esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Free Method for Damage Localization of Grid Structure
## 3 Damage Identification of Grid Structure  
### Characteristic Force and Displacement of Grid Structure  
Generally, the structure shown in Figure 1 was taken as an example to illustrate the characteristic force and displacement of the grid structure, and the essential relationship between the displacement difference and the damage position was analyzed. The node displacement vector in the structure and the element stiffness matrix in the local coordinate system are expressed as:  
\[u^{e}=\left[x_{1}y_{1}x_{2}y_{2}\right]^{T} \tag{12}\]  
\[K_{i}^{e}=\frac{EA}{L}\left[\begin{array}{cccc}1&0&-1&0\\ 0&0&0&0\\ -1&0&1&0\\ 0&0&0&0\end{array}\right] \tag{13}\]  
Figure 1: Grid structure with 11 elements.  
Taking the No. 3 element in Figure 1 as an example, the element stiffness matrix in the global coordinate system of the element and the corresponding \(\Delta K\) are expressed as:  
\[K_{i}^{e}=\frac{EA}{L}\left[\begin{array}{cccc}\frac{1}{4}&-\frac{\sqrt{3}}{4}& -\frac{1}{4}&\frac{\sqrt{3}}{4}\\ -\frac{\sqrt{3}}{4}&\frac{3}{4}&\frac{\sqrt{3}}{4}&-\frac{3}{4}\\ -\frac{1}{4}&\frac{\sqrt{3}}{4}&\frac{1}{4}&-\frac{\sqrt{3}}{4}\\ \frac{\sqrt{3}}{4}&-\frac{3}{4}&-\frac{\sqrt{3}}{4}&\frac{3}{4}\\ \end{array}\right] \tag{14}\]  
\[\Delta K=\sum\alpha_{i}k_{i} \tag{15}\]  
where \(\alpha_{i}\) and \(K_{t}\) are the damage coefficient and element stiffness matrix in the global coordinate system of the \(i\)th element, respectively, and \(E\), \(A\), and \(L\) are the elastic modulus, section area, and length of element, respectively. The first column of the non-zero vector in \(\Delta K\) is applied as the characteristic force to the No. 3 element, as shown in Figure 2:  
It can be seen from Figure 2 that the element 3 was in a state of force equilibrium. The same result can be obtained by using the remaining non-zero column vectors as characteristic forces. The above results are universal, namely: (1) The characteristic force was the balance force system. (2) The characteristic force of an element affects only that element, without affecting other elements. Therefore, when the characteristic force is applied to the structure, the corresponding characteristic displacement has the following characteristics: only the element corresponding to the characteristic force exhibited tensile and compressive deformations, while the other elements were free from the effect of forces and only performed rigid body motions. Furthermore, the change in structural displacement change due to damages was a linear combination of the characteristic displacements associated with the damaged elements. As for the displacement difference vector under any load, except for tensile or compressive deformations of damaged elements, the other elements only performed rigid body movements, without significant deformations. Therefore, the rate of length change of each element can be calculated according to the displacement difference of the structure before and after the damage, and the element with the larger rate of length change was the position where the damage was most likely to occur.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 3 Materials and Methods
### Sentinel SAR Data Preparation  
For both study sites and each date, Sentinel-1 SAR normalized backscatter values (\(\sigma^{0}\)) were calculated at plot scale in both VV and VH polarizations by averaging \(\sigma^{0}\) of all the pixels inside each plot, and for each polarization separately. For each polarization, the Sentinel-1 acquisitions from the morning passes (descending mode) and evening passes (ascending mode) were analyzed separately due to diurnal variations between both acquisitions. The diurnal variation is a result of the difference in the vegetation water content (VWC) between the morning and the evening. This difference in VWC could cause high difference in the radar-backscattering signal over vegetated plots between the morning and the evening acquisitions. In fact, several studies have reported that \(\sigma^{0}\) in the morning overpass registers higher values than \(\sigma^{0}\) in the evening overpass [61; 62]. Therefore, it is advisable to investigate separately each SAR temporal series acquired in the morning and the evening.  
Figure 4: Flowchart for the calibration of threshold values, and freeze detection for each land cover class and each polarization.
