esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 5 ETC-Based Urban Traffic Management: Applications and Practices
### Environmental Impact and Benefit Assessment
Service times, vehicle arrival times, departure times, and vehicle count data were collected before and after installing the E-PASS automatic vehicle identification technology. Results demonstrated that for dedicated E-PASS lanes, the measured capacity increased threefold, the service time decreased by five seconds per vehicle, the average queuing delay decreased by one minute per vehicle, the maximum queuing delay decreased by 2.5-3 min per vehicle, and the total queuing delay for these lanes decreased by 8.5-9.5 vehicle-hours per morning peak hour [62]. [PERSON] and [PERSON] proposed intelligent toll collection systems for moving vehicles based on Indian conditions, proposing effective methods for locating vehicle license plates, creating databases, and linking to test prototype toll collection system performance for moving vehicles. Results demonstrated significant reductions in vehicle waiting times, queue lengths, fuel waste, and pollution emissions at toll stations, with future potential for theft control applications [63]. In economic benefit assessment, [PERSON] et al. conducted a scenario analysis of cost-benefit evaluation for Taiwan's ETC system, using mathematical and engineering economic methods to demonstrate the benefits and costs related to different stakeholders, simulating three traffic scenarios to investigate the parameters by changing assumptions about time savings, fuel savings, environmental improvements, and user costs, as well as outcome variations for different stakeholders. Research found that under current mechanisms, although ETC systems generate overall benefits greater than costs for society as a whole, they are not favorable for investors and highway bureaus [64]. These studies demonstrate that the environmental benefits and economic value of ETC systems have been fully validated. From reducing air pollution to improving traffic efficiency, from lowering operational costs to enhancing user experience, ETC systems exhibit significant positive effects across multiple dimensions. These research findings provide robust scientific foundations and policy support for promoting ETC system applications in urban transportation.
## 1 Introduction
In recent years, ANN has been applied and developed to a certain extent in the simulation of river flow and the calculation of 2D plane flow fields. [PERSON] _et al._[13] combined the ANN theory and the hydrodynamic model; the hydrodynamic model is used to provide learning samples for the ANN model, and the trained ANN is then adopted to predict the navigation depth, and flow motion in some important areas of the 2D flow field, such as water level, flow velocity, flow direction and flow rate. [PERSON] _et al._[14] generalized the whole basin into several reservoirs and used the reservoir water balance principle and ANN to simulate the runoff of the Irwell River. The model shows a preferable simulation effect for the daily and monthly runoff time series. The ANN is also adopted to derive operating policies of a reservoir, such as [15; 16; 17; 18]. [PERSON] and [PERSON] [19] presented a planning model for reservoir operation using a combined backpropagation neural network simulation-optimization (Hooke and Jeeves nonlinear optimization method) process. The combined approach was used for screening the operation policies. [PERSON] and [PERSON] [20] developed a decision support model (DSM) combining a rule based expert system and ANN models to derive operating policies of a reservoir in southern India, and the authors concluded that DSM based on ANN outperforms regression based approaches. Although some studies of ANN have been published in both hydrodynamics and reservoir operations, respectively, its research and application on reservoir sediment erosion and deposition is still deficient. In this study, the BP-ANN model is used to determine the complex non-linear relationship between reservoir sediment flushing efficiency and its influencing factors. On the basis of analysis of 1D unsteady flow and sediment mathematical models, four factors composed of reservoir inflow,incoming sediment concentration, reservoir water level, and reservoir release were selected as the input of the model. As the output of the model, sediment flushing efficiency is used to estimate the simulative and predicting accuracy of the model, which should be as close to the desired values as possible. The historical data of the TGR from 2003 to 2010 are used to train the network, and the data in the year of 2011 is adopted for testing. The results indicate that the established model is able to capture the main feature of the reservoir sediment flushing, especially in the flood season, when the majority of the annual sediment is produced. Although the model can be improved with a larger number of samples, the method is proven to be valid and effective.
