esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Governance
#### 6.3.2 ISPS Code  
Created in the post-9/11 years, the International Ship and Port Security Code (ISPS) focusses on the prevention of a terrorist attack against shipping. Enacted under SOLAS chapter XI-2, on 1 July 2004, the ISPS provides a comprehensive framework for the security of ports and ships, including mandatory (Part A) and recommendatory (Part B) provisions. As one EU-based legal expert interviewee noted, the ISPS was primarily concerned with physical security; however, there has been discussion in recent years about whether it also applies to digital security.  
The European Union did pick up ISPS as a whole and translated it into European law and that had to be picked up by Member States and had to be implemented into the various national legislations in Europe. We sought a robust answer of whether digital security is part of ISPS. There was some nervousness about whether it was or not, if it was not it could require new legislation, and that might not come or it could take a long time. So everybody now takes the stance, 'yes' it is in ISPS and we hope that if there is a case before a court and a judge has to give an opinion, an interpretation, he or she will come to the conclusion, 'yes, digital security is also part of ISPS'. [78]  
The ISPS was clearly not written to give detailed guidance on cybersecurity. The words 'cyber' or 'digital' do not appear in the document. However, as the ISPS is written in fairly broad terms, and the spirit of the document is clear (that is, to comprehensively assess security vulnerabilities and create plans to address them), it is possible to imply its mandatory application to cyber systems. For example, article 8.4.3 of the Ship Security Assessment mandates \"identification of possible threats to the key ship board operations\". Taking account of technological developments in shipping since 2004, this surely must include cyber threats. Indeed, in Part B, the recommendatory provisions specifically include computer networks. In Part B, Section 8 of the Ship Security Assessment (SSA), article 8.3.5 states \"radio and telecommunication systems, including computer systems and networks\" should be addressed in the SSA among other essential systems. Likewise, the Port Facility Security Assessment (PFSA) provisions include, at article 15.3.5, \"computer systems and networks\" in the same way. It is therefore reasonable to conclude that the ISPS does require security assessments to include cyber systems, and this appears to be the approach in the EU; however, a test case has not yet confirmed the point.
## 5 Evaluation
### Results
#### 5.4.4 Results for RQ4  
To answer RQ4, we perform performance predictions for three different refactoring results under the same environment and parameter settings, with the results shown in Table 7.  
For JUnit, the refactoring results are as follows: _getClassName()_ is moved to a new class in a new package under the parent directory, _getAllFields()_ is moved to a new class under the parent directory, and _getStatedClassName()_ is moved to AnnotationCondition; _getClassName()_ remains unchanged, _getAllFields()_ is moved to the inner class WindowsCompileProcess within CompileProcess, and _getStatedClassName()_ is moved to Test2 Benchmark; both _getClassName()_ and _getStatedClassName()_ are moved to Test2 BenchmarkAgent, while _getAllFields()_ is moved to a new class in a new package within the same package.  
For LC-problems, the refactoring results are as follows: _generateUnsortedArray()_ is moved to ArrayTo2 dArrayTest, _printMatrix(int)[]_ is moved to TestUtils, and _printMatrix(List\(<\) List\(<\) T\(>>\))_ is moved to a new class in the same package; all three methods are moved to TestUtils; _generateUnsortedArray()_ and _printMatrix(List\(<\) List \(<\) T \(>>\))_ are moved to a new class in a new package, while _printMatrix(intl[][])_ is moved to another new class.  
For Concurrency, the refactoring results are as follows: _readFile()_ and _sleepMils()_ are moved to RunAsyncDemo, _sleepSecond()_ is moved to a new class, and _printThreadlog()_ is moved to SupplyAsyncDemo02; _readFile()_ is moved to RunAsyncDemo02, while _sleepMils()_, _sleepSecond()_, and _printThreadlog()_ are moved to CommonUtils; all three methods (_readFile()_, _sleepMils()_, and _sleepSecond()_) are moved to CommonUtils, and _printThreadlog()_ is moved to RunAsyncDemo.  