# Effects and Mechanisms of Microbial Remediation of Heavy Metals in Soil: A Critical Review
## 4 Microbial Remediation of Heavy Metal-Contaminated Soil
### Remediation Mechanisms
#### 4.1.3 Plant-Microbial Remediation  
Many microorganisms, including mycorrhizal fungi and other organisms in the rhizosphere, can enhance the ability of plants to absorb or adsorb heavy metals [34]. [PERSON] and [PERSON] [49] showed that when the concentration of Cd\({}^{2+}\) in soil is 1, 10 and 100 mg/kg, the uptake of Cd by mycorrhizal plants was 90%, 127% and 131% higher than that of non-mycorrhizal plants, respectively. Mycorrhizal fungi, for example, have mycelia that extend into the soil and effectively increase the surface area of plant roots [17]. [PERSON] et al. [50] showed that, after inoculation of mycorrhiza, the ability to absorb Cu\({}^{2+}\), Cd\({}^{2+}\) and Zn\({}^{2+}\) is improved. Endophytic mycorrhizala can help host plants develop resistance to heavy metal ions. Plant-endophytic mycorrhizala synergize mainly through  
Figure 2: Biomining mechanism of microbes.  
acidification, production of chelating agents, iron carriers, organic acids, and activation of metal phosphates. When the content of heavy metals in the soil reaches toxic levels, the mucus secreted by the fungal cell wall can combine with the polyphosphate and organic acid ions in the fungal tissue to combine the heavy metal ions and reduce the mobility. [PERSON] et al. [51] showed that the adsorption capacity of arbuscular mycorrhizal fungi on Mn\({}^{2+}\), Zn\({}^{2+}\) and Cd\({}^{2+}\) was equivalent to 1.6%, 2.8% and 13.3% of their dry weights, respectively. Moreover, the number and composition of root exudates change after the fungi infect the plant roots, thereby affecting the oxidation of heavy metals in the vicinity of the rhizosphere [52]. In addition, plant mycorrhizae have a protective mechanism that binds heavy metals to cell walls and prevents them from transferring to the plants [53].
## 3 Overview of the Selected Techniques
### SOGI-Type Adaptive Observer
#### 3.3.2 Observer Design  
Let us consider the following transformation:  
\[\Xi=\mathcal{T}\xi, \tag{32}\]  
where  
\[\mathcal{T}=\frac{1}{2\omega}\biggl{[}\begin{array}{cc}1&1\\ -1&1\end{array}\biggr{]}.\]  
Then, the transformed state and output matrices are given by:  
\[\mathcal{A}_{\Xi}=\mathcal{T}\mathcal{A}^{-1}=\mathcal{A},\mathcal{C}_{\Xi} =\mathcal{C}\mathcal{T}^{-1}=\bigl{[}\begin{array}{cc}\omega&\omega\end{array} \bigr{]}.\]  
Then, the transformed dynamic model for the phase voltage \(U_{a}\) is given by:  
\[\dot{\Xi}=\mathcal{A}_{\Xi}\Xi, \tag{33}\] \[U_{a}=\mathcal{C}_{\Xi}\Xi. \tag{34}\]  
For model (33) and (34), the following Luenberger type linear observer is designed:  
\[\dot{\Xi}=\hat{\mathcal{A}}_{\Xi}\dot{\Xi}+\mathcal{L}\bigl{(}U_{a}-\hat{U}_{ a}\bigr{)}, \tag{35}\]  
where \(\mathcal{L}=\bigl{[}\begin{array}{cc}\mathcal{L}_{1}&\mathcal{L}_{2}\end{array} \bigr{]}^{T}\) is the gain matrix for the observer and  
\[\hat{\mathcal{A}}_{\Xi}=\biggl{[}\begin{array}{cc}0&\omega_{n}+\hat{ \Delta}\omega\\ -(\omega_{n}+\hat{\Delta}\omega)&0\end{array}\biggr{]}. \tag{36}\]Moreover, the gain normalized frequency deviation estimation law is given by:  
\[\hat{\Delta\omega}=-\frac{\gamma(\mathcal{L}_{1}+\mathcal{L}_{2})\hat{\omega}\hat{ \Xi}_{1}\big{(}U_{a}-\hat{U}_{a}\big{)}}{\hat{\Xi}T\hat{\Xi}},\gamma>0. \tag{37}\]  
Similar to the gain normalized adaptive observer presented in Section 3.2, only local stability results are presented in Reference [29] due to the presence of the gain normalization term in Equation (37). From the estimated state vector \(\hat{\Xi}\), the state vector \(\hat{\xi}\) can be calculated through the following equation:  
\[\hat{\xi}=\mathcal{T}^{-1}\hat{\Xi}, \tag{38}\]  
where  
\[\mathcal{T}^{-1}=\hat{\omega}\bigg{[}\begin{array}{cc}1&-1\\ 1&1\end{array}\bigg{]}.\]  
The same observer (35) can be applied to grid phase voltages \(U_{b}\) and \(U_{c}\). From the signals \(U_{a}\), \(U_{b}\), and \(U_{c}\) and their derivatives (i.e., quadrature signal) obtained through the observer (35), the sequences can be easily estimated through the Equations (7)-(9).
