esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Information Extraction Based on Deep Learning
## 2 Entity Relationship Extraction Based on Deep Learning
### Supervised Learning--Joint Extraction
#### 2.2.3 Joint Extraction Based on Knowledge Enhancement  
Currently, pre-trained language models have achieved superior performance in a variety of natural language processing tasks, including entity and relationship extraction. For example, BERT achieved considerable success, but BERT-based models do not perform as well on Chinese domain-specific corpora as on English data sets. When BERT is used with a Chinese corpus, it generates embeddings at the character level; however, Chinese vocabulary contains more semantic information than Chinese characters. Therefore, BERT-based models are limited in their ability to extract dependencies between Chinese words, which are very important in relationship extraction. [PERSON] et al. proposed an extraction architecture for joint entities and relationships to address this problem using external lexical and syntactic knowledge to overcome the limitations encountered by BERT-based models in the joint extraction process of specific Chinese fields [54]. The framework implements a knowledge-enriched and span-based BERT (KSBERT) based on knowledge-rich and span networks, which combines the dependency structure to extract entities and their relationships simultaneously. An end-to-end model of knowledge-enhanced word-embedding BERT (KEWE-BERT) for joint entity and relationship extraction was proposed by [PERSON] et al. [55]. This model can improve the effect of knowledge extraction in the case of limited domain corpora and has good portability. [PERSON] et al. developed an end-to-end knowledge injection deep learning framework (Gated-K-BERT) that utilized a pre-trained BERT language representation model and domain-specific declarative knowledge sources (drug abuse ontology), using a gating, fusion, and sharing mechanism to jointly extract entities and their relationships. Combining knowledge-aware attentional representations with BERT extracts broader cannabis-depression relationships [56]. The model combines entity knowledge in the form of entity location-aware encoding with attention, which helps to perform better relationship classification. Experiments show that location-aware encoding and location attention have a significant impact on the effectiveness of relationship extraction, and the model performance decreases if they are missing. The limitation of the model is that it experiments with tweets on Twitter with short text, which may not be applicable to large text.
# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 2 Study Area and Data
### Impact Factor Data
\begin{table}
\begin{tabular}{c c c c c} \hline
**Category** & **Impact Factor** & **Spatial Resolution** & **Temporal Resolution** & **Data Source** \\ \hline \multirow{4}{*}{Vegetation physiology} & Enhanced vegetation index & 500 m & 8 d & Calculated from MODIS images \\  & Normalized difference & 500 m & 8 d & MOD15A2H \\  & vegetation index & & & Calculated from MODIS images \\  & Leaf area index & 500 m & 8 d & Calculated from MODIS \\ \hline \multirow{4}{*}{Land surface feature} & Land surface water index & 500 m & 8 d & Calculated from MODIS images \\  & Digital elevation model & 90 m & - & SSTM V4 \\  & Land use/land cover & 500 m & 1 a & MCD12Q1 \\  & Land surface temperature & 1 km & 8 d & MOD11A2 \\  & Fraction of photosynthetically active radiation & 500 m & 8 d & MOD15A2H \\ \hline \multirow{4}{*}{Climatic environment} & Precipitation & 0.25\({}^{\circ}\) & 1 d & ERA5 \\  & Minimum air temperature & 0.25\({}^{\circ}\) & 1 d & ERA5 \\ \cline{1-1}  & Maximum air temperature & 0.25\({}^{\circ}\) & 1 d & ERA5 \\ \cline{1-1}  & Evapotranspiration & 500 m & 8 d & MOD16A2 \\ \cline{1-1}  & Potential evapotranspiration & 500 m & 8 d & MOD16A2 \\ \hline \end{tabular}
\end{table} 
Table 1: Information on the impact factor data used in this paper. climate (ERA5, [[https://apps.ecmwf.int/datasets/data/interim-full-daily](https://apps.ecmwf.int/datasets/data/interim-full-daily)]([https://apps.ecmwf.int/datasets/data/interim-full-daily](https://apps.ecmwf.int/datasets/data/interim-full-daily)), accessed on 15 April 2023), with a spatial resolution of 0.25\({}^{\circ}\) and a temporal resolution of 1 d.  
