esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Gene Essential Assessment: A Tool for Drug Target Discovery?
Via noncompetitive inhibition, these drugs block the enzyme and stop beta-1,3-glucan synthesis, compromising the integrity of the cell wall [78]. Other drugs, such as ethionamide, sulfacetamide, azelaic acid, cerulenin or trimethoprim, were identified as targeting proteins from various organisms. These proteins are homologous to the proteins encoded by genes identified as essential in RPMI, in the _C. albicans_' model (Table 6). Altogether, these results suggest that the iRV781 model may prove useful in the prediction of newdrug targets. The predictions of this model may extend to other pathogenic _Candida_ species. In fact, if we search the 12 genes present in Table 6 in emerging non-_albicans Candida_ species, they all have orthologous genes in _C. parapsilosis_ and _C. dubliniensis_, 11 of them have orthologs in _C. auris_ and 8 of them in _C. glabata_. Despite the methodology for the reconstruction of genome-scale metabolic models being standardized, eukaryotic models remain a challenge, due to their large genomes and complexity [79]. These models always seek to get as close as possible to reality; however, given the complexity of the networks, they are always subject to some errors, which may cause small deviations in the predictions. Some errors may include incorrect assignment of GPR associations, reaction directionality or reversibility, incongruuous stoichiometric parameters, missing reactions and inaccurate biomass composition [79]. Additionally, network properties, that go beyond metabolism, cannot be addressed with currently existing modeling tools at a global scale, thus limiting the predictive power that may be drawn from global stoichiometric models. Still, they provide a fresh view of a pathogen's metabolism, while offering a tool to inspect the metabolism itself as a target for new drugs. Genome-scale metabolic reconstructions are effective in drug target prediction and are expected to continue to expand in the future [80]. Gene essentiality assessment is the most common method to identify potential drug targets, and for a better prediction, it is necessary to consider the medium in which the organism is exposed. In this work, gene essentiality was searched for in RPMI medium in order to simulate the natural environment faced by _Candida albicans_ in systemic infections. However, it is important to highlight that in theses reconstructions, it is not considered that cells may need time to adapt to genetic perturbations or environmental variability [79]. Additionally, yeast interactions with other microorganisms and the secretion of compounds that can influence their surrounding environment are not taken into account [81]. Despite these inaccuracies, genome-scale metabolic reconstructions have proved to be very efficient discovering new drug targets, and once a model is built, drug targets can be predicted relatively easily. In fact, the experimental validation of the targets and the identification of the effective drugs represents a more demanding challenge [82].
## 3 Data Augmentation Method Based on the GAN
### S-GAN Model
#### 3.3.3 Objective Function  
Similar to the traditional GAN, the core idea of the S-GAN is still the idea of game theory and confrontation, that is, allow the generator and the discriminator to compete so that the two models are enhanced at the same time, and finally allow the data generated by the generative model to achieve the effect of mixing the fake with the real. Therefore, the optimized objective function is as follows:  
\[\mathrm{min}_{G}\ \mathrm{max}_{D}\ \mathrm{V(D,G)}=\mathrm{E}_{\mathrm{x}\sim \mathrm{P_{data}(x)}}[\mathrm{log}\ \mathrm{D(x)}]+\mathrm{E}_{\mathrm{z}\sim\mathrm{P_{z}(z)}}[\mathrm{log}(1\ -\ \mathrm{D(G(z)))}], \tag{3}\]  
where \(\mathrm{p_{z}(z)}\) represents the prior distribution of the input noise, which is used to learn the probability distribution \(\mathrm{P_{g}}\) of the generator G on the training data x; \(\mathrm{p_{data}(x)}\) represents the distribution of the real data, that is, the distribution that G needs to learn; D(x) represents the probability that the data x come from the real data distribution \(\mathrm{p_{data}}\) instead of \(\mathrm{P_{g}}\); and G(z) is the generating function that represents the mapping of the input noise z to the data.
