esa-sceva/satcom-chunk-collection huggingface dataset:
# The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning
## 2 Materials and Methods
### Improved A* Algorithm
#### 2.2.3 Removing Redundant Turning Points  
As previously discussed, the traditional A* algorithm tends to generate paths with multiple turning points in order to achieve the shortest path. However, the excessive  
Figure 1: The vector representation from the current point to the travel point and the target point.  
number of turns during UAV forest inspection not only leads to unstable flight but also consumes more energy.  
Currently, most redundant turning point removal strategies adopt the approach illustrated in Figure 2a, where \(P_{1}\), \(P_{2}\), \(P_{3}\), and \(P_{4}\) represent the initial turning points of the generated path. According to the rule of redundant turning point removal, node \(P_{1}\) should be connected to turning point \(P_{3}\). After collision detection, if the path segment formed passes through an obstacle, turning point \(P_{2}\) is retained as the initial node of the new path segment and connected to node \(P_{4}\). As the path segment formed by \(P_{2}P_{4}\) does not collide with any obstacles, the redundant turning point \(P_{3}\) can be removed. Finally, a new path \(P_{1}\!\rightarrow\!P_{2}\!\rightarrow\!P_{4}\) is obtained. However, the actual optimal path would be a direct connection between nodes \(P_{1}\) and \(P_{4}\).  
To address this local optimum, as shown in Figure 2b, the target node \(P_{g}\) is connected directly to the initial node \(P_{s}\). If the newly formed path intersects with an obstacle, the target node is connected to the initial node's sub-node (not necessarily a turning point in the path), and collision detection is performed until an obstacle-free path segment connecting \(P_{g}\) to \(P_{n}\) (a node in the middle) is obtained. The nodes, including turning points between \(P_{g}\) and \(P_{n}\) are then removed, and the node \(P_{n}\) is used as the starting point of the next path segment to repeat the above operation until it is connected to \(P_{m}\) (a node in the path close to the initial node, but not necessarily a turning point). Then the node \(P_{m}\) is used as the new starting point of the next path segment to repeat the above steps until it is connected to the initial node \(P_{s}\). This brief introduction outlines the process of removing turning points. In actual applications, it may require multiple iterations of steps to obtain an optimized path with redundant turning points removed.  
The above comparison indicates that the rewiring strategy proposed in this paper allows for a broader selection of nodes when rewiring the path, which is not limited to turning points, resulting in a shorter path with fewer turns. Moreover, our pruning strategy for removing turning points is forward-backward, which enables efficient removal with minimal collision detection and effectively avoids local optima.
## 4 Experiment
### Dataset  
We conducted experiments on six widely used datasets for fine-grained visual classification: FGVC Aircraft, CUB-200-2011, Food-101, Oxford 102 Flowers, Stanford Cars, and Stanford Dogs.  
* **FGVC Aircraft (Aircraft)**[16] contains \(10,000\) images, consisting of 6667 training images and 3333 test images. Each image is annotated with four hierarchical airplane model labels: _model, variant, family, and manufacturer_. We focus on classifying _variant_ annotation that includes 100 different subcategories.
* **Stanford Cars (Car)**[15] contains \(16,185\) images of 196 classes of cars. It is split into 8144 training and 8041 test data. Categories are generally defined according to information about the manufacturer, model, and year of release.
* **Oxford 102 Flowers (Flower)**[39] includes 102 categories of flower images commonly seen in the UK. The training set consists of 20 images per class, and the test set has 6149 images. A single image may contain several flowers. Each image is annotated with a subcategory label.  
Figure 2: Framework overview of the proposed consistency loss. The ‘\(*\)’ symbol represents an extra learnable [CLS] token. We first find the intersection region between the global view (\(g\)) and local view (\(l\)) and generate a corresponding feature map by RoI align pooling. Through the consistency loss, the RoI feature vectors of the two views are to be similar feature vectors.  
* **Food-101 (Food)**[40] consists of \(101,000\) images of \(101\) food categories. There are manually annotated \(250\) test images and \(750\) training images for each class.
