esa-sceva/satcom-chunk-collection huggingface dataset:
# Bioelectrical Impedance Vector Analysis (BIVA) and Somatotype in Female Rugby Players
## 3 Results
Some players are placed out of ellipses at 75% and even at 95% when the impedance individual vectors distribution in the tolerance ellipses for sports populations are performed. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Somatotype Components**} & \multirow{2}{*}{**Total Sample**} & \multicolumn{2}{c}{**Player’s Position**} \\  & _(n = 19)_ & & **Forwards (n = 8)** & **Backs (_n = 11_)** \\ \hline Endomorphy & 5.48 (1.89) & 7.10 (1.35) & 4.30 (1.24) * \\ Mesomorphy & 5.29 (2.27) & 7.21 (1.87) & 3.89 (1.30) * \\ Etomorphy & 1.62 (1.39) & 0.41 (0.36) & 2.50 (1.17) * \\ \hline \hline \end{tabular}  
* \(p<0.05\).  
\end{table} 
Table 2: Somatotype characteristics of rugby players (total sample and player’s position). Figure 1: Rugby players somatochart. (**a**) Total sample (grey dot), forwards (blue dot), and backs (orange dot); (**b**) Individual somatochart of the players. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Electrical Values**} & \multirow{2}{*}{**Total Sample (_n = 19_)**} & \multicolumn{2}{c}{**Players’ Position**} \\  & & **Forwards (_n = 8_)** & **Backs (_n = 11_)** \\ \hline R (Ohm) & 536.7 (64.5) & 498.4 (53.4) & 564.6 (58.7) * \\ Xc (Ohm) & 65.2 (7.7) & 63.5 (7.4) & 66.4 (8.1) \\ Z (Ohm) & 540.7 (64.7) & 502.4 (53.8) & 568.5 (59.0) * \\ PA (\({}^{\circ}\)) & 6.95 (0.59) & 7.26 (0.40) & 6.73 (0.61) * \\ \hline R/H (Ohm/m) & 324.9 (40.8) & 309.2 (41.4) & 336.2 (38.2) \\ Xc/H (Ohm/m) & 39.5 (5.2) & 39.4 (5.5) & 39.6 (5.3) \\ Z/H (Ohm/m) & 327.3 (41.0) & 311.7 (41.7) & 338.6 (38.4) \\ \hline Rsp (Ohm m) & 398.2 (71.2) & 458.9 (64.1) & 354.2 (34.1) * \\ Xcsp (Ohm m) & 48.8 (10.6) & 58.4 (8.0) & 41.8 (5.6) * \\ Zsp (Ohm m) & 401.2 (71.9) & 462.6 (64.5) & 356.6 (34.4) * \\ \hline \hline \end{tabular}  
* R: resistance; Xc: reactance; Z: impedance (vector length); PA: phase angle; H: height; Rsp: specific resistance; Xcsp: specific reactance; Zsp: specific vector length. *_p \(<0.05\)._  
\end{table} 
Table 3: Bioelectrical values of rugby players (total sample and player’s position). Classic BIVA confidence ellipses did not show statistically significant differences in the mean impedance individual vector distribution between forwards and backs (\(p\) = 0.209) (Figure 4a). However, specific BIVA confidence ellipses showed statistically significant differences between both groups (\(p\) < 0.001) (Figure 4b). Figure 4: (**a**) Classic BIVA confidence ellipses by player position; (**b**) Specific BIVA confidence ellipses by player position. Figure 3: Specific BIVA of studied rugby players vs. sports reference population. Green, red and black ellipses are probability graph showing the 50%, 75%, and 95% percentiles, respectively, of the bioelectrical values of the reference population. Figure 2: Classic BIVA of studied rugby players vs. reference sports population. Green, red, and black ellipses are probability graphs showing the 50%, 75%, and 95% percentiles, respectively, of the bioelectrical values of the reference population.
