esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Teager-Kaiser Energy Cepstral Coefficients (TECCs)
#### 2.5.1 Teager-Kaiser Energy (TKE)  
The Teager-Kaiser Energy (TKE) Operator, also simply known as the Teager Energy Operator, was firstly mentioned by Teager & Teager [56] in 1990. However, this first occurrence did not report an analytical definition of it, but only a vaguely defined as the \"energy creating the sound\". Thus, it was [PERSON] [24], from which the complete name, to propose a proper definition of it as it is currently used. An extensive description of it can be found in [57]; the basic concepts are recalled here to provide a minimal context.  
Rather than directly relying on the discretised signal \(x[n]\), extracting the CCs from the nonlinear TKE operator allows for more correctly approximating the actual signal energy. The rationale is that power spectrum/cepstrum approximate the total energy of the system as the square of its amplitude, \(x^{2}\), while the total energy of a vibrating, single degree-of-freedom undamped mechanical system is given by the sum of kinetic and potential energies, that is to say,  
\[E=\frac{1}{2}kx^{2}+\frac{1}{2}m\dot{x}^{2} \tag{6}\]  
Unfortunately, this also means that the TKE is limited by its implicit assumption that the source of the analysed signal can be approximated to a single undamped SDoF oscillator, which is a clearly incomplete model. In its discrete form, the TKE of a given signal can be defined as  
\[\psi(x[n])=x[n]^{2}-x[n-1]x[n+1] \tag{7}\]  
From this definition, the magnitude of the frequency transform is used for the extraction of the CCs. The process is the same as described in Section 2.2. and represented in Figure 1, except for the use of TKE operator and of the Gammatone filter in lieu of acceleration time histories and triangular filters. Noteworthy, the TKE operator can be approximately seen as a high-pass Volterra system, in both continuous- and discrete-time. In the latter case, as used in this work, the operator might be further  
Figure 3: Flowchart of the Teager-Kaiser Energy Cepstral Coefficients (TECCs) extraction procedure.  
approximated as the product of an appropriate finite impulse response (FIR) filter and of the signal local (along time) mean. This approximation is only legitimate if the local mean is much larger than the variance of the signal system; however, this error is much more evident in speech signals, which are commonly strongly non-stationary, rather than in the mechanical vibration of structures.
# Effect of Leaf Grade on Taste and Aroma of Shaken Hunan Black Tea
## 3 Results
### Analysis of Aroma Compounds  
#### 3.3.1 Analysis of Aroma Compounds Identified by GC-MS  
To gain insight into the volatile components present in shaken Hunan black tea, the volatile components were analyzed using HS-SPME/GC-MS. Through the comparison with the NIST 2017 spectral library and retention index validation, a total of 144 volatile components were identified in shaken Hunan black tea at different grade levels (refer to Table 4). Based on the metabolic pathway classification [20], the 144 volatile components can be classified into five distinct groups: fatty acid degradation volatiles (FADVs), volatile terpenoids (VTs), amino acid degradation volatiles (AADVs), carotenoids degradation volatiles (CDVs), and other classes of volatiles. The different categories of volatiles are as follows: FADVs include 61 volatiles, VTs include 34 volatiles, AADVs include 18 volatiles, CDVs include 16 volatiles, and there are 15 components that fall under the category of 'Others'. The mean value of VTs was found to be 103.46 mg/kg, followed by FADVs at 56.93 mg/kg, and AADVs at 33.55 mg/kg. Consequently, VTs, FADVs, and AADVs emerged as the primary volatile components in shaken Hunan black tea. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Components** & **OT (mg/L)** & **First Grade** & \
\begin{tabular}{c} **Second** \\ **Grade** \\ \end{tabular} & **Third Grade** &
\begin{tabular}{c} **Fourth** \\ **Grade** \\ \end{tabular} \\ \hline Bitterness & & & & & \\ D, LC & 290 & 0.32 & 0.26 & 0.17 & 0.14 \\ EGCG & 300 & 3.64 & 3.32 & 2.20 & 2.17 \\ GCG & 180 & 0.73 & 0.68 & 0.73 & 0.60 \\ ECG & 200 & 2.03 & 1.82 & 0.88 & 0.76 \\ Gallic acid & 140 & 4.89 & 4.05 & 2.89 & 2.65 \\ CAF & 100 & 46.74 & 39.65 & 30.77 & 31.87 \\ Theobromine & 144 & 3.31 & 1.95 & 0.78 & 0.71 \\ Astringency & & & & & \\ DL-C & 170 & 0.54 & 0.45 & 0.28 & 0.23 \\ EGCG & 600 & 1.82 & 1.66 & 1.10 & 1.09 \\ GCG & 180 & 0.73 & 0.68 & 0.73 & 0.60 \\ ECG & 500 & 0.81 & 0.73 & 0.35 & 0.30 \\ Theanine & 1050 & 0.60 & 0.67 & 0.88 & 1.09 \\ Sourness & & & & & \\ Gallic acid & 187 & 3.66 & 3.04 & 2.17 & 1.98 \\ Glu & 147 & 1.45 & 1.05 & 1.17 & 1.78 \\ Asp & 143 & 1.72 & 1.27 & 1.06 & 1.37 \\ Unaminess & & & & & \\ Glu & 440 & 0.48 & 0.35 & 0.39 & 0.59 \\ Theanine & 4200 & 0.15 & 0.17 & 0.22 & 0.27 \\ Asp & 530 & 0.46 & 0.34 & 0.28 & 0.37 \\ \hline \end{tabular}
\end{table} 
Table 3: TAV analysis of the main taste components. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.
