esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Global NO2 Retrieval from EMI-II Onboard GF5B/DQ1 and Comparison to Tropopmi
## 2 Data and Methods  
### Emi-Ii  
The EMI-II instruments onboard the GF5B and DQ1 satellites are similar to those onboard the GF5 satellite. Both are nadir-viewing push broom spectrometers flying in a sun-synchronous polar orbit and observe direct and atmospheric backscattered sunlight in the range of 240-710 nm through four channels. The instantaneous field of view (IFOV) and swath width are 114\({}^{\circ}\) and 2600 km, respectively. Table 1 lists the main differences in the technical parameters for the two payloads: (1) Spectral range of the four channels: the four channels: the four channels of GF5 EMI and DQ1 EMI-II are UV1: 240-315 nm, UV2: 311-403 nm, VI1: 401-550 nm, and VI2: 545-470 nm; the four channels of GF5B EMI-II are UV1: 240-311 nm, UV2: 311-401 nm, VI1: 401-550 nm, and VIS2: 550-710 nm. (2) Spectral resolution: the spectral resolution of the GF5 EMI is 0.3-0.5 nm, and the spectral resolution of EMI-II is 0.3-0.6 nm. (3) Spatial resolution: the spatial resolution of GF5 EMI is 13 km \(\times\) 48 km, and that of GF5B and DQ1 EMI-II is 13 km \(\times\) 24 km. (4) Equator crossing time: the local overpass time of GF5 EMI and DQ1 EMI-II is 13:30, and that of GF5B EMI-II is 10:30.  
The EMI-II data have better calibration accuracy and spectral quality than those from GF5 EMI. Figure 1 shows the FWHM variation with the number of rows in the VIS band for DQ1 EMI-II, GF5B EMI-II, GF5 EMI, and TROPOMI. The variation standard deviations of DQ1, GF5B, GF5, and TROPOMI FWHM were 0.042 nm, 0.047 nm, 0.0545 nm, and 0.0041 nm, respectively. The FWHM variation in EMI-II strongly depends on the row number, especially for rows at the edge, but the stability of EMI-II has improved compared with that of GF5 EMI and is inferior to that of TROPOMI.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Instrument Name** & **GF5 EMI** & **GF5B EMI-II** & **DQ1 EMI-II** & **TROPOMI** \\ \hline \multirow{4}{*}{Spectral range} & UV1 band & 240 nm\(\sim\)315 nm & 240 nm\(\sim\)311 nm & 240 nm\(\sim\)315 nm & 270 nm\(\sim\)300 nm \\  & UV2 band & 311 nm\(\sim\)403 nm & 311 nm\(\sim\)403 nm & 311 nm\(\sim\)403 nm & 300 nm\(\sim\)320 nm \\ \cline{1-1}  & VIS1 band & 401 nm\(\sim\)550 nm & 403 nm\(\sim\)550 nm & 401 nm\(\sim\)550 nm & 310 nm\(\sim\)405 nm \\ \cline{1-1}  & VIS2 band & 545 nm\(\sim\)710 nm & 550 nm\(\sim\)710 nm & 545 nm\(\sim\)710 nm & 405 nm\(\sim\)500 nm \\ \cline{1-1}  & Spectral resolution & 0.3–0.5 nm & 0.3–0.6 nm & 0.3–0.6 nm & 0.5–1 nm \\ \cline{1-1}  & Spatial resolution & 48 km \(\times\) 13 km & 24 km \(\times\) 13 km & 24 km \(\times\) 13 km & 3.5 km \(\times\) 5.5 km \\ \cline{1-1}  & \multirow{2}{*}{Ascending node} & \multirow{2}{*}{Descending node} & \multirow{2}{*}{Ascending node} & \multirow{2}{*}{Ascending node} & \multirow{2}{*}{Ascending node} \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & 13:30 & 10:30 & 13:30 & 13:30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic technical parameters of GF5 EMI and EMI-II onboard GF5B and DQ1.
