esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphic Applications of Unmanned Aerial Vehicles (UAVs) in the Study of Industrial Heritage Assets
## Case Study and Results
### UAV Flight Operation Analysis
The values of \(\theta_{cc}\) and \(l_{cc}\) affect the length obtained for the element in the photograph, reducing the value when \(\theta_{cc}\) and \(l_{cc}\) increase. On the other hand, for the same resolution in the UAV camera and in the conventional camera (identical pixel size), the measurement is still a comparison between the number of pixels covered by the element, being lower in the conventional camera. In addition, the error obtained, is related with the measurement that represents a pixel, thus the error in conventional camera is higher than the one obtained with the UAV. Figure 8: Chimney factory cracks (**a**) crack 1; (**b**) crack 2; (**c**) zenith photograph of the top of the chimney. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & **Dimension** & **Value** \\ \hline \multirow{6}{*}{Chimney} & Length & 22.47 m \\  & External bottom diameter & 2.66 m \\  & External upper diameter & 1.46 m \\  & Internal upper diameter & 0.84 m \\  & Crack 1 length & 2.06 m \\  & Crack 2 length & 2.28 m \\ \hline \multirow{2}{*}{Irrigation water pond} & Length & 38.20 m \\  & Width & 20.23 m \\ \cline{1-1}  & Area & 627.35 m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Indirect measurements of chimney and irrigation water pond. It's important to highlight that the UAV has allow obtaining these measurements without the need of external elements, only using the flight operation. Nevertheless, a direct measurement of the chimney dimensions established in Table 3 or the inspection of the upper section requires an external structure or elevation equipment for a person to do that. This fact represents an important cost and a person's risk during the operation that is not generated during a UAV flight operation. Additionally, despite the fact the length and the width of the irrigation water pond can be measurement directly, the geometrical form makes the area measurement difficult. Therefore, the use of UAVs shows a clear opportunity to evaluate the conservation or to obtain measurements of singular elements in heritage assets. Finally, the video recorded in a single shot shows the relationship between the factory and the aqueduct. This video has made it possible to monitor the canal that supplies water from the aqueduct to the factory. In addition, two aspects of interest have been observed, allowing understanding the location of both properties: the mountainous complex to the northeast of the aqueduct and the sugar factory, where the water was collected for irrigation, as well as the extension of the land facing south of the factory, where the sugarcane cultivation was carried out, presenting a downward slope that facilitates the irrigation of the crop without any need for pumping equipment and the distribution of water from the aqueduct to the factory, which is also located at a lower height.
## 2 Materials and Methods
### Patient Studies
#### 2.2.5 Proposed Methodology for Optimization of the Activity to Be Administered
In this regard, a certain number of NLV voxels are allowed to exceed the NLV AD threshold, and identically in the PTV, a certain number of voxels are allowed to receive an AD below the PTV AD threshold. Higher activity tends to increase the number of PTV voxels with AD higher than the threshold, but at the same time, increase the number of NLV voxels with AD higher than is desired. The optimal activity to be administered is determined by the maximization of the sum of the two terms in (1). In other words, the optimal activity is a balance between the two terms. These terms are referred to in this work as the PTV ratio and the NLV ratio. The optimization of the OF was based on the golden section method [50]. This methodology was tested using the patients' pre-treatment SPECT-MAA and post-treatment PET-MS images retrospectively. For both types of images, the optimal activities were estimated using the proposed optimization solution. Then, the optimal values calculated from each patient's SPECT-MAA images were compared against the optimal values calculated from their PET-MS images. The assessment of the optimal activities calculated with SPECT-MAA and PET-MS images was based on AD thresholds of 80 Gy and 70 Gy imposed, respectively, to the PTV and NLV voxels, since these thresholds are the RE clinical reference, as previously referred [29, 30]. The optimal predictive activity (derived from the SPECT-MAA) was compared against the a posteriori optimal activity (derived from the PET-MS). Statistical analysis was based on the Wilcoxon signed-rank statistical test and the ICC for absolute agreement. A significance level of 5% was set. The optimization of the OF can be computed for any pair of AD thresholds. Thus, to test its behavior/robustness to the change of the thresholds, 72 combinations of these were analyzed in additional tests, considering 6 AD threshold values (i.e., 80 to 105 Gy with 5 Gy increments) at the level of the PTV voxels and 12 thresholds (i.e., 15 to 70 Gy with 5 Gyincrements) at the level of the NLV voxels. For this purpose, a vector with 50 test values was generated, from zero to 12 GBq (i.e., the double typical maximum clinical values of 6 GBq).
