esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 4 Experimental Verification
### Case Study 1: Predicting the RUL of Bearings Using the PHM 2012 Dataset
#### 4.2.2 Analysis of Projected Results
Although TCN-SA, TCN-RSA, and TCN-RSCB introduce attention mechanisms and residual connections to improve the traditional TCN models, they have some drawbacks, such as not considering the global nature, not mining the information more deeply, and not utilizing the before and after feature information efficiently. In conclusion, the multi-feature fusion network gets higher forecast results due to the design of the multi-feature fusion network, which enhances the all-round learning of sequence features, the attention mechanism's deep feature mining, and the improvement of the efficiency of residual linkage in the utilization of before and after features. Furthermore, even if the proposed method's prediction curves exhibit some  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Set**} & \multicolumn{3}{c}{**TCN-SA [21]**} & \multicolumn{3}{c}{**TCN-RSA [28]**} & \multicolumn{3}{c}{**Standard-CNN**} & \multicolumn{3}{c}{**TCN-RSCB [29]**} & \multicolumn{3}{c}{**Proposed Method**} \\ \cline{2-13}  & _MAE_ & _RMSE_ & **Score** & _MAE_ & _RMSE_ & **Score** & _MAE_ & _RMSE_ & **Score** & _MAE_ & _RMSE_ & **Score** \\ \hline A1-1 & 10.4 & 11.3 & 0.87 & 11.0 & 13.7 & 0.84 & 8.7 & 10.9 & 0.84 & 8.6 & 10.7 & 0.88 & 8.72 & 11.5 & 0.87 \\ A1-2 & 13.0 & 16.9 & 0.80 & 14.0 & 16.3 & 0.7  
local oscillations, the proposed approach has a good capacity to anticipate the RUL of the bearingss since it can accurately estimate the condition of the bearingss in their latter stages of life. The prediction results for bearings A1-4, A1-5, A2-4, and A2-6 are shown in Figure 11. Figure 11 demonstrates that our proposed multi-feature fusion network successfully captures the bearing degradation information across various operating situations and accurately predicts the RUL. Figure 11: The PHM 2012 dataset contains RUL visualization prediction results for test bearings. Figure 10: Boxplots of MAE, RMSE, and scores on the PHM2012 dataset.
# Effect of Terahertz Antenna Radiation in Hypersonic Plasma Sheaths with Different Vehicle Shapes
## 2 The Plasma Sheath and the THz Array Antenna
### Antenna Position and Suitability of 0.14 THz  
Figure 3 shows the structure of the plasma sheath at three moments during its evolution. In other words, Figure 3 shows the temporal evolution of the plasma sheath.  
According to Figure 3, the plasma distribution is very complicated at the wake. The signal SNR (signal-noise ratio) can be seriously reduced once the signals penetrate the plasma sheath via the wake region. Furthermore, there is an 'electron wall' near the boundary of the wake region. The signal attenuation in that region is severe. In addition, in order to penetrate the plasma sheath via the wake region, the signal propagation path in the plasma sheath is much longer than that in the region near the wall of the vehicle (aft region). The longer propagation path yields more severe total signal attenuation. In such a case, it is not appropriate to mount the communication antenna on the back of the vehicle.  
In addition, in the case of carrier frequencies of 140 GHz and 225 GHz, the order of magnitude of communication system bit error rates is identical. Meanwhile, the plasma sheath has the characteristic of natural evolution (see Figure 3 above). In the time-varying plasma sheath, the bit error rate of the 140 GHz carrier is more stable and the fluctuation of the bit error rate of 225 GHz is more frequent. Thus, we chose 0.14 THz for the communication frequency.  
In summary, when the signal is radiated from the vehicle tail, the total attenuation rate of the signal is similar to that of the sidewall radiation. The main reason is that the signal needs to travel further in the wake area with relatively low electron density and then it needs to pass through the boundary layer with high electron density. In addition, the channel condition near the sidewall is more stable than that in the wake area, so the antenna is mainly considered to be arranged on the sidewall of the aircraft.  
Figure 3: The evolution of the plasma sheath at (**a**) 2.5 ms, (**b**) 7.5 ms, and (**c**) 10 ms.
