esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Performance Evaluation  
Table 3 summarizes the statistical parameters (RMSE and R\({}^{2}\)) of the three proposed models in predicting CO\({}_{2}\) solubility for training, test, and overall data. The graphical representation of the overall RMSE and R\({}^{2}\) through bar plots, as shown in Figure 2. This graphical comparison allows an alternative visualization for the performance of the different models. A concise view of the figure shows that the values of RMSE and R\({}^{2}\) were closest to 0 and 1, respectively, pointing out that the models were more credible and qualified for the forecast.  
Form a deep comparative standing point of view, Table 3 and Figure 2 show that the reliability of the developed models took the following order: MLP-LMA > MLP-BR > GEP. The MLP model optimized using LMA had the highest R\({}^{2}\) coefficients and the lowest RMSE values for both the training and overall data, followed by the MLP-BR model. On the other hand, very satisfactory performances and high predictions potential of the GEP model were asserted by the values reported in Table 3 for training, test, and overall data.  
Cross plots in Figure 3 were used to compare the models' predictions against the experimental data further. In these plots, the predicted values using the three methods were represented versus the previously established experimental data for the solubility of CO\({}_{2}\) in ILs. The criterion of precision for the different models in typical cross plots can be indicated by the compressed gathering of data points cloud nearby the 45\({}^{\circ}\) line, otherwise, all points falling on the unit slope would correspond to a perfect model. The accumulation of data cloud nearby the 45\({}^{\circ}\) line was attributed to the elevated value of R\({}^{2}\) close to the unit and to the low value of RMSE near zero.  
It can be observed in Figure 3, that the MLP-LMA, MLP-BR, and GEP models exhibited distributions of predictions near the unit slope line for both training and test datasets, confirming their high accuracy in estimating the solubility of CO\({}_{2}\) in the considered ionic liquids.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Training Data**} & \multicolumn{2}{c}{**Test Data**} & \multicolumn{2}{c}{**Overall**} \\ \hline  & **RMSE** & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** \\ \hline MLP-LMA & 0.0107 & 0.9971 & 0.0150 & 0.9941 & 0.0116 & 0.9965 \\ MLP-BR & 0.0117 & 0.9966 & 0.0138 & 0.9951 & 0.0121 & 0.9963 \\ GEP & 0.0192 & 0.9907 & 0.0238 & 0.9854 & 0.0201 & 0.9896 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical parameters of the proposed models in predicting CO\({}_{2}\) solubility.  
Figure 2: Performance comparison on the overall dataset for the proposed models: (**a**) Root mean square error (RMSE), and (**b**) R\({}^{2}\).
# Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest
## 4 Discussion
### Future Work  
The proposed method can be expected to be applicable to evergreen broadleaf forests (EBF) with little phenological variation. Although EBF accounts for 48% of the world's forests [38], this study applied the proposed method to these forests with limited conditions. Characteristics of forests where the method is difficult to apply are having prominent tall trees, such as those found in natural forests, and sparsity of trees. The heights of trees in the virtual forests were assumed to follow a normal distribution, but the probability of generating a tree with a prominent height is low. In addition, since the CC was set to 80%, the reflectance of the canopy (leaves) had a large influence on the reflectance simulation. In sparse forests, on the other hand, the soil and forest floor have a greater influence. Therefore, it is necessary to further validate the effectiveness of CSRS in various forests.  
Although only Sentinel-2 was targeted in this study, it is expected that the method could be applied to Landsat as well. It is recommended that the number of available satellite images increase, especially in areas where tropical rainforests are located, because the images are contaminated by clouds. However, when targeting satellite imagery coarser  
Figure 8: Time series of NDVI in 2019 and 2020. The red line represents the mean value (50 th percentile), the upper blue dashed line represents the 84 th percentile and the lower blue dashed line the 16 th percentile.  
than the spatial resolution of Sentinel-2 and Landsat sensors, e.g., MODIS, there may be multiple canopy shapes within a pixel. It is necessary to simulate a virtual forest with multiple canopy shapes within a pixel.  
Furthermore, boreal forest is second only to tropical forest in terms of the proportion of forest area, at 27.3% [1]. Moreover, it is very sensitive to the effects of climate change, and therefore, the GPP of boreal forest should be estimated with high accuracy. However, the canopy shape of boreal forest is asymmetrical, which is called tropism. [PERSON] et al. (2010) [16] suggested that the diversity of canopy shape, such as asymmetry, should be considered when creating virtual forests for boreal forests. Therefore, it is important to improve the CSRS by considering the asymmetric canopy shape, which could be more responsive to real forests and contribute to the shadow fraction estimation.
