esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed PEADO-OFDM Scheme and Full-Duplex Communication  
### The PEADO-OFDM Scheme  
PEADO-OFDM is a modified ADO-OFDM scheme with subcarrier allocation, in which the ACO-OFDM branch occupies only a part of odd subcarriers and the DCO-OFDM branch occupies the remaining subcarriers except the 0_th_ subcarrier, which improves the flexibility of the system. Therefore, the numbers of subcarriers carrying information assigned to the ACO-OFDM branch and DCO-OFDM branch are not fixed anymore.  
In the PEADO-OFDM scheme, the clipping noise from the ACO-OFDM branch will be eliminated at the transmitter. For the ACO-OFDM branch, the time-domain clipped ACO-OFDM signals can be obtained through the traditional ACO-OFDM operations. The noise generated from the clipping operation only falls on the even subcarrier. So, after performing the fast Fourier transform (FFT) operation to the clipped ACO-OFDM signals, the clipping noise can be obtained by extracting the data of the even subcarrier, which will be used as the pre-distorted signals. For the DCO-OFDM branch, before the IFFT operation, the original DCO-OFDM symbols on the even subcarriers should subtract the pre-distorted signals first, which is referred to as the pre-distortion process. Then, the traditional DCO-OFDM operations will be performed to obtain the time-domain DCO-OFDM signals.  
After superimposing the ACO-OFDM signals and the DCO-OFDM signals in the time domain, the clipping noise that falls on the even subcarriers can be eliminated through pre-distortion, which brings independent demodulation of the ACO-OFDM and DCO  
Figure 2: The full-duplex architecture of UOWC system. (**a**) The dual-branch PEADO-OFDM-based transmitter with blue/green LDs. (**b**) The dual-branch PEADO-OFDM-based receiver with double PDs. (**c**) The UOCM. (**d**) The scheme of full-duplex design.  
OFDM branches, leading to a reduction in the processing latency, complexity and error propagation at the receiver.
## 1 Introduction  
Temperature distribution is the one of the most contributing environmental factors which affects the mechanical properties of flexible asphalt paving mixtures and the asphalt pavement structure's bearing capacity [1, 2]. To briefly define, pavement temperature is defined as the changes in pavement surface temperature with the variation in weather parameters over time as influenced by the paving materials and direct solar reflectance, thermal conductivity, thermal emittance, specific heat, and surface convection [3, 4]. It should be noted that, Bituminous and asphalt mixtures are temperature sensitive materials, and therefore their strengths and modulus vary with the change in temperature. As a result of this, different types of damages could occur in flexible pavements. For example,the decreasing temperature can risk pavements crack, while high temperatures can result in rutting (permanent deformation) [5]. It is also been observed that the temperature in one section of an asphalt pavement varies due to several reasons. To better understand, pavement responses are affected primarily by ambient temperature, followed by solar radiation (during the hot season); the effect of wind speed and relative humidity, however, is less significant [6; 7]. Thus, these parameters are considered as the necessary parameters in a pavement temperature prediction model [8; 9]. Furthermore, extensive research on temperature prediction models has been conducted in several regions with different climates to formulate pavement temperature prediction models that provide the highest accuracy [2]. Several researchers have also raised their concern about temperature algorithms' precision and the consequences of using predicted values by emerging technologies of deep learning-based regression models for calculating asphalt pavement temperature [10; 11]. With all the above facts in consideration, this paper aims to provide a comprehensive review of the available pavement temperature prediction models and offer critical evaluation of the models examined. Apart from this, the challenges and limitations of the relevant prediction models will also be discussed in this paper to better visualize the correct outcome.
# On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures
## 4 Performance Assessment
### End-to-End Delays  
Finally, the end-to-end delay was analyzed for all sets of simulations. Figure 13 shows the ECDF of the end-to-end delay experienced by the sensors that successfully complete the random access procedure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(M\) [\#] & Mean & 90 th Percentile & 10 th Percentile \\ \hline
5000 & 1.7 & 3.2 & 0.3 \\
10,000 & 3.4 & 6.5 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Percentage error between success probabilities for Configuration 1.  
