esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic AVOA Inversion for Weak Anisotropy Parameters and Fracture Density in a Monoclinic Medium
## 4 Conclusions  
Assuming that the fractured reservoir has a monoclinic symmetry of VTI background, a new form of P-wave reflection coefficient, in terms of WA parameters and fracture density, was obtained by substituting the stiffness matrix coefficient of the VTI background, and normal and tangential fracture compliances. The new expression of the reflection coefficient for a monoclinic medium can avoid calculation errors due to the assumption of a simple model (such as an HTI medium). Additionally, the inversion parameters contain WA parameters, which avoid the assumption that the parameters of the VTI background are known in the traditional form of a P-wave reflection coefficient. Finally, we achieved the direct inversion of WA parameters and fracture density, and avoided the intrinsic errors introduced by indirect substitution. The synthetic test demonstrated that this method can be used to accurately estimate WA parameters and fracture density, which significantly benefits further calculation of reservoir porosity or permeability, the prediction of the \"sweet spot\", and the evaluation of a reservoir's reconstruction.  
In the approach proposed, the fracture width, radius, and strike are required to be assumed. Incorrect assumptions of fracture width and radius will yield erroneous results, for WA parameters and fracture density, in practice. A priori knowledge about fracture width is important in the prediction of fracture density. However, the interpretation of fracture reservoirs shows that the fracture apertures in reservoir depth are about 1 mm, and have little variation.  
Conceptualization, Z.G. and S.P.; methodology, Z.G. and S.P.; software, J.L.; validation, J.L.; formal analysis, S.P.; investigation, Z.G.; resources, S.P.; data curation, Z.G.; writing--original draft preparation, Z.G.; writing--review and editing, S.P. and J.L.; visualization, S.P.; supervision, J.L.; project administration, S.P.; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We appreciate the helpful comments and suggestions from editors and two anonymous reviewers that led to the substantial improvement of this article. We are grateful for the financial support of the following institutions: the Science and Technology Cooperation Project of the CNPC-SWPU Innovation Alliance and Open Projects Fund of the State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (PLN201733), and the National Natural Science Foundation of China (NSFC 41204101).  
The authors declare no conflicts of interest.  
Figure 7: Fracture density (anisotropy intensity multiplied by a factor) obtained through elliptical inversion using synthetic data (Figure 4a).
## 3 Results
### Electrical Properties
In practical applications, the cyclic stability and durability of the sensor are crucial factors for maintaining mechanical integrity and sensing functionality over long-term use. As shown in Figure 6c, the CB/CNT/PDA/TPU strain sensor underwent durability testing through cyclic stretching at a strain of 70%. During both the early and later stages of the cyclic tests, no significant signal fluctuations were observed, and the relative change in resistance remained stable. This indicates that the CB/CNT/PDA/TPU flexible strain sensor possesses excellent stability and durability, making it suitable for long-term applications.
## 2 Development of the IML  
The initial goal was to develop a device to allow safe SME training. A high priority was that the barbell would have 100% freedom to move in all three planes during SME loading, so that the normal demands of free weight training would be met (see Figure 1).  
A needs analysis concluded that the following conditions must be met:  
* The device will safely and quickly stop the bar if the athlete \"fails\" during a lift.
* The barbell will move freely (in all planes) during the eccentric portion of lift.
* There will be no external adjustments to the barbell load during a multiple repetition set.
* Automated assistance will be provided to safely return the barbell to the starting position of the eccentric phase (no concentric phase for the athlete).
* The range of motion (top and bottom of lift) can be adjusted for each athlete.
* The range of motion for all lifts performed by an athlete will be saved by the device.
* The device will provide training data which can be exported for training documentation.
* The machine can be used as a spotting device during normal lifts.
* The machine will have dimensions similar to a normal power rack.
* The machine cannot be used without a coach or training partner to operate the device.  
The Lifter was originally conceived for SME training, but further developments allow the device to be used for concentric-only training, isometric training, and testing, and as a spotter for normal barbell lifts and jumping. The additional functions that were added to later versions of the Lifter included:  
* Automated assistance will be provided to safely return the barbell to the starting position of the concentric phase (no eccentric phase for the athlete).
