esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper describes a novel wavelet shrinkage scheme for the extraction of PD pulses immersed in noise. Aimed at improving the SNR of denoised PD signal, the optimum wavelet at each scale is selected as the one maximizing the energy ratio of coefficients beside and inside the range formed by the threshold, corresponding to the signal to be reserved and noise to be removed, respectively. Different from most existing studies, the proposed optimum wavelet selection is considered in conjunction with the threshold at each scale. To determine the number of wavelet decomposition levels automatically and get good results for various signals, this paper proposes a novel idea--the energy composition of the approximation coefficients at each scale is analyzed to judge whether to perform the next scale decomposition according to the energy composition of approximation coefficients, rather than before the first scale decomposition. The proposed scheme is applied to simulated PD signals composed of various pulses with different waveforms and measured PD signal on-site and shows obvious advantages over other existing wavelet selection methods. However, the proposed method is more time-consuming; the extra time is mainly spent on the threshold estimation for each candidate mother wavelet.  
Methodology, Y.L.; Software, Y.L.; Validation, Z.L.; Formal Analysis, Z.L.; Investigation, Y.L.; Writing-Original Draft Preparation, Y.L.; Writing-Review & Editing, Z.L.; Supervision, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 14: Denoising results of measured PD signal immersed in noise (**a**) original measured signal (**b**) EBWS (**c**) SNRBWS (**d**) NewEBWS (**e**) proposed method.
## 2 Material and Methods
#### Piezoelectric Materials  
Piezoceramic (\(PZT\)) materials have been widely applied to convert mechanical energy into electrical energy in engineering applications [6; 8; 15; 16]. The constitutive equations of \(PZT\) materials as per IEEE (1987) are expressed as  
\[S_{P}=S_{pq}^{E}T_{q}+d_{kp}E_{k} \tag{1}\]  
\[D_{i}=d_{iq}T_{q}+\zeta_{ik}^{T}E_{k} \tag{2}\]  
where \(S_{p}\) represents the mechanical strain tensor; \(D_{i}\) denotes the electric displacement; \(T_{q}\) is stress tensor; \(d_{kp}\) is strain coefficient matrix; \(E_{k}\) is applied external electric field vector; \(S_{pq}^{E}\) is elastic compliance coefficient at the constant electrical field; and \(\zeta_{ik}^{T}\) dielectric permittivity at constant stress.  
The \(PZT\)-soil coupled system can be modelled as a 1-D system as the constraint along the thickness direction of the \(PZT\) patch. In the vertical direction, assuming the polarization is along the \(z\)-axis. In the transverse direction, the polarization is along the \(x\)-axis. The \(PZT\) material is considered an isotropic material in the \(x\)-\(y\) plane. Thus, the constitutive equations (Equations (1) and (2)) of the \(PZT\) patch are expressed as,  
\[S_{ZP}=S_{33p}^{E}T_{ZP}+d_{33}E_{Z};\ S_{xp}=S_{31p}^{E}T_{xp}+d_{31}E_{x} \tag{3}\]  
\[D_{Z}=d_{33}T_{ZP}+\zeta_{33}^{T}E_{Z};\ D_{x}=d_{31}T_{xp}+\zeta_{31}^{T}E_{x} \tag{4}\]  
The stress-strain in soil material along the \(z\) and \(x\)-direction can be expressed as follows:  
\[\varepsilon_{ZS}=S_{33S}^{E}T_{ZS};\ \varepsilon_{XS}=S_{31S}^{E}T_{XS} \tag{5}\]  
where \(\varepsilon_{ZS}\) and \(T_{ZS}\) are the stress and strain vectors of the granular soil along the \(z\)-direction, respectively. \(\varepsilon_{XS}\) and \(T_{XS}\) are the stress and strain vectors of the granular soil along the \(x\)-direction, respectively. \(S_{33S}^{E}\) represents the elastic compliance coefficient of the granular soil.  
