esa-sceva/satcom-chunk-collection huggingface dataset:
# New Evolutionary-Based Techniques for Image Registration
###### Abstract  
The work reported in this paper aims at the development of evolutionary algorithms to register images for signature recognition purposes. We propose and develop several registration methods in order to obtain accurate and fast algorithms. First, we introduce two variants of the firefly method that proved to have excellent accuracy and fair run times. In order to speed up the computation, we propose two variants of Accelerated Particle Swarm Optimization (APSO) method. The resulted algorithms are significantly faster than the firefly-based ones, but the recognition rates are a little bit lower. In order to find a trade-off between the recognition rate and the computational complexity of the algorithms, we developed a hybrid method that combines the ability of auto-adaptive Evolution Strategies (ES) search to discover a global optimum solution with the strong quick convergence ability of APSO. The accuracy and the efficiency of the resulted algorithms have been experimentally proved by conducting a long series of tests on various pairs of signature images. The comparative analysis concerning the quality of the proposed methods together with conclusions and suggestions for further developments are provided in the final part of the paper.  
Keywords:image registration; image recognition; evolutionary computing; evolution strategies; firefly technique; hybrid techniques; mutual information; affine perturbation +
Footnote †: journal: Applied Sciences
# Prediction of Freeway Traffic Breakdown Using Artificial Neural Networks
## 3 Data Description  
### Study Site  
The study site consists of two sections. The first section is a 3-mile freeway section in Des Moines, IA, USA and has three stationary sensors installed along Interstate 235 (I-235). The second section contains one stationary sensor located along Interstate 80/35 (I-80/35) at mile marker 126. These sensors collect vehicle count, speed, and lane occupancy for mainline and ramp traffic at one-minute intervals. The locations of the stationary sensors in the first section are shown as red dots in Figure 1 and the sensor from the second section is shown as a blue dot. Utilizing the three sensors in the first section serves the purpose of developing and validating the model. For this section, traffic data is divided into training and testing sets based on time periods. In particular, the training subset consists of data from March to May 2022, while the validating subset comprises data from June and July 2022. The second section has purpose of evaluating the performance and effectiveness of the model in other locations except those that were originally utilized for generating model. To show the accuracy and applicability of the model to other locations, the traffic data from this section were collected from March 2023. The inductive capacity of a model can be effectively validated by testing it at a location that is not included in the training process. As traffic breakdown typically occurs during peak hours in the evening at these locations, this study focuses on peak hours in the evening (4 p.m. to 7 p.m.) during the week. Traffic data is aggregated into 5-min time intervals for subsequent analysis.
# A Survey of Deep Learning for Electronic Health Records
## 4 Deep Learning Models
### Graph Neural Network (GNN)  
The Graph Neural Network (GNN) is a neural network that learns graph-structured data. The network has the feature of acquiring data with graph structure and can be applied to many fields such as clustering, classification, and generation. Early GNNs use RNN as the main structure, which simply generates vector representation for each graph node and does not have a good response to complex and variable actual graph structure data.  
The Graph Convolutional Neural network (GCN) constructed by [35] combined with CNN for this problem. Furthermore, they also proposed spectral decomposition convolution and spatial graph convolution. The GCN, shown in Figure 9 and Equation (5), has two input components: the feature matrix \(X\in R^{N\times F}\) and the adjacency matrix \(A\in R^{N\times N}\), \(N\) is the number of nodes in the graph structure and \(F\) is the number of input features per node. \(H_{l}\) is its hidden layer and \(l\) represents the \(l_{th}\) hidden layer. \(W_{l}\) and \(\sigma\) represent the weight matrix of layer \(l\) and the nonlinear activation function of the network, respectively, and \(W_{l}\) are shared among different graph nodes. Matrix \(Z=H_{N}\) is the out  
Figure 8: The model architecture of generative adversarial networks, where the generator generates fake data and the discriminator reasonably identifies the original data and the generated fake data.  
put of this GCN. Because the network itself has properties that enable GCN to perform far better than other methods for tasks such as node classification and edge prediction, the techniques for processing and applying EHR data using graph neural networks have become widespread. Furthermore, there are some common graph neural networks, such as GRN [36], GAE [37], GAT [38], etc. Graph representation learning [39; 40; 41; 42] corresponding to graph neural networks has been of great help for the play of deep learning in medical tasks.  
\[H_{l+1}=\sigma(AH_{l}W_{l}) \tag{5}\]
## 4 Error Analysis of the Laser Processing Platform for MCR Frequency Modulation  
Error Simulation of the Laser Processing Platform for MCR Frequency Modulation Based on Monte Carlo Method  
The Monte Carlo method is a simulation method integrating probability statistics with computer programming. After obtaining the parameters with certain value rules, the probability distribution of the target can be obtained through repeated simulation, so as to obtain the results corresponding to the parameters. The errors of laser processing platform for MCR frequency modulation are mainly geometric errors and assembly errors, which are random. In general, the positioning error at the end of the frequency test probe of the laser processing platform for MCR is difficult to measure, and its positioning accuracy directly affects the processing effect of the MCR.  