* The arms will move fast enough that an athlete can perform a loaded squat jump or countermovement jump and the arms will catch the barbell at the top of the jump.
* Isokinetic movements can be performed with a bar attached to the arms in both concentric and eccentric movements. The force applied by the athlete on the arms will be measured and recorded.
* Isometric testing and training can be performed with a bar coupled to the arms. The force applied to the bar by the athlete will be measured and recorded.
* The velocities of the concentric and eccentric phases of the isokinetic movements can be independent of each other; one phase can be faster than the other.  
These conditions were taken into consideration when developing and constructing the IML. The IML is a safe automated mechatronic SME device which requires one operator and no external load adjustments during training. A prototype of this training device was introduced at the International Conference on Strength Training 2010 [30].  
Figure 1: The Intelligent Motion Lifter® (IML) prototype in 2010. From left to right: bench press, bench pull and squat.
## 3 Results
### HRTEM Study  
HRTEM images of Pt/Ti-PLA-400 and Pt/Ti-P25-400 samples are shown in Figures 2 and 3, respectively. The size of the support particles in the Pt/Ti-PLA-400 catalyst varies in a wide range from 5 to 75 nm. HRTEM shows that the distribution of platinum particles over a support is non-uniform (Figure 2a,b). The average size of platinum particles is 1.4 nm (Figure 2b, insert). The non-uniform particle size distribution leads to the formation of extended structures on the surface due to partial agglomeration of nearby nanoparticles (marked with yellow circles in Figure 2c). The platinum nanoparticles are often localized at the intercristalline boundaries of the support particles (Figure 2d).  
In the Pt/Ti-P25-400 catalyst, the support is represented by nanoparticles of 10-30 nm in size. HRTEM and HAADF-STEM images (Figure 3a,b) clearly show platinum nanoparticles distributed over the entire surface of the support. The average size of the Pt particles is 1.1 nm (Figure 3b, insert). The images uncover that Pt particles smaller than 1 nm are present in the sample. The size distribution curve as well as the contrast heterogeneity of the support lattice fringe in the high-resolution images (Figure 3c,d), also suggest the formation of highly dispersed platinum species. Interplanar distances measured from the high-resolution images of the support nanoparticles correspond to two phases of titanium dioxide: anatase (d\({}_{101}\) = 0.352 nm) and rutile (d\({}_{110}\) = 0.324 nm).  
Figure 2: (**a,c,d**) High-resolution transmission electron microscopy (HRTEM) and (**b**) high-angle annular dark-field imaging scanning transmission electron microscopy (HAADF-STEM) images for Pt/Ti-PLA-400 sample. The inset shows the size distribution of platinum particles.  
HRTEM and XRD data for catalysts calcined at 400 \({}^{\circ}\)C are in a good agreement, pointing to the formation of small Pt particles. The distribution of platinum particles over the surface of the TiO\({}_{2}\)-PLA support suggests that the platinum particles are stabilized on the TiO\({}_{2}\)-brookite or TiO\({}_{2}\)-rutile particles (identified by analysis of interplanar distances) which, according to XRD data, are smaller than TiO\({}_{2}\)-anatase particles. In the Pt/TiO\({}_{2}\)-P25-400 catalyst anatase and rutile TiO\({}_{2}\) are present as highly crystalline particles of similar size. The preferential stabilization of platinum particles, especially particles less than 1 nm, on the surface of rutile particles has also been observed but is less pronounced (Figure 3c,d).
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 4 Methods
### Training Algorithm  
We assume that machine learning models such as feed-forward neural network may identify patterns in the preprocessed EEG data to predict the desired speech and non-speech classes. The preprocessed data fed into the feed-forward neural network are transformed by a number of weights which form the output prediction for each class. At the beginning of the model training phase, all model weights are randomly initialized which results in the erroneous output. Later in this phase, the manually labeled classes, which are paired with the input data, are fed to the feed-forward neural network model. The error backpropagation algorithm is used to optimize the model weights in order to reduce the output error.  
