esa-sceva/satcom-chunk-collection huggingface dataset:
# Antipodal Vivaldi Antenna Arrays Fed by Substrate Integrated Waveguide Right-Angled Power Dividers
## 2 Design Process
### Tapered Slot Antenna  
The widely used antipodal Vivaldi antenna is one of the most popular antennas among tapered printed-circuit slot antennas. The one used here is designed for K-band frequencies based on principles explained in a parametric study [22; 23]. The Vivaldi antenna with same substrate and via dimensions as in Section 2.1 has a reflection coefficient better than \(-15\) dB from 21 GHz to 40 GHz, a directive radiation pattern and gain of about 9.4 dB in the operating frequency range. The antenna structure and its reflection coefficient are shown in Figure 7. The corrugations are introduced to improve the cross-polar performance (c.f. Section 3.3). The comb-like corrugations attenuate the vertical polarization and improve matching and cross coupling as presented, e.g., in [22]. The proposed antenna has a good performance of 99% polarization efficiency in the operating bandwidth because of the corrugations in the top and bottom layers based on simulation results. The polarization efficiency is calculated as the ratio of power radiated in the preferred polarization to the entire radiated power. Figure 8 shows the co-pol and cross-pol radiation in E- and H- planes at 25 GHz.  
Figure 8: Co- and cross-pol performances of Vivaldi antenna at 25 GHz; E-plane (**a**), H-plane (**b**).  
Figure 6: Two-way H-plane SIW power divider with 120-degree phase difference; (**a**) structure, (**b**) output port phase difference, (**c**) power levels between 25 and 29 GHz.  
Figure 7: SIW-fed antipodal Vivaldi antenna and its reflection coefficient.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
###### Abstract  
A short time after the official launch of WiFi 6, IEEE 802.11 working groups along with the WiFi Alliance are already designing its successor in the wireless local area network (WLAN) ecosystem: WiFi 7. With the IEEE 802.11 be amendment as one of its main constituent parts, future WiFi 7 aims to include time-sensitive networking (TSN) capabilities to support low latency and ultra-reliability in license-exempt spectrum bands, enabling many new Internet of Things scenarios. This article first introduces the key features of IEEE 802.11 be, which are then used as the basis to discuss how TSN functionalities could be implemented in WiFi 7. Finally, the benefits and requirements of the most representative Internet of Things low-latency use cases for WiFi 7 are reviewed: multimedia, healthcare, industrial, and transport.  
IEEE 802.11 be; low-latency communications; time-sensitive networking (TSN); WiFi 7
# Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
## 2 Directional Logarithmic Variogram and the VSSIM Model
\[v(x,y)=\frac{k\sum\limits_{j=1}^{M}\sum\limits_{i=1}^{N}\left[r_{X}(i,j) \times r_{Y}(i,j)\right]+c_{1}}{\sum\limits_{j=1}^{M}\sum\limits_{i=1}^{N} \left[r_{X}(i,j)\right]^{2}+\sum\limits_{j=1}^{M}\sum\limits_{i=1}^{N}\left[r_ {Y}(i,j)\right]^{2}+c_{2}} \tag{6}\]  
Through these experiments, we found that the optimal values of the parameters in the above expressions were generally \(k=2\), \(c_{1}=c_{2}=0.001\).
