esa-sceva/satcom-chunk-collection huggingface dataset:
# The Intra-Class and Inter-Class Relationships in Style Transfer
## 2 Background and Related Work
### Demystifying Neural Style Transfer  
Although all the methods mentioned above have improved over the original neural style transfer significantly, they all use the same _Gram matrix_ as the original algorithm to represent the artistic style. As for why the _Gram matrix_ can represent the style of an image in neural style transfer, this fundamental question has never been answered by anyone until, recently, the authors of [5] started to demystify the original neural style transfer algorithm.  
In [5], the authors investigated the original neural style transfer algorithm and argued that the essence of neural style transfer is to match the feature distributions between the style images and the generated images. Furthermore, the authors proved that matching the _Gram matrix_ is equivalent to minimizing maximum mean discrepancy (MMD) with the second order polynomial kernel [26], which means the _Gram matrix_ is not a must for neural style transfer. To verify their theory, the authors also used other kernel functions, such as the linear and Gaussian kernels for the MMD to reproduce the style of the style image onto the content image. Besides the MMD, the authors also tried to use the statistics of Batch Normalization (BN) of a certain layer to represent the style of the image. The experimental results show that, compared to the methods which use the _Gram Matrix_, the methods which use the MMD with the second order polynomial kernel, or the BN to represent the style of the image, can produce stylized images with comparable visual effect.  
In the following sections, the algorithm mentioned in [5] is termed as BN method, while the original neural style transfer algorithm [4] is termed as _Gram-Matrix_ method. Before proposing our style transfer algorithms, the loss functions employed by the Gram-Matrix and BN methods are analyzed in the following section.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 7 The Future of the Spaceborne LiDARs
### Future Missions  
The next decade promises advances in spaceborne LiDAR, with multiple agencies planning new missions and technological improvements on the horizon. Several upcoming missions are in development:
## 4 Conclusions  
In this work, the accuracy of five probability density functions to a large experimental dataset of the received signal strength for an FSO link was studied. The focus of this paper is to identify among those PDFs the one that best fits observed data, based on the available comparison methods. To this end, the Gamma, Lognormal, Extreme-Value, Burr and Weibull distributions were utilized to calculate initially their Kullback-Leibler divergence and a symmetrization of it, the Jensen-Shannon divergence, with the RSSI data. The Burr distribution was found to best fit the experimental data, with a KL and JS divergence values of 2.77 \(\times\) 10\({}^{-2}\) and 6.53 \(\times\) 10\({}^{-3}\), respectively. The Weibull and EV distributions also exhibited a comparable accuracy in contrast with the Gamma and Lognormal, whose \"distance\" from real data was an order of magnitude higher. The Pearson distribution family of continuous probability functions was also used and experimental results showed that the type IV distribution yield the best fit. Both methods offer a straightforward process of comparing among different PDFs for accurately fitting real data. The two aforementioned methods follow a different approach to select the most appropriate PDF to fit to a dataset. The KL and JD metrics allow for the calculation of the performance metric of each candidate PDF, thus designating the best one as the one with the lowest divergence. From a practical point of view, the KL and JD metrics are more useful to apply in a real-world dataset, since they directly provide a certain output that allows any PDF to be examined. On the other hand, the selection of the best Pearson distribution using a moment matching method is preferable when a limited number of options for statistical modeling are available.  
Conceptualization, A.L., K.P.P., H.E.N.; methodology, A.L., K.P.P., H.E.N.; software, A.L.; validation, A.L., K.P.P., H.E.N. and A.T.; formal analysis, A.L., A.T.; investigation, A.L., K.P.P., H.E.N.; resources, A.L., K.P.P., A.T.; writing--original draft preparation, A.L., A.T.;  
Figure 8: The cumulative distribution function for Pearson Type IV against RSSI data.  