## 3 Results
### Associations between Pathogen Occurences  
Simultaneous multiple infections with several combinations of different pathogens have been detected in several hives from the nine apiaries (Figure 3).  
In 2020, honey bees infected by three different combinations of pathogens were found in 3.7% of the hives, while co-infections with several combinations of four or five or more than five different pathogens were found in 21%, 37% and 38.3% of the honey bee samples, respectively. The most detected co-infections in 2020 were due to the simultaneous presence of: _L. passim_ + CBPV + DWV-A + SBV + BQCV in the 12.3% of the hives; _N. cernae_ + _L. passim_ + CBPV + SBV + BQCV in the 11.1% of the hives; _N. cernae_ + CBPV + SBV + BQCV in the 9.9% of the hives.  
In 2021, only the 5.6% of the hives were co-infected with only two pathogens (SBV and BQCV), while three or four or five different pathogens in several combinations were simultaneously found in 6.7%, 21.1% and 34.4% of colonies, respectively. Finally, more than five pathogens were present in 32.2% of honey bees samples. The most detected co-infections in 2021 were due to the simultaneous presence of _N. cernae_ + _L. passim_ + CBPV + SBV + BQCV in the 18.9% of the hives and _L. passim_ + CBPV + SBV + BQCV in the 8.9% of the hives. Statistical analyses showed that in both years of sampling the combination _N. cernae_ + _L. passim_ + CBPV + SBV + BQCV is present in more than 10% of the hives analysed, with an increase in 2021 compared to 2020. However, in 2020 a significant association only between the presence of ABPV and _N. cernae_ (\(p\) = 0.000) was found; in 2021, the significant pathogen associations were three and precisely between APV and _L. passim_ (\(p\) = 0.004),DWV-A and DWV-B (\(p\) = 0.004) and DW-A and _N. ceranae_ (\(p\) = 0.010 significant at a 10% level). No other significant associations were observed in both years.  
Figure 3: Percentage and type of multiple infections found in all nine apiaries in 2020 (**A**) and 2021 (**B**). Co-infections with 2 pathogens are shown in the shades of grey, with 3 pathogens in the shades of yellow, with 4 pathogens in the shades of green, with 5 pathogens in the shades of blue, and with 6 pathogens or more in the shades of red.  
Median values of the number of co-infections have been calculated and stratified for the landscape (plain, hills, and mountains). From the data obtained in 2020, a median decrease from the plain (5.3) and hills (5.7) to the mountains (3.0) could be observed. Similarly, in 2021, co-infections detected in the mountain environment had a lower median value (2.7) than those of the plain (5.1) and hills (5.3).
# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
## 5 Validation and Discussion
### Incipient Failure Detection  
According to the operation handbook, the vibration conditions often evaluated by measuring values directly, the vibration alarm is not triggered until the vibration measure values exceed the threshold, therefore, an early alarm could not be realized, and the condition judgment criteria is shown in Table 6, which means once the vibration measurements exceed 5.1 mm/s, the alarm occurs.  
The exceed-threshold events could be detected by an effective failure early detection, and the incipient failure thresholds for residual and similarity proposed in 4.4 are applied. As shown in Figure 9, the typical vertical vibration alarm events are discussed, and the process is exhibited, residual and similarity are shown correspondingly. There are two vibration alarms in the process, noted as alarm 1 and alarm 2, respectively. According to the data records, the timelines for the event are:  
* Sample 1-Sample 123: Normal
* Sample 124 (2015/11/25 14:01:00): vertical vibration exceeds threshold (5.75 mm/s)
* Sample 139 (2015/11/25 14:06:00): check vibration status and adjust the air volume
* Sample 151 (2015/11/25 14:10:00): vertical vibration recovery
* Sample 152-275: Normal
* Sample 276 (2015/11/25 14:51:40) vertical vibration exceeds threshold (5.20 mm/s)  
The size of sliding window is set as 10, and the effectiveness of residual threshold and similarity threshold are validated in the alarms. The thresholds, current value, and corresponding sample for alarms are compared in Table 7. In the alarm 1, the actual alarm is NO.124 sample, and the failure alarm is earlier detected in NO.120 sample and NO.121 sample by using residual threshold and similarity threshold, respectively, and the failure could be detected 1 minute ahead. Furthermore, in the alarm 2,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Alarm Level** & **Vibration Measurement** & **Condition** \\ \hline / & X \(\leq\) 5.1 mm/s & Normal \\ I & 5.1 mm/s \(<\) x \(\leq\) 7.1 mm/s & Carry out operations, maintenance if necessary \\ II & X \(>\) 7.1 mm/s & Immediately shutdown and maintenance \\ \hline \hline \end{tabular}
\end{table}
Table 6: The vibration threshold for investigated bluer fan.  
