esa-sceva/satcom-chunk-collection huggingface dataset:
# Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
## 4 Implementation and Results
### Adversarial Example Results
#### 4.3.2 Artificial Neural Network Adversarial Examples Result  
In Appendix F, Figure 15, the targeted FGSM is applied to the binary classification problem. The accuracy, precision, F1, and recall scores were reduced as the epsilon value grew. Additionally, the loss score increased as the epsilon value increases. However, the accuracy and precision scores are insignificantly impacted until the epsilon value is 0.8. The recall and precision scores are significantly impacted, with a lower epsilon score of 0.5. Table 5 contains the numerical metrics of zero epsilon as well as 1 epsilon values. Viewing Figure 15 which is the non-targeted FGSM to the binary classification problem, it is seen that the attack is more significant compared to the targeted attack. Figure 15 shows that the accuracy, precision, recall, and F1 scores are all impacted when the epsilon value is 0.2 and higher.  
The Confusion matrices in Figure 3(a) are an alternative representation of the effect that a 1.0 epsilon value had. On the left in Figure 3(a) is the non-targeted method, and on the right is the targeted method. In the non-targeted method, it is seen that there is a greater amount of incorrectly predicted benign records. The greater amount of false-negative cases affects lowering the recall score significantly, as confirmed in Appendix F, Figure 15. On the other hand, viewing Figure 15, the targeted FGSM attack on the multi-class ANN model shows that the accuracy, precision, F1, and recall scores gradually decrease while the epsilon value grows to 1. Interestingly, in the non-targeted FGSM applied to the multi-class ANN model, the accuracy, precision, F1 and recall scores sharply fall until an epsilon value of 0.1, though the impact thereon is insignificant.  
The confusion matrices shown in Figure 3(b) were created using the perturbed testing dataset, with an epsilon value of 1.0 in the multi-class ANN model. On the left side, the non-targeted FGSM is presented, while the targeted FGSM is presented on the right. Comparing these confusion matrices with the baseline multi-class confusion matrix in Figure 2(b), it is observed that the model is lowering its capability of detecting DDoS and DoS attacks while incorrectly predicting a higher number of benign, reconnaissance, and theft attacks.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Scoring** & **Accuracy** & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1**} \\ \hline Percentage of flipped labels (\%) & 0 & 50 & 0 & 50 & 0 & 50 & 0 & 50 \\ \hline Random Flip & 0.999 & 0.441 & 0.999 & 0.610 & 1.0 & 0.613 & 0.999 & 0.612 \\ \hline Targeted Flip & 0.999 & 0.610 & 0.999 & 0.621 & 1.0 & 0.913 & 0.999 & 0.737 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of Zero vs. 50% label flips against the metrics using hyperplane margin method.
# Exact Method for Generating Strategy-Solvable Sudoku Clues
## 9 Conclusions  
Sudokus that appear in books, newspapers, and so on often have a regular pattern in the arrangement of initial digits and are typically made so that all empty cells can be completed using some known techniques, called strategies. We formally defined the problem of generating such Sudoku instances by introducing the concept of strategy-solvability, which means that all empty cells can be filled in with digits using only a given set of strategies. We proposed an exact method for solving this problem. The key is to encode a given problem instance into an equivalent CSP instance and then solve it by applying a CSP solver.  
There are a few existing researches, but all of them are based on the generate-and-test method, which repeatedly generates a set of clues and then tests whether it is strategy-solvable. There are some drawbacks, such as not being able to recognize that a specified set of cells is strategy-unsolvable.  
To the best of our knowledge, our method is the first exact method except for the trivial brute-force search. Our method can eventually find strategy-solvable Sudoku clues if they exist, and otherwise our method can eventually recognize strategy-unsolvability. Besides the clue generation, we presented an application of our method to the problem of determining the minimum number of strategy-solvable Sudoku clues, demonstrating that our method is easily customized with a small modification.  
