esa-sceva/satcom-chunk-collection huggingface dataset:
# Forward Link Optimization for the Design of VHTS Satellite Networks
## 4 Proposed Methodology  
The methodology presented in this paper aims to obtain the optimal VHTS system that achieves the minimum cost per Gbps in orbit obtained the maximum capacity per beam according to a set of technical and commercial requirements. The methodology can be applied to any service area or frequency plan and it provides an idea of the impact of multiple variables on the performance and cost of VHTS satellites.  
The methodology is flexible, so it is necessary to define in advance the technical and commercial requirements and system constraints, such as the service area, the maximum diameter of the satellite antenna, the maximum satellite mass, and the maximum cost that will be invested, taking into account the costs of the satellite and the ground segment.  
An important input into the dimensioning is the finite set of possible \(G/T\) values of the user terminals, which will depend on the commercial values for the service to be provided. For this, the initial \(G/T\) must be the minimum possible according to the user terminals.  
Figure 1: Average \(CINR_{b}\) in Central Asia (27 million km\({}^{2}\)) for a link availability of 99.5%.  
The proposed methodology is divided into two stages (Figure 2): first, the optimum system configuration (user _G_/_T_ and number of satellite beams) is obtained in terms of cost per Gbps in orbit; second, the satellite capacity using diverse transmission schemes (CCM and VCM) is calculated for a set of link availabilities imposed in the service area.
## 7 Conclusions and Future Developments
Secondly, the software tool adopted in this study captures operational performance metrics, but does not include environmental, economic, and social considerations. Future research involving programming-based studies should aim to extend the tool capabilities and provide greater flexibility and customizability. Also, a detailed feasibility study including cost/complexity analysis should be performed before implementing a control system in real scenarios. Specifically, heterarchical control systems require significant investments in technologies (e.g., CPS and MAS) as well as costs related to the required technology competence of employees and top management. Despite this increasing complexity,the literature has shown the huge potential of the smart manufacturing systems in terms of flexibility and improved performance. These systems bring the capability to assist in managing system uncertainties and complexities through simulation scenarios providing valuable managerial insights prior to the deployment of smart manufacturing systems. In conducting this study, some limitations are evident which, however, provide stimulating insights for expanding our research stream. Firstly, this paper is based on different scenarios in controlled experiments in a laboratory environment. In this sense, input from non-academic experts or practitioners may provide greater insights and would further facilitate generalizability of the provided findings. Conceptualization, R.R. and P.C. ; methodology, T.M. ; software, A.R.B. and R.R. ; validation, T.M. and R.R. ; formal analysis, A.R.B. ; investigation, A.R.B. and R.R. ; resources, T.M. and R.R. ; writing--original draft preparation, P.C. ; writing--review and editing, P.C. and R.C. ; visualization, P.C. ; supervision, R.C. and R.R. ; project administration, R.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors would like to express their gratitude to the institutions that have supported this work providing the necessary equipment, software as well as scientific advice, namely the Department of Chemical, Materials and Industrial Production Engineering and the Department of Industrial Engineering of the University of Naples Federico II (Italy) and the Department of Factory Planning and Factory Management of the Technische Universitat Chemnitz (Germany). The authors declare no conflict of interest.
# Firefly Optimized Resource Control and Routing Stability in MANET +
## 4 Results and Discussion  
Simulation experiments were conducted to analyze the performance of the FFROR technique using the NS2 Network simulator. The number of DPs selected for experimental use is 100. In our simulation, the designed technique sends the DPs from sender to receiver using the intermediate mobile nodes randomly distributed in the square area. Table 1 lists the set of input parameters and evaluates the performance of the FFROR technique.
## 4 Results and Discussion
### The Influence of Nozzle Geometry on Internal Steam Condensation
In general, the wet steam condensation phenomenon under other throat radii has similar characteristics to that which is found under the throat radius of 0.05 m, as shown in Figure 6. However, as the throat radius increased from 0.025 to 0.05 m, there were some differences in the following aspects: The peak position of the condensation shock moved further downstream, as illustrated in Figure 6c. The intensity of the condensation shock was also strengthened, which was reflected in the pressure step change (see Figure 6b). In addition, Figure 6d and e show that the saturation ratio (which was larger than 1) and the droplet nucleation ratio (which was larger than 0) were both enlarged along the axis; meanwhile, the peak values of these two parameter curves decreased with the increase of the throat radius. As seen from Figure 6g, the liquid mass fraction in the divergent section under the throat radius of 0.05m was smaller than that of the others, which can contribute to this radius resulting in the least number of droplets per unit volume (see Figure 6f), although an average droplet radius of 0.0175 \(\upmu\)m can be acquired under the throat radius of 0.05 m.  
