esa-sceva/satcom-chunk-collection huggingface dataset:
# Relation-Aware Graph Transformer for SQL-to-Text Generation
## 3 Model
### Encoder Overview  
The input features include trainable embeddings for all nodes and relations. We use \(\mathbf{X}^{L}\in\mathbb{R}^{|V^{L}|\times d_{x}}\) and \(R^{L}=[r^{L}_{ij}]_{|V^{L}|\times|V^{L}|}\) to denote the set of leaf node embeddings and the relation matrix among leaf nodes. Symmetrically, \(\mathbf{X}^{I}\in\mathbb{R}^{|V^{I}|\times d_{x}}\) and \(R^{I}=[r^{I}_{ij}]_{|V^{I}|\times|V^{I}|}\) for intermediate nodes.  
The encoder is composed of \(K\) stacked blocks, as illustrated in Figure 4. The main component is relation-aware graph transformer (RGT), which takes as input the node embedding matrix \(\mathbf{X}\), the relation matrix \(\mathbf{R}\) and a relation function \(\mathbf{E}\) that extracts relation embeddings from \(\mathbf{R}\), and outputs the updated node matrix. Each block contains four modules: one RGT for intermediate nodes, one RGT for leaf nodes, and two cross-attention modules. In each block, node embeddings \(\mathbf{X}^{I}\) and \(\mathbf{X}^{L}\) are updated sequentially via self-attention and cross-attention. According to the dataflow in Figure 4, intermediate nodes are first updated by  
\[\mathbf{X}^{I}_{mid}=\text{RGT}(\mathbf{X}^{I}_{in},E^{I}_{rel},R^{I}).\]  
Then, leaf nodes attend intermediate nodes and update with RGT,  
\[\mathbf{X}^{L}_{mid} =\text{CrossAttention}^{L\gets I}(\mathbf{X}^{L}_{in}, \mathbf{X}^{I}_{mid}),\] \[\mathbf{X}^{L}_{out} =\text{RGT}(\mathbf{X}^{L}_{mid},E^{L}_{rel},R^{L}).\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Node Types**} & \multicolumn{2}{c}{**Enumeration**} \\ \hline \multirow{4}{*}{intermediate} & concept abstractions & SQL, TABLE, COLUMN \\ \cline{2-3}  & \multicolumn{2}{c}{clause keywords} & SELECT, FROM, WHERE, GROUPBY, HAVING, ORDERBY, LIMIT \\ \cline{2-3}  & \multirow{2}{*}{binary operators} & conjuction & AND, OR, INTERSECT, UNION, EXCEPT \\ \cline{2-3}  & & arithmetic & \(+,-,\times,/\) \\ \cline{2-3}  & & condition & \(>,<,=,\geq,\leq,\leq,\) LIKE, IN, BETWEEN \\ \hline \multirow{4}{*}{leaf} & column/table words & e.g., column _state_name_\(\longrightarrow\) nodes _state_ and _name_ \\ \cline{2-3}  & value placeholders & \(val0,val1,val2,\) etc. \\ \cline{2-3}  & \multirow{2}{*}{unary operators} & aggregation & SUM, COUNT, MAX, MIN, AVG \\ \cline{2-3}  & & others & ASC, DESC, DISTINCT, NOT \\ \hline \hline \end{tabular}
\end{table}
Table 1: Enumeration or examples of all types of nodes.  
Figure 3: SQL Tree construction procedure. (**a**) is clause level; (**b**) is schema level; (**c**) is token level.  
Finally, intermediate nodes attend leaf nodes also,  
\[\mathbf{X}_{out}^{I}=\text{CrossAttention}^{I\gets L}(\mathbf{X}_{mid}^{I}, \mathbf{X}_{out}^{L}).\]  
Subscripts \(in,mid,out\) are used to differentiate the inputs and outputs. Definitions of relation embedding functions \(E_{rel}^{I}\) and \(E_{rel}^{L}\), relation matrix \(R^{I}\) and \(R^{L}\), and module \(\text{CrossAttention}^{I\gets L}(\cdot,\cdot)\) and \(\text{CrossAttention}^{L\gets I}(\cdot,\cdot)\) will be elaborated later.
