esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Design  
Recently, SMC has been employed for designing the controller for PAMs or systems powered by PAMs [4; 6; 7; 8; 9; 10; 11]. SMC is able to provide highly accurate tracking performance with a bounded error; however, \"chattering\" problem is a big challenge that SMC must overcome. SMC is a suitable control approach for PAM-based systems to deal with their uncertain, nonlinear and time varying characteristics. In this research, we addressed a DFISMC to improve the tracking performance of the antagonistic actuator powered by PAMs. The fractional order integral is implemented together with disturbance observer to deal with the \"chattering\" problem. Figure 3 illustrates the block diagram of the proposed control system. We consider the following fractional integral sliding surface:  
\[S_{k}=e_{k}+^{\alpha}\Xi_{e,k} \tag{6}\]  
where \(e_{k}=y_{k}^{*}-y_{k}\) is the tracking error with the desired trajectory \(y_{k}^{*}\), and \({}^{\alpha}\Xi_{e,k}\) is the integral of the tracking error with fractional order \(\alpha\) and integral gain \(K_{l}\). \({}^{\alpha}\[PERSON]_{e,k}\) can be calculated as follows:  
\[{}^{\alpha}\[PERSON]_{e,k}=^{\alpha}\[PERSON]_{e,k-1}+K_{l}\left(\sum_{j=2}^{N}\Omega_{j} \tilde{e}_{k-N+j}+\Omega_{1}e_{k-N+1}\right) \tag{7}\]  
and \({}^{\alpha}\[PERSON]_{e,0}=\omega_{N}e_{0}\) at the initial state. Please refer to Appendix A for details about fractional integral approximation. We also obtain  
\[{}^{\alpha}\[PERSON]_{e,k+1}=^{\alpha}\[PERSON]_{e,k}+K_{l}\left(\sum_{j=2}^{N}\Omega_{j} \tilde{e}_{k-N+j+1}+\Omega_{1}\tilde{e}_{k-N+2}\right) \tag{8}\]  
Figure 3: Block diagram of the discrete-time fractional integral sliding mode control. From (6)-(8), we can obtain  
\[S_{k+1}=e_{k+1}+S_{k}-e_{k}+K_{I}\left(\sum_{j=2}^{N}\Omega_{j}\tilde{e}_{k-N+j+1} +\Omega_{1}\tilde{e}_{k-N+2}\right) \tag{9}\]  
Therefore,  
\[S_{k+1}-S_{k}=(1+K_{I}\Omega_{N})e_{k+1}-(1+K_{I}\tilde{\Omega}_{N})e_{k}-K_{I} \sum_{j=2}^{N-1}\tilde{\Omega}_{j}e_{k-N+j} \tag{10}\]  
where \(e_{k}+1\) is one-step-ahead tracking error, which can be computed from the SISO model of the actuator in (4) as  
\[e_{k+1}=y_{k+1}^{*}+\sum_{i=1}^{n}a_{i}y_{k-i+1}-\sum_{j=1}^{m}b_{j}u_{k-d-j+1} -p_{k}, \tag{11}\]  
where \(y_{k+1}^{*}\) is one step ahead of the desired trajectory, which is considered to be known when apply the model to a specific application. In (4), disturbance \(p_{k}\) is unknown and needs to be estimated. In this study, one-step delayed technique was used to estimate \(p_{k}\). This technique is based on the following assumptions:  
**Assumption 1**. : _Sampling time \(T_{s}\) was sufficiently small and system disturbance \(p_{k}\) is bounded, so the difference between two consecutive time samples is also bounded, i.e.,_  
\[p_{k}-p_{k-1}=O(T_{s}) \tag{12}\] \[p_{k}-2p_{k-1}+p_{k-2}=O(T_{s}^{2}), \tag{13}\]  
_where \(O(T_{s})\) is the thickness boundary layer.