* **CUB-200-2011 (CUB)**[14] is the most widely used FGVC dataset. It contains \(11,788\) images of \(200\) species of wild birds, which are divided into \(5994\) for training and \(5794\) for testing.
* **Stanford Dogs (Dog)**[41] is a collection of \(120\) different dog categories from around the world. It has \(12,000\) training data and \(8580\) test data.
# Recovering Microscopic Images in Material Science Documents by Image Inpainting
## 4 Conclusions  
In this paper, we presented a practical use of image inpainting methods for recovering microscopic images in material science documents. In particular, we proposed and compared new methods of preprocessing the inputs with statistical and threshold-based masks in regard to the image inpainting methods. The image inpainting methods that we  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **SSIM** & \(\ell_{1}\)**Error** & \(\ell_{2}\)**Error** \\ \hline None & 0.29 & 7.85\% & 3.94\% \\ Shift-Net & 0.80 (0.09) & 4.40\% (1.91) & 1.99\% (1.08) \\ Global and Local & 0.82 (0.09) & 4.24\% (2.03) & 1.85\% (0.79) \\ Contextual Attention & 0.89 (0.09) & 3.97\% (1.72) & 1.79\% (0.79) \\ Gated convolution & 0.94 (0.08) & 3.22\% (1.69) & 1.57\% (0.83) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Averages and standard deviations in parenthesis of performance metrics (SSIM, \(\ell_{1}\) and \(\ell_{2}\) errors) for the image similarity results.  
Figure 6: Comparison of the results of various image inpainting methods from the three examples of microscopic images (from top to bottom. reprinted with permission from Ref. [12]. Copyright (2020) Elsevier, from Ref. [13]. Copyright (2021) Elsevier, and from Ref. [14]. Copyright (2019) Elsevier): (**a**) represents the input image; (**b**) represents the output image of shift-net; (**c**) represents the output image of global and local; (**d**) represents the output image of contextual attention; (**e**) represents the output image of gated convolution; and (**f**) represents the ground truth.  
used were four models that have appeared in the computer vision field, which are shift-net, global and local, contextual attention, and gated convolution. The gated convolution model showed the best SSIM and \(\ell_{1}/\ell_{2}\) errors, which are the measures of the image similarity between the output and the ground truth. However, this work only shows microscopic images, but it must be said that the figures in the documents are diverse. In future works, we will extend our work to develop an advanced image inpainting method that robustly and efficiently recovers all the images comprising other figures, as well as the material images obtained via microscopy in the documents.  
T.K.: methodology, software, validation, analysis, writing; B.C.Y.: writing-review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Nano & Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2021M3A7C2090586).  
Not applicable.  
The data will be shared upon reasonable request.  
The authors declare no conflict of interest.
## 3 Methodology
### Local Blur Transformation (LBT)
#### 3.2.1 Local-Region Selection (LRS)  
LRS is employed to select an interest range from input sample. Algorithm 1 specifically describes the process of selecting a local rectangular region. The ratio of the selected area is initialized between \(A_{1}\) and \(A_{2}\). A reasonable aspect ratio is set up from \(R_{1}\) to \(R_{2}\) to create a selected area that is more square. Specifically, the area ratio, donated as \(A_{t}\), is set up at a range from \(A_{1}\leq A_{t}\leq A_{2}\), and the aspect ratio as \(R_{t}\) satisfies \(R_{1}\leq R_{t}\leq R_{2}\). With \(A_{t}\) and \(R_{t}\), the width \(W_{t}\) and height \(H_{t}\) of the selected region are calculated. Moreover, the top-left corner of the selected region is also determined by random position \(P_{x,y}\) such that the region is fully located inside the image. Thus, the local region is finally represented by \((P_{x,y},W_{t},H_{t})\).  
Figure 2: Overview of the proposed framework. It consists of two major modules: local blur transformation (red part) and transformation adversarial module (green part). Transformation adversarial module is the main idea of the system. In detail, the parameter generator network learns from the selected filter matrix transformation, and the recognizer learns more to judge the augmented image sample. Both are continuously updating themself to lift network performances. Note that parameter generator is employed both in local blur transformations and the adversarial module.  
