esa-sceva/satcom-chunk-collection huggingface dataset:
# A Femto-Satellite Localization Method Based on TDOA and AOA Using Two CubeSats
## 1 Introduction
We study the performance of this method in the context of space. In this context, the stations are now CubeSats separated by 30 km and moving in a polar orbit. The position of these CubeSats is changing but is also affected by the accuracy of the on-board GNSS receivers. The source is a femto-satellite deployed from one of the CubeSats at a certain speed and direction (which we will determine in the next sections). We evaluate if different positions and directions of deployment change the performance of the localization method. The simulation of the deployment direction considers that the Attitude Determination and Control System (ADCS) is not perfect. The CubeSats combine range-based (TDOA) and angle-based (AOA) localization methods to estimate the femto-satellite position through the communication link. Each CubeSat is assumed to carry an antenna array, an ADCS, and a GNSS receiver. In our study, the femto-satellite transmits a beacon to the CubeSats, and the CubeSats then measure the beacon TDOA and AOA using antenna arrays. These CubeSats relay this information to the ground station, which combines it to estimate the femto-satellite position. We study the effect on the Cramer-Rao Bound (CRB) and the Root-Mean-Square Error (RMSE) of the femto-satellite location for different deployment directions, CubeSats location, and attitude determination and control precision. To evaluate the location precision, we developed a simulation tool that emulates the orbit of the satellites using the SGP4 model [23]. This simulation tool was developed in python and made available to the community as an open-source tool. It is hosted in the SPEL's GitHub site at the following link: [[https://github.com/spel-uchile/Pyrroletic](https://github.com/spel-uchile/Pyrroletic)]([https://github.com/spel-uchile/Pyrroletic](https://github.com/spel-uchile/Pyrroletic)) (last accessed on 15 December 2021).
## 3 Results
### Results of X-ray Diffraction
#### 3.3.3 Results of Compressive Testing  
Figure 5 demonstrates the stress-strain curves of compression testing for Al and Al/Fe\({}_{2}\)O\({}_{3}\) + Ag hybrid nano-composites with different percentages of Fe\({}_{2}\)O\({}_{3}\) and with a constant weight percentage of Ag. The figure shows that the introduction of Fe\({}_{2}\)O\({}_{3}\) and Ag nanoparticles increases compressive strength until reaching 12 wt.% Fe\({}_{2}\)O\({}_{3}\) + 1 wt.% Ag at about 84 MPa. This is a result of decreases in ductility with increasing amounts of Fe\({}_{2}\)O\({}_{3}\) nanoparticles. The highest value of compressive strength is attributed to the strong interfacial bindings between Fe\({}_{2}\)O\({}_{3}\), Ag nanoparticles and the Al matrix [21].  
Figure 4: The variation of wear rate as a function of normal loads for pure Al and Al/Fe\({}_{2}\)O\({}_{3}\) + Ag hybrid nano-composites.  
Figure 5: The stress–strain diagrams of pure Al and Al/Fe\({}_{2}\)O\({}_{3}\) + Ag hybrid nano-composites.  
The increases in the compressive resistances of nano-composites are associated with the introduction of the hard ceramic materials of Fe\({}_{2}\)O\({}_{3}\). However, the increase in Fe\({}_{2}\)O\({}_{3}\) of more than 12 wt.% can increase brittleness and decrease compressive strength, and it can then inhibit elastic deformation and prevent failure at the rimmed area on the surface.
## 2 Theoretical Background  
### Coverage in WSNs  
In accordance to the subject of interest, three types of coverage in WSNs may be identified: area coverage, point coverage, and barrier coverage. Area (or regional) coverage expresses the ability of the network to monitor an area of interest, meaning that all points within this area are always monitored. Point (or target) coverage refers to the ability of the network to guarantee that a predetermined group of points are always observed. Barrier (or path) coverage refers to the ability to always detect the movement across a barrier of sensor nodes [11]. Actually, area coverage is the type of coverage problem that is mostly investigated in WSNs. It may be referred either as 1-coverage or as k-coverage (where k is an integer number greater than 1), depending on the minimum number of sensor nodes that must concurrently sense the area of interest.  
