esa-sceva/satcom-chunk-collection huggingface dataset:
# Unified Performance Analysis of MIMO Mixed RF/FSO Relaying System
## 9 Conclusions  
In this paper, we investigated the performance of the asymmetric dual-hop MIMO mixed RF/FSO DF relaying system, where the RF link suffers from Rayleigh fading and the MIMO FSO link suffered from IT distributed turbulence. For the considered system, we derived the closed form expressions of the system outage probability, BER, and average ergodic capacity. The approximate expression of the system outage probability considering the pointing error is also derived. In addition, the analysis of asymptotic performance and diversity-multiplexing tradeoff was given to provide deeper insight of the system characteristics. We found that the end-to-end diversity gain was mainly determined by the minimum diversity gain of each hop, and the maximal multiplexing gain was dependent on the minimum number of antennas in each link. The model studied in this paper has the characteristics of excellent error performance, high energy efficiency, and strong robustness, attracting attention as an effective solution for future large-capacity and high-speed communication systems.  
Figure 9: DMT of MIMO mixed RF/FSO system with intersection. **(a)** RF link dominates maximum diversity gain; **(b)** FSO link dominates maximum diversity gain.  
**Author Contributions:** Conceptualization, H.L.; funding acquisition, X.L.; methodology, H.L.; writing--original draft preparation, H.L.; writing--review and editing, H.L., Y.L., C.G. and M.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The APC was funded by Xiaofeng Li.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 713694.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Transceiver Architectures' Classification and Basic Concepts
### 3D-Based Architectures  
The bit rate and the SE of self-beating direct detection schemes can be further increased by modulating and recovering a third DoF. To realize this, there are a number of transceiver architectures (as depicted in Figure 12a,b) and modulation formats that can be employed [14]. The signal recovery can then be achieved by means of a Stokes vector receiver [17]. The DP-IM-intensity-and-phase modulation (IPM) (Figure 12a) and PolMux-\(IQ\)-IM (Figure 12b) are instances of the 3D-based architectures. In the schemes, a third RF signal is encoded by employing complex modulation on one polarization component [14], while a real-valued IM is adopted on the other [37; 128]. Therefore, in these schemes, the optical spectrum is conjugate symmetric on one polarization, while it is asymmetric on the other [14]. As the PolMux \(IQ+C\)/Stokes vector receiver scheme, polarization derotation can be realized at the receiver by employing a Stokes vector receiver and the associated MIMO DSP algorithms.
## 4 Experiments and Analyses
### Case Study  
Last but not least, we intuitively demonstrate the effectiveness of CGR-NER through two cases from the test set of the Weibo dataset. Table 12 shows the recognition results of CGR-NER and BERT-BiLSTM-CRF. In sentence 1, BERT-BiLSTM-CRF failed to identify the entity \"\(\#\)\(\#\)\(\#\) (Mengmeng)\", but CGR-NER made a correct identification. In sentence 2, BERT only recognized the entity \"\(\#\) (brother)\", but failed to recognize the entities \"\(\#\)\(\#\) (Lin Rixi)\", \"\(\#\)\(\#\)\(\#\) (Shan Rixi)\" and \"\(\#\)\(\#\)\(\#\) (Xiao yi)\", while CGR-NER correctly recognized all entities. We attribute the correct recognition of CGR-NER to the following reasons. First, entities such as \"\(\#\)\(\#\)\(\#\)\(\#\)\(\#\)\(\#\)\(\#\)\(\#\)\(\#\)\" and \"\(\#\)\(\#\)\(\#\)\" have radicals such as \"\(\#\) (wood)\", \"\(\#\) (sun)\", \"\(\cdots\) (grass)\" and \"\(\#\) (bright)\". The combination of these radicals often appears in Chinese naming, which is very consistent with the naming style of Chinese culture. Consequently, by capturing such features in Chinese character glyphs, CGR-NER can more accurately identify entities such as Chinese people and enterprise names. Secondly, although \"\(\#\)\" and \"\(\#\)\(\#\)\" belong to out-of-vocabulary characters, \"\(\#\)\(\#\)\" and \"\(\#\)\(\#\)\(\#\)\" and \"\(\#\)\(\#\)\(\#\)\(\#\)\" and \"\(\#\)\(\#\)\(\#\)\(\#\)\" have the same radical and a similar glyph structure, respectively. As a result, CGR can infer that they have similar semantics through their similar visual features, thereby reducing the impact of out-of-vocabulary characters on the model recognition performance and correctly identifying the entity \"\(\#\)\(\#\)\(\#\)\".
