esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Aging Resistance of Graphene Oxide Modified Asphalt
## 3 Results and Discussion
### UV Aging Resistance Performance
#### 3.2.3 Evaluating the Rheological Properties
The G*sin\(\delta\) of SBS MA and GO-modified SBS MA after UV aging are shown in Figure 14. With the increment of the GO content, the G*sin\(\delta\) decreases, which shows that the fatigue property is improved after adding the GO, and the results of the 3% GO content is better than 1% GO content. Table 3 gives the FFT results of SBS MA and the GO-modified SBS MA. Before aging, the FFT order of SBS MA is almost the same as 90 A, the1% GO and the 3% GO modified SBS MA are 0.2 \({}^{\circ}\)C and 1.0 \({}^{\circ}\)C smaller than SBS MA. Therefore, the GO can improve the fatigue cracking resistance performance of the SBS MA before aging. After TFOT, the FFT of the 3% GO-modified SBS MA are lower than the SBS MA; the FFT of the 1% GO-modified SBS MA are higher than SBS MA, but the  
Figure 12: Complex modulus of SBS MA and GO modified SBS MA before and after TFOT. Figure 13: Complex modulus of SBS MA and GO-modified SBS MA after TFOT and nine days UV aging. increment is only 0.7 \({}^{\circ}\)C, which will not greatly reduce the low-temperature performance of a binder. Thus, 3% GO can slightly improve the anti-TFOT aging of SBS MA, while 1% GO slightly decreases the anti-TFOT aging of SBS MA. After UV aging, the order of the TTF is 3% GO-modified SBS MA < 1% GO SBS MA < SBS MA, the1% GO and the 3% GO-modified SBS MA are 0.8 \({}^{\circ}\)C and 1.3 \({}^{\circ}\)C lower than SBS MA. Fatigue cracking resistance performances of the modified SBS MA are better than 90 A after UV aging. Therefore, one can conclude that GO can improve the UV aging resistance performance of SBS MA, and the improvement effect of the 3% GO is better than the 1% GO.
# A Blockchain Network Admission Control Mechanism Using Anonymous Identity-Based Cryptography
## 5 Experiments and Results
### Private Key Generation Time  
CKGC was used to generate private key components for users in a distributed manner. Users can generate up to \(2^{m}-1\) private keys by flexibly aggregating private key components. The efficiency comparison of different schemes for generating user private keys is shown in Table 3.  
Assuming \(k=3,n=3\), the user overhead, non-user overhead, and single private key generation overhead of this mechanism are 20.016, 33.822 and 7.69 ms.  
When \(k=3\), the single private key generation time under different numbers of CKGC is as follows (Figure 4).  
When the number of CKGCs is small, the average time for the mechanism to generate a single private key is greater than other schemes. This is because  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Overhead** \\ \hline \(T_{H}\) & SHA256 hash operation & 0.0031 \\ \(T_{M}\) & Point multiplication operation & 0.621 \\ \(T_{A}\) & Point addition operation & 0.0062 \\ \(T_{P}\) & Bilinear pairing & 2.21 \\ \(T_{e}\) & Exponential operation & 0.427 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational overhead times for each operation.  
Figure 4: Single private key generation time when \(k=3\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **User Cost (ms)** & **Non-User Cost (ms)** & **Total Cost of Generating a Single Private** \\  & & & **Key (ms)** \\ \hline Reference [15] & \(\approx 0\) & \(T_{M}+(2k+1)T_{P}=16.091\) & \(T_{M}+(2k+1)T_{P}=16.091\) \\ Reference [17] & \(2T_{M}=1.242\) & \((2n+4)T_{M}=8.694\) & \((2n+6)T_{M}=9.936\) \\ Reference [20] & \(4T_{M}=2.484\) & \(2T_{M}=1.242\) & \(6T_{M}=3.726\) \\ Our scheme & \((3k+n-1)T_{M}+(n+k-1)(2T_{e}+2T_{P})\) & \((6k+2n-1)T_{M}+(n+k-1)(T_{e}+T_{P})\) & CKGC: \(n(T_{M}+2T_{e}+2T_{P})\) & \((3k+3n-3)(T_{e}+T_{P})\)/(2^{m}-1)\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Efficiency comparison of different schemes.  
private key components is too small to balance the total overhead in the process of generating private keys. However, as the number of CKGCs increases, the average overhead decreases. When the number of CKGCs is 5, the average time is 2.327 ms, and the efficiency of generating private keys is higher than other schemes.  
