esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of the Anisotropy Coefficient of Frost Heave Deformation  
Under actual working conditions, the frost heave deformation of the rock and soil masses around the tunnel mainly occurs in the direction of heat flow, but at the same time, the corresponding frost heave deformation also occurs in the direction orthogonal to heat flow, i.e., the volume deformation is spatially distributed along and perpendicular to the heat flow direction [24; 25]. The introduction of the frost heave anisotropy coefficient of the surrounding rock can clarify the calculation and analysis results of the frost heaving force of the tunnel. To investigate the effect of the anisotropy coefficient on the frost heaving force of the tunnel lining, the analysis is performed under four working conditions (the anisotropy coefficient \(\eta\) = 0.7, 0.8, 0.9, and 1.0), with the minimum temperature being \(-10\)\({}^{\circ}\)C and the frost heave ratio being 0.2%.  
Figure 11: Distribution of the maximum principal stress of the primary lining under different frost heave ratios (MPa).  
Figure 12 shows the distribution of the frost heaving force of the tunnel under different anisotropy coefficients of frost heave deformation. The distribution patterns of the frost heaving force at the periphery of the lining under different frost heave deformation anisotropy coefficients are basically the same; the overall frost heaving force follows the order of top arch > spring line > inverted arch, and the frost heaving force is located between the arch waist and the sidewall. As the frost heave deformation anisotropy coefficient increases, a greater proportion of frost heave deformation is concentrated along the direction of heat flow. This shift leads to a corresponding enhancement in the frost heaving force exerted perpendicular to the tunnel lining. As illustrated in Figure 11b, this effect is most pronounced at the vault (top arch), where the increase in stress is greatest. The arch waist experiences a moderate rise in frost heaving force, while the inverted arch shows only a slight increase. Specifically, in comparison to the isotropic case (\(\eta=1/3\)), when \(\eta\) reaches 1.0, the frost heaving force increases by 0.121 MPa at point C (arch waist), by 0.107 MPa at point A (vault), and by just 0.01 MPa at point E (inverted arch). These results are similar to those in previous research [24; 25; 35], which showed that directional deformation and stress concentration often occur around tunnel linings. They found that the uneven thermal and mechanical behaviour of frozen ground can greatly influence how frost forces are distributed.  
Figure 12: Distribution of frost heave pressure under different anisotropy coefficients.
## 2 Experimental
### Preparation and Characterization of PolyHIPE Membrane  
In a typical experiment, the continuous phase was organic solution consisting of St (3.0 g), EHA (6.0 g), DVB (1.0 g), AIBN (0.2 g), and Span 80 (2.4 g). The dispersed phase (40.0 mL of DI-water) was then added dropwise (within 30 min) to the organic solution, stirring at 300 rpm. After the addition of water, the mixture was stirred at 400 rpm for another 30 min to obtain a viscous emulsion solution for membrane preparation. The emulsion solution (HIPE) was placed in a mold with two flat polytetrafluoroethylene (PTFE) plates, and circumscribed by spacers. The thickness of membrane was controlled at 120 \(\pm\) 5 \(\upmu\)m. The mold was then tightly clamped and put into oven pre-set at 60 \({}^{\circ}\)C to start polymerization. After 48 h of polymerization, the PolyHIPE membrane was carefully retrieved. The PolyHIPE membrane was washed three times in ethanol solution for 6 h to remove organic impurities, followed by immersion in water for 1 h to remove inorganic residual. Finally, the PolyHIPE membrane was oven-dried at 50 \({}^{\circ}\)C for 24 h.  
The bulk density was determined by dividing the mass of sample with the bulk volume, as determined from the dimensions of the sample. The membrane dimension was measured by a digital micrometer (Mitutoyo 293-100, Mitutoyo, Aurora, IL, USA) with 0.1 \(\upmu\)m resolution over 10 readings. The chemical structure of PolyHIPE membrane was characterized by FT-IR spectrometer (NICOLET 6700, Thermo Electron, Waltham, MA, USA). The microstructure morphology was examined by SEM (S-4700, Hitachi, Chiyoda-Ku, Tokyo, Japan). The characteristic of pore structure of PolyHIPE was evaluated by mercury porosimetry (Micromeritics AUTOPORE 9520, Micromeritics Instrument, Norcross, GA, USA) with maximum test pressure of 200 MPa.  
