esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Physiological Traits  
Leaf growth rates were significantly higher under the regimes of 31 \({}^{\circ}\)C while there was no influence of CO\({}_{2}\) treatments (Figure 3, Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dependent Variables** & **Factor** & **TSS** & **F-Value** & \(p\)**-Value** \\ \hline \multirow{2}{*}{Leaf number} & Temperature & 0.375 & 1.800 & 0.195 \\  & CO\({}_{2}\) & 0.375 & 1.800 & 0.195 \\  & **Interaction** & **1.042** & **5.000** & **0.037** \\ \hline \multirow{2}{*}{Maximum leaf length} & **Temperature** & **46.482** & **15.381** & **0.001** \\  & CO\({}_{2}\) & 11.482 & 3.799 & 0.065 \\  & Interaction & 0.135 & 0.045 & 0.835 \\ \hline \multirow{2}{*}{Leaf SA} & **Temperature** & **2.059** & **14.429** & **0.001** \\  & CO\({}_{2}\) & 0.075 & 0.528 & 0.476 \\  & Interaction & 0.009 & 0.065 & 0.802 \\ \hline \multirow{2}{*}{Leaf weight} & **Temperature** & **0.058** & **5.542** & **0.029** \\  & CO\({}_{2}\) & 0.000 & 0.007 & 0.934 \\  & Interaction & 0.016 & 1.486 & 0.237 \\ \hline \multirow{2}{*}{Leaf width} & Temperature & 0.001 & 1.494 & 0.236 \\  & CO\({}_{2}\) & 0.000 & 0.477 & 0.498 \\  & Interaction & 0.000 & 0.017 & 0.898 \\ \hline \multirow{2}{*}{Leaf SA growth rate} & **Temperature** & **0.002** & **10.413** & **0.004** \\  & CO\({}_{2}\) & 0.000 & 0.002 & 0.961 \\  & Interaction & 0.000 & 0.292 & 0.595 \\ \hline \end{tabular}
\end{table}
Table 2: Results of analysis of variance (two-way ANOVA test) of the effects of temperature and CO\({}_{2}\) and their interaction on the morphological leaf traits (Figure 2) and growth rate (Figure 3). \(p\)-Values in bold are statistically significant (<0.05) (TSS: Total sum of squares, SA: Surface area).  
Figure 3: The effect of increasing temperature and CO\({}_{2}\) enrichment on the leaf surface area’s growth rate of _Enhalus acoroides_ seedlings at the end of the experimental period. Values are mean \(\pm\) SE (n = 6).  
Additionally, there was an interaction (\(p\)-value = 0.078, Table 4) between the two factors, which are temperature and CO\({}_{2}\) concentration, in \(rETR_{\text{max}}\) and Ek parameters, with the highest values observed under the 27 \({}^{\circ}\)C-enriched CO\({}_{2}\) treatment (Table 3). In contrast, the 31 \({}^{\circ}\)C-enriched CO\({}_{2}\) treatment showed the lowest \(rETR_{\text{max}}\) and Ek values (Table 3). \(Fv/Fm\) and \(\alpha\) measures of the photosynthetic maximum quantum yield and efficiency did not show significant differences among the four treatments, nor any interaction between temperature regimes or CO\({}_{2}\) concentrations (Tables 3 and 4).
## 3 Results and Discussion
### Limit of Detection (LOD) and Limit of Quantification (LOQ)  
The limit of detection (LOD) and limit of quantification (LOQ) were calculated as a signal-to-noise ratio of 3 and 10, respectively. The LOD is the minimum concentration of the analyte that can be detected but not quantitated as an exact value. The LOQ is defined as the minimum concentration of the analyte that can be quantitatively determined with suitable precision and accuracy. LOD and LOQ were calculated according to [PERSON] and [PERSON] (2008) [51]. Table 6 shows the LOD and LOQ for all elements measured in this study.
