esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### G. duodenalis and Other Intestinal Pathogens  
To investigate the influence of _G. duodenalis_ and other enteropathogens, the 67 subjects were divided into seven groups: control group (Group A); 6 subjects positive for _Blackstocystis_ only (group C); 11 subjects positive for _Blackstocystis_ and _E. hartmanni_ without enteric pathogens (group D); 6 subjects infected by _E. hartmanni_, _Blackstocystis_ and _G. duodenalis_, and enteric pathogenic bacteria and viruses (group E1); 6 subjects positive with _En. coli_, _Blackstocystis_ and _G. duodenalis_, and enteric pathogenic bacteria and viruses (group E2); 8 subjects positive with _Blackstocystis_, _G. duodenalis_, and enteric pathogenic bacteria and viruses (group E3); 5 subjects positive for _Blackstocystis_ and enteric pathogenic bacteria and viruses (group M).  
Observed OTUs, Faith's PD, and Shannon index were significantly increased in group D compared to the control group and only for the Shannon index compared to group M (Table S4E).  
The UniFrac PCoA analysis along axis 1 showed some overlap in the composition of the groups and a non-significant separation between the three groups co-infected with _Entamoeba_ species (D, E1, E2) and the other groups (A, E3, M) (Figure 7A); negative subjects (group A) were separated from subjects co-infected with _Entamoeba_ spp. (D, E1, E2) as displayed in Figure 6A. Cladograms generated by LEffSe showed differences in taxa between A, C, E1, E2 groups with LDA score (_p_ < 0.01) (Figure 7B). No significant differences between all groups were observed by the analysis of the relative abundance at the family and genus level (Figure 7C,D).  
Figure 7: Beta diversity analysis for the subjects positive for _G. duodenalis_ and other intestinal pathogens. Groups A: control group; Groups C: positive for _Blackstocystis_ only; Groups D: positive for _Blackstocystis_ and _E. hartmanni_ without other pathogens; Groups E1: positive for _E. hartmanni_, _Blackstocystis_ and _G. duodenalis_, and pathogenic bacteria and viruses; Groups E2: positive for _En. coli_, _Blackstocystis_ and _G. duodenalis_, and pathogenic bacteria and viruses; Groups A3: positive for _Blackstocystis_, _G. duodenalis_, and pathogenic bacteria and viruses; Groups M: positive for _Blackstocystis_ and mixed infection of pathogenic bacteria and viruses, without _G. duodenalis_ and _Entamoeba_ spp. (**A**) PCoA UniFrac showing low divergences along Axis 1 (9,3%) between positives with _Blackstocystis_ and  
_E. hartmanni_ without pathogens (Group D) and Groups E1 and E2 including positives for _G. dudendalis_ and other intestinal microorganisms. (**B**) Histogram of the linear discriminant analysis (LDA scores indicate the higher difference between clades of A, C, E1, and E2 groups. (**C**) The plot of top 15 relative bacterial abundances at the family level. (**D**) The plot of top 15 relative bacterial abundances at the genus level.
## 4 Materials and Methods
### End-Member Modeling Analysis  
End-Member Modeling Analysis (EMMA) has been widely employed in various fields of geoscience. In hydrology, EMMA has been used to identify the sources and flow paths of stormflow in small catchments [68], quantify phosphorus retention and release in rivers [69], determine nitrate concentrations in water flow paths [70], and quantify nutrient dynamics and biological carbon uptake in estuarine zones [71]. In sedimentology, EMMA serves as a statistical approach to identify and quantify sediment transport and deposition processes for sediment samples with multimodal particle size distributions. While EMMA has been applied in marine, lake, and eolian environments, its potential in recognizing transport and deposition processes in low-energy alluvial plains with seemingly sedimentary homogeneity remains unexplored.  