The tensile testing of the PolyHIPE membrane was conducted according to ASTM D412. The Shore hardness of the PolyHIPE membrane was measured with SHAHE/LX-C 0-100 HC (Wenzhou Sanhe Measuring Instrument, Wenzhou, China). The flexibility of PolyHIPE was evaluated by following the standard test method protocol of ASTM F137-08. By placing the specimen over a mandrel with different radius, the bent position of the specimen face was visually examined for breaks, cracks, or other damage. The testing was performed in an ambient environment within 5 min.
# Phase Noise Cancellation in Coherent Communication Systems Using a Radio Frequency Pilot Tone
## 2 EEPN in Optical Communication Systems  
Figure 1 describes the block diagram of a long-haul, coherent optical communication system with EDC and CPR. The LPN from the transmitter (Tx) laser passes through the optical fiber and the dispersion compensation unit, and thus the net CD experienced by the Tx LPN approaches zero. By contrast, the LO LPN goes only through the dispersion compensation unit. Consequently, the LO LPN interplays with the dispersion compensation component in EDC and the interactions will degrade the performance of the optical communication system. This effect is called EEPN [10; 11; 12; 13].  
The variance of the EEPN distortion will increase with fiber dispersion, local oscillator laser linewidth, and signal symbol rate. The noise variance of EEPN can be written as follows [10; 19]:  
\[\sigma_{EEPN}^{2}=\frac{\pi\lambda^{2}}{2c}\cdot\frac{D\cdot L\cdot\Delta f_{ LO}}{T_{S}}, \tag{1}\]  
where \(\lambda\) is the central wavelength of the optical carrier, \(c\) is the speed of the light in vacuum, \(L\) is the length, \(D\) is the CD coefficient of the fiber, \(T_{S}\) is the symbol period of the signal, and \(\Delta f_{LO}\) is the 3-dB linewidth of the LO laser.  
It is noted that the EEPN evaluation in Equation (1) only works for the static time-domain and frequency-domain EDCs, which involve no phase noise compensation functions [22; 23].  
Figure 1: Block diagram of coherent transmission system and equalization-enhanced phase noise (EEPN). \(\phi_{Tx}\): Tx laser phase noise (LPN), \(\phi_{LO}\): LO LPN, ADCs: analogue-to-digital converters.
# An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
## 3 Proposed Scheme
### Mutual Authentication  
Step 1: \(U_{i}\) sends the challenge message \(M_{9}\) to \(S_{j}\). \(M_{9}\) contains \(h\Big{(}n_{CS}^{new}\Big{)}\) as proof of his/her legitimacy and requests the response:  
\[U_{i}\to S_{j}:M_{9}=\Big{\{}E_{SK}\Big{(}h\Big{(}n_{CS}^{new}\Big{)} \parallel serverValue(challenge)\Big{)}\Big{\}}\]  
Step 2: Upon receiving the challenge message \(M_{9}\), \(S_{j}\) computes  
\[h\Big{(}n_{CS}^{new}\Big{)}^{*}\parallel serverValue(challenge)=D_{SK}(M_{9}) \tag{37}\]  
\[\begin{split} h\Big{(}n_{CS}^{new}\Big{)}^{*}?=&\ h \Big{(}n_{CS}^{new}\Big{)}\\ S_{j}\to U_{i}:M_{10}=&\ \Big{\{}E_{SK}\Big{(} serverValue\Big{(}h\Big{(}n_{CS}^{new}\parallel T_{CS}^{new}\Big{)}\oplus h \Big{(}n_{U}^{new}\Big{)}\Big{)}\Big{)}\Big{\}}\end{split} \tag{38}\]  
\(S_{j}\) knows that \(U_{i}\) is the user who requested the user authentication by means of Equations (37) and (38). Then, \(S_{j}\) sends the response to \(U_{i}\).  