writing--review and editing, A.L., K.P.P., H.E.N., A.T.; supervision, K.P.P., A.T., H.E.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
The variance in measurement errors were all positive, and it turned out to be unnecessary to remove questions to improve the goodness-of-fit, as the squared multiple correlations (SMC) were derived to be at 0.4 or higher for all of the questions. The convergent validity was also surveyed to determine whether respondents showed a similar tendency for questions composed to measure latent variables. It was surveyed that most of the \(\beta\) values of the estimates were 0.7 or higher, the average variance extracted (AVE) values, excluding the factor of advantages/disadvantages to the region, were 0.5 or higher, while the AVE for the advantages/disadvantages to the region was 0.495 and the concept reliability of each latent variable was 0.8 or higher. Thus, the final model had convergent validity. All possible combinations of structural model paths were found to be significant at 1%. The derived standardized coefficients show either the combination of positive effects or the combination of negative effects. The combination of paths with technically positive effects indicates that associations with the increased use in air transportation (A) and the effects related to increased exchange (M) were the greatest, and it is divided into a path associated with the regional development (D) and a path associated with the regional economic benefit. However, the differences among the combinations of positively perceived paths were not great compared to the perception paths for negative effects (Table 3). The paths that showed the most significant standardized coefficients (\(\beta\)) in the structural model paths were derived to be the effects related to the increased use in air transportation (A) and the effects related to increased exchange with other regions (M). This means that people perceive that the increased use in air transportation and the increased exchanges are the most relevant effects with regards to airport development and operation. The authors found that the relationships between regional development (D) and the increased use in air transportation (A), and the effects related to increased exchange (M) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**x\({}^{2}\) (CMIN)** & **RMSE** & **TLJ** & **CFI** & **CMIN/DF** \\ \hline
1068.494 & 0.055 & 0.925 & 0.932 & 2.520 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model’s Goodness of Fit Index. and the effects of regional economic benefit (R) were perceived to have the next highest relevance. On the other hand, it was found that people perceived that the relationship between the effects of dis-benefit to the region (B) and other effects had a lower correlation than that between other factors (Figure 2 and Table 4).
# Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l|c|c|c|c|c|c|c|c|c|c|c|c|c} \hline \multirow{2}{*}{**Proposed Models**} & \multicolumn{4}{c|}{**Network Results**} & \multicolumn{4}{c|}{**Ranking the Predicted Models**} & \multicolumn{2}{c|}{**Total**} & \multirow{2}{*}{**Rank**} \\ \cline{2-2} \cline{4-13}  & \multicolumn{1}{c|}{**R\({}^{2}\)**} & & \multicolumn{1}{c|}{**MAE**} & & \multicolumn{1}{c|}{**RMSE**} & & \multicolumn{1}{c|}{**(\%)**} & \multicolumn{1}{c|}{**R\({}^{2}\)**} & \multicolumn{1}{c|}{**MAE**} & \multicolumn{1}{c|}{**RMSE**} & \multicolumn{1}{c|}{**RMSE**} & \multicolumn{1}{c|}{**Ranking**} & \multicolumn{1}{c|}{**Ranking**} \\ \hline lazyLWL & 0.9049 & 3.2345 & 4.2752 & 35.1778 & 42.2953 & 2 & 2 & 2 & 2 & 2 & 10 & 5 \\ \hline Alternating Model & 0.9081 & 0.4869 & 0.6236 & 5.2956 & 6.1693 & 5 & 5 & 5 & 5 & 5 & 25 & 2 \\ \hline Random Forest & 0.9989 & 0.3385 & 0.4649 & 3.6813 & 4.5995 & 6 & 6 & 6 & 6 & 6 & 30 & 1 \\ \hline ElasticNet & 0.896 & 3.2585 & 4.4683 & 35.4392 & 44.2052 & 1 & 1 & 1 & 1 & 1 & 5 & 6 \\ \hline MLP Regressor & 0.9868 & 1.12 & 1.4627 & **12.1811** & **16.0944** & 4 & 4 & 4 & 4 & 4 & 20 & 3 \\ \hline RBF Regressor & 0.9693 & 1.9109 & 2.4641 & 20.7827 & 24.3837 & 3 & 3 & 3 & 3 & 3 & 15 & 4 \\ \hline \end{tabular}
\end{table} 
Table 4: The performance of selected machine learning techniques in the prediction of heating load through several statistical indexes (testing dataset). \begin{table}
