esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intersection Signal Control Mechanism Assisted by Vehicular Ad Hoc Networks
## 3 Intersection Signal Control Mechanism
### Dynamic Phase and Period Signal Control Scheme
#### 3.4.1 Eliminable Waiting Time of the Traffic Flow  
Under the situation that there is a long waiting queue, not all waiting vehicles can pass the intersection within the next green light time (even with the maximum possible value). Therefore, sometimes it is not applicable and appropriate to use the total waiting time of all vehicles in the queue as the evaluation reference. Based on this point, the solution in the scheme is to provide an eliminable waiting time (\(ET_{i}^{T_{g}}\)). It is the sum of the waiting time of vehicles (in the \(i\)th traffic flow) that can pass the intersection during the given green light period \(T_{g}\). In other words, we should instead consider how much waiting time can be eliminated in the next green light period.  
First, we need to determine the maximum number of vehicles in the \(i\)th traffic flow (\(k_{i}^{T_{g}}\)) that can pass the intersection in \(T_{g}\) in the current environment using:  
\[k_{i}^{T_{g}}=T_{g}\times\overline{k_{i}}, \tag{13}\]where \(\overline{k_{i}}\) is the maximum number of vehicles that can pass in a unit time (as described in Section 3.1). Compared with the unstopped vehicles, a vehicle that has stopped in the waiting queue before the decision-making time (\(t_{c}\)) can pass the intersection earlier. If the number of stopped vehicles \(q_{i}\) is greater than (or equal to) \(k_{i}^{T_{g}}\), the front \(k_{i}^{T_{g}}\) vehicles in the queue should be considered. The sum of their waiting time, which is the eliminable waiting time of traffic flow \(i\), is calculated as follows:  
\[ET_{i}^{T_{g}}=\sum_{j=1}^{k_{i}^{T_{g}}}T_{d^{\prime}}^{j},\,q_{i}\geq k_{i}^{ T_{g}}. \tag{14}\]  
If \(q_{i}<k_{i}^{T_{g}}\), this means that all the vehicles in the queue can pass the intersection in \(T_{g}\). In this case, we should take the unstopped vehicles that can reach and pass the intersection into account. Based on the vehicle information collected using the VANET, we can calculate the position (\(pos_{t_{f}}\)) of the vehicle at \(t_{f}\) using Equation (15) if there are no waiting vehicles ahead, and as such, it does not have to stop:  
\[pos_{t_{f}}=\begin{cases}\left(t_{f}-t_{c}-\left(R-pos^{t_{c}}\right)/v^{t_{c} }\right)\times v_{s}&R>pos^{t_{c}}\\ pos^{t_{c}}+\left(t_{f}-t_{c}\right)\times v_{s}&R\leq pos^{t_{c}}\end{cases}, \tag{15}\]  
where \(pos^{t_{c}}\) and \(v^{t_{c}}\) are the position and the velocity of the vehicle, respectively, at the current moment \(t_{c}\); \(v_{s}\) is the velocity limit within the intersection area; and \(R\) is the range of the intersection. If \(pos_{t_{f}}\) is beyond the stop line, we consider that the vehicle can pass the intersection in \(T_{g}\). The decision-making system sequentially calculates the position of each vehicle per unit time until \(t_{e}+T_{g}\) or the number of calculated passable vehicles is equal to \(k_{i}^{T_{g}}\). Eventually, the sum of the waiting time of these passable vehicles is the eliminable waiting time of the traffic flow.
###### Abstract  
Computer-aided design/computer-aided manufacturing (CAD/CAM)-based maxillary templates can transfer a surgical plan accurately only when the template is positioned correctly. Our study aimed to evaluate the positioning accuracy of the CAD/CAM-based template for maxillary orthognathic surgery using dry skulls. After reconstruction of a three-dimensional (3D) virtual skull model, a surface-based surgical template for Le Fort I osteotomy was designed and fabricated using CAD/CAM and 3D printing technology. To determine accuracy, the deviation of the template between the planned and the actual position and the fitness of the template were evaluated. The mean deviation was 0.41 \(\pm\) 0.30 mm in the medio-lateral direction, 0.55 \(\pm\) 0.59 mm in the antero-posterior direction, and 0.69 \(\pm\) 0.59 mm in the supero-inferior direction. The root mean square deviation between the planned and the actual position of the template was 1.21 \(\pm\) 0.54 mm. With respect to the fitness of the template, the mean distance between the inner surface of the template and the underlying bone surface was 0.76 \(\pm\) 0.24 mm. CAD/CAM-based templates showed precise positioning and good fitness. These results suggest that surface topography-based CAD-CAM templates can be considered as an alternative solution in replacing the traditional intermediate splints for the transfer of surgical plans.  
