esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 BLRFS Principles  
The principles of the band-limited reference-free speckle spectroscopy considered in this work are illustrated in Figure 1. The fluorescent random multiple-scattering medium with the slab geometry is illuminated by a continuous-wave laser radiation penetrating into the medium for a certain depth and inducing fluorescence. In turn, the fluorescence radiation diffusively propagating in the slab is partially acquired by a light-collecting system from a small area of the slab surface. The collected fluorescence light passes through a narrow-band filter and is detected by a photodetector unit. In the real system, the filtering and detection functions can be combined and a spectrometer with a linear photosensor can be applied as a narrow-band photodetector. In addition, a cutoff optical filter is used in the light-collecting system to block the pumping laser light. In the course of the medium characterization, the sample surface is scanned by the light-collecting system, and portions of fluorescence radiation are acquired from the various zones of the surface. Due to the narrow-band filtering of the incoming optical signal, the light waves emitted by any fluorescent site and propagating over the various paths in the medium can interfere with each other if the following condition is satisfied: \(\Delta s_{i,j}<l_{c}\) (\(\Delta s_{i,j}\) is the path-length difference for the superposing waves). Occurrence of a stochastic interference contribution in the detected signal should cause its random changes during transitions from one scan position to another due to position-induced changes of an ensemble of \(\Delta s_{i,j}\) values. These random changes can be characterized by the wavelength-dependent normalized second- and third-order statistical moments of intensity fluctuations in the detected signal \(\left\langle l^{2}\right\rangle/\left\langle l\right\rangle^{2}\) and \(\left\langle l^{3}\right\rangle/\left\langle l\right\rangle^{3}\) (the angular brackets mean an ensemble averaging over all the scan positions).  
Figure 1: The scheme of the band-limited reference-free speckle spectroscopy (BLRFS) instrument used in this work: 1—a continuous wave (CW) pumping laser; 2—a concave lens; 3—a probed sample (a fluorescent dye-doped layer consisting of TiO2 or SiO2 nanoparticles); 4—a microscope objective; 5—a pump-blocking filter; 6—a light-collecting lens; 7—an input tip of the fiber-optic patch-cord as the entrance aperture; 8–a spectrometer; 9–a PC.”A” denotes a light-collecting unit (see Figure 2).  
Figure 2: A diagram displaying the operation principle of the light-collecting unit (“A” in Figure 1); I—input tip of the fiber-optic patch-cord; II—lens; III—microscope objective (Nikon E plan 100X, N.A.1.25); IV—sample under study; V—pump = blocking filter (OG590).
# Systematic Review on Individualized Versus Standardized Parental Nutrition in Preterm Infants
## 4 Discussion
### Conclusion 4: SPN vs. IPN Did Not Reduce Sepsis or NEC Incidence, Mortality, or PN Duration  
Beyond improved nutrient intake, the umbrella aim in introduction of SPN is to improve patient safety (minimization of hospital associated incidents) possibly at the cost of increased PN solution wastage [29]. Due to the complexity of the supply chain much of the variations in actual nutrient intake are unintended and SPN may contribute to prevent ordering and compounding errors [7]. Several in-hospital SPN production quality and stability control studies have been published more recently [30; 31; 32; 33]. In a longitudinal quality improvement study in pediatric patients and infants > 1500 g standardizing TPN and transitioning to electronic ordering was associated with reduced ordering errors, reduced processing time and most importantly substantially reduced number of blood draws [34].  
In junior residents, introduction of SPN reduced medication errors and improved time management [35]. Commercial SPN bags in addition, guarantee sterility.  
However, SPN did not significantly prevent hospital associated complications (Figure 3). Especially supported by one very large observation trial [19] it is tempting to interpret the data that there may be a trend that SPN prevents nosocomial sepsis, but there was also a trend in the opposite direction with regard to NEC and no effect on mortality or duration of PN (Figure 3). SPN still requires carful nutritional individualization and increasing the number of local SPN solutions did not reduce the number of fluid imbalances, electrolyte derangements and glucose derangements [36].
