esa-sceva/satcom-chunk-collection huggingface dataset:
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 2 Related Work
Safety conditions are the main consideration of previous studies on road intersections controlled by stop signs. [PERSON] et al. [15] proposed a set of protocols to manage vehicles' movements at these intersections safely. After that, these protocols were investigated in more realistic models and higher-concurrency aspect scenarios [16]. This is in order to prepare these protocols for autonomous driving scenarios. [PERSON] [17] proposed a traffic control system for autonomous vehicles at stop sign intersections. This system first detects all vehicles and pedestrians approaching the investigated road intersection. Then, it setsthe rules regarding the priorities assigned for vehicles to pass through that intersection. Moreover, [PERSON] et al. [PERSON] et al. (2018) introduced another comprehensive traffic control system for autonomous vehicles following the traffic rules while traveling over the road network. It includes detecting road intersections controlled by stop signs and investigating the competing traffic flows. Then, it recommends that vehicles apply the driving rules there to ensure the safe sharing of that intersection among competing flows. On the other hand, several research studies have tested smart stop-controlled intersection systems on naturalistic and GPS traffic data. [PERSON] and [PERSON] [PERSON] and [PERSON] (2019) explored the driver's behaviors at all-way stop-controlled intersections with SHRP2 naturalistic driving data. From this study, the full stop rate at these intersections is only 20.2%. The main factors that influence stop decisions are drivers, vehicles, intersection geometry, maneuvering, and environmental features. Data collection methods could not determine when and where drivers began to decelerate. The normal deceleration behavior of current passenger vehicles is evaluated at stop-sign-controlled intersections on urban streets on the basis of in-vehicle Global Positioning System data [PERSON] and [PERSON] (2019). Finally, the traffic data are utilized for predicting crash-relevant violations at stop-sign-controlled intersections [PERSON] and [PERSON] (2019). This mainly helps to develop safer intersection driver assistance systems. In this work, we are introducing a new driving assistance protocol for intelligent connected vehicles. This protocol alerts drivers of the stop signs' existence. Then, it declares the driving rules around these stop signs. The proposed protocol assists drivers in making smooth and safe stops. It recommends the optimal deceleration rate for each vehicle based on the distance between that vehicle and the targeted intersection. Moreover, the protocol accurately considers the traveling speed of the recommended vehicle. Several stop sign intersection scenarios have been investigated and considered in this work. This provides a more comprehensive solution compared to previous studies in this field.
# Single-Sheet Separation from Paper Stack Based on Friction Uncertainty Using High-Speed Robot Hand
## 3 Trajectory Generation of Robot Hand for Paper Separation
### Trajectory Generation of Fingers  
The fingertip behaviors of the feeding and returning fingers are generated using periodic trajectories. The trajectory is designed using a simple sinusoidal function for the joint trajectory, and the parameters are determined such that the fingertip performs a pseudo-elliptical motion. This allows the finger to move backward at the start of the stroke, enabling the paper to move repeatedly in the same direction. To prioritize high-speed movement, an elliptical trajectory is adopted rather than a linear trajectory parallel to the paper surface. For the two fingers of the robotic hand, let \(\theta_{i}\) be the proximal and distal joint angles of the feed and return fingers, respectively. For amplitude \(a_{i}\), offset \(\beta_{i}\), angular velocity \(\omega_{i}\), and phase \(\phi_{i}\), the joint motion of \(\theta_{i}\) can be expressed as follows for \(i=1,\cdots,4\):  
\[\theta_{i}=\alpha_{i}\sin(\omega_{i}t+\phi_{i})+\beta_{i}\;. \tag{7}\]  
The angular velocities of the finger joints are set to the same value, and the phases of the joint angles on the proximal and distal parts are shifted by \(\frac{\pi}{2}\).  
