esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of Cell Viability, Nitric Oxide (NO), and Melanin Production  
To analyze the NO level in the cultured medium, RAW264.7 cells were treated with or without lipopolysaccharide-asacharide (LPS) in the presence of extracts (50, 100, 150, and 200 ug/mL). After 24 h, 100 uL of Griess reagent (Promega Co., Ltd., Madison, WI, USA) was mixed with an equal volume of cell supernatant and incubated at room temperature for 10 min. The optical density at 540 nm was measured, and NO concentrations were calculated using a nitrite standard curve. The IC\({}_{50}\) value (the concentration of 50% inhibition) was calculated from the graph of inhibition percentage against different extract concentrations.  
The antimelanogenic activity was determined by treating B16F10 cells with each extract (50, 100, 150, and 200 \(\upmu\)g/mL) and 100 \(\upmu\)M of 3-isobutyl-I-methylxanthine (IBMX). Cultured B16F10 cells were plated at a density of 1 \(\times\) 105 cells/mL in 6-well plates, followed by incubation at 37 \({}^{\circ}\)C for 24 h. Subsequently, the B16F10 cells were treated with each extract and IBMX and incubated at 37 \({}^{\circ}\)C for 48 h. Afterward, the cells were washed twice with ice-cold PBS and harvested by centrifugation at 4000 rpm for 10 min. The resulting cell pellets were solubilized in 1 N NaOH with 10% dimethyl sulfoxide at 65 \({}^{\circ}\)C for 1 h. The concentration of melanin was determined using a microplate reader at 490 nm. Data are expressed in terms of melanin-synthesis inhibitory activity compared to the mock control. IC\({}_{50}\) value is the concentration of extract at which IBMX-induced melanin production is inhibited by 50%.  
Using a previously described protocol, the cell viability was assessed using a 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) solution, and the absorbance was measured at 520 nm [6]. RAW264.7 or B16F10 cells were treated in each concentration of leaf or root extracts and stimulated with LPS or IBMX. After 24 h (for RAW264.7 cells) or 48 h (for B16F10 cells), 20 \(\upmu\)L of MTT (5 mg/mL in PBS) solution was added to each well. Incubation was continued for 4 h more. The formazan crystals were dissolved in DMSO, and the optical density was measured at 520 nm.
# Heterologous Production of Acrylic Acid: Current Challenges and Perspectives
## 2 Heterologous Production of 3-HP, a Frequent Intermediary from the AA Pathway
### Glycerol Route
Surprisingly, other genetic modifications that were performed in previous studies did not lead to an improvement in 3-HP concentrations (e.g., deletion of _yqhD_, _ackA_ and _pta_). 3-HP heterologous production using the glycerol route has also been reported in _K. pneumoniae_[37; 39; 47], _C. glutamicum_[49], _P. denitrificans_[44; 45; 78] and _B. subtilis_[85]. In one of the studies focused on 3-HP production in _P. denitrificans_, KGSADH was engineered [78]. The mutated enzyme showed higher substrate specificity and led to lower accumulation of 3-HPA and, consequently, less enzyme inactivation and higher production of 3-HP than the control. Table 1 also includes some of the most promising reports on these strains. However, these studies will not be thoroughly explored, as so far there are no reports on AA production using these hosts. Nevertheless, considering the obtained concentrations (up to 102 g/L), and the fact that they naturally synthesize vitamin B\({}_{12}\) (and are easier to engineer to increase its availability), these hosts may be selected in the future for AA production. From these three strains, _P. denitrificans_ would be the most appealing since it does not present a biosafety hazard, such as _K. pneumoniae_, and similar concentrations were reported. For this reason, the work regarding 3-HP production in this organism has been patented [44; 45]. Additionally, to improve the concentrations, a co-culture engineering strategy with _Lactobacillus reuteri_ and _E. coli_ was recently used [48]. _L. reuteri_ is able to naturally synthesize 3-HP but with high accumulation of 3-HPA. Additionally, it can naturally synthesize vitamin B\({}_{12}\). Therefore, _E. coli_ was modified to overexpress _gabD4_ and _pduQ_ (1,3-propanediol oxidoreductasetase from _L. reuteri_) to convert 3-HPA to 3-HP (125.93 g/L) and 1,3-propanediol (88.46 g/L), respectively [48]. This 3-HP concentration (and productivity) was higher than the maximum obtained when only _E. coli_ (76.2 g/L) [36] or _P. denitrificans_/_K. pneumoniae_ (102 g/L) [44; 47] was used.
