esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Algorithm
### Enhanced POA Algorithm  
The initial positions of the population in the search space are uniformly distributed, which contributes to improving the algorithm's global search capability and search efficiency. Unlike the standard POA algorithm that initializes the population randomly, which can reduce population diversity, we utilize the logistic-sine chaotic map for population initialization in this paper.  
Logistic-sine chaotic mapping combines the characteristics of logistic mapping and sine mapping. This mapping differs from sinusoidal and logical mappings because it has a larger chaotic interval. We guarantee the unpredictability of individuals inside the population by creating an initial population that is randomly distributed using the logistic sine map. Equations (13) through (15) give the mathematical formulations for the logistic map, the sine map, and the logistic-sine map, respectively.  
\[Z_{i+1}=\mu\ Z_{i}(1-Z_{i}) \tag{11}\]  
\[Z_{i+1}=\sin{(\pi Z_{i})} \tag{12}\]  
\[Z_{i+1}=(\mu\ Z_{i}(1-Z_{i})+\frac{(4-\mu)\sin{(\pi Z_{i})}}{4})(mod1) \tag{13}\]  
where \(\mu\) is a multiplier of chaos and \(Z\) is a series of numbers created at random. Logistic map and sine map are formally defined in Equations (13) and (14), respectively. The logistic-sine map is given mathematical representation in Equation (15). The logistic-sine map is given mathematical representation in Equation (15).  
\[x_{i}=l_{b}+(u_{b}-l_{b})*\ Z_{i} \tag{14}\]  
levy flight is a non-Gaussian stochastic process, also known as Levy motion, which performs random walks obtained in Levy stabilization. The balance between exploration and exploitation can be achieved according to the Levy flight based jumps, which allows pelicans catch more fish in the hunting area. This distribution follows a power law formula \(L(s)\sim|s|^{-l-\beta}\), where \(0<\beta<2\) represents an index and \(s\) denotes the step length [26].  
One may determine the step length by:  
\[s=\frac{u}{|v|^{\frac{1}{\beta}}} \tag{15}\]where normal distributions serve as the source for \(u\) and \(v\). Which is  
\[u\ N(0,\sigma_{u}^{2}),v\ N(0,\sigma_{v}^{2}) \tag{16}\]  
where  
\[\sigma_{u}=\left\{\begin{aligned} &\frac{\Gamma(1+\beta)\times\sin \left(\frac{\pi\beta}{2}\right)}{\Gamma\left(\frac{1+\beta}{2}\right)\times \beta\times 2^{\left(\frac{\beta-1}{2}\right)}}\\ \end{aligned}\right\}^{\frac{1}{\beta}}\quad,\quad \sigma_{v}=1 \tag{17}\]  
In the EPOA algorithm, the Levy function is added while winging. The new pelican position is evaluated in the following:  
\[X_{i}=\left\{\begin{aligned} X_{i}^{P_{2}}+\alpha\oplus Levy,&\text{if}\ F_{i}^{P_{2}}<F_{i}\\ X_{i},&\text{else}\\ \end{aligned}\right. \tag{18}\]  
where  
\[\alpha=0.01\times\ s\times(X_{i}^{P_{2}}-X_{best}) \tag{19}\]
# Hybrid Adaptive Controller Design with Hysteresis Compensator for a Piezo-Actuated Stage
## 5 Conclusions  
A hysteresis modeling and compensation method for P-AS was developed. We propose a Hammerstein model comprising a BW static hysteresis sub-model and a dynamic linear sub-model. An improved differential evolution algorithm and a least-squares algorithm are used to identify the static hysteresis sub-model and dynamic linear sub-model for the Hammerstein model, respectively. To suppress the influence of hysteresis on the precision of P-AS, a hysteresis controller based on the inverse BW model was designed to depress the static hysteresis of the stage. However, modeling errors, environmental noise, and measurement errors also reduce the control accuracy. To resolve these complications, a HAC method based on a hysteresis compensator is proposed for increasing the control accuracy. By constructing a reference model of the system and considering the unmodeled dynamics at the input and output, the effects of model uncertainty and perturbations on the system accuracy can be significantly reduced. With the help of the Lyapunov stability theory, the stability of the system is proved. Finally, the effectiveness of the raised approach was verified by a series of experiments involving comparisons with the hysteresis compensation controller based on the inverse BW model and MRAC. The experimental results indicated that the HAC method is efficient for the precise positioning of P-AS.  
Figure 8: Tracking performance of the HAC approach in comparison with the compensator and MRAC under different complex reference trajectories: (**a**) mixed sinusoidal signal; (**b**) mixed triangular signal.  