CAD/CAM; template; orthognathic surgery; positioning; accuracy +
Footnote †: journal: Applied
# A Self-Adaptive Approximated-Gradient-Simulation Method for Black-Box Adversarial Sample Generation
## 4 Experiment and Evaluation
### Experimental Setting
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**DNN** & **Norm** & **FGM** & **PGD** & **CW** & **BMI-FGSM** & **SAGM** \\ \hline \multirow{3}{*}{LeNet5} & 1 & \(\mathbf{1.61\times 10^{1}}\) & \(2.23\times 10^{1}\) & \(2.66\times 10^{1}\) & \(1.40\times 10^{2}\) & \(1.39\times 10^{2}\) \\ \cline{2-7}  & 2 & \(9.47\times 10\) & \(\mathbf{1.32\times 10^{-1}}\) & \(1.38\times 10^{1}\) & \(1.230\times 10^{1}\) & \(1.52\times 10^{1}\) \\ \cline{2-7}  & Inf & \(1.43\times 10^{1}\) & \(1.96\times 10^{1}\) & \(2.60\times 10^{1}\) & \(2.03\times 10^{1}\) & \(\mathbf{3.73\times 10}\) \\ \hline \multirow{3}{*}{ResNet18} & 1 & \(9.70\times 10^{1}\) & \(\mathbf{5.87\times 10^{1}}\) & \(2.22\times 10^{2}\) & \(6.16\times 10^{1}\) & \(1.11\times 10^{2}\) \\ \cline{2-7}  & 2 & \(3.24\times 10^{1}\) & \(2.18\times 10\) & \(1.74\times 10^{1}\) & \(\mathbf{1.32\times 10}\) & \(3.34\times 10\) \\ \cline{2-7}  & Inf & \(2.90\times 10^{-1}\) & \(2.97\times 10^{-1}\) & \(2.54\times 10\) & \(2.65\times 10^{-1}\) & \(\mathbf{2.41\times 10^{-1}}\) \\ \hline \multirow{3}{*}{AlexNet} & 1 & \(9.18\times 10^{2}\) & \(\mathbf{5.18\times 10^{2}}\) & \(2.09\times 10^{3}\) & \(5.85\times 10^{2}\) & \(1.56\times 10^{3}\) \\ \cline{2-7}  & 2 & \(1.33\times 10^{1}\) & \(\mathbf{7.66\times 10}\) & \(5.43\times 10^{4}\) & \(9.39\times 10^{1}\) & \(1.57\times 10^{1}\) \\ \cline{2-7}  & Inf & \(5.33\times 10^{-1}\) & \(\mathbf{3.00\times 10^{-1}}\) & \(2.53\times 10\) & \(8.32\times 10^{-1}\) & \(4.10\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Robustness of DNNs. Figure 11: The adversarial examples of PGD for Cifar10 and AlexNet. Figure 12: The adversarial examples of BMI-FGSM for Cifar10 and AlexNet. Figure 14: The adversarial examples of FGM for Cifar10 and ResNet18. Figure 13: The adversarial examples of SAGM for Cifar10 and AlexNet. Figure 16: The adversarial examples of PGD for Cifar10 and ResNet18. Figure 15: The adversarial examples of CW for Cifar10 and ResNet18. Figure 17: The adversarial examples of BMI-FGSM for Cifar10 and ResNet18. Figure 18: The adversarial examples of SAGM for Cifar10 and ResNet18. Structural similarity (SSIM) measurement, consisting of three comparisons of luminance, contrast, and structure, was proposed to evaluate the similarity between two images. The SSIM between image \(x\) and \(x^{\prime}\) is modeled as Equation (23). \[SSIM\big{(}x,x^{\prime}\big{)}=\frac{1}{m}\sum_{n=1}^{m}\left[L\big{(}x_{n},x_{n }^{\prime}\big{)}^{\alpha}C\big{(}x_{n},x_{n}^{\prime}\big{)}^{\beta}S\big{(}x _{n},x_{n}^{\prime}\big{)}^{\gamma}\right] \tag{23}\]  
where \(m\) is the number of pixels and \(L\), \(C\), and \(S\) are the image's luminance, contrast, and structure, respectively, \(\alpha=\beta=\gamma=1\). The comparison of structural similarity is shown in Table 4, and the best mean values are shown in bold. The results show that CW has a clear advantage on LeNet5 but is almost similar in the other two DNNs.
