esa-sceva/satcom-chunk-collection huggingface dataset:
# Sustainability as a Challenge and Driver for Novel Ecosystem 6G Business Scenarios
## 3 Results
### Developed Scenarios for 6G
#### 3.2.1 User Experience Theme #1
The next-generation intelligent industrial systems will be based on the interaction with hybrid bio-industrial automation. The industrial market segment will have a short to medium change horizon calling for agile and scalable local presence. Furthermore, growing requirements on independence, assurance, and resilience, combined with the emphasis on sustainability and circular economy models, have led to an increase of localized demand-supply models. Decentralized agile manufacturing composes a multi-tenant production ecosystem with producers of different scope and scale. Managing the dynamic network of robotized production plants will utilize distributed ledger technologies for the provenance tracking, inter-organizational record keeping, and integration in the supply chain management. 6G extends HMI to robotization across future industries via connecting human intelligence with advances in cognitive computing. With online data, AI, and a digital twin of the manufacturing process, industries transform towards the servitization of products. Novel digital automation platform with open interfaces will reduce complexity friction and have a positive impact on scalability, replicability,and sustainability. I5.0 facilitates the long tail of mass customization, localization, and closer user interaction. Trustworthy quantum-enhanced I5.0 networking and services deploy the highest standards of safety and environmental protection. Genetic engineering and synthetic biology allow the creation of programmed and modified organisms for specific purposes across industries, e.g., in food, chemical, pharmaceutical, textile, and construction processes. This will decouple growth from cost and resource usage. The interaction and synchronicity with these novel bio-nano-artificial intelligence technologies call for new dynamic capabilities for employees and society.
## 4 Methodology
### Comprehensive Trajectory Metrics Analysis
#### 4.6.3 Methodological Implications and Validation  
The comprehensive metrics analysis demonstrates several key capabilities of the Hamming diversification index framework:  
Pattern Discrimination  
The framework successfully distinguishes between systematic progression (Entity \(X\)), dual-pattern evolution (Entity \(Y\)), and high-frequency adjustment (Entity \(Z\)) patterns, providing nuanced characterization of different evolutionary strategies.  
Multi-Dimensional Analysis  
The feature-specific metric variations (particularly Entity Y's contrasting \(f_{1}\) and \(f_{2}\) patterns) demonstrate the framework's capacity to capture complex, multi-dimensional evolutionary dynamics that would be obscured by univariate approaches.  
* Ordinal Structure Preservation: The consistent differences between regular and modified Hamming distances validate the importance of incorporating ordinal proximity in categorical trajectory analysis, providing more accurate similarity assessments.
* Scalability and Interpretability: All metrics remain interpretable within [0,1] bounds or provide meaningful count-based measures, ensuring accessibility for policy analysis while maintaining analytical rigor.  
This comprehensive analysis establishes the foundation for applying the Hamming diversification index to real-world datasets, where similar metric patterns can provide insights into institutional performance, policy effectiveness, and comparative evolutionary dynamics across diverse entities and time periods.
# Detection of _Camellia oleifera_ Fruit in Complex Scenes by Using YOLOv7 and Data Augmentation
## 4 Conclusions  
A real-time and accurate detection method based on YOLOv7 target detection network and multiple data augmentation was proposed to realize the detection of _Camellia oleifera_ fruit in complex scenes of orchard. Firstly, the images of _Camellia oleifera_ fruits were collected, and the detection model of _Camellia oleifera_ fruits was established by YOLOv7 network, which was compared with YOLOv5s, YOLOv3-spp and Faster R-CNN target detection networks. The results showed that the YOLOv7 model has the best performance with mAP of 95.74%, F1 score of 93.67%, Precision of 94.21%, Recall of 93.13% and the average detection time of 0.025 s. The dataset was further augmented by rotation, mirroring, adding Gaussian noise, increasing or decreasing image brightness and mosaic  
Figure 10: Examples of the detection results between YOLOv7 and the DA-YOLOv7 models. (**a**) side-light angle; (**b**) backlight angle; (**c**) Slight occlusion; (**d**) Heavy occlusion.  
augmentation methods, and the DA-YOLOv7 detection model was established by using the augmented dataset and the YOLOv7 network. Data augmentation can effectively improve the detection ability of the model. The optimal _Camelia oleifera_ fruit detection model was DA-YOLOv7 model with mAP of 96.03%, Precision of 94.76%, Recall of 95.54% and F1 score of 95.15%. In summary, the YOLOv7 target detection network combined with multiple data augmentation can accurately and quickly detect _Camelia oleifera_ fruit in complex scenes. This method has a good application prospect in mechanical harvesting operation. In the future work, we plan to combine the proposed model with the end-effector to realize detection and positioning of fruit, and further adjust the picking angle and the position of the end-effector. At the same time, this study provides a theoretical reference for detection and automatic harvesting of other fruits.  
