esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical 3D Modeling: Microwave Plasma Torch at Intermediate Pressure
## 3 Governing Equations  
Generally, three approaches are used to model plasmas--the kinetic method, fluid approximation, and hybrid approaches. Among the three, fluid approximation was used in this study for the simulation of our model because of its efficiency in solving partial differential equations, modeling significant arbitrarily complex plasma chemistry, and modeling electron dynamics to electromagnetic areas. The fluid approximation method requires complex modeling to take account of gas discharge processes, boundary conditions, thermodynamics, gas reactions, collision processes, electromagnetic phenomena, plasma dynamics, and power conservation of electrons, as well as heavy particles. The absorption of electromagnetic wave energy is the major cause of plasma excitation. Thus, it is necessary to incorporate the dynamical changes of the electromagnetic waves involved in the effects of microwave on the reaction process. The model is not time-independent and quasi-stationary accomplishment is the expectation using the wave equations. Therefore, the electromagnetic wave formula in the plasma needs to be established to perform cover analysis of the influence of various related plasma parameters on electromagnetic wave propagation. Then, under the assumption of ambipolar diffusion, a fluid equation for the plasma was established to analyze the changes in electron density with time, while the Boltzmann equation was used to analyze the heating effect of strong electromagnetic pulses on the electrons. The accelerated electrons transfer power to heavy particles via collisions. The heavy particles are expected to be in thermal balance at a specific temperature \(T\), and the electrons are categorized by the Maxwellian power spreading with a specific temperature \(T_{\mathrm{e}}\). The heat conduction formula was used to compute the rate of change of particle energy, which is affected by the electromagnetic field. Finally, an iterative approach was utilized to analyze the behavior of various parameters of the plasma [34; 35].
# Review of Automatic Estimation of Emotions in Speech
## 4 Models to Use Measures of Analysis for Speech Emotion Classification  
The aim of all methods of analysis for speech (FBE, DFT, LPC, MFCC, intonation) is to yield data representations that are more useful than the (raw) waveform time samples. Neural methods applied to speech inherently \"learn\" a version of this through their automatic training, but SER may be more accurate and efficient if such networks could be guided by direct use of intonation and spectral features. SER and ASR can benefit from lexical data in speech, i.e., information from words and phonemes, which is seen in intonation and VT resonances. Applications that instead produce speech output (e.g., enhancement, coding, synthesis) also retain phase, but such is not relevant for ASR, and also of less utility for SER [51]. The requirements of ASR differ much from those of SER, because ASR has to identify a sequence of phones and words, whereas SER looks for (less obvious) clues to emotion, which may include different acoustical information.  
Most current SER uses neural networks that directly learn from data, and does not employ specific techniques such as MFCCs [52]. Before we discuss neural approaches, we first review other methods used recently for SER. Early SER research was largely developed from prior ASR methods. Such SER methods use this process: (1) front-end acoustic analysis to obtain a phonetic representation, and (2) a back-end classifier. Other recent SER methods may use a single-stage E2E method [53; 54]; we discuss those in Section 7.2.
## 3 Results and Discussion  
### FTIR Spectra  
The FTIR spectrum of superplasticizer J is delineated in Figure 1. The asymmetric absorption peak in the range of 3115 to 3655 cm\({}^{-1}\) is ascribed to -OH group, indicating that the peak at 3481 cm\({}^{-1}\) may be the stretching vibration of carboxyl (-COOH) and hydroxyl (-OH) groups in superplasticizer J. The peaks at 2876, 1452, and 1356 cm\({}^{-1}\) denote the characteristic absorption of methyl and methylene groups, and the peak at 1729 cm\({}^{-1}\) represents the stretching vibration of -C=O- in the ester group. The characteristic absorption peak at 1107 cm\({}^{-1}\) is attributed to the polyether -C-O-C- stretching vibration in superplasticizer J [18].  
Figure 2 depicts the FTIR spectrum of superplasticizer H. The asymmetric peak of hydroxyl (-OH) stretching vibration lies in the range of 3115-3655 cm\({}^{-1}\), indicating that the absorption peak at 3448 cm\({}^{-1}\) may be related to carboxyl (-COOH) and hydroxyl (-OH) groups. The peaks at 2880, 1456, and 1352 cm\({}^{-1}\) represent the characteristic absorption of methyl and methylene groups, and the peak at 1729 cm\({}^{-1}\) denotes the stretching vibration of -C=O- in the ester group. The characteristic absorption peak at 1107 cm\({}^{-1}\) is also ascribed to the polyether -C-O-C- stretching vibration in superplasticizer H.  
