esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
###### Abstract
Therefore, software-defined networking (SDN) and machine learning techniques represent a precise and efficient classification method [15]. Recently, the integration of SDN and the use of Deep Learning (DL) methods have provided viable alternatives that have improved the classification of traffic flows by application type [16; 17]. As a result, this enhances the user experience and optimizes network performance by assigning priority based on quality of service (QoS) and network security requirements. Despite the numerous studies on traffic classification using deep learning (DL) in software-defined networking (SDN) environments, challenges still need to be addressed. These include classifying new traffic categories, optimizing classification approaches, and fine-tuning hyperparameter settings. DL is a practical approach that offers various algorithms that have proven highly effective in traffic classification tasks. Therefore, this research aims to design a traffic classification model using DL algorithms, explicitly focusing on classifying traffic flows by application type and network attacks. The main contributions of this work can be summarized as follows:  
* We have developed four classifiers with DL algorithms to evaluate which offers better results based on two open datasets. Specifically, we used the Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (BiGRU), Long Short-Term Memory(LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) algorithms. In related work, we observed a lack of classifiers that specifically use GRU, BiGRU, and BiLSTM algorithms for multiclass traffic classification in SDNs. * The classifiers aim to improve the SDN performance and security by using the traffic flow to identify application types and attacks in five classes: Multimedia, VoIP, Instant message, File transfer, and Attacks. One of the main innovations of our approach lies in integrating attack detection into the classification process, thus addressing a gap identified in the related work. The experiments demonstrate the accuracy of detecting different application types and attacks, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), Brute-force-attack, Exploitation (R2L), Web_attack, Botnet, and Probe. * The selection of features based on analysis of the SDN controllers and sequence length enabled an appropriate generalization of deep learning models. This choice facilitates implementing classifiers in SDN with any controller because northern interfaces can obtain the selected features, thus reducing the complexity of model training, improving traffic classification accuracy, and decreasing the associated computational cost. The paper is structured as follows: Section 2 discusses related work, Section 3 presents the proposed methodology, Section 4 shows the results and discussion, and finally, Section 5 explains the conclusions and lines of future work.
## 4 Mobile Localization Algorithms
### Distributed Localization Algorithms
#### 4.2.3 \"Multi-Stage DNR\" (MS-DNR) Positioning Scheme  
To improve performance of DNR, the authors proposed \"multi-stage DNR\" (MS-DNR) [47] to speed up the localization process at cost of less accuracy and more messaging. Once unknown nodes become localized, they start to act as reference nodes. Therefore, unknown nodes lying out of the communication range of DNR anchor nodes can be localized by the localized unknown nodes. Similar iterative schemes such as LSHL have been proposed. Communication overhead and energy consumption of MS-DNR are relatively high due to the iterative scheme. For this reason, MS-DNR is less energy-efficient than DNR. Compared with DNR, MS-DNR uses a more realistic underwater mobility model, named Meandering Current Mobility (MCM) model, which has been studied in [48]. The model considers sensor nodes' movement by the effect of meandering sub-surface currents and vortexes. Unlike previous works where nodes are deployed in a small bounded geographic domain, the domain model in MS-DNR is representative of a large coastal environment spanning several kilometers. In this case, assuming sensor nodes uniformly distributed over the large domain is unrealistic. Therefore, the authors consider an initial deployment of sensor nodes in a small subarea where they are released and thereafter move according to the mobility model. A kinematic approach [49; 50; 51] is employed to represent the mobility of underwater sensor nodes drifting with subsurface currents. To the best of our knowledge, this is the first physically-inspired mobility model used in the analysis of mobile UWSNs.  
