esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 3 Materials and Methods
### Explore  
The dataset for this research was organized into five traffic classes, created by combining the two datasets detailed in Table 2. The ICSX VPN-nonVPN dataset excluded  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **Content** & **Samples** \\ \hline \multirow{8}{*}{ICSX VPN-nonVPN} & AIM chat & 1033 \\  & BitTorrent & 777 \\  & Email Client & 6976 \\  & Facebook Audio & 62,544 \\  & Facebook Chat & 2285 \\  & Facebook Video & 1226 \\  & FITS & 2045 \\  & Gmail Chat & 1053 \\  & Hangouts Audio & 72,397 \\  & Hangouts Chat & 4033 \\  & Hangouts Video & 2882 \\  & ICQ & 1131 \\  & Netflix & 2084 \\  & SCP & 9546 \\  & SFTP & 449 \\  & Skype Audio & 27,756 \\  & Skype Chat & 6703 \\  & Skype File & 42,640 \\  & Skype Video & 1594 \\  & Spotify & 1498 \\  & Vimeo & 1752 \\  & VoIP Buster & 6217 \\  & YouTube & 2962 \\ \hline \multirow{8}{*}{InSDN} & DDoS & 121,942 \\  & DoS & 53,616 \\ \cline{1-1}  & Probe & 98,129 \\ \cline{1-1}  & Brute-force-attack & 1405 \\ \cline{1-1}  & Exploitation (R2L) & 17 \\ \cline{1-1}  & Web\_attack & 192 \\ \cline{1-1}  & Botnet & 164 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Preprocessed dataset.  
applications such as BitTorrent, Email Client, and Spotify since they could not be assigned to any of the five classes or needed sufficient samples. On the other hand, in the InSDN dataset, attacks with limited samples, such as Botnet, Web_attack, and Exploitation (R2L), were not discarded. Ultimately, a random and equitable selection process of examples was conducted for applications and attacks to balance the five classes. The result of this sample selection process is presented in Table 3. In addition, Table 4 shows all the features of the dataset.
## 5 Conclusions  
In summary, we successfully demonstrated that LNO surface modification can significantly influence the ORR activity of epitaxial LSCO thin films. We showed that the partial LNO decoration (\(\sim\)3 nm) led to enhancement of the \(k_{\text{chem}}\) values of the LSCO films compared with the undecorated LSCO and LSCO with the LNO decoration of full coverage (\(\sim\)15 nm). We also found a change in the rate-limiting step to charge transfer from dissociative adsorption with partial LNO decorations on LSCO. In addition, the activation energies for oxygen exchange in the LSCO films with partial LNO coverages were smaller than those of the undecorated LSCO and LSCO with full LNO coverage. These changes resulted from the increased oxygen vacancies by decorating LNO on LSCO, as confirmed by the optical spectroscopy results. Therefore, the increased oxygen vacancies in the LSCO film promoted the oxygen absorption at the surface, greatly improving the ORR activity. Our results demonstrate the key role of heterointerfaces on the oxygen surface exchange process in RP phases, and a new strategy to design highly active RP oxides for SOFC cathodes.  
[PERSON] and [PERSON] designed the idea in this paper. [PERSON] performed the experiments and analyzed the data. S.-Y.K. and C.S. performed the optical conductivity experiments. [PERSON] and [PERSON] prepared the first draft. [PERSON] consulted the relevant studies and references. [PERSON] supervised the paper preparation and finalization. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the U.S. Department of Energy (DOE), Office of Science (OS), Basic Energy Sciences (BES), grant number DE-SC0021363. Support for C.S. was provided by Creative Materials Discovery Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (NRF-2017M3D1A1040828).  
Not applicable.  
All data are already provided in the manuscript.  
Sample synthesis and structural characterization were performed at the Center for Nanophase Materials Sciences (CNMS), which is sponsored at Oak Ridge National Laboratory (ORNL) by the Scientific User Facilities Division, BES, DOE. Optical spectroscopic studies were conducted at the IBS Center for Correlated Electron Systems, Seoul National University.  
The authors declare no conflict of interest.  
Figure 5: Schematic of the surface exchange kinetics of LSCO films with (**a**) partial LNO coverage and (**b**) full LNO coverage. The active oxygen vacancies in LSCO with partial LNO coverage lead to fast oxygen incorporation into the LSCO film while full LNO coverage hinders the oxygen incorporation into LSCO.
