esa-sceva/satcom-chunk-collection huggingface dataset:
# YOLO Model-Based Eye Movement Detection During Closed-Eye State
## 3 Experiments and Results
### Eye Movement Tracker
#### 3.4.1 Simulation Testbed  
We captured real-time updated video streams from the camera acquisition system and fed them into the tracker for analysis. The processing flow of the real-time video stream is as follows:  
* Frame Extraction: Images were extracted from the video stream frame by frame and resized to the model's required input size (640 \(\times\) 640 pixels);
* Target Detection and Tracking: The YOLO model was applied to each frame to detect the target position, and the detection results from consecutive frames were correlated with the tracker;
* Trajectory Calculation: The target movement trajectory was calculated based on changes in the target position across consecutive frames. The trajectory was represented as a series of coordinate points and could be further converted into motion parameters such as distance, speed, and acceleration;
* Result Display: The detection frame, tracking trajectory, and motion parameters were displayed on the video screen in real time, allowing users to observe and analyze the results.  
The experiments were conducted using a physical model designed to simulate eye movements, as illustrated in Figure 5a. The camera, positioned atop the physical model, captures images where 640 pixel points correspond to a 100 mm span on the plane. The displacement and velocity of the ball are derived from the pixel coordinates of its trajectory. Figure 5c presents the software interface of the tracker, showcasing the current target tracking.  
As a supplementary note, the weights utilized for testing the simulation model were derived through training on simulated ball images rather than eye images. This simulation model was adopted to validate the integrity of the tracker's hardware and software system. Meanwhile, the next experiment tested the pre-recorded and real-time eye video streams using the optimal model weights trained on actual eye images.  
The experimental results from the simulation platform demonstrate that our tracker achieves robust real-time target-tracking performance. The acquisition system, target detection module, and trajectory measurement unit all operate seamlessly and successfully.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Detection Model** & **P** & **R** & **F1 Score** & **AP0.5** & **Inference Time/ms** \\ \hline YOLOv5s & 0.899 & 0.597 & 0.718 & 0.722 & 13.0 \\ YOLOv8s & 0.880 & 0.549 & 0.676 & 0.693 & 11.6 \\ YOLOv9s & 0.882 & 0.739 & 0.804 & 0.798 & 28.4 \\ YOLOv10s & 0.892 & 0.237 & 0.375 & 0.486 & 15.7 \\ YOLO11n & 0.924 & 0.625 & 0.746 & 0.811 & 14.3 \\ YOLO11s & 0.902 & 0.824 & 0.861 & 0.832 & 14.6 \\ YOLO11m & 0.884 & 0.891 & 0.887 & 0.838 & 18.7 \\ YOLO111 & 0.885 & 0.837 & 0.861 & 0.787 & 27.0 \\ YOLO11x & 0.885 & 0.837 & 0.861 & 0.785 & 28.1 \\ \hline \end{tabular}
\end{table}
Table 2: Performance evaluation of different versions of YOLO (v5\(\backslash\)v8\(\backslash\)v9\(\backslash\)v10) and various pre-trained weights for YOLOv11.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 5 Discussion
### Real-Time Performance of Proposed Algorithm  
The time consumption of the proposed algorithm was tested in the static experiment. The time consumption was lower when the detection distance decreased. There was no relation between the time consumption and the rotation angle of the pallet. Computational resources are the main cost during template matching, and they are determined by the sizes of the template and category matrix. Their sizes are determined by the compression unit size, which is calculated based on the distance according to Equation (5). To accelerate the proposed algorithm, the matching process should be optimized. The time consumption results indicate that the algorithm has the ability to detect pallets in real time when the detection distance is less than 4 m.  
However, the developed algorithm still has some shortcomings. First, illumination changes throughout the entire day, and the training samples hardly cover all illumination conditions. Second, when the pallet color is similar to that of the ground or goods, the color space of each category overlaps and leads to incorrect classification. Third, the compression unit size has a significant impact on pallet recognition performance, and it should be carefully selected by considering the size of the pallet, the camera parameters, and the pallet distance.
# Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model
## 3 Synthetic Failure Data Sets Using Matlab/Simulink  
Using Matlab/Simulink, it is possible to model the monitored components of the rotating machine for failure prediction purposes. The virtual model simulates the IM under a broken rotor bar fault by generating synthetic healthy and faulty data, as shown in Figure 1.  
