esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antenna Configuration
### Simulated Analysis  
The simulated performance of the proposed antenna is presented with the help of the reflection coefficient curve, surface-current distribution, and the 3D radiation patterns (RPs). The proposed triple-band UWB antenna with a dual-notch band characteristics reflection coefficient curve is illustrated in Figure 6. From Figure 6, it was understood that the proposed dual-notch band antenna has three operational frequency bands centered at 3.5 GHz, 6.1 GHz, and 9.25 GHz with a return-loss of \(-26\) dB, \(-31\) dB, and \(-43\) dB, respectively.  
The surface current distributions at 3.5 GHz, 6.1 GHz, and 9.25 GHz are shown in Figure 7a-c. From the graphs, it can be understood that the surface current at 3.5 GHz is greater across the microstrip feed-line and along the edges of the inverted triangular patch. At 6.1 GHz, the surface current distribution is strong at SSRR and edges of the radiating patch. Similarly, at 9.25 GHz, the surface current distribution is stronger at the triangular slot in the middle of the radiating patch. The 3D RPs for the same resonant frequencies are also shown in Figure 8a-c. From the 3D RP plots, it can be observed that the proposed triple-band UWB antenna has peak gains of 2.95 dBi, 5.23 dBi, and 6.2 dBi, at 3.5 GHz, 6.1 GHz, and 9.25 GHz, respectively.  
Figure 6: Simulated reflection coefficient curve of the proposed triple-band UWB antenna with dual-notch.  
Figure 7: Surface current distributions of the proposed triple-band UWB antenna (**a**) 3.5 GHz (**b**) 6.1 GHz (**c**) 9.25 GHz.
# A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping
## 7 Conclusions and Future Work  
In this paper, a method of sensing dynamic gradient only by adjacent robots is proposed, which can effectively realize swarm robots to cover unknown areas by self-organization. Furthermore, the swarm robots are grouped and each group covers the area in parallel to improve the coverage speed. This coverage method can be applied to robots with very simple functions and lack of computing and storage capacity, and the communication cost between robots is very low. Through the experimental analysis, the following conclusions can be drawn: (1) The coverage method based on gradient can make the large-scale robot system self-organize to cover the unknown area effectively; (2) The grouping-based coverage method can effectively improve the regional coverage speed of the swarm robot system and reduce the total moving distance of the swarm robots. However, the number of groups should not be too large, and the coverage distance of the robot should also be taken as a reasonable value. In our future work, other related algorithms will be used to solve the problem of adaptive coverage of dynamically changing regions. Finally, through the application of very simple swarm robots for distributed area coverage, our method can further realize self-organizing control of the collective behavior of submillimeter robots. This will lay the foundation for future microscopic fields such as medicine.  
**Author Contributions:** Conceptualization, methodology, Q.W.; Data curation, validation, writing--review and editing, H.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by the Natural Science Foundation of China under grants 61876164.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithms** & \begin{tabular}{c} **Complex** \\ **Environment** \\ \end{tabular} & \begin{tabular}{c} **Communication** \\ **Delay** \\ \end{tabular} & \
\begin{tabular}{c} **Computing** \\ **Cost** \\ \end{tabular} & **Stability** & **Coverage Rate** &
\begin{tabular}{c} **Speed of** \\ **Convergence** \\ \end{tabular} \\ \hline CVT [37] & \(\times\) & exist & large & poor & higher & fast \\ LFS [39] & \(\times\) & exist & large & poor & high & slow \\ DD [40] & ✓ & exist & large & poor & high & fast \\ CADT [38] & \(\times\) & exist & large & poor & higher & fast \\ SCWC [33] & \(\times\) & not exist & small & good & high & fast \\ DASH [42] & ✓ & not exist & small & good & low & slow \\ FCDFS [43] & ✓ & not exist & small & poor & high & fast \\ Kilobot [41] & ✓ & not exist & small & good & higher & slow \\ GGC & ✓ & not exist & small & good & higher & fast \\ \hline \hline \end{tabular}
\end{table}
Table 3: Qualitative analysis table of coverage algorithm.
