esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks
## 3 Methodology
### Reward  
The reward function used in this paper is as follows: If the channel selection action of each SU is the same, resulting in a successful rendezvous, each agent is awarded a reward of \(R_{p}\). In all other cases, the rendezvous is considered unsuccessful, and each agent is given a reward of \(R_{n}\).  
\[R_{t}=\begin{cases}R_{p},&\text{if }A^{1}_{t}=A^{2}_{t},A^{1}_{t}\in \text{Common channels}\\ R_{n},&\text{otherwise}.\end{cases} \tag{7}\]  
where \(R_{p}\), \(R_{n}\), and \(A^{i}_{t}\) are the positive and negative rewards and the action of the \(i_{th}\) agent for time-slot \(t\), respectively. By using negative rewards, each SU can limit excessive rendezvous attempts.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 3 Results  
### Quality of Photogrammetric Processing  
The aerial image orthorectification and mosaicking process involved a total of 33 flights, with three diachronic flights (November 2018, January 2019, and April 2019) for each of the 11 study sites. A total of 33 orthomosaics and DSMs were produced with different levels of satisfaction. The proportions of correctly aligned images and reprojection errors from the Photoscan software ranged from 59% to 100% and from 0.551 pixels to 0.813 pixels across all study sites (Table 2). The proportion of orthomosaics not retained in this study for reasons of poor quality (blurred images, presence of areas without data, artifacts, etc.) was 24% (i.e., 8 out of the 33 orthomosaics produced). Following this evaluation, Sites 1, 6, 8, 9, and 11 were selected because their orthomosaics and DSMs were of good quality and were available for both November 2018 and April 2019.  
Figure 7: Illustration of the principle of estimating the tree crown area (modified by [35]).
## 4 Comparative Analysis
#### 4.1.1 When Top-k Nearest Neighbor Is Applied  
The Figure 2 shows the comparison between traditional CF algorithms (CF_TMC, CF_TWA, and CF_TZS) and CF algorithms using items' categorical attributes (CF_UIMC, CF_UIWA and CF_UIZS), based on MAE value at various datasets ML\({}_{1}\) and ML\({}_{2}\). In the graph CF algorithms using items' categorical attributes attain low prediction error than traditional CF algorithms for all considerable values of k in top-k neighbors in all traditional prediction approaches. Therefore, CF_UIMC, CF_UIWA, and CF_UIZS provide more accurate recommendation results than CF_TMC, CF_TWA, and CF_TZS respectively.  
The Figure 3 depicts the comparison between traditional CF algorithms and CF algorithms using items' categorical attributes, based on MAE value at various datasets ML\({}_{3}\), ML\({}_{4}\) and ML\({}_{5}\). We can notice that CF algorithms using items' categorical attributes provide less prediction error in all traditional prediction approaches (i.e., MC, WA, and ZS) at all considerable values of k in top-k neighbors. Therefore, CF_UIMC, CF_UIWA and CF_UIZS outperform CF_TMC, CF_TWA and CF_TZS respectively.  
Figure 2: MAE Value at Dataset1.  
Figure 4 shows the comparison between traditional CF algorithms and CF algorithms using items' categorical attributes, based on RMSE value at various datasets ML\({}_{1}\) and ML\({}_{2}\). As shown in the above graph, at all considerable values of k in top-k neighbors, CF algorithms using items' categorical attributes have comparatively low RMSE values than traditional CF algorithms. Therefore, the comparative results of the above graph uphold the fact that CF_UIMC, CF_UIWA, and CF_UIZS are better CF algorithms than CF_TMC, CF_TWA, and CF_TZS respectively.  
Figure 3: MAE Value at Dataset2.  
The Figure 5 represents the comparison between traditional CF algorithms and CF algorithms using items' categorical attributes, based on RMSE value at various datasets ML\({}_{3}\), ML\({}_{4}\) and ML\({}_{5}\). The CF algorithms using items' categorical attributes have a minimal prediction error than traditional CF algorithms for all traditional prediction approaches (i.e., MC, WA, and ZS) at all considerable values of k in top-k neighbors. Therefore, on the basis of the accuracy of recommendation, CF_UIMC, CF_UIWA, and CF_UIZS are preferable algorithms to CF_TMC, CF_TWA, and CF_TZS, respectively.  
Figure 4: RMSE Value at Dataset1.
