esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Algorithm
### Optimal Action Selection Strategy
If there is conflict or interference between nodes, the corresponding matrix element is represented by 1; otherwise, it is represented by 0._  
The conflict interference matrix MC is constructed based on the WSNs topology, as shown in Figure 3 is the conflict interference matrix based on Figure 1.  
**Definition 2**. : _Data urgency indicates the urgency of data to be sent in a certain time slot, represented by \(u_{i}\). It is related to the number of remaining hops \(h_{i}\) and the remaining deadline \(t_{i}\). The data urgency can be expressed as follows:_  
\[u_{i}=h_{i}/(t_{i}*(t_{i}-h_{i})) \tag{8}\]  
The steps to determine the concurrent node set are as follows:  
Step 1: Construct the conflict interference matrix, determine the most urgent node N by the best action, and add this node to the concurrent node-set;  
Step 2: Add the node (excluding node N) with column coordinates corresponding to the value of 0 in the \(n\)-th row of matrix MC to the list to be transmitted;  
Step 3: Calculate the urgency of other data on the network. If the node with the highest urgency is in the list to be transmitted, add the node to the concurrent node-set. Then, remove the node from the list to transfer. If the data are not in the list to be transmitted, ignore the data and repeat Step 3. Step 4: If the list to be transmitted is not empty, continue step 3 until the list to be transmitted is empty and obtain the final concurrent node-set based on the most urgent data. Taking Figures 1 and 3 as an example, assuming the current time slot, the data _data_g_ generated by node g are selected as the most urgent data through the exploration and development strategy, and the node where the data _data_g_ are currently located is \(b\). In step 1, it is determined that node \(b\) is added to the concurrent node-set. In step 2 and the conflict interference matrix, nodes \(d\), \(e\), \(i\), \(j\), \(k\), \(l\), and \(m\) are added to the list to be transmitted. In step 3, calculate the data state urgency evaluation \(ct\) generated by other nodes except the data generated by node \(g\), select the data with the highest evaluation value and the  
Figure 3: Conflict interference matrix. node where the data are currently located in the transmission list, and add the node to the concurrent node-set. Assuming that the node is \(j\), update the transmission queue and remove nodes \(e\), \(i\), \(j\), and \(m\). Continue to select the node with the most urgent data in the list to be transmitted to join the concurrent node-set and update the list to be transmitted. Repeat step 3 until the list to be transmitted is empty, then the final of concurrent node-set \(\{b,d,j,k,l\}\) can be obtained.
## Appendix A Extended Experiments
### Comparison Using More Metrics  
To present an overall validation, we adopted three additional metrics, precision, recall (also sensitivity), and F1-score (weighted average of precision and recall) in the section. The datasets used include MNIST, Fashion-MNIST, and CIFAR-10. The learning rate was set as \(0.01,0.1,1\). For MNIST and Fashion-MNIST, the network architecture and parameter settings were same as the corresponding experiments in Section 4. For CIFAR-10, the network architecture was LeNet. Detailed results are shown in Table 14, where precision, recall, and F1-score are average on all classes. Two conclusions are observed.  
First, each algorithm has a similar precision, recall, and F1-score when accuracy is high. For example, when accuracy is greater than 90% on MNIST (or 80% on Fashion-MNIST, 60% on CIFAR-10), the performance under different metrics almost remains unchanged. This is mainly because these datasets have even class distribution. Second, although Adacomp does not always achieve the highest accuracy or F1-score, it is the most robust to learning rate. For a large learning rate (\(lr=1\)), only Adadelta and Adacomp (ours) work well. Within the two algorithms, Adacomp is more robust than Adadelta. Note that on CIFAR-10, Adadelta achieves 0.45, 0.61, 0.65 F1-score when \(lr=0.01,0.1,1\), respectively. Meanwhile, Adacomp achieves 0.65, 0.67, 0.65 F1-score. This can be also observed on Fashion-MNIST.
## 5 Estimation of Aggressive Intensity of Cavitating Jet  
As mentioned above, in the constant downstream pressure condition, too high an injection pressure, i.e., too low a cavitation number, reduces the aggressive intensity of the cavitating jet. In this section, the mechanism is discussed, and a method to estimate the aggressive intensity of the cavitation jet as a function of cavitation number is proposed.  
