esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Theory of MET in Electrically Conductive Fluid
#### 2.2.4 Mass Transfer of Antigen and Immune Response Enhancement  
The antigen sample is freely suspended in the buffer medium, and its concentration field obeys the conventional convection-diffusion equation:  
\[\frac{\partial c}{\partial t}+\
abla\cdot J=0 \tag{15}\]  
\[J=uc-D\
abla c \tag{16}\]  
where \(c\) represents the concentration of antigens in the bulk liquid, and \(D=10^{-11}\)m\({}^{2}\cdot\) s\({}^{-1}\) is the antigen mass diffusivity.  
It can be assumed that the specific binding interaction between the antigens in solution and the antibodies immobilized on the functionalized electrode surface follows a first-order Langmuir adsorption model:  
\[\frac{\partial B}{\partial t}=k_{on}C_{w}(R_{T}-B)-k_{off}B \tag{17}\]  
where B denotes the surface concentration of the antigen absorbed on the reaction surface, with units in mol\(\cdot\)m\({}^{-2}\), and \(k_{on}\) and \(k_{off}\) stand for the association rate constant and the dissociation rate constant, respectively. \(C_{w}\) is the volumetric concentration of the free antigen in the immediate vicinity of the reaction surface, and \(R_{T}\) is the surface concentration of the immobilized antibody probe with unit in mol\(\cdot\)m\({}^{-2}\). To appraise the feasibility of MET-induced fluid stirring and pumping in enhancing the antigen-antibody binding rate, we define the transient binding enhancement factor as Be(t) = Be\({}_{V}\)(t)/B\({}_{0}\)(t), where B\({}_{V}\)(t) and B\({}_{0}\)(t) are the bound antigen concentrations with and without applying external hybrid AC electric fields, respectively. It is reported that the binding rate strongly relies on the dimensionless Damkohler number:  
\[Da=\frac{k_{on}R_{T}H_{C}}{D} \tag{18}\]  
which measures the relative magnitude of the reaction speed to the diffusion speed. The Da number is often used to determine whether the biosensor is limited by diffusion or by reaction. If the reaction rate is faster than the diffusion transport of the target analyte to the sensor surface, the entire binding process is diffusion-limited. Conversely, the binding rate will be reaction-limited when the analyte diffusion is rapid but the reaction speed cannot keep up with the diffusion rate.
## 2 Materials and Methods
### Monitoring of Decolorization, Quantification of COD, and Measurement of Azo Dye Biodegradation  
Samples were collected from the anolyte every 48 h and were centrifuged at 12,000 rpm for 5 min.The decolorization rate was monitored by measuring the decrease in absorbance of the anolyte free of suspensions with a UV-VIS spectrophotometer (Jenway 7315, Antylia Scientific, Villepinte, France) at the wavelengths of 556 nm for TB and 520 nm for BR.  
A sample of 1 mL was taken every 48 h from each anolyte, diluted at 1/6, and filtered through the syringe of the chloride elimination LCW925 kit(Hach, Loveland, CO, USA). The COD removal efficiencywas determined using the LCK 514 kit (100-2000 mg/O\({}_{2}\))(Hach, Loveland, CO, USA).  
Dye biodegradation was monitored by Fourier Transform Infrared spectroscopy FTIR (Perkin Elmer model). Samples from anolyte were taken at the beginning and at the end of the experiment. FTIR analyses were conductedin the mid IR region of 400-4000 cm\({}^{-1}\)
# A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes
## 9 Conclusions  
In this paper, we propose a threshold-based flexible OSD decoding algorithm to reduce the complexity of the OSD algorithm while maintaining an acceptable WER under various thresholds of short polar codes. Compared with other decoding algorithms, our proposed TH-OSD algorithm shows better WER performance than SC and runs faster than the OSD algorithm with negligible WER performance loss with appropriate threshold settings. We also provide a method for determining an appropriate threshold value and derive an upper-bound on the WER performance of the proposed TH-OSD algorithm. We implement a list approach to improve the WER performance of TH-OSD to a near MLD performance. A CRC-aided TH-OSD algorithm is also presented, which outperforms MLD. The TH-OSD can be easily extended to longer codes using the structure provided by [PERSON] et al. [16]. In the future, we plan to simplify further the expression for the upper-bound of WER performance.  