## 4 Discussion
In another study [37] that conducted angular evaluations in the XZ plane, it is observed that the reference teeth used were maxillary anterior teeth, and bracketing was performed on phantom teeth. The study's differences, such as conducting it on maxillary anterior and phantom teeth, ensuring the desired viewing angle, and eliminating patient-dependent negative factors, contribute to its uniqueness. Studies have highlighted that the additional time required for rubber dam placement is one of the barriers to its use [3]. For this reason, our study decided to calculate the time required during bracket placement using the RDI and TI methods. Studies indicate that the average time for direct bonding procedures using the TI method ranges from 22 to 42 min [39; 40; 41; 42]. In our study, the average procedure time was found to be 20 min and 38 s for the RDI group and 16 min and 47 s for the TI group. These findings support the idea that the time considered as lost during rubber dam placement is compensated due to the improved working conditions provided by its use, such as the retraction of the tongue and lips, the elimination of saliva contamination, the need for fewer changes in cotton rolls, and the reduction in the patient's need for rinsing minutes [8; 9]. Image losses may have occurred due to reasons such as the inability of the sprays used to reduce the reflections caused by the light of the scanning device on the brackets, and the inability to achieve complete elimination of reflections due to the uneven distribution of sprays. This factor can be considered as a limitation of this study. Future research could be conducted with advanced scanners that provide high image quality across different types and models. If matte-surfaced brackets are provided by the manufacturer, the glare and scattering occurring on the brackets would be minimized, potentially leading to higher image and scanning results. Additionally, in future studies, the use of indirect bonding techniques instead of direct bonding, as well as the implementation of different scanners, may result in variations in the outcomes.
## 3 Materials and Methods
### Supervised Machine Learning Classifiers  
Machine learning is a discipline within artificial intelligence (AI) that allows the development of algorithms to classify and identify patterns in different types of databases.  
The classification models addressed in this research correspond to subfield supervised learning, since the database is labelled (the response variable corresponds to the event: collision or avoidance).  
The classifiers evaluated can be divided into distance-based models and tree-based models. The distance-based classifiers are:  
* Support Vector Machine: consisting in the construction of a hyperplane or set of them in a space of very high dimensionality (or even infinite) to obtain a good separation and, therefore, classification of classes of the output variable. The main hyperparameters are the regularization parameter C, the Gamma coefficient, and the type of Kernel function. A high value of C reduces the classification margin, making the algorithm focus more on classifying the training sample. A low value, however, increases the margin, although this may result in a loss of training accuracy. On the other hand, if Gamma is very high, the area of influence is larger, while a smaller value may be too restrictive and not capture the complexity of the data. Kernel functions can be linear, polynomial, with a radial basis or RBF, sigmoidal, etc.
* K-Nearest Neighbors: this method consists of estimating the density function of the observations for each class of the response variable, approximating locally from a simple majority vote of the K nearest neighbors to the observation. The proximity criterion is generally based on distance.  
The tree-based models are:  
* Individual Decision Tree: this classifier splits the sample space through decision rules based on recursive partitioning, with the objective that the final nodes contain classified observations with the same objective value. The partitioning criteria can be by Gini impurity (frequency of misclassification for each observation) or by entropy (disorder of the features with respect to the entropy variable).
* Random Forest: consists of the bootstrapping generation of a set of independent decision trees, the result of which is obtained by majority vote. This classifier allows to reduce the variance without increasing the bias, reducing the sensitivity to noise of the single tree model.  
The workflow for the development of these models includes variable extraction and data preprocessing (including the coding of categorical variables and feature scaling of explanatory variables for distance-based models), feature selection and model fitting (including the splitting into a training set and a test set, and the calculation of hyperparameters through an optimization function). Finally, performance metrics will be discussed to assess the ability of the different classifiers to capture true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN).  
Figure 11: Distribution by gender and age group of the sample of users who carried out the experimental session in virtual reality.
