esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantifying Moss Response to Metal Contaminant Exposure Using Laser-Induced Fluorescence
###### Abstract  
Tracing sources of contamination, including potentially toxic elements (PTEs), has historically been achieved through sampling and analysis of soil or biota, which are labor-intensive, costly, and destructive methods. Thus, availability of a non-destructive in situ remote sensing method for monitoring metals deposited in biota is of great interest. Laser-induced fluorescence (LIF) is an emerging spectroscopic and imaging technique that documents changes in molecular energy level in plants as a biological response to metal contamination. For a proof-of-concept study and preliminary experiment, moss was selected for experimentation due to its long history of use in tracing atmospheric deposition of PTEs. Consecutive treatments of copper chloride (CuCl\({}_{2}\)) were administered to three moss samples, simulating wet deposition every 48 h over 10 days until reaching cumulative Cu concentrations of 2.690 to 8.075 umol/cm\({}^{2}\). While these Cu amounts are above environmentally relevant concentrations, they allowed the best conditions for testing and fine tuning of the imaging and data processing protocols presented in this paper. Moss fluorescence was induced using both 532 nm green and 355 nm UV lasers. A CMOS camera captured images of the LIF response, and red-green-blue (RGB) decimal code values were extracted for each pixel in the images, and pixel densities of color channels from treated and untreated moss samples were compared. Results show a shift towards lower color decimal codes corresponding to increased Cu concentration. We developed and contrasted multiple quantitative analyses of color distributions and demonstrated that LIF shows great promise for remote sensing of Cu accumulation in moss at umol/cm\({}^{2}\) levels. Though currently, the method would be limited to highly toxic sites, it illustrates the possibility and provides a framework for development of higher-sensitivity methods to detect nmol/cm\({}^{2}\) that are viable for urban contamination level monitoring.  
b  
Myophyte; copper; environmental contamination; metal detection
## 2 Experimental  
MWCNT were synthesized via the chemical vapor deposition (CVD) method. Aligned MWCNT arrays were grown on silicon substrates as a result of the thermal decomposition of toluene at a temperature of 800 \({}^{\circ}\)C. The preparation method details can be found in [32], for example, and scanning and tunnelling electron microscopy of the nanotubes used have been presented in our previous works [27; 33].  
Commercially available [34] silicon carbide whiskers were used for ceramics preparation. Whiskers have a diameter less than 2.5 \(\upmu\)m, and the average length of 50-80 \(\upmu\)m.  
Aluminium phosphate ceramic matrices are made of two components: A filler is a homogeneous mixture of Al\({}_{2}\)O\({}_{3}\) and AlN with mass ratio m(Al\({}_{2}\)O\({}_{3}\)):m(AlN)=9:1, and a binder, a dissolution of the aluminium hydroxide in a diluted phosphoric acid with a mass fraction of 60%. The molar ratio of H\({}_{3}\)PO\({}_{4}\)/Al(OH)\({}_{3}\) is equal to 3. A binder and filler were mixed together and a 10 mm diameter, 1 mm and 2 mm thick tablets were formed. After 24 h treatment at ambient conditions, samples were heated to 300 \({}^{\circ}\)C.  
During the mixing procedure MWCNTs and SiC whiskers were added and a series of hybrid samples with 10 wt. % of SiC, and 0.5, 1, 1.5, 2 wt. % of MWCNT was prepared. For comparison, a second series filled only with MWCNTs of the same fraction was fabricated (see Figure 1).  
Frequency dependencies of complex dielectric permittivity were measured using an HP4284A precision LCR meter in the frequency range 20 Hz-1 MHz, by a vector network analyser Agilent 8714 ET in the frequency range 1 MHz-3 GHz, and using a waveguide technique with a scalar network  
Figure 1: Scanning electron microscopy of the sample: SiC whiskers (**a**) and (**b**) MWCNTs embedded into phosphate matrix.  
analyser R2-408R (ELMIKA, Vilnius, Lithuania) in the range of 26-37 GHz. The real part of complex electrical conductivity \(S^{\prime}\) was calculated as \(S=w\psi_{0}\psi^{\prime\prime}\), where \(\psi_{0}\) is the permittivity of vacuum.
## 5 Analysis of Experimental Results
### Analysis of Two Improved Applications of Neural Network Models  
This article discusses the principles and methods of traditional backpropagation neural networks (BPNN) and graph convolutional neural networks (GCNN) for predicting soil heavy-metal pollution, and validates the accuracy and reliability of the predictions of both models through experiments. Overall, the predicted values obtained by these models indicate a good fit with the actual soil heavy-metal values. Thus, neural network models have good practical value in predicting heavy-metal pollution in soil.  
