esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, an FE model has been developed to predict the ballistic response of thermoplastic fiber-metal laminates (TFMLs) subjected to projectile impacts at varying energy levels. The dynamic constitutive models for metal have been described. The effects of the Lode angle, temperature, and strain rate are considered in the strength model, while  
Figure 10: Numerically predicted residual velocity for FMLs (based on a PP/PP composite) struck transversely by the 12.7 mm diameter, 46.7 g projectiles at 300 m/s with different nose shapes.  
Figure 9: Numerically predicted cross-sectional as well as rear surface damage patterns for the FMLs (based on a PP/PP composite) struck transversely by the 12.7 mm diameter, 46.7 g projectiles at 300 m/s with different nose shapes. (**a**) Flat-nosed; (**b**) ogival-nosed; (**c**) hemispheric-nosed.  
the effects of stress triaxiality, Lode angle, temperature, and strain rate are taken into account in the failure criteria. Additionally, the impact of the projectile's nose shape has been investigated. The main conclusions are as follows:  
1. The present model predicts the experimental results well for TFMLs subjected to impact by hemispheric-nosed projectiles in terms of perforation energy, cross-sectional deformation, and failure patterns at various impact energies.
2. It has been demonstrated that the nose shape of projectiles significantly influences the perforation behavior of TFMLs in terms of both residual velocity and damage patterns. This is attributable to the distinct deformation mechanisms induced by each projectile type. Specifically, flat-ended projectiles exhibit the highest residual velocities, whereas ogival-nosed projectiles exhibit the lowest.
3. The present model exhibits an enhanced performance regarding failure patterns when compared to existing models, such as the elastoplastic constitutive model and the JC + BW constitutive model, despite yielding comparable deformation results.  
Conceptualization, Y.Z. and Y.L.; methodology, Y.L.; software, Y.L.; validation, Y.Z., Y.L., H.Z. and C.L.; formal analysis, Y.L.; investigation, Y.Z. and Y.L.; resources, Y.L.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.Z., Y.L., H.Z. and C.L.; visualization, Y.L.; supervision, H.Z. and C.L.; project administration, Y.L.; funding acquisition, Y.Z. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Guizhou University Doctoral Fund [grant (2022) 34], Basic Research Project of Guizhou University (grant (2023) 42).  
Not applicable.  
Data is contained within the article.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TFMLs & thermoplastic fiber-reinforced metal laminates \\ VUMAT & vectorized user material subroutine \\ JC & Johnson-Cook \\ PP/PP & polypropylene fiber-reinforced polypropylene matrix composites \\ DIF & Dynamic Increase Factor \\ FE & finite element \\ \end{tabular}
# Non-Contact and Real-Time Measurement of Kolsky Bar with Temporal Speckle Interferometry
## 4 Conclusions  
With rapid development in the machinery industry and defense industry, the demand for dynamic mechanical properties of materials under higher strain rate and smaller size conditions is getting higher and higher. When the diameter of the bar is less than 3 mm, a traditional Kolsky bar is unable to provide reliable measurement because of the strain gauge and the bar cannot be reliably connected. A measurement system using a Kolsky bar with 2 mm diameter based on temporal speckle interferometry is presented in this paper. Compared with typical Kolsky bars, it can capture and process the dynamic mechanical properties of materials under higher strain rates and smaller size conditions. This method does not require any intermediate media, and has the advantages of being non-contact, real-time, and more reliable. It can be used for dynamic mechanical measurements of various solid materials, including metals, ceramics, and various composite materials, but it cannot be used for soft materials such as biological tissues. We believe that the application scope of the Kolsky bar based on temporal speckle interferometry will become more and more extensive through further research.  
All works with relation to this paper has been accomplished by all authors' efforts. [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
Figure 16: Three-dimensional surface map of stress, strain, and strain rate.  
**Funding:** This research was funded by National Natural Science Foundation of China (Grant No. 51675038) and the Internal Research Funds (4-ZZF) and G-YBWT) of the Hong Kong Polytechnic University.  
**Acknowledgments:** The authors acknowledge the financial supported from National Natural Science Foundation of China (Grant No. 51675038) and the Internal Research Funds (4-ZZF) and G-YBWT) of the Hong Kong Polytechnic University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing
## 2 Research Methodology and EWMA-KAN-Transformer Model
### Structure of KAN-Transformer Prediction Model Based on EWMA Data Processing
#### 2.2.5 Decoder Layer  
The flow of the Decoder is illustrated in Figure 9. The output from the Embedding layer is passed through three linear layers to obtain \(Q\), \(K\), and \(V\). To prevent information leakage during inference, a masked multi-head attention mechanism is used to compute correlations, and the output is then fed into an Add & Norm layer. A linear layer generates the query matrix \(Q^{\prime}\), while the Encoder's output is processed through two linear layers to obtain \(K^{\prime}\) and \(V^{\prime}\). The matrices \(Q^{\prime}\), \(K^{\prime}\), and \(V^{\prime}\) are input into the multi-head attention mechanism to facilitate the interaction between the Encoder and Decoder. The output then passes through an Add & Norm layer, a feedforward neural network, and another Add & Norm layer. It is important to note that the last dimension of the Decoder layer's input and output remains unchanged at 512.  