## 3 Results and Discussion
### Analysis of Flow Characteristics
#### 3.1.3 Discussion on the Wall Shear Stress Profile  
Wall shear stress (WSS) vector illustration properties in arterioles are richly characterized by complex blood flow. Blood flow dynamics and the biology of several cardiovascular disorders are linked by WSS. WSS has attracted significant attention in a variety of research and has become the most often used indicator to link blood circulation to heart diseases. Wall shear stress profiles such as pressure profiles show a positive outcome for introducing the graphene layer to the flow field. As far as the healthy arteriole segment is considered, the WSS profiles for the lower and upper walls are different to a greater extent even though  
Figure 51: Recirculation zones in blood flow through 6.5% porous graphene (Case: Blood flow).  
Figure 50: Recirculation zones in blood flow through 4.5% porous graphene (Case: Blood flow).  
Figure 57: Upper wall WSS for 10% porosity (Case: Blood flow).  
Figure 58: Lower wall WSS for 5.7% porosity (Case: Blood flow).  
Figure 55: Upper wall WSS for 8.2% porosity (Case: Water flow).  
Figure 61: Upper wall WSS for 6% porosity (Case: Blood flow).  
Figure 59: Upper wall WSS for 5.7% porosity (Case: Blood flow).  
Figure 60: Lower wall WSS for 6% porosity (Case: Blood flow).  
Figure 62: Lower wall WSS for 7% porosity (Case: Blood flow).  
Figure 66-71 enclose the WSS profiles for blood in a stenosed arteriole segment. A significant difference is not seen in the WSS values along the lower wall, as there is no disturbance in the planarity of the lower wall. Again, at the foot of the graphene layer, low and oscillatory shear stress segments of graphs are seen. Graphs of Figures 72-74 denote the wall shear stress profiles along the upper arteriole wall. The graph highlights the presence of nearly zero WSS regions (near the foot of the graphene layer) along the upper wall. These regions are more vulnerable to undergoing endothelial dysfunction. Figures 75-77 show the shear stress pattern along the lower arteriole wall. It is noted that with the porosity progression, minimum wall shear stress attained in each of these cases is seen to decrease. It has been encountered from the clinical and theoretical literature [50; 51; 52] that the regions of low WSS are more prone to undergo endothelial dysfunction. The implication obvious to the above observations on WSS profiles is that the PG region is a safer zone void of chances of undergoing wall damage.
## 6 Local Search Algorithms
### Hill Climbing  
Hill climbing is a straightforward local search algorithm that incrementally enhances a solution to a problem by making small iterative adjustments. The process begins with an initial solution, and then, it continually updates the solution to a neighboring one that demonstrates improvement over the current solution. This iterative refinement persists until no neighboring solution surpasses the current one, signifying the algorithm has reached a local optimum.  
The following steps are involved in hill climbing:  
Initialize a solution. This initial solution is given by the BACA.  
Repeat the following steps until no better solution is found:  
Generating a neighboring solution.  
If the neighboring solution is better than the current solution, then update the current solution to the neighboring solution.  
Algorithm 2 illustrates the hill climbing algorithm with the BACA as pseudo-code.  
```
Input :best solution from BACA Output :the best solution obtained during the iterations Initialization:
1// Initialize parameters // Initialize BACA solution (current solution) whileconvergence condition not metdo
2 generate a new neighbor solution by making a small change to current solution ifneighbor solution\(>\) current solutionthen
3 current solution = neighbor solution return the best solution obtained during the iteration
```  
**Algorithm 2**Binary Ant Colony Algorithm with Hill Climbing  
A neighboring solution is a solution that is one small change away from the current solution. The way that neighboring solutions are generated depends on the problem being solved. For example, in the traveling salesman problem, a neighboring solution can be generated by swapping two cities in the tour.  
Hill climbing employs various stopping criteria, such as a fixed number of iterations, a specified level of convergence, or a certain value of the objective function. Once any of these criteria are met, the algorithm concludes, providing the best solution it has found.  
Hill climbing is favored for its simplicity and ease of implementation. Additionally, it exhibits relatively efficient performance by examining only a limited number of neighboring solutions during each iteration. However, one potential drawback of hill climbing is its vulnerability to becoming trapped in local optima. This occurs when the algorithm explores only neighboring solutions that outperform the current one. In situations where the current solution is already a local optimum, the algorithm may not be able to discover a better solution, hindering its ability to reach the global optimum.