Figure 9: Process of typical vertical vibration alarm event.  
the actual alarm is NO.276 sample, in comparison, the failure alarm is detected in NO.263 sample and NO.260 sample, respectively, and the failure is detected over 5 minutes ahead.  
For a further discussion, the other variables in vector used for similarity calculation are investigated, and the results indicated that the deviations of the temperature of end-side bearings \(x_{3}\) and current of driving motor \(x_{6}\) lead to the similarity descend in the early stage of failure, as shown in Figure 10. The results indicate that failure information reflected by the other variables indicates the prognosis of failure.
## 2 Methods
### Optimization Method  
The optimization loop of the developed method is shown in Figure 4. At the beginning of each iteration, the current design, represented by the level set functions on the level set mesh, is remeshed with tetrahedra using the method described below in Section 2.4.5. The result of the remeshing is the FE mesh of the current design. This is then used to determine the system response using linear FEM and to calculate the sensitivities of the objective function and the volume constraints (see Section 2.2.2). The calculated sensitivities serve as input for the MMA [18], which updates the design variables (normal velocities). The implementation of the MMA follows [31]. Since the normal velocities are only non-zero in the immediate vicinity (on the surface) of the level set functions, they are extrapolated to the entire design domain (constant level set mesh) using the velocity extension method described in Section 2.4.1. To stabilize the optimization method, the extrapolated normal velocities are then filtered using the Helmholtz equation (see Section 2.4.2). The filtered normal velocities are then used to evolve the level set functions (see Section 2.4.3). The evolved level set functions are then corrected using the MBO operator, and subsequently, their SDF property is restored using redistanc  
Figure 3: Visualization of the MBO operator using two level set functions \(\varphi_{1}(\mathbf{x})\) (gray) and \(\varphi_{2}(\mathbf{x})\) (blue) moving towards each other based on [22]. (**a**) \(\varphi_{1}(\mathbf{x})\) and \(\varphi_{2}(\mathbf{x})\) are moving towards each other; there is no overlap. (**b**) \(\varphi_{1}(\mathbf{x})\) and \(\varphi_{2}(\mathbf{x})\) overlap in the orange area. (**c**) Correction of \(\varphi_{1}(\mathbf{x})\) and \(\varphi_{2}(\mathbf{x})\) by applying the MBO operator; there is no longer any overlap.
# Simulation Study of Dynamic Rotation and Deformation for Plasmonic Electric Field-Skyrmions
## 4 Conclusions  
The optical skyrmion, as an active new field in nano-photonics, has enormous application prospects in multiple directions, such as polarization sensing [28], spin photonics [9], and high-order topological morphology [29]. Specifically, plasmonic field skyrmions have an application in deformation sensing, possessing the ability to achieve a high sensitivity (>4.82%) and extreme accuracy (~99.99%) in a deep-subwavelength area [30].  
