esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms
## 6 Discussion
### Future Work  
The insights and findings presented in the current research study open up avenues for potential future work on blockchain applications for secure communication and control in drone swarms. Below, we outline some of these potential paths for future research and suggest other potential research directions that could be pursued to enhance and extend the current study's findings.  
One area of future work is to apply the results of this work to real-world implementations. The functionality of the smart contract has been developed, but it is still necessary to put it into practice in order to obtain a clearer picture and assess the performance of the proposed model (scalability, latency, and congestion).  
Another problem that could be further investigated is related to storage. The storage space in such models is an issue, as already indicated. Since drones will be able to securely store enormous amounts of data on the proposed file system, the construction of an off-chain IPFS decentralized network and its integration into this system could offer a solution to this specific problem.  
Furthermore, a topic for future research is private key management in these systems. Private keys are used for the encryption and signing of transactions. The potential leak of these keys could lead to unauthorized actions that would be crucial and devastating to the system's performance. The development of a mechanism for securing and managing these keys could enhance this work and contribute to a more secure and robust scheme.  
Finally, in these systems, the development of a recovery mechanism is essential. In situations where a drone (or a group of drones) becomes compromised, the deployment of a recovery mechanism is necessary. This ensures that the drone is safely returned to the base after being isolated from the blockchain so that it is not lost, as any loss from the system incurs costs.  
In conclusion, drone swarm technology is gaining popularity due to its adaptability and effectiveness in a variety of activities. By strengthening security and enhancing autonomous decision making, the proposed model has the potential to contribute to the rapid development of UAV cluster technology.  
Conceptualization, A.L. and A.K.; Methodology, A.L. and A.K.; Software, A.K.; Validation, A.L. and A.K.; Formal Analysis, A.K.; Investigation, A.K.; Resources, A.K.; Data Curation, A.K.; Writing--Original Draft Preparation, A.K.; Writing--Review & Editing, A.L.; Visualization, A.K.; Supervision, A.L.; Project Administration, A.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 2 Materials and Methods  
### Materials  
2-amino-9,9-diphenylfluorene **(1)**, 2-(4-Bromophenyl)-4,6-diphenyl-1,3,5-triazine (7), 2-Chloro-4,6-diphenyl-1,3,5-triazine **(6)** and 4-Iodottriphenylamine **(2)** were obtained from TCI chemicals (Seoul Korea). Pd(OAc)\({}_{2}\), Pd(PPh\({}_{3}\))\({}_{4}\) and NaOfBu were purchased from Sigma-Aldrich (Seoul, Korea). Toluene, dichloromethane, \(n\)-hexane and ethyl acetate were obtained from SK chemicals (Seongnam-si, Gyeonggi-do, Korea). Toluene was distilled from sodium/benzophenone before use. Analytical thin layer chromatography (TLC) was conducted by using an aluminum-backed Merck Kieselgel 60 coated TLC plate with visualizations of 254 and 365 nm (Seoul, Korea). Column chromatography was performed with silica gel (mesh size of 200-300). Reverse osmosis (RO) water was used for all purposes related to this work.
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
###### Abstract  
Cyberscrime has become more pervasive and sophisticated over the years. Cyber ranges have emerged as a solution to keep pace with the rapid evolution of cybersecurity threats and attacks. Cyber ranges have evolved to virtual environments that allow various IT and network infrastructures to be simulated to conduct cybersecurity exercises in a secure, flexible, and scalable manner. With these training environments, organizations or individuals can increase their preparedness and proficiency in cybersecurity-related tasks while helping to maintain a high level of situational awareness. SPIDER is an innovative cyber range as a Service (CRaaS) platform for 5G networks that offer infrastructure emulation, training, and decision support for cybersecurity-related tasks. In this paper, we present the integration in SPIDER of defensive exercises based on the utilization of machine learning models as key components of attack detectors. Two recently appeared network attacks, cryptomining using botnets of compromised devices and vulnerability exploit of the DoH protocol (DNS over HTTP), are used as the support use cases for the proposed exercises in order to exemplify the way in which other attacks and the corresponding ML-based detectors can be integrated into SPIDER defensive exercises. The two attacks were emulated, respectively, to appear in the control and data planes of a 5G network. The exercises use realistic 5G network traffic generated in a new environment based on a fully virtualized 5G network. We provide an in-depth explanation of the integration and deployment of these exercises and a complete walkthrough of them and their results. The machine learning models that act as attack detectors are deployed using container technology and standard interfaces in a new component called Smart Traffic Analyzer (STA). We propose a solution to integrate STAs in a standardized way in SPIDER for the use of trainees in exercises. Finally, this work proposes the application of Generative Adversarial Networks (GANs) to obtain on-demand synthetic flow-based network traffic that can be seamlessly integrated into SPIDER exercises to be used instead of real traffic and attacks.  
