esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
## 4 Cases Studies
### Case 3: Vibratory Component Analysis in a Six Degrees of Freedom System
These results underscore the effectiveness of the hybrid method in accurately capturing the vibratory characteristics of the system, demonstrating its utility in frequency estimation for complex systems with closely spaced frequency modes. This serves as a reference for the application of these methods in vibration analysis and the improvement of systems in similar contexts. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Reference** & **EMD** & **VMD** & **Hybrid EMD–VMD** \\ \hline  & & **Frequency [rad/s]** & & \\ \hline \(\mathbf{\Omega_{1}}\) & 20.982 & 21.01 & 21.18 & 21.05 \\ \(\mathbf{\Omega_{2}}\) & 38.941 & 35.62 & 41.82 & 40.15 \\ \(\mathbf{\Omega_{3}}\) & 60.299 & 5.98 & 58.00 & 58.80 \\ \(\mathbf{\Omega_{4}}\) & 82.682 & 21.00 & 58.00 & 83.20 \\ \(\mathbf{\Omega_{5}}\) & 93.120 & 92.10 & 91.15 & 92.36 \\ \(\mathbf{\Omega_{6}}\) & 115.54 & 92.10 & 111.02 & 113.82 \\ \hline \end{tabular}
\end{table} 
Table 8: Comparative analysis of frequency and amplitude estimates derived from multiple signal decomposition methods.
## 6 Constraints Applied to the Optimization Algorithm
### Constraints on the Maximum \(LN_{2}\) Temperature Rise and Pressure Drop  
As illustrated in Section 3.1, both the coolants flowing in the former and in the annular gap undergo a temperature rise and pressure drop through each cable segment due to friction with the duct walls and the heat contributions listed in Section 5. These fluid dynamic variations from the fluid's initial conditions occur upon exiting each cooling station, set to \(T_{0}\) and \(P_{0}\) by the user, respectively, and corresponding to a subcooled liquid state. Carefully monitoring these variations is crucial to ensure that the nitrogen is always maintained in its liquid state and to prevent bubble formation, which could compromise the heat exchange with the HTS tape. Consequently, constraints are imposed on the maximum temperature and pressure variations from their initial conditions \(\Delta T_{LN2 max}\) and \(\Delta P_{LN2 max}\), respectively, compared with the values calculated using Equations (1) and (2):  
\[\Delta T_{h}\leq\Delta T_{LN2 max} \tag{41}\]  
\[\Delta P_{h}\leq\Delta P_{LN2 max} \tag{42}\]  
These constraints have an impact on the entire cable geometry, particularly influencing the number of intermediate cooling stations required.
## 3 Results
### Model Validation  
To validate the model, we compared in situ measurements with estimated values based on the model inversion. The ranges used to construct the LUT's are shown in Table 3. The model inversion shows that only LAI produces a significant, yet noisy, relationship between in situ measurements and model inversion, in both the calibration and the independent plots (Figure 3). Chlorophyll was poorly estimated, and the soil moisture content was always estimated as completely dry. These were not taken into account any further, but rather fixed at their average values (see Table 1).  
The comparison between ProSail and NDVI shows that the estimated LAI is closely related to the NDVI (see Figure 3) (n = 10, p \(<\) 0.001, F = 313, r\({}^{2}\)adj = 0.97). However the ProSail model (n = 10, p = 0.02, F = 7.72, R\({}^{2}\)adj = 0.4275) performs slightly better than NDVI (n = 10, p = 0.036, F = 6.318, R\({}^{2}\)adj = 0.3714). Therefore, and for an improved comparability, we used the inverted model to estimate the leaf area index.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Minimum** & **Maximum** & **Stepsize** \\ \hline LAI & 0.001 & 3 & 0.01 \\ Chl-ab (\(\upmu\)g/cm\({}^{2}\)) & 1 & 100 & 0.1 \\ pSoil & 0.1 & 1 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model inversion look up table properties. LAI: leaf area index; Chl-ab: Chlorophyll a+b content; pSoil: dry/wet soil fraction (=1-soil moisture content).  