Figure 6: The steam parametric curves along the axis under different nozzle throat radii: (**a**) temperature, (**b**) pressure, (**c**) Mach number, (**d**) saturation ratio, (**e**) droplet nucleation rate, (**f**) droplets per unit volume, (**g**) liquid mass fraction, (**h**) droplet average radius.
## 3 Construction of Coherent Integration Time Optimization Model  
The altimetric precision is related to the power uncertainty of the incoherent average waveform at the SP. The uncertainty value of the incoherent average waveform will be affected by the correlation between the waveforms. To more accurately reflect the variation of precision with the coherent integration time, the waveform correlation is considered in the precision prediction model. Since the correlation between the waveforms varies with the coherent integration parameters, variable conversion is performed on the reconstructed precision model to obtain the coherent integration time optimization model whose precision varies with the coherent integration time in this section.
## 3 Simulation Studies and Results  
### Data Preparation  
In this study, the data was acquired using an IoT-based noise monitoring system [54]. Shaoxing is located in the central northern part of Zhejiang Province and is among the key developing areas in China's coastal economic zone. In this study, four sets of the system were installed in an administrative district of Shaoxing City, and the data acquisition interval was 1 s. Figure 5 shows the distribution of monitoring points (01, 02, 03, and 04). Point 01 is adjacent to a construction site and the main road. The primary noise sources are construction noise and traffic noise. Point 02 is near a small water surface (a river about 25 m wide) and a residential area, which means that in general, the noise level that can be monitored at this point is low. Point 03 is situated at a construction site and a residential area. The primary noise sources are construction noise and residential noise. Point 04 is close to the main road, and the primary noise source is traffic noise. The basic situation of noise monitoring points is represented in Table 3.  
According to China's technical specifications for the division of acoustic environment functional zones, the monitoring points are located in the administrative district types belonging to the same acoustic environment functional zones, and there are some comparabilities in urban functions and urban land types. The continuous 24-h noise monitoring was carried out in Shaoxing on August 15-21 (monitoring period a) and September 15-21 in 2015 (monitoring period b). After eliminating the null value, a total of 3,592,076 valid raw data points were obtained.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyper-Parameter** & \multicolumn{2}{c}{**Train Set Performance (RMSE)**} & **Test Set Performance (RMSE)** \\ \hline \multirow{4}{*}{Hidden layer neuron} & 32 & 2.187496 & 2.235420 \\  & 48 & 2.190944 & 2.240577 \\  & 64 & 2.131022 & 2.190727 \\  & 128 & 2.245721 & 2.293271 \\  & 256 & 2.314685 & 2.42026 \\ \hline \multirow{4}{*}{Batch size} & 32 & 2.107715 & 2.175924 \\  & 48 & 2.131022 & 2.190727 \\  & 64 & 2.212008 & 2.252307 \\  & 126 & 2.059974 & 2.135513 \\  & 504 & 2.093715 & 2.045401 \\ \hline \multirow{4}{*}{Dropout} & 0.3 & 2.099123 & 2.179599 \\  & 0.5 & 2.059974 & 2.135513 \\ \cline{1-1}  & 0.7 & 2.083521 & 2.143835 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of different parameter settings on model fitting effect/dB(A).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Point Number** & **Near a Road** & **Near a River** & **Near a Construction Site** & **Near a Residential Area** \\ \hline
01 & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\
02 & \(\times\) & \(\surd\) & \(\times\) & \(\times\) \\
03 & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\
04 & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic situation of monitoring points.
# A Comprehensive Review of Face Morph Generation and Detection of Fraudulent Identities
## 2 Materials and Methods
### Image Morphing  
Earlier in the 1980s and 1990s, image morphing was employed to establish graphical effects in animated movies and entertainment videos [4]. Two or more faces are required to create the image morphs. First, spatial correlation between the two subjects is established. Then warping and intensity interpolation is used to create the morphs. Thus the resultant image is a combination of two underlying subjects and therefore resembles both. Transition control parameters are used to produce different quality outputs. These controls include warping and interpolation parameters. Many methodologies are used in the literature for image morphing. The list includes mesh warping [7], field morphing [8], and radial basis morphing. Control points are determined through the mesh grid of face landmarks. The input subject is transformed into the resultant face by freezing a few points of the face while warping the rest of the face image. Field morphing uses a corresponding set of lines to correlate the subjects. Distinct elements in the illustrations were plotted and established by calculating the distance from the specified line.  