Various approaches have been proposed in the literature to model the behavior of an IM under faults. The dynamic d-q model simulates a SICM operating under a healthy and broken rotor bar fault in this work. The d-q model is implemented using Matlab/Simulink, as explained with sufficient details in [38] for rotor fault prognostic purposes.  
The bar breakage is the common fault in the rotor of the SICMs. Therefore, when the bars start to crack, some of the typical symptoms that can appear are unbalanced currents and torque pulsation, a decrease in the average torque, and disturbances in the voltage. Besides that, one broken bar can also overheat the adjacent bars due to the unbalanced rotor current distribution, which makes the fault worse. Such a variation in the heating around the rotor can damage the insulation and create an eccentricity, resulting in a costly repair and production loss.  
This fault leads to a resistance and inductance variation in the rotor phases. This variation generates an asymmetric air gap between the stator and rotor since the rotor is not in its normal position anymore. In an ideal SCIM, the rotor resistance per phase is calculated as follows:  
\[r_{r}\approx\frac{(2N_{b})^{2}}{N_{b}/3}r_{b} \tag{1}\]  
Figure 1: General process of healthy and faulty data acquisition.  
where \(r_{b}\) represents the rotor bar resistances, and \(N_{s}\) and \(N_{b}\) instead are the equivalent stator winding turns and the number of total bars, respectively.  
In this paper, by neglecting the inductance variations, the impact of one broken rotor bar is modeled by varying the rotor resistance per phase, which leads to an increase in the resistance by \(\Delta r_{r}\)[38].  
\[r_{r}+\Delta_{r_{r}}=\frac{(2N_{s})^{2}r_{b}}{N_{b}/3-n_{bb}} \tag{2}\]  
where \(\Delta r_{r}\) stands for the changes in the rotor resistance, and \(n_{bb}\) and \(N_{b}/3-n_{bb}\) present the number of broken bars (in our case \(n_{bb}=1\)) and the number of healthy bars, respectively. The change \(\Delta r_{r}\) in the rotor resistance can be calculated as follows:  
\[\Delta r_{r}=\frac{(2N_{s})^{2}r_{b}}{N_{b}/3-1}-\frac{(2N_{s})^{2}}{N_{b}/3}r _{b} \tag{3}\]  
The modeled fault is performed by varying the resistance over time. The correspondent electrical torque Te, stator current labc-s, and speed Wr signals are collected from the Matlab/Simulink model shown in Figure 2. The implemented model has as inputs the load torques TL; the three-phase voltages Van, Vbn, and Vcn; and their fundamental frequency We. In this study, the synthetic data generated by the simulation is assumed to be related to the health and faulty conditions of the monitored motor [39].
# Influence of Micro-Textures on Cutting Insert Heat Dissipation
## 3 Experimental Study
### Surface Micro-Textures Implementation  
The main motivation for introducing micro-textures on cutting insert surfaces is their potential contribution to reducing the cutting insert temperature. The best way of creating custom surface textures is through laser ablation of WC-Co green compact inserts. Contrarily to sintered WC-Co, this method minimizes the problems of creating spatter, heat-affected zones, and microcracks, with the additional advantages of improving machining efficiency and reducing cost [21, 42]. WC-10 wt.% Co green compacts inserts were pressed at Palbit S.A. company. The surface micro-textures were created by laser surface texturing, using an Nd: YAG laser, being the square grid pattern produced in accordance with the work previously reported in the literature [43]. The green inserts were then post-dewaxing and sintering, according to Palbit S.A. specifications [43]. In Figure 6, the images of four sintered WC-Co inserts, model CNMG 120408-GS are presented, and the respective magnified view of the surface  
Figure 5: Experimental device images: (**a**) laid out components; (**b**–**d**) assembly main steps.  
micro-textures. One has a standard surface topology, and the three others have a square grid pattern of 100 \(\upmu\)m \(\times\) 100 \(\upmu\)m with an average depth of 50, 100, and 150 \(\upmu\)m, being, respectively, designated by Grid depth 1, Grid depth 2, and Grid depth 3. The experimental tests were performed with uncoated inserts to avoid interference in the heat transfer rate assessment.
# Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
## 2 Fundamental Principles
### Reinforcement Learning  
Reinforcement learning [30; 31] is a type of machine learning algorithm. It is a self-teaching system that learns through autonomous trial and error. It works through actions to optimize rewards and receive knowledge via practical experience to obtain the highest return, which comprises fundamental factors, as shown in Figure 1.  
