esa-sceva/satcom-chunk-collection huggingface dataset:
# C-V2X Centralized Resource Allocation with Spectrum Re-Partitioning in Highway Scenario
## 3 Bandwidth Re-Partitioning Techniques among RSUs  
Bandwidth re-partitioning techniques are necessary to reduce or eliminate BCZ. Such a reduction or elimination can be obtained by increasing the frequency reuse distance for each transmitter pair. Therefore, two techniques are proposed in this section based on fractional and soft frequency reuse methods [20] from centralized vehicular communication perspective: Full Frequency Reuse (FFR) and Partial Frequency Reuse (PFR). The cooperating RSUs have been assumed in a highway scenario to support resource allocation for vehicular communication, focusing on increasing the minimum frequency reuse distances and selection and re-selection of radio resources for high reliability broadcasting. We assumed that the cooperating RSUs are connected through the X2 interface using a fibre-optic backhaul network to exchange information related to the busy and free resources, making RSUs ready when new vehicles just enter or exit their coverage area. Each vehicle in the network broadcasts CAM messages to the surrounding vehicles via radio resources selected by RSU by using the SPS method with some proposed modifications. The CAMs are transmitted periodically in current sub-channels until the RC value reaches zero in the RSU and the vehicle. When RC equals 0, the RSU generates a new RC value in a specific range, depending on the used CAM interval. When a new vehicle has just entered a new RSU coverage area, the new RSU should be waiting until this vehicle's RC reaches zero to be notified about the new re-selected resource. Moreover, when a vehicle broadcasts the last packet (RC reaches zero) at the time instant \(T\), RSU must know in advance when RC would reach zero and must prepare the new resources for broadcasting and generate the new RC value. At \(T+T_{cycle}\), the RSU needs to send one message that contains the new address of the resource and the new RC value that will be decremented by one in both the RSU and vehicle in each broadcast.  
It is worth mentioning that the remaining resource blocks \(RB_{free}\) given in Equation (2) are assumed to facilitate the vehicles' necessary signalling to RSUs. These signals are used to exchange control information when a vehicle enters a new RSU coverage area and in emergent situations.
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 2 Materials and Methods
### Case Study: The 2015 Gorkha Earthquake
#### 2.2.4 Masks  
In order to test the classification ability of each surface, it was necessary to mask pixels which were either not mapped in the landslide inventory or which were not well imaged by the SAR system.  
Figure 6: Timeline of Sentinel-1a image acquisition for the study area (see Figure 1) over the months preceding the Gorkha earthquake (red star), showing the imagery used in each method. Black diamonds show satellite overpasses at intervals of 12 days. Those where imagery was acquired are labelled with dates.  
Based on the data of [PERSON] et al. [2], we applied masks to remove pixels that were either outside the mapped area or obscured by cloud in the optical satellite imagery (Figure 7).  
We also masked areas where the SAR system was not likely to capture useable data based on viewing angle of the satellite and topography. The SAR system images the Earth's surface at an oblique angle, locating pixels according to their two-way travel time and Doppler shift, and projecting them onto a two-dimensional image. Steep topography can distort this image, leading to phenomena known as shadowing, foreshortening and layover (detailed in [47]). In order to assess this effect, we used the \(\sigma_{0}\) radar backscatter normalisation area calculated using the pixel area integration method described in [48]. This method divides the DEM surface covering the SAR image into small patches. The patches of the DEM surface that correspond spatially to each radar pixel are then integrated in order to approximate the area on the ground that contributes to each pixel. We used this \(\sigma_{0}\) normalisation area to identify distorted pixels. A mask was applied to remove all pixels for which this contributing area is 0 and those for which the contributing area was \(>\)1000 m\({}^{2}\) (around 6 times larger than the pixel spacing in radar coordinates) since these were expected to contain little information on landsl. The selection of this threshold of 1000 m\({}^{2}\) is justified in Appendix A.