# Investigation of Crosstalk between Data Signals and Feed Light in Power-over-Fiber
## 4 Evaluation of Signal Degradation in PWoF Using MMFs  
### Experimental Setup  
To evaluate the signal quality degradation of data signals in PWoF, we experimentally demonstrate simultaneous data signal and power transmission using an MMF. Figure 6 shows the experimental setup used for the evaluation. In this experiment, we used two types of optical data signals commonly used for short-reach optical transmission systems. One is an analog data signal, which is directly intensity-modulated by an electrical data signal. The electrical data signal generated by a signal generator (SG) was based on an IEEE802.11g wireless LAN standard, and the modulation format was orthogonally frequency division multiplexing (OFDM), 64-quadrature amplitude modulation (64-QAM). The carrier frequency and bitrate were 2.45 GHz and 54-Mbit/s, respectively. For the signal evaluation, we used a signal analyzer (SA) at the receiver side, and we measured the electrical signal spectra and the EVM characteristics. The second is a digital data signal, so called on-off keying (OOK), and commonly used to optical digital transmission systems. The electrical data were generated by a pulse pattern generator (PPG). The data pattern was based on pseudo-random bit sequence (PRBS) with a pattern length of \(2^{31}\)-\(1\), and the bitrate was 500 Mbit/s. For the signal evaluation, we used a BER tester (BERT), and measured the BER characteristics. In addition, we monitored the eye-pattern of the received signal to observe the effect of the transferred noise on the intensity fluctuation of the data signal. As a seed light source, we used a common LD at a wavelength of 1310 nm. The seed light was injected into a LiNbO\({}_{3}\) modulator (LNM) and intensity-modulated by the electrical analog or digital data signal after adjusting the state of polarization of the seed light to optimize the modulation performance. After amplification by a 1310 nm semiconductor optical amplifier (SOA), the data signal was injected into a WDM coupler. Isolators (ISOs) at the input and output of the SOA were used to prevent the laser oscillation by the SOA. A bandpass filter was used to remove the amplified spontaneous noise from the SOA. As a feed light source, we used a 1550-nm RFL with an output power of up to 10 W, as shown in Figure 2. The following setup was the same as that shown in Figure 2, except for the SA and BERT at the receiver side.
# Investigating and Modeling of Cooperative Vehicle-to-Vehicle Safety Stopping Distance
## 6 Simulation of Density Losses
### Node Density Simulation
The simulation provides the number of expected consecutive packet collision probability per node density/traveling speed, when all nodes are competing for channel access with BSM messages being broadcast. The simulation is based upon node density over DSRC communication and contains the parameters used in DSRC for MAC configuration. This includes collision window timings, slot allocation, and acknowledgements for packet reception having been removed. These settings have been gathered through various sources [29; 54; 55; 56]. Further information relating to the simulation and the settings have been collated from References [56; 57], and these relate to data rate and transmission scheme. The simulation has kept as many variables the same as the tests in the distance-based experiments to maintain consistency, comparability, and compatibility. The acknowledgements for packet reception have been removed. Table 6 is used to emphasize important simulation parameters we used. We chose to display only ADAS results as we will be using this to compare with the results of distance-based losses. Figure 14 shows the probability of consecutive packet collisions for the node of interest when a specific number of nodes are within its communication range. We can see from these results that the number of losses due to density is more severe than those of distance-based. The DSRC system suffers up to 15% losses, which is approximately 150 packets with 21 nodes in range. When compared to the distance-based, we saw a maximum loss of 18 packets. This highlights the importance of the capabilities of the network in terms of traffic load and an adequate allocation scheme to reduce these losses in order to meet stringent requirements for safety. We will show the linear regression curve equations produced that enable the calculation of number of packets lost due to collisions, for each different safety distance algorithm, and each will be shown individually in Equations (18)-(21), where x represents the number of nodes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of Simulations per cycle & 5 \\ Packets Per Node & 1000 \\ Transmission Rate & 6 Mbps \\ Number of Nodes & Equations (15) and (16) \\ Packet Size & 256 bytes \\ Number of retransmissions & 0 \\ Contention Parameters (Safety-Related) & Cvmin = 3, Cvmax = 7 \\ Slot time & 13 us \\ MAC SIFS & 32 us \\ MAC DIFS & 58 us \\ MAC PHY Header & 32 us \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Simulation parameters. Figure 14: Node density. \[ADAS=0.72*x-0.57, \tag{18}\] \[PATH=0.57*x+1.8,\] (19) \[PATH_{\mathrm{BS}}=0.54*x+2.9,\] (20) \[HC=0.49*x+4.7. \tag{21}\]
