esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT +
## Appendix A  
\(\Delta\theta\), \(\Delta\phi\), and \(\Delta\tau\) can be calculated from the augmented matrices in (18) and (19), by the following rules: \(a_{22}\{a\}_{1}-a_{12}\{a\}_{2}\), \(a_{22}\{a\}_{3}-a_{32}\{a\}_{1}\), \(a_{21}\{a\}_{1}-a_{11}\{a\}_{2}\), and \(a_{31}\{a\}_{2}-a_{21}\{a\}_{3}\). Denoting the derived matrices with \(b\) and \(c\), we have  
\[\mathbf{b}=\begin{bmatrix}b_{11}&b_{12}&b_{13}\\ a_{21}&a_{22}&a_{23}\\ b_{31}&b_{32}&b_{33}\end{bmatrix}\begin{bmatrix}b_{14}\\ a_{24}\\ b_{34}\end{bmatrix}=\begin{bmatrix}a_{22}a_{11}-a_{12}a_{21}&0&a_{22}a_{13}-a_{ 12}a_{23}&a_{22}a_{14}-a_{12}a_{24}\\ a_{21}&a_{22}&a_{23}&a_{24}\\ a_{22}a_{31}-a_{32}a_{21}&0&a_{22}a_{33}-a_{32}a_{23}&a_{22}a_{34}-a_{32}a_{24} \end{bmatrix}, \tag{11}\]  
\[\mathbf{c}=\begin{bmatrix}c_{11}&c_{12}&c_{13}\\ c_{21}&c_{22}&c_{23}\\ a_{31}&a_{32}&a_{33}\end{bmatrix}\begin{bmatrix}c_{14}\\ c_{24}\\ a_{34}\end{bmatrix}=\begin{bmatrix}0&a_{21}a_{12}-a_{11}a_{22}&a_{21}a_{13}-a_{ 11}a_{23}&a_{21}a_{14}-a_{11}a_{24}\\ 0&a_{31}a_{22}-a_{21}a_{32}&a_{31}a_{23}-a_{21}a_{33}&a_{31}a_{24}-a_{21}a_{34} \\ a_{31}&a_{32}&a_{33}&a_{33}\end{bmatrix}. \tag{12}\]  
Consequently, \(\Delta\theta\), \(\Delta\phi\), and \(\Delta\tau\) can be calculated by the above augmented matrices as  
\[\Delta\theta=\mathbb{R}\bigg{\{}\frac{b_{33}b_{14}-b_{13}b_{34}}{b_{33}b_{11}- b_{13}b_{31}}\bigg{\}}, \tag{13}\]  
\[\Delta\phi=\mathbb{R}\bigg{\{}\frac{c_{23}c_{14}-c_{13}c_{24}}{c_{23}c_{12}-c_ {13}c_{22}}\bigg{\}}, \tag{14}\]  
\[\Delta\tau=\mathbb{R}\bigg{\{}\frac{b_{31}b_{14}-b_{11}b_{34}}{b_{31}b_{13}-b_{ 11}b_{33}}\bigg{\}}. \tag{15}\]  
Hence, the results in (20)-(22) can be obtained by substituting (11) and (12) into (13)-(15).
# The Role of the Directivity of Various THz Detectors in Multiplexing Systems
## 3 Results
### Examination of the Three-Focal-Spot Lenses
The blue, orange and green lines denote the configurations with the detectors placed at an angle of \(0^{\circ}\), \(+2^{\circ}\) and \(-2^{\circ}\), respectively. These geometries reflect the situations in which the detectors face the center of the lens from the positions of M, L and R focal spots. An analogous structure has also been manufactured for the DWL corresponding to the frequency of 260 GHz. This frequency has been dictated by the bandwidth of this particular FET detector used in our laboratory. The results can be seen in Figure 10.  