## 3 Protocols Analysis
### Protocols Introduction  
IEC 60870-5-101/104 [28; 29]: The International Electrotechnical Commission has developed IEC 60870-5-101 based on Enhanced performance architecture (EPA) from the IEC 60870-5 series communication standard, and network communication version 104 is proposed on the basis of 101.  
GB/T26865.2-2011 [30]: 2011 version of real-time dynamic monitoring systems of power system-Part 2: Protocols for data transferring. This specifies the data transmission protocol of the real-time dynamic monitoring system of the power system, including real-time data transmission and offline data transmission. Suitable for real-time data transmission and historical file transfer between the main station and sub-station. A previous study [31] introduced the development of the version of the Synchronous Phasor Transmission Protocol, and another compared GB/T 26865.2-2011 with IEEE C37.118.2-2011 [32]. Their definitions in real-time data transmission are basically the same. The analysis and extension of GB/T 26865.2-2011 can be easily used by IEEE C37.118.2-2011.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 3 Methods  
The technical workflow for tea plantation mapping is illustrated in Figure 6. Firstly, Sentinel-1, Sentinel-2, and DEM data within the study area are collected and processed to form three different image combinations: optical, SAR, and optical + SAR, serving as input data sources for tea plantation mapping. Secondly, based on UAV imagery, ground survey data, and high-resolution imagery from Google Earth, point-based random sample labels covering the entire county and area-based sample labels of typical regions are created for model training, validation, and testing purposes. Thirdly, multiple deep learning models for tea plantation mapping based on semantic segmentation are constructed, leveraging skip connections, attention mechanisms, and other techniques to fully exploit the rich spatiotemporal spectral features in multi-source satellite imagery for precise tea plantation mapping. Finally, by comparing the accuracy of datasets and models, the optimal dataset and model for tea plantation mapping are selected to achieve high-precision mapping, and  
Figure 5: The drone imagery (**a**) and on-site photographs (**b–d**) of the tea garden in Anji. (**b**) illustrates the tea garden in its pre-pruning state, whereas (**c**,**d**) depict the post-pruning condition of the tea garden.  
techniques such as spatial analysis, spectral analysis, and temporal analysis are employed to summarize the unique spatial, spectral, and phenological characteristics of Anji white tea, providing valuable insights for enhancing tea plantation mapping capabilities.
# Design of a Laparoscopic Robot System Based on Spherical Magnetic Field
## 4. **Mathematic Modeling**
### Magnetc Moment of Rotor  
In order to solve the comprehensive force \(F\) and torque \(T\) acting on the rotor, the force and torque acting on every single IPM should be obtained and then superposed together.  
In the rotor coordinate \(CS_{i}\), position and orientation of IPM j can be expressed by \(T_{j}^{r}\) and \(R_{j}^{r}\) respectively (\(j=1,\,2,\,3,\,4,\,\text{or}\,\,5\)). As mentioned before, the size of magnetic moment \(M\) of IPM is constant, and the direction of which is determined by orientation of every IPM. Thus, the magnetic moment of IPM \(j\) in rotor coordinate \(CS_{r}\)\(M_{j}^{r}\) can be expressed as:  
\[M_{j}^{r}=m_{j}\cdot r_{j} \tag{28}\]  
where \(m_{j}\) denotes the size of magnetic moment of IPM \(j\), and \(r_{j}\) represents its direction.  
In Equation (27), the size of magnetic moment of IPM \(j\) can be expressed as:  
\[m_{j}=k_{j}\frac{B_{r}\pi{D_{j}}^{2}L_{j}}{4\mu_{0}} \tag{29}\]  
where \(B_{r}\) is the remanence of every IPM, \(D_{j}\) and \(L_{j}\) respectively denote the diameter and length of every IPM. Similar to the coefficient of correction \(K_{i}\), coefficient of correction \(k_{j}\) are utilized to eliminate the effect between different IPMs, which can be obtained by finite-element simulation.  
