esa-sceva/satcom-chunk-collection huggingface dataset:
applied sciences Article  
Predefined-Time Nonsingular Attitude Control for Vertical-Takeoff Horizontal-Landing Reusable Launch Vehicle [PERSON], [PERSON] (c), [PERSON] (c) and [PERSON]  
This paper presents a novel predefined-time nonsingular tracking control system for a vertical-takeoff horizontal-landing (VTHL) reusable launch vehicle (RLV) in the face of parameter uncertainties, model couplings and external disturbances. Firstly, this paper proposes a novel predefined-time prescribed performance function (PTPPF) with desired steady-state and transient performance. The convergence time of PTPPF from the transient state to the steady state can be flexibly adjusted by changing one parameter. Moreover, the decreasing rate of PTPPF in the transient phase can also be adjusted by changing one parameter on the premise of not changing the convergence time of PPF to reach steady state. A novel predefined-time terminal sliding mode surface (SMS) is designed to avoid the singularity, and the attitude tracking errors on SMS are predefined-time stable. By utilizing PTPPF and error transformation, this paper designs a novel nonsingular sliding mode controller to guarantee the attitudes of RLV with desired tracking performance. Without using piecewise functions, the phenomenon of singularity can be avoided. The Lyapunov method is used to verify the stability of the controller. Lastly, a numerical simulation is presented to validate the efficiency of the proposed controller.  
reusable launch vehicle; attitude tracking control; predefined-time prescribed performance function; predefined-time terminal sliding mode surface; nonsingular sliding mode controller
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 3 OBFNs with TTD Architectures
### Time Delay Selection Architectures
To realize small-scale integration, integrated de-multiplexers have been applied to simplify the time delay selection architectures with a wavelength multiplexer [32; 33]. Furthermore, when using one AWG as a de-multiplexer and the multiplexer simultaneously, the architecture has the potential for more compact integration than does its counterpart with two AWGs [49; 78; 79]. \[\Delta\tau_{\text{g1}}=\beta(\lambda_{2}-\lambda_{1}) \tag{14}\]  
Figure 14: Schematic diagrams of OBFNs based on time delay selection with wavelength demultiplexer and dispersion components [84]. Copyright 2002, IEEE, #5491100280556. Figure 13: Schematic diagrams of OBFNs based on time delay selection by **(a)** optical switches, **(b)** wavelength de-multiplexer, and **(c)** SLMs. As addressed earlier, 2D optical beamforming is required for 2D planar PAAs. Among the OBFNs with time delay selection architectures, 2D optical beamforming has been achieved by the architectures combined wavelength multiplexers and optical switches or dispersion components. As plotted in Figure 15a, a 2D time delay selection architecture implements wavelength-dependent (WD) TTD and wavelength-independent (WI) TTD to realize horizontal beamforming and vertical beamforming, respectively [48; 86]. The WD-TTD adopts fiber Bragg gratings (FBGs) to control time delays for different wavelengths, which is induced by the reflection of the FBGs with various periods, while the WI-TTD uses optical switches to set progressive delays for antenna elements in different rows. It is obvious that a large number of optical switches is demanded in this architecture. For an \(l\)-bit \(\times\)\(n\)-bit beamforming system to support a 2D \(p\times q\) PAA, a total number of \(l+n\times q\) optical switches is needed. Alternatively, a 2D OBFN architecture with wavelength multiplexers and chirped FBGs (CFBGs), as shown in Figure 15b [87], can be used to reduce the components needed. Owing to the multi-wavelength operation capability of tunable CFBGs, this architecture can tune time delays for antenna elements in one row of PAAs by changing the dispersion of the corresponding CFBG. Meanwhile, the time delay difference between adjacent rows of PAAs is controlled by the center wavelength of the CFBG [88]. Note that, for a similar \(l\)-bit \(\times\)\(n\)-bit beamforming system to support a 2D \(p\times q\) PAA, a total number of \(p\) CFBGs is needed in this 2D OBFN architecture in case the tunable CFBG has an \(l\)-bit tuning ability. For the small-scale integration of this architecture, the CFBG can be substituted by integrated chirped Bragg grating or chirped sub-wavelength grating [89; 90], while the de-multiplexer can be replaced by integrated AWG.
