esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Relationship between Fractal Dimensions and Physical Properties  
The correlation analysis of pore structure parameters and fractal dimensions of shale samples from the Wufeng-Longmaxi Formation was conducted. The physical characteristics of shale reservoirs can be evaluated by key parameters such as porosity and permeability. According to Q1's pore size distribution (Figure 3) and fluid saturation  
Figure 8: Relationship between volume and depth for micro-, meso- and macropores.  
Figure 9: Correlation between the pore volume of different scale pores and TOC and quartz contents in shale samples from the Z-3 well.  
features (Figure 4), the Q1 sample exhibits strong peculiarities. Except for the Q1 sample, the relationship between permeability and porosity of the other shale samples is shown in Figure 10. There is a strong exponential increase (R\({}^{2}\) = 0.9229) in correlation between shale permeability and porosity.  
The bound fluid fractal dimension (D\({}_{1}\)) and movable fluid fractal dimension (D\({}_{2}\)) have no obvious correlation with porosity and permeability in the Z-3 well (Figure 11). Scholars confirmed that as the movable fluid fractal dimension increased, the reservoir pore structure became more complex and the seepage capacity deteriorated [33]. In this study, the porosity and permeability of shale reservoirs are speculated to be influenced by multiple and complex factors that need further study.
# A Novel SINS/SRS/CNS Multi-Information Fusion Global Autonomous Navigation Method
## 5 Experiment and Discussion
### Experiment and Analysis  
In view of the restriction of experimental conditions, actual high-altitude and long-endurance flight experiments cannot be conducted in our research center. Considering that RTK (real-time kinematics) can provide high-accuracy velocity and position, the following experimental scheme was designed. The high-accuracy velocity and position offered by RTK were taken as the true values of the SRS velocity, CNS position, and BA altitude. The CNS position error, BA altitude error, and SRS velocity error simulated in Section 5.1 could be superimposed on the true values to obtain the experimental data. On this basis, a SINS/RTK integrated navigation vehicle experiment in a natural system was conducted to verify the parameter conversion method proposed in Section 4. The experimental vehicle is shown in Figure 6.  
Figure 5: The RMSEs of multi-information fusion autonomous navigation: (**a**) the RMSEs of the position; (**b**) the RMSEs of the velocity; and (**c**) the RMSEs of the attitude.  
The parameters of the vehicle experiment are listed in Table 2.  
The experiment trajectory is demonstrated by the red line in Figure 7. The approximate location was east longitude 118.790\({}^{\circ}\) and north latitude 31.939\({}^{\circ}\). The navigation coordinate frame was the \(g\) frame in the beginning, and it switched to the \(G\) frame after 300 s. The transpolar navigation was imitated by coordinate system switching.  
Figure 8 indicates that the filter parameter conversion method proposed in this article can achieve smooth switching between different coordinate systems without oscillation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Gyro constant bias (deg/h) & 0.015 \\ Accelerometer constant bias (ug) & 85 \\ Gyro angular random walk (deg/sqrt(h)) & 0.0007 \\ Accelerometer velocity random walk & 10 \\ (ug/sqrt(Hz)) & 20 \\ Horizontal positioning error (m) & 10 \\ Altitude error (m) & 0.5 \\ Velocity error (m/s) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of the vehicle experiment.  
Figure 6: The experimental vehicle.  
Figure 8: The positions of the two methods with and without filter parameter conversion.  
Figure 7: The experimental trajectory.  
Figure 9 also shows that the proposed multi-information fusion autonomous navigation method with filter parameter conversion can effectively reduce the navigation errors caused by coordinate frame switching. Compared with the method without filter parameter conversion, the horizontal position accuracy of the proposed method was enhanced by about 29%.
# Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding
## 3 Methodology
### Metric Learning Module
#### 3.2.2 Metric Learning  
Metric learning is a method of spatial mapping that enables the learning of a feature space. In this space, all data are transformed into feature vectors, with similar samples having feature vectors close together and dissimilar samples having feature vectors far apart, resulting in a distinct separation between them. Considering that entities should manifest markedly different feature information under distinct relationships and positions, this paper projects the head and tail entities of the triplets onto hyperplanes determined by relationships. Through measuring the semantic similarity of projected head and tail entities, distinctly separate embedding representations are learned for entities under different relationships. This allows for clearer distinctions between entities, leading to enhanced model accuracy.  
