esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Public authorities and private companies have used video cameras as part of surveillance systems, and one of their objectives is the rapid detection of physically violent actions. This task is usually performed by human visual inspection, which is labor-intensive. For this reason, different deep learning models have been implemented to remove the human eye from this task, yielding positive results. One of the main problems in detecting physical violence in videos is the variety of scenarios that can exist, which leads to different models being trained on datasets, leading them to detect physical violence in only one or a few types of videos. In this work, we present an approach for physical violence detection on images obtained from video based on threshold active learning, that increases the classifier's robustness in environments where it was not trained. The proposed approach consists of two stages: In the first stage, pre-trained neural network models are trained on initial datasets, and we use a threshold (\(\mu\)) to identify those images that the classifier considers ambiguous or hard to classify. Then, they are included in the training dataset, and the model is retrained to improve its classification performance. In the second stage, we test the model with video images from other environments, and we again employ (\(\mu\)) to detect ambiguous images that a human expert analyzes to determine the real class or delete the ambiguity on them. After that, the ambiguous images are added to the original training set and the classifier is retrained; this process is repeated while ambiguous images exist. The model is a hybrid neural network that uses transfer learning and a threshold \(\mu\) to detect physical violence on images obtained from video files successfully. In this active learning process, the classifier can detect physical violence in different environments, where the main contribution is the method used to obtain a threshold \(\mu\) (which is based on the neural network output) that allows human experts to contribute to the classification process to obtain more robust neural networks and high-quality datasets. The experimental results show the proposed approach's effectiveness in detecting physical violence, where it is trained using an initial dataset, and new images are added to improve its robustness in diverse environments.  
physical violence detection; active learning; video images processing; deep learning; convolutional neural network +
Footnote †: journal:  
Article
## 4 Aeroelastic Stability and Response Analyses
### Test Case 1
Finally, Figure 9 shows comparisons between the responses of the first two added sloshing modes (in the \(x\)-direction) in the linear and full sloshing models. The modes in question do not appear to be influenced by aeroelastic or rigid-body dynamics, but solely by the initial condition imposed by the external gust. The vertical sloshing effect dampens the second mode response for a few seconds, before becoming irrelevant. Sloshing modes in the \(y\)-direction are not affected by the gust and therefore do not exhibit active dynamics. Figure 6: Root locus with linear sloshing—Test Case 1. (**a**) Root locus of the integrated system. (**b**) Focus on the rigid body and sloshing modes. A response analysis is then performed at \(U_{\infty}=19.35\,\mathrm{m/s}\) in the developed flutter condition with a gust amplitude of \(w_{g_{a}}=0.3\,\mathrm{m/s}\). Under flutter conditions (with speeds 2% and 3% higher than flutter speeds of the frozen and lateral sloshing models, respectively), the response is expected to be unstable with the exponential envelope. The local angle of attack and the first mode responses are shown in Figure 10a,b comparing the effects that the four considered models have on the post-critical response of the flying wing. The linear sloshing model and the frozen fuel configuration results in an unstable response. In contrast, vertical sloshing helps prevent the response from growing indefinitely, favoring the onset of limit cycle oscillations (LCOs). This behaviour is determined by the nature of the nonlinear sloshing forces, which become highly dissipative when the acceleration of the flow is driven by the nonlinear sloshing forces. Figure 8: Dynamic sloshing force for \(U_{\infty}=18\,\mathrm{m/s}\) and \(w_{g_{s}}=2.5\,\mathrm{m/s}\)—Test Case 1.  
of the tank increases. In the case of a combined effect of the two sloshing dynamics, the oscillation amplitude is greater than that in the case of vertical sloshing alone.
