esa-sceva/satcom-chunk-collection huggingface dataset:
# Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry
## 3 Proposed Model
### Method  
This methodology contemplates the analysis of the opinions of the experts, through this method it is sought to measure the impact on the level of demand. It is a tool implemented in a wide variety of investigations due to its simplicity and the results generated. It is structured in four stages: background evaluation, conclusions, aggregation of conclusions, and defuzzification.  
Evaluate the preceding for each rule (input fuzzyfication). It corresponds to the evaluation of the input variables. These entries are assessed by rankings and membership functions that follow a particular trend. These functions can be smooth or abrupt depending on the study. At this stage, they are experimented with different input values and the variation in each rule behavior is interpreted as in the Equation (1), the Gaussian membership function, it is a soft function, seeking to minimize abrupt changes in the level of demand.  
\[\mu A_{h}(x)=exp\left\{-\left(\frac{x-m_{h}}{\sigma_{h}}\right)^{2}\right\} \tag{1}\]  
Obtain each rule's conclusion. The background evaluation allows to generate consequents that correspond to the results of the study of the fuzzy rules. This can study two or more variables to which the type of diffuse AND operator is applied, which contemplates the minimum values according to [53]. This is represented in the Equation (2).  
\[\mu_{AuB}=min\left[\mu_{A}(x),\mu_{B}(x)\right] \tag{2}\]  
Aggregate conclusions. This process aims to unify results. The output rules are combined to obtain a result as a consequence cut resulting from the output variable. The development of the method corresponds to the integration of the set of consequents of the total fuzzy rules. Aggregation is the overall integration of fuzzy values through unification, the goal is to generate only one.  
Defuzzyfication. The final result of this method corresponds to the output value. There are different defuzzyfication methods. The area center method (COA) is one of the most commonly used defuzzification methods, which is also known as the centroid method. This method determines the center of the fuzzy set area and returns the corresponding crisp value. The sums center method (COS) and the maximum mean method are two alternative methods in defuzzification [54]. For convenience, in this study the centroid is used, Equation (3) [55, 56, 57].  
\[\textit{centroid}=\frac{\sum_{x=a}^{b}\mu A(x)x}{\sum_{x=a}^{b}\mu A(x)} \tag{3}\]  
Once the fuzzy values are obtained, the next stage is to execute the time series artificial neuronal network. Through this machine learning technique, it allows to integrate the factors and the historical data.
## 3 Results  
Participants' mean 5 RM load was 94.7 \(\pm\) 20.3 kg. No significant differences were found between CMJ height measured under the CON and PAPE conditions (30.8 \(\pm\) 5.1 vs. 31.0 \(\pm\) 5.5 cm, respectively; \(p\) = 0.28) (Figure 2A). The smallest worthwhile change for CMJ height in the present study was calculated as \(\pm\) 1.03 cm, and the typical error and mean coefficient of variation were 0.45 cm and 1.4%, respectively. Of the 66 participants included in the sample, 18 showed decreases in CMJ height under the PAPE condition, 20 showed increases, and 28 did not present any change in CMJ height in the same condition (Figure 2B).  
Figure 2: Countermovement jump (CMJ) performance under control (CON) and potentiated (PAPE) conditions (panel (**A**)) and individual changes in CMJ (\(\Delta\)CMJ) in response to the pre-activation protocol (panel (**B**)). In panel (**B**), red, grey, and green bars represent participants that responded negatively, maintained CMJ height, and responded positively to the pre-activation protocol, respectively. The typical error and mean coefficient of variation for CMJ height estimation were 0.45 cm and 1.4%, respectively.  
