esa-sceva/satcom-chunk-collection huggingface dataset:
# A Self-Supervised Learning Model for Unknown Internet Traffic Identification Based on Surge Period
## 3 Methods
### The Surge Period-Based Feature Extraction Method in Unknown Internet Traffic Identification
```
0: Internet traffic flow, the number of surge periods: \(n\), the number of data packets: \(k\), bit stream speed in network session: \(v_{0}\)
0: Traffic features
1: Remove data packets of non TCP/UDP protocols
2:for Each data packet in TCP/UDP protocols do
3: Read in the information of five tuples
4: Reorganize the traffic flows according to five tuples
5: Exchange the source port, destination port, source address and destination address in the tuple to find the up-stream or down-stream of the same session
6:endfor
7:for Each network session do
8: Sort data packets in its up-stream and down-stream in a chronological order
9:for The beginning time \(t\) of each network session to \(t+t_{0}\)do
10: Calculate the sum of packet length _sumlen_ for all data packets in the time unit
11:if\(sumlen>v_{0}\)then
12: Mark \(t+t_{0}\) as a surge period
13: Extract \(k\) packet length sequentially in the surge period as features
14:if The number of already counted surge periods >\(n\)then
15: Break
16:else
17: Look for the next surge period
18:endif
19:endif
20:endfor
21:endfor
22: Return features
```  
**Algorithm 1** The surge period-based feature extraction method of unknown Internet traffic identification  
Compared with other traditional feature extraction methods, the surge period-based one does not select a fixed network session as the feature extraction unit, but regards a dense period with a high flow rate as a surge period, and further analyzes and extracts the flow characteristics of surge periods. This method shows great advantages in efficiently extracting features of unknown Internet traffic. On the one hand, it avoids the influence of noise, such as background traffic and improves the accuracy of features. On the other hand, it also greatly expands the number of flow features that can be extracted, laying a solid foundation for the efficient identification of unknown traffic.
# An Optically Transparent Metasurface for Microwave Amplitude-Phase Manipulation
## 3 Discussion
Thus, future research may focus on integrating stimuli-responsive elements (e.g., varactor, graphene, and vanadium dioxide) with meta-atoms to realize dynamical tunability [48], or utilizing polarization-multiplexed and angle-multiplexed technologies to expand the information capacity [49; 50; 51].
###### Abstract  
In order to understand the evaporation and particle formation processes of sprays in technical applications such as fuel injectors or drying processes in the food and pharmaceutical industries in detail, single droplet drying experiments, for example, acoustic levitation, are widely used as model experiments. We combined acoustic levitation and tunable diode laser absorption spectroscopy (TDLAS) to measure the absolute H\({}_{2}\)O concentration in the exhaust gas of a levitation chamber to investigate drying and particle formation processes from single droplets of pure water and protein-water solutions. To that end, we designed and developed a non-invasive, calibration-free TDLAS-based hygrometer to analyze the 1.4 \(\upmu\)m overtone band. To increase the detection range of the developed hygrometer and to track the complete drying process of protein solution droplets even after the critical point of drying, the absorption length was extended to a path length of 18 m using an astigmatic multipass cell of the Herriott type. The setup was validated by drying pure water droplets, resulting in a determination of the water mole fraction in a range from 73 ppm to 1314 ppm, with a single scan resolution of 1.7 ppm. For protein solution droplets, the entire drying process, even beyond the critical point of drying, can be tracked and the different phases of the drying process can be characterized at different drying temperatures.  
