esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Applications
### Flavors and Fragrance Analysis  
TF-SPME has various applications for the analysis of flavors and fragrances in a variety of different matrices. In this regard, [PERSON] and colleagues applied carbon mesh-supported DVB/PDMS as an extraction phase for the sampling of various volatile compounds, such as alcohols and ethyl esters [66]. The improved sensitivity for these various classes of compounds, afforded by TF-SPME, is essential for the rigorous demands of quality control in the beverage industry. A similar study by [PERSON] et al. investigated the efficacy of TF-SPME technology for the analysis of foodstuffs using DVB/PDMS TF-SPME devices and analyzed dark chocolate, cheeses and Caesar dressing [34]. These studies [34; 66] compared the performance of TF-SPME to fiber SPME with the same stationary phase, revealing enhanced extraction efficiency and capacity for the TF-SPME devices. In 2016, another study investigated the fragrance of various essential volatile compounds from grapes, including linalool and 3-isobutyl-2-methoxypryrazine (IBMP), which were measured using PDMS-based TF-SPME devices [38]. This novel solid-phase mesh-enhanced sorption from headspace (SPMESH) method developed by [PERSON] and colleagues resulted in greater throughput and enhanced limits of quantitation (ppb) during the analysis of volatile compounds (such as odorants) compared to other traditional methods. Sol-gel coating of a stainless steel mesh substrate with a thin film of PDMS provided great thermal stability and high sensitivity for the direct analysis with SPME-DART [38]. In 2007, [PERSON] and coworkers compared the results of headspace and direct contact sorptive tape extraction (STE), all the while employing fiber HS-SPME as a reference standard [62]. The analytes extracted, volatile compounds found in various solid biological matrices such as apple, perfume on human skin, rosmary and spearmint, all demonstrated increased sensitivity and extraction efficiency compared to the more conventional fiber HS-SPME method.
# T-Spline Based Unifying Registration Procedure for Free-Form Surface Workpieces in Intelligent CMM
## 3 Technology of the System
### Overall Registration and Error Evaluation  
This section is the last step of the intelligent CMM. The rotation translation matrix **RT** between two different coordinate systems can be obtained as detailed in Section 3.2. The measured surface, position and orientation are obtained by matching the point cloud. Then, a safe path is planned, and the workpiece is detected. The error is calculated through the registration of the point cloud collected by the CMM and the T-spline surface formula as follows  
\[E_{dis}(s_{i},t_{i})=\sum_{i=1}^{m}\bigl{(}S(s_{i},t_{i})-q_{i}^{\prime}\bigr{)} ^{2} \tag{8}\]  
where \(m\) is the number of points collected by the CMM.  
\[\left(\begin{array}{c}x_{d}\\ y_{d}\\ z_{d}\end{array}\right)=\textbf{R}\left(\begin{array}{c}x_{w}\\ y_{w}\\ z_{w}\end{array}\right)+\textbf{T} \tag{9}\]  
\[\text{where, }\textbf{R}=\left(\begin{array}{ccc}\cos\beta\cos\gamma&\sin \alpha\sin\beta\cos\gamma-\cos\alpha\sin\gamma&\cos\alpha\sin\beta\cos\gamma+ \sin\alpha\sin\gamma\\ \cos\beta\sin\gamma&\sin\alpha\sin\beta\sin\gamma+\cos\alpha\cos\gamma&\cos \alpha\sin\beta\sin\gamma-\sin\alpha\cos\gamma\\ -\sin\beta&\sin\alpha\cos\beta&\cos\alpha\cos\beta\end{array}\right), \textbf{T}=\left(\begin{array}{c}t_{x}\\ t_{y}\\ t_{z}\end{array}\right).\]  
The rotation transformation between the designed free-from surface workpiece coordinate system \(o_{d}\)-\(x_{d}\)\(y_{d}\)\(z_{d}\) and the CMM coordinate system \(o_{w}\)-\(x_{w}\)\(y_{w}\)\(z_{w}\) is executed as follows: first, the rotation angle around the \(x_{d}\) axis is \(\alpha\), the rotation angle around the \(y_{d}\) axis is \(\beta\), and the rotation angle around the \(z_{d}\) axis is \(\gamma\).  
The specific steps of accurate registration are as follows:  
1. **RT** solved through the point cloud registration is substituted as the initial solution.
2. Based on the following formula to optimize \(s_{i},t_{i}\), the sum of the distance of the collected points to the T-spline surface, is calculated according to Formula (8) and (9).
