esa-sceva/satcom-chunk-collection huggingface dataset:
# Uncertainties and Perspectives on Forest Height Estimates by Sentinel-1 Interferometry
## 4 Conclusions  
This work proved that S1 interferometric data can be effectively adopted to retrieve forest height. In particular, adopting the topographic levelling approach based on local differencing between interferograms from a forest pixel and a neighbour bare soil, one is permitted to operate with no phase unwrapping, resulting in an unambiguous height estimate. The authors proved that tuning the optimal baseline and multilooking factor can improve the accuracy of forest height retrieval. In particular, forest heights in the range of 10-30 m require an optimal baseline between 250 and 100 m. Furthermore, contrarily to the general rule that suggests large \(B\) values improve InSAR-derived DEM accuracy, this work demonstrates that, in forest areas, the same rule fails. In fact, by increasing \(B\), forest coherence decreases, reducing interferometric phase accuracy. A simple model was additionally proposed and calibrated, aimed at predicting the optimal \(B\) value (and therefore image pair selection) for \(\sigma_{dh}\) minimization. Regarding the spatial density of forest height estimates, it was found that SGRP values between 50 and 100 m can guarantee a \(\sigma_{dh}\) < 3 m. This resolution is certainly not appropriate for fine-scale analysis, suggesting the adoption of this method when small-scale (wide areas) mapping of forest heights is required. Finally, in this paper, authors proved that S1 InSAR data processed by focusing on height differencing computation (see Equation (2)) is effective in retrieving forest heights with medium-high accuracy. This has been necessarily achieved opportunely by tuning user-dependent parameters (\(B\), \(NL\), \(B_{temp}\)) according to the above-mentioned maximization criteria.  
Conceptualization, S.D.P.; methodology, S.D.P.; software, F.S. and S.D.P.; validation, S.D.P. and F.S.; formal analysis, S.D.P. and F.S.; investigation, S.D.P. and F.S.; resources, S.D.P. and F.S.; data curation, S.D.P.; writing--original draft preparation, S.D.P. and F.S.; writing--review and editing, E.B.-M.; visualization, S.D.P. and F.S., supervision, E.B.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c} \hline _Baseline (m)_ & _Expected dh (m)_ & \(\sigma_{dh}\) (m)_ \\ \hline
50 & 10–30 & 2 \\
100 & 10–30 & 2 \\
150 & 10–30 & 1 \\
200 & 10–30 & 0.5 \\ \hline \end{tabular}
\end{table}
Table 3: Best cases from simulations in a typical Italian forest context (tree height in the range of 10–30 m).
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 2 Background and Related Work
### Previous Studies on Alert Fatigue
#### 2.2.2 Alert Prioritization  
By taking too many irrelevant, low-quality alerts as inputs, preprocessing techniques based on filtering and correlation can provide imprecise results due to the noisy input. To solve this problem, alert prioritization attempts to address the difficulty in managing a large number of alerts and improve the accuracy of results by reducing the number of unrelated alerts.  
[PERSON] et al. [39] proposed an alert prioritization model that employs risk assessment to assess the impact of IDS-generated alerts on the security status of an information system. This model incorporates various factors, e.g., priority, reliability, and asset value, to calculate the risk associated with each alert and prioritize the most critical alerts based on their risk levels. The model was evaluated on the KDD Cup 1999 dataset to assess its effectiveness. [PERSON] et al. [40] utilized an isolation forest technique to filter out low-level threat alerts. Here, they considered temporal information in alerts, allowing for real-time prediction of incoming threats. This study demonstrated the potential of unsupervised learning in reducing alert fatigue. The method was then improved [41] by adding a one-class stacked autoencoder that reduced false positives by selecting outliers with high reconstruction error rates as candidates.
## 5 Discussion
### Implications and Applications  
This study presents significant implications for the field of geotechnical engineering by exploring the potential of soft computing techniques in predicting the ultimate bearing capacity (UBC) of shallow foundations on cohesionless soils. The inclusion of multiple linear regression (MLR), Genetic Programming (GP), Classification and Regression Trees (CART), and GA-ENN (Genetic Algorithm-Emotional Neuron Network) models in this research represents a novel approach, as they are used for the first time as grey box AI models in the literature for UBC prediction. The application of these advanced AI models expands the range of predictive tools available to engineers and practitioners, offering diverse options for accurate and reliable UBC estimation.  
