esa-sceva/satcom-chunk-collection huggingface dataset:
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Dataset Description
#### 3.1.2 Pavia University  
The ROSIS sensor was instrumental to the collection of this dataset over the University of Pavia, Italy. The dataset is called \"Pavia University\" (PU). It has a spatial resolution of 1.3 m and originally comprises 115 spectral bands covering wavelength ranges from 0.43 to 0.86 \(\upmu\)m. In the final analysis, 103 bands are used after the elimination of noisy channels. The image has a size of \(610\times 340\) pixels, and it has nine challenging classes with nearly similar spectral reflectances. Detailed information about the false-color image, Ground Truth, and class names is displayed in Figure 6.
## 3 Results and Discussion
### Effect of Inlet Length Scale on the Integral Length Scale in the Domain  
Figure 12 shows the longitudinal integral length scale of the streamwise and vertical velocity (i.e., \(L_{x}(u)\) and \(L_{x}(w)\)) at the inlet section (Figure 12a,c) and at \(x/d=50\) (Figure 12b,d). As shown in Figure 12a,c, in the cases close to the inlet, the magnitude of the integral length scale \(L_{x}(u)/d\) close to the wall surface presented a large discrepancy when compared with existing DNS data. On the contrary, the length scales away from the wall (i.e., \(y/d>0.5\)) were more similar. In particular, Case 09 (i.e., the case having the largest integral length scale at the inlet) had values very similar to the existing DNS data. The discrepancy between the length scales close to the wall may be due to the fact that the streamwise length scale obtained from the actual DNS was for the fully developed profile;  
Figure 11: Mean and turbulent stress profiles at \(x/d=50\). In the figure, (**a**) \(\langle u^{\prime}u^{\prime}\rangle^{+}\), (**b**) \(\langle v^{\prime}v^{\prime}\rangle^{+}\), (**c**) \(\langle w^{\prime}w^{\prime}\rangle^{+}\), (**d**) \(\langle-u^{\prime}v^{\prime}\rangle^{+}\).  
therefore, it was higher than that of the current study close to the wall and gradually decreased in the freestream. Interestingly, even though the length scale was different at the inlet (see Case 02 having no integral length scale at the inlet), the boundary layer developed, and the integral length scale profiles were almost equivalent downstream. This region was fully developed, except for regions that were proximal to the wall. As shown in Figure 12a,c, the longitudinal integral length scale of the vertical velocity (i.e., \(L_{x}(w)\)) at the inlet section would have the same tendency as the length scale of the streamwise velocity (i.e., \(L_{x}(u)\)).  
Figure 12: Longitudinal integral length scale profiles at the inlet section (**a**,**c**) and at \(x/d=50\) (**b**,**d**), \(L_{x}(u)/d\): (**a**,**b**), \(L_{x}(w)/d\): (**a**,**b**). In the figure, (**a**) denotes longitudinal integral length scales of the streamwise velocity component at inlet section, (**b**) longitudinal integral length scales of the streamwise velocity component at \(x/d=50\), (**c**) longitudinal integral length scales at the inlet section of the spanwise velocity component, and (**d**) longitudinal integral length scales of the spanwise velocity component at \(x/d=50\), (\(\circ\): Case01, \(+\): Case09, \(\bullet\): DNS database).  
Figure 13 shows the lateral integral length scale of the streamwise and vertical velocity (i.e., \(L_{z}(u)\) and \(L_{z}(w)\)) at the inlet section (Figure 13) and at \(x/d=50\) (Figure 13,c). As mentioned earlier, regardless of the integral length scale at the inlet, the profiles observed downstream were similar, that is they were fully developed.
## 4 Case Study  
### Case route Overview  
#### 4.1.1 Line Operation Data  
A specific urban rail transit line, operating during the morning peak hours from 7:00 to 9:00 a.m., with the last train departing from the first station at 9:00 a.m., is selected for analysis. This line extends over 26.65 km and encompasses 23 stations, which are numbered from 1 to 23 for up trains and from 24 to 46 for down trains. Depots are located at each end of the line. Short-turn route trains turn back at station 15, covering a section of 17.13 km, as depicted in Figure 6. The line utilizes 4-car B-type trains as the standard operating unit, with each car having with a capacity of 250 passengers. All platforms are capable of accommodating larger train formations after coupling. Parameter values are provided in Table 5.
