esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Governing Equations and Numerical Methods
### Governing Equations  
The density-based, unsteady, compressible, two-dimensional, or axisymmetric RANS equations are expressed as follows:  
\[\frac{\partial\hat{q}}{\partial t}+\frac{\partial(\hat{E}-\hat{E}_{v})}{ \partial\xi}+\frac{\partial(\hat{F}-\hat{F}_{v})}{\partial\eta}=\hat{S} \tag{1}\]  
where:  
\[\begin{array}{l}\hat{q}=\frac{1}{7}\big{(}\rho_{m}Y_{t},\rho_{m}u,\rho_{m}v, \rho_{m}h_{t},\rho_{m}Y_{v},\rho_{m}Y_{g}\big{)}^{T}\\ \hat{E}=\frac{1}{7}\big{(}\rho_{m}Y_{t}U,\rho_{m}uU+\xi_{x}y,\rho_{m}vU+\xi_{y }y,\rho_{m}h_{t}U,\rho_{m}Y_{v}U,\rho_{m}Y_{g}U\big{)}^{T}\\ \hat{F}=\frac{1}{7}\big{(}\rho_{m}Y_{t}V,\rho_{m}uV+\eta_{x}p,\rho_{m}vV+\eta_ {y}p,\rho_{m}h_{t}V,\rho_{m}Y_{v}V,\rho_{m}Y_{g}V\big{)}^{T}\\ \hat{E}_{v}=\frac{1}{7}(0,\xi_{x}\tau_{xx}\quad+\xi_{y}\tau_{xy},\xi_{x}\tau_{ yx}+\xi_{y}\tau_{yy},\xi_{x}(u\tau_{xx}+v\tau_{xy}-q_{x})\\ \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad+\xi_{y }\big{(}u\tau_{yx}+v\tau_{yy}-q_{y}\big{)},0,0\big{)}^{T}\\ \hat{F}_{v}=\frac{1}{7}(0,\eta_{x}\tau_{xx}\quad+\eta_{y}\tau_{xy},\eta_{x}\tau _{yx}+\eta_{y}\tau_{yy},\eta_{x}(u\tau_{xx}+v\tau_{xy}-q_{x})\\ \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad+\eta_{y} \big{(}u\tau_{yx}+v\tau_{yy}-q_{y}\big{)},0,0\big{)}^{T}\\ \hat{S}=\frac{1}{7}((\dot{m}^{+}+\dot{m}^{-})\quad-c_{a}Y_{t}\rho_{m}v/y,\rho_ {m}g_{x}-c_{a}(\rho_{m}uv-\tau_{xy})/y,\rho_{m}g_{y}\\ \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad  
where \(C_{dest}\) and \(C_{prod}\) denote empirical constants, and are set as \(C_{dest}=1\) and \(C_{prod}=80\), respectively, in the present study.
# Analysis on the Spatial-Temporal Distribution Patterns of Major Mine Debris Flows in China
## 3 Analysis of the Time Variation Pattern of Mine Debris Flow
### Interannual Variation Pattern of Mine Debris Flow  
The trends in the number of major debris flow disasters and the number of major mine debris flow disasters occurring over a long period of time, obtained based on the collected data, are shown in Figure 2. The percentage of the number of mine debris flow in the total number of debris flow occurrences in each period is also shown in the figure.  
As can be seen from Figure 2, the number of major debris flow disasters and the number of major mine debris flow disasters in China show a trend of first increasing and then decreasing, while the proportion of mine debris flow in the total number of debris flow disasters also shows a trend of first increasing and then decreasing. The number of major mine debris flows gradually increased after 1950, with the peak number of major mine debris flows occurring between 1990 and 1999, and an average of 2.85 accidents per year between 1990 and 2010, during which the mining industry in China flourished, the number of mines increased steeply, the safety system construction was not perfect, and the ecological environment was seriously damaged, leading to the frequent occurrence of mine debris flows. After 2010, with the attention of the Chinese government, the number of major mine debris flow disasters turned and started to decrease. From 2016 to 2020 alone, more than 19,000 non-coal mines and tailings dumps were rectified and closed nationwide, reducing the risk of mines providing raw materials to debris flows from the root, and playing a positive role in the reduction of major mine debris flow accidents.
