esa-sceva/satcom-chunk-collection huggingface dataset:
# RUL Prediction of Rolling Bearings Based on a DCAE and CNN
## 3 Degradation Modeling of Rolling Bearings
#### Experimental Data
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**Structure 1**} & \multicolumn{2}{c}{**Structure 2**} & \multicolumn{2}{c}{**Structure 3**} & \multicolumn{2}{c}{**Structure 4**} & \multicolumn{2}{c}{**Structure 5**} \\ \cline{2-11}  & **Mon** & **Corr** & **Mon** & **Corr** & **Mon** & **Corr** & **Mon** & **Corr** & **Mon** & **Corr** \\ \hline Bearing1\_1 & 0.21 & 0.97 & 0.14 & 0.85 & 0.20 & 0.85 & 0.10 & 0.35 & 0.16 & 0.85 \\ Bearing1\_2 & 0.27 & 0.94 & 0.15 & 0.94 & 0.25 & 0.87 & 0.16 & 0.32 & 0.14 & 0.87 \\ Bearing1\_3 & 0.35 & 0.97 & 0.19 & 0.95 & 0.21 & 0.94 & 0.23 & 0.11 & 0.19 & 0.86 \\ Bearing1\_4 & 0.41 & 0.91 & 0.37 & 0.90 & 0.17 & 0.91 & 0.15 & 0.04 & 0.15 & 0.84 \\ Bearing1\_5 & 0.19 & 0.92 & 0.15 & 0.90 & 0.19 & 0.85 & 0.14 & 0.32 & 0.11 & 0.81 \\ Bearing1\_6 & 0.21 & 0.97 & 0.18 & 0.85 & 0.15 & 0.87 & 0.10 & 0.31 & 0.11 & 0.87 \\ Bearing1\_7 & 0.21 & 0.96 & 0.19 & 0.85 & 0.11 & 0.55 & 0.11 & 0.11 & 0.13 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of HI curves trained by five structures. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Bearing1\_1**} & \multicolumn{2}{c}{**Bearing1\_2**} & \multicolumn{2}{c}{**Bearing1\_3**} & \multicolumn{2}{c}{**Bearing1\_4**} & \multicolumn{2}{c}{**Bearing1\_5**} & \multicolumn{2}{c}{**Bearing1\_6**} & \multicolumn{2}{c}{**Bearing1\_7**} \\ \hline DCAE + SOM & Mon & 0.21 & 0.27 & 0.35 & 0.41 & 0.19 & 0.21 & 0.21 \\  & Corr & 0.97 & 0.94 & 0.97 & 0.91 & 0.92 & 0.97 & 0.96 \\ \hline \multirow{2}{*}{PCA} & Mon & 0.13 & 0.07 & 0.13 & 0.09 & 0.21 & 0.04 & 0.06 \\  & Corr & 0.86 & 0.19 & 0.73 & 0.32 & 0.19 & 0.19 & 0.56 \\ \hline \multirow{2}{*}{RMS} & Mon & 0.17 & 0.13 & 0.14 & 0.16 & 0.12 & 0.10 & 0.11 \\  & Corr & 0.83 & 0.29 & 0.77 & 0.32 & 0.16 & 0.10 & 0.23 \\ \hline \multirow{2}{*}{ELM\_AE} & Mon & 0.15 & 0.14 & 0.14 & 0.15 & 0.12 & 0.11 & 0.10 \\  & Corr & 0.86 & 0.33 & 0.77 & 0.31 & 0.28 & 0.18 & 0.34 \\ \hline \multirow{2}{*}{SAE} & Mon & 0.21 & 0.16 & 0.36 & 0.33 & 0.21 & 0.17 & 0.21 \\  & Corr & 0.96 & 0.91 & 0.91 & 0.92 & 0.91 & 0.93 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: HI curve evaluation results.
