esa-sceva/satcom-chunk-collection huggingface dataset:
# MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments
## 3 Numerical Example
### Data Preprocessing  
The input data to the neural network are noisy microseismic records, generated by adding Gaussian noise to clean microseismic records. The main task is to localize the source location of microseismic events from noisy data. Traditional deep learning approaches typically predict the exact coordinates of seismic sources, necessitating a predefined number of sources to structure the model's output layer. However, with an increasing count of  
Figure 9: Velocity model.  
seismic sources, there arises a need for developing multiple models, each with a unique architecture. To address this, we employ a Gaussian mixture model to both represent the microseismic source locations and serve as the output label for this key task. The probability distribution of the source location of each microseismic event can be generated by a Gaussian distribution [34]:  
\[f_{i}(x,z)=\exp\left(-\frac{\left(x-x_{0}\right)^{2}+\left(z-z_{0}\right)^{2}}{2 \sigma^{2}}\right) \tag{3}\]  
where \((x_{0},y_{0})\) are the coordinates of the epicenter point and \(\sigma\) is the variance.  
A Gaussian mixture model is essentially a composite of K Gaussian distributions. In scenarios where the microseismic record encompasses multiple events, this model is formulated by the linear aggregation of Gaussian distributions attributed to each event. Within the resultant Gaussian mixture model, the peak's location signifies the microseismic event's source location. To ensure high resolution across all sources, the standard deviation for the Gaussian distribution is uniform for each source.  
For enhanced network learning, each composite Gaussian model undergoes normalization to a range of [0, 1].  
Traditional deep learning approaches for denoising employ supervised learning, utilizing noisy data for input and pristine data for labels. However, this method faces significant challenges in the denoising of seismic data, primarily due to the difficulty in acquiring clean seismic data for labels. [PERSON] et al. [35] introduced a novel self-supervised learning technique for denoising that relies solely on noisy data for training. This method hinges on an L2 paradigm characteristic: the L2 norm remains constant for two vectors substituted with random numbers anticipated to be identical. Consequently, utilizing the L2 loss function for training ensures that network parameters remain stable, even in the presence of noise-contaminated label data. Thus, obtaining clean seismic data is not a prerequisite for effective denoising.
## 2 Operation Principle  
Our algorithm differs from traditional QPSK partitioning methods in three fundamental ways: First, it employs time-tagging to accurately record the time intervals between signals, effectively mitigating cumulative effects and significantly enhancing estimation accuracy. Second, using 16 QAM signals as an example, traditional methods rely on 4 th power operations to remove the modulation phase, which introduces higher computational complexity and implementation challenges. In contrast, our approach utilizes a low-complexity 4 th power operation, employing absolute value operations to achieve a fourfold magnification of the phase angle and thereby remove the modulation phase. Finally, our algorithm is designed as a parallel processing framework suitable for hardware implementation, providing a solid foundation for offline algorithm design and validation, and ensuring seamless integration with hardware systems.  
The principle of the TTQP parallel algorithm is illustrated in Figure 2. In the figure, the symbol * denotes the conjugate operation. This paper provides a detailed explanation of one of the processing paths. The TTQP frequency offset estimation (FOE) algorithm consists of three stages: the first stage involves data parallelization and time-tagging, the second stage focuses on frequency offset estimation, and the third stage addresses frequency offset compensation. This section will provide a comprehensive description of each of these stages.
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
###### Abstract  
Low-Power Wide-Area Network (LPWAN) is the technology that the Internet-of-Things (IoT) uses in long-distance, wide-coverage scenarios. As one of the ultra-narrowband (UNB) modulation techniques, M-ary position phase shift keying (MPPSK) modulation can provide high coverage and high reliability for LPWAN. This paper proposes a multipath separation method based on MPPSK modulation, which aims to eliminate the influence of multipath on the main path without increasing the spectrum overhead and system complexity. Specifically, the modulation parameter of the system is adjusted according to the delay value, so that the phase jump of the multipath signal falls outside the phase jump of the main path symbol to achieve separation of the multipath from the main path. Moreover, a normalized symbol joint decision method is proposed in order to reduce the introduced noise while using multipath information for decisions. The simulation results indicate that the multipath separation conditions given in this paper can meet the requirements of multipath separation of MPPSK signals. Compared with the existing mainstream decision scheme, the normalized symbol joint decision improves the demodulation performance of the system.  
