esa-sceva/satcom-chunk-collection huggingface dataset:
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 5 Evaluation
### Under No Attacks  
Figure 5 compares the performance of FTSP and RSTS in a benign environment, i.e., the difference of clock skew and offset, and the error bars of clock offset that indicate the estimation error average and standard deviation. We can see that both FTSP and RSTS eventually achieve synchronization. Since FTSP adopts a hop-by-hop synchronization manner, it can be observed that as the hop gets larger, the estimated synchronization error between ordinary nodes and the time source gradually increases. Figure 5b shows that under the same network scale, the synchronization accuracy of RSTS is significantly higher than that of FTSP, as the public neighbor forwarding mechanism shortens the synchronization path between the farther node and the time source. To further explain, in FTSP, the farther node 9 synchronizes with source node 0 after 9 hops, while its synchronization path is just 5 in RSTS, and its synchronization accuracy is almost improved by one order of magnitude.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 11 Conclusions  
In recent years, mobile networks have experienced an increase in the number of scenarios with challenging requirements, especially due to rapid growth in traffic demand and the arrival of new services, such as eMBB. Likewise, user behavior has changed, creating scenarios that are hard to predict. This paper discussed the challenges related to a dynamic hotspot scenario and how to mitigate the resulting performance loss. A solution was presented to coordinate ICI using FFR-based techniques. The algorithm dynamically regulates the Strict FR parameters, using Q-Learning (QL).  
The results indicate that the solution effectively optimizes the SINR in the evaluated scenarios, which also resulted in throughput gain, even though this relationship is not always true. Depending on the scenario, the SINR gain may only result in a decrease of the PLR, which is also important for the overall performance of the system.  
In some cases, the gain was higher for hotspot users, even though the algorithm was not configured to act based on their performance. Besides, even in these cases, the algorithm also improved the overall system performance. The results also indicate a tendency of greater SINR gain when the scenario suffers with more interference. The higher SINR gain obtained was 180% for hotspot users, 167.1% for average SINR, and 140% for users outside hotspots.
## 4 Discussion
The practical application of the presented results lies in controlling toolpath quality and precision at the interpolator level. Thus, the machine tool operator can be certain that the potentially observed lack of precision or surface quality is caused by unique properties of the machine tool or the machining process and not by the interpolator output. The presented research is subject to several limitations that should be addressed in future research. First, to the author's best knowledge, no research focusing on the effects of interpolator settings on the quality and precision of the output of commercial interpolators (without consideration of the feed drive system and machine dynamics) was yet performed. Second, there exist multiple other sections of toolpath geometry, in addition to corners, at which the effects of interpolator settings on toolpath quality and precision could be studied, most notably arcs and freeform surfaces. Third, this study considers only the use of spline transition curves at corners. However, the arc transition curves are also a popular option available in commercial interpolators (including the iTNC530) and their effect on the quality of the interpolator output also merits further study. Finally, different choices of filters in addition to the AHSC filter (such as the single, double, and HSC filters) should be studied to obtain a more complete understanding of the iTNC interpolator's behavior.
# Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature
## 5 Discussion
### Physiological Modalities  
Physiological modalities offer another approach to enabling ERS. Among these modalities, ECG and EEG have been the most studied. Physiological modalities can involve the stimulation of blood pressure, heart rate, skin sensors, and brain activity. Previous studies have shown that ECG and EEG can help understand human emotional states [17; 18; 61; 62; 63]. A previous study has been conducted on brain-computer interaction (BCI) systems that allow disabled individuals to control their environment using robots and manipulators [53]. The study focused on the use of EEG signals generated through Motor Imagery (MI), a method that produces signals related to motor movements. The study also explored channel selection for MI classification using a common spatial pattern (CSP) as it maximises the variance difference between the two classes. CSP was solved using an optimisation process that minimised the cost function, defined as the variance of the projected class of one signal while keeping the sum of the variance of both classes fixed. However, it has been noted in a previous study that the generalisation of EEG decoding methods is difficult as optimal channel selection is highly dependent on the application, features, criteria, and classifiers used [53]. They suggested that a deep and extensive analysis of each technique is necessary to design the optimal channel selection algorithm.  
