esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines
## 4 RUL Prediction Experiment
### Experimental Results  
Table 4 shows the computational cost and number of training parameters required to conduct experiments with MSA-FCN on FD002 and FD004, respectively.  
Table 5 presents the results of the RUL prediction performance metrics for MSA-FCN on the FD002 and FD004 datasets. Clearly, MSA-FCN performs better in RUL prediction on FD002 compared with FD004. This difference may stem from the varying number of failure modes contained in the two datasets. FD004 includes two failure modes, while FD002 has only one. Generally speaking, datasets with multiple failure modes pose greater challenges to RUL prediction models.  
Figure 8a,b respectively showcase the RUL prediction results of MSA-FCN for all engines in the FD002 and FD004 datasets. The horizontal axis represents engine codes, while the vertical axis denotes RUL values. Overall, the RUL curves predicted by MSA-FCN (orange) closely align with the actual RUL curves (blue). Specifically, the RUL curves in Figure 8a are more tightly clustered than those in Figure 8b, further verifying the difference in prediction performance of MSA-FCN across the two datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sub-Dataset** & **Training** & **Testing** & **Trainable Parameters** \\ \hline FD002 & 4.19 s/epoch & 0.81 s & 66,311 (259.03 KB) \\ FD004 & 4.55 s/epoch & 0.82 s & 68,831 (268.87 KB) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computational cost and number of trainable parameters.  
Figure 8: RUL prediction results for the tested engines: (**a**) FD002 Engines and (**b**) FD004 Engines.  
Table 6 and Figure 9a-f exhibit the RUL prediction results of MSA-FCN for six test engines (numbered #45, #177, #236, #8, #135, and #216) from the FD002 and FD004 datasets. According to the table, despite slight lags in the predictions for #236, #8, and #135, the RUL values predicted by MSA-FCN are generally close to the actual RUL values. From the figures, it is evident that the RUL curves predicted by MSA-FCN (blue) closely follow the actual RUL curves (green), and these curves nearly overlap during the linear degradation phase of the engines.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sub-Dataset** & \multicolumn{2}{c}{**FD002**} & \multicolumn{2}{c}{**FD004**} \\ \hline
**Tested Engine** & **\#45** & **\#177** & **\#236** & **\#8** & **\#135** & **\#216** \\ \hline Actual RUL & 15 & 6 & 12 & 14 & 7 & 17 \\ Predicted RUL & 14.147 & 4.241 & 12.967 & 14.054 & 7.177 & 16.821 \\ \hline \hline \end{tabular}
\end{table}
Table 6: RUL prediction results for the six different tested engines from FD002 and FD004.  
Figure 9: RUL prediction results for different tested engines. (**a**) Test engine #45 from FD002; (**b**) test engine #177 from FD002; (**c**) test engine #236 from FD002; (**d**) test engine #8 from FD004; (**e**) test engine #135 from FD004; and (**f**) test engine #216 from FD004.  
In conclusion, these results fully demonstrate the effectiveness of MSA-FCN in predicting RUL values for turbofan engines.
