esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation
### States Space Explosion Mitigation  
The capability of the proposed multi-priority scheduling technique for state space explosion mitigation is another test point of this paper. Therefore, in this part of the experiment, the programs we chose to test programs caused a state space explosion. Therefore, we chose \"guetzli\", \"jhead\" and \"pdfdetach\", \"pdfimages\", \"pdinfo\", \"pdftotext\" in the xpdf as the program under test.  
In Figure 4, the x-axis represents the symbolic execution time in minutes, and the y-axis represents the number of states stored in the symbolic execution engine. The meaning represented by the lines of each color in the figure is the same as in Figure 3.  
Figure 3: Performance overhead when exploring programs that do not cause state space explosion.  
As we can see from the graph, many programs encounter the problem of state space explosion when performing the symbolic execution. Many path exploration techniques encounter the state space explosion problem when they just start to explore the execution path of a program. Then, their memory usage reaches the threshold we set and exits execution. To clarify here, Veritesting requires the construction of local control flow graphs during the runtime. Moreover, precise local control flow graphs are difficult to construct. Therefore, in many cases, Veritesting will not only encounter the problem of state space explosion of the program during execution but also the difficulty of control flow graph construction. As a result, the memory threshold is reached at the start of the program. Therefore, Veritesting exits execution when the number of states is still small.  
This is the most common situation in our symbolic execution, when the state space of the program expands dramatically, the computer's memory cannot satisfy it, and the symbolic execution cannot continue. In contrast, our proposed multi-priority scheduling strategy based on abstract syntax tree analysis can effectively mitigate the state space explosion problem in symbolic execution. Moreover, it can maintain the symbolic execution to continue.  
Finally, we calculated a mean value by adding up the results obtained from the six procedures. The state space explosion mitigation capability ratio of path exploration is EtWExplorer technique:Coverage-based technique:Angr's Explorer technique:Lengthlimiter technique:Veritesting technique = 1035:15,952:19,553:20,329:55. EtWExplorer's state space explosion mitigation capability improved by about 95%, excluding Veritesting. We can clearly conclude that EtWExplorer can effectively mitigate the state space explosion problem in symbolic execution. This is even if we limit memory and the control group techniques exit at the beginning of execution. In a memory unlimited environment, EtWExplorer's state space explosion mitigation capabilities would be exponentially improved.  
Figure 4: State space explosion mitigation capability when exploring programs that cause state space explosions.
# Near-Space Wide-Area and High-Resolution Imaging System Design and Implementation
## 3 Results  
In May 2022, a flight experiment was conducted to verify the functionality of the wide-area high-resolution imaging system. The focal length of the camera is set to 320 mm to achieve a resolution of 0.2 m@20 km, as shown in Figure 16. The car in Figure 16 occupies approximately 9 \(\times\) 22 pixels, based on the resolution, and the actual size is approximately 1.8 m \(\times\) 4.4 m, which is consistent with the actual situation.  
We adopted the SIFT method to achieve the image stitching, where the coverage is 33.3\({}^{\circ}\) and the image stitching result is shown in Figure 17, indicating that the wide-area and high-resolution imaging system has a good image stitching effect, and the overlap rate meets the seamless stitching requirements.
## 2 Local Positioning Systems  
LPS, as a radio-frequency technology, is based on quite similar principles to those of GPS [19; 21]. In this case, the satellite network is replaced by a set of antennae placed around the court, in order to alleviate any satellite reference problems by using time-based positioning techniques [19], in which the signal propagates from the transmitter (antenna) to the receiver carried by the players (device) [16; 17; 20]. The antennae continually send information to LPS receivers and the positioning is calculated with different algorithms classified into five categories based on estimating measurements: (1) time of arrival (TOA); (2) angle of arrival (AOA); (3) received signal strength (RSS); (4) time difference of arrival (TDOA); and, (5) hybrid algorithm [19]. At least three antennas that collect the information on three axes (i.e., x, y, z) are necessary to determine the positioning [19; 20]. Several types of LPS have been used based on ultra-wide band (UWB) technology [17] or based on glass fiber technology [25].
