esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Automatic Monitoring and Control Method for Middle Rock Pillars
### Determination of the Stability of the Middle Rock Pillar  
The monitoring data of six monitoring points arranged on the vault and arches of the tunnel section were selected as independent variables. According to the calculation formula obtained in a previous study [31], the displacement value of the upper and central regions of the middle rock pillar could be calculated. The calculated displacement was compared with the corresponding limit value to determine the stable state of the middle rock pillar.  
In a previous study [31], the relation between the horizontal displacement \(H\) of the central region of the middle rock pillar and the convergence of the horizontal clearance \(X_{2}\) of the ramp and horizontal clearance \(X_{3}\) of the main line tunnel was Formula (2).  
\[H=0.296X_{2}-0.240X_{3}-0.190 \tag{2}\]  
where \(X_{2}\) is the difference between the horizontal displacement \(H_{5}\) of the right arch waist of the ramp and the horizontal displacement \(H_{6}\) of the left arch waist of the ramp. \(X_{3}\) is the difference between the horizontal displacement \(H_{4}\) of the right arch waist of the main tunnel and the \(H_{3}\) of the right arch waist of the main tunnel.  
Figure 11: Schematic diagram of displacement numerical processing.  
According to a previous study [31], the relationship between the vertical displacement of the upper region of the middle rock pillar \(V\) and the ramp vault settlement \(V_{2}\) and the arch settlement \(V_{1}\) of the main tunnel is shown in Formula (3).  
\[V=1.216V_{2}+0.183V_{1}+6.069 \tag{3}\]  
In a previous study [31], the horizontal and vertical displacement criteria for the stability of the middle rock pillar were given, as shown in Formula (4).  
\[\left\{\begin{array}{l}H=0.296X_{2}-0.240X_{3}-0.190\leq-1.21\\ V=1.216V_{2}+0.183V_{1}+6.069\leq-7.00\end{array}\right. \tag{4}\]  
When the horizontal displacement of the central region of the middle rock pillar \(H\) calculated by the convergence of the horizontal clearance of the ramp and the main line tunnel was \(\leq\)\(-\)1.21 mm, and the vertical displacement of the central region of the middle rock pillar \(V\) calculated by the settlement of the ramp and the arch of the main line tunnel was \(\leq\)\(-\)7.0 mm, the middle rock pillar was in a stable state.  
The distance data measured by the laser rangefinder were entered into the computer remotely through the device interface to complete the calculation and the stability judgment of the middle rock pillar. If \(H\) and \(V\) were less than the limit values, it was considered that the middle rock pillar was stable. If either \(H\) or \(V\) was greater than the limit value, it was considered that the middle rock pillar may already be in an unstable state and needs to be strengthened by auxiliary measures.
## 4 Conclusions and Outlook  
In this article we have presented a sensor web based approach which combines earth observation and _in situ_ sensor data to derive near real-time vegetation productivity products. A prototype application for monitoring GPP over the Netherlands was successfully developed and implemented within an automated processing facility. Continuous GPP maps are provided to the user through a web mapping service which not only provides functionality for spatial analysis but also includes functionality to present time-series for selected locations.  
In order to achieve an added value for end-user applications using increasingly available real time earth observation data, they need to be combined with _in situ_ sensor data and environmental models to derive higher level products relevant for environmental resource management. In this study, interoperability between sensor data streams and connection with the information system was achieved by using open-source standards for SWE and WMS. For example, meteorological data were obtained in a standardized way through a Sensor Observation Service as developed within OGC-SWE. Use of common standards is an important requirement for upscaling of the developed facility, both in terms of number of sensors and inclusion of new sensor types.  
Further development of the presented approach will focus on the establishment of an integration platform for near real-time assimilation of sensor data sources into simulation models. Focus will be on integration of multi-source sensor data streams and the opportunities for remote sensing data fusion to improve spatial resolutions to relevant management units (\(<30\) m).
