esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Construction of Non-Gaussian Sea Surface  
We used the Tayfun model [45] to construct a non-Gaussian large-scale sea surface. The Tayfun model can be expressed as:  
\[z(x)=z_{1}+\frac{1}{2}\overset{\cdot}{K}\bigg{[}\bigg{(}z_{1}^{2}-\overset{\cdot 2 }{z}\bigg{)}+2z_{1}\overset{\cdot}{z}\bigg{]} \tag{16}\]  
where \(\overset{\cdot}{K}\) is the mean wavenumber, \(\overset{\cdot}{K}=m_{1}/m_{0}\), \(m_{j}=\int KjS(K)dK\), \(S(\cdot)\) is the ocean wave spectrum, and  
\[\overset{\cdot}{z}(x_{m},y_{n},t)=\sum_{x=1}^{M}\sum_{y=1}^{N}\sqrt{2S(K,\varphi )dK}\sin\left(K_{x}x_{m}+K_{y}y_{n}-\omega_{x_{x},x_{y}}t+\varepsilon_{x_{x},x_ {y}}\right) \tag{17}\]  
and \(z_{1}\) is the height of the Gaussian sea surface.  
\[\overset{\cdot}{z}(x_{m},y_{n},t)=\sum_{x=1}^{M}\sum_{y=1}^{N}\sqrt{2S(K, \varphi)dK}cos\left(K_{x}x_{m}+K_{y}y_{n}-\omega_{x_{x},x_{y}}t+\varepsilon_{ x_{x},x_{y}}\right) \tag{18}\]  
where \(M\) and \(N\) are the facets' numbers in range and azimuth directions. \(K_{x}\) and \(K_{y}\) represent the components of wave number \(K\) on the range and azimuth coordinates. \(x\) is the range direction, parallel to the antenna look direction; \(y\) is the azimuth direction, perpendicular to the range direction. \(\omega_{x_{x},x_{y}}\) is the angular frequency. \(\varepsilon_{x_{x},x_{y}}\) is uniformly distributed over (0, 2\(\pi\)). The coordinate system is:  
\[\begin{array}{rl}x_{m}=&\Delta x(m-M/2)|_{m=1,2,\ldots,M}\\ y_{n}=&\Delta y(n-N/2)|_{n=1,2,\ldots,N}\end{array} \tag{19}\]  
in which \(\Delta x\) and \(\Delta y\) are the facet sizes in ground range and azimuth directions.  
Figure 3 compares the probability density functions (pdfs) of the Gaussian and Tayfun sea surfaces based on the A spectrum and Cosine spreading function at wind speed of 10 m/s and incidence angle of 30\({}^{\circ}\) in the upwind direction. The size of the sea surface is 2 km \(\times\) 2 km, and the facet size is 10 m \(\times\) 10 m. From Figure 3a, it can be seen that the distribution of the elevation of Tayfun sea surface deviates from that of the Gaussian surface owing to the inclusion of nonlinear effects, and the mean value shifts to a negative value, which is consistent with the results in [46]. Figure 3b shows that the Tayfun sea surface has higher probability near small slopes in comparison with the Gaussian surface, and the pdfs of the Gaussian and Tayfun sea surface slopes have little difference near large slopes. All these results are similar to the performance of the CWM (Choppy Wave Model) reported in [46].  
Figure 3: Comparison of distributions of Gaussian sea surface and Tayfun sea surface based on the A spectrum and Cosine spreading function at 10 m/s wind speed and 30\({}^{\circ}\) incidence angle along upwind direction. (**a**) Pdfs for surface elevation; (**b**) Pdfs for slope along range direction. Blue solid curve, Gaussian sea surface; red dashed curve, non\(-\)Gaussian sea surface constructed by Tayfun model.
## 4 Repetition-Based Cooperative Broadcasting Scheme
### Cooperative Rebroadcasting  
In the next slot of the source node, the source node transmits an acknowledge for cooperation (AoC) at the beginning of the slot. The AoC message indicates the ID of the selected helpers and the time to cooperate. Then, in the remaining part of the slot, the selected helpers retransmit the packet with the source node cooperatively, as shown in Figure 6. If there is no helper, the source node retransmits the packet in its slot alone. Then, the cooperative retransmission goes back to the original way of repetition-based broadcasting. The cooperative broadcast is performed \(M_{R}\) times in the coming slots of the source node.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 9 Tools for Performance Evaluation of Wi-Fi 6
### Real-World Experiments
#### 9.1.1 Hardware  
The Wi-Fi Alliance is the organization in charge of the certification of Wi-Fi standard products. It has decided to release the implementations of Wi-Fi 6 features for access points in two waves as listed in Table 7. However, some vendors include some features from Wave 2 in Wave 1. In addition, Wi-Fi 6 clients, including laptops, smartphones, etc., are gradually released in the market.  