**Author Contributions:** Y.Z.: conceptualization, data curation, methodology, writing--original draft, formal analysis; M.S.: validation, investigation; Y.S.: formal analysis, resources; C.Z.: software, project administration; M.Z.: writing--review and editing, supervision, visualization. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of Chongqing City of China CSTB2022 NSCQ-MSX0297, and in part by the Program of Science and Technology Development Plan of Jilin Province, China, under Grant 20220508139 RC and Grant 20220201058 GX.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 4 Experiments
### Performance Evaluation
#### 4.4.1 Imbalanced Data  
Table 4 shows the results obtained using imbalanced security alert data samples from the enterprise dataset. All classifiers achieved an accuracy rate of >99.341%. However, accuracy is not a reliable metric for measuring model performance when dealing with highly imbalanced datasets, as a model tends to be biased towards majority class samples. Therefore, metrics that are more appropriate for skewed data scenarios, such as recall,  
FPR, and BCR, were adopted. LightGBM achieved the best recall and BCR of 98.317% and 99.095%, respectively, whereas XGBoost achieved the best FPR of 0.054%. Table 5 shows the results obtained using imbalanced data samples from the benchmark dataset. XGBoost had the best recall and BCR values of 95.940% and 97.881%, respectively, whereas LightGBM yielded the best FPR of 0.176%. Further performance improvement is desired to ensure that the system can accurately detect positive alert samples, thereby minimizing the risk of FNs while avoiding alert fatigue.
# Multivariate Algorithmics for Finding Cohesive Subnetworks
## Average \(s\)-Plex
### Relative Relaxation of Vertex-Connectivity  
A natural way to relax the connectivity is to introduce a relative slack parameter \(\gamma\in(0,1]\) that can be set to vary between perfect vertex-connectivity and \"mere\" connectivity.  
**Definition 11**.: A set \(S\subseteq V\) in a graph \(G=(V,E)\) is \(\gamma\)-relative-vertex-connected if \(\kappa(G[S])\geq\gamma\cdot(|S|-1)\).  
This clique relaxation was proposed quite recently [55]; Figure 6 shows a 0.5-relative-vertex-connected set. There is the following relation between \(\gamma\)-relative-vertex-connected sets and \(\gamma\)-complete sets: if a set is \(\gamma\)-relative-vertex-connected, then it is also \(\gamma\)-complete (and consequently, also a \(\gamma\)-quasi-clique). This relation follows from the fact that it is possible to isolate a vertex by deleting all of its neighbors. Moreover, being \(\gamma\)-relative-vertex-connected is not hereditary and not even quasi-hereditary: consider a cycle on five vertices. This graph is 2-vertex-connected and, thus, 0.5-relative-vertex-connected. Deleting one vertex results in a path on four vertices. This graph is only one-vertex-connected and, thus, not 0.5-relative-vertex-connected.  
The computational problem of finding large \(\gamma\)-relative-vertex-connected sets is formulated as follows.  
\(\gamma\)-Relative-Vertex-Connected Subgraph  
**Input:** An undirected graph \(G=(V,E)\) and an integer \(k\).  
**Question:** Does \(G\) contain a \(\gamma\)-relative-vertex-connected set of size at least \(k\)?  
Unsurprisingly, \(\gamma\)-Relative-Vertex-Connected Subgraph is NP-hard for all fixed rational values \(\gamma\in(0,1]\)[55]. To our knowledge, this is the only known complexity result for \(\gamma\)-Relative-Vertex-Connected Subgraph.  
**Open Topic 4**.: Perform a multivariate complexity analysis for \(\gamma\)-Relative-Vertex-Connected Subgraph.  
Observe that since \(\gamma\)-relative-vertex-connected sets are also \(\gamma\)-quasi-cliques, Observation 4 applies also to \(\gamma\)-relative-vertex-connected sets. Hence, \(k\in O(\Delta)\) for fixed \(\gamma\). Thus, by Proposition 1,  
Figure 6: A part of the highland tribes social network [4]. The black vertices form the maximum 0.5-relative-vertex-connected set in this network. This set is also a maximum-0.5-relative-vertex-connected set, but it is not highly connected.  
\(\gamma\)-Relative-Vertex-Connected Subgraph can be solved in \(\Delta^{O(\Delta)}\cdot n^{O(1)}\) time; for all other parameters, the parameterized complexity is open.
