esa-sceva/satcom-chunk-collection huggingface dataset:
# Line of Sight and Image Motion Compensation for Step and Stare Imaging System
## 3 LOS and Motion Compensation Kinematics and Algorithm
### Analysis of Coordinate Transformation  
The transformation among coordinate frames are shown in Figure 4.  
Figure 3: Imaging optical path of the system.  
From the geographical coordinate frame to the aircraft body coordinate frame, the angle of yaw is represented with \(\Psi\), pitch is represented with \(\theta\), and roll is represented with \(\varphi\). The transform of \(C_{N}^{b}\) is:  
\[C_{N}^{b}=\left(\begin{array}{ccc}1&0&0\\ 0&\cos\varphi&-\sin\varphi\\ 0&\sin\varphi&\cos\varphi\end{array}\right)\left(\begin{array}{ccc}\cos \theta&0&-\sin\theta\\ 0&1&0\\ \sin\theta&0&\cos\theta\end{array}\right)\left(\begin{array}{ccc}\cos \psi&\sin\psi&0\\ -\sin\psi&\cos\psi&0\\ 0&0&1\end{array}\right) \tag{1}\]  
From the aircraft body coordinate frame to the gimbal coordinate frame, the angle of the outer gimbal is represented with \(\alpha\), and the angle of the inner gimbal is represented with \(\beta\). Then \(C_{b}^{G}\) is:  
\[C_{b}^{G}=\left(\begin{array}{ccc}1&0&0\\ 0&\cos\beta&-\sin\beta\\ 0&\sin\beta&\cos\beta\end{array}\right)\left(\begin{array}{ccc}\cos\alpha&0& -\sin\alpha\\ 0&1&0\\ \sin\alpha&0&\cos\alpha\end{array}\right) \tag{2}\]  
According to Snell's Law, the transformation of the incident vector Pi to the reflected vector Po by the mirror can be represented by the Equation (3) [17; 18; 19]:  
\[P_{o}=T\times P_{i} \tag{3}\]  
The matrix T is a transform in terms of the mirror normal components, Nx, Ny, and Nz written as:  
\[T=\left(\begin{array}{ccc}1-2N_{x}^{2}&-2N_{x}N_{y}&-2N_{x}N_{z}\\ -2N_{x}N_{y}&1-2N_{y}^{2}&-2N_{y}N_{z}\\ -2N_{x}N_{z}&-2N_{y}N_{z}&1-2N_{z}^{2}\end{array}\right) \tag{4}\]  
Figure 4: Transformation relationship (**a**) from the geographical coordinate frame to the aircraft body coordinate frame, denoted by \(C_{N}^{b}\); (**b**) from the aircraft body coordinate frame to the gimbal coordinate frame, denoted by \(C_{b}^{G}\); (**c**) from the gimbal coordinate frame to the mirror coordinate frame, denoted by \(C_{G}^{M}\).  
Thus, \(\rm C_{G}^{M}\) is:  
\[\rm C_{G}^{M}=\underbrace{\left(\begin{array}{ccc}0&1&0\\ 1&0&0\\ 0&0&1\end{array}\right)\left(\begin{array}{ccc}0&-1&0\\ -1&0&0\\ 0&0&1\end{array}\right)\left(\begin{array}{ccc}\sin 2\lambda&\cos 2\lambda&0\\ \cos 2\lambda&-\sin 2\lambda&0\\ 0&0&1\end{array}\right)\left(\begin{array}{ccc}1&0&0\\ 0&\sin 2\delta&\cos 2\delta\\ 0&\cos 2\delta&-\sin 2\delta\end{array}\right)}_{T1\ from\ N1} \tag{5}\]  
The matrices in the Equation (5) are calculated from the normal vectors according to Equation (4), which are as follows:  
\[\rm N1=\left(\begin{array}{ccc}0&-\cos(\delta+45^{\circ})&\sin(\delta+45^{ \circ})\end{array}\right)^{T} \tag{6}\]  
\[\rm N2=\left(\begin{array}{ccc}-\cos(\lambda+45^{\circ})&\sin(\lambda+45^{ \circ})&0\end{array}\right)^{T} \tag{7}\]  
\[\rm N3=\left(\begin{array}{ccc}\frac{\sqrt{2}}{2}&\frac{\sqrt{2}}{2}&0\end{array} \right)^{T} \tag{8}\]  
