esa-sceva/satcom-chunk-collection huggingface dataset:
# Polarization Transmission of Visible Light in Inhomogeneous Sea Fog Particle Environment
## 1 Introduction
Moreover, the simulation calculation and experimental verification of the subsequent polarization characteristic parameters of the outgoing light are lacking. Given the above problems, we improved the Monte Carlo method, and established an inhomogeneous particle scattering model. It is based on the equivalent analysis method [8] and the relative humidity formula [7]. This research simulated the complete process of polarization transmission under different wavelengths and relative humidity conditions. Then we seriously analysed the trends and causes of DOP when wavelength and relative humidity changed. Compared with the accuracy of LowTran, ModTran, 6S, RT3, and other models at approximately 60% [13; 14; 15; 16], the accuracy of the inhomogeneous particle scattering model in this research can reach more than 75%. We also use the preset concentration of salt water as the generating liquid to create simulated sea fog. Moreover, we established an experimental system close to the actual sea fog environment. Finally, we verify the inhomogeneous particle scattering model through laboratory experiments and replenish the solution to polarized transmission in inhomogeneous sea fog particle environments. We also provide theoretical support for polarization imaging and target detection.
# One-Step Synthesis of Cross-Linked Esterified Starch and Its Properties
## 2 Materials and Methods
### Determination of Phosphorus Content in CES  
Each 0.5 g sample was taken into the digestion bottle, and 15 mL of digested solution (sulfuric acid and nitric acid at 1:1) were added and mixed with the sample. Then, the bottle was heated until the color of the inner gas became white and the liquid was clarified. To reduce the side effect of the remaining nitric acid on the following reaction, 10 mL of sterilized water were added and the mixture was heated until the color of the inner gas became white. After cooling for 1 h, 45 mL of sterilized water was added into the system and the pH was adjusted to 7.0 with 10 mol/L NaOH. Finally, the solution was  
\begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Cross-linking Temperature (\(\circ\)C)**} & \multirow{2}{*}{**pH**} & \multicolumn{2}{c}{**Cross-Linking Agent (\%)**} & \multirow{2}{*}{**Reaction Time (\%)**} & \multirow{2}{*}{**Anetic Amplitude (\%)**} & \multirow{2}{*}{**Esterification Time (h)**} & \multicolumn{2}{c}{**Esterification Time (\%)**} & \multirow{2}{*}{**Acetyl (\%)**} & \multirow{2}{*}{**Substitution of Refitization (\%)**} & \multirow{2}{*}{**Combined Phosphorus (\%)**} \\  & & & & & & & & & & \\ \hline Waxy maize starch & 0 & 0 & 0 & 0 & 0 & 1.5 & 25 & 0 & 0 & 0.007 \\ CLAC-1 & 45 & 10 & 0.7 & 1 & 3 & 1.5 & 25 & 0.89 & 0.034 & 0.017 \\ CLAC-2 & 40 & 10.5 & 0.9 & 2 & 3 & 1.5 & 25 & 0.83 & 0.032 & 0.025 \\ CLAC-3 & 40 & 10 & 0.7 & 2 & 3 & 1.5 & 25 & 1.02 & 0.039 & 0.022 \\ CLAC-4 & 35 & 10.5 & 0.7 & 2 & 3 & 1.5 & 25 & 1.17 & 0.045 & 0.018 \\ CLAC-5 & 40 & 10 & 0.9 & 3 & 3 & 1.5 & 25 & 0.87 & 0.033 & 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Preparation conditions, acetyl content, and bound phosphorus content of CES.  
transferred into a 100 mL volumetric bottle. After supply of water was adjusted to maintain the prescribed volume, the solution was preserved for further analysis.  
