esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion  
Our investigation started off with the chemical reduction of \(\alpha\)-acetylbutyrateolactone **1** by NaBH\({}_{4}\). The reaction was carried out according to the procedure worked out by [PERSON] et al. [17] and we derived _anti_-to-_syn_ isomers of \(\alpha\)'-1'-hydroxyethyl-\(\gamma\)-butylolactone **2** in a ratio of 1:7 (Figure 1), which were used as GC standards for product **2**. Various yeast strains were examined for their ability to conduct the enantioselective reduction of \(\alpha\)-acetylbutyrateolactone **1**. We commenced with a screening procedure using the following yeast strains: _Y. lipolytica_ P26A, _Y. lipolytica_ AM71, _Y. lipolytica_ AM72, _C. viswanathi_ AM120, _H. anomala_ C2, _S. cerevisiae_ K1, and _S. pombe_ C1. In our preliminary studies, we used microorganisms grown in medium YPG (glucose (20 g/L), peptone (20 g/L), yeast extract (10 g/L, pH = 6). Biotransformations were carried out for a maximum of 4 days. In the case where compound **1** was transformed completely, the reaction was then interrupted earlier (Table 1). Based on the results, _Yarrowia_ strains prove to be very good biocatalysts for the reduction of \(\alpha\)-acetylbutyrateolactone **1**. However, in all cases, only the formation of the _anti_ diastereoisomer was observed. It should be emphasized that \(\alpha\)-acetylbutyrateolactone **1** was transformed completely by _Y. lipolytica_ P26A after 1 day. The _C. viswanathi_ AM120 strain bioreduced **1** after 2 days and with less diastereoselectivity. In general, the _Saccharomyces_ yeast did not transform the substrate at all. This observation is in line with literature data, where [PERSON] et al. [2] indicated that _S. cerevisiae_ wasnot able to transform this substrate. On the other hand, [PERSON] et al. [19] and [PERSON] et al. [20] successfully transformed **1** using this particular microorganism. It should be highlighted that different strains of yeast transform \(\alpha\)-acetylbutyrateolactone **1** with different stereoselectivity. The subsequent stage of our research was to investigate the evaluation of the stereoselectivity of bitotransformations. Tests were performed using two strains of _Y. lipolytica_ and one strain of _C. viswanathi_, because these strains were capable of bioreducing **1** within 1 or 2 days (Table 2). The results presented above indicate that both strains of _Y. lipolytica_ biorecduced **1** to only one isomer, namely **2b**, with 100% enantiomeric excess. The strain of _C. viswanathi_ AM120 transformed \(\alpha\)-acetylbutyrateolactone to a mixture of **2a** and **2b** isomers, with a large predominance of **2a** and trace amounts of optically pure **2d** enantiomer. Next, biotransformation of **1** by _Y. lipolytica_ P26A was repeated in two flasks (summarily 0.12 g of substrate) and product of biotransformation of **1** was purified by means of column chromatography (61 mg after isolation, yield 51%) and optical rotation was determined. The obtained value of optical
## 1 Introduction
It is worth noting that all BDS-3 satellites are equipped with laser retro-reflector arrays (LRAs), which support ground-to-satellite laser ranging with high accuracy. Unlike GNSS carrier observation, they are not affected by theambiguities. Thus, combining the SLR and ISL measurements can be regarded as a good choice for POD. Limited to only a small amount of publicly available SLR measurements in BDS-3, previous studies in China on SLR measurements mainly focused on verifying the determined orbit for BDS and low earth orbit (LEO) satellites [14; 15; 16; 17]. As a matter of fact, the SLR measurements have been fully applied to the POD, clock offset estimation and prediction, and orbit accuracy verification in other global satellite navigation systems, including GLONASS, Galileo, and GPS [18; 19; 20]. In recent years, benefiting from the development of the hardware devices and LEO satellites, SLR measurements in BDS-3 have gradually become public. Compared with GNSS observations, SLR observations have the advantages of high accuracy and are not affected by ambiguity parameters. Thus, integrating ISL and SLR for orbit determination is an effective means to avoid complex ambiguity parameters and overcome the lack of ground reference in AOD. Numerous studies based on SLR observations have subsequently emerged, typically represented by verifications of the orbit accuracy of LEO satellites [21] and POD of GNSS satellites by combining other observations [22]. Among them, some scholars explored the performance of orbit determination by processing the SLR and ISL measurements of several BDS-3 satellites together, called combined ISL/SLR POD in this paper, and analyzed the influence on the orbit accuracy and earth rotation parameters [23; 24; 25; 26]. The results showed that a 3D orbital precision of about 30 cm was achieved. This indicates that the addition of SLR measurements can not only improve the orbit accuracy but also benefit the autonomous and controllable spatial reference parameters. However, there is currently no relevant research on the impact of the a priori orbit with various accuracies on the combined ISL/SLR POD for all BDS-3 satellites, as well as the on the translation and rotation of the constellation. In response to the limitations of the current research, to explore the impact of the a priori orbit with various accuracies on orbit determination for BDS-3 satellites using ISL and SLR osbervations, this study uses the method of combined ISL/SLR POD for all BDS-3 satellites, with a focus on analyzing the influence of the a priori orbit on the orbit accuracy and the overall rotation and translation of the constellation. The paper is organized as follows: after an overview of existing studies, the basic combined ISL/SLR POD functional models and methods are derived. The experimental data, processing strategies, and experimental design are then explained. Subsequently, the orbital accuracy with various a priori orbit and observations is analyzed, and the performance of the overall rotation and translation of the constellation is further explored. Some conclusions and disscussion are given at the end of the article.
# Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means
## 5 Conclusions  
We have proposed a novel method based on LGM and the FCM for fabric defect detection of a wide variety of textures. Extensive experimental results demonstrate that the proposed method could be applied to detect and segment fabric defects from a broad range of fabric defects datasets: Plain fabric, twill fabric, star-patterned fabric, dot-patterned fabric, box-patterned fabric, striped fabric, and statistical-texture fabric, with different defect types and shapes. It can achieve more accurate defect detection than other state-of-art competitors. Despite the effectiveness of the proposed method for fabric images with complicated patterns, it is still clumsy in computational time for detecting the defects. Our future work will be to improve our algorithm, to reduce the computational time in a real-time fabric defect detection system.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
This research is supported in part by National Natural Science Foundation of China under Grant No.61902302; in part by grants from the Shaanxi Provincial College of Science and Technology Youth Talent Support Project (Grant Number 20180115), the Science Research Program Funded by Shaanxi Provincial Education Department (Grant number 18 JK0338), and the Youth Innovation Team of Shaanxi Universities.  
The authors declare no conflict of interest.
# Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions
## 4 Results
### Differences in CFE between Different Stand Types  
In both forest types, the average value of the \(\beta_{GPP}\) calculated by the two remote sensing products also showed a trend of first increasing, then decreasing and eventually stabilizing with age (Figure 8a). In broad-leaved forests and coniferous forests, the highest average \(\beta_{GPP}\) occurred at 10 (\(\beta_{max}^{BF}=181.60\)) and 18 (\(\beta_{max}^{CF}=203.64\)) years of stand age, respectively, whereas the minimum appeared at 100 (\(\beta_{min}^{BF}=41.29\)) and 97 (\(\beta_{max}^{CF}=48.26\)) years of stand age, respectively. Comparing the nonlinear regression results between the two stand types, the \(\beta_{GPP}\) of coniferous forests had a longer increasing phase, and the maximum value was higher than that of broad-leaved forests (Figure 8a). The \(\beta_{GPP}\) of the two stand types began to decline at a stand age of 11 and 16 years, respectively. The \(\beta_{GPP}\) of both stand types reached the same level at about 60 years of stand age. Finally, the \(\beta_{GPP}\) of broad-leaved forests was stable around 89.70 gC100 ppm\({}^{-1}\)m\({}^{-2}\)y\({}^{-1}\), and the coniferous forest \(\beta_{GPP}\) was stable around 82.00 gC100 ppm\({}^{-1}\)m\({}^{-2}\)y\({}^{-1}\). Overall, there was no significant difference in the \(\beta_{GPP}\) between the coniferous forest and broad-leaved forest in the age range of 1-100 years (\(p=0.45\), \(\overline{\beta}_{GPP}^{CF}=115.57\), \(\overline{\beta}_{GPP}^{BF}=111.78\)) (Figure 8b).