With the help of the Monte Carlo method, the actual error can be simulated by random sampling within the value range of error variables, so as to obtain the approximate solution of a large number of error data, so as to simulate the random error in the actual situation. The specific simulation steps are as follows:  
1. The established laser processing platform error model mainly considers the influence of geometric error and assembly error. These errors should be approximately normally distributed for the tuned equipment, and the measurement error also approximately obeys normal distribution when the same measurement tool is used for repeated measurement. Therefore, this paper takes the normal distribution as the probability distribution function of the variable.  
2 Combined with the range of platform error variables measured repeatedly, random sampling is carried out according to the normal distribution with MATLAB to generate random number samples for statistical analysis.
3 The samples are analyzed to obtain the position error probability statistics of the end of the frequency test probe on the laser processing platform, and the error is analyzed accordingly.  
Based on the error model of the laser processing platform for MCR frequency modulation, the Monte Carlo method is used to simulate the positioning error of the end of frequency test probe. The simulation is carried out 250,000 times. The simulation results of the end of frequency test probe are shown in Figure 11. Ex, Ey, and Ez are the positioning error of the end of frequency test probe in the X, Y, and Z directions, respectively.  
The mean and standard deviation of the errors are shown in Table 2. The negative sign indicates that the error direction of the point is opposite to the generalized coordinate system.
## 2 Materials and Methods
### Baseline Network Architecture--Single Task Learning  
The architecture of the model is an obvious factor to improve the performance of downstream applications. In this paper, we focused on the above CNN backbones in our proposed network architecture to extract features for HV prediction. As mentioned before, the conventional CNN is not fully convolutional because it often contains FCLs as the last layers, leading to the requirement of a fixed-sized input. In order to enable the model to adapt to various sizes of images, a baseline network architecture based on fully CNN (FCN) [23] is exhibited in Figure 6, which replaces FCLs with convolutions and subsampling operations. We dropped the FCL of the backbone and the extracted feature map is adaptively convolved into a \(1\times 1\) vector and finally generates the HV as its output. The advantage of FCN is that it is not limited by the input size of the image, so we can accept the arbitrary size of the input image after developing the model.  
The loss function is used to identify whether the neural regression model has learned important features to predict the HV. Therefore, the lower the loss function value, the better the model is. For training of the baseline model, we used the absolute error loss function, L1 loss, to estimate the HV errors predicted by the developed models. The principle is to take the average of the absolute error of the predicted HV\(f(x_{i})\) and the real HV (\(y_{i}\)), as in Equation (2).  
\[L1 Loss(\mathbf{x},\mathbf{y})=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-f(x_{i})| \tag{2}\]
## 4 Analysis of the Simulation and Experimental Results
### Multi-Node Collaborative Jamming Recognition Performance Based on MAML and Time-Frequency Analysis  
In order to test the performance of the multi-node collaborative jamming recognition algorithm based on the time-frequency graph and MAML, we carried out a statistical experiment focusing on the accuracy of jamming recognition with different amounts of nodes. In order to ensure the accuracy of the experimental results, this article organized 100 Monte Carlo experiments under each JNR, and drew the comparison curve of recognition accuracy under different amounts of nodes.  
As shown in Figure 12, one node has very poor identification performance. Especially at low JNR, the accuracy of jamming recognition results based on MAML and time-frequency graphs is obviously very low. For example, when JNR = \(-18\) dB, the recognition accuracy was only 59.75%. With the increase in the amount of cognitive nodes, the accuracy of jamming recognition showed an increasing trend after the integration of the multi-node collaborative algorithm. When there were 21 cognitive nodes, the jamming recognition accuracy of JNR = \(-18\) dB was improved to 73%, which is 13.25% higher than  
Figure 11: Recognition performance of MAML algorithm.  
that of a single node. This is because the jamming information observed by the multi-node cooperative system has diversity, that is, the same jamming signal will be observed by multiple nodes. This diversity can improve the reliability of the jamming recognition system, thereby reducing the global jamming recognition error caused by the single node identification error. At the same time, we discovered that with the number of nodes increased to 9, the improvement of jamming recognition accuracy will become limited by further increasing the number of cognitive nodes. By observing the confusion matrix, as shown in Figure 13, when JNR = \(-18\) dB, the jamming recognition algorithm based on multi-node cooperation not only guaranteed the recognition accuracy of STJ-PJ and STJ-LFM, but also effectively improved the recognition accuracy of STJ-SFMJ and MTJ-PJ.  
