esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining Keyframes and Image Classification for Violent Behavior Recognition
## 5 Conclusions  
We combine keyframes and image classification to implement a robust and adjustable violent behavior recognition system. The number of keyframes can control the recognition efficiency of the model, while probabilistic thresholds can control the robustness and sensitivity of the system. In addition, we propose a new training method to improve the model's recognition rate. Our system performance can surpass or approach that of current violent behavior recognition systems without increasing the complexity of the model structure.  
Data curation, D.L.; Validation, D.L.; Writing--original draft, Y.B.; Writing--review & editing, D.L. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by College of Computer, National University of Defense Technology. And The APC was funded by College of Computer, National University of Defense Technology.  
The study did not require ethical approval.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Please contact [EMAIL_ADDRESS] for experimental data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Threshold = 0.5**} & \multicolumn{3}{c}{**Threshold = 0.7**} \\ \hline  & **A** & **V** & **N** & **A** & **V** & **N** \\ \hline RLVS & 94.6\% & 93.2\% & 96.00\% & 91.50\% & 89.50\% & 93.5\% \\ \hline Hockey & 98.5\% & 98.0\% & 99.0\% & 97.5\% & 96.0\% & 99.0\% \\ \hline Violent Flow & 95.0\% & 90.0\% & 100\% & 93.75\% & 87.50\% & 100\% \\ \hline \hline \end{tabular}  
A: average, V: violence, N: nonviolence, Window Length = 5.  
\end{table}
Table 7: The relationship between recognition rate and threshold.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(K\) & **RLVS** & **Hockey Fights** & **Violent Flow** \\ \hline \multirow{3}{*}{Threshold = 0.5} & 5 & 94.6\% & 98.50\% & 95.0\% \\ \cline{2-5}  & 10 & 96.75\% & 97.5\% & 95.0\% \\ \cline{2-5}  & 15 & 96.25\% & 94.0\% & 95.0\% \\ \cline{2-5}  & 20 & 96.0\% & 92.0\% & 95.0\% \\ \hline \multirow{3}{*}{Threshold = 0.7} & 5 & 91.50\% & 91.5\% & 91.5\% \\ \cline{2-5}  & 10 & 93.0\% & 98.0\% & 93.5\% \\ \cline{2-5}  & 15 & 93.75\% & 95.5\% & 95.0\% \\ \cline{2-5}  & 20 & 92.5\% & 91.5\% & 93.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The relationship between window length and recognition rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Hockey Fights** & **RLVS** & **Violent Flow** \\ \hline \hline FightNet [10] & 97.00\% & – & – \\ \hline
3D CNN [37] & 99.62\% & – & 94.30\% \\ \hline CNN + LSTM [3] & 97.10\% & – & 94.57\% \\ \hline C3D [6] & 96.50\% & – & 84.44\% \\ \hline I3D [38] & 97.50\% & – & 86.89\% \\ \hline VGG + LSTM [17] & 95.10\% & 88.20\% & 90.01\% \\ \hline Ours & 98.50\% & 94.60\% & 95.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparisons between the proposed method and others on previous datasets.
# Optimization of Effective Throughput in NOMA-Based Cognitive UAV Short-Packet Communication
## 2 System Model and Problem Formulation
### Transmission Model
Since \(u_{2}\) decodes \(x_{2}\) directly by treating \(x_{1}\) as interference, \(\varepsilon_{2}\) is equal to the effective decoding error probability \(\bar{\varepsilon}_{2}\) for \(u_{2}\), i.e., \(\bar{\varepsilon}_{2}=\varepsilon_{2}\).
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 4 Proposed Strategy
### Slice Management through Resource Transfer (SMART)
#### 4.3.1 VNF for Dynamic Provisioning  
The life time of the NSRs that are currently in service is being checked on a continual basis. As soon as an NSR's life cycle is over, VNF-M releases the physical resources that have been occupied by the NSR, enabling the next generation of NSRs to profit from the resources. For example, at time \(t\) = 0, the NSR\({}_{1}\) with a life time of 3 consumes the resources of PI and the occupied resources (i.e., e1, m1, and u1) are withdrawn at time \(t\) = 3 according to its life cycle, as shown in Figure 3a.
