esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Feature Fusion Event Argument Entity Recognition Method for Industrial Robot Fault Diagnosis
## 4 Experiment
### Ablation Experiment  
We demonstrate the effectiveness of each module in improving the performance of the model by removing each module from the model and comparing it with the original recognition effect. The results are shown in Table 3. When the context feature is removed, the F1 score is reduced to 92.95%, which shows that the con-text feature can enable each entity in the sentence to capture long-distance de- pendency information and improve the performance of the model. When the context salient feature is removed, the F1 score is reduced to 93.32%, which proves that combining local context information and long-distance dependency information can improve the effect of entity recognition. When the local context features are removed, although the Recall is increased to 94.35%, the Precision is only 92.53%, and the F1 score is reduced to 93.44%, which indicates that the use of local context features to infer entity semantic information has improved model performance. In the absence of the relationship information, that is, the Bi-GCN module, the F1 score is reduced to 90.30%, indicating that the addition of the event argument entity relationship  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P** & **R** & **F1 Score** \\ \hline Lattice LSTM [11] & 90.67 & 92.12 & 91.39 \\ Simplified Lattice [17] & 92.06 & 92.69 & 92.37 \\ LR-CNN [12] & 92.29 & 93.44 & 92.86 \\ Bert-CRF [27] & 92.31 & 93.72 & 93.01 \\
**MF-GCN** & **93.78** & **93.38** & **93.58** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the effect of different models on the event argument entity recognition of the mechanical fault detection dataset (unit:%).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **NE** & **NM** & **Overall** \\ \hline Lattice LSTM [11] & 53.04 & 62.25 & 59.79 \\ WC-LSTM [18] & 52.55 & 67.41 & 59.84 \\ Simplified Lattice [17] & 56.99 & 61.41 & 61.24 \\ LR-CNN [12] & 57.14 & 66.67 & 59.92 \\ ME-CNER [22] & 75.17 & 64.39 & 68.93 \\
**MF-GCN(Remove Bi-GCN module)** & **74.93** & **64.84** & **69.52** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the recognition effects of different models on the Weibo dataset (unit:%). NE, NM, and Overall represent the F1 scores for named entities, nominal entities (excluding named entities), and both.  
information greatly improves the effect of event argument entity recognition. In the case of Bi-LSTM with different stacked layers, when the stacked layer is 2, the F1 score is the highest. During the experiment, the model that removes the context feature, context highlight feature, local context feature, and Bi-GCN module all adopt the Bi-LSTM that is stacked with two layers.
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 3 Experiments and Evaluation
### Dataset Segmentation and Model Training  
Figure 5 illustrates the data segmentation process. The different colored lines represent multi-dimensional data from different sensors. This chapter utilizes the preprocessed NGSIM US101 trajectory dataset, which is discretized and mapped according to the road network structure shown in Figure 1. The NGSIM dataset records vehicle movement data on highways, primarily capturing forward-driving and lane-changing behaviors. The US101 dataset includes three 15 min trajectory segments (7:50-8:05 am, 8:05-8:20 am, 8:20-8:35 am), which are divided into training, validation, and test sets in a 7:1:2 ratio. During model training, a specific vehicle is selected as the objective vehicle (OV), while the surrounding vehicles within a 3 \(\times\) 13 road grid are considered neighboring vehicles (NVs).  
In the HTSA-LSTM model, the historical trajectory data of the OV and NVs over T seconds is used to predict the OV's trajectory over the next H seconds. Therefore, each feature sequence segment must be at least T + H seconds long (as illustrated in Figure 5). In this study, the vehicle trajectory data are segmented into time-series fragments of T + H seconds, where T seconds represents the OV's historical driving data and H seconds represents the model's future trajectory prediction range. Both are sampled at a frequency of 10 Hz. Data segmentation is performed using a sliding time window of length T + H, allowing the predicted results to be merged into a continuous sequence, thus enabling predictions over longer time horizons. Before inputting the data into the HTSA-LSTM model, each vehicle's trajectory is downsampled by a factor of 2 to reduce model complexity.  
