esa-sceva/satcom-chunk-collection huggingface dataset:
Molecules_**2014**, _19_, 881-895; doi:10.3390/molecules19010881  
_molecules_  
**ISSN 1420-3049**  
www.mdpi.com/journal/molecules  
**Volatile Profile, Phytochemicals and Antioxidant Activity of Virgin Olive Oils from Croatian Autochthonous Varieties**  
_Masnjaca_ **and** _Krvavica_ **in Comparison with Italian**  
**Variety _Leccino_**  
**[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3-*,**  
**[PERSON] 1, [PERSON] 1 and [PERSON] 4**  
1 Department of Food Technology, Marko Marulic Polytechnic in Knin, Petra Kresimira IV, 30, Knin 22300, Croatia; E-Mails: [EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (M.G.); [EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (Z.M.)  
2 Department of Life and Environmental Sciences, University of Cagliari, via Ospedale 72, Cagliari 09124, Italy; E-Mail: [EMAIL_ADDRESS]  
3 Department of Organic Chemistry, Faculty of Chemistry and Technology, University of Split, N. Tesle 10/V, Split 21000, Croatia  
4 Department of Pharmacognosy, Wroclaw Medical University, ul. Borowska 211a, Wroclaw 50-556, Poland; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +385-21-329-420; Fax: +385-21-329-461.  
_Received: 14 November 2013; in revised form: 3 January 2014 / Accepted: 8 January 2014 / Published: 14 January 2014_  
**Abstract:** Virgin olive oils (VOOs) obtained from the fruits of Croatian autochthonous varieties _Masnjaca_ and _Krvavica_ were extensively characterized for the first time. Investigated oils were compared with the oil obtained from Italian variety _Leccino_, grown and processed under the same conditions. Headspace volatile profile, tocopherols, chlorophylls, carotenoids and total phenolic content, peroxide value, % acidity, K\({}_{232}\), K\({}_{270}\) as well as antioxidant activity (DPPH) of the oils' hydrophilic fractions (HFs) including their phenolic composition were assessed by means of HS-SPME/GC-MS, HPLC-FL, HPLC-DAD and spectrophotometric methods, respectively. Most of the studied quality parameters varied between the cultivars. The main volatile compounds detected in all tested olive oils were the C6 compounds derived from polyunsaturated fatty acids through the lipoxygenase pathway. _Krvavica_ oil was characterized by hexanal (8.8%-9.4%). _Leccino_ oil contained the highest percentage of (_E_)-hex-2-enal (73.4%-74.0%), whereas (_Z_)-hex-3-enal (21.9%-25.0%) and (_E_)-hex-2-enal (27.6%-28.9%) dominated in _Masnjaca_ oil. _Leccino_ oil contained the highest amount of tocopherols (312.4 mg/kg), chlorophylls (7.3 mg/kg), carotenoids (4.2 mg/kg) and total phenols (246.6 mg/kg). The HF of _Leccino_ oil showed the highest antioxidant capacity (1.3 mmol TEAC/kg), while the HFs of _Masnjaca_ and _Krvavica_ oils exhibited the activity of 0.5 mmol TEAC/kg.  
