esa-sceva/satcom-chunk-collection huggingface dataset:
# Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification
## 2 Related Work  
### Few-Shot Learning  
Few-Shot Learning (FSL) is a critical research area in machine learning, aimed at solving learning problems under conditions of scarce data [12]. In contrast to traditional machine learning approaches that necessitate substantial volumes of labeled data, FSL is dedicated to utilizing a limited number of labeled samples to quickly learn and generalize to new tasks. Among the numerous FSL methods, prototypical networks [21] have received widespread attention due to their simple and effective design. The central concept of this method is to learn an embedding function that maps input data into a metric space, where each class is represented by a prototype. This prototype is usually the mean vector of all samples of that class in the embedding space. During the testing phase, new samples are classified into the category corresponding to the prototype closest to them in the embedding space.  
Considering the specificity of hyperspectral data, [PERSON] et al. [13] applied prototypical networks to the few-shot classification of HSI. By learning the prototype representation of each category, they achieved significant results in situations with few samples. This research provides an effective benchmark method for few-shot learning with hyperspectral data. Following [PERSON]'s DFSL, methods like DCFSL [14], GIA-CFSL [15], and ADAFSL [16] follow the same paradigm based on prototypical networks and meta-learning. They all enhance model performance, to some extent, through cross-domain adaptation. Our DIFSCL method follows the same paradigm as these four few-shot learning methods. It improves model performance under conditions of very few samples by reducing intra-class variability and inter-class similarity, based on a multi-scale adaptive attention feature extraction network, which enhances the feature discriminability of the prototypical networks.
# Real-Time Tomography of Gas-Jets with a Wollaston Interferometer
###### Abstract  
A tomographic gas-density diagnostic using a Single-Beam Wollaston Interferometer able to characterize non-symmetric density distributions in gas jets is presented. A real-time tomographic algorithm is able to reconstruct three-dimensional density distributions. A Maximum Likelihood-Expectation Maximization algorithm, an iterative method with good convergence properties compared to simple back projection, is used. With the use of graphical processing units, real-time computation and high resolution are achieved. Two different gas jets are characterized: a kHz, piezo-driven jet for lower densities and a solenoid valve-based jet producing higher densities. While the first jet is used for free electron laser photon beam characterization, the second jet is used in laser wake field acceleration experiments. In this latter application, well-tailored and non-symmetric density distributions produced by a supersonic shock front generated by a razor blade inserted laterally to the gas flow, which breaks cylindrical symmetry, need to be characterized.  
interferometry; gas-jets; LWFA; knife-edge; shock-front; real-time
# Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN
###### Abstract  
Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link, which may severely reduce the quality of network services due to long delay and low throughput. The flexibility and agility of SDN can effectively ameliorate the aforementioned problem. However, the utilization of link resources across data centers is still insufficient, and has not yet been well addressed. In this paper, we focused on this issue and proposed an intelligent approach of real-time processing and dynamic scheduling that could make full use of the network resources. The traffic among the data centers could be classified into different types, and different strategies were proposed for these types of real-time traffic. Considering the prolonged occupation of the bandwidth by malicious flows, we employed the multilevel feedback queue mechanism and proposed an effective congestion control algorithm. Simulation experiments showed that our scheme exhibited the favorable feasibility and demonstrated a better traffic scheduling effect and great improvement in bandwidth utilization across data centers.  
Keywords:data center; dynamic scheduling; congestion control; OpenFlow; software-defined networking +
Footnote †: journal:
# Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models
## 4 Results and Discussion
### Comparison of Different Models Evaluation Metrics  
The evaluation of the XGBoost, SVM, and RF models for predicting SHmax and SHmin reveals significant differences in their accuracy and robustness. As shown in Table 2, XGBoost outperforms the other models in SHmax prediction with MAE, MSE, and RMSE values of 0.250, 0.125, and 0.353, respectively. These values are markedly lower than those of SVM (MAE = 0.345, MSE = 0.242, RMSE = 0.492) and RF (MAE = 0.621, MSE = 0.763, RMSE = 0.874). XGBoost also achieves an R\({}^{2}\) of 0.978, indicating it can explain 97.8% of the variance in SHmax data, outperforming SVM (R\({}^{2}\) = 0.958) and RF (R\({}^{2}\) = 0.866). In SHmin prediction, XGBoost continues to lead with the lowest MAE, MSE, and RMSE values (0.304,  
Figure 6: Comparison of Prediction Accuracy and Stability of Different Models. (**a,b**) Scatter plots for XGBoost’s SHmin and SHmax predictions within \(\pm\)1% error. (**c,d**) The same for SVM. (**e,f**) are for RF.  