\[\rm N4=\left(\begin{array}{ccc}\frac{\sqrt{2}}{2}&-\frac{\sqrt{2}}{2}&0\end{array} \right)^{T} \tag{9}\]
# A Novel Integrated Gliding and Flapping Propulsion Biomimetic Mana-Ray Robot
## 2 The Design of the Manta Ray Robot
### The Gliding System Design of the Manta Ray Robot
#### 2.3.2 The Buoyancy-Adjustment System Design  
As an essential part of the gliding system of the manta ray robot, the buoyancy-adjustment system plays a vital role in the depth control, attitude adjustment, compensation for carrier buoyancy, and load changes. The primary function of the buoyancy-adjustment system is to adjust the buoyancy of the watercraft itself, and the purpose is to realize the functions of the watercraft's floating and diving by changing its buoyancy. The system adopts the form of a piston hydraulic cylinder, and the outline structure is shown in Figure 2c. The system comprises an inner and outer oil cavity. The piston rod connects the inner and outer pistons to realize synchronous movement. The outer oil cavity and the internal oil cavity are connected through the external oil pipeline, and the control function of the power control system is used to adjust the outer oil cavity. The volume distribution ratio of hydraulic oil in the inner oil cavity realizes the system's overall drainage volume change control, and finally realizes the buoyancy adjustment control.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DOF** & \begin{tabular}{c} **Inertial Position** \\ **and Euler Angles** \\ \end{tabular} & \
\begin{tabular}{c} **Body-Fixed** \\ **Velocities** \\ \end{tabular} &
\begin{tabular}{c} **Forces and** \\ **Moments** \\ \end{tabular} \\ \hline Motion in the x-direction(surge) & \(x\) & \(u\) & \(X\) \\ Motion in the y-direction(sway) & \(y\) & \(v\) & \(Y\) \\ Motion in the z-direction(heave) & \(z\) & \(w\) & \(Z\) \\ Rotation about the x-axis & \(\varphi\) & \(p\) & \(K\) \\ Rotation about the y-axis & \(\theta\) & \(q\) & \(M\) \\ Rotation about the y-axis & \(\psi\) & \(r\) & \(n\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Six DOF motion components of the manta ray robot.  
Figure 3: Earth-fixed (inertial) reference and body-fixed reference frames.
## 2 Materials and Methods
### Control Protocol and Stability Analysis
From (6) and (12), one gets  
\[\dot{q_{i}}=-\sum\limits_{j\in N_{i}}(q_{i}-q_{j})-\sum\limits_{j\in N_{i}} \
abla_{p_{ij}}V(\|p_{ij}\|),\hskip 28.452756 pti=1,\cdots,N \tag{13}\]  
Let \(p=[p_{1}^{T},\cdots,p_{N}^{T}]^{T}\) and \(q=[q_{1}{}^{T},\cdots,q_{N}{}^{T}]^{T}\). According to (6) and (13), one gets  
\[\dot{q}=-(L_{N}\otimes I_{3})q-\sum\limits_{i=1}^{N}\sum\limits_{j\in N_{i}} \
abla_{p_{ij}}V(\|p_{ij}\|) \tag{14}\]  
where \(I_{3}\) denotes the \(3\times 3\) identity matrix, and \(L_{N}(t)=[l_{ij}]_{N\times N}\) is defined as \(\xi(t)\)'s Laplacian matrix. Let \(\psi=[\psi_{1}^{T},\cdots,\psi_{N}^{T}]^{T}\) and \(\gamma=[\gamma_{1}{}^{T},\cdots,\gamma_{N}{}^{T}]^{T}\). Consider the following energy function as the common Lyapunov function  
\[E(p,q)=\frac{1}{2}V+\frac{1}{2}q^{T}q. \tag{15}\]  
