esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review on Virtual Reality Technology for Ancient Ceramic Restoration
## 4 Discussion
### A New Virtual Reality E-Learning Platform on Ceramic Restoration
[PERSON] and [PERSON] surveyed learning efficiency as an indicator for validating the VR technology adoption for the Qingdao Agricultural University (QAU) museum education platform [61]. The 5-point Likert scale measured all potential variables, with 1 indicating strong disapproval and 5 indicating strong approval. The researchers collected formal data through an online questionnaire in July 2023. The participants refer to users who are interested in ceramic restoration. In this case, a preliminary survey was conducted with 35 undergraduates and postgraduates to ensure that the reliability and validity of the scale met the requirements. The questionnaires collected that did not meet the basic quality requirements for responses were screened and excluded. Ultimately, 34 valid questionnaires were obtained. Most of the respondents agree that this VR platform provides a good interactivity experience (86.66%), superb usability of system function (91.14%), and satisfies their emotional commitment (87.61%). The majority of respondents are convinced that VR has promoted their learning efficiency and learning willingness, and among them, 41.71% strongly agree with this view. The next section will discuss how interactivity, usability, and emotional commitment impact learning efficiency. And the scale will be adjusted according to the preliminary survey results to make it more suitable for the Chinese context.
# QoE Modeling on Split Features with Distributed Deep Learning
## 3 Dataset and Feature Extraction  
The publicly available QoE dataset _Waterloo Streaming QoE Database III_ (SQoE-III) [31] is used in this article. It has been collected using a well-grounded user study methodology. The MOS scores on the dataset are highly correlated (higher than 0.8) with well-known QoE models such as P.NATS [32]. The dataset consists of 20 raw HD reference DASH videos, where the video length is 13 s on average. The experiments are performed while changing the video streaming bandwidth amongst 13 different bitrate levels ranging from 0.2 Mbps to 7.2 Mbps. The switch is performed in six categories; stable, ramp-up, ramp-down, step-up, step-down,and fluctuation. The video sequences vary in spatio-temporal complexity. _TI_ (Temporal Information) is a measure of how the consecutive frames differ from each other in time, while _SI_ (Spatial Information) is a measure of how the pixels in a given frame differ in between each other [33]. In the dataset, _SI_ values are between 35 and 160; while _TI_ values are between 11 and 114. There were 34 subjects (ages range between 18 and 35) involved in the study. Four of which provided outlier ratings that had to be removed. The user ratings are collected using a rating scale, where the scores are between 0 (worst) and 100 (best). The number of samples in the dataset was 450.  
We extracted the 9 features as described in Table 1 from the raw dataset. The feature names are also depicted with a split index such that the features that are less than the applied split index are placed in feature subset S0, while the remaining features are placed in feature subset S1, respectively. For example, if the split index is set to 4, the features _TI_, _SI_, _FPS_ and _lastBitrate_ are placed in feature subset S0; and the remaining features _nstalls_, _stallTimelInitialTotal_, _stallTimeIntermediateTotal_, _bitrateTrend_, and _meanBitrate_ (as far as it exists in the scenario) are placed in feature subset S1. In \(DEL\), the group 0 (target domain) has feature set S0, and the group 1 (source domain) has feature set S1. In _vFL_, S0 features are in group 0 (worker 0), and S1 features are in group 1 (worker 1).
# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
[PERSON] and [PERSON] [27] proposed a methodology to be used as a guide for constructing electric power load forecasting models based on segmentation and decomposition of the load time series and used real daily load data from the Kuwaiti electric network and a model with a mean absolute percentage error (MAPE) value of 3.84% was reported. [PERSON] and [PERSON] [28] employed the particle swarm optimization to minimize the error associated with the estimated model parameters for annual peak load forecasting for Kuwaiti and Egyptian powerplants using the least error squares estimation technique to forecast the annual peak load for the years 2006 to 2010. [PERSON] et al. [29] used the cuckoo search algorithm to minimize the error associated with the estimated parameters of three long-term forecasting methods used to forecast the annual peak demands. [PERSON] and [PERSON] [30] presented long-term forecasting for peak loads of Gulf Cooperation Council (GCC) countries using Multiple Linear Regression (MLR) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods. A recent study for the residential sector in Kuwait was carried out by [PERSON], and [PERSON] [31] created a baseline using a bottom-up approach for the end-use energy profile for the residential sector in Kuwait until 2040. With reference to the aforementioned relevant literature, research to date has not yet investigated the performance of the Prophet model in long-term forecasting of maximum load. In this paper, real recorded data from the National Control Center (NCC) in Kuwait were used for long-term electric load forecasting using the Prophet model. The proposed method is simple yet powerful in prediction because of its features of adjusting parameters without exploring the specifications of the underlying model [22]. It consists of a decomposable time series model with three main model components: seasonality, trend, and holidays. The model is compared with a well-established Holt-Winters model for evaluation and to obtain the best forecasting model. This study aims to contribute to the literature by investigating long-term maximum peak load forecasting performance of Kuwait peak loads using the Prophet method based on comparative quantitative analysis with the Holt-Winters forecasting method. To the best of our knowledge, no prior study has investigated the use of Prophet model in long-term forecasting of the maximum electricity load either in Kuwait's electrical national grid or elsewhere. Furthermore, the generalizability and robustness of the Prophet and Holt-Winters methods for the long-term electric peak loads forecasting are explored and presented.
## 4 Discussion
### Monitoring of Gas Explosions in Pipes  
In the set of experiments carried out to detect and localize propagating pressure waves in pipes it was found that the fully distributed fiber-optic sensor configuration is indeed able to track such waves continuously along the pipe. Besides the intended use in condition monitoring this opens new perspectives in the fundamental study of gas explosions.  
In general, gas explosions can be subdivided into two groups with tremendous differences in their consequences on pipeline structure. While deflagrations are characterized by velocities below the speed of sound in the medium and explosion pressures on the order of 10 bar above initial pressure, detonations propagate with supersonic velocities and pressures an order of magnitude larger usually causing catastrophic damage. Due to the complex interplay between fluid dynamics, heat transfer and turbulences depending on the specific gas mixture and pipeline configuration, determination of occurrence and location of deflagration-to-detonation transitions (DDT) is still a topic of current research ([PERSON] et al., 2018; [PERSON] et al., 2018).  
Here, both measurement techniques, DAS and piezoelectric pressure transducers, confirmed a (constant) velocity in the range of the acoustic speed inside the medium after ignition of a gas mixture of propane in air. Together with the observed pressure values of \(p_{\mathrm{ex}}~{}-~{}p_{0}=~{}13\) bar, with \(p_{\mathrm{ex}}\) being the explosion and \(p_{0}\) the initial overpressure, this is characteristic of a deflagration. A similar situation was found in the experiment with a gas mixture of hydrogen in air ignited at ambient pressure.  
The advantage of using a fully distributed sensor as represented by the distributed acoustic sensing system, emerges in cases where a non-linear propagation takes place. This is illustrated in the experiment where a gas mixture of hydrogen in air was ignited at 2 bar initial overpressure. The supersonic propagation velocity and pressure values of \(p_{\mathrm{ex}}~{}-~{}p_{0}=~{}67\) bar and 83 bar, registered by the two piezoelectric pressure transducers, are indicative of a detonation. Due to their positioning it is however not possible to capture the point of DDT. A large number of closely spaced conventional point sensors would be necessary to generally determine this point with sufficient spatial resolution. Only the fully distributed system enables observation of this point without a priori knowledge of its location.  
Therefore, DAS, together with an appropriate sensor configuration, may be used as tool for fundamental studies on the behavior of gas explosions in pipes. As shown in the first part of this paper, the present helical sensor configuration may allow to localize such events with a spatial resolution down to \(\sim\)20 cm. This would correspond to installing 30 piezoelectric pressure transducers on a pipe of 6 m length to achieve the same resolution.