# Maintenance Management of a Transmission Substation with Optimization
## 2 Maintenance Model Based on the RCM Concept  
RCM is a maintenance concept that, in addition to the basic maintenance methodologies, is focused on reliability. The objective of the RCM concept is management of maintenance costs and associated risks, having in mind the provision of adequate reliability of operation of the EPS. It is important not to treat reliability at the level of individual elements, but at the level of the entire system. Maintenance tasks carried out during maintenance are as follows: periodical inspection of elements performed on the energized state of the equipment; revision intended to retain elements' functionality, performed on the de-energized state of equipment; and repair or replacement of elements. Elements with lower importance and good technical condition are maintained in the RCM concept using TBM in a longer time period. The main aim of RCM in this paper was to determine the maintenance periods of EPS elements with regard to their importance and condition, considering reliability and costs. This is the main change with regard to the TBM concept, where maintenance periods are determined in advance. The RCM concept in the paper is based on [1], while the basic concept uses the standard [11].  
Section 2 is dedicated entirely to the RCM concept and consists of seven subsections. The elements are defined in Section 2.1 EPS. This includes a general set of elements and the term switching bay. The structure of the model of input data for optimization in the maintenance process is presented in Section 2.2. Since we wished to include ecology in the optimization process, Section 2.3 defines the characteristic variable of diagnostics of drops in pressure of the SF\({}_{6}\) insulation gas. One of the key parameters in the RCM process is the index of technical condition. For each type of EPS element, there is a methodology for assessing their technical condition, which is described in Section 2.4. The next key parameter in the RCM concept is the index of importance of an EPS element, which is described in detail in Section 2.5. The cost-related part of our concept is dealt with in Sections 2.6 and 2.7.
## 2 Literature Review
### Smart Mobility  
Smart mobility aims to make transportation systems \"smarter.\" Smart transportation networks, in particular, can better serve the public by improving the safety, speed, and reliability for local and international accessibility [29]. Information and communication technologies from modern and sustainable transportation systems help consumers plan their schedules and find the cheapest and fastest routes using transportation-oriented mobile applications [30]. Driver's passports, license recognition systems, car parking searching, and prediction are typical applications in smart mobility facilities [31].  
A depiction of a smart mobility platform is presented in Figure 2.  
Figure 2: Smart mobility application.  
Traffic safety is a significant concern in crowded cities that harms residents [32]. In this regard, IoT can be more proactive in detecting human errors and reducing traffic accidents. In the literature, for example, particular research gives valuable insights into allowing smart mobility systems. In [33], an IoT-based system was created by combining internet platforms and low-cost antenna technologies. The suggested research in [34] demonstrates the viability of monitoring road safety using developments in IoT. It offers a low-cost IoT framework for assessing the safety of a road network. In addition, Ref. [35] presents an overview of the key IoT technologies suggested for smart mobility in smart city scenarios.  
Furthermore, IoT-based solutions must be employed for various applications and modes of transportation, such as smart traffic, parking, and mobility, to create safer and cleaner streets. For instance, in [36], an intelligent transportation system was designed to recognize, locate, track, and monitor buses through exchanging information and communication. This system is based on the IoT, radio frequency identification (RFID), general packet radio services (GPRSs), geographic information system (GIS), and global positioning system (GPS), among other technologies. Another project that is mentioned in [37] aims to construct a prototype for intelligent transportation that makes use of a GPS, near field communication (NFC), and temperature and humidity sensors to monitor automobiles and commuter information and the atmosphere inside buses. The [38] research suggests a real-time traffic-monitoring system to solve the issues associated with traffic management and monitoring. The study used the information gathered from real-time traffic monitoring to determine roadway problems. In addition, Ref. [39] creates an intelligent transportation system application that uses internet-of-things platforms, Intel Edison, and Raspberry Pi. It has been proposed that information on traffic conditions may be distributed via standard instant messaging services, such as WhatsApp.
# Acceleration of a Production-Level Unstructured Grid Finite Volume CFD Code on GPU
## 3 Parallelization on GPUs
### LU-SGS Algorithm Parallelization
#### 3.2.2 Hyperplane Partition  
Cell colouring could solve the data dependency problem, but it degrades the implicit property of the LU-SGS algorithm. Because different colours are executed in sequential order, the value \(\Delta W_{j}\) appearing on the right-hand side may not be the latest update. Therefore, this paper proposes a reordering method based on the hyperplane. This method was originally applied to the vectorization of the LU-SGS algorithm on unstructured grids [26].  
