esa-sceva/satcom-chunk-collection huggingface dataset:
# Distribution of Magnetic Flux Density under Stress and Its Application in Nondestructive Testing
## 3 Results, Analysis, and Discussion
### Finite Element Analysis  
Ansys FEM simulation software was deployed to perform and analyze the stress distribution and concentration in the X, Y, and Z directions of the SA 106 specimen under different compressive loads. The free boundary condition was applied on one side of the model, and the pushing tensile force was applied on the other side. The three-dimensional model automatic meshing methods adopted tetrahedral and hexahedral elements. The size of mesh elements was chosen to be 0.5 mm in order to create an efficient mesh with regular sizing through the volume and the faces. As three-dimensional FEM models are time-consuming, to save mesh and resolution time, only a half-symmetry of the plate was simulated, and it is shown in Figure 4.  
Figure 4: The 3D geometry of the specimen and the pins. (**a**) Half of the geometry; (**b**) the simulated geometry shape.  
Figure 3: Experimental results: (**a**) distribution of the magnetic flux density, \(B_{X}\), \(B_{Y}\), and \(B_{Z}\); (**b**) distribution of the subtract magnetic flux density, \(\Delta B_{X}\), \(\Delta B_{Y}\) and \(\Delta B_{Z}\)The distributions and intensities of stress and shear stress on the SA 106 plate applied by the four-point loading system were simulated using the Ansys software. The FEM was conducted for 0.1 mm displacement along the three directions, as shown in Figure 5. It can be observed that there is a concentration around the \(-60\) mm position on the bottom and upper sides, which corresponds to the position of the two left pins, where the range of \(\sigma_{X}\) and \(\sigma_{Y}\) is higher than \(\sigma_{Z}\).
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 2 Theoretical Background
### Atmospheric Attenuation in FSO Systems  
As an optical beam propagates through the atmosphere, it experiences attenuation due to scattering and absorption by air molecules and suspended particles. The total specific attenuation, denoted as \(\gamma_{\text{total}}\) (in dB/km), can be expressed as follows [1, 2, 3]:  
\[\gamma_{\text{total}}\ =\gamma_{\text{abs}}+\ \gamma_{\text{scat}} \tag{1}\]  
where  
\(\gamma_{\text{abs}}\) accounts for molecular and aerosol absorption;  
\(\gamma_{\text{scat}}\) represents scattering losses due to Rayleigh and Mie scattering mechanisms.  
In the visible spectrum, Mie scattering dominates in environments with fog, haze, and smoke, where particle diameters are comparable to the optical wavelength. Mie scattering is wavelength-dependent and generally follows an inverse power-law relationship [1; 2; 3].  
\[\gamma_{\text{Mie}}\propto\lambda^{-\text{q}} \tag{2}\]  
Here, \(\lambda\) is the wavelength (in nm), and q is an empirically determined exponent related to particle size distribution, typically ranging from 0.7 to 2.  
In contrast, Rayleigh scattering, caused by particles significantly smaller than the wavelength (e.g., air molecules), follows a steeper inverse relationship [1; 2; 3]:  
\[\gamma_{\text{Rayleigh}}\propto\lambda^{-4} \tag{3}\]  
As a result, shorter wavelengths such as 405 nm (violet) are more susceptible to scattering losses compared to longer wavelengths such as 650 nm (red), especially in foggy or dusty atmospheric conditions.
Accordingly, efforts are being made to apply photonic microwaves to large-scale industrial metrology [21] and space missions such as multiple satellite formation flying [22; 23]. In this investigation, we describe a 1 GHz microwave distance interferometer based on the 10 th inter-mode harmonic of a 100 MHz pulse repetition rate of a fiber mode-locked laser. A comprehensive analysis is made for correction of the systematic error through pre-calibration with respect to a commercial incremental He-Ne laser interferometer. The photonic microwave created in this study is able to offer an accurate length ruler with a 0.3 m wavelength stabilized to the Rb clock, with a fractional instability of 10\({}^{-11}\) at 1 s averaging. The repeatable systematic measurement error attributable to the optical and radio frequency (RF) electrical disturbance embedded in the interferometer system is identified and corrected by passive and active suppression schemes. Experimental results reveal that absolute positioning can be achieved within a residual error of 3.5 \(\upmu\)m (1 \(\sigma\)) over a 2.0 m distance, which corresponds to a phase resolution of \(\sim\)0.0084\({}^{\circ}\), which is in fact comparable to the phase resolving power of the phasemeter configured in this study using a lock-in amplifier.
