esa-sceva/satcom-chunk-collection huggingface dataset:
# GPU-Accelerated Algorithm for Polygon Reconstruction
## 4 Complexity Analysis  
### Time Complexity  
Given a dataset contains \(n\) line segments, the time complexity of the algorithm can be divided into three parts: (1) data transmission from the host to the device, (2) computation on GPU, and (3) data transmission from the device back to the host.  
The time required for data transmission is proportional to the size of the dataset, so the time complexity for both transfers is \(O(n)\).  
The computation phase consists of several key steps: inserting key-value pairs, constructing polygon vertex sequences, and searching for outer contours.  
For the key-value pair insertion step, since the hash function can be designed according to the characteristics of the integrated circuit layout to minimize hash conflicts, if the number of threads that can perform computing tasks simultaneously is \(m\), then the time complexity of this step is \(O(n/m)\). In practical applications, \(m\) is usually a very large value. When individual threads encounter hash collisions while inserting key-value pairs, they perform linear probing or quadratic probing, which only adds a constant factor to the time complexity of this part. Only in very extreme cases, where each key-value insertion causes collisions on the order of \(n\), will this affect the time complexity of this part. Such extreme cases can be avoided by configuring the load factor and adjusting the parameters of the hash function mentioned earlier. Therefore, we consider the time complexity of this part to be \(O(n/m)\).  
The time complexity of polygon vertex sequence construction depends on the thread responsible for the polygon with the most vertices. The time complexity of this phase is \(O(p)<O(n)\), and p is the number of vertices of the polygon with the most vertices.  
The time complexity of determining the inclusion relationship between inner and outer contours using the ray-casting method is dependent on the number of edges in the outer contour. In the worst case, with a single inner contour and the bounding box of all outer contours containing the inner contour's bounding box, the time complexity is proportional to the sum of the vertices of all outer polygons, resulting in \(O(n)\). In summary, the total time complexity of the algorithm can be expressed as follows:  
\[O(n)+O(n/m)+O(p)+O(n)=O(n)\]
## 3 Proposed Approach
### Generating GPI from Cubemap Image  
This section describes how to generate the proposed ground plane image (GPI) from the cubemap image. As shown in Figure 8, the front and the bottom faces in the cubemap model are used to generate GPI, which is an ortho-rectified image from a virtual top-view camera. To generate a GPI in the front face, as shown in Figure 8, the front-face image is warped to  
Figure 6: Illustration of enhanced unified camera model based cubemap projection model.  
Figure 7: Illustration of relation between fisheye image and cubemap image projection.  
the ground plane using a homography matrix. We project a point \(\mathbf{q}\) on the front-face image to a homogeneous point \(\mathbf{Q}\) on the ground plane image using Equation (22).  
\[\mathbf{Q} = \begin{bmatrix}X\\ Y\\ 1\end{bmatrix}, \tag{19}\] \[\mathbf{q} = \begin{bmatrix}u\\ v\\ 1\end{bmatrix},\] (20) \[H = \begin{bmatrix}h_{11}&h_{12}&h_{13}\\ h_{21}&h_{22}&h_{23}\\ h_{31}&h_{32}&1\end{bmatrix},\] (21) \[\mathbf{Q} \cong H\mathbf{q}. \tag{22}\]  
In Equation (22), the symbol \(\cong\) means that the left and the right terms are homogeneously the same. The homography matrix \(H\) is calculated from the relationship between the four corner positions of a chessboard. The chessboard is placed on the ground, as shown in Figure 8, and the cubemap of the chessboard is generated. The four corners of the chessboard are selected from the cubemap image.  
The bottom-face image is just cropped because the bottom-face is already obtained using a virtual top-view camera, as shown in Figure 5a. Figure 9 shows an example of GPI generation from a fisheye image. A trapezoid ground area in the front face of the cubemap image is warped to a rectangular area in a GPI, and a cropped bottom-face area is concatenated at the bottom of the GPI. In an ideal case, the generated ground plane image shows the ortho-rectified image from a virtual top-view camera.  
Figure 8: Illustration of the relationship between the ground plane and front-face and bottom-face images, respectively.  
Figure 9: Generation of a ground plane image from a cubemap image.
