esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 3 GNSS Tropospheric Tomography Methodology
### Tomography Parameter Estimation Method
#### 3.2.2 Initial Value and Gaussian Smoothing Filter  
Due to the geometry of the ground GNSS stations and the satellite constellation, the projection matrix is generally sparse and ill-conditioned. This issue can be solved by adding additional constraints, for example, introducing initial values in tomography equations [34]. The initial value can be obtained in several ways, for example, by using the standard atmosphere, by spatially interpolating the ground-based meteorological measurements, or by utilizing the NWP products. Reasonable initial values can not only solve the rank deficient issue, but also speed up the convergence and improve the quality of the tomographic results. In this work, the global forecast products (GFS) produced by National Centers for Environmental Prediction (NCEP), which can be accessed in (near) real time, is taken as initial value.  
As mentioned previously, some voxels in tropospheric tomography cannot be touched by any GNSS signal due to the geometry of ground GNSS stations and satellite constellation. In addition, the signals passed out of the tomographic region from the side faces are generally removed, which makes the ill-condition issue worse. In tomography, by using the algebraic reconstruction technique as described in Section 2.2, Nw in voxels without signal passing through will remain unchanged, which may induce unrealistic tropospheric fields with spurious values. The Gaussian filter will therefore be used in this work to reduce the influence of this issue [28]. The filter operator can be expressed as follows:  
\[\mathrm{G(x,y)=}\frac{1}{2\pi\sigma^{2}}\mathrm{e}^{-\frac{\sigma^{2}}{2\sigma^{ 2}}} \tag{13}\]  
where \(s\) is the distance between the surrounding grid node and the center node, and \(\sigma\) represents the standard deviation. Two kinds of Gaussian template with order of five are used where \(\sigma=1\) and \(\sigma=0.3\) are used for voxel without and with GNSS signals passed, respectively.
Sensors_**2012**, _12_, 14262-14291; doi:10.3390/s121114262  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Reliable Freestanding Position-Based Routing in Highway Scenarios**  
**[PERSON] 1, [PERSON] 2, [PERSON] 1,*,**  
**[PERSON] 1, [PERSON] 3 and [PERSON] 2**  
1 Electronics and Telecommunications Department, CICESE Research Center,  
Ensenada-Tijuana Highway, Km. 3918, Playitas, C. P. 22860, Ensenada, Baja California, Mexico;  
E-Mails: [EMAIL_ADDRESS] (G.A.G.-M.); [EMAIL_ADDRESS] (R.R.-R.)  
2 Faculty of Telematics, University of Colima, Av. University 333, C. P. 28040, Colima, Col.,  
Mexico; E-Mails: [EMAIL_ADDRESS] (R.A.-S.); [EMAIL_ADDRESS] (A.E.)  
3 Plantronics Inc. Mexico, Av. Producci@ No. 216 Parque Industrial Internacional Tijuana, Mexico;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +52-646-175-0500 (ext. 25326).  
_Received: 30 August 2012; in revised form: 11 October 2012 / Accepted: 15 October 2012 / Published: 24 October 2012_  
**Abstract:** Vehicular _Ad Hoc_ Networks (VANETs) are considered by car manufacturers and the research community as the enabling technology to radically improve the safety, efficiency and comfort of everyday driving. However, before VANET technology can fulfill all its expected potential, several difficulties must be addressed. One key issue arising when working with VANETs is the complexity of the networking protocols compared to those used by traditional infrastructure networks. Therefore, proper design of the routing strategy becomes a main issue for the effective deployment of VANETs. In this paper, a reliable freestanding position-based routing algorithm (FPBR) for highway scenarios is proposed. For this scenario, several important issues such as the high mobility of vehicles and the propagation conditions may affect the performance of the routing strategy. These constraints have only been partially addressed in previous proposals. In contrast, the design approach used for developing FPBR considered the constraints imposed by a highway scenario and implements mechanisms to overcome them. FPBR performance is compared to one of the leading protocols for highway scenarios. Performance metrics show that FPBR yields similar results when considering freespacepropagation conditions, and outperforms the leading protocol when considering a realistic highway path loss model.  
