esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The generalization error of conventional support vector machine (SVM) depends on the ratio of two factors; radius and margin. The traditional SVM aims to maximize margin but ignore minimization of radius, which decreases the overall performance of the SVM classifier. However, different approaches are developed to achieve a trade-off between the margin and radius. Still, the computational cost of all these approaches is high due to the requirements of matrix transformation. Furthermore, a conventional SVM tries to set the best hyperplane between classes, and due to some robust kernel tricks, an SVM is used in many non-linear and complex problems. The configuration of the best hyperplane between classes is not effective; therefore, it is required to bind a class within its limited area to enhance the performance of the SVM classifier. The area enclosed by a class is called its Minimum Enclosing Ball (MEB), and it is one of the emerging problems of SVM. Therefore, a robust solution is needed to improve the performance of the conventional SVM to overcome the highlighted issues. In this research study, a novel weighted radius SVM (WR-SVM) is proposed to determine the tighter bounds of MEB. The proposed solution uses a weighted mean to find tighter bounds of radius, due to which the size of MEB decreases. Experiments are conducted on nine different benchmark datasets and one synthetic dataset to demonstrate the effectiveness of our proposed model. The experimental results reveal that the proposed WR-SVM significantly performed well compared to the conventional SVM classifier. Furthermore, experimental results are compared with F-SVM and traditional SVM in terms of classification accuracy to demonstrate the significance of the proposed WR-SVM.  
radius-margin error bound; weighted-radius support vector machine; minimum enclosing ball; support vector machine (SVM) +
Footnote †: journal: Applied Sciences
## 3 Scaling Laws of Scheduling Gain--Perfect CSI
### Individual Rate Analysis
For the ZF receiver, because \(\left|\mathbf{w}_{k}^{H}\mathbf{h}_{k}\right|^{2}=1/\left\|\mathbf{g}_{k} \right\|^{2}\) and \(\left|\mathbf{w}_{k}^{H}\mathbf{h}_{j}\right|^{2}=0\) for \(\forall j\
eq k\), the SINR is obtained as  
\[\log_{2}\left(1+\gamma_{k}\right)=\log_{2}e\left(\log\left(1+\mu_{ \gamma}\right)+\sum_{n=1}^{\infty}\frac{\left(-1\right)^{n-1}}{n}\frac{\left( \gamma_{k}-\mu_{\gamma}\right)^{n}}{\left(1+\mu_{\gamma}\right)^{n}}\right), \tag{4}\]4  
Because \(\gamma_{k,ZF}\) follows the chi-square distribution, with \(2(M-K+1)\) degrees of freedom [31], the first and second moments of the SINR can be represented in terms of \(M\) and \(K\) from the MGF of a chi-square distribution as follows:  
\[\log_{2}\left(1+\gamma_{k}\right)=\log_{2}e\left(\log\left(1+\mu_{ \gamma}\right)+\sum_{n=1}^{\infty}\frac{\left(-1\right)^{n-1}}{n}\frac{\left( \gamma_{k}-\mu_{\gamma}\right)^{n}}{\left(1+\mu_{\gamma}\right)^{n}}\right), \tag{4}\]5By substituting (13) into (7), we can approximate the variance of the individual rate for the ZF receiver as:  
\[\log_{2}\left(1+\gamma_{k}\right)=\log_{2}e\left(\log\left(1+\mu_{ \gamma}\right)+\sum_{n=1}^{\infty}\frac{\left(-1\right)^{n-1}}{n}\frac{\left( \gamma_{k}-\mu_{\gamma}\right)^{n}}{\left(1+\mu_{\gamma}\right)^{n}}\right), \tag{4}\]6  
From the analysis, the following scaling laws of the individual rate with perfect CSI can be observed. For the MF receiver, the variance of the individual rate \(\kappa_{2,MF}\) is a monotonically increasing function of \(M\), which then scales with \(\mathcal{O}(1)\) as \(M\rightarrow\infty\). Conversely, the variance of the individual rate for the ZF receiver \(\kappa_{2,ZF}\) decreases with \(\mathcal{O}(\frac{1}{M})\) and converges to zero as \(M\) increases. Thus, the analysis indicates that the channel hardening effect occurs only for the ZF receiver, while the fluctuation of mutual information for the MF receiver becomes large as the number of antennas increases.