# Drought: A Common Environmental Disaster
## 1 Introduction
The most commonly used drought indices in the literature include the Vegetation Condition Index (VCI) [24], Normalized Difference Vegetation Index (NDVI) [25], Standardized Precipitation Index (SPI) [26], Palmer drought severity index (PDSI) [27; 28; 29], US Drought Monitor (USDM) [30] and Effective Drought Index (EDI) by [PERSON] and [PERSON] [31], among others. As defined by [PERSON] [28], drought is the lack or shortage in hydrologic cycle or persistent low climatological index; this can quantify the scope, severity and frequency of prolonged periods of abnormally dry weather. Defining drought is a difficult task. It has to do with relatively dry weather conditions and the effects they have on the ecosystem, including human activities, such as agriculture and water management [22; 23]. This study presents historical drought events, drought impacts, remote sensing and indices suitable for drought assessment, among others. The use of satellite remote sensing technology has proved to be a powerful and reliable tool for drought monitoring [32; 33]. Remote sensing is most useful for describing the regional and temporal evolution of drought [33; 34]. NDVI and VCI, on the other hand, are used to represent the state of the vegetation and drought severity [35].
## 4 Performance Evaluation of Parameter Estimation
### Tests with Real Data
We can see from the results of T = 250 s that the multipath signal is totally orthogonal to the LOS signal, so the vast majority of the multipath signal energy is distributed in the Q channel, but this phase difference will not affect the parameter estimation of multipath delay when we use the absolute value of I/Q channel impulses as shown in the subfigures on the right side. However, compared with the theoretical results provided in Figures 3 and 4, the impulses seem noisier. This probably due to the non-ideal characteristic of real data and the specific receiver setting. Besides, the carrier phase difference between LOS signal and multipath signal would not be a constant value compared with the ideal condition. It is changing continuously. All the above factors lead to a slight performance loss. And all multipath estimation techniques, not just the aFFT method, have to face these challenges. Nevertheless, the results still show the effectiveness and feasibility of the proposed method on real data even in the dynamic environments. We can easily estimate the code delay using the impulses processed by the averaging-FFT method. Besides, if we enlarge the signal length for each estimation (here is 20 ms) or choose a shorter window size (here is 128 points), as analyzed in the paper, the total performance can be improved further. Figure 6: In-Phase correlation function of TEXBAT dataset.
# Prediction of Vibration-Mode-Induced Noise of Structure-Acoustic Coupled Systems
## 4 Results and Discussion
### Acoustic Mode Prediction
A previous study [42] reported that although  
Figure 4: The time-history characteristics of vibration and impact sound generated by vibration; (a) time history of vibration after shocking (b) time history of impact sound generated by vibration. the impact sound could be accurately predicted using acoustic data, the performance was limited in different boundary conditions. In general, low frequencies corresponded to a high prediction complexity. A previous study based on the FEM-SEA method [25] indicated that the error rate was approximately 10% at a low frequency, although the predictions at other frequencies were reasonable. Although the proposed approach requires additional verification, it can effectively obtain predictions for low and high frequencies. Figure 6 shows the time history and spectrum of the acoustic mode. The comparison was conducted between decomposed data from measurement and analyzed data from FEM. Figure 5(a) presents the acoustic mode decomposed from the measured sound pressure and that obtained using the vibration mode. Figure 5(b) shows the magnified view of the maximum pressure. The results indicated that the most of two modes were similar except for some parts. Figure 5(c) shows the spectra of the two modes, which was shown that two spectra were almost similar. This demonstrated that the predictions for the acoustic mode are reliable. Figure 5: Power spectrum of modes: (**a**) Vibration mode from measured data; (**b**) Acoustic mode from vibration; The frequencies contained two modes. (1) Induced by vibration mode directly (blue dotted lines); (2) induced from vibro-acoustic coupling effect (red lines); (Dashed: vibration mode induced pressure. Solid: acoustic mode induced pressure).