0.181, and 0.426) and the highest R\({}^{2}\) value (0.959). While RF's R\({}^{2}\) for SHmin (0.885) is slightly better than for SHmax, it still trails behind XGBoost and SVM.  
Figure 7 further validates these conclusions through a comprehensive analysis of multiple metrics via radar charts. In the chart, a model demonstrates superior comprehensive performance if its R\({}^{2}\) dimension is closer to the outer edge and its MAE, MSE, and RMSE dimensions are closer to the center. XGBoost demonstrates near-maximum R\({}^{2}\) values and near-minimum error values, highlighting its superior prediction accuracy and goodness of fit. SVM, while outperforming RF, shows higher error metrics and lower R\({}^{2}\) values compared to XGBoost, indicating less favorable predictive performance. This suggests that SVM, though capable in nonlinear regression tasks, is not as effective as XGBoost in this specific application. Conversely, RF exhibits the largest radar chart coverage, with higher error metric dispersion and the smallest R\({}^{2}\) value. This indicates RF's predictions are more sensitive to outliers and have weaker generalization performance. Overall, XGBoost, with its integrated learning mechanism and hyperparameter optimization strategy, shows higher reliability and engineering applicability in the nonlinear mapping of complex geological features and target variables, and provides an optimal solution for horizontal in situ stress prediction.  
Figure 7: Radar Chart of Performance Metrics for XGBoost, SVM, and RF Models. **(a)** Evaluation metrics for SHmax prediction. **(b)** Evaluation metrics for SHmin prediction.
## 4 Results and Analysis
### Analysis of Variance (ANOVA)  
To understand a detailed visualisation of the impact of various factors influencing the depth of cut, i.e., material removal, in the belt grinding of Aluminium 6061 specimen, ANOVA was used. Based on the ANOVA, the comparative significance of the grinding parameters on the depth of cut was examined to define more accurately the optimum combination of the grinding parameters for higher material removal. The analysis is carried out for the level of significance of 5% (the level of confidence is 95%); the results are shown in Table 3. The last column of Table 3 shows the percentage impact of each parameter on the total variation, signifying their degree of effect on the results. The larger the percentage contribution, the greater the influence a parameter has on the material removal. It can be observed from Table 3 that the grit size (F = 124.93) has the greater static influence of 63.93%, followed by RPM (F = 26.42), which has an influence of 13.54%, and Force (F = 25.00), which has an influence of 12.79% on material removal rate. The feed and wheel hardnesses have an insignificant effect on the material removal of 3.34% and 2.31%.  
Interaction plots are most often used to visualise interactions during ANOVA. The effect of one factor depends on the level of the other factor, and such possible interactions can be envisioned using an interaction plot. The two way interaction effects are shown as line plots in Figure 10. Figure 10, below, indicates the interaction plot among the five belt grinding parameters that have been considered in our study on the depth of cut.  
Figure 10: Two way interaction effect plots between RPM, feed, force, hardness, and grit and their influence on depth of cut at different initial levels of parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Machining Parameter** & **Degrees of Freedom** & **Sum of Squares** & **Mean Square** & **F Ratio** & **Contribution (\%)** \\ \hline RFM & 2 & 5055.5 & 2527.7 & 26.42 & 13.54 \\ Feed & 2 & 1249.1 & 624.5 & 6.53 & 3.34 \\ Force & 2 & 4782.8 & 2391.4 & 25.00 & 12.79 \\ Hardness & 2 & 867.1 & 433.6 & 4.53 & 2.31 \\ Grit & 2 & 23,903.7 & 11,951.9 & 124.93 & 63.93 \\ Error & 16 & 1530.7 & 95.7 & - & 4.09 \\ Total & 26 & 37,388.8 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of analysis of variance (ANOVA) for depth of cut.