The data set (X) used in EMMA consists of sediment samples described by m grain size distributions and n grain size classes. EMMA generates a modeled data set that represents the linear combination of end-member loadings (individual grain size distributions) and end-member scores (the contribution of each loading to each sample) [25, 28]. The principles and procedures of the EMMA algorithm have been detailed in previous works [72, 73]. For this study, the data set comprised 158 samples described by 69 grain size classes after removing classes with zero values. The grain size data were processed using the R package EMMAgeo [25, 26] with an extended protocol to parameterize all steps of robust EMMA. In this analysis, a five-end-member mode was selected based on the samples' pre-processing.
## 2 Methodology
### FSC Network Based on Global and Local Knowledge Distillation
#### 2.2.2 Global and Local Knowledge Distillation  
Firstly, a student network is constructed, which consists of a backbone neural network \(B_{\phi}(\bullet)\) composed of a small number of convolutional layers and a linear classifier \(C_{H}(\bullet)\). Next, a batch of M images randomly selected from the base dataset \(I_{train}\) is inputted into both the teacher network and the student network. The \(m\)-th image is represented by feature maps \(z_{m}^{t}=f_{\phi}(I_{m})\) and \(z_{m}^{s}=B_{\phi}(I_{m})\) obtained from the backbone of the teacher network and the student network, respectively. Finally, the features are fed into the linear classifier to obtain the output value of the student network, as shown in Equation (4):  
\[S_{m}=C_{H}(z_{m}^{s}). \tag{4}\]  
Furthermore, the above output classification scores are transformed into classification prediction probabilities through the Softmax layer, as shown in Equation (5):  
\[p_{s}(y_{m}=c|x_{m})=\frac{e^{S_{mc}}}{\sum\limits_{c=1}^{C_{b}}e^{S_{mc}}}, \tag{5}\]  
where \(S_{mc}\) denotes the \(c\)-th element of the score vector \(S_{m}\).  
The equation for calculating the cross-entropy loss function between the output values of a student network and the true labels is shown in Equation (6):  
\[l_{1}(\phi,H)=-\sum\limits_{m=1}^{M}\sum\limits_{c=1}^{C_{b}}y_{mc}\log p_{s}( y_{m}=c|x_{m}). \tag{6}\]  
In order to enable students to learn the representation of global features of images by the teacher network through online learning, we adopt the maximum mean discrepancy between the feature spaces of the two networks as the global loss function, which is calculated by Equation (7):  
\[l_{2}(\phi,H)=\frac{1}{M^{2}}\sum\limits_{m=1}^{M}\sum\limits_{m^{\prime}=1}^{ M}z_{m}^{t}z_{m^{\prime}}^{t}{}^{T}+\frac{1}{M^{2}}\sum\limits_{m=1}^{M} \sum\limits_{m^{\prime}=1}^{M}z_{m}^{s}z_{m^{\prime}}^{s}{}^{T}-\frac{1}{M^{2 }}\sum\limits_{m=1}^{M}\sum\limits_{m^{\prime}=1}^{M}z_{m}^{s}{}^{T}. \tag{7}\]  
In addition, we calculate the Euclidean distance between each sample feature in the two networks as the local loss function, and its calculation formula is shown in Equation (8):  
\[l_{3}(\phi,H)=\frac{1}{M}\sum\limits_{m=1}^{M}\|z_{m}^{t}-z_{m}^{s}\|^{2}. \tag{8}\]  
In summary, the total loss function for the student network is shown in Equation (9). Based on Equation (9), the student network is trained, and the parameters in the network are updated until optimal, thereby completing the knowledge distillation process from the teacher network to the student network.  