3. Cyclically optimize **RT** and perform step (2) until a certain level of accuracy is achieved.
4. The distance data of each point corresponding to the formula surface model are retained as the result of the error evaluation through Levenberg-Marquardt method.  
When the amount of data is relatively large, the point cloud must be simplified according to the relevant literature [44] to save time and in the case of error tolerance. Then, the rotation translation matrix **RT** is solved by optimization, and finally, the distances of all of the collected points to the T-spline surface are calculated.
## 6 Conclusions  
The largest chromite deposit in China is located in the mantle peridotite of the Luobusa ophiolite. In this study, Landsat 8 OLI multispectral and WorldView-2 high spatial resolution remote-sensing data have been employed to delineate the ophiolite complexes for exploring high-potential chromite zones using several image-processing techniques for the first time, which were proven to be successful in discriminating lithological units and delineating chromite host rock within ophiolites, especially in harsh plateau areas, and is of great significance for realizing modern geological surveys.  
Seven spectral enhancement methods were comprehensively used to delineate mantle periodtites in the area, including decorrelation stretching, band ratio, ICA, PCA, MNF, and FCC, which detected different rock units and mantle peridotite as a host rock of chromitite within ophiolite complexes at a regional scale. Meanwhile, the spatial resolution advantage of WorldView-2 remote-sensing data was used to establish interpretation signs for the main rock units in the study area. The support vector machine classification method was applied  
Figure 16: (**a**) Field photograph; (**b**) directional filtering of OLI image; (**c**) structure sliding plane.  
to the digital Landsat multispectral OLI data, which gave rise to the supervised classified thematic map of the investigated area. The overall accuracy assessment of the produced classification map was accepted. At the same time, the \"De-interfered Anomalous Principal Component Thresholding Technique\" was used to extract abnormal information, and the result shows a good matching relationship with the chromite mineralized belt, especially in areas where ophiolitic peridotites are exposed, indicating good significance. Finally, image-processing results were verified by comprehensive fieldwork. The results of this study demonstrate the applicability of remote-sensing data for the delineation of harzburgite or peridotite as host rocks of chromite mineralization in the Luobusa zone and lithological mapping in mountainous and inaccessible regions.  
Conceptualization, W.Y.; methodology, W.Y.; software, W.Y.; validation, W.Y. and Y.Z. ([PERSON]); formal analysis, W.Y.; investigation, W.Y.; resources, Y.Z. ([PERSON]); data curation, W.Y.; writing--original draft preparation, W.Y.; writing--review and editing, S.C., X.X. and X.D.; visualization, W.Y.; supervision, Y.Z. ([PERSON]); project administration, W.Y.; funding acquisition, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by Secondary Project of China Geological Survey [DD20211550] and Natural Science Foundation of Xinjiang Uygur Autonomous Region [2022D01A148].  
Not applicable.  
The datasets presented in this study can be obtained upon request to the corresponding author.  
Constructive comments from the editors, and the revisions from anonymous reviewers greatly improved the manuscript of this paper.  
The authors declare no conflict of interest.  
References
# Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications
## 3 Results
### Final PCA Step  
Figure 6 shows the output signal of the previous BPF step (i.e., blue line) and the next PCA processing output (i.e., red line). On average, the four largest components of PCA were deleted according to 80% of the variance in the data. The average number of cancelled components was decreased to three in SC-MARA-Spline and MC-MARA-Spline. It is highly likely that the local spline corrections resulted in high power in the highest rank components including the large baseline drifts poorly reduced by this approach. Accordingly, an augmented power of cancelled components could be considered for such algorithms. Further, all algorithms displayed a similar and noticeable individual dispersion in the number of cancelled highest rank components, ranging from a minimum of two to a maximum of seven (IQR = 2 for MC/SC-MARA-spline, IQR = 4 for TDDR, IQR = 3 for other methods).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\mathbf{PR_{wb}}\) & \(\mathbf{PR_{ob}}\) \\ \hline SC-MARA-spline & 80.43 (10.88) & 0.95 (0.51) \\ MC-MARA-spline & 77.30 (13.98) & 0.53 (0.44) \\ SC-MARA-tPCA & 77.62 (7.39) & 0.95 (0.53) \\ MC-MARA-tPCA & 74.87 (10.03) & 0.57 (0.47) \\ HybridMAR-spline SG & 76.13 (7.38) & 1.28 (0.63) \\ HybridMAR-tPCA SG & 77.56 (7.02) & 1.34 (0.66) \\ TDDR & 91.28 (2.95) & 0.89 (0.46) \\ Wavelet & 79.67 (5.70) & 0.95 (0.39) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean (std) Power Ratio at the passband (\(PR_{wb}\)) and stopband (\(PR_{ob}\)) in the \(f=[0.01-0.2]\) Hz range across subjects and measurement channels. The leftmost column indicates the adopted MA reduction algorithm prior to the BPF step.  