# High Density RF-DC Plasma Nitriding under Optimized Conditions by Plasma-Diagnosis
## 3 Experimental Results and Discussion  
N\({}_{2}\) + H\({}_{2}\) plasma diagnosis was performed by controlling the plasma processing parameters (RF-voltage, DC-bias and N\({}_{2}\) + H\({}_{2}\) gas pressure) to describe their effects on the mother nitrogen molecule ion or N\({}_{2}^{+}\). OES measurement was adjusted to accurately measure the NH-radicals by diagnosis. The hydrogen content was controlled to describe the effect of hydrogen-to-nitrogen molar ratio on the intensity of NH-radicals. AISI304 stainless steel substrates were plasma nitridted, respectively, at 673 K and 623 K for 14.4 ks to demonstrate that optimization of the hydrogen-to-nitrogen molar ratio was needed to ensure nitrogen supersaturation with sufficient nitrogen content and to harden the stainless steels, even at a low holding temperature.  
\begin{table}
\begin{tabular}{c c c} \hline
**Species** & **Transition Array** & **Peak Position (nm)** \\ \hline N\({}_{2}\)\({}^{*}\) & B\({}^{3}\)\(\prod\)I\({}_{8}\) \(\rightarrow\) A\({}^{3}\)\(\sum\)U & 580.4 (\(v^{\prime}\) = 11, \(v^{\prime\prime}\) = 7) \\ \hline  & & 775.3 (\(v^{\prime}\) = 2, \(v^{\prime\prime}\) = 0) \\ \hline N\({}_{2}\)\({}^{*}\) & C\({}^{3}\)\(\prod\)I\({}_{u}\)\(-\)B\({}^{3}\)\(\prod\)I\({}_{8}\) & 337.1 (\(v^{\prime}\) = 0, \(v^{\prime\prime}\) = 0) \\ \hline  & & 380.5 (\(v^{\prime}\) = 0, \(v^{\prime\prime}\) = 2) \\ \hline N\({}_{2}^{+}\) & B\({}^{2}\)\(\sum\)\({}_{u}^{+}\) - \(\chi^{2}\)\(\sum\)\({}_{8}^{+}\) & 391.4 (\(v^{\prime}\) = 0, \(v^{\prime\prime}\) = 0) \\ \hline  & & 427.8 (\(v^{\prime}\) = 0, \(v^{\prime\prime}\) = 1) \\ \hline H & Balmer series & 656.3 (H\({}_{\alpha}\)) \\ \hline  & & 486.1 (H\({}_{\beta}\)) \\ \hline NH & \(\Lambda\)-sys a\({}^{3}\)\(\prod\)-X\({}^{3}\)\(\sum\)\({}^{-}\) & 336.01 (\(v^{\prime}\) = 0, \(v^{\prime\prime}\) = 0) \\ \hline \end{tabular} Where N\({}_{2}\)\({}^{*}\) denotes for the activated nitrogen molecule.  
\end{table}
Table 1: Reference data for generated species by N\({}_{2}\) + H\({}_{2}\) plasmas.
###### Abstract  
For some IoV-based collision-avoidance architectures, it is not necessary that all vehicles have communication abilities. Hence, they need some particular designs and extra components. In the literature, one of them uses a camera mounted onto the infrastructure at an intersection to realize collision detection. Consequently, technologies for real-time object detection and dynamic prediction are required for the purposes of collision avoidance. In this paper, we propose an interesting method to predict the future position of a vehicle based on a well-known, real-time object detection project, YOLOv3. Our algorithm utilizes the concept of vehicle dynamics and the confidence region to predict the future position on vehicles. This will help us to realize real-time dynamic prediction and Internet of Vehicles (IoV)-based collision detection. Lastly, in accordance with the experimental results, our design shows the performance for predicting the future position of a vehicle.  
IoV-based collision avoidance; vehicle's position estimation; computer vision; machine learning; confidence region +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### IDEAL Test
#### 3.1.4 Effect of Asphalt Type on the IDEAL Test Result  
As shown in Figure 13, the \(G_{\text{initial}}\) value of sample 5 is less than that of sample 7 and the increasing extent of sample 5 is also less than that of sample 7 which shows that SBS modified asphalt can better reinforce the anti-cracking ability of asphalt mixtures. As shown in Figure 14, the \(CT_{\text{index}}\) value of sample 5 is less than that of sample 7 which shows that SBS modified asphalt can better decrease the crack propagation rate of asphalt mixtures. The reason for this result is that the SBS modified asphalt is produced using the pure asphalt and SBS modifier and the SBS modifier is a kind of polymer modifier [36] and could disperse evenly inside the pure asphalt. The chemical bonding of the SBS modifier and the asphalt itself could help to better enhance the property of SBS modified asphalt. The SBS modified asphalt have better mechanical property than the pure asphalt which could help to better transfer the inner stress inside the asphalt mixture. Generally, asphalt with better property could better enhance the anti-crack ability of asphalt mixtures.  