Model Parameter Settings: Based on existing research results in trajectory prediction and considering subsequent comparative experiments, the length of the output window H in the HTSA-LSTM model is incrementally increased from 1, selecting the optimal future prediction step length according to the evaluation metrics. Hyperparameters in the model are optimized using grid search, with the final parameter settings provided in Table 2. For the parameter setting of SICC-SC, we refer to previous work [30].  
Figure 5: The data segmentation process.
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 4 Benchmark Execution and Evaluation
### High-Performance Computing Benchmark Experiment Results  
Based on the benchmark methods described in Section 3.3, experiments are carried out on each of the three platforms, where GPU computing is implemented in the CUDA framework and CPU computing is implemented in the OpenMP framework, meanwhile, with the following parameters being set: \"DATATYPE = FLOAT\" and \"BLOCKSIZE = 32\". It also includes their respective optimization implementations. In the CUDA framework, CUDA's data parallelism and model parallelism techniques are used to accelerate the training process, while relevant acceleration libraries (cuDNN, cuBLAS, etc.) are called for optimization. In the OpenMP framework, critical computational loops are marked as parallel regions using compile directives to achieve thread-level parallel computation, and data locality optimization is used to improve computation speed.
# A Review of Oil Spill Remote Sensing
## 4 Slick Thickness Measurements  
Slick thickness is a strong need for various applications ranging from countermeasures effectiveness assessment to legal purposes for prosecution [57]. Slick thickness measurement is problematic because there are very few operational methods. Second, the visual color methods currently tried may be incorrect. There are a few simple techniques to estimate slick thickness in the range 0.1-5 \(\upmu\)m (0.0001-0.0005 mm), but these do not relate to most oil spill needs. Spill work requires thickness measurements greater than 0.5 mm up to about 10 mm [57].
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
## 4 Limitations on the Control Gain  
The definition of the control gains represents one of the main aspects of this work, since in this way the control system can adapt the control law to any situation in an automated way. For example, if the nature of the disturbance changes during time, the control system will tune the control gains in a different way.  
The use of inertial actuator implies a restriction on the maximum feasible gain. This limitation has been highlighted in many works [15; 17; 18] and an evaluation of the maximum gain related to the actuator dynamics for the Direct Velocity Feedback (DVF) control law has been presented in [19]. However, the limitation on the control gain is affected not only from instability phenomena, but also from stroke and current saturations. As described in the previous sections, the maximum feasible gain \(g_{i,j}\) which can be set by the actuator is the minimum between the maximum gain related to the actuator dynamics and the saturation one.  
In this work, the problem of instability has been addressed from a more general point of view. The effect of the actuator dynamics has been studied not only on the controlled mode, but also on the others. For both cases, it was demonstrated a way to evaluate the maximum gain related to the actuator dynamics, when a SNDF control logic is used. At this stage, the assumption is that just one PMA works on a given mode to be controlled. Moreover, a mathematical expression to calculate the maximum gain that prevents the actuator saturations is derived.
## 2 Materials and Methods
### Testing Procedure  
To guarantee the repeatability of the tests and to provide reliable data to be used, for example, as input for calculation methods or the development of numerical models, a specific testing procedure has been developed during the preliminary phase of the activity.  
The test is performed as follows: after all the set levels of temperature have been defined, the adsorption cycles are started and oscillation in temperatures is monitored. Once a steady-state condition has been reached, a record is started and all the energy flows are measured for 1 h or 6 cycles (for the longest cycle times), by evaluating the thermal power exchanged across each adsorption machine component. The last three cycles are then analyzed for performance evaluation.  
