esa-sceva/satcom-chunk-collection huggingface dataset:
# Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision
## 5 Evaluation
### Comparison to Training on Manually Labeled Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Re-Created Features**} & \multicolumn{3}{c}{**Features from Large Set**} \\ \hline
**Learner** & **Precision** & **Recall** & **F1-Measure** & **Precision** & **Recall** & **F1-Measure** \\ \hline NB & 0.5912 & 0.6983 & 0.6403 & 0.4425 & 0.4310 & 0.4367 \\ \hline DT & 0.6944 & 0.6466 & 0.6696 & 0.7156 & 0.6724 & 0.6933 \\ \hline NN & 0.7500 & **0.8017** & 0.7750 & 0.6640 & 0.7155 & 0.6888 \\ \hline SVM & 0.7311 & 0.7500 & 0.7404 & **0.8585** & 0.7845 & **0.8198** \\ \hline XGB & **0.8000** & 0.7586 & **0.7788** & 0.7797 & **0.7931** & 0.7863 \\ \hline RF & 0.7524 & 0.6810 & 0.7149 & 0.7938 & 0.6638 & 0.7230 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Cross validation results on manually labeled gold standard, using tweet features only. We distinguish a setup with re-created features for the manually labeled dataset, and re-using features from the large dataset. The best results are marked in bold. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Re-Created Features**} & \multicolumn{3}{c}{**Features from Large Set**} \\ \hline
**Learner** & **Precision** & **Recall** & **F1-Measure** & **Precision** & **Recall** & **F1-Measure** \\ \hline NB & 0.8235 & 0.7241 & 0.7706 & 0.5619 & 0.6983 & 0.6255 \\ \hline DT & 0.8750 & 0.9052 & **0.8898** & 0.8571 & 0.8793 & 0.8681 \\ \hline NN & 0.8547 & 0.8621 & 0.8584 & 0.8110 & 0.8879 & 0.8477 \\ \hline SVM & 0.8584 & 0.8362 & 0.8472 & **0.9299** & **0.9138** & **0.9217** \\ \hline XGB & **0.8947** & 0.8793 & 0.8870 & 0.8983 & **0.9138** & 0.9059 \\ \hline RF & 0.8618 & **0.9138** & 0.8870 & 0.8522 & 0.8448 & 0.8485 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Cross validation results on manually labeled gold standard, using user and tweet features. We distinguish a setup with re-created features for the manually labeled dataset, and re-using features from the large dataset. The best results are marked in bold. of a tweet being fake news. The network of a user also matters: the strongest indicator is the containment of a user in lists created by other users. When looking at tweet level features only, as in Scenario 1, more features with lower weights are selected by XGBoost, indicating that this classification problem is inherently harder. We can see that text surface features, such as capitalization, character repetitions, and exclamation marks play a more crucial role in this respect. Moreover, quite a few doc2 vec features for the contents are selected, while topics and sentiment features are not considered helpful by XGBoost. The fact that the month of the year is selected as a relevant feature is probably an artifact of the dataset distribution, as shown in Figure 1. From this distribution, we can see that the months February-June 2017 have a higher ratio of real to fake news tweets than the other months, which is an effect of the data collection and the rate limitations of the Twitter API, as discussed above.
## 6 Result Discussion
### Simulation Environment  
The first step in developing a simulation strategy is to create a comprehensive IoT environment in a clustered form, as shown in Figure 5a, which depicts a sample of four IoT clustered zones. As shown in Figure 5, the RIN communicates with other nodes via single-hop communication, if it is closer to their transmission range, or it requires the assistance of CIN, whose selection process was discussed in the previous section. It should be noted that depending on the density of nodes in each cluster, there may be multiple IoT access points. However, the absence of a CIN will cause the RIN to communicate with the IoT hotspot, which will then search for the nearest CIN. The address of the new CIN is shared with RIN. An IoT hotspot is assumed to offer sufficient coverage for all types of nodes within each cluster. Figure 5a highlights the intra-cluster operation where the control message is propagated to look for the destination node. If the destination node is located within the cluster, the target mobile node follows the path (shared by CIN) to share the data with its destination node. Figure 5b highlights the inter-clustering operation where the source and destination node are located in different clusters. In such cases, the IoT hotspots assist in sharing the address of the next relay node located in different clusters and finally assist in offering proper path information. The proposed study considers Optimized LinkTable 1 highlights the simulation parameters considered for the analysis. It should be noted that the values of the simulation parameters were configured from observation carried out on the frequently implemented research work in the existing system.  
