esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Individual Tree Canopy Dataset  
To create the ground truth dataset, a methodology was adopted aiming to reduce the time and effort demanded by manual activity for image processing techniques. This methodology provides a possibility of simplifying the process, and it can be used in future works to create a dataset of identification of tree canopies not only with the punctual location or squared borders of the instances but with masks that outline its limits. From mask, the point information and the surrounding rectangle can be derived from the extraction of the centroid and bounding box, respectively.  
The proposed methodology for the delineation of the canopies (Figure 4) was performed according to the following sequence:  
1. Extraction of the Color Index of Vegetation Extraction (CIVE) [33] whose equation is given by: \[\text{CIVE}=(0.441\text{''red''})-(0.811\text{''green''})+(0.385\text{''blue''})+18.78 745\] (1) where red, green, and blue are the respective bands of the image;
2. Application of the morphological opening with two different window sizes (3 and 5 pixels) and smoothing in the CIVE image;
3. CIVE image threshold operation from a specific threshold for each image;
4. Conversion of the threshold image from raster to vector (polygon) and selection of the area represented by the set of canopies;
5. Individualization of the polygon representing the set of canopies by manual clipping.  
As a result of the above sequence, the manual work (Step 5) was only necessary to determine the separation line (or eventually the intersection) between two canopies in regions where there was some level of contact between them, and so manual identification and delimitation became unnecessary.  
Figure 4: Framework of process adopted for crown delimitation.  
At the point of contact between two very dense trees, visual identification of the line that separates both can be a difficult and a skewed task if performed only from the RGB image. However, when considering the morphological aspect of the canopies, the occurrence of a saddle point formed by neighboring trees is common, regardless of the proximity between them, which facilitates the individual discrimination. Therefore, for the visual identification of the borders between the canopies, in addition to the RGB image, the CHM was used, which provided information on the relief of the treetops, needed especially in those areas where it is not possible to individualize the trees based on color aspects and texture from the RGB image.  
Furthermore, to increase the feasibility of manual canopy delimitation, an algorithm was developed to convert geographic data into a coco-type annotation, the format used in the Mask R-CNN. Thus, all the steps mentioned in this topic were carried out within the GIS environment (QGIS version 3.16) and afterwards, automatically the masks represented by the vector features (polygons) were converted directly into annotations (JSON format) referenced and geo-located in its respective images.
## 5 Simulation Characterization
### Simulation Context
#### 5.1.1 Routing Algorithms  
The Epidemic routing approach [31] is one of the first schemes and is commonly used as a benchmark for most routing protocols in opportunistic mobile social networks (OMSNs). It relies on continuous flooding where all nodes can act as first senders or re-layers of messages. When nodes encounter each other, they exchange message vectors to identify new messages to exchange copies of. This achieves high message-delivery rates when node buffers are unlimited, but performance deteriorates when buffers are limited, as messages are dumped due to insufficient memory. To control the flooding, some variations set conditions before transferring messages, like n-epidemic, which requires a node to have at least n neighbors before relaying.  
Prophet is a prediction-based routing approach that relies on two main metrics: predictability of message delivery and transitivity in forwarding decisions [32]. It uses delivery probability to indicate how likely node A is to deliver a message to a destination node B when encountering each other. Nodes that meet more frequently have higher delivery probabilities. When nodes do not meet for a while, the probability decreases. It also uses transitivity--if A meets B and B meets C, then C is a more forwardable node. When two nodes meet, they exchange message lists and identify new messages. The message is copied and transferred if the new encounter has a better delivery prediction for a destination. This ensures efficient overhead and resource usage by intelligently calculating delivery predictability.  
The Spray and Wait routing [33] approach limits flooding by controlling the number of copies per message, similar to Epidemic routing but with two phases to balance flooding with directed propagation: a spray phase, where a source node spreads L message copies to the first L distinct nodes encountered, and nodes with copies can further spread to other nodes without copies, and a wait phase, where once a node only has one copy left, it switches to direct transmission, only forwarding the copy to the destination when encountered. Its main advantages are simplicity, low latency, and overhead, while disadvantages include a lower delivery ratio than other flooding protocols.