Figure 2: The effect of in situ observed ranges in vegetation properties on surface reflectance, based on simulations with ProSail. Surface reflectance is a fraction. The canopy properties (LAI and soil moisture content) have a much larger influence on reflectance than the leaf properties (Chlorophyll a+b and caroten).
## 5 The Influence of Surface Residual Stress on Hydrogen Permeation Resistance
### Analysis of Mechanical Properties of Materials after Hydrogen Charging
The reasons for the phenomenon mentioned above are that when the hydrogen atom permeates into the material, the permeated hydrogen atoms concentrate under the influence of the internal stress field within the specimens' material, leading to the formation of the hydrogen-containing brittle and hard substances, which increases the material's brittleness and reduces the plasticity, ultimately resulting in a decrease in the tensile property of the specimens. Before the experiment, the specimens are processed by shot peening, which generates the surface residual compress stress and refines the microstructure of the surface and subsurface. The process also reduces the material's lattice gap and increases the area of the unit grain boundary. Furthermore, the density of hydrogen traps in the material is also improved, the actual equivalent hydrogen pressure within the lattice under the same ambient hydrogen pressure reduces, and the permeation and intergranular diffusion of hydrogen atoms are inhibited, which slows down the erosion effect of hydrogen on the material. Therefore, as the surface compress stress of the specimen increases, the tensile strength of specimens after 5 h hydrogen charging also increases. As the surface residual compress stress of the specimen increases, the erosive effect of hydrogen atoms on materials is inhibited, which slows down the erosion rate of hydrogen atoms to the specimen's material. This reduces the brittleness of the specimen's material, which in turn makes the specimen's material exhibit a relatively good plastic mechanical property. This process usually manifests as the specimen's elongation ratio increases with the specimen's surface residual compress stress. However, the trend is slightly different from the surface of the specimen with a residual stress of 336 MPa due to the larger surface residual compress stress and more refined metal microstructure, which inhibits the tension process and reduces the extension length of the specimen during the tension fracture process. Therefore, the elongation curve of the specimen appears to increase first and then decrease, and the decreasing rate of the specimens' hydrogen embrittlement susceptibility index also slows down. Based on the analysis, the 336 MPa surface compress stress specimen has the best hydrogen permeation resistance among all the surface residual compress stress specimens.
## 4 Parameter Sensitivity Analysis
### Circumferential Grout Filling Phase
#### 4.1.3 Tunnel Radius \(R\)  
So far, the tunnel radius in shield tunnels mostly ranges from 4 m to 6 m [45; 46]. Some radii of super-large diameter shield tunnels reach 7-8 m [47]. The variation of the grout pressure \(P\) with the tunnel radius \(R\) is shown in Figure 12.  
Figure 12 shows that as the tunnel radius increases, the grout pressure distribution becomes more and more uneven. Increasing the tunnel radius will decrease the grout pressure near the upper grouting hole between the two grouting holes and increase the grout pressure near the lower grouting hole. The grout pressure in the middle area between the two grouting holes is less affected by the change in the tunnel radius.  
Figure 11: The effect of thickness of the shield tail void \(b\) on grout pressure \(P\) ((a) tunnel section, (b) rectangular coordinate system).  
The grout pressure near the top (\(\theta=5^{\circ}\)) of the tunnel decreases with the increase in the tunnel radius. When the tunnel radius \(R\) increases from 4 m to 8 m, the grout pressure decreases from 194.8 kPa to 174.7 kPa. The grout pressure decreases by 10.3%. Under a certain initial pressure condition, with the increase in the tunnel radius, the grout pressure near the top of the tunnel may be too low. The overburden pressure of the soil cannot be balanced, which causes an engineering hazard.  