Linear stepwise multiple regression models were performed by including independent variables hierarchically according to their association with \(\Delta\)CMJ. Four models were performed: (1) including only IPT (\(n\) = 38); (2) including IPT and VLMT (\(n\) = 17); (3) including IPT, VLMT, and POCON (\(n\) = 17); and (4) including all independent variables (\(n\) = 17). None of the proposed models were significant (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **F** & \(\mathbf{p}\) & **R2** & **Adjusted R2** \\ \hline
**Model 1** & & & & \\ Predictors: IPT & 0.05 & 0.83 & 0.003 & \(-\)0.063 \\ \hline
**Model 2** & & & & \\ Predictors: IPT \& VLMT \& & & \\ \hline
**Model 3** & & & & \\ Predictors: IPT, VLMT
## 4 Experiments and Results
### Experimental Results
#### 4.4.1 Usability and Utility of HybridFilm
The resulting word cloud is presented in Figure 14. The analysis of the word cloud indicates that the majority of the participant comments are concentrated in the positive feedback category (orange), with a small proportion of neutral feedback (blue), and no negative feedback. This suggests that the system received an overall favorable response from the participants.
## 1 Introduction
A nonhomogeneous Poisson process for the arrival rate of the EV during 24 h is used to create a suitable load profile [7]. The arrival rate is modeled based on the vehicle owner's price preference whereas the probability of arriving vehicle in charging station is increased at lower prices and vice versa [7]. The electricity price is going to be affected by the integration of non-dispatchable renewable energy resources resulting in a dynamic electricity tariff [41]. The impacts of wind power generation on the electricity price in Germany is reported in a comprehensive study showing that wind power generation reduces the wholesale electricity price [42]. Recent research implementation using the queue model emphasizes the EV satisfaction level and preserves power networks [13]. In addition, incentive prices not only achieve reduced cost but improve the utilization of charging infrastructure [43]. In Ref. [44], the controlled charging demand is calculated by using a queuing model and integrated into the economic dispatch algorithm to find the optimal charging plan of each EV. In this paper, a virtual queue is continuously developed to account for EVs owner satisfaction and to avoid excessive EVs charging load. The developed model is quite simple and any EVs plugged into the system can participate in the queue. The owner satisfaction is based on saving both the time and cost of the charging process during two scenarios for wind power generation. The main contributions of this work could be summarized as follows:  
* Developing queue model to account for owner preferences such as electricity tariff rate (ETR) and estimated parking time duration (EPTD) (the time that the owner could keep his vehicle plugged in the grid). Using incentive electricity tariffs with wind power generation and investigating their impact on both owner and the grid in terms of charging cost and peak load shifting respectively. * Simulation of 24-h scenarios using accelerated quasi-static time series (QSTS) engine [45; 46]. The organization of the paper is as follows: Section 2 describes the mathematical model of queuing theory. Section 3 proposes the DSM using queuing theory. Section 4 presents the numerical results based on the standard IEEE 123-node test system. Finally, the conclusions are presented in Section 5.
## 5 Hardware Deployment
### AI-Accelerator VTA  
The Versatile Tensor Accelerator (VTA) is an open-source hardware accelerator designed by Apache TVM to enable the efficient execution of machine learning models on FPGA. In addition, VTA supports quantization, a key feature to adapting the models efficiently for deployment in specific hardware resources. For efficient deployment on FPGA using the VTA, its backend workflow for ANN deployment involves designing and training the model, performing quantization, compiling the model, configuring the hardware, taking input for testing, running inference, and the final post-processing stage for providing output. The advantage of VTA is that it is fully customizable, i.e., the hardware intrinsic properties, memories, and data types can be customized to adapt to the hardware backend requirements [14].  
Quantization of the ANN model is a crucial step for successful deployment. It is the process of reducing the bit width of a deep learning model's weights and activation functions by sharing parameters, decreasing hardware resource usage, and consequently optimizing the model for the target FPGA [40]. Apache TVM can convert a high-level ANN model into a deployable quantized module on a range of hardware platforms. It enables the quantization and efficient execution of models from deep learning frameworks with almost the same accuracy or just a little decline from the original accuracy. The compiler uses an 8-bit integer quantization to efficiently increase the resources of specific target hardware platforms.  
