esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Based on the proven evidence that large earthquakes are often preceded by ionospheric anomalies, the idea of an earthquake warning system based on periodically trained neural networks using GIMs has been presented. The necessary information to be collected for this purpose--three-dimensional matrices from the GIM files for the analysis of the space-time anomalies--has been introduced. The training and validation data--also using an earthquake catalog--for the whole period from 1 January 2011 to 1 October 2024 have been offered to the readers, as well as the neural network that has been trained for this purpose. The network has reached an accuracy of around 60% in the validation data for a division into eight categories of different earthquake magnitudes, a percentage that increases considerably if the classification in the neighboring categories is also accepted, something that could be clearly admissible for the purposes of a warning system.  
Many ideas to enhance the network design may be possible, these are left to the reader along with the network and dataset used for the current study in order to see how much the performance can be improved, either with or without the inclusion of additional information such as, for example, indices of geomagnetic activity. The periodic updating of the network with updated GIMs, for example once a month, is in any case necessary for its application to an earthquake warning service. Although the research leading to a feasible earthquake warning system is clearly unfinished, it is firmly believed that such a system will be developed sooner or later and that one of its key components will be based, in one way or another, on a significant amount of information from the ionosphere.  
The following supporting information can be downloaded at: [[https://doi.org/10.5281/zenodo.14196868](https://doi.org/10.5281/zenodo.14196868)]([https://doi.org/10.5281/zenodo.14196868](https://doi.org/10.5281/zenodo.14196868)), Data file S1: Xtraining.mat, X variables used for training; Data file S2: Ytraining.mat, Y variables used for training; Data file S3: Xvalidation.mat, X variables used for validation; Data file S4: Yvalidation.mat, Y variables used for validation; Data file S5: net24f.mat, trained network.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the Supplementary Materials, further inquiries can be directed to the author.  
The author declares no conflicts of interest.
# Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients
## 5 Conclusions  
1. Rapid detection technologies of tree nutrients based on RGB images and multispectral have the characteristics of a low cost and low computational complexity, but their prediction accuracy is not high and they are susceptible to interference. Hyperspectral technology has high prediction accuracy due to its large number of characteristic variables. The application of filtering, transformation, and sensitive band selection algorithms solves the problem of data redundancy, improves the correlation of feature variables, and reduces computational complexity. However, there are few field applications, and preprocessing algorithms that consider the impact of changes in the field environment should be further studied in depth.
2. Hyperspectral technology also plays an important role in the rapid detection of soil nutrients, demonstrating good prediction accuracy. Laser breakdown-induced spectroscopy has good prospects, as it can simultaneously detect multiple nutrients, including those with lower content. However, spectral analysis is still full of challenges.
3. The existing rapid detection technologies for nutrients are mostly aimed at nitrogen with high content, and research on the rapid detection of small and trace elements needs to be strengthened.
4. Many suitable nutrient standards for soil and leaf have been established, but they are often obtained through long-term and extensive experimentation, which is time-consuming and laborious. A universal and rapid method needs to be studied to meet the construction needs of suitable nutrient standards for different varieties of fruit trees.  
Conceptualization, Q.Y. and X.L.; formal analysis, Y.Q. and K.H.; investigation, Y.S. and W.W.; writing--original draft preparation, Q.Y.; writing--review and editing, Q.Y.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Technology Research and Development Program of China (2022 YFD2001400), China Agriculture Research System of MOF and MARA (CARS-28-21), and Jiangsu Agricultural Science and Technology Innovation Fund (CX(21)2025).  
Not applicable.  
The authors declare no conflicts of interest.
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 3 Results and Discussion
### Logging Response Characterization  
After selecting the logging parameters, PCA was used to eliminate the more complex covariance relationship between the logging data, which obtained the principal component results, as shown in Figures 4 and 5. According to the analysis, the cumulative contribution rate of the first four principal components (PC) reached 95.37%, i.e., PC1, PC2, PC3, and PC4 contain more than 95% of the information in the original data. Furthermore, PC1 mainly reflects the characteristics of acoustic (AC) and compensated neutrons (CNL). PC2  
Figure 3: Sensitive parameter ranking of total organic carbon content.  
