esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Example and Verification Experiment  
### Verification of the Extraction of Workspace Boundary Based on 3D-Layering Concept  
Figure 1 is a structural diagram of the modeled multijoint industrial robot for 3D printing. This position is its initial position. At this position, \(\mathrm{V}_{\mathrm{A}}=\left[-292.0,\,0\right]\) mm, \(\mathrm{V}_{\mathrm{B}}=\left[-230.0,\,-76\right]\) mm, \(\mathrm{V}_{\mathrm{C}}=\left[-230.0,\,79\right]\) mm, and \(\mathrm{V}_{\mathrm{W}}=\left[-292.0,\,-179\right]\) mm. In the experiment, \(\mathrm{V}_{\mathrm{U}}=\left[225,\,-70,12\right]\) mm. The range of joint angles is \(\alpha\in\left[-180^{\circ},\,180^{\circ}\right]\), \(\beta\in\left[-40^{\circ},\,100^{\circ}\right]\), and \(\gamma\in\left[-150^{\circ},\,-55^{\circ}\right]\). According to Equation (30), f(\(-40^{\circ},\,-150^{\circ}\)) = \(-34.6246\) mm, f(\(-40^{\circ},\,-55^{\circ}\)) = \(-175.9471\) mm, f(\(100^{\circ},\,-150^{\circ}\)) = \(117.7572\) mm, and f(\(100^{\circ},\,-55^{\circ}\)) = \(382.9244\) mm. Thus, the maximum of \(Z\) is \(382.9244\) mm, and the minimum is \(-175.9471\) mm. The contour boundary in Equation (31) was imported into the modeling software to generate a 3D printing layering model of the entire workspace, as shown in Figure 9. The layer height was then set to \(0.2\)mm and the 3D-printing codes for the entire workspace were exported for physical printing of the workspace. From the 3D-printing layering model, the boundary curves of each layer were obtained in the entire workspace, as shown in Figures 10 and 11.  
Figure 9: The 3D-printing model of the entire workspace.  
As in Section 4.1, in order to compare with other methods, especially the Monte Carlo method, the boundary curve of the workspace when P\({}_{\rm z}\) = 200 mm was extracted using the method in this paper, as shown in Figure 12. By comparing Figure 12 with Figure 7, it was observed that the boundary curve of the workspace extracted by Monte Carlo method was irregular, which affected the accuracy and efficiency of the solution of the workspace. The boundary curve extracted by the method proposed in this paper was closer to the real boundary and had a better fitting degree and smoothness compared to the actual workspace.  
Figure 11: 3D workspace composed of boundary curves (281 layers).  
Figure 10: 3D workspace composed of boundary curves (nine layers).
## 3 Results and Discussion  
### Crystal Structure and Morphologies  
Figure 1a-f demonstrates the back scattered electron (BSE) images of the as-cast La\({}_{1-x}\)Y\({}_{x}\)Fe\({}_{11.5}\)Si\({}_{1.5}\) (x = 0, 0.03, 0.07, 0.1, 0.15, 0.2) ingots. From Figure 1 and the EDS data, the black parts correspond to the \(\alpha\)-Fe phase, the white parts represent the LaFeSi phase, and the gray parts correspond to the Y\({}_{2}\)Fe\({}_{17}\) phase. The microstructures of the as-cast samples were composed of the LaFeSi phase and \(\alpha\)-Fe phase from the beginning at x = 0 to x = 0.1. Y only existed in the LaFeSi phase. The Y\({}_{2}\)Fe\({}_{17}\) phase was detected as x increased from 0.15 to 0.2, indicating that the solid solubility of Y in the LaFeSi phase was much smaller. Y element was found in both the Y\({}_{2}\)Fe\({}_{17}\) phase and the LaFeSi phase. The Y\({}_{2}\)Fe\({}_{17}\) phase in the ingots reduced the amount of the 1:13 phase generated from the peritectoid reaction of the LaFeSi phase and \(\alpha\)-Fe phase during the annealing process, which will be discussed below.  
Figure 2 shows the X-ray diffraction patterns of annealed La\({}_{1-x}\)Y\({}_{x}\)Fe\({}_{11.5}\)Si\({}_{1.5}\) compounds. Most of the Bragg peaks observed in this figure match the NaZn\({}_{13}\)-type structure angular positions. It indicates that the 1:13 phase is the main phase. For x \(\geq\) 0.1, a certain amount of impurities related to the \(\alpha\)-Fe phase and the Y\({}_{2}\)Fe\({}_{17}\)-type structure phase can also be observed in small sets of the Bragg peaks. The diffraction intensities from the \(\alpha\)-Fe phase and Y\({}_{2}\)Fe\({}_{17}\) phase increase with increasing x.  