\begin{tabular}{l|c|c|c|c|c|c|c|c|c|c|c|c} \hline \multirow{2}{*}{**Proposed Models**} & \multicolumn{4}{c|}{**Network Results**} & \multicolumn{4}{c|}{**Ranking the Predicted Models**} & \multicolumn{2}{c|}{**Total**} & \multirow{2}{*}{**Rank**} \\ \cline{2-2} \cline{4-13}  & \multicolumn{1}{c|}{**R\({}^{2}\)**} & \multicolumn{1}{c|}{**MAE**} & \multicolumn{1}{c|}{**RMSE**} & \multicolumn{1}{c|}{**RMSE**} & \multicolumn{1}{c|}{**R\({}^{2}\)**} & \multicolumn{1}{c|}{**MAE**} & \multicolumn{1}{c|}{**RMSE**} & \multicolumn{1}{c|}{**RMSE**} & \multicolumn{1}{c|}{**RMSE**} & \multicolumn{1}{c|}{**Ranking**} \\ \hline lazyLWL & 0.903 & 3.2838 & 4.3335 & 35.9104 & 42.9757 & 2 & 1 & 2 & 1 & 2 & 8 & 5 \\ \hline Alternating Model & 0.9985 & 0.4096 & 0.5449 & **4.4788** & 5.4036 & 5 & 5 & 5 & 5 & 5 & 25 & 2 \\ \hline Random Forest & 0.9997 & 0.19 & 0.2399 & 2.078 & 2.3975 & 6 & 6 & 6 & 6 & 6 & 30 & 1 \\ \hline ElasticNet & 0.8915 & 3.233 & 4.5678 & 35.3566 & 45.2993 & 1 & 2 & 1 & 2 & 1 & 7 & 6 \\ \hline MLP Regressor & 0.9915 & 0.9795 & 3.1556 & 10.7117 & **13.0465** & 4 & 4 & 4 & 4 & 4 & 20 & 3 \\ \hline RBF Regressor & 0.9647 & 1.8226 & 2.6555 & 19.9307 & 26.3348 & 3 & 3 & 3 & 3 & 3 & 15 & 4 \\ \hline \end{tabular}
\end{table} 
Table 3: The performance of selected machine learning techniques in the prediction of heating load through several statistical indexes (training dataset).
## 2 Related Work  
Efforts to automate vegetation monitoring in railways include monocular camera systems, ranging from UAV-based setups operating purely in the image domain [4] to train-mounted systems capturing close-up tracked vegetation [5; 6; 7; 8], which already rely on visible tracks to maintain a metric reference. The adoption of LiDAR-based solutions by operators and companies in France and Belgium [9; 10] underlines the importance of this task, while the growing interest in camera-only systems [11] reflects the drive for more scalable and cost-effective solutions.  
Due to their lower cost and ease of integration, camera-only systems support reduced maintenance intervals and multipurpose monitoring, and are increasingly used for driver assistance [12] and infrastructure inspection [13] in the railway domain. However, their effectiveness hinges on robust metric 3D reconstruction and extrinsic calibration to meet both technical and legal requirements.
## 3 Case Study
### Comparison with Other Algorithms
#### 3.6.1 Algorithm Descriptions  
(1) RF Algorithm  
The RF algorithm is an ensemble learning method mainly used for classification, regression, and other tasks. It makes predictions by constructing multiple decision trees. Its core concept is the aggregation of predictions from multiple decision trees to improve the overall algorithm accuracy and stability [36]. The optimal hyperparameter settings for the RF algorithm are presented in Table 6.  
(2) LSTM Algorithm  
The LSTM network is a special type of recurrent neural network. By introducing a complex gating mechanism, it effectively retains information over long time intervals, making it well-suited for time-series prediction and related tasks [37]. For this study, the LSTM model was implemented with the following architecture and hyperparameters:  
Network Structure: Two stacked LSTM layers with 64 hidden units each, followed by a fully connected output layer.  
Training Configuration: Adam optimizer with a learning rate of 0.001, batch size of 32, and dropout rate of 0.2 to mitigate overfitting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Hyperparameter Optimization Range** & **Optimal Value** \\ \hline n\_estimators & [2, 50] & 17 \\ criterion & [’gini’,’entropy’] & ’gini’ \\ max\_depth & [1, 15] & 6 \\ min\_samples\_split & [2, 10] & 2 \\ min\_samples\_leaf & [1, 4] & 1 \\ min\_weight\_fraction\_leaf & [0, 0.5] & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal hyperparameter settings for RF algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Improvement Measure** & **MSE** & **MAE** & **RMSE** & **R\({}^{2}\)** & **Memory (GB)** \\ \hline Baseline (Original Informer) & 5.22 & 5.91 & 2.28 & 0.79 & 3.2 \\ + Dilated Causal Convolution & 5.15 & 4.66 & 2.27 & 0.80 & 3.0 \\ + Stratum Classification & 5.13 & 5.76 & 2.26 & 0.82 & 2.2 \\ + Moving Prediction Window & 4.99 & 4.66 & 2.23 & 0.83 & 2.3 \\ + Special Factors & 4.31 & 3.98 & 2.08 & 0.84 & 2.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study.  
