esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Overview of the ANSYS  
The simulation of physical processes using specialized commercial software constitutes an advanced scientific method employed to analyze fluid flow, heat and mass transfer, as well as other related phenomena, through the numerical solution of fundamental equations. This technique provides essential support throughout the entire lifecycle of a building, from the initial conceptual design to the detailed engineering of structural elements and up to the identification and resolution of potential operational issues. Moreover, numerical simulation complements traditional experimental methods, significantly contributing to the reduction in time, effort, and costs required for testing and obtaining experimental data.  
Addressing a problem within the field of fluid mechanics involves essential steps: defining the mathematical model, selecting an appropriate numerical method, developing the computational algorithm, and verifying the accuracy of the obtained results. Thermal analysis plays a crucial role in this process, as it enables the determination of temperature distribution and key thermal parameters such as heat loss, temperature variations, and heat fluxes [27]. In the context of increasing demands for building energy efficiency, there is a growing use of specialized software for thermal system modeling and computational fluid dynamics (CFD) analysis. These tools provide detailed, essential information during the design phase, contributing to the optimization of building energy performance.  
The simulation methodology in ANSYS Fluent 17 is based on solving conservation differential equations, complemented by semi-empirical models addressing aspects such as turbulence, pressure, cavitation, heat transfer, and chemical species transport. These equations are discretized using techniques such as finite difference, finite element, finite volume, or boundary element methods. The simulation domain is divided into a mesh, and the nodal equations are solved as an integrated global system.  
Within the finite volume method [51], the equations are solved for small control volumes, where conservation laws govern the transport of physical properties. This approach is computationally intensive because the balance of transported quantities must be maintained between adjacent volumes. The Navier-Stokes equations form the foundation of most fluid flow simulations, and their simplified variants, such as the Euler equations and potential flow, are obtained by neglecting specific terms. CFD is part of computer-aided design (CAD) and computer-aided engineering (CAE), utilizing computational power to analyze fluid behavior and their interactions with system boundaries or walls. The accuracy and speed of solutions depend on available processing resources, and the validity of results is confirmed through comparison with experimental data or real-world measurements.
# Multi-Attention-Guided Cascading Network for End-to-End Person Search
## 3 Proposed Method
### Trustworthy Bounding Box Regression  
It can be found that the transformer-based feature representation model focuses on extracting the global discriminant representation from the person. However, for some occlusion or complex backgrounds of the image, it is difficult for the re-id model to learndiscriminative feature embedding from person search datasets. It can be found that simultaneously predicting a person's bounding boxes and re-id results usually produce poor results in a single search network. This is because the detection network fails to provide accurate candidate boxes for re-id tasks, which has an impact on the following feature embedding. To this end, we adopt a Faster R-CNN [6] network for the labeled person and generate a trustworthy bounding box in our proposed person search task.  
A Faster R-CNN network is introduced in our paper, which sets Resnet-50 as the backbone network, which mainly includes five residual blocks, Region of Interest (ROI) pooling layer, and a region proposal net (RPN). Given the multiple people in complex scenes, we first adopt the first four residual blocks of the network to obtain the feature map of 1024 channels.  
In the one-step person search method, the regression box may be inaccurate. Therefore, we added the box generation model regression in the detection part to obtain more reliable bounding boxes. Moreover, MGCN generates a set of pedestrian proposals through the RPN module, which is subsequently fed into an ROI-Align layer to extract discriminative features from candidate proposals. Then the network passes the fifth residual block in the ResNet50 network and is processed to generate 2048 dimensional feature representation. In our paper, the detection head is set after 2048 dimensional feature to obtain trustworthy bounding box regression, which judges whether the current characteristics are people and regression loss to generate a bounding box. To enhance the detection frame quality, we utilize the non-maximum suppression algorithm to decrease the number of candidate frames for repeated detection.
## 4 Fault Identification Method Based on IPSO-WNN
### Fault Identification Method Based on IPSO-WNN  
In order to reduce the prediction error, the mean squared error function in Equation (15) is used as the fitness function of the IPSO algorithm in this paper. Whenever the fitted value is near the actual value, the recognition model error is smaller, and the particle's fitness is higher, and the current particle is in the best position when the recognition error reaches the expected value.  
Figure 9: The topology of WNN.  