###### Abstract
Experimental results show that the proposed hybrid algorithm has a better ability to jump out of local optima with faster convergence. Although the SMA, as a new algorithm, is competitive with other algorithms, it also suffers from some shortcomings. The SMA, similarly to many other swarm-based metaheuristic algorithms, suffers from slow convergence and premature convergence to a local optimum solution [44]. In addition, the update strategy of SMA reduces exploration capabilities and reduces population diversity. To improve the above problems, an improved algorithm based on SMA, called the dynamic-quantum-rotation-gate- and opposition-based learning SMA (DQOBLSMA), is proposed. In this paper, we introduce two mechanisms, the dynamic quantum rotation gate (DQGR) and opposition-based learning (OBL), into the SMA simultaneously. Both mechanisms improve the shortcomings of the original algorithm in terms of slow convergence and the tendency to fall into local optima. First, DQGR rotates the search individuals to the direction of the optimum, improving the diversity of the population and enhancing the global exploration capability of the algorithm. At the same time, OBL explores the partial solution in the opposite direction, improving the algorithm's ability to jump out of local optima. The performance of the DQOBLSMA was evaluated by comparing it with the original SMA algorithm and with other advanced algorithms. In addition, three different constraint engineering problems were used to verify the performance of the DQOBLSMA further: the welded beam design problem, the tension/compression spring design problem, and pressure vessel design. The main contributions of this paper are summarized as follows:  
1. DQRG and OBL strategies were introduced into SMA to improve the exploration capabilities of SMA. 2. The DQRG strategy is proposed in order to balance the exploration and exploitation phases. By comparing five well-known metaheuristic algorithms, experiments show that the proposed DQOBLSMA is more robust and effective. 4. Experiments on three engineering design optimization problems show that the DQOBLSMA can be effectively applied to practical engineering problems. This paper is organized as follows. Section 2 describes the slime mold algorithm, quantum rotation gate, and opposition-based learning. Section 3 presents the proposed improved slime mold algorithm. Section 4 show the experimental study and discussion using benchmark functions. The DQOBLSMA is applied to solve the three engineering problems in Section 5. Finally, the conclusion and future work are given in Section 6.
# A Review on Clustering Techniques: Creating Better User Experience for Online Roadshow
## 3 Discussion  
Among the existing works, three perspectives are considered when choosing the clustering techniques, namely the similarity measures, the evaluation metrics, and the functional purposes of clustering. Table 1 summarizes the usage of different clustering techniques in web usage mining based on their similarity measures, evaluation metrics, and functional purposes. The * symbol represents the improved version of either the similarity measures, evaluation metrics, or clustering purposes.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Clustering Technique** & **Similarity Measure** & **Evaluation Metric** & **Functional Purpose** \\ \hline \multirow{4}{*}{_k-means_} & \multirow{4}{*}{Cosine similarity [25], Euclidean distance [23; 24; 26].} & Residual SSE [25], Accuracy [22; 23], Percentage error [24], Precision [22; 26], Recall [22], F-measure [22]. & User group discovery [25], Page categorization [23], Web queries categorization [24], Page recommendation [26], Web personalization [22]. \\ \hline \multirow{4}{*}{Improved _k-means_} & \multirow{4}{*}{Cosine similarity [28], Euclidean distance [29], Variable Length Vector Distance * [27].} & Jaccard index [27], Purity [29], Entropy [29], Dunn index [29], Silhouette index [29]. \\ \hline \multirow{4}{*}{_k-medoids_} & \multirow{4}{*}{Euclidean distance [31; 32], Cosine similarity [30], Hamming distance [30].} & DB index [31; 32], C index [31], SSE [31], Percentage of recommendation quality [30]. & User group discovery [31; 62], Page recommendation [30], Web personalization [32]. \\ \hline \multirow{2}{*}{Improved _k-medoids_} & \multirow{2}{*}{Euclidean distance [33; 34].} & Accuracy [33; 34], Recall [33]. & Page categorization [33], Page recommendation [34]. \\ \hline \multirow{4}{*}{_CURE_} & \multirow{4}{*}{Projected Euclidean distance [63], Cosine similarity [63], Fuzzy similarity [63].} & Inter-cluster distance [63], Intra-cluster distance [63]. & Web personalization [63]. \\ \cline{1-1} \cline{2-4}  & & Manhattan distance [36], Precision [36], Recall [36], Accuracy [36]. & User group discovery [36]. \\ \hline \multirow{2}{*}{_CHAMELEON_} & \multirow{2}{*}{-} & MAE [64]. & User group discovery [65], Page recommendation [64]. \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \end{tabular}
\end{table}
Table 1: The usage of clustering techniques in web usage mining.
