esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers
## 3 Proposed Scheme
### First Layer: Traffic Reduction  
Reducing network traffic to detect malevolent behaviors is extremely important to manage a large amount of network traffic with limited resources, e.g., hard drive and memory. The hardest step in  
Figure 3: Architecture of the proposed method.  
the process is to determine the network activity behavior by only scanning several packets for each flow. Our research therefore brings a new model to reduce congestion so that botnet detection systems can be deployed more easily on congested networks. In currently available identification methods for botnets, most of them use deep packet inspection (DPI) to evaluate packets content that is complicated and expensive and ineffective to simulate unidentified payload signatures [30]. It is assumed that the payload of each packet in DPI can be accessed by the system. This strategy can be especially accurate when the payload is in decrypted form. Most of the new malicious code generators, however, use the concealment strategies, such as encapsulation of protocols, obstruction and encrypting the payloads [31].  
In addition, it is a costly task to evaluate all the packets on the congested networks because the amount of the packets transmitted through the networks are increasing day by day. Consequently, the DPI detection system can suffer from efficiency limited to the processing of the traffic from high-speed networks [30]. This study aims to improve the efficiency by reducing the amount of packets without hampering the precision rate. To accomplish an ultimate objective of this study, the selection of only Transmission Control Protocol TCP packets proposes a new reduction in traffic for a Botnet identification paradigm. This detection technique is used to reduce the volume of the network traffic and will increase the efficiency of the Botnet detection model.  
In this study, TCP traffic control packets are filtered to reduce network traffic volumes and improve the efficiency of the envisaged strategy. Filtering involves mainly two steps: filtering the network flow associated with the TCP protocol and obtaining the control packets SYN, ACK, FIN and RST TCP. Figure 4 displays the network traffic reduction process (PCAP files). An array of TCP Control Packets lists is initialized in the flowchart. New packets (TCP Control Packets List) are added to the array by tuples through the packets until the last packet in the file is reached. After the first loop i.e., for loop, the next loop examines the TCP packet header for loop, and the third loop selects packets without payload data. The flowchart's final loop gets the packet header.  
Figure 4: Flow chart for network traffic reduction.
# Energy Cost Minimization with Hybrid Energy Storage System Using Optimization Algorithm
###### Abstract  
The purpose of this study is to develop an effective control method for a hybrid energy storage system composed by a flow battery for daily energy balancing and a lithium-ion battery to provide peak power. It is assumed that the system operates behind the meter, the goal is to minimize the energy cost in the presence of a PV installation (as an example of a local renewable source) and energy prices are determined by 3-zone tariffs. The article presents the application of an optimization method to schedule the operation of each battery in the system. The authors have defined an optimization method aimed at minimizing the total cost of the system, taking into account energy costs and batteries depreciation. The techno-economical model of the system, including battery degradation, is constructed and the cost optimization methods are implemented in Python. The results are validated with real energy and price profiles and compared with conventional control strategies. The advantages of optimization in terms of energy cost are discussed. The experiment shows that not only is a hybrid energy system successful in lowering the total operation cost and in increasing self-consumption but also that the implemented methods have slightly different properties, benefits and issues.  
hybrid energy storage system; optimization algorithm; peak shaving +
Footnote †: journal: Applied sciences
# Synchronization, Control and Data Assimilation of the Lorenz System
## 2 Master-Slave Synchronization
### Generalized Synchronization  
What happens if the two coupled replicas evolve using different values of parameters \(\sigma,\rho,\beta\)[6; 19]? Even when the coupling parameters (directions and intensity) are above the threshold, the distance \(d\) remains finite. Let us indicate with \(\sigma^{\prime},\rho^{\prime},\beta^{\prime}\) the parameters of the slave system.  
