esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Re-Visiting Ventilation Factor  
It was observed during the experiments that the post-flashover external flow was variable in its behaviour based on the size of the horizontal opening. For horizontal openings with total size of up to 0.04 m\({}^{2}\), the external flow was from both the horizontal and vertical openings, while for horizontal openings above 0.04 m\({}^{2}\), the external flow was totally through the horizontal opening. This was obvious from the radiative heat flux at 0.3 m from the door; for cases with horizontal openings larger than 0.04 m\({}^{2}\), the radiative heat flux decreased by around 60-80%. This behaviour changes our understanding of the flow field of a typical post-flashover under-ventilated compartment fire, where some criteria vanished (e.g., the gas layer temperature for flashover and the neutral plane at the vertical opening). Therefore, the current empirical factor that describes the flow field in post-flashover (e.g., the ventilation factor) also does not fit these conditions. As discussed in the introduction, the ventilation factor was updated by [PERSON] et al. [31] to fit for compartment fires with both vertical and horizontal openings. The main assumptions for this method are that there is only one vertical and one horizontal opening and that the hot gases leave the compartment only through the horizontal opening. These assumptions do not cover the cases in this study where there are more than one horizontal opening and the hot gases leave through all openings (horizontal and vertical), depending on the size of the horizontal opening.  
Based on that, in this study the experimental compartment fires were used to update an empirical ventilation factor for such scenarios. The updated ventilation factor \(\left[v_{f}\right]_{hz}\) was then coupled well with the mass of inlet air and fits in the following equation (e.g., Figure 22):  
\[\dot{m}_{in}=0.2764\left[v_{f}\right]_{hz}-0.018 \tag{4}\]  
\[\left[v_{f}\right]_{hz}=\left(A_{v}+A_{hz}\right)\sqrt{h_{v}} \tag{5}\]  
where, \(\dot{m}_{in}\) is the inlet fresh air, \(A_{v}\) is the total area of the vertical openings, \(A_{hz}\) is the total area of the horizontal openings, and \(h_{v}\) is the vertical opening height.  
This new form of ventilation factor still needs to be tested using further research; this form was conducted to be general and could cover most cases with both horizontal and vertical opening conditions.
## 2 System Model and Problem Formulation
### Energy Consumption Model  
The energy consumption of the UAV mainly includes propulsion energy and communication energy [28]. The propulsion energy is to keep the UAV hovering and support the flight movement. The communication energy consumption is mainly caused by data transmission, signal processing, the communication circuit, etc. The order of magnitude of the propulsion energy is much larger than that of the communication energy. Hence, in this paper, we mainly considered the propulsion energy consumption and set the transmitpower of the UAVs \(P_{u,0,k}\) to be a constant. Then, the communication energy caused by the data transmission from the UAVs to the OBS is \(E_{u}^{\rm{cm}}=\sum_{k=1}^{K}\alpha_{u,0,k}P_{u,0,k}\delta\).  
The propulsion power of UAV \(u\) mainly depends on the flight velocity, given by  
\[P_{u}(V_{u,k})=P_{\rm{o}}\Bigg{(}1+\frac{3V_{u,k}^{2}}{U_{\rm{tip}}^{2}}\Bigg{)} +P_{i}\Bigg{(}\sqrt{1+\frac{V_{u,k}^{4}}{4v_{\rm{o}}^{4}}}-\frac{V_{u,k}^{2}}{2 v_{\rm{o}}^{2}}\Bigg{)}^{1/2}+\frac{1}{2}d_{\rm{o}}\rho lfV_{u,k}^{3}, \tag{17}\]  
where \(U_{\rm{tip}}\) denotes the tip speed of the rotor blade, \(v_{\rm{o}}\) is the mean rotor-induced velocity, \(d_{\rm{o}}\) is the fuselage drag ratio, \(P_{\rm{o}}\) and \(P_{i}\) are the blade profile power and induced power, respectively, and \(\rho\), \(l\), and \(f\) denote the air density, rotor solidity, and blade angular velocity, respectively. The propulsion energy consumption of UAV \(u\) can be expressed as  
\[E_{u}^{\rm{pr}}=\sum_{k=1}^{K}P(V_{u,k})\delta. \tag{18}\]  
Then, the total energy consumption of UAV \(u\) is given by \(E_{u}^{\rm{total}}=E_{u}^{\rm{cm}}+E_{u}^{\rm{pr}}\), where \(E_{u}^{\rm{total}}\leq\bar{E}_{u}\) and \(\bar{E}_{u}\) is the energy threshold of UAV \(u\).  