# Co-Design of Mechanical and Vibration Properties of a Star Polygon-Coupled Honeycomb Metamaterial
## 4 Comparison and Analysis of the Bandgap Properties
### Effects of the Configuration Parameters  
#### 4.3.1 Materials of the Inserted Pins  
Drawn in Figure 7 are the bandgap properties of the SPCHM with different materials of pins, including aluminum, copper, and tungsten; the first bandgaps are marked in red and the rest in orange. It can be clearly seen that the addition of pins has an effect on the vibration mode of the proposed metamaterial and, thereby, changes the bandgap distribution. Comparing with Figure 4b, the insertion of metal pins significantly increases the mass at the nodes; since the Young's modulus of metal is much higher than that of the WL8000, the pins could be equivalent to rigid bodies in contrast with the external plastic material. Therefore, the interaction between the two materials changes the modes of points F1 and F2, causing the variation in bandgaps. For the first 30 dispersion curves of the three kinds of pins, the frequencies vary from 0 to 9100 Hz, 6300 Hz, and 4400 Hz, respectively. From aluminum to copper and then to tungsten, the mass of pin increases as density gradually raises and the dispersion curves move downward from the aluminum pins more than 9000 Hz to the tungsten pins less than 4500 Hz. Furthermore, all the frequencies of the four bandgaps decrease with the increase in pin mass.  
With regard to the first bandgaps generated by the SPCHM with different pins, the frequency ranges are from 1640 Hz to 1880 Hz, 1000 Hz to 1300 Hz, and 680 Hz to 980 Hz, respectively. The frequency range of the first bandgap, where the vibration is attenuated dramatically, decreases strongly from the SPCHM with aluminum pins to that of tungsten pins. On the other hand, there are some differences between the widths of the first bandgap of the three structures, and, to be precise, the width values of the three candidates are 240 Hz, 300 Hz, and 300 Hz, accordingly. Although the width of the first bandgap of the SPCHM with copper pins has been slightly broadened compared with that of the SPCHM with aluminum pins, the width of the SPCHM with tungsten pins does not continue to increase. As a consequence, for the same size of pins, the width of the first bandgap rises with the increase in pin mass until the mass reaches a certain threshold, where the width will basically remain unchanged.  
The adoption of pins of different materials can effectively control the frequency and width of the bandgap, which offers the potential to adjust and optimize the bandgaps.  
This approach can also be applied for vibration control and energy absorption, which requires a more precise control of the bandgap properties.
# Two-Phase Deep Learning-Based EDoS Detection System
## 4 System Design
### Internal Modules
#### 4.3.4 LSTM Model-Flow Detector  
The LSTM model classifies each individual flow during the attack time period detected from ANN model as a normal or abnormal flow. For each flow, the LSTM model considers a time series \(X=\{x^{(1)},x^{(2)},x^{(3)},\ldots,x^{(n)}\}\) where each step \(x^{(t)}\) in the time series is an m-dimensional feature vector \(x_{1}^{(t)},x_{2}^{(t)},x_{3}^{(t)},\ldots,x_{m}^{(t)}\), whose elements correspond to each feature in Table 4. For our proposed LSTM model, the value of parameter \(n\) in a considered time series is five. In other words, the sequential length of input data is five, which is the shortest length compared to other LSTM models applying for the same problem. By using the period detector, we can determine when an attack occurs in network traffic. Then, we only need focus on a time series of each flow during that attacked time period. Thus, we can reduce the sequential length of input data for the LSTM model.  
Figure 6 shows the model architecture of the proposed LSTM. Our proposed model includes an LSTM layer, a dropout layer to avoid over-fitting the problem and a fully connected layer and an output layer of a single node. The sigmoid function is used as the activation function for the output layer because the LSTM model is a binary classifier. Time series data \(X=\{x^{(t-4)},x^{(t-3)},x^{(t-2)},x^{(t-1)},x^{(t)}\}\) are fitted into the LSTM layer. The LSTM layer learns both the temporal and spacial representation from the input sequential data and outputs an output vector \(h=\{h^{(t-4)},h^{(t-3)},h^{(t-2)},h^{(t-1)},h^{(t)}\}\) following the equations from (4) to (9). Each element in the output vector play acts as a unit in the input layer of the fully connected layer. The fully connected layer works as an artificial neural network. It outputs a value of \(z\) and this value then goes through the sigmoid function to output a value of zero (normal flow) or one (abnormal flow). The model parameters are presented in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Description** \\ \hline _avg\_length_ & The average length of a flow \\ _pcf_ & Number of correlative flows \\ _odgs_ & One-direction flows generation speeding \\ _nb\_incoming\_pkts_ & Number of incoming packets \\ _nb\_outgoing\_pkts_ & Number of outgoing packets \\ _nb\_bytes\_flow_ & Average number of bytes per flow \\ _flow\_duration_ & Duration of a flow \\ _cpu\_load_ & CPU usage of victim server \\ _memory\_usage_ & Memory usage of victim server \\ _bandwidth_ & Network bandwidth \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key Features for ANN model.