Wi-Fi 6 products from different vendors may differ by their supported features and their type of antenna, port, and radio-frequency connectors. The high-performance wireless access points are mainly categorized according to their deployment environment, namely indoor and outdoor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Waves** & **Features** \\ \hline \multirow{7}{*}{Wave 1} & * UL/DL OFDMA \\  & * DL MU-MIMO ( \(\geq\) 4 spatial streams) \\  & * TWT \\  & * 20/40/80 MHz channel width on the 5 GHz band \\  & * 1024-QAM \\  & * WPA 3 Encryption (non-802.11 ax feature) \\ \hline \multirow{7}{*}{Wave 2} & * 160 MHz channel width on 5 GHz band \\  & * UL MU-MIMO \\  & * Combining OFDMA and MU-MIMO \\ \cline{1-1}  & BSS coloring \\ \hline \hline \end{tabular}
\end{table}
Table 7: The two waves of Wi-Fi 6 access point features.  
Many vendors are producing Wi-Fi 6 access points with various capabilities. Among these vendors, Aruba, Cisco, and Huawei are the leading vendors that support most of the features. These vendors release the hardware in different series with different levels of capabilities. Aruba's ([[https://www.arubanetworks.com/products/wireless/access-points/](https://www.arubanetworks.com/products/wireless/access-points/)]([https://www.arubanetworks.com/products/wireless/access-points/](https://www.arubanetworks.com/products/wireless/access-points/)), accessed on 1 September 2022) access point series meets most of the requirements in Wi-Fi 6 and Wi-Fi 6 E, namely, TWT, OFDMA with 37 RUs, MCS 0-11, and MU MIMO with up to four antennas. Moreover, Cisco and Huawei products support OFDMA, TWT, MCS 0-11, BSS coloring, and MU MIMO with up to eight antennas.
## 3 Proposed Method
### Cross-Layer Routing Using ECTSA
#### 3.4.2 Fitness Metric Estimation  
In this stage, the fitness metric for the ECTSA is estimated using the residual energy, communication cost, DSR, and mobility. These fitness metrics are used to identify the optimal path to accomplish the cross-layer routing over the MANET.  
The traffic load of each node is required to be considered while calculating the residual energy. The total energy consumption of the node is expressed in Equation (10).  
\[TE_{i}(t)=L(t)\times\left(E_{tx}+E_{rx}\right) \tag{10}\]  
where the traffic load of the node \(N_{i}\) at time \(t\) is denoted as \(L(t)\), i.e., \(L(t)=NP/QL_{i}\); the number of packets saved in the queue \(Q_{i}\) is denoted as \(NP\); and the length of the queue is represented as \(QL_{i}\). Moreover, the utilized energy while transmitting and receiving packets is denoted as \(E_{tx}\) and \(E_{rx}\), respectively. Hence, the primary fitness metric, i.e., residual energy \((f_{1})\), of the node \(N_{i}\) is calculated using Equation (11).  
\[f_{1}=RE_{i}(t)-TE_{i}(t) \tag{11}\]  
where the residual energy of the node \(N_{i}\) after transmitting \(n\) number of packets is denoted as \(RE_{i}(t)\).  
The communication cost \((f_{2})\) required to communicate with a neighbor node is calculated based on distance, as shown in Equation (12).  
\[f_{2}=\frac{d_{avg}^{2}}{d_{0}^{2}} \tag{12}\]  
where \(d_{avg}^{2}\) denotes the average distance among the node and adjacent node and \(d_{0}^{2}\) denotes the node's radius.  