Then the derivative of \(E(p,q,\dot{p},\gamma)\) w.r.t time \(t\in[t_{r},t_{r+1})\) is  
\[ \begin{array}{l}\frac{dE}{dt}=\frac{1}{2}\sum\limits_{i=1}^{N}\sum\limits_{j \in N_{i}}\dot{p}_{ij}^{T}\
abla_{p_{ij}}V(\|p_{ij}\|)+q^{T}\dot{q}\\ \hskip 14.226378 pt=\sum\limits_{i=1}^{N}\dot{p_{i}}^{T}\sum\limits_{j\in N_{i} }\
abla_{p_{ij}}V(\|p_{ij}\|)+q^{T}(-(L_{N}\otimes I_{3})q-\sum\limits_{i=1}^{ N}\sum\limits_{j\in N_{i}}\
abla_{p_{ij}}V(\|p_{ij}\|))\\ \hskip 14.226378 pt=\sum\limits_{i=1}^{N}q_{i}^{T}\sum\limits_{j\in N_{i}} \
abla_{p_{ij}}V(\|p_{ij}\|)-q^{T}(L_{N}\otimes I_{3})q-\sum\limits_{i=1}^{N} q_{i}^{T}\sum\limits_{j\in N_{i}}\
abla_{p_{ij}}V(\|p_{ij}\|)\\ \hskip 14.226378 pt=-q^{T}(L_{N}\otimes I_{3})q.\end{array}  \tag{16}\]  
Undirected graph \(\xi(t)\)'s Laplacian matrix \(L_{N}\) is symmetric and positive semi-definite, thus we have  
\[dE/dt\leq 0. \tag{17}\]  
The potential energy of the system at initial time is finite, and the velocities and attitudes of all UAVs at initial time are also finite. Thus, \(E(p(0),q(0))\) (the energy of the system at initial time) is finite. Since \(\frac{dE}{dt}\leq 0\) on each time-interval \([t_{r},t_{r+1})\), \(r=0,1,\cdots\), the supremum of \(E(t)\) is obviously its initial value \(E(p(0),q(0))\), for all \(t\). Furthermore, both \(V(t)=\sum\limits_{i=1}^{N}\sum\limits_{j\in N_{i}}V(\|p_{ij}\|)\) and \(V(\|p_{ij}\|)\) are bounded. Definition 1's rule (1) indicates that if \(\|p_{ij}\|\to 0\), then we have \(V(\|p_{ij}\|)\to\infty\), which violates the above conclusion (\(V(\|p_{ij}\|)\) is bounded). Thus, one gets \(\|p_{ij}\|>0\), that is, no collision happens between any two UAVs. According to rule (2) of definition 1, if \(\|p_{ij}\|\to D((i,j)\in\epsilon)\), then one gets \(V(\|p_{ij}\|)\to\infty\), which is also not consistent with the above conclusion (that is, \(V(\|p_{ij}\|)\) is bounded). Therefore,
# Prospects for the Development of High Energy Density Dielectric Capacitors
## 2 A Review of the Literature on Energy Storage Using Dielectric Materials  
There is extensive literature available dealing with the use of dielectric materials for energy storage. Most of that literature is concerned with increasing the dielectric constant of polymer materials. Most of the research involves the analysis and testing of composite polymers consisting of a host polymer with nano-structured molecular or solid particles dispersed as nano-charge storage elements. Since this is the approach suggested in Sections 3 and 4 to develop high energy density capacitors, it is appropriate to survey that literature to determine the present state-of-the-art of high dielectric materials using various techniques to increase their dielectric constant. About least 200 papers were surveyed to assess the present state-of-the-art. There are also a number of recent books [5; 6; 7; 8; 9] that discuss in detail various aspects of the development and applications of dielectric polymer nanocomposites. Each of the books written in the last ten years treats in detail aspects of polymer nanocomposite technology and its importance in the capacitor industry.  
