esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mixed Method with Effective Color Reduction
## 1 Introduction
Some methods of this type applied to color quantization are the Particle swarm optimization algorithm [37; 38], the Ant-tree for color quantization (ATCQ) method [39], the Iterative ant-tree for color quantization (ITATCQ) method [40], the Artificial bee colony algorithm combined with K-means [41], the Artificial bee colony algorithm combined with ATCQ [42], the Firefly algorithm combined with ATCQ [43] and the Shuffled-frog leaping algorithm [44]. Other swarm-based methods applied to color quantization are summarized in [45]. Some other methods combine the splitting approach with the clustering-based approach, such as the solution proposed in [46], which combines the Binary splitting (BS) method with the ATCQ algorithm. The resulting method, called BS+ATCQ, combines two existing color quantization techniques and generates better images than each of the two methods applied separately. In addition, it also generates better quality images than other well-known color quantization methods. BS and ATCQ define a tree structure to solve the color quantization problem. When BS+ATCQ is applied, the leaves of the binary tree generated by BS are used to define the initial nodes of the tree considered to apply ATCQ. In general, splitting methods are faster, but clustering-based methods can generate better images. BS+ATCQ takes advantage of both features, since it uses BS to quickly generate an initial solution and then applies ATCQ to improve this solution. This article proposes the introduction of an improved version of the BS+ATCQ algorithm, by replacing the ATCQ algorithm with the ITATCQ algorithm. The ITATCQ method is based on the ATCQ algorithm but generates better quantized images [40]. Therefore, it is expected that the result obtained by combining ITATCQ with BS will generate better images than those obtained using the initial combination of BS and ATCQ. This introduction describes some interesting color quantization methods, but obviously it cannot discuss all existing solutions. For a more detailed description of color quantization methods and their operations, [47] can be consulted. This article describes other methods that have been applied to reduce the colors of an image, such as vector quantization, genetic algorithms or competitive learning. The rest of the article is organized as follows. Section 2.1 describes the BS+ATCQ method, along with the two algorithms used to define it. The ITATCQ algorithm is described in Section 2.2. Then, Section 3 describes the method proposed in this article, while the results obtained after applying it to a set of 24 color images are discussed in Section 4. The proposed method is compared to other color quantification techniques. The quality of the images generated by the different methods is not calculated by means of an error measure based on human perception, but by an error measure that considers the images as arrays of points that are compared with each other. The article finishes by outlining the conclusions drawn.
## 2 Research Data and Research Methods
### Research Methods  
#### 2.3.1 Sen's and Mann-Kendall Trend Test  
Sen's Slope Estimator is a robust non-parametric statistical method for trend analysis in long time-series data. It is favored for its resilience to outliers and lack of assumptions about data distribution, allowing it to extract trend information directly from data. Due to these features, it is widely applied in climate and vegetation change studies [18]. The calculation formula is as follows:  
\[Slope=median(\frac{x_{i}-x_{j}}{i-j}),\forall i>j \tag{1}\]  
In the formula, \(x_{i}\) and \(x_{j}\) are the values of meteorological elements for the \(i\) and \(j\) years, respectively, and _median_ belongs to the median function. When \(Slope>0\), it indicates an upward trend. When \(Slope=0\), it means that the elements remain basically unchanged. If \(Slope<0\), it indicates a downward trend.  
