esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Example Simulation and Analysis
### Algorithm Testing  
To verify the superiority and effectiveness of the improved algorithm, assuming that the actual changes of wind power, photovoltaic power generation, and load are consistent with the forecast. Then the original bat algorithm and the improved bat algorithm are used to solve the day-ahead scheduling model, and the program is independently simulated 30 times. It is important to note that the depth peak state of thermal power unit and demand response is not considered in this power system. The average value and standard deviation of the total system operating cost, and simulation time are shown in Table 3.  
According to Table 3, the convergence accuracy of the improved bat algorithm is obviously better than the original algorithm. Incorporating genetic characteristics of genetic algorithms increases the diversity of bat populations, and it can effectively avoid local optimization in the high-dimensional situation and get a global optimum solution quickly.
## 3 Proposed System Model  
### Physical Infrastructure Model  
The most important aspect of network slicing is the physical infrastructure. The Watts-Strogatz [32] small-world network model is best suited for a network with a fixed number of nodes. Small-world networks feature sub-networks with connections between any two nodes that have a high clustering coefficient, allowing for many pathways between nodes. This network model is built in two phases, which are as follows:  
**Step 1**: Create an undirected graph with \(N\) nodes linked to \(M\) nearby nodes, assuming \(M/2\) on either side of nodes. Any edge (\(i\), \(j\)) might be created in a network of nodes (\(0,1,2,3.....N-1\)) only if it satisfies the following condition:  
\[0<|i-j|mod(N-1-\frac{K}{2})\leq\frac{K}{2} \tag{1}\]  
**Step 2**: Rewire the probability \(\beta\) for each edge (\(i,j\ modN\)) with \(i<j\leq i+\frac{K}{2}\). This is accomplished by substituting (\(i,jmodN\)) for (\(i,k\)), where \(k\) is evenly picked at random from all nodes. The value of \(\beta\) is considered as any number between 0 and 1. The foregoing two procedures result in the creation of an undirected physical network with \(N\) nodes and \(NK/2\) connections, \(G_{p}=(N_{p},E_{p})\), where \(N_{p}\) indicates the set of nodes and \(E_{p}\) denotes the set of linkages in the physical structure. As with earlier research, this approach evaluates the node capacity and security power level for each node. The capacity of a node is determined by the number of CPUs that are available. The total number of CPUs available in the \(i^{th}\) node is defined as \(CPU_{available,i}\). The security of the \(i^{th}\) physical node is denoted by \(SPL_{i}\), which represents the highest level of security that a physical node may provide to the NSR node. The primary parameter for connections linking nodes \(i\) and \(j\) is the link's bandwidth, \(BW_{available,i}\). The lengths of the various connections are supplied in order to discover the shortest route between any two nodes. For example, the variable \(L_{i,j}\) stores the length of the line between the nodes \(i\) and \(j\).
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 3 Preliminaries and Model Definitions
### Bilinear Pairing  
Let \(\mathbb{G}_{1}\), \(\mathbb{G}_{2}\) and \(\mathbb{G}_{S}\) be multiplicative cyclic groups with the prime order \(\mathcal{P}\). A map function \(\hat{\varrho}:\mathbb{G}_{1}\times\mathbb{G}_{2}\rightarrow\mathbb{G}_{S}\) is defined as the bilinear pairing if the following three properties is satisfied:  
1. [leftmargin=1.5 cm]
2. Bilinearity: \(\forall g_{1}\in\mathbb{G}_{1}\), \(\forall g_{2}\in\mathbb{G}_{2}\), \(\forall a,b\in\mathbb{Z}\), there is \(\hat{\varrho}(g_{1}^{a},g_{2}^{b})=\hat{\varrho}(g_{1},g_{2})^{ab}\).
3. Non-degeneracy: \(\exists g_{1}\in\mathbb{G}_{1}\), \(\exists g_{2}\in\mathbb{G}_{2}\), there is \(\hat{\varrho}(g_{1},g_{2})\
eq 1\).