## 2 Strategy
### Ppp  
For PPP, the ionosphere-free combination of GPS, GLONASS or BDS measurements recorded in 30 s intervals from about 12 CMONOC (Crustal Movement Observation Network of China), five MGEX, and three GA (Geoscience Australia) stations observed during DOY (Day of Year) 252 to 282 in 2014 are used in this study (Figure 1). All of these stations are not used for both precise orbit determination and fractional cycle biases (FCB) generation (the latter will be described below). These stations are equipped with Trimble NetR9 receivers except for UNX3 that uses a Septentrio ASTERX3 receiver. The monthly average coordinates estimated by GPS only PPP are used as values of \"ground-truth\" for comparison later. For each station, the 24-h data set is divided into eight sessions. Considering the positioning errors introduced by orbit and clock interpolation, particular for BDS satellites, the first and last 3 h data are abandoned for processing. Hence, there are six sessions available starting from 3:00 a.m. to 9:00 p.m. in each day. For GPS only and combined PPP, the aforementioned data length is long enough to ensure the convergence of positioning. However, it may not be valid for BDS only PPP; in that case, the length of each session is set as 18 h instead of 3 h. Due to elevation dependent code errors of BDS IGSO and MEO satellites, the raw code observations are corrected using the piece-wise linear model developed in [12]. Considering no published receiver antenna Phase Center Offset (PCO) and Phase Center Variation (PCV) available for BDS signals for receiver antennas, the PCO and PCV corrections for GPS L1 and L2 are also used for BDS B1 and B2, respectively. Furthermore, the mask elevation is set to 10\({}^{\circ}\), and the Square Root Information Filter (SRIF) is used as the estimation approach. The spectral density value for zenith tropospheric delay (ZTD) parameter is empirically set as 0.02 m\({}^{2}\)/s. The ambiguity parameters, inter-system biases (ISB), inter-frequency biases (IFB), and static position coordinates are considered as arc-dependent constants in the static mode. But in kinematic mode, the position coordinates are estimated in epoch wise. The initial standard deviations for raw code and phase observables of GPS, GLONASS as well as BDS IGSO and MEO are equally set to 2.0 m and 2.0 cm, respectively. However, we have down-weighted raw code and phase observations of BDS GEO to 4.0 m and 4.0 cm in this study. Table 1 summarizes the strategy used for PPP in detail, and Table 2 lists the PPP solutions used in this study and the corresponding abbreviations
# Fermentation Strategies for Production of Pharmaceutical Ternenoids in Engineered Yeast
###### Abstract
Nonconventional yeast _Yarrovia lipolytica_ has been also considered as a suitable yeast to synthesize terpenoids due to its capacity to produce large amount of acetyl-CoA, the initial substrate of the MVA pathway [23]. In addition, carotenogenic yeast _Rhodosporidium toruloides_ can naturally accumulate several carotenoids (C40 terpenoids), indicating that it might have high carbon flux through MVA pathway, ensuring pools of intermediates for producing diverse types of terpenes [24]. This yeast can metabolize efficiently both xylose and glucose, and tolerates high osmotic stress,enabling the use of lignocellulosic hydrolysates as feedstock in contrast to _S. cerevisiae_[24]. Cyanobacteria have also the potential to produce sustainable terpenoids using light and CO2 instead of sugar feedstocks. However, terpenoids titer and productivity obtained are still below industrial levels and further studies to overcome the barriers for efficient conversion of CO2 to terpenoids are needed [25]. Overall, _S. cerevisiae_ has as main advantage over _E. coli_ and cyanobacteria hosts its intrinsic MVA pathway, and the disadvantage over _Rhodosporidium toruloides_ host the incapacity of using directly lignocellulosic hydrolysates as feedstock. Nevertheless, _S. cerevisiae_ is quite superior to the other microorganisms in respect to higher process robustness, fermentation capacity, plenty of available genetic tools in pathway engineering and genome editing, and proven capacity to attain industrial levels of relevant terpenoids [23]. To date, there has been a strong effort for terpenoid biosynthesis through metabolic engineering of microbes, however, production levels are at the mg/L scale in scientific literature, which are generally too low and commercially insufficient. Economically meaningful metrics of titer (g product per L broth), yield (g product per g substrate), and productivity (g product per L broth per hour) should be provided for industrial production [11]. Fermentation development at scale has a crucial importance to improve terpenoids production. For example, Amyris has reached titers of more than 130 g/L of \(\beta\)-farresene and 25 g/L of artemisinin acid (precursor of artemisinin, antimalarial drug) from sugar cane feedstock in engineered yeast _S. cerevisiae_ through optimized fed-batch fermentation [26; 27; 28]. Fermentation strategies can increase productivity and reduce the cost of production via improving medium composition, optimizing physicochemical conditions, and applying efficient downstream processing. However, a complete overview of the current approaches for obtaining terpenes relevant for the field of pharmaceuticals by yeast fermentation has not yet been reviewed in the literature. Therefore, this review details the production of pharmaceutical terpenoids by engineered yeast _S. cerevisiae_ and focuses attention on fermentation strategies to improve their production scale. Different fermentation factors and processes are discussed.
