esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Computational Method
The structures optimized at the QM/MM level were then used as an input for the final DFT optimizations, but in this case, the effect of the bulk solvent was considered through the quantum continuum COnductor-like Screening MOdel (COSMO) [34]. While explicit water molecules were considered in the QM/MM calculations, in COSMO, the solvent is considered as continuous, characterized by a specific dielectric constant, with the solute placed in a cavity defined by the van der Walls radii of its atoms. Herein, the surfactants and a few water molecules closer to the hydrogens of the ammonium group were considered as solute, and the relative dielectric constant was set equal to four, in order to simulate the effect of the confined water molecules. The partial charges were calculated using both the Lowdin and the electro static potential (ESP) methods [24], in addition to a larger basis set for all of the atoms of the surfactants (i.e., 6-311*). The ESP method evaluates the atomic charges from the fit of the quantum mechanical electrostatic potential of the surfactant on a grid point centered on each atom. Different empirical formulas are available in the literature in order to calculate the maximum length (_lt_) as a function of the molecular weights. However, these formulas are not applicable for the conformational conformers that obviously show the same molecular weight. Furthermore, these formulas depend on the specific experimental conditions used, thus they are not general. Herein, _lt_ and the molecular aspect ratios were calculated by a bespoke algorithm previously validated [35; 36; 37] without resorting any empirical parameters, but only using the QM/MM and full DFT optimized geometries. Briefly, the effective diameter and key molecular sizes are evaluated according to specific dimensions, as follows: the height, _lt_, and size of the minimum rectangle enclosing the orthogonal projections of all of the atoms perpendicular to the principal molecular axis. The height, _lt_, is defined as the distance between the two most distant atoms, taking into account their van der Waals radii. The molecular size, \(\mathrm{d_{min}}\), is defined as the smallest size of the rectangle enclosing the orthogonal projections, while the second dimension of this rectangle, \(\mathrm{d_{max}}\), is used to calculate the aspect ratio (i.e., \(\mathrm{h/d_{max}}\)). Details on the bespoke algorithm used for the calculations and its validation can be found in reference [37]. A flow chart is reported in Scheme 1 to summarize the computational methodology used to investigate the relationships between surfactant's structure and proprieties correlated to the antibacterial activity. As shown in Scheme 1, the computational procedure is quite general and can be applied to predict the analogous properties of functional groups, in order to improve the biofouling resistance of the membrane surfaces.
## 4 Numerical Simulation
### Individual Accessory Parameter Sensitivity Analysis
#### 4.4.1 Buoy Volume and Installation Location  
Adding a buoy to the catenary mooring system can provide an upward force that reduces the weight of the o catenary. In order to study the influence of the change of buoy parameters on the overall response of the wind turbine, the influence of different volumes of buoys on the performance of the mooring system was first compared. The three buoy volumes were 10 m\({}^{3}\), 20 m\({}^{3}\) and 30 m\({}^{3}\) respectively, and the buoys were installed 15 m away from the fairlead; Secondly, consider the optimization effect of float installation position change on the overall motion response of the wind turbine, the buoy with a volume of 10 m\({}^{3}\) is selected, and the installation position is 15 m, 25 m, and 35 m from the fairlead respectively. Using SIMA to simulate the motion response of the floating wind turbine with different buoy parameters under extreme sea conditions for 3 h, the motion response and tension statistics of the floating wind turbine under different buoy parameters were obtained.  
The motion response and tension of the floating wind turbine with different buoy parameters can be seen in Figure 7, and the motion response of triangle tag represents the response statistics of maximum value, round mark on behalf of the mean, the error bars represent the standard deviation. It can be seen from the figure that when the buoy is installed separately on the floating wind turbine mooring system, increasing the volume of the buoy under the condition of determining the installation position will increase the mean and maximum value of the surge, pitch and heave, the maximum value of the No. 2 mooring line tension increases, but the mean value of the mooring line tension changes very little; When determining the volume of the buoy, the distance between the installation position of the buoy and the fairlead increases, which will lead to an increase in the maximum and mean value of the surge and pitch, but will reduce the mean and maximum value of the heave, the maximum tension of mooring line 2 will also increase, and the average value will remain basically unchanged.  