keywords:_Masnjaca_; _Krvavica_; _Leccino_; virgin olive oil; volatile compounds; phenols; antioxidant activity (DPPH)
## 3 Results and Discussion
The algorithm can be applied to other procedures performed at other stages of an investment and construction project or when analyzing construction facilities that have other functions. As a result of the first phase of the study, the authors substantiated 29 factors, affecting the duration of procedures performed by the project manager at the stages of pre-project preparation and design development. The experts have picked these factors from the original list made on the basis of an analysis of the research literature and current legislation. In the course of the questionnaire survey, the experts could formulate their own factors, influencing the duration of the stages. However, the experts picked the factors from the list. Alternatively, the experts formulated the factors which meanings are similar to those available in the initial list, although they were worded in a different way. The responses were processed using the method of least squares. The sequence is provided in Section 2 of this article. The substantiated factors are systematized into four groups:  
* Organizational and managerial factors (\(\delta^{\text{oy}}\));
* Procedural and executive factors (\(\delta^{\text{IIn}}\));
* Economic factors (\(\delta^{\text{s}}\));
* Design factors (\(\delta^{\text{II}}\)). At the second stage of the study, as a result of ranking, the values of the factors' affect the duration of the procedures at the stages of pre-project preparation and design development for the facilities to be constructed (Table 3). The duration increment \(Y\) (\(\Delta T\)) under the impact of the group of factors was determined with due account to the established dependencies (Equations (6) and (7)):  
\[Y=A0+A1\cdot\delta^{\text{oy}}+A2\cdot\delta^{\text{IIn}}+A3\cdot\delta^{ \text{s}}+A4\cdot\delta^{\text{II}} \tag{6}\]  
\[\Delta T=16+0.043\delta^{\text{oy}}+0.013\delta^{\text{IIn}}+0.042\delta^{ \text{s}}+0.027\delta^{\text{II}} \tag{7}\]  
Provided \(\Delta T\geq 0\),  
where:  
\(\delta^{\text{yo}}\), \(\delta^{\text{IIn}}\), \(\delta^{\text{s}}\) and \(\delta^{\text{II}}\)--point assessment of the impact of the group factors for the object in a given range of values from the minimum to maximum (dimensionless value). Conducted by a specialist who performs the functions of the developer (technical customer) for a private facility:  
\(A0\)--shift value;  
\(A1\) and \(A2\)--multiple regression coefficients;  
\(\Delta T\)--increment of the duration under the influence of factors. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Procedure Cipher** & **Procedures** & **Boundary Intervals of Duration Deviations (Months)** \\ \hline \multirow{3}{*}{P11} & Control (supervision) of the project & \multirow{3}{*}{1–3} \\  & documentation development by the design organization & \\ \cline{1-1} \cl
## 3 Experimental Results  
For performance comparison, all microwave amplifiers in this work were designed at \(f_{0}\) = 2.45 GHz with a target operational BW of 200 MHz. The type of microwave transistor used in this work is GaAs MESFET, specifically AH102A-G from Qorvo, which operates at frequencies from 350 MHz to 3 GHz. The optimum input (\(Z_{\text{IN}}\)) and output (\(Z_{\text{OUT}}\)) impedances of microwave transistors can be obtained by performing source and load impedance analyses based on the provided S2P file using ADS software. The \(S\)-parameters of the transistor are chosen under the bias conditions of \(V_{DS}\) = 9 V, \(I_{DS}\) = 200 mA.  
Figure 8a shows a small signal equivalent model of GaAs MESFET [25]. Additionally, Figure 8b illustrates gain circles plotted at the three frequencies (i.e., \(f_{L}\) = 2.35 GHz; \(f_{0}\) = 2.45 GHz; and \(f_{H}\) = 2.55 GHz). The flat gain performance can be achieved by selecting \(Z_{\text{OUT}}\) at the point where these three gain circles are closest to each other. Therefore, the impedances \(Z_{\text{IN}}\) = \(18-j\overline{\gamma}\)\(\Omega\) and \(Z_{\text{OUT}}\) = \(47+j18\)\(\Omega\) are selected. The source impedance (\(Z_{S}\)) and the load impedance (\(Z_{L}\)) are complex conjugates of \(Z_{\text{IN}}\) and \(Z_{\text{OUT}}\), respectively.  
