esa-sceva/satcom-chunk-collection huggingface dataset:
# Terahertz Broadband Adjustable Absorber Based on VO2 Multiple Ring Structure
## 5 Conclusions  
A THz ultra-wideband tunable absorber is proposed based on the multiple ring structure of the phase-change material VO\({}_{2}\). By using the phase-change characteristics of VO\({}_{2}\) affected by temperature, the absorber can not only regulate the absorption efficiency, but also control the absorption bandwidth. The simulation results show that when the conductivities of VO\({}_{2}\) is affected by an external temperature change from 200 S/m to 2 \(\times\) 10\({}^{5}\) S/m, the absorption rate varies from 4% to 100%. The optimal absorption bandwidth of the absorber reaches 4.26 THz, with the relative bandwidth of 82.9%, which is better than that of the absorbers mentioned in Table 1. The optimal impedance-matching analysis and the  
Figure 8: Absorption spectrum of different geometric parameters such as (**a**) the length of outer ring \(l_{1}\), (**b**) the length of middle ring \(l_{2}\), (**c**) the length of inter block \(l_{3}\), (**d**) the width of rings \(w\), (**e**) the gap width \(t\) on the rings, and (**f**) four different structures.  
Figure 9: Absorption spectrum at oblique incident under different polarizations. (**a**) TE mode and (**b**) TM mode.  
different electric field distribution under different conductivities of VO\({}_{2}\) can qualitatively explicate the broadband absorption mechanism. In the meantime, we can quantitatively study the absorption principle by employing the wave-interference cancellation theory from analyzing the influence of the top two layers' thickness. Finally, by analyzing the influence of oblique incidence on the absorptivity, this paper shows that the designed absorber is little affected by small incident angles. Therefore, the research has certain guiding significance for the application of tunable absorbers in the fields of detection, modulation, and switching.  
Conceptualization, X.W.; methodology, X.W.; formal analysis, X.W.; investigation, X.W.; data curation, X.W. and G.W.; writing--original draft preparation, [PERSON] and G.W.; writing--review and editing, X.W., G.W. and Y.W.; supervision, [PERSON] and G.W.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China (No. 2019 YFB2203700 and No. 2018 YFB2200500), and Natural Science Foundation of Shandong Province (No. ZR2020 MF106).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions  
The GNSS orbit and clock corrections from the real-time stream transform the real-time or low-latency PPP time transfer into a possible time transfer that has practical significance for timing laboratories. To escape the embarrassment of a receiver clock offset re-convergence caused by a data interruption, in this study, a new algorithm is proposed to resolve the discontinuity induced by the re-convergence. A combination of a between-epoch constraint receiver clock model and an adaptive clock prediction model is added to the ionosphere-free observation equation. A comparison is also made with the results obtained using the white noise model, regardless of the physical design of the atomic clock. All the results calculated from the single station and the links show that the statistical uncertainty with the between-epoch constraint receiver clock model was less than that of the white noise model. The simulation results show that the average RMS values at all the stations are significantly reduced, i.e., by 66.03% from 0.43 to 0.14 ns, by 64.91% from 0.44 to 0.15 ns, by 57.47% from 0.43 to 0.18 ns, and by 51.67% from 0.44 to 0.21 ns for 10-, 30-, 60-, and 90-min data interruptions, respectively. Furthermore, the time series adopted by the new model was more stable than that of a white noise model. The observed detail shows that the improvement magnitude of the stability acquired with the single station is significantly correlated with the averaging time. The between-epoch constraint receiver clock model is more effective for short-term stability than for long-term stability. The extent of the improvement at the six stations was greater than 50% prior to 10000 s, even reaching 100% at the initial averaging time. The data do not show a close correlation between the distance of the two stations and the level of improvement. The frequency stability of the time link is boosted by at least 5% at each averaging time. The results gained from the PPP strategy conducted using the simulation data and the IGS real-time data indicate that the new method can satisfy low-latency time transfer.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments, analysed the data, and wrote the paper; [PERSON], [PERSON] and [PERSON] contributed to discussions and revisions.  
This work is supported by the National Natural Science Foundation of China (No.11703033), the Natural Science Foundation of Shaanxi (No.2018 JQ1020) and the Youth Innovation and Promotion Committee of the Chinese Academy of Science.  
Many thanks to the IGS for providing precise orbit, clock products and observations.  
The authors declare no conflict of interest.  