The semantic affinity amid the projected entities is evaluated to assess whether they correspond to a specific relation, thus determining the validity of the resulting triple. This evaluation applies the cosine similarity between the head and tail entities, resulting in a triplet score, defined as follows:  
\[\phi(h,t)=\frac{\sum_{l=1}^{n}h_{l}\times t_{i}}{\sqrt{\sum_{l=1}^{de}(h_{l})^{ 2}}\times\sqrt{\sum_{l=1}^{n}(t_{i})^{2}}} \tag{5}\]  
Figure 3: Vector projection diagram.  
Here, _de_ represents the dimension of the entity vector. We apply a _sigmoid_ to calculate the similarity score to obtain the predicted value.  
\[p=\frac{1}{1+e^{-\phi(h_{1},t_{1})}} \tag{6}\]  
Finally, the model utilizes binary cross-entropy as the loss function for optimization, calculated as follows:  
\[L(p,y)=-\frac{1}{n_{e}}\sum_{i=1}^{n_{e}}\left(y^{i}log(p^{i})+(1-y^{i})log(1-p ^{i})\right) \tag{7}\]  
In the given formula, \(y^{i}\) represents whether there is a triplet \(i\) in reality, \(y^{i}\in 0,1\). Additionally, \(n_{e}\) denotes the number of entities in the KG.
## 3 Numerical Simulation of Oil Spill
### Characteristics of Oil Spill in Icy and Ice-Free Waters  
The oil spill behavior was simulated for both open (ice-free) and icy water conditions under the same sea state. The oil diffusion trajectories for open waters and icy waters are shown in Figures 6 and 7, respectively. The blue color represents the oil film and the other colors represent the ice floes on the sea surface. In icy waters, the extent of the oil spill was obviously suppressed and its distribution was relatively narrow. Its maximum drift distance and maximum diffusion area radius were much smaller than those in open waters. At the time of 500 s, in open waters, the maximum drift distance of the oil spill was 1.2 km in the horizontal direction and 0.6 km in the vertical direction; in icy waters, the maximum oil-drift distance was only 0.7 km in the horizontal direction and 0.1 km in the vertical direction. The main direction of oil-spill diffusion was affected by wind and waves under both conditions. In ice-free waters, the oil film was widely spread on the sea surface, whereas in icy waters, a band-like distribution was observed.  
Figure 6: Oil diffusion trajectories for open waters.  
Figure 7: Oil diffusion trajectories for icy waters.  
Although the presence of ice was observed slowing down the oil diffusion rate, the blending of ice and oil would increase the difficulty of oil recovery. The response measures for the oil spill should, therefore, be adopted in a timely manner according to the oil-spill trajectory. In the forecast area of the oil spill, the enclosure and control facilities should be deployed to prevent the wide-area oil-spill accidents.  
In icy waters, the oil diffusion was affected by wave action on the free surface. These diffusion trajectories are shown in Figures 8 and 9, where the red portion represents the seawater, the blue portion represents air and the interface between the two is the free surface. The black parts indicated the ice floe particles that rose and fell with the waves. The oil diffusion was represented by contours and the oil spout was located at y = 0 m. Oil drift could be observed along the horizontal direction with time and the thickness of the oil film changed continuously, representing a simultaneous process of drift and diffusion.  
The width and thickness of the oil film are shown in Table 2. Note that the oil particles presented not only on the free surface but also below-the-water surface. This portion of oil gradually rose within the water until contacting the ice, and then spread to the surrounding water. The drift of the oil spill was evident in the simulation and the drift distance changed significantly. However, the change in the thickness of the oil film was not prominent. Therefore, it was concluded that the expansion mode of an oil spill in medium icy waters was dominated by drift motion.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Encryption Quality
#### 3.5.3 Peak Signal-to-Noise Ratio  
In image encryption, the PSNR metric is used to measure the noise ratio between the plaintext image and its cipher image [55]. A PSNR value of 0 indicates that the tested image is equivalent to random noise, and a higher value means a higher quality image, which is closer to the plaintext image. The lower the PSNR value in the cipher images produced by an encryption algorithm, the better the quality of the encryption. PSNR can be calculated as follows:  
\[PSNR=10\log_{10}\frac{MAX_{p}}{MSE}, \tag{27}\]  
where \(MSE\) is the mean square error value, which is calculated using Equation (23), and \(MAX_{p}\) is the maximum value that a pixel can have (which is typically 255 in 8-bit pixels).