M-ary position phase shift keying; multipath; joint judgment +
Footnote †: journal: Sensors
# Realisation of a Novel Functionally Redundant Actuation System for a Railway Track-Switch
## 3 Development of Simulation Models
### The Actuator Models in Simulink  
This model is based on physical laws. Each actuator consists of two identical sets of electrical motor, gearbox and cam assembly as shown previously in Figure 3. The motor is connected to a bevel gear-head which converts the axis of rotation of the motor with an attendant speed reduction and increase in torque. The output of the gear-head is connected to drive the cam. These individual subsystems are modelled and the output of the actuator model is the actuation force applied to the two switch rails which are moved with the cams. A cam-carrier mechanism moves the rail carrier depending on the rotation of the cam. The first part of the actuator modelling is to model the electrical motor, which is of the servo type. The governing equations of the motor are shown in Equation (1) for the electrical components of the motor. The motor electrical torque can be calculated in Equation (2). All of the mathematical symbols are explained in Table 1 along with the parameter values. \[V_{M}=i_{a}R_{a}+L_{a}\dot{i_{a}}+K_{\varphi}\dot{a_{M}} \tag{1}\]  
\[T_{M}=K_{T}i_{a} \tag{2}\]  
Figure 4: Modelling approach. The shaft connecting the motor and the gear-head is assumed to be rigid in the modelling process. Hence motor and gear-head lumped together and the mechanical governing equation of the combined system is written as  
\[\big{(}J_{M}+J_{g}\big{)}\ddot{\theta_{M}}+\big{(}B_{M}+B_{g}\big{)}\dot{\theta_ {M}}=T_{M}-T_{g^{0}}/n_{g} \tag{3}\]  
The shaft speed relationship across the gear-head can be calculated from Equation (4). Back-lash in the gearbox is not considered in the modelling as this is negligible on this system. \[\omega_{gi}/\omega_{go}=n_{g} \tag{4}\]  
The two cams support and guide the rail carrier such that the rails are moved through a semi-circle. Therefore a 2D reaction force is generated (due to inertia and the elastic bending of the rails which is feedback as a load onto the cams/ gear-head/drive motor through the rail carrier and rail assembly. The force vectors that act on a single cam (e.g., cam-A) are shown in Figure 5 and the governing equations are expressed in Equation (5) where the torque on the cam is calculated in Equation (6).
## 3 Results and Discussion
### Binding Interactions of Fibrillar Tau with TauRP1-14 Peptide  
#### 3.7.1 AFM  
We first examined the morphologies of the aggregates using AFM. The results are shown in Figure 8. As seen in Figure 8a, neat Tau fibrils showed the formation of fibrillary structures with short Tau oligomers packed together to form larger fibrillary structures (as shown in the inset). This corroborates with previous work, where it has been shown that Tau fibrils tend to form by self-assembly and by the organization of shorter protofibrils arranged side-by-side with a lateral packing forming polymorphic structures [110]. From the morphologies seen, the formation of fibrillar Tau was confirmed. The roughness average, Ra, which is indicative of the average of the absolute values of the profile heights, was found to be 0.224 nm, while the RMS roughness, Rq, which is the root mean square average of the profile heights, was found to be 0.280 nm. The Rz (indicative of the difference in height between the average of five highest peaks and the lowest values) was found to be 1.06 nm. The average diameter of the fibrils was found to be around 150 nm, which aligns with the results obtained from DLS. Upon binding to TauRP (1-14) (Figure 8b), large fibrillary structures were not observed. Instead, relatively shorter fibrils and spherical structures which aggregated together were seen. The Rq and Ra values were found to be 0.565 nm and 0.454 nm, respectively, while the Rz was found to be 1.95 nm, indicating an increase in roughness due to the incorporation of TauRP (1-14). These studies confirm the association of Tau fibrils with TauRP (1-14). It is likely that binding occurred due to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Average \(\Delta\)G** & **Average** & **Average** & **Average** & **Average** \\
**Name of** & **Binding** & **Electrostatic** & **H-Bond** & **Lipophilic** & **Av large** \\
**Peptle** & **Energy** & **Energy** & **Energy** & **Energy** & **Energy** & **vdW Energy** \\  & **(kcal/mol)** & **(kcal/mol)** & **(kcal/mol)** & **(kcal/mol)** & **(kcal/mol)** & **(kcal/mol)** \\ \hline TauRP1-2 & \(-\)75.71 & 6.73 & \(-\)5.20 & \(-\)9.59 & \(-\)58.42 \\ \hline TauRP1-4 & \(-\)53.43 & \(-\)215.64 & \(-\)5.01 & \(-\)8.74 & \(-\)43.89 \\ \hline TauRP1-8 & \(-\)42.05 & \(-\)235.13 & \(-\)5.57 & \(-\)4.01 & \(-\)52.34 \\ \hline TauRP1-14 & \(-\)70.70 & \(-\)342.01 & \(-\)10.17 & \(-\)5.61 & \(-\)53.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: MMGBSA studies.  
