esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design Overview
### Technology and Method Selection
#### 3.6.3 AI Model Selection for Expert Matchmaking  
SPARK-IT leverages a hybrid AI-driven matchmaking system that combines natural language processing (NLP), machine learning (ML), and graph-based recommendation algorithms. The bidirectional encoder representations from transformers (BERT) model is selected for semantic analysis of business proposals and mentor profiles, ensuring that AI recommendations consider contextual nuances rather than relying solely on keyword matching. Sentence-BERT (S-BERT) is used to generate dense vector embeddings, which help measure the similarity between mentor expertise and innovator needs more accurately.  
Additionally, graph-based recommendation models such as Node2 Vec and graph convolutional networks (GCNs) analyze expert networks to identify the most well-connected, highly-rated mentors for a given business challenge. A reinforcement learning (RL) feedback loop continuously refines matchmaking accuracy by incorporating user satisfaction ratings and engagement metrics to improve recommendations over time.
## 2 Health Condition Monitoring
### Data Acquisition
#### 2.1.3 Data Storage  
Through the gateway, all the measured and processed parameters are stored in a local server. The storage time of all data stored in a database or files depends on the model and application software [18]. Not all data generated by ship operation need to be stored. Some important operation parameters need to be stored on the ship for more than half a year, and some non-important historical data should be deleted regularly to reserve enough server storage space.  
In this case, the onboard server will periodically generate a large number of files. Before shoreside transmission, these data need to be compressed by a certain algorithm to reduce the number of data transmissions.
## 3 Factors Influencing Volatile Composition
### Genetics  
Evaluation of volatiles at the germplasm level is useful for future breeding efforts, aimed at improvement of fruit quality, via effects on fruit aroma. The composition and concentration of grape volatiles largely varied with genetic background. In grape cultivars belonging to seven genotypic groups, C6 compounds were the dominant volatiles in _Vitis. amurensis_ grape, neutral cultivars _V. vinifera_ grapes and hybrid grapes between _V. vinifera_ with _V. thunbergi_ or _V. amurensis_. Alcohols and carbonyls were relatively low in all _Vitis_ germplasm studied. Terpenoids were abundant in _V. vinifera_ with muscat aroma, while esters were dominant in _V. labrusca_ and its hybrids with _V. vinifera_ or _V. amurensis_[87]. The main aromatic components in three early apple cultivars (\"Zaofengtian\", \"Vista Bella\" and \"Liaofu\") belonging to 12 categories were 1-hexanol, (\(E\))-2-hexenal, acetic acid, butyl ester, acetic acid, hexyl ester, _etc._, representing 93.81% of the total aromatic content. \(\beta\)-Damascenone and estragole in \"Zaofengtian\", which weren't detected in \"Vista Bella\" or \"Liaofu\", could be unique components to \"Zaofengtian\" [88]. In strawberry, differences have been observed between cultivated and wild-type varieties with the monoterpene-linalool and the sesquiterpene nerolidol being the most abundant in cultivated varieties, while oleafinic monoterenes and myrenyl acetate are more important in the wild-type varieties [89,90].  
Insertion of the _rin_ gene to reduce ethylene production and slow tomato fruit softening, resulted in some deterioration in flavor quality and reduction in flavor volatiles [91]. Transgenic fruit with antisense amino cyclopropanecarboxylic acid (ACC) synthase had lowered levels of many important flavor volatiles [91]. Fruit with antisense pectinmethylesterase had lowered levels of methanol, while those with downregulated phytoeen synthase had lowered levels of carotenoid-derived volatiles [91]. The transformed melon plants displayed a range of decreased alcohol acyltransferase (AAT) activities in the ripening fruit. Therefore, the relative content of volatile esters was reduced, and the average relative content in the transgenic fruit was 78% of the wild-type fruit. However, the relative contents of aldehydes and alcohols increased, the average relative contents were 3.2 times and 2.4 times of those in the wild-type fruit, respectively [92].
# Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
## 2 Related Work
### Single Image Super-Resolution  
The process of SR is an ill-posed inverse problem, which can reconstruct an HR image. Due to the advantage of deep learning, we briefly review several significant works using deep learning for this task. Among them, [PERSON] et al. [17; 18] proposed a seminal network called SRCNN to achieve SISR by utilizing the powerful representation capability of CNN. Compared with the shallow architecture of SRCNN, [PERSON] et al. [8] proposed a residual learning network named VDSR, which mainly learned the high-frequency residual information. [PERSON] et al. [19] proposed an enhanced deep SR network (EDSR), which contained the local and global residual connection. Recently, many SR networks with attention mechanisms had superior performance. [PERSON] et al. [20] proposed a second-order attention network (SAN), which applied the trainable second-order attention module to capture spatial information. To improve the performance of remote-sensing images, [PERSON] et al. [21] proposed a contextual transformation network (CTN), which had a lightweight convolution layer to extract and enrich features. These methods have achieved promising performance in SISR reconstruction. It is noted that the SISR can be applied directly to SR for each SAIs. However, the performance of LFSR is hindered because of the deficient use of complementary information from different views.
# System Dynamics Modeling for Estimating the Locations of Road Icing Using GIS
## 2 Materials and Methods
### Modeling Road Icing  
The system dynamics theory was used for modeling to estimate the location of road icing. This theory lists the elements that cause a specific phenomenon in a block diagram and reveals relationships among them over time [9; 19]. When a natural phenomenon, such as road icing, is modeled based on the system dynamics theory, natural scientific equations can show changes in the phenomenon over time [19]. Figure 4 shows the system dynamics model structure (Please refer to the Appendix A).  
Figure 3: Freezing rain analysis for scenario feasibility: (**a**) road (and air) temperature of ASOS (Suncheon, 2018/12/11); (**b**) precipitation of ASOS (Suncheon, 2018/12/11).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **Road Surface** & \multicolumn{2}{c}{**Freezing Rain**} \\  & **Temperature** & **Air Temperature** & **Occurrence Time** & **Precipitation Type** \\ \hline
2020/12/29 & Afternoon below 0 \({}^{\circ}\)C & Afternoon above 0 \({}^{\circ}\)C & PM 20:00–PM 21:00 & Freezing Rain \\
2019/01/31 & Morning below 0 \({}^{\circ}\)C & Morning below 0 \({}^{\circ}\)C & AM 3:00–PM 15:00 & Snow \\
2018/12/16 & Morning below 0 \({}^{\circ}\)C & Morning above 0 \({}^{\circ}\)C & AM 9:00–AM 15:00 & Freezing Rain \\
2018/01/16 & Morning below 0 \({}^{\circ}\)C & Morning above 0 \({}^{\circ}\)C & AM 9:00–PM 12:00 & Freezing Rain \\
2018/01/12 & Morning below 0 \({}^{\circ}\)C & Morning below 0 \({}^{\circ}\)C & AM 11:00–AM 12:00 & Snow \\
2018/01/10 & Morning below 0 \({}^{\circ}\)C & Morning below 0 \({}^{\circ}\)C & AM 3:00–PM 18:00 & Snow \\ \hline \hline \end{tabular}
\end{table}
Table 5: Past scenarios from precipitation history in winter (Suncheon, last three years).  
Figure 5 shows the logic flow of data that expresses only the relationship between the input and output factors in Figure 4 and summarizes the overall flow of the model. The main output factors are (1) road temperature, (2) road moisture, and (3) road icing (g/m\({}^{2}\)). The main equations of the model are constructed to derive these three factors. The model is based on (1) road temperature and (2) road moisture and derives (3) road icing (g/m\({}^{2}\)) for GIS simulation.  
The road icing model consists of various input and output factors. These factors and their information are shown in Table 6. Each symbol is the expression method of the corresponding factor in the equation in the numerical model, and the numerical expression represents the range and unit of values. This information was used, and the derivation process and equations for (1) road temperature, (2) road moisture, and (3) road icing (g/m\({}^{2}\)), which are the main output factors, were explained at the end of Section 2. All the factors have a range of 14 h from AM 00:00 to PM 13:00.  
Figure 4: System dynamics model for predicting road icing.  
Figure 5: Logic flow of data composing the system dynamics model.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 4 Results
### Transmission Line Detection Algorithm  
Figure 10 presents the resultant images in all stages of the transmission line detection approach, with regards to: (a) images analyzed under ideal lighting conditions; (b) image brightness increased by 20%; (c) image brightness increased by 60%; and (d) image brightness reduced by 50%.  
