esa-sceva/satcom-chunk-collection huggingface dataset:
# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 2 Materials and Methods
### Construction of the MMDL-Net Loss Function  
In TensorFlow, we use the binary cross-entropy loss function, which is easy to compute gradients for and is suitable for multi-label, multi-class tasks without the need for any encoding conversion of labels. In the MMDL-Net model, the output of the fully connected layer is activated through the sigmoid function to obtain the predicted probabilities for each category. Afterward, the binary cross-entropy loss function is used to calculate the discrepancy between the predicted probabilities and the true labels. In multi-label, multi-class tasks, the cross-entropy loss function can effectively be used to compute the loss value of the network and update the network parameters through backpropagation. The formula for the cross-entropy loss function in multi-label, multi-class tasks is as follows:  
\[L=-\sum[y\log x+(1-y)\log(1-x)] \tag{3}\]  
Here, \(y\) represents the true labels and \(x\) represents the predicted values by the network. Both \(y\) and \(x\) are processed through appropriate encoding to fit the requirements of multi-label multi-class tasks. The formula calculates the prediction \(x\) based on the network's output and the true labels. The predicted results \(x\) and the true labels \(y\) are input into the cross-entropy loss function formula to calculate the loss value \(L\). Based on the loss value \(L\), the gradient is computed using the backpropagation algorithm, and the network parameters are updated.  
During the calculation process, the predicted values need to be processed by an activation function, typically in combination with the sigmoid function, to ensure that their values are between 0 and 1 and that the sum of the predicted probabilities for each class is 1. The Adam algorithm is used for optimization, which updates the network parameters based on the first-order moment estimate and the second-order moment estimate of the parameter  
Figure 6: The multi-classification basic structure of the MMDL-Net mod.  
gradients. It combines the properties of momentum and adaptive learning rate, enabling efficient optimization of network parameters and improving network performance.
## 3 Methodology
### Laplace Bottleneck (LB)  
#### 3.2.1 Laplace Operator  
As a typical high-pass filter, the Laplace operator enhances the edge contour of an image by increasing the difference between adjacent pixels. The Laplace operator is the sum of second-order differentials in the \(x\) and \(y\) directions, as shown in (1). When applied to a digital image, we can extend it to the discrete domain and obtain (2), the filter template of which is shown as Figure 1a. The filter template with diagonal terms is expanded as shown in Figure 1b, for which the version used in practical applications is Figure 1c.  
\[\
abla^{2}f(x,y)=\frac{\partial^{2}f}{\partial x^{2}}+\frac{\partial^{2}f}{ \partial y^{2}} \tag{1}\]  
\[\
abla^{2}f(x,y)=f(x+1,y)+f(x-1,y)+f(x,y+1)+f(x,y-1)-4f(x,y) \tag{2}\]  
In practical applications, the Laplace operator is often inverted. For this, we need to superimpose the filtered image onto the original image, as shown in (3), where \(f(x,y)\) and \(g(x,y)\) are the input image and sharpened image, respectively. When using templates such as Figure 1a,b for filtering, the constant \(c=1\). When a template such as Figure 1c is used for filtering, the constant \(c=-1\).  
\[g(x,y)=f(x,y)+c[\
abla^{2}f(x,y)] \tag{3}\]  
Figure 1: Common Laplace filter templates: (**a**) filter for Formula (2), (**b**) diagonal expansion, and (**c**) practical application.
# A 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror
## 5 3D Estimation of Observation Scene
### Corresponding Point Search by Color Histogram  
When the incident ray \(\mathbf{v_{i}}\) from an object is projected onto the image plane of the omnidirectional camera, the projection image of the ray draws as an epipolar curve. The search process is simplified by rotating the omnidirectional image on the image plane so that the epipolar curve overlaps the center of the vertical axis of the image. The process of the search is shown in Figure 7. Due to the shape of the mirror, the mirror image shot by the camera contains curved surface distortion. As a result, even at the same location, the mirror and omnidirectional images have significantly different appearances. In this paper, a histogram is used, which is less affected by the distortion than the feature that does the corresponding point matching. In order to deal with the distortion, the mask used to obtain the histogram is configured according to the position of the reflection point on the image and the position relationship between the reflection point and the object (i.e. the ratio of the optical path length indicated by the red line and the green line in Figure 7). Additionally, the search range is limited to balance the estimation accuracy and computational cost of the system. The spatial resolution decreases due to the distance between the spherical mirror and the camera. Thus, the search range is terminated when the resolution decreases below  
Figure 6: Backward Projection (projection from 2D to 3D): Estimation of the light path from an object to the omnidirectional camera via reflection point on the spherical mirror.  