Figure 12: Collision and Success probabilities of RAOs for Configuration 2.  
The end-to-end delay is the time interval between the time instant when the devices application layer generates a data packet, and the time instant when that data packet is received by the the base station. Consequently, it includes the access delay as well as the delay introduced by scheduling decisions and the physical transmission of the packet. The most noticeable aspect is that different configurations have different trends. In addition, as expected, the end-to-end delay grows with the number of sensors deployed in the cell, M.  
Configuration 1 presents both the best and the worst performance in terms of delay, for \(M=5000\) and \(M=10{,}000\), respectively. As a matter of fact, in the first case, there are few user accessing each RAO and a significantly high success probability, as highlighted in the previous sections. Consequently, the end-to-end delay is notably improved, since less preamble retransmission occurs and the uplink scheduler successfully manages the queued terminals. On the contrary, if \(M=10{,}000\), the access delay dramatically increase due to the multiple collisions leading to several preamble retransmissions. At the same time, the higher number of successful devices has a negative effect on the waiting time in the scheduling queue as well, hence causing the rise of the end-to-end delay.  
Differently, Configuration 2 exhibits an intermediate behavior. This is mainly because longer Backoff times and RAR windows are counterbalanced by the difference in the NPRACH resources structure and higher success probabilities.
# A Comparative Analysis of the TDCGAN Model for Data Balancing and Intrusion Detection
## 3 Experiments Setup
### The Experimental Setup  
The class distribution for each dataset is described in Table 1. In the experiment, initially, the TDCGAN model is applied on the training data for each dataset to generate samples and improve data balancing. The output of this training is then preserved for the data balancing evaluation issue. With visual Inspection, we examine the generated samples to ensure they resemble real data and capture important features. To assess the classification performance of the TDCGAN model, we employed it on the testing data for each dataset and conducted evaluation metrics.  
After that, for the classifier evaluation and comparison, each machine learning method will be applied twice for each dataset: (1) Training without TDCGAN-generated samples: Train the classifier using only the original training data, without adding any TDCGAN-generated samples, then evaluate its performance on the testing data. (2) Training with TDCGAN-generated samples: Train the classifier using the original training data augmented with TDCGAN-generated samples, then evaluate its performance on the same testing dataset. By comparing the performance metrics (accuracy, precision, recall, and F1-score) for each classifier, we assess the impact of data augmentation with TDCGAN-generated samples on the classification performance.  
Suppose the result of classifier training with TDCGAN-generated samples outperforms the results trained without TDCGAN. In that case, it suggests that the TDCGAN-generated samples have effectively improved the classifier's ability to generalize to unseen data or to capture the underlying data distribution better. Conversely, if there is no significant improvement or if performance deteriorates, it may indicate that the TDCGAN-generated samples are not beneficial for the task or that they introduce noise or bias.  
The final step involves comparing the evaluation metrics of each classifier with the TDCGAN model for the classification task applied to the testing data of each dataset. This comparison allows us to evaluate the classification performance of each model and identify the best-performing one.
# Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
## 3 Methodology
### Euclidean to \(\lambda\)-Space Transformation  
Using the pairwise distance matrix, the next step to clustering the points is to identify the region of points that have a higher density and find a separation between them. A good way to do this is by transforming the vector space from the Euclidean space to a density/sparsity space. In this paper, the density/sparsity space is referred to as the \(\lambda\)-space and the distance between the points in this new transformed space is called the mutual reachability distance. The mutual reachability distance \(d_{unreach}\) is calculated using formula [21]:  
\[d_{unreach-k}(a-b)=max\{core_{k}(a),core_{k}(b),d(a,b)\} \tag{4}\]  
where \(d(a,b)\) is the Euclidean distance between the point \(a\) and \(b\); \(core_{k}(a)\) and \(core_{k}(b)\) are the core distance for point \(a\) and point \(b\), respectively. Core distance for the point is defined as the radius of the smallest circle that covers \(k\) points. The core distance of the points is easier understood visually, as shown in Figure 2 below.  
Figure 2: Visual representation of the core distance for the center point.