The principle of this method is to partition and reorder all grid cells into different groups (hyperplanes), achieving the decoupling of intra-group dependencies. The majority of cells from a group have connections to cells from groups with lesser numbers as well as to cells from groups with greater numbers. Every cell from one group has no connections with other cells of the same group. As shown in Figure 4, first, partition the grid cells into preliminary hyperplanes. Select the initial grid cell as the first plane, and according to the grid topology, spread from the initial cell to the whole grid from near to far. The external grid cells with the same topological distance are divided into the same plane, that is, the distance between the grid cells in the same plane and the initial grid cell is the same. Following that, further group the cells in each hyperplane. For each hyperplane, no cells of the same group are connected.  
Thus, the lower matrix in Equation (5) was computed by surrounding cells \(j(i)\) whose group indexes are less than a group of cell \(i\), \(j\in L(i):(group(j)<group(i))\), while the upper matrix is computed by surrounding cells \(j(i)\) whose group index is greater than the current group of \(i\), \(j\in U(i):(group(j)>group(i))\). The forward sweep is performed by the group numbers from 1 to the maximum and vice versa for the backward sweep. For all  
Figure 3: Cell coloring for unstructured grid. (**a**) The original mesh topology. (**b**) Painted with multiple colors. Elements of the same colour can be solved in parallel.  
cells of the same group, the computation can be done concurrently, and thus the LU-SGS algorithm can be parallelized on GPU.
## 1 Introduction
Based on cross-correlation technology, fibre elimination is realized by calculating the coordinate transformation parameters between separated cotton images, and the Kalman filter tracking is utilized to predict the estimated location to improve efficiency [9]. Although PIV matching algorithms based on the cross-correlation method are continuously improved, they usually require high image quality, good correlation between images, and long computation time [10]. In recent years, particle image velocimetry algorithms based on the optical flow method have received increasing attention from researchers [11; 12]. The optical flow method, which considers the motion vector field as a whole, is suitable for the calculation and analysis of image sequences with highly concentrated particles and continuous deformation. With a dense optical flow algorithm, its resolution can reach pixel scale, which provides a useful tool for accurate flow field measurements [13]. In summary, an ultrasound particle image velocimetry algorithm is proposed to effectively measure the two-dimensional flow field distribution of highly concentrated sand  
Figure 1: The In-Plane Motion and Out-of-Plane Motion of particles. bearing water streams. The 2D flow field measurement method combined with the UIV measurement system can significantly improve the environmental tolerance and accuracy of flow field measurement, and reduce the computational consumption by pyramidal hierarchical algorithm. Experiments have been conducted to verify the performance of the algorithm proposed in the paper. The results demonstrate that UIV is a promising technique to generate instantaneous two-dimensional fields of flow velocities in sediment-laden fluids [14]. The quantitative performance assessment of the algorithm has been made using ultrasound images acquired in seeded flow. The algorithm accurately measured the 2D flow field in turbid water with 10% volume concentration and outperformed the cross correlation method.
# Lateral Control Calibration and Testing in a Co-Simulation Framework for Automated Vehicles
## 3 Simulation and Results  
### Model-in-the-Loop Simulation  
In this study, a BMW5 model is used for simulation. The model was calibrated with experiments performed at the Institute of Automotive Engineering laboratory and on a proving ground. Firstly, test the BMW 5 car on the real road to measure the parameters and save these in the datasheet. Then, a simple model with parameter adjustment from the datasheet is created. After that, the car was simulated on the test road, and the parameters were measured and the compared with the simulation. Finally, calibrate the steering model and stabilizer model for good fitting of the curves and choose the best tire model. The results of the model calibration refer to [46]. The BMW5 model is simulated in the CarMaker environment on a 50 m circular road, as depicted in Figure 6. The car drives in 48 s, with a maximum speed of 50 km/h and the speed step is 0.01. Therefore, there are 5000 values of matrix K corresponding to each speed step, and the lateral errors are evaluated during driving.  
Figure 4: Flow chart of GA and LQR controller.  
Figure 5: Flow chart of PSO and LQR controller.  
processing unit called \"Xpack4\". Both CarMaker and Xpack 4 are from IPG Automotive GmbH [48]. Moreover, the whole vehicle test bench is controlled by the automation-system software Tornado 6 from KS Engineers [49]. The users have the capacity to configure virtual vehicle test bench using Tornado. In addition, ADAC (5 kHz) from KS Engineers [49], which is a real-time testing, controlling, and monitoring system which was developed for the virtual vehicle test bench in real-time. The EtherCAT topology protocol enables real-time synchronous simulation communication between Tornado, ADAC, and XPack4 real-time systems. In this study, the same framework is used, which comprises a virtual environment and a virtual vehicle test bench. This framework (RT-ADF) allows multiple software to be integrated and executed on real-time hardware platforms. This work demonstrates an offline virtual vehicle test bench. If the simulation model is successfully executed and validated offline, it can be transferred to the real test bench. Therefore, this is a good approach to improve the efficiency and optimize the work. Table 2 depicts the results of the vehicle model in a real-time system.  