###### Abstract
The CRITIC method, on the other hand, compensates for this deficiency, which matches the data characteristics of the organic system of transportation [36]. To address the capacity bottleneck of the diverging area and provide sustainable expressway transportation, the authors explore from the perspective of the interchange horizontal layout, carriageway elements, and traffic organization optimization. Specifically, the paper discusses PVTS and the lane restriction (LR) strategy that is commonly used in China for eight-lane expressways, and presents two innovative design strategies that minimize lane changes in diverging areas. Traffic data from LOS-A to LOS-D is collected and processed to develop and calibrate the VISSIM model. Aiming at achieving an objective and scientific understanding of the sustainable performance of different designs, the authors used the CRITIC method to assign weights and score to different indicators and compared it with the traditional entropy method, pointing out the outstanding advantages of the CRITIC method used in the transportation field. This initiative achieves a breakthrough in research methodology in the transportation field. For one thing, the organic combination of micro-simulation and comprehensive evaluation provides an effective approach for engineers to make efficient scheme decisions. For another, it explores the potential of the CRITIC method in the transportation discipline, addresses the deficiencies of the traditional entropy method, and effectively upgrades the research method for related problems. The final results demonstrate the advantages and adaptability of each design under different traffic conditions. The remainder of this paper is structured as follows: Section 2 provides an overview of the research methodology, including design description, data collection and analysis,VISSIM simulation, and the process of CE. Section 3 presents the simulation and CE results of the investigation scenarios and analyzes the results for dynamic traffic conditions. In Section 4, the research findings are discussed in detail. Finally, Section 5 summarizes the conclusions drawn from the study.
# A Fast and Simple Method for Absolute Orientation Estimation Using a Single Vanishing Point
## 3 Experiments and Results
### Synthetic Data
#### 3.1.4 Computational Speed  
To analyze the performance, not only the accuracy is needed, but also the computational speed. Although our proposed method has better results for numerical stability and noise sensitivity, the computational speed still needs to be given for thoroughly testing our proposed method.  
Hence, 50,000 trials are performed independently for our proposed method, P3P solver, and RPnP solver, individually, on a 3.3 GHz 2-core laptop and no noise is added onto the 2D-3D point/line correspondences. Then, the mean computational speed is reported in Table 1. Here, we program and compare the computational time in MATLAB, which is carried out at the same time without opening other processes. At the same time, we do not add the noise of IMU to facilitate the comparison of the computational speed.  
From Table 1, it can be seen that our proposed method is significantly faster than the other two methods, and the computational speed is 2.86 times and 3.07 times as fast as the other two methods, respectively.
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 3 RF Energy Harvesting Application in Medical Devices
### RF Energy Harvesting for Implantable Medical Devices  
The highlight of IMDs is in the continuous monitoring of human body biological signals to enhance healthcare quality. Due to the favorable inherent low power consumption of IMDs, the goal of recent research works is to extend the battery lifetime of the devices in use. Many of these devices are enabled through a battery that needs frequent replacement, and in the case of invasive devices, surgical intervention is necessary which affects the comfort of patients. Supplying the IMDs with stable and permanent renewable energy sources remains an ongoing challenge. Ambient RFEH is considered as an appropriate solution in addressing this bottleneck. RFEH for IMDs can be achieved in the near-field or far-field scavenging. Some works have also reported midfield energy harvesting [85; 165; 166]. Therefore, the subsequent part of this paper discusses the RF energy harvesting for IMDs in the near field, midfield, and far field.
# A Pineapple Target Detection Method in a Field Environment Based on Improved YOLOv7
## 3 Results and Discussion
### The Results of the Detection Performance of the Proposed Network in Different Field Scenarios  
In order to explore the advantages of the improved model in detection performance under different complex environments in the field, YOLOv4-tiny, YOLOv5s, and YOLOv7 were selected for a comparison of their detection performances, and the results are shown in Figure 13. In this test, 30 pictures with a single fruit, multiple fruit, exposure, backlight, occlusion, and overlap were selected from the test set as test pictures. As can be seen from Figure 13, occlusion had the greatest impact on the detection precision of the model. When there were many pineapples in the scene, the detection precision was also reduced. From the overall trend analysis of Figure 13a-d, the curve at the top represents its better detection ability. The detection model proposed in this paper was generally at the top of the four charts, while YOLOv4-tiny was at the bottom, so the detection performance of the model proposed in this paper was better than that of the other three. When there was only one pineapple in the image, there was little interference information and the image features were obvious, so the four models had a high detection accuracy for a single pineapple. When there were many pineapples in the image, because the close-range pineapples occupied a large pixel area in the image and the far-range pineapples occupied a small pixel area in the image, it was difficult to detect the far-range pineapples correctly by the model. When the pineapple was exposed, the light changed the color of the pineapple in the picture, making detection more difficult. On the contrary, when backlighting, the light had little influence on the color of the pineapple, and the camera could better capture the information of the pineapple correctly, so the four models had a better detection performance in the backlighting scenes. The occlusion and overlap were similar. They were part of the pineapple information loss in the image. The detection model needed to rely on incomplete pineapple pixel information to detect a complete pineapple. Compared with YOLOv4-tiny and YOLOv5s, YOLOv7 and our model had better pineapple detection performance for the occluded and overlapping scenes. Among them, our model  
Figure 12: Comparison bar graphs of the \(AP\) of the four models for pineapple with different maturities.  