\begin{table}
\begin{tabular}{c c c c} \hline
**SNR(dB)** & \(\mathbf{p_{th}=0.6}\) & \(\mathbf{p_{th}=0.7}\) & \(\mathbf{p_{th}=0.8}\) \\ \hline
1 & 53.6\% & 74.9\% & 90.1\% \\
1.5 & 36.8\% & 58.3\% & 78.0\% \\
2 & 20.1\% & 38.3\% & 59.5\% \\
2.5 & 11.9\% & 20.9\% & 37.6\% \\
3 & 7.5\% & 12.3\% & 19.0\% \\
3.5 & 4.2\% & 6.9\% & 11.0\% \\ \hline \end{tabular}
\end{table}
Table 2: Reduction in the number of tested codewords compared to OSD (Codeword length 64 and rate 1/2).  
Figure 6: Comparison of total tested codewords of OSD-2, the algorithm proposed in [10] and TH-OSD-2 with different threshold settings, code length 64, and rate 0.5.  
**Author Contributions:** Conceptualization, Y.X.; methodology, Y.X. and G.T.; formal analysis, Y.X.; Investigation, Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, Y.X. and G.T.; Supervision, G.T.; and Funding acquisition, G.T.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant Nos. 61571416 and 61271282) and the Award Foundation of Chinese Academy of Sciences (Grant No. 2017-6-17).  
**Acknowledgments:** We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Opportunities for Industry 4.0 to Support Remanufacturing
## 3 Smart Remanufacturing in the Digital Age
### Smart Services  
One of the challenges that remanufacturers face, as explained earlier, is the control of the timing, quality, and quantity of cores returned. In this regard, the product service system has provided opportunities to cope with the complexity of core return for remanufacturing [17]. In this emerging and disruptive business model, ownership of the product is usually retained by the OEMs or retailers and only the service or usage is offered to customers (e.g., selling \"flying hours of the engine\" instead of selling \"engines\"). Hence, it creates a mandate for manufacturers or retailers to monitor their product's performance during its runtime and to forecast remanufacturing operations on the cores returned based on the predicted remaining life of the product. On the other hand, from the consumers' perspective, as they pay for the service rather than the ownership of the product, market acceptance for remanufactured goods will likely be increased, leading to a successful remanufacturing model. Real-time monitoring of product in-use and data analysis via embedded sensor networks and cloud-based computation could enable predictive maintenance by the early detection of problems. Increased connectivity among products, customers, and manufacturers, promoted by Industry 4.0, presents immense opportunities for boosting the product service model. Take, for example, machines that are leased to the power plant sector. Power plants rely heavily on the continuous availability of their machinery. To increase the reliability of machines, smart sensors are embedded into the machines to monitor critical factors, such as temperature, pressure, switches, energy consumption, and vibration in real time [18]. Sensor data are collected and logged into a central server through a network for prediction of potential wear and estimation of components' useful life, and for scheduling maintenance or remanufacturing of components in a timely manner.
# Statistical Characterization of the Magnetic Field in Space during Magnetic Storms
## 5 Results and Discussions  
The results show that the probability distribution of the magnitude of each component of the magnetic field is a near-Gaussian distribution when the Dst index is low; however, this distribution deviates from the Gaussian distribution when the Dst index is high, i.e., when magnetic storms are relatively strong. This is probably because the energy  
Figure 4: Dynamic power spectrum of the magnitude of each component of the magnetic field. (a) 0:00 on 6 March 2016, to 23:00 on 7 March 2016; (b) 0:00 on 13 October 2016, to 23:00 on 14 October 2016; (c) 0:00 on 27 May 2017, to 23:00 on 28 May 2017; (d) 00:00 on 8 September 2017, to 23:00 on 9 September 2017; (e) 0:00 on 27 September 2020, to 23:00 on 28 September 2020; (f) 00:00 on 4 November 2021, to 23:00 on 5 November 2021.  
injection during a magnetic storm brought some disturbances. Thus some of the values of observed data might deviate from that in a calm space environment, which would probably violate the necessary condition for the central limit theorem and have some impacts on the information entropy of the magnetic field.  
The auto-correlation function of each component of the magnetic field is non-stationary during a magnetic storm. The auto-correlation function is simultaneously a binary function related to the initial observation moment and the time delay between two observations. This property might result from both the electromagnetic pulses with a non-stationary second moment because of the disturbances during the magnetic storm and the non-Gaussian property of the probability density function of the magnetic fields.  
