esa-sceva/satcom-chunk-collection huggingface dataset:
# Hydraulic Vibration and Possible Exciting Sources Analysis in a Hydropower System
## 1 Introduction
However, the experimental method is restricted owing to its high costs and security risk, and the numerical simulation, including the one-dimensional method of characteristics (1D-MOC) and three dimensional (3D) numerical simulations [26] based on computational fluid dynamics (CFD), sometimes requires plenty of computation time, since the process of convergence to a stable state is very slow for long-distance water conveyance systems [27; 28]. Hydraulic vibration is a periodic hydraulic transient that exists in various water conveyance systems and may result in instabilities and local destructions [29; 30]. With complex condition switches under frequency regulation tasks, both the initial and boundary conditions become extremely complicated, especially for off-design conditions. If frequencies of exciting sources are coincident with the natural frequencies of the hydropower system and continuous working, hydraulic resonance will inevitably develop, and severe pressure oscillations may occur. Consequently, intense flow-induced pressure oscillations may lead to strong vibrations and structural damages [31]. As hydraulic turbines are subjected to increasing off-design operating conditions, the resonance may originate from the vortex rope in the draft tube when its frequency is approximately close to the intrinsic frequency of the water oscillation [32]. However, the exciting sources of hydraulic vibration in hydropower systems have not yet been revealed, and it is imperative to have a clear understanding of the exciting sources of hydraulic vibration in hydropower systems so as to ensure safety and stability during the operating process. In this paper, pressure fluctuations in the turbine are analyzed, and possible exciting sources that may induce hydraulic vibration in hydropower systems are identified. Firstly, experimental tests were carried out for three off-design conditions by utilizing a reduced scale model runner to monitor pressure fluctuation characteristics in the spiral case, vaneless space, draft tube cone, and draft tube elbow. Secondly, based on transfer matrix and hydraulic impedance methods, free vibration analyses were performed to assess the vibration characteristics, including natural frequencies with attenuating factors of each order, and corresponding vibration mode shapes. Finally, the pressure fluctuations were analyzed in both the time domain and the frequency domain. Then, by comparing the frequency characteristics of pressure fluctuations with natural frequencies of the hydropower system, the correlations between flow instabilities in the hydraulic turbine and hydraulic vibration were revealed, and possible exciting sources were identified.
# The Effect of Different Filament Arrangements on Thermal and Optical Performances of LED Bulss
## 4 Conclusions  
This paper investigated the thermal and optical performances for examining heat transfer characteristics of LED bulbs with three different filament arrangements, which including a horizontal arrangement and two vertical arrangements with different stretch heights. The average junction temperature, temperature of surface bulb, thermal resistance, luminous flux, and efficiency of these devices were measured and compared. The average junction temperature of three different filament arrangements all increases with rises of power and are linear. The junction temperature of sample 1 and sample 2 at a power of 3.5 W were improved by 47.24% and 10.78% compared with sample 3 at \(-90^{\circ}\). The luminous flux of the three different filament arrangements also increases with increasing power, but the thermal resistance and luminous efficiency of three different filament arrangements decreased due to the increased power consumption. Meanwhile, the optical characteristics of samples 2 and 3 are close to each other and higher than that of sample 1, indicating that the vertical filament arrangement has the best thermal and optical performances.  
Simulation models of the LED bulbs with three different filament arrangements were built to analyze the temperature distribution of the cross section and air flow path of the inner bulb. The simulation of average junction temperature of three different filament arrangements were 120.99, 101.83, and 96.12 \({}^{\circ}\)C. Compared to sample 1, the gas inside the bulbs of samples 2 and 3 is subject to forming a thermal cycle. From the results of the analysis, the vertical filament with a stretching height of 5 cm showed excellent thermal and illuminance performance characteristics. The presented results may serve as a guide in the heat dissipation design of high-power LED filament bulbs.  