# Finite Element Analysis on Block Shear Mechanism of Lean Duplex Stainless Steel Welded Connections
## 3 Experimental Investigation
### Specimen Design and Test Setup  
Based on the previous experimental results of austenitic stainless steel fillet welded connections failing in block shear, the geometric configuration and test setup illustrated in Figure 2 were designed. The fillet welded connection specimens consisted of two 6.0 mm thick lap steel plates welded on both sides of 3.0 mm thick steel sheets. All fillet welds were manually fabricated by only one skillful welder to ensure the welding quality and to minimize the variation of weld profiles. A gas tungsten arc welding (GTAW) process was used in the present work with an inverter DC pulse welding machine (TIG 500). The weld size of the fillet weld was designed to be 5 mm. The weld length on the test side was shorter than the other side, so that the fillet welded connection would always fail in the test region.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen** & \(\mathbf{f_{base}}\) **(mm)** & \(E\) **(GPa)** & \(\mathbf{F_{y}}\) **(MPa)** & \(\mathbf{F_{u}}\) **(MPa)** & \(\mathbf{F_{y}}\) **(\%)** & \(\mathbf{\varepsilon_{50}}\) **(\%)** \\ \hline SDT30-1 & 2.81 & 205 & 446 & 726 & 1.63 & 43.7 \\ SDT30-2 & 2.81 & 207 & 444 & 726 & 1.63 & 44.0 \\ SDT30-3 & 2.80 & 221 & 443 & 731 & 1.65 & 43.8 \\ Average & 2.81 & 211 & 444 & 728 & 1.64 & 43.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Averaged material properties of lean duplex stainless steels.  
Figure 1: Stress-strain response of lean duplex stainless steel.  
To investigate the block shear response of various welded configurations, block length (\(L\)) was varied with a constant value of block width (\(W\)), which results in the block shear aspect ratios (\(L/W\)) ranging from 0.69 to 1.47. Before testing, block length and block width that include the weld size were carefully measured at three different locations. The test specimens were labelled to easily identify nominal dimensions. For example, the prefix \"\(L\)\" in the label denotes block length, while the letter \"W\" denotes block width. For repeated tests, the suffix \"a\" or \"b\" is presented in the label. All welded connection specimens were tested in the same manner as the tension coupons described in the preceding section. Two linear variable different transformers (LVDTs) were used to measure the displacements of the connection specimen.
## 4 Conclusions  
In this study, we successfully extruded a multichannel cylindrical-tube-type support using alumina powder of various particle sizes with minimum linear shrinkage. An alumina coating was deposited twice to prepare a homogeneous microfiltration membrane. The mean pore size of the alumina support (0.8 \(\upmu\)m) was reduced to 0.07 \(\upmu\)m after the microfiltration membrane coating. The flux performance of the extruded support and microfiltration membranes was analysed. The multichannel extruded support showed a pure water permeability of 614 L\(\cdot\)m\({}^{-2}\).h\({}^{-1}\).bar\({}^{-1}\), while the double-layered microfiltration membrane exhibited a pure water permeability of 311 L\(\cdot\)m\({}^{-2}\).h\({}^{-1}\).bar\({}^{-1}\).  
The effectiveness of the SiO\({}_{2}\) modification on the pristine alumina membranes was demonstrated. The SiO\({}_{2}\)-coated membranes exhibited higher flux than the pristine MF membranes. The negatively charged SiO\({}_{2}\)-modified membrane inhibited the deposition of HA by strong electrostatic repulsive forces. Finally, our multichannel cylindrical tube type membrane support was effective for microfiltration.  
[PERSON]-[PERSON] conceived of and designed the experiments; [PERSON] performed the experiments, analysed the data, and wrote the paper; [PERSON]-[PERSON] and [PERSON] contributed to the formal analysis and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by (1) the National R&D Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (grant number 2020M3H4A3106359) and (2) the Technology Innovation Program (20003782) of the Ministry of Trade, Industry and Energy.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: Normalised flux behaviour of pristine microfiltration (MF) membrane and surface-modified microfiltration (MF) membrane with humic acid (HA) solution (10 mg/L, pH 6.5).
## 1 Introduction
In addition, it discusses the impact of environmental factors such as turbidity, salinity, temperature, and pressure on VLC performance, giving an outlook on the design of robust communication systems for diverse aquatic environments. Realistic applications of underwater VLC in scenarios such as environmental monitoring, underwater discovery, and AUVs are also discussed in the paper while keeping in view the security and privacy concerns of underwater communication. Lastly, the paper concludes with future research prospects, such as the integration of other new emerging technologies like quantum communication and Reconfigurable Intelligent Surfaces (RISs) for further enhancing underwater VLC systems' performance and reliability. The remainder of this paper is structured as follows.