In our experiment, the 2-layer feed-forward neural network was used. Min-max scaling was performed by network pre-processing. The neural network consists of a single tanh activated hidden and binary output sigmoid activated output layer. We trained the network with Scaled conjugate gradient backpropagation, with binary cross-entropy as the loss function. Output pseudo probabilities were thresholded with a 0.5 decision boundary. The first layer of the network consisted of one hundred hidden neurons.  
The models were created based on 18 features calculated for the frame of 10 signal samples. In previous experiments, it turned out that such a window size is sufficient and does not unnecessarily reduce the amount of information in the signal. In the experiment, a model was created using machine learning modelling, which would be able to recognize the state of speech production and the state of brain inactivity. Therefore, in the above classification, all words were explicitly considered as one class.  
The experiment was divided into two main parts, in the first we tried to create single-subject models. In the second part, we created cross-subject models.
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 5 Simulated Experiments and Analysis
### Experiment Results and Analysis
#### 5.3.2 The Impact of the Number of Services  
In this subsection, we vary the number of service invoked by each user to examine the impact on the total waiting time. The user number was set 14, and the number of service invoked by each user was changed from four to 10. Figure 5 illustrates the total waiting time affected by the number of services invoked. We can see that with the increasing of the service number, all users' waiting time generated by the three methods increased almost linearly. The reason was that with increasing number of services in a single-service composition, there were more services that needed to be invoked. More services mean that more data needed to be transmitted, and a greater response time of the service will be obtained. The response time and data transmission time are the main components of the total waiting time. It is obvious that MESP could obtain the least total waiting time compared with the other two baseline approaches under the same conditions.
## 4 Discussion  
Temperature difference between hot and cold junctions in TE circuit was investigated. Voltage in TE circuit is found as \(\Delta V=(|S_{n}|+|S_{p}|)\cdot\Delta T\). \(Sb\)-\(Bi_{88}Sb_{12}\) circuit can produce \((|S_{n}|+|S_{p}|)\) of around 100 \(\mu\)V/K. For an excitation port power of 500 \(\mu\)W, a possible response is expected to be around 1 \(\mu\)V. At lower power (e.g., \(P_{ex}=\)1 \(\mu\)W) the expected response is lower. At \(P_{ex}\) from 1 to 50 mW, the voltage is expected to be from 10 \(\mu\) V to 0.1 mV, which is possible to detect.  
The calculated responsivity is from 1 to 2.5 mVW\({}^{-1}\). The increase in frequency leads to the increase in responsivity. Higher values of TE Seebeck coefficients of thermocouple (up to 300 \(VK^{-1}\)) can increase the responsivity by 2-3 times. The dependence of responsivity on the distance between excitation port and detector has smoother behaviour when the \(h_{port}\)-value is higher than 1 mm.  
Thin layer of \(Bi_{88}Sb_{12}\) showed higher sensitivity to THz radiation with higher resulting temperatures. The temperature of hot junction is lower than in the \(Bi_{88}Sb_{12}\) layer. Hence, it is possible to do a thermoelectric circuit containing one TE material--\(Bi_{88}Sb_{12}\) and metal, for example \(Ni\). Due to the rather high temperature increase (1-2 K at single \(Bi_{88}Sb_{12}\)) and the significant Seebeck coefficient (around 100 \(\mu\)VK\({}^{-1}\)), a voltage of around 0.1 mV can be expected in such a circuit.  
Moreover, other charge-density-wave compounds with high thermoelectric properties at room temperature may be potentially used for THz-detecting purposes. Thin films of well-known thermoelectrics based on Bi, Te, Sb, Sn, and their compounds are of great interest for such applications.  