cybersecurity; cyber range; 5G; machine learning; cryptomining; DoH published maps and institutional affiliations. Article
# Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector
## 6 Discussion  
Table 6 shows the prediction results before and after the application of dynamic feature selection on NSL-KDD datasets. The accuracy, precision, F1-score and the Area Under Curve (AUC) in every experiment resulted in consistent performance improvement using the Dynamic Feature Selection algorithm. We did observe the random forest algorithm provide the best performance in the  
Figure 4: Accuracy and Loss values generated from final feature list selected by meta-learner. (a) NSL-KDD Accuracy. (b) NSL-KDD Loss. (c) UNSW Accuracy. (d) UNSW Loss.  
meta-learner. Accuracy while using random forest jumped from 99.54% to 99.64% while reducing the feature size from 123 to 50.  
Table 7 represents the output of Dynamic Feature Selector algorithm of UNSW-NB15 dataset. The UNSW-NB15 dataset also showed consistent performance increase using the best algorithm as random forest from 90.98% to 92.46% while reducing the feature size from 196 to 47. GRU produced a better precision, but the meta-learner selected random forest algorithm to produce output based on the overall performance metrics such as F1 score, accuracy and AUC. Accuracy is the most intuitive performance measure and a simple way to observe prediction, but it is often misleading due to specificity and sensitivity [69]. Also, since the class distribution is uneven, F1 score is a better indicator to select robustness of the model. For both datasets, dynamic feature selector shows an increment of F1 score and accuracy which clearly states that, this method reduces the feature size effectively with increment in performance as well.  
We also applied Naive Bayes algorithm on the NSL-KDD and UNSW-NB15 datasets directly without using feature selection and a meta-learner to validate if our proposed approach enhances prediction accuracy. An accuracy of 53.32% was observed on NSL-KDD and 73.58% on the UNSW-NB15 test datasets after fitting the model on training datasets. These results further reveal the significance of our proposed approach.
## 2 Materials and Methods
### Evaluation Strategies of Clustering  
To evaluate the clustering results, we selected three evaluation indicators: adjusted Rand index (_ARI_), normalized mutual information (_NMI_), and purity. These are three evaluation indicators designed from different perspectives. Each indicator has its own advantages and disadvantages, so we stack the three indicators as the final cluster evaluation results.  
The adjusted Rand index solved the problem of _RI_, which cannot guarantee a randomly divided clustering result close to 0. Let \(n_{ij}\) be the number in both real label \(i\) and cluster \(j\). The adjusted Rand index can be calculated as follows:  
\[ARI=\frac{RI-E(RI)}{max(RI)-E(RI)} \tag{4}\]  
where \(E(RI)\) is the expected value of _RI_, which is \(E\bigg{[}\Sigma_{i,j}\bigg{(}\begin{matrix}n_{ij}\\ 2\end{matrix}\bigg{)}\bigg{]}=\bigg{[}\Sigma_{i}\bigg{(}\begin{matrix}n_{i,j} \\ 2\end{matrix}\bigg{)}\Sigma_{j}\bigg{(}\begin{matrix}n_{j,j}\\ 2\end{matrix}\bigg{)}\bigg{]}/\bigg{(}\begin{matrix}n\\ 2\end{matrix}\bigg{)}\). The _ARI_ ranges from \(-1\) to 1.  
Figure 2: For each gene \(i\), random forest was used for regression training on data with high confidence cells (Set A), and then predictions were made for data with low confidence cells (Set B). The set B in the left part is imputed with the information of similar cells, and the set B in the right is the predicted value of the random forest, which is also the final imputation result. The darkness of the color represents the level of gene expression.  