Conceptualization, D.W. and S.J.; methodology, S.J.; software, S.J. and E.Z.; validation, D.W., E.Z. and Y.L.; formal analysis, Y.L.; investigation, H.Z. and D.W.; resources, W.W.; data curation, D.W., Y.L. and W.W.; writing--original draft preparation, S.J. and Y.L.; writing--review and editing, R.W. and S.J.; visualization, D.W. and S.J.; supervision, D.W. and E.Z.; project administration, S.J., D.W. and E.Z.; funding acquisition, D.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Anhui Province (NO.2208085 ME132) and the National Key Research and Development Program of China (NO.2016 YFD0702105).  
Not applicable.  
The authors would like to thank the editors and all the reviewers who participated in the review.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Methods
#### 2.2.3 Selection of the Spectral Index  
A single pixel does not contain enough information and, therefore, features other than the pixel value must be considered to perform the phenological normalization [28; 38; 39]. The spectral indices are considered as features in this study, which is influenced by phenological properties [39; 40; 41]. At this time, the spectral indices calculated through reflectance, such as the normalized difference vegetation index, soil-adjusted vegetation index, or enhanced vegetation index, cannot be utilized, since the relative radiometric normalization is usually performed using the DN [3; 41; 42]. Therefore, the proposed method utilizes spectral indices that can be calculated through the DN values, among which the greenness indices of the excess green index (ExG), excess green minus excess red index (ExGR), vegetative index (VEG), color index of vegetation extraction (CIVE), and combined index (COM) are selected [43; 44; 45]. The greenness indices are obtained by converting the red-green-blue (RGB) color space to RGB chromatic coordinates, as defined in Equation (5):  
\[R_{cc}\ =\ \ \frac{R}{(R+G+B)};\ G_{cc}\ =\ \ \frac{G}{(R+G+B)};\ B_{cc}\ =\ \ \frac{B}{(R+G+B)} \tag{5}\]  
where \(B\), \(G\), and \(R\) are the blue, green, and red bands, respectively, and \(B_{cc}\), \(G_{cc}\), and \(R_{cc}\) are the chromatic coordinates of the blue, green, and red bands, respectively. Subsequently, the ExG, ExGR, VEG, CIVE, and COM are defined as in Equations (6)-(10):  
\[\text{ExG}\ =\ 2G_{cc}-R_{cc}-B_{cc} \tag{6}\]  
\[\text{ExGR}\ =\ \text{ExG}-(1.4R_{cc}-G_{cc}) \tag{7}\]  
\[\text{VEG}\ =\ \frac{G_{cc}}{R_{cc}^{0.667}B_{cc}^{0.333}} \tag{8}\]  
\[\text{CIVE}\ =\ 0.441\text{R}_{cc}-0.881G_{cc}-0.385B_{cc}+18.78745 \tag{9}\]  
\[\text{COM}\ =\ 0.25\text{ExG}+0.30\text{ExGR}+0.33\text{CIVE}+0.12\text{VEG} \tag{10}\]  
At this time, feature selection is independently performed for each band, since the features affecting each band are different [25]. Furthermore, as the number of input features increases, the complexity of the model and noise increases, and thus it is important to select optimal features [46; 47]. Therefore, one optimal spectral index for each band is selected in this study.
# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 4 Experiment Analysis
### Datasets and Pre-Processing  
The first step for accurate air quality prediction was to obtain high-quality data. In this paper, four real air quality datasets of Xi'an City were used to evaluate the performance and convergence speed of the APSO-CNN-Bi-LSTM model. To effectively predict air quality and solve the problem, the collected raw data must be pre-processed to remove irrelevant, redundant, noisy, and unreliable data to prevent misleading results. It also produced more accurate results.  
