esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Assessment of the Developed Algorithm Efficiency in Some Case Studies
### Bay of Biscay
The specific absorption coefficient of coccoliths, \(a*_{cc}\) can be assumed zero because coccoliths practically do not absorb light. The coccolithophore spectral coefficients \(a*_{coc}\) and \(b_{b}*_{coc}\) were taken from [37, 38, 39]. Thus, the second algorithm is intended not only to flag the coccolithophore blooming areas, but also to differentiate _chl_ in diatoms and coccolithophores as well as to quantify the concentration of coccoliths. The spatial distributions of _chl_, _cdom_ and _tsm_ (not illustrated here) retrieved with the LM-algorithm explicitly exhibit the impact of the river discharge along the coastline, especially in the area adjacent to the estuaries of Rivers Loire and Gironde, the most full-flowing on the French seaboard. Validation of the algorithm might be done through the analysis of spatial distributions of coccolithophores, coccoliths, and diatoms (Figure 7). As seen, in 2005 there was a very extensive coccolithophore bloom covering most of the pelagic area, but in the coastward direction it is distinctly restricted by the 200 m isobath that corresponds to a sharp shelf break. This is a clear manifestation of the importance of the bay's hydrography, and the influence of the associated fronts mentioned above. Figure 6: Comparison of _chl_ (A) and _tsm_ (B) concentrations measured _in situ_ and retrieved from remote sensing data obtained for the coastal zone of the Biscay Bay for the time period 1998 – 2004. Importantly, inasmuch as the employed algorithm yields the concentrations of coccoliths, it was possible to assess the content of suspended carbon in the surface layer. It is known that the mean concentration of carbon in a coccolith is about 0.2\(\cdot\)10\({}^{-12}\) g [40]. For the situation exemplified in Fig. 7, the area occupied by the _E. [PERSON]_ mounted up to 800 km\({}^{2}\). With the thickness of the mixing layer equaling 10 m, the entire volume in question makes up 8\(\cdot\)10\({}^{11}\) m\({}^{3}\). The mean concentration of coccoliths as determined from the satellite image constituted 8\(\cdot\)10\({}^{12}\) coccoliths/m\({}^{3}\). Thus, the total amount of inorganic carbon in the plume was assessed at 17\(\cdot\)10\({}^{12}\) g. Such assessments are important in light of the global carbon's role in the climate status of the planet [40]. Figure 7: Retrieval of spatial distributions of _chl_ of diatoms (**A**) and _E. huxley_ (**B**) as well as coccoliths (**C**) from a MODIS image taken on 5 May, 2005 over the Bay of Biscay. The maps are given in the geographic projection.
## 1 Introduction
In summary, the generation of vibrations has a significant impact on the braking performance of the brake system, posing safety risks during the braking process of high-speed trains. Currently, most studies on braking vibration are rely solely on dynamic methods, and the studies on thermal mechanical coupling effects in braking have not extensively explored the vibration mechanism. The majority of models disregard the role that thermal-mechanical coupling effects play in the braking process. Therefore, it is necessary to study the vibration characteristics under the action of thermo-mechanical coupling and the dynamic relationship among brake friction, heat, and vibration. This paper takes into account the simultaneity of the thermal coupling effect, as well as the impact of its value and change pattern on the temperature field and stress field distribution, to obtain more accurate vibration patterns and data obtained during the braking process. This paper takes the ventilated brake disc of high-speed trains as the research object. Based on the multi-body dynamics and thermal coupling method, combined with the working characteristics and actual working conditions of the brake, a multi-physics coupling simulation study was conducted on the braking process. Through thermal mechanical coupling simulation analysis of disc brakes, the temperature field, stress field distribution, and their changing rules during the braking process were obtained. By establishing a dynamic model under thermal-mechanical coupling effect, the changing rules of vibration characteristics of high speed train brake discs were studied. Additionally, the vibration of the brake disc was characterized by the vibration stability index, and the changing rules of vibration stability under different working conditions were analyzed. This paper is organized as follows: Section 2 describes the multi-body dynamics analysis method and formulation of disc brake under thermo-solid coupling. In Section 3, a finite element model of disc brake coupling is established, and a braking bench test is set up to verify the rationality of the model. Section 4 discusses the results of simulation and the rules of thermo-solid coupling on the vibration characteristics. Section 5 summarizes the key features and achievements of the work.