a predefined threshold. The color histogram uses three planes of the HSI color space. The Bhattacharyya distance is used to measure of the similarity of the color histograms.
# Using Game Engines for Visuo-Haptic Learning Simulations
## 4 HaDIU Scenarios
### Cloth Interaction  
The last scenario was developed to implement deformable object interactions with haptic devices. Developers could use Unity's plane object; however, the backside of the plane is normally not rendered. Therefore, textures will not be seen from its backside. Consequently, a cloth model was created as a flat, rectangular cube made in Autodesk Maya (Autodesk, Inc., CA, USA). This cube was subdivided into 64 segments (Figure 11a).  
The challenge of this scenario was to recreate proper interactions between the cloth and the haptic device. The scenario was created as a sheet mounted on a clothing rack (Figure 11b). Then, a cloth component has to be added to the model. This component enables developers to constrain some parameters of its behavior. This feature was used to attach the cloth model to the upper pole. Finally, for collision detection between the haptic device and the cloth model, a mesh collider was added to the model and the attributes in the cloth component called \"Capsule collider\" and \"Sphere collider\" were used. The latter indicates the colliders that will affect the cloth instead of the colliders on the cloth. This attribute is used to perform collisions handling between the haptic devices and the cloth model.  
Moreover, to enable haptic devices to grab the cloth model, a collision detection algorithm was programmed to detect interactions between the vertexes and the HIP. First, the HIP C# script calculates the nearest vertex to the haptic device object. This is performed by using the K-d-tree technique, which is a space partitioning algorithm. K-d-tree divides the scene into two subsets that do not overlap. Any point in the space can then be identified to lie in exactly one of the regions. The pseudo-code for the K-d-tree technique can be seen Algorithm 2.  
Figure 11: (a) Cloth model created using Maya software and (b) Cloth interaction scenario.  
```
1:if cloth.vertexSize is 1 then
2:return nearest vertex.
3:else
4: Split the scene into two subsets with a line through the median x-coordinate of the vertexes.
5:if HIP.position is on the left side then
6: Check left cloth model's vertex to find the nearest vertex according to HIP's position and radius.
7:return nearest vertex.
8:else
9: Check right cloth model's vertex to find the nearest vertex according to HIP's position and radius.
10:return nearest vertex.
11:endif
12:endif
```  
**Algorithm 2** K-d-tree algorithm.  
After finding the nearest vertex to the HIP, according to the Pythagorean Theorem, one can get the distance between the HIP and the vertexes. In 3D, this theorem can be imagined as a cuboid (Figure 12). First, the triangle ABC is used to obtain the distance between AC. Next, this value is used to solve triangle ACF to find distance AF. Finally, if the distance between the point and the center of the circle is less than the radius of the circle, they are colliding. Once the collision detection is obtained, the force feedback calculation from the shape manipulation scenario can be applied.
# A Fast Hole-Filling Method for Triangular Mesh in Additive Repair
## 7 Conclusions  
In this paper, a fast hole-filling method for triangular mesh based on hole size towards additive manufacturing repair is proposed. The detected holes are first classified into three categories: small-sized, middle-sized, and large-sized ones, and then filled by the centroid-based, projection-based, and improved advancing front methods respectively. In the end, two experiments are carried out to verify the efficiency, robustness, and the ability to shape recovery of our method.  
