esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Long Short-Term Memory Networks  
Long short-term memory networks are extended algorithms that improve the gradient vanishing problems in the recurrent neural networks when processing long sequences. They save essential information for a long time so that earlier information can be associated with current tasks. Long short-term memory networks provide fusion memory units, allowing the network to update hidden units on the basis of new information before learning. Thus, they are especially able to remember the most important information for future tasks. In addition, long short-term memory networks can select the tanh function as an activation function, which is a stable method that is beneficial in regression problems. Figure 1 shows the neural network architecture of long short-term memory networks. There are many variants in use called bidirectional long short-term memory networks. The cell or memory area of long short-term memory networks is composed of the input gate, forget gate, output gate, and cell state. The forget gate controls whether the memory should be cleared or how much information is entered into the next cell's memory. The input gate controls whether the input value enters the memory. On the other hand, the output gate controls whether the updated value should be output to the next layer of networks. These layers all operate in a specific way, of which three gates control all additions or deletions of the unit state. The long short-term memory network calculation is from the input sequence \(x\), represented by Equation (1):  
\[x=\left(x_{t-1},x_{t},x_{t+1}\ldots,x_{t+n}\right) \tag{1}\]  
to the output sequence \(y\), denoted by Equation (2).  
\[y=\left(y_{t-1},y_{t},y_{t+1}\ldots,y_{t+n}\right) \tag{2}\]For mapping, the first step is to input the activation vectors \(i_{t}\) and \(\widetilde{c}_{t}\), represented by Equations (3) and (4), respectively.  
\[i_{t}=sigmoid(W_{t}[x_{t},h_{t-1}]+b_{i}) \tag{3}\]  
\[\widetilde{c}_{t}=tanh(W_{c}[x_{t},h_{t-1}]+b_{c}) \tag{4}\]  
The sigmoid represents the activation function and maps the variable to values between zero and one, where W is the weight matrix, and b is the bias, \(\widetilde{c}_{t}\) is created by each tanh layer, and the forgetting gate \(f_{t}\) is created by Equation (5).  
\[f_{t}=sigmoid\Big{(}W_{f}[x_{t},h_{t-1}]+b_{f}) \tag{5}\]  
\(\widetilde{c}_{t}\) and \(f_{t}\) are employed to generate the new state of the memory cell \(C_{t}\), which can be expressed by Equation (6):  
\[C_{t}=i_{t}\widetilde{c}_{t}+f_{t}c_{t-1} \tag{6}\]  
The cell state vector \(C_{t}\) is utilized to calculate the output gate \(o_{t}\), which is illustrated in Equation (7).  
\[o_{t}=sigmoid(W_{o}[x_{t},h_{t-1}]+b_{o}) \tag{7}\]  
Finally, the output vector \(h_{t}\) of long short-term memory networks is determined by Equation (8).  
\[h_{t}=tanh(c_{t})o_{t} \tag{8}\]  
The output gate uses the cell state and activation vector to calculate the output, and the weight matrix is used in the training phase to learn with the bias vector. The long short-term memory network architecture has been successful and effective in dealing with vanishing gradient problems [32; 33; 34; 35].
## 3 Data Preparation
### Experimental Dataset
#### 3.1.2 Oxford Battery Degradation Dataset  
The Oxford Battery Degradation Dataset (Hereinafter referred to as the Oxford dataset) was released by [PERSON] et al. using the SLPB533459H4 LCO battery manufactured by Kokam. The rated capacity of this battery is 740 mAh [29]. The specifications of the battery are shown in Table 3.  
