esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Uncertainty Analysis and Simulation
### Uncertainty Model  
Based on the above analysis, only the uncertainties from WPP and PV are considered. In order to simulate the uncertainties, suitable probability distribution functions are needed. Once the simulation results of the wind speed and solar irradiance have been attained, the power outputs of WPP and PV can be calculated.  
(1) WPP Uncertainty Model  
The output of WPP has a high level of uncertainty. The simulation of wind speed is the key to calculating the output of WPP. It has been proven that the Rayleigh distribution function can be used to simulate wind speed as follows:  
\[f(v_{t})=\frac{\varphi_{t}}{\vartheta_{t}}\bigg{(}\frac{v_{t}}{\vartheta_{t}} \bigg{)}^{\vartheta_{t}-1}e^{-(v_{t}/\vartheta_{t})\varphi_{t}} \tag{1}\]  
where \(v_{t}\) is the wind speed at time \(t\). \(\varphi_{t}\) and \(\vartheta_{t}\) are the shape factor and the scale factor at time \(t\). Then, the WPP output can be calculated by Equation (2), as follows:  
\[g^{*}_{WPP,t}=\left\{\begin{array}{l}0,\,0\leq v_{t}<v_{in},\,v_{t}>v_{out} \\ \frac{v_{t}-v_{in}}{v_{rad}-v_{in}}g_{R},\,v_{in}\leq v_{t}\leq v_{rated}\\ g_{R},\,\,\,v_{rated}\leq v_{t}\leq v_{out}\end{array}\right. \tag{2}\]  
where \(g^{*}_{WPP,t}\) is the available output of the WPP at time \(t\). \(g_{R}\) is the rated output of WPP. \(v_{t}\) is the real-time wind speed at time \(t\). \(v_{in}\), \(v_{out}\) and \(v_{rated}\) are the cut-in, cut-out, and rated speeds, respectively. (2) PV uncertainty model  
Similarly, existing research has proven that the beta distribution function can be used to describe solar irradiance as follows:  
\[f(\theta_{t})=\left\{\begin{array}{l}\frac{\Gamma(\alpha_{t})\Gamma(\beta_ {t})}{\Gamma(\alpha_{t})+\Gamma(\beta_{t})}\theta^{\alpha_{t}-1}(1-\theta_{t} )^{\beta_{t}-1},\,0\leq\theta_{t}\leq 1,\alpha_{t}\geq 0,\beta_{t}\geq 0\\ 0,\emph{otherwise}\end{array}\right. \tag{3}\]  
where \(\theta_{t}\) is the solar irradiance at time \(t\). \(\alpha_{t}\) and \(\beta_{t}\) are the shape parameters of the beta distribution at time \(t\), which can be calculated by the mean value and the standard deviation of irradiance [15]. After simulating the solar irradiance, the PV output can be calculated as follows:  
\[g^{*}_{PV,t}=\eta_{PV}\times S_{PV}\times\theta_{t} \tag{4}\]  
where \(g^{*}_{PV,t}\) is the available output of PV at time \(t\). \(\eta_{PV}\) and \(S_{PV}\) are the efficiency and total area, respectively. \(\theta_{t}\) is the solar radiation intensity at time \(t\).
## 3 Results and Discussion
### Effect of Raster Angle on Flexural Properties
#### 3.1.2 Edgewise Testing  
Edgewise testing was carried out with three raster angles, i.e., 0\({}^{\circ}\), 90\({}^{\circ}\) and 0/90\({}^{\circ}\). Results for the maximum edgewise flexural load and strength have been given in Figure 9 and Table 3.  
