esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
In the field of navigation technology, it is necessary to design an autonomous navigation system with high accuracy, stability, and reliability, focusing on exploring potential technologies related to path planning and positioning. In the process of R&D and application of unmanned ship navigation technology, autonomous navigation systems are carefully designed and optimized according to different mission requirements and environmental conditions. In particular, it is necessary to conduct in-depth research on positioning technology to improve the precise positioning and navigation capabilities of unmanned ships. In addition, current research focuses on optimizing the path planning algorithm for unmanned vessels, strengthening the planning and decision-making capabilities of unmanned ship navigation paths, and avoiding obstacles and dangerous areas to achieve efficient and reliable navigation in various complex and dynamic environments. In the field of control technology, it is necessary to strengthen the research and development of unmanned ship control technology, focusing on exploring potential technologies related to remote control, speed control, motion optimization, and movable platforms. In the process of R&D and unmanned ship control technology application, it is necessary to strengthen real-time monitoring of the ship's dynamics and position and promptly obtain parameters and information. At the same time, we actively develop specific control algorithms and actuators to adjust and control the speed of unmanned ships accurately. We need to strengthen the optimization and adjustment of the motion parameters of unmanned ships to improve the navigation performance and mission execution capabilities of unmanned ships in different waters and environments. In addition, it is also necessary to improve the stability and maneuverability of the movable platform and actively carry out the design and development of equipment such as hulls, propellers, and navigation sensors to achieve stable navigation and precise control of unmanned ships in the water. In the field of power technology, it is necessary to focus on exploring potential technologies related to propulsion devices, power batteries, and solar batteries. In the field of power technology, it is essential to focus on exploring potential technologies related to propulsion devices, power batteries, and solar batteries. The propulsion device is the equipment that provides power for the unmanned ship. Different propulsion device types must be accurately selected, such as motor propulsion, water jet propulsion, propeller propulsion, etc. Power batteries are the energy source for unmanned ships. It is also necessary to develop efficient energy systems such as wave energy, lithium batteries, solar charging systems, solar batteries, or fuel cells to extend the endurance of unmanned ships.
## 2 Materials and Methods
### Data Preprocessing and Evaluation Metrics  
To train the ANN, input and output values were normalized. The purpose was to make data comparable or to scale them in a way that simplifies analysis or training processes.  
Data normalization is the process of organizing data entries to ensure uniformity across all fields and records, making information easier to find, group, and analyze.  
Figure 3: Artificial Neural Network architecture.  
The data were normalized in the range 0-1 according to the following formula:  
\[x_{n}=\frac{value_{n}-min_{i}}{max_{i}-min_{i}} \tag{1}\]  
concerning the maximum and minimum value of the i-th column.  
The R-squared (R\({}^{2}\)) metric is used to evaluate the proportion of variance in the dependent variable that can be predicted from independent variables [46]. As an additional method of evaluation, the mean absolute error (MAE) measures predictive model accuracy by calculating the average absolute difference between predicted and actual values. Similarly, the mean squared error (MSE) provides insight into predictive performance by measuring the average of squared differences between predicted and actual values.  
\[R^{2}=1-\frac{SS_{\text{res}}}{SS_{\text{tot}}} \tag{2}\]  
\[MAE=\frac{\sum_{i=1}^{n}\lvert y_{i}-x_{i}\rvert}{n} \tag{3}\]  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-x_{i})^{2} \tag{4}\]  
where \(\text{SS}_{\text{res}}\) is the residual sum of squares, \(\text{SS}_{\text{tot}}\) represents the total sum of squares, n is the number of observations, x is the observed value, and y is the predicted value.  