## 3 Results
### Comparing with the Literature
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types** & **Simulation** & **Literature** & **Sources** & **Species** & **Locations** \\ \hline  & 410 & 109–516 & Estimation [35; 69; 70] & Oak & Kasuya \\  & 511 & 550 & [PERSON] (2011) [36] & Oak & Gwangneung \\  & 154 & 78–159 & [PERSON] et al. (2009) [43] & Oak & Seolmacheon \\  & 65 & 56–66 & [PERSON] et al. (2016) [45] & Oak & Sohung \\  & 153 & 154 & [PERSON] et al. (2008) [46] & Oak & Bikinsky \\ GSV & 391 & 378–645 & [PERSON] et al. (2013) [38] & Pine & Gwangneung \\ (m\({}^{3}\) ha\({}^{-1}\)) & 146 & 117–173 & [PERSON] et al. (2003) [50] & Pine & Honghuaerji \\  & 282 & 282 & [PERSON] et al. (2008) [46] & Pine & Oblast \\  & 124 & 102 & [PERSON] et al. (2008) [46] & Pine & Pribaykalsky \\  & 127 & 118–133 & [PERSON] et al. (2001) [73] & Larch & Laoshan \\  & 227 & 227 & [PERSON] et al. (2008) [46] & [PERSON] & Yeravninsky \\  & 116 & 117 & [PERSON] et al. (2008) [46] & Larch & Buryatia \\ \hline  & 4.28 & 4.30–6.05 & [PERSON] et al. (2003) [71] & Oak & Gwangneung \\  & 5.39 & 4.00–6.55 & [PERSON] et al. (2010) [42] & Oak & Gwangneung \\  & 3.70 & 5.05 & [PERSON] et al. (2008) [46] & [PERSON] & Bikinsky \\  & 6.39 & 5.37–7.20 & [PERSON] et al. (2005) [48] & Pine & Gwangneung \\ NPP & 6.75 & 4.30–6.03 & [PERSON] et al. (2014) [16] & Pine & Wonsan \\ (Mg C ha\({}^{-1}\)) & 3.49 & 3.06 & [PERSON] et al. (2008) [46] & Pine & Oblast \\  & 2.50 & 2.72 & [PERSON] et al. (2008) [46] & Pine & Pribaykalsky \\  & 5.23 & 7.18–7.89 & [PERSON] et al. (2013) [26] & [PERSON] & [PERSON] \\  & 1.28 & 3.38 & [PERSON] et al. (2008) [46] & [PERSON] & [PERSON] \\  & 0.49 & 2.81 & [PERSON] et al. (2008) [46] & [PERSON] & [PERSON] \\ \hline GPP & 8.72 & 11.93–12.06 & [PERSON] et al. (2012) [72] & [PERSON] & Seolmacheon \\ (Mg C ha\({}^{-1}\)) & 2.85 & 5.62–6.10 & [PERSON] et al. (2015) [24] & [PERSON] & Khentei \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparing BGC-MAN simulations and previous literature. Furthermore, the simulated NPPs were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS)'s NPP (MOD17A3H) product from the National Aeronautics and Space Administration (NASA), the United States [74]. Since the MOIDIS NPP product data are available from 2000 and our study has the BGC-MAN results until 2005, the six years of overlapping NPP results were compared to find the relationship (Figure 5). The NPP from the BGC-MAN simulation with climatic variations and the MODIS NPP from different characteristics of spectral reflectance may vary from time to time, but overall, they showed a positive correlation. The BGC-MAN results were underestimated compared to the NPPs from the MODIS NPP product, and the variations were shown in some larch forests because there was under estimation of the NPP from BGC-MAN.