The major drawback of the DNR and MS-DNR scheme is the high expense of DNR anchor nodes. There are 25 DNR anchor nodes in 1 km \(\times\) 1 km \(\times\) 1 km underwater area, so 25 GPS and 25 moving equipments are needed, which is very expensive. Moreover, under actual underwater environments, the DNR anchor nodes are strongly affected by the surface currents, which will degrade localization accuracy. Therefore, Multi-stage AUV-aided Localization (MS-AUV) scheme is proposed in [52] aimed at improving MS-DNR scheme by replacing the DNR anchor node with an AUV. MS-AUV is introduced in Section 4.2.4. MS-AUV is proposed based on AUV-Aided localization technique, therefore, we first introduce the AUV-Aided localization technique in Section 4.2.3.
# Real-Time Information Fusion System Implementation Based on ARM-Based FPGA
## 1 Introduction
It consists of an optimized IP, tools, libraries, models, and example designs, and supports mainstream frameworks as well as the latest models. This platform is capable of executing various deep-learning tasks. VITIS AI also provides a range of pretrained models that can be retrained for specific applications. CPU- and GPU-based computers are convenient for rapidly implementing fusion information systems. However, the computing speed, power consumption, size, and flexibility of these computers are inferior to those of FPGA-based computing systems. Notably, the AI computation power capacity and tradeoff between the computing power and power consumption are the most critical limitations of information fusion systems. Thus, in this study, we used an ARM-based FPGA to integrate complex works and increase the computation speed of fusion information systems. In addition, we compared the computation speed of the information fusions performed by an ARM-based FPGA and a computer with a GPU RTX206. Figure 1 shows the hardware of the information fusion system used in our study. In this paper, Section 2 introduces the architecture and method employed by the information fusion system. Section 3 elucidates the implementation of the information fusion system on an ARM-based FPGA. Section 4 presents the experimental details and results obtained using the fusion information system implemented by the ARM-based FPGA. Finally, Section 5 provides a comprehensive discussion on the obtained results and highlights the major conclusions and inferences drawn from them.
# Line Structure Extraction from LiDAR Point Cloud Based on the Persistence of Tensor Feature
## 3 Methodology
### Initial Tensor Voting and Dimensional Feature Presentation  
According to the manifold theory, the \(N\)-dimensional space \(S\) is composed of \(d\)-dimensional normal subspace \(S_{N}\) and \((N-d)\)-dimensional tangent subspace \(S_{N-d}\), which are orthogonal and complementary to each other, as denoted in Equation (1). The geometric feature in each dimension can be detected based on the normal subspace \(S_{d}\), where the normal vector depicts the orthogonal direction of geometric structure. For example, in the 3-dimensional space (\(N=3\)), the geometric structure with 1-dimensional normal space \(S_{1}\) can be detected as \"surface,\" the geometric structure with 2-dimensional normal space \(S_{2}\) can be detected as \"line\", and the geometric structure with 3-dimensional normal space \(S_{3}\) can be detected as \"point\".  
\[S=S_{d}+S_{N-d}\ \&\ S_{d}\cdot S_{N-d}=0 \tag{1}\]  
However, if there is no normal recorded, the normal space can be computed using tangent information from its neighborhood. Suppose a point \(p\) with a point \(p_{i}\) fromneighborhood \(\Omega\), and the vector from \(p_{i}\) to \(p\) can be denoted as \(v_{i}\), which contains the tangent information of \(p_{i}\). Then \(v_{i}\) is normalized as \(\hat{v}_{i}\) and the tangent subspace \(S^{i}_{N-d}\) can be computed based on the Kronecker delta of \(\hat{v}_{i}\), as denoted in Equation (2).  
\[S^{i}_{N-d}=kron\Big{(}\hat{v}_{i}\hat{v}_{i}^{T}\Big{)} \tag{2}\]  
Since there is no normal vector recorded and no preferred geometric features set for space \(S\), it can be denoted as the identity matrix \(I\) for space \(S\). Hence, the geometric structure encoded by normal subspace \(S^{i}_{d}\) that will be transmitted from point \(p_{i}\) to \(p\) can be denoted  
Figure 1: The pipeline of the line structure extraction framework.  
as complementary form of \(S_{N-d}^{i}\) according to Equation (1), i.e., \(S_{d}^{i}=I-S_{N-d}^{i}\). As seen in Figure 2, the geometric feature of each point in neighborhood of \(p\) is voted to the point, with geometric information transmitted by the normal subspace.  
