esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors
## 3 Results and Discussion
### Radiometric Capability Evaluation of FY-3/Visible and Infrared Radiometer (VIRR)  
The same plots of the FY-3/VIRRs as the MODISs are presented in Figure 10. The temporal range of FY-3A/VIRR is 2008\(-\)2015 and that of FY-3B/VIRR is 2010\(-\)2015. The radiometric characteristics of the VIRRs are as follows:  
1. Both VIRRs have a tendency toward obvious decreasing in their first calibration stages. The reason for the trend can probably be attributed to the degradation of the instruments. At the beginning of the operations of VIRRs, the TOA reflectance in the four bands are abnormally high, especially in the blue band and even higher in the green band. After further investigated and constant adjustment, the instruments became normal and stable in the second stage. The statistics are calculated and listed in Table 13.
2. For the FY-3A/VIRR, the variations of the four bands are 39.42%, 24.48%, 18.60%, and 15.01% respectively. The differences in four bands between the mean TOA reflectance after spectral matching and corresponding bands of MODIS are all less than 0.02. All the standard deviations of the TOA reflectance are within 0.02.
3. For the FY-3B/VIRR, the variations of the four bands are 43.27%, 31.65%, 21.68%, and 23.90% respectively. Only the difference in blue band between the mean TOA reflectance after spectral matching and MODIS is close to 0 (0.0004). The mean TOA reflectance of the FY-3B/VIRR after spectral matching in other three bands are less than that of MODIS and the differences are higher than 0.02 (0.023 for green band, 0.022 for red band and 0.028 for NIR band). All the standard deviations of the TOA reflectance are within 0.02.
4. Low frequency of calibrations is still a problem for VIRRS and a high frequent procedure for calibration could be a solution for the decreasing trend's instability.  
Figure 10: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sensor** & **Band** & **Slope** & **Maximum** & **Minimum** & **Mean** & **Mean\_a** & **Mean\_a\_*-** & **Standard** \\  & Blue & \(2.04\times 10^{-5}\) & 0.2238 & 0.1235 & 0.1598 & 0.1547 & 0.0147 & 0.0179 \\ FY-3A/MERSI & Green & \(-8.74\times 10^{-5}\) & 0.2012 & 0.1449 & 0.1670 & 0.1593 & \(-0.0067\) & 0.0115 \\  & Red & \(4.62\times 10^{-5}\) & 0.2663 & 0.2084 & 0.2326 & 0.2306 & 0.0076 & 0.0103 \\  & NIR & \(-3.58\times 10^{-5}\) & 0.3013 & 0.2342 & 0.2630 & 0.2626 & \(-0.0114\) & 0.0113 \\ \hline  & Blue & \(8.80\times 10^{-5}\) & 0.2042 & 0.1354 & 0.1648 & 0.1708 & 0.0118 & 0.0174 \\ FY-3B/MERSI & Green & \(2.77\times 10^{-4}\) & 0.2024 & 0.1534 & 0.1718 & 0.1716 & \(-0.0234\) & 0.0123 \\  & Red & \(1.39\times 10^{-4}\) & 0.2638 & 0.2159 & 0.2308 & 0.2360 & 0.0080 & 0.0114 \\  & NIR & \(-8.27\times 10^{-5}\) & 0.3136 & 0.2624 & 0.2784 & 0.2838 & 0.0048 & 0.0125 \\ \hline \hline \end{tabular}
\end{table}
Table 12: The statistics from the time series of the TOA reflectance of FY-3/MERSIs.
# Functionalization Strategies and Fabrication of Solvent-Cast PLLA for Bioresorbable Stents
## 2 Materials and Methods
### Surface Characterization  
Topography was evaluated with white light interferometry (WLI) (Optical interferometer Veeco Wyko 9300 NT, Veeco Instruments, Plainview, NY, USA) in a vertical scanninginterferometry mode (VSI) at 100\(\times\) magnification. Data analysis was performed with the Wyko Vision 4.10 software (Veeco Instruments) to obtain the roughness parameter Ra (the arithmetic average height). The curvature and tilt were eliminated with a Gaussian filter.  
The water static contact angle was measured by the sessile drop method with a Contact Angle System OCA15 plus (Databases, Filderstadt, Germany) [43]. Milli-Q(r) water drops of 3 \(\upmu\)L were deposited on the PLLA films and the contact angle between the water and different PLLA films was measured. An average of 15 drops was studied for each condition. The contact angle values were obtained after applying Laplace-Young fitting of the drop profile with the SCA 20 software (Databases). For roughness and wettability results, three biological replicas, each of them with three technical replicates, were performed.  
