esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Vibration and Post-Buckling Behaviors of Metal and FGM Pipes Transporting Heavy Crude Oil
## 5 Post-Buckling Regime: Nonlinear Equation Derivation  
FGM pipes with simple supports transporting heavy crude oil may become unstable due to divergence or buckling when the fluid velocity reaches a critical value [33]. To analyze their post-buckling response, the dynamic (time-dependent) effects in Equation (21) are omitted, allowing the equation to be reformulated using only its static terms, as follows:  
\[\left[\overline{EI}\left(1-\frac{m_{f}\Gamma^{2}}{K_{SF}\overline{GA}}+\frac{P _{0}}{K_{SF}\overline{GA}}\right)+\frac{\overline{EI}\ \overline{EA}}{2 LK_{SF}\overline{GA}}\int\limits_{0}^{L}w_{x,x}^{2}dx \right]w_{,xxx}+\left[m_{f}\Gamma^{2}-P_{0}-\frac{\overline{EA}}{2L}\int \limits_{0}^{L}w_{x,x}^{2}dx\right]w_{,xx}=0 \tag{23}\]  
In this regard, the integral \(\int\limits_{0}^{L}w_{x,x}^{2}dx\) in Equation (23), for a given mode shape of \(w(x)\), yields a constant value. To simplify the expression, we introduce the constants \(\beta_{1}\) and \(\beta_{2}\) as follows:  
\[\beta_{1}=\frac{\overline{EA}}{2 LK_{SF}\overline{GA}}\int\limits_{0}^{L}w_{,x} ^{2}dx;\ \beta_{2}=\frac{\overline{EA}}{2L}\int\limits_{0}^{L}w_{,x}^{2}dx \tag{24}\]  
Accordingly, Equation (23) can be rewritten as follows:  
\[w_{,xxxxx}+\xi^{2}w_{,xx}=0 \tag{25}\]  
where  
\[\xi^{2}=\frac{m_{f}\Gamma^{2}-P_{0}-\beta_{2}}{\overline{EI}\left(1-\frac{m_{f }\Gamma^{2}}{K_{SF}\overline{GA}}+\frac{P_{0}}{K_{SF}\overline{GA}}+\beta_{1 }\right)} \tag{26}\]Equation (26) was previously solved by [PERSON] and [PERSON] [34], who obtained the following solution:  
\[w(x)=\Lambda\sin(\lambda x) \tag{27}\]  
where \(\Lambda\) is an unknown constant. Based on Equations (14) and (27), we obtain the following:  
\[\sin\left(\lambda L\right)=0\ \rightarrow\ \lambda=\frac{m\pi}{L}\ (m=1,\ 2,\ 3,\ \ldots) \tag{28}\]  
Utilizing Equations (24) and (26), \(\Lambda\) can be determined as follows:  
\[\Lambda=\pm 2\sqrt{\frac{K_{SF}\overline{GA}\Big{(}m_{f}\Gamma^{2}-P_{0} \Big{)}+\xi^{2}\overline{EI}\Big{(}m_{f}\Gamma^{2}-P_{0}-K_{SF}\overline{GA} \Big{)}}{\xi^{2}\overline{EA}\big{(}\xi^{2}\overline{EI}+K_{SF}\overline{GA} \big{)}}} \tag{29}\]  
To derive the post-buckling configuration of the pipes made of FGM, we can insert Equations (28) and (29) into Equation (27), leading to the following closed-form solution:  
\[w(x)=\pm 2\sqrt{\frac{K_{SF}\overline{GA}\Big{(}m_{f}\Gamma^{2}-P_{0}\Big{)}+ \xi^{2}\overline{EI}\Big{(}m_{f}\Gamma^{2}-P_{0}-K_{SF}\overline{GA}\Big{)}} {\xi^{2}\overline{EA}\big{(}\xi^{2}\overline{EI}+K_{SF}\overline{GA}\big{)}} }\sin\Big{(}\frac{m\pi x}{L}\Big{)}} \tag{30}\]  
Therefore, critical flow velocity (CFV) for the pipes made of FGM is determined using Equation (30), as shown below:  
\[\Gamma_{cr}=\sqrt{\frac{P_{0}K_{SF}\overline{GA}+\big{(}\frac{m\pi}{L}\big{)} ^{2}\overline{EI}\big{(}P_{0}+K_{SF}\overline{GA}\big{)}}{m_{f}\Big{(}K_{SF} \overline{GA}+\big{(}\frac{m\pi}{L}\big{)}^{2}\overline{EI}\Big{)}}}} \tag{31}\]
# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 6 Conclusions  
The rapid development of VANET systems will lead to the greater usage of intelligent transportation systems. This type of network has attracted attention in recent years because of its enormous influence on improving traffic management systems and road safety. Many studies have been conducted to improve many elements of VANETs, including their coverage, protocols, and other relevant factors. Although VANET security is a major concern, the architecture of these networks appears to render appropriate and effective protection a real challenge.  