It is obvious that for every WSN consisting of a specific number of sensor nodes, the maximization of the area coverage is desired. However, there are various factors that affect area coverage. For example, the deployment of sensor nodes can be either random or deterministic. Likewise, the sensitivity of sensor nodes may be either Boolean or probabilistic. Also, the sensing area may be deterministic or probabilistic. Similarly, the communication range of sensor nodes may be invariable or variable. Additionally, sensor nodes may be static or mobile. Moreover, the coverage scheme adopted may be either centralized or distributed [12]. For all these reasons, the area coverage problem is considered to be a non-trivial problem.
# An Optimized Flexible Accelerator for Elliptic Curve Point Multiplication over NIST Binary Fields
## 2 Lopez-Dahab Projective Form of ECC over \(GF(2^{m})\)  
In projective coordinates, a starting point \(P\) over the binary field is shown with a combination of three variables (\(X\), \(Y\), and \(Z\)). The \(X\) and \(Y\) correspond to the affine \(x\) and \(y\) coordinates. The variable \(Z\) ensures a projective point with \(Z\
eq 0\). \[E:Y^{2}+XYZ=X^{3}Z+aX^{2}Z^{2}+bZ^{4} \tag{1}\]  
In order to execute the point multiplication, the PA and PD operations are essential. Let us illustrate these operations with an example: consider an initial and final point on the curve. These initial and final points are represented as \(P\) and \(Q\), respectively. The PA operation denoted as \(R=P+Q\), generates a new point \(R\). Similarly, adding a point with itself, i.e., \(P+P\), represents the PD operation, denoted as \(2P\). Consequently, the point multiplication simply adds \(k\) copies of PA and PD operations. This phenomenon is shown in Equation (2). \[Q=k.P=k.(P+P\cdots+P) \tag{2}\]  
In the above equation, \(P\) is the starting point. The value of the scalar multiplier is represented by \(k\). Similarly, the variable \(Q\) represents the resultant point. Various PM algorithms have been proposed for the implementation of the above equation. A discussion on these existing algorithms can be found in [11]. According to the findings of [11], the Montgomery PM algorithm is considered a better choice. The reason behind the preference for the Montgomery PM algorithm over other existing algorithms is its ability to offer uniform PA and PD instructions. In other words, the Montgomery algorithm inherently offers protection against various side-channel attacks. Consequently, we have also opted Montgomery PM algorithm to achieve a side-channel-protected solution. Algorithm 1 takes a starting point \(P\) along with a sequence of binary numbers. In addition to it, there is another input \(k\) that represents the scalar multiplier. It outputs the coordinates of the endpoint (\(Q\)). The coordinates of the endpoint are \(x\) and \(y\). The first line of the Algorithm handles the conversions operations. The conversion is made from affine coordinates to Lopez-Dahab coordinates. Inside the \(for\) loop, the \(if\)and \(else\) portions of the algorithm determine whether a PA or PD operation should be performed. The instructions labeled from \(Inst_{1}\) to \(Inst_{7}\) are used for PA computations, while \(Inst_{8}\) to \(Inst_{14}\) are dedicated to PD computations. The selection between the \(if\) and \(else\) statements is based on the status of \(k\). Finally, the Algorithm reconverts the coordinate system. The reconversion is made from Lopez-Dahab coordinates to affine coordinates.
# Spatial Grid-Based Position Calculation Method for Satellite-Ground Communication Links
## 3 Experiment and Result  
In order to validate the proposed GCSGP model in this paper, we develop a feasibility verification experiment for selecting the longest service time satellite using grid computing. In addition, we also validate the computational accuracy error and time efficiency of the GCSGP model. The overall experimental architecture is shown in Figure 7.
## 3 Experiments and Results
### Image Preprocessing and Feature Detection  
The proposed system is evaluated by assessing the 2D error made by estimating the 2D feature location using handcrafted and learned features. Thirty-four hyperspectral data cubes, from 17 healthy volunteers, were acquired using the snapshot HS camera. For each subject, the protocol described in Section 2.2 was applied, consisting of the acquisition of two hyperspectral data cubes corresponding to the inhaling and exhaling phases. For each data cube, the dimensionality reduction step was applied and the features were detected by using the described approaches, such as PCA, VGBS and SSBS. Lastly, feature matching was performed to estimate the 2D feature location after breathing. In spinal surgery applications, breathing movements should be compensated. The distances when the optical markers are detected are computed and the errors made when the skin features are employed are calculated and expressed in millimeters (mm).  
