esa-sceva/satcom-chunk-collection huggingface dataset:
# Orbital Angular Momentum Generation and Detection by Geometric-Phase Based Metasurfaces
## 5 Conclusions  
In summary, we reviewed the research work on the geometric-phase based metasurfaces for OAM generation and detection. The metasurfaces achieve wavefront manipulation by the spin-induced geometric phase and show high flexibilities. Most importantly, they can be multifunctional. Besides the OAM generation and detection, they are designed for realizing beam multiplexing and demultiplexing and manipulating polarization. Therefore, the geometric-phase metasurfaces are promising candidates for practical applications of OAM beams.  
**Acknowledgments:** This work was supported in part by the Research Grants Council of Hong Kong (GRF 716713, GRF 17207114, and GRF 17210815), NSFC 61271158, Hong Kong UGC AoE/P04/08, AOARD FA2386-17-1-0010, Hong Kong ITP/045/14 LP, and Hundred Talents Program of Zhejiang University (No. 188020*194231701/208).  
**Author Contributions:** [PERSON] drafted the manusciprt. [PERSON] and [PERSON] revised and finalized the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 13: Full-wave simulated far-field power patterns when the incident wave carries OAM of order (**a**) \(-2\); (**b**) \(-1\); (**c**) 0; (**d**) 1; (**e**) 2. (**f**) Original and optimized far-field power patterns at \(\theta=40^{\circ}\) for the five cases (**a**–**e**). Reproduced with permission from [89], Copyright IEEE, 2017.
## 2 Materials and Methods
### HEK-Blue TGF-\(\beta\) SEAP Reporter Assay  
The HEK-Blue TGF-\(\beta\) cell line (InvivoGen, San Diego, CA, USA) was used for in vitro assessment of TGF-\(\beta\) neutralization. Stimulation of these cells with TGF-\(\beta\) induces the Smad dependent pathway and secretion of secreted embryonic alkaline phosphatase (SEAP) into media that can be measured via QUANTI-Blue and spectrophotometry. Cells were maintained and assayed according to the manufacturer's protocol with modifications. Briefly, alginate hydrogels were incubated for 2 h in 1 mL Dulbecco's Modified Eagle Medium (DMEM) to release antibodies, then 2.5 \(\times\) 10\({}^{4}\) cells (90 \(\upmu\)L) were seeded into a 96-well plate before 20 pM (in 10 \(\upmu\)L) recombinant human TGF-\(\beta\)1 (Peprotech, Cranbury, NJ, USA) was used to stimulate the cells. Next, 100 \(\upmu\)L alginate hydrogels pre-conditioned media was added to the cells. Two activity controls included TGF-\(\beta\) treatment of cells with DMEM without hydrogels and cells incubated only with DMEM (without TGF-\(\beta\)). Cells were incubated for 24 h at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). Media (10 \(\upmu\)L) were transferred to a new plate and mixed with 90 \(\upmu\)L QUANTI-Blue reagent and incubated for 1 h at 37 \({}^{\circ}\)C before absorbance was measured at 655 nm. The percentage of neutralization was estimated with the equation below, where absorbance of TGF-\(\beta\) control (A\({}_{\text{TGF}}\)) was considered as 0% of neutralization (full effect of cytokine), and absorbance of media control without TGF-\(\beta\) (A\({}_{\text{media}}\)) was considered as 100% of neutralization (no effect of cytokine).  
\[\text{TGF}\beta\text{ neutralization }(\%)=\frac{\text{A}_{\text{sample}}- \text{A}_{\text{TGF}}\beta}{\text{A}_{\text{media}}-\text{A}_{\text{TGF}}\beta}\times 100\% \tag{1}\]
# Pointer Meter Reading Recognition by Joint Detection and Segmentation
## 3 Proposed Method
### Reading Dial Numbers  
In this stage, the pointer and grid points segmented from the previous stage are further used to recognize the dial numbers. In general, we need to first determine the center point  
Figure 3: The architectural details of the reference point segmentation network adopted in the second stage.  