**Author Contributions:** Resources, software and writing--original draft, W.W.; Funding acquisition, J.Z.; Data Curation, Q.Z., B.Y., M.S., Y.L. ([PERSON]), X.L., C.Z., C.L. and D.C.; Software and Data Curation, Y.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work was supported by the Science and Technology Planning Project of Zhejiang Province, China (2018C01046), Enterprise-funded Latitudinal Research Projects (J2016-141), (J2017-171), (J2017-293), (J2017-243).  
**Conflicts of Interest:** The authors declared that they have no conflict of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. The funders had provided equipment and data analysis in the study.
## 3 Results
### The Effect of Landsat Reconstruction on Model Accuracy  
The Landsat-footprint NDVI was severely affected by cloud contamination, which accounted for 25-47% of the data that were acquired over the ten vegetation types (Figure 10a). In contrast, cloud contamination had a relatively small influence on the MODIS data, and only 5% to 19% were affected. The proportions of the Landsat-footprint NDVI data and the MODIS-footprint NDVI data that were affected by cloud at each site are illustrated in Figure 10. In this study, the missing Landsat-footprint NDVI data were not gapfilled or used for parameterization or evaluation of the model due to the serious data loss; the remaining Landsat-footprint NDVI was reconstructed. We compared the accuracy values of the model driven by the Landsat-footprint NDVI and reconstructed Landsat-footprint NDVI. For ENF, DBF, MF, GRA, SHR, and WET, the R\({}^{2}\) values were slightly higher and the RMSE values were slightly smaller when GPP was derived from the reconstructed Landsat-footprint NDVI than GPP driven by the Landsat-footprint NDVI (Figure 6). We found that the reconstruction of the Landsat-footprint NDVI resulted in a slight improvement in model performance at the ecosystem level, which indicated that the effect of the reconstruction on GPP estimation was small.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Sites** \\ **Name** \\ \end{tabular} } & \multirow{2}{*}{**Long (\({}^{\circ}\))**} & \multirow{2}{*}{**Lat (\({}^{\circ}\))**} & \multirow{2}{*}{**V Vegetation**} & \multirow{2}{*}{**Slope**} & \multirow{2}{*}{**Elevation**} & \multicolumn{2}{c}{**GPPMC-30 m**} & & \multicolumn{2}{c}{**GPPMC-500 m**} \\ \cline{4-9}  & & & & & **(\({}^{\circ}\))** & & **(m)** & **R\({}^{2}\)** & **RMSE** & **RMSE** \\  & & & & & & **(g C/m\({}^{2}\)/16 Day)** & & & **(g C/m\({}^{2}\)/16 Day)** & & **(g C/m\({}^{2}\)/16 Day)** \\ \hline AT-Neu & 47.12 & 11.32 & GRA & 14.83 & 961–1307 & 0.76 & 3.11 & 0.69 & 3.42 \\ CH-Lae & 47.48 & 8.37 & MF & 21.44 & 489–846 & 0.76 & 1.87 & 0.75 & 1.82 \\ JP-MBF & 44.39 & 142.32 & DBF & 14.48 & 478–601 & 0.63 & 3.25 & 0.55 & 3.58 \\ CZ-BK1 & 49.50 & 18.54 & ENF & 13.74 & 761–941 & 0.77 & 1.72 & 0.50 & 2.41 \\ IT-Lav & 45.96 & 11.28 & ENF & 12.10 & 1315–1466 & 0.93 & 2.58 & 0.90 & 2.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mountain site information and the value of R\({}^{2}\) and RMSE of the model performance at 30 m and 500 m resolution, respectively.  
Figure 10: **(a)**: The cloud proportion of Landsat NDVI and MODIS NDVI at the 78 sites; (**b,c**): the value of R\({}^{2}\) and RMSE of GPP estimation driven by Landsat NDVI and reconstructed NDVI among the ten vegetation types.