Figure 9a shows the structure of the conventional microwave amplifier, where the conventional input matching network and conventional output matching network are realized using T-type TL topology. The conventional input matching network is implemented using an open-circuited stub (TL\({}_{1}\)) in series with a transmission line (TL\({}_{2}\)) to match the optimum source impedance \(Z_{S}\). Similarly, the conventional output matching network is realized using a short-circuited stub (TL\({}_{3}\)) in series with TL\({}_{4}\) to match the optimum load impedance \(Z_{L}\). The TL\({}_{3}\) of conventional output matching network is also used as a DC feed line by shorting TL\({}_{3}\) with the bypass capacitor (\(C_{bp}\)). All circuits presented in this paper are implemented using Taconic-TLY PCB with \(\varepsilon_{r}\) = 2.2 and \(h\) = 0.787 mm. For soldering and realization purposes, three TLs with \(Z_{add}\) = 50 \(\Omega\) and \(\theta_{add}\) = 10\({}^{\circ}\) are added to the network. The circuit layout with the physical dimensions of the conventional microwave amplifier is presented in Figure 9b. Figure 9c demonstrates the comparison of \(S\)-parameters obtained from EM simulation by using high-frequency structure simulator (HFSS) software and measurements. The simulation and measurement results are agreed well, and a minimum gain of 14.13 dB and a RL of 20.1 dB are achieved within the desired BW.
## 1 Introduction
RCU, RLU, and MV-RLU are non-blocking synchronization mechanisms that improve the previous mechanisms by improving its scalability and easy-to-use API. We call them RCU-style synchronization mechanisms because the design principle and APIs for the three mechanisms are similar to each other. Although RCU has been utilized and evaluated in Linux and other applications [21; 26; 27; 28; 29], RLU and MV-RLU have not been applied nor tested rigorously by the community. To expand the applicability of RCU-style synchronization mechanisms, the performance of these mechanisms have to be evaluated and analyzed in different environments. As systems with hundreds of cores are being introduced in the market, RCU-style mechanisms have to be rigorously tested in order to exploit them in such systems. In this work, we evaluate and analyze the performance of RCU-style synchronization mechanisms in sv6 variant [30], which is a research operating system designed for manycore machines. To attain the purpose of the paper, we first compare the structure, pros, and cons of RCU-style synchronization mechanisms. Then, we implement RCU, RLU, and MV-RLU on sv6 variant and measure its performance using micro-benchmarks. The result shows that the scalability of the mechanisms are very different from each other. We find that the performance depends on the number of executed threads, write ratio, and type of shared data structure. In most of the experiments, MV-RLU exhibits better performance and scalability than RCU and RLU, but it requires care in reporting and evaluating in some of the platforms. In the case of running seventy threads in MV-RLU, it showed about thirteen times faster performance than RCU. We discuss the performance and limitations in Sections 5 and 6,respectively. The rest of the paper is organized as follows. Section 2 presents related work. Section 3 explains how RCU-style synchronization mechanisms operate, their pros and cons, and describes the APIs. Section 4 describes the implementation of RCU-style mechanisms on sv6 variant. Section 5 evaluates and analyzes the performance of the mechanisms, and Section 6 discusses the result. Finally, Section 7 concludes the paper.
## 5 Results
### Calculation of the Geodetically Based Parameters
#### 5.1.1 Maximum (MaHE) and Minimum (MiHE) Horizontal Extension
Regarding the respective MiHE analysis (Figure 4c), the absolute high-value locations (\(-\)631--350 \(\times\) 10\({}^{-9}\)) almost correspond to the MaHE ones; however, a significant difference between the magnitude of the MiHE and MaHE values is documented. Moreover, the lack of the high values of MiHE at the northwestern part of the study area confirms the pure, dip-slip, normal faulting regime of this part, while, on the other hand, the significant MiHE high values along the eastern Strymon basin boundary indicates increased shear, related to the oblique-slip normal faulting. Moreover, the similar MaHE and MiHE values at the southern part of the study area, which are approaching the North Aegean Sea, indicate the potential occurrence of strike-slip tectonic structures, probably related to the major tectonically active strike-slip structures located within the North Aegean Trough (NAT).