Figure 6: Schematic diagram of mooring.  
Figure 7: _Cont._
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Nigella sativa L.
#### 3.3.2 Antioxidant Properties  
Black cumin seed oil, extracts, and thymoquinone are all known to possess antioxidant properties [86; 87]. The research findings indicate that _N. sativa_ oil and its active component, thymoquinone, exhibit significant potential in protecting brain and kidney tissues from radiation-induced oxidative stress in animal models [88; 89]. The protective effects of nigella oil were also evident in the salivary gland of rats subjected to total cranial irradiation [90]. Additionally, black cumin oil may hold promise as an adjunct in anticancer therapy, particularly in mitigating chemotherapy-induced gonadal toxicity. Pre-administration of black cumin oil before carboplatin treatment resulted in a substantial reduction in oxidative stress, particularly in lowering MDA levels [91]. In a randomized, triple-blind clinical trial, supplementation of 1000 mg of black seed oil daily for 8 weeks in postmenopausal women did not have a significant impact on serum levels of oxidative stress markers, i.e., MDA and total antioxidant capacity (TAC) [92]. In a study conducted by [PERSON] et al. [93], postmenopausal women were given a daily supplement of 1g of black cumin seeds for 8 weeks. The results showed a significant increase in serum glutathione (GSH) levels, which indicates a reduction in oxidative stress. However, it is important to note that the study had some limitations, such as the absence of a control group [93].
# Mechanical and Thermal Properties for Uranium and U-6 Nb Alloy from First-Principles Theory
###### Abstract  
Elasticity, lattice dynamics, and thermal expansion for uranium and U-6 Nb alloy (elastic moduli) are calculated from density functional theory that is extended to include orbital polarization (DFT+OP). Introducing 12.5 at.% of niobium, substitutionally, in uranium softens all the \(\mathrm{ci_{i}}\) elastic moduli, resulting in a significantly softer shear modulus (G). Combined with a nearly invariant bulk modulus (B), the quotient B/G increases dramatically for U-6 Nb, suggesting a more ductile material. Lattice dynamics from a harmonic model coupled with a DFT+OP electronic structure is applied for \(\alpha\) uranium, and the obtained phonon density of states compares well with inelastic neutron-scattering measurements. The Debye temperature associated with the lattice dynamics falls within the range of experimentally observed Debye temperatures and it also validates our quasi-harmonic (QH) phonon model. The QH Debye-Gruneisen phonon method is combined with a DFT+OP electronic structure and used to explore the anisotropic thermal expansion in \(\alpha\) uranium. The anomalous negative thermal expansion (contraction) of the b lattice parameter of the \(\alpha\)-phase orthorhombic cell is relatively well reproduced from a free-energy model consisting of QH-phonon and DFT+OP electronic structure contributions.  
U-6 Nb; elastic constants; negative thermal expansion; actinides; phonons Article
# Optimization of Inventory Management to Prevent Drug Shortages in the Hospital Supply Chain
## 5 Experiment and Numerical Results
### Results
#### 5.2.3 Evaluation of the Shortage Situation  
We evaluate the shortage situation in a time horizon by applying four methods within a finite horizon: over-provisioning, DRLD, ski-rental, and max-min. During 30 timeslots, four-point-three-seven percent of the drugs hit a shortage level if applying ski-rental, while over-provisioning is slightly better with 4.09%. The max-min method has an even better result with 3.41% drugs at the shortage level. Our method, the DRLD, obtains the best result in this evaluation with only 2.21% of the drugs in a shortage situation in the considered timeslots. Figure 6 shows the shortage situations and their evolution for all four approaches. In detail, over-provisioning often hits serious shortage points, for example at Timeslots 14, 24, and 27 h, as the average value of the previous usage does not react well with the peak demand. The ski-rental method can obtain a better result compared to the over-provisioning method, but it also often reaches a shortage level. This method can react better to the changes in demand than the over-provisioning method, but it is too difficult to implement a correct setting of the \(c\)-competitive value for all the drugs in the system. The max-min method does not hit some peak points, but the shortage situation still occurs frequently during the evaluation period, similar to the over-provisioning method. As mentioned before, the intuition of the max-min approach is not that different than that of the over-provisioning method. Compared to all the baselines, the DRLD has the least amount of shortage points in the figure. At the peak shortage point (Time Slot 27), the DRLD has only 1.7% of its drugs that reach shortage, the lowest rate in the evaluation.  