As can be seen, the intensity distributions obtained with the QOD and WR34 detectors placed at different angles also differ strongly in this case. At this frequency, however, the highest intensity in all focal spots has been observed for the direct detector configuration. On the other hand, the relative intensity of the focal spots changes for different detector angles, which shows that the reliable determination of the actual or relative intensities is extremely hard, if not infeasible. The intensity distributions obtained with the FET detector (Figure 10c) are, however, much more reliable. Firstly, it must be noted that the relative intensities of the M, L and 2L focal spots are almost equal (1.00, 0.97 and 0.96, respectively). Secondly, the influence of the detector rotation, in comparison to the other two detectors,  
Figure 10: Cross sections along the spots measured in the focal plane of the \(f=100\) mm and \(\
u=260\) GHz structure with **(a)** QOD, **(b)** WR34 and **(c)** FET detectors. The blue, orange and green lines denote the configurations with the detectors placed at an angle of 0\({}^{\circ}\), 14\({}^{\circ}\) and 27\({}^{\circ}\), respectively. These geometries reflect the situations in which the detectors face the center of the lens from the positions of M, L and 2L focal spots. Figure 9: Cross sections along the spots measured in the focal plane of the \(f=100\) mm and \(\
u=150\) GHz structure with **(a)** QOD and **(b)** WR51 detectors. Blue, orange and green lines denote the configurations with the detectors placed at an angle of 0\({}^{\circ}\), 14\({}^{\circ}\) and 27\({}^{\circ}\), respectively. These geometries reflect the situations in which the detectors face the center of the lens from the positions of M, L and 2L focal spots. is far less significant. The relative intensities of the L and 2L focal spots compared to the intensity of the M spot dropped to 0.93, 0.93 and 0.87, 0.87 when observed with the detector rotated by 14\({}^{\circ}\) and 27\({}^{\circ}\), respectively. Moreover, it should be noted that the intensities of the shifted focal spots (L and 2L) are reduced in the theoretically favored configurations with the FET detector facing the center of the lens.
## 2 Results and Discussion
### Chemical Composition of Headspace Samples from C. dactylon, C. exaltatus, and P. repens
It has been suggested that compound blends are significant in the development of ovipositional attractive odors, and that not only must the individual compounds be determined, but also the ratio at which they occur in relation to each other [30; 31]. This would suggest that a different response could be elicited for a blend of the same compounds with different ratios. Thus, compounds that appear in several plant samples could induce a positive ovipositional response, if the ratio of the compounds is suitable for the specific target. On the other hand, the response could vary between plant species due to the different abundances of the same compound. Therefore, it is valuable to determine if there is a significant difference in the relative peak area of the detected compounds between the different plants. From the Venn diagram in Figure 1C, it can be seen that there are six compounds common to the headspace samples of all three species when the results from the roots and shoots are combined. These compounds are \(\alpha\)-pinene, \(\beta\)-pinene, 3-octanone, \(\beta\)-myrorene, limonene, and citronello. Three compounds were shared between _C. dactylon_ and _C. exaltatus_: 2-pentyl-furan, nonanal, and 2,2,6-trimethyl-cyclohexanone, while _C. dactylon_ and _P. repens_ only shared two compounds, namely decane and \(\beta\)-ionone. The only compound shared by _C. exaltatus_ and _P. repens_ was 3-carene. Since the results are also split into roots and shoot samples, a similar comparison can be made for compounds common in both parts of the plants, to investigate if there are significant differences in the abundance of VOCs in the different plant parts. It has been suggested that compounds emitted into the rhizosphere play a role in plant-plant interaction [32], as well as plant-herbivore interactions [33]. However, the interaction of rhizosphere compounds and the malaria vector has not been fully explored, with regards to whether they contribute to long-range cues or short-range cues. Knowledge of abundance variation could offer additional insight into which compounds are important for oviposition. For _C. dactylon_, the compounds \(\alpha\)-pinene, \(\beta\)-pinene, 3-octanone, decane, and limonene were present in both root and shoot samples. In the same sense, the compounds \(\beta\)-pinene, cyperene, and \(\beta\)-selinene were present in the samples of _C. exaltatus_. For _P. repens_, the compounds \(\beta\)-pinene and 3-octanone were present.
