esa-sceva/satcom-chunk-collection huggingface dataset:
# A Game Player Expertise Level Classification System Using Electroencephalography (EEG)
## 2 Proposed Methodology
### EEG Data Acquisition
#### 2.1.3 Apparatus  
The raw EEG data are recorded using a commercially-available EMOTIV (San Francisco, CA, USA) EPOC EEG headset with the EMOTIV Premium SDK software development kit (SDK) v 3.3.3, San Francisco, CA, USA. The observed data are stored in the European data format (EDF). The headset provides fourteen micro-electrodes for recording EEG activity including F3, F4, F7, F8, AF3, AF4, P7, P8, T7, T8, FC5, FC6, O1 and O2. The even- and odd-numbered channels represent electrodes for the right and left hemispheres of the brain, respectively. In addition to the fourteen channels, there are two additional electrodes that act as the reference for each hemisphere of the head. Saline liquid is used to hydrate the electrodes to reduce the resistance of connection between electrode and skull. Figure 2 shows the reference location of electrodes on the player's scalp providing coverage in the frontal, temporal, parietal and occipital regions of the brain. The spatial placement of electrodes of the EMOTIV EPOCH EEG fourteen-channel headset follows the international 10-20 electrode positioning system [24]. The exact location of these channels can vary depending on the head size and hair length of participants. The EMOTIV headset is made flexible to adjust to all head sizes. The recorded brain waves represent the activity in a local brain region, and with the sparse placement of electrodes, it is not effected by slight displacement in location. The recorded data are transmitted over a wireless Bluetooth interface using a proprietary dongle that is connected through a USB connection to a computer system running the EMOTIV SDK.
## 2 Materials and Methods
### Data Recording and Processing  
EMG signals were recorded with a sample frequency of 1 kHz using surface EMG electrodes placed on cleaned skin sites. Muscles included in the EMG data collection were left and right leg tibialis anterior (LTA, RTA), soleus (LSOL, RSQL), gastrocnemius medialis (LGASM, RGASM), gastrocnemius lateralis (LGASL, RGASL), rectus femoris (LRF, RRF), hamstring (LHAM, RHAM), gluteus medius (LGMED, RGMED), and gluteus maximus (LGMAX, RGMAX). For subjects H6 to H12, hip adductors (LADD, RADD) were included instead of GASL. Recordings of the center-of-pressure were obtained by means of four precision force transducers placed underneath the treadmill.  
Raw EMG data were Notch filtered (49-61 Hz) and band pass filtered (20-300 Hz) to remove the mean value and noise. Subsequently, the EMG data were full-wave rectified and filtered with a moving average window of 150 ms to obtain EMG envelopes. The data were segmented into gait cycles defined as the period between two consecutive heel strikes, as detected from the center-of-pressure recordings. For left-aligned perturbations, left heel strikes defined the gait cycle. For right-aligned perturbations, right heel strikes defined the gait cycle. Data for each gait cycle were resampled to a 0-100% range consisting of 200 samples.
## 2 Equations Derivation of the Direct Calculation Method (DCM)
### Procedure of Numerical Analysis for the DCM  
The algorithmic process used in the analysis of DCM is to solve the displacement/stress at the equilibrium state (point \(E\)). The computation flowchart of DCM is represented in Figure 2. The process and steps of the calculation performed by DCM include (1) input of calculation data (including tunnel geometry, in situ stress, surrounding rock material, support member material, unsupported span, and other related parameters), (2) according to the unsupported span calculate the value of \(\lambda_{d}\), \(\lambda_{e}\), and \(\lambda_{s}\), (3) determine whether the stress change of surrounding rock (i.e., the corresponding \(\lambda\) value) caused by the continuous excavation of the tunnel is in the elastic zone or in the plastic zone, (4) according to Equations (3)-(6), calculate the solution at the equilibrium point, and (5) record the displacement/stress corresponding to all incremental values and plot the graphs or tables.  
In the analysis of DCM, the unknown parameters obtained by the calculation with the known parameters shown in Table 1 are the radial stress (\(\sigma_{R}\)), the radial displacement (\(u_{R}\)), and the tangential stress (\(\sigma_{\theta}\)). Corresponding to a specified value of the confinement loss, these unknown parameters are the behavior at the intrados of the tunnel in different steps of excavation. In this study, the Mohr-Coulomb failure criterion is used to determine whether the stress reaches the plastic region, and the plastic strain adopts the plastic associated flow rule.
