esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Device Structure and Mechanism  
In order to achieve the desired charge-coupling, the split-gate electrode within the oxide coated trenches is designed to connect the source during operation in the blocking mode, as shown in Figure 1. The cross-sectional view of the HDSGRSO UMOSFET is presented in Figure 1b. Conventionally, the doping concentration of the n-drift is uniform. In this study, the epitaxial layer of HDGRSO UMOSFET has been divided into three parts with different doping concentration and thickness: the upper, the middle and lower epitaxial layers. The middle epitaxial layer has a higher doping concentration compared with the upper and lower epitaxial. In order to emphasize the modulation effect of the high doped epitaxial layer on the electric field, the doping concentrations of the upper and lower epitaxial layers are set to same, and the thickness of the high doped epitaxial layer is \(L_{2}\).  
In this work, we used the TCAD to compare the performance of two structures. The potential contours and the electric field distribution along the trench surface (cut2) and in the middle of Mesa active area (cut1) for two structures at \(V_{\rm DS}=240\) V are represented as shown in the Figure 2. The doping concentration of the n-drift (\(N_{\rm D}\) and \(N_{2}\)) was \(1\times 10^{15}\)/cm\({}^{-3}\) and \(5\times 10^{15}\)/cm\({}^{-3}\) for SGRSO and HDSGRSO UMOSFET, respectively, and the doping concentration of the high doped epitaxial layer was \(1.5\times 10^{16}\)/cm\({}^{-3}\). It can be seen from the picture that the potential lines originally concentrated on the lower part moves to the upper part of the n-drift region, and the electric field of HDSGRSO UMOSFET distribution was more uniform than that of SGRSO UMOSFET. Furthermore, a small electric field peak appeared at the interface between the upper and the middle epitaxial layers, as shown in Figure 2, at \(-13.5\)\(\upmu\)m in the y-axis. Therefore, the high doped epitaxial layer can modulate the electric field distribution and reduce the internal high electric field to improve the BV.  
Figure 3 shows the three-dimensional view of the electric field distributions for two structures with the same n-drift doping concentration (\(N_{\rm D}\) and \(N_{2}\)) \(5\times 10^{15}\)/cm\({}^{-3}\). The small electric field peak (at point A) brought by the high doped epitaxial layer is shown in the Figure 3b. It is obvious that the electric field in the drift region of HDSGRSO UMOSFET is more uniform than that of the SGRSO UMOSFET at the same n-drift doping concentration, and the volume under the electric field curved surface of HDSGRSO UMOSFET is bigger contrast to the SGRSO UMOSFET. As a result, the BV of HDSGRSO UMOSFET is 240 V, while that of SGRSO UMOSFET is 190 V.  
Figure 1: Cross section view of (**a**) split-gate resurf stepped oxide (SGRSO) and (**b**) split-gate resurf stepped oxide with high doped epitaxial layer (HDSGRSO) u-shape metal oxide semiconductor field effect transistor (UMOSFET).  
Figure 2: Potential distribution and electric field from cutlines comparing of the (**a**) SGRSO and (**b**) HDSGRSO UMOSFET at \(V_{\rm DS}=240\) V.
###### Abstract  
The present paper reports for the first time the comparison of radar-derived eddy dissipation rate (EDR) and vertical velocity with measurements from six aircraft for an intense squall line crossing Hong Kong. The study objectives are three-fold: (i) to characterise the structural dynamics of the intense squall line; (ii) to identify the dynamical change in EDR and vertical velocity during its eastward propagation across Hong Kong with a view to gaining insight into the intensity change of the squall line and the severity of its impact on aircraft flying near it; (iii) to carry out quantitative comparison of EDR and vertical velocity derived from remote sensing instruments, i.e., weather radars and in situ measurements from aircraft, so that the quality of the former dataset can be evaluated by the latter. During the passage of the squall line and taking reference of the radar reflectivity, vertical circulation and the subsiding flow at the rear, it appeared to be weakening in crossing over Hong Kong, possibly due to land friction by terrain and urban morphology. This is also consistent with the maximum gusts recorded by the dense network of ground-based anemometers in Hong Kong. However, from the EDR and the vertical velocity of the aircraft, the weakening trend was not very apparent, and rather severe turbulence was still recorded by the aircraft flying through the squall line into the region with stratiform precipitation when the latter reached the eastern coast of Hong Kong. In general, the radar-based and the aircraft-based EDRs are consistent with each other. The radar-retrieved maximum vertical velocity may be smaller in magnitude at times, possibly arising from the limited spatial and temporal resolutions of the aircraft data. The results of this paper could be a useful reference for the development of radar-based turbulence products for aviation applications.  