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
###### Abstract  
Various measurement monitoring techniques are investigated to mitigate the effect of global navigation satellite systems (GNSS) code multipath through error correction, stochastic weighting of measurements and detection and exclusion (or de-weighting) of affected measurements. Following a comprehensive review of each approach, the paper focuses on detection/exclusion and detection/de-weighting techniques where several single and dual-frequency monitoring metrics are employed in a combination with time-averaging and the _M of N_ detection strategy. A new Geometry-Free (GF) detection metric is proposed given its capability to be combined with a preceding Code-Minus-Carrier (CMC)-based error correction to reduce the number of excluded or de-weighted measurements and thus preserve the measurement geometry. Three geometry-based algorithms, namely measurement subset testing, consecutive exclusion and iterative change of measurement weights are investigated to address multipath scenarios with multiple simultaneously affected measurements. Experimental results are provided using GPS L1, L2C and L5 data collected in multipath environments for static and kinematic scenarios. For GPS L1, the proposed combined method shows more than 38% improvement over a conventional Carrier-to-Noise-density ratio (C/N\({}_{0}\))-based Least-Squares (LS) solution in all but deep urban canyons. Lower performance was observed for L2C and L5 frequencies with a limited number of satellites in view.  
global navigation satellite systems (GNSS); multipath mitigation; measurement monitoring; multipath error estimation and measurement correction; stochastic model of measurements and weighting approach; multipath detection for excluding or de-weighing affected measurements +
Footnote †: journal: Sensors  
_Article_
# Brain Implantable End-Fire Antenna with Enhanced Gain and Bandwidth
###### Abstract  
An end-fire radiating implantable antenna with a small footprint and broadband operation at the frequency range of 3-5 GHz is proposed for high-data-rate wireless communication in a brain-machine interface. The proposed Vivaldi antenna was implanted vertically along the height of the skull to avoid deformation in the radiation pattern and to compensate for a gain-loss caused by surrounding lossy brain tissues. It was shown that the vertically implanted end-fire antenna had a 3 dB higher antenna gain than a horizontally implanted broadside radiating antenna discussed in recent literature. Additionally, comb-shaped slot arrays imprinted on the Vivaldi antenna lowered the resonant frequency by approximately 2 GHz and improved the antenna gain by more than 2 dB compared to an ordinary Vivaldi antenna. An antenna prototype was fabricated and then tested for verification inside a seven-layered semi-solid brain phantom where each layer had similar electromagnetic material properties as actual brain tissues. The measured data showed that the antenna radiated toward the end-fire direction with an average gain of \(-15.7\) dBi under the frequency of interest, 3-5 GHz. A link budget analysis shows that reliable wireless communication can be achieved over a distance of 10.8 cm despite the electromagnetically harsh environment.  
brain-machine interface; implantable antenna; link budget analysis; specific absorption rate; tissue-emulating phantom; ultra-wideband antenna; Vivaldi antenna +
Footnote †: journal: Sensors
## 4 Simulated Annealing Algorithm
### Parameters Tuning of the SA Method  
Since the simulated annealing algorithm is a parametric approach, the setting of values of these parameters has a significant effect on the objective function. For this, we ran a set of experiments to determine the most suitable values to reach good solutions. The controller parameters of the developed simulated annealing are the initial temperature \(T_{0}\), the cooling rate \(\delta\), the neighbor moves rate \(\gamma\) and the minimal temperature \(T_{min}\). The correspondent intervals of these parameters are presented in Table 3.  
The Tagushi design of MINITAB software [53] was used for parameters tuning of the simulated annealing. Equation (12) defines the signal-to-noise ratio \((S/N)\) of Tagushi method. This ratio expresses the effect of parameters on the objective function. A maximum value of this ratio minimizes the effect of uncontrollable factors. This ratio is given by:  
\[S/N=-10\log(\frac{1}{z}\sum_{i=1}^{z}y_{i}^{2}) \tag{12}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Interval** \\ \hline Initial temperature & \(T_{0}\) & \([100,10^{6}]\) \\ Cooling rate & \(\delta\) & \([0.03,0.9]\) \\ Neighbor rate & \(\gamma\) & \([0.05,0.9]\) \\ Minimum temperature & \(T_{min}\) & \([0.1,10]\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: SA parameters.  
The constant \(z\) represents the total number of experiments and \(y_{i}\) is the result of the experiment \(i\). The results of parameters tuning using Tagushi design for a typical set of training instances are illustrated by charts of Figures 3 and 4.