The camera detects the position of the partially separated paper bundle \(\mathcal{P}^{i}_{\text{move}}\) and switches from a feed operation to a return operation when two or more sheets of paper pass through a certain threshold position \(x_{\text{thd}}\) set in the \(x\) direction. The robot repeats the return operations until only A\({}_{1}\) remains among the papers that have passed the position \(x_{\text{thd}}\) and then switches to the feed operation. By continuously performing these operations, it is possible to remove the topmost sheets of paper sequentially.  
Figure 5: State transition of the paper bundle. The figure is top view and different color boxes indicate individual subsets of the paper bundle.
## 3 System Overview
### Bandwidth Allocation Framework Based on Congestion Control
When CUBIC is in a deterministic loss model with packet loss rate \(p\), the relationship between Cubic's average sending window size \(AVG\_C\) and the multiplicative subtraction factor \(\beta\) can be expressed by the following function [14]:  
\[AVG\_C=0.4(\frac{3+\beta}{4(1-\beta)})^{\frac{1}{2}}(\frac{RTT}{p})^{\frac{3} {4}} \tag{4}\]  
where \(p\) is the packet loss rate, and \(RTT\) is the round-trip time. For delay-based congestion control FAST, the video stream can set the congestion window size to:  
\[cwnd^{\prime}=(cwnd\frac{RTT}{RTT_{min}}+w_{i}) \tag{5}\]  
Research has proved that this method can also achieve bandwidth allocation proportional to the weight \(w_{i}\).
## 4 Results and Discussion
### Case 1: Minimization of Fuel Cost  
The ESDO algorithm has been applied to find the minimum total fuel cost of generating units, which is needed to meet the system load demands at each time interval and satisfy the generator limits. The quadratic nature of the fuel cost function involves the optimization of active power output at each generator. ESDO served efficiently in balancing exploration and exploitation while determining an optimal set of generator outputs to minimize the fuel consumption cost over the power system. The solution of Case 1 illustrated that ESDO outperformed the traditional algorithms such as PSO and GA by determining a minimum total operating cost along with faster convergence and more reliable avoidance of local optima. Being able to dynamically explore the search space, ESDO found cost-effective generation schedules that minimized fuel expenditure overall and were equally applicable for large-scale power systems where fuel cost minimization is one of the major economic concerns. The suggested algorithms attempt to decrease operating costs by decreasing generator fuel consumption; hence, Equation (21) is used as the objective function for the OPF algorithm. In Table 4, the optimal values of the control parameters for ESDO are shown and compared to other algorithms. These results show that the proposed algorithm gives the lowest fuel cost most quickly, indicating its superior efficiency and effectiveness on all other techniques. The convergence curves shown in Figure 4 expressed the performances of PSO, MVO, GOA, HHO, HO, and ESDO in reducing fuel costs with respect to the iterations. The \(y\)-axis is fuel cost in USD/h, and the \(x\)-axis is the number of iterations. The black curve is representative of ESDO, which has the lowest cost of fuel achieved in the fewest number \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Line Tag** & **From Bus** & **To Bus** & **R (p.u.)** & **X (p.u.)** & **B (p.u.)** & **Tap Settings** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._of iterations; therefore, this shows better efficiency and optimization efficacy. All of the mentioned nature-inspired optimization algorithms converge more slowly and result in a higher final cost compared with ESDO. HHO and HO show intermediate performance, they converge faster than PSO, MVO, and GOA, but do not achieve as low a cost as ESDO. The results obtained show that, overall, ESDO has an edge over the other algorithms in reaching the optimal solution with a smaller number of iterations.
## 4 A Comparison between the Two Measurement Systems  
At first, a comparison between the results obtained from the uncertainty analysis for both image acquisition systems has been carried out; from the results obtained through the analysis software it has been possible to collect a series of measurements attributable to the comb-drive angular displacement depending on the voltage supply. In particular, 12 measurements were obtained for each applied voltage value, for the OPS, and 16 measurements for each applied voltage value, for the LTM system. In a second stage, it has been necessary to verify whether the two sets of results are comparable, within the interval of the experimental uncertainties.  
