esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## 1 Introduction  
Since its inception 25 years ago, the field of functional near-infrared spectroscopy (fNIRS) has grown rapidly, with the number of publications doubling every 3.5 years [1]. The number of publications per year is expected to approach 500 by the year 2020 [2]. Advances in technology and hardware have allowed fNIRS researchers to non-invasively probe neurovascular physiology, with increasing resolution and signal quality. However, with the rapid increase in new users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods [3]. Furthermore, an apparent lack of standardized and automated processing and analysis methods makes it difficult to reproduce studies, and may result in the misinterpretation of data, even by experienced fNIRS researchers. In this manuscript, we set out to provide visualizations to describe how small differences in individual processing steps can significantly influence study results.  
There are a vast number of signal processing approaches that have been developed to deal with potential confounders of fNIRS data, including various methods for addressing signal quality (channel exclusion, motion correction) and removing physiological noise (filtering, de-noising). With the wide variety of processing strategies, each with its own advantages and disadvantages, it can be difficult to determine the most appropriate algorithm or methodology to implement.  
We attempt to address this issue by first evaluating commonly used and widely available preprocessing methods for channel exclusion, motion correction, and filtering. Simultaneously, we evaluate the performance of several de-noising methods and their interaction with a popular statistical model for detecting activation, namely the general linear model (GLM), under varying hemodynamic response conditions. The purpose of this study is to provide a manuscript with visual descriptionsof the effects of different processing methods, to help inform researchers with less fNIRS expertise in setting up a processing pipeline.  
In the following sections, we will briefly describe some of the potential sources of noise (a more in-depth discussion of this topic can be found elsewhere [4; 5]), and outline some of the commonly used methods for addressing signal quality and removing physiological noise.
## 4 Results and Discussion
By increasing the side resistance between 20 and 50 feet, the value of centroid for side resistance values \"\(C\)\" increased, as shown in Figure 15. Considering the new \"\(C\)\" value in the Equations (1) and (2) reduced the elastic shortening of the shaft. Therefore, the settlement was decreased based on the reported and constructed equivalent top-down load-settlement curves, while the load-bearing capacity of the drilled shaft for certain settlement values were increased. In Case Study II, other soil layers above caliche were partially developed to their failure capacity. During the upward load application, excessive movement between the soil layers and caliche deposit was recorded; however, an additional side resistance was not developed. The transferred load distribution, based on the load application by the top-down test in the caliche, was comparable to the Osterberg test results. Due to negligible changes in the side resistance based on the different loading directions, the \(C\) value remained constant. Subsequently, the load-settlement diagram for the test drilled shaft is similar for both loading directions, as presented in Figure 16.  
Figure 15: Unit Side Resistance and Load–settlement Comparison for O-cell test and Conventional Test (Case Study I).
## 4 Results and Discussion
### Membrane Performance
#### 4.2.2 Effect of Relative Humidity  
In these tests, the relative humidity was changed from 30% to 70% and the error value due to membrane distortion during the experiment was corrected. After converting the measurements into a performance factor, the average value was calculated. As shown in Figure 6, the permeance of both H\({}_{2}\)O and N\({}_{2}\) increased with increasing relative humidity; however, the change in permeance of H\({}_{2}\)O was greater than that of N\({}_{2}\), indicating the selectivity of the membrane for water vapor. The equation for calculating the water vapor permeance used in this study is linearly proportional to changes in the water vapor pressure, and the experimental curves showed linearity within the experimental uncertainty. Similarly, the nitrogen permeance increased with increasing humidity, which is attributed to the effect of the absolute pressure difference in the equation decreasing as the water vapor pressure increased. A MATLAB curve fitting tool was used to extrapolate the curves to determine the permeance and selectivity values of nitrogen and water vapor at a relative humidity of 0%. The permeance value of H\({}_{2}\)O was 2409 GPU and that of N\({}_{2}\) was 3.818 GPU, with a water vapor selectivity of 630.96.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time (h)**} & \multicolumn{2}{c}{**Test C**} & \multicolumn{2}{c}{**Test D**} \\ \cline{2-5}  & _p(abs) (kPa)_ & **Absorbent Mass (g)** & _p(abs) (kPa)_ & **Absorbent Mass (g)** \\ \hline
0 & 42.25 & 89.19 & 162.46 & 75.78 \\
12 & 58.24 & - & 153.40 & - \\
24 & 68.95 & - & 144.95 & - \\
36 & 76.58 & - & 138.03 & - \\
48 & 82.43 & 92.38 & 132.22 & 77.76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Real-time measurement data of Test C and Test D, which were performed considering opposite directions of nitrogen permeation.  