The geometric relationship equations of the \(PZT\) patch and granular soil composite system [17] are expressed as,  
\[\varepsilon_{ZS}=\varepsilon_{PS}=\frac{\partial w_{PS}}{\partial Z};\ \varepsilon_{XS}= \varepsilon_{PS}=\frac{\partial w_{PS}}{\partial X} \tag{6}\]  
\[E_{Z}=-\frac{\partial\phi}{\partial Z}=0;\ E_{x}=-\frac{\partial\phi}{ \partial X}=0 \tag{7}\]  
where \(w_{PS}\) is the displacement of the \(PZT\) patch and soil composite in the \(z\) direction, and \(\phi\) is the electric potential. In the present study, the external electric field is considered negligible.
## 4 Simulation Analysis  
### Simulation Environment  
For simulation, the dynamic model of the PCM is used. The dynamic model of the PCM can be expressed as the mapping between the current time control input \(u_{t}\), the previous control inputs \(u_{t-1},\ldots,u_{t-T}\), the position at the current time \(\mathbf{x}_{t}\), and the position at the previous time \(\mathbf{x}_{t-1},\ldots,\mathbf{x}_{t-T}\). Specifically, it can be expressed as  
\[\mathbf{x}_{t+1} = f(u_{t},u_{t-1},\ldots,u_{t-T},\mathbf{x}_{t},\mathbf{x}_{t-1}, \ldots,\mathbf{x}_{t-T}) \tag{19}\]  
where \(T\) is the horizon length and \(f\) represents the dynamics model of the PCM.  
This paper uses an LSTM network to model the dynamics of the PCM. The input of the LSTM network dynamic model is the pressure value of each pneumatic muscle, and the output is the end-effector position of the PCM.  
Figure 2 illustrates the architecture of the proposed LSTM network for the dynamics model of the PCM. The network comprises an LSTM layer and a fully connected layer. The LSTM layer has 200 neurons, while the fully connected layer has three neurons, and the horizon length \(T=3\).  
Figure 2: The architecture of the dynamics model using LSTM network.  
The dataset is sampled using pseudorandom motor babbling, yielding 21,000 data points. Normalizing the data, 80% is used for training, with the remainder serving as a validation set. The LSTM network is trained using the Adam optimization algorithm, with the root mean square error (RMSE) as the loss function. The initial learning rate is set to 0.1, the learning rate decay factor is 0.5, the learning rate decay period is 5, the batch size is 32, and the number of training iterations is 30. The change in RMSE during training is shown in Figure 3.  
As shown in Figure 3, on the training set, the RMSE converges to 0.02 after training. On the validation set, the RMSE converges to 0.03, and the model's prediction accuracy is 91.2%. Therefore, the LSTM model is capable of representing the dynamics model of the PCM.
## 2 Related Work
### State-of-the-Art on Networking  
Real interventions on hazardous environments, such as underwater and radioactive environments, present limitations on communication, which need special attention to guarantee cooperation between the robots, also allowing supervision of the human operator.  
Scenarios such as the ones present at CERN can be affected by radiation, shielding, and huge magnetic fields, which can limit communications and can also leave the robot without connectivity. Underwater scenarios are affected by energy absorption of the water, limiting the communication distance of visual and radio modems and presenting difficulties in sonar-based systems, such as reflections and multi-path. Sonar needs to be used in the open sea, presenting problems in the presence of nearby structures [34].  
Figure 8: Dual-arm pipe recovery at a Large Hadron Collide (LHC) simulation server using the following behaviours and auxiliary robots for external views.  
Communications in these environments are an interesting line of research which really need an effort to enable the system to work safely. Also, the communication system is very related to fine-grained localization of the robots, especially when working with robot teams, a functionality that has not yet been solved in underwater and symmetrical indoor environments.  
Previous experiments with robot teams at CERN have been performed by using a leader-follower configuration, using a local Wi-Fi network to link the robots and a 3G/4G unique connection to the surface. The system faces relative localization of the robots by using RSSI (Radio Signal Strength Indicator) techniques [35]. It is necessary to work on improving the localization of robots and the recovery behaviours in the presence of communication failure.  
Some current communications experiments in underwater research systems use RF (Radio Frequency) and VLC (Visual Light Communications) modems for low distances. RF systems are able to provide a rough localization of the nodes and to communicate even with a lack of visibility [36; 37], while VLC systems are very convenient when visibility is good and in the presence of magnetic fields [38].  