Figure 13: Percent of improvement of the frequency stability of scheme 2, compared to scheme 1.
## 3 Results
### Phantom Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Depths** & \multicolumn{6}{c}{**Phantom B—Inclusion**} \\ \hline \multicolumn{5}{c}{**Hybrid DOI**} & \multicolumn{6}{c}{**Topographic DOI**} \\ \hline \multirow{5}{*}{**Wavelengths (mm\({}^{-1}\))**} & **Mean Difference from True Over all** & **Mean\% Change from True over All** & **Mean\% Change True Over all** & **True Over all** & **from True over All** \\  & **Wavelengths (mm\({}^{-1}\))** & **Wavelengths (\%)** & **Wavelengths (\%)** & **Wavelengths (mm\({}^{-1}\))** & **Wavelengths (\%)** \\ \hline \multirow{5}{*}{**Wavelengths (mm\({}^{-1}\))**} & **\(\mu_{a}\)** & \(\mu_{s}\)’ & \(\mu_{a}\)** & \(\mu_{s}\)’ & \(\mu_{a}\)’ & \(\mu_{a}\)’ \\ \cline{1-1} \cline{2-7}  & **\(1.8\times 10^{-3}\)** & **0.14** & **13.89** & **13.67** & \(1.8\times 10^{-3}\) & **0.25** & **13.88** & **24.12** \\ \cline{1-1} \cline{2-7}
**9** & **\(1.5\times 10^{-3}\)** & **0.49** & **10.64** & **51.99** & \(4.3\times 10^{-3}\) & **0.43** & **32.05** & **43.49** \\ \hline \multicolumn{5}{c}{**Phantom B—Substrate**} \\ \hline \multicolumn{5}{c}{**Hybrid DOI**} & \multicolumn{6}{c}{**Topographic DOI**} \\ \hline \multirow{5}{*}{**Wavelengths (mm\({}^{-1}\))**} & **Mean difference from true over all** & **Mean\% change from true over all** & **Mean\% change from true over all** & **true over all** & **true over all** \\  & **wavelengths (mm\({}^{-1}\))** & **wavelengths (\%)** & **wavelengths (mm\({}^{-1}\))** & **wavelengths (\%)** \\ \cline{1-1} \cline{2-7}  & **\(\mu_{a}\)** & \(\mu_{s}\)’ & \(\mu_{a}\)** & \(\mu_{s}\)’ & \(\mu_{a}\)** & \(\mu_{s}\)’ & \(\mu_{a}\) & \(\mu_{s}\)’ \\ \cline{1-1} \cline{2-7}  & **\(1.8\times 10^{-4}\)** & **0.03** & **2.62** & **5.58** & \(1.7\times 10^{-5}\) & **0.005** & **0.22** & **0.90** \\ \hline \multirow{5}{*}{**Wavelengths (mm\({}^{-1}\))**} & **\(\mu_{a}\)** & **\(\mu_{a}\)** & \(\mu_{s}\)’ & \(\mu_{a}\)** & \(\mu_{s}\)’ & \(\mu_{a}\) & \(\mu_{s}\)’ \\ \cline{1-1} \cline{2-7}
**2** & **\(1.8\times 10^{-4}\)** & **0.03** & **2.62** & **5.58** & \(1.7\times 10^{-5}\) & **0.005** & **0.22** & **0.90** \\ \cline{1-1} \cline{2-7}
**9** & **\(1.9\times 10^{-4}\)** & **0.004** & **2.64** & **0.67** & \(1.66\times 10^{-5}\) & **0.0052** & **0.23** & **0.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean difference and mean percentage change of recovered (\(\mu_{a}\), \(\mu_{s}\)’) from true (\(\mu_{a}\), \(\mu_{s}\)’) over all wavelengths for phantom B.  
absorption of 8.37% versus 38.43% and 10.64% versus 32.05% for hybrid DOI versus topographic DOI methods at deep ends of phantom A and B, respectively. Similar to simulated \(\mu_{\text{s}}\)' results (Table 3), the phantom experimental results also demonstrate a poorer accuracy in the scattering estimation with 15.34% versus 3.52% and 51.99% versus 43.49% for hybrid DOI versus topographic DOI methods at deep ends of phantom A and B, respectively. Overall, the hybrid DOI accuracy in the simulated results agrees well with the phantom experimental results (Table 3 versus Tables 4 and 5).