The technical literature is extensive and detailed. It can be divided into the following categories:  
1. Analytical papers dealing primarily with theory and polymer mixing rules
2. Composites of mixtures of different polymers
3. Nanocomposites using core-shell coated ceramic particles and metal inserts
4. Nanocomposites using Carbons, graphene, and nano-sheets
5. Breakdown strength characteristics of polymer composites and films  
The literature indicates that many approaches [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29] have been used to increase the dielectric constant of polymer composites with some success. However, the magnitude of the increase in dielectric constant achieved varied greatly from paper to paper, even for the same approach. Many of the increases achieved were less than a factor of ten which is much less than the goal of at least 500-1000 needed to develop the devices of interest in the present paper. In addition, the increase in dielectric constant is needed at high voltage (>1000 V) with a breakdown strength to sustain that voltage with very small leakage current.  
The papers were surveyed to identify studies with large increases in the magnitude of the dielectric constant, very high DC resistance and low loss tangent, and some evidence of high voltage capability. These are difficult requirements to meet, but there were some studies that provided data that indicated they have met some of the target requirements. One of the problems with evaluating the various studies is that the authors do not always specify the dimensions of the elements inserted into the host polymer and the thickness of the material layers being tested. Both factors can have a large influence on the useable dielectric properties of the composites being tested.
# A High-Density Crowd Counting Method Based on Convolutional Feature Fusion
## 2 Feature Fusion CNN for Crowd Counting  
### Density Map Based Crowd Counting  
There are two options for estimating the head count of a crowd image with CNN: (1) Input the crowd image and output the estimated head count directly; (2) input the crowd image, and then output the crowd density map which shows the head count of each pixel in the image, and then integrate any area in the density map to get a final head count in the area. Compared with outputting head count directly, the estimated crowd density map may show the spatial distribution information of a given image. In addition, when learning density maps with a CNN model, the learned filters are more suitable for different sizes of human heads, which can improve the robustness to perspective changes. Thus, these filters have more semantic information and can improve the accuracy of crowd counting [16]. Therefore, we will learn the nonlinear mapping between the crowd image and the corresponding density map with CNN, and get the head count by integrating the density map.
###### Abstract  
Index modulation (IM) has been attracting considerable research efforts in recent years as it is considered a promising technology that can enhance spectral and energy efficiency and help cope with the rising demand of mobile traffic in future wireless networks. In this paper, we propose a cloud radio access network (C-RAN) suitable for fifth-generation (5G) and beyond systems, where the base stations (BSs) and access points (APs) transmit multidimensional IM symbols, which we refer to as precoding-aided transmitter-side generalized space-frequency IM (PT-GSFIM). The adopted PT-GSFIM approach is an alternative multiuser multiple-input multiple-output (MU-MIMO) scheme that avoids multiuser interference (MUI) while exploiting the inherent diversity in frequency-selective channels. To validate the potential gains of the proposed PT-GSFIM-based C-RAN, a thorough system-level assessment is presented for three different three-dimensional scenarios taken from standardized 5G New Radio (5G NR), using two different numerologies and frequency ranges. Throughput performance results indicate that the 28 GHz band in spite of its higher bandwidth and higher achieved throughput presents lower spectral efficiency (SE). The 3.5 GHz band having lower bandwidth and lower achieved throughput attains higher SE. Overall, the results indicate that a C-RAN based on the proposed PT-GSFIM scheme clearly outperforms both generalized spatial modulation (GSM) and conventional MU-MIMO, exploiting its additional inherent frequency diversity.  
index modulation; precoding; PT-GSFIM; multiuser MIMO; system-level simulation; 5G and beyond 5G
## 3 Results
### Generation of an EMP Classifier for NMuMG Cells  
We next generated a simple linear classifier to combine each of the selected four morphological feature values into a single EMP score. Features were normalized to NMuMG cells grown overnight in standard conditions, scaled so that each provided a value between 0 and 1, and summed. As designed, the EMP score would be lower for more epithelial cells and higher for more mesenchymal cells. We first applied the classifier to time-lapsed DHC images of NMuMG cells grown in normal conditions and in the presence of 0.5 ng/mL TGF\(\beta\) for 72 h (Figure 3a). We observed that immediately after addition of TGF\(\beta\), the EMP score rapidly increased then peaked around 16 hours after exposure. After this initial declination, the EMP score remained significantly different (\(p<0.00005\)) in the TGF\(\beta\)-treated conditions as compared to control. In contrast, control NMuMG cells presented a peak EMP score directly after plating, which then gradually declined over the first two days in culture, indicating that the act of passaging these epithelial cells induces a temporary mesenchymal-like morphology. Notably, we observed substantial variation in the EMP score in all conditions, consistent with the expected heterogeneity of EMP within a single culture. To further validate the EMP score, we applied the classifier to DHC images of NMuMG cells grown with different concentrations and combinations of TGF\(\beta\) and TGF\(\beta\)RI for 24 h (Figure 3b). Exposure to less TGF\(\beta\) (0.2 ng/mL) increased the EMP score, but to a lesser degree. Also, as expected, the score was attenuated by the combination of TGF\(\beta\) with 0.5 \(\upmu\)M TGF\(\beta\)RI, whereas exposure to the inhibitor alone presented more epithelial scores similar to control conditions.
