esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Traffic Analysis Methods
### Network Protocol Recognition and Identification Techniques  
The basic principle in protocol recognition and identification is to extract important traffic information from the traffic, based on which the protocol can be identified. There are several methods that can be combined in protocol recognition and identification.  
One division is into manual and automatic analysis. Manual analysis depends on the knowledge and experience of the person who performs the analysis [9]. Automatic analysis is based on the automatic extraction of protocol information from network traffic. Based on the extracted information, patterns of protocol behavior are created, and the techniques that enable automatic analysis create and operate on these patterns. Recognition by automatic analysis can be performed using several techniques, namely preset rules recognition, payload feature recognition, host behavior, and machine learning [9].  
The preset rules recognition technique works with set rules such as port number. This method is not very reliable in terms of user customization of network settings. The payload features technique takes advantage of the deep packet inspection method, which means that it recognizes protocols using the data inside the packets. This method is very simple and easy to implement, but it cannot identify encrypted traffic and is computationally intensive. The host behavior technique works based on statistical parameters of network traffic. The method effectively avoids the process of extracting information from packets. However, the results are often inaccurate due to non-standard traffic parameters [37].  
Machine learning is an important artificial intelligence technique that is used to analyze large-volume datasets based on features and associations between parameters [9]. Machine learning can be divided into shallow learning and deep learning. Shallow learning is used for modeling and analyzing. These are algorithms that cannot fully express complex nonlinear problems. At the same time, the quality of data preparation is crucial and affects the training and results of the model. Deep learning algorithms are able to solve more complex nonlinear problems. The disadvantage of classification based on shallow learning is that the feature extraction and learning process must be repeated after the dataset is changed. In deep learning-based classification, the model does not always need to be re-learned and takes advantage of the original parameters. Shallow learning algorithms include Support Vector Machine (SVM), Naive Bayes (NB), etc. Deep learning algorithms include deep neural networks, Long Short Term Memory algorithms [39], and Generative Adversarial Networks algorithms [16].  
The use of deep learning methods has its application in IoT applications [40]. These methods can be used not only for anomaly detection within industrial networks but also for various operations requiring a high level of abstraction and the ability to understand complex structures.
# Joint Uplink and Downlink Resource Allocation for D2D Communications System
## 3 Resource Allocation Algorithm  
The built optimization function (11) with both continuous and discrete variables is a mixed integer nonlinear programming (MINLP), therefore it is difficult to directly obtain the close-form solution. To solve the above optimization problem (11), the JUAD resource allocation algorithm is proposed, in which the objective function is transformed into two sub-problems to reduce computational complexity cost. The first one is the power allocation for a single D2D pair and its reuse partner, where we allocate transmission power to maximize the total system capacity for the D2D pair and its reuse partner. The second one is the resource allocation for multiple D2D pairs, where we construct the system capacity matrix and improve the Hungarian algorithm to obtain the optimal channel for each D2D pair.
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 5 Path Loss and Shadowing Modelling  
This section describes how this dataset can be used for path loss and shadowing modelling. Although path loss is caused by the attenuation of the power radiated by the transmitter and the channel effects, shadowing is caused by wave phenomena like absorption, reflection, scattering, and diffraction [12]. Both effects are shown in Figure 10. In that way, we fit an LDPLM and a multiple linear regression (MLR), including the environmental variables. Furthermore, we compare both approaches based on the RMSE and the correlation coefficient R\({}^{2}\). Of course, different models can be used for path loss modelling [12; 13; 14], so this procedure just serves as an example of how to use this dataset. Moreover, some machine learning techniques can also be used to estimate path loss and shadowing [40].  
Figure 9: Line of sight for the communication between the GW and the ENs. Credits: Radio Mobile.
# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 2 The Proposed Methods  
### Traditional CU Partition  
The high complexity of HEVC is mainly attributed to the CU partition in which RDO is the core process, as previously mentioned. According to the image content of each LCU, each is divided into four CU sizes with varying depths, and the specific CU sizes of the four are shown in Figure 1. As already noted, the RDO technique is utilized to traverse in order to find the ideal CU sizes, and the procedure consumes the majority of the intra-frame coding time, realizing a full LCU delineation. However, only one CU partition from 1 (maximum size 64 \(\times\) 64 CU are not partitioned) to 64 (CTU are fully partitioned into 8 \(\times\) 8 CU with depth = 3) is chosen in the final CU partition, which significantly increases the amount of redundant coding time.