## 5 A Hysteresis Model with Damage Accumulation of Welding Materials
### Parameter Fitting of the Model
#### 5.5.1 The Parameter \(\lambda\) of EvolutionEquation of Damage Accumulation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multicolumn{4}{c}{**I + II**} & \multicolumn{4}{c}{**III**} \\ \cline{3-8}  & & \(\mathbf{h_{2}}\) & \(\mathbf{g_{2}}\) & \(\mathbf{D_{2}}\) & \(\mathbf{h_{3}}\) & \(\mathbf{g_{3}}\) \\ \hline \multirow{4}{*}{Butt weld of Q235 steel} & WA1(1) & 0.045 & 0.991 & 0.889 & 2.083 & 2.466 \\  & WA1(2) & 0.024 & 0.982 & 0.881 & 1.694 & 2.433 \\  & WA1(3) & 0.024 & 0.989 & 0.879 & 1.668 & 2.453 \\  & Model Parameters & 0.031 & 0.987 & 0.883 & 1.815 & 2.446 \\ \hline \multirow{4}{*}{Left fillet weld of Q235 steel} & WA2(1) & 0.020 & 0.997 & 0.875 & 1.751 & 2.553 \\  & WA2(2) & 0.021 & 0.998 & 0.882 & 1.751 & 2.503 \\  & WA2(3) & 0.020 & 0.997 & 0.879 & 1.709 & 2.513 \\  & Model Parameters & 0.020 & 0.997 & 0.879 & 1.737 & 2.523 \\ \hline \multirow{4}{*}{Right fillet weld of Q235 steel} & WA3(1) & 0.056 & 1.000 & 0.887 & 1.345 & 2.134 \\  & WA3(2) & 0.020 & 0.997 & 0.886 & 1.384 & 2.196 \\  & WA3(3) & 0.113 & 1.000 & 0.886 & 0.981 & 1.796 \\  & Model Parameters & 0.063 & 0.999 & 0.886 & 1.225 & 2.044 \\ \hline \multirow{4}{*}{Butt weld of Q345 steel} & WB1(1) & 0.109 & 0.999 & 0.890 & 1.519 & 2.269 \\  & WB1(2) & 0.111 & 1.000 & 0.882 & 1.807 & 2.499 \\ \cline{1-1}  & W1(3) & 0.103 & 1.002 & 0.886 & 1.445 & 2.196 \\ \cline{1-1}  & Model Parameters & 0.107 & 1.000 & 0.886 & 1.509 & 2.321 \\ \hline \multirow{4}{*}{Left fillet weld of Q345 steel} & WB2(1) & 0.113 & 0.997 & 0.879 & 1.005 & 1.825 \\ \cline{1-1}  & WA2(2) & 0.094 & 0.999 & 0.881 & 1.083 & 1.878 \\ \cline{1-1}  & WA2(3) & 0.104 & 1.001 & 0.877 & 1.054 & 1.883 \\ \cline{1-1}  & Model Parameters & 0.104 & 0.999 & 0.879 & 1.047 & 1.862 \\ \hline \multirow{4}{*}{Right fillet weld of Q345 steel} & WB3(1) & 0.083 & 0.999 & 0.857 & 1.198 & 1.956 \\ \cline{1-1}  & WA3(2) & 0.057 & 1.000 & 0.887 & 2.365 & 3.043 \\ \cline{1-1}  & WA3(3) & 0.089 & 0.999 & 0.890 & 1.789 & 2.513 \\ \cline{1-1}  & Model Parameters & 0.076 & 0.999 & 0.878 & 1.784 & 2.504 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Values of parameters \(\eta\), \(\xi\).
## 2 Materials and Methods
### Chlorophyll Fluorescence at Leaf Level  
Simultaneously with the spectral measurements, one or two leaves around the FOV of the fiber were clipped and immediately delivered to the lab. When the detached leaves were delivered to the lab, they were first placed on a platform in the dark for 30 minutes. Then, the dark-adapted leaves were exposed to a weak measuring pulse (0.32 \(\upmu\)mol\(\cdot m^{-2}\cdot s^{-1}\)) to detect the initial minimum fluorescence (\(F_{o}\)). The maximum fluorescence of the dark-adapted leaves (\(F_{m}\)) was recorded during a short pulse at an actinic light (800 ms, 6174 \(\upmu\)mol\(\cdot m^{-2}\cdot s^{-1}\)). The maximum fluorescence of the light-adapted leaves (\(F_{m}^{\prime}\)) was also measured at the same light intensity as that of the \(F_{m}\), and the fluorescence at a stable state (\(F^{\prime}\)) was measured at a light intensity of 80 \(\upmu\)mol\(\cdot m^{-2}\cdot s^{-1}\). The FluorImager chlorophyll fluorescence imaging system (Technologica Ltd., Colchester, UK) was used to obtain the images of chlorophyll fluorescence of the isolated leaves [34; 35]. The excitation beam of FluorImager was produced by 16 banks of 100 blue pulsed diodes (peak output of 470 nm). The pulsed diode was modulated at a constant voltage and could maintain a constant spectral output. Fluorescent images were taken by