esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 DEM-FDM Coupled Modeling of Pullout Test  
### FDM Biaxial and Triaxial Geogrids Using FLAC3D  
Previous DEM studies [18; 26; 27] have simulated the geogrid with precise geometry using strings of parallel bonded particles in PFC3D. In the authors' previous study, a two-layer geogrid model with a realistic shape in PFC3D was used to replace a single-layer geogrid, as shown in Figure 2a,b. The performance of the two-layer geogrid was better than that of the geogrid composed of single-layer spheres or a row of spheres. To reflect the thickness change of the geogrid rib, the particle radius from the junction to the centre of the rib continuously decreases slightly. These bonded geogrid models, although very detailed and accurate, were found to be very computationally demanding, and the acquisition of results was found to be slow.  
In this study, the precise geometries of the geogrids were modeled using the solid element operation in FLAC3D. Compared with the complex geogrid model with discontinuous elements in PFC3D, the geogrid with continuum elements can reduce computational time and can also be easily generated. Moreover, using the DEM-FDM coupled numerical method, not only can the interaction between geogrids and the surrounding ballast be studied, the strains and stresses of the FDM geogrid model can also be well analysed. Figure 2c,d show the FDM models of biaxial and triaxial geogrids. Firstly, referring to the real geogrid, the geogrid geometric model was created and meshed in FEM software, and then the geogrid mesh file was exported into FLAC3D. Similarly to the generation method of a geogrid in PFC3D, the geogrid model in FLAC3D is also extended by means of some basic components such as ribs and junctions. The whole geogrid model is created through the assembly of the two basic components: ribs and connecting junctions. Both square and triangular geogrids were modeled in the large pullout test simulations. In addition, the buckling characteristics of geogrids are related to the flexural stiffness of the rib, which depends on the material properties and its geometrical properties. As shown in Figure 2e, the geogrid has a rib with a rectangular-shaped cross-section so that the bending stiffness of the geogrid rib along horizontal and vertical planes is different, which makes the deformation characteristics of geogrid more real [17].  
Figure 1: Large box pullout test set-up in the laboratory.
## 2 Data and Methods
### Methodology  
#### 2.3.1 Linear Regression (LR)  
To extract the correlations between \(V_{s}\) and other parameters, the corresponding cross-plots for all well logs have been drawn in Figure 4.  
Based on Figure 4, it can be seen that the \(V_{p}\) and \(PIGT\)(porosity) logs show good correlations with the \(V_{s}\) data. The linear correlation between the \(V_{s}\) and \(V_{p}\) was obtained as  
\[V_{s}=0.45~{}V_{p}+0.001, \tag{1}\]  
where \(V_{s}\) (ft/ us) and \(V_{p}\) (ft/ us) are shear and compressional wave velocities, respectively. Moreover, for the above equation, the \(R^{2}\) was 0.95, the \(RMSE\) was equal to 0.00032, and the \(MAE\) was equal to 0.00029. Such a high correlation coefficient shows that Equation (1) is appropriate for predicting the \(V_{s}\) in the Aboozar limestone oilfield. It is noteworthy that the \(V_{s},~{}V_{p}\), and rock density can be utilized for estimation of the elastic moduli of different rocks [54; 55].  
Figure 4: _Cont._  
Figure 3: The well logging data utilized to predict the \(V_{s}\) in the research.  
The inclusion of linear regression in this study serves a dual purpose. Firstly, it allows for a baseline comparison with traditional linear methods, providing a clear contrast to highlight the superior predictive performance of our chosen nonlinear algorithms (gene expression programming and multivariate regression). Additionally, linear regression models offer inherent interpretability, contributing to a nuanced understanding of the predictive capabilities of both linear and nonlinear approaches. This choice facilitates a comprehensive analysis and comparison, demonstrating the advantages of employing nonlinear methods for predicting shear wave velocity while acknowledging the historical significance of linear regression in empirical correlations such as the Pickett equation.