Training Process: The model was trained for 200 epochs with early stopping (patience = 10) based on validation loss.  
Input/Output: A sliding window of 10 historical rings (aligned with the Informer's input length) was used to predict the next settlement value.  
These hyperparameters were optimized via a grid search on the training set to ensure a fair comparison with the improved Informer algorithm. The optimal hyperparameter settings for the LSTM algorithm are presented in Table 7.
# Handling Skewed Data: A Comparison of Two Popular Methods
## 3 Results
### Comparisons between the Two Methods
#### 3.1.2 Comparison Regarding Power  
Table 3 presents the parameters and power results for the beta-distributed data. P\({}_{0}\) is the power values calculated for the two-group \(t\)-test using the information provided by knowing the two distributions and their parameters that are used in the simulation process, P\({}_{1}\) is the empirical power of the two-group \(t\)-test conducted on the log-transformed groups, and P\({}_{2}\) is the empirical power of the GLM-fitted data. Here we note that the GLM power rates were higher than those for the \(t\)-test with log transformation in 68% of the settings with absolute values of differences ranging from 0 to 0.0884. In 74% of the settings, the GLM power rates exceeded or equalled the calculated power, while the \(t\)-test power rates exceeded the calculated power in only 58% of the settings. Although the percentage of GLM power rates that exceeded or equalled the computed power was higher than the percentage of \(t\)-test power rates, the estimated power rates produced by the \(t\)-test were not that different to those that were produced by the GLM, with a difference of less than 0.1.  
Table 4 contains summary information for the power results of gamma, exponential and beta distributed data tested using both methods. In about 86% of the exponentially distributed data examples, the power for the GLM was higher than that for the \(t\)-test, with absolute values of differences ranging from 0.001 to 0.108. Moreover, in 41% of the settings, GLM power rates exceeded or equalled the calculated power. Then again, in just 10% of the settings, \(t\)-test power rates exceeded the calculated power, while in the gamma-distributed data examples, the power for the GLM was higher in about 85% of the settings, with absolute values of differences ranging from 0.000 to 0.363. In addition, in 41% of the settings, GLM power rates exceeded or equalled the calculated power. Still, in just 15% of the settings, \(t\)-test power rates exceeded the calculated power. Figure 4 shows powers for both methods compared to the computed power rates for three distributions.  
Figure 3: Type I error rates for the \(t\)-test and GLM compared to 0.05 for the three distributions.
## 7 Economic Analysis of Solution Cost  
### Solution Cost  
This section analyses the extra installation cost of upgrading a new USPVP to allow for the proposed EL maintenance technique. In addition, installation costs will be compared to current plant costs in order to evaluate the additional needed investment. As Section 3 shows, power inverters allow bidirectional power flow; therefore, only some minor additional features have to be added.  
Firstly, an inspection control is required in order to perform the new features. Due to the low computational requirements of the needed software, hardware upgrades are not required by the control system. Besides, software development costs will be amortised for all manufactured inverters worldwide, so its impact on the final price will be negligible.  
Secondly, the AC pre-charge system is required to apply EL inspections. The device is implemented in the inverter and allows to raise the DC-link voltage from the grid side. Pre-charge is carried out by means of the following components; an auxiliary power transformer to level-up the input voltage, a breaker to activate the pre-charge process, a resistor to limit the peak current and a diode-based bridge to rectify the AC voltage. These components must be chosen for each case, but their cost will not differ significantly. For instance, a grid-tied inverter with an output grid voltage of 400 V, pre-charge system cost would be zero if the PV plant had a functionality that requires it, for example, reactive power injection at night feature; otherwise, it would be approximately 135 E according to up to date market values [65; 66; 67].  
Finally, string sequencing requires several breakers in order to energise a small panel area and to minimise the energy consumption. Thus, the number of breakers is not predefined, and its estimation is carried out in the following section.  
On the other hand, plant power generation will not be affected as the best accuracy for the EL technique is obtained at night, sunrise or sunset. Thus, the inspection will be carried out during non-productive hours, and therefore there will not be any power production losses and the operational costs of the solution will not increase.
## 6 Implementation Scenarios
### Moderate to Many Obstacles  
The last and most realistic wireless communication system scenario is an area with a high density of obstacles; downlink communication faces increased complexities in environments characterised by dense obstacles, such as urban areas with tall buildings or densely wooded regions featuring reflections, shadowing, and multipath fading impeding the signal propagation.  
