esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Preservation Studies of As(III/V) Stability in Natural Water Samples
The oxidation of As(III), in conjunction with the photochemical reduction of Fe(III), takes place according to the following net reaction [37]:  
\[2\;\mathrm{Fe^{3+}}+\mathrm{H_{3}AsO_{3}}+\mathrm{H_{2}O}+\mathrm{hv}\rightleftharpoons 2\; \mathrm{Fe^{2+}}+\mathrm{H_{3}AsO_{4}}+2\;\mathrm{H^{+}} \tag{1}\]  
Hydroxyl radicals are produced when the pH > 2 and a water sample containing Fe(III) is exposed to light. These radicals react with As(III) to produce an intermediate As(IV) species, which then reacts with Fe(III) to produce Fe(II) and As(V). However, As(III) oxidation by Fe(III) is slowed with the presence of Fe(II) and Fe-complexing anions, and effectively stopped when light is excluded [38]. Finally, the total inorganic As(III)/As(V) concentrations were stable over the 7-day period for the CA-treated subsamples. Therefore, without treatment, the formation of the Fe coprecipitates caused a significant drop in the native As concentrations, while the CA treatment inhibited this process and preserved the native arsenic distribution in the four waters utilized in this study. The acidification of aqueous samples is a widely practiced protocol aimed at suppressing the precipitation of Fe by increasing its solubility. Acidification also helps in reducing microbial activity. Sample acidification is necessary to bring all Fe(III) to solution. [PERSON] et al. used EDTA to preserve groundwater samples for 3 months without adding acetic acid [11]. However, other studies have shown that EDTA alone cannot preserve arsenic speciation, and pH adjustment to 3.2 is necessary to inhibit the oxidation of Fe(II) [39]. Acid preservation could be used for a wide range of surface water and groundwater samples containing dissolved Fe. However, though complexing agents are useful to preserve Fe-rich samples, the complexing agents-HAc combination is more effective in preserving arsenic speciation. The selection of a proper preservation strategy for arsenic species also depends on the analytical methodology to be followed. Tables 3 and 4 show the summary results of As(III) concentrations on the first and sixth days of sampling, as well as the percent of deviation from the initial As(III) concentration in natural water samples. It can be seen that combination of CA/HAC, as a preservative, can preserve the arsenic species for at least 6 days at room temperature in samples with lower arsenic and iron concentrations (not more than 10% deviation). This combination was successful for sample A, too, but not for sample B, which had the highest arsenic and iron concentrations. It is possible that the amount of added preservatives was not sufficient to complex this concentration of iron, but it is appropriate for lower concentrations. On the other hand, NaC was successful in preserving the distribution for about 6 days in samples C and D, but not in samples A and B.
## 3 Results and Discussion
### Brad Acceptability  
The highest acceptability in terms of overall appearance was achieved by the control bread, and the modification of the recipe influenced a decrease in the acceptability of this attribute, but no significant (\(p>0.05\)) differences were found.  
In the evaluation of crust color, a downward trend in acceptability was noted, but also the differences were non-significant (\(p>0.05\)). The crust of the enriched bread was characterized by a darker than control bread, greenish color, less characteristic of wheat bread, which reduced its acceptability. The presence of lyophilized kale also had a non-significant (\(p>0.05\)) impact on the acceptability of crust thickness. In contrast, other crust characteristics were rated significantly (\(p<0.05\)) lower than that of the control sample but with no variation in terms of lyophilized kale level. However, it can be assumed that the decisive influence on the results of this evaluation was the greenish color of the crust. A similar trend was found when evaluating the acceptability of crumb characteristics: springiness, porosity, and other properties. It was found, in all cases, a decreasing trend in  
Figure 4: Color parameters (**a**) and chlorophyll and carotenoids content (**b**) of the investigated breadcrumbs; means marked with the same letters do not differ statistically at 0.05 level of confidence.  