an asynchronous progressive scan CCD camera, which was synchronized to the measuring pulses from the emitting light. With FluorImager, many parameters of chlorophyll fluorescence could be measured, including the \(F_{o}\) and \(F_{m}\) in dark-adapted leaves; the minimum fluorescence (\(F_{o}^{\prime}\)), \(F_{m}^{\prime}\), and \(F^{\prime}\) in light-adapted leaves; and the ratios of these fluorescence measures, e.g., \(F_{o}/F_{m}\) (\(F_{v}=F_{m}-F_{o}\)), \(F_{q}^{\prime}/F_{m}^{\prime}\) (\(F_{q}^{\prime}=F_{m}^{\prime}-F^{\prime}\)), \(F_{v}^{\prime}/F_{m}^{\prime}\) (\(F_{v}^{\prime}=F_{m}^{\prime}-F_{o}^{\prime}\), where \(F_{o}^{\prime}=F_{o}/(F_{v}/F_{m}+F_{o}/F_{m}^{\prime})\)), \(F_{q}^{\prime}/F_{v}^{\prime}\), and \(F_{m}/F_{m}^{\prime}-1\) (a fluorescence parameter to quantify the capacity of non-photochemical quenching of excitation energy via regulated thermal energy dissipation, _NPQ_). Each chlorophyll fluorescence parameter could reflect physiological states. To provide a better understanding for readers, details about their significance in the physiological state are described in Table 1, which has been described by [PERSON] and [PERSON] (2004) [35].
# Magnetic Resonance-Electrical Properties Tomography by Directly Solving Maxwell's Curl Equations
## 2 Theory and Method
### Experimental Setup  
To evaluate the practicality of the proposed EPT method, a phantom-based experiment at 9.4T preclinical MRI system (BioSpec 94/30 USR, Bruker, Germany) located at the Centre for Advanced Imaging, the University of Queensland, was conducted. A Bruker birdcage coil (\(\mathcal{O}\)outer/\(\mathcal{O}\)inner = 112/86 mm) was used to acquire images and the \(B_{1}^{+}\) field. The phantom described in Section 2.3 was fabricated, with conductivities of 0.43 S/m and 0.78 S/m for the inner and outer compartments respectively, and permittivity of 78 for both compartments. The two compartments were separated by a wall (thickness = 2 mm) made of acrylic material. The EPs of the liquid solution were measured using a dielectric probe (Kit DAK-12, SPEAG Ltd., Zurich, Switzerland). The probe uncertainty and temperature-associated variation in dielectric properties were 3.3% and 2%, respectively. After careful calibration, the total measurement error can be considered less than 6.6%. The aqueous solutions consist of distilled water, NaCl, and CuSO4\(\cdot\)5H2O with mass ratios of 100:0.18:0.025 and 100:0.5:0.025, respectively.  
Figure 3 shows the phantom and its cross-section, acquired with a gradient echo sequence. Two MR images with flip angles equal to 45\({}^{\circ}\) and 90\({}^{\circ}\) were obtained by using the fast low-angle shot (FLASH) sequence (echo time (TE) equals 5 ms and repetition time (TR) equals 5000 ms). The field-of-view in the experiment was set to 48 \(\times\) 48 \(\times\) 48 mm, and a 0.5 mm isotropic resolution was used in the image acquisition. The double-angle-method (DAM) [35; 36] was applied to calculate the amplitude of the \(B_{1}^{+}\) field, while the phase was calculated by using the transceive phase assumption (TPA) [6; 37]. To implement the TPA, two images with different TEs (TE1 = 2.5 ms and TE2 = 3.5 ms) were used to estimate the phase difference due to the inhomogeneous \(B_{0}\) field. Another two images, generated using identical TE/TR but reversed gradient directions, were used to cancel the gradient-induced phase (e.g., eddy current) [6]. It should be noted that the \(B_{1}^{+}\) phase was not used in the EP reconstruction but only for the purpose of evaluating the accuracy of the forward solver, as illustrated in the following section.
