esa-sceva/satcom-chunk-collection huggingface dataset:
# Changes in Soil Chemical Properties Due to Long-Term
###### Abstract
However, these models are prone to uncertainties due to different factors such as data measurement or availability of data and the interactions to multiple processes [1]. For instance, the conditions and links between soil chemistry and the abundance of methane-related functional genes occurring during the non-growing period of rice (fallow period) established due to long-term compost fertilization have been understudied as most investigations mainly focused on the rice planting season. Fallow paddy fields show potential fluxes for greenhouse gas emissions, which could extend from one to three months [12] and could be exacerbated when submerged, leading to significant methane emissions [13]. Paddy fields with a history of fertilization may respond more actively and strongly to methanogenesis and methane oxidation [14; 15]. Potential responses to different fertilizations are mainly linked with the microbial communities present in these paddy fields [16]. Methanogenic archaea [4; 17] and methane oxidizing bacteria from type I and type II methanotrophs [4; 18] complicate the processes as they respond differently to fertilization and other contributing factors and may lead to varying results [14]. Therefore, monitoring soil biological and chemical conditions in fallow paddy fields should be strengthened as these conditions may drive actual methanogenesis and methane oxidation occurring during the fallow period. Soil conditions established in fallow paddy fields also greatly influence the paddy field responses to continued long-term fertilization in relation to methane emission. Thus, the study was conducted to gain more insight into the potential contributions and conditions of fallow paddy field agricultural systems under long-term fertilization related to methane emission. Particularly, this study was conducted to evaluate the changes in soil chemical properties that may subsequently affect the abundance of methanogenesis and methane oxidation functional genes present, even during fallow periods in paddy fields under long-term compost fertilization. We hypothesized that long-term compost fertilization results in alterations of the soil chemistry, concurrently driving the changes in functional gene abundance that could become baseline soil conditions in long-term compost-amended paddy fields. Specifically, we posited that long-term compost fertilization will result in the accumulation of organic matter relative to unfertilized paddy fields, which could also be true for other soil chemical properties. In addition, the combination of NPK and compost application may also result in significant changes in the chemical contents of paddy fields especially N, P, and K, which could also differently affect the abundance of methane-related functional genes. These changes under long-term compost fertilization potentially become established even after the growing season, especially during fallow periods.
## 4 Results and Discussion
### Characterization of Soil and Stabilizer
#### 4.1.2 Grain Size Distribution of Soil and CACW  
Through a series of geotechnical tests, including specific gravity, Arterberg limits, and sieve analysis, the fundamental geotechnical characterization of the soil employed in this study was determined. According to the results of the specific gravity test, the soil has a value of 2.67. Contrarily, the findings of the Arterberg test indicate that the soil is non-plastic, which prevents us from rolling it to a thread with a diameter of 3.18 mm and from calculating the number of blows required to reach the liquid limit. The findings of the sieve analysis are shown in Figure 3. According to the figure, 2.3% of the soils pass through sieve No. 200, and these soils are categorized as silty sand \"SM\" according to USCS and A-3 as well as the AASHTO soil classification system.
# Advances in Three Dimensional Bioprinting for Wound Healing: A Comprehensive Review
## 5 Recent Advances in Hydrogel-Based 3D Printing for Wound Healing
### Imaging (MRI, PET, etc.)  
In tissue engineering, the 3D manufacturing processes of modeled body tissues and organs start primarily with acquiring sliced anatomical images via modalities in the radiology department, such as MRI, CT, etc. Like artificial organs and tissues, 3D hydrogel scaffolds designed for wound healing can also easily be produced using imaging modalities and computer-aided design software programs [254]. SEM images allow the analysis of the cross-sectional morphologies of 3D-printed hydrogels [130,255, 256, 257, 258, 259].  
Since manufactured hydrogel models contain an abundance of water molecules, they present an excellent high signal-to-noise ratio (SNR) for MRI based on the magnetic precession of hydrogen atoms in tissues. MRI also provides for the easy characterization of hydrogel-based wound dressings through its non-invasive, high spatial resolution, multi-planar, and ionizing radiation-free imaging characteristics. The chemical structure and physical properties of hydrogels (including distribution of materials, pore size, stiffness, water diffusion, and gelation process of different compositions) can be analyzed in detail during the manufacturing process under MRI [249,260, 261, 262, 263, 264]. Moreover, the level of skin reconstruction and wound healing can also be tracked and assessed in terms of the spatial distribution of the surviving transplanted cells on a hydrogel matrix, acute and chronic inflammation, or epidermal and dermal formation using MRI and fluorescence microscopy images [262,263]. For instance, [PERSON] et al. prepared a kartogenin (KGN) and gelatin-/dextran-based hydrogel for regenerating cartilage defects, and combined this with Fe-chelated synthetic melanin nanoparticles for monitoring the hydrogel state and regenerated cartilage in real time under MRI (Figure 3(B1-B7)) [249].  
