esa-sceva/satcom-chunk-collection huggingface dataset:
# Screening of Some Romanian Raw Honeys and Their Probiotic Potential Evaluation
## 3 Results
### Physico-Chemical Parameters of Raw Honey Samples
In the present study, higher amounts of potassium was identified (375.30 \(\pm\) 19.3 mg/kg), as well as calcium (83.0 \(\pm\) 4.26 mg/kg) and magnesium (69.7 \(\pm\) 3.59 mg/kg), in P1 sample. Low concentrations of selenium (0.13-0.79 mg/kg), zinc (0.32-0.50 mg/kg), and manganese (0.07-7.26 mg/kg) were identified. Other microelements identified were Cu, Fe, Cr, and Ni, only in sample P3. The level of Na was below the detection limit of the method (4.0 ug/L). No heavy metals, such as cadmium and lead, were identified in the analyzed raw honey samples. As shown in Figure 5, the dendrogram obtained from the Cluster analysis (Euclidean distance) reveals the formation of two clusters, with the exception of sample P1, which is an 'outlier', being distinct from the others due to its high content in the minerals K, Ca, and Mg. At the same time, a high content in sucrose and lower in lipids and trehalose is noticed. The following cluster comprises the samples P8 and P5, followed by samples P10 and P4, which display similar concentrations in fructose, glucose, and lipids, as well as in Ca, Se, and Cr. The next sub-cluster comprises samples P7, P9, and P3, which have similar protein values, as well as in the mineral K. The last sub-cluster comprises samples P6 and P2, with relatively close values in fructose, glucose, and moisture content. The same is noticed in the minerals Ca, K but also in those with lower values, such as Zn and Cr. Figure 4: Chromatogram of sucrose standard solution 10% (black) and analyzed samples (colored signals). Low honey pH and acidic substances, such as aromatic acids and royal jelly acids, have been reported to play an important role on antibacterial activity of honeys [5,46]. In the present study (Table 4), the pH value range between 3.78 (P1)-4.21 (P6), and total acidity value between 15.5 (P4)-65.2 (P8) \(\mathrm{meq/kg}\). The recommended value for free acidity, as stated in the European regulations, is 50 \(\mathrm{mEq/kg}\)[47]. Higher amounts in free acidity were obtained in sample P8 (34.1 \(\mathrm{meq/kg}\)), followed by P1 (25.3 \(\mathrm{meq/kg}\)). Higher values are indication of incipient sugar fermentation, leading to the presence of acetic acid, formed by alcohol hydrolysis [48].
# A Multiscale Approach to the Numerical Simulation of the Solid Oxide Fuel Cell
## 4 Mathematical Model
### Thermo-Electric Model
To calculate the volumetric current density, generated at the triple phase boundaries (on the anode) \(j^{\mathrm{ano}}_{\mathrm{TPB}}\) (A m\({}^{-3}\)) and the double phase boundaries (on the cathode) \(j^{\mathrm{cath}}_{\mathrm{DPB}}\) (A m\({}^{-3}\)), the Butler-Volmer equations are utilized:  
\[j^{\mathrm{ano}}_{\mathrm{TPB}}=j^{\mathrm{ano,TPB}}_{0}\rho^{L}_{\mathrm{TPB }}\left[\exp\left(\frac{1.4F}{RT}\eta_{\mathrm{act}}\right)-\exp\left(-\frac {0.8F}{RT}\eta_{\mathrm{act}}\right)\right], \tag{3a}\]  
cathode:  
\[j^{\mathrm{cath}}_{\mathrm{DPB}}=j^{\mathrm{cath,DPB}}_{0}\rho^{A}_{\mathrm{ DPB}}\left[\exp\left(\frac{1.2F}{RT}\eta_{\mathrm{act}}\right)-\exp\left(-\frac {F}{RT}\eta_{\mathrm{act}}\right)\right]. \tag{3b}\]  
Here, \(j^{\mathrm{ano,TPB}}_{0}\) (A m\({}^{-1}\)) is the exchange current density per unit length of the TPB and \(j^{\mathrm{cath,DPB}}_{0}\) (A m\({}^{-2}\)) is the exchange current density per unit area of the DPB; \(\rho^{L}_{\mathrm{TPB}}\) (m\({}^{3}\) m\({}^{-1}\)) is the TPB length density and \(\rho^{L}_{\mathrm{DPB}}\) (m\({}^{3}\) m\({}^{-2}\)) is the DPB area density. The anodic and cathodic charge-transfer coefficients are taken from the porous Ni/YSZ anode measurements of [PERSON] [51] and from the LSCF cathode investigation of [PERSON] et al. [52]. The activation overpotentials \(\eta_{\mathrm{act}}\) (V) are calculated as follows:  