The chemical composition of the surface of PLLA films before and after topographical modification was analyzed by X-ray photoelectron spectroscopy (XPS) (SPECS Surface NanoAnalysis GmbH, Berlin, Germany). The XPS spectra were obtained using an Mg anode XR50 operating at 150 W and a Phoibos 150 MCD-9 detector to record high-resolution spectra at a pressure below 7.5 \(\times\) 10\({}^{-9}\) mbar. The elements present on the surface were evaluated by low-resolution survey spectra, whereas a high-resolution of C 1s was recorded with a detector pass energy fixed at 25 eV with 0.1 eV step at a pressure below 7.5 \(\times\) 10\({}^{-9}\) mbar. The Casa XPS software (version 2.3.16, Casa Software Ltd., Teignmouth, UK) was used to do fitting, peak integration of spectra, and peaks deconvolution. Binding energies were referred to the C-C bond energy at 284.8 eV.
## 3 Results and Discussion
### Comparison of Real-Time Sampling Devices  
Given the different sampling frequency of the three devices (ELPI+ 1 Hz, KORA 0.2 Hz, and AirVisual Pro 0.1 Hz), it was necessary to compare average values over the same time interval. Daily average PM\({}_{2.5}\) concentrations, including descriptive statistics and uncertainty estimation, are reported in Table 3. The uncertainty estimation in this case was complicated because it was not possible to adopt the error propagation law and there was no data available from the manufacturer. We thus decided to assume the standard deviation based on instrumental sensitivities, equal to 0.5 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) and 0.05 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) for AirVisual Pro and KORA, respectively, and calculate the corresponding relative percent error (RE%) of the average values. The comparison among real-time sampling devices confirmed the underestimation of PM\({}_{2.5}\) concentrations of both optical systems with respect to the cascade impactor. Given the alignment observed between ELPI+ and SKC data, the above-mentioned underestimation was expected [24; 44]. The data reported in Table 3 show the same trend among average concentrations. KORA PM2.5 concentrations lay in  
Figure 5: Size distribution curves of normalized particle concentrations in different environmental conditions reported in terms of (a) number and (b) volume of particles (ELPI+ data). Specific expansions of the curves are shown in the insets. the range 65-88%, and those of AirVisual Pro in the range 73-98% with respect to data monitored by the ELPI+. The comparison based on daily mean concentrations was not representative of the observed PM\({}_{2.5}\) concentration trend. Thus, a more in-depth discussion was based on hourly average concentrations. Figure 6 shows the scatter plot and regression lines of the KORA and AirVisual Pro datasets for the whole sampling campaign compared to ELPI+. Data correlation is evident, with linear regression slopes deviating from the unit and decreasing from AirVisual Pro to KORA. The differences among the datasets increases with PM\({}_{2.5}\) concentrations. Since the ELPI+ was our reference real-time monitoring device, the AirVisual Pro, despite its small dimensions and easy use, seemed to be more accurate, even less precise, than KORA, but no information about reliability is known. The related Bland-Altman plots [45] are also reported in Figure S6, where the difference between PM\({}_{2.5}\) concentration values estimated by specific couples of instruments (ELPI+ and KORA (a), ELPI+ and AirVisual Pro (b), and AirVisual Pro and KORA (c)) are plotted with respect to the average hourly concentrations of measurements collected by the two technical devices.
## 2 Conventional Digital Image Correlation (DIC) Applications
### Bending and Buckling Analyses of Composite Conical Shells for Helicopter Tail-Boom Applications
#### 2.2.2 Experimental Work  
**Sample manufacturing and preparation:** The test article, a composite conical shell, which was a full-scale section of a helicopter tail-boom, was made out of advanced thermoplastic composite material (carbon fiber and poly-ether-ether-ketone (PEEK) with the commercial name of AS4/APC-2 from Cytec Engineered Materials, Anaheim, CA, USA). It was manufactured at National Research Council Canada's facility in Montreal using an automated fiber placement (AFP) machine on a steel mandrel with an internal heating system (Figure 5).  
Figure 4: DIC strain measurement versus strain gage results for L-shape composite angle [16].  