capable: 2025 15 8562
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
## 1 Introduction
### C-ITS Initiatives  
The start of the journey in Europe for I2V and V2I communication began with the EU-funded project COOPERS [1] in 2006, where the focus was on information exchange of safety-related traffic and infrastructure status between infrastructure and vehicles. The project with 38 participants from all over Europe delivered valuable specifications and tests of the system design and possible communication technologies.  
Based on the results of COOPERS, but also SAFESPOT [2] and CIVIS [3], the need for further European-wide cooperation was obvious because the C-ITS services shall work across borders and among all traffic participants. Therefore, in 2012, the representatives of the Dutch, German, and Austrian governments signed a memorandum of understandingto work on a European C-ITS corridor from Rotterdam to Vienna. In addition, the different stakeholders recognized that a sustainable solution needs cooperation across industry sectors and public authorities, leading to the non-formal strategic alliance called the Amsterdam Group. The process of cooperation evolved, resulting in the C-Roads platform, which is a cooperation of member states and road operators [4].  
In parallel, the technical aspects have been considered in different projects, stakeholder groups, and standardization bodies. ECo-AT was the Austrian project of the European C-ITS corridor [5]. The joint project of the road operator, industry, and academia laid the foundation for solid specifications and technical tests between the members of the vehicle and traffic infrastructure sector. Scoop@F was a project in France dealing with the deployment of cooperative intelligent transport systems [6]. The project simTD [7] was the largest test field for C-ITS in Germany, where realistic traffic scenarios were addressed. The outcome and lessons learnt from these projects and many others, like the EU-funded project InterCor [8], were used as a starting point for a European harmonization of C-ITS Services in the C-Roads platform.  
In the end, the technical framework and the specification of messages are mainly covered in ETSI (European Telecommunications Standards Institute) [9; 10; 11]. The Car-2-Car Communication Consortium (C2C-CC) [12] brings together the vehicle industry and focuses on V2X (Vehicle-to-everything) use cases. Moreover, technology-related interest groups like 5 GAA [13] focus on use case deployment seen from a 5G perspective. The European platform C-Roads is now responsible for harmonising the traffic infrastructure-related Use Cases and the message profiles to guarantee a cross-border and cross-vendor interoperability.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Requirements on Tools in MLOps
#### 5.1.4 Model Deployment Phase (DP)  
As the deployment of the ML model is organization-specific, restrictions in deployment options of a model may hinder the utilization of supportive tools.  
DP-1: Support for Different Deployment Patterns  
Concerning the physical location of where the prediction should be executed, different deployment strategies are possible. Next to offering the prediction as a service, deploying the edge or inside a container structure or cluster is thinkable.
## 3 Results and Discussion
### Effect of Nitrogen Fertilizer Reduction on Volatile Metabolomics of Suijing 18
#### 3.2.6. Hierarchical Cluster Analysis of Differential Volatile Metabolites  
The heatmap clearly exhibited the changes in the content of the 16 volatile metabolites in each group. Z-score standardization is the standardization of data based on the mean and standard deviation of the raw data. The different colors were filled with the different values obtained after normalization for different relative contents (red for high content, green for low content). As shown in Figure 7, the aromatic components were largely clustered in four categories [34]. Zone I was dominated by 3-methylcycloheptanone and (z)-3,7-dimethylocta-2,6-dienal, and these volatile metabolites were significantly higher in A3 than in A1 and A2. The lowest content was found in A1, which basically showed a decreasing trend with an increase in the amount of nitrogen fertilizer applied. Zone II was dominated by 1,4-dichlorobenzene, 4,6,8-trimethyl-1-nonene, and 1-methylnaphthalene, which showed basically the same trend as zone I and could be clearly distinguished from A3. The compounds detected in zone III were 2-methyl-2-propenoic acid anhy, 3,4-dimethylcycloocta-1,5-diene, and trans-piperitol, which were found at low levels in most of the samples of A1 and detected in all other samples. The compounds present in zone IV were 2-methylnaphthalene, 1-methylnaphthalene, 2-furanpropanoic acid, and ethyl ester, which did not differ significantly; the only exception to this rule were the low levels detected in a portion of A3, which suggests that when nitrogen fertilizer is reduced by 40% or more, the levels of these compounds decrease significantly.  