The direction of magnetic moment \(r_{j}\) can be solve by the following equation:  
\[r_{j}=R_{j}^{r}\bullet(0,0,1)^{T} \tag{30}\]  
The transformation equation from the rotor coordinate to the global coordinate can be expressed as:  
\[P=R^{r}P_{r}+T^{r} \tag{31}\]  
In the above equation, \(R^{r}\) is the rotation matrix from global coordinate \(CS_{0}\) to the rotor coordinate \(CS_{r}\), and \(T^{r}\) represents the translation matrix from global coordinate \(CS_{0}\) to the rotor coordinate \(CS_{i}\). Combining Equations (24) and (30), one can obtain:  
\[P_{i}=R_{i}(R^{r}P_{r}+T^{r})+T_{i} \tag{32}\]  
and:  
\[M_{j}^{i}=R_{j}R^{r}M_{j}^{r} \tag{33}\]  
Hence, \(M\) and \(B\) can be expressed in the same coordinate. \(M_{j}^{i}\) is the magnetic moment of the IPM \(j\) in coil coordinate \(CS_{i}\), and \(M_{j}^{r}\) is the magnetic moment of permanent \(j\) in its rotor coordinate.
## 5 Advanced MPPT Techniques
### Smart MPPT Techniques  
Smart MPPT techniques offer significant advantages in terms of accuracy, adaptability, and efficiency, making them increasingly important for optimizing PV systems in increasingly complex and variable conditions.
## 4 Choice of Polymeric Carriers
### Chitosan  
Chitosan can also be used as a formulation carrier for plant-beneficial microorganisms [40]. Chitosan is made up of long chains of N-acetylglucosamine (GlcNAc) units. It also includes N-glucosamine (GlcNAc) units, which are more abundant than GlcNAc [41]. This oligosaccharides the same characteristics of biodegradability, nontoxicity, ease of handling, and low cost with alginate [42]. Chitosan can also induce the production of osmoregulatory in plants and exhibit significant antimicrobial activities [7]. The major drawback of using chitosan as a carrier for the encapsulation of microbes is its limited mechanical resistance and its low chain flexibility [43]. Its combination with other polymers, such as alginate and starch, can improve these characteristics by strengthening its structure and physicochemical stability [44]. In a recent study, chitosan at different concentrations (0.3-3%) was used in combination with 2% alginate for the encapsulation of _Methylobacterium oryzae_, with a formulation-maintained viability of 10\({}^{7}\) CFU/mL and 80% survival after 3 months of storage. The application showed better promotion of tomato seedling growth compared with formulations of alginate alone [40]. Another research team successfully encapsulated _Azospirillum brasilense_ and _Pseudomonas fluorescens_ in a formulation of chitosan (3%) and starch (8%). The formulation ensured the viability of 10\({}^{9}\) CFU/g for _A. brasilense_ and 10\({}^{8}\) CFU/g for _P. fluorescens_ after 12 months of storage at room temperature [45].
## 3 Numerical Scheme
### Energy Analysis
#### 3.5.1 Vertical Polarisation  
For the vertical polarisation, multiplying Equation (47) by \(h\left(\delta_{t}.\mathbf{w}^{n}\right)^{\mathsf{T}}\) and Equation (53a) by \(\left(\delta_{t}.\mathbf{w}_{FB}^{n}\right)^{\mathsf{T}}\) gives the power balance:  
\[\delta_{t-}\mathrm{H}_{w}^{n}=\mathrm{P}_{w}^{n}-\mathrm{Q}_{w}^{n} \tag{58}\]  
The numerical energy \(\mathrm{H}_{w}^{n}\) is defined as:  
\[\mathrm{H}_{w}^{n} =\mathrm{H}_{w,s}^{n}+\mathrm{H}_{\Phi}^{n} \tag{59a}\] \[\mathrm{H}_{w,s}^{n} =\frac{\rho h}{2}|\delta_{t+}\mathbf{w}^{n}|^{2}+\frac{Th}{2}( \mathbf{D}_{x-}\mathbf{w}^{n})^{\mathsf{T}}\mathbf{D}_{x-}\mathbf{w}^{n+1}\ \ +\frac{EI_{0}h}{2}(\mathbf{D}_{xx}\mathbf{w}^{n})^{ \mathsf{T}}\mathbf{D}_{xx}\mathbf{w}^{n+1}\] \[-\frac{\lambda_{2}\rho kh}{4}|\delta_{t+}\mathbf{D}_{x-}\mathbf{w }^{n}|^{2}\] (59b) \[\mathrm{H}_{\Phi}^{n} =\mathbf{h}^{\mathsf{T}}\mu_{t+}\Phi^{n}+\frac{1}{2}\left( \mathbf{M}_{FB}\delta_{t+}\mathbf{w}_{FB}^{n}\right)^{\mathsf{T}}\delta_{t+} \mathbf{w}_{FB}^{n} \tag{59c}\]  
where \(\mathbf{h}=[\ldots h\ldots|1|1]^{\mathsf{T}}\).  