4. Computability: \(\forall g_{1}\in\mathbb{G}_{1}\), \(\forall g_{2}\in\mathbb{G}_{2}\), there exists an efficient algorithm so that \(\hat{\varrho}(g_{1},g_{2})\) can be calculated.  
**Definition 1** (Decisional Bilinear Diffie-Hellman (DBDH) Problem).: _Given a tuple \((g,g^{a},g^{b},g^{c},Z)\) for \(a,b,c\in\mathbb{Z}_{P}^{*}\), output \(1\) if \(Z=\hat{\varrho}(g,g)^{abc}\) and \(0\) otherwise. \(\mathcal{A}\) is defined as a probabilistic algorithm. Hence the advantage in solving the DBDH problem is defined as:_  
\[Adv_{\mathcal{A}}^{DBDH}=\left|\Pr\left[\mathcal{A}(g,g^{a},g^{b},g^{c},\hat{ \varrho}(g,g)^{abc})=1\right]-\Pr\left[\mathcal{A}(g,g^{a},g^{b},g^{c},Z)=1 \right]\right|,\]  
_where \(Z\in\mathbb{G}_{\mathcal{S}}\) and \(g\) is a random generator in \(\mathbb{G}_{1}\). If all probabilistic polynomial-time (PPT) algorithms have the negligible advantage in solving the DBDH problem, the DBDH assumption holds in the related bilinear map \((\mathcal{P},\mathbb{G}_{1},\mathbb{G}_{\mathcal{S}},\hat{\varrho})\)._
# Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation
## 4 Experiments
### Quantitative Results
The best dice score (0.8389) was achieved when 10,000 Images (5000 Camera catheter + 5000 Synthetic catheter) were used to train model compared to the model that was only trained with 10,000 Synthetic catheter (see Figure 5(a)), the model trained with this split (50-50) achieved better accuracy, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Training Images** & **Dice Score** \\ \hline \multirow{2}{*}{UNet [26]} & 5000 Camera catheter + 5000 Synthetic catheter & 0.8389 \\  & 140 labeled images + 5000 Camera catheter + 5000 Synthetic catheter & 0.8974 \\  & 140 labeled images + 10,000 Synthetic catheter & 0.8544 \\ \hline \multirow{2}{*}{PSFNet [43]} & 5000 Camera catheter + 5000 Synthetic catheter & 0.7220 \\  & 140 labeled images + 5000 Camera catheter + 5000 Synthetic catheter & 0.8112 \\  & 140 labeled images + 10,000 Synthetic catheter & 0.8074 \\ \hline \multirow{2}{*}{PAN [44]} & 5000 Camera catheter + 5000 Synthetic catheter & 0.8210 \\  & 140 labeled images + 5000 Camera catheter + 5000 Synthetic catheter & 0.8764 \\  & 140 labeled images + 10,000 Synthetic catheter & 0.8598 \\ \hline \multirow{2}{*}{Linknet [42]} & 5000 Camera catheter + 5000 Synthetic catheter & 0.8273 \\  & 140 labeled images + 5000 Camera catheter + 5000 Synthetic catheter & 0.8894 \\ \multicolumn{2}{c}{140 labeled images + 10,000 Synthetic catheter} & 0.8797 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of segmentation models using camera and synthetic catheters including labeled images. All the models are evaluated on real catheter X-Ray test set. Figure 5: Test results of the U-Net model trained on (**a**) synthetic data only and a combination of (**b**) camera catheter and synthetic catheter images. since the camera catheter images enable the model to precisely localize and distinguish the thin part of the catheter in real x-ray images. However, the training images are further increased to 20,000 images (10,000 Camera catheter + 10,000 Synthetic catheter), performance decreased by 0.0018 points.