All the values tabulated in Table 1 can be amended to suit any application demands in IoT. While analysed with a different range of values, the variation in numerical outcomes was found to be only a mere 5.27%, which exhibits the consistency of the proposed model implementation.
# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 5 Discussion
### Discussion and Analysis Based on Performance Metrics
#### 5.2.6 Analysis Based on Sharpe Ratio  
In this part, the values of the Sharpe ratio are discussed. The Sharpe ratio refers to the amount of return generated relative to the risk taken, where risk is calculated using the standard deviation of return. Table 20 shows the details of the papers that used the Sharpe ratio as a performance metric. In this table, papers [14,18] used a CNN-based model; papers [71,75] used a DNN-based model; papers [77] and [82] used a reinforcement learning-based model; and papers [84,88,90,91] used other deep learning methods.  
It could be found that papers [75,84,91] performed best, which used a DNN model, and Sharpe ratio was in the range of 5-10. Paper [77] also had a great performance, but papers [14,18,82,88,90] did not have high Sharpe ratios.  
Among the paper that used the Sharpe ratio, all CNN papers were in the range of 0.1-1. The two DNN papers were in the ranges 1-2 and 5-10; 50% of the reinforcement learning papers were in the range of 0.1-1; 50% of the reinforcement learning papers were in the range of 2-5; 50% of the other deep learning method papers were in the range of 0.1-1; and 50% of the other deep learning method papers were in the range of 5-10.
## 2 Model and Method
### Numerical Method  
The software ANSYS2020R1 Fluent is used to investigate the complicated oil-air two-phase flow inside the under-race lubrication ball bearing. In numerical calculations, the inlet boundary of the computational region is set as a velocity inlet condition, and the outlet boundaries on both sides of the bearing are set as pressure outlet boundary conditions. Two assumptions in the calculation process are isothermal conditions and hydrodynamics. The sliding mesh method is used to simulate the motion of various regions inside the bearing. At initialization, the gas phase volume fraction of the entire bearing fluid region is set to 1, and the oil phase volume fraction is set to 0. Air is set as the main phase, and oil is set as the secondary phase. The type of lubricating oil is Mobil Jet Oil II. The SIMPLE algorithm is used for pressure-velocity coupling of the two-phase flow inside the bearing. The pressure term uses the PRESTOf format, and the momentum, turbulent kinetic energy, and turbulent dissipation rate are discretized using the upwind scheme. To avoid divergence issues that may occur during the solution of nonlinear equations, under-relaxation iteration is used. During the calculation process, the inlet and outlet flow rates of the flow field and the average oil phase volume fraction inside the bearing are monitored. When the difference in inlet and outlet flow rates is around 1% and the average oil volume fraction inside the bearing remains basically unchanged, it is considered as converged, and the calculation is stopped to determine the final result.
## 4 Proposed Modulation Recognition Scheme  
### CNN-Based Scheme  
The CNN-based modulation recognition scheme can extract and learn spatial features from weak signals, thereby generating a feature map of the weak signal [21]. In DNNs, each layer processes the image data \(x_{0}\) by performing a series of non-linear transformations. These transformations may involve operations such as Batch Normalization (BN), activation function, pooling, and convolution (Conv).  
The activation function is the Rectified Linear Units (ReLUs) in Equation (10).  
\[f(x)=\max(0,x) \tag{10}\]  
The BN layer is used to normalize the input of each layer, which is defined as follows.  
\[\text{BN}(x)=\gamma\bigg{(}\frac{x-\mu}{\sqrt{\sigma^{2}+\epsilon}}\bigg{)}+\beta \tag{11}\]  
where \(\gamma\) and \(\beta\) are learnable parameters, \(\mu\) and \(\sigma\) are the mean and standard deviation of the input, and \(\epsilon\) is a small constant to avoid division by zero.  
