esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 4 Background  
### Information Fusion, IoT, and Sensors  
Information fusion is composed of two steps, namely pre-classification and post-classification fusion. Pre-classification fusion deals with combining the information prior to the application of any classifier or matching fusion algorithm [28]. During the post-classification fusion phase, the information is combined after the decisions of the classifiers have been obtained [28].  
The emergence of IoT vision comes as a result of the integration of different enabler technologies together such as sensory, communication, service-oriented architecture, networking, and intelligent information processing technologies. IoT systems have a wide range of implications and objectives. One of these objectives is to interconnect different objects such that these objects are addressable, locatable, and readable. Furthermore, each IoT object can be used either to fulfill specific goals or produce and consume IoT services [29]. These IoT objects vary from limited sensing capability sensors such as wearable devices, consumer goods, complicated endpoints, and advanced systems [30].  
According to the 2020 conceptual framework [31], IoT architecture needs four components as expressed in Equation (1):  
\[IoT=Services+Data+Networks+Sensors \tag{1}\]  
Sensors, as data sources, form Wireless Sensor Networks (WSNs) and are an integral part of IoT environments. Sensors are located at the bottom layer (i.e., the things layer), where data is produced and consumed as shown in Figure 1. An edge layer, also known as fog layer, uses edge devices to perform a substantial amount of computation, storage, and communication to ensure seamless accessibility, reliability, and timeliness of the data for its users. Powerful devices are located at the Cloud layer to perform heavyweight tasks such as data mining. Services are provided to users through IoT applications in a ubiquitous manner [32].  
Reliability of smart applications is threatened due to the lack of standard methods for addressing their data sources trust concerns [33]. To achieve the full benefits of smart applications, we have to manage the trust of data sources. For example, data sources can act deceitfully by providing misleading or false information and this type of threat cannot be mitigated using traditional security mechanisms [13]. Trust, unlike security, is concerned with sensors' features or attributes such as reputation, honesty, and accuracy [17; 34].  
As most IoT sensors have been used to either monitor or perform some actions to a large number of time-critical decision-making systems, any malicious behaviour of such sensors may result in harmful consequences. For example, the malicious behaviour of smart vehicles could put passengers' safety in danger. Therefore, trustworthiness is an indispensable requirement to mitigate such risks in such systems [13; 35]. A successful trust management system detects malicious activities, improves the success rate of information fusion techniques, and hence, provides qualified and reliable smart applications.
# A Photoplethysmogram Dataset for Emotional Analysis
## 3 Results and Discussion
### Statistics of Frequency and Time Domain Features  
Table 7 lists the statistics of frequency-domain features. There were no significant differences in the mean value of frequency domain features in the analysis of variance (ANOVA) test, because of high variations. The only difference in frequency-domain features between different emotions was found between joy and relaxed. The VLFa of joy was higher than relaxed (\(p<0.05\)) in [PERSON]'s signed rank test. This means that the VLFa of joy of a person is expected to be higher than the VLFa of relaxed with a high possibility.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sadness** & **Relaxed** & **Joy** & **Anger** \\ \hline
80.65 \(\pm\) 10.39 & 81.41 \(\pm\) 10.17 & 82.89 \(\pm\) 10.20 & 79.75 \(\pm\) 10.04 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average and standard deviation of BPM.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable Block** & **Description * \\ \hline B1 & None \\ B2 & LSTM (32) \\ B3 & LSTM (64) \\ B4 & Bi-LSTM (32) \\ B5 & Bi-LSTM (64) \\ B6 & GRU (32) \\ B7 & GRU (64) \\ B8 & Bi-GRU (32) \\ B9 & Bi-GRU (64) \\ B10 & LSTM (32), Dropout (0.2), LSTM (16) \\ B11 & LSTM (64), Dropout (0.2), LSTM (32) \\ B12 & Bi-LSTM (32), Dropout (0.2), Bi-LSTM (16) \\ B13 & Bi-LSTM (64), Dropout (0.2), Bi-LSTM (32) \\ B14 & GRU (32), Dropout (0.2), GRU (16) \\ B15 & GRU (64), Dropout (0.2), GRU (32) \\ B16 & Bi-GRU (32), Dropout (0.2), Bi-GRU (16) \\ B17 & Bi-GRU (64), Dropout (0.2), Bi-GRU (32) \\ \hline \hline \end{tabular}  
* The numbers in parentheses indicate numbers of nodes (for LSTM or GRU) or dropout ratio (for Dropout).  
