esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Freeway Merging Framework  
Multilane freeways are widely present in city transportation networks. In this study, we took a two-lane freeway with an on-ramp lane as an example, as shown in Figure 1. The mainstream traffic flow contains two lanes, and the incoming traffic flow is a single lane. CAVs merge at the intersection of the main lane and the ramp.  
To simplify the entire model and facilitate optimization, we made the following assumptions for this scenario [PERSON] et al. (2019):  
Figure 1: Schematic illustration of a typical two-lane freeway merging area.  
1. All vehicles on the network are fully automated and equipped with V2V/V2I communication capabilities.
2. The lane-changing behavior is instantaneous. At the same time, we did not consider lateral vehicle control.
3. Communication delay and packet loss were not considered in this study.
4. Overtaking is not allowed throughout the merging process.  
As shown in Figure 2, the two lanes of the mainstream traffic flow are called the Outer Lane and the Inner Lane, respectively, while the ramp lane is called the Ramp Lane. The Outer Lane is the lane close to the ramp, and the Inner Lane is the lane away from the ramp. The intersection of the mainstream traffic flow and the incoming traffic flow is defined as the merging point, which corresponds to the one-dimensional coordinate system's origin. In this merging area, we set up a centralized control unit called traffic controller. CAVs periodically send their information, including vehicle ID, lane ID, position, speed, and acceleration, to the traffic controller. The traffic controller uses this information to control all input vehicles' speed, acceleration, and lane-change behavior.  
In our framework, the entire scenario was divided into three zones: lane-changing zone, trajectory optimization zone, and adjustment zone, where we mainly focused on the lane-changing and trajectory optimization zones. Lane-changing operation was only allowed in the lane-changing zone when the traffic controller ordered vehicles, while free lane changes were not allowed in other areas. In the trajectory optimization zone, the mainstream traffic vehicles and ramp vehicles adjusted their trajectories according to the corresponding optimal control model. Note that there was no additional optimization operation in the adjustment zone. The vehicle only needed to drive at the optimized speed and acceleration in the trajectory optimization zone before finally passing the ramp smoothly at a constant speed without collision.
## 4 Discussion
### Effects of Different Factors on the Capillary Water Absorption of Concrete
#### 4.1.1 Effect of the W/B  
The W/B is an index that can reflect the permeability and compactness of cement-based materials and exerts a statistically significant effect on the capillary water absorption characteristics of cement-based materials. Through the investigation of the literature [11; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46] and the analysis of the test results, the values of the capillary water absorption coefficient of cement-based materials with different W/B values were obtained, as shown in Figure 5. The W/B exerted a statistically significant effect on \(S_{1}\), which increased with an increasing W/B. The exponential function was used to fit the curve, and the result is shown in  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Code**} & \multirow{2}{*}{\(S_{1}\) **(10\({}^{-4}\) mm\(\cdot\)s\({}^{-1/2}\))**} & \multicolumn{6}{c}{**Pore-Size Range**} \\ \cline{3-8}  & & **3–10** & **10–50** & **50–100** & **100–1000** & **1000–1000** & **1000–20,000** \\ \cline{3-8}  & & **nm** & **nm** & **nm** & **nm** & **nm** & **nm** \\ \hline NC-2-1 & 59.65 & 2.81 & 12.03 & 5.60 & 6.46 & 2.82 & 0.92 \\ NC-2-2 & 56.20 & 2.80 & 11.04 & 3.40 & 5.20 & 3.00 & 0.80 \\ NC-2-3 & 50.80 & 2.60 & 9.76 & 3.59 & 4.56 & 2.47 & 1.00 \\ UHPC-3 & 54.73 & 6.70 & 3.30 & 1.50 & 5.20 & 3.10 & 1.10 \\ UHPC-4 & 47.50 & 6.85 & 2.05 & 0.90 & 2.70 & 4.10 & 1.60 \\ \hline \hline \end{tabular}  
* The first three groups are the NC-2 concrete specimens after 0, 90, and 330 ETF cycles, and the last two groups are UHPC-3 and UHPC-4 without ETF cycles.  