## 3 Design of the Wide Passband SIW-SSPP Filters with Multi-Sub-Bands  
To achieve a multi-band filter operating in the 5G n257, n258, and n261 frequency bands, this section first describes the proposed wide bandpass filter structure covering 24-30 GHz, which is based on the hybrid SIW-SSPP structure in Section 2. Then the wideband filter structure is improved by loading an Archimedean spiral to obtain a single wide bandpass SIW-SSPP filter of optimized performance with improved S-parameters in the passband and enhanced roll-off rate at high frequencies, which significantly upgrades the overall filter performance. Finally, the multi-sub-band filter design and the final filter structure are given.
## 3 Experimental Results
### Energy Efficiency of Locomotion  
As a second metric, the energy efficiency \(E_{E}\) corresponds to the distance \(D\) that the fishbot can swim autonomously with a given energy (e.g., stored in its batteries) \(E_{B}\):  
\[E_{E}=D/E_{B} \tag{8}\]  
Equation (8) can be rewritten as  
\[E_{E}=(V\times T)/(I_{AVR}\times U_{CC}\times T) \tag{9}\]  
and  
\[E_{E}=V/(I_{AVR}\times U_{CC}) \tag{10}\]  
where \(T\), \(V\), \(I_{AVR}\), and \(Ucc\) in Equation (9) denote the time that the robot would eventually travel before it completely exhausts the available energy, swimming speed (obtained from the optical tracker, as elaborated in the previous Section 3.2), average electrical current consumed by the robot during the trial (obtained from the current sensor connected serially to the power supply line of the robot, as illustrated in Figure 8), and constant power supply voltage (5 V), respectively. In our experiments, we obtained the energy efficiency according to Equation (10) as the ratio of the speed of locomotion to the electrical power consumed by the robot during the trial.  
As shown in Figure 10, similar to the results obtained for the maximum speed of locomotion, compared to the fully actuated robot, the limited underactuation results in improved energy efficiency. As Figure 10c depicts, compared to the fully actuated robot, a maximum increase in energy efficiency of about 1.37 times is achieved for the maximum angle of underactuation \(\delta=6.3^{\circ}\) at a frequency of oscillation of 0.8 Hz. For the whole range of tested frequencies, however, and for all tested values of the maximum angle of underactuation \(\delta\), the fishbot featuring a limited underactuated fin is superior (in terms of its energy efficiency) to that with the fully actuated joint (Figure 10a,b).  
Analogous to the results obtained for the maximum speed of the robot, as Figure 10c illustrates, the fully underactuated joint (\(\delta=180^{\circ}\)) is somehow superior to the fully actuated one (\(\delta=0^{\circ}\)) for lower frequencies of oscillations (up to 1 Hz). As the frequency increases, however, the energy efficiency of the robot featuring a fully underactuated joint deteriorates sharply and becomes inferior to both fully underactuated and limited underactuated robots.
## Appendix C Optimal Parameter Configuration of the TRSA Model on Two Datasets  
For easy understanding, Table A2 shows the important mathematical notations used throughout the paper.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameters** & **Descriptions** & **Values** \\ \hline LEARNING\_RATE & the initial learning rate of the model & \(2\times 10^{-5}\) \\ \hline BATCH\_SIZE & num. of training samples in one session & 8 \\ \hline EPOCH & num. of iterations & 15 \\ \hline MAX\_SEQUENCE\_LENGTH & the maximum number of tokens contained in the news required by model & 70 \\ \hline LEN\_COM & the maximum number of posts associated with the news required by model & 50 \\ \hline NHEADS & number of heads with multi-head attention & 8 \\ \hline LSTM\_hidden size & the number of hidden units in the LSTM, which are used to control the dimensions of \(\mathbf{h_{i}}\) & 384 \\ \hline GAT\_hidden size & the number of hidden units in the GAT, which are used to control the dimensions of \(\mathbf{m_{i}}\) & 96 \\ \hline Multi\_Head Attention\_outsize & the number of hidden units in Multi\_Head Attention, which are used to control the dimensions of \(\mathbf{e_{i}}\) & 200 \\ \hline Capsule\_out\_dim & the number of hidden units in category capsule, which are used to control the dimensions of \(\mathbf{v_{j}}\) & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Important notations and descriptions.  