Another study has investigated EEG and physiological signal-based brain-computer interfaces that utilise MI data, which could potentially revolutionise technology [49]. The paper examines the selection of signal processing techniques using MI-Based EEG, with a particular focus on feature extraction and selection in such systems. The study also emphasises the use of PSO as a metaheuristic method, which showed a mean classification accuracy of 90.4% with strong directional search and population-based search strategies for both exploration and exploitation. The authors suggest that these methods could have numerous biomedical applications, including the replacement and restoration of central nervous system (CNS) functionality lost due to injury or disease. Lastly, a study was conducted on the classification of ECG using a two-dimensional convolutional neural network (CNN) model [47]. The proposed model was compared with other recent techniques for the automatic classification of arrhythmia and achieved an accuracy of 90%. The study concluded that deep CNNs could enhance the accuracy of diagnosis algorithms in modern machine-learning technologies and that 2-D images can be used to classify eight types of arrhythmia.
# Simulink Model of a Thermoelectric Generator for Vehicle Waste Heat Recovery
## 4 TEG Model for Exhaust Gas Waste Heat Recovery
### Equations and Simulink Model
To test the possible output of such a TEG, 16 test cases (Table 3) were evaluated with varying air temperatures (T\({}_{\mathrm{air}}\)) and air flow velocities (V\({}_{\mathrm{air}}\)). For the hot side, the temperature of the exhaust gases was set at 250 \({}^{\circ}\)C with a flow velocity of 19.1 m/s (12 L) engine running at 1500 min\({}^{-1}\), with an exhaust pipe diameter of 100 mm). For brevity only the results for 50 and 90 km/h are presented here with the others are available upon request. Regarding case notation, \"V\" stands for velocity and \"T\" for temperature. The values following \"V\" and \"T\" are the velocity (in km/h) and temperature (in \({}^{\circ}\)C) values for that case (e.g., V50T-5 = velocity of 50 km/h and temperature of \(-5\)\({}^{\circ}\)C). For this study it was assumed that the hot and cold side fluid temperatures and flow velocities are uniform and constant, but also that there is a uniform temperature distribution on the TEG and TEM surface. Furthermore, the simulation was performed for only one TEM. Consequently when considering the desired number of TEMs this will result in an overprediction of the power output. Figure 14: TEG model validation highlighting the difference between the simulated and measured values of (**a**) the electrical power output, (**b**) hot and cold junction temperatures, (**c**) current, and (**d**) voltage for two different types of cold side heat exchangers: Water (W\({}_{\mathrm{sim}}\)) and air (air\({}_{\mathrm{sim}}\)). Table 4 and Figures 15 and 16 show the results for the two different velocities 50 and 90 km/h. Analysis of the results finds that both the higher air flow velocity on the cold side and a lower ambient temperature increase the output of the TEG. Decreasing the air temperature raises the temperature gradient and the power output, but also reduces the temperature of the hot side junction to that below the maximum allowable value of the TEM. This is because the hot side heat flow rate is constant. To further augment the output of the TEG, it would be necessary to increase the heat flow rate on the hot side (either by increasing the exhaust temperature or the flow velocity). Consequently, a high TEG output requires not only a good HX, but also a suitable heat extraction strategy.
## 5 Experiment
### Interpretability of Scenarios in the Medical Field  
To verify the applicability and interpretability advantages of the DRKG framework in real-world scenarios, this study constructs a vertical domain knowledge graph based on the de-identified electronic medical record data of a certain hospital and designs a multi-hop diagnosis reasoning task for a special assessment. Specifically, the knowledge graph covers 48,612 medical entities and 18 types of core relationships. The question-answering data generation automatically generates 8,806 multi-hop questions based on the large model API, covering typical scenarios such as diagnostic path reasoning (e.g., \"What are the symptoms and preventive measures of cholecystitis?\") and treatment plan tracing (e.g., \"What are the examination items and treatment methods for pulmonary alveolar proteniosis?\").  