## 3 Materials
### Sentinel-2 Satellite Imagery  
The earth observations satellite Sentinel-2 series are able to provide remote sensing images of high spatial, spectral and temporal resolutions due to its customized Multispectral Instrument (MSI) sensor. The spatial resolutions of Band1, Band9, Band10 (60 m),Band5, Band6, Band7, Band8A, Band11, Band12 (20 m) and Band2, Band3, Band4, Band8 (10 m) can meet various requirements in atmospheric and geophysical parameters correction, vegetation detecting, and land classification [8; 10; 21]. Moreover, the 13 bands provided by Sentinel-2 series cover visible bands, near Infrared (NIR) bands, and short waved Infrared information (SWIR) bands.  
In particular, compared against the popular Landsat 8 and other freely accessible mainstream satellites [5; 22; 23], Sentinel-2 satellites are able to provide more details in NIR and SWIR bands, which can promote the land cover classification performance in urban monitoring, forest monitoring, and smart farming, among many others [4; 24]. Moreover, Sentinel-2 series satellites also improve the temporal resolution, where a 5-day revisit time is available with the introduction of Sentinel-2-B. The Sentinel-2 satellite information in terms of band characteristics, wavelength, and spatial resolution are summarized in Table 1, where the band wavelength information is at the central wavelength. It is also noted that Band 10 is particularly for cirrus; therefore, this band is omitted in the land cover classification problem in this study.
## 4 Experimental Results and Analysis
### Comparison of EMD, FT, and DF
The PSD curves obtained by the three methods have obvious peaks, and the extracted structural natural frequency is consistent with that extracted from the measurement of the fixed camera, indicating the good performance of the three methods in extracting the structural natural frequency. Figure 17 shows the mode shape obtained from the acceleration signals of three correction methods and the fixed camera measurement. The modal assurance criterion (MAC) [42] is used to evaluate the fitting degree between the mode shape curves. The mode shape obtained by the fixed camera is taken as the reference, the MAC values of EMD, FT, and DF are 0.994, 0.986, and 0.981, respectively. Based on the above analysis, EMD is the best in the three correction techniques of signal measured by UAVs on processing effect and modal parameter identification of bridge models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **EAC** & **TRAC** & **STD** \\ \hline EMD & 24.897 & 0.941 & 98.604 \\ \hline FT & 25.310 & 0.942 & 99.914 \\ \hline DF & 26.456 & 0.918 & 106.508 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Indexes of three methods. Figure 15: Corrected signals of static point of three methods: (**a**) EMD; (**b**) FT; (**c**) DF.
## 2 Isolation and Characterization of the Sub-6 GHz Multi-Port MIMO Antenna
### Summary of the Trade-Offs  
In summary, radiation-pattern-based ECC calculations deliver very reliable results. Such methods are optimal in terms of final validation. The S-parameter-based method serves as a fast and practical tool during early-stage design. The selection of an approach should be guided by the required level of accuracy, the current phase of the design process, and the availability (or not) of measurement or simulation data.  
Table 2 highlights the key differences between the two primary ECC extraction methods: the S-parameter-based and the radiation-pattern-based approaches. The S-parameter method, which uses reflection and transmission coefficients (e.g., S\({}_{11}\) and S\({}_{12}\)), is widely used during early-stage design given its simplicity and minimal computational overhead. This enables direct ECC estimation using either measured or simulated two-port  
S-parameters, rendering the approach well-suited for rapid analysis and design comparisons. However, this method approximates the ECC based solely on port coupling and is therefore very sensitive to losses and impedance mismatches that can significantly reduce accuracy in practical scenarios. In contrast, the radiation pattern method uses complete, 3D complex radiation field data from each antenna port to compute the ECC based on actual spatial correlations. This offers superior accuracy and, therefore, a more realistic evaluation of system level performance, particularly in terms of diversity and spatial multiplexing. However, the method requires extensive measurement or simulation data and the computational demands of spherical integration and far-field data processing are substantial. Therefore, the method is more appropriate for final-stage validation and conformance testing than the evaluation of iterative design cycles. In practice, the S-parameter method serves as a convenient and efficient tool for initial optimization, whereas the radiation-pattern method is indispensable when rigorously validating the final antenna configuration against real-world MIMO performance criteria such as the diversity gain and robustness in terms of user-hand interaction.