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 5 Conclusions  
From the summary of the main aspects of this study, we found that the differential ISBs of both code and phase observations remain stable even in the long term. Thus, the ISBs can be regarded as random noise within the accuracy of code and phase observations. The zero-mean characteristics of the ISB of identical-type receivers enable the ISBs to be ignored in inter-system DD observations, so the TCM RTK can be achieved with overlapping frequency observations applied. The TCM RTK need only one pivot satellite in the mixed constellation and more redundancy can be obtained in the function model. For instance, in the overlapping-frequency TS-DF RTK, three pivot satellites are necessary in the LCM RTK, whereas only one pivot satellite is required in the TCM RTK. Thus, four (2 \(\times\) 2) more redundant observations can be obtained in the TCM than in LCM, resulting in a stronger functional model and improved performance in multi-GNSS positioning. The improvement in SR in TCM RTK is over 49% with an increasing cutoff elevation angle with respect to LCM RTK, with an obvious improvement in the positioning accuracy also found.  
Conceptualization, W.L. and Z.M.; Software, S.Z.; Validation, W.L. and S.Z.; Writing--original draft preparation, W.L. and S.Z.; Writing--review and editing, W.L. and Z.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Science Foundation of China (grant No. 41804033); the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (grant No. CUGL180831); the Open Foundation of Teaching Laboratory of China University of Geosciences (Wuhan) (grant No. SKJ2020209); and the Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Ministry of Natural Resources (grant No. 201908).  
Not applicable.  
All the data presented in this study are openly available in IGS at [[https://cddis.nasa.gov/archive/gnss/data/](https://cddis.nasa.gov/archive/gnss/data/)]([https://cddis.nasa.gov/archive/gnss/data/](https://cddis.nasa.gov/archive/gnss/data/)) (accessed on 8 June 2021).  
The authors would like to thank IGS for providing GNSS datasets, which allowed this study. All this support is gratefully acknowledged.  
The authors declare no conflict of interest.
## 3 Methodology
### Residual Block  
Deep residual learning is characterized by the addition of shortcut connections between every few stacked layers to build residual blocks. Generally, each residual block can be expressed with the following two formulations:  
\[y_{l}=h(x_{l}+F(x_{l},W_{i})) \tag{1}\]  
and  
\[x_{l+1}=f(y_{l}) \tag{2}\]  
where \(F(x_{l},W_{i})\) is the residual mapping which needs to be learned, \(x_{l}\) and \(x_{l+1}\) are the input and output of the \(l\)-th residual block, \(f(y_{l})\) is an activation function, and \(h(x_{l})\) is the identity mapping function. The mapping function has a typical form that can be expressed as  
\[h(x_{l})=x_{l} \tag{3}\]  
Figure 1: An illustration of the proposed AB-ResUNet+ architecture. The encoder consists of squeeze-and-excitation and residual blocks, while the decoder includes only residual blocks. The channel attention block is inserted into the skip connection to optimize the coding ability of each layer. The ASPP block is placed at the bottom of the network and acts as a bridge between the encoder and decoder.  
In this work, we employ residual blocks in both contracting path and expansive path, as illustrated in Figure 1. Additionally, the output of residual blocks in the encoder part is passed through the squeeze and excitation block to increase the network's representational power. Figure 2 shows the structure of residual and squeeze and excitation blocks.
## 6 Conclusions  
This study investigated the seismic performance of existing soft-first-story RC frames that were seismic-retrofitted with hysteretic dampers under near-fault ground motions. Three prototype frames designed for the generic seismic hazard conditions prescribed by the Dominican Republic code were subjected to 20 near-fault ground motions. The results of the analyses yielded the following conclusions:  
* All prototype structures exhibit satisfactory performances from the viewpoint of (i) not exceeding the maximum story drifts prescribed by SEAOC for the operational seismic performance level and (ii) not exceeding in most cases the maximum residual drift of 0.2% recommended in FEMA-P-58. However, the maximum first-story drifts exhibited by the structures under the near-fault records exceeded by about 30% the counterpart drifts obtained in previous studies with far-field ground motions.  
Figure 10: First-story drift predicted with Equation (19) using \(n_{eq,NTH}\) (\(\delta_{max1,p\_{meltion}}\)) and results from nonlinear time history analyses (\(\delta_{max1,NTH}\)).  
* The maximum first-story drift obtained by establishing the energy balance of the structure and using the number of equivalent cycles proposed by [PERSON] for generic earthquakes leads to an underestimation of the maximum first-story drifts. Based on the results of the analyses conducted in this study, a new expression for the number of equivalent cycles is proposed for predicting the maximum first-story drift of RC frames with a soft first story that are seismically retrofitted with hysteretic dampers under near-fault ground motions. The prediction obtained by establishing the energy balance of the structure and employing the new number of equivalent cycles surpasses more than 90% of the results obtained from the nonlinear time history analyses. Two limitations of the present study are as follows. First, the frames investigated correspond to low- and mid-rise buildings; the behaviour of higher soft-first-story frames with hysteretic dampers under near-fault earthquakes is left to future studies. Second, the models investigated are 2D, and therefore the torsional effects have not been considered.  