# Privacy Protection Based on Special Identifiers of Intersection Base Computing Technology
## 5 Conclusions  
This article proposes a new perfect computing protocol to solve the PSI-CA/PSU-CA problem and proves its security in a semi-honest model. It uses Paillier semi-homomorphic encryption technology, and compared with the most advanced protocol A, this protocol has fewer constant rounds of communication and a lower computational complexity in  
Figure 4: Protocol runtime comparison diagram.  
\begin{table}
\begin{tabular}{c c c c c} \hline \((X,Y)\) & **Protocol** & **Online Time (s)** & **Offline Time (s)** & **Total Time** \\ \hline \multirow{2}{*}{(100,100)} & Text’s protocol & 83.39 & 166.78 & 250.17 \\  & Reference [23]’s protocol & 83.33 & 10,021.83 & 10,106.16 \\ \hline \multirow{2}{*}{(100,300)} & Text’s protocol & 88.64 & 265.92 & 354.56 \\  & Reference [23]’s protocol & 265.92 & 90,177.28 & 90,265.92 \\ \hline \multirow{2}{*}{(100,500)} & Text’s protocol & 73.82 & 295.28 & 369.10 \\  & Reference [23]’s protocol & 369.10 & 250,221.46 & 250,590.56 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of online time and offline time of different protocols.  
the offline stage, and has a wide range of application scenarios. In addition, the protocol has more room for optimization in the future, and the computing efficiency of the protocol should be improved on the premise of ensuring correctness. Can the protocol be combined with other advanced technologies to improve the computing efficiency?  
Methodology, T.M.; software, T.M., P.Z. and Q.Z.; validation, P.Z. and Q.Z.; writing--original draft preparation, T.M. and J.Z.; writing--review and editing, P.Z.; visualization, J.W.; funding acquisition, T.M. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Major Science and Technology Projects of Longmen Laboratory (No. 231100220300), the National Natural Science Foundation of China (No. 62102134), the Key Scientific Research Project in Colleges and Universities of Henan Province of China (No. 21A510003, 23A520046, and 23A413005) and the Key Science and Technology Project of Henan Province of China (No. 222102210053, 232102210130, and 232102210138).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 3 Proposed Lightweight Model for Intrusion Detection
### Anomaly Detection by a Classifier  
Anomaly detection by classifier is a technique used to detect DDoS attacks in IoT devices by categorizing network traffic as normal (Class 0) or attack (Class 1). The classifier is trained on labeled data, encompassing normal traffic patterns and known DDoS attack patterns. This enables the classifier to identify anomalous behaviors associated with attacks. It leverages features extracted from network traffic, such as packet sizes, flow rates, and communication patterns, to differentiate between normal and attack instances. The classifier's performance is evaluated using metrics like accuracy, precision, recall, and F1 score to measure its effectiveness in minimizing false positives and false negatives.  
Figure 4: (**a**) Fifteen important features of BOT-IOT; (**b**) fifteen important features of TON-IOT.  
Anomaly detection by classifier complements signature-based methods by identifying previously unseen or zero-day attacks that lack known patterns. It strengthens network security in IoT devices by providing an additional layer of defense against DDoS attacks, ensuring a timely response and preserving the availability and performance of IoT services.
## 3 Results and Discussion
### Adsorption Isotherms  
The isotherms of NO\({}_{3}\)\({}^{-}\)-N adsorption by AEE-3 are displayed in Figure 6. The experimental data were fitted by both Langmuir and Freundlich equations which are represented as follows [25; 26]:  
\[Q_{\rm e}\ =\ \frac{Q_{\rm m}K_{\rm L}C_{\rm e}}{1+K_{\rm L}C_{\rm e}} \tag{6}\]  
\[Q_{\rm e}\ =\ K_{\rm F}C_{\rm e}^{1/n} \tag{7}\]  
Figure 4: Effect of competitive anions Cl\({}^{-}\) and SO\({}_{4}\)\({}^{2-}\) on NO\({}_{3}\)\({}^{-}\)-N removal by two resins.  
Figure 5: Effect of pH on NO\({}_{3}\)\({}^{-}\)-N removal by AEE-3.  
where \(C_{\rm e}\) (mg/L) is the concentration at equilibrium state, \(Q_{\rm e}\) (mg/g) is the adsorption capacity at equilibrium state, \(Q_{\rm m}\) (mg/g) is the maximum adsorption capacity, \(K_{\rm L}\) is the Langmuir constant and \(K_{\rm F}\) and n are the Freundlich constants. The \(R^{2}\) of the isothermal fit are presented in Table 3. In view of the \(R^{2}\) values, the equilibrium adsorption of NO3\({}^{-}\)-N onto AEE-3 was better described by the Langmuir isotherm, which coincided with the prevalent ion-exchange mechanism [7; 23]. Moreover, the \(Q_{\rm m}\) values of AEE-3 dropped with the increase of temperature, revealing that the NO3\({}^{-}\)-N adsorption by the resin was an exothermic process, and therefore, reducing the temperature benefited NO3\({}^{-}\)-N uptake.  