Figure 12: Multi-node collaborative compound jamming recognition based on MAML and time-frequency image.  
Figure 13: Confusion matrix with different amounts of nodes when JNR = \(-18\) dB. **(a)** Confusion matrix with one node. **(b)** Confusion matrix under 9 nodes. **(c)** Confusion matrix under 15 nodes. **(d)** Confusion matrix under 21 nodes.
## 4 Results
### Experimental Results
#### 4.3.2 Four Quadrant Control Capability  
The objective of this section is showing the capability of the inverter to control unbalanced phase currents as mentioned in Section 1 and required for the proper use of the over-load supervisor strategy suggested in Section 4.2.2. In this scenario, the inverter is operating in grid-connected mode with rated (non over-loaded) unbalanced \(PQ\) set-points. The set-points per phase are \(P_{u}=P_{w}=30\) kW, \(P_{v}=-30\) kW and \(Q_{uvw}\) all nulls. Figure 17 shows that the inverter is able to synthesize non-balanced currents from non-balanced \(PQ\) references.  
Figure 17a shows a simulation of the expected active phase and neutral wire output currents, respectively. Figure 17b presents the captured oscilloscope active currents. It is demonstrated that there is no problem to track unbalanced \(PQ\) set-points. Thus, it is possible to ensure that if an over-load supervisor manages the inner converter references, called \(s_{int}^{*}\), in Figure 11, the inverter can provide autonomous over-load capability per phase.
## 3 Results and Discussion  
### Crystallinity of the AZ Thin Films  
Figure 3 shows the XRD diffraction pattern of raw ZnO powder and the aerosol-deposited ZnO films. The XRD peaks at the diffraction angles of 31.55\({}^{\circ}\), 34.20\({}^{\circ}\), 36.04\({}^{\circ}\), 47.33\({}^{\circ}\), 56.39\({}^{\circ}\), 62.67\({}^{\circ}\), 66.19\({}^{\circ}\), 67.76\({}^{\circ}\), 68.90\({}^{\circ}\), 72.3\({}^{\circ}\) and 76.78\({}^{\circ}\) correlate to (100), (002), (101), (012), (110), (103), (200), (112), (210), (004), and (202) planes of ZnO hexagonal wurtzite structure (JCPDS- 96-901-1663). Peaks from the FTO substrates were also detected at 26.50\({}^{\circ}\), 33.74\({}^{\circ}\), 37.86\({}^{\circ}\), 51.80\({}^{\circ}\), 54.66\({}^{\circ}\), 61.74\({}^{\circ}\), and 65.69\({}^{\circ}\) correspond to the (110), (011), (020), (121), (220), (130) and (031) planes of tin (IV) oxide (JCPDF-98-006-3707), respectively. No significant shift on the 2\(\theta\) of the diffraction peaks of ZnO films as compared to ZnO powder, suggesting that the films have identical crystal quality. A few additional diffraction peaks were found, matching with Zn-SnO (JCPDF-96-101-0111). The presence of this secondary phase, i.e., Zn-SnO, suggests the formation of a new compound during the AZ deposition. The secondary phase was likely from the interfaces between ZnO particles and FTO substrates. The energy released from the ZnO particles facilitated the formation of Zn-SnO at the contact points between ZnO particles and FTO substrate during the bombardment (deposition).  
The average crystallite size of ZnO powder was 43 nm. The average crystal size of ZnO films slightly increased to 45, 48 and 50 nm for AZ-I, AZ-II and AZ-III films, respectively as shown in Figure 4. The increment in crystallite size suggests that consolidation and recrystallization occurred during the AD process. The kinetic energy of ZnO powder could generate localized heating and energy conversion resulting in the growth of ZnO crystallites through densification and particle bonding when the powder impinges on the FTO glass substrate [38]. The continuous supply of kinetic energy with an increased number of cycles (three deposition cycles) could cause formation of higher crystallite size due to fusion of fine particle fragments.  
Figure 3: X-ray diffraction pattern of (**a**) FTO, (**b**) raw ZnO powder, (**c**) AZ-I, (**d**) AZ-II and (**e**) AZ-III films.
