esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Description of the Flux Sites  
As illustrated in Figure 1, the study used two forest sites with long-term continuous flux measurements to represent the typical evergreen needleleaf forest and evergreen broadleaf forest in the subtropical humid region of Southeast China, which also belonged to Chinese Terrestrial Ecosystem Flux Research Network (ChinaFLUX). Both sites started to continuously measure the long-term CO\({}_{2}\) and H\({}_{2}\)O fluxes from autumn 2002 via the EC-based method.  
The Qianyanzhou (QYZ) Ecological Research station (115\({}^{\circ}\)03\({}^{\prime}\)29.2\({}^{\prime\prime}\) E, 26\({}^{\circ}\)44\({}^{\prime}\)29.1\({}^{\prime\prime}\) N) is located in Jiangxi Province on the typical red-soil hilly area in the mid-subtropical monsoon climate region with planted coniferous forests. The terrain of the underlying surface is gentle, with the slope gradient ranging from 2.8\({}^{\circ}\) to 13.5\({}^{\circ}\). Multi-year mean temperature, precipitation, and relative humility at this site are 17.8 \({}^{\circ}\)C, 1471.2 mm, and 84%, respectively [33; 34]. The soil type is comprised of red soil, fluvo-aquic soil, paddy soil, and meadow soil, and the parent materials generally include rotten rock, mudstone, glutinite, and river alluvium. Coniferous forests planted inhabit 1985 are the dominant vegetation type. The major tree species consist of masson pine, splash pine, and cedarwood, with some Schima superba andorange. The canopy is nearly close with evergreen vegetation occupying approximately 76% of total land area.  
The Dinghushan (DHS) flux site (112\({}^{\circ}\)32\({}^{\prime}\)3.8\({}^{\prime\prime}\) E, 23\({}^{\circ}\)10\({}^{\prime}\)24\({}^{\prime\prime}\) N) is situated in the evergreen broadleaf forest, in the kernel area of the Dinghushan Biosphere Reserve, Guangdong Province, within the southern subtropical region. This site has abundant resources of radiation, heat, and rainfall. Multi-year mean temperature, precipitation, and relative humidity are 20.9 \({}^{\circ}\)C, 1956 mm, and 82%, respectively [35; 36]. The terrain is basically flat, especially in the prevailing northeast wind direction. The soil type is mostly lateritic, and a humus layer is covered. Monsoon evergreen broadleaf forest is the typical vegetation, with the dominant species of Castanopis chinensis, Schim asuperba, Cryptocarya chnensis. The  
Figure 1: Geographical location of the two subtropical forest flux sites (QYZ and DHS) used in the present study. The base map is derived from AVHRR 1 km land-cover map.  
stand is about 400 years old, with complicated forest structure including six layers, namely, three sub-layers of arbor, sapling shrub layer, herbaceous seedling layer, and interlayer plant layer. The mean canopy height is about 17 m, and the leaf area index (LAI) spans from 3.8 to 4.2 m\({}^{-2}\) m\({}^{-2}\)[37].
## 3 Results and Discussion of Biosensor Formation and Its Validation
### Precision  
To determine the consistency of the results of the measurement series for a given sample, a series of five quantitative determinations was carried out for the following concentration ranges: 4-80 pg/mL for VEGF-A and 5-80 pg/mL for FGF-2. These values were the cut-off points and midpoints of the rectilinear ranges of the calibration curves. The precision of these determinations was expressed as SD and CV. The results of the analysis are summarized in the table below (Table 2).  
The low SD values suggest that the developed method is precise, regardless of the concentration of the analyte. Coefficients of variation (CV) did not exceed 12%. Their highest values were observed at concentrations close to the LOQ.