## 3 Results
### Gene Association Predictions Using Differential Gene Expression and Gene Embedding Profiles
These two classifiers performed worse, with average AUCs of 0.6946 and 0.6539, respectively. Thus, the XGBoost classifier, which used features from both the differential gene expression and gene embedding profiles, demonstrated the best performance. A summary of the performances is shown in Figure 2. Figure S1 displays the ROC curves of each classifier, and Table S3 provides a comprehensive summary of the evaluation metrics, including F1 measure, precision, and recall, for each classifier. Figure 2: Comparison of performances of XGBoost, random forest, naive Bayes, and neural network classifiers using bar charts. AUC stands for area under the ROC curve, and ACC represents the accuracy performance. Error bars indicate the standard deviations of the performances obtained from the ten-times five-fold cross-validation. To understand the relevant association features for distinguishing positive pairs and negative pairs, we investigated the top features obtained from the RFE algorithm for all classifiers. We observed that 80% (8 out of 10) of the top 10 features were association features calculated from gene embedding profiles. As we extended the analysis to the top 20, 30, 40, and 50 features, we found that the proportion of association features calculated from gene embedding profiles decreased to 65%, 50%, 40%, and 34%, respectively. Supplementary Table S2 provides a comprehensive overview of the percentages of association features calculated from the top selected features (ranging from 10 to 100) in both the gene embedding profiles and the differential expression profiles. We further investigated the distribution of the top 10 features between the positive and negative sets by calculating the correlation between the feature values and the class labels. The positive class was labeled as 1, and the negative class was labeled as 0. The results indicate that 9 out of these 10 features exhibited negative correlations. When these features have higher values, it indicates a lower possibility that the gene pairs are associated with the positive class. However, the absolute correlation values were relatively low. This suggests that using only one feature may not be sufficient to accurately predict an association, and combining various features could lead to a more effective predictive model. It is important to note that these top ten features were derived from voting or counting based on the top selected features obtained from the RFE algorithm. They were not directly combined to train a machine learning model. Supplementary Figure S2 shows a plot of the correlation values of these top ten features.
# Decorrelation-Based Deep Learning for Bias Mitigation
## 4 Results
### Reversed Color-Biased MNIST Dataset  
To analyze the reversed effect of bias and the proposed approach, we use the reversed color-biased MNIST dataset where the bias present in the testing dataset is exactly the opposite of the bias present in the training dataset. This is to validate how well the proposed approach generalizes to the unseen test dataset. In the paper [26], [PERSON] et.al. used an Invariant Risk Minimization (IRM) causal-invariant based approach in multiple training environments to promote out-of-distribution (OOD) generalization by assuming the different environments share the same underlying structural equation model. An ANN classifier is implemented to achieve the same. However, for comparison purposes, we use the same CNN architecture as mentioned in Section 3.4 for all existing methods. Empirical  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Var = 0.02** & **Var = 0.03** & **Var = 0.035** & **Var = 0.045** & **Var = 0.05** \\ \hline Baseline & 0.4055 & 0.5996 & 0.6626 & 0.7973 & 0.845 \\ BlindEye & 0.6741 & 0.7883 & 0.8203 & 0.8927 & 0.9159 \\ Advers Training & **0.8185** & **0.9137** & **0.9306** & **0.9555** & **0.9618** \\ Advers Training-no Pretrain & 0.7336 & 0.8516 & 0.8781 & 0.9277 & 0.9429 \\
**DcCNN** & **0.8100** & **0.8910** & **0.9250** & **0.9500** & **0.9604** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of accuracies for color-biased MNIST dataset for different values of variances among existing methods. Results are calculated on the testing dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **SPD** & **EOD** & **AOD** & **DI** & **BA** \\ \hline Baseline & \(-\)0.3752 & \(-\)0.3716 & \(-\)0.3258 & 0.2876 & 0.7472 \\ Reweighing & \(-\)0.2924 & \(-\)0.3815 & \(-\)0.3234 & 0.3831 & 0.7110 \\ Optimized pre & \(-\)0.2144 & \(-\)0.1991 & \(-\)0.1945 & 0.568 & 0.7231 \\ Advers-Debias & \(-\)**0.0876** & \(-\)**0.0592** & \(-\)0.0373 & 0.5775 & 0.6656 \\ DIR + Advers-Debias + CEO & \(-\)**0.0301** & 0.0785 & 0.051 & - & **0.8113** \\ B-DcANN (\(\lambda=0\)) & \(-\)0.3394 & \(-\)0.1545 & \(-\)0.1965 & 0.3025 & 0.8226 \\
**DcANN** & \(-\)**0.0964** & **0.0657** & **0.0325** & **0.8063** & **0.7747** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fairness scores and balanced accuracies of predictions for gender-biased adult dataset for different models.  