## 3 Trajectory Planning
### Path Planning
So,  
\[\kappa^{\prime}(0.5)=\frac{-4u_{0}v_{2}(v(\gamma)v^{\prime}(\gamma)+u(\gamma) u^{\prime}(\gamma))(v^{2}(\gamma)+u^{2}(\gamma))}{\big{(}u^{2}(\gamma)+v^{2}( \gamma)\big{)}^{4}}=0. \tag{9}\]  
When \(\gamma\) is less than 0.5, \(\kappa^{\prime}(\gamma)\) is positive, and when \(\gamma\) is greater than 0.5, \(\kappa^{\prime}(\gamma)\) is negative. Therefore, the curvature takes a maximum value at \(\gamma=0.5\). It can be obtained from the curvature calculation (7) that the curvature is symmetric with respect to \(\gamma=0.5\). Point \(N\) in Figure 3 is the point when \(\gamma\) in PH curve is equal to 0.5, which means the coordinates of point \(N\) are the coordinates of \(P(0.5)\). From the above derivation we can get the PH curve shown in Figure 3 is symmetrical about the line \(HN\). Hence the maximum deviation between the right angle and PH curve can be obtained when \(\gamma=0.5\). The coordinates of point \(N\) can be get by  
\[\left\{ \begin{aligned} x_{N}&=x(0.5)=\frac{1}{80}u_{ 2}v_{2}+\frac{1}{30}u_{0}v_{2}\\ y_{N}&=y(0.5)=\frac{31}{160}u_{0}^{2}+\frac{1}{30}u_{ 0}u_{2}.\end{aligned} \right. \tag{10}\]  
The coordinates of point \(H\) are calculated by  
\[\left\{ \begin{aligned} x_{H}&=0\\ y_{H}&=\frac{1}{5}u_{0}^{2}+\frac{1}{15}u_{0}u_{2}. \end{aligned} \right. \tag{11}\]Because \(m\) is equal to \(k\), we can get the following equation. \[ \begin{cases}u_{0}=\sqrt{\frac{5(18m-3\sqrt{2m^{2}})}{17}}\\ u_{2}=\sqrt{\frac{5(18m-3\sqrt{2m^{2}})}{34}}\\ v_{2}=\sqrt{\frac{5(18m-3\sqrt{2m^{2}})}{34}}.\end{cases}  \tag{12}\]  
So, \(d\), the distance between point \(N\) and point \(H\) is as follows. \[ \begin{split} d&=\sqrt{\left(x_{N}-x_{H}\right)^{2}+ \left(y_{N}-y_{H}\right)^{2}}\\ &=\sqrt{\left(\frac{1}{80}u_{2}v_{2}+\frac{1}{30}u_{0}v_{2}\right) ^{2}+\left(\frac{1}{160}u_{0}^{2}+\frac{1}{30}u_{0}u_{2}\right)^{2}}\\ &=u_{2}^{2}\sqrt{\left(\frac{1}{80}+\frac{1}{30}\sqrt{2}\right) ^{2}+\left(\frac{1}{80}+\frac{1}{30}\sqrt{2}\right)^{2}}\\ &=\frac{5(18-3\sqrt{2})}{34}(\frac{\sqrt{2}}{80}+\frac{1}{15})m\\ &=0.17064m,\end{split}  \tag{13}\]  
which is the maximum deviation between the right angle and PH curve. Therefore, \(m\) can be determined once the required maximum deviation \(d\) between the right angle and PH curve is determined. As a result, the PH curve can be obtained according to (3). This completes the proof. Once \(d\) is determined, PH curve can be determined based on Proposition 1, then the workspace required for this PPO is determined, which is smaller than that in the PPO transitioned by right angle. It helps to make full use of the Delta robot's workspace.
# A New SEPIC-Based DC-DC Converter with Coupled Inductors Suitable for High Step-Up Applications
## 4 Design Example for the Proposed Converter
The voltage and current stresses associated to diode \(D_{1}\) can be calculated from (15) and (16), respectively, resulting in: \(V_{D1}=108\) V and \(I_{D1}=1.12\) A. The smallest voltage and current stresses are related to the diode \(D_{2}\): \(V_{D2}=70\) V and \(I_{D2}=0.21\) A, calculated from (17) and (18). The value of the voltage stress on diode \(D_{4}\) is equal to the voltage stress of the transistor, \(V_{D4}=197\) V. From (20) diode \(D_{4}\) current stress is \(I_{D4}=0.33\) A.