## 1 Introduction
With such properties, applications can be found in the petroleum, chemical and food industries. It is noteworthy that the use of bentonite in industry and environmental engineering is increasing year by year. One of their fundamental applications is the stabilization of boreholes and deep trenches. They are also employed as stabilizing and softening agents as well as absorbents [24]. Bentonite is often the main component of the modified barrier system, due to its good sorption capacity, microporous structure, low hydraulic conductivity, and high ion exchange capacity (JAEA) [25]. Horizontal directional drilling (HDD) is a minimally-intrusive construction method for installing underground utilities and pipelines. Today, HDD is becoming widely accepted as a cost-effective alternative to traditional open-cut construction [26]. This method is considered environmentally friendly, as long as the appropriate working standards are maintained. Horizontal directional drilling (HDD) contractors are currently facing heightened environmental scrutiny due to concerns regarding hydraulic fracturing and the subsequent migration of drilling fluids to the surface [27]. Risk factors connected with the use of the HDD drilling method were discussed, among other things, in the works of [PERSON] et al. [28]. The risk of drilling fluid loss is a significant problem in horizontal drilling (HDD) projects that involve drilling in a cracked rock layer [29]. The aim of the presented research is to evaluate the scale of the introduction of drilling fluid into the environment, based on the analysis of bentonite loss during drilling works using the horizontal directional drilling (HDD) method, and to determine the correlations between the loss, fluid recovery rate, and the soil fraction. The tests were conducted during the drilling stage, using low-waste HDD technology in valuable natural areas. The scope of the research includes, first of all, the scale of fluid loss and the determination of the correlation between the drilling fluid (bentonite) loss and soil type.
## Materials and Methods
### MOCT Laboratory Hardware
#### 2.1.4 Avalanche Photodetector  
The avalanche photodiode (APD), as seen in Figure 9, is responsible for detecting the 1531 nm optical pulses, and outputting the electrical pulses quickly and efficiently to be time stamped by the time-to-digital converter. The current APD is a Voxtel RIP1 series free-space InGaAs detector (Voxtel, Inc., Beaverton, OR, USA), with a 200 \(\upmu\)m active element and a transimpedance amplifier integrated within the housing to reduce noise and retain its 1 GHz bandwidth. Utilizing two programmable gain amplifiers (PGA) in series, the 30 mm \(\times\) 30 mm board can perform over a dynamic range of 60 dB while only consuming about 1 W. The APD board interfaces directly with a Raspberry Pi (emulating an FPGA board) via Serial Peripheral Interface (SPI), as well as with a daughter board via AC coupled SubMiniature version A (SMA) cables, which, using a comparator with a digital-to-analog converter configured threshold, sends a digital pulse to the time-to-digital converter board.
## 4 Performance Evaluation
### Outage Probability
#### 4.1.2 Outage Analysis of Source-Relay-Select Relaying  
In case 2, the relay \(i=I_{2}\) is selected according to the SNR of the first hop, as given in Equation (8); the maximum instantaneous SNR between AWACS-to-UAV links is  
\[\gamma_{SR_{2}}=\underset{i=1,2\cdots,N}{\text{max}}\Big{\{}\gamma_{SR_{i}}^{ FSO},\gamma_{SR_{i}}^{RF}\Big{\}} \tag{36}\]  
In the second hop, the SNR in the selected UAV-to-GS link is  
\[\gamma_{R_{I_{2}}D}=\max\!\left\{\gamma_{R_{I_{2}}D}^{FSO},\gamma_{R_{I_{2}}D}^ {RF}\right\} \tag{37}\]  
The maximum end-to-end instantaneous SNR of case 2 obtained is  
\[\gamma_{SD2}=\min\!\left\{\gamma_{SR_{I_{2}}},\gamma_{R_{I_{2}}D}\right\} \tag{38}\]  
As a result, the outage probability of case 2 can be given as  
\[P_{out2}=1-\Pr\!\left(\gamma_{SR_{I_{2}}}\geq\gamma_{th}\right)\!\Pr\!\left( \gamma_{R_{I_{2}}D}\geq\gamma_{th}\right)=1-(1-\prod_{i=1}^{N}\!\left(F_{\gamma _{SR_{i}}^{FSO}}(\gamma)F_{\gamma_{SR_{i}}^{RF}}(\gamma)\right))(1-F_{\gamma_{ R_{I_{2}}D}^{FSO}}(\gamma)F_{\gamma_{R_{I_{2}}D}^{RF}}(\gamma)) \tag{39}\]  
After substituting (28) and (31) in (39), the outage probability can be obtained.