\end{table}
Table 5: Tested method and Description.  
The means and standard deviations of time-domain features among the participants are shown in Figure 4. As shown in the figure, there were no significant differences in the mean value of statistical features in the ANOVA test. However, there were individual differences between anger and other emotions. According to [PERSON]'s signed rank test, the meanNN and pNN40 of anger were bigger than joy (\(p<0.05\)); the SDNN of anger was smaller than joy (\(p<0.05\)); the RMSSD of anger was bigger than joy and relaxed (\(p<0.05\)); the meanNN and pNN40 of anger was bigger than relaxed (\(p<0.01\)); the medianNN of anger was bigger than relaxed (\(p<0.05\)); and the NN40 of anger was smaller than sadness (\(p<0.01\)).
# Analysis of Variables Influencing Scour on Large Sand-Bed Rivers Conducted Using Field Data
## 3 Results
### Variable Sensitivity Analysis  
The Pearson correlation coefficient (\(r\)) (Figure 6) interprets the strength of the correlation between selected variables in the filtered subset and measured scour depth (\(d_{s}\)). The Pearson correlation coefficient varies in range from \(-1\) to \(1\). A value of \(-1\) indicates a perfect negative correlation, a value of \(1\) a perfect positive correlation, and a value of \(0\) indicates no correlation at all. If variables are classified into four categories: pier geometry (\(b_{\phi f}\)), hydraulic (\(y\), \(v\), \(\tau\), and \(Fr\)), compounded hydraulic-sediment variables (\(v_{c}\), \(Fr_{c}\), and \(Fr_{d}\)), and sediment (\(\tau_{c}\), and \(Re_{p}\)), then the pier geometric variables are the most influential, followed by compound hydraulic-sediment, hydraulic, and finally the sediment variables. Based on Pearson correlation coefficients, \(b_{\textit{of}}\) proved to be the most influential variable for estimating \(d_{s}\) (\(r\) = 0.625), which is the same observation that was already stated in previous investigations (28,30). The second most influential variable is \(y\) (\(r\) = 0.492), followed closely by \(v_{c}\) and \(v\), \(r\) = 0.474 and \(r\) = 0.436, respectively. In the compound hydraulic-sediment category, \(v_{c}\) has the greatest impact on scour depth in comparison to \(Fr_{d}\) and \(Fr_{c}\), which are defined by \(r\) = 0.427 and \(r\) = 0.323, respectively. The remaining four variables (\(\tau\), \(\tau_{c}\), \(Fr\), and \(Re_{p}\)) show a very weak correlation with scour depth, with \(r\) < 0.3.
# Research on a Strategy for Prediction Methods of Submarine Self-Propulsion Hydrodynamic Performance
## 1 Introduction
Therefore, this work studied commonly used methods for numerical simulations of submarine self-propulsion hydrodynamic performance, analyzing the following four calculation strategies:  
* Strategy 1: Using the steady-state MRF method for the submarine with propeller self-propulsion hydrodynamic performance simulation, where the propeller's rotational speed is initially determined based on submarine resistance and the propeller's open-water performance curve. After the convergence of the hydrodynamic loads, the simulation continues using the unsteady RBM method. This method is referred to as \"MRF + RBM--submarine with propeller\". * Strategy 2: Using the unsteady RBM method with a propeller rotational speed of \(n=0\) r/s to calculate the submarine resistance. After the submarine resistance convergence, the propeller rotational speed is set, and the submarine with propeller self-propulsion hydrodynamic performance simulation is conducted. This method is referred to as \"RBM--submarine first, then propeller\". * Strategy 3: Using the unsteady RBM method with the propeller rotational speed set near the self-propulsion point for the submarine with propeller self-propulsion hydrodynamic performance simulation. This method is referred to as \"RBM--submarine with propeller\". * Strategy 4: Using the unsteady RBM method with a propeller rotational speed of \(n=0\) r/s, conducting a self-propulsion hydrodynamic performance simulation, and then changing the propeller rotational speed after the submarine resistance convergence. The propeller rotational speed is adjusted iteratively in the same file, conducting the submarine with propeller self-propulsion simulation at different propeller rotational speeds. This method is referred to as \"RBM--propeller rotational speed iteration, submarine first, then propeller\". These methods are compared based on submarine resistance, unsteady propeller forces, total solver actual runtime, and propeller excitation forces at different propeller rotational speeds to determine the optimal prediction method for submarine self-propulsion hydrodynamic performance. The commercial computational fluid dynamics software used was Star CCM + 2020.1 (Build 15.02.007-R8), with a 28-cores Intel(R) Core(TM) i9-7980 XE [EMAIL_ADDRESS] GHz 2.59 GHz.