## 2 Materials and Methods
### Cell Design  
A schematic diagram of the new sample cell used for NR measurements is shown in Figure 1. The liquid-liquid interface was prepared inside a cuboid quartz liquid cell (60 mm \(\times\) 40 mm \(\times\) 40 mm). A polytetrafluoroethylene (PTFE) frame was placed at the bottom of the cell to maintain a flat and smooth interface with the aqueous liquid.  
The interface appeared at the plateau of the convex meniscus of the aqueous phase with respect to the organic phase. The top of the sample cell was capped with a PTFE cap to prevent evaporation of the aqueous and organic solutions and to avoid additional moisture intrusion. The cell was mounted on the reflectometer's sample stage and then an appropriate volume (8.5-9.0 mL, depending on the combination of organic solvent, aqueous solution and surfactant) of aqueous and organic solutions was added to the sample cell in order of the density of the liquid. The thickness of the upper layer was \(\sim\)3 mm (it depends on the amount of the upper layer solvent) and the neutron beam was injected through the side of the sample cell. The thickness of the upper layer (3 mm) was sufficient for the measurement of the NR data, because the width of the beam slit in front of the sample was 0.17 mm. Note that, in the neutron transmittance measurements of the sample cell system, 20 mL of toluene-\(d_{8}\) was added to the sample cell to increase the thickness of the upper layer. Because of the problem of neutron transmittance, the solvent of the upper layer must be deuterated to provide high neutron transmittance.
# A Deconvolutional Deblurring Algorithm Based on Dual-Channel Images
## 6 Conclusions  
In this paper, the advantages and disadvantages of the existing blind deconvolution deblur algorithms for single-channel images and multi-channel images were first analyzed, and then the characteristics of large-scale space-variant blurred images were studied. Based on synthesizing single-channel blind deconvolution and multi-channel blind deconvolution algorithms, and through theoretical derivation and experimental verification, a practical dual-channel blind deconvolution algorithm was proposed. By dividing the two-channel blurred image into blocks, the algorithm cleverly uses all the information of the image block as the correlation coefficient, takes into account the correlation of the local image and the utilization rate of the global information, and also deals with the space-variant problem of space-variant images. In the kernel estimation step, the correlation coefficient and correlation information of the dual-channel image is used to reconstruct the two iterative parts of the kernel. In the restoration part of the block image, the clustering algorithm is used to calculate the weight of the kernel, to restore and reconstruct the entire image. The final experimental results showed that the algorithm fulfilled the design expectations, and the improvement to the existing algorithm is obvious.  
In the process of derivation, it was found that the algorithm still has space for further development. First, the number of methods for further enhancement can be increased. According to the derivation of [11], the more channels involved in the deblurring process, the better the restoration effect may be. A specific theoretical derivation and implementation need to be performed in the next step. Second, the block clustering restoration part of the image can also be optimized, and the weight of the corresponding pixels in the block image can be more accurately determined by introducing regularization.  
Conceptualization, Z.T., Y.B. and Q.L.; methodology, M.H.; software, Y.B., P.H.; investigation, Z.T., M.H. and Q.L.; resources, Z.T.; data curation, Y.B.; writing--original draft preparation, Y.B.; writing--review and editing, Y.B. and Z.T.; supervision, Z.T., M.H. and Q.L.; project administration, Z.T., M.H. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by: the National Natural Science Foundation of China (No. 61635002); the National Defense Science and technology innovation fund of the Chinese Academy of Sciences (No. CXJJ-20S016); Science Technology Foundation Strengthening Field Fund Project (No. 2020-JCJCJ-JJ-492).  
Not applicable.  
The authors declare no conflict of interest.
