esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Latency Comparison  
The latency comparison shown in Figure 7 depicts how long each framework took to respond, which is one of the widely compared key Performance Indices (KPIs) [48; 49; 50]. The latency of ACRLPC was 30 ms, which was much lower than the others in the framework. The latencies for FDU-MAC and TCH-MAC were 50 ms and 40 ms, respectively, and 60 ms for UW-ALOHA-QM. In real-time applications, a lower latency is much preferred; thus, ACRLPC outperformed the other algorithms in this event and can be considered a good choice for time-sensitive tasks. FDU-MAC is competitive with TCH-MAC, but UW-ALOHA-QM suffers from a much higher latency that could limit its usefulness in these types of applications.
## 2 Related Work
### Query Dissemination  
A common approach for disseminating query messages in a WSN is a flooding mechanism, which requires any intermediate receiver to rebroadcast a non-duplicated interest packet to all its neighbors. For example, in directed diffusion [9] one of the well-known query-based routing protocols in wireless sensor networks, a sink node initiates dissemination of an interest packet throughout the entire network by flooding. A node receiving the interest sets up a gradient which indicates from whom this interest message has previously been forwarded. Although some additional features such as a gradient reinforcement have been proposed, the directed diffusion with such a flooding of interest messages obviously increases network traffic and leads to inefficient energy consumption on sensor nodes. There are some other proposals for query dissemination in WSNs such as minimum broadcast tree algorithms [10] or epidemic approaches like gossiping [11]. In this paper, the presented approach differs from the discussed approaches by using the position information of the sensors to form sensor node's coverage area. The coverage areas [5] are used to scope the query dissemination between sinks and the target region.
# Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses
## 1 Introduction
Choice behavior in new mobility services is usually assessed and modeled using traditional statistical models, such as regression, mixed logit, multinomial and binary logit models [13; 14]. Recently, [15] used the Chi\({}^{2}\) and Kruskal-Wallis tests to analyze the frequency of e-scooter use. Given the strict assumptions of statistical models, they have limited capabilities to capture the complex relationships between factors and choices, nonlinear correlations among factors, and to deal with factors with various categories [16]. Machine learning (ML) methods have been widely utilized in civil engineering [17; 18; 19; 20; 21; 22] and transportation studies [24; 25]. They can model the nonlinear associations between independent and target variables as well as among independent variables [26; 27]. Therefore, it can be argued that the current study is one of the first attempts to predict SFFES usage frequency and identify significant factors impacting its use by adopting ML techniques. Malaysian universities are currently adopting new sustainable strategies in moving their campuses towards becoming green campuses. Specifically, the management of the University of Malaya is planning to launch SFFES service in the near future. This paper aims to predict the usage frequency of SFFESs among the students and staff on the campus. As a summary of above discussion, the main contributions are:1. This research study is one of the first efforts made to scrutinize the usage of SFFESs on a large university campus. In addition, this is perhaps the first study on SFFES services in \"developing countries\" such as Malaysia. 2. This research is one of the first studies which aims to predict the usage frequency of SFFESs and pinpoint significant attributes affecting the use of SFFESs by adopting various supervised and unsupervised machine learning techniques. The remaining of the article is organized as follows: Section 2 concerns a literature review on related works, followed by the survey design and data collection in Section 3. Section 4 proposes the analysis methodology, including feature selection and model development. Sections 5 and 6 present the model output, analysis results and simulation-based optimization and discussion. The final part offers the obtained findings of the study and suggests future directions.