## 5 Case Study
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**(a) Estimated Process Mean**} & \multicolumn{6}{c}{**(b) Estimated Process Standard Deviation**} \\ \hline \multicolumn{1}{c}{**Weights**} & \multicolumn{6}{c}{**Biases**} & \multicolumn{6}{c}{**Weights**} & \multicolumn{6}{c}{**Biases**} \\ \hline \multicolumn{1}{c}{} & \(W_{ij}^{mean}\) & \multicolumn{1}{c}{\(\left(\mathbf{\
u}_{j}^{mean}\right)^{T}\)} & \(\mathbf{a}_{j}^{mean}\) & \(b_{mean}^{NN}\) & \multicolumn{1}{c}{\(W_{ij}^{std}\)} & \multicolumn{1}{c}{\(\left(\mathbf{\
u}_{j}^{std}\right)^{T}\)} & \(\mathbf{a}_{j}^{std}\) & \(b_{std}^{NN}\) \\ \hline
7.048 & 3.571 & \(-\)1.123 & 0.134 & \(-\)7.259 & & 6.693 & 3.771 & \(-\)0.855 & 0.0516 & \(-\)7.531 & \\
4.726 & \(-\)5.71 & \(-\)1.371 & \(-\)0.212 & \(-\)6.831 & & 4.928 & \(-\)5.169 & \(-\)1.884 & \(-\)0.038 & \(-\)7.045 & \\
4.981 & 4.672 & 3.442 & \(-\)0.789 & 6.003 & & \(-\)4.907 & 4.677 & 3.435 & \(-\)0.147 & 5.755 & \\
4.459 & 3.990 & 3.498 & 1.079 & \(-\)6.208 & & 4.251 & 4.486 & 3.809 & 0.467 & \(-\)5.651 & \\
2.679 & 3.682 & \(-\)6.266 & \(-\)0.199 & \(-\)4.201 & & 2.864 & 3.086 & \(-\)5.745 & 0.330 & \(-\)4.851 & \\
6.436 & 4.389 & 0.793 & 0.209 & 3.175 & & \(-\)6.849 & 3.647 & 0.288 & 0.467 & 3.440 & \\
4.908 & 5.623 & \(-\)1.339 & 0.124 & 2.863 & & \(-\)5.123 & 5.469 & \(-\)1.166 & \(-\)0.356 & 2.874 & \\
1.562 & \(-\)4.993 & 5.438 & 0.190 & \(-\)2.048 & & 1.447 & \(-\)4.815 & 5.474 & \(-\)0.271 & \(-\)2.451 & \\
6.692 & 2.115 & 3.035 & \(-\)0.139 & \(-\)0.859 & & 6.152 & 2.694 & 2.570 & \(-\)0.250 & \(-\)1.183 & \\
5.521 & \(-\)4.127 & 3.079 & 0.099 & \(-\)0.243 & & 6.115 & \(-\)3.555 & 2.652 & \(-\)0.034 & 0.050 & \\
5.977 & 3.126 & \(-\)3.407 & 0.325 & \(-\)0.669 & \(-\)0.343 & & \(-\)6.131 & 2.967 & \(-\)3.265 & 0.230 & \(-\)0.850 & \\
5.256 & \(-\)5.103 & 1.936 & 0.633 & 1.256 & & 5.651 & \(-\)4.723 & 1.535 & \(-\)0.040 & 1.651 & \\
6.483 & \(-\)3.230 & 2.033 & \(-\)0.294 & 2.105 & & 6.082 & \(-\)3.433 & 2.667 & 0.697 & 1.926 & \\
0.589 & \(-\)5.911 & \(-\)4.442 & \(-\)0.462 & \(-\)3.229 & & \(-\)0.441 & \(-\)5.646 & \(-\)4.983 & \(-\)0.529 & \(-\)2.783 & \\
3.818 & \(-\)4.951 & \(-\)4.368 & \(-\)0.443 & 3.550 & & 4.070 & \(-\)5.306 & \(-\)4.141 & 0.407 & 3.526 & \\ \(-\)6.018 & 4.983 & \(-\)0.135 & \(-\)0.385 & \(-\)4.140 & & \(-\)6.091 & 4.643 & \(-\)0.476 & \(-\)0.545 & \(-\)4.469 & \\ \(-\)1.158 & 2.826 & 6.948 & \(-\)0.294 & \(-\)5.321 & & \(-\)1.107 & 3.380 & 6.462 & \(-\)0.155 & \(-\)5.651 & \\
6.528 & \(-\)3.113 & \(-\)2.385 & 0.419 & 6.011 & & 6.112 & \(-\)2.436 & \(-\)2.970 & \(-\)0.514 & 6.074 & \\
4.157 & 6.574 & 1.314 & 0.425 & 6.397 & & 4.160 & 5.845 & 1.643 & 0.072 & 7.251 & \\
4.931 & \(-\)5.012 & \(-\)2.952 & \(-\)0.233 & 7.576 & & 4.467 & \(-\)4.909 & \(-\)3.407 & \(-\)0.070 & 7.674 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Weight and bias values from the NNs approach by using the conventional log-sigmoid transfer function.