To verify the initial hypothesis that, using two different measurement systems, the angular displacement measurement of the comb-drive is the same, the approach described in Ref. [50] has been followed. Considering the average values, the total uncertainties \(\delta_{T}\) of OPS and LTM have been obtained by using Equation (2) and then reported in the form of standard deviations in Table 4. The available data are expressed in the form:  
\[\hat{X} =\bar{X}\pm\delta_{X} \tag{9}\] \[\hat{Y} =\bar{Y}\pm\delta_{Y} \tag{10}\]  
where \(\bar{X}\) and \(\bar{Y}\) are the mean values of \(X\) and \(Y\) (OPS and LTM measurements, respectively), while \(\hat{X}\) and \(\hat{Y}\) represent the measured values according to the standard [47]. To evaluate whether the two different measurements can be considered consistent or not, it is necessary to find the best estimate for the difference \(\Delta_{XY}=\hat{X}-\hat{Y}\) and establish the highest and the lowest probable values of \(\Delta_{XY}\). The highest probable value for \(\Delta_{XY}\) is obtained if \(\bar{X}\) assumes its higher probable value, \(\bar{X}+\delta_{X}\), and at the same time \(\hat{Y}\) assumes its lowest probable value, \(\bar{Y}-\delta_{Y}\). In this way the highest probable value for \(\Delta_{XY}\) is  
\[p_{\text{max}}=\left(\bar{X}+\delta_{X}\right)-\left(\bar{Y}-\delta_{Y}\right) =\left(\bar{X}-\bar{Y}\right)+\left(\delta_{X}+\delta_{Y}\right)\;.\]  
Combining (11) and (12), the difference between the measured values is  
\[\Delta_{XY}=(\bar{X}-\bar{Y})\pm(\delta_{X}+\delta_{Y})\;. \tag{13}\]  
The evaluation of the difference \(\left|\bar{X}-\bar{Y}\right|\) and the sum \(\delta_{X}+\delta_{Y}\) is fundamental for the sake of our investigation. In fact, if the difference \(\left|\bar{X}-\bar{Y}\right|\) has the same order of magnitude as, or even less than, the sum \(\left|\bar{X}-\bar{Y}\right|\), then the two different systems, namely OPS and LTM, measure the angular displacement of the comb-drive without significant difference.
## 3 Results and Discussion
### Thickness Changes of Interfacial IMC Layer during Isothermal Aging
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Aging Hour (h)** & **72** & **240** & **528** & **840** \\ \hline
**Solder** & **Interfacial IMC Layer** & \multicolumn{4}{c}{**Thickness (\(\mu\)m)**} \\ \hline  & Total (Cu\({}_{6}\)Sn\({}_{5}\)+Cu\({}_{5}\)Sn) & 6.42 & 8.04 & 8.9 & 10.2 \\ SAC0307 & Cu\({}_{5}\)Sn & 1.93 & 3.05 & 3.96 & 4.9 \\  & Cu\({}_{6}\)Sn\({}_{5}\) & 4.49 & 4.99 & 4.94 & 5.3 \\ \hline  & Total (Cu\({}_{6}\)Sn\({}_{5}\)+Cu\({}_{5}\)Sn) & 3.9 & 4.47 & 5.56 & 6.5 \\ SAC0307-0.05 Pr & Cu\({}_{5}\)Sn & 1.47 & 2.11 & 3.34 & 3.9 \\  & Cu\({}_{6}\)Sn\({}_{5}\) & 2.43 & 2.36 & 2.22 & 2.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed thickness change with aged time at the interfaces of solders/Cu. Figure 6: Thickness of the (**a**) total interfacial IMC layers (IMLs) and (**b**) Cu\({}_{5}\)Sn IML at (**a**) SAC0307 solder/Cu interface and (**b**) SAC0307-0.05 Pr solder/Cu interface aged at 150 \({}^{\circ}\)C for different hrs. The other is through sacrificing interfacial Cu\({}_{3}\)Sn,  
\[2\text{Cu}_{3}\text{Sn}+3\text{Sn}\text{$\rightarrow$Cu}_{6}\text{Sn}_{5} \tag{5}\]  
For producing interfacial Cu\({}_{3}\)Sn IMCs, two approaches also can be reached. One is also forming directly:  
\[3\text{Cu}+\text{Sn}\text{$\rightarrow$Cu}_{3}\text{Sn} \tag{6}\]  
The other is developing through consuming interfacial Cu\({}_{6}\)Sn\({}_{5}\) IMCs:  