keywords: geographic routing; VANET; highway; location service
# The Usage of Virtual and Augmented Reality in Underwater Archeology
## 3 Virtual and Augmented Reality Technologies
### Development of Technology, Including Underwater Archeology
At the same time, Sony developed a new PS4 game console, where it introduced elements of VR, and Google released the so-called \"Google Cardboard\"--a cheap version of a VR headset. In response to the dynamic market development, in 2015 Samsung released a set that is compatible only with Samsung smartphones, called \"Samsung Gear VR\", and in 2016, HTC released advanced \"VIVE VR\" goggles [24; 28]. In 2016, we also saw the premiere of the game \"Pokemon Go\", published by Niantic. The popularity that the game gained was important in terms of increasing the use of AR [36]. During this period, the market development was very dynamic, as well as the development of applications of VR and AR in underwater archaeology. In 2014, full-scale underwater archeology projects began to be implemented using VR and AR. These were primarily the VISAS and iMARECULTURE projects, where underwater tablets with AR functions were used for the first time [9; 37]. Various types of games based on VR, i.e., \"Ocean Rift\", developed in 2014-2019, also contributed to popularizing the topic of underwater archeology. The game was developed by a team from Bangor University, and one of the game's elements is a virtual dive into the Zenobia wreck [31]. It should be noted, however, that apart from popularizing the topic, the game does not contribute much to the development of underwater archeology in terms of science or education, due to the fact that it is not based on thorough archaeological research [31]. In terms of technology development, in the context of potential applications in underwater archaeology, and in particular in education in the field of underwater work, the \"Amphibian\" project launched in 2016 is also worth a brief mention. It is a 3D scuba diving simulator using a motion platform [38]. Full \"immersion\" in VR is provided to users by a platform on which they lie on their torso with their arms and legs placed on a suspended harness, which is intended to provide a movement experience similar to that of a real dive. VR equipment, in the form of \"Oculus Rift\" goggles, provides them with appropriate visual and auditory experiences. Additionally, the device also simulates buoyancy, drag, and temperature changes through various sensors [38; 39]. Following the development of VR and AR and their application in underwater archeology (Figure 1), one can notice the relatively early use of modern technologies in this field. Nowadays, VR and AR technologies are still used in underwater archeology for both research purposes and the popularization of underwater cultural heritage. They are also being used more and more often in education at all its levels [40; 41].
# Performance Prediction and Design of Stratospheric Propeller
## 4 Performance Prediction  
The aerodynamic loss of the propeller is mainly concentrated on the root and tip. As the root must be strong enough to withstand loads such as the rotating centrifugal force and the bending moment of the whole propeller, it is usually too thick to exhibit good aerodynamic performance. The vortex at the tip increases the induced aerodynamic resistance and reduces the thrust, and if the rotating speed is too high, the tip is prone to generating shock loss. Therefore, the thrust is mainly generated from 0.7\(R\) to 0.9\(R\) of the blade. The performance of the propeller is determined among these sections, and the propeller performance can be predicted through the characteristic blade element \(r_{char}\). The thrust and torque of the propeller can be given by:  
\[\left\{\begin{array}{c}T=\frac{1}{2}\rho V_{\infty}^{2}N_{B}\frac{\left(1+ \alpha\right)^{2}}{\sin^{2}\theta}C_{T}V_{r_{char}}\int_{r_{char}-r_{0}}^{r_{char }+r_{0}}dr\\ M=\frac{1}{2}\rho V_{\infty}^{2}N_{B}b\frac{\left(1+\alpha\right)^{2}}{\sin^{2} \theta}C_{F}V_{r_{char}}\int_{r_{char}-r_{0}}^{r_{char}+r_{0}  
where \(r_{0}\) is the prediction parameter that indicated the coverage on both sides of the characteristic blade element. In this article, 0.75\(R\) is selected as the characteristic blade element. Equation (22) and the efficiency of the propeller can be given as:  
\[\left\{\begin{array}{c}T=\frac{1}{2}\rho V_{0}^{2}N_{B}\frac{(1+a)^{2}}{\sin^{ 2}\phi}C_{TV}\big{|}_{0.75R}\cdot 2r_{0}\\ M=\frac{1}{2}\rho V_{0}^{2}N_{B}\frac{(1+a)^{2}}{\sin^{2}\phi}C_{TV}\big{|}_{0.75R}\cdot 1.5 Rr_{0}\end{array}\right. \tag{23}\]  
\[\eta=\frac{4\lambda}{3\pi b}\frac{C_{TV}}{C_{FV}}\big{|}_{0.75R} \tag{24}\]  
The thrust \(T\) and the torque \(M\) are predicted by the 0.75\(R\) element and \(r_{0}\); the 0.75\(R\) element performance is obtained by iterate calculation (\(r_{0}\) will be discussed later). The efficiency \(\eta\) is independent of \(r_{0}\) and depends on the 0.75\(R\) element performance. This prediction method only needs an iterative calculation of the performance of \(r_{char}\), rather than of the whole section of the propeller; a lower amount of calculation is also required to determine the airfoil performance.