###### Abstract
Within this study, Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series of the meteorological vegetation period (March to November) from 2000 to 2018 were investigated. The aim of the study was to reveal the drought characteristics (strength, variability, and patterns) in Germany in 2018, to identify potential regional differences, and compare the situation to other dry and hot years, like the drought year 2003. To examine the drought effects in various land cover types, the analysis was also conducted for cropland, grassland, and forest areas individually. In addition, annual crop statistics are analyzed to assess the impact of agricultural drought on crop production.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 3 Materials and Methods
### A Framework of the Algorithm for Automatic Generation of Navigation Directions  
The process of generating navigational directions has been divided into five stages, making the whole process transparent as well as easy to design, analyse, and maintain. Such an approach eases modifications of each stage independently after analysis of the outcomes of the tests following initial implementation; see Figure 4.  
Figure 3: Data layers used in the system: (**a**) transport network—_Path Node_ as a green circle and _Path Edge_ as a green line; (**b**) room topology—_Interior Space_ as a blue circle and _Space Connection_ as a blue diamond; (**c**) building topography—_Indoor Installation_ as a purple rectangle, _Furniture_ as a red rectangle, _Point of Interest_ and _Landmark_ as red stars, and _Space Connection_ as a red diamond.  
The algorithm starts with route calculation (stage 1) and identification of all potential decision points (stage 2), which, in most cases, involves listing all nodes in the transport graph belonging to the route. The generation of navigation guidance is divided into two steps--the creation of an extensive list of directions for all decision points (stage 3) and the generalisation of the list (stage 4) to obtain the description of the route. In this stage, some directions are joined, useless directions are removed, and some other directions are left unchanged. The final step of the algorithm is to add topographic information to the direction (stage 5) to provide a comprehensive description of manoeuvres.  
Stage 3 and stage 4 are implemented using a well-known design pattern, 'chain of responsibility'. The algorithm is represented by a set of independent rules. In stage 3, for each decision point, the framework checks whether each rule can be applied and whether the application of a particular rule should complete the direction generation process for the decision point; see Figure 5. With this approach, different rules can be applied independently to create various types of directions and in addition, a given direction can be built following several rules that supplement it with new elements, e.g., making it more specific. An analogous rule-based process is used in stage 4. The consecutive rules try to apply different generalisations to the output of stage 3.
