esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review Paper on \"Graphene Field Emission for Electron Microscopy\"
## 3 Graphene-Based Cold Field Emitters
### Graphene Ring-Cathode Field Emitters  
[PERSON] first proposed the idea of using a ring-cathode emitter for combining high spatial resolution with high beam current for focused electron beam applications in 2013 [(79; 80)]. [PERSON] presented electron focused beam designs based upon using ring-cathodes together with off-axis aberration correction techniques. The first step towards a practical implementation of this idea is to develop a ring-cathode emitter. The proposal of a field emission ring-cathode gun unit has the advantage of intrinsically being a large area field emitter, and it is therefore expected to overcome the well-known disadvantage of conventional point-cathode field emitters having relatively low total current (compared to earlier larger cathode electron gun designs). The much larger emission area of a ring-cathode field emitter, compared to a point-cathode field emitter is also expected to provide it with greater current stability. [PERSON] et al. [(81)] reported a versatile and robust method based on graphene growth on Ni templates and thermo-assisted removal of poly(methyl methacrylate) (PMMA) for fabrication of macroscopic and freestanding tubular graphene (TG) architectures. The TG architectures have a diameter of 50 \(\upmu\)m with a wall thickness in the range of 2.1-2.9 nm (Figure 6A). The individual TG emitter can produce an extremely high emission current, up to 0.46 mA at a very low external applied electric field of 0.68 V \(\upmu\)m\({}^{-1}\). This most likely comes from ultrathin edges on graphitic walls, which consist of a great number of field emission sites. Another important observation is that the TG CFE electron source has good current stability and the stability is maintainable for days under HV conditions (2.2 \(\times\) 10\({}^{-8}\) Torr). However, one limitation of TG architectures fabricated by [PERSON] et al. is that the edges are very jagged rather than being smooth, making it hard to be directly used as a CFE electron source for electron microscopy applications. Moreover, transfer and alignment of such a large diameter graphene tube is an extremely delicate process. [PERSON] et al. [(82)] reported an in-situ fabrication technique, eliminating any transfer and alignment process. They obtained a graphene ring emitter, which consists of a graphene tube grown in situ on a sharp nickel wire tip (Figure 6B). The diameter of the tubular graphene is about 5 \(\upmu\)m while the edge of the tube is approximately 2.7 nm. They have obtained high emission current of \(\sim\)30 \(\upmu\)A at the relatively low applied field of \(\sim\)1.75 V/mm. The ring-cathode field emitter is a new type of field emitter source, and suitable electron ring-focused beam columns will first need to be designed for it. When that is achieved, it is expected to a wide variety of applications in subjects such as lithography, mass spectrometer, X-ray tube and accelerator physics.
# PET/CT Radiomics in Lung Cancer: An Overview
## 3 Applications
### Prediction of Survival  
Prediction of survival plays an important role for triaging and, consequently, for determining the suitability of subjects for different treatment options. Survival studies, however, are notoriously difficult due to the long follow-up required and the presence of many confounding factors. It is not surprising then that results in this topic are less clear-cut than in the other potential applications.  
In [53], tumor heterogeneity evaluated by texture features at CT was a significant predictor of overall survival (OS) in NSCLC, but radiotracer uptake was not. CT- and PET-derived heterogeneity were both significant predictors of OS in [73], while [PERSON] et al. [28] found an association between OS and PET sphericity, although their results also showed dependency on lesion volume and method used for segmentation. Positive association with different survival metrics and PET/CT texture features [37; 54; 73; 74] or CT features alone [32; 36; 53] were also reported in other studies. [PERSON] et al. [55], however, determined that PET/CT texture features were insignificant in predicting progression-free survival beyond tumor volume and clinical stage in lung cancer patients. Similarly, [PERSON] et al. [56] did not observe any significant correlation between CT texture features and OS in lung adenocarcinoma.