# Dynamic Analysis of Fuzzy Systems
## 3 Dynamic Analysis of Non-Linear Fuzzy Systems: First-Order Model
### First-Order Fuzzy Model with Linguistic Hedges  
A model with triangular membership functions is developed, which will now be modified with the linguistic hedges type mentioned above, such as dilation and concentration. We will use a non-linear model that is represented in this way: \(\dot{x}=-\frac{\dot{x}|x|}{\tau}+\frac{\dot{x}}{\tau}u\), in a fuzzy system [39]. It will be a concentration operation \(\mu_{X}{}^{2}(x)\), and it is shown in Figure 6. If the model is \(\dot{x}=-\frac{\sqrt{|x|}}{\tau}+\frac{K}{\tau}u\)[37], this represents a dilation \(\mu_{X}{}^{0.5}(x)\) (see Figure 7). The linguistic hedges used for the concentration at input x will be \(X_{1}^{(1)}=VN\), \(X_{1}^{(2)}=VZ\), \(X_{1}^{(3)}=VP\). The dilation of x is expressed by linguistic hedges \(X_{1}^{(1)}=MN\), \(X_{1}^{(2)}=MZ\), \(X_{1}^{(3)}=MP\).  
The resultant Mamdani model:  
\[R^{(11)} :\text{IF $u$ is $N$ AND $x$ is $X_{1}^{(1)}$ THEN $\dot{x}=0$}\] \[R^{(12)} :\text{IF $u$ is $Z$ AND $x$ is $X_{1}^{(1)}$ THEN $\dot{x}=0.5$}\] \[R^{(13)} :\text{IF $u$ is $P$ AND $x$ is $X_{1}^{(1)}$ THEN $\dot{x}=1$}\] \[R^{(21)} :\text{IF $u$ is $N$ AND $x$ is $X_{1}^{(2)}$ THEN $\dot{x}=-0.5$}\] \[R^{(22)} :\text{IF $u$ is $Z$ AND $x$ is $X_{1}^{(2)}$ THEN $\dot{x}=0$}\] \[R^{(23)} :\text{IF $u$ is $P$ AND $x$ is $X_{1}^{(2)}$ THEN $\dot{x}=0.5$}\] \[R^{(31)} :\text{IF $u$ is $N$ AND $x$ is $X_{1}^{(3)}$ THEN $\dot{x}=-1$}\] \[R^{(32)} :\text{IF $u$ is $Z$ AND $x$ is $X_{1}^{(3)}$ THEN $\dot{x}=-0.5$}\] \[R^{(33)} :\text{IF $u$ is $P$ AND $x$ is $X_{1}^{(3)}$ THEN $\dot{x}=0$}\]  
The scaling factors for the concentration and dilation will be as follows for the input \(u=K\) valid for \(0\leq K\leq 1\), at the output \(\dot{x}=\frac{2}{\tau}\) (see Figure 5).  
The response of a unit step using linguistic operators, concentration and dilation are compared to a first-order linear system in Figure 8. This graph indicates that the settling time (\(t_{s}\)) is less than that of the first-order linear system in concentration, which implies that it is faster than the first-order linear system. On the contrary, the \(t_{s}\) in dilation is higher than that of the first-order linear system. Therefore, the system is slower compared to the  
Figure 6: The concentration of the input membership functions x and the resulting inference map.  
first-order system. This is valid only for the first 10 s. After that, it is observed that the dilation is faster than the concentration.
## 3 Operational Requirements
### Navigation and Mobility  
Navigation and mobility are crucial aspects of fruit harvesting robots for their efficient and effective operation in agricultural environments. These robots must be capable of autonomously moving through crop fields, navigating between rows of plants, and accessing fruits for harvesting without causing damage [35].  
To achieve efficient movement in the field, a harvesting robot requires an advanced navigation and positioning system. High-precision GPS and inertial navigation systems (INS) enable the robot to determine its exact location in the field and plan the optimal route for harvesting [36]. Additionally, the use of LiDAR (Light Detection and Ranging) technology helps create three-dimensional maps of the environment, allowing the robot to detect and avoid obstacles while moving toward the selected fruits. The following section explains how this process works in detail [37].