## 2 Results
### Assessing if Selecting Rif\({}^{\text{R}}\) CFU co-Selects for P\({}^{\text{R}}\) Cells
\begin{table}
\begin{tabular}{c c c} \hline
**Strains/Mutants** & **Transformants at 25 \({}^{\circ}\)C** & **Transformants at 37 \({}^{\circ}\)C** \\ \hline
594 & 520 & \textless{}0.17 \\
594 Rif\({}^{\text{R}}\)**mutants selected from cells never exposed to gene \(P\)\({}^{\text{b}}\)** & \\
1-25A2 & 7900 & \textless{}0.18 \\
3-25E & 570 & \textless{}0.17 \\
1-37A2 & 17,000 & \textless{}0.19 \\
3-37D & 270 & \textless{}0.18 \\ T-3-37-B10 & 580 & \textless{}0.2 \\ T-3-37-C5 & 1200 & \textless{}0.2 \\ T-3-25-D10 & 780 & \textless{}0.3 \\ Td-3-25-A7,scg31 & 2800 & \textless{}0.23 \\ Td-3-37-C7,sc1f45 & 1200 & \textless{}0.3 \\ Td-3-25-C10 & 3800 & 1.2 \\ Td-3-25-D9 & 1000 & 2.4 \\
**594 Rif\({}^{\text{R}}\) mutants selected at 37 \({}^{\circ}\)C from 594[pclpR-\(P\)-timm] cultures that had lost the plasmid \({}^{\text{c}}\)** & \\
3-37-A10 & 270 & \textless{}0.3 \\
3-37-B1 & 410 & 6.0 \\
3-37-B8 & 1200 & 430 \\
3-37-C1, 3-37-C5 \({}^{\text{d}}\) & 180 & \textless{}0.12 \\
3-37-C4 & 860 & 140 \\
3-37-D1 & 100 & \textless{}0.17 \\
3-37-C6, 3-37-D2 \({}^{\text{e}}\) & 1100 & 390 \\
3-37-D3 & 190 & \textless{}0.2 \\
3-37-D6 & 630 & 13 \\ \hline \end{tabular} \({}^{\text{a}}\) Rif\({}^{\text{R}}\) isolates were streaked for sc’s on fresh LB agar plates with 50 ug/mL rifampicin, A CFU was selected, inoculated into LB broth and grown overnight to saturation. These cells were used for transformation with pclpR-\(P\)-timm as described in Materials and Methods. Values with “\(<^{\text{c}}\) had no recovered Amp\({}^{\text{R}}\)-transformants and the frequency shown was obtained by dividing “1” by the cell titer on LB agar plates. The results for one experiment are shown, but are representative of numerous repeats;\({}^{\text{b}}\) Refer to Table S2 for isolates employed;\({}^{\text{c}}\) Refer to Table 5 for isolates employed;\({}^{\text{d}}\) Both mutants with Rif\({}^{\text{R}}\) mutation at 1691:CtoI, P564L, were P\({}^{\text{S}}\), data for 3-37-C1;\({}^{\text{e}}\) Both mutants with Rif\({}^{\text{R}}\) mutation at 1351:CtoA, R451S, were P\({}^{\text{R}}\), data for 3-37-D2.  
\end{table} 
Table 6: Transformation (\(\times 10^{-7}\)) of pclpR-\(P\)-timm/100 ng plasmid into Rif\({}^{\text{R}}\) mutants of 594 \({}^{\text{a}}\). main DNA/RNA channel a Rif binding pocket in RpoB (Figure 5 in reference [79]), which for _E. coli_ represents amino acids (clockwise circular orientation) 511, 510, 512, 513, 514, 515, 526, 516, 518, 522, 521, 686, 563, 564, 573, 529, 572, 531, 532, 533, and 534. Thirty-four of the 52 _rpoB_ mutations we sequenced were missense mutations within codons for these amino acids (Figure 4). The two deletions \(\Delta\)PGGL535-538P (Figure 4) fell just outside of this pocket. The seven P\({}^{\mathrm{R}}\) mutations (Figure 4) Q148L, Q148P, \(\Delta\)_GEV440-442V_, \(R451S\), S509R, G537C, and \(L57 IQ\) (italicized showing strong P\({}^{\mathrm{R}}\)) fell outside of the pocket and may define independent contact points between RpoB and P.
## 2 Related Works and Problems  
In response to the demand for such high-resolution and high-speed signals, research is being conducted at home and abroad. Reference [13] proposes a way to improve the resolution of the system by using two programmable gain amplifiers (PGA) at the back end of the DAC [13]. The PGA is controlled by a digital signal processor (DSP) and can amplify or attenuate the output signal of DAC. In this way, a smaller amount of variation than the minimum signal step output by the DAC itself can be obtained. The structure diagram of the method is shown in Figure 2. The relative resolution of the system is improved by amplifying the output voltage amplitude of the system, but in fact, its absolute resolution, that is, the minimum output voltage step, remains unchanged. When measuring a small range ADC, the problem still exists that the resolution of the signal's resolution is not high enough. In addition, the system will send digital quantities to DAC and PGA at the same time, but the response is not synchronized. It will affect the quality of the output waveform.  