# RISC-Based 10K+ Core Finite Difference Method Accelerator for CFD
## 3 Design and Implementation of Multi-Core Accelerator Architecture
### The FCore Implementation
#### 3.1.2 FCore, Four-Stage Pipeline Microprocessor  
The designed microprocessor architecture for CFD, termed FCore, is structured as a four-stage pipeline, as illustrated in Figure 4. The pipeline stages are defined as follows:  
* Instruction Fetch (IF) Stage: This stage incorporates a 128-depth instruction memory (IM) and addressing logic. It fetches the current instruction based on the value of the instruction address controller and determines the next instruction address for the subsequent clock cycle, supporting both sequential execution and branch/jump operations.
* Instruction Decode (ID) Stage: Equipped with a 60-entry general-purpose register (GPR) file, this stage decodes the instruction retrieved from the IF stage by interpreting its opcode and operand fields.
* Execute (EX) Stage: The arithmetic logic unit (ALU) performs operations such as fixed/floating-point addition, subtraction, multiplication, comparison, and branch target address calculation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Instruction** & **Description** \\ \hline \multirow{6}{*}{Control instruction} & NOP & No Operation \\  & HALT & Stop Execution \\  & JUMPI & Jump with Fixed-Point Immediate \\ \cline{1-1} \cline{2-3}  & BEQZ & \\  & BNEZ & \\  & BEQN & Conditional Branch \\ \cline{1-1}  & BNEN & \\ \hline \multirow{6}{*}{Calculating instruction} & ADDI & Add Fixed-Point Immediate \\ \cline{1-1}  & SUBI & Subtract Fixed-Point Immediate \\ \cline{1-1}  & CMP & Compare Fixed-Point \\ \cline{1-1}  & ADDF & Floating-Point Add Register \\ \cline{1-1}  & SUBF & Floating-Point Subtract Register \\ \cline{1-1}  & MULF & Floating-Point Multiply Register \\ \hline Data migration & MOV & Data Move \\ \hline \hline \end{tabular}
\end{table}
Table 1: Instruction listing for CFD-Reduced Instruction Set Computer (F-RISC).  
The FCore architecture intentionally omits techniques such as branch prediction, out-of-order execution, and multi-level memory hierarchy. While these features generally enhance performance in conventional processors, they provide marginal benefits for FDM-specific computations. Instead, they introduce unnecessary control logic complexity and resource overhead, which constrain the scalability of integrated core arrays. The capacity of the GPR file and IM serves as critical design parameters in the FCore architecture, directly determining its ability to support a broad range of FDM-based programs. Through extensive algorithmic analysis and a design philosophy optimized to minimize logic complexity while maintaining support for finite difference computations, the GPR and IM capacities were determined as 60 entries and 128 instructions, respectively. Experimental validation in Section 4 demonstrates that this configuration suffices for solving complex N-S equations.
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 3 Technical Challenges and Requirements
### Optimal UAV Placement and Trajectory Optimization  
UAVs are expected to operate in highly dynamic and heterogeneous environments, since they must serve users moving with different speeds and characterized by different communication requirements. Moreover, some network parameters--such as cell load and backhaul conditions--can be changing as well. In this respect, 2D/3D optimal placement and trajectory optimization of UAVs are crucial issues, which are even more challenging if the UAVs are allowed to cooperate with each other. In particular, the altitude of UAVs, based on the antenna beamwidth, size and user density of the area to be covered, as well as the number of deployed UAVs are other issues that should be considered, which tremendously increase the complexity of system design [53; 54].  
Many state-of-the-art solutions do not provide the required flexibility or adaptability to cope with environmental changes, since mobility of users is not adequately taken into account and optimized UAV placement is carried out only for specific and/or static scenarios. In this case, any environmental change imposes a redesign of the whole system in order to adapt it to the new situation. For these reasons, these types of solutions might not be suitable for practical situations, wherein the environment is frequently changing [53; 54; 55].