## 2 Materials and Methods
### Services and Performance Indicators
#### 2.3.4 Service 4: Reduction of Grid Contracted-Power (\(Gpp\))  
If the SB-MG surpasses a certain power threshold \(P_{grid}^{peak}\), the utility grid must be ready to deliver that power, even if it is only required few hours of the year. This forces the Transmission System Operator to have expensive fast-responding generation units idling, that will be required a very small percentage of time. Having these idling plants ready to respond to high power peaks, can increase significantly the cost of the electricity. Hence, the service hereby proposed is meant to decrease this peak power requirements from the MG, an its general added value, if thought in a high penetration scenario, is the reduction of the required installed capacity in the utility grid, which, in turn, implies a reduction in the cost of the electricity. The performance indicator chosen to quantify this service corresponds to required contracted power according to the EDF-Tempo tariff (see Figure 3[32]), which allow for contract ingeither 9 kW, 12 kW, 15 kW, 18 kW, 30 kW, or 36 kW. This is directly conditioned by the maximum (peak) power in which a given EMS strategy incurs during the test period, hence its designation as the grid-peak-power (GPP). The lower the GPP, the lower the required contracted power; hence, the lower the annual fee to be paid by the SB users. This is translated in a decrease on the final cost of the electricity consumed by the users of the MG. Countries or electricity markets where the contracted power is charged while using a similar fee schedule as the one that is presented in Figure 3, could benefit from this service.
## Abbreviations  
CubeSat \(=\)CubeSatellite  
\(\rm{CalPoly}=\)California State Polytechnic University  
\(\rm{OD}=\)Orbital Deployer  
\(\rm{P-POD}=\)Poly Picosatellite Orbital Deployer  
\(\rm{DC}=\)Direct Current  
\(\rm{RAVAN}=\)Radiometer Assessment Using Vertically Aligned Nanotubes  
\(\rm{LEO}=\)Low Earth Orbit  
\(\rm{PPT}=\)Pulsed Plasma Thruster  
\(\rm{GEVS}=\)General Environment Verification Standard  
\(\rm{RF}=\)Radio Frequency  
\(\rm{UHF}=\)Ultra High Frequency  
\(\rm{VHF}=\)Very High Frequency  
\(\rm{AD\&C}=\)Altitude Determination and Control  
\(\rm{Li-Ion}=\)Lithium Ion  
\(\rm{PMAD}=\)Power Management and Distribution  
\(\rm{IR}=\)Infra \(-\)Red  
\(\rm{rms}=\)Root Mean Squared  
\(\rm{CGT}=\)Cold Gas Thruster  
\(\rm{OD}=\)orbital deployer  
\(\rm{GPS}=\)Global positioning System  
\(\rm{EOL}=\)End-of-life  
\(\rm{PPTCUP}=\)Pulsed Plasma Thruster for CubeSat Propulsion  
\(\rm{P-POD}=\)Poly Picosatellite Orbital Deployer  
\(\rm{OBC}=\)Onboard Computer  
\(\rm{MLI}=\)Multi-Layer Insulation  
\(\rm{EP}=\)Electric Propulsion
# Short Landing for Flying-Wing Unmanned Aircraft with Thrust Vector
## 2 Aircraft Dynamics Model
### Aerodynamic Model  
Computational fluid dynamics (CFD) methods are used to determine the UAV's aerodynamic properties. However, for the sake of computational efficiency, the effects of the lift fans and thrust vector are not considered. Figure 5 displays the pressure contour of the aircraft, and three different grids are employed to check the mesh sensitivity of the CFD calculation: a fine mesh with \(3.1~{}\times~{}10^{6}\) nodes, a medium mesh with \(2.2~{}\times~{}10^{6}\) nodes, and a coarse mesh with \(8~{}\times~{}10^{5}\) nodes. In Figure 6a, the lift coefficient, \(C_{L}\), increases with an increase in the angle of attack, but it does not reach the stall region within 12 degrees. The drag coefficient, \(C_{D}\), on the other hand, rises monotonically as the angle of attack increases, as depicted in Figure 6b. It is worth noting that the drag coefficient obtained using the medium mesh is quite close to that obtained by the fine mesh. Therefore, unless otherwise stated in subsequent discussions, most of the results reported below are obtained on the medium mesh.  
The pitch moment coefficient, \(C_{m}\), in Figure 6c indicates that the UAV gains longitudinal static stability before 10 degrees, so the maximum angle of attack for safe flight is set to 10 degrees. However, the UAV exhibits yaw static instability with a negative static directional-stability derivative \(C_{n\beta}<~{}0\)[27], which makes yaw control more challenging. This is shown in Figure 6d.  