Environment: The specific environment in which we desire to operate.  
Agent: The worker who acts.  
Action: The possible actions involved can be conducted in that specific environment.  
State: The decision-making process uses the current state or situation of the environment to make decisions.  
Reward: The reward value is a measure of the performance of an action.  
Policy: Rules or methods for determining the best choice of action based on the current state.  
The Markov Decision Process [32] is used to determine the selection of actions. The selection process involves determining the action believed to provide the highest return or cumulative reward. The cumulative reward refers to the sum of all the rewards obtained from the current moment to the future. However, owing to the natural unpredictability of the future when calculating cumulative rewards, it is necessary to reduce the importance of expected rewards. The discount factor (\(\gamma\)), with a value ranging from zero to 1 [33], is applied under Equation (1).  
\[G_{t}=R_{t+1}+\gamma R_{t+2}+\ldots=\sum_{k=0}^{\infty}\gamma^{k}R_{t+k+1} \tag{1}\]  
where \(R_{t+1}\) is the reward for transitioning from state \(S_{t}\) to \(S_{t+1}\).
# CycleDiffusion: Voice Conversion Using Cycle-Consistent Diffusion Models
## 3 Proposed Method
### Training Algorithm  
A pseudo-code of CycleDiffusion training for many-to-many voice conversion is shown in Algorithm 1, where \(\eta\) denotes the learning rate. The decrease in loss values may serve as a termination criterion for the algorithm.  
```
1 Initialize the DNN parameters \(\theta\), learning rate \(\eta\), and iteration count \(i\).
2 repeat
3 Initialize \(\Delta\theta\)
4 for each speaker \(\zeta\)
5\(\mathbf{x}^{\zeta}\leftarrow\) randomly selected data from speaker \(\zeta\)
6\(\Delta\theta\leftarrow\Delta\theta+\
abla L_{\text{diffusion}}\big{(}\mathbf{x}^{ \zeta},\zeta\big{)}\)
7 end
8 for each speaker \(\zeta\)
9\(\mathbf{x}^{\zeta}\leftarrow\) randomly selected data from speaker \(\zeta\)
10 for each speaker \(\xi\) (excluding \(\zeta\))
11\(\mathbf{x}^{\zeta-\xi-\zeta}\leftarrow\mathcal{R}\left(\mathcal{F}\left( \mathcal{R}\big{(}\mathcal{F}\big{(}\mathbf{x}^{\zeta}\big{)},\xi\big{)}\big{)},\zeta\right)\)
12\(\Delta\theta\leftarrow\Delta\theta+\lambda_{i}\
abla L_{\text{cycle}}\big{(} \mathbf{x}^{\zeta},\mathbf{x}^{\zeta-\xi-\zeta}\big{)}\)
13 end
14 end
15\(\theta\leftarrow\theta-\eta\Delta\theta\)
16\(i\gets i+1\)
17 until convergence
```  
**Algorithm 1**CycleDiffusion model training
# Model for Quantifying the Quality of Secure Service
## 5 Case Studies of Multi-Channel QoS
### Single-Channel Network  
The first example is a network that utilizes a single wireless channel to provide a realistic baseline. With \(n=1\), there is \(\sigma=1\) listener, and \(\rho=1\) disruptor. \(AL=1\) because the message cannot be split, and \(DF=1\) since, for this architecture, the message is only sent once. Table 3 shows notional probabilities for a network that has no encryption, standard data encoding, and a moderate probability of interception because it uses a standard broadcast frequency and a moderately strong broadcast signal, which also results in a low probability of noise.  
We assign a high probability of suppression under the assumption of an omnidirectional receiver, susceptible to jamming. The probability for injection is moderately high, though not as high as the probability of suppression, because injection is more challenging than suppression. These values serve as a baseline to demonstrate the effects of multiple channels in the subsequent examples.  
Based on these constraints, the single-channel network has a high probability of leakage, with a corresponding probability of confidentiality. The probability of corruption is also very high, with a correspondingly low probability of integrity. These probabilities may be improved by using encryption and by using directional receivers or a wired connection.
