esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Brix Measurement for Watermelon Breeding
## 3 Results  
### Watermelon Diameter Measurement Performance  
To evaluate the measurement performance of the distance sensor when facing the surface material of the watermelon, we obtained the diameter of 25 watermelon samples  
Figure 4: Measuring gaps and the robotic arm.  
Figure 5: The image analysis procedure for digital tube recognition.  
using the direct distance detection method as follows: (i) fixing the distance sensor perpendicular to a plane; (ii) after measuring the distance of the plane, placing the watermelon and measuring the distance between the watermelon and the sensor. The difference between the two values is the measurement result of the watermelon diameter.  
We compared the measurement results using an RGB-D camera, RealSense D455, manufactured by Intel, as well as the laser rangefinder which was previously mentioned. As shown in Table 2, RealSense D455 showed _RMSE_ of 1.136, which is lower than that of the laser rangefinder. However, since RealSense D455 has a relatively higher cost than the laser rangefinder, we chose the latter for further experiments.  
We placed every watermelon sample on the V-groove, then measured the diameters along the three directions in Table 1 using the laser rangefinder. The result showed that, as shown in Figure 6, there was a high correlation between the measurements and the ground truths, with \(R^{2}~{}=~{}0.9608\) and \(RMSE~{}=~{}4.321\).
## 7 Route Maintenance
### Route Hand-Off  
When the target node has detected that the current route trust is going to be un-trusted, a Flow-Hand-off message is generated by the target and propagated using FLOW-REQ message. After the source receives a Flow-Hand-off message, it chooses the best route to forward the received information.  
After detecting another best route that completes the minimum trust threshold requirement, the source node sends the FLOW-SETUP message. This FLOW-SETUP message is the same as it was sent the first time during route setup, except it has changed the path from source to target node. The Route Hand-off process is depicted in Figure 5. If the RouteTV (Route trust value) for the requirement of packet forwarding is above 0.5. In Figure 5a, the old route is along of nodes (i.e., \(A\to B,\to C,\to E,\to F,RouteTv=0.53\)) and if this route trust value RTv is about to un-trusted \((RouteTVp=0.43)\), so the Route Hand-off is performed and the new route is now (i.e., \(A\to B,\to D,\to E,\to F,RouteTv=0.55\)).
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 2 Methods
### Encoder  
As a widely used benchmark network, we can see the existence of residual networks [32] in many studies. As one of them, ResNet18 can accurately extract multi-scale semantic features while incurring relatively lower computational costs, which is more favorable to enhancing the model's real-time application ability. Therefore, the ResNet18 is used in our encoder to extract multi-scale semantic information from urban scene images. ResNet18 consists mainly of 4 residual blocks, and the size of each stage's feature map is 1/2 of the previous stage. In our model, the feature maps generated in the encoder are fused with the counterparts in the decoder by using a \(1\times 1\) convolution. Specifically, the feature information generated in the encoder is efficiently fused with the context information generated by GLIM in the decoder by introducing a weighted sum operation. The weighting operation selectively fuses the features that are more beneficial to the final segmentation results; as a result, a more representative aggregated feature is obtained. The formula is shown in Equation (1):  
\[AF=\alpha\times EF+(1-\alpha)\times DF, \tag{1}\]  
where \(AF\) denotes the aggregated feature after fusion, \(EF\) denotes the features at different encoding stages, and \(DF\) denotes the features at different decoding stages.  