Figure 2: Logging parameters and total organic carbon content correlation.  
mainly reflects the characteristics of natural gamma (GR) and compensated density (DEN). PC3 reflects the characteristics of shallow lateral resistivity (LLS) and deep lateral resistivity (LLD). PC4 reflects the characteristics of acoustic (AC) and compensation density (DEN). Therefore, according to the principle that the cumulative contribution rate is greater than 90%, the first four principal components were selected as new input variables to build the TOC content prediction model.
## 2 Materials and Methods
### Isolates  
Consortia enriched under aerobic conditions from Neot Hovav groundwater were formerly established and used in this study for isolating TBNPA and DBNPG degrading strains. In short, enrichment included a groundwater inoculum (10 mL) mixed with 90 mL growth medium and either TBNPA or DBNPG at an initial concentration of 100 mg/L [28]. Consortia were streaked on R2A agar plates (BD Difco, Sparks, MD, USA) without DBNPG and TBNPA, and distinctive colonies were further purified on the R2A medium. Strains were identified by 165 rRNA gene sequencing (HyLab, Rehovot Israel). A total of four and five isolates were obtained from the DBNPG and TBNPA original groundwater consortia, respectively.  
To assess whether the isolates can degrade TBNPA and DBNPG, each strain was grown in a liquid growth medium by amendment of yeast extract (100 mg L\({}^{-1}\))/glucose (100 mg L\({}^{-1}\)) and TBNPA/DBNPG (100 mg L\({}^{-1}\)). Individual strains did not exhibit TBNPA/DBNPG degradation, leading to the investigation of different strain combinations. Finally, a four-strain consortia, which successfully degraded TBNPA and DBNPG, was selected for further study. More detail is available in the Supplemental Materials on how strains were screened for degradation. Two techniques measured individual strain biomass of the four-strain consortium's (before combining the strains to the consortium) colony-forming units (CFU/mL) with a series of six serial dilutions as described in [37] and by optical density measurements (OD600) (BioMate 5, Thermo Spectronic, Thermo Fisher Scientific, Waltham, MA USA). The correlation between the two was calculated through the dual measurements of both. The calibration of CFU to OD yields the following relationships: CFU mL\({}^{-1}\) = 10\({}^{8}\)\(\times\) OD600. In all experiments, an inoculum (with growth medium ratio of 1:10) was kept throughout all experiments.
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 4 Proposed Methodology
### Motion Feature Using GSTCAN  
Similarly to the skeleton feature process mentioned in the previous section, we extract the motion feature using another stream. Concretely, the skeleton data were provided by AlphaPose and fed to the Motion Generation module, which calculates the displacement of each coordinate along the temporal dimension. After that, motion data are passed to the GSTCAN [46; 60] modules that are in another stream. The motion of joints among consecutive frames of the human skeleton is used as nodes of the graph, and the human body structures and their connections in time are used as edges of the graph. The final motion feature \(f_{\text{motion}}\) is computed as:  
\[f_{\text{motion}}=\mathcal{P}\left(\sum_{i=1}^{N}\mathcal{A}(\mathcal{T}( \mathcal{G}(\mathcal{B}(X_{motion}))))\oplus X\right) \tag{6}\]  
where \(X_{motion}\) denotes input motion feature of shape \((N,C,T,V)\), \(N\) is the batch size, \(C\) is the number of channels, \(T\) is the length of the sequence, \(V\) is the number of nodes (joints in the skeleton). \(\mathcal{G}(X)\) denotes GCN, which extracts spatial features based on the skeleton's graph structure. \(\mathcal{T}(\cdot)\) denotes TCN, which captures temporal dependencies in joint movements. \(\mathcal{A}(\cdot)\) denotes the Channel Attention (CA) module, which assigns importance to features by weighting different channels. \(\oplus\) denotes Skip connection, which concatenates or adds the refined features to the original input. \(\mathcal{P}(\cdot)\) denotes Global Average Pooling (GAP), which computes the final compact representation of the motion features.
## 2 CHS T-joints
### Numerical Modeling  
The joints were made from steel with elastic-perfectly plastic behavior assuming Young's Modulus of 219 GPa, Poisson's ratio of 0.3, the density of 7800 kg/m\({}^{3}\), and the Yielding stress of 383 MPa. The steel material obeys the von Mises yield criterion and the associated flow rule as the classical plasticity model. For retrofitting of the selected CHS joints, the carbon fiber-reinforced polymer (CFRP) material was utilized due to its popularity with the thickness of 0.125 mm; the properties of the composite material are presented in Table 2. In this table, subscripts 1 and 2 show the fiber longitudinal and transversal directions, respectively. The Hashin damage criteria [22; 23] are used to consider the FRP failure with the properties listed in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Geometric Property** & **Varying Range** \\ \hline \(r/\hbar\) & 8–65 \\ \(R/T\) & 8–24 \\ \(l/r\) & 5–17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The geometric properties of the tubular CHS T-joints and their varying range in this study.  