## 3 Dynamic Error Parameter Estimation
 \tag{29}\]  
By assuming that the difference between the actual \(\hat{\varphi}_{y}(\omega)\) and the ideal \(\phi_{y}(\omega)\) phase shift value is small (i.e., when \(|\hat{\varphi}_{y}(\omega)-\hat{\varphi}_{y}(\omega)|<0.25\) rad), this equation can be simplified even further:  
\[\sigma_{x,d,k}^{2}=\frac{1}{2}\Bigl{(}E_{k}\cos\bigl{(}\tilde{\varphi}(\omega _{k})\bigr{)}-E_{k}\Bigr{)}^{2}+\frac{1}{2}\biggl{(}E_{k}\sin\Bigl{(}\tilde{ \varphi}_{y}(\omega_{k})\Bigr{)}\biggr{)}^{2}. \tag{30}\]  
The simplification contained in Equation (30) results from the properties of the sine and cosine functions, where for small angle measures the sine function is close to a linear function, while the cosine function assumes similar values in the indicated range of arguments. Throughout the remainder of the paper, Equation (27), without simplifications, is used. During the discussion of conclusions, selected results obtained using Equation (30) are also presented. The analytically implemented method presented so far allows for a very accurate estimation of the dynamic error signal parameters. However, its actual implementation may encounter several problems typical when using the DFT algorithm. The most important problems in the analyzed case are aliasing, spectral leakage, and noise in the processed quantity \(x(i)\). Aliasing can be reduced by using appropriate filters [38]. In the case of spectral leakage, an appropriate measurement window [39] can be used, while in the case of noise, it is proposed to use a method similar to the one described in [40; 41], used to reduce noise in the measurement signal using the discrete wavelet transform (DWT) algorithm. The method proposed for handling noise in the measurement signal involves rejecting harmonics with amplitude values \(E_{k}\) lower than a threshold determined by the following equation [41]:  
\[\lambda=\text{med}\big{(}E_{0},E_{1},\ldots,E_{N-1}\big{)}\frac{\sqrt{2\text{ ln}\Big{(}\frac{1}{2}N\Big{)}}}{0.6745}. \tag{31}\]  
Thus, in Equation (28), the corrected amplitude values \(E_{k}^{\prime}\) are used, defined as follows:  
\[E_{k}^{\prime}= \begin{cases}E_{k}&\text{if }E_{k}>\lambda\\ 0&\text{otherwise}\end{cases} . \tag{32}\]  
The application of the measurement window consists of feeding the input of the DFT algorithm, described by Equation (20), with the modified signal \(x^{\prime}(i)\), described as follows:  
\[x^{\prime}(i)=\frac{x(i)w(i)}{\sqrt{\frac{1}{N}\sum_{n=0}^{N-1}w^{2}(n)}}, \tag{33}\]  
where \(w(n)\) is the applied window function [42]. The term in the denominator normalizes the window energy to preserve the original energy of the processed signal [24]. This normalization is necessary because the estimated energy of the error signal depends on the estimated energy of the processed signal, as shown in Equation (27).
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 4 Formalization of the Problem
### Problem Formulation  
According to the proposed model, the residual energy of each MD in any time block is expressed as a function as:  
\[E_{i,j}^{res}(f_{i,j},R_{i,j}^{off},t_{i}^{har},t_{i,j}^{off})=E_{i,j}-(E_{i,j} ^{loc}+E_{i,j}^{off})) \tag{8}\]  
where the \(E_{i,j}\), \(E_{i,j}^{off}\), \(E_{i,j}^{loc}\) are given by (6), (4) and (1), respectively.  
