esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Risk of Bias Within Studies  
The QualSyst tool was employed to evaluate the potential risk of bias across the included studies, and the findings are presented in Table 6. This tool comprehensively examines various aspects of each study, such as study design, sample size adequacy, control of confounding variables, and the alignment of conclusions with the obtained results. Notably, all the studies demonstrated a similar level of quality. [PERSON] et al. (2012) achieved the highest score of 0.95, while both [PERSON] et al. (2014) and [PERSON] et al. (2015) received slightly lower scores of 0.91. The average score across all papers was 0.93, indicating a low likelihood of bias in the included studies.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Criteria** & & **[PERSON]** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** \\  & **et al.** (2021) [(11)] & **(2019) [(12)]** & **(2022) [(13)]** & **(2007) [(14)]** & **(2008) [(15)]** \\ \hline
1 & Question/objective sufficiently & 2 & 2 & 2 & 2 & 1 \\  & described? & 2 & 2 & 2 & 2 & 1 \\ \hline
2 & Study design evident and & 2 & 2 & 2 & 2 & 2 \\  & appropriate? & 2 & 2 & 2 & 2 & 2 \\ \hline
3 & Method of subject/comparison & & & & & \\  & group selection or source of & & & & & \\  & information/input variables & 2 & 2 & 2 & 1 & 1 \\  & described and appropriate? & & & & & \\ \hline
4 & Subject (and comparison group, if & & & & \\  & applicable) characteristics & 2 & 2 & 2 & 2 & 2 \\  & sufficiently described? & & & & & \\ \hline
5 & If interventional and random & & & & & \\  & allocation was possible, was it & 1 & N/A & 0 & N/A & N/A \\  & described? & & & & & \\ \hline
6 & If interventional and blinding of & & & & & \\  & investigators was possible, was it & N/A & N/A & N/A & N/A & N/A \\  & reported? & & & & & \\ \hline
7 & If interventional and blinding of & & & & & \\  & subjects was possible, was it & N/A & N/A & N/A & N/A & N/A \\  & reported? & & & & & \\ \hline
8 & Outcome and (if applicable) & & & & & \\  & exposure measure(s) well defined & & & & & \\  & and robust to measurement/ & 2 & 2 & 2 & 2 & 2 \\  & misclassification bias? Means of & & & & & \\  & assessment reported? & & & & & \\ \hline
9 & Sample size appropriate? & 1 & 2 & 2 & 1 & 2 \\ \hline
10 & Analytic methods & & & & & \\  & described/justified and & 2 & 2 & 2 & 2 & 2 \\  & appropriate? & & & & & \\ \hline
11 & Some estimate of variance is & & & & & \\  & reported for the main results? & 2 & 2 & 2 & 2 & 2 \\ \hline
12 & Controlled for confounding? & 2 & 1 & 2 & 2 & 2 \\ \hline
13 & Results reported in sufficient detail? & 2 & 2 & 2 & 2 & 2 \\ \hline
14 & Conclusions supported by the & & 2 & 2 & 2 & 2 \\  & results? & & & & & \\ \hline Total & 22 & 21 & 22 & 20 & 20 \\ \hline Total\(/(28-(\text{Number of `N/A^{\prime}\times 2}))\) & 0.92 & 0.95 & 0.92 & 0.91 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Risk of bias of included studies evaluated through QualSyst tool.
## 2 Theory of Full Waveform Inversion  
A detailed description of the FWI algorithm can be found in the literature [13; 15]; thus, only the key techniques and steps are briefly introduced here. Generally, FWI imaging can be regarded as a composition of three segments: forward modeling to generate calculated data by solving a wave equation, the computation of the gradient and Hessian matrix of the misfit function and updating the model with a suitable optimization method.