The normalized mutual information measures the similarity between clustering results and real labels from the perspective of information entropy. The normalized mutual information can be calculated as follows:  
\[NMI=2\frac{I(C;T)}{H(C)+H(T)} \tag{5}\]  
where \(C\) and \(T\) are defined as the predicted clustering result and the true category, respectively. \(I(C;T)\) is the mutual entropy, and \(H(C)\) and \(H(T)\) are the information entropy of \(C\) and \(T\), respectively. The NMI ranges from 0 to 1.  
The purity can be calculated as follows:  
\[\frac{1}{N}{\sum_{i}max_{j}|c_{i}\cap t_{j}|} \tag{6}\]  
where \(N\) is the total number of samples, \(c_{i}\) represents cluster \(i\) and \(t_{j}\) represents category \(j\). The purity ranges from 0 to 1.
# A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT
###### Abstract  
At present, new data sharing technologies, such as those used in the Internet of Things (IoT) paradigm, are being extensively adopted. For this reason, intelligent security controls have become imperative. According to good practices and security information standards, particularly those regarding security in depth, several defensive layers are required to protect information assets. Within the context of IoT cyber-attacks, it is fundamental to continuously adapt new detection mechanisms for growing IoT threats, specifically for those becoming more sophisticated within mesh networks, such as identity theft and cloning. Therefore, current applications, such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and Security Information and Event Management Systems (SIEM), are becoming inadequate for accurately handling novel security incidents, due to their signature-based detection procedures using the matching and flagging of anomalous patterns. This project focuses on a seldom-investigated identity attack--the Clone ID attack--directed at the Routing Protocol for Low Power and Lossy Networks (RPL), the underlying technology for most IoT devices. Hence, a robust Artificial Intelligence-based protection framework is proposed, in order to tackle major identity impersonation attacks, which classical applications are prone to misidentifying. On this basis, unsupervised pre-training techniques are employed to select key characteristics from RPL network samples. Then, a Dense Neural Network (DNN) is trained to maximize deep feature engineering, with the aim of improving classification results to protect against malicious counterfeiting attempts.  
Clone ID attack; deep learning; Internet of Things; IoT; intrusion detection; IDS; machine learning; RPL +
Footnote †: journal: Physics Letters  
1
# Partial Discharge Wideband Full-Band High-Gain Resonant Cavity UHF Sensor Research
## 3 Resonant Cavity UHF Antenna Sensor Design  
The resonant cavity UHF antenna sensor designed in this paper is a Fabry-Perot resonant cavity monopole antenna sensor, which contains two parts: the feed antenna and the partial reflection cladding. The feed antenna is a microstrip monopole patch antenna with optimized VSWR using curved flow technique and optimized gain using slot dual resonant structure.
## 2 Various PPF Strategies
### Miscellaneous  
Porous poly(styrene-_co_-divinylbenzene) monoliths with controlled pore size distribution have been prepared using new types of stable free radical (SFR) initiators in HPLC [43]. The origin monolith was prepared via in situ polymerization of a mixture consisting of divinylbenzene and styrene mixed in 1-decanol and poly(ethylene glycol) as porogenic solution in the presence of benzoyl peroxide as initiator and different types of SFR, see the following structures (Figure 2). The pore size distribution was studied for all types of SFR. The study demonstrated that the carboxy-2,2,6,6-tetramethylpiperidinyl-1-oxy (carboxy-TEMPO) and 3-carboxy-2,2,5,5-tetramethyllypyrrolidinyl-1-oxy (carboxy-PROXYL) improved the permeability of the monolith compared with other SFR. The poly(styrene-_co_-divinylbenzene) monolith using carboxy functional SFRs was grafted with 2-hydroxyethyl methacrylate and also with 3-sulfopropyl methacrylate individually to obtain a variety of monolithic surfaces.  