# Efficient LDPC Encoder Design for IoT-Type Devices
## 5 LDPC Encoder Design for a Microcontroller Implementation
### Implementation of Elementary Operations  
The elementary operations of the circular shift of QC-LDPC encoding algorithm can be implemented in the C language by using a bit-wise shift operation and bit-wise OR operation, as in the following example:  
\[\texttt{output}=\texttt{(input}<\texttt{offset)}\ \texttt{|}\ \texttt{(input}> \texttt{(size}-\texttt{offset))};\]  
where offset is the cyclic shift value and size is equivalent to the submatrix size \(P\).  
Using this instruction for the circular shift, the input value needs to be of the uint32_t type (if \(P\leq 32\)), while the size and offset can usually be of the uint8_t type. The output value is in the same format as the input. An example for shifting 8-bit values with offset 2 is presented in Figure 4.  
The possible circular shift is limited to \(P\leq 32\), because the typical microcontrollers use 32-bit cores, and hence the elementary operations are efficiently executed for up to 32b  
Figure 4: Example of a circular shift operation.  
Figure 3: Illustration of the size of the \(\mathbf{H}\) matrix with three different representations in the microcontroller memory.  
subvector sizes. However, this limitation does not influence the achievable performance, because the LDPC codes with short-to-moderate submatrix sizes can be optimized at least as effectively as the codes with greater submatrix sizes if the codeword length remains the same [24].  
The second elementary operation, the addition of the \(\mathrm{GF}(2)\) subvectors, can be realized directly with an XOR operator in the C language applied to variables representing the subvectors.
## 5 Conclusions  
In this paper, soil nutrient and hyperspectral data of different SN and moisture content were collected in laboratory and field. Then, the triple wavelet denoising and PCA dimensionality reduction were carried out to preprocess the hyperspectral data and eliminate environmental interference. Tenfold cross validation was used to compare the classification and recognition effects of CNN convolution layer and SVR kernel function types on soil moisture and nutrients. Evaluation indicators, including confidence interval and prediction accuracy, were given. A GA-optimized CNN-LSTM hybrid neural network (GA-CNN-LSTM) algorithm model was proposed to predict SN and moisture. The prediction performance was compared with those of CNN, SVR, and CNN-LSTM hybrid neural network without GA optimization. The GA-CNN-LSTM algorithm was superior to other models in all indicators; it had the highest accuracy rates of 94.5% and 92.9% for soil moisture and organic matter, respectively. The accuracy of GA-CNN-LSTM model for predicting soil moisture was better than that for predicting SN. Finally, the precision and recall curves of the modified model were obtained, and the applicable methods of the model were discussed. By comparing the measured values and predicted values of each model, the predicted values of the optimized GA-CNN-LSTM model are more closely distributed at about 1:1, and the data-fitting ability and stability are better than CNN-LSTC and other models. However, it is still necessary to further study the relationship between soil moisture and organic matter, verify the robustness of GA-CNN-LSTM model under different soil types, and verify its ability to explore the contents of macroelements and microelements in different soils.  
This study was conceptualized by [PERSON] and [PERSON]; software was designed by [PERSON] and validated by [PERSON], [PERSON], and [PERSON]; [PERSON] provided resources, and [PERSON] curated the data; the original draft of the manuscript was prepared by [PERSON] and [PERSON]; [PERSON] reviewed and edited the manuscript; [PERSON] and [PERSON] assisted with project administration; [PERSON] and [PERSON] managed funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China No.51365048.  