## 4 Security Analysis
### Informal analysis
#### 4.2.4 Privileged Internal Attacks  
This attack is carried out by entities in the UAV communication network, eavesdropping and obtaining valuable information from the communication. In the proposed protocol, only two communicating entities exchange information, regardless of whether they are in the authentication or communication phase, and no other entity is involved in the process. At the same time, the CA is already equipped with an insider threat program [30] that captures and neutralizes any insider threat, preventing it from sabotage, espionage, theft, and fraud. The model thus prevents unauthorized users from gaining authentication and access to system resources and prevents attacks by privileged insiders.
## 3 Results and Discussion
### Structural Improvement of the Differential Motion Assembly of the IPP-Coring System  
From the above results, the differential motion assembly with a 40 mm diameter ball seat and the arc structure in the ball seat outlet shows the lowest pressure drop and fluid velocity. To increase the operational reliability and service life of the differential motion assembly and further increase the success rate of IPP-Coring, this structure was chosen as the improved structure of the differential motion assembly. To quantitatively analyze the erosion of the improved structure, we further calculated the particle trajectories and the erosion of the original and improved differential motion assemblies when the fluid  
Figure 14: (**a**) Velocity distributions at the black velocity monitoring line with different diameters of the ball seat; (**b**) velocity distributions at the white velocity monitoring line with different diameters of the ball seat; the fluid viscosity was set as 100 mPa-s.  
Figure 15: Effect of the outlet structure of the ball seat on the pressure distribution; the fluid viscosity was set as 100 mPa-s.  
viscosity was set as 100 mPa-s and the inlet flow rate was 0.04 m\({}^{3}\)/s. As shown in Figure 16, compared with the original structure, the particles in the improved structure have a lower velocity and less impact on the wall. Therefore, the improved structure shows lower surface erosion, and the average erosion rate is only 0.42 times the value of the original structure. Due to the lower pressure drop and erosion rate, the improved structure has a smaller risk of sticking and triggering the assembly at the wrong time.
## 2 Materials and Methods
### Model Structures and Hyperparameters  
We selected U-Net [48] as an encoder-decoder structure with ResNet-34 [49] as a backbone (ResNet34-UNet) for all experiments. This architecture has been proven to work well in satellite image segmentation tasks [50].  
Figure 5 presents its general structure. Overall, there are five encoder units (Encoder 1, 2, 3, 4, and 5) and five decoder units (Decoder 1, 2, 3, 4, and 5) in ResNet34-UNet. Encoder 1 (seen in Figure 6) converts an input image with a shape of (128, 128, 3) to a set of feature maps with a shape of (32, 32, 64) after a batch normalization (BN) layer, an activation layer, a zero-padding (ZP) layer, a convolution (Conv) layer, a second BN layer, and an activation layer together with a max-pooling layer. The output of the second ReLU layer will be connected to the first layer of Decoder 4. Here, we used rectified linear unit (ReLU) as the activation function. Encoders 2, 3, 4, and 5 are residual units, and they share the same basic structure as Encoder 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Refugee Camp**} & \multirow{2}{*}{**Season**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Validation**} \\ \cline{3-6}  & & **OBIA** & **Manual** & **OBIA** & **Manual** \\ \hline \multirow{2}{*}{Kule} & Dry & 8253 & 8286 & 917 & 921 \\  & Wet & 7930 & 8109 & 881 & 901 \\ \hline \multirow{2}{*}{Nguenyviel} & Dry & 6806 & 6745 & 756 & 749 \\  & Wet & 8106 & 8145 & 901 & 905 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of training and validation patches for the OBIA labels and Manual labels.  
Figure 4: The brief process of data preparation.  
Decoders 1, 2, 3, and 4 share similar structures. Figure 6 shows the structure of Decoder 4. It starts from an upsampling layer converting the shape of the input layer from (32, 32, 64) to (64, 64, 64). Then, it will be concatenated with the output of the first ReLU layer. After that, the first Conv layer reduces the number of features from 128 to 32. Encoder 5 uses a convolution layer to replace a concatenation layer and then connects to a SoftMax operation in the end to produce prediction results.