In addition, the trend of the time required for generating a single private key with \(k\) and \(n\) is calculated in Figure 5.
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 5 Experiment
### Processing Flow  
Within two consecutive PRTs, the system completed noninterrupted phase synchronization between the two satellites and the internal calibration of each satellite. The synchronization transceiver receives the synchronization signal, which is then transmitted to the receiving channel through microwave combination, and further processed digitally to generate synchronization data. The calibration signal processed by the internal calibrator is transmitted to the receiving channel through microwave combination, and further processed digitally to generate internal calibration data. Subsequently, the synchronization performance of the BiSAR system is obtained by analyzing the downlinked internal calibration data and synchronization data, and the processing flow is as follows.  
Step 1: Pulse compression of the internal calibration data. In this step, the matched filter is used for pulse compression. The FFT (Fast Fourier Transform) and IFFT (Inverse Fast Fourier Transform) operation are performed. And, the matched filtering is carried out in the frequency domain.  
Step 2: Extraction of peak position and peak phase. After pulse compression, the signal of internal calibration is compressed. The peak position and peak phase can be accurately obtained through the interpolation.  
Step 3: Deduct the phase introduced by other external components of the radar instrument (such as the echo simulation unit).  
Step 4: Phase compensation based on the synchronization data of the two satellites. The matched filter is performed for compressing the synchronization data. The peak position and peak phase are also extracted.  
Step 5: Introduce the temperature compensation coefficient \(K_{T}\) to obtain the phase \(\varphi_{C}\) that needs to be compensated. The phase is obtained through Formulas (10) and (11).  
Step 6: Perform phase compensation on the auxiliary satellite echo.  
And, the processing flowchart is shown in Figure 14.  
Figure 13: The actual scene of the ground verification system operation.
## 4 Seismic Hazard Analysis  
The annual exceedance probability of the design level PGA suggested in [70] was used for seismic risk analyses. Probabilistic seismic hazard analyses define the seismic sources, formation rates for the size distribution and sources, formation of ground motion, and finally involve probability analysis. Here, all provincial centers in the region were used for analysis. Only the earthquake ground motion level with a probability of exceedance of 10% in 50 years and a reputation period of 475 years was defined in the previous seismic design code. On the other hand, the ground motion levels were defined in four different types in the updated code. Here, these levels were considered for seismic hazard analysis  
Figure 4: Distribution of logMo in Eastern Anatolia. Red (low) and gray (high) counters present the Mo intensity distribution. Small circles represent epicenters. Yellow-colored stars show major earthquakes between 6.8 and 7.9 in the region. The magnitudes of the earthquakes are shown above the yellow-colored stars. by using the updated TBEC-2018 [31] (Table 3). The PGA, PGV, S\({}_{\text{S}}\), and S\({}_{1}\) were obtained separately for different probabilities of exceedance in 50 years from the Turkish Earthquake Hazard Map Interactive Web Application for each province (Tables 4 and 5). The PGA values were in the range of 0.38-1.14 g for 2%, 0.2-0.65 g for 10%, 0.076-0.28 g for 50%, and 0.055-0.18 g for 68% probabilities of exceedance in the region. It is clearly seen that the highest and lowest parameter values were obtained in the Bingol and Kars provinces, respectively. Additionally, the Erzincan and Erzurum provinces were also characterized by high values. The spectral acceleration coefficient (S\({}_{\text{DS}}\)) was calculated on a regional basis in the previous code. However, it was obtained by using site-specific analysis in the updated code. A comparison between these two codes was performed with S\({}_{\text{DS}}\) for the design earthquake (DD-2) with a 10% probability of exceedance per 50 years (Table 6). It was observed that some of the studied provinces produced the same coefficient values through the previous code, but very different values were obtained for the provinces when using the updated code. Therefore, site-specific analysis caused the seismic parameters obtained for each province to differ from each other, as expected. This finding reveals the importance of microzonation once again.
