esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Classification
#### 2.5.1 Support Vector Machine (SVM)  
In classification problems, the SVM is utilized as a supervised ML algorithm. It is used in many applications, including medical systems [47; 48], pattern recognition [60], and machine learning [61]. Recently, SVM has been utilized in speech recognition, text recognition, biometrics, and image retrieval problems. To separate the nonlinear problems, SVM using the largest margin constructed a hyperplane. The good margin produced the lowest generalization error.
# Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
###### Abstract  
Accurate classification and identification of Internet traffic are crucial for maintaining network security. However, unknown network traffic in the real world can affect the accuracy of current machine learning models, reducing the efficiency of traffic classification. Existing unknown traffic classification algorithms are unable to optimize traffic features and require the entire system to be retrained each time new traffic data are collected. This results in low recognition efficiency, making the algoritms unsuitable for real-time application detection. To solve the above issues, we suggest a multi-feature fusion-based incremental technique for detecting unknown traffic in this paper. The approach employs a multiple-channel parallel architecture to extract temporal and spatial traffic features. It then uses the mRMR algorithm to rank and fuse the features extracted from each channel to overcome the issue of redundant encrypted traffic features. In addition, we combine the density-ratio-based clustering algorithm to identify the unknown traffic features and update the model via incremental learning. The cassifier enables real-time classification of known and unknown traffic by learning newly acquired class knowledge. Our model can identify encrypted unknown Internet traffic with at least 86% accuracy in various scenarios, using the public ISCX-VPN-Tor datasets. Furthermore, it achieves 90% accuracy on the intrusion detection dataset NSL-KDD. In our self-collected dataset from a real-world environment, the accuracy of our model exceeds 96%. This work offers a novel method for identifying unknown network traffic, contributing to the security preservation of network environments.  
unknown traffic recognition; multi-feature fusion; deep neural networks; incremental learning +
Footnote †: journal: Applied Sciences  
Article
## 4 BER Computational Strategies for Decision Unit--Background Tutorial
### The Method of Quasi-analytical Estimation  
The abovementioned methods analyse the received signal components (data and noise) at the receiver's output. At this point, we consider solving the BER estimation problem utilizing the succeeding two stages:  
1. One handles the transmitted signal \(r_{f}(t)\) in Equation (4);
2. The other handles the noise component \(r_{th}(t)\).  
First, we presume that the noise is denoted as the Equivalent Noise Source (ENS) and, second, that the ENS probability density function is known and determinable.  
Therefore, we can assume that an ENS with an appropriate distribution can closely evaluate the receiver's performance. This approach is known as quasi-analytical (QA) estimation [96]. We can calculate the BER with ENS statistics using the noiseless waveform. More precisely, we can allow the simulation to calculate the influence of signal changes in the non-existence of \(r_{th}(t)\) and superimpose the \(r_{th}(t)\) on the noiseless signal component.  
The noise statistics assumption results in a significant drop in computation run time. Nevertheless, this may create a risk of complete miscalculation. The appropriateness of the QA estimation will rely on how well the assumption matches actuality [97]. Hence, predicting ENS statistics before they occur for a linear system may be challenging.
# Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift
## 3 Forgetful Data Structures
### Adaptive Calculation of Retain Size and Max Tree Height  
Retaining a substantial amount of historical data will result in higher accuracy when there is no concept drift, because the old information is useful. When concept drift occurs, \(rSize\) (the retained data) should be small, because old information will not reflect the new concept (which is some new mapping from input to label). Smaller \(rSize\) will result in increased speed. Thus, changing \(rSize\) can improve accuracy and reduce time. We use the following rules:  
* When accuracy increases (i.e., the more recent predictions have been more accurate than previous ones) a lot, the model can make good use of more data. We want \(rSize\) to increase with the effect so that we discard little or no data. When the accuracy increase is mild, the model has perhaps achieved an accuracy plateau, so we increase \(rSize\), but only slightly.
* When accuracy changes little or not at all, we allow \(rSize\) to slowly increase.
* When accuracy decreases, we want to decrease \(rSize\) to forget old data, because this indicates that concept drift has happened. When concept drift is mild and accuracy decreases only a little, we want to retain more old data, so \(rSize\) should decrease only a little. When accuracy decreases a lot, the new data may follow a completely different functional mapping from the old data, so we want to forget most of the old data, suggesting \(rSize\) should be very small.  
