esa-sceva/satcom-chunk-collection huggingface dataset:
# Fractional-Order LQR and State Observer for a Fractional-Order Vibratory System
## 2 Background  
### Stability of Fractional-Order Dynamical System  
Denoting the differential operator as \(D\), a linear fractional-order autonomous system can be given as follows:  
\[D^{\alpha}\mathbf{x}(t)=\mathbf{A}\mathbf{x}(t),\;\mathbf{x}(0)=\mathbf{x}_{0}, \tag{1}\]  
where \(\alpha\) is the non-integer order of differentiation, \(\mathbf{x}(t)\) is the state vector and \(\mathbf{A}\) is the system matrix.  
The dynamical behavior and stability of a fractional-order dynamical system can be analyzed by investigating the poles of the system on the complex plane (\(s\)-plane). However, in the case of a fractional-order dynamical system, such as Equation (1), the poles need to be investigated on a new \(s^{\alpha}\)-plane. In the present paper, the \(s^{\alpha}\)-plane is called the \(\omega\)-plane. Here, \(\omega\) can be defined as follows:  
\[\omega=s^{\alpha}, \tag{2}\]  
where \(s\) is given as:  
\[s=re^{i\theta}. \tag{3}\]Then, the definitions given by Equations (2) and (3) yield the following:  
\[\omega=s^{\alpha}=\left(re^{i\theta}\right)^{\alpha}=r^{\alpha}e^{i\alpha\theta}. \tag{4}\]  
Using Equation (4), lines on the \(s\)-plane can be mapped to corresponding lines on the \(\omega\)-plane. Practically, let us consider the case in which \(\alpha=0.5\). The imaginary axis on the \(s\)-plane is expressed as \(\theta=\pm\pi/2\), and \(0<r<+\infty\). Then, the imaginary axis on the \(\omega\)-plane can be expressed as follows:  
\[\omega=r^{\frac{1}{2}}e^{\pm i\frac{\pi}{4}}. \tag{5}\]  
Furthermore, the negative part of the real axis on the \(s\)-plane is expressed as \(\theta=\pm\pi\), and \(0<r<+\infty\). Then, the negative part of the real axis on the \(\omega\)-plane can be expressed as follows:  
\[\omega=r^{\frac{1}{2}}e^{\pm i\frac{\pi}{2}}. \tag{6}\]  
Thus, the imaginary axis and the negative part of the real axis can be mapped on the \(\omega\)-plane as illustrated in Figure 1.  
Classification of the dynamical behavior and stability in each region in Figure 1 is as follows [24]: unstable in region 1, damped oscillation in region 2, and overdamping in region 3. Therefore, the necessary and sufficient condition for the linear autonomous system given in Equation (1) to be asymptotically stable for an arbitrary initial value \(\mathbf{x}_{0}\) is that the arguments of the eigenvalues \(\lambda_{i}\) of the system matrix \(\mathbf{A}\) satisfy the following inequality [25; 26]:  
\[|\arg(\lambda_{i})|>\frac{\alpha\pi}{2}\,,\ (i=1,\ 2,\ \cdots n), \tag{7}\]  
where \(n\) is the order of the system.
###### Abstract  
Icebergs and ice islands (large, tabular icebergs) present a significant hazard to marine vessels and infrastructure at a time when demand for access to Arctic waters is increasing. There is a growing demand for in situ iceberg tracking data to monitor their drift trajectories and improve models used for operational forecasting of ice hazards, yet the high cost of commercial tracking devices often prevents monitoring at optimal spatial and temporal resolutions. Here, we provide a detailed description of the Cryologger Ice Tracking Beacon (ITB), a low-cost, robust, and user-friendly data logger and telemeter for tracking icebergs and ice islands based on the Arduino open-source electronics platform. Designed for deployments of at least 2 years with an hourly sampling interval that is remotely modifiable by the end user, the Cryologger ITB provides long-term measurements of position, temperature, pressure, pitch, roll, heading, and battery voltage. Data are transmitted via the Iridium satellite network at user-specified intervals. We present the results of field campaigns in 2018 and 2019, which saw the deployment of 16 ITBs along the coasts of Greenland and Ellesmere and Baffin islands. The overall success of these ITB deployments has demonstrated that inexpensive, open-source hardware and software can provide a reliable and cost-effective method of monitoring icebergs and ice islands in the polar regions.  