# Single Image Reflection Removal Based on Residual Attention Mechanism
###### Abstract  
Affected by shooting angle and light intensity, shooting through transparent media may cause light reflections in an image and influence picture quality, which has a negative effect on the research of computer vision tasks. In this paper, we propose a Residual Attention Based Reflection Removal Network (RABRRN) to tackle the issue of single image reflection removal. We hold that reflection removal is essentially an image separation problem sensitive to both spatial and channel features. Therefore, we integrate spatial attention and channel attention into the model to enhance spatial and channel feature representation. For a more feasible solution to solve the problem of gradient disappearance in the iterative training of deep neural networks, the attention module is combined with a residual network to design a residual attention module so that the performance of reflection removal can be ameliorated. In addition, we establish a reflection image dataset named the SCAU Reflection Image Dataset (SCAU-RID), providing sufficient real training data. The experimental results show that the proposed method achieves a PSNR of 23.787 dB and an SSIM value of 0.885 from four benchmark datasets. Compared with the other most advanced methods, our method has only 18.524M parameters, but it obtains the best results from test datasets.  
artificial neural network; image processing; image restoration; computer vision; artificial intelligence; supervised learning; multi-layer neural network +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Single Image Reflection Removal Based on Residual Attention Mechanism. _Appl. Sci._**2023**, 13, 1618. [[https://doi.org/10.3390/app13031618](https://doi.org/10.3390/app13031618)]([https://doi.org/10.3390/app13031618](https://doi.org/10.3390/app13031618))
# Towards Automatic License Plate Recognition in Challenging Conditions
## 4 Simulation Results
### Limitations  
As with any other algorithm for machine learning, we discovered several shortcomings and failures in our method. Figure 10 depicts a handful of these instances with the following observations:  
Figure 10: Few failures cases examples.  
* It is clear from the rightmost image in the first row of Figure 10 that the input image is extremely blurry with a non-clear license plate. In such a case, our developed method struggles to distinguish the actual words and reads \"A\" from the license plate as \"0\".
* Similar is the case for the next two images in the first row of Figure 10. We also observe that there is no specific rule for license plate fonts. Therefore, such cases are very hard to identify correctly. As shown in the first image in the second row of Figure 10, the extreme blur is also a very challenging situation for any algorithm to deal with.
* We observe that occlusion, either partial or full, is also a challenging factor for the machine learning-based license plate identification method. One such case is shown in the third image in the second row of Figure 10, where high intensity light beams have created occlusion in the license plate area and thereby a hurdle for the algorithm to handle with. Therefore, before processing the license plate, such factors should be carefully analyzed.
* We also note that light that falls on the license plate area due to reflection from the vehicle's surface also reduces the recognition ability of the algorithm. One such case is seen in the middle image of the second row in Figure 10. Therefore, before a test license plate is fed to the recognition algorithm, this issue should also be noted. In such cases, an image enhancement or contrast rectification method might be useful to improve the quality of the appearance of the license plate.
## 2 Materials and Methods
### The Degree of Freedom in the Matrix Architecture  
The degree of freedom in the matrix architecture encompasses the flexibility to arrange the matrix elements in various configurations while adhering to the six requirements raised for the PMLD matrix. Those requirements ensure that the ordering of elements in the matrix maintains a consistent pattern, which is crucial for the integrity of the local matrix structure. For example, when considering a second-order matrix derived from two synchronously recorded time series \((x_{n}:n=0,1,2,3\ldots)\) and \((y_{n}:n=0,1,2,3\ldots)\), there are four different ways to arrange all the elements of the matrix while preserving the requirements. Each arrangement represents a unique structure of the matrix:  
\[A_{1} =\left[\begin{array}{cc}x_{n}&x_{n+1}-y_{n+1}\\ x_{n-1}-y_{n-1}&y_{n}\end{array}\right] \tag{6}\] \[A_{2} =\left[\begin{array}{cc}x_{n}&y_{n+1}-x_{n+1}\\ y_{n-1}-x_{n-1}&y_{n}\end{array}\right]\] (7) \[A_{3} =\left[\begin{array}{cc}x_{n}&x_{n+1}-y_{n+1}\\ y_{n-1}-x_{n-1}&y_{n}\end{array}\right]\] (8) \[A_{4} =\left[\begin{array}{cc}x_{n}&y_{n+1}-x_{n+1}\\ x_{n-1}-y_{n-1}&y_{n}\end{array}\right] \tag{9}\]  
While the MA1 and PMLD matrices allow for this degree of freedom due to their lexicographical balance, the same degree of freedom is not achievable for MA2. Also, in the research conducted by [PERSON] et al., the specific arrangement depicted in Equation (7) is employed for analytical purposes. In accordance with this precedent, this paper also adopts the same arrangement for subsequent analysis.