DSR  
The DSR \((f_{3})\) of each node is calculated based on the Bit Error Rate (BER) of the received data, which is expressed in Equation (13).  
\[f_{3}=1-BER \tag{13}\]  
The last fitness metric, i.e., node mobility \((f_{4})\), is denoted by means of changes in the node degree \((R_{ND})\). A number of adjacent nodes for node \(N_{i}\) is discovered by the Node Degree (ND). The minimum ND of \(N_{i}\) at a network graph \(NG\) is shown in Equation (14).  
\[ND_{min}(NG)=\min\{ND(i)\},\ \forall i\in NG \tag{14}\]Equation (15) shows an average node degree.  
\[ND_{avg}(NG)=\frac{\sum_{i}^{n}ND(i)}{n} \tag{15}\]  
The rate of changes in the ND is computed using Equation (16).  
\[f_{4}=\frac{ND_{avg}(t)-ND_{avg}(t-1)}{t} \tag{16}\]  
The derived multiple fitness metrics are converted into a single objective function using Equation (17).  
\[Fitness=\alpha_{1}f_{1}+\alpha_{2}f_{2}+\alpha_{3}f_{3}+\alpha_{4}\frac{1}{f_ {4}} \tag{17}\]  
where the \(\alpha_{1},\ \ \alpha_{2},\ \ \alpha_{3},\) and \(\alpha_{4}\) are weight values assigned to each fitness metric. Since this conversion is done because these fitness metrics conflict with each other, the node with higher residual energy is preferred for avoiding node/link failure while broadcasting the packets, which helps to avoid packet loss. A path with less distance to the destination is required for improving energy efficiency. Next, the estimation of the DSR is used to find the path which causes less loss over the MANET. Furthermore, the lesser ND helps to minimize the energy consumption of the node.
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 3 Proposed Fog-Based CDN Framework
### Fog-Based CDN Framework Components
#### 3.1.2 Fog Service Locator (\(Fsl\))  
The Fog Service Locator (\(FSL\)) is a web server extension installed in the cloud environment for web services. The main function of the \(FSL\) is to decide whether the service requested is registered in the fog-based CDN or not and whether it is offloaded to a nearby fog node. The \(FSL\) intercepts the HTTP request headers coming to the cloud server. If the fog headers exist, the \(FSL\) asynchronously checks whether a nearby fog node to the requester can provide quick access for the requested service resources or not. The \(FSL\) plays an important role in enabling the fog-based CDN to perform seamlessly for the cloud provider.
# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 2 Methodology  
### Introduction to the Baseline Model  
This paper utilizes RT-DETR-R18 as the baseline model, an advanced real-time end-to-end object detection model. Compared to traditional YOLO models, RT-DETR incorporates the self-attention mechanism of the Transformer [19] architecture, significantly enhancing its ability to capture interrelations within images, especially improving detection accuracy in complex scenarios. Additionally, RT-DETR is optimized for real-time processing, making it more suitable for applications requiring rapid response.  
The structure of the RT-DETR-R18 model, as shown in Figure 1, consists of four main components: a feature extraction backbone network, an efficient hybrid encoder, an IoU-aware Query Selection mechanism, and a Transformer decoder with an auxiliary prediction head. The feature extraction backbone network uses an improved ResNet18, which includes ConvBNReLU (CBR), max pooling, and BasicBlock modules. The efficient hybrid encoder processes high-level features from the backbone network using an Attention-based Intra-scale Feature Interaction module (AIFI) and fuses features across scales using a CNN-based Cross-Scale Feature Fusion module (CCFF). This design significantly reduces computational demands while enhancing processing speed and maintaining detection performance. The IoU-aware Query Selection mechanism selects image features with high classification and IoU scores from the encoder output as initial queries for the decoder. The Transformer decoder, equipped with an auxiliary prediction head, continually refines object queries to generate more accurate bounding boxes and confidence scores.
# Tunnel Traffic Evolution during Capacity Drop Based on High-Resolution Vehicle Trajectory Data
## 3 Tunnel Vehicle Trajectory Data Extraction  
### Extraction of Fixed-Point Vehicle Trajectory in the Tunnel  
To obtain each vehicle's spatial and temporal coordinate data in the tunnel monitoring field of view, it is necessary to identify and track each vehicle in the video. Firstly, each image frame is binarized, and the threshold value of vehicle identification size is set to realize each vehicle's rough detection and identification. To further improve the accuracy and speed of detection, this paper adopts the emerging deep learning algorithm You Only Look Once (YOLO) v5 to recognize and detect moving targets in monitoring [40; 41]. Then, the intermittent trajectory matching and missing trajectory completion are performed based on the speed limit judgment data association algorithm for the primary construction of vehicle trajectories. Then, the coordinate conversion method is applied to map the image coordinates to the Frenet coordinates along the lane and perpendicular to the direction of the lane. Finally, the overall algorithm of empirical mode decomposition (EMD) noise reduction is used to reduce the trajectory error and improve the trajectory accuracy at the fixed point of the tunnel [42].
