esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Crop Variables Estimation Using Remote Sensing Data
#### 2.4.2 Fraction of Green Vegetation Cover  
The evolution of fCover was measured in situ on four specific dates (1 January, 12 February, 22 March, and 10 May) corresponding to key stages in the crop growth, including sowing and grain development mentioned in Section 2.2. These dates were chosen to capture the dynamic changes in vegetation cover during the growing season. The in situ measurements were then compared with the remote sensing data, specifically, the vegetation indices in Table 2, calculated for both PS and MS datasets. For vegetation indexes, we established linear relationships between the in situ vegetation cover measurements and the PS and MS indexes. In the case of PS, the evolution of the whole time series over the growing season was analyzed to investigate how different treatments affected the vegetation cover at different phenological stages. This analysis provided insights into the temporal dynamics of vegetation cover and the impact of different treatments on crop development, as observed from both ground measurements and remote sensing data.  
Validation of vegetation cover predictions was conducted using standardized metrics to assess model performance. These included the coefficient of determination (R2), which measures the strength of the relationship between observed and predicted values; the mean of the residuals (bias), which evaluates systematic deviations; the standard deviation of the residuals (\(\sigma\)), which quantifies data spread; and the root mean square error (RMSE), which assesses overall accuracy. These metrics were applied to compare predicted and observed fractional vegetation cover values across different treatments and phenological stages.
## 4 Numerical Comparisons between FPBs and SRBs
### Comparison Results and Discussions
It is because of its larger sloping angle (or higher potential energy, as explained in Section 3.3) at the design displacement shown in Figure 11. Another reason might be its smaller static residual displacement to which the theoretical or statistical permanent residual displacement is proportional [24; 36]. However, it is emphasized again that with the same horizontal acceleration control performance, as clearly observed from Figure 13, SRB-6 almost had the largest, and even practically unacceptable, horizontal isolation displacement among all the SRB and FPB models, regardless of being subjected to far-field or pulse-like near-fault ground motions. In addition, by comparing FPB-6-2M (FPB-6-2) with FPB-3-2M (FPB-3-2) and FPB-6-3M (FPB-6-3) with FPB-3-3M (FPB-3-3) in terms of residual displacement, with the same acceleration control performance, the FPB models designed with a larger design displacement, a smaller curvature radius, and a smaller damping force might potentially have a better self-centering performance than those designed with a smaller design displacement, a larger curvature radius, and a larger damping force.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 3 Thermoplastic Resin Toughening Method
### Advancements in Thermoplastic Resin Toughening
#### 3.4.2 Polyether Sulfone  
Polyether sulfone (PES) is one of the earliest thermoplastic resins used to toughen epoxy resins, known for its excellent thermal stability and chemical resistance. [PERSON]'s research on PES-toughened epoxy resins revealed that the toughest results are achieved when the modified system exhibits a cocontinuous phase structure [65].  
[PERSON] et al. [66] utilized hydroxyl-terminated polyether sulfone as a toughen agent and employed a blending method to modify a single-component high-performance epoxy resin. The study indicated that the toughening of the blend is primarily achieved through bridging effects and crack pinning effects. As the PES content increased, there was a significant improvement in both the fracture toughness and impact strength of the samples compared to pure epoxy resin. When the PES content reached 15 parts, the fracture toughness of the blend increased to 2.90 MPa\(\cdot\)m\({}^{1/2}\), a 32% improvement over the unmodified resin; simultaneously, the impact strength rose to 83.7 J/m.  
To enhance the toughening effect, [PERSON] et al. [67] synthesized epoxy-functionalized polyether sulfone (PES-E) (Figure 5A), which can participate in the curing reaction of epoxy resins. PES-E was mixed into bisphenol A diglycidyl ether (DGEBA) at weight ratios of 5%, 10%, 15%, and 20% and then cured with diethyltoluenediamine (DETDA) (Figure 5B). The study found that, at 5% PES-E content, the tensile strength, elongation at break, and impact strength of the PES-E/DGEBA composites were maximized, showing increases of 5.5%, 21.9%, and 74.1%, respectively, compared to pure epoxy resin. As depicted in Figure 5C, PES-E not only dispersed uniformly within the epoxy resin matrix but also crosslinked with the epoxy resin through occuring reactions, forming a crosslinked semi-interpenetrating polymer network that bears stress along with the epoxy molecular chains.  