## 2 Overview of Process Workflow
### Adjusting the Projection Factor  
The projection factor \(p_{f}\) controls the distance between the two adjacent nodes (see Section 4), and is used to control the amount of gap or overlap between the viewpoints. Generally speaking, there should be no gaps between the viewpoints for complete coverage over the panel. However, for ease of understanding/preview, it is more helpful if there are some gaps between the outlines of the neighboring viewpoints. We set the projection factor to a default of \(p_{f}=1.1\) for preview purposes and the user can adjust it by sending appropriate commands to change it in increments of 0.05.  
Figure 10 shows the impact of adjusting projection factor. The default value of \(p_{f}=1.1\) is shown in Figure 10a. On increasing it to \(p_{f}=1.2\) in Figure 10b, we can see that the gaps between the neighboring viewpoint outlines have increased. Decreasing it to \(p_{f}=1.0\) (Figure 10c) reduces the gaps to almost non-existent (in fact there will exactly be no gap for a developable surface) and decreasing it further to \(p_{f}=0.9\) (Figure 10d) creates an overlap between the neighboring viewpoint outlines, which is favorable for complete coverage. Unfortunately, Figure 10d is difficult to interpret at first glance. The user is recommended to use the default value of \(p_{f}=1.1\) and then decrease it based on the requirement to generate the preview.  
After the coverage plan (viewpoints) have been generated to the user's satisfaction, the travel path planning (Section 6) can be performed, and accordingly, the G-code or robot code for the manipulator to reach these viewpoints can be generated. Then the manipulator can be moved according to the code for doing the actual NDT inspection on the panel. These processes are not covered in detail in this paper.  
Figure 9: Saving the current set of viewpoints and generating preview from another point. (**a**) Selecting the second starting point; (**b**) Generating preview from the second point.
## 2 Materials and Methods
### Data Analysis and Derivation  
Based on the pressure frequency-domain transient wave transmission function described in [26], we adopted this model to establish the transmission law of pressure within the rock mass. Reference [12] proposed a pressure function related to position and angular frequency within the tunnel:  
\[h_{m}^{0}(\omega)=-Z(\omega)\text{sinh}(\mu(\omega)s_{m})q_{U}(\omega), \tag{1}\]  
here, \(q_{U}(\omega)\) represents the head oscillation, \(Z(\omega)\) is the characteristic impedance, \(\mu(\omega)\) is the propagation function, consisting of two constants with angular frequency as the independent variable, \(s_{m}\) is the distance from the loading point to the rock mass, and \(\omega\) is the angular frequency of the applied dynamic load. This function is position- and angular frequency-dependent, representing the theoretical pressure value in the absence of cracks at the distance \(s_{m}\) from the loading point. The specific physical parameters of the formula are provided in Appendix A.  
Normalizing the two sets of observations \(Z^{0}(d_{i};\omega)\) and \(Z(d_{i};\omega)\). Normalization can provide several benefits, including: (1) accelerating convergence; (2) preventing bias in feature weighting; (3) improving model performance; and (4) enhancing interpretability.  
Proposing an expression for the pressure frequency-domain transient wave model of the tunnel rock mass:  
\[H_{M}^{0}(\omega)=-\Bigg{(}\frac{a\sqrt{-\omega^{2}+ib\omega}}{ic\omega} \Bigg{)}\text{sinh}\Bigg{(}\frac{\sqrt{-\omega^{2}+ib\omega}}{a}*s_{m}\Bigg{)} \text{d}, \tag{2}\]  
This expression is derived by simplifying Equation (1) and representing the constants as four unknown constants: a, b, c, and d. These four constants are the unknowns, which we aim to determine through experimental data. The observed data sets are used for machine learning training using the least squares method, with 20% of the data set used for testing.  
