esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Analysis and Calibration Improvement of the Imaging Section in a Mueller Matrix Microscope
###### Abstract  
Currently, there are various calibration methods available to reduce the errors caused by the polarizing section of a dual-rotating-retarder polarimeter. Although these methods have high measurement accuracy, their robustness must be improved and the influence of the imaging section needs be discussed when they are applied in Mueller matrix microscopes. In this paper, a method of error source analysis and element calibration for the Mueller matrix microscope is proposed by using error transform coefficient matrices to account for the polarizing effect of the imaging section. Using Taylor expansion, an approximate linear relationship is established between the sources of errors and the Mueller matrix elements of the measured sample. From this relationship, error magnification coefficient matrices are calculated to determine the specific parameter errors in both the polarizing and imaging sections. Furthermore, elements in the fourth row or column of the error magnification coefficient matrix are especially important for the imaging section. The measurement and simulation results for an air sample and a quarter-wave plate sample as the standard samples, as well as a Daphnia organism sample with complex internal structure, are investigated and discussed. Furthermore, the comparison results reveal the effect of the imaging section on the birefringence characteristics of the Mueller matrix. With the proposed method, the maximum error can be reduced to be less than 0.01 for all the matrix elements and for the amplitude parameter of birefringence, even when the two system parameters \(a_{2}\) and \(a_{3}\) of the rotating mechanical part deviate from the default.  
Mueller matrix; Mueller microscopic imaging; calibration +
Footnote †: journal: Applied Sciences
# Growth and Photosynthetic Response of _Capsicum annum_ L. in Biochar Amended Soil
## 1 Introduction
Continuously measured with spectral sensors, the photochemical reflectance index (PRI) can be used to evaluate changes in leaf reflectance as a result of water related stress [27]. At low light intensity high PRI can be expected as most light is being utilized by the plants, while at high light intensities the PRI decreases [28]. Therefore, the effects of biochar on the plants' light use efficiency can be estimated by continuously measuring PRI. While biochar related studies increased in recent decades especially in agricultural systems, there are very limited studies available in literature on evaluating canopy response to biochar using spectral sensors, such as PRI. Gaining more information on the effects of biochar on crop growth and functioning can contribute to more effective decision making processes of biochar application in agricultural systems. This paper is mainly focusing on plant responses to different amount of biochar amendments in silt loam soil under field conditions using discrete and continuous measurements. Therefore, the key objectives of this study were (i) to examine the impact of biochar addition on canopy structural properties of plant height, stem thickness, leaf area, and overall biomass production (comprising root and aboveground dry matter), concurrent with fruit yield; and (ii) to determine the photosynthetic and photochemical response of _Capsicum annuum_ L. growth in biochar amended soils based on fAPAR and PRI data. We hypothesized that changes in the soil's chemical and physical parameters as a result of biochar amendment will be detectable on the plants' photochemical and photosynthetic responses. Findings of the present study can help better understand the changes in the soil-plant-water system as a result of biochar application.
## 2 Theoretical Framework
### Propagation through Free Space  
In free-space propagation modeling, the signal transmission between a receiving and transmitting antenna is assumed to be located in an empty environmental setting devoid of obstructions [47; 48; 49; 50; 51]. Neither reflecting surfaces nor absorbing stumbling blocks or the like are considered. The power density \(w_{p}\) at a given transmitting distance \(d\) with power \(p_{T}\) in correspondence with the antenna gain \(G_{at}\) is given by Equation (1) [52]:  
\[w_{p}=\frac{P_{T}G_{at}}{4\pi d^{2}} \tag{1}\]  
The received power \(P_{R}\) that can be accessed at the receiving antenna with Gain \(G_{ar}\) is given in Equation (2) as:  
\[P_{R}=\frac{P_{T}G_{at}}{4\pi d^{2}}.A_{ef} \tag{2}\]  
and the effective aperture area \(A_{ef}\) is given by Equation (3):  
\[A_{ef}=\frac{G_{ar}\lambda^{2}}{4\pi}. \tag{3}\]  
With reference to Equation (3), the received power \(P_{R}\) can be rewritten as Equation (4):  
\[P_{R}=\frac{P_{T}G_{at}}{4\pi d^{2}}.A=\frac{\lambda^{2}}{\left(4\pi d\right)^ {2}}.G_{at}P_{T}G_{ar} \tag{4}\]  
where \(\lambda\) = transmission wavelength.  