Overall, A3 could be distinguished from A1 and A2 more clearly, and A1 and A2 could be distinguished from a few differential metabolites, although they did not differ much in their content of some metabolites; this observation is consistent with the results of the OPLS-DA model described above.
# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 3 Method
### DeepLabv3+-ResNet101-ECA  
The improved DeepLabv3+ network structure is illustrated in Figure 6 and is divided into two main parts: the encoder and the decoder. At the encoder end, the landslide image is preprocessed and input into the backbone feature network, ResNet101. The feature map obtained after multi-level feature extraction by the backbone network is further processed by the ASPP module. This processing extracts and fuses information from five different scales. At the decoder end, the low-dimensional feature map obtained from the backbone network is fused with the feature map passed through the ASPP module. The ECA module is used to emphasize the important features in the fused feature map. Finally, a prediction segmentation mask of the same size as the original image is generated through convolution and up-sampling operations. In the experiments, the number of training epochs is set to 100. The cross-entropy loss is used as the loss function, and the Adam optimizer is selected with a learning rate of 0.0001.  
Figure 6: DeepLabv3+-ResNet101-ECA network architecture. Inside the blue box is the encoder and inside the red box is the decoder.
## 2 Materials and Methods
### Isometric Testing and Surface EMG  
Participants sat in an isokinetic dynamometer chair (Biodex System 2, 900-800) with restraints around the shoulders, torso, and thigh. All of the participants' right legs (dominant) were used. The knee was positioned to 90 degrees and locked in place. Prior to testing, the subjects' legs were cleaned and prepped with alcohol and marked with permanent marker where the EMG sensor will be placed to ensure the same placement for each test. A wireless surface EMG (Trigno, Delsys, Inc., Natick, MA, USA) was placed over the vastus lateralis at the same place as the ultrasound measurement [53] and in accordance with the Non-Invasive Assessment of Muscle project [54]. Participants were then instructed to kick as hard and as fast as they could for three seconds. This was repeated three times. Peak torque was calculated using Biodex software V2.0.10. The EMG signal was processed using EMG works (Delsys, Inc., Natick, MA, USA), and smoothed with a window size of a 0.025 s root mean square (RMS). The RMS signal was used to calculate the peak EMG amplitude. The low baseline noise and interface was visually inspected to be less than 1.0 so that a between-session comparison can be made. Previous research has demonstrated the excellent intrasession reliability [55] of surface EMG as well as good [56] intersession reliability when looking at maximal voluntary contractions.
## 4 Results and Discussion
### Discussion on Security  
In this section, we discuss the security mechanisms in our model, addressing potential vulnerabilities and adversarial attacks, as well as suggesting optimization strategies to enhance model robustness. Our design uses a skip attention mechanism with skip connections and a hash tree structure, aimed at securing data privacy while improving feature extraction and computational efficiency. However, adversarial attacks in deep learning can exploit certain weaknesses. For instance, subtle adversarial noise--imperceptible to humans but disruptive to model predictions--can affect the attention mechanism's focus, leading to biased feature extraction. Similarly, while the hash tree is efficient for data integrity checks, it may require significant computational resources to respond to rapid changes if malicious nodes are inserted or removed.  
To understand the model's behavior under adversarial conditions, we analyzed its response to common attacks like FGSM, PGD, and CW, which introduce slight data disturbances to skew model outputs. In FGSM, for example, the model is attacked using gradients to create minimal but effective changes in the data. Our skip attention mechanism uses multi-level feature extraction, where skip connections allow features to propagate across levels. This setup disperses disturbances, making it harder for small perturbations to compromise the entire feature set. The hash tree also verifies data integrity at multiple levels, which hinders perturbation propagation throughout the network, further reinforcing model robustness against FGSM attacks. In response to the iterative PGD attack, which applies stronger, multi-step perturbations, our model's skip connections spread out and minimize the cumulative effect of disturbances, while the hash tree's multi-level validation filters out changes at each level. This layered approach effectively limits PGD's impact, as accumulated perturbations are checked at multiple stages, reducing their effectiveness. Finally, the CW attack, designed to apply minimal but impactful perturbations, is also challenged by our multi-level validation system. Even the smallest perturbations face layered scrutiny through both hash validation and multi-level feature focusing in skip attention, which makes it difficult for consistent disturbances to evade detection across all layers. In summary, the combination of skip attention and hash tree structures strengthens our model's defenses against adversarial attacks. The multi-level structure in skip attention and hash tree validation ensures that disturbances cannot easily affect all feature layers, providing robust protection against FGSM, PGD, and CW attacks.