To optimize the neural network for specific hardware and thereby reduce the memory footprint, Apache TVM quantizes the 32-bit float values to 8-bit integers. In addition, it utilizes VTA to perform further quantization to optimize the ANN models by creating a balance between model accuracy and FPGA resource constraints. The purpose of such a quantization process is to enhance time efficiency and better resource utilization since quantization affects the performance of a model as a function of the model depth [40]. Other than quantization, VTA also performs other operations, including the fetch, load, compute, and store, that work together to manage the data flow and optimize the performance of the inference process.  
The modules inside VTA, as shown in Figure 6, have specific work functions as below:  
1. The fetch module loads a stream of instructions from the dynamic random-access memory (DRAM), decodes them and transfers them into the corresponding FIFO queues.
2. The load module loads the DRAM's input, weight, and bias tensors into data-dedicated on-chip memories.  
Figure 5: TVM Compilation Flow.  
3. The compute module performs various computations involving matrix multiplication and activation functions. It also loads data and micro-op kernels from the DRAM into the register file of on-chip memory and the micro-op (UoP) cache, respectively.
4. The store module then retrieves the results generated by the compute module and sends them back into the DRAM.
## 2 Methods
### WDR Simulation by Eulerian Multiphase Model  
Under severe convective weather conditions, the coupled effect of strong wind and extreme rainfall will increase the load on transmission lines, and the rainwater adhering to the conductor surface can significantly change its aerodynamic characteristics. The WDR loads is related to the interaction of raindrops of different sizes with the wind flow. In the EM model, rain is regarded as various continuum and dilute phases with equivalent raindrop sizes, reducing the computation cost compared to Lagrangian particle tracking (LPT) model. Raindrops are divided into \(N\) groups according to the diameter \(D\), and the \(k\)th rain phase represents the collection of raindrops of which the diameter \(D\in[D_{k}-\Delta D/2,\ D_{k}+\Delta D/2]\), \(k=1,2,\ldots,\) N, where \(D_{k}\) is the \(k\)th diameter and \(\Delta D=D_{k}-D_{k-1}\). The mass and momentum conservation equations for \(k\)th rain phase are presented as follows [30]:  
\[\frac{\partial\rho_{\rm w}\alpha_{k}}{\partial t}+\frac{\partial(\rho_{\rm w} \alpha_{k}u_{kj})}{\partial x_{j}}=0, \tag{6}\]  
\[\frac{\partial\rho_{\rm w}\alpha_{k}u_{ki}}{\partial t}+\frac{\partial(\rho_{ \rm w}\alpha_{k}u_{ki}u_{kj})}{\partial x_{j}}=\rho_{\rm w}\alpha_{k}g_{i}+ \rho_{\rm w}\alpha_{k}\frac{3\mu{\rm C_{D}}R\rho_{\rm R}}{4\rho_{\rm w}{D_{k} }^{2}}(u_{i}-u_{ki}), \tag{7}\]  
\[Re_{\rm R}=\frac{\rho_{\rm a}D_{k}}{\mu}|\mathbf{u}-\mathbf{u}_{k}|, \tag{8}\]  
where \(\rho_{\rm w}\) and \(\rho_{a}\) are the density of water and air, respectively; \(\alpha_{k}\) is the volume fraction of the \(k\)th rain phase; \(\mathbf{u}\) and \(\mathbf{u}_{k}\) represent the velocity vector of wind and the \(k\)th rain phase, respectively; \(g_{i}\) is the gravity along \(i\) direction; \(\mu\) is the dynamic viscosity of air; \({\rm C_{D}}\) is the drag coefficient; \(Re_{\rm R}\) represents the relative Reynolds number.  