The next step was as follows: a 25 mL sample was taken into a 50 mL volumetric bottle, and 4 mL of ammonium molybdate and 2 mL ascorbic acid (50 g/L) were added into the bottle. Subsequently, the bottle was boiled for 10 min and water was added to maintain the volume of the bottle after cooling down. The absorbance of the solution at 825 nm was determined, and the standard curve was plotted for analysis. The content of combined phosphorus was calculated as the ratio, according to Formula (1).  
\[P(\%)=\frac{m_{1}\times v_{1}\times 100}{m_{0}\times v_{2}\times 10^{6}} \tag{1}\]  
where \(P\) represented the content of combined phosphorus in the sample. The symbols \(v_{1}\) and \(v_{2}\) represented the dilution volume and the final volume used for determination of absorbance. The symbols \(m_{1}\) and \(m_{0}\) represented the amount of the sample used in in the beginning and the amount of calculated phosphorus.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 6 Current Challenges and Future Research Directions
### Application Challenges  
#### 6.2.1 Coverage and Penetration Rate Enhancement Challenges  
Although ETC user numbers have achieved rapid growth under policy promotion, the coverage and penetration rates on urban roads still face numerous challenges. Unlike the relatively singular application scenarios of highways, the complexity and diversity of urban traffic environments present greater difficulties for ETC system promotion and application.  
Unbalanced infrastructure construction represents the primary factor affecting coverage rates. Urban road networks are dense with numerous nodes, requiring the consideration of multiple factors, including road classifications, traffic volumes, technical feasibility, and construction costs when deploying ETC gantries and reading-writing devices. The current ETC infrastructure is primarily concentrated on highways and urban expressways, with limited coverage on ordinary urban roads. This coverage imbalance not only restricts the application scope of ETC data in urban traffic management but also affects user experience and acceptance.  
Changing user habits requires lengthy adaptation processes. Compared to the rigid demands for highway tolls, ETC applications on urban roads are often optional, making user willingness for active usage a key factor affecting penetration rates. How to enhance user willingness and satisfaction through technical improvements, service optimization, and incentive measures represents an important challenge for promotion and application.  
Immature business models also constrain ETC promotion and application in urban scenarios. Unlike highway tolls, which possess clear business models, profitability models for urban ETC applications remain unclear. Application scenarios such as parking fees, congestion charges, and environmental fees possess enormous market potential but still face uncertainties in policy environments, technological maturity, and user acceptance. This business model uncertainty affects social capital investment enthusiasm and constrains the rapid development of related technologies and applications.
# LIransformer: A Transformer-Based Framework for Task Offloading in Vehicular Edge Computing
## 3 Method
### Task Offloading
#### 3.1.2 Search Stage and Energy Management  
In edge computing, we propose a task offloading strategy that combines the dynamism of edge computing networks with the hardware environment. Let \(r_{s}\) represent the remaining computational resources of the server. \(f_{s}\) represents the computing efficiency. \(P_{s}\) is the server's computational power. Computational tasks typically consist of several sub-tasks.  
Figure 2: (**a**) Training process; (**b**) prediction and optimization process.  
Therefore, we define \(N\) consecutive sub-tasks as \(n=\{1,2,\ldots,N\}\), and the computational resource required for task \(n\) as \(r_{n}\). After receiving a computational task, there are two scenarios that need to be considered in each edge server:  
1. The current edge server is capable of completing the computational task on its own. In that case, the energy consumption required for the edge server to complete computational task \(n\) is \[E=\frac{r_{n}P_{s}}{f_{s}},\ r_{s}>r_{n}\] (1)
2. The remaining computational resources on this edge server cannot meet the minimum requirements of this computational task. The edge server transfers this computational task to an edge server in the direction of the vehicle's movement. In that case, the total energy consumption required for the edge server to complete computational task n is \[E=E_{T}+E_{p}+\frac{r_{n}P_{s}}{f_{s}},\ r_{s}\leq r_{n}\] (2) where \(E_{T}\) represents the energy consumption for task transmission, and \(E_{p}\) represents the energy consumption for trajectory prediction. Namely, if each edge server completes the computational task in the first scenario, the energy consumption is minimized. However, if an edge server handles a lot of computational tasks in the first scenario, it may lead to the depletion of resources on this edge server. Therefore, this strategy can be used to search for idle resources on edge servers and reduce the occurrence of denial-of-service incidents caused by computational task accumulation. The relevant pseudocode is presented in Algorithm 1 below.  