We can define a parameter distance \(D\),  
\[D=\sqrt{(\sigma-\sigma^{\prime})^{2}+(\rho-\rho^{\prime})^{2}+(\beta-\beta^{ \prime})^{2}}, \tag{22}\]  
which is zero for the previous coupling schemes. We can also generalize the coupling among replicas, similar to the approach that was followed for state variables in Equation (6), introducing a parameter coupling direction \(\mathbf{\chi}=[\chi_{\sigma},\chi_{\rho},\chi_{\beta}]\) and a strength \(\pi\) so that the parameters of the slave replica are  
\[\begin{cases}\sigma^{\prime}=\sigma+\chi_{\sigma}\pi(\sigma_{1}-\sigma),\\ \rho^{\prime}=\rho+\chi_{\rho}\pi(\rho_{1}-\rho),\\ \beta^{\prime}=\beta+\chi_{\beta}\pi(\rho_{1}-\beta),\end{cases}\]  
where \(\sigma_{1},\rho_{1},\beta_{1}\) are the values of parameters corresponding to \(\pi=1\), reachable according to the \"direction\" \(\chi\).  
We can notice that the distance among state variables \(d\) decreases smoothly with \(p-p_{c}\) and \(D\) only for a small interval of \(D\) near zero. Clearly, \(d>0\) for \(p<p_{c}\), even for \(D=0\), which is what we have seen in Section 2.3.  
Some simulation results are presented in Figure 4, in which the asymptotic state-variable distance \(d\) is reported as a function of the distance between parameters \(D=\pi D_{0}\) and state-variable coupling \(p\). The parameter coupling \(\pi\) always goes from 0 to 1, so the initial parameter distance \(D_{0}\) corresponds to the larger value of \(D\). The line \(D=0\) corresponds to the distance reported in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(\mathbf{C=[1,1,1]}\) & \(\mathbf{C_{x}=[1,0,0]}\) & \(\mathbf{C_{y}=[0,1,0]}\) \\ \hline \(\lambda_{\text{lin}}\) & 0.995 & 8.840 & 2.670 \\ \(\lambda_{\text{fit}}\) & 0.961 & 8.619 & 2.555 \\ \hline \end{tabular}
\end{table}
Table 1: Maximum conditional Lyapunov exponent estimated using the asymptotic distance \(|d|\) between master and coupled slave systems (\(\lambda_{\text{sim}}\)) and derived from linear fit (\(\lambda_{\text{fit}}\)).  
Clearly, in the absence of synchronization for \(D=0\) (\(C=[0,0,1]\), Figure 5c), there is no synchronization for \(D>0\). In the other cases, there is a region near the synchronized phase in which \(d\) goes smoothly to zero. Notice that, in Figure 5c, the larger distance \(d\) occurs for \(p=1\) and large \(D\). This is probably due to the fact that, when coupled along the \(z\) directions, the two replicas may stay on different \"leaves\" of the attractor, which is almost perpendicular to \(z\).  
Notice also that the \(d\) landscape is not smooth far from the synchronized phase. We consider this aspect in the following section.
## Materials and Methods
### Penalized Maximum Likelihood (PML) Method in Multi-Locus Screening Stage  
#### 2.3.1 Multi-Locus Linear Mixed Model and Penalized Likelihood Function  
All the markers passing the REML step are placed into a multi-locus model [20] as  
\[\mathbf{y}=\mathbf{F}\mathbf{b}+\sum_{i=1}^{t}\mathbf{x}_{i}\beta_{i}+\epsilon \tag{12}\]  
where \(\mathbf{y}\), \(\mathbf{F}\), \(\mathbf{b}\), and \(\epsilon\) are the same definitions as Equation (1). \(\mathbf{x}_{i}\) is the \(i\)th \(n\times 1\) genotypic vector, and \(\beta_{i}\) is the fixed effect of corresponding marker. \(t\) denotes the total number of selective markers from the REML step.  