Figure 5: Refilling cost evaluation.
## 4 Conclusions  
We have designed an SPR sensor for the detection of concentrations of CO\({}_{2}\) which works both indoors and outdoors. The sensor has a sensitivity of almost 25 RIU\({}^{-1}\) and a resolution of 5.15 \(\times\) 10\({}^{-5}\) RIU. It could have better sensitivity if the ambient conditions are known because the refractive index of this gas depends on pressure, temperature, humidity and concentration. This is an improvement compared to other SPR sensors designed to work indoors and outdoors.  
Measurements are made continuously and no time is needed to achieve the measurement, and there is no time lapse between measurements.  
Today, it is vital to ventilate closed spaces to avoid the spread of COVID-19. We cannot be sure if the ventilation time is correct or if a building is well ventilated because we do not have sensors that measure the presence of the virus in the air, but we can indirectly know the correct ventilation of the rooms from the measurement of CO\({}_{2}\) expelled by humans, so we can know the distribution of this residual CO\({}_{2}\). By placing our sensors in the right places, we can know if the ventilation is adequate and, therefore, we will know if the ventilation time is adequate. As our sensor is so tiny, many can be placed and at any point in a room to see the data of concentration of CO\({}_{2}\) in real time.  
The overall system can be connected to a 5G module to send data to a remote point and there is an alarm if the data is superior and potentially damaging for health, the environment, etc.  
Finally, it is not necessary to carry out periodic calibrations.  
Conceptualization, A.M.P. and F.P.-O.; methodology, J.C.; software, J.C. and O.R.; validation, A.M.P. and F.P.-O.; formal analysis, J.C.; investigation, A.M.P., F.P.-O., J.C. and O.R.; resources, A.M.P., F.P.-O., J.C. and O.R.; data curation, J.C. and O.R.; writing--original draft preparation, F.P.-O.; writing--review and editing, A.M.P., J.C. and O.R.; visualization, A.M.P., F.P.-O., J.C. and O.R.; supervision, A.M.P. and F.P.-O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors wish to thank [PERSON], a native English speaker and expert in translations of scientific papers, for her assistance with the English version.  
The authors declare no conflict of interest.
# Underwater Target Recognition Based on Multi-Decision
## 3 LOFAR Spectral Line Enhancement Based on Multi-Step Decision
### Analysis and Construction of Line Spectrum Cost Function  
The definition of the line spectrum feature cost function is as follows:  
\[O(\eta)=\frac{\lambda F(\eta)+\mu T(\eta)}{A(\eta)}, \tag{18}\]  
where \(\eta\) represents a summation path along the time axis in the observation window of the LOFAR graph, and the length of the path is \(N\). \(A(\eta)\) characterize the amplitude characteristics of the line spectrum, \(F(\eta)\) is the frequency continuity of the line spectrum, and \(T(\eta)\) is the trajectory continuity of the line spectrum, \(\lambda\) and \(\mu\) are weighting coefficients. The definitions of \(A(\eta)\), \(F(\eta)\), and \(T(\eta)\) are as follows:  
\[A(\eta)=\sum_{i=1}^{N}a(P_{i}), \tag{19}\]\[F(\eta)=\sum_{i=3}^{N}|d(P_{i-2},P_{i-1})-d(P_{i-1},P_{i})|, \tag{20}\]  
\[T(\eta)=\sum_{i=1}^{N}g(P_{i}). \tag{21}\]  
Each pixel on the summing path is \(P_{i}(1\leq i\leq N)\), which means a point on the \(i\) line of the time axis. \(a(P_{i})\) characterizes the amplitude of the point \(P_{i}\). \(d(P_{i-1},P_{i})\) characterizes the frequency gradient at two points in the path, which is defined as follows:  
\[d(P_{i-1},P_{i})=f(P_{i-1})-f(P_{i}), \tag{22}\]  
where \(f(P_{i})\) represents the frequency of the point \(P_{i}\). \(g(P_{i})\) characterizes the breakpoint identification, which is defined as follows:  
