esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments
## 3 Proposed System
### Data Processing  
We will process non-numerical and numerical features from IP flow records in data preprocessing. For the non-numerical features, it is necessary to use one-hot encoding to transform the numerical features. For original numerical features, they need to be normalized in the range of (0,1) to reduce the impact of the different scales of features. The processing above assists the next module in the anomaly detection approach.  
According to the public dataset CICDDoS 2019, this paper received 88 attributes from IP flow records as its features. The following attributes are included: bits/s, packets/s, Source IP Entropy, Source Port Entropy, Destination IP Entropy, Destination Port Entropy, and 82 other digital features, which include time stamp.  
We define the number of bits and packets, source IP address and port number, and destination IP address and port number, which belong to \(\alpha_{i}\), as \(k_{\in\alpha_{i}}^{bits}\), \(k_{\in\alpha_{i}}^{packets}\), \(k_{\in\alpha_{i}}^{srcIP}\), \(k_{\in\alpha_{i}}^{srcPort}\), \(k_{\in\alpha_{i}}^{dstIP}\), and \(k_{\in\alpha_{i}}^{dstPort}\). Other 82 digital features are the same as these examples.  
As non-numerical features, IP addresses and port numbers differentiate addresses. Under a DDoS attack occurrence, the destination IP address and destination Port number distribution may become concentrated due to the high number of connections requested by the attackers [15]. So, we need to process the data through the Shannon entropy formula to evaluate whether the address of IP flow records is centralized. We define \(k_{\in\alpha_{i}}^{srcIP}=\{k_{1},k_{2},\ldots,k_{n}\}\) as a set of the source IP feature belonging to \(\alpha_{i}\). \(k_{j}\) means the frequency of occurrences of a source IP in \(\alpha_{i}\). S is the sum of all occurrences of all source IPs in \(\alpha_{i}\). \(n\) means the number of source IPs in \(\alpha_{i}\).  
For each \(\alpha_{i}\), the Shannon entropy \(\mathrm{H}(\cdot)\) for the flow feature \(k_{\in\alpha_{i}}^{srcIP}\) is defined as source IP entropy, which can be processed as follows:  
\[H\Big{(}k_{\in\alpha_{i}}^{srcIP}\Big{)}=-\sum_{j=1}^{n}\left(\frac{k_{j}}{S} \right)log_{2}\left(\frac{k_{j}}{S}\right),\]  
\[S=\sum_{j=1}^{n}k_{j}\]  
The source port number, destination IP address, and port number are the same as the source IP.  
As the numerical features, the rate of bits and packets per second can be processed as follows:  
\[\sum_{j=1}^{n}k_{\in\alpha_{i}}^{bits},\]  
\[\sum_{j=1}^{n}k_{\in\alpha_{i}}^{packets}.\]  
Moreover, the other 82 digital features that can be processed are the same as the above features.  
Figure 2: The process of data collection.
## 5 Results and Discussion
### Comparison with the State-of-the-Art Methods
Compared with fully convolution neural networks such as FCN, ConvNet, the U-Net model could obtain a better performance, since U-Net not only has deeper layers, but also has a feature fusion policy that could be used to obtain more discriminative features. Compared with SVM and CrackForest, FCN could obtain a relatively better result, as deep learning models could automatically extract high-level features, which are more generalized than handcrafted features. Here, we present some examples of our PHCNet's detection results for crack images, which were taken from the CRACK500 datasets. These crack images and their detection results are shown in Figure 8.  
Figure 8: Example of the results of our PHCNet when detecting crack images.
