esa-sceva/satcom-chunk-collection huggingface dataset:
# Sol-Gel Silica Nanoparticles in Medicine: A Natural Choice. Design, Synthesis and Products
## 3 Silica NP Design
### NPs Synthesis Methodologies  
During the last decades, remarkable efforts have been made to develop novel NPs synthesis methodologies. Today, it is generally accepted that nanosize cannot be efficiently achieved by the traditional _top-down_ methodologies (such as ball milling and lithography) but rather by _bottom-up_ techniques. A _bottom-up_ strategy looks faster, precise, and cost-effective.  
A _bottom-up_ strategy holds a large number of techniques (flame spray pyrolysis, chemical vapor deposition, and wet-chemistry methodologies like co-precipitation, hydrothermal, solvothermal and sol-gel) but research has been focused on sol-gel, as the synthesis is straightforward, scalable, easily controllable, time and energy saving. The sol-gel chemistry comprises chemical reactions involvingcolloidal particles in a sol, or between alkoxide-precursors and water, in a solution, leading to a highly porous amorphous gel product, in which a liquid phase (solvent, catalyst and eventually excess reactants) may be retained in bulks (3D), films (2D), fibers (1D), powders, and NPs (0D) products [39; 92]. The sol-gel SiO\({}_{2}\) NPs synthesis comprises four common methods: (i) colloidal routes, (ii) biomimetic syntheses, (iii) solution routes (base- and acid catalyzed)) and (iv) templated syntheses (the last one dedicated to mesoporous silica NPs, a topic outside the scope of this short review).
## 4 Verification of the Present Solutions  
First, the derived solutions are compared with the existing solutions and models. The comparison of analytical and numerical solutions is depicted in Figure 2, in which \(m=1\) and \(c=1\), thus [PERSON]'s solution [17] and [PERSON]'s solution [44] are degenerated into 1D nonlinear analytical solutions under the Terzaghi drainage boundary (TDB) and the CDB, respectively. As shown in Figure 2, when \(\alpha=8\), the derived solution of ACD obtained by the finite difference method is consistent with [PERSON]'s analytical solution [44] and when \(\alpha=10^{6}\), the present solution of ACD derived by the finite difference method is consistent with [PERSON]'s analytical solution [17]; thus, the correctness of the present solution obtained by the finite difference method is preliminarily verified. In addition, in [PERSON]'s solution [17], the value of \(b\) has no effect on the ACDS, while in the present solution, \(U_{\rm s}\) increases with the increase of the value of \(b\). When the interface parameter \(\alpha\) is large enough (i.e., \(\alpha=10^{6}\) in this case), the present solution with the CDB is consistent with [PERSON]'s solution [17], which indicates that the CDB can be degenerated into the TDB. In other words, the consolidation solution based on the TDB is a special case of the consolidation solution based on the CDB.  
The comparison of ACDs obtained from different drainage boundaries is illustrated in Figure 3. When \(\alpha=10^{6}\), the consolidation solutions based on the CDB are consistent with [PERSON]'s solution [23] based on the TDB, which again verifies the rationality of the derived solutions. Furthermore, Figure 3 also illustrates the influence of \(I_{1}\) on \(U_{\rm s}\) and \(U_{\rm p}\), and the value of \(I_{1}\) depends on the ratio of soil thickness to external load. It is found that both \(U_{\rm s}\) and \(U_{\rm p}\) decrease as the value of \(I_{1}\) increases, which indicates that the smaller the ratio of soil thickness to external load, the faster the soil consolidation process. According to Equation (11), with the increase of \(I_{1}\), the coefficient on the left side of Equation (11) will decrease, so the soil consolidation rate will decrease. In addition, it can also be found that when the values of model parameters are the same, \(U_{\rm s}\) is greater that \(U_{\rm p}\), indicating that the soil settlement rate is greater than the dissipation rate of EPWP during the consolidation process.  
Figure 2: Comparison of analytical solutions and numerical solutions [17; 44].