Figure 8: Workflow of the Encoder.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 3 Proposed UAV Assisted Dynamic Clustering Architecture
### Uav  
In the developed system, the UAV plays a vital role in cluster formation, localization, communication and data gathering and the whole system is trigged by the UAV beacon. The UAV under consideration is a quad copter equipped with a localization system with the following specifications:  
* Minimum operating flight height = 20 m,
* Maximum operating flight height = 500 m,
* Maximum speed = 7 m/s.  
The role of UAV is very critical in the developed system hence it is designed in such way that it bears most of the processing burden to simplify the tasks of the sensor nodes. It performs many key functions throughout its working cycle: activating the sensor nodes, shunting them for further processing, getting nodes' initial information, locating all activated nodes, evaluating them, nominating the CH and finally collecting data from the selected CH. The working cycle of the UAV is given in Figure 2.  
Figure 2: Flow chart of the UAV working.  
The UAV sends a beacon message (see Table 1) to activate sensor nodes, and the parameters set in the beacon are as follows:  
* _Sensor type_: Type of sensor nodes or combination need to be activated in response of this beacon.
* _Height_: it is current data collection height of UAV not the flying height. The sensor nodes that are capable to communicate at this distance will be considered as candidate for a CH.
* _Threshold_: This threshold will be used to limit the number of sensor nodes contesting for CH.
* _Trailer_: A trailer contains Error Detection Code (EDC) or any other information.
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 4 Methodology
### User Traffic Prediction Module
#### 4.1.2 Transformer and Temporal Convolutional Network Variant  
The Transformer Neural Network is a hybrid model consisting of a Transformer and a TCN model. The transformer component is effective at capturing long-term temporal dependencies and seasonal patterns [30] due to its self-attention mechanism, which enables focusing on crucial information regardless of its position in the time-series data. Hence, the transformer processes all time steps of a time-series sequence simultaneously, unlike traditional RNN-based models, which process time-series data sequentially. In contrast, the TCN is aimed at extracting local short-term patterns [31] since convolutions are performed on windows of data. The TCN model comprises 4 attention heads and a 64-dimensional key space. Furthermore, the model also incorporates a normalization layer and a 256-unit feed-forward layer with a ReLU activation function for stability during training. The encoding used is a Positional Encoding so that the sequence of temporal information is retained. Finally, 4 sequential transformer blocks for hierarchical feature extraction are included. The TCN model comprises Dilated Convolutions with Causal Padding and dilation rates that are exponentially increasing. This ensures that only past information is used for predictions. Moreover, it consists of Residual Connections for gradient flow and two 1D Convolutional Layers, each with its own ReLU activation function. The integration between the two models is achieved with 2 fully connected layers of 128 and 64 units each, followed by a ReLU activation layer and an output of 60 for the 60 predicted time steps. In the output layer, an average pooling is applied.  
For this network, the same loss function and optimizer (Adam [29]) that were used to train our LSTM model were utilized.
# Piston Error Measurement for Segmented Telescopes with an Artificial Neural Network
## 3 Piston Error Detection Method Using BP Artificial Neural Network
### Piston Error Detection Approach with BP Artificial Neural Network  
The piston error detection for multiple sub-mirrors of a segmented telescope with a BP artificial neural network can be divided into three steps:  
1. Determine the system parameters and generate the datasets for training the neural network under the specified system parameters. From Equation (16), we can see that the peak heights of the MTF sidelobes are only related to the input broadband light, the number of sub-pupils and the piston errors between the multiple sub-mirrors. When the segmented telescope and its working wavelength are determined, within the half of the coherent length of input light, a set of piston errors between segments is randomly introduced, and by taking the corresponding piston errors into Equation (16), the peak heights of MTF sidelobes can be obtained. The peak heights of MTF sidelobes directly calculated from Equation (16) served as one column of input matrix and the corresponding piston errors served as one column of the output matrix. Thus the input dataset and output dataset of the network could be generated.  
Figure 4: Sketch graph of a back-propagation (BP) artificial neural network with two hidden layers.  