## 3 Results
### Results of Dissipation Factor Measurements
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **PLA** & **ABS** & **ASA** & **PETG** \\ \hline
50 Hz & 0.6733 & 1.1376 & 2.2501 & 0.3546 \\ \hline
100 Hz & 0.5872 & 1.0286 & 1.6305 & 0.3064 \\ \hline
150 Hz & 0.5429 & 0.9496 & 1.3777 & 0.3066 \\ \hline
200 Hz & 0.5107 & 0.8848 & 1.2302 & 0.3167 \\ \hline
250 Hz & 0.4851 & 0.8365 & 1.1306 & 0.3280 \\ \hline
300 Hz & 0.4679 & 0.7978 & 1.0643 & 0.3412 \\ \hline
350 Hz & 0.4550 & 0.7701 & 1.0131 & 0.3538 \\ \hline
400 Hz & 0.4459 & 0.7468 & 0.9726 & 0.3664 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of Tan\(\delta\) (%) measurement at 2 kV. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **PLA** & **ABS** & **ASA** & **PETG** \\ \hline
50 Hz & 1.1956 & 2.1402 & 3.6904 & 0.8767 \\ \hline
100 Hz & 1.0870 & 2.1500 & 3.1378 & 0.7876 \\ \hline
150 Hz & 1.0336 & 2.1385 & 2.9114 & 0.7595 \\ \hline
200 Hz & 0.9894 & 2.1153 & 2.7688 & 0.7498 \\ \hline
2  
respectively), but the dissipation factor of silver-colored PLA was significantly higher. This shows that pigments or additives introduced into the material can have significant impact on the dissipation factor. In addition to pigmentation, factors such as porosity, impurities, internal structure, and the molecular structure of the material contribute to these variations. Furthermore, the FDM printing process can also introduce inconsistencies in material density, further affecting the dissipation factor.
## 3 Constraint-Handling Techniques in Metaheuristic Algorithms
### Constraint-Handling Techniques
#### 3.2.1 Penalty Function  
The PF [52] has been frequently used to solve constrained optimization problems in the mechanism synthesis. The PF transforms an optimization problem with constraints into an unconstrained problem. The most used transformation equation is:  
\[\bar{J}^{\prime}(x)=\overbrace{\sum_{i=1}^{n}w_{i}l_{i}(x)}^{\underline{J}}+ \overbrace{\sum_{j=1}^{m}\varpi_{j}\gamma_{j}(x)}^{Penalty\ term} \tag{18}\]  
where \(\bar{J}^{\prime}(x)\) is the new function to minimize, \(\bar{J}\) is the weighted objective function, \(x\) is the vector of design variables, \(m\) is the number of constraints, \(\gamma_{j}(x)\) is the infeasible constraint distance defined by (19), and \(\varpi_{j}\) is the penalty factor that can be static, dynamic, or adaptive. In engineering optimization problems, the term \(\varpi_{j}\) is usually defined with a static value.  
\[\gamma_{j}(x)=max(0,g_{j}(x)) \tag{19}\]  
Algorithm 4 describes the operation of PF. This constraint-handling technique requires three sets of solutions (three input arguments). At each iteration \(G\), those solutions are compared according to the new function \(\bar{J}^{\prime}(x)\) (18). The best solutions are stored in the output arguments.  
```
1:Function \(fncCHT(\bar{a},\bar{b},\bar{c})\)
2:Input: \(\bar{a}\in X_{G}\) and \(\bar{b}\in U_{G}\)
3:Output: \(X_{G+1}\) and \(x^{best}\)
4:\(X_{G+1}=\bar{a}\) and \(x^{best}=\bar{c}\)
5:for\(i=1\) to \(NP\)do
6:if\(\bar{J}^{\prime}(\bar{b}_{i})\) \(\bar{J}^{\prime}(\bar{a}_{i})\)then
7:\(X_{G+1}=\bar{b}_{i}\)
8:if\(\bar{J}^{\prime}(\bar{b}_{i})\) \(\bar{J}^{\prime}(x^{best})\)then
9:\(x^{best}\leftarrow\bar{b}_{i}\)
