esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The initial orthodontic diagnosis is essential for correct preventive or corrective treatment, and a wrong diagnosis can lead to a longer treatment or have unforeseen results [1]. The use of appropriate tools for the initial diagnosis allows for appropriate planning and treatment evaluation [2]. Indices and instruments are available to allow the orthodontist to perform evaluations at the beginning of treatment and for completed cases [3].  
Standardization of diagnoses is important, as reliable records of the measurements are made; the ability to compare accurate data with unalterable records will facilitate the evaluation during and after treatment and allow to recognize and correct failures. For an orthodontic diagnosis, photographs, radiographs and study models are necessary. Study models in particular are a fundamental piece of the initial diagnosis. However, usually taking the physical form of plaster models, these have the major disadvantage of requiring space for storage, in addition to carrying the risk of being fractured, becoming degraded, or being misplaced. For these reasons, digital records and models offer an excellent alternative for orthodontic treatment [4].  
At present, digital records are obtained via intraoral scanning of the patient [5, 6]. This type of dental record has become increasingly popular and is being used frequently. Digitalrecords eliminate the storage problems and risk of damage that characterize plaster models; additionally, they improve comprehensive patient care because they can be shared more easily with other dentists who are caring for the patient [7].  
Digital records can be visualized and used with various specialized computer programs to develop and analyze different indices that facilitate diagnosis and decision-making in orthodontic treatment [8].  
The accuracy of digitally obtained orthodontic indices and measurements has been questioned. Previous studies have compared the accuracy of various digital measurements obtained with different devices [9; 10]. However, most studies have been limited to analyzing or comparing only one type of diagnostic record, such as study models or cephalometry. Additionally, it is necessary to know the reliability, reproducibility and precision of digitally obtained measurements compared to manually obtained ones. The discrepancy index developed by the American Board of Orthodontics (ABO) evaluates the complexity of the initial evaluation, based on the measurement of the obtained observations through standardized orthodontic pre-treatments that include dental cast, cephalometric measurements and panoramic radiography, which allow a precise evaluation for the clinical condition of the patient, providing an objective and quantifiable list of the most important disorders. For each disorder, a numerical value is assigned; the highest the value of the discrepancy index, the more complex and challenging orthodontic treatment becomes [11; 12; 13; 14]. The objective of this study is to determine whether there is a difference in the final ABO discrepancy index score when it is obtained using digital versus manual measurements.
## 2 Materials and Methods  
This study was conducted with four one-stage RECDCs and four one-stage REBDCs, at four hydraulic retention times, i.e., 4, 8, 12, and 24 h. The densities of electric current used for each hydraulic retention time were: 0.63, 1.25, 2.50, 5.00, and 10.00 A/m\({}^{2}\)[10; 11]. Over the experimental period, the temperature at the laboratory was about 20.0 \(\pm\) 1 \({}^{\circ}\)C. Wastewater was fed to the reactors with Minipuls 3 peristaltic pumps (Gilson, Middleton, WI, USA). An aluminum anode was mounted in the flow tank of each reactor. Disks made of stainless steel served as cathodes and both electrodes were connected to a laboratory power supply (HANTEK PPS2116A, Zunhua, China), which was a source of direct electric current for maintaining its desired intensity (Figure 1) [11].  
The synthetic wastewater used in this study had the composition characteristics of wastewater from soilless cultivation of tomatoes, and was adopted after [PERSON] and [PERSON] [4] as well as [PERSON] et al. [5]. To provide appropriate conditions for biofilm development, sodium acetate as a source of carbon was added to the wastewater treated in the REBDC. This produced a C:N ratio of 0.5, which ensured the growth of biofilm biomass and, simultaneously, allowed for autotrophic denitrification. The composition of the synthetic raw wastewater used in this study is presented in Table 2[11; 12].  
The analytic control of processes began after adaptation of the reactors. Samples were collected for analyses in 24-hour intervals. Physicochemical analyses of 20 samples were conducted at each of the electric current densities and HRTs applied in both reactors.  
