esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Control of Biofilms in Diabetic Foot Infections: A Narrative Review
## 9 Biofilm Formation of Gram-Positive Organisms in Diabetic Foot Ulcer
### Ica-Dependent Biofilm Formation  
The _ica_-dependent method of biofilm formation is prevalent in both _Staphylococcus epidermidis_ and _Staphylococcus aureus_, relying on the overproduction of extracellular polysaccharide adhesins known as PIA (polysaccharide intercellular adhesin) and PNAG (polymereric N-acetylglucosamine) [62]. These polysaccharides are produced by the _ica_ operon, which helps the bacteria evade the host immune system, making it beneficial during the early stages of infection.  
The _ica_ operon consists of several components, including the _icaAD_ complex, as well as the _icaB_ and _icaC_ genes. The _icaA_ and _icaD_ genes function synergistically as activators. IcaA, IcaD, and IcaC are transmembrane proteins, while IcaB is a surface-attached protein involved in the deacetylation and transport of PIA or PNAG [63].  
Ica-dependent biofilm formation is primarily triggered by high osmotic stress resulting from increased NaCl concentrations, which upregulates the sigma factor B. This, in turn, leads to the upregulation of the SarA protein and subsequently the icaA gene. The IcaA protein acts as an N-acetylglucosamine/hamyl transferase and functions optimally in the presence of IcaD. The IcaAD complex produces a 20-subunit oligomeric peptide chain, while IcaC elongates the peptide chain and translocates it outside the cell [63]. IcaB, a surface-bound protein, deacetylates the peptide chain, generating short peptide chains of poly-\(\beta\)-(1,6)-acetylglucosamine/ [64].  
Several downregulators of the _icaADBC_ operon exist, including TcaR (teicoplanin-associated regulator) and the IcaR repressor protein, which is produced following the upregulation of TcaR by the Spx protein [65]. Another downregulator is the luxS gene, which inhibits biofilm formation by suppressing the _icaADBC_ operon [66]. RsbU is another regulator that upregulates the global stress response regulator sigma B in methicillin-sensitive _Staphylococcus epidermidis_ and represses the _icaR_ gene [67]. Mutations in the Spx protein can lead to upregulation of the _icaR_ gene, resulting in reduced expression of the _ica_ operon; however, RsbU does not exert the same effect in _S. aureus_[68].  
Spx is cleaved proteolytically by the ClpXP enzyme in methicillin-sensitive _Staphylococcus aureus_ (MSSA), and any mutations in this enzyme can impair biofilm formation [69]. Accessory components, such as the ArIRS protein, also play a role by upregulating icaADBC and suppressing _icaR_, aiding in the autolysis of older cells and promoting biofilm formation. The mechanism of _ica_-mediated biofilm formation is depicted in Figure 3.  
Figure 3: _Ica_-dependent biofilm formation in diabetic foot infections.
## 3 Many-Objective Marine Predators Algorithm
### Marine Predators Algorithm Formulation
#### 3.3.1 MPA Scenarios  
In the optimization process, the MPA consists of three primary phases, each of which considers a particular velocity ratio while simulating the whole life cycle of a predator and prey. The particular iteration period is defined for each phase, and the movement of a predator and prey can be mathematically formulated as described below.  
Phase A: In the first phase, a predator is moving faster than the prey or in a high ratio of velocity in the initial iterations, normally called the exploration phase. The prey begins to explore the search space by employing the Brownian approach to locate potential areas that may contain a feasible solution. So, the prey's position is mathematically updated as the given equations:  
If Iteration < \(\frac{1}{3}\) maximum_iteration:  
\[\textit{stepsize}_{i}=\overset{\rightarrow}{R}_{B}\otimes\left(\textit{ Elite}_{i}-\overset{\rightarrow}{R}_{B}\otimes\textit{Prey}_{i}\right),i=1,\ldots,n \tag{30}\]  
\[\textit{Prey}_{i}=\textit{Prey}_{i}+P\cdot\overset{\rightarrow}{R}\otimes \textit{stepsize}_{i}\]  
where \(\overset{\rightarrow}{R}_{B}\) is a vector of numbers randomly generated based on the normal distribution presenting the Brownian motion; \(\otimes\) is the entry-wise multiplication; \(P\) is a constant number set to 0.5; and \(\overset{\rightarrow}{R}\) is a vector of numbers randomly uniformly generated between 0 and 1;  
Phase B: This phase occurs in the middle of the process when predator and prey have the same movement or are in unit velocity ratio because both are searching for their prey. In this phase, the optimization process is changing from exploration to exploitation. So, half of the population (prey) is assigned for exploitation and the other half (predator) is imposed to explore the search space. To mathematically formulate this phase, the prey adopts the Levy motion for exploration, and the predator applies the Brownian motion for exploitation as in the following equations:  
where _FADs_ = 0.2 represent the impact of FADs on the optimization process; \(U\) is a binary vector with arrays consisting of 0 and 1; where the binary vector is randomly generated between 0 and 1, and the array is changed to 0 if the array is less than _FADs_ and 1 if the array is more than _FADs_; \(r\) is the uniformly randomly generated number between 0 and 1; \(X_{\min}\) and \(X_{\max}\) are the minimum and maximum limits; and \(r1\) and \(r2\) represent random indexes of the prey matrix.
# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 3 Results
### Feature Selection Results by BMA  
Feature selection is a technique that involves minimizing the number of input variables in a model by retaining only the pertinent data and eliminating irrelevant or noisy information. Based on the findings of the correlation study, it is evident that the correlation coefficients between the SPEI, SPEI3, SPEI6, SPEI9, and SPEI12 indices were quite high, ranging from 0.57 to 0.96. The correlation coefficient values for the pairs of variables were as follows: 0.79 for SPEI1 and SPEI3, 0.88 for SPEI3 and SPEI6, 0.93 for SPEI6 and SPEI9, and 0.96 for SPEI9 and SPEI12. Thus, this study exclusively chose the indicators SPEI1, SPEI3, SPEI6, and SPEI12 to construct the models. The correlation coefficient between the SPEI and meteorological parameters exhibited a relatively low range, varying from 0.09 to 0.48. Hence, the task of selecting ideal parameters for calculating the SPEI using machine learning models is a challenge, necessitating the use of a method for identifying crucial factors (Figure 4).  
The nonlinear association between SPEI and climatic parameters is evident. This study utilized the Bayesian Model Averaging method (BMA) to choose the most suitable parameters. The statistical analysis results obtained using the BMA are presented in Figure 5. The BMA technique identified the five best models by selecting the essential parameters:  
Figure 3: Chart of SPEI of \(11\) meteorological stations in the Mekong Delta (1985–2022).  
* The model for SPEI-1: seven parameters were selected: Rainfall, Avg_Tmax, Avg_Tmin, Avg_Hum, PET, SOI_Anomaly, and SST_NINO4 (posterior probability was 100%).
* The model for SPEI-3: four parameters were selected: Rainfall, Avg_Tmin, Avg_Hum, and SST_NINO4 (posterior probability was 92.5%).
* The model for SPEI-6: four parameters were selected: Rainfall, Avg_Tmin, Avg_Hum, and SST_NINO4 (posterior probability was 100%).  
The model for SPEI-12:5 parameters were selected as Rainfall, Avg_Tmin, Avg_Hum, SOI_Anomaly, SST_NINO4 (posterior probability was 88.4%).
## 2 Materials and Methods  
### Spectral Measurements  
The methodology of this research was based on the analysis of the radiometric behaviour of the daylight at a location, which serves to model their circadian contribution at this same point. We conducted several spectral measurements of the skydome over one year from December 2018 to November 2019. We sought a sunny day around the 15 th of each month, and measurements were performed from 10:00 to 11:00 a.m. The experiment was located at the roof of the Sciences Faculty at the University of Zaragoza in Spain (41\({}^{\circ}\)38\({}^{\circ}\)31\({}^{\prime\prime}\) N, 0\({}^{\circ}\)53\({}^{\prime}\)60\({}^{\prime\prime}\) W; 243 m above sea level). The SPD measurements were performed with a calibrated spectroradiometer (Avaspec-1024, Avantes, Apeldoorn, The Netherlands, NPL E01110063/DDK calibration and NIST traceability). A tripod was used to ensure accurate measurements of the spectra in the four cardinal directions (north, south, east and west) 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\) from the horizontal plane. To register information about the weather, photos of the sky were taken. The sky conditions were always clear except intermediate skies with visible sun and a few clouds.