## 2 Materials and Methods
### Characterization Techniques
#### 2.3.3 Thermogravimetric Analysis (TGA)  
A Thermogravimetric analysis was performed using a SETARAM SETSYS TG-DTA 16/18, Caluire, France. Samples weighing 4 \(\pm\) 0.5 mg were placed in alumina pans. An empty pan was used as a reference. Fibers were heated from room temperature up to 1000 \({}^{\circ}\)C, while composites were heated from room temperature up to 580 \({}^{\circ}\)C. All samples were measured with a 50 mL/min nitrogen gas flow and a 20 \({}^{\circ}\)C/min heating rate. For each sample, triplicate runs were performed.
# Pulsed Nanoelectrospray Ionization Boosts Ion Signal in Whole Protein Mass Spectrometry
## 3 Results
### Pulsed Nano Electrospray Ionization of Protein Mixture  
Pulsed and DC nESI mass spectra of a solution mixture containing all four proteins are shown in Figure 5. These data were collected using an LTQ XL with an unmodified ion source; i.e., a skimmer-capillary atmospheric pressure ion source rather than a capillary-ion funnel interface. The use of pulsed nESI-MS resulted in a respective increase in the ion counts for the most abundant charge states of Ubq, Cyt C, Myo, and CAII of 158%, 74%, 47%, and 184% compared to when DC nESI-MS was used (Table 2). The extent of protein ion charging was comparable to that obtained using the ion funnel interface (Figure 2). Overall, these results indicate that the enhanced signal obtained by use of pulsed nESI-MS compared to DC nESI-MS under these conditions is likely to apply more generally to additional types of atmospheric pressure ion sources and more complex mixtures of proteins.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Analyytes**} & \multicolumn{2}{c}{**Most Abundant**} & \multicolumn{2}{c}{**Ion Count (\(\times 10^{5}\)) for the Most**} & \multirow{2}{*}{**\% Increase**} & \multirow{2}{*}{**Average Charge State a**} \\  & \multicolumn{2}{c}{**Charge State**} & & **Abundant Charge State a** & & & **in Signal b** & \\ \cline{2-7}  & **Pulsed nESI** & **nESI** & **Pulsed nESI** & **nESI** & & **Pulsed nESI** & **nESI** \\ \hline Ubq & 9+ & 8+ & 2.83 \(\pm\) 0.11 & 1.10 \(\pm\) 0.10 & 158 & 9.19 \(\pm\) 0.05 & 8.99 \(\pm\) 0.13 \\ Cyt C & 13+ & 13+ & 1.75 \(\pm\) 0.08 & 1.01 \(\pm\) 0.10 & 74.0 & 13.2 \(\pm\) 0.21 & 12.9 \(\pm\) 0.07 \\ Myo & 18+ & 18+ & 3.04 \(\pm\) 0.09 & 2.07 \(\pm\) 0.09 & 47.1 & 17.4 \(\pm\) 0.08 & 17.3 \(\pm\) 0.04 \\ CAII & 31+ & 34+ & 2.02 \(\pm\) 0.10 & 0.72 \(\pm\) 0.06 & 184 & 31.8 \(\pm\) 1.03 & 34.0 \(\pm\) 0.79 \\ \hline \hline \end{tabular}  
* Values correspond to the mean \(\pm\) standard deviation of at least three replicate measurements; b -value \(<\) 0.05.  
\end{table}
Table 2: Most abundant charge state, ion count of the most abundant charge state (arbitrary units), percentage increase in ion count, and average charge state of ubiquitin, cytochrome C, myoglobin, and carbonic anhydrase II obtained from pulsed and conventional direct current nESI mass spectra of a solution mixture containing all four proteins. Refer to Figure 5 for the corresponding mass spectra.  
Figure 5: Pulsed (**a**) and direct current nESI (**b**) mass spectra of a mixture of ubiquitin (\(\bullet\)), cytochrome C (\(\blacktriangle\)), myoglobin (\(\blacktriangle\)), and carbonic anhydrase II (\(\blacktriangleright\)) formed from a solution containing 20 uM of each protein. The frequency and duty cycle of the pulsed high voltage was 200 kHz and 60% respectively.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 2 Related Work
### 5G Network Environment
#### 2.2.3 5G Network Framework with ML  
This section describes studies that have proposed new 5G frameworks with improved ML-based anomaly detection. [PERSON] et al. [24] proposed FlowSentinel, a new 5G anomaly detection framework based on SELENET. This study uses ETSI-NFV, a 5G network environment. Using the ML model in a 5G environment, the model analyzes the outbound traffic flow to identify the characteristics of malicious activities and devices attempting flooding-based DDoS attacks. The ML algorithms used to analyze the traffic flow were RF and classification and regression trees (CART). The dataset used designated labels for training and testing purposes, and used the traffic collected from 61 devices with different characteristics. Considering the traffic dataset observed in an actual device, preliminary observations indicate that normal activities were effectively distinguished from DDoS activities of varying intensities.  
