esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results and Discussion
### Reduction in GHGs' Emissions  
The use of nuclear energy for electricity generation is considered cleaner compared to other traditional fuels such as fossil. In using nuclear power, greenhouse gases such as carbon dioxide and methane are deliberated, as by [45]. A nuclear advocacy group, the World Nuclear Association, revealed that, per gigawatt hour (GWh), the average emission of carbon dioxide is 29 tonnes, which is favorably compared with renewable energy sources such as wind, 26 tonnes per GWh and solar, 85 tonnes per GWh, and more favorably with fossil fuels such as coal, 888 tonnes per GWh, and ignite, 1054 tonnes per GWh. Roughly,nuclear produces less or the same emission as renewable energy and, thus, it is considered an environmentally friendly source of energy [45].  
Strong Partnerships' Integration  
The successful deployment of nuclear power production lies within strong partnerships and integration of manpower, which will guarantee the impetus to protect the use of nuclear power. Although, historically, South Africa has pursued self-sufficiency in its energy cycle, which is becoming a priority, the government has established notable partnerships to achieve its energy goal [46]. These prominent partnerships include Russia, China, Korea, and the United States of America. The South African government's agreement with Russia is in the provision of a strategic partnership in the development of nuclear power plants and waste management. A similar inter-governmental agreement was reached with China, which initiated the introductory phases for the potential utilization of Chinese nuclear technology in the country. The partnership integration with Korea and the United States of America is in the area of research and development. This is to facilitate development in the field of nuclear energy and radiation sciences and technology and to process source material, restricted material, and special nuclear material [47].
## 3 Energy Management Strategy
### Cooperative Eco-Driving (CED) Guidance
#### 3.1.1 Curve Speed Assistance (CSA)  
When a vehicle encounters a sharp curve, it needs to slow down to stay safe. CSA takes into account the curvature of the curve and calculates a safe speed limit, as shown in Figure 7. The goal is to provide this information to the driver to encourage safe driving on sharp curves. It also guides the driver when coasting before entering a curve to minimize brake use. This means that you can avoid sudden stops and reduce unnecessary use of the accelerator and brake pedals, which not only improves safety but also improves fuel economy. The curve speed limit is calculated as shown in Equation (1).  
\[v_{lim,\text{CSA}}=\text{max}\bigg{(}\sqrt{\frac{\mu_{road}\;g}{\kappa}}-\Delta v _{safety},v_{lim,min}\bigg{)} \tag{1}\]  
\(v_{\text{lim,CSA}}\) is the curve speed limit for CSA. \(\mu_{road}\) is the friction coefficient of the road and uses the default value from the dSPACE ASM. \(g\) is gravity acceleration. \(\kappa\) is road curvature. \(\Delta v_{safety}\) is the additional speed margin for safety (=5 km/h). \(v_{lim,min}\) is the minimum speed limit (=12 km/h).
# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 1 Introduction
### AI Used in Air Quality Measurement
[39], who applied recursive neural networks to model PM\({}_{2.5}\) emissions, demonstrating the possibility of integrating the DL models to capture the intricate dynamics of AQ variations [40]. Traditional empirical models frequently struggle with accuracy because they cannot adjust to fluctuating environmental conditions. Conversely, ML methods analyze intricate, non-linear interactions among the variables affecting air quality [41]. Feature selection techniques in ML models, a fundamental step in model development, have been shown to optimize performance by identifying the most relevant predictors of AQ. [PERSON] et al. [42] introduced a regional feature selection-based ML system that utilizes AQ observations to improve forecasting accuracy across China.
# Opportunistically Exploiting Internet of Things for Wireless Sensor Network Routing in Smart Cities
## 3 Problem Details
### Requirements  
Considering the above scenario, our IT Platform should be able to satisfy the following requirements:  
1. **Interaction with the IoT devices:** The IT Platform should enable WSN to interact with IoT devices when they are available.
2. **Independent routing:** The platform must allow WSN to continue delivering data to the sink using their normal routing protocol, if there is no IoT devices in the vicinity. This is important since WSN should not be fully dependent on IoT devices, as IoT devices are not a part of their network. Rather they are exploited opportunistically.
3. **Energy efficiency:** By exploiting IoT devices, energy consumption in WSN is conserved.
4. **Network lifetime:** The energy of WSN should be consumed in such a way that a few sensor nodes shouldn't be drained quicker than rest of the network, rather all sensor nodes should be used in a balanced way. This means that along with energy conservation the time duration between first dieing node and the most dieing nodes (making network non-functional) shouldn't be long.
5. **Data delivery:** Typically when IoT devices are exploited, the amount of received packets should either be the same or should be increased as compared to routed only using WSN. Thus, packet loss could be minimized.  