A dedicated step involves assessing the prognosis quality using an independent coefficient to evaluate the approximation's quality thoroughly. This value assesses the agreement between actual test data and the predictions made by the meta-model. The coefficient of prognosis (COP) is applied for this purpose and offers an advantage over other error measures. It is worth noting that the COP automatic scale allows for a more nuanced interpretation of the results [47].  
\[COP=\left(\frac{\sum_{i=1}^{n}\left(x_{i}-\lvert\overline{x}\rvert\right) \cdot\left(y_{i}-\lvert\overline{y}\rvert\right)}{\sigma_{\text{x}}\sigma_{y }\cdot\left(n-1\right)}\right)^{2} \tag{5}\]  
where \(\overline{\text{x}}\) and \(\overline{y}\) are the average values and \(\sigma_{\text{x}}\) and \(\sigma_{y}\) are the standard deviations of the samples.
## 3 Results
### Properties
#### 3.2.2 Self-Diffusion Coefficient  
The self-diffusion coefficient is an important parameter in electrokinetics, metal extraction, and pyrochemical processing of nuclear waste; it is important to understand how the diffusion coefficient value is affected by changes in the solvent, and how it depends on the ionic charge and size.  
The self-diffusion coefficients were calculated from the slope of the mean square displacement in the linear region for each ion in the melt. The simulated data are presented in Table 5. The results were in good agreement with corresponding experiments' data of pure salts [27; 28]. The values fluctuated as the component changed, and showed an overall positive dependence on the LiCl concentration and temperature. The fluctuation may be attributed for two reasons: First, according to Furth hole theory [29], holes were formed by the action of the irregular thermal movement. Larger holes increased as the temperature increased. Second, ion clusters may form and the very short existence of ion clusters influences the mobility of ions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Composition** & **Slope** & **Intercept** & **Pure** & **Slope** & **Intercept** \\ \hline
**A-0** & \(-5.66\times 10^{-4}\) & 2.03415 & & & \\
**A-2** & \(-5.41\times 10^{-4}\) & 2.00025 & & & \\
**A-4** & \(-5.15\times 10^{-4}\) & 1.96612 & & & \\
**A-6** & \(-4.89\times 10^{-4}\) & 1.92813 & & & \\
**A-8** & \(-4.62\times 10^{-4}\) & 1.89015 & & & \\
**A-10** & \(-4.33\times 10^{-4}\) & 1.84863 & & & \\ \hline \end{tabular}
\end{table}
Table 4: Linear fit of the simulated density for six typical systems (900 K–1200 K).  
Figure 7: Surface map of simulated density results.  
It could be observed from Table 5 that ions in melts were less diffused in ternary systems than in the corresponding pure melt [28,30] and the binary systems (LiCl-KCl [30] and LiCl-NaCl [16]). The Li ions' diffusivity was smaller than the Na ions' and K ions' at a low LiCl concentration and exceeded them at a higher concentration, which indicated the chemla effect. The diffusion coefficient of each ion exhibited an overall increase tendency as the LiCl concentration increased, which was consistent with the h\({}_{\text{max}}\) decrease in PDFs. The increasing rate of D(Li\({}^{+}\)) become greater when the molar fraction of LiCl was beyond 60%. The anions' mobility incensement was smaller than the cations'.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 4 Results
### Genetic Algorithm Tests
#### 4.2.2 Crossover and Mutation Probability  
The crossover probability and the mutation probability are also very important parameters for the GA [30]. They should be selected after a trial phase where various combinations must be tested. The proposed GA was tested for many combinations. The results are presented in Figure 10, the two red lines indicate the lowest value of the objective function and the value of the fitness function when the algorithm stops. When there is one line, the GA converges to the minimal point.  
Figure 10: Experimental tests for GA performance setting crossover probability and mutation equal to 0.5 and 1.0.  
Figure 9: Relationship between population size, generations, and execution time.  
To start with, two rather rare combinations were selected where crossover probability and mutation were equal and set to 0.5 and 1.0. In the first case, it is observed that the GA does not converge until generation 100, and the algorithm often has high picks. This is due to the high rate of both selected probabilities. In the second case, where the probabilities are set to 1.0, it may be observed that the algorithm seems to be mixed up and going all over the search space.  