The dataset includes a total of 8 batteries, which were placed on the Bio-Logic MPG-205 battery test equipment. These cells were tested in a thermal chamber at 40 degrees Celsius. The charging strategy adopted the CCCV, and the discharge mode was constant current discharge to cut-off voltage. It is worth noting that this dataset only includes the constant-current charge and constant-current discharge stages of the battery charging process, both at 1C current. Therefore, this dataset does not capture current data. The eight cells in the Oxford dataset are named Cell1 to Cell8 in sequence. Taking Cell7 as an example, the charging voltage and temperature curves are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline
**Number** & **The First Four Steps** & **Cell Number** \\ \hline
1 & \(3.6\text{A}\to 6\text{A}\to 5.6\text{A}\to 4.755\text{A}\) & g1c11, g1c12, g1c27, g1c29, g1c38 \\
2 & \(4.4\text{A}\to 5.6\text{A}\to 5.2\text{A}\to 4.252\text{A}\) & g2c8, g2c15, g2c18, g2c32, g2c48 \\
3 & \(4.8\text{A}\to 5.2\text{A}\to 5.2\text{A}\to 4.16\text{A}\) & g3c1, g3c2, g3c10, g3c20, g3c42 \\
4 & \(5.2\text{A}\to 5.2\text{A}\to 4.8\text{A}\to 4.16\text{A}\) & g4c6, g4c7, g4c37, g4c14, g4c45 \\
5 & \(6\text{A}\to 5.6\text{A}\to 4.4\text{A}\to 3.834\text{A}\) & g5e9, g5c21, g5c22, g5c31, g5c36 \\
6 & \(7\text{A}\to 4.8\text{A}\to 4.8\text{A}\to 4.6\text{A}\to 3.652\text{A}\) & g6c3, g6c25, g6c26, g6c28, g6c44 \\
7 & \(8\text{A}\to 4.4\text{A}\to 4.4\text{A}\to 3.94\text{A}\) & g7c13, g7c16, g7c23, g7c24, g7c47 \\
8 & \(8\text{A}\to 6\text{A}\to 4.8\text{A}\to 3\text{A}\) & g8c14, g8c17, g8c30, g8c35, g8c39 \\
9 & \(8\text{A}\to 7\text{A}\to 5.2\text{A}\to 2.68\text{A}\) & g9c19, g9c33, g9c34, g9c40, g9c43 \\ \hline \end{tabular}
\end{table}
Table 2: The working condition of the battery in the Stanford–MIT–Toyota dataset.  
Figure 4: Charging curve of g1c38. (**a**) Voltage. (**b**) Temperature.  
\begin{table}
\begin{tabular}{c c} \hline
**Oxford Battery Degradation Dataset** \\ \hline Manufactures & Kokam (SLPB533459H4) \\ Battery Type & LiCoO\({}_{2}\) \\ Nominal Capacity & 740 mAh \\ Nominal Voltage & 3.7 V \\ Charging Upper Limit Voltage & 4.2 V \\ Discharging termination voltage & 2.7 V \\ \hline \end{tabular}
\end{table}
Table 3: The specifications of battery in Oxford dataset.
# Nascent Rice Husk as an Adsorbent for Removing Cationic Dyes from Textile Wastewater
###### Abstract  
We assessed the applicability of rice husk (RH) to remove cationic dyes, i.e., methylene blue (MB) and crystal violet (CV), from water. RH thermally treated at 75 \({}^{\circ}\)C showed a higher adsorption capacity than that at high temperatures (300-700 \({}^{\circ}\)C). For a suitable CV-adsorption model, a pseudo-first-order model for MB adsorption was followed by the kinetics adsorption process; however, a pseudo-second-order model was then suggested. In the qt versus t1/2 plot, the MB line passed through the origin, but that of CV did not. The Langmuir isotherm model was better than the Freundlich model for both dye adsorptions; furthermore, the adsorption capacity for MB and CV was 24.48 mg/g and 25.46 mg/g, respectively. Thermodynamically, the adsorption of both MB and CV onto the RH was found to be spontaneous and endothermic. This adsorption increased insignificantly on increasing the solution pH from 4 to 10. With an increasing dosage of the RH, there was an increase in the removal percentages of MB and CV; however, adsorption capacity per unit mass of the RH was observed to decrease. Therefore, we conclude that utilizing RH as an available and affordable adsorbent is feasible to remove MB and CV from wastewater.  
