esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Ion Chromatography (IC) Measurements  
Soluble salt determination was conducted using an ultrasound-assisted extraction method [44], which is an effective alternative to the standard EN 16455/2014 [45] procedure. A single representative aliquot was used for each of SP1 and SP2. These were obtained by combining material from both the surface and bulk portions of the respective stones, to provide an integrated overview of their ionic contents. The lithic fragments were finely ground, homogenized, and dried. Then, 0.1 g of each powder was suspended in 100 mL of Milli-Q water. The samples were introduced in an ultrasound bath for 2 h with a frequency of 40 kHz. For the extract filtration, Millipore 0.45 \(\upmu\)m PTFE disk filters were used, and the solutions were stored at 4 \({}^{\circ}\)C in polypropylene tubes until the analysis.  
A modular chromatographic and titrimetric system was used for the simultaneous quantitative analysis of dissolved cations, anions, carbonate, and bicarbonate in the prepared solutions. The Metrohm analytical system consisted of two 930 Compact IC Flex systems configured as a dual-channel ion chromatograph, with IC conductivity detectors, an 888 Titrando automatic titrator, and an 815 Robotic USB Sample Processor for high-throughput automation.  
In the case of anions, a Metrosep A Supp 7-250/4.0 column was used for the separation, and 3.6 mM Na\({}_{2}\)CO\({}_{3}\) was used as the mobile phase, at a flow rate of 0.8 mL/min, with an MSM (Metrohm Suppress Module) for chemical suppression to achieve a baseline of 12 uS/cm. Moreover, an MCS (Metrohm CO\({}_{2}\) Suppressor) was also used to obtain a baseline below 2 uS/cm and, therefore, to improve the limits of detection. The quantification of cations was conducted using a Metrosep C 6-150/4.0 column from Metrohm. HNO\({}_{3}\) 1.7 mM/dipicolini acid 1.7 mM was used as the mobile phase at a flow rate of 1 mL/min. The system has inline ultrafiltration technology, which filters the samples down to 0.2 um particle size, with generic cellulose filters to protect the modular system before the injection. Once filtered, samples were injected into the two injectors, for anion and cation analysis, respectively, each with an injection volume of 20 uL. For the carbonate/bicarbonate analysis, an 888 Titrando Titrator with an 801 magnetic stirrer, 20 mL exchange unit, and a combined \"Ecotrode Plus\" pH glass electrode and AgCl/Ag reference electrode were used. Moreover, an automatic dosing system that makes highly accurate dosing possible (down to 10 uL additions) was used. The software used was MagIC 3.2 and Tiamo 2.5 for the IC and titration systems, respectively. The measurements were performed in triplicate, and the presented data correspond to the average values.
## 4 Results from the Literature Review
### Emissions' Monitoring and Control Systems  
Transport has a significant environmental impact, producing noise and pollutant emissions that degrade air quality and contribute to climate change. Modern technologies provide opportunities to sustainably develop this sector, helping to reduce its adverse effects on the climate. Intelligent emissions' monitoring and control systems offer a solution for tracking, analyzing, and managing transport-related pollution. These systems use integrated technologies, including advanced sensors, artificial intelligence (AI), and the Internet of Things (IoT), to enable real-time data collection and analysis. Besides monitoring emissions, such as carbon dioxide, nitrogen oxides, and particulate matter from vehicle exhaust, these systems assess air quality and noise levels [138]. Continuous monitoring and real-time reporting facilitate swift interventions to curb greenhouse gas emissions [139; 140], which is crucial for mitigating global warming and addressing the environmental crisis. Emissions' monitoring and control systems are also vital in health crises, where air quality is critical for public health [141]. During the COVID-19 pandemic, these systems were widely implemented, helping to reduce pollution levels and potentially influencing disease outcomes [142; 143].
## 3 System Composition  
The HEPD includes four single units. There are three high-energy probes and a shared electronics box. Each probe includes a high-energy proton probe and a high-energy electron probe. The system scheme composition is shown in Figure 1.  