## 3 Results  
No significant between-trial differences were found for any parameters measured using the SIBPAIR method (\(p>0.05\), Table 1). No interactions between rater and session factors were found. The SIBPAIR method showed a very high level of reliability of repeated measurements for all foot parameters (ICC \(>0.98\)) (Table 1). Very high intra-rater reliability was also found for all foot parameters measured by both raters following the measurement protocol (ICC \(>0.84\); ICC \(=0.84\) for SAI and HVA; Table 2). No significant rater or session effects were found for any foot parameters (\(p>0.05\), Tables 3 and 4). Very high inter-rater (ICC \(>0.96\)) and inter-session (ICC \(>0.86\)) reliability were also found for the measurement protocol for all foot parameters (Tables 3 and 4). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Trial**} & **FL** & **FW** & \multirow{2}{*}{**AI**} & **SAI** & **CSI (\%)** & **AH** & \multirow{2}{*}{**NAH**} & **HVA** \\  & **(mm)** & **(mm)** & & & & & **(mm)** & & **(Degree)** \\ \hline \multirow{2}{*}{1} & 254.5 & 103.5 & 0.30 & 1.02 & 54.8 & 96.6 & 0.05 & 9.9 \\  & (12.2) & (6.3) & (0.05) & (0.25) & (15.1) & (38.8) & (0.02) & (4.7) \\ \multirow{2}{*}{2} & 254.6 & 103.4 & 0.30 & 1.02 & 54.8 & 96.4 & 0.05 & 10.0 \\  & (12.2) & (6.1) & (0.05) & (0.24) & (15.4) & (38.4) & (0.02) & (4.6) \\ \multirow{2}{*}{3} & 254.6 & 103.5 & 0.30 & 1.02 & 54.9 & 96.0 & 0.05 & 10.2 \\  & (12.1) & (6.1) & (0.05) & (0.24) & (15.7) & (38.1) & (0.02) & (4.7) \\ \multirow{2}{*}{4} & 254.6 & 103.3 & 0.30 & 1.02 & 54.1 & 97.5 & 0.05 & 10.1 \\  & (12.2) & (6.1) & (0.05) & (0.26) & (15.9) & (39.3) & (0.02) & (4.5) \\ \multirow{2}{*}{5} & 254.8 & 103.3 & 0.30 & 1.02 & 54.2 & 96.1 & 0.05 & 10.0 \\  & (12.4) & (6.1) & (0.05) & (0.24) & (15.9) & (39.1) & (0.02) & (4.4) \\ \multirow{2}{*}{\(p\)-value} & 0.29 & 0.73 & 0.48 & 0.08 & 0.16 & 0.537 & 0.553 & 0.054 \\ \multirow{2}{*}{ICC} & 0.99 & 0.99 & 0.98 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 \\  & SEM & 0.85 & 0.43 & 0.003 & 0.002 & 1.10 & 2.7 & 0.001 & 0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Means (standard deviations, SD) of the measured foot parameters for each of the five trials, as well as the ICC values for the reliability of repeated measurements of the SIBPAIR method (n = 30). The SEM values for the SIBPAIR method were 0.85 mm, 0.43 mm, 0.31\({}^{\circ}\), 2.7 mm, and 0.001 for FL, FW and HVA, AH, and NAH, respectively (Table 1). Those for AI, SAI, and CSI were 0.003, 0.002, and 1.10%, respectively (Table 1). When the experimental protocol was used, the SEM values were slightly increased. For the senior rater the SEM were 1.75 mm, 1.19 mm, 1.25\({}^{\circ}\), 10.1 mm, and 0.005 for FL, FW and HVA, AH, and NAH, respectively (Table 2). Those for AI, SAI and CSI were 0.009, 0.006, and 3.28%, respectively (Table 2).
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
###### Abstract  
Time-domain backprojection algorithms are widely used in state-of-the-art synthetic aperture radar (SAR) imaging systems that are designed for applications where motion error compensation is required. These algorithms include an interpolation procedure, under which an unknown SAR range-compressed data parameter is estimated based on complex-valued SAR data samples and backprojected into a defined image plane. However, the phase of complex-valued SAR parameters estimated based on existing interpolators does not contain correct information about the range distance between the SAR imaging system and the given point of space in a defined image plane, which affects the quality of reconstructed SAR scenes. Thus, a phase-control procedure is required. This paper introduces extensions of existing linear, cubic, and sinc interpolation algorithms to interpolate complex-valued SAR data, where the phase of the interpolated SAR data value is controlled through the assigned a priori known range time that is needed for a signal to reach the given point of the defined image plane and return back. The efficiency of the extended algorithms is tested at the Nyquist rate on simulated and real data at THz frequencies and compared with existing algorithms. In comparison to the widely used nearest-neighbor interpolation algorithm, the proposed extended algorithms are beneficial from the lower computational complexity perspective, which is directly related to the offering of smaller memory requirements for SAR image reconstruction at THz frequencies.  
complex-valued SAR data interpolation; complex SAR interpolation; THz SAR; backprojection; GBP