## 2 Materials and Methods
### Power Measurements  
Energy consumption was tracked via the DIRIS Digiware D-50 (Socomec SAS, 67235 Benfeld, France), excluding auxiliary cooling, with an accuracy of 0.2%. Data were logged in watts at 1 min intervals. To isolate the energy impact of each MRI sequence, 2 min breaks were used between sequences. Hourly consumption was validated using PACS timestamps.
## 2 Methodology  
Before developing and addressing the problem posed, it is essential to review certain key concepts related to power systems, the most common electrical disturbances, algorithms based on artificial neural networks, and the application of the Hilbert-Huang transform (HHT), an effective tool for the decomposition and analysis of nonlinear and non-stationary signals. This section briefly presents the essential theoretical principles underlying the approach used in this work.  
The study is based on the IEEE 33-bus distribution system simulation, a standard network widely used for power system analysis in academic and technical settings. In this model, sag, swell, and interruption disturbances were introduced in a controlled manner at various system nodes to evaluate the performance of the proposed algorithm under different operating scenarios. To this end, three specific disturbance duration intervals were established, 0.2 s, 0.4 s, and 0.8 s, which allows for the analysis of the dynamic behavior of the signal as a function of the disturbance time.  
The proposed methodology is based on artificial neural networks trained in MATLAB/Simulink R2024a, developed by MathWorks Inc. (Natick, MA, USA). These networks process voltage signals previously processed using the Hilbert-Huang transform. This processing facilitates extracting relevant features that allow for accurate event classification. The model was trained using the disturbances generated in the simulation, evaluating its ability to correctly identify and classify the type of event present in the distribution system.  
The following sections detail the methodological procedure developed, which includes data preparation, the configuration of the neural network used, and the implementation of the disturbances, along with their respective evaluation metrics. Finally, the results are presented, and the performance of the proposed system under different operating scenarios is analyzed.
## 3 Results and Accuracy Analysis
### Using ROC Curve  
Receiver operating characteristic (ROC) curve can effectively analyze the performance of the landslide susceptibility models [36], which can overcome the error caused by setting breakpoints in advance to reclassify the susceptibility index. ROC curves are plotted by taking the false positive rate (sensitivity) of different cut-off thresholds as the y-axis and the real positive rate (specificity) as the x-axis. The area under the ROC curve (AUC) is the area between the curve and the axis, and its value is between 1.0 and 0.5; the closer the value of AUC is to 1, the better the classification effect of the model. The ROC curves of training and verifying performance of the used models are shown in Figure 5.  
In model training, the AUC of the SVM model was 0.927, which was better than the ANN, LR, and CART models of 0.866, 0.860, and 0.842 (Table 8), respectively. It was indicated that the SVM model can more accurately fit the nonlinear relationship between landslide occurrence and its causal factors. In model verifying, the predictive performance of the SVM model was also superior, with the highest AUC of 0.922, which was better than the ANN, LR, and CART of 0.875, 0.863, and 0.837, respectively (Table 8).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Area Under the**} & \multicolumn{2}{c}{**95\% Confidence Interval**} \\ \cline{3-5}  & **ROC Curve (AUC)** & **Standard Error** & **Lower Limit** & **Upper Limit** \\ \hline Training group & & & & \\ SVM & 0.927 & 0.002 & 0.923 & 0.930 \\ ANN & 0.866 & 0.002 & 0.962 & 0.871 \\ LR & 0.860 & 0.002 & 0.855 & 0.864 \\ CART & 0.842 & 0.003 & 0.837 & 0.847 \\ Prediction group & & & & \\ SVM & 0.922 & 0.001 & 0.920 & 0.923 \\ ANN & 0.875 & 0.001 & 0.873 & 0.877 \\ LR & 0.863 & 0.001 & 0.860 & 0.865 \\ CART & 0.837 & 0.001 & 0.835 & 0.840 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The prediction performance comparison.  
Figure 5: The receiver operating characteristic (ROC) curves of the SVM, ANN, LR, and CART models in landslide susceptibility assessment: (**a**) training and (**b**) verifying.  