Subsequently, to test the effect of the crossover probability on the GA, the mutation probability is kept fixed and equal to \(p_{m}=0.8\). As it may be observed in Figure 11, the GA never converges with a high mutation probability, regardless of the \(p_{c}\) value. The GA had sharp fluctuations during the 100-generation deployment. It is suggested that a high rate of mutation disorganizes the GA.  
Figure 11: Crossover and mutation tests with mutation fixed to \(p_{m}=0.8\).  
Moreover, to test the effect of the mutation probability on the GA, the crossover is kept fixed and equal to \(p_{c}=0.8\). In contrast to the previous case where the \(p_{m}\) was high, when the \(p_{c}\) is high, the GA generally performs better (Figure 12). Furthermore, the lower the \(p_{m}\), the better. A \(p_{m}\) greater than 0.05 starts to deteriorate the GA's ability to converge to the optimal solution.  
To sum up, the best performance of the suggested GA is observed when \(p_{c}=0.8\) and \(p_{m}=0.01\). Therefore, this is the combination that is used for the GA because it ensures that the GA will find the best solution while quickly converging.
## 3 Experiment and Analysis
### Ablation Experiments  
To validate the rationality of the designed network, this study conducted ablation experiments to assess the impact of introducing different attention mechanisms on the segmentation accuracy of the model. The experimental results are shown in Table 4, where the checkmark (\"\(\mathscr{A}\)\") indicates whether a particular module was used. Compared to the standard Deeplabv3+ model, the introduction of the SENet module improved mIoU and mPA by 0.62 and 0.23 percentage points, respectively. The introduction of the CBAM module increased mIoU and mPA by 0.65 and 0.1 percentage points, respectively. When SENet and CBAM were combined, the mIoU and mPA were improved by 3.23 and 1.96 percentage points, respectively, compared to the original model. These experimental results are shown in Table 4.  
Figure 11: Comparison of different segmentation models. (**a**) Tilt; (**b**) Horizontal; (**c**) Multi-fish; (**d**) Swimming in opposite directions; (**e**) Partial occlusion of fish.  
sults further demonstrate that the proposed method has good feature extraction capabilities and higher segmentation accuracy.
# MD-TransUNet: An Image Segmentation Network for Car Front Face Design
## 1 Introduction
To enhance the edge continuity and overall definition of headlights and grilles, the MSAG module is integrated within the skip connections linking dual encoders and decoders, capturing multi-scale features to enhance detail sensitivity during reconstruction. This provides additional local specific information while suppressing irrelevant interference. The DCGCN module captures channel topological dependencies and aggregates effective features, enhancing information interaction and fusion between channels, thus addressing Transformer's information loss during sequence encoding. Combining the MSAG and DCGCN modules effectively improves segmentation accuracy and generalization. By first performing image segmentation and then using the stable diffusion model for image restoration, design schemes are quickly generated, thereby improving design process efficiency and effectiveness, and providing a new technical approach for automotive design. The primary contributions of this paper are as follows:  
1. For complex intricate areas such as headlights and grilles, the shortcomings of existing methods in feature extraction, edge information retention, and multi-scale feature fusion are analyzed, and a new car front face segmentation network is proposed. 2. The improved segmentation model MD-TransUNet has a good segmentation effect on headlights and grilles. 3. The innovative combination of the segmentation network and image restoration is proposed, which allows details such as the headlights and air intake grille to show diversity while maintaining the overall consistency of the front face of the car. This improves the efficiency and effectiveness of the design process and provides an innovative technical approach for the field of automotive design. The remaining sections of this paper are outlined below: Section 2 presents the overall architecture of the TransUNet network and two improved modules; Section 3 describes the experimental design process; Section 4 discusses the comparative experiment and the innovative combination of the segmentation network and the stable diffusion model; and Section 5 explains the conclusions of this study.
# Design and Analysis of a Floor Radiant Heating System Based on Energy Substitution Technology
## 5 Results and Discussion  
### Indoor Temperature Distribution  
It can be shown that there is a large temperature difference between the maximum and the minimum of the indoor floor surface temperature (Table 1). There are some reasons:  
1. The test point of the minimum is set near the wall and close to the floor, which means that the position is quite far away from the heat source;
2. The test point of the maximum is in the middle of the heating system, which has less heat loss and a smaller heat change, so the measured temperature is relatively high.  