## 3 Results and Discussion
### Nitrogen Changes and Model Validation
However, due to the strong adsorption of the soil, the concentration of NH\({}_{4}\)\({}^{+}\)-N in the free water of the soil solution decreases [43; 44]. Nitrite nitrogen, in the three media, fluctuated and eventually stabilized, but its concentration was much smaller than the concentrations of NO\({}_{3}\)\({}^{-}\)-N and NH\({}_{4}\)\({}^{+}\)-N. Before the water level rises, when the dissolved oxygen is sufficient, the reproduction rate of nitrifying bacteria is slower than that of nitrosating bacteria; at this time, the nitrosation reaction dominates, causing the accumulation of NO\({}_{2}\)\({}^{-}\)-N, resulting in an increased concentration of NO\({}_{2}\)\({}^{-}\)-N in the soil solution during the rising water level stage. During the falling phase of the water level, nitrification dominates, a lot of H\({}^{+}\) is produced in the solution, and the soil solution becomes weakly acidic, which strengthens the conversion of nitrite to nitrate. NO\({}_{2}\)\({}^{-}\)-N no longer accumulates, and the concentration gradually decreases [29; 45; 46]. In the initial stage, the increase in NO\({}_{2}\)\({}^{-}\)-N satisfies coarse sand, then medium sand, then fine sand. The more significant the particle size is, the more particle surfaces there are that microbial flocs can come into contact with, and more microbes can participate in the nitrification reaction. With a stronger microbial nitrification ability, it is understood that the nitrification ability of microbial flocs will increase with the increase in particle size [47].
## 6 Conclusions  
In this paper, a novel double coordinate free-interface mode synthesis-based method is proposed for analyzing the modal characteristics of irregularly-shaped bridges. Taking the numerical model of a typical irregularly-shaped structure as an example, the effects of four design parameters including ramp radius, crotch diaphragm stiffness, cross-section features, and bearing condition on modal characteristics are demonstrated and the following conclusions can be drawn.  
First, a comparative analysis with the traditional finite element method reveals that the accuracy of the double coordinates free-interface mode synthesis method is favorable and is suitable for analyzing the modal characteristics of irregularly-shaped bridges.  
Second, the effect of ramp radius on the modal characteristics of the irregularly-shaped bridge is not obvious. It has a relatively significant effect on modal frequencies such as the third and fifth modes in which ramp vertical bending vibration is the main vibration form. The capability to resist lateral bending of the ramp continues to weaken with the increasing of the ramp radius.  
Third, the effect of crotch diaphragm stiffness on modal frequencies and low-order mode shapes is negligible. The changes of crotch diaphragm stiffness can weaken the third and fourth vertical vibration of the irregularly-shaped bridge to a certain extent.  
Fourth, the effect of the cross-section stiffness of the whole bridge on modal frequencies is obvious; modal frequencies increase as the stiffness increases. However, this effect is negligible on mode shapes. As for the change of the cross-section stiffness of substructures, substructures 2 and 3 have greater impact on the modal characteristics of the whole bridge. Additionally, the results also indicate that mode shapes have nothing to do with the whole cross-section stiffness but do influence its relative distribution.  
Fifth, different bearing conditions will affect structural vibration. Unreasonable conditions can lead to a decrease in the capability to resist lateral bending vibration. The double fixed bearing condition is set at three ends, and the bifurcation position of the bridge can strengthen the lateral bending stiffness, which is more satisfactory.  
**Acknowledgments:** The authors express their appreciation for the financial supports of National Natural Science Foundation of China (No. 51408258, 51378236); China Postdoctoral Science Foundation funded project (2014M560237, 2015T80305); Fundamental Research Funds for the Central Universities (JCKY-QKJC06); Science & Technology Development Program of Jilin Province.  