## 5 Conclusions
The scope of the literature reviewed is confined to existing studies available up to a certain point, which may exclude recent advancements in the field. Additionally, this paper primarily focuses on selected variants of the TMD, potentially overlooking other relevant adaptations or applications. The field of multi-criteria decision-making is rapidly evolving, and new methodologies or frameworks are continually being developed. As such, the relevance of the TMD may change over time, and the findings of this paper may need to be revisited in light of future advancements. This study aims to enrich both the theoretical foundations and practical applications of the TMD. However, the emphasis on theoretical exploration may overshadow practical considerations, making it less applicable to practitioners seeking straightforward guidance for implementation. The practical applications discussed in this paper may not fully encapsulate the complexities and nuances of real-world decision-making scenarios. Each application context can introduce unique challenges that may not be addressed by the generalizations made in the review. Finally, the lack of extensive comparative analyses with other multi-criteria decision-making methods may limit insights into the TMD's relative strengths and weaknesses. Acknowledging these limitations is essential for understanding the context and implications of the findings. Future research may address these limitations and contribute to a deeper understanding of the TMD's role in multi-criteria decision-making. Future research could also explore the development of hybrid MCDA methods that integrate the Hellwig approach with other established techniques, such as AHP, DEMATEL, PROMETHEE, and ELECTRE. These hybrid methods could be further enhanced by incorporating various uncertainty theories, including fuzzy set theory, rough set theory, and cloud theory. This could expand the applicability of the Hellwig method in more complex decision-making scenarios, especially in the fuzzy or intuitionistic fuzzy context. The contribution was supported by grant WZ/WI-IIT/2/22 from Bialystok University of Technology and founded by the Ministry of Education and Science. The author declares no conflicts of interest.
## 2 Results and Discussion
### Discrimination of Different Flower Stages by Electronic Nose
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Peak**} & \multicolumn{2}{c}{_RT \({}^{*}\)_} & \multirow{2}{*}{**Length \({}^{*}\)**} & \multirow{2}{*}{**Compounds**} & \multicolumn{6}{c}{**Relative Content (\%) \(\pm\) SD**} \\ \cline{3-10}  & & & & **1** & **II** & **III** & **IV** & **V** & **IV** & **VII** \\ \hline
21 & 9.365 & 1032 & & (a)-1,2-ethanediol, & & & & & & & \\  & & & 1,2-diphenyl-, (_R_*, _R_*) & 0.159 \(\pm\) 0.017 & - & - & - & - & - & - \\
22 & 9.425 & 1034 & _p_-toluckool & - & - & - & 0.061 \(\pm\) 0.006 & - & - & - \\
23 & 11.364 & 1099 & _p_-linalool & - & - & 0.053 \(\pm\) 0.010 & 0.062 \(\pm\) 0.001 & - & - & - \\
24 & 15.819 & 1250 & _p_-germinol & 1.396 \(\pm\) 0.101 & 2.262 \(\pm\) 0.102 & - & - & - & - \\
25 & 23.698 & 1550 & _c_lemol & 0.310 \(\pm\) 0.045 & - & - & - & - & - \\
26 & 24.402 & 1579 & _g_ermermee \(D\)-4-ol & - & 0.387 \(\pm\) 0.106 & 0.335 \(\pm\) 0.042 & - & - & - \\  & & & & 10,10-dimethyl-2,6- & & & & & & \\
27 & 25.815 & 1640 & _i_-dcinol/therebeicycl(?.2.0)undecan- & - & - & - & - & - & 0.425 \(\pm\) 0.108 \\  & & & _5-g_-olol & & & & & & & \\
28 & 25.939 & 1646 & _i_-dcinol & - & - & - & 0.090 \(\pm\) 0.010 & - & - & - \\
29 & 26.214 & 1658 & _i_-dcinol & - & 0.146 \(\pm\) 0.049 & - & - & - & - & - \\
30 & 27.500 & 1715 & _g_-germinol & - & - & 0.736 \(\pm\) 0.129 & - & 0.027 \(\pm\) 0.000 & - \\