[PERSON] et al. [25] presented a 5G-oriented architecture to identify cyberthreats using deep learning (DL). This study used ETSI-NFV, which is a 5G network environment. LSTM was used as the DL algorithm, whereas the CTU dataset (a botnet dataset) was applied to analyze 5G anomaly detection. The results were acceptable for traffic evaluation and classification under 5G scenarios.  
Refs. [24; 25] is a 5G network model based on ETSI and SELENET. In this study, the differences in feature selection performance were examined according to various ML algorithms for anomaly detection in a 3 GPP-based 5G mobile network environment.
# Adaptive Emergency Call Service for Disaster Management
## 6 Conclusions  
Simulation results using 5G-SOS over Traverse city of Michigan, USA, with a 15,000 population, demonstrated that 5G-SOS succeeds in transfering more than 80% of the emergency calls when the victims represent <5% of the devices. Further, it was observed that 5G-SOS provided a higher success rate, higher average residual energy per node, and lower average  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Parameter** & **Gain in vs. M-HELP** & **5G-SOS vs. FINDER** & **5G-SOS** & **Comparison** \\ \hline \multirow{2}{*}{Success rate} & \multirow{2}{*}{24.9\%} & \multirow{2}{*}{73.90\%} & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & \\ \cline{5-5}  
number of sent messages per node than FINDER. Moreover, 5G-SOS enhanced M-HELP performance in terms of success rate and end-end latency. In addition, 5G-SOS provided satisfactory performance (success rate of 50%) even when the number of simultaneous emergency calls became very high (5000 calls over 10 min). On average, 5G-SOS performed 24.9% better than M-HELP and 73.9% than FINDER in terms of success rate. Additionally, 5G-SOS reduced the average end-end latency of the emergency calls transfer by 20.8% compared to M-HELP and 61.7% compared to FINDER. The 5G-SOS protocol is characterized by adaptive behavior that uses locally available data provided by sidelink (from neighbors) and downlink (from gNBs) signals. This adaptation allowed adjusting the expected performance of the service (latency and success rate) to the current charge of the network. Limitations of 5G-SOS are having a higher energy consumption than M-HELP by around 29.1% and slightly higher average messages per node than M-HELP by 6.2%. Thereby, designing energy-efficient mechanisms for 5G-SOS-enabled emergency devices is an interesting extension to this work.  
Conceptualization, V.B. and H.M.; original draft preparation, V.B.; supervision, H.M. and D.N.K.J.; co-supervision, P.C. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundacao para a Ciencia e a Tecnologia, Portugal under Grant No.: UIDB/04111/2020 (COPELABS).  
Not applicable.  
The data has been collected using a self-generated simulation model given in [[https://github.com/Chandracnapa/AM_HELP](https://github.com/Chandracnapa/AM_HELP)]([https://github.com/Chandracnapa/AM_HELP](https://github.com/Chandracnapa/AM_HELP)) (accessed on 13 September 2022).  
This research was funded by the Fundacao para a Ciencia e a Tecnologia, Portugal under Grant No.: UIDB/04111/2020 (COPELABS).  
The authors declare no conflict of interest.
# An Experimental Study on Repairing of Reinforced Concrete Beams Having Damaged Longitudinal Bars
## 3 Results and Discussion
### Load-Displacement Curves
#### 3.2.1 First Cracking and Ultimate Loading Capacity
The RB2-SNSM-G specimen (SNSM method with GFRP rebars) was found to be the least effective repair method, with only 237% of the damaged beam (RefB-D) capacity. Using the same GFRP bars in an external layer with the EBR method (RB8-EBR-G) showed a slight improvement of 313% compared to the damaged beam strength. The RB5-EBR-CS specimen without a U boundary sheet resulted in anincrease of 457% in load capacity compared to the damaged beam, while the load capacity of the RB6-EBR-CS-U specimen which is added U boundaries had an increase 650%.