Figure 2: The architectural details of the meter dial location network used in the first stage.  
of the meter dial region that is generated by the meter dial location network. For ease of presentation, let the center point coordinate be \((x,y)\). Based on this coordinate, it is easy to find the pointer by searching similar pixels around the center point. After determining the position and direction of the pointer, a simple method for reading the dial number is to utilize the angle ratio to compute the value, as shown in Figure 4a. This method has been used in some existing works [15; 54].  
Unlike the above method that obtains the dial value of the meter dial by calculating the angle between the pointer direction and the minimum scale direction, we consider the center point \((x,y)\) to be the origin of a polar coordinate system. Based upon the transform relationship between the polar and Cartesian coordinates, circular grid points are projected into uniformly aligned points along with a straight line, which is illustrated in Figure 4b,c. At last, we achieve the final scale value of the meter dial (i.e., reading of dial numbers) by computing the relative position between the pointer and the aligned reference points, which is expressed as  
\[value=\frac{p}{d}\cdot scale, \tag{1}\]  
where \(d\) denotes the distance between the starting and end points shown in Figure 4c, \(p\) is the distance between the starting point and the pointer, and \(scale\) represents the largest dial value of the target meter and needs to be known in advance. It is necessary to point out that our distance-based method for reading dial numbers can achieve higher accuracy than the angle-based method. The major reason is that the latter is very sensitive to the distortion of the meter image. Unfortunately, image distortion is inevitable in practical applications.
# CFD Analysis on the Heat Dissipation of a Dry-Lubricated Gear Stage
## 3 Results
### Analysis of the Heat Transfer Coefficient
#### 3.2.1 Local Heat Transfer Coefficient  
Figure 7 shows the heat transfer coefficient \(h\) over the gear surfaces for the circumferential speeds \(v_{t}\) = [2.0; 5.0; 10.0] m/s. It is visualized by the local coloring of the gear surfaces. Figure 7a refers to gear-C and in Figure 7b to gear-LL.  
Figure 7: Heat transfer coefficient h between the gear surfaces and air for \(\rm v_{t}\) = {2.0; 5.0; 10.0} m/s in 3D and top view for gear-C (**a**) and for gear-LL (**b**).  
For both gears, HTC features the highest values on the tooth surfaces. The maximum values increase with circumferential speed. A comparison of the flow fields in Figure 5 with the HTC values in Figure 7 confirms that high HTC values are present in regions with distinct air flow and high flow velocity. Consequently, gear-C shows a symmetrical HTC distribution over the gear surfaces in the axial direction, with relatively low HTC values are shown in the middle of the tooth flanks. For gear-LL, the highest HTC values are predominant at the tooth tips on the front side of the gears. This indicates the strong influence of the interaction between the gear surfaces and the inflowing air on the HTC.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 2 Literature Review
### Basic Operations of MANET  
The underlying principle of a mobile ad hoc network is that each device can contribute to the formation of the network by acting as a router, passing messages (data packets) to its neighbors, as well as being either a data source, data sink, or both. With such cooperation, nodes that are outside direct communication range from each other can exchange data using intermediate nodes as routers ([PERSON] and [PERSON], 1996, [10]). This cooperative communication allows a network to be connected without relying on any existing infrastructure. The presence of any particular node in the network is not guaranteed, because any node can join or leave at any time. Due to the mobile nature of the nodes and the absence of a fixed infrastructure, a MANET requires wireless communication. This is because wired links would prevent movement, and a wired link would constitute a fixed infrastructure. The development of low-cost wireless data communications in the form of IEEE 802.11 wireless Ethernet (Wi-Fi)-based systems has significantly helped in the research and development of MANETs by providing a usable, lower protocol layer of technology for building MANETs. This is evidenced by the almost universal adoption of IEEE 802.11 as the MAC layer in papers published after the release of the IEEE 802.11 standard in 1997 (IEEE, 2013, [5]) and its inclusion in the ns-2 extensions provided by the Monarch Project (Monarch Project, 2004, [43]).  
