esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Audio-Visual Factors in the Evaluation of Crowd Noise
## 3 Results
### Effect of Visual Crowd Density on Sound Environment Evaluation  
Table 3 shows the overall evaluations under the simultaneous effect of sound and vision. When the crowd density is constant, participants' visual comfort and preference as well as acoustic comfort and sound preference decreased as the vocal volume increased. Figure 5 shows the sound preference values for different crowd densities in the same scene with different conversational sound pressures; that is, the values in the presence of both auditory and visual stimuli. (The error bars represent the S.D. of the average values.) Only when the sound volume was medium, were the loudness evaluations significantly different for the empty scene and the scene with a moderate crowd; the empty scene appeared louder (\(p<0.05\)).  
Figure 5: The mean ratings of evaluation indicators under the effect of visual crowd density.  
There was an interaction between crowd density and volume level for acoustic comfort and visual comfort. In scenes with low and high crowd densities, the scores for both types of comfort were higher for moderate volume levels than for high (\(p<0.01\)). In scenes with medium and high volumes, visual comfort scores were higher when the crowd density was moderate than when it was high (\(p<0.05\)). In scenes with low volume, acoustic comfort scores were higher for scenes with low crowd density than those for with high crowd density. That indicates that even though the sound is not loud, the crowd density of the visual environment still significantly affects the overall sound preference evaluation (\(p<0.05\)). There is also an interactive relationship between sound preference and visual preference. There was no mutual influence between low and moderate volumes in scenes with low and moderate crowd densities. However, the relationships between high volume and low and moderate crowd densities were significant, with higher scores for visual and sound preferences when the volume level was low (\(p<0.01\)). Scenes with low crowd density had higher visual preference scores than the other two scenes for all three volume levels.
## 2 Materials and Methods
### Parameter Selection
#### 2.2.3 Region Classification  
The region classification is used to categorize fixed-wing UAVs, based on the continent of origin. Note that the authors decided to exclude the United States of America and Israel from their respective continents and categorize them as separate regions, since both countries exhibit a large number of different UAV designs throughout the years and are widely recognized as pioneering nations in this field. This claim is supported by the data  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **Category** & **GTOW** & **Employment** & **Altitude** & **Mission Radius** & **Example** \\ \hline \multirow{3}{*}{Class I} & Micro & \textless{}2 kg & Tactical subunit & \textless{}200 ft & \textless{}5 km & Dragon Eye \\  & Mini & 2 kg–15 kg & Tactical subunit & \textless{}3000 ft & \textless{}25 km & EOS \\  & Small & 15 kg–150 kg & Tactical unit & \textless{}5000 ft & \textless{}50 km & Orbiter \\ \hline \multirow{2}{*}{Class II} & \multirow{2}{*}{Tactical} & \multirow{2}{*}{150 kg–600 kg} & Tactical & \multirow{2}{*}{\textless{}18,000 ft} & \multirow{2}{*}{LOS} & \multirow{2}{*}{Shadow} \\  & & & formation & & & \\ \hline \multirow{3}{*}{Class III} & MALE & \textgreater{}600 kg & Operational & \textless{}45,000 ft & BLOS & X47 \\  & HALE & \textgreater{}600 kg & Strategic & \textless{}65,000 ft & BLOS & Global Hawk \\ \cline{1-1}  & Strike & \textgreater{}600 kg & Strategic & \textless{}65,000 ft & BLOS & Avenger \\ \hline \hline \end{tabular}
\end{table}
Table 2: NATO UAV classification.  
shown in Figure 2, where the UAV models per region are presented as a percentage of the total sample. USA and Israel feature double digits, surpassing entire continents, such as Africa and Oceania.  