## 1 Introduction
Single-receiver integer ambiguity resolution (IAR) has often been implemented in numerous LEO missions, and improved precision can be inferred from SLR validation [13; 16; 20]. Unlike the fixed double-difference ambiguity of terrestrial stations, a single-receiver IAR depends on the dedicated GNSS orbit, clocks, and wide-lane bias products [16; 29; 30], which were implemented in this study to improve orbit solutions. The single-receiver IAR of the Sentinel-3 and Swarm missions based on the GRG (Groupe de Recherche de Geodesie Spatiale) products of the CNES/CLS (Centre National d'Etudes Spatiales/Collecte Localisation Satellites) [31] analysis center is supported by previous studies [16; 20]. Furthermore, GNSS orbits and an IAR-enabled clock with the accompanying Observation-Specific Bias (OSB) provided by the Center for Orbit Determination in Europe (CODE) [30] was used to implement the single-receiver IAR of LEO missions [13]. For improved orbit solutions for the HY-2B, a single-receiver IAR was implemented in this study. This manuscript is organized as follows. Section 2 provides a brief introduction to the background dynamic model and the data process strategy used in HY-2B POD. An assessment and a discussion of the different orbit solutions are presented in Section 3. A discussion about the results is provided in in Section 4. Finally, Section 5 concludes the study and provides a general outlook.
## 1 Introduction
### Related Work  
LDA [6] is a Bayesian model for extracting latent semantic topics from text. It can be considered as the categorical/discrete analog of principal component analysis (PCA) to extract latent aspects from collections of discrete data. In LDA, the required posterior distribution is intractable, and hence it is not possible to apply exact inference to calculate it. Therefore, approximate inference techniques are usually employed. For the text topic modeling in our case study, where there is a large topic overlap that makes a difficult inference, we consider both variational Bayes (VB) and collapsed Gibbs sampling (CGS) techniques.  
VB techniques, which are the original inference technique used in LDA [6], is a family of optimization-based techniques for approximate Bayesian inference. VB belongs to the class of variational inference (VI) methods, which can also be used in the frequentist context for maximum likelihood estimation when there are missing data.  
VI inference techniques directly optimize the accuracy of an approximate posterior distribution. This is parameterized by free variational parameters [21]. In VI, we choose a family of distributions and then we find the member of this family (by finding the setting of the parameters) for which a divergence measure is minimized. In practice, the inference problem is translated into an optimization problem and solved with the standard expectation-maximization (EM) algorithm [21; 22]. The EM performs two steps: the E-step, which estimates the topic distribution of each training document using current model parameters, and the M-step, which updates the model parameters. The success of VB as approximate inference strategies for LDA has resulted in their widespread use for TM by practitioners [23]. Two of the main Python implementations are Scikit-learn[24] and Gensim[25]. However, when working with a large topic overlap, which complicates the inference, the quality of extracted aspects can be compromised since that variational EM can lead to inaccurate inferences and biased learning.  
[PERSON] and [PERSON] in [26] presented the CGS technique, which is a Markov chain Monte Carlo procedure. Given that CGS is a straightforward approach and rapidly converges to known ground truth, it can be considered an alternative to VB approaches for many LDA variants. However, CGS may present the critical drawback of high computational complexity that makes it inefficient on large data sets. In [27], a method was further presented for improving the computational performance of CGS-based LDA while providing similar results of the original LDA algorithm.  
In this article, the objective is to provide the reader with a comparison of different VB and CGS inference techniques used in LDA fitting. To this aim, we do not alter the LDAmodel, nor do we use document pooling or transfer learning, but instead we present a method that employs the full joint distributions arising from the standard LDA model.
# An Integrated Adaptive Kalman Filter for High-Speed UAVs
## 3 Verification and Analysis
### Practical Data Based Verification
#### 3.2.4 Results and Analysis of Experiment  
The whole process lasted 2873 s including take-off, flying and landing. Based on GNSS data, the diagrams of 3D trajectory and velocity curve in E-N-U frame are shown in Figure 5.  
The red point in the 3D trajectory diagram is the starting point, and the sections marked with (1)-(13) correspond to the flight in Section 3.2.2. The label \"GNSS Misalignment\" in the diagram of the curve represents the anomaly of GNSS during 1470 s-1505 s (lock-loss, the number of satellite is less than 4). So GNSS information is not used for measurement update in this period.  
