esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation-Based Design for Inlet Nozzle of Vortex Tube to Enhance Energy Separation Effect
## 3 Verification and Validation
### Validation Result  
Based on the results of the GCI and turbulence model tests, a grid system with approximately 1.65 million cells was selected, and the standard \(k-\varepsilon\) turbulence model was used for the numerical simulations to analyze the effect of varying the outlet pressure. Figure 9a,b illustrate the streamlines inside the vortex tube when the outlet pressure (\(P_{ho}\)) was set to 1.7 atm, with the streamlines colored by velocity magnitude and temperature, respectively. It can be observed that the flow entering through the inlet nozzle rapidly rotates within the tube, generating a strong vortex flow that results in the separation of temperature. The separated streams are discharged through the hot and cold outlets on the left and right sides. Figure 10 shows the velocity distribution at the central across-section, where the incoming gas exhibits the highest velocity near the vortex generator. As the vortex flow moves along the wall and decelerates, the velocity near the center of the tube increases until the gas is discharged through the cold outlet.  
The temperature distributions at the central cross-section, resulting from varying hot outlet pressures, are compared in Figure 11. As the pressure at the hot outlet increases, the temperature at both the hot outlet and inside the vortex tube rises, whereas the temperature at the cold outlet shows relatively minor change. This trend is further demonstrated in Figure 12, which compares the temperatures at the cold and hot outlets under different pressures. Figure 13 presents the temperature difference between the hot and cold outlets for varying hot outlet pressure, showing that the temperature difference increases as the hot outlet pressure rises, which indicates an enhancement in the temperature separation effect.  
Figure 10: Streamlines colored by velocity magnitude in the center section of the vortex tube.  
Figure 9: Distribution of streamlines.  
Figure 11: Comparison of temperature distribution in the center section of the vortex tube due to pressure changes at the hot outlet.  
Figure 12: Comparison of the temperature of cold and hot outlets.  
Figure 13: Comparison of the temperature difference between cold and hot outlets.  
Figure 14 compares the temperature differences between the hot and cold outlets for various cold fractions obtained from the present simulation, experimental data, and other simulation results. The current simulation shows a significant improvement in accuracy compared to previous simulation results, with good agreement with the experimental data and an average error of approximately 4.72%. Some of remaining error may be attributed to factors not considered in the simulation, such as the box installed in the experimental setup to prevent gas discharge at the hot outlet and the external thermal environment, as well as potential experimental uncertainties.
## 3 Results and Discussion
### Thermal-Mechanical Properties and Free Volume  
Dynamic mechanical analysis (DMA) was used to characterize the \(\mathrm{T_{g}}\) and the crosslink density (\(v_{e}\)) of the samples. The storage modulus (\(E^{\prime}\)) curves and the tan\(\delta\) curves as a function of temperature for samples with different initiator mass fractions were shown in Figure 8a,b. The peak temperature of the tan\(\delta\) was denoted as \(\mathrm{T_{g}}\). The storage modulus (\(E^{\prime}\)) of DMA in the rubber plateau area (40 \({}^{\circ}\)C above \(\mathrm{T_{g}}\)) was used to calculate the \(v_{e}\) based on the following formula [55; 56; 57]:  
\[v_{e}=\frac{E^{\prime}}{3 RT}\]  
where \(E^{\prime}\) was the storage modulus of the thermoset in the rubbery plateau region at \(\mathrm{Tg}+4\)\({}^{\circ}\)C, R was the gas constant, and T was the absolute temperature at \(\mathrm{Tg}+40\)\({}^{\circ}\)C.  
The Tg and \(v_{e}\) of samples were shown in Table 5. As expected, with more initiator added into the system, the crosslinking reaction advanced more extensively, which resulted in a higher Tg and higher crosslink density.  
PALS was used to measure the free volume of different samples in the system. As shown in Figure 9 and Table 6, the free volume became larger with the increased initiator content.
