esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Performance Evaluation
### Performance Evaluation  
To evaluate the performance of the cyber attack modeling method and analysis system proposed herein, we constructed a small real-world network and performed a SYN (Synchronize) flooding attack. The scenario of a SYN flooding attack is shown in Figure 10. An attacker builds a C&C server and infects a web server. Additionally, terminals accessing a web server become BotNets. Using BotNets, a SYN flooding distributed denial-of-service (DDoS) attack is performed against the target network.  
Figure 10: SYN flooding scenario.  
Figure 9: Part of a large-scale network with 4444 nodes and 200 subnets.  
The network configuration consists of an attacker, C&C server, Webhard server, target server, and hosts infected with BotNet, as shown in Figure 10. The small real-world network is constructed based on virtual machines, as shown in Figure 11, and the hierarchical multi-stage scenario based network proposed herein is constructed as shown in Figure 12.  
Because of the attack, the total number of infected hosts is 11. Nine of them are BotNet hosts, the other two are C&C server and Webhard server. In the small real-world network, the total number of BotNet in the C&C server can be verified, as shown in Figure 13a. In the hierarchical multi-stage scenario network proposed herein, the total number of infected hosts shown in Figure 13b can be confirmed.  
Figure 11: Small real-world network based on virtual machines.  
Figure 12: Hierarchical multi-stage scenario-based network based on proposed analysis system.  
As shown, the DoS traffic transmitted from each BotNet to the target server is approximately 1000 bits/s, as shown in Figure 14.  
Based on the results of the SYN flooding attack, we can confirm that the attack pattern of the proposed system is similar to the real one. In addition, the number of infected hosts, and the amount of transmitted DoS traffic are almost the same as those of the small real-world network, as shown in Table 4. Through the performance evaluation results, we can quantitatively confirm that the proposed analysis system is almost identical to the real world system.  
Figure 14: _Cont._  
Figure 13: This is numbers of infected hosts: (**a**) total number of bots in real-world network of Figure 11; (**b**) total number of infected hosts in the proposed analysis system of Figure 12.
# Stability and Dynamics of Dark-Bright Soliton Bound States Away from the Integrable Limit
## 2 Setup and Prior Background
### Interactions of Symbiotic Matter Waves beyond the Integrable Limit
In panels (a)-(c) both the full and the approximate asymptotic, i.e., expanded to second order with respect to \(\exp(-2 Dx_{0})\), forms of the variationally obtained total interaction energy, \(E_{tot}=E_{dd}+E_{bb}+E_{db}\), are shown with solid  
green and dashed yellow lines, respectively. As it can be seen in all cases, the two results coincide at large distances as they should. Strikingly, in all three cases, a pronounced local energy maximum is identified. The identification of this extremum suggests, if we further take into account the effective negative mass of the DB soliton [7], the existence of an effective _stable_ (with respect to variations in \(x_{0}\)) fixed point, thus a bound state for the two OP dark-bright solitons. Such an anti-symmetric two-DB soliton bound state can indeed be identified also in the full Gross-Pitaevskii system (for the numerical methods used, we refer the reader to the following section). The predictive strength of the variational approximation is directly checked by comparing with the full numerical computation of the respective equilibria \(x_{0}\) on the level of Equations (2) and (3), and the outcome is illustrated in panel (d), showing good agreement. It is worth mentioning at this point that, in order to evaluate the energy functional, we need as input the width \(D\) and amplitude \(\eta\) occurring in Equations (8) and (9). These are obtained by numerically identifying the exact single-DB state at the given \(g_{12}\) and fitting to it the profile known from the integrable limit, i.e., Equations (6) and (7). There are thus two main sources of error of this scheme, namely: (i) the imperfect fit of the _tanh-sech_ profile (strictly valid only in the integrable limit) to the single DB soliton mode and (ii) the limited accuracy of the two-DB soliton ansatz of Equations (8) and (9), especially at small separations \(x_{0}\). For details regarding this and also a discussion of the case of in-phase bright solitons, we refer the interested reader to Ref. [50]. We note also that as \(g_{12}\) increases (while \(\mu\) and \(g_{22}\) are kept fixed), the norm \(N_{b}\) of the bright species is increasingly suppressed and eventually vanishes [50], beyond which point there is no bound two-soliton state anymore, since the presence of the bright component is a necessary ingredient for holding the dark solitons together [24; 37]. This suppression of the bright norm is compatible with the overall decrease of the total energies from panel (a) towards (b) and (c) in Figure 1.  