rice husk; adsorption; crystal violet; methylene blue; thermal treatment +
Footnote †: journal: Applied Sciences
## 3 Design of the Control Scheme  
### Design of the High-Speed Train Speed Control System  
The four carriages of a HST are selected as the research objects in this paper, and the first and the fourth carriages have traction units. Figure 1 shows the structure of the control scheme. In order to solve the speed tracking control problem of the high-speed train, a control scheme combining IPSO and ALADRC is proposed. Since the two LADRC work in different carriages during HST operation, the two LADRC parameters may be inconsistent due to the interaction between carriages. In known lines, the parameters of two LADRC are optimized and adjusted by the IPSO algorithm according to the principle of minimum tracking error. This method can ensure that two LADRC have better parameters at the same time. In addition, APC is introduced to ensure that LADRC still has good control performance under unknown disturbance. This makes LADRC compensate for the observation error caused by the bandwidth limitation of LESO according to the HST tracking error. Meanwhile, the parameters of the PD controller can be adjusted to eliminate the speed fluctuation caused by disturbance. This can also effectively improve the control accuracy and anti-disturbance performance of LADRC.  
**Remark 2**.: _In this paper, LESO can observe each state variable of the HST system and the total system disturbance according to the input and output data. Due to the discretization of the observer, the bandwidth value of LESO cannot exceed the sampling period of the system, as it will lead to the divergence of the LESO observation [30]. If the bandwidth value is minimal, it will lead to low LESO observation accuracy. APC is introduced to compensate for the observation error caused by bandwidth selection to improve the control accuracy and dynamic performance of LADRC. This method will make LADRC more adaptable and extensive._  
Figure 1: Control scheme structure diagram.
## 4 Conclusions  
Thus, for optimal operation, it is necessary that in the stationary mode the operating speed of the system be in the interval between the first linear and the second nonlinear resonance, i.e., \(0.051<\omega_{f}<1.301\) (the first working region), or between the second and ultraharmonic resonances, i.e., \(1.301<\omega_{f}<3\) (the second working region), or in the region behind the ultraharmonic resonance (the third working region), where, due to the self-centering effect, the values of the rotor and foundation amplitudes decrease.  
An increase in the nonlinearity parameter up to \(\varepsilon=10\) has a positive effect on the amplitude values. In this case, it should be taken into account that the frequency of the amplitude breakdown is shifted towards higher rotor speeds.  
The operation of the system is also positively affected by an increase in the damping coefficients (\(\zeta_{1}\), \(\zeta_{2}\)) and an increase in the mass of the foundation, as in this case the foundation acts as an anti-load and contributes to vibration damping.  
An increase in the linear rigidity parameter \(\lambda\) leads to a decrease in the first resonance and a shift in the second resonance, which also contributes to a smoother transition through the resonant frequencies.  
In all of the above cases, it should be taken into account that the limit of the maximum values of parameters of nonlinearity, damping, foundation mass and linear rigidity is limited by the design and technical features of a particular industrial centrifuge.  
A technique that enables us to calculate the maximum value of the amplitudes and construct the frequency characteristics of forced oscillations of the \"rotor-foundation\" system on rolling bearings with a nonlinear characteristic based on Jacobi elliptic functions has been developed. The optimal parameters associated with the mass of the foundation, the coefficients of linear and nonlinear stiffness, as well as the damping coefficients, at which the magnitudes of the amplitudes have optimal values without the need for fundamental structural changes, are determined. The specific features of linear and nonlinear behavior of the system with many degrees of freedom caused by foundation vibrations are considered.  
Not applicable.  
Data sharing not applicable to this article as no datasets were generated or analysed during the current study. The authors declare no conflict of interest.