In terms of experimental settings, the stratified sampling method was used to divide the training set (70%), validation set (20%), and test set (10%). Additionally, 30% of the samples (a total of 260) were randomly selected from the test set for manual interpretability verification. In the manually evaluated samples, the complete coincidence rate between the reasoning plans generated by DRKG and the paths marked by physicians reached 100%, and its step-by-step logic strictly followed the clinical decision tree. For example, for the question \"After treatment, what are the cure rate and treatment course for patients with symptoms such as cyanosis and chest pain?\", the model completed the reasoning process in three steps. Step 1: Through the feature entities \"cyanosis\" and \"chest pain\" and the relationship \"disease symptoms\", the disease name of the entity \"pulmonary alveolar proteniosis\" was queried. Step 2: Through the relationship between the entity \"pulmonary alveolar proteniosis\" and \"cure rate\", the cure rate of this disease was found to be \"about 40%\". Step 3: Through the relationship between the entity \"pulmonary alveolar  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Temperature** & **Top-k** & **Top-p** & \multicolumn{2}{c}{**MetaQA**} & \multicolumn{2}{c}{**PathQuestion**} \\  & & & **ACR** & **AV** & **ACR** & **AV** \\ \hline
0.1 & 10 & 0.1 & 100.0 & 0.0 & 87.65 & \(\pm\)3.20 \\
0.1 & 10 & 0.5 & 100.0 & 0.0 & 83.29 & \(\pm\)4.92 \\
0.5 & 50 & 0.1 & 98.72 & \(\approx\)\(\pm\)0.00 & 76.82 & \(\pm\)8.69 \\
0.5 & 50 & 0.5 & 98.13 & \(\approx\)\(\pm\)0.00 & 76.42 & \(\pm\)8.09 \\
0.8 & 10 & 0.95 & 87.63 & \(\pm\)3.92 & 53.15 & \(\pm\)13.09 \\
1.0 & 100 & 1.0 & 84.63 & \(\pm\)5.32 & 50.30 & \(\pm\)15.09 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Reliability experiment results.  
proteinosis\" and \"treatment course\", the treatment course of this disease was found to be \"about 3 months\".
# Decision Framework for Predictive Maintenance Method Selection
## 5 Evaluation of the Proposed Framework
### Case 1: Electrical Components of a Naval Vessel  
_Case description:_ This department maintains all electronic equipment aboard naval vessels. Due to the large differences between naval vessels, many one-of-a-kind systems are installed. Collecting accurate and representative failure data is, therefore, difficult. As the systems are critical for the operational effectiveness of the vessels, the maintainer aims to prevent unplanned downtime. Traditionally, maintenance plans are based on advice given by the OEM. _Challenge:_ The department recently executed a trial-and-error process in selecting the optimal maintenance method for their situation. In the (recent) past, the department relied on reliability statistics methods. These analyses, however, provided inaccurate results due to incomplete data, unreliable input and poorly filled recording systems. The department,therefore, decided to fall back on using expert knowledge for making maintenance decisions. Since the department conducts maintenance for a large number of different systems, it is too difficult and time-consuming to develop more accurate physical or data models. Using the proposed framework, the analysis in Table 3 demonstrates that this trial-and-error process could have been accelerated considerably. _Conclusion:_ This structured way of comparing the ambition (decision pull, AL3) with the data availability, i.e., expert knowledge and low-quality historical data, reveals a clear mismatch. The framework explains why the attempt to use reliability statistics has failed: no sufficient _high-quality historical data_ are available. Subsequently, the framework guides the user to alternative approaches. Firstly, it is checked whether the data required for the ambitioned type of approach can be collected. This appeared to be infeasible (no positive business case) at the moment. A suggested reduction in the ambition level to AL1, based on only expert knowledge, appeared to make the _experience-based_ type of maintenance approach feasible. This is also the approach that is currently successfully applied. The trial-and-error process the company went through could thus have been accelerated considerably by using the proposed framework.