# Recent Strategies for Environmental Remediation of Organochlorine Pesticides
## 5 Photocatalysts for Pesticides Degradation
### Titanium Oxide-Based Photocatalyst for Pesticide Degradation  
The most widely used heterogeneous photocatalyst is titanium oxide. This is because its holes are selective to oxidation and reduction reaction. In addition, it is highly oxidizing, non-toxic, stable under light and it is not expensive [72; 73]. The major challenge with its usage is that due to its wide band gap energy, it cannot absorb visible light which makes up almost 40% of the solar spectrum. To surmount this challenge, capping, coupling, doping and dye sensitization approaches have been  
Figure 7: Mechanism of decomposition of pesticide involving heterogeneous photocatalysts. intensely studied [72]. [PERSON] et al. [74] used light emitting diodes (LEDs) as light source to irradiate titanium oxide photocatalyst, which was used to destroy four different organochlorine pesticides [2,4-dichlorophenol, 4-chlorophenol, 2-methyl-4-chlorophenoxyayacetic acid, 2,4-dichlorophenoxyayacetic acid] in water. Further evaluation was done on 2,4-dichlorophenoxyayacetic acid in order to determine the effect of light intensity and photocatalyst loading on the rate of degradation. Furthermore, the use of LED irradiation was compared with irradiation from mercury discharge lamp and complete pesticides mineralization was observed with the LED. A reduction in the rate of degradation was observed when mixture of pesticides was degraded. In a related study, [PERSON] et al. [75] selectively degraded the insecticide imidacloprid and the herbicide 2,4-D. The photocatalyst used for the degradation was molecularly imprinted titanium oxide made via sol-gel method with the pesticide molecule as template. The uniqueness of their approach was achieving the selective trapping of the pesticides by molecular imprinting process, while pesticide degradation was simultaneously achieved through heterogeneous photocatalysts. Other works that utilized titanium oxide-containing nanocomposite as heterogeneous photocatalyst for degrading pesticides and other pollutants with more than 50% removal efficiencies are summarized in Table 2. Particularly, up to 85% mineralization efficiency was reported for neat titanium oxide photocatalyst [76].
## 2 The ITU-R P.1546-6 Wave Propagation Model
Once \(u\) is determined, TCA can be calculated using the following formula:  
\[\theta=\tan^{-1}\Big{(}\frac{h_{p}-h_{ant}-h_{ter}}{d}\Big{)}. \tag{5}\]  
It is worth noting that, in addition to using the propagation curves directly for inter- or extrapolation, research has been conducted on using them with a data-driven machine learning method as symbolic regression, which provides closed-form analytical formulas that approximate the model with an error of up to 2 dB within a land propagation path of up to 20 km [34].
# Evaluation of PID-Based Algorithms for UGVs
## 4 Experimental Results  
The various control algorithms derived from the PID controller that were proposed were successfully analyzed and evaluated. It was possible to determine which control algorithms offer greater efficiency and precision. By placing the UGV on a forest terrain (i.e., unstructured) to investigate the differences between the presented controllers, real-time navigation data were collected during these practical experiments.  
Thus, this section aims to present the experimental results regarding the process of tuning the PID coefficients (Kp, Ki, and Kd), followed by an analysis of the proposed control algorithms, highlighting the characteristics of each one.
## 5 Simulation and Analysis
### Performance of the Routing Protocols with the Mobility Models
#### 5.2.1 Aodv  
Figures 9 and 10 show that AODV clearly performs better under RWP, and the performance under RD is the worst. The PDR ranges from about 73% to about 78%, thus a range length of about five percentage points. We already noted that RD presented a few statistics that suggested that the routing protocols would perform their worst under it. The performance of AODV ranking last under RD therefore comes as no surprise here. Similarly, the performance under RWP ranking first is no surprise either.
### Summary of Key Findings  
To provide a clear overview of the key findings and their consistency with previous studies, we constructed a summary table as shown in Table 17. This helps in understanding how the current study aligns with or differs from existing literature.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **T Statistics** & \(p\) **Values** \\ \hline H1 & Perceived Ease of Use -> Perceived Usefulness & 12.732 & 0.000 \\ H2 & Perceived Ease of Use -> Attitude & 4.821 & 0.000 \\ H3 & Perceived Usefulness -> Perceived Security & 8.083 & 0.000 \\ H4 & Perceived Usefulness -> Attitude & 3.658 & 0.000 \\ H5 & Perceived Security -> Perceived Trust & 17.083 & 0.000 \\ H6 & Innovativeness -> Attitude & 2.457 & 0.014 \\ H7 & Innovativeness -> Usage Intention & 8.612 & 0.000 \\ H8 & Perceived Trust -> Usage Intention & 3.094 & 0.002 \\ H9 & Attitude -> Usage Intention & 3.968 & 0.000 \\ H10 & Perceived Control -> Usage Intention & 1.405 & 0.161 \\ H11 & Subjective Norm -> Usage Intention & 1.251 & 0.212 \\ H12 & Perceived Control -> Perceived Ease of Use & 12.017 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Hypotheses testing values.