# Low Power EEG Data Encoding for Brain Neurostimulation Implants +
## 2 Materials and Methods
### Materials
#### 2.1.3 Bluetooth Low Energy  
According to current literature, WIFI, Bluetooth, Bluetooth Low Energy (BLE), and Zigbee are the four primary protocols utilised for the communication of medical implant devices [28]. Zigbee is concerned with the formation of device networks and is used when several implants are in the same body, but in this study, only one implant is accessible.  
On the other hand, BLE is the chosen technology for low-consumption applications. In particular, it assures up to ten times less consumption than WIFI.  
BLE is a communication protocol similar to Bluetooth Classic that operates at 2.4 GHz. This type of protocol can be found in various devices, including intelligent home automation systems, medical devices, fitness and navigation systems. These systems have one thing in common: they all transfer small amounts of data at slow speeds. Furthermore, they are divided into two parts. The first is concerned with a collection of sensors and other information recording devices (peripheral). The second is concerned with the computer systems (central) that process them (laptops, smartphones, PCs, and more). The peripheral part enters the'sleep' mode more frequently to consume less energy.  
To understand why BLE is used, the features that make it unique and necessary will be considered. Initially, low power consumption is a sufficient reason to use it, as there is a need for a wide range of devices with long battery life and durability. Due to the low cost of the modules, construction and installation are also kept to a minimum. Furthermore, there is a variety of open-access information on this technology that many devices have adopted. Therefore, in terms of consumption, cost, access to information, and compatibility, BLE becomes a competitive medium for providing small amounts of data transmission devices, where high transfer rates are not required. It should be mentioned that the assessment of the impact of wireless technology and radiation on patients' health is not the focus of this work. Finally, BLE is a serial communication protocol, which benefits the transmission of low magnitude values, such as the case of this work.
# Vortex Breakdown Control by the Plasma Swirl Injector
## 2 Layout of the Experimental Setup  
The plasma swirl injector with a helical-shaped electrode was adopted for this research. Figure 1 shows the sketch and the photograph of the plasma swirl injector used in the experiment. Design parameters and advantages of the helical shape electrode were discussed previously [55]. The length and inner diameter of the quartz glass tube are 100 mm and 36 mm, respectively. In this study, the actuator was further optimized. The electrodes were arranged perpendicular to the blades at the inlet of the electrode. Compared to that of 170 mm in the earlier study [55], the helix pitch was shortened to 100 mm, which means the electrode was longer and more plasma could be generated at the same voltage. The length of the electrode was approximately 130 mm. The activated and grounded electrodes were stuck to the inside and outside wall of the injector with a width of 2.5 mm and 7.5 mm, respectively.  
In the experiment, air volumetric flow was 175 1/min and the corresponding bulk flow velocity in the injector was approximately 3 m/s. The power inputs of the plasma generator were approximately 31-76 W and the electrodes on the inner wall were activated with a peak-to-peak amplitude of 12-21 kV. Parameters of the five test cases are listed in Table 1.  
Figure 2 shows the schematic of the experimental setup in this research. The flowfields of the five test cases were captured by 2D PIV, manufactured by Lavision (Gottingen, Genmany) [55]. A screw compressor supplied compressed air, which was then settled into two tanks of 1.2 m\({}^{3}\). A small wind tunnel, manufactured by 3D printing, was installed on a displacement device. Through the bottom inlet of the wind tunnel, air flowed into the settling chamber, in which two layers of honeycomb were used to improve flow uniformity. A contraction section, with an area ratio of 6:1, was used to further improve the flow quality. A mass flowmeter was utilized to regulate and measure the flow rate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Case**} & **Actuator** & **Waveform** & **Voltage** & **Voltage** & **Power** \\  & **Status** & **Type** & **Amplitude** & **Frequency** & **Inputs** \\  & & & **(kV)** & **(kHz)** & **(W)** \\ \hline
1 & OFF & - & - & - & - \\
2 & ON & Sinusoidal & 12 & 9 & 31 \\
3 & ON & Sinusoidal & 15 & 9 & 45 \\
4 & ON & Sinusoidal & 18 & 9 & 60 \\
5 & ON & Sinusoidal & 21 & 9 & 76 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The five test cases and their experimental conditions.  
Figure 1: Sketch and photograph of plasma swirler with helical shape electrodes.
# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
## 3 Proposed FLDHDT
### Defuzzifier  
The defuzzifier transfers a linguistic input into a crisp output after the linguistic input is generated by the inference engine on the basis of the rule base. The center of gravity (CoG) method [29] is the most commonly used for defuzzification. The CoG method calculates the center point of all overlapping areas that are obtained from the fuzzy outputs for HOM (TTT) in the corresponding MF, as plotted in Figures 8 and 9 of FLDHDT. In Figure 8, the range of crisp HOM is [0, 5] in units of dB. Moreover, Figure 9 illustrates that the range of crisp TTT is [0, 200] in terms of milliseconds.
## 2 Robustness Analysis Theory for Flexible Cable-Bar Tensile Structures
### Fundamental Robustness Analysis Method Based on \(H_{\infty}\) Theory
During the design process, the gain from input interference \(w(t)\) to output \(\Delta y(t)\) should be sufficiently small; that is, the \(H_{\infty}\) norm of the system transfer function \(G_{w\Delta y}(s)\) should be extremely small, which can be expressed as:  
\[min\|G_{w\Delta y}(s)\|_{\infty}=\gamma_{0} \tag{7}\]  
\(H_{\infty}\) optimizes the performance indicator in the worst state to enable the interference inhibition of the structural system to meet the requirements. Therefore, the evaluation indicator of structural robustness can be defined as:  
\[I_{R}=\|G(s)_{w\Delta y}\|_{\infty} \tag{8}\]For the flexible cable-bar tensile structures, input disturbances will affect structural properties due to its strong geometrical nonlinearity. Therefore, the further introduction of performance criteria \(L_{2}\) is needed, and the input interference \(w(t)\) and the output interference \(\Delta y(t)\) belong to \(L_{2}\). \(W(t)\) and \(\Delta Y(t)\) can be obtained by \(w(t)\) and \(\Delta y(t)\) through Laplace transform. The transfer function matrix of the structural system \(G_{w\Delta y}(s)\ \in\ H_{\infty}\), the input interference vector \(W(s)\ \in\ H_{2}^{m}\), the output interference vector \(\Delta Y(s)\ \in\ H_{2}^{m}\), and the infinite norm of the matrix \(G_{w\Delta y}(s)\) can be calculated via the induced norm \(L_{2}\):  
\[I_{R}=\left\|G_{w\Delta y}(s)\right\|_{\infty}=\sup_{\left\|W\right\|_{2}\
eq 0 }\frac{\left\|\Delta Y(s)\right\|_{2}}{\left\|W(s)\right\|_{2}} \tag{9}\]  
For any function, \(F\), \(G\ \in\ L_{2}^{n}\), and the inner product of functions can be displayed as:  
\[\left\langle F,G\right\rangle=\int\limits_{0}^{\infty}F^{T}(t)G(t)dt \tag{10}\]  
\(w(t)\) and \(\Delta y(t)\) are the inverse Laplace transform of \(W(s)\) and \(\Delta Y(s)\), respectively. In accordance with the definition of the inner product of functions,  
\[\left\|W(s)\right\|_{2}=\left\|w(s)\right\|_{2} \tag{11}\]  
\[\left\|\Delta Y(s)\right\|_{2}=\left\|\Delta y(t)\right\|_{2} \tag{12}\]  
By substituting Equations (11) and (12) into Equation (9), the robustness of nonlinear structural system can be displayed as:  
\[G_{0 ij}(s)=\frac{Y_{i}(s)}{U_{j}(s)} \tag{4}\]0  
Among them: \(\left\|(\cdot)\right\|_{2}=\left(\int_{0}^{\infty}\left\|(\cdot)\right\|^{2} \mathrm{dt}\right)^{1/2}=\left(\int_{0}^{\infty}(\cdot)^{\mathrm{T}}\mathrm{Q }(\cdot)\mathrm{dt}\right)^{1/2}\), \(\mathrm{Q}\) was a weighed matrix. According to Equation (13), the quantitative robustness evaluation indicator \(I_{R}\) of the nonlinear structure can be calculated. Since the indicator \(I_{R}\) indicates the sensitivity of the structural performance to resist the unexpected input interference, the greater the indicator \(I_{R}\), the weaker the resistance of the overall structure to the input interference, namely, the weaker the structural robustness, and vice versa.
