esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
A number of literature reviews on prognosis methods have been conducted in the last few decades and each review paper has presented its own terminology of prognosis approaches. This paper aims to consolidate all the prognosis approaches to provide a clearer understanding and guidelines in selecting the particular prognosis approach for specific needs. Following the review, a study on the time-series feature prediction has also been presented in this paper. The PANFIS-based method, which is considered as one of the data-driven prognosis approaches, is developed and presented in this paper. PANFIS provides a solution for the adaptive prognostic requirement, where it characterizes a fully open structure and operates in a single-pass learning mode. This trait makes it possible to handle non-stationary characteristics of the system in a sample-wise manner. PANFIS is tested for run-to-failure low speed slew bearing vibration data to predict the time domain vibration features. A comparative study of PANFIS and three other prominent algorithms, known as eTS, simp_eTS, and ANFIS, is also presented. It is shown that PANFIS offers a better prediction performance compared to the other three methods. In order to apply the proposed method for real-time prediction, some future research to simplify the complexity of the algorithm is necessary. A feasibility study to embed the PANFIS method into real-time measurement, for example, using the LabVIEW platform, is also expected to be conducted in the future.  
The MATLAB code of Parsimonious Network Based on Fuzzy Inference System (PANFIS) and the time-domain features of slew bearing vibration data can be downloaded from the following weblink: [[http://www.ntu.edu.sg/home/mpratama/Publication.html](http://www.ntu.edu.sg/home/mpratama/Publication.html)]([http://www.ntu.edu.sg/home/mpratama/Publication.html](http://www.ntu.edu.sg/home/mpratama/Publication.html)).  
Conceptualization, W.C., M.P. and B.K.; methodology, W.C. and M.P.; software, W.C. and M.P.; validation, W.C. and M.P.; formal analysis, W.C. and M.P.; investigation, W.C., M.P. and B.K.; resources, W.C. and B.K.; data curation, W.C.; writing--original draft preparation, W.C. and M.P.; writing--review and editing, W.C., M.P., B.K., T.T. and A.G.; visualization, W.C. and M.P.; supervision, B.K. and T.T.; project administration, W.C.; funding acquisition, B.K.  
The first author thanks the University of Wollongong, Australia for the financial support through International Postgraduate Research Scholarship (IPRS) during the test rig construction and experimental setup. The second author acknowledges the support of Nanyang Technological University start-up grant and MOE Tier-1 grant.  
The data used in this paper were collected from Tribology Laboratory, School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Australia.  
The authors declare no conflict of interest.  
Figure 13: Fuzzy rule evolution of histogram lower feature.
# Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text
## 5 Conclusions
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & \multicolumn{3}{c}{**Without TVH**} & \multicolumn{3}{c}{**With TVH**} \\ \hline \multicolumn{2}{c}{**Application**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F-Score**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F-Score**} & \multicolumn{1}{c}{**Accuracy**} \\ \hline \multirow{4}{*}{LSTM} & Sentiment Analysis & 54.76 & 52.48 & 53.59 & 64.04 & 73.04 & 69.67 & 71.31 & 76.59 \\  & Cyberbullying Identification & 81.01 & 74.09 & 77.40 & 77.35 & 83.05 & 80.64 & 81.83 & 82.04 \\  & SPAM & 74.30 & 80.67 & 77.35 & 91.62 & 86.39 & 93.46 & 89.79 & 95.55 \\  & Reviews & 76.05 & 72.26 & 74.33 & 72.69 & 85.04 & 84.05 & 84.54 & 84.05 \\  & Topic Modeling & 71.02 & 69.03 & 70.01 & 68.97 & 76.89 & 76.43 & 76.66 & 76.44 \\  & Covid Apps Reviews & 59.88 & 58.70 & 59.28 & 80.31 & 71.73 & 65.01 & 68.21 & 87.06 \\ \hline \multirow{4}{*}{BERT} & Sentiment Analysis & 61.83 & 56.94 & 59.28 & 68.94 & 77.82 & 74.91 & 76.34 & 80.87 \\  & Cyberbullying Identification & 86.65 & 88.96 & 87.80 & 86.91 & 92.90 & 91.17 & 92.03 & 91.36 \\  & SPAM & 96.40 & 97.66 & 97.03 & 98.68 & 94.59 & 96.39 & 95.48 & 97.96 \\  & Reviews & 87.04 & 87.80 & 87.42 & 87.33 & 90.95 & 90.91 & 90.93 & 90.91 \\  & Topic Modeling & 87.33 & 86.29 & 86.81 & 86.78 & 88.71 & 87.75 & 88.23 & 88.51 \\  & Covid Apps Reviews & 66.93 & 74.79 & 70.64 & 88.40 & 83.18 & 73.96 & 78.30 & 90.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of the proposed TVH in the classification of text in various applications. The performance of our model depends on both the performance of the TVH and text analysis system. Our normalization method and text analysis model outperforms the existing methods in terms of precision, recall, accuracy, and F1-scores. All authors contributed equally to this work. Both authors have read and agreed to the published version of the manuscript. This research was supported by National Research Foundation of Korea (NRF) Grant funded by the Korean Government (Ministry of Science and ICT) NRF- 2020K1A3A1A47110830. The authors declare no conflict of interest.