SURF and DELF are used as feature detection and extraction algorithms. A pre-trained DELF network for learned feature extraction is deployed. The network is trained with landmark images which are different from HS images. However, these experiments, using features trained on different images, merely serve as a preliminary test of the system. When SURF is selected as feature detection algorithm, a feature threshold equal to 500--two octaves and four scale levels per octave--is employed.
# Recurrent Deep Learning for Beam Pattern Synthesis in Optimized Antenna Arrays
## 3 Numerical Experiments  
### Antenna Pre-Synthesis Process  
In this study, preliminary beam pattern synthesis in the antenna system was achieved using genetic algorithms, which explore multiple combinations of potential solutions to  
Figure 1: Proposed deep neural network architectures for prediction of the amplitude and phase of radiation patterns. (**a**) FCNN composed of four fully connected layers with 128 ReLU neurons and an output layer composed of 16 linear neurons. (**b**) 1D-CNN made up of two 1D-convolutional layers with 1024 and 512 hyperbolic tangent activation functions, two fully connected layers with 128 ReLU neurons, and an output layer of 16 linear neurons. (**c**) LSTM built using two LSTM layers with 1024 and 512 hyperbolic tangent activation functions, two fully connected layers with 128 ReLU neurons, and an output layer consisting of 16 linear neurons.  
identify a quasi-optimal solution for an equally spaced linear antenna array in order to fulfill specific objectives prior to training the DNN. GAs are robust and trustworthy evolutionary algorithms categorized as population-based optimizers, which have been extensively used on electromagnetics problems, including beam pattern synthesis and antenna design.  
A GA approach was utilized to assess the array factor across the azimuthal plane within a window spanning (\(50^{\circ}\leq\theta\leq 130^{\circ}\)). The training data's radiation patterns were obtained by incrementing the phase by \(20^{\circ}\). The number of instances generated per angle was 847, resulting in a total of 7623 radiation patterns. As a case study, a uniform antenna array with a linear geometry consisting of 8 equidistant elements spaced at intervals of \(d=0.5\)\(\lambda\) was employed (Figure 2). The approach used standard genetic operators [29], including a selection method that combines fitness ranking and elitist selection, as well as two-point crossover (\(P_{c}=1.0\)). Additionally, a single mutation operation was applied (\(P_{m}=0.1\)), wherein a locus was randomly selected, and the allele was replaced with a random number uniformly distributed within the feasible region of the search space. The GA algorithm begins by initializing a population comprising 250 individuals, all subjected to 500 iterations, ensuring comprehensive sampling of the solution space. In this work, the GA approach was implemented using MATLAB R2019b.
By using reflected signals from multi-GNSS constellations and multi-frequency bands rather than only GPS L1 signal, the spatial-temporal sampling efficiency of further GNSS-R missions and the geophysical information included in the data products can be further improved. Lessons learned and data collected from the previous missions can directly benefit the development of new GNSS-R applications, the definition and optimization of future GNSS-R spaceborne missions and advanced GNSS-R instruments. In addition to the standard data products (mainly the DDM of reflected signal power), most of the previous missions have collected a large amount of raw IF data, which are the bit streams of raw signal samples recorded after the analog-to-digital converters (ADCs) and prior to any onboard digital processing. The unprocessed nature of these raw IF data provides an unique opportunity to fully explore the potential of GNSS-R technique for new geophysical applications and future spaceborne missions. To facilitate such explorations, a dedicated GNSS-R software receiver was developed by the Institute of Space Sciences (IEEC, CSIC), and corresponding data products have been generated from the raw IF datasets collected by different missions. Although some preliminary results have been already obtained with these datasets by ICE-CSIC/IEEC (e.g., [24; 25; 26; 27]) as well as the other teams (e.g., [28; 29; 30; 31; 32]), more extensive efforts are still needed by engaging a broader community into such explorations. In this context, we have recently made the data products publicly available through ICE-CSIC/IEEC's public open-data server. With the aim of encouraging new users and new studies, the paper soughts to explain in an understandably way the raw IF data processing chain, the data products, together with their potential applications. Section 3 continues with a brief description of the raw IF data sets collected by different spaceborne missions, together with the processing chain of the raw IF data. The data products extracted from the raw IF data, i.e., the complex waveform of the reflected signals, are explained in detail in Section 4, including their availability, the data format and structure, as well as the scheme and methods to process these products. In Section 5, a set of applications of the data products, such as inland water detection and surface altimetry, are demonstrated preliminarily. Lastly, the conclusions and future prospects are summarized in Section 6.