The energy consumption of the buoys is mainly generated by data transmission, expressed as \(E_{m}=\sum_{u=1}^{U}\sum_{k=1}^{K}\alpha_{u,m,k}P_{u,m,k}\delta\), where \(E_{m}\leq\bar{E}_{m}\), and \(\bar{E}_{m}\) is the energy threshold of buoy \(m\).
## 2 Related Work
### Depth-Wise Separable Convolution and Standard Convolution  
Depth-wise separable convolution (DSC) [40] is composed of depth-wise convolution (DWC) and pointwise convolution (PWC). Among them, DWC operates independently on each channel of the input image, effectively extracting spatial characteristics along each dimension, with one convolution kernel responsible for one channel, combining all output feature maps to generate the final result. This operation greatly reduces the number of parameters compared with standard convolution. PWC is actually 1 \(\times\) 1 standard convolution, which has two functions including the freedom to change the number of output channels and the fusion of feature map channels after DWC. The operation of PWC has a less computationally intensive nature, which makes depth-wise separable convolution possess significant advantages in lightweight models and environments with limited computing resources.  
Figure 1 is a contrast diagram of standard convolution and depth-wise separable convolution. Compared with standard convolution, DSC can be decomposed into two steps. We assume the input image size is \(D_{I}\times D_{I}\times k\), where \(D_{I}\) is the height and width of the input image, and \(k\) represents the number of channels of the image, ignoring other convolution parameters. Given a kernel of size \(D_{K}\times D_{K}\) and a number of kernels (\(n\)), we can obtain an output feature map of \(D_{O}\times D_{O}\times k\) (\(D_{O}\) is the final output image size). Respectively, the computational complexity of the standard convolution and DSC is as follows:  
\[P_{1}= D_{O}\times D_{O}\times D_{K}\times D_{K}\times k\times n \tag{1}\] \[P_{2}= D_{O}\times D_{O}\times D_{K}\times D_{K}\times k+k\times n\times D _{O}\times D_{O} \tag{2}\]  
From Equation (2), the first part is the calculational complexity of depth-wise convolution, and the second part refers to pointwise convolution. Relative to standard convolution, the ratio of the computational complexity of DSC to standard convolution is shown below:  
\[R=\frac{P_{2}}{P_{1}}=\frac{1}{n}+\frac{1}{{D_{K}}^{2}} \tag{3}\]  
It is evident that the parameters and computations involved in depth-wise separable convolution are merely a fraction, specifically \(R\) times, of those in standard convolution, significantly enhancing model training speed and reducing computational cost. Therefore, instead of full 3D convolutions, we used mixed 2D and 3D depth-wise separable convolutions, offering computational efficiency.
## 3 Mathematical and Problem Formulations
### Power System Modeling with UPFCs and PSSs
#### 3.1.1 Generator Modeling  
The analysis of the power system under consideration in this study considers the dynamics of the stator, field, and damper windings. The quantities associated with the stator and rotor are expressed using the two-axis reference frames, also known as the two-axis d-q frames. The system equations for this power system are provided in references [4; 8; 15].  
\[V_{d}=R_{s}i_{d}+\frac{d}{dt}\phi_{q}-\omega_{R}\phi_{q} \tag{1}\]  
\[V_{q}=R_{s}i_{q}+\frac{d}{dt}\phi_{q}+\omega_{R}\phi_{d} \tag{2}\]  
\[V^{\prime}_{fd}=R^{\prime}_{fd}i^{\prime}_{fd}+\frac{d}{dt}\phi^{\prime}_{fd} \tag{3}\]  
\[V^{\prime}_{kd}=R^{\prime}_{kd}i^{\prime}_{kd}+\frac{d}{dt}\phi^{\prime}_{kd} \tag{4}\]  
\[V^{\prime}_{kq1}=R^{\prime}_{kq1}i^{\prime}_{kq1}+\frac{d}{dt}\phi^{\prime}_{ kq1} \tag{5}\]  
\[V^{\prime}_{kq2}=R^{\prime}_{kq2}i^{\prime}_{kq2}+\frac{d}{dt}\phi^{\prime}_{ kq2} \tag{6}\]  
where:  
\[\phi_{d}=L_{d}i_{d}+L_{md}(i_{fd}+i_{kd}),\hskip 56.905512 pt\phi_{q}=L_{q}i_{q }+L_{mq}+i^{\prime}_{kq}\]  
\[\phi^{\prime}_{fd}=L^{\prime}_{fd}i^{\prime}_{d}+L_{md}(i_{d}+i^{\prime}_{kd}),\hskip 56.905512 pt\phi^{\prime}_{kd}=L^{\prime}_{kd}i^{\prime}_{kd}+L_{md}(i_{ d}+i^{\prime}_{fd})\]  
\[\phi^{\prime}_{kq1}=L^{\prime}_{kq1}i^{\prime}_{kq1}+L^{\prime}_{mq}i^{\prime }_{q},\hskip 56.905512 pt\phi^{\prime}_{kq2}=L^{\prime}_{kq2}i^{\prime}_{kq2}+L_{mq }i_{q}\]  