# On the Contribution of Wind Farms in Automatic Generation Control: Review and New Control Approach
## 5 Simulation Results
### Second Scenario: Comparison of Results in Different Level of Wind Penetration  
In this scenario, the performance of the DFIGs equipped with the SCA optimized FOPID controller is investigated under different levels of wind power penetration. Herein, 10%, 15% and 20% penetration levels are considered for the wind power. The penetration level of wind power is increased through reducing the existing generator units by x%, i.e., an x% reduction in system inertia constant. In the other words, an x% increase in wind power is fulfilled through decreasing the inertia by x% [72]. In Figure 6, the system dynamic responses of 10%, 15% and 20% penetration levels of wind power are demonstrated. As can be seen, by increasing the wind power penetration level, although the inertia of wind system decreases, the proposed FOPID controller provides better frequency performance in high penetration level.
# Trimodal Watch-Type Wearable Health Monitoring Device
## 2 System Design
### Firmware Development  
The firmware has been developed using embedded C and compiles with the PSoC Creator 4.4 compiler from Cypress Semiconductor. Communication between the device and a PC is facilitated through the MCU's internal USB peripheral interface. For wireless communication, the BLE module interfaces with the MCU via UART communication. The 'BME680' and 'TMD26353' sensors transmit data to the MCU using the I2C communication protocol. A cyclic voltammetry (CV) technique is implemented using the MCU's internal blocks to interpret the output current from the SPES.  
\[I_{sen}=\frac{V_{ref}-\left(V_{adc}+V_{ref}\right)}{R_{f}}=\frac{-V_{adc}}{R_{ f}} \tag{1}\]  
Figure 3 showcases a block diagram of the CV firmware. The CV circuit utilizes internal MCU components such as an 8 bit digital-to-analog converter (DAC), an operational amplifier (op-amp), a transimpedance amplifier (TIA), and a 12 bit delta sigma ADC. The SPES comprises three electrodes: counter electrode (CE), reference electrode (RE), and working electrode (WE). A potentiostat, realized with a DAC and an op-amp, provide a stable reference voltage to the RE, ensuring current flow through the CE until the RE potential matches the DAC output. The op-amp's positive input is connected to the DAC, its negative input to the RE, and its output to the CE, to form the potentiostat.  
Figure 2: System on PCB: (**a**) top of PCB; (**b**) bottom of PCB; (**c**) watch-type wristband.  
To accommodate measurements involving negative voltage or current using a single-ended power supply, a virtual ground is established. This concept exploits the op-amp's property of maintaining zero potential difference between its input terminals, with no current flowing into them. As a result, a reference voltage of 2.048 V is supplied to the ADC's negative input and the TIA's positive input, creating a virtual ground at 2.048 V via the internal DAC. This configuration supports a potential range of \(\pm\)2.048 V between the WE and RE, with a resolution of 1 mV. The TIA, consisting of an op-amp and a feedback resistor (\(R_{f}\)) of 20 K\(\Omega\), converts the current signal into a corresponding voltage. This voltage is then fed into a differential ADC to digitize the voltage difference. The ADC is capable of interpreting voltages within \(\pm\)2 V with a resolution of 1 mV, allowing difference as small as a 0.05 \(\mu\)A generated by the SPES to be detected. The ADC output voltage (\(V_{adc}\)) is employed to calculate the SPES output current (\(I_{sen}\)) using the reference voltage of the TIA and ADC (\(V_{ref}\)), as described in Equation (1). Figure 4 shows a comprehensive flowchart for the firmware.
# Multi-User Scheduling for 6G V2X Ultra-Massive MIMO System
## 2 System Model
### G V2X Ultra-Massive MIMO System Model Based on Multi-User Scheduling
If proper precoding is used, the data interference between users can be eliminated at the receiving end, and Formula (3) is expressed as  
\[\mathbf{\widetilde{y}}_{k}(t)=\mathbf{W}_{k}^{\ \ \ \ \ H}\mathbf{H}_{k}(t) \mathbf{F}_{k}\mathbf{s}_{k}+\mathbf{W}_{k}^{\ \ \ \ H}\mathbf{n}_{k}(t),k=1,\ 2,\ \ldots R \tag{4}\]
## 2 Pantograph Arcing Data
### Pantograph Arcing Signal Collection  
Field measurements of electromagnetic disturbance induced by off-line discharges of the grid mainly refer to measuring the time-domain waveform of the radiation field through an antenna or an electric field probe on a railway site, and then analyzing its radiation properties. The measurement instruments used are shown in Table 1, and the test layout and installation locations of the broadband electric field probe are shown in Figure 1.  