acceptability, but with non-significant (\(p>0.05\)) differences. On the other hand, the decline in acceptability in the case of taste and aroma was significant (Table 3), but the large values of standard deviations for the mean values resulted in no significant differences between them. This indicates that the panelists did not agree in assessing the acceptability of aroma and taste, which may be due to individual differences in preferences. These results are reflected in the work of other authors. [PERSON] et al. [34] observed a significant adverse effect of the share of celery powder on the acceptability, both overall and of individual sensory attributes. Similarly, [PERSON] et al. [45] noted that the share of broccoli-based powder in gluten-free cookies results in a significant decrease in product acceptability due to its green color, increase in hardness, and intense smell and taste. [PERSON] et al. [33] also observed a decrease in the acceptability of individual organoleptic characteristics of baked goods enriched with yellow bell pepper flour. Also, [PERSON] et al. [39] observed a decrease in the acceptability of the taste of bread enriched with onion extract. On the other hand, [PERSON] et al. [46] indicate that enriching baked goods with vegetable-based raw materials is an appropriate strategy for promoting and increasing their consumption, but the sensory acceptability of such products is closely connected with the kind of vegetables used. Probably, the appropriate promotion and health information for this type of product could favorably influence its sensory acceptability.
## 5 Random Channel Selection Intelligence Algorithm for CR in BNC for the WBAN System
**Step 2:**: In this step, the fitness function is made to run; the channel with the maximum BER obtained in the previous calculation is obtained from the LUT. The maximum number of times the channel is used by the BNC is counted and stored in a table. The maximum number of times the channel is used is kept as a reference and a decreasing order in the number of usage of licensed channels created. The highest maximum number of times the channel is used is marked as 'gbest', and the values are 19, 18, 15, 8, 2 respectively. Figure 5: Central Cognitive Controller with an inpatient and out-patient based hospital scenario. * A random number is arbitrarily chosen from a uniform distribution and compared with the Switch Probability. Then, the condition (rand \(>\) SP) decides the threshold and which channel in the search area must undergo either global or local channel selection. In the given example, if the threshold is set as (T = 5), four channels undergo the global channel selection, and one channel undergoes the local channel selection. **Global channel search:** During the first iteration, C1, C2, C3, and C5 undergo a global channel search, and C2 channel is first considered for a global search in Equation (1). The next optimum channel to be allocated for the IMD is in another direction toward the current 'gbest' channel. \[\text{Best~{}chaff point (global Best) Solution f(z)}=\left\{ \begin{array}{c}Z_{\text{f}}^{ \text{P}+1}=Z_{\text{f}}^{\text{P}+1}\,\ Z_{\text{f}}^{\text{P}+1}<Z_{\text{f}}^{\text{P}}\\ \\ Z_{\text{f}}^{\text{P}+1}=Z_{\text{f}}^{\text{P}+1}\,Z_{\text{f}}^{\text{P}+1}>Z_{ \text{f}}^{\text{P}}\end{array} \right. \tag{1}\]  
**Local channel search:** Only one point undergoes a local point search C4. The local channel search occurs between the threshold channel points close to it. The threshold channel C3 and point C1, another channel, combine to arrive at the free channel. This is the new channel C4, which is quite free for a long time. * Steps 1, 2, and 3 are repeated until all the free channels are searched, and a single free channel is selected. It is understood that the switch probability and fitness of the free channel value make a suitable condition for channel allocation to the OP-IMD by introducing the maximum randomness. In addition, the optimal channel is selected faster from the LUT of free channels due to the minimum convergence and small step of the variables. The EFPOC is best suited for channel selection application, as it explores globally and exploits locally within a single iteration. This process is concurrently performed for all the free channels shown in Figure 6, which is freely available at that time. Once the channel is selected, it is transferred to the OP-IMD immediately for communication with the hospital server. We attain an energy-saving method from a dynamic spectrum selection algorithm and improved the overall performance by simulating the EFPOC.