# An Evaluation of Orbital Angular Momentum Multiplexing Technology
## 4 Experimental Demonstration Wireless OAM Multiplexing Technology using 28 GHz
### OAM-MIMO Multiplexing Using Multiple UCAs  
This subsection presents wireless OAM-MIMO multiplexing that combines the OAM and MIMO concepts. The number of usable OAM modes is limited due to their mode-dependent beam divergence. In particular, higher OAM modes have a practical limitation due to their nature of large attenuation caused by beam divergence. To address this problem, we present OAM-MIMO multiplexing using multiple UCAs. Unlike OAM multiplexing, OAM-MIMO multiplexing exploits multiple sets of the same OAM modes with receiver equalizations. Consequently, the number of concurrently transmitted data streams can be increased without using higher OAM modes that have large beam divergence.  
To enable OAM-MIMO multiplexing, we also used multiple UCAs such as OAM multiplexing. To achieve superposition-based simultaneous OAM beam generation and separation, we, respectively, implemented wideband analog \(5\times 16\) and \(16\times 5\) Butler matrices for Tx and Rx UCAs.
# Waterproof Performance of Sealing Gasket in Shield Tunnel: A Review
## 4 Waterproof State Simulation Methods Development
### Simplified Method  
In terms of the simplified numerical simulation method, the key point is to collect the average contact stress under specific joint deformation conditions, such as the water proofing threshold due to the material nonlinearity of gaskets, large geometric deformation, and complex contact relations. [PERSON] et al. [57] proposed the concept of taking average contact stress as a reference for waterproofing ability. [PERSON] et al. [58] proposed empirical formulas for contact stress and waterproofing capacity of seven typical gaskets based on experimental results and numerical analysis data. [PERSON] [59] analyzed the stress deformation of gaskets considering water pressure and studied the corresponding contact stress distribution and waterproof failure mode. Due to the nonlinear nature, large deformation, and complex contact of gaskets, the research of numerical simulation analysis is focused on accurately reproducing the compression deformation process of gaskets in grooves [33; 60]. The latest research extends to how to simulate the leaking failure process of joint gaskets: Based on the concept of average contact stress, [PERSON] et al. [13] compared the contact stress on different contact surfaces under the joint opening, offset, and rotation modes to predict the location of leakage. [PERSON] et al. [61] proposed the concept of effective contact stress and refined the failure mechanism of different contact surfaces. In essence, the above methods are simplified models that equate the lateral water pressure to uniform pressure and do not consider the mesoscopic opening process of water pressure invading the gasket contact surface. In the preliminary design stage, the simplified numerical simulation method can better predict the deformation performance and contact stress of gaskets.
## 2 Navigation and Localization
### Simultaneous Location And Mapping (SLAM)
[85] formed their own datasets from an underwater sensor suite--equipped with a 100 Hz IMU and a 15 fps, 1600 \(\times\) 1200 px stereo camera--operated by a diver, an underwater sensor suite mounted on a diver propulsion vehicle, and an AUV. Experiments were conducted for each dataset considering the following combinations: monocular; monocular with IMU; stereo; and stereo with IMU, based on the modes supported by each Visual Odometry (VO) or Visual Inertial Odometry (VIO) algorithm. Results showed that DSO and SVO, despite quite often fail to track the complete trajectory, had the best reconstructions for the tracked parts and, as expected, stereo performed better than monocular. The results confirmed that incorporating IMU measurements drastically lead to higher performance, in comparison to the pure VO packages.
# Effects of Temperature on Growth and Isoprene Metabolism Pathway in _Eucomnia ulmoides_ [PERSON]
###### Abstract  
This study aimed to explore the influence of temperature on the growth and isoprene metabolism pathways of _Eucomnia ulmoides_ seedlings. Growth indices and levels of rubber and total triterpenoids in leaves were assessed under three temperature treatments: low (15 \({}^{\circ}\)C/10 \({}^{\circ}\)C), control (25 \({}^{\circ}\)C/20 \({}^{\circ}\)C), and high (35 \({}^{\circ}\)C/30 \({}^{\circ}\)C). High-throughput sequencing identified 2309 differentially expressed genes, of which 1608 were upregulated and 701 were downregulated. After 40 days, leaf length, leaf width, and plant height were significantly lower in low- and high-temperature treatments compared with the control. Specific leaf weight was higher in LT-treated leaves. Chlorophyll a and b contents were 1.372 mg.g\({}^{-1}\) and 0.594 mg.g\({}^{-1}\), respectively, in control leaves, followed by low- and high-temperature treatments. Caroenoid content was the highest in LT treatment. The rubber content of LT and HT groups significantly decreased by 16.5% and 38%, respectively, compared with that of the control group. Total triterpene content was the lowest in control leaves at 1.02%, which was 30% and 20% less than that in low- and high-temperature treatments, respectively. This study provides insights into the efficient cultivation of _E. ulmoides_ and the regulatory network of secondary metabolic pathways.  