As can be seen from Figure 2, the single pantograph arcing signal is composed of a series of oscillating and attenuating pulses in the time domain. The rising edge time is at ns level and the duration is within 1 \(\upmu\)s. The radiation disturbance caused by pantograph arcing is a wide-frequency electromagnetic field, ranging from tens of kHz to a few GHz, with the main power spectrum concentrated at a few hundred MHz. According to China's frequency planning and usage, 400 MHz is available for the LTE-R system. At 400 MHz, the field strength is about 47 dB\(\upmu\)V/m. However, there are some differences in the pulse signal data for each measurement, which have strong randomness. Therefore, it is not accurate to analyze the impact of the LTE-R system using only the results of one measurement.  
\begin{table}
\begin{tabular}{c c c} \hline
**Instrument** & **Model Number** & **Performance** \\ \hline digital storage oscilloscope & Tektronix TDS3052 & 500 MHz, 5 GS/s \\ electric field probe & HI-6105 & 100 kHz–6 GHz \\ \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the instruments and equipment required for field measurements.  
Figure 1: Test layout.  
Figure 2: Pantograph arcing measured at the site.
# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 2 Literature Review
### Multi-Level Caching  
Multi-level caching strategies aim to improve data availability by leveraging cache storage across different tiers of the network. One widely used approach, Leave Copy  
Figure 4: The progression historically.  
Everywhere (LCE), stores a copy of the requested content at every node along the delivery path. Although this method reduces content retrieval delays and overall bandwidth usage, it often leads to excessive data redundancy and inefficient cache utilization. To mitigate this, probabilistic caching (Prob) introduces randomness into caching decisions--allowing nodes to cache data with a predefined probability, thereby controlling content replication. More advanced hierarchical caching schemes, such as Collaborative Hierarchical Caching and Efficient Hierarchical Content Placement (EHCP), extend cache coordination across multiple network levels, optimizing both edge and core cache placements. However, these approaches often rely on static topologies and lack the ability to adapt based on node centrality, real-time resource availability, or social dynamics among nodes. SARAC4N addresses these gaps by introducing a dynamic, socially aware clustering mechanism that adapts cache placement not only by location but also by interaction relevance and resource capacity.  
Although multi-level caching strategies such as LCE and EHCP improve data availability and reduce retrieval latency across hierarchical layers, they often lead to excessive redundancy and inefficient cache utilization, especially when storage resources are limited. Probabilistic variants partially address this by controlling replication rates, yet they lack intelligent decision-making based on network dynamics or node importance. SARAC4N surpasses these limitations by using social centrality metrics and resource profiling to selectively cache content in strategically influential nodes, significantly minimizing redundancy while preserving access efficiency.
## 4 Conclusions  
We successfully formed uniform monolayer arrays of TiO\({}_{2}\) NSs on LED chips via theicing transfer method. After the transfer of TiO\({}_{2}\) monolayers with different crystal phases onto LED chips, enhancements in the light extraction of LEDs were observed. The LEDs covered with Am- and An-TiO\({}_{2}\) NS monolayers showed 3.0- and 1.05-fold enhancement of LOP intensity, respectively, compared with the reference LEDs without the NSs at a fixed injection current of 100 mA. The experimentally measured results are entirely consistent with those of 3D-FDTD simulations, and the large enhancement in the optical output power can be attributed to the superior suppression of the TIR and backscattering between the GaN layer and air by Am-TiO\({}_{2}\) NS arrays and the concentration of light on the apex of spheres within light propagation path. Based on our results, the transferable uniform arrays of Am- and An-TiO\({}_{2}\) NS monolayers will open a new path to improve the optical performance of various optoelectronic devices, such as solar cells, photodetectors, and LED chips.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13053042/s1](https://www.mdpi.com/article/10.3390/app13053042/s1)]([https://www.mdpi.com/article/10.3390/app13053042/s1](https://www.mdpi.com/article/10.3390/app13053042/s1)), Figure S1: (a,b) Optical microscope image of the TiO\({}_{2}\) nanosphere arrays on blue LED chip with Am- and An-TiO\({}_{2}\) nanosphere. (c,d) and (e,f) show the SEM images of 500 nm size Am- and An-TiO\({}_{2}\) nanosphere arrays on p-GaN substrate, respectively; Figure S2: (a-c) FDTD simulation results of compares cross sections of the power field for general planar LED and LEDs with Anatase-, Amorphous-TiO\({}_{2}\) sphere array (d,e) Anatase, Amorphous  
Figure 6: Far-field intensity mappings of (**a**) a reference LED and LEDs with (**b**) An-TiO\({}_{2}\) and (c) Am-TiO\({}_{2}\) NS monolayers.  
Figure 7: Evolution of travelling light via An-TiO\({}_{2}\) NSs (**a–f**) and Am-TiO\({}_{2}\) NSs (**g–l**) with respect to change of time. The red arrows represent the backscattering from the interface of air/TiO\({}_{2}\) sphere to p-GaN.  