# System-Theoretic Process Analysis Based on SysML/MARTE and NuSMV
## 3 STPA-SN Application Example
### Step 4: Identify the Loss Scenarios
#### 3.4.4 Model Checking  
The NuSMV model is used to analyze the CTL specifications and the results show that the system model does not satisfy the two CTL specifications. Below is the analysis result of SPEC_SC2, which is output as a counterexample, i.e., a loss scenario.  
1. In the beginning, the door is completely closed, i.e., in the state of Dr_Closed, and there is no obstacle in the doorway, i.e., in the state of Obs_NoExisting.
2. The driver sends the command of opening the door (Drv_Open_CA), triggering the door controller to send Ctrlr_Open_CA.
3. After receiving Ctrlr_Open_CA, the door actuator performs the door opening action (Act_Open_CA).
4. Triggered by Act_Open_CA, the door changes its state from physicalClosed to physicalOpening. After 10 units of time, it changes to the state of physicalOpened.
5. The driver sends the command of closing the door (Drv_Close_CA), triggering the door controller to send Ctrlr_Close_CA.
6. The door actuator executes the closing action (Act_Close_CA) after receiving Ctrlr_Close_CA.
7. The door changes its state from physicalOpened to physicalClosing.
8. The obstacle appears in the doorway and physicalExisting is true. When the door is closing and an obstacle exists, clkDoorOpenSinceObstacle is set to zero and starts the timing.
9. A total of 2 units of time delay happens to the obstacle sensor and clkDoorOpenSinceObstacle increases by 2 units of time.
10. The door controller sends the command of door opening (Ctrlr_Open_CA), and the door actuator delays 2 units of time from receiving Ctrlr_Open_CA to sending Act_Open_CA. The clkDoorOpenSinceObstacle adds 2 units of time, reaching 4 units of time in total.  
At this point, the door of the train is in the physicalClosing state and the obstacle is in the physicalExisting state. The total duration (clkDoorOpenSinceObstacle) from the moment when an obstacle appears in the doorway to the moment when the door receives the command to open the door, is 4 units of time, which is longer than 3 units of time. Hence, SPEC_SC2 is violated and the system becomes hazardous.
# Numerical Analysis on Self-Propulsion of a Waterjet-Propelled Ship with Different Propulsion Models
## 2 Numerical Model
### Governing Equations and Turbulence Model  
The numerical simulations are performed with the platform of the commercial software ANSYS Fluent. The governing equations are the Reynolds-averaged Navier-Stokes (RANS) equations including the mass and momentum conservation equations of incompressible fluid:  
\[\frac{\partial\overline{u}_{i}}{\partial x_{i}}=0, \tag{1}\]  
\[\frac{\partial\overline{u}_{i}}{\partial t}+\overline{u}_{j}\frac{\partial \overline{u}_{i}}{\partial x_{j}}=-\frac{1}{\rho}\frac{\partial\overline{p}}{ \partial x_{i}}+\
u\frac{\partial}{\partial x_{j}}\left(\frac{\partial \overline{u}_{i}}{\partial x_{j}}+\frac{\partial\overline{u}_{j}}{\partial x_ {i}}\right)+\frac{\partial\tau_{ij}^{R}}{\partial x_{j}}+f_{i}, \tag{2}\]  
where \(\rho\), \(\overline{p}\), \(v\) and \(f_{i}\) denote the fluid density, mean pressure, kinematic viscosity coefficient and body force, respectively. \(\overline{u}_{i}\) and \(\overline{u}_{j}\) are the mean values of velocity components, and \(\tau_{ij}^{R}=-\overline{u_{i}^{\prime}u_{j}^{\prime}}\) is the Reynolds-stress tensor, which represents the transfer of momentum  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Parameter** & **Particulars** & **Value** \\ \hline
1. Hull model: & & & \\ Length between perpendiculars & L\({}_{\text{PP}}\) (m) & 6.467 \\ Breadth & B (m) & 0.813 \\ Molded depth & D (m) & 0.387 \\ Draft & T (m) & 0.241 \\ Displacement & \(\Delta\) (t) & 0.579 \\ Wetted surface & S\({}_{\text{W}}\) (m\({}^{2}\)) & 5.453 \\ \hline
2. Waterjet model & & \\ Diameter of pump inflow plane & D\({}_{\text{pump-inflow}}\) (mm) & 180.00 \\ Diameter of impeller & D\({}_{\text{impeller}}\) (mm) & 179.64 \\ Width of tip clearance & \(\delta_{\text{tip}}\) (mm) & 0.18 \\ Diameter of nozzle & D\({}_{\text{nozzle}}\) (mm) & 108.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the hull and waterjet models.  