## 3 Results and Discussion
The elongation of the MFC flocs (from spheres into prolates) has probably happened already during the constriction conditions of the flow of the suspension from the plastic container into the rubber hose [36]. Furthermore, the data in Figure 8 suggest that when the wall shear stress exceeds 6 Pa, the floc aspect ratio starts to decrease monotonically reaching approximately \(\alpha\approx 1\) with the highest shear stress. Unfortunately, there are no measurement points in the wall shear stress range of 6-9 Pa, and thus the more accurate onset of this phenomenon remains unclear. Figure 8: Floc aspect ratio (axial size/radial size) as a function of wall shear stress. The yield stress \(\tau_{y}\) = 3.4 Pa is shown with a vertical dashed line. Figure 7: The radial and axial floc size as a function of wall shear stress. The floc sizes remain approximately constant until wall shear stress exceeds the yield stress \(\tau_{y}\) = 3.4 Pa (dashed line). The radial floc size \(L_{r}\) appears to follow a power law (solid line). The dynamics of the axial floc size is more complicated. The arrow shows the abrupt drop of the axial floc size at \(\tau_{w}\) =10 Pa.  
As pointed in [8], geometry gap dimensions and material may affect flocculation. Especially with small gaps, wall depletion may hinder the breakdown of the flocs, and the floc sizes can reach dimensions of the gap. Probably due to the rheometer geometry and the imaging setup, the floc size varied for an identical MFC suspension between 0.1-1 mm in [9], being in a completely different length scale when compared with the present study. Also, the dynamic behavior of flocculation was qualitatively very different when compared with our results for the pipe flow. During the rheometer measurements, the floc size usually increased until the yield stress was reached above which the floc size decreased with increasing shear stress. While the number of flocculation studies with MFC is rather low, dynamics of flocculation and floc rupture of wood fibers and other materials has been studied extensively [37; 38; 39; 40; 41]. The observations on the flocculation and breaking mechanisms have varied a lot, but the relation between the floc size \(L\) and shear stress \(\tau\) has typically been a power law  
\[L=G\tau^{-\beta}. \tag{3}\]  
As we see from Figure 7, this is also the case here for the radial floc size \(L_{r}\) with \(\beta\) = 0.32. Depending on the floc breaking mechanism and inertial range, one can find in literature various values for \(\beta\)[38; 42]. Similar values (\(\beta\sim\)0.3) have been reported, for example, in [43; 44]. The dynamics of the axial floc size \(L_{a}\) is more complicated; the slope for \(L_{a}\) is up to 6 Pa approximately the same as for \(L_{r}\), but above 6 Pa it is twice as big. It is probable that the elongational forces due to the constriction conditions in the outflow from the container not only stretch, but also break flocs, when wall shear rate exceeds 6 Pa [45; 46].
# Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar
## 4 Conclusions  
Passive Forward Scatter Radar provides a new emerging area of research that can be utilized for low profile targets as well as 'Stealth' targets detection crossing the FS baseline. This paper presents the first experimental result on target detection and classification using passive FSR by exploiting LTE signals as the illuminator of opportunity. The LTE-based passive FSR experiment setup was illustrated in detail. Furthermore, the detection and classification procedures were explained. The selected target's Doppler spectrum as the target's feature vectors shows excellent classification result. In addition, the experimental analysis and results prove that the baseline recognition of moving vehicle in passive FSR can be clustered using the Principal Component Analysis. It was found that the speed of the ground moving vehicle influence to the classification result, where higher speed offered finer variance of training data. Moreover, the target's crossing baseline features by using the target's power spectrum density had shown good clustering performance. Despite the significant results achieved, an additional pre-processing signal can be included for future analysis to strengthen the target's baseline crossing recognition in passive FSR. Due to the fact that, this is the first classification analysis of passive FSR to  
Figure 13: Location of training and testing data for all vehicle categories at different R\({}_{\text{R}}\) in PCA space for (**a**) R = 5 m; (**b**) R = 10 m and (**c**) R = 20 m.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Range, R\({}_{\text{R}}\) (m)** & \multicolumn{3}{c}{**5**} & \multicolumn{3}{c}{**10**} & \multicolumn{3}{c}{**20**} \\ \hline
**Type of Vehicle** & **Compact** & **Saloon** & **SUV** & **Compact** & **Saloon** & **SUV** & **Compact** & **Saloon** & **SUV** \\ \hline Training Data & 40 & 40 & 40 & 40 & 40 & 40 & 40 & 40 & 40 \\ Testing Data & 30 & 69 & 33 & 27 & 61 & 35 & 32 & 39 & 25 \\ Classification & 30 & 67 & 33 & 21 & 57 & 32 & 30 & 33 & 17 \\ Classification (\%) & 100 & 97.1 & 100 & 77.78 & 93.44 & 91.4 & 93.75 & 84.62 & 68.0 \\ \hline
**Average Classification (\%)** & \multicolumn{3}{c}{**99.0**} & \multicolumn{3}{c}{**87.54**} & \multicolumn{3}{c}{**82.12**} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Vehicle’s category classification performance at different \(R_{\text{R}}\).  