Having variationally predicted and numerically confirmed the existence of anti-symmetric stationary dark-bright soliton bound states for a large interval of values of the inter-species interaction parameter \(g_{12}\) (typically, we study \(0.75\leq g_{12}\leq 1.5\) in the present work), our main goal in the following is to address their stability and (where applicable) decay dynamics.
## 2 Materials and Methods
### Construction of Moisture Content Prediction Model
#### 2.3.1 Multiple Linear Regression Model  
MLR is a statistical model that establishes and analyzes the relationship between multiple independent variables and a dependent variable. It is an extension of linear regression analysis and can simultaneously consider the effects of multiple independent variables on the dependent variable [38; 39]. The objective of the model is to estimate the coefficients of the independent variables by minimizing the difference between the observed values and the predicted values of the model and to build a model that can explain the variation of the dependent variable. The model equation is as follows:  
\[\text{Corn moisture content}(\%)=\alpha+\sum\beta_{i}X_{i}+\varepsilon \tag{3}\]  
(\(\alpha\): intercept term; \(\beta_{i}\): regression coefficients; \(X_{i}\): observations of the dependent variables; \(\varepsilon\): random errors generated by the model).  
Meanwhile, the variance inflation factor (_VIF_) was used to detect the presence of multicollinearity in the Multiple Linear Regression model. The _VIF_ value for independent variable \(i\) was calculated as follows:  
\[VIF_{i}=\frac{1}{1-{R_{i}}^{2}} \tag{4}\]  
(\(R_{i}\): correlation coefficient of the variable \(i\) with the remaining independent variables).
## 2 Challenges for Modeling of Five-Axis Coordinate Measuring Systems
### Determination and Modeling of Other Sources of Errors
#### 2.3.3 Measured Workpiece's Form Errors Influence  
An interesting problem that occurs during measurement with five-axis measuring system is the accuracy loss in case of measurement of objects characterized by large form errors. The authors proposed an experiment to assess the impact of this factor. Two standards in the form of cylinders were designed with preliminarily determined form errors described using the relevant cosine functions. The first standard was characterized by lower form error (roundness) value equal to 0.5 mm, the form deviation of the second one was 1.6 mm. The diameter of both of them equaled 39 mm. They were manufactured using additive manufacturing technology. In the next step, both standards and a standard ring of similar diameter were calibrated according to calibrated workpiece method on  
Figure 3: Procedures used during probe hysteresis error determination. Procedure 1 was treated as a reference. In all cases the coordinate system was set as in Procedure 1.  
the highly accurate CMM machine. After the calibration process, standards and the reference ring were measured on a five-axis measuring system, using only rotational movements of head. The measurements were performed using the same strategy that was utilized during calibration. Figure 4 presents the mean difference between radial error for certain \(\alpha\) angle obtained during calibration and measurements on the five-axis system. The biggest differences were obtained for manufactured standards burdened with the largest form errors. As one possible cause of such accuracy loss, some simplifications of algorithms utilized in the metrological software may be taken. The predefined form errors cause the tip of stylus to hit the surface of the measured object in different points that are expected for generated measurement code. That means that the rotation angle in one of the probe head's axes reaches higher or lower values than in case of measurement of points generated for nominal features. Thus, the radial correction of the tip ball may be done in a different direction than it should be, which can introduce additional errors.
