esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of P+ Source Pattern in 4H-SiC Trench-Gate MOSFETs on Low Specific On-Resistance
###### Abstract  
Novel 1.7-kV 4H-SiC trench-gate MOSFETs (TMOSFETs) with a grid pattern and a smaller specific on-resistance are proposed and demonstrated via numerical simulations. The proposed TMOSFETs provide a reduced cell pitch compared with TMOSFETs with square and stripe patterns. Although TMOSFETs with a grid pattern reduce the channel area by approximately 10%, the cell density is increased by approximately 35%. Consequently, the specific on-resistance of the grid pattern is less than that of the square and stripe patterns. The forward blocking characteristics of the grid pattern are increased by the reduced impact ionization rate at the P/N junction. As a result, the figure-of-merit (FOM) of the grid pattern is increased by approximately 33%.  
silicon carbide; 4H-SiC; trench-gate MOSFETs; 3D-simulation; design consideration; specific on-resistance; forward blocking; figure-of-merit +
Footnote †: journal: Applied Sciences
## 2 Definitions, Representation and Assumptions
### Representation of SDSE Estimates  
According to [9], it is important that metrics are represented by numeric values or percentage, which are more accurate and can better capture the security state of data than qualitative values. Moreover, qualitative high-medium-low values, besides generating the loss of information, introduce subjectivity to metrics. For that reasons, the security estimates computed by SDSE are probability values between zero and one (inclusive), where zero indicates totally insecure data and one indicates totally secure data.
## 3 Results
### Handcrafted Feature Extraction  
The handcrafted feature extraction process demonstrated its capability to capture essential facial features indicative of drowsiness. Dlib's precision in detecting facial landmarks ensures accurate measurements of eyelid and mouth openings as shown by Figure 3. The integration of head pose estimation using HOPEnet offers a comprehensive 3D perspective, potentially enhancing the accuracy of drowsiness detection. The combination of these handcrafted features, including the three opening values, PERCLOS and POM calculations, and Euler angles, provides a robust dataset for subsequent modeling and analysis of the data.  
To evaluate the impact of different features, the table below shows a series of trials that employed various methods. The use of frontalized landmarks alongside PERCLOS, POM, and head pose, replacing the use of landmarks instead of using pairwise features alongside PERCLOS, POM, and head pose, or even only using PERCLOS, POM, and head pose, as well as the use of eye cropping, to potentially increase the accuracy of eye state classification.  
The results in Table 3 indicate that including both facial landmarks and pairwise differences generally leads to higher accuracy in drowsiness detection across all regions (eyes, mouth, and head) and models (SVM, Random Forest, XGBoost) compared to using only pairwise differences. The use of facial landmarks appeared to be particularly beneficial for the eye region, where a significant drop in accuracy was observed when landmarks were excluded. However, for the mouth and head regions, the effect of excluding landmarks was less pronounced. This suggests that while facial landmarks provide valuable information for drowsiness detection, pairwise differences alone can achieve comparable results for certain regions and models. Overall, the combination of these features offers a more robust approach for accurately detecting drowsiness.  
Figure 3: Results of head pose estimation using HOPEnet and landmark detection using DLIB in a sample.
# Coordinated Multi-Platooning Planning for Resolving Sudden Congestion on Multi-Lane Freeways
## 4 Proposed Platoon-Based Coordination Scheme to Minimize Traffic Congestion
### The Overall Platoon Protocol and Algorithm
#### 4.6.1 Join Maneuver  
Recall that the front-most vehicle in a platoon is the platoon leader. In the join maneuver, a vehicle requests to join an existing platoon, and the platoon leader will decide whether to allow the vehicle to join. The join maneuver is illustrated as follows:  
Figure 2: Illustration of forming a platoon in each lane and lane-change operations to maintain safety conditions if a platoon is changing its moving direction from the blocked lane to the free lane.  
Platoon Status: Each platoon leader maintains the timely status of its platoon and periodically groupcasts status messages to its platoon members or non-member vehicles. In particular, the platoon size has an upper bound that determines whether the platoon can accept more members or not. The platoon status includes platoon member information, such as the number of members, each member's information, and whether the platoon can accept more members.
* Join Request: If the platoon status message indicates that the platoon is open for joining, the platoon leader will wait to receive join requests from non-member vehicles. The join request will include information such as the requesting vehicle ID and its position.