## 4 Discussion
### Stride Length  
Similar to previous studies, we observed that the spatial cues, the SI and SD conditions, had a significant effect on the stride lengths of the participants. The significant difference between the Before and After stride length values in both the SI and SD conditions indicates an immediate main effect of the spatial cues on the stride length. The increases and decreases in distance, though gradual and uninstructed, led to similar increases and decreases in the stride length as was shown in the previous study with healthy young participants [25]. The stride length in the SD condition after the change was significantly shorter than before the change. It was the largest change in stride length amongst the conditions, potentially because the participants feared passing through the avatar and made an effort to reduce their strides even though not instructed to do so.  
Additionally, though not explored in the previous study, we observed a learning carry-over effect in the SI condition with a significant increase in the Post section compared  
Figure 10: The gait speed change ratio values showing the comparison between the Base and Post sections (Base-Post) \(=\frac{\text{Post}-\text{Base}}{\text{Base}}\) and the change between the Before and After sections (Before-After) \(=\frac{\text{After}-\text{Before}}{\text{Before}}\). SI—Spatial Increase; SD—Spatial Decrease; PI—Phase Increase; PD—Phase Decrease. **—\(p<0.01\); ***—\(p<0.001\).  
with the Base section. Though there was a slight decrease in the SD condition, the difference was not significant, which could have been due to the shorter strides being more unstable, so the participants reverted to their stable and comfortable stride length [33; 34].  
The temporal conditions, PI and PD, did not have the same effect strength as the spatial cues on the stride length. This is perhaps due to the temporal cues being mainly auditory and the spatial cues being mainly visual; the latter tend to have dominance in spatial guidance and are more effective in triggering gait adjustments [35; 36]. The significant difference found between the Before and After sections in the PI condition could be attributed to the relationship between gait parameters; so, the increase in cycle time caused an increase in stride length [37].
# Response Analysis of Asymmetric Monostable Harvesters Driven by Color Noise and Band-Limited Noise
## 5 Conclusions  
This paper presents a detailed analysis of the response of asymmetric potential monostable energy harvesters (MEHs) excited by exponentially correlated color noise and band-limited noise excitations. For the excitation of exponentially correlated color noise, the mean square voltage and output power of the asymmetric potential MEHs are determined theoretically by the method of moment differential equation. Numerical and theoretical results are in good agreement and the results show that the increase in noise intensity and degree of asymmetry can enhance the power output. However, the increase in parameters such as damping ratio, electromechanical coupling coefficient, cubic coefficient, and corre  
Figure 14: Influence of center frequency on the output performance of the asymmetric MEH excited by band-limited noise when with different values of the quadratic coefficient.  
Figure 13: Influence of frequency bandwidth on the output performance of the asymmetric MEH excited by band-limited noise with different center frequency.  
lation time results in less power generated. Regarding the time constant ratio, there is an optimum value to maximize the output performance of the asymmetric MEHs.  
Numerical simulations were carried out to consider the response of asymmetric MEHs under band-limited noise excitation. Our simulated data illustrate that the degree of asymmetry influences the output power significantly. With the increase in the degree of asymmetry, the MEH tends to generate a larger output power at lower frequency ranges, and this phenomenon is similar to the response of asymmetric MEHs under down-sweep frequency harmonic excitation. Regarding the center frequency, the asymmetric MEHs generate considerable average output power near the resonance frequency, which is determined by the nonlinear restoring forces. For the frequency bandwidth, its appropriate increase could enhance the performance and its influence will be small for a large frequency bandwidth. This work provides insight into strategies to design MEHs accounting for the response characteristics of random excitations.  
Both authors contributed equally to this work. Both authors have read and agreed to the published version of the manuscript. This study was supported by the Science and Technology Project of Henan Province (grant no. 212102210438; 212102310248; 202102210281) and the China Postdoctoral Science Foundation (2020M682336).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 1 Introduction
The paper is organized as follows: in Section 2, the NOMA-based S-MIM system is described; in Section 3, the evolution from S-MIM to F-SIM is summarized jointly with key performance laboratory results; Section 4 provides an overview of the F-SIM system ground segment elements developed; Section 5 shows the ongoing R&D activities aiming to further improve the performance and to expand the technology use cases; Section 6 discusses the possible satellite technology commonalities with 5G eMTC requirements; finally, Section 7 provides the conclusions.