Three main figures of merit have been considered for the assessment of the performance of the small storage:  
* The average discharge power during each cycle, calculated as the mean power during the discharge phase. \[\dot{Q}_{\text{are}}=\frac{\int_{0}^{\text{discharge}}\dot{m}_{\text{e}}c_{p}(T_{in,\text{e}}-T_{out,\text{e}})dt}{t_{\text{discharge}}}\] (1)  
Figure 4: The cold storage prototype (highlighted) connected to the testing bench. (1) composite adsorber based on an aluminum, porous structure and a SAPO-34 coating; (2) phase-changer; (3) switching valves; (4) pumps.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Monitored Parameter**} \\ \hline T in/out Adsorber; & T liquid phase into Phase Changer; \\ T in/out Phase Changer (CON/EVAP); & Adsorter HTF (Heat Transfer Fluid) flow rate; \\ Phase changer HTF flow rate; & Adsorber pressure; \\ Phase changer Pressure; & T shell; \\ T ambient. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters monitored during the tests.  
* The stored energy for mass unit of the adsorbent, calculated as the ratio of the total energy stored during each test and the total mass of adsorbent: \[E_{ev}=\frac{\int_{0}^{t_{discharge}}\dot{m}_{ev}c_{p}(T_{in,ev}-T_{out,ev})dt}{m_{adsorbent}}\] (2)
* The thermal COP of the system; in this case, corresponds to the storage efficiency of the system, since it is the ratio between the cooling energy recovered and the energy used as input. \[COP=\frac{\int_{0}^{t_{discharge}}inc_{p}(T_{in,ev}-T_{out,ev})\cdot dt}{\int_{0}^{t_{ charge}}inc_{p}\Big{(}T_{in,ads}-T_{out,ads}\Big{)}\cdot dt}\] (3)
# Parallel Complex Quadrature Spatial Modulation
## 5 Computation Complexity of the Receiver  
In addition to the performance analysis, the computation complexity of the ML receiver employed in PCQSM is analyzed and compared with that of PQSM and CQSM. The number of complex operations, specifically addition and multiplication operations, that are involved in the proposed PCQSM is determined from (7) and (8). To compute \(\mathbf{g}\) in (8), \(2 PN_{r}\) multiplications and \((2P-1)N_{r}\) additions are required and in (7), \(\|\mathbf{y}-\mathbf{g}\|^{2}\) requires \(N_{r}\) additions and \(2N_{r}-1\) multiplications. The ML search is performed over the \(R\)-dimensional space. Therefore, the overall computation complexity of the ML receiver in PCQSM is given as  
\[\eta_{PCQSM}=(4 PN_{r}+2N_{r}-1)2^{R}. \tag{15}\]  
The complexity of ML detector in PQSM, \(\eta_{PQSM}\) is provided in (Equation (26) in [18]). Assume that the spectral efficiency in PCQSM and PQSM are the same, the complexity ratio, \(\eta_{PCQSM}/\eta_{PQSM}=1\). This implies that the two transmission scheme have the same complexity at the same transmission rate while PCQSM can significantly reduce the required number of transmit antennas.  
Compared to CQSM, the difference in complexities is in the computation of \(\mathbf{g}\), which requires \(2N_{r}\) multiplications and \(N_{r}\) addition. Altogether, CQSM requires \(6N_{r}-1\) complex operations. Therefore, the ML receiver complexity ratio of PCQSM to CQSM is given as follows.  
\[\frac{\eta_{PCQSM}}{\eta_{CQSM}}=\frac{4 PN_{r}+2N_{r}-1}{6N_{r}-1}. \tag{16}\]  
The expression in (16) is expected as PCQSM involves antenna groups and the implication is that the ML complexity grows as \(P\) increases. This slight increase in the computational complexity is tolerable, given the huge gains achieved by the PCQSM in terms of SNR and number of required transmit antennas.
## 3 Modeling
### Satellite Products  
Different satellite products, namely MODIS land use/land cover (LULC), Global Map of Irrigation Areas (GMIA), and GlobCover LC v2, monthly irrigated and rainfed crop areas (MIRCA), World Database on Protected Areas (WDPA) from (UNEP-WCMC), world data on permanent water bodies (perWB), and population density data derived from Indian census, were used for the analysis. The MODIS 8_daily LAI products (MOD15A2H, version 6) were used to estimate LAI. Remotely sensed information using artificial neural networks (PERSIANN) and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) for the period of 15 years (2003-2019) were used for the estimation of the number of rainy days. The CHIRPS is also free-of-cost, quasi-global rainfall data, which covers 50\({}^{\circ}\) S to 50\({}^{\circ}\) N and has been available from 1981 to the near-present. CHIRPS was developed by the United States Geological Survey (USGS) to support the United States Agency for International Development Famine Early Warning Systems Network (FEWS NET). The MODIS satellite 8_daily GPP and NPP products were converted into monthly data. Further, monthly GPP and NPP data were used to derive the yearly net dry matter (NDM).