However, through experiments and comparative analysis, it is observed that compared with traditional neural networks, graph convolutional neural networks have a lower overall average error in predicting soil heavy-metal pollution. This is because graph convolutional neural networks are a type of deep learning model that extracts features from graph data that can be used for accurate prediction. Traditional neural networks, with their inherent problems, produce a higher overall average error in predicting soil heavy-metal pollution using this method. Both models demonstrate good efficiency and relatively high fitting accuracy.  
Through experiments and analysis, the following conclusions have been drawn:  
Figure 12: Absolute error between actual and predicted values of Ni for different models.  
(1) This study used a simulated annealing algorithm to optimize the fruit fly algorithm and replace the traditional BP neural network parameter optimization method, improving the traditional BP neural network with better prediction results for missing values of heavy metals in soil. Although the traditional neural network has fewer layers and lower simulation accuracy, it can still be used as a soil heavy metal prediction model when few data samples are available.  
(2) The graph convolutional network that integrates spatial information has a high accuracy in predicting soil heavy-metal content. However, research has found that predictive accuracy distribution is not uniform. For areas with flat terrain, the predictive accuracy for soil heavy-metal content was relatively high, whereas for areas with significant terrain changes, the predictive accuracy was relatively low. Thus, prediction of soil heavy-metal content is related to spatial distribution and also to the elevation of the sample points.  
(3) Different neural network models are suitable for different application scenarios and data sample sizes. Traditional neural network models are more appropriate for scenarios with limited data samples and few predicted variables.  
(4) Models such as the GCNN are applicable for application scenarios with relatively large sample sizes and significant prediction tasks.
## 2 Tunnel Description and Geological Conditions
### DEM-Based Numerical Simulation  
3 DEC 5.20 software is a 3D discrete element programme focusing on discontinuous media, and it provides a powerful support for the in-depth study of deformation damage and movement of rock masses by employing an explicit solution method to compute the discontinuous motions of blocks. The M-C model is used for the calculation of surrounding rock blocks in the model. The joint constitutive model adopts the Mohr Coulomb slip model, which considers the shear failure and tensile failure of joints as well as the shear dilation of joints. The Mohr Coulomb joint slip model is applicable to the contact between rigid blocks and the contact between deformable blocks. The block can be further divided using DNF. Tetrahedral mesh is automatically generated in blocks, and each element mesh responds to a specified linear or nonlinear stress-strain relationship. The average length of the tetrahedral area after meshing was 2 m in this article. In this case, seepage in rock mass was regarded as a hydrodynamic coupling problem, which meant that changes in seepage pressure affected the mechanical deformation. At the same time, the mechanical deformation would in turn affect the seepage pressure. This interrelationship was accurately expressed in the hydrodynamic coupling formulation for discrete fracture networks, especially the effect of mechanical deformation on fracture permeability, which was mainly reflected by the change in fracture openness. The numerical simulation calculation process of tunnel excavation approaching water-rich faults is shown in Figure 2.  
Figure 1: Overview of 2# edit of Xianglushan tunnel: (**a**) Location of 2# edit of Xianglushan tunnel; (**b**) Longitudinal geological profile of Xianglushan Tunnel; (**c**) Surrounding rock with fractured structure; (**d**) Misalignment situation; (**e**) Water leakage occurs in the tunnel.
# Models of HF Interference over Cyprus
## 3 Model Specification and Performance
In fact, the examples were randomized before each experiment, and the predictions obtained were then mapped back to their original order. The performance of the developed models was evaluated based on the Root Mean Squared Error (RMSE) and the Correlation Coefficient (CC) between the predicted and actual congestion values. Both RMSE and CC quantify how well a model fits a dataset. However, the RMSE indicates how well a model can predict in absolute terms, while the CC indicates how well a model can predict in relative terms. The profiles of the CC, RMSE, and CC variation across the spectrum reflect a distinctive pattern that is characterized by the superior performance of the NN approach, as shown in Figures 8 and 9. This is indicated by the fact that the CC for NN models is equal to or higher (this is mostly evident for allocations <40) than the CC for the non-linear regression models for most of the allocations, with the exception of allocations in the range of 64-80. With respect to the RMSE profile, we can observe that RMSE is notably low for both model approaches for allocations in the range of 50-70. This can be attributed to the fact that most of the congestion values for higher incident field thresholds for these allocations are zero due to the lack of ionospheric propagation support at these frequency allocations. This implies that diurnal and seasonal variations appearing at the lowest threshold will not appear at higher thresholds, and therefore the models will tend to saturate at these thresholds. The higher frequency allocations are actually also used for groundwater transmissions, and this is reflected in the higher RMSE values for allocations 80-95. Figure 8: Correlation coefficient versus allocation number. Examples of model fits for indicative days are shown in Figures 10-14, distributed across the HF spectrum. These plots show that both approaches can express the 24 h in interference variation with reasonable accuracy and capture the dynamic variability during a 24 h day interval across the HF spectrum. Figure 11: Allocation 34 at a threshold of 20 dB\(\mu\)V/m. Figure 9: RMSE versus allocation number.