Due to these reasons, the temperature at the position that is near the heat source is higher than in other locations. These results indicate that the difference in the indoor floor surface temperature is larger and the thermal comfort is poor. The inhomogeneity of the indoor floor surface temperature distribution is larger than in other locations, and the standard deviation of the indoor floor surface temperature can reach 1.87 \({}^{\circ}\)C.  
At the positions of 0.6 m, 1.2 m, 1.8 m and 2.4 m, the temperature homogeneity of the test points is better than that of the indoor floor surface, and the average temperature is 26 \({}^{\circ}\)C (ranged from 25 \({}^{\circ}\)C to 27 \({}^{\circ}\)C), which is also shown in Table 1. The maximum temperature deviation is almost the same  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Temperature (\({}^{\circ}\)C)**} & **0** & **0.6 m** & **1.2 m** & **1.8 m** & **2.4 m** \\ \hline Minimum & 24.13 & 25.69 & 25.7 & 25.66 & 25.2 \\ Maximum & 29.96 & 26.94 & 26.92 & 26.89 & 26.71 \\ Average & 27.21 & 26.27 & 26.25 & 26.25 & 25.97 \\ The max temperature difference & 5.83 & 1.25 & 1.22 & 1.23 & 1.51 \\ Standard deviation & 1.8737 & 0.3587 & 0.3563 & 0.3633 & 0.4323 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of 125 temperature measurement points.  
at positions of 0.6 m, 1.2 m and 1.8 m, and the standard deviation is 0.36 \({}^{\circ}\)C, which indicates that the temperature homogeneity of positions 0.6 m to 1.8 m is the best among the different positions. From Table 1, it can be demonstrated that the thermal comfort is the best between positions 0.6 m to 1.8 m, which are the active regions of most people.  
Figure 8: Temperature distribution. (**a**) Temperature distribution of different layers; (**b**) Temperature distribution across all positions.  
The temperature distributions of the different positions are revealed in Figure 8. Because the indoor floor surface is near the heat source, the average of the indoor floor surface temperature is obviously higher than in other locations. As the height of the test points increases, the temperature decreases and the temperature gradient exhibits a small change. With respect to the proposed floor radiant heating system, the temperature homogeneity of the whole room is preferable, as is the thermal comfort.
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 3 System Model
### Channel Allocations  
The gateway is the master node or base station which undertakes the spectrum-related tasks along with data processing and routing. The nodes are sensors which sense and transmit data. Each gateway assigns the transmission channels to its governed nodes. We propose that spectrum management algorithms shall be hosted by the gateway, as it can have a constant power source (e.g., mains). On the other hand, the nodes, which are mainly battery operated, can have only limited functionality related to sensing the data and transmitting it to the gateway. This saves the node's battery to a great deal and is very desirable for many applications of sensor networks.  
We have previously proposed a dynamic channel allocation algorithm for cognitive radio networks [31]. To turn the WBAN into a cognitive body area network (C-BAN), we have revised the algorithm and made it more dynamic and robust for the resource limited computational platform.
