esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Stage Registration Strategy for Thermal-Visible Images in Substations
## 3 Experimental Results and Discussion
### Discussion  
In some special working scenarios, the main reason for the failure of existing registration methods is usually due to the large difference in scale between image pairs. The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **PSO-SIFT** & **MS-PIIFD** & **SI-PIIFD** & **RANSAC** & **Proposed** \\ \hline Avg of TRE & 113.13 & 20.55 & 82.76 & 6.36 & **4.53** \\ Std of TRE & 7.81 & 3.96 & 10.68 & 0.97 & **0.39** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average (Avg) and standard deviation (Std) of TRE across various registration methods.  
Figure 8: Average TRE values of image pairs with various methods.  
Figure 7: Comparision of precision performance according to different related methods.  
two-stage registration strategy proposed in this paper solves this problem well, and we adjusted the image pairs to similar scales during the first registration, which greatly improved the robustness of the proposed method in the different experimental scenarios. After that, through the second, more detailed, adjustment, the problem of complex pictures and blurred images is solved, so that the image pair can be perfectly registered. However, the method proposed in this paper only realizes the adaptive adjustment of some key parameters, and parameters such as feature point extraction threshold still need to be adjusted according to the actual scene. In the future, we will continue to research algorithm optimization and full-parameter adaptive adjustment so that they can be used in more application scenarios.
## 4 Performance Analysis
### Comparison of the Power Patterns for All Beamformers
#### 4.3.2. Comparison of the Power Pattern for the ODL-Based Beamformer  
In Figure 4b, the output power of the LCAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity is \(-\)0.289 dB, \(-\)0.54 dB and \(-\)0.76 dB, respectively. For 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity, the output power of LCPAA is \(-\)0.331 dB, \(-\)0.623 dB, and \(-\)0.885 dB, respectively. Then, the output power of the LRNAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity is \(-\)0.21 dB, \(-\)0.39 dB, and \(-\)0.55 dB respectively. The output power of the LCCAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity is \(-\)0.36 dB, \(-\)0.68 dB, and \(-\)0.95 dB, respectively. We can deduce from this figure that the output power of LCCAA is higher than that of the other beamformers. In this case, the LCCAA beamformer performance is good.
## 2 Materials and Methods  
### Study Sites  
Four study sites were equipped with light trap stations installed near the rice paddy fields. The site were located at (A) Chai Nat Rice Research Centre in the Mueang district of Chai Nat province (15.197\({}^{\circ}\) N, 100.137\({}^{\circ}\) E), (B) Suphan Buri Rice Research Centre in the Mueang district of Suphan Buri province (14.476\({}^{\circ}\) N, 100.086\({}^{\circ}\) E), (C) a private paddy field in the Sai Noi district of Nonthaburi province (14.060\({}^{\circ}\) N, 100.277\({}^{\circ}\) E) and (D) Khlong Luang Rice Research Centre in the Khlong Luang district of Pathum Thani province (14.118\({}^{\circ}\) N, 100.622\({}^{\circ}\) E) (Figure 1). Weather stations were located 0.2, 5.5, 33.8, and 1.5 km from sites A, B, C, and D, respectively.  
All sites were situated within the central plain of Thailand, which is characterized by flat topography, alluvium soil, and a tropical climate. Most agriculture in this area is rice plantation. Two main rice crop seasons pertain to the dry season and the wet season, where the dry season crop is generally planted in November-December and harvested in March-April and the rainy season crop is planted in May-June and harvested in September-October. Water for cultivation is mainly supplied by well-irrigation systems, so that double or continuous rice cropping with staggered planting times is sometimes practiced. The rice varieties most planted in the area are non-photoperiod sensitive and takes around 100 to 140 days to mature, including such types as \"Pathum Thani-1\", \"Chai Nat-1\", and \"Suphan Buri-1\". Due to the suitable environment and continuous rice planting throughout the year, BPH outbreaks occur frequently in this area.
# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 2 Literature Review
### Applications of MoE in NLP  
The concept of mixture-of-experts (MoE) was first introduced in the literature [26]. Due to its flexibility, scalability, low computational cost, strong specialization, and generalization capabilities, MoE has been widely used in AI subfields, such as computer vision [27], multi-modal learning [28], speech recognition [29], and recommendation systems [30], achieving great success. In the natural language processing field, research on MoE models mainly focuses on reducing the computational cost of large language models by decomposing large models into smaller expert components, thereby improving computational efficiency and optimizing resource allocation [31]. MoE research has become one of the effective solutions to reduce the computational costs of large models. Drawing inspiration from the MOE principle and its applications, and considering the significant imbalance in the sample sizes of different relation types in entity-relation extraction tasks, we innovatively introduce a mixed expert architecture into the relation classification layer of our model. By using a gating network, tasks are assigned to each expert based on their specialization, allowing the model to better address the data-imbalance issue and enhance the accuracy of triplet extraction.