However, there were no significant differences in the alpha diversity of bacterial and fungal communities. Also, [PERSON] et al. [26] determined the effect of this type of MP from face masks in concentrations of up to 1% (w w\({}^{-1}\)) on the physical and chemical properties and microbial communities of soil under laboratory conditions for 80 days. They found that this type of MP modified the soil's density, soil aggregates, the level of nutrients such as organic matter (OM), and observed an increase in denitriying bacteria (Rhodanobacteraceae) with respect to microbial communities. However, it is important to use tools to determine the quality of soil once these MPs have been incorporated. In this regard, soil quality indexes (_SQIs_) can be used to evaluate the quality of soil using physicochemical and biological indicators. In general terms, an _SQI_ is a tool consisting of a set of properties or indicators closely related to the phenomena to be monitored that allows us to synthesize information for decision making [27]. The development of _SQIs_ has gained great importance in recent years, especially regarding agricultural soils and their productivity. [PERSON] et al. [27], in a meta-analysis study, indicated that there are different types of _SQIs_, and the following two are the most used for agricultural soils: the Weighted Additive Index (_SQI\({}_{w}\)_) and the Unified Weighted Additive Index (_SQI\({}_{u}\)_). However, in the _SQI\({}_{w}\)_, the selection of indicators may not be correlated with the soil in question, since it is based on indicators previously established by experts. On the other hand, the _SQI\({}_{u}\)_--following the methodology used in the present study--is not influenced by human subjectivity, since the results or the weight of the indicators are based on the variability of the input data when using multivariate statistical techniques or models in the analysis. These statistical tools have also been used to develop _SQIs_ in other contexts, such as soil degradation by pollutants, organic contaminants, deforestation, and desertification [28]. Therefore, the objective of the present study was to develop an _SQI_ based on different treatments of soil mixed with MPs from face masks. The effects on physicochemical, enzymatic, and ecophysiological indicators were also evaluated through microcosm-level experiments on C and N mineralization dynamics over time. It is worth mentioning that ecophysiological indicators have not been evaluated in other similar studies. Our hypothesis was that the developed _SQI_ would be able to differentiate the quality of the soil according to the different treatments and the indicators selected in the minimum set of data through a principal component analysis (PCA). With these results, we will have a broader overview of the effects on soil quality with different concentrations of MPs from face masks, which will allow us to generate strategies for the management of these and other types of MPs, taking into account the characteristics of the soil.
## 3 Rice Phytoalexin Biosynthesis
### Biosynthesis of Rice Diterpenol Phytoaleoxins
Gene clusters associated with the biosynthesis of secondary metabolites have been found in several plant species: benzoxazinone BCG in maize [114], avenacin BCG in oats [115], thalianol, and marneral BGC in Arabidopsis [116; 117], potential BGCs in cucumber [118], as well as a new momilactone BGC in bamyard grass [45]. At least two BGCs associated with rice diterpenoid phytoalexin biosynthesis have been identified in the genome of cultivated rice. They are c4 BGC, involved in momilactone A biosynthesis on chromosome 4, and c2 BGC, associated with phytocassane biosynthesis on chromosome 2 [119; 120; 121; 44; 113] (Figure 7). Several lines of evidence suggest that c2 BGC plays a more general biosynthetic role, e.g., the production of oryzaeins and oryzaeilides [122; 123; 124]. Furthermore, it has been shown that the biosynthesis of momilactones requires the contribution of genes belonging to both c4 BGC and non-clustered genes located on different chromosomes [125].
## 3 Results and Discussion
### Phenol Contents  
Phenols are the important characteristic substances in grape and wine, which play an important role in wine color and astringency [16; 34]. Phenol contents were consistent in three years (Figure 2). Anthocyanin, flavanols, tartaric esters, and total phenols were increased for LR-BF and LR-AF berries in 2017 and 2018, except for tartaric esters in 2017. In 2019, only LR-BR showed a high content of anthocyanin, tartaric esters, and total phenols, as well as a high content of total phenols in LR-V. Leaf removal treatments could broaden fruit exposure area and facilitate light absorption on grape berries. Sunlight had a positive effect on the synthesis of phenols in grapes [35; 36; 37], and leaf removal treatments improve the photosynthetic capacity of the remaining leaves and induce flavonoid synthesis as a stress response in the common grapevine [38]. In this study, LR-BF and LR-AF increased light exposure and temperature difference on berries, resulting in the accumulation of phenols in grapes. In addition, the total sunshine duration was 873.8, 826.4, and 848.6 h in these three years from July to October (veraison to harvest), respectively, which means that the grapes received the most total sunshine duration during the ripening process in 2017.  