###### Abstract  
Silicon is one of the most abundant elements in the Earth's crust and, although it is not an essential element for plant growth, it is considered beneficial as it can help in defense against biotic and abiotic stresses. Research on the effects of foliar sprays containing silicon has been well documented for different plants. However, in olive plants there is considerably less research focused on silicon soluble foliar formulations, which differs from the application of inert materials, like kaolin. The purpose of this study was to evaluate the effects of three increasing levels of foliar-applied soluble silicon on the characteristics of olive fruit and olive oil parameters. Fruit morphology, contents of multiple polyphenols, fatty acid profiles and mineral content in olive oil, olive oil quality parameters, fruit yield, and olive oil production parameters were assessed. Results show significant effects of silicon treatments on palmitic, palmitoleic, and linoleic acids and on the content of mono- and polyunsaturated fatty acids. Polyphenols were generally more influenced by the cultivar rather than by silicon. Apigenin, phenolic acids, and flavonoids were impacted by silicon treatments. Fruit morphology was affected by silicon treatment and, generally, parameters (fruit and seed length, width, and mass) increased at higher silicon application. This was concomitantly seen in both fruit and extra virgin olive oil (EVOO) yield increase, but only for the Leccino cultivar, suggesting specific cultivar x treatment interplay for those traits. Increases in mineral contents in olive oil due to Si treatments were not found, except for silicon, where Si2 and Si3 treatments yielded higher silicon content in olive oil compared to control. Results of this study indicate that Silicon Foliar Application could be used to enhance olive yield and only slightly modifies the fatty acid composition of olive oil and the content of some of the phenols. Although silicon application did affect several parameters, the effect of cultivar was more pronounced in affecting most of the parameters. Further research could be driven towards the effect of foliar silicon on these parameters under environmental stress factors.  
bio-stimulants; Istarska Bjelica cv.; Leccino cv.; EVOO; _Ola europaea_ L.
###### Abstract  
In this paper, we propose a polarization-selective electromagnetic (EM) transparent mesh-type E-shaped antenna unit-cell in a shared aperture. The proposed antenna unit-cell, which can be expanded to a larger array in a modular way, has one S-band antenna on the upper layer and nine X-band antennas on the lower layers. The simple E-shaped structure, which has a low profile with a good bandwidth, is used for the antenna elements. However, due to the limited aperture size of the stacked configuration, the lower-layer elements can be physically blocked by the upper-layer element. To reduce this blockage effect, the S-band element is rotated 90 degrees with respect to X-band elements so that the polarizations between the S- and X-band elements are perpendicular to each other. Moreover, to minimize performance degradation due to the blockage effect, a mesh structure is applied for S-band elements for EM transparent characteristics, thereby improving EM transparency from \(-30\) dB to \(-1.5\) dB. The extended via cavity wall is also employed outside the nine X-band elements to minimize the mutual coupling and to reduce antenna size. To confirm the effectiveness of the proposed design, the proposed antenna unit-cell is fabricated, and the radiation characteristics are measured, in a full anechoic chamber. The average bore-sight gains in the S- and X-band are 5 dBi and 4.5 dBi, respectively. The results confirm that the proposed design is suitable for shared-aperture radar applications.  
Keywords:polarization-selective structure; electromagnetically transparent structure; S-band antenna; X-band antenna; E-shaped antenna; shared aperture +
Footnote †: journal: Applied
## 1 Introduction
Proper selection of the QP solver is crucial to meet the time requirements for fast dynamic systems. In [25], the authors present a benchmark comparing OSQP with MOSEK, GUROBI, and ECOS. This work shows that the OSQP solver, based on the alternating  
Figure 1: Generic representation of the voltage source inverter control system. direction method of multipliers (ADMM), has fast response times and reliable performance compared to other alternatives. Moreover, it is open-source and suitable for embedded implementations. Additionally, other implementations of ADMM have been extensively studied for power electronic systems applications [26, 27, 28]. This work introduces an MPC for the control of a voltage source inverter. The proposed algorithm is formulated based on dynamic performance criteria and incorporates integral action to mitigate unmodeled errors and system disturbances. The MPC is designed as a QP problem, where the predictive dynamic model is included as a linear equality constraint and physical and operational constraints are expressed as linear inequalities. MATLAB simulations demonstrate the superior performance of the proposed approach compared to conventional hierarchical PI control. The optimization problem is solved using the OSQP algorithm, achieving real-time execution with an average computation time of approximately 70 \(\upmu\)s using the MicroLabox controller of dSPACE, confirming its suitability for microgrid applications.
