esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Open domain relation prediction is an important task in triples extraction. When faced with the task of constructing large-scale knowledge graph systems, with the exception of structured data, it is necessary to automatically extract triples from a large amount of unstructured text to expand entities and relations. Although a large number of English open relation prediction methods have achieved good performance, the high-performance system for open domain Chinese triples extraction remains undeveloped due to the lack of large-scale Chinese annotation corpora and the difficulty of Chinese language processing. In this paper, we propose an integrated open domain Chinese triples hierarchical extraction method (CTHE) to solve this problem, considering the advantages of Bi-LSTM-CRF and Att-Bi-GRU models based on the pre-trained BERT encoding model. This method can recognize the named entities from Chinese sentences to establish entity pairs, and implement hierarchical extraction of specific and open relations based on the user-defined schema library and attention mechanism. The experimental results demonstrate the effectiveness of this method, which achieved stable performance on the test dataset, and better precision and F1-score in comparison with state-of-the-art Chinese open domain triples extraction methods. Furthermore, a large-scale annotated dataset for a Chinese named entity recognition (NER) task is established, which provides support for research on Chinese NER tasks.  
named entity recognition; open relation prediction; information extraction; CTHE 2020, 10, 4819; doi:10.3390/app10144819
## 2 Material and Methods
### ASI-AMSR2 Sea-Ice Concentration  
The Advanced Microwave Scanning Radiometer 2 (AMSR2) aboard the Japan Aerospace Exploration Agency's (JAXA's) GCM-W1 measures horizontally and vertically polarised electromagnetic radiation in seven microwave frequency bands between 6.9 GHz and 89 GHz. The polarisation difference at 89 GHz is used by the Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice algorithm (ASI). The ASI-AMSR2 SIC is provided as a daily gridded dataset at 6.25 km and 3.125 km grid spacing at [[https://seaice.uni-bremen.de](https://seaice.uni-bremen.de)]([https://seaice.uni-bremen.de](https://seaice.uni-bremen.de)) (last access 12 September 2020). For a better temporal consistency with the MODIS data, we use swath data processed internally. For the merged dataset, they are interpolated to the same projection and grid spacing as the MODIS data. For the intercomparison with the Sentinel-2 SIC, they are resampled to a Transverse Mercator projection for consistency with the Sentinel-2 data.
# Study of Orientation Error on Robot End Effector and Volumetric Error of Articulated Robot
## 2 Orientation Test of Robot End Effector
### Orientation Test of Roll Motion in the Robot end Effector  
Figure 9 shows the equipment setting for the orientation test of the roll motion. The defined positions and orientation of the robot end effector for the circular contouring are the same as the yaw test position. The difference is that the transducer is rotated by 90 degrees from the position of the yaw motion test. The results are shown in Figure 9(a),b in both clockwise and anticlockwise directions. Comparing the two graphs in Figure 9(a),b, the error shapes are almost identical. Therefore, backlash error has no influence on the roll motion error in the robot axes. If the gear transmission error and backlash error of the robot axes were negligible, the error shape would be a straight line, i.e., the error band would be zero degrees. However, the error band has a value of approximately 1.75 degrees which stems mainly from the gear transmission error in the robot axes.  
The simulation result for the same test is shown in Figure 11. The error band is about 1.76 degree. The error curve shows maximum and minimum points around 180 and 45 degrees on the circle. The simulation error shape and band are very similar to the experimental data.  
Figure 10: Experimental data of orientation error in the roll motion: (**a**) clockwise direction and (**b**) anticlockwise direction.  
Figure 9: Equipment setting for roll motion test.
## 4 Implementation and Results
### Fog Computing Operation  
At fog layers, multi-layer feed-forward NN based on the Levenberg-Marquardt algorithm are used to supervise and learn all customers' electricity consumption, and ultimately to make the prediction with regression analysis. The dataset of this test refers to the load data of German BlOND buildings [35]. The NN settings are as follows: 1) the input data include time, time type, temperature, humidity, illumination; 2) the output date is the prediction value of the customers' continuous load; 3) the training set is 70% of the input data, the validation set is 15% of the input data t and the test set is 15% of the data; 4) this NN consists of one hidden layer with 10 neurons; 5) NN training will stop when the generalization ability of the network is not improved. The regression effects are shown as follows.  
As above in Figure 6, the error of root mean square begins to meet the requirements at 34 generations through NN training, and it stops learning after 40 iterations.  
It is generally believed that the regression effect is good if the value is greater than 0.9. The R-value shown as Figure 7 is more than 0.92, thus it is considered that the regression effect is good after training. The normalized utility feature is shown as Figure 8.  