Figure 9 shows the same trend as in the case of in-plane testing. The highest values obtained were for the 0\({}^{\circ}\) raster angle, i.e., 73.23 \(\pm\) 1.36 N and 1.61 \(\pm\) 0.01 GPa for the flexural load and modulus, respectively, while the lowest values were achieved for the 90\({}^{\circ}\) raster angle, i.e., 67.14 \(\pm\) 2.37 N and 1.51 \(\pm\) 0.02 GPa. A similar change in the material behavior was observed in this case as well. As indicated by Figure 9b, the behavior changed from ductile to brittle as the raster angle changed from 0\({}^{\circ}\) to 90\({}^{\circ}\). The steep decline in the stress-strain curve caused due to the abrupt failure of the samples for the 90\({}^{\circ}\) samples shows this transition. An increase of 24.2% in the value of maximum flexural load was observed for the transition of the raster angle from 90\({}^{\circ}\) to 0\({}^{\circ}\). Again, this can be attributed to the interaction between the material orientation and the loading direction. The rasters in the case of 0\({}^{\circ}\) raster angle were perpendicular to the loading direction, hence the resistance to deformation was high. This also resulted in no fracture for the case of 0\({}^{\circ}\) as presented and tabulated in Figure 9b and Table 3, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Raster Angle** & **Max. Flexural** & **Flexural** & **Flexural** & **Fracture Strain** \\ **(\({}^{\circ}\))** & **Load (N)** & **Modulus (Gpa)** & **Strength (Mpa)** & **(\%)** \\ \hline
0 & 138.42 \(\pm\) 3.19 & 3.2 \(\pm\) 0.01 & 75.87 \(\pm\) 1.88 & Fracture did not occur \\
60 & 62.52 \(\pm\) 1.26 & 1.56 \(\pm\) 0.02 & 34.19 \(\pm\) 0.67 & 3.59 \(\pm\) 0.03 \\
90 & 48.02 \(\pm
## 5 Impact of the Collimator Characteristics
### Collimator Material  
In this experiment, the probe is equipped with either a 10 mm graphite (G.10.E) or a 10 mm molybdenum (Mo.10.E) grounded collimator. Figure 9 shows I-V curves for the two configurations at two different discharge voltages measured on the HT axis (\(\theta\) = 0\({}^{\circ}\)). The slope is of the order of \(10^{-5}\) mA/cm\({}^{2}\) for the two configurations. The ion current saturation is relatively constant indicating that most electrons are properly screened. The main difference between the two designs is the amplitude of the measured current. The FC equipped with a molybdenum collimator gives a current density which is about 20% lower. As can be noticed in Figure 9 the gap between the two curves increases when the voltage is ramped up for a fixed value of \(I_{d}\).  
The total ion current is obtained from the current density profile as explained in Section 4.1. We use Equations (6) and (8) to obtain the current and propellant use. Figure 10  
Figure 8: I–V characterization at different \(\theta\) of the I SCT200 firing at 250 V, 0.66 A with 0.765 mg/s of anode mass flow rate. The measurements are conducted 27.4 cm from the thruster.  
Figure 9: On-axis (\(\theta\) = 0\({}^{\circ}\)) I–V curves \(I_{d}\) = 0.66 A, U\({}_{d}\) = 200 V (**top**) and 250 V (**bottom**). Faraday cup is in G.10.E (**blue**) and Mo.10.E (**red**) configuration.  
shows the value of \(\eta_{b}\) and \(\alpha\) for two thruster operating conditions and two FC configurations. Both parameters are lower for the molybdenum collimator configuration. The difference remains true whatever the thruster discharge voltage with constant discharge current. For the two configurations \(\eta_{b}\) is larger at 250 V because the ionization degree increases with voltage. Numerical simulations are used in Section 6.1 to discuss the behavior observed experimentally.
## 2 Physical Modelling and Numerical Methodology
### Numerical Validation Study
#### 2.2.3 Mesh Sensitivity  
The sensitivity of the results was examined using three different meshes: A coarse (345,000 items), a medium (612,500) and a fine (1,510,000). All meshes consisted of hexahedral elements and mesh refinement on the inner wall using standard wall functions (SWF) for Mesh 1 (coarse) and Mesh 2 (medium) and using enhanced wall treatment (EWT) for Mesh 3 (fine). Figure 6 shows the medium and fine mesh, for the axial velocity profile within the curve. The results for the coarse mesh were omitted, as they were poor. The velocity field is well resolved near the inside wall. This is of special interest, as it determines the migratory movement of the particles transported by the flow and their accumulation inside the bend. In short, no critical difference is perceived between the fine and medium mesh, so it can be concluded that the underestimation of the flow in the inside wall mentioned above is due to the model used and that it occurs rather at the exit of the curve (90\({}^{\circ}\)), due to the detachment of the flow at the point of curvature of the bend on the internal face, extending its effects upstream.
