esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Digital Twin-Based Collision Avoidance:
## 2 Collision Prevention Mechanism
### System Design  
In addition to the architecture depicted in Figure 2, the communication strategy is detailed in Figure 3. The system comprises three primary components: the forklift truck, local IT infrastructure, and cloud-based services with internet connectivity. Beyond collision avoidance, the forklift is fitted with sensors for various applications. An onboard computing unit manages sensor data and controls certain forklift functionalities. The driver  
Figure 1: This is an illustration of a potential collision scenario involving two forklift trucks. The black arrows indicate the movement of the trucks. Trucks transmit their position to the base station (blue arrows). The forklift truck on the left is unable to see the oncoming forklift due to a blocked view. An external system uses the positions of the two forklifts to determine the probability for a collision. It may initiate an alarm or send a deceleration command to a vehicle if necessary (orange arrows).  
receives visual feedback through a display. The computing unit connects with external services, such as Real-Time Location Systems (RTLSs) or edge devices, via a 5G module. This module encompasses a local edge server linked to the 5G network, which is responsible for collating data from forklifts and potentially computing collision scenarios. Additionally, it acts as a gateway to other cloud services or the local network. Enterprise-specific services, like a Warehouse Management System (WMS), are integrated with the local edge server to enhance system capabilities. Internet connectivity allows for the remote monitoring of the logistics system via a web application.
# Application-Aware Network Traffic Management in MEC-Integrated Industrial Environments
## 5 SDN-eINP Interplay for Application-Aware Network Traffic Management
### Data Plane  
The data plane comprises backbone NDs as well as on- and off-premises ENs. It is worth remarking that NDs and ENs pursue different purposes, although working synergistically. On the one hand, NDs enforces flow prioritization and steering. On the other hand, ENs applies processing functions to the data carried by messages belonging to such flows, such as data filtering, aggregation, and encryption. From an operational point of view, both NDs and ENs provide a kind of processing. The difference lies in what they process. Specifically, the former process packet headers, whereas the latter processes packet payloads.  
Figure 3: Sequence diagram detailing how the architectural components interact with each other to deal with a harmful communication pattern. The IAM jointly orchestrates the SDN and eINP controllers, which, in turn, dictate the behavior of NDs and ENs, respectively.  
As messages pass through an EN, the first step is payload deserialization. On top of that, an EN can perform a broad spectrum of actions at different degrees of expressiveness. Note that eINP is not limited to data aggregation, filtering, and encryption, which, to some extent, are our supported primitives. Other more sophisticated capabilities may be easily implemented on top of the primitives, such as pattern-matching detection and message duplication. For instance, an EN may detect hazardous temperature levels and duplicate those messages to alert the affected operators. Although message duplication may promptly improve information circulation, it also increases bandwidth usage. Capabilities that may help in this regard are data aggregation and filtering. Indeed, data filtering permits one to drop messages if the target field is evaluated as of little application interest (e.g., vibration values within a safe range), whereas data aggregation consists of, for example, averaging a target field carried in a certain number of consecutive messages that belong to the same flow and sending out a single message containing the average value. To do so, an EN queues incoming messages on a flow basis and performs the aggregation function as soon as the queue amounts to a given threshold. With data encryption, on the other hand, an EN may use a symmetric key to encrypt packet payloads of a target flow as they leave the sender, and then another EN may decrypt them before they reach the receiver.
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 3 Analysis of the Influence of Horizontal Parameters of the Mega-Sub Isolated Structure
### Analysis of the Influence of the Horizontal Frequency of the Seismic Isolation Layer
#### 3.3.1 Influence of Frequency Ratio on Main Frame Response  
The variation curves of the peak displacement and acceleration dynamic response of the top floor of the main frame with the frequency ratio \(f\) are shown in Figure 5. It can be seen that the displacement and acceleration peaks change with the frequency ratio in a  
Figure 4: Variation of subframe dynamic response: (**a**) Acceleration change of the sub-frame; (**b**) Change in the displacement of the seismic isolation layer.  
relatively consistent manner, with the peak response decreasing first and then increasing, with the displacement and acceleration response peaks reaching a minimum when the frequency ratio \(f\) is around 0.6, which has a very good Isolation effect at this time.