## 4 Results and Analysis
### Comparative Testing of Transmission Mechanisms and Results Analysis
#### 4.2.6 NIC Resource Utilization Analysis  
In terms of RDMA resource utilization, the status-byte-assisted RDMA transmission mechanism demonstrates high efficiency and significantly reduces RNIC resource usage on the receiver end. This is because the status-byte-assisted RDMA transmission mechanism utilizes one-sided operations and implements a stateless transmission strategy on the receiver end, which eliminates the need to maintain QP connection status and requires only maintaining locally registered memory block information. Consequently, it greatly reduces the consumption of RNIC on-chip resources on the receiver end.  
Figures 16 and 17 compare the minimum RNIC resource consumption required during transmission for different RDMA transmission mechanisms. As shown in these figures, the RNIC resource consumption of the status-byte-assisted RDMA transmission mechanism is significantly lower than that of the sliding window RDMA transmission mechanism. In the sliding window transmission mechanism, to prevent the receiver from continuously sending ACKs, the sender initially issues three consecutive receive requests. Additionally, the sender may issue an RDMA Write With Immediate request and a receive request simultaneously, generating two CQEs. Consequently, the receiver might continuously receive three requests, so the minimum capacity required for both the receive queue and the completion queue is three. Overall, to reduce processing latency, the sender in the sliding window RDMA transmission mechanism initiates an ACK receiving thread and sets up two CQ queues to maintain continuous send and receive operations. Both sides issue multiple receive requests during initialization, which increases the consumption of RNIC on-chip resources. In contrast, in the status-byte-assisted RDMA transmission mechanism, the sender needs to issue two RDMA Write requests for each send operation, generating one CQE. An RDMA Read request, which also generates a CQE, is issued only when a status byte of 1 is encountered in the second status byte memory block. Since the sender serializes Write and Read requests, the minimum capacity of the sender's send queue is two and the completion queue's minimum capacity is one. The receiver does not explicitly send or receive any requests, so the capacity required for both the send queue and receive queue is zero. In addition, establishing an RDMA connection necessitates creating a completion queue with a minimum capacity of one.  
Figure 16: Comparison of the minimum RNIC resource consumption at the sending end for different RDMA transmission mechanisms.
# The Response of NDVI to Climate Change in the Lowest and Hottest Basin in China
## 3 Results and Analysis
### Correlation Analysis of NDVI and Climate Factors in the Turpan-Hami Basin  
Figure 8 shows that the spatial distribution of the correlation between NDVI and climatic factors in the Turpan-Hami Basin from 2001 to 2020 exhibits a significant threshold effect and regional heterogeneity. The relationship between mean annual temperature and NDVI is predominantly non-correlative (78.6%) (Figure 8a), indicating that vegetation response to temperature changes in this region is strongly constrained. Potential promoting effects of temperature increases are only observed in a few high-altitude cool areas (2.9% strongly positively correlated) and the foothill transition zone (15.6% weakly positively correlated), whereas the weak negative correlation in low-lying desert areas (2.9%) suggests that high temperatures may further compress vegetation survival space by intensifying evaporation. The correlation between mean annual precipitation and NDVI is also highly limited (Figure 8b), although the northwestern piedmont area and the north-east riparian grassland zone (4.4% strongly positively correlated, 19.4% weakly positively correlated) show a short-term water activation effect due to precipitation redistribution and runoff recharge. Notably, the weak positive correlation (37%) and strong positive correlation (9.8%) between PET and NDVI account for nearly half (46.8%) of the total, predominantly concentrated in the oasis-desert transition zone and the foothill transition zone  
Figure 7: Area ratio statistics of seasonal NDVI changes in Turpan-Hami Basin from 2001 to 2021 (%).  