tunable diode laser absorption spectroscopy; astigmatic multipass cell; acoustic levitation; single droplet drying; protein drying +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 1 Introduction
On the other hand, the microgrid control center-side cognitive control (MACC) solves the problem of data loss through a Q-learning algorithm. MACC evaluates the impact of information gaps on dispatching decisions and, when the information gaps exceed the allowable range, re-estimates the gap between user demand and actual demand and ensures reasonable power resource allocation even when the data are incomplete. By integrating the real-time monitoring and feedback mechanism of CPS, the cognitive control model plays an important role in quantifying information gaps and effectively assessing data uncertainty caused by communication instability. This integration not only enhances the accuracy and flexibility of load forecasting after mall scheduling but also enhances the system's ability to handle uncertainty in a dynamic environment. On this basis, the innovative algorithm based on BiLSTM-Transformer proposed in this paper combines BiLSTM and Transformer architecture, focusing on the charging and discharging management of a large number of electric vehicles in the parking lot of commercial buildings, so as to achieve accurate prediction of load consumption after shopping mall scheduling. The model not only overcomes the limitations of real-time response and accuracy in traditional methods but also enhances the robustness of the system in an incomplete data environment through cognitive control, providing efficient and reliable support for complex and dynamic shopping mall load management [10]. The main contributions of this paper are as follows:  
1. A shopping mall load prediction framework based on BiLSTM-Transformer was constructed. In this framework, the training and prediction processes of the BiLSTM-Transformer network are separated. Offline training enhances the generalization ability of the model, enabling it to quickly generate optimized shopping mall load consumption predictions in real-time applications. The separation of offline training and online prediction ensures real-time performance while enhancing the adaptability and stability of the model. 2. A BEMS model based on BiLSTM-Transformer was proposed. The model combines the powerful feature extraction capabilities of LSTM and the global modeling capabilities of Transformer, enabling the system to efficiently and accurately capture complex patterns and structures in BEMS input time series. This approach significantly improves the computation speed, especially when processing large-scale data, and greatly improves the accuracy and responsiveness of load forecasting. 3. A cognitive control method is introduced to enhance the adaptability and robustness of the system. Cognitive control simulates the learning and adaptive capacity of the human brain, enabling the system to adjust control strategies based on real-time feedback. By combining the long-term predictive power of BiLSTM with the adaptability of cognitive control, the system maintains prediction stability in the event of data loss or unstable communication and makes optimization decisions in a dynamic and uncertain environment, thereby improving the performance of BEMS.
## 3 Methods
### Classification
#### 3.6.2 LS-Svm  
The Least-squares Support Vector Machine (LS-SVM) is a least square version of SVMs that can analyse the data and identify recognisable patterns for classifying the data [29]. An SVM uses data points as input and gives the output in form of a hyper-plane. A decision boundary is used for the classification between the different classes, which will be classified. Instead of quadratic programming, like conventional SVMs, the LS-SVM technique uses linear equations for solving them and they are a type of kernel based learning method [30].
## 2 Background
### An Overview of Intrusion Detection Systems
#### 2.3.2 Intrusion Detection Approaches  
In the field of cybersecurity, IDSs utilize a range of methodologies to detect and counteract potential security breaches. The primary IDS approaches include the following:  
* Signature-based detection: An essential component of an IDS involves comparing known attack patterns, often referred to as signatures, with incoming network traffic or system actions. If there is a match between the observed data and a saved signature, the IDS generates an alert indicating a potential security breach. This method efficiently recognizes well-known attacks that have been documented in the past, including various forms of malware, viruses, and infiltration attempts. However, its most significant limitation is its inability to identify novel or zero-day attacks. These types of security threats exploit vulnerabilities unknown to security professionals. Despite this limitation, signature-based detection remains a vital part of any comprehensive security strategy. When used as one component of a layered security approach, it can be combined with other detection approaches, such as anomaly-based detection.
* Anomaly-based detection: IDSs use this sophisticated method to identify anomalous patterns or behaviors within the network traffic or system operations. Anomaly-based detection establishes a baseline of normal behavior by examining historical data to create a reference point, rather than relying on pre-defined attack signatures. It identifies any behavior that deviates from this baseline, such as unexpected patterns of network traffic or actions that are atypical for the system, as a potential security threat. ML algorithms are frequently utilized to analyze large datasets, detecting subtle variations that may indicate a security breach. Because it is highly effective at identifying entirely new types of attacks, anomaly-based detection is an essential component of contemporary cybersecurity methods. However, it requires accurate baselines and ongoing tuning to minimize false positives and negatives, maximizing the likelihood of identifying serious threats while reducing interference with legitimate network operations.
## 2 Methods and Materials
### Performance Index  
Three key performance indexes were employed to statistically quantify and examine the prediction accuracy of the proposed RF-PS model. These include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Correlation Coefficient (R) [60; 61; 62]. The mathematical definitions of these performance indexes are given in Equations (17)-(19). Specifically, the Mean Absolute Error (MAE) is given in (18), the (RMSE) is shown in (19), and the (R) is given in (20)  
\[MAE=\frac{1}{N}\sum\limits_{q=1}^{N}\left|y_{q}-d_{q}\right| \tag{18}\]  
\[RMSE=\sqrt{MSE}=\frac{1}{N}\sqrt{\sum\limits_{q=1}^{N}\left[y_{q}-d_{q}\right] ^{2}} \tag{19}\]  
\[R=\frac{\sum\limits_{q=1}^{N}\left(y_{q}-\overline{y}_{k}\right)\left(y_{q}- \overline{d}_{k}\right)}{\sqrt{\left[\sum\limits_{q=1}^{N}\left[\left(y_{k}- \overline{y}_{k}\right)^{2}\right]\right]\left[\sum\limits_{q=1}^{N}\left[ \left(y_{k}-\overline{d}_{k}\right)^{2}\right]\right]}} \tag{20}\]  
Figure 2: A three-phase flow chart for the proposed RF-PS path model development and application.  