## 3 Experimental Section
### Synthesis of Compounds 7-**10** from Natural Products
The residue was dried in vacuum, giving the product (0.523 g, quantitative yield) in the form of a yellow powder, mp 97-100 \({}^{\circ}\)C.  
\({}^{1}\)H-NMR (400 MHz, D\({}_{2}\)O): 8 1.43 (d, \(J\) = 7.0 Hz, 3H, CH\({}_{3}\)), 3.65 (s, 3H, OCH\({}_{3}\)), 3.92 (dd, \(J\) = 7.0, 3.1 Hz, 1H, SCH), 6.39 (d, \(J\) = 3.1 Hz, 1H, NCH), 6.70 (d, \(J\) = 9.0 Hz, 2H, Ar), 6.74 (d, \(J\) = 9.0 Hz, 2H, Ar), 7.84-7.88 (m, 1H, Hquino), 7.93-7.95 (m, 1H, Hquino), 8.04-8.08 (m, 1H, Hquino), 8.18-8.20 (m, 1H, Hquino), 9.15-9.16 (m, 1H, Hquino), 9.21-9.23 (m, 1H, Hquino). \({}^{13}\)C-NMR (101 MHz, D\({}_{2}\)O): 8 19.25 (CH\({}_{3}\)), 38.18 (SCH), 55.23 (OCH\({}_{3}\)), 74.33 (NCH), 114.17, 122.18, 124.12, 126.68, 127.75, 129.65, 130.30, 131.09, 133.18, 134.52, 150.10, 150.25, 159.10. Anal. Calcd for C\({}_{19}\)H\({}_{18}\)NCIOS: C 66.36, H 5.28, N 4.07, Cl 10.31, S 9.33. Found: C 66.46, H 5.34, N 4.15, Cl 10.84, S 9.91. _2-[(3,4-Dimethoxyphenyl)methyl]-2H,3H-[1,4]thiazol[2,3,4-il]quinolin-4-ium chloride_ (**10**). A solution of sulfuryl chloride (0.067 g, 0.50 mmol) in chloroform (10 mL) was added dropwise to a solution of di(8-quinolinyl) disulfide (0.160 g, 0.50 mmol) in chloroform (10 mL), and the mixture was stirred for 10 min at room temperature. A solution of methyl eugenol (0.178 g, 1.0 mmol) in chloroform (10 mL) was added dropwise, and the reaction mixture was stirred for 1 h at room temperature and 3 h at reflux temperature. The mixture was filtered, and the solvent was removed by rotary evaporator. The residue was washed with cold hexane and dried in vacuum, giving the product (0.366 g, 98% yield) in the form of a yellow powder, mp 179-180 \({}^{\circ}\)C.  
\({}^{1}\)H-NMR (400 MHz, D\({}_{2}\)O): 8 2.76-2.82 (m, 1H, CH\({}_{2}\)), 2.93-2.98 (m, 1H, CH\({}_{2}\)), 3.56 (s, 3H, OCH\({}_{3}\)), 3.57 (s, 3H, OCH\({}_{3}\)), 3.83-3.86 (m, 1H, SCH), 4.82-4.85 (m, 1H, NCH\({}_{2}\)), 5.12-5.16 (m, 1H, NCH\({}_{2}\)), 6.45-6.51 (m, 2H, Ar), 6.56 (s, 1H, Ar) 7.56-7.65 (m, 2H, Hquino), 7.80-7.82 (m, 1H, Hquino), 7.90-7.92 (m, 1H, Hquino), 8.89-8.94 (m, 2H, Hquino). \({}^{13}\)C-NMR (101 MHz, D\({}_{2}\)O): 8 37.20 (CH\({}_{2}\)), 37.56 (SCH), 55.43 (OCH\({}_{3}\)), 55.53 (OCH\({}_{3}\)), 62.18 (CH\({}_{2}\)N), 111.22, 112.45, 121.79, 121.92, 125.95, 126.85, 128.63, 129.49, 130.72, 132.92, 133.20, 146.92, 147.55, 148.92, 149.03. Anal. Calcd for C\({}_{20}\)H\({}_{20}\)NCIO\({}_{2}\)S: C 64.25, H 5.39, N 3.75, Cl 9.48, S 8.58. Found: C 63.73, H 5.21, N 4.01, Cl 10.01, S 8.99.