## 2 Materials and Methods
### Methodology for Optimizing Ground Data Points  
Data was collected from four villages to conduct crop-cutting experiments. Ground data points have been optimized based on criteria, including soil type, rainfall map, and elevation map [42]. Four different types of soil have been recognized (clay, clay skeletal, loamy, and loamy skeletal) in the selected study area. Clay and clay skeletal are the major soils occupying more than 80% of the area, whereas loamy and loam skeletal are minor soils occupying less than 20%. Rainfall details for the last 20 years were collected from the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). The average rainfall in this area was 890 mm under semi-arid conditions, where it receives most precipitation from southwest monsoons. A significant proportion of the district receives less than 1003-1028 mm of precipitation, while a minority receives > 1061 mm. Based on the digital elevation model (DEM), elevation points for the selected study areas were identified. Most of the study area had an elevation > 292 m. Land use land cover (LULC) classification was incorporated as a criterion as rice has been described as the major crop grown in both seasons [43].  
Based on these criteria, data from the wet season of four villages (Rukmapur, Vedurugattu, Elbaka, and Gangipalle) from different mandals covering entire rice-growing regions of the district (Figure 1) were evaluated. Importantly, these villages had different rainfall levels, soil types, and elevation. A combination of the maps were generated to identify the homogeneity in the villages. Detailed methodology adopted for this study is presented in Figure 2.
# Towards Flexible Distribution Systems: Future Adaptive Management Schemes
## 2 Evolution of Flexible Distribution System
### New Operation and Planning Methods for Resilient Digital Distribution Systems
Resiliency and reliability of the measurements used for protection, control, and management functionalities could be improved, for example, by combined functionalities  
* Only communication-based (disconnect/connect) control commands (e.g., for DER control or islanding detection) could be verified before execution by local frequency, voltage and/or other measurements depending on the control command purpose
* For example, participation of DER on system-wide (TSO) frequency control and/or local (DSO) voltage control/congestion management or islanding detection
* Needs information about the control command purpose, i.e., is it based on which parameter(s) can also be verified locally
* Measurements for only local measurements-based functionalities could also be verified before execution by decentralized monitoring schemes in which the feasibility and correctness of the local measurement is checked with one or more nearby measurements
* This would also increase the reliability in false measurements due to damages in measurement devices, etc.
# Effective Scheme for Inductive Wireless Power Coil Design Using Scan-and-Zoom Optimization
## 3 Proposed Coil Design Method
### Effective Coil Modeling Considering the Multiple-Turn Effect  
It is well known that a multiple-turn coil has different characteristics from a single-turn coil due to the proximity effect [15]. The difference makes calculation errors, especially for the winding resistance and thus efficiency calculation in the IPT system. To consider the multi-turn effect, there are two different approaches. One is to draw the exact shape of the conductor in the FEA tool, but this increases the number of meshes and thus increases the computation burden. If the number of turns is changing, it requires another model of construction of the coil. Therefore, this kind of approach is not suitable for the coil shape parameter design purpose. The more effective way is to consider the Litz wire effect in the single-conductor model. In this paper, the accuracy of the AC resistance value calculation in the FEA program is greatly increased by adopting the latter way.
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 3 Signal Design Scheme
### Modulation Type
#### 3.2.2 Signal Components for High Data Rate  
The modulation type of the high data rate component is the same as the high-precision measurement component. The high-precision measurement component can modulate low data rate messages, while the high data rate component can modulate high data rate messages.  
For message mapping, the traditional direct sequence spread spectrum method--BPSK can be used. However, using BPSK to improve the data rate would reduce the spread spectrum ratio and increase the multiple access interference. For high data rate components, it is advisable to maintain the same code period length and multiple access performance. For this reason, MPSK and CSK schemes can be considered.  
Considering the data rate of 2 kbps/4 ksps, the high data rate signal scheme is shown in Table 2. The demodulation thresholds of BPSK, 16-PSK, and CSK(4,1) signals are presented without considering channel coding schemes. The demodulation performance of MPSK signals is much worse than that of BPSK and CSK signals. The CSK demodulation threshold is better than BPSK by 2.3 dB. Moreover, CSK signals also support non-coherent demodulation, even if the carrier phase is not yet locked, which is suitable for LEO satellite scenarios. High data rate components can be modulated using CSK. At present, CSK signal is also one of the candidate systems for GNSS high data rate, which is adopted in the QZSS L6 signal.