The radial basis function (RBF) morphing technique also employs the mesh of points or curves. These points or curves are established by correlating face images. After that, mapping is performed through these points and patches. FantaMorph is a professional tool used to create such morphs. Figure 2 illustrates the intermediary and final results of morphing using the specified tool.  
Figure 2: Morphed image created by merging two subjects in the tool FantaMorph (Version-5).  
In the recent past, a lot of work has been done on generative adversarial networks (GANs). These methods are also employed for face morphing generation [9; 10]. These morphs are highly dependent upon the properties of GAN used for generation. Moreover, it can pose a serious threat to morph detection if the GAN is new or unknown. Depending upon these factors, these deep learning-based morphs can be very challenging for face recognition systems.
## 6 Actor-Critic Methods
### Soft Actor-Critic (SAC)
```
Input: Replay buffer \(\mathcal{D}\), policy parameters \(\theta\), Q-function parameters \(\phi_{1}\), \(\phi_{2}\), temperature parameter \(\alpha\), learning rates \(\eta_{\pi}\), \(\eta_{Q}\), \(\eta_{\alpha}\) Output: Trained policy \(\pi_{\theta}\)  Initialize Q-function parameters \(\phi_{1}\), \(\phi_{2}\), policy parameters \(\theta\), and replay buffer \(\mathcal{D}\);  Initialize target Q-function parameters \(\phi_{1}^{\text{target}}\leftarrow\phi_{1}\), \(\phi_{2}^{\text{target}}\leftarrow\phi_{2}\); for each training iterationdo for each environment stepdo  Observe state \(s_{t}\);  Sample action \(a_{t}\sim\pi_{\theta}(\cdot|s_{t})\);  Execute \(a_{t}\) and observe reward \(r_{t}\) and next state \(s_{t+1}\);  Store transition \((s_{t},a_{t},r_{t},s_{t+1})\) in replay buffer \(\mathcal{D}\); for each gradient stepdo  Sample a mini-batch of transitions \((s_{t},a_{t},r_{t},s_{t+1})\) from \(\mathcal{D}\);  Compute target Q-value: \(y_{t}=r_{t}+\gamma\Big{(}\min_{i=1,2}Q_{\phi_{i}^{\text{target}}}(s_{t+1},a_{ t+1})-\alpha\log\pi_{\theta}(a_{t+1}|s_{t+1})\Big{)}\), where \(a_{t+1}\sim\pi_{\theta}(\cdot|s_{t+1})\);  Update Q-functions \(\phi_{1}\) and \(\phi_{2}\) by minimizing: \(L_{Q}(\phi_{i})=\mathbb{E}\Big{[}\big{(}Q_{\phi_{i}}(s_{t},a_{t})-y_{t}\big{)} ^{2}\Big{]}\), \(i\in\{1,2\}\). Update policy \(\pi_{\theta}\) by maximizing: \(J_{\pi}(\theta)=\mathbb{E}\big{[}\alpha\log\pi_{\theta}(a_{t}|s_{t})-Q_{\phi} (s_{t},a_{t})\big{]}\),  where \(Q_{\phi}(s_{t},a_{t})=\min_{i=1,2}Q_{\phi_{i}}(s_{t},a_{t})\);  Adjust temperature \(\alpha\) by minimizing: \(L_{\alpha}(\alpha)=\mathbb{E}\big{[}-\alpha\log\pi_{\theta}(a_{t}|s_{t})- \mathcal{H}_{\text{target}}\big{]}\)  Update target Q-functions: \(\phi_{i}^{\text{target}}\leftarrow\tau\phi_{i}+(1-\tau)\phi_{i}^{\text{ target}}\), \(i\in\{1,2\}\),  where \(\tau\) is the soft update parameter. ```  
**Algorithm 11**Soft Actor-Critic (SAC) Algorithm  
The combination of off-policy learning, entropy regularization, and a stochastic actor enables SAC to achieve robust performance in a wide range of robotic control tasks, including locomotion, navigation, and manipulation. The recent advancements related to robotic path planning are reviewed in the following sections.