31 & 27.527 & 1716 & _g_-mersyl alcohol & 0.406 \(\pm\) 0.043 & 1.238 \(\pm\) 0.038 & 0.383 \(\pm\) 0.000 & - & 1.227 \(\pm\) 0.319 & - & - \\
32 & 29.988 & 1832 & _(E)_-neroldold & - & - & - & - & - & - & 0.113 \(\pm\) 0.000 \\ \hline \multicolumn{2}{c}{**Hydrocarbons**} & & & & & & & & \\ \hline
33 & 6.558 & 931 & _a_-pinene & 0.196 \(\pm\) 0.009 & - & - & - & - & - & - \\  & & & & bicycl[3.1.1]/lept-2-ene, & & & & & & \\
34 & 8.034 & 986 & & _3,6.6_-trimethyl & & & & & & \\
35 & 8.057 & 987 & _p_-pinene & 0.132 \(\pm\) 0.024 & - & 4.061 \(\pm\) 0.000 & 2.861 \(\pm\) 0.277 & 6.872 \(\pm\) 1.055 & - & - \\
36 & 8.067 & 988 & _p_-symcreen & - & 0.106 \(\pm\) 0.005 & 0.118 \(\pm\) 0.010 & - & - & - & 0.097 \(\pm\) 0.000 \\
37 & 9.262 & 1029 & _a_-limcene & - & - & - & 0.078 \(\pm\) 0.009 & - & 0.029 \(\pm\) 0.008 & 0.030 \(\pm\) 0.000 \\
38 & 9.283 & 1030 & 3-ethyldenceycloheptene & - & - & - & - & 0.072 \(\pm\) 0.012 & - & - \\
39 & 10.961 & 1086 & isoterpiolence & - & - & - & 0.022 \(\pm\) 0.003 & - & - & - \\
40 & 19.348 & 1376 & _a_-ylangene & 4.093 \(\pm\) 0.099 & - & - & - & - & - & - \\
41 & 19.357 & 1378 & _a_-copane & - & 6.019 \(\pm\) 0.361 & - & - & - & 2.669 \(\pm\) 0.298 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 3 Uhs  
### Uhs Process and Mechanism  
The classic high-temperature synthesis processes are commonly considered to be thermodynamically driven and operate continuously under equilibrium conditions [43; 120]. This is a result of the gradual rates of heating and cooling. Consequently, products derived from conventional synthesis methods typically demonstrate the lowest Gibbs free energy and the most stable atomic configurations. To overcome the limitations imposed by thermodynamics in conventional high-temperature synthesis methods, researchers have redirected their attention toward materials synthesis techniques that are guided by kinetic principles [121]. There are two main methods for accomplishing this: one requires quickly creating high-temperature conditions within a short time frame, while the other permits materials to pass through a consistently hot area [122]. High-temperature Shock (HTS) synthesis is an innovative technique that utilizes the former approach and has the ability to achieve extremely rapid heating/cooling rates (>10\({}^{5}\) K s\({}^{-1}\)) [123]. Commonly used HTS techniques include Joule heating, laser heating, and microwave heating [124]. [PERSON] et al. [43] introduced ultrafast high-temperature sintering (UHS), and they achieved successful sintering of alumina with a density of up to 96% and YSZ with a density exceeding 95% and a grain size of 265 \(\pm\) 85 nm within seconds, as shown in Figure 13. This UHS method eliminates the need for pressure and effectively reduces the occurrence of side reactions. It has the ability to co-sinter various materials at the same time and sinter ceramic structures with complex geometries.  
Since the report, researchers have persistently engaged in discussions and investigations concerning the mechanism of UHS. Based on experimental data and assumptions, the following mechanisms for the rapid sintering and densification of samples through UHS technology are proposed:  
1. The passage of electric current through carbon cloth generates Joule heat, providing an ultra-high heating rate. This heat bypasses the low-temperature region, reducing particle coarsening and maintaining a high capillary driving force for sintering [43].
2. High sintering temperatures and rapid heating rates result in small particles with molten surfaces, which exhibit characteristics of rapid migration. This promotes grain rearrangement and accelerates densification [125].