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 3 Implementing Physics-Informed Surrogate Model
### Data-Driven Objective
#### 3.4.5 Inference Level (Offline-Constrained Techniques--OCT)  
Post-processing techniques play a crucial role in refining model outputs after training, allowing to ensure adherence to established scientific principles and maintaining physical consistency. The effectiveness of this approach depends on an initial understanding of the underlying physics, as discussed in Section 3.3. In certain cases, particularly for identified series models, a final physics-based computation can remain external to the model, provided that its computational cost is not prohibitive.  
A well-documented example of offline-constrained techniques can be found in weather forecasting. Researchers have employed machine learning models, such as neural networks, to predict meteorological variables like temperature and humidity. However, to maintain physical realism, additional post-processing adjustments using established physical laws--such as the ideal gas law and the Clausius-Clapeyron equation--are applied. These corrections, performed separately from the training phase, ensure that predictions remain consistent with fundamental scientific principles [49].  
The utility of offline-constrained techniques is particularly evident in fields where multiple modelling approaches need to be integrated, such as fire safety engineering. A basic application involves predicting the structural performance of simply supported fire-exposed beams, as in [12]. Here, a machine learning model first estimates the peak temperature of embedded steel reinforcement bars. This output is then used as an input for a physics-based calculation, such as a sectional analysis, to determine the beam's moment capacity under fire conditions. This two-stage process guarantees that the final structural performance assessment aligns with established physical laws, even if the initial machine learning model does not explicitly incorporate physical constraints. Similarly, [PERSON] et al. [97] proposed an approach where the temperature of a steel column, predicted by a machine learning model, is linked to its load-bearing capacity through a physical equation. [PERSON] et al. [63] presented a model that predicts fire source properties, which can then be used in a physics-based model to estimate fire-induced temperature distributions.  
There are, however, limits to what can be achieved with OCT. Since corrections occur after training, the model may still learn non-physical relationships. The post-processing can only provide superficial adjustments rather than fundamentally improving model behaviour. Moreover, post-processing cannot practically be used to enforce complex physical principles, particularly PDEs and multi-physics interactions. Without embedding these physical constraints as part of the model training, the model lacks inherent physics awareness, which cannot be resolved through OCT. For problems requiring strict adherence to governing laws, integrating physics directly into the learning process is thus essential.
# Post-Quantum Digital Signature: Verkle-Based HORST
## 8 Horst: HORS with Trees Signature Scheme
If a portion of the private key is made public, its Merkle tree structure stops attackers from creating legitimate signatures. But because HORST makes signatures larger, it is less effective when used alone. In order to solve this, SPHINCS--a hierarchical signature scheme that arranges many HORST instances into a more comprehensive structure--is often used. SPHINCS+, a variation of SPHINCS, is a top contender for post-quantum safe digital signatures and is a component of the NIST Post-Quantum Cryptography Standardization Project. Because of its ease of use and security, HORST is a popular cryptographic system in post-quantum cryptography. Applications that need a limited number of signatures, such as blockchain transactions, secure boot protocols, and firmware upgrades for Internet of Things devices, benefit greatly from it. HORST is included into SPHINCS+, a stateless hash-based signature scheme, which makes it more useful for actual applications.
# Multiple-Parallel Morphological Anti-Aliasing Algorithm Implemented in FPGA
## 3 Implementation of the Algorithm in FPGA  
When implementing the proposed algorithm in FPGA, we found that the edge detection of the image, the classification of the aliasing patterns, the acquisition of the blend weight coefficients, and the calculation of the anti-aliased color can be performed simultaneously in the R, G, and B channels of the image. In addition, the classification of the aliasing patterns and the acquisition of the blend weight coefficients can also be carried out simultaneously in the horizontal and vertical directions of each channel, which improves the parallelism of the algorithm and reduces the time consumption greatly. Figure 7 shows the top-level state machine when the proposed algorithm is implemented in FPGA.  
It can be seen from Figure 7 that the state machine contains six states, of which state GEN_EDGE, state GEN_ALIASING, state GET_WEIGHT, and state CAL_COLOR correspond to the edge detection of the image, the classification of the aliasing patterns, the acquisition of the blend weight coefficients, and the calculation of the anti-aliased color of the proposed algorithm, respectively.  