Penalized maximum likelihood (PML) is a fast approach to use numerical optimization to obtain the maximum posteriori estimates when the penalty function is a probability density on the parameters [20; 22; 42]. Let \(\mathbf{\theta}=\left\{\mathbf{b},\beta_{1},\cdots,\beta_{t},\sigma_{n}^{2}\right\}\) is the interesting vector of the parameters. The log-likelihood function denotes  
\[L(\mathbf{\theta})=\log\varphi\left(\mathbf{y};\mathbf{F}\mathbf{b}+\sum_{i=1}^{t}\mathbf{x}_{i} \beta_{i},\mathbf{I}\sigma_{n}^{2}\right) \tag{13}\]  
where \(\varphi\left(\mathbf{y};\mathbf{F}\mathbf{b}+\sum\limits_{i=1}^{t}\mathbf{x}_{i}\beta_{i},\bm {I}\sigma_{n}^{2}\right)\) is the normal density with the mean \(\mathbf{F}\mathbf{b}+\sum\limits_{i=1}^{t}\mathbf{x}_{i}\beta_{i}\) and covariance \(\mathbf{I}\sigma_{n}^{2}\). A factor is introduced to penalize on the marker effects \(\beta_{i}\)  
\[p(\beta_{i})=\varphi\left(\beta_{i};\mu_{i},\sigma_{i}^{2}\right)\qquad i=1, \cdots,t \tag{14}\]  
where \(\varphi\left(\beta_{i};\mu_{i},\sigma_{i}^{2}\right)\) is a normal prior with a mean \(\mu_{i}\) and variance \(\sigma_{i}^{2}\). Then, \(\mu_{i}\) is also assigned a normal prior distribution  
\[p(\mu_{i})=\varphi\left(\mu_{i};0,\sigma_{i}^{2}/\tau\right)\qquad i=1, \cdots,t;\tau>0 \tag{15}\]  
Let \(\mathbf{\delta}=\left\{\mu_{1},\cdots,\mu_{t},\sigma_{1}^{2},\cdots,\sigma_{t}^{2}\right\}\) be the hyperparameters that can be estimated along with the interested parameters at the same time. The logarithm of the penalized prior distribution is  
\[P(\mathbf{\theta},\mathbf{\delta})=\sum_{i=1}^{t}\left[\log\varphi\left(\beta_{i};\mu _{i},\sigma_{i}^{2}\right)+\log\varphi\left(\mu_{i};0,\sigma_{i}^{2}/\tau \right)\right] \tag{16}\]  
Then, the logarithm of penalized likelihood function can be defined as  
\[L_{p}(\mathbf{\theta},\mathbf{\delta})=L(\mathbf{\theta})+P(\mathbf{\theta},\mathbf{\delta}) \tag{17}\]
# Flight Level Assignment Using Graph Coloring
## 2 Flight-Level Assignment as Spectrum Coloring Problem with Hard Constraints
### Individual and Social Utility Models
#### 2.1.2 Social Utility: Interference between Crossing Airways  
In this section we bring social safety considerations to the flight level assignment, considering flight levels as corridors in which crossings arise with the largest possible vertical distance between the two flight levels involved. Thus, there is no need to handle the possibility of two aircrafts passing through a crossing at the same time, hence not needing to take into account the actual position of aircrafts within the corridor in every time unit.  
We translate the search for the largest possible vertical distance at crossings of routes to an instance of the Spectrum Coloring problem we studied in [5]. There, we introduced the idea of having an abstract graph \(G\) and a spectrum of colors \(C=\{c_{1},\ldots,c_{n}\}\) together with a matrix \(W\) storing weights \(W_{ij}=W(c_{i},c_{j})\) for each pair of colors. Thus, a coloring \(c\) of the graph \(G\) will induce, via the matrix \(W\), an _interference_\(I_{v}\) at each vertex \(v\), according to the following expression:  
\[I_{v}(G,W,c)=\sum_{u\in N(v)}W(c(u),c(v)), \tag{8}\]  
Figure 1: Total fuel consumption of Airbus A320 as a function of distance and flight level (FL).  
where \(N(v)\) is the set of neighbors of vertex \(v\).  
The applicability of Spectrum Coloring to the problem presented in this paper is clear when we map the colors in the spectrum \(C\) to the FLs available for our flights. Here, the entry \(W_{ij}\) of the matrix of interferences represents the impact on safety of assigning FLs \(c_{i}\) and \(c_{j}\) to a pair of crossing routes. Clearly, this interference must have a maximum value when the separation between FLs is zero (that is, in the diagonal of the matrix), and could have decreasing values as the separation between FLs increases.  