## 3 Results
### Sine Sweep Test Results
#### 3.3.2 Effect of Mesh Density on the Damping Capacity of WMD  
To examine the effects of mesh density on the vibration isolation performance, NiTi WMDs fabricated with relative densities of 0.25, 0.31, and 0.38 were subjected to a 1 g sine sweep test from 20 to 200 Hz. Figure 18 shows the acceleration response versus the frequency of the NiTi WMDs with three different mesh densities. As the mesh density increases, the resonant peak shifts to a higher frequency and exhibits a higher acceleration transmissibility.  
Figure 17: Characterization of the vibration isolation performance of WMDs. (**a**) Natural frequency; (**b**) Peak transmissibility; (**c**) Loss factor.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & & & & \\ \hline \multicolumn{5}{c}{**Vibration Magnitude (g)**} \\ \hline  & & **0.25** & **0.5** & **1.0** & **1.5** & **2.0** \\ \hline \multirow{3}{*}{304} & Natural frequency (Hz) & 121.8 \(\pm\) 6.7 & 92.2 \(\pm\) 5.4 & 65.0 \(\pm\) 3.7 & 49.4 \(\pm\) 4.2 & 41.8 \(\pm\) 6.6 \\  & Peak transmissibility & 3.37 \(\pm\) 0.13 & 2.85 \(\pm\) 0.16 & 2.34 \(\pm\) 0.08 & 2.34 \(\pm\) 0.14 & 3.01 \(\pm\) 0.16 \\  & Loss factor & 0.29 \(\pm\) 0.02 & 0.39 \(\pm\) 0.02 & 0.49 \(\pm\) 0.03 & 0.51 \(\pm\) 0.03 & 0.27 \(\pm\) 0.02 \\ \hline \multirow{3}{*}{NiTi} & Natural frequency (Hz) & 85.3 \(\pm\) 3.7 & 62.9 \(\pm\) 6.4 & 44.0 \(\pm\) 4.7 & 35.9 \(\pm\) 5.2 & 31.0 \(\pm\) 7.6 \\  & Peak transmissibility & 2.66 \(\pm\) 0.13 & 2.62 \(\pm\) 0.14 & 2.46 \(\pm\) 0.04 & 2.56 \(\pm\) 0.13 & 3.07 \(\pm\) 0.07 \\  & Loss factor & 0.43 \(\pm\) 0.04 & 0.49 \(\pm\) 0.05 & 0.63 \(\pm\) 0.02 & 0.52 \(\pm\) 0.02 & 0.29 \(\pm\) 0.02 \\ \hline \end{tabular}
\end{table}
Table 11: Sinusoidal sweep test parameters of metal rubber shock absorbers of different materials.  
The natural frequency, transmissibility, and dissipation factor of NiTi WMD subjected to the sine sweep test were calculated and plotted as a function of relative density, As shown in Figure 19, the natural frequency and peak transmissibility increase monotonically with the mesh density. However, the dissipation factor shows an opposite trend. This can be attributed to the increased number of compacted wire coils per unit volume, which improves the stiffness of the entire WMD and reduces the relative slippage distance between individual coils.
## 2 Materials and Methods
### Simulator
#### 2.1.1 Vehicles Parameters  
The default values of the vehicles' parameters within the simulations are described in this section. By default, sigma = 0.5 which is the driver imperfection. It varies in the range of 0 to 1, with 0 indicating perfect driving and 1 indicating the opposite. By default, speedFactor = \"normc(mean,deviation,lowerCutOff,upperCutOff)\" = \"normc(1,0.1,0.7,1.3)\", where speedFactor is a value that comes from a normal distribution with mean = 1, standard deviation = 0.1, lowerCutOff = 0.7, and upperCutOff = 1.3. The top speed of a vehicle is calculated as top_speed = normal_speed \(\times\) speedFactor, with normal_speed = 16.6666 m/s, and speedFactor being a value in the range 0.7 to 1.3. In this study, the parameters sigma and speedFactor are selected and an experiment is performed for each. Thus, sigma is associated with the precision of driving in one experiment and speedFactor is associated with the vehicles' top speeds distribution in other. These variables take different values in the simulations of their respective experiments.  