On the other hand, chemical exchange saturation transfer (CEST) can eliminate the disadvantages of MRI for visualizing solute protons and molecules into water. CEST is a type of molecular magnetic resonance imaging (MRI) method originating from the fast exchange rate of saturated solute protons or molecules through water using selective radio frequency (RF) pulses. Therefore, many recent studies benefit from the enhanced sensitivity property of CEST imaging when monitoring liposomal hydrogels with different concentrations, as well as to evaluate hydrogel degradation and drug release with varied materials [265, 266, 267, 268, 269, 270, 271, 272, 273, 274].
## 2 Materials and Methods
### Identification of Drought Impact in Alpine Mountain Forest  
Temporal variability of forest status under drought conditions was analyzed using the S-mode PCA. The PCA decomposition investigates inter-relationship within a dataset identifying a dominant temporal variability of its elements. Being a statistical method, physical meaning of PCA results should be evaluated against known or expected variability or process [68]. Following findings from [60], NDVI and NDII7 time series were z-score normalized from a composite mean, and truncated to annual vegetation season time-windows corresponding with a period between April and mid October. Each resulting time series (nNDVI and nNDII7, respectively) was introduced into the covariance-matrix S-mode PCA under the forest mask. The first four PCs obtained for each setup were cross-correlated with the scPDSI temporal patterns, the latter approximating regional meteorological drought conditions. Due to a mismatch in temporal resolution of both datasets (vegetation season vs. complete year, and 16-day vs. month, respectively), correlation was performed for yearly averages. A visual comparison between temporal pattern complemented the statistics [60] and allowed to determine NDVI and NDII7 based PCs with the strongest linear relation to identified meteorological drought conditions, hence representing forest response to drought. Selected PCs were subsequently correlated (pixel based Pearson's correlation) with their respective vegetation index time-series, resulting in a correlation map (as many maps as PCs selected). These correlation maps can be understood as drought footprints, i.e., the spatial representations of the temporal variability [68]. Each correlation map was classified into three impact classes using the 5 th and 95 th percentiles of the correlation values distribution. Impact class 1 (0-5 th percentile) comprised the most negative, whereas impact class 3 (95-100 th percentile) the most positive Pearson's correlation values. These were interpreted as forest stands responding respectively in contradiction and in agreement with the specific drought temporal variability. Changes in forest phenology and green biomass production within two contrasting response classes were compared for each footprint. Only pixels with a correlation significance level \(p<0.1\) were considered during analyses.
## 4 Experiment and Result  
To evaluate the accuracy of our proposed HRNet-CFR, we conducted training and evaluation on two datasets, namely the Jilin-1 dataset and the GID dataset. Section 4.1 introduces the details of the cropland high-resolution remote sensing images dataset. Sections 4.2 and 4.3 show the parameter settings and network training conditions, respectively. In addition, Section 4.4 presents the measurement of accuracy evaluation and results extracted by the model over large-area image data.
# A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images
## 4 Deep Learning-Based Ensemble Method for VA Measurement
### ML Models and Classification Performances
#### 4.2.1 ML Model for Step-1: VGG19-Based CNN  
The VGG19 model was developed to study how the depth of the network affects the classification outcomes, such as accuracy and training speed. The convolutional base of the VGG19 consisting of the convolution and pooling is composed of 19 layers. All convolution layers are characterized by fewer parameters, using filters of \(3\times 3\). As a result, it can effectively extract features of images with small parameters, which leads to securing nonlinearity that can flexibly classify images. For Step-1, we build a VGG19 CNN model in which the convolutional base for feature extraction is based on VGG19 by transfer learning and the classification layers for the actual classification is built by ourselves, as shown in Figure 6. Table 6 shows the parameters we use to train DNN of VGG19 and EfficientNet-B7. We use a try-and-error approach to select the hyper-parameters after many experiments.  