## 1 Introduction
### Problem Statement and Research Question  
Previous research [11; 12; 13] has leveraged machine learning (ML) algorithms, extreme learning machine (ELM), and support vector machine (SVM) to detect Sybil attacks. In online social networks (OSNs), ML-based detection methods are promising but remain under-researched. Hence, this study built upon the works of [PERSON] et al. [14] and other scholars, e.g., [15; 16; 17; 18], to delve into Sybil attacks within OSNs. Sybil attacks manifest across various network types, e.g., wireless sensor networks (WSNs), mobile ad hoc networks (MANETs), and online social networks (OSNs) [16]. WSNs consist of interconnected nodes equipped with sensors for data processing and analysis. MANETs, featuring mobile nodes without fixed infrastructure, find applications in vehicular networks. OSNs, encompassing platforms such as Facebook and Twitter, offer attackers opportunities to introduce malicious nodes and undermine the network's integrity [19]. The literature reports on the different patterns exhibited by Sybil attacks, such as direct and indirect attacks, stolen identities, or fabricated attacks [3; 20]. In direct attacks, malicious nodes mimic legitimate ones, directly communicating with authentic nodes. Indirect attacks involve manipulated communications between legitimate nodes, confusing while altering information flow. Stolen identity or fabricated attacks involve malicious nodes claiming legitimate credentials and sometimes replacing authentic nodes without being detected [21; 22; 23]. Amidst the escalating Sybil attacks within OSNs, scholars are studying user identity protection from rampant fake identities (bots) [21; 22]. Notably, networks can be exploited as propaganda tools if Sybil attacks persist; a paramount motivation of this study was to develop an innovative algorithm named \"SybilSocNet\", designed to identify and counteract Sybil nodes within OSNs to address this challenge. The proposed algorithm harnesses ML methodologies such as K-nearest neighbors (KNN), support vector machine (SVM), and random forest, to optimize detection accuracy by extracting useful information from a large amount of raw data.  
The research question in this case was as follows: How can the SybilSocNet algorithm, using machine learning techniques such as KNN, SVM, and random forest, effectively detect and mitigate Sybil attacks in online social networks (OSNs) by efficiently processing and analyzing large-scale raw data into informative matrices?
# Analyzing the Performance of WBAN Links during Physical Activity Using Real Multi-Band Sensor Nodes
## 2 Materials and Methods  
### Test Platform  
All the tests performed within this work were carried out with YetiMote nodes, a custom hardware platform developed at B105 Electronic Systems Lab [18]. This node is based on a high-performance, low-power STM32L4 microcontroller that runs at 48 MHz and is equipped with 512 KB of flash memory and 128 KB of RAM memory. It has several input/output interfaces--UART, SPI, I2C, USB, microSD slot--in order to support the connection of all kinds of sensors.  
For the purposes of this work, the most important feature of YetiMotes is a wireless interface module with transceivers and antennas in three different frequency bands. This shield has two STMicroelectronics SPIRIT1 transceivers and their corresponding RF adaptation hardware for the frequency bands centered in 433 MHz and 868 MHz, respectively. These two sub-1 GHz interfaces end in two connectors for external antennas. On the other hand, the 2.4 GHz wireless interface consists of a CC2500 transceiver from Texas Instruments, its corresponding RF adaptation stage and a built-in printed circuit board (PCB) antenna. Figure 1 shows a YetiMote node with the wireless shield in the foreground.  
YetiMotes include a custom operating system called YetiOS that is based on the well-known real-time system FreeRTOS. It is equipped with a layered network stack that is in charge of the sending and reception of packets and supports several network topologies. All layers of the stack are fully configurable, including all the wireless parameters from the physical layer that are accessible in the corresponding transceivers. Table 1 lists the values set for these physical wireless parameters in all the tests performed in this work.  
Figure 1: YetiMote node.  
All the experiments described during the remainder of the article have been carried out with two or more YeiMote nodes, where one of them acts as a receiver and the other(s) as sender(s). There is absolutely no difference in terms of hardware between a sender node and a receiver node. Out of all the above parameters, the only one that will be modified during tests is the output power of sending nodes.
## 3 Results and Discussion  
### Neural Network Structure  
Several trials have been performed to determine the neural network configuration. Table 3 presents some important parameters for the selected configurations. The decisions have been made based on two imperatives attributes which are: the number of nodes in hidden layers, and the choice of each layer activation function.  
Choosing each layer activation function is proven to be an important issue. Moreover, previous studies showed that multilayer network with linear output neurons and sigmoid hidden neurons are arbitrarily well-fitted to map multi-dimensional problems [36; 49]. Hence, in this study, the activation function of the hyperbolic tangent is implemented for the hidden layers, also the linear activation function is implemented for the output layer. There is an advantage, especially when using the Levenberg-Marquardt training approach, to use the hyperbolic tangent function. The fact that this function is derivable, gives the advantage for easier neural network weights tuning.  