The maximum NO3\({}^{-}\)-N adsorption amount of AEE-3, as evaluated according to the Langmuir model, was compared with other materials including chitosan, activated carbon and commercial anion exchange resins (A520E and D201), etc. As shown in Table 4, the \(Q_{\rm m}\) of AEE-3 for NO3\({}^{-}\)-N uptake was higher than that of these commercially available materials, signifying that the novel anion exchange resin can act as a potential material for the technological application of NO3\({}^{-}\)-N removal from water.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Samples**} & \multicolumn{1}{c}{**\(Q_{\rm m}\) (mg/g)**} & **Reference** \\ \hline \multicolumn{1}{c}{Chitosan/PVA} & 35.00 & [27] \\ \multicolumn{1}{c}{Cationic polymer-modified granular activated carbon (CPMG)} & 27.56 & [28] \\ \multicolumn{1}{c}{Modified commercial activated carbon} & 21.51 & [29] \\ \multicolumn{1}{c}{Magnetic anion-exchange resin MD217} & 30.40 & [30] \\ \multicolumn{1}{c}{A520E} & 36.15 & [31] \\ \multicolumn{1}{c}{D201} & 38.46 & [31] \\ \multicolumn{1}{c}{AEE-3} & 40.32 & This study \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of NO3\({}^{-}\)-N maximum adsorption capacity (\(Q_{\rm m}\)) by different materials.  
Figure 6: Equilibrium isotherm for NO3\({}^{-}\)-N adsorbed using AEE-3.
## 3 Materials and Methods
### HS-SPME-GC-MS Analysis of Volatile Organic Compounds (VOCs)  
The profile of volatile compounds was analyzed by a HS-SPME-GC-MS method, recently developed and validated [24], which allowed quantification of 73 volatile organic compounds (VOCs) using 9 internal standards for area normalization. Briefly, a 4.3 g aliquot of each EVOO and 0.1 g of ISTD (internal standard) solution were weighed into 20 mL screw cap head-space vials, and immediately sealed. A tri-phase Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) 50/30 \(\upmu\)m SPME fiber (Agilent, Palo Alto, CA, USA) was exposed in the vial head-space under orbital shaking for 20 min, after a five-minute equilibration of sample at 45 \({}^{\circ}\)C. The adsorbed VOCs were desorbed in 1.7 min in the injection port (split-less mode, 260 \({}^{\circ}\)C) of a 6890N GC system, equipped with a MS detector (model 5975, Agilent, Palo Alto, CA, USA) and an HP-Innowax capillary column (50 m \(\times\) 0.2 mm i.d., 0.4 \(\upmu\)m film thickness) was used. The fiber was then conditioned at 260 \({}^{\circ}\)C for 20 min.  
Oven temperature program: initial temperature 40 \({}^{\circ}\)C for 2 min, then 4 \({}^{\circ}\)C/min until 156 \({}^{\circ}\)C, then 10 \({}^{\circ}\)C/min until 260 \({}^{\circ}\)C. The carrier gas was helium 1.2 mL/min; temperature of ion source and transfer line was 230 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. MS detector operated in scan mode in the _m/z_ range 30-350 Th, 1500 Th/s.  
Since the response of SPME fibers varies depending upon wearing and sample complexity, the addition of suitable internal standards (ISTDs) to the samples prior to analysis was necessary. An ISTD mix was selected on the basis of chemical similarity to the compounds present in the EVOOs, and contained acetic acid-d\({}_{3}\), 6-chloro-2-hexanone, butanol-d\({}_{10}\), 4-methyl-2-pentanol, 3-octanone, ethyl acetate-d\({}_{8}\), 3,4-dimethylphenol, toluene-d\({}_{8}\), and trimethylacetaldehyde in refined olive oil (IntStd). This IntStd was added to both samples and calibration standards before the analysis. Calibration lines (1 st order) were built for each analyte in ranges including the concentration present in EVOO samples, using 6 diluted solutions constituted of the same amount of IntStd and different amounts of external standard. Each analyte was referred to the most suitable ISTD. The diluted standard solutions were stored in the dark at -20 \({}^{\circ}\)C until chromatographic analyses. Refined olive oil was used as a matrix for building the calibration lines. Mass spectra and retention times of injected authentic standards were compared with those of each peak for identification of the 73 VOCs.