One can devise different approaches to define the matrix \(W_{ij}\): We could take an academic approach and use linear or exponential decays for the interferences, as we did in [5; 28], or we could take a more risk-based approach and use the probabilities for a collision when the corresponding FLs are used (in a similar way to what we did in [6] in a different domain). Finally, we can take a more conservative approach and assume that crossing routes must have FLs with a minimum separation, so that the interference induced by FLs closer than that minimum distance threshold must be infinite. As we will discuss later, this is the approach we have taken in this work.
## 6 Conclusions  
In this paper, the weight of the sky-ground hook control method under various road excitation was studied based on the 2-DOF suspension model. Then, with the proposed extension controller, the effect of the 2-DOF suspension system on ride comfort indexes was studied.  
The following main conclusions can be drawn:  
1. Based on the 2-DOF suspension model, sky-hook, ground-hook, and S-GH control are established. The frequency responses have analyzed to study the influence of S-GH weight.
2. Based on the extension theory, the hybrid damping extension control is established under the simulation. The optimal weight of S-GH control is conducted by correlation function.
3. Compared to the sky-hook control algorithm and the passive suspension, the proposed hybrid damping extension control is simulated based on ve-DYNA vehicle simulation. Notably, the control effectiveness of the hybrid controller is highlighted under complicated conditions.  
In the future, we will make a profound study for the extensive theory and try to use other hybrid control methods for active/semi-active suspension system, in order to improve comprehensive ride comfort performance.  
Conceptualization, G.G., Y.Y. and L.S.; Methodology, G.G., Y.Y. and H.L.; Software, Y.Y.; Formal analysis, G.G., Y.Y. and L.S.; Funding acquisition, G.G.; Investigation, G.G.; Project administration, G.G.; Resources, G.G. and Y.Y.; Validation, G.G., Y.Y. and L.S.; Methodology, G.G., Y.Y. and H.L.; Writing--original draft, Y.Y.; Writing--review and editing G.G., Y.Y., L.S. and H.L. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by the National Natural Science Fund (nos. 51875256, 51675235, and 51575241).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RMS** & **Passive Suspension** & **Sky-Hook Control** & **Extension Control** \\ \hline Body acceleration (m\(\cdot\)s\({}^{-2}\)) & 0.6823 & 0.5945 & 0.5962 \\ Front left suspension dynamic displacement (m) & 0.0068 & 0.0064 & 0.0063 \\ Front right suspension dynamic displacement (m) & 0.0078 & 0.0072 & 0.0072 \\ Rear left suspension dynamic displacement (m) & 0.0067 & 0.0066 & 0.0066 \\ Rear right suspension dynamic displacement (m) & 0.0069 & 0.0068 & 0.0068 \\ Front left tire dynamic deformation (m) & 0.01822 & 0.01803 & 0.01713 \\ Front right tire dynamic deformation (m) & 0.01840 & 0.01820 & 0.01749 \\ Rear left tire dynamic deformation (m) & 0.01773 & 0.01762 & 0.01702 \\ Rear right tire dynamic deformation (m) & 0.01777 & 0.01766 & 0.01702 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of response under different control methods.  
**Acknowledgments:** We are grateful to the valuable comments by the anonymous reviewers, which have helped improve this manuscript.  
**Conflicts of Interest:** The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
# L2-CalSat: A Calibration Satellite for Ultra-Sensitive CMB Polarization Space Missions
## 3 Calibration Mission Impact over Systematic Errors
### Beam Patterns
For such a beam, Jupiter could provide information only until this angle, remaining the rest of it non-characterised. However, due to its more selective profile the produced spurious signal is about one order of magnitude lower than in the previous (red) case. For both beams it has been considered a reference NF level of \(-50\) dB (available using Jupiter), which would allow to characterise them until \(10^{\circ}\) (blue) and \(20^{\circ}\) (red) from the beam center. However, at larger angles powerful signals coming from the galaxy could contaminate the CMB observations. It can be observed that, targeting \(r=0.001\), the spurious BB signal produced by the beam in red would mask the primordial signal for almost all multipole values while the beam in blue would be also an issue for multipole values around 10.