Figure 6: Single-channel graphical representation of the effect of PCA over \(\Delta OD\) signals across pipelines. \(\Delta OD\) signals are represented with respect to the BPF signals (blue line-input signal) and after PCA step (red line-output signal).  
Despite the significant differences displayed in the single steps (MA, BPF, and PCA) the similarity of the final time courses of the eight pipelines (Figure 6, red lines) was remarkable. However, the results must be related to the statistical analysis for inferring activation vs. resting conditions and especially the overall localization of cortical activation as expected from the employed task. This is further evidenced by the final processing steps showing the HRF shapes and the significant group-level response maps, which are shown in Section 3.5.
## 4 Experiment and Results
### Discussion  
In this study, we proposed SPT-AD, a transformer-based anomaly detection model for bearing fault diagnosis, addressing key challenges in industrial applications. Utilizing only normal data and employing data degradation techniques to synthetically generate structural outliers, our approach enables high-performance anomaly detection even in environments with limited labeled data. This is particularly valuable for predictive maintenance, smart manufacturing, and industrial IoT (IIoT) systems, where acquiring labeled anomaly data is often infeasible. Furthermore, our model has a structure capable of multidimensional and multivariate data analysis, which can reliably maintain anomaly detection performance in the event of errors in individual sensors or failures in specific equipment.  
The ability to detect bearing anomalies at an early stage has significant industrial implications. Bearings are critical components in rotating machinery across industries such as aerospace, automotive, power generation, rail transportation, and heavy manufacturing. The failure of bearings can lead to unexpected downtime, increased maintenance costs, and severe safety risks. Thus, accurate and early fault detection not only enhances equipment reliability but also improves overall operational efficiency by enabling proactive maintenance strategies.  
However, despite its advantages, the proposed approach has certain limitations. The reliance on normal data and resolution compression may introduce information loss, which could impact the detection of rare and complex fault patterns. Additionally, since the experiments were conducted in a controlled environment, the model's performance in real-world industrial settings remains to be fully validated. Variations in operating conditions, sensor noise, and atypical fault patterns could affect the generalizability of the model.
## 2 Numerical Methods
### Computational Domain and Boundary Conditions
#### 2.4.1 Open Water Propeller Test  
The overset grid system and computational domain are the same as in the previous work [39] for the wave condition with the deepest propeller immersion depth. Here, it is only for the calm water condition, but the free surface is considered. Although only the propeller is simulated here, for the further application of the behind hull condition, the CFD computational domain is non-dimensionalised by the model length of KCS: \(L=Lpp\) (length between perpendiculars) = 3.2 m. The domain size is \(-0.24<x\)/\(L<1.75\) from upstream to downstream, \(-1<y\)/\(L<1\) from side to side, and \(-0.79<z\)/\(L<0.22\) from top to bottom. The propeller plane is at \(x\)/\(L=0.0\). The domain consisted of two overset grid blocks: the propeller cube and far-field background, as shown in Figure 4. Inside the propeller cube, the grid number is 50 \(\times\) 50 on the \(x\)/\(L=0\) plane to cover the propeller diameter. Table 1 shows the grid number detail. The total grid number is 3.5 M (million). The uniform inflow boundary condition is specified on the inlet, the exit condition for the outlet, the zero-gradient condition on both sides, and the far-field condition on the top and bottom face of the domain. The non-dimensional inflow velocity \(U\) is set as 1, and the open water test for different \(J\) is conducted by adjusting propeller rotation rate \(n\). The mathematical  
Figure 3: Linear body force distribution.  
formulae for the boundary conditions are listed in Table 2 and specified on the boundaries indicated in Figure 4a. There is no solid surface in the open water test here because no physical propeller exits inside the domain. The body force propeller models simplify the propeller. The grid system and CFD setup are the same for the rotating blade and average propeller model, and for KP505 and E1619 propeller.
