esa-sceva/satcom-chunk-collection huggingface dataset:
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 2 Wireless Communication Technology
* **Joint Terrestrial and Non-terrestrial network and possible challenges:** Various new non-terrestrial networking platforms are expected to be integrated into the traditional terrestrial communication network for creating a more reliable 6G communication network. However, proper channel models, mobility management, savior channel loss due to natural causes such as rain fading, and proper resource allocation are some of the main challenges that need to be handled. * **Mobile edge computing (MEC) and corresponding challenges:** MEC has emerged as a promising technology that brings cloud computing resources to the proximity of end-users. However, size and coverage restrictions often limit the MEC servers' computation and communication capabilities. With upcoming 6G technology, various new latency-critical and data-intensive applications are expected to be enabled. Allocating the proper networking services to the edge servers, proper user-server assignments, proper resource allocation, and user mobility issues are some of the main challenges that require proper attention.
## 3 Results and Discussion
### Rheological Measurements  
3.6.1. Flow Curve Tests and Thermal Flow Curve Ramp  
Figure 9a,b, respectively, show the viscosity and the shear stress behaviour (again shear rate) of the OOBC, tested at t\({}_{0}\) and t\({}_{1}\), as well as the viscosity of a commercial mayonnaise used as a benchmark. Figure 10 shows the OOBC viscosity trends at t\({}_{0}\) and t\({}_{1}\) across a range of temperatures between 10 and 80 \({}^{\circ}\)C with a heating velocity of 10 \({}^{\circ}\)C/min.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Centrifugation Step Number** & **T (min)** & \
\begin{tabular}{c} **Separation Grade \%** \\ **OOBC-U** \\ \end{tabular} &
\begin{tabular}{c} **Separation Grade \%** \\ **OOBC-H** \\ \end{tabular} \\ \hline
1 & 5 & 1.1 & 0 \\
2 & 10 & 1.1 & 0 \\
3 & 15 & 2.2 & 3.8 \\
4 & 20 & 3.4 & 4.6 \\
5 & 25 & 4.5 & 5.8 \\
6 & 30 & 4.5 & 6.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Stability index of the OOBC untreated and MW treated up to 70 \({}^{\circ}\)C after 6 centrifugation steps.  
Figure 10: Oil bodies viscosity behaviour under temperature variation between 10 and 80 \({}^{\circ}\)C.  
Figure 9: Oil bodies and mayo viscosity behaviour expressed as (a) flow curve and (b) yield stress curve.  
Figure 9a shows that at the lowest shear rate (0.01 s\({}^{-1}\)), the viscosity values are \(1.5\times 10^{7}\) mPa\(\cdot\)s for the OOBC at t\({}_{0}\) and 8.2 \(\times\) 10\({}^{6}\) mPa\(\cdot\)s for the OOBC at t\({}_{1}\), while it is 1.6 \(\times\) 10\({}^{6}\) mPa\(\cdot\)s for the commercial mayonnaise. Even though the OOBC viscosity is reduced after ten days, it still resembles a commercial food emulsion. The shear stress values, in Figure 9b, at the lowest shear rate are 1.5 \(\times\) 10\({}^{5}\) mPa at t\({}_{0}\) and 8.2 \(\times\) 10\({}^{4}\) mPa at t\({}_{1}\) for the OOBC and 1.6 \(\times\) 10\({}^{4}\) mPa for commercial mayonnaise.  
Figure 10 illustrates that the viscosity is 3.2 \(\times\) 10\({}^{6}\) mPa\(\cdot\)s at the lowest temperature (10 \({}^{\circ}\)C) and 4.2 \(\times\) 10\({}^{4}\) at the highest temperature (80 \({}^{\circ}\)C), with a percentage variation of 86% for sample t\({}_{0}\), while it is 1.3 \(\times\) 10\({}^{6}\) mPa\(\cdot\)s at 10\({}^{\circ}\)C and 1.7 \(\times\) 10\({}^{4}\) mPa\(\cdot\)s at 80 \({}^{\circ}\)C, with a percentage variation of 87% for sample t\({}_{1}\). These results show encouraging signs, indicating that at the lowest shear rate, the OOBC viscosity is approximately five-fold higher than mayonnaise and that they have the same order of magnitude, whereas at the highest shear rate, the viscosity trends overlap. To obtain the best flow for OBs, we need a force of at least five-fold higher than that used for mayonnaise. The temperature is a factor considered to reduce the force applied during flowing, as indicated from the reduced viscosity at the highest temperature evaluated.
