esa-sceva/satcom-chunk-collection huggingface dataset:
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 4 Results
### Analyzing the Image Classification Accuracy of the Proposed System Under Different BERs  
Figure 10 presents CNN image classification accuracy for different BER levels (from 0 to 4.375% BER) under different trained autoencoders. The observation shows that each decoder has high accuracy levels at zero BER. At high BERs, the decoder trained with no noise performs worse compared to the other decoders, whereas all other decoder types demonstrate similar performances. This observation suggests that decoders trained with different noise levels have marginal improvements in classification accuracy under varying channel BERs.  
Figure 10: BER against accuracy (CNN digit classification).  
Figure 9: BER against PSNR.  
Analyzing the Image Quality and Image Classification Accuracy of the Proposed System Under Different BERs with the CIFAR-10 Dataset  
In this experiment, we analyzed the impact of a complex dataset by training a single decoder under several BERs to observe the effects of different noise levels. Figures 11 and 12 present the PSNR and CNN image classification accuracy for different BER levels (ranging from 0% to 0.7% BER) using the CIFAR-10 dataset, with the decoder trained at various BERs. Similar to Figures 9 and 10, the results show that each decoder achieves high accuracy and PSNR at zero BER. However, at high BERs, the decoder trained without noise performs worse compared to the proposed decoder. This suggests that decoders trained with different noise levels exhibit improvements in both classification accuracy and PSNR under varying channel BERs.  
This approach demonstrates the decoder's ability to handle varying BERs with a similarly trained model. The results indicate the robustness of a
# Enhancing Phishing Email Detection through Ensemble Learning and Undersampling
## 6 Conclusions  
To reduce the impact of data imbalance on classification performance, we introduce the idea of undersampling benign emails for the soft-voting ensemble learning algorithm in this paper. We first propose the FMPED algorithm, which removes benign emails in the overlapping region based on the change of the G-mean value, and the decision boundary is subsequently adjusted using the FLD algorithm. Then, Markov sampling is used to selectively receive the retained benign emails. Only the important benign emails that are close to the hyperplane are sampled. The retained benign emails are merged with the phishing emails and trained by the soft-voting ensemble learning algorithm to obtain the final classifier. Based on the inspiration of the FMPED algorithm, we also propose the FMMPED algorithm, which uses Markov sampling for both removing and undersampling benign emails. Unlike FMPED, FMMPED determines the number of undersampled rounds according to the imbalance ratio. The undersampled benign emails are combined with phishing emails to obtain a classifier, and the weight is computed. If the weight is bigger than the last received classifier, it is retained. The final prediction for the testing sample is obtained by integrating the predictions of the retained classifiers with weights above the mean. Experimental results show that FMPED and FMMPED can obtain satisfactory results in F1-score, accuracy, AUC, G-mean, and MCC evaluation metrics compared with other eight phishing email classification algorithms.  
There are still some issues to be investigated, such as exploring an undersampling strategy that applies to all phishing email algorithms and exploring a more effective way of locating the overlapping regions. All these questions are currently being researched and investigated.  
Conceptualization, Q.Q. and Z.W.; methodology, Q.Q. and Z.W.; software, Z.W.; validation, Q.Q., Z.W., and Y.X.; formal analysis, Q.Q.; investigation, Z.W.; resources, Y.F.; data curation, C.W.; writing--original draft preparation, Q.Q. and Z.W.; writing--review and editing, Q.Q. and Z.W.; visualization, Z.W.; supervision, Y.F.; project administration, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation of China (U20B2045).  
Not applicable.  
The authors declare no conflict of interest.
# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 4 Experimental Results and Discussion
### Sensitivity Analysis of Optimization Algorithm  
Several optimization algorithms such as SGD, Adagrad [35], RMSprop [36] and Adam [37] are available to adjust the weights and biases of a network by iterating through the training data. Each algorithm has its own advantages and disadvantages, and there are no clear guidelines for selecting an optimizer for a particular problem. Therefore, in this study, several optimization algorithms were evaluated as we searched for the best to train the proposed RNN model. The evaluated algorithms are SGD, RMSprop, Adagrad, Adadelta, Adam, and Nadam [38]. The parameters of each algorithm were obtained from the aforementioned literature. Table 4 shows the performance of each optimization technique in predicting the severity of traffic accidents. The best validation accuracy (71.77) was achieved by the SGD method with a learning rate of 0.01. The SGD uses a small batch of the total data set to calculate the gradient of the loss function with respect to the weights and biases, using a backpropagation algorithm. The algorithm moves down the gradient by an amount proportional to its learning rate. Optimization methods such as Adam and Nadam have performed reasonably well.