###### Abstract  
Roll angle measurement is an essential technology in the trajectory correction projectiles. In this paper, an algorithm to detect the roll angle and rotational speed of a spinning vehicle is studied by using a GPS (Global Positioning System) receiver with a single side-mounted antenna. A Frequency-Locked Loop (FLL) assisted Phase-Locked Loop (PLL) is designed to obtain the attitude information from GPS signals, and the optimal parameters of this system are discussed when different rotational speeds are considered. The error estimation of this method and signal-to-noise ratio analysis of GPS signals are also studied. Finally, experiments on the rotary table were carried out to verify the proposed method. The experimental results showed that the proposed algorithm can detect the roll angle in a precision of within 5 degrees.  
GPS based roll angle and rotational speed measurement; single-patch antenna; spinning vehicle +
Footnote †: journal: Sensors
## 3 Proposed Methodology
### Data Preprocessing  
Data preprocessing is a pre-requisite task in a number of data-driven applications. The main objective of this operation is to ensure that the data are of high quality before commencing the model construction process. The major operations performed by this component are missing values imputation and feature id-ness detection and removal. For this study, we used a k-nearest neighbor to impute missing values, where k = 3. This technique selects a set of records from the dataset which are similar to the missing value record and subsequently imputes the missing value based on the local information of the selected instances. Furthermore, we used Euclidean distance for computing the similarity of numerical attributes. Numerical values are replaced with taking the mean value of the selected attribute while mode value is used for nominal features. The chronic kidney disease dataset, discussed in the subsequent section, contains 242 records with missing values while the complete dataset is composed of 400 records. Therefore, around 60% of the records contained one or more missing values.
## 5 Case Study
### Results and Discussion
#### 5.3.2 Variation of Transmission Efficiency with Input Speed  
The input speed was varied while the input torque remained constant and the transmission efficiency of the herringbone planetary transmission system was analyzed. Variation of power loss with input speed is shown in Figure 16. Frictional power losses on the tooth surface increased as the rotational speed increased, and the windage power loss increases, suggesting that, when the rotational speed increases, the increase in resistance due to the surrounding air is greater than the increase of frictional power loss. The bearing power loss is different at different positions. The power loss at bearing 2 is the largest. With the speed increment, the total power loss at the bearing increases. As shown in Figure 17, the gear meshing efficiency slowly decreases as the rotational speed increases. However, windage losses rapidly increase, suggesting windage power loss is an essential consideration for gear systems operating at high rotational speeds.  
Figure 16: Cont.  
Figure 16: Variation of power loss with input speed. (**a**) Frictional power loss on tooth surface; (**b**) windage power loss; (**c**) bearing power loss at different positions; (**d**) bearing friction power loss.  
Figure 17: _Cont._
# Key Management Scheme Based on Route Planning of Mobile Sink in Wireless Sensor Networks
## 4 Simulation and Evaluation
### Keys Storage Overhead  
After completing the process of establishing a session key, a cluster head needs to store some keys: cluster key, a pair of public and private keys of the cluster head, the public keys of all nodes in the cluster, the session keys with all nodes in the cluster, and the session key with the MS. A member node also needs to store some keys: cluster key, a pair of public and private keys of itself, and the session key with the associated cluster head. Therefore, the total storage overhead is \(4\left(M+1\right)+2N+\ 4N\), where \(M\) denotes the number of cluster heads, \(N\) is the number of member nodes. In the AP scheme, the number of member nodes is 1000, every node is preloaded with 20 keys, and the number of senior nodes is 20, and every senior node has 500 pre-stored keys. Therefore, the total storage overhead in the scheme is 1000 \(\times\) 20 + 20 \(\times\) 500 = 30,000.  
Figure 7 shows the comparison of the proposed scheme, E-G scheme and AP scheme on the storage overhead performance after the session key establishment. Although the length of an asymmetric key is longer than that of a symmetric key encryption system, a 160 bit asymmetric key length roughly equals the length of 64 bit symmetric key in security, and the total storage overhead of the proposed scheme is smaller. This is mainly because of the introduction of dynamic clustering and the MS node. In the proposed scheme, communication between clusters is not needed, and cluster heads only need to establish the security of communication with the MS. As a result, each cluster head only needs to store the public key of the node within the cluster and the corresponding session key. It means it does not need to store the public key and corresponding session key for the entire sensor network.  
We can conclude from the above that with the same number of keys stored in the nodes, the proposed scheme also has smaller total storage overhead compared with the E-G scheme and the AP scheme as well.  
Figure 6: The comparison of network connectivity among the schemes of AP, E-G (s = 10,000), \(q\)-composite (s = 10,000), and the proposed scheme (R = 25 and 30).