The Mann-Kendall trend test, a non-parametric statistical method, is widely used for analyzing non-linear trends in non-normally distributed variables due to its distribution-free nature [19]. Recognized by the World Meteorological Organization (WMO) as an effective non-parametric trend analysis tool, it is particularly suited for long-term trend analysis of meteorological and hydrological sequences. In this study, the Mann-Kendall test is employed to identify and quantify potential trends in meteorological variables, thereby providing a rigorous statistical framework for understanding the impact of climate change on the stability of arid ecosystems. The calculation formula for the MK trend test statistic is as follows:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\text{sgn}\big{(}x_{j}-x_{i}\big{)} \tag{2}\]  
\[\text{sgn}\big{(}x_{j}-x_{i}\big{)}=\begin{cases}+1&if\big{(}x_{j}-x_{i}\big{)} >0\\ 0&if\big{(}x_{j}-x_{i}\big{)}=0\\ -1&if\big{(}x_{j}-x_{i}\big{)}<0\end{cases} \tag{3}\]  
\[\sqrt{var(S)}=\frac{1}{18}\Bigg{[}n(n-1)(2n+5)-\sum_{q=1}^{p}t_{q}\big{(}t_{q} -1\big{)}\big{(}2t_{q}+5\big{)}\Bigg{]} \tag{4}\]  
\[Z_{mk}=\begin{cases}\frac{S}{\sqrt{Var(S)}}&if\;S>0\\ 0&if\;S=0\\ \frac{S-1}{\sqrt{Var(S)}}&if\;S<0\end{cases} \tag{5}\]In the formula, \(x_{i}\) and \(x_{j}\) represent the values of the time series in the \(i\) and \(j\) years, sgn is a symbolic function, \(n\) is the length of the time-series data, \(t_{q}\) corresponds to the \(q\) term, and \(z_{mk}\) represents the trend of the time-series data.
# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
## 3 Mine Subsidence Susceptibility/Hazard Assessment and Mapping  
Table 2 summarizes the map type, analytical methods and techniques, verification/validation method, and spatial resolution in each study on mine subsidence susceptibility (or hazard) mapping in a GIS environment [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45]. Despite the aforementioned difference between subsidence susceptibility and hazard maps, all studies on the GIS-based mining subsidence assessment used the term subsidence hazard map in their title [23; 25; 28; 30; 31; 34; 35; 36; 39; 40], even though most of the papers used subsidence maps. Thus, the susceptibility and hazard studies were integrated and analyzed together in this paper.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Type of Maps**} & \multicolumn{1}{c}{**Characteristics**} \\ \hline \multirow{2}{*}{Inventory map} & \(\cdot\) & Locations, type, abundance, activity, date of (subsidence) events \\  & \(\cdot\) & Used to validate prediction accuracy of resulting (subsidence) susceptibility map \\ \hline \multirow{2}{*}{Susceptibility map} & \(\cdot\) & Zonation of the relative spatial probability of future (subsidence) events \\  & \(\cdot\) & Ranks the stability of an area in categories that range from stable to unstable \\ \hline \multirow{2}{*}{Hazard map} & \(\cdot\) & Zonation of the spatio-temporal probability of future (subsidence) events \\  & \(\cdot\) & Hazard = Magnitude \(\times\) Probability (of subsidence events) \\ \hline \multirow{2}{*}{Risk map} & \(\cdot\) & Expected damage or losses caused by (subsidence) events \\  & \(\cdot\) & Risk = Probability (of subsidence) \(\times\) Element at risk \(\times\) Vulnerability \\ \hline \hline \end{tabular}
\end{table}
Table 1: Type and characteristics of engineering geological maps in terms of mining subsidence (modified from [PERSON] et al. [17]).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Map Type** & **Analytical Methods and Techniques** & **Verification/Validation** & **Spatial Resolution** & **Country** \\ \hline [PERSON] and [PERSON] [23] & Hazard & Wei 1  & AUC (ROC) & \(1\,\mathrm{m}\times 1\) m & Republic of Korea \\ \hline [PERSON] et al. [24] & Susceptibility & Wei, Sensitivity analysis & AUC (CFD) & \(1\,\mathrm{m}\times 1\) m & Republic of Korea \\ \hline [PERSON] et al. [25] & Hazard & FR 2 & AUC (SRC) & \(1\,\mathrm{m}\times 1\) m & Republic of Korea \\ \hline [PERSON] et al. [26] & Susceptibility & PR, Radius of influence & AUC (CFD) & \(1\,\mathrm{m}\times 1\) m & Republic of Korea \\ \hline [PERSON] et al. [27] & Susceptibility & Certainty factor analysis, & AUC (SRC) & \(1\,\mathrm{m}\times 1\) m & Republic of Korea \\ \hline [PERSON] et al. [28] & Hazard & ANN 3 [FOOTNOTE:3]Footnote 3:
# Multidisciplinary Optimization of Aircraft Aerodynamics for Distributed Propulsion Configurations
## 5 Research Challenges and Future Trends
### Scheme of Aero-Propulsive Configuration  
Optimization cannot be made out of nothing;an idea that does not exist cannot be optimized. In the case of satisfying the node requirements, optimization can modify a  
Figure 6: Conventional and reform (regional/distributed) propulsion configuration in TWB and BWB.  
