esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Theoretical Calculations
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** \\ \hline CAT-TEO (\(\delta_{\text{exp}}\)) & 154.5 & 154.5 & 144.2 & 140.2 & 108.3 & 31.9 & 27.6 \\ CAT-TEO\({}_{1}\) (\(\delta_{\text{calc}}\)) & 154.6 & 155.3 & 150.2 & 138.2 & 114.0 & 38.9 & 35.7 \\ \(\sigma_{\text{iso}}\) & (45.3) & (44.6) & (49.7) & (61.7) & (85.9) & (161.0) & (164.1) \\ CAT-TEO\({}_{2}\) (\(\delta_{\text{calc}}\)) & 157.5 & 151.9 & 151.9 & 139.7 & 113.4 & 37.2 & 36.7 \\ \(\sigma_{\text{iso}}\) & (42.4) & (48.0) & (48.0) & (60.2) & (86.5) & (162.7) & (163.1) \\ \hline  & **C11** & **C12** & **C13** & **C14** & **C15** & **C16** & \\ \hline CAT-TEO (\(\delta_{\text{exp}}\)) & 144.7 & 144.7 & 114.7 & 119.8 & 119.8 & 114.7 & \\ CAT-TEO\({}_{1}\) (\(\delta_{\text{calc}}\)) & 148.8 & 147 & 116 & 121.3 & 122.9 & 117.4 & \\ \(\sigma_{\text{iso}}\) & (51.1) & (52.9) & (83.9) & (78.6) & (77.0) & (82.4) & \\ CAT-TEO\({}_{2}\) (\(\delta_{\text{calc}}\)) & 146.6 & 146.5 & 116.7 & 122.8 & 122.9 & 119.3 \\ \(\sigma_{\text{iso}}\) & (53.3) & (53.4) & (83.2) & (77.1) & (76.0) & (80.5) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculated (\(\delta\)calc) and experimental (\(\delta\)exp) CP/MAS solid state \({}^{13}\)C NMR chemical shifts (ppm) of TEO and CAT in the 1:1 anhydrous ground mixture, and calculated (root mean square error less than 4.0) GIAO isotropic magnetic shielding tensors (\(\sigma\)iso). Figure 5: Possible C=O\(\cdots\)H-O hydrogen bond models and optimized structures of the CAT-TEO corystral. The C=O\(\cdots\)H-O hydrogen bond interaction in the CAT-TEO\({}_{2}\) model is formed by the overlap of the lone pair electrons of the carbonylic oxygen (nO) with the \(\sigma\)*(H-O) anti-bonding orbital (the energetic parameters are shown in Table S1 of supplementary material). The superficial analysis at DFT-B3 LYP/6-31G(d,p) confirmed the preference for the C=O(2)\(\cdots\)H-O in the CAT-TEO\({}_{2}\) model cocrystal (Figure 6), as suggested by the \({}^{13}\)C NMR results because in the spectra the C2 carbon showed a higher shift with respect to the C1. The hydrogen bond distances are similar to the reported for cocrystals of dihydroxybenzenes [15; 24].
## 3 Results and Discussion
### Cyclic Voltammperometry  
To follow the redox reactions in the investigated system in more detail, cyclic voltammetry-perometry measurements have been performed and are shown in Figure 6. For the initial cycle of all composites, the curves present two cathodic peaks related to the lithiation of S\({}_{8}\) to higher order soluble polysulfides (Li\({}_{2}\)S\({}_{\mathrm{x}}\) (4 \(\leq\) x \(\leq\) 8)) and, finally, to solid Li\({}_{2}\)S\({}_{2}\) and Li\({}_{2}\)S, as well as one anodic peak due to the conversion of polysulfides back to elemental sulfur S\({}_{8}\). It is worth noting that the cathodic peak appears at higher potential position also with higher intensity for the SiCN-S composites pyrolyzed at 1100 \({}^{\circ}\)C. This feature, again, indicates that the electrical conductivity of the C-rich SiCN matrix increases, leading to a higher sulfur capacity. Besides, there is a significant increase of the distance between the cathodic and the anodic peak for the sample SiCN-S-800 during the first CV cycle. The cathodic peaks are found at 2.30 and 1.99 V, whereas for the SiCN-S-900 and SiCN-S-1100 composites they are located at 2.33 and 2.03 V, respectively. The anodic peaks of the samples SiCN-S-800 and SiCN-S-900 are at around 2.47 V, whereas for SiCN-S-1100 the anodic peak is shifted to 2.44 V. This feature is caused by the higher ohmic resistance in the case of the samples synthesized at lower temperature [35; 38; 39]. Additionally, it is not more visible, in case of SiCN-S-900, meaning that, in this case, a degree of graphitization of the free carbon phase is enough to provide a sufficient electrical conductivity. After the fifth cycle, the ohmic resistance of SiCN-S-800 and SiCN-S-900 is still a bit higher than that of SiCN-S-1100, but much lower than for the initial cycle. Both cathodic and anodic peak intensity of the samples SiCN-S-1100 decrease compared with the samples SiCN-S-800 and SiCN-S-900. This behavior reveals that the cyclic stability of the electrode material is significantly decreased when the pyrolysis temperature is beyond 900 \({}^{\circ}\)C.
# Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks
## 5 Evaluation and Results
### Results  
As shown in Table 3, all the classification metrics used to evaluate the randomness in the shaped traffic are close to the baseline of random guessing. The values in the last column are bolded to represent the best-performing results. The result demonstrates the reduction in classification accuracy from 98% by the attack to 63% by our defense. Also, our technique achieves a better obfuscation (lower accuracy) than random padding. The attack accuracy under our defense is 8% lower.  
Moreover, Table 4 compares the byte overhead \(B\) of our defense with random padding. The bolded values in the last column represent the most favorable outcomes. Our defense incurs a significantly lower overhead for all devices, which saves nearly 47% of the total overhead. (From Table 4, \(B\) with random padding is 54% more than with random segmentation, which achieves 7%, resulting in a savings of 47% (54-7).)  
Last, we report the latency results from our large file transmission experiments between our client machine and a remote server. As shown in Figure 4, our defense comes with an average time overhead of 20.5%. Compared with random padding, our technique underperforms by only 0.7%, as [15] reported a 19.8% latency. The same figure (Figure 4) also shows a stable \(T\), regardless of whether we perform low or high splitting (using \(Rand_{(low)}\) and \(Rand_{(high)}\)). Although \(B\) has increased by 4.5% due to the intensive splitting using \(Rand_{(high)}\), more splitting seems not to introduce noticeable latency.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Device**} & \multicolumn{2}{c}{**No Obfuscation**} & \multicolumn{2}{c}{**Random Padding**[15]} & \multicolumn{2}{c}{**Random Segmentation**} \\ \cline{2-5}  & \(W_{b}\) **(MB)** & \(D_{b}\) **(MB)** & \(B\) **(\%)** & \(D_{b}\) **(MB)** & \(B\) **(\%)** \\ \hline Bulb & 0.0348 & 0.1936 & 456 & 0.1091 & **214** \\ Plug & 0.0287 & 0.1626 & 467 & 0.0887 & **209** \\ Camera & 549.2 & 766.7 & 40 & 589 & **7** \\ Doorbell & 155.3 & 317.6 & 105 & 168 & **8** \\ \hline Total & 704.6 & 1084.7 & 54 & 757.2 & **7** \\ \hline \hline \end{tabular}  
* Refer to Equation (1) for details regarding the definition of \(W_{b}\), \(D_{b}\), and the specific computation of \(B\).  
\end{table}
Table 4: Byte overhead \(B\) of two defenses *.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**No Obfuscation**} & \multicolumn{2}{c}{**Random Padding**[15]} & \multicolumn{2}{c}{**Random Segmentation**} \\  & **(\%)** & **(\%)** & **(\%)** \\ \hline Accuracy & 98 & 71 & 63 \\ Precision & 98 & 77 & 67 \\ Recall & 98 & 71 & 63 \\ F1 & 98 & 71 & 63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification accuracy, precision, recall and F1 score of two defenses.
## 2 Analysis
### Basic Theory and EE vs. SE Relationship
* In non-linear-region models, significantly better _EE_ is achievable, as stronger received signals inevitably imply the significant reduction of cell dimensions down to tens of meters, giving rise to various cell classes in this regard: micro, nano, pico, and femto, which ensure: \(\odot\) closer-to-uniform _EE_ distribution, \(\odot\) significantly higher _SE_ and so the throughput alike, \(\odot\) rational coverage with satisfactory _EE_, especially in the areas of many active users, and \(\odot\) significantly reduced EM radiation density.