## 2 Spectral Density Estimation
### Spectrum-Adapted Polynomial Interpolation  
In the first method, we take \(y_{k}:=\frac{\cos\left(\frac{k\pi}{K}\right)+1}{2}\), for \(k=0,1,\ldots,K\), which are the \(K+1\) extrema of the degree \(K\) Chebyshev polynomial shifted to the interval \([0,1]\). We then warp these points via the inverse of the estimated cumulative spectral density function by setting \(x_{k}=P_{\lambda}^{-1}(y_{k})\), before finding the unique degree \(K\) polynomial interpolation through the points \(\{(x_{k},f(x_{k}))\}_{k=0,1,\ldots,K}\). The intuition behind warping the interpolation points is that (i) a better approximation is attained in areas of the spectrum (domain) with more interpolation points, (ii) the error in (3) only depends on the errors at the eigenvalues of \(A\), so we would like the approximation to be best in regions with many eigenvalues, and (iii) as shown in Figure 3, using the inverse of the estimated cumulative spectral density function as the warping function leads to a higher density of the warped points \(\{x_{k}\}\) falling in higher density regions of the spectrum of \(\mathbf{A}\). Thus, the warping should ideally lead to more interpolation points in high density regions of the spectrum, better approximation of the target function in these regions, and, in turn, a reduction in the error (3), as compared to interpolations generated from points spread more evenly across the spectrum.  
To find the (unique) degree \(K\) polynomial interpolation, our numerical implementation uses MATLAB's polyfit function, which centers and scales the data and then solves the resulting system of equations via a QR decomposition. Once the interpolating polynomial coefficients are attained, \(p_{K}(\mathbf{A})\mathbf{b}\) can be computed, e.g., via (2) or by representing the interpolating polynomial as a linear combination of Chebyshev polynomials and using Chebyshev coefficients in the associated three-term recurrence [23; 32]. This entire procedure is detailed in Algorithm 1.  
```
0: Hermitian matrix \(\mathbf{A}\in\mathbb{R}^{N\times N}\), vector \(\mathbf{b}\in\mathbb{R}^{N}\), function \(f\), polynomial degree \(K\) Output degree \(K\) approximation \(p_{K}(\mathbf{A})\mathbf{b}\approx f(\mathbf{A})\mathbf{b}\in\mathbb{R}^{N}\)
1: Compute \(\tilde{P}_{\lambda}(z)\), an estimate of the cumulative spectral density of \(\mathbf{A}\)
2:for\(k\) in \(0:K\)do
3:\(y_{k}\leftarrow\frac{1}{2}[\cos(\frac{k\pi}{K})+1]\)
4:\(x_{k}\leftarrow\tilde{P}_{\lambda}^{-1}(y_{k})\)
5:endfor
6: Find the (unique) degree \(K\) polynomial interpolation \(p_{K}\) through the points \(\{(x_{k},f(x_{k}))\}_{k=0,1,\ldots,K}\)
7: Compute \(p_{K}(\mathbf{A})\mathbf{b}\) via (2)
8:return\(p_{K}(\mathbf{A})\mathbf{b}\)
```  
**Algorithm 1** Spectrum-adapted polynomial interpolation.  
Figure 3: Construction of six interpolation points for the same graph Laplacian matrix described in Figure 1. The interpolation points \(\{x_{k}\}\) on the horizontal axis are computed by applying the inverse of the estimated cumulative spectral density function to the initial Chebyshev points \(\{y_{k}\}\) on the vertical axis.
# Intelligent Traffic Management in Next-Generation Networks
## 2 Machine Learning
### Deep Learning  
Deep learning (DL), also known as deep neural networks (DNN) represents one of the most active areas of AI research [13]. It is a branch of ML that evolved from neural network (NNs) which enables an algorithm to make predictions or classifications based on large datasets without being explicitly programmed. In many domains, DL algorithms are able to exceed human accuracy. Using conventional ML-based models requires some feature engineering tasks like feature selection [35], which is not the case with DL models, as these can hierarchically extract knowledge automatically from raw data by stacked layers [23]. The major benefits of DL over conventional ML models are its superior performance for large datasets [36] and the integration of feature learning and model training in one architecture, as illustrated in Figure 2. Therefore, it helps to avoid human intervention and time-wasting as maximum as possible. In the literature, DL has also been referred to as deep structured learning, hierarchical learning, and deep feature learning [37]. This deep architecture enables DL to perform better than conventional ML algorithms; accordingly, DL can learn highly complicated patterns. It uses supervised and unsupervised learning to learn high-level features for the tasks of classification and pattern recognition. Several DL algorithms are presented in Table 4. The growing popularity of DL inspired several compa \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Strengths** & **Weaknesses** \\ \hline Supervised learning & Low computational cost, fast, scalable & Requires data labeling and data training, behaves poorly with highly imbalanced data \\ \hline Unsupervised learning & Requires only the data samples, can detect unknown patterns, generates labeling data & Cannot give precise information \\ \hline Semi-supervised learning & Learns from both labeled and unlabeled data & May lead to worse performance when we choose the wrong rate of unlabeled data \\ \hline Reinforcement learning & Can be used to solve complex problems, efficient when the only way to collect information about the environment is to interact with it & Slow in terms of convergence, needs a lot of data and a lot of computation \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of ML approaches. nies, and open-source initiatives have developed powerful DL libraries and frameworks that can be used to avoid building models from scratch [38]. Many libraries have appeared, and we summarize the most popular ones in Table 5.  