## 2 Materials and Methods  
### Data Collection  
To make use of as much observed data as possible, we utilized the FLUXNET2015 dataset to obtain daily mean eddy-covariance carbon dioxide fluxes and meteorological data ([[https://fluxnet.org/](https://fluxnet.org/)]([https://fluxnet.org/](https://fluxnet.org/)), accessed on 1 May 2021) [36]. Initially, we excluded site-years where missing values in the relevant columns exceeded 30% of the total data, obtaining the dataset to 188 sites (Figure 1) and their corresponding Whittaker biomes (Figure 2). We first reclassified global terrestrial ecosystems into five major categories based on the MODISIGBP land cover product (0.05\({}^{\circ}\), [[https://lpdaac.usgs.gov/products/mcd12q1v006/](https://lpdaac.usgs.gov/products/mcd12q1v006/)]([https://lpdaac.usgs.gov/products/mcd12q1v006/](https://lpdaac.usgs.gov/products/mcd12q1v006/)), accessed on 11 March 2022): forest, grassland, shrubland, wetland, and cropland [37]. Given that forests encompass various types and are distributed across multiple climate zones globally, we further subdivided the forest category into needleleaf forest, broadleaf forest, and mixed forest. To investigate the relationship between precipitation sensitivity and drought severity, we used the global average aridity index (AI) at a 0.05\({}^{\circ}\) resolution from 1970 to 2000. The data were sourced from the Global Aridity Index and Potential Evaporation (ET0) Database v3 [38], where AI = MAP\({}_{\text{rec}}\)/MA\(\_\)ET\({}_{0}\). Here, MAP\({}_{\text{rec}}\) refers to mean annual precipitation (mm year\({}^{-1}\)), and MA\(\_\)ET\({}_{0}\) represents mean annual reference evapotranspiration (mm year\({}^{-1}\)), which is calculated using the fully parameterized FAO-56 Penman-Monteith formula.  
Figure 1: Global distribution of study sites across five ecosystems: cropland, forest, grassland, shrubland, and wetland.  
Figure 2: Whittaker biome classification of global distribution of the sensitivity of gross primary productivity and ecosystem respiration to precipitation (\(S_{\text{GPP}}\) and \(S_{\text{ER}}\)): (**A**) \(S_{\text{GPP}}\), and (**B**) \(S_{\text{ER}}\).
## 4 Results  
### Distribution Features of Fault Slip Rate and Locking Depth  
The strike-slip velocity, compression velocity, and vertical relative deformation rate on both sides of different segments of the Xianshuihe fault (Figure 5) show significant spatial variation. The data (Figure 5a) indicate that the left-lateral strike-slip rate of the Xianshuihe fault is 9.3-14.3 mm/yr. The maximum slip rate is at the southeast end of the Kangding segment of the XSHFZ (14.3 mm/yr), and the minimum slip rate is at the southeast end of the Moxi segment (9.3 mm/yr). The slip rate generally shows a characteristic of being higher in the middle and lower at both ends. The tectonic loading rate of the XSHFZ inverted in this paper is largely consistent with the slip rate (7-15 mm/yr) reported in existing geodetic results [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]; the locking depth primarily ranges between 13 and 26 km, which is in good agreement with the findings of [PERSON] et al. [66]. The probability density distribution of the kinematic parameter estimation is provided in Supplementary Figure S1, demonstrating the accuracy of the parameter estimation. Perpendicular to the fault direction, a significant compression feature is observed, with a fault compression velocity of 0-3.6 mm/yr. The relative vertical rate on both sides of the fault shows a significant segmented characteristic, with the overall vertical relative deformation rate on both sides of the fault being less than 2 mm/yr. Compared with existing research results [12; 22], this study more clearly illustrates the spatial distribution characteristics of the three-dimensional movement rate of the XSHFZ.
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
###### Abstract  
Given the great achievements of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission in providing huge amount of GPS radio occultation (RO) data for weather forecasting, climate research, and ionosphere monitoring, further Global Navigation Satellite System (GNSS) RO missions are being followingly planned. Higher spatial and also temporal sampling rates of RO observations, achievable with higher number of GNSS/receiver satellites or optimization of the Low Earth Orbit (LEO) constellation, are being studied by high number of researches. The objective of this study is to design GNSS RO missions which provide multi-GNSS RO events (ROEs) with the optimal performance over the globe. The navigation signals from GPS, GLONASS, BDS, Galileo, and QZSS are exploited and two constellation patterns, the 2D-lattice flower constellation (2D-LFC) and the 3D-lattice flower constellation (3D-LFC), are used to develop the LEO constellations. To be more specific, two evolutionary algorithms, including the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used for searching the optimal constellation parameters. The fitness function of the evolutionary algorithms takes into account the spatio-temporal sampling rate. The optimal RO constellations are obtained for which consisting of 6-12 LEO satellites. The optimality of the LEO constellations is evaluated in terms of the number of global ROEs observed during 24 h and the coefficient value of variation (COV) representing the uniformity of the point-to-point distributions of ROEs. It is found that for a certain number of LEO satellites, the PSO algorithm generally performs better than the GA, and the optimal 2D-LFC generally outperforms the optimal 3D-LFC with respect to the uniformity of the spatial and temporal distributions of ROEs.  