## 2 Materials and Methods
### Advantages of the FPGA Platform  
For real-time detection embedded systems, low computational latency and computational overhead are important factors. FPGA (Field-Programmable Gate Array) demonstrates significant advantages due to its unique architecture and flexibility [21]. FPGA is a highly flexible integrated circuit that allows users to configure its logic functions through programming after manufacturing, thereby meeting specific application requirements [22; 23]. Unlike traditional fixed-function chips, FPGAs consist of numerous programmable logic units, programmable interconnect networks, and input/output modules, enabling customization and optimization based on different application scenarios [24; 25]. The programmable logic units within FPGAs consist of Look-Up Tables (LUTs), Flip-Flops, and other basic logic elements, capable of performing complex logic operations and data processing. By configuring these logic units, developers can design various digital circuits such as adders, multipliers, and state machines. These logic resources are typically designed using hardware description languages (e.g., Verilog). Developers write code that describes the circuit's functions, mapping the logic functions onto the FPGA's hardware resources. Synthesis tools convert this code into a netlist and, with other design constraints, generate a bitstream file that can be loaded onto the FPGA, implementing the required hardware circuit. FPGAs can achieve highly parallel computation, meaning multiple tasks can be executed simultaneously, significantly reducing overall computational latency [26; 27]. This is particularly important for applications requiring immediate responses, such as fault detection. Additionally, the programmability of FPGAs allows for customization and optimization based on specific needs, ensuring maximum resource utilization and reducing computational overhead.
## 4 Advanced Algorithms in Real-Time Diagnostics
### Real-Time Diagnosis of Operational Parameters of CAP Sources  
Traditional diagnostics (e.g., laser-induced fluorescence, mass spectrometry, spontaneous Raman scattering) of the different operational parameters of CAP sources are complex and expensive, requiring sophisticated instrumentation setups and specialized analysis, making them impractical for real-time use [127,128] and making it challenging to control the plasma consistently and effectively. Using information-rich datasets collected from simpler diagnostic methods like optical emission spectroscopy (OES) [129] and electro-acoustic emission and enabling fast, automated data processing and analysis using AI techniques in real-time monitoring and control improves the feedback control, ensuring effective and optimal performance of CAP sources [127,128]. Simulated data [130,131], although not capturing all the complexities in real-world scenarios, complement real data by providing a controlled environment for analysis and experimentation.  
The manual analysis of the datasets with traditional methods can be challenging due to the complex relations and patterns in the data. ML and other computational approaches (Table 1, Figure 6) are designed to handle such complexity and can identify patterns and trends that may not be apparent to human analysis [128].  
Figure 6: Common ML components employed for the application of CAP in oncology, highlighting their respective advantages and disadvantages.  
ML algorithms can be trained on the operational parameters and their corresponding data, allowing them to learn and generalize their knowledge for making predictions on new, unseen data. As new data are received, the algorithms rapidly process and analyze the data, enabling quick estimation of critical operational parameters from the spectral data or other input data in real time. ML provides a promising pathway through the creation of input-output models that support real-time computations, thus enabling the implementation of more advanced feedback control strategies [120, 138]. Constructing multivariable, nonlinear prediction models to characterize the complex interactions between CAP and cancer cells is crucial for guiding effective cancer treatment approaches [136]. AI models can thus be used for real-time monitoring and diagnostics [132, 133, 134, 135], detecting any deviations from normal behavior [135, 138], and predicting when maintenance or adjustment is required [122] for maintaining optimal real-time control of the operational parameters. Thus, AI-based approaches, specifically ML, can significantly improve the reliability and performance of CAP by adapting to dynamic and uncertain environments.
