esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Damage Mechanism and Evolution Model of Concrete under Freeze-Thaw Cycles
## 2 Damage Variables Reviews
### Damage Factors Established Based on the Dynamic Elastic Modulus  
The longitudinal wave velocity defined by the elastic theory is as follows:  
\[V_{P}=\sqrt{\frac{E_{d}(1-\mu)}{\rho(1+\mu)(1-2\mu)}} \tag{4}\]where \(V_{p}\) is the P-wave velocity of the concrete, \(E_{d}\) is the dynamic elastic modulus before the F-T cycles, and \(\mu\) is [PERSON]'s ratio of the concrete; the damage variable \(D_{t}\) is defined by the dynamic elastic modulus of the concrete as follows:  
\[D_{t}=1-\frac{E_{d}^{\prime}}{E_{d}} \tag{5}\]  
If the change of [PERSON]'s ratio in the F-T cycles is not considered, Equation (4) is substituted into Equation (5) to obtain the following equation:  
\[D_{t}=1-\frac{\rho^{\prime}}{\rho}\frac{V_{p}^{\prime 2}}{V_{p}^{2}}=1-\frac{v}{v ^{\prime}}\frac{V_{p}^{2}}{V_{p}^{2}} \tag{6}\]  
where V is the unit mass volume of the concrete before F-T cycles; \(\rho^{\prime}\), \(v^{\prime}\), and \(V_{p}^{\prime}\) are the density, unit mass volume, and P-wave velocity of the concrete after F-T cycles, respectively.  
The volume of the concrete will be irreversibly expanded and deformed under the F-T cycle action. The change in the concrete volume is the result of the initiation and evolution of micropores and fissures. If the matrix deformation is not considered, the volume change in concrete, before and after the F-T cycles, can be expressed as follows:  
\[\frac{v}{v^{\prime}}=\frac{v_{s}+v_{o}}{v_{s}+v_{o}+\Delta v_{o}} \tag{7}\]  
where \(v_{s}\) and \(v_{o}\) are, respectively, the volume of the matrix and pore of the concrete before the F-T cycles; \(\Delta v_{o}\) is the pore volume variation in the concrete, before and after the F-T cycles.  
The porosity of the concrete before the F-T cycles can be expressed as follows:  
\[\phi_{0}=\frac{v_{o}}{v_{s}+v_{o}} \tag{8}\]  
The porosity of the concrete after the F-T cycles can be expressed as follows:  
\[\phi_{n}=\frac{v_{o}}{v_{s}+v_{o}+\Delta v_{o}} \tag{9}\]  
By substituting Equations (7)-(9) into Equation (6), we can get  
\[D_{t}=1-\frac{1-\phi_{n}}{1-\phi_{0}}\frac{V_{p}^{\prime 2}}{V_{p}^{2}} \tag{10}\]  
Equation (10) is the concrete F-T damage variable expressed by the concrete porosity and the P-wave velocity [80].
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 3 System Model
### The Proposed DLSR Protocol  
The proposed routing protocol is called the DLSR protocol. We assume that the clustering process has already been completed and is running periodically during the routing process. Figure 4 shows an illustration when a blackhole node appears in the network. Each node employs DL to detect the presence of a blackhole node. If a blackhole node is identified along the route, then the fitness value will have a higher value than the other route, and secure routing is employed to avoid it. The flowchart of the proposed DLSR protocol is shown in Figure 5, which can be divided by the route request process and the route reply process.
## 1 Introduction
[17], that is, the multi-hop parallel hybrid FSO/RF cooperation system or the relay-aided hybrid FSO/RF system. Compared with previous work, the innovative content of this article includes the following points:  
* First, we used the gamma-gamma turbulence model in our previous work, while we used the more general Malaga turbulence model in this paper, resulting in a new performance expression. * Second, due to model limitations, we only derived and analyzed the system performance under CBPSK and DBPSK modulation methods in the previous work. Inthis paper, we derived and analyzed the system performance under four modulation methods (CBPSK, DBPSK, CBFSK, NBFSK). * Third, we only derived and analyzed the system performance under direct detection in the previous work. In this paper, we derived and compared the system performance under direct detection and coherent detection. * Finally, we derived and analyzed the relationship between the system outage probability and signal-to-noise ratio in the previous work. In this paper, we derived and analyzed the relationship between the system outage probability and the decision threshold. The remainder of this paper, considers direct detection (DD) and heterodyne detection (HD), the PDF and CDF of the output SNR of only the FSO direct link and only the RF direct link are given, respectively. Furthermore, the CDF of the output SNR of the hybrid FSO/RF parallel link in the one-hop or direct link is obtained. Then, the end-to-end performance of the multi-hop parallel hybrid FSO/RF cooperative system based on the DF protocol is analyzed, and new mathematical expressions of the end-to-end ABER and outage probability of the system under four binary subcarrier modulation schemes are derived. Finally, through numerical analysis, the effects of the turbulence, pointing errors, modulation mode, detection technique, RF fading, and relay-aided structure on the hybrid FSO/RF direct link and the relay-aided hybrid system are compared and analyzed.
