esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Lignin for Slope Bioengineering: Effect on Soil Improvement and Plant Growth
## 3 Materials and Methods  
This study included a series of experiments on three different soil types treated with different solutions of lignin. The change in the soil strength over 40 days was estimated by conducting pocket penetrometer and vane shear tests. Three types of seeds were used to  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil Type** & **Biopolymers** & **Content (\%)** & **Method** & **Vegetation Type** & **Curing Days** & **Outcomes** & **Reference** \\ \hline Sand & Polyurethane & 0–20 & Mixing & _Ryegrasses (60 g of seeds)_ & 10–60 & Polyurethane polymer did not show effective promotion of vegetation growth compared to untreated sand, especially at concentrations above 10\%. & [PERSON] et al. [46] \\ \hline Sand & Xanthan gum, cationamyl, potassium nitrate, carboxy methyl cellulose & 0.5–1 & Mixing & _Festuca, Poa Dridensis, Loium permetine and Trifolium repens seds_ & 1–27 & Biopolymers are capable of improving the overall vegetation growth. & [PERSON] et al. [37] \\ \hline Weathered granite soil & Xanthan gum, starch, \(\beta\)-glucan & 0.45–0.5 & Mixing— spray & _Seeds_ & 30 & Xanthan gum, starch, and \(\beta\)-glucan compounds were implemented using the wet-spraying method to strengthen the structure and promote vegetation growth on levee slopes. & [PERSON] et al. [47] \\ \hline Sand & Hydrophilic polyosaccharide (HPB) & 0.05–1 & Spraying & _Ryegrass and Bermuda grass_ & 12 & HPB concentration of less than 5\% promotes vegetation growth. & [PERSON] et al. [48] \\ \hline Clay soil & Xanthan gum & 0.25–1 & Mixing & _Oats (160 seeds)_ & 1–14 & Xanthan gum promotes vegetation growth at 0.25\% to 0.5\% dosage; above 0.5\% may impede vegetation growth. & [PERSON] et al. [49] \\ \hline Desert sand & MICP & 0.1–0.5 M & Spraying & _Heracleum persicum_ & 7–30 & MICP biopolymer promotes the germination of Heracleum persicum. & [PERSON] et al. [36] \\ \hline Loess & Xanthan gum & 0–1 & Mixing & _Oats (160 seeds)_ & 14 & Xanthan gum yields higher germination from 0.25\% to 1\% dosage and high root content at 0.25\% to 0.75\%. & [PERSON] et al. [50] \\ \hline Clay & Xanthan gum & 0–4 & Mixing & _LP seeds_ & 60 & Xanthan gum promotes LP growth, and increasing Xanthan dosage increases the growth rate. & [PERSON] et al. [51] \\ \hline \hline \end{tabular} Note: IPCs—inter-polyelectrolyte complexes; HPAN—Hydrolyzed Polyacrylonitrile; PDADMAC—PolyDiallyldimethylammonium Chloride; KNO3—potassium nitrate; MICP—microbially induced calcium carbonate precipitation.  
\end{table}
Table 2: _Cont._estimate the effect of lignin on seed germination and growth. The following section details the materials used and the experimental procedure.
## 3 Results
### Patient-Specific Method Results  
The results of the new algorithm presented in Algorithm 1 for the patient-specific method are depicted in Figure 3.  
Figure 2: Results of LSTM-estimated MAP vs. actual MAP values for different patients using the population-specific method.  
For the state activation function (Figure 3a), tanh dominates with an appearance in 83% of patients, while softsign is the best state activation function for 17% of patients. The results of the gate activation function (Figure 3b) show that the sigmoid is the most frequent, with an appearance in 67.1% of patients. This result suggests that the sigmoid activation function is more adept at regulating the flow of information through the LSTM gates, potentially contributing to better model convergence. The results in (Figure 3c) reveal that the adam optimizer is the most prevalent optimizer with presence in 95.7% of patients. By contrast, rmsprop was identified in only three patients. The mini-batch size of 32 is the most prevalent (Figure 3d), occurring in 41% of the patients. The selection of 100 neurons per layer (Figure 3e) is the predominant configuration, appearing in 46% of patients, with variations in other configurations (50 neurons at 20%, 75 neurons at 27%, 10 neurons at 7%). The results (Figure 3f) indicate that 67% of patients fall within the three-layer approach. The variations across parameters emphasize the need for personalized adjustments. This analysis enhances our understanding of prevalent trends and informs future refinements in LSTM architecture selection for physiological parameter estimation during anesthesia.  