Following, assumptions are made that simplify the problem scenario and provide better understanding:  
* IoT devices are Vehicles/cars/passengers;
* WSN is surrounded by IoT devices, but can function without them; and
* WSN nodes send their data to a single sink.  
Figure 2: Sharjah city center, a typical scenario in which 100 sensors (red dots) are being placed at random location in a 200 by 200 m region.
# Smart Product Design Process through the Implementation of a Fuzzy Kano-AHP-DEMATEL-QFD Approach
## Literature Review
### Kano Model  
The first tool QFD is traditionally integrated with, is the Kano Model. The use of the Kano Model aims to debug the expressed customer requirements to make the QFD analysis simpler. [PERSON]'s proposal to assess customer needs has been a great contribution to the evaluation of the impact of the degree of fulfillment of these needs on customer satisfaction. Traditionally, researchers have assumed a linear relationship between the achievement of quality attributes and customer satisfaction [24; 27; 28; 29; 30; 31]. More specifically, before [PERSON]'s approach, it was assumed that customers would be satisfied if a certain quality attribute of a product or service was provided; otherwise, they would be dissatisfied [24; 27; 28; 32]. However, the assumption of linearity has been proven to be inaccurate. This allows understanding that product and service quality is composed of various attributes, and some of them serve to fulfill minimum customer requirements, while others provide additional value. In other words, the achievement of some quality attributes, in particular, does not necessarily involve an improvement of overall customer satisfaction. Additionally, it is important to point out that not all customer needs are viewed as equally important to them [24; 27; 28; 32].  
The situation described above implies that there is a non-linear relationship between the performance of quality attributes and overall customer satisfaction. Therefore, the effects of the fulfillment of the different customer needs have to be taken into consideration from manufacturers or service providers to adequately allocate resources via prioritizing design factors to increase customer satisfaction or minimize dissatisfaction [24; 27; 28; 32].  
Applications of the Kano Model include all types of product and service design [33], healthcare services improvement [34], as well as Information and Communications Technology (ICT) devices improvement [35], among others. Nevertheless, the Kano Model does not work well when implementedalone. The problem is that companies need not only to identify customer needs but also to align these needs with some design features/actions, to identify which actions will have a bigger impact on customer satisfaction, so the implementation of each one of them can be prioritized [31; 33]. Therefore, it is necessary to integrate the Kano model with QFD, and other Multi-criteria Decision Making (MCDM) methods to have a more robust decision support system when prioritizing design features/actions.
## 5 Performance Comparison Before and After Optimization
### No-Load Simulation Analysis  
In the no-load simulation and load simulation, the permanent magnets of the proposed and optimized models are set in a state where demagnetization has not occurred. In the no-load analysis, the flux density and Back-EMF can reflect the ability of PMA-SynRM to generate torque. Higher flux density and Back-EMF mean that the PMA-SynRM is able to produce higher output torque. From Figures 12a and 13a, the RMS values of no-load air-gap flux density and Back-EMF of the optimized model are reduced by 1.41% and 1.81%, respectively, compared to the basic model. This is due to the fact that the optimized model adds the bypass magnetic bridges, which causes some magnetic leakage to occur in the PMA-SynRM, resulting in a slight decrease in the no-load air-gap flux density and Back-EMF. From Figure 12b, it can be seen that the lower harmonics 3 and 5 of the air-gap flux density of the optimized model are slightly larger than that of the basic model, which results in some distortion of the air-gap flux density waveform. The high harmonics 7, 9, 11, and 17 of the air-gap flux density of the basic model are larger than those of the optimized model, and the increase of the high harmonics leads to the increase of the high-frequency loss. From Figure 13b, consistent with the air-gap flux density harmonics, the lower harmonics 3 and 5 of the Back-EMF of the optimized model are slightly larger than those of the basic model, which makes the Back-EMF waveform also have some distortion. In the Back-EMF harmonic decomposition, the higher harmonics 7, 9, 11, and 17 of the basic model's Back-EMF are also larger than those of the optimized model. However, the high harmonics cause a high-frequency component in the Back-EMF waveform, leading to high-frequency electromagnetic noise when the motor is running at high speed.  
Figure 11: Optimized model rotor single-pole topology.
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 2 Related Work
### Meta-Heuristic Algorithms
#### 2.3.2 ACO-Based Meta-Heuristic Algorithms  
ACO was introduced by [PERSON] and was inspired by the behavior of some species of ants. Ants guide each other by pouring pheromones on the ground. This behavior is used for solving optimization problems [26]. ACO as a meta-heuristic algorithm is used for many optimization problems that include scheduling. [PERSON] et al. [27] used the ACO method to deposit pheromones between VMs in order to achieve the past utility of placing pheromones in physical machines. Their algorithm is simulated in a homogeneous environment, and only CPU and memory resources are considered. [PERSON] et al. [28] present task scheduling based on ACO. The main objective of this algorithm is to minimize makespan. Random ACO search engine optimization is a method that allocates VMs to entry tasks. The simulation was performed by CloudSim, and the results show that the performance of this algorithm is better than that of FCFS and RR algorithms. In [29], the authors used ACO for mobile cloud computing that requires specific resources. This method executed offloaded tasks in fog devices (FDs) by delay, complete-time, and energy consumption objectives. The time order of their simulation depends on the number of cycles, tasks, and ants.