```
1. for server in edge_servers:
2. while data = receive_data():
3. if data.\(r_{n}\)> server.\(r_{s}\):
4. trajectory = receive_trajectory(data.vehicle)
5. next_server = predict(trajectory)
6. data.send(next_server)
7. else:
8. results = exec(data.task)
9. results.send(data.vehicle)
```  
**Algorithm 1**LTransformer-based Predictive-mode Task Offloading Algorithm.  
It is worth noting that if the predicted edge server still faces resource constraints, it will continue to follow the second scenario for execution. However, prediction tasks will update the trajectory as the vehicle moves, thus dynamically altering the prediction results in real time.  
As a result, trajectory prediction becomes a crucial component of this optimization approach. To enhance prediction accuracy, we introduce the LTransformer.
## 6 High Frequency Characterization and Modelling
### High Frequency Characterization of InP DHBTs  
#### 6.1.1 Dedicated Test Structure Design  
An on-wafer thru-reflect-line (TRL) calibration kit was developed and implemented in the InP DHBT process. Its purpose is to allow on-wafer RF transistor measurements beyond 110 GHz. In fact, at such high frequencies, conventional calibration methods fail to provide sufficiently accurate measurements as demonstrated in [23], by comparing short-open-load-thru (SOLT) on commercial calibration substrate on alumina and on-wafer TRL calibrations up to 500 GHz. Thus, on-wafer calibrations are of particular interest for device characterization in the sub-millimeter-wave frequency ranges [24].  
In [25], coplanar waveguide (CPW) transmission lines in InP technology proved to be suitable for on-wafer TRL calibration for InP DHBT characterization up to 500 GHz. Hence, the same methodology for designing calibration structures was applied to the III-V Lab InP DHBT process (Figure 12). As shown in Figure 12, the calibration kit contains one 50 \(\Omega\) Thru and two 50 \(\Omega\) Lines of length 180 and 120 \(\upmu\)m, covering, respectively, the bandwidths of 35-315 GHz and 70-630 GHz for the TRL calibration. The layout of the RF pads allowed a probing pitch of 50-100 \(\upmu\)m. After the on-wafer TRL calibration, the reference plane is set at the inner edge of the pads (see Figure 12b), which is also the beginning of the 50 \(\Omega\) Thru standard. Hence, the RF pads are included in the calibration error terms.
## 3 Results
### Thermal Analysis  
The thermal decomposition behavior of the PCL microfibers, containing different GNPs concentrations, was evaluated by TGA. For this analysis, the samples were subjected to a controlled temperature increment up to 600 \({}^{\circ}\)C (Figure 7).  
The TGA curves of the PCL and GNPs-PCL microfibers with different GNPs concentrations (Figure 7a) showed that there was one main loss step common to all samples, between approximately 350 \({}^{\circ}\)C and 450 \({}^{\circ}\)C, corresponding to the PCL degradation, as expected [39]. With the increasing concentration of GNPs, especially for the 1% and 2%, another loss step appeared between 250 \({}^{\circ}\)C and 350 \({}^{\circ}\)C, which was probably due to the loss of the acidic functional groups and residues of the exfoliation process [40; 41; 42]. DTG (Derivative Thermogravimetry) analysis (Figure 7b) demonstrated that the samples did not present a linear thermal behavior in relation to the percentage of GNPs. The maximum degradation peak of PCL was practically the same, at approximately 409 \({}^{\circ}\)C for all samples  
Figure 6: FESEM images of (**a**) and (**b**) PCL microfibers; (**c**) and (**d**) 0.2%GNPs-PCL membranes; (**e**) and (**f**) 2%GNPs-PCL membranes. The magnifications used for these images was of 10,000\(\times\) (10 \(\upmu\)m) and 50,000\(\times\) (2 \(\upmu\)m), from the left to the right.  
