esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Experimental Reproducibility Details
### Statistical Analysis Details  
* **Significance Testing:** Paired \(t\)-tests for performance comparisons* **Confidence Intervals:** Bootstrap sampling with 1000 iterations
* **Effect Size:** Cohen's d for practical significance assessment
* **Multiple Comparisons:** Bonferroni correction applied where appropriate
# Exploring Frontiers of 4D X-ray Tomography
## 2 High-Speed X-ray CT
### X-ray CT  
Since its development in the 1970s, X-ray CT has been used in a wide range of fields including medical diagnosis and non-destructive testing. [PERSON] and [PERSON] were awarded the Nobel Prize in Physiology or Medicine in 1979 for their work on X-ray CT. In CT, a projection of a physical quantity is obtained experimentally in a number of directions. Here, the projection is mathematically just a line integral along a certain direction. For example, the projection \(q(x)\) of the physical quantity \(p(x,y)\) in the direction parallel to the y-axis is given by  
\[q(x)=\int_{-\infty}^{\infty}p(x,y)dy\enspace. \tag{1}\]  
Solving the inverse problem of obtaining a tomogram, \(p(x,y)\), from a set of projections in many directions is called \"tomographic reconstruction\" [3]. We can obtain a three-dimensional image, \(p(x,y,z)\), by stacking tomograms \(p(x,y)\) in the \(z\) direction.  
In the case of visible light transmitted through a transparent object, the deviation in the refractive index from 1 is generally large, so it is not easy to obtain a projection experimentally. However, in the case of X-rays, the interpretation of an experimentally  
Figure 3: Three independent projection images obtained by X-ray grating interferometry. (**a**) Absorption, (**b**) differential-phase, and (**c**) visibility-contrast images of cherry.  
Figure 2: Typical X-ray phase-contrast imaging techniques. (**a**) Crystal interferometry, (**b**) diffraction enhanced imaging (DEI) using analyzer crystals, (**c**) propagation-based method, and (**d**) X-ray grating interferometry.  
acquired projection image becomes much easier since X-rays are only slightly refracted by materials.  
In X-ray CT using absorption, which is historically the oldest, projections of the linear absorption coefficient are obtained experimentally from X-ray transmission, and a three-dimensional image of the linear absorption coefficient is reconstructed. In the case of X-ray CT using phase shift, projections of the refractive index decrement \(\delta\) are obtained, and a three-dimensional image of \(\delta\), which is approximately proportional to the number density of electrons, is reconstructed. Note that, for a synchrotron radiation beam, a tomographic reconstruction algorithm for a plane wave can be used because it can be approximated as a plane wave, while CT measurement using a laboratory X-ray source requires a tomographic reconstruction algorithm [25] for a cone beam for a large sample.
## 6 Simulation Results
### Sparse Comparison  
We compare the DFT sparse basis, diffusion wavelets based on degree (the first scheme), and diffusion wavelets based on distance (the second scheme) presented in Section 4.2. However, since there are not enough real data, we choose synthetic datasets [(42)] to accomplish the following simulations. Thus, Figure 12 is plotted in Matlab software. In Figure 12a denotes DFT coefficients, Figure 12b is the diffusion wavelet coefficients based on degree, and Figure 12c represents diffusion wavelet coefficients based on distance. Here, we select 650 frame length data. As can be seen from Figure 12, DFT does not sparisty the sensor signal, and the value of most of the coefficients is approximately 0.1 instead of 0. However, Figure 12b,c can all sparsity the sensory data, while the energy of the latter is more concentrated without interference signal compared with the former. On the whole, diffusion wavelets based on distance represent better performance because they exploit the spatial correlation features among sensor nodes of the networks.