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 5 Discussion
Digital twins allow for the generation of simulated data and thereby make it possible to test anomaly detection techniques on the ground before applying them to the spacecraft once it is launched. Profiting from such digital twins [84] in detecting anomalous events for spacecraft telemetry constitutes an interesting research trend.
# Haptic Texture Rendering of 2D Image Based on Adaptive Fractional Differential Method
## 2 The Advantage of Fractional Differential  
Fractional differential processing of signals can not only enhance the high-frequency components of a signal nonlinearly, but also enhance the intermediate frequency components of the signal nonlinearly to a certain extent, while retaining the low-frequency components of the signal nonlinearly [29; 30]. Using this property of fractional differentiation, we can preserve the low-frequency contour information of digital images while nonlinearly augmenting high-frequency detailed texture patterns with wider gray distributions. Finally, the enhanced image is subtracted from the original image to obtain the texture extraction result.  
The Grunvald-Letnikov (G-L) definition, Kaputo definition, and Riemann Liouville (RL) definition are three commonly used definitions of fractional differential under Euclidean Metric [29]. Recent research indicates that the implementation of differentials in digital image processing is almost always based on the G-L concept. The Tiansi operator mask that is constructed according to the definition of G-L will actually have inaccuracy in image processing, because it is discrete in digital images and is an approximate expression of functions, so the effect of image texture extraction is often unsatisfactory.  
Therefore, we add adaptive augmentation to the G-L fractional differentiation definition, and find it is suitable for image texture acquisition.  
The low-frequency components of the image can be well preserved under the fractional differential operator mask filtering. The output gradation value is dramatically enhanced for nearby pixels whose gray value fluctuates rapidly in the area (including picture borders and texture regions), showing that the fractional differential operator mask may significantly improve the original image's high-frequency component.
## 5 Conclusions
### Limitations and Future Research Directions  
First, the current study only used patent data as the basis for analyzing technology development trends. However, the output of the development of 5G optical networks is not limited to registered patent data; its output is also presented in other forms, such as journal articles, conference papers, exchanges between talents, and trade fairs. This study did not include other forms of outputs, which is a major limitation of this research. Second, the researchers used the patent keywords compiled in the Derwent database as the basis for filtering patent data. Despite the Derwent database having been established through a complete reading of official patent disclosures by hundreds of experts and compiled through human reading, the domain of 5G optical networks changes rapidly with each passing day. Certain patents from rapidly developing technology domains may contain technologies that belong to the scope of 5G optical networks; however, such data were not included in the current study's analysis. Thus, future researchers are advised to expand their analysis scope by incorporating expert interviews and other research designs that could aid the incorporation of more relevant patent data. Third, this study relied solely on the USPTO database, the world's largest database for commercial transactions, for its patent information. Therefore, although this database is highly representative and has a long history of use [21; 32], researchers of future studies are advised to expand their sources of data by including other patent agencies, if time and budget allow it. The Worldwide Patent Statistical Database (PATSTAT) managed by the European Patent Office, for example, can be a suitable addition to increase the width of research scope. Fourth, future research may include the high-impact peer-reviewed journal sources within a reasonable technology readiness level period of the reported patents to increase research contributions. Finally, because of time constraints and macro-level examination of the technology network of 5G optical networks, the researchers only used the number of patents as the basis for their empirical examination and failed to further analyze the content of each individual patent. Therefore, to obtain a more accurate observation of technology trends in line with market needs, future studies should analyze the content of individual patents.  
**Funding:** This research was funded by the Ministry of Science and Technology of the Taiwan, grant number MOST 108-2410-H-492-001.  
**Acknowledgments:** The authors would like to thank the Ministry of Science and Technology of the Taiwan for financially supporting this research under Contract No. MOST 108-2410-H-492-001.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 2 Background of Process Automation
### Process Control Automation
#### 2.1.1 Safety and Supervisory Control  
The transmission of sensor data to the controller in safety and supervisory control is very much essential. Additionally, issues such as packet loss and latency cannot be tolerated because these are emergency control systems, and their failure will lead to catastrophic accidents. Thus, sensors connected to these systems are always in standby mode, with a maximum permitted latency of 10 ms.