## 4 Evaluation Method and Simulation Requirements
### Digital Twin City and AI
The asynchronous state of the digital twin offers the flexibility of embedding and testing one or more scenarios and conditions to imitate particular real-life events and provide output for prediction models [49; 54; 55; 56; 57]. For the development of the digital twin 3D model, the aforementioned road network was modelled with a combination of satellite images, photogrammetry, and videoc recordings to provide a photorealistic outcome that could enhance the driver's immersion  
Figure 10: Section of the digital twin model of the city of Glasgow used in the VR driving simulator. Figure 9: Creation of collision prone scenarios by Artificial Intelligence (AI)—controlled vehicles. as illustrated in Figure 11. To avoid visualising every road extension stemming from the main motorways, the exits were blocked with police cones which were defined as immovable objects in the simulation. In this way, a continues path of the three connected motorway sections produced a triangular circuit that kept the drivers from wandering in the neighbouring areas. Other minor approximations were performed to the 3D digital twin model to reduce the graphics' computational intensity of the real-time VR simulation. Objects, irrelevant to the simulation, such as light-poles, pavements, electric station boxes, and other road furniture, were originally introduced to the simulation scenes but removed as they were affecting the frame per second performance of the VR environment. Maintaining a minimum of 90 FPS was essential to avoid or reduce the participants' disorientation, nausea, and overall motion sickness. To improve further the immersion of the users to the simulation environment, the vehicles selected to populate the traffic flow were chosen based on their commonality to the existing vehicles that are typically encountered in these motorways. Police vehicles, lorries, and other distinctive vehicles were also used to seamlessly appear in the three motorways and provide a diverse group of vehicles, to improve the drivers' familiarisation and immersion in the VR environment.
## 4 Real Ship Experimental Verification  
### Experimental Equipment  
In the dynamic response experiment of the low-speed diesel engine propulsion shaft system, the resistance strain measurement method was used. Resistance strain gauges were attached to the determined measurement points and connected to the DH5905N wireless node modules. The sensor nodes were connected to a laptop via a router, which was set up and controlled by the DHDAS dynamic signal acquisition and analysis system to collect the actual strain curves under different operating conditions of the propulsion shaft system. The schematic diagram and physical image of the experimental system are shown in Figure 7.  
Figure 5: Vibration stress and torque curves at intermediate shaft: (a) vibration stress; and (b) vibration torque.  
Figure 6: Vibration stress and torque curves at propeller shaft: (a) vibration stress; and (b) vibration torque.  
The strain gauges used are manufactured by Aviation Electric Measurement Co., Ltd, located in Xi'an, China. and are secured to the polished shaft surface with special adhesive before testing and protected with silicone. The specification of the single coil resistance is 350 \(\Omega\), and the sensitivity factor is 2.09. Since the strain gauges are fixed on the propulsion shafting system, when the shafting system undergoes torsional vibration, the sensitive grid of the strain gauge will deform by an equivalent amount, thereby causing changes in the resistance value of the sensitive grid. This change in resistance represents the torsional deformation of the propulsion shafting system. In strain measurement, the measurement circuit is composed of resistance strain gauges arranged in a certain order to form a circuit with certain characteristics for experimental measurement. The resistance change of the bridge strain gauges during the measurement process is output in the form of voltage or current. Before starting the measurement, it is necessary to zero the offset to eliminate any initial imbalance or error. Each set of strain gauges is connected to the differential analog channel input of the DH5905N wireless data transmitter manufactured by Donghua Testing Co., Ltd., located in Taizhou City, China. The bridge voltage of DH5905N is set to 3 V, with an indication error not exceeding 5%, and the zero drift is less than 3 \(\mu\)e/2 h [33]. Due to the split design of the equipment, the power module and the acquisition module are symmetrically installed on the propulsion shafting system, which does not affect the balance of the shafting system during measurement; there is no wire connection between the upper computer and the acquisition module, and WiFi wireless transmission is used to facilitate the acquisition of torsional vibration data from the propulsion shafting system.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 6 Conclusions  
This work presents DK-SLAM, a monocular visual SLAM with deep keypoint meta-learning, a coarse-to-fine matching strategy, and an online binary learned feature BoW. To enhance generalization, we employ MAML for local feature training, yielding a feature extractor with robust capabilities in unseen scenarios. Coarse relative poses between frames are estimated via a semi-direct method for accurate feature point matching. An online binary learned feature BoW corrects SLAM accumulation errors. Our proposed DK-SLAM outperforms representative baselines, e.g., ORB-SLAM3, on the KITTI and EuRoC datasets. However, the GPU-based online front-end faces efficiency challenges during information transmission to the CPU-based back-end. Future work will explore knowledge distillation for neural network parameters compression and further SLAM framework modifications to enhance efficiency.  
