esa-sceva/satcom-chunk-collection huggingface dataset:
# Named Entity Recognition Method Based on Multi-Feature Fusion
## 4 Experimental Results
### Named Entity Recognition
#### 4.3.4 Results of NER  
To verify the effectiveness of the model, the proposed model is compared with other existing mainstream NER models. The comparison models are divided into the following three types:  
1. Existing mainstream NER model: BERT-BiLSTM-CRF model. This model is one of the commonly used models in named entity recognition tasks. It has been widely used in many fields and has achieved good results.
2. Models that splice BiLSTM and DGCNN in the feature extraction layer: BERT-BiLSTM-DGCNN-MHA-CRF and BERT-DGCNN-BiLSTM-MHA-CRF. Unlike the proposed model, these two models use the method of splicing BiLSTM and DGCNN in the feature extraction layer. Comparing the proposed model with these two models can prove the effectiveness and scientificity of the fusion of BiLSTM and DGCNN in the feature extraction layer.
3. Other pre-trained models: To explore the impact of pre-trained models on experimental results, this study compares the different effects of different pre-trained models. RoBERTa-wmm and ELECTRA will be used.  
The performance of the different models on the task of Named Entity Recognition (NER) for online reviews of tourist attractions is shown in Table 4. As observed from Table 4, the BERT-BiLSTM-CRF model, a mainstream NER model, achieved a baseline performance of 69.7527% on this dataset. Although it attained a relatively high precision of 81.518%, the low recall dragged down the overall performance. After introducing DGCNN and MHA, a significant improvement in recall was achieved, boosting the overall performance to 85.1132%. In order to explore the different impacts of the different extraction orders of BiLSTM and DGCNN, the third comparison model was selected (BERT-DGCNN-BiLSTM-MHA-CRF. Compared with the effect of BiLSTM first and then DGCNN, the recall rate of this model is compared with the baseline model. The improvement shows that DGCNN plays an important role in the named entity recognition task of online reviews of scenic spots. Regardless of the order, introducing DGCNN yielded a significant improvement over the baseline, though the performance did not surpass the BiLSTM-first configuration, with the overall performance reaching 80.0807%. To verify the impact of different pre-trained models on the experiment, ELECTRA and RoBERTa-wmm were in  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline dropout & 0.5 \\ learning rate & 0.0001 \\ batch size & 32 \\ epoch & 64 \\ optimizer & Adam \\ \hline \end{tabular}
\end{table}
Table 3: Parameter settings.  
troduced for comparison with BERT used in this study. Under identical conditions, the model in this study outperformed these two pre-trained models by 0.1276% and 1.6694%, respectively, achieving an F1 score of 86.8383%. These results demonstrate that the model in this study performs well in NER tasks for online review texts of tourist attractions.
## 3 Experimental Verification and Discussion
### Discussion  
The present study explores the optimization of valve effective opening, with the objective of attaining thermodynamic targets. This is achieved by maintaining maximum acceleration in the opening region. However, this approach resulted in separation between the cam lobe and the follower. To address this issue, both the spring tension and lift acceleration was modified. The Version 1 design features no-contact points, where the force between the camshaft and the follower is zero, shown in Figure 10. The Version 2 design indicates that the contact between the cam lobe and the follower is maintained during the lift phase, and concurrently, the amount of force fluctuation is diminished, signifying that the system is more stable.  
The reduction in force variations provides a more uniform load on the cam lobe and follower, which reduces Hertzian pressure. Since this force is transferred directly from the crankshaft gear to the camshaft gear, the power demand on the crankshaft is reduced thus increasing the power output of the engine. The elimination of sudden force fluctuations ensures that the valve lift functions with complete accuracy.  
Although increased spring forces enabled greater maximum acceleration, this method introduced challenges in manufacturability. The desired curve in the opening and closing regions was achieved by staying within the maximum allowable concavity limits of the machining equipment. However, high acceleration values during the closing phase, particularly at high engine speeds, resulted in valve bounce issue optimized result is shown in  
Figure 10: Force between the cam lobe and the follower.  