## 5 Analysis of Cracking and Damage in Wharf Structures under Impact Loading
### Structural Stress Analysis under Scenario 1 (20,000 t, 0.2 m/s)  
When a 20,000 t ship collided with the high-piled wharf structure at a speed of 0.2 m/s, the maximum tensile stress in the concrete at the top and below the pile body of the #6 pile reached the ultimate tensile strength, as depicted in Figure 15a. The concrete on the tension side exhibits various degrees of plastic damage. At this point, this section of the concrete has incurred tensile cracking and started unloading. The tensile stress released by the concrete is borne by the steel bars, with the maximum tensile stress reaching 261.2 MPa, which is 87% of the ultimate tensile strength of the steel bars, as depicted in Figure 15b. Additionally, a small segment of the #6 pile body beneath the mudline is also subject to tensile cracking, illustrated in Figure 16a,b. The tensile stress of the steel bars in the affected zone reaches 201.1 MPa. Under Scenario 1, the remaining five straight columns did not exhibit concrete cracking. However, due to the impact force, the concrete's tensile stress in each pile increased. The maximum concrete tensile stress on each pile body included the following: the #1 pile reached 2.194 MPa, the #2 pile reached 1.629 MPa, the #3 pile reached 1.678 MPa, the #4 pile reached 2.073 MPa, and the #5 pile reached 2.031 MPa. The high-stress areas in each straight pile are primarily near the pile top, followed by stress values near the mud surface line. With the multiple reciprocal impacts of ships wharfing at high-piled wharfs, these high-stress zones in straight piles may induce fatigue cracks. Additionally, the expansion of some original cracks in the concrete pile body can be attributed to these higher tensile stresses.  
Figure 14: The cloud map of stress distribution at the most critical section of pile #6.  
Figure 15: The structural damage status near the top of pile #6 under Scenario 1. **(a)** The cloud map of tensile damage in concrete near the pile top; **(b)** The cloud map of reinforcement damage near the pile top.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 6 Results and Evaluation
### Parameter Analysis
#### 6.4.4 Reference Tags  
Only two reference tags are involved in the experiment, and we explored all of the reference tags combinations. The result is in Figure 20, as the horizontal distance difference (X-axis) and height difference (Y-axis) between the two reference tags decreases, the error significantly increases. Therefore, the best placement of the reference tags is the diagonal corners, because the difference of data between the two reference tags is the largest, which is beneficial for subsequent calculations. Other placement methods yield large error, so simply adding reference tags will only increase the average error. Unless the reference tags are placed on all four corners, and the diagonal line is used as a group, which averages the results of the two groups and can reduce the upper limit of the positioning error to a certain extent and improve the stability of the system.
# Trend Prediction of Event Popularity from Microblogs
## 3 Methodology  
In order to solve the issue of trend prediction of event popularity from microblogs, this paper presents an effective predictive algorithm based on Bi-LSTM network model. Figure 1 shows the framework of our predictive system. Specifically, the system framework has three main modules, namely, data acquisition, data processing and modeling. Here, the modeling module contains the main core idea of this paper. For the data acquisition module, firstly, this paper uses the method of simulated login to enter the microblog platform, namely, Sina Weibo platform. And then the parameters and time window for retrieval are set using the advanced search function. In the end, our proposal uses Python to write a web crawler according to the functional requirements to crawl the microblog information derived from the keywords. In the data processing stage, this paper screened the results collected by the crawler to eliminate repeated and missing items. On the basis of the first two steps, this paper designs a model framework to solve the trend prediction of microblog event popularity. Compared to existing methods, our proposal proposes to model the microblog event popularity by selecting the influence factors and quantifying the weight of each factor with information entropy algorithm in order to deal with time series modeling for nonlinear data. Meanwhile, aimed at the issue of the trend prediction of nonlinear time series, the singular spectrum analysis is carried out to decompose and reconstruct the time series of the popularity of a microblog event while a learning method to deal with trend prediction with a sequence to label model is proposed in our proposal. The details will be discussed in the following four subsections.