To achieve the above requirements, we adaptively change \(rSize\). Besides that, we also adaptively change the maximum height of the tree (\(maxHeight\)). To avoid overfitting or underfitting, we will set \(maxHeight\) to be monotonic with \(rSize\). In addition, to handle the cold start at the very beginning, we will not forget any data until more than a certain size of data (call it \(warmSize\)) has arrived. We explain the details of adaptive parameters in Appendix B.
## 3 Scientometric Analysis: Results and Discussion
### Science Mapping
#### 3.2.3 Mapping of Authors  
In our preliminary analysis, we identified a total of 974 authors. Figure 8 shows the relationship between the number of authors and the number of documents published in the field. There is a high dispersion in the production of papers, which could be explained by the novelty and interest of the field; moreover, the largest number of documents published per author is 10, followed by 7 and 6; in addition, 818 of the authors (making up 84% of the total) have published only 1 paper. As a result of the lower number of papers published per author, only seven authors have published five papers or more (Table 3). The ranking in terms of publication count is led by [PERSON], with 10 documents published, followed by [PERSON], with 7 documents. As the number of citations received by a researcher in a certain discipline is used to quantify his/her influence, we also analyzed the number of authors in terms of citations. Table 3 shows that 11 authors have more than 200 citations; in this case, the ranking is clearly led by [PERSON], [PERSON], and [PERSON], all of them with 764 citations and only five documents, followed by [PERSON] with the same number of documents and 745 citations. Positions 7 to 10 are occupied by authors with only 1 or 2 documents, but with a number of citations that varies between 261 and 284; the high number of citations reveals the great impact of these authors, especially when taking into account the short time horizon of this analysis. Finally, in the case of the average normalized citation ranking,  
Figure 7: Network visualization of countries with greater importance in the field in terms of total citations. the results include authors with only one document published, but with 177 to 282 citations. In all these cases, the average normalized citation index is higher than nine, which means that these authors with only one document have made a considerable contribution to the research field. With the objective of analyzing the collaboration between authors, we developed a mapping of co-authorship by author with a minimum of one document and 50 citations. Figure 8: Relationship between the number of authors and the number of documents published.
## 4 6G and Dependability
### Dependability in 6G Networks  
In this part, we analyze the dependability of future 6G networks from the perspective of their distributed and data-driven nature. Concepts such as EdgeAI help reduce latency between the end-devices and edge servers. However, at the same time, they might be points of failure and interesting targets for security attacks, if its weaknesses are not properly addressed before deployment. Below we discuss the dependability of 6G networks in the four dimensions, i.e., reliability, availability, safety, and security from the perspectives of EdgeAI.
## 2 Channel Model and Signal-to-Noise Ratio  
In optical wireless links, the most viable method is to employ intensity modulation (IM), in which the instantaneous power of the optical carrier is modulated by the signal. The receiver makes use of direct detection (DD), where a photodetector generates a current that is proportional to the instantaneous received optical power. The channel characteristics in an indoor optical wireless channel using IM/DD can be fully characterised by the impulse response \(h(t)\) of the channel [4]:  
\[I(t)=R\,x(t)\otimes h(t)+n(t) \tag{1}\]  
where \(I(t)\) represents the received instantaneous current at the output of the photodetector, t is the time, \(x(t)\) is the transmitted instantaneous optical power, \(\bigotimes\) denotes convolution, \(R\) is the photodetector responsivity and \(n(t)\) is the background noise, which is modelled as white and Gaussian, and independent of the received signal.
## 4 Bromelain Use as an Exfoliant Agent in Cosmetic Applications
### Chemical Properties and Structure of Bromelain  
#### 4.3.1 Molecular Structure  
Bromelain is primarily recognized as a cysteine protease, also known as thiol endopeptidase, and it has a very low toxicity [36]. Its molecular structure is glycoprotein-based, containing an oligosaccharide chain with sugars such as xylose, fucose, mannose, and N-acetyl glucosamine [52]. Bromelain's molecular weight (MW) varies slightly depending on the source and extraction method. For stem bromelain, the MW is approximately 23.8 kDa, with some studies reporting values around 28.5-37 kDa when analyzed through different techniques [36]. Fruit bromelain, on the other hand, varies in molecular weight from 24.5 to 32 kDa (acidic protein) [35], while crown-derived bromelain revealed a MW-28 kDa [38]. Its enzymatic activity is facilitated by specific catalytic residues, such as cysteine, histidine, and asparagine that are crucial for its proteolytic function, as they are involved in the enzyme's ability to cleave peptide bonds. Bromelain's distinct binding sites allow it to interact with several substrates, contributing to its capability of catalyzing different reactions and enhancing various cosmetic formulas [52].