## 6 A Damage Quantification Based on SAW
### Damage Quantification Based on Longitudinal Waves  
From numerical results, the speed of sound in non-piezo stiffened and piezo stiffened specimens were determined to be 7696 m/s and 7873 m/s, respectively. Referring to Figure 6a, the experimental speed of the longitudinal wave in reference state was determined to be 7692 m/s. Similarly, the experimental speed of the longitudinal wave in damage state was determined to be 6666 m/s. The normalized frequency domain signal in the reference state shows a distinct peak amplitude at 107 MHz at unit amplitude (Figure 7a). For the damaged state, the peak amplitude is reduced to 0.38 and the frequency is shifted to 109 MHz.  
A similar procedure is adapted to quantify the nonlocal intrinsic length scale parameter using longitudinal waves. Due to changes in TOF, the speed of longitudinal wave velocity in reference state and damage state is 7692 m/s and 6666 m/s, respectively. In Figure 12, a point \"A\" is marked having abscissa and ordinate as 107 MHz and 7692 m/s respectively. The point \"A\" overlay on the nonlocal line has a nonlocal damage parameter of 0.4. Similarly, in Figure 12, a point \"B\" is marked having an abscissa and ordinate as 109 MHz and 6666 m/s. The point \"B\" lies on the nonlocal damage parameter line between 0.9 and 1.0. The nonlocal line index was interpolated and the has an index of 0.96. The final value of nonlocal intrinsic length scale parameter is \(\Delta\varepsilon=0.56\).  
The results are summarized in Table 2 showing numerical and experimental speed of sound for piezo and non-piezo stiffening. The Table 2 also summarized information of shift in frequency content for reference state and damage state which are used to quantify the nonlocal damage parameter.
# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 4 Experimental Results
### Reconstruction Quality Evaluation  
To further assess the efficacy of the proposed scheme in point cloud attribute reconstruction quality following lossy compression, we conduct subjective quality evaluations on decoded point clouds with distorted attributes generated by our scheme and comparative platforms. Specifically, we select datasets _Longdress_vox10_1300_ and _Thaidancer_viewdep_vox12_, characterized by different attribute characteristics and rich textures, for this quality assessment.  
In Figure 5, we present a subjective quality comparison of decoded point clouds between our scheme and state-of-the-art methods at comparable attribute bitrates. To facilitate a detailed examination, we zoom in on specific regions of interest marked by red boxes. Each sub-figure in Figure 5 showcases the reconstructed attribute details within the red box, alongside the associated attribute bitrate (bpp) and combined YUV-PSNR (dB). Notably, local details within white circles allow for a clear depiction of quality differences among decoded point clouds. Examining the contents within the yellow circles, we observe that the proposed method better preserves attribute details, particularly in areas characterized  
Figure 4: R-D performance comparison of the proposed method, G-PCC PLT, G-PCC RAHT, and BAAC on point cloud attribute lossy compression.  
by complex textures and rich edges. Moreover, our scheme achieves the highest PSNR values at the lowest bitrate expenses compared to comparative platforms. Consequently, the results demonstrate that the proposed scheme outperforms other platforms in point cloud attribute lossy compression, yielding superior subjective quality.
## 3 Image Acquisition and Distortion Correction  
### Image Acquisition Scheme  
The image acquisition setup is depicted in Figure 6a. The hole part should be placed on a workbench that can move along the X, Y and Z direction. The endoscope probe should be fixed downward at the front end of the workbench cantilever. The diameter of the hole is 20 mm, and its depth is 80 mm. The calibration paper close to the inner wall of the hole serves as the shooting target. This paper has transparent standard paper with 9 \(\times\) 12 black squares measuring 1.5 mm \(\times\) 1.5 mm (as shown in Figure 6b). To ensure that the main optical axis of the lens always coincides with the hole axis, a self-made endoscopic probe sleeve with an external diameter of 19.5 mm is used to fix its position, as shown in Figure 6c.  