For Kevin, since the project contains two _benchmark()_ methods, one in NorbitBenchmark and one in OrbitBenchmark, we rename these two methods as _benchmarkN()_ and _benchmarkO()_, respectively. In this experiment, the refactoring results are as follows: _benchmarkN()_ is moved to Benchmarks, _prepare()_ is moved to OrbitEvent, and the _benchmarkO()_ method remains in place; _benchmarkN()_ and _benchmarkO()_ remain in place, and _prepare()_ is moved to a new class within the same package; _benchmarkN()_ and _prepare()_ are moved to OrbitListener, and _benchmarkO()_ remains in place.  
From the results in Table 7, it can be observed that MovePerf consistently maintains high accuracy in its predictions across these datasets. It is capable of effectively predicting the execution time for any restructured results, with its predicted values providing meaningful reference points.
# Intelligent Space Object Detection Driven by Data from Space Objects
## 2 Dataset Construction and Labeling
### Data Enhancement  
In this study, 8282 simulated images captured by 49 satellites are used for scene simulation. Firstly, the color images are converted into grayscale images, and Gaussian filtering is performed to remove noise from the images [29]. Secondly, the Sobel operator is employed to calculate the gradient magnitude and direction of the images [30], while non-maximum suppression is performed to eliminate non-edge points and refine the edges. The upper and lower threshold values of binarization are set to 30 and 150, respectively, which removes redundant pixels from the images, with a sharpened edge image obtained. Then, the binary image is combined with the original image at a 0.2:0.8 ratio to capture a feature image with distinct edge characteristics. There are sixteen background images selected, including the color images of the Earth and deep space backgrounds, as well as the grayscale images of the Earth and deep space backgrounds. To evenly distribute the backgrounds of the satellites and better perform the simulation, the dataset is randomly divided into 16 groups comprising about 500 images each. Each group is fused with the background image of the same type [31]. During the fusion process, an appropriate proportion of the background, ranging from 0.17 to 0.25, is chosen. Gaussian noise is also added to this part to simulate the sources of various natural and artificial noises, such as the thermal noise made by electronic devices and the celestial noise made in astronomical observations. The reason why Gaussian noise is chosen lies in the Gaussian distribution, followed by the random noise introduced by various factors in the process of image acquisition, transmission, and processing which follows [32]. After multiple experimental comparisons performed to minimize image distortion, the Gaussian noise with a mean of 0 and a variance of 0.001 is added into this part of the dataset. The process of image fusion is illustrated in Figure 5, and the final results are presented in Figure 6.  
Figure 4: A proportion of the images obtained from the collected dataset of 500 real satellite images.  
Figure 5: Image enhancement flowchart.
# Droop Control Strategy of Utility-Scale Photovoltaic Systems Using Adaptive Dead Band
## 3 Simulation Study and Analysis
### Simulation Results
However, if the dead band length is set too long, the rise time exceeds 0.2 s, which is the tolerance value, so the criterion is not satisfied. Figure 13 shows a graph of the voltage, active power, and reactive power output of the utility-scale PV controlled by the plant controller during the time when the voltage profile change occurs due to a large disturbance. It is confirmed that the reactive power is produced to recover the voltage after the fault, and accordingly, the value of the active power is instantly reduced. As there is a small oscillation in the voltage profile after a large voltage variation, a small change occurs in the reactive power output for the remaining time. After the voltage is stabilized, the active and reactive power outputs are the same as those before the fault. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Dead Band (p.u)**} \\ \cline{2-4}  & **0.02** & **0.04** & **0.06** \\ \hline \(t_{\text{rise}}\) (s) & 0.161 & 0.162 & 0.163 \\ Overshoot (\%) & 3.0909 & 2.9691 & 2.7517 \\ Stability Indicator(SI) & 0.0417 & 0.0417 & 0.0417 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance indicators result according to dead band range. Figure 11: Voltage profile due to small disturbance. Figure 12: Voltage profile due to large disturbance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Dead Band (p.u)**} \\ \cline{2-5}
**Parameter** & **0.00** & **0.032** & **0.08** & **0.20** \\ \hline \(t_{\text{rise}}\) (s) & 0.177 & 0.179 & 0.185 & 0.235 \\ Overshoot (\%) & 3.0985 & 2.9866 & 2.5117 & 2.9359 \\ Stability Indicator(SI) & 0.0437 & 0.0438 & 0.0438 & 0.0440 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance indicators result according to dead band range. Figure 13: (**a**) Voltage profile, and (**b**) reactive and (**c**) active power result obtained by large disturbance. A simulation corresponding to a large disturbance is performed for all hours to calculate an appropriate dead band range. Therefore, the dead band information for each time zone is entered into each plant controller for each hour. Figure 14 shows the net load curve and dead band value. The below part of the figure shows the result of dead band by time. Each time, the adaptive dead band value derived by the proposed scheme is specified. It can be confirmed that the dead band differs with time, and when the net load curve, which is the sum of the amount of renewable energy generated and load data, is compared with the value of the dead band by hour, they are found to be similar.