\[\eta^{\mathrm{ano}}_{\mathrm{act}}=\phi^{\mathrm{ano}}_{\mathrm{el}}-\phi^{ \mathrm{ano}}_{\mathrm{lon}}-\frac{RT}{2F}\ln\left(\frac{p^{\mathrm{bulk}}_{ \mathrm{H_{2}}}}{p_{\mathrm{H_{2}}}}\frac{p_{\mathrm{H_{2}O}}}{p^{\mathrm{bulk}}_ {\mathrm{H_{2}O}}}\right), \tag{4a}\]  
cathode:  
\[\eta^{\mathrm{cath}}_{\mathrm{act}}=\phi^{\mathrm{cath}}_{\mathrm{ion}}-\phi^{ \mathrm{cath}}_{\mathrm{el}}-\frac{RT}{4F}\ln\left(\frac{p^{\mathrm{bulk}}_{ \mathrm{O_{2}}}}{p_{\mathrm{O_{2}}}}\right). \tag{4b}\]  
In Equations (4a) and (4b), the symbols \(\phi_{\mathrm{el/ion}}\) (V) are the electric potentials of the electronic and ionic conducting phases, \(p_{i}\) (Pa) means the partial pressure of species \(i\), and the superscript bulk indicates that the partial pressure value is the bulk inlet partial pressure. The exchange current densities per unit length of the TPB/unit area of the DPB are formulated in accordance with the literature data [51; 53]. The pre-exponential factors are the fitting parameters, proposed by [PERSON] et al. (for the andoe) [27] and [PERSON] et al. (for the cathode) [28]. The exchange current densities are calculated as:  
\[j^{\mathrm{ano,TPB}}_{0}=31.4p^{-0.06}_{\mathrm{H_{2}}}p^{0.43}_{\mathrm{H_{2 }O}}\exp\left(-\frac{152,000}{RT}\right), \tag{5a}\]cathode:  
\[j_{0}^{\text{cath,DPB}}=1.47\cdot 10^{6}\cdot p_{\text{O}_{2}}^{0.23}\exp\left(- \frac{133,150}{RT}\right), \tag{5b}\]  
where the partial pressures \(p_{i}\) are taken in pascals.
## 3 Results and Discussions
### Physical and Mechanical Performance
#### 3.2.3 Relationship between the Elastic Wave Velocity and Age  
From the elastic wave velocity age curve for three samples (Figure 6), it can be observed that the general tendency of the entire curves is primarily coincident for different specimens. The general tendency is that there is a decline, and afterward, continuous growth emerges. In detail, for SK, declines occur during 1-4 d, while SL2 and SL5 increase. This was attributed to SK interiors, which still contained liquid water during the initial stages and caused absorption and scattering attenuation of the elastic waves. With volatilization and water hydration in the specimens, the solid phases and crystal particles continued to grow. Afterward, the waves began to propagate through the solid-phase path, and the wave velocities continued to increase. SL2 and SL5 contain CaO and consume more water. Therefore, there will be no large volatilization of water in the early stages of the specimens, and there will be no decrease in wave velocity. It can also be observed that the wave velocity of SL2 and SL5 increased rapidly starting at 2 days.
## 2 Materials and Methods
### Model Comparison and Selection  
To identify the significance of the difference between the implemented models, a significance test was performed. For this, a paired \(t\)-Test (parametric) combined with \(5\times 2\) cross-validation [41; 42] was conducted to assess the significance of the difference between all the established models. Therefore, the significance level \(\alpha\) was set to 0.05 so that a \(p\)-value over 0.05 means no significant difference, indicating that the results of the two tested models are samples drawn from the same distribution. This means that the difference between the accuracy results is not due to an overall better-performing model but may be due to statistical anomalies in the data. Based on the results of the significance test as well as on the results from the test set, the best-performing models for the striking technique and target object classification were selected.
