esa-sceva/satcom-chunk-collection huggingface dataset:
# A Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks
## 4 Experiments
### Analysis in 2D-Space
#### 4.2.1 Continuous Smooth Conformal Mapping  
To test the consistency approach proposed in Equation (4), we generated 1000 random paths in two-dimensional space using the uniform distribution. The \(x\)-axis of the paths is chosen in the interval \((0.5,1)\) and the \(y\)-axis of the paths is chosen in the interval \((0,0.5)\) with 20 randomly chosen points, as shown by the black lines in Figure 4. The conformal transformation [41] is applied to these randomly generated paths using Equation (7), also shown by the red lines in Figure 4.  
Figure 4: Consistency analysis results in 2D-Space with the transformation of input paths through the objective function. As shown in the figures, as the number of increments increases along the input paths, the GRU-based architecture’s predictions deviate from the ground truth. In contrast, the ConsRNN-based architecture’s estimations remain consistent.  
The primary goal for an NN architecture is to learn these transformed paths using the randomly generated paths as input. Accordingly, the 1000 synthetic paths are divided into 800 for the training set and 200 for the test set. Additionally, in the same domain, \(10^{2}\), \(10^{3}\), and \(10^{4}\) random points are generated 100 times to create different incremental paths to quantitatively test the consistency.  
\[F:R^{2}\to R^{2},\ F(x,y)=(x^{2}-y^{2},2 xy) \tag{7}\]  
The same structured NN architectures as in the 1D space case are trained for 1000 epochs on the synthetically generated 2D dataset, where the GRU-built architecture yielded an MSE of \(1.0\times 10^{-3}\) on the test set, and the ConsRNN-built architecture yielded an MSE of \(1.2\times 10^{-3}\). The loss function and the optimizer were also chosen to be the same as in the 1D space case. The trained NN architectures are then tested on the different incremented paths, as shown in Figure 4. As can be seen from both the visual and MSE results, as the number of increments increases, the estimates from the GRU-built architecture diverge from the ground truth, unlike the ConsRNN-built architecture. The ConsRNN-built architecture provided fairly stable MSE results for the 100 paths as the number of increments increased.
## 5 Extraction Deformation and Discussion
Compared with the dial gauge measurement and finite-element results, the maximum errors of our method were \(11.11\%\) on the 11 th measuring point and \(12.12\%\) on the 12 th measuring point, indicating that the global holographic deformation curves obtained through the docking analysis of contours are accurate enough for engineering practices. In order to more clearly reflect the advantages of dense full-field displacement, the deformation of the whole bridge girder obtained from NRS under condition 6 is compared with the deformation of the whole bridge girder obtained from nine dial indicators, as shown in Figure 19. The comparison of Figure 19 and Table 3, it can be derived that the position with the maximum deformation in Table 3 is the measuring point 4, but the position with the maximum deformation of the actual test bridge is the position with the length of 10.71m between the measuring points 5 and 4; the comparison of the six working conditions, it can be derived that the position with the maximum deformation of the girder changes under different damage conditions, as shown in Figure 20. If the contact sensor, such as the dial gauge is used for measurement, a reasonable arrangement way of obtaining the deformation characteristics of the bridge structure from the finite points is difficult to determine. One arrangement approach cannot meet the deformation characteristics of the bridge structure caused by the change of structural stiffness at different positions. The noncontact remote measurement method used in this paper can capture the pixel level change of any position of the bridge structure, the accuracy can meet the requirements of engineering application, and the advantages are obvious. Meanwhile, model updating according to more accurate line shape can make the model closer to the real bridge, increasing the authenticity and credibility of the finite element calculation results, and exerting the same positive significance on the application of digital twin. The dense full-field displacement monitoring provides a larger amount of real data, which is the basis of machine learning. Using this method for regular or long-term monitoring can obtain a larger amount of real data than traditional ways, laying the foundation for using machine learning for structural health monitoring. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No. **} & \multicolumn{2}{c}{**Deformation of Stacking Analysis (mm)**} & \multicolumn{2}{c}{**Measured**} & \multicolumn{2}{c}{**Relative Error**} \\  & \multicolumn{2}{c}{**Dial gauge Measurement**} & \multicolumn{2}{c}{**Finite-Element Method**} & \multicolumn{2}{c}{**Noncontact Remote Sensing**} \\  & **R1** & **R2** & **R3** & **[R3-R1]/R1** & **[R3-R2]/R2** \\ \hline \(1\) & \(0.11\) & \(0\) & \(0.1\) & \(9.09\%\) & / \\ \(2\) & \(0.99\) & \(1\) & \(1.08\) & \(9.09\%\) & \(8.00\%\)
## 2 Materials and Methods
### Data
#### 2.1.3 On-the-Ground GPP Observations  
On-the-ground GPP observations were obtained from the eddy covariance-based carbon flux towers in China (ChinaFLUX) and applied to evaluate the accuracy of the FPARAN in terms of seasonal changes. Daily data were acquired for the period of 2004 to 2010 for the eight sites (Table 2 and Figure 1a). The vegetation types are the temperate deciduous forest (CBS), the sub-tropic evergreen needle-leaf forest (QYZ), the tropic evergreen broadleaf forest (DHS and XSBN), the temperate meadow (NMG), the alpine shrub (HBGC), the alpine steppe (DX), and crop (YC). The details for observation instrumentsand data processing can be found in the literature [41]. In this study, the 8-day average was calculated to match the remote sensing-based FPAR data.  
Figure 1: The ANN-based FPAR developed in this study (\(\rm FPAR_{ANN}\)) over Chinese terrestrial ecosystems from 1980 to 2018 and its validations and evaluations. (**a**) the mean FPAR from 1980 to 2018 and the sites map for the flux towers. (**b**) The \(\rm FPAR_{ANN}\) was validated through the observed GPP on the eight flux towers of ChinaFLUX and the results were shown by the coefficient of determination (\(R^{2}\)). (**c**) the mean FPAR of each vegetation classification was compared among the \(\rm FPAR_{ANN}\), the product of the MODIS (\(\rm FPAR_{MCD15\AA2}\)) and the GLASS (\(\rm FPAR_{BNU}\)). These eight flux towers are Yucheng cropland (YC), Dangxiong alpine steppe-meadow (DX), Haibei alpine shrub-meadow (HBGC), Inner Mongolia temperate steppe (NMG), Dinghushan evergreen mixed forest (DHS), Changbaishan temperate mixed forest (CBS), Qianyanzhou evergreen needleleaf forest (QYZ), Xishuangbanna evergreen broadleaf forest (XSBN).
# Stomatal Response of Maize (_Zea mays_ L.) to Crude Oil Contamination in Soils
## 4 Discussion
### Stomata and Other Leaf Parameters  
Stomata are the pores in the epidermis of botany leaf controlling gas exchange, mainly CO\({}_{2}\) and water vapor, between the atmosphere and plants [15]. In the present work, stomatal length and density of maize leaf in contaminated soil treatments decreased compared with those in the control, indicating that crude oil contaminated soil harmed stomatal structures. And the downtrend showed stomatal length to be more sensitive than stomatal density to contaminated soil. Previous work showed stomata in plants were not only influenced by air pollution of automobile emissions [35], but also by soil or water contamination of environmental stress [19; 36]. It is interesting to observe the changes in maize stomata induced by the contamination of air or soil in the present work.  
Plasticity of stomatal development may be determined by many exogenous and environmental cues, of which abscisic acid (ABA) is considered as a vital regulator of environmentally determined stomatal development [37]. According to previous work, ABA can increase progressively in the root with responses to abiotic environmental stress [38; 39]. In particular, the level of endogenous ABA significantly increased in pea (_Pisum sativum_ L.) plant with increasing fluoranthene concentrations [40]. Therefore, crude oil contaminated soil might stimulate the synthesis of ABA in maize root and then increase the amount of ABA, thus decreased stomatal length and density.  