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 3 Design and Implementation
### Call Types  
When a call has arrived at the carrier network for the first time from the LPWAN, it should be classified as an emergency call type, as it is an emergency in the case of a disaster situation. However, when it is not an emergency, the call may be treated as a normal call. To differentiate the calls, there are three types of calls from the LPWAN to the carrier network.  
1. Scheduled calls: LPWAN sensors transmit sensed data on a regular basis as pre-programmed, for example, every hour, for checking of the sensor status.
2. Urgent calls: When a disaster situation occurs, LPWAN sensors will trigger the gateway with sensed data as compared to the predefined threshold values. The gateway in this case transmits the emergency data to the destination.
3. Recovery calls: When sensors capture measurements that have become within the normal ranges against the threshold values, they transmit the data as recovered and the gateway transmits them to the disaster centre to be marked as normal.  
The type of call can also be in the form of short message service (SMS), PSTN or Mobile termination, Answered or Not answered, emergency call (000), and so on.
## 4 Discussion
### Construction Costs
For this reason, the scientific community and the public and private sectors must collaboratively address current challenges and develop new (or improve existing) industrialized solutions at the lowest cost, considering the project's location and local conditions (country), regulatory aspects, initial investment, advanced human capital, mastery of tools and technologies, and logistics.
# An Immersive Virtual Reality Game for Predicting Risk Taking through the Use of Implicit Measures
## 5 Discussion
### Accuracy of the Models to Discriminate RT Domains  
#### 5.1.1 Personality Recognition  
Regarding the final models on personality recognition, the dimensions of neuroticism, extraversion and openness to experience have been properly recognized. The validation set using 88 subjects achieved 72.6% accuracy (kappa: 0.447), 75.4% accuracy (kappa: 0.506) and 70.8% accuracy (kappa 0.402) respectively. The selected models for predicting agreeableness and conscientiousness have not overcome the chance level.  
Interestingly, these results show that neuroticism, extraversion and openness to experience are the better predicted personality dimensions. On the first hand, neuroticism has been related to negative affect and sensitivity to punishment [15], but its relationship with RT seems to be more complex and context-related. Therefore, although high levels of neuroticism may lead to risk aversion in most domains, as a way of avoiding guilt or anxiety about negative outcomes, the relation between neuroticism and RT seems to be inverse in the health domain [18], in which some studies identified a tendency to take risks to alleviate anxiety and other emotions in subjects with high neuroticism [89]. On the other hand, high extraversion and openness to experience have been related to risk approach across domains, due to a generalized need for stimulation and cognitive risk seeking, acceptance of experimentation, tolerance of uncertainty, change and innovation [14; 20]. In the light of these findings, we could conclude that suitably our tool brings out the personality dimensions most context-dependent and related to the approach to risk, and not so much those related to general risk avoidance.
# Low Power Consumption Signal Detector Based on Adaptive DFSD in MIMO-OFDM Systems
## 6 Conclusions  
In this paper, the adaptive DFSD detection scheme is proposed in MIMO-OFDM system of wireless communication to reduce the power consumption. The proposed detection scheme uses thresholds based on channel conditions to vary the number of the \(N_{l}\)-th layer nodes. At the \(N_{l}\)-th layer, the nodes are sorted according to ascending order of squared ED. Then, the proposed detection scheme  
Figure 14: The complexity of the detection schemes according to number of transmit antennas with 64-QAM.  
Figure 13: The complexity of the detection schemes according to number of transmit antennas with 16-QAM.  
selects the number of nodes according to thresholds based on channel condition. Finally the DFSD detection scheme is processed from the \((N_{t}-1)\)-th layer to the last layer.  
It is shown that the proposed detection scheme is similar to the error performance of the ML detection scheme and the conventional DFSD detection scheme. Also, the proposed detection scheme has lower complexity and lower increase rate of complexity according to the number of transmit antennas than the conventional DFSD detection scheme. The proposed detection scheme has lower complexity compared to the adaptive QRD-\(M\)[21; 22] detection schemes at the high SNR and all SNR ranges, respectively. Therefore, this proposed detection scheme is an efficient detection scheme to lower the power consumption by reducing the complexity.  
[PERSON] proposed the adaptive DFSD detection scheme algorithm and processed the simulation; [PERSON] analyzed the simulation results and made the figure; [PERSON]-[PERSON] reviewed the adaptive DFSD detection scheme algorithm and provided the experimental materials for better computational simulations and revised critical errors of the manuscript.  
This research was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00217, Development of Immersive Signage Based on Variable Transparency and Multiple Layers) and was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2018-0-01423) supervised by the IITP (Institute for Information & communications Technology Promotion).  