The use of input parameters such as the width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (\(\gamma_{d}\) or \(\gamma^{\prime}\)), and internal friction angle (f) further enhances the versatility and practicality of the developed AI models. Engineers can leverage these models to perform UBC predictions for a wide range of shallow foundation designs on cohesionless soils, optimizing foundation designs and ensuring structural stability. The comparison of the AI models with existing theoretical approaches demonstrates their superiority in UBC prediction, underscoring their potential to outperform traditional methods. Consequently, the adoption of AI models like GP, CART, and GA-ENN can lead to more accurate and efficient foundation design practices, resulting in cost-effective and safer construction projects. Moreover, the sensitivity analysis conducted in this study empowers engineers to gain a deeper understanding of the influence of individual input parameters on UBC predictions, facilitating the identification of critical factors that impact the foundation's load-bearing capacity. Overall, the successful integration of AI models in UBC prediction for shallow foundations on cohesionless soil contributes to the advancement of geotechnical engineering practices and promotes more reliable and informed decision making during the design and construction phases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Input Parameters**} \\ \cline{2-6}  & **B** & **D** & **L/B** & **Unit Weight (\(\gamma_{d}\) or \(\gamma^{\prime}\))** & **Friction Angle** \\ \hline MLR & 3 & 1 & 5 & 4 & 2 \\ GP & 1 & 3 & 4 & 5 & 2 \\ CART & 3 & 1 & 5 & 4 & 2 \\ GA-ENN & 3 & 1 & 5 & 4 & 2 \\ Total Score & 10 & 6 & 19 & 17 & 8 \\ Ranking & 3 & 1 & 5 & 4 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Ranking results of variable importance for the proposed mathematical models to predict q\({}_{u}\).
# Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System
## 3 Accurate Objects' Height from Focus Measurement
### Optimised Polynomial Fitting
#### 3.1.2 Validation on Real Data  
Our method was validated experimentally. We employed a 3 rd, a 4 th and a 5 th order polynomial function to understand if higher order interpolators led to better results. Note that these require from 4 up to 6 coefficients \(a_{i}\in[l_{i},\ u_{i}]\), with \(i=0,1,2,3,4,5\). The corresponding search spaces for the problems are indicated via the following lower bound vector \(\mathbf{l}=[-400,\ 0,\ -3,\ -0.003,\ -0.00003,\ -0.000001]\) and upper bound vector \(\mathbf{u}=[0,\ 50,\ 3,\ 0.003,\ 300,000,\ 1,0000,000]\). Before applying the PSO approach, a camera was randomly selected and calibrated five times using accurate data measured in the factory to produce the 'ground truth' described in Section 3.1.1. Results showing the height of the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(SZ\) & 25 & 50 & 75 & 100 & 150 & 200 & **250** & 500 & 1000 \\ \hline Max Step Error Average & 1.33 & 0.48 & 0.46 & 0.43 & 0.39 & 0.33 & **0.22** & 0.22 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Finding \(SZ\) for best polynomial match. Selected values are shown in boldface.  
camera (in terms of steps) are reported in Table 2. These show errors varying from 3.38 steps to 7.65 steps. Subsequently, we fit the three polynomial functions with the PSO algorithm and used it to add calibration points as previously explained. The second approach led to better results, in particle when using the 4 th order polynomial function, which led to a reduction in the error to the range of 0.24-0.53 steps.  
It must be remarked that the proposed approach, with the suggested settings for the PSO and the use of a 4 th order polynomial for the regression problem at hand, has shown to be very robust also in presence of outliers. Indeed, we replicated the experimental phase by manually adding five outliers in the calibration data and were able to achieve the 0.24 steps error after the optimisation process.  