## 4 Discussion
### Comparison with Other Structures of Sensors
#### 4.2.1 Bragg Grating Structure Biochemical Sensing  
The reflection wavelength of a Bragg grating varies with the refractive index, and its effective refractive index decreases with a decrease in the fiber core radius and ambient refractive index. The sensitivity of the effective refractive index increases with the ambient refractive index. Therefore, the sensitivity of the sensor can be obtained by fitting the wavelength response to the range of the refractive index. In previous studies [159], the hydrofluoric acid solution chemical corrosion method was used to detect glycerin solution in the refractive index range of 1.33-1.43, and the refractive index sensitivity was 172 nm/RIU. In another study [160], the fiber radius was designed to be 0.5 um, the sensitivities measured under the refractive index conditions of 1.20-1.30 and 1.33-1.43 are 477.33 nm/RIU and 856.30 nm/RIU, respectively. By optimizing the production parameters of the nanofiber grating, the linear sensitivity can reach 539.15 nm/RIU in the refractive index range of 1.348-1.397 [161]. It can be seen that the sensitivity and integration of Bragg grating refractive index sensing is not as good as that of optical waveguide refractive index sensing.
## 3 Results
### Parameters' Influence  
Figure 7 shows the tornado plot in which parameters are ranked by influence. An example of these results is shown for one batch during the rainy season (4 June 2012). According to the sensitivity analysis, the albedo has the most significant influence, and the other parameters have little effect. This means that at a higher albedo, lower air temperatures are expected. In the case of the instantaneous mass of paddy rice during drying, albedo is also the main parameter of influence, and the other parameters have less impact. Higher albedo values result in lower energy. Therefore, using a constant albedo value in the model may result in the over/underestimation of paddy rice temperature, and consequently, drying rate. Similar results were found for all the experiments and they showed a significant influence of albedo, with a correlation R ranging from \(-0.81\) to \(-0.67\) for the estimation of the exit temperature and from 0.92 to 0.98 for the instantaneous mass during drying.
# Lifetime Prediction of Single Crystal Nickel-Based Superalloys
## 1 Introduction
This model is particularly effective for single crystals, aligning with DCA under complex loading and critical slip plane stress formulations, and its mathematical formulation allows for straightforward integration into computational tools, enabling efficient fatigue analysis compared to other approaches [15; 17]. Integrating artificial neural networks (ANNs) into fatigue life assessment has significantly enhanced prediction accuracy and efficiency. ANNs are adept at handling complex, nonlinear interactions between variables, which are common in fatigue scenarios. Researchers like [PERSON] et al. [18] have demonstrated that machine learning can predict solder joint reliability under cyclic stress with higher precision than traditional methods. Moreover, ANNs have been applied to interpret multiaxial fatigue data, offering new insights into the critical stress and strain thresholds that precede material failure [19]. These advancements underscore the potential of ANNs to refine predictive models in high-stakes applications such as aerospace engineering, where many material failure mechanisms occurs simultaneously. In this study, the fatigue life of CMSX-4 is predicted, initially, by considering both crystal plasticity and multiaxial fatigue properties. The small strain constitutive crystal plasticity model, described later on, is used for modelling the stress-strain behaviour of the single crystal on geometric irregularities. Lifetime assessment modelling is conducted on the stress history obtained from the single crystal model by considering the cleavage stress as the main failure. Parameter optimization is performed for the Chaboche model at three different temperatures, and the fatigue curves provided by these models are fed into ANNs, where lifetime assessment is conducted for different temperatures with a single optimized model, unlike the Chaboche model.
###### Abstract  
In this study, natural clay minerals with green appearance were treated with sulfuric acid. Mass percentage of acid (wt%), temperature (T), contact time (t) and liquid-to-solid mass ratio (R) are used as the prevailing factors that determine the extent of acid-activation. The values of these factors range from 15-50%, 60-90 \({}^{\circ}\)C, 1.5-6 h and 4-7, respectively. The study has focused on the structural changes as well as textural characteristics of the clay. Three activated clay samples were prepared under different treatment conditions. The samples were characterized using X-ray powder diffraction (XRD), fourier transform infrared (FTIR), scanning electron microscope (SEM), chemical analysis and N\({}_{2}\) adsorption techniques. Characterization of the treated clay minerals exhibited significant structural changes to a greater extent of acid-activation, from being partially crystalline to being amorphous silica. The surface area and total pore volume of clay increased proportionally with the level of acid treatment. The average pore diameter behaved differently. During the strong acid treatment, a large increase in pore volume and the enlargement of the pore size distribution were observed. This suggests that considerable structural changes and partial destruction may have occurred in this condition. The removal of methylene blue, used as cationic dye, from aqueous solution by the batch adsorption technique on three prepared acid-activated clay samples was studied. The Langmuir model was found to agree well with the experimental data.  