be reported, therefore, future work should also investigate different classification techniques such as, Neural Networks and some Artificial Intelligence can be included in the system.  
[PERSON] created the main ideas, analyzed the results and served as an advisor, while [PERSON], [PERSON] and [PERSON] performed the simulations and analyzed the results.  
The authors declare no conflict of interest.
## 5 Establishment of 3D Geological Model
### Analysis of Variation Function
#### 5.2.1 Geological Probability Surface-Constrained Lithofacies Model  
The geological probability surface is a sophisticated tool developed based on single-well logging interpretation results, which serve as empirical data. It is guided by the reservoir development model and constrained by various parameters, including reservoir configuration, formation thickness percentage maps, and vertical reservoir probability distributions. This surface is instrumental in establishing a geological lithofacies probability surface that aids in reservoir development. It is created by juxtaposing the thicknesses of sandstone and mudstone with the overall formation thickness. A probability surface is a three-dimensional parameter surface composed of probability values (0-1) that directly assigns a value to each grid in the form of a probability. This approach differs from traditional boundary constraints and two-dimensional trend surface constraints, as it extends across the entire spatial distribution of the model. It circumvents the in-layer homogenization that can arise from human bias, thereby preserving detailed constraints both vertically and within boundaries. This method effectively captures the inherent variability of reservoir parameters. In the lithofacies modeling of the target layer in the research area, the calculated percentages of the thickness maps of layers \(7_{1}^{1},7_{1}^{2},7_{2}^{1},7_{2}^{2}\), and \(7_{3}\) (Figure 4) were projected onto a plane, and then the sandstone thickness map (Figure 5) was compared with the formation thickness map to obtain the sandstone probability map (Figure 6). Meanwhile, by comparing the mudstone thickness map (Figure 7) with the stratigraphic thickness map, a mudstone probability map (Figure 8) was obtained and used as a condition for controlling and constraining lithology modeling. The constraint conditions for vertical application were the thickness distribution curve interpreted by the sedimentary facies of a single well and the vertical proportion curve of a single well. The projected lithofacies thickness percentage was used on the plane. Finally, the random modeling sequential indication method was used to carry out random simulations. Two lithologies (mudstone and sandstone) are listed in the research area. Thus, the three-dimensional lithofacies model of each small layer of the Chang 7 member in the study area (Figure 9) and the three-dimensional lithofacies model of each small layer after the probability surface constraints (Figure 10) were obtained. The method of geological probability surface constraint, combining individual well data, sand and mudstone thickness, and reservoir plane thickness, and combining certainty and randomness to accurately construct the reservoir matrix lithofacies model, further improved the accuracy of the model.
## 2 Simulation Methodology
### Global Cohesive Element Model  
Generally, cohesive elements are considered as being composed of two faces separated by a thickness, as depicted in Figure 3a. The relative motion of the bottom and top faces measured along the thickness direction represents the opening and closing of fractures. The relative change in position of the bottom and top faces measured in the plane orthogonal to the thickness direction quantifies the transverse shear behavior of the cohesive element. The bottom surface and the top surface of the element will internally form intermediate pore pressure nodes, which can be observed in Figure 3a (node 5 and node 6). When the mid-surface is separated, driven by the fluid pressure, the fracturing fluid fills the fracture space; then, as shown in Figure 3b, normal flow occurs towards the top and bottom surface while tangential flow takes place at the intermediate node. In the CZM, the fractures are restricted to propagate along the cohesive element layer without being deflected into the rock matrix, as shown in Figure 4, which requires how the fracture will propagate be known. However, it is quite difficult to prefabricate all the fracture propagation paths in a naturally fractured formation, as fully developed natural fractures will greatly increase the morphology complexity of hydraulic fractures. In this work, we realize the fracture to extend along any element boundary after it initiates by using global cohesive elements. The cohesive element COH2D4P with pore pressure DOF was inserted arbitrarily into two  
Figure 2: Cohesive traction–separation law with linear damage evolution.  
adjacent rock elements and a natural fracture element. The cohesive elements were zero geometric thickness, which guaranteed the mechanical properties of the rock would not be affected. Since the fluid pressure is transmitted through intermediate pore pressure nodes, pore pressure nodes are shared between any two adjacent elements by converging all the intermediate nodes together, as schematically shown in Figure 5.  
