esa-sceva/satcom-chunk-collection huggingface dataset:
# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 5 Pointing Correction Verification  
While the methods utilised here have a strong theoretical underpinning and have previously been implemented successfully for identifying radar defects, there still remains a degree of underlying uncertainty for the results shown in 4 that is difficult to quantify due to the data sparsity in many time periods and the location of the radar at a high latitude that limits the observed solar hits to a narrow azimuth range in winter. As a result, post-correction verification is an essential step for understanding the results and the limitations of the underlying methods for high-latitude locations. Here we present two approaches: the first based on reanalysis of the solar hits themselves and the second being an analysis of radar quantitative precipitation estimates (QPEs) before and after correction. The first approach can be considered a simple sense check of the results, while the second approach provides a much wider verification but is prone to its own uncertainties. Alternative approaches were also considered, such as an analysis of retrieved melting layers (again prone to a wide degree of uncertainty due to atmospheric variations), but the two presented here are more robust and provide additional verification to support the derived corrections.
## 4 Experimentation and Results
### Ablation Test  
We conducted a decomposition of the SAFEPA into its fundamental models, as depicted in Figure 12. The fundamental model comprises a single branch of InceptionV3, which is subsequently followed by dense and dropout layers. In contrast, the FEAPAS consists of two branches of InceptionV3, where the outputs of these branches are concatenated and forwarded to dense and dropout layers. As an extension to the FEAPAS, the SAFEPA introduces the inclusion of SAE into the first branch.  
To evaluate the performance of each model, we utilized the UNBC-McMaster dataset for pain assessment, along with three sets of KDEF. The KDEF dataset encompassed three distinct variations: (1) only frontal view, comprising 980 samples, (2) full and half left and right poses, amounting to 3920 samples, and (3) the complete dataset encompassing 4900 samples for facial expression recognition (FER). Table 7 shows the accuracy for each case.  
Table 7 does not provide the results for the FEAPAS in the last two cases of the KDEF dataset, as the FEAPAS relies on MTCNN which is unable to handle full left/right profile samples. Consequently, the FEAPAS cannot be applied to these specific samples within the KDEF dataset. However, while the FEAPAS exhibits satisfactory performance with front view samples in both the UNBC-McMaster and KDEF datasets, it fails to deliver successful outcomes in other scenarios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**The Model**} & **Percentage of** & **Use Face** & **Cross Validation** & **Accuracy \%** & **Processing Time** \\  & **Used Data** & **Detection** & **Cross Validation** & **Accuracy \%** & **in Second** \\ \hline [PERSON], et al. [37] & 100\% & Yes & leave-one-out & 30.80 & Not available \\ [PERSON], et al. [38] & 100\% & Yes & 10-fold & 27.13 & Not available \\ [PERSON], et al. [39] & 33.71\% (unknown selection method) & Yes & k-fold & 37.42 & Not available \\ SAFEPA & 100\% & No & Testing all the data & 33.28 & 17.82 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of SAFEPA system on pain assessment task when evaluated on BioVid dataset.  
Overall, the findings presented in Table 7 demonstrate that all models perform optimally when confronted with frontal view samples in both the UNBC-McMaster and KDEF datasets. In the case of full left and right profile samples, the FEAPAS encounters limitations and the base model's performance experiences a substantial decline. In contrast, the SAFEPA operates effectively without any complications and demonstrates superior performance, surpassing the base model's capabilities.
# Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain
## 3 Simulation Results and Discussion
### Signal-to-Noise Ratio (SNR)  
Figure 11 shows the SNR against the coverage area under very clear and snowy weather at different snow rates, estimating the minimum SNR corresponding to minimum BER = 10\({}^{-6}\) for different modulation schemes. It is clear that OOK has a moderate SNR value between L-PPM and I-LPPM, while SC-BPSK can cover less distance but a vehicle can receive the data with a higher SNR value than OOK. The I-LPPM achieves the best SNR, while L-PPM achieves the best coverage area.  
The highest SNR that a vehicle will receive on the first lane under very clear weather conditions is 44.21 dB at 18 m, when the data rate is 10 Mbps. After 18 m and 25 m, the SNR drops in the first lane and the second lane, respectively. The second lane can be covered by a traffic light, but the vehicle can receive data with 6 dB SNR lower than the first lane.  
The SNR decreases with the increase of perception rate. In addition, dry snow yields less SNR than wet snow because of the high attenuation, which affects the system. The wet snow varies from 43.41 dB to 41.64 dB, while dry snow varies from 42.12 dB to 30.11 dB, ranging from lower rates to higher rates. In the case of using \(\theta=40^{\circ}\) and FOV = 20\({}^{\circ}\), the SNR values is decreased by almost 13.64 dB. This is also obtained at all other attenuation cases.  