outperformed YOLOv7 in detecting unripe pineapple and semi-ripe pineapple in the occlusion and overlapping scenes. The detection performance of ripe pineapple was very close. Our model was superior to YOLOv7 for the average detection performance of pineapple with three maturities in a variety of complex field scenes. Therefore, our model is more suitable for actual detection tasks.
# Performance of Numerically Optimized Tuned Mass Damper with Inerter (TMDI)
## 2 Modelling
### Primary Structure with TMDI with Fly Wheel Inerter in Series with Stiffness and ViscousDamper  
For the TMDI with a serial arrangement of spring, inerter and viscous damper, an inerter model based on the fly wheel principle s computed. The fly wheel that rotates accelerated when the relative motion \((x_{3}-x_{4})\) between the housing and the steel rod of the fly wheel was accelerated (Figure 2). The mass of the housing including the gear rod is denoted as \(m_{3}\) and the mass of the fly wheel including the steel rod and the axle is \(m_{4}\) (Table 5).  
The moment of inertia \(I\) of the fly wheel with mass \(m_{wheel}\) and radius \(R\) is equal to the moment of inertia resulting from the envisaged inertance \(b\) and the gear radius \(r\).  
\[I=\frac{m_{wheel}\ R^{2}}{2}=b\ r^{2} \tag{13}\]  
\(r\) can be derived to obtain the envisaged inertance, which yields \(r=0.288\) m for \(m_{wheel}=3388\) kg and \(R_{wheel}=0.7\) m (Table 5). For this TMDI type, the equations of motion, which avoid the zeros in the fourth line of the mass matrix in (12), are as follows:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Damper Type** & \begin{tabular}{c} **Mass Ratio** \\ \(\mu=m_{2}/m_{1}\) \\ \end{tabular} & \begin{tabular}{c} **Inertance** \\ **Ratio** \\ \(\beta=b/m_{1}\)** \\ \end{tabular} & \begin{tabular}{c} **Tuning** \\ \end{tabular} & \begin{tabular}{c} **Stiffness Coefficient** \\ **\(k_{2}\) (kN/m)** \\ \end{tabular} & \
\begin{tabular}{c} **Stiffness Coefficient** \\ **\(k_{b}\) (kN/m)** \\ \end{tabular} &
\begin{tabular}{c} **Viscous Damping** \\ **Coefficient** \\ \(c_{b}\) (kN/m)** \\ \end{tabular} \\ \hline Serial TMDI & 1\% & 1\% & Numerically optimized & 347.47 & 80.103 & 76.288 \\ \hline Serial TMDI & 1\% & 0.02\% & Numerically optimized & 378.17 & 7.6402 & 50.441 \\ \hline Serial TMDI & 1\% & 0.02\% & Numerically optimized & 377.40 & 8.1045 & 50.275 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of TMDIs with a serial arrangement of stiffness, inerter and viscous damper.  
Figure 2: Fly wheel (without friction) as an ideal realization of an inerter.  
\[\left[\begin{array}{cccc}m_{1}&0&0&0\\ 0&m_{2}&0&0\\ 0&0&b+m_{3}&-b\\ 0&0&-b&b+m_{4}\end{array}\right]\left[\begin{array}{c}\ddot{x}_{1}\\ \ddot{x}_{2}\\ \ddot{x}_{3}\\ \ddot{x}_{4}\end{array}\right]+\left[\begin{array}{cccc}c_{1}&0&0&0\\ 0&c_{b}&0&-c_{b}\\ 0&0&0&0\\ 0&-c_{b}&0&c_{b}\end{array}\right]\left[\begin{array}{c}\dot{x}_{1}\\ \dot{x}_{2}\\ \dot{x}_{3}\\ \dot{x}_{4}\end{array}\right]+\left[\begin{array}{cccc}(k_{1}+k_{2}+k_{b}) &-k_{2}-k_{b}&0\\ -k_{2}&k_{2}&0&0\\ -k_{b}&0&k_{b}&0\\ 0&0&0&0\end{array}\right]\left[\begin{array}{c}x_{1}\\ x_{2}\\ x_{3}\\ x_{4}\end{array}\right]=\left[\begin{array}{c}f_{ex}\\ 0\\ 0\end{array}\right] \tag{14}\]  
The TMDI model (14) is computed for the inertance ratio \(\beta=0.02\%\), which turns out to be much more favourable than \(\beta=1\%\), with numerically optimized parameters \(k_{2}\), \(k_{b}\) and \(c_{b}\) (Table 4).