(Figure 8c). This may be attributed to the regulation of shallow groundwater and human irrigation vegetation through evapotranspiration or the synchronicity of evapotranspiration and photosynthesis during brief wet periods.
# A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
## 2 Preliminaries  
### Dempster-Shafer Evidence Theory  
The D-S evidence theory [22; 23], as introduced by [PERSON] and then developed by [PERSON], has emerged from their works on statistical inference and uncertain reasoning. This theory is widely used in decision making [57; 58; 59; 60; 61] and information fusion [62; 63; 64; 65]. In this part, a few concepts about D-S evidence theory are given.  
Let \(\Theta\) be a finite nonempty set of mutually exclusive alternatives and \(\Theta\) is called the frame of discernment  
\[\Theta=\left\{\theta_{1},\theta_{2},\ldots\theta_{i},\ldots,\theta_{N}\right\}. \tag{1}\]  
The power set of \(\Theta\) is indicated by \(2^{\Theta}\), namely:  
\[2^{\Theta}=\left\{\emptyset,\left\{\theta_{1}\right\},\left\{\theta_{2}\right\}...,\left\{\theta_{N}\right\},\left\{\theta_{1},\theta_{2}\right\},...,\left\{ \theta_{1},...,\theta_{i}\right\},...,\left\{\theta_{1},...,\theta_{N}\right\} \right\}. \tag{2}\]  
For a frame of discernment \(\Theta\), a basic probability assignment (BPA) is a mapping \(m\) from \(2^{\Theta}\) to [0,1], formally defined by  
\[m:2^{\Theta}\rightarrow\left[0,1\right], \tag{3}\]  
which satisfies the following condition:  
\[m\left(\emptyset\right)=0, \tag{4}\]  
\[\sum\limits_{A\in 2^{\Theta}}m\left(A\right)=1. \tag{5}\]  
The mass \(m\left(A\right)\) represents how strongly the evidence support \(A\). \(A\) is called a focal element when \(m\left(A\right)>0\left(A\in 2^{\Theta}\right)\).  
Two BPAs \(m_{1}\) and \(m_{2}\) can be combined with [PERSON]'s combination rule as follows:  
\[m\left(A\right)=\left\{\begin{array}{ll}0,&A=\emptyset,\\ \frac{1}{1-K}\sum\limits_{B\cap C=A}m_{1}\left(B\right)m_{2}\left(C\right),&A \
eq\emptyset,\end{array}\right. \tag{6}\]  
\[K=\sum\limits_{B\cap C=\emptyset}m_{1}\left(B\right)m_{2}\left(C\right), \tag{7}\]  
where \(K\) is a normalization constant, reflecting the conflict between the two BPAs \(m_{1}\) and \(m_{2}\).  
For the sake of understanding, an illustrative example is given as follows to show how to combine two BPAs according to Equations (6) and (7).  
**Example 1**.: _Assuming \(m_{1}(\left\{a\right\},\left\{a,b\right\},\left\{c\right\})=\left\{0.6,0.3,0.1 \right\},m_{2}(\left\{a\right\},\left\{b\right\},\left\{c\right\})=\left\{0.7,0.2,0.1\right\}\), computational method of \(K\) and \(m(\left\{a\right\},\left\{b\right\},\left\{c\right\})\) is as follows:_  
\(K=m_{1}(\left\{a\right\})\times m_{2}(\left\{b\right\})+m_{1}(\left\{a\right\} )\times m_{2}(\left\{c\right\})+m_{1}(\left\{a,b\right\})\times m_{2}(\left\{ c\right\})+\)__  
\(m_{1}(\left\{c\right\})\times m_{2}(\left\{a\right\})+m_{1}(\left\{c\right\} )\times m_{2}(\left\{b\right\})\)  
\(=0.6\times 0.2+0.6\times 0.1+0.3\times 0.1+0.1\times 0.7+0.1\times 0.2=0.3\)  
\(m(\left\{a\right\})=\frac{m_{1}(a)\times m_{2}(a)+m_{1}(a,b)\times m_{2}(a)}{ 1-0.3}\)  
\(=0.6\times 0.7+0.3\times 0.2\over 1-0.3}\)  
\(=0.9\)  
\(m(\left\{b\right\})=\frac{m_{1}(a,b)\times m_{2}(b)}{1-0.3}\)  
\(=0.9\)  
\(m(\left\{c\right\})=\frac{m_{1}(c)\times m_{2}(c)}{1-0.3}\)  
\(=0.01\).