Figure 1: Geometric features of a typical circular hollow section (CHS) T-joint without retrofit. \(D=2R\); \(d=2r\).  
The joints were modeled using the finite element method with the ABAQUS software package. Two types of analysis were carried out. First, the linear perturbation analysis was adopted in order to investigate the geometric effects on the buckling modes and the critical loads of the selected CHS T-joints. In this analysis, the loading scenario was a static monotonic compressive loading along the brace as shown in Figure 1. Then the post-buckling analysis was done to evaluate the post-buckling behavior in different buckling modes. In this step, the loading scenario is displacement-based as a static monotonic compressive displacement along the brace. The displacement-based approach was selected because the post-buckling behavior and ductility can be entirely evaluated. The governing equations were displacement-based equations with regards to the static analysis. In these equations, the stiffness and mass matrices as well as the loading vector was constructed based on the discretized finite element mesh. For the purpose of retrofitting, three joints with different governing buckling modes were considered. Their properties will be represented in the next sections.  
The CHS joints were three-dimensionally modeled with brick elements while the FRP patches were modeled using 3D shell elements, which were fully bonded to the steel surface. The used solid and shell elements were first-order with linear shape functions. The brace's end was constrained in the plane of its cross-section, but no constraint was applied along its length for possibility of applying static compressive load. All rotational degrees of freedom at the brace end were also constrained. Both ends of the chord were pinned, hence the transitional degrees of freedom along with the rotational degree of freedom about the longitudinal chord axis were constrained.
## 3 Controllable and Uncontrollable Modes for AVC
### Theoretical Analysis
When the disturbance frequency, \(\omega\), is close to the first (m, n) natural frequency, \(\omega_{mn}\), substituting Equations (8) and (12) into Equation (14) approximately yields:  
\[\gamma\approx\left(v_{s}^{2}+1\right)\left[\frac{v_{a}-v_{F}}{v_{\omega}+v_{a }v_{s}}\right]^{2} \tag{15}\]  
in which \(v_{\omega}=\frac{\omega_{m}^{2}-\omega^{2}}{\omega_{mn}^{2}-\omega^{2}}\) is the gap ratio of adjacent natural frequencies; the mode at \(\omega_{c}\) is the closest mode to the (m, n) mode; \(v_{F}=\frac{\Phi_{s}\left(x_{F},y_{F}\right)}{\phi_{mn}\left(x_{F},y_{F}\right)}\) is the ratio of adjacent mode shapes at the location of the disturbance; \(v_{a}\) and \(v_{s}\) are the ratios of adjacent modal strains at the actuator and sensor positions, respectively; and \(v_{a}=\frac{\phi_{c}\big{(}x_{a},y_{a},\Delta_{x},\Delta_{y}\big{)}}{\phi_{mn} \left(x_{a},y_{a},\Delta_{x},\Delta_{y}\right)}\); \(v_{s}=\frac{\phi_{c}\big{(}x_{s},y_{s},\Delta_{x},\Delta_{y}\big{)}}{\phi_{mn} \left(x_{s},y_{s},\Delta_{x},\Delta_{y}\right)}\). In Equation (15), the value of \(\gamma\) is determined by the ratio of adjacent modal strains, the ratio of adjacent mode shapes, and the gap ratio of adjacent natural frequencies. When the ratios \(v_{F}\), \(v_{a}\), and \(v_{s}\) are large enough, the value of \(\gamma\) is greater than one. Thus, the total closed-loop energy in Equation (12) is greatly enlarged and AVC fails. Therefore, the \((m,n)\) mode is denoted as an uncontrollable mode. Conversely, when \(v_{F}\), \(v_{a}\), and \(v_{s}\) are less than one, the \((m,n)\) mode is denoted as a controllable mode for AVC. In addition, when \(v_{a}\) is large enough, \(\gamma\) is less than one. In this condition, the \((m,n)\) mode is denoted as a controllable mode. Hence, the value of \(\gamma\) can be used as a criterion in advance to distinguish the controllable and uncontrollable modes in AVC.