Comparing the performance of SHAKF, \(\mathrm{H_{\infty}}\) filter, STF and SAKF, the results are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Error** & **Value** \\ \hline Horizontal attitude & \(30^{\prime}\) \\ Yaw & \(50^{\prime}\) \\ Horizontal position & \(10\) m \\ Height & \(20\) m \\ Horizontal velocity & \(0.1\) m/s \\ Vertical velocity & \(0.5\) m/s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Initial Error of UAV.  
Figure 5: (a) 3D trajectory diagram; (b) diagram of eastward velocity, northward velocity and upward velocity.  
We choose some representative moments and use GNSS data as the reference, in order to compare the filtering accuracy of H\({}_{\infty}\) filter, STF and SAKF (divergence appears using SHAKF). The comparison is showed in Table 5.  
Table 5 shows the accuracy comparison of the H\({}_{\infty}\) filter, STF and SAKF (divergence appears using SHAKF).  
In Figure 6, the SHAKF method shows the filtering divergence, so it is not fitful in real high-dynamic environment. STF, whose accuracy closely depends on measurement information, has better tracking ability, but its accuracy will deteriorate when GNSS misalignment happens. The H\({}_{\infty}\) filter has great availability, even in the worst case, but its average accuracy is poor. SAKF has the best performance in the experiment. It maintains high stability and accuracy even when an anomaly appears. It can be seen from Table 5 that the error varies with time. The value of error in high-dynamic flight (500, 800, and 1800 s) is much larger than that in level flight (1000, 2500 s). Evaluating the accuracy of different filtering algorithms, SAKF is much better than STK and H\({}_{\infty}\) filter.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Time (s)** & **Error of STF (m)** & **Error of H\({}_{\infty}\) (m)** & **Error of SAKF (m)** \\ \hline
500 & 10.4 & 21.5 & 9.7 \\
800 & 13.7 & 34.9 & 12.4 \\
1000 & 8.4 & 14.4 & 7.9 \\
1800 & 14.5 & 32.4 & 10.6 \\
2500 & 4.6 & 15.5 & 4.1 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of filtering errors.  
In summary, the proposed SAKF algorithm, which combines the advantages of SHAKF, STF and H\({}_{\infty}\) filter, fits the high-dynamic flight of a UAV well.
# Feasibility of Wave Simulation in Typhoon Using WaveWATCH-III Forced by Remote-Sensed Wind
## 3 Results
### Applicability of Assembling Winds  
The comparison between the maximum wind speed (\(V_{max}\)) in 30 typhoons and the measurements from NHC was statistically analyzed, yielding an RMSE of 4.79 m/s and an SI of 0.20 for the assembling wind and an RMSE of 9.20 m/s and an SI of 0.37 for the ERA-5, as exhibited in Figure 4a. Similarly, the comparison between the assembling wind and ERA-5 data in the 30-typhoon period is depicted in Figure 4b. It is found that the underestimation in the results from the ERA-5 data is reduced in a typhoon period. In addition, the assembling wind speeds are close to that of ERA-5 under low-to-moderate atmospheric conditions.
