esa-sceva/satcom-chunk-collection huggingface dataset:
# The Application of Computed Tomography to Study the Soil Porosity of Mountain Red Earth
## 3 Results and Discussion
### The Relationship between the Parameters of Pores  
The parameters of pore quantity, area, roundness, and fractal dimension can provide a multi-faceted, quantitative examination of pore characteristics, with each parameter  
Figure 11: Linear fitting plots of lnr and lnNr for box-counting dimension calculation.  
Figure 12: The box-counting dimension varies with depth.  
exerting some degree of influence over the others. The size of the pore area (percentage of total area) does not significantly affect the roundness (Figure 13). An analysis of individual pores on the CT slices also shows that the size of a pore's area has no bearing on its roundness; it is only determined by the pore's equivalent diameter. However, the number of pores does impact roundness; as the quantity of pores increases, the overall roundness tends to decrease gradually. This is because smaller pores are more likely to approximate a circular shape, resulting in a higher roundness value. When the number of pores is high, such as in the topsoil layer, there are more large- and medium-scale pores, leading to a smaller roundness value. As depth gradually increases, the number of large pores decreases, the quantity of medium- and small-scale pores increases, and the overall shape is more circular, resulting in a larger roundness value. There is a clear correlation between the number of pores, the percentage of pore area, and the box dimension, with all displaying a clear positive relationship (Figure 14). This relationship is most evident in the way the pore area percentage relates linearly to the box dimension. The more widely the pores are distributed on the CT slices and the larger their area, the stronger the heterogeneity, complexity, and irregularity will be. This makes it more conducive to water infiltration under rainy conditions. Therefore, in the topsoil layer, where the distribution of pores is extensive, there is also strong spatial heterogeneity of the pores, which is more favorable for the absorption and infiltration of water. Finally, a correlation analysis was conducted between the D value and the parameters of pore count, roundness, and pore area percentage using the Pearson correlation coefficient; the results are shown in Table 1. It can be seen that the correlation coefficient values between the box dimension D and the number of pores, roundness, and pore area percentage all show significance. Specifically, the correlation coefficient between the D value and the number of pores is 0.711, indicating a significant positive correlation between the D value and the number of pores.
## 2 Materials and Methods
### Differential Scanning Calorimetry (DSC)  
Thermal analyses were carried out using a Mettler Toledo DSC 1 STARe system equipped with a Poly-Science temperature controller (PolyScience, Niles, IL, USA). The detection system was a HSS8 high sensitivity sensor (120 gold-gold/palladium-palladium thermocouples) and the ceramic sensor (Mettler Full Range; FRS5) with 56 thermocouples. A pure indium sample was used to calibrate the instrument. Each DSC scan had an accuracy of \(\pm\)0.4 \({}^{\circ}\)C and a reproducibility and resolution of 0.1 \({}^{\circ}\)C. To determine the thermotropic behavior, about 5 mg of each sample (RS100, RL100, RS/RL physical mixture (p.m.), DIC, DIEP, RS/RL-DIC p.m., RS/RL-DIEP p.m., EUD3A, EUD3A_DIEP3, EUD3A_DIC4), was sealed in a 40-\(\mu\)L aluminum pan; an empty pan was used as a reference. In the case of EUD NP specimens, the NP suspensions were lyophilized for 24 h ([PERSON], Thermo Fisher Scientific Italia, Rodano, Italy), after a quick freezing in liquid nitrogen. Each sample was submitted to a heating cycle between 25 and 210 \({}^{\circ}\)C, at a scan rate of 5 \({}^{\circ}\)C/min. The thermal behavior was extrapolated using the software provided (Mettler STARe Evaluation software system, version 13.00, installed on an Optiplex3020 DELL PC).