# A Study on \(H_{\infty}\)-Fuzzy Controller for a Non-Linear Wind Turbine with Uncertainty
## 4 Controller Validation
### Wind Tunnel Experiments
In addition, the blade pitch angles were maintained to be a fine-pitch angle with all three controllers, and the thrust force was slightly improved using LQR and \(H_{\infty}\) controllers. Similar to the results confirmed in the simulation results, Figure 7b clearly shows the performance differences between the three controllers in the above-rated wind speed region. The results showed that, when using LQR and \(H_{\infty}\) controllers on the data of the rotor speed, power, and thrust, both regulating and load reduction performance were improved compared with those of PI controllers. Additionally, the proposed \(H_{\infty}\) controllers showed the best-regulating performance while reducing the load. The generator torque was well maintained at the fixed rated value for all three controllers. Table 3 shows the quantitative values of control performances obtained in the wind tunnel experiments shown in Figure 7. Like the simulation results shown in Table 1, the performances of the three controllers obtained in the wind tunnel experiments were also similar in the region where the wind speed was lower than the rated wind speed (behavioral region). Compared with the PI controllers, the mean power differences of LQR and \(H_{\infty}\) controllers were \(0.90\%\) and \(-1.03\%\), respectively, while fatigue load reductions were \(-5.25\%\) and \(-5.46\%\), respectively. In regions with a higher wind speed than the rated wind speed, the rotor speed deviation of the LQR and \(H_{\infty}\) controllers was decreased by \(29.39\%\) and \(57.58\%\), respectively, compared with the result obtained using the PI controller. Similarly, the power deviation was reduced by \(-31.60\%\) and \(-53.49\%\), respectively, and the fatigue loads were decreased by \(-19.58\%\) and \(-21.48\%\).
# Design of an Eye-in-Hand Smart Gripper for Visual and Mechanical Adaptation in Grasping
## 4 Mechanism Adaptive Results
As the orientation of each of the three adaptive fingers of the gripper can be controlled independently, high spatial adaptivity was also observed when grasping cylindrical, spherical, and irregularly shaped objects, as shown in the photos in Figure 17. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Range of Motion at End**} & \multicolumn{3}{c}{**Double-Section Structures**} & \multicolumn{3}{c}{**Triple-Section Structures**} \\ \cline{2-7}
**Point (mm\({}^{2}\))** & \(\mathbf{h_{1}=0.5}\) & \(\mathbf{h_{1}=1.0}\) & \(\mathbf{h_{1}=1.5}\) & \(\mathbf{h_{1}=0.5}\) & \(\mathbf{h_{1}=1.0}\) & \(\mathbf{h_{1}=1.5}\) \\ \hline \multirow{4}{*}{\(R=0.5\)} & \(\varphi\) \& \(\psi=45^{\circ}\) & 249.0 & 234.2 & 252.7 & 652.8 & 661.0 & 667.9 \\ \cline{2-7}  & \(\varphi\) \& \(\psi=90^{\circ}\) & 238.5 & 233.2 & 248.2 & 535.7 & 630.3 & 663.3 \\ \cline{2-7}  & \(\varphi\) \& \(\psi=135^{\circ}\) & 75.1 & 229.6 & 198.2 & 161.6 & 538.8 & 484.5 \\ \hline \multirow{4}{*}{\(R=1.0\)} & \(\varphi\) \& \(\psi=45^{\circ}\) & 212.1 & 215.3 & 214.0 & 199.7 & 195.9 & 195.0 \\  & \(\varphi\) \& \(\psi=90^{\circ}\) & 105.3 & 95.1 & 219.8 & 160.6 & 159.2 & 158.9 \\ \cline{1-1}  & \(\varphi\) \& \(\psi=135^{\circ}\) & 19.6 & 14.7 & 19.4 & 28.4 & 26.0 & 26.2 \\ \hline \multirow{4}{*}{\(R=1.2\)} & \(\varphi\) \& \(\psi=45^{\circ}\) & 54.1 & 37.2 & 33.5 & 64.0 & 51.4 & 40.5 \\ \cline{1-1}  & \(\varphi\) \& \(\psi=90^{\circ}\) & 20.2 & 10.3 & 11.5 & 17.5 & 14.0 & 12.6 \\ \cline{1-1} \cline{2-7}  & \(\varphi\) \& \(\psi=135^{\circ}\) & 4.2 & 2.9 & 2.0 & 3.4 & 2.2 & 1.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Range of motion for different linkage parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operation Range** & **Speed** & **Force** & **Load** & **Weight** \\ \hline
160 mm & 100 mm/s & 50 N & 5 kg & 1.5 kg \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specifications of the gripper.