Keywords:clay minerals; acid activation; characterization; structural changes; cationic dye; adsorption
# Strategies for Enhancing Extracellular Electron Transfer in Environmental Biotechnology: A Review
## 3 Strategies for Enhancing EET in Environmental Biotechnology
### Enhancing EET by Magnetism
The resultant findings indicated that consistent with prior research, the operation of MFCs is negatively impacted by MFs at elevated intensities. The appropriate selection of MF intensity may have a gradual impact on the electrochemical performance of BESs. Nonetheless, such an effect must be maintained over an extended duration. To assess the impact, four distinct intensities of MF specifically, 20, 120, 220, and 360 mT were investigated by [PERSON] et al. [93]. The findings of the study demonstrated a notable enhancement in electricity generation and overall efficiency across various levels of intensity, with the most optimal performance observed at an intensity of 220 mT. An in-depth analysis of biofilm development also unveiled a noticeable disparity in the adhesion of microorganisms onto the carbon-felt surface used. Their findings illustrated that the presence of biofilm is significantly reduced at 360 mT compared to the control and bare carbon felt. This suggests that elevated intensities may have a detrimental effect on the formation of biofilm. The utilization of an external magnetic field and an applied potential in MES exhibited a mutually beneficial effect in stimulating EPS secretion and promoting the proliferation of EAB on the surface of the electrode. Microalgae was employed in a bioachode as a means to address the issue of membrane resistance resulting from catholyte accumulation [96]. From the study, the utilization of a magnetic field with an intensity of 200 mT resulted in a greater biomass concentration and led to enhanced cell performance through expedited oxygen reduction reactions. The aforementioned observation suggests that the utilization of MF exhibits a considerable capacity and is appropriate for augmenting bioelectrochemical reactions.
## 2 Experiment Setup and Data Analysis  
### Wind Tunnel Test Setup  
The schematic wind tunnel experimental study program was conducted in the Building Aerodynamics Laboratory of Hunan University of Science and Technology. Consideringthe simulated flow properties for the specific wind tunnel, a geometric scale of 1:400 is selected, and the wind tunnel tests are performed for the most critical suburb terrain exposure D in load code for the design of building structures (GB50009-2012). The atmospheric boundary layer was modelled at a geometric scale of 1:400. Some devices (spires, vortex generators, and roughness elements) were placed at the entrance to the test section to generate acceptable mean and turbulent flow condition [20]. The mean velocity and turbulence intensity profiles matched the specification of GB50009-2012 with power law of \(\alpha=0.30\) and I\({}_{0}=0.39\) for exposure D, which are shown in Figure 1a. \(\alpha\) is the power law index. I\({}_{0}\) is the turbulence intensity at the height of 10 m, and H is the height of building. Figure 1b shows the spectrum of the longitudinal wind velocity fluctuations, compared with the Karman spectrum.  
The full-scale dimensions of the modelled Commonwealth Advisory Aeronautical Research Council (CAARC) high-rise building are 30.48 m \(\times\) 45.72 m \(\times\) 182.88 m (length \(\times\) width \(\times\) height). The scale ratio for the wind tunnel test model is 1:400. The size in model scale was 76.20 mm \(\times\) 114.30 mm \(\times\) 457.20 mm (length \(\times\) width \(\times\) height). The dimensions of test section for the wind tunnel are 4 m width \(\times\) 3 m height, resulting in the blockage ratio of wind tunnel tests being 0.29-0.44%. Figure 2 shows the experimental model for the wind tunnel test. The model building is equipped with a total of 308 pressure taps, and the swarmed pressure taps were arranged at 2/3 the height of the building. Figure 3 shows the building sizes, the positions of the pressure measuring taps, the number of the test points in the swarmed area, and the wind direction arrangement. In the corner diagram, the four facades of the building are named East E, West W, South S, and North N. The wind direction angle is 0\({}^{\circ}\) when facing northward.  
Figure 1: Approach flow condition for exposure D. (**a**) Mean velocity and turbulence intensity profiles; (**b**) Longitudinal wind velocity fluctuations spectrum at 2/3 building height.  
The sampling frequency of the test is 330 Hz, and each measurement tap is sampled for about 60 s. Sixty seconds for wind tunnel tests is equivalent to one hour in full scale. The total length of one sample is 20,000 data points, and each wind direction angle is independently sampled 20 times repeatedly. In the tests, the reference height is selected at 45.7 cm to the bottom of the test model, which is the height of the building.