This paper improves PSO algorithm to make the global search ability and local search ability in a balanced state by acquiring a faster convergence speed and making the algorithm more resistant to falling into local minima.  
\[w_{i}=w_{max}-\frac{i(w_{max}-w_{min})}{I_{max}} \tag{16}\]  
where \(w_{max}\), \(w_{min}\) are the maximum and minimum values of inertia weights, respectively. \(I_{max}\) is the preset maximum number of iterations. \(w\) is the number of iterations with \(I\) linearly decreasing, but to consider the effects of the evolutionary speed and aggregation degree of the particle population, a nonlinear decreasing strategy of inertia weights is proposed in this paper, as given in Equation (17).  
\[w_{i}=\left\{\begin{array}{l}w_{max},f\geq f_{avg}\\ w_{min}+\frac{w_{max}w_{min}}{1+e^{i\pi\beta-f}},f<f_{avg}\end{array}\right. \tag{17}\]  
where \(f\) is the fitness of the particle, \(f_{avg}\) is the average fitness of all particles. This method adjusts the inertia weights of the particles based on the relationship between \(f\) and \(f_{avg}\) in order to enhance the ability of the particles to find the global optimal solution.  
The learning factors \(c_{1}\) and \(c_{2}\) in PSO algorithm affect the superiority seeking ability of particles and the information interaction ability between particles. In the standard PSO algorithm \(c_{1}\), and \(c_{2}\) are equal fixed values, which have a large impact on the convergence speed and the merit-seeking ability of the algorithm. Therefore, in this paper, the learning factor is optimized based on the nonlinear decreasing strategy for the inertial weight.  
\[\left\{\begin{array}{l}c_{1}(t)=2-e^{g_{t}-\frac{1}{N}\sum_{l=1}^{N}p_{i}}\\ c_{2}(t)=2-c_{1}(t)\end{array}\right. \tag{18}\]  
where \(N\) is the total number of particles, \(c_{1}\) is the number of particles in the first \(t\) at the second iteration, and \(c_{2}\) is the value of \(g_{t}\), which is the optimal solution of the current particle swarm. \(p_{i}\) is the optimal solution of the first \(i\) optimal solution of the first \(i\) particle.  
From Equation (18), it can be seen that as the iterations proceed, the learning factor \(c_{1}\) decreases nonlinearly and \(c_{2}\) nonlinearly increases, which significantly increases the convergence speed of the algorithm and achieves the goal of enhancing the algorithm's ability to find the global optimal solution.
## 1 Introduction  
Bauxite is abundant, totaling 30 billion tons globally in 2018, according to the data from the United States Geological Survey (USGS). The natural distribution of bauxite is extremely uneven, mainly concentrated in Africa, Oceania, South America, and Southeast Asia. China's demand for imported bauxite increased sharply from about 2.3 million tons in 2007 to 82.62 million tons in 2018 [1]. Panamax and Handy carriers transport 90% of the bauxite via shipping [2]. Here, Handy carrier is the collective term that refers to Handysize and Handymax bulk carriers. Handy carriers play an important role in bauxite shipping, accounting for 48% of the total industry. Meanwhile, 74% of deaths in the industry were linked to accidents involving Handy bulk carriers [3; 4; 5; 6]. The liquefaction of bauxite during transportation is an important cause of ship accidents, directly responsible for more than 80 casualties of seafarers [7].  
The basic reason for liquefaction is that the moisture content of bauxite exceeds the transportable moisture limit (TML). Influenced by the effect of the ship's stability and its cargo properties in a complex shipping process, bauxite with a high moisture content carried by Handy carriers tends to liquefy, which threatens the stability and safety of the ship. The bauxite performance, dynamic ship stability, and maritime environment have important effects on the risk level of the carrier during the transportation process. Risk identification and monitoring can improve the capacity for risk prevention on bauxite carriers. The study of risk reasoning of the transportation process based on cargo information and uncertain weather and sea conditions may allow more time for an emergency response, thus reducing the risk of loss or damage. A hidden Markov model (HMM)-based approach is introduced here to reveal the factor and time correlations of the observation index and hidden risk, thereby achieving risk reasoning for the bauxite transportation process.  
The organization of this paper is as follows: recent studies related to bauxite liquefaction and its risk reasoning are reviewed in Section 2. The research theory and the model for reasoning are presented in Section 3. The model is applied to specific cases in Section 4, where the results are analyzed for effectiveness and accuracy. Section 5 presents the analysis and discussion of this study, encompassing scenario planning. Conclusions are drawn in Section 6.
