esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this article, we have briefly examined the entropy generation in magnetohydrodynamic (MHD) Eyring-Powell fluid over an unsteady oscillating porous stretching sheet. The impact of thermal radiation and heat source/sink are taken in this investigation. The impact of embedded parameters on velocity function, temperature function, entropy generation rate, and Bejan number are deliberated through graphs, and discussed as well. By studying the entropy generation in magnetohydrodynamic Eyring-Powell fluid over an unsteady oscillating porous stretching sheet, the entropy generation rate is reduced with escalation in porosity, thermal radiation, and magnetic parameters, while increased with the escalation in Reynolds number. Also, the Bejan number is increased with the escalation in porosity and magnetic parameter, while increased with the escalation in thermal radiation parameter. The impact of skin fraction coefficient and local Nusselt number are discussed through tables. The partial differential equations are converted to ordinary differential equation with the help of similarity variables. The homotopy analysis method (HAM) is used for the solution of the problem. The results of this investigation agree, satisfactorily, with past studies.  
entropy; MHD; Eyring-Powell fluid; thermal radiation; porosity; oscillatory stretched sheet; HAM +
Footnote †: journal: Applied Sciences
## 2 Study Area and Data Sources
### Data
#### 2.2.2 GPP Data  
The GPP data used in this study is the global GPP dataset published by [PERSON] et al. [33], which is derived from the improved EC-LUE model. This model integrates several key environmental variables, including atmospheric CO\({}_{2}\) concentration, radiation components, and atmospheric vapor pressure difference. The dataset offers high spatial and temporal resolution (0.05\({}^{\circ}\), 8 days) and is widely regarded as highly reliable [34].
# An Easily Understandable Grey Wolf Optimizer and Its Application to Fuzzy Controller Tuning
## 3 Grey Wolf Optimizer, Analysis, and Optimal Tuning Approach
### Optimal Tuning Approach  
The GWO is mapped onto the optimization problems defined in Equation (13) by the relationships given in Equations (20), (26) and (27). The optimal tuning approach for T-S PI-FC consists of the following steps built around the approach suggested in [21] and the GWO implementation according to the previous sub-section:  
Step A. The ESO method is applied to tune the parameters of the linear PI controllers, \(T_{s}\) is set, and the discrete-time PI controllers expressed in Equation (9) are obtained. The particular expressions of the state sensitivity models of the fuzzy control system are derived in accordance with Equations (14) and (15).  
Step B. The weighting parameters \(\gamma_{\alpha_{i}},j\in\{1,2\}\), specified in Equation (13) are set such that they meet the performance specifications of the fuzzy control system. The parameter \(t_{df}\) is set according to step 6 of GWO, and the domain \(D_{\rho}\) is set to include all constraints imposed to the elements of \(\rho\).  
Step C. GWO implemented according to Sub-Section 3.1 is applied to solve the optimization problems defined in Equation (13), which produce the optimal values of the parameter vector \(\rho^{*}\) and the optimal parameters \(\beta^{*}\), \({B_{e}}^{*}\), and \(\eta^{*}\):  
\[\rho^{*}=\big{[}\;\;\beta^{*}\;\;\;{B_{e}}^{*}\;\;\;\;\eta^{*}\;\;\big{]}= \big{[}\;\;{\rho_{1}}^{*}\;\;\;{\rho_{2}}^{*}\;\;\;{\rho_{3}}^{*}\;\big{]}^{T}. \tag{50}\]  
Step D. The optimal value of the parameter \(B_{\Delta e}\), namely \({B_{\Delta e}}^{*}\), results from the following particular expression of Equation (11):  
\[{B_{\Delta e}}^{*}=\mu\;{B_{e}}^{*}. \tag{51}\]  
Step E. The transfer function of the reference input filter is obtained from the following particular expression of Equation (8):  
\[F(s)=1/(1+\beta^{*}\;T_{\Sigma 0}s). \tag{52}\]  
As illustrated in Equation (52), the steps A to E have been applied for the controllers tuned using the nominal values of the process that belongs to the family of nonlinear servo systems modeled in Equation (1). However, other control approaches can be used [55; 56; 29; 33; 57; 58].