## 1 Introduction
### Co-Registration and Individual Tree Matching
#### Automated Individual Tree Matching  
Available automated algorithms for matching survey trees and aerial tree detections are very similar, and are all based on rules defined by an expert user. For each terrestrial reference tree, neighbored detections (candidates) are selected within a predefined [11] or adapting [3; 10; 12] radius. Filters are applied (e.g., based on height difference: [10; 11]) to exclude implausible candidates. The closest detected tree [12; 21], the detected tree with lowest 3D distance [10], or the lowest value of a combined criterion (e.g., 2D/3D distance or height difference: [3; 11; 20]) is then assigned to the reference tree.
# Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning
## 1 Introduction
Accordingly, the potential patterns between structural types and other building inventory data (e.g., story area, story height, story number, and construction year) can be identified using ML. Consequently, the structural type of each building in a city can be predicted using such patterns. In the field of buildings and constructions, ML is primarily adopted for automatic designing and detection [17; 18; 19; 20; 21]. For instance, [PERSON] et al. proposed a self-learning algorithm for the automatic selections of structures based on building information models [17]. [PERSON] et al. presented a generic framework that exploited recent advances in image segmentation and region descriptor extraction for the automatic and accurate detection of buildings on aerial orthophotos [18]. [PERSON] et al. designed a deep convolutional network with a simple structure that integrates the activation from multiple layers for pixel-wise prediction; furthermore, they trained the network to extract buildings from aerial scene images [19; 20]. [PERSON] et al. proposed an automatic building recognition method based on ML for point cloud models created by three-dimensional laser scanners [21]. However, the existing ML-based studies on the predictions of structural types of buildings are few. Herein, a method to predict the structural type of buildings based on ML is proposed. Specifically, using the training data of 230,683 buildings in Tangshan city, China, a supervised ML solution based on a decision forest model was designed for the prediction. The scale sensitivity and regional applicability of the designed solution are discussed, respectively, and the results show that the supervised ML solution can maintain high accuracy for different scales; however, it is only suitable for cities similar to the sample city. For wide applicability for various cities, a semi-supervised ML solution was designed based on sampling investigation and self-training procedures. The downtowns of Daxing and Tongzhou districts in Beijing were selected as a case study for the designed semi-supervised ML solution. The overall prediction accuracies of structural types for Daxing and Tongzhou downtowns can reach 94.8% and 99.5%, respectively, which are acceptable for seismic damage simulations. Based on the predicted results, the distributions of seismic damage in Daxing and Tongzhou downtown were output. This study provides a smart and efficient method for obtaining structural types for a city-scale seismic damage simulation.
# Overview on Different Types of Solar Cells: An Update
## 4 Dye-Sensitized Solar Cells
### Commercialization of DSSC  
Dye-sensitized solar cells have attracted increasing attention (cost effectiveness and ease of production) in the last few years. Research is ongoing to improve the efficiency and stability of solar cells. Technically speaking, natural dyes are inefficient and show very weak binding properties with titanium dioxide. Therefore, synthetic dye was used to create the most stable and effective dyes. Based on the analysis, the world DSSC market size was valued at approximately USD 90.5 million, in 2019, and is expected to reach an annual growth rate of 12.4% (from 2020 to 2027). This solar cell could be used in building-integrated photovoltaic, portable charging, automobile, electronic gadgets, and mobile devices. Several factors such as the installation of off-grid solar cells in African countries, the European Union cutting down its carbon emissions, the movement towards renewable energy sources, and enhanced technological development in the solar energy system increase the demand for DSSC.