## 3 Numerical Test Scheme  
This work analyzes the influence of the number and thickness of sand layers, designated by \(n\) and \(t\), respectively, on the deviatoric stress-axial strain relationship and the shear strength of the LGSCR samples. To this end, the effect of four sand layers (\(n=0\), 1, 2, and 3) on the triaxial compression test results of the LGSCR samples was examined. The geogrid-reinforced sand layers in the four samples were evenly laid vertically at equal intervals, and the thickness of each layer was 8 mm. Moreover, the effect of the five thicknesses of sand layers (\(t=0\), 8, 12, 16, and 20 mm) on the triaxial compression test results of the LGSCR samples was investigated. Only one layer of the reinforced sand (\(n=1\)) was laid in the middle of the five samples. The numerical test scheme is displayed in Figure 6. In addition, the numerical model of the sand-clay-layered (SCL) structure is established by ensuring the sizes of the sand layer and the clay layer remain unchanged but removing the geogrid in the sand layer. The effect of laying the geogrid in the sand was also studied by comparing the test results of the SCL samples with those of the corresponding LGSCR specimens.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Material** & **Clay** & **Sand** & **Geogrid** \\ \hline Specific gravity of particle & 2.00 & 2.63 & 1.00 \\ Porosity of assembly & 0.40 & 0.40 & \\ Minimum nominal radius of particle (mm) & 0.70 & 0.75 & 0.50 \\ Maximum nominal radius of particle (mm) & 0.95 & 1.00 & 0.50 \\ Contact normal stiffness (N/m) & 6.00 \(\times\) 10\({}^{4}\) & 8.00 \(\times\) 10\({}^{4}\) & 4.00 \(\times\) 10\({}^{4}\) \\ Contact shear stiffness (N/m) & 6.00 \(\times\) 10\({}^{4}\) & 8.00 \(\times\) 10\({}^{4}\) & 4.00 \(\times\) 10\({}^{4}\) \\ Coefficient of friction & 0.30 & 0.60 & 0.60 \\ Contact bond normal strength (N/m\({}^{2}\)) & 1.00 \(\times\)
## 3 Kinematic Parameter Identification Method
### Parameter Identification with an Improved PSO Algorithm
The iterative process is repeated until meeting the iterative termination conditions and the global optimal position vector is obtained. In general, the PSO algorithm is used to solve unconstrained optimization problems. Therefore, the PSO algorithm lacks the mechanism to handle the boundary constraints. In addition, the actual kinematic parameters of the parallel robot that need to be identified are up to forty-two, so the convergence speed and identification precision need to be improved while guaranteeing the excellent global search capability. Several modification strategies were adopted and applied to the PSO algorithm to obtain the excellent actual kinematic parameter identification result. The boundary processing strategy of the PSO algorithm is to adjust the velocity direction of the out-of-boundary particles to the opposite direction in order to prevent the search from entering a stagnation state and falling into a local optimal trap. A new boundary processing strategy [50] is adopted and expressed as follows:  
\[\textbf{X}_{i}^{t+1}= \begin{cases}\textbf{X}_{i}^{t}-\lambda\textbf{V}_{i}^{t+1}& \textbf{X}_{i}^{t+1}\geq\textbf{X}_{max}\ or\ \textbf{X}_{i}^{t+1}\leq\textbf{X}_{min}\\ \textbf{X}_{i}^{t}+\lambda\textbf{V}_{i}^{t+1}&\textbf{X}_{min}\leq\textbf{X}_ {i}^{t+1}\leq\textbf{X}_{max}\end{cases}  \tag{25}\]  
where \(\textbf{X}_{max}\) and \(\textbf{X}_{min}\) are the upper and lower boundary of the search space, respectively. For the sake of strengthening the local and global optimal search ability and enhancing convergence speed, a nonlinear inertia weight adjustment strategy [56] is adopted and expressed as follows:  
\[w= \begin{cases}w_{max}-(w_{max}-w_{min})\frac{E(\textbf{X}_{i})-E_{min}}{E_{ave }-E_{min}}&E(\textbf{X}_{i})\leq E_{ave}\\ w_{min}&E(\textbf{X}_{i})>E_{ave}\end{cases}  \tag{26}\]  
where \(w_{max}\) and \(w_{min}\) are the maximum and minimum values of the inertia weight, respectively, \(E_{min}\) is the minimum fitness of all particles in each iteration, \(E_{ave}\) is the average fitness of all particles in each iteration, and \(E(\textbf{X}_{i})\) is the fitness value of the current particle in each iteration. According to the description above, the actual kinematic parameter identification procedure of the parallel robot based on the improved PSO algorithm is outlined in Figure 2.  