# Decentralizing Private Blockchain-IoT Network with OLSR
## 3 Background
### Ethereum  
Ethereum is well-known in the form of a cryptocurrency over a public blockchain network, namely Mainnet. At the same time, Ethereum also supports private blockchain, which can be deployed and edited by users. The private blockchain networks are isolated to the public Ethereum. Only the blockchain nodes with permissions can access the private blockchain. The nodes in the Ethereum blockchain construct their P2P communication entirely in the transport and application layers of the networking stack. More specifically, they use Recursive Length Prefix transport protocol (RLPx) and Ethereum Wire Protocol (ETH) [2]. RLPx is responsible for serializing messages transmitted in blockchain connections. ETH defines how peers are enabled to interact with different messages. The synchronization of transactions and blocks is one of the most significant parts during the interaction [36]. The nodes propagate transactions and blocks to keep synchronizing the latest actions and events. Moreover, transactions that have not been admitted into blocks are propagated and managed with the _TxPool_ module ([[https://github.com/ethereum/go-ethereum/blob/master/core/bx_pool.go](https://github.com/ethereum/go-ethereum/blob/master/core/bx_pool.go)]([https://github.com/ethereum/go-ethereum/blob/master/core/bx_pool.go](https://github.com/ethereum/go-ethereum/blob/master/core/bx_pool.go)) (accessed on 24 June 2021)). The received transactions are arranged in _TxPool_ that is located on every node and further propagated to cover the entire network as rapidly as possible. The blocks, which contain block headers and various admitted transactions in block bodies, are disseminated according to the protocol defined in the _Downloader_ package ([[https://github.com/ethereum/go-ethereum/blob/master/eth/downloader/downloader.go](https://github.com/ethereum/go-ethereum/blob/master/eth/downloader/downloader.go)]([https://github.com/ethereum/go-ethereum/blob/master/eth/downloader/downloader.go](https://github.com/ethereum/go-ethereum/blob/master/eth/downloader/downloader.go)) (accessed on 24 June 2021)). Using an ad hoc routing protocol such as OLSR, a blockchain connection could leverage the multi-hop characteristic in the ad hoc network. Directed by the routing protocol, each node could reach more peers rather than in the case of infrastructure restriction. It may accelerate information propagation to the entire blockchain network [37].
# Influence of Frost Growth and Migration in Cryogenic Heat Exchanger on Air Refrigerator
## 4 Result and Discussion  
### Experimental Study  
#### 4.1.1 Heat Transfer Characteristics  
To investigate the effect of air humidity on the refrigerator, the experimental comparison of dry and wet air was carried out under the same operating conditions. As shown in Figure 1a, when the freezing dryer and molecular sieve were working, the tested moisture content at inlet of the heat exchanger was lower than 0.0075 g (kg dry air)\({}^{-1}\). Therefore, the air could be considered as dry at this time. When the freezing dryer and the molecular sieve were off, most of the moisture in the air was removed by the oil-water filter. Currently, the inlet air of the heat exchanger was wet with small amounts of moisture. Operation parameters of the wet and dry air experiments are shown in Table 2. Despite the inlet air humidity, the inlet and outlet boundary conditions of the heat exchanger in two tests were consistent, which were the design parameters of the heat exchanger.  
Figure 5: Flowchart of the frosting model.  
As shown in Figure 6, the cooling curves of two conditions basically coincide in the initial stage of cooling. As the cooling went on, the inlet and outlet temperatures of the heat exchanger with wet air were gradually higher than those under the dry air condition after about 50 mins. At the 120 th min, in the wet air condition, temperatures of the hot fluid outlet and cold fluid inlet were 5.1 K and 3.9 K higher than the other ones. As shown in Figure 6b, the pressure losses at the hot side of the heat exchanger were higher with the wet air applied. That is because there was frosting in the hot channel, which increased the flow resistance. The pressure loses at the cold side were nearly the same under two different conditions. It showed that even the frosting might occur in the cold side, the phenomena were not obvious. Therefore, it can be concluded that the assumption of frost formation in the hot side was reasonable.
# Effects of the Top Edge Impedance on Sound Barrier Diffraction
## 4 A Simple Example of Implementing the Good Top Edge Impedance  
According to Section 3, the good top edge impedance can be pure reactance, which can be obtained by digging a well on top of the barrier in a specific frequency band because the theoretical normal admittance at the opening of a well with a depth of \(l\) is \(\beta=\text{j}\beta_{0}\text{tan}(kl)\). A model was established in COMSOL, in which the depth of the well was 0.1684 m, and the opening size was consistent with the barrier thickness, which was 0.01 m. The maximum element size was set to 0.048 m, which corresponds to the one-seventh of the acoustic wavelength at 1 kHz, and the minimum element size was set to one-thirtieth the thickness of the barrier.  