# Clustering-Based Segmented Regression for Particulate Matter Sensor Calibration
## 2 Materials and Methods
### Related Work  
In practice, sensor calibration faces challenges posed by sensor type variety and individual differences within the same sensor type [19]. Deployment of the sensing system requires high efficiency, and it is difficult to fully learn sensor's characteristics under a controlled environment and figure out corresponding calibration model in laboratory calibration before deployment. Instead in-field calibration [20] make it possible to calibrate sensor under practical environment after deployment and improve sensor performance continuously.  
Regression methods are widely applied in in-field sensor calibration against a series of measurement interference including drift [21], cross-sensitivity [19] and environment factors such as temperature and relative humidity [19]. One of the major challenges of in-field calibration is to determine the structure of regression model, since the mechanism of interference factors is complex and it is difficult to directly design a global fitting function for a specific sensor type based on prior knowledge, especially when there are non-linear responding characteristics [22]. In this case, simple linear [23; 24] or multivariate linear regression [4; 23] methods cannot perform well either on the whole range of the sensor or under some working conditions. Some ANN-based methods such as [25] use multilayer back-propagation artificial neural network to consider the multiple environmental factors that affect low-cost air temperature sensors. In addition, random forest model is often used as a non-linear model for in-field calibration such as [26]. They may generate non-linear fitting function for calibration or achieve a relatively satisfying result, and its generalization performance, interpretability and calculation cost still remain bottlenecks for use across various in-field sensor calibration scenarios. For in-field sensor calibration, a regression is required to deal with sensor's non-linear response characteristics under variable interference factors with small-scale training data and simple model structure.
## 3 The Study
### Pollution Simulation  
The primary goal is to simulate the effect of a pollutant on the sensors' measurements and then its propagation along the river. In our simulation, we adopted a simple U-shape model in which the presence of the pollutant leads to a temporary decrease in the \(pH\) measurement of the sensor while the pollutant flows along the river. We imagine placing and ordering the sensors toward the water flow. Accordingly, as the pollutant flows along the river, the sensor \(s_{1}\) will start reporting a reduction in its \(pH\) measurements to the server at time \(t=T_{1}\). At time \(t=T_{1}+\Delta T\), UE2 starts sensing the reduction in its measurements too. The interval \(\Delta T>0\) depends on the river water's speed (assumed as constant, for the sake of simplicity) and the distance between the sensors. To make the simulation more realistic, we adopt a model of the \(pH\) sensor based on which the value of a sensor reading is assumed to be affected by an error (Equation (1)):  
\[pH_{read}=pH_{ideal}+N(0,\sigma) \tag{1}\]  
where \(pH_{read}\) is the reading value of the WSN node which will be transmitted, \(pH_{ideal}\) is the value generated according to our U-shape model of the pollutant as described above, and \(N(0,\sigma)\) represents the error as a random value according to a Gaussian distribution having 0 mean and standard deviation \(\sigma\) (e.g., \(\sigma=0.01\)).