## 4 Energy and Power Optimization Allocation
### Integrated Optimization Scheme
#### 4.1.2 Optimization for Asynchronous Networks  
For the single-slot asynchronous ISAL network, the FIM of \(\boldsymbol{\theta}=[\mathbf{p}_{\mathrm{r}}^{\mathrm{T}},\mathbf{\tau}^{ \mathrm{T}},\mathbf{p}_{\mathrm{t}}^{\mathrm{T}}]^{\mathrm{T}}\) is as in (23), and the power optimization allocation strategy is as follows:  
\[\mathfrak{A}_{3}^{\mathrm{SDP}}: \min\quad\mathrm{tr}(\mathbf{H})_{\mathbf{p}_{\mathrm{t}}}\] (42) s.t. \[\mathbf{H}\succeq 0 \tag{43}\] \[\left[\begin{array}{cc}\mathbf{H}&\mathbf{I}\\ \mathbf{I}&\mathbf{I}_{\theta}^{\mathrm{syn}}\end{array}\right]\succeq 0\] (44) \[\sum_{j\in\mathcal{N}_{\mathrm{a}}\cup\mathcal{N}_{\mathrm{r}}}P_{j} \leq E_{\mathrm{total}}/1\] (45) \[0\leq P_{j}\leq P_{\mathrm{r,max}},\forall j\in\mathcal{N}_{ \mathrm{r}}\] (46) \[0\leq P_{j}\leq P_{\mathrm{a,max}},\forall j\in\mathcal{N}_{ \mathrm{a}}. \tag{47}\]  
For the dual-slot asynchronous ISAL network, the FIM is expressed as in (24). Similar to the synchronous network, we search for the optimal energy allocation between two time slots through traversal. The power optimization allocation strategy within slot 1 is the same as that of the single-slot asynchronous network. The power optimization allocation within slot 2 applies the result in slot 1 as prior knowledge, which is shown as follows:  
\[\mathfrak{A}_{4}^{\rm SDP}: \min \quad\operatorname{tr}(\mathbf{H})_{\mathbf{p}_{t}^{(2)}}\] (48) s.t. \[\quad\mathbf{H}\succeq 0 \tag{49}\] \[\left[\begin{array}{cc}\mathbf{H}&\mathbf{I}\\ \mathbf{I}&\mathbf{I}_{\boldsymbol{\theta}}^{\rm asyn}(2)\end{array}\right] \succeq 0\] (50) \[\sum_{j\in\mathcal{N}_{\mathrm{a}}\cup\mathcal{N}_{\mathrm{r}}}P_{ j}\leq E_{\rm slot2}/1\] (51) \[0\leq P_{j}\leq P_{\rm r,max},\forall j\in\mathcal{N}_{\mathrm{r}}\] (52) \[0\leq P_{j}\leq P_{\rm a,max},\forall j\in\mathcal{N}_{\mathrm{a}}. \tag{53}\]
# A Reversed-Phase HPLC Method for Determination of Osteopontin in Infant Formula
## Materials and Methods
### Instrumental and Chromatographic Conditions  
The chromatographic analysis was performed using a Shimadzu HPLC system (Kyoto, Japan). The system consisted of an in-line vacuum degasser DGU-20A5, quaternary delivery pump LC-20 AT, an ultraviolet detector SPD-20A, an autosampler SIL-20A and a column oven CTO-20 AC. The software (LabSolutions) associated with the system was used for data collection. In this work, 0.1% TFA was used as a mobile phase A, while a number of options were considered for the selection of the mobile phase B (hydrophobic stationary phase). For instance, different concentrations (40-90%) of ACN in 0.1% (_v/v_) TFA and 75% (_v/v_) 2-propanol in 0.1% (_v/v_) TFA were investigated to select a suitable mobile phase B. The HPLC system was operated isocaratically, with a flow rate of 0.3 mL/min and the column temperature at 40 \({}^{\circ}\)C. The injection volume was 10 \(\upmu\)L and a ZORBAX Eclipse C18 column (Agilent, Santa Clara, CA, USA; 4.6 \(\times\) 100 mm \(\times\) 5 micron) was used. The absorbance was read at 214 nm.
# Design and Calibration of a Tri-Directional Contact Force Measurement System
## 3 Calibration
### Calibration Matrix  
It is expected that, due to the mechanical coupling between the three orthogonal branches, a linear relationship between the measured force values \(\left\{F^{*}\right\}=\left(F_{1},F_{2},F_{3}\right)^{T}\) and the actual applied loads \(\left\{F\right\}=\left(F_{x},F_{y},F_{z}\right)^{T}\) can be expressed as  
\[\left\{F^{*}\right\}=\left[C\right]\left\{F\right\} \tag{1}\]  
Figure 3: (**a**) Force sensors in tripod configuration, (**b**) Locknut Collar (above) and Necked collar (below).  