where \(y_{q}\) denotes the desired target output, \(d_{q}\) indicates the actual network output, \(\overline{y}_{q}\) is the mean of the actual network output, \(q=1,2,\ldots,N\), and \(N\) is the actual network output number.
## 4 Results and Discussion  
### Driving Stability Analysis on Single-Factor  
#### 4.1.1 Effects of Various Driving Conditions on LA  
Figure 8 shows the effects of different factors on the LA of the vehicle. During the driving process, it mainly contains four stages: smooth driving, initiating lane change, crossing rutting, and ending lane change with smooth driving. It was found that the LA maintains at around 0.1 g in the smooth driving state, with only a minor variation before and after. For a short period of time, after starting a lane change, the lateral tractive force generated during lane change demonstrates a remarkable decrease of LA. When the vehicle reaches around 500 m distance travelled (approximate 16 s), the vehicle starts to cross the rutting, and the LA increases in the opposite direction. When it came to the termination of a lane change, LA developed back to a steady trend. The LA fluctuated up and down, due to the compound effects of lateral wind and rutting.  
Figure 7: Diagram of lane-change trajectory.  
To compare the effects of different factors from the perspective of spacing between the curves in Figure 8, it is indicated that the vehicle speed, WHR of rutting, radius of the circular curve, and friction coefficient of the road surface have a greater impact on LA, while the superelevation, lateral wind angle, and speed have a relativly weak influence on LA. As shown in Figure 8a,c, LA increases with the increase of driving speed, concurrently with the decrease of the radius of circular curve. The LA reaches a high level, with a maximum value approximately 0.2 g, when the driving speed and radius of the circular curve are  
Figure 8: Change of LA over time under various driving conditions: (**a**) Vehicle speed; (**b**) WHR of the rutting; (**c**) Radius of circular curve; (**d**) Superelevation; (**e**) Crosswind angle; (**f**) Wind speed; (**g**) Friction coefficient between road & tire.  
120 km/h and 600 m, respectively. From Figure 8b, it is found that the LA dramatically and suddenly rises to a peak when the WHR reaches 5. In addition, the maximum LA is far greater than that of other width-to-height ratios, comprising a larger amplitude of variation. As a result, the vehicle shows a higher risk of lateral offset when driving across rutting in adverse weather.
# Antioxidant Properties of _Jatropha curcas_ L. Seed Shell and Kernel Extracts
## 3 Results and Discussion
### DPPH Radical Scavenging Assay  
Oxidative stress due to the accumulation of free radicals may cause heart disease, cancer, diabetes, ischemia, and aging. The negative effect of free radicals on the body can be resisted by antioxidants [29]. Figure 2 displays the DPPH radical scavenging activity of seed shell and kernel extracts obtained from different extraction solutions in this study. The result indicates that the DPPH radical scavenging activity increased with the concentration of the extracts, irrespective of the extraction solution used. The seed shells extracted from concentrated ethanol solutions had high DPPH radical scavenging activity. The half-maximal inhibitory concentration (IC\({}_{50}\)) values in the DPPH radical scavenging assay of the shell were 721.84 \(\pm\) 8.04, 101.66 \(\pm\) 8.92, 40.82 \(\pm\) 1.65, 28.09 \(\pm\) 3.41, and 13.63 \(\pm\) 0.15 \(\mu\)g/mL for solutions obtained using 0%, 25%, 50%, 75%, and 95% ethanol, respectively (Figure 2a). The corresponding IC\({}_{50}\) values for the kernel were 1195.60 \(\pm\) 87.56, 1409.19 \(\pm\) 65.83, 1239.22 \(\pm\) 44.79, 1698.33 \(\pm\) 54.28, and 1729.31 \(\pm\) 118.46 \(\mu\)g/mL, respectively (Figure 2b). The IC\({}_{50}\) of ascorbic acid in a water solution was  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ent** & **Ethanol conc. (\%)** & **0** & **25** & **50** & **75** & **95** \\ \hline Seed shell & 12.58\(\pm\)0.25 \({}^{\ast}\) & 14.81\(\pm\)0.92 \({}^{\ast}\) & 13.35\(\pm\)0.78 \({}^{\ast}\) & 8.35\(\pm\)0.29 \({}^{\circ}\) & 3.00\(\pm\)0.45 \({}^{\ddagger}\) \\ Seed kernel & 24.23\(\pm\)0.78 \({}^{\ast}\) & 23.27\(\pm\)0.57 \({}^{\ast}\) & 20.45\(\pm\)0.26 \({}^{\ddagger}\) & 17.60\(\pm\)0.82 \({}^{\circ}\) & 8.19\(\pm\)1.26 \({}^{\ddagger}\) \\ \(p\) value \({}^{\ast\ast}\) & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 \\ \hline \end{tabular} \({}^{\ast}\)Mean within a row followed by the same lower case letter are not significantly different at 5% level by Tukey test.  
