esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Exploring TaPS Catalytic Mechanism by Homology Modeling and Mutagenesis  
To explore the catalytic mechanism of TaPS, homology modeling and docking was performed. As shown in Figure 5A, the residues I392 and C393 were closely localized to the final product \(\beta\)-patchoulene in the kink of Helix G, which was proposed to be involved in stabilization of carbocation in Nt5 EAS [40]. Amino acid sequence alignment indicated that both residues of I392 and C393 in TaPS were distinct from equivalent residues of other sTPSs (Figure 5B and Figure S4). Particularly, the patchoulol synthase from _P. cablin_ (PatTPS177) has a Cys and Gly at the equivalent positions, respectively [41]. Patchoulol has only one more hydroxyl group than \(\beta\)-patchoulene and their corresponding biosynthetic enzymes should involve some key residues to generate these two similar products. We mutated I392 and C393 of TaPS to the equivalent C and G of PatTps177, respectively. The product profiles of both mutants were changed with FFP as the substrate (Figure 5C and Figure S5). Notably, some hydroxylated sesquiterpenoids were produced by TaPS mutants. Although none of them was identified as (\(-\))-patchoulol by comparing with the authentic standard, generation of  
Figure 4: Dual choice assays. (**A**) Arabidopsis leaves fed by beet armyworm larvae for 24 h. Seven pieces of leaves for wild type (WT) or overexpression lines (TaPS-OE) were placed on the opposite side of the same plate. Five larvae were placed at the middle line in each plate. Two independent lines (TaPS-OE#2 and #5) were used for assays; (**B**) leaf consumption of beet armyworm larvae on WT and transgenic Arabidopsis leaves. The consumed leaf area (cm\({}^{2}\)) was calculated based on leaf images before and after feeding with analysis by Image J. The lowercase letters indicate significant difference (Tukey HSD test, \(p<0.05\)). Error bars indicate SE (\(n=3\)).  
hydroxyl products by these mutants suggests involvement of I392 and C393 in TaPS catalysis and further investigation is needed to clarify the underlying mechanism.
# Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
## 2 Literature Review
### LiDAR-Based Detection Networks  
Developing LiDAR-based object detection has seen numerous contributions [4; 37]. Earlier approaches utilized hand-crafted features when detailed 3D information was available. Since the objects vary in shape and size, this approach becomes tedious. To minimize the complexity, recent developments focus on voxels (grid-like structures), point-based and voxel-point-based. PointRCNN [38] proposes a two-stage detector network similar to RCNN, where a Regional Proposal Network (RPN) has a point-based feature extractor. The approach provides high accuracy in detection, but the network significantly suffers from the amount of power computation and storage. To reduce the model complexity and improve network speed, PointNet [16] and PointNet++ [39] were proposed. PointNet architectures utilize MLPs to independently extract features from each point cloud and create a global feature vector. This vector space represents the entire point cloud information for the particular scene. These networks underperform in the case of sparse point cloud data, which is commonly observed in autonomous platforms. PointPillars [40] uses PointNet to represent the point cloud information in vertical columns known as pillars. An encoder structure operates on the stacked pillars to extract features and are followed by CNN layers. F-ConvNet [41] proposed an end-to-end learning framework that utilizes PointNet to aggregate local points from frustum structures, known as frustum-level feature vectors. This approach addresses the shortcomings of PointNet, which relies on a few foreground points. VoxelNet [4] networks prepossess the point clouds by dividing them into 3D small cubes to address the sparsity issue. These cubes are passed through 2D CNN layers and 3D sparse CNNs to generate feature maps. These feature maps are, in turn, utilized for developing network predictions. This network shows a significant increase in performance in detection tasks but is computationally expensive as it uses CNNs. MGAF-3 DSSD [42] network converts voxel-based 3D feature volumes into sparse 2D feature maps to address the unordered sparsity of point clouds. Second [43], an improvement of VoxelNet reduces the number of parameters in 3D sparse convolutional layers while still maintaining the network performance. DOPS [44] incorporates PointNet++ with VoxelNet to gather local geometric features to reduce the noisy data and significantly improve network performance. The Point-GNN [45] down samples the density of point clouds using voxels and represents the LIDAR point clouds in graph structures. LiDAR sensors suffer from sensor interference from other LiDAR and are vulnerable to adverse weather conditions, resulting in poor performance.