Therefore, the problem of maximizing residual energy in multi-user and multi-time slots can be expressed as:  
\[\begin{split} P1&\max_{f,R^{off},f^{har},^{pdf}}\sum_{i= 1}^{N}\sum_{j=1}^{M}E_{i,j}^{res}(f_{i,j},R_{i,j}^{off},t_{i}^{har},t_{i,j}^{ off})\\ s.t.& C1:\frac{(R_{i,j}-R_{i,j}^{off})}{f_{i,j}} \leq T\quad i\in\mathbf{N},j\in\mathbf{M}\\ & C2:0<f_{i,j}\leq f_{i,j}^{max}\quad i\in\mathbf{N},j\in\mathbf{M}\\ & C3:t_{i,j}^{har}+\sum_{j=1}^{M}t_{i,j}^{off}\leq T\quad i\in\mathbf{N }\\ & C4:\sum_{j=1}^{M}R_{i,j}^{off}C_{j}\leq F\quad i\in\mathbf{N}\\ & C5:t_{i}^{har}\geq 0\quad i=1,2,3,\ldots,N\\ & C6:t_{i,j}^{off}\geq 0\quad i=1,2,3,\ldots,N,j\in\mathbf{M}\\ & C7:0<R_{i,j}^{off}\leq R_{i,j}\quad i=1,2,3,\ldots,N,j\in\mathbf{M}\\ & C8:E_{i,j}-(E_{i,j}^{loc}+E_{i,j}^{off})\geq 0\quad i\in\mathbf{N},j \in\mathbf{M}\end{split} \tag{9}\]  
In order to simplify the formula, we define \(f(i,j)=v_{j}p_{i,j}^{a}I_{i,j}^{a}\), then the complete expression of:  
\[E_{i,j}^{res}(f_{i,j},R_{i,j}^{off},t_{i}^{har},t_{i,j}^{off})=f(i,j)t_{i}^{har }-((R_{i,j}-R_{i,j}^{off})C_{j}\kappa_{j}f_{i,j}^{2}+\frac{t_{i,j}^{off}}{h_{i, j}^{m}}f(\frac{R_{i,j}^{off}}{t_{i,j}^{off}})+p_{m,j}t_{i,j}^{off}) \tag{10}\]  
In \(P1\), \(C1\) and \(C3\) respectively represent the time constraints of local computation and edge offloading, the total time of offloading time and energy harvesting time cannot be greater than each time block \(T\), and local computation can use the entire time block \(T\). \(C2\) is the CPU frequency constraint of each MD, and \(C4\) is the computing power constraint of the edge server. \(C5\), \(C6\) and \(C7\) give the optimization variable range of the problem. \(C8\) ensures the residual energy of each MD in each time block.  
The optimal solution of problem \(P1\) contains four solution vectors, where \(f_{i,j}=[[f_{1,1},f_{1,2},\ldots,f_{1,M}],[f_{2,1},f_{2,2},\ldots,f_{2,M}], \ldots,[f_{N,1},f_{N,2},\ldots,f_{N,M}]]\) consists of the CPU frequency of each MD, \(R_{i,j}^{off}=[[R_{1,1}^{off},R_{1,2}^{off},\ldots,R_{1,M}^{off}],[R_{2,1}^{ off},R_{2,2}^{off},\ldots,R_{2,M}^{off}],\ldots,[R_{N,1}^{off},R_{N,2}^{off}, \ldots,R_{N,M}^{off}]]\) consists of the size of each MD's offload data, \(t_{i}^{har}=[t_{1}^{har},t_{2}^{har},\ldots,t_{N}^{har}]\) consists of the energy harvesting time of the MD in each time block, \(t_{i,j}^{off}=[[t_{1,1}^{off},t_{1,2}^{off},\ldots,t_{1,M}^{off}][t_{2,1}^{off},t_{2,2}^{off},\ldots,t_{2,M}^{off}],\ldots,[t_{N,1}^{off},t_{N,2}^{off},\ldots,t_{N,M}^{off}]]\) consists of task offload time for each MD.
## 4 Discussion
### Single-blind Test  
For the single-blind test, patients in malignant (98) and benign groups (110) were used to train the MLP classifier. After training, pathologists initiated a single-blind test, including 17 patients with definite malignant or benign diagnosis. The accuracy of the MLP classifier obtained an accuracy of 82.4% (14/17) that is comparable with the accuracy during model optimization and proved the good generalization ability of the trained classifier.