## 3 LB-MPC in Path Tracking of Mobile Platforms
### Learning and Optimizing Prediction Model
#### 3.1.1 Theoretical Basis for Modeling  
The prediction models of different governing equations in MPC have a different effect on the path tracking control. The Gaussian model considers the Gaussian distribution as the model parameter [74]. It has a better control performance when dealing with periodically time-varying disturbances. The robust model predictive control (RMPC) and stochastic model predictive control (SMPC) are commonly used in uncertain systems. RMPC is especially suitable for control systems in which the stability and reliability are the primary objectives because the dynamic characteristics in the process are known and the range of uncertain factors is predictable. Moreover, RMPC does not require an accurate process model [76; 77]. On the other hand, SMPC is generally intended to guarantee stability and performance of the closed-loop system in probabilistic terms by explicitly incorporating the probabilistic description of model uncertainty into an optimal control problem [78]. The disturbance rejection model predictive control (DRMPC) is based on MPC and is used for compensating the disturbances in real time. It tries to bring the disturbed system as close as possible to the calibrated system and find the nominal optimal solution by using compensation methods [79]. This is the main difference between RMPC and SMPC. In this work, both RMPC and SMPC are designed on the basis of the upper bound of disturbance. As a result, both methods are highly conservative and sacrifice some performance. The control equations of the aforementioned methods are shown in Table 2.
## 4 Results and Discussions
### The Assessment of Carbon Intensity  
In Figure 9, the CII of the target ship fluctuates significantly in January, stabilizing within 7 to 8 subsequently. This fluctuation is due to limited initial voyages and relatively large CO\({}_{2}\) emissions. As cumulative voyage values increase, CII stabilizes when calculated with deadweight tonnage.  
By attaining the optimal state of each model through comparative analysis, we predict the fuel consumption within one year and transform the predictions into carbon intensity. The CII distribution for each model's predictions is shown in Figure 10.  
Figure 10 shows that the CII distribution from the SA-LSTM closely aligns with the actual distribution of the target ship, while LSTM and ANN models show larger discrepancies. To visually compare the fit of each model's carbon intensity predictions to the actual values, a scatter plot is shown in Figure 11.  
Figure 10: CII assessment results of different models.  
Figure 9: The true carbon intensity distribution of the case ship.  
Figure 11 reveals that LASSO and SVR models have high dispersion and more outliers, deviating significantly from actual values. The ANN and LSTM models show reduced dispersion but still have numerous outliers. Ensemble models (XGBoost, RF, LGB, and ET) exhibit similar, more concentrated scatter distributions. The scatter points of SA-LSTM are concentrated near the fitting curve, indicating minimal dispersion and prediction deviation.
## 6 Research Approach
### Empirical Data Collection and Analysis  
Empirical data were collected by conducting interviews to capture the adoption of CLC in C-ITSs. Interviews were selected to obtain a first-person account of the social reality of the subject [39]. In this case, the interviews were used to provide insights into affecting factors when deciding on new, innovative cryptographic security controls. Selected for interviews were three subject experts. These three were selected because of their involvement with either regulation of or standardization within C-ITSs and, therefore, in a position to elaborate on potential, future directions and security controls. All interviews were recorded and lasted approximately half an hour each. The interviewed subject specialists are described in Table 2 below.  
To enable a more natural conversation, semi-structured interviews were used. The structure of the interviews took inspiration from the laddering technique, so as to gain a richer understanding of underlying reasoning and motives. The laddering technique implies that the interviewer repeatedly asks additional, elaborating type of questions and follow up on answers given in order to find nuances in the answers [40]. To help direct the interviews, an interview guide was developed that consisted of a series of questions based around the five phases from the Innovation-Decision Model. A snippet from the interview guide with respect to the decision phase is illustrated below.  