## 4 Numerical Simulation Considering the Stress Ratio \(R\) Based on the XFEM
### Effect of Mesh Size  
Figure 9a shows four different mesh sizes: 0.5 mm, 1 mm, 1.5 mm, and 2 mm. Calculation results of these four different meshing models under \(\Delta P\) = 15 kN and \(R\) = 0.1 are  
Figure 8: Flow chart of FCP considering \(R\) effects based on the XFEM.  
shown in Figure 9b. Under the same cycles, the crack length decreases as the mesh size increases. For example, under \(N=2\times 10^{5}\), the crack lengths are 22.5 mm, 22.3 mm, 19 mm, and 18 mm for mesh sizes of 0.5 mm, 1 mm, 1.5 mm and 2 mm, respectively. The results are towards insecurity with the increase of mesh size. However, the calculation results obtained when using a mesh size of 1 mm are close to those obtained when using a mesh size of 0.5 mm, while the calculation efficiency is obviously improved. Therefore, a 1 mm mesh size is more appropriate to use in the simulation.  
The maximum principal stress distributions of the FCP process with a mesh size of 1 mm under \(\Delta P=15\) kN and \(R=0.1\) are shown in Figure 10. The stress is large near the crack tip and decreases rapidly away from it. The stress at the crack tip increases with the degree of crack propagation. As shown in Figure 10b-d, the stress at the crack tip is about 500-2200 MPa at the steady crack propagation stage. When the model is approaching the fracture point, the stress reaches 1.2 \(\times\) 10\({}^{5}\) MPa, as shown in Figure 10f. The increment number of cycles decreases while the crack propagation length remains constant, indicating that the growth rate of the fatigue crack increases.  
Figure 9: Effect of mesh size. (**a**) Mesh size; (**b**) \(a\)-\(N\) curve under different mesh sizes.
## 6 Conclusions  
Given the real-time dynamic imaging and identification requirements of natural and artificial objects, metal and non-metal objects in material evidence searches, and camouflage target identification, the infrared polarization imaging method of objects based on micropolarizers is proposed. A polarization optical system for long-wave infrared imaging and a micropolarizer array for the long-wave infrared band is designed; it achieves all-weather detection and imaging of typical targets with a resolution of 0.25 m @ 1 km. The structural parameters of the metal polarization grating determined in the simulation analysis provide theoretical guidance for the preparation of the long-wave infrared integrated polarization detector, which can reduce the number of process verification and structure optimization, improve the efficiency of process verification, and reduce the research and development cost. However, the conditions of simulation analysis are ideal, and the effect of process errors should be considered in the actual preparation and testing. It can be concluded from the long-wave infrared polarization imaging experiment that long-wave infrared polarization images can weaken the background, highlight the target's details, make the target outline more prominent, and be more easily detected and recognized in the complex background. Long-wave infrared polarization imaging can also make high-temperature targets easier to be detected and recognized in complex backgrounds. The results show that infrared polarization is significantly advantageous in material evidence searches and camouflage target recognition.  
Conceptualization, Q.F., Y.L. ([PERSON]) and J.Z.; Data curation, H.S. and J.Z.; Methodology, Y.L. ([PERSON]), S.Z. and Y.L. ([PERSON]); Project administration, Q.F. and Y.L. ([PERSON]); Resources, H.S.; Supervision, H.J.; Validation, S.Z. and H.J.; Writing - original draft, Y.Z.; Writing - review & editing, Q.F. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Contrast of visible light imaging and fusion effect. (**a**) Ordinary intensity imaging. (**b**) Fusion effect picture 1. (**c**) Fusion effect picture 2. (**d**) Fusion effect picture 3.  
**Funding:** This research was funded by Changchun University of Science and Technology, China. [PERSON], National Natural Science Foundation of China, grant number: 61890960, 61890963, JJKH20220744 KJ, 62127813.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The study did not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improvement of Electrical Performance in Heterostructure Junctions TFET Based on Dual Material Gate
## 3 Results and Discussion
### The Input Characteristics  
The log scale and linear scale transfer characteristics of JLTFET, HJLTFET and DMMG-HJLTFET are illustrated in Figure 4a. It is very clear that DMMG-HJLTFET can generate a higher current  
Figure 3: **(a)** The energy band diagram of JLTFET, HJLTFET and DMMG-HJLTFET at the device surface region; **(b)** The energy band diagram of DMMG-HJLTFET at the device middle region.  
