esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The aim of this study was to evaluate the influence of applying the Deep Learning-based Super-Resolution model on low resolution thermal video sequences for the purpose of improving remote measurement of a respiratory rate. While telemedicine was originally intended as a tool to treat patient in remote or not accessible locations, it is becoming increasingly popular as a tool to receive health care in a convenient way. Respiration rate is one of the useful metrics that can help in determining remote patient's health condition, and one of the means to obtain this signal is with the use of thermal cameras. Thermal cameras, while still expensive in comparison with standard web cameras, are becoming more affordable due to technological advances. Their modules are also becoming smaller, which allows for embedding them in more portable form factors, and some day we might even see them implemented in our personal computers just next to the web camera. The preliminary results indicate that applying such technique is beneficial and can lead to the decrease of the respiratory rate estimation error when compared with other enhancement methods, i.e., EVM by 41.2% and bicubic by 45.3% in the best case, as proven by tests on two independent databases. The performed experiments proved that DL can help to improve accuracy of vital signs estimation, however, we believe that further SR improvements could lead to achieving even higher accuracy and thus we will focus on this area in the future work. Another element of our work that could be improved is the selection of ROI and the window of samples used for RR estimation. Both parameters could be selected automatically, as described earlier. In our study we also identified other limitations of the proposed method, such as short acquisition time or specific conditions for data collection, i.e., visibility of the nostril area and provided ideas for addressing these issues in future studies.  
The authors specify following individual contribution to this study: conceptualization, A.K. and J.R.; methodology, A.K. and J.R.; software, A.K., J.R. and M.S.; validation, A.K.; formal analysis, A.K.; data acquisition, A.K. and J.R.; data curation, M.S.; writing-original draft preparation, A.K., J.R. and M.S.; writing-review and editing, A.K., J.R. and M.S.; visualization, A.K. and M.S.; supervision, J.R.; funding acquisition, J.R., A.K. and M.S.  
This work has been partially supported by Statutory Funds of Electronics, Telecommunications and Informatics Faculty, Gdansk University of Technology and by Intel Corporation, USA. We thank all our colleagues, who provided insight and expertise that greatly assisted the research.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 2 Data and Methods
### Methodology
#### 2.3.2 Satellite Phenological Evaluation Based on Ground-Based Observations  
We compared phenology data extracted from satellite SIFs with GPP-based phenology data and used mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) as evaluation metrics.  
\[\text{ME}=\frac{1}{\text{n}}\sum_{\text{i=1}}^{\text{n}}(\text{y}_{\text{i}}- \hat{\text{y}}_{\text{i}}) \tag{3}\]  
\[\text{MAE}=\frac{1}{\text{n}}\sum_{\text{i=1}}^{\text{n}}|\text{y}_{\text{i}} -\hat{\text{y}}_{\text{i}}| \tag{4}\]  
\[\text{RMSE}=\sqrt{\frac{1}{\text{n}}\sum_{\text{i=1}}^{\text{n}}(\text{y}_{ \text{i}}-\hat{\text{y}}_{\text{i}})^{2}} \tag{5}\]  
In Equations (3)-(5), n is the number of years, y\({}_{\text{i}}\) is the extracted phenological metrics based on different scales of SIF products, \(\hat{\text{y}}_{\text{i}}\) is the extracted phenological metrics based on GPP, and i is the given year. The closer these evaluation metrics are to 0, the higher the accuracy of the extracted phenological metrics.  
Figure 3: Schematic diagram of the dynamic threshold method.