Figure 2: Flow chart of the kinematic parameter identification method of the parallel robot.
## 2 Routing Protocol Types  
In MANET, there are different routing mechanisms. Three sorts of routing systems are available: proactive, reactive, and hybrid. The MANET routing methods are used for dealing with a considerable number of limited-resource nodes. The true concern in routing is the entry/departure of the network nodes [13; 14]. Despite the development of several mobile nodes, it is vital to reduce the overhead routing message. The measurement of the routing table is another critical concern. The extent of the routing technique is more significant than the control packets that are transmitted inside the system can affect. The routing procedure determines the optimal route to the destination and how and when courses are determined. Figure 2 demonstrates the case of the kind of routing method.  
A trust-based multi-authenticated routing protocol is a protocol in which a node considers a routing decision based on the behaviour of a candidate router and its validation status. This view is quantified and referred to as the Trusted Validation metric. The route from source to destination is determined based on trust metrics [15]. Trust based routing is vital to ensure the information collected, to preserve network performance against inappropriate consumption and network resources. Most uses of WSN convey and provide very vital information and secrecy, such as for military and health purposes [16]. WSN infected with misbehaving nodes is misrouting traffic to misrepresentation or is not transmitting packets towards the destination that cause information loss. A trustworthy protocol on routing can protect the interchange of data, provide safety information, and protect the value of the data. However, the classic routing methods based on trust have certain essential limitations [17]. The trust based systems deal with threats inherent in wireless networks and also create additional hazards that need to be paid particular attention [18]. In this manuscript, a trust based multi authenticated routing model is proposed to deal with the problems mentioned earlier. The routing algorithm in our system considers the characteristics of the trust measure and other path selection quality requirements.
## 3 Results and Discussion
### Numerical Validation
#### 3.1.1 Validation and Comparison of Air-Blast Model  
Firstly, three air-blast models were validated and compared: (1) CONWEP, (2) SPH, and (3) multi-material Arbitrary Lagrangian-Eulerian (MMALE). The CONWEP and SPH models are detailed in Section 2.2.3. MMALE represents an advanced method in which air-blast dynamics are modeled using Eulerian formulations. This approach allows blast waves to propagate through the air domain and interact with Lagrangian structures. In this validation, the contact algorithm used between the Eulerian and Lagrangian parts is a penalty-based method through the keyword CONSTRAINED_LAGRANGE_IN_SOLID.  
Experimental data from [PERSON] et al. [6] used as validation for the air-blast models. A single AL-6 XN steel plate specimen with dimension of 406 \(\times\) 406 \(\times\) 3.4 mm\({}^{3}\) was subjected to a 0.15 kg C-4 explosive charge at an SoD of 150, 200, and 250 mm. For the CONWEP model, 0.15 kg C-4 was equated to 0.18 kg TNT based on mass equivalence [65]. The J-C material model parameters from reference [6] were implemented in the numerical simulations. The SPH and MMALE methods utilized the MAT_HIGH_EXPLOSIVE_BURN model and JWL EOS for modeling the C-4 charge, with material properties and JWL EOS details provided in [59]. The primary comparative metric was the permanent displacement (\(\delta_{p}\)) of the central node of the plate. Figure 5 illustrates the FE models of the three air-blast models.  