# Study on Nitrogen-Doped Graphene Ink and Its Effects on the Heat Dissipation for the LED Lamps
## 3 Experiment Description
### Experiment of Nitrogen-Doped Graphene
#### 3.1.3 Micro-Raman Spectroscopy  
In this study, the micro-Raman spectroscopy system consisted of a spectrometer (TRIAX 320, Jobin-Yvon, 1200 gr/mm grating), a thermoelectric cooling charged-coupled device (TE-cooling CCD) (ANDOR DU401A-F1, cooling temperature: \(-\)70 \({}^{\circ}\)C), a microscope (GX-71, OLYMPUS), and an unpolarized DPSS (Diode Pumping Solid State) laser [21]. The wavelength of the Raman spectrum experiment was 514 nm, and the scanning range was 1000 cm\({}^{-1}\)-3000 cm\({}^{-1}\) with the accuracy of \(\pm\)1 cm\({}^{-1}\).
# On the Effect of Filling Rate on Positive Macrosegregation Patterns in Large Size Cast Steel [PERSON]
## 3 Results and Discussions
### Effect of Filling Rate on Fluid Flow and Solidification Time
In addition, secondary dendrite arm spacing (SDAS) measurements along the cutting section of the LFR ingot revealed SDAS values as large as 536 \(\upmu\)m in the 1/2 radius position from the center and 474 \(\upmu\)m in the ingot center. The two observed regions solidified in the intermediate or final stage of the solidification process. The SDAS values were also in the same range for the HFR ingot but slightly smaller (530 \(\upmu\)m and 470 \(\upmu\)m, respectively, in the corresponding regions), as presented in Figure 7. Lower SDAS values are generally correlated with shorter solidification times [26]. Furthermore, as reported by [PERSON] et al. [35], higher filling rates result in increased dissipation rate of the melt superheat with the mold and accelerated solidification rate due to the higher strength of convection. Therefore, the findings of the shorter solidification time resulting from higher filling velocity could result in less time available for solutes to transport, and could be another source for the lower segregation intensity in the casting body of HFR. It should be noted that further experimental work on measuring the SDAS values in different locations of the ingots will contribute to confirm and better quantify the differences in the SDAS values for each condition. Figure 6: Grain morphologies in the bordering zones (the chill walls are on the left) of (**a**) LFR; (**b**) HFR. Figure 7: Micrographs along the bottom of the cutting section (30 cm below the hot-top/casting body separation position) of ingots LFR (left) and HFR (right): (**a,c**) In the 1/2 radius position from the center; (**b,d**) In the center. The shorter solidification time and the lower diffusion coefficient of the solutes are probably the main sources for the observed lower location (~5 cm) of the positive manganese and chromium concentration cores in HFR (as reported in Figures 1 and 4). Besides, no sufficient diffusion time could be the origin for the observed higher fluctuations in the concentrations of manganese and chromium in the radial and axial distributions in HFR (as seen in Figures 4 and 5). The reduced segregation in the casting body of HFR, produced under fast filling conditions, is probably the root cause for the higher segregation intensity observed in the hot-top of HFR. Indeed, a lower segregation level in the casting body results in more partitioning of solutes into the melt and their accumulation towards the end of solidification in the upper part of the hot-top [4]. The axial less severe segregation of chromium in both the casting body and the hot-top of HFR could be due to less available time for the diffusion of the solute.