## 4 Discussion
### Limitations and Uncertainties  
Firstly, this study focuses primarily on how plant physiology and phenology affect GPP. However, ecohydrological factors, such as precipitation, temperature, and soil moisture, are also crucial for GPP growth. The situation and determinants vary across different vegetation types. For instance, [PERSON] et al. found that temperature is the primary factor influencing grassland growth in China. As latitude decreases, the correlation between GPP and temperature weakens, and moisture becomes the most significant limiting factor [51]. For cropland, [PERSON] et al. observed a significant positive correlation between GPP and precipitation in over half of the agricultural areas. Soil moisture and temperature were key determinants of GPP sensitivity to precipitation in Chinese agricultural systems. Additionally, changes in management practices and cropping patterns may contribute to increased GPP [52]. For forest areas, [PERSON] et al. identified precipitation, rather than temperature, as the climatic factor most affecting the interannual variation of forest GPP [53]. Additionally, deforestation projects promoted a shift from lower to higher forest GPP and supported a consistent increase in GPP, thereby enhancing its stability [54]. This study focuses on the effects of plant physiology and phenology on GPP changes but does not consider the response of GPP to various ecological factors. Future studies should integrate these ecological factors into the model for comprehensive analysis.  
Secondly, we used remotely sensed and modelled data as a source of data for the GPP in this study. Terrestrial ecosystem models have significant uncertainties in model structure, parameterization, and driving data, and the correction and deterministic assessment of model data become important scientific issues [55; 56]. [PERSON] et al. found significant overestimation or underestimation of GPP in both ISIMIP and MsTMIP models, particularly in low-GPP regions [11]. This discrepancy may stem from uncertainties in model input data as well as the model itself. Factors such as climate information, system noise, and processing bias can affect the accurate simulation of GPP at the regional scale [13; 56]. Variations in climate input parameters can lead to differing GPP outputs for Chinese grasslands between the two model datasets. Additionally, differences in environmental drivers and related data--such as time-varying atmospheric CO\({}_{2}\) concentrations, nitrogen deposition, C3/C4 fractions, major crop distributions, phenology, and soil characteristics--also contribute to GPP over- or underestimation in model simulations [57]. We used remotely sensed and modelled data as a source of data for the GPP in this study; while ensemble average data reduced data uncertainty to some extent, the impact of internal model elements on GPP data was not addressed. Future uncertainty analyses should evaluate the influence of different model components to identify sources of uncertainty in GPP data.
# Power Modeling and Experiment Study of Large Flapping-Wing Flying Robot during Forward Flight
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**V\_Wind (m/s)**} & \multicolumn{3}{c}{**Wing \#1**} & \multicolumn{3}{c}{**Wing \#2**} \\ \cline{2-7}  & **3** & **5** & **8** & **3** & **5** & **8** \\ \hline \(D1\) (\(N\)) & \(-0.1822\) & \(-0.5117\) & \(-1.369\) & \(-0.1316\) & \(-0.3763\) & \(-0.9549\) \\ \(D2\) (\(N\)) & \(-0.0479\) & \(-0.1350\) & \(-0.3457\) & \(-0.0479\) & \(-0.1350\) & \(-0.3457\) \\ \(D3\) (\(N\)) & \(-0.1343\) & \(-0.3767\) & \(-1.0233\) & \(-0.0837\) & \(-0.2413\) & \(-0.6092\) \\ \(C_{D,prw}\) & & 0.045 & & & 0.037 & \\ \(C_{D,par}\) & & & & 0.15 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data for drag coefficient calculation. Figure 8: Using AOA = 15\({}^{\circ}\), f = 3.5 Hz, and V_wing = 5 m/s: (**a**) lift measured by modeling and experiment, and thrust measured by experiment; (**b**) power comparison of the model and experimental results. Note that the experimental data are much smaller than the model results at low flapping frequency, i.e., f = 1.5 Hz. This part of the gap could be the fact that in the model, we default the motor to rotate at constant speed. In reality, because of the soft mechanical characteristics of the brushless