square to a more suitable shape (circle) for rolling, but it is unable to turn a circle into a donut. The actual mechanism of DP would be limited if the general propulsor is composed and conceived as a DP system. Nowadays, DP is regarded as a scheme of dividing a large propulsor into several propulsors [8]. In fact, DP should not be limited by quantity. As long as the wake can be completely filled by thrust without generating excess wake, it can be a perfect DP. An ideal DP is one filling the wake at the trailing edge of an aircraft by nearly 100% [85]. However, when thrust is activated, a residual called \"anergy\" is leftover. This phenomenon causes the launch energy to not be fully converted into thrust. Furthermore, the circular propulsors are not in contact with each other when juxtaposed to form a DP [41; 86; 87]. Thus, achieving perfect thrust-drag balance with standard propulsors is unrealistic.  
The cross-flow fan proposed by FanWing Ltd could produce airflow and evenly cover the entire trailing edge. This configuration does not present discontinuous thrust distribution. Its shape and energy method also completely subverts the conventional form [39]. Currently, there are two difficulties in implementing cross-flow fans on BWBs. One is that it has not yet met the thrust requirements of large size BWBs. The second is that its thrust can only be output monotonically and uniformly through the elongated cross-flow fan, which does not solve the problem of the thrust-drag balance between the shape and DP of a BWB. From the perspective of a conventional DP layout, the position and distance of the propulsors, exhaust nozzles, fan distribution, jet flow, and control systems affect the thrust magnitude and thrust-drag balance in the different areas of an aircraft. A feasible approach that can improve these variables to improve propulsion efficiency, noise reduction, and flight range is to adjust the thrust-drag balance using MDO.
## 4 Model Implementation and Simulation
### Simulation Settings  
In our simulations, we analyze the system behavior with the nodes placed in the positions presented in topology 1 (Figure 8a) and topology 2 (Figure 8b). The simulation lasted 18,000 s, and we switched from topology 2 to topology 1 in the middle of the simulation (i.e., at time 9000 s) by adding the link from node 1 to node 3 and changing the packet success probability per link and the transition probabilities of every link accordingly. The behavior of the three communication stacks depicted in Figure 8 is analyzed. All stacks use a constant bitrate application layer, static routing with all nodes transmitting to their 1-hop neighbors and a time division multiple access (TDMA) MAC layer. The first stack (Figure 8a) uses the legacy DESERT physical layer, the second stack (Figure 8b) uses the two-state HMM-based physical layer and, finally, the third stack (Figure 8c) employs the three-state HMM-based physical layer.  
The network is composed of 4 nodes and each node generates 28 bytes packets every 60 s. Bandwidth and carrier frequency are set to 5 kHz and 25 kHz, respectively, in order to best simulate the behavior of the modems used in the field experiment presented in Section 2. The simulation parameters are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nodes** & **Pkt Size** & **Tx Duration** & **Tx Power** \\ \hline
4 & 28 B & 18,000 s & 165 dB \\ \hline
**Frequency** & **Bandwidth** & **Bitrate** & **Cbr Period** \\ \hline
25 kHz & 5 kHz & 600 bps & 60 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
Figure 8: The three communication stacks compared in simulation, all composed of a constant bitrate application layer, static routing, and TDMA, and a different physical layer: UwHMMPHysical (**a**), UWHMMPhysical (**b**), and UWHMMPhysicalExt (**c**).  
The TDMA MAC is configured with a frame duration of 8 s, equally divided between the four nodes that have a time slot of 2 s each to transmit their packets. A guard time of 0.8 s is used to avoid interference caused by the propagation time and to consider possible synchronization errors between the nodes.  
At the end of the simulations we observed the performance of each link of the network by computing PER and throughput averaging over 50 simulation runs and presenting the 95% confidence interval (CI).
# Exploring the Evolution of Autonomous Vehicle Acceptance through Hands-On Demonstrations
## 4 Results and Discussion
### Future Users Survey Results
#### 4.2.1 Last-Mile Delivery Use Case  
In this section, the analysis of results focuses on the survey segment dedicated to the technology of autonomous mobile systems used as a parcel delivery service for what in the logistics sector is called the \"last mile\", i.e., the final part of a product's supply chain to a final customer.  