# Optical Wireless Communications Using Signal Space Diversity with Spatial Modulation
## 1 Introduction
### Theoretical BER Expressions
Similarly, the distances between two diagonal symbols, denoted as \(d_{3}\) and \(d_{4}\), respectively, can be expressed as  
\[\mathrm{y_{2-QAM}}=\mathrm{a_{2}}+\mathrm{b_{2}}\times j \tag{2}\]4  
\[\mathrm{y_{2-QAM}}=\mathrm{a_{2}}+\mathrm{b_{2}}\times j \tag{2}\]5  
It can be deduced that \(d_{3}<d_{4}\) is obtained when \(0\leq\varphi\leq 45^{\circ}\). Due to \(\mathrm{h}_{1}>\mathrm{h}_{2}\), it can also be deduced that \(d_{2}<d_{4}\). However, the values of \(d_{1}\) and \(d_{3}\) need to be further discussed. When \(d_{1}<d_{3}\), the BER performance of the proposed SSD scheme, denoted as BER\({}_{\text{SSD}}\), is determined by \(d_{1}\) and \(d_{2}\), which can be written as [22]. \[\mathrm{y_{2-QAM}}=\mathrm{a_{2}}+\mathrm{b_{2}}\times j \tag{2}\]6  
where \(\sigma^{2}=\frac{N_{0}}{2}\) is the variance of noise per signal dimension (real and complex). Here, an approximation is applied in Equation (15) when the two Q (\(\cdot\)) terms are dominant for the expected higher \(\frac{E_{s}}{N_{0}}\) values in the indoor LOS optical wireless links [23]. On the other hand, when \(d_{3}\leq d_{1}\), the BER performance of the proposed SSD scheme is determined by \(d_{1}\), \(d_{2}\), and \(d_{3}\), which can be written as  
\[\mathrm{y_{2-QAM}}=\mathrm{a_{2}}+\mathrm{b_{2}}\times j \tag{2}\]7 \[\mathrm{y_{2-QAM}}=\mathrm{a_{2}}+\mathrm{b_{2}}\times j \tag{2}\]8  
Here, another Q (\(\cdot\)) term appears due to the distance of neighboring constellation symbols not being limited to the edge symbols but extended to the diagonal symbols under the condition of \(d_{3}\leq d_{1}<d_{2}\). \[\mathrm{y_{2-QAM}}=\mathrm{a_{2}}+\mathrm{b_{2}}\times j \tag{2}\]9 Signal constellation after the SSD diversity deinterleaving at the receiver. Equations (15) and (16) are used as the theoretical BER expressions of the SSD scheme. The theoretical BER results are further investigated and compared with numerical simulation results in the following section.
## 2 Materials and Methods
### Instruments  
Three instruments were used to analyze the effect of gamified DGBL on academic performance and motivation.  
Academic performance: to assess the level of learning acquired by the students, a specific test was designed for the contents selected for this experience, created and validated by the subject teacher. The test consisted of 10 random questions from a bank of 30 that were conceptual and procedural.  
Motivation: the validated Motivation for Cooperative Learning Strategies questionnaire (CMELAC) [76] was used to measure perceived motivational orientation after the completion of the intervention toward task, learning and Flow. The questionnaire contains a total of 14 items assessing task motivation (5 items), learning motivation (6 items) and flow (3 items). A 5-point Likert scale (1: strongly disagree and 5: strongly agree) was applied (Table 1). The data-gathering adaptation is based on eliminating two items that refer exclusively to teamwork. This instrument was very well-adapted to the proposed experience, as it analyses the motivation aroused in game-related situations.  