## 2 Materials and Methods
### Modelling
#### 2.2.3 LS Factor  
CZ: In Czechia, the \(L\) and \(S\) factor is derived only for agricultural land (excluding forests and paved areas). The agricultural land for \(LS\) computation is defined by a digital map layer of the Land Parcel Identification System [35]. Before calculating the \(L\) factor, all neighboring parcels without any other land use in-between (trails, field road, etc.) are arranged in unified field blocks. Slope length is hence not affected by adjacent field borders. For each unified field block the \(L\) and \(S\) factor are calculated following the approach of [PERSON] and [PERSON] [36] using the Usle2D tool provided by the KU Leuven [36; 37] with the equation of [PERSON] [38; 39] and moderate rill/inter-rill ratio. Multiple flow routing (MFR) is applied for definition of contributing areas.  
BY: Following the approach used in Bavaria, the \(L\) and \(S\) factor is solely derived for arable land. Before calculating the \(L\) factor, all neighboring fields without any other land use in-between are arranged in field blocks. For each field block, the \(L\) and \(S\) factor is calculated using the Usle2D tool [36; 37] with the equation of Nearing [40] and the slope length exponent following [PERSON] and [PERSON] [16]. MFR is applied for definition of contributing areas.  
AT: For the estimation of \(L\) and \(S\) factors following the calculation method used in Austria, only cultivated plots are considered for computation in order to simulate overland water flow that is interrupted by linear features (e.g., roads) or suspended by plots that are not assigned as cultivated. The two-dimensional method presented in [PERSON] and [PERSON] [36] with a slope length exponent m after [PERSON] et al. [41] and the MFR algorithm by [PERSON] et al. [42] were used for the spatial \(L\) factor computation. The \(S\) factor was calculated according to Nearing [40].
## 4 Wear and Corrugation Simulation
### Analysis of Longitudinal and Vertical Rail Modes
In other words, when the vertical and longitudinal modes are inconsistent, the differential wear caused by the respective modes will suppress each other so that there will be no corrugation, no matter high or low adhesion, as long as the consistency condition is not satisfied. Certain track parameters could satisfy the required consistency requirement; thus, corrugation will appear in some places, but not in others. In summary, the longitudinal vibration modes are probably dominant for short pitch corrugation initiation. Then, the interaction and consistency between longitudinal and vertical modes should determine the development of short pitch corrugation, and once a certain severity is reached, vertical modes become dominant. This global perspective of the consistency conditions that govern the evolution of short pitch corrugation represents a novel contribution. The literature has so far only considered the effects of the vertical, lateral and torsional modes [1; 39; 40; 56; 57]. In [42], the relative longitudinal vibration of the two rails of a track is considered for corrugation initiation, for which each half track is modelled as an inertia, and longitudinal vibration modes of the rail as a flexible body are not considered. Figure 10: Modal analysis of the two major frequencies (1185 and 1296 Hz) shown in Figure 9: (**a**) rail vertical mode at 1185 Hz and (**b**) rail longitudinal mode at 1291 Hz, in comparison with the closest vertical pin-pin mode at 1100 Hz shown in (**c**).
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
## 3 System Control and Modelling with the Proposed Compensator
### Modeling Analysis of the Active Compensator
#### 3.2.2 Calculation of the LV-VSC Output Voltage, \(\mathrm{u_{o,B}}\)  
By combining the ac voltage regulation and the active damping function as presented in Figure 8, the generated output information, \(u_{a}\), is derived.  
\[\Delta U_{a}(s)=G_{\mathrm{NF}}(s)\ e^{-0.5 sT_{S}}\ \Big{\{}\Delta U_{ \mathrm{pcc}}(s)+R_{\mathrm{\tiny P}}\ \Delta I_{\mathrm{C}}(s)\Big{\}} \tag{6}\]  
where \(e^{-0.5 sT_{S}}\) represents the equivalent sampling delay during processing \(u_{\mathrm{pcc}}\) and \(i_{\mathrm{C}}\), and the transfer function of the notch filter, \(G_{\mathrm{NF}}(s)\), is written as  
\[G_{\mathrm{NF}}(s)=\Big{(}s^{2}+{\omega_{0}}^{2}\Big{)}/\Big{(}s^{2}+2\omega_{ b}s+{\omega_{0}}^{2}\Big{)} \tag{7}\]  
where the center frequency \(\omega_{0}\) is obtained from the PLL term and the filter bandwidth \(\omega_{b}\) determines the responding time.  