As we make use of a heuristic method for fitting the polynomial function, we also compared the performances of the employed PSO to those obtained of another state-of-the-art stochastic optimisation algorithm, namely the CMAES [35]. The latter has a higher computational complexity and it has been applied to five data sets acquired from a single camera run through the calibration process and compared to the PSO when fitting the 4 th order polynomial function to the data set; however, as shown in Table 3, only very marginal improvements were registered on the objective functions. Such small variations do not lead to better calibration processes, in favour of using PSO over CMAES due to its faster and simpler algorithmic structure.
## 5 Results and Discussion
### Experimental Analysis  
After the actual detection and literature research analysis, the optimal height of welding defect detection lift-off should be 5 mm. Therefore, this thesis elected to use a 5 mm lift-off value as the detection height from the center of the weld position in the scan to extract magnetic signal distortion information. We obtain three directions of magnetic memory signal components, as shown in Figure 11.  
From Figure 11, we can see that the tangential component X at 580 mm produced a peak, and the tangential component Y at 65 mm and 380 mm and 630 mm produced a peak. However, peaks and ultrasonic detection of defects correspond to a large error. In contrast, the normal component Z did not produce the phenomenon of over-zero points, so we can see that the tangential and normal way of judging the position of welding defects is not particularly reliable. At the same time, the extracted magnetic signal is brought into the three-dimensional modal gradient polar characteristic Formula (8). The results are shown in Figure 12.  
It can be seen from Figure 12 that the extreme value of the normal gradient has extreme signal distortion at 92 mm, 314 mm, 555 mm, and 741 mm. The value of distortion has a large error due to the accuracy of ultrasonic signal detection, and there is a misjudgment amplitude at 741 mm, and there is no change in the amplitude of residual stress.  
Figure 10: Variation curve of three-dimensional magnetic modulus gradient with defect size.  
Figure 11: Weld’s magnetic signal diagram. (**a**) Tangential component X; (**b**) Tangential component Y; (**c**) Normal component Z.  
Figure 12: Normal gradient extremum dz.  
Figure 13 shows that the three-dimensional magnetic modulus gradient extremes at 145 mm, 321 mm, and 526 mm represent distortions of the signal. The accuracy of ultrasonic detection coincides with high-degree residual stress-generated amplitudes at 417-457 mm, 659-705 mm, and 705-797 mm; the amplitude of the impact of residual stress is much lower than the signal generated by the defect amplitude, before 81 mm and after 800 mm. Because of the boundary effect of the welded components, ferromagnetic components at the boundary are in contact with the air, resulting in a change in magnetic permeability, so the three-dimensional magnetic gradient modulus polarity generated a very high signal amplitude. However, in the actual test, due to the noisy magnetic field in the test environment, the initial signal after the artificial processing of the component surface and the actual measured signal contain a large amount of random noise and strong impulse noise, which causes the test data to produce a certain error.
## 2 Materials and Methods
### Methodology
Both architectures are based on the U-Net model, with the only difference on the softmax activation function [48] at the output layer of the Binary Classification U-Net, which is absent in the Regression U-Net, and the loss functions used during the training process, as described in Section 2.6. By combining the two mentioned building blocks differently, we have considered three different approaches:  
* A regression-only approach, in which only the Regression U-Net is used. * A parallel approach, namely, Parallel U-Net, in which the two building blocks are used separately in parallel. The final output is obtained by the mathematical multiplication of the two outputs as shown in Figure 4.
* A two-step approach, namely, Double-step U-Net, in which the building blocks are concatenated. First, the binary segmentation model is used to predict the burned area regions in the input tile. Figure 4: Parallel U-Net simplified architecture. The damage severity estimation is computed by filtering the Regression U-Net output with the burned/unburned binary mask. Figure 5: Double-Step U-Net simplified architecture. The damage severity estimation is computed in two steps: burned area identification through the Binary Class U-Net and damage severity estimation by means of Regression U-Net. The Regression U-Net receives as input the Sentinel-2 L2A image filtered with the binary segmentation mask. The binary prediction is used to filter the input and isolate the burned regions, thus generating the input tile for the Regression U-Net, which provides the damage severity estimation. Figure 5 shows the simplified architecture of the proposed solution with sample images, where for satellite imagery only the RGB channels are shown for simplicity. A detailed version of the Double-step U-Net architecture is shown in Appendix C.
