esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### Transcriptome Sequencing and De Novo Assembly of M. canadensis  
Transcriptome sequencing was performed for _M. canadensis_ under control check (CK) and MeJA treatment using Illumina HiSeq(tm) 4000. Six sequencing libraries were constructed including three replicates for both CK and MeJA treatment. As a result, each library produced at least 54 Mb clean reads and 8 Gb clean bases. The percentage of clean reads was more than 98% and Q20 percentage was at least 98.69% for the six libraries (Table 1). Then, clean reads of the six libraries were de novoassembled using Trinity program and finally 81,843 unigenes were generated with a GC percentage of 43.92%. The average length and N50 length for the assembled unigenes were 724 and 1126 bp, respectively (Table 2). The length distribution of the unigenes is indicated in Supplementary Figure S1 and 70.63% of all unigenes show lengths longer than 300 bp.
# Effect of Zr Modification on NH\({}_{3}\)-SCR Reaction Performance of Cu-Ce/SAPO-34 Catalysts
## 3 Results and Discussion
### In Situ DRIFTS Measurements
#### 3.5.3 NO+O\({}_{2}\) Adsorption Experiment  
Figure 7 shows the in situ DRIFTS spectra of NO+O\({}_{2}\) adsorption on the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst. The experimental temperature was kept at 150 \({}^{\circ}\)C, the characteristic curves of the samples were recorded at 3, 6, 10, 20, 30, and 40 min, and characteristic peaks appeared at 1244, 1384, 2268, 2397, and 3109 cm\({}^{-1}\). The characteristic peak at 1384 cm\({}^{-1}\) was the strongest, which was classified as belonging to a CIS-N\({}_{2}\)O\({}_{2}\)\({}^{2-}\) species, the peak at 1244 cm\({}^{-1}\) was considered to be due to a bridging nitrate species [31; 32], and the characteristic peaks at 2268 and 2397 cm\({}^{-1}\) were mainly composed of NOH\({}^{+}\) and HNO\({}_{2}\)\({}^{+}\) and were formed by NO adsorption on the catalyst surface [33]. The peak at 3109 cm\({}^{-1}\) was the hydroxyl vibration peak formed by the oxygen in the mixed gas and the H bond in the molecular sieve framework. The in situ DRIFTS spectrum of NO+O\({}_{2}\) adsorption on the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst hardly changed with time, indicating that the adsorption of NO onto the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst reached a saturated state at the beginning, and this may be related to metallic Zr.  
Figure 8 shows the in situ DRIFTS spectra of 4 Cu-4 Ce/SAPO-34 and 4 Cu-4 Ce-4 Zr/SAPO-34 after adsorbing NO+O\({}_{2}\) for 40 min up to saturation. Compared with 4 Cu-4 Ce/SAPO-34, 4 Cu-4 Ce-4 Zr/SAPO-34 had more adsorption peaks and higher peaks, indicating that there were more nitrate species adsorbed onto the surface of 4 Cu-4 Ce-4 Zr/SAPO-34, which proved that metal Zr could enhance the adsorption capacity of the catalyst for NO\({}_{x}\)[11; 34], which facilitated the reaction with NH\({}_{3}\)-SCR.  
Figure 6: In situ DRIFTS of NO+O\({}_{2}\) reacting with pre-adsorbed NH\({}_{3}\) over the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst.
## 3 Results and Discussion
### Evaluation of Volatile Aroma Compounds
#### 3.1.7 Other Esters  
Among young wines, the highest concentration of ethyl lactate was found in TM21-Y wine, although not drastically different from those in TM7-Y and T10-Y wines (Table 1). TPHT-Y wine contained a significantly reduced level. The applied treatments influenced the concentration of diethyl succinate with significant differences between all the treatments. The highest concentration was determined in control TM7-Y wine, followed, in decreasing order, by TM10-Y, TPHT-Y, and TM21-Y wine. Prolonged maceration significantly reduced the diethyl succinate concentration. Previous research found no significant differences in diethyl succinate levels between 5 and 15 days of maceration but noted a lower concentration after 10 days of maceration [41]. The same authors observed that ethyl lactate concentrations decreased with longer maceration. A significant increase in ethyl lactate and diethyl succinate was observed after aging in wooden barrels in wines of all the treatments (Table 1). Certain amounts of ethyl lactate are formed in alcoholic fermentation, but malolactic fermentation can produce much higher concentrations [53]. A strong positive correlation between prolonged maceration and aging with increased levels of these esters was found previously [30; 55]. Other studies confirmed this increase, with higher concentrations observed during a one-year bottle aging [33; 37; 56; 57]. The increase in ethyl lactate and diethyl succinate concentrations during aging likely results from spontaneous esterification to balance precursor concentrations [10; 58].
