esa-sceva/satcom-chunk-collection huggingface dataset:
# Antioxidant and Antimicrobial Activity of Porcine Liver Hydrolyates Using Flavourzyme
## 2 Materials and Methods
### Antimicrobial Activity  
A modified Kirby-Bauer medium with an agar well diffusion method was used to assess the antimicrobial capacity [28]. This qualitative test measures the microorganism resistance to the antimicrobial agent, correlating the antimicrobial concentration of substance with the inhibition halo area created on the surface of the agar plate. A specific microorganism is homogeneously inoculated into the agar plate with an appropriate growth medium. A total of six bacterial strains were selected and as culture media we employed trypticase soy agar (TSA) for _Escherichia coli_, _Salmonella enterica_ and Listeria monocytogenes_, Baird-Parker agar medium for _Staphylococcus aureus_, Streptomycin Thallous Acetate (STAA) for _Brochothrix thermosphacta_, and Pseudomonas agar base for _Pseudomonas aeruginosa_. The preparation of the strains was performed according to the commercial indications. 1 mL of _E. coli_, _S. enterica_, and _L. monocytogenes_ were poured into sterile Petri dishes. Next, 0.2 mL of _S. aureus_, _B. thermosphacta_, and _P. aeruginosa_ were spread onto plates by utilizing a sterile cell spreader (Drigralsky spatula). To ensure the maximum growth of the microorganism in the culture medium, the bacterial population must be roughly 108 colony-forming unit (CFU)/mL. Seven holes of 6 mm diameter were punched aseptically with a sterile cork-borer in the solid nutrient agar. In one of the holes, 25 \(\upmu\)L of positive control is added. For Gram-positive bacteria, the control was 105 IU nisin/mL and for Gram-negative bacteria was 50 ppm of gentamicin. Another hole contained 25 \(\upmu\)L of sterile water, as negative control. The remaining holes were destinated for the extracts, where 25 \(\upmu\)L of each hydrolysate were introduced. Before the addition of the samples and controls onto the wells, they were passed through a sterile cellulose acetate filter with a pore size of 0.22 \(\upmu\)m (Filter Lab, La Rioja, Spain). Agar plates were incubated for 24 h at 37 \({}^{\circ}\)C, optimum temperature for the microorganism growth. For those antimicrobial peptides that exerted a growth inhibitory effect, a halo appeared surrounding the wells. Diameter of the inhibitory area was measured using a manual calliper. The percentage of inhibition was calculated following the formula shown below:  
\[\text{Inhibition }(\%)=\frac{(\text{Diameter hydrolysate}-\text{Diameter blank})}{( \text{Diameter positive control}-\text{Diameter blank})}\times 100.\]
# Analysis of Pressure Effect on Three-Dimensional Flame Surface Density Estimation
## 4 Modeling and Approximations of \(\langle\cos\phi\rangle_{s}\)  
### DNS Results and the Measurements  
The direct way to evaluate \(\langle\cos\phi\rangle_{s}\) is through Direct Numerical Simulation (DNS). [PERSON] et al. [26] firstly tested the difference of 2D and 3D flame surface density using their 3D DNS results. They first computed \(\Sigma\), and then identified one plane to compute \(\Sigma^{2D}\) as the approximation, to mimic the planar laser measurement in experiments. Their conclusion is that \(\Sigma\) from 2D approximation underestimates the real value and it should be multiplied by a coefficient of 1.35 to yield 3D values. [PERSON] et al. [16] proposed a cosine value of the flame front orientation angle of 0.69, based on the experimental measurements in  
Figure 3: (a) The sketch of a 3D surface measured on a 2D plane; (b) the relation of 3D and 2D normals; (c) the definition of \(\theta\). _xoy_ represents the measurement plane and \(z\) is the normal direction of _xoy_.  
a Bunsen burner and an optical engine. This cosine value will be larger at lower engine speed, namely:  
\[\Sigma=\frac{1}{0.69}\Sigma^{\text{2D}} \tag{10}\]  
The DNS data only provide a global coefficient to distinguish the deficiency of 2D approximation, while the measurement can only provide an averaged 2D value.