## 2 Materials and Methods  
### Hysteretic Behavior  
Figure 1 shows the flag-shaped hysteretic model that depicts the equivalent SDOF system's hysteretic behavior. The yield force of the system is represented by \(F_{y}\) in this figure; the hysteretic energy dissipation capacity of the system is defined by the energy dissipation ratio (\(\beta\)), and the post-yield stiffness ratio (\(\alpha\)) represents the ratio of secondary stiffness (k\({}_{2}\)) to initial stiffness (k\({}_{1}\)). A flag-shaped hysteretic model can only remain self-centered if \(\beta\) = 0.5, whereas a self-centering system with \(\beta\) = 0 is equivalent to a bilinear elastic system that is incapable of dissipating energy. The bilinear elastoplastic hysteretic model is represented by the extreme case of \(\beta\) = 1.0. Figure 2 displays the hysteretic model for different values of \(\beta\).  
Figure 1: A schematical view for the flag-shaped hysteretic model.  
A post-yield stiffness ratio (\(\alpha\)) value greater than zero corresponds to hardening behavior, while a value softening behavior is defined for smaller values of it. The P-\(\delta\) effect, caused by gravitational loads on the deformed structure, inevitably results in a reduction in effective strength and stiffness, as well as an increase in lateral displacements. Interstory drift may be significantly impacted by the P-delta effect if it causes a negative post-yield stiffness in any story. If the structure is not strong enough, this might also lead to incremental collapse.
## 1 Introduction
Calving detection requires the following information: dam identification, time, and location when the calf is expelled, and these data should be transmitted in near real-time to allow for attending the birth site before the cow and offspring move away. The hypothesis of the present study was that devices containing GNSS, two-way satellite communication, temperature sensors and accelerometers can be used to remotely detect changes at the time of calving and this information allows for the accurate detection of calving events and location in beef cattle extensively grazing. The objectives of the study were to (1) evaluate the effectiveness detecting calving events of GNSS collars with satellite communication and VIT birth alert devices, and (2) to study behavioral changes with calving measured by these devices in extensive commercial beef cattle.
## 4 Proposed Protocol
### Registration Phase  
The API needs three parameters in the registration phase of our proposed protocol:  
* E-mail;
* Password;
* Vehicle Registration Number.  
The API will get the current time on the vehicle onboard unit side. The other operations include encrypting passwords using MD5, extracting numeric values from vehicle registration numbers, generating updated codes, and creating reference values for vehicles OBU. The \(filter\_var()\) function takes the registration number as input and returns all numeric characters from the input string in our scenario. After extracting the \(NC_{i}\), this \(NC_{i}\) is concatenated with \(ID_{i}\) API from the database, which fetches that. For example, if there is no registered vehicle on the CA Server, the database will return 0, and API will increment the value by 1 in \(ID_{i}\). We have applied the SQL aggregate MAX function that will return the maximum \(ID_{i}\) stored in the table. The combined value of \(ID_{i}\) and \(NC_{i}\) is assigned to \(UC_{i}\). A lightweight block is created to prevent database record tempering in the form of a field that takes \(ID_{i}\), \(t_{i}\), and \(r_{i}\) as inputs, merges these values using the concatenation operator, and produces the vehicle registration reference \(REF_{i}\). The API sends \(\{ID_{i},E_{i},SPW_{i},UC_{i},REF_{i}\}\) on a public channel. The Certification Authority Server generates Updated Vehicle Code \(UC_{s}\) by taking \(UC_{i}\) and a random number \(r_{2}\). The \(UC_{s}\) is shuffled using the function \(shuffle()\) to form \(SVC_{s}\). This \(SVC_{s}\) is then passed to \(SELECT\_RANDOM()\) function in order to produce \(VCA_{s}\). This \(VCA_{s}\) can be used for authentication henceforth. After all this processing, the Certification Authority Server stores \(\{ID_{i},E_{i},SPW_{i},UC_{i},UC_{s},SV_{s},VCA_{s},REF_{i}\}\) in the DB table. The CA Server also generates Session Keys for the onboard vehicle unit. The \(SK_{i}\) is generated by combining \(ID_{i}\) and \(t_{s}\). The CA Server sends \(\{SK_{s},VCA_{s}\}\) to the OBU after handling all the processes mentioned above. Finally, the OBU stores \(\{SK_{s},VCA_{s}\}\) in its shared preferences for session management (see Table 5).