GNSS radio occultation; LEO Constellation; evolutionary algorithms; flower constellation 20191157103390/rs110505710
###### Abstract  
The measurement of soil gas permeability is influenced by the temperature and pressure fluctuation in the low gas pressure region. In order to investigate these influences, a soil temperature-controlled apparatus connected to a low-gas-pressure supply equipment is proposed in this study. The low constant gas pressure is supplied by two Mariotte bottles, by which the airflow rate is measured. Meanwhile, the soil specimen is controlled by a temperature-controlled apparatus. During the test, the negative pore water pressure and volume change of the soil specimen are measured. Through the temperature-controlled apparatus, it is observed that as the temperature increases from 25 \({}^{\circ}\)C to 60 \({}^{\circ}\)C, there is a corresponding increase in soil sample porosity by 5.4%, while the negative pressure of pore water decreases by 11.1%. This can be attributed to the reduction in the surface tension of contractile skin caused by elevated temperatures. Furthermore, due to variations in gas viscosity with temperature, there was a significant decrease in the gas flow rate by 50.5%. And, the relationship between permeability and volumetric gas content at different temperatures in low-pressure regions well confirms the existing power-law model. In addition, the existence of a temperature-independent critical negative pore water pressure is observed, beyond which the intrinsic permeability remains constant. At 36 kPa of negative pore water pressure, the intrinsic permeability at 60 \({}^{\circ}\)C exhibits an 81.8% reduction compared to that at 25 \({}^{\circ}\)C. This decline in intrinsic permeability can be attributed to a diminished pore connectivity, resulting from elevated temperatures.  
Keywords:temperature-controlled triaxial permeameter; gas permeability; soil; constant gas pressure
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
###### Abstract  
In this work, the lumped model of Rogowski coils is briefly reviewed to illustrate that the reduction in stray capacitance expands the bandwidth and improves the high-frequency performance. Then, a network model as well as explicit formulas for the stray capacitance of Rogowski coils are established, and the influence of geometrical parameters of the skeletons on the stray capacitance is investigated. It is found that the stray capacitance of Rogowski coils is approximately proportional to the perimeter of the skeleton cross-section. Based on the above discussion, optimization of the shape of the skeleton cross-section, aiming at minimizing the perimeter without affecting the magnetic flux and mutual inductance of the coils, is carried out. The widely adopted circular and rectangular cross-sections are discussed first. Then, the cross-section of an arbitrary smooth and convex shape is optimized by solving a constrained variational problem, leading to an explicit equation for the optimal skeleton cross-section of Rogowski coils. Numerical results demonstrate that, compared with the common circular and rectangular skeleton cross-sections, the proposed optimal cross-section exhibits the shortest perimeter and thus the highest upper cutoff frequency under fixed magnetic flux. The optimization method developed by this work can provide a theoretical basis and guidance for the design of Rogowski coils.  
constrained variational problem; Rogowski coils; shape optimization; stray capacitance +
Footnote †: journal: Applied Sciences
# A Study on the Applicability of Bio-Sulfur as a Cement Admixture
## 2 Results
### Evaluation Outcomes of the Specimen Characteristics
#### 2.2.2 Sulfuric acid Resistance Evaluation  
Figure 6 shows the evaluation results for sulfuric acid resistance (weight loss rate and compressive strength change rate) after immersion in a 5% sulfuric acid solution for 7 days and 14 days. Overall, when no bio-sulfur was added, the weight reduction rate was the largest, and the weight reduction rate generally decreased with the addition of bio-sulfur. After 14 days of immersion in the 5% sulfuric acid aqueous solution, the weight loss rate was lowest when 7.5% bio-sulfur was utilized, indicating the best sulfuric acid resistance.  