The dynamic power spectrum analysis shows that for each observation moment, the power spectrum is mainly concentrated in the very-low-frequency band. The result is similar to that of previous research, as shown in [34; 35].  
[PERSON] designed the project and wrote the paper; [PERSON]-[PERSON] collected, processed, and analyzed the data; [PERSON]-[PERSON] prepared the original draft with contributions from all authors; [PERSON], [PERSON], [PERSON]L.C., [PERSON] and [PERSON] were responsible for discussion and revisions. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the Strategic Pioneer Program on Space Science, Chinese Academy of Sciences (Grant Nos. XDA17010301, XDA17040505, XDA15052500, and XDA15350201), the National Natural Science Foundation of China (Grant Nos. 41731070 and 41931073), the Specialized Research Fund for State Key Laboratories, the CAS-NSSC-135 project and the NSSC Director Fund (Grant No. E0 PD41A11S).  
Not applicable.  
The datasets used or analyzed in the present work are available from the corresponding author upon reasonable request.  
The authors thank swx.sinp.msu.ru for the data.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Enzymatic Activity  
The API ZYM assay results are presented in Table 2. All the strains presented with the activity of leucine arylamidase, alkaline phosphatase, and acid phosphatase, and none of the strains presented with the activity of \(\alpha\)-fucosidase. All but one (085_Pich_manshurica) presented with the activity of esterase C4 and esterase lipase C8. Also, only two strains (110_Metsch_pulcherrima and 111_Metsch_pulcherrima) presented with the activity of trypsin and \(\beta\)-glucuronidase, and only 063_C_metapsilosis presented with the activity of lipase (C14). In opposition to the results of similar assays published by [PERSON] et al. [44], where nearly no activity of alkaline phosphatase, valine arylamidase, and cysteine arylamidase were presented, these activities are widely present in the strains tested in this article. On the other hand, the results obtained by yeasts in the study by [PERSON] et al. [44] show similar enzymatic activity abundance to strains presented in our study in the aspects of esterase (C4), esterase lipase (C14), and leucine arylamidase. Similar enzymatic activities in probiotic strains were observed by [PERSON] et al. [57] and [PERSON] et al. [58]. The activity of alkaline phosphatase in probiotic strains may positively impact patients with inflammatory disorders of the gastrointestinal tract and, furthermore, the activity of leucine and cysteine arylamidases, acid phosphatase, and naphthol-AS-BI-phosphohydrolase is within the range of an adequate enzymatic activity profile for probiotic strains [57; 58; 59]. On the other hand, only two strains of _M. pulcherrima_ showed the presence of trypsin and \(\beta\)-glucuronidase, which may have detrimental effects on human health [60; 61].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline
052 & + & + & \(-\) & + & \(-\) & \(-\) & + & + & \(-\) & \(-
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
###### Abstract  
Plant genomics have progressed significantly due to advances in information technology, but phenotypic measurement technology has not kept pace, hindering plant breeding. As maize is one of China's three main grain crops, accurately measuring plant height is crucial for assessing crop growth and productivity. This study addresses the challenges of plant segmentation and inaccurate plant height extraction in maize populations under field conditions. A three-dimensional dense point cloud was reconstructed using the structure from motion-multi-view stereo (SFM-MVS) method, based on multi-view image sequences captured by an unmanned aerial vehicle (UAV). To improve plant segmentation, we propose a column space approximate segmentation algorithm, which combines the column space method with the enclosing box technique. The proposed method achieved a segmentation accuracy exceeding 90% in dense canopy conditions, significantly outperforming traditional algorithms, such as region growing (80%) and Euclidean clustering (75%). Furthermore, the extracted plant heights demonstrated a high correlation with manual measurements, with R\({}^{2}\) values ranging from 0.8884 to 0.9989 and RMSE values as low as 0.0148 m. However, the scalability of the method for larger agricultural operations may face challenges due to computational demands when processing large-scale datasets and potential performance variability under different environmental conditions. Addressing these issues through algorithm optimization, parallel processing, and the integration of additional data sources such as multispectral or LiDAR data could enhance its scalability and robustness. The results demonstrate that the method can accurately reflect the heights of maize plants, providing a reliable solution for large-scale, field-based maize phenotyping. The method has potential applications in high-throughput monitoring of crop phenotypes and precision agriculture.  