Not applicable.  
All relevant data presented in the article are stored according to institutional requirements and, as such, are not available online. However, all data used in this manuscript can be made available upon request to the authors.  
We are grateful to [PERSON], [PERSON], and [PERSON] for their data recording and checking work during the process. We are also grateful to [PERSON], [PERSON], and [PERSON] for their help in project management. We thank Shihezi University for providing the experimental conditions for us to successfully complete this experiment. Finally, we thank the instructor for his constructive comments on the earlier version of the manuscript.  
The authors declare no conflict of interest.  
References
## 5 Repair Qualification Study
### Second Stage--Strength Test of the Bonding Systems
#### 5.2.2 Comparative Tests of the Tearing Off the Shield from the Leading Edge  
The test consisted of a comparison of the tearing force F (Figure 17) of the shield parts with the factory bonding and the bonding created according to the proposed technology (Bonding DP490). The test was also carried out on an Instron Fatigue system. The constant speed of actuator movement equal to 2 mm/min was applied. Force was recorded as a function of displacement. Five samples from each series were tested. Distinctive fractures of bonding are given in Figure 18. All the fractures were cohesive; therefore, the rubber was damaged.  
Figure 16: Results of the bonding DP490 obtained during the shear strength test: (**a**) samples without aging, (**b**) samples after aging.  
Figure 17: Comparative tests of the tearing off of the shield from the leading edge: (**a**) Scheme, (**b**) Sample on the test stand.  
The results of test of the learning off of the shield from the leading edge are given in Figure 19.  
The strength of the DP490 bonding was lower by about 30% in comparison to the factory bonding. The determined standard deviation indicated that the dispersion of the results was greater in the case of the DP490 bonding. The exposure in the climatic chamber did not affect the strength of the bond, both in the case of the factory bonding and the DP490 bonding.  
The differences in strength between the factory-bonded samples and samples after the proposed repair technology may be due to the fact that, during preparation for repair, the shield must be folded back, and thus residual stresses are introduced. Due to these stresses, the tearing force is lower than that in the case of factory bonding.  
The test samples were taken from a main rotor blade operated on a helicopter. Therefore, the factory bonding was exposed to actual atmospheric conditions. Similarly, the aged  
Figure 19: Results of the comparative tests of the tearing off of the shield from the leading edge: (**a**) Factory-bonded sample without aging; (**b**) Factory-bonded sample after aging; (**c**) DP490 bonding sample without aging; (**d**) DP490 bonding sample after aging.  
Figure 18: Characteristics of fractures during tests of the tearing off of the shield from the leading edge: (**a**) Factory bonding sample without aging, (**b**) Factory bonding sample after aging, (**c**) Bonding DP490 sample without aging, (**d**) Bonding DP490 sample after aging.  
DP490 bonded samples were exposed to variable climatic chamber conditions. In these cases, the maximum force occurred at a displacement of about 2.5 mm during the tearing off of the shield from the leading edge.  
The maximum force occurred at a displacement of approximately 5 mm during the test of the bonding DP490 samples without aging.  
It was assumed that the faster force increase was due to internal stresses in the bondingply and at the rubber-bonding ply border as the result of aging processes under environmental conditions.