# An FPGA-Based Four-Channel 128k-Point FFT Processor Suitable for Spaceborne SAR
## 2 Fast Fourier Transform (FFT) Operation Analysis and Principle Introduction
### Operation Proportion Analysis of FFT in the Standard Chirp-Scaling (CS) Algorithm
When the number of FFT points is higher, the saving of the multiplier is more obvious, and the radix-23 algorithm inherits the simple butterfly unit in the radix-2 algorithm. In summary, the advantages of the radix-23 algorithm include lower algorithm structure \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Occupancy for** \\ **Multiplier** \\ \end{tabular} & \
\begin{tabular}{c} **Occupancy for** \\ **Memory** \\ \end{tabular} &
\begin{tabular}{c} **Occupancy for** \\ **Butterfly Unit** \\ \end{tabular} \\ \hline R-2 SDF & \(\log_{2}N-2\) & \(N-1\) & \(\log_{2}N(R2)\) \\ R-4 SDF & \(\log_{4}N-1\) & \(N-1\) & \(\log_{4}N(R4)\) \\ R-23 SDF & \(\log_{8}N-1\) & \(N-1\) & \(\log_{2}N(R2)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of hardware unit usage of radix-2 (R-2), radix-4 (R-4), and radix-23 (R-23) algorithms. Figure 5: Structure diagram of the radix-4 algorithm. complexity, easy implementation, saving of hardware resources, etc., as compared with the radix-2 and radix-4 algorithms, and it is more suitable for applications with ultra-long points.
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 2 Robust Statistics Principles  
Classical regression methods assume perfect knowledge of the probability distribution that the data obey. Particularly, parametric models are typically considered [38]. A traditional way to represent \"well-behaved\" data is to assume that the underlying noise is normally distributed, that is, \(\eta\sim\mathcal{N}\left(\mu,\sigma^{2}\right)\), with known mean and variance, \(\mu\) and \(\sigma^{2}\). If this assumption holds, the LS estimate is known to be optimal. However, several real-world measurements have confirmed the presence of heavy-tailed (or _approximately normal_) noise [39; 40; 41], causing estimators derived from the Gaussian probability model to be biased or even to break down [14]. Under these circumstances, the robust estimators become relevant, given their capacity to provide _close-to-optimal_ results in non-nominalconditions. The concept of approximate normality can be formalized by considering a proportion \(1-\varepsilon\) of the observations to be effectively following a Gaussian distribution, while a complementary portion \(0\leq\varepsilon\leq 1\) of the data being contaminated by an unknown (potentially) non-Gaussian distribution,  
\[\eta\sim\left(1-\varepsilon\right)F+\varepsilon\;H \tag{1}\]  
where \(F=\mathcal{N}\left(\mu,\sigma^{2}\right)\) is the nominal Gaussian distribution and \(H\) is an arbitrary contaminating distribution. Observations following the assumed \(F\) distribution are commonly referred to as _inliers_, while the corrupted observations are regarded as _outliers_. Notice that another approach for modeling outliers involves the use of _heavy-tailed_ distributions, whose tails tend to zero at a slower rate than the Gaussian distribution. Cauchy, Laplace, Student-t, or \(\alpha\)-stable distributions are examples of such heavy-tailed densities. The remainder of this section introduces basic notions in robust statistics and details some of the most well-known robust estimators for regression problems. For a detailed theoretical analysis of robust statistics, the reader is referred to classical textbooks [13; 42; 43], or the recent works [14; 44] for its application to a variety of signal processing problems. The peculiarities of applying these methods to GNSS SPP are discussed in Section 3, as well as their validation using both synthetic and real data experiments in Section 5.
# _Legionella pneumophila_--Virulence Factors and the Possibility of Infection in Dental Practice
## 3 Virulence Factors
### Surface Virulence Factors  
Surface virulence factors that affect the virulence of _Legionella_ spp. and enhance infectivity are lipopolysaccharide, flagella, pili, and outer membrane proteins [46; 47; 49; 50; 51].  
The LPS is located on the outer envelope of the outer membrane and is crucial in interacting with various host immune cells, as well as in intracellular traffic modulating [22]. The LPS molecule consists of an O-specific chain, a nucleus, and a lipid A component, combined with an endotoxin with a relatively low toxic potential. LPS contains many extended, branched fatty acids and O- and N-acetyl groups. It is highly hydrophobic and differs from the lipopolysaccharide. of other Gram-negative bacteria [51; 52].  