# Recent Advances in Sample Preparation for Cosmetics and Personal Care Products Analysis
## 2 Sample Preparation Strategies for Cosmetics Analysis
### Microextraction Techniques for Cosmetic Analysis
#### 2.7.2 Sorbent-Based Microextraction Techniques
Among the sorbent-based microextraction techniques, stir bar sorptive dispersive microextraction (SBSDME) is one of the most recent ones. It was introduced by [PERSON] et al. as a hybrid microextraction technique that combines the principles of stir bar sorptive extraction (SBSE) and dispersive u-solid-phase extraction (d\(\upi\)SPE) [148]. It has been mainly applied to determine organic contaminants in the aquatic media [149]. Although it is a very recent technique, its use has been extended to cosmetics matrices using a hybrid magnetic composite made of CoFe\({}_{2}\)O\({}_{4}\) MNPs and MIL-101(Fe) MOF as a sorbent phase to analyse N-nitrosamines [137] and a magnetic carbonaceous composite made of CoFe\({}_{2}\)O\({}_{4}\) and reduced graphene oxide (\(\upi\)GO) to extract PAHs [138]. In both cases, satisfactory recoveries and precision were achieved, showing the use of SBSDME as a promising green microextraction technique.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Experimental Result and Analysis
#### 3.4.1 Results from the Indian Pines Dataset  
Based on the results shown in Table 2, the proposed method achieves the best values across three metrics, and its accuracy exceeded 89% on almost all classes. In the tables, the best results in each row are highlighted in bold font. When comparing SP-SSA with raw HSI data, the OA improved substantially from 76.42% to 98.15%. In addition, comparisons between SVM and other methods indicated that the incorporation of spatial features can enhance the classification performance compared to considering spectral features alone. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Indian Pines, Salinas** & **Pavia University** & **Houston 2018** \\ \hline SVM & NA & NA \\ \hline EPF & \(\delta_{k}-3,\delta_{k}-0,2,r=3,\text{and}c=0.01\) & \(\delta_{k}-3,\delta_{k}-0,2,r=3,\text{and}c=0.01\) & \(\delta_{k}-5,\delta_{k}-0,1  
Superpixel-based methods, such as SCMK, R2 MK, ASMGSSK, and MsuperPCA techniques, yield higher classification accuracy as compared to non-superpixel based techniques (EPF, 2D-SSA, and 2D-MSSA); by grouping spectrally identical regions, superpixels offer a powerful way to exploiting spatial/contextual information. It can also be noted that methods considering multi-scale windows (ASMGSSK, MsuperPCA, and 2D-MSSA) perform better with respect to fixed-window methods. Due to the different window sizes, unique local spatial features can be exploited, which allows better covering of different sizes of land cover classes and different scales of spatial features. On the downside, the use of multiscale approaches involves heavier processing burdens. As the proposed method reconstructs each superpixel individually, better classification results are obtained. Figure 11 displays the classification maps produced by various approaches for the Indian Pines dataset. For the SVM approach, the classification map appears very noisy if spatial features are not considered. Through the use of neighborhood spatial information, the EPF and 2D-SSA techniques can suppress spot-wise misclassification to a large extent, but these methods do not preserve the detailed structures of the HSI well enough. However, by adopting superpixel-based approaches, the generated classification map becomes much smoother, and more accurate estimates are obtained in the detailed region. With the utilization of multi-scale approaches (like ASMGSSK, MsuperPCA, and 2D-MSSA), the amount of misclassification is further reduced. Still, even with multi-scale approaches, landcover boundaries are frequently misplaced. As can be observed from Figure 11, the proposed approach effectively solved the above-mentioned problems due to its considerate utilization of spectral and spatial features.
# An Efficient VQ Codebook Search Algorithm Applied to AMR-WB Speech Coding
## 3 BSS-VQ Algorithm for ISF Quantization
### BSS Generation with Dichotomy Splitting  
As a preliminary step of a training procedure, each dimension is dichotomized into two subspaces, and a dichotomy position is defined as the mean of all the codewords contained in a codebook, formulated as:  
\[dp(j)=\frac{1}{CSize}\sum_{i=1}^{CSize}\mathbf{c}_{i}(j),\ 0\leq j<Dim \tag{8}\]  
where \(\mathbf{c}_{i}(j)\) represents the \(j\)th component of the \(i\)th codeword \(\mathbf{c}_{i}\), \(dp(j)\) the mean value of all the \(j\)th components. Taking the codebook CB1 as an instance, \(CSize=256\), \(Dim=9\). As listed in Table 3, all the \(dp(j)\) values are saved and then presented in a tabular form.  