# Estimation of Aircraft-Dependent Bumpiness Severity in Turbulent Flight
## 3 Experiments and Analysis
### Analysis of Vertical Acceleration Response
#### 3.2.1 Instantaneous Acceleration Response
Quantitative comparison leads to the conclusion that Linear Model was less sensitive and caused more error when encountering severe turbulence. In severe turbulence, the recorded acceleration series can be better recovered by UVLM than by the linear model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Wind Fields** & **Turbulence Severity** & **Max/min Acceleration** \\ \hline WF1 & Light & +1.130 g/+0.980 g \\ WF2 & Light & +1.232 g/+0.972 g \\ WF3 & Light & +1.230 g/+0.969 g \\ WF4 & Light & +1.224 g/+0.952 g \\ WF5 & Moderate & +1.392 g/+0.733 g \\ WF6 & Moderate & +1.579 g/+0.744 g \\ WF7 & Severe & +1.993 g/+0.457 g \\ WF8 & Severe & +2.034 g/+0.413 g \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selected eight wind fields. In order to compare the acceleration response more comprehensively, the MSE and absolute peak acceleration in WFI-WF8 are computed and shown graphically in Figure 11. The Linear Model did a good prediction in light turbulence as UVLM. However, the tracking error increased in moderate and severe turbulence. The increase of turbulence intensity leads to bigger MSE in linear model. On the contrary, UVLM is able to track the acceleration response with better accuracy than the linear model. Figure 10: Turbulence components and acceleration response in WF8. Figure 9: Turbulence components and acceleration response in WF5. It is not unexpected that the accuracy of acceleration response by UVLM is better than by Linear Model. On the one hand, severe turbulence could easily change the governing aerodynamic coefficients of Linear Model, leading to a severe deviation from trim condition. Differing from Linear Model, UVLM is a kind of nonlinear algorithm in which the high-order response feature can be computed, rather than linear approximation. On the other hand, the recorded acceleration response is excited by both turbulence and control surface deflections. The recorded control deflections are input into the UVLM, changing the boundary condition of local vortex ring, while the control deflections are not considered in linear transfer function model.
# Experimental Study on ELID Grinding of Silicon Nitride Ceramics for G5 Class Bearing Balls
###### Abstract  
This study has focused on analyzing the impact of material characteristics and grinding conditions on the surface roughness in ELID grinding of ceramic materials intended for bearing balls. The main research objective was to examine the feasibility of achieving the required surface roughness for G5 class bearing balls through a high-efficiency and high-precision ELID grinding process. Three types of silicon nitride specimens and two types of grinding wheels with cBN and diamond abrasives were prepared for the experiments. An HP (high-pressure) specimen was fabricated through high-temperature and high-pressure sintering at 1700 \({}^{\circ}\)C for 2 h, containing a composition of Y\({}_{2}\)O\({}_{3}\) and MgO in Si\({}_{3}\)N\({}_{4}\), while GPS 1 hr and GPS for specimens were prepared using gas-pressure sintering for 1 h and 6 h, respectively. From the experimental results, it has been confirmed through surface morphology and surface roughness analysis that material characteristics and grinding parameters affect the surface roughness of silicon nitride ceramics during the grinding process. The surface ground with a #2000 diamond wheel is at a level that can satisfy the required surface roughness, 0.014 um or less in G5 class bearing balls. Based on the analysis of surface morphology and roughness in grinding processes, the #325 cBN wheel exhibited excellent performance in rough grinding, while the #2000 diamond wheel demonstrated highly effective surface finishing performance, indicating that the combination of these two abrasives can be effectively utilized for high-efficiency and high-precision nanosurface machining of silicon nitride ceramics.  
surface roughness; ball bearing; silicon nitride (Si\({}_{3}\)N\({}_{4}\)); ELID grinding; nanosurface
###### Abstract  
Spice plants are not only a source of nutrition compounds but also supply secondary plant metabolites, such as polyphenols. Therefore, their bioaccessibility is an important issue. In order to understand the biological activity of polyphenols present in spice plants, it is necessary to broaden knowledge about the factors influencing their bioaccessibility, including nutritional factors. Therefore, the objective of this research was to determine the antioxidative and antimicrobial nutritive properties and potential bioaccessibility of plant seeds and microalgae rich in protein and polyphenolic compounds. Plant seeds rich in protein--i.e., black cumin, milk thistle, fenugreek, almonds, white sesame, white mustard, eggfruit and the two most popular algae, chlorella and spirulina--were analyzed for total polyphenolic compounds (TPC) and antioxidant properties (ABTS, FRAP), as well as their potential bioaccessibility, antimicrobial activity, basic chemical composition and amino acid profiles. With regard to the TPC, the highest levels were found in star anise, followed by milk thistle, white mustard and fenugreek, whereas the lowest were noted in white sesame, allmonds, eggfruit, spirulina and chlorella. White mustard and milk thistle showed the highest antioxidant capacities and almonds, eggfruit, spirulina, and chlorella the lowest according to the ABTS and FRAP assays. The widest spectrum of microbial growth inhibition was detected for fenugreek extract, which showed antimicrobial activity against four analyzed microorganisms: _B. subtilis_, _P. mirabilis_, _V. harveyi_ and _C. albicans_. The protein from seeds of black cumin, milk thistle, white mustard and eggfruit and chlorella was not limited by any essential amino acids. Among all analyzed plants, fenugreek seeds were judged to have potential for use in food formulation operations in view of their antioxidant activity and amino acid profile. Based on the results, intake of polyphenols together with protein in plant seeds does not have a major impact on the potential bioaccessibility of a range of polyphenols and phenolic metabolites.  