The output voltage information of the PI controller for regulating the dc capacitor voltage is calculated as  
\[\Delta U_{d}(s)=G_{\mathrm{PI}}(s)\Big{[}\Delta U_{\mathrm{dc}}(s)-\Delta U_{ \mathrm{dc,ref}}(s)\Big{]}\frac{s}{s^{2}+{\omega_{0}}^{2}}e^{-0.5 sT_{S}} \tag{8}\]  
where \(G_{\mathrm{PI}}(s)\) means the transfer function of PI controller and \(\frac{s}{s^{2}+{\omega_{0}}^{2}}\) is the \(s\)-domain function of the sinusoidal signal, \(\cos\omega_{0}t\).  
By adding Equation (6) with Equation (8), the modulating voltage of the LV-VSC, \(\Delta U_{m}\), is obtained  
\[\Delta U_{m}(s)=\frac{[\Delta U_{a}(s)+\Delta U_{d}(s)]}{U_{\mathrm{dc}}}e^{- sT_{S}}=\frac{[\Delta U_{a}(s)+\Delta U_{d}(s)]}{U_{\mathrm{dc}}}e^{-sT_{S}} \tag{9}\]  
Thus, the converter output voltage of the LV-VSC becomes  
\[\Delta U_{o,B}(s)=\left[\Delta U_{a}(s)+\Delta U_{d}(s)\right]\ e^{-sT_{S}} \tag{10}\]  
Because the control bandwidth of dc capacitor voltage regulation is much lower than the line frequency, \(\Delta U_{d}\) is neglected. Then, (10) is simplified into  
\[\begin{split}\Delta U_{o,B}(s)\big{|}_{\Delta U_{d}(s)\equiv 0} =\Delta U_{a}(s)e^{-sT_{S}}&=G_{\mathrm{NF}}(s)e^{-1.5 sT_{S}} \left[\Delta U_{\mathrm{pcc}}(s)+R_{\mathrm{\tiny P}}\ \Delta I_{\mathrm{C}}(s)\right]\\ &=e^{-1.5 sT_{S}}\ \Delta U_{\mathrm{pcc,h}}(s)+e^{-1.5 sT_{S}}\ R_{ \mathrm{\tiny P}}\ \Delta I_{\mathrm{C,h}}(s)\end{split} \tag{11}\]  
where the voltage source \(\Delta U_{o,B}\) is mainly composed of the feedforwarding voltage for grid impedance cancellation and the voltage for damping filter resonance.
## 2 Materials and Methods
### Methods
#### 2.2.1 Finite Element Method (FEM)
The stress concentration equation is expressed in terms of the stress concentration factor and relevant parameters, considering variations in system stiffness. The following boundary conditions are applied to the central plate, taking advantage of its double symmetry:  
* Sides: restrained in the X, ZZ, and YY directions
* Front side: restrained in the Y, XX, and YY directions
* Back side: restrained in the Z, XX, and YY directions
* Axial loading: applied in the \(-\)Z direction  
The study employs the general formulation of FEM, represented by the static equilibrium Equation:  
\[\mathbf{K_{g}\cdot u}=\mathbf{f_{e}} \tag{1}\]  
where  
* \(\mathbf{K_{g}}\) is the global stiffness matrix combining the stiffness contributions of the plate (\(\mathbf{K_{\text{plate}}}\)) and the stiffener (\(\mathbf{K_{\text{stiffener}}}\))
* \(\mathbf{u}\) is the vector of nodal displacements
* \(\mathbf{f_{e}}\) is the vector of applied nodal forces resulting from the induced equivalent compressive forcesConsidering the different parameters used in the problem under study, the stiffness matrix can be noted as \(\mathbf{K_{g}}(P_{1},P_{2},P_{3},P_{4})\), the force vector as \(\mathbf{f_{e}}(P_{5})\), and the Equation (1), transformed in Equation (2):  
\[\mathbf{K_{g}}(P_{1},P_{2},P_{3},P_{4})\cdot\mathbf{u}=\mathbf{f_{e}}(P_{5}) \tag{2}\]  
The maximum Von Mises stress in the region of interest is determined by:  