The datasets were first normalized using the function MinMaxScaler, to eliminate the effect on the prediction results due to the difference in the scale [45]. The specific mathematical formulas are shown as follows.  
\[\text{y}^{*}=\frac{(\text{y}-\text{y}_{\text{min}})}{\text{y}_{\text{max}}- \text{y}_{\text{min}}}\]  
Among them, y is the sample data to be normalized, while ymin and ymax denote the minimum and maximum values observed in the sample data column, and y* is the result obtained after the normalization of the sample data. Table 3 presents an overview of the four datasets, with detailed dataset information as follows.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Type** & **Data Set** & **Time Scale** & **Time Step** & **Data Size** \\ \hline \multirow{4}{*}{Urban air quality data} & 1036A & & 17,542 \\  & 1037A & & 17,539 \\ \cline{1-1}  & 1038A & 1 January 2019– & 1 h & 17,518 \\ \cline{1-1}  & 1039A & 31 December 2020 & 17,527 \\ \cline{1-1}  & 1040A & & 17,528 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of datasets.  
In this section, we juxtapose the APSO-CNN-Bi-LSTM model introduced in this paper against a cadre of alternate air quality prediction algorithms. The precision of the model was predominantly quantified by the discrepancy between the actual observational data and the predicted data by the model. To appraise the efficacy of the model delineated herein, we employed specific metrics to ascertain the model's predictive accuracy [46]. The calculation formulae used for this purpose are outlined as follows:  
\[\mathrm{E_{MAE}}=\frac{1}{\mathrm{M}}\sum_{j=1}^{\mathrm{M}}\left|\mathrm{x_{ j}-\widetilde{\mathrm{x}_{j}}}\right|\ \mathrm{E_{RMSE}}=\sqrt{\frac{1}{\mathrm{M}}\sum_{j=1}^{\mathrm{M}}\left( \mathrm{x_{j}-\widetilde{\mathrm{x_{j}}}}\right)^{2}}\]  
Among them, \(\mathrm{x_{j}}\) represents the actual value, \(\widetilde{\mathrm{x_{j}}}\) denotes the predicted value generated by the model, and M represents the total number of data points.
# A Phase Angle-Modulated Bat Algorithm with Application to Antenna Topology Optimization
## 3 Phase Angle Modulated Bat Algorithm (P-AMBA)
### P-Amba
To better demonstrate the mapping process, the principle through which P-AMBA obtains discrete \(n\)-dimensional search space results by optimizing the continuous six-dimensional search space is shown in Figure 4.  
```
Initialize \(a\), \(b\), \(c\), \(d\), \(e\), \(g\), and \(\mathbf{V}_{i}=0\) in the continuous bat algorithm  Define pulse frequency \(F_{i}\)Initialize pulse rates \(r_{i}\) and the loudness \(A_{i}\) While (t < Max iteration)  Generate new solutions by adjusting frequency, updating velocities and positions [Equations (1) to (3)]  Calculate the output value \(g(\mathbf{x})\) using Equation (12) to generate \(n\)-dimensional bit stringsUpdate the position vectors using Equation (13) if (\(rand>r_{i}\))  Select a solution among the best solutions randomly  Change some of the dimensions of the position vector with some of the dimensions of \(\mathbf{X}_{\text{gbest}}\) endif  Generate a new solution by flying randomly if (\(rand<A_{i}\,\&\,f(\mathbf{x}_{i})<f(\mathbf{X}_{gbest})\))  Accept the new solutions  Increase \(r_{i}\) and reduce \(A_{i}\) endif  Rank the bats and find the current \(\mathbf{X}_{gbest}\); endwhile
```  
**Algorithm 2** Pseudo-code of P-AMBA  
Figure 4: The process of mapping continuous search space to discrete search space.