Thus, the attenuation propagation \(\gamma_{att}(f)\) (in dB) due to free space can be determined from Equation (4) as given in Equation (5):  
\[\gamma_{att}(f)=20\log\biggl{(}\frac{4\pi d}{\lambda}\biggr{)}.=20\log\biggl{(} \frac{4\pi f_{c}d}{C_{s}}\biggr{)} \tag{5}\]  
where \(f_{c}\) and \(C_{s}\) are the transmission frequency and light speed, respectively.
# Analysis of Life Cycle Environmental Impact of Recycled Aggregate
## 3 Analysis of Life Cycle Impact Assessment (LCIA)
### Process of LCIA  
Life cycle impact assessment is divided into the following [30; 31]: classification, which collects substances drawn from inventory analysis into their relevant environmental impact categories; characterization, which quantifies the impact of items classified as environmental impact categories; normalization, which divides the impact on environmental impact categories by the total environmental impact of a certain region or period; and weighting which determines the relative importance of impact categories [32]. This study assessed all stages up to characterization since the normalization and weighting factors of recycled aggregates that fit the current situation of Korea have not been developed [33; 34].  
The substances discharged from recycled aggregate production impact the atmosphere and water quality, and the resulting environmental issues include global warming potential (GWP), ozone depletion potential (ODP), photo-chemical oxidant creation potential (POCP), abiotic depletion potential (ADP), eutrophication potential (EP), and acidification potential (AP) [35].  
\[\text{CI}_{\text{i}}=\sum\text{CI}_{\text{i,j}}=\sum\left(\text{Load}_{\text{ j}}\times\text{eqv}_{\text{i,j}}\right) \tag{1}\]  
Here, \(\text{CI}_{\text{i}}\) is the value of the impact by all inventory items (j) included in impact category i in the belonging impact categories; \(\text{CI}_{\text{i,j}}\) is the impact size by inventory item j on impact category i; Load\({}_{\text{j}}\) is the environmental load of inventory item j; and \(\text{eqv}_{\text{i,j}}\) is the characterization coefficient value of inventory item j in impact category i.
## 3 Materials and Methods
### Ensemble Classification Techniques  
A special type of classifiers known as ensemble classifiers, combine multiple base classifiers in order to improve the total accuracy of all base classifiers for different circumstances. This presents the main issue of generating different complementary base-learners. Additionally, the combination of the base-learner outputs for maximum accuracy is another problem [32]. There are several ensemble classification techniques developed for combining multiple classifiers. Well-known and widely-used ensemble classification techniques are Bagging [33], Boosting [34], Random Forest [35], and Rotation Forest [36]. The Rotation Forest is an ensemble algorithm like the Random Forest algorithm. The Rotation Forest algorithm retains the same properties as other ensemble algorithms, including the capacity to generate complex models and improve performance by combining predictions of multiple models. However, one of the primary distinctions between Rotation Forest and other ensemble algorithms is that Rotation Forest randomly chooses a subset of features from the data set to utilize in every model it generates. This property of the Rotation Forest algorithm signifies that the properties used to develop each model are different. This feature also allows the algorithm to lessen the bias of the final model. Due to its effectiveness, this study promptly selected the Rotation Forest Ensemble Model and studied its performance in predicting software faults.
# A Multiscale Approach to the Numerical Simulation of the Solid Oxide Fuel Cell
## 4 Mathematical Model
### Transport Model  
The transport model is based on the set of the partial differential equations, which describe species, energy and charge conservation. In addition, the equation of the state--here the ideal gas law--is used:  
\[Pv=\frac{R}{M}T, \tag{6}\]  
where \(P\) (Pa) is the total pressure, \(v\) (m\({}^{3}\) kg\({}^{-1}\)) is the specific volume, \(M\) (kg mol\({}^{-1}\)) is the molar mass and \(T\) (K) is the temperature.  