## 6 Multi-Slab Laser Amplifier
### Wavefront Correction by a Deformable Mirror  
The adaptive optics system for the 10-J cryogenically-cooled multi-slab amplifier was designed starting from the thermo-optical simulations. First, the amplifier head was studied and crucial parameters were estimated by two numerical models, including the aberration induced by thermal loading of the gain media [20,21]. In order to estimate the aberration of the output laser beam, another main source of aberrations was estimated by a different numerical model. The static aberration of optical components was modelled using Miro software and adding to each optical surface the optical aberration typical for the given component and scaled to fit the optics specifications [61]. Thermal aberrations were also included in the simulation. As a result, a sample wavefront profile was acquired (Figure 20a), which was then subject to numerical wavefront correction using another code. The simulation focused on the estimation of the number of actuators in order to provide sufficient wavefront correction. The results  
Figure 19: Calculations of optical path difference for (**a**) the case with the optimized ratio of the active part and absorber part and (**b**) for the same case where the beam is rotated between amplifier heads.  
presented in Figure 20b clearly show that the target wavefront quality of RMS \(<\) 74 nm (set by the Marechal criterion, _i.e._, RMS \(<\) 1/14 \(\lambda\)) is achieved by using actuator arrays with 6 \(\times\) 6 actuators or more.  
An experimental setup was also developed in order to benchmark the numerical simulations [62]. The optical aberrations of the multi-slab amplifier head were simulated in the experiment. The heat-absorbing glass, which was used instead of the laser crystal, was heated by infrared laser diodes and generated the optical aberrations. By using a photo-controlled deformable mirror, irradiated by a mini-projector, various actuator arrays were simulated experimentally. Sufficient correction was achieved with the arrays having 5 \(\times\) 5 actuators or more [62]. Further, the optimum parameters of a deformable mirror for the HiLASE 10-J multi-slab laser were determined. Since the photo-controlled deformable mirror prototype is not yet suitable for use in the high average power lasers, a bimorph deformable mirror with an array of 36 actuators was developed by Adaptica (Italy). Recently, the mirror has been implemented in the real laser amplifier at the Central Laser Facility, Rutherford Appleton Laboratory (U.K.). The feedback for its closed loop operation was provided by the SID4 wavefront sensor from Phasics (France). The results show good performance of the adaptive optics system, especially in terms of the focal spot improvement, delivering more than a 10\(\times\) increase in the peak intensity [63].
## 5 Coto Laurel Case Study
### Proposed Model  
The implemented model describes the dynamics of the PV cell injection, considering the voltage of each array and the irradiance and temperature variations. The inverter was modeled as a continuous ideal AC current source. Additionally, a Direct-Quadrature (DQ) transformation was implemented to control the grid's power injection. Finally, a phase-locked loop (PLL) was used to perform the synchronization with the grid.  
The configuration of each set of arrays was carried out using the information provided by the Coto Laurel farm. The parameters of the PV panels were extracted from the  
Figure 8: Average day global horizontal irradiance (GHI) in Ponce, Puerto Rico, for each month of the year.  
Figure 9: Daily irradiance in Ponce, Puerto Rico on 18 May 2020.  
corresponding solar panel's datasheet, using information available in the standard test conditions (STD), and nominal operating cell temperature (NOCT). Additionally, the irradiance profile was measured in a meteorological station located near the system's arrays. Moreover, the voltage of each set of arrays was obtained from the information provided by the solar farm. Using the farm's measured parameters, a model was created using Matlab/Simulink. This model is composed of solar panels, inverters, transformers, distribution lines, and batteries.  
Coto Laurel's lithium-ion battery bank was modeled using an equivalent circuit model. This model is composed of a voltage source with a series resistor, which represents the battery's open-circuit voltage (OCV) and internal resistance, respectively. Moreover, two RC networks in series were used to represent the electrochemical and concentration polarization of the battery. The voltage source was modeled using a lookup table that describes the relationship between the OCV and the battery level, which can be used to calculate the dynamic values of the battery's internal resistance and the two RC networks.  
Considering the nature of this study and the model's time response (e.g., minutes), a continuous ideal AC current source was used to model the inverters on the entire system. This model is suitable for the proposed analysis since the time response of the power electronic elements and the applied current control techniques is faster (e.g., milliseconds) than the exhibited by the model. Moreover, considering the previous assumptions, a PLL with a continuous ideal AC current source was used, since it offered an accurate approximation for the current system.  
The distribution and internal transmission line connections for the different elements (PV arrays, inverters, and transformers) were modeled using the branch block of the Simulink's SimPower systems library. Moreover, a resistive three-phase branch was used to model the transmission lines available on the Coto Laurel farm, since this model provides accurate approximations for transmission lines shorter than 80 Km.