Summing up, the region classes of this study are:  
1. Africa;
2. America;
3. Asia;
4. Europe;
5. Oceania;
6. USA;
7. Israel.
# Derivation and Validation of Bandgap Equation Using Serepentine Resonator
## 4 Experimental Validation
### Vibration Transmission  
Figure 10 shows the vibration transmission experimental setup to analyze the vibration response of the fabricated serpentine resonators. A steel beam with dimensions \(300\times 50\) mm is used as the host structure. The serpentine resonators are arranged in a \(2\times 10\) array in intervals of 25 mm on a 1 mm-thick steel beam with silicone glue. The surface vibrational velocity of the serpentine resonators is measured using the impact hammer and accelerometer. To minimize the effect of mass, hammer excitation is performed instead of shaker excitation. The accelerometer is attached to a point 20 mm away from the end of the beam and the hammer point also vibrates at the same point. The steel beam is supported by wires so that all the edges are in the free-free boundary condition.  
Figure 11 displays the measurement result of the bare beam and serpentine resonator beam. The red and green lines represent the vibration responses of the two beams, respectively. The measurement result shows that the vibration response of the serpentine resonator is significantly reduced for the bandgap in the range of 200-290 Hz. In frequency  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **1** & **2** & **3** & **4** & **5** & **Average** \\ \hline Resonance [Hz] & 189 & 188 & 193 & 190 & 186 & 189 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measured values of resonance frequency.  
Figure 10: Experimental setup of vibration transmission.  
bands other than the bandgap, the peaks are shifted and reduced due to damping and mass effects. However, in the bandgap, the result shows that the dip is divided into several peaks because of the local modes of the serpentine resonator. The figure also shows that the measurement results for the bandgap region obtained by theoretical implementation and simulation are similar.
## Materials and Methods
### Data Preparation  
#### 2.2.1 Field Plot Data  
The main purpose of field-plot data is to identify and validate classified rice paddies. The field-plots are commonly selected as the area with uniform and homogeneous, which can be easy accessed by the road. Representative samples should be able to represent one of land use/cover types, so as to ensure that the geographic location of each class of pixel is accurate [14]. The spatial resolution of time-series Sentinel-1 data and the final rice map was 10 m. Thus, the representative field plot data is based on our field observations with homogeneous land cover in a minimum radius of 50 m [45] for each plot in this study.  
Sampled field-plot data was gathered from two field trips carried out at 542 locations during 24 February to 10 March (dry season) and 698 locations during 15-24 August (rainy season) in 2017. The 1240 locations in total were all recorded with the geographic coordinates from a handheld Trimble GeoXT3000 GPS receiver along with the observed cropping pattern or land cover categories and digital photographs (Figure 2). Furthermore, interviews with farmers were conducted to collect relevant information about crop planting, including the crop varieties, sowing density, fertilization amount, spraying conditions, crop growth phenology period (such as seeding, jointing, heading, booting, and harvesting), and the rice ecosystem (irrigated or rainfed). Ultimately, all observed field-plots were used as the validation points for verifying the accuracy of the final classification results in this study. In the end, 722 observation samples of rice paddies were obtained, and the remaining 518 sample plots were unified as the other land use/land cover types.  
Figure 1: Location of the study area in the Mun River basin in Thailand.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 2 An Overview of Rail Defects and of the Techniques for Their Detection
### Rail Diagnostics Techniques
#### 2.2.5 Radiographic Techniques  
Radiography is the only technique able to analyze visually the interior of a rail or a weld. This method can detect cracks, flaws, and thickness reduction in detail, but has safety hazard concerns, is expensive, and time consuming. For these reasons, it is often used to analyze rails; only after that have other NDT techniques detected a specific problem [5].
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 5 Performance Evaluation
### Experimental Setup  
The simulation of the proposed TACRP routing protocol was done in the NS2 simulator as well as the PC, where certain configurations were included, such as 4 GB RAM, (Fiat Chrysler Automobiles in Stellantis), Windows 10 OS (from Microsoft) in Redmond Washington, and the Intel I3 processor (Samsung in Suwon-si, south Korea). Network Simulation 2 (NS2) was based on two languages as from end language OTCL (Object Tool Command Language), back and C++. To check the animator view of the constructed network, a NAM (Network Animator) window was used. With the help of the trace file, the outcome of the network was measured. The simulation input parameters are described in Table 2.