# An Efficient Multi-Lane SpaceFibre Core for Spacecraft Data-Handling Networks
## 1 Introduction
Therefore, the Multi-Lane layer recovery (MLRec) time is reduced. Experimental results show that the E-ML-SpFi core supports the full-hardware protocol stack, that the core has low area, that the core allows a high operating clock frequency, and that the core has better performance in terms of recovery time. The rest of this paper is organized as follows. Section 2 briefly introduces the SpaceFibre protocol. Section 3 details the architecture of the proposed E-ML-SpFi core. The results of hardware implementations are discussed in Section 4. Finally, Section 5 summarizes the main conclusions.
## 3 System Performance under Both Weak Turbulence and Moderate-to-Strong Turbulence
### Average BER and Channel Capacity under Weak Turbulence  
To analyze the system performance influenced by various noises under weak turbulence condition, the expressions for the average BER and average capacity are derived. Following the approach detailed in [29], \(Q\)-function can be expressed as \(Q(t)=\frac{1}{\pi}\int_{0}^{\frac{\pi}{2}}\exp\left(-\frac{t^{2}}{2\text{sin} ^{2}(\theta)}\right)d\theta\), \(x>0\). Then, according to Gauss-Hermite polynomials, the integration is approximately written as \(\int_{-\infty}^{\infty}f(x)\exp\left(-x^{2}\right)dx\approx\sum_{i=1}^{n}w_{ i}f(x_{i})\), where \(w_{i}\) and \(x_{i}\) are the weights and zeros of the Hermite polynomials [29]. Finally, an expression for the average BER under weak turbulence can be derived as  
\[P_{e}=\frac{1}{2\sqrt{\pi}}\sum_{i=1}^{n}w_{i}\text{erfc}\left(\sqrt{\frac{ \gamma}{4}}\exp\left(\sqrt{2}\sigma_{x}x_{i}-\frac{\sigma_{x}^{2}}{2}\right) \right), \tag{11}\]  
where \(\sigma_{x}^{2}\) is the log irradiance variance, \(\gamma\) is the normalized SNR expressed as \(\gamma=\frac{R^{2}A^{2}}{\sigma^{2}}\), \(\sigma^{2}\) denotes the total noise which has a significant impact on average BER and has been explained in Equation (8), and \(\text{erfc}(\cdot)\) is the complementary error function defined as \(\text{erfc}(x)=\frac{2}{\sqrt{\pi}}\int_{x}^{\infty}\exp\left(-t^{2}\right)dt\).  
Note that the average BER is related to the log irradiance variance and the normalized SNR. In addition, structural constants, number of waves, binding distance result in log irradiance variance and total noise results in normalized SNR. Therefore, the average BER influenced by structure constants, link distance, and total noise is studied in this study.  
For weak turbulence, the average channel capacity \(C\) can be obtained by substituting Equation (3) to Equation (10). After some mathematical manipulations, a closed-form expression for the channel capacity under weak turbulence can be achieved as [28]  
\[C=\frac{B}{\sqrt{\pi}}\sum_{i=1}^{n}w_{i}\text{log}_{2}\left(1+\gamma\exp \left(2\sqrt{2}\sigma_{x}x_{i}-\sigma_{x}^{2}\right)\right), \tag{12}\]  
where \(B\) is the bandwidth.