We propose a multi-arc symmetric structure that enables the rotation and deformation of SPP electric-field-vector skyrmions. Based on the strong dependence of SPP interference field morphology on the electric field, we discovered and demonstrated the independent and controllable properties of skyrmion deformation and skyrmion rotation state through the linkage control of phase difference and the positions of incident spots in the simulation. Specifically, our results not only enable the deformation of skyrmions in the linear direction, but also endow the deformation process with free rotational states, achieving more diverse patterns. It is worth noting that our rotational states are not limited to the 12 directions, and it can be inferred that more diverse rotation states can be achieved under the condition that more different directions act at the same time, which provides a new idea for freely and flexibly manipulating complex topological forms of skyrmions in the future.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors would like to thank the University of Shanghai for Science and Technology for helping to identify collaborators for this work. All individuals included in this section have consented to the acknowledgments. The authors declare no conflicts of interest.
# HoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network
## 3 Numerical Experiments
### Reconstructing Images of Objects 1  
For example, let us consider training a neural network on holograms containing two planes with phase images of digits. Figure 5 shows examples of objects (b, d) restored by the neural network. On the left side, the object planes closer to the hologram are presented. For comparison, the images of ground truth objects are given (a, c). A training set consisted of 25,000 holograms. Each hologram was matched with two images, which represent two corresponding object planes. The validation dataset consisted of 5000 holograms. The model gives two images corresponding to the object planes for each hologram.  
Figure 5: Ground truth (**a**,**c**) and neural network-reconstructed images (**b**,**d**). The holograms contained scenes consisting of two object planes.  
From a visual point of view, the resulting images are extremely close to the truth. A quantitative evaluation of the results was also performed. The structural similarity index (SSIM) and correlation coefficient (CC) and their STDs were calculated for 100 reconstructed images to assess the performance of the neural network. The results are presented in Table 1. It can be seen that the neural network provides good quality for such a scene's reconstruction.  
The method generates reconstructed object images. In contrast to standard propagation modeling methods, the images are not contaminated by parasitic diffraction orders. This is especially useful when inline digital holograms are considered.
## 2 Theory
### Otsu Algorithm Process for Binarization  
Simplifying thermography images through binarization allows for intuitive evaluation of defects and characterizes automatic detection of various defects. Figure 3 shows the binarization process of the Otsu algorithm. As shown in Figure 3, the Otsu algorithm is a technology that finds the optimal threshold value through the histogram of the input image and performs binarization. This is a method to find an optimal threshold that minimizes the variance or maximizes the difference between classes when classifying pixels in an image into two classes [15].  
Figure 3: Principle of the Otsu algorithm process for binarization.  
Figure 2: Basic concept of the four-point method among phase shifting methods in LIT.  
Binary images obtained by Otsu's processing of images are classified into two classes. In the two classified classes, 'class 1' refers to [0, k] based on the calculated threshold k, and 'class 2' refers to [k, 1]. The image of a 2D matrix can be expressed as a binary image through this series of processes and is useful for evaluating the characteristics of defects in the image. To obtain an appropriate binary image, the optimal threshold must be calculated [26; 27; 28]. The gray level of the M \(\times\) N image with L intensity levels is [0, 1, \(\ldots\), L \(-\) 1]. The threshold t (0 \(\leq\) t \(\leq\) L \(-\) 1) splits pixels with intensity values into two classes: object (class 1) and background (class 2). The object class probability is 'class 1', and the background class probability is 'class 2', and can be expressed by Equations (2) and (3).  
\[\mathrm{P_{1}(k)=\sum_{i=0}^{k}P_{i}} \tag{2}\]  
\[\mathrm{P_{2}(k)=1-P_{1}(k)} \tag{3}\]  
The average intensity values of pixels classified into classes 1 and 2 are defined as Equations (4) and (5).  