# Comparisons of Dynamic Landslide Models on GIS Platforms
## 5 Conclusions  
Based on the above analysis, we can draw the following conclusions:  
1. A suitable model with proper assumptions can reduce uncertainties and simplify calculations. We must select suitable models to simulate different types of landslides. The proposed classification can provide some guidance for model selection. Landslide classification helps us understand landslide phenomena and select suitable dynamic models.
2. Compared with the two different models, the 3-D model can describe more details of the moving process than the depth-averaged model. Landslide runout zone, height, and speed are critical parameters in engineering design. The depth-averaged model meets the actual needs and provides engineering parameters quickly. Therefore, we can use the depth-averaged model to obtain the runout zones. If we pay more attention to the details, such as surge waves, the 3-D landslide simulation model is better.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Motion** & **Rock** & **Earth** & **Debris** \\ \hline \multirow{2}{*}{Falling and toppling} & Force: collision, friction & Force: collision, friction & Force: collision, friction \\  & Motion: Lagrangian & Motion: Eulerian & Motion: Eulerian/Lagrangian \\ \hline \multirow{3}{*}{Sliding, spreading, and flowing} & Force: dry friction & \multirow{3}{*}{Force: dry friction} & \multirow{3}{*}{Force: dry friction} \\  & and viscosity & & viscosity \\ \cline{1-1}  & Motion: Lagrangian & Motion: Eulerian & Motion: Eulerian/Lagrangian \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relationship between models and types.  
A small difference in algorithms can produce a large difference in the runout zones. We should use as many algorithms as possible to obtain the trajectory for engineering design.
* The number of elements, property, and material size determine the model selection. For discrete rigid body motion, models based on the Lagrangian description are suitable; for flow-like motion, models based on the Eulerian description are proper; for materials with various properties, models based on the Eulerian-Lagrangian description are best.  
Conceptualization, H.L. and Y.W.; methodology, Y.W.; software, Y.W. and A.T.; validation, H.L. and Y.W.; formal analysis, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W.; visualization, Y.W.; supervision, Y.W.; funding acquisition, H.L. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (Grant No. XDA23090301), National Natural Science Foundation of China (Grant No. 41941019, 42041006), and the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant No. 2019 QZKK0904).  
Not applicable.  
The authors declare no conflict of interest.
# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
## 4 Discussion and Limitations
### Comparison of Machine Learning Model Performance  
To compare the predictive accuracy of different machine learning models, MLR, random forest, and RNN were employed for training. Figures 15 and 16 illustrate the comparison between actual and predicted data for DLC and SWD simulations at a speed of 80.5 km/h, where the curvature and lateral acceleration data were used for training.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Speed (km/h)**} & \multicolumn{2}{c}{**Mean Squared Error**} \\  & & **Model A** & **Model B** \\ \hline \multirow{4}{*}{DLC} & 40 & 0.0041295 & 0.0000168 \\ \cline{2-4}  & 80.5 & 0.0070954 & 0.0001994 \\ \cline{2-4}  & 50–70 & 0.0033708 & 0.0001438 \\ \hline \multirow{4}{*}{SWD} & 40 & 0.0011664 & 0.0004339 \\ \cline{2-4}  & 80.5 & 0.0005428 & 0.0000835 \\ \cline{1-1} \cline{2-4}  & 50–70 & 0.0006962 & 0.0003175 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of prediction accuracy for LTR (curvature and speed vs. curvature and lateral acceleration).  
Table 6 presents the MSE values of the rollover stability predictions compared to actual data. Among the models, RNN exhibited the lowest MSE, indicating the highest accuracy in predicting rollover stability.  
The MLR model demonstrated the second-lowest error after the RNN model. In Figures 15 and 16, the DLC scenario results were comparable across models. However, in the SWD scenario, from 1.4 s onward, the MLR model overestimated the actual values, with a larger error range than the RNN model. The random forest model, as shown in Figure 17, exhibited minor oscillations in the predicted data between 3.59 and 3.75 s in the DLC scenario, while it completely failed to generate accurate predictions in the SWD scenario.  