Figure 3a-f demonstrates the BSE images of La\({}_{1-x}\)Y\({}_{x}\)Fe\({}_{11.5}\)Si\({}_{1.5}\) compounds annealed at 1353 K for 14 days. The white areas, grayish areas, dark gray areas, and black areas in Figure 3 correspond to the LaFeSi phase, 1:13 phase, Y\({}_{2}\)Fe\({}_{17}\) phase, and \(\alpha\)-Fe phase respectively. In order to find out which phase contained the Y element, an EDS analysis was performed. The results show that Y existed in the 1:13 phase and LaFeSi phase for La\({}_{0.7}\)Y\({}_{0.3}\)Fe\({}_{11.5}\)Si\({}_{1.5}\) compounds. Also, a large amount of the Y\({}_{2}\)Fe\({}_{17}\) phase appeared in the compounds with x greater than 0.07, as shown in Figure 3c-f. It indicates that the solid solubility of Y in the 1:13 phase was much smaller compared with Ce and Nd for the annealing compounds [16]. There was no difference for the phase compositions of x = 0 and 0.03, and very small amount of the LaFeSi phase (white area) can be observed, as shown in Figure 3a,b. The amount of the Y\({}_{2}\)Fe\({}_{17}\) phase and \(\alpha\)-Fe phase increased with increasing x, which agreed with the results of the XRD observations as discussed above. It's interesting to note that no Y\({}_{2}\)Fe\({}_{17}\) phase was observed in the as-cast samples until x > 0.1, as shown in Figure 1. It suggests that the heat
## 3 Proposed Method
### Auction-Based Non-Cooperative Game  
After we have obtained user association by interference mitigation, then we need to allocate power for each user suitable to maximize the EE. We will use auction-based Non-cooperative game to allocate power to our clusters.  
The auction mechanism involves several hierarchical models [11], Single-seller multiple-buyer hierarchical auction model is used in our case, we assume there's a central controller which can manage the resource allocation process. Hence, we introduce an auction-based non-cooperative game to allocate power efficiently. The result from the previous clustering and the interference mitigation process is used, each cluster consists of one base station, then the non-cooperative game is utilized to allocate the power to the clusters based on their submitted bids. Each cell is assumed to be selfish and always chooses a strategy to maximize its utility. In the following lines, we express the mathematical model of the game.
## 2 Classification of Routing Protocols
### Deterministic Energy-Efficient Clustering (DEC) Protocol  
#### 2.3.1 DEC Network Model and Energy Dissipation  
A radio energy dissipation model of the network is assumed [13; 14], as shown in Figure 6.  
Figure 4: The chain is constructed by using the minimum distance between the sensor nodes.  
Figure 5: Method of Token Passing to the base station.  
Figure 6: Basic network energy distribution model.  
The sensor design is known to dissipate energy for each bit around \(E_{T_{x},elce}=E_{R_{x},elce}=50\) nanojoules/bit to run for transmitter or receiver circuits in this model. The amplification energy must be used to reduce either free space losses (fs) or multi-path (mp) losses to transmit the data bits up to distance (d) with a suitable SNR, which is dependent on the total transmission distance (d). Hence, the power used for the transmission of k bits, [15; 16], is:  
\[E_{T_{x}}(k,d)=kE_{T_{x}elce}+k\varepsilon_{T_{x}amp}=\begin{cases}kE_{T_{x}, elce}+k\varepsilon_{fS}d^{2},d<d_{0}\\ kE_{T_{x}elce}+k\varepsilon_{mp}d^{4},d\geq d_{0}\end{cases} \tag{1}\]  
where total transmitter energy dissipated is \(E_{T_{x}}\), the sum of dissipated electronics energy is \(E_{T_{x}elce}\), and amplification energy is \(E_{T_{x}amp}\), in each transceiver unit in the network. \(\varepsilon_{fS}d^{2}\) and \(\varepsilon_{mp}d^{4}\) are the amplifier energies that depend on the transmitter amplifier model. \(d_{0}\) is equal to the distance threshold value for the swapping amplification model, where:  
\[d_{0}=\sqrt{\frac{\varepsilon_{fS}}{\varepsilon_{mp}}} \tag{2}\]  
To receive the k-bit message, the energy expended by the radio given as  
\[E_{R_{x}}(k)=kE_{R_{x},elce} \tag{3}\]  
Under the heterogeneous environment, it may be assumed that the same energy is needed to transmit the k-bit information between the nodes A and B in the network. The network remains energy heterogeneous until all the nodes are fully active.