The three probes of the HEPD are installed on three mutually perpendicular planes of the satellite to realize the measurement of high-energy protons and electrons in three orthogonal directions. The three probes are connected to the electronics box through the cross-cabin cables inside the satellite, and the data processing unit processes the signals of the six probes. Figure 2 below shows the photos of HEPD.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Item** & **Energy Range** & **Detection Field** & **Sensitivity** \\ \hline High-energy protons & 3–300 MeV & 40\({}^{\circ}\) & 5\% (\(\Delta\)N/N) \\ High-energy electrons & 0.15–5.7 MeV & 30\({}^{\circ}\) & 5\% (\(\Delta\)N/N) \\ \hline \end{tabular}
\end{table}
Table 1: Main technical indexes of the HEPD on the FY-3E satellite.  
A collimating structure, added outside the HEPD, limits the detection field of view of the instrument. Furthermore, the shielding effect of the collimating structure is applied to reduce the interference ratio of obliquely incident particles (high-energy protons and electrons). Combined with the field of view limited by the collimator and the actual size and shielding conditions of the instrument, the final geometric factor of the instrument can be obtained by adopting the method of Monte Carlo simulation, which is used as the basis for later data processing. The geometric factor is an important characteristic parameter of the detector. It is necessary for data normalization and data comparison.  
The semiconductor detector measurement system is the key part of the entire instrument, and it measures the energy spectrum of high-energy particles in the form of a telescope formed by stacking multiple pieces of a silicon semiconductor. The energy spectrum of space particles is determined by analyzing the signal amplitude of each sensor in the measurement system and combining with a reasonable logical working mode.  
The electronics system collects the signals of the sensors, executes the logical working mode in the physical design, divides and counts the electronic energy spectrum, and, finally, packs the data and communicates with the satellite.
# Surface Subsidence Monitoring in Kunming City with Time-Series InSAR and GNSS
## 4 Results and Discussion
### Factors of Land Subsidence in Kunming City  
According to the partial settlement analysis of the above three parts, in the northeast of Dianchi Lake, the settlement of the area around the Convention and Exhibition Center is the most obvious. The main reason is related to the soft soil bottom around the Dianchi Lake. In recent years, large-scale projects have been built in this area, and due to its proximity to Dianchi Lake, the bottom layer of soft soil has been compressed, resulting in ground settlement under the action of building load and its own gravity. The area of Fuhai-Hewei village- Yuhu village has a relatively small settlement. Since the introduction of Kunming City groundwater exploitation policy, groundwater exploitation around this area has been restricted. Meanwhile, after the long-term compression of Quaternary sediments, soil consolidation and compression to the extreme result in the disappearance of large settlement area. The subsidence factor of Kunyang Phosphate Mine is due to the exploitation of underground resources. The regional subsidence of Kunming City is influenced by the structure of central Dianchi Lake. The geological structure can make the surface area descend or uplift, displace the rock layer, and land disturbance affect the underground water flow field, which leads to the crustal deformation. Secondly, due to the industrial development, a large amount of groundwater is drawn, which leads to the subsidence phenomenon in the urban center area far away from Dianchi Lake.
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
###### Abstract  
FANET (flying ad-hoc networks) is currently a trending research topic. Unmanned aerial vehicles (UAVs) have two significant challenges: short flight times and inefficient routing due to low battery power and high mobility. Due to these topological restrictions, FANETS routing is considered more complicated than MANETs or VANETs. Clustering approaches based on artificial intelligence (AI) approaches can be used to solve complex routing issues when static and dynamic routings fail. Evolutionary algorithm-based clustering techniques, such as moth flame optimization, and ant colony optimization, can be used to solve these kinds of problems with routes. Moth flame optimization gives excellent coverage while consuming little energy and requiring a minimum number of cluster heads (CHs) for routing. This paper employs a moth flame optimization algorithm for network building and node deployment. Then, we employ a variation of the K-Means Density clustering approach to choosing the cluster head. Choosing the right cluster heads increases the cluster's lifespan and reduces routing traffic. Moreover, it lowers the number of routing overheads. This step is followed by MRCQ image-based compression techniques to reduce the amount of data that must be transmitted. Finally, the reference point group mobility model is used to send data by the most optimal path. Particle swarm optimization (PSO), ant colony optimization (ACO), and grey wolf optimization (GWO) were put to the test against our proposed EECP-MFO. Several metrics are used to gauge the efficiency of our proposed method, including the number of clusters, cluster construction time, cluster lifespan, consistency of cluster heads, and energy consumption. This paper demonstrates that our proposed algorithm performance is superior to the current state-of-the-art approaches using experimental results.  