In summary, there is no general solution, making it necessary to continue working in this field and to use multimodal communication systems. In addition, the communication systems for hazardous environments need more research on compression techniques, especially for applications where a human operator is necessary (e.g., expert manipulation and maintenance) [39].
# Comprehensive Analysis of the Acute Toxicity of Ionic Liquids Using Microtax(r) Bioassays
## 1 Introduction
On the other hand, IL toxicity is more extensively studied with respect to the cation, and therefore, toxicity is primarily attributed to this moiety. The effect of cationic alkyl side chains is one of the most thoroughly investigated aspects of IL toxicity, and numerous studies have established a direct correlation between toxicity and longer alkyl side chain lengths through various trophic levels [11; 12; 13]. Various techniques are currently employed to determine the toxicity of ILs, and the choice depends on the trophic level under consideration. Aquatic ecosystems (algal assays, _Daphnia magna_, _Aliivibrio fischeri_), microorganisms (mainly bacteria due to their short generation time), cytotoxicity (cell lines), enzyme inhibition, and animal tests are some of the most used microorganisms to determine the toxicity of ILs [14]. In addition to the most common methods, new methodologies are being developed to assess the toxicity of ILs as, for example, activated sludge response [15] or soil microbial activity and seed germination [16]. Among all the mentioned techniques, the acute toxicity test towards bioluminescent bacterium _Aliivibrio Fischeri_ stands out as one of the less time-consuming, cost-effective methods, and most sensitive and susceptive bacteria to ILs [11; 14; 17; 18]. To advance the understanding and the deep comprehension of the relationship between toxicity and structure and broadening the database concerning the toxic effects of ionic liquids (ILs), ecotoxicity assessments were carried out on a diverse large set of ILs in this work. The study covered ILs that have various synthetic natures, i.e., PILs and APILs, that have a common anion or cation, and different alkyl chain lengths. The tests were performed by monitoring changes in the bioluminescence of the bacteria _A. fischeri_, utilizing the Microtax(r) standard toxicity test. The effective concentration (EC\({}_{50}\)) of these mixtures was determined over three standard periods of time, namely 5, 15, and 30 min, and compared with the corresponding values for pure ILs.
# Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding
## 2 Experimental Setup  
### Equipment and Sensors  
Laser lap microwelding experiments were conducted on a laser microwelding machine (Figure 1) with an integrated Kistler 8152B121 AE sensor (linear frequency range: 50-400 kHz). A QCW fiber laser with wavelength of 1.064 um was implemented in this study. Two 45 mm \(\times\) 10 mm SUS304 stainless metal sheets with 200 um in thickness was chosen as workpiece. Its thermal expansion (\(10^{-6}\)/K), thermal conductivity (W/m K), and melting point (OC) were 18.4, 16.8, and 1723. In the test, the AE sensor was installed on the bottom plate of the fixture with screw to collected AE signal during welding. To avoid aliasing occurring during data acquisition, an NI 6132 was used to collect AE signals with a sampling rate of 2 MHz.
# Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey
## Appendix A Research Methods
### Non-Negative Matrix Factorization  
Non-negative matrix factorization (NMF) is a constrained version of the more general matrix factorization frameworks. In matrix factorization, the observed data are described by decomposing it into two lower rank matrices whose vector product is the closest approximation of the observed data. To take the effects of noise and any other contributing factors into account, more additive terms are added into the framework, depending upon the application and physical quantities being estimated by the framework. In the case of calcium imaging data, the two lower rank matrices represent the spatial and temporal footprints of the neurons. One additive term describes the baseline fluorescence and another additive term describes the noise. Therefore, the general matrix factorization framework for calcium imaging has one lower rank matrix for spatial footprint, one for temporal traces,one term for baseline fluorescence and one for noise. In order to make the framework follow the physics of calcium fluorescence, a constraint is placed so spatial and temporal components are strictly non-negative as neither spatial coordinates nor the temporal traces of neurons can be negative. This is why the framework is often referred to as non-negative matrix factorization (NMF). The additive term for baseline fluorescence can also take many forms, depending on the assumption about the data and the choice of models. Moreover, additional constraints can be put in place and different methods can be chosen to solve the framework which results in a wide range of NMF variants.