## 2 Calculation of the New Thermal Constitutive Matrix \([M_{\tau}]\)  
In the present section, we obtain the analytical formulation of the matrix \([M_{\tau}]\) that has been developed to improve the results obtained with \([M_{\lambda}]\). In a steady state, and without internal heat sources, the equation of energy balance without mass transfer [10; 11; 12; 13] is in the CM,  
\[\widetilde{D}(-M_{\tau}GT)\ =\ G^{t}(M_{\tau}GT)\ =\ 0. \tag{1}\]  
The domain is meshed by tetrahedral elements. In CM, the tetrahedron of Figure 2 will be taken as the reference cell, with its nodes, and edges and dual surfaces oriented internally and externally, respectively. Figure 2: (**a**) Tetrahedron with primal nodes and primal edges. (**b**) Tetrahedron with dual surfaces. In CM, the discrete gradient operator for the reference tetrahedron is defined as follows:  
\[[G]\ =\ \left[ \begin{array}{cccc}-1&1&0&0\\ -1&0&1&0\\ -1&0&0&1\\ 0&-1&1&0\\ 0&0&-1&1\\ 0&-1&0&1\end{array} \right]. \tag{2}\]  
It is assumed that the temperature is distributed within the tetrahedron, following the affine function of Cartesian spatial coordinates  
\[\tau_{i}(x,\ y,\ z)\ =\ g_{x}\ x+g_{y}\ y+g_{z}\ z+a, \tag{3}\]  
where \(a\) is an auxiliary constant introduced in order to later develop a square matrix. Then, the temperatures in the primary nodes of the reference tetrahedron are as follows:  
\[ \begin{array}{rcl}\tau_{0}&=&g_{x}\ x_{0}+g_{y}\ y_{0}+g_{z}\ z_{0}+a\\ \tau_{1}&=&g_{x}\ x_{1}+g_{y}\ y_{1}+g_{z}\ z_{1}+a\\ \tau_{2}&=&g_{x}\ x_{2}+g_{y}\ y_{2}+g_{z}\ z_{2}+a\\ \tau_{3}&=&g_{x}\ x_{3}+g_{y}\ y_{3}+g_{z}\ z_{3}+a\end{array} , \tag{4}\]  
where the Cartesian coordinates of the nodes \(N\ =\ \{n_{0},n_{1},n_{2},n_{3}\}\) are the following  
\[\left[ \begin{array}{c}n_{0}\\ n_{1}\\ n_{2}\\ n_{3}\end{array} \right]\ =\ \left[ \begin{array}{cccc}x_{0}&y_{0}&z_{0}\\ x_{1}&y_{1}&z_{1}\\ x_{2}&y_{2}&z_{2}\\ x_{3}&y_{3}&z_{3}\end{array} \right]. \tag{5}\]  
The equation system shown in Equation (4), in matrix form, is as follows:  
\[\left[ \begin{array}{cccc}x_{0}&y_{0}&z_{0}&1\\ x_{1}&y_{1}&z_{1}&1\\ x_{2}&y_{2}&z_{2}&1\\ x_{3}&y_{3}&z_{3}&1\end{array} \right]\ \  
where \([Q^{\mu}]\) is the heat flow transmitted and \([M_{\tau}]\) is the constitutive matrix of thermal transmission we propose in this article. Suppose there is a matrix \([A_{\tau}]\), such that  
\[[M_{\tau}]_{6\times 6}\ =\ \left[\overline{S}\right]_{6\times 3}[A_{\tau}]_{3 \times 6}, \tag{19}\]  
where \(\left[\overline{S}\right]\) is the dual faces matrix of the cell  
\[\left[\overline{S}\right]\ =\ \left[ \begin{array}{cccc}S_{0x}&S_{0y}&S_{0z}\\ S_{1x}&S_{1y}&S_{1z}\\ S_{2x}&S_{2y}&S_{2z}\\ S_{3x}&S_{3y}&S_{3z}\\ S_{4x}&S_{4y}&S_{4z}\\ S_{5x}&S_{5y}&S_{5z}\end{array} \right]. \tag{20}\]  
The heat flow transmitted \([q]\) is  
\[[q]_{3\times 1}\ =\ [A_{\tau}]_{3\times 6}[G]_{6\times 4}[\tau]_{4\times 1}. \tag{21}\]  
We define an unknown vector \([X]\), such as  
\[[X]_{6\times 1}\ =\ [G]_{6\times 4}[\tau]_{4\times 1}. \tag{22}\]  
The density heat vector \(\left[\overline{q}\right]\) is  
\[\left[\overline{q}\right]_{3\times 1}\ =\ -\lambda\left[ \begin{array}{c}g_{x} \\ g_{y}\\ g_{z}\end{array}
###### Abstract  
When a platform carrying a space laser communication system moves through the atmosphere, the relative motion of the turret and the air produces fluctuations in the air density, which affects the beam propagation, and, hence, the laser communication performance. In this paper, we propose a performance analysis method for the space laser communication link to the airborne platform. By employing this method, which is based on a flow field simulation, we are able to determine the laser link's communication performance curves for various flying situations. At an altitude of 5 km and a signal-to-noise ratio (SNR) of 10 dB for the laser communication link, the bit error rate (BER) under a flight speed of 0.4 Mach is \(5.1\times 10^{-4}\). With each 0.1 Mach increase in speed, the BER decreases by approximately \(6\times 10^{-5}\). If the flight speed is 0.8 Mach and the flight altitude increases from 5 km to 10 km, the BER decreases from \(7.26\times 10^{-4}\) to \(1.89\times 10^{-4}\), but the system becomes more sensitive to changes in flight speed. Under the same flight altitude conditions, the beam spot on the downwind side is more affected by airflow, resulting in a general increase in the BER by approximately one order of magnitude, compared to the upwind side.  