# Propagation Characteristics of High-Power Vortex Laguerre-Gaussian Laser Beams in Plasma
## 5 Discussion  
From Figures 3 and 5, it is noted that the self-focusing and absorption phenomena of high-power laser beams in plasma are evident as compared with those propagating in free space as illustrated in Figures 1 and 2. The physical model of permittivity with ponderomotive nonlinearity given by Equation (8) determines that the refractive index of plasma is smaller at the place where the light intensity is lower and larger where the light intensity is stronger. The ensuing inhomogeneous distribution of refractive-index attribute to the self-focusing effect and the laser beam is therefore focused when this effect overly counteracts the natural diffractive divergence of the laser beam. This is apparent from Figures 4 and 6 in which the cross-sectional profiles of the light fields of \(\text{LG}_{0}^{1}\) and \(\text{LG}_{1}^{2}\) vortex laser beams within the plasma first experienced self-focusing and then stochastic absorption. Video S2 and Video S4 vividly displayed the rotations and temporal evolutions of the cross-sectional light fields of \(\text{LG}_{0}^{1}\) and \(\text{LG}_{1}^{2}\) laser beams, which demonstrate the vortex features of the LG laser beams. We also note that the field distributions of the \(xOz\) and \(yOz\) planes are different from each other since the two laser beams are both linearly polarized in the \(x\) direction.  
We also highlight the ribbons with half-wavelength intervals in Figures 3 and 5. Since the incident laser beams are assumed spatially coherent, the transient light intensity proportional to \(E^{2}(r,z,t)\) is also harmonic with a half-wavelength period. In each half period of space, the electrons at the place with high transient light intensity are pushed to the place with low transient light intensity. According to Equations (4) and (9), this intensifies the difference of refractive indices at the two places and the half-wavelength electron-free elongated cavities are formed. The ensuing high reflection of light at the walls of cavities causes the ribbon phenomenon. Moreover, the speckles found in Figures 4 and 6 can be attributed to the Rayleigh-Taylor fluid mechanics instability [19] under the local oscillation electronic acceleration mechanism. Once the initial laser beam has a small-scale perturbation or is disturbed by the unevenly medium when it propagates in the plasma, a rapid light intensity growth will occur in the near region that forms a speckle in the cross-sectional patter of the laser beam. It can be estimated that different modulation effects of light fields can be achieved by using LG laser beams of other polarization states, such as the radial and azimuthal polarizations. Thus, the plasma can serve as a new medium for modulating the LG beams into the desired field patterns.
## 2 Materials and Methods
### In Vitro Digestion Model System  
An in vitro digestion model system was used with slight modifications from the previous studies in order to measure the digestive stability of \(\omega\)-3 FAs either alone or combined with GTE35, GTE65, NFGT, and AdiphenonTM[26; 27]. In this study, the \(\omega\)-3 FAswere composed of EPA and DHA in a 1:1 (\(v\):\(v\)) ratio. An aliquot of \(\omega\)-3 FAs (300 \(\mathrm{\SIUnitSymbolMicro g}\)), GTE35 (600 \(\mathrm{\SIUnitSymbolMicro g}\)), GTE65 (600 \(\mathrm{\SIUnitSymbolMicro g}\)), NFFT, which included 180 \(\mathrm{\SIUnitSymbolMicro g}\) of NFFT30 and 600 \(\mathrm{\SIUnitSymbolMicro g}\) of NFFT100, and 300 \(\mathrm{\SIUnitSymbolMicro g}\) of Adiphenon(tm) were suspended in 1 mL of 20 mM PBS. The concentrations of samples were determined based on their recommended daily intake levels, as specified by the Ministry of Food and Drug Safety (MFDS, 2020). The digestive process that simulated human body digestion was conducted as follows: 700 \(\mathrm{\SIUnitSymbolMicro L}\) of \(\alpha\)-amylase, which included 2 unit/mL of 20 mM PBS, was added to the sample for the salivary digestion, and the pH was then adjusted to 6.9 \(\pm\) 0.1. The samples were then mixed in a shaking water bath at 37 \({}^{\circ}\)C at 200 rpm for 5 min in order to mimic the masticatory motion. A volume of 1.4 mL of pepsin, which included 20 mg/mL in 0.1 M HCl, was added when the samples reached the gastric phase, and the pH was adjusted to 2.0 \(\pm\) 0.1 with 1 M HCl. The samples were then shaken at 37 \({}^{\circ}\)C at 200 rpm for 60 min. The mixtures were progressed to a simulated small intestine phase. The pH was adjusted to 5.3 \(\pm\) 0.1 for the upper small intestinal phase using 1 M NaHCO\({}_{3}\). A volume of 700 \(\mathrm{\SIUnitSymbolMicro L}\) of bile acid, 700 \(\mathrm{\SIUnitSymbolMicro L}\) of lipase, and 700 \(\mathrm{\SIUnitSymbolMicro L}\) of pancreatin were then added to the mixture, and the pH was increased to 7.0 \(\pm\) 0.1 using 1 M NaOH. The mixtures were incubated for 120 min in a bath at 37 \({}^{\circ}\)C at 200 rpm. All samples were adjusted to the same volume after digestion by adding 20 mM of a phosphate buffer. The mixtures were subsequently centrifuged for 30 min at 4000 rpm at a refrigerated temperature of 4 \({}^{\circ}\)C. The separated supernatant was used for further analyses. The supernatants of the sample were measured using HPLC-UV.