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 4 Discussion
### Analysis of PACT and RACT Results  
Overall, our simulation results on two different tissue phantoms have shown that ONELAB can effectively simulate photoacoustic computed tomography (PACT) as well as RF-induced acoustic computed tomography (RACT). The optical fluence distribution (Figure 2) and electric field intensity (Figure 4) for the homogeneous phantom were significantly different, though the PACT/RACT reconstruction (Figures 3 and 5) is of similar accuracy. The relative error of the maximum absorption coefficient was 1.25% for the PACT case, and 8.70% in the RACT case. PACT for this phantom had a much lower relative error, since the contrast between absorber was much larger than the same difference in the RACT case. Imperfect reconstruction of the absorbers is due to error in reconstructing the initial pressure, instead of differences in the field intensity distribution. The relative shape of the absorbers, using either PACT or RACT, is easily identified from the reconstruction, with further processing only necessary for very precise characterizations. The successfully simulated results on a generic phantom gave us confidence to further validate our algorithms on a real tissue phantom mimicking the optical and RF properties of a human breast.  
When the approximate optical and RF parameters corresponding to breast tissue are used (Figures 6 and 8), the optical and electric field intensities resemble each other, and have similar accuracy to the homogeneous phantom. The relative error of the maximum absorption coefficient was 23.2% for PACT and 6.92% for RACT. In the realistic tissue, RACT had more contrast, leading to less error inthe reconstruction. The difference in field intensity between the homogeneous and breast phantom did not have a significant effect on the reconstruction accuracy. Both PACT and RACT are able to approximately reconstruct the tumor and the surrounding glandular tissue with no further processing. Even though the tumor had properties similar to the background, the PACT reconstruction was still able to identify the tumor surrounded by the glandular tissue. In RACT, the tumor is readily identified as being significantly different from the background.  
In summary, our work demonstrated that ONELAB is a viable simulation platform for use in PACT and RACT, and is well suited for experiments that exploit both modalities. Reconstructed images of the phantoms provided both qualitative and quantitative information about the tissue optical and conductivity properties, including the size, shape, and location of the target regions. This work laid a foundation for future studies to develop and validate more robust multimodality reconstruction algorithms that will help improve quantitative accuracy.
## 4 Experimental Section
Spectral Properties: We use Singular Value Decomposition (SVD) to calculate the singular values and singular vectors of the graph's adjacency matrix, which act as a signature of the graph structure. In an unweighted graph, the adjacency matrix contains binary values, for \"edge\" or \"no edge\". In a weighted graph, the adjacency matrix contains the edge weights (with 0 indicating no edge). For SVD \(U\Sigma V\), we plot the CCDFs of the singular values \(\Sigma\) and the components of the left singular vector \(U\) corresponding to the highest singular value. Clustering Coefficients: the clustering coefficient \(C\) is an important measure of community structure. It measures the density of triangles in the graph, or the probability that two neighbours of a vertex are themselves neighbours [5]. We extend the notion of clustering coefficients to weighted, directed graphs by defining \(C_{u}\), the weighted clustering coefficient for vertex \(u\)[30]:  
\[C_{u}=\frac{[\textbf{W}_{u}^{[\frac{1}{3}]}+(\textbf{W}_{u}^{T})^{[\frac{1}{3} ]}]_{uu}^{3}}{2[d_{u}^{tot}(d_{u}^{tot}-1)-2d_{u}^{\leftrightarrow}]} \tag{13}\]  
where \(\textbf{W}_{u}\) is the weighted adjacency matrix for \(u\) and its neighbours, \(\textbf{W}^{T}\) is the transpose of **W**, \(d_{u}^{tot}\) is the total degree of a vertex (the sum of its in- and out-degrees) and \(d_{u}^{\leftrightarrow}\) is the number of bilateral edges in \(u\) (the number of neighbours of \(u\) which have both an in-edge and an out-edge between themselves and \(u\)). The notation \(\textbf{A}_{uu}\) means the \(u^{\text{th}}\) element of the main diagonal of **A**. Triad Participation: Closely related to the clustering coefficient is the concept of triangle or triad participation. The number of triangles that a vertex is connected to is a measure of transitivity [5]. For the directed graphs, the triangles have a different interpretation depending on the edge directions. There are four types of triangle pattern [30], as shown in Figure 3. To generalise the concept of triad participation to weighted, directed graphs, we consider each of the four triangle types separately, and sum the total strength of the edges in each triad:  
\[t_{u}^{y}=\sum_{v,z\in\mathbf{W}_{u}\backslash u}\mathbb{W}_{wz}^{y} \tag{14}\]  
where \(y=\{cycle,midleman,in,out\}\) is the triangle type and \(\mathbb{W}_{wz}^{y}\) is calculated as shown in Figure 3 for each triangle type \(y\). To give a more objective measure of the closeness of fit between the generated graphs and the input graph, we use a Kolmogorov-Smirnov (KS) test and the L2 (Euclidean) distance between the CCDFs for each statistic. Details are in Appendix B.