## 2 Mobility Models in FANETs
### Random Mobility Models
#### 2.1.4 Manhattan Grid  
The Manhattan Grid (MG) model employs a grid road structure [43]. This mobility model was designed to simulate vehicle movement in an urban environment with a well-defined street layout. Mobile nodes move horizontally or vertically across an urban map. The MG model employs a random approach to node movement selection since a vehicle must choose whether to continue driving in the same direction or turn at each intersection, as shown in Figure 7. UAVs can follow the same direction as ground nodes to complete a mission under such a mobility model [40], and also this model is also adopted in UAV-assisted mmWave 5G operation in urban environments [44]. Moreover, the MG model can be adopted in FANETs industry applications, such as the mining industry [45].
## 2 Materials and Methods
### Intrinsic Parameters  
In this study, mechanical testing of FTB was conducted using a TMS-Pro texture analyzer (The Food Technology Corporation TMS-Pro texture analyzer from the Fairfax, VA, USA), incorporating both compression and shear tests.  
Poisson's ratio and elastic modulus are the primary intrinsic parameters of a material. As shown in Figure 2, a texture analyzer equipped with a flat probe was utilized to measure the intrinsic parameters of FTB, and its loading rate was set to 25 mm/min [9].  
Considering the anisotropic nature of FTB, whole bulbs were used for the compression tests, which were repeated 20 times to ensure reliability.  
Poisson's ratio refers to the ratio of transverse contraction (or expansion) strain to longitudinal extension strain under uniaxial tension or compression, and functions as an elastic constant that quantifies the lateral deformation of a material. The calculation formula is shown in Equation (1):  
\[\mu=\left|\frac{\delta_{1}}{\delta_{2}}\right|=\frac{W_{1}-W_{2}}{L_{1}-L_{2}} \tag{1}\]  
In Equation (1), \(\delta_{1}\) is the transverse deformation (mm); \(\delta_{2}\) is the axial deformation (mm); \(W_{1}\) is the initial transverse dimension before compression (mm); \(W_{2}\) is the transverse dimension after compression (mm); \(L_{1}\) is the initial axial dimension before compression (mm); and \(L_{2}\) is the axial dimension after compression (mm).  
Figure 1: Fritillariae Thunbergii Bulbus.  
The [PERSON]'s ratio (\(\mu\)) of FTB was obtained as 0.373 through 20 repetitions of mechanical tests.  
The elastic modulus \(E\) serves as an indicator of a material's capacity to withstand elastic deformation. The elastic modulus of FTB was determined to be 7.93 MPa based on texture analyzer measurements conducted across 20 experimental trials. The calculation formula is provided in Equation (2):  
\[E=\frac{F\cdot L}{S\cdot\Delta L}\sqrt{b^{2}-4 ac} \tag{2}\]  
In Equation (2), \(F\) is the maximum bearing capacity of FTB within the elastic deformation range (N); \(L\) is the original length of the FTB sample (mm); \(S\) is the cross-sectional area of the FTB sample (mm\({}^{2}\)); and \(\Delta L\) denotes the variation in the length of the FTB sample before and after compression (mm).