Using a qualitative approach, students answered three open-ended questions to support the information collected in the CMELAC questionnaire. The aim of combining the two methods was to identify the real reasons for the scores obtained in the CMELAC and to deepen the analysis from a common perspective.  
Question 1--After completing gamified digital games, do you think it is motivating that the teacher applies this methodology on the virtual platform? Why?  
Question 2--To make the activities more motivating, what do you think could be added?  
Question 3--Did the gamified digital games motivate you to log into the course platform more frequently?
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
###### Abstract  
The evolution of the IoT (Internet of Things) paradigm applied to new scenarios as VANETs (Vehicular Ad Hoc Networks) has gained momentum in recent years. Both academia and industry have triggered advanced studies in the IoV (Internet of Vehicles), which is understood as an ecosystem where different types of users (vehicles, elements of the infrastructure, pedestrians) are connected. How to efficiently share the available radio resources among the different types of eligible users is one of the important issues to be addressed. This paper briefly analyzes various concepts presented hitherto in the literature and it proposes an enhanced algorithm for ensuring a robust co-existence of the aforementioned system users. Therefore, this paper introduces an underlay RRM (Radio Resource Management) methodology which is capable of (1) improving cellular spectral efficiency while making a minimal impact on cellular communications and (2) ensuring the different QoS (Quality of Service) requirements of ITS (Intelligent Transportation Systems) applications. Simulation results, where we compare the proposed algorithm to the other two RRM, show the promising spectral efficiency performance of the proposed RRM methodology.  
Internet of Vehicles; VANETs; smart wireless technologies; intelligent transportation systems; smart bandwith utilization; QoS requirements +
Footnote †: journal: Sensors
Entropy_**2015**, _17_, 6072-6092; doi:10.3390/e17096072  
_entropy_**ISSN 1099-4300**  
www.mdpi.com/journal/entropy  
Distributing Secret Keys with Quantum Continuous Variables: Principle, Security and Implementations  
**[PERSON] 1,* and [PERSON] 2**  
1 Laboratoire Traitement et Communication de l'Information, CNRS, Telecom ParisTech,  
23 avenue d'Italie, Paris 75013, France  
2 Inria, EPI SECRET, B.P. 105, Le Chesnay Cedex 78153, France; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-1-45817114.  
Academic Editor: [PERSON]  
_Received: 16 June 2015 / Accepted: 27 August 2015 / Published: 31 August 2015_  
**Abstract:** The ability to distribute secret keys between two parties with information-theoretic security, that is regardless of the capacities of a malevolent eavesdropper, is one of the most celebrated results in the field of quantum information processing and communication. Indeed, quantum key distribution illustrates the power of encoding information on the quantum properties of light and has far-reaching implications in high-security applications. Today, quantum key distribution systems operate in real-world conditions and are commercially available. As with most quantum information protocols, quantum key distribution was first designed for qubits, the individual quanta of information. However, the use of quantum continuous variables for this task presents important advantages with respect to qubit-based protocols, in particular from a practical point of view, since it allows for simple implementations that require only standard telecommunication technology. In this review article, we describe the principle of continuous-variable quantum key distribution, focusing in particular on protocols based on coherent states. We discuss the security of these protocols and report on the state-of-the-art in experimental implementations, including the issue of side-channel attacks. We conclude with promising perspectives in this research field.  
**Keywords:** quantum key distribution; quantum information; quantum cryptography; quantum optics; security
## 3 Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy** \\ \hline [PERSON]’s method [19] & 69.27 \\ \hline [PERSON]’s method [20] & 70.5 \\ \hline [PERSON]’s method [21] & 70.7 \\ \hline [PERSON]’s method [22] & 74.46 \\ \hline [PERSON]’s method [23] & 75 \\ \hline [PERSON]’s method [24] & 76.86 \\ \hline [PERSON]’s method [25] & 78.9 \\ \hline [PERSON]’s method [26] & 79.3 \\ \hline Proposed method & 92 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison with state-of-the-art methods [19; 20; 21; 22; 23; 24; 25; 26]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifiers** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Kappa** \\ \hline LDA & 70.21 & 68.15 & 68.2 & 68 & 57.22 \\ \hline SVM & 70.24 & 70.83 & 70.35 & 70 & 60.02 \\ \hline RF & 59.29 & 58.15 & 58.05 & 58 & 44.02 \\ \hline MLP & 64.81 & 64.23 & 63.97 & 64 & 52.02 \\ \hline GNB & 40.18 & 40.18 & 39.45 & 40 & 20.13 \\ \hline Average Ensemble of BO-CNNs & 93 & 92 & 92 & 92 & 89.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with traditional ML algorithms. function, number of dense nodes, batch size, and optimizer) and each model has its own weaknesses. The idea behind employing an ensemble is that by combining various models that represent multiple hypotheses about the data, we can come up with a better hypothesis that is not in the hypothesis space of the models from which the ensemble is constructed. The three models are complementary in that if one fails, the other succeeds. The accuracy of this Ensemble method was higher than when a single model was used. This demonstrates the efficiency of ensemble learning.