The present study provides, for the first time (to our knowledge), significant data about the frequency of occurrence of microplastics in some of the most widely sold fish species (_Dicentrachus labrax_, _Sparus aurata_) collected from aquaculture in the North Ionian Sea while enriching the existing data regarding the presence of microplastics in mussels (_Mytilus galloprovincialis_) from the same area. Chemical identification of selected MPs is performed using Attenuated Total Reflectance--Fourier Transform Infrared spectroscopy (ATR-FTIR) for the larger items, as well as micro-Raman spectroscopy, utilizing the Particle Finder software. Finally, the surface morphology and elemental composition of selected MPs are elucidated utilizing Scanning Electron Microscopy (SEM) in tandem with Energy Dispersive Spectroscopy (EDS). This study is among the rare studies that use SEM-EDS for the surface analysis of MPs in environmental and food samples.
## 4 SAR Raw Signal Processing
### Omega-k Algorithm for LFM-CW SAR System
#### 4.2.1 Fast Fourier Transform in Range  
The range Fourier transform is applied to the LFM-CW SAR raw data, and the fast Fourier transform is used to convert the data from the time domain to the range-Doppler domain, because it is an algorithm to calculate the Fourier transform.
# Maleic Anhydride-Grafted Isotactic Polybutene-1 and Modified Polyamide 6
## 3 Results
### The Grafting Reaction of DVB/MAH onto iPB-1
The initiator DCP had little effect on Gd when its concentration was higher than 0.2 phr, but the MFR of iPB-g-MAH increased significantly (as shown in Figure 5b). It is possible to prepare iPB-g-MAH with the same grafting rate and meeting the different processing requirements in industry. Figure 4: Effect of reaction temperature on Gd and Ge of iPB-g-MAH. Figure 5: Torque/time spectra (**a**) and MFR (**b**) of iPB-g-MAH at different DCP concentrations. (\(T=180\)\({}^{\circ}\)C, MAH = 6 phr, DVB/MAH = 4:6). Figure 6: Effect of different DCP concentrations on Gd and Ge of iPB-g-MAH. It can be seen from Figure 7 that the torque end values of iPB-g-MAH increased with increasing MAH concentration, and the degradation of iPB-1 macromolecules caused by DCP was inhibited by the addition of MAH. Figure 8 shows effect of different MAH concentrations on Gd and Ge of iPB-g-MAH. The increase of Gd of iPB-g-MAH could be attributed to the increase in MAH concentration, owing to an increase in the monomers in the grafting system, as well as the participation in the grafting reaction of more macroradicals. However, the Ge of iPB-g-MAH decreased, and the reason was that the proportion of MAH that participated in the reaction decreased. It can be seen from Table 1 that the effect of DVB as a comonomer enhanced the Gd and Ge of iPB-g-MAH better than St. Table 2 shows the \(Q\)-\(e\) values of different monomers. DVB and St both have negative e values so that both of them can provide one electron to MAH that changed the symmetry of MAH double bond and increased the reactivity of MAH and iPB-1 macroradicals. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Reaction** & **DCP** & **MAH** & **Comonomers** & **Gd** & **Ge** \\  & **Temperature** & **Concentration** & **Concentration** & **Concentration** & **Concentration** & **(\%)** & **(\%)** \\  & (°C) & **(phr)** & **(phr)** & **(phr)** & **(phr)** & **(\%)** \\ \hline iPB-g-MAH & 180 & 0.6 & 6 & 0 & 0.26 & 4.3 \\ iPB-g-MAH-co-DVB & 180 & 0.6 & 6 & 4 & 3.4 & 56.7 \\ iPB-g-MAH-co-St & 180 & 0.6 & 6 & 6 & 1.62 & 27 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Gd and Ge of grafted iPBs with different comonomers. Figure 8: Effect of different MAH concentrations on Gd and Ge of iPB-g-MAH. Figure 7: Torque/time spectra (**a**) and MFR (**b**) of iPB-g-MAH at different MAH concentrations. (\(T\) = 180 \({}^{\circ}\)C, DCP = 0.6 phr, DVB/MAH = 4:6). According to the Alfrey-Price Q-e deformation formula [15], the reactivity ratio can be calculated and used to compare the difficulty degree of reaction between comonomers and MAH. The reactivity ratio is calculated, that \(r_{\text{St}}\) = 0.379, \(r_{\text{DVB}}\) = 0.012. As a result of 0 \(<r_{\text{DVB}}\)\(<r_{\text{St}}\) < 1, compared with St, DVB copolymerizes with MAH more easily, so that the Gd of DVB/MAH onto iPB-1 is higher than that of St/MAH.