We set standards for data input and unified the spatial resolution of all data to 500 m by upscaling or downscaling; we also unified the temporal resolution to 8 d by time series interpolation or aggregation. In addition, considering the relationship between phenological changes and time in grassland ecosystems, we also take time as a factor and add it to the grassland GPP prediction.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 4 Experimental Evaluation
### Capacity and Bounds Checking  
The framework implements several validation mechanisms:  
* Image Size Validation: Before encryption, the client verifies that the flattened image vector does not exceed the FHE scheme's slot capacity (4096 slots for our parameters).
* Parameter Bounds: Operation parameters (e.g., brightness factors) are checked against reasonable ranges to prevent overflow or underflow during homomorphic computation.
* Noise Budget Monitoring: While not shown in the algorithms for brevity, our implementation tracks the remaining noise budget in ciphertexts to prevent decryption failures.
## 4 Discussion
### The Applicability of Different Models  
The different performances of three LUE models at different temporal scales and for different vegetation types suggest that it should be selective when using them. Prior to regional simulations of GPP using these models, we must be careful with the applicability of the optimized parameters.  
In this study, optimized parameters changed significantly among different vegetation types. The across-site variability of these parameters is also sizeable even for a specific vegetation type (Table 3). The sensitivity of simulated GPP to \(\varepsilon_{m}\) and \(\beta\) in TL-LUEn, to \(\varepsilon_{msu}\) and \(\varepsilon_{msh}\) in TL-LUE, and to \(\varepsilon_{max}\) in MOD17 was assessed using the factorial approach described in section 2.2.3. The calculated main effect of \(\varepsilon_{max}\), \(\varepsilon_{m}\), \(\beta\), \(\varepsilon_{msu}\) and \(\varepsilon_{msh}\) are 20%, 11.50%, 8.50%, 8.09% and 11.91% at the half-hourly scale, and 20%, 10.72%, 9.28%, 6.84% and 13.16% at the daily scale, and 20%, 11.26%, 8.74%, 7.52% and 12.48% at the 8-day scale, respectively (Table 5). The sensitivity of simulated GPP to \(\varepsilon_{max}\) in MOD17 is higher than the sensitivity of simulated GPP to individual parameters in TL-LUEn and TL-LUE. The  
Figure 8: The RMSE of modeled GPP against tower-derived GPP within different PAR classes for 6 different vegetation types: (**a**) BF, (**b**) CROP, (**c**) GRASS, (**d**) MF, (**e**) NF and (**f**) SHRUB, at the 8-day scale.  
sensitivity of simulated GPP to the simultaneous uncertainties of \(\varepsilon_{m}\) and \(\beta\) in TL-LUEn and to the simultaneous uncertainties of \(\varepsilon_{msu}\) and \(\varepsilon_{msh}\) in TL-LUE is the same as the sensitivity of simulated GPP to \(\varepsilon_{max}\) in MOD17. In addition, parameters in TL-LUE and MOD17 showed similar variations within each vegetation type, indicated by the similar CV in Table 3. However, as the two parameters in TL-LUEn vary not only with biomes but also with temperature [6], optimized \(\varepsilon_{m}\) and \(\beta\) exhibited larger variations and uncertainties than \(\varepsilon_{max}\), \(\varepsilon_{msu}\), and \(\varepsilon_{msh}\) for all vegetation types (Table 3).  
Above analyses on the performance of three models at different temporal scales and their sensitivity to parameter uncertainties indicate that TL-LUEn is more applicable in individual site at the half-hourly scale. TL-LUE can be used regionally at the half-hourly, daily, and 8-day scales. MOD17 is also a good option for simulating regional GPP at the 8-day temporal scale and it is able to simulate GPP with accuracy close to TL-LUE.  