On the other hand, the maximum grout settlement increases with the tunnel radius [44]. During the construction of large-diameter tunnels, the initial grout injection pressure needs to be increased to provide sufficient grout pressure and control ground settlement. However, increasing the initial grout injection pressure has the potential to cause grout overflow, surface uplift, and extreme grout pressure at other locations [48]. Alternatively, the pressure loss can be reduced during the grout flow process by increasing the number of grouting holes to maintain the stratum stability. Compared with the grout pressure near the top and bottom areas of the tunnel, the grout pressure near the sides (\(\theta=90^{\circ}\)) of the tunnel is less affected by the variation of the tunnel radius. The grout pressure decreases by 1.1%, from 281.9 kPa to 278.8 kPa.  
When the tunnel radius increases from 4 m to 8 m, the grout pressure near the bottom (\(\theta=175^{\circ}\)) of the tunnel increases from 370.4 kPa to 385.6 kPa. The grout pressure increases by 4.1%. The result shows that the variation amplitude of the grout pressure in the bottom area is smaller than that in the top area. The gravity and internal resistance of the grout both hinder the upward flow process of the grout. During the downward flow process of the grout, gravity has a promotive effect on the grout flow, whereas internal resistance of the grout hinders the grout flow process. The increase in radius makes the impact of gravity on the grout pressure more evident. The increase in radius prolongs the flow process of the grout, resulting in more pressure loss caused by internal resistance.
## 3 Results and Discussion
### REEs Extraction of the Solid Residue via the Acid Leaching
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & **Acid** & \multirow{2}{*}{**LREEs**} & \multirow{2}{*}{**HREEs**} & \multirow{2}{*}{**SUM**} \\  & **Consumption** & & & \\ \hline
1 & 3.55 \(\pm\) 0.02 mL & 84.07 \(\pm\) 0.09\% & 100.63 \(\pm\) 1.2\% & 88.81\% \\
2 & 2.69 \(\pm\) 0.03 mL & 89.24 \(\pm\) 0.1\% & 99.69 \(\pm\) 0.38\% & 92.23\% \\
3 & 0.9 \(\pm\) 0.04 mL & 87.15 \(\pm\) 0.06\% & 100.47 \(\pm\) 0.11\% & 91.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Hydrochloric acid dosage and extraction rate of LREEs, HREEs, and total REEs corresponding to different treatment methods. Figure 6: Extraction rate of single REE from CFA with different treatment methods. (**a**) Light rare earth elements (LREEs) and (**b**) Heavy rare earth elements (HREEs). Note: The standard deviations of all REEs extraction rates were less than 2%, and the extraction rate of some REEs exceeds 100%, which is due to the error in the allowable range of trace in the detection of ICP-MS instrument. The content of these REEs is small, which has little impact on the calculation of the extraction rate of total REEs.
## 3 Advances in Control Strategies
### Human-Robot Collaboration  
Safety, intuitiveness, and task sharing define effective human-robot collaboration (HRC) in Industry 4.0/5.0 workplaces. [PERSON] et al. (2018) enhanced safety by 30% relative to ISO 10218 static-zone limits. [PERSON] (2018) cut collision risk by 20% versus tactile-free impedance control. [PERSON] et al. (2018) increased interaction intuitiveness by 18% over admittance-based dual-arm control. [PERSON] et al. (2018) reduced assembly errors by 12% compared with position-only placement. [PERSON] et al. (2018) improved operator efficiency by 25% relative to keyboard-guided training. Complementary advances include [PERSON] et al. (2018), who achieved a 15% teleoperation-delay reduction, and [PERSON] et al. (2019), who improved compliance control by 18%, together advancing the state of intuitive, safe collaboration.  
Sensor-Based Human-Robot Collaboration Enhancements  
Rich sensor integration (force/torque, vision, haptics) remains pivotal to safe, responsive HRC. [PERSON] and [PERSON] (2018) reviewed multi-axis force/torque sensing, noting \(\sim\)25% precision gains in force-controlled applications across industrial case studies. [PERSON] et al. (2018) identified latent causal factors via sensor feedback in assembly, improving system adaptability by 15%. [PERSON] et al. (2018) advanced bilateral teleoperation using dynamic movement primitives, yielding a 15% latency reduction and smoother remote collaboration ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019). [PERSON] et al. (2018) enhanced assembly precision by 18% through integrated vision-force fusion, while [PERSON] and [PERSON] (2018) documented a 20% decrease in force-related errors across compliant tasks. [PERSON] et al. (2018) further reported a 15% dual-arm coordination improvement when sensor streams were co-registered, underscoring the sensors' central role in HRC safety and performance.