In traditional methods, polyether sulfone (PES) is often used in blended forms to toughen epoxy resins. In addition to blending, PES fibers have also been utilized to enhance the toughness of epoxy resins. [PERSON] et al. [68] designed and prepared a uniformly oriented PES fiber mat as an interleaved layer to simultaneously improve both the Mode I and Mode II fracture toughness of carbon fiber/epoxy composites. The study revealed that, compared to laminates without the interleaved layer, the introduction of a 28.3 g/m\({}^{2}\) PES fiber mat increased the Mode I and Mode II fracture toughness by up to 120% and 68.8%, respectively. Additionally, the interleaved layer improved the interlaminar shear strength and compressive after-impact performance by 18.2% and 43.8%, respectively.
# Converting MST to TSP Path by Branch Elimination
## 3 MST-to-TSP Algorithm  
The proposed algorithm resembles Christofides as it also generates minimum spanning tree to give first estimate of the links that might be needed in TSP. Since we focus on the open-loop variant, it is good to notice that the TSP solution is also a spanning tree, although not the minimum one. However, while Christofides adds redundant links to create Euler tour, which are then pruned by shortcuts, we do not add anything. Instead, we change existing links by a series of link swaps. In specific, we detect branches in the tree and eliminate them one by one. This is continued until the solution becomes a TSP path; i.e., spanning tree without any branches. Pseudo codes of the variants are sketched in Algorithm 1 and Algorithm 2.  
```
MST-to-TSP(\(X\)) \(\rightarrow\) TSP: \(T\leftarrow\) GenerateMST(\(X\)); \(\text{WHILE branches DO}\) \(e_{1}\leftarrow\) LongestBranchLink(\(T\)); \(e_{2}\leftarrow\) ShortestConnection(\(T\)); \(T\leftarrow\) SwapLink(\(T\), \(e_{1}\), \(e_{2}\)); RETURN(\(T\));
```  
**Algorithm 1**Greedy variant  
Figure 8: Cross removal heuristic to improve an existing tour. The four end points involved in the cross are re-organized by swapping the end point of the first link with the start point of the second link.  
Both variants start by generating minimum spanning tree by any algorithm such as [PERSON] and [PERSON]. [PERSON] grows the tree from scratch. In each step, it selects the shortest link that connects a new node to the current tree while [PERSON] grows a forest by selecting shortest link that connects two different trees. Actually, we do not need to construct _minimum_ spanning tree, but some sub-optimal spanning tree might also be suitable [33]. For Christofides, it would be beneficial to have a tree with fewer nodes with odd degree [12]. In our algorithm, having spanning tree with fewer branches might be beneficial. However, to keep the algorithm simple, we do not study this possibility further in this paper.  
Both variants operate on the branches, which can be trivially detected during the creation of MST simply by maintaining the degree counter when links are added. Both algorithms also work sequentially by resolving the branches one by one. They differ only in the way the two links are selected for the next swap; all other parts of the algorithm variants are the same. The number of iterations equals the number of branches in the spanning tree. We will next study the details of the two variants.