## 2 System and Channel Model
### Turbulence-Induced Beam Spreading  
When the beacon beam propagates through the atmosphere, it undergoes beam spreading due to small-scale turbulent eddies, in addition to the far-field angular spreading coming from the divergence angle. This beam spreading makes the spot size larger, resulting in a reduction in beam irradiance at the detector. Assuming a Gaussian beam, the effective spot size is approximated in the uplink as follows [32]:  
\[W_{eff}^{2}(z)=W^{2}(z)(1+T), \tag{4}\]  
where  
\[W(z)=W_{0}\sqrt{1+\left(\frac{2z}{kW_{0}^{2}}\right)^{2}}\approx\frac{2z}{kW_ {0}}=z\theta_{D}, \tag{5}\]  
\[T=4.35k^{7/6}(\Lambda H)^{5/6}\sec^{11/6}(\theta_{z})\int_{h_{\rm s}}^{h_{\rm s }+H}C_{n}^{2}(h)\biggl{(}1-\frac{h-h_{\rm s}}{H}\biggr{)}^{5/3}dh. \tag{6}\]  
Here, \(T\) is the additional spreading caused by atmospheric turbulence; \(k\) is the optical wavenumber; \(W_{0}\) is the beam waist (i.e., beam size at \(z=0\)); \(\theta_{D}\) is the far-field divergence half-angle of the beacon beam; \(\Lambda\) is the Fresnel ratio defined as \(\Lambda=2z/kW^{2}(z)\); \(H\) is the difference in altitude between the receiver and the optical ground station at \(h_{\rm s}\); and \(C_{n}^{2}(h)\) is the refractive-index structure parameter, which characterizes the strength of atmospheric turbulence.  
Many models have been proposed to predict the behavior of the refractive-index structure parameter, which is a quantitative measure of atmospheric turbulence. The Hufnagel-Valley model is widely used since it not only allows for easy variation of the day-time and night-time profile by adjusting various site parameters including wind speed, isoplanatic angle, and altitude, but is also best suited for ground-to-satellite uplink [4]. The model is given as  
\[C_{n}^{2}(h)=0.00594\left(\frac{v}{27}\right)^{2}\left(10^{-5}h\right)^{10} \exp\biggl{(}-\frac{h}{1000}\biggr{)}+2.7\times 10^{-16}\exp\biggl{(}-\frac{h}{1500} \biggr{)}+A_{0}\exp\biggl{(}-\frac{h}{100}\biggr{)}, \tag{7}\]  
where \(v\) is the mean square value of the wind speed in m/s, and \(A_{0}\) is the nominal value of the refractive-index structure parameter at the ground in m\({}^{-2/3}\). It is noteworthy that the turbulence strength is dominated by \(A_{0}\) near the ground, but by \(v\) at high altitude (i.e., \(h>5\) km) [36]. The value of \(A_{0}\) is typically in the range from \(10^{-17}\) m\({}^{-2/3}\) for weak turbulence to \(10^{-12}\) m\({}^{-2/3}\) for strong turbulence, with a typical average value being \(10^{-15}\) m\({}^{-2/3}\)[37].
# A Comparative Study for Control of Quadrotor UAVs
## 2 Materials and Methods
### Simulation Setting  
The whole control architecture is implemented in the MATLAB/Simulink(r) software environment, mainly by means of MATLAB function blocks. Table 1 shows the simulation parameters, while the control parameter settings of all the proposed attitude stabilizers are reported in Table 2. All the values are expressed according to the International System of Units.  
\begin{table}
\begin{tabular}{c c c} \hline
**Simulation Parameter Type** & **Parameter** & **Value** \\ \hline \multirow{8}{*}{UAV} & m & 0.60 kg \\  & g & 9.81 m\(\cdot\)s\({}^{-2}\) \\  & b & 3.60\(\cdot\)10\({}^{-6}\) kg\(\cdot\)m \\  & d & 1.60\(\cdot\)10\({}^{-7}\) kg\(\cdot\)m\({}^{2}\) \\  & \(\text{I}_{\text{x}}\) & 5.20\(\cdot\)10\({}^{-3}\) kg\(\cdot\)m\({}^{2}\) \\  & \(\text{I}_{\text{y}}\) & 5.20\(\cdot\)10\({}^{-3}\) kg\(\cdot\)m\({}^{2}\) \\  & \(\text{I}_{\text{z}}\) & 9.40\(\cdot\)10\({}^{-3}\) kg\(\cdot\)m\({}^{2}\) \\  & \(\text{I}_{\text{R}}\) & 4.20\(\cdot\)10\({}^{-5}\) kg\(\cdot\)m\({}^{2}\) \\  & \(\text{L}\) & 0.20 m \\  & \(\text{K}_{\text{d}}^{\text{R}}\) & 0.15 kg\(\cdot\)m\({}^{2}\)\(\cdot\)s\({}^{-1}\) \\  & \(\text{K}_{\text{d}}^{\text{R}}\) & 0.15 kg\(\cdot\)s\({}^{-1}\) \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
## 4 Experimental Results
### Autoencoder  
For the Autoencoder model, the encoding of the images in the training dataset needs to be completed before training in order to predict and classify nutritional deficiencies (e.g., decoding). Figure 8 illustrates the results of the comparison between the original images in the training dataset and the images from the Autoencoder model after being encrypted and used for training. Figure 8a represents the coding results for the nutritional deficiency of Calcium.  