TiO\({}_{2}\) single sphere cross section image; Table S1: Summary of synthesized TiO\({}_{2}\) nanospheres sizes according to amount of the injected DIW.  
Conceptualization, J.P. and D.K.; methodology, D.K.; software, U.J.; validation, N.K. and D.K.; formal analysis, W.H.; investigation, D.K. and U.J.; writing--original draft preparation, D.K. and J.P.; writing--review and editing, N.K. and J.P.; supervision, J.P.; project administration, J.P.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. 2021R1A4A1052085).  
The authors declare no conflict of interest.
# Review of Magnetoelectric Effects on Coaxial Fibers of Ferrites and Ferroelectrics
## 5 Hexagonal Ferrite and Ferroelectric Core-Shell Fibers
### M-Type Hexaferrite-Based Core-Shell Nanofibers  
[PERSON] et.al. reported on the synthesis of coaxial fibers of M-type strontium hexagonal ferrite, SrFe\({}_{12}\)O\({}_{19}\), (SrM), and BTO or PZT [76]. SrM is a good candidate material amongst the M-type hexaferrites due to a large H\({}_{\text{a}}\) of around 20 kOe [46]. The electrospinning procedure was similar to the ones described earlier for spinel ferrites and ferroelectrics. Fibers with either a ferrite core or a ferroelectric core were made and annealed at 700-1000 \({}^{\circ}\)C [76]. An SEM micrograph for the fibers clearly revealed the core-shell structure, as shown in Figure 13a, for a SrM core-BTO shell fiber. XRD data, as shown in Figure 13b, showed strong diffraction peaks for the piezoelectric phase, i.e., PZT/BTO, and relatively weaker peaks for SrM.  
The nature of ME coupling was studied by H-induced P on a disc of the fibers annealed at 900 \({}^{\circ}\)C [77]. In addition, low-frequency MEVC was measured on 2D and 3D films of the fibers assembled in a magnetic field. The fractional change in P\({}_{\text{r}}\) for PZT core-SrM shell and SrM core-BTO shell fibers are shown in Figure 13c,d [76]. The data for PZT-SrM showed a decrease in P\({}_{\text{r}}\) under H and its magnitude increased under H to \(-\)17% for H = 6 kOe. Upon decreasing H, P\({}_{\text{r}}\) continued to decrease further to \(-\)22% at H = 0. A similar behavior for H-induced P\({}_{\text{r}}\) at the same magnitude is evident in Figure 13d for fibers of SrM-BTO. Data on MEVC measured at 100 Hz in 3D films of SrM-BTO and SrM-PZT are shown in Figure 13e.f. A very large zero-bias MEVC, at H = 0, is seen in the data and is due to a large \"built-in\" bias field provided by the magneto-crystalline anisotropy for SrM. The MEVC increased with increasing H, except for the fibers with a PZT core-SrM shell, which showed a decrease under H. The overall magnitude of the MEVC was comparable to results reported for layered composites of single crystal SrM and PZT [78].
## 3 Results and Discussion
### Effect of Initial PH in Adsorption Tests  
The pH of the solution is an important factor that could affect the adsorption process by controlling both the surface chemistry of the adsorbent and the speciation of the adsorbate. The experiments were carried out in a pH range of 3.0-10.0 and the results are reported in Figure 6. It could be observed that MB uptake increased from 37.1 to 82.7 mg g\({}^{-1}\) with increasing the pH solution from 3 to 10. These results are directly related to the chemical surface properties of the adsorbent and MB molecules.  
Thus, the point of zero charge (Ph\({}_{\text{PZC}}\)) of the beads was found to be 7.2 in MB solution [48]. The results showed that a remarkable decreasing of the adsorption capacity was observed at Ph values \(<\) Ph\({}_{\text{PZC}}\), related to the pKa values of alginate (3.38 and 3.65) [46]. At Ph values higher than 3.65, more negative charges are generated on the surface of the beads, while MB molecules showed positive charge, thus promoting the adsorbate-adsorbent electrostatic interactions between the negatively charged groups (-COOH and -OH) in the beads surface and the positively charged molecules of MB. In addition, at Ph \(<\) pKa values of alginate, other interactions such as hydrogen bonding between sulfur and/or nitrogen atoms in MB and protonated carboxylate groups might be dominant in the adsorption process [34]. Same results were found in the literature for MB adsorption onto other composite beads [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 49].