Figure 4: Schematic of arrangement of cohesive elements at the intersection of a hydrofracture with a fixed propagation direction and a pre-existing natural fracture.  
Figure 5: Schematic of element nodes at the intersection.  
Figure 3: (**a**). Schematic of two-dimensional pore pressure cohesive element. (**b**) Fluid flow in cohesive element. The pore pressure cohesive element can simulate a tangential flow and leak-off from fracture to rock matrix.
# A Review of Blast Loading in the Urban Environment
## 4 Experimental Methods and Instrumentation
### Pressure Measurements
#### 4.2.4 Fiber Bragg Grating Pressure Sensors  
[PERSON] et al. [138] explore Fiber Bragg grating (FBG) sensors for space exploration applications. However, experiments were conducted with pressure ranges generally comparable to those from urban blast scenarios. A FBG sensor consists of an optical fibre section containing a repeating pattern of multiple reflection points. This pattern reflects certain wavelengths of incident light and allows all others to transmit through it. FBG sensors measure changes in Bragg wavelength due to an external stimulus, causing a strain that  
Figure 13: Measured reflected overpressures using electrical and optical sensors, adapted from [131].  
alters the grating spacing. This change can be used to accurately measure pressure changes at high frequencies. Temperature compensation can be achieved by using an additional FBG sensor attached to a location insensitive to experimental inputs. Test data can then be calibrated using the control sensor data.  
The pressure sensors were tested through shock tube experiments. A FBG sensor with a sampling rate of 2500 Hz was situated at the open end of the shock tube. The sensor was placed in a manner that would cause a tensile strain. This was achieved by placing the sensor within a housing parallel to a diaphragm, which would deflect outwards in the presence of a positive pressure differential. The sensitivity of the FBG sensor was determined beforehand via static pressure calibration and was found to have a value of 3.64 pm/bar. Two trials were conducted where the peak Bragg wavelength shift was found to be 68 pm and 99 pm, respectively. This corresponded to absolute pressure measurements of 19.59 bar and 27.19 bar, whereas the driver pressures were 18.5 bar and 19.9 bar. This is quoted as _\"observed to be within an error of 1% of full scale (700 bar)\"_.
## 4 Results and Discussion
### Mel-Frequency Cepstrum Coefficients (MFCC) Solving the Nonlinear Problem  
The EEG signals can be deconvoluted into a time-domain signal using the MFCC algorithm; its first series of typical coefficient results are provided in Figures 5 and 6. The MFCCs in PF1 from top to down are clearly very different. It is foreseeable that the MFCCs can be fed into some classifiers, and then the classifiers, containing hundreds of dimensional features, will be built up and can be used to achieve a super recognition result.  
Generally, as mentioned before, the first subcomponent PF1 contains more information than the others, and this opinion was verified by the results of five datasets (datasets A, B, C, D, and E), as shown in Figure 5, and the differences among these five datasets (datasets A, B, C, D, and E) are obvious in MFCC (PF1). The MFCCs of the extracted features are not just these exhibitions, but were numerous (about 144 features from PF1 to PF4). Furthermore, when the datasets were fed into some classifiers, the recognition results (provided in the next subsection in details) were obtained quickly with impressive results. To explaining the causes of this phenomenon, we need to return to our hypothesis. The construction of brain cells is often unique and the motivation signals are large in number. This is consistent with the result obtained from the current biomedical research.  
Although there are some cluttered features that cannot be distinguished, the results of these five different types of classifiers demonstrate the efficiency of these features. One possible explanation is that the dimensions of these 144 features can be employed to form ultra-dimensional spatial-temporal space, which is very hard to describe. Figures 5 and 6 best describe what we know so far.  
Figure 4: Sample Electroencephalographic (EEG) segments of five datasets (datasets A–E) and their corresponding product functions (PFs) (PF1 to PF5).