## 1 Introduction
### Methodology and Criteria  
To ensure a comprehensive and unbiased analysis, an approach was employed to select relevant research papers. The search was conducted using established academic indexing databases, including Scopus, IEEE Xplore, SpringerLink, ACM Digital Library and ScienceDirect, focusing on keywords such as \"Autonomous Forklifts\", \"machine Vision\", \"Perception Technology\", \"Scanning systems\", \"Object Detection\" and \"Forklift Automation\". In addition, technical reports and white papers from official company websites indexed by these platforms were included to provide industry insights. Inclusion criteria prioritized peer-reviewed journal articles, conference papers and technical reports. Studies were selected based on their relevance to the main themes of the manuscript, with a particular emphasis on technological advancements, challenges and industry applications in the field of autonomous vehicle perception and scanning systems. Papers lackingsufficient technical detail or falling outside this scope were excluded. This methodology was designed to provide a focused yet comprehensive overview of the current state of the art in this domain.
# Improved Durability of Wood Treated with Nano Metal Fluorides against Brown-Rot and White-Rot Fungi
## 3 Results  
### Characterization of Nano Metal Fluorides  
The Figure 1a presents the high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) image of nanoparticles of MgF\({}_{2}\) and CaF\({}_{2}\) synthesized in one sol and the corresponding energy dispersive spectroscopy map (Figure 1b-d). From Figure 1a, it is seen that particle sizes are less than 20 nm. The small size of these low-water soluble metal fluorides enables them to be distributed inside wood during treatment. Additionally, elemental mapping presented in Figure 1b-d confirmed that both MgF\({}_{2}\) and CaF\({}_{2}\) particles are present in the sol with the latter forming larger aggregates.
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 2 Materials and Methods
### Parametric Study of Modified Antenna
#### 2.3.2 Changing Parasitic Element Width, W2  
Reducing W2 results in a slightly higher BW but increases the reflection coefficient. It also introduces a second resonant at 8.2 GHz. Increasing W2 enhance the reflection coefficient but reduces the bandwidth. Figure 10 shows the effects of changing W2. It can be seen that W2 = 0.15 mm is the optimum value.
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 3 Methods
### Dual Attention Fusion Enhancement Block (DAFEB)  
In order to better balance the reconstruction performance and model complexity, we design DAFEB with a similar architecture to the DAFEN. As shown in Figure 3, we  
Figure 2: Overview of the proposed Dual Attention Fusion Enhancement Network (DAFEN) architecture. The shallow feature extraction and reconstruction parts are utilized to extract coarse features and enlarge the features \(s\) times (e.g., \(\times 2\), \(\times 3\), \(\times 4\)), respectively. The Feature Refinement and Fusion part with four DAFEBs carries the main feature expression ability. The FFEM can generate more contextual information via forward sequential concatenation.  
progressively refine the complex features through three stacked CSLBs. The features refined by the shallow CSLBs are sent to both the deeper CSLBs and directly to the FFEM for fusion and enhancement. This can be represented as follows:  
\[P_{i}=\mathcal{H}^{i}_{CSLB}(P_{i-1}),\,i=1,2,3 \tag{7}\]  
\[P_{\text{fuse}}=\mathcal{H}_{FFEM}(P_{1},P_{2},P_{3}) \tag{8}\]  
where \(\mathcal{H}^{i}_{CSLB}(\cdot)\) denotes the CSLB function at the \(i^{th}\) layer, \(\mathcal{H}_{FFEM}(\cdot)\) represents the FFEM function, and \(P_{i}\) denotes the features obtained after the \(i^{th}\) CSLB layer. Subsequently, we perform fusion and enhancement on the extracted multi-level features to obtain \(P_{\text{fuse}}\). Finally, we introduce CEM [48] to amplify spatial details within the fused features. It is noteworthy that the CEM has an extremely low parameter count, making it worthwhile to slightly increase the parameter count for effectively enhancing model performance by introducing the CEM.