## 3 Position Tracking Controller Design
### Nonlinear Observer  
In the design of controller (7), (8), (11), and (14), the speed and the load torque are assumed to be known. In reality, it is difficult to exactly know the velocity and the load torque. To estimate the velocity and load torque, a nonlinear observer is designed as  
\[\begin{split}\dot{\theta}=&\dot{\omega}+l_{1}(\theta- \hat{\theta})\\ \dot{\omega}=&-\frac{3}{2}\frac{p\Phi}{J}i_{ \alpha}\sin(p\theta)+\frac{3}{2}\frac{p\Phi}{J}i_{\beta}\cos(p\theta)-\frac{B }{J}\dot{\omega}-\frac{\hat{\tau}_{L}}{J}\\ &+l_{2}(\theta-\hat{\theta})\\ \dot{\tau}_{l}=& l_{3}(\theta-\hat{\theta})\end{split} \tag{17}\]  
where \(\hat{\theta}\), \(\dot{\omega}\), and \(\hat{\tau}_{L}\) are the estimated position, velocity, and load torque, respectively, and \(l_{1}\), \(l_{2}\), and \(l_{3}\) are the observer gains. The estimation errors are defined as  
\[\tilde{x}=\begin{bmatrix}\hat{\theta}\\ \dot{\omega}\\ \tau_{L}\end{bmatrix}=\begin{bmatrix}\theta-\hat{\theta}\\ \omega-\hat{\omega}\\ \tau_{L}-\hat{\tau}_{L}\end{bmatrix}. \tag{18}\]The estimation error dynamics are  
\[\underbrace{\begin{bmatrix}\hat{\theta}\\ \dot{\omega}\\ \dot{t}_{l}\end{bmatrix}}_{\hat{x}}=\underbrace{\begin{bmatrix}-l_{1}&1&0\\ -l_{2}&-\frac{R}{7}&-\frac{1}{7}\\ -l_{3}&0&0\end{bmatrix}}_{A_{o}}\underbrace{\begin{bmatrix}\tilde{\theta}\\ \tilde{\omega}\\ \tilde{\tau}_{L}\end{bmatrix}}_{\hat{x}}. \tag{19}\]  
If \(l_{1}\), \(l_{2}\), and \(l_{3}\) are chosen such that \(A_{o}\) is Hurwitz, then the origin of the estimation error dynamics (19) is exponentially stable.
## 3 Methods  
### Feature Selection  
The objective of feature selection is to identify the most optimal subset of features, comprising only those that are relevant and non-redundant. Feature selection methods have been used to reduce the complexity of datasets in fields such as agriculture, finance and information [40,41]. In this study, a voting-based feature selection strategy was adopted to identify the key influencing factors, which integrates results from the mutual information method, RF-RFE and Lasso regression.  
Specifically, the principles of each method are as follows. Mutual information measures the dependency between features and target variables [42]. Although the method is computationally efficient, it may not handle feature redundancy well and could lead to model overfitting. RF-RFE gradually eliminates less important features based on the feature importance derived from the RF model [43]. It continues until the number of features reaches a predetermined threshold or until no further improvements in model performance are observed. While RF-RFE is more accurate in feature selection, it is computationally more intensive. Lasso regression applies an L1 regularization term, which penalizes large coefficients, forcing some coefficients to zero [44]. This helps with feature selection and prevents overfitting, but it may oversimplify the model, possibly discarding important features.  
The voting-based feature selection process is illustrated in Figure 4. For each feature, a ranking was generated by these three methods, and a final score was calculated by averaging the rankings. Features with the lowest average rankings were selected as the optimal subset, ensuring a more robust and comprehensive feature selection outcome. This voting strategy mitigates potential biases of any single method, offering a more balanced assessment of the feature contributions and reducing the likelihood of selecting irrelevant features.
## 2 Multilevel Edge Network Task Offloading Model  
This study focuses on emergency communication in disaster areas, agricultural data collection in remote residential areas, power network monitoring, and other scenarios where terminals are far away from network servers and cannot directly obtain computing services. This work proposes a multilevel edge network task offloading strategy on the basis of the MDP algorithm. When the terminal has actual computing requirements, the UAV, which undertakes two functions of MEC server computing or relay forwarding, follows a fixed algorithm to approach the terminal and hover flights with position adjustment [16]. The UAV will choose specific functions according to its own energy status and task size. That is, the UAV will determine whether the task is executed on the UAV as a MEC server or on a remote ground-fixed MEC server with a UAV relay. Figure 1 shows the task offloading model.  