The preparation of a continuous-bed column and its PPF with macrocyclic antibiotic as chiral selector was proposed for use in CEC [44]. The continuous bed capillary column was prepared first via in situ polymerization of a mixture containing \(N\)-(hydroxymethyl)acrylamide, piperazine diacylamine, \(N\),\(N^{\prime}\)-diallyltetradiamide, \(N\),\(N\),\(N^{\prime}\),\(N^{\prime}\)-tetramethyllylphenediamine, ammonium persulfate and vinyl sulfonic acid. Then, the obtained column was grafted with vancomycin by a reductive amination process. The enantioseparations of thalidomide, bupivacaine and warfarin were successfully obtained using the vancomycin based nonparticulate bed.  
A molecularly imprinted polymer (MIP) coating was grafted on a poly(TRIM) monolithic capillary column for use in chiral separation by CEC [45]. The origin monolith was prepared via in situ polymerization of a mixture containing TRIM in the presence of a binary porogen under thermal initiator condition. The grafted MIP was obtained by passing a mixture of template, methacrylic acid and EDTAA as monomers, porogen and thermal initiator into the TRIM monolithic column, see Scheme 32. Then the capillary was incubated in water bath at a temperature of 53 \({}^{\circ}\)C until polymerization. The proposed monolith provided high resolution for amlodipine enantiomers separation up to 7.99.  
Figure 2: Porous poly(styrene-co-divinylbenzene) monoliths with controlled pore size distribution have been prepared using new types of stable free radical (SFR) initiators in HPLC [43].
## 3 Proposed CPDAE
### Channel-Wise Average Pooling in Skip Connection  
When the encoder layer maps features into low-dimensional \(\mathbf{z}\) from the input layer, certain tiny but key features disappear as the network deepens. This results in the reconstructed features being unable to perfectly represent the features of the ECG signal [36]. To solve this problem, a skip connection between the corresponding encoder and decoder was added in [34; 35; 36]. However, this requires substantial memory use to hold the output of the encoder layer [46]. In this study, CWAP was proposed as a trade-off between denoising quality and memory requirements. The CWAP passes the average feature of channels from the encoder to the corresponding decoder and reduces the amount of memory usage in the skip connection as follows in Equation (5):  
\[\begin{split}&\hat{a}_{0,n}=\frac{1}{\zeta}\sum\limits_{i=0}^{C-1}a _{i,n}\\ &\hat{\mathbf{a}}=\left[\begin{array}{ccc}a_{0,0}&\cdots&\hat{a }_{0,N-1}\\ a_{0,0}&\cdots&a_{0,N-1}\\ \vdots&\ddots&\vdots\\ a_{C-1,0}&\cdots&a_{C-1,N-1}\end{array}\right]\end{split} \tag{5}\]  
where \(\hat{\mathbf{a}}\) and \(\mathbf{a}\) are the CWAP output and input from the encoder, respectively. In addition, \(C\) is the number of input channels, and \(N\) represents the number of features. The CWAP averages the input channels into one channel (\(\hat{\mathbf{a}}\)) to reduce \(C\) times the memory usage of the skip connection. The memory size of the output \(\tilde{\mathbf{a}}\) of point-wise convolution takes the same dimension as \(\mathbf{a}\), and the output \(\tilde{\mathbf{a}}\) can be fed into the decoder layer, as depicted in Figure 7. To minimize gradient vanishing, a nonlinear activation function, such as a ReLU or sigmoid function, is not available in the skip connection.
# Federated Pseudo-Sample Clustering Algorithm:
## 5 Experiments
### Experimental Settings  
IID Data Setting: For the IID setting, it is usually assumed that the local datasets of each client are independently and identically distributed in terms of data distribution. Taking MNIST as an example, the experimental training set is formed by uniformly and randomly selecting images from each class of the MNIST training set without repetition. The allocation of test data among classes follows the same proportion as that of training data. Each client uniformly and randomly selects images from each class of the MNIST test set without repetition to form the experimental test set.  
Non-IID Data Setting: The IID setting is considered ideal, and current federated learning methods excel in such environments. However, in reality, achieving the IID environment is difficult, especially in personalized federated learning scenarios. Therefore, experiments in a non-IID environment become necessary. In this setting, the local datasets of each client have different data distributions and may exhibit significant variations. Taking MNIST as an example, for the 10 classes of MNIST samples, a local dataset might only have a subset of these classes, rather than the complete set. Furthermore, even within the classes it possesses, the quantities of samples for each class may vary. As a result, experiments in a non-IID environment are more challenging compared with those in the IID setting. This is an unavoidable issue in the context of personalized federated learning.  