Figure 12: Effect of NMAS on \(CT_{\text{index}}\).  
Figure 13: Effect of asphalt type on \(G_{\text{initial}}\).
# Study on Two-Phase Mixing Inside Flow Focusing/Blurring Nozzle by Gray Distribution Analysis Method
## 5 Conclusions  
(1) The feasibility of using the gray distribution curve of the gas-liquid flow images to study the two-phase flow in the mixing zone of the flow focusing/blurring nozzle is analyzed. Preliminary analysis shows that the gray distribution of the flow images can represent the mixing intensity and uniformity of the gas and liquid in the mixing zone;  
Figure 14: Gray scale distribution of local flow field.  
(2) The influence of different parameters on gas-liquid mixing was studied by using the gray distribution of the experimental images. The results show that the increase of the liquid flow rate, the tube hole distance and the orifice diameter is not conducive to the two-phase mixing in the mixing zone, while the increase of the gas flow rate is conducive to the full mixing of the gas and liquid in the mixing zone;  
(3) The effects of different parameters on two-phase mixing in the mixing zone were compared by using the gray distribution curve of the experimental images. The results show that the influence of the gas inertia force on two-phase mixing is weaker than the liquid inertia force. At low flow rate, the influence of the gas inertia force is similar to the tube hole distance. The orifice diameter has a certain effect on the two-phase mixing, but the effect is weak;  
(4) The gas-liquid flow field in the mixing zone of the flow focusing/blurring nozzle under different modes is analyzed and studied by using the gray distribution curves. The results show that the two-phase flow in the mixing zone is stable in the flow focusing mode. In the flow blurring mode, the two-phase mixing in the mixing zone is stable in the radial direction and pulsating in the axial direction. In the transition mode, the two-phase flow is unstable, but its flow characteristics are close to the flow blurring mode.  
Conceptualization, J.Z. and M.L.; methodology, Z.N.; software, C.S.; experiment, J.Z. and C.S.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, M.L. and Z.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 51776016 and 52006136, Beijing Natural Science Foundation, grant number 3182030, and the National Key Research and Development Program, grant number 2017 YFB0103401.  
Not applicable.  
The data that support the findings of this study are available upon reasonable request.  
The authors declare that they have no conflict of interest.
## 2 Materials and Methods
### Particle Swarm Optimization  
The particle swarm optimization algorithm (PSO) stochastic metaheuristic computational method was developed by [PERSON] and [PERSON] in 1995 [17]. PSO and other metaheuristic algorithms do not guarantee a global optimal solution, yet they have proven to be essential in near-optimal solution searches in vast nonlinear multidimensional solution spaces [18]. The algorithm mimics the intelligence of a flock of birds (swarm), based on the socio-psychological correlations [19] expressed by mathematical formulae over the bird's (particle's) position and velocity. The position of a particle represents a possible solution, while velocity can be described as the pulling tendency of a particle in the direction of the next solution search. PSO enables an iterative solution search by moving particles through the search space, trying to improve the candidate's solution regarding the given fitness function representing a measure of quality. Fitness function represents how good the multidimensional position of an \(i\)-th particle is. Particles move according to their personal and global best positions. Conceptually, personal best (\(pbest_{id}\)) imitates a particle's autobiographical memory: it tends to push each particle to the place at which the current particle was most satisfied. Global best (\(gbest\)) is conceptually similar to a collective knowledge: it tends to pull all particles in the direction represented by the best position (achieved until now) of any particle in a swarm.  
In the first iteration, particles' positions are generated randomly as multidimensional constrained vectors, with initial velocities set to zero. Through the iteration process of the solution search, the particles' velocities \(v^{t}_{id}\) are altered with:  
\[v^{t+1}_{id}=wv^{t}_{id}+C_{1}rand_{1}*\left(pbest_{id}-x^{t}_{id}\right)+C_{2 }rand_{2}*\left(gbest-x^{t}_{id}\right) \tag{8}\]  
and positions \(x^{t}_{id}\) with:  
\[x^{t+1}_{id}=x^{t}_{id}+v^{t+1}_{id}. \tag{9}\]  
where:  
* _id_ subscript represents the current particle,
* \(t\) is the current iteration,
* \(w\) is the inertia weight coefficient,
* \(C_{1}\) and \(C_{2}\) are the acceleration coefficients, and
* \(rand_{1}()\) and \(rand_{2}()\) represent random number generators.  
