esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls
## 4 Approach
### Determining Adaptiveness of Switchless Calls  
Based on our metric to determine switchless efficiency, we perform profiling to establish a proper optimization strategy on our target application, SGX-Box. Note that we adopt switchless calls to ECALLs used by SGX-Box only due to its internal implementation. SGX-Box threads rarely call OCALLs because the mOS framework [35] part in the untrusted region handles the packet I/O and packet reassembly procedures by design. Such design makes an enclave of SGX-Box threads not invoking system calls (and OCALLs) to handle I/O operations (e.g., read and write). Instead, we measure the number of enclave transitions and the completion time of seven ECALLs used by SGX-Box. Then, we calculate the switchless efficiency and compare it with the threshold in (4). For estimation, we use Quad-core Intel i7-6700 3.4 GHz CPU machines with Linux 3.19.0 and Linux SGX SDK version 2.2 that supports switchless call functionality [23]. We use four worker threads for ECALLs and enable hyperthreading. Finally, we configure max_retries as 1000, and the enclave transition latency is 2.67 us in our environment.  
Based on our profiling, we adaptively utilize a switchless call for the ECALL and make other ECALLs use the traditional ECALL interface for optimization. Figure 4 shows the calculated switchless efficiency of ECALLs defined in SGX-Box. As the figure shows, a single ECALL, \(\text{ecall\_reproduce\_ssl\_context}\), satisfies the condition among seven ECALLs under our evaluation environment. Note that \(\text{ecall\_reproduce\_ssl\_context}\) reproduces SSL context within an enclave when a packet arrives in the untrusted region. Therefore, it is less effective when applying switchless calls to other ECALLs, except for \(\text{ecall\_reproduce\_ssl\_context}\).  
Figure 4: Switchless efficiency of each ECALL used by SGX-Box.
###### Abstract  
This research aimed to qualitatively analyze the composition of selected extra virgin olive oils from organic and conventional farming available on the Polish market. Determination of the fatty acid profile, determination of the sterol content, and measurement of the acidic and peroxide numbers of the olive oils were performed. Moreover, the content of phenolic acids was determined using the HPLC method, and the antioxidant activity was examined using, e.g., the FRAP and DPPH methods, to establish the differences between the analyzed olive oil samples. The most abundant fatty acids in the olive oil were monounsaturated fatty acids (65.60-78.50%) with oleic acid (59.54-75.36%), saturated fatty acids (14.60-20.49%) with palmitic acid (10.93-16.45%), and polyunsaturated fatty acids (7.80-15.04%) with linoleic acid (6.24-14.34%). The phytosterol fraction consisted of \(\beta\)-sitosterol, campesterol, stigmasterol, clerosterol, \(\Delta\)5-avenasterol, cycloartenol, 24-methylenedycloartenol, and citrostadienol, and its concentration ranged from 775.23 to 1115.70 mg/kg of the olive oils. The conventional method of olive cultivation influenced campesterol concentration in the extra virgin olive oils, and the concentration was higher in such products than in organic. Tests conducted on the reduction of iron ions (FRAP method) showed that the olive oil obtained from conventional farming (except for one product) had slightly higher antioxidant activity (0.23-0.30 \(\upmu\)mol TE/g of olive oil) than that obtained from organic farming (0.19-0.26 \(\upmu\)mol TE/g of olive oil). The total content of phenolic acids (oleuropein, hydroxytyrosol, and tyrosol) in the extra virgin olive oils ranged from 133.20 to 226.82 mg/kg.  
olive oil; organic; conventional farming; fatty acid profile; phenolic compounds; antioxidant activity; GC-MS; HPLC
# Equivalent Modeling and Verification of a High-Steepness and Lightweight Elliptical Aluminum Mirror
## 4 Optomechanical Design and Optimization of Mirror  
### Initial Structure Selection  
According to the equivalent method in Section 3, the thickness of the mirror can be selected to meet the bending stiffness requirements. In order to realize the high lightweight design of the mirror, it is necessary to select the support structure and optimize the lightweight structure.  