## 4 Conclusions  
In the past few years, Raman scattering has been successfully used to investigate the strain state of GaAsN and GaAsN:H layers. The technique is based on the dependence of GaAs-like phonon frequencies on strain through the phonon deformation potentials. Raman scattering microscopic results compare well with the average in-plane strain determination, independently obtained via PR measurements on a macroscopic scale. In particular, it was possible to relate the GaAs-like LO phonon frequency shift to the H dose received by the sample, hence to the amount of in-plane strain. Checking this linear relation takes advantage of the PR well-established compressive strain dependence on H dose. Remarkably, micro-Raman mapping allowed to gain detailed information about the strain distribution in GaAsN/GaAsN:H heterostructures. A Raman shift in heterostructures depends on both biaxial in-plane strain and uniaxial strain components acting at the interface between, e.g., GaAsN wires and GaAsN:H matrix. It is possible, at least in simple cases, to isolate the strain components other than the biaxial strain. The result of such data reduction is in qualitative agreement with finite element method simulations. The techniques described here can help design devices based on the strain engineering of GaAsN/GaAsN:H heterostructures. Work is in progress concerning wire-barrier heterostructures with different orientations with respect to the crystallographic axes.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Microbiological Result  
The microbiological examination results are presented in Figure 8. The trends observed in the diagram align with those observed in the previously determined parameters. Microbiological examinations conducted on the meats of the I. and IV. sample groups exhibited significant similarity. These two groups had the most similar temperature step parameters throughout the aging process. For instance, the I. sample group, which was stored consistently at 5 \({}^{\circ}\)C, showed a higher final microbial count (1.0 \(\times\) 10\({}^{6}\) CFU/g) compared to the IV. sample group (6.2 \(\times\) 10\({}^{5}\) CFU/g), which completed the aging process at 1 \({}^{\circ}\)C in the last week. Despite the similar trends, there was a significant difference in the final microbial count, as evident from the table below the diagram.  
In the case of the II. sample group, a notable increase in microbial count was observed after the first week, which could be correlated with the decrease in  
Figure 8: Total anaerobic viable cell count [CFU/g] results of different sample groups (I., II., III., IV) at each measurement day (0, 7, 14, 21, 28).  
at that time. Higher water retention capacity entails a stronger microbiological risk. The pH value determined after the first week's measurement for this sample group was 6.15, indicating a high probability of DFD (dark, firm, dry) meat defect. It is important to mention the DFD meat defect characteristic of beef, which results in a high pH value. In their research, [PERSON] and [PERSON] (1986) minimized the aging time as they found that meat defects lead to early spoilage ([PERSON] and [PERSON], 1986). They also observed higher microbial counts at higher pH levels during their microbiological examinations. The differences observed within the same sample groups during the aging process may arise from variations between the slices.  
The III sample group exhibited an increasing microbial count, except for the value measured after the third week. Intermediate fluctuations may have been caused by differences between meat and potential meat defects. According to a previous study, beef can be stored under refrigerated conditions for over 10 days ([PERSON] and [PERSON], 1986); however, other researchers found that beef with the DFD meat defect stored under the same conditions started to spoil in less than 6 weeks ([PERSON] and [PERSON], 1986).  
This sample group reached the highest final microbial count (2.9 \(\times\) 10\({}^{6}\) CFU/g), indicating that the aging process at 5 \({}^{\circ}\)C for 2 weeks, followed by 1 \({}^{\circ}\)C for another 2 weeks, posed the greatest microbiological risk.  
According to the literature data, an unpleasant odor occurs in meat when the cell count reaches over 10\({}^{7}\) CFU/g, and further signs of spoilage, such as sticky, slimy surfaces, appear when the microbial count exceeds 10\({}^{8}\) CFU/g for raw meats ([PERSON] and [PERSON], 1986).  
Overall, it can be concluded that significant microbiological changes were not observed in three out of the four sample groups after the first two weeks.
# Topology Inference and Link Parameter Estimation Based on End-to-End Measurements +
## 4 Performance Evaluation  
The experiments presented in this article take place in a fully-controlled testbed to demonstrate the feasibility of our ideas. Exploring how our methods can be further fine-tuned and adapted to specific types of networks (e.g., cloud, overlay, or data center networks) and their particular characteristics is left for future work.