## 2 Numerical Methodology
### Governing Equation  
In the analysis of the flow fields, the continuity equation, momentum equation and energy equation can be expressed as the following unified form:  
\[\
abla\cdot(\rho u\varphi)=\
abla\cdot(\Gamma\text{grad}\varphi)+S, \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**LS on Stator1** & \multicolumn{2}{c}{**LS on Rotor**} \\ \hline
**Items** & **Value** & **Items** & **Value** \\ \hline Leakage passage outer radius/mm & 106.26 & Leakage passage outer radius/mm & 153.2 \\ Leakage passage inner radius/mm & 101.06 & Leakage passage inner radius/mm & 148 \\ Tooth number & 4 & Tooth number & 4 \\ Tooth height/mm & 5 & Tooth height/mm & 5 \\ Tooth width/mm & 1 & Tooth width/mm & 1 \\ Seal clearance (C)/mm & 0.2 & Seal clearance (C)/mm & 0.2 \\ Tooth pitch (_P_)/mm & 5.8 & Tooth pitch (_P_)/mm & 5.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometry parameters of seal structures.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Inlet total pressure/MPa & 15 \\ inlet total temperature/K & 773.15 \\ outlet static pressure/MPa & 11.09 \\ Rotation speed/rpm & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The boundary conditions.  
Figure 2: Schematic diagram of the seal structures.  
where \(\varphi\) is the general form of physical variables, \(\Gamma\) is the generalized diffusion coefficient and \(S\) is the generalized source term. When they are expressed as the following forms in Equations (2), Equation (1) represents the continuous equation, momentum equation and energy equation respectively.  
\[\varphi=\left[\begin{array}{c}1\\ u_{i}\\ T\end{array}\right],\ \Gamma=\left[\begin{array}{c}0\\ \mu\\ k/C\mathrm{P}\end{array}\right],\ S=\left[\begin{array}{c}0\\ -\mathrm{grad}p\\ 2\mu\
abla\cdot\left(\mathrm{grad}u\right)/C\mathrm{P}\end{array}\right], \tag{2}\]  
where \(u\) denotes velocity and subscript \(i\) denotes velocity directions. \(T\) is the temperature and \(k\) is the thermal conductivity. \(p\) represents the pressure and \(C\mathrm{P}\) represents the specific heat capacity. And \(\mu\) is the dynamic viscosity coefficient.
## 4 Post-Processing for Surface Roughness Improvement  
It is a well-known fact that IC manufacturing produces a high-quality surface finish due to the application of ceramic slurry coating. Therefore, it is required for the pattern to also have superior surface quality. In the case of 3D printed parts, surface roughness was always an issue due to the stair-stepping effect. The problem is aggravated even more for materials having low viscosity values at the molten state, such as wax. For example, Figure 7 shows a 20 \(\times\) 20 \(\times\) 20 mm wax cube with the presence of rippling on the side surface. The measured Ra values using the portable surface roughness tester showed values in the range of 25-39 \(\upmu\)m. The parts tested are the cubes used in the Taguchi analysis.  
To overcome the issue of low surface quality, it was decided to apply a chemical treatment using a proper agent that can partially dissolve the material. From the literature, numerous works can be found on enhancing popular plastics such as PLA and ABS through chemical treatment. For example, one popular method for improving the surface roughness of ABS parts is the use of an acetone bath. [PERSON] et al. [27] found that immersing the printed ABS parts in acetone for several minutes significantly decreases the Ra value from 9.42 to 0.84 \(\upmu\)m. However, it was noticed that a longer immersion time may impair mechanical properties. In the current case, the same approach can be utilized by choosing different chemicals such as a white spirit. White spirit is known as a wax dissolver, therefore, it can be used to remove the surface irregularities, provided sufficient immersion time is applied. To see the effect of the chemical on the surface, the wax cubes were immersed in a white spirit bath for several periods of time. All the cubes were manufactured using the same process parameters to ensure that equal starting conditions are maintained for all samples. However, the Ra values of untreated samples were not equal and varied between 25.1 and 28.3 \(\upmu\)m. Nevertheless, the experiment was conducted using three immersion time periods, such as 1, 3, and 5 min. The experiments showed that holding for more than 10 min has a negative effect on the object, and delamination might occur. On the other hand, a significant improvement was noticed when using a 5 min immersion time with the subsequent wiping of the surface using a soft tissue. Figure 8 shows the optical microscopic images of chemically treated and untreated parts. It should be noted that  
Figure 7: (**a**) 3D printed wax cube with visible ripples on the side surface and (**b**) magnified view of the surface.  
regardless of the magnitude of the starting Ra value, the Ra value after a 5 min immersion was approximately 3.2 \(\upmu\)m \(\pm\) 0.05 \(\upmu\)m for all samples.