# A Multi-Layer Attention Knowledge Tracking Method with Self-Supervised Noise Tolerance
## 3 MASKT Model
### Introducing of Dynamic Masking Mechanism  
The input sequence completes the mask conversion in the pre-training stage of MASKT to generate a static mask. However, a sequence corresponds to only one mask form, which reduces the data reuse efficiency and wastes a large amount of feature information in the data. The introduction of dynamic masking technology has changed this situation. In each training round, the position and direction of the mask are calculated in real time, so that the same sequence has different mask modes in different rounds, greatly improving the data reuse rate. The experiment compared the training results of static masking and dynamic masking and found that the two have similar performance, but dynamic masking has a more efficient advantage. Therefore, dynamic masking was selected in subsequent experiments. Figure 7 shows that dynamic masking achieves training data augmentation by changing the mask position of the same sequence in different epochs, thereby improving the model training effect.  
The core advantage of dynamic masking over static masking lies in its ability to achieve coordinated optimization of data utility and model generalization through a periodic mask reconstruction mechanism. During the pre-training stage of MASKT, the static masking strategy predefines a fixed mask pattern for each sequence, resulting in a one-to-one correspondence between sequences and mask patterns. This severely restricts the reuse efficiency of data features. In contrast, dynamic masking generates differentiated mask positions and orientations in real time during each training cycle (epoch), as illustrated in Figure 4. This mechanism enables a single sequence to be represented from multiple perspectives, yielding two key benefits:  
1. Equivalent data augmentation: By randomly transforming the mask patterns during training, dynamic masking effectively expands the diversity of training samples, pushing data utilization closer to its theoretical upper bound.
2. Feature learning smoothing: By forcing the model to decouple its dependence on fixed contextual information, dynamic masking promotes the formation of robust feature representations, significantly reduces the risk of overfitting, and lowers RMSE in noise testing scenarios.  
The efficiency gain in training convergence speed stems from the real-time computation paradigm, which eliminates the need to pre-store mask matrices. Meanwhile, improvements in generalization performance are attributed to the anti-memory effect, introduced by periodic perturbations at the epoch level. Overall, dynamic masking serves  
Figure 7: Comparison of dynamic/static masking mechanisms.  
as a preferred strategy for time-series data modeling, offering a more efficient learning framework for knowledge tracing tasks and enhancing both robustness and adaptability.
# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 5 Experiments
### One-Hour-Ahead Forecasting
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **Metric** & **LR** & **FFN** & **LSTM** & **GRU** & **LSTM-4** \\ \hline \multirow{4}{*}{Cerrillos} & R2 & 0.533 & 0.699 & 0.725 & 0.732 & **0.733** \\ \cline{2-6}  & RMSE & 13.35 & 10.73 & 10.25 & 10.12 & **10.10** \\ \cline{2-6}  & MAE & 7.44 & 4.83 & 4.60 & 4.43 & **4.38** \\ \hline \multirow{4}{*}{Cerro Navia} & R2 & 0.678 & 0.707 & 0.828 & 0.836 & **0.844** \\ \cline{2-6}  & RMSE & 14.27 & 13.62 & 10.43 & 10.19 & **9.93** \\ \cline{2-6}  & MAE & 6.80 & 4.96 & 3.82 & **3.82** & 3.83 \\ \hline \multirow{4}{*}{El Bosque} & R2 & 0.523 & 0.646 & 0.684 & 0.706 & **0.707** \\ \cline{2-6}  & RMSE & 15.52 & 13.37 & 12.63 & 12.18 & **12.17** \\ \cline{2-6}  & MAE & 8.57 & 5.96 & 5.49 & **5.43** & 5.58 \\ \hline \multirow{4}{*}{Independen.