esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Selection of Candidate Reference Genes  
We have performed high-throughput comparative transcriptome of _L.__sargentiae_ tissue culture seedlings at 24 h post-inoculation with _F. oxysporum_ and sterile water, respectively (data unpublished). By examining the expression levels of genes, nine reference genes, including cyclophilin (\(CYP\)), elongation factor 1-\(\alpha\) (\(EF1\)-\(\alpha\)), glyceraldehyde-3-phosphate dehydrogenase (\(GAPDH\)), tubulin (\(TUB\)), ubiquitin (\(UIBQ\)), aquaporin (\(AQP\)), histone (\(HIS\)), phosphoglycerate kinase (\(PGK\)), and 60S ribosomal protein L13-1(\(RPL13\)), were chosen as potential candidate reference genes. These genes exhibited relatively (similar or indistinguishable) stable expression levels in all six samples based on (fragments per kb per million reads) FPKM values (Table S1). Meanwhile, based on the RNA-Seq result, four potential _Fusarium_ wilt resistance candidate genes that showed differential expression patterns were also selected to verify the efficiency of candidate reference genes for qRT-PCR validation (Table S1). They included genes for peroxidase (\(POD\)), phenylalanine ammonia-lyase (\(PAL\)), chitinase (\(CHI\)), and dirigent-like protein (\(DIR\)). Specific primers of each gene were designed in Primer Premier 5.0 software and are listed in Table 1.
# Digital Twin Model Quality Optimization and Control Methods Based on Workflow Management
## 4 Macro-Level Model Quality Control Method  
Traditional workflows do not support workflow state transitions according to model evaluation results. To effectively control the flow of digital twin models among the nodes in the workflow according to the model evaluation results, a macro-level model quality control method is developed based on workflow technology. When each digital twin model \(R\) enters the PDM workflow as a design document, model \(R\) will undergo step-by-step design approval. The flow of model \(R\) among the nodes is navigated according to the pre-defined workflow model. The model evaluation activity is the operation that takes place when the node is changed. If a model evaluation method is configured on the transition between current active nodes of the workflow, the configured model evaluation is executed. If no abnormal index is generated, the flow proceeds to the next workflow node. The control process of the proposed method is shown in Figure 4, and the steps are as follows:  
The inputs include workflow model \(W\), current workflow node \(N_{0}\) of digital twin model \(R\), next workflow node \(N_{1}\), transition \(T_{1}\) between workflow nodes \(N_{0}\) and \(N_{1}\), evaluation set \(O\) = {\(O_{1}\), \(O_{2}\),...,\(O_{m}\)} associated with \(T_{1}\), and the threshold set corresponding to the evaluation set \(G\) = {\(G_{1}\), \(G_{2}\),...,\(G_{m}\)}.  
The outputs include the latest workflow node of model \(R\) and abnormal quality index set \(Q\) = {\(Q_{1}\),...,\(Q_{n}\)}.  
Figure 4: Macro-level model quality control process.  
Figure 3: Mapping relationships between design activity, workflow, and digital twin model evaluation content.  
Step 1: Based on the evaluation method in Section 3.1, model \(R\) is evaluated using evaluation set \(O\) to obtain the evaluation results. First, model \(R\) is evaluated with each method in the evaluation set \(O\), and each evaluation index value is exported. Then, whether the output evaluation index value reaches the corresponding threshold is judged according to the set of thresholds \(G=\{G_{1},\,G_{2},...,\,G_{m}\}\) corresponding to the evaluation set. Finally, the set \(Q\) of evaluation indexes failing to reach the corresponding threshold is output to the workflow management system and the user.  
Step 2: If there is an abnormal index, the current node of model \(R\) remains unchanged, and the process ends by outputting the abnormal index set \(Q\).  
Step 3: If there is no abnormal index, the process ends by allowing the flow of model \(R\) from workflow node \(N_{0}\) to the next node \(N_{1}\).