**Author Contributions:** [PERSON] conceived and designed the investigation; [PERSON] performed the calculation and wrote the paper; [PERSON] and [PERSON] analyzed the data and explained the calculated results; [PERSON] edited and audited the content. All authors have read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Extracted Building Characteristics and Accuracy Assessment
#### 2.3.3 Building Height Validation  
We used 11 field-measured building heights and ICESat-2 altimetry data as an independent check of our DSM-DTM-derived building heights. They were obtained using a laser range finder (TruPulse 360R) in June 2019 for buildings 16-57 m tall (Table S2).  
Buildings common to both the Pleiades (2013) and WorldView-2 (2019) datasets, which were separated by six years, were extracted using a polygon IoU threshold of 0.8 to ensure robust comparisons. Where our gridded ICESat-2 pixels (Section 2.3) intersected with these buildings, the elevation value was assigned to produce three independent height measurements for cross-comparison.
# One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments +
## 2 Distributed GEP Algorithm and Its Extensional Applications
### Discussion  
In some cases, e.g., [21], they consider that the centered data covariance \(M_{0}\) is divided into local agents as \(M_{0}=[M_{1},M_{2},\ldots,M_{N}]\) such that the distributed GEPs are reduced to distributed SVDs. However, in our distributed setting, which is more concrete, we can communicate the data covariance \(A_{i}\) and \(B_{i}\) and obtain the exact solution of the centered GEP as depicted in Figure 2.  
Furthermore, from a data privacy perspective, since data covariance pair \((A_{i},\,B_{i})\) may lose privacy for its simple symmetry, sending local covariance \(M_{i}=(B_{i})^{-\frac{1}{2}}A_{i}(B_{i})^{-\frac{1}{2}}\) to the central server is much safer and more efficient than sending \(A_{i}\) and \(B_{i}\) directly.  
In addition, a dense \(M_{i}\) is regarded as compromising a mix of multi-view data, which are hard to recover. However, there is a divergence between \(\sum_{i}M_{i}\) and \(M_{0}\), and it is decided by the data structure.  
In this paper, we will further analyze the approximation error in this one-shot communication method in a distributed GEP.  
Note that in the one-shot distributed system, it is assumed that node failure and asynchronization are ignored. All the local servers have a similar scale of data to ensure that synchronous communication will not result in a long delay in the whole system.
# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
## 5 Conclusions  
In this paper, a type of novel model SwinGlris has been proposed to enhance iris recognition accuracy by converting low-resolution iris images into high-resolution images.  
Through experimental validation, our method achieves the best performance under different testing sets and backbone networks with different classification capabilities. Apart from that, our model is more capable of recovering high-frequency iris information compared to other mainstream models when more information interference is present.  
It cannot be ignored that there are some limitations to this method. Due to the inherent GAN-based architecture, the methodology is susceptible to training instability issues during the training process. Furthermore, the evaluation indexes present smaller advantages for the image super-resolution task. However, for the iris recognition task, it does improve the accuracy of low-resolution iris image recognition over other mainstream super-scoring models. Overall, we believe our model will have a positive impact in the field of iris recognition.  
**Author Contributions:** Conceptualization, H.L. and H.J.; methodology, H.L.; software, H.L.; validation, H.L., J.C. and H.J.; formal analysis, X.Z.; investigation, H.L.; resources, X.Z.; data curation, H.L.; writing--original draft preparation, H.L.; writing--review and editing, H.L.; visualization, H.L.; supervision, X.Z. and J.C.; project administration, H.L. and X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61471181, the Natural Science Foundation of Jilin Province (grant number YDZJ202101 ZYTS144), the Jilin province Industrial Innovation Special Fund Project (grant number 2019C053.2), and the Science and Technology Project of the Jilin Provincial Education Department (grant number JKJH20180448 KJ).  
**Data Availability Statement:** The Iris datasets in this paper are openly available at [[http://biometrics.idealtest.org/index.jspf/datasetDetail/4](http://biometrics.idealtest.org/index.jspf/datasetDetail/4)]([http://biometrics.idealtest.org/index.jspf/datasetDetail/4](http://biometrics.idealtest.org/index.jspf/datasetDetail/4)) (accessed on 5 October 2022).  