Besides, the intensity of geometric feature decays along distance \(r_{i}\) from \(p_{i}\) to \(p\), and the decay function is usually taken as the Gaussian function, i.e., \(w(r)=e^{-(\frac{r}{p})^{2}}\). Here, the \(\rho\) for \(w\) is set to be 0.1. Finally, tensor \(T_{p}\) at point \(p\) is computed and accumulated using that of the point from its neighborhood, as denoted in Equation (3).  
\[T_{p}=\sum_{p_{i}\in\Omega}w(r_{i})\Big{(}I-S_{N-d}^{i}\Big{)} \tag{3}\]
## 5 Case Studies: Validating the CILS Framework
### Case Study One: Busuu
#### 5.1.3 Enhancing Language Skills through CILS: A Case Study of Busuu  
This case study on Busuu illustrates how the CILS significantly enhances language skills. By integrating the CILS, Busuu users experienced improved grammar, vocabulary, and pronunciation skills, evidenced by pre- and post-assessment comparisons. The adaptive learning technology within the CILS customizes lessons based on individual user  
Figure 6: CEFR Level Progression in Busuu’s Language Learning Platform.  
performance, ensuring that each learner faces challenges tailored to their current skill level. This personalization has led to a 40% increase in grammar accuracy, a 35% enhancement in vocabulary retention, and a 25% improvement in pronunciation clarity among learners. Such results not only underscore the effectiveness of the CILS in enhancing language skills but also demonstrate its potential as a pivotal tool in modern language education platforms.
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## Materials and Methods  
### Theory  
#### 2.1.1 Multi-Step Speckle Tracking  
Tissue displacements were estimated using a multi-step ST algorithm. Such multi-scale methods are able to obtain accurate displacement estimates in highly discontinuous displacement fields [45; 46]. Normalized 2D cross-correlation was used as the pattern matching function. Inter-frame displacements were first estimated using relatively large 2D windows of envelope (demodulated RF) data, where after, in the second step of the algorithm, smaller windows of RF data were used to refine the displacements. Subsample displacement estimates were obtained by performing interpolation of the cross-correlation-function (CCF).
# Research on Space-Based Visible Detection for Conical Space Targets
## 3 Radiation Model of Conical Space Target
Taking the phase angle value of \(80^{\circ}\) and the conical target inclination value of \(60^{\circ}\) as an example, we simulated a three-dimensional plot of the radiation intensity as a function of \(\Theta_{s}\) and \(\Theta_{d}\) for each case under this example condition (see Figure 3). We then simulated the extreme values of radiation intensity of the space target received by the detector under different conical target inclinations, phase angles, and cases, and the simulation results were shown in Table 1.  
Cases 1 and 3 have extremely small incident angles or observation angles, which will make it difficult for the detector to capture the space target and aggravate the difficulty of space detection by the detector. In Case 4, whether the observation angle or the incident angle, the angular span is very large, which leaves sufficient time for the detector to capture the space target, which facilitates the space detection of the detector. Therefore, the following analysis will focus on Case 4. From Table 1, we can clearly see that the increase of the conical target inclination will lead to the increase of the minimum value of the radiation intensity. The increase of the phase angle will lead to a decrease in the minimum value of the radiation intensity. We plot Figure 4 to visually represent this  
Figure 3: Under the condition that the value of the phase angle is \(80^{\circ}\) and the value of the conical target inclination is \(60^{\circ}\), this three-dimensional map shows the radiation intensity in each case. (from left to right: Case 1, Case 3, Case 4). changing trend. According to the findings, the radiation intensity reflected by the conical target has a minimum value of 266.6 W/sr when the conical target inclination is 60\({}^{\circ}\) and the phase angle is 85\({}^{\circ}\).