# Numerical Validation of the Radiative Model for the Solar Cadaster Developed for Greater Geneva
## 2 Materials and Methods
### Modeling Approaches for Radiative Transfers
#### 2.1.2 Radiosity Method  
The radiosity method is based on the calculation of the balance of radiosity, which is the total radiative flux exiting a surface (W m\({}^{-2}\)). This method allows the calculation of diffuse and reflected components of the irradiation only by solving a system of equations involving all the objects of a scene. Taking specular reflections (see Figure 2) into account is not directly possible with this method. However, unlike ray tracing, the calculations are independent of the position of the possible observer (surface of interest)--the calculation is generalized over the whole scene.  
The radiosity method is based on solving the equation introduced by [PERSON] [11] and detailed in the work of [PERSON] and [PERSON] [12], where the exiting power is expressed as:  
\[B(x)=E(x)+\rho\int_{\Omega}L_{i}(x,\theta,\phi)\cos\theta\mathrm{d}\omega \tag{1}\]  
where \(x\) is a point in space, \(B(x)\) is the exiting power (emitted, reflected, and transmitted) at point \(x\) in space (in W m\({}^{-2}\)), \(E(x)\) is the power emitted by point \(x\) (source term, in W m\({}^{-2}\)), \(\rho\) is the coefficient of reflection of the surface, \(L_{i}(x,\theta,\phi)\) is the incident power at point \(x\) from the solid angle determined by the angles \(\theta\) and \(\phi\), \(\Omega\) is the set of directions \((\theta,\phi)\) of the hemisphere, and \(\mathrm{d}\omega\) is the elementary solid angle (sr).  
This equation can be discretized:  
\[B_{i}=E_{i}+\rho_{i}\sum_{j=1}^{N}F_{ij}B_{j} \tag{2}\]  
where \(B_{i}\) and \(B_{j}\) are the power exiting the \(i\)-th cell and \(j\)-th cell, respectively, or the radiosity of the \(i\)-th or \(j\)-th cell (in W m\({}^{-2}\)); this term takes into account both the term emitted and the effect of the reflections. \(E_{i}\) is the power initially emitted by the \(i\)-th cell, \(\rho_{i}\) is the reflection coefficient of the \(i\)-th cell, i.e., the fraction of the energy received that the cell returns (unitless value between 0 and 1), \(N\) is the number of meshes of which the scene is composed, and \(F_{ij}\) is the form factor between the \(i\)-th and the \(j\)-th cells.  
Solving the radiosity equation requires one to first know the source term \(E_{i}\) (known if the source emits directly), the reflexivity \(\rho_{i}\), which is generally known as one of the characteristics of the materials, and the matrix of the form factors \(F_{ij}\), which must be calculated because it is dependent on the studied configuration.