We conducted experiments to compare our method with the generator of Zama and Sasano, using grids varying the positions and the numbers of clues at random. From the results we observed that our method terminated within 1 min for many grids, showing our method to be stable in terms of running time. However, as the number of clues got closer to 20, the running time rapidly increased and exceeded the time limit, which was set to 600 s. On the other hand, the generator of Zama and Sasano could often find solutions much faster even nearing 20 clues, while the performance sharply deteriorated around 45 clues and exceeded the time limit for all grids with more clue positions. We also evaluated our method for several instances with 17 clue positions taken from known minimum Sudokus to see the efficiency for deciding unsolvability. It remains for future work to improve our method for less clues.  
**Author Contributions:** Conceptualization, K.N. and T.T.; methodology, K.N. and T.T.; software, T.T.; validation, T.T.; formal analysis, T.T.; investigation, K.N. and T.T.; resources, T.T.; data curation, T.T.; writing--original draft preparation, K.N. and T.T.; writing--review and editing, T.T.; visualization, T.T.; supervision, T.T.; project administration, T.T.; funding acquisition, T.T. Both authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by JSPS KAKENHI, Grant Number 17K17725.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Study on Characteristic Strength and Constitutive Model of Red Sandstone under Hydraulic Coupling
## 2 Engineering Background and Microcharacteristics of Red Sandstone
### Microcharacteristics  
The composition analysis test of red sandstone was conducted by EDS, as shown in Figure 1; the sample mainly includes O, Si, Pt, C, Al, Mg, Fe, Ca, K, Na, and Ti. The content of the O element is the most abundant, followed by the Si element.  
Figure 2 shows the morphological characteristics of the fracture. Mineral particles are bonded together through cement. The uncoordinated deformation of particles leads to stress concentration; therefore, the particle cross section on the fracture surface presents an irregular orientation in the microview. In the process of deformation, the clay cementitious material is destroyed, the cracks generated will continuously open and close, and the friction between local particles will make the microstructure stagger, causing changes in macromechanical properties of weakly cemented rocks. The cementation material is  
between particles, the skeleton structure is loose, and the degree of compaction is low. The cementation material contains many micropores. After the destruction of the cementation material, the micropores are exposed, resulting in the discrete distribution of concave convex cavities on the failure surface, and there are a large number of loose particles on the fracture surface.  
Figure 3 shows the intensity of the diffraction peak obtained by testing with an X-ray diffractometer. Mineral matching is carried out through the built-in database to obtain the mineral composition and content of the rock, as shown in Figure 4. The basic skeleton structure of red sandstone is composed of minerals such as orthoclase, quartz, plagioclase, and calcite. The minerals that play the role of cementation are mainly clay minerals, such as montmorillonite, chlorite, illite, and kaolinite. Among them, skeleton particles account for 86.8%, cement accounts for 13.2%, and cement accounts for a small proportion.  
Figure 1: EDS energy spectrum.  
Figure 2: Microcosmic appearance.  
Mineral composition and microstructure essentially affect the macromechanical properties of red sandstone. Most clay minerals are hydrophilic. During the water-rock interaction, the cement absorbs water, expands, and disintegrates. The skeleton structure bonded by the cement tends to be loose, indicating that the content of clay minerals is low and the stability is poor, which is the direct reason for the weak cementation ability of Cretaceous red sandstone.
# Low-Complexity Online Calibration for Large-Scale Multi-Beam Antennas
## 6 Conclusions  
An online calibration method for large-scale multi-beam antennas is proposed for high-throughput satellite communication systems. Calibration signals based on cyclic m-sequences are injected into the normal communication traffic flow. The receiver of the proposed method can achieve parallel calibration with only one matched-filter and no matrix inversion operation is required. Meanwhile, the calibration accuracy can be maintained among the strong interference from communication. The most powerful application of the proposed method is for large-scale antenna systems due to calibration accuracy and computational complexity advantages.  
Theory analysis, Y.L., C.H. and Z.S.; simulation, Y.L. and C.H.; writing--original draft, Y.L.; writing--review and editing, Y.L., B.L. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Scientific Foundation of China under grant 62001030.  
The authors declare no conflict of interest.