\[\text{Cu}_{6}\text{Sn}_{5}+9\text{Cu}\text{$\rightarrow$5 Cu}_{3}\text{Sn} \tag{7}\]  
It can be seen that for directly forming interfacial Cu\({}_{6}\)Sn\({}_{5}\) and Cu\({}_{3}\)Sn IMCs (Equations (4) and (6)), per unit of Cu\({}_{6}\)Sn\({}_{5}\) requires more Sn atoms than per unit of Cu\({}_{3}\)Sn. For indirectly forming them (Equations (5) and (7)), Cu\({}_{3}\)Sn just needs abundant supply of Cu atoms, whereas that of Cu\({}_{6}\)Sn\({}_{5}\) still needs the supply of Sn atoms. Therefore, it can be concluded that what controls Cu\({}_{6}\)Sn\({}_{5}\) IMCs' growth is the Sn atom diffusion and the solder/Cu\({}_{6}\)Sn\({}_{5}\) interface state, while Cu atom diffusion determines the interface state of Cu\({}_{3}\)Sn/Cu\({}_{6}\)Sn\({}_{5}\) and determines the development of Cu\({}_{3}\)Sn. Obviously, most Sn source comes from Sn-based solder, while most Cu source is from Cu substrate. Although most Pr atoms were adsorbed on the Cu substrate, hindering Cu diffusion in the initial reaction period, its inhibition effect was mainly on Sn atom diffusion with the growth of Cu\({}_{6}\)Sn\({}_{5}\). Therefore, the interfacial Cu\({}_{6}\)Sn\({}_{5}\) IMC growth was affected more than Cu\({}_{3}\)Sn IMCs.
# Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control
## 1 Introduction
#### Notations and Abbreviations  
All random variables are distinguished from the deterministic variables in notation by the accent \(\hat{\mathbf{\ast}}\). Namely, \(\hat{x}\) is a random variable, whereas, \(x\) is deterministic. When \(\hat{x}\) is normally distributed with expected value \(\mu\) and variance \(\Sigma\), we write that \(\hat{\delta}\sim\mathcal{N}(\mu,\Sigma)\). When \(\hat{\delta}\sim\mathcal{N}(\mu,\sigma^{2})\) is a scalar-valued Gaussian variable, the confidence intervals \(\mu\pm 1\sigma=[\mu-1\sigma,\mu+1\sigma]\) and \(\mu\pm 2\sigma=[\mu-2\sigma,\mu+2\sigma]\) of probability levels \(68.2\%\) and \(95.4\%\) are called the \(1\sigma\) and \(2\sigma\) confidence intervals, respectively. The value of a time series \(x:\{0,1,\dots\}\to\mathbb{R}^{n}\) at time instant \(k\) is denoted by \(x_{k}\). Each constant or variable, which denotes a given number of people, is denoted by a boldface letter, e.g., \(\mathbf{N}\) constitutes the number or people in a community or the population of a country. When \(A\in\mathbb{R}^{n\times m}\) is a matrix, \(\text{He}\{A\}\) stands for \(A^{\top}+A\), where \(A^{\top}\) is the transposition of \(A\). Let \(x=(x_{1},x_{2},\dots,x_{n})\) denote \(x=\left(x_{1}^{\top}\ x_{2}^{\top}\ \dots\ x_{n}^{\top}\right)^{\top}\). The matrix-valued functions \(\frac{\partial f}{\partial x}:\mathbb{R}^{n+m}\to\mathbb{R}^{p\times n}\) and \(\frac{\partial f}{\partial(x,y)}:\mathbb{R}^{n+m}\to\mathbb{R}^{p\times(n+m)}\) (with arguments \((x,y)\in\mathbb{R}^{n+m}\)) denote the Jacobian of function \(f:\mathbb{R}^{n+m}\to\mathbb{R}^{p}\) with respect to (w.r.t.) \(x\in\mathbb{R}^{n}\) and \((x,y)\in\mathbb{R}^{n+m}\), respectively. Furthermore, the value of \(\frac{\partial f}{\partial x}\) at \(x_{0}\in\mathbb{R}^{n}\) and \(y_{0}\in\mathbb{R}^{m}\) is referred to as \(\frac{\partial f}{\partial x}(x_{0},y_{0})\in\mathbb{R}^{p\times n}\). The Euclidean norm of a vector \(\mathbb{R}^{n}\) is \(\left\|x\right\|\), whereas the weighted norm of \(x\) w.r.t. the symmetric and positive definite matrix \(Q\) is referred to as \(\left\|x\right\|_{Q}\), namely \(\left\|x\right\|_{Q}^{2}=x^{\top}Q\ x\). Finally, let \(\mathbb{I}_{a}^{b}=\{a,a+1,\dots,b\}\) denote the set of integers between \(a\) and \(b\).