Further, we analyzed the changes in model performance under different damping coefficients \(\alpha\) (described in Section 4.1.2 and the optimal damping parameter determined. The value range of the damping coefficient is \([0,1]\)). For \(\alpha=0\), random walkers only jump according to user authority, regardless of the actual dispersion network (i.e., the credibility of the obtained evidence is only determined by user own authority). For \(\alpha\to 1\), random walkers largely ignore user authority and jump along the actual dispersion network. In Figure 10, one can see that on the PHEME dataset, when \(\alpha=0.8\) is used, the model performance is optimal, and on the CED dataset the optimal value of the damping coefficient is \(0.7\).
## 3 Methodology  
### AGB Upscaled Procedure  
Figure 2 shows the procedure and evaluation of upscaling AGB based on the gradient boosting regression trees (GBRT) model. The first step was to prepare the MODIS NDVI product (1 km), AVHRR NDVI product (5 km), CMDF Ta, P, Rs (1 km and 5 km), elevation dataset (1 km and 5 km), and ground AGB observation dataset. The second step was upscaling grassland AGB from ground observations to a 1 km spatial resolution based on GBRT model using all the input and output variables. Note that in the training process, the input variables were MODIS NDVI, P, Ta, Rs, and elevation at site scale, and the output was the ground AGB observation dataset collected at 75 sampling sites. Then, the developed model was applied to generate AGB with a 1 km spatial resolution from 2009 to 2013. Then, the upscaled AGB from MODIS NDVI, P, Ta, Rs, and elevation with a 1 km spatial resolution was evaluated using the ground AGB observations at the other 50 sampling sites as reference values. The third step was upscaling grassland AGB from a 1 km to 5 km spatial resolution using all the input and output variables based on the GBRT model. Note that in the training process, the input variables were AVHRR NDVI, P, Ta, Rs, and elevation with a 5 km spatial resolution, and the output was the MODIS-derived AGB with a 5 km spatial resolution from 2009 to 2011, which was aggregated from the MODIS-derived AGB with a 1 km spatial resolution using a simple average method. Then, the developed model was applied to generate AGB with a 5 km spatial resolution during 1982-2018. The upscaled AGB from AVHRR NDVI, P, Ta, Rs, and elevation with 5 km spatial resolution was evaluated using the MODIS-derived AGB with a 5 km spatial resolution during 2012-2013 as reference values. The final step was to detect the spatiotemporal variations in grassland AGB in the TRHR.
## 3 Wireless Communication Schemes for Aircraft
### AF Scheme  
The AF communication mechanism is similar to the DF. However, the relay node receives a signal from the source node, not to decode the signal but to amplify the signal and forward it to the destination node. Thus, the received signal at the destination node can be expressed by:  
\[y_{ij}=\beta\sqrt{P_{i}}h_{ij}x+n_{ij} \tag{27}\]  
where \(\beta\) is amplification factor that is given by:  
\[\beta=\frac{\sqrt{P_{i}}}{\sqrt{P_{i}\left|h_{sr}\right|^{2}+N_{0}}}. \tag{28}\]  
According to this communication mechanism, the total energy consumption [43] of the AF can be expressed by:  
\[E_{AF}= \frac{\left(P_{AMP}+P_{TX}+2P_{RX}\right)\kappa}{\theta R_{b}} \tag{29}\] \[+\frac{\left(P_{AMP}^{*}+P_{TX}+P_{RX}\right)\kappa}{\theta R_{b}}\]  
where \(P_{AMP}^{*}=v\left(P_{i}-\left(P_{i}\gamma_{SR}+N\right)\right)\) is the transmission power from the relay node and \(\gamma_{SR}\) is the pathloss between the source node and the relay node. The first term represents the energy consumption when the source node transmits a packet to the source node and destination node, and the second term is the energy consumption when the relay node conveys the packet.  
In terms of the throughput of the AF, it is the same as the DF.
# Resonance-Driven Passive Folding/Unfolding Flapping Wing Actuator
## 2 Structure and Modelling
### Modelling  
In this section, we describe a simplified theoretical model of the system to clarify the condition of the folding/unfolding motion. In Section 4, we will simulate the folding/unfolding characteristics based on this model and compare it with the measured result. Figure 2 shows the simplified system used to derive the model. The piezoelectric actuator is modeled as a 1 degree-of-freedom (DoF) spring, \(K_{a}\), with the inertia of the actuator with respect to \(\phi\) denoted as \(J_{a}\). The piezoelectric actuation torque is represented by \(\tau_{a}\), the mass of the wing is \(M_{w}\), and the length of the wing is \(L_{w}\). We consider that three forces act on the wing: centrifugal, lift, and gravitational forces. The lift force was modeled as a perpendicular force to the wingspan, \(F_{L}\); the centrifugal forces due to the stroke motion and gravitational force are represented by \(F_{C}\) and \(F_{g}\), respectively. Moreover, the spring for the folding/unfolding motion has a spring constant of \(K_{h}\).  