## 2 Description of MMC-VSG Structure and Modeling
### MMC-VSG Topology and Control Strategy  
#### 2.2.1 MMC-VSG Structure and Modeling  
There are three main components of the SG, which include the prime mover, the synchronous machine, and the power grid [22]. The VSG controls an inverter system that is equivalent to the SG. In contrast with the SG, VSG does not require a complex magnetic field relationship between the stator and rotor; rather, the converter is used to replicate the synchronous motor's exterior features without being constrained by the motor's physical properties. In addition, the electronic parts can enable flexible control of the system. Simultaneously, VSG uses a low-order mathematical model to simplify the high-order motor model, which considerably increases the simulation efficiency by condensing the calculation in the real modeling process. The primary circuit topology and control method of the MMC-VSG is shown in Figure 2.  
The MMC control usually adopts a double closed-loop or current inner loop control mode according to the control requirements. The double closed-loop method contains an outer voltage loop and an inner current loop, and the \(*\) represents the instruction value for the parameters in the control loop. In previous studies, the inner current loop method was used to replace the voltage loop with virtual stator impedance to calculate the line current. In this study, inner current loop control is manipulated for the VSG, accompanied by nearest level modulation (NLM) to produce the reference modulation signal [23]. As the specification of inner current loop control and NLM modulation have already been extensively discussed in other studies [14; 24], this study will mainly focus on providing the VSG-MMC control block diagram instead of going into that in detail.
## 4 Inland and Marine MLH Detections Using Ground-Based CDWL  
The TKEDR threshold method is good at tracing the MLH development and descent processes in the transition periods, including nighttime-daytime and daytime-nighttime. Therefore, it is possible to trace the MLH diurnal evolution. In the ground-based experiments, the MLH diurnal cycles at the inland and marine sites are successfully retrieved. To validate the accuracy of the MLH retrieval result at the marine site, radiosonde-derived MLH is given as a reference.
# Pilot Study of Microplastics in Snow from the Zhetysu Region (Kazakhstan)
## 2 Materials and Methods
### Cleaning and Filtration  
After sieving the material using a set of sieves [37; 38; 39; 40; 41; 42; 43], the solids were transferred to a pre-weighed clean beaker and dried at 90 \({}^{\circ}\)C for 24 h in a desiccator [36; 44; 45; 46; 47]. A 30-35% solution of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) and 20 mL of iron (II) sulfuric acid catalyst was used to remove organic matter [44; 48; 49]. After oxidation, 6 g of sodium chloride [50; 51; 52] was added for every 20 mL of sample to increase the density of the solution. The treatment was carried out at 75 \({}^{\circ}\)C with stirring on a magnetic stirrer [41; 51], until the reaction stopped and the contents were discolored; then, the solution was transferred to a density separator for 12 h. The remaining precipitate was visually inspected for the presence of MP particles, and the residue was filtered through a 0.315 mm sieve. The separated MP particles were visually examined at 40\(\times\) magnification using a Levenhuk MED D25T LCD  
Figure 1: Location of Kazakstan and the Zhetysu region with snow sampling points.  
Digital Trinocular Microscope by Levenhuk Company (Tampa, FL, USA) [36; 52; 53], which allows the visual identification of plastic particles.  
Detected microplastic (MP) particles are examined visually under 40\(\times\) magnification with a microscope [36; 54; 55], whose authors describe protocols for the microscopic visual identification of plastic particles. The principal criteria are lustre, brightness, or an atypical color--features that can indicate plastic, especially when the particles display properties typical of polymeric materials [37; 50]. Elasticity or hardness, which can be assessed with tweezers, is another useful indicator [53]. MP particles larger than 1.0 mm can be isolated visually according to the following rules [50; 54; 56; 57]: absence of cellular structure or other organic forms (to exclude biogenic matter); uniform coloring and fiber thickness; and a clean, even tone, i.e., a homogeneous color without inclusions or blotches. This approach enables the preliminary attribution of a particle to plastics during microscopic inspection.  