The image-based deep learning methods generally use two-dimensional convolution. The Conv layer is expressed as  
\[y(h,w)=\sum_{m}\sum_{n}x(h-m,w-n)h(m,n) \tag{12}\]  
where \(h\) and \(w\) are the length and width of the input \(x\).  
The above series of non-linear operations is denoted as \(H_{l}(\cdot)\). The output feature map of each layer is \(x_{l}\), with dimensions \(C\times H\times W\). The output of the convolutional layer at a certain layer is given by  
\[x_{l}=H_{l}(x_{l-1}) \tag{13}\]  
Figure 1: Time–frequency image of QPSK, SPSK, 16 QAM 64 QAM, GPSK, PAM4, B-FM and DSB-AM. In TFIs, the colors typically indicate signal energy strength at a specific time and frequency. Darker colors mean stronger energy at that moment and frequency.  
This continuous hierarchical structure enables the network to extract and abstract image features in an increasingly deeper manner. Many studies have focused on the combination of CNN and RNN [21]. Although DNNs can still offer strong performance on certain tasks, especially in scenarios that demand simultaneous processing of spatio-temporal information, they are prone to issues such as vanishing or exploding gradients, increased computational complexity, and limited generalization capabilities.
# Observer-Based Distributed Fault Detection for Heterogeneous Multi-Agent Systems
## 5 Conclusions  
Two distributed FD schemes for heterogeneous MAS are proposed in this paper. A state-feedback-based distributed control law is adopted to realise COR when there does not occur any fault, where state-feedback-based observers for FD are designed, and existence of the designed observers is also proved. Furthermore, we consider the condition that state information is unmeasurable, an output-feedback-based distributed control law is designed, as well as output-feedback-based observers for FD, where existence of the above observers are ensured through appropriately designed feedback information. Finally, two simulation examples verify the effectiveness of the proposed FD schemes. However, the above distributed control laws and FD schemes require one agent to obtain information from its neighbours, such as state, compensator state, measurement output and state estimate, which will exert a heavy burden on communication networks.  
Possible future work includes considering the condition that agents suffer from disturbances and faults simultaneously, as well as reducing communication burden.  
Conceptualization, [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 61973005 and 61633001.  
The authors would like to thank the editors and all the anonymous reviewers for their careful reading of this paper and their constructive suggestions.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
Multi-agent systems FD Fault detection COR Cooperative output regulation UIO Unknown input observer UAV Unmanned aerial vehicles
## 2 Case Study: Energy Harvesting Floor
### Testing Piezoelectric Transducers
#### 2.2.1 Macro Fiber Composite Transducers  
Macro fiber composite (MFC) transducers were employed for fault detection and harvesting high frequency small vibrations [16; 17; 18; 19]. MFCs consist of rectangular piezo ceramic rods sandwiched between layers of adhesive, electrodes, and polyimide film [19; 20; 21; 22]. The electrodes are attached to the film in an interdigitated pattern [23; 24; 25].  
Table 1 shows the characteristics of the P1 and P2 piezoelectric transducers. The terms P1 and P2 refer to the type of movement as follows: When the P1 type is excited with electrical power it produces an elongation movement, and vice versa, and when the P2 type is excited it generates a contraction movement, and vice versa.  
This transducer generated the lowest output voltage (250 mV) despite its larger size because it was designed to generate voltage with an elongation motion (P1). However, the force was applied directly to the transducer in the tests and did not produce enough elongation, therefore, it produced a reduced voltage.  
* MFC 2: M-5628-P1  
This transducer was similar to MFC 1, however, the output voltage was better, mainly because of the larger size of MFC 1 was more inefficient for this specific application. The MFC 2 had a smaller area and the weight pressed more area, producing a better output.  
* MFC 3: M-2814-P2  
The MFC 3 is a P2 type, i.e., the optimal direction of movement is a contractor movement. The output voltage is shown in Table 2. Due to its reduced size, together with the P2 type, the output voltage generated was better than previous cases.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Active** & **Active** & **Overall** & **Overall** & **Capacitance** & **Free Strain in** & **Blocking** \\  & **Length** & **Width** & **Length** & **Width** & **in****F** \(\pm\) **20\%** & **ppm** \(\pm\) **10\%** & **Force in** \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline M0714-P2 & 7 & 14 & 16 & 16 & 7.89 & \(-\)600 & \(-\)85 \\ M2814-P2 & 28 & 14 & 37 & 18 & 30.78 & \(-\)700 & \(-\)85 \\ M5628-P1 & 56 & 28 & 67 & 37 & 8.71 & 1350 & 340 \\ M8557-P1 & 85 & 57 & 103 & 64 & 16.00 & 1350 & 693 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Macro fiber composite (MFC) characteristics.  