## 2 Proposed Method
### Training GAN
#### 2.3.1 Data Preparation  
This study was approved by the Institutional Review Board of Korea University Anam Hospital (2019 AN0470). Intraoral scan data of ten patients, who had taken a digital impression as part of an orthodontic diagnosis, were exported and segmented by a trained dental technician. The scans were acquired by one orthodontist by using the Trios 3 intraoral scanner (3 Shape, Denmark). The figure below depicts the dental scan data after the segmentation process and one of the horizontal plane images (Figure 9).  
Figure 8: Example after reconstruction: (**a**) Dental scan data with occlusion area removed, (**b**) reconstructed data by stacking images, and (**c**) dental scan data by merging (**a**,**b**).  
Figure 9: Data after tooth segmentation: (**a**) 3D and (**b**) horizontal views of the data.  
The area to be used for learning is cropped into a 256 (px) \(\times\) 256 (px) square in the horizontally sectioned images, as shown in (b) of Figure 9. In this study, we used 10,000 cropped images for training the GAN. The ratio of the training, validation, and test sets was 28:1:1. In general, approximately 100,000 images are used for training complex images, such as CelebA or Paris Street View. However, in the case of tooth data, the images are not complicated and can be trained using only 10,000 images. The training data was organized such that the tooth images were contained. Figure 10 shows the process of image cropping.  
Five types of data are prepared: Cropped images, edge images, masks, masked images, and masked edge images. The cropped images can already be seen as edge images, but using edge images of the cropped images is important in terms of accuracy. We can apply the Edge Connect method when we consider only the cropped images as a picture, and not the edge images. For the edge images, we used the Canny edge detector with a threshold of 0.5 [25]. We determined the masks based on the dental spaces, where occlusions mainly occur. Any mask shape can be used, and the mask only needs to cover the areas where an occlusion is expected to occur. Herein, we used a rectangular shape. Figure 11 illustrates a few examples of the five types of data.
## 5 Data Preprocessing
### Data Processing  
In the literature, no specific classification has been identified as the most suitable for identifying NTLs [16,59,90]. For this reason, several supervised classifiers are trained and evaluated to determine which one achieves the best results. The classifier models to be trained and evaluated are the following:  
1. K-nearest neighbor (K-NN);
2. Support Vector Machine (SVM);
3. Decision Tree (DT);
4. Random Forest (RF);
5. Artificial Neural Network (ANN);
6. CatBoost (CGB);
7. LightGBM (LGB);
8. XGBoost (XGB);
9. Logistic Regression (LR);
10. Naive Bayes (NB).  
The Preparation Matrix (Equation (11)) is used to train the classifiers mentioned. Each classifier requires hyperparameters for training. Hyperparameters are elements of the classification models that must be specified prior to training and are used to direct the training process [96,97,98].  
To enhance the performance of the classifiers, hyperparameter tuning is conducted. There are several methods for hyperparameter tuning, including (i) grid search; (ii) random search; (iii) derivative-free optimization: Nelder-Mead; and (iv) Bayesian optimization [96,97,99]. In this study, grid search is used for the optimization of the hyperparameters of the different classifiers. Grid search involves an ordered combination of hyperparameters for each classifier and the manual adjustment of each one of them [97]. For example, consider a hyperparameter of the K-NN classifier (argument \"K\"). The model is evaluated with different values of \"K\" using the accuracy metric (Equation (2)). In Figure 10, the results are shown; it can be seen that with K = 18 (red color), the highest effect was achieved with an accuracy of 0.766, and the lowest value was with K = 1 (yellow color) with an accuracy of 0.69.  