Figure 1: Overall structure of our model.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Cameraman**} & \multicolumn{2}{c}{**Pepper**} \\ \cline{3-6}  & & **0.5 m** & **0.3 m** & **0.5 m** & **0.3 m** \\ \hline \multicolumn{2}{c}{Compression ratio} & 7.2113 & 7.1111 & 7.2113 & 7.1608 \\ \hline \multirow{4}{*}{Reconstructed image from compressed holograms} & PSNR & 19.10 & 17.83 & 18.92 & 17.64 \\  & SSIM & 0.1651 & 0.0967 & 0.2007 & 0.1036 \\  & MS-SSIM & 0.6091 & 0.4628 & 0.6907 & 0.5252 \\  & VIF & 0.3946 & 0.2183 & 0.6396 & 0.3438 \\  & IFC & 0.4522 & 0.2519 & 0.5543 & 0.2835 \\ \hline \multirow{4}{*}{Reconstructed image from restored hologram} & PSNR & 28.86 & 26.86 & 29.88 & 27.16 \\  & SSIM & 0.6036 & 0.4465 & 0.6767 & 0.4852 \\ \cline{1-1}  & MS-SSIM & 0.8798 & 0.8022 & 0.9138 & 0.8343 \\ \cline{1-1}  & VIF & 0.5378 & 0.4316 & 0.6841 & 0.6306 \\ \cline{1-1}  & IFC & 0.9027 & 0.5098 & 1.2064 & 0.6379 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PSNR (dB), SSIM, MS-SSIM, VIF and IFC values of reconstructed results from JPEG compressed holograms and restored holograms with our proposed scheme. Figure 5: Ten object images employed for generating training holograms. Figure 6: Two object images (“Peppe” and “Cameraman”) employed for generating testing holograms. Figure 8: Reconstructed images from original holograms, JPEG compressed holograms and restored holograms in numerical simulation. Figure 7: (**a**) Original hologram; (**b**) JPEG compressed hologram; and (**c**) restored hologram with our proposed scheme. Our proposed scheme was further verified in optical experiments and the calculated holograms were optically reconstructed using the optical setup [6] shown in Figure 9. The hologram was reconstructed with a phase-only Holoeye PLUTO spatial light modulator (SLM). The system parameters were the same as the numerical simulation (wavelength: 532 nm; pixel size: 8 \(\upmu\)m; hologram size: 1024 \(\times\) 1024 pixels; object-hologram distance: 0.3 m/0.5 m). The phase-only holograms loaded into the SLM were assigned with an additional carrier phase to transversally shift the desired reconstructions away from the un-diffracted (zero-order) beam component. The projected beam from the SLM is transmitted through a 4-f optical filtering system containing a low-pass iris with 3.3 mm diameter to block the zero-order noise. The optically reconstructed results are shown in Figure 10. It can be observed that the quality of reconstructed images after hologram enhancement with deep learning are improved, compared with the ones from compressed holograms without restoration.
## 3 Results and Discussion
### Protein Content and Enzymatic Activity  
The tested honeydew samples were compared in terms of protein content and enzymatic activity (Table 4). These parameters were formerly proposed as auxiliary indicators of honey quality [33], especially in terms of its overheating. Among the tested enzymes, diastase activity, included in the obligatory set of parameters used for quality control, as well as three further acid glycosidases applied to the honey activity evaluation were determined.  
The obtained results confirmed that honeydew honeys were more abundant in total soluble protein compared to rape honey; however, there were great differences between the tested samples, and even a three-times higher protein content was found for samples H3 and H4 compared to H1. The same was reflected in all studied enzymes: sample H1 was characterized by the lowest glycosides activity and H4 by the highest. In all samples, the glycosidase activity decreased in the following sequence: \(\alpha\)-GLU \(\geq\)\(\beta\)-GAL \(>\) NAG. Nevertheless, all honeydew honey indicated a higher activity of the tested glycosidases than rape honey, excluding diastase.  
It is commonly accepted that dark honeys are richer in protein than pale ones [33]; however, previously we found great variation in this parameter regarding honey variety which ranged from 34.3 (acacia honey) to 452.7 (heather) mg/100 g. In contrast, for honeydew honey, a lower protein value was found in our previous paper (94-102 mg/100 g) [33].  
The data obtained for diastase occurring in honeydew honey were lower than previously reported [33; 51]. As all used honeys were collected as fresh, the possibility of overheating could be excluded. Moreover, the good quality of the tested honeys is supported by other tested enzymes activities which were formerly proposed as a simple sensitive marker of honey processing [52]. However, in all cases, the obtained values were at the upper end of the range found for glycosidases activity in various varietal honeys, which was clearly seen for N-acetyl-\(\beta\)-glucosidase (NAG) activity, which is much more active in honeydew and heather honeys (up to 43.70 mu) compared to other nectar honey varieties (from 4.29 to 10.46 mu). Moreover, the tested honeys also showed an increased activity of \(\alpha\)-glucosidase and \(\beta\)-galactosidase. These enzymes were previously proposed as indicators of honey thermal processing [52; 53]. However, enzymatic activity may also be influenced by other factors, i.e., the degree of honey maturity storage conditions [53; 54; 55].
## 4 Discussion
### Challenges and Limitations
#### 4.4.3 Model Interpretability  
The reviewed studies are also faced with another challenge: interpretability is missing from many advanced forecasting models, e.g., LSTM and CNN [73]. While these models may be precise, they lack interpretability; in other words, it is very difficult to say how exactly the model comes to a particular prediction, which is a significant requirement for agricultural markets. In this case, a lack of clear and coherent explanations of model results poses a problem to the use of such models in practice. In addition, many models show inconsistent performance depending on the forecast horizon and market condition, and this makes it hard to deploy them. For instance, deep learning methods can make good long-term predictions but may perform as well in short-term predictions or on datasets containing significant price fluctuations supported by extreme economic factors that may be beyond humans' control to forecast. This places further refinement and optimization required to enhance the robustness and reliability of forecasting models to more general scenarios and datasets.