The influent and the effluent were analyzed using the following indicators:  
1. Organic matter expressed as the COD value, following the bichromate method;
2. Total nitrogen, using a total organic carbon analyzer TOC-L CPH/CPN with a TNM-L device (Shimadzu Corporation, Kyoto, Japan) and the method of oxidative combustion and chemiluminescence;
3. Ammonia nitrogen, nitrate nitrogen, and nitrite nitrogen, using a VWR UV-3100 PC spectrophotometer (VWR International, Shanghai, China), with a colorimetric method;
4. Total phosphorus (\(\pm\)0.01 mgP/L), using a UV-Vis 5000 DR spectrophotometer (HACH Lange, Duesseldorf, Germany), with an HACH Lange LCK 348-350 method;
5. pH value (\(\pm\)0.01 pH) and temperature (\(\pm\)1 \({}^{\circ}\)C), using a CP-105 pH meter (Elmetron, Zabrze, Poland); redox potential (\(\pm\)1 mV), with a pH 211 m (Hanna Instruments, Eibar, Spain); and electrolytic conductivity (\(\pm\)0.01 mS/cm), using an HQ 440d multimeter (Hach Company, Loveland, CO, USA).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fertilizers** & **Sample Weight** \\ \hline Calcium nitrate & 2.06 g/L \\ Potassium nitrate & 0.89 g/L \\ Potassium monophosphate & 0.353 g/L \\ Potassium sulphate & 0.395 g/L \\ Magnesium sulfate & 1.08 g/L \\ Iron chelate & 0.023 g/L \\ Microelements & 0.2 cm\({}^{3}\)/L \\ \hline \hline \end{tabular}
\end{table}
Table 2: The composition of the synthetic wastewater.  
Figure 1: Scheme of the unit experimental model.
## 2 Methodology
### Definition of the Bayesian Approach  
The Bayesian approach is a statistical inference method in which Bayes' theorem is used to update the probability of a hypothesis as more evidence or information becomes available. The Bayesian approach treats a parameter as a random quantity and is based on the distribution of the parameter conditional on observed data, as provided below:  
\[\mathrm{p}(\theta|y)\propto\mathrm{L}(y|\theta)\cdot\pi(\theta) \tag{1}\]  
where \(\mathrm{p}(\theta|y)\) is the posterior distribution based on given measured data \(y\), \(\mathrm{L}(y|\theta)\) is the likelihood based on given parameter \(\theta\), and \(\pi(\theta)\) is the prior distribution before updating with observed data \(y\).  
The methodology in this study attempts to estimate accurate travel time under low sampling rates of probe data using the Bayesian approach. According to the characteristics of travel time regularity [30; 31], the prior distribution of the parameters can be estimated using a data-driven method. The distribution of travel times during peak periods has a mixture shape of distribution that can be classified into a group of vehicles that have rapidly traveled along the road segment and a group of vehicles that have traveled slowly due to the influence of irregular traffic oscillation [32; 33]. Since the distribution of travel time has the mixture shape of distribution, this study adopts a Gaussian mixture model with two components in the Bayesian approach. The Gaussian mixture model has the advantage of expressing the centroid and forming clusters properly even if the variance is not constant. The details of the model with each step included are as follows.
## 2 Survey of Commercial Small Platforms
### Active Microwave
#### 3.3.1 Real Aperture Radar Altimeter  
Radar altimeters measure the distance of the Earth's surface underneath the spacecraft by measuring the time between transmitting the signal and receiving the echo. Microwave radar altimeters have been used for a wide range of applications that can be grouped as: (a) geodesy and geophysics, study the Earth's shape and size, on the ground as well as on the sea surface [63]; (b) ocean applications (ocean surface currents, wind speed, significant wave height); (c) ice sheets and sea ice (sea ice thickness, and glacier topography) [64]; (d) climate (ocean topography and the heat exchange with the atmosphere); and (e) hydrology.  
Nowadays, altimeter constellations on small platforms are deemed important, since they bring improved temporal resolution, and some ocean phenomena can only be perceived if subject to an almost continuous observation. At the same time, a shorter revisit time represents an increase in the spatial coverage and a finer spatial sampling grid. Equally, SSO should be avoided because of the errors associated with solar tidal effects.  
Examples of recent altimetry missions are presented in Table 10. Typical requirements are: 100 W average power consumption, 1.2 m antenna diameter, 61 kg payload mass. The implementation on nano- platforms for radar altimeters may partially degrade the quality of the measurements. Additionally, nadir looking altimeters do not provide a wide swath. In this way, constellations of small satellites embarking a compact nadir altimeter [65] could improve the temporal/spatial sampling and therefore closing the gap with current planned missions.