Commonly used mirror support methods include center support, back support, peripheral support, etc. For high-steepness and very lightweight mirrors, the use of peripheral support will cause gravity deformation and collapse along the optical axis. Center support and back support are two commonly used support modes for small- and medium-sized mirrors. In order to combine the advantages of the rapid manufacturing and assembly of metal mirrors, the center supporting structure, which is easy to design, is selected as the preferred support scheme of the mirrors.  
In order to reduce the mounting stress caused by the support structure connecting to the mirror, the mounting flange is generally isolated from the mirror, as shown in Figure 6. The diameter of the mirror is 410 mm \(\times\) 310 mm. According to the above equivalent method, the elliptic mirror is equivalent to a circular plate. Formula (5) is used to calculate  
Figure 5: Typical lightweight unit: (**a**) triangular cell; (**b**) square cell; (**c**) hexagonal cell.  
the mirror diameter-thickness ratio using the circular plate, and \(\delta\) is 0.032 \(\upmu\)m (1/20 \(\lambda\), \(\lambda\)= 632.8 nm). According to Formula (1), the equivalent diameter-thickness ratio \(\delta\) = 8.77, the equivalent mirror thickness = 41.25 mm, and \(T_{\text{min}}\) = 28.9 mm. The weight of the solid model of the elliptic mirror designed by UG software is 12.5 kg, and the weight of the equivalent circular mirror is 12.6 kg, which approximately equates to the equivalent of \"equal volume\".
## 3 Results and Discussion
### Superconductors' Properties  
The SC critical temperatures of the bottom Nb electrode, deposited on silicon, and spin-coated with MEH-PPV on top was \(\sim\)8.2 K, see Figure 3a. The transition width (from 90% to 10% of normal resistance) was 0.05 K, and the normal resistance was 2.7 \(\Omega\)/Sq (ohm-square) near TC. Thus, the spin-coating does not affect the bulk Nb properties, but this measurement does not rule out possible interface degradation. In Figure 3b, we compare the resistance vs. temperature measurements of 15 nm NbN films deposited on silicon or on top of the MEH-PPV(40 nm)/Al(5 nm) layers. The TC slightly decreased, from 10.01 K to 9.90 K, when depositing on the organic. These are lower than the reported TC\(\sim\)17 K of NbN, but reasonable considering the imposed deposition conditions, i.e., room temperature deposition, low rate, and thickness. The transition width increased from 0.88 K for NbN on Si to 1.06 K when deposited on MEH-PPV. For temperatures just above TC, the resistance for NbN on MEH-PPV/Al is 20% higher than on Si. NbN acts like a \"bad metal\", in that its resistivity increases with decreasing temperature (in our films from room temperature resistance of \(\sim\)115 \(\Omega\)/Sq, to \(\sim\)60 \(\Omega\)/Sq for temperatures just above TC). The critical currents of the NbN (15 nm) top electrodes, measured for several temperatures below TC, plotted in Figure 3c, show a linear increase with decreasing temperature. The critical current density is approximately an order of magnitude lower than observed for clean NbN. Finally, the optical transmission spectrum was measured for an Al(5 nm)/NbN(15 nm) bilayer, deposited on a glass substrate simultaneously with the MEH-PPV/Al/NbN sample described above, see Figure 3d. More than 27% of incident light is transmitted through the top Al/NbN electrode.  
Figure 3: Characterization of superconducting layers. (**a**) Resistance vs. temperature of Nb film (50 nm) spin-coated with MEH-PPV. (**b**) Resistance vs. temperature of NbN film (15 nm) deposited on Si substrate (red) and MEH-PPV/Al (blue). (**c**) Voltage vs. current across the NbN layer, showing critical current at different temperatures. (**d**) Optical transmission through an Al(5 nm)/NbN(15 nm) bilayer deposited on a glass substrate.  