## 2 Related Work
### Related Work of Cooperative Computing Offloading Scheme  
For the application environment that requires intensive data exchange among nodes, [PERSON] et al. proposed a method called cooperative offload in [26], which considers the cooperation between mobile devices and the communication problems resulting from computing offload. [PERSON] et al. formulated the problem of joint computation offloading, service caching, and resource allocation as a Mixed Integer Non-Linear Programming (MINLP) problem and proposed an efficient collaborative service caching and computation offloading algorithm to improve the Quality of Service (QoS) and reduce the computation complexity [27]. By making full use of the cooperative communication ability among mobile terminals, [PERSON] et al. discussed the Device-to-Device (D2D) joint multi-user cooperative partial unloading, transmission scheduling, and computation allocating based on Mobile Edge Computing (MEC). An online resource coordinating and allocating scheme is proposed by considering random application requests, unpredictable Mobile Terminal (MTs) states, time-varying channel states, and computing resources [28]. [PERSON] et al. proposed a Mobile Edge Computing Offload (MECO) architecture based on multiple Unmanned Aerial Vehicles (UAVs), in which the deep reinforcement learning method was used to deal with the complex problems of computing offload and resource allocation in a dynamic environment [29]. When multiple UAVs cooperate to complete a complex task, the task unloading between multiple UAVs must meet the interdependence of the task and realize parallel processing, so as to reduce the computing cost of terminals.  
Considering the computing requirements of terminals, the delay constraints of computing tasks, and the safe distance of UAVs, [PERSON] et al. constructed a UAV-assisted cooperative offloading system based on mobile edge computing in [30]. The system optimizes the number of local computing tasks, the number of computing unloading tasks, the trajectory of UAVs, and the unloading matching relationship between multiple UAVs and multi-user terminals.
###### Abstract
detected obstacles for unmanned driving in foggy weather and achieved a high recall rate and mAP by fusing features based on YOLOv3 [9]; [PERSON] et al. [10] proposed an algorithm based on Mask R-CNN combined with the Retinex image enhancement algorithm to detect whether there are fallen rocks and potholes in front of the moving vehicle, reducing the false detection rate; [PERSON] et al. [11] tracked and counted mine vehicles based on the YOLOv5 target detection algorithm with a high recognition rate. [PERSON] et al. [12] analyzed and classified traffic monitoring from a macro and micro perspective and provided a more accurate basis for anomaly detection and traffic prediction based on the understanding of traffic semantics. [PERSON] et al. [13] proposed a cascade attention mechanism. They designed a cascade attention feature enhancement module for traffic sign recognition in complex application scenarios, which not only achieved lightweight characteristics but also enhanced the model recognition efficiency and robustness. However, in the research on obstacle detection in open-pit mines, it is difficult to strike a balance between real-time and accuracy (either the detection accuracy is high, but the model is large, or the model is super lightweight, but the detection accuracy is too low). There are even fewer methods that can truly meet high detection accuracy and be deployed on mobile terminals. Moreover, most methods detect fewer obstacles, and it is difficult to ensure the robustness of the algorithm in practical applications. Therefore, this paper analyzes the unstructured road environment and harsh working conditions of open-pit mines and studies a lightweight open-pit mine driving obstacle detection algorithm suitable for deployment, targeting common obstacles in the driving process of unmanned engineering machinery. The main contributions are as follows:  
1. In this paper, we construct an open-pit mine driving obstacle dataset and propose an obstacle detection method that takes into account both high precision and real-time performance. This method provides technical support for smart mine driving. 2. We propose an improved YOLOv88 lightweight obstacle detection network. The lightweight context guidance block strengthens the connection between local features and global context features while reducing the number of parameters and calculations. The efficient squeeze-incentive attention mechanism enhances the feature fusion capability and reduces the missed detection rate. A more suitable dynamic head network improves the network model's perception ability. 3. We use a pruning score algorithm based on layer adaptive amplitude to prune the improved model, reducing parameter redundancy and improving the network model's detection speed.
# Peri-Implantitis Causal Therapy with and Without Doxycycline: Retrospective Cohort Clinical Study
## 2 Materials and Methods
### Setting  
This retrospective clinical study analyzed patients who underwent non-surgical peri-implant therapy before and after the introduction of 14% topical doxycycline gel (Ligosan(r) at the study center. To ensure methodological consistency and minimize temporal biases, patient selection was stratified according to treatment periods as follows:  
1. Control Group (CG): This cohort included patients who received non-surgical peri-implant therapy between January 2021 and December 2022 before the introduction of Ligosan(r) in clinical practice. These individuals underwent a standardized non-surgical treatment protocol consisting of professional mechanical debridement without local or systemic antibiotic drugs.
2. Test Group (TG): This cohort comprised patients who underwent the same non-surgical peri-implant therapy between January 2022 and March 2024, after the introduction of Ligosan(r). In addition to mechanical debridement, these patients were treated with 14% topical doxycycline gel to assess its adjunctive efficacy in peri-implant disease management.  
