esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Three-View Imaging Optical Coherence Tomography for Intraoperative Vascular Evaluation
## Materials and Methods  
### Principle of Cooperative Three-View Imaging  
The principle of cooperative three-view imaging is illustrated in Figure 1. Focal plane1 and focal plane2 are separated by a certain distance H. The rectangle scanning area or FOV (\(a\times b\)) is formed by the deflection of two side beams through two symmetrically placed 45\({}^{\circ}\) mirrors. Central position of the mirrors (M1 and M2) must satisfy the following equations \(X=H+(b-a)/2\), \(Y=H-a/2\) with the constraints \(2a+b\leq L\) and \(a\leq H\). Parameters a and b can be designed to form different sizes of rectangular scanning area to meet the circumferential imaging demands of vessels with various diameters. The benefit of this novel scanning scheme is that the internal structure information of the vessel from three orthogonal views can be acquired within one fast B-mode scan.
## 2 Materials and Methods
### Feature Selection
#### 2.5.3 Sequential Forward Selection  
Sequential search algorithms utilize bootstrap-aggregated decision trees as the base classifier and select the most important features in a sequential manner. These algorithms can provide high classification accuracy and have been widely used in various areas. An SFS algorithm starts with an empty set and proceeds by adding one feature variable at a time [53; 54]. At each iteration, the SFS algorithm selects the feature variable that is most informative in terms of classification accuracy. By sequentially selecting the feature variables, the SFS algorithm advances one step further towards the optimal classification accuracy. The SFS algorithm adds features one at a time to the set until no further improvement in the results is observed or until a prespecified number of feature variables is reached.
# Numerical Analysis of Flow in Building Arrangement: Computational Domain Discretization
## 1 Introduction
### Computational Domain Discretization
#### 1.2.2 Tetrahedral Mesh  
Semi-automatic tetrahedral (TETRA) mesh generation algorithms have been implemented into commercial pre-processors in order to overcome HEX mesh disadvantages. TETRAs (Figure 1--right) are the simplest volume elements as they consist of four faces. The faces are plane segments, thus both face and volume centroid locations are well defined. The unquestionable advantage of TETRA mesh is the ease of its generation even in the case of complicated geometry. On the other hand, TETRAs cannot be stretched excessively, so significantly larger numbers of elements have to be used in comparison to structured HEX mesh in order to achieve acceptable accuracy in representing certain spaces. Moreover, the neighbouring nodes of a TETRA element may lie in nearly one plane and therefore computing gradients can be difficult and lead to convergence problems [25]. In consequence, the numerical diffusion of TETRA mesh is significantly higher than HEX mesh and low-quality TETRAs result in convergence errors. The above-mentioned disadvantages contribute to a reduction of solution accuracy.
## 4 Evaluation
### Experimental Results  
Our first experiment involved evaluating the effectiveness of the proposed model at the architecture level, as shown in Table 2.  
In Table 2, WAE is a conditional WAE model [12] that is the baseline model because it has similar architecture to that of the proposed model. WAE + query encoder (QE) is a modified WAE in which the encoding module of a dialog context is changed into the proposed query encoder. WAE + QE +RR is a modified WAE + QE to which the RR mapper (i.e., WGAN for response generation) is added. As shown in Table 1, WAE + QE showed better BLEU-R and BLEU-F1 than did WAE. This means that the proposed query encoder can provide some assistance in generating words in gold responses by selectively looking up dialogue contexts. The final model, WAE + QE + RR, showed better performance than did WAE + QE at all measures except Intra-dist. This means that WGAN for response generation improves the overall quality of responses.  
In the second experiment, we compared the performance of the proposed model with those of the previous state-of-the-art models, as shown in Table 3.  
In Table 3, HRED is a generalized Seq2 Seq model with a hierarchical RNN encoder [29]. CVAE is a conditional VAE model with KL-annealing [9]. CVAE-BOW is a conditional VAE model with a BOW loss [9]. CVAE-CO is a collaborative conditional VAE model [10]. WAE is a conditional WAE model [12]. As shown in Table 3, the proposed model, WAE + QE + RR, outperformed the comparison models at all measures except Dist-1.