# Selective Removal of Diesel Oil Hydrocarbons in Aerobic Bioremediation
## 1 Introduction  
Biodegradation by indigenous microorganisms present in the soil is a good technique that permits removing pollutants in contaminated soil. With biostimulation, the optimal dosage of nutrients, such as nitrogen and phosphorus, can be added as a mineral salt medium for bacteria (MSMB) to enhance microbial activity [1].  
A detailed knowledge of the pollutant composition is useful to efficiently accomplish bioremediation activity, and for this purpose, a preliminary analytical study is required [2].  
The chemical composition of diesel oil is rather complex because of the presence of a huge number of compounds. For this reason, usually, it is advantageous to analyze the main groups of hydrocarbons that constitute the diesel oil, that is to say paraffins, aromatics, naphtenes, and olefins [3], instead of the individual compound concentration.  
The common technique for the identification of hydrocarbon species is gas chromatography-mass spectrometry (GC-MS) because it allows us to identify and quantify the chemical compounds [4; 5; 6]. However, it is possible to use gas chromatography with a flame ionization detector (GC-FID) and compare the results with a specific standard [7; 8]. This allows simplifying the methodological approach without losses of result reliability.  
Identifying the removal efficiency for a single hydrocarbon permits improving the degradative process. It is possible to decide to add a specific inoculum to improve the degradation of the hydrocarbons more recalcitrant to removal [9; 10; 11; 12] and/or biosurfactants that promote the degradation [13; 14]. For example, polycyclic aromatic hydrocarbons can be removed differently by various bacterial strains [15].  
We performed a preliminary analysis to identify the hydrocarbon compounds through GC-FID to monitor the degradation of C8-C24 alkanes and aromatic hydrocarbons, including some polycyclic aromatic hydrocarbons (PAHs). The test was done in a microcosm with 200 g of soil, polluted with diesel oil at a concentration of around 70 g kg\({}^{-1}\) of soil, carbon to nitrogen ratio (C/N) equal to 180, and water content (u%) equal to 12% by mass. The test lasted 138 days, and the findings showed that the removal of the diesel oil components occurred to a different extent. These findings were confirmed by analyzing other microcosms where the operative conditions were C/N = 120 and 180 and u% = 8%, 12%, 15% by mass.
# A Bagged Ensemble Convolutional Neural Networks Approach to Recognize Insurance Claim Frauds
## 1 Introduction  
The advancement of cutting-edge digital technology in the industries of banking, insurance, communication, and media have introduced many ways to commit financial fraud. To earn unjustified financial gain, financial fraud is committed [1]. Money laundering, corporate fraud, telecom, life insurance, auto insurance, and credit cards are a few examples of financial fraud. According to the agencies, there was a significant loss of revenue as a result of the clients' fraudulent behavior. The loss was estimated by American agencies to be over USD 80 billion annually [2]. According to a survey by the Australian Insurance Bureau, losses tended to rise in 2013. They exceeded the 2012 figures by more than USD 2 billion [3]. According to research by the British Insures Association, fraudulent claims are rising daily [4]. These investigations make it clear that false claims are a severe issue that require immediate attention in order to create a mechanism to stop them.  
Automobile insurance fraud is the practice of deceiving an insurance provider by requesting financial assistance for vehicle theft or damage using fictitious documentation [5]. The vehicle insurance scam has grown to be one of the main issues for both insurance providers and consumers as recipients of payment in the event of an accident do not always act honestly. Theft or accident planning, submitting a fraudulent application, or other methods of auto insurance fraud are all possible. False data representation makes it difficult to detect fraud [6]. Additionally, there are fewer illegitimate claims than legitimate claims, which contributes to the issue of class inequality. The detecting method is further made more challenging by the imbalanced data. Furthermore, the straight classification of an imbalanced dataset takes time and may produce misclassification errors. Therefore, the precise subset of cases is crucial for the method used to detect vehicle insurance fraud. Apowerful method must effectively distinguish between malicious and legitimate situations. Additionally, it ought to reduce the rate of misclassification.  
Eliminating the noise and zero-valued data from the initial imbalanced data-set is one of the crucial jobs in many data sets. Eliminating noise is one of the obstacles we face in our work. Another challenge is the hugely imbalanced dataset. To overcome these issues, this article presents an efficient way to eliminate zero-valued data, by analyzing the impact. Further, 1D Convolutional Neural Network (CNN) along with pre-trained CNN models are utilized, which are empowered by bagged ensemble learning techniques to obtain efficient results.  
The rest of the paper is organized as follows: Section 2 gives a short review of seminal studies and the state of the art of the problem. Section 3 describes the methodology and the tools used in our approach. Section 4 presents a description of our experimental environment and an analysis of our findings. Finally, Section 5 presents conclusions and future directions.