plant species; spirulina; chlorella; bioactive compounds; antiradical activity; ferric reducing antioxidative power; potential bioaccessibility; amino acid profile +
Footnote †: journal: Applied Sciences
# A Quantum-Secure Cryptographic Algorithm Integrating Fractals and Prime Numbers
## 1 Introduction
In [3], we showed that prime numbers can be seen as angles of a multifractal polygon based on a hexagon. All these results indicate that prime numbers follow a multiscale distribution and that starting from two sets (6k + 1 and 6k \(-\) 1) that contains all the prime numbers, we can generate other sets that are more likely to contain primes, and as we go down the decomposition hierarchy, the extent of the research decreases, and the computational time is also reduced. Another feature that is guaranteed in the present work is quantum security, which makes the algorithm quantum-resistant. Quantum-resistant cryptography is cryptography that aims to create functions and protocols that allow the system to remain secure even if there is an attack from a CRQC (Cryptographically Relevant Quantum Computer), a quantum computer with features such that it can break public key cryptography systems, such as RSA [16]. Large quantum computers would theoretically have the ability to break algorithms like RSA using [PERSON]'s algorithm, a quantum algorithm that uses mathematical properties to efficiently factorize composite numbers into prime factors. The potential damage that CRQCs could inflict is motivating researchers in the field of cryptography to develop countermeasures, although at the moment, quantum computers are not so diffused [17]. Therefore, this paper uses as a starting point the technique proposed in [1] and extends it, likewise obtaining an algorithm for Information Fusion (IF) that is more efficient compared to that in the previous work. As is well known, Information Fusion is a relatively new research field, which is considered multidisciplinary and consists of taking data from more than one source and joining them together in order to obtain super-information. In the present work, thanks to our Information Fusion algorithm (IFA), we will obtain the following advantages:* A security key driven by a numeric sub-key obtained via a multiscale algorithm for prime generation;
* A security sub-key based on different fractal algorithms;
* A security key given by fusion of the information from the two sub-keys. The sub-keys follow a new dynamic process of evolution and recombination that gives them a crypto-agile property in the context of quantum computing. Compared to the state of the art, this work is the first to pave the way for a new approach that simultaneously combines fractals, prime numbers, and a cooperative-competitive quantum mechanism involving both relocation and recomposition in key construction. This new approach endows the solution with intrinsic crypto-agility and thus specific resistance to quantum attacks.
###### Abstract  
The extremely-low-frequency (ELF) magnetic fields of submarines serve as key characteristics for target detection, with their formation mechanisms being complex and diverse. Effectively manipulating a submarine to reduce its magnetic signature is crucial for enhancing its magnetic stealth capabilities. However, current research on the impact of various causative factors is insufficient. This study proposes a contribution rate assessment method based on the Analytic Hierarchy Process (AHP) model, aiming to provide a theoretical basis for effective manipulation. Initially, a thorough analysis of the threat causes of a submarine's ELF magnetic fields is conducted, and a corresponding hierarchical threat structure model is established. Subsequently, magnetic field signal characteristics generated by different causes are obtained through simulation, and threat matrices and characteristic matrices are constructed. Finally, the contribution rates of different causative magnetic fields to the total magnetic field are calculated, and the simulation results validate the effectiveness of the method. At the stern detection line, the contribution rate of the wake magnetic field is the highest, reaching 0.7649. Along the radial detection line, the contribution rate of the shaft frequency magnetic field is the highest and gradually decays, eventually falling below the wake magnetic field at 150 m and remaining at an approximately 0.5 contribution rate. This study calculates the contribution rates under different operational conditions and detection scenarios, laying a technical foundation for research on the comprehensive active control strategies of submarine ELF magnetic fields in different scenarios.  
extremely-low-frequency magnetic field; contribution rate assessment; analytical hierarchy process (AHP); feature extraction