* MFC 4: M-0714-P2  
The output voltage, shown in Table 2, is the highest of all the MFCs. The MFC 4 was the smallest of the MFC tested. The surface was pressed in its totality when the weight pressed the transducer. The MFC P2 type had a better performance than the MFC P1 type for this application.
# Bridge Assessment under Earthquake and Flood-Induced Scour
## 2 Methodology
### Fragility Curves and Surfaces Development
#### 2.6.3 Fragility Surfaces  
Fragility surfaces illustrate the cumulative impact of seismic action in the presence of local scour on the likelihood of bridge failure across different damage states. Statistical independence is assumed among random variables of the intensity measures, which depict flood and earthquake hazards. The joint CDF of seismic action and local scour induced by flood yields the structural failure probability in a combined-effect scenario. Fragility surfaces are derived using the maximum likelihood method of bivariate log-normal distribution.  
\[L=\left(\prod_{j=1}^{m}F\big{(}x_{1,j}x_{2,j};\theta_{1,k},\theta_{2,k},\beta_ {1,k},\beta_{2,k}\big{)}\right)\left(1-F\big{(}x_{1,j}x_{2,j};\theta_{1,k}, \theta_{2,k},\beta_{1,k},\beta_{2,k}\big{)}\right)^{n-m} \tag{4}\]  
In this expression, the random variables of \(x_{1,j}\) and \(x_{2,j}\) denote peak ground acceleration and the annual peak flow discharge, respectively. The values of medians \(\theta_{1,k}\) and \(\theta_{2,k}\) and log-standard deviations \(\beta_{1,k}\) and \(\beta_{2,k}\) are given for damage state \(k\). The bivariate log-normal distribution's probability density function is derived using the Jacobian of the transformation and can be articulated as follows:  
\[f\big{(}x_{1,j}x_{2,j};\theta_{1,k},\theta_{2,k},\beta_{1,k},\beta_{2,k}\big{)} =\frac{1}{2\pi x_{1,j}x_{2,j}\beta_{1,k}\beta_{2,k}}\exp\left(- \frac{\left(\frac{\ln\big{(}x_{1,j}\theta_{1,k}\big{)}}{\beta_{1,k}}\right)^{ 2}+\left(\frac{\ln\big{(}x_{2,j}/\theta_{2,k}\big{)}}{\beta_{2,k}}\right)^{2} }{2}\right) \tag{5}\]
## 1 Introduction
The features that are not relevant to the problem or are insensitive to the problem should be removed, and a subset (i.e., the optimized monitoring points) of the most important features of the problem will be selected. Common feature selection methods include the correlation coefficient method [32; 33] and the least absolute shrinkage and selection operator (LASSO) regression method [34; 35]. Moreover, some popular optimization principles are typically harnessed, which are the gauge orthogonality criterion, strain gradient criterion, and distance constraint. When compared to the LASSO method, the correlation coefficient method examines the measured strain data for strain monitoring location optimization, which is more efficient. However, the traditional correlation method based on Pearson correlation can only evaluate the strain correlation degree between any two monitoring points. When the first two monitoring points are determined, it is impossible to select the third one. In order to optimize the strain monitoring points based on a minimum correlation criterion, an improved correlation method is proposed to estimate the correlation degree of a couple of strain monitoring points. The major contribution of this study is that a strain gauge location optimization method based on a minimum correlation method is proposed. To overcome the drawback of the Pearson correlation method, an improved correlation method is proposed to measure the correlation degree of a couple of strain monitoring points. In this study, only the strain data are required for strain gauge location optimization, which can provide accurate load prediction results. The rest of this study is outlined as follows. In Section 2, a strain monitoring location optimization method is proposed based on a minimum correlation criterion. Instead of the traditional Pearson correlation method, an improved correlation method is defined to measure the correlation between a certain monitoring point and many other monitoring points. Moreover, based on an aircraft wing box, the proposed correlation method for strain gauge location optimization is validated and proven to be effective and accurate in Section 3. And then, a comparison with LASSO regression and principal component regression is conducted in Section 4 to demonstrate the advantages of the proposed correlation method. Finally, some key conclusions are summarized in Section 5.
