esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
A good correlation between the presence of hydrocarbon contaminants and a substantial increase of the electrical conductivity, caused by microbiological activities, has been reported in previous studies [6; 12]; eventually, the long-term degradation of hydrocarbons results in a marked attenuation of electromagnetic signals [13]. A geoelectric survey for the characterization and monitoring of a contaminated site showed an interesting relationship between the production of by-products of biodegradation and the response of induced polarization measurements [6]. Other researchers linked the geophysical investigation of the self-potential response to redox reactions from microbiological activity, developing a \"biogeobattery\" model [14; 15]. The need to control the numerous system parameters of a bioremediation process has suggested developing laboratory-scale experiments. [PERSON] et al. [16] measured the evolution of electrical conductivity in pore water, draining it from contaminated soil microcosms. [PERSON] et al. [17], working on a 2 m\({}^{3}\) tank, compared water monitoring with time-lapse electrical resistivity tomography. [PERSON] et al. [4] observed that biofilm production in biostimulated samples was detectable when measuring a loss of amplitude of the acoustic waves that crossed the system and when observing a peak in imaginary electrical conductivity. A study by [PERSON] et al. [18] confirmed the relation between peculiar trends in the imaginary part of electrical conductivity and the clogging of pores by biofilms, and also estimated consequent variations of porosity and hydraulic conductivity of soil. We focus on the geoelectrical properties of soil affected by hydrocarbon contamination, using TDR measurements to provide long-term monitoring of the biological processes in a simple way. Our goal is to validate their use as a support tool for the individuation and characterization of a contaminated area to provide useful information for soil bioremediation applications. Previous works have demonstrated that diesel oil contamination activates the biological processes of degradation by biostimulated indigenous microflora [3; 19]. In such a scenario, we prepared two mesocosms, artificially contaminated by diesel oil, and we monitored the electrical properties for more than three months to assess changes in dielectric permittivity and electrical conductivity. The geophysical monitoring was supported by soil sampling in different points of the columns at the end of the test (t = 105 days) to get diesel oil concentration, viable microbial count, fluorescein diacetate (FDA) analysis, and SEM images.
## 2 Materials and Methods
### Performance Evaluation of SM-OGFC  
#### 2.4.1 High-Temperature Mechanical Performance Test  
Marshall test and rutting test are conducted to evaluate the high-temperature performance of SM-OGFC according to Chinese standards GB/T0709-2011 and GB/T0719-2011 in this study. By assessing Marshall stability (MS) and dynamic stability (DS), the resistance of SM-OGFC to pressure, horizontal and shear stress induced from the loading in the high-temperature environment can be predicted.
## 2 Modern Physical Layer
### Conclusions and Future Challenges  
In UWSNs, energy consumption can be argued to be the largest concern. Because nodes are battery powered, energy efficiency at the physical layer is mandatory. However, at the same time, it is desirable to reach further transmission distances and to achieve better bandwidth usage. Hence, further research should be conducted into developing energy-aware techniques that will make more efficient use of the available bandwidth. On the one hand, effective bandwidth utilization can be improved by reducing the BER. For this purpose, efficient multipath and Doppler correction algorithms, as well as efficient error correction mechanisms should be investigated and executed, even in ultra-low-power resource-constrained modems. On the other hand, current acoustic networks are very bandwidth constrained. Thus, in order to increase the total available bandwidth, basic research on piezoelectric transducers should be conducted to obtain high bandwidth and more affordable transducers. Additionally, studies of modulations with higher spectral efficiency using low-power modems should be conducted.  
Last, a number of recent and ongoing studies are moving towards platforms to facilitate UWSN experiments [59; 60; 61]. Lower layers are implemented using existing commercial modems that cannot be re-configured out of the pre-defined modem settings. These platforms can be remotely reconfigured to perform high-level protocol layer evaluation. Further deployments, with an open access philosophy combined with an easy reconfiguration framework, would lead to faster development of UWSNs.