## 1 Introduction
Kompetitive allele specific PCR (KASP), a single-step genotyping technology, has the major advantage of improved cost-effectiveness and has been widely used in linkage maps accompanied by simple sequence repeats (SSRs), cleaved amplified polymorphic sequences (CAPSs), sequence characterized amplified regions (SCARs), amplified fragment length polymorphism (AFLP), and random amplified polymorphic DNA (RAPD) [45]. Molecular markers are also required in purple plant breeding because of the plants' environmentally sensitive and semi-dominant traits during the introgression process. In purple cauliflower, 17 primers were designed using the homologous sequences of anthocyanin-related genes in the _B. olerca_ genome and _Arabidopsis_. Three polymerase chain reaction (PCR)-based markers, namely _BoMYB2_, _BoMYB3_, and _BoMYB4_ (<0.2 cM), were detected to provide a link to the high-resolution map of the _Pr_ gene in \"Graffiti\" purple cauliflower [27]. However, among the three molecular markers, only the _BoMYB3_ marker could distinguish the Sicilian purple \"PC-1\" and white curding Indian cauliflower parents, but it did not show co-segregation during the investigation in the F2 population [46]. Cauliflower (_B. olerca_ var. _btortytis_) and broccoli (_B. olerca_ var. _italica_) are different botanical varieties of _B. olerca_ and, hence, the evolutionary levels may be different. Identifying molecular markers closely linked to the quantitative trait locus (QTL) can make selection more efficient, effective, rapid, and cost-effective. Therefore, the construction of detailed molecular and genetic maps of the genome of purple broccoli is a prerequisite for MAS. In a previous study, a purple broccoli inbred line \"BT126\" was crossed with a green broccoli inbred line \"SN60\" without anthocyanin accumulation. The genetic analysis of the segregating F2 population revealed two QTLs located on chromosomes C7 and C9 named _Pur7.1_ and _Pur9.1_, respectively. During QTL-seq analysis, the target genes were localized in the intervals of 6.92 MB (36.78 MB-43.70 MB) on chromosome C7 and 73 K (C9:51.71 MB-51.79 MB) on chromosome C9. The blast search of the reference genome revealed that the C9 candidate interval (C9:73 Kb) contained 14 candidate genes, and one candidate gene _Bo9g174880_ was homologous to AT5G07990 in _Arabidopsis_, encoding a flavonoid 3'-hydroxylase (F3'H) involved in anthocyanin synthesis [47]. Since the color distinction between different green broccoli varieties was not obvious at low temperatures and the character of the purple head was investigated only in the mature plant stage, this study aimed to develop new molecular markers using the molecular genetic mechanism of the purple broccoli phenotype and accurately perform early screening for non-anthocyanin and anthocyanin-rich broccoli plants with superior market value.
## 2 Materials and Methods  
### Experimental Design  
Rolled grass sod of approximately 50 cm \(\times\) 200 cm was purchased from Rasenland Rottdorf. The population was a mixture from 40% ryegrass (_Lolium perenne_ L.), 40% Kentucky blue grass (_Poa prtensis_ L.) and 20% red fescue (_Festuca rubra_ L.) grown on a loamy sand. The rolled grass sod was cut in one direction as the width already fit with that of the containers. Each container was filled with 11.5 kg of sandy loam and the patches of grass sod with approximately 2 cm of substrate were put on top and slightly compacted and watered well to flush soil particles away from the grass sod surface.  
Nine containers were prepared on 4 June 2019 and were left to grow in an open cage greenhouse with ambient air temperature and natural light until 9 July to acclimate the plants to the experimental conditions. During that time, the plants were cut 5 times to a height of 5-6 cm. The last cutting was performed on 8 July one day before the experiment started.  
On 9 July 2019, the experiment was set-up: three of the containers were used as control and were continuously watered throughout the experiment according to the crop's water demand. Three containers received a spray application with 13 mL glyphosate solution, which was prepared according to the instruction of the packaging for grass (Dominator 480 TF: 1 mL diluted in 100 mL of distilled water). This treatment was watered according to the crops demand, too. The last three containers were no longer watered from 9 July. All containers were observed until 1 August 2019. During this time, spectral reflectance was acquired using a field spectrometer and a hyperspectral imaging camera. Following these measurements, plant samples were taken for chemical analysis (Figure 2). Finally, growth in the containers was documented using a standard digital camera (Leica D-Lux 2).  
During the first 9 days from 9 July (day 0) to 17 July (day 8) measurements were performed every day. After this period, containers were sampled two more times, on 25 July and on 1 August. This was necessary due to the considerably slower vegetation dieback of the drought treatment than of the glyphosate treatment.  
Figure 1: The field in front shows the characteristic colour of plants treated with the herbicide glyphosate in comparison to flowering oilseed rape in the background (April 2019; Whitby, North Yorkshire, UK, 54\({}^{\circ}\)28\({}^{\prime}\)39\({}^{\prime\prime}\) N; 0\({}^{\circ}\)39\({}^{\prime}\)36\({}^{\prime\prime}\) W; photo: [PERSON]).
## 3 Influencing Factors and Control Variables
### Urbanization  
As an important economic and social transformation, urbanization is treated as one of foremost influencing factors of ecological consumption. However, the impacts of urbanization on ecological consumption are controversial. On the one hand, because urbanization typically goes hand-in-hand with the industrial process, urbanization would increase ecological consumption due to more consumption of fossil fuels, construction land, cars, electric appliances, and so on. On the other hand, urbanization goes in parallel with a high population density and more ecologically oriented institutions, policies, plans, and technologies, which would permit more efficient use of ecological consumption and, thus, reduce ecological consumption.  
