esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 9 Linear Time Series
#### Relevant Papers  
[PERSON] et al. [57] utilized the ARMA time series model to forecast network traffic and examine the impact of data transferring in low and high forecasted traffic scenarios. The authors experimented with the ARMA model using captured network packets representing the network condition in this study. These data were transformed with log return before using the ARMA model to create a network traffic pattern for forecasting. Using both a 10 MB file and a 1 GB file, they initiate the transferring of files when the forecasted traffic is low and high to test their forecasting accuracy and observe the file transfer throughput. In their conclusions, the authors note that initiating data transfer when the next step in the traffic forecast is low would lead to more efficient throughput, and they suggest dividing larger files into smaller sizes. They suggest that using ARMA model traffic prediction could allow for more effective file transfers.  
Similarly, [PERSON] et al. [58] proposed using the ARMA model for traffic prediction in hopes of allowing application developers to find out the best periods for their users to download software patches. Given that downloading software patches can impact bandwidth during activities such as streaming videos or gaming, it would be ideal to use network traffic forecasting to avoid downloading patches or updates during these times. Using captured TCP data, the authors used the ARMA model on the log return values of the collected data and compared the forecasted pattern to the actual traffic data, varying two parameters: the sampling size of the Moving Average and the step size of the sample data. Their study finds that the ARMA model could forecast short-term traffic regardless of historical data size and suggests that this method could be suitable for forecasting software patch download periods.  
[PERSON] et al. [59] proposed using a k-factor Gegenbauer ARMA (GARMA) model for predicting high-speed network traffic and compared it to the traditional autoregressive model. This study captured traffic data for MPEG, JPEG, video, Ethernet, and the Internet. Using the mean absolute error (MAE) and the signal to error ratio (SER), they measured the accuracy of their prediction model. The authors found that the k-factor GARMA model outperformed the autoregressive (AR) model. They suggest that this prediction model could be used to plan congestion control, such as dynamically allocating bandwidth.  
[PERSON] et al. [60] described the use of an ARIMA model for predicting traffic and detecting anomalies. Using a uniform random number distribution, they simulated 15 min of data from a network interface card. Using the ARIMA model to simulate traffic, anomalies were detected in the autocorrelation and partial autocorrelation plots.
## 2 Basic Theory
### Experimental Modal Analysis
#### 2.2.2 Experimental Modal Analysis  
In the research of this paper, the DASP software (Data Acquisition and Signal Processing) ([[http://www.coinv.com/](http://www.coinv.com/)]([http://www.coinv.com/](http://www.coinv.com/)), accessed on 3 July 2023) was used to analyze the modal test data, and Stochastic Subspace Identification (SSI) is used to identify the modal of the test object. The SSI method was proposed by [PERSON] and others in 1995. This method analyzes linear systems using a discrete-time state-space model, constructing the Hankel matrix through the correlation function of response signals in the time domain, thereby achieving modal parameter identification under stationary stochastic excitation. For an  
Figure 2: Distribution of rough surface asperities. n-degree-of-freedom linear system affected by its own noise, its state space equation in discrete form can be described as follows:  
\[ \begin{cases}\mathbf{x}_{k+1}=\mathbf{A}\cdot\mathbf{x}_{k}+\mathbf{B}\cdot\mathbf{u}_{k}\\ \mathbf{y}_{k}=\mathbf{C}\cdot\mathbf{x}_{k}+\mathbf{D}\cdot\mathbf{u}_{k}\end{cases}  \tag{6}\]  
where \(\mathbf{x}_{t}=\mathbf{x}(k\Delta t)\) is the n-dimensional discrete state vector, with n representing the degrees of freedom; \(\mathbf{y}_{k}\) is the N-dimensional output vector, with N representing the number of response points; \(\mathbf{A}\) is the discrete system matrix; and \(\mathbf{B}\) is the discrete input matrix. During the measurement process, we inevitably encounter effects caused by systematic uncertainties, which often manifest in the form of random components. These uncertainty factors can be further divided into process noise \(w_{k}\) and measurement noise \(v_{k}\). When considering process noise \(w_{k}\) and measurement noise \(v_{k}\), Equation (6) is expressed as follows:  