Keywords:icebergs; ice islands; tracking beacon; Arduino; open source; hazards; Arctic; polar; low-cost; real-time data +
Footnote †: journal: Sensors
## 8 System Design
### Open Spacecraft Design
#### 8.6.4 Integration of Boards & Battery Area  
This section considers the integration of boards and batteries into the spacecraft design. The corner post configuration (shown in Figure 15) and receiver (shown in Figure 14) are integrated to produce the bottom part of the CubeSat structure. A top plate, which secures the posts in place and connects to the top of the boards (not shown) completes the design.  
The base configuration of the OPEN design, shown in Figure 17, allows 14.4 cm\({}^{3}\) of battery space in each corner. An alternate supported configuration moves 1.5 mm of space on each side of the payload/subsystem boards from the full-height to the reduced-height area and converts this space into  
Figure 16: Example Corner Post Schematic (lower left corner shown).  
Figure 15: OPEN / OpenOrbiter Corner Post Configuration.  
additional battery area. To support this, two posts are used on each corner (leaving the corner-square unoccupied), instead of a continuous structure. This approach increases the battery volume to 20.4 cm\({}^{3}\), per corner (an increase of 6 cm\({}^{3}\) per corner or 24 cm\({}^{3}\) for the whole spacecraft--more than the amount of volume of a fifth battery). For boards that do not need to fully utilize the full-height area, the reduced full-height area may not be impactful on the boards' functionality. Figure 18 depicts this alternate configuration.  
Figure 17: Overlay of Board Clearance Diagram on combined corner post/receiver and base plate diagram.
## 3 Monte Carlo simulation
### Spatial Structure Analysis and Variogram Calculation Results  
The variogram functions provide an effective solution for the spatial correlation data analysis of printer return. Three commonly used variograms, namely spherical, exponential, and Gaussian variogram, can be used in the Kriging spatial model for return prediction. As shown in Figure 4, three variogram functions are finally fitted after repeated simulation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No. of Samples** & **No. of Sites** & **Repetitions** & **Min.** & **Max.** & **Median** & **Standard** **Deviation** \\ \hline
1 & 100 & 25 & 0 & 9543 & 5421 & 0.0026 \\
2 & 50 & 50 & 0 & 8945 & 4580 & 0.0027 \\
3 & 20 & 50 & 0 & 10000 & 5110 & 0.0025 \\
4 & 10 & 50 & 0 & 7849 & 3459 & 0.0026 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample data of Monte Carlo simulation.  
Figure 4: _Cont._  
Note that the abscissa unit in Figure 4 is 0.5 km, which does not mean that the interval between two recycling sites must be set to 0.5 km in practice. In order to find out the variation law as well as the spatial structure of return amount, the sampling points should be set as densely as possible, within the limit of computing capability. It can be seen from Figure 4 that the variation structure analysis covers the range from 0.5 km to 100 km. The corresponding variation value can be found as long as the distance between recycling sites is within this range (for example, two recycling sites with a distance of 20 km).  
_Simulation conclusion 1:_ As can be seen from Figure 4, the correlation length of these three variogram functions is about 90 km, that is, if the distance between two recycling sites exceeds 90 km, the correlation will decrease quickly.  
Figure 4: Three variogram fitting results of WEEE return in simulation example.  
Figure 4 shows the results of quantitative comparative analysis of structural variance of different site numbers. The larger the structural variance ratio \(\frac{C_{1}}{C_{0}+C_{1}}\), the stronger the spatial correlation will be.  
_Simulation conclusion 2:_ The prediction value given by the Kriging method using exponential variogram is the best and its prediction error is the least. It can be found from Table 3 that the structural variance ratio fitted by exponential variogram is the largest, indicating the WEEE return of each recycling site has the strongest spatial correlation, which is most consistent with the Monte Carlo simulation test data.
# Prevention of Controller Area Network (CAN) Attacks on Electric Autonomous Vehicles
## 5 Data Integrity Algorithms
### RSASASA-PSS 2048-Sha256  
RSASSA-PSS is a digital signature generation and verification scheme that uses the RSA to encrypt a digested message, which is resulted from multiple hashing of the data along with a random salt value. The result of the RSA encryption process is a digital signature, which can be verified by decrypting the signature, extracting the hash of the data stored in the signature, and comparing it to the calculated data hash [51]. The use of random salt in the signature generation gives the scheme its probabilistic characteristic.  