## 5 Conclusions
### Limitations and Futher Research Recommendations  
The main limitation of this study is that it has not fully explored other behavioral parameters, such as loss aversion \((\mu)\) and attitudes towards gains \((\alpha)\) and losses \((\beta)\) in the B-DEA model. The lack of more complex sensitivity analyzes of the weights of the evaluation criteria are another limitation of this study. Therefore, further studies are suggested to perform complex sensitivity analyzes for behavioral parameters. Moreover, further assessments of wave climates could enhance the ranking results of this study. Conceptualization, M.-T.L. and N.-L.N. ; methodology, N.-L.N. ; validation, M.-T.L. and N.-L.N. ; formal analysis, M.-T.L. and N.-L.N. ; investigation, M.-T.L. and N.-L.N. ; data curation, M.-T.L. and N.-L.N. ; writing--original draft preparation, N.-L.N. ; writing--review and editing, M.-T.L. and N.-L.N. ; project administration, N.-L.N. ; funding acquisition, M.-T.L. All authors have read and agreed to the published version of the manuscript. This work belongs to the 2022 project funded by Ho Chi Minh City University of Technology and Education, Vietnam. **Acknowledgments:** The authors appreciate the support from the Ho Chi Minh City University of Technology and Education, Vietnam. The authors sincerely thank the valuable technical comments from the reviewers. **Conflicts of Interest:** The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Location** & **EC1** & **EC2** & **EC3** & **EC4** & **EC5** & **EC6** & **EC7** & **EC8** & **EC9** & **EC10** \\ \hline WWS-1 & M & L & M & SH & VH & H & SH & M & L & VHI \\ WWS-2 & H & L & L & SH & AL & H & L & M & SH & AL \\ WWS-3 & AH & VH & VL & H & AL & M & VL & VL & H & AH \\ WWS-4 & VH & VH & SH & L & VL & AL & VH & H & H & H \\ WWS-5 & SH & SH & L & M & VL & SH & AH & SL & VH & AH \\ WWS-6 & SH & SH & AH & VL & H & SH & SH & AH & H & SH \\ WWS-7 & AH & VL & H & VH & SL & VL & M & VH & AH & SL \\ WWS-8 & VL & VL & VL & AH & M & AH & AL & SL & VH & H \\ WWS-9 & AL & AH & L & H & H & SH & SL & SL & H & VH \\ WWS-10 & AL & SH & AH & VH & VH & M & H & M & VL & M \\ WWS-11 & SH & VL & VL & L & VH & L & AH & VH & VH & SL \\ WWS-12 & H & SH & L & H & L & AL & VH & SH & VL & VL \\ WWS-13 & VH & H & AH & VH & M & SL & VH & VL & M & SL \\ WWS-14 & VH & VH & AL & SL & SL & M & AH & AL & SL & M \\ WWS-15 & AH & AL & M & AH & VH & SL & SL & VL & AH & L \\ WWS-16 & SH & AL & VH & VH & VL & SL & L & SL & VL & H \\ WWS-17 & L & M & VL & VL & SL & AH & SH & H & AL & VH \\ WWS-18 & SL & AH & VH & AH & SL & VL & VL & AL & M & L \\ WWS-19 & SL & AL & L & L & L & AL & VL & VL & H & AL \\ WWS-20 & SH & AH & H & VL & AH & SL & M & SL & VL & AL \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Spherical fuzzy decision matrix.
## 5 Connected Vehicles
### Technological Developments
#### 5.3.3 Mainland China  
In 2016, BAIC EV, a renowned vehicle company in Mainland China, launched \"I-link\", the first intelligent Internet-connected system in China to apply 4G communication technology for interaction between in-vehicle terminals and the cloud. In addition, I-link realizes the first onboard system to integrate voice recognition technology, infotainment technology, and a cabin unified information board in Mainland China, allowing drivers to remotely access query and alert services. BAIC's breakthrough in Telematics technology has changed the situation that traditional CVs were limited to the preliminary integration of the Internet, broadening the scope of Internet resource interaction through various sensor technologies to create a better user experience for vehicle passengers [111]. Meanwhile, other auto brands in Mainland China are strengthening cooperation with information technology companies to advance CV development further.