\[ \begin{cases}\mathbf{x}_{k+1}=\mathbf{A}\cdot\mathbf{x}_{k}+\mathbf{B}\cdot\mathbf{u}_{k}+\mathbf{w}_{ k}\\ \mathbf{y}_{k}=\mathbf{C}\cdot\mathbf{x}_{k}+\mathbf{D}\cdot\mathbf{u}_{k}+\mathbf{v}_{k}\end{cases} . \tag{7}\]  
It is assumed that the noise has a zero mean, and its covariance matrix satisfies  
\[E[ \begin{pmatrix}\mathbf{w}_{p}\\ v_{p}\end{pmatrix} (\mathbf{w}_{q}^{T}&\mathbf{v}_{q}^{T})]= \begin{pmatrix}\mathbf{Q}&\mathbf{S} \\ \mathbf{S}^{T}&\mathbf{R}\end{pmatrix} \delta_{pq}. \tag{8}\]  
Under the excitation of the environment, the input \(\mathbf{u}_{k}\) is not measured, which means Equation (7) can be expressed as:  
\[ \begin{cases}\mathbf{x}_{k+1}=\mathbf{A}\cdot\mathbf{x}_{k}+\mathbf{w}_{k}\\ \mathbf{y}_{k}=\mathbf{C}\cdot\mathbf{x}_{k}+\mathbf{v}_{k}\end{cases} . \tag{9}\]  
At this point, \(\mathbf{A}\) and \(\mathbf{C}\), respectively, represent the n \(\times\) n order state matrix and N \(\times\) n order output matrix, and the characteristics of the system are entirely represented by the eigenvalues and eigenvectors of the characteristic matrix \(\mathbf{A}\).
# Dynamic Frequency Support from a DFIG-Based Wind Turbine Generator via Virtual Inertia Control
## 3 Model and Control of the DFIG-Based WTG
### Control of the DFIG-Based WTG  
The control system of the DFIG-based WTG includes a pitch angle, a rotor-side converter, and grid-side converter controllers. The function of the pitch-angle controller is to prevent the rotor speed (\(\omega_{r}\)) from exceeding the maximum value (\(\omega_{\max}\)), or it is used to enable the de-loading mode according to the command from a high-level controller. The rotor-side converter controller, which achieves decoupled control of reactive power and active power, focuses on maintaining the stator voltage at the regulated value and controls the active power. The gird-side controller focuses on controlling the direct current (DC)-link voltage.  
As shown in Figure 2, a rotor speed error between \(\omega_{r}\) and \(\omega_{\max}\) is passed through a proportional-integral-derivative (PID) controller to calculate the reference of pitch controller. In addition, the rate and the angle limiters are considered; the setting of maximum pitch angle limit is 30\({}^{\circ}\), and the setting of pitch rate is \(\pm\)10\({}^{\circ}\)/s, as in [24].  
In (3), \(c_{p}\) is a function of \(\lambda\) and has a maximum value (\(c_{P,\ max}\)) at the optimal \(\lambda\) (\(\lambda_{opt}\)). At the value of \(\lambda_{opt}\), the DFIG-based WTG can capture the maximum \(P_{m}\) from wind [25; 26]. Substituting (6) in (3), the power reference for MPPT operation can be represented as:  
\[P_{MPPT}=\frac{1}{2}\rho\pi R^{2}(\frac{\omega_{r}R}{\lambda_{opt}}^{3})c_{P, \ max}=k_{g}\omega_{r}^{3}, \tag{10}\]  
where \(k_{g}\) represents a constant and is set to 0.512. In addition, \(c_{P,\ max}\) is set to 0.5; \(\lambda_{opt}\) is set to 9.95 in this study.  
Figure 3 indicates \(P_{m}\) curves and \(P_{MPPT}\) of the DFIG-based WTG. The \(\omega_{r}\) operating range of the DFIG-based WTG ranges from 0.70 p.u. to 1.25 p.u., as represented by black dashed lines. The red and the blue solid lines indicate the MPPT curve and the \(P_{m}\) curves, respectively.  
The DFIG-based WTG can provide virtual inertial response by adding the _df/dt_ loop to the MPPT control loop, as shown at the bottom of Figure 1. Further, the primary frequency control (droop control) loop is not implemented, as this paper solely studies how to improve the frequency stability from the aspect of the virtual inertia control from a DFIG-based WTG.  
Figure 2: Pitch angle controller.
## 3 Proposed Approach
### Classification  
#### 3.3.1 Machine Learning Algorithms  
To select the appropriate classifier, various factors should be considered, including computational cost, classification accuracy for several datasets, and performance [15]. The well-known classification approaches k-NN, SVM, and ANNs were developed specifically to deal with uncertain data. These methods provide a probability measure in order to classify samples with different class memberships [95]. Therefore, in this study, the three most common classifiers--SVM, ANN, and kNN--are employed.  