## 2 VBNC L. _monocytogenes_ Detection Methods
### Optical Detection Methods  
Early studies on VBNC cells compared bacterial cultures with microscopic counting of cells stained with fluorescent dyes, making them able to differentiate viable and dead cells [29]. Immunolabeling with antibodies bound to fluorophores such as fluorescein isothianate (FITC) also allowed the specific detection of bacteria in food. In this case, the antibody and the dye are first incubated in the dark with the target bacteria; then, cells are detected by FC [30].  
Among viability staining dyes, fluorescein diacetate (FDA) and the similar dye carboxfluorescein diacetate (CFDA) can permeate the membrane of metabolically active cells; inside the cells, they are converted by intracellular esterases into fluorescent compounds that remain inside the cells with an intact cytoplasmic membrane [16; 30; 31]. Propidium iodide (PI) is an indicator of membrane integrity and cell death that enters cells with damaged membranes and binds to DNA at either major or minor grooves with red fluorescence emission. Differential staining with FDA, or CFDA, and PI allows distinguishing viable (green fluorescence), sub-injured (brown fluorescence), and dead (red fluorescence) cells [14; 31]. A similar principle is exploited by the commercial staining system LIVE/DEAD BacLight kit (Thermo Fisher Scientific, Waltham, MA, USA) comprising the two fluorescent dyes SYTO9 and PI. Other dyes used for viable cell differential staining are 5-cyano-2,3-ditolyl tetrazolium chloride (CTC), 4\({}^{\prime}\),6-diamidino-2-phenylindole (DAPI), ethidium bromide, Bis-(1,3-Dibutylbarbituric Acid)Trimethine Oxonol (DiBAC4 (3)), calcein, and acridine orange [30; 32; 33].  
The direct epifluorescent filter technique (DEFT) enumerates microbes by filtering samples pre-treated with chemical reagents or enzymes through polycarbonate filters, staining with CFDA or acridine orange dyes, and microscopic analysis. This technique was applied to determine VBNC _L. monocytogenes_ cells in hard cheese but showed limitations such as cell injury caused by the chemical reagents or missed detection of weak signals [30].  
FC allows the detection of viable cells based on cell membrane integrity, energy status, and enzymatic activities such as substrate uptake. In this technique, cells suspended in a liquid pass rapidly through a laser beam and are detected by exploiting light scattering or fluorescence emission. The signals are sent to detectors and converted into digital signals. Before FC, food samples must be pre-treated to decrease background noise. For example, dairy products need pre-treatments such as the dispersion of milk powder in a buffer and the extraction of cheese juice with a hydraulic press. Moreover, proteases and detergents, such as Triton X-100, can be added to degrade proteins and break somatic cells. Filtration and centrifugation are also carried out [30]. FC is not suitable for all food matrices, and optical assays have common disadvantages, such as susceptibility to cell membrane damage, background fluorescence, and the misidentification of dead cells with an intact membrane as viable [29; 33].
## 3 Analysis of Accuracy and Performance  
An basic model of VLF propagation is simulated by using the HIE-FDTD. The results are compared with the computed results of the traditional FDTD method. The schematic diagram of VLF propagation model in spherical coordinate system is shown in Figure 2. The existence of UPML divides the entire VLF propagation model into four regions, as shown in Figure 3. The four regions are, respectively, a computation region, two planar regions, and one edge region. The calculation area is the largest and most important area, starting from the symmetry axis on the left and ending at the UPML boundary on the right.  
MATLAB 2019b simulation software is used in these simulations, and the operating system is Windows 10. The processor is an Intel processor (Intel Corporation, Santa Clara, CA, USA) with 16 GB of RAM and 20 GB of hard disk space.  