Figure 2: HR-TEM and EDS profile. (**a**) TEM and (**b**) EDS of Nb for a device without the Al layer. (**c**) TEM and (**d**) EDS of Nb and Al for a device with 5 nm Al protective layer.
## 3 Method  
This section describes the most important steps in the TOA flux retrieval's methodology. A detailed Algorithm Theoretical Basis Document (2017) provides additional clarifications on the algorithms, technical details and example figures. Figure 4 shows a breakdown of the different processing steps. Part 1 (Section 3.1) performs the retrieval of Level-2 instantaneous TOA albedo. Part 2 (Section 3.2) is responsible for the spatial aggregation from irregular GAC orbit into regular lat-lon grid boxes. Finally, Part 3 (Section 3.3) temporally integrates the instantaneous observations into Level-3 daily mean fluxes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \multicolumn{6}{c}{**Clear-Sky**} & \multicolumn{6}{c}{**Overcast**} \\ \hline
**NTP Surface** & \(b_{0}\) & \(b_{1}\) & \(b_{2}\) & \(b_{3}\) & \(b_{4}\) & \(b_{0}\) & \(b_{1}\) & \(b_{2}\) & \(b_{3}\) & \(b_{4}\) \\
**Type** & & & & & & & & & & & \\ \hline Ocean & 1.811 & 1.148 & \(-\)0.523 & \(-\)0.043 & 0.390 & 4.013 & 0.313 & 0.447 & 0.709 & 1.286 \\ Forests & 1.471 & 0.480 & 0.377 & 1.358 & 1.211 & 3.622 & 0.366 & 0.395 & 0.905 & 1.542 \\ Savannas & 1.421 & 0.463 & 0.382 & 2.696 & 0.769 & 3.328 & 0.403 & 0.355 & 4.127 & 1.059 \\ Grass/crop & 2.575 & 0.443 & 0.339 & 1.101 & 1.430 & 3.704 & 0.393 & 0.368 & 1.093 & 1.893 \\ Dark deserts & 2.384 & 0.367 & 0.376 & 1.279 & 0.724 & 3.031 & 0.291 & 0.473 & 1.377 & 1.597 \\ Bright deserts & 3.225 & 0.365 & 0.335 & 1.467 & 1.291 & 1.305 & 0.462 & 0.317 & 2.648 & 1.419 \\ Perm. snow/ice & 7.511 & 0.202 & 0.479 & \(-\)0.507 & 1.612 & 15.075 & 0.164 & 0.463 & \(-\)1.231 & 6.254 \\ Fresh snow & 1.598 & 0.310 & 0.412 & 1.627 & 3.213 & 2.487 & 0.334 & 0.430 & 1.223 & 3.272 \\ Sea ice 100\% & 7.214 & 0.212 & 0.460 & \(-\)0.163 & 4.139 & 8.590 & 0.270 & 0.423 & 0.029 & 4.896 \\ Sea ice 95–99\% & 8.486 & 0.220 & 0.437 & \(-\)0.424 & 3.847 & 9.553 & 0.238 & 0.449 & \(-\)0.266 & 4.424 \\ Sea ice 90–95\% & 6.376 & 0.253 & 0.430 & \(-\)0.209 & 3.320 & 10.353 & 0.214 & 0.466 & \(-\)0.466 & 4.176 \\ Sea ice 80–90\% & 3.619 & 0.345 & 0.367 & 0.191 & 2.635 & 9.456 & 0.207 & 0.484 & \(-\)0.218 & 3.879 \\ Sea ice 60–80\% & 2.922 & 0.410 & 0.296 & 0.562 & 2.734 & 6.188 & 0.275 & 0.455 & 0.455 & 3.148 \\ Sea ice 10–60\% & 2.540 & 0.458 & 0.227 & 0.492 & 4.172 & 4.259 & 0.290 & 0.472 & 0.444 & 2.518 \\ Sea ice 0–10\% & 1.868 & 0.806 & \(-\)0.121 & 0.382 & 2.267 & 4.195 & 0.326 & 0.436 & 0.392 & 2.839 \\ \hline \end{tabular} \(b_{0}\), \(b_{1}\), \(b_{2}\), \(b_{3}\), \(b_{4}\): coefficients in Equation (3). Similar to the reflectance, these coefficients are expressed as percentage, with a range of 0–100.  