# Research on Temperature Field Analysis Method of High-Speed Bearing Chamber-Bearing System
###### Abstract
Under high-speed rotation, the lubricating oil splashed from the bearing forms an oil film on the inner wall of the bearing chamber, and there is convection heat transfer between the lubricating oil and the inner wall of the shell. The oil-air mixture formed by the mixing of lubricating oil and air in the chamber has convection heat transfer with bearing chamber walls. In the bearing chamber, all parts are combined by a physical connection, and heat transfer between adjacent parts is carried out by heat conduction. In a word, the heat transfer process in a bearing chamber is very complicated. In order to study this problem, this paper puts forward a decoupling method that separates fluid dynamics from thermal analysis of a solid temperature field. Specifically, the characteristics of oil-air two-phase distribution in a bearing chamber under a given bearing speed, bearing load, oil flow rate, and oil inlet temperature are obtained based on the VOF method, and the convective heat transfer boundary conditions obtained from the above analysis are used as the thermal boundary conditions for the subsequent thermal analysis. The three-dimensional models for the solid domain of the bearing chamber-bearing system, as well as the three-dimensional models for the fluid domain and solid domain of each individual bearing chamber within the system, are established independently. Initially, the characteristics of the oil-air two-phase distribution within the bearing chamber are determined under specified conditions of bearing speed, load, oil flow rate, and oil inlet temperature, utilizing the Volume of Fluid (VOF) method. The convective heat transfer boundary conditions derived from this analysis are then employed as the thermal boundary conditions for the subsequent thermal analysis. The heat generation of the bearings and the thermal boundary conditions are taken into account to establish the temperature field analysis model. The article conducts a computational study on the temperature field of the bearing chamber-bearing system under three different working conditions. The calculated results are found to be largely consistent with the actual test results, with an error of less than 10%. This validates the feasibility of the proposed decoupling method for separately calculating the oil-air two-phase flow within the bearing chamber and the temperature field within the bearing chamber-bearing system. This research provides robust technical support for the investigation of bearing chamber-bearing systems.
## 4 Discussion
### Feature Variables Selection, Model Construction, and Variable Contribution to the Model
#### 4.3.1 Model Comparison with SOTA  
In terms of model accuracy, the random forest (RF) model proposed in this study achieved an R\({}^{2}\) of 0.55 on the test set for January 26, outperforming [PERSON]'s SVR model (R\({}^{2}\) = 0.4) [44] and [PERSON]'s model (R\({}^{2}\) = 0.5) on the validation set [45]. The RF model aggregates predictions from multiple decision trees, demonstrating stronger robustness. In contrast, linear models typically require manual feature selection and extensive data preprocessing. Deep learning models, while powerful, are highly sensitive to hyperparameter tuning, requiring adjustments to many parameters. The RF model, however, is less dependent on hyperparameters, requiring fewer settings and offering greater interpretability. Additionally, the feature importance plot in RF provides valuable insights into the key variables critical for model construction.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 2 Methodology of Literature Review
### Data Sources and Search Strategy  
This review drew on multiple reputable databases to achieve both breadth and depth in the analysis. These databases included Scopus, Web of Science, IEEE Xplore, and Google Scholar. Scopus and Web of Science were selected for their extensive multidisciplinary coverage, enabling access to peer-reviewed journal articles across diverse disciplines. IEEE Xplore was utilized to identify technical papers focusing on LLM architectures and AI-driven systems while Google Scholar complemented these sources by including gray literature and conference proceedings. This combination of databases ensured a comprehensive representation of studies addressing AI advancements, maritime safety protocols, and regulatory frameworks.  
To systematically retrieve the relevant literature, a keyword-driven search strategy was employed. Search terms included phrases such as \"Large Language Models\", \"Maritime Safety\", \"Artificial Intelligence in Maritime Operations\", \"AI-driven Risk Assessment in Shipping\", and \"Maritime Security and AI-driven Communication\". The search was confined to studies published between 2010 and 2024 to focus on recent advancements and their applications in the maritime sector. The strategy was iteratively refined to maximize relevance and minimize the inclusion of unrelated studies.
# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 3 Result
### Measured Data, Measured Results, and Analysis
#### 3.2.1 Measured Results and Analysis of the Audio Signal  
The effective bandwidth of the audio signal is within 130 kHz, the carrier frequency is 174.75 MHz, and the sampling frequency is 200 kHz. A total of seven frames of data were collected, and the length of each frame was 2.055 s. The target parameters were [36 km, \(-\)156.8 km/h]. During the simulation, the data in frame five were randomly selected, and the accumulation time was set to 0.5 s and 1 s, respectively. The Doppler resolution (that is, the Doppler frequency corresponding to each Doppler unit) was about 2 Hz and 1 Hz, respectively.  