By substituting the test set data into Equation (2) for solving, the four unknown constants a, b, c, and d are determined as 1.5724, 3.4860 \(\times\) 10\({}^{-6}\), 2.5129, and 9.2144 \(\times\) 10\({}^{-2}\), respectively. Testing based on the training set achieved an MSE of 0.00197, indicating good accuracy of the pressure frequency-domain transient wave model for the tunnel rock mass.  
Based on the simulated pressure frequency-domain transient wave model of the tunnel rock mass, along with the modeling of the rock mass, frequency-domain transient wave analysis can be performed, providing the transmission relationship of pressure frequency-domain transient waves with respect to position (as given by Equation (2)). This  
Figure 2: Dynamic load applied to a perfect rock mass with cracks.  
provides theoretical support for transforming the pressure difference vector into a vector representation of crack position and size.
## 5 Results and Discussion  
### Laboratory Analyses  
The first parameter analyzed by the authors was the health hazard from the RT58 material safety data sheet. According to standards 1272/2008 and 1999/45/EG, it represents no hazard.  
Results from the DSC analysis (Figure 3) showed that the phase change temperature range is 50-61 \({}^{\circ}\)C with a peak temperature at 58.3 \({}^{\circ}\)C. Since RT58 is technical grade paraffin, the fact of observing a temperature range instead of a sharp melting point in the melting is coherent. The phase change enthalpy for the melting temperature range is found, from the DSC analysis, by numerical integration within the melting temperature range and results 120.1 kJ/kg far from the value presented by the manufacturer (Table 1). The reason might lie in the use of different techniques and methodology to determine it [17].  
Figure 4a shows the TGA of RT58. It can be seen that the sample degrades in one step. The paraffin degradation starts at 200 \({}^{\circ}\)C and it is finished at 350 \({}^{\circ}\)C, which allows for the conclusion that usage of RT58 should be avoided above 200 \({}^{\circ}\)C. Since the temperature range of the potential applications and the present experimentation is far away from the beginning of the degradation, no problem regarding this matter was expected during the present experimentation. If this result is compared against the thermo-physical properties presented by the manufacturer (Table 1), the obtained results match with the manufacturer data sheet (Flash point > 200 \({}^{\circ}\)C). However, it has to be noticed that the maximum operational temperature of RT58 is 80 \({}^{\circ}\)C due to its vapor pressure. Hence, an appropriate control system should be used to avoid the operation of the PCM above these thermal conditions.  
Figure 4: RT58 laboratory analyses results: (**a**) Thermogravimetric analysisTGA; (**b**) Infrared (FT-IR) spectroscopy before and after 500, 1000 and 1200 cycles.  
Figure 3: Differential scanning calorimeter (DSC) analysis of RT58.  
Figure 4b shows the FT-IR spectrograms of an RT58 sample before and after 500, 1000 and 1200 cycles and the FT-IR characteristic peaks of this kind of materials. Results show that RT58 presents non-chemical degradation since they show equal FT-IR characteristic peaks with similar profiles.  
Furthermore, samples after 500, 1000 and 1200 cycles were taken to study the evolution of the phase change temperature and latent heat of the studied material. Results are presented in Table 2, and show that after 1200 cycles, the variation on the phase change temperature is about 1.7% and the variation on the latent heat is about 2.7%, meaning that paraffin RT58 presents no degradation after 1200 cycles. The lack of a clear and consistent pattern in the enthalpy values along the cycles may be due to the DSC error itself along with differences between the DSC samples and cycling conditions (mass, pan, sample preparation, or the presence of humidity in the DSC). This kind of behavior has been widely seen in various studies involving different materials [18].
# Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron
###### Abstract  
Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS attacks that are not easily detectable because of impersonating authentic users. In this study, we address novel application layer DDoS attacks by analyzing the characteristics of incoming packets, including the size of HTTP frame packets, the number of Internet Protocol (IP) addresses sent, constant mappings of ports, and the number of IP addresses using proxy IP. We analyzed client behavior in public attacks using standard datasets, the CTU-13 dataset, real weblogs (dataset) from our organization, and experimentally created datasets from DDoS attack tools: Slow Lairs, Hulk, Golden Eyes, and Xerex. A multilayer perceptron (MLP), a deep learning algorithm, is used to evaluate the effectiveness of metrics-based attack detection. Simulation results show that the proposed MLP classification algorithm has an efficiency of 98.99% in detecting DDoS attacks. The performance of our proposed technique provided the lowest value of false positives of 2.11% compared to conventional classifiers, i.e., Naive Bayes, Decision Stump, Logistic Model Tree, Naive Bayes Updateable, Naive Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier, and OneR.  
DDoS attack; attack; attack detection; botnet; MLP classifier Article
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## Theoretical Principle
### Secure Hash Algorithm  
Secure Hash Algorithms (SHA) are a kind of hash functions, which is released by NIST. SHA is mainly applied to the integrity security services [18]. SHA-256 is a popular SHA, which outputs the message digest with the length of 256 bits.  
The proposed algorithm uses SHA-256 to generate the initial value \(x_{0}\) and variable \(q\) of PWLCM. For the plain image, the proposed algorithm adopts SHA-256 to generate hash value \(K\), which can be segmented into 8-bit parts, i.e.,:  
\[K=k_{1},k_{2},\ \cdots,\ k_{\mathrm{i}},\ \cdots,\ k_{32}\,\quad i=3,\ 4,\ \cdots,\ 31 \tag{2}\]  
The variables \(x_{0}\) and \(q\) of PWLCM are computed by:  
\[x_{0}=\frac{k_{1}\bigoplus k_{2}\bigoplus\ \cdots\ k_{\mathrm{i}}\ \cdots\bigoplus k_{16}}{255},\quad i=3,\ 4,\ \cdots,\ 15 \tag{3}\]  
where \(\bigoplus\) denotes the exclusive OR (XOR) operation in the binary system.  
\[q=\frac{k_{17}\ \bigoplus k_{18}\bigoplus\ \cdots\ k_{\mathrm{i}}\ \cdots\bigoplus k_{32}}{510},\quad i=19,\ 20,\ \cdots,\ 31 \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Key Length** & **Block Size** & **Rounds** \\ \hline
128 & 128 & 10 \\
192 & 128 & 12 \\
256 & 128 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advanced Encryption Standard (AES) core parameters.
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
## 1 Introduction
### Exavation Damaged Zone Investigations in ONKALO(r), Olkiluoto, Finland  
Transport of radionuclides with water in fractured bedrock has been identified as one of the key risks to the environment related to the final disposal [3]. Excavation of the deposition and access tunnels with the drill and blast method causes damage to the surrounding rock mass, seen as the excavation damaged zone (EDZ). Characterisation of the EDZ is necessary for understanding the fluid transport properties of the damaged rock mass near the excavation profile of the tunnels.  
Posiva committed a set of research works in ONK-TKU-3620 between 2012 and 2018 to characterise EDZ [4]. Research work consisted of geological, petrophysical, and rock mechanical studies and modelling. This study focuses on the discrete fracture network (DFN) analysis of drill core specimens from the EDZ study area in ONK-TKU-3620 [5] and the relationship between observed fracturing and measured petrophysical [6] and mechanical properties [7]. Specifically, the focus is on the network of micro and macro fractures potentially induced by the excavation, as opposed to pre-existing natural fracture systems.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 7 Evaluation
### Experimental Results and Findings
#### 7.3.1 Experiment 1: Performance Overhead of Reconfigured Features  
In this experiment, we compare the performance of the same application on clusters that natively support our selected features and clusters that have these features enabled using our controller and reconfiguration tactics.  
Experimental Setup  
To evaluate this research question, we first have to create K8s clusters that support the native versions of our selected features as our baselines. This can be achieved through Kubeadm and GKE proprietary interfaces. Then, we use the controller to reconfigure the default GKE and on-premise clusters to obtain clusters supporting our reconfigured features.  