## 3 Materials and Methods  
### Research Design  
The research process was complex and was structured into five distinct phases. The first phase involved defining the research theme and purpose. The second phase entailed a comprehensive analysis of the literature and the development of research hypotheses based on a deep understanding of how technologies, such as AI, IoT, BD, and CC, can contribute to achieving SDGs. The third phase was dedicated to collecting relevant data panels, using the DESI to measure the level of digital technology adoption in EU countries and the Sustainable Development Reports 2023 to illustrate the levels of SDG achievement for each EU country [3].  
In the fourth phase, data panels were processed and interpreted using two statistical methods involving structural equation modeling (SEM). SEM enables the study of complex relationships between multiple variables, both observed, such as the components of DESI and GDP per capita, and latent, such as economic performance. Moreover, SEM is well-suited for handling multidimensional constructs, such as DESI, which comprises diverse components, including digital public services, human capital, and digital technology integration, thus ensuring a comprehensive evaluation of digitalization's impact. Furthermore, SEM facilitates hypothesis testing by assessing the fit of the theoretical model against empirical data, ensuring that the proposed relationships are statistically robust. In the context of this study, SEM allows for the rigorous testing of the research hypotheses, quantifies the mediated effects, and delivers reliable results that enhance the understanding of how digital technologies can accelerate progress toward sustainable development goals in the European Union.  
The final phase involved formulating discussions and conclusions, highlighting the significant influences of emerging technologies on SDGs and the mediating effect of economic performance, measured by GDP per capita. Although the study has limitations, including a regional focus, its conclusions contribute to a deeper understanding of how technological innovations can be leveraged to accelerate progress toward a more sustainable future.
## 3 Database and Data Collection  
The Pahang-Selangor Raw Water Tunnel was constructed by a Japanese firm. Consequently, the JH system was employed in the tunnel rock mass characterization. To do this, the tunnel was divided in three zones: right, left and center sides as shown in Figure 4.  
Figure 3: Schematic cross section of the tunnel alignment.  
Figure 2: Geologic map of the study area.  
Each of these zones were characterized using the JH system described in Section 1.1. For the intact rock strength, Schmidt hammer measurements were made and converted to UCS values. The tunnel face was mapped by geologist to provide the needed information to calculate the grade points for each zone. The final grade point was a weighted average of the grade points of the three zones. The tunnel was mapped every four (4) to ten (10) meters along the length of the tunnel. The database used for this paper consists of 180 rock mass data and 79,813 TBM operating data points. This dataset represents 11.6 km of the tunnel from chainage 6.85 km to chainage 18.59 km.
## 3 Results  
### Prototype Example  
In this section, an example of differential dual-band BPF filter design is reported. The first passband corresponds to the L1 GPS signal at 1.575 GHz. A fractional bandwidth of 8.86% is considered. The second passband is designed to be allocated in the first Wi-Fi frequency at 2.45 GHz, with a fractional bandwidth of 5.69%. The filter is implemented on a substrate with the following features: dielectric constant \(\varepsilon_{r1}=\varepsilon_{r2}=3.0\), and thicknesses \(h_{sub}=1.016\) mm. As explained in detail in previous sections, the first step in the design process is to obtain the resonator dimensions to set the two desired center frequencies, which are controlled by resonator lengths (recall that resonance arises when the length of the resonators is half the guided wavelength at the desired center frequencies). Once resonators are designed, design curves in Figures 6 and 7 are used to set the desired matching level and fractional bandwidth. Following this procedure, the final dimensions of the proposed dual-band differential filter in Figure 2 are those of the caption of Figure 4, where we have represented the final simulated filter response. This figure exhibits two differential passbands well allocated. The common-mode rejection level is very satisfactory too. To verify experimentally these results, a prototype has been fabricated and measured. Results are presented in next subsection.
# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
## 3 Results
### Analysis of Influencing Factors  
Considering the guiding significance to current practice, we selected the newest year, 2018, to construct the influencing factor model. Previous studies commonly divided the above-ground water storage factors into socioeconomic and climatic factors. We selected the population (POP), artificial surface coverage rate (AS), cultivated area (CA), and gross domestic product (GDP) as the socioeconomic factors, and evapotranspiration (ES), precipitation (PRE), temperature (T), and vegetation surface rate (VS) as climatic factors. Correlation analysis shows that these parameters have multicollinearity (Table 6).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Correlation Coefficient** & **AS** & **GDP** & **POP** & **ES** & **PRE** & **T** & **VS** & **CA** \\ \hline AS & \(1.00\) & \(0.77\) & \(0.82\) & \(0.63\) & \(0.09\) & \(0.20\) & \(-0.50\) & \(-0.24\) \\ GDP & \(0.77\) & \(1.00\) & \(0.97\) & \(0.45\) & \(0.02\) & \(0.12\) & \(-0.41\) & \(-0.20\) \\ POP & \(0.82\) & \(0.97\) & \(1.00\) & \(0.46\) & \(0.02\) & \(0.12\) & \(-0.45\) & \(-0.25\) \\ ES & \(0.63\) & \(0.45\) & \(0.46\) & \(1.00\) & \(0.51\) & \(0.74\) & \(0.23\) & \(0.27\) \\ PRE & \(0.09\) & \(0.02\) & \(0.02\) & \(0.51\) & \(1.00\) & \(0.40\) & \(0.49\) & \(-0.02\) \\ T & \(0.20\) & \(0.12\) & \(0.12\) & \(0.74\) & \(0.40\) & \(1.00\) & \(0.56\) & \(0.42\) \\ VS & \(-0.50\) & \(-0.41\) & \(-0.45\) & \(0.23\) & \(0.49\) & \(0.56\) & \(1.00\) & \(0.52\) \\ CA & \(-0.24\) & \(-0.20\) & \(-0.25\) & \(0.27\) & \(-0.02\) & \(0.42\) & \(0.52\) & \(1.00\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Correlation coefficients of influencing factors.  
Figure 16: The spatial distribution of degradation zones.  
We built a principal components regression model to explore the relationship between the influencing factors and the multidimensional water provisioning services in Minnesota. The results are as follows:  
\[P=1.64-2.19 AS-1.06 GDP+0.34 POP-0.22\text{ES}+0.66\text{PRE}-0.89T-1.62 VS-0.43\text{ CA} \tag{14}\]  
\[Q=1.34+1.44 AS+0.80 GDP+1.03 POP-1.53 ES+0.84\text{PRE}-1.98T-0.19 VS-0.67\text{CA} \tag{15}\]  
\[D=-0.47-0.32 AS+0.51 GDP+0.42 POP-0.03\text{ES}+1.84\text{PRE}+0.39T-1.05 VS+0.16\text{ CA} \tag{16}\]  
The F test shows that the above model is credible within a 95% confidence interval (Supplementary Table S2).
# A Study on the Impact of Low-Frequency Random Loading and Unloading on Silo Structures
## 5 Conclusions  
This work conducted a systematic study on the frequent occurrence of concrete cracking and peeling on the walls of large-diameter reinforced concrete silos during the loading and unloading process in practical applications. Through a combination of field monitoring and numerical simulation analysis, the following conclusions were drawn:  
Figure 17: Fatigue life analysis results.  
Based on in situ monitoring results, it was found that during the filling process, the minimum measured wall pressure gradually increases, while it gradually decreases during the unloading process. The timing of the minimum pressure at the same elevation follows a 4:3 ratio. The pressure follows a specific fluctuation pattern during the unloading process, and the time interval between extreme points is approximately 15 min. The load in the non-unloading area is 1.43 times that of the load in the unloading area. Comparing the calculated values from the specification's horizontal pressure at different elevations with the measured values, it was discovered that there are fluctuation and rotation effects in the actual force on the silo wall.