\[\mathrm{m_{1}(k)=\frac{1}{P_{1}(k)}\sum_{i=0}^{k}iP_{i}} \tag{4}\]  
\[\mathrm{m_{2}(k)=\frac{1}{P_{2}(k)}\sum_{i=k+1}^{L-1}iP_{i}} \tag{5}\]  
There are mean intensity values up to the k level, which for all images is  
\[\mathrm{m_{G}=P_{1}m_{1}+P_{2}m_{2}} \tag{6}\]  
In order to calculate the optimal threshold value, the Otsu algorithm should allow the concept of between-class variance. The equation of between-class variance is defined in Equation (7).  
\[\mathrm{\sigma_{b}^{2}=\frac{\{m_{G}P_{1}-m(k)\}^{2}}{P_{1}(1-P_{1})}} \tag{7}\]
# Cmpk2 Gene and Protein Expression in Saliva or Salivary Glands of Dyslipidemic Mice
## 2 Materials and Methods
### Saliva RNA Isolation and qRT-PCR  
To evaluate the efficacy of total RNA isolation methods, we compared three commercially available reagents for RNA extraction: TRIzol reagent (Cat. No. 15596018, Thermo Fisher Scientific, Waltham, MA, USA), RNeasy Protect Saliva Mini Kit (Cat. No. 74004, Qiagen, Hilden, Germany), and NucleoSpin RNA (Cat. No. 740955.10, Takara Bio, Shiga, Japan) (Table S2). TRIzol reagent method (Figure S1B): The saliva samples were thawed at 4 \({}^{\circ}\)C and centrifuged at 12,000\(\times\)\(g\) for 20 min at 4 \({}^{\circ}\)C. The supernatant was carefully removed, and 1 mL of TRIzol reagent (Thermo Fisher Scientific) was added to the precipitate. The mixture was subsequently thoroughly dissociated using a pipette, followed by vortexing to ensure complete homogenization. After incubation at room temperature (RT, 23 \({}^{\circ}\)C) for 5 min, 200 \(\upmu\)L of cold chloroform was added and incubated for 3 min at RT (23 \({}^{\circ}\)C). The samples were then centrifuged at 12,000 \(\times\)\(g\) for 20 min at 4 \({}^{\circ}\)C. Approximately 500 \(\upmu\)L of the upper aqueous phase was transferred to a new RNase-free tube, and 500 \(\upmu\)L of cold isopropanol was added. Following incubation at \(-\)20 \({}^{\circ}\)C for 2 h, the samples were centrifuged at 12,000\(\times\)\(g\) for 20 min at 4 \({}^{\circ}\)C. The supernatant was removed, and cold 75% ethanol was added. The samples were centrifuged at 10,000\(\times\)\(g\) for 5 min at 4 \({}^{\circ}\)C, and the procedure was repeated. Finally, the RNA pellet was air-dried for 5 min and then resuspended in 20 \(\upmu\)L of RNase-free water. The sample was incubated in a 55 \({}^{\circ}\)C water bath for 5 min and stored at \(-\)80 \({}^{\circ}\)C until further analysis. RNeasy Protect Saliva Mini Kit (Qiagen) and NucleoSpin RNA (Takara Bio) were utilized according to the manufacturer's standard protocol (Figure S1C). NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific) was employed to assess the quality of RNA.  
Reverse transcription was conducted using the SuperScript IV VILO Master Mix with eZ DNase Enzyme Kit (Cat. No. 11766050, Thermo Fisher Scientific) in accordance with the manufacturer's standard protocol. In this investigation, _Actb_ (Cat. No. 4331182, Thermo Fisher Scientific, TaqMan Gene Expression Assay, Mm02619580_g1) was selected as the endogenous control gene, while _Cmpk2_ (Cat. No. 4351372, Thermo Fisher Scientific, TaqMan Gene Expression Assay, Mm01287125_m1) was designated as the target transcript. TaqMan Fast Advanced Master Mix (Cat. No. 4444557, Thermo Fisher Scientific) was employed to quantitatively assess the expression levels of salivary transcripts (Table S2). The relative expression of target genes was determined using the 2\({}^{-\Delta\Delta\Gamma}\) method.