Figure 9: Vibration measurement points on engine and dynamic model: (**a**) Contact at midpoint with valve closed. (**b**) Centered position with the valve at half the maximum opening. (**c**) Centered position at the moment of maximum force on the valve.  
Figure 11. In the closing phase 2%, the bouncing is solved. This was mitigated by increasing spring forces, though there was a limit to this solution due to the torque requirements of the camshaft. However, excessive spring forces diverted a substantial portion of the engine's torque to the camshaft, a result that was sub-optimal.  
To address the tilting motion of the valve bridge, which led to uneven opening between the two valves, adjustments were made to the position of the rocker arm. Statically balancing the rocker arm at its midpoint was determined to minimize the tilting motion, ensuring more uniform valve operation.  
As shown in Figure 12, the simulation results of the Version 1 design exhibited two distinct peak behaviors, characterized by the initiation of closure and subsequent reopening. The enhanced positioning of the elephant foot in the Version 2 design facilitated the desired behavior of the valve, which began to close after the lift reached its maximum value.
## 2 Materials and Methods
### Data Sources  
The foundational data used in this study includes: (1) Land Use Data, covering Liaoning Province with a spatial resolution of 30 m, obtained from the Chinese Academy of Sciences Resource and Environment Data Center ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn)), accessed on 1 June 2024); (2) Meteorological Data, including precipitation and potential evapotranspiration for Liaoning Province, sourced from the National Qinghai-Tibet Plateau Science Data Center ([[https://data.tpdc.ac.cn](https://data.tpdc.ac.cn)]([https://data.tpdc.ac.cn](https://data.tpdc.ac.cn)), accessed on 6 June 2024); (3) Soil Database: Soil data for Liaoning Province are derived from the Chinese soil dataset based on the World Soil Database (HWSD); (4) GDP Raster Data: Liaoning Province GDP data are obtained from the Chinese Academy of Sciences Resource and Environment Data Center ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn)), accessed on 15 June 2024); (5) Population Density Data, sourced from the WorldPop population density dataset ([[https://www.worldpop.org](https://www.worldpop.org)]([https://www.worldpop.org](https://www.worldpop.org)), accessed on 28 June 2024) with a spatial resolution of 100 m; (6) Statistical Data, sourced from the Liaoning Provincial Statistical Yearbook ([[https://tj.ln.gov.cn](https://tj.ln.gov.cn)]([https://tj.ln.gov.cn](https://tj.ln.gov.cn)), accessed on 1 July 2024) and The Liaoning Province Water Resources Bulletin ([[https://slt.ln.gov.cn](https://slt.ln.gov.cn)]([https://slt.ln.gov.cn](https://slt.ln.gov.cn)), accessed on 10 July 2024); (7) NDVI Data, calculated from the 16-day composite product of the MODIS13Q1 dataset provided by NASA, with a spatial resolution of 250 m; (8) Digital Elevation Model (DEM) Data: DEM data for Liaoning Province are obtained from the Geospatial Data Cloud ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn)), accessed on 15 July 2024). All data are from the year 2020, and the spatial resolution has been uniformly adjusted to 100 \(\times\) 100 m for analysis. The research analysis flowchart is as follows (Figure 2):  
Figure 1: Study Area Overview Map (**a**) Liaoning Province, China; (**b**) Ecological Restoration Zoning of Liaoning Province; (**c**) land use of Liaoning Province.
# Decoupled MPC Power Balancing Strategy for Coupled Inductor Flying Capacitor DC-DC Converter
## 2 Basic Principle Analysis of Coupled Inductor FCDC
In Figure 2k, \(u_{\text{i}}\) charges \(C_{\text{fb}}\) and the energy in \(C_{\text{fa}}\) is released to the \(u_{\text{o}}\) side. In Figure 2l, \(C_{\text{fa}}\) discharges while \(u_{\text{i}}\) supplies power to \(u_{\text{o}}\) through the upper bridge arm of phase b. \(C_{\text{fa}}\) and \(C_{\text{fb}}\) discharge simultaneously in Figure 2m. In Figure 2n, \(u_{\text{i}}\) supplies power to \(u_{\text{o}}\) simultaneously through the switches on the upper bridge arms of phase a and b. Actually, the converter operating state is determined by the region of the duty ratio. Taking \(0<D<0.5\) as an example, it can be seen from Figure 3 that the coupled inductor FCDC contains a total of eight operating states. According to the analysis method shown in Figure 3, the operating states of coupled inductor FCDC with other duty ratios are shown in Table 1.  