# Adhesion Evaluation of Asphalt-Aggregate Interface Using Surface Free Energy Method
## 4 Adhesion of Asphalt-Aggregate Interface
### Adhesion of Asphalt-Aggregate  
The adhesion energies of asphalt-aggregate interfaces in different conditions are calculated, and the results are shown in Figure 5. In the dry condition, it was observed that the adhesion of the asphalt-aggregate interface of asphalt containing organic additives was higher than that of the base asphalt. This is because the surface free energy of organic additives modified asphalts is lower than that of base asphalt. As a result, the stability of organic additive-asphalt-aggregate is greater than that of asphalt-aggregate. Therefore, the surface free energy of asphalt can be reduced by adding organic additives so that the adhesion of asphalt-aggregate interface in a dry condition can be enhanced. In the wet condition, it was observed that the adhesion of the base asphalt and the asphalt containing organic additives decreased significantly. The reason for this is that water has a higher surface energy that prevents effective bonding between asphalt and aggregate. The asphalt-aggregate adhesive energy of the base asphalt, the Sasobit-modified asphalt, and the RH-modified asphalt, was reduced by 10.8%, 47.9% and 32.9% on average, respectively. This indicates that water has a great influence on the adhesion of asphalt-aggregate. In addition, it was found that water has a greater effect on the asphalt-aggregate adhesion of the asphalt containing organic additives compared to that of the base asphalt. This indicates that after the addition of organic additives, moisture damage can be more severe.  
In fact, previous studies have shown that asphalt mixes containing organic additives have a greater moisture susceptibility than the conventional asphalt mix [30]. This is due to the hydrophobic characteristics of organic additives. As a result, the asphalts containing organic additives refuse to form effective bonding between asphalt and the aggregate surface. The comparison between the two organic additives showed that the asphalt containing Sasobit is more susceptible to moisture damage than the asphalt containing RH. The comparison between the two asphalt binders showed that the SK-70 exhibited greater adhesion than the SK-90. This may be because of the higher surface energy of SK-70, as shown in Table 2. In addition, it was found that the SK-70 is more resistant to water damage than the SK-90. The average reduction in adhesion of SK-70-aggregate and SK-90-aggregate interfaces were 33.4% and 27.7%, respectively. The comparison between the two types of aggregates showed that the adhesion of the limestone-asphal interface was lower than that of the basalt-asphal interface. This can also be attributed to the higher surface free energy of basalt. However, it was found that the limestone-asphal interface was more resistant to water damage than the basalt-asphal interface. The average reductions in adhesion of limestone-asphal and basalt-asphal interfaces were 27.0% and 34.1%, respectively.
# Effects of Voice and Lighting Color on the Social Perception of Home Healthcare Robots
## 3 Results
### Manipulation Check
The results indicated that the participants felt strong discomfort when white lighting and the child or adult male voice were used. If warm lighting was used, the participants felt strong discomfort when the adult male voice was employed. If cool lighting was used, the participants felt strong discomfort when the adult male or female voice was employed. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mean and SD**} & \multicolumn{4}{c}{**F Values and Effect Sizes (np\({}^{2}\))**} \\ \hline  & \multicolumn{4}{c}{**Voice (V)**} & \multicolumn{4}{c}{**Light (L)**} & \multicolumn{4}{c}{**Voice (V)**} & \multicolumn{4}{c}{**Light (L)**} & \multicolumn{4}{c}{**V \(\times\) L**} \\ \cline{2-13}  & **M** & **F** & **C** & **WH** & **WA** & **CO** & **F** & **np\({}^{2}\)** & **F** & **np\({}^{2}\)** & **F** & **np\({}^{2}\)** \\ \hline \multirow{2}{*}{Warmth} & 2.85 & 3.05 & 3.30 & 3.09 & 3.15 & 2.96 & 20.43 * & 0.07 & 3.68 * & 0.01 & 1.19 & 0.01 \\ \cline{2-13}  & (0.62) & (0.72) & (0.71) & (0.77) & (0.66) & (0.68) & \multicolumn{2}{c}{C \(>\) F \(>\) M} & WA, WH \(>\) CO & & & \\ \hline \multirow{2}{*}{Competence} & 3.53 & 3.40 & 3.31 & 3.42 & 3.13 & 3.39 & 31.68 ** & 0.10 & 11.42 ** & 0.04 & 1.97 & 0.01 \\ \cline{2-13}  & (0.63) & (0.63) & (0.70) & (0.68) & (0.66) & (0.73) & \multicolumn{2}{c}{M \(>\) F \(>\) C} & WH, CO \(>\) WA & & & \\ \hline \multirow{2}{*}{Discomfort} & 2.26 & 2.13 & 1.97 & 1.81 & 2.17 & 2.38 & 6.10 * & 0.02 & 23.51 ** & 0.08 & 3.22 * & 0.02 \\ \cline{2-13}  & (0.95) & (0.84) & (0.72) & (0.67) & (0.81) & (0.94) & \multicolumn{2}{c}{M, F \(>\) C} & \multicolumn{2}{c}{C \(>\) WA \(>\) WH} & \multicolumn{2}{c}{M: WH \(>\) WA, CO} \\ \hline \multirow{2}{*}{Overall Acceptance} & 3.12 & 3.25 & 3.06 & 3.58 & 2.84 & 3.01 & 1.8 & 0.01 & 28.56 ** & 0.09 & 0.75 & 0.01 \\ \cline{2-13}  & (1.02) & (0.93) & (1.13) & (0.96) & (0.96) & (1.03) & \multicolumn{2}{c}{WH \(>\) WA, CO} & & \\ \hline \hline \end{tabular} Note: M = male voice; F = female voice; C = child voice; WH = white lighting; WA = warm lighting; CO = cool lighting. * \(p<0.05\), ** \(p<0.001\)**.  