_Legionella_ spp. move, in most cases, by means of a polar and/or lateral flagella consisting of a basal body, a hooked structure, and a filament [53]. Motility may be crucial for dispersing in the lungs of patients, as such forms of _L. pneumophila_ have been detected in alveolar parts [46; 51; 52; 54]. The gene responsible for expression depends on the temperature, availability of nutrients, and viscosity of the medium in which they reside. Although flagella are not a condition for intracellular proliferation, they enhance host cell invasion regardless of attachment to it [51; 52]. Pili can be divided into two forms: the long form, measuring 0.8 to 1.5 um, and the short form, measuring 0.1 to 0.6 um. The Pile protein is an integral part of the long form of type IV pili. It is involved in binding and adhesion to host cells. Prepilin peptidase (PilD) is another protein liable for the production of type IV pili. It is crucial for successful intracellular proliferation. This protein is also involved in secretion type II [51; 52].  
_Legionella_ spp. outer membrane proteins are essential for phagocyte entry and survival. The Macrophage Infectivity Potentiator (MIP) displays peptidyl-prolyl cis/trans isomerase activity and is necessary for the early stages of intracellular infection and survival in macrophages, protozoa, and pulmonary epithelium [55]. Due to the _MIP_ gene's characteristics not being found in other _Legionella_ genes, it is possible to identify clinical and environmental strains of _L. pneumophila_ and other _Legionella_ spp. by _MIP_ sequencing [51; 56].  
The major Outer Membrane Protein (MOMP) has porin properties, and is required for bacterial interaction with CR1 and CR3 receptors on monocytes and other phagocytes [57]. Factors of both the host and bacteria that facilitate initial adherence and entry of _Legionella_ spp. into the cell, are essential [57].
## 2 Materials and Methods
Measurements for laser radiation parameters were taken by the following devices: BeamOn HR (Duma Optronics, Nesher, Israel) for measurements of the laser beam diameter in the plane of the sample, F150A-BB-26 (Ophir, North Logan, UT, USA) for measurements of the radiation power incident on the sample and 0D-08A (Avesta, Ltd., Troitsk, Russia) with an oscilloscope for pulse repetition rate measurements. To obtain data on the average electrical conductivity \(\sigma\) over cross-section of the sintered silver lines, the absolute resistance of the lines, their cross-section profile and length were measured by a 287 multimeter (Fluke, Everett, WA, USA) DCM 3D optical profiler (Leica, Wetzlar, Germany) and VHVX-1000 optical microscope (Keyence, Iasca, IL, USA) respectively. The shapes and sizes of the deposited NP and their agglomerates were studied by transmission electron microscope (TEM) JEM-2100 (JEOL, Ltd., Tokyo, Japan). Additionally, the statistical size \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Laser Type** & **Tech-1053** & **Tech-527** & **DLM-30** & **DTL-413** \\ \hline Wavelength, nm & 1054 & 527 & 980 & 527 \\ \hline Operating mode & Pulse & & CW & \\ \hline Beam spot diameter, \(\mu\)m & & \(>\)70 & & \\ \hline Maximum average power, W & 2 & 0.5 & 25 & 1.5 \\ \hline Pulse repetition rate, kHz & 4 & & – & \\ \hline Pulse duration, ns & 20 & & – & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Lasers operating parameters. Figure 2: The silver NP array in the form of line: (**a**) a model of the line printed between two silver contact pads and (**b**) a 3D image of the printed line taken by an optical profiler. distribution of silver NP agglomerates in the aerosol stream after the focusing nozzle was measured by the aerosol NP analyzer SMPS 3936 (TSI Inc., Shoreview, MN, USA). To study the microstructure of sintered samples in the form of lines along the cross-section, their fractures were prepared. To obtain a brittle fracture and minimize the effects of silver crystallites plastic deformation, the samples were cooled in liquid nitrogen, after which a transverse brittle fracture of the line was made together with the substrate. The transverse line fractures obtained in this way were studied by scanning electron microscope (SEM) JSM 7001F (JEOL, Ltd., Tokyo, Japan). The porosity pattern and its boundaries were determined from the images of fractures, the average crystallites statistical sizes were estimated for processing of over 400 particles, approximating them with a sphere. Crystallite sizes were compared for a pore-free layer near the array outer surface and for a porous microstructure in the central part of the line fracture. The samples surface roughness was studied by atomic force microscopy (AFM) using a Solver PRO-M system (NT-MDT, Zelenograd, Russia).
# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
## 4 Discussion and Conclusions
### Future Perspectives  
The images presented here show that real surface conditions of parts are no longer an impediment to imaging the microstructure. Future generations of SRAS instruments will be able to deal with the complex shapes of parts, allowing the surface microstructure to be determined on the part before being put into service. This will allow decisions on inspection and maintenance schedules at the part level, reducing costs and time associated with inspection. It will also accelerate the adoption of additively manufactured components into safety-critical areas, as the performance of the part will be known because the part can be inspected during the build process, generating a digital twin of the part containing information on the remaining defects and microstructure within the part. The availability of optical detectors that can work on a wide range of surface finishes means that imaging the microstructure of real parts is now possible.  
Figure 13: Ratio of build + scan time to build time, as a function of inspection wavelength and data points captured per second. Marked points show the parameter for three of the detectors, where KED is single-shot and high-resolution but requires smooth surfaces, and SKED was on L-PBF sample with 116 averages with 250 \(\upmu\)m wavelength, and Quartet was from the etched sample, using 16 averages and 80 \(\upmu\)m wavelength.
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 3 GO Model Construction
### GO Containing Different Functional Groups  
In order to analyze the influence of different types of functional groups on the adsorption process in the subsequent calculation, GO models containing different types of functional groups should be constructed. To simplify the calculation, it is assumed that each GO molecule contains only one functional group, that is, a hydroxyl group, carboxyl group or epoxy group, where the hydroxyl group exists on the base plane and on the edge of the base plane. The structure and energy of GO configurations containing different types of functional groups in the stable state are shown in Figure 2. The hydroxyl group is denoted as GO(OH)\({}_{\text{s}}\) when it is present at the base level and GO(OH)\({}_{\text{b}}\) when it is present at the edge of the base level; \(\blacktriangledown\) represents a H atom, \(\blacktriangledown\) represents a C atom and \(\blacktriangledown\) represents an O atom.  
Through energy calculations, it was found that GO(COOH) had the lowest energy in the stable state, indicating that compared with other types of functional groups, combining more carboxyl groups could improve the stability of the adsorbent.
## 4 SDP Solution Method for the FFP-IM  
When the established Monte Carlo simulation (MCS) method to resolve the proposed \(\tau_{i}\), the process will involve a double-layer sampling procedure. The inner layer sampling is to calculate the conditional FFP \(P_{tM|Z_{i}}\) when the membership level of the fuzzy variable \(Z_{i}\) is taken as a realized value \(\alpha_{i}^{*}\), and the sample number is noted as \(N_{i}^{\mathrm{inner}}\). The outer layer is to calculate the mathematical expectation of the membership level \(\alpha_{i}\) within the range [0, 1], and the sample number is noted as \(N_{i}^{\mathrm{outer}}\) samples. Therefore, the quantity of samples necessary for the computation of the FFP-IM for the fuzzy variable \(Z_{i}\) is \(N_{i}^{\mathrm{total}}=N_{i}^{\mathrm{inner}}\times N_{i}^{\mathrm{outer}}\). So, \(\sum_{i=1}^{n}N_{i}^{\mathrm{total}}\) sample points are needed to calculate the FFP-IM index of \(n\) fuzzy variables. Furthermore, the computation of the NPR index (Equation (7)) necessitates the use of an optimization method for non-monotonic LSF. In practical engineering contexts where finite element analysis (FEA) is crucial for determining the response relationship, the computational demand can become extensive and often unmanageable.  
To optimize the computation of the FFP-IM in fuzzy structural systems, a transformative approach is taken. Initially, both the unconditional and conditional FFP are metamorphosed into the mathematical expectations of the failure domain index function. Subsequently, the presented FFP-IM is reframed into a form resembling the variance of the expectation of this conditional failure index function. This essentially represents the variance-based IM of the very same failure domain index function. With the application of the SDP method, this complex equation becomes manageable and capable of being solved with relative efficiency. Consequently, this operational methodology enables the effective calculation of the FFP-IM through the SDP method, streamlining the process.