where \([C]\) represents the 3 \(\times\) 3 calibration matrix in this case and can be defined as follows:  
\[[C]=\left[\begin{array}{cc}F_{1}(F_{x})&F_{1}(F_{y})&F_{1}(F_{2})\\ F_{2}(F_{x})&F_{2}(F_{y})&F_{2}(F_{2})\\ F_{3}(F_{x})&F_{3}(F_{y})&F_{3}(F_{z})\end{array}\right]\]  
where \(F_{1},\ F_{2}\), and \(F_{3}\) represent the force measured by Sensor 1, Sensor 2, and Sensor 3, respectively, when load i.e., \(F_{x},\ F_{y}\), or \(F_{z}\) was applied along the x-axis, y-axis, and z-axis, respectively. The columns of the calibration matrix were determined using the sensor output values recorded during the calibration procedure. In other words, when \(F_{x}\) was applied the coefficients of the first column of \([C]\) were determined and so on for the remaining columns. The above mentioned calibration procedure was performed at multiple force levels obtaining a set of calibration matrices whose coefficients can be interpolated to determine the calibration matrix for a given load.
# Locating the Parameters of RBF Networks Using a Hybrid Particle Swarm Optimization Method
## 2 Method Description
### Optimization of Parameters through Genetic Algorithm  
During the second phase of the proposed method, a genetic algorithm is implemented, which optimizes the parameters of the RBF network within the optimal interval calculated in the first phase. The used genetic algorithm has its roots in the GA\((c_{\intercal},l)\) algorithm from the paper of [PERSON] and [PERSON] [64]. This method was enhanced using the stopping rule suggested by [PERSON] [65]. This genetic algorithm has the following steps:  
1. **Initialization step:** 1. **Set**\(N_{c}\) as the number of chromosomes. Every chromosome is coded as in the case of PSO using the scheme of Figure 2. 2. **Set**\(N_{g}\) as the maximum number of generations allowed. 3. **Set**\(k\) as the weight number of the RBF network. 4. **Obtain** the domain range \(S\) from the procedure of Section 2.2. **Initialize**\(N_{C}\) randomly in \(S\). **Define** the selection rate \(p_{s}\in[0,1]\). **Define** the mutation rate \(p_{m}\in[0,1]\). **Set** iter = 0. **Evaluation step:** **For** every chromosome \(g\)**, calculate** the associated fitness value \(f_{g}=\sum_{i=1}^{m}(y(x_{i},g)-t_{i})^{2}\): 3. **Genetic operations step**: Perform the genetic operations of selection, crossover, and mutation. 1. **Selection procedure:** First, the population of chromosomes is sorted based on the associated fitness values. The first \((1-p_{s})\times N_{c}\) chromosomes are copied unchanged to the next generation, while the rest are replaced by offspring constructed by the crossover procedure. During the selection step, a series of mating pairs is chosen using the well-known procedure of tournament selection for each parent.  
Figure 2: The scheme of the particles in the current PSO algorithm.  
**Crossover procedure**: For each pair \((z,w)\) of chosen parents, two new offspring \(\tilde{z}\) and \(\tilde{w}\) are constructed with the steps:  
\[\tilde{z_{i}} = a_{i}z_{i}+(1-a_{i})w_{i}\] \[\tilde{w_{i}} = a_{i}w_{i}+(1-a_{i})z_{i} \tag{9}\]  
where \(a_{i}\) is a random number with \(a_{i}\in[-0.5,1.5]\)[64].
**Mutation procedure**: For every element of each chromosome, pick a random number \(r\in[0,1]\). **If**\(r\leq p_{m}\), then alter randomly the corresponding element.
4. **Termination check step:** 1. **Set**\(iter=iter+1\).
2. **If** the termination criteria hold, then **Terminate; else, goto** evaluation step.  
The overall process of the two phases is graphically shown in Figure 3.