# PSO Optimized Active Disturbance Rejection Control for Aircraft Anti-Skid Braking System
## 5 Simulation Study for Hybrid Runway Conditions  
The performance of the algorithm proposed in this paper is verified by an aircraft anti-skid semi-physical simulation platform Figure 9.  
Figure 9: Semi-physical simulation platform of anti-skid braking system: (**a**) electrical console; (**b**) hydraulic pump and piping; (**c**) overall configuration.  
Without loss of generality, we choose the mixed runway case as the simulation case for the proposed controller. The mixed runway layout is shown in Figure 10.  
In this test, the initial speed of the anti-skid braking process is 72 m/s, and the failure speed is 7 m/s. Aircraft speed and tire speed are two important variables in the process of anti-skid braking, and they are also two dynamic characteristics to be considered in the design of the controller. The speed at which the body speed decreases to a large extent represents the braking efficiency of the anti-skid controller, and the difference between the tire speed and the body speed can roughly reflect the degree of tire slippage. If deep skidding occurs at high speed, it may lead to a tire blowout phenomenon, and this is an important problem that needs to be prevented in the design of the anti-skid controller.  
As can be seen from Figure 11, under the control of the controller designed in this paper, the speed of the aircraft body and the wheel speed both drop steadily, and there is no deep skid phenomenon. When the runway changes, the controller can quickly perform anti-skid action to prevent wheel lock caused by the instantaneous reduction of ground friction, which reflects a good control effect.  
Figure 12 is a graph of the system slip rate. The slip rate is the most important variable in the anti-skid control, because the slip rate is the most important factor affecting the ground bond coefficient. Generally speaking, 0.08-0.12 means that the system slip rate is relatively good. In this interval, the system stability is better, and the ground friction force utilization rate is higher. It can be seen from Figure 12 that under the control of the controller in this paper, the system slip rate can be maintained in the range of 0.08-0.12 most of the time, and there is no deep slip phenomenon during the whole braking process.  
Figure 11: Aircraft speed and wheel speed curve on hybrid runway.  
Figure 10: Schematic diagram of mixed runway layout.  
Figure 13 is a graph of the braking torque of the system. It can be seen that at the moment of runway switching (10 s, 20 s), the torque output by the controller can be adjusted quickly, because the runway is set to switch from a high-coefficient runway to a ground-coupling coefficient runway, so the controller quickly decompresses when the runway is switched, which avoids the problem of deep skidding and reflects the good adaptability to the runway state.
# Experimental Characterization of the Capacitively Coupled RF-Plasma Thruster
## 5 Conclusions  
Experimental investigations of a novel thruster design based on a CCP RF discharge is performed. The formation of the accelerated ion beam with an energy of 10 eV in shown. The beam energy corresponds to the potential drop in the plasma plume. The estimated thruster parameters indicate a limited efficiency of the proposed design. Furthermore, due to limits of power supplies used, an optimal mass flow/power ratio was not reached. Future investigations will be devoted to the optimization of the design, and improvements in plasma parameters acquisition.  
Conceptualization, P.S.; Data curation, R.K.; Formal analysis, R.K.; Funding acquisition, J.S.; Investigation, P.S. and R.K.; Methodology, R.K.; Project administration, J.S.; Software, P.S.; Supervision, J.S.; Visualization, P.S.; Writing--original draft, R.K.; Writing--review & editing, P.S. All authors have read and agreed to the published version of the manuscript.  
This research has been conducted within the project frame of SeRANIS--Seamless Radio Access Networks in the Internet of Space. The project is funded by dtec.bw--Digitalization and Technology Research Center of the Bundeswehr, grant number 150009910.  
Not applicable.  
Data Availability Statement: Datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Thruster** & **C-STAR** & **MINOTOR** \\ \hline Gas & Ar & Xe \\ Mass flowrate [mg/s] & 1.189 & 0.1 \\ Power absorbed [W] & 20 & 30 \\ Ion energy [eV] & 10 & 248.5 \\ Ion current [mA] & 1.98 & 45.5 \\ Thrust [mN] & 0.0057 & 0.98 \\ Thrust to Power ratio [mN/kW] & 0.285 & 33 \\ Isp [s] & 708.6 & 1001 \\ Mass Utilization efficiency [\%] & 0.069 & 62 \\ Power efficiency [\%] & 0.198 & 38 \\ Thruster efficiency w/o divergence losses [\%] & 0.00014 & 19.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of thruster parameters of C-STAR and MINOTOR [1, 2].  