**Acknowledgments:** Thanks to the Jilin Provincial Key Laboratory of Biometrics New Technology for supporting this research.  
**Conflicts of Interest:** We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
# Investigation of the Influence of Splitter Blades on the Resonance Conditions of Impellers
## 4 Validation and Discussion
### Differences from the Conventional Resonance Conditions  
The differences between the proposed resonance conditions and the conventional ones are caused by the existence of splitter blades, which are reflected in the following points:  
1. The proposed resonance conditions are derived based on the impellers with splitter blades. The influences of splitter blades on the natural structural characteristics and excitation characteristics are taken into consideration. However, the conventional resonance conditions are derived based on the conventionally designed impellers without considering the splitter blades.
2. The shape matching condition of the proposed resonance conditions is dependent with the total number of main and splitter blades, i.e., \(2N\) in Equation (27). For the conventional shape matching condition, it is dependent on the number of sectors, i.e., \(N\) in Equation (29).
3. The proposed shape matching condition is also dependent with the harmonic contents of the mode, i.e., \(h\) in Equation (27), which are analyzed based on the blade-based RMV. For the conventional  
Figure 9: (**a**) Mistuned response under EO1–EO17; (**b**) Comparison of normalized harmonic content amplitude of mistuned mode 192 and normalized mistuned response amplitude under EO1–EO17.  
shape matching condition, it is related to the nodal diameter component \(n_{ND}\) in Equation (29), which is analyzed based on the duplicated sector by cyclic symmetry.
Sensors_**2015**, _15_, 2629-2643; doi:10.3390/s150202629  
_Sensors_  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
_Article_  
A Novel Ultrasensitive ECL Sensor for DNA Detection Based on Nicking endonuclease-Assisted Target Recycling Amplification, Rolling Circle Amplification and Hemin/G-Quadruplex  
**[PERSON] \({}^{\dagger}\), [PERSON] \({}^{\dagger}\), [PERSON] \({}^{\ast}\), [PERSON], [PERSON] and [PERSON]**  
Department of Clinical Laboratory, Xinqiao Hospital, Third Military Medical University,  
Chongqing 400037, China; E-Mails: [EMAIL_ADDRESS] (F.L.); [PERSON]SS] (G.X.); [EMAIL_ADDRESS] (J.Y.); [EMAIL_ADDRESS] (M.C.); [PERSON]ESS] (G.C.)  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\ast}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]na.cn; Tel./Fax: +86-23-6875-5637.  
Academic Editor: [PERSON]  
_Received: 1 December 2014 / Accepted: 19 January 2015 / Published: 26 January 2015_  
**Abstract:** In this study, we describe a novel universal and highly sensitive strategy for the electrochemiluminescent (ECL) detection of sequence specific DNA at the aM level based on Nt.BbvCI (a nicking endonuclease)-assisted target recycling amplification (TRA), rolling circle amplification (RCA) and hemin/G-quadruplex. The target DNAs can hybridize with self-assembled capture probes and assistant probes to form \"Y\" junction structures on the electrode surface, thus triggering the execution of a TRA reaction with the aid of Nt.BbvCI. Then, the RCA reaction and the addition of hemin result in the production of numerous hemin/G-quadruplex, which consume the dissolved oxygen in the detection buffer and result in a significant ECL quenching effect toward the O2/S2O82- system. The proposed strategy combines the amplification ability of TRA, RCA and the inherent high sensitivity of the ECL technique, thus enabling low aM (3.8 aM) detection for sequence-specific DNA and a wide linear range from 10.0 aM to 1.0 pM. At the same time, this novel strategy shows high selectivity against single-base mismatch sequences, which makes our novel universal and highly sensitive method a powerful addition to specific DNA sequence detection.  
**Keywords:** target recycling amplification (TRA); rolling circle amplification (RCA); hemin/G-quadruplex; Nt.BbvCI; ECL