Step 3: Upon receiving the message \(M_{10}\), \(U_{i}\) computes and verifies the legitimacy of \(S_{j}\) as follows:  
\[h\Big{(}n_{CS}^{new}\parallel T_{CS}^{new}\Big{)}^{*}\oplus h\Big{(}n_{U}^{new }\Big{)}=serverValue\Big{(}h\Big{(}n_{CS}^{new}\parallel T_{CS}^{new}\Big{)} \oplus h\Big{(}n_{U}^{new}\Big{)}\Big{)}=D_{SK}(M_{10}) \tag{39}\]  
\[h\Big{(}n_{CS}^{new}\parallel T_{CS}^{new}\Big{)}^{*}?=&\ h\Big{(}n_{ CS}^{new}\parallel T_{CS}^{new}\Big{)} \tag{40}\]  
finally, \(U_{i}\) recovers the response from \(M_{10}\) using Equation (39) and verifies the legitimacy of \(S_{j}\) by means of Equation (40).  
Step 4: \(U_{i}\) replaces \(PID_{i}\), \(h(n_{U})\), and \(C_{4}=h\big{(}ID_{i}\parallel PW_{i}\parallel h(n_{U})\big{)}\) with \(PID_{i}^{new}=\Big{(}h\Big{(}T_{U}^{new}\parallel h(ID_{i}\parallel PID_{i}) \parallel n_{U}^{new}\Big{)}\Big{)}\), \(h\Big{(}n_{U}^{new}\Big{)}\), and \(C_{4}^{new}=h\Big{(}ID_{i}\parallel PW_{i}\parallel h\Big{(}n_{U}^{new}\Big{)} \Big{)}\), respectively.
# Validating and Testing an Agent-Based Model for the Spread of COVID-19 in Ireland
## 2 Materials and Methods
### Model Description  
The model presented here is a version of a previous model [20] that has been scaled to simulate the entire population of the Republic of Ireland. It was created and implemented in the modeling environment, Netlogo [21]. The Republic of Ireland is a country in Northwestern Europe. In 2016, when the last Irish census was completed, there were 4,757,976 people in Ireland; in the scaled model, this would equate to approximately 47,580 agents. There are 26 counties that make up the country. Approximately 40% of the population of the country live in the region of the nation's capital city, Dublin. The model is an agent-based model that was created for the spread of measles in an Irish county [20] and has been adapted to simulate the spread of COVID-19 [22]. The model is made up of four main components: environment, transportation, disease, and society [23]. It uses census data from the Irish Central Statistics Office to create a population that matches the demographic characteristics of the county at the small area level (small areas are the smallest geographic area over which census statistics are aggregated in Ireland, and contain between 50 and 200 dwellings) [24] and transportation patterns [25]. The scaled-up model presented here has the same four components and a similar structure. The largest difference between the two models is that the scaled-up model is designed to simulate the entire population and capture the interactions between counties. There are, however, other differences that are mainly meant to reduce the computing power necessary to run a county-level agent-based model. The more complex an agent-based model is and the more agents in the simulated population, the more computing power that is needed [17]. Thus, to simulate at a larger scale while still being able to produce timely results, we reduce the complexity of the environment matching population distributions at the county level instead of the small area level, and we scale the model so that each agent represents 100 similar people in Ireland. The following sections discuss the different model components used in the model in more detail as well as the model schedule, the initial conditions of the model and the interventions that can be implemented in the model. Further details including model parameters can be found in the model ODD [26].