\[\sigma_{MAX}(K_{t},P_{1},P_{2},P_{3},P_{4},P_{5})=K_{t}\cdot\frac{P_{5}}{\alpha _{A}(P_{1},P_{2},P_{3},P_{4})} \tag{3}\]  
\[\sigma_{VM}=\left(\sqrt{\frac{(\sigma_{xx}-\sigma_{yy})^{2}+(\sigma_{yy}- \sigma_{zz})^{2}+(\sigma_{zz}-\sigma_{xx})^{2}}{2}+3\Big{(}\tau_{xy}^{2}+\tau_ {yz}^{2}+\tau_{zz}^{2}\Big{)}}\right)(K_{t},P_{1},P_{2},P_{3},P_{4},P_{5}) \tag{4}\]  
where  
* \(\sigma_{MAX}\) is the maximum von Mises stress \((\sigma_{VM})\) as a function of the parameters
* \(K_{t}\) is the stress concentration factor
* \(P_{5}\) is the applied force  
The function \(\alpha_{A}\) represents the changes in the combined cross-sectional area of both the stiffener and the web plate, considering the factors that affect the overall stiffness of the system. This function accurately reflects the combined effect on the system's stiffness of both welded bodies in the region of interest, which is influenced by variations in geometric and loading parameters throughout the experimental factorial design process. Furthermore, by introducing a new function \(\beta\), Equation (5) generally describes how variations in geometry, global system stiffness, and applied loading conditions influence the maximum value of the Von Mises stress concentration:  
\[\sigma_{max}=\beta\big{(}K_{t},\mathbf{K_{g}}(P_{1},P_{2},P_{3},P_{4}),P_{5}\big{)} \tag{5}\]
## 3 Results and Discussion
### iNOS, TNF-\(\alpha\), IL-6 and IL-1\(\beta\)in mRNA Level  
The inhibitory effects of okra ethanol extracts, prepared using different cooking methods, were examined to determine their influence on iNOS, TNF-\(\alpha\), IL-6, and IL-1\(\beta\) mRNA expression in LPS-induced RAW246.7 cells. The results are shown in Figure 4. Among the extracts tested, SVOE showed the strongest suppressive effect on LPS-induced iNOS mRNA expression in RAW264.7 cells. We further investigated whether ROE, BOE, SOE, and SVOE influence inflammatory cytokine expression in LPS-treated RAW264.7 cells. Compared to the controls, all four cooking methods reduced production of iNOS and inflammatory cytokines (TNF-\(\alpha\), IL-6, and IL-1\(\beta\)) at 200 mg/mL okra extract, while a marked increase was observed in the LPS-treated group. In addition, a concentration-dependent inhibitory effect of okra extracts on the production of iNOS and inflammatory cytokines (TNF-\(\alpha\), IL-6, and IL-1\(\beta\)) was shown across all four cooking methods.  
Figure 3: Effect of cooking method and okra extract on cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) protein expression in LPS-induced RAW264.7 cells. Cells were treated with ROE (**A**), BOE (**B**), SOE (**C**), and SVOE (**D**) (50, 100, 200 mg/mL), with or without LPS (1 mg/mL) for 24 h. The COX-2 and iNOS expression were normalized to the \(\beta\)-actin signal (the relative band intensities are indicated above each band). iNOS: inducible nitric oxide synthase, LPS: lipopolysaccharide, ROE: raw okra ethanol extract, BOE: blanched okra ethanol extract, SOE: steamed okra ethanol extract, and SVOE: sous-vide cooked okra ethanol extract.