The main hyperparameters obtained from the grid search optimization of the different classifiers are presented in Table 9.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 5 Experimental Results and Comparison
### Lane Detection Dataset
#### 5.1.5 Results Comparison  
To compare the performance of the classifiers used, and taking into consideration the nature of the imbalanced data set, all metric values in the previous tables are combined into a single overall score for each classifier.  
Generally, there are several ways to obtain a single performance score for each classifier. In our case, the overall accuracy, which is the ratio between the true predictions made by classifiers over the overall instances, is initially used to evaluate each classifier and obtain the best parameters for each. Another important score is the macro score, which represents a simple arithmetic mean value (in which equal weights are given to all classes) of the scores of each class. It is also used to evaluate the performance of all algorithms. Moreover, the Weighted score can be useful to compare the performance of the of the classifiers used, where this metric weights the score of each class using the number of its samples. Lastly, the micro score is also used to perform the comparison. The micro score considers all the samples from each class and determines the performance score rather than the average of the scores from each class as in the case of macro scores. This is particularly useful to determine the performance of a model on imbalanced datasets such as the one considered in this paper.  
Table 4 shows that the gradient boosting algorithm outperforms ANN, RF and LR classifiers. This is due to its flexibility and efficiency in using clone decision trees, where all scores of this algorithm are greater than the other classifiers.  
The gradient boosting algorithm has the ability to provide a certain consistency across all classes. On the other hand, the ANN classifier has the worst accuracy results, as all of its metric scores are less than 0.3 (30%). The RF classifier shows acceptable accuracy compared to the logistic regression classifier, where all metric scores are greater than 50%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
**Class 0 (normal)** & 0.53 & 0.64 & 0.58 & 40,320 \\
**Class 1 (aggressive)** & 0.62 & 0.20 & 0.31 & 35,752 \\
**Class 2 (drowsy)** & 0.56 & 0.78 & 0.65 & 42,206 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multi-class evaluation metrics with random forest classifier for lane detection.
## 3 Electric Propulsion System for Multicopter UAVs  
A multicopter is a rotorcraft that has more than two lift-generating rotors. The most prominent feature of the multicopter that distinguishes it from conventional helicopters is its simple configuration. Instead of a large rotor blade and complex hub structures, it simply uses multiple propellers that were directly connected to the motors. Figure 1 shows the typical configuration of the electric propulsion system for multicopter UAVs, which consist of multiple motors and propellers, ESCs, and batteries.  
Each rotor on a multicopter platform typically includes components with the same specifications. For the motor type, a permanent magnet outer rotation brushless direct current (BLDC) motor with a large number of poles is preferred. During operation, the output of the motor moves along the  
Figure 1: Typical configuration of the electric propulsion system of multicopter unmanned aerial vehicles (UAVs).  
torque-to-speed curve of the propeller. For propellers, a two-blade fixed pitch propeller type that is made of carbon fiber reinforced composite material is preferred. Usually, one rotor arm has one propeller; however, some platforms are equipped with two propellers, each coaxially inverted to reduce the overall size. The ESC refers to a motor driver and it controls the output level of the motor by regulating the input voltage. Although the ESC is conventionally called a speed controller, it does not actually quantitatively control the speed of the motor. For type of batteries, lithium-based secondary batteries are commonly used. In this study, only models for lithium-polymer (LiPo) batteries were considered.