## 1 Introduction
With the preprocessed properties for each cell in the ECPM models, the ECPM models could be used for various site-specific conditions and used to quantify the large-scale flow and transport mechanisms in fractured rocks. However, detailed interactions such as the flux, sorption, and desorption between fractures and rock matrix and the characteristic of high-velocity pathways for fractures are not available in the models [7; 8; 12; 13; 14]. The large discrepancy of hydraulic properties between fractures and rock matrix has made the simulations challenging. For a complex three-dimensional (3D) problem, quantifying the fractures and rock matrix interactions is critical to understand the detailed flow and transport in fractured rocks [7; 8]. In many previous investigations, the computational costs had made most studies focus on small-scale or simplified problems if detailed flow dynamics need to be explored [7; 8; 9; 10; 11; 16; 17; 18; 19; 20]. The hybrid domain (HD) approaches that simultaneously consider fractures and rock matrix in a simulation domain have become feasible because of recent developments of computational technologies [21; 22]. The models require complex numerical meshes to discretize the key geometries of fractures and rock matrix in the HD approaches. Recent studies have focused on developing complex numerical models, which enable the separation of the computational domains into fracture and rock matrix domains [23; 24]. However, the local refinement of numerical meshes might be required to maintain the numerical stability and flow continuity near the interfaces of fractures and rock matrices. Such mesh refinement could also limit the efficiency of the hybrid approach for problems with practical scales and complexities. This study employed the hybrid domain concept but used virtual fractures to account for the interactions between fractures and rock matrices. This study also conducted benchmark tests to assess the developed HD model for simulating flow and advective transport in fractured rock systems. The commercial software FracMan and DarcyTools were used to obtain the solutions of DFN and ECPM models for comparison purposes [25; 26; 27; 28; 29]. Based on the same flow conditions, two synthetic cases were designed to evaluate the developed HD model. The first simple case (Case I) was composed of three intersected fractures in a simulation domain. In the second case (Case II), a cylinder object was embedded in the fractured rock to assess the flow and advective transport in the complex flow system. In this study, the generated computation meshes for different cases needed to meet the stability requirement for flow simulation. A systematic comparison was conducted to quantify the differences in heads and particle paths obtained from three different modeling concepts.
## 3 Experimental Analysis
### VMD Results  
In the application of VMD processing, this study anticipated the extraction of 12 IMFs, with the bandwidth constraint factor alpha set to 800, indicating a medium-strength bandwidth setting. The direct current component was set to 0 (meaning that the decomposition modes would not explicitly contain a constant term), and the frequency initialization parameter init was set to 100 (representing a uniform initialization strategy). The detailed results of the IMFs obtained following these settings are shown in the table below.  
As shown in Table 2, the mean variance and average values of the decomposed data are significantly reduced compared to the original data. Meanwhile, the IMFs resulting from the VMD exhibit increasingly smooth and simplified periodic characteristics with the progression of decomposition. The IMFs (Intrinsic Mode Functions) represent different frequency components of the original time series data. Higher-indexed IMFs capture finer, higher-frequency patterns, while lower-indexed IMFs capture broader, low-frequency  
Figure 8: Lag plot of the dataset.  
Figure 7: Autocorrelation of the dataset.  
t trends. IMF1 corresponds to the highest-frequency component, which captures the finest details or rapid fluctuations in the time series; IMF2-IMF12 gradually capture lower-frequency components, representing more long-term patterns in the data; the average of IMFs represents the aggregation of all IMFs to approximate the original time series data.  
At the same time, the spectral images after decomposition are shown in Figure 8: it is evident from the figure that modal decomposition reveals the trends and patterns of passenger flow on different time scales, with each mode reflecting different characteristics of passenger flow variations.  
As shown in Figure 9, the X-axis represents time steps, and the \(Y\)-axis shows the amplitude of the signal. The IMFs are sorted by frequency, with IMF1 representing the highest-frequency component and IMF12 capturing the lowest-frequency component. As you move from IMF1 to IMF12, you will notice that the oscillations (frequency) decrease, representing the long-term trends in the data. Each IMF reveals different aspects of the underlying data: IMF1-IMF4 generally capture short-term fluctuations, while IMF5 and above capture lower-frequency variations and broader trends.  
In summary, the modes decomposed effectively extract the impact of various factors on subway passenger flow.
# Energy Performance of LR-FHSS: Analysis and Evaluation
## 3 Overview of LoRaWAN and LR-FHSS
### LoRaWAN
Figure 2: Class A operation with one uplink transmission followed by two receive windows. LoRaWAN defines the concept of DR, which indicates a PHY layer mechanism and a particular radio interface configuration leading to a specific PHY layer bit rate. For each DR, there is a maximum payload size specified. The maximum MAC payload size and the maximum application PDU (FRM Payload) size, presented as M and N values, respectively, when FOpts is not present are shown in Table 1.