interactions between the positively charged residues such as lysine and Arg present in Tau and the five negatively charged residues in TauRP (1-14), as predicted in the computational studies. Upon binding, there appears to be a change in the conformation of Tau protein, resulting in a change in morphology, as seen in the AFM images.
## 4 Discussion
The in vitro nature of most of the included studies also limits the generalizability of the findings to clinical settings. While in vitro studies provide valuable insights into precision and fit accuracy, they do not fully capture the complex forces and variables present in the oral environment, such as mastication, saliva, and temperature changes, all of which can affect the long-term performance of RPDs [43]. Future research should include clinical trials that assess not only the fit accuracy, but also the durability, patient satisfaction, and overallclinical outcomes of digitally fabricated RPDs. To overcome the limitations of in vitro studies and enhance the real-world applicability of the findings, future research should focus on conducting randomized controlled trials (RCTs) and prospective cohort studies to assess the long-term performance of removable partial dentures (RPDs) fabricated using digital techniques. These studies should evaluate factors like patient comfort, fit accuracy, durability, and clinical outcomes, such as tissue response and bone resorption. A hybrid approach, combining in vitro and clinical testing, could provide a more comprehensive understanding by aligning lab results with real-world clinical performance. In vivo material performance testing is critical to assess the wear, temperature effects, and biocompatibility of RPDs. Standardizing digital workflows across studies, including scanning, software, and fabrication techniques, would help ensure consistent and comparable results. Additionally, incorporating patient-reported outcome measures (PROMs) would provide insights into patient satisfaction regarding comfort, esthetics, and overall quality of life related to RPDs. Finally, multicenter clinical trials across diverse populations and clinical settings would yield more generalizable findings and validate the utility of digital fabrication methods in a broader context. The quality assessment of the included studies, conducted using the OHAT tool, revealed a low-to-moderate risk of bias across most domains. Notably, randomization was lacking in several studies, which could have introduced performance bias, particularly in studies where blinding was not adequately addressed. However, the statistical methods used were generally appropriate, supporting the validity of the findings, despite these methodological limitations. The moderate risks in terms of randomization and reporting bias suggest that future studies should aim to improve these aspects to increase the reliability of the results.
###### Abstract  
Uniaxial compressive strength (UCS) testing requires high-quality core samples, which is a difficult task for weak, highly fractured, thinly bedded, foliated, and weathered rocks. In addition, it is time-consuming and expensive. Because of the good relationship between rock point load strength (PLS) and UCS, the PLS could be used to estimate rock UCS quickly. The lump structure and layer structure of carbonaceous slate are revealed in the tunnels of the China-Laos Railway in the Laos Luang Prabang Suture Zone as one of the important factors leading to tunnel squeezing deformation and support structures. To reveal the relationship between the PLS and UCS of carbonaceous slate in the Luang Prabang Suture Zone, PLS tests and UCS tests of lump-structure carbonaceous slate (lamina plane inconspicuous) and layer-structure carbonaceous slate (lamina plane conspicuous) were performed. Results show that the \(I_{4(50)}\) of lump-structure carbonaceous slate ranged from 0.06 MPa to 0.30 MPa, the \(I_{4(50)}\) of layer-structure carbonaceous slates which were loaded perpendicular to the lamina plane ranged from 0.64 MPa to 1.25 MPa, the \(I_{4(50)}\) of layer-structure carbonaceous slates which were loaded parallel to the lamina plane ranged from 0.49 MPa to 0.71 MPa, and the correction power index m ranged from 0.42 to 0.51 with an average value of 0.47. Four correlation expressions of carbonaceous slate relationships between PLS and UCS were fitted by zero-intercept linear expression, nonzero intercept linear expression, power expression, and logarithmic expression, and the calculation results were compared with results calculated by the International Society of Rock Mechanics (ISRM) correlation equation. It is concluded that the correlation equation between UCS and PLS recommended by ISRM specifications easily causes soft rock strength overestimation, which affects the correct evaluation of the surrounding rock property and the structural design safety of tunnels and underground projects. The zero-intercept linear equation UCS = \(18.45I_{4(50)}\) has better goodness of fit and higher accuracy in predicting the UCS of the carbonaceous slate in the Luang Prabang Suture Zone.  