From the above two methods of accuracy analysis, we can see that the SVM model had the best prediction performance in the susceptibility modelling of the study area, followed by ANN and LR models, and CART had the worst prediction performance.
## 5 Results and Discussion
### Glaucoma Classification Results
#### 5.2.2 Classification Result for ORIGA Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Models** & **Fold** & **TP** & **TN** & **FP** & **FN** & **R** & **S** & **P** & **F1** & **Acc** \\ \hline \multirow{8}{*}{Inception V3+RF} & Fold1 & 6 & 96 & 1 & 27 & 18.18 & \$89.97 & 79.99 & 72.73 & 78.46 \\  & Fold 2 & 7 & 95 & 2 & 26 & 21.21 & 97.94 & 78.33 & 73.49 & 78.46 \\ \cline{1-1}  & Fold 3 & 4 & 89 & 7 & 30 & 11.76 & 92.71 & 64.74 & 65.79 & 71.54 \\ \cline{1-1}  & Fold4 & 2 & 91 & 5 & 32 & 0.05 & 94.79 & 62.11 & 63.92 & 71.54 \\ \cline{1-1}  & Fold 5 & 5 & 91 & 5 & 29 & 14.71 & 94.79 & 69.08 & 68.17 & 73.85 \\ \cline{1-1}  & Overlapped & & & & & & & & \\ \cline{1-1}  & Gluucoma & 24 & 462 & 20 & 144 & 14.29 & 95.85 & 54.55 & 22.64 & \\ \cline{1-1}  & Normal & 462 & 24 & 144 & 20 & 95.85 & 14.29 & 76.24 & 84.93 & \\ \cline{1-1}  & Average & & & & & 55.07 & 55.07 & 65.39 & 53.78 & 74.76 \\ \hline \multirow{4}{*}{VGG19+RF} & Gluucoma & 23 & 462 & 20 & 145 & 13.69 & 95.85 & 53.48 & 21.8 & \\ \cline{1-1}  & Normal & 462 & 23 & 145 & 20 & 95.85 & 13.69 & 76.11 & 84.85 & \\ \cline{1-1}  & AverageThe results from the ORIGA dataset for both cropped and uncropped images suggest that using the Swin Transformer with cropped images and hold-out validation can produce better results for glaucoma identification. Furthermore, utilizing a k-fold validation strategy, the Inception V3 model produced better performance results for uncropped datasets.
## 4 Performance Evaluations
### Experimental Environments
#### 4.1.3 Data Augmentation and Model Training  
To enhance the robustness of our sign language recognition model, we implemented several data augmentation techniques during the training process. In real-world scenarios, the form of sign language utterances can vary significantly depending on the signer. Additionally, the signer may not always perform directly in front of the camera and may even perform signs in reverse. Therefore, data augmentation techniques such as random horizontal flipping, random rotation, and random cropping were applied. Our data loading and augmentation pipeline is implemented in a custom class that is inherited from PyTorch's Dataset class. The three data augmentation techniques used in our training process are as follows.  
* Random horizontal flipping: 50% probability;
* Random rotation: \(\pm\)5 degrees;
* Random cropping: From 256 \(\times\) 256 to 224 \(\times\) 224.  
The data loading process is optimized to handle varying video lengths and to apply transformations efficiently. Our implementation allows for the easy adjustment of parameters such as the number of frames, image size, and augmentation intensity. This robust data preparation and augmentation pipeline, combined with our large and diverse dataset, provides a strong foundation for training our sign language recognition model. It ensures that the model is exposed to a wide variety of input variations, promoting generalization and resilience to real-world variability in sign language performances.
# Versatile Video Coding-Post Processing Feature Fusion:
## 2 Related Works
### Comparative Analysis of Prior Approaches  
Traditional approaches to artifact removal and video quality enhancement rely on mathematical models and algorithms that are transparent and easy to understand. Traditional approaches are not versatile and cannot effectively handle diverse video content and various types of artifacts. In contrast, deep learning-based methods can simplify the artifact removal process by automating it, thereby eliminating the need for manual adjustments and fine-tuning of parameters.  