* Join Response: Upon receiving a join request, the platoon leader examines its platoon status, such as whether the platoon size is less than the limitation, and decides whether to accept the request. If the request is accepted, the platoon leader will send an acceptance response message to the requester. Otherwise, a rejection response is sent.
* Join Execution: Upon receiving an acceptance response from the platoon leader, the requesting vehicle will join the platoon following the platoon-joining protocol described in Section 4.2. After becoming a platoon member, the vehicle will follow the driving instructions from the platoon leader groupcast.
# An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods
## 5 The Spacing Judgement of Two Robotic Fishes
### The Phalanx Formation
#### 5.2.2 The Construction and Validation of Judgement Models  
Through the results on the left side of Figure 19, we found that, at different inlet flow velocity \(U\), the relationship between \(p_{3}^{\prime}\) and the spacing \(dy\) is different. Thus, we also need to use the kinematic pressure \(p1_{0}\) or \(p2_{0}\), which is insensitive to the spacing as a feature for eliminating the effect of inlet flow velocity. For the presetting appropriate spacing \(\Delta d\), we divide the data into two categories: the label is equal to 0 when the current spacing \(dy\) is less than \(\Delta d\), and the label is equal to 1 when the current spacing \(dy\) is greater than \(\Delta d\). We used the same neural network structure above. We used the data simulated by settings in Table 4 to train the classification model, and used the data simulated by settings in Table 6 to validate the model. When we construct the model, the input layer contains two features \(p_{3}^{\prime}\) and \(p1_{0}\) (which is about equal to \(p2_{0}\)). However, when we validated the model, we used the kinematic pressures of each fish, and two features in the input layer are \(p1_{3R}\), \(p1_{0}\) or \(p2_{3L}\), \(p2_{0}\). We used several \(\Delta d\) to construct the judgement models, and the effects of each fish are shown in Table 10.  
In this section, we construct some binary classification models to judge the relationship between the current spacing and the presetting appropriate spacing \(\Delta d\). Through the results of judging, the robotic fishes can adjust their actions to maintain a stable formation at the appropriate spacing \(\Delta d\).
## 2 Materials and Methods
### Training of the ANN Using the Phase Congruence Method
number of neurons in the hidden layer with an interval of [5; 200]. The second parameter is the learning rate with an increment factor of 0.001 [0.005, 0.9]. The allowed error was set to only two possibilities: \(1\times 10^{-4}\) and \(1\times 10^{-5}\). Finally, a stop in training was set through the down-gradient method if the error function did not improve by at least 0.0001 for 150 iterations. For each architecture, its output is recorded in a database to proceed to calculate the mean squared error against the previously established target values and this was the mechanism for selecting the winning topology. The advantage of the proposed procedure is to ensure a global minimum and avoid falling into local minima. The ANN topologies proposed in Table 5 adequately fit the 411 target values of the corresponding metallographic pattern in the training phase, which is why the mean squared error (MSE) used to determine which ANN architecture should be selected for the test phase with the remaining 127 images. The ANN 4 topology presented a better fit in the training phase by registering a lower MSE index (0.088); therefore, it will used in the test phase. See Table 6.  