Figure 6: Permeance of H\({}_{2}\)O and N\({}_{2}\) as a function of relative humidity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Performance Factor**} & **Test C** & **Test D** & **Cup** \\ \hline
**Permeance (H\({}_{2}\)O) (GPU)** & \(3.56\times 10^{3}\) & \(2.08\times 10^{3}\) & \(1.68\times 10^{3}\) \\
**Permeance (N\({}_{2}\)) (GPU)** & \(4.40\) & \(2.52\) & - \\
**Selectivity (\(\star\))** & \(8.08\times 10^{2}\) & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Membrane performance factor values of Test C, Test D, and the cup method.
## 4 Experimental Analysis
### Ablation Study
#### 4.5.3 Inference Efficiency  
As shown in Table 7, we compare the inference time of different methods, where the average inference time is measured on the ShapeNet test set using two NVIDIA RTX 2080  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Decoder** & **3 Views** & **5 Views** & **8 Views** & **12 Views** & **16 Views** & **20 Views** \\ \hline LegoFormer [15] & 0.7536 & 0.7643 & 0.7696 & 0.7729 & 0.7737 & 0.7751 \\
3D-RETR [14] & 0.7592 & 0.7685 & 0.7723 & 0.7775 & 0.7787 & 0.7796 \\ LRGT [11] & 0.7581 & 0.7687 & 0.7721 & 0.7766 & 0.7773 & 0.7785 \\
**WGA-SWIN (Ours)** & **0.7636** & **0.7748** & **0.7786** & **0.7808** & **0.7819** & **0.7827** \\ \hline \hline \end{tabular}  
* The best results are highlighted in bold.  
\end{table}
Table 6: The performance comparison between our decoder and other previous works such as LegoFormer [15], 3D-RETR [14], and LRGT [11]. To handle the variables, the same encoder, WGA-SWIN, is applied in all experiments. Based on ShapeNet, IoU metrics are used to evaluate the experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Grouping Parameters \(G\)** & **3 Views** & **5 Views** & **8 Views** & **12 Views** & **16 Views** & **20 Views** \\ \hline
36 & 0.7592 & 0.7702 & 0.7744 & 0.7787 & 0.7798 & 0.7809 \\
**49** & **0.7636** & **0.7748** & **0.7786** & **0.7808** & **0.7819** & **0.7827** \\
64 & 0.7612 & 0.7734 & 0.7772 & 0.7795 & 0.7808 & 0.7816 \\ \hline \hline \end{tabular}  
* The best results are highlighted in bold.  
\end{table}
Table 5: An ablation experiment examining the different grouping parameters G on ShapeNet using IoU score across views.  
Ti GPU devices for different numbers of views. The results show that as the number of views increases, the inference time also increases due to the larger amount of data to be processed. However, WGA-SWIN consistently exhibits superior efficiency, outperforming both LRGT and UMIFormer across all view configurations.  
For example, when processing three views, WGA-SWIN takes only 0.0302 (s), which is slightly faster than LRGT with 0.0319 (s) and UMIFormer with 0.0328 (s). This trend continues as the number of views increases, with WGA-SWIN maintaining the lowest inference time of 0.0973 (s) for 20 views, which is only slightly lower than the other two methods. Specifically, WGA-SWIN strikes an impressive balance between inference time and model performance, outperforming both LRGT and UMIFormer in terms of performance while maintaining similar inference times.
###### Abstract  
Machine learning (ML) has gained significant attention for unraveling the complex, nonlinear relationships between soil moisture (SM) and various predictive variables, including remote sensing (RS; reflectance, brightness temperature, backscatter coefficients) and biophysical (topographic, soil, vegetation, and weather) variables. We reviewed the literature to extract and synthesize ML algorithms, reliable input features, and challenges in SM estimation using RS data. We analyzed results from 144 articles published from 2010 to 2024. Random forest (40 out of 67 studies), support vector regressor (13 out of 39 studies), and artificial neural networks (12 out of 27 studies) often outperformed other algorithms to estimate SM using RS datasets. Multi-source RS data often outperformed single-source data in SM estimation. Satellite-derived features, such as vegetation indices and backscattering coefficients, provided critical information on surface SM (SSM) variability to estimate SSM. For root zone SM estimation, soil properties and SSM generally were more reliable predictors than surface information derived solely from RS. Two recent advances--the use of semi-empirical models and L-band SAR to mitigate vegetation effects, and transfer learning to improve model transferability--have shown promise in addressing key challenges in SM estimation.  