## 3 Theory Analysis
\tag{4}\]  
where (\(R\), \(\theta\), \(\phi\)) is the observation point coordinate in the spherical coordinate frame, \(k_{0}\) is the wavenumber in free space, \(h\) is the height of the substrate, \(\mathbf{R}\) is the vector from origin of the spherical coordinate frame to the observation point, \(\mathbf{d}\) is the vector annotated in the Figure 5c, and \(w_{m}\) and \(w_{a}\) represent the width of main patch (\(W_{0}\)) and the assistant patch (\(W_{1}\)) as shown in Figure 1a, respectively. Correspondingly, the far-field distribution is determined by electromagnetic duality, and the electric field is  
Figure 4: Distribution of surface current density of antenna. **(a)** Left switch in the up state and right in the down state; **(b)** Left switch in the down state and right in the up state. (Note that switches were replaced by conductor patch without loss of generality)  
Figure 5: Distribution of the surface electric field of the antenna, when the left switch is in the up state and the right is in the down state. **(a)** Switches were replaced by a conductor patch; **(b)** Using RF MEMS switches; **(c)** The coordinate frame for field calculation. The relative value of surface electric field was represented by rainbow color, red and violent represent maximum and minimum respectively. \[E_{\varphi}=[-\
abla\times(\mathbf{F}_{m}+\mathbf{F}_{a})]\propto\left\{ \begin{array}[ ]{c}j\frac{1}{R^{2}-\beta_{b}R}\cdot\frac{\sin(\beta_{b}\text{sinsin}\Theta \cdot\text{cosp})}{\beta_{b}\text{sinsin}\Theta\cdot\text{cosp}}\cdot\frac{\sin \left(\frac{1}{\beta_{b}\text{coscosp}}\right)}{\alpha_{b}\text{cosp}}\sin \theta+\\ j\frac{1}{|R-d|}e^{-j\beta_{b}(|R-d|-\frac{1}{2}\text{lnsinp}\cdot\text{sinp})} \cdot\frac{\sin\left(\beta_{b}\text{sinsin}\Theta\cdot\text{cosp}\right)}{ \alpha_{b}\text{sinsin}\Theta\cdot\text{cosp}}\cdot\frac{\sin\left(\frac{1}{ \beta_{b}\text{cosp}}\right)}{\alpha_{b}\text{cosp}}\sin\theta\cdot\cos\left( \frac{1}{\beta_{b}\text{sin}\theta\text{sin}\theta}\right)\end{array} \right\} \tag{5}\]  
The magnetic field can also be expressed according to the electromagnetic relationship of plane wave in the free space or Maxwell equations. Hereto, the radiating patterns were obtained and can be plotted using MATLAB software (MATLAB 7.10, The Mathworks, Natick, MA, USA) as shown in Figure 6a. Comparing the theoretical evaluation patterns with the simulation patterns, good agreements of maximum radiating directions are obtained. However, pattern simulation results have back lobe because of coupling between the main and assistant patches. Due to ideal assumptions of the radiating model, the theoretical evaluation pattern has no back lobe. However, the back lobe of the patterns have no influences on maximum radiating direction of the reconfigurable antenna--i.e., the effects of the back lobe can be neglected when one focuses on the maximum radiating direction.