In accordance with the described area in Figure 2, U1 and U2 are located in highly obstacle-dense areas with NLoS between users and BS1. In the downlink scenario, BS1 fails to send the signals to their intended users; instead, BS1 sends the signals to the HRIS element on top of B1; the HRIS controller will analyse the incident signals using Algorithm 1 and redirect the signals toward U1 and U2 with full transmission power, resulting in a 100% coverage area.  
The HRIS-DMA employs the novel iterative algorithm to actively and simultaneouslyreconfigure the incident signal, creating focused signal paths and minimising the impact of obstacles on signal strength. Through adopting the HRIS-DMA, the downlink communication remains robust, ensuring effective data delivery to the user devices despite the challenges posed by the obstacle-dense environment.  
As the stated examples mention only four users, the real-time test and performance analysis scenarios, which are shown in the following Section 7, prove the superiority of the proposed HRIS-DMA approach using the performance evaluation test parameters in Table 3, with higher and different density levels and a varied range of THz frequencies, making it a promising approach for deployment in 6G networks with THz applications.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 4 Deployment of the IDS
### Vehicle-Related Networks  
Vehicle-related networks, also known as the Internet of Vehicles (IoV), are an infrastructure based on connectivity between vehicles with road-side units, such as cameras or other electronic devices [52; 53]. Smart vehicular networks improve many factors, such as road safety, driving experience, and decision-making based on the collected information, and are used to reduce accidents and increase the performance of driving. Securing against the alteration, monitoring, and removal of vital messages has been a big concern due to the vehicle's connection over a wireless medium making several types of attacks viable, including DoS/DDoS. Additional difficulties arise due to the traditional design of vehicles, which often lacks comprehensive security considerations, particularly regarding autonomous functionality and communication capabilities. Furthermore, the growing number of networked vehicles increases the attack surface, introduces resource constraints, and adds complexity to modern automotive systems.  
Because of these reasons, reactive/predictive IDSs have recently received more attention. Refs. [52; 53] emphasize real-time communication and low-latency operations. These networks are essential for autonomous vehicles and smart traffic systems, where the speed of data exchange and reliability can impact safety and performance.  
[PERSON] et al. [53] train classifiers on the TON_IoT database and apply it to ad hoc networks of vehicles, showing great results despite the dataset being general, supported by many actions taken against overfitting. Their aim is to employ their model on both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) parts of the network.  
[PERSON] et al. [52] used specific car-hacking datasets. In their paper, they propose an ML model with high accuracy, with the intent for it to be deployed on the vehicles' firewall. In the intra-vehicle network, the internal smart devices of a vehicle communicate with each other and control the communication of the vehicle in the inter-network. The hacker can target the internal network of the vehicle, which is challenging to defend against, as it is already met with high demand and resource requirements due to its low latency and high-pace environment.
## 4 System modeling
### Partial least square regression (PLSR)
#### 4.1.4 Bayes classifier  
Bayes classifier is a maximal probability rule based classification method, aiming to minimize the \"expected risk\" caused by misclassification [59]. Assuming there are N classes (1,2,.., N), the posterior probability of class h for sample x is:  
\[P(h/\,\mathbf{x})=\frac{\lambda_{h}P(\mathbf{x}/\,h)}{\sum_{j=1}^{N}\lambda_{ j}P(\mathbf{x}/\,j)} \tag{5}\]  
where, \(\lambda_{j}\) is the priori probability of class j and \(P(\mathbf{x}/\,j)\) is the conditional probability of \(\mathbf{x}\) given class \(j\). The Bayes decision rule is:  
\[\mathbf{x}=\underset{h}{\arg\max}\big{[}P(h/\,\mathbf{x})\big{]}\ \ \ (h=1,2,..,N) \tag{6}\]  
The priori probability \(\lambda_{h}\) can be estimated as the ratio of the number of the samples in Class \(h\) to the number of all the samples. In most cases, the conditional probability \(P(\mathbf{x}/\,h)\) can be replaced by a continuous probability density function \(f(\mathbf{x}/\,h)\) and usually can be estimated by two methods, namely the parametric approach and non-parametric approach [59]. A Bayes classifier based on parametric approach can also be called quadratic discrimination (QD) process while non-parametric approaches generally apply kernel functions to estimate \(f(\mathbf{x}/\,h)\).  
Bayes classifier was successfully applied to classify the bacterial infections in a hospital environment [59]. In this study, using an adaptive kernel function based probability density function, the Bayes classifier reached an excellent bacterial classification accuracy of 99.8%.