## 5 Biological Activities of Rice Phytolexins
### Bioactivities of Diterpeneoid Phytolexins
#### 5.1.1. Momilactones  
As previously mentioned, momilactones are secondary metabolites belonging to the (9\(\beta\)-H)-pimarane diterpene family, found not only in cultivated and wild rice [27; 28; 44; 127] but also in other Poaceae such as _Echinochla crus-galli_ (barnyard grass) [9], as well as in the mosses _Calolynum plumiforme_[48; 62], and _Plagionnium acutum_[47]. Several biological activities have been attributed to momilactones (Table 4), some of which are directly related to their biological role in plant-pathogen and plant-plant interactions. Momilactones also exhibit pharmacological activities, which make them potential candidates for the development of novel drugs, cosmetics, and additives for health-promoting foods. * Antimicrobial and allebotathic activity: _Magnaporthe grisea_, the causal agent of rice blast disease, is a major devastating pathogen resulting in a loss of 40% of global yield [191]. This ascomycete can infect more than 130 Poaceae species, including barley, wheat, and millet [192; 193]. The anti-blast activity of momilactones A and B was first reported in 1977 by [PERSON] et al. [56]. Following this discovery, several other metabolites isolated from resistant rice strains were tested against this fungal pathogen. Among them, momilactone B exhibited the highest power against both spore germination and germ tube growth of _M. grisea_[28]. The superior anti-fungal activity of this compound was then confirmed by tests carried out on different fungal pathogens, including _Botrytis cinerea, Fusarium solanti_, and _Collectrotriculum glucosporoides_[19]. In addition, momilactone B exhibited significantly higher antibacterial activity than momilactone A against different bacteria such as _Pseudomonas ovals_, _Bacillus cereus_, and _B. pumilus_[19]. The allebotathic properties of momilactones A and B were soon recognized [27]. It has been observed that, when co-cultivated with rice, the growth of other plant species like baryard grass [164; 166; 169] and _Alisma plantago-aquatica_ (the common water plantain) [167], two of the most disruptive rice weeds, was inhibited. Similar results were obtained using model plants such as _Medicago sativa_ (alfalfa) and _Lactuca sativa_ (lettuce) [55; 157; 158]. Momilactone B inhibited the growth of cress (_Lpidium sativum_ L) and lettuce (_Lactua sativa_ L) seedlings at concentrations above 3 and 30 \(\upmu\)M, respectively [58]. Momilactones A and B exhibited strong herbicidal activity against duckwood (_Lenna pauccostata_ Hegelm 381) [160] and quantitatively inhibited the germination and growth of three weed species (_Amananthus retroflexus_ L., _Cyperus difformis_ L., and _Leptoclhoa chinensis_ L.) at concentrations ranging from 4 to 20 ppm [160]. Further studies confirmed that momilactones A and B accumulate in the roots of rice seedlings and can be released into the environment as root exudates [60; 159; 194; 195]. Expression analyses of diterpene cyclase genes involved in the biosynthesis of momilactones
# Method for Calculating of Lightning Impact Level on Overhead Transmission Line
## 3 Results
### Detailed Method Description  
Let the transmission line model consist of 15 towers, the distance between which does not exceed 300 m. The coordinates of the towers are given in Table 1.  