The STORZ SP100 endoscope from Germany was used for photography, with an outer diameter of 6 mm and a V0507 WAHMV lens model providing an 80\({}^{\circ}\) shooting angle and depth of field ranging from 7-80 mm. We determined the lens focal length at 41.6 mm by taking the ratio of foreground depth to back depth of field as 0.9 due to the small dispersion circle compared to shooting distance. Additionally, we found that there is a distance of 1.5 mm between the lens center and point light source emitting less than 5 nm luminous power at precisely 639 nm wavelength.  
When acquiring the image, position the workpiece vertically on the movable platform and align the thread entry point with the aperture's rightmost end. Adjust both \(X\) and \(Y\) axes to align the endoscope probe sleeve with the workpiece aperture, then adjust the \(Z\) axis for smooth insertion of the probe sleeve into the threaded hole while using a magnet to secure the workpiece. Capture the initial image when the threaded mouth reaches the frame edge by the lowering platform; gradually raise it and capture subsequent images at each height adjustment until selecting an ideal image exhibiting uniform illumination  
Figure 5: Principles of Circumferential Correction of Endoscopic Images.  
Figure 6: Image Acquisition Scheme and Equipment. (**a**) experimental scheme, (**b**) calibration plate, (**c**) endoscopic probe sleeve.  
and distinct brightness variation while recording its corresponding platform elevation. The shooting results are shown in Figure 7a.
## 3 Methodology
### Computer Vision Module
#### 3.4.2 Tracking Algorithm
Compared to existing tracking systems, which often rely on fixed zoom levels or less dynamic orientation adjustments, this algorithm provides a significant improvement in responsiveness and accuracy. Its reliance on real-time data and camera specifications allow it to operate effectively without requiring extensive computational resources, making it a robust and efficient solution for UAV tracking. The key innovation is the dynamic zoom adjustment, which continuously analyzes the UAV's size in the frame to balance visibility and coverage, preventing the UAV from becoming too small to detect or moving out of view. Additionally, the algorithm calculates the camera's FoV in real time based on the zoom level, leveraging the camera's optical specifications to fine-tune its orientation with precision. Lastly, to ensure smooth and accurate tracking, continuous recalibration is carried out based on trigonometric angular correction.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 2 Related Work
Lightweight, strongly consistent, low-latency consensus protocols remain necessary. Additionally, traditional blockchain's full ledger replication imposes heavy storage burdens on high-density data environments like ATM. Current mainstream solutions fall into two categories: off-chain and on-chain cooperative storage. Off-chain solutions, such as LightChain [16], IPFS [17], and LayerChain [18], store raw data externally while maintaining hash indices on-chain, reducing redundancy but introducing trust and availability challenges. On-chain cooperative storage reduces individual node loads through ledger sharding, such as BFT-Store [19] using erasure coding and VBG [20] using virtual block groups, but incurs high coordination costs in dynamic node environments. Light client solutions, such as FlyClient [21] based on Merkle Mountain Ranges, improve storage efficiency but heavily rely on full nodes and have limited autonomous validation capabilities. In summary, ATM data sharing faces challenges such as unstable connectivity, high real-time demands, and massive data volumes. Neither purely on-chain nor off-chain storage can fully meet these needs. Constructing a multi-layer hybrid storage architecture that dynamically processes data based on priority is a critical path to enhancing system adaptability and scalability.