In Figure 21, the experimental results of the aggressive intensity of the cavitating jet as a function of cavitation is revealed by blue closed circles [112]. The aggressive intensity is normalized by the maximum value, and it has a peak at \(\sigma\) = 0.016. As the energy of cavitation is proportional to the volume of the cavitation and the pressure difference of the bubble [111], the aggressive intensity of the cavitating jet \(I_{\alpha\
u}\) can be assumed as follows.  
\[I_{\alpha\
u}\ \propto\ \left(I_{\alpha\
u}\right)^{3}\cdot\left(p_{2}-p_{ \
u}\right) \tag{13}\]  
u}\) is affected by the acoustic impedance [95; 111], the term of the sound velocity \(v_{\rm s}\) is included in Equation (13). As \(I_{\alpha\
u}\) is also affected by flow velocity, which is defined by the pressure difference, i.e., \(\sqrt{p_{1}-p_{2}}\), the velocity term is also included in Equation (13).  
\[I_{\alpha\
u}\propto\left(I_{\alpha\
u}\right)^{3}\cdot\left(p_{2}-p_{\
u} \right)\cdot\left(v_{s}-v_{s\ th}\right)\cdot\left(\sqrt{p_{1}-p_{2}}\right)^{n} \tag{14}\]  
Here, \(v_{s\ th}\) is the threshold level of the sound velocity considering the threshold level of \(I_{\alpha\
u}\)[43]. The \(n\) is put as an exponent in Equation (14) to consider the power law of the velocity on \(I_{\alpha\
u}\)[76; 105; 111; 112; 113].  
Figure 21: Aggressive intensity of cavitating jet changing with cavitation number.  
If it was assumed that \(I_{cav}\) was proportional to the flow energy, which was defined by the product of the pressure and the flow rate, the flow energy is proportional to \(p_{1}-p_{2}\) and \(\sqrt{p_{1}-p_{2}}\), as \(\sqrt{p_{1}-p_{2}}\) was proportional to the flow velocity. Namely, the flow energy is proportional to cube of \(\sqrt{p_{1}-p_{2}}\). Thus, in the present review, \(n=3\) is chosen. From Equations (8) and (14), Equation (15) was obtained.  
\[I_{cav}=c_{3}\;\sigma^{-1.8}\cdot(p_{2}-p_{v})\cdot(v_{s}-v_{s\;th})\cdot(\sqrt {p_{1}-p_{2}})^{3} \tag{15}\]  
Here, \(c_{3}\) is constant. When Equation (15) is assumed, \(I_{cav}\) can be estimated by obtaining \(c_{3}\), \(c_{4}\), \(c_{5}\) and \(v_{s\;th}\) by a least-square method.  
\[v_{s}=c_{4}\;\sigma+c_{5} \tag{16}\]  
The estimated \(I_{cav}\) is shown in Figure 21 by empty squares. The correlation coefficient between experimental data and estimated values is 0.924. As the number of the datasets was 11, the probability of non-correlation is less than 0.005%. If the probability of a non-correlation is less than 1%, it can be concluded that the relationship is highly significant. Thus, it can be concluded that the relationship between the experimental data and the estimated values is highly significant. \(I_{cav}\) can be estimated by Equation (15).
## 3 Experimental Scenarios
### Scenario I: Edge Case Evaluation (SlideNN vs. GRU)  
In this scenario, we focused on environments with limited computational resources, where smaller models are preferable (edge and real-time AI). For this reason, the GRU model was tested with a small number of recurrent units (cells) selected from the set \(8,16,32,64\) and \(128\). The experiment showed that the performance difference between using 8 and 16 GRU cells was negligible. As such, they are not treated as distinct cases but are instead grouped into a single category representing the smallest cell configuration.  
To ensure a fair comparison, the number of trainable parameters in the edge GRU model was matched to that of the corresponding slideNN model for each input-output configuration. Specifically, four submodels of both GRU and slideNN were created and trained for input sizes 64, 128, 256, and 512, and their performance was evaluated using the Root Mean Squared Error (RMSE). Table 5 provides each configuration's inputs and outputs, number of parameters, and load memory sizes.  