###### Abstract  
Based on an actual ultra-high voltage (UHV) substation, a finite element (FE) model for a high-voltage (HV)ushing, arrester, and down lead transmission line (DLTL) system was built using ANSYS software. The dynamic responses of the system under different seismic intensities were analyzed and compared with those of the corresponding single bushing and arrester. On this basis, the coupling vibration influence of the upper DLTL on the responses of the HV bashing and arrester is discussed. The results indicate that the DLTL adversely affects the responses of the HV bashing and arrester under seismic loading. As the seismic intensity increases, the structural displacements at the top of the HV bashing and arrester increase, accompanied by a reduction in the geometric length redundancy of the DLTL, resulting in a mutual pulling effect between the HV bashing and the arrester and the quick amplification of their respective dynamic responses in a nonlinear form. Under the action of an earthquake with a peak ground acceleration (PGA) of 0.4 g, the maximum stresses at the roots of the HV bashing and the arrester in the system separately increase by approximately 13.02% and 7.80% compared to the corresponding single HV bashing and the arrester. Overall, a geometric length redundancy of at least 200 mm in the DLTL in engineering design is recommended.  
high voltage bashing lead system; seismic responses; coupling vibration; displacements; maximum stress; geometric length redundancy for the DLTL
## 3 Results and Discussion
### Substrate Characterization  
The commercial materials used for the construction of raceway ponds are fiber reinforced polymers substrates and their surfaces are generally treated with different types of gel coatings. The raceway employed in this study was manufactured in fiber reinforced polyester by PROAGINOR S.A.L. (Asturias, Spain).  
Rectangular specimens of gel coated substrates (dimensions \(5.0\times 2.5\times 0.5\) cm\({}^{3}\); see Figure 3a) were surface treated by applying the different coatings developed in this study before being tested in order to evaluate their antibofouling capacity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metal-NPs** & **Size (nm)** & **Polydispersity Index (PDI)** \\ \hline TiO\({}_{2}\) & 110.0 & 0.127 \\ ZnO & 112.9 & 0.100 \\ CuO & 104.2 & 0.101 \\ AgNPs NPs & 100.9 & 0.152 \\ Ag-TiO\({}_{2}\) & 135.2 & 0.144 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the size (hydrodynamic diameter) and polydispersity index (PDI) values obtained for the synthetized nanoparticles.  
Figure 3: Substrate characterization: (**a**) front and back appearance of the original substrate; (**b**) FTIR characteristic spectrum for gel-coated surface and (**c**) XRD pattern of gel-coated surface.  
The specimen's surfaces were characterized by Fourier transform infrared spectroscopy (FTIR). Figure 3b shows the characteristic bands of alcohol group at 3500 cm\({}^{-1}\), probably from the hardeners or curatives co-reactants. A band around 1750 cm\({}^{-1}\) attributed to the - C = O groups is also observed. Bands in the range of a wavenumber from 1500 to 400 cm\({}^{-1}\) were difficult to assign, but that information is complementary to that given by the X-ray diffraction patterns of the specimen substrate. Figure 3c shows the presence of rutile (TiO\({}_{2}\)) and gibbsite (Al (OH)\({}_{3}\)) as main phases.  
The surface roughness can affect the hydrodynamic performance of antibiofouling coatings and the settlement of microalgae. Therefore, it was considered an important parameter in the evaluation of the novel coatings [49]. Surface roughness measurements were carried out on three substrate specimens, and three different sections of each one, as explained in Section 2.4. The arithmetic mean roughness (Ra) was taken as the representative parameter of the roughness of the substrate surface. The average roughness obtained for the three substrate specimens was 0.46 \(\upmu\)m (see Table 2). This low value for initial roughness of the gel-coat face should favor the uniformity of the subsequently applied coating.
## 2 Material and Methods
### Statistical Analysis  
The following research variables were considered in the experiment: (A)--treatments (5 levels), (B)--growing seasons (2 levels), and (C)--grass growth cycles (3 levels). One factor analysis of variance for replicates in the experiment did not show any significant differences between them.  