# SDN-OpenFlow Topology Discovery: An Overview of Performance Issues
## 2 Topology Discovery in SDN Networks
### Challenges of the Link Discovery  
As we discussed in Section 2.4, on each fixed time interval (10 s) the SDN-Controller sends an LLDP packet encapsulated with a Packet-Out message to each active SDN-Switch port in the network. This discovery mechanism could present serious performance issues to SDN networks, especially of large networks. Therefore, we will summarize the OFDP link discovery challenges as follows:  
Overhead to SDN-Controller and Control Channel  
This point of the challenge has been clarified in details in the OFDP performance subsection, and therefore we will avoid re-explaining it  
Inefficient Link Failure Detection  
In most SDN-Controllers, each round of detections is performed approximately every 10 s and the SDN-Controller will become aware of new topology changes [44]. This is a very long process for dynamic network environments where the changes to the topology occur frequently over a short period [38]. This greatly affects the operation of network applications that depend entirely on the SDN-Controller's network topology [78]. For example, if the learning time is long and there is a link failure on router links, the routing application will still redirect traffic on that path based on a legacy network topology, which means this will cause many packets to be dropped. Figure 7 shows the relationship between learning time and topology changes. When a topology change occurs after the first discovery round, the SDN-Controller will wait for the next round to re-detect changes to the topological structure, which is too long. On the other hand, there is a suggestion to reduce the interval of discovery changes, but this will cause the number of the Packet_Out messages to increase significantly, which in turn will increase the load on the SDN-Controller and uses more bandwidth in the process of discovering the topology. In addition, an increase in the discovery interval means fewer LLDP messages and less overhead, but also means more time required to learn about new changes.  
Regarding large and dynamic network environments as we discussed in Section 2.6.1 these challenges appear clearly, and based on performance metrics, we expect that OFDP performance will perform as shown in Table 3. The table shows that the value of some performance metrics is considerable and cannot be ignored [38; 69].
###### Abstract  
We have demonstrated a multiple-access transfer of time and frequency signal over a fiber and free-space link based on an optical frequency comb (OFC). With this transfer technique, two time-frequency signals were disseminated separately from a master site to two slave sites over a 3.9 km fiber and 100 m free-space link for 10,000 s. The timing fluctuations and instabilities of the time and frequency transfer were measured, estimated, and discussed. The experimental results show that the total root-mean-square (RMS) timing fluctuation of the transfer from site A to B is about 119 ps, with a fractional frequency instability on the order of 3.3 \(\times\) 10\({}^{-11}\) at 1 s and 2.8 \(\times\) 10\({}^{-14}\) at 2000 s. The RMS timing fluctuation of the transfer from site A to C is about 59.5 ps, with a fractional frequency instability on the order of 3.0 \(\times\) 10\({}^{-11}\) at 1 s and 2.6 \(\times\) 10\({}^{-14}\) at 2000 s. These results indicate that the multiple-access transfer technique proposed in this paper can provide important support for the application of a large-scale time-frequency synchronization network.  
time and frequency transfer; fiber link; free-space link; two-way phase comparison Article
## 4 Conclusions  
A new perspective on the change in the angles of pad surface asperities occurring during the CMP process has been proposed in this paper. Experiments were performed to clarify the relationship between the temperature of the pad surface and the asperity angle. It was found that as the pad surface temperature increases, the surface becomes rough and the pad topography changes. Through micro-CT and SEM, it was determined that pad asperities are nearly flat at lower temperatures and become vertical at higher temperatures. It was also ascertained that the change in asperity angle affects the contact area, the contact perimeter, and the number of asperities in contact. Patterns in the polishing rate and the behavior of the asperity angle during continuous polishing without any conditioning were characterized according to the pad temperature. When higher temperatures are maintained, the reduction in polishing rate and the change in asperity angle are slowed, which has a beneficial effect on the conditioning process interval and polishing efficiency. During conditioning, the surface result differs according to the pad temperature under the same conditions. The asperity angle recovery rate is proportional to the pad temperature, and the polishing recovery rate is also proportional.  
Thus, this study determined that changes in the pad asperity angles play an important role in the mechanical aspects of the CMP process. It is true that the CMP process involves many factors, which interact in a complex manner, and there are many behaviors that cannot be evaluated solely in terms of asperity angles. Nevertheless, it is believed that better conclusions can be drawn by considering the angles of asperities in mathematical planarization models, simulation problems, and research on managing and predicting pad shapes.  
Conceptualization and methodology, S.J.; supervision, H.J.; experiment and analysis, S.J. and J.C.; measurement, K.J.; writing--review and editing S.J. All authors have read and agreed to the published version of the manuscript. This research was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2019R1A2B5B01070489). Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data sharing is not applicable to this article. The authors declare no conflict of interest.  
Figure 13: Schematic images of the change of the asperity angle according to the pad surface temperature during conditioning process.