###### Abstract  
Experimental and theoretical assessments of phonon and optical characteristics are methodically accomplished for comprehending the vibrational, structural, and electronic behavior of InP\({}_{\rm 1-x}\)Sb\({}_{\rm x}\)/n-InAs samples grown by Gas-Source Molecular Beam Epitaxy. While the polarization-dependent Raman scattering measurements revealed InP-like doublet covering optical modes (\(\omega_{\rm LD}^{\rm InP}\sim\)350 cm\({}^{-1}\), \(\omega_{\rm TD}^{\rm InP}\sim\)304 cm\({}^{-1}\)) and phonons activated by disorders and impurities, a single unresolved InSb-like broadband is detected near -195 cm\({}^{-1}\). In InP\({}_{\rm 1-x}\)Sb\({}_{\rm x}\), although no local vibrational (InSb:P; x \(\rightarrow\) 1) and gap modes (InP:Sb; x \(\rightarrow\) 0) are observed, the Raman line shapes exhibited large separation between the optical phonons of its binary counterparts, showing features similar to the phonon density of states, confirming \"two-mode-behavior\". Despite the earlier suggestions of large miscibility gaps in InP\({}_{\rm 1-x}\)Sb\({}_{\rm x}\) epilayers for x between 0.02 and 0.97, our photoluminescence (PL) results of energy gaps reinstated achieving high-quality single-phase epilayers with x\(\sim\)-0.3 in the miscibility gap. Complete sets of model dielectric functions (MDFs) are obtained for simulating the optical constants of binary InP, InSb, and ternary InP\({}_{\rm 1-x}\)Sb\({}_{\rm x}\) alloys in the photon energy (0 \(\leq\) E \(\leq\) 6 eV) region. Detailed MDF analyses of refractive indices, extinction coefficients, absorption and reflectance spectra have exhibited results in good agreement with the spectroscopic ellipsometry data. For InP\({}_{\rm 0.67}\)Sb\({}_{\rm 0.33}\) alloy, our calculated lowest energy bandgap E\({}_{\rm 0}\)\(\sim\) 0.46 eV has validated the existing first-principles calculation and PL data. We feel that our results on Raman scattering, PL measurements, and simulations of optical constants provide valuable information for the vibrational and optical traits of InP\({}_{\rm 1-x}\)Sb\({}_{\rm x}\)/n-InAs epilayers and can be extended to many other technologically important materials.  
Keywords:gas-source molecular beam epitaxy; Raman scattering spectroscopy; photoluminescence; model dielectric functions; optical constants; two-phonon-mode behavior +
Footnote †: journal: _crystals_
## 3 Results and Discussion
### Antibacterial Studies  
The antibacterial action of MnFe\({}_{2}\)O\({}_{4}\) and Ce doped MnFe\({}_{2}\)O\({}_{4}\) NCs (Figure 11) were inspected by the Gram-positive _B. subtilis_ and _S. aureus_, and Gram-negative _K. pneumoniae_ and _E. coli_, which are bacterial strains, correspondingly. It was supposed that there is no region of inhibition found over the control (Figure 11). The antibacterial activity showed that all samples have a positive response in killing bacterial strains. The different concentrations of the samples were used to determine the optimized concentration at which bacterial growth stopped. The measured values of radial diameters of ZOI at varying concentrations are presented in Table 5. The zones of inhibition elucidate that the increase in the concentration of Ce ions leads to a bigger zone of inhibition. The trend was followed: the increasing concentration eventually stops the process of replication and ceases the growth of bacteria. A higher activity can be seen in MnCe\({}_{\text{x}}\)Fe\({}_{2-\text{x}}\)O\({}_{4}\) (x = 0.3) NCs compared to the doped and undoped samples. The enhanced antibacterial activity can be defined as the exposure of positively charged ions, such as Ce\({}^{3+}\), Fe\({}^{3+}\), and Mn\({}^{2+}\), which charged bacterial membrane and distorted membrane integrity and can impair electrostatic interaction, leading to the  
Figure 10: Photo-degradation (PCD) mechanism of MB dye using spinel Ce:MnFe\({}_{2}\)O\({}_{4}\) NCs.  
crossing of membrane barriers, thus disrupting the electron transport chain, and causing cell death. The major element that has played a central role is the ROS species \(\bullet\)OH and \(\bullet\)O\({}^{2-}\), and they were higher in MnCe\({}_{x}\)Fe\({}_{2-x}\)O\({}_{4}\) (x = 0.0, 0.1, 0.3 and 0.5) NCs and the highest in the case of MnCe\({}_{x}\)Fe\({}_{2-x}\)O\({}_{4}\) (x = 0.3) NCs (evident from photocatalysis). These species have unpaired electrons and can generate oxidative stress and cytotoxicity in the cellular environment. Further, they can start a series of reactions, such as DNA damage, enzyme protein peroxidation, and membrane lipid peroxidation, and inhibit the functioning of the electron's transport mechanism [61; 62]. Furthermore, a better attentiveness of Ce ions replacement impact improved antibacterial activity than the smaller Ce exchanged MnFe\({}_{2}\)O\({}_{4}\) NCs. The smaller grain size and developed surface area with bulk ratio of the products show an exciting atmosphere in antibacterial activity [63; 64].