esa-sceva/satcom-chunk-collection huggingface dataset:
# Big Data Perspective and Challenges in Next Generation Networks
## 3 Data Analytics Perspective on Next Generation Networks
### Significance of Big Data Analytics
Lastly, this meaningful data are given to the network optimization layer. This layer is able to identify issues and intelligence decision, i.e., efficiently allocation of resources. Hence, the big data-driven model can do performance optimization in the next-generation networks. [PERSON] et al. [53] proposed an architecture for optimizing next-generation network with the help of mobile big data. In this architecture, the authors analyzed the big data collected from subscribers as well as networks and proposed a dynamic algorithm for bandwidth distribution. In the proposed approach, the information obtained from big data is used to form a cluster of the subscribers. A set of users accessing the same content, are put into the same cluster. Initially, the cluster head is chosen. Then, the users adjacent to the cluster head and attempting to access contents same as those accessed by the cluster head, become members of the cluster. Finally, resources are assigned to the cluster head which then distributes these amongst the cluster members. Hence, the throughput of the network will be increased with the aid of such architecture. This framework is applicable in scenario where several users try to access the same content. For example, if a football match is being tele-casted on the internet and is being watched by multiple users, the architecture would be helpful to ensure the best QoS for all the users. Self-organizing network (SON) in next generation networks is another dimension, which can be optimized with big data analytics. A SON architecture based on big data was proposed by [PERSON] et al. [54]. With the help of the proposed SON architecture, a typical SON was transformed from reactive to pro-active state, which is useful for next generation networks. Hence, this proactive  
Figure 5: Big data flow diagram. big data based SON architecture will fulfill close-to-zero requirements for next-generation networks and maximize throughput of the network. The goal of this architecture is to build end-to-end visibility of network through extraction of meaningful information from big data.
## 4 Discussion
### Impact of Maximum Light Use Efficiency on GPP Estimate  
The maximum light use efficiency is one of most important parameter in light use efficiency model to affect the GPP estimates. [PERSON] _et al._[29] suggested that an important reason for underestimated GPP in croplands was that the maximum light use efficiency was underestimated. Using the optimized maximum light use efficiency (17.66 gC/MJ/8-day) replacing MOD17 algorithm recommended value (5.44 gC/MJ/8-day), [PERSON] _et al._[28] found RMSE decreased from 80.87% to 48.43% and bias changed from 4.83 gC/m\({}^{2}\)/8-day to 2.85 gC/m\({}^{2}\)/8-day when estimating GPP, indicating that adjusting the MOD17 maximum light use efficiency is necessary for the GPP estimate of croplands. The maximum light use efficiency of EBF is also underestimated in MOD17 algorithm except that in croplands. But for MF, ENF and grasslands, the maximum light use efficiency values in MOD17 algorithm were close to the optimized values. In addition, this study suggested that optimized maximum light use efficiency was effective for reducing the bias and RMSE between estimated GPP and flux derived GPP (Table 2), owing to that it changed the magnitude of GPP value, however, only optimized maximum light use efficiency did not change the R\({}^{2}\) of estimated GPP with flux derived GPP.