In both IID and non-IID settings, the amount of local data per client is 500. In the IID setup, each client contains all 10 categories, with 50 samples per category; in the non-IID setup, the maximum sample size for each category is 120 and then decreases sequentially until the sample size for some categories is 0. The purpose of this is to create local data based on the client's data, with some categories being the majority and others having little or no data. The test and training sets have the same data distribution regardless of the IID and non-IID settings.  
The experiments in this paper were performed on a 2080 ti GPU.
## 3 Results and Discussion
### Mesoporous Controlled Silica Microcapsules  
BET analysis was also employed to determine the specific surface area and internal pore area of the synthesized silica microcapsules. The adsorption isotherms of the silica shells exhibit type II behavior (Figure 3A), indicating the presence of mesopores (2-50 nm) and macropores (>50 nm) [42]. The size of the macropore is similar to or larger than the measured thickness of the silica shell, therefore does not affect the internal structure of the shell that influences fragrance oil leakage, and can thus be ignored (Figure 3B). The adsorption hysteresis loops are non-overlapping and narrow, exhibiting the characteristics of a slit-like pore structure (H3 type). As the amount of added TEOS increases up to 3%, the overall absorbed volume decreases. Seeded growth occurs after hydrolysis of TEOS, which appears to fill both mesopores and external pores on the surface of the shell.  
The abovementioned data were calculated using the BET and t-plot methods. The total surface areas of the silica microcapsules and their respective surface areas are shown in Table 1. As the additional TEOS content increased from 0% to 3%, the total surface area of the silica shell decreased by 40.5%, and the micropore surface area decreased by a considerable 82.2%. The BJH distribution of average pore size versus incremental pore area and volume also revealed a reduction in both pore area and volume of both the macropores and mesopores (Figure 3B,C). The pores were clearly discernable by their distinct maxima centered at 2.36, 8.69, 44.2, and 101 nm in Figure S1. The pore sizes of 44.2 and 101 nm are not relevant because they are higher or similar to the thickness of the silica microcapsules, as described above. In particular, the reductions of 2.36 and 8.69 nm in the size of the mesopores were presumably directly related to the fragrance retention ability of the microcapsules.  
The absorbed volume increased upon the introduction of additional 5% TEOS (T5) to the immature silica microcapsules (Figures 3A and S2). The pores in the shells of the silica microcapsule were presumably filled by the addition of 3% TEOS as the capsule shell thickened. Further addition of TEOS resulted in a build-up of excess TEOS, forming a rough surface on the surface of the silica microcapsules (Figure 3A). Although the phase and number of additions were different, the results are surprisingly similar to those of [PERSON] et al. [33] when considering the concentration of TEOS added. In this system, multiple additions of TEOS are considered most effective. However, the addition of excess TEOS improves both the simplicity and the productivity of capsule synthesis.  
Figure 3: (**A**) Nitrogen adsorption/desorption isotherms of silica microcapsules synthesized following the addition of various concentrations of TEOS. (**B**) Incremental pore areas and (**C**) incremental pore volumes as functions of average pore size.
# TGN: A Temporal Graph Network for Physics Prediction
## 4 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Rollout**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Cylinder Flow**} & \multicolumn{3}{c}{**Airfoil**} \\  & & \(\mathbf{u}\) & \(\mathbf{v}\) & \(\mathbf{p}\) & \(\mathbf{u}\) & \(\mathbf{v}\) & \(\mathbf{\rho}\) & \(\mathbf{p}\) \\ \hline \multirow{4}{*}{1-step} & MeshGraphNets & 0.1 & 0.77 & 923.15 & 0.41 & 15.53 & 0.01 & 0.07 \\  & Linear & 0.06 & 1.52 & 66.55 & 0.35 & 13.14 & 0.01 & **0.04** \\  & Transformer & 0.02 & 0.37 & 33.07 & 0.37 & 11.09 & 0.01 & 0.17 \\  & TGN & **0.01** & **0.24** & **20.63** & **0.33** & **10.49** & **0.00