## 4 Results and Discussion  
Due to the limited power of the shore infrastructure, it is necessary to minimize the peak load. Moreover, for some electricity suppliers, a fee is charged for the installed power. Thus, this kind of analysis can also have economic benefits. Furthermore, when carrying out the optimization of the peak load in accordance with the repetitive navigation profile of the ships, technical aspects and limitations are not considered. Namely, in order for charging the entire fleet of ships to be feasible, all docks should have connections of appropriate power. However, this research starts from the assumption that the mentioned requirement is fulfilled. The optimization is achieved in such a way that at the moment of connection of high-power ships, ships that are available for a longer period of the day are disconnected from the network.  
The proposed model is applied to optimize the sailing schedule of the analyzed ferry fleet. The optimization model achieves a 24% [62] reduction in the required charging power of the electric ESS of the group of ferries. Savings were achieved by properly scheduling charging, i.e., by disconnecting less energy-intensive ships from the shore energy infrastructure. The problem arises when several ships with similar energy needs are connected to the network at the same time. In this case, a reduction of only 0.76% was achieved. The described schedule is shown in Figure 10 when the energy needs of the most demanding ship are reduced to 0.48 MW daily.  
Figure 9: Schematic representation of electricity flow.
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 5 Evaluation
### Simulation Results
#### 5.5.1 Default Parameter  
Using the default parameters, IACS successfully improved the computation, communication, and energy (Figure 6c-e) costs required for 50 iterations while maintaining the estimation error and closeness (Figure 6a,b) value for each normal node as in MSL* and LCC. Based on the figures above, IACS reduced 25% of computation cost, 25% of communication cost and 6% of energy consumption compared to MSL*, while 15% of computation cost, 13% of communication cost and 3% of energy consumption compared to LCC. The percentage is the difference between the value of each IACS evaluation metrics with respect to MSL* and LCC. For example, the difference percentage between IACS (562.44) and LCC (646.75) communication cost in Figure 6d: ((646.75 - 562.44) \(\div\) 646.75) \(\times\) 100 = 13.04 \(\approx\) 13%.  
Figure 5: Neighbor Information Packet Structure.
## 1 Introduction
This results in three-dimensional (3D) hyperspectral images, also called hypercubes [1; 2; 3; 4; 5; 6; 7; 8]. In filter-based imaging mode, spectral images are obtained from stationary targets for a waveband sequence using either a liquid crystal tuneable filter (LCTF) or an acousto-optical tunable filter (AOTF) [1; 2; 3; 4; 5; 6; 7; 8]. In-line scanning is the most common as it is relatively easy to implement, particularly for real-time and online applications. In comparison, filter-based HSI systems are not suitable for online applications and they require more complicated calibrations [1; 2; 3; 4; 5; 6; 7; 8]. Hyperspectral imaging methods require a high performance digital camera that has the ability to cover the spectral region of interest, in a wide dynamic range with high signal-to-noise-ratio, and good quantum efficiency [1; 2; 3; 4; 5; 6; 7; 8]. An imaging spectrograph, with the ability to disperse the line images into different wavelengths is essential [1; 2; 3; 4; 5; 6; 7; 8]. Optical resolution and spectral response efficiency with minimal aberrations are also key features of a HSI system. Finally, a highly stable, DC-regulated light source with a smooth spectral response is critical [1; 2; 3; 4; 5; 6; 7; 8]. Hyperspectral imaging is particularly attractive for food applications because of the integration of imaging ability with spectroscopy, which enables the simultaneous acquisition of both spectral and spatial information from the target. The gains achieved by this amalgamation can allow for the monitoring of highly spatially variable raw and food products [1; 2; 3; 4; 5; 6; 7; 8]. However, this technology is not widely used by the food industry, with key considerations to user uptake depending on several reasons that include cost and instrument availability, whether the application is online or in-field and training of staff [1; 2; 3; 4; 5; 6; 7; 8]. This paper reviews some of the advantages and the recent applications hyperspectral and chemical imaging to discriminate and authenticate foods.