Figure 1: FTIR spectrum of superplasticizer J.  
According to the FTIR spectrum of superplasticizer Z (Figure 3), the asymmetric stretching vibration peaks in the range of 3115 to 3655 cm\({}^{-1}\) are related to hydroxyl groups (-OH), which indicates that the peak at 3436 cm\({}^{-1}\) may be related to the absorption of carboxyl (-COOH) and hydroxyl (-OH) groups. The peaks at 2880, 1456, and 1350 cm\({}^{-1}\) denote the characteristic absorption of methyl and methylene groups, and the peak at 1729 cm\({}^{-1}\) represents the stretching vibration of -C=O- in the ester group. The characteristic absorption peak at 1107 cm\({}^{-1}\) is attributed to the polyether -C-O-C- stretching vibration in superplasticizer Z [19].
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### Communication Protocols and Interfaces
#### 3.2.4 NAVTEX-Specific  
The 518 kHz, 490 kHz, and 4209.5 kHz frequencies are used for NAVTEX. The International NAVTEX service is provided on 518 kHz. The National NAVTEX service is provided on 490 kHz or 4209.5 kHz. Other national frequencies may be allocated by the ITU [99].
## 5 Results
### The Effect of Cables among Three Ships on the Salvage System
#### 5.3.1 The Effect of Cables among Three Ships on Ships' Motion Responses
With six samples, the score threshold for a single DOF is 18 points without accounting for weighting. The weights of the 6 DOFs are as follows: 1.5, 1.25, 1.25, 1, 1, and 1. After applying the weights, the score thresholds of the 6 DOFs become 27, 22.5, 22.5, 18, 18, and 18. Hence, the total score threshold of the 6 DOFs is 126 points. From Figure 19, it is apparent that the length of the connecting cable between the two lifting barges should be, at most, a certain limit. When the slack length of the cable exceeds this limit, it can adversely affect the safety and stability of the salvage system. For instance, adding connecting cables that are 1.3 times longer than the salvage spacing between the two lifting barges yields the highest scores for the roll, yaw, and sway responses. Figure 20 shows that the total score for the inclined lifting cable is 107.25, which is the lowest among the five schemes and significantly below the score threshold for the 6 DOFs. This scheme is reasonable and effective for restraining the movement of the three ships. The total score for 1.3d is 144, which far exceeds the score threshold, making it an unreasonable solution. The total scores for 1d and 1.1d are 125.5 and 125 points below the score threshold, respectively. The total  
Figure 21: Total scores of 6 DOFs for the five schemes. Figure 20: Scores of single DOFs for five schemes. score for 1.2d is 128.25, which is slightly higher than the score threshold but still within an acceptable range. Hence, from the perspective of restraining the motion responses of the three ships, the most optimal solution is to deploy inclined lifting cables underwater. Simultaneously, it is also reasonable to employ connecting cables with lengths ranging from 1d to 1.2d between the two lifting barges.
## 4 Results
### Analysis of Initial Fracturing, Fracture Propagation, and Oil Well Production Performance
#### 4.3.2 Production Dynamics Analysis of Oil Wells  
When the YP 3 well was first placed into production on 29 December 2012, its water content was 64.8%, its initial daily liquid production was 15.98 m\({}^{3}\), and its daily oil production was 5.62 t, as shown in Figure 11. On 12 September 2018, the well was injected with water vapor for a throughput experiment. Following the implementation of the measures, the horizontal well's daily liquid production reached 16.25 m\({}^{3}\), its daily oil production reached 1.06 t, and its water content reached 93.5%. The CO\({}_{2}\) injection replenishment experiment was then carried out in August 2022. After a month of production following the measures, the average daily fluid production was 8.61 m\({}^{3}\), the daily oil production was 1.95 t, and the water content efficiency of the well was reduced to 76.3%. The well was then shut down for 53 days and opened for 290 days of production. Before refracturing, the daily fluid production was reduced to 3.51 m\({}^{3}\), the daily oil production was 1.63 t, and the water content was 49.5%. These results showed that the injected CO\({}_{2}\) was not completely contacted and exchanged with the crude oil.  