## 3 Results and Discussion
### Effect of Post-Yield Stiffness Ratio  
The strain hardening ratio (\(\alpha\)), which is also called the post-yield stiffness ratio, is one of the key hysteretic parameters that affect the structural response. Analyses have been conducted for \(\alpha=0\%\), \(5\%\), and \(10\%\) in order to examine the effect of the strain hardening ratio. Figure 8 presents the variation in the inelastic displacement ratio with the considered values of strain hardening ratio for fixed-base (a) and interacting (b) cases. Results are presented for \(\beta=0.25\). As shown, the post-yield stiffness ratio has a negligible effect on inelastic displacement ratios for both fixed-base and interacting cases. However, for smaller period ranges, inelastic displacement ratio values tend to decrease with the increase in the post-yield stiffness ratio, which means that using larger post-yield stiffness ratios for self-centering structures can decrease the structural displacement response.  
Variations in residual displacement with the considered values of strain hardening ratio are shown in Figure 9. Results are presented for \(\beta=0.25\). It can be seen from the graphs that, similar to inelastic displacement ratios, the post-yield stiffness ratio has a negligible effect on residual displacement demands for both fixed-base and interacting cases for energy dissipation ratios of 0, 0.25, and 0.5. But for the \(\beta=1\) case, which corresponds to elastoplastic behavior, a limited variation can be observed.  
Figure 7: Mean residual displacement demands computed as a function of T and \(\widetilde{\Upsilon}/\mathrm{T_{p}}\) for interacting systems.  
Figure 8: Variation in inelastic displacement ratios according to post-yield stiffness ratio. (**a**) Fixed-base and (**b**) interacting cases.  
Figure 9: Variations in residual displacement according to post-yield stiffness ratio; (**a**) fixed-base and (**b**) interacting cases.
# Construction Progress and Aviation Flight Test of BDSBAS
## 2 Overview of BDSBAS
### System Architecture  
BDSBAS is composed of three parts: a space segment, ground segment and user segment (as shown in Figure 1).  
The BDSBAS space segment includes three geosynchronous earth orbit (GEO) satellites, which broadcast SF augmentation information through B1C frequency and DFMC augmentation information through B2a frequency. The specific information of BDSBAS GEOs is shown in Table 1.  
The ground segment consists of 1 operation control center (OCC) in Beijing, 2 data processing centers (DPC) in Beijing and Xi'an, 3 uplink stations (US) and 30 monitoring stations (MS) in China and overseas. A total of 27 of the 30 MSs are evenly distributed in cities such as Beijing, Guangzhou, Xi'an, and Changsha in mainland China, as shown in Figure 2. The other 3 MSs are deployed overseas, which are located in Kamchatka, Irkutsk and Obninsk of Russia. The specific coordinates and observation data of the MSs are planned to be released after the system has passed the civil aviation certification. Each MS contains three multi-frequency and multi-mode monitoring receivers, meteorological equipment and a rubidium atomic clock [32]. At present, the ground section facilities of BDSBAS have been almost completed, and the system already has the capability for initial operation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Satellite** & **PRN Code Number** & **Orbital Slot** & **Launch Date** \\ \hline GEO-1 & 130 & 140E & 1 November 2018 \\ GEO-2 & 144 & 80E & 9 March 2020 \\ GEO-3 & 143 & 110.5E & 23 June 2020 \\ \hline \end{tabular}
\end{table}
Table 1: PRN codes and orbital slots of BDSBAS GEOs [31].  
Figure 1: Architecture of BDSBAS.  
The user segment refers to BDSBAS terminals used in civil aviation, maritime, railway and other applications. User uses the correction information from the messages to improve accuracy and the integrity information from the messages to calculate the protection level, providing users with better navigation performance and integrity guarantee. BDSBAS user equipment used in civil aviation shall meet the requirements of RTCA DO-229 [33].