# A Novel High Gain Monopole Antenna Array for 60 GHz Millimeter-Wave Communications
## 3 Results
### The Proposed Microstrip Antenna Array Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Size (mm2)** & **Gain (dB)** & **Bandwidth (\%)** \\ \hline
[28] & \(9.5\times 5.2\) & 10.7 & 7\% \\
[29] & \(28.61\times 25.7\) & 10.67 & 16\% \\
[30] & \(30\times 30\) & 11.8 & 20.7\% \\ Proposed array & \(20.64\times 20\) & 11.6 & 10.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between previously published work and the proposed antenna array. Figure 17: Surface current distribution of the proposed mmWave antenna array (**a**) Top view; (**b**) Bottom view. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Size (mm2)** & **Gain (dB)** & **Bandwidth (\%)** \\ \hline
[25] & \(1\times 1.3\) & 4.24 & 4.1\% \\
[26] & \(3\times 3\) & 7.8 & 7.3\% \\
[27] & \(2.75\times 5\) & 6 & 14.34\% \\ Proposed single antenna & \(12\times 9.6\) & 9.9 & 9.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between previously published work and the proposed single patch antenna.
## 3 Results and Discussion
### Discussion of the Statistical Analysis Results  
Figures 1 and 2 show the boxplots of the PAH content in the plants and infusions, respectively. For the content in the infusion, a zoomed-in view is illustrated. Among the polycyclic aromatic hydrocarbons evaluated, only BaP showed a normally distributed difference between the contents in the two media (the \(p\)-value of the Shapiro-Wilk test is around 0.1188, above the adjusted significance level \(\alpha/16=0.003125\)).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**PAHs** & \begin{tabular}{c} _Hypericum_ \\ _perforatum_ \\ \end{tabular} & \begin{tabular}{c} _Artentiisia_ \\ _abstinthium_ \\ \end{tabular} & _Tilia_ **sp.** & \begin{tabular}{c} _Matricaria_ \\ _chamontilla_ \\ \end{tabular} & \begin{tabular}{c} _Achillea_ \\ _millefolium_ \\ \end{tabular} & \begin{tabular}{c} _Taraxacum_ \\ _officiala_ \\ \end{tabular} & \
\begin{tabular}{c} _Calendula_ &
\begin{tabular}{c} _Cratageus_ \\ _monogyna_ \\ \end{tabular} \\ \hline Na & BLD & BLD & BLD & BLD & BLD & BLD & BLD & BLD \\ Ac & \(0.9\pm 0.01\) & BLD & BLD & BLD & \(15.3\pm 2.67\) & \(13.33\pm 1.89\) & \(1.42\pm 0.11\) & BLD \\  & & & & & \(19.30\) & \(45\pm 4.38\) & \(11.07\pm 20.68\pm 2.04\) & \(3.42\pm 2.04\) & \(3.42\pm 2.04\) \\ Flu & \(\begin{  
The quantile-quantile plot suggests data consistency with the normal distribution (Figure 3). The \(p\)-value of the paired t-test indicates that the content of BaP decreases significantly in the infusion.  
For all the other PAHs, the exploratory analysis and the low \(p\)-values of the Shapiro-Wilk test suggested that the differences do not follow a normal distribution, and hence, the two-sample sign test was applied. The \(p\)-values shown in Table 4 were compared to the adjusted significance level \(\alpha/15\approx 0.0033\). The comparisons indicated that the contents of Ace, Flu, Ph, An, Fln, Pyr, BaA, BbF, BbF and IP in the plants are significantly larger than in the infusions. The other differences (Na, Ac, Chr, Bghi and DahA) are not statistically significant due, in part, to the fact that these PAHs were not detected in all the samples of plants and infusions.  
Figure 1: Boxplots of PAH contents in the medicinal plants.  
Figure 2: Boxplots of PAH contents in the infusion (closer look).
## 3 Results and Discussion
### OWHTO Geometric Planning Optimization
It is also worth noting that for a 25% change in the position of the mechanical axis, an approximately equal distribution of stress is obtained in both the plaque and the bone, but with an increase in the total deformation of about 100% compared to the situation in which the change in the position of the mechanical shaft was only 5%. In fact, with regard to total deformation, it can be seen that for overcorrections of up to 12.5% (Fujisawa Point), the values for total deformation are reasonable, grouped around 0.4 mm, then having a significant increase for large overcorrections (20-25%). The result confirms the research in the papers [18; 25; 26], which emphasized that excessive overcorrection is not recommended as it can lead to wear of the cartilage from the lateral compartment and other degenerations and dysfunctionalities. Analyzing all these results, it can be stated that the optimum point, taking into account the three response functions analyzed, is the Fujisawa Point, which has equivalent reasonable stresses of 113.7 MPa in the tibia bone, 152.85 MPa in the TomoFix plate under  
Figure 17: Variation in the tibia equivalent stress related to the overcorrection positions. Figure 18: Variation in the TomoFix equivalent stress related to the overcorrection positions. the condition of a total deformation of 0.452 mm, quite close to the resulting minimum value. This is a numerical confirmation of the experimental results in the article [44]. Regarding the strengths and limitations of this research, it should be noted that the use of CAD models that take into account the mechanical characteristics and actual structure of human bones may provide a closer assessment of reality compared to possible experimental assessments performed on bovine or porcine bones. It is also possible to analyze the influence of variables that influence the geometric planning of the OWHTO by simulating loads and constraints on the same unique CAD bone model. It is known to limit the importance of experimental methods on real bones, given the impossibility of performing a considerable number of absolutely identical experimental samples. CAD-FEM modeling also proves its usefulness in establishing its optimal position as the point of intersection between the medial-lateral articular line and the corrected mechanical axis. Strengths in this regard would be the accuracy with which loads can be made on the surfaces of the medial and lateral plates of the tibia 3D model and the possibility of accurately adjusting (at any point on the joint line) the load transfer from medial to lateral specific to OWHTO correction. Obviously, the approach has some limitations, such as, first of all, the fact that the study is performed on a CAD model, which, no matter how well it is performed, cannot be 100% identical to the real human bone. The effect of soft tissues (muscles, cartilage, meniscus) is also neglected. Overall, we consider that the strengths highlighted above provide a relevant assessment of the objectives proposed in the paper and with a good degree of generality.