## 3 Simulation and Analysis  
### Data Input  
To validate the effectiveness and applicability of the proposed method across different scenarios, a series of simulation experiments were conducted. According to the calculation process of \(P_{ident}\) shown in Figure 3, the experiment first required the input of data. To simplify the simulation, it was assumed that all points of the MGF possess pronounced electromagnetic characteristics. Based on this assumption, the experiment used points read from the MGF's stl file as the \(B_{i}(X_{Bi},Y_{Bi},Z_{Bi})\) (\(i\) = 1,2,..., \(N\)) for data input. The stl format is a file format that describes the surface geometry of a 3D object with multiple triangular facets. Its binary format is shown in Table 1.  
Since the triangular facets of the MGF were obtained using the file format in Table 1, it was accessible to determine whether the marine location point was within the radar LOS by checking whether the line connecting the radar position point and the marine location point intersected with any of the triangular facets. The \(B_{i}(X_{Bi},Y_{Bi},Z_{Bi})\) (\(i\) = 1, 2,..., \(N\)) data could be used to determine whether the RTA problem occurred. Based on that, two islands of different shapes were used as the MGFs for the experiment. The 3D models and parameters of the MGFs are shown in Figure 4 and Table 2.  
Since the planar geometric center of the experimental MGFs are close to the coordinate origin, for the convenience of subsequent calculations and explanations, the distance, azimuth angle, and elevation angle between the radar and the coordinate origin are considered as those between the radar and the MGFs for the experiment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Data Type** & **Offset/Byte** & **Notes** \\ \hline Header & String & 0 & File header \\ Number of triangles & UInt32 & 80 & Number of triangular facets \\ Normal vector of triangle \(M\) & Single(3) & \(50(M-1)+84\) & Normal vector of the \(M\)-th triangular facet \\ Vertex1 of triangle \(M\) & Single(3) & \(50(M-1)+96\) & Coordinates of the 1 st vertex of the \(M\)-th triangular facet \\ Vertex2 of triangle \(M\) & Single(3) & \(50(M-1)+108\) & Coordinates of the 2 nd vertex of the \(M\)-th triangular facet \\ Vertex3 of triangle \(M\) & Single(3) & \(50(M-1)+120\) & Coordinates of the 3 rd vertex of the \(M\)-th triangular facet \\ Attribute & UInt16 & \(50(M-1)+142\) & Attribute of the \(M\)-th triangular facet \\ \hline \hline \end{tabular}
\end{table} 
Table 1: stl binary format. In addition to the MGF, the experiment simulated reasonable types of virtual maritime aircraft and the onboard radar. The specific parameters of them and other parameters for the simulation are shown in Table 3.  
To verify the applicability of the proposed method to aircraft at any altitude, three groups of radar position points were constructed, as shown in Table 4.
# A Review of Validation Methods for the Intracranial Response of FEHM to Blunt Impacts
## 2 Experimental Procedures Used in FEHM Validation  
The first component of any validation is identifying experimental data to compare numerical performance to. When validating the intracranial response of FEHM, this data are drawn from experimentation performed on either post-mortem human subjects (PMHS), also known as cadavers, or live human subjects. Each has their advantages and limitations. Cadavers can be directly instrumented and subjected to impacts of any severity, including those that would result in injury. On the one hand, they are post-mortem: All the experiments discussed in this work [34; 35; 36; 37; 38; 39; 40] used unembalmed cadavers. However, they still underwent preparation processes and were repressurised with CSF substitute which could alter conditions. Furthermore and the properties of brain tissue may change post-mortem [41]. On the other hand, experimentation on in vivo subjects is ethically limited to scenarios that do not put volunteers at risk of injury, and monitoring the impact response has to be done via external methods, limiting the data that can currently be collected.  
The following sections summarise experimental procedures that have previously been applied in the validation of FEHM, using both cadaver and in vivo subjects. Within each subject type, experimental procedures are grouped according to the impact response metric they predominantly investigate. The experimental procedures used are briefly outlined and discussed, focusing on the data available to both generate impacts in the simulation environment and to assess and compare impact responses. Additional experimental investigations that provide potential alternatives to the impact data are also introduced and compared to the existing options.