## 2 Data and Methods
### Data
#### 2.1.5 Ancillary Data  
Along with the basic data products, in this work a number of ancillary datasets is utilized. OMI/Aura O\({}_{3}\) total column data from the 1\({}^{\circ}\)\(\times\)1\({}^{\circ}\) daily gridded dataset OMTO3d version 3 (v3) [74] were acquired for the period 2005-2019. The OMI science team produces this level-3 product by gridding and averaging only good-quality level-2 total column ozone orbital swath data retrieved by the TOMS version 8 algorithm. Also, satellite-based monthly gridded (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) resolution) surface albedo (SAL) data from the CM SAF Cloud, Albedo, Radiation AVHRR-based edition 2.1 (CLARA-A2.1) product [75] were acquired for the period 1/2005 to 6/2019. The surface albedo data are retrieved from the Advanced Very High Resolution Radiometer (AVHRR) instruments onboard the National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational Satellite Program of Europe (MetOp) satellites and cover the spectral range 0.25-2.5 \(\upmu\)m. Another CM SAF product used for the scopes of this research is the CLoud property dAtAset using SEVIRI--edition 2.1 (CLAAS-2.1)--cloud fractional cover (CFC) [76]. More specifically, daytime CFC data were acquired on a monthly basis at a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) and monthly diurnal data (for 11:00 and 12:00 UTC) which are available at a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). The CFC measurements are retrieved by the SEVIRI instruments onboard the MSG geostationary satellite series. Finally, the surface topography data used here are from the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) product [77] and the population data for the year 2020 from the Gridded Population of the World version 4 (GPWv4.11) dataset [78].
## Appendix A Derivation of an Empirical Porosity Model  
We present the empirical porosity equation based on experimental measurements conducted by [26] in clean sandstones, which relates the \(V_{P}/V_{S}\) ratio to \(\phi\) as follows:  
\[\frac{V_{P}}{V_{S}}=1.494\exp(0.326\phi), \tag{1}\]  
or equivalently:  
\[\frac{V_{P}}{1.494V_{S}}=\exp(0.326\phi). \tag{2}\]  
To isolate \(\phi\), we take the natural logarithm (ln) of both sides of Equation (2). Since \(\ln(\exp(x))=x\) and \(\ln(a/b)=\ln(a)-\ln(b)\), we obtain:  
\[\phi=\frac{1}{0.326}(\ln(V_{P})-\ln(1.494V_{S})). \tag{3}\]For the general case, Equation (A3) can be expressed as:  
\[\phi=\alpha(\ln(V_{P})-\ln(\beta V_{S})).\] (A4)  
It is important to note that this model was developed for predictive seismic analysis, with the parameters \(\alpha\) and \(\beta\) serving as control and adjustment factors to enhance the accuracy of porosity prediction. Specifically, the parameter \(\alpha\) acts as a general calibration parameter, while \(\beta\) adjusts the S-wave velocity values corresponding to the porosity predictions.
## 3 Result and Discussion
### Radiation Attenuation Studies
At gamma-ray energy of 1 MeV, the RPE increases between 11.23-69.63%, 11.82-71.60%, 12.86-74.76%, and 14.93-80.17% for the fabricated glass samples SBT-Ti, SBT-Zn, SBT-Ba, and SBT-Pb, respectively.
## 4 Results and Discussion
### Results Gained through Practical Platform  
A practical platform in the lab was developed with IRF 840 as a switching transistor and RHRG3040 as a diode. The turn-on and turn-off operation of IRF840 is supported by employing four gate-controlling circuits realized with hybrid chip EXB840 and four isolated DC sources. The values of the input voltage, switching frequency, and filtering components are the same as those used in the simulation. A high-speed STM microcontroller is employed for the generation of the control signals that are aligned with the variation in the input voltage. This is ensured by employing the output of a voltage-sensing circuit to the STM controller as a control input. All the practical waveforms are obtained by using a dual-channel RIGOL (DS1052E) oscilloscope.  
Figure 10: Simulation plotting input (blue) and output (red) during (**a**) non-inverting operation; (**b**) inverting operation.  
The recorded waveforms of Figure 12a,b from the practical platform confirm the non-inverting and inverting boost operation. The stabilization of the output voltage with either polarity of the voltage gain can be achieved by the PWM control. Similarly, the practical results of Figure 13a-c prove the validity of the peak inverse voltage that may be developed across the solid-state devices.  
Figure 11: Simulated voltage waveforms developed across the semiconductor devices: (**a**) \(Q_{4}\), \(D_{5}\) (blue) and \(Q_{4}\), \(D_{5}\) (red); (**b**) \(Q_{1}\) (blue) and Q\({}_{3}\) (red); (**c**) \(D_{1}\), \(D_{2}\) (blue) and Q\({}_{3}\), \(D_{4}\) (red).  
Figure 12: Practically gained waveforms of input (blue) and output (red) for (**a**) non-inverted output and (**b**) inverted output.