Based on fracture extension simulation and PEBI grid refinement technology, combined with black oil model three-phase flow simulation and production history fitting, the dynamic evolution law of the pressure field in the YP 3 well is clarified (Figure 12). The initial formation pressure was 15.8 MPa and reached a historical low of 7.8 MPa with continuous extraction before the steam throughput, which occurred in 2018. After the steam throughput (Figure 12d), the formation pressure rebounded to 15.2 MPa, which was close to the original formation pressure, but then started to decline. In 2022, the CO\({}_{2}\) make-up test was implemented, and the pressure increased from 1.4 MPa to 11.4 MPa, but the injected gas and crude oil were not sufficient to create. The contact between the injected gas and the crude oil was insufficient, and the pressure fluctuation was obvious (Figure 12e,f), indicating that the gas diffusion was limited due to the underdevelopment of the fracture network. After 53 days of continuous well smothering, the formation pressure recovered to 85.9% of the original pressure, and the locally high pressure indicated the strong non-homogeneity of the reservoir. In summary, the existing fracture system is difficult to support an efficient CO\({}_{2}\) drive, and repeated fracture modification is required to improve the complexity of the fracture network.  
Figure 11: Production dynamic curve of YP 3 horizontal well before fracturing.
## 3 Results and Findings
### Results from SLR  
This subsection is come up with the issues that affect CRM in GSD as the factors identified in the systematic literature review related to customers. The customers' issues and the factors that affect the CRM in GSD are discussed in detail to answer **RQ1**. From 46 primary studies, 11 factors were identified from the SLR, which were further fed-in frequencies and percentages in Table 3. It can be observed that the frequency of the factor lack of communication achieved a higher frequency of 34 among all other listed factors. Similarly, language differences were also reported in 33 studies out of 46, which significantly affects CRM. Based on the given summary, it was found that the three factors lack of communication, language differences, and cultural differences negatively influence the implementation of CRM. Time zone difference also moderately affects CRM. In contrast, technical issues and policies, rules, and regulations influence CRM slightly compared to other listed factors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **Frequencies** & **Percentages** \\ \hline LC & 34 & 75.1 \\ \hline LD & 33 & 73.3 \\ \hline CD & 32 & 71.1 \\ \hline DEL & 15 & 33.3 \\ \hline EXP & 14 & 31.1 \\ \hline TEC & 7 & 15.5 \\ \hline CC & 15 & 55.5 \\ \hline PRG & 5 & 11.1 \\ \hline TD & 24 & 53.3 \\ \hline MU & 11 & 24.4 \\ \hline GD & 31 & 68.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of factors affecting CRM.  
**H8**: Policies, rules, and regulations affect CRM in GSD.
**H9**: Time zone difference affects CRM in GSD.
**H10**: A lack of mutual understanding affects CRM in GSD.
**H11**: Geographical distance affects CRM in GSD.
## 3 Methodology
### Transmission
#### 3.1.4 Modulation  
Here, \(\bar{\rho}\) was modulated using Gaussian Modulation \((GM)\) to transmit the signal through the public channel to the receiver. \(GM\), which is commonly used in communication systems to modulate the signal before transmission, was applied to modulate \(\bar{\rho}\) as in the equation:  
\[\partial_{\text{mod}}=\Re e^{\frac{\pi^{2}}{2\omega^{2}}}\left(\cos 2\pi r_{c}m +\phi\right.) \tag{16}\]  
Here, \(\partial_{\text{mod}}\) signifies the modulated signal of \(\tilde{\rho}\), \(\Re\) signifies the amplitude of \(\tilde{\rho}\), \(m\) and \(h\) characterize the time variable and standard deviation of the Gaussian pulse, respectively, \(r_{c}\) is the carrier frequency of \(\tilde{\rho}\), \(\phi\) represents the phase of \(\tilde{\rho}\), and \(e\) and \(\cos\) represent the exponential function and cosine function, respectively.
# Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios
## 1 Introduction
For instance, this implementation can be applied in a scenario where devices receive maintenance alerts coming from a data analysis system, as is presented in the Zero Defect Manufacturing Platform (ZDMP) [3], and where the proposed LoRaWAN network is used to support the data acquisition and alarm event applications in industrial plants where communications are restricted to sub-gigahertz bands. The rest of the paper is structured as follows: Section 2 describes the concepts and technologies employed during the design and definition of the work. Section 3 shows the related work relevant to the topic with a description of the main key points. Sections 4 and 5 explain the proposed implementation, scenario, and configurations selected, while Section 6 presents the results of the simulations for the cases under study. Finally, Section 7 summarizes the conclusions and suggests future work.