## 1 Introduction
However, due to the uncertainty of systems in practice, it is difficult or impossible to obtain these precise upper bounds and achieve good control results using the abovementioned methods. Consequently, this presents a challenge and the need to devise a novel control scheme to both achieve the control goals of the platoon and mitigate the influence of disturbances under the condition that the precise upper bounds of disturbances are unknown. According to the above analysis, we investigate the sliding mode control issue of a vehicle platoon affected by matched and mismatched disturbances in this article. A novel FTDO-based sliding mode control strategy is developed to improve platoon control performance and achieve its control goals. The proposed FTDO, which is designed to solve the issue of degraded control performance and enhance the vehicle platoon's stability, can directly estimate disturbances without the need to know the precise upper bounds of the disturbances. A feedforward compensation term based on disturbance estimation is incorporated into the FTDO-based sliding mode controller to effectively reduce the influence of disturbances. The primary contributions of this article are as follows:* Unlike existing studies which focus on vehicle platoons with matched disturbances [20, 21, 22, 23], this work investigates the control issues of a vehicle platoon influenced by matched and mismatched disturbances. * In contrast to [28, 29, 30], in which the assumptions regarding the known precise upper bounds of disturbances are made, this work presents a novel FTDO capable of directly estimating matched and mismatched disturbances without the need to know the precise upper bounds of both types of disturbances, which makes this approach more general and extends applicability to a broader range of practical systems. * For a vehicle platoon affected by matched and mismatched disturbances, a novel FTDO-based sliding mode control approach is developed to achieve the control goals. Moreover, a feedforward compensation term is constructed by utilizing disturbance estimation to effectively reduce the negative effects of disturbances and guarantee the platoon's stability. The rest of the article is organized as follows: A dynamics model of the vehicle and the problem formulation are described in Section 2. Section 3 illustrates the development process of the FTDO-based sliding mode control strategy. A numerical example is outlined in Section 4 to confirm the efficacy of the developed FTDO-based sliding mode control approach. The conclusions are outlined in Section 5.
# Advances in Genome Sequencing and Natural Rubber Biosynthesis in Rubber-Producing Plants
## 2 Progress in the Genome Sequencing of Rubber-Producing Plants
In 2020, a tetraploid guayule was sequenced using Illumina NovaSeq platform, resulting in an assembled genome size of 2.93 Gb and consisting of 1,070,894 scaffolds. The genomic data, expressed sequencing tags, and plastid genome data of the tetraploid guayule have been retrieved in the Guayule Genomic Resources ([[https://probes.pw.usda.gov/Guayule/index.html](https://probes.pw.usda.gov/Guayule/index.html)]([https://probes.pw.usda.gov/Guayule/index.html](https://probes.pw.usda.gov/Guayule/index.html))). In 2021, Bridgestone and NRGene jointly announced the successful sequencing and assembly of a diploid guayule genome. However, the detailed genome sequence has not yet been released, and only limited sequence information can be obtained through sequence alignment [28]. Progress in the genome sequencing of alternative rubber-producing plants provides the basis for elucidating the molecular mechanism of natural rubber biosynthesis.
## Appendix B Hyperparameter Tuning  
In the following, we provide the list of the hyperparameters optimized through Optuna with the possible range of values for every classifier.  
* n_estimators--ranging from 100 to 1000, with a step of 100;
- criterion--to be chosen between gini, entropy and log_loss;
- max_depth--ranging from 2 to 8;
- min_samples_split--ranging from 2 to 5;
- min_samples_leaf--ranging from 1 to 5;
- min_weight_fraction_leaf--ranging from 0 to 0.5, with a step of 0.1. In our case, all the data have the same weight;
- max_features--to be chosen between sqrt and log2;
- max_leaf_nodes--ranging between 4 and 16, with step of 1.