The best architecture for each patient is used to estimate the MAP. The results of patients with the worst and best fitting estimations are presented in Figures 4a and 4b, respectively. The accuracy of the fitting is higher when the correlation between inputs (CeProp, CeRemi) and output (MAP) is strong, as seen in Patient 63. Conversely, for Patient 15, the estimation accuracy is lower due to the weak correlation between inputs and output.  
Figure 4: Results of LSTM-estimated MAP vs. actual MAP values for different patients using the patient-specific method.  
Figure 3: Percentages of appearance of each option among the different iterations and loops for the patient-specific method.
## Methods  
### Model Development  
#### 2.1.1 The Community Earth System Model  
The CESM1.2.0 [29] was developed from the Community Climate System Model, version 4 (CCSM4). It consists of seven geophysical models: atmosphere, land, ocean, land-ice, runoff, sea-ice, and ocean-wave, linked by a coupler that passes information among them. The atmospheric component is the Community Atmosphere Model, version 4 (CAM4) [30], which uses the Lin-Rood finite-volume dynamic core with an atmospheric grid spacing of 0.9\({}^{\circ}\)\(\times\) 1.25\({}^{\circ}\) horizontally, and 26 levels in the vertical direction arranged in a hybrid pressure sigma coordinate system [31]. The land component, CLM4.5, which is a state-of-the-art LSM, is the latest version of the CLM family of models. The spatial heterogeneity of the land surface is represented in CLM as a nested sub-grid hierarchy, and vegetation is classified into 16 plant functional types (PFTs) according to different photosynthesis parameters and optical properties. This model can simulate the exchange of radiation, momentum, heat, and water-vapor flux between the land and the atmosphere, as well as hydrologic processes (including precipitation interception, soil water infiltration, and so on). CLM4.5 also has a biogeochemistry module called the carbon-nitrogen cycle (C-N) model, which is fully prognostic with respect to all C and N state variables in vegetation, litter, and soil organic matter, to provide an explicit description of photosynthesis, respiration, and plant phenology [32]. A detailed description of its biogeophysical and biogeochemical parameterizations and numerical implementation can be found in [PERSON] et al. [33].  
The root distribution function that determines the fraction of roots (\(r_{i}\)) in each soil layer in CLM4.5 can be written as follows [34]:  
\[r_{i}=\left\{\begin{array}{ll}0.5\Bigg{[}\begin{array}{c}\exp(-r_{a}z_{h,i-1 })+\exp(-r_{b}z_{h,i-1})-\\ \exp(-r_{a}z_{h,i})-\exp(-r_{b}z_{h,i})\end{array}\Bigg{]}&\text{for }1\leq i<10\\ 0.5[\exp(-r_{a}z_{h,i-1})+\exp(-r_{b}z_{h,i-1})]&\text{for }i=10\end{array}\right\} \tag{1}\]  
where \(z_{h,i}\) (m) is the depth from the soil surface to the interface between layer \(i\) and \(i+1\); \(r_{a}\) and \(r_{b}\) are two PFT-dependent root parameters.
## 3 Methodology  
### Herschel-Bulkley Rheological Model  
Rheology is the study of the flow of fluids or materials that accounts for the behavior of non-Newtonian fluids. Bingham plastic [57] and the power law [58; 59] are two-parameter models that were initially used in several studies to describe non-Newtonian characteristics of fluids. [PERSON] and [PERSON] [1] suggested a three-parameter model, also known as Yield Power Law (YPL), and later more complex models up to 5 parameters have also been suggested [3]. However, these high dimensional rheological models are uncommon due to the complexity in evaluating the flow parameters such as velocity profile, Reynolds number, pressure drop, and the hydraulic parameters. These parameters could only be determined by numerical simulations [24]. To maintain the simplicity and calculation accuracy, Herschel-Bulkley model is widely used in industry. The literature reveals that it better fits the two-parameter models [2; 3] which describes the drilling fluids rheology precisely [18; 19; 20; 21; 22; 23; 24]. The model is defined in Equation (1).  
\[\tau=\tau_{y}+\kappa y^{n} \tag{1}\]where shear stress is represented by \(\tau\),  
Yield stress by \(\tau_{y}\), fluid consistency by k, n is fluid behavior index and \(\gamma\) denotes shear rate. The pressure loss for the concentric annulus is calculated by Equation (2).  