# Highway Tunnel Defect Detection Based on Mobile GPR Scanning
## 4 Experiment and Results  
### Data Acquisition  
The experiment was conducted in a highway tunnel in Zhejiang, China. This tunnel has been operating safely for about 10 years. The structure of the tunnel is supported by reinforcement and concrete. First, the DMDS was installed on the vehicle. Then, the manipulator's attitude was adjusted to meet the section shape of the tunnel. Finally, the vehicle moved forward, and the GPR scanning was conducted.  
In order to obtain accurate coordinate information, GPS is used for positioning at the beginning of detection. The obtained positioning information corresponds to the mileage of the design to obtain the mileage at the starting point of the detection line. However, the GPS signal is weak in the tunnel. Consequently, the encoder recorded the driving distance and converted it into the mileage in the tunnel.  
The scanning of the GPR profile was 30 km/h. The distance between two adjacent A-scans was 2 cm, and there were 1024 sample points in an A-scan. The time window of scanning was about 50 ns. Consequently, the time distance between every sample point was 0.05 ns. The operation of the RMMS, including attitude adjusting and scanning, took about 10 min to complete. In the end, 1 km GPR profiles with two different frequencies were obtained. The efficiency of the DMDS was significantly higher than that of the traditional detection method, even disregarding the safety guarantee during conducting. Figure 9 depicts the data acquisition process by the DMDS.  
Considering the amount of data, this paper selects a 10 m scale profile (from mileage ZK39 + 752 to 762) in the obtained GPR data for discussion. Figure 10 depicts the preprocessed GPR data with 400 MHz and 900 MHz. After direct wave interception, gain, and background elimination, the horizontal multiple reflection waves caused by the air layer are filtered out. Still, a small amount of interference caused by the reinforcement layer and disease remains, especially in the 900 MHz signal. The undulating reflective interface caused by the reinforcement layer is visible around 10 ns. However, the reflection interface is continuous due to scattered waves. Consequently, the spacing of the reinforcement is difficult to determine. Furthermore, apparent abnormalities can be seen in the range  
Figure 9: When the manipulator is adjusted to a reasonable attitude, start the vehicle to carry out a quick scan along the measurement line.  
from ZK39 + 754 to +760, presumed to be structure diseases. Since the interference of shallow information masks the adequate deep information, it is difficult to obtain the bottom information of the thickness of the second-lined structural layer.
## 4 Conclusions  
In summary, we introduced a flexible methodology for enhancing solar cell performance. By depositing Au NPs and CdSe/ZnS QDs layer-by-layer in Si-based Schottky solar cells, a considerable increase in cell performance is observed. Besides, the effect of different Au NPs diameters is explored. Without coating QDs layers on the cells, the smallest diameter of controlled Au NPs sizes has given the highest efficiency enhancement, up to 8%, corresponding with the photocurrent heightening of 14%. Moreover, the efficiency depicts remarkable improvement by incorporating Au NPs with CdSe/ZnS layer on the cells. In detail, the best cell's performance at the smallest Au NPs attains 28% efficiency enhancement. This can be explained by the downshifting of incident photons and the anti-reflectance property of QDs. This facile method can be well-incorporated with Si-based Schottky solar cells and other types of devices, such as dye-sensitized (DSSCs) and organic solar cells.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Device** & **V\({}_{\text{oc}}\) (V)** & **J\({}_{\text{sc}}\) (mA/cm\({}^{2}\))** & **FF (\%)** & **RS (\(\Omega\))** & **RSH (\(\Omega\))** & \(\eta\) **(\%)** \\ \hline
**n-Si** & 0.383 & 15.535 & 42.493 & 1.101 & 595.854 & 2.528 \\
**n-Si/Au NPs** & 0.380 & 18.220 & 39.689 & 2.326 & 539.865 & 2.747 \\
**n-Si/Au NPs/QDs** & 0.387 & 19.299 & 46.791 & 1.766 & 548.481 & 3.495 \\ \hline \hline \end{tabular}
\end{table}
Table 1: J–V characteristics of reference cell, Au NPs 1 cell, and Au NPs 1/QDs composite cell measured under the artificial light.  