field corn; point cloud processing; column space algorithm; growth parameters 202515236  
H uazhe [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm Agriculture 202515236  
1
## 2 Overview of Random Linear Network Coding
### Unequal Error Protection RLNC  
In typical video delivery scenarios, the source block \(\mathbf{s}\) can be divided into \(L\) sub-blocks or _layers_ such that \(\mathbf{s}=[\mathbf{I}_{1},\mathbf{I}_{2},\ldots,\mathbf{I}_{L}]\), where the \(i\)-th layer \(\mathbf{I}_{i}\) contains \(k_{i}\) source packets and \(\sum_{i=1}^{L}k_{i}=K\). As the layer index \(i\) grows, the packets contained in \(\mathbf{I}_{i}\) have progressively decreasing impact on reconstructed video quality. For this scenario, unequal error protection RLNC (UEP RLNC) schemes offer significant benefits in terms of flexibility, reconstructed video quality and decoding complexity, as compared to the standard RLNC.  
Two generic and well-studied UEP RLNC methods are non-overlapping window RLNC (NOW-RLNC) and expanding window RLNC (EW-RLNC) [22]. In both schemes, a set of \(L\) windows \(\mathbf{w}=[\mathbf{w}_{1},\mathbf{w}_{2},\ldots,\mathbf{w}_{L}]\) is defined over the set of layers: for NOW-RLNC, windows correspond to layers, i.e., \(\mathbf{w}_{i}=\mathbf{I}_{i}\), while for EW-RLNC, the \(i\)-th window \(\mathbf{w}_{i}\) contains the first \(i\) layers, i.e., \(\mathbf{w}_{i}=[\mathbf{I}_{1},\mathbf{I}_{2},\ldots,\mathbf{I}_{i}]\). Coded packets are produced by applying RLNC over a content of a window randomly selected using a window selection distribution \(\mathcal{W}\). Proper design of \(\mathcal{W}\) achieves desired balance of decoding probabilities of source packets belonging to different layers [22].
## 2 Materials and Methods
### Second Scenario SVM Results  
Figure 8 shows the laboratory data distribution and predicted curves of the best composition for the second scenario.  
Figure 8a shows the optimal measured and predicted values for the second scenario for the training phase. It is inferred from the figure that the laboratory data were less dispersed than the predicted data and the output data from the SVM were very well matched with a maximum relative error of \(\pm\)8.97%. Figure 8b compares the laboratory data with the optimal prediction model in the training phase and shows that there is a very good correlation for this scenario. Figure 8c,d also show the distribution and comparison curves of the laboratory data and the predicted energy dissipation in the testing phase, respectively. It is seen that that the laboratory data corresponded very well with the predicted data, with a maximum relative error of \(\pm\)8.63%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Testing**} \\ \hline
**Model** & **R\({}^{2}\)** & **RMSE \(\times\) 100** & **KGE** & **R\({}^{2}\)** & **RMSE \(\times\) 100** & **KGE** & \(\gamma\) \\ \hline Model 1 & 0.648 & 9.45 & 0.988 & 0.733 & 8.36 & 0.975 & 10 \\ Model 2 & 0.974 & 5.42 & 0.991 & 0.97 & 4.88 & 0.982 & 6 \\ Model 3 & 0.988 & 3.95 & 0.998 & 0.988 & 3.89 & 0.993 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The statistical parameters for the SVM model in the second scenario.  
Figure 6: Variation of relative energy dissipation versus relative critical depth.  
Figure 8: _Cont._  
Figure 7: Variation of RMSE and R\({}^{2}\) vs. Gamma (\(\gamma\)) values for best model in the second scenario. (**a**) Training; (**b**) testing.  
Figure 8: Comparison of the observed and predicted energy dissipation values for model 3 in the second scenario. (**a**,**b**) training; (**c**,**d**) testing.  
Pool depth values for the three relative screen diameters are shown in Figure 9. It can be seen from the figure that increasing the relative critical depth increased the pool depth for all three relative diameters. Additionally, the depth values of the present research are in good agreement with the results of the [PERSON] (2018) study for the same relative diameter. Additionally, by increasing the diameter of the holes in the horizontal screen, the depth of the pool decreased. By increasing the diameters of the screen, the angle of the jet falling from the mesh plate was reduced and this reduced the pool depth; the reduction of air-water interference is another factor that can reduce the depth of the pool. Note that reducing the depth of the pool is an economically effective design for stilling basins.  