The acquired image has additional objects, which are not related to the transmission lines. These objects from the scene have to be filtered or eliminated to isolate the conductors. First, the color constraint is applied to obtain a gray-scale image. Then, the image brightness is corrected to highlight power conductors. Finally, the delivered image is filtered using Gabor filters, which simultaneously remove the background noise of power lines as well as generate edge maps. It is possible to observe that the system highlights power conductors. However, the delivered image was noisy due to complex and irregular ground coverage. It is possible to observe that the system highlights power conductors. However, the delivered image was noisy due to complex and irregular ground coverage. This disadvantage increases the risk of the positioning system returns erroneous measurements.  
The direct bright sunshine can affect the positioning system since the system could not detect wires and conductors, as shown in Figure 10b,c. Therefore, the system was not able to find and isolate power-lines in the image. On the other hand, the system could operate under reduced visibility conditions but not in darkness, as shown in Figure 10d.  
Figure 8: Results of the simulation: Stationary flight of the UAV (flight altitude: 55 m, 60 m and 70 m to conductors) with frames where transmission lines were not detected.  
Figure 9: Results of simulation: Comparative analysis between pre-establish path and estimated path by our approach (straight line path).
## 2 Literature Review
### Limitations of JADE Algorithm  
JADE enhances differential evolution (DE) by incorporating self-adaptive control parameters. However, inadequate adaptation can lead to slow convergence or stagnation, particularly in complex optimization scenarios. When confronted with abrupt landscape variations, JADE may require numerous iterations to refine solutions effectively, making it unsuitable for real-time applications. Additionally, the self-adaptive parameter mechanism does not always guarantee sufficient diversity in the early stages, which can cause premature convergence and limit exploration of the search space. Furthermore, JADE employs an external archive to maintain diversity, but the computational burden of managing this archive increases as problem complexity escalates.
# SNR Enhancement of an Electrically Small Antenna Using a Non-Foster Matching Circuit
###### Abstract  
A non-Foster circuit (NFC) is known as an active broadband matching technique to improve the impedance matching bandwidth of an electrically small antenna (ESA). There has been a vast amount of papers that report the generation of negative impedance using an NFC and its effectiveness on broadband antenna matching. However, only a few discussed its impact on the signal-to-noise-ratio (SNR), which is one of the most important figures-of-merit for a wireless communication system. In this paper, the SNR enhancement due to an NFC was measured and discussed. An NFC was carefully designed to have a low dissipation loss and to meet the stability conditions. The optimized NFC design was fabricated and applied to an ESA length of \(\lambda\)15 at a frequency range of 150 to 300 MHz. The measured results showed that the NFC enhanced the received power of the antenna system by more than 17 dB. However, due to the noise added by the NFC, the SNR enhancement was not guaranteed for some frequency points. Nevertheless, an average of 7.3 dB of SNR improvement over the frequency band of interest is possible based on the experiment result.  
broadband impedance matching; electrically small antenna; non-Foster circuit; UHF; VHF
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 5 Discussion and Conclusions
### Discussion
Although the EMA mechanism and C3k2_PConv structure proposed in this study demonstrate superior performance on the cotton boll disease dataset, they still confront inherent limitations in agricultural scenarios: under severe occlusion or chromatic camouflage, EMA's attention weighting mechanism may fail or exhibit attenuated effectiveness; C3k2_PConv requires improvement in capturing subtle morphological variations while maintaining robustness against complex textured backgrounds. Furthermore, the model shows sensitivity to extreme illumination and cross-growth-stage chromatic variations. Future study will prioritize designing occlusion-resistant attention mechanisms, integrating multi-spectral features to mitigate camouflage challenges, optimizing sparse connectivity to enhance fine-grained discrimination, and developing adaptive background suppression modules, thereby improving the model's generalization capability in complex agricultural environments. It should be noted that the four-tier classification framework based on visual lesion coverage (0-50% vs. >50%) carries inherent limitations: weak linkage to pathological mechanisms (where symptom gradation remains unquantified against bio-pathological evidence such as pathogen invasion dynamics or host physiological responses) and experience-dependent threshold setting--while practical for rapid field assessment--risks misclassification of borderline cases due to environmental variability and observer subjectivity, potentially constraining model generalizability in complex agronomic scenarios. Future refinements should integrate objective biomarkers through multi-center pathological validation to strengthen the scientific robustness of the criteria.