## 4 Slater-Koster Parameters from Fitting to Ab Initio Calculations
\begin{table}
\begin{tabular}{l c c c c c} \hline  & & **MoS\({}_{\mathbf{2}}\)** & **MoS\({}_{\mathbf{2}}\)** & **WS\({}_{\mathbf{2}}\)** & **WS\({}_{\mathbf{2}}\)** \\ \hline SOC & \(\lambda_{M}\) & 0.086 & 0.089 & 0.271 & 0.251 \\  & \(\lambda_{X}\) & 0.052 & 0.256 & 0.057 & 0.439 \\ \hline  & \(\Delta_{0}\) & \(-1.094\) & \(-1.144\) & \(-1.155\) & \(-0.935\) \\ Crystal Fields & \(\Delta_{1}\) & \(-0.050\) & \(-0.250\) & \(-0.650\) & \(-1.250\) \\  & \(\Delta_{2}\) & \(-1.511\) & \(-1.488\) & \(-2.279\) & \(-2.321\) \\  & \(\Delta_{p}\) & \(-3.559\) & \(-4.931\) & \(-3.864\) & \(-5.629\) \\  & \(\Delta_{z}\) & \(-6.886\) & \(-7.503\) & \(-7.327\) & \(-6.759\) \\ \hline \(M\)–\(X\) & \(V_{pd\pi}\) & 3.689 & 3.728 & 7.911 & 5.803 \\  & \(V_{pd\pi}\) & \(-1.241\) & \(-1.222\) & \(-1.220\) & \(-1.081\) \\ \hline  & \(V_{dd\pi}\) & \(-0.895\) & \(-0.823\) & \(-1.328\) & \(-1.129\) \\ \(M\)–\(M\) & \(V_{dd\pi}\) & 0.252 & 0.215 & 0.121 & 0.094 \\  & \(V_{dd\pi}\) & 0.228 & 0.192 & 0.442 & 0.317 \\ \hline \(X\)–\(X\) & \(V_{pp\pi}\) & 1.225 & 1.256 & 1.178 & 1.530 \\  & \(V_{pp\pi}\) & \(-0.467\) & \(-0.205\) & \(-0.273\) & \(-0.123\) \\ \hline \end{tabular}
\end{table} 
Table 2: Spin–orbit coupling \(\lambda_{\alpha}\) and tight-binding parameters for single-layer \(MX_{2}\), where the metal \(M\) is Mo or W and \(X\) is S or Se. All of the Slater–Koster parameters are in units of eV. Spin-Orbit (SO) coupling parameters are taken from Reference [37]. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & & \multicolumn{2}{c}{**K\({}^{\pi}\)**} & \multicolumn{2}{c}{**K\({}^{\mathbf{c}}\)**} & \multicolumn{2}{c}{**\(\Gamma^{\mathbf{c}}\)**} \\ \hline  & & **DFT** & **TB** & **DFT** & **TB** & **DFT** & **TB** \\ \hline \multirow{4}{*}{MoS\({}_{\mathbf{2}}\)} & \(d_{0}\) & 0.0 & 0.0 & 0.82 & 0.77 & 0.66 & 0.96 \\  & \(d_{2}\) & 0.76 & 1.0 & 0.0 & 0.0 & 0.0 & 0.0 \\  & \(p_{xy}\) & 0.20 & 0.0 & 0.12 & 0.23 & 0.0 & 0.0 \\  & \(p_{z}\) & 0.0 & 0.0 & 0.0 & 0.00 &
## 2 Materials and Methods
### Data
#### 2.2.2 Drought Index  
To detect drought events in our study area, we used the Standardized Precipitation-Evapotranspiration Index (SPEI). The SPEI is a state-of-the-art meteorological drought index. It considers not only the temperature and precipitation (PPT) as the traditional  
Figure 1: (**a**) The geographic location of the study area, North China, is filled with light green. The entire region of China is filled with light blue. All the colored areas are land. (**b**) Spatial distribution of forests in North China. The emerald pixels are deciduous needleleaf forests (DNFs) which were constant during 2001–2018. The grass-green pixels are deciduous broadleaf forests (DBFs) constant during 2001–2018. The gray-colored pixels are forests where the type was inconstant between the China Vegetation Map and MCD12Q1 or altered during 2001–2018 in the MCD12Q1 dataset.  