## 2 Materials and Methods
### Methods
#### 2.3.4 Factor Detection for Spatial Variation of WUE  
If a dependent variable is influenced by an independent variable, the spatial distributions of the dependent and independent variables should be consistent. Based on the theory of spatial stratified heterogeneity, [PERSON] et al. [27; 50] proposed a novel statistical method, Geodetector, to measure the correlation between the independent and dependent variables. The Geodetector method overcomes the limits of classic statistical methods when handling categorical variables and can detect the linear and nonlinear relationships between two certain variables. It is applicable to quantitative attribution analysis for spatial patterns of geographic variables [51; 52; 53]. The Geodetector method includes four detectors. In this paper, 2 detectors, i.e., the factor detector and interaction detector, were used.  
Figure 4: Example of STL seasonal decomposition for a grassland pixel (38\({}^{\circ}\)34\({}^{\prime}\)N, 101\({}^{\circ}\)45\({}^{\prime}\)E) during 2002–2021.  
The factor detector measures the degree to which a certain factor explains the spatial heterogeneity of the dependent variable using the \(q\) value (Equation (4)),  
\[q=1-\frac{\sum\limits_{h=1}^{L}N_{h}\sigma_{h}^{2}}{N\sigma^{2}} \tag{4}\]  
where \(h\) is the partition of a certain factor \(X\) or the dependent variable \(Y\). Here, \(X\) represents 13 factors (AT, PR, HUM, WS, SR, VT, LAI, SM, ELE, SLP, ASP, SP, and TPI). \(Y\) refers to WUE. \(N_{h}\) and \(N\) are the number of units in partition \(h\) and the entire region, respectively. The \(\sigma_{h}^{2}\) and \(\sigma^{2}\) are the variances in partition \(h\) and the entire region, respectively. The \(q\) value shows the magnitude of the explanatory power of \(X\) to \(Y\) with a range of 0 to 1. The larger the \(q\) value, the stronger the explanatory power.  
A simple transformation of the \(q\) value satisfied the noncentral \(F\) distribution with the 1 st df \(L\)-1, the 2 nd df \(N\)-\(L\), and non-centrality \(\lambda\)[54].  
\[F=\frac{N-L}{L-1}\times\frac{q}{1-q}\sim F(L-1,N-L;\lambda) \tag{5}\]  
The significance of the \(q\) value can be tested by looking up the \(F\)-distribution table.  
The interaction detector estimates whether the interaction of any two factors (\(X_{1}\) and \(X_{2}\)) enhances or weakens their explanatory power on the dependent variable \(Y\)[55]. First, the \(q\) value of \(X_{1}\) and \(X_{2}\) on \(Y\) was calculated separately (\(q(X_{1})\) and \(q(X_{2})\)). Then, the \(q\) value of the interaction, \(q(X_{1}\cap X_{2})\), was calculated. Finally, the values of \(q(X_{1})\), \(q(X_{2})\), and \(q(X_{1}\cap X_{2})\) were compared to show the type of interaction between the two factors [56].  
We use the \(GD\) package in R, which was developed by [PERSON] et al. [57], to implement the Geodetector method.
## 4 Discussion
### Limitations of the Study  
Even though the FEPA-Net model has obtained favorable outcomes in building segmentation, there remain certain aspects requiring further improvement. In actuality, buildings can be influenced by multiple factors like vegetation obstruction and shadow interference. This makes it arduous to precisely extract the features of some buildings. In the future, it is essential to explore ways to further optimize the model structure. This will enable the model to more efficiently handle these intricate scenarios and accurately recognize buildings that are obscured or located in shadow areas. Meanwhile, this study is based on a vast quantity of sample data. In future work, self-learning signals generated from unlabeled data can be utilized to improve the segmentation network. Additionally, the extended applications of FEPA-Net in scenarios such as road extraction and water body extraction should be explored.
# Analysis of Cryptographic Algorithms to Improve Cybersecurity in the Industrial Electrical Sector
## 3 Cybersecurity in the IEC61850 Standard
The HMAC algorithm ensures message authenticity and integrity by using a hash algorithm, such as SHA256, and a symmetric key. The output of the HMAC algorithm is a fixed string value that represents the message's authenticity and integrity [23].