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 3 System Description
### Energy Sharing Model  
If the case is an on-grid condition, energy can be shared between a network connection system and the electrical grid system. The algorithm of the proposed system is illustrated using the flowchart in Figure 2. Three separate scenarios occur for the state of the grid.  
**Case I: Generation is higher than demand (\(E_{Gen}>E_{D}\)):**  
If \(E_{Gen}>E_{D}\), the green energy is enough to meet the BS's own need for energy. Surplus energy is stored in the battery bank for backup power supply and energy for sharing. The amount of energy share and the loss of power supply probability of the system can be expressed as follows:  
\[E_{Share}=E_{Gen}-E_{BS}-E_{Losses} \tag{7}\]  
\[LPS(t)=0 \tag{8}\]  
where \(E_{D}\) is the summation of \(E_{BS}\) and \(E_{Loss}\), and \(LPS\) represents the loss of power supply probability. A reliable system must have an \(LPS\) that is either zero or greater than zero.  
**Case II: Generation is equal to demand (\(E_{Gen}=E_{D}\)):**  
If \(E_{Gen}=E_{D}\), then the green electricity generated is supplied to the base station, and the battery bank is ready to provide backup power, which means \(E_{Share}=0\), \(LPS\) (\(t\)) = 0.  
**Case III: Generation is lower than demand (\(E_{Gen}<E_{D}\)):**  
If \(E_{Gen}<E_{D}\), the harvested green energy cannot fully meet the BS energy need. Therefore, backup power is provided by the battery bank, and the remaining energy is received from the electrical grid.
## 1 Introduction
The main contributions of this paper are as follows:  
* Considering monitoring frequency and path privacy, this study shows how to formulate a multi-UAV cooperative persistent monitoring path planning problem with multiple constraints based on the monitoring of overdue time and of monitoring period entropy. * A multi-group ant colony optimization algorithm, called overdue-aware multiple ant colony optimization (OMACO), is proposed to obtain an optimal flight path for UAV cooperation. The heuristic function and pheromone update method are improved based on the monitoring delay time and overdue time. In addition, a target exclusive mechanism and greedy strategy are proposed for ant node selection. * Simulation experiments are carried out in complete and incomplete environments to verify the effectiveness and advantages of the designed algorithm. The simulation results show that the algorithm proposed in this paper can effectively improve both the monitoring frequency and the monitoring privacy protection.
# The Influence of Grain Size on the Abrasive Wear Resistance of Hardox 500 Steel
## 1 Introduction
For instance, [PERSON] [24] identified prior austenite grain size as a potential factor in abrasive wear intensity, citing a reduction in steel ductility as grain size increases. However, this correlation is not universally applicable, with limited studies examining steels with a homogeneous martensitic microstructure. Some studies [25; 26] found that stresses generated by hard abrasive particle impacts exceeded those experienced during cold plastic deformation, restricting findings to extreme conditions. The authors of [14] concluded that reducing grain size increases wear resistance, though the steels compared varied in composition, and grain size changes did not consistently correlate with hardness changes. Research on the effect of prior austenite grain size on the abrasive wear of Hardox450 steel [1] also revealed no significant changes in tribological resistance, aligning with the findings in [27] for steels with microstructures similar to Hardox. Given the importance of these findings, Hardox 500 steel was selected for research on grain size effects in martensitic boron steels. According to its manufacturer, Hardox 500 is weldable and bendable, making it suitable for a broad range of applications. This versatility outperforms that of Hardox 600 and Hardox Extreme steels, which face stricter limitations in structural applications [28; 29]. Hardox 500 is widely used in plow blades, consistently surpassing 38 GSA steel (548 HBW hardness), commonly applied in agriculture [30; 31]. Additional applications include excavator buckets, cultivator tines, liners, and wear-resistant bars [32; 33]. In abrasive wear tests, Hardox 500 has demonstrated lower mass loss than steels S355 JR, S355J2, and AISIJ304 [34] as well as carburized 20 MnCr5 steel [31]. Furthermore, when exposed to soil-based abrasive materials, its abrasion resistance is comparable to Hardox Extreme steel [35]. Recent investigations into low alloy boron steels underscore the pivotal influence of grain size on wear resistance, highlighting a direct correlation between finer grains and enhanced performance under abrasive conditions [7; 15; 19; 24]. These studies emphasize that the increased density of grain boundaries in finer-grained microstructures impedes dislocation motion and crack propagation, thereby significantly augmenting material durability in high-stress environments. This evidence underscores the imperative to refine grain size through precisely controlled heat treatment protocols, as explored comprehensively in this work.