* _What are the reasons for (not) adopting new cryptographic systems (e.g., certificate-less) to ITS?_
* _What motivates the adoption of (new) cryptographic systems for ITS?_
* _If you were to decide today, would you be in favor of implementing certificate-less cryptography in ITS? Why or why not?_
* _Do you think these motivations/reasons may change in the future?_
* _Why do(n't) you think this will happen?_
* _Have you ever experienced that a decision to adopt a cryptographic system was later reversed (e.g., going from adoption to rejection, or the other way around)?_
* _What was the reason for reversing the decision?_
* _Why do you think that argument was made?_
* _What would have needed to be different for this decision (not) to have been made?_  
After the interviews, the recordings were partially transcribed and analyzed in two steps. First, the transcripts were analyzed using concept-driven coding to extract relevant answers into the five phases of the Innovation-Decision Model. Next, the extracted data under each phase were then lifted into a new document where differences and similarities between answers were identified and synthesized into a coherent result.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Interviewee** & **Role** & **Experience** \\ \hline Alpha & Digital strategist ITS/C-ITS & Cybersecurity, ITS/C-ITS \textgreater{} 20 years \\ Beta & PKI specialist & Cybersecurity, PKI, C-ITS \textgreater{} 10 years \\ Gamma & C-ITS specialist & IT-architecture, ITS/C-ITS \textgreater{} 5 years \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of the interviewees.
## 3 Numerical Tests
### Plate Shear Flow
#### 3.2.2 Effect of Scaling Factor  
Seven hard constraints with different magnitudes of \(a\) (i.e., \(a\) = 1.0 \(\times\) 10\({}^{-6}\), 1.0 \(\times\) 10\({}^{-4}\), 1.0 \(\times\) 10\({}^{-2}\), 1.0, 1.0 \(\times\) 10\({}^{2}\), 1.0 \(\times\) 10\({}^{4}\) and 1.0 \(\times\) 10\({}^{6}\)) and the same degree (i.e., \(k\) = 2.0) are formulated to examine the effect of scaling factor on the PINN prediction accuracy.  
The relative \(L_{2}\)-norm errors of axial velocity at different Reynolds numbers are listed in Table 4. As the values of \(a\) approach 1.0, the relative \(L_{2}\)-norm errors of axial velocity between PINN-predicted results and analytic solutions become smaller. In addition, the large values of relative \(L_{2}\)-norm errors with \(a\) = 1.0 \(\times\) 10\({}^{-6}\) and \(a\) = 1.0 \(\times\) 10\({}^{6}\) in Table 4 are still smaller than those with first-degree hard constraints in Table 3, further demonstrating that the selection of degree is crucial to the PINN prediction accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scaling Factor**} & **Reynolds** & \(\mathbf{L_{2}}\)**-Norm** & **Scaling** & **Reynolds** & \(\mathbf{L_{2}}\)**-Norm** \\  & **Number** & **Error** & **Factor** & **Number** & **Error** \\ \hline \multirow{4}{*}{\(1.0\times 10^{-6}\)} & 60 & 41.15\% & & 60 & 0.140\% \\  & 120 & 41.04\% & & 120 & 3.290\% \\  & 240 & 44.45\% & \(1.0\times 10^{2}\) & 240 & 2.450\% \\  & 480 & 45.68\% & & 480 & 30.60\% \\  & 960 & 46.42\% & & 960 & 40.73\% \\ \hline \multirow{4}{*}{\(1.0\times 10^{-4}\)} & 60 & 1.190\% & & 60 & 3.050\% \\  & 120 & 28.94\% & & 120 & 3.760\% \\  & 240 & 34.28\% & \(1.0\times 10^{4}\) & 240 & 4.342\% \\  & 480 & 41.77\% & & 480 & 9.571\% \\  & 960 & 43.04\% & & 960 & 10.18\% \\ \hline \multirow{4}{*}{\(1.0\times 10^{-2}\)} & 60 & 0.010\% & & 60 & 131.7\% \\  & 120 & 0.750\% & & 120 & 46.33\% \\ \cline{1-1}  & 240 & 29.34\% & \(1.0\times 10^{6}\) & 240 & 54.46\% \\ \cline{1-1}  & 480 & 31.29\% & & 480 & 57.85\% \\ \cline{1-1}  & 960 & 30.76\% & & 960 & 49.78\% \\ \hline \multirow{4}{*}{\(1.0\)} & 60 & 0.020\% & & & \\  & 120 & 3.500\% & & & \\ \cline{1-1}  & 240 & 16.32\% & & & \\ \cline{1-1}  & 480 & 21.11\% & & & \\ \cline{1-1}  & 960 & 1.660\% & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The \(L_{2}\)-norm errors of axial velocity of plate shear flow predicted by the PINN and the analytic solution at different scaling factors and Reynolds numbers.  