## 3 Results
### Fractal Dimensions  
The fractal analysis can evaluate the self-similarity of all B-mode images by calculating their fractal dimensions. The process procedure for the B-mode thyroid nodule image is shown in Figure 3. A benign thyroid nodule (9.5 mm \(\times\) 5.5 mm) and a malignant thyroid nodule (8.6 mm \(\times\) 8.6 mm) were chosen to illustrate the estimation procedure. The thyroid nodule was picked up from the B-mode image for further processing. Then, morphological operations, including erosion and dilation, were applied to remove random noise superimposed on US image. The structuring element used in the morphological operation was a nonflat \"ball-shaped\" with radius r = 3 and height h = 3. Following the morphological operation, histogram equalization was operated to standardize the image and enhance its features, which was necessary for further feature extraction. After the image process, the Hurst coefficient H can be calculated from the standardized images by Equation (2), where the parameter \(n\) = 8. The slope of fitting line in Figure 4 was the Hurst parameters H for the two nodules in Figure 2. Finally, the fractal dimension (D) was deduced according to Equation (3).  
The fractal dimensions for all 60 thyroid nodules are given in Figure 5. A two-sample independent nonparametric Mann-Whitney U test was applied to evaluate whether or not the fractal dimensions of two categories were different. This test returned a parameter of p for null hypothesis that two samples have equal median; a smaller p means the median for one sample is more different from another. A significant difference (\(p\) < 0.05) was observed in fractal dimensions between malignant and benign thyroid nodules.  
Suspicious nodules, which are likely to be malignant, can be identified earlier before FNA by setting a certain threshold for FD. The receiver operating characteristic (ROC) curves analysis was applied using MATLAB program (Math Works). The calculated result is shown in Figure 6. The optimal threshold 2.164 was then determined by Jordan index. The area under the curve (AUC) analysis for the threshold was 0.792, and the sensitivity and specificity were 64.9% and 86.9%, respectively.  
Figure 1: A longitudinal B-mode image of the normal thyroid.  
Figure 2: B-mode US images and corresponding FNAs of benign and malignant thyroid nodules: (**a**) B-mode image of benign thyroid nodule; (**b**) B-mode image of malignant thyroid nodule; (**c**) FNA smears of benign thyroid nodule; and (**d**) FNA smears of malignant thyroid nodule.  
Figure 4: Fractal analysis of images in Figure 2.  
Figure 5: The fractal dimensions of benign and malignant nodules.  
Figure 3: The processing procedure of the fractal analysis for the B-mode image. The malignant thyroid nodule: (**a**) Original image; (**b**) morphologically operated image; (**c**) image equalization. The benign thyroid nodule: (**d**) Original image; (**e**) morphologically operated image; (**f**) image equalization.
## 3 Wireless Sensor Networks
### System Design
#### 3.1.4 Power Management  
As already mentioned, the power management circuit supports three modes of operation: powering from a USB; charging the battery while powering from the USB, or battery charging and powering. A Solems (Solems, Palaiseau, France) 10/072/048 Photovoltaic Panel (PV) built with amorphous substrate is used or energy harvesting. This PV is very appropriate for indoor operations, as it is able to convert light into current even in low-light environments (20-100 Lux).  
The power supply must be constant at 3.3 volts, as the major components work at this voltage. A step-up boost converter or a step-down converter can be used to accomplish this, when using a solar panel. As the step-up boost converter needs a high current, and the use of a step-down converter allows for using a lithium battery with higher load capacity and lighter properties, the latter approach was employed. A block diagram of the SPWS power management circuit is shown in Figure 4.  