As mentioned in Section 3, this technology, directly linked to autonomous driving, will become a reality in society even before systems such as robotaxis [36]. It has been decided to include this technology in the study performed because it is part of autonomous mobility, it is an incipient technology in the market, it is linked to the insurance sector, it interacts with open traffic on public roads, and because of its social impact. Thus, statistics such as those shown in Figure 7 allow us to evaluate the degree of acceptance and inclusion of this technology in society. According to the survey conducted, if we look at the diagram on the left, the results suggest that this type of services linked to autonomous and intelligent mobility are highly demanded by society, so much so that 76% of respondents state that they like the idea of delivery companies using autonomous vehicles to make deliveries.  
In contrast, when it comes to robotaxi services, as shown in the diagram on the right, user confidence drops to 58% when answering whether they would feel comfortable interacting with or taking a ride in an autonomous vehicle. This difference suggests that autonomous driving has yet to overcome the barrier of user acceptance when the use of such technology implies that the user himself is embarked in the vehicle, meaning that in the event of an incident there is a potential risk to human life and safety.  
This analysis becomes clearer when looking at the following diagram, Figure 8. As can be seen in the figure, the fact that companies in the logistics sector implement small autonomous robots for last-mile delivery does not lead to people rejecting the service, given that a large majority, 85%, state that they would continue to use home delivery services to the same extent as if this technology was not introduced.  
In this way, it can be concluded that potential users of autonomous transportation technologies are willing to accept such technologies as they consider them to be beneficial for the progress of society. However, to achieve widespread acceptance of self-driving passenger cars and robotaxis, it is necessary to demonstrate to the public that these vehicles are indeed safer than human drivers.
# Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
## Experiments and Discussion
### Data Preprocessing and Model Design
#### 5.2.2 Model Design  
The configuration of the two-dimensional CNN consisted of two convolutional layers, two pooling layers, three fully connected layers, and a softmax regression. Cross-entropy was employed as the loss function, and several fundamental parameters of the CNN model are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Variable and Dimension** & **Training Parameters** \\ \hline Convolutional layer 1 & CW = 5; CH = 5; CN = 32 & Padding = ‘same’ \\ Pooling layer 1 & S = 2 & MaxPooling \\ Convolutional layer 2 & CW = 5; CH = 5; CN = 64 & Drop out = 0.5 \\ Poling layer 2 & S = 2 & MaxPooling \\ Fully connected layer 1 & Nodes = 1024 & Training sample rate = 80\% \\ Fully connected layer 2 & Nodes = 512 & Max epoch = 60 \\ Fully connected layer 3 & Nodes = 64 & Learning rate = 0.0001 \\ softmax regression layer & Nodes = 4 & active function = ReLU \\ \hline \hline \end{tabular}  
* CW = filter width; CH = filter hight; CN = number of filter; S = width of pooling.  
\end{table}
Table 1: Key parameters of selected CNN model.  
Figure 4: Angular-domain recurrence plots: (**a**) normal (\(m=5\), \(\delta=1\), \(\varepsilon=0.03\)); (**b**) planet gear-root crack (\(m=3\), \(\delta=1\), \(\varepsilon=0.03\)); (**c**) sun gear with a tooth-root crack (\(m=6\), \(\delta=2\), \(\varepsilon=0.01\)); (**d**) planet carrier with crack (\(m=3\), \(\delta=1\), \(\varepsilon=0.03\)).
## 2 Results and Discussion
### Method Validation of HS-SPME GC\(\times\)GC-TOFMS  
The analytical procedure for the extraction of volatile compounds was validated according to the recommendations of ISO 17,025 and the International Council for Harmonisation (ICH) Guideline Q2 (R1) [44; 45]. Under the optimum experimental conditions, the validation of the proposed HS-SPME GC\(\times\)GC-TOFMS method involving HS-SPME followed by GC\(\times\)GC-TOFMS was accomplished.  