\[L(\phi,H)=l_{1}(\phi,H)+\alpha_{1}l_{2}(\phi,H)+\alpha_{2}l_{3}(\phi,H). \tag{9}\]
## 3 Experimental Description  
### Tank Size and Scale Effect  
The boundary effects, L/\(D\) ratio, and geometric similitude ratio were taken into account while modelling the stone column and test tank characteristics (length (L) and diameter (\(D\))). As per [PERSON] et al. [33], the diameter of a prototype stone column varied between 0.6 m and 1 m. Furthermore, the minimal diameter of the column, which can be erected entirely intact, was around 13 mm [28]. Moreover, the L/\(D\) ratio of the prototype varied between 5 and 20 [28]. Thus, the diameters of the columns used in the current study were 50, 75, and 100 mm. In accordance with the above rule, the L/\(D\) ratio for the current analysis was set in the range of 5-10. Moreover, the similitude ratio (D\({}_{\text{model}}\)/D\({}_{\text{prototype}}\)) was found in the range of 0.05 to 0.1. The most crucial characteristic for dimensioning the model tank is that the tank boundaries exhibit low induced stresses. This indicates that the tank's boundaries must be at a distance such that no constraints form, and so that the overestimation of outcomes can be avoided. Considering this, the overall depth of the testing box utilised for the test setup was maintained at 0.6 metres. Similarly, [PERSON] et al. [21] investigated cylindrical tank models for single and multiple reinforced stone columns. The tank was equipped with a robust loading structure and a loading system (Figure 2).  
\begin{table}
\begin{tabular}{c c} \hline Tensile modulus from seam tests (kN/m) & 14.8 \\ \hline Ultimate tensile strength (kN/m) & 8 \\ \hline Tensile modulus (kN/m) & 14 \\ \hline Strain at ultimate strength (\%) & 52 \\ \hline Strain at ultimate strength (\%) from tests with & 46.5 \\ seam & \\ \hline Ultimate tensile strength from tests with seam & 7 \\ (kN/m) & \\ \hline \end{tabular}
\end{table}
Table 3: Attributes of geosynthetics (G).
## 4 Results and Discussion  
### Simulation Results of Natural Gas Storage  
Figure 3 illustrates the variation in natural gas pressure within the salt cavern storage over a period of 1080 days. During the gas injection phase, the compression of the gas within the cavern generates heat, resulting in an increase in the temperature of the gas as the injection proceeds. Conversely, during the storage phase, the temperature of the gas slightly decreases due to thermal exchange with the surrounding formation. The principal reason for the decrease in gas temperature during the withdrawal phase is the reduction in gas pressure. Over the course of 1080 days, the gas temperature rises to 59.5 \({}^{\circ}\)C during the initial injection phase, then slightly decreases to 58.3 \({}^{\circ}\)C during the first storage  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Hydrogen** & **Methane** \\ \hline \hline \multicolumn{1}{c}{Molar mass (g/mol)} & 2.016 & 16.043 \\ \multicolumn{1}{c}{Density (kg/m\({}^{3}\))} & 0.0838 & 0.6512 \\ \multicolumn{1}{c}{Diffusion coefficient (cm\({}^{2}\)/s)} & 0.61 & 0.16 \\ \multicolumn{1}{c}{Heat value per mass (MJ/kg)} & 119.93 & 50.2 \\ \multicolumn{1}{c}{Inversion temperature (K)} & 202 & 968 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of hydrogen and methane [32].  
Figure 2: Boundary conditions of the numerical model and model validation.  
period. Subsequently, the gas temperature rapidly drops to approximately 40 \({}^{\circ}\)C during the withdrawal phase. Each injection and withdrawal cycle exhibits a pattern of \"sharp rise-decrease-steep drop\" in temperature. Overall, throughout the three-year cycle, there is a positive correlation between the gas temperature and pressure within the cavern. The simulated gas temperatures align closely with the actual measured temperatures, thereby validating the accuracy and feasibility of the model.
## 2 Materials and Methods
### Statistical Analysis
#### 2.6.2 Repositioning Task  
The GLMM was computed with log transformation of the target variable and robust covariances. It was computed on repositioning errors (RE) from target positions of 30 and 60 degrees to analyze differences between the groups (group 1: leukocyte; group 2: kinesio tape), measurement times (pre- and post-test), repetitions (1-5), and interactions between these factors. The participants were considered as a random factor, groups as a fixed factor, and measurement time and repetitions as repeated and fixed factors. The participants' intercepts were not included because they caused the model not to converge. The residuals had the following distribution: skewness of 0.44 (SE = 0.22) and kurtosis of 0.07 (SE = 0.44) in the 30-degree target position, and skewness of 0.29 (SE = 0.22) and kurtosis of \(-\)0.54 (SE = 0.44) in the 60-degree target position.  