## 1 Introduction
In fact, in the THz range the carbon nanotubes and the graphene nanoribbons allow the propagation of slowly-decaying surface waves (plasmons), with almost frequency-independent wavenumber and phase velocity usually two order of magnitude smaller than the speed of light in vacuum. These properties make graphene-based materials excellent candidates for THz nano-antennas, realized with technologically-affordable lengths, e.g., [17, 20, 21, 22, 23]. Another interesting mechanism to realize THz narrow-band emitters or detectors is based on the excitation of interband transitions, which can be obtained in two ways: by applying an external magnetic field [24] or by controlling the curvature (in CNTs) or edges (in GNRs) [5]. This paper presents a third way to excite resonances in the THz range, based on the tunneling effect between two graphene nanoribbons. A simple detector is proposed in Section 2, where the electrodynamic model used to study its behavior is also derived. The model is an extension of that presented in [25], here updated to include the tunneling effect. Section 3 first shows that the model is able to describe the well-known plasmon resonances. Then, the model is used to study the new kind of THz resonances arising from the coupling between the two graphene layers, via tunneling.
## 4 Conclusions  
With respect to the mitigation of the environmental noise of aerodynamic origin, the present study aims at enabling the experimental characterization of airfoil noise using closed-vein, aerodynamic wind tunnels. The latter facilities are indeed known to restitute better the aerodynamic flow features (and, thus, noise sources), but at the price of incurring important acoustic installation effects, namely, reverberation by the duct's walls. This question is here explored with respect to the specific albeit important problem of the aerodynamic noise generated by the trailing edge (TE) of airfoils, when the latter are sunk into low-to-moderate Reynolds number flows. To this end, an analytical model is derived, which allows predicting the wall-induced reverberation effects that an as-like airfoil TE noise source (2D harmonic dipole) shall be subjected to, when radiating within a closed-vein, hard-wall, wind tunnel. This is exemplified through a representative test case, which is then explored through a parametric study, thereby allowing to illustrate better how these acoustic installation effects may impact the noise measurements that would be performed using flush-mounted microphones located on the vein's walls.  
From a phenomenological perspective, the study highlights how important the reverberation effects by the vein can be, depending on the TE noise source location (i.e., airfoil incidence) and, to a lesser extent, its frequency. In particular, it is shown how these effects may impact very diversely any given flush-mounted microphone, depending on its position. As demonstrated here, this results from the (constructive or destructive) noise interferences inherited from the way the acoustic wavelength (i.e., TE noise frequency) combines with the propagation distance (i.e., TE and microphone respective locations).  
From a methodological viewpoint, the study illustrates how the acoustic installation effects incurred by the vein can be straightforwardly assessed, using the proposed analytical formalism. The latter may thus constitute a useful diagnosis tool for mitigating the experimental biases that would weigh on airfoil TE noise tests to be performed within a closed-vein facility, whether by preventing them a priori (e.g., exploring where to better locate the flush-mounted microphones) or by remedying them a posteriori (e.g., de-biasing the noise measurements accordingly).  
Of course, the simplicity of the proposed analytical formalism comes at the price of some underlying assumptions. First, as of now, the approach solely incorporates those acoustic installation effects which are incurred by the vein's walls (reverberation), excluding those that may be induced by the flow (e.g., convection, refraction). Second, only primary specular reflections are considered here, all secondary ones being neglected. Although these approximations are deemed to be very acceptable in regard to the particular problem that is considered here (airfoil TE noise), relaxing these two constraints constitute the immediate perspective of this work.  
This research received no external funding.  
The author declares no conflict of interest.