Figure 2: Hull and waterjet geometry.  
by turbulent fluctuations and can be calculated by the turbulent transport equations. In this study, the SST \(k\)-\(\omega\) turbulence model [35] is used to solve the turbulence effect. The RANS equations and the turbulent transport equations are discretized through second-order upwind schemes. The pressure-based solver SIMPLE is used as the pressure-velocity coupling algorithm, and the gradient is discretized using Least Squares Cell based scheme. The transient terms are discretized using second-order implicit time discretization. The physical time step for each rotational speed is maintained at \(1^{\circ}\) per step, and there are 20 inner iterations in each time step. The volume of fluid (VOF) method [36] is applied to capture the free surface. The hull draft is kept constant, and the trim angle is fixed at \(0^{\circ}\) to simplify the calculation and to accelerate numerical convergence.
## 7 Experimentation and Results
### Analysis of Pareto-Optimal Set of the Four Algorithms
Figure 11: Minimizing both end-to-end latency and end-to-end delay and maximizing both network throughput and energy efficiency based packet payload size achieved by the OMOPSO algorithm. Figure 12: Minimizing both end-to-end latency and end-to-end delay and maximizing both network throughput and energy efficiency based packet payload size achieved by the NSGA-II algorithm. Figure 15 presents the Pareto optimal front obtained from all the algorithms at the end of 250 generations where f1 represents the end-to-end delay, f2 represents the end-to-end latency, f3 represents the network throughput, and f4 represents the energy efficiency. Both MOSFP and OMOPSO produce 19 non-dominated solutions related to the Pareto front, while both NSGA-II and SPEA2 produce 18 non-dominated solutions for the same objective functions. This proves the ability of MOSFP which obtained 19 values of getting good results, compared to SPEA2 and NSGA-II. In addition, MOSFP has the best spread and distribution of solutions related to the true Pareto front while the OMOPSO is in the second followed by NSGA-II and SPEA2 as outlined in Figure 15.  
Figure 14: Minimizing both end-to-end latency and end-to-end delay and maximizing both network throughput and energy efficiency based packet payload size achieved by the MOSFP algorithm. Figure 13: Minimizing both end-to-end latency and end-to-end delay and maximizing both network throughput and energy efficiency based packet payload size achieved by the SPEA2 algorithm. Based on Figure 7, we can summarize the procedure of SPEA2 as follows: the algorithm begins the procedure by initializing the packet payload value, which is varied in the range of 0 to 114 bytes based on the IEEE 802.15.4 data frame. After that, SPEA2 uses the first population to evaluate the objective functions to minimize the network end-to-end delay and end-to-end latency and also maximize the  
Figure 15: Pareto optimal front for end-to-end delay and end-to-end latency (f\({}_{1}\) and f\({}_{2}\)) vs. network throughput (f\({}_{3}\)) vs. energy efficiency (f\({}_{4}\)) based on the results of all the algorithms, (**a**) Pareto front of four objectives that obtained by MOSFET; (**b**) Pareto front of four objectives that obtained by OMOPSO; (**c**) Pareto front of four objectives that obtained by NSGA-II; (**d**) Pareto front of four objectives that obtained by SPEA2  
network throughput and energy efficiency. Hence, the algorithm uses the values of the fitness function to perform the selection operator. After selection, the algorithm generates the mating pool. This pool is a set of population that both mutation and crossover operations are applied on them in order to generate a new population.