# Secure and Privacy-Aware Blockchain Design: Requirements, Challenges and Solutions
## 4 Attacks and Countermeasures
### Network Attacks and Countermeasures
[85], proposed BGPcoin based on Ethereum blockchain, a BGP framework that is generated by a smart contract in the network Ethereum. It provides a transparent allocation of sources (e.g., IP prefixes). It also checks source assignments using blockchain to enhance system security. * **Liveness attack:** This attack [86] causes the delay of target transaction time on blockchain. It works in three phases, such as (1) attack preparation phase, in which the attacker creates a private chain longer than the public chain, (2) in-transaction denial phase, where the attacker tries to privately keep the block that contains target transaction, (3) in-blockchain phase, where the attacker will integrate their private blocks into the public chain to slow down the growth rate of the public chain. * **Countermeasures:** To resolve the liveness attack [PERSON] et al. [87] proposed the conflux consensus protocol that allows fast confirmation and ensures the consensus progress. It is a secure, scalable, and decentralized blockchain platform. On the contrary, with passive attacks, an attacker monitors the network and attempts to gain sensitive information about a target. We identified the most well-known passive attacks as follows:  
* **Traffic analysis attack:** An adversary tries to observe the communication route between the sender and receiver. The goal of this attack is to analyze the network communication and from this analysis gather useful information to execute an attack [88]. * **Countermeasures:** Onion routing provides a solution to traffic analysis as it allows to transfer data from a source through multiple onion routers before the data reaches its destination, thus guaranteeing anonymity. Each message has various layers of encryption, where each layer links to one onion router. It prevents the attacker to identify the endpoints of communication [89]. * **Eavesdropping attack:** The aim of this attack is to listen to private information over the network [90]. * **Countermeasures:** Cryptographic techniques such as symmetric key encryption can be used to protect confidential communication against eavesdropping attack. It limits data access only to the authorized user [91]. As a summary, we observe that most cryptographic mechanisms that are used to ensure security on sensitive data rely on key management solutions. Symmetric cryptography is computationally efficient, but is sensitive to attacks during key agreement or key distribution. For instance, the well-known Diffie-Hellman (DH) scheme is vulnerable to the man-in-the-middle attack. Efficient key agreement and distribution are still being researched intensively, though improvements like Station-to-station (STS) addressed the issues by authenticating both parties of the exchange through public-key cryptography. Although DDoS attack remains dangerous, it is generally prevented by implementing firewalls on routers, where networks are interconnected.
# Machine Learning Detection of Cloud Services Abuse as C&C Infrastructure
## 4 Methodology
### Importance of Derived Features  
In our study, we evaluate a total of 38 pertinent features, of which 36 were raw PE features and 2 were derived features. Utilizing various feature selectors, as discussed in Section 4.3, our results identified a combination of 21 raw features and the derived feature \"potential_C&C_api_calls\" to yield the highest classification accuracy. Detailed results and  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**API Call** & **Description** & **Potential Abuse Case** \\ \hline InternetOpenA & Open an Internet session & Establish a connection to CLS \\ \hline InternetConnectA & Connect to a remote server & Connect to the servers of CLS \\ \hline HttpOpenRequestA & Open an HTTP request handle & Open HTTP requests to CLS \\ \hline InternetReadFile & Read data from an open Internet file & Read data from a file on CLS \\ \hline InternetWriteFile & Write data to an open Internet file & Write data to a file on CLS \\ \hline WinHttpOpen & Open an HTTP session & Open HTTP sessions with CLS \\ \hline WinHttpConnect & Connect to a remote server & Connect to the servers of CLS \\ \hline WinHttpOpenRequest & Open an HTTP request handle & Open HTTP requests to CLS \\ \hline WinHttpSendRequest & Send an HTTP request & Send HTTP requests to CLS \\ \hline WinHttpReceiveResponse & Receive an HTTP response & Receive HTTP responses from CLS \\ \hline WinHttpReadData & Read data from an HTTP request & Read data from an HTTP request to CLS \\ \hline WinHttpWriteData & Write data to an HTTP request & Write data to an HTTP request to CLS \\ \hline URLDownloadToFileA & Download a file from the Internet & Download files from CLS \\ \hline HttpSendRequestA & Send an HTTP request & Send HTTP requests to CLS \\ \hline InternetOpenUrlA & Open an HTTP or FTP session & Open HTTP or FTP sessions with CLS \\ \hline InternetReadFileExA & Read data from an open Internet file & Read data from a file on CLS \\ \hline InternetWriteFileExA & Write data to an open Internet file & Write data to a file on CLS \\ \hline \hline \end{tabular}
\end{table}
Table 3: Potential API calls for abusing CLS.  