free-space optical; communication; wavefront aberration; aero-optics; communication performance evaluation
## 3 Results and Discussion  
### Nutrient Status of Azolla  
The nutrient analysis of different _Azolla_ treatments is presented in Table 1. The highest organic matter (OM) was obtained from fermented _Azolla_ (80.66%), while dried _Azolla_ shows the lowest OM (76.58%). Fermented _Azolla_ also provides the highest percentage of nitrogen (6.32%), phosphorus (0.84), and calcium (2.15%); however, the fresh and dried _Azolla_ shows higher values of magnesium (0.58 and 0.59%), and fresh _Azolla_ provides the highest potassium (0.35%). The nutrients of _Azolla_ treatments are similar or higher than some previous report on _Azolla_ nutrient components. [PERSON] et al. [16] assessed the nutritive value of _Azolla pinnata_, and reported that the _Azolla_ contained organic matter at 75.73%, phosphorus at 0.26%, and calcium at 2.58%. [PERSON] and [PERSON] [17] determined the chemical composition of _Azolla pinnata_ used as feeding of egg-type chicks. They reported that the _Azolla_ provided calcium at 1.16%, phosphorus at 1.29%, potassium at 0.35%, and some trace elements (copper, zinc, manganese, and iron) at 16.74-775.73 ppm. The nutritional value of _Azolla pinnata_ as a feed for animals was evaluated by [PERSON] et al. [9]. They found that _Azolla_ contained organic matter at 82.66%, calcium at 1.64%, potassium at 2.71%, phosphorus at 0.34% and other trace elements (nickel, cobalt, copper, boron, zinc, iron, and manganese) at 5.3-2418 ppm. The different nutritive values in _Azolla_ might depend on several factors such as genotype, culture method, fertilizer supply, and water quality [18].
## 1 Introduction
Fortunately, the formulated optimization design is a convex problem and the toolbox CVX in MATLAB can be utilized for effectively solving the convex programs [15]. Simulation results show that better error performance can be obtained when the channel crosstalk problem is considered. Moreover, the proposed power allocation strategy can significantly reduce power consumption compared with uniform power allocation method with fixed chromaticity point, and the optimal power allocation strategy is discussed under different given CCT values for high-quality illumination. To sum up, our major contributions lie in the optimization design of the power allocation strategy for the QLED LED based VLC system under the practical communication and illumination constraints. The reminder of this paper is organized as follows: In Section 2, the system model for the multi-color LED based VLC system is established with channel crosstalk. Section 3 demonstrates the optimal power allocation strategy via collaboratively guaranteeing the practical luminance, chromaticity and SINR constraints. The corresponding simulation results and performance comparison are given in Section 4, and conclusions have been made in Section 5.
# Horizontal Wind Effect on the Aerodynamic Performance of Coaxial Tri-Rotor MAV
## 3 Experiment  
### Experiment Setup  
In order to obtain the performance of the MAV in the horizontal wind, wind tunnel tests were carried out to simulate the environment of MAV at 0 m/s, 2.5 m/s and 4 m/s. Experiment process of the coaxial Tri-rotor MAV considering the horizontal wind is shown in Figure 4.  
The dimensions of rectangular test section of wind tunnel are 3 m (length) \(\times\) 3 m (width) \(\times\) 2.5 m (height), ensuring sufficient space for maneuvering the multirotor platform. A settling chamber is attached before the test section to characterize the output wind. Wind is generated with two 3 m diameter, 45 kW fans. For the current low Reynolds number experiments the maximum testing velocity is 12.5 m/s. According to the theoretical analysis, the power, rotor speed and thrust of the coaxial Tri-rotor MAV with the horizontal wind are obtained accordingly to convert into the power loading and _FM_. In the test, propeller is specially made with unidirectional carbon fiber fabrics as stiffener based on the airfoil of C5.5/4.5, with 15.7 cm of pitch and 2.8 cm of chord at 75% position. The rotational speed range of rotor is 1500-2300 r/min. The motor is brushless DC motor(model: MSYSLRK 195.03).  