**Power imbalance issue:** According to Figure 1, when the energy flows from the left side to the right side and the switches S\({}_{\text{a1}}\) and S\({}_{\text{b1}}\) are conducting simultaneously, the voltage equation of the coupled inductor FCDC can be expressed as:  
\[\left\{ \begin{array}{l}u_{i}=L_{\text{a}}\frac{di_{\text{a}}}{dt}+M\frac{di _{\text{b}}}{dt}+r_{\text{a}}i_{\text{a}}+u_{\text{o}}\\ u_{i}=M\frac{di_{\text{a}}}{dt}+L_{\text{b}}\frac{di_{\text{b}}}{dt}+r_{\text{ b}}i_{\text{b}}+u_{\text{o}}\end{array} \right. \tag{1}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Duty Ratio Range** & **Operating State** \\ \hline \(-1<D<-0.5\) & a, b, c, d, e \\ D = \(-0.5\) & a, c, d, e \\ \(-0.5<D<0\) & a, c, d, e, g, i, k, m \\ D = 0 & g, i, k, m \\ 0 < D < 0.5 & f, g, h, i, j, k, l, m \\ D = 0.5 & f, h, j, l \\ 0.5 \(<D<1\) & f, h, j, l, n \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Operating states of coupled inductor FCDC with different duty ratios. Figure 3: Operating states of coupled inductor FCDC when \(0<D<0.5\). It can be seen that only when \(L_{\rm a}=L_{\rm b}\) and \(r_{\rm a}=r_{\rm b}\) is the two-phase current equal and the power between the two phases balanced. However, manufacturer errors and other reasons lead the parameter differences in the coupled inductor in practical applications, resulting in imbalanced power between phases. It not only leads to a current stress deviation of the switching devices but also easily leads to saturation of the magnetic component. In addition, the mutual inductance leads to the power coupling between the two phases, which increases the difficulty of system power control. Thirdly, the control strategy design based on the converter model should not only consider the operating states shown in Table 1 but should also consider the impact of the coupled inductor on the performance of the control strategy. Therefore, it is necessary to further research the decoupled model and study the decoupled power control strategy.
## 6 Transient Results
### Methane  
As already discussed, in all the considered steady-state conditions, methane was fully converted only at the stoichiometric condition, while an efficiency loss was observed in lean cases, and no conversion occurred in rich ones. On the contrary, as visible in Figure 10, during the dynamic \(\lambda\) transitions, the CH\({}_{4}\) tailpipe concentration displayed a different behavior, TWC shows a high and constant conversion efficiency even in rich conditions. The only exception in Figure 10 was represented by the first spike, but, as already mentioned, the first step could be neglected as TWC pre-conditioning. A similar response was obtained in the experiments at lower engine loads reported in Figures 11 and 12, in which methane concentration profiles at the catalyst outlet appeared nearly unaffected by the inlet concentration dynamics. In order to try to capture the observed TWC behaviors, the reaction mechanism was modified, including some steps involving palladium. In fact, it is known that methane can interact with the oxides of noble metals, such as palladium [18]. For this reason, the following two reactions have been added to the kinetic scheme, identified in the Appendix A with the numbers 23 and 24.  
\[Pd+O_{2}\to PdO_{2}\]  
\[PdO_{2}+CO\to Pd+2 CO_{2}\]  
The link between methane and palladium oxide was created by modifying the expression rate of the methane oxidation reaction (reaction 5 in the Appendix A), introducing an inhibition term \(\Omega\), defined as:  
\[\Omega=\frac{mol\ of\ PdO_{2}}{2\cdot mol\ of\ Pd+mol\ of\ PdO_{2}}. \tag{6}\]  
The kinetic parameters of the added/modified reactions were only calibrated at full engine load, then applied to the other test cases. As visible in Figure 10d, thanks to this modification, the model could reproduce a consistent methane conversion during the AFR sweep, especially in rich phases, despite not being completely abated as shown in the measured profile. At lower engine load values (Figure 11; Figure 12d), the agreement between experimental and predicted methane histories was less satisfying, but it is likely that further calibration work, possibly combined with additional experiments, could significantly improve these results.