Preparation of the composite cone for bending tests included applying the end tabs, creating a random pattern (Figure 6a) on the surface of the cone (for strain and deformation measurements using DIC), installing strain gases, and potting the composite cone inside the installation rings (Figure 6c) using low melting temperature point alloy (LMPA) (Figure 6b). The surface of the thermoplastic composite cone was fairly black after manufacturing, and, to have maximum contrast, a white random pattern was applied on the surface of the composite cone using permanent markers by hand. The size of speckles ranged between 3 to 7 pixels with approximately 50% area of interest covered by white speckles. A 3D DIC system from Correlated Solutions, Irmo, SC, USA equipped with two 5 Megapixel cameras (monochrome charge-coupled device (CCD) with 2/3-inch sensor from Point Grey) attached to Schneider 17 mm lenses were used in this experiment, and results were obtained using VIC-3D software.  
**Test setup:** Despite the bending load being the dominant load on conical shells in many applications (e.g., helicopter tail-boom), experimental setups to study bending and buckling behaviors of cylindrical and conical shells under pure bending loads are scarce. In order to study bending and buckling behaviors of conical shells, a pure bending test setup was developed at Concordia Centre for Composites (CONCOM) laboratory (Figure 7). The test setup was equipped with a 3D DIC system that consisted of four cameras to cover the top and front sections of the test article for full-field deformation and strain measurements.  
Figure 5: Six-axis automated fiber placement (AFP) machine with a steel mandrel (courtesy of National Research Council Canada) [15].  
Figure 6: Test article preparation: (**a**) random pattern, (**b**) low melting temperature point alloy (LMPA), and (**c**) installation ring [15].
## 3 Results and Discussion
### Comparison of Experimental and Numerical Results
Moreover, the results are considered reasonable, given that higher forging load is predicted in the case of Grade A material, due to its higher mechanical strength, as it was experimentally determined and depicted in Figure 3.  
The crack initiation point identified by the DIC system was analyzed in detail during the numerical simulation. An example of such an analysis is shown in Figure 8. The graphs in Figure 8 show the changes in the principal strain (\(\varepsilon\)1) from the beginning of the process to the moment the crack appears. In both analyzed cases, an increase in principal strain in the fracture initiation point (Figure 8) is observed during the process. Although during some part of the hot forging procedure, there is a difference between the experimental and numerical results for principal strain at the point of fracture, mostly for the Grade B material, the average error is still acceptable with the curves showing similar trends. Additionally, due to the considerable importance of fracture prediction, the effect of selected fracture criteria in the analyzed areas of the samples was investigated by comparing two different fracture criteria in the software. Although there are some differences in the distribution of the values of the two different fracture criteria, it was shown that both the Rice and Tracey (Equation(2)) and Cockcroft and Latham (Equation (3)) criteria [22] achieved the highest values for material grade A (as can be seen in Figure 9,b) equal to 0.58 and 156.89, respectively, and for material grade B (as can be seen in Figure 9,d) equal to 0.54 and 153.65, in the locations of the fracture. This shows the correctness of the assumptions made and confirms the good correlation of the results obtained in the laboratory tests using the DIC system. Figure 9: Distribution of fracture criteria on sample surfaces: (**a,b**) grade A; (**c,d**) grade B; (**a,c**) Rice and Tracey fracture criterion; (**b,d**) Cockcroft and Latham fracture criterion. \[\int\limits_{0}^{\overline{z}_{f}}\exp\left(\frac{3}{2}\frac{\sigma_{m}}{\overline {\varphi}}\right)d\overline{\varepsilon}=C_{1} \tag{2}\]  
\[\int\limits_{0}^{\overline{z}_{f}}\sigma_{1}d\overline{\varepsilon}=C_{2} \tag{3}\]  
The above considerations mean that the adopted assumptions and the developed boundary conditions for the FEM simulation were correct. The satisfactory agreement of the results proves that the models were developed in accordance with dedicated experimental tests and may be usefully implemented in the simulation of industrial processes to predict the areas and risk of fracture.
## 3 The Receiving Process of the Composite Signal  
In this section, we present the receiving process of the BOC signal and BPSK signal. First, we show that BPSK signal and BOC signal interfere with each other in the case of spectrum aliasing. Then, we design the receiving system of the composite signal. Finally, the application scenario is proposed.
