esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Benefits and Deployment Considerations of GLI-Split
### Benefits for Upgraded GLI-Nodes  
With GLI-Split, hosts are not configured with any global locators. This simplifies provider changes as it makes renumbering in terms of assigning new global locators obsolete. Renumbering nodes inside a GLI-domain means assigning new local locators. This is necessary, for example, when subnetworks need to be rearranged for administrative reasons. With GLI-Split, this is facilitated because local locators are automatically assigned to nodes and nodes outside a GLI-domain are unaware of the corresponding local addresses. Nodes inside a GLI-domain use only stable identifier addresses for configuration and connection establishment.  
GLI-Split enables multihoming and takes advantage of all benefits associated with multihoming. When the connection from the local GLI-domain to its ISP fails, the local routing system reroutes the traffic to another GLI-gateway. When a destination is not reachable at its default locator, the source may be notified about a failure and may address the traffic to another global GLI-address.  
This represents a host-based rerouting technique that is an alternative to network-based rerouting techniques, as presented in [46]. GLI-hosts can select the GLI-gateways of the source and destination domain and thereby enable multipath routing, which might be useful for host-based load balancing. Traffic engineering for outbound traffic can be performed by enforcing the GLI-gateway of the source domain with gateway selection. In addition, traffic engineering for inbound traffic can be achieved by enforcing the GLI-gateway of destination domains. This is done by activating the GAP-bit in global GLI-addresses for certain services or nodes. Moreover, GLI-Split provides improved mobility support in the sense that corresponding nodes can contact mobile nodes directly without triangle routing over a home agent.  
Most of these advanced networking features are not available in today's Internet or require provider-independent addresses. GLI-Split enables even smallest edge networks to use these features without increasing the routing tables in the DFZ. In contrast to many other future Internet routing proposals, GLI-Split does not suffer from potential problems due to increased packet sizes after encapsulation, and it does not require special interworking techniques with the classic IPv6 Internet.
# A Review of Hydrogen Sensors for ECLSS: Fundamentals, Recent Advances, and Challenges
## 3 Heat Conduction Hydrogen Sensors  
As the thermal conductivity of each gas is different, with the change in the concentration of the measured gas, the thermal conductivity of the thermistor on the thermal conductivity core to the measured gas is different, and the temperature of the sense resistor on the thermal conductivity core changes, which finally leads to the change in the resistance value of the sense resistor on the thermal conductivity core. There will be no chemical reaction, let alone material change; thus, its output is very stable and the drift is very small in long-term use, which can meet the actual application requirements in the aerospace field.
## 2 Detection Principle  
### Acoustoelastic Effect  
The acoustoelastic effect is widely used for ultrasonic stress measurements. In a solid, the ultrasonic velocity varies due to the applied stress, within the elastic limits of the material [21].  
The uniaxial stress and the ultrasonic velocity in a solid have a linear relationship as shown in the expression of Equation (1) [23]. This equation is applicable to uniaxial stress in isotropic materials [2]. South China University of Technology group applied Equation (1) in internal stress measurements for a GIS epoxy composite with the longitudinal wave detection technique [2].  
\[\frac{V-V_{0}}{V_{0}}=K(\sigma-\sigma_{0}) \tag{1}\]  
where \(V\) refers to ultrasonic wave velocity under a stressed state (unit of m/s), \(V_{0}\) refers to ultrasonic wave velocity under the initial stress, \(\sigma\) is stress under a stressed state (unit of MPa), \(\sigma_{0}\) is internal stress under the initial stress, and \(K\) refers to the acoustoelastic coefficient (unit of MPa\({}^{-1}\)) [2]. For basin insulators, differences in the manufacturing technique and material can affect the acoustoelastic coefficient.  
\(K\) can be calculated from \(k\), the slope of \(V\)-\(\sigma\) linear equation, and \(V_{0}\) as shown in Equation (2) below [2].  
\[K=\frac{k_{0}}{V_{0}} \tag{2}\]  
The internal stress \(\sigma\) is derived as the following Equation (3), according to Equations (1) and (2) [2].  