\({}^{\ast\ast}\)The \(p\) value in comparing yield between seed shells and seed kernels using \(t\)-test.  
\end{table}
Table 1: Extraction yield (%) of seed shells and kernels with different ethanol solutions.  
\(8.35\pm 1.12\)\(\mu\)g/mL. Therefore, the shell extract obtained using 95% ethanol had the best DPPH free radical scavenging activity, similar to that of ascorbic acid. This result is in agreement with a previous report [16]. However, the kernel extracts had poor DPPH free radical scavenging activity, considerably lower than that of ascorbic acid. According to the previous study [30], an extract of defatted _J. curcas_ seeds also exhibited DPPH radical scavenging activity.
# Ecotoxicological Assessment of Microplastics in Freshwater Sources--A Review
## 5 Ecotoxicological Effects on Microplastics
### Ecotoxicological Effects on Algae  
Algae (especially phytoplankton) are the primary producers and basis of the aquatic food chain. Therefore, any adverse effect on algae affects the entire ecosystem [115], as presented in Figure 4.  
Various adverse effects of MPs on algae have been reported already; mostly enlightening negative influence on algae growth. [PERSON] et al. [116] reported that PP and PVC MPs at concentrations of 5, 10, 50, 100, 250, and 500 mg/L showed a negative effect on chlorophyll of freshwater algae _Chloorella pyrenoidosa_ and _Microystis flos-aquae_. According to the inhibition values recorded, PVC is more toxic in the water environment in comparison to PP. MPs can decrease photosynthesis efficiency due to adsorption on algae surface [114] or by reducing the chlorophyll level in cells [116] (Table 3). MPs adsorption on algae surface hinders the exchange of nutrients, gasses, and toxic metabolites. [PERSON] et al. [115] reported that PS particles in size range between 0.1 and 1.0 \(\upmu\)m and at concentrations 10, 50, and 100 mg/L induced the inhibition of algal growth (higher effect with smaller particles, Table 3), oxidative stress and caused morphological changes on freshwater algae _Chloella pyrenoidosa_ cell. Morphological changes of the algal cell are commonly related to the occurrence of hetero-aggregation between MPs, yielding structures larger and heavier than common MPs, resulting eventually in physical damage of cell [129]. Such particles can reach easier deeper layers of water, thus, hetero-aggregation supports MPs transfer to the bottom of aquatic ecosystems [115]. Recent studies have focused mainly on marine algae. Therefore, it is necessary to conduct studies on freshwater algae that are present in lakes, rivers, and streams to close the knowledge gap in ecotoxicological MPs assessment in freshwater sources. The environmental characteristics of sources have to be considered as well; such factors may determine relevant direct and indirect effects on algal communities, consequently affecting specific ecotoxicological responses.
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 3 Simulation Results
### Multipath Metrics
#### 3.3.1 Power Delay Profile (PDP)  
The PDP is used to calculate various multipath statistics (mean excess delay, RMS delay spread, maximum excess delay, CB) and quantifies the number and severity of power rays (echoes) in the wireless channel. Figure 5 illustrates the PDP at z-plane of 1.5 m for 3 different locations represented in Figure 10a, while Figure 10b,c depicts the PDP when TX1 or both TX1 and TX2 are transmitting. Figure 10b shows many power rays (echoes) in a time span of 0 to 1200 ns showing a high dispersive nature of this environment causing reflected, refracted, and diffracted rays that arrive to PA, PB and PC points. The highest density of rays arrives to PA which is in LoS and closest (first ray arrives at 100 ns) to TX1; conversely the lowest density is at PC, which farthest from TX1. Figure 10c illustrates the increase in the number of power rays arriving to PB and PC as a contribution effect of TX2 to the jointly PDP mainly at the transition region (refer to Figure 9b). The presence of power rays with low RSS values are mainly those reflected and diffracted by obstacles and the line of trees among AV1 and AV2.  