The results were statistically processed using a three-factor analysis of variance according to the mathematical model [19]:  
\[y_{ijp}=\text{m}+a_{i}+g_{j}+e_{ij}^{\prime 1/}+b_{l}+ab_{il}+e_{ijl}^{\prime 2 /}+c_{p}+ac_{ip}+bc_{lp}+\text{AA}+e_{ijlp}^{\prime 3/}\]  
where \(y_{ijlp}\)--the value of the variable for the i-th level of factor A and p-th level of factor C for the j-th replicate; m--the mean of research; \(a_{i}\), \(b_{l}\), \(c_{p}\)--the effects of factors; \(g_{j}\)--the effect of the j-th replicate; \(ab_{il}\), \(ac_{ip}\), \(bc_{lp}\)--the effects of the interaction of two factors; \(abc_{ijp}\)--the effect of the interaction of three factors; \(e_{ij}^{\prime 1/}\), \(e_{ijl}^{\prime 2/}\), \(e_{ijlp}^{\prime 3/}\)--the effect of random factor; i = 1, 2,..., a, a--the number of levels of factor A; j = 1, 2,..., n, n--the number of replicates; l = 1, 2,..., b, b--the number of levels of factor B; and p = 1, 2,..., c--the number of levels of factor C.  
All calculations were carried out using Statistica 6.0-2001 [20]. In order to compare means, [PERSON]'s test at 0.05 probability level was used.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{8}{c}{**Month**} \\ \cline{2-9}  & **April** & **May** & **June** & **July** & **August** & **September** & **October** \\ \hline
**2015** & 1.36 (o) & 1.87 (mw) & 1.64 (mw) & 0.59 (sd) & 1.92 (mw) & 0.64 (sd) & 0.12 (ed) \\ \hline
**2016** & 1.22 (md) & 2.63 (sw) & 0.87 (d) & 1.08 (md) & 0.18 (ed) & 1.46 (o) & 1.94 (mw) \\ \hline
**2017** & 2.88 (sw) & 1.15 (md) & 1.08 (md) & 0.45 (sd) & 0.96 (d) & 1.92 (mw) & 1.90 (mw) \\ \hline \hline \(K\leq 0.4\) extremely dry (ed); \(0.4<K\leq 0.7\) severely dry (sd); \(0.7<K\leq 1.0\) dry (d); \(1.0<K\leq 1.3\) moderately dry (md); \(1.3<K\leq 1.6\) optimal (o); \(1.6<K\leq 2.0\) moderately wet (mw); \(2.0<K\leq 2.5\) wet (w); \(2.5<K\leq 3.0\) severely wet (sw); \(K>3.0\) extremely wet (ew). & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sielianinov’s hydrothermal coefficient (K).
## 3 Methodology
### Data  
LiDAR point-cloud data were collected on 15 January 2015 at a point density of 8 points/m\({}^{2}\) and frequency pulse rate of 25,000 Hz. The absolute accuracy of the data (root-mean square errors) was restricted to 0.15 m and 0.3 m in the vertical and horizontal axes, respectively. Orthophotos were obtained using the same acquisition system that relied on the abovementioned cloud data. A DEM was derived from LiDAR point clouds with a spatial resolution of 0.5 m after non-ground points were removed using inverse distance weighting with a spatial reference of GDM2000/Peninsula RSO. Subsequently, LiDAR-based DEM was used to generate derived layers to facilitate the identification and characterization of landslide locations [37].  
Figure 2: Overview of the proposed method. LiDAR: light detection and ranging; RNN: recurrent neural networks; MLP-NN: multi-layer perceptron neural networks; CFS: correlation-based feature selection; DEM: digital elevation model.  
According to the authors of [38], slope directly and highly affects landslide phenomenology. The authors of [39] also inferred that slope is the principal factor that affects landslide occurrence. The author of [40] indicated that a hillshade map provides a good image of terrain movements, thus facilitating the development of landslide maps. Texture and geometric features are crucial for improving the classification accuracy of landslide mapping [14]. Landslide intensity and texture derived from LiDAR data are affected by the accuracy of landslide detection [9]. The accuracy and capacity of DEM to represent surface features are determined by terrain morphology, sampling density, and the interpolation algorithm [41]. In this study, hillshade, height (nDSM), slope, and aspect were generated from LiDAR-based DEM. As shown in Figure 4, landslide locations were detected using visible bands and texture features.