## 5 Optimization Problem
}^{N_{S}-1}y_{\{i,p\},\{i,p\},\{b,p\}}^{t\;mod\;T,h}\right] \}=0\] \[\forall i\in[0,\ldots,N_{S}-1],\;\forall h\in[0,\ldots,N_{T}-1],\] \[\forall i\in[f_{t},\ldots,f_{t}^{h}+f_{t}^{h}],\;\forall p\in\{0,1\}\] \[(1-\delta_{f_{t}^{h},0})\cdot y_{\{N_{S}-1,1\},\{N_{S}-1,1\}}^{(f _{t}^{h}+f_{t}^{h}-1)\;mod\;T,h}+y_{\{N_{S}-1,0\},\{N_{S}-1,1\}}^{(f_{t}^{h}+f _{t}^{h})\;mod\;T,h}+\sum_{i=0}^{N_{S}-2}y_{\{i,n\},\{N_{S}-1,1\}}^{(f_{t}^{h}+ f_{t}^{h})\;mod\;T,h}=1,\] (24) \[\forall h\in[0,\ldots,N_{T}-1]\]  
In particular, constraint (20) ensures that the resource allocation does not exceed the capacity of each link (notice that, thanks to the proposed modeling, this expression is valid for all transmission, storage, and processing links). Constraint (21) limits the energy consumption during each time cycle in each node as the maximum energy amount available on the node at the beginning of the time cycle. Constraints (22)-(24) are related to flow conservation. Specifically, following the characteristics of the considered EO applications, constraint (22) sets the source node for each service as the one related to the satellite which acquired the image, dealing with unprocessed data, at the service generation cycle, and ensures that node has only an outgoing flow for each service. In particular, constraint (24) sets the destination node for each service as the \(vGS\) dealing with the processed data in the service delivery deadline time cycle, and ensures that node has only an ingoing flow for each service. Please note that constraint (24) also ensures that all services are processed and does not prevent a service to reach the ground station before the service delivery deadline. Finally, constraint (23) simply imposes flow conservation on intermediate nodes on the path each service crosses from the source to the destination. It could be interesting to underline further details on how the specific application of OEC to EO missions impacts the definition of the presented optimization problem. In particular, since both topology and service generation are cyclostationary, we just have to define and solve the problem in a cyclostationary period, since what happens in that period repeats all mission long. For this reason, constraints (20) and (21) shall be written only for the time cycles composing a cyclostationary period, while in constraints (22)-(24), since a service delivery deadline cycle could exceed the end of the current cyclostationary period, we leverage modulo operation to bring back all that would happen after the end of the cyclostationary period to its beginning, since cyclostationary periods always repeat the same one after another. Finally, we discuss the complexity of the proposed formulation. In particular, it can be noticed that it is analogous to the decision multi-commodity flow problem, to which an additional constraint related to energy is added. For this reason, since the decision of the multi-commodity flow problem is NP hard [39], the same applies to our problem.
# Modified Equation of Shear Strength with Respect to Saturation
## 5 Sensitivity Analysis of Four Parameters  
The parameter sensitivity analysis was conducted to examine the robustness of the proposed model and evaluate whether varying key parameters affected the results. In this model calculation, one parameter was changed while the others remained fixed at their optimal value. The result of the parameter sensitivity analysis is shown in Figure 25. Take \(\rho_{d}=1.5\) t/m\({}^{3}\) as an example, the ranges of four parameters are shown as follows: \(\xi_{2}\), [38912.99, 41929.02]; \(\xi_{3}\), [\(-4.184\), \(-4.049\)]; \(\xi_{4}\), [19883.32, 20763.47]; m, [0.2397, 0.2429]. By comparing the range of shear strength under the variation of the one parameter, we found that this model was much more sensitive regarding the parameter m, which is also related to the material characteristic. When soil is close to a saturated status, the shear strength tends to a stable value. This is because the softening effect of soil against shear strength is much more obvious when soil tends to the saturated status.  