The precision of the method was evaluated by assessing repeatability (intra-day) and intermediate precision (extra-day). Precision was expressed as the coefficient of variation (CV). The method precision values, expressed as % CV, of the developed method ranged from 1.91% (2 PF) to 26.90% (PYR) for repeatability (\(n=9\)), and 7.32% (OCA) to 37.36% (PEN) for intermediate precision (\(n=3\times 3\)). The result confirmed that acceptable precision for the extraction method had been achieved.  
A certified reference material was not available for the studied compounds in rice matrices; consequently, definitive statements cannot be made with regard to accuracy. Nonetheless, the extraction recovery (%R) was determined after evaluating the results from spiked rice samples with standards. The recoveries related to the spiked standards on rice samples ranged from 78.79% (2 PF) to 96.86% (OCT). These results show that the developed extraction method is applicable for the assessment of studied volatile compounds.
# Technology Behavior Model--Beyond Your Sight with Extended Reality in Surgery
## 6 Implications  
With the global spread of COVID-19, healthcare systems in many places are collapsing due to the dramatic increase in patients and to protect medical experts and reduce work stress. Extended reality devices are important to technology development units around the world [51]. Smart healthcare has been explored in-depth and smart glasses have become a prominent representative of many smart products. It can be used in all phases of the healthcare system, including remote consultations, room visits, monitoring, and surgery. In particular, there is a need to ensure contact-free contamination during surgery due to the sterile environment to improve the surgeon's eyesight, free up the surgeon's hands, and better complete the surgery. Specific contributions are illuminated in the following subsections.
## 3 Materials and Methods
### Green Material Characterization
#### 3.6.4 Particle Size Distribution Analysis  
The particle size distribution was determined using a Malvern Instruments Mastersizer 2000 laser scattering analyzer (particle size range: 0.02 to 2000 \(\upmu\)m). The absolute particle size distribution of MSWI-FA and W-FA is presented in Table 7 below.  
The absolute particle size distribution of GGBFS and MS is reported (Figure 8c,d below).  
GGBFS exhibits a monomodal absolute particle size distribution, and the representative diameter of the particle population is 33.2 \(\upmu\)m. The marble sludge appears to have a monomodal particle size distribution, and the representative diameter of the particle population is 15.5 \(\upmu\)m.  
\begin{table}
\begin{tabular}{c c c} \hline
**Particle Size Distribution (\%)** & **MSWI-FA** & **W-FA** \\ \hline \(<\)1 \(\upmu\)m & 0 & 0 \\
1–10.5 \(\upmu\)m & 5.84 & 4.75 \\
10–20 \(\upmu\)m & 9.28 & 6.30 \\
20-49 \(\upmu\)m & 29.35 & 16.73 \\
49–80 \(\upmu\)m & 13.66 & 8.89 \\
80–120 \(\upmu\)m & 8.29 & 6.03 \\ \(>\)120 \(\upmu\)m & 33.58 & 57.30 \\ \hline \end{tabular} Bimodal particle size distribution is observed for both MSWI-FA and W-FA, with the main modal values equal to 66.9 \(\upmu\)m and 454 \(\upmu\)m for MSWI-FA and 42.8 \(\upmu\)m and 916 \(\upmu\)m for W-FA (Figure 8a,b below). In particular, for MSWI-FA the D10 is 14.8 \(\upmu\)m, the D50 is 63.7 \(\upmu\)m, and the D90 is 131 \(\upmu\)m, while for W-FA the D10 is 19.6 \(\upmu\)m, the D50 is 198 \(\upmu\)m, and the D90 is 1320 \(\upmu\)m.  
\end{table}
Table 7: Particle size distribution of MSWI-FA and W-FA.  
Figure 8: Particle size distribution analysis of (**a**) MSWI-FA, (**b**) W-FA, (**c**) GGBFS, and (**d**) MS.  
3.6.5. Leaching and pH Assessment of MSWI-FA  
Table 8 summarizes the results regarding pH, chloride and sulphate concentrations of untreated MSWI-FA and W-FA, as well as the same values regarding the water resulting from the washing procedure.  
MSWI-FA shows a high chloride content and low sulfate content. The residual chloride content in the W-FA was not significantly lower than the initial levels observed in untreated FA. In contrast, the sulfate content in W-FA was even higher compared to MSWI-FA. A notable increase in calcium content in the washed FA can be attributed to the concentration of this element, resulting from mass loss during the washing procedure due to the solubilization of salts.