The power \(\mathrm{P}_{w}^{n}\) supplied or withdrawn through excitation is:  
\[\mathrm{P}_{w}^{n}=\left(\delta_{t}.\mathbf{w}_{FB}^{n}\right)^{\mathsf{T}} \mathbf{f}_{\mathrm{ext}\,w,FB}^{n}-h\left(\left(\mu_{t}.\mathbf{w}^{n}\right) ^{\mathsf{T}}\left(\delta_{t}.\mathbf{j}^{n}\right)\right)\mathbf{f}_{ \mathbf{w}}^{n} \tag{60}\]  
The power \(\mathrm{Q}_{w}^{n}\geqslant 0\) dissipated through damping is:  
\[\mathrm{Q}_{w}^{n} =\mathrm{Q}_{w,s}^{n}+\mathrm{Q}_{\Psi}^{n} \tag{61a}\] \[\mathrm{Q}_{w,s}^{n} =\lambda_{1}\rho h|\delta_{t}.\mathbf{w}^{n}|^{2}+\lambda_{2} \rho h|\delta_{t}.\mathbf{D}_{x-}\mathbf{w}^{n}|^{2}\] (61b) \[\mathrm{Q}_{\Psi}^{n} =\left(\mathbf{h}\odot\delta_{t}.\mathbf{\Delta}^{n}\right)^{ \mathsf{T}}\left(\left(\delta_{t}.\mathbf{\Delta}^{n}\right)\odot\mathbf{\Psi }^{n}\right) \tag{61c}\]  
In the absence of external excitation, the numerical energy \(\mathrm{H}_{w}^{n}\) is strictly decreasing.
# Initial Student Attention-Allocation and Flight-Performance Improvements Based on Eye-Movement Data
## 3 Results
### Correlation between Flight-Performance Indicators and Eye-Movement Indicators  
Fifteen student participants in the experiment were randomly selected for a Spearman correlation analysis. The goal of the analysis was to examine the relationship between the average fixation duration for each AOI and the flight performance. The Spearman correlation coefficient was used to determine the strengths of the correlations. The results, as shown in Figure 9, indicate that a change in the stall duration was positively correlated with the horizontal status indicator. Additionally, speed losses were positively correlated with the airspeed indicator, while altitude losses were positively correlated with both the vertical speedometer and the altimeter. Furthermore, there was a correlation between the mean course deviation and one item, the horizontal status indicator, as depicted in Figure 9.  
In terms of the correlations between the flight performance and the eye-movement indicators, the correlation coefficient between changes in the stall duration and the horizontal status indicator was 0.609, which indicates a positive correlation. The correlation coefficient between speed losses and the airspeed indicator was 0.615, which indicates a negative correlation. Furthermore, the correlation coefficients between altitude losses and the vertical speedometer and between altitude losses and the altimeter were \(-0.667\) and \(-0.521\), respectively, which both indicate negative correlations. Finally, the correlation coefficient between the mean course deviation and the horizontal status indicator was \(-0.700\), which also indicates a negative correlation. These results suggest that gazing longer at the horizontal status indicator was associated with a longer stalling duration and a smaller course deviation. Similarly, longer fixations on the airspeed indicator were associated with smaller speed losses. Additionally, longer fixations on the vertical speedometer and altimeter were associated with smaller altitude losses. These findings align with the conclusions of [PERSON] [39], although the sample size used in that study was small, thereby leading to non-significant correlations between performance indicators and eye-movement indicators. The current study used a larger sample size of 38 subjects and employed immediate post-course training; therefore, the training effect was more significant and was consistent with the research results of [PERSON] [40]. Furthermore, this research demonstrates that timely performance feedback can help pilots to better understand flight tasks.  