Randomly place several lightning strikes in the power line area. Let, for illustration, have two flashes with coordinates \(x_{1}=5.887\), \(y_{1}=5.658\) and \(x_{2}=4.258\), \(y_{2}=4.717\). Let us assume that one flash near a power line tower and a second flash near a buffer boundary, the initial size of which is 0.8 km (Figure 1, left column). Using Equation (1), we obtain the distance matrix \(D\):  
\[D=\left(\begin{array}{cccccc}2.546&2.352&2.077&1.854&\ldots&1.150\\ 0.691&0.478&0.197&0.045&\ldots&2.288\end{array}\right) \tag{5}\]  
Then, for \(b=0.8\) km, we carry out the calculation by Equation (2) and get:  
\[E=b-D=\left(\begin{array}{cccccc}-1.746&-1.552&-1.277&-1.054&\ldots&-0.350\\ 0.109&0.322&0.603&0.755&\ldots&-1.488\end{array}\right) \tag{6}\]  
According to Equation (3), zeroize the negative values of the matrix \(E\):  
\[E[E<0]=0\overset{yields}{\rightarrow}E=\left(\begin{array}{cccccc}0&0&0&0& \ldots&0\\ 0.109&0.322&0.603&0.755&\ldots&0\end{array}\right) \tag{7}\]  
At the final step, by Equation (4), we obtain the desired influence vector \(e\):  
\[e=\overset{15}{\underset{i=1}{\sum}}\left(\frac{E(i)}{0.8}\right)= \tag{8}\] \[=(0.137,\,0.402,\,0.754,\,0.944,\,0.572,\,0.315,\,0.067,\,0,\,0, \,0.014,\,0,\,0,\,0,\,0,\,0).\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** \\ \hline \(x\), km & 3.8 & 3.9 & 4.1 & 4.3 & 4.6 & 4.8 & 5.0 & 5.1 & 5.1 & 5.1 & 5.1 & 5.2 & 5.3 & 5.3 & 5.4 \\ \hline \(y\), km & 4.2 & 4.4 & 4.6 & 4.7 & 4.7 & 4.8 & 4.8 & 5.0 & 5.3 & 5.6 & 5.9 & 6.2 & 6.4 & 6.6 & 6.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The coordinates of the towers.  
As the buffer increases to 1.2 km and then to 1.6 km, each flash will be counted by more towers but with different intensities due to different distances from the towers. The graphical representation of the vector e for the 0.8 km, 1.2 km and 1.6 km buffers is shown in the bottom row of Figure 1.
# MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction
## 2 Materials and Methods
### Materials  
Three publicly available, mainstream datasets of retinal images are used to evaluate the properties of the proposed method. The details of these datasets are provided as follows.  
**DRIVE dataset**[8] includes 40 hand-labeled retinal vessel images with \(565\times 584\) pixel resolution. The images are equally divided into training and testing datasets. In our training and testing, we follow the same image division. Figure 1a shows two of the images in the DRIVE dataset.  
**STARE dataset**[32] consists of 20 images, which are digitalized to \(700\times 605\) pixels. Figure 1b shows images in the STARE dataset. The training and test images are not clearly defined in the STARE dataset; therefore, k-fold (k = 4) cross-validation is used in performance evaluation. Model training is performed four times. Fifteen images are used for training each time, and the remaining five images are used for testing. The average value of the metrics for the four test results are calculated.  
**CHASE dataset**[15] comprises 28 retinal images from 14 different children in the Child Hear and Health Study in England. Each image has a 999 \(\times\) 960 pixel resolution. Similarly, we also conduct k-fold cross-training and testing on this dataset. As shown in Figure 1c, the images in the CHASE dataset have worse contrast.  
Table 1 summarizes the information of the DRVIE, STARE, and CHASE datasets. In addition, we use the skeletonization method mentioned in the literature [28] to skeletonize the blood vessel annotations of the three datasets as the ground truth of the centerline.  
Figure 1: Examples of some images in the three datasets; (**a**) is the DRIVE dataset, (**b**) is the STARE dataset, and (**c**) is the CHASE dataset.
## 3 Results and Discussion
### Adsorption Performances of DE and DE-LDH
#### 3.2.9 Effect of Salt  
Industrial wastewater generally contains water soluble inorganic salts at higher concentrations. Thus, the effect of salts and the study of ionic strength are important parameters in the dye adsorption process. Figure 11 shows the effect of different concentrations of NaCl, Na\({}_{2}\)SO\({}_{4}\) and Na\({}_{2}\)CO\({}_{3}\) salts on the removal efficiency of CR by DE-LDH. From the results, it is evident that the presence of salts in the dye solution slightly decreases the CR adsorption onto DE-LDH. This initial decrease in the efficiency justifies anion deposition on the surface of DE-LDH from 0.1 to 0.3 M concentrations. However, by increasing the salt concentration from 0.5 to 0.9 M, the dye removal efficiency was slightly increased. This observation is attributed to the salt effect on the CR. Thus, the presence of salt at higher concentrations enhances the solubility of CR, which increases the adsorption.  
Figure 9: Schematic representation of CR adsorption mechanism onto DE-LDH.