2. Establish the neural network and train it with the input dataset and the corresponding output dataset. Here we utilized the neural network fitting tool in MATLAB, and by properly setting the number of neurons in each layer, the neural network was established. In the training process, the dataset is separated into three parts including training set, validation set and test set, then a specific training algorithm is set to train the network.
3. Once the network is well trained, we can apply the trained neural network to determine the piston errors with the PSF image collected from the optical system. Note that the image should be Fourier transformed first to get the peak heights of the MTF sidelobes before they can be handled with the neural network. By establishing the correspondence of the MTF sidelobes with their associated sub-pupils, multiple piston error measurements of the whole aperture can be implemented simultaneously by one detection of a CCD broadband image.  
The application procedure of the piston error detection approach with BP artificial neural network is summarized in Figure 5.
## 4 Measurement Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **TYP.** & **Unit** \\ \hline Quiescent current & 0.3 & mA \\ Input offset voltage & 20 & \(\upmu\)V \\ Input offset voltage drift & 0.7 & \(\upmu\)V/\({}^{\circ}\)C \\ Common-mode voltage range & 2.5 & V \\ Open-loop voltage gain & 140 & dB \\ Slew rate & 0.45 & V/\(\upmu\)s \\ Gain-bandwidth product & 100 & KHz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Typical parameters of the amplifier at room temperature. Figure 13: Open-loop gain (with a 100 pF load capacitor at 250 \({}^{\circ}\)C). Table 3 summarizes and compares the performance of the operational amplifier in high-temperature applications. As shown, the amplifier achieves an offset voltage as low as 20 \(\upmu\)V and a high DC gain of 140 dB, demonstrating its suitability for precision applications in demanding environments. These results underscore the amplifier's ability to maintain  
Figure 14: Pulse response (with a 5 V step input signal with a cycle time of 10 ms). Figure 15: Measurement results versus temperature: (**a**) gain; (**b**) GBW; (**c**) power; (**d**) offset. exceptional performance, even under extreme temperature conditions, ensuring stability and reliability across a wide operational range.
# A New Compact Double-Negative Miniaturized Metamaterial for Wideband Operation
## 5 Results and Discussion
### Metamaterial Arrays Configurations
#### 5.2.3 4 \(\times\) 4 Array Analysis  
In Figure 9, the permeability, permittivity, and refractive index for 0-degree and 90-degree rotation of the 4 \(\times\) 4 array structure are depicted. From Figure 9a, negative permeability is shown from 7.94 to 14 GHz, covering almost 6.06 GHz in bandwidth, for 0-degree rotation, and from 7.928 to 14 GHz, covering more than 6 GHz bandwidth, at 90-degree rotation. Moreover, Figure 9b shows negative permittivity from 3.764 to 7.64 GHz, 8.456 to 9.044 GHz, 9.176 to 9.50 GHz, and 9.596 to 10.076 GHz for 0-degree rotation and from 3.824 to 7.664 GHz, 8.468 to 8.876 GHz, 9.044 to 10.268 GHz, and 11.336 to 11.648 GHz for 90-degree rotation. Figure 9c reveals the real magnitude of the refractive index (\(\eta\)) vs. frequency from 3.26 to 7.364 GHz, 7.82 to 10.004 GHz, and 10.832 to 14 GHz, respectively covering bandwidths of 4.104 GHz, 2.184 GHz, and 3.168 GHz for 0-degree rotation, and from 3.404 to 7.148 GHz, 7.832 to 10.004 GHz, and 10.832 to 14 GHz, covering refractive index bandwidths of 3.744 GHz, 2.172 GHz, and 3.168 GHz for the 90-degree rotated array structure. Therefore, it is notable here that one bandwidth is greater than 4 GHz and that all of the bandwidths are almost in the same range for both the 0-degree and 90-degree rotations of the array configuration. Finally, the Z-shape structure also exhibits characteristics of a double-negative metamaterial at 8.60 GHz, e.g., in 1 \(\times\) 1 and 2 \(\times\) 2 array configurations.  
Figure 8: **(a)** 90-degree rotated 2 \(\times\) 2 array geometry; the real magnitude of **(b)** the permeability; **(c)** the permittivity; and **(d)** the refractive index of the 90-degree rotated 2 \(\times\) 2 array configuration.  
Figure 9: Real magnitude of **(a)** the effective permeability; **(b)** the effective permittivity; and **(c)** the refractive index of the 90-degree rotated 4 \(\times\) 4 array configuration.  
Table 2 represents the value of effective medium parameters for unit cell and array configurations. Here, there are some differences in the parameter values between the 0-degree and 90-degree rotation angles, but the unit cell and arrays configuration are exhibited double-negative characteristics at 8.79 GHz and 8.60 GHz, respectively. Moreover, the effective medium parameter values of the 4 \(\times\) 4 array structure are almost the same for both the 0-degree and 90-degree rotation angles.  