Each configuration was tested independently to compare how well each architecture performed, both in terms of training convergence and forecasting accuracy, in resource-constrained settings.
## 3 Multi-Dimensional Fusion Feature-Enhanced Prediction Method
### Deep Fusion Module
#### 3.2.1 Convolutional Neural Network Structure
After three such pooling operations, the sequence length is reduced to 1/8, and the final feature map is flattened. The resulting length \(16*(2101//8)\), where 2101 is a parameter related to the original input data's sequence length, effectively connects with the fully connected layer, transmitting the extracted multi-scale feature information for further integration and mapping. The flattened vector is fed into the fully connected layer. As per formula \(y=Wx+b\), it is transformed into a 100-dimensional feature space, with \(x\) representing the input vector, \(W\) the weight matrix, \(b\) the bias vector, and \(y\) the output vector. Through a combination of global linear and nonlinear transformations, the intricate relationships among features are explored, and deeper insights within the data are extracted, enabling a sophisticated feature representation for the price prediction task. Dropout is applied between the fully connected layers. During training, the outputs of certain neurons are randomly set to zero, which decreases the interdependencies among neurons and mitigates overfitting, allowing the model to learn more robust feature representations and enhancing its predictive performance on unseen data. Finally, formula \(y=Wx+b\) is utilized to map the 100-dimensional features to three distinct output channels, corresponding to various prediction goals, such as the price forecast, the classification of price trends, and other objectives, based on the model's design and application requirements.
###### Abstract  
Surface defect detection plays an important role in particleboard quality control. But it still faces challenges in detecting coexisting multi-scale defects and weak texture defects. To address these issues, this study proposed PBD-YOLO (Particleboard Defect-You Only Look Once), a lightweight YOLO-based algorithm with multi-scale feature fusion and weak texture enhancement capabilities. In order to improve the ability of the algorithm to extract weak texture features, the SPDDEConv (Space to Depth and Difference Enhance Convolution) module was introduced in this study, which reduced the loss of information in the down-sampling process through space-to-depth transformation and enhanced the edge information of weak texture defects through difference convolution. This approach improved the mAP (mean average precision) of weakly featured but edge-sensitive defects (such as scratches) by as much as 20.9%. In order to improve the algorithm's ability to detect multi-scale defects, this study introduced the ShareSepHead (Share Separated Head) and C2f_SAC (C2f module with Switchable Atrous Convolution) modules. ShareSepHead fused feature maps from different scales of the neck network by adding a convolutional layer with shared weights, and the C2f_SAC module adaptively fused multi-rate receptive fields through a switching mechanism. The synergistic effect of ShareSepHead and C2f_SAC improved the detection accuracy of multi-scale defects by 10.6-20.8%. The experimental results demonstrated that PBD-YOLO achieved 85.6% mAP at 50% intersection over union (IoU) and 81.4% recall, surpassing YOLOv10 by 5.5% and 13%, respectively, while reducing parameters by 11.3%. In summary, it could be better to meet the need of accurately detecting surface defects on particleboard.  
particleboard; surface defect detection; multi-scale feature fusion; weak texture enhancement; YOLO +
Footnote †: journal: Applied sciences
## 4 Results and Discussion
### Observations from the Seismic Data Analysis  
The analysis of the seismic data shows that the Saranikos Gulf region is mainly characterized by moderate seismic events and shallow depths, as already discussed in the literature [29]. During the period from 2006 to February 2015, the most important earthquakes occurred in 2008, 2012 and 2014, with magnitudes (Mw) of 3.4 to 4.2 and focal depths of 8 to 16 km. The focal mechanism solutions  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Station**} & \(\mathbf{V_{E}}\) & \(\mathbf{V_{N}}\) & \(\mathbf{V_{U}}\) & \(\mathbf{STDV_{E}}\) & \(\mathbf{STDV_{N}}\) & \(\mathbf{STDV_{U}}\) \\  & **(mm/year)** & **(mm/year)** & **(mm/year)** & **(mm/year)** & **(mm/year)** & **(mm/year)** \\ \hline LYGO & – & – & – & & & \\ MENO & \(-0.49\) & 0.08 & 0.55 & 0.216 & 0.337 & 0.311 \\ MESO & 0.03 & 0.2 & \(-4.48\) & 0.158 & 0.106 & 1.123 \\ METH * & \(-0.22\) & 0.46 & – & 0.453 & 0.435 & \\
010A * & 0.42 & \(-0.04\) & – & 0.433 & 0.445 & \\ \hline \end{tabular}
* Values provided by NTUA and [55].  