## 3 Results and Analysis
Thus, it is necessary to investigate how the wave attenuation varies with the parameters of the dipole field. Figure 3a illustrates how the attenuation coefficient \(Att\) varies with the radius of the coil and the incident wave frequency under the condition of \(B_{c}=1\) T. As shown in the Figure, \(Att\) decreases as the wave frequency increases. Additionally, Figure 3a shows that the \(Att\) decreases as the coil radius increases. Yet, once the coil radius is greater than 0.06 m, the \(Att\) decreases with the coil radius increasing more slowly than that of the coil with a radius smaller than 0.06 m. In such a case, the coil radius is suggested to be around 0.06 m, according to the present study. attenuation of the 225-GHz waves is relatively higher in a neutral atmosphere, which is not a good choice; the \(Att\) of 94-GHz waves can reach 3 dB under a relatively weak external magnetic field, while 77-GHz waves require a strong external magnetic field. When the external magnetic field reaches 5 T, the \(Att\) of 35-GHz waves is still greater than 3 dB. Therefore, the suggested carrier frequency is 94 GHz or 140 GHz, according to the present study. Figure 3c illustrates how the \(Att\) varies with \(B_{c}\) and the radius of the coil when the wave frequency is \(f=94\) GHz. As shown in Figure 3c, the \(Att\) decreases with the increase of the coil radius and the \(B_{c}\). It can be seen that, while \(B_{c}\) is small, the \(Att\) is less affected by the coil radius. The 94-GHz waves obtain a 3-dB absorption coefficient, even if the coil size exceeds 0.1 m; \(B_{c}\) needs to be close to 1 T. This is difficult to achieve for the current onboard magnetic field. Therefore, it is recommended to use 140 GHz as the carrier frequency.
## 3 Results
### Contribution of MISR-HR RPV Model Paramters  
Scenario 5 evaluated the effectiveness of the RPV parameters in predicting the forest structural variables (Figure 7). We first assessed the RPV parameters (\(\rho_{0}\), \(k\), \(\Theta\)) separately for all four spectral bands. The \(\rho_{0}\), parameter produced the highest correlation and lowest estimation errors, with R\({}^{2}\) = 0.65, rRMSE = 38.22% and R\({}^{2}\) = 0.47, rRMSE = 46.69% for H\({}_{\text{mean}}\) and CC, respectively (Figure 6a,b). The poorest performances were obtained for \(\Theta\) with R\({}^{2}\) = 0.48, rRMSE = 46.32% and R\({}^{2}\) = 0.43, rRMSE = 49.42% for H\({}_{\text{mean}}\) and CC, respectively. The best RPV model resulted from the combined use of all 12 MISR-HR RPV parameters for both H\({}_{\text{mean}}\) and CC parameters, with rRMSE = 34.84% for H\({}_{\text{mean}}\) and rRMSE = 40.96% for CC (Figure 7b). The 12 MISR-HR RPV parameter model did not perform as well (statistical significant \(p\)-value \(<0.05\)) as the MISR-HR BRF 36 band model (Scenario 4) and produced a relative rRMSE of 5.1% and 6.2% higher for H\({}_{\text{mean}}\) and CC, respectively. The H\({}_{\text{mean}}\) and CC had similar trends and patterns across all scenarios with H\({}_{\text{mean}}\) models generally performing better than CC models. Overall, for the combined forest type dataset the best model was obtained for the H\({}_{\text{mean}}\) parameter using the 36 BRF data channel (R\({}^{2}\) = 0.73, rRMSE = 33.14%).
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 1 Introduction
However, neither paper discussed the impact of parameter changes and vehicle execution delay on control effects. The vehicle's motion status will influence the control of autonomous driving. Commercial vehicles have a large load and complex driving conditions, and the vehicle's mass varies with differing loads. [PERSON] [12] introduced a two-phase Lyapunov-based approach to estimate the mass of heavy vehicles, utilizing standard signals accessible within the controller area network for vehicles. An adaptive least squares method is used to estimate the vehicle's mass. [PERSON] [13] established suitable operating conditions and devised a confidence factor for mass estimation utilizing fuzzy logic rules and subsequently used recursive least squares for online mass estimation. However, the existing methods fail to consider uncertain factors such as sensor noise interference and error in actual vehicle scenarios. It is essential to improve the robustness of the estimation algorithm against possible disturbances, abnormal errors, and potential faults that may be encountered by the sensor during the driving process of commercial vehicles. The load variation of vehicles is significant, and the mass parameter has a deeper impact on the control effect. Traditional control algorithms usually set the vehicle mass as a constant, which cannot meet the control requirements of corresponding working conditions well. This paper introduces a model predictive control approach equipped with a mass estimator, referred to as MFFRSL + MPC. First, considering the sensor noise and unstable parameter changes in real vehicle scenarios, the recursive least squares method with a forgetting factor is introduced to dynamically adjust the weight of historical data, reflecting the current state and environmental changes. The joint M-estimation further reduces the influence of sensor noise on the estimation results and enhances the robustness of the algorithm, achieving a robust estimation of vehicle mass. Second, based on the incremental control error model of the steering angle, a model predictive controller is established to improve the control accuracy. A steering angle compensator is established for the execution delay of the steering system. Based on the rollover index, stability constraints are applied to the front wheel steering angle. Ultimately, the efficacy of the introduced control approach is confirmed using collaborative simulations in TruckSim/Simulink under dual lane-changing scenarios and real-world vehicle trials on straight roads integrated with roundabout conditions.