## 4 Results
### Texture Profile Analysis of Probetic Cow Yogurt Beverage  
In our results, in blended yogurt beverage, Table 6 illustrates the impact of varying honey concentrations (1%, 3%, and 5%) on the texture profile analysis (TPA). The hardness reaches its maximum at a 1% honey concentration (59.74 \(\pm\) 2.65 a). At 3% honey, hardness decreases marginally (56.49 \(\pm\) 0.03), although the difference is not statistically significant (a). The hardness significantly (p \(<\) 0.05) decreases with 5% honey (34.11 \(\pm\) 2.54 b). Our results correspond with those of ref. [34], who also examined the influence of pine honey on yogurt firmness via alterations to the casein network. Adhesiveness decreases with increasing honey concentrations. At 1% honey, the value is \(-\)19.16 \(\pm\) 0.57 c; at 3% honey, it is \(-\)13.09 \(\pm\) 0.13 b; and at 5% honey, it is \(-\)7.81 \(\pm\) 1.17 a. These findings correspond with the research of ref. [54], which illustrated that honey's hygroscopic characteristics reduce adhesiveness via influencing water retention. Springiness significantly increases with the proportion of honey. At 1% honey, the value is 0.93 \(\pm\) 0.005 c; at 3% honey, it is 0.98 \(\pm\) 0.005 b; and at 5% honey, it is 1.03 \(\pm\) 0.01 a. According to ref. [30], the integration of honey into the matrix augments springiness and boosts elasticity. Cohesiveness significantly increases with the concentration of honey. At 1% honey, the measurement is 0.63 \(\pm\) 0.01 b; at 3% honey, it is 0.65 \(\pm\) 0 b; and at 5% honey, it is 0.69 \(\pm\) 0.01 a. Ref. [29] observed that cohesiveness is enhanced with increased honey concentrations as it strengthens the protein network. Gumminess reaches its maximum at 3% honey (36.82 \(\pm\) 0.02 a) and decreases with both lesser and greater amounts. At 1% honey, the reading is 35.95 \(\pm\) 0.74 b, while at 5% honey, it is 22.01 \(\pm\) 0.03 c. Moreover, ref. [34] contended that honey improves gumminess by increasing viscosity and promoting cross-linking. Chewiness has a pattern like gumminess. At 1% honey, the value is 35.50 \(\pm\) 0.89 b; at 3% honey, it is 36.56 \(\pm\) 0.04 a; and at 5% honey, it is 21.73 \(\pm\) 0.02 c. Moreover, ref. [29] noted that the incorporation of honey enhances chewiness by stabilizing gel properties. Resilience significantly increases with honey content. At 1% honey, the value is 0.27 \(\pm\) 0.02 c; at 3% honey, it is 0.31 \(\pm\) 0.005 b; and at 5% honey, it is 0.35 \(\pm\) 0.005 a. Ref. [30] similarly found that honey improves resistance by strengthening the yogurt matrix. The concentration of honey significantly affects the textural properties of probiotic blended yogurt. Increased honey concentrations (5%) result in reduced hardness, adhesiveness, gumminess, and chewiness while improving springiness, cohesiveness, and resilience. These modifications signify a change in yogurt
## 5 Future Research Directions
This innovative system appears in Figure 14.  