Eucomnia ulmoides; temperature; rubber; total triterpene Article
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 4 Proposed Method
### Distributed Scheme
```  
**Algorithm 2** IT-based decentralized scheme for solving problem \((\mathrm{P1})\)  
This iterative update mechanism guides the system's performance toward the Pareto boundary of the optimality gap region. By carefully selecting step sizes and direction vectors for IT adjustments, the algorithm ensures that each update leads to a non-increasing optimality gap for the involved APs. Over successive iterations, this process converges to a state where no further reductions in optimality gaps are possible without adversely affecting other APs, which signifies Pareto optimality. Compared to the initial configurations or schemes where each AP optimizes independently, this cooperative approach achieves a more efficient global performance. It encourages APs to participate in collaborative adjustments, even when such changes may not immediately benefit their individual performance metrics. This collaborative behavior is facilitated by the algorithm's design, which ensures that any trade-offs made by individual APs contribute to the overall reduction in the system's optimality gap. Such approaches highlight the potential of decentralized coordination mechanisms in enhancing the efficiency and fairness of distributed wireless systems.
## 2 Simulation of Blast Events
### Determination of Pressure Wave Parameter  
Methods for determining the blast propagation for a pressure wave resulting from a vapor cloud explosion can be found in [33]. Ideal explosion sources, such as high explosives like TNT, can be characterized by a single parameter, namely their explosion energy. In Equation (1), this is represented by the mass of TNT. Consequently, the relationship between blast parameters and distance for ideal explosion sources can be represented by a single curve in energy-scaled coordinates. However, when dealing with non-ideal explosion sources like vapor cloud explosions, the situation changes. Non-ideal sources cannot be solely defined by explosion energy alone; both the explosion energy and the energy release rate are necessary to characterize these sources accurately [33]. Therefore, a set of curves describes the relationship between blast parameters and distance for vapor cloud explosions. The two predominant sets of blast curves are the Baker-Strehlow-Tang (BST) method [38; 39] and TNO multi-energy method (MEM) [40], but other methods also exist [33]. For a long time, the TNT equivalence method [33] was used, which is very simple since the only parameter needed to represent the vapor cloud explosion includes the explosion energy, from which the TNT charge weight is calculated. In [33], it is stated that the TNT equivalence methods are not well suited, since blast effects from vapor  
Figure 2: Definition of angle of incidence.  
cloud explosions depend not only on the explosion energy but, more importantly, on the combustion rate. Therefore, methods like BST or TNO MEM have replaced the TNT equivalence method.  
If a vapor cloud explosion scenario is defined and specified in ammonia process units, the described curves of BST or TNO MEM can be used to determine the blast load. The procedure is complex and requires various input parameters. For the blast analysis in tank design, however, often, only the resulting overpressures are specified, which stress the tank structure and must be considered in the design. A risk analysis does look at specific underlying scenarios for a potential blast load. However, often--as already mentioned in the introduction (Section 1)--tank structures and entire process plants are built in such a way that future risks not yet foreseeable at the planning stage are also taken into account. Accordingly, the scenario in which a blast load is applied is secondary for the analysis of tank structures.  
Although the scenario is in principle relevant for the calculation approach developed in Section 3.4, it is derived from the relationships of shock wave propagation inspired by the TNT equivalency. Therefore, the methods BST or TNO MEM for determining the blast propagation for a pressure wave are not relevant for this research and are not explained further.