## 3 Results
### VOCs  
In this study, VOCs in samples were detected on days 1, 5, 9 and 13 by GC-MS. A total of 25 VOCs were detected, which mainly included alcohols, aldehydes, ketones, acid, esters, sulphur compounds and other compounds (Supplementary Table S1). By comparing the composition and content of four groups of VOCs, we determined which compounds increased, decreased, disappeared or fluctuated during storage (Figure 5).  
Figure 3: Rank-abundance curve showing bacterial community diversity during storage at 4 °C.  
Types and concentrations of VOCs changed at the later stage of storage compared with initial VOCs. A total of 5 alcohols were detected in experimental groups; ethanol was high in samples but usually had only a minor effect on food flavor [39]. The substance 3-methyl-1-butanol appeared in samples on days 9 and 13. The relative content reached 23.49% and 22.11% respectively. The substance 3-methyl-1-butanol produces an odor similar to that of whiskey- and is derived mainly from the catabolism of leucine or isoleucine. It  
Figure 4: Percent of major bacterial community abundance at the phylum level (**A**) and the genus level (**B**).  
Figure 5: Heat map analysis was used to analyze the changes of volatile components of samples. Note: Blue represents negative correlation, red represents positive correlation, and the darker the color, the stronger the correlation.  
has been recognized as a potential spoilage indicator of fish [40; 41]. The degradation of phenylalanine mainly includes benzyl alcohol, phenylethyl alcohol, benzaldehyde, phenylacetaldehyde and other small volatile compounds [42]. Phenylethyl alcohol and benzyl alcohol increased progressively during storage. The level of enzaldehyde was higher than benzyl alcohol and phenylethyl alcohol, and the early level of enzaldehyde was higher than later ones.  
Acids are mainly produced by hydrolysis of triglycerides and phospholipids [43]. Acetic acid was just detected on day 9 and 13. The level of n-hexadecanoic acid decreased during storage. Especially in the later stage, the content of n-hexadecanoic acid was significantly lower than acetic acid. The substances 2,3-butanedione and 2-pentanone were not produced in fresh fish, which indicates that two ketones are highly putrid volatile markers of fish. Acetone is produced by the catabolism of glucose [44]. With the extension of storage time, the content of this compound increased. Microorganisms might involve the utilization of glucose during storage. Esters are usually formed through esterification of alcohols and carboxylic acids, and ethanol seems to be used as a preferred substrate for ester synthesis [45]. Ethyl acetate amounts were high at each stage. This might be related to the high level of ethanol detected in this study. The sulfur-containing compounds is usually related to the utilization of sulfur-containing amino acids by microorganisms and plays in an important role in the flavor of fish [46].
# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 3 System Model
### Comparative Analysis  
Here in this section, the proposed IACO is evaluated with existing methods such as D2-QDPO3 [34] and JERA [35]. Table 4 shows an analysis of proposed and existing methods in terms of Energy Efficiency (EE), Throughput, Latency, Power Consumption and Average Response Time.  
Figure 7: Analysis of Convergence.  
Table 4 clearly shows that the proposed IACO outperforms the existing methods by achieving better results in EE (14.3 Mbit/Joule), Throughput (13.6 Kbps/Hz/J), Latency (12.7 s), Power Consumption (34 dBm) and Average Response Time (98 ms). The existing D2-QDPO3 [34] obtained the results of EE of 11.2 Mbit/Joule and Latency of 19.1 s. The existing JEERA [35] obtained the results of EE of 4.8 Mbit/Joule, Throughput of 10 Kbps/Hz/J, Power Consumption of 48 dBm and Average Response Time of 147 ms.