## 2 Experimental
### Pilot Plant Description
#### 2.1.2 Measurement Equipment  
The pilot plant is equipped with measurement equipment for temperatures, pressures, mass flow rates, and flue gas composition at several measurement locations. The temperatures and pressures are measured inside the riser, the hot loop circulation, the flue gas path, and the peripheral systems. Orifice plates and venturi nozzles measure the flow rate of the combustion air and the flue gas. The mass flow rate of solids is measured either by weight decrease (the continuous measurement of fuel and sand) or weight increase (the discontinuous measurement of bottom ash and fly ash). Solid samples are taken from these positions to analyze the chemical composition and the physical/mechanical properties of the solids. The flue gas composition is measured at three locations. The volumetric concentration  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline \hline \multicolumn{1}{c}{Furnace inner diameter [m]} & 0.59 \\ \hline \multicolumn{1}{c}{Furnace outer diameter [m]} & 1.3 \\ \hline \multicolumn{1}{c}{Furnace height [m]} & 8.6 \\ \hline \multicolumn{1}{c}{Furnace free volume [m\({}^{3}\)]} & 2.37 \\ \hline \hline \multicolumn{1}{c}{Height: Solids inlet (fuel, sand) [m]} & 0.481 \\ \hline \hline \multicolumn{1}{c}{Height: Secondary air inlet 1 [m]} & 2.74 \\ \hline \hline \multicolumn{1}{c}{Height: Secondary air inlet 2 [m]} & 6.0 \\ \hline \hline \multicolumn{1}{c}{Height: Loop seal solid recirculation to riser [m]} & 0.481 \\ \hline \hline \multicolumn{1}{c}{Height: Burner air inlet [m]} & 0.699 \\ \hline \hline \multicolumn{1}{c}{Height: Temperature sensors [m]} & 0.25, 1.12, 1.55, 2.38, 6.25, 8.21 \\ \hline \hline \multicolumn{1}{c}{Height: Pressure sensors [m]} & 0.11, 0.22, 0.4, 0.58, 0.91, 1.1, 2.07, 3.42, 7.31, 8.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of the furnace.  
(dry state) of oxygen, carbon dioxide, carbon monoxide, nitrogen oxide, and sulfur dioxide is measured after the cyclone with a paramagnetic sensor for O\({}_{2}\) and an NDIR (nondispersive infrared) sensor for the other gases. Before and after the fabric filter, the gas composition is measured with two FTIR (Fourier-transform infrared spectroscopy) measurement devices, which are additionally equipped with oxygen sensors. Besides the main gas components (O\({}_{2}\), CO\({}_{2}\), H\({}_{2}\)O), the concentration of trace gases (HCl, NOx, SO\({}_{2}\), NO, CH\({}_{4}\), CO) can be measured with these FTIRs.
## 2 Materials and Methods
### Methods
#### 2.3.1 Initial Feature Extraction and Optimization  
Initially, this study acquired spectral features and supplemented them with vegetation indices, terrain factors, and other variables to create a multispectral feature dataset. Subsequently, random forest was utilized to evaluate the importance of these features, and their importance was ranked accordingly. Finally, a new feature set was chosen based on the ranking results. The Sentinel-2 composite imagery comprises nine spectral bands, used to compute a series of vegetation indices. These indices include the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), Normalized Difference Tillage Index (NDTI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Built-up Index (NDBI). The formulas for computing these indices are provided in Table 3. BLUE in the table represents blue band B2, GERRN represents green band B3, RED represents red band B4, NIR represents near-infrared band B8, SWIR1 represents shortwave infrared band B1, and SWIR2 represents shortwave infrared band B12.  
Figure 3: Technical flowchart.  
Initially, this study acquired spectral features and supplemented them with vegetation indices, terrain factors, and other variables to create a multispectral feature dataset. Subsequently, random forest was utilized to evaluate the importance of these features, and their importance was ranked accordingly. Finally, a new feature set was chosen based on the ranking results. The Sentinel-2 composite imagery comprises nine spectral bands, used to compute a series of vegetation indices. The formulas for computing these indices are provided in Table 3.  
The mean, maximum, and standard deviation of each index were computed as classification features after acquisition. Additionally, three terrain features, including slope, aspect, and elevation extracted from DEM data, were incorporated. A total of 17 spectral, vegetation index, and terrain features were obtained.
## 7 Discussion  
This research aimed to firstly analyze the different road user behavior and secondly to find out the objective measures that play a significant role in describing and reflecting human personal understanding of danger in a traffic event under heterogeneous traffic conditions.  
Firstly, after analyzing the road user behavior at multiple intersections, it can be recommended that more attention should be given to educating drivers on road safety, as just focusing on infrastructure will not be sufficient. In education, they can be told about stopping, yielding at intersections and being more observant of traffic and pedestrians coming from different arms. This will aid in reducing the number of critical interactions.  