ML models play a crucial role in guaranteeing the security and dependability of network systems and applications because of their capacity to handle massive amounts of data and detect patterns and abnormalities that may lead to security breaches. Therefore, ML offers a potential security solution for VANET systems.  
In this paper, VANET vulnerabilities, attacks, and countermeasures are thoroughly discussed, with a focus on ML models that have recently been introduced to identify VANET attacks. Their achievements and drawbacks are thoroughly addressed. Accordingly, this study presents an effective ML model for detecting VANET attacks. This model is based on the RF classification model and the information gain feature selection method. It was trained, tested, and evaluated on an enhanced version of the CICIDS2017 dataset. The information gain method was used to select the features with the greatest influence on the prediction, and a random oversampling technique was applied to balance the dataset. As a result, a balanced dataset with information gain was developed, the BCICIDS2017-GI. The proposed model achieved outstanding performance compared with the other classification models investigated in this study. The proposed model achieved an accuracy rate of 99.8%, with acceptable classification errors. In terms of computational cost, the proposed classifier surpasses all other classifiers by requiring the least processing time; hence, it is more appropriate for VANETs. Our future work will focus on evaluating the proposed model by using other datasets.  
Conceptualization, M.A.E.; Formal analysis, M.A.E., A.A., Y.M., A.H.M., A.K. and M.B.; Investigation, M.A.E., A.A., Y.M., A.H.M., A.K. and M.B.; Methodology, M.A.E., A.A., Y.M., A.H.M., A.K. and M.B.; Resources, M.A.E., A.A., Y.M., A.H.M., A.K. and M.B.; Software, M.A.E., A.A., Y.M., A.H.M., A.K. and M.B.; Validation, M.A.E., A.A., Y.M., A.H.M., A.K. and M.B.; Visualization, M.A.E., A.A., Y.M., A.H.M., A.K., M.B. and M.A.E.A.; Writing--original draft, M.A.E., A.A., Y.M., A.H.M., A.K., M.B. and M.A.E.A.; Writing--review & editing, M.A.E., A.A., Y.M., A.H.M., A.K., M.B. and M.A.E.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Available upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Results
### Smart Mobility Impacts  
This section discusses the main innovations identified in the literature that are associated with smart mobility and what impacts these innovations will make to transportation. Understanding the broad impacts each of the innovations will have on the transportation system is important so that the flow on effects can be analyzed against each of the transport disadvantage dimensions. The six major smart mobility innovations identified in the literature are: (a) DRT; (b) shared mobility; (c) ITS; (d) alternative fuel vehicles; (e) AV; and (f) MaaS. ITS, alternative fuel vehicles, and AV represent direct technological advances that will affect vehicles and infrastructure. On the other hand, while technology is critical to the development of DRT, shared mobility, and MaaS, they are more associated with innovations to the way transportation services are provided to the community rather than a direct impact to the vehicles and infrastructure in the transport system. A description of each of these innovations and relevant literature is shown in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Innovation** & **Description** & **Reference** \\ \hline \multirow{8}{*}{DRT} & DRT provides a transportation options distinct from traditional fixed-route & \\  & services in that they utilize dynamic, semi-fixed, or fixed routes with users & \\  & able to pre-book based on travel needs and services operated on-demand. & \\ \cline{1-1}  & The main impacts on the transportation system relate to ability to provide & \\ \cline{1-1}  & greater coverage and flexibility. Although existing DRT services have been & \\ \cline{1-1}  & criticized for their inability to manage high demand and provide the & \\ \cline{1-1}  & required service coverage at an appropriate cost unless supported by other & \\ \cline{1-1}  & innovations including ITS, AV, and shared mobility. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Smart mobility innovations (source: authors). The innovations often overlap to optimize the potential positive impacts. DRT systems and AV enabled by ITS technology are often referred to as real-time or dynamic FTS [61] and connected AV (CAV), respectively [55; 62]. Shared AV (SAV) incorporates elements of shared mobility and AV [44], and FFM is essentially a combination of shared mobility, battery electric vehicles, and DRT [63]. MaaS forms an overarching platform in which each of these services can be bundled together [64]. A conceptual diagram is shown in Figure 3 to better understand the relationships between these innovations. This diagram is by no means exhaustive, for example free floating e-mobility incorporates elements of ITS, and, when offered as a bicycle service, it might not rely on electric powered engines. In addition, DRT are typically offered as shared mobility to improve efficiency and costs [65]. Nonetheless, the figure