## 3 Experimental Results
### Summary  
In conclusion, it can be observed that the proposed SynthSecureNet architecture yields better results as compared with ResNet50V2 and MobileNetV2 in the task of violence detection from videos. SynthSecureNet achieved higher F1 scores, recall rate, precision level, and accuracy in all the sizes of the datasets considered, indicating its efficiency in classifying violence accurately. Despite the fact that the training times were longer than those of the other models, SynthSecureNet presented excellent results that seem to be justified by extra computational time.  
The analysis of the evaluation results shows that SynthSecureNet is a promising violence detection system and can be considered rather effective and accurate. Therefore, the capacity for scaling big data and maintaining high performance makes it a suitable solution for actual use. The viability of the architecture reflects its durability and adaptability, which may make it appropriate for use in different areas with violence identification as a priority, for example, video surveillance, content filtering, safety, and security.  
The consistent superior performance of SynthSecureNet across various dataset sizes demonstrates its robustness and scalability. This is particularly important in real-world applications where the volume of data can vary significantly. The architecture's ability to maintain high accuracy levels even with larger datasets suggests that it could be effectively deployed in large-scale surveillance systems without compromising on detection quality.  
Moreover, the trade-off between increased training time and improved performance is a crucial consideration. While SynthSecureNet requires more computational resources during the training phase, the enhanced accuracy in violence detection could lead to more efficient and effective real-time monitoring systems. This could potentially result in faster response times to violent incidents and improved public safety outcomes.  
The flexibility of SynthSecureNet, as evidenced by its performance across different dataset sizes, also indicates its potential for adaptation to various contexts and scenarios. This versatility could be valuable in different sectors such as law enforcement, private security, and content moderation platforms, where the nature and prevalence of violent content may vary.  
In summary, the experimental results strongly support the efficacy of the SynthSecureNet architecture for violence detection tasks. Its consistent outperformance of established models like ResNet50V2 and MobileNetV2 positions it as a promising advancement in the field of automated violence detection. Future work could focus on further optimizing the training process to reduce computational time while maintaining or even improving the current high levels of accuracy.
# Silver Nanowires: Synthesis, Antibacterialativity and Biomedical Applications
## 5 Emerging Biomedical Applications of Silver Nanowires
### Textiles and Fibrous Membranes  
Due to the history of silver use in wound dressing there is interest in exploring ways of exploiting AgNPs in the design of textiles and other wound dressing materials. [PERSON] et al. [PERSON] et al. (2017) produced silver nanowire-loaded cotton fabric, obtaining a multifunctional textile with antimicrobial properties coupled with electrical conductivity and UV protection. AgNPs, obtained via the soft-template polyol method, were loaded into cotton fibers by repeating iterations of submerging the cotton in an alcoholic suspension of AgNPs with drying at room temperature. This study demonstrated that AgNPs-functionalized cotton induces complete killing of _E. coli_ and _S. aureus_ cultures, compared to the control material, proving that it is possible to effectively create a cellulosic textile with antibacterial properties [PERSON] et al. (2017). These textiles have potential in wound healing applications, and it would be interesting to see more studies in this area to compare the effect of nanowires vs nanoparticles in this application. [PERSON] et al. [PERSON] et al. (2018) used electrospinning to form hybrid membranes of AgNPs and poly(vinyl alcohol) (PVA). The AgNPs arranged into coaxial nanocable structures and demonstrated high antibacterial efficiency, with a higher effect observed against gram positive compared to gram negative. This paper demonstrates a simple manufacturing process that can used for the incorporation of AgNPs into fibrous membranes that can have applications in wound dressing and water or air filtration.