# Optimal Estimation Retrieval of Aerosol Fine-Mode Fraction from Ground-Based Sky Light Measurements
## 4 Results
### Retrieval from Experimental Data  
#### 4.2.1 Validation of V\({}_{0}\) and FMF\({}_{\rm v}\)  
Furthermore, the OE-based algorithm was performed on the experimental data. The validation of \(V_{0}\) and FMF\({}_{\rm v}\) against AERONET inversion products are shown in Figure 6. For an accurate comparison, the SMAC observation time was limited to a range of \(\pm 15\) min based on the AERONET sky radiation measurement time. As seen in the left panel of Figure 6, data from 490 and 670 nm channels were used for the inversion. As seen in the middle panel, data from four channels (490, 550, 670, and 870 nm) were used for inversion, while in the right panel, the channel of 1610 nm was introduced for the retrieval. In regard to \(V_{0}\), the correlation coefficients (\(R\)) were 0.90, 0.95, and 0.98 for the three scenarios, respectively. Correspondingly, the root-mean-square-error (\(RMSE\)) were 0.296, 0.089, and 0.041, respectively. Likewise, the \(R\) values were 0.838, 0.897, and 0.939, while the \(RMSE\) were 0.101, 0.074, and 0.057 for each of the three FMF\({}_{\rm v}\) scenarios, respectively. The results clearly showed a good performance of our algorithm. It can be seen from Figure 6f that the retrieval of FMF\({}_{\rm v}\) was significantly improved since the observation of the 1610 nm wavelength provided extra information on coarse-mode aerosol. Consequently, the inversion accuracy of FMF\({}_{\rm v}\) was further improved. However, the deviations of FMF\({}_{\rm v}\) were larger than total volume, which means that there were still some uncertainties in retrieving FMF\({}_{\rm v}\). On one hand, the AERONET inversion algorithm adopted multi-angle sky light observations, combined with precise direct AOD measurements, which provided sufficient information to constrain the multi-parameter retrieval of aerosol, including the PSD parameters. While the proposed algorithm in this paper used single-viewing measurements, and the aerosol PSD model parameters were considered as a priori knowledge rather than as state vectors. Consequently, the a priori estimation might have affected the validation results. On the other hand, the calibration error of the SMAC instrument might have been another important factor. A previous study showed that for the OE inversion framework, the observation error was the main factor influencing the uncertainties of the inversion results [52].  
Figure 6: The scatter plot of retrieved \(V_{0}\) (upper panel) and FMF\({}_{\rm v}\) (lower panel) compared to the AERONET inversion products. (**a**) and (**d**) denote the scenarios of two channels (490, 670 nm). (**b**) and (**e**) denote the scenarios of four channels (490, 550, 670, and 870 nm). (**c**) and (**f**) denote the scenarios of all the five intensity channels (including 1610 nm), respectively. The solid line, dotted line, and dashed line represent the fit line, 1:1 line, and the expected error line (\(\Delta x=\pm 0.05\pm 0.15x\)), respectively.
# Phenolic Compounds and Antioxidant Activity in Edible Flower Species from Oxaxa
## 3 Results
### Phenolic Compounds and Antioxidant Activity in the Infloresences of A. salmiana  
The comparison of the contents of phenolic compounds and antioxidant activity between samples of maguey pulquero infloresences revealed significant differences depending on the geographical origin. For example, the samples from Chalcatongo, Santa Cruz Nundaco, and Santa Cruz Itundujia had higher contents of polyphenols and flavonoids and greater antioxidant activity than did the samples collected from San Esteban Attalahuca, Santa L. Monte Verde, Santo T. Ocotepec, and San Pedro Molinos. The variation in polyphenol content ranged from 12.1 to 16.6 mg GAE g\({}^{-1}\); for catechin-equivalent flavonoids, it ranged from 3.40 to 4.73 mg CE g\({}^{-1}\); and for quercetin-equivalent flavonoids, it ranged from 2.73 to 4.32 mg QE g\({}^{-1}\). The antioxidant activity evaluated using the DPPH method ranged from 34.4 to 47.5 umol TE g\({}^{-1}\), while the antioxidant activity evaluated using the FRAP method ranged from 47.7 to 68.0 umol TE g\({}^{-1}\) (Table 4). The contents of polyphenols and flavonoids were significantly correlated with the antioxidant activity according to the DPPH (0.65 \(\leq\) r \(\leq\) 0.76; \(p\) <0.01) and the FRAP (0.35 \(\leq\) r \(\leq\) 0.82; \(p\) <0.01) methods, which indicates that part of the antioxidant activity determined using DPPH and FRAP was due to the contents of polyphenols and flavonoids in the infloresences of maguey pulquero. It should be noted that other compounds also influence antioxidant activity. \begin{table}
\begin{tabular}{l c c c c} \hline \multirow{2}{*}{**Communities of Origin of Samples Evaluated**} & \multirow{2}{*}{
\begin{tabular}{c} **Total Polyphenols** \\ **(mg GAE g\({}^{-1}\))** \\ \end{tabular} } & \multicolumn{2}{c}{**Flavonoids**} & \multicolumn{2}{c}{**Antiox. Activity**} \\  & & **(mg g\({}^{-1}\))** & & **(\(\mu\)mol TE g\({}^{-1}\))** \\ \cline{2-5}  & **CE 1** & **QE 1** & **DPPH** & **FRAP** \\ \hline San Esteban Attalahuca & 12.7 of 2 & 3.42 de & 3.57 c & 35.4 d & 47.9 e \\ Chalcatongo de Hidalgo & 16.6 a & 4.73 a & 4.20 a & 47.5 a & 68.0 a \\ Santa Cruz Itundujia & 14.5 c & 3.70 bc & 4.32 a & 45.4 ab & 56.8 b \\ San Pedro Mills & 13.4 d & 3.44 c-e & 3.55 c & 35.0 d & 48.1 de \\ Santa Cruz Nundaco & 15.6 b & 3.95 b & 4.27 a & 42.8 bc & 59.4 b \\ Santo Tomas Ocotepec & 12.8 de & 3.18 e & 3.86 b & 35.1 d & 47.7 e \\ San Jose Monte Verde & 13.3 de & 3.63 cd & 4.26 a & 41.1 c & 57.1 b \\ Santa Lucia Monte Verde & 12.1 f & 3.53 cd & 3.54 c & 36.8 d & 52.1 cd \\ San Juan Topscollua & 14.6 c & 3.40 de & 2.73 d & 34.4 d & 57.3 b \\ Saint Peter Tidaa & 14.3 c & 3.64 cd & 3.61 c & 35.2 d & 55.7 bc \\ \hline \end{tabular}  
* CE and QE, catechin and quercetin equivalents, respectively; 2 within columns, means with the same letter do not differ significantly (Tukey’s test, \(p\leq 0.05\)).  