## 2 Materials and Methods
### Proposed DC/DC Converter  
The sizing of conventional DC/DC converter components is heavily based on the nominal input power. In Table 1, a pre-sizing of inductor values for different current values and duty cycle values is given by Equation (A13) in the Appendix A. It can be observed that the optimal value of the inductor heavily varies according to the input current and to the duty cycle value. It is clear that the duty cycle changes because of the MPPT controller. In Table 1, the values of the table are calculated using \(V_{i}\) = 42 V (the value of \(V_{i}\) corresponds to the nominal MPP value of a SANYO HIP-215 NKHE5 PV panel, which will be used in experimental measurements) with three values of duty cycle (\(d\) = 0.1, 0.5 and 0.9) for different current levels at a switching frequency 20 kHz and a ripple of 10%.  
Indeed, the sizing of the inductor becomes a tedious task for PV applications where the current varies steadily, and using only one value of the inductance results in power loss. To reduce this power loss, the inductor can be sized for the maximum current. However, this design choice results in an increased cost and more area occupation.  
In the following section we propose a solution to fix the above-mentioned problem. We propose to adaptively adjust the inductor value through an auxiliary circuit in order to maximize the power efficiency of the converter. The new proposed topology is presented in Figure 4 where a coupled inductor, \(L_{1}\)-\(L_{2}\), is adopted instead of a simple inductor.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{\(I_{L}\) (A)} & \multicolumn{3}{c}{\(L\) (\(\mu\)H)} \\ \cline{2-4}  & \(d\) = 0.1 & \(d\) = 0.5 & \(d\) = 0.9 \\ \hline
0.5 & 4200 & 21,000 & 37,800 \\ \hline
1 & 2100 & 10,500 & 18,900 \\ \hline
1.5 & 1400 & 7000 & 12,600 \\ \hline
2 & 1100 & 5300 & 9500 \\ \hline
2.5 & 800 & 4200 & 7600 \\ \hline
3 & 700 & 3500 & 6300 \\ \hline
3.5 & 600 & 3000 & 5400 \\ \hline
4 & 525 & 2600 & 4700 \\ \hline
4.5 & 466 & 2300 & 4200 \\ \hline
5 & 400 & 2100 & 3800 \\ \hline \end{tabular}
\end{table}
Table 1: Inductor sizing for different current duty cycle values (\(f\) = 20 kHz, \(V_{i}\) = 42 V).  
Figure 4: PV system using boost converter with a coupled inductor.  
To realize the coupled inductor, a second coil is introduced to the inductor magnetic core, as described in the next subsection. The two electromagnetic fields created by the two coils constitute the total electromagnetic field in the core. Any change in the electromagnetic fields results in a change in the inductors values. To benefit from this feature, an external circuit is added to make \(L_{1}\) adaptive to the input current, \(I_{i}\). The current \(I_{2}\) that flows through \(L_{2}\) is controlled to generate a variable electromagnetic field, therefore changing the value of \(L_{1}\). The reference current \(I_{2 ref}\) that \(I_{2}\) must follow is calculated based only on the measured current \(I_{i}\).
## 2 Principles of New Measurement Methods
### DSS-Based Deformation Measurement
The equipment used in this study is the LIOS Pre.Vent system, which takes readings in 25 cm intervals, computes the average of four numbers, and provides average strain and temperature for every 100 cm intervals. The workflow was simple: connect the LIOS device to the fiber optic cable, turn on measurement software, and wait until the device finishes the data collection procedure. Then, compare the strains computed at each point with the initial readings and find the difference that is the deformation between two different measurement times. In this study, the strain caused by the movement(deformation) of the structure is monitored by tight-buffered cables, as shown in Figure 2, while the temperature is monitored by strain relief loose tube cables. Therefore, this study partially nullifies the so-called strain-temperature coupled effect to a certain degree.