In both the lifting and repositioning tasks, post hoc comparisons were computed using Bonferroni adjustment for multiple comparisons (\(p\)-values were adjusted). [PERSON]'s d was calculated based on the raw data. The effect magnitude was classified as trivial (d \(<\) 0.2), small (0.2 \(<\) d \(<\) 0.5), moderate (0.5 \(<\) d \(<\) 0.8), or large (d \(>\) 0.8).
# Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing
## 3 Proposed Architecture
### Satellite Node Selection
#### 3.3.2 Energy Consumption  
Following this, the average energy consumption \(E_{ij}\) of task execution in the resource pool mode will be calculated, which is composed of two parts: the average transmission energy consumption of the task \(E_{i,j}^{trans}\), and the is the average execution energy consumption of the task \(E_{i,j}^{sat}\). According to the parameters of the above communication model, the average transmission energy consumption of the task can be expressed as:  
\[E_{i,j}=E_{i,j}^{trans}+E_{i,j}^{sat}=\frac{p_{i}t^{send}}{R_{i,j}}+\kappa \Big{(}C_{j}^{(s)}\Big{)}^{2}rc_{j} \tag{10}\]  
\(E_{i,j}^{sat}\) means that the energy consumption of the processor is proportional to the square of the CPU frequency. \(p_{i}\) is the energy consumed by transmitting each unit file, and \(\kappa\) in the \(E_{i,j}^{sat}\) represents the equivalent switching capacitance, which is related to the architecture of the chip [25]. When the task is processed locally, there is no transmission energy consumption, i.e., \(E_{i,j}^{trans}=0\).
## 2 Materials and Methods
### Scapular Kinematics  
Wireless miniature inertial measurement units (MIMU, WISE, NCS Lab, Carpi, Italy) and the software SHoWlder (NCS Lab, Carpi, Italy) were used to evaluate 3D shoulder kinematics bilaterally. MIMU signals were recorded with a sample frequency of 60 Hz. Sensors were placed according to the ISEO-validated protocol [28]. Five MIMUs were fixed with skin tape: one to the center of the manubrium sternum, two on each suprascapular fosse at a medial distance between the medial portion of the acromion and the lateral part of the scapular spine, and two over the lateral aspect of both arms at a medial distance between the acromion process and the olecanum process, as shown in Figure 1. Patients stayed seated in a chair without supporting their backs, with the trunk upright, feet flat on the floor, humerus alongside the trunk, and elbows flexed at 90\({}^{\circ}\). This position was maintained for a few seconds for calibration. Then, patients performed 5 repetitions of elevating both arms simultaneously to maximum and then lowering them to resting position in the sagittal plane (anterior flexion) and in the frontal plane (abduction), with the elbow fully extended and the thumb pointing up, and they were then recorded. The first and last repetitions were excluded, and only the mean of repetitions 2, 3, and 4 was considered.  
Scapular movement on the thorax was recorded during humeral flexion and abduction with both arms: internal-external rotation, also referred to as protraction-retraction (PR), mediolateral upward/downward rotation (UD), and anterior-posterior tilt (TI). Protraction, upward rotation, and posterior tilt were positive values, while retraction, downward rotation, and anterior tilt were negative values. The SHR was measured for shoulder abduction and flexion.  
Figure 1: Five MIMUs were affixed: one to the center of the sternum, two between the medial portion of the acromion and the lateral part of the scapular spine, and two over the middle third of the arm.