Figure 7a,b show the accuracy and loss changes of classifications during training and testing of the VGG19-based CNN model. Figure 7c shows a confusion matrix for 4000 images consisting of 1000 randomly selected from each class (Classes 1 to 4) of the original datasets, not augmented ones. The testing of VGG19-based CNN obtains 93% of the classification accuracy. From this confusion matrix, it is observed that some of Class A are identified as Class B. The reason might be inferred through the classification report in Figure 7d: The precision, which is the ratio of the data that is a true positive of the data determined to be positive, shows an accuracy of more than 80% in both Classes A and B. On the other hand, the recall, the ratio of positive data among the true positive data, is 60% for Class A compared to 98% for Class B. We believe that it is because of the data imbalance problem of the original datasets, thus the training of the VGG19 model is biased to Class B with a higher number of images. However, according to the f1-score in Figure 7d, which  
Figure 5: Proposed ensemble method.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **4-Class Classification** & **Step-1** & **Step-2-1** & **Step-2-2** \\ \hline VGG19 (Tensorflow) & 79\% & **94\%**1  & 79\% & 78\% \\ \hline EfficientNet-B7 (PyTorch) & 78\% & 92\% & 78\% & **79\%** \\ \hline SVM (RBF kernel) & 77\% & 91\% & **94\%** & 79\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy when three machine learning models.  
is the weight harmonic average of precision and recall and mainly used for unbalanced datasets, it can be inferred that the overall learning is well done because Class A shows the high accuracy of 70%.
## 6 Results and Discussion
### Secondary Mass Flow Rate Comparison  
The effect of reducing the radius of the converging mixing section inlet, \(R_{CV,in}\), is analyzed. Reducing this radius means that the angle of the converging mixing section is reduced. The angles for ejector G2, G2.1, and G2.2 are 22.94\({}^{\circ}\), 16.60\({}^{\circ}\), and 9.60\({}^{\circ}\), respectively.  
The CFD results show that the secondary mass flow rate increases as the radius decreases or, in other words, as the angle decreases. This trend can be seen in Table 6. The finding agrees with that of [PERSON] et al. [30], however, they have found an optimum angle which will result in a maximum \(\hat{m}_{s}\). Thus, the \(\hat{m}_{s}\) keeps increasing as the angle decreases until it reaches a certain angle (the optimum angle). A further decrease in the angle will decrease the \(\hat{m}_{s}\).  
In the current work, such an optimum angle of converging mixing section has not been found because, most likely, the range of the angle variation is not wide enough. [PERSON] et al. [30] concluded that the optimum angle is 1\({}^{\circ}\) while the ejector considered in the current research has a minimum angle of 9.60\({}^{\circ}\). Moreover, the dimension of the other parts of their ejector is different which may cause the difference in the optimum angle. [PERSON] et al. [18] have also found an optimum converging mixing section angle. Their results show that the optimum angle cannot be generalized to meet all operating conditions. Therefore, they suggested that research to develop an adjustable ejector should be conducted, especially an ejector with an adjustable primary nozzle exit position and adjustable converging mixing section angle.
# Predicting Location of Tweets Using Machine Learning Approaches
## 4 Methodology
### Model Building  
We conducted this experiment by building four different machine learning models to predict the tweet location based on the aforementioned features. The models are well-known in the field and have been proven effective in supervised machine learning problems. The first algorithm is logistic regression (LR), which is simply a linear regression with binary mapping. The logistic function can be provided as:  
\[f(y)=\frac{1}{1+e^{-y}},\]  
where \(y\) is the linear regression function \(y=wx+b\)[35]. The second Algorithm is MNB. Due to its computational efficiency and proper performance in real-world problems, MNB is one of the most employed algorithms, especially in text classification problems [36]. In our MNB model, we assumed the data to have a multinomial distribution. Therefore, the MNB classifier was evaluated as follows:  
\[p(c_{k}|x)\propto p(c_{k})\Pi_{n}p(t_{i}|c_{k})^{f_{\lambda_{i}}(t_{i})}.\]  
Third, we used a support vector machine (SVM) model with a non-linear kernel function. It is a mathematical trick to allow SVM to separate non-linearly separable data in lower-dimensional space by adding more dimensions [37]. SVM creates a maximum-margin hyperplane by maximizing the distance between the separating line and the nearest points, called the support vectors [38].  
\[y=\mathbf{w}\boldsymbol{\phi}(x)+b,\]  
where \(\boldsymbol{\phi}\) is the kernel function. Finally, we used the random forest (RF) algorithm. It combines multiple decision tree algorithms and aggregate their results to produce better results. It can be considered one of the most efficient divide-and-conquer algorithms [39, 40].  
We utilized the above-mentioned algorithms to build the machine learning models to predict the geo-location of the tweets based on the extracted features. We then experimentally compared the predictability capabilities of these models in this noisy problem.