The authors declare no conflict of interest.
# Experimental Study and Numerical Simulation of W/O Emulsion in Developing Heavy Oil Reservoirs
## 3 Results and Discussion
### Effect of stirring speed  
Figure 3 displays the effect of stirring speed on the stability of the emulsion. Figure 4 shows the effect of stirring speed on the viscosity of the emulsion.  
As shown in Figure 3, with the change in stirring speed, the water bleeding rate in the same time period changed. With the increase in stirring speed, the water bleeding rate of the emulsion decreased gradually, indicating that the stability of the emulsion increased gradually. The water bleeding rate varied greatly with stirring speed when it was less than 7000 r/min. The emulsion was basically stable after 7000 r/min. Because the stirring speed was small and the input energy of the system was small, the dispersion degree of the internal phase was relatively low. With the extension of the standing time, the water bleeding rate increased, but the extent of the increase decreased gradually, due to the gradual sedimentation and coalescence of the incompletely dispersed droplets. With the increase in stirring speed, the dispersed phase droplets were subjected to greater shear, resulting in deformation and rupture, tending to form smaller droplets, and gradually making the droplet size and distribution reach equilibrium. At this time, a further increase in stirring speed did not affect the stability of the emulsion [28].  
As shown in Figure 4, with the increase in stirring speed, the viscosity of the emulsion increased gradually, reaching the highest value at 7000 r/min, which was 2.23 times higher than that of crude oil. An emulsion with high viscosity can play a more important role in plugging the high-permeability formation and improving the mobility ratio.  
Figure 4: Influence of stirring speed on emulsion viscosity; the viscosity of oil is provided as a reference.  
Figure 3: Effect of stirring speed on emulsion stability.
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 3 Flexible Packaging and Testing of Sensors  
Sensors are a critical factor affecting the quality of pulse signal acquisition. Therefore, after designing a reliable measurement method, we developed a packaging method based on parylene and polydimethylsiloxane (PDMS) to address the limitations of using micro-pressure sensors for blood pressure monitoring. The packaged sensor exhibited high accuracy and sensitivity, and pulse signal testing was successfully conducted using the packaged sensor.
## 3 Upper Bounds on the Number of Channels  
In this section, we first convert the channel assignment problem of _DIR_ networks to the vertex coloring problem in graph theory. We then derive the upper bounds on the number of channels.
## 2 Materials and Methods
### Bayesian Network for Diagnosis Refinement through Questioning  
In this section, we describe the methodology used to refine the diagnosis obtained from the language model. Our complete system is based on a dual-step approach that combines the strengths of deep learning models and Bayesian networks. The language model is able to automatically analyze textual descriptions of symptoms, capturing the variability and uncertainty inherent in how symptoms are reported by different users. This first step streamlines the diagnostic process, significantly reducing the number of steps to arrive at a prediction.  
The enhancement process is based on a Bayesian network constructed with base in the DSM-V diagnostic manual. The process of inquiry and diagnosis is framed as a sequential decision-making problem where an agent interacts with a patient. At each step, the agent asks the patient about the presence of a specific symptom \(s_{i}\) from a set of possible symptoms \(S\). The response of the patient, either confirming or denying the symptom \(s_{i}\), guides the next steps of the agent.  
The ultimate objective of the agent is to accurately identify the disease \(d\) from a set of possible diseases \(D\), with as few questions as possible.  
This approach is similar to other methodological frameworks that have been used in deep reinforcement learning studies, such as [61; 62], to model the diagnostic process.  
As mentioned previously, our model is based on a Bayesian Network, which is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph. The network comprises two types of nodes: the symptoms nodes \(S\) and the disease nodes \(D\). The symptom nodes represent the presence or absence of a symptom, while the disease nodes represent the presence or absence of a disease. The edges between the nodes denote the conditional dependencies between symptoms and diseases.  
The inference process in the Bayesian network involves updating the probabilities of the disease nodes based on the responses of the user that confirm or reject the symptom nodes. This process is carried out iteratively, with the agent asking questions about the presence or absence of symptoms to refine the probabilities of the disease nodes. The objective of the agent is to identify the most probable disease given the responses of the patient to the symptoms.  
In the following section, we describe the construction of the Bayesian network and the methodology used to refine the diagnosis obtained from the language model.
## 6 MATLAB Simulation
### Scenario 1
#### 6.3.1 The Effect of Changing System PARAMETERS (Number of Drones and Noise Strength)  
We discuss the effect of the number of drones. We also present the effect of noise on the control performance. Let \(N_{s}\) denote the standard deviation of measurement noise. In Equation (12), the measurement noise \(\mathbf{v}_{k}\) is generated with \(\mathbf{v}_{k}\sim\mathcal{N}(\mathbf{0},\mathbf{R}_{k})\), where \(\mathbf{R}_{k}\) is the diagonal matrix such that every diagonal element is \(N_{s}^{2}\). This implies that the standard deviation of measurement noise is \(N_{s}\) in meters.  