###### Abstract  
High-resolution weather models and large eddy simulations are critical for accurate modelling of wind and wind power production in complex terrain. Standard mesoscale numerical weather modelling tends to underestimate high-wind events, while increased resolution in the model allows for a more accurate terrain representation and capture of localised wind effects in the area.  
This study investigates high-resolution numerical weather modelling and large eddy simulations (LESs) for wind resource assessment in complex coastal mountainous terrain. The main purpose is to investigate strong-wind events, where earlier research indicates that high wind speeds are underestimated. Using the Weather Research and Forecasting model (WRF), simulations were conducted for the Fakken wind power plant in northern Norway, a region characterised by steep mountains, fjords, and challenging wind patterns. The study evaluates the impact of increasing model resolution, from mesoscale to LESs, on wind speed and power production estimates. Results show that higher-resolution models improve the representation of terrain features, leading to better estimations of wind speed and direction, particularly during strong-wind events such as the Ylva storm in 2017. The LES model demonstrated the ability to capture high-wind events, including localised speed-ups and lee-side amplification, which is critical for accurate wind speed modelling. Comparison with power production data shows the potential of WRF LESs to optimise wind farm operations in complex terrains.  
complex terrain; LES; mountains; numerical weather modelling; wind energy; WRF Article
## 2 Mathematical Formulation
### Transfer Matrix for Field Point Pressure Calculation  
The acoustic pressure field radiated from an array can be obtained by the acoustic transfer matrix and the velocity distribution over the transducer heads calculated by the impedance matrix equation in Equation (19). The transfer matrix, \(\mathbf{H}\), indicates the transfer function between the surface velocity of transducer heads, \(\mathbf{u_{h}}\), and the pressure of field points, \(\mathbf{P_{f}}\):  
\[\mathbf{P_{f}}=\mathbf{H}\mathbf{u_{h}}\qquad\quad\mbox{where}\ \ \mathbf{u_{h}}=\left[\begin{array}{ll}u_{1,1}&u_{1,2}&\cdots&u_{1,n}\end{array} \right]^{\mathbf{T}} \tag{30}\]  
The vector \(\mathbf{P_{f}}(n\times 1)\) is the pressure of the field points located in the area of interest. The vector \(\mathbf{u_{h}}(n\times 1)\) is the velocity of the transducer heads. The relation between the velocity of the transducer heads, \(\mathbf{u_{h}}\), and the surface normal velocity, \(\mathbf{v_{n1}}\), is shown in Equation (31):  
\[\mathbf{v_{n1}}=\mathbf{L_{h}}\mathbf{u_{h}} \tag{31}\]  
where \(\mathbf{L_{h}}(s_{1}\times n)\) is a matrix for finding nodes on the surfaces of the transducer head on the radiating surface of the array.  
The pressure of the field points, \(\mathbf{P_{f}}\), can be determined using the surface normal velocity \(\mathbf{v_{n3}}\) and the surface pressure \(\mathbf{p}_{3}\):  
\[\mathbf{P_{f}}\mathbf{=}\mathbf{C_{2}}\,\mathbf{p_{3}}\mathbf{+}\mathbf{D_{2 }}\,\mathbf{v_{n3}} \tag{32}\]  
where \([\mathbf{C_{2}}]\) and \([\mathbf{D_{2}}]\) are the dipole matrix and monopole matrix, respectively, for fluid 2 corresponding to the field points, obtained by the acoustic BEM. Equation (33) shows the relationship between the surface normal velocity, \(\mathbf{v_{n1}}\), and the structure velocity of outer surface, \(\mathbf{v_{3}}\). The transformation matrix \(\mathbf{T}\) between \(\mathbf{v_{n1}}\) and \(\mathbf{v_{3}}\) can be obtain from Equation (27).  