\[\sigma=\frac{V-V_{0}}{KV_{0}} \tag{3}\]
## 2 Research Area and Data Selection
### Data Selection  
[PERSON] et al. [7] selected second-sampled data of the GM4 fluxgate with a suitable quality, which are 5-100 s cycle data of the 71 geomagnetic observation stations of China's geomagnetic network. The data were utilized to conduct earthquake predictions with the polarization method, and favorable results were obtained. In this study, we analyzed data recorded by the HPM payload onboard the CSES referring to ground-based data. HPM vector and scalar data are obtained by the Fluxgate Magnetometer (FGM) and Coupled  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Magnitude**} & & & & & **Depth** & \\  & \multicolumn{2}{c}{**(Ms)**} & **UTC+8** & **Longitude (\({}^{\circ}\))** & **Latitude (\({}^{\circ}\))** & **(km)** & **Location** \\ \hline
1 & 5.1 & 14 October 2022 & & & & & \\  & 08:53:53 & & & & & & \\
2 & 5.2 & 8 June 2022 & & & & & \\  & & 20:24:18 & & & & & \\
3 & 5.3 & 23 May 2022 & & & & & \\  & & 10:01:03 & & & & & \\
4 & 5.5 & 23 September 2021 & & & & & \\  & & 01:01:26 & & & & & \\
5 & 6.6 & 1 May 2021 & & & & & \\  & & 09:27:26 & & & & & \\
6 & 6.3 & 21 December 2020 & & & & & \\  & & 01:23:19 & & & & & \\
7 & 5.1 & 12 July 2020 & & & & & \\  & & 06:38:25 & & & & & \\
8 & 6.2 & 20 April 2020 & & & & & \\  & & 04:39:05 & & & & & \\
9 & 5.4 & 29 November 2019 & & & & & \\  & & 12:01:36 & & & & & \\
10 & 6.5 & 18 June 2019 & & & & & \\  & & 21:22:21 & & & & & \\
11 & 5.1 & 18 May 2019 & & & & & \\  & & 06:24:48 & & & & & \\  & & 11 April 2019 & & & & & \\
12 & 6.0 & 16:18:23 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Catalog of the selected Ms \(>\) 5.0 earthquakes in Northeast Asia, December 2018–January 2023.  
Dark State Magnetometer (CDSM) probes, respectively [30]. In this study, HPM_5 vector data recorded by the FGM probe were selected for seismic analysis. The FGM sampling rate is 1 Hz, the detection band is DC-15 Hz, and the selected data are geomagnetic three-component vector data.
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
## 3 Results
### Sea-Level Performances
#### 3.2.1 Self-Crossing Point Analysis  
The ideal situation would be if the mean differences in the SLA at the self-crossing points were close to zero, and the standard deviation was very small. In this study, the GDR data were screened according to HY-2B data screening criteria. The time range of the HY-2B/C/D satellites' ascending and descending tracks to determine the self-crossing points was three days. In order to achieve assessments with high accuracy, the data were analyzed using the following criteria: between 50\({}^{\circ}\) of the equator north and south latitudes, water depth greater than 1000 m, and SLA lower than 20 cm [24].  
It can be seen in Figure 3 that the standard deviation of the SLA at the self-crossing points for each cycle of the satellite were 3.30 cm, 3.51 cm, 3.72 cm, which indicated that the accuracy of HY-2B altimetry data was high and stable. HY-2C/D data gradually stabilized as the number of cycles increased.
# Displacement-Dependent Damping Inerter System for Seismic Response Control
## 3 Characteristics of DDIS  
### Stochastic Performance Indices  
To characterize the dynamic performance of the SDOF structure equipped with DDIS and further evaluate the vibration mitigation effect of the DDIS, different stochastic performance indices are introduced. First, the dimensionless parameters of the DDIS are defined, including the inertance-mass ratio \(\mu\), DDE stiffness ratio \(\kappa\), and stiffness ratio \(\lambda\):  
\[\mu=\frac{m_{in}}{m},\kappa=\frac{k_{t}}{k},\lambda=\frac{k_{d}}{k} \tag{15}\]  
The commonly used index is the displacement mitigation ratio \(\gamma_{Dis}\), which is the ratio of the RMS displacement of the structure-DDIS system \(\sigma_{Dis,SDOF-DDIS}\) to that of the original structure \(\sigma_{Dis,SDOF-}\). Another index is the acceleration mitigation ratio \(\gamma_{Acc}\), which is the ratio of the RMS acceleration of the structure-DDIS system \(\sigma_{Acc,SDOF-DDIS}\) to that of the original structure \(\sigma_{Acc,SDOF}\). The parameters \(\gamma_{Dis}\) and \(\gamma_{Acc}\) are, therefore, defined as follows:  
\[\begin{split}\gamma_{Dis}\Big{(}\mu,\kappa,\lambda,\alpha,u_{dy} \Big{)}&=\frac{\sigma_{Dis,SDOF-DDIS}}{\sigma_{Dis,SDOF}},\\ \gamma_{Acc}\Big{(}\mu,\kappa,\lambda,\alpha,u_{dy}\Big{)}& =\frac{\sigma_{Acc,SDOF-DDIS}}{\sigma_{Acc,SDOF}}\end{split} \tag{16}\]  