Figure 9:The average differences of modeled daily GPPs with observations for different ranges of clearness index \(Q\). \(\Delta\)GPP means the difference between the simulated and tower-derived daily GPP for certain biome. (**a–f**) denote\(\Delta\)GPP for 6 different vegetation types (BF, CROP, GRASS, MF, NF and SHRUB), respectively.
# Optimizing Airfoil Aerodynamic Characteristics by Using Proposed CSA-KJ Method
## 2 Aerodynamic Optimization Method: CSA-KJ  
### Objective Function of CSA-KJ Method  
Assume that the flow field around the airfoil is uniform, inviscid, incompressible and without turbulence, the circulation lift \(F_{L}\) of the object in the inviscid flow field is calculated according to the KJ theorem as [29]  
\[F_{L}=Ur\Gamma, \tag{1}\]  
where \(U\) is the fluid velocity at infinity, m\(\cdot\)s\({}^{-1}\). \(r\) is the density, kg\(\cdot\)m\({}^{-3}\). \(\Gamma\) is the path integral of fluid velocity along a closed curve, which is the closed curve integral of airfoil surface velocity along the path of the airfoil profile, and it can be expressed as  
\[\Gamma=\oint vds, \tag{2}\]  
in which, \(v\) is the average fluid velocity per unit length \(ds\) of the airfoil profile. According to the differential method, \(ds\) can be written as  
\[ds= \sqrt{\Delta y^{2}+\Delta x^{2}}dx, \tag{3}\]  
where \(\Delta y\) and \(\Delta x\) are the unit lengths in the direction of the \(y\)-axis and \(x\)-axis, respectively.  
For the sake of obtaining the fluid velocity on the airfoil profile surface, a fourth-order constant-free polynomial function is used to describe the upper and lower profiles of the airfoil, and it can be expressed as  
\[y= p_{1}x^{4}+p_{2}x^{3}+p_{3}x^{2}+p_{4}x, \tag{4}\]  
where \(p_{1}\sim p_{4}\) are the polynomial coefficients, which can be obtained by the least square method.  
It is assumed that the radius of the two-dimensional flow tube where the airfoil is located is \(r\), and its fluid flow is a laminar one. According to fluid continuity, when the fluid velocity entering the flow field is \(U\), the fluid velocity \(v\) at the chord length \(x\) of the airfoil profile can be defined as  
\[v=Ur/(r-y), \tag{5}\]  
Substituting Equations (3) and (5) into Equation (2), one can express the airfoil circulation \(\Gamma\) as follows  
\[\Gamma= \oint Ur/(r-y)\sqrt{\Delta y^{2}+\Delta x^{2}}\mathrm{d}x, \tag{6}\]  
then combined Equation (1) with Equation (6), the calculation formula of airfoil lift \(F_{L}\) is written as  
\[F_{L}=U\rho\oint Ur/(r-y)\sqrt{\Delta y^{2}+\Delta x^{2}}\mathrm{d}x, \tag{7}\]  
consequently, the objective function of the CSA-KJ aerodynamic optimization method is obtained.
## 3 Description of MSIMP
### MSIMP Based on Traditional Optimization Algorithms
From the perspective of the objective task requirements, LP, IP, and MIP are suitable for solving optimal MSIMP problems with linear and discrete objective tasks; CG, SA, GA, PSO, and ACO are suitable for solving optimal MSIMP problems with large-scale objective tasks; and DP, B&B, TS, and DE are suitable for optimal MSIMP problems with complex temporal and integer constraints. In terms of the convergence speed of the optimization algorithms used to solve the MSIMP model and the quality of the mission planning scheme, LP, IP, MIP, DP, B&B, and DE converge faster and are able to solve higher-quality MSIMP schemes, while SA, GA, PSO, ACO, CG, and TS converge slower but are able to solve MSIMP schemes in complex situations. However, there are some limitations in solving MSIMP based on traditional optimization algorithms. SA, GA, PSO, ACO, TS, and other methods need to adjust the algorithmic parameters during the optimization search process and present high computational complexity when dealing with large-scale and complex problems, especially complex multi-peak optimization problems; thus, they can easily fall into local optimal solutions. In addition, algorithms such as LP, IP, MIP, B&B, CG, and DP fail to adapt to changes in dynamic environments, making it difficult to meet the demands of highly dynamic and intelligent MSIMP. Therefore, although the traditional optimization algorithms for MSIMP have a certain degree of effectiveness, they still exhibit problems such as difficulty of parameter adjustment, high computational complexity, and easily falling into local optima. Thus, there is an urgent need to enhance the optimization algorithms to improve their efficiency in solving the MSIMP problem.