-drought index does (e.g., PDSI and SPI) but also potential evapotranspiration (PET), which accounts for the real water balance for terrestrial ecosystems [33]. In this study, we downloaded the 12-month windowed SPEI dataset from the global SPEI database ([[https://spei.csic.es/database.html](https://spei.csic.es/database.html)]([https://spei.csic.es/database.html](https://spei.csic.es/database.html)), accessed on 18 January 2021), since SPEI-12 is a good representation for annual water balance [17; 34] and forests are sensitive to annual drought events [35; 36]. The spatial resolution of this dataset was 0.5 degrees. To align the pixels with the MODIS EVI and GPP for spatial statistical analyses, we resampled the SPEI pixel into 500 m following the nearest neighbor rule.  
Generally, the levels of the water balance conditions could be classified by the thresholds of the SPEI as follows (Table 1).
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 5 Review on ECS Methods on WSN
### Other Techniques
consider the use of BICM techniques in WSN where the authors use convolutional code (corresponding Viterbi decoding) followed by a pseudo random interleaver, BPSK modulation and AWGN channel. The paper considers short packet length varying from 100 to 1000 bits. PER results for those settings are presented for different block lengths with and without interleaving. The PER results indicate that the use of convolutional code and interleaving for short packet length decreases the reliability of the communication link. These concluding remarks from the paper are difficult to understand, and there are no reasons provided by the authors. The PER results are standalone results without considering WSN scenarios. The use of AWGN channels for WSN is only realistic if LOS exists between nodes. Our brief review on the BICM technique used in WSN indicates that there are limited investigations in these areas, and the outcomes from the review are also contradicting. The results from two papers showed that the use of BICM in WSN does not enhance in reliability significantly [100; 101]. We found that there is a lack of energy analysis using BICM in WSN. Therefore, we expect further research on performance and energy analysis of BICM in WSN.
## 4 Stationary Solutions
### Case \(g=0\)  
The \(\lambda-\)invariance (3) when \(g=0\) implies that the stationary solution is uniquely determined for \(\psi_{c}=1\). The inverse fitness of the best individual in the genetic algorithm (GA) for \((\psi_{c},g)=(1,0)\) is depicted on the left of Figure 1, illustrating convergence after 120 generations. The fitness reaches an optimal value of approximately \(10^{8}\), corresponding to an error of \(10^{-8}\). This tolerance is used in subsequent solutions. Once the GA finds an optimal solution, the resulting genes yield \((r_{\max},\omega)\approx(12.24,-0.6922)\), consistent with previous well-known results (e.g., [13]).  
The ground state for \(g=0\) can be approximated by the following empirical formula, found from structure formation simulations of Fuzzy Dark Matter [12]:  
\[\rho_{\mathrm{core}}(r)|_{g=0}=\rho_{c}\Bigg{[}1+\left(2^{1/8}-1\right)\left( \frac{r}{r_{c}}\right)^{2}\Bigg{]}^{-8}. \tag{22}\]  
which is a formula found to match the density profile of the ground-state solution of the GPP system for \(g=0\). The core radius \(r_{c}\) is defined as the radius where the density \(\rho(r_{c})\) is half of the central density \(\rho_{c}\). Setting \(r_{c}\approx 1.306\) matches Equation (22) with \(\rho_{c}=1\), specific to this case as illustrated on the right of Figure 1. The relation between \(r_{c}\) and \(\rho_{c}\) is established using the \(\lambda\)-scaling (3), leading to \(\rho_{c}\approx(1.306/r_{c})^{4}\), for either arbitrary central density or core radius. In physical units,  
\[\rho_{c}\approx\frac{\hbar^{2}}{4\pi Gm^{2}}\left(\frac{1.306}{r_{c}}\right)^ {4}\approx 1.983\times 10^{7}\Bigg{(}\frac{\mathrm{kpc}^{4}}{m_{22}^{2}r_{c}^{4 }}\Bigg{)}\,\mathrm{M}_{\odot}, \tag{23}\]  
with \(m=m_{22}\times 10^{-22}\) eV/c\({}^{2}\). This formula practically relates the tightly core radius and central density [9].