## 2 Experiments
### Modeling Tools
#### 2.1.2 Model Set-Up
The EDGAR-HTAP project compiled a global emission dataset with annual inventories for CH\({}_{4}\), NMVOC, CO, SO\({}_{2}\), NOx, NH\({}_{3}\), PM\({}_{10}\), PM\({}_{2.5}\), BC (Black Carbon), OC (Organic Carbon) and covering the period 2008-2010 using, to the extent possible, official or scientific inventories at the national or regional scale that are likely to be acceptable for policy makers in each region of the world. This compilation of different official inventories from EMEP for Europe, EPA for USA, GAINS for China and REAS was first gap-filled with global emission data of EDGARv4.2. For this period, the 2010 dataset has been used; monthly emissions of these macro pollutants are pre-processed in the CHIMERE suite to fit with the simulation grid, the model species and the final time resolution [46]. To evaluate the CHIMERE outputs with observed optical properties, the OPTSIM model [63] has been used to calculate the main properties. This post-processing tool calculates properties such as the aerosol optical depth (AOD), the Angstrom exponent (\(\upalpha\)), single scattering albedo (SSA), the color ratio and the visibility.
## 3 Finite Element Model of a Concrete Gravity System
### Applied Static and Dynamic Loads  
The applied static loads included the self-weight as well as hydrostatic pressure, and the level of still water was 91.75 m. The dynamic loads considered the hydrodynamic pressure and the NF motion input. During the numerical analysis of the dam structure, the input acceleration time history of the ground motion was transformed into equivalent forces on each boundary node in accordance with the viscous-spring artificial boundary theory introduced in Section 2. As shown in Figure 7, the seismic input cases compared the NF, PL, and NP motions. Additionally, the P wave obliquely incident angles were \(-90^{\circ}\) to \(90^{\circ}\) at \(15^{\circ}\) intervals, and a total of 13 types of incident angles were included. Many authors [43; 47] have demonstrated the importance of considering the incident direction. In particular, in this study, the incident angle ranged from \(-90^{\circ}\) to \(-15^{\circ}\), representing the negative incident angle to explore the vibration characteristics of an asymmetric concrete dam under NF motion with asymmetric pulse characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material Parameters** & **Elastic Modulus** & **Bulk Modulus** & **Density** & **Poisson’s Ratio** \\  & **(GPa)** & **(GPa)** & **(kg/m3)** & **Poisson’s Ratio** \\ \hline Concrete & 31 & - & 2643 & 0.2 \\ Foundation rock & 21.6 & - & 2000 & 0.2 \\ Reservoir water & - & 2.07 & 1000 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of concrete gravity dam system.  
Figure 7: PL motion and decomposed NP component for one selected earthquake: (**a**) acceleration time history; (**b**) velocity time history.  
4 Seismic Dynamic Response Analysis Under Oblique P Near-Fault Motion with Different Incident Angles
# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
## 4 Problem Formulation and Performance Metrics
### UAV Communication Latency  
As shown in Figure 1, vehicles arrive at location x0 and randomly establish communication links with the UAV within its communication range. Let us assume that all vehicles transmit data packets of the same size, denoted as \(\beta\) bits. Let T represent the total communication delay between vehicles and the UAV. The specific calculation expressions are described below.