Figure 1: Berry weight (**A**), total soluble solids (**B**), titratable acidity (**C**) and pH (**D**) in Cabernet Sauvignon berries exposed to CN, LR-BF, LR-AF and LR-V in three years (2017, 2018, and 2019). LR-BF: leaf removal at 10 days before flower, LR-AF: leaf removal at 35 days after flowers, LR-V: leaf removal at veraison. TSS: total soluble solid content, TA: total titrated acid content. Different letters indicate significant differences (_p_ < 0.05) among treatments using one-way ANOVA with the least significant different (LSD) test used to examine the means.  
That explains the lower amount of anthocyanins, tartaric esters, and total phenols in 2018 and 2019 than in 2017.
# Numerical Calculation of Slosh Dissipation
## 6 Discussion on the Comparison between Elemental and SPH-Flow Results  
In the previous sections, it was underlined that the problem studied in this work involves complex three-dimension flows involving complex liquid-gas interactions. Furthermore, for the water test-case, the high Reynolds number involved requires very high spatial resolution in order to perform effective large eddy simulations. In a 3D context, the CPU costs of those simulations are still prohibitive for both the adopted solvers, Elemental(r) and SPH-flow. As a consequence, in the present work for the water test-case, convergent results are not achieved, and the effect of the spatial resolution \(N\) is not negligible in the results presented. However, from the point of view of the slosh dissipation, the single-phase model and 2D framework are able to supply a reasonable estimation of \(E_{diss}\). This result is in agreement with previous literature on different kinds of problems (see, e.g., [24; 26; 43; 44]).  
Regarding the SPH-Flow solver when increasing the spatial resolution \(N\), the 3D results for both water and oil test-case show a tendency to overestimate the dissipation compared to experimental measurement. This may be linked to the action of the gas phase, which is neglected in the adopted \(\delta\)-LES-SPH model. Conversely, in the case of the Elemental(r) solver, it is evident that the gas-liquid interface is more complex when using a two-phase model. The liquid and gas phases are much more dispersed compared to the single-phase model. Consequently, very high spatial resolutions are needed to resolve the smallest scales (i.e., the small gas bubbles, the small liquid drops and the thin liquid jets).  
The comparison between the two solvers and, therefore, the effect of the gas-liquid interaction is confirmed in 2D results of the vertical position of the fluid center of mass \(y_{G}(t)\), depicted in Figure 33. In these plots, the SPH-Flow solver manifests a significantly larger motion of the fluid center of mass for both water and oil test-cases. In the water test-case, as expected, the solvers present remarkable differences not only in the fluid amplitude but also in the signal time phases. This is not the case in the oil results, where the two solvers show a good agreement in the time phase of \(y_{G}(t)\) and a reduced difference in the amplitude of liquid motion.  
Figure 34 depicts the vertical motion of the fluid center of mass for the three-dimensional simulations of the two solvers. The result trends are similar to the 2D simulations. In fact, the center of mass motion of the SPH model presents a larger amplitude again, and in the water test-case, a significant time-shifting appears on the \(y_{G}(t)\) curves in the comparison between the two codes. The oil case also presents a significant amplitude difference in center of mass motion, even though the motion is in phase with the SPH results.
## 2 Principles and Methods  
CYGNSS is composed of eight LEO satellites [10], and a 24-satellite polar orbiting constellation was taken into consideration in the simulation work of [PERSON] et al. [2]. Considering that Cubesats have advantages in the development cost and time when compared with larger-size satellites for Earth observation, it is highly likely that in the near future, the LEO constellation in a GNSS-R mission will be composed of a large number of Cubesats [20]. Therefore, we also considered the LEO constellations composed of 60 satellites and 120 satellites. Thus, we carried out the optimal designs for 8-, 24-, 60-, and 120-satellite LEO constellations for GNSS-R observations in the present work. The constellation pattern, the evolutionary algorithm with implemented fitness function, the simulation scenario, and the criteria to evaluate the performance of GNSS reflections are presented in this section.