## 5 FAD Procedure  
The failure assessment diagram (FAD) provides a graphical method of assessing the structural integrity of the pipeline by plotting the relationship between crack dimensions and safe operating conditions. Figure 11 illustrates the FAD diagram through which an evaluation of the dimensions of the cracks located in the safe domain is shown. The estimated assessment points are determined with coordinates \(K\)r and \(L\)r by using the FEA results. The points illustrated lie below the FAD function (dotted line), indicating that they fall within the safe domain of the chart. When a point on a FAD diagram falls within the safe domain, it suggests that the pipeline can continue to operate safely under the current loading conditions. The pipeline is not likely to fail due to crack propagation or yielding until loads remain within the material capacity. The key point to note here is that the relationship between \(K_{\mathrm{r}}\) and \(L_{\mathrm{r}}\) appears approximately linear for these crack configurations within the safe domain (below the dashed line). This linear behavior is common in fracture mechanics when dealing with small-to-moderate crack sizes and load levels. The linearity indicates that, as the load on the pipeline increases (represented by increasing \(L_{\mathrm{r}}\)), the stress intensity factor also increases proportionally (leading to a rise in \(K_{\mathrm{r}}\)). This proportionality holds true until the material begins to yield, or the load approaches the limit load. Once the plastic limit pressure is reached, the material goes through plastic collapse, and the relationship between \(K_{\mathrm{r}}\) and \(L_{\mathrm{r}}\) becomes nonlinear [48]. Additionally, the plastic limit pressure is almost five times higher than the working pressure of the pipe. The point near the FAD function (\(a=1.68\) mm, \(c=3.36\) mm) still falls within the safe domain but is close to the FAD threshold where the structure may fail. For engineers, this proximity to the boundary raises concerns about the reliability of the system under additional loads or unexpected conditions, such as pressure oscillations or further crack growth. In cases like this, there is the option to consider preventative actions, such as reducing the operating pressure, arranging immediate repairs, or even taking the component out of service for a more detailed inspection.  
Figure 10: Plastic limit pressure scatter plot.  
Table 3 presents the failure pressure values, which were calculated by determining the points where the loading paths intersect the FAD function. These values are critical for assessing the integrity of the pipeline and predicting the conditions under which failure might occur.  
Safety depends on the difference between the working and failure pressures. For example, if the working pressure is significantly lower than the failure pressure, it means that the pipeline can operate safely under normal working conditions. However, if the working pressure approaches or exceeds the failure pressure, the risk of failure increases significantly, requiring immediate maintenance measures.
## 3 Proposed Approaches
### Spectrum Attention Mechanism
#### 3.2.4 Inverse Transform  
By applying an inverse fast Fourier transform (IFFT) to the features in the frequency domain, these features are transformed back to the time domain. This process enables the extracted frequency domain features to be integrated with the original time-series data,thereby restoring the temporal characteristics of the signal. The formula for recovering the temporal characteristics is presented by \(X^{\prime}_{t}\), which represents the time domain signal obtained through the IFFT of the weighted computed frequency domain signal, as depicted in (8).
# Multivariate Analysis for Prediction of Splitting Tensile Strength in Concrete Paving Blocks
###### Abstract  
Paving blocks are concrete pieces exposed to the weather and subjected to loads and wear. Hence, quality control in their manufacture is essential to guarantee their properties and durability. In Ecuador, the requirements are described in the technical standard \"NTE INEN 3040\", and tensile splitting strength is a fundamental requirement to guarantee product quality. The objective of the study is to predict the tensile splitting strength using two groups of predictor variables. The first group is the thickness in mm, width in mm, length in mm, mass of the fresh paving block in g, and percentage of water absorption; the second group of predictor variables is the density of the fresh paving block in kg/m\({}^{3}\) and the percentage of water absorption. The data were obtained from a company that can produce 30,000 units per day of rectangular paving blocks with 6 cm thickness. The research involves sampling, analysis of outliers, descriptive and inferential statistics, and the analysis of multivariate models such as multiple linear regression, regression trees, random forests, and neural networks. It is concluded that the multiple linear regression method performs better in predicting the first group of predictor variables with a mean square error (MSE) of 0.110086, followed by the neural network without hidden layers, resulting in an MSE of 0.112198. The best method for the second set of predictors was the neural network without hidden layers, with a mean square error (MSE) of 0.112402, closely followed by the multiple linear regression model, with an MSE of 0.115044.  