Figure 2: **(a)** Nonlocal band to band tunneling (BTBT) rate of electron; **(b)** nonlocal band to band tunneling (BTBT) rate of hole; **(c)** electric field distribution in DMMG-HJLTFET; **(d)** total current density of DMMG-HJLTFET.  
than HJLTFET and JLTFET, the ON-state current of DMMG-HJLTFET is \(1.01\times 10^{-5}\) A/\(\upmu\)m when Vgs = 2.0 V and Vds = 1.0 V, however, the corresponding ON-state currents of HJLTFET and JLTFET are only \(3.82\times 10^{-6}\) A/\(\upmu\)m and \(7.21\times 10^{-7}\) A/\(\upmu\)m, respectively. Moreover, the OFF-state current of DMMG-HJLTFET is much smaller than that of JLTFET and HJLTFET due to a sandwich stack structure (GaAs/Si/GaAs) is introduced at the drain region, which ensures a small OFF-state current in DMMG-HJLTFET on account of large forbidden band width of GaAs.  
The transconductance (g\({}_{\text{m}}\)) is an important parameter to evaluate the analog performance of devices. It can be calculated by first derivative of drain current (Ids) with respect to VGS, Equation (2) is the formula of g\({}_{\text{m}}\):  
\[\text{g}_{\text{m}}=\frac{\text{d}I_{\text{ds}}}{\text{dV}_{\text{GS}}} \tag{2}\]  
Figure 4b shows the transconductance characteristics of JLTFET, HJLTFET and DMMG-HJLTFET. Transconductance of DMMG-HJLTFET increases observably compared with JLTFET and HJLTFET. The maximum transconductance of DMMG-HJLTFET is \(1.26\times 10^{-5}\) S/\(\upmu\)m, while the maximum transconductance of JLTFET and HJLTFET are \(1.22\times 10^{-6}\) S/\(\upmu\)m and \(5.76\times 10^{-6}\) S/\(\upmu\)m.  
Including Figure 4a,b, a comparison of the three devices is presented in Table 2. According to Table 2, the value of Ion/Ioff ratio of DMMG-HJLTFET can be reached at \(1.56\times 10^{13}\), while the Ion/Ioff ratio of JLTFET and HJLTFET are only \(5.3\times 10^{8}\) and \(1.34\times 10^{9}\), respectively. Moreover, average subthreshold swing(SS) of DMMG-HJLTFET is 52 mV/Dec extracted from Vmin to Vt (Vmin is the gate voltage at which drain current equals to Ioff, the gate voltage at which the drain current becomes \(1\times 10^{8}\) A/\(\upmu\)m is taken as the threshold voltage Vt) under Vds = 1.0 V.
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 6 Limitations and Challenges
### Relative Permanence of Soil Properties and Revisit Time for Soil Monitoring  
In Section 2, we described the relative permanencies of soil properties. One question is how these relative permanencies relate to current and future satellite revisit times. Indeed, it is a great advance that satellite revisit times are becoming shorter, especially with some of the commercially available data products with multiple weekly flybys.  
Some applications might expect us to be able to deliver soil information corresponding to this time span. The challenge is the number of cloud-free days due to weather conditions, especially in the northern or mountainous regions of France. Even when there are no clouds, we may be confronted with shadow effects, and the topographic image corrections may not be accurate enough to monitor mountainous regions.  
Another challenge is dealing with the observed spatial and temporal heterogeneity that is inconsistent with our understanding of the relative permanence and natural variability of soil properties. Finally, how do we translate this into effective sampling designs for soil monitoring? At field or farm-scale levels, UAV imagery helps design more efficient field sampling designs for monitoring, especially for SOC. It can also increase the density of observations in space and time and help to select the relevant soil signals and filter/correctsome perturbing factors. One challenge is to combine detailed surveys and RS products (from proximal sensing to UAV, airborne, and satellite data) in order to better calibrate the models obtained by hyperspectral and high-resolution satellite sensors and better design and optimize field sampling.  