L uang Prabang Suture Zone; carbonaceous slate; point load strength; uniaxial compressive strength; size correction factor
# End-to-End Underwater Acoustic Communication Based on Autoencoder with Dense Convolution
## 3 Dense-Convolutional-Autoencoder-Based UWA FBMC Communications System
### System Model
The decoder is highly similar in structure to an encoder and can be seen as its inverse process where the input features dimension is accumulated layer by layer. In addition, the Flatten function is used to transform a higher-dimensional tensor into a one-dimensional tensor, and the fully connected layer is used as the output layer to predict all the binary bits. We record \(\hat{b}\) and the corresponding input \(b\) as a set of training data, and the output tensor is consistent with the input. The cross-entropy function is considered the loss function to measure the gap between \(\hat{b}\) and \(b\), which is expressed as:  
\[J\left(b,\hat{b}\right)=-\sum_{i=1}^{n_{b}}b_{i}\log\left(\hat{b}_{i}\right)+ \left(1-b_{i}\right)\log\left(1-\hat{b}_{i}\right) \tag{11}\]  
where \(b_{i}\) and \(\hat{b}_{i}\) are the supervised value and output prediction of the \(i-\)th node in the output layer and \(n_{b}\) is the number of bits predicted at a time. The Adam stochastic gradient descent algorithm is selected as the optimizer, which is updated in reverse to adjust parameters such as network weights and bias, and the loss function is minimized by iterative finetuning to fit the nonlinear relationship between the input and output. An early stopping mechanism with monitoring on loss is employed to shorten the redundant training epoch. The transmitted data can be recovered without explicit channel estimation and equalization through a completely data-driven approach. The training phase is completed when the value of the loss function or the number of iterations learned reaches a preset value, and the network weights and biases stop being updated at the same time. When a new received signal is input, the well-trained detector directly restores the original transmitted bits.
# A Unified Approach for the Control of Power Electronics Converters. Part II: Tracking
## 7 Approximated State Reference Signal from a Periodic Output Reference Signal
### Approximated State Reference Signal
\right]\leq 0\]  
Multiplying on the left by \(\left[(x_{a}-x_{app})^{T},\bar{x}_{app}^{T}\right]\) and on the right by its transpose, we have  
\[(x_{a}-x_{app})^{T}\Big{(}\mathcal{A}_{A_{app}}^{T}S+\bar{S}\mathcal{A}_{ \lambda_{app}}+\mathcal{C}_{\lambda_{app}}^{T}\mathcal{C}_{\lambda_{app}} \Big{)}(x_{a}-x_{app})-2(x_{a}-x_{app})^{T}\bar{s}x_{app}-\bar{\mu}x_{app}^{T} \bar{x}_{app}\leq 0\]  
Then,  
\[\frac{dV}{dt}+\|y_{a}(t)-y_{ref}(t)\|^{2}-\bar{\mu}\left\|\frac{dx_{appr}(t)}{ dt}\right\|^{2}\leq 0\]  
which, in turn, by integration with \([t,t+T]\), gives  
\[V(x_{a}(t+T),x_{app}(t+T))-V(x_{a}(t),x_{app}(t))+\int_{t}^{t+T}\|y_{a}(t)-y_{ ref}(t)\|^{2}dt-\bar{\mu}\int_{t}^{t+T}\left\|\frac{dx_{appr}(t)}{dt}\right\|^{2}dt\leq 0\]  
When \(t\to\infty\), by the periodicity of \(x_{a}(t)\) and \(x_{app}(t)\), we have  
\[\int_{t}^{t+T}\|y_{a}(t)-y_{ref}(t)\|^{2}dt\leq\bar{\mu}\int_{t}^{t+T}\left\| \frac{dx_{appr}(t)}{dt}\right\|^{2}dt\]  
completing the proof of Theorem 6. If \(x_{appr}(t)\) is constant, the right-hand term of (32) is equal to zero and \(y_{ref}=x_{a}(t)=\) constant. As expected, the error depends on the variations of \(x_{app}(t)\) and \(\bar{\mu}\) can be interpreted as a rejection gain when \(dx_{app}(t)/dt\) is considered a perturbation signal.