comparisons before and after inclusion of the derived features can be perused in Tables 4 and 5. Our final model that delivered the highest accuracy employed the RF classifier in conjunction with the aforementioned feature selector. We also charted the importance of each feature. This plot offers a visual representation of the relative significance of each feature as depicted in Figure 7.  
Although the \"presence_of_CLS_domains\" may not have prominently boosted the classification accuracy, as indicated in Table 4, its relevance stems from our data collection approach, detailed in Section 4.1. In particular, the dynamic analysis phase of our data collection was designed to extract a subset from the VirusTotal dataset that predominantly connects CLS domains.
###### Abstract
In this paper, service, data, data object and content are used interchangeably. Our main contributions are summarized as follows:  
* We explore how D2D communications can be used to offload traffic and push contents to the edge closer to the end user in 5G. * We propose a D2D security solution for authentication, data caching and sharing authorization in network-assisted and non-network-assisted D2D communications scenarios. The solution consists of two security protocols for providing authentication and authorization to D2D users facilitated by Identity-based Encryption (IBE), Elliptic Curve Integrated Encryption Scheme (ECIES) and access control mechanisms. * We model and formally analyze the proposed protocols using formal methods and automated protocol verifier ProVerif with applied pi calculus. * We also analyze the protocols security properties conscientiously using two taxonomies. The rest of the paper is structured as follows. Related works on D2D security in legacy and 5G systems are discussed in Section 2. Section 3 discusses D2D service delivery, system architecture and D2D communications processes. In Section 4, the problem is defined and the proposed D2D security solution is presented. The modeling of the proposed security protocols is presented in Section 5. In Section 6, the formal verification of the protocols is presented. Section 7 analyzes and evaluates the protocols' security properties. The paper is finally concluded in Section 8.
## 3 Results and Discussion
### Detection of Glucose  
Detection of glucose involves glucose oxidation by glucose oxidase to generate H\({}_{2}\)O\({}_{2}\) and the ensuing H\({}_{2}\)O\({}_{2}\) measurement. First, the quantitative relationship between glucose concentration and the FRET ratio was calibrated. The glucose solution was diluted to a serial of concentrations (0, 2, 4, 6, 8, 10, 12 uM), and reacted with GOX. Subsequently, CFP-Prx and mTrx-YFP mixture, both 150 nM, was added to the glucose oxidation system and fluorescence spectra were measured. The glucose oxidation was conducted in a small volume in order to obtain relatively higher enzyme and substrate concentration for fast and complete reaction. Figure 6 showed the corresponding fluorescence spectrum from each measurement, labeled by the glucose concentration. The FRET ratio (518 nm/476 nm) was calculated and plotted versus glucose concentration as a calibration curve (inset in Figure 6). Finally, the calibration equation of R = 0.0039C + 0.822 (R\({}^{2}\) = 0.9809) was obtained for the glucose analysis. Herein, R is the FRET ratio (518 nm/476 nm), R\({}^{2}\) is correlation coefficient and C is the concentration of glucose (nM).  
To verify the applicability of CPmTY for glucose detection, Dulbecco's modified eagle medium (DMEM, Corning Cat. No. 10-013) was tested with labeled glucose concentration of 4500 mg/L, that is, 25 mM. The result was 24.58 mM, indicating a recovery of \(98.32\pm 0.47\%\) and confirming the applicability of CPmTY.  