except one. The incorporation of 2% of GNPs promoted a shift of the peak to higher temperatures, from 409 \({}^{\circ}\)C (neat PCL sample) to 415 \({}^{\circ}\)C, which corresponded to an increase of 8 \({}^{\circ}\)C. Therefore, these results showed that although the thermal behavior was not linear, the tendency was to improve the thermal stability of the PCL membranes with the increase of GNPs concentration [39].  
TGA can also be used as a quantitative analysis, providing information about the amount of the GNPs present in the developed samples. Taking into consideration all the weight losses and giving special attention to the residue percentage at the end of the heating process, the quantity of GNPs was calculated, using the Equation System (2). The sample of only PCL presented 1.80 wt% of residues, and the samples 0.05%GNPs-PCL, 0.1%GNPs-PCL, 0.2%GNPs-PCL, 0.5%GNPs-PCL, 1%GNPs-PCL, and 2%GNPs-PCL presented 8.45 wt%, 17.66 wt%, 20.05 wt%, 27.39 wt%, 24.70 wt%, and 24.76 wt% of total residues, respectively. Assuming that the samples were composed by only PCL and GNPs, the final GNPs percentage present on the samples after the impregnation was of 6.65 wt%, 15.86 wt%, 18.25 wt%, 25.59 wt%, 22.90 wt%, and 22.96 wt% for the samples 0.05%GNPs-PCL, 0.1%GNPs-PCL, 0.2%GNPs-PCL, 0.5%GNPs-PCL, 1%GNPs-PCL and 2%GNPs-PCL, respectively.
## 5 Conclusions and Future Trends
### Key Challenges and Limitation Costs  
Despite these benefits, this study also identifies key challenges and limitations associated with implementing these systems in real-world scenarios. The effectiveness of rerouting heavily depends on the speed and reliability of real-time data transmission. Delays in communication between intersections can disrupt synchronization, leading to inefficient traffic control and safety risks from misaligned signals. Scalability becomes a critical issue as traffic systems expand to larger urban areas, requiring robust computing infrastructure capable of processing and analyzing vast amounts of data without performance degradation. Furthermore, network downtime or instability, particularly in regions with outdated infrastructure, can undermine rerouting efficiency, resulting in operational delays and inefficiencies. Upgrading these systems to accommodate rerouting capabilities often requires significant investment in hardware and software. Even with reliable data, the performance of rerouting systems depends on decision-making algorithms that must process information quickly and accurately. Delays or inefficiencies in algorithmic processing can compromise system effectiveness, leading to suboptimal traffic flow and increased congestion.  
Although precise cost estimates are not calculated due to the research-oriented focus of this study, we address potential barriers and practical considerations for implementing multi-intersection VLC and DRL systems. Key aspects include the following:  
* _Hardware Costs:_ Expenses depend on the deployment scale and performance requirements, encompassing LED transmitters, photodetectors, and edge-computing devices for real-time processing.
* _Maintenance Requirements:_ Regular upkeep involves replacing LEDs, calibrating photodetectors, and updating edge devices, with costs influenced by environmental factors and usage intensity.
* _Compatibility with Existing Systems:_ Deployment may require retrofitting traditional traffic lights and ensuring DRL system integration with centralized traffic management.
# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
## 2 Literature Review
### Visibility Estimation Studies
This approach improves the accuracy of the DCP algorithm by leveraging the distance data of fixed objects, thereby providing a more accurate visibility estimation. [PERSON] [23] further improved the DCP algorithm by integrating it with the Grayscale Image Entropy (GIE) and Support Vector Machine (SVM) techniques to estimate the visibility range more precisely. This integrated approach is employed to determine the visibility in current road and traffic conditions, which helps in setting appropriate speed limits to enhance traffic safety and reduce congestion. The DCP algorithm has been rigorously validated over an extended period and has been embedded in various industrial products, such as car cameras and road CCTVs, making it a highly reliable method for estimating fog intensity. Consequently, research has been conducted using the DCP as a foundational algorithm to develop advanced video-based fog intensity measurement algorithms specifically for estimating sea fog intensity.
# Numerical Analysis of Vibroacoustic Response of Timber Floor Panels Damped with Porous Materials
## 4 Dynamic Analyses and Parametric Study
### Effect of Porous Material
The introduction of the porous material in the panel design leads to notable improvements in the vibroacoustic characteristics. One key observation is the slight shift in the critical eigenfrequencies caused by the presence of the porous layer. This shift indicates that the porous material influences the panel's natural frequencies, potentially altering its modal behavior. Furthermore, the porous material acts as a shock absorber, effectively dampening vibrations within the panel structure, which leads to reduced sound radiation. Figure 7: Panel submitted to load walking, (**a**) comparison of a radiated sound pressure in the narrow band for rigid connection, (**b**) displacement at the middle point of the rigid panel, (**c**) comparison of a radiated sound pressure in the narrow band for hinge connection, and (**d**) displacement at the middle point of the hinge panel. The results reveal that the porous sound-absorbing material minimizes sound transmission and significantly reduces radiated sound energy when compared to the untreated system. This is owed to the porous sound-absorbing material's damping effect on timber floor panels' displacement, in which vibration energy is released as heat and absorbed by the porous sound-absorbing material's solid skeleton. The porous sound-absorbing material also reduces noise due to thermal and viscous effects. It is possible to deduce that the porous sound-absorbing material plays a critical role in controlling the acoustic sound-pressure response, particularly for noise reduction in a coupled system. According to the findings, adding porous material can improve the acoustic performance of a timber floor panel in the low-frequency region.
## 1 Introduction
In the aforementioned circumstances, the study of the optimal number of images to find the critical image acquisition times and temporal resolution along with VIs and their gradient is necessary. This paper will address the issue of how important the temporal resolution of image acquisition for multi-temporal image classification is and what happen when the temporal resolution of Landsat-8 increases to one week. For this purpose, the study area selected is an overlapping area between two Landsat paths. Moreover, missing data problems due to cloudiness is another matter which limits the application of multi-temporal image classification. Indeed, many training pixels or test data are ignored if only one cloudy image exists in the time series. Although considerable research has been previously done on the time-series classification, the problem of missing data caused by cloudy pixels in such images is still an issue to which less attention has been paid. One of the main problems in multi-temporal image classification is that some pixels of training/test areas may be covered by cloud. Hence, those pixels are ignored and neither incorporated as training data in the classifier learning process nor as test data for the classification accuracy. This paper remedies this problem for those cloudy pixels, retrieves the missing data through k-nearest cloudless pixels in the feature space and investigate effect of missing data retrieved on the classification results of multi-temporal images. This paper also addresses the impact of the appropriate image acquisition times and VIs along with the spectral and VIs gradient on the RF classifier when missing data exist in the multi-temporal images. The objective of this paper is, therefore, to investigate the practical considerations of the multi-temporal image classification approach for consultants and agronomists. The specific goals of this study are: (1) to assess the effect of missing data in training areas, due to being cloudy, on RF classification learning; (2) to determine the appropriate data acquisition times and temporal resolution; (3) to assess the effect of VIs and their temporal gradient along with temporal gradient of spectral bands in different times; and (4) to determine the optimum number of classes to be distinguished in the early season. To our knowledge, there are currently no other comprehensive assessments of multi-temporal image classification which takes into consideration these practical aspects. We proposed a developedmethod for image time series processing that improves the classification of winter wheat results even in early crop classification.