\end{table} 
Table 5: Results of the MANOVA.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 4 Case Study
### Step 5: Analyze Results  
Based on the results of Step 4, the case study moves forward and does not need iteration at this point. The proposed nanogrids are as or more resilient than the existing microgrid. Because of the SCADA system deployed at the military installation, and location of runways for military aircraft, cyber attacks and plane crashes can impact both the microgrid and proposed nanogrids. It is important for the base energy manager and stakeholders to stay one step ahead and test all vulnerable SCADA systems within any base energy system, and continually upgrade cyber security.
## 1 Introduction
Although some recent works have attempted to incorporate sentiment analysis and text mining into modeling pipelines, alignment between textual semantics and signal-based representations remains weak, limiting their practical utility [15]. To address these challenges, we propose a deep-learning-driven signal generation framework tailored for high-frequency environments. This framework integrates graph neural networks (GNNs), multi-scale Transformer modules, and macro factor semantic modeling into a unified architecture capable of capturing the structural, temporal, and semantic dimensions of large-scale systems. By jointly modeling the relationships across assets, the evolution of temporal signals, and the influence of exogenous variables, the proposed method significantly enhances predictive performance, stability, and responsiveness. Our key contributions are summarized as follows:  
1. We propose a deep quantitative modeling framework that integrates asset graph learning, cross-scale temporal modeling, and macro-semantic embedding to jointly capture structural dependencies, temporal dynamics, and external disturbances in signal data, thereby improving the accuracy and robustness of predictive models. 2. We introduce the use of large language models (LLMs) to extract macroeconomic policy semantics for strategy construction, aligning textual sentiment embeddings with system signals to improve interpretability and sensitivity to exogenous changes. 3. We conduct extensive empirical evaluations on real-world large-scale datasets, assessing performance from multiple dimensions, including classification accuracy, return prediction error, and backtesting outcomes. Results show that our approach achieved superior robustness under varying conditions. We implement an end-to-end prototype system for signal generation, enabling full-cycle automation from data ingestion to strategy execution, demonstrating scalability and deployment feasibility.
# Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network
## 2 Methods
### Network Architecture  
The architecture adopts a symmetric four-order downsampling and upsampling network structure, as shown in Figure 1. The encoder utilizes MRCM for multi-scale feature extraction, along with a downsampling max-pooling operation using a pooling size of \(2\times 2\) and stride = 2. With each downsampling step, the number of feature channels is doubled. For the first-level MRCM, a convolution which is of quantity 64 with a kernel size of \(3\times 3\) (stride = 1, padding = 1) is performed. Then, three sets of convolution operations are applied to the feature map (\(C\times H\times W\)) at different scales: \(1\times 1\) (stride = 1, padding = 0), \(3\times 3\) (stride = 1, padding = 1), and \(5\times 5\) (stride = 1, padding = 2). Additionally, a \(1\times 1\) convolution operation is employed to adjust the number of channels. In the MRCM, the quantity of filters in each level is equal to the number of feature channels in that level. All other parameters remain consistent with the first level. In the improved decoder section, a channel attention mechanism is added to the concatenated feature maps, while maintaining the original U-Net decoder's structural framework. The decoder consists of a RAM and a \(2\times 2\) transposed convolution upsampling, which reduces feature channels by half and gradually reconstructs a segmented image with the same spatial size as the input. Instead of using simple bilinear interpolation, trainable transposed convolutions are used for upsampling, enabling adaptive recovery of detailed feature information. In Figure 1, the embedding of the SKU in the skip connections of the U-shaped network is depicted using yellow boxes, while a residual structure is employed in this framework. Then, the concatenated feature maps are processed by RAM. In the last layer of the MU-Net, a \(1\times 1\) convolution operation and sigmoid activation function are utilized to achieve the final segmentation graph. In Figure 1, graphical representations of adding attention mechanisms and residual connections to the encoder-decoder section of the U-Net framework are displayed in the bottom right corner. The implementation process is explained in detail in Sections 2.2 and 2.4.
