esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Classifier**} & \multicolumn{1}{c}{**Valience**} & \multicolumn{1}{c}{**Arousal**} & **Description** \\ \hline
**NB** & **by** & **[PERSON] et al. (2017)** & 57.6 & 62.0 & \\
**Bayesian weighted-log-posterior** & **by** & **[PERSON] et al. (2012)** & 70.9 & 70.1 & \\
**SVM+nMRMR** & **by** & **[PERSON] et al. (2013)** & 73.41 & 73.06 & \\
**Segment level decision fusion** & **by** & **[PERSON] et al. (2014)** & 76.9 & 68.4 & 2-class classification for valence and \\
**CNN+RNN** & **by** & **[PERSON] et al. (2015)** & 72.06 & 74.12 & arousal, and within-subject emotion \\
**DNN** & **by** & **[PERSON] et al. (2015)** & 75.78 & 73.12 & recognition. \\
**CNN** & **by** & **S1.41** & 73.36 & \\
**LSTM+RNN** & **by** & **[PERSON] et al. (2017)** & 88.65 & 88.45 & \\
**3D-CNN** & **by** & **[PERSON] et al. (2015)** & 87.44 & 88.49 & \\ \hline \multicolumn{1}{c}{**GELM**} & **by** & **[PERSON] et al. (2018)** & 69.7 & 4-class classification in VA space. \\ \hline \multirow{5}{*}{SVM} & **+Raw** & 0.5500 & 0.7525 & \\  & **+Norm** & 0.5991 & 0.5900 & \\
**SVM** & **+PSD** & 0.7996 & 0.5531 & \\  & **+PSD+Raw** & 0.9234 & 0.9462 & \\  & **+PSD+Norm** & 0.7460 & 0.7383 & \\  & **+Raw** & 0.6221 & 0.6012 & \\  & **+Norm** & 0.6551 & 0.6176 & \\
**CVCNN** & **+PSD** & 0.9907 & 0.8851 & \\  & **+PSD+Raw** & 0.9933 & 0.9988 & 2-class classification for valence and \\  & **+PSD+Norm** & 0.100 & 1.00 & arousal, and within-subject emotion \\  & **+R** & 0.6242 & 0.5902 & recognition, and AUC (Area Under ROC \\  & **+Norm** & 0.6394 & 0.5987 & Curvey used for evaluation. \\
**GSCNN** & **+PSD** & 0.8875 & 0.8802 & \\  & **+PSD+E**aw & 0.9933 & 0.9930 & \\  & **+PSD+Norm** & 1.00 & 1.00 & \\  & **+R** & 0.6717 & 0.6175 & \\  & **+Norm** & 0.6330 & 0.5600 & \\
**GSITCNN** & **+PSD** & 0.8523 & 0.8390 & \\  & **+PSD+Raw** & 0.9946 & 0.9908 & \\  & **+PSD+Norm** & 1.00 & 1.00 & \\ \hline CNN & & 57.2 & 56.3 & 2-class classification for valence and \\ LSTM & **+Raw** & **by** & **[PERSON] et al. (2014)** & 63.7 & 61.9 & arousal, and cross-subject emotion \\
**H-ATT-GRU** & & & 67.9 & 66.5 & recognition \\ \hline NB & & 61.04 & 58.46 & \\ SVM & & 69.55 & 69.92 & 2-class classification for valence and \\ MLR,1 & **+DE** & **in our study** & 33.69 & 43.10 & arousal, and within-subject emotion \\ MLR,12 & & & 33.81 & 43.25 & recognition \\ LORSAL & & 77.17 & 77.03 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the introduced LORSAL methods, the other shallow classifiers, and the deep learning approaches for EEG-based emotion recognition of LV/HV and LA/HA on DEAP dataset.
# Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
## 3 Proposed Approach
### Computer Vision Model  
We have developed CV models to detect the Whitefly (WF) pest. This type of pest is one of the most common; thus, it is one of the most relevant to fight, especially in tomato crops. The life cycle of WF starts as an egg, then passes on to nymph and ends up as an adult propagating to the entire crop. There are several species of whiteflies that are visually similar, such as _Trialeurodes_ vaporariorum and _Bemisia tabaci_. The next subsections describe the dataset used to train several CV models (object detection models), the obtained results, and the chosen CV model for the IoT network.
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 4 Applications of RL in FANET
### Flight Trajectory Selection
#### 4.2.3 Joint Trajectory Design and Power Control  
In [58], [PERSON] et al. predicted the movement of the users and determined trajectories toward the users to deliver data with minimum power. The authors also predict the future positions of the users using an echo state network (ESN) and determine the trajectory in advance using multiagent \(Q\)-learning algorithm. To achieve fast convergence and reduce complexity, the authors trained one agent at a time while maintaining a fixed policy for  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Type** & **Algorithm** & **Advantages** & **Limitations** \\ \hline \multirow{3}{*}{\(Q\)-SQUARE [52]} & Single-agent \(Q\)-learning & **1.** Energy limitation is considered. **2.** Autonomous visit to charging stations is incorporated. & **1.** Fixed RL parameters. **2.** Single UAV is considered for learning. \\ \hline \multirow{3}{*}{Decentralized Trajectory Design [56; 57]} & Decentralized RL/DRL & **1.** POMDP is used to determine the trajectory path. & **1.** Fixed altitude setting. \\ \cline{1-1} \cline{2-3}  & Decentralized RL/DRL & **2** Energy limitation is considered. **3.** Actor-critic method is used for distributed control. & **2.** Autonomous visit to charging stations is not considered. \\ \hline \multirow{3}{*}{Joint Trajectory design and Power Control [58]} & & **1.** Low complexity and fast convergence due to individual agent training. **2.** User location is predictable, and action is possible accordingly. & **1.** Fixed policy for other agents is considered while training an agent. \\ \cline{1-1} \cline{2-3}  & Multi-agent design and Power Control [58] & Multi-agent design and Power \(Q\)-learning & **2.** User location is predictable, and action is possible accordingly. & **2.** Autonomous visit to charging stations is not considered. \\ \cline{1-1} \cline{2-3}  & Control [58] & Multi-agent design & **3.** Fixed RL parameters. **4.** Energy limitation is not considered. \\ \hline \multirow{3}{*}{Multi-UAV Movement Design [59; 60; 61]} & & **1.** User mobility is considered. **1.** User mobility is constrained within the cluster. **2.** Energy limitation is not considered. \\ \cline{1-1} \cline{2-3}  & \(Q\)-learning /Double Movement Design [59; 60; 61] & & **3.** Autonomous visit to charging stations is not considered. **4.** Mobility of UAVs is constrained within 7 directions. \\ \hline \multirow{3}{*}{Trajectory Optimization for UBS [53; 55]} & & **1.**\(\epsilon\)-greedy policy is used. **2.** Obstacles are considered during flying towards destination. **3.** Safety check is incorporated within the system. **4.** Energy limitation is considered in terms of flying time. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis of the selection of flight trajectory based on RL in FANET.  
other agents. Moreover, the agents use a greedy policy to achieve optimal solutions for joint trajectory design and power allocation.
# Real-Time Simulator for Dynamic Systems on FPGA
## 4 Development of the Prototype and Results
### Prototype Implementation  
The implementation of the ODE described in the previous section shows the ideal realization of the ODS prototype, as shown in Figure 12, which has two generic inputs \(P=2\), \(V_{e}(t)\) and \(V_{o}(t)\), and one output \(H=1\), \(v(t)\). Considering Equation (27), the inputs described refer to torque and angle values and the output refers to the velocity.  
The prototype was developed using two DAMs (DAM-1 and DAM-2) and one SGM (SGM-1), translating the signals from the world to the platform and from the platform to the world. DAM-1 and DAM-2, illustrated in Figure 13, are A/D converts responsible for receiving the input signals, \(V_{e}(t)\) and \(V_{o}(t)\), and SGM-1, also shown in Figure 13, is a D/A converter responsible for system output, \(v(t)\). The physical architecture of this system is shown in Figure 13, constituted, as described in Section 3, by one DSM and one SMMODE.  