## 5 Results
### Question VII--Qualitative Results  
The last part of the online survey used three qualitative non-mandatory questions to give the participants the possibility to write from their point of view. To display the answers of Questions i-ii, see Section 4; the word cloud technique from MATLAB C was applied. Results are separated between genders and shown in Figure 9(a)-d.  
Figure 10: _Cont._  
Figure 9: For pattern (**a–c**) nearly unique meanings were identified. Besides, also multiple meanings were selected as shown in (**f**). For (**d,e**) no clear pattern meaning was identified.  
The first question asked which in-vehicle lighting system participants preferred for their future vehicle. Within all male answers, there is an identification for fast-forward, dynamic and information-medium. The female participants answered in a more balanced way with multichromatic colors, nice atmosphere and controlled systems. The answers of the second question, the improvements to their current in-vehicle lighting system, were similar between both groups in terms of not distracting and safety aspects. The male side preferred more functional and perceptional improvements, whereas the female side was more conservative with \"nothing\" to improve.
# Voice Communication Systems Impact on Navigating Officers
## 3 Results
### Calls through Ship's Internal Phone  
This call type represents the incoming call for the navigational bridge via ship's internal phone system form a crew member being elsewhere on the ship during navigation. The collected data (statistical sample) have the following parameters: Range = 239 min (1-240 min), Mean = 42.8, Median = 30, Standard deviation = 49.76 and 90% confidence interval for the mean value [31.36, 54.24]. The data are congruent with the lognormal distribution. By rejection of outliners, beyond three standard  
Figure 4: Time between two incoming mobile phone calls [min].  
deviations, the initial distribution parameters are \(\mu_{l}\) = 40.94 and \(\sigma_{l}\) = 62.18 (Figure 5). The lognormal distribution is given by the following expressions:  
\[\mu=\ln\left(\frac{\mu_{l}^{2}}{\sqrt{\sigma_{l}^{2}+\mu_{l}^{2}}}\right)=\ln \left(\frac{40.94^{2}}{\sqrt{62.18^{2}+40.94^{2}}}\right)=3.11 \tag{8}\]  
\[\sigma=\sqrt{\ln\left(\frac{(\sigma_{l}^{2}+\mu_{l}^{2})}{\mu_{l}^{2}}\right)} =\sqrt{\ln\left(\frac{62.18^{2}+40.94^{2}}{40.94^{2}}\right]}=1.09 \tag{9}\]  
\[f(x)=\frac{1}{\sigma x\sqrt{2\pi}}e^{-\left(\ln\left(x\right)-\mu\right)^{2}/ \left(2\sigma^{2}\right)}\ =\frac{1}{1.09t\sqrt{2\pi}}e^{-\left(\ln\left(t\right)-3.11\right)^{2}/2.39} \tag{10}\]  
\[x>0;\ \mu_{l},\ \sigma_{l}\ \epsilon\ R^{+}\]  
The average time between two consecutive calls may be estimated at one call every 40.9 min or 0.68 h. That frequency generates approximately 35 calls per day or six calls during one standard watch (4 h).  
Regarding the process times of these calls, almost all participants of the interview responded that the calls were rather short, usually lasting up to 1 min. The reason for such a short communication time is the nature of calls, which is often limited to asking or providing one or a smaller set of information related to ship's routine operations.  
Considering the frequency of incoming calls and their duration, it is estimated that during one day, officers of the watch spend approximately 35 min (or 6 min during one watch) talking on the official mobile phone.
## 4 Validation and Analysis
### Data Preparation
#### 4.1.4 Time Feature Construction  
Since tunneling is a dynamic process, data of a certain time are difficult to precisely reflect precise disc cutter conditions. Therefore, features are extracted from time series data. The time window of the data object for feature extraction is selected as 5 min. The average value, peak value, variance, and other augmented features are calculated to represent the 5 min data. Specific chosen features about the eight parameters are shown in Table 3.  
Most of the time, cutterhead cutting ability is above the judgement threshold and no disc cutter replacement is required during tunneling. The size of \"no replacement needed\" data is larger than that of \"replacement needed\" data. Therefore, different sampling ratios are required to equalize the data sets. For \"no replacement needed\" data, 5 min samples are randomly sampled from the data of 414 no-disc-cutter replacement days. For \"replacement needed\" data, a sliding time window is used to acquire more 5 min samples from the time before disc cutter replacements, whose rolling distance is set to 5 s.  
