esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effect of White Space on Traditional Chinese Text-Reading on Smartphones +
## 4 Discussion
### Line Spacing  
This study tested the difference between two sizes of line spacing: 2-F leading and 3-F leading. The result demonstrated that the difference was significant on reading time and ratings of aesthetic appeal and comfort. This study indicated that the text with 2-F leading was faster on reading speed than with 3-F leading. [PERSON] and [PERSON] also found that 2-F leading was faster in reading speed than 1-F leading; however, [PERSON] et al. found no Line spacing effect on reading time when the levels of Line spacing on the experiment were 1.125, 1.25, 1.325 and 1.5-F leadings. Extrapolating these findings, it may indicate that oversized line spacing (i.e., 3-F leading) would degrade reading speed.  
Scrolling may explain why oversized line spacing (i.e., 3-F leading in this study) would degrade reading performance. Due to the limit of the display size of mobile phones, the text on the small display would overflow into multiple screens. Users must slide their fingers to scroll text to read all the time. Scrolling text might negatively affect performance because it would change the locations of all characters [14; 15; 16; 17], resulting in difficulty for users in locating information by recalling their spatial locations [14]. The length of an essay with 3-F leading would almost be 1.5 times the length of that with 2-F leading, resulting in 3-F leading having more scrolling frequencies than 2-F leading. Consequently, the result showed that participants took more time to read an article with 3-F leading than that with 2-F leading.  
However, the outcome showed that ratings of the 3-F leading were higher than 2-F leading on aesthetic appeal and comfort. That is, participants felt that the larger line spacing was more beautiful and comfortable although they had to spend more time reading it. Additionally, [PERSON] and [PERSON] found 2-F leading line reported higher preferences than 1-F leading. [PERSON] et al. also found that participants subjectively preferred to the larger line spacing to the smaller one when comparing the four kinds of leadings (1.125, 1.25, 1.325 and 1.5 leadings) on readability, fatigue, and attractiveness. Extrapolating these findings, we could conclude that participants favored larger line spacing.  
Hence, considering tradeoffs of outcomes from objective and subjective measures, we may conclude that 2-F leading or 1.5-F leading spacing may be the optimal line spacing for Chinese text-reading on a smartphone.
## 3 Methods
### Navigation Risk Assessment Model  
After creating a suitable sea fog data source, the navigation risk assessment model can be constructed according to various environmental elements in the study area. This section mainly includes four parts: first, the accident data are statistically analyzed to select the environmental factors that have the greatest impact on the navigation of ships in the study area. Second, the data of each evaluation factor are preprocessed. Third, we introduce a detailed process of determining the weights of each evaluation factor by the CRITIC objective weighting method, and then construct the navigation risk assessment model. Finally, we use the Delaunay triangulation method to divide the sea area into different navigational regions, and calculate the navigation risks in the specific navigational areas for further risk analysis.
# IoT Botnet Attack Detection Model Based on DBO-Catboost
## 4 Experimental Results
### Experimental Results
Due to the severe class imbalance in the Bot-IoT dataset, where there was a small amount of normal data and a large amount of attack data, the dataset was processed using a combination of the SMOTE technique and an undersampling technique to achieve a balanced dataset. The data preprocessing workflow is shown in Figure 4, and the sample distribution before and after dataset processing is illustrated in Figure 5.  
Figure 4: Imbalanced data handling process. Figure 5: Dataset sample distribution. Table 7 presents the performance of the IDBO-Catboost and the comparison models on the processed Bot-IoT dataset. From the information in Table 7, it can be observed that compared to other models, IDBO-Catboost achieved higher detection accuracy on the Bot-IoT dataset, with an accuracy and F1 score of 98.57%. This indicates that the hyperparameters identified by IDBO-Catboost were highly effective, and the model exhibited good generalization ability. The main reason for this result was the presence of the dynamically changing parameter R in the dung beetle optimizer, which allowed the algorithm to adapt better to different problems and enhance the model's generalization performance. In addition to performance metrics such as accuracy and F1 score, the average time (sum of model training and prediction time) of the proposed model and other detection models on the Botnet dataset and Bot-IoT dataset are compared in Table 8. It can be observed that the IDBO-Catboost detection model requires less time compared to the other detection models. This is because the IDBO-Catboost model can utilize GPU acceleration for training, thereby improving the detection efficiency of the model. Regarding DBO-Catboost and Catboost, the IDBO-Catboost model has fewer iterations, resulting in a shorter training time compared to DBO-Catboost and Catboost models. In summary, this paper conducted experiments using two datasets, Botnet and Bot-IoT, related to Internet of Things (IoT) botnet networks. By comparing with existing detection models, the proposed improved DBO-Catboost detection model demonstrated the best classification performance in terms of high accuracy and detection efficiency. Therefore, it can be effectively applied to the detection tasks of IoT botnet networks.
# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 3 Proposed Algorithm
### JDE Module  
There are three prediction heads at the end of our designed one-stage detection architecture. The detection part combines a CSPDarkNet-53 as backbone and a modified bi-directional feature pyramid network (BiFPN) as the neck layer [13].  
CSPDarkNet-53 is an upgraded version of the DarkNet-53 [9] which is a feature extractor used for object detection. Its core unit is the CSPResNet, which divides a basic feature map into two parts by using CSPNet strategy, and then merges these parts through  
Figure 1: The whole structure of the algorithm consists of a detection module and a post-processing module.  
a cross-stage hierarchical structure. In the way, this convolutional neural network is used as a backbone in YOLOv4 [14] and transferred to our JDE without any modification.  
The BiFPN is a multi-scale feature fusion block which balances efficiency and accuracy. It fuses three feature maps that are output by the backbone block and enables features to flow in both the top-down and bottom-up directions. Different from traditional methods, BiFPN can learn the importance of the input feature maps which have different resolutions by adding an additional weight. Then, these fused features are fed into the corresponding prediction head, which has some convolution layers. As shown in Figure 1, the proposed architecture combines two BiFPN blocks. What is more, we can extend the framework more deeply and widely by increasing the number of BiFPN blocks as well as its input features.  
Each prediction head is composed of a series of convolution layers and outputs a three-dimension map with size \((4A+2A+E)\times H\times W\), where \(A\) is the number of anchors that are assigned to its corresponding feature scale, and \(E\) is the number of appearance embedding. The three-dimension map consists of the box coordinate offsets regression of size \(4A\times H\times W\), the box classification of size \(2A\times H\times W\), and the appearance embedding of size \(E\times H\times W\).
## 6 Simulation and Analysis of Protocol Based on BFGO-C in HWSN
### Comparison of the Lifetime of Network  
The number of rounds in which the last node dies represents the life of the network. Figure 12 shows the changes in the surviving nodes of the SEP protocol, the HCR protocol, the ERP protocol, and the protocol based on BFGO-C with the number of rounds.  
The survival trend of the four clustering protocols SEP, HCR, ERP, and the protocol based on BFGO-C in Figure 12. As the network runs, the consume energy of nodes for data transmission and forwarding. After several operation rounds, some nodes run out of energy and become dead nodes. The graph shows that the nodes of the protocol based on BFGO-C die a little slower, and the network lives a little longer than all three other protocols.  
There are also application scenarios that pay more attention to the comprehensiveness of the node information. Once the first node in the network dies, the data are missing by default. Hence the paper records the number of rounds when the first node dies, half of the nodes die, and the last node dies, as shown in Figure 13.  
In Figure 13, the growth cycles of the four cluster protocols SEP, HCR, ERP, and the protocol based on BFGO-C are 2602, 1771, 1763, and 3909 rounds, respectively. The protocol based on BFGO-C has the longest lifetime of the network. In SEP protocol, the network ran 999 rounds when the first node died and 1385 rounds when half of the nodes died. The first node of the HCR and ERP protocol died in rounds 919 and 1078, and half of the nodes died in rounds 1771 and 1763. The number of rounds when the first node of the protocol based on BFGO-C died was 1134, and the number of rounds when the intermediate node died was 1570.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Network area & \(100\times 100\) m\({}^{2}\) \\ Number of nodes (\(N\)) & 100 \\ Base station position & (50,50) \\ Packet size (I) & 4000 bits \\ Initial energy of node (\(E_{0}\)) & 0.5 J \\ Advanced node scale (\(m\)) & 0.1 or 0.2 \\ Transmitter/Receiver electronics (\(E_{disc}\)) & 50 n/Jbit \\ Transmit amplifier (free space) \(\epsilon_{fs}\) & 10 n/Jbit/m\({}^{2}\) \\ Transmit amplifier (multipath) \(e_{np}\) & 0.0013 n/Jbit/m\({}^{4}\) \\ Data aggregation energy cost \(E_{DA}\) & 5 nJ/bit \\ Number of optimized individuals & 20 \\ Number of iterations & 20 \\ The weight of the fitness function & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation parameters of protocol based on BFGO-C in HWSN.  