The main approximations used to derive the simplified model are summarized as follows.  
* Piezolectric torque \(\tau_{a}\) is approximated to be proportional to the applied voltage; any nonlinear effects of piezoelectricity were neglected.
* Because the CFRP leading edge rod contributes to most of the wing mass, the wing is regarded as a uniform rigid bar in consideration of its inertial characteristics.
* Although the wing has a biomimetic, rounded shape, we approximate it as a simple rectangle to derive the aerodynamic lift force.
* Because the stroke frequency is much faster than the response speed of the folding/unfolding motion, the folding/unfolding system is subjected to the time-averaged forces of \(F_{L}\) and \(F_{C}\).
* We only consider an equilibrium state and not a transient response for the folding/unfolding motion
* The stroke motion system is simply modeled as a second-order system. Thus, the drag force of the wing is approximated as a linear damping force. In addition, we neglect any geometric nonlinearities.  
Figure 1: Structure of the flapping-wing actuator with passive folding/unfolding mechanism: (**a**) photograph of the actuator and spring for folding/unfolding; (**b**) stroke motion and (**c**) folding/unfolding motion.  
The equilibrium folding angle is given by the torque balance equation:  
\[\overline{T_{C}}+\overline{T_{L}}+T_{g}=K_{h}\theta, \tag{1}\]  
where \(T_{C},\ T_{L}\), and \(T_{g}\) are torques due to the centrifugal, lift, and gravitational forces, respectively, which are formulated in the following text. The bar symbol indicates time averaging. These torques also depend on \(\theta\) and other parameters related to the applied driving signal.
## 2 Materials, Experiments, and Numerical Methods  
### Material and Experimental Procedures  
Commercially available 1050 Al alloy was investigated in the present study. This technically pure alloy (99.5 wt% of Al) contains minimal traces of Fe (max. 0.4 wt%) and Si (max. 0.25 wt%), while the concentration of other alloying elements such as Cu, Mg, Mn, V, and Ti is even lower (\(\sim\)0.05 wt%). The volume fraction of inclusions (large particles, secondary phases with a diameter above one micrometer) is very limited due to the low concentration of alloying elements and, therefore, the bulk of this material is expected to deform homogeneously, implying that the strain concentration will not occur in the vicinity of secondary phases. It should be mentioned here that the homogeneity of deformation cannot be guaranteed through the whole process since both necking in tension and inhomogeneous shear strain distribution in rolling account for the inhomogeneous material flow leading to heterogeneous strain/stress distribution.  
The alloy under consideration was investigated via tensile testing to derive the necessary material constants for various constitutive laws. The simulation of cold symmetric and asymmetric rolling processes by finite element model allowed validation of material parameters. Prior to deformation, the as-received material was heat-treated at 550 \({}^{\circ}\)C for 3 min in order to ensure a fully recrystallized and stress-free state. As was already reported, heat treatment at elevated temperatures for a short time ensures a fully recovered microstructure [17].  
The tensile test was performed by Zwick Roell Z100 with a speed of 20 mm/min according to EN ISO 6892-1 standard. The gauge length of the tensile sample (\(l_{0}\)) was 50 mm while the initial cross-section (\(A_{0}\)) was 18.44 mm\({}^{2}\). The load cell and the longitudinal extensometer provided information on the change of force and elongation during the test.  
The cold rolling was performed on a laboratory rolling mill with a roll diameter of 150 mm. Both symmetric and asymmetric rolling trials were carried out. In order to introduce an asymmetry to the conventional rolling process, the angular velocity of the upper and lower rolls was varied.
## 4 Implementation Results and Discussion
### HPC2 Implementations  
If resistance against multivariate attacks is demanded, the KSA shows the worst performance in most categories. It should therefore only be considered in this scenario if the combination of latency and throughput are the primary optimization goals. The SA can effectively be utilized if the latency of the adder and the area requirements are the most important factors. The logic and memory requirements are significantly lower and the randomness requirement is less than half when compared to the KSA, although the throughput is slightly lower. If latency and memory are less important and the randomness consumption is critical, the BKA should be preferred. The logic area is lower and the randomness requirement can be further reduced by almost 40 % in comparison to the SA. Due to the differing asymptotic complexity this difference increases for larger widths.