### Related Work
In these publications, the possibilities of using single-board microcontrollers and computers (Arduino, Raspberry Pi) and their various operating modes in supporting universal automation functions, along with the possibility of their integration in distributed smart home networks, were verified. It was concluded that the proposed framework extends the existing communication technologies to a new level, introducing edge-based data processing enabling the low-cost computation to the IoT smart networks. The working prototype proposed in [6] is efficient in terms of energy consumption, response time, data processing, and bandwidth use. However, the issue of data security in these types of networks, in particular those with distributed and hybrid architectures, should not be forgotten. Some of them are discussed in [17; 12], where innovative methods of data encryption and network attacks' detection are indicated by the authors, considering technical and power consumption aspects to provide secure data transmission among several integrated sensor and actuator nodes in the IoT smart home network over a long convergence range. There are only a few publications focused on the energy efficiency of smart home systems and their sensors and controllers, both battery-operated as well as main-powered. One of them is in [18], where [PERSON] et al. discuss the energy consumption of building automation for several real constructed buildings equipped with different designs of building automation systems (BAS) and diverse products. Moreover, the analysis of energy consumption considers different types of building infrastructure subsystems (different loads) controlled by actuators and sensors within the BAS system. Therefore, the BAS energy consumption results presented in the paper assume values from 2 to 5 kWh/m\({}^{2}\) per year. The authors point to this relationship, but they provide only a general discussion of the energy consumption levels for smart home/building automation systems, without a broader analysis of its connection with the type of controlled loads, subsystems.
# Teleportation-Based Error Correction Protocol of Time-Frequency Qubit States
## 2 Time-Frequency Qubit States
### Time-Frequency Entangled GKP State and Error Correction Protocols
For that, we must first entangle the single photon to correct with a less noisy ancilla single-photon state using the entanglement gate Equation (15). Since now we have two separable single-photon states, we cannot directly entangle them by using a non-linear crystal, which is inefficient. Nevertheless, such a frequency entangled gate could be implemented with a quantum emitter embedded into a wave guide, which assists in the interaction between the two single photons [27; 54]. The second entanglement structure of the time-frequency GKP state that can be considered is to start with two initially separable GKP states, with one being less noisy  
Figure 2: Different joint spectral intensity \(|\langle\omega_{s},\omega_{i}|\psi\rangle|^{2}\) of time–frequency entangled GKP states (**c**,**d**) that can be generated experimentally and their quantum circuit representation (**a**,**b**). The situation (**a**) corresponds to a photon pair produced by a SPDC process, while (**b**) corresponds to two single photons with a frequency comb structure produced by two independent processes, which are then frequency entangled. The position of frequency beam-splitter operations modifies the periodicity of the grid state from a factor \(\sqrt{2}\). \(\hat{D}(\omega)\) and \(\hat{D}(t)\) are frequency and temporal displacement operations defined in [1]. than the other. These states are then entangled using Equation (15). The resulting wave function is  
\[|\psi^{\prime}\rangle=\iint d\omega_{s}d\omega_{i}f_{+}(\omega_{+})f_{-}(\omega_{- })f(\omega_{+})f(\omega_{-})|\omega_{s},\omega_{i}\rangle. \tag{16}\]  
In this new spectral function (see Figure 2d, the cavity function is now dependent on the collective variables, and thus, the periodicity of the grid state is not the same as in Equation (14). The corresponding quantum circuit representation is pictured in Figure 2b. The error correction protocol is followed by a temporally resolved measurement followed by a conditional displacement operation to correct only the temporal noise in the state of interest. A second entanglement operation is performed using a less noisy ancilla state, followed by a resolved frequency measurement followed by a conditional displacement operation to correct the frequency noise. The difference in the joint spectral amplitude of the photon pairs results-- Equations (14) and (16)--in a different wave function when one of the photons undergoes a temporally resolved (and frequency) measurement and a conditional displacement operation.