} & R2 & 0.041 & 0.529 & 0.688 & 0.698 & **0.699** \\ \cline{2-6}  & RMSE & 14.27 & 10.00 & 8.13 & 8.01 & **8.00** \\ \cline{2-6}  & MAE & 7.27 & 4.66 & **4.12** & 4.15 & 4.20 \\ \hline \multirow{4}{*}{La Florida} & R2 & 0.169 & 0.464 & 0.667 & **0.679** & 0.676 \\ \cline{2-6}  & RMSE & 15.41 & 12.38 & 9.75 & **9.57** & 9.63 \\ \cline{2-6}  & MAE & 8.57 & 5.96 & 5.49 & **5.43** & 5.58 \\ \hline \multirow{4}{*}{P.O’Higgins} & R2 & 0.568 & 0.696 & 0.717 & 0.691 & **0.729** \\ \cline{2-6}  & RMSE & 11.69 & 9.81 & 9.46 & 9.88 & **9.26** \\ \cline{2-6}  & MAE & 6.56 & 4.53 & 4.33 & 4.76 & **4.24** \\ \hline \multirow{4}{*}{Pudahuel} & R2 & 0.511 & 0.691 & 0.785 & 0.800 & **0.802** \\ \cline{2-6}  & RMSE & 15.49 & 12.33 & 10.29 & 9.92 & **9.87** \\ \cline{2-6}  & MAE & 9.11 & 4.78 & **3.75** & 4.09 & 3.79 \\ \hline \multirow{4}{*}{Average} & R2 & 0.432 & 0.633 & 0.728 & 0.735 & **0.741** \\ \cline{2-6}  & RMSE & 14.29 & 11.75 & 10.13 & 9.98 & **9.85** \\ \cline{1-1} \cline{2-6}  & MAE & 7.74 & 5.08 & 4.41 & 4.48 & **4.40** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Metrics of space-time predictions considering 7 stations, of 1 h in the future. Bold font indicates best results. Next, we visually analyze the results of the 1 h spatio-temporal prediction of the LSTM-4 neural network considering the Cerro Navia station. Figure 6 shows the forecast for 24 h ahead--9 August 2013. We chose this day because it corresponds to a winter one, which is a season where greater pollution changes occur. In particular, we note that the
## 3 ANN model
### Evaluation of ANN Model
Figure 7: The correlation relationships between the predicted and experimental true stress for the (**a**) training part and (**b**) test part. \[\delta(\%)=\frac{P_{i}-E_{i}}{E_{i}}\times 100\% \tag{7}\]  
where \(P\) is a predicted stress and \(E\) is homologous experimental stress. Figure 9 shows the stress-strain data predicted by the ANN model and their comparison with the initial experimental curves. Obviously, the predicted data can grasp the stress-strain evolution rules precisely, that is, the stress decreases with temperature increasing or strain rate decreasing, predictably indicating that the present ANN model is able to track the work hardening and dynamic softening regions of Inconel 718 superalloy. Moreover, as shown in Figure 9, the predicted points on the 14 training curves almost coincide with the experimental stress-strain curves. Compared with the predicted points on the training curves, the predicted points on the test curves of 1153 K and 0.1 s\({}^{-1}\), 1303 K and 0.1 s\({}^{-1}\), 1153 K and 1 s\({}^{-1}\) and 1253 K and 1 s\({}^{-1}\) have relatively larger deviations. Nevertheless, the maximum relative error is merely 3.82%, a quite acceptable value. By comparing the predicted and experimental true stress corresponding to training points and test points, the learning and generalization capabilities of ANN model were validated respectively. Conclusively, the present ANN model has admirable performance in describing and predicting the flow behaviors of Inconel 718 superalloy. Figure 8: The relative error distributions of the predicted true stress corresponding to (**a**) the training points and (**b**) the test points. Figure 9: _Cont._
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 2 Background
### Indoor Search and Rescue (SAR) Operations for Swarms of Drones
#### 2.1.3 Path Planning in Maze-like Environments  
From an optimization point of view, the search in an ambiguous environment can be modeled as a maze-solving problem where the maze walls denote the presence of unknown obstacles on the way to the rescue target. Path planning through a prior known environment (often considered \"_global path planning_\") is a relatively simple and well documented research topic, with several of its main issues having been addressed [40; 41]. However, maneuvering a maze with only local information on the search space is called \"_local path planning_\", which is performed _on the go_ during the maze-solving process. Searching an unknown or uncertain environment is similar to a local path-planning problem, in which heuristic techniques (cf. Section 2.3) can be employed to optimize the search process [42].