# Fast Normalization for Bilinear Pooling via Eigenvalue Regularization
## 4 Experiments
### Experiments on Small-Scale Datasets
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Backbone** & **A** & **B** & **C** & **Avg.** & **IS (ms)** \\ \hline BCNN [13] & & 83.9 & 84.0 & 90.6 & 86.2 & **167.3** \\ IB-CNN [15] & & 88.5 & 85.8 & 92.0 & 88.8 & 177.5 \\ MoNet [17] & & 86.7 & 86.0 & 90.5 & 87.7 & 1048.6 \\ MPN-COV [57] & & 86.1 & 82.9 & 89.8 & 86.3 & - \\ GP [46] & & 89.8 & 85.8 & 92.8 & 89.5 & - \\ DeepCOV [21] & VGG-16 & 88.7 & 85.4 & 91.7 & 88.6 & - \\ DeepKSPD [52] & & 90.0 & 84.8 & 91.6 & 88.8 & - \\ DropCov [10] & & 90.3 & 81.6 & 89.5 & 87.1 & **167.3** \\ PSRM [9] & & 90.0 & 86.3 & 92.3 & 89.5 & 221.4 \\ iSICE [8] & & **92.2** & 86.5 & **94.0** & **90.9** & 187.2 \\ RegCov (Ours) & & 90.6 & **86.8** & 92.3 & 89.9 & **167.3** \\ \hline CBP [16] & & 81.6 & 81.6 & 88.6 & 83.9 & **96.8** \\ DeepCOV [53] & & 83.9 & 86.0 & 85.0 & 85.0 & - \\ DropCov [10] & & 88.5 & 78.7 & 90.0 & 87.1 & **96.8** \\ PSRM [9] & & 92.3 & **87.0** & **94.1** & **91.1** & 99.8 \\ iSICE [8] & & **92.7** & 85.9 & 93.5 & 90.7 & 110.3 \\ GBP [32] & ResNet-50 & & 89.6 & 87.8 & 93.5 & 90.3 & - \\ PoW-EMLR [70] & & 90.0 & 84.9 & 93.8 & 89.6 & 108.8 \\ Scale PoW-EMLR [70] & & 89.9 & 84.8 & 93.3 & 89.3 & 109.9 \\ PoW-TMLR [70] & & 88.4 & 77.8 & 73.9 & 80.0 & 108.1 \\ RegCov (Ours) & & 91.8 & 86.4 & **94.1** & 90.8 & **96.8** \\ \hline DropCov [10] & & 89.7 & 81.7 & 91.6 & 87.7 & **148.9** \\ iSICE [8] & ResNet-101 & **92.9** & 86.6 & 93.6 & 91.0 & 155.6 \\ RegCov (Ours) & & 92.2 & **87.1** & **94.2** & **91.2** & **148.9** \\ \hline DropCov [10] & & 91.1 & 87.4 & 93.8 & 90.8 & **124.2** \\ iSICE [8] & ConvNext-T & 90.4 & 86.7 & 93.1 & 90.1 & 141.3 \\ RegCov (Ours) & & **92.1** & **89.1** & **94.5** & **91.9** & **124.2** \\ \hline DropCov [10] & & 89.8 & 86.2 & 90.3 & 88.8 & **158.0** \\ iSICE [8] & Swin-T & 89.6 & 86.5 & 91.3 & 89.1 & 175.4 \\ RegCov (Ours) & & **90.1** & **86.6** & **92.2** & **89.6** & **158.0** \\ \hline DropCov [10] & & 90.1 & 87.1 & 92.8 & 90.0 & **374.4** \\ iSICE [8] & Swin-B & **92.9** & 88.3 & **93.3** & **91.5** & 382.3 \\ RegCov (Ours) & & 91.6 & **88.6** & 92.7 & 91.0 & **374.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of our method RegCov with the state-of-the-art bilinear pooling methods on the three fine-grained classification datasets: Aircrafts (A), Birds (B) and Cars (C). Average (Avg.) accuracy in % and inference speed (IS) for one mini-batch (32 images) on an RTX-3090 GPU are also listed.
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 2 Earthquake and Scour Combination in the Failure Probability of Bridge
### Application for a Practical Project
#### 2.2.3 IDA Analysis under the Combined Action of Earthquake and Scour  
After the determination of failure component, failure mode, and the position of the critical section under the combined action of earthquake and scour, the selected PGA was input as the ground motion parameter into the established FE model of the example bridge under different scour depths. To ensure that the relationship between PGA and seismic response can be reflected accurately, 0.1-0.5 g was selected as the variation range of PGA with 0.05 g as the step size, which can guarantee the bridge structure to enter failure stage under seismic action so as to obtain the failure probability under the combined action of scour and earthquake. Amplitude modulation of PGA for 48 seismic ground motions were implemented in accordance with the above range and step size in order to carry out IDA analysis [42; 43; 44], as shown in Figure 14. The average values of the maximum bending moment of pile foundation under different seismic waves and different scour depths were extracted, as shown in Figure 15. It can be concluded that the bending moment of the pile foundation increased rapidly with the growth of scour depth and the speed at which the pile section enters plastic state improved with raising PGA. It proved the weakening effect of soil restraint on pile foundation due to the action of scour, which results in an increase of seismic vulnerability of the pile foundation components.  
Figure 14: Response spectra of 48 time-history curves (PGA = 0.1 g).  