# Bi-Objective Power Optimization of Radio Stripe Uplink Communications
## 3 Uplink Power Optimization Problem
### Differential Evolution  
The DE [21; 22] is a population-based MH algorithm based on a differential mutation operator between pairs of existing individuals (solutions) in the current population and then applied to a third individual, denoted as base vector. The population is randomly initialized, being \(\mathbf{P}^{(t)}=\left\{\mathbf{p}^{(t,t)},i=1,\cdots,N_{pop}\right\}\), where \(t\) is the index of the generation, \(i\) is the index of each individual and \(N_{pop}\) is the population size. We adopt as stopping criteria the maximum number of iterations, \(N_{\text{nr}}\), or a the maximum number of iterations, \(N_{\text{lr}}\), with no improvement of both OFs within tolerance \(\varepsilon\).  
The mutant population \(\tilde{\mathbf{P}}^{(t)}\) with mutant individuals, \(\tilde{\mathbf{p}}^{(t,j)}\), is generated according to [21; 22]  
\[\tilde{\mathbf{p}}^{(t,j)}=\mathbf{p}^{(t,j)}+\lambda\Big{(}\mathbf{p}^{(t,j_{ \text{host}})}-\mathbf{p}^{(t,j)}\Big{)}+\mathbf{F}\Big{(}\mathbf{p}^{(t,j_{2} )}-\mathbf{p}^{(t,j_{3})}\Big{)}, \tag{19}\]  
where \(r_{2},r_{3}\in\big{\{}1,\cdots N_{pop}\big{\}}\) are randomly selected individuals with \(i\
eq r_{2}\
eq r_{3}\), \(\mathbf{p}^{(t,j_{\text{host}})}\) denotes the best individual in the population at generation \(t\), \(\lambda\in[0,1]\) and \(\mathbf{F}=[F_{1},\cdots,F_{K}]^{T}\) is a scaling parameter that acts as a weight coefficient of the difference vectors, defined as  
\[F_{k}=F_{0}+\mathcal{Z}_{\alpha} \tag{20}\]  
with \(\mathcal{Z}_{\alpha}\sim\mathcal{N}[0,\alpha]\) and \(\alpha\ll 1\). The next step includes a discrete recombination operation between \(\mathbf{P}^{(t)}\) and \(\tilde{\mathbf{p}}^{(t)}\), producing the offspring population, \(\tilde{\mathbf{p}}^{(t)}\) whose individuals are  
\[\tilde{p}^{(t,j)}_{k}=\left\{\begin{array}{ll}\tilde{p}^{(t,j)}_{k},&\text{ if }\mathcal{X}\leq C\lor k=\delta_{i}\\ p^{(t,j)}_{k},&\text{otherwise},\end{array}\right. \tag{21}\]  
where \(\mathcal{X}\sim\mathcal{U}[0,1]\), \(\delta_{i}\in\{1,\cdots,K\}\) is a random index. Additionally, \(C\in[0,1]\) is a parameter that controls the fraction of decision variables that are copied from the mutant individual to the offspring individual. It starts with a maximum value \(C_{max}\), and is reduced according to a rate \(C_{r}\). Finally, the selection process for individual survival for the next generation, \(\mathbf{P}^{(t+1)}\), involves the evaluation of the OF for \(\tilde{\mathbf{p}}^{(t,j)}\) and \(\mathbf{p}^{(t,j)}\), with the worst ones being discarded and the best ones surviving  
\[\mathbf{p}^{(t+1,i)}=\left\{\begin{array}{ll}\tilde{\mathbf{p}}^{(t,j)},& \text{if }\text{eval}\Big{(}\tilde{\mathbf{p}}^{(t,j)}\Big{)}\geq\text{eval}\Big{(} \mathbf{p}^{(t,j)}\Big{)}\\ \mathbf{p}^{(t,j)},&\text{otherwise}.\end{array}\right. \tag{22}\]
# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
## 4 Results and Analysis
### Device Pose Neural Network  
The details of the algorithmic study and the results for the antecedent device pose neural network of the RSSI-and-device-pose neural network framework (Figure 4) that utilizes the accelerometer measurements to detect device pose can be found in [24]. This device pose identification network was sized with just five hidden neurons and achieved good accuracy (>90%) for each device pose class. The network's high accuracy is not surprising, as datasets for much more complex activities and even for multiple individuals are also commonly classified at >90% accuracy rates in the human activity detection literature using accelerometers [18].