Figure 4: Comparison of axial velocity distributions in plate shear flow between PINN-predicted results with different degrees of hard constraint and analytic solution.  
Figure 5 further shows the axial velocity distributions at a Reynolds number of 60. The best agreement between the PINN-predicted distribution of axial velocity and the analytic solution is achieved for the case of \(a=1.0\), corresponding to the quantitative comparison results in Table 4.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 4 The Proposed MAC Address Randomization Covert Channel
### Covert Channel Metrics
#### 4.3.3 Covert Channel Delay  
The network channel delay refers to the duration it takes for a message to travel from the source to the destination, measured in time units. This delay is influenced primarily by four factors: propagation time, transmission time, queuing time, and processing delay. Therefore, the delay is defined as the cumulative duration of time from the moment a PR is sent to the time it takes to receive the corresponding probe response. The delay metric carries significant relevance in the context of the covert channel, as it assists in identifying potential bottlenecks within the channel. Since delay is influenced by various parameters, it aids in pinpointing the factors that may result in untimely frame arrival at different stages of the transmission process, including the sender, the channel itself, and the receiver. By analyzing the delay, we gain insights into the external factors that impact frame arrival, such as the number of frames and the offered load. This information allows for effective planning and identifying suitable environments for implementing the covert channel, ensuring optimal performance and reliable frame delivery:  
\[Delay=\frac{Tx\ PR\ Frame\ Delay+Rx\ PR\ Frame\ Delay}{Simulation\ Time}\ [\text{ms}] \tag{3}\]
# Inspection System for Vehicle Headlight Defects Based on Convolutional Neural Network
## 3 Method for Detecting Defective Vehicle Headlight Parts
### CNN-Based Training for Detecting Defects (Step 2)  
The size of the ROI for detecting defects is different for each inspection region, as shown in Figure 9. Therefore, an image size conversion process is required to apply the previously proposed CNNs of various structures (ReNet-5, AlexNet, GooLeNet, REsNet, VGG19, etc. [18; 19]) to an ROI image. The feature map size of the input layer of ReNet-5 is 32 \(\times\) 32 of which the depth is 1. In the case of AlexNet, the feature map size is 227 \(\times\) 227, and the depth is 3. In the cases of GooLeNet, REsNet, and VGG19, the feature map size is 224 \(\times\) 224, and the depth is 3. Therefore, the image size of the ROI needs to be changed  
Figure 9: ROI coordinates setting and loading.  
to fit the input layer. In this study, VGG19 was used, and the image size of the ROI was changed to fit the input layer of VGG19, as shown in Figure 10. Among the verified CNNs, the VGG19 has the advantage of a simple structure and good performance for classification. In addition, it can be easily applied to field applications. Therefore, the VGG19 was used in this paper.  
The detailed structure of VGG19 includes a feature size and stride of the convolution layer of 3 \(\times\) 3 and 1, respectively, and the padding is configured to be the same as shown in Table 1, in which a CNN model for detecting defects is built, and used to detect defects in parts.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Layer** & **Filter Size** & **Strides** & **Padding** & **Activation** & **Feature Map Size** & **Depth** \\ \hline - & Input & - & - & - & - & 224 \(\times\) 224 & 3 \\ \hline C1 & Convolution & 3 \(\times\) 3 & 1 & same & ReLu & 224 \(\times\) 224 & 64 \\ \hline C2 & Convolution & 3 \(\times\) 3 &