## 4 Conclusions  
In this study, a carboxyl-terminated APTMS self-assembled monolayer diffusion barrier was prepared via molecular self-assembly technology. According to the SEM data. the COOH-APTMS diffusion barrier after 3 h of modification was continuous and integrated, containing particles and defects on its surface. The XPS results indicated that the copper atoms were firmly bonded to the COOH-APTMS structure through the Cu-O bonds. Moreover, the interfacial toughness analysis also revealed that the bonding strength between the COOH-APTMS and the Cu film exceeded that between the APTMS and the Cu film. Moreover, the COOH-APTMS self-assembled monolayer diffusion barrier exhibited an outstanding thermal stability, and its failure temperature was 100 \({}^{\circ}\)C higher than that of the APTMS diffusion barrier. This was because the carboxyl groups and sterically hindered benzenes were introduced into the self-assembled monolayer after the carboxyl group modification, which enabled one to effectively inhibit the diffusion of the copper atoms. Therefore, the self-assembled carboxyl-terminated APTMS has a good reliability and thermal stability, opening up new prospects for the application of Cu interconnects in the near future.  
Conceptualization, H.L. and S.P.; methodology, M.F.; software, H.Z.; validation, H.L., Z.W. and M.D.; formal analysis, H.Z.; investigation, Z.W.; resources, S.P.; data curation, H.L., C.Z., L.M., X.L. and M.D.; writing--original draft preparation, H.L. and M.D.; writing--review and editing, H.L.; visualization, Z.W.; supervision, M.D. and Y.F.; project administration, H.L.; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Natural Science Foundation of Heilongjiang Province (LH2021E029).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 9: Change in sheet resistance versus annealed temperatures: (**a**) Cu/APTMS/SiO\({}_{2}\)/Si; (**b**) Cu/COOH-APTMS/SiO\({}_{2}\)/Si.
## 1 Introduction
Through comparing the results of different assessment methods, researchers can evaluate the reliability and accuracy of each method and select the most suitable one for a given situation. Comparative research can also help users better understand the method's limitations and application scenarios, which helps to promote the improvement and refinement of methods. Additionally, the comparative study of evaluation methods can provide decision-making support for earthquake landslide disaster prevention and mitigation. On 5 September 2022, Luding County in Sichuan Province was struck by a magnitude 6.8 earthquake; its epicenter was located at 29.25\({}^{\circ}\) N, 102.08\({}^{\circ}\) E, and it had a focal depth of 16 km. The earthquake reached an intensity of IX according to the China Earthquake Network Center (www.cenc.ac.cn, accessed on 11 September 2022), causing widespread damage to infrastructure, including transportation networks, power systems, and buildings. Additionally, the seismic event triggered a substantial number of landslides, exacerbating the devastation [37; 38; 39; 40] (refer to Figure 1). By the end of the emergency response phase on 11 September, the earthquake had claimed 118 lives, with 86% being attributed to secondary geological disasters, such as landslides and collapses. Following the earthquake, researchers conducted extensive field investigations and utilized remote sensing data to compile distribution data on the earthquake-triggered landslides, providing valuable insights for studying rapid assessment methods in such scenarios. This study focuses on high-intensity areas--notably, Moxi Town and Detuo Town--during the Luding earthquake (rated VII-IX) and employs the difference method and cumulative displacement method to assess the risk of earthquake-induced landslides in the region. The validation of these methods relies on actual landslide data, allowing for a comprehensive analysis of their strengths, weaknesses, and applicability in similar contexts.
# Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane
## 2 Hybrid Enhanced Polarization Image Clarity
### Improved Grayworld Method for Color Compensation  
By observing a large number of underwater scene images, the optical properties of water are analyzed. It can be concluded that the red channel of underwater image has the most serious attenuation, while the retention of the green channel is relatively good. The blue channel's information is not affected. The red channel and green channel of the underwater image have opposite color information. Therefore, this paper compensates the green channel to the red channel to better restore the overall color of the image. In order to  
Figure 1: Algorithm flow chart.  
avoid the subsequent use of the GrayWorld method to produce red channel supersaturation, only the highly attenuated areas in the image are compensated.  