# Comparison of Disturbance Compensators for a Discrete-Time System with Parameter Uncertainty
## 2 Closed-Loop Stability Analysis
### Stability Analysis Considering Parameter Uncertainty  
Considering Equation (1), the parameter uncertainty \(\Delta\Phi\) is coupled with the system state \(x(k)\). Since chattering always exists in the DSMC system [6], the commonly used smooth and slow varying assumptions on the disturbance [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16] are not suitable for the coupled term \(\Delta\Phi x(k)\). Hence, it is reasonable to analyze the closed-loop stability considering the parameter uncertainty separately.  
Taking into account \(z(k)=\mathbb{T}x(k)\), Equation (8) can be obtained by noting Equation (1):  
\[z(k+1)=\big{(}\overline{\Phi}+\Delta\overline{\Phi}\big{)}z(k)+\overline{ \Gamma}u(k)+\overline{d}(k) \tag{8}\]  
where \(\overline{d}(k)\ =\ Td(k)\). \(d_{i}\) (\(i=1,\,2,\,\ldots,\,n\)) is the parameter uncertainty  
\[\Delta\overline{\Phi}=T\Delta\Phi T^{-1}=\left[\begin{array}{ccccc}0&1&0& \cdots&0\\ 0&0&1&\cdots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\cdots&1\\ -d_{1}&-d_{2}&-d_{3}&\cdots&-d_{n}\end{array}\right] \tag{9}\]  
Substituting the controller Equation (5) into Equation (8) yields Equation (10)  
\[z(k+1)=\bigg{[}\overline{\Phi}+\Delta\overline{\Phi}-\overline{\Gamma}\big{(} \overline{CT}\big{)}^{-1}\big{(}\overline{C\Phi}-(1-q)\overline{C}\big{)} \bigg{]}z(k)+\overline{d}(k) \tag{10}\]  
where \(\overline{d}(k)=\overline{d}(k)-\overline{\Gamma}\big{(}\overline{CT}\big{)} ^{-1}\xi\text{sign}(s(k))\). Based on Equation (10), the characteristic equation of the closed-loop system, Equation (11), can be obtained:  
\[\lambda^{n}+(c_{n-1}-(1-q)+d_{n})\lambda^{n-1}+\ldots+(c_{1}-(1-q)c_{2}+d_{2}) \lambda+(d_{1}-(1-q)c_{1})=0 \tag{11}\]Unlike the traditional stability analysis, Equation (7), it follows from Equation (11) that the parameter uncertainty actually affects the closed-loop system stability. Hence, it is not appropriate to mix the external disturbance with parameter uncertainty. Even if the selection of \(q\) and \(\overline{C}\) renders \(|\lambda|<1\) in Equation (7), it may not hold in Equation (9), and the closed-loop system still may be unstable.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 3 Satellite Measurement of Precipitation
### Science and Technology Advances
#### 3.1.6 Future Observations of the Water Cycle as a Whole  
Broader-scoped missions are being planned for measuring several components of the water cycle at once and at very high spatial resolution (Figure 6). The Chinese candidate mission Water Cycle Observation Mission (WCOM, [160]) will host a three-instrument payload: (1) an L-S-C-band Fully-Polarized Interferometric synthetic aperture microwave Radiometer (FPIR), (2) a Polarized Microwave radiometric Imager (PMI), and (3) an X-Ku Dual-Frequency Polarized SCATterometer (DFPSCAT). The mission rationale is to acquire high accuracy and consistent measurements of key elements of the water cycle from space, including soil moisture, ocean salinity, freeze-thaw, and snow water equivalent, among other parameters. A satellite mission for surface water monitoring was first delineated by Alsdorf and Lettenmaier [161] and Alsdorf et al. [162], with very stringent characteristics: (i) a spatial resolution of \(\sim\)100 m to resolve basin discharge; (ii) a few days' temporal resolution to capture short flood events; and (iii) a vertical resolution of a few centimeters to measure the subtle height changes which are responsible for significant discharges. The concept successively evolved into the Surface Water and Ocean Topography (SWOT, [[https://swot.jpl.nasa.gov/home.htm](https://swot.jpl.nasa.gov/home.htm)]([https://swot.jpl.nasa.gov/home.htm](https://swot.jpl.nasa.gov/home.htm)), last accessed 8 August 2019) mission, which is planned to be launched in 2021.  
Figure 6: Concept of the planned missions for the global water cycle observation.