# The Concept of Segmented Wind Turbine Blades: A Review
## 7 Mechanical Joints in Segmented Blades
### Experience from Rotor Tips and Glider Wings  
To reduce turbulence at the tips, aircraft often employ winglets. Similar tips are used on wind turbines to limit noise production [129]. However, such angled blade tips form delicate components during transportation and make manufacturing more complex and expensive. Therefore, they are often made as separate components and connected to the blades at the installation site. The blade tips can be connected by means of tubular guides and locked by means of a bolt, either transversely to the joint as suggested in [PERSON] [129] or longitudinally as suggested in [PERSON] [130]. Furthermore, in the past, tip brakes were used to prevent over-speed on rotors with stall control [8; 99; 131]. These can rotate 90 degrees to create a drag. They are typically connected by means of a tube. Similarly, [PERSON] [50] suggested to alleviate loads with a segmented tip. In addition, the fabrication methods, structural layout and slenderness of gliders and wind turbine blades are similar, making joints used in gliders suitable candidates for blade segmentation [100]. Gliders often have detachable wings to allow easier transport and storage [132]. Modern glider carry-through structure configurations have one or two tongues next to each other to transfer bending loads [133]. Similar spar-bridge strategies with one or more protrusions have been suggested and tested for segmented blades. For example, [PERSON] [134] suggested a segmented blade that relies on joining the shear webs of a number of structural spars with shear pins. Loads are distributed using shear blocks attached to the webs. Segmented blades using spar-bridge joints were suggested in various studies [135; 136; 137; 138]. Further, [PERSON] [45] designed and tested prototype of a segmented version of a 13.4-m blade with a connecting tube. The tube was attached to the blade using two bulkheads, similar to the concept suggested by [PERSON] [139]. The blade underwent three static load tests (flap-wise towards the suction side and both edge-wise directions) followed by a five million cycle fatigue test in the flap-wise direction after which the static tests were repeated. It was concluded that no damage occurred in the blade, but that the loads were sometimes transmitted through the locking device instead of via the fitting, which had resulted in fretting corrosion.
## 4 Conclusions  
In summary, the dynamic manipulation of the metal-graphene hybrid structure has been theoretically proposed and numerically investigated. The transparency window is substantially weakened with the presence of the graphene layer and can be actively modulated by tuning the Fermi energy via external voltages. The distinctive variations in different resonances of the transparency window as Fermi energy changes are elucidated by analyzing the surface current distribution. The different width of the strip is considered to explore the influence of the graphene layer. Moreover, the tunable slow light effect is analyzed in detail with various parameters including Fermi energy, strip width, and carrier mobility. The proposed metamaterial may be useful in designing terahertz modulators and optical buffers, and the presented analysis of the physical manipulation mechanism can be significant in developing devices based on the metal-graphene hybrid structure in the THz regime, which can also be extended to other spectral regimes.  
Conceptualization, C.L.; Data curation, S.Z.; Formal analysis, S.Z. and C.Y.; Investigation, C.L.; Methodology, C.L.; Project administration, S.Z.; Resources, C.Y.; Software, C.L.; Supervision, P.L.; Visualization, Q.Z.; Writing--original draft, C.L.; Writing--review & editing, C.L., S.Z. and P.L.  
This research received no external funding.  
The authors declare no conflict of interest.
## 1 Introduction
[20] utilized laser technology for experimental studies on micro-orifice gas bearings with both circular and rectangular pads, examining the influence of micro-orifice numbers and diameters. [PERSON] [21] introduced inherently compensated rectangular gas bearings to the discourse, adopting a multi-objective optimization framework with genetic algorithms to delve into how pad geometry, supply pressure, and the positioning and size of supply holes impact static performance. [PERSON] et al. [22] developed a parameter model for rectangular gas bearings that facilitated rapid static analysis without sacrificing accuracy and discussed the role of geometric parameters to assist bearing design researchers. They presented the analysis in a dimensionless format, aiming to enhance the model's general applicability. [PERSON] et al. [23] focused on air hydrostatic thrust bearings for ultra-precision micro-machining tools, innovating designs with single- and double-row hole structures and incorporating various pressure-equalizing grooves on the bearing surface. Utilizing computational fluid dynamics, they simulated air film behavior to assess pressure distributions, highlighting the benefits of a double-row hole configuration for these applications. Existing research has largely focused on the characteristics of bearings, but it has not thoroughly investigated their operational stability. This study advances the field by assessing the flow field area within porous aerostatic rectangular thrust bearings, applying both analytical and finite element methods. It also includes an experimental component, allowing for a comparison of results. Through the use of comprehensive finite element software, the study evaluates key metrics such as the bearing's capacity, gas mass flow, stability,and stiffness. The impact of various structural parameters and operational conditions on the bearing's performance is also explored, shedding light on their significance.