After the above analysis, the compensated red channel image (\(I_{RC}(x)\)) can be expressed as:  
\[I_{RC}(x)=I_{R}(x)+\beta\big{(}\overline{I_{G}}-\overline{I_{R}}\big{)}(1-I_{R} (x))I_{G}(x) \tag{3}\]  
In Equation (3), \(I_{R}(x)\) and \(I_{G}(x)\) are the red and green channels of the image, while \(\overline{I_{R}}\) and \(\overline{I_{G}}\) represent the average values of \(I_{R}(x)\) and \(I_{G}(x)\). \(\beta\) is the adjustment parameter. Experiments show that the effect is better when \(\beta\) is 1.  
After compensating the attenuation of the red channel of the image, the GrayWorld method is used to restore the color of the underwater image, as shown in Equation (4).  
\[\overline{Gray}=\overline{I_{RC}+\overline{I_{G}}+\overline{I_{B}}} \tag{4}\]  
In Equation (4), \(\overline{Gray}\) is the uniform gray value defined by the GrayWorld method. \(\overline{I_{RC}}\), \(\overline{I_{G}}\) and \(\overline{I_{B}}\) are the average values of \(I_{RC}\), \(I_{G}\) and \(I_{B}\). \(I_{B}\) is the blue channel of the image.  
The gain coefficients of the three color channels can be obtained by Equation (5).  
\[\begin{cases}gain_{R}=\frac{\overline{Gray}}{I_{RC}}\\ gain_{G}=\frac{\overline{Gray}}{I_{G}}\\ gain_{B}=\frac{\overline{Gray}}{I_{B}}\end{cases} \tag{5}\]  
According to the von Kries diagonal model, the pixel values \(W_{R}\), \(W_{G}\), \(W_{B}\) of the RGB three channels color of image \(W\) processed by GrayWorld method are obtained, as shown in Equation (6).  
\[\begin{cases}W_{R}=gain_{R}\times I_{RC}\\ W_{G}=gain_{G}\times I_{G}\\ W_{B}=gain_{B}\times I_{B}\end{cases} \tag{6}\]  
In the white balance processing of the water degradation image, compared with the traditional method, the improved GrayWorld method proposed in this paper greatly reduces the red artifacts caused by overcorrection of the areas with serious underwater color deviation. At the same time, this method can still effectively restore the original color of the target and improve the contrast in the harsh underwater environment.
## 5 State of the Art in Conversational AI Platforms
### Conversational AI Platforms Backed by Tech Giants
#### 5.2.5 Amazon Lex (Lex)  
Amazon Lex is a Web service for building conversational interfaces into any application using voice and text. Amazon Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) and NLU, enabling lifelike conversational interactions. KGs can be integrated in the chatbot service either with the use of the internal component of Amazon Neptune, or with the use of an external API and Amazon Connect service [137]. The implementation of a distributed and collaborative architecture is not officially supported or stated by Amazon Lex, though it could be implemented with tools provided by Amazon Web Services or third-party modules. A unique in-house feature is that Amazon Lex provides the ability for multi-turn conversation. Amazon Lex chatbots can be published to messaging platforms such as Facebook Messenger, Slack, Kik, and Twilio SMS. Amazon Lex is free for a trial period with limited speech and text requests. After that free period or on the exhaustion of the free requests there is a payment per number of requests [104, 117, 138, 139].
## 4 Results Analysis
### Theoretical Analysis of the Sensitivity of Each Parameter  
In this study, three key influencing factors--porosity of the porous medium, water-cement ratio, and injection pressure--are selected for a quantitative analysis of Bingham fluid infiltration grouting models in porous media with different particle size distributions. This analysis is conducted in comparison with Bingham fluid infiltration grouting models in porous media with the same particle size distribution.  
The porosity of the porous medium was examined at three levels: 0.35, 0.45, and 0.55. The water-cement ratio of the Bingham fluid slurry was tested at three levels: 0.8, 1.0, and 1.25. The injection pressure was tested at three levels: 0.02 MPa, 0.04 MPa, and 0.06 MPa. The impact of these three factors on the diffusion radius of Bingham fluid, both considering and not considering particle size distribution in porous media, is depicted in Figures 9-11.  
Figure 10: Porosity versus slurry diffusion.  