10:endif
11:endif
12:endfor
```  
**Algorithm 4** Penalty function pseudocode.
# Shafting Torsional Vibration Analysis of 1000 MW Unit under Electrical Short-Circuit Fault
###### Abstract  
Taking a 1000 MW turbine generator as the research object, the short-circuit fault in electrical disturbance is analyzed. Since it is very difficult to carry out fault analysis experiments and research on actual systems, simulation analysis is one of the more effective means of electrical fault diagnosis; the simulation's results approach the actual behavior of the system and are ideal tools for power system analysis, and can provide an empirical basis for practical applications. The short-circuit fault model of the SIMULINK power system is built to analyze the two types of faults of generator terminals short-circuit and power grid short-circuit. The impact load spectrum, fault current and speed fluctuation between low-voltage rotors were extracted and analyzed. The conclusion is that the impact value of electromagnetic torque at the generator terminal is greater than that on the power grid side. The impact value of a two-phase short-circuit at the generator terminal is the largest, and that of a three-phase short-circuit on the power grid side is the smallest. The transient impulse current of a three-phase short-circuit at any fault point is greater than that of a two-phase short-circuit; the impulse current of the grid side short-circuit is much greater than that of the generator terminal short-circuit; the speed fluctuation and fluctuation difference caused by the three-phase short-circuit in the grid side are the largest. The alternating frequency of the transient electromagnetic force of the four kinds of faults avoids the natural frequency of the torsional vibration of the shaft system, and the torsional resonance of the shaft system in the time domain of the short-circuit fault will not appear. However, after the fault is removed, the residual small fluctuation torque in the system has a potential impact on the rotor system. This research shows an analysis of the structural integrity and safe operation of turbine generator units after a short-circuit fault, which can not only be applied to engineering practice, but also provide a theoretical basis for subsequent research.  
turbo-generator set; electrical short-circuit; shafting torsional vibration; fault diagnosis; simulation analysis +
Footnote †: journal: Applied Sciences
applied sciences Article  
MSSI System Combined with Dispersion-Managed Link Configured with Random-Based RDPS Differently Controlled by Fiber Length  
[PERSON] and [PERSON],*()  
1 Department of Electronic Engineering, Gachon University, Seongnam 13120, Republic of Korea;  
[EMAIL_ADDRESS]  
2 Division of Navigational Information System, Mokpo National Maritime University,  
Mokpo 58628, Republic of Korea  
* Correspondence: [EMAIL_ADDRESS]  
We numerically demonstrate the dispersion map configured by random-based residual dispersion per span (RDPS) applied into the mid-span spectral inversion (MSSI) system to mitigate the impact of chromatic dispersion and the fiber nonlinearity in wavelength division multiplexed (WDM) signals. The dispersion map proposed was a scheme in which the RDPS of all optical fiber spans in the front section of the midway optical phase conjugator (OPC) are randomly selected, and the arrangement order of the RDPS in the rear section is inverted from that of the front section. Numerical simulations were performed by evaluating the compensation of the distorted 960 Gb/s WDM signal as a function of the variation of the DCF length and the SMF length, which are involved in determining RDPS. It was confirmed that the compensation effect of the proposed dispersion maps has improved compared to the conventional dispersion map since the dispersion maps examined in this paper have antipodal symmetry around the midway OPC. In particular, it was confirmed that the method of randomly determining the RDPSs by varying the DCF length slightly improved system performance compared to the variation of SMF lengths. We also found that the feature of the RDPS random distribution patterns can achieve excellent compensation for the distorted WDM signal through 50 iterations.  
dispersion management; mid-span spectral inversion; dispersion map; optical phase conjugator; residual dispersion per span; random distribution; chromatic dispersion; nonlinear Kerr effect; wavelength division multiplexed
## 2 Experimental Setup
### Test Conditions  
In the baseline experiments, where measurements were performed on the smooth profile, two values of freestream velocity produced by the wind tunnel were investigated: 10 m/s and 20 m/s, corresponding to Reynolds numbers relative to the chord length of 100,000 and 200,000, respectively. The angles of attack investigated were 16\({}^{\circ}\) and 24\({}^{\circ}\).  
At a \(Re=100,000\) and \(\alpha=16^{\circ}\), the frequencies tested were \(f=35.0\), 44.2, 66.6, 133.3, 199.9, 266.6, 500.0, 666.6 and 1058.0 Hz, which correspond to the values of reduced frequency of \(F_{c}^{+}=1\), 2, 3, 4 and 10. At the same \(Re\) and \(\alpha=24^{\circ}\), the frequencies tested were \(f=34.0\), 66.6, 133.3, 199.9, 266.6, 400.0 and 700.0 Hz with values of reduced frequency of \(F_{c}^{+}=1\), 2, 3, and 4.  