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 4 Proposed System for the EEG-based Preference Detection
### Feature Extraction
#### 4.4.2 Valence  
The valence was selected as the measure of preference in this study. Strong valence is reflected in the activation of frontal EEG asymmetry [34]. In DEAP dataset [58], there was high correlation between valence and EEG frequency bands, as shown in Figure 5. The increment in valence led to power increment in alpha, which is consistent with the results in a similar study [34]. We did not use  
Figure 4: Feature extraction block diagram.  
liking rating in the DEAP dataset because the data owners [58] reported conflicting findings between the activation in left alpha power and liking.  
We applied the different valence equations and investigated the relationship with the DEAP self-assessment valence measurement. For the valence calculation, we used the extracted alpha and beta band powers from the DEAP data and considered only the following electrodes: Fz, AF3, F3, AF4, and F4. Finally, we computed the values of the valence by using four different equation (Equations (3)-(6)), which have been well-explained in a previous paper [34] authored by an author of this paper.  
\[\text{Valence}=\frac{beta(AF3,F3)}{alpha(AF3,F3)}-\frac{beta(AF4,F4)}{alpha(AF4,F4)} \tag{3}\]  
\[\text{Valence}=ln[alpha(Fz,AF3,F3)]-ln[alpha(Fz,AF4,F4)] \tag{4}\]  
\[\text{Valence}=alpha(F4)-beta(F3) \tag{5}\]  
\[\text{Valence}=\frac{alpha(F4)}{beta(F4)}-\frac{alpha(F3)}{beta(F3)} \tag{6}\]
## Nomenclature  
\begin{tabular}{l l} ACT & Activity Diagram \\ ASIL & Automotive Safety Integrity Level \\ AV & Autonomous Vehicle \\ AVP & Automated Valet Parking \\ BDD & Block Definition Diagram \\ CAD & Computer-Aided Design \\ CAN & Controller Area Network \\ CAL & Cybersecurity Assurance Level \\ CAV & Cooperative Autonomous Vehicle \\ CCPA & California Consumer Protection Act \\ CM & Cooperative Maneuvering \\ CSM & CAMEO Systems Modeler \\ E/E & Electric/Electronic \\ \end{tabular}  
UNECE  
EU  
EU  
ENISA  
European Union Agency for Cybersecurity  
FAS  
(Driver Assistance System)  
FEM  
Finite Element Method  
FMEA  
Failure Mode and Effects Analysis  
FSR  
Functional Safety Requirement  
FTA  
Failure Tree Analysis  
GDPR  
General Data Protection Regulation  
HARA  
Hazard Analysis and Risk Assessment  
HW  
Hardware  
IBD  
Internal Block Diagram  
IEC  
International Electrotechnical Commission  
IEEE  
Institute of Electrical and Electronics Engineers  
INCOSE  
International Council on Systems Engineering  
IoT  
Internet of Things  
ISA  
Independent Safety Assessor  
ISO  
International Organization for Standardization  
MBSE  
Model-Based Systems Engineering  
MoE  
Measures of Effectiveness  
OMG  
Object Management Group  
PAR  
Parametric Diagram  
PKG  
Package Diagram  
RAM(S)  
Reliability, Availability, Maintainability (Safety)  
RE  
Requirements Engineering  
REQ  
Requirement  
SAE  
Society of Automotive Engineers  
SD  
Sequence Diagram SE Systems Engineering  
SFOP  
Safety, Financial, Operational, Privacy  
Sol  
System of Interest  
SoS  
System of Systems  
SOTIF  
Safety of the Intended Functionality  
STM  
State Machine Diagram  
SW  
Software SysML (Systems Modeling Language)  
TARA  
Threat Analysis and Risk Assessment  
TCU  
Telematics Control Unit  
TSR  
Technical Safety Requirement  
UC  
Use Case Diagram  
UML  
Unified Modeling Language  
UPDM  
Unified Profile for DoDAF/MODAF  
V2X  
Vehicle to X  
VANET  
Vehicular ad hoc Network  
VDA  
Verband der Automobilindustrie  
VDI  
Verein Deutscher Ingenieure
# An Eco-Driving Controller Based on Intelligent Connected Vehicles for Sustainable Transportation
###### Abstract  
The rapid increase in the number of vehicles has brought significant challenges to energy conservation and environmental sustainability. To solve these problems, various frameworks and models based on intelligent connected vehicles (ICVs) have been identified for road capacity improvement and fuel consumption reduction. In this paper, an eco-driving controller with ICVs was first proposed by combining vehicular dynamics with wireless communication technologies, where the nodes that can implement perception and control in