## 5 Conclusions
Contributions of influencing factors to vegetation dynamics in global drylands during 1982-2018 based on the Partial Least Squares (PLS) method. (a) Precipitation, (b) temperature, (c) solar radiation (SRAD), and (d) human activities; Figure S9. The scatter fitting between the simulated LAI from 22 CMIP6 ESMs and its corresponding climatic factors. (a) Precipitation flux (pr, water-related index), (b) relative humidity (hurts, the opposite index of VPD in the satellite-observed analysis), (c) temperature (las, (d) solar radiation (rsds) under historical scenarios (1982-2014);Figure S10. Multi-year averaged groundwater reserves and their interannual trends across global drylands during 1982-2018: (a) multi-year mean, (b) interannual trend; Figure S11. Spatial distribution of interannual trends in different LAI, GPP, and SIR datasets during the period of 1982-2018. (a) GLASS LAI, (b) GLASS GPP, (c) GLOBMAP LAI, (d) NIRv GPP, (e) GIMMS LAI3g, (f) EC_LUE GPP, (g) GIMMS LAI4g, and (h) CSIF. Regions labelled by black dots represent those trends that are statistically significant at a 95% significance interval (Mann-Kendall test; \(p<0.05\)); Table S1. Area percentage of the interannual trends of main climatic factors; Table S2. Area percentage of the interannual trends in soil water (SW) at different soil depths; Table S3. Area percentage of the contributions of heat-related factors and human activities to vegetation growth in global drylands during 1982-2018; Table S4. The area of vegetation changes from different satellite-observed LAI/GPP/SIF datasets. Conceptualization, F.T. and L.W. ; methodology, Y.Y. ; investigation, Y.Y. and J.C.; resources, F.T. ; data curation, F.T. ; writing--original draft preparation, F.T. and L.W. All authors have read and agreed to the published version of the manuscript. This research was fund by the National Natural Science Foundation of China (42401147). The GLASS LAI was is freely available at the National Earth System Science Data Center of China (NESSDC, [[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)), (accessed on 25 August 2023)); the GIMMS LAI4g product (version 1.2) is freely downloaded from the ZENODO website ([[https://zenodo.org/records/8281930](https://zenodo.org/records/8281930)]([https://zenodo.org/records/8281930](https://zenodo.org/records/8281930)), (accessed on 7 March 2019)). GLASS GPP was publicly obtained from the official website of NESSDC ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)), (accessed on 15 September 2022)). The GLASS-GLC product is openly obtained from the official website of the multi-discipline research database ([[https://doi.pangaea.de/10.1594/PANGAEA.913496](https://doi.pangaea.de/10.1594/PANGAEA.913496)]([https://doi.pangaea.de/10.1594/PANGAEA.913496](https://doi.pangaea.de/10.1594/PANGAEA.913496)), (accessed on 23 April 2019)). We thank the TRENDY modelling group for providing the TRENDY data. The authors declare no conflict of interest.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 5 Research Opportunities and Future Directions
### RF Frontend Design  
First, there exists a huge room for improvement and advancements to design efficient RF frontend components. For instance, mmWave power amplifiers are usually less efficient due to the very high frequency of operation. Similarly, the design of wideband PAs and low-noise amplifiers (LNAs) at 60 GHz mmWave is also a challenge [137]. Particularly high PA efficiency will help to extend communication range and enhance battery life. The phase shifters (e.g., to be used for phased array antennas) at mmWave frequencies are quite lossy, and thus, accurate phase shifts are difficult to achieve with low insertion loss. Lossy phase shifters also reduce antenna gain at high frequencies. Thus, research should be carried out to design efficient phase shifters at mmWave bands to reduce antenna losses. In the same way, other RF components, such as mixers and voltage-controlled oscillators, must have a very large tuning range at the 60 GHz band. Likewise, the design of high-speed digital to analog converters (DAC) and analog-to-digital converts (ADC) is also a challenge. ADC is a crucial component of the 60 GHz communication system as it determines the achievable data rates.
## 3 System Overview
### Measurement Device  
In this study, a CDM7160 CO\({}_{2}\) sensor module provided by Figaro was used to measure CO\({}_{2}\) concentration and a temperature sensor, BME280, was used to check the temperature change in the room. The reason for selecting the CDM7160 CO\({}_{2}\) sensor module is that the data output is digital rather than analog, which is often used in conventional CO\({}_{2}\) concentration sensors. The accuracy is \(\pm 50\) ppm plus 3% of the output value in the range of 300 to 5000 ppm, which is higher than that of conventional sensors [11]. The measurement range is limited to a single point in the sensor because the CO\({}_{2}\) concentration in the atmosphere that enters the measurement part of the sensor through the nonwoven fabric is measured. The accuracy of the BME280 is \(\pm 1\)\(\ {}^{\circ}\)C in the range of \(-45\) to \(85\ \ {}^{\circ}\)C. Figure 2 shows the external view of the measurement device.  
To confirm that the acquired data were correctly obtained, we compared and corrected 12 measurement devices using the IES-5000 type indoor environment measurement set developed by Shibata Kagaku Co [12]. We used a two-point calibration curve [13] with two gases, 500 ppm and 1500 ppm, to make adjustments, and the sensor outputs of all the measurement devices were corrected to be within the error range.  
Figure 1: System concept diagram.  
Figure 2: Measurement device appearance.  