## 3 Results
### Relative Effects of \(R_{n},VPD,\) and \(\ REW\ on\ T\)  
The contribution of each factor to T was quite different in various years. The Rn and REW in the 'wet' year exerted negative and positive effects on T, with contribution rates of \(-13.6\%\) and \(11.0\%\), while VPD exerted slight positive effects on T, with a contribution rate of \(3.0\%\) (Figure 6a). The Rn and REW in the 'normal' year exerted negative effects on T, with contribution rates of \(-11.1\%\) and \(-8.9\%\); VPD imposed a lesser positive effect on T, with a contribution rate of \(5.5\%\) (Figure 6b). The REW in the 'dry' year imposed an obvious negative effect on T, with a contribution rate of \(-31.7\%\); Rn also imposed a negative effect on T, with a contribution rate of \(-14.7\%\), while VPD exerted a slight positive effect on T, with a contribution rate of \(4.6\%\) (Figure 6c). The Rn and REW in the 'wetter' year exerted negative and positive effects on T, with contribution rates of \(-18.0\%\) and \(12.8\%\), while VPD exerted slight positive effects on T, with a contribution rate of \(4.0\%\) (Figure 6d). The dominant factor contributing to the changes in T was Rn in the 'wet' year, Rn in the 'normal' year, REW in the 'dry' year, and Rn in the 'wetter' year. Except for the 'dry' year, the contribution rates of Rn and REW to T were not much different in other hydrological years, but REW had a positive impact in the 'wet' and 'wetter' years and a negative impact in the 'normal' and 'dry' years. The VPD had only a small positive effect in each hydrological year. In the 'dry' year, the negative effect of REW on T was very obvious, and the contribution rate was also the highest (\(-31.7\%\), Figure 6c).  
Figure 5: Comparison of the observed and simulated T during the calibration (odd days in 2017 and 2021, (**a**)) and validation (even days in 2017 and 2021, (**b**)) phases.  
Figure 6: The relative contribution rates of Rn, VPD, and REW to T in the 'wet’ (**a**), 'normal’ (**b**), 'dry’ (**c**), and 'wetter’ (**d**) years compared to the reference total T.
## 4 Reliability, Availability and Fault Tree Analysis
### Reliability  
Reliability is a measure used to characterize if a component/system, is properly working according to its specifications during a specific period of the time [6]. Formally, it is defined as the probability that a  
Figure 2: Typical ISA 100.11a network.  
component does not fail in the time interval \((0,t]\). Considering that the _time to failure_ of a component, \(T\), is a random variable defined by a cumulative distribution function \(F(t)\) (CDF), the reliability \(R(t)\) is given by:  
\[R(t)=Pr(T>t)=1-F(t) \tag{1}\]  
The reliability function is closely related with the _failure rate function_\(\lambda(t)\). This function (also known as _hazard rate_) describes the instantaneous failure rate of a component. Formally, this function is defined as the probability that a component fails during the period of the time \([x,x+\Delta t]\), knowing that it is working at time instant \(t=x\). The behavior of this function has been extensively discussed in the literature [40]. For many systems/components this function presents a characteristic shape which is similar to a _bathtub curve_. When the system is young, the failure rate is higher (infant mortality), and then quickly decreases until stabilizes (useful life). As the system/component gets older it increases again (wear out). For electrical/electronic systems it is common to consider that the failure rate is constant during the useful life period, _i.e._, \(\lambda(t)=\lambda\)[41]. It can be proved that \(R(t)\) and \(\lambda(t)\) are related according to the following expression [42]  
\[R(t)=exp\left(-\int_{0}^{t}\lambda(u)du\right) \tag{2}\]  
Therefore \(\lambda(t)\) establishes \(R(t)\). Another metric related with \(R(t)\) is the MTTF (_Mean Time to Failure_). Formally, it is defined as the expected (average) time during which a component is working properly, and is given by [42]  
\[MTTF=E(T)=\int_{0}^{\infty}tf(t)dt \tag{3}\]
## 2 System Architecture Framework
### Frontend Visual-Inertial Odometry  
Frontend odometry is a core module in the SLAM system that is responsible for real-time device pose estimation. In this research, we maintain the classical ORB feature point extraction method from ORB-SLAM3 while introducing a line feature extraction module to better handle sparse textures and prominent geometric features in mining environments. The introduction of line features compensates for the limitations of solely relying on point features, providing additional geometric constraints for the system and enhancing adaptability to complex scenes. By incorporating IMU (Inertial Measurement Unit) data, the frontend odometry achieves a fusion of visual and inertial information, thereby improving pose estimation accuracy across multiple sensor modes and providing more accurate initial poses for subsequent local and global optimizations.