Subsequently, for vector quantization on the \(n\)th input vector \(\mathbf{x}_{n}\), a quantity \(v_{n}(j)\) is defined as:  
\[v_{n}(j)=\left\{\begin{array}{c}2^{j},\ \mathbf{x}_{n}(j)\geq dp(j)\\ 0,\ \mathbf{x}_{n}(j)<dp(j)\end{array}\right.,\ 0\leq j<Dim \tag{9}\]  
where \(\mathbf{x}_{n}(j)\) denotes the \(j\)th component of \(\mathbf{x}_{n}\). Then \(\mathbf{x}_{n}\) is assigned to subspace \(k\) (\(bss_{k}\)), with \(k\) given as the sum of \(v_{n}(j)\) over the entire dimensions, formulated as:  
\[\text{Assigning}:\mathbf{x}_{n}\in bss_{k}|k=\sum_{j=0}^{Dim-1}v_{n}(j). \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline \(j\)**th-Order** & **Mean** \\ \hline
0 & 15.3816 \\
1 & 19.0062 \\
2 & 15.4689 \\
3 & 21.3921 \\
4 & 26.8766 \\
5 & 28.1561 \\
6 & 28.0969 \\
7 & 21.6403 \\
8 & 16.3302 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dichotomy position for each dimension in the codebook CB1.  
In this study, \(0\leq k<BSize\) and \(BSize=2^{9}=512\) represents the total number of subspaces. Taking an input vector \(\mathbf{x}_{n}\) = {20.0, 20.1, 20.2, 20.3, 20.4, 20.5, 20.6, 20.7, 20.8} as an instance, \(v_{n}(j)=\{2^{0},2^{1},2^{2},0,0,0,0,0,2^{8}\}\) for each \(j\), \(0\leq j\leq 8\), and \(k=263\) can be obtained by Equations (9) and (10) respectively. Thus, the input vector \(\mathbf{x}_{n}\) is assigned to the subspace \(bss_{k}\) with \(k=263\).  
By means of Equations (9) and (10), it is noted that this algorithm requires a small number of basic operations, i.e., comparison, shift and addition, such that an input vector is assigned to a subspace in a highly efficient manner.
## 3 Application to the MAGIC Mission
### Recommended Orbits
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**ID** & \multicolumn{2}{c}{**Sats 1 (IP)**} & \multicolumn{2}{c}{**Sats 2 (PP)**} & \multicolumn{2}{c}{\(h_{l,1}\)**(-)**} & \(h_{l,2}\)**(-)** & \(\Delta\lambda_{shift,1}\)**(deg)** & \(\Delta\lambda_{shift,2}\)**(deg)** & **Subcycle (days)** \\ \hline  & **Alt.** & **Incl.** & **Alt.** & **Incl.** & & & & & & \\  & **(km)** & **(deg)** & **(km)** & **(deg)** & & & & & & \\ \hline
3d\_M & 409 & 70 & 440 & 89 & 1.368 & 1.383 & 2.308 & 2.384 & 2, 3, 8, 11, 30 \\
3d\_H & 432 & 70 & 463 & 89 & 1.451 & 1.449 & \(-\)3.076 & \(-\)3.067 & 3,7,31 \\
5d\_Ma & 396 & 65 & 434 & 89 & 1.397 & 1.383 & \(-\)1.499 & \(-\)1.458 & 2, 3, 5, 13, 18, 31 \\
5d\_Mb & 397 & 70 & 425 & 87 & 1.168 & 1.167 & 0.736 & 0.733 & 2, 5, 27, 32 \\
5d\_H & 465 & 75 & 488 & 89 & 1.185 & 1.190 & 0.762 & 0.781 & 4, 5, 29 \\
7d\_M & 389 & 70 & 417 & 87 & 1.238 & 1.253 & 0.743 & 0.786 & 2,7,30 \\
7d\_H & 432 & 70 & 463 & 89 & 1.218 & 1.226 & 0.672 & 0.692 & 3, 7, 31 \\ \hline \end{tabular}
\end{table} 
Table 3: Candidate orbit sets for inclined and polar pairs recommended for further investigation. The ID shows the number of subcycle days for which the set is optimized as a first step and additional information about the altitudes: mid (M) and high (H). Figure 8: Altitude sensitivity for scenario 5d_Ma. Homogeneity (**left**) and longitude shift (**right**) variations within \(\pm\)2 km of the selected altitude. The 3-day and 5-day subcycle lines are shown in yellow and green, respectively. The values are estimated at the scenarios' altitudes, and the ones between brackets are for subcycles with \(2<h_{l}<3\). The consequences of the altitude variation are given to provide relevant information for the system design and further scientific studies. The orbit inclination is a less strict parameter and nearly does not change over the mission lifetime. However, if a change in inclination would be required, it is important to know that inclination changes of \(\pm 1^{\circ}\) for subcycles of 3, 5 and 7 days can modify the ground track homogeneity with an average 0.07, 0.15 and 0.26 deg\({}^{-1}\), respectively. This further confirms the higher stability of 3-day solutions and the low influence of the inclination changes on the homogeneity also for longer subcycles.