\[g(P_{i})=\left\{\begin{array}{ll}1&a(P_{i})<\varepsilon\\ 0&others\end{array}\right.. \tag{23}\]  
If the amplitude of the point \(P_{i}\) is less than \(\varepsilon\), it is regarded as a breakpoint and recorded as 1, otherwise it is recorded as 0. Regarding the calculation of the threshold \(\varepsilon\), the original algorithm is mostly set by empirical values, and a new calculation method is proposed as follows:  
\[P(w)=|STFT\{n(t)\}|^{2}, \tag{24}\]  
\[\varepsilon=A_{average}=\sqrt{\frac{\sum P(w)}{M*N}}, \tag{25}\]  
where \(n(t)\) represents the marine environmental noise. The sampling sequence of the interference noise in the marine environment is subjected to STFT transformation which can obtain the LOFAR spectrum. At the same time, the instantaneous power \(p(w)\) of each time-frequency point is calculated. \(M\), \(N\) represent the points of frequency domain and time domain of LOFAR spectrum. The power of all time and frequency points is summed and averaged to obtain the average power. Take a square to obtain the average amplitude of the LOFAR spectrum of marine environment interference noise, which is the threshold \(\varepsilon\) for determining whether the point \(p_{i}\) is a breakpoint.  
It can be analyzed from the cost function: the problem of line spectrum detection is transformed into the problem of finding the optimal path \(\eta\) and minimizing the cost function about the path \(\eta\).
## 4 Numerical Experimentation and Results
### Example 2  
Consider the logistic equation:  
\[y^{\prime}=\kappa y(1-y),\quad y(0)=y_{0} \tag{33}\]  
with the exact solution \(y(t)=\dfrac{y_{0}}{(1-y_{0})e^{-\kappa t}+y_{0}}\). The problem was integrated on \([0,10]\) using a step size of \(h=0.1\) for the case when \(\kappa=2\).  
Table 4 compares the maximum errors produced by the OOHBM versus the OHBM. Included also are the maximum errors of the corresponding hybrid block methods constructed using intra-step points equally spaced apart. The table clearly illustrates the increase in accuracy brought about by the introduction of overlapping in grids that use both equally spaced and optimal intra-step points.  
Figure 5 provides a comparison of the convergence plots for the standard OHBM and the proposed OOHBM. The illustration demonstrates the speedy convergence of the linear partitioning iterative approach and the superior accuracy of the proposed OOHBM schemes in comparison to the OHBM schemes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{\(m=3\)} & \multicolumn{2}{c}{\(m=5\)} \\ \cline{2-5}  & **Original HBM** & **Proposed HBM** & **Original HBM** & **Proposed HBM** \\ \hline Equally spaced points & \(1.3918\times 10^{-7}\) & \(1.5292\times 10^{-8}\) & \(7.2577\times 10^{-12}\) & \(8.5256\times 10^{-13}\) \\ Optimal points & \(6.3022\times 10^{-9}\) & \(4.4041\times 10^{-10}\) & \(1.2751\times 10^{-12}\) & \(6.2343\times 10^{-14}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum errors for Example 2 using different choices of intra-step points.  
Figure 4: Maximum error profile for Example 1.  
Figure 6 illustrates the maximum error profile \(E_{n}\) across all blocks as a function of the block index \(n\). The decrease in error shown in Figure 6 when switching from the OHBM to the OOHBM is consistent with the pattern observed in Figure 4 of the preceding case. This is additional proof that the concept of overlapping grids improves the accuracy of hybrid methods.  
Next, we discuss the implementation of the overlapping HBM on systems of nonlinear equations that are notoriously difficult to numerically solve due to their stiff characteristics or their propensity to change rapidly over short time intervals.