Furthermore, the NN embedded in the fog computing layers can also be used to predict the output power of RES in prosumers. The process is similar to the load prediction through regression analysis. Although we do not predict the output power of RES with NN here. In this test, the daily storage operation for arbitrage and prediction of the RES of prosumers are out of our scope as the prediction of the RES with NN is another research topic. To simplify the test, the data of feed-in RES to the grid from Open Energy Information (OpenEI) [36] are referred to directly, and this is shown in Table 1 of Appendix B.  
Figure 8: Customers’ utility feature in one day.  
Figure 6: NN learning generalization trajectory.  
Figure 7: Regression effect of training/test/validation (set) regression effect.
## 7 Conclusions  
This article puts forth a proposal for an SOP and energy storage optimization configuration method for the snowflake-shaped grid. Firstly, the location of the SOP is determined based on the characteristics of the interconnection nodes between snowflake websites, which are fully utilized in accordance with the snowflake-shaped grid structure. Secondly, the nodes that have a significant impact on the system voltage distribution are selected based on the voltage sensitivity analysis method, which is used to achieve the energy storage site selection. Subsequently, a multi-objective optimization configuration model for SOP and energy storage is established, taking into account the economic efficiency and load balancing of the power grid, in order to achieve a suitable capacity for SOP and energy storage. Finally, the method proposed in this paper is verified and analyzed using a snowflake-shaped grid in Tianjin as an example. The case analysis demonstrates that integrating SOP and energy storage can effectively enhance the power flow distribution between different feeders, balance the load, reinforce the reliability and stability of the power grid, and facilitate the integration of renewable energy in the future. In comparison with the plan that solely considers the economic aspects of the power grid, the methodology proposed in this article has the potential to reduce the degree of load balancing by 50.93% while simultaneously increasing the annual comprehensive cost by only 24.35%. The method proposed in this article can achieve the configuration of SOP and energy storage at the lowest possible economic cost while also optimizing the load balancing situation of the snowflake-shaped power grid to the greatest extent possible, and it has long-term  
Figure 8: SOP reactive power output curve.  
Figure 9: Energy storage charge/discharge curve at node 40.  
economic sustainability. Further research could consider the high proportion of renewable energy integration with a view to further achieving economic and safe operation of the snowflake-shaped grid and fully tapping into its operational potential.  
Conceptualization, Z.W. and Z.Z.; methodology, X.Z. and J.D.; software, X.Q.; validation, F.L.; formal analysis, Z.W.; investigation, Z.Z.; resources, Z.W., Z.Z., F.L., and X.Q.; data curation, X.Q.; writing--original draft preparation, X.Q.; writing--review and editing, F.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Projects of State Grid Tianjin Electric Power Company (No. SCTJ)Y00 GHJS2400036).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
Authors [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company State Grid Tianjin Electric Power Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Enhanced Mathematical Model with Atmospheric and Environmental Effects
### Two-Ray Model Overview  
The two-ray model considers both the direct line-of-sight (LOS) signal and a reflected non-line-of-sight (NLOS) signal (as shown in Figure 1). This model is particularly significant in analysing and predicting the behaviour of radio wave propagation in scenarios where both direct and reflected signals contribute to the received signal [12, 13, 14]. In such scenarios, especially in environments with large reflective surfaces like oceans or flat terrain, the two-ray model becomes crucial for accurately modelling signal strength, fading, and phase shift, which are all important factors for reliable communication between the ground station and the low-Earth-orbit (LEO) satellite [23].  
In the two-ray geometry model, the signal from a transmitter (e.g., a ground station) to a receiver (e.g., a satellite) reaches the receiver through two distinct paths:  
1. **Direct path** (\(r_{t}\)): This is the line-of-sight (LOS) path that directly connects the ground station to the satellite.
2. **Ground station reflected path** (\(r_{1,t}\)): This path involves the signal reflecting off the Earth's surface before reaching the ground station.
3. **Satellite reflected path** (\(r_{2,t}\)): This path involves the signal reflecting off the Earth's surface before reaching the satellite.  
The constructive or destructive interference of these components is determined by the phase difference between them, resulting in either signal augmentation or fading.  
Figure 1: Two-ray geometry for 5G–satellite network.
## 2 Materials and Methods
### EOs Characterization  
#### 2.2.1 Samples and Equipment  
To determine the total phenolic content, TPC, the following materials, equipment, reagents, and solvents were used:  
* Materials: EOs from eucalyptus, rosemary, and thymus leaves;
* Equipment: UV-Vis Spectrophotometer, Thermo Electron Corporation, Type Helios Alpha, Part No 9423 UVA 1002E, Ser No UVA 150211, made in England; Vortex, Scientific Industries Inc., New York, USA, Model No G560E; Double boiler, Thermo electron, Type003-2859 1200700108006, made in Germany;
* Reagents and solutions: saturated solution of sodium carbonate; gallic acid monohydrate, 98+%; methanol, 99.8+%; [PERSON] and Ciocalteu's phenol reagent, 2M, SIGMA, 47641 500 mL, Lot No BCBN0326V; N-Hexane, 99%, MERCK, Lot No 298067; deionized water.
# Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications
## 4 Problem Formulation
Figures 3 and 4 show the distance error calculated using the above model and simulated with the considered platoon controller for a single disturbance (\(z_{3}(0)=2\) m/s\({}^{2}\)) and for random disturbances over time, respectively. The results clearly indicate the correctness of the derived model (i.e., both the curves for the simulation and the theoretical model overlap ideally). Figure 3: Distance error between leader and the first follower for a single disturbance. The derivation can be continued in a similar way to obtain the distance error representations for other vehicle pairs. However, one should note that here, the relations will be more complicated, as these will account for the information received from different sources (the platoon leader and the preceding car). Hence, the impact of leader acceleration disturbance on the distance error will depend on the vehicle position in the platoon, as well as on the individual latencies when passing the information between the vehicles. One should also note that (10) assumes a fixed and equal delay when transmitting the intra-platoon messages, which might not be the case in a real scenario. Furthermore, the problem defined in (7) is a mixed-integer programming problem, where the constraint (8) relies on indicator function values (\(I[\epsilon_{i}(t)>d_{d}-d_{Th}]\)), which are discrete (in fact, Boolean). Hence, the optimal solution cannot be easily found, even when applying relaxation techniques. Therefore, in the next section, we propose a heuristic approach that is based on the iterative prediction of the distance error.
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 4 Applications of RL in FANET
### Flight Trajectory Selection
#### 4.2.4 Multi-UAV Deployment and Movement Design  
Multiple UAVs are deployed in a 3D space to serve mobile users in [59]. The \(Q\)-learning algorithm is used to solve the NP-hard problem [60] of 3D deployment and movement toward the users considering users' mobility. The main goal is to maximize the sum mean opinion score (MOS) of the users while maintaining the QoE.  
[PERSON] et al. [59] proposed a three-step solution in which they used the \(k\)-means algorithm to cluster the users, and then trained the UAV agents using a \(Q\)-learning algorithm to find its optimal 3D positioning with respect to the mobile users. Finally, they also used a \(Q\)-learning algorithm to determine the flying trajectory toward the moving users. However, there is a huge scope for implementing deep \(Q\)-learning to overcome constraints such as intercluster users' mobility and UAVs flying in all possible directions. [PERSON] et al. also adopted a similar kind of approach to maintain QoS in [61]. However, a double \(Q\)-learning approach was used instead of simple \(Q\)-learning in [62] for similar 3D scenarios and achieved a 14.1% gain in user satisfaction compared to simple \(Q\)-learning.
# Entanglement Distillation Optimization Using Fuzzy Relations for Quantum State Tomography
## 3 Chaotic Dual Annealing Optimization
Solve \(P(\delta)\) using dual annealing algorithm to find optimal quantum state, \(\rho_{A^{\prime}B^{\prime}}^{T}\) and Choi states: \(\hat{C}_{1,A^{\prime}}\) and \(\hat{C}_{1,B^{\prime}}\). 4. Initiate and run CML simulation. ```  
**Algorithm 1**Combined Coupled Map Lattice and Dual Annealing Optimization Approach```  
**Algorithm 1**Cont. The bilevel optimization problem presented in Equation (3) is solved within a Stackelberg game-theoretic framework [34]. The fidelity objective function, \(F\) is the upper level/leader, while the probability of success, \(P(\delta)\) is the lower level/follower. The leader's strategy is to optimize the objective of the sub-problem (\(P(\delta)\). This influences the follower's strategy, optimizing the objective (\(F\)). The numerical framework iteratively solves every level of the optimization problem as a Stackelberg game reaches the optimal solution. In this work, the entanglement distillation problem was solved using: Combined Coupled Map Lattice and Dual Annealing (CML-DA) technique and dual annealing method using pseudo-random number generators (PRNG-DA). The sub-problem problem is solved using the DA-PRNG approach by searching for the optimal dimension, \(d\) that maximizes \(P(\delta)\). The quantum state, \(\rho^{T}_{A^{\prime}B^{\prime}}\), and the Choi states, \(\hat{C}_{1,A^{\prime}}\) and \(\hat{C}_{1,B^{\prime}}\) are generated using the PRNG. Consequently, using the obtained dimension \(d\), and the density states, \(\rho^{T}_{A^{\prime}B^{\prime}}\) and the probability of success, \(P(\delta)\), the upper-level problem is solved by searching for the best Choi states, \(\hat{C}_{1,\hat{A}^{\prime}A^{\prime}}\) and \(\hat{C}_{1,\hat{B}B^{\prime}}\) using the PRNG. As for the proposed CML-DA technique, the Stackelberg framework is employed, and each level of the problem is solved iteratively. In the CML-DA approach, the quantum state of the qubit before conversion (\(\rho^{T}_{A^{\prime}B^{\prime}}\)) is estimated using the CML and PRNG techniques. The parameter settings employed for the numerical experiments in this work, for both the CML and DA techniques, are given in Tables 1 and 2: \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Initial Temperature, \(T(1)\) & \(15\) \\ Restart temperature ratio & \(0.0002\) \\ Shape controlling parameter, \(q\) & \(2.62\) \\ Acceptance parameter, \(q^{\prime}\) & \(-5\) \\ Maximum iterations & \(100\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter settings for the DA technique.