The transition of damper performance from controlled laboratory environments to real-world applications presents a considerable challenge. According to [PERSON] et al. [163], although small-scale experiments and numerical analyses yield important insights, there is a notable deficiency in comprehensive data derived from large-scale, practical implementations of supplementary dampers. This lack of information complicates the accurate forecasting of damper efficacy in actual structures and highlights the necessity for more thorough full-scale testing and evaluations of performance following seismic events. Additionally, there is a pressing need for the establishment of standardized performance metrics and testing protocols for supplementary dampers. The incorporation of additional dampers alongside other advanced seismic protection technologies, such as base isolation systems and structural health monitoring frameworks, reveals a significant gap in current knowledge. According to [PERSON] et al. [164], while these technologies have been examined in isolation, their collective impacts and possible synergies remain inadequately explored. Investigating hybrid systems that utilize the advantages of various protective strategies may result in more robust and adaptable structural designs. Furthermore, there exists a considerable deficiency in comprehending the socio-economic ramifications of the extensive deployment of supplementary dampers in urban settings. The integration of sophisticated data analytics and machine learning methodologies for the optimization of damper design and performance forecasting is an emerging domain characterized by considerable knowledge deficiencies. [PERSON] and [PERSON] [165] and [PERSON] et al. [166] indicate that, although these technologies hold promise for deepening our comprehension of intricate damper dynamics, their complete potential within the realms of structural dynamics and earthquake engineering remains largely untapped. In summary, while additional dampers have demonstrated effectiveness in improving the seismic resilience of structures, the existing knowledge gaps present significant avenues for future inquiry. Addressing these deficiencies is essential for the advancement of the discipline, enhancing the reliability and efficiency of damping systems and ultimately bolstering the resilience of structures against seismic threats. As structural designs become increasingly complex and performance expectations rise, bridging these knowledge gaps will be critical in influencing the future of earthquake-resistant design methodologies.
## 3 Results
### Relative Variable Importance  
#### 3.3.1 Modeling for Each Scenario  
Figure 6 displays the relative importance of the variables selected by MGFS for each scenario. For scenarios 1-3, using Sentinel-1/2 alone or together led to different variables deemed important. When using them together, the VV backscatter composite 37 (September to October 2021) and the composites 12 of the red edge band (BSA) and NDVI (May to August 2021) were identified as the most important variables. Furthermore, there were almost the same number of Sentinel-1 and Sentinel-2 composites in scenario 3. For scenarios 4-5, only three variables were selected by MGFS from 370- variables. For scenarios 6-7, the main crop rotation systems in 2021 were identified as the most critical variables, especially the third, which represented the orchard system, outperforming the Sentinel composites. The crop rotation subsystems in 2021 contributed to scenario 6, while the historical cropland maps contributed to scenario 7. Additionally, more optical images, including B2, BSA, B11, and SAVI, than radar images (VH backscatter) were selected by scenario 6. Surprisingly, the natural variables, i.e., soil, climate, and topography, were deemed insignificant by all the scenarios, except for soil thickness, which was chosen by scenario 7.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 5 Energy and Delay Aware Data Aggregation in RPL
### Parent Selection  
In EDADA-RPL, the DODAG root node broadcasts the DODAG Information Object (DIO) to the neighbor nodes in the network. The DODAG Advertisement Object (DAO) message is sent to the parent or DODAG root by the participant node. The DODAG root or parent node sends the signal of the DODAG Advertisement Object-Acknowledgement (DAO-ACK) to the child node within the trickle interval. The participant node chooses the parent node depended on the DODAG node-level routing remaining metric energy (RER).  
Residual energy shows the present energy available in the RPL routers. The RER measures the difference between the original energy and the energy currently consumed by the node [32]. The formula for calculating the remaining energy is provided in Equation (5).  
\[\text{RER}(\text{Ni})=\frac{\text{E}_{\text{Initial}}-\text{E}_{\text{ Depleted}}}{\text{E}_{\text{Initial}}} \tag{5}\]
# Integrated Precise Orbit Determination of Multi-Gnss and Large LEO Constellations
## 3 Integrated POD Method and Strategy
### Processing Strategy  
We select about 120 globally-distributed stations from the Multi-GNSS Experiment (MGEX) [1], and 22 global stations from the International GNSS Monitoring and Assessment System (iGMAS) [32], to simulate one week of ground GNSS observations from day of year (DOY) 001-007, 2018. Figure 2 indicates the distribution of ground stations we used. The onboard GNSS measurements are also simulated based on the LEO constellations described in Section 2.1. The simulated observation interval is set to 30 s for both ground and onboard observations. Table 3 lists the processing strategy of integrated multi-GNSS POD in detail. Due to the large amount of observations to be processed, we selected an arc length of 24 h and a processing interval of 300 s. The cut-off elevation angle is set to 7\({}^{\circ}\) and 1\({}^{\circ}\) for ground stations and LEO satellites respectively. In terms of force model, GNSS and LEO satellites suffer from different perturbative forces, since they move at different orbital altitudes, especially in the aspect of non-gravitational forces. For GNSS satellites, the solar radiation pressure serves as the primary source of non-gravitational forces due to the thin atmosphere at the GNSS altitude; the atmosphere drag is neglected in the GNSS POD processing. Different from GNSS satellites, as the trajectories of LEO satellites undergo perturbation from both solar radiation pressure and atmosphere drag, the atmosphere drag plays a dominant role in the non-gravitational forces for LEO. All the POD computations are performed in the single-thread mode on HP Apollo2000 machines with 16-core CPU and 96-GB memory.