\[\mathbf{v_{3}}\mathbf{=}\mathbf{T}\mathbf{v_{n1}} \tag{33}\]  
By using Equations (22), (25), (31) and (33), Equation (32) can be expressed as  
\[\mathbf{P_{f}}\mathbf{=}\left(\mathbf{C_{2}}\,\mathbf{A_{2}}^{-1}\mathbf{B_{2 }+}\mathbf{D_{2}}\right)\mathbf{Q_{3}^{T}}\,\mathbf{T}\,\mathbf{L_{h}}\, \mathbf{u_{h}} \tag{34}\]  
Thus, when there are surrounding structures, the transfer matrix \(\mathbf{H}\) is  
\[\mathbf{H}\mathbf{=}\left(\mathbf{C_{2}}\,\mathbf{A_{2}}^{-1}\mathbf{B_{2}+} \mathbf{D_{2}}\right)\mathbf{Q_{3}^{T}}\,\mathbf{T}\,\mathbf{L_{h}} \tag{35}\]
## 5 Experiments and Results
### Qualitative Analysis of Prediction Results  
To further examine the performance of TPASTGCN, several representative trajectories predicted for different scenes are visualized in Figure 10. The LocalX along with LocalY in  
Figure 9: Comparison of RMSE for proposal model and baselines.  
Figure 10 represent the traveling displacement distance of the vehicle in two directions on the ground, which is the trajectory of the vehicle on the ground. The multiple tracks the proposed model generates with 5 s are expressed in blue, while the historical paths with 3 s in yellow and red curves denote the ground truth. Figure 10a depicts the scenario of a vehicle traveling straight, which shows that the future potential trajectory determined by the proposed model is strongly consistent with the ground truth. This implies that the proposed scheme has produced the correct anticipated intention and then accurately works in trajectory construction in the next time periods. As for the vehicle's slow turn in Figure 10b, a good agreement is also found between the predictions and real-world track. The reason that our predicted future trajectory is shorter than the truth in this sample is because the vehicle travels fast when it leaves the intersection, which is beyond our concerned ranges.  
Especially, Figure 10c,d present two extreme scenes, in which the vehicle suddenly and massively engages in complicated movements without any prior knowledge. As can be found in Figure 10e,f, LocalX along with LocalY in the historical track do not present obvious turning and acceleration behavior. However, our proposal successfully captures the changes and delivers the prediction trend, which is similar to the ground truth. The results displayed in Figure 10d,g together with Figure 10h demonstrate the same observation, where the predicted paths have the correct traveling intention and the velocity arrow. Combined with these intuitive analyses and previous ablation tests, we indicate the derived results are learning from multi-agent interaction mechanisms and are perfectly disposed by our developed model, as there is no prior knowledge. The above series consequence highlights that our network not only provides an adequate solution in prediction tasks with robustness but also is competent in the work of deducing vehicle traveling intentions.
## 4 Analysis and Performance Comparison  
The first part of this section analyses the performance of the CoAP protocol on 6 LoWPAN nodes. The study aimed at conducting a worst case analysis on the effects that large resource representation transfers in CoAP have onto the lower layers in terms of fragmentation and rejection risk (a relevant issue within the CORE WG [15]). Subsequently, we illustrate simulative results that compare the CoAP model with the proposed model in terms of energy consumption; this demonstrating the higher performance of the latter.
# A Presentation of Ionic Liquids as Lubricants: Some Critical Comments
## 5 Some Applications
### Steel Surfaces
#### 5.1.4 Low-Vapour Pressure  
The advantageous properties of ILs and, in particular, their low-vapour pressure makes them interesting for testing purposes in extreme conditions, such as high vacuum and low temperatures to which space mechanisms are subjected. Common lubricants used in space are generally based on perfluorinatedpolyethers (PFPE), such as Fomblin(r)Z25 [103]. In 2019, its tribological properties were compared to seven purchased or synthesized ionic liquids with different chemical structures [104]. Among all tested lubricants, almost all ILs showed good antifriction and wear-resistance properties, but only an IL based on 1-butyl-1-methyl-pyrrolidiniumbis(trifluoro-methylsulfonyl)amide (named IL2 in that work) widely overcame the performance of the reference lubricant, as illustrated in Figure 11. Lifetime experiments (long-term lubricity and endurance in vacuum) confirmed the excellent results of IL2, even if it showed an insufficient fluidity at a low temperature. It is clear that this is a weak point for its lubricating applicability in space, considering the extreme conditions to which mechanism parts are subjected, and therefore, it is worth thinking about how it is necessary to study the largest number of ILs with tuneable properties.  
Figure 10: Average coefficient of friction (COF) of [(NEMM)MOE][FAP], [BMP][FAP], and [BMP][NTF2] at 40 and 100 \({}^{\circ}\)C.