## 2 Materials and Methods
### Energy-Dispersive X-ray (EDX) Analysis  
All of the specimens were examined by energy-dispersive X-ray microanalysis (QUANTAX QX2, BRUKER/ROENTEC, Berlin, Germany) for chemical determinations. Standard chemical composition determinations using PB-ZAF database on a selected area were used. Qualitative evaluation of the chemical elements revealed carbon (C), calcium (Ca), phosphorus (P), and oxygen (O) as dominant components. Quantitative evaluations of ion concentrations (wt%) were performed in ten different areas of each dentin sample. The ion concentrations on each sample were reported as the average value of ten determinations.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multirow{3}{*}{KAREX} & Hydroxyapatite, Xylitol, Hydrated Silica, Tetrapotassium Pyrophosphate, \\  & Zinc Chloride, Cetlypyridinium Chloride, Sodium Methyl Cocoyl Taurate, \\  & Sodium Cocyl Glycinate, Aqua, Glycerin, Phosphoric Acid, Silica, \\  & Hydrogenated Starch Hydrolysate, Cellulose Gum, Aroma \\ \hline \multirow{3}{*}{BIOREPAIR PLUS SENSITIVE COSWELL} & Aqua, Zinc Hydroxyapatite, Glycerin, Sorbitol, Cellulose Gum, PEG-32, \\  & Silica, Sodium Myristoyl Sarcosinate, Sodium Methyl Cocoyl Taurate, \\  & Aroma, Sodium Saccharin, Citric Acid, Phenoxyethanol, Benzyl Alcohol, \\  & Sodium Benzoate. \\ \hline \multirow{3}{*}{DR. WOLFF’S BIOREPAIR} & Aqua, Zinc Hydroxyapatite, Hydrated Silica, Glycerin, Sorbitol, Silica, \\  & Aroma, Cellulose Gum, Sodium Myristoyl Sarcosinate, \\ \cline{1-1}  & Sodium Methyl Cocoyl Taurate, Tetrapotassium Pyrophosphate, Zinc Pca, \\ \cline{1-1}  & Sodium Sucacharin, Phenoxyethanol, Benzyl Alcohol, Propylparaben, \\ \cline{1-1}  & Methylparaben, Citric Acid, Sodium Benzoate. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical ingredients of the desensitizing toothpastes.
## 2 Methods
### Data Collection, Processing, and Gap-Filling Fluxes  
All data were acquired using a solid-state data logger (CR3000, Campbell Scientific Inc., USA). The data were stored on a 2 GB CompactFlash card, which was retrieved monthly. The raw flux data were grouped into individual 30 min files and converted into TOA5 format before processing. These unprocessed data were screened for quality control and gap-filled using LoggerNet, EddyPro, TOVI, and REddyProc R packages [19]. To determine the periods of low mixing, which can lead to the underestimation of water and heat fluxes, the frictional velocity (u*) threshold was identified using the Moving Point Test approach in TOVI [20]. Further quality control screening was carried out to filter potential data outside the u* threshold value (0.4 m/s).  