\[\Delta P=\frac{dp_{f}}{dL}=\frac{2f\rho v^{2}}{D_{o}-D_{i}}=\frac{4\tau_{w}}{D_ {o}-D_{i}} \tag{2}\]  
where, D\({}_{o}\) is outer diameter of annulus (m), D\({}_{i}\) is inner tube's diameter of annulus (m), \(\rho\) is density of fluid (kg/m\({}^{3}\)), \(\tau_{w}\) is shear stress of wall (Pa), \(f\) is the friction factor (dimensionless). Determination of the friction factor (\(f\)) for different flow regimes requires an iterative trial and error method.  
The non-Newtonian fluids represented by Herschel-Bulkley model pose few challenges while performing numerical simulations because the flow around the centre is characterized as plug region and near the wall as non-plug flow. There are certain parametric conditions imposed under which the above-mentioned model can approximate the behaviour of such fluids, such as K \(>\) 0, \(\tau_{y}\) \(>\) 0 and 0 \(<\) n \(<\infty\). Due to such restrictions, the complexity of the derived governing equations increases making it highly non-linear in nature and requires strenuous effort to solve it. Many analytical and experimental work conducted in the past have been outlined in aforementioned literature that clearly illustrate the difficulty associated with approximating the behaviour of such nonlinear three parameter fluid model.
## 1 Introduction  
The evaluation and monitoring of tooth wear are essential for therapeutic strategies and scientific purposes. For scientific investigations, wear measurement is still predominantly based on a conventional workflow [1, 2] by taking impressions and fabricating casts. Depending on the analytical method, different subsequent steps are essential. The cast can be digitized with a laboratory or an industrial high-resolution scanner [3, 4, 5, 6]. The obtained 3D data files of different wear statuses are superimposed with 3D analyzing software to calculate metrical wear values. If optical profilometry is applied, the wear facets have to be identified by scanning electron microscopy (SEM). Then, the identified areas are captured by profilometry. The digital data files are superimposed, and vertical height loss is analyzed between two different stages of wear. In summary, conventional workflows are time-consuming and, therefore, far away from real-time analysis. Although single steps of the conventional workflow like SEM and profilometry are highly accurate, some other essential procedures like conventional impression taking and producing the casts limit the overall accuracy. Intraoral scanning offers direct access to 3D data files without the necessity of intermediate steps. In literature, the first insights towards implementing optical impression systems are reported [7, 8]. [PERSON] et al. showed that in vitro wear analysis based on data files obtained with an intraoral scanner was comparable to optical profilometry. The Lava C.O.S.'s (Chairside Oral Scanner, 3M ESPE) optical principle was based on wavefront sampling, and \"dusting\" of the scanned surface with titanium dioxide was required. Therefore, the study aimed to test if wear measurements based on data files obtained with powder-free scanners are comparable to results based on profilometry.  
The null hypothesis stated was:  
The exclusively digital workflow with data acquired on the basis of intraoral optical impression systems is equal to the white light profilometer for wear measurement within deviations of a maximum of 10%.
## 2 Models and Methods  
### Models  
In order to investigate the effect of barriers on the propagation process of spontaneous fault rupture, an analysis was conducted on whether the research conclusions are consistent when using different friction laws. First, we established a two-dimensional linear elastic fault rupture model, as shown in Figure 1. The model space geometry was set as \(300\times 300\) km square with a fault length of 120 km, in which the asterisk represents the nucleation zone in which the stress state exceeds the critical state that was required by the rupture criterion before the rupture begins, and its length (L\({}_{nz}\)) was 3 km; the thick blue line represents the barrier, the length of which (L\({}_{rz}\)) was 5 km, and the distance between the barrier and the nucleation zone was 70 km. Moreover, uniform compressive normal stresses -\(\sigma_{0}\) (positive in tension) and shear stress \(\tau_{0}\) were applied in the whole model region as initial stress conditions. An additional effective normal stress \(\Delta\sigma_{n}\) was applied in the barrier so that the initial normal stress in the barrier was higher than that in other areas of the fault. To prevent seismic waves from affecting the results by boundary reflection, an infinite element-based absorbing boundary (gray part in Figure 1) was set around the model.  
Figure 1: Model geometry, the initial conditions, and boundary conditions. The model domain is 300 by 300 km. The black line in the figure represents the location of the fault, and the red star marks the nucleation patch. The blue line denotes the barrier, and the gray region around the model is the absorbing boundary.  