The SVM classifier usually outperforms other soft-computing approaches in terms of efficiency, scalability, and performance [96]. SVM is rather insensitive to feature correlation and is typically adopted to handle high-dimensional feature spaces [87]. This classifier minimises the number of misclassifications. However, the training time is very high [56]. Also, the provided kernel matrix of input samples must be stored in its memory. The desired optimal solution for non-linear mapping depends on selecting an appropriate kernel function as well [52].  
It has been reported that the Artificial Neural Network (ANN) performs very well on complicated data, noisy fields, and multivariate nonlinear domains [56]. ANN demonstrates complex global behaviour to provide the desired outputs in a supervised or unsupervised manner [97]. One basic feature that distinguishes this method from an algorithmic program is the ability to classify unseen data [98]. Additionally, the potential of nonlinear mapping increases the flexibility of the ANN method for modelling complex relationships [56]. However, due to the use of gradient methods in the learning process, ANN is very time consuming [56]. Iteration number, learning rate, and activation function affect the accuracy rate of ANN as well [99].  
K-NN is a very simple algorithm. Therefore, its implementation and debugging are relatively easy. This algorithm searches the training set to select the \(k\) nearest neighbours of the unseen features and estimates their class label based on the majority voting technique. K-NN can be used for large numbers of training sets. However, its classification and estimation are moderately slow. Additionally, the choice of \(k\) affects the performance of this algorithm. Moreover, the accuracy of this algorithm may be degraded in the presence of noisy or irrelevant features [56].
## 6 Discussion  
### Research Objectives  
This research addresses the practical challenges faced by the development of TIZCB. Anchored in the climatic conditions of high temperature, humidity, and salinity prevalent in tropical islands, the study employs a comprehensive approach involving architectural parameter performance simulation, machine learning predictive techniques, and intelligent optimization methods. The theoretical framework of an intelligent energy-saving and capacity-enhancing system for TIZCB is established. The research aims to explore the quantification mechanisms of the system and formulate optimization methods. The specific objectives are discussed as follows:  
1. Establish a theoretical framework for the quantitative study of energy utilization, energy conservation, energy production, and intelligent systems in TIZCB. By defining the intrinsic characteristics and technical systems of ZCB, theoretical models based on systems theory, synergy theory, and control theory are constructed. The research proposes a macro-meso-micro perspective for constructing a quantitative analytical framework for energy utilization, energy conservation, energy production, and intelligent systems. Technical methods such as energy consumption simulation, sensitivity analysis, integrated learning, and intelligent optimization are introduced to support the theoretical framework and technical solutions for the mechanism and optimization of ZCB energy utilization, energy conservation, energy production, and intelligent systems.
2. Analyze the impact of design parameters on the energy utilization, energy conservation, energy production, and intelligent systems of TIZCB. Building upon the correlation analysis of input parameters, uncertainty analysis tools such as frequency distribution, standard deviation, variance, and fitting are employed. Key parameters, including thermal environment simulation, energy flow analysis, and cost-benefit assessment, are calculated and simulated. The goal is to reveal the relationship between variations in design parameter combinations and building performance, providing real-time and accurate data support for the design and operation of ZCB.
3. Develop performance prediction and multi-objective optimization design methods for energy utilization, energy conservation, energy production, and intelligent systems in TIZCB. Integrating various machine learning regression prediction algorithms, a data-driven building performance prediction model is developed to explore the relationship between design parameters and building performance. Through intelligent optimization multi-objective analysis, optimal combinations of design parameters for energy systems are derived. Optimization design considerations include geographical location, structure, material selection, energy system design, and the application of intelligent management systems, ensuring high energy efficiency, low environmental impact, and high residential comfort in ZCB within the unique climate of tropical islands.