## 3 Results and Discussion
### Surface Morphology and Elemental Analysis Using SEM-EDS
[98], who recovered MPs from sea slugs, which were also collected from the Ionian Sea, reported the presence of both aluminum and silicon in some of their samples. This fact is interesting since the simultaneous presence of these two elements at similar concentrations may indicate contamination of the MPs by aluminum silicate [73]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{**F2**} & \multicolumn{2}{c}{**F3**} & \multicolumn{2}{c}{**F4**} & \multicolumn{2}{c}{**F5**} & \multicolumn{2}{c}{**F6**} \\ \cline{2-13}  & **W \%** & **A \%** & **W \%** & **A \%** & **W \%** & **A \%** & **W \%** & **A \%** & **W \%** & **A \%** & **W \%** & **A \%** \\ \hline Carbon & \(64.5\pm 2.5\) & \(76.7\pm 1.9\) & \(67.8\pm 3.2\) & \(77.4\pm 2.5\) & \(79.4\pm 3.5\) & \(84.5\pm 2.8\) & \(68.4\pm 4.0\) & \(76.3\pm 3.2\) & \(68.7\pm 3.2\) & \(80.0\pm 2.5\) & \(39.5\pm 4.5\) & \(55.7\pm 3.6\) \\ Oxygen & \(14.9\pm 1.3\) & \(13.3\pm 1.1\) & \(19.3\pm 1.7\) & \(16.5\pm 1.5\) & \(17.2\pm 2.5\) & \(14.1\pm 2.1\) & \(18.6\pm 2.7\) & \(16.2\pm 2.4\) & \(13.6\pm 1.4\) & \(11.9\pm 1.2\) & \(24.2\pm 2.0\) & \(25.6\pm 1.8\) \\ Sodium & \(7.9\pm 0.4\) & \(4.9\pm 0.3\) & \(5.1\pm 0.7\) & \(3.0\pm 0.5\) & \(1.4\pm 0.5\) & \(0.8\pm 0.4\) & \(5.7\pm 0.6\) & \(3.4\pm 0.4\) & \(5.9\pm 0.7\) & \(3.6\pm 0.6\) & \(10.2\pm 0.7\) & \(7.5\pm 0.6\) \\ Chlorine & \(12.7\pm 0.8\) & \(51\pm 0.5\) & \(7.8\pm 0.8\) & \(3.0\pm 0.5\) & \(1.5\pm 0.5\) & \(0.6\pm 0.3\) & \(8.7\pm 0.7\) & \(3.4\pm 0.4\) & \(10.7\pm 0.9\) & \(4.2\pm 0.6\) & \(15.3\pm 0.9\) & \(7.3\pm 0.6\) \\ Silicon & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & 1.1 & 0.5 & n.d. & n.d. & n.d. & n.d. \\ Chlorine & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & \(2.9\pm 0.4\) & \(1.6\pm 0.3\) \\ Aluminum & n.d. & n.d. & n.d. & n.d. & n.d. & 0.5 & 0.2 & n.d. & n.d. & n.d. & n.d. \\ \hline nm & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & \(1.1\pm 0.2\) & \(0.3\pm 0.1\) & \(7.8\pm 0.5\) & \(2.4\pm 0.3\) \\ \hline \end{tabular} n.d. = not detected.  
\end{table} 
Table 2: Average weight concentrations (W %) and atomic concentrations (A %) of the elements detected on the surface of the selected MPs recovered from fish guts. The color of each element is the same as the color of the corresponding symbol in Figure 11.