Calcium-silicate-hydrate (C-S-H) can react with acids to form low-strength phases. In addition, CH reacting with sulfate radical ions can form gypsum, extending and damaging the C-S-H matrix. Gypsum can also react further with hydrated calcium aluminate to form ettringle [24]. The reason that the addition of bio-sulfur increases the sulfuric acid resistance, as shown in Figure 7, is thought to be because the components contained in bio-sulfur reduce the formation of gypsum or ettringite.  
Figure 6: Weight loss rate (**a**) and compression strength reduction rate (**b**) by immersion time in the aqueous sulfate solution. (**a**) is the weight loss rate after 7 days and 14 days. (**b**) is the rate of decrease in compressive strength after 7 days and 14 days.  
Figure 7: Hydrate XRD changes after 7 and 28 days. The degree of hydrate formation by age according to the rate of binding resistance is shown. The left side represents results for the specimen 7 days after specimen preparation, and the right side results for the specimen 28 days after specimen preparation.  
If the sulfuric acid resistance is excellent, it may be used in concrete structures and in structures where concerns over corrosion by sulfuric acid bacteria exist, such as water and sewage pipes [25]. Porous cement is inevitably more susceptible to sulfuric acid erosion, and its use as a binder may be considered for such cement [26].  
Exposure of cement and concrete to sulfuric acid causes the formation of gypsum hydrate, which causes the structure to expand and weaken [27]. As shown in Figure 6, when immersed in the 5% sulfuric acid aqueous solution, the compressive strength of the samples decreased with age. When bio-sulfur was not added, the compressive strength reduction rate was evaluated to be the lowest, and, overall, when bio-sulfur was added, the compressive strength reduction rate was evaluated to be high.
## 5 Experimental Validation and Results  
### Calibration Results  
Following the calibration procedure, the triaxial calibration of the sensor was conducted within the calibration system, and the corresponding calibration curves are presented in Figure 8. As shown in Figure 8, during \(X\)-axis calibration, the differential configuration of the sensing electrodes ensures that the outputs of sensing electrodes 1 and 2 exhibit opposite polarity, achieving differential decoupling along the \(X\)-axis. Simultaneously, sensing electrodes 3 and 4 suppress coupling interference from the \(X\)-axis electric field through a differential structure. Similarly, during \(Y\)-axis calibration, sensing electrodes 3 and 4 output signals with opposite polarity, achieving differential decoupling along the \(Y\)-axis, while sensing electrodes 1 and 2 effectively suppress \(Y\)-axis coupling interference. For \(Z\)-axis calibration, the four sensing electrodes are mutually orthogonal with a height difference between the electrode pairs. Consequently, sensing electrodes 1 and 2 produce signals in the same direction, whereas sensing electrodes 3 and 4 generate signals with opposite polarity, enabling differential decoupling along the \(Z\)-axis.  
Based on the acquired calibration data, two decoupling methods were employed to compute the calibration matrix: the CRLB-based optimization method and the sensitivity matrix inversion method. The resulting decoupling calibration matrices are as follows:  
The optimized decoupled calibration matrix **C\({}_{opt}\)** based on the CRLB is  
\[\textbf{C}_{opt}=\begin{bmatrix}12.3824&-9.1911&4.0723&4.8480\\ -0.2127&0.1589&-14.7831&15.2420\\ 5.3764&8.7850&-12.7045&-12.6108\\ \end{bmatrix} \tag{29}\]  
Figure 7: Decoupling calibration procedure for the 3D electric field.  
The decoupled calibration matrix \(\mathbf{C}_{mi}\) obtained via the sensitivity matrix inversion method is  
\[\mathbf{C}_{mi}=\begin{bmatrix}11.1543&-11.2116&2.6360&3.2646\\ -0.5705&-0.4298&-15.2016&14.7906\\ 7.0518&11.5413&-10.7452&-10.4509\end{bmatrix} \tag{30}\]  
Figure 8: Quantization output curves of the sensing chips during three-axis calibration. (**a**) Calibration curve of the sensing chips under the electric field parallel to the \(X\)-axis direction; (**b**) calibration curve of the sensing chips under the electric field parallel to the \(Y\)-axis direction; (**c**) calibration curve of the sensing chips under the electric field parallel to the \(Z\)-axis direction.