Figure 12: Variation of the number of live nodes with the number of rounds.  
It can be shown that the protocol based on BFGO-C has a higher number of rounds at the death of the first node and a higher number of rounds at the end of half of the nodes than the other three protocols.
## 3 Results and Discussion
### Stability and Wear Resistance of Superhydrophobic Surfaces
This may be due to the degradation of the PVDF surface layer by prolonged contact with alkaline solution, resulting in a decrease in plasticity and loss of superhydrophobicity of the coating [28]. The coating adhesion cross-cut method test standard (ASTM D 3359) is used to assess the adhesion between the superhydrophobic film and the magnesium alloy substrate [29]. The experiment was carried out with Elcometer 107, which was used to make six parallel cuts with equal spacing (1 mm) on the coating, and then cut perpendicularly the cuts with the same number and spacing as the former, and the adhesion was evaluated by the peeling area of the coating surface. The crosshatch tape adhesion test for composite coatings is shown in photos in Figure 7. The lattice does not fall off and the cut edge is smooth after the cross-cut tape adhesion test. Therefore, the ASTM 5B rating for the superhydrophobic coating denotes the highest substrate adhesion. Figure 6: Changes CA and the SA of the superhydrophobic surfaces as a function of immersion time in aqueous solutions at pHs (**a**) 4.0, (**b**) 7.0, and (**c**) 10.0, measured at five different points on the same samples. The error bars for each plot indicate the maximum and minimum water contact angles. Figure 7: Crosshatch tape adhesion test for the composite coating. The superhydrophobic coating's abrasion resistance was tested using sandpaper. Scheme 2 illustrates the test device. A 50 g weight and 1200-grit sandpaper make up most of the test device. The superhydrophobic film is first placed in contact with the sandpaper, then the weight is placed on the magnesium alloy that has been prepared. The sample is then rotated 90\({}^{\circ}\) (the side of the film facing the sandpaper) and moved another 10 cm in the opposite direction after the superhydrophobic film has been pushed 10 cm in the opposite direction. A wear cycle is a term for the entire process described above. Following the abrasion test, the superhydrophobic film's contact angle and sliding angle were measured. Figure 8 shows that after 10 cycles of abrasion test, the contact angle of the coating dropped from 161.3\({}^{\circ}\) to 154.2\({}^{\circ}\), and the sliding angle increased from 1.6\({}^{\circ}\) to 4.5\({}^{\circ}\), indicating that the composite coating still maintains superhydrophilicity.
# Evolution of the Residual Pollution in Soils after Bioremediation Treatments
## 3 Results and Discussion
### Evolution in Soil Properties over Time
The most significant change in this sector affected to the EC; in this case, the initial mean values of this property were above 4 dS m\({}^{-1}\) in all studied depths (up to 50 cm) and twenty years after the recovery and bioremediation treatments these mean values significantly decreased below 0.4 dS m\({}^{-1}\) for all studied depths. The decrease in the EC over time in \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{3}{*}{**Depth (cm)**} & \multirow{3}{*}{**Year**} & \multicolumn{4}{c}{**Acidic Sector (S1)**} & \multicolumn{4}{c}{**Neutral-Alkaline Sector (S2)**} \\ \cline{3-10}  & & & **pH** & **EC** & **CaCO\({}_{3}\)** & **OC** & **pH** & **EC** & **CaCO\({}_{3}\)** & **OC** \\  & & & **(dS m\({}^{-1}\))** & **(\%)** & **(\%)** & **(\%)** & **(dS m\({}^{-1}\))** & **(\%)** & **(\%)** \\ \hline \multirow{3}{*}{0–10} & \multirow{3}{*}{1998} & 5.02 a & 5.65 b & 1.51 a & 0.85 a & 7.49 A & 5.72 A & 9.58 A & 1.20 A \\  & & (1.86) & (3.79) & (4.09) & (0.50) & (0.55) & (5.93) & (5.25) & (0.54) \\ \cline{2-10}  & \multirow{3}{*}{2018} & 7.04 b & 1.03 a & 3.41 a & 1.63 b & 8.19 B & 0.26 B & 13.72 B & 1.92 B \\  & & (1.66) & (0.83) & (5.72) & (0.58) & (0.35) & (0.25) & (6.54) & (0.52) \\ \hline \multirow{3}{*}{10–30} & \multirow{3}{*}{1998} & 6.09 & 2.89 ab & 1.17 a & 0.80 a & 7.88 & 4.07 A & 9.70 AB & 1.03 A \\  & & (1.98) & (1.57) & (2.04) & (0.49) & AB & (4.72) & (6.11) & (0.47) \\ \cline{2-10}  & \multirow{3}{*}{2018} & 6.79 b & 0.99 a & 2.31 a & 0.88 a & 8.30 B & 0.25B & 12.42 B & 1.06 A \\  & & (1.75) & (0.78) & (3.57) & (0.28) & (0.52) & (0.39) & (7.41) & (0.32) \\ \hline \multirow{3}{*}{30–50} & \multirow{3}{*}{1998} & 6.69 & 2.17 ab & 2.10 a & 0.58 a & 8.02 B & 4.35 A & 9.81 AB & 0.94 A \\  & & (2.00) & (1.71) & (5.48) & (0.40) & (0.45) & (6.75) & (7.12) & (0.42) \\ \cline{1-1} \cline{2-10}  & \multirow{3}{*}{2018} & 6.37 ab & 0.69 a & 2.35 a & 0.59 a & 8.32 B & 0.36 B & 11.88 & 1.09 A \\  & & (1.88) & (0.69) & (3.97) & (0.32) & (0.60) & (0.63) & (7.56) & (0.29) \\ \hline \end{tabular}  
* Letters indicate significant differences between years and depth, lowercase for the acidic sector and capital letters for the neutral-alkaline sector ([PERSON]’s HDS test \(p<0.05\)).  
\end{table} 
Table 1: Change in depth and over time in soil properties, mean (standard deviation), in the affected area according to different sectors (acidic: S1 and neutral-alkaline: S2 soil sectors). EC: electrical conductivity. OC: organic carbon. this type of pollution is considered an indicator of the degree of pollution [27], the significant reduction in S2 in relation to S1 could be related to the better evolution of the soils in the S2 and the presence of residual pollution in the S1 soils.
# Cell/Module Integration Technology with Wire-Embedded EVA Sheet
###### Abstract
To fabricate the wire-embedded EVA sheet module, a low-melting-point solder such as SnBiAg (melting point \(<\) 140 \({}^{\circ}\)C) was used instead of SnPb (melting point \(>\) 190 \({}^{\circ}\)C) solder coating ribbon used in existing modules. This is because the solder coating on the wire melts at a low temperature of 140 \({}^{\circ}\)C or less and contacts the finger electrode of the cell. The proposed module was expected to exhibit high efficiency owing to a decrease in the electrical resistance within the cell. Based on the three busbar modules, it was possible to reduce the shadow loss by approximately 2% of the cell area through re-incidence of the reflected light from the sphere of the wire, making it possible to manufacture a high-power module. Several other companies have adopted this type of module. The material cost of the proposed module can be significantly reduced by using a low-cost solder such as SnBi instead of SnIn; this is not the case for existing modules. Furthermore, the thermal effect of the cell can be minimized because the process is performed at a low temperature of 160 \({}^{\circ}\)C or lower, instead of at the tabbing process temperature of 200 \({}^{\circ}\)C or higher. In addition, because the module is manufactured using a wire-embedded EVA sheet, wherein the wire is attached to the EVA sheet rather than directly to the cell, direct heat damage is not inflicted on the cell [16; 17; 18; 19; 20]. The melting point of the solder coating on the wire should be controlled to increase the adhesion between the wire and the cell; however, designing the wire electrode on the front is also essential. Variables such as the number and diameter of wires interconnected with the finger electrodes of the cell are important factors in determining the characteristics of the module. In this study, we designed an optimal electrode considering the round shape of the wire using a simulation, and analyzed the effect of variables that can act on the module characteristics. Finally, the wire-embedded EVA sheet module manufactured with the optimized number and diameter of wires and the existing five busbar tabbing modules were compared, and it was confirmed that the wire-embedded EVA sheet module is a module manufacturing method that can overcome the limitations of the five busbar tabbing modules.