Figure 24: Comparison of the calculated shear strength of the proposed model and the experimental data [27].  
Figure 25: _Cont._
## 2 Methodology
### The Best-Worst Method (BWM)  
#### 2.2.1 Overview of the BWM  
The AHP is a well-established method for obtaining subjective weights, involving pairwise comparisons between various evaluation indicators to establish a hierarchy of their importance. However, AHP can be cumbersome, requiring \(n(n-1)/2\) comparisons, and when there are many evaluation indicators, the process can lead to significant errors and inconsistencies.  
In contrast, the BWM, introduced in 2015 by [PERSON], a scholar from the Netherlands, is a newer multi-attribute decision-making approach. BWM, which stands for the Best-Worst MCDM method, features multi-criteria, qualitative and quantitative aspects, mathematical expression, and statistical processing [38]. As a novel weighting method based on pairwise comparisons, BWM simplifies this process by only requiring comparisons between the best indicator against all others and all indicators against the worst. Compared to AHP, BWM is simpler and more accurate, reducing redundant parts. In handling multi-level mathematical models, BWM uses limited data to mathematize the decision-making process. It only requires \(2n-3\) comparisons among the indicators to obtain optimal weight results, significantly reducing the number of comparisons needed. This not only ensures reliability,but also minimizes inconsistencies in the comparison process. The results obtained from BWM are closer to real situations and exhibit stronger consistency.
# A Novel Hybrid Model for Cantonese Rumor Detection on Twitter
## 4 Experiments and Evaluation  
In this section, we evaluate the performance of the proposed XGA model based on CR-Dataset. All experiments were undertaken on a workstation with two Tesla-V100 32G GPUs. In the experiments, we held out 80% of CR-Dataset for training, 10% for validation, and 10% for testing. The results shown in this section are the average value of each experiment that was repeated ten times independently.  
Four metrics are used to evaluate the performance of the embedding layer and the proposed detection model, including _Accuracy_, _Precision_, _Recall_, and _F-score_. The _True Positive (TP)_ is the number of rumors that are correctly detected, the _False Negative (FN)_ is the number of rumors that are incorrectly detected, the _False Positive (FP)_ is the number of non-rumors that are incorrectly detected, and the _True Negative (TN)_ is the number of non-rumors that are correctly detected. _Accuracy_, _Precision_, _Recall_, and _F-score_ can be computed by  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}, \tag{12}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{13}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{14}\]  
\[F-score=\frac{2\times Precision\times Recall}{Precision+Recall}. \tag{15}\]  
In addition, we plot a Receiver Operating Characteristic (ROC) curve based on _True Positive Rate (TPR)_ and _False Positive Rate (FPR)_ and compute the Area Under Curve (AUC) score to evaluate the performance of our proposed model. _TPR_ and _FPR_ can be computed by  
\[TPR=\frac{TP}{TP+FN}, \tag{16}\]  
\[FPR=\frac{FP}{FP+TN}. \tag{17}\]
## 4 Discussion
### Comparison of Three Approaches
#### 4.1.2 Comparison between Parameterization Approaches and Regression Tree Approach
The Cubist regression tree approach belongs to the empirical linear regression method, which could be easily affected by a specific parameter and produced a fluctuating line. For the parameterization approaches, the LUE showed a gradual increase from the Arctic to equatorial regions. The three approaches all witnessed a local minimum of 0.5 gC/MJ at 12\({}^{\circ}\)N, which was caused by the large area of low LUE in Africa. In the second season, there were few changes (small drops) in the Southern Hemisphere; however, the Northern Hemisphere witnessed large increases up to 1.0 gC/MJ at 50\({}^{\circ}\)N. Similar to the first season, the curve generated by V3 showed more fluctuations. In the third season, vegetation in the Southern Hemisphere remained roughly unchanged. However, the LUE in the Northern Hemisphere declined. The peaks produced by V1 and V2 fell to 0.7 gC/MJ, while those produced by V3 remained at 1.0 gC/MJ. In the last season, the Southern Hemisphere saw a slight increase in LUE, with a peak of almost 1.0 gC/MJ. However. The LUE in the Northern Hemisphere went through a slump, and the latitude of the peak moved from 55\({}^{\circ}\) to 25\({}^{\circ}\), and the peak LUE declined to 0.5 gC/MJ. Compared with the Southern Hemisphere, the Northern Hemisphere presented much more variation. The main reason for this variation was the distribution of vegetation in the Southern Hemisphere, which was mainly located in low-latitude areas, while the vegetation in the Northern Hemisphere was spread in low  