# New Methods of Series Expansions between Three Anomalies
## 2 Materials and Methods
### The Geometric Relationship among True Anomaly, Elliptic Anomaly, and Mean Anomaly
Then, the iteration of the formula stops. We can obtain the result of the ninth iteration, which is the trigonometric series expansion of the elliptic anomaly we obtained. \[E_{9}=M+\sum_{n=1}^{8}c_{n}\sin nM \tag{24}\]  
\[\left\{ \begin{array}{l}c_{1}=e-\frac{e^{3}}{8}+\frac{e^{5}}{192}-\frac{e^{7} }{9216}\\ c_{2}=\frac{e^{2}}{2}-\frac{e^{4}}{6}+\frac{e^{2}}{8}-\frac{e^{2}}{720}\\ c_{3}=\frac{3e^{3}}{8}-\frac{27e^{7}}{128}+\frac{243e^{7}}{5128}\\ c_{4}=\frac{e^{4}}{4}-\frac{4e^{6}}{45}+\frac{e^{4}}{45}\\ c_{5}=\frac{125e^{5}}{384}-\frac{3125e^{5}}{9216}\\ c_{6}=\frac{27e^{6}}{800}-\frac{243e^{6}}{560}\\ c_{7}=\frac{16807e^{7}}{46080}\\ c_{8}=\frac{128e^{8}}{315}\end{array} \right. \tag{25}\]  
We substitute \(e=\frac{2m}{1+m^{2}}\) into Formula (24), and then we can obtain new coefficients in the form of \(m\) power series:  
\[\left\{ \begin{array}{l}c_{1}=2m-3m^{3}+\frac{31m^{8}}{6}-\frac{63m^{7}}{72} \\ c_{2}=2m^{2}-\frac{20m^{4}}{3}+\frac{18m^{6}}{18m^{6}}-\frac{1936m^{8}}{45}\\ c_{3}=3m^{3}+\frac{63m^{7}}{4}+\frac{2313m^{7}}{4}\\ c_{4}=\frac{16m^{4}}{3}-\frac{192m^{6}}{5}+\frac{8032m^{8}}{45}\\ c_{5}=\frac{125m^{5}}{122}\frac{685m^{7}}{72}\\ c_{6}=\frac{108m^{6}}{360}-\frac{8424m^{8}}{35}\\ c_{7}=\frac{16807m^{7}}{360}\\ c_{8}=\frac{32768m^{8}}{315}\end{array} \right. \tag{26}\]  
Comparison of Efficiency  
All algorithms were implemented in Python and run on a Lenovo XiaoXinPro 14 IHU 2021 with a 4-core 3.11 GHz Intel i5-11300H processor. The manufacturer of the computer is Lenovo (Beijing) Co., Ltd., located in Beijing, China. According to the literature, it is known that [PERSON]'s Goat Herd is superior to the [PERSON], [PERSON] (1988), and Series methods ([PERSON], 1992; [PERSON], 1992). Therefore, this article compares [PERSON]'s Goat Herd method. The method in this article is only applicable to situations where \(e\ll 1\). Computation time (in milliseconds) required [PERSON]'s equation to be solved for \(10^{6}\) points (equally spaced in \(E\)) using [PERSON]'s Goat Herd and that of this work. The number of \(N\) was chosen by repeating the calculation until a mean absolute error below \(10^{-12}\) (relative to the true solution) was obtained for the sample. We can find that when \(e\ll 1\), the efficiency of the two methods has their own advantages and disadvantages. The comparison results are shown in Table 1. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{_e = 0.01_} & \multicolumn{2}{c}{_e = 0.05_} & \multicolumn{2}{c}{_e = 0.1_} & \multicolumn{2}{c}{_e = 0.2_} \\ \cline{2-9}  & \(N\) & **Time** & \(N\) & **Time** & \(N\) & **Time** & \(N\) & **Time** \\ \hline Kepler’s & 3 & 137.5 & 5 & 269.3 & 5 & 249.2 & 5 & 219.3 \\ Goat Herd & 5 & 114.6 & 8 & 155.6 & 11 & 194.4 & 18 & 299.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of efficiency between two methods.