During an MANET operation, node A may wish to send data to node B. Note that the MANET itself does not have any knowledge of the data contents, the reason of its transmission, or the relationship between the nodes. Just like the more common, fixed infrastructure-based network model, MANETs operate on a layer model, and the network is only concerned with data delivery. The generation and processing of the data is the responsibility of the source or sink nodes. If nodes A and B are within each other's communication range, then A can send directly to B in a single hop. However, in most cases, there will be no direct link, so a mobile ad hoc network is necessary. Node A will send the data to an intermediate node, which will pass the data on to another node, and so on, until the destination, node B, is reached. However, this example has several assumptions. They are as follows: node A knows how to reach node B; node A either knows the logical location of B or at least has next hop information; there are intermediate nodes that are willing and able to pass the data on; and there is a connected route between A and B. This will be made possible if a route between A and B is established. Whilst routing across fixed infrastructure networks is well understood and robustly implemented, MANETs present a number of key issues in route management. However, MANETs also present several key opportunities by not relying on pre-installed infrastructure and allowing nodes to move.
## 4 Security
### Availability of Information  
Such routing attacks also threaten the last criterion, as the data may not be delivered timely or even at all and the information are therefore not available. Additional attacks can be conducted at different layers to disturb the availability of information. At the physical layer, jamming may cause interference at different frequencies in an intermittent or constant manner that make the communication impossible. Jamming may be caused by malicious attacks or unintentionally by surrounding equipment. To fight against malicious jamming, the physical protection of the industrial sites is one of the first measures to adopt. However, most of the industrial sites still accept external visitors. Even if their visits may be strictly controlled, attackers might benefit from security weaknesses to introduce jammers within the factory. Additionally, uninterrupted transmission of data by the attacker can generate collisions and force retransmissions at data link layer. The energy budget of the node decreases rapidly due to the retransmissions and the sensor is made inoperable. Additional energy consumption can also be caused by flooding the network with many connection requests at transport layer for example.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Plant Extract**} & \multirow{2}{*}{**Bacterial Strain**} & \multicolumn{6}{c}{**Mean Zone of Inhibition (mm) \(\pm\) SD**} \\ \cline{3-8}  & & **50.0 mg/mL** & **25.0 mg/mL** & **12.50 mg/mL** & **6.25 mg/mL** & **3.125 mg/mL** & **CPF (100 mg/mL)** \\ \hline \multirow{8}{*}{_V. domains_ crude saponin extract_} & _E. coli_ & 200 \(\pm\) 1.41 & 15.0 \(\pm\) 1.41 & 10.5 \(\pm\) 0.71 & 8.0 \(\pm\) 0 & 7.0 \(\pm\) 0 & 29.25 \\  & _E. coli ATCC_ & 11775 & 17.0 \(\pm\) 0 & 12.5 \(\pm\) 0.71 & 10.5 \(\pm\) 0.71 & 8.0 \(\pm\) 0 & - & 28.85 \\  & _S. aureus_ & 130 \(\pm\) 1.41 & 10.0 \(\pm\) 0 & 8.5 \(\pm\) 0.71 & - & - & 24.85 \\  & _S. aureus_ ATCC 12600 & - & - & - & - & - & 22.25 \\  & _P. aeruginosa_ & 185 \(\pm\) 0.71 & 16.0 \(\pm\) 0.13 & 13.5 \(\pm\) 0.71 & 10.0 \(\pm\) 1.41 & 7.5 \(\pm\) 0.71 & 26.85 \\  & _P. aeruginosa_ ATCC 10145 & 15.0 \(\pm\) 0 & 12.0 \(\pm\) 1.41 & 9.5 \(\pm\) 0.71 & - & - & 25.25 \\ \hline \multirow{8}{*}{_P. macrophylla_ crude saponin extract_} & _E. coli_ & 18.0 \(\pm\) 1.41 & 14.5 \(\pm\) 0.71 & 12.0 \(\pm\) 1.41 & 9.5 \(\pm\) 0.71 & 7.5 \(\pm\) 0.71 & 29.25 \\  & _E. coli_ ATCC 11775 & 13.0 \(\pm\) 1.41 & 10.0 \(\pm\) 0 & 8.0 \(\pm\) 1.41 & - & - & 28.85 \\  & _S. aureus_ & 105 \(\pm\) 0.71 & 8.0 \(\pm\) 0 & 7.0 \(\pm\) 0 & - & - & 24.85 \\  & _S. aureus_ ATCC 12600 & - & - & - & - & - & 22.25 \\  & _P. aeruginosa_ & 185 \(\pm\) 0.71 & 13.5 \(\pm\) 0.71 & 12.0 \(\pm\) 0 & 10.0 \(\pm\) 1.41 & 7.5 \(\pm\) 0.71 & 26.85 \\  & _P. aeruginosa_ ATCC 10145 & 13.5 \(\pm\) 0.71 & 10.5 \(\pm\) 0.71 & 9.0 \(\pm\) 0 & 7.0 \(\pm\) 0 & - & 25.25 \\ \hline \hline \end{tabular}  
* CPF: electrode; values are mean zone of inhibition (mm) \(\pm\) standard deviation of three replicates ; no activity.  