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 2 VNE Problem Decomposition and Modeling
### Problem Description
#### 2.1.2 Configuring the Element Parameters  
The existing resources of the computational and networking SN elements can be summarized by a vector space \(\widehat{R}=\prod_{j=1}^{m}R_{j}\), which is constituted by the resource sets \(R1,\ldots,Rm\). Their capacity thresholds can be defined by the function \(rc:N_{s}\cup L_{s}\to\widehat{R}\). In the same sense, function \(rd:N^{i}\cup L^{i}\to\widehat{R}\) represents the thresholds of the demanded resources of a VN included in a VNR. The most commonly used elements defining the thresholds of node and link resources are CPU cycles and bandwidth (BW) units, respectively.
# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
## 4 Discussion  
Analyzing the behavior of cognitive data networks, especially wireless networks, is a great challenge. Several aspects must be considered regarding the environments where the networks are deployed: the number of devices (i.e., IoT devices [43; 44; 45]), the characteristics of the applications (i.e., smart environments [46; 47; 48]), and the transmission technologies [26; 49; 50], among others. The nonlinearity and complexity of data traffic flow in such networks, for example, are characterized by temporal and spatial correlation. Therefore, their study is complex and even more so if one wants to predict its behavior.  
In addition, in the case of spectrum usage, it is necessary to extract meaningful information that leads, as a result, to a set of massive and complex data that requires sophisticated and advanced algorithms for its analysis. Moreover, in data networks,learning models must allow sequential decisions to be made based on continuous feedback or prior knowledge using algorithms that can work with multiple objectives in conflict with the flexibility and speed to make observations within a changing environment and take actions accordingly.  
For this reason, summarizing the review works, most include the use of combined learning methods, highlighting deep reinforcement learning (DRL) and the convolutional neural network (CNN), for example, the multilayer perceptron (MLP) in conjunction with reinforcement learning (RL), MLP with the CNN itself, residual network with the recurrent neural network (RNN), RL and long-short-term memory LSTM, and double deep reinforcement learning, among others. However, even with this clarity, it is not an easy task to select the appropriate deep learning method for each communication objective within the network, linked to the great challenge that implies the input parameters selection and tuning of the model's hyper-parameters.  
Table 3 presents a summary of the previous challenges and the application of deep learning for addressing them.
## 4 Experiments
### Experiment Settings
#### 4.1.2 Hyperparameters and Experimental Environment  
Based on relevant research and experience, we selected the following parameter settings: The baseline models are trained by 100 epochs and save the one with the best valuation performance, and the optimizer is set as Adam [37] with a learning rate of 0.001 and weight decay of 0.0. Moreover, the batch size in our experiments is set to 128, and hidden layers in LSTM are set to 64. PESO and the baselines are implemented in the environment of Python 3.6.9 under the deep learning framework of PyTorch. We train the models on the server with the Ubuntu operating system with an NVIDIA GeForce RTX 3090 Ti GPU.  
In order to obtain optimal hyperparameters, such as the training epochs and the network layers, we conducted the following experiments. Considering time and computational consumption, we conducted the exploration experiments on hyperparameters within a limited circumstance. We learned the changing trend of training loss and testing loss during training in 100 epochs and try to find the optimal epochs. The details are shown in Figure 4, where we can learn that the losses decrease as the number of epochs increases. We chose to ultimately train 100 epochs. Meanwhile, we explored the impact of the number of LSTM layers in PESO. Table 1 shows the evaluation metrics of PESO under different layers. After comprehensive consideration, we adopted a five-layer LSTM in both encoder and decoder.