###### Abstract  
Solvent-based CO\({}_{2}\) capture technologies hold promise for future implementation but conventional solvents incur significant energy penalties and capture costs. Phase-change solvents enable a significant reduction in the regeneration energy but their performance has only been investigated under steady-state operation. In the current work, we employed a systematic approach for the evaluation of conventional solvents and mixtures, as well as phase-change solvents under the influence of disturbances. Sensitivity analysis was used to identify the impact that operating parameter variations and different solvents exert on multiple CO\({}_{2}\) capture performance indicators within a wide operating range. The resulting capture process performance was then assessed for each solvent within a multi-criteria approach, which simultaneously accounted for off-design conditions and nominal operation. The considered performance criteria included the regeneration energy, solvent mass flow rate, cost and cyclic capacity, net energy penalty from integration with an upstream power plant, and lost revenue from parasitic losses. The 10 investigated solvents included the phase-change solvents methyl-cyclohexamine (MCA) and 2-(diethylamino)ethanol/3-(methylamino)propylamine (DEEA/MAPA). We found that the conventional mixture diethanolamine/methyldiethanolamine (DEA/MDEA) and the phase-change solvent DEEA/MAPA exhibited both resilience to disturbances and desirable nominal operation for multiple performance indicators simultaneously.  
Keywords:CO\({}_{2}\) capture process; solvent-based absorption/desorption; off-design operation; phase-change solvents; sensitivity analysis +
Footnote †: journal: Applied Soft. 2020, 10, 5316; doi:10.3390/app10155316
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## 2 Sources and Impacts of Training Data Error
### Impacts of Training Data Error
However, previous studies have shown the that this product shows higher uncertainty in urban areas [191] and has a tendency to underestimate urban canopy cover compared to high resolution datasets. Therefore, to quantify the potential impact of NLCD PTC error on canopy biomass estimates, we compared the accuracy of the NLCD PTC dataset to canopy cover estimates derived from manually digitized VHR Imagery for a suburb of Washington, D.C., USA. We found that NLCD PTC underestimated canopy cover by 15.9%, particularly along forest edges (Figure 5) where it underestimated canopy cover by 27%. This discrepancy is particularly important in heterogeneous urban landscapes, where forest edges comprise a high proportion of total forest area. Scaling field data from forest plots to the entire study yielded an estimate of 8164 Mg C stored in aboveground forest biomass, based on our manually digitized canopy cover map, compared to only 5960 Mg C based on the NLCD PTC. This finding indicates the significance of these map errors for carbon accounting, as temperate forest carbon storage and rates of sequestration are much larger (64% and 89%, respectively) than in forest interiors [192]. Quantifying errors in the NLCD is thus important for correcting subsequent estimates trained on these data. Figure 5: Spatial variations in canopy cover (**A**) and uncertainty in canopy cover estimates (**B**) in forested and non-forested areas of the heterogeneous suburban landscape of the National Institute of Standards and Technology campus in Gaithersburg, Maryland. Percent canopy cover at a 30-m resolution from the commonly used National Land Cover Database (NLCD) Percent Canopy Cover product (and its uncertainty) is superimposed over a high-resolution map of forested areas (hollow outlined polygons) and non-forest trees (e.g., street trees; solid polygons) that were manually mapped using \(<\)1-m resolution Wayback World Imagery. Note the lower estimates of percent canopy cover along forest edges (**A**) and the associated higher levels of uncertainty (**B**) using the NLCD product. These brief examples help illustrate the potential problems of TD error, but the range of potential impacts is as varied as the number of mapping projects underway across academic research, commercial operations, and the public sphere. To represent the growing set of remote-sensing applications in which TD error may be encountered, we present a set of case studies below. To help lay a common framework, we show a typical methods sequence for a ML-based remote-sensing analysis in Figure 6, which also helps clarify the terminology used in this paper. The figure shows the various sources and implications of error in the modeling and mapping process, beginning with issues in the data sources and sample design, and continuing through-model training, validation, and ultimately in map accuracy assessment.