The second term on the RHS of Equation (7) stands for the dragging of the wind flow. A raindrop in equilibrium state gives  
\[g_{i}+\frac{3\mu C_{\rm D}Re_{\rm R}}{4\rho_{\rm w}{D_{k}}^{2}}V_{\rm t}(D)=0, \tag{9}\]  
where \(V_{\rm t}(D)\) is the terminal velocity of raindrops with diameter \(D\), and can be calculated by [39]:  
\[V_{\rm t}(D)=\left\{\begin{array}{ll}0,&D\leq 0.03\;{\rm mm};\\ 4.323(D-0.03),&0.03\leq D\leq 0.6\;{\rm mm};\\ 9.65-10.3\exp(-0.6D),&D\geq 0.6\;{\rm mm}\end{array}\right., \tag{10}\]  
Then, the drag coefficient \(C_{\rm D}\) is constructed in the following form to hold Equation (9):  
\[C_{\rm D}=\frac{24}{Re_{\rm R}}\exp\left(-0.1 FN+0.201 FN^{2}-0.03537 FN^{3}+0.0 02537 FN^{4}\right), \tag{11}\]\[FN=\ln\,(1+Re_{\rm F}). \tag{12}\]
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 2 Related Works
[17] adopted phone-based accelerator data for human activity recognition, where six daily activities from 29 volunteers are involved. Note that the time series data are segmented into examples over 10-s example duration (ED), achieving accuracy over 90% for most activities. Similarly, [PERSON] et al. [23] developed a SVM-based classifier for recognition over seven common physical activities. Both the time- and frequency-domain information are taken into consideration. Subsequently, [PERSON] et al. [24] evaluated thoroughly the impacts of various types of sensor data and demonstrated that performance improvement is available by combining individual parameters together. The health status measurement and prediction can be achieved with the user behavior profiles generated by smartphone sensors. Hence, several existing studies have been conducted, emphasizing practical applications for medical purposes. In Reference [25], the data derived from accelerometer are processed to measure the behavior regarding a patient's actual stress levels, indicating the generalized and prosperous utilization for healthcare environment with smartphone sensor data analysis. Thereafter, [PERSON] et al. [14] developed a health status measuring design so as to objectively monitor the real-time physical condition of patients, which provides a unique approach for the clinicians to conduct in-time treatments. Specifically, the studies on continuous authentication combing user's behavioral properties have been presented in existing papers. [PERSON] et al. [13] emphasized on the reliability on the usage of motion-sensor behaviors for continuous authentication conducted on smartphones. Subsequently, several human activity recognition for authentication purposes have been proposed [15; 26]. Accordingly, continuous authentication towards identification for smartphone users is performed in our authentication scheme, resulting in advanced security properties.
Remote Sens._**2015**, \(7\), 7378-7401; doi:10.3390/rs70607378  
_remote sensing_  
ISSN 2072-4292  
www.mdpi.com/journal/remotesensing  
_Article_  
Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 1, [PERSON] 3 and [PERSON] 2**  
1 Department of Engineering, University of Almeria, Ctra. de Sacramento s/n, La Canada de San Urbano, Almeria 04120, Spain; E-Mail: [EMAIL_ADDRESS]  
2 Department of Sciences and Technologies, University of Naples \"Parthenope\", Centro Direzionale Isola C4, Naples 80143, Italy; E-Mails: [EMAIL_ADDRESS] (A.V.); [EMAIL_ADDRESS] (C.P.)  
3 Department of Geography, University of Almeria, Ctra Sacramento s/n, La Canada de San Urbano, Almeria 04120, Spain; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-950-015-997; Fax: +34-950-015-994.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 24 April 2015 / Accepted: 29 May 2015 / Published: 3 June 2015_  
results were hardly improved by adding the information of the WorldView-2 image. The most important information for correct classification of different crops under greenhouses was related to the greenhouse management practices and not the spectral properties of the crops themselves.  