## Appendix A Multiple Read Dominant Pole (MRDP) Controller Tuning  
The MRDP method (see [17]) avoids \"slow\" closed-loop poles by requiring all poles to be equally located. From (10), an \(m+2\)-tuple real dominant pole \(s_{o}\) of the quasi-polynomial  
\[P(s)=T_{i}s^{2}e^{T_{i}s}+K_{c}K_{s}(1+sT_{i})(1+T_{1}s+\dots+T_{m}s^{m}) \tag{11}\]  
has to fulfil the requirements  
\[\left[P(s);\ \ \frac{dP(s)}{ds};\dots\ \frac{d^{m+2}P(s)}{ds^{m+2}}\right]_{s=s _{o}}=\mathbf{0} \tag{12}\]  
For dimensionless parameters  
\[\sigma=sT_{d};\ \kappa=K_{c}K_{s}T_{d};\ \tau_{i}=\frac{T_{i}}{T_{d}};\ \tau_{j}=\frac{T_{j}}{T_{d}^{j}};\ j=1,2,\dots,m \tag{13}\]  
one obtains  
\[\frac{d^{m+2}P(\sigma)}{d\sigma^{m+2}}=e^{\sigma}\tau_{i}\left[\sigma^{2}+2( m+2)\sigma+(m+2)(m+1)\right]=0 \tag{14}\]  
Solving (14) yields an \(m+2\)-tuple real \"**dominant**\" root (i.e., located in the left half-plane closer to the origin/imaginary axis)  
\[\sigma_{o}=\sqrt{m+2}-(m+2)<0 \tag{15}\]This means that the equivalent dominant closed-loop time constant  
\[\tau_{o}=-\frac{1}{\sigma_{o}}=\frac{T_{o}}{T_{d}} \tag{10}\]  
is decreasing with increasing \(m\). Solutions meeting the remaining conditions (11) and found using computer algebra support are in Table 11. The maximum \(m=7\) corresponds to the so-far calculated and verified solutions.
# A Review of Pool-Boiling Processes Based on Bubble-Dynamics Parameters
## 5 Bubble Departure Frequency
### Bubble Waiting Time \(t_{w}\)  
The bubble waiting time is the time interval from the detachment of the bubble to the start of next nucleation from the same cavity, representing the process by which the superheated wall activates the nucleation site by natural convection. [PERSON] et al. [93] suggested a waiting-time correlation according to the guidelines of bubble nucleation and flow theory, and Equation (42) was proposed for the waiting period by simplifying the temperature distribution equation. The irregularity in the bubble waiting periods was attributed to random differences in the temperature of the liquid that flows in after the bubbles have left.  
\[t_{w}=\frac{9}{4\pi a_{l}}\bigg{\{}\frac{(T_{w}-T_{l})R_{c}}{T_{w}-T_{sat}[1+ (2\sigma/R_{c}\rho_{v}h_{lv})]}\bigg{\}}^{2} \tag{42}\]  
[PERSON] et al. [94] developed a detailed analysis of the relationship between cavity size and bubble waiting time for a given wall temperature. The exact radius value of the nucleation cavity in the experiment is difficult to calculate. Thus, they analyzed the liquid-temperature line and the system-equilibrium-temperature profile to obtain the minimum bubble waiting time, and their model is shown in Equation (43).  
\[t_{w,min}=\frac{144(T_{w}-T_{l})T_{sat}^{2}\sigma^{2}}{\pi a_{l}\rho_{v}^{2}h_ {lv}^{2}(T_{w}-T_{sat})^{4}} \tag{43}\]  
[PERSON] et al. [95] proposed the relationship between the bubble waiting time and the growth time about monolithic fluids, and the bubble waiting period can be simply estimated by tripling the bubble growth period, i.e.,:  
\[t_{w}=3t_{g} \tag{44}\]