## 3 System Implementation
### Active Self-Interference Cancel  
The schematic of the proposed active SIC is shown in Figure 5. It consists of two parallel transconductance amplifiers (AMP\({}_{1}\) and AMP\({}_{Q}\)) with independently programmable gains and quadrature output phases. To achieve the 360\({}^{\circ}\) phase coverage, a replica of the AMP\({}_{1}\) and AMP\({}_{Q}\) with inverted polarities is used, but only one of the two instances is active at any given time. Each transconductance amplifier consists of an array of identical elements (slices) that can be independently enabled and disabled, providing a total transconductance gain of 20 ms, digitally programmable with 5-bits of resolution. The cancellation current produced by these amplifiers is injected directly at the secondary terminals of the LNTA input transformer. The canceler is designed to cancel up to \(-4\) dBm of SI signal at the LNTA input and consumes a maximum of 6 mA. Each slice consists of a degenerated complementary transconductance stage. Resistive and capacitive degeneration allow to generate nearly quadrature signals.  
In reality, the gate drain-capacitance determines a dominant right-hand side zero in the gain of AMP\({}_{1}\) (G\({}_{\mathrm{ml}}\)) while the finite transistors transconductance determines a dominant pole in the gain of AMP\({}_{Q}\) (G\({}_{\mathrm{mQ}}\)), causing a large phase deviation from ideal quadrature. The expressions of G\({}_{\mathrm{ml}}\) and G\({}_{\mathrm{mQ}}\) are  
\[\mathrm{G}\mathrm{m}_{1}=\frac{\mathrm{g}\mathrm{m}_{1}}{1+\mathrm{g}\mathrm{m }_{1}\mathrm{R}_{\mathrm{C}}}\bigg{(}1-\frac{\mathrm{s}}{\omega_{Z}}\bigg{)} \tag{7}\]  
\[\mathrm{G}\mathrm{m}_{Q}=\frac{\mathrm{s}\mathrm{C}_{\mathrm{c}}}{1+\frac{ \mathrm{s}}{\omega_{\mathrm{p}}}} \tag{8}\]  
where \(\omega_{\mathrm{z}}=\mathrm{g}\mathrm{m}_{1}/\mathrm{C}^{\prime}_{\mathrm{g}d}\) and \(\omega_{\mathrm{p}}=\mathrm{g}\mathrm{m}_{Q}/\mathrm{C}_{\mathrm{c}}\) at the frequency of interest. \(\omega_{\mathrm{z}}\) is typically at a much higher frequency compared with \(\omega_{\mathrm{p}}\). As a result, a significant phase error results that creates a tilt in the canceler output vector constellation and reduces the covered range. Adding an explicit capacitance C\({}_{\mathrm{g}d}\) in parallel to the intrinsic gate-drain capacitance of AMP\({}_{1}\) lowers \(\omega_{\mathrm{z}}\), eventually canceling out the quadrature phase error (phase compensation). The phase difference between AMP\({}_{1}\) and AMP\({}_{Q}\) before and after phase compensation is plotted in Figure 7.  
Figure 6: (a) LNA IIP3; (b) LNA NF vs. RF frequency.  
Figure 8 shows the simulated normalized vector gain of the active canceler for all combinations of the control bits before and after the phase error compensation.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 6 Simulation Results
### Comparison of Computational Complexity  
In order to evaluate the computational complexity of the proposed algorithms, Figure 12 compares the average computational time of the optimal and greedy algorithms for MWSC and MMCDR. The average total requested capacity and the average total traffic demand of the satellite system are set to 5 Gbps, and the minimum required capacity and the average traffic demand of each terrestrial system are evenly distributed from 250 to 1050 Mbps. When solving problems P1' and P2' by the optimal algorithms proposed in Sections 4.1 and 5.1, the computational time increases dramatically with the number of satellite terminals. By comparison, the computational time of the greedy algorithms is insensitive to the number of satellite terminals and is four to five orders of magnitude lower than that of the optimal algorithms. The simulation results in Sections 6.1 and 6.2 show that the spectrum efficiency of the greedy algorithm for MWSC and the minimum CDR of the greedy algorithm for MMCDR are close to those of the optimal algorithms. Therefore, the proposed greedy algorithms can be adopted to achieve desirable performance with much lower computational complexity, which is attractive for real-time resource scheduling in the ISTN.  
Figure 11: CDF of the CDR of the terrestrial system.  
Figure 12: Computational time with varying numbers of satellite terminals.