# Classification of Sleep Stage with Biosignal Images Using Convolutional Neural Networks
## 5 Experimental Results
### Experiments
The reason why it is difficult for the CNN to distinguish between S1 and R is because they have similar characteristics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stage** & **Precision** & **Recall** & **F1 Score** & **Weighted F1 Score** & **Accuracy** \\ \hline S1 & 0.78 & 0.91 & 0.84 & & \\ S2 & 1.00 & 1.00 & 1.00 & & \\ SWS & 1.00 & 1.00 & 1.00 & & \\ R & 0.91 & 0.78 & 0.84 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experiment results for test data (Exp3). Figure 6: Confusion matrix of the classification results (Exp2). Figure 7: Training and validation set accuracy over epochs (Exp3). Table 5 shows a comparison of the results of the three experiments. It shows the average, the range of accuracy, and the F1 score for each experiment. The highest accuracy was shown when EEG and EOG images in the time domain and frequency domain were used. In this research, only EEG and EOG channels were used, but in general, we can obtain better results by adding more channels. It was experimentally confirmed that using only EEG and EEG channels can achieve similar results to using more channels. We showed that it is possible to classify sleep stages using the EEG and EOG channel and a CNN working on biosignals, without any feature extraction processes. Training after simple preprocessing was conducted without requiring any expert knowledge of features. This is an advantage, because the CNN can learn the features in the EEG and EOG images, wherein the images are a kind of dataset that somnologists are familiar with when classifying sleep stages. When analyzing the kinds of errors in the classification, the errors mostly happened in stages that are contiguous in the sleep cycle. For example, S1 is most often confused with R or vice versa. Similarly, S1 is known as the stage for which inter-somnologist agreement is the lowest. It can be misclassified as R, which can contain patterns similar to S1.
###### Abstract
To this end, the sparse coding technique can save a significant amount of sampling time and sample storage space, and it is favorable and advantageous. The aim of this study was to detect some pests and diseases of apples using digital image processing and sparse coding based on computational intelligence. In this study, to increase the processing speed, sparse coding was used, in which only a portion of the image is analyzed instead of the whole image. The advantage of this case is that, instead of using the extracted features from the sample images, the extracted features from the sub-images via the sparse method were used to train the neural network. Therefore, instead of analyzing the whole image, examining a part of the fruit, which includes damages caused by pests or diseases, minimizes processing time.
# Integrating Interpolation and Extrapolation: A Hybrid Predictive Framework for Supervised Learning
## 2 Problem Statement and Model Setup
### Optimization Model M0
#### 2.1.2 The Linearization of Absolute Terms  
Objective Function (4) is non-linear due to the absolute terms. To linearize them, we introduce auxiliary non-negative decision variables for categorical and numerical features, respectively. For categorical features \(c\) (\(c\in C\)), we define  
\[z_{i}^{c,k}=\Big{|}x_{i}^{c,k}-x_{0}^{c,k}\Big{|},\quad\ c\in C,\ i\in\{1,\dots,N \},k\in\{1,\dots,l_{c}\},\]  
and add the following constraints:  
\[x_{i}^{c,k}-x_{0}^{c,k}\leq z_{i}^{c,k},\quad\ c\in C,\ i\in\{1,\dots,N\},k\in \{1,\dots,l_{c}\}, \tag{5}\]  
\[-\Big{(}x_{i}^{c,k}-x_{0}^{c,k}\Big{)}\leq z_{i}^{c,k},\quad\ c\in C,\ i\in\{1, \dots,N\},k\in\{1,\dots,l_{c}\}. \tag{6}\]  
For numerical features \(u\) (\(u\in U\)), we define  
\[z_{u}=\left|\sum_{i=1}^{N}\lambda_{i}(x_{0})x_{i}^{u}-x_{0}^{u}\right|,\quad \ u\in U,\]  
and add the following constraints:  
\[\sum_{i=1}^{N}\lambda_{i}(x_{0})x_{i}^{u}-x_{0}^{u}\leq z_{u},\quad\ u\in U, \tag{7}\]  
\[-\left(\sum_{i=1}^{N}\lambda_{i}(x_{0})x_{i}^{u}-x_{0}^{u}\right)\leq z_{u}, \quad\ u\in U. \tag{8}\]  
After linearization, we obtain the following linear optimization model:  
**Model M0**:  
\[\min_{\lambda(x_{0})}\sum_{i=1}^{N}\lambda_{i}(x_{0})\sum_{c\in C}\sum_{k=1}^ {l_{c},k}z_{i}^{c,k}+\sum_{u\in U}z_{u} \tag{9}\]  
subject to Constraints (2)-(3) and (5)-(8).  