One of the important findings of this study is that the existence of evasive action plays a critical role in determining the degree of perceived danger. As seen in Model A, the proxy indicator associated with evasive action was deemed significant and ranked second in significance. It appears that the severity of a situation can be concluded by the relevant information already present in its initial conditions. Secondly, no individual indicator could adequately explain the perceived level of risk in an interaction. The results presented in Tables 5-8 above indicate that the variables related to the proximity (time, distance) and severity of the consequences (speed, deceleration rate) have the highest contribution for all the models.  
Figure 7: Confusion matrix for developed models.  
Model D incorporates a traffic situation from all three phases (preliminary, peak and end), and it was observed that the most significant contributions were estimated in the preliminary phase, as seen in Figure 7. However, the peak phase made some notable contributions, while the end phase did not offer much important information. This observation is similar to the study conducted in 1991 and a recent study (2022), in which the author mentioned that the observers could judge the risk of a traffic conflict based on the first few seconds of its development [11; 46].
## 4 Results  
The initial screening results of the segmentation points based on the adaptive chord inclination angle are shown in Figure 9 and Section 3.2. The precise screening results of the segmentation points based on the improved NMS are shown in Figure 18 and Section 3.3.  
After confirming the location of the segmentation point of the graphic element, the graphic element needed to be fitted to determine its key parameters. If we directly fitted the graphic element contour, it resulted in a situation where the fitting result was not closed, as shown in Figure 21. Therefore, the graphic element was fitted with a two-point constraint. The two endpoints were noted as \((x_{0},y_{0})\) and \((x_{1},y_{1})\).  
For a straight-line segment, two points can determine all the parameters of the straight line. The equation of a straight line is given in Equation (20):  
\[\left\{\begin{array}{l}y=y_{0}\quad(y_{0}=y_{1})\\ x=x_{0}\quad(x_{0}=x_{1})\\ y=k\cdot x+b\end{array}\right.\quad k=\frac{y_{1}-y_{0}}{x_{1}-x_{0}},\ b=y_{0}-k \cdot x_{0} \tag{20}\]  
The inclination angle of a straight-line segment is given in Equation (21):  
\[angle=\text{arc}\tan\left(\frac{\overset{\rightarrow}{P_{0}}\overset{ \rightarrow}{\underset{x}{\rightarrow}}\overset{\rightarrow}{\underset{y}{ \rightarrow}}}{\overset{\rightarrow}{P_{0}}\overset{\rightarrow}{\underset{x }{\rightarrow}}}\right)\times 180^{\circ}\div\pi \tag{21}\]  
where \(\overset{\rightarrow}{x}\) is the positive direction of the x-axis.  
For a circular arc segment, the equation of a circle is as follows: \(x^{2}+y^{2}+a\cdot x+b\cdot y+c=0\). Then, the coordinates of the center of the circle are \(\left(-\frac{a}{2},-\frac{b}{2}\right)\), and the radius is \(R=\frac{\sqrt{a^{2}+b^{2}-4c}}{2}\). We consider \(x\) as the set of x-coordinates of all contour points in the element and \(y\) as the set of y-coordinates of all contour points in the element.  
When \(y_{0}\
eq y_{1}\), the coefficients in the circle equation can be calculated by Equations (22)-(24).  
\[a=-\frac{J}{\sum H^{2}} \tag{22}\]  
\[b=D+a\cdot E \tag{23}\]  
\[c=F+a\cdot G \tag{24}\]  
\[D=\frac{\left({x_{0}}^{2}-{x_{1}}^{2}\right)+\left({y_{0}}^{2}-{y_{1}}^{2} \right)}{y_{1}-y_{0}}\]  
\[E=\frac{x_{0}-x_{1}}{y_{1}-y_{0}}\]  
\[F=\frac{\left({y_{1}}{\cdot}{x_{0}}^{2}-{y_{0}}{\cdot}{x_{1}}^{2}\right)+\left( {y_{1}}{\cdot}{y_{0}}^{2}-{y_{0}}{\cdot}{y_{1}}^{2}\right)}{y_{0}-y_{1}}\]  
\[G=\frac{\left({x_{0}}{\cdot}{y_{1}}-{x_{1}}{\cdot}{y_{0}}\right)}{y_{0}-y_{1}}\]  
\[H=x+E\cdot y+G\]  
Figure 21: The graphic element fitting results were not closed.  