At a \(Re=200,000\) and \(\alpha=16^{\circ}\), the frequencies tested were \(f=33.6\), 41.0, 133.3, 174.4, 266.6 and 533.3, which give the values of reduced frequency of \(F_{c}^{+}=1\), 2, 4. At the same \(Re\) and \(\alpha=24^{\circ}\), the frequencies tested were \(f=69.0\), 133.3, 266.6, 400.0, 470.0, 533.3 and 1333.3 Hz and included the values of reduced frequency of \(F_{c}^{+}=1\), 2, 3, 4 and 10.  
The choice for the frequencies was made by examining the power spectra of the velocity field, without plasma actuation, obtained by using Hot Wire Anemometry and taking values of reduced frequency that were known to be effective. Further details of this procedure follow in the next section.
# Real-Time Image Analysis for Intelligent Aircraft De-Icing Decision Support Systems
## 3 Research Methodology
### The Process of Training, Testing, and Validating Models  
Taking technological capabilities into account, it was decided to conduct two AI model training processes: an automatic process and a manual process. The first was carried out using the Roboflow training platform. The second was performed manually using custom code and the Google Colab computational environment.
# Towards a Catalogue of Self-Sovereign Identity Design Patterns
## 3 Self-Sovereign Identity Design Patterns and Their Classification
### Methodology  
To verify that no further progress has been made in this area in the years since the SMS was conducted in 2021, and that no additional research has been carried out, we searched the digital databases IEEE Xplore, Science Direct, and ACM DL, using the search string _(\"Self-Sovereign Identity\" OR \"Self Sovereign Identity\" OR \"Decentralised Identity\" OR \"Blockchain Identity\") AND (\"Design Pattern\" OR \"Design Patterns\" OR \"Design Principle\" OR \"Design Principles\" OR \"Good Practice\" OR \"Good Practices\")_. Thus, in the initial phase, we conducted a literature review which resulted in the identification of two sources, from which we obtained a set of twelve SSI design patterns [2,14]. Based on preliminary research and existing knowledge, we identified shortcomings in the defined patterns and recognised the possibility of expanding or supplementing the set of patterns. Consequently, a review and analysis of the technical documentation followed. We focused on the extensive European Self-Sovereign Identity Framework (ESSIF) documentation by the European Blockchain Services Infrastructure (EBSI) [26], Decentralised Identity Foundation (DIF) [27] and the World Wide Web Consortium (W3C) [7,8,16] documentation and standards, and, later, the Veramo [28] and Sovrin documentation [29]. The review and analysis were carried out in March and April 2022, and resulted in the identification of an additional twenty-three patterns. We synthesised the findings carefully and defined all the patterns based on the [PERSON] template [17]. For each identified pattern, we defined (i) name, (ii) summary, (iii) context, (iv) problem, (v) forces, (vi) solution, (vii) example of application, (viii) resulting context, (ix) related patterns, and (x) known use cases. Additionally, each pattern was also classified into a representative category. The process described is visualised in Figure 3.
# Big-Delay Estimation for Speech Separation in Assisted Living Environments
## Appendix A Hough Transform as a Log-Likelihood  
Consider a data set of \(N\) collinear points in the image feature space in Figure 2a,  
\[\mathbf{x}=((x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{N},y_{N}))^{T}. \tag{1}\]Given this set of points, we consider the problem of maximizing the likelihood function with respect to the pair of parameters, \([\rho,\phi]\). Assuming a Gaussian i.i.d. noise model with variance \(\sigma\), the likelihood function is given by:  
\[p\big{(}\mathbf{x}\big{|}[\rho,\phi]\big{)}=L\big{(}\mathbf{x}\big{|}(\rho,\phi )\big{)}=\prod_{i=1}^{N}p\big{(}(x_{i},y_{i})\big{|}(\rho,\phi)\big{)}=\prod_{i =1}^{N}\frac{1}{\sqrt{2\pi\sigma^{2}}}\exp\bigg{(}\frac{(\rho-x_{i}\cos(\phi)- y_{i}\sin(\phi))^{2}}{2\sigma^{2}}\bigg{)}. \tag{10}\]  
The log-likelihood, \(\mathcal{L}\big{(}\mathbf{x}\big{|}(\rho,\phi)\big{)}\), is a more convenient form to work with. The voting procedure that maximizes the log-likelihood function, the MLE is,  
\[[\hat{\rho},\hat{\phi}]=\operatorname*{argmax}_{[\rho,\phi]}\mathcal{L} \big{(}\mathbf{x}\big{|}(\rho,\phi)\big{)}, \tag{11}\]  
and corresponds to the accumulator cell that receives the highest vote.