## 3 Numerical Method and Validation
### Validation
#### 3.3.2 Experimental validation  
The experimental test scene and the simulated nodules extracted by this hydraulic polymetallic nodule collector are shown in Figure 11. The experimental and numerically simulated collection efficiencies of the collector at different collection heights are shown in Table 3. As the collection height increased, the collection efficiencies in both the simulation and experiment decreased. The error between the two was small at all collection heights, with a maximum error of no more than 5%. Therefore, it could be verified that the established simulation model of the hydraulic collector, which was based on the solid-liquid two-phase flow method, has high accuracy.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Collection Height \(H\) (mm)** & **Simulated Efficiency** & **Collection Efficiency** & **Experimental Collection** \\ \hline
70 & 99.3\% & 100\% & \(-\)0.7\% \\
90 & 99.0\% & 96.0\% & 3.0\% \\
110 & 97.3\% & 99.2\% & \(-\)1.9\% \\
150 & 90.7\% & 89.4\% & 1.3\% \\
180 & 84.0\% & 88.3\% & \(-\)4.3\% \\
200 & 68.3\% & 72.1\% & \(-\)3.8\% \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of collection efficiency.  
Figure 10: Experimental test system and polymetallic nodules. (**a**) Experimental platform of the hydraulic collector. (**b**) Simulated polymetallic nodules on the bottom of the test pool.
# Multi-Object Deep-Field Digital Holographic Imaging Based on Inverse Cross-Correlation
## 3 Numerical Calculation Results
### Comparison with Single-Depth Estimation Algorithms  
During the reconstruction process, a single-depth estimation operator is commonly employed to determine the numerical reconstruction distance. However, for large-depth-of-field multiplanar objects, several challenges arise when employing such approaches.  
Figure 6 illustrates the estimation results of various estimation operators, and the results are discussed and analyzed herein. The operator types are divided into different types of depth estimation operators, where type 1 is based on an image gradient operator, type 2 is a frequency domain evaluation operator, and type 3 is an autocorrelation operator based on image statistics theory. The red cross section indicates the actual plane where the three subjects are located. After a comprehensive comparison, the single-depth estimation operator has two major disadvantages compared with the inverse cross-correlation operator proposed in this article: (1) The reconstruction distance normalization coefficient for single-depth estimation operators appears to be disorganized. It can be clearly seen in Figure 6 that the ideal quadratic function curve is not present; even in the case of minimal values of the interval, it appears at the second measured object plane position, such as in the case of Laplace, EOG, Brenner, Roberts, and Schaar operators in type 1, as well as in DCT and Wave operators in type 2. (2) The reconstructed image corresponding to the distance under the extremum point is blurred, and only the general outline of the object can be seen; thus, the detailed information of the subject has been lost. Figure 6b shows the reconstructed images corresponding to the extreme value points of the different single-depth estimation operators.  
After comparing Figure 4 to Figure 6, it is obvious that the reconstructed images of each object produced when using the inverse cross-correlation algorithm are very clear and that their planes are accurate, thus indicating that the limitations of the traditional single-depth estimation operator have been overcome. Therefore, the inverse cross-correlation algorithm proposed in this article provides a reference value with respect to the data processing for large-depth-of-field digital holographic imaging in multiple planes.  
Figure 5: Fused image.
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 4 Core Based Multicast Tree
### Tree Maintenance
Thus, we can find an alternative path between \(S\) and its core \(C\) without passing through \(S1\) (or using the port connected between \(S\) and \(S1\)). We handle this case by slightly varying Subcase 1.2. As like Subcase 1.2, \(S\) will multicast _CBT-disconnected_ message to every descendant of \(S\) followed by a _CBT-rejoin_ message. On hearing _CBT-disconnected_ message, every descendant of \(S\), including \(S\), will follow like Subcase 1.2 to reestablish connectivity with the CBT delivery tree. From the link-layer activities, \(S1\) will also note that either \(S\) or the link connecting \(S\) and \(S1\) is down. \(S1\) issues _CBT-disconnected_ message on behalf of \(S\) to its core node. The core node and \(S1\) will remove its forward pointer to \(S\) and wait for it to get reconnected. **Case 3: Switch S is Down Due to Abrupt Failure**  
Let \(H1\), \(H2\), \(\cdots\), \(Hr\) be the children of \(S\). As soon as \(S\) is down, these children will notice it from the link-layer activities with \(S\). As like Case 2, these switches multicast _CBT-disconnected_ message to every descendant of them followed by _CBT-rejoin_ message. Since the underlying topology is 2-connected, they will get reconnected to the CBT delivery tree. Similar to Case 2, \(S1\) will inform its core switch and remove its forward pointer to \(S\). We listed all possible CBT-messages in Appendix A.