Thus, conforming to Equation (27), the dynamic system was implemented in FPGA, based on the generic circuit illustrated in Figure 14. This circuit is constituted of sub-circuits, corresponding to parts of Equation (27). The sub-circuit responsible for the aerodynamic friction force, described in Equation (22), is shown in Figure 15, called ADF. The sub-circuit responsible for the rolling resistance force, described in Equation (23), is shown in Figure 16, called RRF.  
Figure 12: ODS with two inputs, \(P=2\) (torque, \(V_{e}(t)\), and inclination angle \(V_{o}(t)\)), and one output, \(H=1\) (vehicle speed in a longitudinal direction, \(v(t)\)).  
Figure 13: ODS implemented for the prototype.
## 3 Results  
### Biogeochemical and Optical Characterization  
The data measured during the seven surveys conducted between 2008 and 2017 show that each main color parameter of the waters of Lake Taihu varied over a wide range (Table 1). The Chla concentration ranged from 0.46 to 157.05 \(\mathrm{\SIUnitSymbolMicro g}\)/L with a mean value of 33.90 \(\pm\) 63.78 \(\mathrm{\SIUnitSymbolMicro g}\)/L, and the total SPM concentration ranged from 1.57 to 180.00 \(\mathrm{\SIUnitSymbolMicro g}\)/L with a mean value of 45.15 \(\pm\) 36.05 \(\mathrm{\SIUnitSymbolMicro g}\)/L. The \(a_{\mathrm{g}}\)(440) values of CDOM at the wavelength of 440 \(\mathrm{\SIUnitSymbolMicro g}\) ranged from 0.40 to 2.42 \(\mathrm{\SIUnitSymbolMicro m}^{-1}\) with a mean value of 0.98 \(\pm\) 0.46 \(\mathrm{\SIUnitSymbolMicro m}^{-1}\). The mean SPIM concentration was 29.04 \(\pm\) 22.55 \(\mathrm{\SIUnitSymbolMicro g}\)/L, and the mean SPOM concentration was 10.16 \(\pm\) 6.31 \(\mathrm{\SIUnitSymbolMicro g}\)/L. The Pearson correlation coefficient between the SPIM concentration and the total SPM concentration was as high as 0.87 (\(p<0.001\)), and SPIM accounted for more than 60% of the total SPM at 71% of the sampling sites, which indicates that SPIM is the dominant suspended matter in the waters of Lake Taihu. This finding agrees with a previous report [27]. Note that the samples with algal blooms and errors were not included in this analysis.  
Figure 2: Processing procedure of the satellite-derived \(K_{\mathrm{d}}\)(490) product.  
The magnitude of \(K_{\rm d}\) is mainly influenced by changes in the absorption of dissolved matter, together with the absorption and scattering of particulate matter, over a background \(K_{\rm d}\) related to pure water [52]. An analysis of the correlations between \(K_{\rm d}\)(490) and the main color parameters (Figure 3) shows significant statistical correlations between \(K_{\rm d}\)(490) and all of the main color parameters (\(p<0.001\)) except for CDOM's \(a_{\rm g}\)(440) (\(p>0.1\)). It is worth noting that the correlation coefficient between \(K_{\rm d}\)(490) and the SPIM concentration (\(r=0.88\)) is significantly higher than that between \(K_{\rm d}\)(490) and the SPOM concentration (\(r=0.22\)) and that between \(K_{\rm d}\)(490) and the Chla concentration (\(r=0.15\)). There is also a significant correlation coefficient between \(K_{\rm d}\)(490) and the total SPM concentration (\(r=0.88\), \(p<0.001\)) as SPIM is the dominant suspended matter in the waters of Lake Taihu. This suggests that in the absence of algal blooms, \(K_{\rm d}\)(490) for the waters of Lake Taihu is mainly controlled by SPIM, followed by SPOM and phytoplankton pigments, and is insignificantly affected by CDOM.  