## 6 Results and Discussion  
### Comparative Performance Analysis  
Table 7 presents the classification metrics for both simulated and experimental PD signals across six time-frequency analysis methods. The key findings reveal the following:  
* The superiority of the Scatter Wavelet Transform (SWT) : \[\text{SWT}_{\text{Exp}}\rightarrow\text{Accuracy}=100\%,\quad\text{F1}=1.000\]  
achieved perfect classification with ResNet50, confirming its robustness in handling real-world noise and multi-source PD patterns.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Optimizer & Adam \\ Initial learning rate & \(5\times 10^{-5}\) \\ Batch size & 64 \\ Epochs & 80 \\ Learning rate schedule & Step decay (factor = 0.1 every 20 epochs) \\ Weight initialization & ImageNet pretrained \\ Data augmentation & Random horizontal flip \\ \hline \hline \end{tabular}
\end{table}
Table 6: Training hyperparameters.  
Figure 39: Convolutional neural network (CNN) detailed architecture.  
* The consistency of the Wigner-Ville Distribution (WVD): \[\Delta\text{Accuracy}_{\text{Sim}\rightarrow\text{Exp}}=1.41\%\quad(93.33\% \to 94.74\%)\] demonstrated stable performance, though its quadratic computation introduces trade-offs between resolution and cross-term artifacts.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 5 Proof of Concept
### Scenario II--External Link Failure
Once this occurred, the traffic automatically shifted from path [200,400] to  
Figure 11: An execution of link failure in the 15 th second. Figure 12: Details of network traffic for the link failure around the 15 th second. Figure 10: The average of multiple measurements for the network traffic during a link failure around the 15 th second with a confidence interval of 95%. path [300,400]. This process was done exclusively in the data plane (OpenFlow switches) and took less than ten milliseconds, which is an acceptable value for disruption in an inter-domain environment (less than 50 ms [34]). The measured millisecond interval was coherent with the FF mechanism recovery times found in the literature [35,36]. The main result of this scenario was that, by exploring the valid and available multiple paths of BGP, the proposed architecture avoided the application of the BGP decision process for selecting a new best next hop when an external link failed. The BGP convergence is a time consuming process (dozens or hundreds of seconds [37,38]) when deciding a new route, and with the application of the proposed architecture mechanisms, if it exists, a valid next hop will then always be already computed and installed in the data plane, and then the domain can immediately use the spare route. Therefore, the proposed architecture can bring the unavailability of paths to the milliseconds time after an external link failure, instead of being subject to the long BGP convergence time.
## 3 Results and Discussion
### Physical Properties of the Biochars
The drop size was 2 \(\mu\)L. Notice that initial contact angle was determined at initial time. biochars, as seen from the initial slopes in Figure 3. Greater hydrophobicity coincided with higher average stove temperatures for the bamboo biochars. For pinewood biochars, the stove temperatures were not significantly different. The trend in the hydrophobicity of the bamboo biochars mirrored the results in A-BET (Table 2), where BB3 showed the lowest A-BET as well as lowest hydrophobicity. For the pinewood biochars, A-BET values were very similar. The trend in the bamboo biochars marks a contrast to previous statements relating low specific surface area values to the presence of aliphatic functional groups and greater hydrophobicity [51; 52]. Our results for the bamboo chars show that A-BET values do not necessarily hold a direct relationship to hydrophobicity, but can actually be inversely related. [PERSON] et al. [22] considered hydrophobicity induced by biochar in soils to be most significant in the first years after application, due to 'fresh' biochar comprising a great amount of hydrophobic surfaces. However, hydrophobicity is also affected by relative humidity, surface roughness and surface tension of liquid water, which can change with the osmotic force; these are factors that can contribute to reducing hydrophobicity of a biochar in a soil. Besides, the persistence of water repellency is time dependent and can reach the subcritical range in short times, causing the surface to be easily wetted. Moreover, exposure to water during analytical tests is short and does not reflect genuine environmental conditions as exposure time, surface tension, dissolved ions, dissolved organic matter, suspended particles as clays and other physical aspects. For instance, capillary water content in the soil and soil amendments can move into the macroporosity space.