## 2 Materials and Methods
### Permeability  
The permeability was calculated using [PERSON]'s law of the relationship between the pressure difference and flow rate from the simulation results using the LBM. [PERSON]'s law describes the flow in porous media as follows:  
\[q=-KA\frac{\Delta P}{\mu L} \tag{14}\]  
where \(q\) is the flow rate in the \(x\) direction, \(K\) is the permeability, \(A\) is the area of the porous medium, \(\Delta P\) is the pressure drop, \(\mu\) is the dynamic viscosity of the fluid, and \(L\) is the flow length of the fluid. The LBM simulates [PERSON]'s experiment by applying a pressure gradient to the inlet-outlet of a saturated porous medium using the OpenLB Open Source Lattice Boltzmann. OpenLB utilizes the D2Q9 scheme, which describes motion in two dimensions and 9 associated velocity vectors. Based on Equation (14), the permeability can be calculated as follows:  
\[K=-\frac{q\mu L}{A\Delta P} \tag{15}\]  
The flow rate in a certain direction (in this case, \(x\) direction) can be expressed as the rate volume of fluid passing through the cross-sectional area, A, perpendicular to the flow direction as follows:  
\[K=\frac{\langle v\rangle\mu L}{\Delta P} \tag{16}\]  
where \(\langle v\rangle\) is the average velocity. Permeability has units of area (m\({}^{2}\) in SI units), but sometimes, in terms of the geophysical aspect, it is more common to use the Darcy, where \(1\)\(\text{Darcy}=0.987\times 10^{-12}\) m\({}^{2}\) or the millidary (\(mD\)) where \(1\)\(mD=0.001\) Darcy. In this study, permeability was also calculated using the Kozeny-Carman permeability:  
\[K=\frac{\phi_{\text{eff}}^{3}}{c\tau^{2}S^{2}} \tag{17}\]  
where \(\phi_{\text{eff}}\) is the effective porosity, \(\tau\) is the tortuosity, \(c\) is a constant, and \(S\) is the specific surface area. Kozeny-Carman permeability is used to calculate the permeability of a porous medium based on its pore structure. This theoretical model relates permeability to the geometric characteristics of the pores and fluid flow through the medium.
## 7 Conclusions  
Environmental applications such as fog clearance, air-pollution control, and smoke clearance need to be solved using an advanced system; accordingly, in this study, the use of an octocopter is proposed. This work's predominant contribution is a unique octocopter design. Standard analytical formulae are used to estimate the conceptual design parameters. The design of the arm, design of the landing stick, and payload design play an essential role in the analytical estimation. The aerodynamic performance parameters, such as the lift and drag coefficients, were estimated for both vertical-climb and forward-speed operations using standard analytical methods. The attitude dynamics of the octocopter were introduced with a linearized model along with the motor dynamics, and the controller-design and -stability analyses were performed for attitude control. The simulation results gave a faster response from the octocopter with acceptable overshoot and zero steady-state error. In addition, we suggest the implementation of sensor-fusion algorithms to improve the performance of the control system, owing to the presence of gyro errors. An advanced CFD tool-based methodology was implemented for the estimation of the aerostatic fluid property variations over the octocopter. The coupled computation [CFD-MRF] is involved in estimating the aerodynamic properties in intended foggy environments, in which the fluid properties are monitored. The fluid velocities are increased by multiples of 16.2, and the static pressures are decreased, which can help manage foggy environments by reducing the foggy-fluid density. Three effects (C-D duct effect, rotodynamic effect of eight rotors, and high turbulence) imparted a large amount of kinetic energy to the fluid from the exit of the tank. Owing to this large kinetic energy, the highly dense seawater dominates the foggy fluid movement and forces the foggy fluid towards the ground. Finally, FSI analyses were performed for the three necessary composite materials, and the GFRP-based composite was determined to be the most suitable. Through FSI, it was strongly observed that the environment does not affect the structural parts of the octocopter because the induced stress values (0.40 MPa) are 250 times lesser than the ultimate stresses of the implemented lightweight materials. Thus, the proposed octocopter is structurally stable and can be efficiently used in many types of environmental applications.  