# Recent Advances in Hydrothermal Oxidation Technology for Sludge Treatment
## 2 Results and Discussion
### Issues and Optimization Strategies in HTO of Sludge
#### 2.5.2 Optimizing the Solution of HTO Technology
It has been reported that, at pressures and temperatures above the critical point (P = 22.064 MPa, T = 373.946 \({}^{\circ}\)C), water behaves as a typical fluid known as supercritical water. SCWO offers advantages over conventional hydrothermal oxidation, including faster reaction rates, thorough treatment, and no secondary pollution. It can convert heteroatoms such as chlorine, sulfur, and phosphorus into their respective inorganic acids or salts, and it can also mineralize heavy metals, depositing them as stable solid residues [36]. [PERSON] et al. reviewed the treatment effects and kinetic parameters of various nitrogen-containing organic compounds and NH\({}_{3}\)-N in the SCWO environment, analyzing the transformation pathways of organic nitrogen in different nitrogen groups to understand its degradation mechanisms. Their results indicated that nitro-containing organic compounds primarily convert nitrogen into nitrogen oxides (NO\({}_{\text{x}}\)\({}^{-}\)) in the form of nitrogenous intermediates, while the main intermediate for amino-containing or nitrogen-heteroatom organic compounds is NH\({}_{3}\)-N. For organic compounds containing both amino and nitro groups, the generated NO\({}_{\text{x}}\)\({}^{-}\) can react with NH\({}_{3}\)-N through redox processes to form N\({}_{2}\). Furthermore, supercritical water oxidation, when coupled with conventional oxidants like H\({}_{2}\)O\({}_{2}\) or O\({}_{2}\) under relatively mild SCWO conditions, enhances the synergistic removal efficiency of COD or TOC and NH\({}_{3}\)-N while avoiding the generation of NO\({}_{\text{x}}\), NO\({}_{\text{3}}\)\({}^{-}\), and NO\({}_{\text{2}}\)\({}^{-}\), converting organic nitrogen into environmentally friendly products such as N\({}_{2}\) and N\({}_{2}\)O [36]. [PERSON] et al. investigated the effect of adding a Ni/Al\({}_{2}\)O\({}_{3}\) catalyst in the SCWO process, with reaction conditions set at 450 \({}^{\circ}\)C for 20 min, achieving TOC removal and carbon conversion to gas phase efficiency of 95.2% and 68.2%, respectively [48]. The study revealed that the advantage of SCWO technology lies in its ability to achieve efficient conversion of organic matter in a short time, far surpassing traditional hydrothermal oxidation in treatment efficiency. Additionally, SCWO does not produce harmful by-products such as dioxins, and inorganic salts can be recovered in solid form, reducing the complexity and cost of subsequent treatment processes.
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 7 Conclusions  
In this paper, we discuss the theoretical analysis and modeling of the 6G satellite twin network, in which the key issues of the communication-twin-computing integration network model are investigated. A hypergraph hierarchical nested kriging model and a hierarchical unified feature description method are proposed to describe the communication network and twin network and computing network. Meanwhile, the basis function matrix of the local flexible connection of the global network is introduced to map the functions to the resource. In addition, the optimal mapping problem is described as a three-dimensional packing problem, and the multi-constraint multi-objective genetic algorithm is proposed to find the optimal computational resource allocation. Finally, the three-dimensional hierarchical simulation framework and the decentralized adaptive development model are proposed.  
Conceptualization, X.Y. and Z.D.; methodology, X.Y.; software, X.Y.; validation, X.Y. and Z.D.; formal analysis, Z.D.; investigation, Z.D.; resources, Z.D.; data curation, Z.D.; writing--original draft preparation, X.Y.; writing--review and editing, X.Y.; visualization, Z.D.; supervision, X.Y.; project administration, X.Y.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the China National Key Research and Development Plan (2020 YFB1808005 and 2019 YFB1803105).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 11: Computing performance under different architectures.