## 3 Results
### Soil Bulk Density  
Bulk density (g\(\cdot\)cm\({}^{-3}\)) ranged widely throughout the sampled sites (Figure 2). The highest bulk density was found in the basin environments of both tidal creeks; bulk density exceeded 1.0 g\(\cdot\)cm\({}^{-3}\) below a depth of 18 cm, where the soil core transitioned to predominately sand. The lowest value (0.070 g\(\cdot\)cm\({}^{-3}\)) was also found in Susan's Creek basin mangroves at the surface layer (2 cm depth), accounting for the unconsolidated surficial detritus. Bulk density values in the riverine environments did not show any appreciable trend. Values in the dwarf mangrove setting increased substantially below a depth of 10 cm. Bulk density (mean \(\pm\) standard error) for the past 50 years (using \({}^{210}\)Pb) in Susan's and Hamilton Avenue Creeks are: 0.13 \(\pm\) 0.009 and 0.78 \(\pm\) 0.09 g\(\cdot\)cm\({}^{-3}\) in the basin mangroves, 0.25 \(\pm\) 0.02 and 0.25 \(\pm\) 0.004 g\(\cdot\)cm\({}^{-3}\) in the fringe mangroves, and 0.22 \(\pm\) 0.02 and 0.13 \(\pm\) 0.005 g\(\cdot\)cm\({}^{-3}\) in the riverine mangroves. Mean soil bulk density in the dwarf mangroves was 0.16 \(\pm\) 0.04 g\(\cdot\)cm\({}^{-3}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**SC**} & \multicolumn{3}{c}{**HA**} \\ \hline  & **Basin** & **Fringe** & **Riverine** & **Basin** & **Fringe** & **Riverine** \\ \hline Tree density (stems m\({}^{-2}\)) & & & & & & \\ \hline _Rhizophora mangle_ & 1.2 & 0.39 & 0.51 & 0.18 & 0.46 & 0.77 \\ _Avicentia germinans_ & 0.04 & 0.01 & 0 & 0 & 0 & 0 \\ _Laguncularia racemosa_ & 0.08 & 0.03 & 0.01 & 0.02 & 0.18 & 0.21 \\ \hline Mean DBH (cm) \(\pm\) SE. & & & & & & \\ \hline _Rhizophora mangle_ & 3.6 \(\pm\) 0.6 & 5.5 \(\pm\) 0.7 & 5.9 \(\pm\) 0.8 & 7.4 \(\pm\) 0.9 & 3.2 \(\pm\) 0.5 & 3.3 \(\pm\) 0.2 \\ _Avicentia germinans_ & 10.7 ** & 24.4 ** & - * & - * & - * & - * \\ _Laguncularia racemosa_ & 4.5 \(\pm\) 0.2 & 18.2 \(\pm\) 1.1 & 25 ** & 8.1 \(\pm\) 0.8 & 1.4 \(\pm\) 0.2 & 7.0 \(\pm\) 0.4 \\ \hline plot size (m) & 5 \(\times\) 5 & 10 \(\times\) 10 & 10 \(\times\) 10 & 10 \(\times\) 10 & 5 \(\times\) 10 & 5 \(\times\) 7.5 \\ \hline \multicolumn{7}{c}{* species not present; ** one single specimen.} \\ \end{tabular}
\end{table}
Table 1: Mangrove tree stem density and diameter at breast height (DBH) of sampled mangrove hydrogeomorphic sites at Susan’s Creek (SC; reference) and Hamilton Avenue Creek (HA; disturbed) adjacent to Naples Bay, Naples, Florida.
# Topological and Fractal Analysis of Nanostructured Metal-Dielectric Films
###### Abstract  
The surface topology and fractal dimension of ultrathin silver and gold films have been investigated utilizing atomic force microscopy. These films were formed at the early stages of metal deposition through thermal evaporation and have pre-percolation thicknesses. They contain both metallic and insulating (void) phases, making them metal-dielectric composites. We identified the main parameters of the microstructure, such as the size of the metallic particles and surface roughness, as well as the dependence of these parameters on the film thickness and substrate parameters. Approaches to processing data, including correlation analysis, were employed. An analysis of dependencies and an explanation of their appearance were conducted. The discussion also addressed the limitations of using atomic force microscopy for studying ultrathin metal films. We determined the various types of fractal dimensions, considering the film topology for two-as well as three-dimensional objects. Depending on the actual dimensions of the phase boundary for silver films, a maximum was found. Different approaches to determining the fractal dimensions in 3 Ds case show a similar dependence, but different values.  