Figure 4: Longitudinal force analysis.  
The SSD is used to control the yaw motion of the UAV. However, the control derivative, \(C_{\mu\delta_{SOD}}\), exhibits nonlinear behavior at deflection angles below 10 degrees due to interactions between the SSD and the wing's boundary layer [28], as shown in Figure 7. This nonlinearity makes yaw control more challenging. To address this, a bias angle [29] of 10 degrees is chosen for the SSD during yaw control.  
Figure 5: Pressure contour of the aircraft.  
Figure 6: Longitudinal aerodynamic coefficients: (**a**) lift coefficient (sideslip angle \(\beta=0\)), (**b**) drag coefficient (sideslip angle \(\beta=0\)), (**c**) pitch-moment coefficient, and (**d**) directional static stability derivative (per radian).  
Equations of motion with six degrees of freedom are developed for the UAV based on the previous analysis. These equations are then used for the performance analysis, control system design, and simulation of short landings. Table 3 summarizes the simulation inputs, and the outputs are the UAV's states during simulation, including position, attitude angles, and angular rates.
## 3 Materials and Methods
### Entropy Water Quality Index (EWQI)  
EWQI is an effective method to quantitatively appraise the groundwater drinking quality. It is information entropy-based so it could avoid the subjective weights given by experts in the traditional assessing method. Hence, EWQI has been widely adopted in studies on groundwater quality due to its more objective idea. It calculates the groundwater drinking quality using the hydrochemical parameters. The calculation procedure of EWQI usually follows the steps of creating an eigenvalue matrix, standardizing the eigenvalue matrix, calculating the information entropy and entropy weight, and quantifying the EWQI results, specifically listed as the following Equations (1)-(8):  
\[X=\begin{pmatrix}x_{11}&x_{12}&\dots&x_{1n}\\ x_{21}&x_{22}&\dots&x_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ x_{m1}&x_{m2}&\dots&x_{mn}\\ \end{pmatrix} \tag{1}\]\[y_{ij}=\frac{x_{ij}-\left(x_{ij}\right)_{min}}{\left(x_{ij}\right)_{max}-\left(x_{ ij}\right)_{min}} \tag{2}\]  
\[Y=\begin{pmatrix}y_{11}&y_{12}&\dots&y_{1n}\\ y_{21}&y_{22}&\dots&y_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ y_{m1}&y_{m2}&\dots&y_{mn}\end{pmatrix} \tag{3}\]  
\[P_{ij}=\frac{y_{ij}}{\sum\limits_{i=1}^{m}y_{ij}} \tag{4}\]  
\[e_{j}=-\frac{1}{\ln m}\sum\limits_{i=1}^{m}\left(P_{ij}\times\ln P_{ij}\right) \tag{5}\]  
\[w_{j}=\frac{1-e_{j}}{\sum\limits_{j=1}^{n}\left(1-e_{j}\right)} \tag{6}\]  
\[q_{j}=\frac{C_{j}}{S_{j}}\times 100 \tag{7}\]  
\[EWQI=\sum\limits_{j=1}^{m}\left(w_{j}\times q_{j}\right) \tag{8}\]  
where \(X\) is the eigenvalue matrix, \(Y\) is the standardized matrix, \(m\) represents the ground-water sample amount, \(n\) represents the hydrochemical parameter amount, \(P_{ij}\) denotes the probability of different parameters, \(e_{j}\) denotes the information entropy, \(w_{j}\) signifies the entropy weight, and \(q_{j}\) signifies the quality rating scale of different parameters.