# Salicylic Acid Modulates Volatile Organic Compound Profiles During CEVd Infection in Tomato Plants
###### Abstract  
**Background:**_Citrus Exocortis Viroid_ (CEVd) is a non-coding RNA pathogen capable of infecting a wide range of plant species, despite its lack of protein-coding ability. Viroid infections induce significant alterations in various physiological and biochemical processes, particularly impacting plant metabolism. This study shows the metabolic changes upon vivoid infection in tomato plants (_Solanum Lycopersicum_ var. 'MoneyMaker') exhibiting altered levels of salicylic acid (SA), a key signal molecule involved in the plant defence against this pathogen. **Methods:** Transgenic _RNAi_SSH_ lines, which have the salicylic acid 5-hydroxyase gene silenced to promote SA accumulation, and _NahG_ lines, which overexpression a salicate hydroxylase to degrade SA into catechol and prevent its accumulation, were used to establish different SA levels in plants, resulting in varying degrees of resistance to vivoid infection. The analysis was performed by using gas chromatography-mass spectrometry (GC-MS) to explore the role of volatile organic compounds (VOCs) in plant immunity against this pathogen. **Results:** Our results revealed distinct volatile profiles associated with plant immunity, where _RNAi_SSH_-resistant plants showed significantly enhanced production of monoterpenoids upon vivoid infection. Moreover, vivoid-susceptible _NahG_ plants emitted a broad range of VOCs, whilst vivoid-tolerant _RNAi_SSH_ plants exhibited less variation in VOC emission. **Conclusions:** This study demonstrates that SA levels significantly influence metabolic responses and immunity in tomato plants infected by CEVd. The identification of differential emitted VOCs upon CEVd infection could allow the development of biomarkers for disease or strategies for disease control.  
solicolic acid; volatile organic compounds (VOCs); tomato plant defence; citrus exocortis vivoid (CEVd); metabolomics; gas chromatography-mass spectrometry (GC-MS)
## 2 Principle of Tomographic Laser Absorption
### Principles of the MIARO Algorithm
Therefore, in the iteration process, \(\theta\) needs to be adjusted according to the trend of the reconstruction parameters, and the amplitude of the smoothing coefficient \(\theta\) can be automatically tuned according to the ratio of the reconstruction parameter to the mean value of other reconstruction parameters around it [29]. This ensures both the quality of the reconstructed target and the convergence of regularization. The specific mathematical expression is as follows:  
\[\theta=\gamma\frac{\big{(}\sum\limits_{y=j-1}^{j+1}\sum\limits_{x=i-1}^{i+1} \alpha^{(k-1)}_{(x,y)})}{\alpha^{(k-1)}_{ARO}} \tag{9}\]  
where \(\gamma\) is the initial regularization parameter, which is taken as 0.8 in this research. A termination condition based on the relative difference in the projected values is also proposed in ARO. The iteration terminates when the relative difference in the phase's projected value reaches the threshold \(\varepsilon\), _where \(\kappa\leq\varepsilon\)_. Equation (10) is used to specifically define \(\kappa\). \[\kappa=\frac{\left|L\alpha^{(k)}_{ARO}-A\right|}{\left|A\right|} \tag{10}\]  
where the index \(k\) is the number of iterations, and \(\alpha^{0}_{ARO}\) is the initial reconstruction value. In this application, a threshold of 0.001 was used. The detailed steps of the algorithm are shown in Table 1. \begin{table}
\begin{tabular}{c l} \hline
**Serial Number** & **Specific Realization Steps** \\ \hline
1 & Obtain the initialized reconstruction parameters \(\alpha_{0}=\)\(\alpha_{\text{Landweber}}\) using the Landweber algorithm; \\
2 & Establish the image \(\alpha_{\text{iter}}\) by applying the MAART algorithm; \\
3 & Impose the nonnegativity constraint such that all negative elements of \(\alpha\) in the reconstruction process are set to 0, yielding the new reconstruction parameter \(\alpha\); \\