## 3 Results
### Dynamic Analysis
#### 3.3.3 Accelerations  
The results obtained from the dynamic analysis also included the accelerations over a considered time period. The relationship between the corrugation amplitude and the maximum acceleration of the element over a given period of time for the considered thicknesses of a steel sheet is shown in Figure 17.  
The analysis of the dynamic acceleration over time for plates of various thicknesses and corrugations provides several important observations. Firstly, it is noted that, for thicker plates, the differences in acceleration due to various corrugations become less pronounced. This suggests that an increased plate thickness diminishes the impact of corrugation on dynamic acceleration, making these differences less noticeable.  
Furthermore, the graphs indicate that higher natural vibration frequencies result in a greater density of oscillations on the acceleration plots. This means that, as the natural frequency of the corrugated plates increases, the frequency of the acceleration waveforms becomes more tightly packed, reflecting the more rapid changes in acceleration experienced by the plates.  
A significant finding from the analysis is that, compared to deflection and velocity, the effect of corrugation on acceleration is the least pronounced. While corrugation substantially influences deflections and velocities, its impact on accelerations is relatively minimal. This suggests that, while corrugation enhances overall structural performance, its benefits are more pronounced in terms of reducing deflections and velocities rather than accelerations.  
Figure 16: The relationship between the corrugation amplitude and the maximum velocity of the element over a given period of time for the chosen thickness of a steel sheet: (**a**) \(t\) = 3.0 mm; (**b**) \(t\) = 4.0 mm; (**c**) \(t\) = 5.0 mm; and (**d**) \(t\) = 6.0 mm.
## 2 Materials and Methods
### Model Parameter Obtaining Process  
Due to the small variability in the time constant parameters of the membrane and delays in the dendritic tree (\(a\) and \(b\), [10]), it was assumed that the model can feature changes in eight parameters, i.e., \(A\), \(B\), \(C\), \(e_{0}\), \(v_{0}\), \(r\), and \(p\)'s lower limit and range. These parameters mostly affect the form of the signal. Due to the relatively high number of arguments in the minimized function, the genetic algorithm (GA) was used for the minimization process. Along with other heuristics, it is often used for parameter estimation [27].  
Preliminary study was done comparing GA to different common heuristics, i.e., simulated annealing (SA), particle swarm (PS), and surrogate optimization (SO). In the task of J-R model parameter obtaining, the GA and the PS proved to be more accurate than the rest of the compared algorithms. Despite the longest time to compute, genetic algorithm was chosen because of its lowest value of found optimum and vast tuning capabilities. Specific values of the computation time and the found optimum are presented in Table 1. Note, that both, time and optimal value, were normalized to the highest value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Time** & **Found Optimum** \\ \hline Genetic algorithm & 1.000 & 0.161 \\ Simulated annealing & 0.440 & 1.000 \\ Particle swarm & 0.978 & 0.189 \\ Surrogate optimization & 0.236 & 0.275 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the preliminary study comparing common heuristics in task of obtaining Jansen Rit (J-R) model parameters. Values of time and optimum are normalized to the highest value.  
Figure 2: Sigmoidal function changing the population’s membrane potential into fluctuation rate.  
Function minimized in this comparison, and in the rest of this paper, is described below.