## 2 Study Area and Input Data
### Global Lakes and Wetlands Database  
For comparison, information provided by the Global Lakes and Wetlands Database (GLWD) ([PERSON] et al., 2018) is used in this study; namely, shorelines and surface areas of Level 1 and Level 2. Level 1 provides  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Orbit** & **Missions** & **Period** & **Height** & **Repeat Cycle** & **Track Dist.** \\  & & & **[km]** & **[days]** & **at Equator [km]** \\ \hline Jason & TOPEX, Jason-1/2/3 & 1992-today & 1336 & 9.9 & 315 \\ Envisat & ERS-1/2, Envisat, Saral & 1991–2010/2013–2016 & 800 & 35 & 80 \\ Sentinel-3A & Sentinel-3A & 2016-today & 815 & 27 & 104 \\ Sentinel-3B & Sentinel-3B & 2018-today & 815 & 27 & 104 \\ Cryosat-2 & Cryosat-2 & 2010-today & 717 & 369 & 8 \\ Saral-DP & Saral-DP & 2016-today & changing & drifting & irregular \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orbit configuration of satellite altimetry missions used in this study.  
metadata for the largest lakes (with a surface area larger than 50 km\({}^{2}\)) and reservoirs (storage capacity larger than 0.5 km\({}^{3}\)) worldwide. Level 2 contains additional smaller lakes with surface areas larger than 0.1 km\({}^{2}\). For the MRB, the database provides 120 level 1 lakes and 4527 lakes of level 2, with a total area of 33,687.66 km\({}^{2}\) (19,041.73 km\({}^{2}\) for level 1 and 14,645.83 km\({}^{2}\) for level 2). GLWD is used for comparison, since it is the most commonly used and freely accessible global dataset available today.
## 6 Conclusions  
In summary, our prototype system has successfully demonstrated that the addition of a DC layer composed of silica hafnia glass and glass-ceramic doped with Tb\({}^{3+}\) and Yb\({}^{3+}\) can enhance the performance of a crystalline silicon (c-Si) solar cell. The electrical characterization results show an improvement in photocurrent generation, as well as an increase in short circuit current density and external quantum efficiency (EQE). The effect is particularly remarkable, with an improvement of 10.79% and 10.78% when the solar cell is, respectively, coated with 70 SiO\({}_{2}\)-30H\({}_{2}\)-3 Tb\({}^{3+}-12\)Yb\({}^{3+}\) glass and glass-ceramic. Furthermore, by combining a DC layer with the c-Si solar cell, the system exhibits an additional EQE ranging from 2.64% to 3.44%.  
Overall, these results demonstrate that incorporating a layer of silica hafnia glass and glass-ceramic doped Tb\({}^{3+}\) and Yb\({}^{3+}\) as a down-cutting material significantly improves the performance of a crystalline silicon (c-Si) solar cell in terms of energy production and efficiency.  
Conceptualization, L.O.; methodology, L.O.; software, L.O. and S.E.A.; validation, K.B., A.B. and M.R.B.; formal analysis, K.B., A.B., M.R.B. and F.E.; investigation, K.B. and A.B.; resources, S.E.A.; data curation, L.O.; writing--original draft preparation, L.O. and S.E.A.; writing--review and editing, L.O. and F.E.; visualization, K.B., A.B. and M.R.B.; supervision, K.B., A.B. and M.R.B.; project administration, K.B., A.B. and M.R.B.; funding acquisition, F.E. and M.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Material Preparation and Properties
* Skin effect: At higher frequencies, electric currents tend to stay near the surface of conductors, a phenomenon known as the \"skin effect\". This is caused by an increase in reactance at higher frequencies, which restricts the current flow to the outer layers [24]. The \"skin depth\", the depth within which the current is confined, decreases with rising frequency and is influenced by the material's properties. * Inductive and capacitive effects: In conductive materials, higher frequencies can increase the inductive reactance, affecting the current-voltage relationship and potentially influencing EIT measurements [25]. For non-conductive materials or dielectrics, frequency interactions become more complex:
* Dielectric relaxation: Dielectrics, especially those with polar molecules, exhibit a lag in the polarization in response to oscillating electric fields. This lag, which is more pronounced at certain frequencies, can lead to changes in the impedance spectrum, affecting the clarity and precision of the EIT image [26]. * Dispersion phenomena: Dielectrics may display varying impedance values at different frequencies as a result of diverse polarization mechanisms within the material. This\"dielectric dispersion\" arises from different polarization processes, such as electronic, atomic, or molecular, becoming dominant at varying frequencies [27]. * Capacitive effects: Unlike conductive materials, dielectrics demonstrate capacitive behavior in circuits. With increasing frequency, their capacitive reactance decreases, altering their representation in EIT images [28]. Copper and aluminum, with their notable conductivity, consistently showcase a high contrast with non-conductive materials in EIT images. Acrylic resin, which is characterized by its high resistivity and impedance, presents a distinctive impedance profile compared to that of copper. This clear contrast, particularly at different frequencies in EIT, underscores the method's capability to differentiate materials with varying electrical properties, enabling a detailed analysis of frequency-dependent contrasts in EIT imaging.