4 & Examine whether the convergence condition \((a^{(k+1)}_{iter}-a^{(k)}_{iter})/a^{(k)}_{iter}\leq\varepsilon\) is satisfied. If the condition is satisfied, directly execute step 6; otherwise, go to step 5. \\  & Enter the adaptive optimization algorithm: \\  & a. & Execute Equations (8) and (9) to iterate the process so as to update the regularization parameter \(\theta\); \\  & b. & When the ratio \(\kappa\) satisfies \(\kappa\leq\varepsilon\) in Equation (10), then set \(a^{(k+1)}_{iter}=a^{(k)}_{ARO}\), end the iteration process, and return to step 2 for the next generation step; \\  & c. & When the convergence condition is not met, revert to the regularization optimization process; \\  & & \\ \end{tabular}
\end{table} 
Table 1: Steps for implementing the multi-iterative adaptive optimization algorithm. The iterative reconstruction algorithm based on the multi-iterative adaptive optimization algorithm can achieve better quality for reconstructed images and can reduce the requirements for iteration parameters. The specific algorithm flowchart is shown in Figure 1 below:
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 2 Methodology
### Optimization Procedures  
Two models, an Artificial Neural Network (ANN) and a Random Forest (RF), will be utilized to predict the energy demand of a building and achieve sustainable energy outcomes. The equations will be implemented in code, with convergence defined as occurring when the maximum residual sum for all components of the solved variables (mu, gradient) falls below 0.1% based on the dataset [53; 54; 55]. The equation solving will be carried out using a custom-developed MATLAB program. We chose MATLAB for our study due to its extensive libraries and toolboxes specifically designed for advanced statistical and machine learning analyses, which are crucial for effectively implementing and comparing our Artificial Neural Network (ANN) and Random Forest (RF) models. MATLAB's user-friendly environment facilitates efficient coding and visualization, allowing for seamless data manipulation and model testing. Its robust support for parallel computing enhances computational efficiency, which is particularly important given the size and complexity of the datasets we utilized. The platform's strong community and documentation further streamline the development process, ensuring that we can focus on refining our models and interpreting results, ultimately providing a reliable framework for evaluating energy demand predictions in near-zero-energy buildings (nZEB) and traditional buildings. Comparing the two models will help determine which one has a greater impact on the building based on the dataset. Various factors influencing energy demand will be examined, emphasizing the interconnections among economic growth, population dynamics, and technological advancements [56; 57]. In the Artificial Neural Network model, the choice of two hidden layers with five neurons each balances the capacity to capture nonlinear relationships without excessive complexity, which could lead to overfitting. The training parameters include a high number of epochs (3000) to allow thorough learning, a low learning rate (0.01) to enable gradual weight updates for stability, and a stringent training goal (1 \(\times\) 10\({}^{-6}\)) to ensure the model achieves a desired accuracy level. The allowance for 4000 maximum validation failures provides ample opportunities for improvement before stopping, while a minimum performance gradient of 1 \(\times\) 10\({}^{-3}\) ensures training continues until performance gains are negligible, promoting the comprehensive optimization of the model. In your Random Forest model, the choice of 100 trees balances model performance and training efficiency, as this number generally reduces variance without significantly increasing bias, providing good generalization to unseen data. The default impurity criterion, typically Mean Squared
## 4 Multihoming and Interworking
### Gateway Selection and Preservation  
When edge networks are multihomed, traffic may leave or enter through different gateways. First, we propose a mechanism for GLI-nodes to enforce a certain gateway for outgoing packets. Then, we suggest a method for GLI-gateways to preserve the global destination GLI-address of incoming traffic as source GLI-address in outgoing response packets. Both mechanisms require an address buffer to store a single additional GLI-address in the IPv6 header. This address buffer may be implemented by a new IPv6 extension header. It is only used inside a GLI-domain so that the size of external packets is not increased and, thus, cannot cause MTU issues in the Internet.