First, three different algorithms are utilized to fill the small-sized, middle-sized, and large-sized holes respectively. In the case of small-sized holes, a simple and fast algorithm is used to iteratively create new triangles by directly connecting each pair of two continuous hole boundary vertices and the centroid vertex of the hole loop. As for middle-sized holes, to achieve a good balance between the efficiency and the ability to shape recovery of the hole-filling algorithm, the detected holes are first closed by an initial filling patch generated by using the minimum area triangulation algorithm. Then the patch refinement and fairing operations are applied to maintain a Delaunay-like triangulation and make the filling patches assort with surrounding meshes well via a Laplace-Beltrami mesh deformation technique. Regarding large-sized holes, an improved advancing front method is used to close holes fastly without any further refinement and fairing steps while ensuring the patching mesh to be compatible with the surrounding mesh well.  
Second, two experiments specifically including the quantitative evaluation and other example testings are carried out to verify the effectiveness, robustness and shape-recovery ability of our  
Figure 20: Vertex valence statistics of the filling patch for the head model.  
hole-filling algorithm. Besides, the advantages and disadvantages of our approach, as well as the future work, are also given.  
Our approach can also be used in the fields of 3D geometry measurement and geometry modeling, since the complete 3D model of one measured physical object is usually the input for its downstream geometry processing steps, such as mesh alignment, Boolean difference, etc.  
Conceptualization, C.F.; methodology, M.R.; software, G.Q.; validation, W.L.; formal analysis, C.F.; investigation, C.F.; resources, G.Q.; data curation, C.F.; writing--original draft preparation, C.F.; writing--review and editing, C.F. and Q.G.; visualization, S.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China grant number 51675404.  
The authors declare no conflict of interest.
# Improved Matching Algorithm with Anchor Argument for Rotate Target Detection
## 4 Results
### Experiment Results  
We compared the performance of our proposed model with that of Rotation RetinaNet on the DOTA and HRSC2016 datasets, as shown in Tables 2 and 3. Our proposed model showed a significantly better performance than the baseline in the target category of the large aspect ratio, such as LV in DOTA and ships in HRSC2016, at about 6.94 and 9.90%, respectively. In Table 2, there are some categories that did not improve, but we still achieved a 1.46 mean AP increase compared with the baseline. Figure 5 illustrates the RP curves of the large aspect ratio targets of the two datasets. Under the same accuracy, the recall rates of our proposed model are higher than the baseline. Meanwhile, in order to verify the superiority of our model for the oriented target in some angle regions, we compared the recall improvement of our proposed model on the targets that are not sensitive to the anchor angle set by Rotation RetinaNet, such as the target oriented at 5-10, 20-25, 35-40, 50-55, 65-70 and 80-85 degrees, with the baseline model. The anchors set
# Design and Test of Seeding-Picking Mechanism of Fully Automatic Transplanting Machine
## 5 Simulation and Test
### Test Results and Their Analysis  
Comparison between the actual trajectory with the kinematic simulation trajectory of the end movement of the seedling claw during the seedling-picking operation, as depicted in Figure 11, reveals that the actual trajectory exhibits a non-smooth curve with minor fluctuations. These fluctuations are primarily attributed to the overall vibration of the device, in addition to certain errors in the post-processing of the captured points, which result in discrepancies when compared to the simulated trajectories. Despite these discrepancies, the overall consistency between the trajectory of seedling extraction and the simulated trajectory as well as the state of the clip seedlings in Figure 12, supports the validity of the seedling extraction process design and demonstrates the practical feasibility of the seedling extraction device.  
Initially, the seedling pickup speed of the delivery mechanism was set at 60 plants/min, 72 plants/min, and 84 plants/min, corresponding to a complete row pickup cycle time of 6 s/row, 5 s/row, and 4.3 s/row, respectively. Three trays of pepper seedlings were selected to establish the order for the pickup and casting tests. The evaluation metrics included the success rate of seedling pickup, the success rate of seedling casting, the substrate crushing rate, the overall success rate, and the seedling injury rate. The criterion for evaluating damaged seedlings is based on the stem compression characteristics test. By designing the compression displacement to be less than 50% of the stem diameter, it can be ensured that the stems of pepper plug seedlings experience only elastic deformation. This approach minimizes damage to the epidermal cells of the stems and does not adversely affect the subsequent growth of the seedlings [27].  