## 2 Proposing a New Failure Threshold
### Proposing a New Criterion for the FT
As mentioned in Section 2.2, \(\text{PC}_{1}\), which was proportional to the defect growth, was used as the degradation indicator. To provide a new suggestion for the FT, the relationship between vibration features and \(\text{PC}_{1}\) must be studied. With this aim, a conditional probability distribution of the vibration feature given variable \(\text{PC}_{1}\) was obtained through a proper copula model. The conditional probability distribution function, in which 95% of the data are scattered in the range of \(\pm\)25% around the mode of distribution, was used as the indicator of the initiation of high fluctuation in the vibration trend in this study. In other words, this distribution, \(\text{H}\left(\text{F}\mid\text{PC}_{1}=\text{pc}_{1}\right)\), should satisfy the equation below:  
\[\int\limits_{0.75}^{1.25}\text{f}_{\text{m}}\text{H}\left(\text{F}\mid\text{PC }_{1}=\text{pc}_{1}\right)\text{df}=0.95 \tag{9}\]  
In Equation (9), F is the vibration feature most highly correlated with \(\text{PC}_{1}\), which was described in the previous subsections. The parameter \(\text{f}_{\text{m}}\) is the mode of \(\text{H}\left(\text{F}\mid\text{PC}_{1}=\text{pc}_{1}\right)\). Since H was assumed to correspond to high fluctuation initiation, it represents the FT probability distribution and \(\text{f}_{\text{m}}\) is suggested as an FT. The procedure proposed in this paper to estimate the FT of REBs is summarized in the flowchart depicted in Figure 2. First, different vibration features in the time and frequency domains were extracted from the run-to-failure history. These features were the inputs to the PCA algorithm that produced a feature as a degradation indicator (\(\text{PC}_{1}\)). Afterward, the vibration feature most highly correlated with \(\text{PC}_{1}\) was selected. By using the copula models, a suitable joint probability distribution was fitted for the selected feature and \(\text{PC}_{1}\). Finally, the FT probability distribution was obtained based on the proposed criterion.
###### Abstract  
Xinyang Maojian (XYM) is one of the premium green teas and originates from Xinyang, which is the northern green tea production area in China. The special geographic location, environmental conditions, and manufacturing process contribute to the unique flavor and rich nutrition of XYMJ green tea. Aroma is an important quality indicator in XYMJ green tea. In order to illustrate the aroma of XYMJ green tea, the key odorants in XYMJ green tea and their dynamic changes during the manufacturing processes were analyzed by headspace solid-phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS). A total of 73 volatile compounds of six different chemical classes were identified in the processed XYMJ green tea samples, and the manufacturing processes resulted in the losses of total volatile compounds. Among the identified volatile compounds, twenty-four aroma-active compounds, such as trans-nerolidol, geranylacetrone, nonanal, (+)-\(\delta\)-cadinene, linalool, (Z)-ismone, cis-3-hexenyl butyrate, cis-3-hexenyl hexanoate, methyl jasmonate, and \(\beta\)-ocimene, were identified as the key odorants of XYMJ green tea based on odor activity value (OAV). The key odorants are mainly volatile terpenes (VIs) and fatty acid-derived volatiles (FADVs). Except for (+)-\(\delta\)-cadinene, copaene, cis-\(\beta\)-farmesene, (Z,E)-\(\alpha\)-farmesene and phytol acetate, the key odorants significantly decreased after fixing. The principal coordinate analysis (PCoA) and the hierarchical cluster analysis (HCA) analyses suggested that fixing was the most important manufacturing process for the aroma formation of XYMJ green tea. These findings of this study provide meaningful information for the manufacturing and quality control of XYMJ green tea.  
Xinyang Maojian (XYMJ) green tea; volatile compounds; key odorants; manufacturing process 2022
## 2 Materials and Methods
### Evaluation of the Viability of S. cerevisiae Yeast in Bread Dough by Flow Cytometry  
To assess the effect of TW and BMW on the viability of _S. cerevisiae_ during the dough fermentation process using flow cytometry, 15 g of each type of dough was weighed after fermentation but before baking in a BagFilter bag (Interscience, Saint-Nom-la-Breteche, France), and a 1/10 dilution was made with buffered peptone water. Each bag, with its diluted dough sample, was placed in the BagMixer (Interscience) and the maximum speed (level 4) was selected for 3 min to process the sample. Next, 1 mL of the processed sample was collected in a 1.5 mL Eppendorf tube and centrifuged at 3000\(\times\)\(g\) for 1 min to sediment any debris collected. After centrifugation, 200 uL of the supernatant obtained was pipetted into a new 1.5 mL Eppendorf tube, centrifuged again at 3000\(\times\)\(g\) for 1 min, and the pellet was resuspended in 500 uL of 0.01% (_v/v_) TWEEN 20 solution/PBS 1\(\times\). Finally, 5 uL of Thiazole Orange (TO) and 5 uL of Propidium Iodide (PI) were added to the contents of each tube and then incubated at room temperature for 5 min in the dark. After incubation, the contents of the tubes were analyzed by flow cytometry. To obtain uniform and easily comparable results, 10 uL of the total content of each tube, previously homogenized, was acquired.