## 2 Materials and Methods
### Atmospheric Filtering
#### 2.4.2 GACOS Correction  
We used GACOS tropospheric delay products as a second approach to predict and correct errors caused by atmospheric interactions. Developed by Newcastle University in England in June 2017, GACOS is based on numerical weather models [57]. GACOS has become frequently used by many researchers to mitigate or eliminate the effect of atmospheric error, owing to its advantages such as near real-time usability, simplicity, and free implementation [58; 59; 60; 61; 62; 63]. In the processing of GACOS delay products, SRTM DEM (90 m, S60-N60) and ASTER GDEM (90 m, N60-N83, S60-S83) data are used, which are also high-definition weather models, created by the European Centre for medium-range weather forecasts [64]. GACOS uses an iterative tropospheric decomposition model to separate stratified and turbulent components from tropospheric total delays, and high spatial resolution zenith total delay (ZTD) maps are generated. Then, the ZTD maps obtained at different dates are converted to LOS interferograms and applied after phase correction to minimize or eliminate the effect of atmospheric errors on the interferograms. Finally, the SCLA error is removed after the GACOS-based atmospheric correction and as a result deformation maps and time series in the LOS direction are produced. The procedure of GACOS-based atmospheric correction approaches is shown in Figure 3. In addition, further details about these steps can be found in the references [53; 55; 56].
# Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking
## 2 Related Work
### Name-Resolution Server Placement in Information-Centric Networking (ICN)
They used a hierarchical clustering algorithm with good scalability that only adapts to the K server placement problem. The studies above have some limitations if directly applied, because they do not take into account both deterministic latency constraint and multiple latency demands when name-resolution servers are placed in ICN. The most relevant studies are [23; 51]. In [51], [PERSON] et al. first defined the multilayercontrol problem in terms of the minimum dominating set (MDS) controllability framework. Their target was to minimize the total dominator count, which is similar to the server placement problem. However, there are no more constraints between layers in their model, and it is not suitable for direct application in name-resolution server placement in our situation. [PERSON] et al. [23] proposed the DLNR framework, and an algorithm called the latency-aware hierarchical elastic area partitioning (LHP) algorithm was also proposed. However, the LHP is just a feasible algorithm that outputs a placement and partition solution to satisfy the structural constraints, and its costs are not minimized.
## 2 Spectral Analysis of HY-2B Empirical Accelerations
### Refined PEA Model of HY-2B  
Figure 2 shows the presence of a low-frequency signal and a 5.5 CPR signal, and the power spectrum magnitudes of the two signals are almost equivalent. Due to the existence of high- and low-frequency signals, it may be necessary to make the empirical acceleration in the cross-track direction in the form of a combination of constant and periodic signals. Moreover, the conventional 1 CPR signal in the cross-track direction can be considered as a comprehensive scheme. Based on these results, the PEA models of the empirical acceleration in the along-track and normal directions can be obtained as:  
\[\begin{array}{l}\tilde{r}_{A}=a_{1}\cos(k\cdot v)+a_{2}\sin(k\cdot v),\ k=4\\ \tilde{r}_{c}=a_{0}+a_{1}\cos(k\cdot v)+a_{2}\sin(k\cdot v),\ k=5.5\end{array} \tag{2}\]  
and:  
\[\begin{array}{l}\tilde{r}_{A}=a_{1}\cos(k\cdot v)+a_{2}\sin(k\cdot v),\ k=4\\ \tilde{r}_{C}=a_{1}\cos(k\cdot v)+a_{2}\sin(k\cdot v),\ k=1\end{array} \tag{3}\]  
Likewise, the individual empirical accelerations in three directions can be expressed as:  
\[\begin{array}{l}\tilde{r}_{A}=a_{1}\cos(k\cdot v)+a_{2}\sin(k\cdot v),\ k=4\\ \tilde{r}_{c}=a_{0}+a_{1}\cos(k\cdot v)+a_{2}\sin(k\cdot v),\ k=5.5\\ \tilde{r}_{R}=a_{1}\cos(k\cdot v)+a_{2}\sin(k\cdot v),\ k=2\end{array} \tag{4}\]  
and  
\[\begin{array}{l}\tilde{r}_{A}=a_{1}\cos(k\cdot v)+a_{2}\sin(k\cdot v),\ k=4\\ \tilde{r}_{C}=a_{1}\cos(k\cdot v)+a_{2}\sin(k\cdot v),\ k=1\\ \tilde{r}_{R}=a_{1}\cos(k\cdot v)+a_{2}\sin(k\cdot v),\ k=2\end{array} \tag{5}\]  
The periodic term of the empirical acceleration in three directions defined by the local orbital reference frame has been revealed, and the power spectrum of the cross-track direction is worth discussing. The spectrum results show that there are two differences between the normal direction and the other two directions. The power spectrum's amplitude of acceleration in the normal direction is smaller than that in the along-track and radial directions, which is related to the magnitude of acceleration and the intensity of the signal. On the other hand, there is no obvious dominant signal in the normal direction, and there are  
Figure 2: Amplitude spectrum of unmodeled accelerations in the along-track (red), cross-track (blue), and radial (green) directions. Values for the cross-track and radial directions are shifted by +10 nm/s\({}^{2}\) and +20 nm/s\({}^{2}\), respectively.  
coexisting high- and low-frequency signals. The optimal frequency combination for PEA is discussed in the following section, and a refined PEA combination can independently be inferred from the SLR validation.