A final challenge is to make a clear distinction between what the changes in carbon storage and the changes in carbon sequestration are [179; 215; 260]. A promising way, at least for topsoil, could be to relate soil spectra to pools of SOC with different residence times. Several studies have shown the potential of laboratory Vis-NIR and MIR spectroscopy for an efficient estimation of SOC fractions, SOC monitoring, and modeling (e.g., [338; 339; 340]). To our knowledge, the feasibility of upscaling this kind of relationship to RS data needs to be assessed. This may increase the usefulness of RS to map and monitor SOC pools.
# Optimal Energy Consumption Tasks Scheduling Strategy for Multi-Radio WSNs
## 2 Related Work
Moreover, the GA based modified LEACH algorithm was proposed for the energy harvesting WSNs (EH-WSNs) in order to minimize energy consumption and to prolong the network lifetime of the EH-WSNs [30]. Similarly, a self-organizing network clustering method based on GA was proposed for searching an optimal network structure, and further for extending the network lifetime [31]. In [32], the authors proposed a distributed GA to solve the energy efficient coverage problem in WSNs. In [33], the authors proposed GA based approaches for clustering and routing in WSNs in order to prolong the lifetime of a sensor and to increase the quality of service. Inspired by the social behaviour of birds, PSO has became one of the most popular optimization algorithms and has been widely applied to solve complex optimization problems. A hybrid approach based on combining PSO with random transition moves was proposed to create the maximum possible number of disjoint sets in order to maximize the lifetime of WSNs [34]. In [35], the authors applied the PSO algorithm to decrease the location error and improve the location accuracy. In [36], [PERSON] et al. presented an energy center searching method using the PSO algorithm to avoid energy holes near the cluster cased by the heavy burden of forwarding. In order to solve the resource allocation and energy consumption problem in multi-agent clustering WSNs, a hierarchical resource allocation strategy using the adaptive PSO algorithm was presented to address the issue of resource allocation in these types of networks [37]. In [38], the authors presented three different energy efficient routing strategies using the PSO algorithm; one for maximizing the energy of the node with the lowest energy, one for maximizing the overall WSN energy, and one for maximizing the energy of the worst performing node. In summary, on one hand, it is great that the energy consumption optimization in WSNs has been researched and has been proven to be an NP-Complete problem. On the other hand, a heuristic algorithm has been applied to solve those optimization problems, including routing, cluster head selection, and energy consumption optimizaiton, etc. However, to our knowledge, such a heuristic algorithm is rarely applied for achieving energy save for MR-WSNs. Therefore, this paper introduces the PSO algorithm to the MR-WSNs and presents an optimal energy consumption multi-task scheduling strategy in order to reduce energy consumption and prolong the network lifetime for the MR-WSNs.
## 1 Introduction
It has been reported that B can be transported through plants by an active process mediated by the specific transporter BOR1 localized in membrane cells [17]. In addition, some aquaporins--such as NIPS;1--were observed to be able to transport B [19]. Additionally, the deficiency of B leads to a reduction in water transport from root to leaf, and an associated increased in NIPS;1 and NIPs;1 expression in _A. thaliana_ plants [16]. The increases in the expression of other NIPs aquaporins, such as NIPS;1 in rice and NIPs;1 in barley, have been associated with B transport, suggesting the involvement of NIPs aquaporins in B uptake and transport [20; 21]. In the same way, the expression of B transporters, BOR1, and BOR2, was regulated for maintaining plant and cell B concentration [18]. Likewise, B has been reported to reach long distance from leaves to roots via phloem, by the B-sucrose complex [22]. The nanoencapsulation of boron has been applied to almond trees [23; 24], demonstrating that the efficiency in B transport into tissues was much higher than when free B was applied. In fact, a higher nutrient penetration through the leaf cuticle was demonstrated [23] by the fact that nanocapsules containing fluorescein disodium salt (FNA) were able to penetrate into leaf tissues in 2 h whereas no penetration was observed in free form. Additionally, they were able to penetrate and reach other tissues separated from those where B was applied. Likewise, nanoencapsulation with proteoliposomes from natural membranes has been shown to behave as an effective carrier [25], as these were able to deliver mineral nutrients into cells [26]. Therefore, the aim of this work was to establish the regulation of B transporters (aquaporins and specific transporters) when boron concentration increased in leaves. We determined the changes in metabolism that could be associated with the regulation in these transporters. For this, different applications of free B and encapsulated B were subjected to plants, then measurements of mineral nutrients, pigment concentration, and a metabolomic analysis were associated to the expression of NIP aquaporins and BOR transporters. We expected to find that primary metabolite changes, in conjunction with an increased expression of aquaporins, was a method to determine the possible connections.