Figures 16a and 17a show that there are many clutters with Doppler spread in the region near the receiver, so the target in Doppler dimension will be covered by the clutter. The estimated target position and Doppler presented in Figures 16b and 17b are consistent, and the output SNR increases with an increase in the accumulation time. The output SNR of the target shown in Figures 16b and 17b is about 2 dB and 2.8 dB, respectively.  
Figure 16: Integration results with traditional R-D processing for the audio signal: \(N=10^{5}\); (**a**) Doppler dimension; (**b**) Range dimension.  
Figure 17: Integration results obtained with traditional R-D processing for the audio signal: \(N=2\times 10^{5}\); (**a**) Doppler dimension; (**b**) Range dimension.  
As can be seen from Figures 18 and 19, the algorithm in this paper can obtain the peak value in the Range-Doppler unit corresponding to the target. However, it is also affected by the clutter in the Doppler spread in the near region, and the target will be covered by clutter. When the accumulation time was 0.5 s, the output SNR of the algorithm in this paper was 6 dB, which is about 4 dB higher than the value shown in Figure 16. Similarly, when the accumulation time was 1s, the output SNR of the algorithm in this paper was 9 dB, which is about 6.2 dB higher than the value shown in Figure 17.
## 3 Results
### Segmentation Tasks
#### 3.1.4 Active Contour Models  
The model-based segmentation methods, also known as active contours, can be divided into parametric models, or snakes, and geometric models, which are based on the level set method. These deformable models rely on the definition of both an internal and external energy and an initial contour which evolves until the two energy functions reach a balance.  
The five studies that employed a model-based segmentation framework were all focused on ocular applications, either segmenting the retinal vessels [69; 70; 71] or the FAZ [72; 73]. In the first case, the best results were achieved by [PERSON] et al. [70] using a database of 100 images and obtaining a final DSC of 0.9502 \(\pm\) 0.0443. In the same study, the best results were also obtained for FAZ determination, with a DSC equal to 0.93 \(\pm\) 0.06. Both parametric and geometric active contours were found. One study compared two different ImageJ macros that implement the level set method and the Kanno-Saitama macro [72] with the built-in software for FAZ segmentation, whereas the other three studies used custom-written software implementing the Global Minimization of the Active Contour/Snake model (GMAC) [71], a generalized gradient vector flow (GGVF) snake model [73], and a joint Markov-Gibbs random field (MGRF) model [69].
## 1 Introduction
Based on an analysis of the hangers of a real TAB with a floating deck structure, we find that the bending of hangers is mainly caused by the difference in longitudinal displacement between the arch and the deck under the effect of temperature. Thus, the following study is conducted under the consideration of lateral bending of hangers that is caused by the difference in  
Figure 1: (**a**) Fracture of hangers of half-through and through arch bridges (HTABs and TABs); (**b**) Hangers under lateral bending. longitudinal displacement between the arch and the deck. Based on an analysis of the mechanical model of hangers, it is concluded that, besides the maximum bending stress \(\sigma_{b}\) in hangers, the ultimate tensile strength \(\sigma_{u}\) of the seven-wire strand under lateral bending is also the primary influential factor of the safety of hangers when hangers have lateral bending deformation. The influence of lateral bending deformation on the ultimate tensile strength of the seven-wire strands is obtained, according to finite element analysis and tests on the seven-wire strands. It is found that the ultimate tensile strength of the seven-wire strands under lateral bending decreases significantly. Thus, a method for checking the strength of the seven-wire strands in hangers under lateral bending is proposed. In addition, the S-N curve of the seven-wire strands under lateral bending can also be obtained based on the ultimate tensile strength under lateral bending. The fatigue properties of seven-wire strands will be weakened with an increase in lateral bending \(\theta\) according to the S-N curve of the seven-wire strands under lateral bending. When the lateral bending \(\theta\) is over 40 mrad, the fatigue properties of seven-wire strands will fall dramatically. In conclusion, lateral bending can exert a significant adverse influence on the seven-wire strands in hangers, so it is advised to take several measures to mitigate this adverse influence, such as using a tied arch structure as much as possible, replacing the fixed connections with hinged connections for short hangers, and using a jointless bridge structure. Using a tied arch structure for HTABs and TABs might have fewer side effects when compared with using the other methods above, so using a tied arch structure is more strongly recommended.