# Adsorption of Metolachlor and Its Transformation Products, ESA and OXA, on Activated Carbons
## Materials and Methods  
### Materials  
Metolachlor (MET) (racemic mixture), ESA and OXA were purchased from LGC Standards. Potassium carbonate (purity > 98%) and H\({}_{3}\)PO\({}_{4}\) (85%) were obtained from Across Organics and Alfa Aesar, respectively. The chemicals were used without further purification. For clarity, the physico-chemical properties of the adsorbates including the molecular structures are shown in Table S1. All solutions were prepared with ultra-pure water obtained from Milli-Q water purification systems.  
A commercial activated carbon (AQ630) was supplied by JACOBI Carbons (Vierzon, France). The rest of the activated carbons were obtained by chemical activation of two lignocellulosic precursors, rape straw and sunflower seed shell, supplied by Region Centre-Val de Loire. Two different activation protocols were employed. Firstly, the precursors were activated with K\({}_{2}\)CO\({}_{3}\) following the activation process described in [19], briefly, a mass of 1 g of precursor was impregnated with a mass of 1 g of K\({}_{2}\)CO\({}_{3}\) (1:1), then the activation was performed at 800 \({}^{\circ}\)C for 1 h under N\({}_{2}\) flow of 160 mL/min (heating rate 10 \({}^{\circ}\)C/min) in a horizontal furnace. Two impregnation procedures were followed as a function of the used precursor. For the impregnation of the rape straw, which was received as fine powder (10-100 \(\mu\)m), a physical impregnation in a mortar was made at room temperature. On the other hand, the sunflower seed shells were used as flakes (due to the nature of the shells) and the activating agent impregnation was made by means of a wet process. The impregnation consisted in stirring the precursor and activating agent (1:1) with ca. 10 mL of water overnight, then the mixture was dried in an oven at 80 \({}^{\circ}\)C up to constant weight to ensure the well contact between the precursor flakes and K\({}_{2}\)CO\({}_{3}\). Secondly, the rape straw was also activated by H\({}_{3}\)PO\({}_{4}\); in this case, a mass of 1 g of residue was impregnated with phosphoric acid 85% (ratio 1:1 \(w/w\)) and heated to 80 \({}^{\circ}\)C under manual shaking for 30 min. It was then dried in an oven at 110 \({}^{\circ}\)C for 15 h, then the activation was performed at 500 \({}^{\circ}\)C for 1 h, applying a heating rate of 20 \({}^{\circ}\)C/min under N\({}_{2}\) flow of 160 mL/min. The activated carbon obtained was immersed in a hydrochloric acid solution (37%) for 20 min to eliminate the inorganic residues. All the materials were washed with distilled water until constant pH to remove any water-soluble species and the excess of activating agent, dried at 60 \({}^{\circ}\)C overnight and stored in a desiccator.  
The nomenclature of samples was as follows: X-YY, with X being related to the precursor (R for rape and S for sunflower) and YY being the activating agent (KC and PO for K\({}_{2}\)CO\({}_{3}\) and H\({}_{3}\)PO\({}_{4}\), respectively). For example, R-PO is rape straw activated by phosphoric acid.
## 7 Conclusions  
As can be seen in the previous section, the approach gives good results not only classifying the parts of the environment that are desired to be segmented into classes but also reducing the tedious labor of generating a data set by human hand. As seen on the results image, the proposal can classify with more detail than a SVM or a human using basic image editing tools.  
The CNN for pixel classification commonly needs a big data set to train; in this paper, a CNN is pre-trained with the prediction of an SPS-SVM. Then, the SPS-SVM can be considered as a data augmentation process to generate synthetic labeled data.  
The approach is fast enough to provide sensitive information in a short time, so a UAV can take decisions with recent information. Future work will focus on improving the classification by adding estimations on the different types of clouds that can be found in the environment and the risk they could represent for a UAV.  
Conceptualization, C.V. and N.A.-D.; methodology, C.V.; software, A.A.G.; validation, G.L-G., C.V. and J.G.-A.; formal analysis, N.A.-D. and A.A.G.; investigation, J.D.R.; writing--original draft preparation, J.G.-A.; writing--review and editing, G.L.-G., J.D.R. and J.G.-A.; visualization, A.A.G. and G.L.-G., supervision, J.D.R.; project administration, C.V. and N.A.-D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CONACYT Mexico grants numbers CB256769, CB258068, and PN-4107.  