As shown in Figure 1, this study constructs a three-level network offloading model, including the terminal equipment, the UAV layer, and the distal G-MEC layer. The UAV can consider offloading the task to terminals, the UAV, or the G-MEC according to the actual situation, such as the task size and UAV energy level. The UAV acts as a relay node for the offloading of the task by the G-MEC and connects the terminal device to the remote G-MEC.  
This study considers a multilevel offloading model of UAV relay to achieve rapid deployment, reduce the deployment cost, extend the network service range, and facilitate improved flexibility of the edge computing network layout. The UAV can provide the edge cloud service for many regions and realize the efficient utilization of network resources. During resource optimization, the UAV WPT technology, which is considered in the model to realize solar energy charging and solve its own energy  
Figure 1: Multilevel offloading network environment.  
level problems, ensures the reliability and Quality of Service (QOS)of the system and increases the working hours and system stability of the UAV.
# NAS-CRE: Neural Architecture Search for Context-Based Relation Extraction
## 5 Discussion  
The NAS-CRE model stands out in the field of relation extraction primarily due to its integration of cutting-edge NAS technology, which automates the fine-tuning of RNN structures to precisely match the specific requirements of the task. This significantly enhances the model's performance in complex relation extraction tasks. Additionally, the NAS-CRE model employs the advanced XLNet pre-trained model as its encoder, which demonstrates exceptional ability in decoding the deep semantic features of sentences. The self-attention mechanism of XLNet enables the model to capture long-range dependencies in text, which is crucial for accurately identifying complex relationships between entities. Compared to baseline models that focus only on the target entities, the NAS-CRE model comprehensively considers the potential influence of other entities in the sentence on the target entity. This makes the model not only more efficient but also more accurate when extracting cross-entity relationships.  
When evaluating the time complexity of models, the time complexity of NAS-RNN is particularly crucial. Therefore, the following section will focus on comparing the time complexity of NAS-RNN with that of traditional RNN.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Chemical Methods
#### 2.2.5 Sol-Gel Method  
The sol-gel method is a wet chemistry procedure involving the formation of a gel at room temperature through hydrolysis and polycondensation reactions of metal alkoxides [19; 42]. Metallic salts are dissolved in water or other solvents, forming a homogeneously dispersed sol or colloidal solution [48]. Van der Waals forces between the particles are induced. The interaction between the particles intensifies through stirring and temperature increase [19]. The mixture undergoes heating until the solvent is evaporated, resulting in the solution drying and the ultimate formation of a gel [19; 54].  
This method is particularly effective for the synthesis of iron oxide MNPs and silica coated MNPs. It enables the production of MNPs in significant quantities with controlled sizes and well-defined shapes [19].
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 4 Results and Analysis
### Comparative Analysis of Model Prediction Performance
#### 4.3.1 Comparison of Different Prediction Sequence Lengths
In summary, the LSPP model performs well under different prediction sequence lengths, especially in long-distance prediction with significant advantages. Compared with the traditional LSTM and GRU models, the LSPP can better handle long sequence data and improve prediction accuracy. This advantage stems from LSPP's effective modelling of long-term dependencies and its ability to capture complex patterns.
# Wide Area Key Distribution Network Based on a Quantum Key Distribution System
## 3 Public-XOR-Key Scheme for QKD Network
### The Complex QKD Network Scheme with Multiple Endpoints
Since all QKD keys are used only once and are independent of each other, the third party can never access the keys he should not have. Figure 3: The communications of three endpoints with Protocol 2 with repeated keys. The solid line stands for the QKD process. The relay node calculates and publishes \(Qk_{AB}=Qk_{A}\oplus Qk_{B}\), \(Qk_{AC}=Qk_{A}\oplus Qk_{C}\), \(Qk_{BC}=Qk_{B}\oplus Qk_{C}\), and then dismisses the QKD keys immediately. The dotted line shows the internal attack, where the shared key is not one-time-pad.
## Abbreviations  
CMG control moment gyro  
EEMD ensemble empirical mode decomposition  
EMD empirical mode decomposition  
IMF intrinsic mode function  
MISO multiple input single output  
MWA moment wheel assembly  
RMS root mean square  
SDP symmetrical dot pattern