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
###### Abstract
The contributions of this paper can be summarized as follows: (1) by means of a constrained stochastic game model, the transmission policy of each observation satellite can be optimized by considering the other observation satellites' transmission policies in a distributed manner; (2) since the optimal transmission policy can be attained with little overhead, the proposed scheme can readily achieve the best performance in real systems;  
Figure 1: Motivating example. and (3) we evaluate RNA-OTS in a variety of situations, and the results may be used as design guides for observation satellite systems. The remainder of this paper is constructed as follows. First, we describe related works in Section 2 and elaborate RNA-OTS in Section 3. The constrained stochastic game model is defined in Section 4. Then, the evaluation results are given in Section 5. The conclusion is presented in Section 6.
## 5 Discussion
### Vegetation Green-Up and Its Effects on Air Temperature  
Increasing vegetation decreases land surface albedo [55] and increases evapotranspiration (ET) [56]. Decreasing land surface albedo tends to amplify the land surface  
Figure 11: Trends of the growing season precipitation (**a**) and temperature (**b**) in the study area for the years from 2000 to 2019.  
temperature by absorbing more solar energy, while increasing ET tends to decrease the land surface temperature by increasing turbulent energy fluxes. How these two mechanisms influence the land surface temperature depends on which of them dominates the influence [29]. [PERSON] et al. [29] suggested that in dry regions, the magnitude of cooling from the enhanced ET is larger than that of warming from the decreased albedo.  
In our investigation in the semi-arid region of Northwest China, some of the sites have a warming trend with increasing vegetation greenness, while some others have a cooling trend with vegetation green-up (Figure S2). At only one of the nine sites, which is barren land, growing season air temperature decreased with increasing vegetation greenness at the annual scale, while the other sites experienced moderate warm-up or no change with increasing vegetation greenness. This suggests that the mechanisms controlling the trends of interannual air temperature are more complex--factors other than changes in albedo and ET are also important, such as rising atmospheric CO\({}_{2}\) concentration, elevation, topography, microclimate, etc. [57].  
Nevertheless, the correlation coefficients between the detrended growing season vegetation greenness and the detrended growing season air temperature at the nine sites were all negative (Figure 7). This suggests that the mechanism of land surface cooling by enhanced ET from vegetation green-up generally dominates in this semi-arid region, which agrees with [29]. Hence, vegetation green-up in the semi-arid regions of Northwest China helps to mitigate climate warming, especially during the growing season. [PERSON] et al. [58] suggested that there are air temperature optima of vegetation productivity across global biomes, and the optimum air temperature for the primary production of tropical forests are projected to decrease under all future climate scenarios, implying a decrease in tropical forests' productivity, given the projected warming of the tropics. As for this semi-arid region in China, the cooling effect of increasing vegetation greenness slows down the increase in the growing season air temperature in the context of global warming, which alleviates the issue of ecosystem-level optimum air temperature change; nevertheless, more research is needed with regard to the projection of the optimum air temperature in this region.
# Sensitivity Analysis of P-Wave Polarization Direction and Velocity Gradient Inversion
## 5 Tests and Discussion
### Validity of the Sensitivity Analysis of Polarization Direction Data to Velocity Gradient  
The velocity model used in this section is shown in Figure 1. The model size is \(320\times 320\) m\({}^{2}\) and is described by a \(16\times 16\) grid of uniformly spaced nodes in both horizontal and vertical directions. On the left side of the model, eight sources are placed at equal intervals between depths of 10 m and 150 m. Additionally, eight receivers are positioned at equal intervals on the right side of the model, between depths of 170 m and 310 m.  
We conducted our analysis under two conditions: homogeneous velocity (where both layers have a velocity of 5000 m/s) and heterogeneous velocity (where the first layer maintains a velocity of 5000 m/s, with velocity differences between the two layers set at 50 m/s, 100 m/s, 150 m/s, 200 m/s, and 250 m/s, respectively). Under these conditions, we selected eight parallel rays generated from the first source to the first receiver, the second source to the second receiver, and so on, up to the eighth source to the eighth receiver. We focused on the partial derivative values at the eighth row for these mutually parallel rays. The results are shown in Figure 2.  