a simulated complex traffic environment have been deployed. Then, the information of the surrounding environment, including the preceding vehicles, was obtained through a wireless communication module based on the technology of vehicle to everything (V2X). Besides, the advanced model predictive control (MPC) strategy was integrated into the ICV controller with the objectives of minimizing the driving spacing and improving environmental sustainability. Finally, a co-simulation platform for ICVs based on a robot operating system (ROS) and PreScan software was constructed, and the dynamic characteristics of the controller were verified in three aspects, including car-following behaviors, fuel efficiency improvement, and carbon dioxide emission reduction. The proposed controller showed that it can reduce fuel consumption by 3.71% and reduce carbon dioxide emissions by 3.42%, in the scenarios of a preceding vehicle with constant velocity, and by 6.77% and 7.91%, respectively, in a preceding vehicle with variable velocity scenario. The demonstrated experimental results show that the proposed controller can effectively reduce fuel consumption and emissions during car-following.  
car-following model; eco-driving; intelligent connected vehicles (ICVs); model predictive control (MPC); sustainable transportation +
Footnote †: journal: Applied Sciences
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 7 Applications of HRV Analysis
### Psychiatric Disorder Detection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author, Year** & **Feature Extraction Methods Used** & **Feature Selection/Reduction Methods Employed** & **Classifiers Used** & **Inference** \\ \hline [PERSON] et al. (2019) [16] & Frequency-domain methods & - & SVM & The study employed an HRV analysis and an SVM classifier for detecting psychiatric disorders. The projected scheme attained a categorization accuracy of 87.0% between depression patients and healthy persons. \\ \hline [PERSON] et al. (2021) & Time-domain, frequency-domain, and non-linear methods & - & RF, gradient boosting machine (GBM), SVM, ANN, and regularized logistic regression (LR) & The study presented ML approaches using HRV features as input to detect the panic disorder. L1-regularized logistic regression showed the best accuracy of 78.4\%. \\ \hline [PERSON] et al. (2020) [17] & Time-domain and frequency-domain methods & - & - & The study found that individuals with post-traumatic stress disorder (PTSD) exhibited pointedly advanced HR. Throughout stress, people with PTSD displayed increased HR and decreased IF values. \\ \hline [PERSON] et al. (2020) [172] & Joint T-F analysis method, i.e., SPWVD & Student \(t\)-test or Wilcoxon test & - & The research showed that sympathetic dominance decreased significantly (\(p<0.05\)) in major depressive disorder (MDD) patients compared to control participants under stress, implying that ANS responsiveness to stress stimuli is weaker in MDD patients. \\ \hline [PERSON] et al. (2019) & Entropy computation methods & SVM-RF algorithm & SVM, linear discriminant analysis (LDA), K-NN, and NB & The values of HRV entropy attributes were inferior in MDD patients. It also achieved 70\% accuracy in classifying MDD and healthy groups with three optimal features. \\ \hline [PERSON] et al. (2022) & Time-domain, frequency-domain, and non-linear methods & [PERSON]’s correlation coefficient analysis and \(t\)-test & - & The study revealed that in a condition of mental strain, the subjective survey score amplified considerably (\(p<0.01\)), the time perception fault value increased dramatically (\(p<0.01\)), and the relative fault rate improved exponentially (\(p<0.05\)), indicating that the individuals were experiencing a mental load. \\ \hline [PERSON] et al. (2019) [175] & Time-domain and frequency-domain methods & Minimum redundancy maximum relevance (mRMR) selection algorithm & KNN, GLM, NB, LDA, SVM, and RF classifiers. & The study projected a stress recognition system utilizing HRV features. The SVM classifier achieved the highest categorization accuracy of 84.4\% in a 10-fold cross-validation approach. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recent published papers on the application of HRV analysis in psychiatric disorders.