In a number of cases, it is recommended to disregard particles that are white, transparent, or black because they may be confused with biological debris or other substances that can be mistakenly identified as plastic [54; 56]. For the reliable and rapid identification of larger MP particles, methods such as pyrolysis-gas chromatography/mass spectrometry (Pyr-GC/MS), Fourier-transform infrared spectroscopy (FTIR, \(\mu\)-FTIR), Raman spectroscopy (Raman, \(\mu\)-Raman), several modes of scanning electron microscopy (SEM, FESEM, SEM-EDS, ESEM-EDS), Nile Red fluorescence staining, holographic imaging coupled with machine learning, and various portable optical-sensor systems (laser, interferometric, diffraction-based, etc.) are advisable.
# Rain Removal of Single Image Based on Directional Gradient Priors
## 4 Experiments
### Experiments on Simulated Sets
Obviously, compared with other methods, the results obtained by the proposed algorithm retain more structural details, have a more realistic visual effect, and are closer to the ground truth. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**IMAGES** & \begin{tabular}{c} **GF** \\ **2013 TIP** \\ \end{tabular} & \begin{tabular}{c} **GMM** \\ **2016 CVPR** \\ \end{tabular} & \begin{tabular}{c} **JCAS** \\ **2017 ICCVR** \\ \end{tabular} & \begin{tabular}{c} **DetailsNet-H** \\ **2017 CVPR** \\ \end{tabular} & \begin{tabular}{c} **DetailsNet-L** \\ **(19 TNNLS)** \\ \end{tabular} & \
\begin{tabular}{c} **LPNet-L** \\ **(19 TNNLS)** \\ \end{tabular} &
\begin{tabular}{c} **OURS** \\ **OURS** \\ \end{tabular} \\ \hline IMAGE1 & 29.57/0.7 & 32.06/0.8 & 33.72/0.91 & 19.45/0.83 & 18.06/0.80 & 21.58/0.83 & 32.35/0.86 & **34.42/0.9241** \\ IMAGE2 & 27.27/0.7 & 34.87/0.9 & 35.21/0.95 & 32.63/0.94 & 33.98/**0.95** & 22.04/0.86 & 33.22/0.91 & **35.40**/0.9549 \\ IMAGE3 & 28.29/0.7 & 32.23/0.9 & 32.53/0.91 & 26.83/0.94 & 30.91/0.96 & 25.91/0.93 & **35.92/0.96** & 35.34/0.9558 \\ IMAGE4 & 29.02/0.7 & 35.26/0.9 & 40.75/**0.98** & 23.08/0.95 & 21.89/0.96 & 24.72/0.92 & 35.81/0.92 & **40.77**/0.9819 \\ IMAGE5 & 24.49/0.6 & 29.48/0.9 & 30.57/0.94 & 30.01/0.94 & **32.99/0.96** & 23.09/0.88 & 30.04/0.89 & 30.64/0.9442 \\ IMAGE6 & 24.13/0.6 & 31.46/0.9 & 33.38/0.96 & 28.09/0.94 & 29.74/**0.96** & 24.22/0.91 & 31.60/0.92 & **33.40**/0.9626 \\ IMAGE7 & 24.49/0.6 & 30.58/0.9 & 30.59/0.90 & 28.85/0.90 & 29.1/0.893 & 24.36/0.84 & 29.25/0.89 & **31.11**/**0.9194** \\ IMAGE8 & 27.25/0.7 & 29.93/0.8 & 31.56/0.91 & 30.88/0.91 & 28.41/**0.91** & 24.08/0.87 & 31.21/0.90 & **31.58**/0.9150 \\ IMAGE9 & 27.58/0.7 & 32.05/0.9 & **32.61**/0.91 & 28.66/0.92 & 25.42/**0.91** & 23.32/0.86 & 31.69/0.88 & **32.57**/0.9161 \\ IMAGE10 & 25.83/0.6 & 30.63/0.8 & 31.90/**0.93** & 26.73/0.89 & 31.85/0.93 & 25.91/0.88 & 29.50/0.89 & **31.96**/0.9342 \\ AVERAGE & 26.79/0.6 & 31.86/0.9 & 33.28/0.93 & 27.52/0.92 & 28.24/0.92 & 23.92/0.88 & 32.06/0.90 & **33.72**/**0.9408** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PSNR (dB)/SSIM Results of Images after Rain Removal by Different Methods.