# A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Improvement Dimension** & **Reference** & **Year** & **Core Methods** \\ \hline \multirow{8}{*}{Environment Adaptability Optimization} & \multirow{3}{*}{[PERSON] et al. [27]} & \multirow{3}{*}{2020} & Proposed the back-looking distance driver model, which integrates a backward-looking distance optimization module with linear stability theory to enhance the stability of homogeneous CAV queues. \\ \cline{2-4}  & & & Developed adaptive IDMs that dynamically adjust parameters based on real-time traffic conditions. \\ \cline{2-4}  & & & Introduced the concept of maximum deceleration competition between main lane and converging vehicles into IDM. \\ \hline \multirow{8}{*}{Vehicle Performance Coupling Modeling} & \multirow{3}{*}{[PERSON] et al. [31]} & \multirow{3}{*}{2018} & Optimized queue cooperative control by dynamically correcting IDM acceleration function based on front vehicle speed and time. \\ \cline{2-4}  & & & Coupled the IDM with a generalized autoregressive conditional heteroskedasticity model to predict sudden changes in acceleration associated with risky driving behavior. \\ \cline{2-4}  & & & Utilized residual dense networks to extract driving state features and made collaborative decisions with IDM through optimal weight assignment. \\ \hline \multirow{8}{*}{Driver Behavior Integration} & \multirow{3}{*}{[PERSON] et al. [33]} & \multirow{3}{*}{2020} & Quantified the nonlinear inhibitory effect of psychological stress on acceleration by embedding panic level coefficients into IDM safety distance calculations. \\ \cline{2-4}  & & & Introduced a multi-target prognostic logic and a stimulus perception parameter calibration mechanism into IDM to account for bus driver driving characteristics. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification and Comparison of IDM Improvement Models.
# Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars
Bayesian methods are popular in genomic prediction because they can incorporate prior knowledge, handle high-dimensional and correlated data, and provide a probabilistic interpretation of predictions. This flexibility allows for more accurate and robust predictions, accommodating the complex genetic architecture and uncertainty inherent in genomic data. Among the numerous Bayesian methods available, such as BayesA, BayesB, BayesC, and Bayesian Lasso, Bayesian GBLUP is widely used for its robustness and computational flexibility, but in recent years, we have seen the emergence of deep learning models in genomic prediction. Deep learning models can sometimes achieve higher prediction accuracy by learning directly from raw data, such as text, sound, and images. These models use large, labeled datasets to automatically extract features, eliminating the need for manual feature extraction typical of traditional machine learning. However, substantial and effective data are needed to prevent overfitting, especially when data are limited. Developing effective deep learning models requires minimizing errors within the training set, which impacts the validation set. Data augmentation (DA) is a powerful technique used to reduce training set errors and combat model overfitting [13; 14]. DA involves artificially increasing the size of the training dataset through techniques like data warping or oversampling, thereby enhancing the generalizability and overall performance of training models. Data augmentation techniques such as flipping, rotating, and cropping have proven to be successful in image classification, speech recognition, and natural language processing. The average gain in prediction performance from DA depends on the specific dataset and augmentation techniques used. In genomic selection for plant breeding, especially with limited large-scale training and genomic data, DA can maximize the utility of existing data. For example, [PERSON] and [PERSON] [15] developed a plant disease recognition system using deep convolutional neural networks, achieving higher accuracy by combining offline training with data augmentation techniques. [PERSON] [16] evaluated five regression models using three datasets (primary, with additional features, and augmented), finding that cross-validation showed an overall performance increase with augmented data. The challenges in implementing GS due to insufficiently accurate predictions make data augmentation a promising solution to enhance predictive performance. This research aims to leverage data augmentation algorithms to improve prediction accuracy, which is essential for the successful adoption of GS methodologies in plant breeding.
## 3 Secure FSO Link in GNU Radio Simulation  
The implementation of the secure FSO link in the GNU Radio platform is split into four main parts: the initial setup, the transmitter, the FSO channel, and finally the receiver. Each part is explained as follows.