The last important parameter, which must be studied to complete the structures of the neural networks, is the number of nodes in each hidden layer. Table 4 compares the best MSE results to various hidden layer's number of neurons for both NARX and DFF network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**NARX Model**} & \multicolumn{3}{c}{**DFF Model**} \\ \hline
**Number of Nodes/Neurons** & **MSE Train** & **MSE Test** & **Number of Nodes/Neurons** & **MSE Train** & **MSE Test** \\ \hline
2 & 0.0365 & 0.0465 & \(2\times 2\) & 1.5618 & 2.9642 \\
3 & 0.0131 & 0.0315 & \(3\times 3\) & 0.7764 & 0.8642 \\
4 & 0.00964 & 0.010065 & \(4\times 4\) & 0.5831 & 0.6945 \\
5 & 0.007589 & 0.006415 & \(4\times 5\) & 0.4816 & 0.4951 \\
6 & 0.010832 & 0.00921 & \(5\times 5\) & 0.4167 & 0.4283 \\
7 & 0.009412 & 0.007052 & \(5\times 6\) & 0.4576 & 0.4891 \\
8 & 0.008641 & 0.007731 & \(6\times 5\) & 0.4317 & 0.5172 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Best-obtained results vs.the hidden layer’number of neurons.
As shown in Table 4 the best-obtained structure of the DFF model is when having five neurons in each hidden layer. The MSE for both training and testing with this structure are 0.4167 and 0.4283, respectively. However, using the NARX model the results were much better. Result shows that when having five neurons in one hidden layer, the MSE for both testing and training are 0.006415 and 0.007589, respectively.
## 5 Performance Comparison and Validation
### Results and Discussion
#### 5.2.1 Lifetime Evaluation  
Figure 5 shows the change in the number of surviving nodes for each routing protocol with the network running time. For the high-speed railway monitoring network, the main task is to collect as much on-site information as possible to ensure the safety of railway operation, so we first focus on the life cycle of the network. Figure 4(a) shows that DQAAR has a longer running time than MTERR, MVECR, and AUMRP under the same conditions. Because MTERR only pays attention to the overall energy loss of the network and does not care about the energy balance of the network, the death time of each node occurs in about 1000 s. MTERR, MVECR, and AUMRP impose some constraints on the overall energy consumption balance of the network, but while prolonging the death time of the first node, they also cause a large area of low-energy nodes to die in the network around 1500 s.The DQAAR proposed in this paper delays the death time of the first node in the network to about 2000 s, and then there is no continuous death of large-scale nodes, but a relatively slow trend is maintained. This is because DQAAR offers dynamic rewards based on data aggregation while paying attention to the balanced use of network energy, and each node learns the best next-hop node. Efficient data aggregation paths greatly reduce the amount of data transmitted over the network, which greatly delays node death times. Figure 4(b) shows the FND and HND data of the four routing protocols, respectively. The FND and HND of the worst-performing MTERR are 985 and 2024, respectively, and the AUMRP data of FND are only 1565 and 2155, although it is effective. The node death time is delayed, but the process from the death of the first node to the death of half of the nodes is not very slow. The FND and HND of the DQAAR proposed in this paper are 1982 and 3016, respectively. It can be seen that DQAAR provides better answers in terms of balancing network energy consumption and improving network life.