The equations include different subscripts to represent various quantities, such as \(d\) for d-axis, \(q\) for q-axis, \(R\) for rotor, \(S\) for stator, \(f\) for field, and \(k\) for damper winding. Additionally, \(l\) represents leakage, and \(m\) represents magnetizing inductance. In addition to these equations, the following mechanical equations are also relevant to this system:  
\[\frac{d}{dt}\omega_{r}=\frac{1}{J}(P_{e}-F\omega_{r}-P_{m}) \tag{7}\]  
\[\frac{d}{dt}\theta=\omega_{r} \tag{8}\]  
where the rotor angular velocity is denoted by \(\omega_{r}\), while \(\theta\) represents the angular position. The electrical power is referred to as \((p_{e})\), while \((p_{m})\) stands for mechanical power. On the other hand, \((J)\) is the inertia of the rotor, while \((F)\) represents its friction. These parameters are crucial in analyzing and optimizing the performance of a power system under different operating conditions.  
The symbols used in Equations (1)-(8) are defined as follows:  
\(V_{d}\): d-axis voltage of the generator;  
\(V_{q}\): q-axis voltage of the generator;  
\(R_{s}\): equivalent resistance of the stator in the generator;  
\(i_{d}\): d-axis current of the stator in the generator;  
\(i_{q}\): q-axis current of the stator in the generator;  
\(\phi_{d}\): d-axis magnetic flux of the stator in the generator;  
\(\phi_{q}\): q-axis magnetic flux of the stator in the generator;  
\(\omega_{r}\): rotor angular velocity;  
\(V^{\prime}_{fd}\): derivative d-axis voltage of the generator;\(V^{\prime}_{fq}\): derivative q-axis voltage of the generator;
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### ETX-Based Solutions
#### 4.2.3 A Congestion Avoidance Multipath Routing Protocol (CA-RPL)  
Recall that RPL is a single path routing protocol where packets are transmitted by a node to a preferred parent and upon the failure of the existing path, the network selects new links to make a new topology, which creates unnecessary cost. RPL works on a single routing decision metric, hence, during network overload, large data traffic causes significant packet loss and latency due to network congestion. Therefore, the authors [63] proposed a Congestion Avoidance multipath routing protocol (CA-RPL) which minimizes the average delay towards DAG root. Each path weight is computed by metrics rank, ETX, the number of received packets, link reliability, the time factor, and load balance. Based on Contiki MAC radio duty and cycling protocol, the authors proposed a minimized delay metric where the node learns the wake-up phase of its parents and packets are sent to the first awaken parent. Therefore, it minimizes the time delay between DOAG root and DODAG nodes as it reduces the time the node takes to wait until the receiver wakes up. During network building, the number of packets received in a period of time is recorded by each node.  
Subsequently, the node broadcasts its DIO message where fields like REC, RANK value, ETX, cycle time (CT), \(DELAY_{R}OOT\) values, and wake-up phase are included. Upon receiving the DIO message by the child node from the parent node, it extracts REC value, RANK value, ETX, and then calculates the \(DELAY_{R}OOT\) the minimized delay metric. Following, the child node computes the path weight from the parent node to the root node. The distinction of DIO messages from different parents is done by the child node by computing each path weight, ranks the parent nodes according to their weights, and a link table is formed in which the best two parent nodes are selected. As a result, the child node sends a DAO message to its selected parents by forming a link to the DAG root. The authors carry out a series of simulations: run-time 6 min, Interpacket interval is kept to 4 s. During the short inter-packet interval, the Packet Reception Ratio (PRN) in CA-RPL increases by 50% as compared to basic RPL, on the other hand, during network congestion when the network size is big, the PRN of CA-RPL increases by 20% compared to basic RPL. The packet loss rate reduces to 25% in CA-RPL as compared to basic RPL. Similarly, latency is less in CA-RPL as compared to RPL since, in CA-RPL, nodes avoid congestion links while sending packets and consider time factors for choosing the first-awake parent to receive packets.