The velocity \(u\left(y\right)\) inside the gas channels is described by the following form of the Darcy-Brinkmann equation [54], obtained for the fully-developed, one-dimensional, axial, incompressible flow:  
\[\frac{\text{d}\,P}{\text{d}\,x}=\mu\varepsilon\frac{\text{d}^{2}\,u}{\text{d} \,y^{2}}-\frac{\varepsilon\mu}{K}u. \tag{7}\]  
This partial differential equation is possible to be solved analytically with the no-slip boundary conditions. Moreover, the pressure gradient may be replaced by the mass flow rate, defined as \(\dot{n}=\rho W\int_{0}^{H}u\left(y\right)\text{d}\,y\). The velocity profile is then described by:  
\[u\left(y\right)=\frac{\dot{m}}{\rho W}\frac{f\left(y\right)}{C}. \tag{8}\]  
The function \(f\left(y\right)\) in Equation (8) is given by:  
\[f\left(y\right)=\frac{1-\exp\left(-HK^{-\
icefrac{{1}}{{2}}}\right)}{2\sinh \left(HK^{-\
icefrac{{1}}{{2}}}\right)}\exp\left(yK^{\
icefrac{{1}}{{2}}} \right)+\left(1-\frac{1-\exp\left(-HK^{-\
icefrac{{1}}{{2}}}\right)}{2\sinh \left(HK^{-\
icefrac{{1}}{{2}}}\right)}\right)\exp\left(-yK^{\
icefrac{{1}}{{2 }}}\right)-1, \tag{9}\]  
and the constant \(C\), which appears in Equation (8), is evaluated as follows:  
\[C=\frac{1-\exp\left(-HK^{-\
icefrac{{1}}{{2}}}\right)}{2\sinh \left(HK^{-\
icefrac{{1}}{{2}}}\right)}\sqrt{K}\left(\exp\left(HK^{-\
icefrac{ {1}}{{2}}}\right)-1\right)\\ -\left(1-\frac{1-\exp\left(-HK^{-\
icefrac{{1}}{{2}}}\right)}{2 \sinh\left(HK^{-\
icefrac{{1}}{{2}}}\right)}\right)\sqrt{K}\left(\exp\left(- HK^{-\
icefrac{{1}}{{2}}}\right)-1\right)-H. \tag{10}\]  
In the above equations, \(\dot{m}\) (kg s\({}^{-1}\)) is the mass flow rate, \(\rho\) is the averaged cross-sectional density, calculated with the ideal gas law (Equation (6)), \(W\) and \(H\) (m) stand for the width and height of the channels, respectively. \(K\) (m\({}^{2}\)) is the permeability of a porous medium, calculated with the Kozeny-Carman model [55]:  
\[K=\frac{d_{\text{pore}}^{2}}{180}\,\frac{\varepsilon^{3}}{\left(1-\varepsilon \right)^{2}}. \tag{11}\]  
The porosity of the gas channels \(\varepsilon\) is 0.55 and the mean pore diameters are: \(1.05\times 10^{-4}\) m for the anode channel and \(1.24\times 10^{-4}\) m for the cathode channel.