# Application of Polymers as a Tool in Crystallization--A Review
## 2 Basic Research
### Crystallization on Polymeric Surfaces/Nucleation Induction  
Now, the application of functionalized surfaces as well as synthesized substrates for nucleation induction will be discussed. For example, [PERSON] et al. investigated the influence of the morphological parameters of microporous poly(vinylidene fluoride) membranes on the heterogeneous nucleation rate of hen egg white lysozyme crystals [22]. They obtained a mathematical correlation between the energy nucleation barrier, membrane porosity and the contact angle between protein solution and polymeric substrate. On the other hand, [PERSON] et al. studied the surface crystallization of calcium sulfate dihydrate (gypsum) on a series of polymeric surfaces using a quartz microbalance system [23]. They discovered that the extent of surface mineral scaling was the combined result of the rate of nucleation and crystal growth kinetics. In turn, [PERSON] et al. in their paper described a new method called Predicting Efficacy Through Intermolecular Interactions (PETI) for predicting the effectiveness of different polymer surfaces in promoting heterogeneous nucleation [24]. [PERSON] et al. crystallized diphenylurea via slow evaporation from a range of solvents [25]. The experiments demonstrated the utility of template-directed sublimation methods as a way to obtain metastable phases with high phase purity. [PERSON] et al. tested the influence of various commercial polymer films on the crystallization of CaCO\({}_{3}\) from a supersaturated mixture containing other salts [26]. Such methodologies and data can be useful for designs of crystallizers and mixing devices. Finally, it is worth mentioning the crystallization of polymers with the use of other polymers. At this point it has to be emphasized that this research topic is extremely popular and one may easily find multiplepapers dedicated to this phenomenon. Therefore, here the work of [PERSON] et al. is presented just as an example of such research. These authors in their experiments proved that the crystallization rate of poly(ethylene terephthalate) was significantly accelerated and melt-crystallization temperature was increased with the addition of ionomers of poly(styrene-alt-maleic anhydride)/organic montmorillonite nanocomposites [27]. The nanocomposites acted as heterogeneous nucleating agents which enhanced the crystallization process. According to these authors the synergy was achieved for a combination of ionomers and organic montmorillonite platelets.
# Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks
## 3 CNNMVN Learning Algorithm and Error Backpropagation
### CNNMVN Error Backpropagation
#### 3.2.3 CNNMVN: The General Case
It is important to mention that we neither apply zero padding nor flip the depth dimension of a feature map, because a respective kernel does not slide over it in the feedforward process. Thus, we obtain  
\[\delta_{ij}^{l,g-1}=\frac{1}{Q_{g-1}}\sum_{h=1}^{H_{g}}\sum_{u=1}^{r_{g}}\sum_ {v=1}^{r_{g}}\delta_{i+u-1,j+v-1}^{h,g*}\left(w_{r_{g}-u+1,r_{g}-v+1,l}^{h,g} \right)^{-1}, \tag{26}\]  
where \(\delta^{l,g-1}\) are the errors of the feature map obtained by the \(l^{th}\) kernel in the \(g-1^{st}\) convolutional layer (\(l=\overline{1,H_{g-1}}\)), \(\delta^{h,g*}\) are the elements of the zero-padded matrix of the errors of the feature map obtained by the \(h^{th}\) kernel in the \(g^{th}\) convolutional layer, \(w^{h,g}\) are the weights of the \(h^{th}\) kernel in the \(g^{th}\) convolutional layer, and \(Q_{g-1}\) is a normalization factor which is equal to the size of the kernel in \(g-1^{th}\) layer (note that \(Q_{2}=1\) as there is no further error backpropagation from the first convolutional layer). Hence, the CNNMVN error backpropagation for its fully connected part is determined by (8)-(10) and (20), and the error backpropagation for its convolutional part is determined by (24)-(26). It is also interesting to mention that if the kernels' size is equal to the input image or feature map size (excluding the neurons' biases), MLMVN can be considered as a specific subcase of CNNMVN.
## 5 Conclusions  
The gait-analysis research area is expanding quickly due to its fast-growing demand in areas such as health services and robotics. Due to the rapid advancement in sensing technology and artificial intelligence, gait analysis has become possible using only a few wearable sensors. However, there is less consensus on the sensor quantity and placement for better lower-leg pose estimation. Therefore, in this study, the placement of a single inertial sensor on the lower half of the body for the leg-joint angle estimation using neural networks was investigated. Four neural-network models were compared using walking-motion data collection from 16 multiracial subjects. Among the neural networks, BLSTM networks performed better with MAE ranging from 3.02\({}^{\circ}\) to 4.33\({}^{\circ}\) for the four dominant sagittal-plane leg-joint angles. The results were improved with the increment of sensors and the introduction of biometric information. From the investigation of single senor placement, it was found that the shank or thigh is the optimal position for leg-joint angle estimation. Both achieve similar results with an overall average error of 3.84\({}^{\circ}\) and 3.65\({}^{\circ}\) for the thigh and shank, respectively. Others positions such as the pelvis would not be close enough to capture whole-leg kinematics from the hip to the toe. Furthermore, it was confirmed from the estimation results that a single inertial sensor can be enough to estimate the extension/flexion angles of the hip and knee joints. However, it was challengingto accurately estimate the coronal-plane joint angles of the lower limb and ankle joints owing to the inherent small lateral movement during walking foot-ground impact during heal strike.  