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 3 Affordable Broadband for Rural Communities
### Training and Human Resource Development  
The sustainability issue is raised mainly due to the lack of skilled human resources to maintain and operate network systems in order to provide broadband services in rural areas. Similarly, the lack of knowledge of consumers about access and use of the internet, including other factors like socioeconomic and demographic factors, are regarded as the challenges of sustainable broadband services for rural communities. Hence, public and private stakeholders in the rural communities are to be trained through consumer education with training initiatives and a broadband affordability program, including other incentives to attract people to use the services [37].
# Formal Modeling and Improvement in the Random Path Routing Network Scheme Using Colored Petri Nets
## 2 Related Work
### Source-Location Privacy Addressing Scheme
#### 2.2.3 Dynamic Fake Source Routing  
Dynamic fake source routing is also a distributed solution of fake source routing called, phantom source routing [16]. There are many paths available for the source node to send data to the sink node, and the data can be sent to the sink node from every possible path. Source-location privacy (SLP) is essential in WSNs as, in most cases, the source node can be exposed to diversity [17]. Fake random path routing provides a significant source of location privacy, but it is too expensive computationally [18]. The information sent from node to sink is exposed to the cyber city and the source's location, which needs source-location privacy [19]. One of the possible solutions for source-location privacy is a random path routing protocol [19].  
This research paper proposes an improvement to the random path routing scheme to remove its deficiencies by adding the novelty factor of distance calculation of paths. Furthermore, a Petri net-based formal modeling of the improved routing scheme is proposed, which leads to verifying and validating our proposed routing scheme CRPR. The proposed technique is based on the strategy that the random selection of the path will be made from the top three shortest paths. Most research papers have focused on source-location privacy and ignored the computational cost [20]. While working with source-location privacy, all the nodes in geographically different locations have limited memory, energy, and computational capacity [21]. So, establishing an optimal tradeoff between source-location privacy and computational cost has made the system more secure and efficient. Many protocols have been proposed to address SPL, and one of the fundamental protocols is random path routing (RPR). RPR is used in the adversary to protect tokens from malicious attackers [16]. Although RPR ensures SLP, it becomes costly when it starts sending tokens along the longest path each time.
# Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods
## 6 Results
### Analyses of Feature Importance
The XGBoost model with selected features demonstrated excellent performance on the training data, with high accuracy, recall, precision, F-1 score, and AUC score and low balanced error rate (Table 9). However, the model's performance on the testing data was slightly lower, suggesting that it may have some difficulty in generalizing to new, unseen data. Further model refinement and evaluation may be necessary to improve its generalization capabilities. The techniques that showed signs of overfitting were Naive Bayes, ANN, XGBoost, Random Forest, CatBoost, and LightGBM. These models showed high performance on the training data, but significantly lower performance on the test data, indicating that they learned specific patterns in the training data that did not generalize well to the new data.
###### Abstract
And also, they proposed a session key agreement for secure transmitting information. However, we figure out that the proposed protocol of [PERSON] et al. is vulnerable to side channel attacks of TPD and cannot defeat various attacks, including session key disclosure and impersonation attacks. Their protocol is also unable to provide secure mutual authentication and privacy-preserving. This paper suggests a secure key agreement and authentication protocol for message confirmation in the VCC environment in order to overcome their security flaws. We design the protocol to use only the hash function and XOR operation, and assume that realistic OBUs can be deployed realistic environments. We also assume that an attacker can perform side channel attacks on TPDs to obtain secret values stored in TPDs. Consequently, our proposed protocol does not strongly rely on TPDs; instead, it uses only OBUs. Furthermore, we propose a key agreement protocol for secure data transmission. We analyze the security aspects of our proposed protocol using Real-Or-Random (ROR) model and the Automated Validation of Internet Security Protocols and Applications (AVISPA) software for the formal analysis. This paper also compares the computation cost and security features with [4] and previous similar protocols. Finally, this paper demonstrates that the proposed protocol is able to be deployed in a real VANET. The rest of this paper is as follows: Section 2 reviews related works and presents the network model, threat model and notations used in this paper. At Section 3, we review the [PERSON] et al. 's protocol. We cryptanalyze its security flaws in Section 4. At Sections 5 and 6, we propose a secure key agreement and authentication protocol for VCC environment in VANET and perform informal and formal security analysis. We use ROR model, AVISPA simulation, and informal analysis for verification. Subsequently, we compare the security properties and computational cost with related previous researches in Section 7. Finally, in Section 8, we present our conclusions with the results of the proposed protocol.
## 2 Materials and Methods  
In this section, we depict the experimental setup and main parameters of five V-shaped notches in an ASTM-A36 steel pipe.  