## 2 Materials and Methods
### Performance Test  
The oil/water separation efficiency of the fabricated new membranes was evaluated by a filtration device (Nalgene 300-4050, Rochester, NY, USA) with its effective membrane area of 11.3 cm\({}^{2}\). The device was operated under vacuum filtration condition. The fabricated TNF/CNF membrane was placed in the vacuum filtration apparatus. The separation of oil and water was conducted in the dead-end filtration mode. In the filtering device, 100 mL of the prepared oil-in-water emulsions or oil-in-saline water mixtures was poured. The oil emulsion was then allowed to pass through the TNF/CNF membrane using vacuum pressure. The operating vacuum pressure throughout all the experiments was \(-30\) KPa.
## 3 Results
### Characterization Methods
#### 3.3.6 BET Analysis  
The surface area (SA) of RPNS and thermally treated samples was measured as shown in Table 7. The collected data show that Langmuir SA has increased from 4.24 to 76.94 m\({}^{2}\)/g. This huge increase in the SA could be attributed to the thermal treatment process. Furthermore, the total pore volume has increased from 0.008174 to 0.07234 cm\({}^{3}\)/g for RPNS and PNS500 samples, respectively. The average pore radius was found to be 61.7 \({}^{\circ}\)A for RPNS and 89.8 \({}^{\circ}\)A for RPNS350. The obtained data indicate that the thermal treatment process of PNS samples did not have a great impact on the average pore radius and the total pore volume in contrast to the SA.  
Figure 7: SEM micrographs of (**A**) RPNS, (**B**) PNS300, (**C**) PNS400, and (**D**) PNS500.
## 4 Conclusions  
This work presents an investigation on the performance of G-26 and MTS9570 resins for the adsorption of lead ions from synthesized wastewater. The resin dosage, pH of solution, contact time, initial metal concentration, and temperature were the main operation parameters that affected the adsorption of Pb(II). For all the systems studied, the pseudo-second-order chemical reaction kinetics provided the best correlation of the experimental data for both resins. The Langmuir model better described the adsorption equilibrium. The maximum adsorption capacities to Pb(II) were 45.45 and 38.46 mg/g for G-26 and MTS9570, respectively. The negative value of free energy \(\Delta G^{o}\)and the positive value of \(\Delta H^{o}\) obtained indicate that the adsorption process is spontaneous and endothermic in nature. The resin recycling study shows that the most efficient Pb(II) desorption for both resins was obtained using 40 mL volume of HCl with 15% (\(v/v\)) concentration and 20 min of elution time at room temperature. Results of consecutive adsorption/desorption studies show that G-26 resin exhibited excellent stability in all the three cycles, with 100% recovery for adsorption and 100% efficiency for elution of Pb(II) ions. Furthermore for the MTS9570 resin, its performance degradation might be due to its macroporous S-DVB structure (low surface area) and negative swelling factor. Gel-structure resin G-26 is more effective than macroporous-structure resin (MTS9570) for the removal/recovery of lead ions from wastewater.  
All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; the first draft of the manuscript was written by [PERSON] and [PERSON]. All authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.  
Discovery Program Grant # RGPIN/004354-2017, Natural Sciences and Engineering Research Council of Canada (NSERC).  
The datasets generated during the current study are available from the corresponding author on reasonable request.  
The authors gratefully acknowledge the financial support of this work by the Natural Sciences and Engineering Research Council of Canada (NSERC) and [PERSON] in the Core Research Equipment and Instrument Training Network (CREAIT) at Memorial University of Newfoundland for metal concentration analysis.  
The authors have no relevant financial or non-financial interests to disclose.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Pb(II) Removal \%**} & \multicolumn{3}{c}{**Pb(II) Recovery \%**} \\ \cline{2-7}
**Resin** & \multicolumn{3}{c}{**Adsorption Cycle**} & \multicolumn{3}{c}{**Elution Cycle**} \\ \cline{2-7}  & **First Adsorption** & **Second** & **Third** & **Adsorption** & **First Elution** & **Second Elution** & **Third Elution** \\ \hline G-26 & 100 & 100 & 100 & 100 & 100 & 100 \\ MTS9570 & 100 & 97 & 96 & 90 & 86 & 76 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Adsorption and desorption cycles of Pb(II) on G-26 and MTS9570 resins.