\end{table} 
Table 3: Bacteria growth inhibition zones of saponin extract of _V. domains_ and _P. macrophylla_ stem bark.
## 2 Results and Discussion
### Materials Characteristics
#### 2.1.2 FTIR/ATR Analysis  
FTIR/ATR spectroscopy was applied to assess the advancement in the phase bonding of the organic-inorganic microspheres. The building of dual systems is associated with the content of both polymeric as well as silane phases. Therefore, the characteristic vibrations from divinylbenzene and triethoxyvinylsilane have been observed (Figure 2). The spectra show bands at 1000-1110 cm\({}^{-1}\) associated with the asymmetrical stretching vibrations of Si-O-C groups, which confirm the incorporation of TEVS into the polymeric structure of the synthesized material. This peak is not observed for divinylbenzene (Figure S1, Supplementary Material). The TEVS introduction into the polymeric phase has been found for all studied materials. However, the peak with the highest intensity is observed for the sample with the highest amount of triethoxyvinylsilane [33]. The existence of stretching bonds C-H of methyl (-CH\({}_{3}\)), ethyl (-CH\({}_{2}\)-CH\({}_{3}\)) -2900 cm\({}^{-1}\) and vinyl groups (-CH=CH\({}_{2}\)) -3080 cm\({}^{-1}\) and -2998 cm\({}^{-1}\) and aromatic ring of divinylbenzene -3020 cm\({}^{-1}\) are observed. The peak intensity of aromatic ring for DVB:TEVS materials is associated with the sample composition. For DVB:TEVS = 1:2 this peak intensity is the lowest. The stretching vibrations of C=C from vinyl groups are found at 1650 cm\({}^{-1}\). The ring stretches of aromatic ring are visible at 1600, 1500 and 1450 cm\({}^{-1}\), respectively. The deformation vibrations of methyl and ethyl groups are observed in the range of 1470-1350 cm\({}^{-1}\). Moreover, in the range of 900-650 cm\({}^{-1}\) is possible to see deformation bands of C-H groups at benzene ring, therefore, the presence out of plane ring and H bending vibrations at 695 and 750 cm\({}^{-1}\) is detected. Besides, the out of plane bending and twisted vibrations of vinyl groups at 908 and 991 cm\({}^{-1}\) are observed [36; 37; 38; 39; 40; 41; 42].
## 3 Data and Results
### Results  
#### 3.2.1 Predicting Smartphone Technology  
We inference ROC using smartphone data until 2017 and forecast a first launch date afterward. A result of tfdea using smartphone data is shown in Table 3.  