The authors would like to thank Hydra Technologies de Mexico for providing the data for the development of this work.  
The authors declare no conflict of interest. The founders 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:  
\begin{tabular}{l l} CNN & Convolutional Neural Networks \\ UAV & Unmanned Aerial Vehicle \\ ReLU & Rectified Linear Unit \\ SILC & Simple Linear Iterative Clustering \\ SPS & Superpixel Segmentation \\ SURF & Speeded Up Robust Features \\ SVM & Support Vector Machine \\ \end{tabular}
###### Abstract  
Efficient and suitable animal models directed to skeletal muscle hypertrophy are highly needed; nevertheless, the currently available models have limitations, such as restricted hypertrophy outcome and prolonged protocols; thus, additional research is required. In this study, we developed an improved muscle training protocol for mice by directly stimulating the tibialis anterior (TA) muscle motor point using electrical stimulation. C57 BL/6 adult male mice were separated into four groups: CTR (control groups for one and two weeks), ES1 (electrical stimulation for one week), and ES2 (electrical stimulation for two weeks). Following muscle training, TA was taken for further examination. The results demonstrated a steady increase in the fiber cross-sectional area as a result of muscle training (ES1, 14.6% and ES2, 28.9%, \(p<0.0001\)). Two weeks of muscle training enhanced muscle mass and maximal tetrace force by 18 (\(p=0.0205\)) and 30%, respectively (\(p=0.0260\)). To assess the tissue remodeling response in this model, we evaluated satellite cell activity and observed an increase in the number of Pax-7-positive nuclei after one and two weeks of muscle training (both \(>\)2-fold, \(p<0.0001\)). In addition, we observed an increase in the number of positive nuclei for Myo after two weeks (2.6-fold, \(p=0.0057\)) without fiber damage. Accordingly, phosphorylation of mTOR and p70 increased following two weeks of muscle training (17%, \(p=0.0215\) and 66%, \(p=0.0364\), respectively). The results indicate that this muscle training strategy is appropriate for promoting quick and intense hypertrophy.  
skeletal muscle hypertrophy; neuromuscular electrical stimulation; resistance exercise; tibialis anterior; mice Article
## 3 Materials and Methods
### PTR-ToF-MS Measurements  
A commercial PTR-ToF-MS 8000 instrument (Ionicon Analytik GmbH, Innsbruck, Austria) was used for the headspace measurements. The instrumental conditions in the drift tube were as following--drift voltage 550 V, drift temperature 110 \({}^{\circ}\)C, drift pressure 2.30 mbar affording an E/N value of 140 Townsend (1 Td = 10\({}^{-17}\) V.cm\({}^{2}\)). Sampling was performed with a flow rate of 40 sccm. The mass resolution (m/\(\Delta\)m) was at least 3800. Measurements were performed in an automated way by using a multipurpose GC automatic sampler (Gerstel GmbH, Mulheim am Ruhr, Germany) as previously described [11]. The measurement order, both samples and replicates, was randomized to avoid memory effects. All vials were incubated at 40 \({}^{\circ}\)C for 30 min before PTR-MS analysis. Each sample was measured for 30 s, at an acquisition rate of 1 spectrum per second with an overall throughput of one sample every 5 min. The experiment was repeated at 60 \({}^{\circ}\)C, the temperature at which HS-SPME GC-MS provided better results. The entire experiment was repeated three times and empty vials, containing lab air, were measured together with the sample set and considered as \"blanks\". Data processing of PTR-ToF-MS spectra included dead time correction, external calibration and peak extraction steps performed according to a procedure described elsewhere [40]. The baseline of the mass spectra was removed after averaging the whole measurement and peak detection and peak area extraction was performed by using modified Gaussian to fit the data [41]. To determine the concentrations of volatile compounds in ppbv (part per billion by volume) the formulas described by [PERSON] et al. were used by assuming a constant reaction rate coefficient (k\({}_{R}\)=\(2\times 10^{-9}\) cm\({}^{3}\)/s) for H\({}_{3}\)O\({}^{+}\) as primary ion [42].