The step-down 1 and 2 blocks are implemented by a Microchip MCP1700T-33 [30] and a Texas Instruments (Texas Instruments, Dallas, TX, USA) TPS79901 [31] low quiescent current voltage regulators, respectively.  
A simple configuration with two jumpers (J1 and J2) is used to select the power management mode of operation. Powering from the USB is achieved by opening J1 and closing J2. In this case the power supply is regulated first by step-down 2 and then by step-down 1, which feeds the SPWS. By closing both jumpers, allows us to simultaneously charge the battery and power the SPWS. Finally, in the case that J1 is closed and J2 open, the third mode of operation is obtained, as the battery is connected to the input of the MCP1700 step-down, whose output is used to provide the voltage regulation for the SPWS circuit. To prevent battery current dissipation by the PV or by the step-down 2 component, the two diodes D1 and D2 are used.
# Smooth Optimised A*-Guided DWA for Mobile Robot Path Planning
## 2 Global Path Planning
### Traditional A* Algorithm  
Mobile robots need to plan a path from the starting point to the target point within limited time, and consider factors such as the distance and traffic conditions. Thus, selecting an efficient algorithm that ensures high-quality paths is critical. The A* algorithm can comprehensively consider the actual cost from the starting point to the current node and the estimated cost from the current node to the target node. By minimising the total cost at each node, A* identifies the optimal search path and efficiently computes the shortest path within a constrained search space. The total cost of the node is as shown in Equation (1).  
\[f(n)=g(n)+h(n) \tag{1}\]  
The heuristic function value \(h(n)\) has a significant impact on the performance of the A* algorithm. If \(h(n)\) is lower than the actual cost from the current node to the target node, the A* algorithm will traverse more nodes, which will make the algorithm slower, but it can be guaranteed that the obtained path is the shortest path; if \(h(n)\) is equal to the actual cost from the current node to the target node, then all nodes traversed by the A* algorithm are on the shortest path, and the search speed is very fast, but it is difficult to accurately calculate how far the current node is from the target node; if \(h(n)\) is higher than the actual cost from the current node to the target node, then the A* algorithm is more inclined to search for paths closer to the target, thereby reducing the search range and the search speed is faster, but it cannot guarantee that the shortest path is found. In summary, the heuristic function value should be as close as possible to the actual cost from the current node to the target node to ensure that the A* algorithm can effectively balance the search speed and path quality during the search process. The road has a certain curvature. To avoid unnecessary turns and twists when planning the path, this article uses the Euclidean distance between the current node and the target node as the heuristic function:  
\[h(n)=\sqrt{\left(x_{i}-x_{n}\right)^{2}+\left(y_{i}-y_{n}\right)^{2}} \tag{2}\]  
where \((x_{i},\ y_{i})\), \((x_{n},\ y_{n})\) denote the coordinates of the current node and the target node, respectively.  
Although the traditional A * algorithm can search the shortest path from the starting node to the target node faster, there are still problems with redundant path points and multiple turns in path planning. Therefore, this paper proposes to improve the traditional A * algorithm through path pruning and path smoothing.  
Figure 1: Raster environment: (**a**) 30 \(\times\) 30 simple environment; (**b**) 30 \(\times\) 30 complex environments.
## 2 Materials and Methods
### Analytical Methods  
The self-organizing map (SOM) is an unsupervised neural network as machine learning, and it is commonly known as a powerful tool for pattern recognition from complex data [38]. In ecological research, the SOM has recently been considered as a more appropriate multivariate analysis than other conventional statistical approaches [39]. The SOM is robust and suitable in providing comprehensive views on highly complex and multi-dimensional data through reducing the data dimension. The efficiency of SOMs in information extraction was demonstrated across different hierarchical levels of life from molecules to ecosystems [40]. Several studies showed that the SOM was robust enough to capture the nonlinear pattern of an ecosystem [39; 41; 42]. For these reasons, the SOM has been extensively applied to pattern recognition in various ecological domains including benthic macroinvertebrates [43; 44], plankton communities [45; 46; 47; 48], dissolved organic matters [49], fish assemblages [50; 51], and biomanipulation assessment [52; 53].  