3. The shorter processing time increases the number of defects, leading to a reduction in activation energy and an increase in conductivity, which in turn promotes sintering [126].
# Integration of ERT, IP and SP Methods in Hard Rock Engineering
## 4 Results and Discussion
### Water-Clay Distinction  
Electrical resistivity obtained from ERT models cannot distinguish between clay and weathered rock saturated with water, since low resistivity suggests water as well as clay. Thus, ERT models alone cause ambiguity in the interpretation of the subsurface geological model. On the other hand, high IP suggests a higher likelihood of clays while low IP suggests fluid-filled voids. Therefore, such uncertainty in the model was removed by the integration of ERT and IP. Based on the integration between ERT and IP models, and the borehole information, clay was identified with IP greater than 40 ms and resistivity less than 1000 \(\Omega\)m. The results suggest that mostly clay was delineated along the fractures/faults near the surface (Figure 5). The prominent clay content along profile A was delineated at 220 m, 490 m, 1000 m and 1300 m. Clay was identified at four places along profile B, such as at 30 m, 190 m, 280 m and 480 m distance. Profile C revealed clay at three locations, such  
Figure 4: Integration of 2D ERT models along all profiles interpreted for the weathered rock (blue color), weathered/unweathered transition zone or partly weathered rock (yellow color) unweathered rock (pink color), and fractures/faults (dotted reds lines).  
as at 40 m, 210 m and 520 m distance. Profile D revealed thin clay content at several places near the surface. Profile E detected the high clay content at 550 m on the east side. The main clay content along profile F was revealed at a 500 m distance. Along profile G, minor clay content was identified at a 75 m distance. Along profile H, two places of high clay were identified at the integration of profiles E and F. Along profile I, clay was detected at four places, at 70 m, 250 m, 300 m and 390 m distance. Compared with other profiles, mostly the topsoil layer in profile J is dominant with clay, especially along the fractures/faults. The locations identified by clay may cause the collapse of the engineered structures. Therefore, clay identified along the 2D IP profiles should be avoided in the construction.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Holding Time and Activation Time Calculation
#### 4.1.1 Activation Time (AT) calculation  
Activation time for the master node is the time before spreading the control packet in its neighborhood. For \(M1\), \(AT_{M1}\), the value is zero, because \(M1\) has high privileges, and it does not wait for other's permission. It just sends a control packet in its surroundings, as illustrated in Equation (11) as:  
\[AT_{M1}=0 \tag{9}\]  
\[AT_{M2}=PD_{1000}+PD_{M1M2} \tag{10}\]  
\[AT_{M3}=AT_{M2}+PD_{M2M3} \tag{11}\]  
Figure 7: Sequence Diagram of ERPMSA-UWSN.  
The above equations demonstrate activation time for \(M2\), where the first term is propagation time taken by a data packet to travel over 1000 m, and the other term is for propagation delay of the data packet from \(M2\) to \(M3\). If we closely look at Figure 8, it demonstrates the first term i.e., when node S sends data packet in its neighborhood, node B receives data packet far earlier than node A. Similarly, the \(AT_{M2}\) and for activating \(M2\), it must wait for the data packet to reach M1 and for the control packet to travel from \(M1\) to \(M2\). Equation (13) shows the activation time for \(M2\), whose 1 st term is the activation time of \(M2\), while the 2 nd term is the control packet's propagation delay from \(M2\) to \(M3\). Activation time contributes a little to the E2E delay of the whole network.