## 2 Methods
### Context-Aware Strategy  
FCOS proposed an effective strategy for suppressing low-quality predicted bounding boxes, called center-ness. By calculating the degree to which each pixel deviates from the center of the ground-truth box, the predicted bounding boxes far from the center can be filtered. In FCOS, all proposal points inside the ground-truth boxes are regarded as positive samples.  
Location(\(x,y\)) is defined as a positive point if it lies within the range of any ground-truth box. \(left,right,top,bottom\) refer to the distances from the positive point to the four edges of ground-truth bounding boxes. The center-ness is defined as:  
\[Centerness=\sqrt{\frac{\min(left,right)}{\max(left,right)}\times\frac{\min(top,bottom)}{\max(top,bottom)}} \tag{5}\]  
However, the edge of bounding boxes usually contains background information and the points on the edge of bounding boxes are usually corresponded to negative samples. Based on center-ness branch, we use a context-aware strategy that restrains the range of positive samples. As shown in Figure 4, the dashed yellow rectangle represents the bounding box, named ground-truth box, and the red circle represents the context region, named context-aware region. Taking the center point of the ground-truth box as the center, a circular part with a radius is taken, and only the points falling into the circular region can be regarded as positive samples. Then, the model calculates the _Centerness_ of each point to constrain the predicted bounding boxes, which ensures that the low-quality predicted bounding boxes will be close to the center of the ground-truth box. The circular area can filter the negative samples to improve the discrimination ability of the model.  
Each layer \(E_{i}\) is used to detect objects at different scales. According to the sizes of the bounding boxes, objects are assigned to the corresponding \(E_{i}\) for detection, and the radius multiplied by the stride of layer is the true radius of this layer. Due to the multi-scale feature pyramid structure, each layer has a different area for filtering negative samples.  
\[area=\pi(radius*stride)^{2} \tag{6}\]  
In our experiment, the radius is set to 2.0, and stride is [8; 16; 32; 64; 128]. In Section 3.5, we conducted experiments to validate the effectiveness of context-aware strategy in different radii.  
Figure 4: Context-aware strategy. Yellow box: ground-truth. Red circle: context-aware region. Black boxes: possible proposals without context-aware strategy.
## 2 Theoretical Fundamentals
### Encoder Stacks  
The encoder layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward network. A residual connection was employed around each of the two sub-layers, followed by layer normalization. That is, the output of each sub-layer is LayerNorm. (\(x+\text{Sublayer}(x)\)), where \(\text{Sublayer}(x)\) is the function implemented by the sublayer itself. To facilitate these residual connections, all sublayers in the model, as well as the embedding layers, are taken into account. The encoder block is shown in Figure S1.  
The parameters used in the fault-diagnosis task are shown in Table S2. In this task, considering that the complexity of the fault data is greater than that of the fault-prediction task, sufficient attention was needed to learn the correlation between data of each fault type in the training process, so it increased the parameter head to 4. In the middle layer, the classical transformer model was used without any changes. The dropout operation in Layer 3 and Layer 4 was used to prevent the model from being overfitted during training, resulting in poor performance on the test set.  
The parameters used in the fault-prediction task are also shown in Table S3. The traditional embedding layer was changed to tranquilized time-series decomposition embedding, which linearized the structure, and our prediction data were the matrix [L, 3]. Therefore, the value of \(\text{d}_{\text{Model}}\) could only be 3. Considering that the predicted data were not particularly complex, the encoder was changed from N to 1 and maintained head as 3, so that the model could learn the correlation between the fault data and its trend and seasonal value. The traditional transformer model settings were maintained for the hidden layer, with a \(\text{d}_{\text{rf}}\) value of 128. Similarly, the dropout techniques were used in Layer 3 and Layer 4 to prevent overfitting the model, which would lead to low performance.