\end{table}
Table 3: NTB (Narrowband-to-Broadband) regression coefficients to be used in Equation (3), generated from all matched NTB pairs (update of Table 3 in Akkermans and Clerbaux (2017)).
## 3 Results and Discussion
### Wound Assay  
The chemotherapy failure and the difficulties in glioma treatment are also caused by the cell's ability to infiltrate adjacent healthy tissues [28]. [PERSON] et al. [29], studied the anti-invasive activities of four citrus flavonoids, demonstrating that tangeretin and chokeberry extracts can reduce the migration and invasiveness of glioma cells. In order to propose another integrative treatment for glioma, after the previous results demonstrating the absence of _C. australasia_ cytotoxicity against U87 and LN18 cancer cells, we have focused attention on the use of peel extract as anti-invasive phytocomplex for glioma management. For this reason, wound-healing experiments were conducted to evaluate the effects of 100, 200, and 400 ug/mL of citrus peel extract on the migration ability of U87 and LN18 cells. Figures 5 and 6 illustrate the wound healing of U87 and LN18 cells, respectively, treated with _C. australasia_ peel extract compared to their controls. The results indicate that as the concentration of peel extract increases, the migration ability of U87 cells decreases (85% control vs. 50% at 400 ug/mL), and this effect was even more pronounced in resistant LN18 cells (93% control vs. 15% at 400 ug/mL).  
Figure 4: Cell cycle analysis. Percentages of U87 (**A**) and LN18 (**B**) cells in the different phases of the cell cycle after treatment with 100, 200, and 400 ug/mL of peel extract at 48 and 72 h by DNA flow cytometry analysis using [PERSON] software for analysis v. 2.2 (Beckman Coulter). Values are the means \(\pm\) SD of three individual measurements.  
Figure 5: (**A**) Representative wound-healing profiles in U87 cells after _C. australasia_ peel extract treatments (Control, 100, 200, and 400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, respectively). (**B**) Graphical profile of the migration ability of U87 cells. All experiments were conducted in triplicate and the results are expressed as mean \(\pm\) SD values. According to GraphPad Prism10, unpaired _t_-test, *_p_-value \(<\) 0.05, **_p_-value \(<\) 0.01.  
Figure 6: (**A**) Representative wound-healing profiles in LN18 cells after peel extract treatments (Control, 100, 200, and 400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, respectively). (**B**) Graphical profile of the migration ability of LN18 cells. All experiments were conducted in triplicate and the results are expressed as mean \(\pm\) SD values. According to GraphPad Prism10, unpaired _t_-test, *_p_-value \(<\) 0.05, **_p_-value \(<\) 0.01, ***_p_-value \(<\) 0.001.
# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
## 2 Materials and Methods
### Data  
#### 2.2.1 Original Soil Moisture  
SMAP is a satellite launched by the National Aeronautics and Space Administration (NASA) in 2015 to observe the global surface soil moisture and soil freezing and thawing status. L-band detectors that combine active and passive modes are widely used for soil moisture monitoring. SMAP provides soil moisture datasets with multiple resolutions. The SMAP data selected in this study include the SPL4 SMGP dataset released by NASA and provided by GEE (for the sake of distinction, the original soil moisture in this study is uniformly referred to as SMAP_10 km) [8]. Taking into account the freeze-thaw conditions of soil moisture, data quality, and weather influences, combined with the actual measurement dates, images from July to October 2019 and 2020 were selected.
## 4 Proposed Scheme
### Enrollment  
Before the user leaves the factory in 5G-enabled vehicular networks, the vehicle needs to execute the enrollment process with the system (TA) through the secure channel as follows.  