# Data-Driven Network Latency Processing for Auxiliary Services in Virtual Power Plant
## 5 Evaluations
### Prediction Error  
We use the mean square error (MSE) to characterize the error between the predicted network latency and the smoothed network latency. Figure 10 shows the prediction error under different settings. The MSE of both uplink and downlink latency prediction will increase gradually with the prediction time interval. The closer the interval, the higher the prediction accuracy can be obtained. This is due to the autocorrelation characteristics of the network latency sequence and is consistent with our intuition. It is noted that the prediction MSE of downlink latency increases faster with the increase in prediction time interval than that of uplink latency. This means that the downlink data may contain more mutations, making the proposed method less effective.
## 9 Topology Optimization Results
### L-Bracket: Stress-Constrained Compliance Minimization Results  
The stress-constrained compliance-minimization problem statement considered a maximum allowable stress \(\sigma^{*}=200.0\) MPa. Imposing this constraint caused the MMA to converge towards a curved geometry, shown in Figure 6B, avoiding the creation of a stress concentration point in the inner corner of the L-bracket. This transition is in agreement with the results presented in [53].  
It is important to note that the transition from an angular geometry to a curved geometry (from case A to B of Figure 6) is dependent on several factors and not guaranteed to occur. First, the use of the modified p-norm approximation (Equation (47) proposed in [53]) may lead to an underestimate of the maximum stress installed. This fact may cause the optimization algorithm to overlook the influence of the stress concentration point and justifies the selection of \(\sigma^{*}=200.0\) MPa, a relatively low maximum value for the stress constraint. Second, the algorithm selected may adopt a strategy that is more or less prone to explore and accept solutions that do not respect the imposed constraints. The solution obtained by the SLSQP algorithm had a slightly curved shape closer to the region where the load was applied, but did not avoid the internal angular geometry. The solution obtained by the Trust-constr region was equal to the one obtained in the compliance-minimization problem statement, as the implementation available in SciPy only determined the maximum stress sensitivity for the first iteration. Although this strategy reduced the computational cost, it did not allow a rigorous evaluation of the influence of each element in the maximum stress.  
Finally, it is also important to note that the p-norm exponential factor had a significant influence on the result obtained, especially in the use of elements with intermediate densities.
## 3 Results and Discussion
### P3 Target Pressure Evaluation  
The aim of the new model proposed is to be used as an alternative to the current open-loop critical point control based on the flow signal at the inlet of the WDN. Figure 11 shows the difference between the target _P3_ pressure and the real measurement obtained after implementing the new model.  
Since data are logged every 15 min for 11 days, there are a total of 1056 datapoints during the whole measurement period. The measured _P3_ value is higher than the target in 770 datapoints, 73% of the total, and below the target in 286 datapoints, 27% of the total.  
When _P3_ is above the _P3_ target, the average deviation is 0.53 m. When _P3_ is below the _P3_ target, the average deviation is \(-\)0.85 m. A total of 89% of the measurements fall in the range of \(\pm\)1 m deviation from the target, which is considered acceptable for the purpose of this model. Figure 12 shows the histogram with the deviation per range.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**FLOW-BASED PM**} & \multicolumn{3}{c}{**SARIMA PM**} \\ \hline  & **Maximum** & **Minimum** & **Average** & **Maximum** & **Minimum** & **Average** \\ \hline _P1_ (m) & 60.84 & 59.85 & 60.59 & 60.82 (0.02; 0.03\%) & 59.51 (0.34; 0.57\%) & 60.42 (0.17; 0.28\%) \\ _P2_ (m) & 28.54 & 16.45 & 24.96 & 28.19 (0.35; 1.24\%) & 16.15 (0.30; 1.86\%) & 24.67 (0.29; 1.18\%) \\ _Flow_ (l/s) & 31.11 & 10.00 & 17.09 & 26.67 (4.44; 16.65\%) & 9.99 (0.01; 0.10\%) & 16.75 (0.34; 2.00\%) \\ _P3_ (m) & 34.07 & 22.18 & 30.55 & 33.80 (0.27; 0.80\%) & 21.89 (0.29; 1.32\%) & 30.27 (0.28; 0.93\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hydraulic data under SARIMA and flow-based PM schemes.  
Figure 11: _P3_ target versus _P3_ real measurement.  
Figure 13 represents the value of target and measured _P3_ pressures by box plots. To confirm what the box plot seems to indicate, an ANOVA test is performed, comparing both sets of pressures. A _p_-value of 0.412 is obtained, indicating that there is no significant difference between the two sets of pressures.  
Figure 12: Histogram with deviation from the target pressure.  
Figure 13: Box plot of real measured values and target _P3_ values.