Data recorded from 1 January 2014 to 31 December 2021 were used in this analysis. There was a major missing data gap due to instrument failure from January 2016 to July 2016 and from November 2018 to December 2020, and these gaps were not included in the analysis. Small gaps (<2 h) due to lower-quality data were gap-filled using the Marginal Distribution Sampling Technique using the R package, REddyProc. Meteorological data from a nearby meteorological station (Monroe Airport--MLU) and NASA's Prediction of Worldwide Energy Resources (POWER) [21] database were used for gap-filling temperature and precipitation data when necessary. The gap-filled data accounted for less than 20% of the whole EC data. The water losses associated with ET were quantified by the conversion of LE values from (W/m\({}^{2}\)) to mm/day [15]. To compare the means and assess the interrelations among variables, we performed an ANOVA test and the associated post hoc tests and an SEM analysis as required, using R (v4.1.1, R Core Team 2023) [22].  
The data were aggregated into hourly, daily, and weekly datasets spanning the growing (March-August) and non-growing (September-February) seasons. Variables with more than 50% of their half-hourly data missing were not included in the SEM analysis. The missing data were then adjusted as described in [23] by dividing each daily and weekly value by the fraction of the data present for that particular unit of time. RH and soil moisture, two of the primary drivers of ET, were not included in our analysis since RH is redundant to the VPD, and soil moisture was not measured in our study.
## 2 Related Work and Background
### Related Work
#### 2.2.2 Regularization-Based Methods  
The second class of watermarking techniques modifies the model's loss function by adding a regularization term. This approach allows for a principled trade-off between watermark strength and overall model performance. By adjusting the regularization hyperparameter, one can control the balance between embedding a strong watermark and maintaining the model's accuracy. This flexibility ensures effective watermark embedding without sacrificing the model's predictive capabilities. Although we examine these schemes in detail within our unified framework in Section 3, we provide an overview of their technical evolution here.  
[PERSON] et al. [45] proposed one of the first explicit watermarking schemes of this kind. They insert a binary string as a watermark in model parameters by training or fine-tuning the model using a loss function composed of the model's original loss, along with an embedding regularizer. Embedding the watermark by introducing a regularizer term manages a trade-off between watermark robustness and preservation of model accuracy. However, this method is not robust against overwriting attacks (OWA). [PERSON] et al. [47] extended this approach and proposed a DNN watermarking algorithm based on spread-transform dither modulation (ST-DM). The DNN is trained with a loss function explicitly designed with a regularizer to ensure the correlation of the DNN weights with a given spreading sequence. [PERSON] et al. [55] propose inserting the watermark into fused kernels instead of using them separately to resist parameter-shuffling attacks. Because this strategy reduces capacity, the authors introduce a capacity expansion mechanism using a MappingNet to map fused kernels into a higher-dimensional space capable of hosting the watermark. However, it should be noted that the shuffling attack is not effective, since it degrades model performance.  
[PERSON] and [PERSON] [56] demonstrated that the aforementioned approaches based on the scheme by [PERSON] et al. [45] do not fulfill the requirement of watermark secrecy because they introduce easily detectable changes in the statistical distribution of model parameters. To address this limitation, [PERSON] and [PERSON] [57] proposed a strategy to create undetectable watermarks in a white-box setting using generative adversarial networks (GANs). In this approach, the watermarked model serves as the generator while the watermark detector acts as a discriminator, detecting changes in the statistical distribution of model parameters.
## 4 Discussion
### Available Plant Nutrients Concentration in the Soil  
As shown in Figure 1c, biochar incorporation into soil increased organic matter concentration, possibly water-extractable organic carbon, and stimulates soil microbial biomass and activities [44], ultimately increasing nutrient availability [33]. Organic amendments had higher concentrations of organic matter at first (Figure 1c), which may have contributed to increased nutrient availability in the soil. This is demonstrated by a linear increase in soil nutrient availability as application rate increases (Table 3). Furthermore, biochar's high aromatic composition is suitable for carbon sequestration due to more C=O and C-H functional groups that serve as nutrient exchange sites after oxidation [33], which may increase nutrient availability in the soil. The higher nutrient content in soil incorporated with biochar produced at lower pyrolysis temperatures (BC350) compared to BC600 may be attributed to surface charge [33, 50, 51] reducing reactivity surfaces in soils [40]. Furthermore, biochar made at lower temperatures has aliphatic and cellulose structures that are suitable substrates for mineralization by bacteria and fungi during the nutrient turnover process [33] that increases nutrient availability in the soil.  