microwave; synthetic aperture radar; optical sensor; input features; surface soil moisture; root zone soil moisture 2025172397  
1
## 2 Sensor Calibration
### Camera Parameter Calibration
#### 2.1.2 Traditional Camera Imaging Model  
The pinhole model is the most widely used imaging model in traditional cameras. It is based on the physical concept of pinhole imaging, describing the transformation relationship from a point in space to a pixel on the camera's image plane. This model involves the mapping relationships among the image physical coordinate system \(o-x_{\mathrm{c}}y_{\mathrm{c}}\), the image pixel coordinate system \(M-uv\), the camera coordinate system \(O_{\mathrm{C}}-X_{\mathrm{C}}Y_{\mathrm{C}}Z_{\mathrm{C}}\), and the world coordinate system \(O_{\mathrm{W}}-X_{\mathrm{W}}Y_{\mathrm{W}}Z_{\mathrm{W}}\).  
In the traditional camera imaging model, any point \(P_{\mathrm{W}}\) in the world coordinate system is projected as point \(P_{\mathrm{C}}\) on the image plane along a ray passing through the optical center \(O_{\mathrm{C}}\) of the camera lens. The coordinates of point \(P_{\mathrm{C}}\) in the image pixel coordinate system are \((u,v)\). Without considering distortion, the traditional camera imaging model is as follows:  
\[\lambda_{0}\begin{bmatrix}u\\ v\\ 1\end{bmatrix}=\mathbf{K}(\mathbf{R}\begin{bmatrix}X_{\mathrm{W}}\\ Y_{\mathrm{W}}\\ Z_{\mathrm{W}}\end{bmatrix}+\mathbf{T}) \tag{3}\]  
\[\mathbf{K}=\begin{bmatrix}f_{x}&0&u_{0}\\ 0&f_{y}&v_{0}\\ 0&0&1\end{bmatrix},\]  
\[f_{x}=\frac{f}{\Delta x},\]  
\[f_{y}=\frac{f}{\Delta y} \tag{4}\]  
where the rotation matrix \(\mathbf{R}(3\times 3)\) and the translation vector \(\mathbf{T}(3\times 1)\) represent the rigid body transformation from \(O_{\mathrm{W}}-X_{\mathrm{W}}Y_{\mathrm{W}}Z_{\mathrm{W}}\) to \(O_{\mathrm{C}}-X_{\mathrm{C}}Y_{\mathrm{C}}Z_{\mathrm{C}}\). The intrinsic parameter matrix \(\mathbf{K}\) is determined by the camera's intrinsic properties. The physical sizes of each pixel in the \(x_{c}\) and \(y_{c}\) directions are \(\Delta x\) and \(\Delta y\), respectively. The scale factor \(\lambda_{0}\) is related to the projection depth. The effective focal lengths of the camera in the \(x_{c}\) and \(y_{c}\) directions are \(f_{x}\) and \(f_{y}\), respectively. Finally, \(u_{0}\) and \(v_{0}\) are the coordinates of the principal point in the image pixel coordinate system.
## 3 Results  
### Acute Load  
Figure 1 shows the effect of training and matches in pre- and post-values in the biochemical variables evaluated during the congested-fixture period. Higher values after sessions were found in total protein with moderate effect in both contexts (training: \(t=-6.85\), \(p<\) 0.01, \(d=0.73\); match: \(t=-4.15\), \(p<\) 0.01, \(d=0.52\)) and in SlgA with high effect in matches (\(t=-3.09\), \(p<\) 0.01, \(d=1.09\)) and moderate effect in training (\(t=-2.95\), \(p<\) 0.01, \(d=0.52\)). In addition, a low effect with high after-match values were found in TAC (\(t=-3.67\), \(p<\) 0.01, \(d=0.46\)). No effect was found in TAC during training sessions and in TOS and oxidative stress index in both scenarios.  