\end{table} 
Table 4: Average total polyphenol and flavonoid contents and antioxidant activity in infloresences of _A. salmiana_ collected from different communities in Oaxaca, Mexico.
# Sub-Diffraction Focusing Using Metamaterial-Based Terahertz Super-Oscillatory Lens
###### Abstract  
This paper presents a metamaterial-based super-oscillatory lens (SOL) fabricated by photolithography on a glass substrate and designed to operate at sub-terahertz (sub-THz) frequencies. The lens consists of repeating crisscross patterns of five-ring slits with sub-wavelength diameter. The lens is capable of generating multiple focal points smaller than the diffraction limit, thereby allowing many points to be inspected simultaneously with sub-wavelength resolution. After elucidating the influence of the lens parameters on light collection through calculations by the finite element method, the fabricated lens was then evaluated through actual experiments and found to have a focal length of 7.5 mm (2.5\(\lambda\)) and a hot spot size of 2.01 mm (0.67\(\lambda\)) at 0.1 THz (\(\lambda\) = 3 mm), which is 0.27 times the diffraction limit of the lens. This demonstrated sub-diffraction focusing capability is highly effective for industrial inspection applications utilizing terahertz waves.  
super-oscillatory lens; sub-diffraction focusing; THz; metamaterials 2022
## 1 Introduction
### Objectives and Contributions  
Usable ERP systems tend to reduce the training cost, usage frustration among the users, time to complete a task, and successful completion of projects. If the usability experts, interface designers, and developers are provided with comprehensive details of potential ERP problems and recommendations, it can help them to avoid these problems in designing and developing the ERP system. In addition, this data may help companies considering the usability evaluation of existing ERP systems to make improvement plans. For this purpose, we have proposed an approach for identifying ERP usability issues and developing usability improvement recommendations with the following objectives.  
* Study ERP usability problems in the previous research papers and collect them for further analysis.
* Applying a machine learning approach to aggregate the major ERP system's usability issues found by the users. Consequently, utilize the results for developing a precise checklist related to ERP user-system interaction problems.
* Validate the reliability of the proposed ERP usability problem checklist by performing usability evaluations of existing ERP systems according to the proposed checklist.  
* Develop the recommendations for the stakeholders to avoid frequent usability issues and report them to the community.  
This paper proposed a state of the art approach to identifying the main usability issues in ERP systems using the machine learning technique of topic modeling based on previously reported usability evaluation results. Consequently, a precise list of frequent usability issues is developed based on the findings of the topic modeling in the previous step. ERP systems are evaluated using the methods of heuristic evaluation, cognitive walkthrough, and usability testing to validate the proposed ERP usability checklist. The output is used to develop the recommendations of developing usability interfaces of ERP systems.  
For the remainder of this paper, Section 2 describes the previous contributions in this area. Section 3 explains our research methodology and usability evaluation, and usability recommendations. Finally, we close this paper with a discussion of our results and conclusion in Section 4.