## 4 Workload and Data Center Modeling
### Analytical Modeling Approach  
Workload demand and data center resource provisioning are modeled using statistical probability and stochastic random variables. The workload model section focuses on modeling the demands using statistical analysis and probability random variable theory, whereas, for data center modeling, a queuing theory is used based on a stochastic model.
## 2 Experimental
### Binding Parameters Determination and Isotherm Fitting Models
#### 2.4.2 Isotherm Fitting Models  
The plotting of \(B\) versus \(F\) yields a binding isotherm, making possible to determine apparent parameters through fitting. Adsorption isotherm models evaluated through non-linear fitting were as follows:  
\[\text{Langmuir }B=\frac{F\times B_{max}}{Kd+F} \tag{2}\]  
\[\text{Freundlich }B=A\times F^{m} \tag{3}\]  
In Equation (2), \(B_{max}\) and \(Kd\) are the density of binding sites and the dissociation constant of these sites, respectively. The Langmuir model considers all binding sites are equivalent. When \(F=Kd\), then \(B\) = 0.5 \(\times\)\(Bmax\). \(Kd\) can be defined as the concentration of free template at which 50% of imprinted sites are occupied, and \(Bmax\) as the value of \(B\) when all of the binding sites are occupied. In Freundlich model (Equation (3)), \(A\) and \(m\) are dimensionless constants that can be used to evaluate the binding site heterogeneity and determine dissociation constants and binding site densities [26]. The best fit was assessed by [PERSON]'s method and presented as the \"probability of correct model\" [27].
## 4 Results and Discussion
### Geochemical Characterization
The ratio of (La/Yb)\({}_{n}\) is 4.77 (average) reflecting the fractionated nature of ElSibai-Abu EITiyur alkaline granites. They manifest a moderate negative Eu anomaly where Eu/Eu\({}^{*}\) ranges from 0.16 to 0.63 (average 0.44). These REE patterns are consistent with the patterns of the alkaline A-type granites worldwide [54; 55]. The REE pattern of the abitized granite (La/Yb)\({}_{n}\) = 1.54) is the most enriched in HREE relative to the LREE, showing the least negative Eu-anomaly (Eu/Eu\({}^{*}\) = 0.16). This HREE enrichment parallels those of Na, Nb, Ga, U, Ta, and Y (sample S1 in Table S1), most likely related to the late-stage processes of albitization and fluorine reaction [56]. The correlation coefficients for the major oxides and trace elements including U, Th, and REEs, were calculated for the ElSibai-Abu EITiyur granites (Table S2 in Supplementary Material). As per [PERSON] [57], the Pearson correlation coefficient is weak (0.20-0.39), moderate (0.40-0.59), strong (0.60-0.79), and very strong (0.80-1.00). According to the correlation matrix, all the interesting elements showed strong (>0.65) to very strong (>0.8) positive correlations with each other. LREEs and HREEs exhibit a good association with the incompatible elements. Uranium is strongly associated with SiO\({}_{2}\), Rb, and Th in rock-forming minerals, while thorium is highly linked to the elements Cs, Li, Pb, and Rb in the mineralogical phases. All the samples of ElSibai-Abu EITiyur granites plot in the A-type granite field [44], using the standard tectonic discrimination diagram (Figure 6f). They occupy the A2 field or OIB Ocean island setting (Figure 6g) on the Nb-Y-Ga\({}^{*}\)3 ternary diagram (Eby [45]). They were emplaced in a within-plate setting (Figure 6h) [46], except for the two alkaline samples (S7 and S9) which experienced further fractionation, falling in the volcanic arc setting. We believe that they were emplaced with the opening of the Red Sea. The ratio Y/Nb varied between 0.75 and 2.26, i.e., they are derived from sources chemically similar to those of oceanic island basalts, while those with Y/Nb > 1.2 are derived from sources chemically similar to island arc or continental margin basalts. From all the above, we suggest that many processes including fractionation from mantle-derived basaltic magmas, during the phase of fracturing and crustal fading, following the termination of the Pan-African orogen generated the granites of ElSibai-Abu EITiyur.