Figure 8b,c shows the results of the encryption for Potassium shortage and Nitrogen deficiency, respectively. As shown in Figure 8, the coding and decoding can recognize the training results and produce images that are almost the same as the original images. This makes it possible for predictive results based on decoded and trained images to achieve high accuracy in the classification process.  
Figure 8: Comparison between original training dataset and pre-training dataset with Autoencoder.  
(a) Pre-training of Calcium; (b) Pre-train for Potassium; (c) Nitrogen Pre-training.  
Figure 7: Prediction results of Inception-ResNet v2. (a-c) Prediction of Calcium deficiency; (d-f) Potassium shortage forecast; (g,h) Nitrogen lack prediction.  
Accordingly, the results of the Autoencoder model are shown in Figure 9. It can be seen that the prediction performance was not able to achieve a good result in Figure 9a due to an inaccurate forecast expression of the symptoms of Calcium deficiency on the tomato fruit in the test case. Moreover, Autoencoder was not able to achieve a good prediction for the expression of Nitrogen deficiency in Figure 9g. Figure 9b,c show the highly predictable for classifying Calcium deficiency, and Autoencoder model achieves the high predictive accuracy for Potassium shortage as shown in Figure 9d-f. Therefore, the validation value for the forecast in this model is about 79.09%. Similar to the Inception-ResNet v2, a confusion matrix is also used to calculate the accuracy of the training and validation datasets for the assessment of forecasting accuracy using Autoencoder as mentioned in Section 5.
# Active Learning for Biomedical Article Classification with Bag of Words and _FastText_ Embeddings
### Contributions  
Although related prior work has contributed several useful ideas and results, not only exploring different classification algorithms and active learning setups, but also proposing novel custom variations thereof, it has left some noticeable gaps that this article is intended to partially fulfill. It examines how classification algorithms and text representation methods that have been found particularly useful by prior work handle the challenges associated with active learning of text classification models and with what active learning setups they work best. While the scope of the presented study is limited to only two popular and highly successful algorithms and two text representation methods, this limitation is well justified by the recent evaluation [13] in which these were found to provide the best level of prediction quality. Limiting the scope of classification algorithms and text representations to the most promising ones makes it possible to experiment with several different active learning setups on multiple datasets and systematically evaluate the predictive performance.  
The experiments presented in this article are supposed to verify the scale of data labeling effort savings possible by different active learning configurations without sacrificing classification quality, i.e., to determine how the predictive performance of models obtained by active learning at different stages of this process compares to that of models that could be obtained by standard passive learning if all class labels were available. They also investigate the possibility of reliably determining when to stop the active learning process and avoid further labeling queries without sacrificing the predictive performance. The distinguishing features of this work are summarized below.  
* The random forest [25] and support vector machines [26] algorithms are coupled with the bag of words [27] and _FastText_ word embeddings [28] representations. It is noteworthy that the latter has probably never been used for active learning before.
* Several different active learning setups are examined, including not only different query selection strategies, but also different initialization methods and early stopping criteria.
* A collection of 15 datasets consisting of scientific article abstracts from biomedical systematic literature review studies, of varying size and class imbalance ratio, is used for the experiments.
* The prediction quality is rigorously evaluated using \(n\times k\)-fold cross-validation with the area under the precision-recall curve adopted as the quality measure. In addition, potential workload reduction in an SLR process is estimated using the WSS measure.