Figure 5: Interference of common oxidant on FRET signal of CPmTY. Concentration of oxidized glutathione (GSSG), cysteine, tert-butyl hydroperoxide (t-BOOH): 1 uM, H\({}_{2}\)O\({}_{2}\): 100 nM, superoxide radicals (O\({}_{2}^{-}\)) and peroxynitrite anion (ONOO\({}^{-}\)) were produced in situ by specific enzymatic reactions as described in experimental section.
# A Comparison of Faulty Antenna Detection Methodologies in Planar Array
## 5 Results and Discussion
### Optimization Results  
The initial Failed Radiation Pattern is then compared with the new failed radiation pattern, with the failed location optimized by optimization methods elaborated earlier. The optimization flow shown in Figure 1 is followed to obtain the results. The results show that the optimization algorithm developed managed to identify the failed locations in a planar array. Figure 11: (a) Initial Failed Radiation Pattern. (b) The weight distribution of Initial Failed Radiation Pattern. Next, the simulated annealing algorithm in 8 \(\times\) 8 planar array detected 6 locations correctly out of 16 failed elements, giving 37.50% accuracy for random and row type failures; 5 correct detections in column type of failure with 12.50% accuracy; and only 2 correct location detections with 25% accuracy in the group type of failure. Similarly, the PSO algorithm for the 8 \(\times\) 8 planar array detected seven failed locations correctly in the random type of failure, which represents 43.75% accuracy. For the row type  
Figure 14: Column type of failed location found by pattern search method for 8 \(\times\) 8 planar array. Figure 15: Group type of failed location found by pattern search method for 8 \(\times\) 8 planar array. of failure, this algorithm failed to give any correct detection. For the column type of failure, this algorithm detected four locations correctly, which gives 25% accuracy. Lastly, for the group type of failure, this algorithm detected three locations correctly, which results in 18.75% accuracy. The same optimization algorithms and the type of failure described above were developed for the 6 \(\times\) 6 planar array. The results from using pattern search, simulated annealing, and particle swarm optimization methods for the 8 \(\times\) 8 array size and 6 \(\times\) 6 array size in planar arrays are listed in Table 2. The time taken for each of the optimization methods to identify the failed location is recorded for all types of failure in Table 2 below. It is observed that the pattern search method provides better accuracy percentages for all types of failure in both 8 \(\times\) 8 and 6 \(\times\) 6 planar arrays. It managed to provide 100% accuracy for all types of failure for both array sizes. This method provides the best accuracy percentages compared to other methods for all types of failure. The success of this method of finding failure locations is due to the restricted search method, which operates in a mesh form, representing a matrix to find the global convergence. The current mesh is reduced
## 2 Problem Statement
### Problem Description  
Research in the construction of linear facilities such as pipelines, transmission lines, and communication routes is essential to define the land corridor where they will be placed. A corridor is a geographic area between two points joined using several intermediate points, providing the necessary space for guaranteeing the right conditions for the construction and use of the facilities. In particular, the corridor-width depends on local regulations, according to the installation type. The corridor planning problem can be modeled as an optimization problem subject to topographical constraints [1; 5; 12]. Corridors are usually addressed by one or more selection criteria such as distance, environmental impact, safety, etc. The design of a topographical network representing the study region is part of the corridor planning process. Once this network is defined, a corridor between two points is selected from all possible paths.  
The corridor planning process considers numerous factors affecting the project proposal [3; 12]. In general, an initial corridor route is first determined and then refined using the design criteria [13]. In the first phase, the parties involved in the project must approve the proposed route according to the input data used in the spatial analysis [14]. This route can be modeled using a geographic information system (GIS) to assign the impact costs in the study area. GIS provides several algorithms to find the shortest path, but what is required in this work is to find multiple optimized routes within a corridor near the shortest route which is the global optimum. Therefore, a route refinement is necessary, where other competitive and near-optimal but spatially different solutions are considered [14]. The shortest path can be obtained by penalizing the distance of each step taken when crossing the region. However, a real problem may have obstacles complicating the movements in certain directions. Therefore, it is crucial to exploit the knowledge of the study region through spatial exploration and analysis [15], to classify the factors being part of the route selection criteria. Some of them are the ground elevation, soil type, vegetation type, and the presence of water bodies, roads, and archaeological zones. Other factors that should be considered are those disturbing humans and wild animals. Once the constraints have been defined using these factors, a feasible route can be found.