# Neutralization of Toxic Malodorous Gases from Cattle Slurry
## 4 Discussion
Our studies, as well as the findings of other authors, indicate the possibility of using many types of microorganisms (_Bacillus, Pseudomonas, Alcaligenes, Enterococcus_) to reduce odors emitted from organic fertilizers. As stated in the literature, _Bacillus amyloidioefaciens_ strains effectively decreased the ammonia, hydrogen sulfide, and sulfur dioxide concentration from pig manure [48]. [PERSON] et al. [48] obtained a 43.5% decrease in ammonia emissions on the third day after the application of the preparation and a decrease of 81.6% after 7 days. Slightly better results were obtained in our studies, where the application of biopreparation led to a 77% reduction compared to the initial sample on Day 4 of the incubation and 94% after 7 days. In our results, the decrease in hydrogen sulfide emission was 95% after 4 days of deodorization, compared to 98.2% (4 days) in [PERSON]'s study. The authors concluded that odor neutralization may be related to the secretion of bacteriocins by bacteria. Similar experiments were conducted by [PERSON] et al. [49] using _Bacillus megaterium_, _Candida tropicalis_, and _Streptomyces griseus_. They conducted their research on farm animals and poultry. They reported that the use of the microbial consortium decomposed more than 80% of NH\({}_{3}\) and more than 65% of H\({}_{2}\)S. In turn, [PERSON] et al. [50] found that the use of the microbial preparation made of _Bacillus megaterium_, _Lactobacillus acidophilus_, and _Alcaligenes faecalis_ reduced the content of ammonia and hydrogen sulfide by 83.56% and 70.25%, respectively. Similar studies to our experiments were performed by [PERSON] et al. [51]. They isolated 52 microbial strains from pig manure and selected three strains for ammonia reduction among them. Their study found that NH\({}_{3}\) was completely removed after 48 h. Compared to our study, the authors observed a shorter time required for ammonia removal. This may be due to the use of biofoam on the surface of the slurry in their studies. In our results, the prepared microbial consortium caused a reduction of ammonia by 42.2% (R1-reduction) after 48 h of incubation. The literature review showed that microbial deodorization of manure gives high odor removal effects on the scale, even up to 90-99%. Therefore, further studies are needed to evaluate the impact of various environmental parameters such as changes in ambient temperature, slurry freshness, storage duration, and the intensity of airflow over the slurry on the deodorization process under real-world conditions on a semi-industrial scale. Based on the research findings, it will be possible to develop recommendations for the application of microbial preparation in litter-less cowsheds. In order to investigate different application methods of biopreparation to slurry, a dry form will also be developed in the future.
# Policy-Based Smart Contracts Management for IoT Privacy Preservation
## 4 Implementation and Validation
### Implementing the Blockchain Network
#### 4.2.1 Creating the Network  
The network consists of one organization providing healthcare services. A service care provider (a hospital, for instance) could define any number of peers to verify the patient's requests. Peers could be the doctor, the pharmacist, the manager, or any party required for the transaction. Upon organization creation, A Certificate Authority (CA) is also created to issue certificates to all network participants (peers, ordering services, clients, admins, and so on).  
These identities, which include a signing certificate and private key, allow network participants to communicate, authenticate, and, ultimately, transect. Those keys are provided by the Cryptogen fabric tool which are cryptographic materials using an x509 certificate. In Hyperledger Fabric, the cryptogen tool generates a set of certificates and keys for organizations, peers, orderers, and users, based on the configuration specified in a file typically named crypto-config.yaml. The fundamental architecture of the network is also contained in crypto-config.yaml. Any information sent to Fabric must be signed with a private key and confirmed with a corresponding public key.  
Furthermore, the Genesis block, which is the first block in any blockchain, and the ordering service that knows all entities joining the network are created.
## 4 Use Case and Simulation of the Proposed System
### PMIPv6/AAND
#### 4.4.4. PMIPv6/SAND--Handover Analysis  
The PMIPv6/SAND, the HOD is determined as follows in the Equation (5). There is no need for a pause in authentication, because the authentication of the registration takes place in the predictive mode after the MN connection.  
\[\text{HOD}_{\text{PMIPv6/SAND}}=\text{Layer 2 connection}+2(\text{ t}_{\text{PBA}}+\text{t}_{\text{PBU}})+2(\text{ t}_{\text{RS}}+\text{t}_{\text{RA}})+\text{TTD}_{\text{Data}} \tag{5}\]  
PMIPv6/SAND Handover Simulation Result  
Here, the MN begins with its present interface if_1 i.e., WLAN. Then the if_2 Wi-Max comes into the simulation at 11 th second, yet the MN hands over its sign to if_2 (Wi-Max) at 14.5 seconds. Then, the if_3 i.e., 3G, comes into the simulation at the 24.5 th second, however the MN hands over its sign to 3G at the 25.5 th second. Figure 16 portrays the handover diagram of PMIPv6/SAND.