Multivariate analysis; prediction of tensile splitting strength; quality in concrete paving blocks; density of the fresh paving block; water absorption of concrete paving blocks; weight of the fresh paving blocks
# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis
### Multi-Focus Fusion Methods Based on Boundary Segmentation
#### 2.2.3 Pixel-Based Methods  
Beyond 2012, the pixel-based fusion method has become a popular direction of multi-focus image fusion. The main reason is that this method can obtain accurate pixel-weighted images. Most pixel-based approaches' core problem is obtaining a weight map for each source image. In these methods, the activity level measurement is first adopted, and then the focus values obtained from different source images are compared to generate pixel-level weight maps. Weight graphs are also known as decision graphs because multi-focus image fusion can be viewed as a classification problem in which each pixel's focus attributes (focusing and defocusing) are determined. In some methods, the source image is also divided into regions with different points (such as focus/defocus/border, texture/smooth). Different fusion rules are applied according to their characteristics.  
[PERSON] et al. [50] proposed a more focused image fusion algorithm based on image segmentation; the task of decision graph detection was regarded as the image segmentation between the focus region and the defocus region, and the feature images on the boundaries of the focus region and the defocus region were obtained by the multi-scale convolutional neural network. Then the initial segmentation, morphological operation, and watershed processing were performed on the fused image to get the segmentation graph and decision graph. This method proved that the decision graph obtained by a multi-scale convolutional neural network is reliable and can produce high-quality fusion images. [PERSON] et al. [51] proposed a dual-scale multi-focus image fusion algorithm based on an enhanced random walk. Using the complementary characteristics of dual-scale measurement can better align boundaries and solve noise problems, thus achieving a more robust fusion.  
Other pixel-based fusion methods involve robust principal component analysis (RPCA) [22], random field [23], morphological filtering [24], etc. The pixel-level fusion method has fast fusion speed and real-time solid performance. However, operating pixels are susceptible to noise, which will reduce the signal-to-noise ratio and contrast of the image.
###### Abstract  
Post-weaning diarrhoea (PWD) is one of the long-standing challenges in pig husbandry. Due to the risks of resistance caused by antibiotics (AB) misuse, conventional treatments against _Escherichia coli_ K88 (_E. coli_ K88), the PWD etiological agent, urgently need to be replaced. Organic acids (OA) and nature-identical compounds (NIC) are currently finding a central role in infection management thanks to their recognized antimicrobial activity. This study investigated the susceptibility of an _E. coli_ K88 field strain to a wide panel of AB, NIC, and OA. Secondly, we evaluated the ability of sub-lethal doses of the most active compounds to modulate the expression of _E. coli_ K88 virulence genes. Results showed that the bacterial strain was resistant to many of the tested antibiotics, but an antimicrobial action was registered for selected NIC and OA. The quantitative PCR analysis revealed that thymol, carvacrol, eugenol, and benzoic acid were able to downregulate (\(p<0.05\)) the expression of bacterial genes related to motility, adhesion to enterocytes, heat-labile (LT) and heat-stable (ST) toxin secretion, quorum sensing, and biofilm formation. Therefore, this study demonstrated that selected OA and NIC not only control _E. coli_ K88 growth but also modulate the expression of many virulence genes at sub-lethal doses, thus offering new insights on their mechanism of action and suggesting a powerful tool to manage PWD.  
post-weaning diarrhoea; pigs; _Escherichia coli_ K88; antibiotics; nature identical compounds; organic acids; virulence regulation; enterotoxins  
Post-weaning diarrhoea main etiological agent--_Escherichia coli_ K88--is affected by bioactive molecules--such as nature identical compounds and organic acids--which represent effective alternatives to inhibit microbial growth. Moreover, thymol; carvacrol; eugenol; and benzoic acid can downregulate _E. coli_ K88 virulence gene expression related not only to motility, cellular adhesion, quorum sensing, and biofilm formation, but also to LT and ST toxin secretion, to the same extent as efficacious antibiotics.