## 3 Results
### Image Analysis  
Burn severity was computed from dNBR and RdNBR. The final image was derived (Figure 6), where red areas depicted high severity, yellow areas depicted moderate severity, and the green areas depicted low severity. The threshold values were determined based on the USGS Fire Effects Monitoring and Inventory Protocol (FireMON) program [17]. The charred vegetation/soil characteristics obtained from the endmember collection were considered when identifying burn severity classes. The gradient of RdNBR has been stratified to identify burn severity levels, including: unburned or low change, low (green), moderate-low (yellow), moderate-high (orange), and high (red).The values from dNBR ranging from \(-0.1\) to \(0.66\) were classified into categories listed in Table 1 where, \(-0.1\) was rated as unburned and above \(0.66\) was marked as a high severity area, appearing as red on the map. Unburned areas were not visible within the study area.  
Figure 5: Reflectance spectra from Dark Object Subtraction. Landsat images showing green vegetation and uncharred soil reflectance curves from pre-fire image year 2013 and charred soil and charred/scorched vegetation reflectance curves from post-fire image year 2015.
# Make predictions from new data. y_pred = clf.predict(X_test),
## 7 Results  
The algorithms have been trained to predict the blow values of the DPSH penetrometer. These values are discrete in nature and the problem can be treated as both a regression or a classification. In this case, it was decided to use the classification algorithms with 31 classes (from 0 to 30 blow values). However, since the target variable values are numbers, regression metrics can also be used.  
To evaluate the performance of the predictions, the accuracy (Equation (1)), [PERSON]'s correlation coefficient (Equation (2)), MAE, MSE and R2 were used.  
\[accuracy\left(y,\hat{y}\right)=\frac{1}{n_{samples}}\sum_{i=0}^{n_{samples}-1} I(\hat{y}_{i}=y_{i}) \tag{1}\]  
\[\rho_{X,Y}=\frac{\sigma_{XY}}{\sigma_{X}\cdot\sigma_{Y}} \tag{2}\]  
\[MAE=\frac{\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert}{n} \tag{3}\]  
\[MSE=\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{n} \tag{4}\]  
\[R^{2}(y,\hat{y})=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n} (y_{i}-\overline{y})^{2}} \tag{5}\]  
Accuracy measures the proportion of correctly classified samples out of the total. In this case, the Pearson correlation coefficient measures the linear relationship between the real values of the target variable and the values calculated by the algorithms.  
In the various sites analyzed, the models have been trained for a single diameter of those available since it is not the objective of this article to evaluate the effect of the different diameters.  
In addition, only those perforations made less than 2 m from its associated penetrometer have been considered. In this way, an attempt is made to avoid errors due to changes in the terrain with the distance to the penetrometer. The effect of separation is evaluated later in this article. This limitation greatly affects the number of samples available (Table 5).  
Figure 9: Rotation torque vs torque pressure on site 1 reviewed. To the left depending on the speed of rotation and to the right depending on the depth.  
The input variables are the drilling speed (m/h), rotation speed (rpm), rotation torque (t\(\cdot\)m) and thrust (t). Therefore, the same parameters as in traditional formulas are considered. The variable to predict is the blow values per 20 cm of the penetrometers.  
For every calculation, the data set is divided randomly into a train set with 70% of the data and a validation set with the remaining 30%.
# Can Walnut Serve as a Magic Bullet for the Management of Non-Alcoholic Fatty Liver Disease?
## 3 Results and Discussion
### Pathogenesis of NAFLD and Progression to NASH, Fibrosis, and Primary Liver Cancer
#### 3.1.4 Insulin Resistance  
Insulin resistance contributes to the dysfunction of adipose tissue and secretion of adipokines and inflammatory cytokines. Insulin resistance has been noted as one of key factors in the pathophysiology of NAFLD because this is frequently associated with obesity, T2 DM, and metabolic syndrome, all of which can manifest as insulin resistance [10]. Insulin signaling in hepatocytes induces gluconeogenesis and plays an important role in regulating the fatty acid levels by inhibiting lipolysis as well as lipoprotein export. Development of insulin resistance in the adipose tissue increases peripheral lipolysis and free fatty acids can accumulate as triglycerides; the resulting hyperinsulin combined with diet can promote lipogenesis [51]. Progression of insulin resistance results in the release of non-esterified fatty acids by the adipose tissue into the bloodstream, resulting in postprandial lipidemia and fat deposition in the liver [52].  