## 6 Research Trends and Open Research Issues  
Although a number of papers have been published in this area, still many research issues remain to be addressed. Figure 7 shows the number of research papers published over the last few years. Less than 15 papers were published in IEEE journals in 2011 and 2012.  
No clear standard exists and there have been several unclear proposals. Many areas need to be explored, such as low computational and energy efficient spectrum sensing, spectrum management, clustering, energy consumption, spectrum handoff, channel allocation, channel access, geo-location information sharing, self-topology generation, cross-layer optimization of protocol stacks, _etc._ In addition, many issues remain to be resolved, such as coexistence with other CR systems, legal issues to access incumbent channels, limit of interference with PUs, trans
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 4 Performance Metrics
### Jain's Fairness Index (\(\gamma_{\text{Jain}}\))  
In the literature, indexes were developed in order to assess the fairness of resource assignment in networks. A well-known fairness index was proposed by [PERSON] (2008), which yields a positive real value, which is \(k/n\) in case \(k\) out of \(n\) users get an equal allocation, whereas the other \(n-k\) users are not assigned any capacity. The minimum is \(1/n\) if only one user out of \(n\) gets an assignment at all, the maximum is \(1\) in the case all users get the same assignment:  
\[\gamma_{\text{Jain}}=\frac{(\sum_{[s,t]\in K}\varphi^{st})^{2}}{|K|\cdot\sum_{ [s,t]\in K}(\varphi^{st})^{2}}. \tag{29}\]
# Build-Launch-Consolidate Framework and Toolkit for Impact Analysis on Wireless Sensor Networks
## 4 Case Study: Analyzing Flooding Attack
### Simulation Environment  
We carried out the flooding attack in a simulated environment using Cooja and Contiki, leveraging our aforementioned framework tools to build the network and analyze our simulations. Our virtual simulation consists of two layouts (binary and grid), as discussed in Section 3, and two scenarios (one with an attacker and another without an attacker as a baseline scenario), making a total of four simulations. Each simulation consists of 35 nodes (34 sensors and one sink). Each sensor node records some observations and sends its supposed readings to the sink, which in a real application would analyze the different readings and act accordingly. The position of the attacker in central areas of each network, as illustrated in Figure 6, is neither directly adjacent to the root node nor at the edge of the network. Further details on the specifications of our setup are summarized in Table 3. After we had set up the environment, we ran all of our four scenarios using the launcher script.  
Figure 5: Consolidator process map.  
Afterward, we compared the performance of our network when attacked to our original baseline. This experiment aims to compare the effect of a treatment (applying flooding attack) while controlling one potential confounding variable (network topology).
# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 6 Simulation Results  
The suggested routing protocol's performance is assessed using the Network Simulators, discrete-event simulator. Each simulation runs for 300 s, and averages are calculated from multiple observations for analysis.  
The performance of the suggested routing protocol is evaluated using Network Simulators, a discrete-event simulator, with each simulation running for 300 s and averages calculated from multiple observations to ensure reliable analysis. UAVs have a transmission range of 550 m, a value chosen to represent a typical communication range for effective coverage. They use Constant Bit Rate (CBR) traffic with 512-byte packet sizes, selected for balancing efficient data transfer and manageable packet processing, and a 2 Mbps data transmission rate, reflecting common speeds in UAV networks. The simulation area is set at 800 \(\times\) 800 m to simulate a moderately sized operational environment, while UAV counts range from 100 to 200 to assess the protocol's scalability across different network densities. The assessment metrics include throughput, measured as the total size of data packets successfully delivered per unit time, and end-to-end latency, which describes the time taken for data packets to travel from source to destination. These initial values and metrics provide a comprehensive and realistic evaluation of the proposed routing protocol's performance in UAV network scenarios. Table 1 includes the parameters such as simulation time, UAV transmission range, traffic type, packet size, data transmission rate, simulation area, number of UAVs, and assessment metrics.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulation Time & 300 s \\ UAV Transmission Range & 550 m \\ Traffic Type & Constant Bit Rate (CBR) \\ Packet Size & 512 bytes \\ Data Transmission Rate & 2 Mbps \\ Simulation Area & 800 \(\times\) 800 m \\ Number of UAVs & 100–200 \\ Assessment Metrics & Throughput, End-to-End Latency \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.