Figure 15: IDA curve of seismic action under different scour conditions.
# Addressing the Algorithm Selection Problem through an Attention-Based Meta-Learner Approach
## 3 Solution Model
### Multi-Head Attention for Problem-Algorithm Mapping
The objective here is to generate, once again, an improved representation of the \(h_{i}^{(0)}\) vector such that, by leveraging the attention mechanism, it is weighted to better encode the features characterizing the given problem instance in regards to the algorithm space mapping. As mentioned, the attention block is similar to the encoder block used in the transformer [9]. However, it differs from the original model in that, since there is no use in capturing information from the input order, no positional encodings are added [9]. In addition to that, the attention block follows the original encoder block implementation [9], consisting of a multi-head attention layer (MLA) and a dense layer (FF), each with a corresponding skip-connection and batch normalization (BN) [38; 39]. This work uses \(M=8\) attention heads as the optimal value found when optimizing the model hyperparameters via Bayesian optimization, as described in Section 4. This work uses only one block; however, as for the original transformer architecture, \(n\) attention blocks can be defined sequentially so that \(h_{i}^{(\ell)}\) represents the output obtained after a particular block \(\ell\) (where \(\ell\in 1,\ldots,n\)). Nevertheless, since we only use one block, the initial linear projection \(h_{i}^{(0)}\) is equivalent to the general input of an attention block \(h_{i}^{(\ell-1)}\), as defined by [PERSON] et al. [38]. Hence, following their notation, where \(\hat{h_{i}}\) and \(h_{i}^{(1)}\) are the outputs from the MHA and FF sub-layers, respectively, both sub-layers can be simplified and expressed as in Equations (1) and (2). \[\hat{h_{i}}=\text{BN}(h_{i}^{(0)}+\text{MHA}_{i}(h_{i}^{(0)})) \tag{1}\] \[h_{i}^{(1)}=\text{BN}(\hat{h_{i}}+\text{FF}(\hat{h_{i}})) \tag{2}\]
* Finally, in the third step, the output layer takes the resulting \(h_{i}^{(1)}\) as input representation and generates the final probability vector via log softmax after applying global average pooling.
## 3 Demand-Response Aggregator Frameworks in Future US Electricity Markets
### Prosumer-Aggregator Market Bidding Frameworks  
The problem of market bidding, often formulated as a separate game, considers the market-clearing mechanism with only the total market capacity cleared and the market-clearing prices as the available information. In electricity markets, firms (power producers, aggregators, etc.) compete for both quantity and price, and they all play simultaneously. However, bidding strategies are usually simplified. Examples include auction-like mechanisms [207], piece-wise, constant-quantity price-bidding mechanisms [213; 246], a finite pool of past (quantity and price) successful actions, and sampling bids from a nominal bidding curve and running scheduling algorithms [247], among others [246]. Market bidding strategies have also been developed using RL [248; 249; 250]. In the other literature, players decide to form tacit collusion by withholding capacity [251] in order to minimize a response from another firm (avoid the opportunity to price cut an opponent) without explicitly stating so. These are modeled as auction games with repeated play where the firm that sets the initial price is the leading firm. In [252], RL is used, and the market mechanism is modeled as a double auction between energy sellers and consumers. In [252], microgrids with private information compete in the energy market using RL. Stochastic optimization approaches, such as MILP [123; 139; 253] and scenario-based stochastic programming [254; 255], have been proposed before, in addition to robust optimization [256; 257], theoretical information gap decisions [258; 259], and genetic algorithms [260; 261] according to [69].