atomic force microscopy; thin films; topology; surface; nanostructures; fractal; percolation Article
## 3 Results and Discussion
### Bactericidal Effect of L-Phe Treated With AAGD
The AAGD system could be introduced to existing hydroponics systems in several ways. A possible approach is to produce a suitable amount of plasma-treated L-Phe, dilute it to the desired concentration, and supply it directly to a hydroponic-plant-growth cell, as was done in the present work. Another approach is to organize liquid circulation between hydroponic-plant-growth cell and plasma-treatment device, and continuously treat circulating liquid or during a fixed amount of time per day. Direct treatment of the liquid medium during plant growth is advantageous for on-site liquid sterilization considering the stronger bactericidal effect of direct plasma treatment of suspension containing bacteria compared to the bactericidal effect of liquid treated with plasma, as was reported elsewhere [19]. Bacteria suspended in DI water could be sterilized after 30 s of treatment of suspension with AAGD, when it requires 60 min for bacterial sterilization by suspending it to DI water treated by AAGD for  
Figure 7: Viability of _E. coli_ incubated in pristine L-Phe, L-Phe treated with AAGD, and L-Phe treated with oxygen radicals using nonequilibrium atmospheric-pressure radical source (NEAPRS) [18] as function of incubation time. 1 min, which could be related to the presence of short-living radicals (such as peroxynitrite and HOO\({}^{\bullet}\)) and the strong electric field and ultraviolet emission during the plasma treatment [19]. Despite the use of air as a process gas, low cost and simple setup, and the high concentration of RONS in treated liquid, power consumption of the present system (28 W) is higher compared to that of analogs, which could be a problem in large-scale application [16; 40; 48; 54; 57]. A problem with power consumption could be partially solved by the improvement of plasma-generator efficiency (currently only about 50% of power is absorbed in plasma) and fine-tuning the treatment parameters (matching liquid-flow rate and discharge power to achieve desired RONS concentration in the liquid without following dilution). On the other hand, it is not yet clear which components of the treated solution are responsible for plant-growth promotion and which for the bactericidal effect. Treatment by plasma results in RONS delivery to the liquid, triggering the chemical reactions and decomposition of organic components [17; 68]. Products of plasma treatment could vary in a wide range depending on plasma type and parameters, resulting in different properties of produced PAM (e.g., stronger bactericidal effect after treatment by AAGD compared to NEAPRS reported elsewhere). Therefore, it is necessary to perform precise analysis of products produced by plasma and components responsible for plant-growth promotion and bactericidal effect for reliable comparison to treatment with alternative plasma sources or chemical methods.
# Defining Breast Tumor Location Using a Four-Element Wearable Circular UWB MIMO Antenna Array
## 2 The Proposed Antenna Design
### Effect of Antenna Bending  
The proposed wearable UWB antenna is subjected to bending in both the horizontal and vertical directions around cylinders with varying radii (50, 60, 70, 80, 90, and 100 mm) positioned around the center of the breast phantom to investigate the bending effect [22]. Figure 5 provides a visualization of the 3D antenna considering different bending radii. The reflection coefficient results for the different bending radii, considering both vertical and horizontal bending, are presented in Figure 6.  
In the flat case, the antenna demonstrates a bandwidth ranging from 2.57 to 12.6 GHz. When bent with a radius of 50 mm, a slight mismatch is observed within the frequency band from 3 GHz to 3.37 GHz for horizontal bending, and from 3.05 GHz to 3.47 GHz for vertical bending. Within these frequency bands, the reflection coefficient remains below 8.6 dB for horizontal bending, and below 7.6 dB for vertical bending. The suggested antenna exhibits only minor variations in the bandwidth before and after bending, indicating its robustness to bending effects.  
Figure 5: The 2D flat antenna bends with 50, 60, 70, 80, 90, and 100 mm radii. (**a**) Horizontal bending; (**b**) vertical bending.  
Figure 7 illustrates the dispersive properties of each phantom layer in terms of complex permittivity. The real part of the permittivity (\(Eps^{\prime}\)) and the imaginary part (\(Eps^{\prime\prime}\)) are shown, along with the loss tangent (\(Eps\)\(D\)), which indicates the frequency dependency of the imaginary part. These parameters are crucial in the simulation process to ensure that the breast phantom closely resembles the real-life scenario, capturing the frequency-dependent behavior of the breast tissue layers.