Soil pH has a significant effect on plant nutrient availability in the soil. According to [37], K, Ca, and Mg are more available to plants within a pH range of 6.5 to 8, and nutrients are available to plants in lesser amounts outside of these optimal ranges. As a result, the initial soil samples with a slightly acidic pH of 6.36 showed lower concentrations of macronutrients. Additionally, base cations (Ca, K, and Mg) are more weakly bound to the soil and more prone to leaching at low pH, which may have contributed to lower base cations concentration in control samples. Phosphorus (P) is most available between pH range of 6 to 7 [37], hence its concentration levels were lower than those of K, Ca, and Mg (Table 3). Additionally, as soil pH values approach strongly alkaline pH ranges, micronutrient availability decreases due to cations being more strongly bound to the soil and not as easily exchangeable. This was observed because micronutrient concentrations decreased as soil pH increased (Table 3). Furthermore, blended organic amendments showed a slight increase in soil nutrients, particularly for PLBC600. The significant interaction effect of biochar and PL indicates the feedstock's efficacy of the feedstock as organic soil amendments.
## 1 Introduction
Percentage of eyelid closure versus pupil change over time (PERCLOS) is one of the most popular algorithms [26], which is accurate but takes a relatively long time to compute fatigue results [27]. A method to calculate blinking frequency (BF) by analyzing specific image frames was proposed in [28]. Although the method gives appropriate criteria for determining the level of fatigue, the number of blinks in a given period of time needs to be counted prior to the computation, which prolongs the response time. As reported in [29; 30; 31], eye aspect ratio (EAR) calculation is simple to perform and has a fast response; however, a single detection parameter is difficult to reflect an accurate and reliable fatigue level. In practical applications, fatigue detection must be accurate, reliable, and responsive. However, existing methods often rely on single or limited behavioral features, or apply uniform thresholds across all subjects, which limits their adaptability and leads to potential misjudgments. To address these limitations, this article proposes a fatigue monitoring algorithm based on facial multi-feature fusion, which combines three evaluation indexes: blinking frequency (BF), yawning frequency (YF), and nodding frequency (NF). Additionally, considering individual driver characteristics, a personalized threshold is introduced to assess the driver's eye, mouth, and head status by normalizing the degree of eye and mouth openness, rather than using a traditional average threshold. Compared to previous approaches, the proposed method provides improved adaptability to individual differences and more comprehensive behavioral representation, thereby enhancing overall detection robustness. It demonstrates significant applicability in a wide range of fatigue detection scenarios. The main innovations of this article include the following:The introduction of a personalized threshold model based on the normalization of eye and mouth openness, which accounts for individual differences in facial features. 2. The use of a sliding window model to dynamically track changes in fatigue indicators (BF, YF, and NF) in real time, enhancing detection accuracy. 3. The development of a multi-feature fusion approach that integrates multiple fatigue-related behaviors (blinking, yawning, and nodding) to improve overall system reliability and response time.
## 1 Introduction
### Key Definitions  
#### 1.3.1 Cybersecurity  
Cybersecurity refers to the process of protecting and recovering computer systems, programs, networks, and devices from cyber-attacks. There are numerous cyber threats that are incidental to e-government systems, such as that of Saudi Arabia, that are yet to be identified due to the sophistication of such issues [15]. The importance of cybersecurity on e-government platforms is enormous, since the systems contain personal data, for instance, that could result in identity theft, among other issues [16]. The e-government systems of Saudi Arabia are susceptible to cyber-attacks from adversaries and criminals with different intentions and have to be protected from a multi-stakeholder and multi-dimension perspective [8]. Cyber attackers employ new methods driven by social engineering and artificial intelligence in circumventing traditional security systems [17]. Cyber-attacks have increasingly become sophisticated, posing danger to sensitive data in contemporary society.