esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Recommendations
### Recommendations  
Several strategies are recommended to mitigate the environmental impacts identified in this study. First, optimizing the synthesis of AuNPs is critical. This can be achieved by developing more resource- and energy-efficient methods and incorporating greener chemistry principles to minimize chemical emissions and waste. Energy efficiency across the production processes must also be improved, particularly in the USP synthesis cycle, while transitioning to renewable energy sources to reduce reliance on fossil fuels and their associated emissions. Advanced water management practices are necessary to address the significant water consumption during nanoparticle synthesis. Implementing water recycling and reuse systems, alongside upgrading equipment such as rotary evaporators with flow-through cooling systems, can enhance water efficiency significantly.  
Additionally, exploring alternative materials with lower environmental impacts can reduce the burden of AuNPs. Research into less resource-intensive functional ingredients could provide a sustainable path forward. Finally, adopting advanced waste management strategies is essential. Recycling systems to recover valuable by-products from waste streams and reducing overall waste generation will improve resource efficiency and overall environmental performance.  
The findings of this study highlight critical environmental impacts associated with the production of cosmetic creams containing gold nanoparticles (AuNPs), particularly in freshwater eutrophication, resource depletion and greenhouse gas emissions. Addressing these challenges aligns with several United Nations Sustainable Development Goals (SDGs 6, 7, 12 and 13) [42].  
We recognize the importance of extending this study to include downstream stages. Future research will adopt a cradle-to-cradle approach, evaluating impacts from consumer use and disposal stages, to ensure a holistic understanding of the product's life cycle. Specific areas for future investigation include assessing the environmental impacts of packaging, transportation and end-of-life scenarios.
# A Review on Reconfigurable Liquid Dielectric Antennas
## 2 Liquid-Based Antennas  
A liquid-based reconfigurable antenna is a type of dielectric resonator antenna (DRA). DRAs have been actively used since the first systematic reviews were conducted by [PERSON], [PERSON], and [PERSON] in 1980s [33; 34; 35]. DRAs became the center of interest for antenna applications due to their high radiation efficiency, compact size, and good match to most commonly used transmission lines [36]. Most of the designs use a slab of dielectric material mounted on a ground plane and excited by an aperture feed or by a probe inserted in the dielectric [36]. Ceramics are typically used for DRAs due to their high permittivity and low loss. On the other hand, liquid dielectrics can be used to build a DRA. Liquid-based DRAs are a type of reconfigurable liquid dielectric antennas. They offer a number of advantages such as dynamic frequency reconfigurability by changing the liquid volume, easy access to probes, possibility to have virtually any shape, and faster prototyping.  
Water is the most widely used liquid dielectric applied for liquid dielectric based DRAs. Pure water, seawater or more generally saline water with different percentages of salts are used. Even though the electrical properties of saline water are more similar to a conductor (due to a higher conductivity in the presence of salts), we will consider antennas based on this type of liquid along with antennas based on pure water.
# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 1 Introduction
The dataset was collected by the authors, manually labeled, and made publicly available for future research, being accessible at [21]. The images were taken from different angles and under various visibility conditions. The study proposed and presented in this article consists of three main stages, each of which is composed of sub-stages detailed in Section 4. The first stage consists of locating the license plate in the image, the second stage involves determining the alphanumeric characters that make up the license plate number from the detected plate, including recognizing the county in which the vehicle is registered, and the third stage consists of detecting the type of vehicle (car, motorcycle, bus, etc.) in which the detected license plate is found. In the first stage, in order to locate the license plate in the image, a YOLOv12 model was trained and tested using the dataset containing images of vehicles registered in Romania. In the second stage, the license plate was cropped from the image, and the alphanumeric text forming the license plate number was detected using PaddleOCR [22]. The county in which the vehicle was registered was also detected. In the third stage, the YOLOv12 model pretrained on the COCO dataset was used to determine the type of vehicle in which the detected license plate was located. The paper is structured as follows: the Related Work Section summarizes the approaches that other authors have applied in similar studies. The Section Data Preparation and Technical Resources presents the types of Romanian license plates and their characteristics, the dataset collected and labeled by the authors that was used in this study, the image labeling process and its output, the characteristics of the YOLOv12 model used, a comparison among the five YOLOv12 versions, a comparison between YOLOv12 and YOLOv10/YOLOv11 models, and details about the hardware and software support used in this study. The Section Detection and Classification Method presents how YOLOv12 was trained on the constructed dataset, the values chosen for the training parameters, data augmentation, the metrics used for performance evaluation, and the method used for vehicle type detection. The Section Results and Validation presents the performance achieved by the proposed model, both during training and after its completion. The performance of the proposed model is also compared with the performance of the previous model, YOLOv11, on the same dataset. The Conclusions Section summarizes the main results obtained, the novelties and contributions of the study, and possible directions for future development.