## 4 Results and Discussion
Studies have shown that wearable optical fiber and inertial systems can achieve comparable clinical performance to optical motion capture, especially for single-joint assessments like the knee, while offering superior usability and cost-effectiveness [18; 46; 47]. These advantages position the system as a viable tool for personalized gait tracking, post-operative recovery, and remote physiotherapy applications. To complement the qualitative discussion above, Table 2 presents a comparative overview of the proposed system and other existing gait monitoring technologies. This comparison summarizes key parameters such as sensor type, resolution, infrastructure requirements, portability, and cost. The analysis emphasizes the practical advantages of the proposed photonic-inertial hybrid system over traditional laboratory-based setups, particularly in terms of wearability, ease of use, and applicability in diverse real-world environments.
## 6 Evaluation
### Comparison with Existing Automated Sample Construction Methods  
#### 6.2.1 Effectiveness Comparison under Windows  
At present, there is no research on automated traffic sample construction in academia, but an open-source process-based packet capture tool named OpenQPA [6] has been developed in the Windows environment on GitHub. We compared our A3C system and OpenQPA tool in the actual network environment in the national education network under environment P. A typical result is shown in Table 3.  
We take the Wireshark [5] traffic capture record as the baseline. Although it cannot be used for sample labeling, it can effectively count the number of current packets without packet loss as the baseline for capture. Compared with Wireshark, A3C-Windows has no packet loss when the bandwidth peak is achieved in environment P, while OpenQPA has  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Encrypted Flow Number** & **Unencrypted Flow Number** \\ \hline Aim & 10 & 4 \\ BaiduNetdisk & 863 & 12 \\ Chrome & 5994 & 603 \\ Email & 9 & 4 \\ Facebook & 146 & 9 \\ Firefox & 792 & 19 \\ FTPs & 1 & 2 \\ Hangouts & 142 & 3 \\ Icq & 10 & 1 \\ Msedge & 6219 & 286 \\ Netfix & 54 & 140 \\ QQ & 70 & 126 \\ QQBrowser & 385 & 60 \\ QQMMusic & 314 & 112 \\ IQYI & 26 & 2358 \\ ScpDown & 3 & 0 \\ SFTP & 6 & 0 \\ Skype & 139 & 116 \\ Spotify & 77 & 20 \\ Vimeo & 239 & 18 \\ VoipBuster & 2 & 11 \\ VPN & 13 & 6 \\ WeChat & 15 & 208 \\ Youtube & 243 & 191 \\ \hline Total & 15,772 & 4101 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistic information on the data used in encrypted traffic detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Estimator** & **OpenQPA** & **A3C** & **Baseline** \\ \hline Implementation & & & \\ Rationale & & & \\ Label Granularity & Process & Process/App & - \\ Packet Count & 8463 & 9641 & 9641 \\ Duration (s) & 310 & 311 & 311 \\ Captured Bytes & 8,194,421 & 8,999,105 & 8,999,105 \\ Packet Loss Rate & 12.22\% & 0 & - \\ Bandwidth Peak & & & \\ (Mbps) & 28.4 & 28.4 & 28.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative comparison of traffic sample construction in Windows.  
a serious packet loss rate of 12.22% (1178 loss in 9641). The result means that A3C can effectively construct traffic samples in any end.  
It is worth noting that although the duration of the two methods is different (only one second) from the table, they compare the same data under the same environment. Hence, they meet the standard of quantitative controlled experiments.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion  
To rigorously evaluate the performance of our methodology, we designed a comprehensive empirical validation framework. This section systematically outlines the experimental design, encompassing four critical components: (1) benchmark datasets employed for validation, (2) implementation protocols detailing technical configurations, (3) quantitative assessment criteria, and (4) comparative analysis of experimental outcomes with critical discussions. The methodological framework is structured across these dimensions to ensure thorough verification of the proposed approach.