The default values of the following parameters never change: vClass = \"passenger\", length = 5 m (the vehicle length), minGap = 2.5 m (the minimum gap between vehicles at rest), accel = 2.6 m/s2 (acceleration ability), decel = 4.5 m/s2 (deceleration ability), emergencyDecel = 9 m/s2 (emergency deceleration), and tau = 1 s (the driver's desired minimum time headway). The parameter vClass = \"passenger\" indicates that the default SUMO parameters' values for a passenger car (presented above) will be used; sigma and tau are parameters of the Krauss Car-following model (with modifications), which is the default Car-following model in SUMO.  
Our intention is to select the parameters' values so likely traffic conditions can emerge. For this reason, we choose the default SUMO parameters' values as the base configuration for the simulations. Nevertheless, the parameters can be adjusted with empirical data for modeling observed traffic.
## 7 Conclusions  
In this paper, we present a novel approach to partial discharge (PD) classification by merging time-frequency analysis (TFA) techniques with convolutional neural networks (CNNs). Our results demonstrate that wavelet-based methodologies, particularly Wavelet Scattering, significantly enhance the classification of PD sources. This approach outperforms other TFA methods in terms of accuracy and F1-score, thereby improving both precision and recall in intricate PD scenarios. The Scatter Wavelet Transform (SWT) achieves a simulation accuracy of 96.67% and an impressive F1-score of 0.967. Remarkably, in experimental settings, it attains perfect classification outcomes with 100% accuracy and an F1-score of 1.000, far surpassing methods such as the Hilbert-Huang Transform (HHT), which recorded merely 70.00% accuracy. While the Wigner-Ville Distribution (WVD) reaches an accuracy of 94.74% with an AUC of 0.947, its substantial computational requirements limit its applicability in real-time scenarios. Consequently, the SWT emerges as an exceptionally effective technique for noise reduction and the management of multi-source PD signals, establishing itself as a formidable diagnostic tool for power system insulation assessment.  
Conceptualization, R.P.; methodology, A.A.; software, A.A.; validation, R.P.; formal analysis, A.A.; investigation, A.A.; resources, A.A.; data curation, A.A.; writing--original draft preparation, A.A. and R.P.; writing--review and editing, R.P.; visualization, A.A.; supervision, R.P.; project administration, A.A.; funding acquisition, R.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Czech Office for Standards, Metrology and Testing, under the Metrology Development Program 2025, Task number VIII/6/25. This research was also supported by Czech Technical University in Prague, grant number 24/133/OHK3/3T/13.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Simulation Confusion Matrix** & **Experimental Confusion Matrix** \\ Wigner–Ville Distribution (WVD) & & \\ Hilbert–Huang Transform (HHT) & & \\ Scatter Wavelet Transform (SWT) & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Noise Attenuation Based on Wave Vector Characteristics
## 5 Conclusions  
We have developed a noise attenuation method based on wave vector characteristics. The key part of our method is the discrimination of polarizations of valid seismic signals and noise based mean (or median) wave vectors. In our method, the wave vectors of ground roll, valid signal, random noise and non-ground-roll related coherent noise in 3C records are separated through the smoothing of wave vectors with different moving window sizes and directions. Generally, the moving-time-window sizes adopted to suppress ground roll and random noise should be close to the maximum apparent period and half of the reciprocal of high-cutoff frequency of the valid signal, respectively. Our noise attenuation can be applied to both land and ocean bottom multi-component records. Specially, the ground roll received on the \(Y\)-component can also be filtered out, although ground roll is often assumed to be polarised on a two-dimensional vertical plane. Meanwhile, 2C or 3C records together can help to discriminate the spatial particle motion of body waves from a noisy background. Tests show that our noise attenuation method is effective for both numerical and field data applications and the effect of the proposed method is better than that of the instantaneous polarization filter. This method will be useful for obtaining pure elastic wavefields in multi-component seismic surveys.  