Risk Minimization (ERM) combines the data from all the training environments and uses all features which is similar to the baseline model principle.  
The Table 5 presents the comparison where the ERM method classifies digits based on color bias and hence the lowest accuracy whereas IRM and DcCNN remove the color bias information from the features and classify based on features relevant to digits. The results show that DcCNN achieved an accuracy of 66.30% which is the best across all methods and can successfully mitigate the color bias by learning more digit-relevant features.
## 6 Results and Discussion
### Model Implementation and Training Protocols
#### 6.2.5 Comparative Overview
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison** & \(p\)**-Value** & **Adjusted (\(p\)) Significant** \\ \hline RBF vs. BR & 0.000012 & 0.000037 * \\ \hline RBF vs. SCG & 0.000002 & 0.000010 * \\ \hline RBF vs. LM & 0.130592 & 0.156710 * \\ \hline BR vs. SCG & 0.599936 & 0.599936 \\ \hline BR vs. LM & 0.000136 & 0.000204 * \\ \hline SCG vs. LM & 0.000082 & 0.000164 * \\ \hline \hline \end{tabular}  
* indicates significant difference at \(\alpha=0.01\) level.  
\end{table} 
Table 6: Pairwise Friedman test results for test-set MSE with Benjamini-Hochberg correction. Raw and adjusted \(p\)-values are reported; an asterisk (*) denotes rejection of the null hypothesis at the \(\alpha=0.01\) level. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Median MSE\_Train** & **Median MSE\_Test** \\ \hline RBF & 0.0008 & 0.0128 \\ Bayesian & 0.0010 & 0.0017 \\ SCG & 0.0013 & 0.0019 \\ LM & 0.0046 & 0.0068 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of MSE by each algorithm on the training and testing sets, showing the median (30 runs). These R\({}^{2}\) results corroborate the MSE findings: the RBF network achieves the highest median coefficient of determination, closely followed by the Bayesian regularized model, both markedly outperforming the SCG and LM algorithms. In addition, we assessed statistical significance using the Friedman test with Benjamini-Hochberg correction, and the adjusted \(p\)-values in Table 8 confirm the conclusions drawn above. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Median R2\_Train** & **Median R2\_Test** \\ \hline RBF & 0.9753 & 0.5932 \\ Bayesian & 0.9690 & 0.9286 \\ SCG & 0.9575 & 0.9274 \\ LM & 0.8532 & 0.8043 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of R\({}^{2}\) by each algorithm on the training and testing sets, showing the median (30 runs). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison** & \(p\)**-Value** & **Adjusted (\(p\)) Significant** \\ \hline RBF vs. BR & 0.000006 & 0.000017 * \\ \hline RBF vs. SCG & 0.000002 & 0.000010 * \\ \hline RBF vs. LM & 0.059836 & 0.071803 \\ \hline BR vs. SCG & 0.530440 & 0.530440 \\ \hline BR vs. LM & 0.000075 & 0.000113 * \\ \hline SCG vs. LM & 0.000063 & 0.000113 * \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Pairwise Friedman test results for test-set MSE with Benjamini-Hochberg correction. Raw and adjusted \(p\)-values are reported; an asterisk (*) denotes rejection of the null hypothesis at the \(\alpha\) = 0.01 level. Figure 18: Comparison of R\({}^{2}\) on training and testing sets across neural network algorithms.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 5 Evaluation and Results
### Open Challenges  
The promising findings presented by our approach still have significant limitations since the utilized dataset comprises simulated spoofing attacks. Such environments do not reflect the full complexity and uncertainty of real-world spoofing, which may not capture the impact of such a threat. Cyber-adversaries may employ adaptive, stealthy, or coordinated strategies that are difficult to reproduce in structured simulations. Furthermore, there exist external factors that may affect the performance of our ML models, which are not expressed in the dataset; these include urban obstructions, atmospheric disturbances, and electromagnetic interference. Therefore, generalizing our proposed approach to production UAV systems may be impractical. Consequently, it is essential to explore real-world datasets with more sophisticated and dynamic spoofing methods to validate UAVs' resistance against GPS spoofing attacks.  