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
## 5 Numerical Examples
### Preliminary Application of Adaptive Algorithm to \(1d\) Problem  
Consider the two-point boundary value problem in Section 5.1. The prescribed tolerance is set to be \(T_{l}=10^{-4}\). Using one element as the initial mesh, the mesh \(\pi^{*}\) for the first adaptivity phase is obtained by six adaptivity steps and the computed results are shown in Figures 11 and 12, where the mesh \(\pi^{*}\) is demonstrated by stepped line segments with each horizontal and vertical line segment representing the corresponding element size. The errors of FE solutions estimated by using the EEP solution are shown in Figure 11. Compared with real errors shown in Figure 12, the error estimate using the EEP solution cannot guarantee that the real error can meet the requirement of the error tolerance, \(T_{l}  
analysis is fully successful. In addition, for this example, \(N_{\text{dof}}\) is only 16 from the adaptive refinement, while \(N_{\text{dof}}\) is 64 from the uniform refinement, reflecting the remarkable efficiency gained from the adaptivity algorithm.
## 4 Enabling Technologies for FSO Communication
### Adaptive Detection Thresholds  
This section introduces Adaptive Detection Threshold (ADT) schemes, which are essential for optimizing communication system performance in varying conditions. ADT schemes dynamically adjust detection thresholds based on real-time environmental and signal quality metrics, thereby enhancing reliability and efficiency. By continuously adapting to changing conditions, these schemes mitigate the impact of noise, interference, and signal fading, ensuring robust and efficient data transmission.
# A Wideband Termination Based on Laser-Scribed Lossy Microstrip Line Structures
## 5 Conclusions  
In this article, we proposed a tapered open-end termination design with an overlapped lossy resistive rGO film and a strip conductor. This design provides a well-matched sheet-resistance range between device design (20 \(\Omega\)/\(\square\)-1.2 k\(\Omega\)/\(\square\)) and laser-scripted rGO (175 \(\Omega\)/\(\square\)-1.2 k\(\Omega\)/\(\square\) with 150 mW-25 mW laser power, respectively). The large sheet-resistance range can also reduce the fabrication difficulties and give more choices of materials. The fabricated termination provides broadband absorption for high frequencies, with a return loss of >-15 dB at above 6.0 GHz. The termination can also be used for attenuation over a \(-\)10 dB attenuation level (>8.5 GHz) with a low reflection level better than \(-\)15 dB (>2.0 GHz). Moreover, the laser-direct writing-based technique enables fast prototyping or modification, which promotes flexible and low-cost manufacturing and will be beneficial for device design.  
[PERSON]-[PERSON] and [PERSON] conceived and designed the experiments; [PERSON]-[PERSON], [PERSON] and [PERSON] performed the simulations; [PERSON] analyzed the data; and [PERSON] contributed materials. [PERSON]-[PERSON] and [PERSON] wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61704090, 11904177), the Natural Science Foundation of Jiangsu Province (BK20170908, BK20170903).  
The authors declare no conflict of interest.  
Figure 9: The measured S-parameters of the laser-scribed termination at two-port configurations, (**a**) transmissions \(\mathrm{1S_{21}}\)\(\mathrm{1}\) and (**b**) reflections \(\mathrm{1S_{11}}\)\(\mathrm{1}\).