## 5 Cost Analysis  
A further comparison of the proposed solutions has been conducted in terms of direct cost of the materials where economies of scale are not considered (Figure 7). All the solutions are equipped with two electric thrusters (3 kW at 48 V). In addition, (Solution A) adopts a couple of reversible electro pumps (150 W at 24 V), (Solution B) two water electro-valves and two one-way electro-pumps (132 W  
Figure 5: Control system layout. Solid lines: power signals, dashed lines: control signals.  
Figure 6: Buoyant communication system. 1: On board underwater transceiver. 2: Data cable. 3: Buoy.  
4: Antenna. 5: Remote operations transceiver. 6: Remote operator.  
at 12 V), (Solution C) two linear actuators (350 W at 24 V), (Solution D) a vacuum generator, a pressure reducer, an electro-valve before pressure reducer, an electro-valve inflating line, two tanks to be certified and a couple of dinghies, (Solution E) a water electro-valve, a one-way electro-pump, a reversible electro-pump, two electric motors (400 W at 24 V) for the two vertical propellers, and (Solution F) two droplanes with the related electric motors (1.8 kW at 48 V). The battery pack cost difference between the solutions is due to the different power and thus to the different battery nominal capacity required by each configuration.
## 2 About the Frequency Symbolic Method
The features of the frequency symbolic method of analyzing LPTV circuits described above are as follows:  
1. Regarding the choice of the number of \(k\) harmonics (step 3), it should be noted that, as a rule, the first \(k\) harmonics are typically chosen. However, there are algorithms that allow solutions to be obtained even when the number of equations is greater than the number of unknowns. In our opinion, the choice of harmonics and the number of equations in each case should be left to the specialist who designs and studies the given LPTV circuit. 2. The solution obtained in step 4 of the SSLAE (in the form of \(\mathbf{M}\times\mathbf{W}=\mathbf{P}\) matrix) has its own peculiarities, as some or all elements of the matrix \(\mathbf{M}\) and vector \(\mathbf{P}\) are symbolic. Therefore, symbolic methods should be used to solve the SSLAE. 3. The approximation selection of Equations (11) or (12) is not fundamentally important. However, empirical experience of using the approximation described by Equation (12), has shown that the matrix \(\mathbf{M}\) is sparser than with approximation (11) of the same order. This is significant in the symbolic solution of the SLAP. Therefore, approximation (12) is more beneficial, in our opinion. The values of \(W_{0}\)(s), \(W_{ci}\)(s), \(W_{si}\)(s) or \(W_{0}\)(s), \(W_{+i}\)(s), \(W_{-i}\)(s) in this case are fractional rational expressions, where the denominators are identical and correspond to the determinant of the \(\mathbf{M}\) matrix. The numerators are the determinants of the modified matrices \(\mathbf{M}\), where the corresponding column is replaced by the vector \(\mathbf{P}\). The peculiarity of steps 1-4 is that several (or even all) of the parameters of the analyzed circuit, including \(s\), \(\Omega\), and other parametric element coefficients, are specified symbolically. Therefore, the differentiation of the approximating function, substitution of it and its derivatives into the equation (6), determination of \(k\) harmonics with (2\(k+1\))-fold integration of the expression (13) products into the corresponding orthogonal functions, and solving the SSLAE in symbolically is very cumbersome. In this context, the sophisticated symbolic computation capabilities of contemporary CAD software (MATLAB 2014a) proved invaluable, enabling the calculations described in this manuscript to be carried out with a high degree of feasibility [15]. 4. The symbolic solution of the SLAE is performed in the system user-defined functions MAOPCs [15] using standard functions of the MATLAB environment. Figure 1 shows the most commonly used steps of the FSM procedure. There are other possible steps depending on the input data for FSM, but they are not detailed in this manuscript. Figure 1 also presents the division of feature sets that could be extracted, which are described in Section 3.