## 5 Conclusions  
The best results for seizure detection were obtained with features related to iEEG signal energy, as well as features related to chaos, the Lyapunov exponent, and the fractal dimension. The application of CNN and LSTM networks yielded significantly better results (CNN: Accuracy of 0.99, precision of 0.98, sensitivity of 1, and specificity of 0.99; LSTM: Accuracy of 0.98, precision of 0.96, sensitivity of 1, and specificity of 0.99). The results indicate that even CNN and LSTM networks with a simple structure are capable of handling the problem of epileptic seizure detection. The use of the gradient-weighted class activation mapping algorithm identified iEEG signal fragments that played a significant role in seizure detection. The study was conducted based on iEEG signals that contained well-described and relatively easy-to-detect cases of epileptic seizures. To assess the detection quality more accurately, future experiments should be repeated on a much larger dataset containing a greater number of examples recorded from a larger number of patients. In the future, it is necessary to expand the research to explore the possibility of using shorter iEEG signal windows.  
Conceptualization, A.M., A.R. and M.K.; methodology, M.K., A.R. and A.M.; software, M.K.; validation, M.K. and A.M.; formal analysis, A.M.; investigation, A.M. and M.K.; resources, M.K.; data curation, A.M., A.R. and M.K.; writing--original draft preparation, A.M. and M.K.; writing--review and editing, A.M. and M.K.; visualization, M.K.; supervision, A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable, a publicly available database was used.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 3 Model Structure
### Overall Model Implementation  
The overall flow chart of the Chinese medical entity recognition model of the improved Tri-Training algorithm proposed in this paper is shown in Figure 2 below: it mainly includes training of labeled data, expanding the sample set of labeled data with unlabeled data, and integrating learning to obtain the final classifier model.  
In the process of this algorithm, \(L\) denotes the labeled training dataset, U denotes the unlabeled dataset, and \(C_{i}(i=1,2,3)\) denotes the base classifier model, where base classifier \(C_{1}\) is the BERT-IDCNN-CRF model (BBC), base classifier \(C_{2}\) is the BERT-BiLSTM-CRF model (BBC), and base classifier \(C_{3}\) is the BERT-IDCNN-BiLSTM-CRF model (BIBC).  
According to the model structure proposed in this paper, the specific algorithmic procedure is as follows.  
(1) The labeled training set \(L\) is evenly divided into three sub-training sample sets \(L_{i}(i=1,2,3)\).  
(2) The three base classifiers \(C_{i}(i=1,2,3)\) are trained using three labeled sub-training samples \(L_{i}(i=1,2,3)\), where the three base classifier models are different.  
(3) Use the unlabeled dataset \(U\) to expand the sub-training set \(L_{i}\) of the base classifier; for the base classifier \(C_{1}\), randomly select sample instances from \(U\) if the labeled results of the base classifiers \(C_{2}\) and \(C_{3}\) on the sample instance are the same, the prediction result is considered to be credible, the sample instance is removed from \(U\) and placed in the sub-training sample set \(L_{1}\) to form a new sub-training set \(L_{1}^{{}^{\prime}}\). If the labeled results of \(C_{2}\) and \(C_{3}\) are different, the sample instance is removed from \(U\).  
(4) Repeat step (3) for base classifiers \(C_{2}\) and \(C_{3}\) to expand their sub-training sets to obtain new sub-training sets \(L_{2}^{{}^{\prime}}\) and \(L_{3}^{{}^{\prime}}\).  
(5) Retrain the base classifiers \(C_{1}\), \(C_{2}\), and \(C_{3}\) using the new sub-training sets \(L_{1}^{{}^{\prime}}\), \(L_{2}^{{}^{\prime}}\), and \(L_{3}^{{}^{\prime}}\), respectively.  
(6) When the unlabeled sample set \(U\) is empty, terminate the algorithm to obtain three final classifier models, \(C_{1}\), \(C_{1}\), and \(C_{1}\); otherwise, repeat steps 3-6 for training.  
(7) In the final model recognition stage, the integrated learning strategy adopted is a voting method that combines the simple voting method and weighted voting method. The three base classifiers are integrated into the final classifier model according to this voting method.