## 3 Dataset Overview
### Addressing Dataset Imbalance  
The following strategies were applied to improve the predictive power of the model:  
1. Synthetic Data Generation: Creating artificial samples to simulate low-risk conditions where wildfires did not occur.
2. Probabilistic Labeling: Assigning probabilistic values to real and synthetic samples to reflect the spectrum of fire risk scenarios.
3. Feature Normalization: Standardizing numerical attributes to ensure uniformity across scales, facilitating model convergence.  
To evaluate redundancy and potential multicollinearity, a Pearson correlation matrix was generated among all input features. No pair exceeded a correlation coefficient of 0.75, suggesting a sufficiently independent set of predictors. The correlation matrix is provided in Figure 3.  
Figure 3: Correlation matrix among environmental variables used in the wildfire prediction models. Values represent Pearson correlation coefficients.  
The original dataset exhibited a pronounced class imbalance, with approximately 78% of the entries corresponding to wildfire-positive instances. This bias posed a risk of overfitting, particularly under high-capacity models. Synthetic data were generated to simulate low-risk conditions and enrich the feature space with negative cases for model training.
# NTT and Inverse NTT Quantum Circuits in CRYSTALS-Kyber for Post-Quantum Security Evaluation
## 1 Introduction
### Extended Version of MobiSec 2022  
The work presented in MobiSec 2022 is revisited in this paper. Research on the NTT quantum circuits in CRYSTALS-Kyber was presented in [25]. This time, we further present an efficient inverse NTT quantum circuit. Using this implementation, we conducted research on estimating, comparing, and analyzing all quantum resources required for inverse NTT in CRYSTALS-Kyber.
# A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection
## 5 Experiments and Discussion
### Ablation Analysis  
#### 5.2.1 Structural Analysis  
In this subsection, we perform an ablation study to analyze the seven configurations of the proposed NURCNet using mean absolute error (MAE), the weighted F-measure (WF), and structure-measure (SM) metrics, as shown in Figure 7 and Table 1. From Figure 7, we observe that the proposed model with all components, i.e., attention, URB, and RRS_ID, captures more detailed information concerning defect objects and, thus, achieves the best performance. Table 1 further validates the observation, as the performance of the model gradually improves with the addition of each key component, and the model with all components has the best prediction results in terms of MAE, WF, and SM. In addition, Table 1 shows that NURCNet reduces MAE by 26% and, respectively, improves WF and SM by 3.2% and 1.4% compared to the baseline model. This observation indicates that all the key components in the proposed model are useful and necessary for obtaining the best defective object detection
## 3 Road-Based Multi-Metric Forwarder Evaluation Scheme
### Multi-Metric Video Streaming with Dynamic Self-Weightting Score
#### 3.2.2 Multi-Metric Video Streaming with Dynamic Self-Weighting Score
The \(VR_{m}\) vector values are normalized to values between 0 and 1 and the sum of the weight values are equalized to a value of 1, which is in line 6. In line 7, the whole vector weight is normalized using Equation (20). Then, lines 8 and 9 test the variation of the metric value and then assign a higher weight value to the metric that tends to zero, otherwise the optimal NFV is selected based on the dynamic self-weighting of the metrics, which is in line 11. Lines 12 and 13 terminate the algorithm procedure. Figure 4 depicts the flow representation of the dynamic self-weighting score. ```
1:Input : Multimetricvalues Output : Dynamic self-weighting score for NFV selection
2:ProcedureWeigthingscore forNFVs
3:ComputeVDR, VTJ, ABE, H_mRr, SINR
4:ComputeMultimetricscoresusingEquation 16
5:EvaluateVCRmVariationvaluesofmetriscussingEquation 17
6:NormalizeVectorVRm \(\in(0\geq VR_{m}\leq 1)\) usingEquation 18
7:Expresshesumofweightsofmetrics \(==1\)usingEquation 19
8:NormalizeVector/WtusingEquation 20
9:Ifmetricsscorevariationishigh \((VR_{m}\to 0)\)then
10:AssignhigherWttohametricforoptimalNFVselection
11:ElselectoptimalNFVbasedontheWiscore
12:Endif
13:Endprocedure
```  
**Algorithm 2** Dynamic Self-Weighting Score  
Figure 4: Dynamic Self-Weighting Score Flowchart. The whole concepts discussed in this section are implemented based on greedy-based geographic routing. The complete implementation process including simulation settings and performance evaluation of the proposed scheme are presented in Section 4.