## 3 Blockchain and Tracking Technologies in the Raw Material Sector
### Considerations on the Value of the Data in a Blockchain Environment
\begin{table}
\begin{tabular}{c c} \hline \hline Typical Use Cases & Taggants are chemical or physical markers that are added to materials, which enables them to be tested for authenticity. In this way, they also mitigate risks for counterfeiting and forgery in products. There are several types of taggants/physical markers, and are generally microscopic particles. They are added to the materials in small quantities and are easy to detect with a “reader” mechanism. Markers can combine visible or concealed security features and have several form factors, such as DNA encoded information, or micro-dust made out of ceramics or diamonds that can withstand harsh processing treatments within the supply chain. Similarly to the unique fingerprints of humans, taggants can go beyond the level of barcodes since they can provide objects with an identity that cannot be tampered with [26]. A multilayer taggant system has been described based on phase change nanoparticles of metals and eutectic alloys, which can be added in matrix materials and printed on objects to form microscale features containing fluorescence and thermal signatures [27]. The fluorescence additive could be used for initial screening. Micro features could add an extra layer of protection, and phase change nanoparticles could be used for high capacity serialization or forensic investigation [27].  
\begin{tabular}{c c} \hline \hline Robustness & Chemical taggants, biological taggants, DNA taggants and isotope ratios may be too sensitive to the environment, may degrade over time, and may only function over a limited period of time [27]. They often may not stand smelting processes of raw materials. However, as mentioned, some markers or taggants, such as micro-dust made out of ceramics or diamonds, can withstand harsh processing treatments. Also, a durable colored polymer sandwich can be capable of surviving detonation events of explosives. The color sequence of the requisite layers can determine the origin and ownership of the materials [26]. \\ \hline Range & Depending on the type of marker or taggant and the detection method, the ranges and performances can vary. \\ \hline Energy Requirements & Besides the energy needed to generate and detect the marker or taggant, there are usually no further energy requirements. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of Markers and Taggants.
## 6 Conclusions  
This paper presented an in-depth analysis and evaluation of SGX-based TEE technologies in IoT and edge intelligence scenarios. Through comprehensive performance testing of various SGX implementations, including those based on SGX SDK and LibOS, we identified key challenges, such as performance degradation and I/O security issues, that arise when applying TEE in resource-constrained and latency-sensitive edge environments. Our experimental results highlight significant performance bottlenecks, particularly in areas like enclave transitions and secure I/O operations. These findings offer critical insights into the limitations of current SGX solutions and provide valuable benchmarks for improving the integration of TEE in IoT scenarios. Additionally, it proposes corresponding performance optimization strategies, offering practical approaches for deploying TEE in IoT and edge intelligence scenarios.  
However, this study has some limitations. Primarily, it focuses on SGX-based TEE implementations and does not include evaluation with real workloads for other kinds of TEE technologies. Second, some security concerns related to TEE usage in edge environments, such as Man-in-the-Middle and Denial of Service attacks, are not covered in this work. Future work will focus on implementing the proposed performance optimizations and I/O security enhancements, while also broadening our experimental scope to cover other TEE technologies. This will provide a more comprehensive understanding of TEE utilization and find potential improvements in IoT-Edge Cloud Continuum.  
Conceptualization, investigation and funding acquisition Y.Z. ; methodology, software, data curation, visualization and draft preparation, Z.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China under Grant No. 2023 YFB4503000.  
**Data Availability Statement:** The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.
## 2 Materials and Methods
### Fabrication of the Simplified Test Cabin
#### 2.2.1 Vibration Design of the Cabin Module  
A modal analysis in ANSYS(r) software, version 2024 R2 determined the operational vibration range of the cabin, considering fixed boundary conditions at the structure's base. The analysis identified that the vibration modes fell within a range of 0 to 35 Hz, which prevents structural resonance phenomena within the operational range. Piezoelectric sensors connected to an SVAN 958A vibration analyzer, SVANTEK, Warsaw, Poland performed experimental measurements to validate the modal analysis. The sensors were placed at the cabin's base and seating area, as illustrated in Figure 5. An unbalanced mass motor installed at the base induced the vibration, which excited the entire system (see Figure 4a). By comparing the simulation results with experimental data, two representative values for vibration frequency were shown: 9.5 associated with a low or ergonomic level, and 12 Hz, corresponding to an elevated level.  
Figure 4: 3D model of the participant interacting with the cabin and a photograph of the actual constructed cabin: (**a**) simplified cabin schematic depicting its principal mechanical components and the laboratory’s global coordinate reference system; (**b**) simplified cabin setup with participant equipped with reflective markers and inertial sensors.  
Figure 3: Dimensions and component layout in the simplified test cabin: all dimensions are in centimeters.