There has been a growing trend towards the use of deep learning-based techniques for artifact removal in recent times. By training on a vast collection of paired input-output images, a CNN network develops the ability to recognize patterns and correlations between the two, allowing it to effectively extrapolate and perform well on novel, unseen data. Employing a hierarchical feature extraction method, CNN networks begin by detecting low-level features (e.g., edges, textures) in early layers and progress to high-level features (e.g., objects, patterns) in later layers, empowering them to identify and eliminate a diverse array of artifacts throughout the image-processing pipeline. With their vast learning capacity, CNN networks can capture and model intricate patterns, empowering them to eliminate artifacts that are too complex or intractable for traditional filters to handle, thereby achieving superior artifact removal capabilities.  
In previous deep learning-based VVC post-processing filters, almost all research developed a residual style model to obtain the residual between compressed frame and original frame. We also followed the same method while we attempted to keep the residual block as minimal as possible to obtain the optimum bitrate saving. Our proposed network structure utilizes both shallow and deep features through feature fusion, while most other studies use very simple or very complex structures. According to the input of networks, most previous research is focused only on compressed frames. Since QP value is one the deciding factors in the compression level, we used QP map as the input along with the compressed frame.
## 3 Methodology
### Design and Development Process
#### 3.2.1 Nursery Rhyme  
Nursery Rhyme is an AR application for the learning purpose of the English alphabet. This application is based on an academic book named \"Nursery Rhyme\" for the students of grade one. Teaching the English alphabet to children at the age of five to eight, especially non-native children, is difficult. Letters from A to Z are taught utilizing attractive 3D visuals, animations, and melodic sounds. AR technology can enhance alphabet learning in students. We target a primary level academic book as AR markers for visualizing AR content on the screen. The user needs a smartphone or tablet to scan the target images and observe virtual content on the surface of those target images. The content of the \"Nursery Rhyme\" AR-based application has two sections of learning. The first section is the students learning section, in which students take a session of learning the alphabet. Pages of the book are scanned with an AR camera to provide animated 3D virtual learning content. Students learn with the vocabulary of words associated with the specific letter. For example, for the letter \"A\", we have virtual models of the objects and animals that start with the letter \"A\", such as Ant and Apple. Students also learn the pronunciation of each letter and associated words. Similarly, for the letter \"B\", corresponding objects are augmented in the real world environment. Students interact with these models with the help of touch and observe from different aspects. Users can zoom in, zoom out, rotate, translate these models. Students can play the corresponding melodic rhyme of each letter. Students can take screenshots of the 3D models that are saved in the application gallery for viewing and sharing with friends.  
The second section is a quiz that evaluates children's performance. In this session, students are tested with image-based Multiple-Choice Questions (MCQs) given in the book. The quiz session consists of twenty-six questions, one question per letter. Based on the correct answers, the students are awarded stars. Based on these stars, students learning performance is evaluated. Figure 5 shows screenshots of the application.  
Figure 5: Screenshots of different scenes from the application “Nursery Rhyme” for learning of English alphabet.
## 3 Results and Discussion
### Aerosol Optical Properties
#### 3.3.3 Single Scattering Albedo  
The single scattering albedo (SSA) \(\omega_{0}\) is defined as the ratio of the scattering coefficient to the extinction coefficient of particles; the latter is the sum of the absorption and scattering coefficients. The value of \(\omega_{0}\) can reflect directly the aerosol radiative forcing and reveal the relationship between scattering and absorption coefficients [70]. For example, the value of \(\omega_{0}\) for a pure BC particle is only 0.2, which indicates that the BC particle has stronger light absorption ability than scattering ability. The value of \(\omega_{0}\) is related to the size and composition of the aerosols [23] and it can be obtained using integrated nephelometer and aethalometer. When calculating the SSA, the absorption and scattering coefficients should be corrected to the uniform wavelength. In the present study, the SSA was calculated at the wavelength of 532 nm. The details of the specific algorithm used can be found in a previous study [71].  