RSASSA-PSS 2048 uses a 2048-bit modulus to output a 2048-bit signature. Although the length of the key pair in RSA 2048 can vary, the most commonly used lengths are 4096 bits for the private key and 2072 bits for the public key. Key size is the main disadvantage of RSASASA-PSS because algorithms with a smaller key size, such as RSA 256 and RSA 512, do not provide adequate security and should not be used. Despite using a modulus size eight times larger than the modulus size used in the ECDSA P256 algorithm, RSA 2048 still provides less security strength [52].  
As RSASASA-PSS is a digital signature scheme, it can be used in the same applications as the ECDSA algorithm but at the expense of increased key size. One of the most common applications of RSASASA-PSS in the automotive field is in secure flashing to authenticate the update code. In secure flashing schemes, the code to be updated is signed using RSASASA-PSS, and the generated signature is appended to the code. The code with the appended signature is then sent to the ECU bootloader, which verifies the signature and flashes the code if the signature is valid.
## 3 Detailed Implementation of Layer-1 BoD Services
### Path Calculation Based on Route Preference  
Upon receiving a user request, the layer-1 BoD server selects an appropriate route from among several candidate routes in the network by referring to the user's route preference. When the user specifies the \"minimum delay route\" [Figure 4 (d)], the layer-1 BoD server finds this route by using the Dijkstra algorithm, which uses the delay of each link as the link metric (see Figure 3). When \"none\" is specified, the layer-1 BoD server selects the shortest route with the maximum available end-to-end bandwidth by using the Edmonds-Karp algorithm [17], which uses the available bandwidth for the layer-1 services of each link as the link metric. The layer-1 BoD server gathers the required bandwidth from the parallel links between the layer-1 switches to enable the remaining bandwidth for the layer-2/3 services of each link to be as balanced as possible, thereby maximizing the packet forwarding performance. The layer-1 BoD server usually gets the required bandwidth from a single route but, if there is no single route that can meet the requested end-to-to-end bandwidth, it gathers enough bandwidth from multiple routes.
## 3 Valve Opening Characteristics
### Investigation Methods and Boundary Conditions
In CFD simulations, although the \"convergence\" based on RMS error values, monitoring points and imbalances can ensure the accuracy of the simulation results to a certain extent, the grid-independent inspection of each model is necessary to minimize the effect of different grid resolutions on the simulation outcomes [38]. Therefore, in the simulation of the opening characteristics of the CFFCS, we used different numbers of grids of the fluid domain (4.3 \(\times\) 10\({}^{6}\) (coarse), 5.8 \(\times\) 10\({}^{6}\) (medium) and 6.6 \(\times\) 10\({}^{6}\) (fine)). Considering the complexity of the internal geometry of the coupling system and the computability of the dynamic mesh simulation, it is difficult to refine the boundary layer separately. Therefore, in this study, a method based on the global size control of unstructured grids is used to ensure the correctness of the calculation results of viscous flow near the wall. The special control method for moving zones in dynamic mesh simulation is described in detail in the following text. The average skewness of the grid was limited to less than 0.5. Then, through comparison of the required thrust during the opening process of the CFFCS under the three grid models (coarse, medium and fine), it was found that the maximum deviation of the coarse model was 5.62% relative to the fine model, while the maximum deviation of the medium model was only 1.72%. Thus, considering both the economics of the calculation and the accuracy of the simulation, the medium model was selected for further simulations carried out in this research. The same grid independence check strategy was also adopted for all the other simulation models mentioned in this article. Figure 7a,b respectively show the meshed cross-sections of the CFFCS and NFFCS, that were obtained after the grid independence verification. According to the rules of the dynamic meshing method, the volume of the grid cannot be zero. Therefore, a gap must  
be left between the valve and the glove during modeling. In order to simulate the true opening process to the greatest extent, this gap between the valve and the glove must be very small. Thus, the initial gap in this study is defined as 0.2 mm. For the CFFCS, the wall contacting the valve is set as a moving rigid body, and its velocity is defined by the profile programming. In the NFFCS, the instantaneous pressure relief pin valve will be opened before the larger valve is opened. For this process, the velocity at the flow area of the pin valve and the larger valve were defined separately. The total coupling duration of the two valves is set as 2.0 s.