* n_estimators--ranging from 100 to 1000, with a step of 100;
- max_depth--ranging from 2 to 8;
- max_leaves--ranging from 0 to 16, with step of 1;
- learning_rate--ranging from 0.10 to 1.00, with step of 0.01;
- booster--to be chosen between gbtree and dart;
- gamma--ranging from 0 to 2.0, with step of 0.2;
- min_child_weight--ranging from 0 to 5, with step of 1;
- max_delta_step--ranging from 0 to 10, with step of 1.
* hidden_layer_sizes--ranging from 10 to 200, with a step of 10. This size refers to the number of nodes of the single hidden layer added to the model;
- solver--to be chosen between sgd and adam;
- alpha--ranging from 0.0001 to 0.01, with a step of 0.0005;
- batch_size--ranging from 4 to 32, with a step of 4;
- learning_rate--to be chosen between constant, invscaling, and adaptive;
- learning_rate_init--ranging from 0.001 to 0.05, with step of 0.001;
- power_t--ranging from 0.1 to 1.0, with a step of 0.1;
- max_iter--ranging from 100 to 1000, with a step of 100;
- tol--ranging from \(0.5\times 10^{-4}\) to \(10^{-3}\), with step of \(0.5\times 10^{-4}\);
- momentum--ranging from 0.1 to 1.0, with step of 0.2.
- c--ranging from 0.1 to 9.6, with step of 0.5;
- gamma--to be chosen between scale and auto;
- tol--ranging from \(10^{-4}\) to \(0.99\times 10^{-2}\), with a step of \(2\times 10^{-4}\);
- max_iter--ranging from 10 to 10,010, with a step of 100;  
During hyperparameter optimization, the importance of each hyperparameter was computed by the Optuna function optuna.importance.get_param_importances. We used the importance estimation and the optimized hyperparameter value collected for each fold to adjust the ranges shown. Figure 14 shows the values of hyperparameter importance averaged over the folds for every classifier.
[48] introduced an artificial neural network (ANN) algorithm for investigating the fatigue problem of chassis components. In addition, the durability of structural components subjected to cyclic loading was evaluated using a neural network in the work of [PERSON] et al. [49], whereas the failure of concrete columns under fire using a data-driven technique was reported by [PERSON] et al. [50]. The investigation on instability of columns under axial compression using an AI-based approach was promising but still limited, particularly for accounting initial imperfections of structural elements. The ANN models have been introduced in several studies for predicting critical buckling load, such as in the work of [PERSON] et al. [51] on beam-columns, in the studies of [PERSON] et al. [52] on thin cylindrical shells, in work by [PERSON] et al. [53] on arch-shells, and in work by [PERSON] et al. [54] for laminated panels. Last but not least, [PERSON] et al. [55] developed an adaptive neuro-fuzzy inference system (ANFIS) to estimate the column buckling load that takes into account initial cracks. Above all, several remaining issues need to be clarified: (i) the parameter optimization of AI methods, (ii) the possibility of treating the problem with other AI algorithms, especially the use of hybrid models, and (iii) sensitivity analysis of factors that affect the buckling responses of structural members under compression. Therefore, the main objective of this work is to develop hybrid AI approaches involving ANFIS, ANFIS optimized with simulated annealing (SA), and ANFIS optimized with biogeography-based optimization (BBO) for predicting critical buckling load, or the maximum load capacity, of compression members, taking into account local, flexural-torsional, flexural failure modes and initial geometric imperfections. The SA and BBO optimization techniques were implemented to increase the prediction performance of the initial ANFIS model. The developed hybrid AI models were constructed and performed with data available in the literature on the buckling behavior of Y-section steel columns. Input parameters of the models were divided into two main groups--slenderness ratios (including 2 inputs) and initial geometric imperfections (with 9 inputs), respectively--while the output was the critical buckling load of the columns. Statistical criteria, namely the correlation coefficient (R), the root mean squared error (RMSE), and the mean absolute error (MAE), were evaluated at the end of the numerical simulations in order to investigate the prediction capability and performance of the AI models. Sensitivity analysis was finally performed with the aim of estimating the influence of input parameters on the predicted results, particularly focused on the initial imperfections.