However, an energy-based performance index is more robust against various types of input ground motion than displacement- and acceleration-based performance indices. Referring to the energy balance in Equation (14), the excitation-induced input energy is eventually dissipated by the structural inherent damping and DDE of the DDIS. The portion of the energy dissipated by the DDE is maximized, whereas the portion filtered into the primary structure and dissipated by the structural inherent damping should be minimized. A filtered energy ratio \(\gamma_{E}\) is thus defined here to quantify the portion of energy dissipated by DDIS out of the total input energy:  
\[\begin{split}\gamma_{E}\Big{(}\mu,\kappa,\lambda,\alpha,u_{dy} \Big{)}&=\frac{\mathrm{E}[e_{total}(t)]-\mathrm{E}[e_{d,DDIS}(t)]} {\mathrm{E}[e_{total}(t)]}\\ &=1-\frac{(1-\alpha)k_{d}\sigma_{z_{ii}\dot{u}_{d}}}{\alpha_{z_{ ii}\dot{u}_{d}}+(1-\alpha)k_{d}\sigma_{z_{ii}\dot{u}_{d}}}\end{split} \tag{17}\]A low value of \(\gamma_{E}\) indicates that only a small portion of the vibrational energy is filtered into the primary structure, indicating the energy dissipation efficiency of the DDIS.
# Ferroelectric Materials: A Novel Pathway for Efficient Solar Water Splitting
## 4 Ferroelectric Materials for Photoelectrochemical (PEC) Water-Splitting Devices
### BiFeO\({}_{3}\)  
The BiFeO\({}_{3}\) (BFO) ferroelectric material shows unique photovoltaic effects that result from large spontaneous polarization. This prominent characteristic develops a high built-in potential that effectively enhances the separation and drift of photogenerated carriers for application in PEC devices composed of BFO-containing photoelectrodes. In comparison with photocathodic materials, there are few reports regarding the application of BFO as photoanodic materials in PEC water splitting.  
Very recently, [PERSON] et al. [4] reported on epitaxial BFO thin-film photoanodes with different crystallographic orientations including \((111)_{\text{pc}}\), \((110)_{\text{pc}}\), \((001)_{\text{pc}}\), and the consequence of the ferroelectric domain structures was also investigated. To investigate the difference among various crystallographic orientations on ferroelectric properties and the effect of different polarization states on photovoltaic performance, ferroelectric P-E hysteresis loop and polarization switching measurements were performed, respectively, as shown in Figure 10. The results revealed that in the absence of polarization, the charge energy band bending, which induces inefficient charge separation, was not observed. In contrast, an optimal PEC performance with \((111)_{\text{pc}}\) BFO thin film under a downward polarization state was revealed. Previously, enhanced efficiency in a polycrystalline BFO photoanode also exhibited a similar mechanism when switching the polling bias from +8 V to \(-8\) V [149]. This BFO thin film was based on the cost-effective technology of spin coating, instead of growing epitaxial BFO thin film using more sophisticated techniques including radio frequency (RF) and pulsed laser deposition (PLD) [150; 151]. However, this limits their application in large-scale technology.  
Figure 9: SrTiO\({}_{3}\)–TiO\({}_{2}\) eutectic composite-based PEC cell. (**a**) A simplified PEC cell employing a eutectic system as the photoactive anode material. (**b**) Charge-carrier separation mechanism at the phase boundary, with bandgap positions and values with respect to electrochemical potential (E) vs. NHE based on [139; 147; 148]. Figure 9 [139] from Copyright 2017 Elsevier.
## 2 Structure and Modeling of the Proposed System
### Equivalent Model Analysis of the System  
The equivalent circuit of the system is shown in Figure 3. \(U_{\text{P}}\) is the output voltage of the inverter and \(R_{\text{L}}\) is the load. \(I_{\text{P}}\) and \(I_{\text{S}}\) are the current on the emitting side and the receiving side, respectively. \(L_{\text{P}}\) and \(L_{\text{S}}\) are the inductances of the transmitting coil and the receiving coil, respectively. \(R_{\text{P}}\) and \(R_{\text{S}}\) are the internal resistance of the transmitting coil and the receiving coil, respectively. \(C_{\text{P}}\) and \(C_{\text{S}}\) are the resonant capacitors of the transmitting side and the receiving side, respectively.  