Conceptual Design, V.R. and P.R.; Modeling and Control, S.K.S. and S.J.; CFD and FSI, V.R. and S.K.M.; writing--original draft preparation, S.J. and P.R. All authors have read and agreed to the published version of the manuscript.  
Following are results of a study on the \"Leaders in INdustry-university Cooperation +\" Project, supported by the Ministry of Education and National Research Foundation of Korea (No. 2021-C-O031-010109).  
Not applicable.  
The authors declare no conflict of interest.
## 6 Discussion  
### Interpretation of Findings  
The experimental results provide compelling evidence for the efficacy of the proposed composite strategy. The time-domain analysis demonstrated that the synchronous sampling method consistently reduces the error between the measured and theoretical rotational speeds across all tested conditions. However, the most significant finding is the marked improvement in the correlation coefficient (R). This indicates that the method does more than simply reduce noise; it actively reconstructs the waveform, restoring the signal's fidelity to the true torsional dynamics. The reduction in overall errors, quantified by lower RMSE and MAE values, corroborates this enhancement in data accuracy.  
In the frequency domain, the analysis reveals the method's nuanced effectiveness. The runout introduced during the experiments primarily generated interference in the medium and high-frequency bands. The synchronous sampling method showed superior anti-interference capabilities in these specific bands, significantly reducing noise where it was most dominant. Critically, this was achieved while preserving the primary low-frequency torsional vibration characteristics, which are often of greatest interest in powertrain analysis. This ability to selectively cancel interference without distorting the underlying signal addresses a key challenge that traditional low-pass filtering methods often fail to overcome, as they can inadvertently attenuate valuable high-frequency features or leave low-frequency noise untouched.
# An Examination of Thermal Features' Relevance in the Task of Battery-Fault Detection
## 2 Experiment Design
### Methodology of Data Exploration
#### 2.2.2 Unsupervised Data Processing  
Principal component analysis (PCA) is a linear transformation of matrix data, which reduces their dimension with regulated loss of information [16; 17]. Such a process is achieved by embedding the original data into the linear subspace with a lower dimension in a way to preserve as much of relevant information as possible. It finds a mapping matrix \(M\) to lower the dimension with maximum variance, which is defined as a solution of an eigenproblem.  
\[cov(X)M=\lambda M \tag{2}\]  
where \(cov(X)\) is the covariance matrix of input data \(X\). The mapping matrix \(M\) is orthogonal and is formed by principal components-eigenvectors of the covariance matrix \(cov(M)\). The matrix \(\lambda\) contains eigenvalues (the principal components) of the covariance matrix on diagonal. To provide the dimensionality reduction, the columns of the mapping matrix \(M\) are sorted according to the decreasing eigenvalues in the matrix \(\lambda\). The mapping matrix is then truncated to keep only the first \(d\) principal components (the most significant components to keep the highest amount of information). A new data set \(Y\) reduced into the dimension \(d\) is then computed accordingly.  
\[Y=XM_{d} \tag{3}\]  
Principal Component Analysis was successfully applied in the area of feature extraction and signal processing [18], feature extraction [19] and it was also compared with other reduction techniques [20].  
The cluster analysis is a grouping mechanism of samples into clusters according to their similarity that depends on a distance function and a given representation of the samples [21]. Such process was found helpful in various applications and, in our case, this process used to examine the relevancy of samples representation. The clear clusters with minimum overlap can imply lower noisiness and uncertainty, while the inseparable highly overlapped clusters imply the presence of irrelevant data representation.  
The number of clusters can be determined by gap statistic [22]. It compares the total within intra-cluster variation for different values of \(k\) with their expected values under null reference distribution of the data. The estimate of the optimal clusters will be a value that maximizes the gap statistic (i.e., one that yields the largest gap statistic). This means that the clustering structure is far away from the random uniform distribution of points.  
The Silhouette score may be calculated using the mean intra-cluster distance \(a\) and the mean nearest-cluster distance \(b\) for each sample [23] in order to evaluate the clarity of the solution.  
The Silhouette Coefficient of the solution is than \((b-a)/max(a,b)\). To be specific, \(b\) is the distance between \(a\) sample and the nearest cluster of which the sample is not a part.