# Determination of Nonlinear Creep Parameters for Hereditary Materials
## 2 Method of Isochronous creep Curves of [PERSON]
### Nonlinear Equation and Creep Kernel  
Considering the similarity property of isochronous creep curves, [PERSON] proposed the following nonlinear integral equation to describe the process of nonlinear deformation of hereditary materials [4; 5; 6; 11].  
\[\varphi[\varepsilon(t)]\ \ =\ \ \sigma(t)\ +\ \int\limits_{0}^{t}K(t-\tau)\ \sigma(\tau)\ dt, \tag{1}\]  
where \(\varepsilon(t)\) is strain at time moment \(t\);  
\(\sigma(t)\) is stress at time moment \(t\);  
\(\sigma(\tau)\) is stress at time moment \(\tau\);  
\(K(t-\tau)\) is creep kernel;  
\(t\) is observation time;  
\(\tau\) is time preceding observation time \(t\).  
Expression \(\varphi[\varepsilon(t)]\) in the left part of integral Equation (1) represents by itself the so-called \"instantaneous deformation curve\", determination of which will be given below.  
Figure 1: Creep curves of a material at various constant stresses.  
Figure 2: Isochronous deformation curves of a material.  
Creep kernel is described by Rabotnov's fractional exponential function [4; 5; 6]:  
\[K(t-\tau)\ =\ \ \lambda E_{-\alpha}(-\beta,t-\tau)\ =\ \ \lambda(t-\tau)^{- \alpha}\sum_{n\ =\ 0}^{\infty}\frac{(-\beta)^{n}\ (t-\tau)^{(1-\alpha)n}}{\Gamma[(1-\alpha)(1+n)]}, \tag{2}\]  
where \(E_{-\alpha}(-\beta,t-\tau)\) is [PERSON]'s fractional exponential function; \(\lambda\), \(\alpha\), \(\beta\) are creep kernel parameters (\(\lambda>0\), \(0<\alpha<1\), \(\beta>0\));  
\(\Gamma(\cdot)\) is gamma-function.  
Inserting the expression for creep kernel (2) into integral Equation (1), and considering \(\sigma\) = const at creep, we obtain:  
\[\varphi[\varepsilon(t)]\ \ =\ \sigma\Bigg{[}1+\lambda\sum_{n\ =\ 0}^{\infty} \frac{(-\beta)^{n}t^{(1-\alpha)(1+n)}}{\Gamma[(1-\alpha)(1+n)+1]}\Bigg{]}. \tag{3}\]
# A Roadmap for the Integration of Active Solar Systems into Buildings
## 4 Roadmap Development
### Step 1--Passive Analysis  
Step 1 of the roadmap deals with the passive energy analysis of the building, or cluster of buildings, at an urban level. The process is based on previous work by [PERSON] et al. [60; 61], and starts with the three-dimensional representation and modelling of the building, or building cluster, at an urban level. This model is used for simulations that should be made in order to calculate the solar incidence on a building's shell. The total insolation value represents a first attempt to quantify the building's solar potential, while the three-dimensional model can also provide an accurate estimation of any interfering shadows that could affect the building's solar performance.  
Subsequently, the incident radiation on a building's shell is thoroughly analysed, which provides a first indication of the surfaces on which an active solar system could be viably integrated in terms of energy production. It also an early attempt to identify which surfaces of a building might require passive solar protection to avoid possible overheating issues. All the above help in the initial optimisation of a building's geometry.  
At the same time, because incident radiation may not be used on its own to fully determine the viability of a building to reasonably accept the integration of an active solar system, the least possible amount of incident radiation on a surface that would allow for this should be calculated. [PERSON] [26] reports that a building may exhibit a high value for average incident radiation, but a number of its constituent facades might exhibit comparatively lower incidence values, rendering the integration of an active solar system non-viable, a fact exhibited by an equation that provides the lowest radiation needed for a viable integration of an active solar system on a surface. The parameters for that figure's calculation is the system's performance, the energy cost in euros per kWh, the years for repayment and the system installation cost in euros per square meter of installation. The equation is presented below:  
\[Radiation\left(\frac{\frac{kWh}{m^{2}}}{yr}\right)=\frac{System\;Installation\;and\;Maintenance\;Cost\left(\frac{m^{2}}{m^{2}}\right)}{System\;Performance\;\times Energy\;Cost\left(\frac{kWh}{kWh}\right)\;\times Years\;for\;Repayment}. \tag{1}\]  
The first methodological step of the roadmap described above is presented in Figure 3.