\end{table}
Table 6: Pore volumes for various concrete samples with different pore size ranges (10\({}^{-3}\) mL-g\({}^{-1}\)).  
Figure 4: Relationship between the capillary water absorption coefficient and porosity.  
Equation (3) (correlation coefficient R\({}^{2}\) = 0.790). When the W/B increased from 0.30 to 0.40 and 0.50, the first-stage capillary water absorption coefficient increased by approximately 89.82 and 260.31%, respectively. The explanation for this finding is that the total porosity of concrete increased, the ability of cement-based materials to absorb and accommodate water increased, the microscopic pore structure changed, the pore size became larger, and the connectivity between the pores improved as the W/B increased, which increased the water absorption capacity of the cement-based materials. Therefore, an appropriate W/B should be selected to ensure good impermeability of cement-based materials.  
\[S_{\rm W/B} =7.946e^{6.409W/B}-0.01, \tag{3}\]  
where \(S_{\rm W/B}\) is the first-stage capillary water absorption coefficient at different W/B; (10\({}^{-4}\) mm\(\cdot\)s\({}^{-1/2}\)).
## 4 Dataset Development  
To the best of our knowledge, there is no public dataset available for specular free-form surface reconstruction; therefore, we generate our own synthetic dataset.  
By creating a virtual environment that approximates key optical aspects of real-world conditions for a reflective textured surface according to physical laws of reflection embedded in the Blender Cycles ray-tracing engine, we can systematically explore the nuances of specular surface reconstruction. This section details the precise setup and execution of our simulation environment, ensuring that every aspect, from lighting and camera positioningto surface properties, is meticulously configured to generate reliable and varied datasets necessary for robust training.
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 3 Measurement Setups
### Offshore Ground-Based Comparative Measurement Campaign  
In the framework of the German research project \"GW Wakes\", three scanning long-range Doppler LiDAR systems of type Leosphere Windcube WLS-200S [6] were operated in the offshore wind farm \"alpha ventus\" in the German North Sea. The wind farm comprises six 5 MW wind turbines Senvion 5 M with rotor diameter of \(D_{S}=126\) m and hub height of \(h_{S}=92\) m that are located in the northerly two rows and six 5 MW wind turbines Adwen AD5-116, formerly called M5000-116, with rotor diameter of \(D_{A}=116\) m and hub height of \(h_{A}=90\) m in the two southerly rows (Figure 6). The LiDAR used for the measurements was operated on the substation of the wind farm in the south east corner. \"alpha ventus\" is located close to the research platform FINO1 that is equipped with a meteorological mast [25]. In the following, all directions in the context of the offshore measurement campaign refer to the meteorological reference system, if not explicitly mentioned.
## 5 Simulation Results
### Experiments with One Transmitter Unsynchronized  
In this part, we simulate the scenario in which there is one transmitter unsynchronized. We suppose emitter 1 is unsynchronized with the other three emitters, which are synchronized with GPS time during the position calculation. Different time offsets are tested; while in each simulation, the time offset stays constant. The four trajectories used in Section 5.3 are also simulated here.  
In Figure 18, the mean errors with respect to different time offsets are plotted for the four trajectories. We can see that the position error increases, with the time offset increasing for all four trajectories.