# Semantic Decomposition and Anomaly Detection of Tympan Membrane Endoscopic Images
## 3 Semantic Decomposition  
Tympanic membrane endoscopic images comprise plenty of information, including the degree of inflammatory distribution, transparency of the tympanic membrane, presence of fluid, and degree of perforation. Therefore, semantically decomposing this information so the model can better comprehend it is vital. This section describes the techniques used for the semantic segmentation of tympanic membrane endoscopic images and the implications of each dataset. First, AHE highlights the tympanic membrane transparency and structural depth data. Then Canny edge detection is used to emphasize the degree and size of inflammation and perforation distribution.
# Application of Quantum Dots for Photocatalytic Hydrogen Evolution Reaction
## 2 Quantum Dots-Based Photocatalyst for Hydrogen Evolution
### Semiconductor-Quantum-Dot-Based Hybrid Photocatalyst
#### 2.2.4 Mxenes-QDs-Based Hybrid Photocatalyst  
As new 2D materials, MXenes have attracted remarkable interest due to their excellent electrical conductivity, hydrophilic property, and abundant surface functional groups [98, 99, 100]. Therefore, MXene can be used as a novel occatalyst. As a well-known MXene, Ti\({}_{3}\)C\({}_{2}\) has been widely explored for photocatalytic H\({}_{2}\) generation [53]. In particular, 0D Ti\({}_{3}\)C\({}_{2}\) QDs have attracted more notable attention due to their abundant active edge sites compared with the 2D Ti\({}_{3}\)C\({}_{2}\) sheets [53,100]. [PERSON] et al. [101] categorized Mxene QD synthesis methods as top-down and bottom-up, with precursors in top-down methods typically split by physical, chemical or electrochemical methods, while bottom-up methods use small-molecule organic and inorganic precursors, which are then converted into appropriately sized QDs. Thus far, Ti\({}_{3}\)C\({}_{2}\) has generally been prepared in 2D NSs and combined with other semiconductors to enable efficient photocatalytic H\({}_{2}\) production. Few studies have focused on the design and photocatalytic applications of Ti\({}_{3}\)C\({}_{2}\) QDs. [PERSON] et al. [53] mixed and freeze-dried g-C\({}_{3}\)N\({}_{4}\) with Ti\({}_{3}\)C\({}_{2}\) MXene QDs in order to use them as an efficient catalyst for photocatalytic H\({}_{2}\) generation. The results show that g-C\({}_{3}\)N\({}_{4}\)@Ti\({}_{3}\)C\({}_{2}\) QDs hybrid photocatalysts exhibit a remarkably enhanced solar light conversion efficiency, with a H\({}_{2}\) generation rate of 5111.8 umol g\({}^{-1}\).h\({}^{-1}\), which is nearly 26, 3, and 10 times greater than those of g-C\({}_{3}\)N\({}_{4}\) NSs, Pt/g-C\({}_{3}\)N\({}_{4}\), and the Ti\({}_{3}\)C\({}_{2}\) MXene sheet/g-C\({}_{3}\)N\({}_{4}\), respectively. Using a Ti\({}_{3}\)C\({}_{2}\) QD with tiny particle size as the occatalyst led to an increased number of  
Figure 6: The photocatalytic mechanism of the Ni\({}_{2}\)P QD/red P nanosheets hybrid used for photocatalytic hydrogen production. Reprinted with permission from Ref. [51].  
surface active sites, and improved the migration of photoexcited electrons due to the close contact between Ti\({}_{3}\)C\({}_{2}\) QDs and g-C\({}_{3}\)N\({}_{4}\) NSs; thus, the electrons could participate in rapid photocatalytic H\({}_{2}\) evolution (Figure 7).