###### Abstract  
Ammonia is an ideal renewable, carbon-free fuel and hydrogen carrier, which produces nitrogen and water after complete combustion in the presence of oxygen. However, ammonia has low reactivity, slow flame-propagation speed, and carries risks of high nitrogen oxide (NO\({}_{x}\)) emissions. Co-firing ammonia with an industrial by-product gas (with CH\({}_{4}\) and CO being the main combustible materials) is a cost-effective and convenient method of improving the combustion characteristics of ammonia, but attention still needs to be paid to the NO\({}_{x}\) generation. Currently, the research on NO\({}_{x}\) formation during co-firing of ammonia with other fuel gases is still insufficient. In this study, a high-temperature furnace reaction system was used to investigate the NO\({}_{x}\) formation and inhibition mechanisms during the combustion of NH\({}_{3}\)/CH\({}_{4}\) and NH\({}_{3}\)/CO mixtures. By varying the ammonia blending ratio, excess air coefficient (\(\alpha\)), temperature, residence time, and fuel concentration, the key factors influencing NO\({}_{x}\) generation and inhibition were further analyzed. The results showed that when \(\alpha\) was no less than 1, the production of NO\({}_{x}\) initially increased and then decreased with an increasing proportion of ammonia in the fuel gas. Within the temperature range of 900 \({}^{\circ}\)C to 1500 \({}^{\circ}\)C, the amount of NO\({}_{x}\) generated during the combustion of the mixed gas gradually decreased with the increase in temperature. Under the conditions of NH\({}_{3}\)/CH\({}_{4}\) and NH\({}_{3}\)/CO, the emissions of NO\({}_{x}\) were higher than those during pure ammonia combustion.  
ammonia; nitrogen oxide; blending combustion; formation mechanism +
Footnote †: journal: Applied
_Applied_  
_sciences_  
_Insights on Niobium Micro-Alloyed Laser In Situ Synthesized Gamma Titanium Aluminide Alloys_  
**[PERSON]\({}^{1,2,*}\)\({}^{\copyright}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{3}\)**  
\({}^{1}\) Laser Enabled Manufacturing Group, Photonics Centre, Council for Scientific and Industrial Research,  
Pretoria 0001, South Africa  
\({}^{2}\) Department of Mechanical Engineering Science, Faculty of Engineering and the Built Environment,  
University of Johannesburg, Auckland Park Campus, Johannesburg 2006, South Africa  
\({}^{3}\) School of Chemical and Metallurgical Engineering, University of the Witwatersrand, 1 Jan Smuts Avenue,  
Johannesburg 2050, South Africa  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]; Tel: +27-12-841-2395  
**Abstract: The effects of micro-alloying gamma titanium aluminide (\(\gamma\)-TiAl) with niobium (Nb) using a laser melt pool as a melting pot are reported. The Optomec LENS machine was used to carry out the laser in situ alloying experiments where Nb, ranging from 6 to 10 (at. %), was added to the stable binary \(\gamma\)-TiAl alloy. The results of this study concluded that when a stable binary \(\gamma\)-TiAl alloy is micro-alloyed with Nb, there is a definite microstructural transformation, anneal twinning, promotion, and retardation of aluminium solubility in the dual and pure \(\gamma\) phases, respectively. Twinning in the as-built in situ alloyed ternary Ti-48 Al-Nb was for the first time reported in this study. It was observed that 6 at. % Nb promoted twinning in the as-built sample, which inferred that the sample might have room temperature ductility. In fact, it was shown that the twins formed in the as-built sample dissipated with the addition of Nb. A heat treatment temperature of 1200 \({}^{\circ}\)C promoted anneal twinning only in the binary alloy, as confirmed by XRD data. Meanwhile, this twinning was absent in all the ternary alloys when they were heat treated to a temperature of 1200 \({}^{\circ}\)C. Anneal twinning was confirmed only for the alloy containing 8 Nb (at. %) at 1400 \({}^{\circ}\)C. Stalk faults, dislocations, and dislocation pile-ups were observed in the \(\alpha_{2}\) phase of the alloys. Aluminium solubility was seen to increase in the \(\alpha_{2}\) + \(\gamma\) (\(\pm\)49 at. %) phase alloy and sharply decrease in the pure \(\gamma\) (\(>\)49 at. %) phase alloys. Most importantly, this study determined that the laser in situ alloying process was highly exothermic. The heat gained by the reaction was found to increase with the increase in niobium content.  
aluminium; anneal twinning; gamma titanium aluminides; heat treatment; laser in situ alloying; niobium
## 2 Geometric Noise Analysis  
Precision laser interferometers have many common noise sources. Among them, TTL noise is a coupling noise caused by angle jitter and lateral and longitudinal jitter. It is an important noise source in precision laser interferometers and exists in any type of interferometer.  
Figure 1 below is a schematic diagram of the geometric TTL noise analysis generated when the laser is incident on a steering mirror. For ease of explanation, we divide the noise into lever arm noise and piston noise.