Keywords:Landsat 8; WorldView-2; time series; object-based classification; greenhouse crops; decision tree
## 5 Results
### Comparison between Configurations Results  
This section displays a comparison between the highest accuracy reached in each configuration along with the number of features used to train the SVMs. Figure 5 shows that the maximum accuracy attained in configuration II was higher than the one obtained in configuration I. These results prove that fusing multiple handcrafted features acquired from several domains with a single DL feature set could improve diagnostic performance. In addition, the peak accuracy attained in configuration III was greater than that achieved in configuration II. This confirms that fusing numerous DL features with multiple handcrafted descriptors mined from numerous domains with PCA was superior to employing either one individual DL feature set of a single CNN or one set of handcrafted features. The performance of configuration III also verifies that PCA is capable of reducing the dimension of features with an enhancement in diagnostic accuracy.  
Figure 4: ROC curves of the quadratic SVM constructed with PCs generated after fusing the entire DL features with the combined handcrafted features when the positive class is: **(a)** High squamous intraepithelial, **(b)** Negative for intraepithelial **(c)** Low squamous intraepithelial, **(d)** Squamous cell carcinoma.
# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
## 3 Simulation Analysis of Factors Affecting Detection Effect  
In laser thermography methods, key parameters, such as laser power and scanning speed, have an important influence on the detection quality [29; 30]. In order to guide the selection of experimental parameters of laser thermography, based on finite element method, a simulation model with a vertical surface crack is established. Circular Gaussian heat source is used to simulate the point laser, and the surface temperature response of the model under different heat source parameters is calculated. According to the simulation results, the effects of material properties, laser power and laser scanning speed, thermal image acquisition frequency on the temperature gradient at the crack are analyzed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multirow{2}{*}{\begin{tabular}{c} **Density** \\ **\(\rho\) (tonne/mm\({}^{3}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Specific Heat** \\ **\(C\) (m)\(\cdot\)(tonne\(\cdot\)K)\({}^{-1}\)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Thermal** \\ **Conductivity** \\ **\(\lambda\) (mW/(mm\(\cdot\)K))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Diffusivity** \\ **\(k\) (mm\({}^{2}\)/s)** \\ \end{tabular} } \\ \hline Aluminum alloy & \multirow{2}{*}{\(2.82\times 10^{-9}\)} & \multirow{2}{*}{\(963\times 10^{6}\)} & \multirow{2}{*}{\(157\)} & \multirow{2}{*}{\(57.8\)} \\ (7050) & & & & \\ Titanium alloy & \multirow{2}{*}{\(4.44\times 10^{-9}\)} & \multirow{2}{*}{\(611\times 10^{6}\)} & \multirow{2}{*}{\(6.8\)} & \multirow{2}{*}{\(4.8\)} \\ (TC4) & & & & \\ Nickel-based Alloys & \multirow{2}{*}{\(8.16\times 10^{-9}\)} & \multirow{2}{*}{\(465\times 10^{6}\)} & \multirow{2}{*}{\(11.85\)} & \multirow{2}{*}{\(3.12\)} \\ (K438) & & & & \\ Iron-based alloys & \multirow{2}{*}{\(8.23\times 10^{-9}\)} & \multirow{2}{*}{\(422\times 10^{6}\)} & \multirow{2}{*}{\(16.7\)} & \multirow{2}{*}{\(2.5\)} \\ (GH903) & & & & \\ Air & \multirow{2}{*}{\(1.0\times 10^{-14}\)} & \multirow{2}{*}{\(1.0\times 10^{6}\)} & \multirow{2}{*}{\(0.023\)} & \multirow{2}{*}{\(2.3\times 10^{6}\)} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermodynamic parameters of commonly used repair materials.  
Figure 3: Analysis of temperature response based on circular Gaussian pulse excitation: (**a**) Temperature distribution along the y-direction of different materials; (**b**) Temperature gradient distribution of different materials along the y-direction; (**c**) Temperature distribution along the y-direction at different times; (**d**) Temperature distribution along the y-direction of different \(Q_{0}\).