## 8 Future Opportunities and Research Directions
### AI-Based Energy-Harvesting Management Systems  
Artificial intelligence (AI) and machine learning (ML) are set to revolutionize the way RF energy-harvesting systems operate. Intelligent energy management systems, powered by AI, can dynamically optimize the allocation, storage, and distribution of harvested energy based on real-time network conditions and user demands. Predictive models using deep reinforcement learning (DRL) have been proposed to adaptively control matching circuits, select optimal harvesting frequencies, and manage energy storage to maximize system uptime and efficiency [126; 127]. Moreover, federated learning techniques are emerging as a solution for distributed RFEH networks, allowing devices to collaboratively learn optimal harvesting strategies without centralized control, preserving data privacy. AI-based optimization also supports the realization of cognitive radio energy harvesting, where devices not only sense and adapt to RF environments but intelligently forecast RF spectrum usage patterns to maximize energy capture [11; 128]. Recent works have introduced implementation frameworks where DRL agents are deployed on edge devices using TensorFlow Lite or PyTorch 2.0 Mobile to minimize computational overhead while enabling real-time decision-making in dynamic RF environments. Additionally, federated learning prototypes have been demonstrated on embedded platforms like Raspberry Pi and NVIDIA Jetson Nano for decentralized energy prediction tasks in IoT clusters. To address power constraints, ongoing research is focusing on quantized and pruned neural networks tailored to microcontrollers such as STM32 or ESP32, enabling AI-driven harvesting with sub-milliwatt power consumption. These lightweight models can be trained on cloud servers and deployed on nodes for inference, offering a feasible roadmap for integrating intelligence into ultra-low-power harvesting systems [129]. Future research is likely to explore lightweight AI models specifically designed for ultra-low-power energy-harvesting systems.
## 4 Model-in-the-Loop Simulation Test and Analysis  
### Build a Model-in-the-Loop Test Platform  
Model-in-the-loop (MIL) testing is a simulation testing method that uses a mathematical model of the controlled object instead of the real physical hardware for integration testing with the control algorithm or software. MIL testing reduces development costs by eliminating the need to build expensive physical prototypes. Early in the development process, MIL testing can be safely used without fear of real hardware damage. Moreover, MIL testing reduces the randomness and influence of environmental factors that are present in physical experiments [20; 21].  
The MIL test process is shown in Figure 20. In this study, the SimulationX 3.5 and NI VeriStand 2018 software were used to build the MIL test platform. In the SimulationX model, the engine speed, torque, and clutch hydraulic control module are determined as input variables, and the output shaft speed, torque, and impact are determined as output variables. FMU (Functional Mock-up Unit) files can be exported through \"FMI for co-Simulation\" which is provided by SimulationX. In the VeriStand project, you can add \"System Explore\" and select the FMU file. VeriStand will automatically identify the variables in the FMU file and map the variables to VeriStand's channels. In the simulation setting interface of VeriStand, you can configure parameters such as the simulation step size and target rate of FMU. After you start the VeriStand project, VeriStand automatically loads the FMU file and starts the simulation; VeriStand periodically reads the output variables of the FMU and sends the control instructions to the FMU [22].  
Figure 20: MIL test scheme.
# Computational Modeling of the Thermal Behavior of a Greenhouse
## 1 Introduction
Mathematical models that were developed for specific greenhouse assessment and subsequently solved with the computer application MATLAB/Simulink have also demonstrated ability to correctly estimate the thermal performance of a greenhouse [16]. In this paper, a computational model of an existing small-scale greenhouse was built using the computer software EnergyPlus and DesignBuilder. The model was then simulated under the same conditions that were applied to the physical greenhouse to evaluate the reliability of the simulation results. Subsequently, a group of case studies was defined to evaluate the impact of different design choices in the thermal behavior of the greenhouse.
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 2 Methods
### Grid Network Topology Model and Routing Algorithm  
A low-orbit satellite constellation is composed of hundreds of satellites. If only a time-sliced routing algorithm is used in the system, this centralized routing algorithm will result in great pressure on the storage and calculation abilities of the satellite. At the same time, in the event of an emergency, the efficiency and reliability are not high enough. For situations such as emergency communications, disaster warning, and maritime rescue, a set of low-cost and high-efficiency on-board route calculation methods is urgently needed to achieve efficient communication [9]. Therefore, it is necessary to deeply consider the characteristics of low-orbit satellite constellations and design a set of models and routing algorithms to improve the efficiency of satellite network topology. This new proposed method for network modeling and routing is clever and innovative, by mapping the complex dynamic topological relationships between satellites into a series of static grid topological models.  
Figure 5: Documents of grid codes: (**a**) with data; (**b**) without data.