# Fast-, Light-Cured Scintillating Plastic for 3D-Printing Applications
## 3 Results and Discussion
### Leading and Ethanol Treatment  
While the initial results with the formulations presented in this work do not seem to indicate a significant performance degradation over time, a potential advantage over EJ-276, the leaching of some formulations poses a challenge for printing and application use. The leaching is primarily from the PPO near the surface that crystallizes, creating the hazy film. In the Lawrence Livermore National Laboratory's development of PSD plastic scintillators, leaching was also observed on the surface of the scintillators [15]. To prevent the leaching, [PERSON] et al. developed a method to treat the scintillators with ethanol post cure. While the specifics of this treatment were not described in [15], a study was performed on the effects of an ethanol treatment on the light-cured scintillators in an attempt to reduce the leaching of PPO over time.  
Four formulations were produced for the study that contained 49 wt% IBOA, 0.2 wt% Exalite 416, 0.1 wt% TPO, and 0-10%DIN, 20-30%PPO, 14-21%HDDMA, and 0-7% BPADMA as listed in Table 14. Each \(\sim\)20 g formulation was prepared as described in Section 2.1 before being split equally into two separate glass containers inside the glove box. Each scintillator was cured for 5 min in the Formlabs Form Cure and 4 min in the Dymax BlueWave AX-550 VisiCure, sanded and polished. One of the scintillators from each pair was placed in a 200-proof ethanol bath for 1 h, and the other was left at room temperature in a standard atmosphere. Each treated sample showed no evidence of leaching, while the untreated sample leached as expected. The untreated sample was washed with mild soap and water (as described previously), and both samples were characterized.  
The scintillators were weighed before and after the ethanol treatment to measure the amount of material that was removed by treatment before mounting them to PMTs as described in Section 2.2. The ethanol treatment resulted in relative light yield and PSD performance statistically consistent with the untreated scintillators as shown in Table 1.  
Figure 13: Relative light yield of AFIT101 and AFIT102 compared to EJ-276 from 30 August to 15 November. The error bars represent the standard deviation of the measurements.  
The ethanol treatment did not remove a significant amount of material (0.2-0.37% by mass); this is an expected result, as the overall composition is similar and the removed material likely was poorly incorporated into the matrix. Importantly, no visual observation of subsequent leaching has been observed in any ethanol-treated scintillator to date (\(>\)5 months), while the non-treated samples continue to leach.
# A Doppler Frequency-Offset Estimation Method Based on the Beam Pointing of LEO Satellites
###### Abstract  
With the advancement of 5G-Advanced Non-Terrestrial Network (5G-A NTN) mobile communication technologies, direct satellite connectivity for mobile devices has been increasingly adopted. In the highly dynamic environment of low-Earth-orbit (LEO) satellite communications, the synchronization of satellite-ground signals remains a critical challenge. In this study, a Doppler frequency-shift estimation method applicable to high-mobility LEO scenarios is proposed, without reliance on the Global Navigation Satellite System (GNSS). Rapid access to satellite systems by mobile devices is enabled without the need for additional time-frequency synchronization infrastructure. The generation mechanism of satellite-ground Doppler frequency shifts is analyzed, and a relationship between satellite velocity and beam-pointing direction is established. Based on this relationship, a Doppler frequency-shift estimation method, referred to as DFS-BP (Doppler frequency-shift estimation using beam pointing), is developed. The effects of Earth's latitude and satellite orbital inclination are systematically investigated and optimized. Through simulation, the estimation performance under varying minimum satellite elevation angles and terminal geographic locations is evaluated. The algorithm may provide a novel solution for Doppler frequency-shift compensation in Non-Terrestrial Networks (NTNs).  