# A Review of Small-Scale Vapor Compression Refrigeration Technologies
## 3 Small-Scale Vapor Compression Refrigeration (VCR) Systems
### Components  
A vapor compression cooling system, whether on a small or large scale, consists of four main components (compressor, condenser, evaporator, and expansion device) that cause the refrigerant to experience a sequence of thermodynamic states that make up the refrigeration cycle. These four components are important and necessary, which is why continued research is key for innovating and redesigning these systems, leading to improved performance. Small-scale vapor compression systems are not the exception and there are several publications focusing on specific components, evaluating different designs, proposing modifications, etc. For example, diverse types of compressors and heat exchangers for both the condenser and the evaporator have been evaluated, as well as different expansion devices made of diverse materials and varied dimensions and characteristics.  
An important aspect is that even though some miniature vapor compression refrigeration systems and individual components for such systems may be commercially available, their manufacture and distribution is still limited, making it difficult or even impossible to purchase them. Therefore, the authors of previously cited publications typically manufacture their own experimental components due to lack of availability.  
We next consider each of the components of small-scale vapor compression refrigeration systems separately to describe the different types of components that are being evaluated and define their characteristics, performance, and identified areas of opportunity.
## 3 Proposed Algorithms
### Steady-State Analysis  
According to [24], the weight-error vector is defined as  
\[\tilde{\mathbf{w}}(n)=\mathbf{w}^{o}-\mathbf{w}(n). \tag{34}\]  
Using (12), we can obtain:  
\[\tilde{\mathbf{w}}(n)=\tilde{\mathbf{w}}(n-1)-\mu_{12}\frac{\mathbf{U}_{s}(n-1 )}{\sqrt{\|\mathbf{U}_{s}(n-1)\|_{2}^{2}}} \tag{35}\]\[\mathbb{E}[\|\tilde{\mathbf{w}}(n)\|^{2}] =\mathbb{E}[\|\tilde{\mathbf{w}}(n-1)\|^{2}]-2\mu_{12}\mathbb{E}[ \tilde{\mathbf{w}}^{T}(n-1)\frac{\mathbf{U}_{s}(n-1)}{\sqrt{\|\mathbf{U}_{s}(n -1)\|_{2}^{2}}}]\] \[+\mu_{12}^{2}\mathbb{E}[\frac{\|\mathbf{U}_{s}(n-1)\|^{2}}{\| \mathbf{U}_{s}(n-1)\|_{2}^{2}}], \tag{36}\]  
where \(\mathbb{E}\) is the expectation operator. The mean-square-deviation \(\mathbb{E}[\|\tilde{\mathbf{w}}(n)\|^{2}]\) must be less than \(\mathbb{E}[\|\tilde{\mathbf{w}}(n-1)\|^{2}]\) to ensure the algorithm is stable, so the step-size \(\mu_{12}\) must satisfy the following condition:  