# An Efficient Optimization of the Monte Carlo Tree Search Algorithm for Amazons
## 3 Research Methodology
### Overall Framework of Algorithm  
We propose an MG-PEO algorithm optimization strategy based on the Monte Carlo tree search [22]. As shown in Figure 2, the optimized Monte Carlo tree search algorithm involves iteratively constructing a search tree, creating a new tree each round until the time constraint is reached or the game ends. At this point, the search stops and returns the best-performing root operation (a combination of actions from certain nodes in the second and third layers).  
Specifically, the following four steps are performed for each search iteration:  
1. Selection: Starting from the root node, a certain strategy is applied to recursively go down through the tree for expandable node selection. A node is expandable if it represents a non-terminal state and has unvisited children.
2. Expansion: Expand the tree by adding one (or more) child node(s), depending on the available operations.
3. Simulation: Run a simulation from a new node to produce results according to the default strategy.
4. Backpropagation: Back up the simulation results through the selected nodes to update their statistics.  
As seen in Figure 2, due to the use of the Move Groups strategy, the algorithm operates under two scenarios during the simulation phase: if the current node is an Arrow Shot layer node, it executes Rollout and Parallel Evaluation; if the current node is an Amazon Movement layer node, it performs Random Arrow Shot, Rollout, and Parallel Evaluation.  
The pseudo-code for the overall framework is described in Algorithm 1.  
```
0:\(s_{0}\) (The current game State)
0: Optimal decision
1:function MCTSSEARCH (\(s_{0}\))
2: create root node \(v_{0}\) with state \(s_{0}\)
3:while within the computational budget do
4:\(v_{1}\leftarrow\) Selection (\(v_{0}\))
5:\(v_{2}\leftarrow\) Expansion (\(v_{1}\), \(s_{0}\))
6:\(\Delta\leftarrow\)Simulation (\(v_{2}\), \(s_{0}\))
```  
**Algorithm 1**General Algorithm Framework  
Figure 2: MCTS plus MG-PEO general framework.  
7: Backup (\(v_{2}\),\(\Delta\))
8: **return** BESTCHILD(\(v_{0}\))
# Event-Triggered Fault-Tolerant ADRC for Variable-Load Quadrotor with Prescribed Performance
## 1 Introduction
The research of [26] proposes a real-time actuator fault detection and isolation (FDI) method for quadrotor UAVs using a linear parameter-varying model and directional residuals to balance fault sensitivity and disturbance robustness. The scholars of [27] presented a fault-tolerant tracking control method for quadrotors, combining recursive least squares-based motor parameter identification, an augmented state observer, and nonlinear model predictive control (NMPC) to handle actuator faults and disturbances. The study of [28] developed an output-feedback dynamic surface sliding mode passive fault  
Figure 1: Quadrotors with different payloads. tolerant control method for quadrotors under actuator controllable failures, significantly enhancing system robustness against both faults and uncertainties. Active disturbance rejection control (ADRC) has attracted significant attention due to its unique disturbance estimation and compensation mechanism. This innovative control strategy employs an ESO to achieve the real-time estimation and compensation of both internal and external disturbances [29]. Compared with conventional control methods, ADRC demonstrates distinct advantages including model independence, strong robustness, and enhanced disturbance rejection capabilities. Significant theoretical advancements were achieved in ADRC. Observer design evolved from the original nonlinear ESO to linear ESO [30, 31], adaptive ESO [32, 33], fuzzy ESO [34, 35, 36], and neural network-based ESO [37]. The innovations of control algorithm integrated ADRC with sliding mode control [38], model predictive control [39], and adaptive control [40], creating hybrid strategies with improved dynamic performance and robustness. Meanwhile, intelligent parameter tuning techniques employing particle swarm optimization (PSO) [41], grey wolf optimization [42], and reinforcement learning [43] enhanced the practicality of ADRC in engineering applications. This study proposes an event-triggered ADRC for variable-load quadrotors with actuator failures, introducing PSO for online parameter tuning. This study makes the following key contributions:  
1. A cascaded ADRC approach is developed by decoupling the system into position and velocity subsystems, each with dedicated ESOs and controllers. Combined with the event triggered mechanism, it effectively reduces the consumption of computing resources. 2. To address actuator failures, a fault-tolerant control method integrating prescribed performance functions with ADRC is proposed, which dynamically constrains tracking errors within predefined bounds through an error transformation mechanism. 3. The controller introduces PSO for online parameter adaptation, enhancing both system adaptability and robustness.