## 2 Data and Methods
### Vertical TEC from the International GNSS Service and Empirical Models
#### 2.4.2 The International Reference Ionosphere Model  
The International Reference Ionosphere (IRI) empirical model [12] is the most widely used model and the standard for ionosphere applications and research. The version of the IRI-2012 with the coefficients of the 13 th version of the International Geomagnetic Reference Field is used; the model is available at the IRI-MODEL website ([[https://irimodel.org/](https://irimodel.org/)]([https://irimodel.org/](https://irimodel.org/)), accessed date: 3 September 2021). The IRI-2012 model provides vTEC estimates, and many studies have investigated the accuracy to predict ionospheric parameters (e.g., [58]). The vTEC is estimated from 100 to 2000 km height with the 13-month running mean of sunspot number index, the IG12 (an index based on foF2 measurements from a dozen ionosondes  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Code** & **Name** & **Country** & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** & **MLat (\({}^{\circ}\))** \\ \hline STJ & St John & Canada & 47.60 & \(-\)52.68 & 53.59 \\ PST & Port Stanly & Falkland & \(-\)51.70 & \(-\)57.89 & \(-\)38.12 \\ HRB & Hurbanovo & Slovakia & 47.87 & 18.19 & \(-\)43.02 \\ HER & Hermanus & South Africa & \(-\)34.43 & 19.23 & \(-\)41.90 \\ BMT & Beijing Ming Tombs & China & 40.3 & 116.2 & 34.16 \\ CNB & Canberra & Australia & \(-\)35.32 & 149.36 & \(-\)45.24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Geomagnetic observatories for CI calculation.  
correlated with foF2 maps) [59], the F10.7 radio flux at 10.7 cm wavelength, the topside Ne from NeQuick [60], and the F peak model of [61].
## 4 Bi-Level Dispatch and Control Architecture  
### Structure and Composition  
As mentioned above, the GVPP is designed as a grid-friendly and autonomous virtual unit, which interacts with the power system through the VPCC. Thus, the inner operation of the GVPP and the output characteristics of the VPCC should be controlled. Based on the cloud platform of the China state grid, a bi-level dispatch and control architecture is proposed to realize the integration of GVPPs into the power system.  
The cloud platform of the China state grid is shown in Figure 8, and \(N\) is the number of provincial platforms. The VPN for power system dispatch and control is established. Furthermore, the national platform is set as the master node, and the \(N\) provincial platforms are collaborative nodes, which constitutes a \"1+\(N\)\" framework. The national platform is the core and performs the analysis, dispatch, and control functions at the voltage level of 220 kV. The provincial platforms carry out these functions at the voltage level of 10 kV.  
According to the above cloud structure, GVPPs can be integrated into the provincial platform and participate in power system dispatch and control. In order to respond to the commands from the provincial platform, the GVPP should aggregate enough DERs to assure sufficient control capability and be integrated into the power system at the voltage level of 10 kV or 35 kV through step-up transformers. Moreover, considering the grid-friendly characteristics, the GVPP can operate as traditional thermal and hydro units, and even have superior performance. So, the GVPP is integrated into the provincial cloud platforms like traditional power plants, and a bi-level dispatch and control architecture is established, as shown in Figure 9, and \(N_{G}\) is the number of GVPPs.  
From Figure 9, the inner level and outer level of the proposed architecture are the GVPPs and provincial cloud platform, respectively. In the inner level, the GVPP operates autonomously and preserves a certain amount of adjustment capability for the power system. So, GVPPs can participate in power system stability control under major disturbances. In the outer level, the cloud platform coordinates the \(N_{G}\) GVPPs, traditional power plants, and other controllable resources for economic and stable operation.  
Furthermore, the web layer is the interface that GVPPs and other units interact with the cloud platform. The mission allocation layer distributes the computation and storage requests to corresponding devices. Moreover, the GVPP is controlled by the inner optimal dispatch agent and emergency response agent, which communicate with the cloud platform  
Figure 8: Schematic diagram of the cloud platform of the China state grid.  
Figure 9: Bi-level dispatch and control architecture of GVPPs based on the cloud platform.  
through the VPN and web layer. The application layer performs the decision-making on dispatch and control, of which the process is supported by the platform and fundamental infrastructure layers.