FANETS; energy efficiency; clustering; routing; WSN; Cloud; transmission range; bio-inspired +
Footnote †: journal: _Sustainability_
# Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model
## 2 Data  
Table 1 provides an overview the data used by the study and their specific use. Detailed description of the datasets is given in Sections 2.1-2.3.
## 4 Multilevel Inverters (MLIs)
### Classification of Multilevel Inverters
#### 4.2.1 Diode-Clamped Multilevel Inverter  
The neutral-point-clamped (_NPC_) _PWM_ topology is the first practical generation of _MLI_ (Figure 31); this multilevel inverter is clamped by a diode using clamping diodes. It helps to reduce the electronic devices' voltage tension. It was considered the first-generation _MLC_, called the three-level _NPC_, and was first introduced in 1981 by [PERSON] et al. [110, 111]. Nonetheless, this topology suffers technical problems if used as high-power converters. It needs diodes with high-speed clamping that are subject to the stress of reverse recovery. Due to the series connection of the diodes, the design complexity is a key concern. Half of the input _DC_ voltage is the maximum output voltage. This problem is simply eliminated by increasing the number of components, such as switches and diodes [113; 120; 121; 122]. This topology, like other techniques, possesses advantages and drawbacks; one of the benefits is that the control technique is basic, and it utilizes back-to-back inverters. Another positive point is that the switch voltage is just half the _DC_-link voltage. An additional benefit is that material declines when the number of levels rises along with the distortion. Furthermore, at the fundamental frequency, the efficiency is great, aside from capacitor capacity being small and preloaded. Despite these positive effects, the diode-clamped multilevel inverter has some weak points, such as the need for more clamping diodes when the number of levels increases, and when control and monitoring are not correct, a considerably large amount of _DC_ will be discharged [113; 120].  
Figure 31: Diode-clamped multilevel inverter [23].  
Figure 30: Classification of multilevel inverters [119].
## 5 Grasp
### Latency GRASP
#### 5.1.2 Search Phase  
The second or search phase of the GRASP algorithm is responsible for improving, if possible, the solution created by the construction phase. In order to do so, the search explores the solution neighborhood to check if there are any with better cost in terms of the objective function under use.  
Algorithm 3 shows the code for the local search. Regarding its parameters, \(G_{current}\) contains the solution to be improved, and \(maxNoImprovIter\) specifies the value of the stopping criteria. It limits the number of consecutive iterations to perform without solution improvement. Once this threshold is reached, we consider that \(G_{improved}\) contains the local best.  
For improving the solution, the algorithm firstly gets a _random_ node \(n\) from the solution. Then, it computes the current cost of sending data through its path until reaching the base station. Since this path has been greedily selected by the constructive phase, we check whether there are alternative paths through any of the neighbors of \(n\) ending at the base station that have better cost. If so, the path is updated and the local search continues. In addition, the consecutive iterations without improvement are reset.  
```
1:\(G_{current}\): current solution
2:\(maxNoImprovIter\): maximum number of consecutive iterations to perform without improvement
3:
4:\(G_{improved}\): improved solution
5:\(stop=0\)
6:while\(stop<maxNoImprovIter\)do
7:\(n=\text{getRandomNode}(G_{current})\)
8:\(best=\text{calculatePathCost}(n,G_{current})\)
9:for\(m\in\text{Neighbours}(n)\)do
10:\(cur=\text{cost}(n,m)+\text{calculatePathCost}(m,G_{current})\)
11:\(\text{update}(G_{current})\)
12:if\(cur<best\)then
13:\(stop=0\)
14:\(G_{improved}\gets G_{current}\)
15:endif
16:endfor
17:\(stop=stop+1\)
18:endwhile
19:return\(G_{improved}\)
```  
**Algorithm 3**\(\text{localSearch}(G_{current},maxNoImprovIter)\)
## 4 Visual Pathways  
Uncovering the role of optic flow in postural control, and more specifically, elucidating the functional differences between the central and peripheral visual fields, is challenging due to the complex anatomo-physiological organization of the mammalian retina. Anatomical studies showed that each retinal area projects to different cortical pathways. It is thought that the primate retina contains more than 20 types of ganglion cells, most of which are unstudied. [PERSON] reported that there are three main types of ganglion cells projecting to the lateral geniculate nucleus: the parasol, the midget and the small bistratified cells [51]. As reported by [PERSON] in 1994, the relative densities of the three types of ganglion cells vary in eccentricity. In the fovea, the midget cells represent about 90% of the total ganglion cells, the parasol cells about 5% and small bistratified cells about 1%. On the contrary, the peripheral retina is formed for the major part by the midget cells, which are the 45-50% of the total ganglion cells, the parasol cells are about 20% and the small bistratified cells are about 10%. Thus, from the peripheral to the central retina, the number of midget ganglion cells progressively increases relative to the parasol and the small bistratified types [51]. The parasol cells project to the geniculate magnocellular layers while the midget and the bistratified cells project to the geniculate parvocellular layers.  