## 5 Result and Discussion
The findings of the Deep-Hist with AHT optimization algorithm and the other pretrained models such as ResNet, DenseNet, and MobileNetV2 with BO approaches indicate that our methodology outperforms the well-known Multi-Objective Bayesian hyperparameter optimization method while delivering significantly smaller structures. Furthermore, it illustrates that using the proposed AHT optimization algorithm to tweak the hyperparameters in the CNN architectures greatly increases the F1-Score, Specificity, and Sensitivity of healthcare datasets. Figure 4: Our proposed CNN model (Deep-Hist) is compared with pretrained models like as ResNet, DenseNet and MobileNetV2 on combined dataset (images acquired and combined from BreakHis, COVID-19 X-ray, NIX X-ray and BraTS). The acquired accuracy of pretrained models are ResNet (0.87), DenseNet (0.88) and MobileNetV2 (0.84).
## 3 Multimedia
### Applications and Use-Cases
Researchers need to explore innovative approaches to improve bandwidth utilization, reduce latency, and ensure reliable multimedia transmission within VANETs/DSRC. By tackling these challenges, researchers can contribute to the continued advancement of multimedia applications in vehicular communication systems, ultimately enhancing safety, entertainment, and advertising capabilities for a seamless driving experience.
# Optimization Procedure for Computing Sampling Time for Induction Machine Parameter Estimation
## 3 Optimal Induction Machine Poles and Sampling Time
### Optimal Sampling Time  
When motor poles are calculated by fitting the damped complex exponentials to the decimated and interval divided data, the computation of the optimal sampling time is straightforward following the criteria introduced by [PERSON] [15] and applied in synchronous generator parameter estimation in [26].  
The method describes the optimal sampling time as the longest interval \(T_{s,opt}\) for which the Tustin bilinear transformation used to approximate continuous systems with discrete systems is not loosing accuracy.  
Put in numbers, given the largest magnitude of the continuous system eigenvalues \(|\lambda_{max}|\), and the sampling time \(T_{s}\), the bilinear transformation holds if  
\[|\lambda_{max}|T_{s}\leq 0.5 \tag{25}\]  
Without presenting the details that can be found in [15; 26], the optimal sampling time is obtained from the minimum magnitude discrete pole \(z_{min}=|z_{min}|e^{-\hat{y}/\hat{z}}\) of the system by numerical solving of the Equation (26) for unknown \(K\), and computing the optimal sampling time as \(T_{s,opt}=KT_{s,fit}\).  
\[\frac{\left[|z_{min}|^{4K}+2|z_{min}|^{2K}\cos(K\phi_{z})+1\right]^{1/2}}{|z_ {min}|^{k2}-2|z_{min}|^{K}\cos(K\phi)+1}=5 \tag{26}\]
## 2 Materials and Methods
### Quantitative Evaluation Indicators  
\(R^{2}\), Evar, RMSE, and MAE, the evaluation indicators used in this study, are commonly used to evaluate the prediction performance of the regression model, where \(R^{2}\) and Evar explain the variance score of the regression model, and their values take the range of [0,1]; values closer to 1 indicate that the independent variable is more able to explain the variance change in the dependent variable, and smaller values indicate the opposite [47; 48; 49].  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}} \tag{20}\]  
\[\text{Evar}=\text{Explain variance}=1-\frac{\text{Var}\{y_{i}-\hat{y}_{i}\}}{ \text{Var}\{y_{i}\}} \tag{21}\]  
RMSE indicates the average value of the square root of the error of the fitted data and the original data corresponding to the sample points; MAE assesses the degree of closeness between the prediction results and the real dataset. The smaller the value of these two, the better the fitting effect. The statistical indicators are computed as follows:  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{n}} \tag{22}\]  
\[\text{MAE}=\frac{\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert}{n} \tag{23}\]  
where \(y_{i}\) denotes the true value of the undrained shear strength of the clay sample, \(\hat{y}_{i}\) denotes the predicted value of the undrained shear strength of the sample clay, \(\overline{y}\) denotes the value of the undrained shear strength of the clay sample, and \(n\) denotes the number of the clay sample. The coefficient of variation (COV) is commonly used in engineering to perform an analysis of variance, showing the variability of a sample population relative to the sample mean.  
\[\text{COV}=\frac{\sigma}{\mu} \tag{24}\]  
where \(\sigma\) represents the standard deviation of the undrained shear strength of the sample clay, and \(\mu\) represents the mean undrained shear strength of the sample clay.