Overall, the waters of Lake Taihu are characterized by high SPM and Chla concentrations, and SPIM is the dominant component of SPM. In addition, \(K_{\rm d}\)(490) for the waters of Lake Taihu is mainly controlled by SPIM, followed by SPOM and phytoplankton pigments, and is insignificantly affected by CDOM.
## 2 Material and Methods
### System Description
The radiation intensity was measured by using an Eppley Black and White Model 4-48 Pyranometer (The Eppley Laboratory, Inc., Newport, USA), Hukseflux LP02 pyranometers (Hukseflux Thermal Sensors B.V., Delft, The Netherlands) and the global horizontal irradiation was taken by an EMS 11 Silicon PV detector (Energy XPRT, Brno, Czech Republic) (Figures 5 and 6). The angle information of the photovoltaic modules and pyranometers was measured using a digital angle gauge, while the humidity of the air was measured by a HYTE-ANA-1735 (B+B Thermo-Technik GmbH, Donaueschingen, Germany) meter and by an OTT TRH relative humidity sensor (OTT Hydromet GmbH, Kempten, Germany) (Figure 5). Pt 100 sensors (Conrad Electronic SE, Wernberg-Koblitz, Germany) were used to detect the PV module and air temperatures with the help of the PicoLog devices. A digital LM 35-based precision thermometer (B+B Thermo-Technik GmbH, Donaueschingen, Germany) was used to calibrate the entire measuring circuit. In this research, the temperature values were for informational purposes only, because the temperature values of the PV modules remained stable throughout the experiments (see chapter \"2.3. Data Processing and Analysis\"). In 2017, 2018 and 2019 measuring stations 'A' and 'B' were used at the same places and times (Figures 5 and 6). Figure 5: Schematic illustration of PV module measuring station ‘A’. For positioning the photovoltaic modules two linear actuators were used. By a handheld remote control, it was possible to carry out a set of attempts, which were suitable for both horizontal and/or vertical control. The accurate adjustment to the sun was checked by the visible focus point (FP) of the CPV solar module (Figure 7) because at this position the rays of the sun are ideally positioned, perpendicularly to the photovoltaic modules. The measurements at the FP position were used as a reference in order to obtain the power loss as a function of the solar tracking uncertainty angle. Thus, it was possible to measure the technology-specific performance losses in the case of inaccurate sun tracking compared to the ideal position. With inadequate dual tracking devices, it is difficult to provide appropriate measurement conditions, which may lead to erroneous evaluation of the data.
## 2 System and Channel Models
### \(\kappa-\mu\) Shadowed Fading Model  
The \(\kappa-\mu\) shadowed fading model concurrently emulates both the shadowing and the multi-path fading. If \(\kappa-\mu\) shadowed fading is experienced by the \(\ell\)-th relaying hop of the relaying system, then we can write the PDF of the \(\ell\)-th hop instantaneous SNR as [4] (Equation (3)):  
\[f_{\gamma_{\ell}}(\gamma)= \underbrace{\left(\frac{\mu_{\ell}(1+\kappa_{\ell})}{\hat{\tau}_{ \ell}}\right)^{N_{\ell}\mu_{\ell}}\left(\frac{m_{\ell}}{m_{\ell}+\kappa_{\ell} \mu_{\ell}}\right)^{N_{\ell}m_{\ell}}\frac{1}{\Gamma(N_{\ell}\mu_{\ell})}}_{ \stackrel{{\Lambda}}{{=}}\xi_{\ell}}\gamma_{\ell}^{N_{\ell}\mu_{ \ell}-1}\exp\left(-\underbrace{\frac{\mu_{\ell}(1+\kappa_{\ell})}{\hat{\tau}_{ \ell}}}_{\stackrel{{\Lambda}}{{=}}\psi_{\ell}}\gamma_{\ell}\right)\] \[\times_{1}F_{1}\left(N_{\ell}m_{\ell};\,N_{\ell}\mu_{\ell};\, \underbrace{\frac{\kappa_{\ell}\mu_{\ell}^{2}(1+\kappa_{\ell})}{(m_{\ell}+ \kappa_{\ell}\mu_{\ell})\hat{\tau}_{\ell}}}_{\stackrel{{\Lambda} }{{=}}\delta_{\ell}}\gamma_{\ell}\right) \tag{2}\]  