Figure 9: Coverage map: (**a**) scenario; (**b**) along AV2.
###### Abstract  
The ionosphere can be perturbed by solar and geomagnetic activity, earthquakes, thunderstorms, etc. In particular, electromagnetic pulses produced by thunderstorms can generate wave structures in the ionospheric plasma, which are known as atmospheric gravity waves (AGWs), which can be detected by measuring the total electron content (TEC). We studied ionospheric variations resulting from thunderstorms on 10 November 2018, between 00:00 and 08:00 UTC, in the central region of Argentina, site of the RELAMPAGO-CACTI Project (Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations; Clouds, Aerosols, and Complex Terrain Interactions). Atmospheric electrical activity data were provided by the Earth Networks Total Lightning Network (ENTLN) and the TEC was computed from Global Navigation Satellite System (GNSS) measurements provided by the Argentinian Continuous Satellite Monitoring Network (RAMSAC by its Spanish acronym). We found AGWs with periods less than or equal to 100 min and peak-to-peak Differential Vertical Total Electron Content (DVTEC) amplitude values up to 1.35 TECU (1 total electron content \(=10^{16}\) electrons/m\({}^{2}\)). We observed that AGWs show the highest peak-to-peak amplitudes during intense thunderstorm periods. On a day without thunderstorms, the peak-to-peak amplitudes were approximately 2.91 times lower.  
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# An Empirical Study on Software Defect Prediction Using CodeBERT Model
## 6 Experiments and Results
### RQ1: Does the Pre-Trained CodeBERT Model Outperform a Newly-Trained CodeBERT Model?
We investigate the fluctuating level of F-measure on the CodeBERT-NT and CodeBERT-PT model for each project, as shown in Figure 7. The \(F1\) level fluctuates more severely on the whole compared with Figure 6 in RQ1a, indicating that the prediction performance of cross-project defect prediction is not as good as cross-version defect prediction, which is often the case in software defect prediction. Specifically, the CodeBERT-PT model in solid blue line converges more quickly and generally has fewer fluctuations, indicating less time cost. Statistics show that training a CodeBERT-PT model could be 1.5 times faster than training a CodeBERT-NT model. Although the CodeBERT-PT model also has advantages in time cost, the ratio decreases from 4 to 1.5 due to cross-project defect prediction. Summary of RQ1b: The CodeBERT-PT outperforms the CodeBERT-NT model in cross-project software defect prediction. The CodeBERT-PT model also trains 1.5 times faster than the CodeBERT-NT model with lower fluctuations. Summarizing RQ1a and RQ1b, we can conclude RQ1. Summary of RQ1: The CodeBERT-PT model outperforms the CodeBERT-NT model in software defect prediction. The CodeBERT-PT model also trains much faster than the CodeBERT-NT model with lower fluctuations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Set** & **Test Set** & **CodeBERT-NT** & **CodeBERT-PT** & **RANDOM** \\ \hline JEdit 4.1 & Camel 1.4 & 0.293 & **0.354** & 0.255 \\ Camel 1.4 & JEdit 4.1 & 0.342 & **0.36** & 0.267 \\ Lucene 2.2 & Xalan 2.5 & **0.707** & 0.66 & 0.502 \\ Xerces 1.3 & Xalan 2.5 & 0.627 & **0.659** & 0.502 \\ Xalan 2.5 & Lucene 2.2 & 0.321 & **0.648** & 0.546 \\ Poi 2.5 & Synapse 1.1 & **0.443** & 0.431 & 0.349 \\ Poi 3.0 & Synapse 1.2 & **0.72** & 0.531 & 0.402 \\ Synapse 1.2 & Poi 3.0 & 0.493 & **0.587** & 0.562 \\ Xalan 2.5 & Xerces 1.3 & **0.642** & 0.445 & 0.231 \\ \hline \multicolumn{5}{c}{Average} & 0.51 & 0.519 & 0.402 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of CodeBERT-NT, CodeBERT-PT, and RANDOM models on the CPPSC dataset for cross-project defect prediction. The float values are the average F-measure values. The best F-measure values are highlighted in bold.