###### Abstract  
Mapping and identifying allergenic tree species in densely urbanized regions is vital for understanding their distribution and prevalence. However, accurately detecting individual allergenic tree species in urban green spaces remains challenging due to their smaller site and patchiness. To overcome these issues, PlanetScope (PS) satellite imagery offers significant benefits compared with moderate or high-resolution RS imagery due to its daily temporal resolution and 3 m spatial resolution. Therefore, the primary objectives of this research were to: assess the feasibility of mapping allergenic tree species in the highly urbanized area using high-resolution PS imagery; evaluate and compare the performance of the most important machine learning and feature selection methods for accurate detection of individual allergenic tree species. The research incorporated three classification scenarios based on ground truth data: The first scenario (CS1) used single-date PS imagery with vegetation indices (VI), while the second and third scenarios (CS2 and CS3) used multitemporal PS imagery with VI, and GLCM and VI, respectively. The study demonstrated the feasibility of using multitemporal eight-band PlanetScope imagery to detect allergenic tree species, with the XGB method outperforming others with an overall accuracy of 73.13% in CS3. However, the classification accuracy varied between the scenarios and species, revealing limitations including the inherent heterogeneity of urban green spaces. Future research should integrate high-resolution satellite imagery with aerial photography or LiDAR data along with deep learning methods. This approach has the potential to classify dominant tree species in highly complex urban environments with increased accuracy, which is essential for urban planning and public health.  
urban green area; random forest; neural network; extreme gradient boosting; PlanetScope; feature selection +
Footnote †: journal: Medical Image Computing  
1
###### Abstract  
Remote sensing technology plays an important role in monitoring rapid changes of the Earth's surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven't been designed yet. This paper proposes an improved spatial and temporal adaptive reflectance fusion model (STARFM) with the help of an Unmixing-based method (USTARFM) to generate the high spatial and temporal data needed for the study of heterogeneous areas. The results showed that the USTARFM had higher accuracy than STARFM methods in two aspects of analysis: individual bands and of heterogeneity analysis. Taking the predicted NIR band as an example, the correlation coefficients (\(r\)) for the USTARFM, STARFM and unmixing methods were 0.96, 0.95, 0.90, respectively (\(p\)-value \(<\) 0.001); Root Mean Square Error (RMSE) values were 0.0245, 0.0300, 0.0401, respectively; and ERGAS values were 0.5416, 0.6507, 0.8737, respectively. The USTARM showed consistently higher performance than STARM when the degree of heterogeneity ranged from 2 to 10, highlighting that the use of this method provides the capacity to solve the data fusion problems faced when using STARFM. Additionally, the USTARFM method could help researchers achieve better performance than STARFM at a smaller window size from its heterogeneous land surface quantitative representation.  