# Usefulness of OSM and BDOT10k Data for Developing Tactile Maps of Historic Parks
## 4 Discussion
The slight advantage of BDOT does not mean, however, that it is better in all aspects. It is worth noting that it is often the case that when one of the databases is poorer in data, the other one has more available. This suggests that the best solution is to use both of these sources when developing park maps. As an example, the OSM has many more proper names of objects, which make it possible to identify unique objects characterising park styles. The obtained results also bring the important suggestion for the tactile maps development process. It clearly shows that the most specific and the most unique elements, which are characteristic for the Baroque, Renaissance, and Oriental/Japanese styles, have to be collected from other sources. The results showed that the thematic scope of the abovementioned resources does not include all the qualitative features of the content elements presented on park tactile maps. Nor does it contain the precise location of some of the content elements necessary to present the features of various garden styles on a low-scale map (e.g., location of a waterfall, Torii gate, dry garden) or on a large-scale map (composition of a dry garden, garden parterre). A literature analysis has revealed that there are no satisfactory uniform sources of cartographic data covering the qualitative characteristics of content elements of different garden styles. Therefore, when developing the content of tactile maps of parks, it is necessary to use high-resolution orthophotomaps, to obtain both the location of content elements and their qualitative features and to update all data. Finally, the best way to collect quality features, especially for the less-known areas, may be obtaining this data in person in the field. Gardens in different styles have their own distinctive features. The results show that BDOT10k and OSM data are of the highest usefulness for developing maps of Landscape/English and Romantic/Sentimental parks. This provides an answer to the third research question. This is due to a certain specificity of the characteristic elements of each style. Landscape/English and Romantic/Sentimental are more natural parks; therefore, typical topographic elements are suitable to define these styles. Moreover, these parks have fewer elements characterising style, so basic database elements are enough to fulfil map content. This also confirms that these parks can be presented only at two levels of map details. The other parks, i.e., Oriental/Japanese, Baroque, and Renaissance, have a greater number of unique elements that are very specific for the given style; therefore, they are difficult to find in the databases. For example, the Oriental/Japanese park has such elements as a tea house, a stone lantern, a Torii gate, or a Karesansui--a dry garden. These are not typical topographic elements; hence, they are absent in the OSM database and even more so in the BDOT10k database.
## 4 Experiments
### Robustness  
To account for the differences in various environments, such as different computer, server, or network conditions, we need to evaluate the stability of technical performance. To achieve this, we conducted tests on multiple hardware devices and found that the differences in test results were not significant. However, based on past experience, we recognize that data sets significantly impact technical performance. Therefore, during the testing process, we paid special attention to using data sets of different types, sizes, and qualities. It is gratifying to note that the proposed technique shows good applicability and generalization. One thing to note is that certain technologies may depend on specific parameter settings. Therefore, during the test, we attempted multiple parameter combinations to determine the optimal setting and verified the robustness of its adjusted results. We use metrics such as accuracy and F1-measure to evaluate the technology's performance and cross-validation methods to verify the reliability of the results.
# EHANet: An Effective Hierarchical Aggregation Network for Face Parsing
## 3 Methodology
### Semantic Gap Compensation Block  
In the networks with encoder-decoder architecture, skip connections are often employed to fuse shallow and deep layers. ExFuse [30] points out that simple fusion of deep and shallow layers of the network may lead to semantic gaps, which should be alleviated in order to obtain robust multi-scale features. Motivated by this work, we construct a semantic gap compensation block (SGCB) to compensate semantic gaps in a hierarchical aggregation manner. Our insight is that shallow layers utilize receptive field enhancement to bridge the semantic gap with deep layers. In detail, it contains two points: (1) increasing the receptive field can capture richer context to enhance the shallow representation ability; (2) by adjusting different rates, the receptive fields in the deep and shallow layers are generally consistent.  
ERFNet [38] indicates that 1D factorized convolution greatly reduces the number of parameters (only 33% of the conventional convolution), while the accuracy is close to 2D convolution. Motivated by ERFNet and Inception series [6; 7], we design an efficient SGAB module (Figure 4a) that captures hierarchical features of different scales in parallel. ShuffleNet v2 [21] points out that the FLOPs (float-point operations) are the smallest when the dimensions of the input and output channels are the same. Following this principle, we use _Channel Equalization_ module (channel dimensions of different branches are equally divided) and _Concatenation_ module to guarantee the consistency of the input and output channels. Take the first branch as an example, which consists of \(\{3\times 1,\ 1\times 3\}\) convolution pairs, where each convolution is equipped with a dilation rate \(r_{i}\) that determines the span of the convolution interval. Setting different \(r_{i}\) values (i.e., \((1,\ 2,\ 4)\)) for different branches can acquire multi-scale context. For stage-level feature fusion (Figure 4b), \(Add\) is used to perform element-wise summation of feature maps at different stages after semantic compensation.  
Figure 3: Stage contextual attention mechanism. “Conv” denotes convolutional operation.
## 4 Data Transformation Impact on Gray Values Compression
### Possible Combinations of Transformation and Compression  
Using the information in the last two sections, which describe all compression methods used and all data transformations, a table of all possible combinations can be created, Table 5.  