Table 1 summarizes the MC simulation results representing the effect of system parameters. We apply three metrics (\(captureRate(cR)\), \(endDist(eD)\), and \(simTime(sT)\)) to analyze the proposed controls. As the number of drones increases, \(captureRate\) increases in general. This is due to the fact that as we deploy more drones, the area covered by the drones increases. Furthermore, as the measurement noise \(N_{s}\) increases to 10 meters, the \(captureRate\) decreases. Note that an MC simulation ends when the distance between a drone and the target is less than \(\Delta=10\) m.  
Figure 7: The result of one MC simulation using grid formation. The proposed guidance law is applied to hit the target. (**a**) depicts the distance between the virtual agent and the target as time elapses. The relative distance keeps decreasing as time elapses. (**b**) plots the distance between the virtual agent and the ship as time elapses. See that the virtual agent stays close to the safety distance \(\beta\) while protecting the ship. (**c**) depicts the side length as time elapses.
## 4 Experimental Results
### Comparison of Trading Strategies
#### 4.3.1 Analysis after Price Correction  
The test set data start predicting stock prices from 9 April 2019. In the first half of 2020, the COVID-19 event affected the financial market, causing the model's predicted stock price errors to increase. After correction, as shown by the black dots in Figure 11, the red dots indicate the times when stocks were bought, and the green dots indicate the times  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Trading Strategy** & **Return on Investment (\%)** \\ \hline One-time Conversion & 4.70 \\ Fuzzy System (using initial values) & 4.72 \\ Genetic Fuzzy System & 32.00 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of returns for different trading strategies.  
Figure 10: Optimal membership function of _RSI_ indicator.  
Figure 9: Optimal membership function of \(D\) indicator.  
when stocks were sold. The period marked by the yellow box is when COVID-19 had the most severe impact on Taiwan.  
It can be observed that despite COVID-19 significantly affecting stock prices, the Genetic Fuzzy System found the optimal buying and selling points. Although there was only one round of trading, the selling point was at an upward trend, which is the desired high point for selling, thus the return rate is relatively high.
# Predicting the Life of Varistors via a Nonlinear Coefficient Based on a Small-Scale Data Model
## 3 Experiment and Analysis
### Prediction Performance of Life Model under Different Hyper-Parameters  
In the life prediction model proposed in this paper, in addition to the network training parameters, there are still a large number of hyper-parameters, among which the training sample length n and the number of BiLSTM network layers have the greatest impact on the model. In order to verify the influence of different hyper-parameters on the life model, the root mean square error (RMSE) was originally used to evaluate the prediction accuracy of the model, namely:  
\[\text{RMSE}=\sqrt{(\sum_{1}^{\text{N}-\text{n}}\Bigl{(}y_{1}-\hat{\bar{y}} \Bigr{)}^{2}})/(\text{N}-\text{n}), \tag{14}\]  
where \(\hat{\bar{y}}\) is the value predicted by the model.  
The data set is divided into the training set and the test set according to the ratio of 80% to 20%. The training set data are used to train the life model under different preset hyperparameters by cyclic iteration method. Test set data include test set 1 and test set 2. The data from test set 1 and the training set are collected from the same batch of varistors of the same model, while the data from test set 2 and the training set are from different batches.  
Figure 14 shows statistics for the prediction errors in the life curves of test set 1 and test set 2 generated by the models under different hyper-parameters after 100 independent trainings, in which the prediction accuracy of test set 2 reflects the generalization performance of the model.  
From the statistics for 100 independent test errors under each hyper-parametric condition, it can be seen that the model has good stability and reliability. For test set 1, when the network contains a 1-layer BiLSTM layer and a 2-layer BiLSTM layer, the performance is close, but the performance decreases when there are three layers. For test set 2, layer one performance is the best. In the case of the same number of layers, when the training sample length is two, it performs better on test set 1, but it performs worse on test set 2. When the sample length is three and four, the model has a relatively balanced performance on the two data sets, and when the sample length is four, the performance is more stable. If the sample length continues to increase, the accuracy of the model will decline. Clearly, the multi-layer BiLSTM layer in this model cannot further increase the accuracy of prediction and will definitely occupy more computing resources. An appropriate increase in the sample length will help to enhance the stability of the model, but if it is too long, it will lead to an excessively high degree of model fitting and a lack of generalization and mobility.