## 6 Experiments
### Results
Contrarily, both DRL approaches cannot reach PSR performance as competitively as the CI methodologies. This aspect explains why their objective functions visible in Table 7 ranked among the lowest. Nevertheless, both PPO and the DQN provided very good outcomes in terms of the average latency for each micro-service. Despite certain shortcomings in the PSR of specific services, particularly Audio and Video, PPO consistently outperformed most of them in terms of latency. On the other hand, looking at the average service latency of the other approaches, Table 8 shows that the algorithms performed quite differently. Indeed, it is clear how the ECT methodologies consistently outperformed their counterparts in the majority of cases. Among them, QPSO-ECT emerged as the most-efficient overall in minimizing the average latency, particularly for mission-critical services. Specifically, QPSO-ECT overcame the GA-ECT by an average of 50%, PSO-ECT by 37%, GWO-ECT by 48%, and MPSO-ECT by 19% for these services. Oppositely, both variants of the GA registered the worst performance, with a significant number of micro-services registering a latency between 100 and 200 ms. However, let us specify that minimizing the average service latency was not within the scope of this manuscript, which instead aimed at maximizing the PSR, as is visible in (9). To conclude, we can suggest that PPO emerged as the best DRL algorithm. It can find a competitive value in terms of the objective function along with the best results in terms of sample efficiency and latency at the price of a longer training procedure--when compared to the CI algorithms. While the ECT variants of the metaheuristics included in this comparison demonstrated great performance as well, they require much more samples to find the best solution.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results
### Noise-free Performance Analysis  
Figure 16 demonstrates the complete PPM generation and demodulation chain under ideal noise-free conditions. The signal processing sequence begins with the original input signal (first trace) and its mathematical derivative (second trace), which serves as the control input for frequency modulation. The VCO output (third trace) exhibits clear frequency variations corresponding to the derivative amplitude, while the monostable output (fourth trace) produces the desired PPM signal with position-modulated pulses.  
Figure 15: Complete PPM signal processing chain with conditioning and demodulation. (**a**) Original information signal for reference comparison; (**b**) PPM signal after reference pulse subtraction; (**c**) PPM signal with reference pulse removed and DC offset added; (**d**) Successfully demodulated output signal demonstrating accurate information recovery through the complete PPM processing chain.  
The demodulation process, illustrated in traces five and six, shows successful signal recovery through the Quasi-FM-PWM technique. The monostable processing of the received PPM signal (fifth trace) and subsequent filtering produces a demodulated output (sixth trace) that closely matches the original input signal, demonstrating effective signal reconstruction under optimal conditions.
## 2 Related Work
### MPDIoI Loss Function  
Bounding box regression (BBR) [24] has an important influence on the accurate positioning and recognition of the model and is the key link to achieving efficient and accurate object detection. At present, most of the existing BBR loss functions can be divided into the following two categories: loss function based on ln norm and loss function based on intersection over union (IoU). The traditional bounding box regression loss function has the same aspect ratio in the prediction box and the actual annotation box, so it cannot be optimized. The MPDIoU loss function combines the concept of minimum point distance and improves the regression efficiency and accuracy by minimizing the distance between the upper left and lower right points between the prediction box and the real box. This process can be described as follows:  
\[d_{1}^{2}=\left(x_{1}^{B}-x_{1}^{A}\right)^{2}+\left(y_{1}^{B}-y_{1}^{A} \right)^{2} \tag{1}\]  
\[d_{2}^{2}=\left(x_{2}^{B}-x_{2}^{A}\right)^{2}+\left(y_{2}^{B}-y_{2}^{A} \right)^{2} \tag{2}\]  
\[MPDIou=\frac{A\bigcap B}{A\bigcup B}-\frac{d_{1}^{2}}{w^{2}+h^{2}}-\frac{d_{2 }^{2}}{w^{2}+h^{2}} \tag{3}\]  
The parameters \(A\) and \(B\) denote two arbitrary convex images, \(w\) is the width, and \(h\) is the height; \((x_{1}^{A},y_{1}^{A})\) and \((x_{2}^{A},y_{2}^{A})\) represent the coordinates of the upper left corner and the lower right corner of \(A\), respectively; \((x_{1}^{B},y_{1}^{B})\) and \((x_{2}^{B},y_{2}^{B})\) represent the coordinates of the upper left corner and the lower right corner of \(B\), respectively; \(d_{1}^{2}\) is the square of Euclidean distance between the upper left corner of \(A\) and \(B\); \(d_{2}^{2}\) is the square of Euclidean distance between the lower right corner points of \(A\) and \(B\); and MPDIou is the intersection and union ratio (IoU) of \(A\) and \(B\) minus the normalized minimum point distance.