```
0: image width \(W\); image height \(H\);  area of image \(A\);  ratio of width and height \(R\);  area ratio ranges from \(A_{1}\) to \(A_{2}\);  aspect ratio ranges from \(R_{1}\) to \(R_{2}\).
0: selected rectangle region \((P_{x,y},W_{t},H_{t})\).
1:repeat
2:\(A_{t}\leftarrow\textbf{rand}(A_{1},A_{2})\times A\)
3:\(R_{t}\leftarrow\textbf{rand}(R_{1},R_{2})\)
4:\(H_{t}=\sqrt{A_{t}\times R_{t}}\)
5:\(W_{t}\leftarrow\sqrt{A_{t}\times R_{t}}\)
6:\(P_{x}\leftarrow\textbf{rand}(0,W)\)
7:\(P_{y}\leftarrow\textbf{rand}(0,H)\)
8:until\(P_{x}+W_{t}\leq W\wedge P_{y}+H_{t}\leq H\)
9:return rectangle region \((P_{x,y},W_{t},H_{t})\)
```  
**Algorithm 1** Process of Local-Region Selection
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 4 Experimental Results
### Subjects and Apparatus  
The indoor transmission line test environment, shown in Figure 10a, was built by simulating external transmission lines using a small tower, wire, and tower tilt-monitoring terminal.  
Figure 10: Experimental environment construction: (**a**) Indoor monocular vision camera setting; (**b**) The picture was taken by monitoring terminal.  
Figure 9: Pole and tower pixel coordinate traversal process.  
According to the actual measurement, the camera installation height was 2.82 m, the tower height was 3.40 m, the tower width was 1.4 m, and the distance between the two towers was 16.22 m. During the test, the photo interval was set, and the tower was given a tilt angle after each shot. The pictures taken are shown in Figure 10b. The tree was used as a reference for the calibration of the pictures. The distance between the camera and the tower and the axial eccentricity distance between the camera and the tower components were calculated using the established tower tilt state model and algorithm.  
The detailed steps are as follows:  
1. Obtain the first picture and use it as the reference image to find the vertical distance between the camera and the tower (P1) and the horizontal distance between the camera and the tower components (P2) using a laser rangefinder.
2. Process the captured image, calibrate the pixels, and find the pixel distance. The processed pictures are shown in Figure 11. After traversing, the coordinates obtained for the top of the tower and the base point are (277, 309) and (240, 590), respectively. Thus, the pixel height of the tower can be found according to the formula.
3. Obtain the camera installation and tower heights and find the vertical distance between the camera and the pole and tower (PZ) using the distance combined with the pole and tower imaging model obtained in Step 2.
4. Find the distance between the camera and the horizontal component (PQ) using OI;
5. Compare the desired distance PZ and PQ with P1 and P2 and find the error rate.  
The position information of tower key points and distance results are shown in Figure 12.
# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
## 4 Discussion
studied the period 2000-2007 in Serbia, addressing the necessity of irrigation due to frequent drought occurrences in the region, and they reported an RMSE value of 0.03 t\(\cdot\)ha\({}^{-1}\)[32]. The likely explanation for the higher RMSE value compared to the other two studies could be the variation in fields allocated for sugar beet cultivation each year. This suggests that differences in the datasets may also contribute to these outcomes. For winter rapeseed, the LAI-based RMSE value was determined to be 0.40 t\(\cdot\)ha\({}^{-1}\), while the NDVI-based RMSE value was found to be 0.36 t\(\cdot\)ha\({}^{-1}\) in 2018, representing the year with the highest observed values for these errors. The research conducted by [PERSON] et al. from 2005 to 2008 in Iran aimed to simulate real conditions where water stress could be applied during various growth periods of winter rapeseed. The precision of the model they formulated was ascertained, yielding an RMSE value of 0.92 t\(\cdot\)ha\({}^{-1}\) for crop yield [33]. The influence of severe water stress conditions was overlooked in this study, whereas the inclusion of this parameter in the researchers' investigation might account for the disparities in findings. Particularly, the significant impact of severe water stress throughout the entire growth cycle, facilitated by supplementary irrigation, could substantially affect the accuracy of predictions. While our study has provided valuable insights into crop yield prediction using the AquaCrop model and satellite data, it is essential to acknowledge certain limitations. Firstly, the accuracy of predictions may be influenced by factors such as soil heterogeneity, which was not extensively accounted for in our modeling approach. Additionally, while satellite data offer valuable information, they may not capture certain micro-level variations in crop conditions, leading to potential discrepancies between predicted and observed yields. Furthermore, the calibration and validation of the AquaCrop model rely on historical data, which may not fully account for future changes in climate patterns or agricultural practices. Moreover, the transferability of our findings to other regions or crops should be approached with caution, as agricultural systems vary widely in their environmental and management contexts. Future research could focus on refining the model to incorporate more dynamic factors, such as real-time climate data and pest or disease outbreaks, to enhance prediction accuracy and robustness. However, it is crucial to also consider alternative approaches, such as the utilization of gridded data, as suggested by [PERSON] and [PERSON] (2023) [34], which could offer valuable insights in crop simulation modeling.