Figure 1: Block diagram of the ADC test scheme.  
Figure 2: Schematic diagram of PGA amplification method [13].  
Reference [14] proposes a method of superimposing the output of several DACs together to improve system resolution [14]. Its system structure is shown in Figure 3. It uses four 8-bit DACs to achieve the effect of a 32-bit resolution digital-to-analog (DA) conversion circuit. The principle is to use the difference of the resistance in the addition circuit to achieve different magnifications. This method can improve the resolution of the DA conversion circuit. However, there is a problem in the actual circuit that it is difficult to achieve a strict multiple relationship of the resistance value due to the accuracy of the resistance. In addition, the thermal noise of the resistance [15], the asynchronous transmission delay of the amplifier [16] and the asynchronous output of different DACs will also have a greater impact on the system output, resulting in a reduction in the signal-to-noise ratio (SNR) of the output signal.  
Considering the influence of power supply on DAC reference voltage and amplifier [17; 18; 19], the influence of temperature on resistance noise [20; 21; 22], without considering the additional processing of power supply, the time alternating circuit we designed needs to use the same type of DACs and the same channel structure and avoid the use of resistance weighted network [23]. Therefore, in view of the above problems, this article proposes a method to generate high-resolution and high-speed signals for ADC testing based on the principle of time-interleaving by using high-resolution DAC.
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 2 Literature Review  
### Review of Temporal Instability Research  
For traffic safety research, it is crucial to explore the temporal stability of variables. Suppose the effects of factors are temporarily unstable. In that case, it cannot be determined to what extent changes in injury severity are due to temporal changes and how much depends on the implementation of specific safety countermeasures [21]. Ignoring the time factor can lead to false conclusions and ineffective or even dangerous safety policies [22]. The effectiveness of various data analysis methods is also potentially affected by temporal instability. When modeling crash data, numerous road safety studies have proposed the assumption that the impacts of influencing factors do not vary over time. However, there is growing evidence that explanatory factors' influence on crash injury outcomes is temporally unstable.  
Through research in psychology, neuroscience, economics, and cognitive science, [PERSON] [22] found that the instability of the influence of explanatory variables over time exists due to some basic behaviors. Subsequently, [PERSON] and [PERSON] [11] tested the difference in injury severity of motorcycle drivers at different times based on two crash datasets. As expected, the results indicated temporal instabilities in the statistical model. Temporal instability of factors affecting injury severity was also found in truck crashes [12]. In addition, the study by [PERSON] et al. [23] showed that crash injury severity was unstable over time by estimating a Markov transformation model. The findings of [PERSON] et al. [24] and [PERSON] et al. [25] further confirm this conclusion. The non-negligible effect of temporal instability on model estimation is shown in the above studies, which are increasingly becoming one of the focuses of traffic safety research [26].
# Research on Synthetic Aperture Radar Processing for the Spaceborne Sliding Spotlight Mode
## 5 PFA Algorithm for Sliding Spotlight SAR
### Simulation Results  
The simulation parameters are same with Table 2, and the distribution of 225 simulation points are given in Figure 15. The space between each points is 800 m, and the imaging area is 11,200 m\(\times\) 11,200 m, which is far beyond the effective imaging scene size. According to the relationship between the actual scene range and the effective scene range, we can find that only one level segmentation of 4 sub-images can be controlled within the effective imaging scene radius.  
The echo signal after matching filter and motion compensation is a two dimensional matrix of 16,384 \(\times\) 16,384 points. Image obtained by PFA is shown in Figure 16a, while the corresponding imaging range is beyond the actual scene, it can be seen that the image has obvious geometric distortion, and error position of the point target is max to 15 and 16 m in range and azimuth domain compared to the original point target distribution in region A.  
The imaging result of one sub-image is shown in Figure 16b, corresponding to region A. It can be seen from the figure, the distortion has been preliminarily corrected. The full image after mosaic is shown in Figure 16c, and the geometric distortion has been all corrected, well focus and with no error position. The target response characteristics are shown in Figures 17 and 18.  
Figure 16: (**a**) Result of PFA; (**b**) Local sub-image; (**c**) Full image after mosaic.  
Figure 15: Simulation point targets distribution.  
Evaluation results of point targets simulation are shown in Table 5.  
Figure 17: Two dimensional characteristic of point target. (**a**) PFA; (**b**) PFA based on sub-images.  