# Experimental Methods to Evaluate the Carbonation Degree in Concrete--State of the Art Review
## 2 Qualitative Methods
### Phase Analysis
#### 2.3.4 Ultrasonic Phase Velocity Technique  
The ability to use ultrasonic wave transmission velocity to predict the microstructure and properties of materials was investigated in several previous studies [78; 79]. To assess characteristics of concrete, such as porosity, density and pore structure, the surface wave-based technique is highly recommended due to the fact that the wave's energy is intensified significantly on the surface of material obviously affected by carbonation [80]. This technique could monitor, automatically and continuously, the effect of the carbonation process without any destruction based on the transmission ultrasound. Acoustic phase velocity analysis was conducted on numerous acquired signals mainly obtained from the controlled carbonation process of cementitious composites. In detail, typical ultrasonic signals obtained from reference (non-carbonated) and carbonated specimens at different ratios of water and cement are indicated in Figure 8, according to [PERSON] et al. [81]. As can be seen from this figure, after carbonation, scattering effect was observed with larger pulse width because of the decrease in the granular noise and the group velocities.  
Figure 7: The variation of CH and CC content in non-carbonated (Ref, figure (**a**)) and carbonated (0.55, figure (**b**)) concretes. The dashed lines show the carbonation depth measured by indicator [26]. Note: BRC: Belite-rich cement paste; OQ: ordinary Portland cement and quartz paste, BQ: Belite-rich cement and quartz; BL: Belite-rich cement and limestone.  
The diffusion of the ultrasonic energy based on phase velocity measurement could give information on variation of the microstructure in cement paste. The effect of carbonation during the carbonation period is determined based on the experimental pulse parameters and group velocity data. In addition, the increment of wave dispersion reflects the change in the porosity and increase in microscopic phases due to non-constant diffusion of CO\({}_{2}\) in concrete. It is a fact that increased density and decreased pore spaces in the concrete structure could be achieved after carbonation [82]. Modification of the microstructure with exposure duration could be obtained by increasing the pulse width and the appearance of structural noise, in which the frequency capacity of the wave becomes more expansive, due to the diffusion effects of CO\({}_{2}\). The result obtained from this technique is well in agreement with the FTIR result, according to [PERSON] et al. [81].
# An Overview of Intelligent Transportation Systems in Europe
## 2 A Brief History of ITSs in Europe
He highlights the role of the European standardization organizations (CEN, CENELEC and ETSI) in creating a solid technical and legal framework for ITS applications in different transport domains. Going on, [PERSON] et al. present the strategic vision of ITSs for safe, efficient and sustainable transport in Europe [13]. They examined how legislation such as Directive 2010/40/EU and the 2008 ITS Action Plans influenced national implementation, promoting interoperable and seamless services between Member States and improving the efficiency of international transport [14]. The study by [PERSON] [3] examines ITS standardization in Europe and the United States between 1991 and 2012. Ultimately, Europe's government-led and policy-based strategy has encouraged strong top-down cooperation and faster standardization among stakeholders. Europe's proactive policies and cooperative frameworks have enabled the rapid integration of ITS. These frameworks underscore the importance of deliberate policy support in the growth of these technologies. The European Commission's Directorate-General for Mobility and Transport has highlighted the strategic importance of ITSs for creating new services and jobs in the transport sector by 2030. The report highlighted the importance of data sharing and digital technologies in improving the safety and efficiency of travel in Europe, which benefitsall stakeholders, including governments, private companies and consumers [15]. As ITSs continue to evolve, the integration of emerging technologies such as 5G connectivity, artificial intelligence and the blockchain will further transform the transport landscape in Europe, making it more adaptable, safe and sustainable [16].
# 6G Mobile Networks: Key Technologies, Directions, and Advances
## 3 6G Market Overview and Current Status
### 6G Architecture
#### 3.4.1 From Terrestrial to Ubiquitous 3D Coverage  
Increasing the comprehensiveness of communication coverage becomes a goal of the next-generation network design. The prevailing network design is built upon ageing terrestrial cellular infrastructures. Non-terrestrial networks would be integrated into 6G to offer complete wireless connectivity.  
* Space-Network: High throughput satellite (HTS) devices are known to provide broadband Internet solutions with prices and capacity that are equivalent to terrestrial offerings. Geostationary orbit (GEO), where the majority of communications satellites are located, is at a height of 35,786 km, which inevitably causes significant latency and makes connectivity with terrestrial mobile networks impossible. A non-geostationary orbit (NGSO) satellite system is proposed, and multiple satellite constellations are about to begin making a lot of money to provide low-latency, extremely high Internet connectivity. compared to a network of terrestrial optical fibres. Reduced latency connectivity may be available with an LEO system using co-routing radio-frequency and laser technologies.