Figure 12 shows the SNR against the coverage area with various rain attenuation models. As it appears, the SNR is diminishing at higher prescription rates. In addition, for different rain attenuation models, the SNR has very close behavioral curves at small distances. The maximum SNR that a vehicle can receive in the case of the Marshall model varies from 43.89 dB to 44.25 dB at 18 m ranging from  
Figure 11: Signal-to-noise ratio vs. distance for (**a**) very clear weather and 2 mm/h wet, dry snow in the first lane, (**b**) very clear weather and 2 mm/h wet, dry snow in the second lane, (**c**) 4, 8 mm/h wet, dry snow in the first lane, and (**d**) 4, 8 mm/h wet, dry snow in the second lane.  
higher to lower rates. In the case of the France model, the maximum SNR varies from 42.92 dB to 44.05 dB, while the Japan model varies from 42.44 dB to 43.97 dB.  
Figure 12: Signal-to-noise ratio vs. distance for (**a**) very clear weather and different rain models at 25 mm/h in the first lane, (**b**) very clear weather and different rain models at 25 mm/h in the second lane, (**c**) different rain models at 10 mm/h in the first lane, (**d**) different rain models at 10 mm/h in the second lane, (**e**) different rain models at 2 mm/h in the first lane, and (**f**) different rain models at 2 mm/h in the second lane.
# Wheel-Based MDM-PON System Incorporating OCDMA for Secure Network Resiliency
## 3 Results and Discussion
### Power Budget (PB) Analysis  
PB calculates the total loss in the system due to power splitters, power combiner and mode selectors. The expression for total loss, \(L_{T}\), of the system is given as [41]:  
\[L_{T}=L_{c}+L_{s}+aL_{fiber}+L_{MS} \tag{6}\]  
where \(L_{c}\), \(L_{s}\), \(a\), \(L_{fiber}\) and \(L_{MS}\) represent combiner loss, splitter loss, fiber attenuation, fiber loss and mode selector loss, respectively. In the proposed system, mode selector loss is considered zero as its basic function is to draw a single mode from the multimode signal in MMF. Table 5 presents derailed loss calculations of the proposed scheme at an input power of 10 dBm and 0.2 dB/km attenuation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Length (km)** & \(\Delta t\) **(ps)** & \(B_{r}\) **(Gbit/s) [Downlink]** & \(B_{r}\) **(Gbit/s) [Uplink]** \\ \hline
0.2 & 3.4 & 0.03 & 0.008 \\
0.6 & 10.2 & 0.10 & 0.2 \\
1 & 17 & 0.17 & 0.4 \\
1.2 & 20.4 & 0.20 & 0.5 \\
1.6 & 27.2 & 0.27 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measured pulse broadening and supportable bit rate in the proposed scheme.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transmission** & **Wavelength (nm)** & **Length (km)** & **Chromatic Tolerance (ps/nm)** \\ \hline \multirow{6}{*}{Downlink} & & 0.2 & 200 \\  & & 0.6 & 400 \\  & 1596 & 1 & 600 \\  & & 1.2 & 800 \\  & & 1.6 & 1000 \\ \multirow{2}{*}{Uplink} & & 0.2 & 150 \\  & & 0.6 & 300 \\  & 1527 & 1 & 450 \\  & & 1.2 & 600 \\  & & 1.6 & 700 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculated CD for downlink and uplink transmission wavelengths.  
Table 6 presents that the proposed scheme based on wheel topology offers the highest number of connected ONUs (=58) with high security by employing MNZCC code with high scalability and fewer losses at a high data rate (=58 \(\times\) 10/2.5 Gbit/s) than other existing schemes. Except for reference [34], no other scheme uses code for enhancing the system security at high data. Although the transmission distance of the proposed scheme is less than other schemes, other ring/tree-based schemes include losses and lack in scalability as well as security of the system. Furthermore, to improve the transmission distance and data rate in the proposed schemes, amplifiers can be employed successfully.
# DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism
## 5 Experimental Evaluation
### Experimental Results  
This paper performed random sampling to generate sets for our evaluations. First, the sample set was randomly divided into 10 parts on average, and one of them was taken as a test set. One of the remaining nine samples was used as a verification set, and the remaining eight were used as a training set. The ratio of the final training set, verification set, and test set is 8:1:1. After training, the test set was used to test the classification model. The results of the experiment are shown in Table 3.  