From Table 3, [PERSON] et al. [14] analyzed the 1 \(\times\) 1 and 2 \(\times\) 2 arrays in an orthogonal position. In addition, [PERSON] et al. [18] presented 1 \(\times\) 1 and 2 \(\times\) 2 array structures in his framework. Further, [PERSON] et al. [19] showed a 2 \(\times\) 2 array configuration at an open and interconnecting position. However, in this paper, 1 \(\times\) 1, 2 \(\times\) 2 and 4 \(\times\) 4 open array configurations were analyzed for 0-degree and 90-degree rotation, which is a new type of analysis from the previous work.
# Reinventing Mobility Paradigms: Flying Car Scenarios and Challenges for Urban Mobility
## 4 Experiments
### Transportation Test-Networks  
The experiments have been carried out on simulated test transportation networks given that current urban contexts do not consider UAM needs at all, such as suitable safe take-off and landing spaces for flying cars inside the cities also according to flying rules in the urban context, which could vary within and among cities. Any of them can be defined at this time, also because they will depend on the adopted standards for commercial flying cars.  
The test transportation networks have been set by adopting a modular approach, which is based on a suitable symmetric \"module\". The designed module (depicted in Figure 5) is \(L\times L\), there are 25 nodes and all links have the same capacity and the same length \(l=L/4\). For simplicity, the figure reports just one link to simulate the two-way roads connecting each pair of nodes, although oriented links, as in this case, should explicitly consider the direction for each pair of nodes. As depicted in Figure 5, links are represented with a light blue continuous line, while the red links represent the virtual connection between demand points, i.e., trip origin/destination red nodes \(V\), and transport supply (see for example [52]). Aerial take-off and landing activities can happen only at the transition node \(T\) (green circle in Figure 5) for each module. Take-off and landing operations are suitably separated at the transition node in order to keep two distinct traffic flows entering/exiting the transition node in ground mode for reaching the destination. Each \(A_{i}\) starts the trip from its origin node \(V_{k}\) and will reach the transition node \(T\) in ground mode. In flying mode, \(A_{i}\) will move only between transition nodes of the modules forming the transportation network and will, again, use the ground links to reach the destination \(V_{h}\).  
It is worthwhile to note that the simplified topological symmetry and the hypothesis on link features do not influence the preliminary results, as the link features (such as travel times, capacity, length) may be changed even though the graph topological structure remains the same. For these first experiments, the simplified structure has been considered more suitable than a more complex one in terms of link features. In addition, it is not the aim of this work to explore the optimal location of the transition nodes and for this reason too the modular, symmetric structure of the test network has been considered.
# Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
## 4 Evaluation
### Necessity of Multiple-Channel and Feature Fusion  
In order to demonstrate the effectiveness of the multiple-channel architecture and the necessity of the feature fusion method, we conducted 10 experiments on the extracted test set using different architectures. The compared architectures included single-channel AlexNet, single-channel VGG16, single-channel LSTM, and three-channel parallel network structures, where each single-channel network is directly connected to the classifier without feature selection and fusion. The multiple-channel structure is further divided into two types: with and without the feature fusion algorithm. The average test accuracy and average test time results for the five different approaches are shown in Figure 6.  
Figure 4: The relationship between the number of fused features and the accuracy of model detection.  
The experimental results showed that compared with the multi-channel structure, the three single-channel architectures consume less time, but their test accuracy is also very low, below 0.75. This is because the single-channel structures are more simple, and the extracted effective features are limited. The single-channel structures produced varying results. AlexNet consumes less time; VGG16, with a deeper network, can extract more complex features.  
Figure 5: Histogram of the distance distribution of all features from the average feature.  
Figure 6: Test accuracy and time of single-channel and multiple-channel structures.  
features, thereby improving accuracy; and LSTM does not have efficient feature extraction capabilities like CNNs, so the accuracy is low when used alone. The multiple-channel structure can combine the extracted features using different deep neural networks, including spatial and temporal features, significantly improving accuracy, but, it takes more time than the single-channel structure. Since the features extracted by different channels may have different degrees of relevance to the classification task, and there may be redundant information between them, a large number of features can affect the time performance and accuracy of the overall model. Therefore, we considered selecting and fusing the features from both correlation and redundancy perspectives. After adding feature selection and fusion algorithms, the average test accuracy of the multi-channel structure improved by more than 10%, reaching 0.942, while the time consumption only increased by 0.87 seconds. Considering both accuracy and time consumption, we think that using a multiple-channel structure and a feature fusion algorithm is necessary.