## 3 Experimental Results
### Performance Evaluation
#### 3.3.5 Dataset of 2000 Videos  
In Table 5, the 2000-video dataset was used to train, validate, and test the models. It can again be observed that SynthSecureNet outperformed both MobileNetV2 and ResNet50V2. The results in Table 5 were obtained during the testing phase, and the training duration that was observed during the training phase is also included in Table 5.  
The SynthSecureNet architecture achieved an F1 score of 99.24%, a recall of 99.23%, a precision of 99.26%, and an accuracy of 99.22%. On the other hand, ResNet50V2 achieved an F1 score of 94%, while MobileNetV2 achieved an F1 score of 91%, which means that ResNet50V2 is more reliable when compared with MobileNetV2.  
The training duration of the SynthSecureNet model (6 h and 5 min) took longer than that of ResNet50V2 (3 h and 9 min) and MobileNetV2 (2 h and 30 min).  
Figure 6 shows the confusion matrices obtained in the testing process of all three models. SynthSecureNet made 12,454 correct predictions and only 98 wrong predictions, demonstrating its effectiveness in accurately classifying violent and non-violent activities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **F1 Score** & **Recall** & **Precision** & **Accuracy** & **Training Duration** \\ \hline SynthSecureNet & 98.88\% & 98.89\% & 98.87\% & 98.77\% & 1 h 40 min \\ ResNetV2 & 91\% & 90\% & 91\% & 91\% & 30 min \\ MobileNetV2 & 91\% & 91\% & 90\% & 90\% & 1 h 11 min \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison using the 1600-video dataset.  
Figure 5: Confusion matrices for SynthSecureNet, ResNetV2, and MobileNetV2 (1600-video dataset).
# Examination of Intermolecular Forces Influencing Headspace Analysis of Biological Samples
###### Abstract  
Headspace analysis is an effective method for assessing the concentrations of volatile and semi-volatile metabolites in biological samples. In particular, solid-phase microextraction (SPME) is an efficient tool for headspace analyses. Metabolites present in the sample are the typical targets of headspace analysis (rather than the vapor phase concentration) for making measurements on sample donors (e.g., biomarkers of health or disease). Accordingly, intermolecular forces between metabolites and matrix may prevent a complete profile of the metabolite composition in the biosamples from being revealed. To assess sources of such interactions, several volatile compounds in various sample mediums were examined. Small volatile metabolites typical of human biosamples were the volatile compounds selected for this study. Test media included lipid or serum solution to simulate biological samples commonly encouraged in biomarker discovery. Headspace concentrations of volatile analytes were compared using solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS). Observed levels of metabolites in headspace varied among the different media, despite being fortified at equal concentrations in the samples. Overall, lower headspace responses were observed in samples containing proteins or lipids. It was found that these strong intermolecular interactions arose from irreversible chemical bonds between the volatile molecules and component of the sample matrix. However, headspace responses could be maximized when the analysis was performed at temperatures ranging from 60 to 70 \({}^{\circ}\)C. Furthermore, normalization of peak responses to an internal standard did not always account for these interactions.  
headspace analysis; SPME; volatile metabolites; GC-MS; sample matrix effects Article
## 3 Results and Discussion
### Characterization
#### 3.2.2 SEM  
An organic-inorganic composite flocculant is a simple superposition of organic matter and inorganic matter. In this study, a composite flocculant derived from mixing PAC and PDMDAAC was compared to the PAC-PDMDAAC hybrid flocculant prepared. The composite flocculant and the prepared hybrid flocculant were observed by SEM.  
The SEM images of the PAC-PDMDAAC composite flocculant (a) and the PAC-PDMDAAC hybrid flocculant (b) are presented in Figure 9. It can be observed that the composite flocculant exhibited a relatively smooth surface with a blocky shape, featuring a few bulges that facilitate adsorption. On the other hand, the hybrid flocculant possessed a larger particle size and an irregular surface morphology with more convex folds, presenting an obvious spatial network structure. The increased surface area of the molecular particles  
Figure 8: FTIR spectrum of hybrid flocculant.  