Figure 10: Trajectory of the seedling-picking jaw. (**a**) Side view trajectory diagram. (**b**) Inclined plane trajectory diagram. (**c**) Overhead trajectory chart.  
Take-and-drop trials were conducted in accordance with the test protocol, with a total of nine sets of trials, recording the trial data, and organizing and analyzing them. The success rates of seedling taking, seedling casting, substrate fragmentation, and overall success were calculated individually. Subsequently, the mean value for each index was determined. The test results are presented in Figures 13 and 14 below.  
Figure 11: Seedling retrieval trajectory curve.  
Figure 12: Seedling pickup status.  
Figure 13: Statistical table of the results of the seedling sampling trials.
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 5 Discussion
### Classification Results
The validation of the S-1 FS results, which were supplemented with the results of the developed SAR time series approach, show that this addition led to an improvement in the classification results of the S-1 FS. Although the results of the fully automatic near-real time S-1 FS have been significantly improved, it should be noted that the developed SAR time series approach requires training data for the initialization, and that several images (time series) are used to derive the TFV. Thereby, preprocessing is more time-consuming compared to the S-1 FS and depends on the number of images used in the time series, the classification basis applied (pixel- or object-based), and the duration for the extraction of training data by an expert. Apart from the above-mentioned steps, the actual classification process only takes a few seconds. A minimum number of data sets in the time series depends on the duration of the flood and the number of S-1 images that have been acquired during the flood. The less non-flooded dates used in the time series, the lower the classification accuracy. If available, it is recommended to include at least one vegetation cycle in order to consider the entire seasonality or phenology. Thus, the statistical distribution of the backscatter values over the entire vegetation cycle is considered (19). Considering the above-mentioned prerequisite for the SAR time series approach, it was possible to achieve a significant improvement in classification accuracy of S-1 FS for PA Flood and UA Dry Land. This was accomplished by supplementing the TOW of S-1 FV with the TFV areas (Figure 13).
## 4 Evaluation
### KPIs on the User Updates to the Risk Assessment  
After the study, the risks documented by the users were analyzed to compare the performance of the users based on which tool they used. The following KPIs were monitored: true positives (e.g., fields that were changed, which were supposed to have been changed, and for which the updated content was correct), false negatives (e.g., omissions, in which fields that should have been updated, were not), and false positives (e.g., fields that should have been changed, but for which the updated content was incorrect). The authors formulated the hypothesis that the RA tool usage would lead to fewer false negative and false positive answers, as well as a higher percentage of true positives. In other words, the authors expect that the RA tool will completely eliminate errors wherein the safety expert forgets to add a change. The results for the different KPIs measuring these impacts can be seen in Figure 13. Looking at the figure, it is possible to see that, on average, the RA had lower performance in cases of true positive entries and false positive entries. However, RA scored better in the category of false negative entries. To check the difference among distributions, a [PERSON]'s t-test was conducted after ensuring preconditions to the test. The test yielded a statistical significance for the group of false positive entries (\(p<0.05\), CI = 95%) and false negative entries (\(p<0.05\), CI = 95%). On one hand, RA had a score of M = 43 (SD = 44) and the legacy tool had a score of M = 3 (SD = 5) for the false positive entries. On the other hand, RA had a score of M = 0 (SD = 0) and the legacy tool had a score of M = 19  
(SD = 17) for the false negative entries. No further statistical significance was found for the true positive entries.