## 3 Methodology
### Utilizing Machine Learning for Outlier Detection  
Machine Learning (ML) is applied in a wide area of applications to discover patterns from given data and make predictions, such as anomaly detection. Several approaches, reported in the literature, utilize Machine Learning as an effective method for detecting anomalies. For the purpose of this paper, we developed a Machine Learning system, focused on user-centred analysis to distinguish malicious activities from the legitimate ones. The system utilizes the Local Outlier Factor (LOF), to detect the outlier or rare instances from the given data. LOF is fitted to the subset data frame, generated after feature selection optimization, to detect the outliers. For every detected outlier, the system marks the corresponding insider as malicious. The system marks an outlier based on the entire subset dataframe, based on the selected features and not based on CERT's accompanied scenarios.  
Local Outlier Factor (LOF), is an unsupervised anomaly detection algorithm, which uses a score to determine if a certain point is an anomaly. Each datapoint is assigned this score, which is the result of the computation of local density deviation of the given datapoint with respect to its neighbor data oints. If a given datapoint has a substantially lower density than its neighbors, then it is a considered as an outlier [26]. The LOF algorithm is considered as an efficient method to detect outliers in high dimensional datasets. In our system, we employed sklearn.neighbors.LocalOutlierFactor from the scikit-learn module, which utilizes K-nearest neighbours to compute the local density of a datapoint. LOF score value is determined from the ratio of the average local density of the observation's neighbors and its own local density [27]. In LOF, we need to define the values of n_neighbors, contamination and n_jobs [27]:  
* n_neighbors: the number of neighbors to take into consideration to detect the outliers. If the value is larger than the number of provided samples, then all samples will be used;
* Contamination: the proportion of outliers in the dataset. Contamination is used to define the threshold on the scores of the samples we fit LOF to;
* n_jobs: number of parallel jobs to run or neighbors search. \"\(-1\)\" value uses all available processors.  
We fit LOF to the \"candidate\" optimal subset data frame, generated after performing feature selection optimization using bio-inspired models, to detect outliers.
## 1 Introduction
Despite the widespread use of dynamometers, there is limited research on how variations in the position of the measurement pad, whether anterior or posterior, affect knee strength outcomes, particularly in athletes. This gap in the literature highlights the need for studies that investigate the effect of pad positioning on the accuracy and consistency of knee strength measurements. Some devices, such as the Byodex 3, utilize distal fixation, where the force is exerted against a pad at the anterior and posterior parts, as shown in the study by [PERSON] and [PERSON] [10]. Other devices, such as the Isomed 2000 [12], the PRIMUS RS [8], and the HUMAC NORM [5], only have cushioned areas in the anterior part of the fixation, and the posterior part simply has a belt to adjust the pad. Consequently, the support surface on which the anterior aspect of the leg has to exert the knee extension force on the device differs from the one on which the posterior aspect has to exert the flexion force. In the 1940s, Dr. [PERSON] described proprioceptive neuromuscular facilitation as a set of methods to promote neuromuscular response by stimulating receptors [16]. This method uses the body's receptors (including skin pressure and tactile exteroceptors) to inhibit or facilitate muscle contraction [17]. Visual, auditory, somatosensory, and proprioceptive information are necessary to plan a movement. If the brain receives sufficient afferents, the movement will be more coordinated. Therefore, a deficit of the above information may cause functional alterations [18]. In addition, a larger fixed point to exert force will increase the force generated [15]. Therefore, when measuring with the pad placed on the anterior aspect of the leg, theoretically, the quadricp musculature will be capable of generating a greater force than the hamstring musculature because it has a larger contact surface and, therefore, will receive a greater tactile and pressure stimulus and have a greater fixed point of support. As a result, it is plausible that the muscular force generated could vary depending on where we place the pad (anterior or posterior aspect of the leg). Thus, the main objective of this study is to analyze whether the position of the pad in distal fixation influences knee flexion/extension strength values, addressing the gap in the existing literature and providing valuable insights into the optimization of dynamometer use in sports and rehabilitation settings. It is hypothesized that the position of the pad in distal fixation (anterior versus posterior) significantly affects knee flexion and extension strength measurements, with the posterior pad position resulting in higher knee flexion strength values compared to the anterior pad position.