In the SOM analysis, a total of 33 variables were used including six physicochemical parameters, 27 dominant plankton populations (10 from the eDNA, and 17 from the CMI samples). In selecting the number of variables, we only included the plankton communities, of which abundance was greater than 5% of the total abundance. That is, otherwise, the variables would contain too many zero values which could lead to topological biases in the SOM visualization. The SOM size was determined by the rule of 5 \(\sqrt{\text{sample size}}\)[54]. The SOM model was developed using MATLAB 6.1 (The MathWorks Inc., Natick, MA, USA) and the SOM Toolbox (Helsinki University of Technology, Espoo, Finland).  
For assessment of richness and diversity, the former simply equals to number of species, while the latter is based on the Shannon-Weaver index (H\({}^{\prime}=-\sum p_{i}\ln p_{i}\), \(p_{i}\) indicates a fraction of \(i^{\text{th}}\) species) [55]. In calculating those biological indices, we excluded the taxonomical groups from eDNA samples, such as bacteria, mammals, reptiles, terrestrial plants, and amphibians, because the comparison between two different methods should be done at the same level of analytical resolution.
# Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks
## 2 Related Works
### Fault Tolerance and Node Discovery  
The reactive fault-tolerant systems respond to failures after the occurrence of a fault. The failures are rectified after the reception of the request service, and then the responses are implemented. To detect the fault, the cloud or Fog statuses are regularly monitored and various responses, such as replication, checkpoint, and resubmission, are recorded [8]. [PERSON] et al. [9] suggested the use of a Fault-Tolerance Generic Adaptive Interaction Architecture (FT-GAIA) software-based FT method for parallel and distributed environments. Mainly, this method deals with Byzantine faults and crash errors with the help of server restoration in the cloud layer, and this is achieved with the help of a replication mechanism. [PERSON] et al. [10] suggested the use of a Replication and Pre-emptive Migration based Fault-Tolerance (RPMFT) technique to identify this fault through a distributed load-balancing algorithm by combining proactive and reactive fault-tolerance techniques. The authors in [11; 12] suggested a fault-tolerance approach to reductions in cost and deadlines in order to reduce the failure rate in clustering networks. [PERSON] et al. [13] proposed a WOA algorithm based on a multi-objective model without a fault-tolerance mechanism for optimal task allocation via maximized system performance and efficiency. The authors in [14] proposed a scheduling algorithm that is efficient, low-cost, and FT to minimize the uncertainty and cost of these resources. They used spot and block-spot instances as hybrid instances to reduce the execution costs. [PERSON] et al. [15] suggested a new task scheduling strategy based on Dynamic Fault-Tolerant Learning Automata (DFTLA). They used variable-structure learning automata to identify the most efficient task assignment to fog nodes. [PERSON] et al. [16] proposed a multi-objective fault-tolerant optimization algorithm to reduce bandwidth wastage in a distributed environment.  
[PERSON] et al. [17] used a hierarchical ranking algorithm with several centrality variables to find the appropriate means of (key) Fog node discovery in complicated networks for task-processing after the occurrence of a fault. [PERSON] et al. [18] developed a new technique to measure the importance of key nodes in networks with more than two layers. They also checked the measurements in single/multi-relationship networks and combined networks. [PERSON] et al. [19] proposed the Multi-critEria Decision support meChanism for IoT offloading (MEDICI) method to make a dynamic decision regarding task-processing, which could be either edge or cloud processing based on the requirements. The authors of [20] applied sequential decision-making with the Markova process, improved using the Lyapunov optimization method, to minimize an IoT system's operational costs while offering strict performance assurances.