The circuit equivalent total impedance \(Z_{\text{P}}\) and the receiver total impedance \(Z_{\text{S}}\) can be expressed as:  
\[\begin{cases}Z_{\text{S}}=j\omega L_{\text{S}}+\frac{1}{j\omega C_{\text{S}}} +R_{\text{L}}+R_{\text{S}}\\ Z_{\text{P}}=j\omega L_{\text{P}}+\frac{1}{j\omega C_{\text{P}}}+R_{\text{P}}+ \frac{\omega^{2}M^{2}}{Z_{\text{S}}}.\end{cases} \tag{4}\]  
Figure 3: System equivalent circuit diagram.  
In Equation (4), \(\omega\) is the operating frequency of the system, and \(M\) is the mutual inductance between the transceiver coils. The current at the transmitter and receiver can be expressed as:  
\[\left\{\begin{aligned} \dot{I}_{\text{P}}&=\frac{\dot{U}_{ \text{P}}}{Z_{\text{P}}}\\ \dot{I}_{\text{s}}&=\frac{\dot{I}_{\text{P}}\ \cdot\ j \omega M}{Z_{\text{s}}}.\end{aligned}\right. \tag{5}\]  
The output power can be expressed as:  
\[P_{\text{out}}=\left|\dot{I}_{\text{s}}^{2}\cdot R_{\text{L}}\right|=\frac{8 \Omega_{\text{in}}^{2}\omega^{2}M^{2}R_{\text{L}}}{\pi^{2}Z_{\text{P}}^{2}Z_{ \text{s}}^{2}}\cos^{2}\frac{\alpha}{2}, \tag{6}\]  
and the output efficiency can be expressed as:  
\[\eta=\frac{P_{\text{out}}}{P_{\text{in}}}=\left|\frac{\dot{I}_{\text{s}}^{2} \cdot R_{\text{L}}}{\dot{I}_{\text{P}}^{2}\cdot Z_{\text{P}}}\right|=\frac{ \omega^{2}M^{2}R_{\text{L}}}{Z_{\text{S}}^{2}Z_{\text{P}}}. \tag{7}\]  
It can be seen from Equations (6) and (7) that the transmission power of the system is affected when the phase shift angle is changed, but the transmission efficiency of the system will not be affected. Therefore, during the simultaneous transmission of power and information, the output power fluctuation should be minimized while ensuring the transmission rate and controlling the bit error rate.
## Results
### VIS/NIR Spectrometer for TSS Detection of Orange
#### 3.1.2 Spectral Data Processing  
Data processing is a very important step to discern the differences among spectral sampling data, and different processing methods have their own advantages. To find an optimal data processing method, reference to previous researches of [PERSON] et al. [23] and [PERSON] et al. [24], 16 data processing methods were applied for comparison in this research. They are first derivative, second derivative, square root, square root + first derivative, square root + second derivative, logarithm, logarithm + first derivative, logarithm + second derivative, reciprocal + logarithm, reciprocal + logarithm + second derivative, reciprocal, reciprocal + first derivative, reciprocal + second derivative, Savitzky-Golay, and multiplicative scatter correction. These 16 data processing methods were labeled as DP 1 to 16, respectively. After the data processing operations, PLSR was applied for orange TSS prediction to test the treatment effect of the different data processing methods. The PLSR detection results are shown in Table 2. Compared to the TSS detection result based on original spectral data (Figure 2b), Savitzky-Golay method (DP 15) can improve the detection accuracy the most, with an R\({}^{2}\) for calibration and validation sets of 0.8991 and 0.6600, respectively.  
The PLSR detection results based on Savitzky-Golay processed data are shown in Figure 3. Even though the orange TSS detection accuracy has been improved efficiently after Savitzky-Golay data processing, the accuracy is still not enough.
## 4 Conclusions  
Sulfate-bearing soils cause major issues in pavement and various civil engineering infrastructures due to their causing significant swelling and strength damage. The results of this study portrayed the impacts of (M) as a non-calcium-based stabilizer, rice husk ash, and GGBS as abundant byproducts in treating gypseous soil with a high level of sulfate. Based on the results of the study, the following conclusions were made:  
* The minimum swelling values of M-RHA-, M-GGBS-, and M-GGBS-RHA-stabilized gypseous soils with 20% doses (0.011%, 0.001%, and 0.001%) were lower than that of lime-stabilized soil.