In addition, the changed stomata might affect other leaf parameters. Stomatal length has an extremely positive correlation with leaf K content (r = 0.92, \(p<0.01\)), and positive correlation with leaf Na content (r = 0.75, \(p<0.01\)). It seems that K and Na content in leaf may be influenced by the stomata structure. But on the other hand, K\({}^{+}\) is considered to involve in controlling stomatal movements, in which guard cell K\({}^{+}\) uptake from the apoplast is mediated by a proton-extruding adenosine triphosphate on the plasmalemma [41]. Moreover, tonoplast-localized NHX proteins as Na\({}^{+}\), K\({}^{+}\)/H\({}^{+}\) antiporters are essential for active K\({}^{+}\) uptake at the tonoplast for stomatal function [42], so that K\({}^{+}\) and Na\({}^{+}\) in plant are considered as twins [43]. The contents of K and Na in maize leaf might also affect the length and density of stomata. The relation between those ions and stomata structure still needs further study. Additionally, leaf water content and transpiration rate did not significantly change in different treatments, indicating that the response of water balance in maize plant was different from nutrient ions in contaminated soil.
# Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
###### Abstract  
Coded modulation (CM), a combination of forward error correction (FEC) and high order modulation formats, has become a key part of modern optical communication systems. Designing CM schemes with strict complexity requirements for optical communications (e.g., data center interconnects) is still challenging mainly because of the expected low latency, low overhead, and the stringent high data rate requirements. In this paper, we propose a CM scheme with bit-wise hard-decision FEC and geometric shaping. In particular, we propose to combine the recently introduced soft-aided bit-marking decoding algorithm for staircase codes (SCCs) with geometrically-shaped constellations. The main goal of this CM scheme is to jointly boost the coding gain and provide shaping gain, while keeping the complexity low. When compared to existing CM systems based on \(M\)-ary quadrature-amplitude modulation (\(M\)QAM, \(M=64,128,256\)) and conventional decoding of SCCs, the proposed scheme shows improvements of up to 0.83 dB at a bit-error rate of \(10^{-6}\) in the additive white Gaussian noise channel. For a nonlinear optical fiber system, simulation results show up to 24% reach increase. In addition, the proposed CM scheme enables rate adaptivity in single-wavelength systems, offering six different data rates between 450 Gbit/s and 666 Gbit/s.  
coded modulation; error correcting codes; hard decision decoding; geometric shaping; staircase codes; optical fiber communications +
Footnote †: journal: _Entropy_  
_Article_  
Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
[14] incorporated Coordinate Attention [15] into a grasp model for capturing position-related features; however, their method ignores mutual features across the entire spatial position, and the convolution is not conducive to cross-channel interactions. Furthermore, many grasp detection networks rely on encoders and decoders. While the encoding process yields advanced grasp semantic features, the gradual reduction in resolution during encoding results in the loss of spatial features. It is noteworthy that the spatial features of objects are closely related to grasp poses, and multi-scale features can help the learning system gain a comprehensive understanding of the object's appearance and structure, which significantly impacts robot grasping quality. To overcome these problems, we developed the Feature-Augmented Grasp Detection Network (FAGD-Net) based on encoder-decoder architecture. Within this network, we introduced two modules to enhance its focus on grasp-related features. Firstly, we proposed a Residual Efficient Multi-Scale Attention (Res-EMA) module, aimed at effectively regulating the significance of feature channels while preserving precise spatial information. Additionally, we introduced a Feature Fusion Pyramidal Module (FFPM) as an intermediary between the encoder and decoder, effectively addressing potential oversights or losses of grasp-related features as the encoder network deepens. Summarizing, the main contributions of this paper are as follows:  
1. A new module termed Residual Efficient Multi-Scale Attention (Res-EMA) was proposed to adjust the importance of the feature channels and ensure the preservation of accurate grasping space information within the channels. 2. A Feature Fusion Pyramid Module (FFPM) was constructed between the encoder and decoder. This module aims to enhance the grasp-related features that may otherwise be overlooked or lost during the encoding process. 3. A Feature-Augmented Grasp Detection Network (FAGD-Net) was developed based on the Res-EMA and FFPM network frameworks, which is used for the real-time prediction of optimal grasp configuration. Experimental evaluations on the Cornell dataset and Jacquard dataset demonstrate exceptional performance, where the proposed method achieved high accuracy rates of 98.9% and 96.5%, respectively. 4. We integrated the network into an actual robot grasping system and conducted real-world grasping tests to validating the performance of our approach. It achieves advanced performance in both public datasets and real-world grasping tasks.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 4 Solution Using Matching Game-Based U-S. A and U-S. P Algorithms