# Enhancing Traceability of Italian Almonds through IR Spectroscopy and Chemometric Classifiers
## 2 Materials and Methods
### Simca  
Soft Independent Modeling of Class Analogy (SIMCA) is a class modeling method that allows for the individual modeling of categories by defining the region of space where it is likely to find samples belonging to the same class [17].  
To achieve this goal, a model is constructed for each class of interest, thus determining the possible belonging of an unknown object to one of the considered categories. Using SIMCA, a PCA [18] model is built for each known class. From this, it is possible to estimate, for each \(i\)-th observation, a distance \(d_{i}\), a combination of the sample's distance in the scores space (\(T^{2}\)), and the distance in the residual space (\(Q\)):  
\[d_{i}=\sqrt{\left(T_{i,\ red}^{2}\right)^{2}+\left(Q_{i,\ red}\right)^{2}} \tag{1}\]  
Subsequently, an unknown sample is assigned to a category according to \(d_{i}\)[19]. In particular, it is attributed to the modeled category if \(d<\sqrt{2}\); otherwise, it is rejected.  
This approach, unlike discriminant methods, does not allow for a unique prediction of class-belonging. As a consequence, samples may be confused (attributed to more than one class) or predicted as not belonging to any of the investigated categories.
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 4 Sensors for Water Quality Monitoring
Their introduction and early development dates back to the pioneering work of the group of [PERSON] and [PERSON] [95; 96]. Their sensor units often rely on broadly selective electrochemical (potentiometric or amperometric/voltammetric reading) or optical processes, for which selectivity could be controlled with the appropriate choice of sensor architecture (e.g., miniaturized ISFETs) or coating membrane materials and processes, including the use of biosensors [97]. Raw sensors data are often collected by feature extraction techniques and then processed with data-driven or machine learning components to accomplish the given task [98], their use for water quality monitoring being reported in the literature since the first years of this century [99]. Natural basins have been, of course, a sensitive target for the development of such devices, as shown in [100] along with wastewater treatment plants [101; 102]. Starting from lab-based equipment architecture, they are evolving towards point-of-care measurement devices, potentially addressing continuous monitoring tasks. In view of this, the capability of monitoring bacteria, algae activity, or the health status of ecosystem agents could be targeted. A relevant example is reported in [PERSON] et al. 2018 [103], where authors show the possibility of detecting and quantifying shellfish toxins with potentiometric multisensory platforms. The integration of these concepts into autonomous vehicles and the IoT framework has been recently shown by [104; 105; 106; 107]. Particularly revealing is the case of the Mayflower, an IBM trans-oceanic autonomous navigation vehicle equipped with an e-tongue for complementing on-board measurement instrumentation in a long-term and long-range ocean water quality measurement campaign [108]. In general, in the case of long-term operative in-water deployment, drift effects due to physical, chemical, or biological activity-driven degradation often occurs [100]. Frequent recalibration is hence perceived as mandatory to maintain high accuracy of the online measurement process, as highlighted in [109], which also reviews several relevant remediation methodologies. Frequency of the recalibration interventions may of course vary due to several factors, including environmental and biological conditions of the basin. Finally, unless mounted on ROVs, when a multiparametric probe is submerged at fixed locations for long periods, wipers are adopted to clean the sensors regularly. In conclusion, the ideal multiparameter probe for FPV application should be selected based on the water body's nature, dimensions, depth, FPV features, and sensor distribution (fixed or mobile). Available sensors and their technical specifications (range, accuracy, and resolution), weight, dimensions, maximum operating depth, and cost will guide the selection of the best multiparameter probe for each application.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 3 Element Design
### CMOS-Based Switch Design and Parameter Extraction  
In standard CMOS processes, equivalent modeling of switches is typically accurate below 30 GHz. Foundries generally do not provide models for switch devices in the high-frequency range, especially in the terahertz range. This makes the design and verification of the equivalent parameters for a terahertz switch a necessary procedure.