# Development of a Multiplex PCR Assay to Monitor Living Modified Cottons in South Korea
###### Abstract  
Cotton has been cultivated worldwide and is a useful crop for humans. However, all living modified organisms (LMOs), including living modified (LM) cotton, are not cultivated in South Korea and are imported from overseas. LM cotton imports are on the rise and most of the imported cotton is used as livestock feed. In particular, it is commonly used to feed Holstein breeds that produce milk, because cotton improves the quality of milk. However, as the cotton imports increase, the possibility of unintentional outflows in the distribution process also increases. Consequently, there is an increasing concern about unintentional release of LM cotton into the natural environment. Therefore, environmental monitoring and post-management of LMOs are very important steps. Recently, a total of 30 LM crop events were approved for LM cotton import in South Korea. A single detection method has been used to monitor individual events. However, a single method of detection for collected samples requires a large number of PCRs, with obvious disadvantages. Therefore, a simultaneous detection method was developed for 8 representative events (GHB119, GHB614, MON88913, MON15985, LLCOTTON25, MON1445, 281-3006, and MON531) in an effort to monitor 26 of them and facilitate the identification of LM cotton. The results suggest that our new multiplex PCR method may be useful for monitoring and post-management of LM cotton.  
living modified organism; monitoring; multiplex PCR; feedstuffs Vol. 1, No.  
(herein referred to as The Law). According to The Law, the Ministry of Environment (MOE) is in charge of affairs related to living modified organisms (LMOs) \"used to reduce or remove environmental pollutants or to restore the environment,\" and \"effects on natural ecosystems.\"  
Since 2009, the MOE and National Institute of Ecology (NIE) have been conducting The Environmental LMO Monitoring and Post-Management Project [9,10]. In the project, the natural environmental leakage of LM crops such as cotton, maize, soy bean, and canola was monitored and the risk assessment and management of released LMOs was performed [11, 12, 13]. A specific LMO detection method is crucial to determine whether the samples collected in this project contain LMOs [14,15].  
Recently, a total of 30 LM cotton events including single and stack LMOs were approved in South Korea. A previous study reported LM cotton multiplex detection methods for six events [13]. As approval and unintentional release of LM cotton events have been increasing every year, we needed to develop a new method which can detect almost approved LM cotton events. In this study, we developed a multiplex PCR method to detect eight single LM cotton events to make up for 26 approved LM cotton events. Additionally, we applied our new detection method to suspect LM samples collected during the monitoring project and found that it was more cost-effective and time-saving than conventional methods of analyzing LM samples.
# Optimization Design of RV Reducer Crankshaft Bearing
## 2 Performance of crankshaft Bearings of Rotate Vector (RV) Reducers
### Fatigue Life of Crankshaft Bearings
#### 2.3.1 Load Distribution of Crankshaft Bearings  
As shown in Figure 4, the radial load \(F_{m}\) from the cycloidal gear acts on the crankshaft bearings. It is assumed that the relative radial displacement between the crankshaft and the cycloidal gear is \(\delta_{r}\). It can be seen from the deformation coordination relationship that the radial deflection at any roller angular position \(\psi_{j}\) is  
\[\delta_{rj}=\delta_{r}\cos\psi_{j}-\frac{\mathrm{P}_{d}}{2} \tag{7}\]  
where \(\psi_{j}\) is the angular position of the \(j\)th roller and \(P_{d}\) is the radial clearance.  
The total normal contact deformation between two raceways separated by a rolling element under a load is the sum of the deformations between the rolling element and each raceway. Hence,  
\[\delta_{rj}=\delta_{ij}+\delta_{oj} \tag{8}\]  
where \(\delta_{ij}\) is the normal contact deformation between inner raceway and the \(j\)th roller, and \(\delta_{oj}\) is the contact deformation between the outer raceway and the \(j\)th roller.  
The roller at the angular position \(\psi_{j}\) is subjected to forces from the raceways \(Q_{oj}/Q_{ij}\). The normal roller loads are related to normal contact deformations as follows [19]:  
\[\left\{\begin{array}{l}Q_{oj}=K_{c}\delta_{oj}^{10/9}\\ Q_{ij}=K_{c}\delta_{ij}^{10/9}\end{array}\right. \tag{9}\]  
According to [PERSON] [19], for the steel roller-steel raceway contact,  
\[K_{c}=8.06\times 10^{4}L_{we}^{8/9} \tag{10}\]  
The roller is subjected to the forces from the raceways and the centrifugal force \(F_{c}\). Thus, considering the force equilibrium,  
\[Q_{oj}-Q_{ij}-F_{c}=0 \tag{11}\]  
The centrifugal force for a steel roller orbiting at speed \(n_{m}\) about a bearing axis is given by  
\[F_{c}=3.39\times 10^{-11}D_{we}^{2}L_{we}d_{m}n_{m}^{2} \tag{12}\]  
Figure 4: Load distribution of bearing: (**a**) Load distribution of crankshaft bearing; (**b**) Bearing ring displacement.  