Another critical issue relates to post-detection strategies. The current studies based on machine learning lack techniques enabling UAVs to predict their position and maintain navigation autonomy when reliable GPS signals are absent in order to prevent mission failure. Furthermore, the existing models are often trained on static datasets and lack adaptability to evolving spoofing tactics and dynamic environmental conditions. Developing adaptive and self-learning models that can continuously be updated based on real-time environmental feedback is crucial for improving the resilience of UAV navigation systems.  
Consequently, it is imperative that researchers tackle these challenges in the future to provide resilient and operational GPS spoofing defense systems capable of ensuring the safety of UAVs in adversarial environments.
## 3 Configuration of the System and Conditions
### Configuration of the Measuring Sensors  
As the schematic system was supposed to be constructed, a number of measurement sensors were necessary to be deployed all over the system. Their types and positions are shown in Figure 3, and their uncertainties are listed in Table 4. Temperature sensors had the largest number, as they were the most crucial to deciding the thermodynamic state of S-CO\({}_{2}\). Pressure sensors were fewer than temperature sensors, and there were only four mass flow sensors for the S-CO\({}_{2}\) part of the system. As for S-CO\({}_{2}\) measurements, the pressure sensors (supposed to be pressure transmitters [35]) took 1% of uncertainty, and temperature sensors (supposed to be thermocouples [36]) used an absolute uncertainty of 1 \({}^{\circ}\)C. For mass flow measurement, they were usually low-precision for gas [37], so the uncertainty was supposed to be 2.5%. As for cooling water, the pressure measurements were supposed to be constants, as their values almost caused no impact on the enthalpy of the water under normal conditions. The uncertainty of temperature was set as the same value as S-CO\({}_{2}\) (1 \({}^{\circ}\)C), and the uncertainty of the mass flow rate was supposed to be a little bit lower [38], configured as 1%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**State Point** & **Pressure** & **Temperature** & **Mass Flow Rate** \\ \hline
1 & 1\% & 1 \({}^{\circ}\)C & 2.5\% \\
2 & 1\% & - & - \\
3 & - & - & 2.5\% \\
4 & 1\% & - & - \\
5 & - & - & - \\
6 & - & 1 \({}^{\circ}\)C & - \\
7 & - & 1 \({}^{\circ}\)C & 2.5\% \\
8 & 1\% & 1 \({}^{\circ}\)C & 2.5\% \\
9 & 1\% & 1 \({}^{\circ}\)C & - \\
10 & - & 1 \({}^{\circ}\)C & - \\
11 & - & 1 \({}^{\circ}\)C & - \\
12 & 1\% & - & - \\
13 & const. & 1 \({}^{\circ}\)C & 1\% \\
14 & const. & 1 \({}^{\circ}\)C & 1\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensors deployed in the S-CO\({}_{2}\) recompression cycle and their uncertainties.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Parameter** & **Value** \\ \hline RTE & 43.55\% \\ C1 isentropic efficiency & 85\% \\ C2 isentropic efficiency & 85\% \\ T isentropic efficiency & 90\% \\ Generator power & 431.27 kW \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the cycle and key device parameters under the standard condition.  
Apart from these three types of sensors, the power generated by G (converted from the shaft power generated by T; part of them were also consumed by C1 and C2 for compression) was also supposed to be measured through a dynamometer. It was a high-precision meter, compared with the thermal sensors, so its uncertainty was set as 0.25%, much lower than any of the sensors deployed in the system.