From Table 5, it can be seen that, with delta coding, Burrows-Wheeler transform, and Prediction by Partial Matching, all compression methods can be used. With the context tree weighting, all entropy encoders can be used, while with Dynamic Markov coding, only certain entropy encoders can be used, i.e., arithmetic coding and range coding. For all possible combinations, an analysis of the AAE parameter is performed for the image _horse_ with a compression ratio of 0.2 bpp, Table 6.  
For the compression to be more efficient than the original \(EEDC\), the value of the AAE parameter must be lower than 3.80. From the results in Table 6, it can be seen that the entropy coders achieve better results compared to dictionary coders, which do not use their full potential due to the small size of the input data. The best result was obtained by a combination of range coding with Burrows-Wheeler transform; an AAE value of 3.61 was achieved. In further analysis, this combination will be \(EEDC\)-\(RANGE\) (\(BWT\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Input Stream Transformation**} \\
**Compression Method** & **Delta** & **CTW** & **BWT** & **PPM** & **DMC** \\ \hline Huffman coding & 3.89 & 3.91 & 3.84 & 4.12 & / \\ Arithmetic coding & 4.02 & 3.91 & 3.88 & 4.09 & 4.53 \\ Range coding & 3.91 & 3.84 & **3.61** & 3.93 & 4.11 \\ LZ77 & 4.21 & / & 3.91 & 4.51 & / \\ LZ78 & 4.55 & / & 4.17 & 5.01 & / \\ LZW & 4.34 & / & 3.99 & 4.89 & / \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(AAE\) for all possible combinations of the compression and transformation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Input Stream Transformation**} \\
**Compression Method** & **Delta** & **CTW** & **BWT** & **PPM** & **DMC** \\ \hline Huffman coding & ✓ & ✓ & ✓ & ✓ & \\ Arithmetic coding & ✓ & ✓ & ✓ & ✓ & ✓ \\ Range coding & ✓ & ✓ & ✓ & ✓ & ✓ \\ LZ77 & ✓ & & ✓ & ✓ & \\ LZ78 & ✓ & & ✓ & ✓ & \\ LZW & ✓ & & ✓ & ✓ & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Possible combinations of coders and transformations.
_Article_  
A Transitional Connection Method for the Design of Functionally Graded Cellular Materials  
[PERSON] 1, [PERSON] 1\({}^{\copyright}\), [PERSON] 2 and [PERSON] 1\({}^{,}\)\({}^{\copyright}\)  
Received: 31 August 2020; Accepted: 21 October 2020; Published: 23 October 2020  
**Featured Application: Functionally graded materials composed of cellular structures have the characteristics of high specific stiffness/strength, energy absorption, etc. On the occasion of their structural lightweight and energy absorption, functionally graded materials are often used in engineering.**  
**Abstract:** In recent years, the functionally graded materials (FGM) with cellular structure have become a hot spot in the field of materials research. For the continuously varying cellular structure in the layer-wise FGM, the connection of gradient cellular structures has become the main problem. Unfortunately, the effect of gradient connection method on the overall structural performance lacks attention, and the boundary mismatch has enormous implications. Using the homogenization theory and the level set method, this article presents an efficient topology optimization method to solve the connection issue. Firstly, a simple but efficient hybrid level set scheme is developed to generate a new level set surface that has the partial features of two candidate level sets. Then, when the new level set surface is formed by considering the level set functions of two gradient base cells, a special transitional cell can be constructed by finding the zero level set of this generated level set surface. Since the transitional cell has the geometric features of two gradient base cells, the shape of the transitional cell fits perfectly with its connected gradient cells on both sides. Thus, the design of FGM can have a smooth connectivity with C\({}^{1}\) continuity without any complex numerical treatments during the optimization. A number of examples on both 2D and 3D are provided to demonstrate the characteristics of the proposed method. Finite element simulation has also been employed to calculate the mechanical properties of the designs. The simulation results show that the FGM devised by the proposed method exhibits better mechanical performances than conventional FGM with only C\({}^{0}\) continuity.  
**Keywords:** functional graded material; level set; topology optimization; homogenization