# Random Fiber Array Generation Considering Actual Noncircular Fibers with a Particle-Shape Library
## 3 Results and Discussion
### Comparison of the Results between Actual Noncircular Fibers and Circular Fibers  
In this section, we compared the performance of the proposed RVEs of actual noncircular fibers with conventional RVEs of circular fibers. To generate RVEs consisting of circular fibers, the RFR and RSE algorithms were also used. The minimum distance between fibers used in the RSE algorithm was the same as that of noncircular fibers. Furthermore, the radius of circular fibers was set to \(r_{f}\). Figure 6 shows the RVE samples of \(V_{f}=45\%\) according to RFR and RSE algorithms with conventional circular fibers and actual noncircular fibers by a shape-library approach.  
Table 2 shows the \(\mu\), standard deviation (SD, \(\sigma\)), and relative standard deviation (RSD, \(\sigma/\mu\)) of \(V_{f}\) of the RVE samples corresponding to \(V_{f}^{rq}\), and it can be seen that \(V_{f}^{rq}\) and its corresponding \(V_{f}\) of RVE samples are not exactly identical. In particular, the RVE samples with noncircular fibers show some deviation from those with circular fibers because they have large RSD values in comparison to the RSD values of circular fibers. Given that the volume of each noncircular fiber is not identical, RVE samples with the same \(V_{f}\) cannot be produced. The \(V_{f}^{rq}\) will be approximately satisfied.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**RFR**} \\ \cline{2-3} \(V_{f}^{req}\) & \multicolumn{2}{c}{**Circular Fiber**} & \multicolumn{2}{c}{**Noncircular Fiber**} \\ \cline{2-3} \cline{5-3}  & (\(\mu\) (\%), \(\sigma\)(\(\times 10^{-3}\)),\(\sigma\)/(\(\times 10^{-5}\))) & (\(\mu\) (\%), \(\sigma\)(\(\times 10^{-3}\)),\(\sigma\)/(\(\mu\) (\(\times 10^{-5}\))) \\ \hline
60\% & (60.2403, 0.0059, 0.0098) & (59.9544, 1.9157, 3.1952) \\
55\% & (55.0584, 0.0097, 0.0177) & (55.0125, 2.0399, 3.7081) \\
45\% & (44.6943, 0.0101, 0.0225) & (45.0108, 2.0356, 4.5225) \\
35\% & (34.9782, 0.0141, 0.0402) & (34.9971, 1.9286, 5.5107) \\
25\% & (25.2621, 0.0123, 0.0487) & (25.0233, 1.7587, 7.0282) \\
15\% & (14.8982, 0.0098, 0.0656) & (14.9566, 1.8338, 12.2610) \\
5\% & (5.1819, 0.0074, 0.1433) & (5.0967, 1.7048, 33.4482) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean, SD, and RSD of \(V_{f}\) of RVE samples corresponding to \(V_{f}^{req}\).  
Figure 6: RVE samples of \(V_{f}\) = 45%: (**a**) conventional circular approach and (**b**) shape-library approach.
# A High Full Quality CMOS Image Sensor for Space Applications
## 2 Sensor Architecture  
Figure 1 shows a block diagram of the CIS and simplified readout circuit. The chip is comprised of a 3000 (H) \(\times\) 3000 (V) pixel array, column readout circuits with buffer amplifiers, row shift registers, and Tx controllers.  
There are 10 channel readout circuits in the CIS. The one channel of column readout circuits consists of a switched capacitor amplifier and unit gain buffer, which use a high gain operational amplifier. In one channel, 300 sets of two capacitors each were used for sampling reset and signal values out of the 300-pixel columns. A column shift register (CSR) sequentially selects one of 300 capacitor sets, and the sampled values are outputted in the form of analog voltage. The output voltages from the 10 channels are converted to digital values by ten off-chip analog to digital converters (ADCs). A row shift register (RSR) approaches each row in sequence starting at the first. The multiple charge transfer is controlled by the Tx controllers, which is similar to RSR. All pixel control signals are buffered by digital drivers.  
Figure 1: CMOS image sensor (CIS) block diagram. CSR, column shift register; ADCs, analog to digital converters.