By employing the EF as the proxy of ecological consumption and based on panel datasets, [PERSON] and [PERSON] confirmed a positive relationship between urbanization and ecological consumption in 11 emerging countries during 1971-2014 [19], [PERSON] et al. confirmed a positive relationship in 59 Belt and Road countries for the time period 1990-2016 [20], and [PERSON] and [PERSON] confirmed a positive relationship in 14 sub-Saharan Africa countries for the time period 1990-2014 [21].  
By employing the EF as the proxy of ecological consumption, some literature confirmed a negative relationship between urbanization and ecological consumption. Based on time series datasets, [PERSON] et al. confirmed a negative relationship in South Africa for the time period 1965-2014 [22], [PERSON] and [PERSON] confirmed a negative relationship in India for the time period 1971-2014 [23], and [PERSON] et al. confirmed a negative relationship in Nigeria for the time period 1971-2013 [24]. Based on a panel dataset during 1990-2013, [PERSON] et al. confirmed a negative relationship in MINT countries (Mexico, Indonesia, Nigeria, and Turkey) [25]. Based on three panel datasets for the time period 1975-2007, [PERSON] and [PERSON] found that urbanization would reduce ecological consumption in 15 Middle East and North African (MENA) countries, eight oil-exporting MENA countries and seven non-oil-exporting MENA countries [26].  
It is expected that the negative impacts of urbanization on ecological consumption are stronger than the positive impacts and would dominate the relationships between urbanization and ecological consumption. High-density, compact, and modern urban lifestyles are more likely to bring lower levels of ecological consumption. Therefore, the following hypothesis is established:  
**Hypothesis 1 (H1)**. _Urbanization would reduce ecological consumption._
# An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
## 3 Proposed Scheme
### Improved Energy-Efficient LEACH Protocol
#### 3.2.3 Cluster Formation Algorithm of the Proposed IEE-LEACH  
Once the election of CHs is completed, the CHs will inform the other nodes of the information that they have become the CH in this round. To complete this function, each CH node will send an advertisement message to all the other nodes in the form of broadcast with a non-persistent carrier-sense multiple access (CSMA) MAC protocol [33].  
Each member node decides whether to participate in cluster formation, according to the signal intensity of the message that is transmitted from the BS and each CH node. Since the symmetric propagation channel model is used by the proposed IEE-LEACH protocol, the election of a cluster head is only related to the pure signal strength [15]. The stronger the received signal is, the closer the distance between member nodes to the sink node or the CH is. Comparing the distance of the nodes to the CHs and that of the nodes to the BS, the nodes closer to the base station do not participate in cluster formation and directly send data information to the BS.  
As shown in Figure 4, when the cluster head in the network is elected and the cluster head broadcasts messages to other nodes, the non-cluster head node A finds the nearest cluster head B according to the received signal strength, and calculates the distance between them, i.e., \(d_{AB}\). When \(d_{AB}>d_{AtoBS}\) (\(d_{AtoBS}\) denotes the distance between the node A and BS), node A does not join any cluster and communicates directly with the base station during the data communication. When \(d_{AB}<d_{AtoBS}\), node A joins the cluster where cluster head B is located and acts as a member node in the cluster.  
This means that not all nodes in the WSN participate in cluster formation. A node closer to the CH decides to join the cluster whose CH transmits the strongest signal to the nodes by comparing all the signal intensities of the messages sent by the CHs. After each node closer to the CH determines which CH will join, it needs to inform the CH that it has become a member node of this cluster. Each node closer to the CH sends a join-request message (Join-REQ) to the chosen CH, which includes the node's identification and the CH's identification.  
Figure 5 shows the process of CH selection and cluster formation of the proposed IEE-LEACH protocol. First, the related energy and the distance to the BS are calculated. Then, the threshold \(T(s_{i})\) in each round is determined by Equation (17). Each node closer to the CH needs to generate a random number and then compare it with \(T(s_{i})\). If the result is less than or equal to \(T(s_{i})\), then the node will be chosen as the CH. The distance to CH is calculated. The nodes whose distance is closer to the BS rather than the CH will not take part in the process of cluster formation and will directly transmit data to the BS. Finally, the transmitted data adopt the method of single hop, multi-hop and hybrid communications.