## 6 Discussion  
Robotics and autonomous driving both face a basic challenge with point cloud registration. It is used in medical imaging, panorama stitching, object localization and recognition, motion estimates, 3-D reconstruction, and object recognition. Common methods for registration with unknown or uncertain correspondences depend on the existence of an initial hypothesis for the unknown transformation. These methods could stop working at any time and return estimates that are arbitrarily far from the transformation of the ground  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loss** & **Precision** & **Accuracy** & **Recall** & **F1-Score** \\ \hline SuperGlue loss & 54.6 & 75.7 & 47.6 & 0.509 \\ Triplet loss & 56.3 & 77.8 & 51.6 & 0.538 \\
**Gap loss** & **80.4** & **90.2** & **76.1** & **0.782** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation of loss function.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Descriptor Encoder** & **Precision** & **Accuracy** & **Recall** & **F1-Score** \\ \hline PointNet & 59.4 & 80.3 & 62.3 & 0.608 \\ Global Desc. & 79.1 & 89.7 & 75.2 & 0.771 \\ MDGAT & 70.3 & 86.7 & 69.4 & 0.698 \\
**DFGAT** & **80.4** & **90.2** & **76.1** & **0.782** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation of descriptor encoder.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mather** & **Precision** & **Accuracy** & **Recall** & **F1-Score** \\ \hline DFGAT variant 1 & 77.9 & 89.6 & **76.3** & 0.771 \\ DFGAT variant 2 & 78.2 & 89.1 & 73.6 & 0.758 \\
**DFGAT** & **80.4** & **90.2** & 76.1 & **0.782** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation of GAT.  
truth. In our proposed study, the keypoint detector, feature descriptor, and the dynamic graph attention network are the three components of our proposed DFGAT. The detector uses deep learning to achieve repeatability, specificity, and computing efficiency in its capacity to identify key locations. The learned descriptor can preserve intricate local geometric patterns and is rotation invariant. The graph architecture focuses on useful information with self and cross-attention. To demonstrate the efficiency and uniqueness of our network, experiments using the KITTI odometry are carried out. Our methodology surpasses previous registration methods with a success ratio of almost 99.88 % and achieves the best results in evaluation matching metrics when compared with existing methods.
# Pantograph-Catenary Interaction Prediction Model Based on SCSA-RBF Network
## 3 Pantograph-Catenary Interaction Prediction Model
### Model Architecture
In this paper, the above prediction method for the time-domain signal of the dynamic response for the pantograph-catenary system, based on the SCSA-RBF network and considering the physical process of pantograph-catenary interaction, is referred to as the pantograph-catenary interaction prediction model (Figure 2). To evaluate the generalization ability of the pantograph-catenary interaction prediction model, set C was designated as the test set to calculate the determination coefficient between the model-predicted value and the FEM-calculated value of the time-domain signals of the panhead vertical acceleration and pantograph-catenary contact force in the test set. The average values and the interval within the probability of 0.95 of these coefficients were obtained. According to the trade standard EN50367:2020 [28], the SDCF, denoted as \(\sigma\), is a critical index to evaluate the current collection quality and represents the oscillation degree of the time-domain signal of the contact force. Based on the model-predicted value \(\widehat{F}_{\text{c}}\left(t_{j}\right)\) and the FEM-calculated value \(F_{\text{c}}(t_{j})\) of the time-domain signal of contact force, their standard deviation \(\hat{\sigma}_{q}\) and \(\sigma_{q}\) were calculated. In addition, the determination coefficient between the \(\hat{\sigma}_{q}\) and the \(\sigma_{q}\) was calculated, and the interval of the model errors \(\hat{\sigma}_{q}-\sigma_{q}\) within the probability of 0.95 was obtained. Among them, the subscript \(q\) represents the sample \(\mathbf{X}_{q}\) (\(q=1\), \(\ldots\), and \(N_{q}\)) in the test set.
# A Study on the Surrogate-Based Optimization of Flexible Wings Considering a Flutter Constraint +
## 2 Methodology
### Surrogate Model Training, Validation, and Testing  
This subsection provides an overview of surrogate model development, from training to testing. The goal is to find a machine learning-based surrogate model that meets computational needs and offers a high predictive accuracy. Our criteria for selecting the model include accuracy, efficiency, and robustness.  
We considered various regression-based machine learning models from the scikit-learn Python library [31], including Bayesian Ridge, Decision Tree Regressor, Extra Trees Regressor, Lasso, ARD Regression, and Linear Regression. For the model generation, we considered a 60%-30%-10% split for training, validation, and testing, respectively, based on experimenting with different combinations for generating surrogates of \(C_{L}\), \(C_{D}\), mass, and flutter speed. A random procedure was followed to assign the data points to each  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**DVs** & **Initial** & **LB** & **UB** & **Units** \\ \hline AR & 6.67 & 6 & 16 & - \\ \(\Lambda\) & 0 & 0 & 40 & deg \\ GJ & 0.99 \(\times\) 10\({}^{6}\) & 0.70 \(\times\) 10\({}^{6}\) & 1.70 \(\times\) 10\({}^{6}\) & N.m\({}^{2}\) \\ \(\alpha\) & 0.05 & -5 & 15 & deg \\ \hline \hline \end{tabular}
\end{table}
Table 1: The design variables and respective boundaries used for the dataset generation and optimization problem.  
set. These machine learning models were set with random hyperparameters to gauge their baseline performance. Each was then assessed on the validation subset.  