# Predicting Runoff from the Weigan River under Climate Change
## 4 Discussion
### Model Applicability and Prediction Results  
In this study, the hydrological processes in the upper watershed of the Weigan River outflow in historical years were recreated by constructing a SWAT model. And the model data processed by using the \(r\)-MME averaging method were also input into the model to predict the trend of runoff evolution in the study area for the next 30 years. When evaluating the simulation results, the NSE coefficient of the calibration period and validation period are all greater than 0.75, and the R2 reaches higher than 0.8, suggesting that the simulation results are excellent, and the simulation results of the runoff curves in the historical period are better fitted. It can be seen that the SWAT model constructed in this study has good applicability in the upper watershed of the Weigan River outflow.  
The SWAT model predicts that the average annual runoff values during the next 30 years under four different radiative forcing scenarios, namely, SSP1-2,6, 2-4.5, 3-7.0 and 5-8.5, are 84.58 m\({}^{3}\cdot\)s\({}^{-1}\), 85.52 m\({}^{3}\cdot\)s\({}^{-1}\), 87.13 m\({}^{3}\cdot\)s\({}^{-1}\) and 90.01 m\({}^{3}\cdot\)s\({}^{-1}\), respectively. The runoff of the Weigan River is predicted to be maintained at 25.68 to 30.89 \(\times\) 10\({}^{8}\) m\({}^{3}\) in 2030 and 2050, in which the contribution of temperature change and precipitation change to future runoff increase is 68.71% and 27.24%, respectively, and the contribution of human activities to future runoff increase is 4.05%. With the continuation of global warming, the increase in temperature directly affects the evaporation rate, the glacier ablation rate and the frequency of extreme precipitation. The study area is located in an alpine mountainous region, and the impacts on glaciers under warming will be more pronounced, so the obtained results are improved compared to past research results (the percentage of increase in incoming water due to temperature increase during the period 1965-2016 was 58.55%) [21]. In terms of interdecadal variation, the average annual runoff in the study area under the four different radiative forcing scenarios shows an increasing trend. This is more in line with the findings of [PERSON] et al. [46]. The results of the current study suggest that with global warming and the gradual warming and humidification of Northwest China [47, 48], the outflow runoff from the Weigan River will likely show an increasing trend in the future. However, it should be noted that warming and humidification will not change the basic characteristics of aridity and low rainfall in Northwest China and will only improve the degree of wetness in this arid region to a certain extent [49]. The results of this study can be applied to relevant research on typical inland river basins in the arid region of Northwest China, while providing scientific support for regional climate estimation to mitigate the potential risks of climate change and formulate timely response policies.
## 4 Related Work
### Distributed Networking Security  
Adding distributed, ad hoc, peer-to-peer networking capabilities beyond the centralized backend-infrastructure-accessible networking can enable greater connectivity coverage and implement redundancy in controlling the flying base stations to improve the connectivity provision reliability, as described in Section 3.4. Previous research studied the flying ad hoc network (FANET) or UAV ad hoc network to implement communications between the UAV drones, e.g., [63; 64; 65].  
The Blockchain can also enable the secure dissemination of security-critical information to secure integrity and message authenticity against base-station threats. These threats are described in Section 4.2 although in a more centralized environment. The Blockchain can replace the centralized key management to enable/share the networking root of trust (from which other security functionalities and properties can be derived). Blockchain-based designs have implemented distributed key management and establishment in other computing/networking applications, e.g., general digital networking [66; 67; 68; 69], vehicular communications [70; 71], electronic voting system [72; 73], and software-defined networking or SDN [74; 75; 76]. The previous research can provide initial building references for designing such solutions for the applications of securing ground/terrestrial and flying base stations. More specifically, as demonstrated by the previous research in other computing applications, the Blockchain can be used for the following security purposes: disseminating the public key while replacing the centralized-server-based public-key infrastructure (PKI) to authenticate the base station; identifying/detecting a malicious or rogue base station; and using the public-key to construct channels with message authenticity (e.g., the Blockchain can be used to securely disseminate the core network's public key, which can be used for the source integrity protection of the delivery of the core-network generated credentials, such as \(E(\text{MSIN})\) or TMSI in Section 2.1).
## 4 Results
### Off-Grid Operation  
Several tests and measurements were carried out to verify the correct operation of the power supply system, the quality parameters of electricity, and the dynamics of regulation at variable load power and various types of loads (symmetrical, asymmetric and non-linear).