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 4 Experimental Setup  
To depict a real-world use case scenario, this research study replicates the deployment of IoT sensors in a smart hospital. Due to significant and critical assets at stake, including patients' life, network security is a key issue for smart hospitals. There are several networked medical devices enabling remote patient monitoring. Stationary as well as mobile devices are used a lot in the smart hospital context. These devices include mobile devices, wearable external devices, stationary, implantable, and supportive devices [53].  
Two types of network architectures can be considered for the smart hospital application: centralized and distributed. This research considers the centralized architecture where each node communicates with a sink or root node, also referred to as a controlling node. This type of setting is well supported by Contiki/Cooja simulator for a simple evaluation, according to the acknowledgment that exhaustive evaluation of the scenario will be too daunting to undertake. SMTrust carefully analyses and selects the simulation parameters values as adopted by [14; 15; 16] with justification for the assumptions suitable according to the real-world scenario. Table 4 shows the parameters setting in the Cooja simulator. Attacker nodes are positioned randomly among the normal nodes to illustrate a real use-case scenario where attacker nodes can infiltrate the internal network of the smart environment.  
To evaluate the performance of SMTrust in a mobile IoT environment, a mobility plugin with Cooja is used. BonnMotion-3.0.1 tool [54] was used to generate the mobility scenario for nodes using the Random Waypoint mobility model. For a mobile IoT environment, mobility speed was kept as 0 to 6.23 km/h and mobile to static node ratio as 1:3 approximately [16]. Figure 6 shows the distribution of normal and attacker nodes in the network simulated using Cooja, with 1 sink node (node id: 1), 26 sender nodes (node id: 2-27), and 3 attacker nodes (node id: 28, 29, 30). Similarly, Figure 7 illustrates the network after 60 min simulation run with mobile sink and sender nodes in the network.  
Figure 5: Detection of Blackhole Attack.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulation Tool & InstantContiki2.7/Cooja \\ Simulation coverage area & \(110\) m \(\times\)\(110\) m \\ Total number of nodes & 30 \\ Malicious nodes & 3 \\ Deployment of nodes & Random positioning \\ TX range & 50 m \\ INT range & 60 m \\ TX ratio & 100\% \\ RX ratio & 30–100\% \\ Routing Protocol & MRHOF, SMTTrusOF \\ Network protocol & IPv6 based \\ Start-up Delay & 5000 milliseconds \\ Radio Medium & UDGM Distance Loss \\ Simulation period & 60 min \\ Environment & Static, Mobile \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation Parameters.  
Figure 6: RPL Network of 30 nodes in Cooja simulator: sink node (1), sender nodes (2–27), attacker nodes (28, 29, 30).  
Figure 7: Network screenshot after 60 min simulation run, with mobile sink and Sender nodes in the network.
## Materials and Methods
### Data Analysis  
Two indices, including the root-mean-square error (RMSE) and the coefficient of determination (R\({}^{2}\)), were used to comprehensively evaluate the performance of three remotely-derived GPP products at 8-day period. The linear trend (\(k\)) was also calculated to represent long-term interannual variations over diverse climate zones. Subsequently, to reveal the dominant environmental factors controlling the dynamics in GPP \(\upsilon\) 2001-2015, the Pearson correlation coefficient (\(r\)) was calculated to examine the relationships between the reliable GPP product and spatial temperature (T) and precipitation (P) patterns. The equations of R\({}^{2}\), RMSE, \(k\), and \(r\) are as follows:  
\[\text{R}^{2}=1-\sum{(\hat{y}-y)^{2}}/\sum{y^{2}} \tag{9}\]  
\[\text{RMSE}=\sqrt{\sum{(\hat{y}-y)^{2}}/n} \tag{10}\]  
\[k=(\sum{{  
where \(y\) and \(\hat{y}\) represent the VPM GPP product and the GPP estimates of MODIS or BESS, respectively; t\({}_{i}\) referred to the year \(i\), \(x_{i}\) and \(y_{i}\) represented the interannual GPP and the associated annual mean T and accumulated P of the year \(i\), and \(\overline{x}\) and \(\overline{y}\) represented multiyear average GPP, T or P, respectively. A minimum RMSE and a maximum R\({}^{2}\) were obtained by the optimal satellite-derived GPP products in general. All the statistics were performed through SPSS 19.0 (IBM, Chicago, IL, USA). Moreover, the MODIS reprojection tool (MRT) and the interactive data language (IDL) in ENVI 5.3 were used to process a large number of remote sensing data sets.