# Finite Element Analysis of Thermal Stress and Thermal Deformation in Typical Part during SLM
## 4 Emulation Results and Discussion
### Effective Stress and Strain  
In the formation analysis of heat stress subsequent to different levels of height, each floor included four locations (Points 1-4, as shown in Figure 3b). As presented in Figure 5a, the printing started from the support layers to the height position of the plate layers at 4 mm, and the thermal stress gradually increased from the upper plate surface to the lower layer near the substrate as the layers accumulated. Since the heat transfer temperature gradient was the highest, it led to faster solidification and higher thermal stress at the base plate layer due to \"metal solidification theory\" [40; 41]. As the other powders were formed through laser exposure, which caused the layer-by-layer circulation heating, the current printing layer produced a \"micro annealing effect\" of stress release before each layer. In contrast, its influence was lower than that of the stress-induced rapid solidification. Consequently, the bottom stress was the highest and the upward stress became slightly lower with the forming height. Moreover, water nozzles with barbs existed at the height of 4 mm. These were added with the new support, but the heat transfer medium was a panel of considerable temperature, which led to a reduction in the part cooling rate and the stress decrease in the curves. Following this, the curve entered the normal stress accumulation phase as before. Simultaneously, the thermal stress caused the thermal strain of the material, which constituted microcosmic thermal deformation [35]. From Figure 5a,b, it could be observed that the thermal plastic strain and thermal stress variations had the same tendency.  
Furthermore, the effective stress extension from 0 to 4 mm in the large panel structure that was printed from the substrate was analyzed (single support layer was accumulated on a single basis), while the increase occurred as follows:  
Through the least square method cubic polynomial fitting function curve,  
\[f(x)=P_{3}x^{3}+P_{2}x^{2}+P_{1}x+P_{0}. \tag{21}\]  
Through MATLAB calculations, \(P=0.0196\), \(-0.9584\), \(7.2315\), and \(289.6667\) because  
\[f^{\prime}(x)<0,x\in(1,20) \tag{22}\]  
Therefore, the thermal stress curve monotonically decreases from the substrate to the upper layer, and vice versa.  
Figure 5: Stress–strain curve from the bottom to the surface of the part. (**a**) Thermal stress; (**b**) thermal strain.
## 5 Results
### Position Accuracy  
The position error in millimeters was calculated using the L2 norm between the positions of the target and objective objects. Figure 7 illustrates the graph of position error for each task, except Manipulation 3. Manipulation 3 did not calculate the position error because it was the task to only rotate the virtual object. The position error for each task did not find a statistically significant difference.  
Figure 6: Mean completion time for each interaction (+/- standard deviation of the average).  
Furthermore, we analyzed the position error of each axis. The Friedman test showed no statistically significant difference in the \(x\)- and \(z\)-axis positional errors among the interaction methods, but in the Manipulation 4 task, the \(y\)-axis position error showed a significant difference (\(\chi^{2}(2)=6.778,p=0.034\)). In the Manipulation 4 task, the average of \(y\)-axis position error for Hand Interaction (M = 5.36 mm) was significantly less than those for Finger Interaction (M = 9.46 mm, Z = -2.243, \(p=0.025\)) and Head Interaction (M = 7.29 mm, Z = -2.417, \(p=0.016\)). However, there was no statistically significant difference between Head Interaction and Finger Interaction.
## 3 Navigation Framework
### Transition Model  
The position of the \(m\)-th user in the \(t+1\) slot is \(\mathbf{\mathrm{p}}_{m}(t+1)=\mathbf{\mathrm{p}}_{m}(t)+\dot{\mathbf{\mathrm{p}}}_{m}(t)T\), where \(T\) denotes the sampling interval. Denote the random disturbance received during the target movement as \(\mathbf{\mathrm{u}}_{m}(t)=\big{[}u_{x_{m}}(t),u_{y_{m}}(t),u_{z_{m}}(t)\big{]}^{ \mathsf{T}}\). For the \(m\)-th user, the transition dynamics can be modelled as  