## 5 Distributed Heterogeneous Cluster Task Planning Based on Nash Equilibrium
### Description of the Simulation Scenario  
A heterogeneous satellite cluster composed of two intelligent satellites (D1-D2) is deployed in geostationary orbit. These satellites possess space situational awareness capabilities and are responsible for safeguarding the operational security of critical space facility X. The orbital parameters of facility X and the two intelligent satellites are listed in Table 1. Additionally, two detection satellites (A1-A2) are present in orbit, tasked with conducting scientific observations of the intelligent satellites. In this scenario, the intelligent satellites must optimize their orbital maneuvers to maintain a safe distance from the detection satellites, while the detection satellites adjust their orbits to achieve optimal observation conditions. Through intelligent task planning, both satellite types fulfill their respective mission requirements while ensuring space safety. Figure 5 shows the satellite cluster cooperative mission scenario.  
Figure 4: Solution process for distributed task planning based on Nash equilibrium theory.  
Each intelligent satellite (D1-D2) has a maximum single impulse of 2 m/s, with a minimum interval of 200 s between maneuvers and a maximum of 30 maneuvers. Their orbital data are provided in Table 1. The detection satellites (A1-A2) are adapted for two mission scenarios, with specific parameters listed in Table 2. Each detection satellite has a maximum single impulse of 1 m/s, a minimum interval of 400 s, and a maximum of 30 maneuvers (orbital data in Table 3). Four performance metrics are evaluated: detection success rate, fuel consumption, time cost, and target coverage. The proposed Zebra Optimization Algorithm is applied, with parameter settings detailed in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & **Semi-Major Axis (km)** & **Eccentricity** & **Inclination (deg)** & **Right Ascension of Ascending** & **Argument of Perigee (deg)** & **Mean Anomaly (deg)** \\ \hline X & 42,464.169 & 0 & 0 & 0 & 0 & 136.500 \\ D1 & 42,464.169 & 0 & 0 & 0 & 0 & 136.542 \\ D2 & 42,464.169 & 0 & 0 & 0 & 0 & 136.565 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The orbital parameters of facility X and the two intelligent satellites.  
Figure 5: The satellite cluster cooperative mission scenario.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**ID**} & **Mission Scenario** & **Mission Parameter Settings** \\ \hline A1 & Scenario 1 & \(d_{1}^{\text{M}}=20\text{ km},\theta_{2}^{\text{S}}=40^{\circ},T_{1}=1000\) \\ A2 & Scenario 2 & \(d_{2}^{\text{M}}=20\text{ km},\theta_{2}^{\text{S}}=10^{\circ},T_{2}=1000\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detection satellite attributes.
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
## 5 Experiments
### Performance Metrics  
The D1-DCAE model was tested and reported on its detection performance in terms of F1 score, accuracy, precision, recall, Area Under the ROC Curve (AUC), and False Positive Rate (FPR).  
The most important metrics that are utilized to evaluate the 1D-DCAE model are F1 score and AUC [37]. The F1 score is made up of precision and recall values.  
\[\text{Precision}=\frac{\text{True Positive}}{\text{True Positive + False Positive}} \tag{6}\]  
\[\text{Recall}=\frac{\text{True Positive}}{\text{True Positive + False Negative}} \tag{7}\]  
\[\text{F1}=2*\frac{\text{Precision}*\text{Recall}}{\text{Precision + Recall}} \tag{8}\]  
Precision, computed in Equation (6), measures the proportion of true positive cases identified out of all predicted positive cases, and recall, computed in Equation (7), measures the proportion of true positive cases identified out of all actual positive cases. The F1 score is calculated in Equation (8) based on these two metrics and is considered a more accurate measure of model evaluation.  