This model can be directly solved by commercial solvers (e.g., Gurobi and Cplex) to obtain \(d^{*}(x_{0})\) for each new data point. Next, we discuss how to choose the best prediction method for new observations based on their centralities to the original dataset.
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 3 Numerical Analysis
### Sensitivity of Receiver  
Receiver sensitivity (\(RS\)) at the receiver side is presented as the lowest tolerable value of received signals strengths is [48]:  
\[RS=RS(\text{dB})-\{IL_{total}+PP\}>-32.5\text{ dBm} \tag{23}\]  
where \(IL_{total}\) and \(PP\) represent total insertion loss as well as power penalty, respectively. The insertion loss for DS and UP such as \(IL_{dn}\) and \(IL_{up}\) are given as [48]:  
\[IL_{dn}(\text{dB})=IL(MZM+MUX+Circulator+DEMUX) \tag{24}\]  
and  
\[IL_{up}(\text{dB})=IL(DynamicY+MUX+Circulator) \tag{25}\]  
Thus, the calculated \(RS_{dn}\) and \(RS_{up}\) for DS and UP transmission, respectively, over 10 km in 16 \(\times\) 10/10 Gbps TWDM-PON is given as [48]:  
\[IL_{dn}(\text{dB})=3+2+1+3=9\text{ dB} \tag{26}\]  
\[RS_{dn}=-24-\{9+2\}=-35>-32.5\text{ dBm} \tag{27}\]  
\[IL_{up}(\text{dB})=3+2+1=6\text{ dB} \tag{28}\]  
\[RS_{up}=-24-\{6+2\}=-32>-32.5\text{ dBm} \tag{29}\]  
Table 8 summarizes the system performance parameters QF, BER, RS and OSNR obtained for TWDM-PON using MNZCC code at 1596 nm for DS and 1527 nm for UP channels operating at symmetric 10 Gbps per channel rate. The suggested system is a symmetric system for diverse link distances with acceptable log (BER) of \(-9\). The obtained RS for DS and UP transmission easily achieve minimum value sensitivity conditions endorsed by ITU-T 989.2 [48; 49].
# A Review on Upper Limb Rehabilitation Robots
## EMG-Driven Exoskeleton Robots
### SUEFUL-7 Exoskeleton Based on the Muscle-Model-Oriented Control Method  
SUEFUL-7 is an upper limb exoskeleton that is used to help patients with weak motions [27]. SUEFUL-7 uses a unique controlling method named muscle-model-oriented that also depends on the fuzzy principal, where a high number of degrees of freedom require a high number of fuzzy mathematical models [71]. SUEFUL-7 employs impedance control in combination with the muscle-model-oriented method, and the parameters of the impedance are modified in real time, according to the extracted EMG signal and limb posture [27]. The root mean square (RMS) is the feature that is extracted from the EMG signal to control the SUEFUL-7 [55]. SUEFUL-7 is characterized by a hybrid controlling nature, where other variables such as forearm force, forearm torque and hand force are used as input signals in addition to the EMG signal [55]. The controller depends mainly on the EMG signal when it is high and the RMS is accurate; otherwise, the controller uses all other aforementioned variables [71].
## 3 Results
### Technologies and Implementation
#### 3.3.3 Manufacturing Execution Systems  
MESs are tools designed to help on the shop floor and to follow the processes from the raw material until the finished product (or sub-product). These tools enable the decision makers to act in real time to improve processes. MESs are nowadays supported by IIoT where all kinds of sensors (and actuators) can be connected and interacted with from high- to low-level systems. In this way, there is a holistic approach from tools like ERP and LCA or PLCs and workers.  
The use of MES can reduce time and costs on the shop floor if the information flows properly and if both the machines and processes from the factory are fully digitalized.  
Figure 19: (**a**) ProductTemplate class; (**b**) Production class; (**c**) BillOfMaterial class.  
Figure 18: (**a**) PurchaseOrder class; (**b**) Product class; (**c**) SaleOrder class; (**d**) SaleOrderLine.  