Compared to the horizontal screen with a relative pore diameter of 0.067, the use of a screen with a relative diameter of 0.2 reduced the relative depth of the pool by 8.5%.
# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 4 Methodology
### Classification Module
#### 4.3.3 Sparse Representation Based Classification (SRC)  
In SRC, a testing sample is expressed as a linear sparse combination of all the training samples [22]. The sparse representation coefficients can be obtained by solving \(L_{1}\) optimization problem. We characterise the training samples for \(i\)th class as \(F_{i}=\{f_{i1},f_{i2},\ldots,f_{iN_{i}}\}\), such that \(N=\sum_{i=1}^{t}N_{i}\). Now assume a matrix \(F=\{F_{1},F_{2},\ldots,F_{t}\}\) in which all training samples of entire activity classes are being concatenated.  
The testing sample \(y\) is defined as \(y=Fv\) for the sparsest solution. The coefficient vector \(v\) has nonzero values only for the entries that are associated to class \(i\). The sparse solution for coefficient vector \(v\) may be optimized with \(L_{0}\) norm constraint as:  
\[\begin{cases}\hat{v_{0}}=arg\ min\|v\|_{0}\\ s.t.\ Fv=y.\end{cases} \tag{27}\]  
Assuming the solution of \(L_{0}\) norm constraint is equivalent to \(L_{1}\) norm constraint, we obtain  
\[\begin{cases}\hat{v_{1}}=arg\ min\|v\|_{1}\\ s.t.\ Fv=y.\end{cases} \tag{28}\]  
After getting the optimal \(\hat{v_{1}}\) solution for sparsity, SRC can be designed for class specific reconstruction residual.  
Let \(\delta_{i}(v)\) is the vector associated with \(i\)th class that selects non-zero entries corresponding to \(v\). Based on the coefficients of \(i\)th class, we can reconstruct the test sample \(y\) as:  
\[\hat{y_{i}}=F\delta_{i}(v), \tag{29}\]  
where \(\hat{y_{i}}\) indicates the reconstructed testing sample. The residual of reconstructed class can be obtained as:  
\[r_{i}(y)=\|y-\hat{y_{i}}\|_{2}=\|y-F\delta_{i}(v)\|_{2}. \tag{30}\]  
The decision of class is based on the following principle:  
\[class(y)=\min_{i}r_{i}(y). \tag{31}\]  
In our proposed framework, we used the decision rule of SRC to supervise MNN and sparse representation coefficients were computed from the mean vector of nearest neighbors.
## 3 Results and Discussion
### Effect of Electrode Diameter on NO Oxidation
Comparing the change of NO\({}_{\text{X}}\) concentration of DBD reactors with different electrode diameter during the discharge process (as shown in Figure 12), we can find that the NO\({}_{\text{X}}\) concentration at the outlet of DBD reactor with 14-mm electrode diameter is lower than that at the inlet during the whole discharge process, which indicates that increasing the electrode diameter can improve the production of O radicals and inhibit the generation of NO\({}_{\text{X}}\) at the same time. To analyze the mechanism, the distribution of E/N in the gas gap of DBD reactor with different inner electrode diameter was simulated, as shown in Figure 13. The simulation results show that E/N changes (\(\Delta\)E/N) in the gas gap of DBD reactors with 14-, 12- and 10-mm electrode diameter are 21.6, 51.8 and 68.5 Td, respectively. Increasing the electrode diameter does not simply improve the E/N, but also makes the distribution of E/N more centralized, which makes it easier for DBD reactor to operate at high O radical generation efficiency. It is difficult for the DBD reactor with 10-mm electrode diameter to inhibit the generation of \(N_{2}(X,v\geq 13)\) and N radicals simultaneously due to the large \(\Delta\)E/N in the gas gap. Therefore, the DBD reactor with 10-mm electrode diameter is easier to generation NO\({}_{\text{X}}\) and has a lower NO oxidation efficiency. Figure 12: The change of NO\({}_{\text{X}}\) concentration at the outlet of DBD reactor during the discharge.