## 4 Protocol Description
### Performance Compared with Other Protocols in Fire Hazard  
We then compare EAR with two related routing mechanisms: RPAR and minimal hop count routing. Figure 12 shows the end-to-end delay as delay bound increases from 10 to 100 ms when there is one sink (node 99). We can see that EAR has the minimal end-to-end delay as we relax the bound, then RPAR, and minimal hop count routing has the worst result.  
Because EAR adapt power level to try to increase the probability of real-time delivery and it is adaptive to fire spreading by choosing the real-time route path avoiding the dangerous area in fire. RPAR also uses power adaptation to try to increase the real-time delivery, but they are not suitable for fired, and easily chooses a minimal delay path but in the fire area. There is a real-time guarantee mechanism in minimal hop count routing and it is not suitable for fire situations.  
Figure 12 shows the miss ratio of real-time packet delivery with one sink. EAR achieves the best real-time data delivery. RPAR is not suitable for fire hazard. Even it adapts power level to try to find a real-time delivery path, but the performance is bad in fire. Figure 14 shows the average node energy in simulation time when delay bound is 50 ms. From the results, three routing mechanisms have similar energy efficiency. EAR has no obvious better energy efficiency, because it increase its power level to increase real-time packet delivery and incur energy consumption.  
Figure 12: End-to-end delay as delay bound increases.  
Figure 14: Average node energy when delay bound = 50 ms.  
Figure 13: Miss ratio as delay bound increase.
## 2 Background on the Calibration of Satellite Sensors  
### Absolute Radiometric Calibration and Approaches  
After launch, a satellite starts to operate in an extremely harsh environment; consequently, a degradation process in all of the components begins. Conditions, such as exposure to mechanical stress, ultra-violet (UV) radiation, cosmic rays, energy generated during solar flares, charged particles trapped in radiation belts and outgassing, affect all optical and electronic devices of a platform [22; 23; 24; 25]. These factors degrade the imaging instrument, modifying the radiometric response measured in the laboratory prior to launch and generating variations that must be periodically studied and compensated. This is mandatory when the collected data need to be used to derive biogeophysical parameters of different land covers and to quantitatively monitor our planet's ecosystems [26; 27; 28; 29; 30].  
The absolute radiometric calibration is defined as the process of quantifying the response of a sensor to known and controlled signal inputs [30; 31]. The calibration plays a key role in the detection and monitoring capability of a satellite sensor [32], since the collected information that will be used (1) either to spectrally characterize a surface or (2) as an input to a model relies on how accurately the registered signal can be related to the respective incoming radiance. If the calibration parameters are not well known, any change in the response of the sensor could incorrectly be attributed to variations in the observed scene [33]. Hence, the success of any satellite mission and the feasibility of quantitative remote sensing applications are necessarily tied to Cal/Val activities [34].  
The calibration parameters can be determined either directly, by an OBC device that provides a known signal to the sensor [35], or using indirect or vicarious methods that are totally independent from calibrations conducted in the laboratory or via OBC. Among the vicarious calibration methods, two categories can be differentiated, depending on the availability and usage of in situ measurements. On the one hand, the following approaches do require in situ measurements: reflectance-based [3; 34; 35; 36], radiance-based [37] and irradiance-based approaches [38]. On the other hand, there are other approaches that only use remote measurements based on: (1) Rayleigh or molecular scattering over oligotrophic oceanic areas [39; 40; 41]; (2) deep convective clouds [42; 43]; (3) Pseudo-Invariant Calibration Sites (PICS) [44; 45]; (4) the surface of the Moon [46; 47]; and (5) radiometrically well-characterized stellar sources [48; 49; 50].  
Another widely-used alternative is cross-calibration, which consists of estimating the calibration coefficients of a sensor using as a reference another well-known instrument [19; 20; 51; 52; 53; 54; 55; 56; 57; 58]. A cross-calibration can be applied when the OBC source is not available on the satellite [52; 55; 56; 57; 58], to proceed with a combined approach [51; 56; 59] or to tie data from different missions to a common radiometric scale [5; 60].