# A Survey on Hardware Accelerators for Large Language Models
## 2 Computational and Energy Requirements
### Computational Complexity in Training  
The training phase of large language models (LLMs) is inherently complex due to the extensive computational resources required to process and learn from massive datasets. This complexity stems from several key factors:  
* **Volume of parameters:** LLMs, such as GPT-3, comprise an extraordinarily high number of parameters (often in the billions), each requiring computation during the training process. Adjusting these parameters to minimize errors involves extensive computational efforts.
* **Training data sizes:** LLMs are trained on vast datasets, necessitating significant computational power to process and learn from the data. This process includes tasks like tokenization, encoding, and the application of complex algorithms to understand linguistic patterns and structures.
* **Depth of neural networks:** Deep neural network architectures employed in LLMs, consisting of multiple layers, add to the computational load. Each layer contributes to the model's ability to understand and generate language, requiring substantial computation for both forward and backward propagation during training.
# Flexural Performance of Reinforced Concrete Members with Steel Bars
## 2 Materials and Methods
### Experimental Materials  
In this experiment, ready-mixed concrete was poured and naturally cured with a mixing strength of 21 MPa. The details of the concrete mix are summarised in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Specimen** & **f\_cu** & **f\_y** & **Tensile Steel** & **A\_s** & **Required Steel A\_req** & **Error Rate** & \\  & **Name** & **(MPa)** & **(MPa)** & **p\_b** & **Ration** & **(mm2)** & **(mm2)** & **(\%)** & **A\_req/A\_s** \\  & & & & & \(\rho\) & & & & \\ \hline
1 & 21-400-1.0 & & & \(\rho_{\text{max}}=0.01625\) & 1652.6 & 4-D22 + 1-D13 = 1675.1 & 1.36 & 1.01 \\
2 & 21-400-0.75 & & 400 & 0.02276 & 0.75\(\rho_{\text{max}}=0.01218\) & 1239.4 & 3-D22 = 1161.3 & -6.31 & 0.93 \\
3 & 21-400-0.5 & & & \(\rho_{\text{max}}=0.00813\) & 826.3 & 2-D22 + 1-D13 = 900.9 & 9.03 & 1.09 \\
4 & 21-600-1.0 & & & \(\rho_{\text{max}}=0.00843\) & 857.7 & 2-D22 + 1-D13 = 900.9 & 5.04 & 1.05 \\
5 & 21-600-0.75 & & 600 & 0.01264 & 0.75\(\rho_{\text{max}}=0.00633\) & 643.3 & 1-D22 + 2-D13 = 640.5 & -0.43 & 0.99 \\
6 & 21-600-0.5 & & & \(0.5\rho_{\text{max}}=0.00422\) & 428.8 & 2-D13 + 2-D10 = 396.06 & -7.64 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental variables of flexural members.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mixing Strength** (**MPa**)} & **Water-Cement** & **Fine Aggregate** & \multicolumn{4}{c}{**Unit Material Requirements (kg/m3)**} \\ \cline{3-8}  & **Ratio** & **Ratio** (\%)** & **Water** & **Cement** & **Fine** & **Coarse** & **Aggregate** & **Admixture** \\ \hline
21 & 56.2 & 48.8 & 174 & 310 & 866 & 926 & 1.55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Concrete mix.  
Columnnar specimens with a diameter of 100 mm and a height of 200 mm were fabricated simultaneously with the concrete placement according to KS F 2405. The compressive strength test result of the average 28-day concrete for the columnar specimens was 19.2 MPa, as listed in Table 3.  
The steel bars used in specimen fabrication were high-tension bars (SD400) and ultra-high-tension bars (SD600). The strength measurement results of the ultra-high-tension bars according to the diameter were 653.3 MPa for average yield strength and 806.6 MPa for average tensile strength, as shown in Table 4.