Figure 10: Global GPP in each type of vegetation. V1: Parameterization approach without the CI; V2: Parameterization approach with the CI; V3: Cubist regression tree approach. and high-latitude regions. High-latitude areas underwent great changes in temperature and solar radiation, which were essential for vegetation growth. Therefore, the LUE in the Northern Hemisphere varied greatly during different seasons. The two parameterization approaches showed few differences except in equatorial regions and other local maximum values where V2 had a higher LUE than V1. A better explanation for this increase can be found in Section 3.2.2. The Cubist regression tree approach, however, presented a clear difference from the former parameterization approaches. V3 produced a higher LUE in high-latitude areas (50\({}^{\circ}\)N and 50\({}^{\circ}\)S) and a lower LUE at low-latitude areas (between 30\({}^{\circ}\)N and 30\({}^{\circ}\)S). Looking at the GPP curves with latitude, there were two clear peaks reaching 75 TgC/yr at 50\({}^{\circ}\)N and 130 TgC/yr in equatorial regions. The annual GPP curves from the 3 approaches showed fewer variations than those of LUE.
## 2 Related Work
### Sequence Tagging Based Method  
[PERSON] et al. [9] first propose the truly joint entity and relation extraction model which uses a sequence tagging scheme for joint extraction. [PERSON] et al. [10] propose a hierarchical reinforcement learning-based (RL) deep neural model for joint extraction. High-level reinforcement learning is used to determine relationships based on relation-specific tokens. After determining the relationship, the low-level reinforcement learning extracts the connection between two entities and the relation by using a sequential annotation method. To address the problem that the CopyR [11] model can only extract the last token of an entity, the CopyMTL [12] model improves CopyR and is able to extract the entity name completely using the sequence annotation method. [PERSON] et al. [13] decompose the joint extraction into two sub-tasks, the first task is head entity extraction and the second task is tail entity and relation extraction. They also use the sequence tagging approach to accomplish these two sub-tasks. [PERSON] et al. [14] added BERT to the model proposed by [PERSON] et al. [13] to improve the performance of the model. [PERSON] et al. [15] add relation-specific attention mechanism to the joint extraction model based on sequence tagging and achieve a good improvement. Multi-head+AT [16] treats the relation extraction task as a multi-head selection problem. Each entity is combined with all other entities to form entity pairs, and then the relationships of these entity pairs are predicted. Each relationship is independent to achieve multiple relationship predictions for entity pair. SciIE [17] reduces the error propagation problem between named entity recognition and relation extraction by introducing a multi-task mechanism and disambiguation task. In addition, joint entity and relation extraction based on question answering systems or reading comprehension have emerged. Multi-turn QA [18] treats joint entity and relation extraction as a multi-turn question answering task. MRC4 ERE++ [19] proposes a multi-turn question answering-based diversity question answering mechanism and designs two selection strategies to integrate different answers. [PERSON] et al. [20] propose a unified multi-task learning framework, which decomposes the task into three interactive subtasks, and they present a problem-based method to generate extracted objects.  
Sequence tagging-based methods mark words with labels (BIO/BILOU). In this way, each word can only be assigned one label, which leads to the problem that overlapping entities can not be well extracted. In addition, methods based on question-answering depend on the quality of question generation and the performance of the question-answering model. Therefore, span-based joint entity and relation extraction methods have emerged.