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 4 Charging Type
### Conductive Charging
#### 4.1.3 Charging Standards
* Type 2-AC/DC Type 2-AC can be generalized as the European charging standard that supports both a single phase as well as three phase (Level 1, Level 2, Level 3) power supply to EVs [75]. The Type 2-AC charging standard associated with the three phase charging level can charge EVs (2W, 3W, 4W) with a variable charging power of 120 V for Level 1, 240 V for Level 2, and 410 V for Level 3. Thus, the charging standard is used for slow EV charging at specific locations, i.e., gyms, parks, shopping centres, etc. Therefore, it can also be utilized for community charging in which EVs have sufficient time to charge. However, Tesla is an exception that utilizes the Type 2 charging standard as the DC fast charger. These chargers are available for Tesla EVs only [76]. Regardless of the limited utilization of Type 2-AC for slow charging, many manufacturers, including Citroen C-Zero, Renault Zoe, and Smart EQ (For-Four, For-Two), have invested in designing the charging standard to expand their business further and provide flexible charging standards for EVs. * Bharat AC-001/ Bharat DC-001 India attempted to deploy EV charging standards with the help of Bharat chargers, i.e., Bharat AC-001 for low-powered charging vehicles. It can be equipped with 2W, 3W, and 4W EVs embedded with on-board chargers of low voltage. The variable charging power required for Bharat slow chargers ranges from 110 V to 450 V, based on the different charging levels [77]. On the other hand, to enhance the power supply to the EVs for fast charging, Bharat AC-001 can be upgraded to the Bharat DC-001 charging standard. The upgraded Bharat charging standard is built based on the IEC61851 that can transfer a maximum power of 72 V to the EVs. Moreover, the power supply for charging level 2 and charging level 3 can vary between 30 V and 48 V. To provide fast EV charging, [PERSON] et al. [78] investigated the conversion of the Bharat AC-001 to Bharat DC-001 charging standard considering the delta Vienna rectifier and a unidirectional DC/DC converter. Therefore, the aforementioned charging standards with variable charging power can be utilized for community charging based on the requirement and travelling destination of the users. Table 3 presents a comparative analysis of various EV conductive charging standards based on parameters such as charging level, charging time, voltage, and type of vehicle. Figure 5: Taxonomy of charging connectors based on the manufacturers. Figure 6: Taxonomy of conductive charging standards based on the type of EV.
# Preparation and Characterization of Gold Nanorods Coated with Gellan Gum and Lipoic Acid
## 2 Materials and Methods
### Synthesis and Characterization of the Gellan Gur Derivative GG-LA  
The grafting of lipoic acid (LA) onto the gellan gum (GG) backbone was carried out in aqueous environment at controlled pH and temperature. More specifically, 100 mg of gellan gum sodium salt (GG, Mw \(\approx\) 50 KDa) was solubilized in 3 mL of bidistilled water. Separately, lipoic acid (LA to GG repeating units' molar ratio equal to 0.3) was dispersed in ultrapure water and dissolved adjusting the pH to 6.8 with TEA. The activation of LA occurred by addition of EDC-HCl and NHSS (mol EDC-HCl\(\equiv\)NHSS/mol LA equal to 1.2:1) and the pH was once again adjusted to 6.8. The reaction mixture was kept stirring at 40 \({}^{\circ}\)C for 18 h, checking the pH for the first 2 h. Unreacted chemicals were removed through dialysis tubing (cut-off 12-14 KDa) and then, the product was further purified by gel permeation chromatography using Sephadex G-15 resin. The final derivative, called GG-LA, was collected after freeze-drying (yield: 85% calculated on the starting GG weight). Here, \({}^{1}\)H NMR was (300 MHz, D\({}_{2}\)O/NaOD, 25 \({}^{\circ}\)C, 6): 1.2 (3H GG,-CH\({}_{3}\) of rhamn), 1.55 and 2.2 (4H LA,-HO-CO-CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{2}\)-SC-), 2.23 (2H LA -HO-CO-CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{2}\)-SC-), 2.97 (2H LA, -HO-CO-CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{2}\)-CH\({}_{2}\)-SC-), 3.1-4.1 (1H GG, -CH of glucose), 5.03 (1H GG, -CH of glucuronic acid), 5.2 (1H GG, -CH of rhamn).  
SEC analyses were performed using a Phenomenex PolySep-GFC-P4000 (Torrance, CA, USA) column connected to a light scattering detector (LALS, 15\({}^{\circ}\) and RALS, 90\({}^{\circ}\)) and a refractive index detector (Agilent 1260 Infinity). TRIS buffer 0.15 mM at pH 9 was used as mobile phase and the flow was set at 0.8 mL\(\cdot\)min\({}^{-1}\). Samples were incubated overnight in mobile phase under stirring (15 mg\(\cdot\)mL\({}^{-1}\)) and filtrated through a syringe filter (0.45 \(\upmu\)m cut-off) before injection.