Table 4 summarizes the numerical results compared to the experimental results. MMALE shows the smallest error for a SoD of 150 and 200 mm, while CONWEP performs best for a SoD of 250 mm. On average, MMALE exhibits the smallest error, followed by SPH and CONWEP. Figure 6 shows the bar plot of \(\delta_{p}\) for each air-blast model alongside experimental data. This observation is consistent with the fact that CONWEP relies solely on empirical formulas with inherent limitations. In contrast, SPH and MMALE provide more accurate and detailed simulations by solving the governing equations of fluid and solid dynamics. However, the computational time required by CONWEP is significantly faster, approximately 55 times faster than SPH and 305 times faster than MMALE. Considering computational efficiency, the CONWEP method is chosen for the data sampling process, while the SPH model is used for final evaluation.  
Figure 5: Quarter-symmetric model of a single plate subjected to air-blast loading using three types of air-blast models: (**a**) Conventional Weapons Effects Program (CONWEP), (**b**) smoothed-particle hydrodynamics (SPH), and (**c**) multi-material Arbitrary Lagrangian–Eulerian (MMALE). In the SPH model, the black line represents the SPH calculation domain. In the MMALE model, the C-4 charge and air are modeled using Eulerian formulations, while the single plate is modeled using Lagrangian formulations.
# Prediction of Soil Compaction Parameters Using Machine Learning Models
## 2 Materials and Methods
### Machine Learning Algorithms  
#### 2.3.1 Support Vector Machines  
SVM is a classic model for the binary classification of data, which is based on the principle of distinguishing data into two classes in an optimal way through hyperplanes [30]. SVM has good performance and advantages for small- and medium-sized data samples, nonlinear, and high-dimensional problems and can be used to handle regression and classification tasks [31]. As shown in Figure 2, the SVM hyperplane equation expression is  
\[\omega^{T}x+b=0 \tag{1}\]  
where \(\omega\) is the weight vector and \(b\) is the offset.  
The marginal hyperplane spacing between the two sides of the hyperplane is \(\frac{2}{\|\omega\|}\), which is required to maximize the spacing:  
\[\min\frac{1}{2}\parallel\omega\parallel^{2}\text{s.t.}y_{i}\Big{(}\omega^{T}x _{i}+b\Big{)}\geq 1,i=1,2,\cdots,n \tag{2}\]  
where \(x_{i}\) is the input variable and \(y_{i}\) is the output indicator.  
The maximum interval problem is then converted to a simpler dyadic problem using the Lagrange optimization method, which can be used to find the unique optimal solutions \(\omega^{*}\) and \(b^{*}\). The Lagrange optimization function is  
\[L(\omega,b,\alpha)=\frac{1}{2}\parallel\omega\parallel^{2}-\sum_{i=1}^{n} \alpha_{i}\Big{[}y_{i}\Big{(}\omega^{T}x_{i}+b\Big{)}-1\Big{]} \tag{3}\]  
where \(\alpha\) is the Lagrange multiplier and \(\alpha_{i}\geq 0\).  
Figure 2: Schematic diagram of SVM.  
The optimal solution is brought in to obtain the separation decision function  
\[f(x)=sign(\omega^{*}x+b^{*})=sign\Bigg{(}\sum_{i=1}^{n}\alpha_{i}^{*}y_{i}K(x,z)+ b^{*}\Bigg{)} \tag{4}\]  
where \(K(x,z)\) is the kernel function.  
SVM built-in kernel function expressions are as follows:  
Linear kernel function:  
\[K(x,z)=x^{T}z \tag{5}\]  
Gaussian kernel function:  
\[K(x,z)=\exp\biggl{(}-\frac{\parallel x-z\parallel^{2}}{2\sigma^{2}}\biggr{)} \tag{6}\]
## 3 System Design and Implementation
### Hardware Design  
Figure 4 shows the hardware architecture diagram, which includes a USART to RS485 conversion module, USART module, timer module, SPI interface module, SPI driver with DMA module, W5500 register configuration module, and data-processing module.  
* USART to RS48 Module: This is used to convert between USART signals and RS485 bus differential signals to realize the transmission between STM32 microcontroller and RS485 bus.