Conceptualization, H.Q. and C.C.; methodology, H.Q. and C.C.; software, H.Q. and C.C.; validation, H.Q. and C.C.; writing--original draft preparation, H.Q. and C.C.; writing--review and editing, H.Q., J.M., X.H. ([PERSON]), X.H. ([PERSON]), C.C., and L.Z.; supervision, L.Z., Y.S. and J.T.; funding acquisition, L.Z. and C.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NFSC) (Nos. 62103427, 62073331, and 62103430). Natural Science Foundation of Hunan Province,China under (No. 2023 JJ20051) and a Major Project of the Natural Science Foundation of Hunan Province under (No. 2021 JC0004). [PERSON] is funded by the Young Elite Scientist Sponsorship Program by CAST (No. YESS20220181). And supported by the Science and Technology Innovation Program of Hunan Province,China (2023 RC3011).  
Not applicable.  
Data Availability Statement: The original data presented in this study are openly available at [[https://www.cvlibs.net/datasets/kitti/eval_odometry.php](https://www.cvlibs.net/datasets/kitti/eval_odometry.php)]([https://www.cvlibs.net/datasets/kitti/eval_odometry.php](https://www.cvlibs.net/datasets/kitti/eval_odometry.php)) and [[https://projects.asl.ethz.ch/datasets/](https://projects.asl.ethz.ch/datasets/)]([https://projects.asl.ethz.ch/datasets/](https://projects.asl.ethz.ch/datasets/)).
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 1 Introduction
Networks devoid of semantic priors tend to distort original color distributions, exemplified by erroneous transformations like converting black hair to gray during enhancement. [PERSON] et al. [7] introduced semantic brightness consistency loss to ensure smooth and natural luminance recovery within identical semantic categories. Although the enhanced images improve downstream semantic segmentation performance, semantic information remains underutilized. The method proposed by [PERSON] et al. focuses on enhancement for maintaining color consistency, which deviates from simplistic global brightness adjustment. However, it still processes color attributes holistically. To address these challenges, we propose a Semantic-Aware Knowledge-Guided Framework (SKF) that systematically integrates semantic priors to improve illumination recovery and enhance visual quality. The specific contributions of our work can be summarized as follows:  
(1) Semantic Feature Enhancement (SE) Module: This module integrates hierarchical semantic features with the enhancement network, enabling more precise and context-aware image enhancement. (2) Semantic-Guided Color Histogram (SCH) Loss: By leveraging multi-color space constraints (RGB, LAB, and LCH), this loss function ensures color consistency and naturalness while preserving local color information. (3) Semantic-Guided Adversarial (SA) Loss: This loss function enhances the perceptual realism of the enhanced images through adversarial training, ensuring global consistency and semantic integrity.
## 1 Introduction
### How Does It Look from the Process Side?
Often such a monolithic approach is inherited from the framework being used as part of the re-use strategy. Knowledge of the development time and technical skill constraints in a typical CubeSat software development team, such a monolithic and tightly bounded structure, has never been challenged or changed. So how can the problem of a waterfall development model that is \"reinforced\" by a tightly coupled data/code and monolithic structured source code be solved? The answer is simple and clearly stipulated by the NASA \"CubeSat 101 Handbook\" [13]--it involves carrying out proper testing! Testing and quality control and assurance are by themselves very skill-demanding areas, and thus, in the majority of CubeSat projects, software, and hardware testing are pushed to the very last moment and undergo only integration testing. If we take the more mature development teams (mainly those who are part of commercial companies), they are using the so-called V-model that comes from the functional safety world where every step of the SDLC shall be properly verified after being accomplished. The V-model is widely used in applications driven by the following standards: IEC 61508 [29]--\"electronic functional safety package\", IEC 62304 [30]/ISO 14971 [31]--\"medical device software\", ISO 26262 [32]--\"automotive functional safety\", etc. It is represented below in Figure 3.  
With the use of the V-model SDLC, the development team can better decompose the \"design phase\" and \"integration and system testing phase\" mentioned above and have more time to design/test the system at different abstraction levels. This in return helps one to obtain a better overview of its functions and addresses the functions' complexity better. However, it is important to state that the use of the V-model does not change the fact that the integration is pushed to the very end of the development process and still requires a lot of development and testing efforts in the very end of the development. Using such an approach gives a closer look into the verification and later validation of the software but does not change either the design thinking or design approach of building the CubeSat software. Generally, both \"waterfall\" and \"V-model\" are widely criticized by the software industry due to their rigidity, simplicity, inflexibility, and linearity. It is important to emphasize that the use of the V-model does not improve the speed of the integration simplicity of the source code, it just helps to use more time for the design considerations at the different abstraction levels.