## 3 Results and Discussions
### UV-VIS Spectroscopic Analysis  
In the present investigations, the UV-VIS spectroscopic studies of Au-20 and Au-PVP-20 were accomplished by using Agilent Technologies Cary 60 ranging from 200-800 nm, with a resolution of 1 nm. The absorbance spectrum of Au-20 exhibited a maximum peak at the wavelength of 541 nm, whereas Au-PVP-20 exhibited a maximum peak at 542 nm,  
Figure 6: Intensity-particle size distribution with Dynamic Light Scattering.  
as shown in Figure 7, which lies within the range previously reported (520 nm-580 nm) for gold nanostructured particles with a size range of 10 nm-100 nm [28; 35]. There was no reasonable shift in the maximum absorption in Au-PVP-20 as compared to Au-20. This is because the immediate mixing of PVP with gold nanoparticles gives no sufficient time for Au-20 nanostructured particles to agglomerate or to change the effective cores of nanostructured particles [36]. PVP itself is best known for its stability, which has prevented the Au-20 nanostructured particles to agglomerate [37]. Particularly, previous investigations suggest that the maximum absorption peak is red-shifted with an increase in the size of the nanostructured particles and blue-shifted with the decrease in nanostructured particles' size [28; 37].  
However, in our case, the DLS analysis (Figure 6) revealed that the sizes of the metallic cores of the nanostructured particles in the distribution did not significantly change with PVP capping. Eventually, the surface plasmon resonance response of conduction electrons in both Au-20 and Au-PVP-20 remains almost identical [38].  
By using the UV-Vis absorption spectra of Au-20 and Au-PVP-20, the optical bandgap was determined by exploiting the Tauc equation, which is a relation between the absorption coefficient \(\alpha\), and \(\mathrm{E_{g}}\) is the bandgap energy \(\mathrm{E_{g}}\). [39; 40].  
\[(\alpha\mathrm{h}\mathrm{v})^{2}=\mathrm{k}(\mathrm{h}\mathrm{v}-\mathrm{E_{ g}}) \tag{1}\]  
where \(\mathrm{h}\mathrm{v}\) is the energy of the incident photon, \(\mathrm{k}\) is a constant, and the Tauc plot is a graph of \(\mathrm{h}\mathrm{o}\mathrm{h}\mathrm{e}\mathrm{( abscissa)}\) Versus \((\alpha\mathrm{h}\mathrm{v})^{2}\) (ordinate). The extrapolation of the tangent in the Tauc plot to \((\alpha\mathrm{h}\mathrm{v})^{2}=0\) gives the value of the direct bandgap energy.  
The optical bandgap values extracted from the Tauc plot (Figure 8) were found to be the same both for Au-20 and Au-PVP-20, i.e., 2.58 eV, which justifies the identical absorption response. However, with the PVP linkage, the Intensity of absorption increased reasonably. This difference in the intensity of absorption is usually associated with the nanoparticles' concentration affected by the mild acidic environment of PVP (3 > pH < 4.8) [41].