DL is capable of learning high-level features better than shallow neural networks. Shallow ANN contains very few hidden layers (i.e., with one hidden layer) while DL contains many more layers (deep). Each hidden layer comprises a set of learning units called neurons. These neurons are organized in successive layers, and every layer takes as input the output produced by the previous layer, except for the first layer, which consumes the input.
## 3 Methodology
### ISR Tracking Dataset  
The ISR RGB-D Dataset [28] is a non-object centric RGB-D dataset, recorded at the Institute of Systems and Robotics (ISR-UC) facilities using a camera sensor onboard the ISR-InterBot [43] mobile platform. The dataset presents a mission performed by the platform in a real scenario setting, representing object conditions under which mobile robot platforms may navigate. The ISR RGB-D dataset contains a total of 10,000 RGB-D raw images capturedat 30 FPS with a resolution of 640 \(\times\) 480. Moreover, ten object classes (unknown, person, laptop, tvmonitor, chair, toilet, sink, desk, door-open, and door-closed) were annotated at every fourth frame, reaching a total of 7832 object-centric images.  
As aforementioned, the main goal of this work is to study and compare the KF-based SORT and Deep-SORT object tracking methods to be applied in real-time mobile robot applications. To pursue that goal, a dataset representing the object conditions from the mobile robot platform's point of view during their navigation tasks is required. Due to the lack of publicly available datasets for such requirements, the labels of ISR RGB-D Dataset ([[https://github.com/rmca16/ISR_RGB-D_Dataset](https://github.com/rmca16/ISR_RGB-D_Dataset)]([https://github.com/rmca16/ISR_RGB-D_Dataset](https://github.com/rmca16/ISR_RGB-D_Dataset)) (accessed on 15 October 2021)) were rearranged to be used as a multi-object tracking dataset, the ISR Tracking Dataset. First, the labels for the remaining images were annotated for the described ten object classes. Then, a unique tracking ID was associated with the same objects throughout the images, except for the \"unknown\" object class that was not considered for tracking tasks. However, if an object disappeared or was occluded for more than 15 frames, it was considered as a new object, and a new tracking ID was associated. Each image has an associated \".txt\" file that contains all object labels for that image, and each object label is organized as follows: <object class>, <tracking ID>, <bounding box center \(\times\), <bounding box center \(>\)>, <bounding box width>, and <bounding box height>. ISR Tracking dataset has in total 32,635 object bounding boxes and 329 object sequences.
## 4 Numerical Simulation
### Numerical Model Validation  
In order to test the rationality of parameter selection for the finite element numerical model, the numerical simulation value of the vertical ground pressure peak distribution rule at a height of 6.9 m at a load cycle number of 300,000 was selected for comparison and verification with the measured value in a field test, as shown in Figure 11. As can be seen from the figure, the numerical simulation value is basically consistent with the measured value in the field test. The slight difference between the two values is since the compaction of the fill throughout the retaining wall cannot be accurately controlled  
Figure 10: Finite element model diagram of a DK315+913 reinforced soil-retaining wall.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Material** & **Modulus of** & **Cohesion** & **Natural Unit Weight** & **Internal Friction** \\  & **MODEL** & **Elasticity (kN/m2)** & **(kN/m3)** & **Angle (°)** \\ \hline Bedrock & Line elasticity & \(1.2\times 10^{7}\) & — & 27 & — \\ Powdered clay & Mohr–Coulomb & 8950 & 15 & 19 & 30 \\ Gravel matting & Mohr–Coulomb & \(5\times 10^{5}\) & 1 & 20 & 40 \\ Pile cap & Line elasticity & \(3.5\times 10^{7}\) & — & 25 & — \\ Bold stone and foundation & Line elasticity & \(3\times 10^{7}\) & — & 24 & — \\ Filling & Mohr–Coulomb & \(5\times 10^{4}\) & 30 & 18 & 35 \\ CFG pile & Line elasticity & \(3\times 10^{7}\) & — & 25 & — \\ Panel & Line elasticity & \(1.2\times 10^{7}\) & — & 24 & — \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of geotechnical and concrete materials.  