## 5 Conclusions  
1. Within the examined CBM reservoirs, which are associated with complex conditions in China, eight resources and productivity characterization indices, namely, the reservoir depth, gas content, reservoir thickness, reservoir pressure gradient, critical reservoir ratio, permeability, reservoir temperature, and Young's modulus, were selected as the main evaluation indices of development blocks;
2. The importance of an evaluation index was determined by the combination of the subjective experience of the experts, the objective deviation of the data through the BWM and the entropy method. The importance of the evaluation indices, ordered from high to low, is permeability, reservoir pressure gradient, gas content, reservoir thickness, reservoir depth, critical reservoir ratio, Young's modulus, and reservoir temperature, and the weights are 0.5877, 0.2911, 0.0443, 0.0261, 0.0220, 0.0115, 0.0105, and 0.0067, respectively;
3. Based on the gray system theory, a multi-index gray relational analysis optimization model was established, and the development potentials of CBM blocks in complex geological conditions with interval values were ranked. The ranking results, from most to least optimal, are Block 2 (Sanjiao block), Block 1 (Fanzhuang-Zhengzhuang block), Block 4 (Daning-Jixian block), Block 3 (Gujiao block), and Block 5 (Fengrun block). The optimum membership degrees are 0.8936, 0.7500, 0.5123, 0.2808, and 0.1112, respectively. The results of the evaluation are in accordance with the actual conditions. The multi-index gray relational analysis optimization model with interval numbers for evaluation indices has practicability and can be used for quantitative evaluation and optimization of CBM blocks under complex conditions. This model is suitable for the CBM blocks in the range of 547 km\({}^{2}\)-675 km\({}^{2}\). When using this model, the index values must be representative and accurate.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Author Contributions:** Writing--original draft, G.Z.; Supervision, T.K.; Methodology, J.G.; Data curation, R.Z.; Formal analysis, L.L.  
**Funding:** This research was funded by [the NSFC-Shanxi Coal Based Low Carbon Joint Fund] grant number [U1810102], [the National Natural Science Foundation of China] grant number [51174141, 50974093] And The APC was funded by [U1810102].  
**Conflicts of Interest:** The authors declare there is no conflicts of interest regarding the publication of this paper.  
Figure 3: The trend chart of daily gas production per well and optimal membership degree in CBM blocks.
# Theory of Edge Effects and Conductance for Applications in Graphene-Based Nanoantennas
## 4 Conclusions and Outlook  
The main results of the paper can be summarized as follows:  
1. We developed a new theory of interaction of electromagnetic field with a graphene sheet for nanoantenna applications in the THz, infrared, and optical frequency ranges. The main characteristic feature of our theory is accounting for edge effects in a self-consistent manner. It is based on the concept of optical conductance considered as a general susceptibility and calculated by the Kubo approach. The model is based on the concept of Dirac pseudo-spins founded via solving the boundary value problem for the Dirac equation with the appropriate boundary conditions satisfying the physical model, including edge effects of the sheet;2. The main manifestation of the importance of edge effects is demonstrated by the inhomogeneity of the optical conductance. The amplitude and period of its oscillations depend on the length of the sheet and on the electrochemical potential. It is defined by the number of pseudo-spin modes supporting the conductance;
3. The developed theory is applied for the simulation of the sheet conductance in a wide range of sample parameters (length 2.1-800 nm and electrochemical potential 0.1-1.0 eV). It is shown that for a length exceeding 800 nm our model and the widely used Drude model of conductivity agree to a high degree of accuracy. However, for small geometric sizes (i.e., smaller than 50 nm), the physical picture of conductivity with respect to the Drude model changes dramatically due to the influence of edge effects. This circumstance should be accounted for in the design of graphene-based resonant THz antennas and other types of photonic and plasmonic nanodevices;
4. It is shown that the qualitative distribution of the conductivity along the sheet strongly depends on the electrochemical potential. Thus, it is possible to control the conductivity and performance of graphene nanoantennas, by means of varying the gate voltage.  
Our theory allows reformulation of the effective boundary conditions for the electromagnetic field at the surface of the graphene sheet with accounting of the edge effects. It requires the modification of integral equations of antenna theory and the methods of their solution. This should be one of the subjects of future research activity as well as their application to nanoantennas and other nanodevices.  
Developments of the physical models, derivation of the basis equations, interpretation of the physical results and righting the paper have been performed by [PERSON], [PERSON], [PERSON] and [PERSON] jointly. The numerical simulations were produced by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by NATO grant number NATO SPS-G5860 and by H2020, project TERASSE 823878.  
Not applicable.  
The authors declare no conflict of interest.