# Advanced Bayesian Network for Task Effort Estimation in Agile Software Development
## 3 The BN Model
### The New Proposed BN Model
#### 3.2.3 Prediction Accuracy
The instance lasted 0.5 h and was accurately classified into class '0-2' in the old model (5 output intervals), i.e., into class '0-1' in the new model (7 output intervals, Figure 4). Pred. (m) measures the percentage of estimates for which the magnitude of the relative error MRE is less than or equal to m (usually m = 25) [41]. This BN model estimates effort as a set of probability distributions for all possible classes, so a conversion method is used to obtain the estimated effort as a discrete value [42; 43; 44]. The probabilities of the classes should be normalized so that their sum is equal to one. The estimated effort is then calculated as follows:  
\[\text{Effort}=\sum_{i=1}^{n}\rho_{\text{classi}}\mu_{\text{classi}} \tag{2}\]  
where \(\mu_{\text{classi}}\) is the mean of class i, and \(\rho_{\text{classi}}\) is its respective class probability. Figure 4: Number of tasks per interval. Figure 3: Confusion matrix and accuracy measures per classes a–g. Each class probability of the selected instance is shown in Table 6 and the MRE is calculated according to the following equation:  
\[\text{MRE}=\frac{|\text{y}_{\text{i}}-\text{f(x}_{\text{i}})|}{\text{y}_{\text{i }}}=\frac{|\text{y}_{\text{i}}-\sum_{i=1}^{n}\rho_{\text{classi}}\mu_{\text{ classi}}|}{\text{y}_{\text{i}}} \tag{3}\]  
where yi is the actual value and f(xi) is the estimated value. The magnitude of the relative error for a correctly classified instance is 324.4% in the old model and 359.4% in the new one. It turns out that Pred (MRE < 25) is not a measure suitable for evaluating the performance of models with output classes. It is suitable for a relative comparison between two models with the same data set [45] or for the relative comparison of two data sets for the same model. Comparing the results of Pred. (25), the old model is more precise. Consequently, it can be concluded that the BN model is suitable for estimating efforts on agile projects, and by increasing empirical data, it is easy to increase the number of output intervals, without affecting the accuracy of the estimate.
## 3 Results and Discussion
### Deficit Irrigation Effect on Titratable Acidity, Total Soluble Solids, Sugars, and Organic Acid
Because this noncyclic carotenoid found in tomatoes, was reported to be the most effective free radical scavenger of all carotenoids; thus, can protect DNA, lipids, and other macromolecules with its antioxidant activity ([PERSON] et al., 2011). Is important to highlight that European Food Safety Authority ([PERSON] et al., 2011), adopted the decision on 30 of April 2009, authorizing the _\"Synthetic lycopene... may be placed on the market in the Community as a Novel Food ingredient to be used in the foods listed in Anne II\"_ under Regulation (EC) No 258/97 of the European Parliament and of the Council. A clear relationship was found for the contents of lycopene and \(\beta\)-carotene (\(R^{2}=0.64,p<0.001\)), demonstrating a similar accumulation pattern of these two carotenoids in tomatoes (Figure 3). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**ABTS\({}^{**}\)**} & \multicolumn{1}{c}{**DPPH\({}^{*}\)**} & \multicolumn{1}{c}{**FRAP**} & \multicolumn{1}{c}{**TPC**} & \multicolumn{1}{c}{**Lycopene**} & \multicolumn{1}{c}{\(\beta\)**-Carotene**} \\ \hline  & \multicolumn{3}{c}{**(mmol Trolox kg\({}^{-1}\))**} & \multicolumn{3}{c}{**(g GAE kg\({}^{-1}\))**} & \multicolumn{3}{c}{**(mg 100 g\({}^{-1}\))**} \\ \hline  & \multicolumn{6}{c}{**Tukey Multiple Range Test \({}^{\ddagger}\)**} \\
**Week** & & & & & & \\
**4** & & & & & & \\ _Control_ & 1.47 & 1.88 b & 2.41 & 0.72 & 0.19 & 0.09 \\ _RDI_ & 1.25 & 2.06 a & 2.27 & 0.68 & 0.25 & 0.09 \\
**Week** & & & & & & \\
**6** & & & & & & \\ _Control_ & 1.41 & 1.91 & 2.66 & 0.92 & 0.34 b & 0.10 b \\ _RDI_ & 1.39 & 2.05 & 2.72 & 0.99 & 0.55 a & 0.20 a \\ \hline \hline \end{tabular} \({}^{\dagger}\) NS = not significant at \(p<0.05\); * and ** significant at \(p<0.05\) and 0.01, respectively. \({}^{\ddagger}\) The values (mean of 4 replications) followed by the same letter within the same column do not present statistically significant differences (\(p<0.05\)) according to the Tukey Multiple Range Test. RDI, Regulated deficit irrigation.  
\end{table} 
Table 4: _Cont._Finally, the TPC presented a mean value of 0.66 g GAE kg\({}^{-1}\) without significant differences between treatments. The amount was higher than that reported by [PERSON] et al. [31]. in Ryna(r) cultivar in the most stressed treatment (0.55 g GAE kg\({}^{-1}\)); and same authors showed an increase of TPC under deficit irrigation conditions (0.40-0.55 g GAE kg\({}^{-1}\)) which was not observed in the present work.