It can be seen from Table 3 that the average precision rate is 95.05%, the average recall rate is 95.14%, and the average \(F_{1}\) score is 95.48%. The accuracy rate is 95.14%, which is very high. The confusion matrix for the LSTM multiclass classifier with attention mechanism is show in Figure 4. Blocks in the figure represent the fraction of domains belonging to the DGA families on the vertical axis, classified as DGA families on the horizontal axis, where 0 is depicted as white and 1 depicted as black. As can be seen in Figure 4, a large number of Cryptolocker DGAs, Locky DGAs, and Necurs DGAs are classified as Ramnit. We analyzed these DGAs and found that they are very similar. [PERSON] [26] pointed out that the Ramnit acquired parts of the Zeus code and became a banking trojan after the Zeus source code leak. [PERSON] [27] supposed that Necurs operators were linked to the very centrum of Zeus elite in the early days, whereas [PERSON] [28] told us that the Locky ransomware roared back to life via Necurs botnet. We could infer that there is some relationship among Cryptolocker,  
\begin{table}
\begin{tabular}{c c} \hline
**DGA** & **Amount** \\ \hline banjori & 439,223 \\ Post & 66,000 \\ tinba & 65,603 \\ ramnit & 47,510 \\ necurs & 32,768 \\ qakbot & 20,000 \\ murofet & 14,260 \\ pykspa & 14,215 \\ ranbyus & 13,960 \\ simda & 13,681 \\ shiotob/urlzone/bebloh & 12,521 \\ dyre & 7998 \\ Cryptolocker & 6000 \\ nymaim & 6000 \\ locky & 5352 \\ Alex & 910,313 \\ \hline \end{tabular}
\end{table}
Table 2: The details of the dataset.  
Locky, Necurs, and Ramnit; that is why some DGAs of Cryptolocker, Locky, and Necurs are classified as Ramnit.
# Selective Removal of Diesel Oil Hydrocarbons in Aerobic Bioremediation
## 3 Results
### Removal Efficiency  
The identification of peaks allowed evaluating the degradation of a single hydrocarbon, according to Equation (2). For more practical usability, we preferred to show the results by grouping the alkanes into four clusters (C8-C11; C12-C15; C16-C19; C20-C24), while for the aromatic hydrocarbons the overall degradation efficiency was reported.  
The removal efficiency for four hydrocarbon clusters and aromatics, \(\eta_{\rm H}\), can be calculated with the summation:  
\[\eta_{\rm H}\ =\ \sum_{1}^{n}\eta_{i} \tag{6}\]  
where \(n\) is the last hydrocarbon compound of each cluster.  
Figure 4 reports the results for the tested microcosm.  
In general, the trend of the removal efficiency was similar for all hydrocarbon clusters, the percentage of removal increased much in the first 70 days; then, between the 71 st and the 138 th day, \(\eta_{\rm H}\) became almost constant. From Figure 4, it is evident that the hydrocarbons with short chains (alkanes C8-C11) were the most degraded, while as the number of carbon atoms increased, the removal efficiency decreased.  
The C8-C11 cluster was reduced by 30% after 7 days from the beginning of the test and over 80% after 138 days. The trend of the removal efficiency for this cluster was always higher than for the others.  
The removal efficiency of the C12-C15 cluster was equal to 63%, and this percentage remained unchanged from the 70 th day to the end.  
The C16-C19 cluster showed an evident minimum at t = 112 days: this means that at this time, its concentration was higher than at t < 112 days. As a hypothesis, the reason could be the generation of C16-C19 compounds deriving from the degradation of compounds with a higher carbon number.  
From the starting time to the 112 th day, the removal kinetics for the C20-C24 compounds were very similar to those of the C12-C15 compounds.  
The removal trend of aromatic hydrocarbons was slower than for the other compounds, due to the molecular complexity and their difficulty to be degraded.