## 2 Proposed Algorithms  
### Preliminary  
The best-known single channel or one-dimensional ANC system uses a secondary signal to attenuate the primary noise in a duct. In this paper, this is the fundamental model to test the performance of the proposed method. The diagram of the standard ANC system is shown in Figure 1, where \(P\left(z\right)\) denotes the primary path from the reference signal \(x\left(n\right)\) to the error sensor \(e\left(n\right)\). \(S\left(z\right)\) denotes the secondary path from the secondary source to the error sensor. \(\hat{S}\left(z\right)\) is the estimate one of \(S\left(z\right)\), which can be estimated using either on-line or off-line techniques [1]. \(\mathbf{\Theta}\left(n\right)\) is the weight vector of the adaptive filter and \(y\left(n\right)\) is the output of adaptive filter.  
Based on this structure, several methods of M-estimate have been introduced into active impulsive noise control [6; 7; 8; 9; 10; 11; 12; 13]. However, the main problem of applications of these methods is that it cannot works well in the variable-\(\alpha\) impulsive noise environment, because these methods use a nonlinear transform function with a certain threshold. Especially for the post normalized filtered-x least mean square (PFxNLMS) algorithm in [13], it must select a proper value of \(\rho(-1<\rho<1)\) for a certain environment.
# Docker Performance Evaluation across Operating Systems
## 2 Docker Architectures
### Docker on Linux
#### 2.1.4 User Namespace  
An important feature of Docker containers in terms of security and isolation is the ability to map the UID in a container to a higher value of the user ID on the host system. Using a user namespace can prevent container breakout, even when a user in a container has root privileges. A high UID that is mapped to the host system prevents harmful actions as a user with such a UID does not have enough privileges. This feature also helps to distinguish container processes from host processes.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 2 Full-Scale Test of Corrugated Steel
### Equivalent Stiffness Coefficient  
#### 2.3.1 Proposal of Equivalent Stiffness Coefficient  
According to the principle of virtual work, the structural deformation of CSP can be calculated using the following equation:  
\[\Delta=\sum\int\overline{M}d\theta+\sum\int\overline{Q}dv+\sum\int\overline{N} du-\sum\overline{R}_{c} \tag{1}\]  
Figure 5: Comparison between full-scale test and numerical simulation of midspan deflection of corrugated steel plate.  
When calculating beam deflection, bending deflection can be considered only without shear and axial forces. In addition, the theoretical calculation equation of deflection can be obtained as follows by substituting load-induced \(d\theta=\frac{M_{P}}{EI}ds\) into Equation (1):  
\[\omega_{\text{theory}}=\sum\int\frac{\overline{M}M_{P}}{EI}ds \tag{2}\]  
In Equation (2), \(\omega_{\text{theory}}\) refers to the theoretical deflection, \(M_{P}\) refers to the section bending moment caused by the vertical load, and \(E\) and \(I\) refer to the modulus of elasticity and theoretical inertia moment, respectively. In the elastic stage, deformation and internal force are linearly elastic, so the incremental calculation is adopted in the follow-up calculation to reduce error.  
[PERSON] et al. [21] have highlighted the unreliability of directly applying theoretical stiffness values to represent the actual stiffness of CSP. According to the Design and Construction Manual for Embedded Structures of CSP [24], the theoretical moment of inertia for a CSP plate (Model: 380 mm \(\times\) 140 mm \(\times\) 5 mm) is calculated as 15,117.8 mm\({}^{4}\)/mm, yielding a theoretical stiffness of 3023.6 kN\(\cdot\)m\({}^{2}\)/m. Applying Equation (2), the predicted midspan deflection increment for CSP under a vertical force increment \(\Delta\)F = 30 kN is \(\Delta\omega_{\text{theory}}=1.05\) mm.  
Through numerical simulation, the actual midspan deflection increment (\(\Delta\omega_{\text{true}}\)) is approximately determined to be \(\Delta\omega_{\text{true}}=1.20\) mm, resulting in a relative error of 14% compared to \(\Delta\omega_{\text{theory}}\). Therefore, an \"equivalent stiffness coefficient\" \(\eta\) is proposed to adjust Equation (2), refining the theoretical deflection calculation for CSP. The modified equation for calculating the actual deflection of CSP is thus derived as follows:  
\[\omega_{\text{true}}=\sum\int\frac{\overline{M}M_{P}}{\eta EI}ds \tag{3}\]  
To sum up, in combination with Equations (2) and (3), the calculation equation of the \"equivalent stiffness coefficient\" of CSP can be obtained as follows:  
\[\eta=\frac{\omega_{\text{theory}}}{\omega_{\text{true}}}=\frac{\Delta\omega_{ \text{theory}}}{\Delta\omega_{\text{true}}} \tag{4}\]