4 Analysis of CO\({}_{2}\) Diffusion Trend to the Position of the Subject and the Measurement Device
## 3 Materials and Methods
### Transition Design Equations and Parameters
#### 3.1.1 Microstrip Line Calculations  
The following design Equations (1)-(5) were used for the design calculations of the microstrip line [14];  
The effective dielectric constant;  
\[\varepsilon_{eff}=\frac{\varepsilon_{r}+1}{2}+\frac{\varepsilon_{r}-1}{2} \bigg{(}1+\frac{12h}{w}\bigg{)}^{-0.5} \tag{1}\]  
where \(\varepsilon_{r}\) = dielectric constant of the substrate,  
\(w\) = width of the microstrip,  
\(h\) = height of the substrate  
\[\text{Chosen characteristics impedance};\ Z_{0}=\frac{60}{\sqrt{\varepsilon_{ eff}}}ln\bigg{(}\frac{8h}{w}+\frac{w}{4h}\bigg{)}\cdot\cdot\cdot\cdot\cdot\frac{w}{h}\leq 1 \tag{2}\]  
\[Z_{0}=\frac{120\pi}{\Big{(}\sqrt{\varepsilon_{eff}}\Big{)}\big
# On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
## 5 Numerical Results  
In this section, we first present the numerical results of the probability of eavesdropping attacks \(P(E)\) with comparisons among different schemes in Section 5.1. Then we will show the impacts of friendly jammers on the legitimate communications in Section 5.2.
## 5 Results and Discussion
### Simulation Results
#### 5.1.3 Scenario 3: Ocean Current and Dangerous Area Information  
This part compares and analyzes the operational effects of three algorithms in scenario 3 that contains both ocean current information and danger area information. Figure 10 compares the results of PSO, FFA, and SHH algorithms in such a scenario. The solid line was the result of the PSO, the dash-dot line was the result of the FFA, and the dashed line was the result of the SHH. Apparently, the paths planned by the three algorithms satisfy the constraints of no collision, smoothness, and to some extent along the direction of the ocean current.  
Table 4 is the result comparison of PSO, FFA and SHH algorithms in the case of scenario 3. In terms of computing time, the SHH algorithm used the shortest computing time, with the average time of 0.6241 s, which was 0.1992 s faster than the FFA. The total cost value of the final path using the FFA was optimized to 935.7844, while for the SHH, the optimal path cost was 893.2590. The SHH had significant advantages in terms of time cost and ocean current cost, but the FFA had a smaller curvature value. PSO had the worst performance in the aspect of computing time, final total cost, and curvature cost value. It shows that the path obtained by the SHH algorithm is shorter and more in line with the ocean current constraint.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PSO** & **FFA** & **SHH** \\ \hline Computing time & 2.4215s & 1.2700s & 0.6217s \\ Final total cost value & 588.3817 & 322.0653 & 306.4266 \\ Time cost value & 185.6156 & 197.4390 & 196.8662 \\ Map cost value & 0 & 0 & 0 \\ Curvature cost value & 402.7660 & 124.6264 & 109.6048 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results comparison using PSO, FFA and SHH algorithms in scenario 2.  
Figure 10: Comparison of the path planning results of PSO, FFA and SHH algorithms in scenario 3.
## 3 Example Analysis
### Performance Comparison of Different Algorithms  
Under the condition of a large sample, different algorithms are compared on the overall precision rate, F1 score, attack detection rate, and fault detection rate. The attack detection rate is mathematically equal to the average F1 score of different types of network attacks, and the fault detection rate is mathematically equal to the average F1 score of different points of faults caused by different types of network attacks. The comparison results are shown in Figure 10. The comparison algorithms include K-nearest neighbors (KNN), decision tree (DT), voting classifier (VC), eXtreme Gradient Boosting (Xgboost), GBDT algorithm, random forest (RF), adaptive boosting (Adaboost), and extreme forest algorithm (EF). Excepting the decision tree and voting classifier algorithms, the parameter \(k\) of KNN and the number of base classifiers of other algorithms are obtained by the grid search algorithm.  