# Securing Construction Workers' Data Security and Privacy with Blockchain Technology
## 3 Methodology
### Experimental Design of the Proposed Blockchain
#### 3.1.2 System Structure of Proposed Blockchain
It included APIs, which were tested successfully in Postman (an API platform), a middleware, and connectors that enabled communication and data exchange between the blockchain network and other external applications. This layer ensured seamless integration and interoperability between the blockchain network and existing infrastructure. Application Layer: The application layer was made up of business-specific applications and chaincode developed with Java version 11.10.18. Java was chosen for its broad adoption and versatility, thus streamlining the implementation process and ensuring the flexibility of the chaincode. The chaincode was built to be highly secure, deterministic, and transactional. It served as the foundation for the construction workers' information management system, offering various functions. These included adding workers' biodata and safety information, retrieving data, and making updates to details like qualifications, certifications,and work history. These features were customized to cater to the construction industry's specific requirements. Representation layer: The representation layer was a vital part of the proposed blockchain application's user interface. It included the Controller, User Interface (UI), and Hyperledger Explorer. The Controller acted as a link between the UI and the application's logic, facilitating communication and coordination. The UI offered a graphical or command-line way for users to engage with the blockchain app, such as submitting transactions and accessing ledger data. The Hyperledger Explorer, a visualization tool, gave a comprehensive overview of the blockchain network, displaying block, transaction, and participant details. This layer was crucial for stakeholders to interact with the blockchain effectively while ensuring user-friendliness and transparency.
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Meander Design** & **Mark** & **Description** & **Measurement** & **Unit** \\ \hline \multirow{8}{*}{Design 1} & 1\({}_{1}\) & Width of the meander bus & 11.4 & mm \\  & 1\({}_{2}\) & The length of the straight part connecting the & 4.8 & mm \\  & \({}_{1}\) & Total width of meander including 4 Cu tracks & 0.9 & mm \\  & \({}_{1}\) & Half of the period between the middle top & 5 & mm \\  & \({}_{1}\) & and middle bottom part & 2.45 & mm \\  & \({}_{1}\) & Meander angle & 90 & \({}^{\circ}\) \\  & \({}_{1}\) & Spacing between copper tracks & 0.1 & mm \\  & & Copper track width & 150 & \(\mu\)m \\ \hline \multirow{8}{*}{Design 2} & 1\({}_{2}\) & The total length of the sample & 9.4 & mm \\  & \({}_{2}\) & Total width of sample including 4 Cu tracks & 0.9 & mm \\  & \({}_{2}\) & The period between the middle top and & 3.5 & mm \\  & \({}_{2}\) & Middle bottom part & 2.55 & mm \\  & \({}_{2}\) & Meander radius & 2.55 & mm \\  & \({}_{2}\) & Meander angle & 45 & \({}^{\circ}\) \\  & \({}_{2}\) & Spacing between copper tracks & 0.1 & mm \\  & \({}_{2}\) & Copper track width & 150 & \(\mu\)m \\ \hline \multirow{8}{*}{Design 3} & 1\({}_{3}\) & The total length of the sample & 5.9 & mm \\  & \({}_{3}\) & Total width of sample including 4 Cu tracks & 0.9 & mm \\  & \({}_{3}\) & The period between the middle top and & 5 & mm \\  & \({}_{3}\) & middle bottom part & 2.45 & mm \\  & \({}_{3}\) & Meander radius & 90 & \({}^{\circ}\) \\  & \({}_{3}\) & Spacing between copper tracks & 0.1 & mm \\  & & Copper track width & 150 & \(\mu\)m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dimensions of meanders. Figure 3: (**a**) Different meander designs. From the top: Meander type 1, type 2 and type 3 and Meander type 1-1, 2-2 and 3-3. (**b**): Dummy PCB (printed circuit board) that was soldered to the central interposer. The most straight-forward design of adding 4 tracks to a polyimide meander is shown in Figure 4a: here all tracks are evenly spaced and centered on the polyimide meander. However, as our initial results clearly indicated that failures mainly occur at the top of the meander, we also introduced a secondary design, where this region is reinforced with additional copper tracks [20] (Figure 4b). The additional track is not connected to the circuit but merely added to support the durability of connected copper tracks. This was done for all three designs presented in Figure 3. The corresponding designs are labeled as \"1-1\", \"2-2\" and \"3-3\". By combining the above-mentioned options in materials, textiles and designs, the first set of 24 samples was composed and tested according to the washing procedure of Table 3. Next, based on the initial results, the second set of 9 samples was constructed and tested. This set comprises the aforementioned reinforced meanders on the one hand but also differs from the first set in the way the layering was composed.
# Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
## 3 The Limits of Multi-Agent Reinforcement Learning
### Nonstationarity  
The environment in a multi-agent setting can be modified by the actions of all agents; thus, from the single-agent perspective, the environment becomes non-stationary. The effectiveness of most reinforcement learning algorithms is tied to the Markov property, which does not hold in non-stationary environments [16]. Policies created in a non-stationary environment are deemed to have become outdated. Despite the loss of theoretical support, algorithms designed for the single-agent setting have been applied in multi-agent settings, such as independent learners (IL), occasionally achieving desirable results [22]. A naive approach to tackle the non-stationarity is the use of joint action learners (JAL). This uses a single-agent RL but with the joint action instead of the local action used to compute its value functions. This approach eliminates the problem of non-stationarity entirely; however, it is computationally ineffective, and the action space dimension becomes \(|A|^{N}\), where N is the number of agents, making it difficult to scale this type of approach to more than a few agents. In addition, to ensure that every agent knows the actions of others, some type of centralized controller or communication network is required [23]. In [24], a variation of Q-learning for swarm systems is presented, called Q-RTS. The key idea is to use a centralized aggregation center to combine all the Q-value tables of the agents to form a global swarm matrix (\(Q_{sw}(s,a)\) containing the highest and lowest Q-values representing the most interesting iterations by the agents. The swarm matrix is then linearly combined into the local Q-value matrix of each agent \(Q_{i}(s,a)\) using an independence factor \(\beta\in[0,1)\), controlling the tradeoff of local and global knowledge. The Q-learning update then proceeds independently on \(Q_{i}^{\prime}\) for each agent.  
\[Q_{i}^{\prime}(s_{t},a_{t})=\beta Q_{i}(s_{t},a_{t})+(1-\beta)Q_{sw}(s_{t},a_{ t}) \tag{14}\]  
An FPGA implementation was also proposed [25].
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 7 Applications of AV Traffic Modeling
### Environmental Impact Assessment  
The field of Environmental Impact Assessments (EIAs) in the context of autonomous vehicle (AV) traffic modeling is rich with diverse research contributions.  
[PERSON] and [PERSON] explored the use of Vehicle Sensor Networks (VSNs) embedded in public transportation for air quality tracking, significantly improving data accuracy and coverage. Their work provides a foundational approach to monitoring environmental conditions in real-time, which is critical for assessing the direct impact of AV operations on air quality [281]. This study was complemented by [PERSON] et al., who highlighted the often-overlooked energy demands associated with AV data management. Their analysis suggests that the energy use required for processing AV data could result in emissions comparable to millions of additional vehicles by 2030, thereby emphasizing the importance of considering data management in environmental impact assessments [282].  
[PERSON] et al. contributed a quantitative framework for evaluating AV intelligence in complex environments using a topology structure and analytic hierarchy process. This framework is essential for understanding how AV decision-making processes impact environmental outcomes, particularly in terms of energy consumption and emissions [283]. Building on this, [PERSON] et al. discussed enhancements in traffic simulation models aimed at assessing vehicle energy consumption and CO\({}_{2}\) emissions. Their focus on the implementation of intelligent transportation systems (ITSs) and Information and Communication Technology (ICT) systems, such as Advanced Driver Assistance Systems (ADASs) and Eco-Driving applications, illustrates the potential for technology to mitigate environmental impacts [284].  
[PERSON] et al. further developed this discourse by creating an evaluation framework that incorporates energy, traffic, and air quality impacts using advanced fuzzy evaluation methods. This integrative approach allows for a more nuanced assessment of AV environmental effects, considering multiple factors simultaneously to provide a holistic view of AV impacts [285]. Lastly, [PERSON] and [PERSON] proposed a neural network approach for traffic noise analysis, demonstrating superior performance over classical statistical models in urban settings. Their work highlights the importance of innovative methodologies in accurately assessing noise pollution, a critical component of environmental impact that is often overlooked [286].  
These studies collectively advance the understanding of AV traffic modeling's environmental impacts, offering innovative methodologies and comprehensive assessments across various dimensions of AV and transportation systems.