Figure 8: Comparison of entropy of fixation between experimental group and control group (each point represents one individual data, *** \(p<0.00\)).
# Using Machine Learning for Quantum Annealing Accuracy Prediction
## 2 Methods  
We aim to predict both if an instance of MC is solvable by D-Wave 2000Q to optimality, as well as the size of the clique which will be found by the annealer.  
We first generate a set of 47000 Erdos-Renyi random graphs. For each graph, we first choose its number of vertices \(|V|\) uniformly at random in \(\{20,\ldots,65\}\), and its density \(p\) uniformly at random in \([0.01,0.99]\). We then insert any of the \(|V|(|V|+1)/2\) pairwise edges after an independent coin flip with probability \(p\). Using these 47000 Erdos-Renyi random graphs, we generate a dataset of 47000 MC instances by applying the QUBO formulation of Equation (1). We use 90% of those 47000 MC instances for training, the remaining 10% are used for validation. The upper bound of 65 vertices is determined by the largest size of a complete graph embeddable on D-Wave's Chimera architecture. We specifically do not use any graphs with zero degree nodes.  
Next, we compute an embedding \(E\) of a complete 65 node graph onto the Chimera architecture with the help of the _minorniner_ module of the D-Wave Ocean API [17]. Since any graph with at most 65 nodes is a subgraph of the complete 65 node graph, we can re-use the embedding \(E\) to map any problem instance of up to 65 vertices onto the D-Wave 2000Q annealer. Different embeddings typically use different hardware qubits, and map logical qubits to chains of different lengths, both of which can alter the performance of the D-Wave 2000Q. In order to be able to compare and interpret results across different graphs, we thus keep the embedding fixed in all experiments. Moreover, the D-Wave API provides a tool to compute the chain strengths for an embedding, called _uniform torque compensation_[18]. We always compute all chain strengths using this feature. For each graph on which MC is to be solved, we request 1000 anneals from D-Wave 2000Q after embedding. The best D-Wave result (i.e., the largest clique found) among those 1000 anneals is saved for each graph after unembedding with the majority vote option (see Section 1).
# Research on Slope Stability Based on Bayesian Gaussian Mixture Model and Random Reduction Method
###### Abstract  
Slope stability analysis is conventionally performed using the strength reduction method with the proportional reduction in shear strength parameters. However, during actual slope failure processes, the attenuation characteristics of rock mass cohesion (\(c\)) and internal friction angle (\(\varphi\)) are often inconsistent, and their reduction paths exhibit clear nonlinearity. Relying solely on proportional reduction paths to calculate safety factors may therefore lack scientific rigor and fail to reflect true slope behavior. To address this limitation, this study proposes a novel approach that considers the non-proportional reduction of \(c\) and \(\varphi\), without dependence on predefined reduction paths. The method begins with an analysis of slope stability states based on energy dissipation theory. A Bayesian Gaussian Mixture Model (BGMM) is employed for intelligent interpretation of the dissipated energy data, and, combined with energy mutation theory, is used to identify instability states under various reduction parameter combinations. To compute the safety factor, the concept of a \"reference slope\" is introduced. This reference slope represents the state at which the slope reaches limit equilibrium under strength reduction. The safety factor is then defined as the ratio of the shear strength of the target analyzed slope to that of the reference slope, providing a physically meaningful and interpretable safety index. Compared with traditional proportional reduction methods, the proposed approach offers more accurate estimation of safety factors, demonstrates superior sensitivity in identifying critical slopes, and significantly improves the reliability and precision of slope stability assessments. These advantages contribute to enhanced safety management and risk control in slope engineering practice.  