\[\mathbf{\mathrm{s}}_{m}(t+1)=\mathbf{\mathrm{F}}_{m}\mathbf{\mathrm{s}}_{m}(t)+\mathbf{\mathrm{ \Gamma}}_{m}\mathbf{\mathrm{u}}_{m}(t), \tag{9}\]where  
\[\mathbf{F}_{m}=\begin{bmatrix}\mathbf{I}_{3\times 3}&T\mathbf{I}_{3\times 3}\\ \mathbf{0}_{3\times 3}&\mathbf{I}_{3\times 3}\end{bmatrix}, \tag{10}\]  
\[\mathbf{\Gamma}_{m}=\begin{bmatrix}\frac{1}{2}T^{2}\mathbf{I}_{3\times 3}&T \mathbf{I}_{3\times 3}\end{bmatrix}^{\mathsf{T}}. \tag{11}\]  
The random disturbance \(\mathbf{u}_{m}(t)\) can be characterized by the covariance matrix  
\[\mathbf{U}_{m}=\text{diag}(\sigma_{x_{m}}^{2},\sigma_{y_{m}}^{2},\sigma_{z_{m} }^{2}), \tag{12}\]  
where \(\sigma_{x_{m}}^{2},\sigma_{y_{m}}^{2},\sigma_{z_{m}}^{2}\) are adjustable parameters that denote process noise in the moving process. Thus,  
\[\mathbf{Q}_{m}=\mathbf{\Gamma}_{m}\mathbf{U}_{m}\mathbf{\Gamma}_{m}^{\mathsf{ T}}. \tag{13}\]  
Accordingly, \(\mathbf{F}=\text{diag}(\mathbf{F}_{1},\dots,\mathbf{F}_{m},\dots,\mathbf{F}_ {M})\) and \(\mathbf{Q}=\text{diag}(\mathbf{Q}_{1},\dots,\mathbf{Q}_{m},\dots,\mathbf{Q}_ {M})\).
# Spectroscopic Studies of Laser-Based Far-Ultraviolet Plasma Light Source
## 4 Results and Discussion
In the simulation model, a single laser beam illuminates a planar target in the radius-Z (R-Z) cylindrical geometry. The laser is focused on the Z axis and enters the domain at a 90-degree angle, i.e., laser beam is normal to the target. It is assumed that the Si target is in a low-density air environment related to the pressure of 1.3 \(\times\) 10\({}^{-4}\) Pa at the vacuum chamber. The laser spot size diameter is assumed to be \(\approx\)120 \(\upmu\)m (1/e\({}^{2}\) width \(\approx\)200 \(\upmu\)m). The FLASH code predicts a maximum of electron temperature \(\approx\)20 eV for the plasma core at the peak laser irradiance. This plasma temperature is high enough to produce an average ionic charge state higher than \(\approx\)4, especially for elements with a high atomic number such as Mo or Ta. Our calculations (not shown here) predict that at such a high temperature, the plasma should also strongly emit at the wavelengths shorter than 120 nm. The calculation also shows at the maximum laser irradiance, this dense high-temperature plasma close to the target radiates intense continuum emission. Figure 5a also presents a dashed-lines contour to emphasize plasma regions with the electron temperatures in the range of 0.5-8 eV. Our study reveals that the dense low-temperature plasma, especially close to the target (as can be seen in Figure 5b for the region of \(\rho\geq 10^{-4}\) g cm\({}^{-3}\)), strongly radiates in the FUV region. The reason may be understood based on a comparison between the calculated Si spectral radiance (Figure 2b) and measured signal (Figure 4a). Due to the lack of experimental information, an effective plasma size may be estimated by the simulation as \(\approx\)300 \(\upmu\)m. Indeed, more experiments and the radiation-hydrodynamic calculations coupled to the CR solver are required to investigate the conversion efficiency versus the laser spot diameter, pulse width and emission angle (angle-resolved spectra) in the laser plasma FUV light source. These features are now under investigation. Figure 5: (**a**) The spatial distributions of the plasma electron temperature (in units of eV); (**b**) the plasma density (in units of g cm\({}^{-3}\)); and (**c**) the plasma electron density (in units of cm\({}^{-3}\)) for a 10 ns LPP near the peak irradiance of \(\approx\)10\({}^{10}\) Wcm\({}^{-2}\) over the Radius-Z space calculated using a 2D Eulerian FLASH code. The laser beam propagates along the Z direction as shown in (**c**). Here, a dashed-lines contour in Figure 5a shows the locations with the electron temperatures in the range of \(\approx\)0.5–8 eV.