###### Abstract  
Subway station projects are characterized by complex construction technology, complex site conditions, and being easily influenced by the surrounding environment; thus, construction safety accidents occur frequently. In order to improve the computing performance of the early risk warning system in subway station construction, a novel model based on least-squares support vector machines (LSSVM) optimized by quantum-behaved particle swarm optimization (QPSO) was proposed. First, early warning factors from five aspects (man, machine, management, material, and the environment) were selected based on accident causation theory and literature research. The data acquisition method of each risk factor was provided in detail. Then, the LSSVM with strong small sample analysis and nonlinear analysis abilities was chosen to give the early warning. To further ameliorate the early warning accuracy of the LSSVM, QPSO with a strong global retrieval ability was used to find the optimal calculation parameters of the LSSVM. Seventeen subway stations of Chengdu Metro Line 11 in China were picked as the empirical objects. The results demonstrated that the best regularization parameter was 1.742, and the best width parameter was 14.167. The number of misjudged samples of the proposed model was 1, and the early warning error rate was only 4.41%, which met the needs of engineering practice. Compared with the classic and latest methods, the proposed model was found to have a faster prediction speed and higher prediction accuracy.  
early warning; construction safety risk; subway station; LSSVM; QPSO; accident causation theory +
Footnote †: journal: Applied Sciences  
0  
Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO
###### Abstract  
Conventional approaches to global localization and navigation mainly rely on metric maps to provide precise geometric coordinates, which may cause the problem of large-scale structural ambiguity and lack semantic information of the environment. This paper presents a scalable vision-based topological mapping and navigation method for a mobile robot to work robustly and flexibly in large-scale environment. In the vision-based topological navigation, an image-based Monte Carlo localization method is presented to realize global topological localization based on image retrieval, in which fine-tuned local region features from an object detection convolutional neural network (CNN) are adopted to perform image matching. The combination of image retrieval and Monte Carlo provide the robot with the ability to effectively avoid perceptual aliasing. Additionally, we propose an effective visual localization method, simultaneously employing the global and local CNN features of images to construct discriminative representation for environment, which makes the navigation system more robust to the interference of occlusion, translation, and illumination. Extensive experimental results demonstrate that ERF-IMCS exhibits great performance in the robustness and efficiency of navigation.  
vision-based topological mapping and navigation; image-based monte carlo localization; image retrieval; CNN features +
Footnote †: journal: Applied Sciences
# Dielectric Elastomer Actuator for Soft Robotics Applications and Challenges
## 2 Working Principle and Characteristics
### DE Materials  
The selection of DE material is one of the key factors of DEAs. To enhance the performance of the DEA, the DE materials should have a low elastic modulus, low viscosity, high dielectric constant, and high electrical breakdown strength. Since many DE materials were tested, acrylic elastomers (3M VHB 4910 and VHB 4905), polyurethane (PUs), and silicone elastomers are most commonly used. Using 3M VHB acrylic elastomer as a DE has benefits in generating a large strain. DEA using 3M VHB acrylic elastomer with large pre-strain shows the strain over 380% [3]. Recently, various silicone elastomers, such as Dow Corning Sylgard 186, Dow Corning HS3, Nusil CF19-2186, Wacker Elastosil RT 625, Wacker Elastosil P7670, THERABAND YELLOW 11726, and OPPO BAND GREEN 8003 were tested as DE materials [11; 12]. Compared to the acrylic elastomers, silicone elastomers have relatively low DE constants, and thereby require higher electrical fields to achieve high strain. However, as acrylic elastomers have a higher viscosity than silicone elastomers, the silicone elastomers are more suitable in the applications operating at higher frequencies [4].  
The pre-strain of the DE materials is also an important factor to improve the actuation performance. Pre-strain can improve breakdown strength [5] and mechanical stability but can also cause a decrease in the DE constant [6]. Table 1 compared the properties of DEAs of various DE materials and other actuators.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Type** & **Pre-Strain (\(\mathbf{v}\), \(\mathbf{y}\)) (\(\mathbf{c}\))** & **Maximum Strain (\(\mathbf{c}\))** & **Work Density per** & **Maximum Energy** \\  & & & & & **Cyde (\(\mathbf{f}\)(cm\({}^{3}\))**)** & **Efficiency** \\ \hline
[13; 14; 15] & Dielectric elastomer (VHB 4910) & 300, 300 & 158 (Circular area strain) & \(\sim\)4.0 (15. typical) & 80 (00 typical) \\
[13; 14; 15] & Dielectric elastomer (VHB 4910) & 540,75 & 215 (Linear area strain) & \(\sim\)1.56 & 80 (00 typical) \\
[16; 17] & Dielectric elastomer (polyurethane acrylate) & 0, 0 & 67.7 (Circular area strain) & 0.087 & 80 \\
[17] & Dielectric elastomer (Syglard 186) & 0, 0 & 32 (Circular area strain) & 0.082 & 90 \\
[3; 14] & Dielectric elastomer (CF19-2186) & 45, 45 & 64 (Circular area strain) & 0.75 & 79 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the properties of actuators.