Conceptualization, M.K.K., N.S.K., and A.S.T.; methodology, A.V.A., A.S.T., N.S.K., and P.S.D.; formal analysis, A.S.T., A.V.A., and A.V.N.; investigation, A.S.T., P.S.D.; data curation, A.D.Z. and A.V.N.; writing--original draft preparation, A.S.T. and A.V.A.; writing--review and editing, M.K.K.; visualization, I.L.T.; supervision, M.K.K.; funding acquisition, M.K.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Russian Science Foundation, grant number 19-72-10141.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
The Thermoelectric  
THz Terahertz  
Sb Antimony  
Bi Bismuth  
Ni Nickel
## 3 Comparative Analyses Results
### Rain-Gauge vs. Satellite Precipitation Products
#### 3.2.1 IR Sensor-Based Product
However, we did not see high precipitation estimations with GHE over NMC, unlike RADOME; for example, in Table 2 where GHE maximum was 83.68 mm against 136.7 mm for RADOME. These two maximums were located at a distance of 9.44 km, revealing that GHE tends to better localise the maximum during the June case than the April case. The bias distribution reveals that the overestimation signal was present more to the north of the observed location of the June case, while the position where the event took place was underestimated by the RADOME estimations. This point is confirmed when we look at the median of GHE estimations, which was 7 mm smaller than the median observed over the NMC area and the mean bias of \(-\)12.86 mm over the area. We can see that PDIR had difficulty in to correctly identifying the precipitation patterns during the April case and for GHE to quantify the precipitation patterns over the east parts of the country. In general, the satellite-based precipitation estimations differed from the reference data (as in [64]) in low-intensity rainfall events (such as for our April case). Note that the majority of the studies which have used PDIR have been conducted over mountainous areas in lower latitudes, such as in California [64], Taiwan [65], Morocco [66], or Turkey [67], compared to the higher latitudes of France. One hypothesis that partially explains the north shift of both case studies' estimations is the parallax effect. This effect is caused by satellites with a high distance from the nadir, thus leading them to affect the clouds located in higher latitudes from the nadir more, such as the case in observed over Poland by using the Meteosat dataset [68]. The Meteosat dataset is included in PDIR and GHE algorithms as one of the GEO satellites inputs. For GHE, the algorithm might have observed the cold clouds present over France during the April event, which could explain the overestimation over the region.
## 2 Materials and Methods
### Background
#### 2.1.3 Proximal Policy Optimization  
Proximal Policy Optimization (PPO) [34] is a family of policy gradient methods for RL that optimize an objective function using stochastic gradient ascent while simultaneously gathering data through interactions with the environment. PPO balances exploration and exploitation by updating the policy iteratively and learning to maximize long-term rewards.  
PPO is built within an actor-critic architecture, where the actor is a neural network that represents the policy (mapping states to actions), and the critic is another neural network that estimates the state value function \(V(s)\), providing feedback on the expected rewards. Using trajectories gathered from interacting with the environment, PPO performs multiple updates with these samples, improving sample efficiency and data usage. This batch update process makes PPO more efficient than algorithms that update the policy after every single interaction.  
The main innovation in PPO is its clipped objective function, which limits policy updates to prevent drastic changes. Denoting the probability ratio by \(r_{t}(\theta)=\frac{\pi_{\theta}(a_{t}|s_{t})}{\pi_{\theta_{old}}(a_{t}|s_{ t})}\), the surrogate loss is defined as  
\[L^{CLIP}(\theta)=\hat{\mathbb{E}}_{t}\big{[}min(r_{t}(\theta)\hat{A}_{t},clip (r_{t}(\theta),1-e,1+e)\hat{A}_{t})\big{]}, \tag{9}\]  
where \(t\) is the time-step, \(e\) is a hyperparameter, and \(\hat{A}_{t}\) is the advantage, which can be computed as  
\[\hat{A}_{t}=-V(s_{t})+r_{t}+\gamma r_{t+1}+...+\gamma^{T-t+1}r_{T-1}+\gamma^{T -t}V(s_{T}) \tag{10}\]  
The clipping mechanism constrains the probability ratio between the new and old policies, ensuring that updates remain within a specified range. This prevents large, destabilizing updates while still allowing for effective policy improvements. As a result, PPO is a stable and sample-efficient algorithm that has demonstrated robust performance across a wide range of applications. The complete algorithm of PPO is shown in Algorithm 2.  
```
1:for each episode = 1 to \(E\)do
2:for each agent = 1 to \(N\)do
3: Run policy \(\pi_{\theta_{old}}\) in environment for \(T\) time-steps.
4: Compute \(\hat{A}_{1},\cdots,\hat{A}_{T}\).
5:endfor
6: Optimize surrogate \(L\) with respect to \(\theta\), with \(K\) epochs.