Hence, adding low-dimensional sensors, such as pressure sensors, could potentially improve the obtained result. However, this study has achieved a promising result that could serve as a springboard for the further extension of the study to other human activities. If a robust estimation mechanism for various human activities is developed, it can be implemented to solve real-world issues, particularly in healthcare services, assistive robotics, and collaborative robotics.  
Conceptualization, J.H.L.; Data curation, T.T.A.; Formal analysis, T.T.A.; Investigation, T.T.A. and J.H.L.; Methodology, T.T.A. and J.H.L.; Project administration, J.H.L.; Software, T.T.A.; Supervision, J.H.L. and S.O.; Validation, J.H.L. and S.O.; Writing--original draft, T.T.A.; Writing--review and editing, J.H.L. and S.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by JSPS KAKENHI, Grant Number JP22K04012.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Partial resources of this research can be found here. [[https://github.com/tsgtdss583/JointAngleEstimation](https://github.com/tsgtdss583/JointAngleEstimation)]([https://github.com/tsgtdss583/JointAngleEstimation](https://github.com/tsgtdss583/JointAngleEstimation)), accessed on 23 February 2023.  
The authors declare no conflict of interest.
# POL-MUX System for Noncoherent Optical Networks
## 2 Temperature Influence on the POL-MUX Signal
### Principle of Measurement  
The measurement testbed is shown in Figure 2. As a tested sample, a 10 m long optical patch cord with G.652.D fiber was used. The ambient temperature was kept at 20 \(\pm\) 1 \({}^{\circ}\)C.  
As a light source, two laser diodes with different wavelengths (\(\lambda_{1}=1550.1\) nm and \(\lambda_{2}=1555.2\) nm) were used. Although it is possible to use the same wavelengths, we  
Figure 2: Block scheme of the measurement test bed.  
used different wavelengths to enable the distinction of optical signals aligned with both polarization planes by a standard optical spectral analyzer (OSA). The optical signals from lasers TX1 and TX2 were set by polarization controllers PC1 and PC2, respectively, for optimal multiplexing by polarization beam splitter PBS1, e.g., the signal from one laser must be parallel to one of the polarization plane axes \(x\), and the signal from the second laser must be parallel with polarization plane \(y\) of polarization beam splitter PBS1. The multiplexed signal is shown in Figure 3a. The optical powers of both lasers were set to \(TX1=5.61\) dBm and \(TX2=5.65\) dBm.  
A multiplexed signal was fed into the testing optical patch cord, which was placed in a thermo chamber. The output of the tested fiber was connected to polarization controller PC3 and then to polarization beam splitter PBS2. Both outputs were checked by an optical spectral analyzer (OSA). The optimal setup of the state and especially the angle of polarization at the end of the measured optical link was carried out by PC3, so the polarization extension ratio was set to \(\geq\)22 dB according to the PBS datasheet. The demultiplexed signal is shown in Figure 3b. The default difference between the optical powers in each polarization plane was set to 23.22 dBm (measured by OSA\(\lambda\)1). We can say that it is optional to determine the noise ratio or crosstalk between polarization planes, which is given by the PER of PBS2.
# High-Accuracy Clock Offsets Estimation Strategy of BDS-3 Using Using Multi-Source Observations
## 2 Materials and Methods  
### Two-Way Superimposition Strategy  
#### 2.1.1 Fundamental Principles Process  
BDS-3 owns a time synchronization, which is independent of satellite orbit determination. The fundamental measurements of BDS-3 two measurements include L-band two-way satellite-station measurements, C-band two-way inter-station measurements, and Ka-band inter-satellite measurements.  
The fundamental equation for using two-way measurements to calculate two-way clock offsets is given as Equation (1) [15]:  
\[clk_{BA}(t_{0})=[(\rho_{AB}(t_{0})-r_{AB}(t_{0}))-(\rho_{BA}(t_{0})-r_{BA}(t_{0} ))]/2+\sum corr+Err+\varepsilon \tag{1}\]  
where, \(clk(t_{0})_{BA}\) is the clock offsets of \(A\) with respect to \(B\); \(\rho_{BA}(t_{0})\) and \(\rho_{AB}(t_{0})\) are the measurement values \(r_{AB}(t_{0})\) and \(r_{BA}(t_{0})\) are the signal propagation distance between two atomic clock \(A\) and \(B\). \(\sum corr\) is all kinds of errors correction in two-way measurements. They include phase center correction, relativity correction [23], time delay correction [24]. For L-band and C-band measurements, their signal passes through the ionosphere and troposphere, So, the errors correction of L-band and C-band measurements also include troposphere delay correction and ionosphere delay correction [25]. \(Err\) are the unknown residual errors. \(\varepsilon\) is two-way noise.  