In the lateral geniculate nucleus, the parvocellular and magnocellular layers originate from two different pathways, namely the ventral and dorsal visual streams, respectively. The ventral stream, which mostly originates from the fovea, processes information related to the color and shape of objects; it is also called the \"what\" pathway. On the other hand, the dorsal stream, which mostly originates from the peripheral regions of the retina, processes information related to self-motion perception, depth, and spatial orientation; it is also called the \"where\" pathway [52; 53; 54; 55].  
[PERSON] and [PERSON] already reported that the view that the peripheral visual field is crucial in generating section has been challenged [56]. A reasonable behavioral explanation for the periphery being more important in perceptual orientation arises from the observation that the peripheral visual field is not usually occluded by objects of interest. In addition, salient features that are relevant for motion perception, like walls or floors, are more visible in this region.  
It thus becomes clear that a precise anatomical distinction between the foveal (or central) and peripheral retinal fields is necessary for developing an appropriate experimental protocol to study the contribution of optic flow in postural control. Even the inclusion of a few degrees of visual angle in either the peripheral or central visual fields could change the results and lead to contradictory findings.
# Numerical Analysis of a Spiral Tube Damping Busbar to Suppress VFTO in 1000 kV GIS
## 3 Simulation Results  
### Modelling of a 1000 kV GIS  
The equivalent circuit diagram of a 1000 kV GIS was simulated by using Emitter (Electromagnetic Transients Program) simulation in order to study the VFTO with and without installing the damping busbar [30]. However, the high-frequency characteristics of VFTO led to simulating GIS components as capacitances dominating the other parameters. In order to model the GIS bus duct, distributed parameters and lumped elements can be utilized. Surge impedance and wave velocity could be calculated for a GIS section of any length by using the physical dimensions of the bus duct [31,32]. In the following equations, C and L are self-capacitance and inductance, Z is surge impedance, and V is wave velocity in the GIS [33,34]:  
\[C=\frac{2\pi\varepsilon_{0}\varepsilon_{r}}{\ln R/r}, \tag{2}\]  
\[L=\frac{\mu_{0}\mu_{r}}{2\pi}\ln\frac{2R}{r}, \tag{3}\]  
\[Z=\sqrt{\frac{L}{C}}=\frac{1}{2\pi}\sqrt{\frac{\mu_{0}\mu_{r}}{\varepsilon_{0} \varepsilon_{r}}}\ln\frac{R}{r}, \tag{4}\]  
\[V=\frac{1}{\sqrt{LC}}. \tag{5}\]  
Figure 6 shows the equivalent circuit of a 1000 kV GIS using Emitter simulation after installing the equivalent circuit of the damping busbar, as illustrated in Table 2.  
Let r be the inner radius of the GIS shell and R be the inner radius of the GIS bus. In addition, \(\varepsilon_{0}=8.85418782\times 10^{-12}\) F/m, \(\varepsilon_{r}=1.0024\) and \(\mu_{0}=4\pi\times 10^{-7}\) H/m. According to the dimensions of the GIS busbar, the calculations showed that the propagation velocity is \(295\,\mathrm{m}/\,\mathrm{\SIUnitSymbolMicro s}\) and the surge impedance is about 93.382 \(\mathrm{ohm}/\,\mathrm{m}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & **Parameters** & \\ \cline{2-4}  & **Number of Turns** & **Units** & **Resistance per Unit [\(\Omega\)]** & **Inductance [mH]** \\ \hline Value & 28 & 14 & 6 Resistors in parallel & \\  & & & & \(\mathrm{Req./unit}=20\ \Omega\) & 0.33656 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Damping busbar parameters.  