7:\(\theta_{old}\leftarrow\theta\)
8:endfor
```  
**Algorithm 2** Proximal Policy Optimization.
# A Novel Interpretable Deep Learning Model for Ozone Prediction
## 5 Conclusions  
In this paper, we propose a novel model called the Spatiotemporal Gate Recurrent Unit (STAGRU), which captures spatiotemporal information using two types of attention mechanisms: temporal attention and spatial attention. Temporal attention captures information from the past sequence, while spatial attention captures information from the surrounding monitoring stations. We demonstrated the effectiveness of the STAGRU model compared to Seq2 Seq and Seq2 Seq+Attention models in five major cities: Nanjing, Beijing, Wuhan, Guangzhou and Chengdu. Statistically, our proposed method is 14% better than Seq2 Seq-based methods and 4% better than Seq2 Seq+Attention-based methods. Furthermore, we proposed another model that captures spatial information during decoding. This model was able to apply forecasting to multiple stations simultaneously, but it came at the cost of stability. In addition, it provided insight into our proposed model, enabling a discussion on interpretability from the perspective of statistical temporality and spatiality. The analysis shows that the temporality of ozone variation involves short-term dependency and long-term periodicity and that the spatiality of ozone transportation is mainly affected by wind, including speed and direction. By utilizing our model, policy decision-makers can make accurate ozone predictions in advance for specific regions of interest. If it is predicted that there will be ozone pollution in this area, decision-makers can provide early warnings and take appropriate measures to control the area. Current results presented in this manuscript are considered preliminary. For future work, the two major objectives are to extend the domain by including more observational stations (e.g., mainland China) and to further improve accuracy. It is worth noting that the amount of CPU time required for the calculation is directly proportional to the number of monitoring stations being considered. This implies that larger domain tests necessitate significant computational resources.  
X.C.: Conceptualization, Writing--review and editing and supervision. Y.L.: conceptualization, methodology, validation, investigation, data curation, writing--original draft and visualization. X.X.: methodology, investigation, writing--original draft, visualization and data curation. M.S.: writing--review and editing, resources and supervision. All authors have read and agreed to the published version of the manuscript.  
This paper was partially supported by the National Natural Science Foundation of China (No.62276142, 62206133, 62202240, 62192783), the Special Science and Technology Innovation Program for Carbon Peak and Carbon Neutralization of Jiangsu Province (No. BE2022612), and the Natural Science Foundation of Jiangsu Province (No. BK20210574).  
Not applicable.  
The data presented in this study are openly available in FigShare accessed on 18 September 2023. [PERSON], [PERSON] (2023). Air pollutant and meteorological data of five major cities in China. figshare. Dataset.  
The authors declare no conflict of interest.
# Identification Approach for Nonlinear MIMO Dynamics of Closed-Loop Active Magnetic Bearing System
## 4 System Identification of Rotor/RAMB Dynamics
The fit values are summarized in Table 1. The averaged fit values of the Hammerstein-Wiener model, NARX model, and NARMAX model are 93.25%, 88.36%, and 76.91%, respectively. Figure 20: Estimated inputs to the rotor/RAMB model for validation. The significant limitations of this study are addressed as follows:  
* By applying the proposed modeling approach presented in Section 4, the mathematical model, i.e., the nonlinear element of the Wiener model, should be estimated prior to the recursive identification algorithm being undertaken. For instance, the static relations between the inputs and outputs of the gap sensor are measured with a high-precision positioning platform. * As the perturbation signals are being injected into the rotor/RAMB system, the responses of the rotor/RAMB system will be distorted by the vibrations of the surrounding objects. How the engineering of vibration isolation performs will affect the accuracy of the identified model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fit (\%)** & **Hammerstein–Wiener Model** & **NARX Model** & **NARMAX Model** \\ \hline UX & 92.11\% & 87.63\% & 75.91\% \\ UY & 93.38\% & 88.88\% & 77.88\% \\ LX & 91.51\% & 85.57\% & 78.32\% \\ LY & 95.99\% & 91.34\% & 75.54\% \\ Average & 93.25\% & 88.36\% & 76.91\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fit values between validation data and responses of Hammerstein–Wiener model, NARX model, and NARMAX model. Figure 21: Comparison of the proposed Hammerstein-Wiener model, NARX model, and NARMAX model.