In Table 3, the first Column is the name of each DMUs' manufacturer. The Release year is DMU k's first release date. Column Efficiency release is a DEA efficiency estimator at the time of each DMU released. Column theta_tf is an efficiency estimator at the time of tf. Column current (Effective date) is the effective date of each DMU. Additionally, column ROC is a measured ROC of DMUs, which was an SOA at tf but changed to a non-SOA when new DMUs appeared. SROC is an average of each ROC and the number of DMUs chosen to infer ROC. There were 80 DMUs between 2005 and 2017, and 37 DMUs were changed from SOA to non-SOA. In total, 22 among 37 DMUs belonged to the period from 2005 to 2011, which means that there were many cases of new smartphone products being released and withdrawn at that time. A total of 43 other DMUs stayed as SOAs in 2017, so they were excluded when measuring ROC. The SROC of smartphones from 2005 to 2017 is 1.07. The A1200 smartphone made by Motorola has an efficiency value of 1 at the time of release in 2005 and an efficiency value of 1.7837 for the production frontier in 2017, so the rate of technological change is calculated as 1.08. The ROC from 2005 to 2012 was analyzed as 1.09, and the ROC from 2013 to 2017 was analyzed as 1.05. The column \"date forecast (tf_exp)\" shows the forecasting results of target DMUs. There was no difference between the two groups as a result of statistical testing between the predictions from 2018 to 2020 and the release year. From the output of the two-sample Wilcoxon rank-sum (Mann-Whitney) test, we see that we fail to reject the null hypothesis that the populations are the same at a 0.05 significance level. These results indicate that the rate of SOA progress up to 2017 surpasses the rate of technological change from 2018 to the present. Samsung's Galaxy S20 5G, launched in 2020, is in use by the ROK armed forces, and it can be assumed that the 2021 release year is to meet high military requirements. On the other hand, Apple's expected release year of the iPhone SE (2000) model was 2017, but it was actually released in 2020. Although the iPhone SE (2000) is not a flagship model, it can be assumed that it is intended to broaden the base of consumers. To find out in more detail about the technological change of smartphones by year, the following section analyzes the productivity change. \begin{table}
\begin{tabular}{c c} \hline
**Input** & **Outputs** \\ \hline Price in EUD (price) & Network bands-2G–5G (ntwk) \\  & Display size in inches (scrsnize) \\ \hline  & Display resolution in pixels (resolution) \\  & Sensors (sensor) \\  & Network speed in Mbps(ntwk.1) \\ Body weight in g (bdywgt) & CPU speed in MHz (cpus) \\  & Main camera performance (pcamp) \\  & Battery capacity in mAh (btycpty) \\  & Communication WLAN (comlev) \\ \hline \end{tabular}
\end{table} 
Table 2: Selected inputs and outputs.
## 4 Case Study
### Comparison Method  
To verify the effectiveness and feasibility of the SDRD method, it is compared with several advanced methods.  
1. SNADM: This method is based on an equivalent approach of the cumulative damage theory to derive the piecewise expression for the overall cumulative degradation function. It then combines the nonlinear function to obtain a Segmented Nonlinear Accelerated Degradation Model (SNADM). Subsequently, numerical iterative methods are used to estimate the model parameters [(22)].
2. Simple IG process: This method uses an IG process-based approach described in Section 2.2 of this article, which ignores the randomness of the degradation paths among different samples. It assumes that all samples share the same performance degradation process and uses the MLE method to estimate the model parameters.
3. IG model considering random effects (IG-RE): This method only considers the randomness of the product degradation paths and uses the Markov Chain Monte Carlo (MCMC) method to estimate the unknown parameters in the degradation model [(21)].
## 4 Results and Discussion  
In this section, we investigate the system performance in terms of OP, AT, BER, and the PS factor in connection with the main system parameters: \(\eta\), \(\rho\), \(P_{s}\)/\(N_{0}\) and \(\sigma_{1}\), \(\sigma_{2}\). The primary system simulation parameters are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Name** & **Values** \\ \hline \(\eta\) & Energy harvesting efficiency & 0.7 \\ \(\lambda_{h}\) & Mean of \(|\mu|^{2}\) & 0.5 \\ \(\lambda_{g}\) & Mean of \(|g|^{2}\) & 0.5 \\ K & Rician K-factor & 3 \\ \(\gamma_{th}\) & SNR threshold & 7 \\ \(P_{s}\)/\(N_{0}\) & Source power-to-noise ratio & 0–30 dB \\ \(\sigma_{1}\) & Distortion error & 0.01 \\ \(\sigma_{2}\) & Distortion error & 0.05 \\ R & Source rate & 3 bit/s/Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main simulation parameters.  