As shown in Figure 4d, \(\omega_{0}\) increased rapidly to \(\sim\)0.97 from the morning of 11 June and remained high throughout the haze days. During polluted conditions, the values of \(\omega_{0}\) were 0.89 at Wuqing [69] and \(\sim\)0.86 in Beijing [72], both of which are lower than that found in Wuhan. This indicates that pollutants in Wuhan produced by biomass burning, rather than by local emissions, are more serious. [PERSON] _et al._ found that larger values of \(\omega_{0}\) are observed when RH is higher [23], _i.e._, the water uptake by aerosol particles results in higher \(\omega_{0}\)[73], and the increasing trend in particle size produces non-light-absorbing components. In summary, the aged aerosols could generate coating effects, which increase light absorption. The solidification and condensation processes and secondary particles could increase light scattering, overcoming the increase of absorption resulting from increases of \(\omega_{0}\)[74]. The high values of \(\omega_{0}\) coincide with the decreases of \(\alpha_{\rm s}\) and b, both of which are related to the growth and aging characteristics of aerosols. Mie theory demonstrates that as aerosols grow in size and age by condensation during transport, their mass scattering efficiency increases, leading to an increase in \(\omega_{0}\)[55]. Overall, changes in the composition and size of the aerosols caused the higher values of \(\omega_{0}\) observed during the haze episode.
## 1 Introduction
In this type of method, the classification accuracy of feature selection is high but consumes more time due to the introduction of classifiers. On the other hand, this type of method has a great relationship with the performance of the learning algorithm. Akin to some previous works [7; 8; 9], we aim to adopt the swarm intelligence algorithm as the learning algorithm in the wrapper-based feature selection method. Specifically, a swarm intelligence algorithm studies the complex behaviors of a swarm consisting of several simple agents. By iteratively updating the swarm, the agents will have a more powerful performance than before, so that the algorithm can provide a sub-optimal solution. Swarm intelligence has the benefits of high convergence and powerful solving ability. Moreover, swarm intelligence can also handle NP-hard problems such as feature selection. Thus, it can be seen as an effective method to overcome the challenges of feature selection. For instance, some well-known swarm intelligence, i.e., genetic algorithm (GA) [10], particle swarm optimization (PSO) [11], dragonfly algorithm (DA) [12], ant-lion optimizer (ALO) [13], and grey wolf optimizer (GWO) [14] has been applied in feature selection. Bat algorithm (BA) and binary BA (BBA) are promising forms of swarm intelligence and have been demonstrated to be better than other algorithms in some applications due to their effectiveness and high performance. However, as suggested in no-free lunch (NFL) theory, there are no algorithms that can suitably solve all optimization problems. In addition, BA also has some shortcomings in solving feature selection problems. Thus, we aim to enhance the performance of BA for solving feature selection. The contributions of this work are summarized as follows:  
* We show that the feature selection is a multi-objective optimization problem, and we present the decision variables and the optimization goals of the feature selection problem. * We propose an enhanced binary BA (EBBA) for solving the feature selection problem. In EBBA, we propose Levy flight-based global search method, which enables the algorithm to jump out of the local optimum. Moreover, we propose a population diversity boosting method so that the exploration capability of the algorithm can be further enhanced. In addition, we use a recently proposed chaotic mapping to assign values to the key parameter of the algorithm, thus enhancing the exploitation capability of the algorithm. * Simulations are conducted based on open datasets of UC Irvine machine learning repository to verify the solving ability of the proposed EBBA. First, we introduce some benchmark algorithms for comparisons. Then, we show the effectiveness of the proposed improved factors. The rest of this work is arranged as follows. Section 2 reviews some key related works about swarm intelligence algorithms and feature selection. Section 3 gives the model of feature selection. Section 4 proposes the EBBA and details the improved factors. Section 5 provides simulation results and Section 6 concludes this work.