unmixing-based method; spatial and temporal adaptive reflectance fusion model (STARFM); heterogeneity; data fusion; Landsat 8; MODIS +
Footnote †: journal: Sensors
## 4 Model Simulations with FEM-3D
### Simulation Results in Standstill Conditions
#### 4.3.1 Simulation of the Original Model  
In this section, we describe the experiences with model 1. Figure 11 shows the principal forces in this model: thrust, levitation, and transversal. In this case, the levitation force (red line) was higher than the thrust force (blue line). If the average value of the forces was taken as a representative value of their evolution, the levitation force reached around 400 N, in comparison with the thrust force which was nearly to 80 N. It denotes that the levitation force is very important in a LIM, instead, it does not exist in a rotatory induction motor. The transverse force (green line) was not relevant in this model, its value is only 12 N, because the moving and fixed parts were aligned.  
Equation (16) explains the generation of the thrust force. It is important to remark that during the simulation, the layer of iron located over the aluminum plate was assigned an electrical conductivity equal to zero, \(\sigma_{Fe}\) = 0 S/m. Consequently, eddy currents were canceled inside this region, therefore  
Figure 11: Forces in model 1.  
Figure 10: Transversal view of magnetic flux in model 1.  
Figure 13 shows the electric currents, which present an exclusive phenomenon of an LIM. Due to the finite length of the motor, the main magnetic circuit did not have a symmetry for each phase. This phenomenon is called the longitudinal end-effect. Therefore, the maximum value of the electric current will be different due to the relative position of each phase inside the primary winding. Precisely, the RMS values of the electric currents in phase A, phase B, and phase C were 84.811, 93.583, and 87.938 A, respectively.  
A system where the electric currents per phase are all different (\(I_{Phase\_a}\
eq I_{Phase\_b}\
eq I_{Phase\_c}\)) is called an unbalanced three-phase system [3] and presents two relevant consequences. First, as presented in Figure 14, the spatial phasor of the magnetomotive force (MMF) was not uniform [21] because it described an ellipsoid instead of a circumference. For this reason, the instantaneous velocity of the first harmonic of the traveling magnetic wave never reached the synchronous velocity. The value of the synchronous velocity calculated in Equation (21), \(v_{Synchronous}=20\ m/s\), corresponded to its average value not to the instantaneous value. This effect produced a huge variation in the principal forces of the LIM. Second, it is important to take into account the differences among electric currents when we controlled the LIM and when the insulation of the primary winding conductors was selected.  
In order to increment the thrust force, the goal of this paper was to increase term 1 and to reduce term 2 in Equation (16), by adding changes into the geometric parameters of model 1.  
Figure 14: MMF generated by an unbalanced system of electric currents [21].  
Figure 13: Electric current per phase in model 1.
# Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
## 1 Introduction
The reference models of antenna patterns complimented with real base station positions are considered in the analysis. Next, we provide a detailed performance comparison of the LTE and mmWave cellular networks. We present a method to improve the interference situation for mmWave networks by tuning the number of sectors per BS as a basic form of beamforming. The remainder of this paper is organized as follows. Section 2 introduces the system model and the semi-deterministic channel model as well as the description of our simulator and its key parameters. The simulation results for LTE and mmWave cellular networks (with and without interference mitigation applied) are presented in Section 3. Section 4 concludes the paper.