where \(\Gamma(\cdot)\) designates the Gamma function, \(\overline{\gamma}_{\ell}\) symbolizes the average SNR of the \(\ell\)-th hop, \(\kappa_{\ell}\) designates the power ratio of the dominant component to the scattered waves, \(m_{\ell}\) denotes the shadowing parameter, and \(\mu_{\ell}\) denotes the fading (i.e., several clusters). Additionally, \({}_{1}F_{1}(\cdot;\cdot;\cdot)\) represents the confluent hypergeometric function [46], and it is defined in the form of an infinite series as \({}_{1}F_{1}(a;b;z)=\sum_{i=0}^{\infty}\frac{(a),z^{i}}{h(b)_{i}}\)[46] (Equation (9.210.1)), where \((z)_{n}=\frac{\Gamma(z+n)}{\Gamma(z)}\) shows the Pochhammer symbol [46].  
The \(\kappa-\mu\) shadowed fading model contains the distribution of some general fading models, namely, Rician, Rician-shadowed, One-sided Gaussian, \(\kappa-\mu\), Nakagami-\(\hat{m}\), and Rayleigh, as special cases [26]. Table 1 summarizes these special cases, where \(K\) and \(\hat{m}\), show the fading parameters of the Rician (also for Rician-shadowed) and Nakagami-\(\hat{m}\) fading links, respectively.
## 1 Introduction
The results showed that the improved method has better rapidity and immunity. Linear Active Disturbance Rejection (LADRC) is widely used because of the small number of controller parameters and convenient adjustment [18; 19; 20; 21]. Although the number of controller parameters that need to be adjusted by LADRC is much smaller than that of ADRC, it is still a difficult task to readjust the controller parameters for different systems or the same system in different working scenarios. At the same time, Linear Extension State Observer (LESO) in LADRC often has the problem of limited bandwidth, which may cause poor control effect in practical applications [22]. For the problem of quadrotor attitude control, this paper designs a quadrotor attitude control strategy based on LADRC. The control strategy not only considers the internal and external disturbances of the system, but also has a strong adaptability to load quality changes. In order to solve the problem of LADRC controller having too many parameters and being difficult to adjust manually, BP neural network and adaptive control algorithm are proposed in this paper. The BP neural network is used to dynamically optimize and adjust the bandwidth value of LESO in real time, so that the controller can still maintain good control effect in the complex and changeable working environment. By adjusting the values of \(k_{p}\) and \(k_{d}\) in the PD controller in real time, adaptive control reduces the parameter error caused by manual parameter adjustment, and compensates the observation error caused by bandwidth selection, which improves the control accuracy of LADRC and the dynamic performance of the system. Finally, in order to verify the effectiveness of the control scheme, a simulation experiment is designed to verify the attitude tracking ability of the controller.
## 3 Results
### Acoustic Waveguide Modeling Results
As can be seen from the figures, the signals have a similar waveform; the wavelet spectrograms (Figure 14c,d) only show a change in the phase spectrum of the signal. Figure 13: Simulated signals at a distance 0.1 m from the AE source (**a**), 0.3 m (**b**), 0.5 m (**c**); wavelet spectrogram for the signals at a distance 0.1 m (**d**), 0.3 m (**e**), and 0.5 m (**f**). Changing the source position by depth, as well as source orientation, should change the ratio of energies of the asymmetric (A0) and symmetric (S0) Lamb wave modes. However, in the considered frequency range of 30-300 kHz, typical for industrial AE testing, the S0 mode is significantly attenuated [25] and, as a rule, has several times smaller amplitude than the A0 mode. As a result, the location of the source by depth and the change in its orientation did not lead to significant changes in the signal waveform.