In recent years, the cause-and-effect relationship between insulin resistance and NAFLD has been reported to be bidirectional [53]. Systemic insulin resistance associated with central obesity and the metabolic syndrome is a major risk factor in NAFLD, while hepatic steatosis may aggravate metabolic disturbances through the hepatic insulin resistance, resulting in inactivation of insulin receptors [54].
## Case Study: Data Analysis and Results
### Bayesian Statistics and Hierarchical Model Fitting
#### 4.3.1 Daily RADARSAT-2 and Transponder Drifts  
A plot of the original data from the first processing step was show in Figure 8. On first impression, the data non-uniformity is dominated by a daily systematic drift. For instance, independent of the radar target group (transponder, 3.0 m, or 1.5 m corners), a radiometric drift of more than 0.5 dB can  
Figure 8: Uncompensated and unmasked data, the immediate result of the processing described in Section 4.2. The data points lie on a common ordinate; for better visibility, one region per target group is plotted.  
be observed between the last and the previous overpass. Therefore, a daily RADARSAT-2 radiometric drift compensation had to be included in the analysis.  
Also, there is one immediately apparent data outlier: The corner at site D26g on the third day was clearly not aligned and was masked out prior to further analysis.  
Besides the RADARSAT-2 drift, the (mostly temperature-dependent) transponder drift can be estimated and compensated. The compensation is based on internal calibration data recorded by the transponder itself. Exemplary transponder loop gain and temperature drift data are shown in Figure 9. The dashed red line in the upper plot defines the point in time when the transponder RCS was corrected to its nominal RCS. The blue markers, on the other hand, indicate times at which the transponder RCS was merely estimated. In the case of Figure 9, the drift at the overpass time can be estimated to be nonexistent (0.0 dB). An upper bound on the uncertainty of this estimated drift can be stated from the plot as 0.02 dB, _i.e._, the true drift should lie, with high probability, in the range \([-0.02,0.02]\) dB.  
A similar assessment has been performed for all other days. The estimated transponder drifts and their estimated error bounds are listed in Table 4. A higher drift and/or error is stated for overpasses when a transponder software problem prohibited a nominal, temperature controlled operation of the prototype.
## 3 Results and Discussion
### Volatile Compounds Analysis Using GC-qMS  
A total of 78 volatile compounds were detected in these passion fruit wine samples, including 32 esters, 17 terpenes, 8 alcohols, 7 acids, 6 norisoprenoids, 2 ketones, 2 benzenes, 2 phenols, 1 furan compound and 1 lactone volatile compound (Table 3). Among these volatile compounds, esters, terpenes and alcohols were found to be the dominant volatiles in these wine samples. These strain-fermented wine samples possessed a similar volatiles composition. However, the concentration of the volatile compounds showed differences in these wine samples.