# Analysis of a Novel Fluidic Oscillator under Several Dimensional Modifications
###### Abstract
The link between the output frequency and the feedback channel dimensional modifications was explored. CFD simulations using the k-omega (GECO) turbulence model with the aim of understanding thermal pollution in water jets was studied in [32]; they observed that the use of sweeping jet oscillators created a thermal dilution effect, drastically reducing the thermal pollution. The jet structures generated and their associated decay patterns were obtained. This effect was attributed to the fact that the sweeping jet converts more streamwise momentum into spanwise momentum, also increasing the homogeneity of the flow turbulence. Geometry modifications and scale variations of fluidic oscillators were considered in [33] to enhance mixing. The work was mostly performed via 2D-CFD simulations which were compared with 3D ones and experimental results. The most important design parameters were revealed, setting the basis of design modifications for other future applications. A supersonic fluidic oscillator employed to produce self-sustained oscillations in gas-pressurized chambers was studied in [34]. They observed that 2D-CFD simulations were not capable of reproducing the transients as accurately as the very computationally expensive 3D ones and decided to generate a low-order fluid circuit model which was quite capable of predicting the performance of the prototype. In this study, a newly designed fluidic oscillator (FO), previously studied by [28] at very low Reynolds numbers is analyzed using numerical methods at relatively high Reynolds numbers, and flow is assumed to be incompressible. Following the establishment of a typical linear correlation between the Reynolds number and the oscillating frequency of the outlet flow, the study addresses the impact of dimensional modifications in outlet width and mixing chamber wedge inclination angle. Notably, alterations in the outlet width are observed to have a substantial influence on the performance of the fluidic oscillator. The manuscript also delves into an analysis of the origin of self-sustained oscillations, providing valuable insights into the forces acting on the jet within the mixing chamber. Ultimately, it is deduced that the current FO configuration operates under pressure-driven conditions, although mass flow forces appear to be much more significant than in previously studied FO configurations [25; 26].
## 2 Related Work
For the selection of nodes and their route towards the destination, the hop-by-hop dynamic addressing-based routing protocol for pipeline monitoring is discussed (H2-DARPPM) [14]. The parametric value for the selection of nodes is energy consumption. It uses the dynamic addressing scheme to each node address so that the PDR is increased, but this approach is costly for network topology. In [15], the vector-based void avoidance (VBVA) works for mobile underwater sensor networks to address the void routing problem. It works with two novel features vector shift and back pressure to handle the void empty regions. For the empty regions that occur in the boundary of the network, the vector shift is used to deal with these regions; meanwhile, back pressure is used to avoid the concave empty regions. The primary strategy is that it does not need any prior information about the network due to VBVA's work with an on-demand approach. It is the first void empty region protocol that works with 3-dimensional topology with mobile nodes. The simulation results show that the PDR is achieved and minimizes the Packet Lose Ratio (PLR). An energy-efficient watchman-based algorithm (EE-WBA) [16] uses a novel node called watchman node that applies in a distributed based network for detection and avoidance of empty regions near the sink neighboring nodes. Those nodes that died early due to high deprecation of the bottom nodes can rescue with the help of watchman nodes. Watchman nodes are dedicated nodes that deploy for this task. The data are transfer successfully from dying nodes to watchman nodes and the responsibility of the data storage to the sink comes from watchman nodes. Another article in which introduced the approach for reliable data delivery underwater. The neighbor node approaches distinct energy efficient mates (NADEEM) [17] with two invariants like fallback and transmission. Both these are following the greedy approach to forwarding the data among the nodes of the network. The neighboring node is only eligible to send the data when the route towards the destination is not avoided. Five parameters are considered for this work like PDR, PLR, energy consumption, throughput, and a fraction of void nodes for simulation. The proposed technique performed better than the baseline approach GEDAR [9] in terms of void nodes and corresponding consumption of energy. The major limitation of this work, the route is not every time clear with respect to void nodes, so most of the time, NADEEM does not detect the void nodes.