* Aerial-Network: High altitude platforms (HAP), which normally work the stratosphere, and low altitude platforms (LAP), which are usually at a height little and over a few kilometres, can be generally categorised as two types of aerial networks. HAP networks can provide more coverage and can last longer than LAP networks. Unmanned aerial vehicle (UAV)-based LAP networks, on either hand, could be deployed faster, more easily modified to properly serve the communication system, and function great narrow communication. When facilities are severely damaged or absent altogether, such as in disaster emergency circumstances, UAV networks offer mobile communication. The suggested new trajectory optimisation & path guidance techniques considerably help save energy.  
Figure 3: 6G Architecture.  
* Undersea-Network: The three main categories of underwater wireless networks are radio frequency, acoustical, and optical communications. Due to the unexpected and complex undersea setting, which results in challenging network coverage, severe signal attenuation, and mechanical damage to the equipment. There are numerous issues to be solved.
## 3 Results and Discussion
After obtaining the best-matching position, with local resamples of the adjacent images in the arrangement direction, we can obtain the adjacent images with more accurate positions. Thus, the geometric stitching of the two images can be completed by stitching according to their geometric positions. Finally, loop processing was conducted. The concatenated result and the image to be concatenated are processed in a cyclic manner according to the first four processing steps, ultimately obtaining the concatenated large-area image. A total of 21 aerial infrared images were used for the region mosaic processing experiment in order to verify the effectiveness of the processing method. Using a high-performance server and VC, a fast processing program, including an image restoration module, geometric correction module, and a mosaic module, is developed. The size of each original image is 11,500 \(\times\) 480, and each pixel is 16 bits, so the regional geometric mosaic (including geometric correction, matching, and resampling) can be completed within 80 s. The final mosaic image is shown in Figures 9 and 10. The two figures show the effect of local zoom in a display of
## 2 Risk Modeling Techniques
### Stochastic Modeling  
Stochastic models incorporate randomness directly into their calculations to account for the variability inherent in risk factors. These models are especially prevalent in electricity markets for option pricing or risk assessment in investment portfolios. In the power system sector, stochastic models can be used for modeling the uncertainties in the generation of renewable energies. Stochastic models are vital for understanding the implications of random fluctuations in market prices or interest rates.  
Value-at-risk (VaR) is one of the stochastic risk modeling techniques and a widely used risk management tool in finance and power systems. It provides an estimate of the maximum potential loss in value of a risky asset or portfolio over a defined period for a given confidence interval. This technique is commonly employed by banks, investment firms, and corporate risk managers to gauge the amount of assets needed to cover potential losses.  
Conditional value-at-risk (CVaR) is raised based on the concept of VaR subject to additional constraints that limit the risk consequences [17]. The CVaR based on a linear function can be expressed by (16) [18].  
\[CVaR=VaR-\frac{1}{1-\alpha}\sum_{i_{1},i_{2},\ldots,i_{n}}\left[\xi_{i_{1}}^{ uncertain_{par}}\cdot\xi_{i_{2}}^{uncertain_{par}}\cdot\ldots\cdot\xi_{i_{n}}^{ uncertain_{par}}\cdot\eta_{i_{1},i_{2},\ldots,i_{n}}\right] \tag{16}\]  
where \(\xi_{i_{n}}^{uncertain_{par}}\) is the probability of the n-th uncertain parameter.  
The constraints for CVaR are presented in (17) and (18).  
\[VaR-f_{i_{1},i_{2},\ldots,i_{n}}\leq\eta_{i_{1},i_{2},\ldots,i_{n}} \tag{17}\]  
\[\eta_{i_{1},i_{2},\ldots,i_{n}}\geq 0 \tag{18}\]  
Figure 2 demonstrates the calculation of CVaR, showing the probability distribution of losses in an investment portfolio. The area shaded in red represents the tail of the distribution beyond the VaR threshold, where CVaR is calculated. This shaded area corresponds to the expected average loss in the worst-case scenarios beyond the VaR point, effectively highlighting the extreme losses that could occur, which CVaR aims to quantify and manage.  
Figure 2: Conditional value-at-risk.