Figure 9: Relationship between infiltration grouting pressure and slurry diffusion.  
Analyzing Figures 9-11 shows the following:  
1. As the injection time increases, the diffusion radius of the Bingham fluid in porous media, both considering and not considering particle size distribution, exhibits a nonlinear growth relationship. In the initial stage of injection, the growth rate of the slurry diffusion radius decreases rapidly. Subsequently, with the increase in time, the rate of increase in the slurry diffusion radius gradually slows down. It can be observed that during the infiltration grouting process, the expansion of the slurry diffusion radius mainly occurs in the first half of the process. As the slurry diffusion radius increases, the diffusion rate of the slurry gradually decreases.
2. At the same injection time, both theoretical models yield diffusion radii that are positively correlated with the infiltration grouting pressure, porous media porosity, and the water-cement ratio of the Bingham fluid. Additionally, the spherical diffusion radius of the Bingham fluid in porous media with particle size distribution is smaller than that in porous media without particle size distribution. The obtained slurry diffusion patterns are consistent with the laboratory experimental results. Furthermore, as time progresses, the difference between the theoretical diffusion radii of the two models continues to increase.
###### Abstract  
The concept of synthetic aperture radar (SAR) has the advantage of being able to obtain high-quality images even when the target area is at night or covered with obstacles such as clouds or fog. These imaging capabilities have led to a rapid increase in demand for space SAR imagery across a variety of sectors, including government, military, and commercial sectors. The SAR-based deployable reflector antenna was developed in this series of paper. The satellite performance is influenced by the aperture size of an antenna. To improve the image acquisition performance, the SAR antenna has the configuration of several foldable CFRP reflectors. In this paper, the experimental investigation of the Structural-thermal model deployable reflector antenna is performed. During the launch condition, the satellite and payload are subjected to the dynamic load. In the STM phase, the acoustic test was conducted to evaluate the structural stability of the deployable reflector antenna within the acoustic environment. The sinusoidal vibration test was implemented to investigate the fundamental frequency for inplane/normal directions and evaluate the structural stability of reflector antenna. By using experimental data obtained from the thermal-balance test, the well-correlated thermal analysis model was established to execute the orbital thermal analysis. The experimental results of the environmental test in STM phase show that the deployable reflector antenna has structural stability for the structural/thermal environments. The configuration of the deployable reflector antenna determined in STM phase can be applied to the qualification model.  
deployable reflector antenna (DR-A); structural-thermal model (STM); environment test; sinusoidal vibration test; acoustic load test; thermal-balance test +
Footnote †: journal: Applied Sciences
# ndInIoT-FC: IoT Devices as First-Class Traffic in Name Data Networks
## 3 ICN Architecture for IoT Environments
### ndnlIoT-FC Pub-Sub Architecture
#### 3.4.3 Subscription Requests Workflow  
As shown in Figure 6, at the beginning of communications a subscriber node, which is interested in subscribing a specific content, sends an NDN interest packet with a special \"subscription\" tag (mainly used to differentiate a native NDN content request from a publisher-subscriber node) towards its neighborhood according to existing FIB entries. Upon receiving this Interest packet, nodes intervening in the communication create a PIT entry as per usual NDN forwarding using an extended \"lifetime\" value defined by the subscriber node upon Interest creation. Then, likewise to the subscriber nodes, they forward the content to their known paths towards the publisher. When a subscription request reaches a publisher node, a PIT entry is created, as is dictated by the NDN stack and the underlying services installed at the node are informed of the occurrence, initiating a content creation and delivery process, marking the end of the subscription request.  
Figure 5: ndnIoT-FC pub-sub workflow.  
When nearing the expiry of a PIT entry, as established by the lifetime value chosen during interest creation, if a subscriber still aims to maintain data receipts, it proceeds to send a new Interest packet for the same content, with the same Name, in order to relay this intent to the network. When this Interest is received by another node, if a PIT entry already exists for such content, the entry is updated with the received lifetime value, otherwise a new entry is created.