Once the information is processed through ANN, the following weights and bias matrices are obtained:  
\(\text{W}_{1}\): synaptic weights of the first layer (\(55\times 30\)):  
\[\text{W}_{1}= \begin{bmatrix}-0.0767&-0.0553&\cdots&-0.0477&-0.0235\\ 0.1308&-0.0414&\cdots&0.0383&0.0386\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ 0.0514&0.0064&\cdots&-0.0094&-0.2929\\ -0.0671&-0.0197&\cdots&0.0684&-0.0362\\ \end{bmatrix} \]  
\(\text{b}_{1}\): bias of the first layer (\(1\times 55\)):  
\[\text{b}_{1}=\begin{bmatrix}2.7675&-2.7125&\cdots&2.6527&-1.8140\\ \text{W}_{2}\text{: synaptic weights of the hidden layer ($1\times 55$):}\\ \text{W}_{2}= \begin{bmatrix}-0.2024&-0.0995&\cdots&-0.5504&0.2546\\ \end{bmatrix} \] \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Specification** \\ \hline Neurons hidden layer & 55 \\ Activation function & Sigmoidal tangent \\ Learning rate & 0.005 \\ Allowed error & \(1\times 10^{-5}\) \\ Number of iterations & 1632 \\ MSE & 0.088 \\ Convergence time & 00:01:13 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Selected topology. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Topologies** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Number of neurons & 54 & 54 & 55 & 55 & 56 & 56 \\ Learning rate & 0.01 & 0.005 & 0.01 & 0.005 & 0.01 & 0.005 \\ Allowed error & \(1\times 10^{-4}\) & \(1\times 10^{-5}\) & \(1\times 10^{-4}\) & \(1\times 10^{-5}\) & \(1\times 10^{-4}\) & \(1\times 10^{-5}\) \\ Number of iterations & 1293 & 2420 & 1457 & 1632 & 1050 & 2130 \\ MSE & 0.091 & 0.106 & 0.095 & 0.088 & 0.095 & 0.103 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Topologies of artificial neural networks developed for the training phase. \(\text{b}_{2}\): hidden layer bias (1 \(\times\) 1):  
\[\text{b}_{2}=[-0.0523]\]
## 5 Discussion
### Future Directions  
The wireless capability of sensors to detect and transmit signals across a wide range of frequencies, and ensure that the data are transferred successfully, has been tested with a simulator. Deploying and testing the real-time environment is planned as future work. Moreover, we have planned to use fuzzy rules for trust modeling for security and effective clustering in WBAN.  
Figure 11: Energy consumption vs. the number of sensor nodes.
## 3 Materials and Methods
### Computational Complexity  
To analyze the time complexity of the CFR algorithm, we need to consider several factors, including the size of the game tree, the number of iterations, and the operations performed during each iteration. Let \(|I|\) denote the number of information sets in the game tree, and let \(A_{i}\) represent the set of actions available at information set \(i\). The total number of actions across all information sets can be denoted as \(|A|=\sum_{i\in I}|A_{i}|\).  
In extensive-form games, the size of the game tree is exponential in the depth of the tree. However, the CFR algorithm does not require exploring the entire game tree explicitly; instead, it traverses the game tree implicitly during each iteration.  
During each iteration \(t\), the CFR algorithm performs the following operations:  
1. For each information set \(i\): * Compute the counterfactual regret for each action \(a\in A_{i}\).  
Figure 3: (**a**) shows the strategy iteration curves for different information sets of the first player, and (**b**) shows the strategy iteration curves for different information sets of the second player.  
* Update the strategy using regret matching.
* Accumulate the cumulative regrets and update the average strategy.  
The computation of counterfactual regret involves summing over all possible histories \(h\in I\), which requires \(O(|I|\cdot|A|)\) operations per iteration. Let \(T\) denote the number of iterations required for convergence. The overall time complexity of the CFR algorithm can be expressed as: \(O(T\cdot|I|\cdot|A|)\). This complexity arises from performing \(O(|I|\cdot|A|)\) operations during each of the \(T\) iterations.
## 3 IoT Standards and Protocols for Precision Agriculture
### IoT Protocols in Precision Agriculture (RQ2)
#### 3.2.3 Communication Between Non-IP and IP Protocols
These gateways include an operating system that manages the forwarding of LoRa packets to the target network. While the data rate between the end device and the LoRaWAN gateway is relatively low, this is a necessary trade-off to ensure long battery life and extended radio range. The gateway is then linked to a high-bandwidth IP network, such as WiFi, Ethernet, or cellular, enabling end-to-end connectivity between LoRaWAN end devices and the application server [77]. Figure 1 shows how communication occurs between a LoRaWAN network and the Internet, from the IoT nodes to the application server in precision agriculture. It shows how end devices, such as low-power IoT sensors, collect data from the environment and communicate with gateways. These gateways, equipped with LoRa concentrators, receive transmissions from the end devices and forward the data to the cloud [78; 79]. The network server acts as the central hub, routing messages between the end devices and the application layer. It ensures that data packets reach the correct destination, facilitating communication in both directions: from devices to applications, and vice versa. Finally, the application, running on a server, processes the data, providing insights or automated actions, such as controlling irrigation systems or generating alerts. This architecture allows efficient long-range communication with low power consumption, making it ideal for precision agriculture. In summary, all LoRaWAN gateways within the range of a specific end device receive data from the device. These gateways subsequently forward the data to the network server, which handles tasks such as removing duplicate packets, verifying data integrity, and conducting security checks before passing the message to the application server.