## 1 Introduction
Experimental results demonstrate that the neural networks generated by Darts_Tiny can save at least an order of magnitude in model volume, the amount of parameter, and the amount of computation. A lighter model yields a faster detection speed: by inputting \(800\times 800\) SAR images, more than 750 frames per second (FPS) can be achieved. The main contributions are summarized as follows:  
1. We customized a new differentiable neural architecture search model, Darts_Tiny, to automatically generate neural network-based ship detectors. Compared with manual design, our methodology can explore more possibilities in the network design space and thus can handle well both the ship detection accuracy and efficiency. 2. The neural architecture search process of Darts_Tiny is computation aware, where a channel-pruning scheme and a synthetic loss function are adopted. The channel-pruning scheme suppresses excessive channels, and the synthetic loss function combines cross-entropy loss and the amount of computation. Compared with existing models, Darts_Tiny can generate remarkably lightweight neural networks that not only have significant advantages in terms of model scale, computation complexity and detection speed but are also competitive in detection accuracy. The rest of this paper is organized as follows. Section 2 sketches out the related work. Section 3 introduces the ship detection problem and necessary evaluation metrics. In Section 4, we introduce Darts and apply it to ship detection. By conducting experiments, we show that directly applying Darts can hardly obtain good results. In Section 5, we propose a new model, Darts_Tiny. The experimental setup and results are given in Section 6. Finally, Section 7 brings concluding remarks and discusses future work.
# The Effectiveness of an Authentic E-Learning Environment for Deaf Learners
## 2 Background  
### Distance Learning and Authentic E-Learning  
Learning from a distance is a way of transferring knowledge without having face-to-face connections and currently happens through digital devices and applications, such as web-based learning. E-learning is a platform for distance learning in which the learners can join online tutorials via smartphone or laptop. E-learning allows learners to be tough on their schedule (self-paced) and provides a flexible learning setting. E-learning promotes the flexibility of education and encourages learners to progress learning at their own pace. Additionally, e-learning improves the digital literacy of both teachers and learners and increases collaboration between learners by integrating technology tools into the teaching environment [13; 14; 15].  
Technology tools to design e-learning platforms by preparing an interactive learning setting enhance learners' interest and increase their motivation. Additionally, to provide collaboration opportunities in learning concepts, employing technology tools is the critical component [16]. Technology and user-friendly learning platforms allow learners to access teaching materials in an easy way, anywhere and anytime. E-learning can support the learner's needs, and it is particularly valuable in contexts where traditional educational resources, such as classrooms and face-to-face instruction, are limited or inaccessible [17].  
Authentic e-learning increases the engagement between learners, and creating collaborative learning is the key to increasing the effectiveness of e-learning [18; 19]. The studies provide significant results to show the impact of employing authentic learning in the design of e-learning settings that enhance engagement while involving learners in real-world situations. Learning from real-world contexts increases problem-solving skills and develops critical thinking and collaboration [1; 2].
# Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction
## 2 CEEMDAN Denoising Technology
### Calculation of Correlation Coefficient  
Although the CEEMDAN decomposition method can address the issue of modal aliasing, it still poses the problem of generating pseudo-modes. Furthermore, noise typically represents a non-dominant component within signals. Therefore, in order to eliminate unnecessary components arising during the decomposition process, retain the primary components pertinent to the grinding of bearing rings within the signal, select modal components sensitive to the grinding process, and reduce data dimensionality, it can effectively eliminate noise interference in the signal and enhance its quality by calculating the correlation between each modal component and the original signal, retaining modal components with high correlation levels.  
To represent the degree of correlation between each modal component and the original signal, this section introduces the Pearson Correlation Coefficient (PCC) as a measurement standard. Its value ranges from \(-1\) to \(1\), and the higher the correlation between the two, the larger the correlation coefficient.  
The Pearson correlation coefficient between the \(n\)th-order modal component \(IMF_{n}(t)\) and the original signal \(X(t)\), calculated using Equation (7):  
\[R_{n}=\frac{\sum\limits_{t=1}^{T}\left(IMF_{n}(t)-\overline{IMF_{n}(t)}\right) \left(X(t)-\overline{X(t)}\right)}{\sqrt{\frac{\sum\limits_{t=1}^{T}\left(IMF _{n}(t)-\overline{IMF_{n}(t)}\right)^{2}}\left(X(t)-\overline{X(t)}\right)^{ 2}}} \tag{7}\]  
The decomposed modal components are ranked according to the calculated Pearson Correlation Coefficient (PCC) values. The top eight modal components are retained, while those with lower correlation to the original signal are removed. This approach helps to preserve the main characteristic information related to the grinding process of bearing rings, facilitating subsequent extraction of signal features.