The average detection precision of the CIKS-MFS-FLGB method for attack detection is 94.09% and the average F1 score is 93.61%. In general, the four indexes of the CIKS-MFS-FLGB are higher than the above eight algorithms. Among them, the accuracy increased by 21.75% on average, the F1 score increased by 24.87% on average, the attack detection rate increased by 23.96%, and the fault detection rate increased by 25.25%. After data balance  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Type and Cause** & **Class** & **Precision** & **F1 score** \\ \hline Fault from 10–19\% (FLG) on L1 & 1 & 0.8814 & 0.9123 \\ Fault from 20–79\% (FLG) on L1 & 2 & 0.9286 & 0.9630 \\ Fault from 80–90\% (FLG) on L1 & 3 & 0.9091 & 0.8352 \\ Fault from 10–19\% (FLG) on L2 & 4 & 0.9792 & 0.9691 \\ Fault from 20–79\% (FLG) on L2 & 5 & 1.0000 & 1.0000 \\ Fault from 80–90\% (FLG) on L2 & 6 & 0.9091 & 0.9302 \\ Fault from 10–19\% (FDIA) on L1 & 7 & 0.7778 & 0.8235 \\ Fault from 20–79\% (FDIA) on L1 & 8 & 0.9189 & 0.9315 \\ Fault from 80–90\% (FDIA) on L1 & 9 & 0.9750 & 0.9398 \\ Fault from 10–19\% (FDIA) on L2 & 10 & 1.0000 & 1.0000 \\ Fault from 20–79\% (FDIA) on L2 & 11 & 1.0000 & 1.0000 \\ Fault from 80–90\% (FDIA) on L2 & 12 & 0.9592 & 0.9307 \\ Fault (RSC) on L1 (RSC) on L1 & 0, 21–25, 33–36 & 0.9488 & 0.9409 \\ Fault (RSC) on L2 (RSC) on L2 & 26–30 & 0.9194 & 0.9011 \\ Average & — & 93.62\% & 93.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis of fault detection performance.  
Figure 10: Performance comparison of different algorithms.  
processing, the CIKS-MFS-FLGB reduces the false alarm rate of the power CPS network attack identification model. Through comparative analysis, the CIKS-MFS-FLGB method realizes the detection of the CPPS network attacks with higher precision and improves the stability of the power system operation.
# Design and Applications of GLANCE: GLanceable Alarm Notification for a User Centered Experience
## 3 Results of the 1 st Macro-Phase: The GLANCE Project
### First Iteration: Dealing with Individual Alarms
#### 3.1.1 Definition of the Abstract Model and Application to the Organizational Situation
Application to the specific problem situationThe five severity levels considered in the TLC network naturally maps to different areas of the attention-awareness graph, as illustrated in Figure 5: _Cleared_ and _warning_ are associated with the _inattention_ zone and the _change-blind_ notification level, _minor_ is associated with the _divided attention_ zone and the _make-aware_ notification level, _major_ is associated with the _focused attention_ zone and the _interrupt_ notification level, and _critical_ is associated with the _focused attention_ zone and the _demand-attention_ notification level. Figure 4: Application of the first GLANCE model to fault notification: (**a**) Mapping the five severity levels onto three abstract schemata and (**b**) sample fault notifications with different severity levels.
## 3 Experimental Results
### Experimental Results for Handwritten Receipt Characters  
A precaptured template image was used for template matching for the handwritten characters. After each receipt image is completely scanned, the matching image position was determined, and the text area was extracted using the relative position of the matching image. Figure 14 displays the template matching results.  
Figure 10: Receipt information required for tax filing.  
Figure 11: Various receipt formats: (**a**) electronic receipt certification sheet, (**b**) cash register unified receipt, (**c**) triple unified receipt, (**d**) double unified receipt, (**e**) special unified receipt, and (**f**) electronic computer unified receipt.  
Figure 12: (a) Numerical and (b) alphabetical characters in the printed dataset.  
Figure 13: Accuracy and loss function of (a) numerical and (b) alphabetical characters in the printed dataset.  
The erosion method was used to extract the vertical lines in the table after the text area can be obtained. Subsequently, a projection method that determines the position of the vertical line was used to extract each box. Next, the character segmentation initiation point was identified, and the original receipt image was segmented to obtain the handwritten character images. Then, the handwritten character images were processed by using the  
Figure 16: Template matching results.  
Figure 14: Numbers in the handwritten dataset.  
Figure 15: (**a**) Accuracy and (**b**) loss function of the numbers in the handwritten dataset.  
binarization method to binary images (Figure 17). Finally, CNN was adopted to identify relevant characters. Figure 17 shows the projection and segmentation results.  
The derived character image was used as the input of the CNN model for recognition. The recognition results were stored in the database for subsequent review and error correction. Figure 18 displays the CNN recognition results.  
A total of 145 handwritten receipt characters were obtained in the verification set. Moreover, a total of 6815 characters were noted, of which 5516 were successfully recognized, resulting in a recognition accuracy of 80.93%.  
Figure 17: Projection and segmentation results.