## 4 Experiments
### Experiment Settings  
#### 4.1.1 Dataset  
We downloaded the raw AIS data from the southeastern and southwestern coastal waters of the United States for the year 2021 on the website for training, validation, and testing. The geographical range is 59.56\({}^{\circ}\) to 125.35\({}^{\circ}\) east longitude and 20.91\({}^{\circ}\) to 49.21\({}^{\circ}\) north latitude. The eastern area is from 62.47\({}^{\circ}\) to 125.35\({}^{\circ}\) east longitude and 20.91\({}^{\circ}\) to 49.21\({}^{\circ}\) north latitude, while the western area is 59.56\({}^{\circ}\) to 79.9\({}^{\circ}\) east longitude and 25\({}^{\circ}\) to 45.53\({}^{\circ}\) north latitude. There are 68 types of vessels and 28,645 vessels in total, which include 60 types and 15,496 vessels in the eastern area, and 62 types and 20,497 vessels in the western area. The original AIS data contain information of MMSI, longitude, latitude, speed, course, departure time, departure port, and so on. We comprehensively preprocess the raw AIS data, including classifying, denoising, dividing time intervals, and segmenting. After data preprocessing, there remain 34,142 trajectories, 33,652 vessels, and 50 types of ships. We divide the normalized trajectory data into three datasets, training, validation, and testing. Specifically, the training, validation, and testing sets contain 27,313, 3046, and 3027 trajectories, respectively. The trajectories of the three datasets contain the vessel numbers of 6927, 1633, and 1615.
## 3 Preliminaries
### Capture Time  
In order to evaluate whether a solution provides SLP protection services, the concept of Security Time [14] was proposed, which is first used to evaluate the parameters of how many messages an attacker needs to trace to the source node. But the original concept raises some questions, because the number can be very large or infinite (and can never be traced), making it difficult to evaluate the performance of the solution. A similar concept is used in this paper to evaluate the performance of an algorithm called Capture Time [19], which describes the minimum time required for an attacker to trace the source node location in a given network.  
In the CT algorithm, for the same network \(G\ =\ (V\), \(E)\), the capture time under normal DAS (Data Aggregation Scheduling) is T\({}_{D}\), and in the case of scheduling algorithm \(S\), it is \(T_{S}\). In general, \(T_{S}=\ E\ \times T_{D}\ (1<E<2)\), where \(E\) is the expected value of the schedule, representing the user's expectation of the effect of the scheduling algorithm \(S\) in extending the capture time. In the experiment, if the actual time \(T\) used by attacker \(\mathcal{A}\) to track the source node is greater than the capture time \(T_{S}\), it is considered that this scheduling \(S\) successfully protects the SLP.
###### Abstract
The effect of PEF treatment on amino acids and fatty acids content have been reported in beef ([PERSON] et al., 2018), seafood ([PERSON] et al., 2018), and lamb cuts ([PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] (2018)). [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] (2018) recently showed that PEF treatment significantly changed the fatty acid composition of meat, especially, n-6 or n-3 PUFA. In contrast, PEF treatment of all chilled cuts at 0 and 7 days of storage significantly decreased fatty acids. Moreover, PEF treatment significantly increased the amino acid content of chilled knuckle, rump, topside, and loin cuts that were stored for 7 days. PEF treatment also increased the amino acid content of all the frozen meat cuts except the rib cut that were stored for 7 days. It is therefore crucial to understand how these changes in fatty acids and amino acids resulting from PEF treatments can influence volatile profiles and sensory quality of PEF processed lamb meat. Temporal Dominance of Sensations (TDS) has been used to understand the temporal flavour profile of meat. TDS, a temporal method was chosen over a static evaluation of sensory attributes like Quantitative Descriptive Analysis (QDA) technique in order to study the progression of dominant sensory attributes in different lamb cuts with time thus providing more realistic results about flavour changes during consumption. The breakthrough idea of TDS was to no longer score intensities, but to elicit \"dominance\". As dominance is far simpler to determine than scoring intensities, the TDS task is feasible for use with consumers that have some training. Consequently, TDS can be correlated to liking, wanting and/or satiation evaluated quantitatively and dynamically during consumption of food. Recently, [PERSON] et al. (2018) investigated the effect of PEF processing on sensory properties of beef muscles (_biceps femoris_ and _semitendinosus_) using TDS. The authors demonstrated that temporal flavour changes described changes in sensory quality of meat samples very well with PEF processing and storage. The current study set out to assess the effects of PEF processing of seven different lamb meat cuts (knuckle, loin, rump, rib, shank, shoulder and topside), and effects of chilled storage (0 and 7 days) of meat on free fatty acid, amino acid, flavour volatile composition, and temporal flavour profile of cooked lamb meat. Partial least Squares Regression (PLSR) analysis was further carried out to determine how chemical changes in terms of amino acids, fatty acids, and volatile composition with PEF processing and storage influenced temporal sensory perception of cooked meat during consumption.
# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
## 2 Models Developed for Network Coverage and Capacity Problems
### Capacitated Network Area Coverage  
Let us assume that each customer location is assigned directly to the one service centre with sufficient signal strength. This is guaranteed by Equation (4).  
\[\forall j\in J:\ \sum_{i\in I}a_{ij}y_{ij}=1. \tag{4}\]  
By Equation (5), each selected service centre must have its capacity sufficient for all the devices of customer locations that are assigned to it.  
\[\forall i\in I:\ c_{i}x_{i}\geq\sum_{j\in J}a_{ij}y_{ij}b_{j}. \tag{5}\]  
If a service centre is selected to be removed from the network infrastructure, none of the customer locations should be assigned to it, this is given by Equation (6).  
\[(\forall i\in I)(\forall j\in J):\ y_{ij}\leq x_{i}. \tag{6}\]  
All selected service centres must have a sufficient sum of their capacities to cover all devices (or facilities) in all customer locations.  
This is guaranteed by constraints (7).  
\[\forall i\in I:\ c_{i}x_{i}\geq\sum_{j\in J}a_{ij}y_{ij}b_{j}. \tag{7}\]  
Now, we can summarize all the previous considerations in the following model.  
Minimize  
\[z=\sum_{i\in I}w_{i}x_{i} \tag{8}\]  
subject to:  
\[\forall j\in J:\ \sum_{i\in I}a_{ij}x_{i}\geq 1 \tag{9}\]  
\[\forall j\in J:\ \sum_{i\in I}a_{ij}y_{ij}=1 \tag{10}\]  
\[\forall i\in I:\ c_{i}x_{i}\geq\sum_{j\in J}a_{ij}y_{ij}b_{j} \tag{11}\]  
\[(\forall i\in I)(\forall j\in J):\ y_{ij}\leq x_{i} \tag{12}\]  
\[\forall i\in I:\ x_{i}\in\{0,1\} \tag{13}\]  
Figure 3: The illustrated scenario where each customer location is assigned exactly to one service centre.  
\[(\forall i\in I)(\forall j\in I):\;y_{ij}\in\{0,1\}. \tag{14}\]  
A necessary precondition for finding a solution is that the sum of all capacities is sufficient to cover all demands, i.e., \(\sum_{i=1}^{m}c_{i}\geq\sum_{j\in I}b_{j}\), with each customer having a reachable distance to at least one centre, i.e., \(\forall j\in J:\;\sum_{i\in I}a_{ij}>0\).  
This model is applicable especially in those use-cases in which we are deploying the gNodeB nodes to the new area or if we can rebuild the whole existing network. However, in many scenarios, we need to take into account the existing gNodeB nodes (existing services), for that reason, we have created the next model considering that use-case.
## 2 Experimental Programs
### Test Setup, Loading Protocol, and Instrumentation  
In this study, a cyclic loading test was performed using the loading apparatus depicted in Figure 2. Load and displacement measurements were obtained using load cells (MISUO, DWG.NO. HC-15391, Japan) and LVDTs (displacement meters) integrated into the vertical and horizontal jacks. Additionally, a 160 kN axial force was applied using a vertical hydraulic jack (KYOWA, LUR-B-1 MNASA1, Japan).  
A positive and negative alternating load was applied in the horizontal loading direction by incrementally increasing the displacement. Figure 3 illustrates the horizontal displacements applied during these tests. The reference displacement was set to 0.5% of the effective height for both experimental specimens (hereafter referred to as \"0.5% Drift\"), resulting in 6.75 mm for Case-1 and 8 mm for Case-2. These values are roughly equal to the design yield displacement, and integer multiples of this displacement were manually applied repeatedly. The number of repetitions was set to three for the same displacement  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multicolumn{10}{c}{**Strength Parameters (Mpa)**} \\ \hline  & \(f_{e}\) & \(f_{y}^{\prime}\) & \(f_{u}^{\prime}\) & \(f_{hy}^{\prime}\) & \(f_{hu}^{\prime}\) & \(D_{Ir}\) & \(\rho\) & \(\rho_{w}\) & \(I_{re}\) \\ \hline Case-1 & 30.1 & 357 & 486 & 321 & 492 & D13 (12.7 mm) & 1.27\% & 0.79\% & \(3.11\times 10^{9}\) mm\({}^{4}\) \\ Case-2 & 27.6 & 351 & 483 & 332 & 500 & D10 (9.53 mm) & 1.25\% & 0.79\% & \(3.13\times 10^{9}\) mm\({}^{4}\) \\ \hline  & & & & & & & & & \\ \end{tabular} \(f_{c}\): Concrete strength on the day of loading test. \(f_{y}\), \(f_{hy}\): Average yield strength and average tensile strength of longitudinal reinforcements. \(f_{d}\), \(f_{hy}\): Average yield strength and average tensile strength of hoop reinforcements. \(D_{Ir}\): Diameter of longitudinal reinforcements. \(\rho\), \(\rho_{w}\): Longitudinal reinforcement ratio and hoop reinforcement volume ratio. \(I_{re}\): Inertia moment of each section.  