The state IDLE is the initial state of the system; when the key representing the reset signal rst on the FPGA is pressed, the asynchronous reset signal rst is active (active low), and the system completes the initialization of the internal registers.  
When the key representing the start signal Start on the FPGA is pressed, the asynchronous start signal Start is active (active low). At this time, the state of the state machine changes from IDLE to GEN_EDGE, the synchronous signal Start1 is active, and the system starts the edge detection of the image.  
After the edge detection of the image is completed, the state of the state machine changes from GEN_EDGE to GEN_ALIASING, the synchronous signal Start2 is active, and the system starts the classification of the aliasing patterns.  
By analogy, after the calculation of the anti-aliased color is completed, the state of the state machine changes from CAL_COLOR to DISPLAY. At this time, the anti-aliased image and the original image will be output to an LCD screen connected to the FPGA.  
Figure 8 takes the R channel as an example to demonstrate the diagram of the whole system implemented in FPGA. Next, we will describe the FPGA implementation of each step of the proposed algorithm in detail.  
Figure 7: State machine on the top level.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 6 Discussion
### Limitations and Future Directions  
While our study provides valuable insights, several limitations should be considered:  
1. Laboratory Setting: The controlled environment may not fully replicate the complexity of actual match conditions. Future studies should validate these findings in competitive scenarios.
2. Sample Size: While adequate for our analyses, the sample size may limit generalizability. Larger-scale studies across diverse populations would further validate our findings.
3. Cross-sectional Design: Our study provides a snapshot of QE behaviors but does not capture how these might change over time or with training. Longitudinal studies are needed to understand the causal relationships between QE training, skill development, and performance improvements.  
Future research directions include:  
1. Exploring the temporal dynamics of QE during actual matches to understand how it adapts to varying game situations.
2. Developing and testing real-time feedback systems based on our predictive model.
3. Investigating the potential transfer of QE training effects to other racket sports.
4. Integrating additional physiological and psychological measures to create a more comprehensive model of badminon performance.
# A Cloud-Based Real-Time Mechanism to Protect End Hosts against Malware
## 4 Evaluation
### Effective Evaluation  
To analyze the effectiveness of Skywalker, 200 malware that we collected from the Internet were downloaded to a host with Skywalker. When clicking the malware to execute these malicious programs, Skywalker successfully blocked their execution and showed warning messages to the users.
## 5 Experimental Results  
The proposed fault diagnosis method was also verified through field tests using an experimental setup in which the characteristics of the motor can be seen in Table 3. The drive was feed by a _DC_ power supply of 100 V. A current chopping control was used to control the motor. Hall-effect sensors were used for the acquisition of the motor winding currents. The performance of the fault diagnosis method was tested considering several condition modes, namely: healthy operation, open and short-circuit failure modes. All these conditions were also performed at different _SRM_ speeds.  
Figure 17: Diagnostic variables associated with the motor winding currents of the _SRM_ in normal operation and short-circuit failure of power device (**a**) 1200 rpm and _S1B_ (**b**) 2840 rpm and _S1C_ (simulation result).  
A similar experimental test, considering 1200 rpm was also performed. This experimental test is similar to the previous one, where the motor is operating in normal condition when an open-circuit failure suddenly occurs in the power device \(S_{1B}\). Figure 20 shows the _SRM_ winding currents before and after the failure. As expected, in this experimental test the amplitude of the currents is slightly larger when compared with the previous test due to higher speed operation. It is also possible to confirm that, after the fault, the current in the faulty phase remains always at zero value. The corresponding diagnostic variables are shown in Figure 21. From this figure, it is possible to verify that the behavior of the diagnostic variables is identical to the one obtained in the previous test.  
Experimental tests considering a short-circuit failure in power devices were also made. Thus, regarding this failure mode, a first test was made at 600 rpm. The results of an experimental test were the motor is operating in normal condition followed by a short-circuit failure in power device \(S_{1C}\) is presented in Figure 22. From this figure is possible to see that initially, the currents are balanced, but after the short-circuit failure the current in the motor winding connected to the faulty leg increases uncontrollably. The behavior of the associated diagnostic variables can be seen in Figure 23. As shown by this figure, during the normal operation the diagnostic variables are constant and with the value of one. However, after this failure these variables gradually change. In this situation the diagnostic variable associated with the faulty leg become higher and reach the value of 2.6. The other variables will become smaller, stabilizing at the value of 0.5. From this experimental test is possible to confirm that the obtained pattern of the diagnostic variables is different from the open-circuit failure mode, but in accordance with the conditions presented in Table 1.  