Figure 5: Inductance calculation of the damping busbar at high frequency.
## 3 Results
### GPP Validation
#### 3.1.2 Annual GPP  
The Flux_Tower GPP werecompared with the calculations from MOD_GMAO GPP, MOD_Tower GPP, GMAO_GLASS GPP and Tower_GLASS GPP during 2001-2014. (Figure 3, Table 3). The annual average value of the Flux_Tower GPP and the above four algorithms across all the sites was 1460 gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\), 976 gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\), 869 gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\), 1126 gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\) and 987 gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\), respectively. The Flux_Tower GPP for AR-Vir, BR-Sa3, GH-Ank and PA-SPs were especially severely underestimated (Figure 3a,b) by the MOD_GMAO GPP and MOD_Tower GPP algorithms, which led to large biases (\(-484\) gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\) for MOD_GMAO GPP and \(-591\) gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\) for MOD_Tower GPP). With respect to the estimating effectiveness, the GMAO_GLASS GPP algorithm showed the greatest performance because it yielded the highest value of R\({}^{2}\) (0.79) and the lowest values of bias (\(-334\) gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\)) and RMSE (32%).  
The flux derived GPP values (Flux_Tower GPP) at various biome types were also compared with the calculations of the four algorithms (MOD_GMAO GPP, MOD_Tower GPP, GMAO_GLASS GPP and Tower_GLASS GPP) over several years (Table 3). In the case of ENF, the Tower_GLASS GPP algorithm exhibited a significant correlation (R\({}^{2}\) = 0.91) and relatively underestimated the Flux_Tower GPP less (bias = \(-458\) gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\)). For EBF and GRA, the GMAO_GLASS GPP was the most effective algorithm and exhibited significant correlations (R\({}^{2}\) = 0.91, R\({}^{2}\) = 0.95) and the smallest biases (\(-222\) gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\), \(-173\) gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\)) and RMSEs (21%, 33%). Similarly, for DBF and MF, the GMAO_GLASS GPP algorithm also outperformed the other algorithms because of its most significant correlations (R\({}^{2}\) = 0.56, R\({}^{2}\) = 0.61), smallest biases (\(-530\) gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\), \(-411\) gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\)) and RMSEs (31%, 36%). None of the algorithms showed significant correlations for croplands (CRO); however, the GMAO_GLASS GPP algorithm exhibited the smallest bias (\(-228\) gC\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\)) and RMSE (27%).  
Figure 3: Comparisons of the annual Flux_Tower GPP with: (**a**) MOD_GMAO GPP; (**b**) MOD_Tower GPP; (**c**) GMAO_GLASS GPP; and (**d**) Tower_GLASS GPP. Significance levels: \({}^{**}p<0.01\).
# Extra-Tidal Members and Dynamics of the Open Cluster NGC 6705
###### Abstract  
In this study, we utilized Gaia-DR3 astrometric data combined with the density-based spatial clustering of applications with noise (DBSCAN) algorithm to thoroughly investigate the dynamics and extra-tidal members of the open cluster NGC 6705. We determined more than 1900 cluster members within \(\sim\)12 pc of the cluster. We estimated the core and tidal radii to be 3.11 \(\pm\) 0.21 arcmin (\(\sim\)2 pc) and 20.02 \(\pm\) 0.71 arcmin (\(\sim\)13 pc), respectively, based on the cluster members. A Gaussian mixture model (GMM) was used to segregate the core and halo components of the cluster. The major and minor axes of the core and halo were determined through principal component analysis (PCA). The semi-major axis lengths of the core and halo were estimated to be 7 (\(\sim\)4.5 pc) and 21 (\(\sim\)13.6 pc), respectively. Additionally, the axis ratios of the core and halo were found to be \(e\)\(\sim\)0.89 and \(e\)\(\sim\)0.80, respectively, suggesting that the halo was significantly affected by the external tidal field. We detected a clear mass segregation effect within the cluster. Furthermore, we also detected some extra-tidal members around the cluster, implying that these stars are being lost from the cluster because of gravitational interactions with the Milky Way. This work provided a comprehensive characterization of NGC 6705, revealing its tighter structure, ongoing mass segregation, and potential star loss.  
open clusters and associations; NGC 6705; parallaxes; proper motions; stellar kinematics; C-M diagram +
Footnote †: journal: Applied Sciences