## 3 Results and Discussion  
### Recovery of Lycopene from Tomato Peels  
The extraction of lycopene from tomato peels was investigated using the UAE-MAE method, while Soxhlet extraction was conducted as a reference method, serving as an exhaustive extraction technique to determine the maximum achievable lycopene yield. The lycopene content of the extract obtained from Soxhlet extraction was 40.66 \(\pm\) 3.08 mg/100 g of tomato peels. Various conditions, including the solid/liquid ratio, microwave power, and ultrasound power, were tested during the UAE-MAE process. The solid/liquid ratio ranged from 0.03 to 0.10 g/mL, microwave power from 0 to 500 W, and ultrasound power from 0 to 600 W. The optimal extraction conditions for lycopene recovery were found at a solid/liquid ratio of 0.03 g/mL with 500 W microwave power and 600 W ultrasound power, resulting in the highest lycopene content (37.08 \(\pm\) 2.09 mg/100 g of tomato peels) and extraction yield (91.20 \(\pm\) 2.90%). Although Soxhlet extraction provided slightly higher recovery, UAE-MAE offered several key advantages, particularly in terms of processing time and energy efficiency. Soxhlet extraction required 4 h of continuous heating, leading to higher energy consumption and an increased risk of the thermal degradation of bioactive compounds. In contrast, UAE-MAE significantly reduced the extraction time to 8 min, minimizingexposure to heat and preserving the structural integrity of lycopene. Furthermore, UAE-MAE has demonstrated greater industrial scalability potential. Unlike Soxhlet extraction, which is primarily a batch-based laboratory technique and not inherently designed for large-scale continuous operations, UAE-MAE offers a more adaptable framework for industrial applications. The extensive processing time and high solvent consumption associated with Soxhlet extraction, without achieving any considerable increase in lycopene recovery, limit its feasibility for large-scale extraction setups. Studies have shown that UAE-MAE can be integrated into continuous extraction systems, further enhancing its feasibility for industrial production. Future research should focus on optimizing large-scale UAE-MAE equipment and evaluating its long-term economic viability.
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 6 Defenses
### Data Encryption  
Data encryption is essential for protecting data both in transit and at rest [64]. Implementing strong encryption algorithms such as AES-256 can safeguard sensitive information from data breaches [65]. Secure communication protocols like TLS (Transport Layer Security) should be used to protect data during transmission [66]. This ensures that even if data are intercepted, they cannot be read or tampered with, thereby mitigating the risks associated with eavesdropping and data breaches.
# An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments
## 5 Conclusions  
This paper proposed an adversarial DBN-LSTM method for detecting and defending DDoS attacks. We designed four modules on the application plane: data collection, data processing, adversarial DBN-LSTM anomaly detection, and abnormal defending. First, the SDN controller realizes the fine-grained monitoring and collection of IP flow records in a complex network environment. Then, the IP flow records are preprocessed by normalization and the Shannon entropy formula. Finally, we designed the adversarial DBN-LSTM anomaly detection method. We can quickly generate many adversarial samples and build adversarial datasets using the GAN model. Furthermore, we introduced DBN for data dimensionality reduction and used LSTM to extract sample timing features to detect IP  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Accuracy** & **False** \\ \hline Our method & 91.23\% & 8.77\% \\ DBN + LSTM & 7.62\% & 92.38\% \\ LSTM & 6.34\% & 93.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison outcomes on our method, DBN + LSTM, and LSTM.  
Figure 9: Comparison outcomes between our method and the compared methods through the evaluating indicator.  
flow records and identify adversarial DDoS attacks. Finally, we designed an abnormal defending strategy to provide SDN controllers. Our experiments show that our method can effectively detect DDoS attacks and make the system less sensitive to adversarial attacks.  
Writing--original draft preparation, L.C. and Z.W.; writing--review and editing, R.H.; supervision, T.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China, grant number 2019 YFB1804403.  
The data are not publicly available due to privacy.  
The authors declare no conflict of interest.