# Temporal Subtraction Technique for Thoracic MDCT Based on Residual VoxelMorph
## 2 Literature Review
### Conventional Temporal Subtraction Technology and the Positioning of This Research  
[PERSON] proposed a technique to generate temporal subtraction images for multi-detector row computed tomography (MDCT) volume images [23]. Although the image quality of the subtraction images was generally good, the problem was that many subtraction image artifacts appeared in these images due to misregistration. They often exist on temporal subtraction images which are generated from thoracic MDCT images and can be less effective in the detection of pulmonary nodules. We previously proposed a temporal subtraction technique based on nonrigid image registration using the finite element method in selected local regions [24]. In principle, the finite element method can handle any geometry, but it is expensive when attempting to increase accuracy. Therefore, using the FEM in the local region selected, the computational cost can be significantly reduced and artifacts can be reduced by flexible deformation with sufficient nodal placement in the volume of interest (VOI). However, the computational cost is still high, artifacts remain, and further improvements are expected.  
Regarding our challenge, the limitation of these deep neural network-based methods is that the transformations in different directions do not significantly resemble actual lung movements. In particular, one of the problems is that the loss of structural smoothness in the image promotes deformation irregularities. To solve this problem, deep neural networks require huge amounts of data to train. However, excluded medical imaging, such huge amounts of data are often already available, and in some situations, transition learning can be used to avoid problems. On the other hand, transition learning is not possible with 3D medical images such as thoracic MDCT images [14]. In addition, the other limit is that networks trained by inputting entire 3D images require oversized memory units [25]. In previous studies, the image was scaled down to work around this limitation, but the problem of blurring the image remains. In this research, we propose a high-precision 3D image registration method that can be transformed smoothly at low computational cost and can be learned efficiently even with a small amount of training data. With this method, we aim to establish an efficient and clear 3D temporal subtraction image generation method.
## 1 Introduction  
Researchers who have so far used deep learning models to classify plant leaf disease severities have used training datasets that were categorized into classes that relied on human decisions by making observations. This method is biased and unreliable because some human decisions may not be accurate due to possibilities such as impaired eyesight. For instance, in a study by [PERSON], [PERSON] and [PERSON] [1], the VGG-16 was used for the classification of apple disease severities. In their study, they made use of a botanist to decide on severity stages of the diseased apple leaves. The botanist they used, made the following discretions to make decisions on severity classes: the healthy-stage leaves are free of spots; the early-stage leaves have small circular spots with diameters less than 5 mm; the middle-stage leaves have more than three spots with at least one frog-eye spot enlarging to irregular or lobed shape; the end-stage leaves are so heavily infected that they will drop from the tree. With these decisions that were made by their botanist, they developed the training datasets that were used to train the VGG-16 network with a testing accuracy of 90.4%. However, the approach they used was not generic, in the sense that the methods used to assign diseased images to their severity classes were exclusive to apple leaf diseases, and the severity class assignments were made based on the observation of a human eye, hence in this study we introduce a novel approach that uses the decisions of computerized fuzzy decision rules to assign the maize Common Rust images to their severity classes of data sets that were used to train the VGG-16 network. The proposedmethod is based on thresholding the images of the diseased maize (Common Rust in this case) and use the percentages of the diseased leaf area to derive fuzzy decision rules for the assignment to their severity classes. Once the severity classes were developed, we trained the fine-tuned VGG-16 network to classify the tested maize common rust images among four disease severity classes: Early Stage, Middle Stage, Late Stage, and Healthy Stage. Using this approach, the RGB images of the maize Common Rust disease were first converted to grayscale. This enabled the use of Otsu threshold-segmentation, that segmented the images into two classes of dark intensity pixels (Background) and light intensity pixels (Fore ground). To find more about the maize common rust disease, we approached the researchers of the Agricultural Research Council (ARC), South Africa. _Puccinia sorghi_ is the fungus that causes maize common rust. Maize common rust disease development is favoured by conditions with cool and moist weather of around (59-72 \({}^{\circ}\)F). Mostly, it affects the coastal parts of Durban, South Africa. In Section 2, we review the necessary topics that made this study possible. Section 3 dwells more on the methods and materials. The results are presented in Section 4, while Section 5 discusses the findings and finally draws the conclusions.