## 2 Materials and Methods
### Characterization Techniques  
The crystalline phases of the synthesized materials were determined by the X-ray diffraction method (XRD). The analysis was performed with a Bruker D8 Advance (Billerica, MA, USA) X-ray diffractometer with Cu K\(\alpha\) (\(\alpha\) = 1.5418 A). The patterns were obtained in the step-scanning mode (\(\Delta\)20 = 0.05\({}^{\circ}\)) over an angular range of 10-80\({}^{\circ}\). The optical absorption spectra of the powdered samples in the wavelength range of 200-800 nm were recorded by the UV-Vis diffused reflectance spectrophotometer \"Evolution 300\" (Madison, WI, USA) using a magnesium oxide reflectance standard as the baseline. The band gap energies (Eg) of the samples were calculated by [PERSON]'s equation: Eg = h.c/\(\lambda\), where Eg is the band gap energy (eV), h is the Planck's constant, c is the light velocity (m/s), and \(\lambda\) is the wavelength (nm). To study the morphology and microstructure, the as-obtained samples were imaged by the scanning electron microscope (SEM) JSM-5510 (JEOL), operated at 10 kV of acceleration voltage. The investigated samples were coated with gold by the JFC-1200 fine coater (JEOL) (Krefeld, Germany) before observation. The energy dispersive X-ray spectroscopy (EDS) analysis was carried out on a Zeiss Evo 15 microscope (Bruker Resolution 126 eV, Berlin, Germany). The specific surface areas (BETs) of the catalysts treated at 500 and 700 \({}^{\circ}\)C were determined by low-temperature (77.4 K) nitrogen adsorption in the NOVA 1200e surface area and pore analyzer at relative pressures p/p0 = 0.1-0.3 using the BET equation.
# Real-Time Anomaly Detection with Subspace Periodic Clustering Approach
## 5 Complexity Analysis
Since the time-complexity of the method depends on \(n\) and \(n_{1}\), and not on \(d\) (dimension), the worst-case complexity of the method can be rewritten as \(\mathrm{O}(n^{3})\). Thus, the method runs in cubic time.
###### Abstract  
The hybrid cable-stayed suspension bridge is used to combine the advantages of cable-stayed and suspension bridges and hence has a broad prospect for application. The conventional simplified analytical models of the hybrid bridge are usually developed based on a schematic with the cable-stayed and suspension systems working separately without any overlapping zone, which cannot represent the modern hybrid bridge system. In this study, a novel analytical model is proposed based on the modified suspension-elastic foundation beam theory to estimate the mechanical performance and deflection of the hybrid bridge system with the consideration of the overlapped section between the suspension and stayed cables. The governing equations of the hybrid bridge system are developed based on the elastic foundation beam theory and the deflection theory, which are derived separately in the hybrid section, the pure suspension section and the cable-stayed section. The general solution of each section is presented. The Transfer Matrix Method is then employed to solve the unknowns from one end to the other, which are in turn used to solve the internal forces of the hybrid bridge system caused by the concentrated load. In addition, in view of no variation in the unstressed length of the main cable, the compatibility equation of the main cable is established with consideration of the longitudinal displacement of the main tower, which is used to derive the formulas for the internal force and deflection of the hybrid system. The model can be easily complied in any programming platform, such as Matlab, with simple input parameters, which can eliminate the complex finite element modeling process. Hence, it can be easily used in the preliminary design stage to determine the optimal size and layout of the bridge. Then, a case study is presented for the verification of the proposed model under a vertical load, which is simplified from the Xihoumen Bridge, a combined highway and railway bridge with a main span of 1488 m. Good agreement is obtained between the proposed model and the finite element method. Meanwhile, it is found that there exists a negative deflection zone for the main beam at a distance from the concentrated vertical load, which is mainly caused by the deflection of the main cables, leading to the cambering of the beam.  
hybrid cable-stayed suspension bridge; analytical model; influence line; elastic foundation beam theory; FEM analysis +
Footnote †: journal: Applied sciences
## Materials and Methods  
### Lamb Meat Samples, Packaging and Analysis Conditions  
Leg of lamb meat (hind shank) samples in chunks, of dimensions ca. 2 \(\times\) 2 \(\times\) 2 cm, were provided from different chilled carcasses, 24 h post slaughter, by a local meat processing company (SVEKI S.A., Rodotopi, Ioannina, Greece). Lambs belonged to the Fries-Arta breed, which is a cross breed with high-yielding animals, farmed intensively in the lowlands. Lambs were 3-6 months old, male, with a slaughter weight of 12-13 kg. To avoid any source of contamination, lamb meat samples were transported to the laboratory in insulated polystyrene boxes on ice within 1 h of the chopping process. Prepared samples consisted of 150 \(\pm\) 10 g in weight, were immediately placed in low density polyethylene/polyamide/low density polyethylene (LDPE/PA/LDPE) barrier pouches (6-7 chunks per pouch), 75 \(\upmu\)m in thickness, having an oxygen permeability of 52.2 cm\({}^{3}\) m\({}^{-2}\) day\({}^{-1}\) atm\({}^{-1}\), at 75% relative humidity (RH), 23 \({}^{\circ}\)C and a water vapour permeability of 2.4 g m\({}^{-2}\) day\({}^{-1}\) at 100% RH, 23 \({}^{\circ}\)C. The experimental procedure was carried out on 36 samples: 2 bags \(\times\) 2 replicates \(\times\) 9 sampling days. Samples were stored at \(-\)18 \({}^{\circ}\)C in order to avoid any source of deterioration and prepared daily prior to headspace solid phase microextraction coupled to gas chromatography/mass spectrometry analysis (HS-SPME-GC/MS).