* The user sends the vehicle's original identity \(OID_{v_{i}}\) to the TA.
* The TA first tests the legitimacy of \(OID_{v_{i}}\); if it is true, the TA continues with the subsequent steps; otherwise, it stops.
* The TA chooses a short valid period \(SVP_{v_{i}}\), such as 1 January 2022-1 February 2022.
* The TA computes \(A_{v_{i}}=h(s||ev_{i})\) and inter-pseudonym identity \(IPID_{v_{i}}=h(OID_{v_{i}}||A_{v_{i}}||SVP_{v_{i}})\) to the vehicle. At the same time, it puts the tuples into the list of members of the TA.
* The vehicle stores the tuples into TPD.  
Note that sensitive data are stored in TPD, which could be disclosed by a third party when these data are still saved for a long time. Thereby, the adversary can successfully launch a side-channel attack to retrieve the data for impersonating a legal vehicle and sending fake messages.
## 4 Results and Discussion
### Suppression of the Twin-Image Effect
#### 4.1.3 Boundary Condition for the Spatial-Frequency Method  
The experiments on the digital hologram recording for the spatial-frequency method were conducted for model opaque circle particles with a diameter of 89.7 \(\upmu\)m and 133.2 \(\upmu\)m. Figure 11 shows the dependencies PSNR, K, and \(\Delta\)I on the distance between the particle and the recording plane (Figure 11a,c--for a particle with a diameter of 89.7 \(\upmu\)m, Figure 11b,d--for a particle with a diameter of 133.2 \(\upmu\)m), which confirm that the use of this filtering improves the quality of the particle image boundary according to a boundary intensity jump by at least 1.7 times if the same condition as in Section 4.1.2 is satisfied: \(z_{R}\geq\frac{2d^{2}}{\lambda}\).
## 5 Discussion  
### Prediction Model Analysis  
Both the polynomial and nonlinear models are fitted by the nonlinear least-square method. According to the goodness-of-fit results and the images of the prediction model, the second-order polynomial model can better explain the discrete test data. The prediction of other working conditions in the scope of the test is higher, and the vibration exposure can be predicted more accurately. However, due to the influence of the extreme point, the applicability of the model outside the scope of the test is poor. Although some high-order polynomial models can obtain better goodness-of-fit index in the scope of the test,  
Figure 6: Comparison between the test data of vibration exposure using the 600 mm operating rod and the results of the nonlinear prediction model: (**a**) Palm; (**b**) Opisthenar.  
Figure 7: Comparison between the test data of vibration exposure using the 400 mm operating rod and the results of the nonlinear prediction model: (**a**) Palm; (**b**) Opisthenar.  
the Runge phenomenon occurs when the high-order polynomial fitting is used, and it is difficult for high-order complex models to achieve ideal fitting results.  
The quadratic cumulative nonlinear model has the best fitting results in different cumulative terms, and it can also predict the vibration exposure in the scope of the test, but compared with the above second-order polynomial model, its predictability and explanation are slightly worse. The test results show that outside the scope of the test, the nonlinear model has certain applicability for the prediction of vibration exposure. However, it should be noted that when the length of the operating rod and the number of the non-detaching fastening device further increase, the deviation of vibration exposure predicted by the model tends to increase.