The SWL flowing from \(\Sigma_{2}\) to Space III calculated by COMSOL simulation is shown in Figure 7a. The peak value appeared at 482 Hz, and the valley value appeared at 500 Hz. Figure 7b shows the comparison of the equivalent normal admittance at the opening of the well at the peak and valley frequencies of the SWL curve and the theoretical bad and good top edge impedance. Although the equivalent normal admittance at the opening of the well was not uniform, the results at most measurement points agreed roughly with that from the theoretical values.  
The sound pressure level and sound intensity distribution near the top edge of the barrier at 482 Hz and 500 Hz are shown in Figure 8a,b, respectively. It can be seen from Figure 8 that the reflected sound wave of the well interfered constructively with the incident wave at 482 Hz, while it interfered destructively with the incident wave at 500 Hz. These two sound field distributions shown in Figure 8 are basically the same as those in Figure 6a,c, indicating that the mechanisms at these two frequencies can be equivalent to obtaining a bad top edge impedance or a good top edge impedance. This shows that the bad and the good top edge impedance can be realized in a certain frequency by digging a well at the top of the barrier.  
Figure 6: The SPL and sound intensity distribution at 500 Hz near the top edge of the barrier with (**a**) bad impedance (\(\beta=11.2\text{j}\beta_{0}\)), (**b**) rigid boundary (\(\beta=10^{-10}\)), (**c**) good impedance (\(\beta=27.3\text{j}\beta_{0}\)) and (**d**) soft boundary (\(\beta=10^{10}\)).  
According to the above results and discussion, the system with good impedance can have larger noise control performance than that with soft boundary. However, the required impedance is frequency-dependent, which is difficult to implement in broadband control with passive materials. Active noise control (ANC) techniques have been used to realize broadband soft boundary near the top edge of the barrier by minimizing the near-field sound pressure [33], and the feasibility of controlling the near-field acoustic impedance has been demonstrated [34]. Future research can use the ANC system to achieve the designed impedance in a broadband frequency range.
## 4 The Trust Evaluation Process
### Trust Evidence Generation  
In the first part of trust model, trust evidences are collected. As analyzed in the Table 1, there are many malicious attacks against localization and the main attack results can be classified into two categories: heavy packet loss and packet error. On one hand, malicious nodes can selectively  
Figure 3: A sliding time window.  
Figure 2: The five sub-processes in Collaborative Secure Localization algorithm based on Trust model (CSLT).  
forward or discard localization beacons, and they also can be selfish nodes refusing to take part in the localization process. In this case, unknown nodes may fail to localize themselves due to missing beacons, which introduces high packet loss. On the other hand, malicious nodes can monitor localization beacons from normal anchor nodes, then modify the beacons or send wrong localization information to mislead unknown nodes' localization, which introduces high packet error. Therefore, packet loss and packet error are taken as two main trust evidences in the CSLT algorithm to detect malicious anchor beacons and calculate trust values of sensor nodes. If packet error of loss is detected, the related sensor nodes will be assigned with a lower trust values and will not be used in the localization process. The trust model is not used to detect malicious node, but used to choose sensor nodes with higher trust level to participate into localization to ensure localization ratio and accuracy. As a kind of intrusion tolerance technology, the trust model is used to ensure network security allowing existence of malicious node, which is a little different from the intrusion detection technology. The intrusion detection technology is always used to detect and remove malicious nodes. However, in the trust model, the malicious nodes are kept in the network while may not be used until they become trust ones.  