slope stability; dissipated energy; non-proportional reduction; machine learning 2021065840.cn
# A Review on Local Failure Probability Sensitivity Analysis
## 4 Local RSA in the Standard Normal Space
### Sensitivity with Finite Difference Schemes
#### 4.4.2 Statistical Properties of the Finite Difference Scheme Estimates  
Both approaches are biased since \(h\
eq 0\). The expression of the bias and variance of both approaches, for both finite difference schemes can be found in [50]. The bias \(e\) of both approaches is proportional to \(h\) with the forward formula and \(h^{2}\) with the central formula. The variance of the Direct approach is proportional to \(1/Nh^{2}\). Whereas the variance of the CRV approach is proportional to \(1/Nh\). Consequently, the CRV is theoretically superior to the Direct approach. Furthermore, among the two formulas, the central formula is the best theoretically as the bias converges more rapidly, for small \(h\). The evolution of the mean square error as a function of \(h\) can be separated into two stages for both methods: the variance stage for small values of \(h\) and the bias stage for greater values of \(h\), as presented for the Weak approach in Section 4.3.2.  
An optimal value of \(h\) is displayed in [20] for the central formula in the CKV approach. This value is proportional to \(1/N^{1/5}\) and depends on the quantity of interest \(\partial P_{f}(\mathbf{s})/\partial s_{\ell}\) as well as the derivative of order 3 of \(P_{f}\) with respect to \(s_{\ell}\); thus, its computation is intractable in practice. With this optimal \(h\), the value of the mean square error is computed and \(\widetilde{E}_{\mathrm{max}}^{\mathrm{MC}}(\widetilde{\sigma})\propto 1/N^{4/5}\), which is the same convergence rate than the Weak approach with the optimal \(\widetilde{\sigma}\). The same conclusion can be drawn: this convergence rate is slower than the error convergence rate of the MC estimate obtained with the score function method, which is \(1/N\), see Section 3.2. Consequently, for sensitivities with respect to distribution parameters, the score function method is more efficient.
## 5 Case Studies
### Validation of Inertia Evaluation Method for Wind Farms  
After identifying the controller parameters and obtaining the wind farm's basic parameters, the equivalent virtual inertia of the DFIG wind farm following a frequency disturbance can be calculated using Equation (11). The proposed method for evaluating the wind farm's inertia is then compared with the simulation results. The equivalent inertia time constant is obtained by extracting the ratio \(\Delta\omega_{r}/\Delta\omega_{s}\) and substituting it into Equation (7). Figure 6 demonstrates a comparative validation between the identified evaluation results and the actual simulated values under two distinct control strategies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Actual Values** & **Identified Values** & **Error/\%** \\ \hline \(H_{wind}\) & 5.04 & 4.95 & 1.79 \\ \hline \(k_{p1}\) & 6 & 6.144 & 2.4 \\ \hline \(k_{i1}\) & 2 & 2.073 & 3.65 \\ \hline \(k_{p}\) & 15 & 14.268 & 4.88 \\ \hline \(k_{d2}\) & 10 & 10.587 & 5.87 \\ \hline \(T_{f}\) & 0.5 & 0.516 & 3.20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Partial parameter identification results with integrated inertia control.  
Figure 6: Simulated and evaluated inertia values at different inertia control.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Actual Values** & **Identified Values** & **Error/\%** \\ \hline \(H_{wind}\) & 5.04 & 4.99 & \(-\)0.99 \\ \hline \(k_{p1}\) & 6 & 6.17 & 2.83 \\ \hline \(k_{i1}\) & 2 & 2.01 & 0.5 \\ \hline \(k_{d}\) & 10 & 10.561 & 5.61 \\ \hline \(T_{f}\) & 0.5 & 0.514 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Partial parameter identification results with virtual inertia control.  
From the above figure, it can be observed that:  
1. The virtual inertia values derived from the proposed methodology closely align with the simulation outcomes, thereby confirming its precision and efficacy. Following a frequency disturbance, the inertia demonstrated by the wind farm equipped with inertia control is characterized by temporal variability, with the integrated inertia control exhibiting greater inertia support compared to the virtual inertia control;
2. In response to disturbances, the inertia control mechanism of the wind farm promptly discharges rotor kinetic energy to address the power shortfall. This results in a rapid increase in the equivalent virtual inertia, which effectively mitigates the rate of change in grid frequency. As the frequency reaches a state of stability, the virtual inertia gradually diminishes to zero, thereby concluding the inertia response.