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
###### Abstract  
Nowadays, broadband applications that use the licensed spectrum of the cellular network are growing fast. For this reason, Long-Term Evolution-Unlicensed (LTE-U) technology is expected to offload its traffic to the unlicensed spectrum. However, LTE-U transmissions have to coexist with the existing WiFi networks. Most existing coexistence schemes consider coordinated LTE-U and WiFi networks where there is a central coordinator that communicates traffic demand of the co-located networks. However, such a method of WiFi traffic estimation raises the complexity, traffic overhead, and reaction time of the coexistence schemes. In this article, we propose Experience Replay (ER) and Reward selective Experience Replay (RER) based Q-learning techniques as a solution for the coexistence of uncoordinated LTE-U and WiFi networks. In the proposed schemes, the LTE-U deploys a WiFi saturation sensing model to estimate the traffic demand of co-located WiFi networks. We also made a performance comparison between the proposed schemes and other rule-based and Q-learning based coexistence schemes implemented in non-coordinated LTE-U and WiFi networks. The simulation results show that the RER Q-learning scheme converges faster than the ER Q-learning scheme. The RER Q-learning scheme also gives 19.1% and 5.2% enhancement in aggregated throughput and 16.4% and 10.9% enhancement in fairness performance as compared to the rule-based and Q-learning coexistence schemes, respectively.  
LTE-U; IEEE802.11; experience replay; Q-learning; coexistence IEEE/RSJ Web Conference 2021 21 21 6977
# BigGRU-Based Adaptive Receiver for Indoor DCO-OFDM Visible Light Communication
## 5 Conclusions  
This study introduces a novel direct post-equalization approach based on BiGRU technology within the framework of the DCO-OFDM-VLC system. The goal is to achieve low BER reception with minimal spectrum occupancy. The proposed BiGRU model operates by utilizing the transmitted signal as the label and directly employing the received signal at the receiver's end as the sample. This approach eliminates the need for CE, interpolation, and element division, as required by traditional receivers. Additionally, it streamlines the input data and tag processing procedures. The performance evaluation of the BiGRU receiver was conducted within a commercial LED-based single-light 4 QAM DCO-OFDM-VLC system, with a reception distance of 3 m. The experimental results revealed that the BiGRU model yields comparable performance to BiLSTM under reduced computational demands. Both algorithms exhibit similar KL values and BER. Moreover, the proposed BiGRU-based approach demonstrates potential for significant BER performance enhancement, while also offering a 20% reduction in computational costs compared to BiLSTM. BiGRU's capacity to learn and analyze channel characteristics contributes to signal recovery, despite contamination. This approach presents a highly efficient method of spectrum utilization with low complexity. By directly extracting information from the original received signal, this study introduces a fresh perspective to receiver research within the realm of indoor VLC.  
Conceptualization D.H. and M.Z. methodology, Y.H.; software Y.Z.; validation, D.H., M.Z., Y.Z. and L.W.; formal analysis, Y.H.; investigation, Y.Z.; resources, Y.H.; data curation, Y.H.; writing--original draft preparation, Y.H.; writing--review and editing, M., Y.Z. and L.W.; visualization, Y.H.; supervision, D.H. and M.Z.; project administration, D.H. and M.Z.; funding acquisition, D.H. and M.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China grant number 2022 YFB2901100.  
Data Availability StatementData underlying the results presented in this paper are not publicly available. However, they may be obtained from the authors upon reasonable request.  
This work was partly supported by China's National Key R&D Program (2022 YFB2901100).  
The authors declare no conflict of interest.  
Figure 7: BER performances for different receiver schemes: traditional method using LS, MMSE; BiLSTM/BiGRU-based typical receive; BiLSTM/BiGRU-based direct receive. (**a**) BER performance for LS and MMSE model with eight pilots. (**b**) BER performance for LS and MMSE model with 16 pilots. (**c**) BER performance for LS and MMSE model with 32 pilots.