# Current Harmonic Suppression Algorithm for Asymmetric Dual Three-Phase PMSM
## 1 Introduction
Most methods detect the current polarities with current sensors, which are easily polluted by the noises and harmonics. In Reference [22], a specific hardware circuit is designed to detect which antiparallel diode conducts in a one switching cycle to judge the current polarity. However, it adds burden to design the hardware. Similar to the current harmonics suppression of the ADT-PMSM, the resonant controller or the PI controller in the synchronous frames is also adopted [23; 24; 25; 26]. Another approach that utilizes the integral term of the d-axis current as feedback to obtain the voltage distortion can only be used on condition that the d-axis reference current is a constant value [27]. Some methods adopt a disturbance observer to estimate the voltage error, which does not need current polarities and the specifications of the inverter [28; 29; 30; 31]. However, these methods require lots of efforts to obtain the observer gains and need to have a good knowledge of the motor parameters. This paper proposes a method that combines the feedforward voltage compensation and the feedback voltage compensation methods together to suppress the current harmonics. The feedforward voltage compensation is obtained by the voltage-second balance theory, and the current detection scheme is independent of current sensors. In order to overcome the problems caused by the junction temperature and some other factors of the inverter, a feedback voltage compensation that employs a resonant controller is put forward. The proposed scheme can achieve the desired results without additional hardware or a good knowledge of the load parameters. The effectiveness of the proposed method is verified by a set of experimental results. This paper is structured as follows: Section 2 describes the mathematical model of ADT-PMSM. The analysis of dead-time effects is given in Section 3. The feedforward voltage compensation and feedback current regulator designed in \(x\)-\(y\) subspace are discussed in Section 4. Section 5 gives the pulse width modulation (PWM) techniques to implement the four-dimension current control of ADT-PMSM. Experimental results are carried out on an ADT-PMSM experimental platform to verify the effectiveness of the proposed method in Section 6. Finally, the conclusions are summarized in Section 7.
## 5 Comparison and Discussion Between KANs and MLPs  
### MLP Network Architecture and Parameters  
MLP-A/B/C were designed by mirroring the architectures of KAN-A/B/C, with their network structures and parameters listed in Table 8. Network diagrams of the three MLPs are shown in Figure 7. MLP-A has totally same initial structure as KAN-A, and this correspondence applies to MLP-B/C, as well.  
Figure 6: Comparison of error and runtime among KAN-A, KAN-B, and KAN-C on the test set.  
Figure 7: Structure of MLP-A/B/CMLP-A/B/C. (**a**) Architecture of MLP-A, which corresponds to KAN-A. (**b**) Architecture of MLP-B, which corresponds to KAN-B. It represents a very simple neural network structure. (**c**) Architecture of MLP-C, which corresponds to KAN-C. It is a more complex structure that can also be viewed as an extended version of MLP-A/B.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Name** & **MLP-A** & **MLP-B** & **MLP-C** \\ \hline \multirow{3}{*}{Network Structure} & 1 output variable & 1 output variable & 1 output variable \\  & 10 nodes & 3 nodes & 20 nodes \\  & 3 input variables & 3 input variables & 3 input variables \\ \hline \multirow{3}{*}{Parameters} & Optimization: [PERSON] & Optimization: [PERSON] & Optimization: [PERSON] \\  & L2 regularization: 0.01 & L2 regularization: 0.01 & L2 regularization: 0.01 \\  & epochs = 8000 & epochs = 10,000 & epochs = 8000 \\ \cline{1-1}  & Learning rate = 0.001 & Learning rate = 0.001 & Learning rate = 0.001 \\ \hline \end{tabular}
\end{table}
Table 8: Network structures and parameters of MLP-A/B/C.  
Compared to KANs, MLPs were assigned higher epochs due to two key reasons:  
1. The dense weight matrices in MLPs' fully connected architecture require more gradient propagation iterations to overcome convergence delays caused by symmetric weight initialization.
2. KANs incorporates local parameter regularization through its divide-and-conquer strategy (guided by KART topological constraints), whereas MLPs lack such structural priors. Extended training cycles are therefore necessary for MLPs to achieve comparable generalization via implicit regularization.