# A Ship Trajectory Prediction Method Based on an Optuna-BILSTM Model
## 3 BILSTM neural network
### Pre-Processing of Data
#### 2.3.3 Partitioning of the Trajectory Data Set  
This study selected historical trajectory data from over 3200 vessels in the Zhoushan sea area, sourced from the Navigation Project research team at Ningbo University, as the foundation for model training. To ensure data representativeness, this study analyzed AIS data from a randomly selected day each month between March and September 2022. these vessel trajectory data consist of continuous sequences of positional points, meticulously recording key navigation information such as longitude, latitude, heading, and speed. Within this dataset, each unique vessel identifier corresponds to a specific vessel trajectory, while certain vessel identifiers are associated with multiple trajectories. Through analysis of the initial segments of each trajectory, the model trained during the training phase can capture both general navigation patterns and specific behaviors, such as changes in speed and turning maneuvers. This enables the trained model to accurately predict the future  
Figure 5: Comparison of track points before and after interpolation processing of the trajectory data.  
positions of vessels, particularly at critical turning points, based on previously learned patterns when receiving new trajectory data.  
After cleaning the original trajectory data and interpolating the data, the training, validation, and test sets of trajectory data need to be divided. Firstly, the historical ship trajectory data are divided into samples, i.e., data samples corresponding to the input form of the prediction model. The sliding window length set in this paper is 12, i.e., the observed time, latitude and longitude, speed, and heading of the first 12 moments are set as known information, and the position information of the 13 th moment is set as unknown information, and all sample data are generated according to this rule. The first 70% of all sample data are divided into the training data set, 10% of the articulated data are divided into the validation data set, and the last 20% are divided into the test data set. This completes the pre-processing of the ship trajectory data.
# Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure
## 4 Experimental Results and Analysis
### Results  
Results are illustrated in the Table 3. After conducting a considerable number of experiments, we can confidently state that state-of-the-art models such as MuDeep and ResNet50 performance will dramatically deteriorate when trained on datasets with clothes variation. Other observations worth mentioning:  
* ResNet-50 model performed relatively better than MuDeep (from Table 3 MuDeep Rank-1 accuracy: 58.5%, ResNet50 Rank-1 accuracy: 62.9%) which is why it was used as a backbone network to the PCB achieving 59.2%.
* To improve the performance of ResNet50 with PCB, the model was augmented with the RPP module, but it seemed to harm the model's performance decreasing the accuracy from 59.2 % to 51.2% accuracy on Rank-1.
* The outstanding performance of OSNet producing an accuracy of 63.6% on Rank-1 was the inspiration to replace the ResNet-50 used as a feature extraction for PCB with OSNet. This combination improved the accuracy on Rank-1 to 73.5%.
* The proposed architecture produced an accuracy of 81.4% on Rank-1. Our model was able to outperform some of the recent state-of-the-art approaches such as HACNN, MLFN and SCRNet in terms of accuracy and mAP.
* Our model does not consume an enormous amount of training time due to its lightweight architecture and exploiting pre-trained models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** & **mAP** & **Training Time** \\ \hline HACNN & 64.1 & 68.1 & 70.2 & 72.1 & 41.5 & 2:48:49 \\ MLFN & 50.2 & 56.1 & 59.4 & 62.7 & 29.6 & 2:54:21 \\ MuDeep & 58.5 & 64.6 & 67.6 & 71.6 & 34.1 & 3:46:54 \\ SCRNet & 78.1 & 79.5 & 81.7 & 83.2 & 58.2 & 1:57:32 \\ ResNet50 & 62.9 & 67.1 & 68.3 & 70.7 & 35.5 & 1:49:11 \\ PCB + ResNet50 & 59.2 & 66.4 & 69.7 & 71.4 & 31.8 & 2:14:49 \\ PCB + ResNet50 (RPP) & 51.2 & 52.3 & 52.5 & 53.4 & 28.0 & 2:47:12 \\ OSNet & 63.6 & 67.6 & 70.4 & 73.2 & 33.7 & 1:12:08 \\ PCB + OSNet & 73.5 & 75.1 & 75.8 & 76.8 & 48.6 & 2:10:01 \\ Our model & 81.4 & 82.3 & 83.1 & 83.7 & 60.2 & 2:35:42 \\ \hline \end{tabular}
\end{table}
Table 3: Performance (%) of our approach compared to other methods on the PRCC dataset.  
Figure 9: Sample 2 of results: 2 query images and our model’s predicted matches from the gallery (Green frames indicate correct matches, Red frames indicate incorrect matches).