The finite element model was divided by four-node element. To meet the purpose of calculation accuracy and eliminating the effect of an uneven grid size on the model calculation, all the elements in the model had a uniform side length of 100 m. The finite element model had 9,019,234 nodes and 9,012,000 elements. The model parameters are summarized in Table 1.
## 2 Materials and Methods
### Statistical Analysis and Accuracy Validation  
We used the metrics of \(R\) (correlation coefficient), \(SD\) (standard deviation), \(RMSE\) (root-mean-square error), \(RMSD\) (root-mean-square difference), \(R^{2}\) (coefficient of determination),  
Figure 2: Workflow of GPP estimation through the integration of LUE models based on ground measurements, remote sensing observations, and Köppen–Geiger climate zones.  
and _RPE_ (relative predictive error) in Taylor diagrams [53] and scatter plots to evaluate the LUE models, SVM, and RF. The _RMSE_, _RMSD_, and _RPE_ are defined as:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{m}\left(\hat{y}_{i}-y_{i}\right)^{2}}{m}} \tag{5}\]  
\[RMSD=\sqrt{\frac{\sum_{i=1}^{m}\left[\left(\hat{y}_{i}-\overline{\hat{y}} \right)-\left(y_{i}-\overline{y}\right)\right]^{2}}{m}} \tag{6}\]  
\[RPE=\frac{\overline{\hat{y}}-\overline{\hat{y}}}{\overline{\hat{y}}}*100\% \tag{7}\]  
where \(\hat{y}_{i}\) and \(y_{i}\) represent the GPP estimated by the LUE models or machine learning methods, and FLUXNET2015 sites' GPP; \(\overline{\hat{y}}\) is the average of modeled values; \(\overline{\hat{y}}\) is the average of observed values; and \(m\) denotes the sample size. Better model performance is indicated by higher \(R\) or \(R^{2}\), lower SD or _RMSE_/_RMSD_, and lower absolute _RPE_.  
The RF and SVM were evaluated by a method named 5-fold cross validation. All data were partitioned randomly into five groups with equal samples, and one group was chosen as a validation set while the remaining four groups were taken as a training set. We trained models with four of the five groups and then validated GPP estimates of the machine learning by using the remaining group. Then, the average of \(R^{2}\), _RMSE_, and _RPE_ for the SVM and RF was calculated. We also used site-derived and simulated data to test two kinds of GPP variability across forest types and ground sites.  
The index of _AIC_ and _BIC_ were used here to evaluate models from candidates. The definitions of _AIC_ and _BIC_ are as follows [54; 55]:  
\[AIC=-2*ln(H)+2*k \tag{8}\]  
\[BIC=-2*ln(H)+k*ln(m) \tag{9}\]  
where \(H\) denotes the maximum likelihood function, \(k\) represents how many parameters are needed, and \(m\) represents sample size. Lower values of _AIC_ and _BIC_ indicate a better choice.
# Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing
## 5 Agent-Based VM Migration
### VM Migration Policies  
The VM migration policy is used to decide when to initiate a VM migration from one Server Agent (SA) to another. This policy comprises two essential components: a migration threshold and an activation threshold.  
The migration threshold indicates that the resource utilization level is about to degrade the performance of the host or that a VM does not match the security level of the current host. SAs employ VM migration thresholds to initiate VM migrations. SAs are provided with both resource-based and security-based migration thresholds and can activate either one or both simultaneously. For instance, a host may face a considerable performance decline when the CPU resource is relatively exhausted while there is still idle memory. Consequently, its SA uses the migration threshold of CPUs to trigger VM migrations.  
The activation threshold prevents unnecessary VM migrations. That is, peaks in resource usage that last only for a short period of time. It indicates the maximum time that the resource usage surpasses the migration thresholds (based on the host monitoring rate) before attempting to initiate VM migrations, as in [27].  
Both thresholds are decided by the administrator based on the resources of each host. Hosts with more computing resources will of course have higher resource-based thresholds, but the security-based threshold is only related to the matching of the security level of the host and the VM.