## 3 Results
### Final Filter Design  
#### 3.2.1 Structures Parameters  
Three probable causes for the high losses can be identified (please note that all the experiments were performed using low optical power, so that nonlinear effects can be excluded)--(i) the scattering due to waveguide roughness; (ii) the curvature loss due to waveguide bending; and (iii) the discontinuity of the effective refractive index due to the presence of the coupling region. While the first cause can only be addressed by improving the fabrication processes at the Si-Photonic foundry, the second and third can be mitigated by modifying the filter design.  
In our initial design the radius-of-curvature could have been increased, while still satisfying the FSR requirement, but only for the rings-based configuration. This is due to the fact that the effective index in the reduced-height 100-nm waveguides is significantly lower than that in standard 220-nm waveguides, because of the reduced modal confinement in the silicon core. As a consequence, once the optical path length is fixed, the corresponding physical length of resonators based on 100-nm waveguides is longer than that of structures based on 220-nm waveguides. This allows us to increase the ring radius from 6.0 to 8.4 \(\upmu\)m, with the expectation of reducing the possible IL-contribution from the waveguide curvature. It is worth mentioning that the biggest contribution to curvature loss is not represented by radiation loss, which is in our case negligible (<0.01 dB/cm even for a ring radius of 6.0 \(\upmu\)m), but by the scattering induced because of the interaction of the optical mode, propagating in the bending section with the waveguide lateral-surface roughness.  
We can reduce the mode index contrast experienced by light propagating in the coupling region, by increasing the gap between the two waveguides. This is also useful because some areas of the wafer, such as the 200-nm gap, were not fully etched, resulting in a small layer of Si being present between waveguides, which can completely compromise the filter transfer function. In the case of the racetrack configuration, increasing the gap forced an increase in the length of the straight-leg, and a reduction in the radius-of-curvature (to keep the total ring length constant) by approximately 20%, to 3.6 \(\upmu\)m. For the modified rings-based configuration, the optimal gap increased by about 25% (from 400 to 515 nm). The final structure parameters of the modified design are reported in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Units** & **Racetracks** & **Rings** \\ \hline Insertion Loss & dB & 2.0 & 4.6 \\ \(-\)3 dB Bandwidth & GHz & 47 & 73 \\ \(-\)20 dB Bandwidth & GHz & 158 & 208 \\ Suppression @ 100 GHz & dB & 24 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Best values measured on filters based on the first design.
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
## 4 Analysis of Normal Section Capacity
### Ultimate Load-Bending Moment Curve  
The ultimate load-bending moment (\(N_{\text{u}}\)-\(M\)) curves of SSRC columns can be calculated using Equation (1) and the optimization model. For \(f_{\text{c}}\) = 20.1 MPa and \(A_{\text{S}}\) = \(A^{\prime}\)\(\text{S}\) = 30 mm\({}^{2}\), the \(N_{\text{u}}\)-\(M\) curves for various eccentricities are depicted in Figure 11. The \(N_{\text{u}}\)-\(M\) curve of the optimization model is consistent with the test and FEA results, and the predicted results always remain safe. In addition,  
Figure 10: Schematic of the second-order effect: (**a**) additional eccentricity and (**b**) amplification factor.  
these results indicate that the mechanical properties of SSRC columns perform better when the relative eccentricity is smaller.