### One-Sided Matching Based Solution Approach Sub-Problem (19)
#### 4.1.1 UEs Utility Function  
Since eMBB UEs are data-rate-hungry, we utilize the DL attainable data rate as the utility function \(\Psi_{e}(b)\), which can be expressed as follows:  
\[\Psi_{e}(b)=R_{be}, \tag{31}\]  
Meanwhile, uRLLC UEs are deemed urgent-latency UEs; thus, the utility attained by an uRLLC UE when it is associated with the BS is introduced as a function of the DL transmitted latency and reliability as follows:  
\[\Psi_{l}^{1}(b)= R_{bl}\ \&\ \Psi_{l}^{2}(b)=\overline{T}_{bl}, \tag{32}\]
###### Abstract  
This paper presents the design and optimization of a dual-band polarization-dependent metasurface capable of dynamically switching transmission and reflection characteristics. The metasurface is composed of three metallic patterns, with the bottom layer governing the reflection and transmission phase for both TE-polarization and TM-polarization states. The middle and top layers are strategically employed to ensure optimal transmission and reflection performance. The results confirm that the metasurface enables the transformation of the transmission band into a complete reflection band, and vice versa, through variations in the incident wave polarization. Remarkable transmission and reflection characteristics are achieved within the frequency ranges of 6.1-6.55 GHz and 8.9-9.3 GHz, respectively. The proposed metasurface offers promising applications in advanced communication systems and radar technology, enabling dynamic manipulation of electromagnetic waves.  
metasurface; polarization-dependent; incident wave polarization; communication systems +
Footnote †: journal: _Applied_  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming
## 4 Results and Discussion
### Authentication and Traffic Isolation  
In order to validate the implemented architecture, the user authentication procedure and the network slice traffic isolation were tested for both types of clients: operator and non-operator. A simple test scenario with the infrastructure shared by one operator client and one non-operator client was designed, and we measured the user authentication time and verified the traffic isolation among clients.  
Regarding the authentication time for the two types of clients, 10 measurements were taken from a user of each client, the averages were calculated, and the difference was analyzed. The average value obtained for the authentication of the operator-client user was 262.4 ms, and for the non-operator-client user, it was 239.9 ms. Thus, a slight difference in the authentication time of 22.5 ms was observed. This difference is a consequence of the extra network hops in the authentication of the operator-client user as it requires the core of its operator to authenticate it via roaming, while the non-operator-client user authenticates directly on the neutral host CN. This time difference can become more significant according to the communication delay between the operator CN and the neutral host CN.  
Open5 GS creates a tunnel interface for each slice, so to verify traffic isolation in each slice, traffic was generated using iperf from two different users of different clients. On the core network side, tcpdump was used on each interface to monitor the traffic of each user. Through filters, we checked if the traffic passing through one user's interface also passed through the other user's interface. If it happened, it would indicate interference between the interfaces of each user. However, we observed that this was not the case, leading us to conclude that each interface is specifically dedicated to each client, thus ensuring traffic isolation between the slices.  
Figure 6: Architecture of the evaluation scenario used for the tests.
# Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe
## 3 Results
### Improvement of GPP Estimation by Considering the Aerosol-Radiation Effect  
Compared with the EC-LUE model, the improved GPP model that considered aerosol-radiation effects (i.e., the AOD-LUE model) can more accurately estimate GPP (Figure 5a). The AOD-LUE model had higher R\({}^{2}\) (0.68) and slope (0.93) than the EC-LUE model (R\({}^{2}\) = 0.6 and slope = 0.89). The data points of the AOD-LUE model values were more concentrated in the vicinity of the 1:1 line than those of the EC-LUE model, which indicated that the GPP values estimated by the AOD-LUE model were closer to the true values (i.e., flux tower GPP). Additionally, the simulated value of the AOD-LUE model decreased when the tower's GPP was less than 7.5, and vice versa. The degree of improvement of the AOD-LUE model differed across EC sites (Figure 5b). To be more specific, the AOD-LUE model was better optimized at the EC sites where the R\({}^{2}\) for the EC-LUE model was lower (Figure 5b). The AOD-LUE model can be greatly improved (is about 40-120%) when the R\({}^{2}\) value of the EC-LUE model is in the range of 0-0.4., but when the R\({}^{2}\) value of the EC-LUE model was high (i.e., R\({}^{2}\) greater than 0.6), the R\({}^{2}\) value of the AOD-LUE model was not greatly improved (less than 10%).  