Figure 2: Schematic diagram of VLF propagation model.  
The three UPML regions in the model are two planar regions and one edge region, and the parameters of the planar region can be obtained by degrading the edge region. Based on this, the parameters of three UPML boundaries can be derived from the parameters of the aforementioned edge regions, which can determine the dielectric constant and magnetic permeability within the region, as shown in Table 1.
## 6 Results and Discussion  
RQ1: To What Extent Is Our Approach Effective in Reducing Test Execution Time (cost) Compared to jGenProg?  
We investigated the effectiveness of MPPEngine against jGenProg in terms of reducing test execution time and cost. We ran experiments on the Defects4j [14] dataset used in Astor [11; 12; 13] to compare the results more effectively. So, we filtered out 48 bugs from 4 Defects4j (Chart, Math, Lang, and Time) subjects for which jGenProg said to have generated at least one test suite adequate patch. As per the experiments conducted among other tools in Astor [11; 12; 13], jGenProg generated patches faster using test suites. There were 19 patches out of 48 patches generated within 3 h. So, we decided to check that condition with our approach. Randomness in the results was reduced by running all the experiments 3 times.  
In terms of test case reduction with respect to time, MPPEngine is better for 42 bugs (42/48) and jGenProg is better for 6 bugs (6/48). Table 2 depicts that Minimum time to validate a program variant in MPPEngine is 0.86 s where jGenProg is 4.80 s. Meanwhile, Median and Maximum time consumed to validate a patch in MPPEngine is lesser (19.53 s, 119.75 s) than jGenProg (30.96 s, 792.47 s). Also, the standard deviation of MPPEngine (34.29 s) is lesser than jGenProg (153.86 s). On average, jGenProg takes up to 79.27 s to validate one program variant, whereas MPPEngine consumes only 33.70 s. We calculated this average value using executed test case count and the number of generations iterated for the bug. So, the time consumption on patch validation is reduced to 57.50%. This shows our approach is better in terms of reducing test execution time and cost, effectively.
# Method to Remove Tilt-to-Length Coupling Caused by Interference of Flat-Top Beam and Gaussian Beam
## 1 Introduction  
In the detection processes of space-borne and long-baseline laser interferometers such as the Laser Interferometer Space Antenna (LISA) [1] and Taiji program [2; 3], the coupling between the beam jitter and the path length readout represents a significant noise source, this coupling is called tilt-to-length (TTL) coupling [4]. In their current designs, the interferometer arm length of the LISA is 2.5 million kilometers, and Taiji is 3 million kilometers, meaning that a fundamental-mode Gaussian beam would travel millions of kilometers in space and expand into a huge fundamental-mode Gaussian wavefront. (The above is a simplified assumption; interested readers can find more detailed discussion in [5; 6], but these discussions are beyond the scope of this paper). The wavefront would then be clipped into a flat-top beam and imaged via the local telescope to the local optical bench to interfere with the local Gaussian beam. The wavefront will suffer diffraction and its propagation direction will jitter because of beam clipping and pointing accuracy limitations, and when the obvious beam type differences between the two interfering beams are considered, the above factors will produce more complex noise in the length readout.  
In this paper, both non-diffracted and diffracted models are constructed to research the interference between a flat-top beam and a Gaussian beam. Then a special case, in which the TTL noise can be made negligible by placing the beam waist in an appropriate position and using a suitably large detector without clipping, is found and is verified via both analytical solutions and numerical simulations.