# Physical, Thermal, and Mechanical Characterization of PMMA Foils Fabricated by Solution Casting
## 3 Results
### Shape Memory Effect of Commercial PMMA Foil and Cast Foils  
Figure 8a-c represents the shape memory effect of PMMA commercial foil and prepared foil from two different solutions (S/L ratio of 1/18 and 1/24 mL). The PMMA foils are deformed at 25 \({}^{\circ}\)C with a follow-up deformed shape restored at cooling of the sample at \(-\)5 \({}^{\circ}\)C. The sample holds a deformed shape. The recovery to the original step is followed by heating to 25 \({}^{\circ}\)C with subsequent heating to 50 \({}^{\circ}\)C in sequence steps, showing partial recovery of the sample. The commercial sample and prepared sample show a shape recovery effect without full recovery to the original position.  
PMMA shape fixed ratio and shape recovery are the two significant parameters that represent the shape memory effect of PMMA. Therefore, we studied the shape memory properties of PMMA in bending mode [26]. The shape recovery of PMMA was recorded by a camera and a program that was used to measure the angles of the bending polymer. The sample was in a temporary shape after cooling down. The fixed angle \(\theta_{F}\) (the angle between the horizontal line and the free side of the sample) was measured. Thus, we could calculate the shape fixed ratio Rf according to Equation (1). Then the temporary shape was heated up again at 50 \({}^{\circ}\)C, and the recovery of the permanent shape was achieved. The sample shows recovery angle \(\theta_{R}\). Using \(\theta_{R}\), we could obtain the shape recovery R\({}_{r}\) from Equation (2). These two significant indexes determine the shape memory effect in PMMA polymer in bending. The value of the fixed ratio represents the percentage of bending deformation for the temporary shape, and the value of the recovery ratio represents the maximum percentage of recovery from the deformed shape to the permanent shape. The equations are presented below.  
\[R_{f}=\frac{\theta_{F}}{180}\times 100\% \tag{1}\]  
\[R_{r}=\frac{\theta_{F}-\theta_{R}}{\theta_{F}}\times 100 \tag{2}\]  
where \(R_{f}\) represents the percentage of shape fixity ratio, and \(R_{r}\) represents the shape recovery ratio with respective shape fix angle \(\theta_{F}\) and shape recovery angle \(\theta_{R}\), respectively.  
Figure 7: Tensile strength of PMMA commercial foil in both normal and parallel directions and prepared foils (1 and 2 from 1/24 and 1/18 mL). (Three samples are tested for the analysis.) However, two-sample test results are presented.
## 5 Discussion  
In this paper, we showed that \(1\leq\lim_{n\rightarrow\infty}\frac{\textit{diam}(\mathscr{M}_{n,n})}{n^{2}} \leq 4\). It would be interesting to improve the lower or upper bounds, especially the lower bound. One way to obtain upper bounds is to find integers \(p\), \(q\), \(d_{1}\), \(d_{2}\), \(b\), and a \"potential\" function \(\phi:\mathscr{M}_{n,n}\mapsto\{0,1,2,\ldots\}\) satisfying the following conditions: (1) \(\phi(\mathbf{X})=0\) iff \(\mathbf{X}=\mathbf{A}\) (the potential of the initial configuration is zero), (2) \(\forall\mathbf{X}\
eq\mathbf{A}\) with \(\phi(\mathbf{X})\geq b\), \(\exists\mathbf{Y}\) such that \([dn(\mathbf{X},\mathbf{Y})\leq d_{1}]\) and \([\phi(\mathbf{X})-\phi(\mathbf{Y})\geq q]\) (if the potential is at least some threshold, say \(b\), then one can decrease it by \(q\) using at most \(d_{1}\) steps.), (3) if \(\phi(\mathbf{X})<b\), then \(dn(\mathbf{X},\mathbf{A})\leq d_{2}\) (if the potential is less than \(b\), then we can always reach \(\mathbf{A}\) within \(d_{2}\) steps.), and (4) \(\phi(\mathbf{Y})\leq p\) for every \(\mathbf{Y}\) (the maximum potential is \(p\).) This would imply that the upper bound of \(dn(\mathscr{M}_{n,n})\leq[\frac{p-b}{q}]d_{1}+d_{2}\). Actually, our upper bound was obtained in this manner by setting \(\phi(\mathbf{X})=r\) iff the pieces numbered \(q\leq r\) are placed at their right positions and the piece numbered \(r+1\) is not placed at its right position. We believe that our upper bound can still be improved to 3, even if one uses the same \(\phi\). However, we suspect that more sophisticated \(\phi\) will be needed to find a better upper bound. On the other hand, our lower bound was obtained by considering the graph \(G=(\mathscr{M}_{m,n},\{\{\mathbf{X},\mathbf{Y}\}\mid\mathbf{Y}\in N(\mathbf{X})\})\). It would be interesting to improve the lower bound by using the structural information of \(G\).  