## 4 Discussion
### Policy Context
#### 4.3.1 Status in REDII  
Oil crops and lignocellulosic crops have distinct statuses under the RED II, which affects the potential value proposition. If \"produced on agricultural land as a main crop\", oil crops fall under the RED II definition of food and feed crops. Despite the name, the RED II definition of a food and feed crop is not linked to edibility, and so an inedible oil crop grown as a main crop for bioenergy purposes would still count as a food and feed crop under this definition. Food and feed crop-based biofuels are counted once towards RED II targets, and there is a cap on their total use, which can vary by Member State. Oil crops grown as intermediate crops, however, are not identified as food and feed crops providing that their production does not \"trigger demand for additional land\". Low ILUC-certified oil crops grown in cover cropping or intercropping systems would therefore not be subject to the limitations on use applied to food and feed crops.  
Lignocellulosic material is treated more favorably under the RED II. Biofuels produced from lignoellulosic materials are treated as advanced and are eligible to be counted twice towards RED II targets, and there is no limit on the contribution of fuels from lignocellulosic material.  
The RED II includes 'provisional estimated' ILUC emissions values for different feedstock types, which inform decision making by the Commission and Member States. Oil crops are assigned a mean ILUC value of 55 gCO\({}_{2}\)e/MJ, but lignocellulosic crops are considered under the Directive to have no associated ILUC emissions. In the context of the RED II, low ILUC-risk certification is therefore more immediately relevant for oil crops than for lignocellulosic crops.  
The RED II provides two opportunities for additional support to be offered to low ILUC-risk certified biofuels. First, it allows for additional feedstock production systems to be made eligible to be counted twice towards targets by being added to the list in Annex IX. At present, none of the entries on Annex IX is conditional on low ILUC-risk certification, but in principle it would be possible for new entries with this type of conditionality to be added. Second, Article 26 (1) gives Member States leeway to distinguish between biofuels based on best evidence on ILUC emissions when creating national support systems. This would allow a Member State to provide a more favourable treatment under national legislation to biofuels that have reduced their ILUC impact by receiving low ILUC-risk certification. This opportunity is clear for oil, starch, and sugar crops, as the Directive includes estimates of ILUC for those feedstocks. For lignocellulosic crops, however, a Member State would need to argue that best evidence suggested that uncertified material was likely to be associated with non-zero ILUC emissions (contrary to the assumption enshrined in Annex VIII), as low ILUC-risk certification would not deliver any prima facie emission benefit if a crop was already expected to have zero ILUC emissions.
# A Systematic Literature Review of Reliable Provisioning for Virtual Network Function Chaining
## 5 Discussion
### Failure Type  
The reliable provisioning algorithm can be categorized by the type of failure they consider and the number of occurrences of those failures. Many reliable provisioning algorithms consider only VNF failure while others study link failure solely or consider both VNF and link failures. Furthermore, the assumed number of failure occurrences can vary for each of these algorithms. Generally, algorithms consider a single failure to be able to build their reliable algorithm with less complications. However, failure is unpredictable and can occur multiple times. Therefore, recently, more researchers have been studying reliable provisioning algorithms that can address multiple failures. However, there is still a gap for a comprehensive approach that manages multiple types of failure.
# A Copula-Based Attack Prediction Model for Vehicle-to-Grid Networks
## 5 V2G Network Simulation
### Selection of Significant Variables  
The contribution of each explanatory variable in the prediction of the binary variable ATT is not the same. Some variables participate better in the prediction of the ATT response variable than others. In order to avoid the manipulation of the 24 variables (obtained after the cleaning operation) during the production phase of our model, it was important to determine the significant variables. Determining the significant variables allowed us to avoid data redundancy.  
The StepWise method under R software was used to determine the significant variables. However, the results were not exploitable because only one significant variable was found. Thus, the PCA method under the TANAGRA software was used in order to bring out the significant variables. The results of the PCA were conclusive since several significant variables were found, as shown in Figure 3 below.  
From this figure, axis 1 and axis 2 represent the most important axes because they alone constitute 95% of the variability of the data, namely 85% (slight rounding) for axis 1 and 10% for axis 2. Moreover, the significant variables are Flow IAT Min, Bwd Pkts/s, Fwd Pkts/s, Flow IAT Max, Flow IAT Mean, Flow Pkts/s, which are related to axis 1, and the Flow Duration variable, which is linked to axis 2.  