The results indicate that the partial derivatives decrease with increasing distance from the receiver, which aligns with the theoretical prediction. Specifically, the sensitivity of P-wave polarization direction data to the relative velocity gradient is related to the distance of the relative velocity gradient from the receiver. The farther the relative velocity gradient is from the receiver, the lower the sensitivity. This trend is observed both in homogeneous velocity fields and near velocity interfaces. Furthermore, the test results also suggest that the magnitude of the velocity gradient has a significant impact on the partial derivatives. As the velocity difference between the two layers increases, the decrease in the partial derivative values in areas closer to the receiver (such as from column 15 to column 13) becomes gradually more pronounced. This suggests that larger velocity differences lead to greater variations in the partial derivatives in areas near the receiver. Moreover, the P-wave  
Figure 1: Velocity model used in Section 5.1.  
polarization direction data shows greater sensitivity to changes in the relative velocity gradient in regions closer to the receiver. These findings provide valuable insights into the behavior of P-wave polarization data in different geological settings and have important implications for seismic data analysis and interpretation.
## 5 Results Discussion
### Effect of Darcy Number  
Darcy number is an indicator of how easy the fluid is allowed to flow within the medium characterized by the permeability of the medium through which the fluid flows with respect to its cross-section. Figure 21 investigates that at \(\phi\) = 0.02, Ra = 10\({}^{6}\), Ha = 0 and \(\varepsilon_{p}\) = 0.4 by providing visualization of the streamlines, isotherms, and entropy. Fora high Da number (last row of the figure, \(Da\) = 0.01), permeability is granted to the fluid rendering the vortices in the middle of the cross-section between the lower hot surface and the upper cold surface and with high velocity. However, decreasing the Da reveals attempts of the medium to stagnate or resist at least the fluid motion, and vortices are pushed downward in the direction of gravity. The former is accompanied by an enhanced heat transfer evidenced by the jump in the thermo lines, where the latter shows smoothed uniform temperature gradient revealing decreased heat transfer. Thus, entropy follows as explained earlier due to the dominance of the thermal entropy.  
Figures 22 and 23 study Nu and total entropy values at variations of Da and \(\varepsilon_{p}\) at the a high value of Ra. Not surprisingly, increasing \(\varepsilon_{p}\) and Da work, hand in hand, providing more freedom of fluid motion and enhancing convective heat transfer, and thus the total entropy. Finally, Figures 24 and 25 show that the role of \(\phi\) on Nu and total entropy is not significant, especially for high values of Da for Nu and low values of Da for \(\mathrm{Stot}\). Still, it is of interest that Figure 25 shows the flipping behavior for different values of \(\phi\) across the Da range. For example, at low values of Da, the largest \(\phi\) is marginally the dominant, where at high values of Da, the smallest \(\phi\) is marginally dominant. Physically, that can be attributed to the fact that, on the one hand, high values of Da enhance permeability for nanoparticles up to a certain size, giving them more movement, more heat transfer, and producing more entropy. On the other hand, at low values of Da, almost all nanoparticle sizes are hindered, and those with more size create marginally more entropy in the fluid friction. All in all, increased values of Da will always provide higher Nu, due to permittable fluid motion, and hence increase generated entropy.  
Figure 20: Total entropy as a function of Hartmann number (Ha) at various values of porosity \(\varepsilon_{p}\). Ra = 10\({}^{6}\), \(\phi\) = 0.02, and Da = 0.01.  
Figure 21: Variations of the streamlines (**left**), isotherms (**middle**), and general entropy (**right**) with various values of Darcy number (Da). \(\phi=0.02\), Ra=\(10^{6}\), Ha= 0, and \(\epsilon=0.4\).  
Figure 23: Total entropy as a function of Darcy number (Da) at various values of \(\varepsilon\). Ra = \(10^{6}\), \(\phi\) = 0.02 and Ha = 0.  
Figure 24: Average Nusselt number (Nuave) as a function of Darcy number (Da) at various values of \(\phi\). Ra = \(10^{6}\), \(\varepsilon\) = 0.4 and Ha = 0.