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 2 Novel Hybrid Data-Fusion System  
### Hybrid System Structures  
Figure 1 shows the interior structure of the hybrid damage detection system, which reveals the technology of combining FD and PNN models with the data fusion. The system contains three models, namely, a single PNN classifier model, an eigen-level fusion model and a decision-level fusion model and the system can get three kinds of reasonable  
detection results correspondingly, according to the different cases of structures to choose the suitable model. Furthermore, the proposed system can realize three damage identification functions simultaneously.  
The left side of Figure 1 includes two models: a single PNN classifier model (namely, without data-fusion tactics, after fractal features extraction to make PNN decision directly, as shown in Figure 2) and an eigen-level data-fusion model. The essential difference between them is the process of having data fusion or not. Meanwhile, on the right of Figure 1, it shows the details of decision-level data-fusion model. This makes decisions based on data fusion of multiple single-PNN classifiers, as the eventual damage detection results output.  
The proposed system has three damage identification functions and can even detect damage simultaneously. However, if the characteristics of the data to be detected are clearly understood, it would be a wise choice to select one of them for damage detection, as a single PNN is more suitable for the situation where the limited data of a few sensors are collected; when the reliability of each sensor in the multi-sensor system is known, a decision-level data fusion damage detection model would be the most appropriate choice; otherwise, an eigen-level data fusion damage detection model can be adopted.
# Seismic Vulnerability Assessment of a Shallow Two-Story Underground RC Box Structure
## 4 Conclusions  
This study examines the fragility curves of a shallow two-story reinforced concrete underground box structure subjected to seismic loading. A total of 1150 artificial ground motions in 23 PGAs ranging from 0.02 g to 1.3 g (with 50 ground motions per PGA level) were applied to a two-dimensional plane strain FEM model.  
The effect of the near-field ground domain adjacent to the tunnel structures was investigated to estimate the relative displacement between the top and bottom of the tunnel following the GRAMBS method. An efficient and practical guide based on GRAMBS method, nonlinear static analysis (or pushover) with Latin hypercube sampling, and maximum likelihood method was proposed to construct fragility curves of a RC box structure in a highly-weathered soil. In addition, the definition of damage states was provided by conducting comprehensive pushover analyses with considering the uncertainties of material properties and concrete cover, to help in making a good decision for the recovery time and repair cost, without taking much time for dynamic analysis.  
In the comparison of the required earthquakes per PGA level for developing fragility curves, the results suggest that the analysis with equal to or larger than 20 ground motions in each PGA is a reasonable value for performing the structural analyses to derive the fragility curves of tunnels.  
The shapes of fragility curves of both hypotheses, the shear force developed at the exterior surface of the roof (point load) and the racking deformation developed along the side walls (invert distributed load), are similar. However, at the same PGA level, the probability of exceeding the damage state of the point load is larger than the distributed load in the minor and extensive damage state, except for the moderate damage state, it shows nearly same results.  
In this study, the tunnel material properties and concrete cover had been considered as random variables. This approach can be extended further by considering other uncertainties such as the difference in geometric shapes of underground tunnels owing to a possible incorrect construction or the variability of soil properties.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2058765) and was a part of the project titled 'Development of performance-based seismic design technologies for advancement in design codes for port structures', funded by the Ministry of Oceans and Fisheries, Korea.  
[PERSON] and [PERSON] conceived the idea and designed the framework; [PERSON], [PERSON] and [PERSON] designed the numerical models; [PERSON], [PERSON] and [PERSON] performed the analyses and analyzed the data; and [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest[PERSON]
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 4 Advances in Computational Modeling Method
### ALE and CEL  
To overcome the limitations in FEM, various computing techniques, such as the Arbitrary Lagrangian-Eulerian (ALE) and coupled Eulerian-Lagrangian (CEL) approaches, have been developed to model penetration-related problems. Severe mesh distortion complications can be somewhat sorted by these methods [94; 95; 96; 97]. [PERSON] et al. [79] used the 3D large deformation finite-element (LDFE) method based on the Coupled Eulerian-Lagrangian (CEL) approach to explored the soil deformation characteristics and installation mechanism of GDCO caissons. [PERSON] et al. [98] created an innovative static simulating method for caisson sinking that successfully models complex seepage circumstances by combining the coupled Euler-Lagrange method. As CEL was time-consuming, to increase computational efficiency and accuracy in complicated soil conditions to simulate open caisson shafts, future research should concentrate on improving these techniques.