## 4 Case Study: The Kimberley Climate Adaptation Project (KCAP)
### Evaluation of Visualization Rankings and Responses to Virtual Globes in KCAP
#### 4.6.2 User Orientation  
Twice, users as well as CALP team members got lost in 3D space because handling was sometimes cumbersome and there were few embedded navigational constraints. The usability of virtual globes could further be improved through more constraints and \"smart navigation\" [53] that guides the user. However, it helped that Google Earth provides bookmarks which enable re-orientation within the virtual globe when a user 'gets lost'. They can also re-orient by re-selecting a layer (observations from the video recordings).  
In order to choose different views, orientation is crucial. Various landscape elements are mentioned in the transcriptions. It is particularly interesting that two stakeholders orientated with regard to features such as the city's administrative boundaries, features usually non-visible to lay people. It should be noted that both stakeholders work with planning documents and therefore are familiar with these administrative features. After summarizing and abstracting all elements used by participants, the following types of landscape elements (Figure 4; cf. [54]), can be distinguished:  
* Topographic features (creeks, mountains, ski hill);
* Built structures (city districts);
* Linear built structures (roads, trails);
* Administrative, only partly physical, boundaries (city boundary, watershed boundary).
# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
## 6 Simulation Results
### Scenario 3  
In this scenario, the FC and battery are assumed to be in the OFF mode. The Scenario 3 simulation results are shown in Table 8. The best result of the optimum power dispatch with the beetle antenna search algorithm is presented in Table 9. The generations of all the units are within their limitations and the grid supplies the load for 24 h. For 9-21 h, both the MT and WT are working at their maximum power generation level. Since the bid of these generators is cheaper than the PV, the VPP encountered the load with the MT and WT. In this scenario, the power is purchased from the grid during the first 8 h and the last 9 h when the market price is comparatively low.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time (h)**} & \multicolumn{3}{c}{**DG Sources (kW)**} & \multicolumn{3}{c}{**Battery**} & **Utility** \\  & **MT** & **FC** & **PV** & **WT** & **(kW)** & **(kW)** \\ \hline
1 & 20.5 & 0 & 0 & 1.5 & 0 & 30 \\
2 & 18.5 & 0 & 0 & 1.5 & 0 & 30 \\
3 & 19.5 & 0 & 0 & 1.5 & 0 & 30 \\
4 & 20.5 & 0 &  
The corresponding characteristic of the BAS algorithm is compared with that of the BFOA algorithm for Scenario 3, which is shown in Figure 14. The BAS converges with the best solution in 10 iterations. Moreover, it shows a quick convergence to find the optimal cost. The optimal scheduling strategy for the DA market is shown in this scenario by considering the FC and the battery storage system in the OFF condition. The detailing of the power transaction with the grid is shown by considering the power purchase price from the grid. There is less available literature with this approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Best Solution**} & \multirow{2}{*}{**Mean Simulation Time (s)**} \\  & & **(Euro Cent)** & & & \\ \hline BFOA & 1498.2 & 7.41 \\
**Proposed algorithm BAS** & **1493.17** & **6.441** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of the cost of generation between BFOA and the proposed BAS algorithm corresponding to Scenario 3.  
Figure 13: Power generation for Scenario 3.
## 3 Case Study
### Decomposition Results  
From the perspective of dam deformation characteristics, the setting of the sequence decomposition frequency is critical for subsequent model construction, and the dam body displacement changes annually. Therefore, the sequence decomposition frequency of the STL method is set to 365, and then the observed radial displacement data are decomposed into three components. The decomposition results show that all the components of the radial deformation time series are displayed as a cycle depending on the demand. Moreover, the trend component of G2-HY shows a gentle downward trend, whereas the trend component of G3-HY shows a much gentler growth. The residual component that shows a high degree of irregular complexity can be interpreted as the part where periodicity and the changing trend of the target quantity are eliminated. Because G0-HY and G3-HY have similar phenomena, G1-HY and G2-HY have similar phenomena; hence, Figure 8 shows only the representative components of the radial displacement data from G2-HY and G3-HY; the other measuring point results are not given. The components obtained by decomposition at each measuring point have evident differences, which shows the feasibility and variability of using STL to extract intrinsic information from the radial displacement time series.