* The optimum strength values of M-RHA-, M-GGBS, and M-GGBS-RHA-stabilized soils after soaking with 20% (2.7, 12.8, and 9.7 MPa) were notably higher than that of lime-stabilized soil (0.9 MPa).
* The curing time had a significant impact on the resistance to sulfate attack as a result of the hydration process; an increase in the curing period from 7 to 90 days resulted in adequate hydration and improved strength in the presence of sulfate (gypsum).
* The results of the SEM and EDX showed that no ettrinigite was formed in the M-RHA-, M-GGBS-, or M-GGBS-RHA-stabilized soils.
* The formation of cementing gels such as MSH, MAH, CSH, and CAH improved the UCS values of the stabilized samples, as these gels occupied the voids and improved the bonding between the soil particles. As a result, the treated soils' UCS values were improved.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11146618/s1](https://www.mdpi.com/article/10.3390/app11146618/s1)]([https://www.mdpi.com/article/10.3390/app11146618/s1](https://www.mdpi.com/article/10.3390/app11146618/s1)), Figure S1. Particle size distribution (PSD) of RHA and GGBS used in this study.  
[PERSON] and A.M.A.E. designed and performed the experimental program; [PERSON] and M.A.M.I. collected and analyzed the experimental data; A.M.A.E. wrote, reviewed, and edited the manuscript; and M.A.M.I. provided supervision of the manuscript writing. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Universit Sains Malaysia, under grant No. 1001.PAWAM.8014090.  
Not applicable.  
The authors would like to acknowledge the Universiti Sains Malaysia for its financial support.  
No conflict of interest to declare.
# A Co-Rotational Meshfree Method for the Geometrically Nonlinear Analysis of Structures
## 4 Numerical Examples
### Cantilever Curved Beam Subjected to End Moment  
Figure 4 represents the geometrically nonlinear analysis of a cantilever curved beam with an initial circle shape such that the fixed and free ends can be reduplicated. The geometry and material properties of the curved beam are: L = 10.0 m, b = 1.0 m, h = 0.1 m, and E = 1.2 \(\times\) 10\({}^{5}\) kN/m\({}^{2}\). The basic equation of the fixed global coordinates and end moment can be written as follows:  
\[Z^{2}+X^{2}=\left(\frac{EI}{M}\right)^{2} \tag{56}\]  
Hence, a curved beam with an initial circular shape will bend into a straight beam when the end moment, \(M=\left(\frac{2\pi EI}{L}\right)\), is applied. If the end moment reaches \(M=\left(\frac{4\pi EI}{L}\right)\), the beam will bend into a circle in the opposite direction.  
Figure 3: The incremental–iterative steps adopted in the present scheme.  
To find the optimal number of neighboring nodes and order of basis function, the L2 error for a combination of neighboring nodes 3, 5, and 7 with respect to the basis functions of quadratic, cubic, and quartic was plotted in Figure 5. It shows that the optimal relationship between the number of the neighboring nodes and basis functions can be given as follows:  
\[Ne=(2m-1), \tag{57}\]  
where \(Ne\) is the number of the neighboring nodes and \(m\) is the number of basis functions. The computational time of the above combinations shown in Figure 5 was less than 10 s. Additionally, the above optimal combination of neighboring nodes with order of basis function was adopted to plot Figure 6, which shows the convergence corresponding to the number of nodal points of 21, 31, 41, and 51 with quadratic, cubic, and quartic basis functions, respectively. It should be noted that h, shown in Figure 6, means the spacing of nodal points. The results show that the DRK approximations with a higher degree basis and more nodal points can effectively improve the solution accuracy. Moreover, the higher degree basis functions will achieve better solution accuracy even if the same number of nodal points is adopted. A combination of quadratic basis functions and five neighboring nodes was adopted in the following examples.  
Figure 4: Cantilever curved beam of a circle subjected to an end moment.  
Figure 5: Convergence of L2 norms of displacement with a different number of neighboring nodes and order of basis.  
The moment-displacement curves of the curved cantilever beam are plotted in Figure 7, solved by 31 nodal points with 10 loading increments. The number of iterations of each increment is less than three. As can be seen, the present theory can provide accurate solutions.