Figure 21: Diagnostic variables associated with the motor winding currents of the _SRM_ at 1200 rpm in normal operation and open-circuit failure of power device \(S_{1B}\) (experimental result).  
Figure 20: Motor winding currents of the _SRM_ at 1200 rpm in normal operation and open-circuit failure of power device \(S_{1B}\) (experimental result).
## 2 Materials and Methods
### Missing Values Interpolation
As shown in Figure 3 the difference between the on-time and on-duration approaches is the order of the processing steps: in the on-duration approach the timestamps are converted to durations as the first processing step; in the on-time approach this step is performed after interpolation is performed. To better illustrate the differences between interpolation on time and duration, we simulated 100 heartbeats and then we randomly generated 10% of missing RR-intervals in this artificial timeseries by using Gilbert burst approach. The length of the RR-intervals timeseries changes when we interpolate the missing values on duration, while it remains the same when we interpolate on time (Table 1). This result suggests that the interpolation on duration moves beats away from their original position in time, introducing changes to the spectrum, while interpolation on time preserves the position on time of retained heartbeats. In particular, Table 1 shows that the low RR-intervals error (i.e., average difference between heartbeats duration) is obtained with linear interpolation on time. The nearest interpolation on time was not performed because interpolating with this approach is useless, as the interpolated values introduced into the timeseries would have the same time as the closest beat, creating physiologically impossible data. Figure 4 provide more detailed analyses of the difference between linear interpolation on both duration and time. The cumulative error when the missing values are interpolated on duration increases as the time series goes by because it creates RR-intervals in accordance with the closest interval values (i.e., the higher is the number of missing values, the higher is the cumulative error) depending on the interpolation type used (e.g., linear, quadratic and cubic spline). Differently, time interpolation did not introduce change in time series length due to the fact that this approach estimates intermediate values between the time when two observed beats happen in accordance with interpolation type. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Window Time (s)**} & \multicolumn{2}{c}{**RMSE (s)**} & \multicolumn{2}{c}{**RE (\%)**} \\ \hline
**Interpolation** & **Time** & **Duration** & **Time** & **Duration** & **Time** & **Duration** \\ \hline No-missing values & \multicolumn{2}{c}{90.11} & \multicolumn{2}{c}{—} & \multicolumn{2}{c}{—} \\ Nearest & — & 91.95 & — & 0.096 & — & 5.11 \\ Linear & 90.11 & 91.83 & 0.075 & 0.090 & 3.70 & 4.86 \\ Quadratic & 90.11 & 92.13 & 0.084 & 0.107 & 4.35 & 5.83 \\ Cubic spline & 90.11 & 92.24 & 0.085 & 0.109 & 3.46 & 6.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Difference between duration and time interpolation by using different approach (i.e., no-missing values, nearest neighbor, linear, quadratic, and cubic spline). Figure 3: Processing steps in the on-time and in the on-duration approaches.
# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 2 TinyML
### Use Cases of the TinyML
#### 2.7.6 Autonomous Vehicle and Traffic Management  
However, it is presently a challenging task to drive a little automobile autonomously on such a small scale. The TinyML method is being researched to improve the autonomy of such tiny cars, according to [135]. Convolution neural network architecture is used in its construction in MCI GAP8. Additionally, the STM32L4 and NXP k64f platforms have been used for testing. The integration increases power consumption by 92% and reduces processing delay by 13x, according to the results.  
Recently, automated traffic programming has been investigated for prospective integration with the TinyML framework in order to improve the real-time traffic management system [52]. Piezoelectric sensors are used in the system indicated, and they are placed in various traffic lanes. To identify a vehicle using piezo sensor data, a two-point time ratio approach is needed. In order to forecast when the green light will emerge, the categorization of cars is then employed. The random forest regressor (RFR) of the study considers the number of cars entering each lane when predicting signal duration. It is built on an Arduino Uno and uses scikit-learn's help along with the m2 gen library. Only 1754 KB are needed for the created algorithm.