\end{table}
Table 2: Local velocity values of the benchmark stations in Methana Peninsula with respect to LYGO station. Notation: \(V\), velocity; \(STDV\), standard deviation; E, east–west; N, north-south; and U, vertical/up.  
Figure 3: Local horizontal velocity field calculated with respect to the LYGO cGNSS site.  
Figure 2: Horizontal velocity vectors from cGNSS and local GPS benchmark stations in the broader area of Methana peninsula with respect to the IGb08 reference frame. Stations METH and 010A were retrieved from other sources (NTUA and [55]).  
indicate the activation of NW-SE or NE-SW striking normal faults (Figure 4). Two clusters of intense seismic activity are located offshore: between Methana and Poros Island, and NE of Aegina Island. Shallow microseismicity is observed NW of Saronikos Gulf (in an area where the 2012 and Mw 4.1 earthquake occurred), offshore NW of Aegina island and at the southern coast of Attica (Figure 4).  
For the period 2015-2019, there were three significant seismic events in the area: two moderate shallow earthquakes with magnitudes Mw = 3.8 and Mw = 3.6 and a very deep one (depth = 142 km) with magnitude Mw = 4.2 (Figure 5). In this period, a significant seismic swarm was observed NE of Poros Island. The seismic activity in this cluster started in 2016 and continued with few events up to 2019. Additional to the surface seismicity, very deep (80-100 km) sporadic micro-events took place on a wider area extending from Methana towards Hydra Island. These deep micro-events may be attributed to the NE-subduction zone of the Ionian oceanic plate that reaches very deep in this area [5; 54; 59; 60].  
Figure 4: Seismic activity recorded in 2006–February 2015.  
Figure 5: Seismic activity recorded in March 2015–August 2019.
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 3 Materials and Methods
### Classifier Models
#### 3.4.3 Long Short-Term Memory (LSTM)  
LSTM has \"forget gates\" in addition to the simple RNN architecture to handle vanishing and exploding problems. Figure 6 shows the LSTM structure.  
Unlike the simple RNN, LSTM back-propagates errors through a limitless number of time steps [11].
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 4 Recent Advancement on UAV Tracking Antenna
### Tracking Orientation
#### 4.2.2 Air-to-Ground Tracking Antenna  
This technique is not as popular as the ground-to-air type of tracking systems. In this design, the tracking is performed by the airborne platform. For the case of the UAV, the UAV uses a directional antenna to send the signal while an omnidirectional antenna is solely for the tracking antenna. The directional antenna on the UAV is desirable because it reduces the burden of detecting the angle of arrival (AoA) of the arrived signal. Despite its challenges, some authors have deployed this scheme [89; 90]. Their result shows that the tracking antenna continuously keeps track of and aligns with the base station and GCS, respectively, with the minimum error possible. However, this design is limited to ground based vehicles provided there is an available LoS between the sender and receiver. Reference [91] presented a brief overview of waveforms for UAV air-to-ground communication systems. The researchers broadly divided the modulation schemes into single carrier and multicarrier. The single carrier modulation are: QPSK, APSK, OFDM, MQAM, GMSK, MSK, SOQPSK, SC-FDE. However, the multicarrier is the OFDM. Additionally, [92] conducted air-to-ground UAV tracking although using a MIMO beam-forming technique which showed promising results.
## 3 Sensing Per Crop
### Sensing Potato Diseases
#### 3.5.1 Sensors and Algorithms
Late blight tuber rot increased significantly as more irrigation water was applied, and was significantly greater within 30 m of the pivot than at greater distances [220]. Long duration sprinkler irrigation also favored late blight in Oregon and California [221]. Under overhead sprinkler irrigation the proportion of potato leaflets containing late blight oospores and the number of oospores per leaflet were dependent on the soil water regime [222]. Potato vines that remain wet for long periods create a propper microenvironment for early blight (_Alternaria solani_), late blight (_Phytophthora infestans_), white mold (_Sclerotinia scleroorum_), and blackleg (_Rhizoctonia solani_) [223]. The timing of these diseases and associated crop losses vary regionally with yearly weather patterns, and can be affected by irrigation methods, which increase or decrease the duration of high humidity in the crop canopy. Long periods of leaf wetness or high relative humidity within the potato canopy favor infection by white mold [224]. Avoiding light, frequent irrigation of coarse-textured soils, and avoiding heavy, less frequent irrigation of fine-textured soils can diminish the risk of white mold.
# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
## 2 Basic Theory
### Random Forest Model  
Random Forest [17] is an integrated classification method. This paper defines a strategy for filling in missing values in time series data using Random Forest. It is assumed that a value in a time series (e.g., 'cumulative failure rate?) is affected not only by its intrinsic trend, but also by the time (year, month) in which it occurs. The relationship between time and this value is learned by constructing a Random Forest regression model that uses the temporal characteristics corresponding to known values to predict this value at the point in time corresponding to the missing value.  
The method uses temporal information as a predictor to estimate the value at the missing point in the time series through the powerful nonlinear fitting ability of Random Forest and improves the reasonableness of the filled value through simple constraints. It captures the complex patterns that may exist in the time series better than simple linear interpolation or mean interpolation.
## 5 Implementation and Results
### Data Exploration
Therefore, in each iteration of the algorithm, the groups in the testing set were adjusted to three to ensure that all artworks in the testing set pass, and a final score was calculated by combining their individual scores. Five distinct datasets were constructed for this research query. This method helped each artwork pass the entire test set. Consequently, separate evaluation scores were extracted for each of them. To determine which group is the most popular, it is incorrect to determine which post is the most popular because a group may not be popular but have several popular posts. The objective is to determine which group is the most popular overall. Using the weighted average of all post scores for each group may be an appropriate criterion for determining the most popular group. However, because an excessive number of posts garnered negative ratings, a different method was implemented. Therefore, it was optimal to select 10% of the most popular posts. In a group of sixty posts, for instance, the average of the best six is chosen. With each algorithm run, the most popular artworks were thus identified. The final ranking is displayed in Table 6 below. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Artwork**} & \multirow{2}{*}{**Count**} & \multicolumn{2}{c}{**av**} & \multicolumn{2}{c}{**Max**} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Std**} \\  & & & & **Comments** & & **Comm** & & **Comm** \\ \hline
1 & Orange, Red, Yellow & 201 & 41.35 & 335 & 0 & 51.07 \\
2 & The Gates of the Hell & 209 & 6.85 & 199 & 0 & 18.41 \\
3 & IKB-191 & 389 & 13.67 & 255 & 0 & 370.65 \\
4 & Tower & 66 & 150.00 & 199 & 0 & 40.15 \\
5 & Solid Sky & 124 & 5.96 & 206 & 0 & 22.22 \\
6 & Water Tower & 130 & 5.13 & 99 & 0 & 12.65 \\
7 & Angel of the North & 110 & 6.21 & 110 & 0 & 15.42 \\
8 & Maman & 391 & 9.54 & 182 & 0 & 20.35 \\
9 & Cloud Gate & 233 & 5.61 & 131 & 0 & 13.36 \\
10 & Waiting for the & 101 & 16.42 & 199 & 0 & 41.51 \\
11 & Melting House & 72 & 4.81 & 21 & 0 & 5.36 \\
12 & The London Mastaba & 212 & 5.83 & 144 & 0 & 12.18 \\
13 & ParaPivot & 108 & 10.70 & 167 & 0 & 26.91 \\
14 & Illuminated River & 116 & 12.49 & 281 & 0 & 35.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical description of the comments feature of the non-controversial artwork’s dataset. The scores of the most popular controversial artworks range from 6.92 (the highest-ranking value) to \(-\)3.29 (the lowest and negative ranking value) according to the ranking task's overall measurement data. Given that both datasets contained renowned works of art, this improved both the research questions and the model's reliability. As shown in the table, the three most popular controversial works are Comedian by [PERSON], Fountain by [PERSON], and Girl with Balloon (Love is in the Bin) by [PERSON]. These works have been primarily characterized as controversial. We briefly examine the reason for this. Comedian was created by the well-known artist [PERSON].