* USART Module: This is composed of a software FIFO buffer, USART3 peripheral, and interrupt service mechanism. The data is captured in real-time via receive completion interrupt and stored in the FIFO. Frame termination is determined using idle interrupt. The main loop asynchronously reads FIFO data for protocol parsing/processing, enabling transceiver status monitoring and data flow scheduling.
* TIMER2: This utilizes TIM2 to configure the auto-reload value (ARR) and prescaler (PSC) to generate timeout interrupts. It detects the end of a Modbus RTU frame transmission by measuring a 3.5-character time interval.
* SPI Module: It comprises a SPI interface (Mode 0, 8-bit frame @18 MHz) and SPI-DMA driver with double buffering. It enables zero-CPU-overhead full-duplex data transfer between STM32 and W5500 through cyclic DMA channel configuration.
* W5500 Register Configuration Module: The physical layer (PHY) configuration and interrupt enable of W5500 is realized through the configuration of W5500-related registers by STM32.
* Data-Processing Module: It performs CRC-16 validation and bidirectional Modbus AUDP/Modbus RTU protocol conversion.
* Interrupt Management Module: It handles centralized interrupt events (e.g., USART idle, SPI transfer complete, and W5500 status) with priority-based response logic via NVIC, ensuring the timely clearing of interrupt flags through hardware-level register operations.  
Figure 4: Hardware architecture diagram.
## 3 Control Method and Control Characteristics of the Switching Process of the DFVSPS Unit
### Generating Mode  
The switching process of generating mode can be divided into start-up, load ramping, stable operation, load rejection, orderly shutdown and other stages. From the switching process in the full characteristic curve, the start-up is mainly to load the unit speed to the synchronous speed and complete voltage regulation and grid connection. In the stage of load ramping, the output power of the unit is increased from no-load power to full power by adjusting guide vane opening. Stable operation stage refers to the high efficiency operation of the unit near the rated operating point. The load rejection stage is mainly to regulate the unit from full power to no-load power by adjusting guide vane opening. Orderly shutdown refers to the stage when the unit is disconnected from the power grid, the unit speed drops to 0, and the unit turns to static standby. The control method is described in stages.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline rated power & 300 MW & stator resistance \(R_{s}\) & 0.00103 \(\Omega\) \\ frequency & 50 Hz & rotor resistance \(R_{r}\) & 0.00065 \(\Omega\) \\ rated voltage & 18 kV & stator inductance \(L_{s}\) & 0.000194 H \\ inertia \(J\) & 4,000,000 kg-m\({}^{2}\) & rotor inductance \(L_{r}\) & 0.000267 H \\ pole pairs \(p\) & 12 & mutual inductance \(L_{r}\) & 0.00567 H \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of DFIM.
# Investigating and Modeling of Cooperative Vehicle-to-Vehicle Safety Stopping Distance
## 5 Simulation of Field Tests
### Distance Dependent Consecutive Packet Loss  
A second scenario to be analyzed is the impact of distance dependent packet losses. This scenario is based upon the studied vehicle being at different distances from the vehicle of interest. In this case, we measured how the stopping distance is reduced when vehicles are at different communication distances. We first show Figure 7, where we can see the receiver power over distance from 0-250 m. Figure 8 is used to show the number of packet losses at each distance and data rate. It can be seen in this figure that, at distances of 30 m and between 60-90 m, there is a spike in packet losses at the 120 and 140 pps curves. We can refer to Figure 7, where we see that, due to the fading nature of the channel, the received power suffers degradation over these distances, and we can attribute the number of packets being transmitted that cause this fading to have a larger effect of the reception percentage.  
Figure 4: Thirty miles per hour (48.27 km/h) reduction in stopping distance.  
Figure 5: Fifty miles per hour (80.45 km/h) reduction in stopping distance.  
Figure 6: Seventy miles per hour (112.63 km/h) reduction in stopping distance.