Patients were retrospectively selected through clinical records, ensuring that all clinical procedures, data collection, and follow-up evaluations adhered to the same standardized protocol across both groups. This study design allowed for a direct comparison of peri-implant clinical outcomes before and after the implementation of topical doxycycline, providing insights into its therapeutic impact on peri-implant health.  
The protocol procedures, including data recording, were performed by three operators (dentists) with more than 5 years of clinical experience in periodonontology and implantology.
## 3 The Results
### Discussion  
Research has highlighted the importance of considering the psychological and behavioral aspects of users in the design and implementation of V2G strategies.  
Key factors in predicting EV charging demand include the number of trips, distance traveled, and energy consumption, as well as the location of the parking lot, power, and duration. Accurate demand forecasting requires an understanding of the temporal (relative to time) and spatial (relative to place) usage patterns, which can be identified through surveys and operational data. However, the need to identify strategies and policies for aggregate V2G participants becomes relevant, given that it is not possible to participate in the electricity market individually [34].  
Additionally, to fully achieve success of V2G services, it is necessary to combine the energy storage characteristics and the traffic characteristics of electric vehicles and fully consider the user behavior to achieve a more accurate description of the V2G model in order to further analyze the role of the control of the energy storage of the EV cluster and study the impact of V2G on the grid load. The majority of existing studies solely take into account traffic and travel statistics; they do not carry out a thorough assessment of a variety of criteria, including user behavior, willingness preferences, charging and discharging methods, and the effect of charging on the grid [35]. It is challenging to point out users' acceptance. Thus, a pilot survey carried out in Italy where about 300 people were interviewed can be recalled. Up to 74% of interviewees declared themselves to be interested in participating in V2G, and half of them (50.4%) indicated that they would like to maintain, at the end of the day, a residual level of the battery not less than 60%. Finally, only 74.8% were willing to receive compensation equal or higher to the energy cost paid for charging, although 2.9%, pushed by environmental feelings, were willing to receive less than what they paid for. The remaining ones preferred not to give an evaluation.  
Concerns about battery degradation and economic issues are the main barriers to the widespread adoption of V2G. The frequent charging and discharging required for V2G services can lead to reduced battery capacity, which discourages users. Privacy and security concerns also limit the adoption of V2G, as users must share detailed data with EV aggregators, exposing themselves to potential risks. Addressing these issues is crucial for the successful integration of electric vehicles into the grid.
## 3 Model-Driven Approach to QoS Estimation Using Neo4j Graph Database Aided by ChatGPT
### Simulations and Evaluation
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Aspect** & **Component** & **Condition** & **Result** \\ \hline \multirow{3}{*}{Classification} & Predictor model & Learning rate: 0.001 & F1—0.95 \\  & Neo4j & 75\% training data & Ac—0.98 \\ \hline \multirow{6}{*}{Processing time} & Import CSV & & 5.5 min \\ \cline{2-4}  & Train & 100,000 records, 6 features & 11.2 s \\ \cline{2-4}  & Prompt construction & & 0.12 s \\ \cline{2-4}  & & Approach & Auto & Manual \\ \cline{2-4}  & ML query generation & Import data & 4.32 s & 154 s \\ \cline{2-4}  & & Graph construction & 4.76 s & 467 s \\ \cline{2-4}  & & Classification & 5.13 s & 643 s \\ \hline \multirow{6}{*}{Acceleration} & Model creation & User-created in Eclipse tool & 296 s & \\ \cline{2-4}  & Query generator & ChatGPT generation & 3.48 s & \\ \cline{1-1} \cline{2-4}  & Query manual & High-skill & 154 + 467 + 643 & \\ \cline{1-1} \cline{2-4}  & Speed-up & Manual/ & 4.07 & \\ \cline{1-1} \cline{2-4}  & Auto & (times) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulations and approach evaluation. According to the achieved results, the proposed classification method for QoS estimation shows promising results regarding both the prediction performance (high accuracy) and execution speed. Considering the other approaches to QoS estimation, and taking into account the network performance metrics, it outperformed the Pytorch-based implementation from [16; 22], resulting in both faster execution speed and higher accuracy compared to deep neural networks, which are more complex but less efficient for classification tasks based on few numeric values. Furthermore, compared to Weka-based classification implementation in the Java programming language using a level crossing rate [34], it outperformed all the methods in accuracy, but it required a longer overall execution time. This can be explained by the overhead of the data import and graph construction in the Neo4j graph database environment. Regarding code generation, the ChatGPT-based Neo4j query generation time is about 1 s. Prompt construction based on a user-defined model is an order of magnitude faster. The longest execution time for query generation was in the case of the GDS classification command. Compared to the traditional approach using model-driven code generation [22], this method is slower, as it relies on ChatGPT's huge LLM and internet service. However,  
Figure 7: Experiment illustration: from Ecore model instance to classification in Neo4j Desktop (version 1.5.8) [33] graph database software environment. this approach brings additional value, as ChatGPT is pre-trained on a huge number of textual documents and can be further specialized using proper prompts and context. Unlike in traditional workflows, it is not required to develop a new code generation algorithm each time the simulation setup, the underlying machine learning technology, or the data format is changed. Thus, the main advantages of adopting ChatGPT in such a manner is the adaptability and extendibility.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 6 Method Comparison and Analysis
### Experimental Results and Method Comparison  
This study uses the traffic flow characteristics of PEMS08 and the speed data of the METR-LA dataset for experiments, with a sampling period of 5 min. Table 2 shows the prediction errors of the ARIMA, MLP, LSTM, GRU, and ASTGN models on different datasets.  