## Materials and Methods
### Data Processing  
#### 2.3.1 Multisource Remote Sensing Data and Their Processing  
Four types of remote sensing data from three satellite platforms and DSM/DOM data from unmanned aerial vehicles (UAV) were used in this study (Table 1). MOD13A2, MOD17A2, MOD10A2 data from the MODIS satellite provide plant information, which includes EVI, GPP and snowfall, and their spatial resolution is 500 m, and the temporal resolution is 8 days (GPP, snow) and 16 days (EVI). There were two scenes of MODIS data in the Sanjiang Plain, and their track numbers are named h26v04 and h27v04. The raw nadir bidirectional reflectance distribution function adjusted reflectance (MODIS NBAR) data from NASA ([[https://ladsweb.modaps.eosdis.nasa.gov/search/](https://ladsweb.modaps.eosdis.nasa.gov/search/)]([https://ladsweb.modaps.eosdis.nasa.gov/search/](https://ladsweb.modaps.eosdis.nasa.gov/search/)) (accessed on 8 January 2021)) were processed by using the MODIS reprojection tool (MRT) software to adjust the coordinate and projection system. We used the WGS-1984 as the coordinate system, and the projection system was Albers equal area, which unified the data format for more convenient data processing. Then, the data were clipped in bulk by using the interactive data language (IDL), the scale factor of the raw data from the hierarchical data format (HDF) files, which should be focused on when extracting the correct data, and filling values were assigned a value of zero. The MODIS data were applied to calculate forest and grass phenological periods in the snow drought and snow-abundant periods. Landsat data ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 8 January 2021)) with 30 m resolution were also used to obtain information about the main vegetation types (forest, grass) in the study area by using the visual interpretation method. The DOM from the UAV was applied to check and assess the land use data interpreted by the Landsat data. The DSM obtained by the UAV was used to calculate vegetation volume data, and then, the relationship was set between the vegetation volume and biomass by matching the UAV and GPP data [41]. This relationship was employed to evaluate and to improve the quality of the GPP data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline No. & **Data Name** & **Sensor** & **SR 1** & **TR 2** & **Date Range** & **Number** & **Role** \\ \hline
1 & MOD13A2-EVI & MODIS & 500 m & 16 d & 10/12/14/15/16/19 & 224 & Phenological period \\
2 & MOD17A2-GPP & MODIS & 500 m & 8 d & May 2003 & 432 & Vegetation growth \\
3 & MOD10A2-Snow & MODIS & 500 m & 8 d & 10/12/14/15/16/19 & 432 & Snowfall \\
4 & Landsat & TM 3/OLI 4  & 30 m & 16 d & 10/12/14/15/16/19 & 72 & Land use \\
5 & DOM/DSM & UAV & 5 cm & 1 d & 2019 & 272 & Land use/vegetation \\ \hline \end{tabular}  
* \({}^{1}\) SR: spatial resolution; 2 TR: temporal resolution. 3 TM: thematic mapper; 4 OLI: operational land imager.  
\end{table}
Table 1: Remote sensing data used in this research.  
Figure 2: The flow chart of key research steps and multisource data.