**Author Contributions:** [PERSON] conceived the idea of this research. [PERSON] and [PERSON] designed and programmed the codes. [PERSON] simulated the synthetic data and tested the proposed method. [PERSON] applied the proposed method to the field data. The paper was written by all the authors.  
**Acknowledgments:** The work presented in this paper has been supported by the National Natural Science Foundation of China (Nos. 41574126, 41425017) and the Fundamental Research Funds for the Central Universities (2-9-2017-452).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Deployment Overview
### Network Operation
Prior to transmitting, the raw values are converted to their respective physical values and multiplied by 10 to provide single-digit decimal resolution;
* The CSMA_RETRIES value is 1 byte and indicates the number of times that the CSMA/CA process has been repeated before the transmission is allowed;  
Figure 6: Sensing and packet transmission timeline. Sensors are sampled once at the start of the active sub-period, and then each data packet is transmitted nine times (three times for each IEEE 802.15.4g SUN modulation). * The CSMA_RSSI value is 1 byte and indicates the RSSI level (dBm) that has been measured during the CSMA/CA process. Notice that, regardless of the modulation, the packet length is always 21 bytes. However, before transmission, packets are encrypted using AES-128 and, hence, the length of the PSDU transmitted by each node is always 32 bytes. Thus, considering that packets are transmitted at 50 kbps, each packet requires 5.12 ms to be transmitted. Since each node transmits nine packets every minute and there are 11 nodes in the network, the total channel occupation is 506.88 ms per minute (about 0.85%). Hence, considering the low channel occupation and the use of CSMA/CA prior to transmission, we did not expect a high impact on the results caused by self-interference. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SUN-FSK** & **SUN-OQPSK** & **SUN-OFDM** \\ \hline
**Name** & **Mode 1** & **Mode 3** & **Option 2** \\
**Data rate** & & & **MCS0** \\
**(kbps)** & 50 & 50 & 50 \\ \hline
**Modulation** & & & \\
**type** & & & \\ \hline
**Modulation** & & & \\
**index** & & & \\ \hline
**Chip rate** & & & \\
**(kchips/s)** & & & \\ \hline
**Spreading** & & & \\
**mode** & & & \\ \hline
**Pilots** & & & \\
**Total/Data/Pilot** & & & \\ \hline
**Center** & & & \\
**frequency** & & & \\
**(MHz)** & & & \\ \hline
**Channel** & & & \\
**spacing** & & & \\
**(kHz)** & & & \\ \hline
**Available** & & & \\
**channels** & & & \\ \hline
**Transmit** & & & \\
**power** & & & \\ \hline
**Sensitivity** & & & \\
**(dBm)** & & & \\ \hline
**CCA** & & & \\
**Threshold** & & & \\ \hline
**Notes** & FEC: yes & \
\begin{tabular}{c} Coding rate: 1/2 \\ Interleaver: yes \\ \end{tabular} &
\begin{tabular}{c} Coding rate: 1/2 \\ Freq.repetition: 4\(\times\) \\ \end{tabular} \\ \hline
**UINGFR** & **PAN ID** & **EUB-16** & **TX MODE** & **TX** \\  & & & **COUNTER** \\ \hline
1/2 bytes & 2 bytes & 1 byte & 1 byte & 4 bytes & 2 bytes & 2 bytes & 2 bytes & 1 byte & 1 byte \\ \hline
0.255 & **OOK/FE** & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: IEEE 802.15.4-2015 SUN (Smart Utility Network) modes used in the deployment. Notice that the presented values for the transmit power are the theoretical values according to the configuration of the Atmel AT86 RF215 radio transceiver, but have been validated in the laboratory prior to the deployment. MCS, Modulation and Coding Schemes; SHR, Synchronization Header; PHR, PHY Header; DSSS, Direct Sequence Spread Spectrum; PSDU, PHY Service Data Unit; FEC, Forward Error Correction.