## Acoustic Wave Theory and Finite Element Formulation  
### Acoustic Wave Theory  
Acoustic wave propagation in a homogenous fluid media at rest is governed by the wave equation:  
\[\varrho^{2}\varPhi=\frac{1}{\varc^{2}}\frac{\partial^{2}\varPhi}{\partial t^{2}} \tag{1}\]  
For Cartesian and polar coordinates, (\(x\), \(y\), \(z\)) and (\(r\), \(\theta\), \(z\)), respectively, the three-dimensional Laplacian operator \(\varnabla\) is defined, respectively by:  
\[\varvarvar^{2}=\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{ \partial y^{2}}+\frac{\partial^{2}}{\partial z^{2}} \tag{2}\]\[\bar{\
u}^{2}=\frac{\partial^{2}}{\partial r^{2}}+\frac{1}{r}\frac{\partial^{2}}{ \partial r^{2}}+\frac{1}{r^{2}}\frac{\partial^{2}}{\partial\theta^{2}}+\frac{ \partial^{2}}{\partial z^{2}} \tag{3}\]  
and the equations for coordinate transformations are given by:  
\[x=rcos\;\theta,\;y=rsin\;\theta,\;r=\sqrt{x^{2}+y^{2}} \tag{4}\]  
where \(c\) is the velocity of acoustic wave propagation, \(\Phi\) velocity potential and \(x\), \(y\), and \(z\) are standard Cartesian coordinates. The corresponding acoustic pressure wave propagation is governed by:  
\[\bar{\
u}^{2}p=\frac{1}{c^{2}}\frac{\partial^{2}p}{\partial t^{2}} \tag{5}\]  
where the pressure, \(p\) and instantaneous particle velocity, \(u\) are related to the velocity potential, \(\Phi\) by:  
\[u=\bar{\
u}\Phi;\;p=-\rho\frac{\partial\Phi}{\partial t} \tag{6}\]  
For an open-ended air-filled pipe without defect, with uniform cross-sectional area, driven by a sound pulse, the general solution of Equation (1) is of the form [18]:  
\[q_{k_{x},k_{y},k_{z}}(x,y,z,t)=Ae^{i(k_{x}x+k_{y}y+k_{z}z-\omega t)} \tag{7}\]  
or  
\[q_{k_{r},k_{\theta},k_{z}}(r,\theta,z,t)=Ae^{i(k_{r}r+k_{\theta}\theta+k_{z}z- \omega t)} \tag{8}\]  
where \(q\) represents the longitudinal displacement of the fluid as the wave propagates through it, \(t\) is time, \(\omega\) is the frequency and \(k\) is the wave number. The FEA method based on Equation (1) leads to a symmetric matrix formulation for fluid structure interaction [1; 3] which is used in several numerical computational analysis programs for acoustics. For direct integration transient analysis, FEA solutions are based on Equation (7).
## 3 Main Results
### The Improved Dynamic Allocation Method  
When the static metric allocation method mentioned above is applied to a real large-scale hierarchical industrial wireless sensor networks, the capacity of neighboring nodes may be close to saturation and can not bear the excess load. If the load redistribution is forced according to the static initial metric allocation method of the nodes, it will undoubtedly lead to the failure of the neighboring nodes.  
Furthermore, when a node determines that there is a load to be redistributed, the node with higher degree among the neighboring nodes has a higher probability to take on more load-distribution tasks. To solve this problem, two real-time dynamic allocation methods are used in this paper, the first one is the average degree distribution (ADD). For each time step \(t\), the load is allocated in real time according to the residual load ratio \(\psi\):  
\[\psi_{j}(t)=\frac{C_{j}(t)-L_{j}(t)}{C_{j}(t)}, \tag{10}\]  
The load distribution rates are as follows:  
\[\eta_{j}(t)=\frac{\psi_{j}(t)C_{j}(t)}{\sum_{n\in\Gamma_{i}}\psi_{n}(t)C_{n}(t)}. \tag{11}\]  
The second way is surplus load distribution (SLD). The load distribution ratio is as follows:  
\[\eta_{j}(t)=\frac{k_{j}}{\sum_{n\in\Gamma_{i}}k_{n}(t)}, \tag{12}\]  
where \(k_{n}(t)\) represents the degree of the node \(n\) at time \(t\). \(k_{n}(t)\) dynamic real-time detection is required, rather than being decided at the initial network construction.  
When the dynamic load distribution is complete, the current load of each node is as follows:  
\[L_{j}(t)=L_{j}(t-1)+\eta_{j}(t)L_{i}(t-1), \tag{13}\]  
when \(L_{j}(t)>C_{j}\), the cascading failure process occurs until \(L_{j}(t)\leq C_{j}\).
## 3 Model Evaluation
### Descriptions of Site and Data  
The study site used for empirical analysis is two segments of the Busan-bound Gyeongbu Expressway connecting Giheung IC to Dongtan IC, as illustrated in Figure 1a. The selected freeway sections typically experience high traffic volume and heavy congestion on weekends since they are main routes to tourist attractions. Therefore, travelers need efficient and accurate travel time estimates when planning trips and changing travel routes, especially during peak periods. The evaluation of travel time estimation on the test site is conducted based on individual probe data from Korean Expressway Corporation. The dataset provided by Korean Expressway Corporation is mainly collected by Korea Expressway's ETC system, Hi-Pass, using two different types of transponder technologies, radio frequency (RF) and infrared ray (IR), through dedicated short-range communication (DSRC) ([PERSON] et al., 2013). The individual probe data at the test site covers two freeway segments with a total length of 5.3-km. The raw probe data provides individual travel times for each segment and are collected in different time intervals. In this study, the raw data are used at five-minute intervals to estimate travel time and their distribution of travel times. The individual probe data between 6:30 and 13:00 from 1 March 2017 to 28 April 2017 are used for the training dataset, and from 29 April and 5 May 2017 are used for the test dataset because the most congested periods are observed in this time frame. Therefore, the estimation performances using different methods are investigated during the peak period.  