At the beginning of localization, there are only three types of sensor nodes in the UWSN: surface buoys, mobile AUVs, and underwater unknown nodes. A real number from 0 to 1 is used to denote the trust value, 1 means completely trust and 0 means the opposite. We assume that all the anchor nodes are trusty at the beginning of network deployment, and set their trust value to 1. Then, based on the main idea of [30], we calculate trust value for anchor nodes according to packet loss and packet error. In the process of unknown nodes' initial localization process, only one-hop anchor nodes are used. Therefore, we first introduce trust evaluation for one-hop neighbor nodes. Then, in the process of unknown nodes' secondary localization process, two-hop anchor nodes and reference nodes are used. Thus, we will further present trust evaluation for two-hop neighbor nodes.
# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 2 Methodology
### Parallel/Distributed K-Means
In the context of node allocation in complex network topologies, such as those found in MEC, we could suppose that each base station is a point represented as \(x_{i}\), the initial centroids, \(\
u_{j}\), represent the initial guess of the EDC locations, and the parameter \(K\) is the number of edge data centers to deploy. The final EDC positions (centroids) after the execution are the optimal locations for the data centers to ensure efficient and low-latency service. Further details of the implementation are described in [21]. Figure 2: Graphical scheme of Spark architecture for K-Means II algorithm. ```
Input: Dataset \(D\) (partitioned across nodes), Number of EDCs \(k\), Maximum iterations \(maxIter\), Convergence threshold \(\epsilon\) Output: Set of \(k\) centroids representing EDCs Initialize the centroids \(C=\{c_{1},c_{2},\ldots,c_{k}\}\) randomly from dataset \(D\) ; foriter\(\gets\) 1 to maxIterdo Step 1: Assign points to nearest centroid foreach data partition \(P_{i}\) in \(D\) (parallelized)do Assign each point \(p\in P_{i}\) to the nearest centroid \(c_{j}\in C\) ; Record the assignment \((p,c_{j})\) ; Step 2: Recompute centroids foreach centroid \(c_{j}\in C\)do Aggregate assignments from all partitions in parallel: ; foreachpoint \(p\) assigned to \(c_{j}\) (across all partitions)do Calculate the new centroid \(c^{\prime}_{j}=\frac{1}{|S_{j}|}\sum_{p\in S_{j}}p\), where \(S_{j}\) is the set of points assigned to centroid \(c_{j}\); Step 3: Check for convergence if\(\max_{j}||c^{\prime}_{j}-c_{j}||<\epsilon\)then Break the loop (convergence reached); Step 4: Update centroids Update \(C=\{c^{\prime}_{1},c^{\prime}_{2},\ldots,c^{\prime}_{k}\}\); return\(C\) ;
```  
**Algorithm 1**Distributed K-Means in Apache Spark
## 2 Related Works
[27] employed a Diffie-Hellman key exchange algorithm using normal version; (b) using the ACK/NACK messages for delivery of packets to the base station; and (c) using Macro station to send a verification code to verify the reception of data and provide authentication. It was shown that the techniques were able to mitigate the Man-in-the-middle attack as well as the problem of key distribution. In [28], the authors implemented Elliptic Curve Cryptography to secure against blackhole attacks. The authors used the scheme using Adhoc on Demand Multipath Distance Vector as it is a more reliable scheme. The authors claim that packet delivery can be secured against attacks using a simulated environment. [PERSON] et al. [29] presented an algorithm with ECC and Elgamal and compared it with the ElGamal scheme to prove that usage of ECC with Elgamal is a better option. The comparison was only performed in terms of space and time. The comparison showed that their proposed algorithm performed better on small devices. [PERSON] et al. [30; 31] used a scheme based on Elgamal for securing the microgrid communication and ensured that it is helpful in the protection of nonrepudiation and replay attacks. [PERSON] et al. [32] also highlighted the issues that can occur in device-to-device communication, and it emphasizes the requirement of central control. It uses receive signal strength and channel state information to design the key, as well as an algorithm based on DH to perform key management and sharing. It has proved to be a good defense against man-in-the-middle attacks. In [33], the authors used a Deffie-Hellman and ECC scheme together to ensure that the key exchange is performed securely and is not vulnerable to known attacks. Being a lightweight, ECC can be adapted in lightweight communication. They have used this scheme for image conversion as images are considered memory intensive, and the result shows that the scheme can be adapted for transmission of images securely.