## 1 Introduction
As compared with the FDTD method, BPM has improved convergence speed and reduced running time, and the nonlinear self-focusing processes of laser beams with beam sizes up to 10 wavelengths can be calculated. However, since the typical sizes of laser beams output from modern large laser facilities varies from a few millimeters to several decimeters, which are beyond the capabilities of both FDTD and BPM. As for the calculation of beam propagation in free space and lens focusing, many researchers have done relevant research in this area, and there were some mature calculation approaches can be used. In 2001, [PERSON] and [PERSON] showed a fast Fourier transform (FFT)-based method for calculating the Rayleigh-Sommerfeld full diffraction integral [10]. By comparing of that method with direct integration (DI) of the Rayleigh-Sommerfeld integral, they proved that the new method based on FFT was effective. In 2003, [PERSON] et al. analyzed the fast algorithms using the Fourier transform and the fractional Fourier transform (FRT) [11]. They also studied the calculation of Fresnel diffraction patterns under convergent illumination. Double-step Fresnel diffraction (DSF) is an efficient diffraction calculation method at the expense of computer memory and calculation time. In 2013, [PERSON] et al. described a band-limited DSF method and mitigated the aliasing noise of the DSF method [12]. In 2009, [PERSON] et al. presented a novel method for simulating the free-space propagation of laser beams, which was an improvement of the angular spectrum method (AS) [13]. They resolved the sampling problem of transfer function by limiting the bandwidth of the propagation fields. However, the above methods are only used to calculate the propagation of laser beams in free space without considering the Kerr effect. In this work, we try to analyze the influence of Kerr effect on the focusing performance of optical lens under the incidence of high-power laser beams with beam sizes up to 100 mm. For this purpose,Both the two-dimensional fast Fourier transform (2D-FFT) algorithm, and the angular spectrum algorithm are utilized according to the theory of Fourier optics. The presented calculation methodology was employed to reduce the computation resource for the focus of large laser beams and the Kerr effect is considered by adding phase term proportional to the Kerr effect in the 2D-FFT method to get the light field distributions in the nominal focal plane and in several planes adjacent to the focal plane. The results show that the different types of incident laser beams have different focusing characteristics for the same optical lens when the Kerr effect is taken into consideration.
## 3 Results
### The Effect of Flow Velocity  
In order to keep the flow disturbance in the storage tank within a certain range and maintain the good working performance of the TES system, the inlet flow rate is usually set at a low level during the charging and discharging processes. However, the inlet velocity is related to the flow and heat transfer parameters such as inlet flow rate and Reynolds number in the storage tank. The variation within its reasonable range may cause the change in charging and discharging characteristics of the TES system. It's necessary to investigate the influence of the change of inlet velocity on the thermocline in the storage tank, for selecting the best inlet velocity to improve the efficiency of the TES system.  
In the simulation of flow rate, other parameters remain unchanged. The inlet flow velocity in the charging process is 4.0-4.8 \(\times\) 10\({}^{-3}\) m/s, and the inlet flow velocity in the discharging process is 8.0-9.6 \(\times\) 10\({}^{-3}\) m/s. Figure 7 presents the variation of thermocline thickness with flow rate during charging and discharging processes at different times.  
Figure 7: The variation of thermocline thickness with flow velocity ((**a**)-charging, (**b**)-discharging).  
It can be seen in Figure 7 that the thermocline thickness increases with the increase of the inlet flow velocity, whether charging or discharging. When the inlet velocity increases, the average velocity in the storage tank increases, and the Reynolds number of turbulent flow in the storage tank increases accordingly. The increase of Re will lead to the increase of Nu. It means that the convective heat transfer of water in the storage tank is enhanced, which enhances the thermal disturbance in the storage tank and thickens the thermocline.  
Under different inlet flow velocities, the thermocline thickness in the storage tank increases with the increase of inlet flow velocity during charging and discharging processes, but the system performance index defined by Equation (11) combined with Table 1 shows that the performance of the TES system has almost no change. This is because this study is based on the change of velocity under different flow rates. That is, when the velocity increases, the flow rate increases accordingly. When the charging time and height to diameter ratio are certain, it means that the diameter and height of the tank body increase. In this way, it is found that the ratio of the thermocline thickness to the height of the tank body and the evaluation index are approximately unchanged.
# An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
## 2 Materials and Methods
### Remote Sensing Impact Factor (RSIF)  
We constructed an impact index (1) to measure the contribution of each satellite data to the remote sensing community and to predict the possible future development of related trends. The indicators selected included the number of articles, user countries, and research areas. These three indicators provide a representation of the development and potential of satellites. The equation used to calculate the index was  
\[\text{RSIF}=\sqrt[3]{a\times b\times c} \tag{1}\]  
where \(a\), \(b\) and \(c\) are the number of articles, user countries, and research areas where EO data are applied, respectively.