2. Focusing on the problem of the rotation effect, an elastoplastic finite element numerical simulation analysis of the silo wall structure under uneven loading conditions was carried out. The analysis results show that, when zones 5 and 7 are used as unloading areas, the silo wall experiences the most severe tensile damage, which is defined as the most unfavorable loading condition.
3. Addressing the fluctuation effect, this study adopted a co-simulation method using Abaqus and Fe-safe. It is the first study to propose a fatigue life prediction model for reinforced concrete silo walls, and we verified the applicability of this prediction method. This has significant implications for the design, usage, and life analysis of reinforced concrete silos.  
Conceptualization, methodology, writing--review and editing, investigation, and validation, R.L. and D.L.; software, formal analysis, writing--original draft preparation, data curation, and visualization, R.L.; supervision, D.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this research are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Sixty-Nine-Element Voice Coil Deformable Mirror for Visible Light Communication
## 2 Theory
### Power Dissipation and Efficiency of Micro VCA  
To quantify the performance of the micro VCA, the efficiency \(\varepsilon\) as the factor of metrics is used [17]. It is defined as  
\[\varepsilon=\frac{F}{\sqrt{P}}. \tag{3}\]  
In Equation (3), the unit of \(\varepsilon\) is \(\mathrm{N}/\mathrm{W}^{1/2}\), \(F\) is the electromagnetic force, and \(P\) is the power dissipation of the micro VCA that is equal to \(I^{2}R\). From Equations (2) and (3), \(\varepsilon\) could be written as  
\[\varepsilon=\frac{F}{\sqrt{I^{2}R}}=\int_{V}B_{r}\cdot\
abla\int_{\mathrm{L}} \frac{1}{4\pi}\frac{\mathrm{d}\mathbf{l}\times\mathbf{e_{r}}}{\mathbf{r}^{2}\sqrt{R}}d\mathbf{ v}. \tag{4}\]  
Figure 1: Structure of micro VCA.  
According to Equation (4), the efficiency is a physical quantity independent of the coil current, evaluating the structural parameters of the actuator. A larger value of efficiency means that VCA could generate a larger force with less power dissipation. Therefore, the larger the efficiency of VCA is, the better VCA will be.  
The optimized VCA is also assembled into the DM for thermal analysis. Meanwhile, the response speed of the DM is also very important, so it is necessary to carry out a force-velocity analysis of the VCA and a modal analysis of the mirror. Finally, the statics of the mirror is analyzed, and the influence function related to the correction ability is obtained.
## 3 Results
### Potential NPP Calculated by Miami Model  
The total potential NPP (PNPP) calculated by the Miami model in eastern Inner Mongolia ranged from 163.5 Tg C yr\({}^{-1}\) to 210.2 Tg C yr\({}^{-1}\)(1 Tg = 10\({}^{12}\) g) during the period of 2000-2017, with an average of 187.2 Tg C yr\({}^{-1}\). The total PNPP of the grasslands changed with fluctuations from 99.6 Tg C yr\({}^{-1}\) to 136.3 Tg C yr\({}^{-1}\) in the same period, with an average of 120.5 Tg C yr\({}^{-1}\), accounting for 64.3% of the total PNPP. The multi-year (2000-2017) averaged annual PNPP showed a gradient increasing from the west to the east (Figure 5a), which ranged from 74.5 to 146.1 g C m\({}^{2}\) yr\({}^{-1}\). Compared with Xilingol League and Hulunbuir City, the more abundant precipitation and warmer temperature in Xing'an League (Figure 2) makes the PNPP there larger than that in other regions. Meanwhile, as shown in Figure 5b, the average PNPP showed a slight increase trend in most areas of eastern Inner Mongolia except the west of Xilingol League.  
Figure 3: The variation in the total ANPP (blue) and total grasslands NPP (green) of eastern Inner Mon-golia from 2000 to 2017.