The differences of acute effects between training and match contexts in biochemical markers are shown in Table 2. Higher acute fatigue in the match context was found with a moderate effect in SlgA and a low effect in TAC and TOS. No effect was found in the total protein and oxidative stress index between session contexts.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \begin{tabular}{c} **Training** \\ **M**\(\pm\)** **SD** \\ \end{tabular} & \begin{tabular}{c} **Match** \\ **M**\(\pm\)** **SD** \\ \end{tabular} & \
\begin{tabular}{c} **_p_-Value** \\ \end{tabular} &
\begin{tabular}{c} **Cohen’s**\(d\) \\ **(Effect)** \\ \end{tabular} \\ \hline
**Total Protein** & 0.76 \(\pm\) 1.04 & 0.50 \(\pm\) 0.97 & 1.55 & 0.13 & 0.15 (trivial) \\
**SlgA** & 63.81 \(\pm\) 122.46 & 162.73 \(\pm\) 149.01 & \(-\)1.96 & **0.04** & 0.77 (moderate) \\
**TAC** & 0.09 \(\pm\) 4.73 & 2.37 \(\pm\) 5.12 & \(-\)2.84 & **<0.01** & 0.47 (low) \\
**TOS** & \(-\)0.85 \(\pm\) 3.37 & 1.37 \(\pm\) 5.66 & \(-\)2.37 & **0.02** & 0.49 (low) \\
**TOS/TAC ratio** & \(-\)0.04 \(\pm\) 0.20 & 0.03 \(\pm\) 0.32 & \(-\)1.18 & 0.24 & 0.13 (trivial) \\ \hline \hline \end{tabular}  
* **Notes: SlgA: salivary immunoglobulin **A**; TOS: total oxidant status; TAC: total antioxidant capacity. \(t\) = t-statistic; \(p\) = _p_-value. **Bold** indicates statistical significance set at a _p_-value of \(\leq\)0.05. Cohen’s d is interpreted by the authors as (_d_ = 0.2 to 0.49) small, (_d_ = 0.5 to 0.79) medium, and (_d_ = 0.8) large.  
\end{table}
Table 2: Independent measures \(t\)-test to evaluate the differences in acute effect between training and match contexts.  
Figure 1: Acute fatigue (pre- vs. post-session) in training and match contexts in immunological and oxidative variables. **Notes:** SlgA: salivary immunoglobulin A; TOS: total oxidant status; TAC: total antioxidant capacity. * indicates statistical significance set at a _p_-value of \(\leq\) 0.05. [PERSON]’s d is interpreted by the authors as: * small (_d_ = 0.2 to 0.49), ** medium (_d_ = 0.5 to 0.79), and *** large (_d_ \(\geq\) 0.8).
## 3 Results and Discussion
### Photonic Center for Visible-Light Absorption
Since the defect levels are lower than the conduction band edge of rutile, most of the electrons cannot overcome the energy barrier of rutile and are forced to flow away from the anatase/rutile interface. Thus, those electrons attempting to flow towards rutile are mainly turned away by the interface so as to reduce electron-hole recombination on the rutile side. The holes on the rutile side are mostly free from recombination with electrons and are allowed to proceed with their photocatalytic reaction on the rutile surface. With the help of the anatase/rutile interface, the particles generated on anatase are separated from rutile effectively, and the visible-light photocatalytic activity of the TiO2 nanopowder is enhanced. Figure 7: A schematic diagram of photoelectron generation and movement in TiO2 with an anatase/rutile interface under visible light illumination (**a**) without a visible-light-absorbing photonic center and (**b**) with a visible-light-absorbing photonic center.
In our previous study [27], we showed that the transparent and antireflective coatings on polyvinyl chloride (PVC) substrates can be obtained through the sol-gel method under mild conditions, using methyltriethoxysilane (MTES) and vinyltrimethoxysilane (VTMES). The obtained results indicated a reduction of reflectance (10%) for the sol-gel silica coatings, at the wavelength of 550 nm, compared with uncoated PVC substrate. In the present study, different silica materials were synthesized using the acid-catalyzed sol-gel method, using silane precursors with various alkyl groups (tetraethoxysilane (TEOS), ethoxdimethylvinylsilane (DMVES), triethoxyoctylsilane (OTES), and trimethoxyhexadecylsilane (HDMTSE)). The sol-gel method was chosen to obtain the silica materials due to its cost-effectiveness, low processing temperature, and the possibility of controlling the product's chemical composition. These silica materials were deposited onto plastic substrates (polyvinyl chloride (PVC)) in order to obtain transparent and hydrophobic hybrid coatings. Physicochemical and morphological properties of final silica materials and of hybrid coatings were examined by Fourier-transform infrared spectroscopy (FTIR), UV/Vis spectroscopy, transmission electron microscopy (TEM), thermogravimetric analysis (TGA), atomic force microscopy (AFM), spectroscopic ellipsometry (SE), and water contact-angle measurements. Treating clear PVC films with hybrid silica coatings will increase photo- and thermal stability and transparency, improving their properties for outdoor applications, such as energy-generating solar panel window blinds or PVC clear Windmaster outdoor blinds.