## 3 Materials and Methods  
### Wetland Classification  
The scientific classification of wetlands is not only one of the core problems in wetland science theory, but also the important marks of wetland science development level [15]. However, there are no unified criteria for wetland classification. Many classification systems have been proposed based on different perspectives and various purposes. For the purpose of wetland mapping and classification extraction based on the Landsat Thematic Mapper (TM) imagery, [PERSON] [16] classified wetlands into inland swamps, wet peat swamps, developed peat swamps, other peat swamps and salt swamps. [PERSON] _et al._[17] used Landsat Enhanced Thematic Mapper Plus (ETM+) and Satellites Pour l'Observation de la Terre or Earth-observing Satellites (SPOT) 5 satellite images to monitor and assess wetlands in Missouri, USA, and they classified wetlands into permanent water bodies, seasonal water bodies, deep swamps, shallow swamps and wet meadows. Chinese researchers have also conducted many studies and proposed some practical wetland classification systems according to China's wetland features, actual conditions and research objectives. In 1995-2001, the Chinese State Forestry Administration organized the first investigation of national wetland resources, and classified wetlands into 5 first level classes and 28 second level classes, including coastal wetlands, rivers, lakes, swamps, reservoirs & ponds [18]. [PERSON] _et al._[19] classified wetlands into lakes (perennial and seasonal), swamps (reed and sedge), salt swamps (saline-alkaline land, alluvial land), paddy fields, reservoirs & ponds, when extracting wetlands in the Zhalong Nature Reserve using ETM+ satellite images.  
In our research, a RS classification system of wetlands was drafted (Table 1). Wetlands in the TRH region are classified into two first categories, which include water bodies and swamps, and then the two first categories can be further classified into four categories, which include streams & rivers, lakes, reservoirs & ponds, and swamps. Detailed information of the classification and the description for each category are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**First level categories** & \multicolumn{2}{c}{**Second level categories**} & \\ \hline
**Name** & **Code** & **Name** & **Descriptions** \\ \hline \multirow{4}{*}{**Water body**} & - & - & Lands covered by natural water bodies or lands with facilities for irrigation and water reservation. \\ \cline{2-4}  & 41 & Stream \& rivers & Lands covered by rivers including canals. \\ \cline{2-4}  & 42 & Lake & Lands covered by lakes. \\ \cline{2-4}  & 43 & Reservoir \& ponds & Man-made facilities for water reservation. \\ \hline
**Swamp** & 64 & Swamp & Lands with a permanent mixture of water and herbaceous or woody vegetation that cover extensive areas. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wetland classification and code based on remote sensing (RS) images.
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 3 Methodology
### The Focaler-GloU  
In remote sensing image object detection tasks, sample imbalance often poses a challenge. For instance, a large number of small target samples may be difficult to detect and  
Figure 4: The architecture of DySample.  
Figure 5: The architecture of adaptive fusion.  
accurately localize. Thus, the bounding box regression loss function is crucial in remote sensing image object detection. RT-DETR employs generalized intersection over union (GIoU) [39] as the bounding box regression loss function, but this approach performs poorly in complex remote sensing scenes. To address this issue, we propose a novel bounding box regression loss function, Focaler-GIoU, which combines Focaler-IoU and GIoU. Focaler-IoU [40] emphasizes difficult samples during training by adjusting their contribution to the loss function based on sample difficulty. By employing Focaler-GIoU, our proposed model focuses on small or dense objects during training, enhancing detection performance in complex environments. The definitions of Focaler-GIoU are  
\[GIoU=IoU-\frac{A^{c}-U}{A^{c}}, \tag{3}\]  
\[L_{GIoU}=1-GIoU, \tag{4}\]  
\[IoU^{focaler}=\left\{\begin{array}{ll}0,&IoU<d\\ \frac{IoU-d}{u-d},&d\ll IoU\ll u\\ 1,&IoU>u\end{array}\right., \tag{5}\]  
\[L_{Focaler-GIoU}=L_{GIoU}+IoU-IoU^{Focaler}, \tag{6}\]  
where \(IoU\) denotes the intersection over union (IoU) value, \(A^{c}\) denotes the area of the smallest enclosing convex object, \(U\) represents the union area of predicted and ground truth bounding box, \(IoU^{focaler}\) represents the reconstructed Focaler-IoU through linear interval mapping, and \([d,u]\in[0,1]\). By adjusting the parameters \(d\) and \(u\), our proposed method can focus on various regression samples, enhancing detection performance in complex remote sensing scenes.