# Guard Band Protection Scheme to Facilitate Coexistence of 5G Base Stations and Radar Altimeters
## 4 Conclusions  
This paper proposes an interference-analysis model based on the guard band protection method to investigate the possibility of coexistence between 5G base stations and radar altimeters. For accurate simulation, we use the Monte Carlo method, model the 5G base station using 4-by-4, 8-by-8, and 16-by-16 antenna arrays, distribute the interference scenario in different environments, including rural, suburban, and urban areas, and consider various types of losses. The results indicate that using a 150 MHz guard band can facilitate the coexistence of 5G base stations and the radar altimeter, except in the case of the 16-by-16 antenna array in the rural environment. Notably, in the urban environment, coexistence can be achieved with only a 50 MHz guard band for the 5G base station using the 4-by-4 antenna array. These simulation results can provide a valuable reference for researchers, and the results without protection align well with our previous findings.  
Motivated by these promising results, future work focused on interference mitigation can be aimed at shutting off the partial vertical beamforming angle, limiting the deployment location of the 5G base stations, and decreasing the antenna height of the 5G base stations. Additionally, the proposed protection methods may be confirmed with using practical measured data.  
Figure 10: CDF of interference-to-noise ratio in the urban environment with: (**a**) 4-by-4; (**b**) 8-by-8; and (**c**) 16-by-16 antenna arrays.  
**Author Contributions:** Conceptualization, J.L. and S.-H.H.; methodology, J.L. and S.-H.H.; software, J.L.; validation, J.L.; formal analysis, J.L.; investigation, J.L. and S.-H.H.; resources, J.L.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, J.L. and S.-H.H.; visualisation, J.L.; supervision, S.-H.H.; project administration, S.-H.H.; funding acquisition, S.-H.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Electronics and Telecommunications Research Institute (ETRI) grant funded by ICT R&D program of MSIT/IITP (No. 2017-0-00066, Development of time-space-based spectrum engineering technologies for the pre-emptive use of frequency).  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 8 Future Opportunities and Research Directions
### Advanced Materials for High-Efficiency Rectennas  
One of the most promising future directions in RFEH is the development of advanced materials to improve rectenna efficiency. Traditional rectenna designs, typically based on conventional metallic conductors and semiconductor diodes, face efficiency bottlenecks due to material limitations. Recent research has explored the use of two-dimensional materials such as graphene and molybdenum disulfide (MoS\({}_{2}\)) for ultrafast, low-threshold Schottky diodes, resulting in significant improvements in RF-to-DC conversion efficiency. Future research should explore scalable fabrication techniques such as chemical vapour deposition (CVD) for synthesizing high-quality graphene films with controlled doping and defect minimization to optimize carrier transport properties. Studies have demonstrated that graphene-based flexible rectennas can maintain high conversion efficiency, even when subjected to mechanical deformation, making them ideal for wearable and biomedical applications [116]. Implementing conformal rectenna structures based on flexible substrates such as polyimide or PDMS, using printable conductive materials like silver nanowires or graphene inks, would support the development of miniaturized and stretchable designs. These should be experimentally validated under mechanical strain conditions (e.g., bending or stretching) to assess real-world performance reliability [34]. Moreover, the exploration of metamaterials and meta-surfaces, integrated into rectenna structures, has opened new avenues for enhanced energy capture and impedance matching across wide frequency ranges [117,118]. A focused research agenda should include the design and simulation of tunable meta-surfaces using embedded MEMS or varactor-based elements, enabling real-time frequency agility in dynamic RF environments. These reconfigurable structures can be modelled using full-wave electromagnetic solvers and optimized through machine learning algorithms to tailor performance to specific use cases [79]. Research efforts focusing on nanomaterial-enhanced substrates and tunable smart surfaces are expected to drive the development of a new generation of rectennas with record-breaking efficiency and flexibility. Additionally, developing standardized testing protocols for evaluating rectenna performance across a range of frequencies, input power levels, and mechanical deformation scenarios will support the benchmarking and commercialization of next-generation rectenna technologies [5].