## 1 Introduction
It was shown that the extended formulations can clearly describe wave-body dynamics, including momentum and energy transfer. [PERSON] et al. proposed a method for numerically modeling 2D and 3D nonlinear sloshing responses using a new boundary integral method (BIM) [49]. Using an artificial linear damping coefficient proportional to the fluid particle velocity, they simulated liquid motion energy dissipation. They conducted several small-scaled shaking table tests to show the efficiency and reliability of the new BIM model. [PERSON] et al. extended a two-dimensional numerical scheme to a three-dimensional model to simulate the free-surface waves in liquid storage tanks with different shapes. The extended scheme enabled the precise prediction of the trajectory of each free-surface node based on the accurate estimation of the partial derivatives of the velocity potentials, shown by comparing with experimental results [50]. The FE method developed by [PERSON] et al. was shown to be highly effective in modeling the fluid-structure interaction and tracking free-surface fluid [51]. This method updates the mesh every time step to handle fluid changes in the spatial domain. The Lagrangian-Lagrangian method is another numerical method that enables the precise modeling of water-tank systems and the tracking of free-surface water nodes' movement over time. [PERSON] et al. used this method to study the seismic behavior of a liquid storage tank considering the fluid-soil-structure interaction [52]. They validated their model by comparing the first frequency of their numerical model with the one calculated using ACI regulation. In this study, the sloshing response of a real-scale rectangular liquid storage tank under seven three-dimensional near-field earthquakes was numerically studied. In order to numerically model the liquid storage tank, the Lagrangian-Lagrangian method was applied. This method was validated using the experimental results provided by [53]. In the subsequent step, a real-scale rectangular liquid storage tank was modeled using the same numerical method, and its dynamic properties, including the first five sloshing periods of vibration, were calculated. The tank was then subjected to seven near-field earthquakes under two different conditions. In the first condition, the major and minor horizontal earthquake components were applied perpendicularly to the shorter and longer tank walls, respectively. These two components were then rotated 90 degrees around the vertical earthquake component and applied to the tank. The sloshing response of the tank to the seven earthquakes was recorded at its corners, in the middle of the shorter tank wall, and in the middle of the longer tank wall at the water's surface. Finally, the relationship between the maximum sloshing response and the frequency content of the earthquakes was studied.
# Improving a Rapid Alignment Method of Tomography Projections by a Parallel Approach
## 1 Introduction
### Post-Acquisition Alignment  
Marker-based alignment methods are a common solution to the problem, especially in the case of biological samples which exhibit low absorption contrast and/or not well-defined features; at the cost of decreasing the sample visibility (this is the measure of how critical this step is), gold nano-beads are added to the sample solutions before cryo-fixation [2] and are tracked in a post-acquisition procedure among each projection. IMOD [18, 19, 20] and many other software frameworks [14, 21, 22, 23, 24, 25, 26] reviewed in, e.g., [27] are used to manage this delicate pre-processing step. However, this approach is questionable, as for many biological specimens, it is extremely difficult to control the spatial distribution of such fiducials. Large areas of the sample can be completely void of markers, or, conversely, the distribution can be so dense that many different beads agglomerate one to another, determining no reliable tracking information. Automatic feature-based approaches are proven to work only in the presence of high quality images [28].  
_Tomography self-consistency_ or _bootstrap_ methods [29, 30] instead try to infer the alignment parameters while reconstructing the volume (Section 2). Paired with coarse alignment [31], these methods are usually the best choice to solve the misalignment problem. While in great part automatic, these techniques are for their nature extremely demanding from a computational point of view and can require both commercial software and highly experienced users [16]. The rapid projection alignment method presented in [12] belongs to this class of algorithms, is implemented in the advanced _Tomory_[7] software framework, and can be simply introduced in the pipeline by just invoking one line of code. Unfortunately, the implementation can be slow on high-resolution datasets such as the one acquired with newer setups [4], undermining its use as a fast method.