Conceptualisation, A.B.-C.; methodology, S.M.-P.; software, S.M.-P.; validation, S.M.-P., D.E.-M. and A.B.-C.; investigation, S.M.-P., D.E.-M. and A.B.-C.; resources, S.M.-P., [PERSON]-C. and [PERSON]-M.; writing--original draft preparation, D.E.-M.; writing--review and editing, S.M.-P., [PERSON]-M. and A.B.-C.; visualisation, S.M.-P. and D.E.-M.; supervision, A.B.-C.; project administration, A.B.-C.; and funding acquisition, A.B.-C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Spanish Ministry of Economy and Competitivity (research project reference MEC BIA2017 88814 R) and received funds from the European Union (Fonds Europeen de Development Regional).  
Not applicable.  
Data available on request due to restrictions e.g. privacy or ethical.  
The authors declare no conflict of interest.
## 3 Results
### Findings Based on Previous Focused Questions
#### 3.3.2 Secondary Outcome Measures  
* _Soft tissue healing_ Six SRs [1; 15; 16; 17; 18; 19] reported a favorable effect on the enhancement of the soft tissue healing and epithelization in the first weeks after extraction.
* _Pain_ One SR [17] evaluated the pain parameter through meta-analysis. The use of PRF significantly relieved pain (\(p=0.01\)). Six SRs [1; 14; 16; 17; 18; 20] evaluated qualitatively the pain outcome. Five studies [14; 16; 17; 18; 20] observed a lower incidence of pain or consumption of analgesic medication for patients with sockets preserved with PRF.
* _Swelling_ Three SRs [14; 17; 20] evaluated the swelling parameter. In all, there was a favored effect for the use of PRF.
* _Osteitis_ Three SRs [14; 17; 20] evaluated the effect of PRF on alveolar osteeitis through meta-analysis. In all, the use of PRF significantly decreased the occurrence of osteeitis after the extractions (\(p=0.01\), \(p=0.009\), and \(p<0.0001\), respectively).
* _Trismus_ One SR [17] evaluated the effect of PRF on trismus through meta-analysis. The use of PRF did not significantly affect the trismus (\(p=0.20\)). Another SR [16] reported a qualitatively positive effect of PRF on trismus.
## 4 Conclusions  
A CFD model is developed to illuminate the hydrodynamics and mass transfer characteristics of an ejector by employing the Eulerian-Eulerian multiphase fluid model and Standard k-\(\varepsilon\) model with 2D (air/CO\({}_{2}\)-MEA solution) and 3D (air-water) modeling. For 3D geometry, the model was validated initially by altering the rate of liquid fluid flow in the range of 2.17 \(\times\) 10\({}^{-4}\) to 2.64 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s and outlet pressure in the range of 3000 to 6500 Pa, then the effects of these two parameters on gas phase hold-up, recirculation, the location of mixing shock, and negative pressure were investigated. Experimental data and CFD results are in good agreement. Mixing shock and position of its occurrence were investigated in this study as important factors. This three-dimensional phenomenon could be explained satisfactorily based on the results obtained from the 3D simulation. For 2D geometry and by considering the User-Defined Functions code, the effect of different parameters on CO\({}_{2}\) removal was considered. The following are the main findings: for the 3D ejector, it is found that with increasing the outlet pressure by about 70%, the rate of gas entrainment and gas hold up decrease by about 37% and 20%, respectively. The mentioned parameters showed increasing behavior of about 74% and 15%, respectively, when the mass flow rate of the liquid increased from 2.17 \(\times\) 10\({}^{-4}\) to 2.64 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s. It is observed that the location of mixing shock comes about at the end of the mixing tube. With an increasing mass flow rate of the liquid, the mixing shock is transferred from the ejector's throat to the diffuser side. A greater negative pressure is observed when the mass flow rate of the liquid is increased. 2D ejector's findings showed that %CO\({}_{2}\) removal is increased with increasing outlet pressure, absorbent concentration, and liquid-to-gas ratio.  