When \(y_{0}=y_{1}\),the coefficients in the circle equation can be calculated by Equations (25)-(27).  
\[a=\frac{\left({x_{0}}^{2}-{x_{1}}^{2}\right)+\left({y_{0}}^{2}-{y_{1}}^{2}\right) }{x-x_{0}} \tag{25}\]  
\[b=\frac{E-F}{\sum y^{2}-\frac{1}{n}{\left(\sum y\right)}^{2}} \tag{26}\]  
\[c=-\sum\frac{D+b\cdot y}{n} \tag{27}\]  
\[D=x^{2}+y^{2}+a\cdot x\]  
\[E=\frac{1}{n}\sum D\cdot\sum y\]  
\[F=\sum(D\cdot y)\]  
\[n=\text{length}(x)\]  
The results of fitting to the pending inspection image are shown in Table 4.
## 2 Materials and Methods
### Settings of EDEM Contact Model and Theoretical Basis
The general calculative equation could be expressed as:  
\[V=k_{\rm a}\frac{W}{H}s \tag{9}\]  
where \(V\) is the volume of geometric wear, \(W\) is the normal load, \(s\) is the friction distance, \(H\) is the hardness of the material, \(k_{\rm a}\) is the abrasive wear constant which is usually determined by the hardness, shape and the number of abrasive particles for cutting. The EDEM software simplified the setting of Archard Wear model parameters, which means it only needed to set the value of \(k_{\rm a}/H\). In this study, by referring to investigations by [PERSON] [49], the value of \(k_{\rm a}/H\) was set as 4.4 \(\times\) 10\({}^{-11}\). In the EDEM approach, as presented in Equation (10), to show the wear morphology of the geometry surface, each particle contact unit sets a wear depth \(d\) to represent the wear amount of the geometry. \[d=\frac{V}{S} \tag{10}\]  
where \(S\) is the contact area between the geometry and the particle. Considering the calculation time of the simulation and the size of the geometry grid, the radius of the particles was set to 0.2 mm; the angle between the particle flow and the test surfaces was 30\({}^{\circ}\); the particle velocity was 1 m/s, the number of particles generated per second in the particle factory was 2.0 \(\times\) 10\({}^{4}\); and the total number of particles that participated in the calculation was 4.0 \(\times\) 10\({}^{5}\).
## 3 Mathematical Model for Optimal Trajectory Planning  
A technique in this paper is introduced based on a distributed path planning, where each UAV is equipped with a PSO optimization algorithm. The trajectories are, thus, not computed in a central unit, but in a distributed manner on the swarm of UAV. Therefore, the communication between the swarm of UAV is only used to share position and location among themselves, which is considered as a target to be tracked; each UAV then computes its trajectory. Therefore, this results in a distributed path planning. To design a mathematical model for an optimal trajectory planner we designed the required functions to get dynamic fitness values. The design of function consists of three types, such as fitness function, objective function, and constraint function. The mathematical model for these optimal functions is discussed in the following sections.
## 2 Theoretical Description
### Parameter Estimation  
After the model is established, parameters \(k_{1},k_{2}\), \(\sigma_{1}\) and \(\sigma_{2}\) need to be estimated according to the wavelet coefficients of the noisy bearing fault signals. At present, there are many mature iterative optimization algorithms to estimate the parameters of the model, such as gradient descent method, Gibbs sampling method and EM algorithm. The expectation-maximization (EM) algorithm has been successfully applied to the optimization of the sparse representation model and parameter estimation of mixture models in many fields [31]. Usually, the computational complexity of the EM algorithm for the one-dimensional signal is not very high. So, the EM algorithm is adopted in this paper to estimate the four parameters.  