## 3 Results
### Ga Nanoparticles on Graphene/Cu
Ga NP/G/Cu system were obtained for Ga NP/G/Ni, although in the latter, the graphene layer is a non-homogeneous mixture of bilayer and multilayer regions. Figure 4a shows the real-time evolution of the pseudo-extinction coefficient \(\langle k\rangle\) during Ga NP deposition on G/Ni. For comparison, the \(\langle k\rangle\) spectrum of equivalent Ga NPs grown simultaneously on G/glass is also shown. As in the case of G/Cu, there is no plasmon resonance peak coming from the excitation of the LSPR LO mode of the Ga NPs, although Ga NPs are clearly formed on the substrate, as demonstrated by the AFM image in the inset of Figure 4c. This behavior is consistent with the fact that G/Ni is also a conductor, like G/Cu. The experimental spectra show a damping of the Ni's characteristic Drude-like behavior at low photon energies as well as of the peak above 4 eV coming from the interband transition of Ni [18] due to the effective thickness of the Ga NPs. The absence of any localized surface plasmon resonance in the extinction coefficient spectra is consistent with the absence of any enhancement of the Raman signal of the graphene layer, as shown in Figure 4b. Here, the variation of the intensity ratio between the G and 2D peak is not speculated because it might be due to the non-homogeneity of the graphene layer thickness. Moreover, the XPS analysis of the photoelectron core lever Ga3\(d\) also shows, in this case, a strong Ga\({}^{3+}\) (BE = 20.8 eV) component and a quenched metallic Ga component (BE = 18 eV) [21]. This puts in evidence the loss of the metallic character of the Ga NPs due to electron transfer from the Ga NPs into the graphene and finally into the Ni substrate, according to the schema sketched in Figure 3. As shown in Figure 3b, Ga, G, and Ni have values of their work functions of \(\phi_{F}^{Ga}\) = 4.2 eV, \(\phi_{F}^{G}\) = 4.5 eV, and \(\phi_{F}^{Cu}\) = 5.4 eV, respectively; thus, electrons transfer from the Ga NPs to graphene and to nickel.
## 2 Materials and Methods
### GA-BP Neural Network  
#### 2.5.1 BP Neural Network  
Backpropagation (BP) neural network is a multilayer feedforward network model consisting of two processes: forward propagation of information and backward propagation of error [48]. It is a more widely used neural network with solid adaptability and learning ability and can better solve nonlinear problems. Its essence is learning by stochastic gradient descent solving algorithm. The input and output layers output data are called forward propagation, called backward propagation, using the weights and deviations calculated in each layer to update the model for iteration. BP neural network mainly consists of the input layer, hidden layer, and output layer. Its network structure is shown in Figure 3. Our study's input signals are latitude, longitude, NDVI, rainfall, air temperature, land cover type, and four topographic factors (elevation, slope, slope direction, and shading); the output parameter is GNSS-IR soil moisture. BP neural network model is implemented by using the neural network toolbox of MATLAB. The number of neurons m in the implicit layer of the BP neural network takes values between \(\sqrt{2n}+1\) and \(2n+1\). n is the number of neurons in the input layer, so we tested hidden layer neurons ranging from 7 to 21.  
Figure 2: Flow chart of multi-data fusion model.  
Using a 10-fold coefficient performs best by validating that the best model performance is obtained when the number of hidden layer neurons is 19. Also, set the number of training steps for the BP neural network to 1000, the training accuracy to 0.001, and the learning rate to 0.0001.  
However, the number of neurons in the hidden layer of the BP neural network needs to be manually selected, and the weights and thresholds are also randomly generated, which causes the BP neural network to converge slowly and quickly fall into a local minimum [49]. For this reason, this paper uses the GA algorithm to optimize the BP neural network.
# Acquisition of Weak BDS Signal in the Lunar Environment
## 4 Discussion  
To further explain the significance of the side lobe signals for the navigation of a lunar rover, Figure 16 presents the GDOP values in the cases of using the main lobe signals only (blue line) and all the signals (orange line). In the circumlunar mission, the GDOP value can descend from \(10^{5}\) to less than \(10^{2}\) when side lobe signals are considered. Furthermore, due to the small number of visible satellites, the discontinuity of the GDOP values using only the main lobe signals is evident. It can be seen that the low number of visible satellites and poor geometry are the main reasons for the large GDOP values.  