The methods were evaluated for flutter speed, mass, drag, and lift coefficients, i.e., the output parameters from SHARPy that were used for the optimization problem definition. All the considered models were able to well represent the structural mass, drag, and lift coefficients, with values of R\({}^{2}\) higher than 0.99 and of Root Mean Squared Error (RMSE) lower than 1%. However, the models evidenced difficulty in provide accurate predictions of flutter speed, as shown in Table 2.  
While, for this model, only the Decision Tree method approached the performance of Extra Trees (still remaining inferior), for the other models, the difference between the methods was minimal. With these considerations in mind and for consistency and simplicity in the workflow, we chose to use the Extra Trees Regressor as the surrogate model method for all output variables. After determining the Extra Trees Regressor's aptitude, the training and validation processes began.  
Alternatively, another SBO strategy could be explored in the future, where new infill points are added alongside the optimization using an appropriate acquisition function and Bayesian Optimization [32]. This approach was recently applied to an aero-structural problem by [PERSON] et al. [33].
## 5 Conclusions  
This paper proposed a cutting energy-absorbing structure that can control the buffer force according to the actual situation and studied the influence factors of the buffer force of the metal-cutting energy-absorbing structure under high speed and large cutting depth. A calculation formula of cutting buffer force considering parameters such as material length, width and cutting inclination was obtained, providing a design basis for variable cutting-force energy-absorbing structures.  
1. A controllable buffering energy-absorbing structure whose buffering force can change with the stroke was proposed. The mechanical properties and deformation process of the material were studied by means of experiments and simulations. The influencing factors of the average force of the metal cutting buffer structure and its influence trend were described.
2. The calculation formula of an average buffer force considering material strength and friction coefficient was obtained. As \(A\) increases, the average force of the material decreases gradually. When \(W\) increases, the average force of the material gradually increases, and when the width of the cutting material reaches a value, the average force does not change significantly. \(D\) can greatly affect the properties of the material.
3. Through experiments, it was found that the buffer force of the structure will not change significantly due to the change of cutting speed. Finally, we obtained the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{D_{2}/mm}\) & \(\mathbf{W/mm}\) & \(\mathbf{A}^{D}\) & **Average Force/kN** & **Energy Absorption/MJ** \\ \hline
4 & 20 & 5 & 114.025 & 34.207 \\
4 & 30 & 5 & 252.78 & 75.8 \\
4 & 40 & 5 & 355.023 & 106.507 \\
4 & 50 & 5 & 352.693 & 105.808 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Influence of \(W\) on material properties.  
Figure 11: Three-dimensional figure of A–energy–force.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{D_{2}/mm}\) & \(\mathbf{W/mm}\) & \(\mathbf{A}^{D}\) & **Average Force/kN** & **Energy Absorption/MJ** \\ \hline
4 & 20 & 5 & 114.025 & 34.207 \\
4 & 20 & 10 & 100.066 & 30.019 \\
4 & 20 & 20 & 99.568 & 29.87 \\
4 & 20 & 30 & 77.865 & 23.359 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Influence of A on material properties.  
significant degree of influence \(W>D_{2}>A\). We fit the formula to obtain the average buffer force \(F=235\times 0.7859\times D_{2}\)\({}^{0.759}\times W^{1.2246}\times(\sin A)^{-0.1584}\).  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13031596/s1](https://www.mdpi.com/article/10.3390/app13031596/s1)]([https://www.mdpi.com/article/10.3390/app13031596/s1](https://www.mdpi.com/article/10.3390/app13031596/s1)).  
Methodology, H.W.; Software, F.Z.; Formal analysis, R.L.; Investigation, Y.X.; Writing--original draft, W.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
The authors declare no conflict of interest.