## 4 Proposed Method
### Feature Extraction  
The main goal was to determine which features and feature combinations would correctly classify fixations, saccades, PSOs, and smooth pursuit events simultaneously.  
Figure 1: Architecture of the proposed network.  
The features used for classification were velocity, acceleration, direction, and jerk. It was hypothesized that velocity and direction alone were insufficient to distinguish PSOs due to the overlapping nature of different event types. For example, velocity and direction were used in [21], and it worked well, but only for fixation, saccade, and smooth pursuit detection. The velocity feature can distinguish saccades from fixations and smooth pursuits because fixations and smooth pursuits are low-velocity, and saccades are high-velocity movement types. Additionally, direction can distinguish smooth pursuits from fixations due to the uniform distribution of direction in fixations but not in smooth pursuits. However, using only velocity parameters to distinguish fixations from smooth pursuits will not work correctly, as both fixations and SPs are low-velocity movement types. Saccades and PSOs will also be misclassified, as both are high-speed movement types.  
Because different features were important in classifying different events, tests were performed to verify which combination of features gave the best event detection results. Velocity, acceleration, and jerk were calculated from the raw x, y, coordinates, as shown in Figure 2, while the direction was calculated as shown in Equation (1).  
\[Direction_{i}=atan2(x_{i+1}-x_{i},y_{i+1}-y_{i}) \tag{1}\]
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 7 UOC Near-Future Network Scenarios and Research Challenges
### UOC--Today's Research Challenges  
UOC technology has already gained attention in addressing high-speed communication links in terrestrial- and space-based (satellite/airborne) terminals, and underwater optical communication. UOC is a feasible method for creating a 3D global communication grid. UOC technology is a powerful tool to address connectivity bottlenecks and can provide worldwide internet access independent of terrestrial limitations, by establishing wireless links via air and satellite communications, providing access to fixed and mobile services. A complete solution should manage outdoor, terrestrial, space, indoor and underwater linkages for successful secure worldwide internet access. This section will point out some of the challenges and critical issues, as briefed below, that must be addressed and solved to explore the whole viability of implementing the technology for various applications.
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 3 Dissolution of Biomass and Its Components
### Deep Eutectic Solvents (DESs) in Biomass Processing
#### 3.7.3. Cellulose Dissolution in DESs  
Cellulose is the most difficult biomass fraction to dissolve in DESs. Recent reports show that it is still problematic to dissolve cellulose in DESs [147]. The amount of dissolved polymer depends on the source--microcrystalline cellulose is more soluble in DESs compared with that isolated from plant waste. For instance, the solubility of microcrystalline cellulose in ChCl:Lys 1:2 (\(n/n\)) (choline chloride:lysine) was approximately 8% of the initial amount, while in the case of cellulose isolated from wheat straw, it was only 6% (24 h, 90 \({}^{\circ}\)C). It is worth mentioning that these yields were obtained by applying ultrasound assistance in order to increase the solubility [148; 149]. The poor solubility of cellulose and significantly better solubility of lignin in DESs allows one to separate biomass fractions, which is one of the main goals of plant waste pretreatment before further transformation into valuable chemicals.
# UAV Formation Trajectory Planning Algorithms: A Review
## 5 Local Trajectory Planning Algorithm
### Artificial Potential Field
Then, the position and velocity variables in the consensus protocol are improved to solve the problem of UAV formation in 3D obstacle environment trajectory planning and position-velocity consistency problems. [PERSON] et al. [138] proposed a consensus algorithm for distributed cooperative formation trajectory planning. The algorithm introduces the potential field function including distance items and communication effects into APF (IAPF) and then combines the second-order system dynamic model, consistency theory and IAPF for UAV formation collaborative trajectory planning. This solves the problems that traditional APF encounters in UAV including poor convergence problems related to consistency, relative distance and velocity in formation cooperative trajectory planning. [PERSON] et al. [139] proposed a new trajectory planning method using the improved artificial potential field algorithm (IAPF). This method increases the repulsive force between UAVs and defines the front center of mass of the cluster as another source of gravity, which solves the problem where the traditional APF target is unreachable and easily falls into a local minimum. [PERSON] et al. [140] proposed a UAV time-varying formation trajectory planning method with an interactive topology. This method introduces the improved potential field into APF (IAPF) and then combines distributed time-varying formation control, IAPF and model predictive control (MPC) for UAV formation trajectory planning. It solved the problem where the UAV formation has poor ability to deal with complex environments during flight. [PERSON] et al. [141] introduced a multi-UAV trajectory planning method based on an adaptive extended potential field. In this method, the gravitational influence factor and the repulsive force influence factor are introduced into the layered potential field function, and the auxiliary force is added to improve the APF. It solves the problems related to the slow convergence speed of a layered potential field algorithm and unreachable target of a traditional APF, easily falling into local minimum, inability to avoid obstacles and lack of a trajectory optimization strategy. [PERSON] et al. [142] proposed a trajectory planning method based on artificial potential functions (IAPF) for multi-UAV systems. This method introduces the improved artificial potential function (IAPF) of the rotating potential field and at the same time adds the leader-follower UAV model for UAV formation trajectory planning. It solves the problems of poor stability, local minimum and oscillation in the traditional APF system. Table 8 summarizes the contents of our survey regarding artificial potential field methods.
# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 3 Methodology
### Decoding Layer  
To decode entities and relationships from the output matrix accurately and effectively, a specific relation-based indexing strategy is employed to determine the boundary markers of the head and tail entities. For a given sentence, a classifier is designed to assign labels to all possible combinations. A three-dimensional matrix is maintained to store the classification results. An entity can be identified by detecting its boundary markers, and four types of labels are used in the labeling strategy.  
1. HB-TB: The head entity's starting token is connected with the tail entity's starting token. For example, there is a relationship between the entities \"New York City\" and \"New York State\", so the combined classification label (\"New\", \"Located in\", \"New\") is assigned as \"HB-TB.\"
2. HB-TE: The head entity's starting token is connected with the tail entity's ending token. For instance, \"New\" is the starting token of \"New York City\", and \"State\" is the ending token of \"New York State\", so the combination (\"New\", \"Located in\", \"State\") is designated as \"HB-TE.\"
3. HE-TE: The head entity's ending token is connected with the tail entity's ending token. For example, the combination (\"City\", \"Located in\", \"State\") is designated as \"HE-TE.\"
4. \"-\": No connecting relationship exists.  
Using the triangular labeling information from the matrix, the entity pairs for each relation can be determined. For each relation, the span of the head entity extends from \"HB-TE\" to \"HE-TE\"; the span of the tail entity extends from \"HB-TB\" to \"HB-TE.\" The two paired entities share the same \"HB-TE\", thus satisfying the conditions for the relation triplet (s, r, o).
## 4 Recent Advances and Future Directions
### Integration of Quantum Computing  
Quantum computing represents one of the most exciting frontiers for the future of ML, particularly in the context of big data. Quantum computing offers the potential to solve certain types of problems much faster than classical computers, which could revolutionize fields such as cryptography, optimization, and complex simulations. In ML, quantum algorithms like quantum SVMs and quantum neural networks are being explored for their potential to handle high-dimensional data and perform computations that are infeasible for classical algorithms [362; 363; 364; 365].  
Recent advancements in quantum ML (QML) have demonstrated the feasibility of implementing basic quantum algorithms on existing quantum hardware. While current quantum computers are still in the early stages of development and are prone to errors, significant progress is being made in improving their stability and scalability. Hybrid quantum-classical algorithms, which combine quantum computing with classical ML methods, are also being developed to take advantage of quantum speedups where possible while relying on classical computation for tasks where quantum advantages are less clear [366; 367; 368].  
Looking forward, integrating quantum computing with big data and ML is likely to become a major area of research. As quantum hardware continues to improve, the development of quantum ML frameworks and algorithms will accelerate, potentially leading to breakthroughs in areas that require the processing of large, complex datasets. The future will likely see a growing synergy between quantum computing and ML, opening up new possibilities in solving some of the most challenging problems in science, engineering, and beyond [369; 370; 371; 372].
## 2 Study Area and Data
### Data
#### 2.2.1 Satellite Data
However, it is affected by orbital constraints, resulting in parts of the images being blocked by the orbit. To address this, a time-series analysis method based on a Singular Spectrum Analysis (SSA) was employed to interpolate the missing regions. To match the spatial resolution of VIRR LST, machine learning methods were used to downscale the interpolated MWRI LST data. The DEM data obtained from the Shuttle Radar Topography Mission (SRTM) and the slope data derived from the DEM both have a spatial resolution of 900 m.
## 5 Conclusions  
With the increase in the number of air quality monitoring sites and the explosion in the volume of data, centralized training of urban \(PM_{2.5}\) prediction methods has led to several issues, such as computational pressures and security risks. To tackle these challenges, a FedDeep model was developed for multi-urban \(PM_{2.5}\) forecasting in this paper. FedDeep uses a federated learning architecture. We first allocated an ECS corresponding to each urban region. ESTNets were then deployed on each ECS and the central cloud. We uploaded the data generated by each urban region to the corresponding ECS for training, instead of the central cloud, and then uploaded the trained gradients to the central cloud for parameter updating. Subsequently, all urban \(PM_{2.5}\) predictions were carried out through the ESTNet on the central cloud, thus reaching a \"distributed training, centralized prediction\" mode. In this way, the computational load on the central cloud was alleviated, and the risk level of data leakage was reduced. Second, in ESTNet, a gated fusion layer was proposed to allow the model to consider the various impacts produced by different external factors (e.g., weather conditions, temperature, and wind speed). Third, we conducted an experiment on 13 prefecture-level cities in Jiangsu Province. The experimental results indicated that our proposed model outperformed the other state-of-the-art baselines, in both prediction accuracy and efficiency.  
The authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 62271190, and Research on Distribution Room Condition Sensing Early Warning and Distribution Cable Operation and Inspection Smart Decision Making Technology No. 524609220092.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
Authors [PERSON] and [PERSON] were employed by the company NARI Technology Co., Ltd., Author [PERSON] was employed by the company Shenzhen Urban Transport Planning Center Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN-LSTM & Convolutional Neural Network and Long Short-Term Memory \\ ECS & Edge Computing Server \\ ESTNet & External Spatio-Temporal Network \\ FedDeep & Federated Deep Learning Network \\ GAT-LSTM & Graph Attention Network and Long Short-Term Memory \\ GPU & GPU memory \\ HA & Historical Average \\ MAE & Mean Absolute Error \\ MHSA & Multi-Head Self Attention \\ RMSE & Root Mean Squared Error \\ \(R^{2}\) & Coefficient of determination \\ SVR & Support Vector Regression \\ TT & Training Time \\ \end{tabular}
## 3 Materials and Methods
### Data Analysis
#### 3.3.2 Application Validation and Analysis of the System  
To validate the sustainable application of this system, a representative case analysis was conducted using video-recorded \"pass-and-cut coordination\" drills from a high school basketball lesson as the exemplary case. The instructor, holding a Master of Education degree, conducted the lesson with 32 Grade 10 male students (Level V according to China's Physical Education Curriculum Standards) [4]. The instructional content progressed through three sequenced components: (a) fundamental drills for cutting routes and passing timing in give-and-go plays, (b) tactical application through constrained practice progressing to modified games, and (c) sport-specific conditioning exercises  
Aligned with the pedagogical principles of China's Physical Education and Health Curriculum Standards, this lesson employed instructional strategies that balanced efficiency with long-term goals--a strength explicitly recognized by instructional evaluation experts. The CoSVLM2-Video system performed real-time analysis of teaching behaviors with optimized computational efficiency, illustrating how sustainable AI technologies can support scalable and continuous assessment in physical education.
# Seismic Fragility Analysis of the Transmission Tower-Line System Considering Bol Slippage
## 2 Fragility Analysis
### FEM of the TTLS  
A 500 kV TTLS with 2 towers (each with a total height of 99.9 m) and 3 span lines is employed. The members of the towers consist of Q420-type, Q345-type, and Q235-type steels, which are simulated by the BEAM189 element in ANSYS, and the bilinear isotropic hardening plasticity model is employed, as shown in Figure 1, where \(E\) denoteselastic modulus of members [23; 24]. To consider the bolt reciprocating slippage effect, the nonlinear spring element COMBIN39 is employed in the FEM of the tower. Specifically, overlapping nodes are established at the joints between the tower members, and the zero-length spring element COMBIN39 is inserted between these overlapping nodes. It should be noted that since the torsional deformation behavior between joints is not considered, the characteristic of the COMBIN39 element is set as the axial spring element. The real constants (loading curves) of the COMBIN39 element, which are used to simulate slippage behavior for bolted joints, are obtained through a combination of the reciprocating load test on bolted joints, numerical simulation, and BP neural network. The skeleton curve of the joint consisting of 8 M16 bolts with grade 6.8 is shown in Figure 2, which is the real constant of the corresponding COMBIN39 element. In addition, the unloading path of the COMBIN39 element is defined by setting KEYOPT (1) = 1, which indicates that it unloads along a line parallel to the slope at the origin of the loading curve. More specific details regarding the JM establishment method are described in [PERSON] et al.'s studies [12].  
In addition, the LGJ630/45 conductors and the JLB20A-150 ground wires are adopted. Both of them are simulated by the LINK10 element, and their design parameters are provided in Table 1. The FEM of the TTLS presented in Figure 3 includes three spans, each with a length of 500 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Diameter (mm)** & **Mass (kg/m)** & **Area (mm2)** & **Elastic Modulus (MPa)** \\ \hline LGJ630/45 & 33.60 & 2.06 & 666.55 & 63,000 \\ JLB20A-150 & 15.75 & 0.9894 & 148.07 & 147,200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of transmission lines.  
Figure 1: Material model.  
Figure 2: The skeleton curve of the bolt joint.