# A Hybrid Adaptive Particle Swarm Optimization Algorithm for Enhanced Performance
## 4 Experiments
### Comparative Analysis of the Enhanced Algorithm and Standard PSO Algorithm  
The efficacy of the algorithm's optimization can be evaluated through both the mean and optimal values, while the standard deviation serves as an indicator of the algorithm's stability. The results obtained from testing the enhanced PSO algorithm in comparison to the standard PSO algorithm are presented in Table 3.  
The comparative experiments conducted between the standard PSO algorithm and the enhanced PSO algorithm reveal a marked improvement in the optimization performance of the latter. Evaluations of various test functions demonstrate that the optimal values achieved by the improved algorithm consistently approximate the theoretical optimal values of the respective test functions, indicating effective optimization results. As illustrated in Table 3, both algorithms exhibit a standard deviation of 0.00E+00, suggesting minimal variability and relatively stable performance. Notably, with the exceptions of \(F_{6}\) and \(F_{10}\), where the relative gap in results is not substantial, the APSO algorithm demonstrates a significant enhancement in solution accuracy across the remaining functions. This further substantiates the efficacy of the improved algorithm. The convergence curves presented below illustrate a comparison between the enhanced algorithm, APSO, and the conventional PSO across various test functions.  
Figure 3 indicates that the improved algorithm exhibits a convergence rate comparable to that of the standard PSO on functions \(F_{1}\) and \(F_{8}\), while requiring fewer iterations and achieving convergence at a faster pace. Furthermore, for the remaining test functions, the APSO demonstrates a marked improvement in speed relative to the standard PSO, thereby exhibiting a more pronounced overall convergence effect.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & \multicolumn{2}{c}{\(V_{avg}\)} & \multicolumn{2}{c}{\(V_{opt}\)} \\ \hline  & **APSO** & **PSO** & **APSO** & **PSO** \\ \hline \(F_{1}\) & 1.15E-15 & 7.07E+04 & **1.15E-15** & 7.07E+04 \\ \(F_{2}\) & 3.26E+41 & 3.52E-74 & **3.52E-74** & 3.26E+41 \\ \(F_{3}\) & 1.43E+27 & 8.23E-69 & **8.23E-69** & 1.43E+27 \\ \(F_{4}\) & 7.87E+01 & 9.89E-76 & **9.89E-76** & 7.87E+01 \\ \(F_{5}\) & 6.96E+01 & 2.34E-77 & **2.34E-77** & 6.96E+01 \\ \(F_{6}\) & 5.72E+04 & 1.06E+00 & **1.06E+00** & 5.72E+04 \\ \(F_{7}\) & 4.54E+09 & 1.13E-02 & **1.13E-02** & 4.54E+09 \\ \(F_{8}\) & 6.05E+04 & 0.00E+00 & **0.00E+00** & 6.05E+04 \\ \(F_{9}\) & 2.42E+00 & 0.00E+00 & **0.00E+00** & 2.42E+00 \\ \(F_{10}\) & 2.59E+10 & 3.80E+00 & **3.80E+00** & 2.59E+10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the enhanced algorithm APSO and the standard algorithm PSO, where \(V_{avg}\) and \(V_{opt}\) denote the average and optimal values of the problem, respectively.
## 3 State-of-the-Art Review on Energy Optimization Techniques in UIoT
### Battery Swapping Approaches in UIoT  
In UIoT networks, battery swapping methods are generally used for underwater vehicles such as AUVs, UUVs, or ROVs to replace their batteries when they land on surface water. Although the processing time for battery replacement is fast, the downtime allied with resurfacing, swapping, and plunging into underwater vehicles is a huge process. In addition, the battery-swapping process needs supporting crew and vessels. Therefore, this process needs more manpower and, similarly, it is costly and difficult to manage. In one example, a battery replacement approach is adopted in Bluefin [23], a model shown in Figure 8. Bluefin is the AUV that is adapted with a battery replacement mechanism, the total processing time for battery replacement takes around 30 min, which includes resurfacing, battery swapping, and plugging into the vehicle.
## 5 Experiment Results and Discussion
### Identification Results by IHHT
Tabulated in Table 3 are the identification results based on such four features using an extension method. It is suggested that in the absence of noise interference, an average identification ratio up to 99.2% is reached, a figure superior to that provided by HHT, and the ratio drops to 78.3%, still a satisfactory figure, in the presence of \(\pm\)30% noise interference.
# Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
## 1 Introduction
The second challenge is that most existing research focuses on specific and single-port traffic flow scenarios, while studies on wide-scale, networked, and multi-port ship traffic flow prediction are relatively scarce. For example, ref. [27] combines Kalman filtering with regression analysis to improve short-term ship traffic flow prediction performance. Ref. [28] proposes a multi-variable extended CNN model based on convolutional methods, which specifically considers the impact of extreme weather events on ship traffic flow changes. However, the experimental settings in [28] involve geographically close ship traffic statistical areas, failing to consider the complex correlation structures among multiple regions. Ref. [29] uses AIS data to analyze the hourly ship traffic volume in a specific area near Ningbo and achieves ship traffic flow prediction for multiple time periods using an improved GRU-based time series prediction model. Nevertheless, it is also limited to the task of predicting traffic flow in a single maritime area. This article tackles the challenge of ship traffic forecasting in a scenario involving multiple ports, employing a spatiotemporal graph model. Our proposed approach adopts a data-driven methodology that leverages actual data to construct a comprehensive multi-port graph structure, thereby establishing a realistic representation of the traffic network for the accurate prediction of maritime traffic flow. Subsequently, we utilize graph neural networks to capture the intricate message-passing patterns occurring between nodes within the port network. This enables the timely identification of abnormal traffic fluctuations in neighboring port nodes, thus enhancing the model's capacity to capture and exploit complex temporal dependencies. The incorporation of these techniques leads to improved prediction performance and effectively addresses the need for coordinated management in the context of multiple ports. In summary, this paper contributes to the research in the following ways:  
* It proposes a data-driven method for the construction of a multi-port network based on historical data and creates a realistic port ship traffic dataset using AIS data. * It presents a traffic prediction model specifically designed for maritime scenarios, utilizing a spatiotemporal graph neural network. The novel model addresses the issue of imbalanced temporal and spatial features in the spatiotemporal dataset. * The improved model's effectiveness is validated through experiments conducted on multiple port group datasets.
## 4 Related Research on Service Discovery Protocols
### Directory-Based Architecture  
The directory-based architecture can be also divided into two categories: centralized directory architecture and distributed directory-based architecture. A centralized directory architecture relies on central directories to store services of node providers. Most of the existing service discovery protocols based on a directory architecture are designed for fixed and infrastructure-based networks, and thus, they are neither efficient, nor practical for mobile _ad hoc_ applications. SLP, Jini [26], Universal Plug and Play (UPnP) [27], Salutation [28] and Service Discovery Protocol (SDP) [29] are some examples of such protocols.  
In MANETs, however, the network topology is very dynamic, and nodes have limited energy and bandwidth resources. It is not possible to statically configure nodes with a list of central directories or servers. Taking into account these characteristics, we hold that distributed directory architectures are more appropriate for MANETs than centralized directory architectures.  
Recently, a considerable amount of work has been done on this research area. There are solutions that offer a cluster of service providers to be a service directory for answering service queries and accepting service registrations. [PERSON] _et al._[30] propose a model for energy efficiency and scalability. Service discovery messages are flooded over the cluster head nodes, which form a service repository for service registration. [PERSON] [31] proposes a solution where nearby nodes are grouped together to form clusters. Clusters are two hops in diameter, and they are overlapping or disjoint. Each cluster chooses a cluster head that coordinates the cluster members. Inter-cluster routes are dynamically discovered. Each node maintains a neighbor table and a cluster adjacency table. [PERSON] _et al._[32] propose a service discovery architecture using swarm intelligence. Swarm intelligence is used to establish the intra- and inter-cluster shortest path routing. Clients send a service request with its required quality of service (QoS) parameters to its source cluster head. Each cluster head searches the QoS-aware server with matching QoS constraints by means of a service table and a server table. [PERSON] _et al._[33] present an adaptive service discovery protocol that enhances the performance of service discovery. They use a distributed directory-based service discovery mechanism that operates in a proactive mode with service advertisements to the core node and selects a provider based both on the distance and service capability of the provider.