Eventually, a total of 46,629 instances from 691 days were used to construct the training and testing sets. Among them, 16,852 instances were \"replacement needed\" data and 29,777 instances were \"no replacement needed\" data. Each instance had 18 features and a class label. The numerical output value of normal operation was set to 1 and that of predicted failure was set to 0. To evaluate the model performance during training, a 10-fold cross-validation was used to obtain the errors.  
To reproduce our process, the original data of the studied project can be found at [[https://github.com/ChenZuyuIWHR/YS-IWHR](https://github.com/ChenZuyuIWHR/YS-IWHR)]([https://github.com/ChenZuyuIWHR/YS-IWHR](https://github.com/ChenZuyuIWHR/YS-IWHR)), accessed on 16 February 2022.
# An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
## 2 Materials and Methods  
### Data Collection and Processing  
We selected articles relevant to widely used satellites and sensors as the basis of this research. Considering the prevalence, the amount of data accumulated and the diversity of sources, the satellites, and sensors included Advanced Land Observing Satellite (ALOS), the AVHRR, Cartosat, Constellation of Small Satellites for Mediterranean basin Observation (COSMO-SkyMed), Fengyun (FY), Gaofen (GF), Indian Remote Sensing Satellites (IRS), Landsat, MODIS, Radarsat, Sentinel, Systeme Probaotive d'Observation de la Terre (SPOT), TerraSAR, WorldView, and Ziyuan (ZY)/CBERS. We selected relevant articles and review articles from the Web of Science (WoS) [62] Core Collection including from the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI). The filter criterion used was \"Topic = satellite/sensor name (e.g., Landsat, AVHRR, Sentinel), Document Types = Articles or Review Articles; Publication Years until 2020\". To ensure that the selection was as accurate and comprehensive as possible, we selected articles with the topic in the title, abstract, or keywords.
# Chaos-Based Underwater Communication With Arbitrary Transducers and Bandwidth
###### Abstract  
In this work, an enhanced differential chaos shift keying (DCSK), based on a first order hybrid chaotic system, is being proposed for a high reliability underwater acoustic communication system. It can be integrated into systems that use standard existing transducers. We show that a coherent operation between the received signal and the time reversal of the basis function in a first order hybrid chaotic system maximizes the signal to noise ratio at the receiver. Concurrently, DCSK configuration is used to resist the distortion caused by the complex underwater acoustic channel. Our simulation results show that the proposed method has lower bit error rate (BER). In addition, it shows higher communication reliability over underwater acoustic channel as compared to the conventional DCSK using logistic map and its variant forms such as Correlation Delay Shift Keying (CDSK), Phase-Separate DCSK (PS-DCSK), High Efficiency DCSK (HE-DCSK), and Reference Modulated DCSK (RM-DCSK).  
Keywords:first order hybrid system; communicating with chaos; DCSK; underwater acoustic communication +
Footnote †: journal: Applied sciences
###### Abstract  
Remote sensing data plays an important role in classifying land use/land cover (LULC) information from various sensors having different spectral, spatial and temporal resolutions. The fusion of an optical image and a synthetic aperture radar (SAR) image is significant for the study of LULC change and simulation in cloudy mountain areas. This paper proposes a novel feature-level fusion framework, in which the Landsat operational land imager (OLI) images with different cloud covers, and a fully polarized Advanced Land Observing Satellite-2 (ALOS-2) image are selected to conduct LULC classification experiments. We take the karst mountain in Chongqing as a study area, following which the features of the spectrum, texture, and space of the optical and SAR images are extracted, respectively, supplemented by the normalized difference vegetation index (NDVI), elevation, slope and other relevant information. Furthermore, the fused feature image is subjected to object-oriented multi-scale segmentation, subsequently, an improved support vector machine (SVM) model is used to conduct the experiment. The results showed that the proposed framework has the advantages of multi-source data feature fusion, high classification performance and can be applied in mountain areas. The overall accuracy (OA) was more than 85%, with the Kappa coefficient values of 0.845. In terms of forest, gardenland, water, and artificial surfaces, the precision of fusion image was higher compared to single data source. In addition, ALOS-2 data have a comparative advantage in the extraction of shrubland, water, and artificial surfaces. This work aims to provide a reference for selecting the suitable data and methods for LULC classification in cloudy mountain areas. When in cloudy mountain areas, the fusion features of images should be preferred, during the period of low cloudiness, the Landsat OLI data should be selected, when no optical remote sensing data are available, and the fully polarized ALOS-2 data are an appropriate substitute.  
Keywords:land use/land cover (LULC); optical remote sensing images; SAR; feature fusion; mountain area +
Footnote †: journal: Applied Soft. 2020, 10, 2928; doi:10.3390/app10082928