Smaller MAE and RMSE values indicate superior model performance. As shown in the table, the ARIMA model exhibits suboptimal predictive performance due to its limited data mining capabilities. Deep learning models, such as LSTM and the GRU, significantly enhance the predictive accuracy. The hybrid neural network model ASTGCN achieves the highest overall accuracy by incorporating both temporal and spatial correlations.  
Traditional statistical methods struggle to capture complex traffic flow patterns, model both short- and long-term dependencies, and represent spatial relationships. In contrast, deep learning leverages multilayer neural networks (e.g., CNN, LSTM, Transformer) to automatically learn intricate data patterns, capture nonlinear dependencies, and enhance the predictive accuracy. CNNs and GNNs further enable spatial modeling, improving urban traffic flow forecasting.  
Machine learning methods rely on manual feature engineering, increasing the costs and limiting their generalizability. Deep learning autonomously extracts hierarchical features from raw data, enhancing the model stability and adaptability.  
In conclusion, deep learning outperforms traditional methods in terms of predictive accuracy and computational efficiency, enabling high-precision real-time forecasting. For data scenarios where statistical methods like ARIMA are inadequate, deep learning remains an effective approach for short-term traffic flow prediction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**PEMS08**} & \multicolumn{2}{c}{**METR-LA**} \\ \cline{2-5}  & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline ARIMA & 33.04 & 50.41 & 2.04 & 5.89 \\ MLP & 26.73 & 35.81 & 1.84 & 4.62 \\ LSTM & 27.34 & 37.43 & 1.86 & 4.66 \\ GRU & 26.95 & 36.61 & 1.86 & 4.71 \\ ASTGCN & 19.63 & 27.50 & 1.31 & 3.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of model performance on different datasets.
###### Abstract  
With the rapid development of terahertz technology, tunable high-efficiency broadband functional devices have become a research trend. In this research, a dynamically tunable dual broadband terahertz absorber based on the metamaterial structure of vanadium dioxide (VO\({}_{2}\)) is proposed and analyzed. The metamaterial is composed of patterned VO\({}_{2}\) on the top layer, gold on the bottom layer and silicon dioxide (SiO\({}_{2}\)) as the middle dielectric layer. Simulation results show that two bandwidths of 90% absorption reach as wide as 2.32 THz from 1.87 to 4.19 THz and 2.03 THz from 8.70 to 10.73 THz under normal incidence. By changing the conductivity of VO\({}_{2}\), the absorptance dynamically tuned from 2% to 94%. Moreover, it is verified that absorptance is insensitive to the polarization angle. The physical origin of this absorber is revealed through interference theory and matching impedance theory. We further investigate the physical mechanism of dual broadband absorption through electric field analysis. This design has potential applications in imaging, modulation and stealth technology.  
**Keywords:** terahertz; metamaterial; vanadium dioxide; perfect absorber
## 4 Discussion  
### Electrical Change Following Quantitative Straining  
The resistance rise originates from lattice distortion and area reduction at \(\varepsilon\) < 5% and from dislocations and defects at \(\varepsilon\) > 10% [34]. Crucially, within the elastic-to-early-plastic window (\(\varepsilon\leq\) 5%), resistance variation remains minimal, confirming that moderate deformation, even beyond the purely elastic limit, does not compromise electrical stability or fuse current.
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 3 Materials and Methods
### YOLO Object Detection  
The accurate identification and localization of fire incidents in real time are crucial for effective fire detection systems. To achieve this, our unified Unmanned Ground Vehicle (UGV) and Unmanned Aerial Vehicle (UAV) system leverages the You Only Look Once (YOLO) family of object detection algorithms. YOLO algorithms have demonstrated exceptional performance and efficiency in the field of computer vision.