## 3 Results
### Spatial Distribution of Fishing Vessel Tracks  
Figure 4 presents spatial maps illustrating the cumulative time of occurrence (without distinguishing between operational and non-operational periods) of all the fishing vessels in each grid for 2021 and 2022. The findings revealed that throughout the year, fishing vessel activity predominantly occurred near the southern high seas where the EEZ of Oman and the EEZ of Pakistan intersect. Several patchy areas with a high cumulative time of vessel occurrence were observed. Upon comparing 2021 and 2022, it became apparent that the majority of the areas with high cumulative times of vessel occurrence in both years did not coincide. Only a few areas exhibited consistently high cumulative times in both years. For instance, fishing vessels showed high cumulative times near the coordinates of 64\({}^{\circ}\)12\({}^{\prime}\) E, 15\({}^{\circ}\)18\({}^{\prime}\) N, and 63\({}^{\circ}\)24\({}^{\prime}\) E, 21\({}^{\circ}\) N during both years.  
The distribution of vessel trajectory points by month (depicted in Figure S3) revealed discernible seasonal variations in vessel activity. Notably, the area at approximately 64\({}^{\circ}\) E, 16\({}^{\circ}\) N exhibited a consistent year-round aggregation of vessel activity, suggesting heightened resource richness in this region. The spatial maps of trajectories for January, May, October, and November illustrate sailing departures from and arrivals at the fishing grounds. Specifically, in May, all the vessels left the operational area, while in October and November, almost all the vessels entered the fishing grounds. In November, virtually all the vessels accessed the fishing grounds, indicating a concentrated period of increased fishing activity.
## 3 Results
A moderate, positive correlation was found between the two variables, r = 0.524, _p_! 0.001, suggesting that higher levels of active time were associated with greater MVPA. When analyzed by group, a significant, moderate-to-strong correlation was observed in G1, r = 0.586, _p_! 0.001. In G2, a moderate and significant correlation was also identified, r = 0.523, _p_! 0.001. A strong positive correlation was found in G3, r = 0.668, _p_! 0.001 (Figure 2). Significant differences were observed across all physical activity metrics: NPA (F(2, 154) = 7.139, \(p\) = 0.01), LPA (F(2, 154) = 24.800, _p_! 0.001), MPA (F(2, 152) = 10.137, _p_! 0.01), Vigorous PA (F(2, 152) = 25.550, _p_! 0.001), and MVPA (F(2, 152) = 15.134, _p_! 0.01) (Table 4). Post hoc analyses using the Bonferroni correction revealed that participants in G2 spent significantly more time in No PA and Low PA compared to the G1 (_p_! 0.01). Additionally, participants in the G3 spent significantly more time in LPA than those in the G2 (_p_! 0.01). MPA, VPA, and MVPA were significantly lower in the G3 compared to the other two groups (_p_! 0.01). The G2 also showed reduced levels of VPA compared to the G1 (_p_! 0.01). These results highlight significant age-related differences in physical activity levels, with older groups engaging in less vigorous activity and spending more time in low-intensity or no physical activity. \begin{table}
\begin{tabular}{c c c c c} \hline
**Distance-Based Metrics** & **G1 (n = 48)** & **G2 (n = 70)** & **G3 (n = 60)** & **F(2, 177)** & \(p\) \\ \hline Total Distance (m/min) & 82.45 \(\pm\) 12.85 & 61.70 \(\pm\) 15.03 * & 52.71 \(\pm\) 14.15 *,\# & 60.627 & 0.000 \\ Distance in speed zone 1 & 24.98 \(\pm\) 4.20 & 24.37 \(\pm\) 5.62 & 24.97 \(\pm\) 5.71 & 0.280 & 0.756 \\ Distance in speed zone 2 & 29.79 \(\pm\) 6.99 & 21.54 \(\pm\) 5.98 * & 17.30 \(\pm\) 5.66 *,\# & 55.643 & 0.000 \\ Distance in speed zone 3 & 23.83 \(\pm\) 8.33 & 14.47 \(\pm\) 6.69 * & 9.73 \(\pm\) 6.68 *,\# & 52.615 & 0.000 \\ Distance in speed zone 4 & 3.84 \(\pm\) 3.37 & 1.29 \(\pm\) 1.33 * & 0.69 \(\pm\) 0.84 * & 36.575 & 0.000 \\ \hline \end{tabular} Data are presented as means \(\pm\) SD. * Significant difference between G1 and G2, + Significant difference between G1 and G3, and # Significant difference between G2 and G3.  
\end{table} 
Table 3: Distance-based metrics among age groups. Figure 2: Correlation between active time (min) and MVPA (min).