Based on the combined analysis of MSE values and prediction trends, the RNN model was determined to be the most suitable for predicting the rollover stability of heavy-duty trucks.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Mean Squared Error**} \\ \cline{2-3}  & **DLC** & **SWD** \\ \hline MLR & 0.0003264 & 0.0007587 \\ \hline Random forest & 0.0003354 & 0.0044262 \\ \hline RNN & 0.0001994 & 0.0000835 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of mean squared error with various machine learning models.  
Figure 16: Comparison of LTR prediction results with various machine learning models (driving scenario: SWD test).  
Figure 15: Comparison of LTR prediction results with various machine learning models (driving scenario: DLC test).
# Effect of Zr Modification on NH\({}_{3}\)-SCR Reaction Performance of Cu-Ce/SAPO-34 Catalysts
## 3 Results and Discussion
### Sem  
Figure 4 shows SEM photos of some of the prepared catalysts. SEM characterization is mainly used to explore the crystal state, microstructure, and adhesion state of active components on catalyst surfaces. As shown in Figure 4, all the catalysts presented a cube state related to the CHA structure of SAPO-34. The grain size was about 4 \(\upmu\)m. The surface of the 4 Cu-4 Ce/SAPO-34(a) catalyst was covered with randomly shaped materials, and it was speculated that Cu and Ce oxides were attached to the crystal surface after interaction [21]. The XRD results showed that the characteristic peaks of the 4 Cu-4 Ce-2 Zr/SAPO-34(b) and 4 Cu-4 Ce-4 Zr/SAPO-34(c) samples were high, that is, both the crystallinity and strength were high. Compared with the SEM photos, it was found that the  
Figure 3: NH\({}_{3}\)-TPD spectra of catalysts with different Zr loadings.  
Figure 2: XRD spectra of catalysts with different Zr loadings.  
4 Cu-4 Ce-2 Zr/SAPO-34(b) and 4 Cu-4 Ce-4 Zr/SAPO-34(c) cubes had complete structures and flat surfaces, and each particle was in an independent state, which confirmed the conclusion of the XRD analysis. For the 4 Cu-4 Ce-6 Zr/SAPO-34(d) catalyst, it was found that most of cubic structures were damaged, the surface was rough, and most of the particles were bonded together after being damaged, which was consistent with the conclusion that the XRD results showed that the characteristic peaks disappeared, the crystallization decreased, and the grain structure was damaged.  
Figure 4: SEM images of 4 Cu-4 Ce/SAPO-34 (a), 4 Cu-4 Ce-2 Zr/SAPO-34 (b), 4 Cu-4 Ce-4 Zr/SAPO-34 (c), and 4 Cu-4 Ce-6 Zr/SAPO-34 (d) (the magnifications is 10,000 and 1000 times).
# Post-Authorship Attribution Using Regularized Deep Neural Network
###### Abstract  
Post-authorship attribution is a scientific process of using stylometric features to identify the genuine writer of an online text snippet such as an email, blog, forum post, or chat log. It has useful applications in manifold domains, for instance, in a verification process to proactively detect misogynistic, misandrist, xenophobic, and abusive posts on the internet or social networks. The process assumes that texts can be characterized by sequences of words that agglutinate the functional and content lyrics of a writer. However, defining an appropriate characterization of text to capture the unique writing style of an author is a complex endeavor in the discipline of computational linguistics. Moreover, posts are typically short texts with obfuscating vocabularies that might impact the accuracy of authorship attribution. The vocabularies include idioms, omomatopoeias, homophones, phonemes, synonyms, acronyms, anaphora, and polysemy. The method of the regularized deep neural network (RDNN) is introduced in this paper to circumvent the intrinsic challenges of post-authorship attribution. It is based on a convolutional neural network, bidirectional long short-term memory encoder, and distributed highway network. The neural network was used to extract lexical stylometric features that are fed into the bidirectional encoder to extract a syntactic feature-vector representation. The feature vector was then supplied as input to the distributed high networks for regularization to minimize the network-generalization error. The regularized feature vector was ultimately passed to the bidirectional decoder to learn the writing style of an author. The feature-classification layer consists of a fully connected network and a SoftMax function to make the prediction. The RDNN method was tested against thirteen state-of-the-art methods using four benchmark experimental datasets to validate its performance. Experimental results have demonstrated the effectiveness of the method when compared to the existing state-of-the-art methods on three datasets while producing comparable results on one dataset.  
authorship attribution; character embedding; bidirectional decoder; bidirectional encoder; deep learning; neural network; social media +
Footnote †: journal: _Applied Sciences_