Figure 4: Experimental process.  
The main measuring equipment is as follows: (1) speed controller (model: BL-6); (2) tachometer (model: DT-2234C, accuracy: \(6\pm(0.05\%+1\mathrm{D})\)); (3) thrust sensor (model: CZL605, accuracy: 0.02% F.S.).
## 5 Optimization and Scheduling Model of the Complementary System  
In order to verify the rationality of the above capacity configuration method, this paper establishes the optimization and scheduling model of the complementary system to minimize the deviation power of the complementary system in the daily cycle period. The safety and economy of the complementary system and the cascade small water-wind-PV system without pumped storage transformation are compared and analyzed based on different extreme scenarios of wind and PV output fluctuation.
## 3 Results
### Applications Across Industrial Domains
#### 3.3.3 Transportation  
Maritime and shipyard systems are using hybrid models to detect faults in low-sensor environments. A multi-model approach (GB, LSTM, one-class SVM) enabled early fault detection in vessel bearings using entropy-based features [56]. Similarly, an SVM with principal component analysis (PCA) achieved predictive accuracy for ballast pump failures with only historical data [57].  
Railway applications demonstrate the value of legacy data and sensor fusion. DTs trained on historical records achieved 90% accuracy without real-time sensors [58]. Leveraging locomotive sensor data, another study proposed a hybrid framework combining SVM, RF, and digital twin technology, emphasizing the role of digital logging and IoT integration [59].
## 1 Introduction
Reference [33] explored the distributed fixed-time observer and fixed-time tracking controller of heterogeneous multi-agent systems. For the integrator-based multi-agent system, reference [34] proposed a fixed-time consensus technology based on TBG, which has more advantages than traditional fixed-time consensus technology. The above literature has taken into account the introduction of fixed time in order to better achieve the control objectives, but the method adopted has yet to be improved. Therefore, this paper studies the combination of the time base generator and event triggering. This article's primary contributions can be summed up in two ways. (1) We discussed the containment control protocol based on TBG and achieved practical timing consensus. The confinement control protocol is an efficient solution to the multi-agent system's temporal consensus issue because it can indicate the system's settling time in advance and reduce the initial control input. (2) A fully distributed event-triggered control mechanism was used in the architecture of the control protocol to reduce system utilization. In this mechanism, each controller samples only based on local information and does not require global information. Once the leader has created a convex hull using a fully distributed technique, the controller ultimately modifies the state of the followers to match it. This control method not only enhances the system's robustness and collaboration but also elevates the efficacy of the system. The following is the structure for the remaining portions of this paper: The second segment addresses a few key ideas and provides a thorough description of the issue. The third section describes the proposed containment control protocol, including TBG and a fully distributed event-triggered control mechanism, and validates the theoretical findings presented in the fourth section with simulation examples. The fifth and final section concludes by summarizing the findings. Symbols: The notations \(R^{n\times m}\) and \(R^{n}\) represent n-vectors of order \(n\times m\) and real moments. \(||\cdot||\) denotes a vector's Euclidean norm. \(\lambda_{max()}\) and \(\lambda_{min()}\) designate the matrix's maximum and minimum eigenvalues, respectively. \(\otimes\) is a representation of the Kronecker product. \(I_{n}\) denotes an \(n\times n\) identity matrix. The identity matrix is a square matrix with 1's on the diagonal and 0's everywhere else. A diagonal matrix is represented by \(diag(,)\).
## 2 Study Area and Data Sources
### Landslide Conditioning Factors
#### 2.3.3 Geological Factors  
Lithology is the key factor influencing landslide occurrence. Lithological units such as clayey and maryl sediments exhibit strong positive contrast values, indicating a higher likelihood of landslides, while hard rocks like sandstones and metamorphic rocks show negative values, suggesting that they contribute to slope stability [40]. Different lithologies have distinct physical and mechanical properties, such as strength, weathering rate, permeability, and shear strength. These characteristics directly influence slope stability [41]. Lithology (Figure 2k) also affects groundwater flow and storage, increasing pore water pressure and reducing effective stress--both critical factors triggering landslides.  
Fault density impacts surface structure and permeability, influencing landslide susceptibility. It is an essential factor of geological complexity and plays a significant role in the likelihood of landslides [42]. Fault density can be calculated by using the line density analysis tool in ArcGIS (Figure 2l).