# Reviews on Corrugated Diaphrags in Miniature Fiber-Optic Pressure Sensors
## 2 The Principles of Operation of Miniature Fiber-Optic Pressure Sensors  
### The Basic Principles of EFPI Pressure Sensors  
The typical structure of a diaphragm-based EFPI fiber-optic pressure sensor is shown in Figure 1. The F-P cavity, either the fiber-tip structure (Figure 1a) or the fiber-end structure (Figure 1b), is usually composed of a fiber end and a sensitive diaphragm. The diaphragm's deformation due to external pressure will cause the F-P cavity's length to change, then modulate the reflected light signal, and then the external pressure information will be detected [16]. According to the F-P interference principle, the cavity length \(L\) could be written as  
\[L=\frac{\lambda_{1}\lambda_{2}}{2\pi(\lambda_{1}-\lambda_{2})}, \tag{1}\]  
where \(\lambda_{1}\), \(\lambda_{2}\) are the two center wavelengths of adjacent resonance peaks in interference spectra of the F-P cavity, \(n\) is the refractive index of medium in the F-P cavity, here, for air, \(n=1\). Thus, the cavity length \(L\) can be determined by obtaining the wavelengths \(\lambda_{1}\) and \(\lambda_{2}\) from the interference spectra, thereby calculating the pressure applied to the diaphragm.  
Figure 1: The typical structure of a diaphragm-based extrinsic Fabry-Perot interferometer (EFPI) fiber-optic pressure sensor. (**a**) Fiber-tip structure; (**b**) Fiber-end structure.
## 3 The Proposed Channel Estimation Methods
### Computation Complexity Analysis  
Since the proposed method is symbol-wise adaptive, the complexity of each iteration is described in Equation (17), which namely shows linear growth with the length of pilot symbols.  
Since decision feedback equalization (DFE) based on proposed channel estimations is a symbol level equalizer, we can deduce that its complexity experiences linear growth with the symbols in one OTFS frame. Meanwhile, the equalization computation burden of each symbol is linearly increasing with the size of filters; thus, the computation burden of the proposed DFE can be written as \(\mathcal{O}(MN(QL_{w}+L_{u}))\). The computation burden order of the single-channel matching pursuit-based (MP-based) method is described as \(\mathcal{O}(n_{\text{iter}}MNS_{L}\Theta)\), in which \(n_{\text{iter}}\), \(S_{L}\), and \(\Theta\) denote the number of iterations, the number of nonzero channel taps, and the alphabet size, respectively. Thus, we can conclude that the computation burden of the MP-based method is mainly determined by the sparsity level of channels. The computation burden induced by channel estimation also should be considered of course. We can deduce that the proposed 2D DFE receiver achieves the lowest complexity with large \(M\) and \(N\) compared with traditional methods.
## 4 Results and Discussion
### The MLR Model  
The objective of the MLR is to capture the mathematical relationship between the two independent variables (the dust accumulation and the average ambient temperature values) and the dependent variable (the daily system conversion efficiency). To this end, the multivariate regression analysis software package, Minitab (Minitab, Ltd., Coventry, UK), is employed by [PERSON] et al. [54] to estimate the regression model intercept and coefficients (\(b^{\prime}s\)) associated with each variable as reported in Equation (2). Thus, the best mathematical equation that can represent the collected data (\(n=143\)) accurately is found to be  
\[\hat{\eta}_{i}=16.0513-0.024133i-0.078743T_{i}, \tag{8}\]  
where \(\hat{\eta}_{i}\) is the \(i\)-th predicted daily efficiency, \(D_{i}\) is the \(i\)-th exposure day, and \(T_{i}\) is the \(i\)-th average daily ambient temperature.  