\end{table}
Table 1: Summary of Cyclic Loading Test Specimens.  
Figure 2: Test Setup.  
amplitude. To ensure proper adaptation of the specimen to the loading device, a load was applied once in both positive and negative directions at 0.25% Drift before the loading test commenced.  
Due to experimental constraints, only the damage to the longitudinal reinforcements in Case-2 was examined. Strain gauges were attached to the central longitudinal reinforcements on both the N-face and S-face sides of the loading direction, as illustrated in Figure 4. The strain gauges were placed from the base up to a height of 800 mm, and the strain at each loading step was measured.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
###### Abstract  
(1) Background: Forklifts are used widely in factories, but it shows the problem of large uncertainties when using an RGB-D sensor to recognize and locate pallets in warehouse environments. To enhance the flexibility of current autonomous forklifts in unstructured environments, the improved labeled template matching algorithm was proposed to recognize pallets. (2) Methods: The algorithm comprises four steps: (i) classifying each pixel of a color image with the color feature and obtaining the category matrix; (ii) building a labeled template containing the goods, pallet, and ground category information; (iii) compressing and matching the category matrix and template to determine the region of the pallet; and (iv) extracting the pallet pose from information in respect of the pallet feet. (3) Results: The results show that the proposed algorithm is robust against environmental influences and obstacles and that it can precisely recognize and segment multiple pallets in a warehouse with a 92.6% detection rate. The time consumptions were 72.44, 85.45, 117.63, and 182.84 ms for detection distances of 1000, 2000, 3000, and 4000 mm, respectively. (4) Conclusions: Both static and dynamic experiments were conducted, and the results demonstrate that the detection accuracy is directly related to the detection angle and distance.  
Keywords:pallet recognition; autonomous forklift; labeled template; RGB-D sensor +
Footnote †: journal: Applied
## 2 Method
### Experimental Data  
The SEED-V database delved into an examination of five distinct emotions--happiness, sadness, fear, disgust, and neutral. A curated selection of 15 film clips, with 3 clips representing each emotion, served as stimuli. As shown in Figure 2, five specific emotional stimulation videos correspond to five different emotions, namely fear, happiness, disgust, sadness, and neutral. Each of the 16 subjects, comprising 6 males and 10 females, partook in the experiment 3 times, with each session spanning 50 min. The ages of the 16 subjects were 21, 21, 23, 21, 20, 23, 23, 21, 23, 24, 20, 22, 22, 19, and 19, respectively. The average age is 21.63, with a standard deviation of 1.63. Throughout the experiment, a meticulously designed protocol was adhered to, as follows: a 15 s initial pause preceded each stimulus, followed by 2 to 4 min of stimulus exposure, with intervals lasting either 15 or 30 s, depending on the emotion portrayed [14].  
EEG signals were captured using a sophisticated 62-channel EEG device with a sampling frequency set at 1000 Hz, later downsampled to a 200 Hz rate to facilitate processing. Preprocessing of EEG data involved the application of a band-pass filter spanning 1 to 75 Hz, aimed at eliminating noise and artifacts. Subsequent to the experiments, participants engaged in a self-assessment segment, where they were prompted to rate the impact of the stimuli on a scale ranging from 0 to 5. A score of 5 indicated the most favorable response, while 0 denoted the least favorable.  
Figure 1: Framework for EEG-based emotion classification using ResNet18 combined with the _DE_ method.