## 4 Preliminary Background  
### Blind Elliptic Curve DSA Signatures  
In this cryptoscheme, a _user_ checks if a signature on a message \(M\) is valid from the _signer_ while hiding \(M\) from the signer. In our voting scheme, the blind signature cryptoscheme in [37] is leveraged so that voters can obtain anonymous voting tokens so they can cast their votes to the blockchain while keeping their real identity private. The aforementioned cryptoscheme is used because it is efficient in terms of computations with shorter signatures and it contains the following steps:All parties are assumed to use an elliptic curve of order \(n\) with generator \(G\). \(P=d\cdot G\) is the signers' public key, where \(d\in\mathbb{Z}_{n}^{*}\) is the private key.
2. The signer first selects a random element \(k\in\mathbb{Z}_{n}^{*}\) and then he/she sends \(R=k\cdot G\) to the requester.
3. Then, the requester selects the following random elements \(\gamma,\delta\in\mathbb{Z}_{n}^{*}\) to compute \(A=R+\gamma\cdot G+\delta\cdot P\). Let \(x\) be the \(x\)-coordinate of point \(A\), and \(t=x\bmod n\). The requester then computes \(c=H(M||t)\bmod n\) and sends \(c^{\prime}=(c-\delta)\bmod n\) to the signer. \(H(\cdot)\) is a cryptographically secure hash function, and \(H:\{\}^{*}\to\mathbb{Z}_{n}^{*}\).
4. The signer then computes \(s^{\prime}=(k-c^{\prime}\cdot\mathrm{d})\bmod n\) and sends the result back to the requester.
5. The requester then computes \(s=(s^{\prime}+\gamma)\bmod n\) and the signature of \(M\) is stored as \((s,c)\). Finally, to validate the signature, the _verifier_ calculates \(A=c\cdot P\ +\ s\cdot G\). Then, \(t=x\bmod n\) is computed, where \(x\) is the \(x\)-coordinate of point \(A\). The verifier verifies if \(c\stackrel{{?}}{{=}}H(M||t)\bmod n\).
###### Abstract  
The pressure gain across a rotating detonation combustor offers an efficiency rise and potential architecture simplification of compact gas turbine engines. However, the combustor walls of the rotating detonation combustor are periodically swept by both detonation and oblique shock waves at several kilohertz, disrupting the boundary layer, resulting in a rather complex convective heat transfer between the fluid and the solid walls. A computationally fast procedure is presented to calculate this extraordinary convective heat flux along the detonation combustor. First, a numerical model combining a two-dimensional method of characteristics approach with a monodimensional reaction model is used to compute the combustor flow field. Then, an integral boundary layer routine is used to predict the main boundary layer parameters. Finally, an empirical correlation is adopted to predict the convective heat-transfer coefficient to obtain the bulk and local heat flux. The procedure has been applied to a combustor operating with premixed hydrogen-air fuel. The results of this approach compare well with high-fidelity unsteady Reynolds-averaged Navier-Stokes three-dimensional simulations, which included wall refinement in an unrolled combustor. The model demonstrates that total pressure has an important influence on heat flux within the combustor and is less dependent on the inlet total temperature. For an inlet total pressure of 0.5 MPa and an inlet total temperature of 300 K, a peak time-averaged heat flux of 6 MW/m\({}^{2}\) was identified at the location of the triple point, followed by a decrease downstream of the oblique shock, to about 4 MW/m\({}^{2}\). Maximum discrepancy between the reduced-order model and the high-fidelity solver was 16%, but the present reduced-order model required a computational time of only 200 s, that is, about 7000 times faster than the high-fidelity three-dimensional unsteady solver. Therefore, the present tool can be used to optimize the combustor cooling system.  
rotating detonation combustor; heat flux; reduced model; pressure-gain combustion 2018 893; doi:10.3390/app8060893