Acknowledgments: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
## 3 Preliminaries
### Mcf  
SDM technology can significantly improve the current EON capacity by utilizing the space dimension. It mainly includes [18] MCF, few-mode fiber, few-mode multi-core fiber, and SMF. MCF is the most intuitive and effective way to increase the capacity of a single fiber. Compared with the optical fiber cable that places multiple SMFs in the same cladding, MCF can effectively increase the spatial density of optical fiber [19]. In terms of the parameter design and product manufacturing of an optical fiber, MCF is relatively mature compared with other technologies. Currently, there are commercial seven-core fiber  
Figure 1: Spectrum allocation and utilization in optical WDM networks and EONs.  
products, whose characteristics are very close to those made by SMF. Therefore, we focus on the resource allocation in MCF-EONs.  
As shown in Figure 2, the RSCA-CS problem is studied with three-core, five-core, and seven-core optical fibers in our work.
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 4 Blockchain-Based Architecture and System Design
### Collaborative DDoS Mitigation
```
BEGIN // Step 1: Start monitoring network traffic START:  WHILE (system_running) DO  traffic_data = MonitorNetworkTraffic() // Step 2: Detect and filter suspicious traffic  filtered_traffic = DetectAndFilter(traffic_data) // Step 3: Use LSTM-based Deep Learning model to detect DDoS  attack_detected = DetectDDoSUsingLSTM(filtered_traffic) IF attack_detected THEN // Step 4: Identify malicious traffic  malicious_traffic = IdentifyMaliciousTraffic(filtered_traffic) // Step 5: Forward to Fog Nodes for processing  SendToFogNodes(malicious_traffic) // Step 6: Fog nodes filter malicious traffic  clean_traffic = FogNodeFiltering(malicious_traffic) // Step 7: Forward cleaned traffic to the Cloud Controller  SendToCloudController(clean_traffic) // Step 8: Cloud controller validates traffic  validation_result = ValidateTrafficInCloud(clean_traffic) IF validation_result == \"malicious\" THEN // Step 9: Execute Smart Contract  defense_actions = ExecuteSmartContract(validation_result) // Step 10: Initiate smart contract for mitigation  InitiateMitigation(defense_actions) // Step 11: Record actions on the blockchain  LogOnBlockchain(defense_actions) // Step 12: Collaborative defense by other participants  TriggerCollaborativeDefense() // Step 13: Ensure transparency and accountability  EnsureTransparencyAndAccountability() // Step 14: Attack mitigated, maintain normal traffic  MaintainNormalTraffic() ELSE // Step 15: Allow normal traffic  AllowTraffic(clean_traffic) END IF  ELSE // Step 16: No attack detected, allow traffic  AllowTraffic(filtered_traffic) END IF  END WHILE // Step 17: End  END
```  
**Algorithm 2**Pseudocode for DDoS Detection and Mitigation Using Fog-Cloud and Blockchain
# A Study on Cyber Target Importance Quantification and Ranking Algorithm
## 3 Design of Target Importance Rank Algorithm
### TIR Algorithm Design and Prioritization Method
#### 3.2.3 Cyber Target Exposure Score Calculation Method  
Previously, the target connectivity and target criticality were derived among the target importance criteria, and finally the target exposure was derived from Equation (2) for the HE value in Section 2.4.2. First, the definition of HE is changed to the \"potential attack risk value that synthesizes various vulnerabilities on the target.\" The derived HE value is integrated into the PageRank-based target importance assignment formula derived as shown in Equation (3) and defined as TIR. Accordingly, PR is changed to TIR, as shown in Figure 7.