Considering all satellite clock offsets with respect to BDT. BDS-3 needs to use the two-way superimposition strategy to calculate final satellite clock offsets with respect to BDT. The procedure of the two-way superimposition strategy is given in Figure 2. For the intraregional satellites that are tracked by MCS antennas, BDS-3 can directly obtain the clock offsets by the L-band two-way satellite-station clock offsets of MCS. We present it as L in Figures 2 and 3. For the satellites that are tracked by TSS antennas, the two-way superimposition strategy subs the TSS's C-band inter-station clock offsets and L-band two-way satellite-station clock offsets to obtain the final satellite clock offsets. We present it as L + C in Figures 2 and 3. For the overseas satellites, the two-way superimposition strategy subs the two-way clock offsets of intraregional satellite and Ka-band inter-satellite clock offsets to obtain the overseas satellite two-way clock offsets. We present it as L + C + Ka or L + Ka in Figures 2 and 3. According to the antenna track messages and ISL plans [23; 24], the two-way superimposition strategy can real-time calculate all satellite's two-way clock offsets [19]. Then, by 3-day comparison, BDS-3 estimate and deduct the bias between the two-way clock offsets and GNSS L-band downlinks offsets. Last, the final clock offset parameters of BDS-3 can be fitted by the linear model.  
Figure 2: The procedure of the BDS-3 two-way superimposition strategy
# A Novel Structure of Rubber Ring for Hydraulic Buffer Seal Based on Numerical Simulation
## 5 Discussion  
### Evaluation of Installation Mode  
It can be drawn that the contact length and contact peak pressure caused by installation mode of RA are higher than that caused by installation mode of RL and GL. Considered the function between piston rod and rubber ring [35], the different interaction force for the RA, RL and GL installation mode are given in Figure 12.  
The deformation of the rubber ring under RA mode is caused by shear force and squeeze force together. The deformation of the rubber ring under RL and GL is nearly the same, due to the deformation are both caused by squeeze force as illustrated in Figure 12b [36]. The deformation of the rubber ring is affected by the stiffness of rubber. For rubber material, the stiffness caused by squeeze force are different with that caused by shear force. [PERSON]'s research shows that the compression stiffness is more sensitive to displacement than the shear stiffness, and the relationship between the compression stiffness and displacement is of the fifth power, while the relationship between the shear stiffness and displacement is of the first power.  
Thus, the pressure caused by compression stiffness would be much higher than the pressure caused by shear stiffness. It is consistent with the previous results that the contact peak pressure under RA is slightly higher than the contact peak pressure under RL and GL. Due to the contact peak pressure and contact length caused by RA, RL and GL are different, considered the three-dimensional hydraulic buffer as shown in Figure 1, the RA installation mode is approximately to the actual assemble [37]. Therefore, RA installation mode is adopted as the installation mode for FEA of rubber ring.  
In addition, the installation stroke should be considered in the simulation of the rubber ring. As shown in Figure 8, the contact peak pressure arrives at the limit value with installation stroke 9 mm. Additionally, the limit value of contact length for groove side, piston rod side, and bottom side are also got stable at the installation stroke 9 mm. From the perspective of load transmission, the deformation and stress of the rubber ring is mainly triggered by installation stroke via the squeeze state. The squeeze state is simulated with contact pair defined among piston rod, rubber ring, groove as Figure 3 presented. Then, the squeeze state is calculated via the contact pair with Augmented Lagrange. In this method, the stiffness of the contact pair is rebalanced after each iteration, so the rubber ring needs enough installation stroke to reach a stable state. Therefore, the installation stroke should be 9 mm in this study [38]. For different structure of the rubber rings, the installation stroke should be obtained through trial calculation.