in actual projects and may be uneven, whereas the compaction of the fill in the PLAXIS retaining-wall model is uniform by default. Uneven compaction will result in a weakening of the internal load-carrying capacity of the retaining wall and an increase in the stress-transfer capacity, resulting in larger field values for vertical stresses in the retaining wall than the simulated values. The error between the field measured value and the numerical simulation value is acceptable. Within a certain range, the coincidence degree is good, so the established model is reliable.  
The simulation analysis of the load test by using PLAXIS shows that the PLAXIS simulation curve is in good agreement with the actual test curve. Although PLAXIS is a reasonable simulation of the test because the soil is a non-homogeneous body, its nature is randomly variable, so it still needs to accumulate and summarize the simulation of the real situation. With the continuous optimization of PLAXIS software and the accumulation of practical engineering experience, the application of PLAXIS in load tests will be more extensive.
## 1 Introduction
Other successful methods that have been reported are the reproducing kernel methods [14; 78; 88], Tau methods [50; 55], modified hat functions method [56], optimal control method [28], scaling function interpolation wavelet method [39], hybrid contractive mapping and parameter continuation method [72], sinusoidal basis functions and a neural network approach [74]. Finally, existence and uniqueness results are considered in [6; 7; 8; 9; 11; 20; 83; 91]. The above analysis shows that there are no procedures for the exact closed-form solution of VFIE. It is understandable that such methods are very difficult to develop due to the nature of the problem, especially in the nonlinear case, and whether they exist they will relate to specific categories of VFIE. Methods for solving Volterra IE and Fredholm IE exactly are given in [104], which describes the method for integral transforms, the method for differentiation and the direct method for integral equations in both the Urysohn form and the Hammerstein form. Motivated by this work and based on our previous experience with the closed-form solution of the Fredholm-type integro-differential equations [105; 106; 107; 108], we consider the VFIE of the second kind in the form  
\[y(x)=f(x)+\sum_{i=1}^{m}\int_{0}^{x}k_{i}(x,t)y(t)dt+\sum_{j=1}^{m}\int_{0}^{b }\bar{k}_{j}(x,t,y(t))dt,\quad 0\leq x\leq b, \tag{1}\]  
where  
\[\bar{k}_{j}(x,t,y(t))=\bar{q}_{j}(x,t)\bar{\varphi}_{j}(t,y(t)),\quad j=1,2, \ldots,m. \tag{2}\]  
The kernels \(k_{i}(x,t)\), \(i=1,2,\ldots,n\), and \(\bar{q}_{j}(x,t)\), \(j=1,2,\ldots,m\), the functions \(\bar{\varphi}_{j}(t,y(t))\), \(j=1,2,\ldots,m\), and the input function \(f(x)\) are given continuous functions, and \(y(x)\) is the unknown function. Equation (1) with a kernel of the form (2) is called the Hammerstein equation of the second kind. When all functions \(\bar{\varphi}_{j}(t,y)\), \(j=1,2,\ldots,m\), are linear in \(y\), then Equation (1) is a linear integral equation. We formulate (1) in a convenient operator form and derive its closed-form solution when the inverse of the associated integral Volterra operator of the second kind is available in explicit closed form, and the kernels \(\bar{q}_{j}(x,t)\), \(j=1,2,\ldots,m\), are degenerate. In the linear case, i.e., when all functions \(\bar{\varphi}_{j}(t,y)\), \(j=1,2,\ldots,m\), are linear in \(y\), we establish results of the existence and uniqueness. We elaborate on the Volterra operators of convolution type (CVFIE) and their inversion by the Laplace transform method. A complete algorithm for the proposed solution method is provided. The rest of the paper is organized as follows: In Section 2, an operator formulation of the problem is given, and the solution technique is explained. In Section 3, the case of convolution-type kernels is studied, and an algorithm is presented. In Section 4, we solve various illustrative examples to show the efficiency of the method. Lastly, some conclusions are given in Section 5.