The total number of significant variables is 07. The manipulation of these 07 significant variables during the estimation phase of the copula of our model could be tedious. For this, three variables were chosen, among those that were related to axis 1, namely the variables Flow IAT Min, Bwd Pkts/s, Fwd Pkts/s, were those used because they were the most correlated with axis 1.  
Table 1 presents an overview of the features of CICFlowMeter. More features of CICFlowMeter are presented in [25].  
\begin{table}
\begin{tabular}{l l} \hline
**Variables** & **Description** \\ \hline Bwd Pkts/s & Number of backward packets per second \\ Flow Duration & Flow time \\ Flow IAT Max & Maximum time between two flows \\ Flow IAT Mean & Average time between two flows \\ Flow IAT Min & Minimum time between two flows \\ Flow Pkts/s & Flow packets rate that is number of packets transferred per second \\ Fwd Pkts/s & Number of forward packets per second \\ \hline \end{tabular}
\end{table}
Table 1: Some features of CICFlowMeter.  
Figure 3: PCA results.  
Summary  
The use of tools such as MiniV2G, Wireshark, and CICFlowMeter led to obtaining the V2G attack database. MiniV2G was used to simulate three types of scenarios (without attack, MitM type attack, DoS-type attack); Wireshark was used to assess the network in each scenario, and CICFlowMeter relied on Wireshark's PCAP files to generate the databases, which were then cleaned. PCA facilitated the detection of significant variables.  
The following section presents the obtained results.
## 5 Conclusions  
Based on the previous breakage index, the improved breakage index \(B_{C}\) was established in this paper. Through the quantitative relationship with the fractal dimension, the rationality of the improved breakage index \(B_{C}\) was explained. Based on large-scale triaxial shear test results of soil-rock mixtures under different dry densities, the following conclusions were drawn:  
(1) Combined with the phase transformation stress ratio \(M_{pt}\), when the dry density increases, the phase transformation stress ratio \(M_{pt}\) also increases, leading to an increase in the volume variable. As the confining pressure increases, \(M_{pt}\) decreases progressively, suggesting a decrease in the volume variable, and the specimen exhibits dilatancy hysteresis.  
(2) The particle breakage increases with increasing confining pressure before and after particle-grading shear. The largest changes in particle breakage occur in the particle group ranging from 20 to 60 mm. A higher dry density leads to more noticeable particle breakage. The breakage index \(B_{C}\) and fractal dimension \(D\) are fitted, resulting in well-fitting parameters that effectively describe and quantify the overall degree of particle breakage.  
(3) In the study of deformation characteristics, the failure dilatancy rate \(\zeta_{f}\) shows a strong power function correlation with the particle breakage index \(B_{C}\). The absolute value of the failure dilatancy rate \(\zeta_{f}\) decreases with increasing crushing index \(B_{C}\), transitioning gradually from dilatancy to shear shrinkage.  
(4) When investigating the strength characteristics, increasing the confining pressure also increases the breakage index \(B_{C}\). This leads to a decrease in the internal friction angle and a decrease in the shear strength, exhibiting a nonlinear trend. Particle breakage is the direct cause that affects the nonlinearity of the internal friction angle.  
Supervision, Funding acquisition, Writing--review and editing: P.X.; Investigation, Methodology, Writing--original draft: Z.C.; Conceptualization, data curation: L.F.; Funding acquisition, Investigation, Revision: Z.X.; Supervision, Revision, Conceptualization: Q.L.; Supervision, Revision, Conceptualization: Z.L. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (52179143); Natural Science Basic Research Program of Shaanxi Province (2023-JC-YB-383); Key Industry Innovation Chain of Shaanxi (2022 ZDLNY02-04).  
Not applicable.  
The data presented in this study are openly available in China National Knowledge Infrastructure at [[https://doi.org/10.27398/d.cnki.ggalu.2023.001199](https://doi.org/10.27398/d.cnki.ggalu.2023.001199)]([https://doi.org/10.27398/d.cnki.ggalu.2023.001199](https://doi.org/10.27398/d.cnki.ggalu.2023.001199)) (accessed on 2 July 2024).  
The authors declare no conflicts of interest.