DC motor, it cannot output enough torque at low speed because the aerodynamic damping torque is variable during the flapping cycle. Furthermore, the power slightly increases with a greater angle of attack for the same wind speed and flapping frequency. On the one hand, the increase in the angle of attack leads to an increase in the frontal projected area \(Sb\) of the fuselage along the incoming flow direction, thus increasing the parasite power. On the other hand, increasing the angle of attack also causes an increase in the lift coefficient \(C_{L}\), so that increases the induced coefficient \(C_{D,ind}\) with increasing induced power. As shown in Figure 10, the results of different wind speeds and flapping frequencies are shown for the wing #1 and wing #2 prototypes at an angle of attack of 5\({}^{\circ}\). In Figure 10a,b, the model trends well with the experimental results. However, it can still be seen in Figure 10c,d that at low flapping frequencies, the model results are similar to the experimental results in Figure 9, which are analyzed in detail in the conclusion section. When the flapping frequency is greater than 3 Hz, as the flapping frequency increases, as shown in Figure 10d, the flexible deformation of the wing is somewhat different from the  
Figure 9: (**a**) Power comparison of wing #1 with different AOA and frequency in V_wing = 5 m/s; (**b**) power comparison of wing #2 with different AOA and frequency in V_wing = 5 m/s; (**c**) power difference in percentage between the experimental and model result of wing #1; (**d**) power difference in percentage between the experimental and model result of wing #2.  
deformation assumed in the paper, and the flexible deformation is difficult to measure in the experiment. Therefore, the difference percentage increases if the flapping frequency is higher than 3.0 Hz.
# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 3 Materials and Methods: A Pipeline for Dynamic Range Expansion of Video Sequences
### 3.3. Dequantization  
This step focuses on removing \"false contours\" or \"banding artifacts\", commonly observed in smooth transition areas within an LDR frame. To achieve this, we employ our fast iterative false-contour removal method, which we previously introduced in [31]. Ourproposed \"decontouring\" method is specifically tailored to mitigate the emergence of false contours arising from the dynamic range expansion process. It considers the false-contour removal operation as a signal reconstruction problem that can be solved using an iterative projection onto convex sets (POCS)-based minimization algorithm.  
The decontouring method operates jointly with the dynamic range expansion function. That is, both false-contour removal and dynamic range expansion are performed simultaneously in an iterative manner until converging to an acceptable solution, i.e., an inverse tone-mapped LDR frame without visible false contours. An illustrative example of this iterative convergence using cascaded projection operators is shown in Figure 5. In this example, the pixel value in \(x_{i}\) is filtered using a low-pass filter (first operator), and the resulting value is clamped (second operator) to keep it between the quantization boundaries. The low-pass filter is a two-dimensional average filter with a radius equal to \(b\).  
As presented in [31], our decontouring method is characterized by three key parameters: the difference between two consecutive pixels within a smooth region (\(q\)), the radius  
Figure 5: False-contour removal method based on iterative projection onto convex sets (POCS). In this example, the pixel value in \(X_{i}\) is filtered using a low-pass filter (first projection operator) and clamped between the quantization boundaries (second projection operator) iteratively.  
of the average filter (\(b\)), and the number of iterations (\(i\)). The difference between two consecutive pixels in an image to be perceived as a smooth transition depends on multiple factors. For a standard 8-bit LDR image, a typical difference threshold ranges from 1 to 5 units. However, this is merely a general guideline. The optimal threshold can vary based on specific contexts and applications. Within our specific context, we determined that a range between 3 and 11 units effectively filters out the majority of false contours.