l 2025  
25 May 2025  
16 June 2025  
17 June 2025  
23 June 2025  
###### Abstract  
With the advancement of 5G-Advanced Non-Terrestrial Network (5G-A NTN) mobile communication technologies, direct satellite connectivity for mobile devices has been increasingly adopted. In the highly dynamic environment of low-Earth-orbit (LEO) satellite communications, the synchronization of satellite-ground signals remains a critical challenge. In this study, a Doppler frequency-shift estimation method applicable to high-mobility LEO scenarios is proposed, without reliance on the Global Navigation Satellite System (GNSS). Rapid access to satellite systems by mobile devices is enabled without the need for additional time-frequency synchronization infrastructure. The generation mechanism of satellite-ground Doppler frequency shifts is analyzed, and a relationship between satellite velocity and beam-pointing direction is established. Based on this relationship, a Doppler frequency-shift estimation method, referred to as DFS-BP (Doppler frequency-shift estimation using beam pointing), is developed. The effects of Earth's latitude and satellite orbital inclination are systematically investigated and optimized. Through simulation, the estimation performance under varying minimum satellite elevation angles and terminal geographic locations is evaluated. The algorithm may provide a novel solution for Doppler frequency-shift compensation in Non-Terrestrial Networks (NTNs).
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 5 Evaluation
### Case Study of Image Processing with D-SWIM
#### 5.4.1 Conv2D  
2D convolution (Conv2D) is a popular operation for image feature extraction. Figure 1 shows a typical Conv2D operation with \(3\times 3\) convolutional kernels. Pixels in a kernel-sized window were fetched and multiplied to the kernel weights and then accumulated to the Conv2D result. The subsequent operation moves the window 1 pixel to the right, and performs the same arithmetic. In the high-throughput scenario of D-SWIM, multiple overlapped windows are processed in the same clock cycle. Thus, the logic components are connected as Figure 13. In every clock cycle, the buffer output pixel block has a height of \(H\), and width of \(N_{blk}\). The pixels were directly delivered to the parallel operators (OP) which performed the MACC operation. The results of OPs were concatenated to the output block. Note that there are windows, such as Win0 and Win1, in Figure 13, containing pixels from two consecutive output blocks of the buffer. Thus, we set registers to store the last pixel-columns of the previous cycle to construct these windows.  
Following the architecture above, we implemented the Conv2D with \(N_{blk}=16\) and \(H=3\). For a fair comparison, the hardware of OP was simply implemented with naive RTL with a pre-defined pipeline stage. The implementation results are listed in Table 5. We name the works in [7; 10] Design2 and Design1, respectively, in the following content. The devices adopted in each work have been listed as a reference. Note that the size of BRAM in Intel FPGA is 20 Kbits, whereas it is 36 Kbits in Xilinx FPGA. \(N_{blk}\) represents the hardware throughput (pixel/cycle). Meanwhile, the \(f_{max}\) of each design has been given, and we obtained the system pixel throughput (giga pixel per second (GPPS)) by \(N_{blk}\times f_{max}\).  
Because D-SWIM and Design1-2 have different throughputs, it was unfair to compare the resource number in Table 5 directly. Thus, we obtained the hardware efficiency of FPGA logic (LUT and REG) with Equation (5). Comparing with the highest-throughput design (Design1), the hardware efficiency of D-SWIM is 4.8\(\times\) and 8.2\(\times\) in LUT and REG, respectively. Comparing with the smallest design (Design2), D-SWIM also achieves competitive hardware efficiency. Note that Design2 achieves higher hardware efficiency on LUT, as it has traded off throughput severely for simpler logic. Moreover, it does  
Figure 13: D-SWIM-based architecture for Conv2D (\(3\times 3\) window).  
not consider the issues in the multi-pixel input scenario (such as BRAM-misalignment), which allows further reduction in overall hardware usage.  
\[Efficiency=Throughput/Hardware\,Consumption\times 10^{5} \tag{5}\]