasks. Despite OLBT, OLWT does not have a meaningful influence on the overall power consumption.  
The store-and-forward packet-switching strategy adopted for this series of experiments is not the best option in the sense of link wait time. This is a major contributor to the relatively high OLWT in many of the experiments. This method was chosen only because of its simplicity of implementation. It is anticipated that the link access wait time will improve if a cut-through or wormhole technique is used.  
Figure 12: Link busy time (OLBT in ms) for FFTTM on a 5D grid with two sizes: 1000 nodes (**a**,**c**,**e**) and 2000 nodes (**b**,**d**,**f**). Data sizes are 100 KB (**a**,**b**), 1 MB (**c**,**d**) and 10 MB (**e**,**f**). Blue, red, green, orange and black graphs represent computational ability of 0.01, 0.1, 1, 10 and 100 GIPS respectively.  
Figure 14: _Cont_.  
Figure 13: Link wait time (OLWT in ms) for FFTTM on ABC with two sizes: 1000 nodes (**a**,**c**,**e**) and 2000 nodes (**b**,**d**,**f**). Data sizes are 100 KB (**a**,**b**), 1 MB (**c**,**d**) and 10 MB (**e**,**f**). Blue, red, green, orange and black graphs represent computational ability of 0.01, 0.1, 1, 10 and 100 GIPS, respectively.