Figure 7: The influence of reaction time on intrinsic viscosity.  
in the hybrid flocculant contributed to its enhanced adsorption capacity, while its spatial network structure made it have a superior bridging effect.
# Logistics Service Provider Lifecycle Model in Industry 4.0: A Review
## 4 Concept of Logistic Service Provider Lifecycle Model (LSLM)  
For the definition of LSLM, we will describe the stages, criteria, and sub-criteria. Lifecycle Management is not only a concept for accomplishing customer satisfaction, but also for maintaining the creation, design, production, and decomposition of products and services in companies. PSS, SLM, and PLM are concerned with activities of the whole lifecycle to attain customer acceptance, for instance, quick responses to customers, quick delivery, after-sale service, maintenance, reuse, and recycling. LSLM covers the whole concept of lifecycle management, which associates all stages with improving performance efficiency and services. The LSP Lifecycle Model (LSLM) consists of three phases, eight criteria, and seventeen sub-criteria for customer satisfaction, as shown in Figure 3.  
Figure 3 shows the LSP Lifecycle Model, which consists of three main phases, namely the Beginning of Life (BOL), the Middle of Life (MOL), and the End of Life (EOL). The LSP Lifecycle Model focuses on both, physical flows (material flows) and information flows throughout every phase of LSLM. BOL focuses on creating service innovation, design and testing and long-term relationships. MOL emphasizes operational performance and financial performance. EOL prioritizes risk management, evaluation of customer satisfaction and decomposition.
## 1 Introduction  
As an important group of the most utilitarian biocatalysts, lipolytic enzymes, with their ability to catalyze the cleavage and synthesis of ester bonds, have been widely used in many applications including pharmaceuticals, paper, biofuels, and detergent manufacturing [1]. The two main categories of lipolytic enzymes, esterases and lipases, are differentiated by their substrate preference. Esterases prefer hydrolyzing small esters with short fatty acid side chains (\(\leq\)C10), conversely, lipase prefers long fatty acid chains triglycerides (>C10). Esterases and lipases can also be distinguished according to their substrate hydrophobicity, interfacial activation, solvent stability and enantioselectivity [2, 3]. The tertiary structure of lipolytic enzymes is highly conserved with a typical \(\alpha/\beta\) hydrolase fold structure. The catalytic amino acid triplet from esterases of different sources generally consists of Ser-Asp-His, usually with a conserved motif (Gly-x-Ser-x-Gly) around the active site serine. Microbial lipolytic enzymes have been classified into eight families according to their conservative amino acid sequence motifs and biochemical event properties. Among them, lipases belong to family I, which can be divided into 6 subfamilies, and esterases belong to families II-VIII [4]. Generally, esterases demonstrate optimal reaction temperatures around 30 \({}^{\circ}\)C and those that display catalytic activity even at 0 \({}^{\circ}\)C are called cold-adapted esterases. Cold-adapted esterases always have high catalytic activity and low activation energy in low temperature environments, which advantageously can prevent product decomposition, structural damage and save energy cost. Additionally, chemical processes such as bioremediation, fermentation, and biomass conversion are best performed at low temperatures to reduce energy consumption and risk of contamination by mesophiles. Therefore, the development and application of cold-adapted esterase have important research values to broaden the industrial applications of esterases [5; 6; 7].  
To date, many different lipolytic enzymes have been reported. Esteras with special properties have a more and broad application prospect: detergent-resistant esterases are applied as detergent additives; cold-adapted esterases are important in the manufacture of thermolabile pharmaceutical products; organic solvent-stable esterases are necessary for biotransformation processes in organic solvents such as biodiesel production. However, few esterases with specific properties have been reported, implying the necessity to find more novel esterases. This discovery process yields additional information regarding the structural and functional adaptation of lipolytic enzymes. The aim of the current study was to identify the gene encoding lipolytic enzymes in \(D\). _radiodurans_ (ATCC 13939), an extremophilic bacterium, and then purify and biochemically characterize the corresponding enzyme to provides a potential biocatalyst for industrial biotechnological applications.