To evaluate the estimation accuracy of the methodology, this study assumes the travel time estimated on the Gyeongbu expressway to be the ground truth. The Gyeongbu expressway, which relatively includes a high penetration rate of on-board devices in South Korea, is selected to test the estimation algorithm. The loop detector collects a full volume of traffic during the time interval, and it is possible to compare those data with the amount of traffic volume collected by ETC readers. The traffic volume from loop detectors was compared with the penetration rate of ETC between Giheung IC to Dongtan IC, as illustrated in Figure 1b. The traffic volume of passenger cars was used to compare between loop detectors and ETC, and traffic volume from different types of vehicles was excluded. Since the ratio traffic volume between loop detectors and ETC is approximately 50% in the study site, the estimation results obtained by assuming the travel time collected by ETC as ground truth were compared.
# MOCA: A Network Intrusion Monitoring and Classification System
## 4 MOCA Design
### Feature Importance  
This study measures feature importance using the following algorithms: information gain, correlation coefficients, and SHAP (SHapley Additive exPlanations) values. Consider \(S\) a set of training-set samples with corresponding labels. Assuming there are \(m\) classes, the training set contains \(c_{i}\) samples of class \(C_{i}\), and \(N\) is the total number of samples in the training set. The information needed to classify a given sample is calculated by the following equation:  
\[I(c_{i},c_{2},\ldots,c_{m})=-\sum_{i=1}^{m}\frac{c_{i}}{N}\log_{2}(\frac{c_{i} }{N}) \tag{1}\]  
Feature \(F\) with values \(\{f_{1},f_{2},\ldots,f_{v}\}\) can divide the training set into \(v\) subsets \(\{S_{1},S_{2},\ldots,S_{v}\}\), where \(S_{j}\) is the subset with value \(f_{j}\) for feature \(F\). Furthermore, let \(S_{j}\) contain \(s_{ij}\) samples of class \(i\). Entropy of feature \(F\) is:  
\[E(F)=\sum_{j=1}^{v}\frac{s_{1j}+\ldots+s_{mj}}{N}\times I(s_{1j},\ldots,s_{mj}) \tag{2}\]  
Information gain for \(F\) can be calculated as:  
\[Gain(F)=I(s_{i},s_{2},\ldots,s_{m})-E(F) \tag{3}\]  
It is noticeable that the information metric is a good measurement among different feature selection metrics to quantify the importance of features.  
Correlation coefficients evaluate a subset of highly correlated features within an attack class but not correlated with each other. The correlation coefficient of a feature subset \(F\) containing k features is given by [5]:  
\[Merit_{s}=\frac{k\times\overline{r_{cf}}}{\sqrt{k+k\times(k-1)\times\overline {r_{ff}}}}\]  
where \(\overline{r_{cf}}\) is the mean feature-class correlation, and \(\overline{r_{ff}}\) is the average feature inter-correlation  
The SHAP value [26] of a feature is it contribution to the payout, it is weighted and summed over all possible feature combinations:  
\[\phi_{j}(val)=\sum_{S\subseteq X\setminus x_{j}}\frac{|S|!(p-|S|-1)!}{p!}( val(S\cup x_{j}))-val(S)) \tag{4}\]  
where \(S\) is a subset of all features used in the model, \(X=\{x_{1},\ldots,x_{p}\}\) describes the vector of feature values of the instance to be explained, and \(p\) is the number of features; \(val_{x}(S)\) is the prediction of feature values in set \(S\) that are marginalized over features not included in set \(S\).
## 6 Implications and Future Directions
### Scalability Considerations  
Linear scalability characteristics were observed in simulations involving up to 1000 devices, with all performance metrics remaining within acceptable operational thresholds. Theoretical analysis suggests that larger deployments could be accommodated through  
* Hierarchical blockchain architectures for efficient identity verification at scale;
* Edge computing infrastructure deployment to distribute computational loads;
* Context-aware security policies with dynamic resource allocation based on network size;
* Federated learning paradigms to maintain distributed intelligence without centralized bottlenecks.  
The empirical validation of these scalability mechanisms remains a critical objective for future research.