###### Abstract
In [11; 12; 13], converters were regulated based on the comprehensive mathematical model covering all the dynamics of synchronous generators to achieve power regulation and load sharing. However, the presented model suffers from its inherent complexity. In addition, models regarding frequency regulators, i.e., the speed governor and turbine, are excluded from the analysis. References [14; 15; 16; 17; 18] claimed that inverters were not necessarily controlled exactly as synchronous generators. As long as the essential electrical-mechanical behavior of synchronous generators, which is described by the well-known swing-equation [5], was incorporated in VSMs, it would exhibit a similar inertial response as synchronous generators under frequency events. In order to supplement this model, behaviors of frequency regulators, such as speed governor, were added to VSMs in [16; 19; 20]. This completed the frequency regulation structure. Moreover, it was identified in [21; 22; 23; 24; 25] that the frequency stability of power electronics-based power systems could be improved since VSMs are capable of responding much faster to frequency events than conventional synchronous generators. In order words, VSMs enable fast frequency regulation and power balance. However, this may cause remarkable differences in the influence of inertia on frequency regulation from traditional power systems, which was not discussed in previous research. Moreover, it is possible to dynamically change the inertia coefficient of VSMs even during frequency events, although this is achieved at the expense of increased control complexity [26; 27; 28; 29]. Therefore, it is quite necessary to have a guideline for the design of inertia coefficients. This paper aims to analyze the influence of inertia provided by grid-tied converters in EVs on frequency regulation performances, where synchronous generators are replaced by fast-response VSMs. Furthermore, a design method for the optimal inertia coefficient will be presented. The system schematics and structure of frequency regulation will be provided in Section 2. In Section 3, the relationships between several important performance indices, such as the rate-of-change-of-frequency (ROCOF), frequency nadir (peak), overshoot,settling time, and inertia coefficient, will be derived. Based on these indices, the design of optimal inertia coefficients will be discussed in Section 4. Experiment results will be demonstrated in Section 5 to validate the correctness of the theoretical analysis and feasibility of the inertia design. Finally, Section 6 concludes the main contribution of this paper.
# An Eye-Tracking Study on Text Comprehension While Listening to Music: Preliminary Results
## 2 Materials and Methods
### Measurements
#### 2.4.3 Reading Attitudes Measurements  
In the experiment, participants were invited to answer a survey questionnaire designed to assess their attitudes towards reading. The experiment leader employed a fourth-grade Likert scale to calculate the scores for five perceived reading attitudes: difficulty, understanding, confidence, fatigue, and immersion. The following section provides an explanation of the methodology used to calculate each reader's attitude score. Firstly, perceived difficulty refers to the level of difficulty associated with each reading task, as evaluated by the participant. This was assessed through the following question: \"How difficult did you perceive this reading task to be?\". A score was calculated for the perceived difficulty of difficulty of radio task. A score was calculated to quantify the perceived difficulty of each reading task, ranging from 1 (very easy) to 4 (very difficult). Secondly, perceived understanding was assessed through the question: \"How clearly did you understand the reading text?\". Responses were recorded on a scale from 1 (poorly understood) to 4 (completely understood), providing a measure of the participants' self-assessed comprehension of the reading task. Thirdly, the levels of perceived confidence were evaluated through the following question: \"How confident are you about your text comprehension?\". The scale ranged from 1 (lack of confidence) to 4 (complete confidence). Fourth, perceived fatigue was assessed using two statements: \"My eyes became tired as I read the text\" and \"I felt comfortable reading the text\" (reverse-scored). Responses were recorded on a scale from 1 (very unlikely) to 4 (very likely), capturing the extent to which participants experienced fatigue during reading. Similarly, perceived immersion was evaluated through two statements: \"I became really involved in reading the text\" and \"I could not focus on reading the text\" (reverse-scored). This scale, ranging from 1 (strongly disagree) to 4 (strongly agree), was employed to measure the degree of engagement participants experienced while reading. The content of the survey questionnaire was based on the relevant literature concerning reading attitudes [10; 33].