In [50], a quantitative comparison method is the relative error criterion (REC) used to validate results as:  
\[Error_{L}=\frac{|Peak_{test}-Peak_{sim}|}{|Peak_{test}|}\times 100\%=53.23\% \tag{27}\]  
\[Error_{dL}=\frac{|Peak_{test}-Peak_{sim}|}{|Peak_{test}|}\times 100\%=78.89\% \tag{28}\]  
We calculate the maximum absolute value of the error as:  
\[Error_{L}=max(|L_{test}-L_{sim}|)=2.35 \tag{29}\]  
\[Error_{dL}=max(|dL_{test}-dL_{sim}|)=0.28 \tag{30}\]
## 3 Results
### Variability and Drivers
#### 3.3.1 EOF Analysis of the Raw Versus Detrended Data
The steric response of the surface layers of the Caribbean Sea to changes in atmospheric heat fluxes has been highlighted as a major reason for variability in sea levels by [20]. This suggests that continued ocean warming because of climate change will result in increasing sea levels in the Caribbean. Correlations with Carib SST and the AMO are smaller but still significant for PC1_detrended (+0.18 and +0.23 respectively) in comparison to PC1_raw. This reinforces the idea of the long-term trends in north Atlantic SSTs being a significant background factor. The PC1_detrended correlations are, however, higher (than for PC1_raw) for both Pac-Atl and sea level pressure (\(-\)0.61 and \(-\)0.46 respectively) and comparable for NINO3 and NINO3.4 (+0.2 and +0.25 respectively). The annual cycle of the interbasin SST gradient associates with climatological variation in both Caribbean basin sea level pressures and surface wind strengths through changes in large scale circulation features in the Caribbean [66]. A significant correlation, albeit small (\(-\)0.18), exists between PC1_detrended and the CLLJ index. It is noted that the spatial pattern for PC2_detrended (Figure A2) depicts high but oppositely signed spatial loadings around Jamaica (and extending westward to Cayman and south and south-westward) versus off the north Coast of South America and westward to the Panama-Colombia Gyre. (PC2_raw has an almost identical spatial pattern). The corresponding timeseries captures seasonal and interannual variations. The CLLJ and Jamaica wind indices show highest correlations with the timeseries for PC2_raw (magnitudes 0.50 and 0.49, respectively) and PC2_detrended (magnitudes 0.54 and 0.51 respectively) (Table 4a). These correlation magnitudes increase with lag, e.g., when PC2_detrended lags CLLJ and Jamaica wind by one month, the correlations increase to 0.65 and 0.59, respectively. The CLLJ has a July peak [72] and, as previously shown, sea levels on the south coast of Jamaica peak in August. The Pac-Atl gradient and ENSO, through its influence on the gradient, also impact the CLLJ summer strength [55; 73], likely accounting for the significant correlations seen with the index in Table 4a. This suggests a significant role for the CLLJ in driving sea level change on seasonal and interannual timescales. Based on the signs of the loadings, trend, and correlations, the suggestion is that a strengthened CLLJ associates with higher sea levels in the vicinity of Jamaica.
## 2 Materials and Methods
### Antibacterial Assay of Biosynthesisized AgNPs  
The antibacterial assessment of biosynthesized AgNPs was performed by agar well diffusion method against Gram-positive (_Staphylococcus aureus_ and _Staphylococcus epidermidis_) and Gram-negative (_Escherichia coli_ and _Salmonella_ sp.) pathogens. The powder form of biosynthesized AgNPs was suspended in sterilized deionized water before use. The inoculum of each pathogen was grown overnight in nutrient broth at 37 \({}^{\circ}\)C. Sterilized swabs were used to streak each pathogen onto the nutrient agar. Simultaneously, wells were punched with sterile borer and about 100 \(\upmu\)L of biosynthesized AgNPs at different concentrations (12.5, 25, 50, 100, and 200 \(\upmu\)g/mL) were added to each well. The plate was incubated at 37 \({}^{\circ}\)C for 24 h, and the diameter of the inhibition zone was measured. The experiment was performed in triplicates.