In addition, the OP and AT versus the PS factor \(\rho\) are shown in Figures 7 and 8. It can be observed that the AT increased and OP fell at the D with factor \(\rho\) from 0 to 0.6. After that, the OP and AT had the opposite effect when ratio \(\rho\) from 0.6 to 1.0. We can find the optimal value of factor \(\rho\) from 0.6 to 0.7 in this situation. It can be observed that when \(\rho\) is too small, R cannot harvest enough energy from S to operate reliably, but when \(\rho\) is too large then the reliability of the communications link from S to R is impaired. Furthermore, the OP and AT of the model system versus \(\sigma_{1}\) = \(\sigma_{2}\) varied from 0 to 0.2 and are plotted in Figures 9 and 10. In the same way, OP increased and AT decreased with the increasing of \(\sigma_{1}\) = \(\sigma_{2}\). In all the above figures, the simulation and analytical results agree well with each other.  
Figure 5: The outage probability versus ratio of \(P_{s}/N_{0}\).  
Figure 6: The throughput versus ratio \(P_{s}/N_{0}\).  
Figure 8: The throughput versus \(\rho\) for the PS protocol.  
Figure 7: The outage probability versus \(\rho\) for the PS protocol.  
Figure 9: The outage probability versus \(\sigma_{1}=\sigma_{2}\).
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## 4 Discussion
### Limitations and Future Work  
The bias of the training datasets to train the machine learning methods may have introduced some errors into the up-scaling of GPP. In this paper, NDVI, FVC, SWR, RH and Ta were used to train the machine learning method. In the future, some other variables, such as soil moisture, may also be taken into account in the model training. Moreover, we may carry out some research about the error transfers in GPP up-scaling when using the machine learning models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **ANN (\%)** & **Cubist (\%)** & **RF (\%)** & **SVM (\%)** & **DBN (\%)** \\ \hline FVC & 22 & 21 & 22 & 24 & 20 \\ SWR & 21 & 16 & 22 & 21 & 25 \\ Ta & 17 & 21 & 21 & 18 & 22 \\ RH & 8 & 11 & 6 & 9 & 7 \\ NDVI & 32 & 31 & 29 & 28 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Global sensitivity analysis of the input data to the up-scaled GPP. FVC (fractional vegetation cover), SWR (shortwave radiation), Ta (air temperature), RH (relative humidity), NDVI (normalized difference vegetation index), ANN (artificial neural network), RF (random forest), SVM (support vector machine), DBN (deep belief network).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **ANN (\%)** & **Cubist (\%)** & **RF (\%)** & **SVM (\%)** & **DBN (\%)** \\ \hline FVC \(\pm\) 10\% & 4.69 & 5.03 & 5.71 & 4.62 & 5.82 \\ FVC \(\pm\) 50\% & 16.83 & 20.61 & 21.92 & 16.21 & 22.23 \\ SWR \(\pm\) 10\% & 6.77 & 8.55 & 7.63 & 7.23 & 8.02 \\ SWR \(\pm\) 50\% & 18.69 & 36.81 & 22.34 & 20.08 & 30.21 \\ Ta \(\pm\) 10\% & 2.63 & 3.24 & 2.98 & 3.65 & 2.74 \\ Ta \(\pm\)The errors induced by scale transformation of the input data would bring some uncertainty to the up-scaled results. Interpolation of the meteorological data, aggregation of the land-cover data, and the method to match the field observation data and satellite data would cause some errors in the up-scaling of GPP. Improving the method to match the scales of the training data in the up-scaling of GPP, would be another way to improve the accuracy of the up-scaling results.  
Moreover, it is still challenging to assess the performance of the scale transform models. In this paper, up-scaled GPP were validated at the field scale, footprint scale and regional scale. Some cross validation methods would also be adopted in the future to analyze the effects of the up-scaling models.