It is worth mentioning that the significance of the obtained MLR model has been evaluated and verified by resorting to the analysis of variance (ANOVA) based on the least-squares method. In addition, the adequacy and performance of the model are verified through the following [54]:  
* Assumption validation: the validity and significance of the model have been examined based on some assumptions, such as residuals being normally distributed and having constant variance;
* Multicollinearity: this indicates the near-linear dependencies among the regression variables, which can lead to misleading results. To examine whether the multicollinearity does not exist in the obtained model, large variation inflation factors (VIFs) have been calculated;
* Independency of variables: to examine the correlation between the systems' conversion efficiency and the two predictors (the dust exposure days and the average daily ambient temperature), the correlation matrix has been calculated;
* Goodness-of-fit: to verify whether the model reasonably represent the behavior of the data, the \(R^{2}\) and its adjusted value have been computed;
* Analysis of model coefficient signs: due to the fact that the dust accumulation and the increase in the ambient average temperature will lead to a decrease in the performance of the PV system, the signs of the models' coefficients have been verified to be negative;
* Best subsets regression: this identifies whether the obtained model can predict the conversion efficiency accurately by including all of the necessary independent variables. To this end, the \(R^{2}\) metric has been calculated and found to achieve the highest value among the whole subset model candidates.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 4 Data Processing
### Data Dissemination  
Based on the analysis results, the short- to medium-term hazard level is assessed, and--if deformation is detected--early warnings and alarms are issued. The main information dissemination tool will be a newly developed mobile app.
## 4 Effect of Essential Factors on CE
### Temperature  
A change in the solution temperature during cavitation affects the cavitation intensity owing to variations in the physiochemical properties of the liquid medium [76]. Due to the cavitation effect, the rising temperature during the extraction process needs to be controlled via external methods, such as by circulating cold water or by using a chiller. Reported data indicate that the extraction temperature varies between 10 to 80 \({}^{\circ}\)C depending on the type of solvent and ultrasonic intensity. The diffusion rate enhances at higher solvent temperature, thereby assisting in breaking the solute-matrix interaction. The mass transfer, in general, enhances with an increase in the extractiontemperature, but the extraction yield and quality could vary for every individual extract depending on its property. The extraction yield of phenolic compounds from defatted oat increased with an increase in temperature. The solubility of phenolic compounds, as well as their diffusivity, might get improved at high temperature, resulting in higher yield [16], whereas, during the extraction of phenolic compounds from blueberry wine pomace, an increase in temperature from 61.03 to 70 \({}^{\circ}\)C resulted in a decline in the yield [17]. Varying the temperature of the solution affects the cavitational intensity and thereby affecting the penetration capability of solvent into the cells. An increase in the solution temperature is associated with less intense bubble collapse. Moreover, an increase in the extraction temperature beyond threshold value can also affect the extraction yield. Considering the correlation between the operational parameters and the [PERSON] in extraction temperature, it is advisable to consider the boiling point of the solvent, power density, etc. to establish the optimum extraction temperature for better outcomes. An overview of the previous reports indicated that UAE could be effectively operated at lower operating temperature compared to conventional methods, which makes it ideal for the extraction of heat sensitive materials.  
While a temperature range of 10-70 \({}^{\circ}\)C was reported for the extraction of [PERSON] and anthocyanins from mulberry pulp [21], a range of 40-70 \({}^{\circ}\)C was opted for the extraction of polysaccharides from fungus during UAE [26]. The operating temperature was 25-45 \({}^{\circ}\)C for the extraction of carotenoids from tomato waste [49], whereas for aromas it was reported to be in the range of 10-50 \({}^{\circ}\)C [55]. While an increase in temperature from 20 to 70 \({}^{\circ}\)C [1] has shown a favorable effect for the extraction of natural products, Esclapez et al. [77] suggested low temperature (below 30 \({}^{\circ}\)C) for better outcomes in the extraction of natural products. From these observations, it can be concluded that a substantial extent of extraction can be achieved using UAE within the temperature range of 30-40 \({}^{\circ}\)C only.
# Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces
## 6 Discussion
As a limitation of the methodology for individual frequency selection in general, it is important to note that the individual calibration process might need to be repeated after a while, since the human body is prone to changes, especially over longer periods of time (months and years). And, indeed, the individual reactions might be different due to a user's condition. However, this deserves a specific study and is beyond the scope of our current paper. It is also important to note that some frequencies may be mathematically incompatible. For example, if a 5 Hz photostimulus does not evoke a response at the fundamental but at the second (10 Hz) harmonic, then such a photostimulus may be indistinguishable from a 10 Hz stimulation frequency, which evokes a response only at the fundamental frequency. However, this fact is more of a limitation of the SSVEP paradigm itself than of our proposed method.
# Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction
###### Abstract
First, based on the proactive method, this study targets the real task arrival workload of the Wikimedia service [10], which is a typical Internet workload, in order to research workload prediction. Then, based on stochastic process and queueing theory, we maximize the cloud service's profit as the optimization objective to design a computing resource search algorithm. The primary contributions of this study are as follows:  
Figure 1: Situations of resource under- and over-provision. VMs, virtual machines. - Introduces first-order gradient features and applies the K-means algorithm to effectively classify workload patterns into uphill and downhill trends, eliminating the need for manual labeling. - A binary classifier is used for trend categorization, with specialized LSTM networks assigned to each trend category to improve prediction accuracy. * Optimizes virtual machine provisioning by incorporating predicted workload patterns, VM rental costs, and task rejection penalties. - Maximizes service profit while ensuring system stability and QoS guarantees. * Demonstrates improved workload prediction accuracy. - Identifies the optimal number of VMs required to maximize profit while maintaining QoS constraints. The remainder of this paper is organized as follows. The related work is summarized in Section 2. Section 3 introduces the system architecture. The prediction method and resource allocation policy are detailed in Sections 4 and 5, respectively. Section 6 evaluates the performance of the proposed prediction method and resource allocation policy. Finally, Section 7 concludes this paper.
## 6 Proposed Model Validation  
The proposed model is designed to enhance the simulation of coverage estimation for deploying 5G services in the bustling metropolitan cities of India. This model seeks to streamline the computational process involved in simulations, making it quicker and more efficient. The accuracy of any channel model is fundamentally tied to the mathematical assumptions it employs. For instance, if the actual layout of buildings in the target area diverges significantly from the assumptions made by the model, the predictions regarding signal behavior and coverage may suffer. To provide a robust validation of the proposed channel model presented in this paper, path loss comparisons were conducted against established models: Model 1 [27] and Model 2 [31] are specifically designed for dense urban environments. The simulation parameters detailed in Table 6 are utilized for validation.  
The results shown in Figure 10 demonstrate a close alignment between the existing model and the proposed model. Notably, the existing models do not account for the effect of polarization on path loss. Furthermore, other models' scenarios are validated against the estimates found in [27], where a similar close correlation is also observed. The close alignment of the proposed model with the existing model ensures that the mathematical framework of the model effectively reproduces key aspects of path loss and the propagation characteristics essential for successful 5G deployment.
## 4 Discussion
GPR penetration in the natural coastal landform is shallower than in the anthropogenic coastal landform because of attenuation caused by the saltwater intrusion on Batu Ferringhi beach, while the saltwater content in the filling reclamation sand of the Queensbay Mall area is not that much since it is anthropogenic and less exposed compare to the natural coastline. Hence, it is less influenced by signal attenuation. The use of sieve analysis gives evidence to confirm the previous GPR conclusions about the differences between the natural and anthropogenic coastal landforms. At Batu Ferringhi, which represents the natural coastal landform, although the sediment grain size ranges widely from medium sand to gravel size, they are evenly distributed. Other characteristics of such a sediment is the heterogeneous sediment visible as gravel content [45]. The even distribution is moderately sorted to well sorted at every sampling point. Natural sedimentation makes it possible for the sediment materials to sort evenly over time. As the Batu Ferringhi area is laid by medium to coarse-grained biotite granite or Ferringhi granite [2], the sediments along the coastline correspond to the weathering transportation product of this formation. The degradation characteristics of Malaysia's West Coast, including in Penang, formed a reverse grading of sediments, including gravel in the deposits. Unfortunately, the stratigraphic reverse graded bedding of deposition was not clearly recorded in this study. This is different from an anthropogenic coastal landform, represented by the Persiaran Bayan Indah (the Queensbay Mall area), where the site displays an uneven grain size distribution. This frequency distribution can be seen from the graphic skewness and kurtosis. Reclamation sediment ranging from medium to very coarse sand and mostly poorly sorted in the middle, is moderately sorted at the starting point and the end. This shows unclear trends for the sample content. Substantial variability of texture was observed since it is a filling material for coastal reclamation.