To control the system's convergence tendencies, a set of restriction coefficients \(w\), \(C_{1}\) and \(C_{2}\) was adopted, according to [PERSON] and [PERSON]'s recommendations [20], that has proven to work sufficiently well on a variety of different optimization problems. The positions \(x^{t}_{id}\) were represented as external ANFIS input parameters. The PSO algorithm was set to stop after a certain number of iterations.
## 3 UAV Platform for Multi-Sensor Photogrammetry Aerial Mapping
### Multi-Sensors System for Photogrammetry Data Acquisition  
The aforementioned advances in technology and sensor minimization give us the opportunity to the development of unique, highly specialized multi-sensors system for photogrammetry data acquisition. Today, these systems can contain four independent types of cameras: RGB camera, thermal, multispectral, and hyperspectral camera. All these cameras complement each other in spectral,  
Figure 1: Photogrammetric multi-sensor systems on UAVs enable a fast and accurate collection of large amounts of spatial data for building damage detection and mapping. **(a)** Undamaged building, **(b)** visual inspection from street level, **(c)** post-earthquake assessment with UAV systems, **(d–h)** possible damages on a building after an earthquake.  
radiometric, and geometric characteristics. The new multi-sensor system is capable for the detection of damage and anomalies on a building that is not visible to the human eye. The importance of using various kinds of different cameras, from thermal to multispectral cameras for damage assessment and crack detection on buildings, was emphasized by numerous researchers [48; 52]. Although all these cameras were already used for building diagnostics assessment, so far, a multi-sensor system has not been developed for this purpose. It should be noted that the multi-sensor system should be modular, and it should be able to use different camera lenses, depending on building size and a priori accuracy. The newly built multi-sensor system should enable accurate data acquisition for building damage assessment, and the system harness the potential of all implemented cameras.  
After the design and development of the multi-sensor system, it is necessary to develop new methods for calibration, synchronization, and fusion of the collected data. For each camera, the development of the new calibration method based on photogrammetric methods must be obtained. The geometric calibration for internal camera parameters calculation must be done [68; 69] and followed by the radiometric calibration for each camera [70; 71; 72; 73]. Further, for the autonomous spatial data acquisition from all cameras in a multi-sensor system, the system synchronization is crucial [70; 74; 75]. Adequately synchronized multi-sensor system are able to autonomously and/or remotely capture images from all cameras at the same time. Once the data has been properly collected, it is necessary to research and develop new imagery fusion methods. The new fusion methods developed for the multi-sensor system with various independent cameras allow the collection of a fused image that contains the data of all the above sensors (visible part of the spectrum, thermal, multispectral and hyperspectral data). The fusion process increases the spatial resolution of final multi-band imagery. The usage of fused, high-resolution, multi-band imagery enables better and more accurate damage detection on buildings.
## 2 Materials and Methods
### Canopy Measurements
#### 2.4.2 Fractional Vegetation Cover Estimation  
Fractional vegetation cover (FVC), defined as the projected vegetated percentage of the total study area [61], was here used to track the development of the potato crop canopy between early and late measurements as well as between treatments. FVC was estimated from multispectral images collected from two separate flights on July 10 and 25, 2018, with a MicaSense RedEdge M (AgEagle Sensor Systems Inc., Wichita, KS, USA) sensor. The flight height was approximately 54 m above ground level, providing a ground sample distance of 3.55 cm. The images were processed to orthomosaics with a GSD of 3.5 cm using Agisoft PhotoScan Professional commercial software (AgiSoft LLC, St. Petersburg, Russia). From the multispectral data collected by the camera on board the UAV, we chose to use the NIR band (centre wavelength 842 nm, bandwidth 57 nm) to classify each point in each plot into two categories, 0 s and 1 s, based on the reflectance of each pixel, using histogram-based segmentation with the QGIS-program (QGIS Geographic Information System. QGIS Association. [[http://www.qgis.org](http://www.qgis.org)]([http://www.qgis.org](http://www.qgis.org)) (accessed on 1 September 2022)). On each plot, the ground consisted of 0 pixels and the vegetation consisted of 1 pixels. This segmentation from histogram data allowed us to estimate the fractional vegetation cover for each plot as the ratio of the number of canopy pixels to the total (canopy + ground) pixels. More sophisticated approaches to estimating FVC are commonly applied, as NIR may be problematic under full canopy development [61]. However, due to the uncertainty of the data captured by the Micasense RedEge M--sensor in the visible wavelength in our study, we selected the NIR--band only.