# Blind Image Deblurring Based on Local Edges Selection
## 3 Results and Discussion
### Discussion of the Parameters in the Deblurring Model  
In this subsection, we will discuss how the parameters in the model effect the deblurring results. First, the size of local image patch will affect the edges selection results and the final image deblurring results. When the size of the image patch \(N(w)\) in Formulas (6) and (7) is less than \(9\times 9\), for normal images, it nearly has no influence on the deblurring results. However, for low illumination images or saturated images, when choosing larger sizes of image patches, the edges cannot be selected well, thus leading to bad kernel estimating results. Figure 3 shows an example of how the window size influence the deblurring result. Based on exhaustive experiments, we set the patch size to be \(5\times 5\).  
Figure 1: Comparison of different edges selection method: (**a**) Original image, (**b**) first-order gradients of image, (**c**) edges extracted by Canny operator, (**d**) Laplace gradients, (**e**) local edges of gray value image obtained by the proposed method, (**f**) local edges of color image obtained by the proposed method.  
Figure 2: Image edges comparison of blurred image (from left to right are clear image, blurred image, horizontal first-order gradients, vertical first-order gradients, second-order gradients, edges extracted by Canny operator, Laplace gradients, local edges of gray image, and local edges of color image).  
In addition, the proposed model involves two parameters, \(\alpha\) and \(\beta\). In order to analyze the effects of the two parameters on the proposed image deblurring method, we test the sensitivity of \(\alpha\) and \(\beta\). The sensitivity analysis is similar to the method proposed by [PERSON] et al. [8]. In the analysis of each parameter, other parameters remain unchanged. We set the value of \(\alpha\) from 0.0001 to 0.2 with the step size of 0.005, the value of \(\beta\) ranges from 0.1 to 3 with the step size of 0.05. In the sensitivity analysis test, kernel similarity is the metric to measure the accuracy of estimated kernels. Figure 4 shows the average kernel similarity of the 20 test images in the test, and the results show that the proposed method performs well with a wide range of parameter settings, and the algorithm has certain robustness for parameter selection.
# Blind Deconvolution with Scale Ambiguity
## 4 Discussion and Experimental Results
### Discussion
#### 4.1.2 Convergence Property  
We present the convergence property of the proposed method when \(p=2\) in this section. For better demonstration, we quantitatively evaluate the convergence property of the proposed method on the benchmark dataset introduced by [19]. The results shown in Figure 5 demonstrate that the proposed method converges after less than 10 iterations, in terms of the average kernel similarity and average energies computed from model (5).
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work
### Data Limitation  
The most important aspect of deep learning is its capability to model highly complex mathematical calculations, which makes it different from the rest of artificial intelligence algorithms. Generally, more layers are introduced to learn more complex functions; however, a deeper network must also be trained over a large number of parameters. Such a model can only learn well if we correspondingly use a large amount of data to infer the parameter values. A complex model trained using a small dataset normally overfits and becomes data-dependent. Such modeling gives a false impression of performance and is not reliable. The lack of availability of the required data has been a limitation for various research works ([PERSON], [PERSON]; [PERSON], Year; [PERSON], Year). This is the reason that makes the medical field challenging for deep learning. Crowd-sourcing can be used to label the data of daily life objects, but for medical videos, experts in the field are required. Privacy and consent of patients are the other two aspects contributing to the lack of publicly available medical data.
# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 6 Conclusions  
In this paper, we presented a simple yet efficient retinex-based method for low-light images. The research mainly included the following steps: firstly, an initial illumination map estimation algorithm based on neighborhood information was proposed. Then, in order to further enhance brightness and local contrast of the initial illumination map, two input images, which were obtained by BEF and IACE, respectively, were fused by means of the Gaussian-Laplacian pyramid. Finally, the enhanced image was obtained based on retinex theory. Images from different datasets were used to verify the effectiveness of our method from both subjective and objective aspects, that is, our method can not only improve the brightness, but also retain more detailed information. In the future, we aim to enhance low light images by extracting rich image features in conjunction with the currently hotter deep learning.  