EM algorithm needs to set initial values for each parameter. In the proposed method, \(k_{1}\), \(k_{2}\), \(\sigma_{1}\) and \(\sigma_{2}\) is needed to be initialized. And then a series of expectation and maximization steps will alternate. The E-step updates the responsibility factors  
\[q_{i}(n)\leftarrow\frac{k_{i}\text{LapGauss}(y_{n},\sigma_{1},\sigma_{n})}{k_ {1}\text{LapGauss}(y_{n},\sigma_{1},\sigma_{n})+k_{2}\text{LapGauss}(y_{n}, \sigma_{2},\sigma_{n})},\ i=1,\ 2. \tag{26}\]  
Next the updated parameters \(k_{1}\), \(k_{2}\), \(\sigma_{1}\) and \(\sigma_{2}\) are estimated in the M-step. \(k_{1}\) and \(k_{2}\) are estimated by  
\[k_{i}\leftarrow\frac{1}{N}\sum\limits_{n=1}^{N}q_{i}(n),\ i=1,\ 2. \tag{27}\]  
Then \(\sigma_{y(i)}^{2},\ i=1,\ 2\) are estimated as weighted sum of the data values [32]. As to \(\sigma_{y(i)}^{2}\), the weight for \(y_{n}\) is the probability that the data point \(y_{n}\) is generated by \(\text{LapGauss}(y,\sigma_{w},\sigma_{n})\)  
\[\sigma_{y(i)}^{2}\leftarrow\frac{\sum\limits_{n=1}^{N}q_{i}(n)y_{n}^{2}}{ \sum\limits_{n=1}^{N}q_{i}(n)},\ i=1,\ 2. \tag{28}\]  
Finally, as the wavelet coefficients \(w\) and \(n\) are independent, the standard deviation \(\sigma_{1}\) and \(\sigma_{2}\) can be calculated by  
\[\sigma_{i}^{2}\ =\ \sigma_{y(i)}^{2}-\sigma_{n}^{2},\ i=1,\ 2. \tag{29}\]  
And it is possible that the estimated variance \(\sigma_{1}^{2}\) and \(\sigma_{2}^{2}\) will be negative because these parameters are all estimated. So, in order to let the value under the radical sign not to be negative and the denominator not to be zero, \(\sigma_{1}\) and \(\sigma_{2}\) are estimated as [32]:  
\[\sigma_{i}=\sqrt{\text{max}\bigg{(}\sigma_{y(i)}^{2}-\sigma_{n}^{2},\frac{ \sigma_{n}^{2}}{250}\bigg{)}},\ i=1,\ 2. \tag{30}\]
## 3 Experimental Results
### Impact Behavior
#### 3.2.3 Local Pre-Stressing Effect  
Heating the composite specimens by up to 150 \({}^{\circ}\)C led to the formation of multiple hairline cracks of less than 10 \(\upmu\)m in width at the front surface of the specimen, as shown in Figure 10a. The heat treatment also led to strength degradation in the ECC specimens incorporating PVA fibers due to the melting of the fibers in the proximity of the heating nozzle, as shown in Figure 10b. Figure 11 exhibits the accumulated energy dissipated by heat-treated and non-heat-treated ECC specimens. For instance, the energy dissipation capability of the heated ECC2-0 specimens decreased by about 9.9% compared to that of its non-heated counterpart. Conversely, the hybrid SMA-PVA fiber-reinforced specimens displayed different performance after the heating process, as demonstrated in Figure 11. For example, the energy absorption capacity of the heated ECC2-0.5, ECC2-1, and ECC2-1.5 specimens increased by about 20.1%, 32%, and 26.56% compared to that of its non-heated counterparts, respectively. This can be ascribed to the shape memory effect of the heated SMA fibers, which led to a local pre-stressing effect of the specimens, consequently enhancing its energy dissipation capability. This highlights the crack arresting capability of SMA fibers in concrete structures, even when affected by high temperature.  
Figure 9: Impact load-time history curves for different composite specimens at different drop levels: (**a**) 1 st hit, (**b**) 2 nd hit, and (**c**) 3 rd hit.
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics were computed for all the variables. Data distribution was evaluated through a Shapiro-Wilk normality test and using histograms, boxplots and Q-Q plots. The presence of statistically significant differences between the T1 and the T0 measurements for all the variables was assessed using a paired samples \(t\)-test or a Wilcoxon signed rank test, depending on the type of data distribution. In order to evaluate the presence of factors that could explain the onset or the persistence of CSA, the sample was divided into two groups, one composed of subjects showing the outbreak or the increase of CSA events from T0 to T1, and another composed of subjects that never showed or discontinued CSA events; next, an independent samples \(t\)-test or a Mann-Whitney U-test was used to compare OSA, AHI, ODI, age, BMI, and the amount of mandibular advancement between the two groups. The three outlier cases were included in the latter analysis. The statistical analysis was performed using SPSS software (SPSS Statistics for Windows, Version 20.0., IBM Corp, Armonk, NY, USA). Type I error was set as \(p=0.05\).  
Figure 1: Mandibular Advancement Device. (**a**) left lateral view, (**b**) frontal view, (**c**) right lateral view.  
A power analysis (G*Power 3.1.9.2, [PERSON], Universitat Kiel, Kiel, Germany) revealed that to detect a large effect size of 0.5 with a Wilcoxon signed rank test, considering an alpha of 0.05 and a power of 0.95, 47 subjects would be needed [26].