# The Influence of Co-Firing Coal with Biomass Syngas on the Thermodynamic Parameters of a Boiler
## 4 Results and Discussion
### Effect of Co-Firing on Heat Transfer of the Convective Heating Surfaces  
The heat transfer coefficient (\(K\)), temperature difference (\(\Delta t\)), and heat transfer quantity (\(Q_{\text{tr}}\)) of a certain convective heating surface with various biomass syngas consumption rates under BMCR are displayed in Figure 5. \(K\) is affected by many factors, such as the radiative characteristics of the flue gas, and the physical characteristics and velocity of the flue gas and working fluid. Since the convective heat transfer on the working fluid side is scarcely affected, \(K\) is primarily impacted by the heat transfer (including convective and radiant heat transfer) on the flue-gas side. The inflection point occurs when only the coal is burned, the result is similar to that in our previous study [24]. The heat transfer coefficient decreases (for the palm and wood syngases) or increases (for the straw syngas) with ascending biomass syngas consumption rates (see Figure 5a). A possible explanation might be the alterations of the temperature, component, and quantity. The temperature difference is influenced by the inlet and outlet temperatures of the working fluid and flue gas of a certain heating surface. The temperature of the working fluid could be barely impacted. Although \(T_{1}^{\prime\prime}\) declines with rising biomass syngas consumption rates, the flue-gas temperature of the low-temperature superheater rises slightly. Thus, \(\Delta t\) climbs moderately in Figure 5b. \(Q_{\text{tr}}\) is related to \(K\), \(\Delta t\), and geometric parameters according to Equation (18), while the geometric parameters remain unchanged. The variations in the heat transfer quantity are displayed in Figure 5c. It can be seen from Figure 6 that all of these three thermodynamic parameters decrease when the boiler load declines, while the introduction of the wood syngas could not lead to a dramatic variation.  
Figure 5: Some parameters related to heat transfer of a low-temperature superheater under various biomass syngas consumption rates (BMCR). (**a**) Heat transfer coefficient; (**b**) temperature difference; (**c**) heat transfer quantity.
# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
## 4 Experiments
### Baselines  
In our experiments, we compared the predictive capabilities of the proposed AMGC-AT model and classical time-series models (ARIMA and HA), deep-learning-based models (LSTM, TCN and GCN), and existing advanced methods (MTGNN, AGCRN and STGCN).  
ARIMA [2]: It is one of the most common statistical models used to predict time series. It represents a standard ARIMA model that uses historical data-fitting parameters to predict future metro passenger flow.  
HA [1]: Historical average model. We use the average value of the last time step of three patterns to predict the value of the next time step.  
LSTM [3]: LSTM was first introduced in transport in 2015. The long-term temporal characteristics of traffic flow sequences can be modeled by a time-series prediction model with three more gate control units than a typical RNN.  
TCN [25]: A Convolution Neural Network with Dilation Causal Convolution for Sequence Modeling.  
GCN [12]: Graph Convolution Neural Network is a prediction method for mining the spatial correlation of traffic flow. It should be noted that we performed GCN operations on all graphs in a comparative experiment.  
MTGNN [21]: Multi-variable time-series prediction based on a graph neural network. MTGNN has designed a graphical learning module to learn spatial unidirectional correlations between variables. It proposes a convolution of graphs with mix-hop propagation and an extended starting layer for multi-variable time-series prediction.  
Figure 4: The RMSE distribution with different batch size.  
Figure 5: The MAE distribution with different time step.  
STGCN [17]: spatial-temporal graph convolution network. STGCN is the fastest model to train in recent years, At the spatial feature level, the spatial dependencies between nodes are achieved using graph convolution neural networks, and sequences of traffic flows in terms of temporal characteristics are modelled using one-dimensional convolution.  
AGCRN [26]: AGCRN proposes two adaptive learning modules to generate new adjacencies to better represent spatial correlations of nodes, enabling the use of GRU-based graph convolution networks to perform some multi-variable time-series prediction tasks without input of predefined adjacency matrices.  
We compared our AMGC-AT model to the baseline and measured the performance of all methods using three widely used metrics: mean square root error (**RMSE**), mean absolute error (**MAE**), and weighted mean absolute percentage errors (**WMAPE**).  
\[\textbf{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(\hat{Y}_{i}-Y_{i})^{2}} \tag{25}\]  
\[\textbf{MAE}=\frac{1}{N}\sum_{i=1}^{N}\left|\hat{Y}_{i}-Y_{i}\right| \tag{26}\]  
\[\textbf{WMAPE}=\sum_{i=1}^{N}\left(\frac{\left|\hat{Y}_{i}-Y_{i}\right|}{\sum _{n}Y_{i}}\right) \tag{27}\]  
where \(N\) denotes the total number of values that need to be predicted, \(\hat{Y}_{i}\) is the predicted value and \(Y_{i}\) is the actual value.