Figure 18: Comparison of point target profile. (**a**) Azimuth profile of PFA; (**b**) Azimuth profile of PFA based on sub-images; (**c**) Range profile of PFA; (**d**) Range profile of PFA based on sub-images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Conventional PFA** & **PFA with Sub-Images** \\ \hline Theoretical Range Resolution & \(1.00\) m & \\ \hline Simulation Range Resolution & \(1.18\) m & \(1.05\) m \\ \hline Range PSLR & \(-13.23\) dB & \(-13.26\) dB \\ \hline Theoretical Azimuth Resolution & \(1.19\) m & \\ \hline Simulation Azimuth Resolution & \(-\) & \(1.22\) m \\ \hline Azimuth PSLR & \(-7.15\) dB & \(-12.86\) dB \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation results of point targets simulation.
## 6 Conclusions  
Through this study, three holding area concepts have been designed based on various airspace, terrain conditions and the performance of UAM vehicles. By analyzing the turning performance of two winged-type UAM vehicles, it has been verified as to whether the turning radius could be satisfied within the defined cruise speed. The result is that the wingless-type UAM vehicle was able to turn because it flew at a low speed, but the winged-type UAM vehicles were unable to turn within the specified turning radius.  
However, the results of this study do not mean that winged-type UAM vehicles cannot be operated in the holding area. It is possible for them to operate by decelerating to a speed lower than the cruise speed, by changing the cruise mode of the UAM vehicle to the turning mode, or by performing the turning procedure for winged-type UAM vehicles. In addition, if the turning radius of the holding area is larger than the studied radius, it is possible to turn at a higher speed. In other words, the conclusion presented in this study is meaningful in that it is necessary to satisfy the performance factors and establish holding procedures for UAM vehicles to operate safely, rather than considering the possibility of operating winged-type UAM vehicles. In addition, the results of this study can be used as guidelines when designing UAM corridors or UAM flight routes. In particular, the designed holding area concepts can be used to determine the appropriate shape and design according to the cruise speed by adjusting the turning radius and distance.  
Conceptualization, Y.L. and J.L.; validation, Y.L. and J.L.; formal analysis, Y.L. and J.L.; investigation, Y.L.; resources, J.L.; data curation, Y.L. and J.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L., J.L. and J.-W.L.; visualization, Y.L. and J.L.; supervision, J.-W.L.; project administration, J.-W.L.; funding acquisition, J.-W.L. All authors have read and agreed to the published version of the manuscript.  
This study has been carried out with the support of the 2020 National Research Foundation Basic Research Project (No. 2020R1A6A1A03046811) and the Ministry of Land, Infrastructure and Transport/Research Project of the Land, Infrastructure and Korea Agency for Infrastructure Technology Advancement (No. 21 ACTO-B151661-03).  
Not applicable.  
The authors declare on conflict of interest.
# Stents in Congenital Heart Disease: State of the Art and Future Scenarios
## 3 Congenital Heart Disease
### Defects Amenable to Stenting
#### 3.2.3 Aortic Coarctation  
Aortic coarctation is a birth defect consisting of a narrowing of the aorta, which does not form correctly during gestation. The narrowing typically manifests just beyond the left subclavian artery [53], thus, before the aortic isthmus and the ductus arteriosus insertion (Figure 3c), but it may occur in various anatomical locations of the aortic arch or in the thoracic or abdominal aorta [54]. Moreover, CoA may occur in isolation, or it can be associated with other complex CHD lesions such as HLHS. This pathology represents a quite common form of congenital lesion if compared to other malformations, comprising approximately 7% of all CHD forms [55; 56], meaning an incidence of 50,000 over the total live births worldwide in 2022 [57; 58]. As for the other congenital malformations, the parallel circulation allowed by the fetal shunts permits the fetus to survive gestation. Indeed, the ductus arteriosus provides a route with less resistance for the blood flow in the right ventricle to bypass the aorta stenosis and perfuse the downstream districts, while the left ventricle feeds the upper portions of the body. In extra-utero life, when adult circulation is established, the work of the left ventricle has to increase to generate sufficient pressure to force the blood into the narrowed aorta. In the most severe cases with marked stenosis or interrupted aortic arch, the lower body districts are poorly perfused. The treatment of CoA may vary according to anatomical features and age of presentation. In this review, attention is placed on native CoA forms requiring treatment in the neonatal period. Typically, surgical treatment is retained as the first-choice option in younger patients up to 18 months, while transcatheter treatment through balloon angioplasty or stent implantation in the aortic isthmus is preferred for older children and adolescents [55; 59]. Indeed, the main limitation of stenting in infants nowadays is related to the dimension of sheath needed for implantation of stents that can be redilated to adult size following somatic growth [54].