## 5 Conclusions  
The antimicrobial activities of the selected plants against _S. aureus_ and NAS isolated from clinical cases of bovine mastitis are not well known, although some of the plants in this study are recognized for their activities against other microbes. However, the crude extracts of these plants exhibited good to moderate antibacterial activity against drug-resistant staphylococcal isolates, and it generally appears that the isolates were more susceptible to the extracts than the ATCC strain. Among the plants tested, _S. lancea_ exhibited the most potent antibacterial activity, suggesting its potential usefulness as a broad-spectrum antibacterial agent against staphylococci associated with bovine mastitis, at relatively non-cytotoxic concentrations. The ethanol extract of _E. caffra_ appears to be active against the organisms at a concentration less than half of its acetone counterpart. This favors the preference for ethanol in industrial scale extraction of plant materials. Moreover, the ethanol extracts of _A. venosum_, _E. caffra_, and _S. lancea_ demonstrated low toxicity to mammalian cells, making them attractive candidates for possible development into herbal products or for isolating novel pure compounds that can serve as templates for new antimicrobial drugs, which can be viable alternatives for managing bovine mastitis. Future studies will investigate the synergistic antibacterial activity of these extracts and their constituents, leading to the development of sustainable, polyhedral products useful in managing bovine mastitis.  
Conceptualization, A.O.A., I.M.F., I.-M.P. and L.J.M.; methodology, A.O.A. and I.M.F.; formal analysis, A.O.A. and I.M.F.; investigation, A.O.A. and I.M.F.; resources, I.-M.P. and L.J.M.; data curation, A.O.A. and I.M.F.; writing--original draft preparation, A.O.A.; writing--review and editing, I.M.F., I.-M.P. and L.J.M.; supervision, I.-M.P. and L.J.M.; project administration, A.O.A.; funding acquisition, L.J.M. All authors have read and agreed to the published version of the manuscript.  
The bacterial identification is based on the research supported in part by the National Research Foundation (NRF) of South Africa (Grant specific unique reference number (UID) 74426). [PERSON], [PERSON], and the staff of the University of Pretoria's Department of Plant and Soil Sciences are hereby acknowledged for their support. The National Research Foundation (Grant number 111945) provided research funding to L.J.M. The University of Pretoria is acknowledged for bursary support to A.O.A.  
This study received ethical permission from the Ethics Committee, Faculty of Veterinary Science, University of Pretoria (reference number REC236-19).  
Not applicable.  
All research data are shared in this article.  
The authors declare no conflict of interest.
## 2 Impervious Surface Extraction Method
### Extraction of Built-Up Areas  
The extraction of built-up areas is accomplished using the integrated and improved DMSP-OLS data using the spatio-temporal normalized thresholding method based on pseudo-invariant features. The method is mainly divided into the following three steps to realize the extraction of built-up areas: (1) Selection of the base year and determination of the optimal threshold for the base year. Using 2013 as the base year, nighttime light imagery and Landsat TM imagery were overlaid to determine an appropriate threshold, and a threshold of 45 was selected for 2013. (2) Determining desaturated pseudo-invariant features. Nighttime light intensity in most urban patches peaks at the city center and gradually decreases from the city center to the periphery of the city; the process of urbanization is irreversible, and it is assumed that these urban areas delineated by the base year will remain urban in the succeeding years. (3) Normalization method for built-up area extraction. A linear regression model was used to relate the \(DN\) values within the region of desaturated pseudo-invariant features between the reference year and the following year, as shown in Equation (1):  
\[DN_{t}=\alpha+\beta\times DN_{0} \tag{1}\]  
where \(DN_{0}\) is the gray value of lights in the reference year, \(DN_{t}\) is the gray value of lights in the desired year, and \(\alpha\) and \(\beta\) are the intercept and slope of the linear regression model, respectively. The urban threshold \(T_{0}\) for the reference year is used to determine the built-up area extraction threshold \(T_{0}\) for year t through Equation (2).  
\[T_{t}=\alpha+\beta\times T_{0} \tag{2}\]  
Figure 1: Technology roadmap for impervious surface extraction.  
(4) Eliminate outliers. To ensure the reliability of the resulting linear regression equation to eliminate outliers, all the original data are substituted into Formula (2) to obtain the predicted \(DN\) value, the residuals between the \(DN\) value and the actual value are calculated, the residuals should obey the normal distribution, the standardized residuals are calculated through Formula (3), and the sample points below the 95% confidence level are eliminated as outliers. The final values are obtained to determine the built-up area extraction thresholds for the study year and to complete the extraction of built-up areas:  
\[\delta*=\frac{\delta-\overline{\delta}}{\sigma(\delta)} \tag{3}\]  
where \(\delta\) is the residual between the \(DN\) value of the predicted value and the actual value, \(\sigma(\delta)\) is the standard deviation of \(\delta\), and \(\delta*\) is the standardized residual.