# Behavior of Circular Hollow Steel-Reinforced Concrete Columns under Axial Compression
## Appendix A Method 1
### Method 2
#### a.2.3 Long Columns (\(H/D>12\))  
\[N_{\text{uc2}}^{\text{2}}=N_{\text{uc2}}^{\text{rc2}}+N_{\text{uc2}}^{\text{ s2}}=N_{\text{uc2}}^{\text{c2}}+N_{\text{uc2}}^{\text{l2}}+N_{\text{uc2}}^{\text{s2}} \tag{17}\]\[N_{\text{uc2}}^{\text{l2}}=\begin{cases} \begin{array}{cc}\Sigma A_{\text{li}} \times F_{\text{I}}&\left(\lambda_{\text{l}}{}^{\prime}<0.3\right)\\ \left[1-0.545\big{(}\lambda_{\text{l}}{}^{\prime}-0.3\big{)}\right]\cdot N_{ \text{yl}}&\left(0.3<\lambda_{\text{l}}{}^{\prime}<1.3\right)\\ N_{\text{yl}}{}^{\prime}\Big{[}1.3\times\left(\lambda_{\text{l}}{}^{\prime} \right)^{2}\Big{]}=N_{\text{cr}}/1.3&\left(\lambda_{\text{l}}{}^{\prime}>1.3 \right)\end{array} \\ N_{\text{uc2}}^{\text{s2}}=\begin{cases} \begin{array}{cc}A_{\text{s}}\times F _{\text{s}}&\left(\lambda_{\text{s}}{}^{\prime}<0.3\right)\\ \left[1-0.545\big{(}\lambda_{\text{s}}{}^{\prime}-0.3\big{)}\right]\cdot N_{ \text{ys}}&\left(0.3<\lambda_{\text{s}}{}^{\prime}<1.3\right)\\ N_{\text{ys}}\Big{/}\left[1.3\times\left(\lambda_{\text{s}}{}^{\prime}\right)^{2 }\right]=N_{\text{cr}}/1.3&\left(\lambda_{\text{s}}{}^{\prime}>1.3\right) \end{array} \\ \lambda_{\text{l}}=\frac{H}{\sqrt{\frac{I_{\text{l}}}{A_{\text{li}}}}}\text{, } \lambda_{\text{s}}=\frac{H}{\sqrt{\frac{I_{\text{s}}}{A_{\text{s}}}}}\text{, } \lambda_{\text{c}}=\frac{H}{\sqrt{\frac{I_{\text{c}}}{A_{\text{c}}}}} \tag{101}\]  
\[\lambda_{\text{l}}{}^{\prime}=\frac{\lambda_{\text{l}}}{\pi}\cdot\sqrt{\frac{ N_{\text{l}}^{2}}{E_{\text{s}}}}=\sqrt{\frac{N_{\text{l}}^{2}}{N_{\text{cr}}^{ \text{l}}}}\text{, }\lambda_{\text{s}}{}^{\prime}=\frac{\lambda_{\text{s}}}{\pi}\cdot\sqrt{\frac {N_{\text{s}}^{2}}{E_{\text{s}}}}=\sqrt{\frac{N_{\text{s}}^{2}}{N_{\text{cr}} ^{\text{s}}}}\text{, }\lambda_{\text{c}}{}^{\prime}=\frac{\lambda_{\text{c}}}{\pi}\cdot\sqrt{\varepsilon _{\text{cu}}} \tag{102}\]  
\[N_{\text{cr}}^{\text{l}}=\pi^{2}\cdot E_{\text{I}}I_{\text{l}}/(\mu\cdot H)^{ 2}\text{, }N_{\text{cr}}^{\text{s}}=\pi^{2}\cdot E_{\text{s}}I_{\text{s}}/(\mu\cdot H)^ {2} \tag{103}\]  
\[N_{\text{uc2}}^{\text{c2}}=A_{\text{c}}\cdot\sigma_{\text{c}} \tag{104}\]  
\[\sigma_{\text{c}}= \begin{cases}\frac{2}{1+\sqrt{\left(\lambda_{\text{c}}{}^{ \prime}\right)^{4}_{\text{l}}}+1}\times\sigma_{\text{B}}&\lambda_{\text{c}}{}^{ \prime}<1.0\\ 0.83\cdot\text{esp}\left[\text{C}_{\text{c}}\cdot\left(1-\lambda_{\text{c}}{} ^{\prime}\right)\right]\cdot\sigma_{\text{B}}&\lambda_{\text{c}}{}^{\prime}>1.0\end{cases}