Figure 5: Variation of photovoltaic characteristics extracted for all devices with different diameters of Au NPs (**a**) \(\mathrm{J_{SC}}\) and \(\mathrm{V_{OC}}\). (**b**) Fill factor and Efficiency. (**c**) Series resistance and Shunt resistance.  
**Author Contributions:** [PERSON] conducted experiments, analyzed the data and wrote the manuscript; [PERSON], [PERSON] and [PERSON] provided suggestions for the research method and revised the article. [PERSON] and [PERSON] conceptualized, validated, reviewed-edited, and revised the original draft. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Incheon National University Research Grant in 2018.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors acknowledge the work supported by Incheon National University Research Grant in 2018.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Hybrid Multi-Agent Reinforcement Learning (Hybrid MARL)  
### Spatio-Temporal Information Extraction  
In large-scale road networks, traffic conditions [29] exhibit strong spatial and temporal correlations due to dynamic vehicle movements. Effectively capturing these relationships is crucial for optimizing traffic signal control, as it enables reinforcement learning (RL) agents to make more informed decisions [30]. Traditional methods often rely on manually designed traffic features, which may fail to fully represent the complex traffic dynamics in large-scale networks [31]. To address this limitation, this study integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Attention Networks (GATs) to extract spatial and temporal dependencies from traffic data [32].  
The proposed spatio-temporal feature extraction framework is illustrated in Figure 1. This framework consists of three key modules: state embedding, spatial module, and temporal module. The state embedding module processes raw traffic data into structured feature representations that capture key characteristics of vehicle movements. The spatial module employs Graph Attention Networks (GATs) to model interdependencies between intersections, allowing the system to learn dynamic traffic interactions. The temporal module utilizes Long Short-Term Memory (LSTM) networks to extract sequential patterns from historical traffic data, enabling the model to anticipate congestion trends. By integrating these three modules, the extracted spatio-temporal features serve as informative inputs for reinforcement learning agents, leading to improved decision-making in traffic signal control.
## 2 Materials and Methods  
### Electrode Design and Fabrication  
The electrodes were designed using 3D design software. Autodesk Fusion 360 was used for the preliminary design, then an STL format was created for the use of CuraUlt-maker 5.1.1, in which the printing parameters were set, and a GCode file was generated for subsequent printing on a Creality CR10-SE printer. Two materials, PLA/carbon black (Protopasta) with a reported resistance of 15 \(\Omega\)-cm and TPU (Fili conductive) with a reported resistance of 27.44 \(\Omega\)-cm, were used for the printing of the electrodes. For electrodeposition, a 4 mg L\({}^{-1}\) suspension of graphene oxide (GO) in H\({}_{2}\)O (SIGMA-ALDRICH Chemie GmbH, Taufkirchen, Germany) was used as the raw material, diluting this original solution in 150 mM NaCl to a final concentration of 0.5 mg mL\({}^{-1}\). The solution was then neutralized with 0.5 M KOH and sonicated for 15 min. Subsequently, cyclic voltammetry was used for the electrodeposition (ED) from 0.8 V to \(-\)1.5 V, at a rate of 20 mV s\({}^{-1}\) with 16 repetitions. A 3-electrode configuration was used, with the electrodeposited electrode being the working electrode. A platinum wire mesh (2 cm \(\times\) 2 cm, Goodfellow, UK) was used as a counter electrode and as a reference Ag/AgCl 3M KCl, with all this in an oxygen-free atmosphere by means of nitrogen bubbling [24].  