Conception and design of study, developing the idea, and simulation, M.M.P.; acquisition of data, M.M.P.; analysis and/or interpretation of data and figure extractions, M.M.P. and M.A. Supervision, M.R., Writing manuscript, M.M.P., drafting the manuscript and team management, M.M.P. and M.A.; reviewing the manuscript, M.M.P., M.R. and M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Seismic Reliability Assessment of Water Supply Systems Considering Critical Paths
## 3 Application and Results
### Determination of Critical Paths  
In this study, critical paths were determined such that all pipelines were included in a path connecting each water source to the final customer. The target industrial water supply systems operates  
Figure 3: Pipe Network in the Target Area.  
based on nine water supply reservoirs. Each reservoir is responsible for an independent area if the emergency connection routes are excluded. As a result, a total of nine critical paths (RES(Reservoir)1-9 Pipelines) were identified. The nine critical paths are visually represented in Figure 4, as shown in red area. An analysis of the customer demand that is fulfilled by each of the nine water sources showed that A-City's water supply network supplies the greatest amount of water through the RES2 Pipeline.  
Figure 4: _Cont._  
Table 2 shows pipe length and diameter data for each critical path. Of all nine pipelines, the RES2 Pipeline is the longest and provides the greatest amount of water. With regard to the pipe diameter distribution, the average pipe diameter is 1099 mm; it ranges from a minimum of 50 mm to a maximum of 2400 mm in each critical path.  
Figure 4: The Nine Critical Paths in the A-City Industrial Water Supply Network.  
The distribution of the number of pipes according to pipe diameter in each critical path is shown in Figure 5. The RES7 Pipeline consists largely of small-diameter pipes (D < 300 mm), which account for 53.61% of the total number of pipes in the pipeline. This represents an environment in which seismic damage to pipelines can be expected to occur relatively frequently. In the RES9 Pipeline, most pipes are between 300 mm and 1000 mm, which are medium-diameter pipes. Additionally, the RES3 Pipeline was found to have the highest distribution (37.23%) of large-diameter pipes (1700 mm or larger). The distribution of pipe diameter and the length of the pipes are the main factors that affect the reliability of water supply after an earthquake. In general, the smaller the pipe diameter, the higher the probability of pipe breakage, and in the case of a single route, the longer the pipeline, the greater the probability of disconnection of the water supply due to damage.
## 3 Specimens and Experimental Techniques
### Impedance Analysis  
We derived material constants using the theoretical equations in RM. The resonant and anti-resonant frequencies substituted into the equations were obtained by measuring the mode shapes with specific structures, namely the TE, LE, TS, LTE, and RAD modes, using an impedance analyzer.  
An impedance analyzer uses fixed voltages to excite vibrations in the target object and then measures the continuous changes in electrical impedance at various frequencies, as shown in Figure 1. The electrical impedance can be regarded as the total impedance applied by the resistors, inductors, and capacitors in the target object on the alternating current. Coupling characteristics are evident in the conversion between electrical and mechanical energy in piezoelectric materials. Thus, the electrical impedance of piezoelectric materials differs from that of components without piezoelectric properties. Generally, the results will display a resonant frequency (\(f_{resonance}\), \(f_{r}\)) or anti-resonant frequency (\(f_{anti-resonance}\), \(f_{a}\)) at certain resonant frequencies in frequency-impedance characteristic diagrams.
# ADS-B Like UTM Surveillance Using APRS Infrastructure
## 4 Discussion  
Through 19 flight tests, the CJCU GTS collected 1330 packets of data out of the 1331 transmitted from the APRS OBU. Each data packet has a complete 90 bytes for surveillance with position and 6 DoF to the UTM cloud. These tests are based on the APRS OBU with 0.5 W radio power and GTS using 6 dBi antenna. From authors' experience, carefully adjusting the voice volume of RF receiver in GTS is an important step to reduce the rate of missing data. The rate of missing data is about 0.07% in these tests. It is concluded that the APRS ADS-B like system works well for UTM operation in transmission reliability and data continuity. Moreover, the APRS ADS-B like system collects 6-DoF data for UAV flying onto the UTM cloud. The big data will form the basis for further analysis and study on UAV flight operation quality assurance (FOQA).  
Since the reporting interval of the APRS OBU is around 10 s, the 90 byte surveillance data reveals conditions of the UAV in-flight quickly. They are valuable not only for identifying the flight direction, position, operation, etc., but also for predicting the health of a UAV in-flight using the surveillance  
Figure 17: Flight data no. 36 and no. 37, UAV yaw turns from 126.9\({}^{\circ}\) to 218.1\({}^{\circ}\).  
Figure 18: UTM surveillance display with checking UAV attitude.  
data stored in the UTM cloud. For example, the excessive drop in voltage indicates damage on the UAV battery.  
The UTM system can offer UAV service providers with in-flight data as redundancy if any malfunction or lost contact during the flight occurs. Under request, the UTM system can offer the pilots or UAV service providers to back up the flight data.