Conceptualization, X.P. and C.L.; methodology, X.P., C.L., Z.P. and J.Y.; software, X.P. and S.T.; validation, X.P. and S.T.; writing--original draft preparation, X.P.; writing --review and editing, C.L.; supervision, C.L. and X.Y.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Startup Foundation for Introducing Talent of NUIST under grant No. 2022r081, the open fund of Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Technology under grant No. 2020 GDDSIPL-02, and in part by the National Natural Science Foundation of China under grant No. 61871174.  
Not applicable.  
Figure 24: Graphs of six quantitative indicators at different \(\gamma\) values.  
**Acknowledgments:** We would like to thank the anonymous reviewers for their valuable suggestions, which have helped us to improve the manuscript greatly.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Finite Element Theory of Acoustic-Vibration Coupling
### Inner Acoustic Field  
The issue of acousto-vibrational coupling in the system pertains to the limited extent of the acoustic field within the fluid domain, which is surrounded by a vibrating enclosed boundary surface. As shown in Figure 1, in the acoustic-vibration coupling system of the interior acoustic field, the fluid is located within the bounded acoustic domain V1.  
Figure 1: Interior acoustic–vibration coupling system.  
By combining the modified finite element model for the structure and acoustics, as discussed in reference [21], it is possible to obtain the coupled finite element equations for the acoustic-vibration coupling system of the interior acoustic field:  
\[\left(\begin{pmatrix}K_{s}&K_{c}\\ 0&K_{a}\end{pmatrix}+j\omega\begin{pmatrix}C_{s}&0\\ 0&C_{a}\end{pmatrix}-\omega^{2}\begin{pmatrix}M_{s}&0\\ -\rho_{0}K_{c}^{T}&M_{a}\end{pmatrix}\right)\cdot\begin{Bmatrix}w_{i}\\ p_{i}\end{Bmatrix}=\begin{Bmatrix}F_{si}\\ F_{ai}\end{Bmatrix} \tag{1}\]  
In which \(K_{s}(n_{s}\times n_{s})\), \(M_{s}\), and \(C_{s}\) are the unstressed stiffness matrix, mass matrix, and damping matrix on the structural grid; \(C_{a}\), \(K_{a}\), and \(M_{a}\) are the modal damping matrix, modal stiffness matrix, and modal mass matrix; \(K_{c}(n_{s}\times n_{a})\) is the coupled stiffness matrix and \(F_{si}(n_{s}\times 1)\) and \(F_{ai}(n_{a}\times 1)\) are the excitation load matrices; \(w_{i}\) represents unknown displacements and \(p_{i}\) represents unknown sound pressures.
# Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques
## 3 Proposed Model
### Feature Extraction
#### 3.4.3. ResNet  
The intuition behind ResNet is that deep neural networks are hard to train due to their huge number of layers, especially where the problem of vanishing gradient occurs [37]. ResNet50 is the variant that is used in this paper. It is 50 layers deep as the name indicates and its pretrained version from the ImageNet dataset is used. Images of size 224 \(\times\) 224 with 3 color channels were used. For ResNet, batch size and epochs were used as tunable parameters. Here, a batch size of 16 with a learning rate of 0.001 and 40 epochs achieved the best results. The learning curves show a good generalization between training and validation data on the Urdu numeral dataset. They are plotted in Figure 12.  
\begin{table}
\begin{tabular}{r r r} \hline \hline
**Layer** & **Output Shape** & **Number of Parameters** \\ \hline Conv & 32 \(\times\) 32 \(\times\) 256 & 7168 \\ Batch Norm & 32 \(\times\) 32 \(\times\) 256 & 1024 \\ MaxPool & 16 \(\times\) 16 \(\times\) 256 & 0 \\ Conv & 16 \(\times\) 16 \(\times\) 128 & 295,040 \\ Batch Norm & 16 \(\times\) 16 \(\times\) 128 & 512 \\ MaxPool & 8 \(\times\) 8 \(\times\) 128 & 0 \\ Flatten & 8192 & 0 \\ Dense & 90 & 737,370 \\ Dense & 64 & 5824 \\ Dense & 10 & 650 \\ \hline Total parameters & & 1,047,588 \\ Trainable parameters & & 1,046,820 \\ Nontrainable parameters & & 768 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of proposed CNN model.  
Figure 10: Architecture of CNN for Urdu handwritten numeral recognition.