## 2 Antenna Design and Analysis
### Antenna Array Configuration
However, with a further increase in spacing (D = 50 mm), the total coverage of the intended bands (WiMAX and 5G New Radio (NR) n78 and n96) was lost. Beyond the optimal spacing, the reduction in mutual coupling compromised the antenna's ability to resonate effectively in both bands, leading to reduced gain and inadequate coverage.
# C Band 360\({}^{\circ}\) Triangular Phase Shift Detector for Precise Vertical Landing RF System
## 3 Circuit Design
### Circuit Adjustments  
In the first tests, oscillations in the passband occurred when each of the branches was connected independently. The unstable behavior increased when all three branches were connected. Therefore, attenuators were placed between the amplifier stages to improve stability in addition to reducing the gain of each amplifier (23 dB per amplifier) to a maximum gain of 52 dB, i.e., a theoretical dynamic range of the input signal varying from \(-52\) dBm to 3 dBm. However, the maximum level is reduced to \(-2\) dBm to avoid damage to the first input amplifier, leaving a dynamic range of 50 dB. This dynamic range is 20 dB higher than that achieved in [25], which uses the AD8302 circuit and had a proper performance between \(-10\) dBm and \(-40\) dBm. Figure 9 shows a photograph of the circuit board where the triangular layout of the RF input connectors (white lines) and the three areas containing the devices that make up each of the branches of the circuit (red lines) have been highlighted. The location of the main components of each branch is detailed in Figure 10.
# Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems
## 2 Background  
The design of fault diagnostic engines has to address many of the challenges common to ICPSs ([PERSON] et al., 2013). By the start of the 1960s, the first general-purpose ECSs had been developed for General Motors' automotive assembly lines ([PERSON] and [PERSON], 2006; [PERSON] and [PERSON], 2006). Referred to as Programmable Logic Controllers (PLCs), they controlled individual machines in isolation. Industrial plants rapidly became _systems-of-systems_([PERSON] and [PERSON], 2006), with connected production planning and management that demanded up-to-date information from the factory floor. Increasingly sophisticated manufacturing processes required more complex PLC capabilities. However, connecting multiple PLCs together and co-ordinating them was in itself complex. The term Cyber-Physical System (CPS) originated in the work of [PERSON] and [PERSON] in 2006 to describe the new device architectures that were emerging to network devices together to better control their environments ([PERSON] and [PERSON], 2006). As the scale of CPS architectures expanded, the term Industrial Cyber-Physical System (ICPS) was adopted to distinguish them from simpler consumer CPSs ([PERSON] and [PERSON], 2006).  
ICPSs bridge the divide between their \"cyber\"computational parts and their \"physical\" real-world environments. At this boundary, the discrete behavior of computational programs has to co-exist with the non-discrete, time-critical behaviors that drive real-world activities ([PERSON] and [PERSON], 2006). Figure 1 shows a typical entity in an ICPS, a Franka Emika Panda Collaborative Robot ([PERSON] and [PERSON], 2006). Collaborative Robots or \"cobots\" often share production line assembly operations with humans. Cobots perform a wide range of pre-programmed tasks but can autonomously modify their behavior in real-time. They detect the presence of nearby objects in their work area and capture feedback from their environment. They make control decisions, often exchanging telemetry over networks. Sensors convert physical characteristics such as orientation, proximity and velocity into electrical signals that can be processed by their control computers ([PERSON] and [PERSON], 2006).  
Mechanical actuators are transducers that enable an ICPS to move physical objects in its environment [1]. Motors are special classes of actuators that create translational or rotational movement. They allow cobots to rotate through multiple degrees of freedom with high precision. At all times, cobots have to honor hazard prevention protocols when working in close collaboration with humans or other cobots. This ability to operate in safety-critical environments is a core requirement demanded of many ICPSs. [PERSON] comments that the emergence of distributed ICPSs and CPSs is far more ubiquitous [25]. The same cyber-physical concepts that implement large-scale ICPSs also apply to smaller, less complex consumer and medical CPSs.