# Energy-Efficient Wireless Hopping Sensor Relocation Based on Prediction of Terrain Conditions
## 3 The Proposed Relocation Protocol for Rugged Terrains
### Descriptions of the Proposed Protocol
Since the proposed relocation protocol reduces the number of REQ messages compared with the previous one, it could be verified that our protocol is energy efficient for wireless hopping sensor networks.
## 3 Results  
### Data Preprocessing  
The PubMed search yielded 6829 articles. They were manually curated, and information about 5199 human cancer antigens was obtained. Among them, only those with experimental data for immunogenicity were collected, resulting in 548 antigenic and immunogenic proteins. In addition to them, following the previously described methodology, 548 antigenic but non-immunogenic proteins were compiled. Thus, the final dataset consisted of 546 immunogenic and 548 non-immunogenic proteins. The dataset was partitioned into balanced training (436 immunogenic and 439 non-immunogenic proteins) and test (110 immunogenic and 109 non-immunogenic proteins) sets. Each protein was encoded as a numerical sequence using 5\(n\) E-descriptors, where n represents the amino acid sequence length. We applied auto- and cross-covariance (ACC) transformation with a lag value of \(L\) = 5 to handle variable-length sequences, generating fixed-dimensional feature vectors. This processing converted the training set into an 875 \(\times\) 125 matrix (5 descriptors \(\times\) 5\({}^{2}\) auto-correlation terms) and the test set into a 219 \(\times\) 125 matrix. Figure 1 illustrates the complete data preprocessing pipeline.
# Segmented Hybrid Impedance Control for Hyper-Redundant Space Manipulators
## 5 Object Manipulation Control
### Object Collision Avoidance  
In the operation of hyper-redundant manipulators, avoiding collisions with surrounding obstacles is critical to ensure safe and efficient execution of tasks. The obstacle avoidance torque is designed to generate forces that push the manipulator away from detected obstacles, preventing contact while preserving the planned trajectory as much as possible. The collision detection and avoidance system continuously monitors the distances between the manipulator's joints and nearby obstacles. When the manipulator approaches an obstacle, a repulsive force is generated to guide it away, with the torque being applied in the direction of the closest joint to the obstacle. The avoidance strategy consists of several steps, as detailed below.  
For each joint, the Euclidean distance to the nearest obstacle is calculated. To enhance the collision detection resolution and monitor the whole length of the object, the positions of each joint are projected along the Z-axis in both directions (above and below the joint). This creates two reference points to account for the obstacle height. The repulsive force is designed using an exponential relationship to ensure that the closer the manipulator gets to an obstacle, the stronger the force pushing it away becomes. The potential force magnitude due to the obstacle and the force vector are defined as follows:  
\[\mathrm{h}_{\mathrm{avoidance}}=\mathrm{k}_{\mathrm{pl}}\left(\mathrm{e}^{ \mathrm{k}_{\mathrm{p}2}(\mathrm{d}_{\mathrm{safety}}+\mathrm{d}_{0}-\mathrm{ d}_{\mathrm{min}})}-1\right), \tag{56}\]  
\[\mathrm{f}_{\mathrm{avoidance}}=\frac{\mathrm{h}_{\mathrm{avoidance}}}{ \mathrm{d}_{\mathrm{min}}}\left(\mathrm{p}_{\mathrm{closest}}-\mathrm{p}_{ \mathrm{desired}}\right), \tag{57}\]  
\(\mathrm{d}_{\mathrm{min}}\) is the minimum distance between the projected points and the obstacle, \(\mathrm{d}_{\mathrm{safety}}+\mathrm{d}_{0}\) defines the distance at which the repulsive force becomes active, \(\mathrm{p}_{\mathrm{closest}}\) is the closest joint to the obstacle position and \(\mathrm{p}_{\mathrm{desired}}\) is the desired position on the trajectory. The torque exerted by the force is given by:  
\[\tau_{\mathrm{avoidance}}=\mathrm{M}(\mathrm{q})\mathrm{J}^{\mathrm{T}}( \mathrm{q})\mathrm{f}_{\mathrm{avoidance}}, \tag{58}\]  
Since the manipulator wraps around the obstacle, any movement of the manipulator joints to avoid contact with external objects automatically guides the obstacle along the intended path.