For each vegetation type, the simulation performance of the AOD-LUE model outperformed the EC-LUE model (Table 1). As compared with the EC-LUE model, the AOD-LUE model produced higher R\({}^{2}\) and less SD across all vegetation types. The improvement was more obvious in EBF and CSH, in which the EC-LUE model has low R\({}^{2}\) (0.35, 0.18) and high RMSE (2.21, 1.76), and the AOD-LUE model increased R\({}^{2}\) by 0.1 and 0.11 and decreased RMSE by 0.12 and 0.17 and SD by 0.2 and 0.18. For other vegetation types such as MF, DBF, and ENF, the AOD-LUE model had slightly higher performance than the EC-LUE model.  
Figure 4: Scatter plot of daily diffuse fraction (D\({}_{\rm f}\)) and daily AOD of flux tower sites globally. The red line is fitted by the Michaelis–Menten equation in clear sky. V\({}_{\rm max}\) represents the maximum D\({}_{\rm f}\), and K\({}_{\rm m}\) represents the AOD value at half of the maximum D\({}_{\rm f}\).  
The results show that the AOD-LUE model has higher potential in some poorly simulated vegetation types.
## 4 Discussion  
### Comparison of LEO Constellation Scheme with GNSS  
In order to further verify the feasibility of the optimization schemes, the five optimization schemes were compared at different latitudes around the world as well as with the four major GNSS operating in orbit, and the PDOP comparison analysis is shown in Figure 14.  
As can be seen from Figure 14a, the constellation performance between 40\({}^{\circ}\) north and south latitude is in reverse order, with Scheme 5 being better than the other schemes and the constellation orbital altitude having a greater influence. Between 60\({}^{\circ}\) north and south latitude, the influence of incline on the constellation performance gradually increases.  
Figure 14: Five optimization schemes are compared with the PDOP of GNSS constellations: (**a**) LEO tilt constellation scheme and (**b**) LEO hybrid constellation scheme.  
Figure 13: Staged deployment spatial configuration of the LEO hybrid navigation constellation. (**a**) Stage I compensation. (**b**) Stage II global navigation augmentation. (**c**) Stage III global BDS-3 level.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Event** & **PDOP** & **Event** & **PDOP** \\ \hline
24 MEO & 2.348 & 24 MEO + \(tilt_{6}+polar_{4}\) & 1.562 \\
14-2235 & 5.208 & 24 MEO + \(tilt_{8}+polar_{4}\) & 1.479 \\
14-22-35 + \(tilt_{2}+polar_{2}\) & 2.828 & 24 MEO + \(tilt_{10}+polar_{4}\) & 1.456 \\
14-22-35 + \(tilt_{4}+polar_{2}\) & 2.206 & 24 MEO + \(tilt_{10}+polar_{4}\) & 1.3 \\
14-22-35 + \(tilt_{4}+polar_{4}\) & 2.039 & \(tilt_{18}+polar_{8}\) & 1.676 \\ \hline \end{tabular}
\end{table}
Table 3: Event denotes the PDOP of the constellation in different scenarios.  
Combining the orbit altitude, incline, and constellation performance, Scheme 3 was selected as the final optimized design result, and at the same time, the constellation performance of Scheme 3 between 20\({}^{\circ}\) north and south latitude is close to that of BDS 24 MEO, Galileo, and GLONASS.  
LEO tilt constellations are utilized for navigation services in mid-latitudes. To achieve global navigation, the design of a polar constellation is essential to enhance coverage and navigation accuracy in polar regions. As depicted in Figure 14b, the quantity of GPS satellites and the distinctive configuration of BDS-3 constellations exhibit a slight advantage over LEO hybrid navigation constellations within latitudes 0\({}^{\circ}\)-20\({}^{\circ}\) north and south, with the average PDOP of LEO hybrid navigation constellations being 1.676, surpassing that of the four GNSS navigation constellations. The performance and arrangement of LEO hybrid navigation constellations satisfactorily meet the design requirements.