# Characterization of Nanoparticle Batch-To-Batch Variability
## Materials and Methods
### Production Methods for Silica NMs
#### 2.1.2 Dry Bulk Silica NMs from Flame Synthesis  
Flame synthesis is one of the most widely used industrial processes to produce NMs. NMs produced at high volume (soot, metal oxides) are frequently manufactured from gaseous precursors by flame synthesis, which provides high purity but invariably yields agglomerates and aggregates of NMs [22]. The degree of agglomeration, however, has a large influence on the possible applications of the particles. The lack of structural and compositional control makes these processes unsuitable for high quality applications, yet these products are widely used as fillers for polymers (polymers, paints) to improve their mechanical and optical properties. Recent laboratory studies attempting to overcome the agglomeration have reduced the flame temperature and applied staged pressure reactors.  
To produce silica NMs, gaseous TEOS was injected into a methane flame via a nitrogen carrier gas stream. The precursor (TEOS) was metered by a syringe pump and sprayed utilizing the nitrogen through a 2-phase nozzle into a heated packed column. There it was evaporated into the gas stream. In this production the mass flow of the precursor, temperature and the stoichiometry of the flame were varied.  
Downstream of the flame reactor the hot flue gas was diluted by injection of air resulting in a temperature decrease to around 200 \({}^{\circ}\)C. The particles were filtered from the gas by polytetrafluoroethylene membrane bag filters (Type 8328, Gore(r)). The NM concentration, as monitored by a condensation particle counter downstream of the fabric filter, was below the detection limit. After the end of the experiment, the filter housing was removed from the setup and opened. By ramping and careful scraping with a metal spoon, the NMs were removed from the filter bags. The recovered NMs were stored in plastic or glass bottles and homogenized with the spoon. Afterwards the containers were sealed and the yield was determined by the weight difference compared to the empty containers. The bag filters were thoroughly cleaned with clean pressurized air and reused for the next production run. Altogether, NMs were produced at two different experimental conditions. Three batches were prepared at each condition, as outlined in Tables S1-S2.  
During the production of the first two batches the clean gas leaving the filter unit was drawn off by a fluid ring pump. To compensate for the rising pressure drop over the filter, the vacuum was periodically increased by a valve resulting in a saw tooth like pressure-time-profile. For all other batches, no pump was used, resulting in a constantly increasing pressure drop in the filter unit.  
Aerosil(r)200 (Evonik, Frankfurt, Germany) was added to this group (batch name SilNP_001) to optimize the characterization methods.
# Digital Shearography for NDT: Phase Measurement Technique and Recent Developments
## 4 Recent Developments of Shearographic NDT
### Multidirectional Sheared SPS-DS  
The sensitivity direction of digital shearography is related to its shearing direction, which leads to the problem of missed detection of directional flaws with the single shearing direction system. Therefore, shearographic setup with double shearing directions was developed. In 1997, [PERSON] proposed  
Figure 10: (**a**) The SPS-DS setup for out-of-plane deformation and its first derivative measurement; (**b**) phase map of a hologram; (**c**) phase map of a shearogram.  
a TPS dual-shearing direction shearography system based on the Michelson interferometer [47]. This system is composed of two modified Michelson interferometers superimposed on the vertical plane, and the different shearing directions are generated by the upper and lower system. In 2016, [PERSON] et al. proposed an SPS dual-shearing direction shearography system based on the Mach-Zehnder interferometer, which is a polarized system recording shearograms in the two orthogonal directions simultaneously [48]. In 2016, [PERSON] et al. proposed an SPS dual-shearing direction shearography system based on the Michelson interferometer as shown in Figure 11a [49]. The system is composed of two modified Michelson interferometers on the horizontal plane. Only one camera is required using the carrier frequency technique. Due to the different shearing directions, the carrier frequency of the two parts are different, and the phase map can be separated from one intensity map. The experimental results are shown in Figure 11b,c. In addition, in 2018, [PERSON] et al. proposed an aperture-based SPS multishearing direction system that can acquire information in three different shearing directions simultaneously [50]. A set of three wedge prisms combined with three apertures were used to obtain the interferograms in three directions.
## 3 Resource Allocation Considering NS and D2D
### Network Energy Efficiency  
The power allocated to the UE is a crucial factor that directly influences the quality of the communication channel. However, UEs communicating with D2D pairs using the same channel resources creates the possibility of interference. This interference can reduce the quality of the communication and even lead to a total loss of communication. Therefore, power transmission control can be used to reduce the power of the interfering device, which will reduce interference and improve communication quality [10].  
D2D communication can reduce the total power consumption of UEs while providing a reliable and efficient communication service. One metric by which efficient power consumption can be assessed is energy efficiency, which in this work is given by the ratio of total throughput to total power consumed, given by the equation below:  
\[EE=\frac{\sum_{j=1}^{F}C_{j}+\sum_{d=1}^{D}C_{d}}{\sum_{j=1}^{Z}p_{j}}. \tag{17}\]