## 2 Experimental Setup
### Emission Spectroscopy  
The spectroscopic setup is based on a Czerny-Turner spectrograph of type THR1000 made by Jobin-Yvon (now Horiba), Oberursel, Germany, with a focal length of 1000 mm and a diffraction grating of 1200 L/mm. The horizontally placed effluent plasma jet is projected through a 100 mm planoconvex fused silica lens onto the vertically oriented entrance slit. Similar to the scattering setup, the same intensified camera of type 4 Picos is used as a detector placed in the imaging plane of the spectrograph. In this work, the camera is triggered at peak current by the circuit described in the section above (exactly by the  
Figure 1: Simplified schematic of the triggering setup used for laser scattering.  
\"out A\" output of the pulse-delay generator). The electron density \(n_{e}\) is estimated from the linear Stark effect after recording the hydrogen beta line at 486.13 nm. The following equation, given in [47], is used for the estimation:  
\[\text{FWHA }[\text{nm}]=1.666\left(\frac{n_{e}\left[\text{m}^{-3}\right]}{10^{2 3}}\right)^{0.68777}. \tag{1}\]  
The heavy particle temperature, on the other hand, is determined by fitting the synthetic spectra to the measured spectra of the \(C^{3}\Pi_{u}\to B^{3}\Pi_{g}\) transition for the excited nitrogen molecule and of the \(B^{2}\Sigma_{u}^{+}\to X^{2}\Sigma_{g}^{+}\) transition for the ionized nitrogen molecule. For more details on the spectroscopic setup and data evaluation, it should be referred to [40].
# A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM
## 4 VMD Optimization Based on the Improved MFO
### IMF Screening Based on the EWCS  
When the value of \(K\) is greater than the number of components in the signal, over-decomposition occurs. False IMFs may exist among the K IMFs components of the original signal when decomposed by the MFO-optimized VMD method. These false components can adversely affect subsequent fault classification. Therefore, it is necessary to separate these false IMF components. While correlation coefficients or kurtosis criteria are frequently employed in separating false IMFs by existing methods [41], these indicators may not fully capture the intricate vibration characteristics of signals [42]. To address this limitation, we propose a novel evaluation index, known as the \(EWCS\), to screen IMFs. A modal component with a calculated \(EWCS\) greater than 0 is identified as an effective one. The \(EWCS\) calculation involves both the correlation coefficient and sparsity of the IMF, providing a more comprehensive assessment of its suitability. The \(EWCS\) is calculated using the formula:  
\[Spa=\frac{\sqrt{\frac{1}{N}\sum_{i=1}^{N}x^{2}(t)}}{\frac{1}{N}\sum_{i=1}^{N} \lvert x(t)\rvert} \tag{22}\]  
\[Cor=\frac{E[(x-\overline{x})(y-\overline{y})]}{E[(x-\overline{x})^{2}]E[(y- \overline{y})^{2}]} \tag{23}\]  
\[WCS=Spa\cdot Cor \tag{24}\]  
\[EWCS(i)=WCS(i)-\frac{1}{K}\sum_{i=1}^{K}WCS(i) \tag{25}\]  
where; \(Spa\) is the sparsity of signal \(x(t)\), and \(Cor\) is the correlation coefficient between signal \(x\) and \(y\).
## 2 Background and Preliminaries
### Federated Learning: Peer-to-Peer Architecture  
The architecture of federated learning based on peer-to-peer interaction operates without the need for a central server to coordinate the learning and parameter sharing process. Participants engage in direct communication without relying on an intermediary. This results in an equitable standing for each participant within the architecture, enabling any participant to initiate a model exchange request with others [22]. Due to the absence of a central server, participants must establish a prior consensus regarding the sequence in which models are to be transmitted and received.  
Figure 3 illustrates P2P FL. It shows clients directly communicate with one another instead of any central authority. A group of clients with a common goal collaborate to improve their models by sharing information from peer to peer.  
Figure 3: The P2P FL architecture inspired by [23].  
Figure 2: The centralized FL architecture inspired by [21]. Step1: Participant selection and global model dissemination. Step2: Local computation. Step3: Local models aggregation. Step4: Global model update.  
When assessing vulnerabilities, the P2P FL architecture proves superior due to its avoidance of a central server, mitigating the risks associated with a single point of failure. Nonetheless, the efficiency of this approach can be influenced by the manner in which clients are interconnected [24], potentially impacting communication costs. Hence, achieving an equilibrium between performance and communication expenses becomes imperative within the P2P FL framework.