Our test applications should use the feature we evaluate. For the static CPU manager policy, both are assigned with two dedicated cores. When evaluating the PodPresets feature, we inject environment variables into our test applications. As for the CNI network plugin, we configure network policies stating that only the traffic generator K8-Scalar can communicate with the test applications.  
We only deploy a single replica of the test applications on the clusters. To make sure our traffic generator does not become a bottleneck, we deploy it on a different node than the test application, and it generates loads of 25 to 200 requests per second in steps of 25. We run each load 1800 seconds.  
As introduced in Section 7.2.2, our GKE clusters have two e2-highcpu-4 worker nodes and our on-premise clusters have two c4m4 worker nodes and one c4m4 control plane node. The Kubernetes version that we use is 1.21, except for experiments about PodPresets, where we use Kubernetes 1.19, which is the latest version that supports PodPresets API. As stated in Section 7.2.2, we do not conduct experiments about network plugins in on-premise clusters.  
Experimental Results and Findings  
Figure 9 presents our the results of our experiments for each type of reconfigurator.  
For all selected features, we find no significant difference in performance overhead between the reconfigured features and the native versions supported by K8s. For the API server reconfiguration with PodPresets, this is because CRDs and webhook servers only work when a new Pod is being created; therefore, the impact is only visible in case of autoscaling Pods. The additional webhook server consumes almost no resources when no pods are being created. For Kubelet reconfiguration with the static CPU manager policy and the CNI plugin reconfiguration with the Calico CNI network plugin, the actual Kubelet and CNI configuration implemented by our reconfigurators do not differ from the configuration files in the base line clusters with natively configured features as prescribed in the documentation.
## 5 Experiment
### Accuracy of Edge Computing
#### 5.3.2 Effect of Occlusion on the Accuracy  
The experimental assessment of the occlusion effects on the accuracy of an embedded development board is illustrated in Figure 10. The green line represents the recognition accuracy, while the yellow line signifies the occlusion proportion. The left y-axis corresponds to the recognition accuracy, whereas the right y-axis corresponds to the occlusion rate. The discernible influence of occlusion ratios induced by varying population scenarios and formations necessitates a certain threshold being reached before a perceptible impact is manifested on the technological outcomes.  
The graphical representation illustrates that, under most circumstances, there is a significant probability of observing an impact on the counting accuracy when the occlusion within the imaged targets exceeds a range of 20% to 50%. This phenomenon can be attributed to two plausible explanations. Firstly, within these formations, specific angles of the target object facilitate rapid recognition by the image model from other angles. This accelerated recognition prompts immediate localization and tracking through positioning algorithms, ensuring that the counting accuracy is maintained, even when the target object is subsequently occluded. Secondly, the initial absence of individuals within the waiting zone can lead to a scenario where individuals simultaneously enter the area, inducing a temporal misalignment. This misalignment can result in pre-identification of the target  
Figure 10: Relationship between the accuracy and the occlusion rate under different conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of People in Waiting Area** & **Horizontal** & **Vertical** & **Diagonal** & **Criss-Cross** \\ \hline
1 & 100\% & 100\% & 100\% & 100\% \\ \hline
2 & 100\% & 100\% & 100\% & 100\% \\ \hline
3 & 100\% & 100\% & 100\% & 100\% \\ \hline
4 & 50\% & 100\% & 100\% & 100\% \\ \hline
5 & 40\% & 80\% & 100\% & 80\% \\ \hline
6 & 67\% & 67\% & 83\% & 83\% \\ \hline
7 & 57\% & 71\% & 86\% & 86\% \\ \hline
8 & 63\% & 63\% & 75\% & 88\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy when counting eight people sequentially entering the elevator waiting area.  
object's position upon entry into the waiting area. Consequently, subsequent occlusion of the target object within the identified region does not alter the count, as the target's position is established and remains within the defined LOI boundary. Furthermore, due to the inherent computational limitations of embedded development boards relative to more powerful computational platforms, the processing capacity available for tracking is constrained. This constraint augments the likelihood of losing track of an object's location during tracking.