Substituting Equations (7)-(10) into Equation (11), Equation (11) can be rewritten as follows:  
\[K_{c}\bigg{(}\delta_{r}\cos\psi_{j}-\frac{\mathrm{P}_{d}}{2}-\delta_{ij}\bigg{)} ^{10/9}-K_{c}\delta_{ij}^{10/9}-F_{c}=0 \tag{13}\]  
For static equilibrium, the radial load must be equal to the sum of the vertical components of the roller loads:  
\[F_{m}-K_{c}\sum_{j}^{Z}\delta_{ij}^{10/9}\cos\psi_{j}=0 \tag{14}\]  
Equations (13) and (14) are a system of simultaneous nonlinear equations. \(\delta r\) and \(\delta_{ij}\) are unknown and need to be solved. The equations are solved by Newton-Raphson method. Thereafter, roller loads are obtained using Equation (9).
## 2 Materials and Methods
### Evaluation of Gene Expression by Real-Time PCR with Reverse Transcription  
The transcription level of the _ilvBNC_ genes in the obtained strains was studied using real-time PCR technology. _C. glutamicum_ cells were grown in BH broth until the mid-log phase. Total RNAs were isolated using the RNeasy Mini Kit (QIAGEN, Hilden, Germany). Cell lysis was performed using liquid nitrogen. To thoroughly remove genome remnants,the extracted RNA was treated with DNase I (ThermoFisher Scientific) for 30 min. cDNA synthesis was carried out using the MMLV RT kit (Evrogen, Moscow, Russia) according to the manufacturer's protocol using a random decanucleotide primer. PCR was carried out on an ABI 7500 PCR System Fast device (Applied Biosystems, Carlsbad, CA, USA) using the qPCRmix-HS SYBR Low LowBox reagent kit (Evrogen, Moscow, Russia). PCR was carried out using primers based on the sequences of the genes being studied (Supplementary Table S1). Relative mRNA quantities were determined using the \(\Delta\)\(\Delta\)CT method, with standard calculation algorithms provided by 7500 Fast Software version 2.3 (Applied Biosystems). As a housekeeping gene, we used the gene _fusA_ coding ribosome elongation factor G. All quantifications of target genes were performed in triplicate and are presented as the mean \(\pm\) standard deviation (SD).
## 2 Materials and Methods
### Data Sources
#### 2.2.1 CI Products  
The remote sensing estimated CAS (Chinese Academy of Sciences) CI products were acquired from the National Earth System Science Data Center, National Science & Technology Infrastructure of China, NESSDC ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)) accessed on 11 May 2021). Their spatial resolution was 500 m with a sinusoidal projection, and the temporal resolution was 8 days. Here, we used the 2001\(-\)2015 CAS CI. The CAS CI algorithm is introduced briefly, as follows: firstly, the LUT (lookup table) database was generated using the Ross\(-\)Li model based on the MODIS BRDF (bidirectional reflectance distribution function) products, and daily CI products were then estimated based on the LUT database; subsequently, the snow contaminated pixels were removed, and the daily products were synthesized into 8-day products [18]. CAS CI products considered different zenith angles, from 0\({}^{\circ}\) to 60\({}^{\circ}\) (in increments of 10\({}^{\circ}\)), and different crown shapes (cone/cylinder, ellipsoid, and half ellipsoid). Validation indicated that the CAS CI was consistent in time with moderate accuracy. In this study, Arcpy was used to mosaic, reproject, and clip the CAS CI. Subsequently, we used the mean composition method to obtain the temporal CI dataset for the 2001\(-\)2015 growing season mean CI (CI\({}_{\text{GS}}\)) and seasonal mean CI (CI\({}_{\text{SP}}\), CI\({}_{\text{SU}}\), CI\({}_{\text{AU}}\) CI\({}_{\text{WI}}\)) in the study area.