Figure 1 shows an ASTM-A36 steel pipe (280 mm length, 89 mm outer diameter and 5.45 mm thickness) that has five different V-shaped notches on its outer surface. The dimensions of the five V-shaped notches are shown in Table 1. The first notch has minimum values of 1000 \(\upmu\)m width and 500 \(\upmu\)m depth. The fifth notch however has maximum dimensions of 3500 \(\upmu\)m width and 2500 \(\upmu\)m depth.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**V-Notch Defect** & **Width (\(\upmu\)m)** & **Depth (\(\upmu\)m)** \\ \hline S1 & 1000.0 & 500.0 \\ S2 & 1500.0 & 1000.0 \\ S3 & 2000.0 & 1500.0 \\ S4 & 3000.0 & 2000.0 \\ S5 & 3500.0 & 2500.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of five V-shaped notches on the surface of an ASTM-A36 steel pipe.  
Figure 2 depicts the experimental setup to measure the MMM signals around five small V-shaped notches on the outer surface of an ASTM-A36 steel pipe. This system contains a rotational mechanism; a (2 mm \(\times\) 2 mm \(\times\) 0.85 mm) commercial three-axis magnetoresistive sensor MAG3110 (Austin, Texas, USA, Frerescale semiconductor) of high sensitivity (0.1 \(\upmu\)T), an Arduino mega 2560 (Santiago, Chile, Arduino) and data processing through an Arduino board. The rotational mechanism uses a motor with encoder (12 VDC precision Faulhaber model 2342L012 CR, Clearwater, Florida, USA) that generates a rotational motion of 2 rpm. In addition, the mechanical structure of the rotational mechanism is fabricated with non-magnetic materials, such as nylamide and aluminum. Thus, the residual magnetic field of the pipe is not affected by the rotational mechanism. The steel pipe is collocated in the rotational mechanism, which is supplied with a DC power supply (B&K Precision 1761, Yorba linda, California, USA). The magnetoresistive sensor employs an I\({}^{2}\)C (inter-integrated circuit protocol) interface to communicate with Arduino mega. The distance between the magnetoresistive sensor and the outer surface of each steel pipe was kept constant at 2 mm. This distance must have a minimum value to ensure a better detection of the magnetic signals around the defects. This is due to the fact that the magnitudes of these signals significantly decrease with an increase in the distance with respect to the surface of the steel pipe. Thus, the MMM method is most sensitive for monitoring small variations of the magnetic fields close to defects on the steel pipe. The measured MMM signals around the five V-shaped notches in the steel pipe are processed using a laptop with MATLAB software. The proposed system has a simple operation that can measure tangential and normal MMM signals of the V-shaped notches on the surface of an ASTM-A36 steel pipe.  
Figure 2: Measurement system of tangential and normal MMM signals around five different V-notch defects on the outer surface of an ASTM-A36 steel pipe.
## 4 Results and Discussion
### Uncertainty Analysis  
Although a successive-frequency FWI shows a high accuracy in quantitative defect evaluation in a complex structure, an accurate or reliable initial model is very important for FWI. In previous studies, the exact wave speed was applied in the initial model for the inversion process. However, accurate material properties are not always available and might vary with the practical environments. Therefore, an uncertainty analysis should be conducted to assess the robustness of the proposed method. Here, two initial models with a \(-2\%\) and \(+2\%\) error for the velocity of the turbine blade were considered. Without a loss of generality, an internal defect with a diameter of 4 mm, slightly larger than the half wavelength, was introduced for FWI.  
The tomograph results are shown in Figure 16a,c. In both cases, this defect could be accurately reconstructed. Compared with Figure 9 in Section 4.1, the reconstruction results showed more background noise. From the comparison of the vertical and horizontal profiles, as shown in Figure 16b,d, the RMS error of the FWHM was 5.42% for the \(-2\%\) error in the initial model and 8.23% for the \(+2\%\) error in the initial model. Compared with the relative error, which was smaller than 2% in Section 4.1, the inaccuracy of the initial model increased the relative error for the inversion. Vice versa, accurate parameters for the initial model could improve the final result. Nevertheless, the overall error was smaller  
Figure 15: (**a**) FWI tomograph of three circular holes and a notch defect. Cross section (**b**) through the incline notch along the width direction, (**c**) through #1 and #2 defects along the horizontal direction and (**d**) through #3 defect along the horizontal direction. The positions are depicted in (**a**) with red dashed lines.  
than 10% and it was acceptable for the practical application in NDT even with a slight variation in the initial model.