# Correction of Field-Measured Wind Speed Affected by Deterministic Interference Factors
## 1 Introduction
They selected the standard \(k\)-\(\epsilon\) turbulence model for RANS steady-state analysis in numerical simulation after considering the calculation cost and the accuracy balance. In addition, the normalized wind speed ratios at the re-entrant corners by the two methods were compared. The results show that the fluctuation of the WTT results is more evident. [PERSON] et al. [15] simulated the air flow changes of two non-parallel walls (with an angle of 100\({}^{\circ}\)) atop the bell tower under divergent and converging wind directions by CFD, and then verified them with the field measurement results. The results also show that the effect of wind speed amplification or contraction observed in numerical simulation and full-scale measurement are consistent. [PERSON] et al. [16] proposed a combination of WTT and CFD to predict the indoor air flow of wind-induced cross ventilation and introduced the wind pressure test data of the WTT as the boundary condition into the CFD numerical simulation. Then, the steady-state and transient analysis were used to compare the mean flow rate and the instantaneous velocity, which were verified by the full-scale measurement results to assess the cross-ventilation potential of the building. In general, the WTT uses wind speed sensors to measure air flow changes under the analog simulation air flow. However, it is often limited by the model scale ratio, the blockage ratio, and the wall Reynolds number. Nevertheless, compared with the dependence of the simulation precision and the computational parameters [17], such as the size of the computational model, the resolution of the computational grid, the inlet turbulent kinetic energy profile of the atmospheric boundary layer, the turbulence model, the order of the discretization schemes, and the iterative convergence criteria, the stable implementation conditions of the WTT increase result recognition. Two stations with relatively stable surrounding environment, namely, Noi Ling Ding Island (NLDI) and hollow-layer Ping An International Financial Center (PAFC), are considered the research objects to obtain the accurate wind speed in the Shenzhen area during typhoon landfall. The WTT and CFD numerical simulation were conducted to study the local wind environment characteristics of the sites. The influence of NLDI and structural components and instruments in the hollow-layer of PAFC on the measured results of the corresponding anemometers was analyzed, and the correlation between the wind speed influence coefficient (\(C_{\text{VI}}\)) and wind direction was established. The measured meteorological wind speeds were corrected, and the corresponding maximum reference wind pressures were converted based on the revised results.
## 5 Future Recommendation  
For this project, a single battery cell was used but, in practical application, a larger battery pack is required; hence, the next, future step would be to expand the number of battery cells in the battery pack. However, there are challenges to using a larger battery pack, including the irregularities and inconsistency of constant performance between the large number of cells; some cells may perform better, while others might lag behind. Due to this, there are uneven charge accumulations throughout the cells in the battery pack. Hence, to tackle this problem, a battery cell balancing system (BBS) is essential for a multi-cell Li-ion battery pack used in EV applications, as it plays a critical role in ensuring the uniformity, safety, and longevity of the battery pack while in operation, and it works in conjunction with the BMS to further optimise the battery performance and health, thereby enhancing its reliability and efficiency in energy storage. A BBS tracks the multi-battery cells, which are charging or discharging at different rates, having different SOC levels and redistributing the charge evenly among all the cells to have a similar charge so that the battery can provide a constant smooth performance; hence, before the BBS, the cells charge and the SOC levels are uneven, but after the BBS they are even. The reason charge redistribution is important is to prevent the dominant SOC level cells from overcharging or discharging and accumulating excessive heat which badly affects the battery performance and lifespan. It also increases the risk of battery explosion or damage.  
The BBS consists of sensing battery cells using algorithms or controllers for routing charging or discharging current to the cell which is typically conducted by using ASICs (battery sense chips and a system charge controller). The types of battery balancing methods are passive and active battery balancing. In passive battery balancing (PBB), the charge distribution consists of discharging current from one battery cell which has most dominant SOC level to all the other battery cells, producing a naturally balanced charge across all cells in the battery, which will distribute charge even to cells which do not require it. Hence, there is energy wastage. Whereas, in active battery balancing (ABB), it uses an intentionally routed path from the most dominant SOC level cell to the lowest SOC level cell and, due to this selective cell path method and controlled balancing, the battery's charge is more efficiently distributed. Thus, ABB is preferable for large battery applications [26].