## 4 Abnormal Measurement Exclusion Using RAIM Multiple Fault Detection and Exclusion
However, it is very difficult for the algorithm to identify which one is clean or abnormal. Note that the optimal receiver clock bias calculated by the proposed method is also estimated by the two satellites. In this case, it has a high probability that the consistency check excludes a clean LOS signal. Thus, the proposed method uses the RAIM when at least three  
Figure 5: RAIM multiple fault exclusion used in the proposed 3D map method. TS, test statistic; and DOF, degree of freedom. satellites from a same constellation are received. In the case of two LOS signals and one abnormal NLOS signal are received, the proposed method is capable of excluding the abnormal NLOS. Even if in a special case that one LOS and two abnormal NLOS are received, the two abnormal NLOS do not seem to agree with each other. Generally speaking, the LOS will not be excluded by the proposed RAIM algorithm. Thus, the proposed RAIM is only used when the \(DOF\) is larger than 1. The probability of false alarm (P\({}_{\text{FA}}\)) used in this paper is \(10^{-4}\). By giving the P\({}_{\text{FA}}\), the value of the chi-square threshold (\(T\)) can be calculated as [28, 31, 39]:  
\[1-P_{FA}=\frac{1}{\Gamma(DOF/2)}\int_{0}^{T^{2}}e^{-s}s^{\frac{DOF}{2}}ds \tag{5}\]  
To reduce the computational load, the value of the chi-square (consistency) threshold is calculated in advance and saved in the program as listed in Table 2.  
If the test statistic is smaller than the value of the chi-square threshold, all the valid satellites will be used in the position estimation. On the other hand, if the test statistic is larger than the chi-square threshold, it implies one of the valid satellites used in the subset may be deteriorated by double or multiple reflected NLOS. In this case, the satellite exclusion by RAIM is required. We apply the greedy search algorithm to remove the satellite one by one until it meets the chi-square threshold [37]. The fault exclusion used in this paper assumes only one satellite is biased at one iteration. It is achieved by trying every subset. \[\hat{\rho}_{n}^{(i)}=R_{n}^{(i)}+c\big{(}\delta t^{\tau(i)}-\delta t_{n}^{ \text{sv}}\big{)}+I_{n}+T_{n}+\varepsilon_{n}^{\text{refl}(i)} \tag{1}\]0  
\[\hat{\rho}_{n}^{(i)}=R_{n}^{(i)}+c\big{(}\delta t^{\tau(i)}-\delta t_{n}^{ \text{sv}}\big{)}+I_{n}+T_{n}+\varepsilon_{n}^{\text{refl}(i)} \tag{1}\]1  
Again, if the _TS(K)_ is smaller than the chi-square threshold, the \(k\)th satellite will be excluded if the test statistic of the subset without it is the minimum. As a result, the developed method can exclude the abnormal NLOS signal to estimate a more accurate position solution. However, if the _TS(K)_ is still larger than the threshold, we exclude the satellite measurement that has the largest impact on the test statistic. In other words, the \(k_{excluded}\) satellite will be excluded from the subset of the next iteration. The iteration will stop until the remaining set of the measurements is self-consistent or the DOF is not enough.
# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
## 1 Introduction
However, there is still a gap that need to be filled for the accurate detection of the maximum operating point under partial shading conditions. In addition, convergence time, tracking accuracy, and undershoot voltage rate issues under rapidly changing irradiance levels seem to be significant performance parameters that need to be improved. In this context, a hybrid MPPT method based on Perturb & Observe and Particle Swarm Optimization is proposed to determine the global maximum power point under partial shading conditions. The proposed hybrid MPPT method is tested under different partial shading conditions and rapidly changing irradiance levels. The obtained results reveal that the dynamic response of the system is remarkably increased by the proposed MPPT method. Moreover, the maximum operating point is accurately determined to obtain the maximum power for partial shading conditions. The main contributions of this study are listed as follows;  
* The dynamic response of the system is remarkably improved for rapidly changing irradiance levels,
* Maximum operating point determination capability of the developed algorithm is high when compared to existing methods in the current literature,
* The number of oscillations under steady-state operation is significantly reduced,
* The accuracy of the perturbation direction is increased by the proposed method. The proposed method seems to be a promising method in terms of reduced under-shoot voltage, improved convergence time, and increased tracking accuracy under partial shading conditions. The rest of the paper is organized as follows. In Section 2, the characteristics of the PV system, which is created in MATLAB (version R2016b)/Simulink environment, are mentioned. In Section 3, the features of the simulation scenarios are discussed. In Section 4, the purpose of the study, the method of the study, and the characteristics of the PV system are discussed. Finally, in Section 4, the study results are given and the outputs are interpreted.