# Anti-Biofilm Agents to Overcome _Pseudomonas aeruginosa Antibiotic Resistance_
## 2 The Biofilm Life Cycle
### Cell Attachment  
The initial attachment of _P. aeruginosa_ to a biotic or abiotic surface relies on its physicochemical properties (mechanical roughness, hydrophobicity) and other different environmental factors such as pH, temperature or nutrient levels. The flagellum plays a major role in the establishment of the first reversible van der Waals interactions by overcoming  
Figure 1: The biofilm life cycle (**A**) and related signaling pathways (**B**).  
the repulsive forces between the cell and the surface [26; 27]. Then, upon recognition of a surface, type-IV pili (T4P) activate the production of bis-(3',5')-cyclic dimeric guanosine monophosphate (c-di-GMP) that is a secondary messenger regulating biofilm production in several bacterial species [28]. According to environmental stimuli, diguanylate cyclase (DGCs) synthesize c-di-GMP or phosphodiesterase (PDEs) catalyze its degradation (Figure 2). The production of c-di-GMP is regulated by the two-component system GAC (Global activator of Antibiotic and Cyanide synthesis) composed of the regulator GacA and the transmembrane kinase GacS. GacA activation mediated by the phosphorylation of GacS stimulates the production of small RNAs RsmZ and RsmY. Those bind to the RsmA protein to inhibit its transcriptional activity and lead to the biosynthesis of DGC. The c-di-GMP signaling pathway is also in charge of flagellum expression. Increasing levels of c-di-GMP brake flagellum-based motility and allow the cell transition from a motile to a sessile state [29]. This phenotypic change includes modifications in the lipopolysaccharideacharide (LPS) composition, promoting cell-cell aggregation and cell surface adhesion. The LPS is composed of a polysaccharide region called O-antigen, an oligosaccharidesacharidic core and lipid A inserted in the outer membrane. _P. aeruginosa_ produces two types of O-side chains. The most common type termed \"A-band\" is electroneutral at physiological pH, whereas the \"B-band\" O-side chain contains electronegative sites [30]. The bacterium can thus tune the cell surface hydrophobicity (A-band) or hydrophilicity (B-band) in the function of the physicochemical properties of the external medium [31]. Finally, high c-di-GMP levels trigger the production of extracellular matrix components, mainly exopolysaccharideacharides which will maintain the biofilm's irreversible adhesion to the surface.
## 4 Experimental Results
### Accuracy of Non-Compliant Group Localization  
This evaluation metric was used to measure the accuracy of localization based on comparison between the ground truth location of the non-compliant group and the location estimated using our method. Prior to the experiments, ground truth locations were manually marked on the 2D map of the ground plane based on the Cartesian coordinate system used by the robot for navigation and localization. Participants were planted at those ground truth locations in order to test the accuracy of our system with respect to the localization of non-compliant groups. The plot of the ground truths as green circles and estimated locations of non-compliant groups as orange circles is shown in Figure 7.  
The above plot shows the non-compliant groups being localized with respect to the Cartesian coordinate system fixed to the robot. The maximum error observed between the ground truth and the estimated location was 0.24 m and the average error observed between them was approximately equal to 0.12 m. The detection and localization of the non-compliant groups during two different experiments performed with the proposed system and navigation of the robot to these groups are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multirow{2}{*}{\begin{tabular}{c} **Total Experiments** \\ **(Violations Made)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Number of Violations Detected** \\ **Enforcements** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Enforcements** \\ \end{tabular} } \\ \hline Without Group & \multirow{2}{*}{19} & Single Group at & Multiple Groups \\ Re-identification & & a Time & at a Time \\ \cline{3-4}  & & \(11\) & \(6\) \\ \hline With Group & \multirow{2}{*}{9} & \multirow{2}{*}{8} & \multirow{2}{*}{8} \\ Re-identification & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall performance of proposed system with respect to different configurations.  
Figure 7: Localization of non-compliant groups.