## 2 Materials and Methods
### Natural Nanophotonic Structures
#### 2.1.3 Diatoms  
Diatoms are unicellular and eukaryotic algae, which can live in isolation or form colonies either in freshwater or marine environments. Diatoms are hundreds of \(\upmu\)m in size [46], with a distinctive shell wall consisting of two hydrated silica valves, interconnected in a structure called a frustule. Frustules present patterns of regular arrays of holes, the apealae, which are characterized by sub-micrometric dimensions. There are about \(10^{5}\) species of diatoms, whose frustules differ in shape, morphology and size. The dimensions of the frustule pores, typically hundreds of nanometres, provide diatom frustules with peculiar optical properties. In the present work, we considered C. wailesii diatoms, in which organic matter was removed by means of strong acid solutions [47]. The diameter of the valves ranged from 100 to 200 \(\upmu\)m, while the average thickness of the wall was about 1 \(\upmu\)m. Every single valve is formed by two co-joined plates. The external plate comprises a complex hexagonal arrangement of hollow pores with a diameter (\(\cong\)200 nm) below the visible wavelength. On the other side, the internal plate of the valve is characterized by hexagonally-spaced pores with diameters in the range of 50 nm and by a lattice constant of about 50 nm.
## 3 Results
### SIF Correlates Better with Drought Than EVI and NDVI
Furthermore, the areas of EVI, NDVI, and sc_PDSI with a significantly positive correlation in Central and Eastern Yunnan accounted for less than SIF. For a more accurate description, we selected four uniformly distributed sites in Yunnan Province: Lijiang, Lincang, Wenshan, and Zhaotong (distribution can be seen in Figure 1). Then, we extracted their multi-year growth peak (May-September) SIF, NDVI, and EVI data and sc_PDSI data for correlation analysis. The results are shown in Figure 5. It can be seen that all three indices and sc_PDSI were significantly and positively correlated, with R\({}^{2}\) means of 0.68, 0.43 and 0.50, respectively. The correlation between SIF and sc_PDSI was the highest, and the correlation between EVI and sc_PDSI was slightly better than that of NDVI. Thus, the correlation between SIF and the drought index sc_PDSI was found to be significantly better than that of EVI and NDVI, both in all of Yunnan and at individual sites.
## 3 Artificial Intelligence (AI) Techniques in Power Systems
### Evolutionary Methods
#### 3.4.2 Metaheuristic Optimization Technique
In the initial discussion of evolutionary methods, it might have been understated how much expertise is required to effectively apply these algorithms within complex power systems. While evolutionary algorithms (EAs) such as genetic algorithms and differential evolution are conceptually straightforward, involving principles such as mutation, crossover, and selection, their practical application demands significant expertise in optimization techniques and power systems' domain-specific challenges. These challenges include accommodating nonlinear loads, adhering to regulatory constraints, and understanding the physical limitations of grid infrastructure. Moreover, optimizing the parameters of EAs, such as population size, mutation rates, and crossover methods, requires a deep understanding of both the algorithms and the system being optimized. Therefore, while the foundational elements of evolutionary algorithms are simple to grasp, their successful implementation in the context of power systems is a sophisticated process that necessitates substantial domain-specific expertise and meticulous customization. The genetic algorithm is extensively utilized for reactive power optimization due to its ability to search for global optimal solutions through multiple paths, effectively addressing discrete and nonlinear problems. In [59], an enhanced genetic algorithm is introduced to tackle reactive power optimization issues, enhancing optimization outcomes. Using this improved genetic algorithm, standard test systems simulate reactive power optimization and compare results between the standard and enhanced genetic algorithms. Simulation outcomes indicate the proposed algorithm's feasibility and effectiveness, with the enhanced genetic algorithm exhibiting lower active network loss and superior global convergence performance and speed. Similarly, the study in [49] aimed to assess the efficiency of various versions of the differential evolution (DE) method concerning accuracy and speed for adequacy assessment in electrical power systems. Different DE methods were evaluated, including standard DE, composite DE, JDE, chaotic DE, and adaptive DE, employing mutation strategies like DE/rand/1, DE/best/1, DE/rand/2, etc. Independent software developed in C++ facilitated experimentation on systems with three and seven adequacy zones. The most effective bundles, aDE and DE/rand/1, exhibited universality, stability in objective function values, and computational efficiency, highlighting the necessity for precise method selection and parameter tuning in evolutionary algorithms to achieve optimal results in complex power systems. Various metaheuristic algorithms in power system aspects are highlighted in Table 3.