The electrode dimensions were 3 cm long \(\times\) 2 cm wide \(\times\) 3 mm thick. We decided to use 2 mm isosceles triangles (Figure 1) as the pattern as a compromise between the optimization of the electroactive surface and the fluid shear response. The electrodes were printed with six different pattern depths in millimeters: no pattern (NP), 0.2, 0.6, 1, 1.4 and through hole (TH). Thus, 6 different depths were used for each material. Prior to ED and characterization, the electrodes were pre-treated with pure acetone for 30 s to ensure their activation [25].
## 4 Transfer Impedance  
The transfer impedance is utilized to calculate the contribution of each PV to the negative sequence voltage. Transfer impedance is a \(i\) by \(j\) matrix, that transfers PV contribution column of length \(i\) into voltage drop column of length \(j\). The magnitude of the injected current is the value of the negative sequence current and the angle of the negative sequence current depends on the phase that is chosen. It is assumed that connected PVs are synchronized with background voltage phasors and their injected current follows the angle of background voltage phasors.  
The main concept of the transfer impedance utilization in this paper is inspired by [3]. However, minor changes were introduced to the transfer impedance matrix. In order to include several customers with PVs per one bus, additional columns were included in the matrix. Customers, that are connected  
Figure 4: Flowchart of the Monte Carlo simulation.  
to one bus, have the same contribution to the VU. Additionally, customer nodes, that have no PV, thus no contribution to the negative sequence voltage, are pruned from the transfer impedance matrix, as well as from the current injection column. The negative sequence voltage, as a sum of background and PV-caused negative sequence voltage, can be found as followed in Equation (8).  
\[\underline{U}_{j}= \sum_{i}\underline{Z}_{ij}\cdot\underline{I}_{i}+\underline{U}^{ \text{bg}} \tag{8}\] \[j\in E_{\text{b}},\ i\in E_{\text{PV}}\]  
where \(\underline{U}_{j}\) is total negative sequence voltage, \(\underline{Z}_{ij}\) is transfer impedance, \(\underline{I}_{i}\) is PV-injected negative sequence current, \(\underline{U}^{\text{bg}}\) is background negative sequence component obtained from phase voltages in Equation (5), \(E_{b}\) is a set of network buses and \(E_{PV}\) is a set of buses with PVs. Note that the variables presented are phasors, and constitute magnitude and angle values.
# Rare Earth Magnet Recycling and Materialization for a Circular Economy--A Korean Perspective
## 3 Recycling and Materialization (ReMat)  
ReMAT is an environmentally friendly dry extraction process that can recycle high-purity REEs from end-of-life products (EoL) containing permanent magnets as a supplementary source of REE supply. Recycling (Re) is a two-step process, i.e., liquid metal extraction (LME) followed by vacuum distillation (VD), whereas MAT focuses on the application of the resultant products from each step of the recycling process.  
The LME method was developed as an alternate to the environmentally hazardous aqueous processing techniques. LME is a pyrometallurgical technique that can extract clean materials with small nitrogen and carbon impurities and unwanted metallic contamination from a wide variety of scrap materials [29, 30]. Furthermore, it offers the added advantage of the separation of metallic species without metallothermic reduction. The phase system in the LME process is primarily liquid-solid instead of the conventional liquid-liquid solvent extraction. Essentially, this process consists of selective dissolution of the REE alloy by a liquid alloy system, in which the REEs distribute themselves between two immiscible liquid metal phases [31]. Mg is a good candidate as an agent for the extraction of REEs because the resulting intermetallic alloy can either be used directly or can be refined further to recover pure REEs. We used VD for the further recovery of REEs from Mg-RE alloy [32, 33, 34]. These processes are followed by materialization, which is the utilization of the products of these experiments for different applications. A process flow diagram for the ReMaT process is shown in Figure 5.  
Figure 4: Material flows in in Republic of Korea: Nd (**a**); Dy (**b**) [28].