# Exploring the Feasibility of Autonomous Lighting Systems for Pedestrian Crossings in Off-Grid Areas
## 1 Introduction
### Description of the Research Problem  
This paper presents key features of the design and performance. The technical solution of the lighting system must fulfill the illumination requirements and should be maintenance-free and economically acceptable. This paper examines the problem of supplying power for a pedestrian-crossing lighting installation under Polish climatic conditions, using an electricity storage bank and PV installation.  
Depending on the year and season, the availability of solar radiation changes significantly and has a direct impact on the operating time of the lighting system. In the summer, the amount of solar radiation is sufficient to provide enough energy to fulfill illumination requirements of the system. In wintertime, battery discharging time increases and PV energy production decreases. In addition, the ambient temperature reaches negative values, which reduces the availability of energy in the energy bank. These factors are crucial for the proper operation of the lighting system.  
After reviewing the most interesting literature positions, authors concluded that the appropriate selection of the parameters of autonomous lighting system is possible.  
Authors hypothesize that improving lighting conditions, particularly in the autumn and winter, will directly affect the safety of pedestrian crossings, resulting greater pedestrian visibility and a smaller number of accidents.  
The authors of this article performed research on issues related to street lighting, pedestrian safety, light sources' energy-efficient power supply, electric energy storage systems and control algorithms. In collaboration with the Ministry of Infrastructure of the Republic of Poland, a new standards of traffic safety regulations turned into legal requirements and existing laws were developed.  
Scientists analyzed data accidents at pedestrian crossings and concluded that it is advisable to undertake research to verify the reasons for autonomous lighting systems' incorrect operation during periods of low solar radiation.  
The main purpose of this article is the verification of the possibility of autonomous lighting system operation throughout the year for different power levels of LED luminaires.
## 2 Materials and Methods
### Recruitment and Data Collection  
In collaboration with investigators from the Swinburne University of Technology, a local non-government organization in the Narail district of Bangladesh, the Organization for Rural Community Development, recruited the participants. The Organization for Rural Community Development investigators and trained data collectors communicated with the potential participants by telephone or direct contact. Once potential voluntary participants were identified, their blood pressure levels were taken to assess their eligibility for the study. The data were collected through face-to-face interviews, and equal numbers of men and women were recruited. The chief investigator conducted four Zoom meetings to train the data collectors and local investigators on the steps to be followed to conduct the research. A pilot study of ten adults who were not included in the main research was conducted before the final data collection to familiarize the data collectors with all study procedures and to ensure they were conversant with the questionnaire. The Organization for Rural Community Development's investigator monitored the data collection process for quality assurance. The Organization for Rural Community Development investigator revisited 10% of the households to cross-check data quality.
## 2 Related Work
### Synthesis and Research Gaps  
This review of existing approaches reveals several critical gaps that our Multi-Examiner system addresses. By identifying these limitations, we establish the foundation for our research questions and system design.  
The review of QGSs reveals an evolution from rule-based methods to sophisticated integrations of KGs, machine learning, and LLMs. While initial systems achieved moderate success, contemporary models that integrate LLMs and intelligent agents show promise for complex question generation but still face technical integration challenges. Cognitive complexity remains a barrier, with current systems maintaining good accuracy for knowledge-level questions but struggling with higher-order questions [56]. Domain-specific adaptability, particularly in technical education, also shows performance declines as complexity and specialization increase [57].  
Three significant research gaps emerge from our review, directly aligning with our research questions. First, existing systems struggle to generate contextually relevant and accurate distractors, particularly for technical subjects like IT. This limitation undermines assessment validity and connects directly to our first research question. Second, current approaches produce limited question diversity across cognitive levels, with most systems focusing on lower-order questions. This gap relates to our second research question on improving question diversity. Third, there is a notable deficiency in generating higher-order thinking questions, with accuracy rates declining sharply for complex cognitive tasks. This limitation addresses our third research question on supporting critical thinking in IT education.  
Our Multi-Examiner system addresses these gaps through a novel integration of KGs, specialized agents, and educational taxonomies. By combining structured domain knowledge with targeted generation capabilities and systematic cognitive alignment, our approach offers potential improvements in distractor quality, question diversity, and higher-order thinking assessment. The following sections detail our methodology for implementing and evaluating this system.