## 3 Results
### Analysis of the Fracture Mechanisms
For tension along zigzag, the situation is a bit simpler: only two stages can be found--before the point I and point II (II' for graphene with SW and DD). Ripples did not disappear until the fracture and the elongation of graphene is much lower. From Figure 8 it can be seen that the length of the structure is not enough to obtain equilibrium ripples with the same parameters for graphene--there are three (at the point I) and two ripples of different amplitude. However, the presence of defects affects the ripple configuration and for graphene with SW and DD, ripples with close amplitude and wavelength appeared. Again, a continuous increase in the ripple amplitude can be seen before the point I with a slow decrease until points II and II'. Bonds in graphene and defective graphene are also considered. For defect-free graphene, bond \(b\), which is aligned in almost the same direction as tensile strain is weaker and rapidly increases before the point I. After that, the whole changes of the bonds and valent angles result in a continuous increase of both \(a\) and \(b\) bonds until the pre-critical stage. For graphene with DD, at the first deformation stage (even at \(\epsilon^{2}\) = 0.1), a sharp increase of the bond 3' which is also aligned in almost the same direction as tensile strain (as bond \(b\) in graphene). Since ripples did not totally disappear from the structure, no structural transformations to elongated hexagons took place. Bonds oriented along which the tensile strain is applied are the weakest bonds. Fracture starts near the defect (see Figure 8). The process of fracture of defect-free graphene and graphene with SW and DD is also analyzed at elevated temperatures. In common, the same situation took place: ripple formation under tension and transformation to the planar state at pre-critical strain, when the bonds are considerably stretched. Figure 7: (**a**) Stress-strain curves for graphene, graphene with SW, and DD\({}_{2}\) under tension along zigzag direction at \(T\) = 0 K. (**b**) Average amplitude \(\overline{A}\) of ripples for defect-free graphene, graphene with SW, and DD\({}_{2}\) as the function of strain under tension along zigzag direction at \(T\) = 0 K. Numerical data are shown by dots, while approximations are shown by solid lines. One more structure with the dipole (DD\({}_{6}\)) is added for comparison with the approximation shown by a thick dashed line. The thin black dashed lines show different deformation regimes. (**c**) The bond length in graphene and graphene with DD\({}_{2}\) as the function of applied tensile strain.
## 8 Conclusions  
In this paper, the proposed ECKF filter has been introduced to estimate the positive and negative sequence components of biased, unbalanced, and distorted grid conditions of grid-tie transtormerless converters while considering the existence of a DC offset generated from measurement and an A/D conversion. The proposed ECKF estimator presents better estimated results compared with the conventional ECKF used in the literature. The current harmonic distortion is reduced by 80% and, consequentially, yields a better performance in the overall current control structure.  
Conceptualization, M.E.-N.; methodology, M.E.-N., K.A. and A.S.A.-K.; software, M.E.-N.; validation, M.E.-N.; formal analysis, E.H. and M.S.H.; investigation, M.E.-N.; resources, A.S.A.-K.; data curation, M.E.-N., K.A. and A.S.A.-K.; writing--original draft preparation, M.E.-N.; writing--review and editing, M.E.-N. and A.S.A.-K.; visualization, M.E.-N.; supervision, M.E.-N., K.A. and A.S.A.-K.; project administration, K.A. and A.S.A.-K.; funding acquisition, S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 14: The estimated grid frequency in case of conventional and modified ECKF.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Conventional ECKF** & \multicolumn{2}{c}{**Modified ECKF**} & \multicolumn{2}{c}{**Reduction Percentage**} \\ \hline THD & 7.81 & THD & 1.58 & Reduction & 80\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: THD results.