The MES architecture proposed is presented in Figure 20. MES is a type of software that can be used by a manager and has two interfaces: one with REST API, so machines and shop-floor workers can interact with the system (local), and another based on publish/subscribe methods, so external software can interact with the system (cloud-enabled). The local REST API can be seen as a gateway that was directly built in MES to simplify the operations on the shop floor. This MES software is also supported by a database that records all the actions relevant to the production environment.  
With the publish/subscribe interface, MES can read or write on external software without changing their internal state and, if needed, can postpone this task giving priority to shop-floor control and management through the REST API (both communications are bi-directional as can be seen in Figure 21).  
The REST API of MES enables the shop-floor machines to interact with the system but also enables the HMI to be connected, and a worker can add more information manually about the production processes. This API features the following services: Get Production, Add Production, Getal Production, Get Events, Set Events, Alter Event, Get Worker Registers, Set Worker Registers, Alter Worker Registers, Get Production Unit, Set Production Unit, and Alter Production Unit (as depicted in Figure 22). With this data, MES can create and show graphics and metrics available to managers to help with decision support.  
Figure 21: MES interfaces (no user input).  
Figure 20: MES architecture.
# Geotechnical Evaluation of Diesel Contaminated Clayey Soil
## 3 Results and Discussion
### Impact of Diesel Contamination on the Soil's Particle Size Distribution  
Grains' size content of both soils is presented in Table 4, and their granulometric curves are shown in Figure 2. Both natural and diesel contaminated soils corresponded to a fine-grained soil. After diesel soil contamination, a significative (\(p\) < 0.01) change in fine-grained particles sizes' content was observed. The silt-like sized granules increased by 37% while the clay-like sized granules decreased by 46%. This change was due to both the reduction of the dielectric constant and the acidification of the pore fluid of the soil, which caused that the clay-like sized particles agglomerated and increased their size reaching a silt-like size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grain Size** & **Natural Soil** & **Contaminated Soil** \\ \hline Gravel (\%) & 0.25 \(\pm\) 0.06 & 0.22 \(\pm\) 0.03 \\ Sand (\%) & 7.34 \(\pm\) 0.49 & 6.43 \(\pm\) 0.21 \\ Silt (\%) & 52.03 \(\pm\) 1.21 & 71.72 \(\pm\) 1.44 \\ Clay (\%) & 40.38 \(\pm\) 2.30 & 21.63 \(\pm\) 0.70 \\ \hline \hline \end{tabular} Values in the table: mean value \(\pm\) standard deviation (\(n\) = 5).  
\end{table}
Table 4: Impact of diesel contamination in grain-like size distribution of soil.  
Figure 2: Impact of diesel contamination on particle size distribution of soil. Error bars denote standard deviations.  
[PERSON] and [PERSON] [38] indicated that the reduction of the dielectric constant and/or pH drop, as well as ion valence reduction and/or electrolyte concentration decrease, promotes soil flocculation. In low pH environments, the thickness of the double layer around clay particles can decrease [39]. The diffuse double layer theory indicates that the thickness of the double layer (\(1/\chi\)) can be calculated as indicated in Equation (8):  
\[\frac{1}{\chi}=\sqrt{\frac{\varepsilon kT}{8\pi z^{2}\ell^{2}\eta}} \tag{8}\]  
where \(\varepsilon\) corresponds to the dielectric constant of the pore fluid, \(k\) is the Boltzman constant, \(T\) is the absolute temperature, \(z\) is the valence of the cations, \(e\) is the electric charge, and \(\eta\) is the concentration in number of ions per millilliter.  
When the soil was contaminated with diesel (\(\varepsilon_{\mathit{diesel}}=2.3\)), it interacted with the water (\(\varepsilon_{\mathit{water}}=80.3\)) contained in the pores of the soil and reduced the dielectric constant of the pore fluid. As a result, the thickness of the double layer was reduced and the soil particles began to join together due to the influence of the van der Waals forces. As the soil particles got closer to each other, the effect of the van der Waals attractive forces increased. This promoted the agglomeration of clay particles by forming flocs, which was appreciated as an increment in the silt-like size fraction content, with a correspondent decrement in the clay-like size fraction.
# Dynamic Optimization of Data Packet-based Communication for PLC Visual Monitoring
## 3 Experimental Studies and Discussions  
As discussed in Section 2, the dynamic optimization of the data packet-based communication under study is mainly concerned with optimizing the packet size for updating the PLC visual monitoring views.