# Q-Learning Empowered Highly Dynamic and Latency-Aware Routing Algorithm for Flying Ad-Hoc Networks
## 1 Introduction
However, in scenarios with nodes frequent entries and exits, leading to obvious topology changes, these protocols may restrict information dissemination in highly dynamic networks, ignore link stability, and easily select routes that fail to perform packet transmission. These issues weaken the protocol's efficiency and eventually lead to increases in high packet loss rates and latency. New routing protocols based on intelligent algorithms tend to fix the mobility model, which has problems such as routing failure and computational complexity, leading to increasing in scenarios with low flight density. Based on the above-mentioned issues, this paper focuses on the UAV ad-hoc network routing problem in highly dynamic 3D topology scenarios. The protocol designed in this paper needs to learn the network topology state adaptively, improve the stability of routing, and maintain high QoS at low complexity. Therefore, we employ a Q-learning method to learn the link status in the network and effectively select routes through Q-values to avoid connection loss. Additionally, the remaining time of the link or path lifespan is incorporated into the routing protocol to improve the routing table. QEHLR can delete predicted failed links based on network status, thereby reducing packet loss caused by failed route selection and improving link stability. This paper demonstrates that the enhanced algorithm significantly improves the packet transmission rate. It addresses the challenge of routing protocols' inability to adapt various mobility scenarios in FANETs with dynamic topology by introducing a calculation factor based on the QEHLR protocol. Experimental results indicate that the improved routing algorithm achieves superior network performance.
## 3 Evolution Towards Next Generation Networks (NGN)  
This section provides an overview of the NGN and how it evolved from the PSTN approach, using IP technologies.  
ITU-T (world-wide known standardization body) defines Next Generation Network (NGN) as follows [5]:  
_\"Next Generation Network (NGN): A packet-based network able to provide telecommunication services and able to make use of multiple broadband, QoS-enabled transport technologies and in which service-related functions are independent from underlying transport-related technologies. It enables unfettered access for users to networks and to competing service providers and/or services of their choice. It supports generalized mobility which will allow consistent and ubiquitous provision of services to users.\"_
###### Abstract  
Assessing potential impacts of 1.5 \({}^{\circ}\)C and 2 \({}^{\circ}\)C global warming and identifying the risks of further 0.5 \({}^{\circ}\)C warming are crucial for climate adaptation and disaster risk management. Four earth system models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and a process-based ecosystem model are used in this study to assess the impacts and potential risks of the two warming targets on the carbon cycle of China's terrestrial ecosystems. Results show that warming generally stimulates the increase of net primary productivity (NPP) and net ecosystem productivity (NEP) under both representative concentration pathway (RCP) 4.5 and RCP8.5 scenarios. The projected increments of NPP are higher at 2 \({}^{\circ}\)C warming than that at 1.5 \({}^{\circ}\)C warming for both RCP4.5 and RCP8.5 scenarios; approximately 13% and 19% under RCP4.5, and 12.5% and 20% under RCP8.5 at 1.5 \({}^{\circ}\)C and 2 \({}^{\circ}\)C warming, respectively. However, the increasing rate of NPP was projected to decline at 2 \({}^{\circ}\)C warming under the RCP4.5 scenario, and the further 0.5 \({}^{\circ}\)C temperature rising induces the decreased NPP linear slopes in more than 81% areas of China's ecosystems. The total NEP is projected to be increased by 53% at 1.5 \({}^{\circ}\)C, and by 81% at 2 \({}^{\circ}\)C warming. NEP was projected to increase approximately by 28% with the additional 0.5 \({}^{\circ}\)C warming. Furthermore, the increasing rate of NEP weakens at 2 \({}^{\circ}\)C warming, especially under the RCP8.5 scenario. In summary, China's total NPP and NEP were projected to increase under both 1.5 \({}^{\circ}\)C and 2 \({}^{\circ}\)C warming scenarios, although adverse effects (i.e., the drop of NPP growth and the reduction of carbon sequestration capacity) would occur in some regions such as northern China in the process of global warming.  
1.5 \({}^{\circ}\)C and 2 \({}^{\circ}\)C global warming; climate risks; earth system models of CIMP5; process-based ecosystem model; China 1