# A Biased Proportional-Integral-Derivative-Incorporated Latent Factor Analysis Model
## 3 Preliminaries
### Svd  
#### 3.1.1 Conventional Matrix Decomposition SVD Model  
The SVD in the recommendation system is slightly different from the mathematical singular value decomposition [30]. In linear algebra, SVD decomposes a matrix into three matrices. As shown in (1), \(W\) is the original matrix, \(U\) is the left singular matrix, \(V\) is the right singular matrix, and \(\sum\) is the singular value diagonal matrix.  
\[W=U\sum V. \tag{1}\]  
However, in the recommendation algorithm, SVD decomposes a matrix into two matrices \(P_{u*k}\) and \(Q_{i*k}\), the user latent factor matrix and the item latent factor matrix, respectively. Then it gains a matrix full of data which is a predicted matrix filled with user ratings for items through the product of two matrices like (2) and (3). This SVD method is the basis of the LFA.  
\[R_{u*i}=PQ^{T}. \tag{2}\]  
\[\hat{r}_{u,j}=\sum_{k=1}^{K}p_{u,k}q_{i,k}. \tag{3}\]  
The recommendation system aims to minimize the error between the real score and the predicted score, thus the objective function of the traditional SVD model is (4). To avoid the problem of gradient disappearance of P and Q in the process of gradient descent, the regularization term should be added. \(\lambda\) expresses the regularization parameter.  
\[\min_{Q^{*},P^{*}}\sum_{(u,i)\in R}(r_{ui}-\sum_{k=1}^{K}p_{u,k}q_{i,k})^{2}+ \lambda(\|Q_{i}\|^{2}+\|P_{u}\|^{2}). \tag{4}\]  
The iterative formulas (5) for P and Q are obtained by taking the derivative of the objective function.  
\[\left\{\begin{array}{l}\frac{\partial\mathcal{C}}{\partial P_{uk}}=-2(r_{ui }-\sum_{k=1}^{K}p_{u,k}q_{i,k})Q_{ki}+2\lambda P_{uk},\\ \\ \frac{\partial\mathcal{C}}{\partial Q_{ki}}=-2(r_{ui}-\sum_{k=1}^{K}p_{u,k}q_ {i,k})P_{uk}+2\lambda Q_{ki}.\end{array}\right. \tag{5}\]  
Then the stochastic gradient descent (SGD) method (6) is used to acquire the final solution where \(\alpha\) represents the learning rate [31].  
\[\left\{\begin{array}{l}P_{uk}^{t+1}=P_{uk}^{t+1}+\alpha((r_{ui}-\sum_{k=1}^{ K}p_{u,k}q_{i,k})Q_{ki}-\lambda P_{uk}^{t}),\\ \\ Q_{ki}^{t+1}=Q_{ki}^{t+1}+\alpha((r_{ui}-\sum_{k=1}^{K}p_{u,k}q_{i,k})P_{uk}- \lambda Q_{ki}^{t}).\end{array}\right. \tag{6}\]
## 2 Monte Carlo Dropout Neural Networks
### MCDO as Approximation  
A Bayesian neural network (BNN) extends traditional neural networks by placing a probability distribution over weights, enabling a comprehensive quantification of uncertainty in predictions. Using [PERSON] theorem, the posterior distribution over the weights \(W\), given the dataset \(D=\{(x_{i},y_{i})\}_{i=1}^{N}\) is computed as:  
\[P(W|D)=\left.P(W)P(D|W)\right/P(D). \tag{3}\]  
However, directly computing \(P(W|D)\) is computationally intractable for deep neural networks due to the high dimensionality of \(W\) and the complexity of \(P(D|W)\)[27].  
To approximate this, MCDO applies dropout during both training and inference, randomly deactivating a subset of activations. This effectively samples from a distribution of thinned networks, which can be viewed as drawing from an approximate posterior distribution \(q(W)\). The predictive distribution for a new input \(x^{*}\) is then obtained by marginalizing over this approximate posterior [28]:  
\[p(y^{*}|x^{*},D)\propto\int p(y^{*}|x^{*},W)\ q(W)\ dW. \tag{4}\]  
Since evaluating this integral is computationally expensive, Monte Carlo integration is used to approximate it. By performing \(M\) stochastic forward passes with dropout, MCDO generates a set of predictions \(\{y^{*}_{1},y^{*}_{2},\dots,y^{*}_{M}\}\). The empirical mean and variance of these predictions serve as approximations for the predictive mean and uncertainty, respectively [29]:  
\[\overline{y}^{*}=\frac{1}{M}{\sum_{m=1}^{M}y^{*}_{m}},\ \sigma^{2}_{y^{*}}= \frac{1}{M}{\sum_{m=1}^{M}(y^{*}_{m}-\overline{y}^{*})^{2}}. \tag{5}\]  
The 95% confidence intervals for predictions are then computed as:  
\[\overline{y}^{*}\pm 1.96\sqrt{\sigma^{2}_{y^{*}}}. \tag{6}\]