\[0<\mu_{12}<2\times\frac{\mathbb{E}[\tilde{\mathbf{w}}^{T}(n-1)\mathbf{U}_{s}( n-1)]}{\mathbb{E}[\|\mathbf{U}_{s}(n-1)\|^{2}/\sqrt{\|\mathbf{U}_{s}(n-1)\|_{2}^{ 2}}]}. \tag{37}\]  
For the SCSS-APSA, the step-size should satisfy the condition below:  
\[0<\mu_{s12}=\lambda_{s}(n)\mu_{2}+(1-\lambda_{s}(n))\mu_{1}<2\times\frac{ \mathbb{E}[\tilde{\mathbf{w}}^{T}(n-1)\mathbf{U}_{s}(n-1)]}{\mathbb{E}[\| \mathbf{U}_{s}(n-1)\|^{2}/\sqrt{\|\mathbf{U}_{s}(n-1)\|_{2}^{2}}]}. \tag{38}\]  
For the MCSS-APSA, the step-size should satisfy the condition below:  
\[0<\mu_{\beta 12}=\lambda_{\beta}(n)\mu_{2}+(1-\lambda_{\beta}(n))\mu_{1}<2 \times\frac{\mathbb{E}[\tilde{\mathbf{w}}^{T}(n-1)\mathbf{U}_{s}(n-1)]}{ \mathbb{E}[\|\mathbf{U}_{s}(n-1)\|^{2}/\sqrt{\|\mathbf{U}_{s}(n-1)\|_{2}^{2} }]}. \tag{39}\]  
For the SMCSS-APSA, the step-size should satisfy the condition below:  
\[0<\mu_{s\beta 12}==\lambda_{s\beta}(n)\mu_{2}+(1-\lambda_{s\beta}(n))\mu_{1}<2 \times\frac{\mathbb{E}[\tilde{\mathbf{w}}^{T}(n-1)\mathbf{U}_{s}(n-1)]}{ \mathbb{E}[\|\mathbf{U}_{s}(n-1)\|^{2}/\sqrt{\|\mathbf{U}_{s}(n-1)\|_{2}^{2} }]}. \tag{40}\]  
In addition, \(\lambda(n)=0\) in the transient state and \(\lambda(n)=1(n\rightarrow\infty)\) in the steady state, such that \(\mu_{1}\) or \(\mu_{2}\), should also satisfy condition (36). Also, from [19], we know that \(0<\mu_{2},\mu_{1}\ll 1\), so the ranges of \(\mu_{s12},\mu_{\beta 12}\), and \(\mu_{s\beta 12}\) are restricted as  
\[0<\mu_{s12},\mu_{\beta 12},\mu_{s\beta 12}\ll 1. \tag{41}\]
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
###### Abstract  
In this paper, we study the offloading decision of collaborative task execution between platoon and Mobile Edge Computing (MEC) server. The mobile application is represented by a series of fine-grained tasks that form a linear topology, each of which is either executed on a local vehicle, offloaded to other members of the platoon, or offloaded to a MEC server. The objective of the design is to minimize the cost of tasks offloading and meets the deadline of tasks execution. The cost minimized task decision problem is transformed into the shortest path problem, which is limited by the deadline of the tasks on a directed acyclic graph. The classical Lagrangian Relaxation-based Aggregated Cost (LARAC) algorithm is adopted to solve the problem approximately. Numerical analysis shows that the scheduling method of the tasks decision can be well applied to the platoon scenario and execute the tasks in cooperation with the MEC server. In addition, compared with task local execution, platoon execution and MEC server execution, the optimal offloading decision for collaborative task execution can significantly reduce the cost of task execution and meet deadlines.  