## 4 Discussion  
### Model Selection  
Among the four models considered herein (AlexNet, ResNet-50, MobileNet, and VGG-19), all models gave satisfactory classification performance in Round 5 on the three databases (91.9-99.0%). Thus, these four models are all good candidates for developing AI-assisted COVID-19 diagnostic systems, as also suggested by many previous studies. Among them, ResNet-50 consistently had higher overall performance than other networks across the five training stages and four levels of testing. It also outperformed other networks in learning new features when progressively exposed to more diverse training images, as evidenced by the more upper-left skewed ROC curves in Figure 5 when compared to other models and the quickly improved ROC curves across Rounds 1-5 in Figure 6.  
VGG-19 showed the lowest performance in this study. However, it still had a classification accuracy of 95.0% on the first database (Level 1), 93.5% on the second database (Level 2), and 91.9% on the third database (Level 4). It performed poorly on the outlier images collected from the first and second databases, even after being trained on 50% of them. The classification accuracy in Round 5 was 73.6%, compared to 83.1-86.8% achieved using other networks. Considering its persistent underperformance in five rounds of training and four levels of testing, we recommended ResNet-50 or MobileNet over VGG-19 for the future development of automatic diagnostic systems using X-ray images. MobileNet was recommended because of its simpler architecture (thus fewer computational requirements), overall satisfactory classification accuracy, robust performance, and good learnability.
# Characteristics of BeiDou-3 Experimental Satellite Clocks
## 3 Pre-Processing for Clock Offset  
The pre-processing quality of the original clock offsets determines the reliability of the on-board clock characteristics. In general, there are three types of anomalies in a set of raw clock offsets, which are the outliers, jumps, and gaps. For the outliers and jumps, the median detection method is applied. First, the phase data in the time domain is converted into frequency data. Second, for the obvious frequency drifts of the BeiDou clocks (shown below), from the authors' experience, the clock offsets are segmented into a single day, and the frequency data of each day is individually detected by the median method.  
A deviation limit in terms of the median absolute deviation (MAD) is described as follows [27],  
\[MAD=Median\left(\frac{|y_{i}-Median(y_{i})|}{0.6745}\right) \tag{4}\]  
where \(y_{i}\) is the clock frequency series, \(Median(y_{i})\) is the median value of \(y_{i}\), and the factor 0.6745 makes the MAD equal to the standard deviation for the normally distributed data. Here, a three times MAD is adopted as a deviation limit for judgment. The anomalies--jumps, outliers, and mixed jumps and outliers--in the original clock offsets are plotted in Figure 3. It can be the concluded that one outlier of the frequency demonstrates a clock jump in the phase sequence, two outliers signify that there is a phase outlier in the time domain, and three or more outliers indicate that there may be the potential for both clock offset jumps and outliers. We found no difficulty in detecting and removing the clock offset outlier shown in a(2) as well as fixing the clock offset jump shown in a(1) with a linear fitting. As for the mixed cases of a(3), three clock offset outliers related to the four frequency outliers shown in b(3) were detected and removed. Thereafter, we recalculated the new frequency sequence and determined whether there was a need to fix the clock jump, like for a(1). The time series of the clock frequency  
Figure 3: Three types of anomalies in the original clock offsets, b(1), b(2), and b(3), are the frequency sequences corresponding to the clock time series of a(1), a(2), and a(3), respectively.  
after pre-processing is plotted in Figure 4. The frequency amplitudes of the experimental satellites clocks are smaller than those of the BeiDou-2 satellites, and the obvious abnormalities of C04 result in the poor stability reflected in the following analysis.
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
###### Abstract  
Optical wireless communications have attracted widespread attention in the traditional power industry because of the advantages of large spectrum resources, strong confidentiality, and freedom from traditional electromagnetic interference. This paper mainly summarizes the major classification and frontier development of power industry optical wireless technologies, including the indoor and outdoor channel characteristics of power industry optical wireless communication system, modulation scheme, the performance of hybrid power line, and indoor wireless optical communications system. Furthermore, this article compares domestic and foreign experiments, analyzes parameters for instance transmission rate, and reviews different application scenarios such as power wireless optical positioning and monitoring. In addition, in view of the shortcomings of traditional power technology, optical wireless power transfer technology is proposed and combined with unmanned aerial vehicles to achieve remote communication. At last, the main challenges and possible solutions faced by power industry wireless optical technologies are proposed.  
Keywords:optical wireless technologies; power industry; power line communications; indoor wireless optical communications; optical wireless power transfer +
Footnote †: journal: Applied Sciences