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 4 Combination of Optical and Thermal Remote Sensing
### The Concept of Temperature-Vegetation Space
There are generally three ways to solve this issue: visual recognition, automatic fitting, and theoretical calculation [212]. Visual recognition relies on human experience and judgment and has great subjectivity and uncertainties. Automatic fitting is usually based on a set of rules to find representative pixels for the dry edge and the wet edge or exclude outliners (usually water and cloud pixels). The chosen (if representative pixels are picked) or remaining (if outliers are excluded) pixels are used in the regression of a linear or nonlinear expression of the dry/wet edges. [PERSON] et al. [213] is a good example of such methods. However, these methods still have some empirical parameters which require fine-tuning for different regions and datasets, and they also lack physical interpretability. Moreover, if the study area does not have extremely dry and wet pixels as well as bare and fully vegetation-covered pixels at the same time, these methods may yield biased outputs. In contrast, the theoretical calculation is directly based on the energy balance equation, facilitating the understanding and interpretation of the dry/wet edges' physical meanings. They can be applied to any area, or even a single pixel [214], regardless of its coverage of the Temperature-Vegetation space [212; 215; 216]. A major drawback of theoretical methods is their dependence on meteorological data such as surface air temperature, wind speed, or roughness length, which are difficult to obtain from remote sensing. The capability of detecting both surface and root-zone soil moisture status ensures the value of the Temperature-Vegetation space in drought monitoring. Nevertheless,  
Figure 7: Evolution of the Temperature-Vegetation space (adapted from Sun [211]). (**a**) Triangle; (**b**) trapezoid; (**c**) two-stage trapezoid. one should take caution that the Temperature-Vegetation space is not suitable for certain subarctic or arctic regions where temperature, instead of water, is the primary limit of vegetation growth [217].
## 2 Materials and Methods
### Sample Preparation  
The natural moisture content of the soil was employed for the sample preparation in order to simulate a hypothetical real mixing of the soil, hydraulic binder, and industrial by-product at the field. Firstly, a moisture content of 31% was added to the oven-dried soil. Afterwards, dry limestone powder was added and mixed until a homogeneous mixture was reached. Samples were prepared using 0 and 25% of industrial waste by the total dry weight of the soil. Finally, 9% of the hydraulic binder (lime or cement) was added and combined until a homogeneous mixture was obtained. The time to obtain homogeneous mixtures was set at 5 min based on the observation of the process for the highest percentage of addition. The percentages of industrial waste and commercial binder employed in this research were decided based on previous results, where the unconfined compressive strength was determined for samples made with 3, 6, and 9% commercial binder and 0 to 25% waste. The highest unconfined compressive strength was obtained for 25% waste and 9% commercial binder [41]. In addition to the samples made with distilled water, other samples were made with Na\({}_{2}\)SO\({}_{4}\) solution to investigate the effect of sulfate-aggressive water on the stabilized soil. The solution used had a concentration of 3000 mg SO\({}_{4}\)\({}^{2}\)-per liter. This concentration was employed because it is the threshold for considering groundwater as highly aggressive for concrete according to the Spanish Standards [42; 43].  
The samples were prepared in metal molds 50 mm in diameter and 100 mm in height using standard Proctor compaction energy. The samples were tested at 7 days and at 3 and 6 months to understand the evolution of the mixed soil characteristics over time. The samples were stored in a curing room with a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C and 95% relative humidity until being tested.  
A summary of the samples prepared for the study is presented in Table 2.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Sample** & **Binder** & **Industrial Waste** & **Sulfate Solution** \\ \hline LM9 LP0 & Lime 9\% & 0\% & No \\ LM9 LP25 & Lime 9\% & 25\% & No \\ LM9 LP0 Sulf & Lime 9\% & 0\% & Na\({}_{2}\)SO\({}_{4}\) \\ LM9 LP25 Sulf & Lime 9\% & 25\% & Na\({}_{2}\)SO\({}_{4}\) \\ \hline CM9 LP0 & Cement 9\% & 0\% & No \\ CM9 LP25 & Cement 9\% & 25\% & No \\ CM9 LP0 Sulf & Cement 9\% & 0\% & Na\({}_{2}\)SO\({}_{4}\) \\ CM9 LP25 Sulf & Cement 9\% & 25\% & Na\({}_{2}\)SO\({}_{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the samples prepared.