Figure 17 displays a histogram of the \(C/N_{0}\) statistical distribution with a width of 1 dB-Hz for different global positioning systems. The left and right subplots give the distribution of the carrier-to-noise ratios of the main and side lobe signals, respectively.  
Figure 16: GDOP values of LLO using quad-GNSS at a \(C/N_{0}\) threshold of 10 dB-Hz.  
Figure 15: Correlation values of different methods for a 15 dB-Hz signal using coherent integration of 200 ms.  
The \(C/N_{0}\) distribution of the main lobe signals is relatively uniform, especially when the value is greater than 18 dB-Hz, but it can also be found that they are aggregated in the range of 14-18 dB-Hz. For the side lobe signals, the signals are concentrated around 10-20 dB-Hz, and only 11.6% of the signals are greater than 20 dB-Hz when quad GNSS is used, which is consistent with the results in Figure 10.  
Multi-GNSS is beneficial for space users, especially those far from Earth; thus, developing interoperable multi-GNSS systems is worthwhile. Accurate simulation results and better recommendations for onboard receiver designs can be achieved once GLONASS and Galileo satellite antenna patterns are published. In addition to GNSS satellites, the navigation performance can be further improved once regional satellite navigation systems are used, such as Japan's Quasi-Zenith Satellite System (QZSS), the Navigation with Indian Constellation (NavIC) system, and the Korean Positioning System (KPS). These abundant signals will help GNSS-based spacecraft to position themselves at longer distances, such as at Earth-Moon Lagrange points, contributing to deep space exploration. The interruption of GNSS signals on the lunar backside also needs to be noted, and the future prospects of using lunar-GNSS or relay satellites for lunar probe navigation are bright.  
Furthermore, the current paper only considers one type of GNSS signal. Future studies can explore the navigation performance using different signal types, especially new signals from BDS-3 such as B1C. It would be valuable to investigate high-sensitivity acquisition algorithms for signals that contain both data and pilot components [39] or dual-frequency joint signals [40].
## 3 AI Techniques and Their Applications
### Deep Learning Techniques
#### 3.2.3 Volumetric and 3D CNNs  
The growing adoption of 3D GPR systems, such as multi-channel arrays and step-frequency radars, has driven the need for advanced models capable of processing volumetric data. Unlike traditional 2D approaches, these systems generate three-dimensional datasets that encompass spatial variations across both slices and depth. To address this, researchers have turned to 3D CNNs and VNet-like architectures, which are specifically designed to leverage the spatial correlations inherent in volumetric data. These models enable more effective feature extraction and pattern recognition in complex underground environments. As a result, they are particularly suited to applications requiring high-resolution interpretation, such as detecting archeological remains, identifying buried walls, and mapping intricate tree root systems. By incorporating depth-wise context and inter-slice continuity, 3D models offer a significant advantage over their 2D counterparts in terms of accuracy and reliability [47, 48, 49].  
Despite these benefits, the application of 3D models presents several challenges. Chief among them are the substantial computational costs and memory requirements associated with training and deploying 3D CNNs. Processing large-scale volumetric GPR datasets demands high-performance hardware and optimized algorithms to maintain efficiency. Furthermore, the increased data dimensionality can lead to extended training times and the risk of overfitting, especially in scenarios with limited annotated samples. Nonetheless, the superior capability of 3D models to capture complex subsurface structures continues to justify their growing use in the field. Ongoing research efforts aim to mitigate these challenges through model compression, hardware acceleration, and data augmentation techniques, making 3D GPR interpretation increasingly accessible and practical for a wide range of geophysical applications [50].
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 4 Security Analysis and Performance Evaluation
### Security Analysis
#### 4.1.4 Data Availability  
In our proposed model, the legitimate vehicles can securely download and forward packets in terms of data storing and sharing, which always available due to the distributed nature of blockchain. Moreover, they can communicate with the RSUs and request information on another vehicle's trustworthiness in the network. As shown in Figure 4, distributed RSUs collect a vehicle's reputation based on trust value ratings in the smart contract. Upon receiving a request message, the RSU verifies the identity of the requester'svehicle. Consequently, RSU responds the request by sending the information of the target vehicle's reputation to the requester vehicle.