# Deep Learning for Visual Leading of Ships: AI for Human Factor Accident Prevention
## 3 Results and Discussion
### Network Training Accuracy and Hyperparameters: Specialized CNN  
The Adam algorithm was selected for the training process due to the large size of the image dataset, and because it offers efficient convergence and high accuracy within a reduced training time. As an extension of the Stochastic Gradient Descent (SGD) algorithm, [PERSON] dynamically updates the neural network weights during training by combining momentum and adaptive learning rates.  
In this study, the dataset was divided as follows: 70% for training, 15% for validation, and 15% for testing.  
As is common in neural network training, accuracy and loss were recorded for each epoch. As shown in Figure 11, the training process achieved 100% accuracy, while the validation process reached 86%, which indicates effective generalization and strong predictive performance on new data. These results suggest that overfitting was successfully avoided, as the validation accuracy remained stable across epochs and closely tracked the training accuracy.  
Finally, Figure 12 presents the confusion matrix, showing the performance of the network in classifying the three output classes: OK, NOT OK, and DOUBT. Out of the 177 test images, 111 were correctly classified as OK (correct leading), corresponding to 63% of the dataset. Additionally, 40 images were correctly identified as NOT OK.  
Only 12 and 14 images were misclassified between the OK and NOT OK categories, which demonstrates the reliability of the model's predictions.  
Figure 11: Accuracy trends for training (blue) and validation (red) sets using a specialized CNN trained for night navigation images. Validation accuracy reached 86%.  
It is worth noting that, although 13 images were labeled and included in training as DOUBT, the network did not classify any image into this category during testing. This suggests that the model consistently assigned a decision (either OK or NOT OK), even in cases with potentially ambiguous visual conditions, such as interference from lighthouses or lights on the horizon.
# Video Super-Resolution with Regional Focus for Recurrent Network
## 4 Experiments
### Experimental Details  
Label video frames are first down-sampled using a Gaussian fuzzy kernel of \(\sigma=1.6\) before being sent to the network. Since we mainly verify the results of the \(\times 4\) times video  
Figure 6: Feature aggregation structure.  
frame reconstruction in this paper, we obtain low-resolution video frames via the \(\times 4\) times down-sampling of label frames. During the training process, a video frame is randomly extracted from a low-resolution video sequence, and then a \(64\times 64\) patch is randomly cropped into the network, and the corresponding region is cropped in the labeled video frame for the input network. We use random flips and mirrors to increase the amount of training data, which can improve the generalization ability of the network.  
Because our network is a recurrent structure, we initialize both the previous frame reconstruction result and the hidden state to zero. The network model is trained for a total of 130 epochs, and the learning rate of each 60 epochs decays to 0.1 times of the original. Here, we set the learning rate to \(1\times 10^{-4}\). The Adam optimizer is used in the training process, and we set \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and the weight decay rate is \(5\times 10^{-4}\). The experiment was performed on NIVIDIA GTX 1080 Ti GPU in Python 3.6.4 and Pytorch 1.1. The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) were used as the evaluation indexes. All the evaluation indexes were performed on the brightness channel of the video frame YCbCr space.