When evaluating the performance of an ML model, the F1 score and accuracy are often compared. In this work, the F1 score was chosen over accuracy for two reasons [38]. First, the accuracy score is influenced by the rate of true negatives, which may not be significant in real-life scenarios. In contrast, the F1 score is only affected by true positives, false positives, and false negatives, making it a more appropriate metric for the proposed model, which focuses on classifying intrusion attempts as positive. Second, the F1 score is more appropriate for datasets with imbalanced distributions of positive and negative cases. For imbalanced datasets, accuracy may be biased towards more frequent classes, resulting in lower classification accuracy for less frequent classes. This is particularly important for positive instances, which represent attacks or intrusion attempts and are often a minority class in intrusion detection datasets.  
In addition to the F1 score, the Receiver Operating Characteristics (ROCs) metric is a widely accepted metric for assessing the performance of ML models [37]. ROC is used to evaluate the relationship between recall (i.e., sensitivity) and precision (i.e., specificity) for different threshold values in classification. The Area Under the Curve (AUC) is a measure of the ROC evaluation. A higher AUC value indicates better model performance.
## 5 Lacunae
### Fault Detection and Recovery  
Self-managing and reliable software and configuration updating requires fully automatic detection of faults and recovery. A _one-symptom_ approach to this is described in the Update Failure Detection and Recovery Model presented in this paper. There are however three general classes of faults associated with a software update:  
1. a fault in the original software,
2. a fault in the updated software, and
3. a fault in the interaction between the two versions.  
Further research is required, extending the work presented in Section 3.4, to validate the model, and provide reference implementations to determine performance characteristics. Research is also required into these three general classes of fault, not only to discover whether identifying the symptoms associated with the different classes can be of value in fault _detection_, but also whether they can be of value in fault _recovery_. For a particular recovery policy, there may be different actions available for handling recovery depending on the class of fault.
# Prediction of Pollutant Emissions in Various Cases in Road Transport
## 1 Introduction
The first potential solution to this problem is biofuel, usually treated as a carbon-negative energy carrier [15]. Therefore, it is essential to promote, in parallel with the promotion of electric options, alternative fuels that can reduce GHG emissions. In particular, the contribution of alternative fuels in the EU countries is relatively small. Apart from the issue of GHG emissions, it is crucial to reduce air pollutant emissions. In this article, nitrogen oxides (NO\({}_{\text{X}}\)) and particulate matter (PM2.5) were chosen (because of the associated smog and health problems). Taking into consideration the foundations of climate neutrality, there are a few questions that need to be answered:  
* Does a change in fuel type affect CO\({}_{2}\), NO\({}_{\text{X}}\), and PM2.5 emissions? * Does a difference in the vehicle fleet structure impact emissions? * Which fuel is the most ecologically suitable and will help to achieve goals related to climate neutrality, especially in countries where the energy mix is dominated by hard coal and the development of electromobility may not bring the assumed reduction of CO\({}_{2}\) and pollution? * Does the climate neutrality trend affect the emission of other air pollutants?
## 3 Results for LSTM and 1-D CNN
### Results for LSTM
Initially, the training accuracy sharply increases, indicating rapid learning in the early stages. As iterations progress, the training accuracy shows a gradual upward trend, eventually stabilizing. The validation accuracy, however, starts off high but shows a slight decreasing trend, indicating potential overfitting as the model learns to perform well on the training data at the expense of its generalization to new data. The bottom graph illustrates the loss during training (blue line) and validation (red line). The training loss decreases sharply at first and then gradually levels off, suggesting that the model is effectively learning from the training data. However, the validation loss decreases initially but then begins to increase, which is a typical sign of overfitting. The model's increasing loss on the validation set implies that the adjustments made to the model parameters fit the training data more closely, but do not improve its performance on unseen data. Overall, these trends suggest that while the LSTM model is learning and improving its performance on the training set over time, it is also becoming more specialized to the training data. This specialization reduces its ability to generalize, as evidenced by the increasing validation loss and decreasing validation accuracy after a certain number of iterations. It may be beneficial to introduce regularization techniques, adjust the model's complexity, or provide more varied training data to improve the model's generalization capabilities. Figure 4: Training and validation accuracy and loss of the LSTM model over iterations.