It would also be interest to determine \(\textit{diam}(\mathscr{M}_{n,n})\), even if \(n\) is 4 or 5. This is because 4 by 4 or 5 by 5 may be popular sizes when playing the torus puzzle on touch-panel devices. We performed computer experiments on \(\textit{diam}(\mathscr{M}_{m,n})\) for small values of \(m\) and \(n\): \(\textit{diam}(\mathscr{M}_{2,3})=7\), \(\textit{diam}(\mathscr{M}_{2,4})=8\), \(\textit{diam}(\mathscr{M}_{2,5})=12\), \(\textit{diam}(\mathscr{M}_{2,6})=14\), \(\textit{diam}(\mathscr{M}_{3,3})=8\), and \(\textit{diam}(\mathscr{M}_{3,4})=13\).
# Nutritional Support for Liver Diseases
## 2 Nutritional Support for Liver Diseases
### Liver Transplantation  
The only available treatment for the end-stage of liver disease is transplantation. Patients with acute liver failure, end-stage chronic liver disease, primary liver cancer, or congenital metabolic disorders are eligible for surgery [(143)]. For most patients, transplantation is a life-saving and life-prolonging procedure [(144)]. In order for a patient to be qualified for surgery, they must meet a number of criteria, including being prepared for nutrition. The success of organ transplantation depends largely on the nutritional status of the patient. One of the primary problems in liver cirrhosis is malnutrition [(145)] caused by, for example, the following factors:  
* A reduction in the amount of food consumed by the patient and an inadequate diet (i.a. low calorie diet low in protein (<0.8 g/kg bw) [(146)];
* metabolic process disorders (patients are characterized by a state of hypermetabolism; there is an acceleration of protein, fat and carbohydrate metabolism);* the impaired absorption of fats associated with reduced bile secretion. This condition also leads to deficiencies in fat-soluble vitamins;
* the premature feeling of satiety (patients experience a delay in gastric emptying and ascites compressing the gastrointestinal tract) [93].  
Due to the development of malnutrition as a result of liver cirrhosis, the risk of failure of the operation increases. Therefore, it is essential that each patient qualified for surgery undergoes a screening assessment of their nutritional status. If a state of emergency or malnutrition is found, it is necessary to treat it [147].  
Among those patients qualified for liver transplantation, the risk of developing sarcopenia should be assessed, as this condition is a strong predictor of mortality and morbidity [39]. The assessment of the phase angle measured by bioelectrical impedance analysis is also mentioned as a prognostic factor of mortality among patients. In patients with cirrhosis, a low phase angle is associated with higher mortality [148]. Ways to control the adequate supply of protein using diet include anthropometric methods (e.g., measuring arm circumference) and biochemical methods (determining the level of albumin, prealbumin, total lymphocyte count (CLL), or nitrogen balance) [149].
# A Communication Framework for Image Transmission through LPWAN Technology
## 3 Proposed Framework
### Stage IV: Postprocessing and Reconstruction  
The data sent by the radio transmitter is received by the reception node at this stage. These data are processed through data frame decomposition in order to organize and deliver classification information, as well as image reconstruction. The classification result and reconstructed images are displayed by the reception node. The received data is processed, and once rebuilt, the vector with its features is used in a sparse representation reconstruction algorithm to recover a large number of samples of the original signal and recover the original information vector via wavelet inverse transform. It is now necessary to change the matrix's original size from \(M\times N\) to \(n\times n\) and display the image. To compare the original image with the received image, the PSNR function was used, which can be calculated as (1).  
\[PSNR=10 log\bigg{(}\frac{S^{2}}{MSE}\bigg{)} \tag{1}\]  
where \(s\) is 255 in an image of 8 bits and \(MSE\) is the mean squared error, which is the average of the squared difference in the intensity pixels in the original and the output images. \(MSE\) is calculated with (2).  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=1}^{n-1}[I(i,j)-K(i,j)]^{2} \tag{2}\]  
where \(m\) and \(n\) are the respective length and width of the image in pixels, and \(I(i,j)\) and \(K(i,j)\) are functions that describe the intensity of individual pixels in the transmitted and received image. PSNR allows calculating the relation between transmitted and received images. It is expressed in a logarithmic scale where values of among 30 and 50 dB are acceptable [87; 95]. In wireless communication systems, acceptable values are considered from 20 to 25 dB [107; 108]. If the image on analysis is the same as the reference, the PSNR is infinite [86].