# Studies and Rejection of Intercrystal Crosstalk on FPGA in a High-Energy Photon-Counting System
## 3 Studies of Intercrystal Scatter
### Characters
#### 3.2.1 Energy  
The energies of one incident event and its corresponding first crosstalk event are dotted in Figure 7, in which the second and subsequent crosstalk events with a very low likelihood of arising are disregarded. In accordance with the Compton scattering law stating that the combined energies are equal to the energies of photons decayed by \({}^{60}\)Co, a pair of intercrystal events have a combined energy of 1.17 or 1.33 MeV (see Figure 7). The density is greater below the dotted line, which means incident events tend to have higher energies than crosstalk events. However, there is no definite relationship between the energies of the two events, and the energies are distributed in a wide range from 0 to the maximum value of 1.17 or 1.33 MeV.
## 5 Research Directions
### 6G Physical Layer Security and THz Spectrum  
Physical layer security implementations have received massive attention from researchers, especially related to mm-waves in 6G communication. However, resource-constrained 6G-enabled IoTs, heterogeneous integration of cloud computing-based emerging technologies [169] and HNV-based evolution require research to design optimum PLS security protocols. PLS protocols face significant challenges related to seamless fusion with higher layers, especially cloud infrastructures [24]. Moreover, security control and key management between higher-level network layers and massive physical communication is a major challenge in the 6G-enabled network. Similarly, in combination with 6G-enabled emerging technologies, PLS brings several challenges related to conventional cryptographic algorithms and physical layer signature-based protocols. Likewise, various material-based advancements, such as molecular communication, atomic communication, etc., are also open research areas for optimum cyber-secure 6G-enabled infrastructure at the physical layer.
# Malicious Powershell Detection Using Graph Convolution Network
## 4 Experimental Results  
### Setup  
We used 1000 powershell scripts, including 500 normal and 500 malicious powershell scripts for malicious powershell detection provided by the Electronics and Telecommunication Research Institute (ETRI) [19].  
Figure 3 shows that for malicious powershell detection using GCN, we first implemented a feature extraction module using PSParser and Python. Figure 5 shows that the feature data were transformed into frequency data. Second, we implemented the Jaccard similarity computing module. Third, we implemented an adjacency matrix generation module based on Jaccard similarity. Fourth, we modified keras-gcn [20] for malicious powershell detection using an adjacency matrix.  
In addition, we used 5-fold cross validation [21]. Thus, we split 1000 powershell scripts into five subsets and in the \(i\)-th experiment, we used \(i\)-th subset for the test and the other subsets for training; we completed five experiments in total. In each experiment, we used 800 powershell scripts for training and 200 powershell scripts for testing.  
The experimental environment was as follows: we used Windows 10 pro, Intel i7 3.7 GHz CPU, 16 GB RAM, and GeForce 1080 GPU. For the deep learning framework, we used Keras 2.3.1 [22].  
We used the following performance metrics. The recall (detection rate), false positive rate (FPR), and accuracy were defined as the following.  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN})\]  
Figure 4: GCN model.  
Figure 5: Powershell frequency data.  
\[\text{FPR}=\text{FP}/(\text{FP}+\text{TN})\]  
\[\text{Accuracy}=(\text{TP}+\text{FP})/(\text{TP}+\text{FP}+\text{FN}+\text{TN}).\]  
Here, True Positive (TP) was the number of malicious scripts predicted as malicious; False Negative (FN) was the number of malicious scripts predicted as normal; True Negative (TN) was the number of normal scripts predicted as normal; False Positive (FP) was the number of normal scripts predicted as malicious.  
The second performance metric was the adjacency matrix generation time. To use GCN for malicious powershell detection, we needed an adjacency matrix to find powershell scripts such as a new powershell script. In addition, we had to determine as soon as possible whether a new powershell script was malicious. Therefore, we generated an adjacency matrix within a reasonable time period.  
Note that we attempted to continue training for 200 epochs and if the loss did not improve for 10 successive epochs, then training was ceased.  
In the experiment, the first goal was to increase recall and accuracy and decrease FPR using GCN. The second goal was to generate an adjacency matrix within a reasonable time.