# Tsunami Modeling and Satellite-Based Emergency Mapping: Workflow Integration Opportunities
## 2 Materials and Methods: Tsunami Modelling and Satellite-Based Emergency Mapping
### Copernicus Emergency Management Service (CEMS)
#### 2.2.1 Rapid Mapping
Despite technical limitations of tasking satellites for a new acquisition (orbits, cut-off-times for scheduling a new tasking), experience over the past six years has shown that time can be gained by submitting a tasking as soon as there is a very high probability that an event will occur (e.g., floods, tropical storms) or as soon as an event has occurred (e.g., earthquakes, tsunamis, volcanic eruptions). Statistics (from 2012 to 2019) for CEMS-RM activations for earthquakes which started within 24 h after the event show that the time lapse between the earthquake and the reception of an activation request is 12 h on average (in a range from 0.5 to 21 h considering 16 activations). This time could lead to a delay of up to one day as the request often comes too late for an acquisition on the same day (due to the aforementioned tasking limitations). Figure 2 shows the potential benefit of a satellite early tasking based on early warning information compared to the standard workflow. The example shows the timeline for a hypothetic event which occurs in the first hours of the day. In this example up to one day can be gained when early warning information is used. Results: GDACS Tsunami Model and CEMS Rapid Mapping Activation Outputs for the 2015 Tsunami in Chile  
In the aftermath of the tsunami generated by the earthquake on 16 September 2015 in Chile, 30,000 e-mails and 18,000 SMS messages were dispatched by the GDACS few minutes after the event, providing information on the potential impact along the Chilean coast. The European Union's Emergency Response Coordination Centre in Brussels (DG ECHO-ERCC) activated the Rapid Mapping component of CEMS about nine hours after the event.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Sensor Parameter** & **Value** \\ \hline \multirow{4}{*}{Satellite} & Total satellite mass & 67 kg \\  & Orbit height & 500 km \\  & Orbit inclination angle & 98\({}^{\circ}\) \\  & Regression cycle & 2.5 days \\ \hline \multirow{8}{*}{Sensor} & Field of view (FOV) & 20.5\({}^{\circ}\) \\  & Signal-to-noise ratio (SNR) & \(\geq\)300 dB \\  & Number of active detectors & 5056 \(\times\) 2968 \\  & Detector size & 4.25 \(\upmu\)m \(\times\) 4.25 \(\upmu\)m \\ \cline{1-1}  & Sensor technology & Spectral filter \\ \cline{1-1}  & Imaging mode & Push-broom imaging \\ \cline{1-1}  & Resolution & 10 m \\ \cline{1-1}  & Spectral range & 400 nm–1000 nm \\ \cline{1-1}  & Number of bands & 32 \\ \cline{1-1}  & Quantization bits & 10 \\ \cline{1-1}  & Width & 50 km \(\times\) 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensor parameters of OHS. RadCalNet product. This allows users to understand the error and uncertainty related to RadCalNet-based calibration results. At present, the RadCalNet product, which includes BOA reflectance, TOA reflectance, and atmosphere parameters, has been widely used in the on-orbit radiometric calibration and validation of many optical satellite sensors, such as Landsat-8/OLI [12], Sentinel-2A/2B/MSI [13; 14; 15], SV-1/MUX [16], ZY-3/MUX [17], etc. As they benefit from time continuity and multi-site collaboration, the RadCalNet product is suitable for use in the on-orbit radiometric calibration of a constellation of identical sensors such as the Zhuhai-1 satellite constellation. The standardization, traceability and high frequency of the RadCalNet product mean it is useful in ensuring radiometric consistency between different satellite measurements. With the aim of calibrating the Zhuhai-1 satellite constellation with multi-hyperspectral sensors and multi-integration stages, an on-orbit radiometric calibration model considering all of the integration stages of hyperspectral sensors was proposed based on the RadCalNet product (i.e., BOA reflectance and atmosphere parameters) from four sites. In this paper, we analyzed the consistency of the calibration results from different RadCalNet sites, and then, we also conducted the on-orbit radiometric stability evaluation of OHSs. To further validate the reliability of the calibration results, we compared the retrieved hyperspectral reflectance from OHSs with site surface reflectance measurements.
## 2 Performance Analysis of WAAS
When comparing the 99.9% Localizer Performance with Vertical guidance (LPV) [6] availability contours in Figure 1 (_i.e._, the dark purple region in the left plot and the red region in the right plot), the MAAST simulation result is more optimistic and less smooth than the actual long-term average WAAS result (July 1 st to September 30 th, 2009), but it could determine the limits of availability to within a few degrees of the correct location [11]. MAAST is intended for use as a fast, accurate, and highly customizable experimental test bed for WAAS algorithm development. The remainder of this paper shows only the VPL simulation results, because in general GPS has more difficulties meeting the vertical guidance requirement.