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline
**Authors** & **Component Analyzed** & **Misalignment FF Creation Process** & **Detection Method** \\ \hline \multirow{8}{*}{[36; 37]} & \multirow{8}{*}{Leadscrew} & \multirow{8}{*}{Light and left misalignment (0.5 mm, 1 mm) was introduced at the second support block of the leadscrew.} & Ref. [36] Applied various combinations of machine learning models including convolutional neural network (CNN), autoencoder (AE), temporal convolutional network (TCN), and long short-term memory (LSTM). \\  & & & Ref. [37] Applied fast Fourier transform (FFT) alongside statistical feature analysis on the collected current data. \\ \hline \multirow{8}{*}{[27]} & \multirow{8}{*}{Linear guide rail} & \multirow{8}{*}{Linear guide rail} & Vertical misalignment was introduced by removing bolts and applying a specialized pushing mechanism underneath the guide rail to cause maximum deformation (max 91 μm). \\  & & & \\ \cline{1-1}  & & & Horizontal misalignment was introduced on the evaluated linear guide rail by loosening the end bolts and placing feeler gauges ranging from 20 μm to 200 μm. \\ \hline \multirow{8}{*}{[38]} & \multirow{8}{*}{Leadscrew} & \multirow{8}{*}{Light and left misalignment} & \multirow{8}{*}{Light and left misalignment} \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of linear axis component FF diagnosis studies. Figure 2: Misalignment types. (**a**) Vertical, (**b**) Horizontal. Nevertheless, all these different works focused in analyzing the full stroke data in a holistic manner without considering detecting the location of the misalignment within the stroke. Furthermore, the reviewed literature works concluded their experiments once the misalignment was detected. However, there is no mention as to how the repair of the misalignment varies from their baseline dataset. Given that the linear axis travels a specified distance throughout its operation, detecting where a misalignment could be present in the stroke can be beneficial in its maintenance, since, with a location, the need to troubleshoot may decrease. Finally, localizing the misalignment in the axis stroke can aid in making well-informed maintenance decisions, and determine if the machine requires immediate repair based on the fault's severity.
## 3 Results
### [HbO], [HbR], and [HbO & HbR]  
Overall, the compositions incorporating both [HbO] and [HbR] (FC-1, 4, 7, and 10) performed better than the other compositions on both indices (Figure 4 and Table 2). The compositions with only [HbR] (FC-3, 6, 9, and 12) generated the lowest performance. Particularly, the compositions using both types of hemoglobin enhanced MCC\({}_{\text{RS}}\) against the compositions using only one type (13.55% (\(p<0.000001\)) against [HbR], 3.71% (\(p<0.0001\)) against [HbO]). Furthermore, the compositions with both hemoglobins lowered detection latency by 19.24% (\(p<0.05\)) against those with [HbR] only and by 6.01% (\(p<0.01\)) against those with [HbO] only (Figure 5 and Table 2).
# Road-Side Unit Anomaly Detection
###### Abstract  
Actors of the Cooperative Intelligent Transport Systems (C-ITS) generate various amounts of data. Useful information on various issues such as anomalies, failures, road profiles, etc., could be revealed from the analysis of these data. The analysis, could be managed by operators and vehicles, and its output could be very helpful for future decision making. In this study, we collected real data extracted from road operators. We analyzed these streams in order to verify whether abnormal behaviors could be observed in the data. Our main target was a very sensitive C-ITS failure, which is when a road-side unit (RSU) experiences transmission failure. The detection of such failure is to be achieved by end users (vehicles), which in turn would inform road operators which would then recover the failure. The data we analyzed were collected from various roads in Europe (France, Germany, and Italy) with the aim of studying the RSUs' behavior. Our mechanism offers compelling results regarding the early detection of RSU failures. We also proposed a new C-ITS message dedicated to raise alerts to road operators when required.  
C-ITS; failure detection; road-side unit; data analysis; machine learning Article  
Road-Side Unit Anomaly Detection