## 3 Results
### Forest Monitoring Results
To assess the accuracy of the monitoring results, 54 sample areas are selected from the high spatial resolution remote sensing images, Google Earth, Landsat time series. A total of 1131 pixels are identified to evaluate the accuracy. Since there is no unanimous method to verify the results of the dynamic monitoring based on time series remote sensing images currently, we adopt the overall accuracy, user's accuracy and producer's accuracy, which are commonly used indices for accuracy assessment in remote sensing [34; 35; 36]. Figures 7 and 8 show two sample areas with forest attenuation during the 1999-2013 period. Figure 7 illustrates the woodland decay caused by human production and construction
# IoT-Based Medical Image Monitoring System Using HL7 in a Hospital Database
## 2 Materials and Methods
### Ultrasound Machine to Raspberry Pi HL7 Messaging Process
#### 2.1.1 HL7 Block Formation and Messages
* _Available online: [[https://thingsboard.io/docs/user-guide/install/rhel/](https://thingsboard.io/docs/user-guide/install/rhel/)]([https://thingsboard.io/docs/user-guide/install/rhel/](https://thingsboard.io/docs/user-guide/install/rhel/)) (accessed on 27 July 2022). * [PERSON] (2015) [PERSON] Wireless communication of real-time ultrasound data and control. In Medical Imaging 2015: Ultrasonic Imaging and Tomography. _Int. Soc. Opt. Photonics_**2015**, _9419_, 94190M. * _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Performance Evaluation of the Transmission Quality of the Internet of Things in Farmland Environmental Monitoring Based on ns-3. _Mob. Inf. Syst._**2022**, _2022_, 3148348. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Development of smart healthcare monitoring system in IoT environment. _SN Comput. Sci._**2020**, \(1\), 185. [CrossRef]
* _[PERSON] et al. (2018)_ [PERSON]; [PERSON] Emotion-Aware Connected Healthcare Big Data Towards 5G. _IEEE Internet Things J._**2018**, \(5\), 2399-2406. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Lightweight and Anonymity-Preserving User Authentication Scheme for IoT-based Healthcare. _IEEE Internet Things J._**2022**, \(9\), 2649-2656. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Medical Image-based Detection of COVID-19 using Deep Convolution Neural Networks. _Multimed. Syst._**2022**, 1-10. [CrossRef] [PubMed]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] A health monitoring system for vital signs using IoT. _Internet Things_**2019**, \(5\), 116-129. [CrossRef]
* _Disclaimer/Publisher's Note_: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
# Nonlinear Vibrations of Low Pressure Turbine Bladed Disks: Tests and Simulations
## 2 Experiment
### Modal Hammer Testing  
Initially, a hammer test is performed on the bladed disk without UPDs, which are already connected to the flange of the rotating shaft in the spinning rig (Figure 2b). Hammer test measurements are a non-destructive testing method commonly employed in engineering to understand the dynamic behavior of systems. During the test, a hammer equipped with a load cell at its tip is used to measure the impact force on the component being tested. The structure's recorded response to the impact allows us to determine Frequency Response Functions (FRFs). These data are crucial for understanding the structure's behavior in terms of natural frequencies and mode shapes if multiple output measurements are collected. In this study, the response of the bladed disk is recorded with a non-contact, optical (laser)-scanning measurement technique. The hammer testing method consists of the separate excitation of each blade with an impulse and measuring the corresponding response of the blades. The post-processing software allows users to convert the input force and output response via Fast Fourier Transform (FFT) into the frequency domain and the Frequency Response Function (FRF) is calculated based on input and output data. Several impacts are applied for the same measurement to collect multiple FRFs from different measurement target points; averages for each measurement point are calculated in order to minimize  
Figure 2: (**a**) Vertical axis spinning test rig (top view); (**b**) bladed disk within spinning test rig.  
random errors (three hits per target point). As an example, the measured data on the 38 th blade are represented in Figure 3.  
Afterwards, the ND8 mode related to the third modal family is identified and the corresponding maximum response and corresponding critical frequency are plotted as a function of the blade number, as presented in Figure 4.  
It is clear from Figure 4 that the values identified for each blade are not the same. This happens due to the phenomenon called natural or intrinsic mistuning [24], which may occur due, for example, to manufacturing inaccuracies or assembly procedures. Therefore,  
Figure 4: Critical frequency and maximum response for 3 rd family ND8 mode.  
Figure 3: Excitation, response, and frequency response function measurements performed on 38 th blade by hammer test.  
it is reasonable to summarize the results for all the blades with the mean value and standard deviation. These values are 503.2 and 1 Hz for the critical frequency and \(1.9\times 10^{-3}\) and \(6.7\times\ 10^{-4}\) m/s/N for the response.