## 3 Results
### White Matter Structure Data  
The effect of football juggling learning on white matter integrity (FA) was analyzed by repeated measurement ANOVA. Notably, only results on interactions were reported because this study focused on interactions between the groups and test time i.e., whether football juggling learning caused changes in WMI.  
After family-wise error (FWE) correction, ANOVAs in FA revealed significant group by time interactions in the R.ACR (\(F\) (1, 92) = 11.05, \(p\) = 0.001 \(<\) 0.05, _partial_\(\eta^{2}\) = 0.107) and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Control Group** & **Experimental Group** \\ \hline N & 41 & 53 \\ Gender (male/female) & 24/17 & 38/15 \\ Age (years) & 18.49 \(\pm\) 0.746 & 18.26 \(\pm\) 0.524 \\ BMI (height/weight2) & 21.81 \(\pm\) 3.145 & 20.61 \(\pm\) 2.737 \\
4 \(\times\) 10-m shuttle run (s) & 11.47 \(\pm\) 1.042 & 11.71 \(\pm\) 0.860 \\ Standing broad jump (m) & 1.99 \(\pm\) 0.331 & 1.98 \(\pm\) 0.283 \\ Sit-and-reach (cm) & 13.64 \(\pm\) 8.185 & 12.37 \(\pm\) 6.018 \\ \hline \hline \end{tabular} Note: Values are presented as mean \(\pm\) SD or percentages unless otherwise indicated.  
\end{table}
Table 1: Demographic characteristics of participants (M \(\pm\) SD).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Control Group**} & \multicolumn{2}{c}{**Experimental Group**} \\ \cline{2-5}  & **Baseline** & **Posttest** & **Baseline** & **Posttest** \\ \hline \multicolumn{5}{c}{1-back task} \\ RT & 680.954 \(\pm\) 182.830 & & 669.432 \(\pm\) 120.663 & \\ ACC & 0.948 \(\pm\) 0.034 & & 0.959 \(\pm\) 0.044 & \\ \multicolumn{5}{c}{2-back task} \\ RT & 1109.885 \(\pm\) 179.623 & 1069.991 \(\pm\) 215.187 & 1101.474 \(\pm\) 160.020 & 911.799 \(\pm\) 184.143 \\ ACC & 0.740 \(\pm\) 0.167 & 0.815 \(\pm\) 0.119 & 0.791 \(\pm\) 0.171 & 0.894 \(\pm\) 0.114 \\ \multicolumn{5}{c}{FA} \\ R.ACR & 0.419 \(\pm\) 0.029 & 0.411 \(\pm\) 0.023 & 0.419 \(\pm\) 0.023 & 0.427 \(\pm\) 0.027 \\ GOCC & 0.618 \(\pm\) 0.022 & 0.611 \(\pm\) 0.018 & 0.617 \(\pm\) 0.023 & 0.623 \(\pm\) 0.021 \\ \hline \hline \end{tabular} Note: RT, reaction time; ACC, accuracy; FA, fraction anisotropy; R.ACR, right anterior corona radiate; GOCC, genu of corpus callosum.  
\end{table}
Table 2: Analysis of experimental and control groups for WM and FA (M \(\pm\) SD).  
GOCC (\(F\) (1, 92) = 11.41, \(p\) = 0.001 \(<\) 0.05, _partial_\(\eta^{2}\) = 0.110). However, follow-up analysis showed no significant differences (\(p\) \(>\) 0.05) between the groups at baseline (Figure 1).  
Moreover, simple effect analysis revealed that the FA of the R.ACR exhibited an upward trend in the exercise group (\(p\) = 0.017 \(<\) 0.05) and a downward trend in the control category (\(p\) = 0.024 \(<\) 0.05). Additionally, the FA of GOCC showed an upward trend in the exercise group (\(p\) = 0.028 \(<\) 0.05) and a downward trend in the control category (\(p\) = 0.013 \(<\) 0.05). Specific FA values of the brain regions with significant interactions are shown in Table 2.