## 3 Results and Discussion
### Analysis of VOCs Obtained by GC-IMS and GC-MS

Table 2: Volatile compounds in four stages of Zhenba bacon by GC-MS.  
Figure 4: Change of VOCs species of Zhenba bacon with different stages using GC-MS. Fresh: raw sample; YZ: cured sample; XZ: smoking sample; CP: finished sample; VOCs: volatile organic compounds. Ketone was a product of lipid oxidation and hence was used as an indicator of lipid peroxidation [40]. 2,3-butanedione is a byproduct of the Maillard reaction. Because its odor threshold is low, it is likely to be an important contributor to provide buttery aroma with bacon. In the four stages, the content of 2,3-butanedione was observed only in the YZ stage. In this study, 3-Methyl-1,2-Cyclopentanedione was detected in XZ and CP stages. These methyl ketones, which are caused by autoxidation and \(\beta\) oxidation of fatty acids or microbial metabolism, are considered to be important contributors to the complex flavor and aroma properties of pork [41]. Most alcohols are produced by the degradation of unsaturated fats, and some may be produced by the reduction of aldehydes. In the XZ and CP samples, the concentration of 3-octenol (grass aroma and mushroom aroma) [42] and furfuryl alcohol (mildew aroma and caramel aroma) indicated a significant contribution to the bacon flavor. 3-octenol has been associated with the pathway of arachidonic acid. The content of ethanol, 1-pentanol, and 3-methyl-1-butanol has a high level in YZ, and a low level in XZ and CP samples. This could be due to the large contribution of liquor flavor substances in the YZ stage. Previous studies have shown that linear alcohols were known to originate from lipid oxidation; branched-chain alcohols were produced by a Millard-type reaction and Strecker degradation under fermentation conditions and then reduced [43]. The proportion of esters in the volatile components of Zhenba bacon was the largest, which contributed to the flavor of bacon. The formation of esters usually requires a complex reaction chain, which may come from the esterification of alcohols and carboxylic acids under the action of microorganisms [44]. Among the identified substances of esters were ethyl valerate, ethyl caproate, methyl hexanoate, ethyl butyrate, and ethyl lactate. However, content of ethyl caproate is the highest in the YZ stage, which is mainly produced in the fermentation process, and has the aroma of pineapple and banana fruits [45]. Phenolic substances are mainly generated by the decomposition of lignin in fumigating materials, which are the unique volatile flavor substances of smoked products. It is attributed mainly to the volatile compounds generated during the heating and decomposing of the smoked material, and most of the phenolic substances formed after being smoked. The findings from the present study are consistent with this idea. Phenols are only present during the XZ and CP samples, which contained 2-methylphenol, 3-methylphenol, 4-methyl guaiacol, 4-ethylguaiacol, guaiacol, and phenol. Phenolic did not differ much between these two stages.
## 2 Materials and Methods
### Development and Design Process of the Control System Hardware
#### 2.2.5 Design and Manufacture of the Printed Circuit Board  
A double-sided circuit board was designed, because too many circuits needed to be connected to the same controller, and this board aimed to arrange all the circuits together. The board included a motor drive circuit, a main control unit interface, a display interface, and a signal acquisition interface. After obtaining the EMG signals with an SCSI I-to-II cable from the Delsys(r) system, the data are collected by an AD7606 circuit board via a Terminal Block and SCSI-II 68-Pin Connector and are then transmitted to the STM32. An illustration of the designed board and connections is shown in Figure 4.