platooning; collaborative task execution; mobile edge computing; offloading decision +
Footnote †: journal:
# EXOLOX: An Efficient One-Stage Object Detection Network Based on YOLOX
## 3 Results and Analysis
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Methods** & **Size** & **AP(\%)\({}_{\text{val}}\)** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** & **Parameters** \\ \hline RetinaNet-R50 [15] * & 640 & 39.2 & – & – & – & – & – & 34M \\ RetinaNet-R101 [15] * & 640 & 39.9 & – & – & – & – & – & 53M \\ DETR-R50\({}^{500e}\)[34] & 640 & 42.0 & **62.4** & 44.2 & 20.5 & 45.8 & **61.1** & 44M \\ YOLOF-R50\({}^{72e}\)[35] & 640 & 41.6 & 60.5 & 45.0 & 22.4 & **46.2** & 57.6 & 44M \\ YOLOv5-s [1] & 640 & 37.4 & 57.1 & 40.3 & 21.2 & 42.3 & 49.0 & 7.2M \\ YOLOv7-tiny-SiLU [43] & 640 & 38.7 & 56.7 & 41.7 & 18.8 & 42.4 & 51.9 & 6.2M \\ EfficientDet-DI [30] & 640 & 39.6 & 58.6 & 42.3 & 17.9 & 44.3 & 56.0 & 6.6M \\ YOLOv5-PD-s [58] & 640 & 40.0 & 59.5 & 43.5 & **23.5** & 44.9 & 50.4 & 8.7M \\ YOLOv6-T\({}^{300e}\)[42] & 640 & 40.3 & – & – & – & – & – & 15.0M \\ YOLOv6-T\({}^{400e}\)[42] & 640 & 41.1 & 57.5 & 44.3 & 21.0 & 45.9 & 58.1 & 15.0M \\ PAI-YOLOX-s [57] & 640 & 41.4 & 60.0 & 45.0 & 18.3 & 40.0 & 55.0 & 15.9M \\ YOLOX-s [16] & 640 & 40.5 & 59.3 & 43.7 & 23.2 & 44.8 & 54.1 & 9.0M \\ EYOLOX & 640 & **42.2** & 61.6 & **45.4** & **23.5** & 46.0 & 58.4 & 13.54M \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the accuracy of different object detectors on COCO-2017 val. The methods marked with “*” are the results reproduced in other papers. Without the associated annotations, all our results are derived under 300 epochs. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **Size** & **AP(\%)\({}_{\text{test}}\)** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **Parameters** \\ \hline YOLOv5-s [19] & 640 & 55.3 & 77.8 & 63.3 & 7.2M \\ YOLOX-s [16] & 640 & 59.3 & 79.5 & 65.4 & 9.0M \\ EYOLOX & 640 & **62.5** & **81.5** & **69.3** & **13.54M** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the accuracy of different object detectors on PASCAL VOC 07+12 test. Without the associated annotations, all our results are derived under 165 epochs. Figure 8: The AP comparison chart of EXOLOX training PASCAL VOC 07+12 on the validation set. The blue curve represents the result of iteration 165 epochs, and the red curve represents the result of iteration 300 epochs. Figure 9: Comparison of the prediction results between EXOLOX (**a**,**c**), YOLOX-s (**b**) and YOLOv5-s (**d**).
## 5 Model Evaluation
### Model Experiment 3: Weather Integrated Speed Prediction
For the \"LSTM_No weather\" scenario, the results also showed a high level of accuracy above 91% for multiple prediction horizons up to 60 min into the future. For the second scenario, \"BiLSTM_Rain\" and \"LSTM_Rain\", the input data included two features: speed (km/h) and rain intensity (mm). The results for the BiLSTM model showed a similar accuracy of above 99% for multiple prediction horizons and above 91% for the LSTM model for up to 60 min into the future (Table 5). The third scenario, \"BiLSTM_Rain and Wind\" and \"LSTM_Rain and Wind\", incorporated three inputs: speed data with rain and wind speed. The results showed prediction accuracies above 99% and 91% for BiLSTM and LSTM, respectively. The last scenario, \"BiLSTM_Weather\" and \"LSTM_Weather\", involved five inputs: speed, rain, wind speed and humidity and air temperature. The results for both models demonstrate the robustness of the models when tested under multiple weather  
Figure 14: Volume prediction results for 30-min horizons using the 4-BiLSTM model. Source: authors. Note: The first 500 conservative data points in the northbound direction were selected for presentation. Figure 15: Volume prediction results for 45-min horizons using the 4-BiLSTM model. Source: authors. Note: The first 500 conservative data points in the northbound direction were selected for presentation. conditions with an accuracy above 99% for the BiLSTM model and 91% for the LSTM model. The green cells in Table 5 represent the best-performing models, while the yellow cells represent the worst-performing models.