# CRISPR-Cas Systems: Prospects for Use in Medicine
## 2 Structure, Classification, and Function of CRISPR-Cas Systems  
### Structure  
CRISPR-Cas systems are diverse in structure, but include several mandatory elements. They usually consist of two parts: the CRISPR cassette, which consists of a number of unique sequences of about the same length (spacers), separated by short repeating segments (direct repeats, DRs), and a locus of CRISPR-associated genes (_cas_). The spacer sequences in CRISPR cassettes were found in phage genomes, plasmids, and transposons. Spacers are involved in the recognition of foreign DNA. Their length can vary and usually consists of 20-40 nucleotides; the length of direct repeats corresponds approximately to the length of the spacers. The _cas_ genes encode proteins with nuclease activity that are involved in the digestion of foreign DNA and its integration into the genome as new spacers. Another obligatory element is a leader sequence [18]. The most important role belongs to the so-called adaptation module. In the majority of CRISPR-Cas systems it consists of two proteins--Cas1 and Cas2--which participate in the integration of new spacers in the CRISPR cassette, complementary to phages' genomes and transposons encountered by the cell and its ancestors during their existence. New spacers are integrated into the cassette next to the leader sequence containing the promoter. The adaptation module is mandatory for the system to function. However the type IV CRISPR-Cas systems are an exception to this rule: they are mostly located on plasmids, prophages, and mobile genetic elements [19]; these systems probably use adaptation modules from type I systems [20]. Cas1 is responsible for the integration of the new spacer, while Cas2 is almost not involved neither in Cas1-Cas2 complex formation, nor in spacer integration. Cas2 represents an mRNA-interfearase, which specifically cleaves ribosome-associated mRNA, that appears useless for new spacers integration. There is a hypothesis about the possible origin of Cas2 from the toxin-antitoxin (TA) systems; in this case, it can be assumed that Cas2 retains the ancestral function of endo-RNase, whose activity is reversibly inhibitedby interaction with Cas1 and the formation of the Cas1-Cas2 complex. According to this hypothesis, when CRISPR-Cas systems fail to prevent viral replication in a cell, Cas2 can be activated to stop translation, presumably leading to cell death [21; 22].
# End-of-Life Impact on the Cradle-to-Grave LCA of Light-Duty Commercial Vehicles in Europe
## 3 Results
### Comparison of the Cradle-to-Grave LCA of the Three Vehicles under Study  
In Figure 8a, the entire cradle-to-grave GWP impact for each vehicle under study is reported in gCO2 eq/km. Although the EoL stage (light green bars) reduces the total GWP impact, the reduction is not significant. In fact, it accounts for \(-3.6\%\) for both the DIE-ICEV and BEV configurations and \(-5.3\%\) for the CNG-ICEV configuration. Nevertheless, as is shown in Figure 8b, the role of the EoL stage is significant in the following impact categories:  
* Abiotic depletion accounts for \(-59\%\), \(-78\%\), and \(-35\%\) of the DIE-ICEV, CNG-ICEV, and BEV configurations, respectively;
* Human toxicity accounts for \(-33\%\), \(-31\%\), and \(-48\%\) of the DIE-ICEV, CNG-ICEV, and BEV configurations, respectively;
* Freshwater aquatic ecotoxicity accounts for \(-48\%\), \(-49\%\), and \(-35\%\) of the DIE-ICEV, CNG-ICEV, and BEV configurations, respectively;
* Marine aquatic ecotoxicity accounts for \(-32\%\), \(-30\%\), and \(-27\%\) of the DIE-ICEV, CNG-ICEV, and BEV configurations, respectively;
* Photochemical oxidation accounts for \(-13\%\), \(-24\%\), and \(-23\%\) of the DIE-ICEV, CNG-ICEV, and BEV configurations, respectively;
* Acidification accounts for \(-24\%\), \(-34\%\), and \(-18\%\) of the DIE-ICEV, CNG-ICEV, and BEV configurations, respectively;
* Eutrophication accounts for \(-23\%\), \(-28\%\), and \(-11\%\) of the DIE-ICEV, CNG-ICEV, and BEV configurations, respectively.  
Figure 8: (a) Cradle-to-grave results for global warming for the three vehicles analyzed; (b) Cradle-to-grave results for the impact categories other than global warming for the three vehicles analyzed.
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 2 Related Work
developed an autoencoder-based anomaly detection framework [15]. The autoencoder model in their work has been improved by introducing a variational self-attention mechanism. In view of reducing the training cost of model, the unsupervised-classification-based method shows great potencies; however, that also means that the real application of this method can be limited, because of the absence of rational references. Under this, a semi-supervised classification-based detection method is proposed to balance supervised classification and unsupervised classification. This approach addresses the training cost problem of supervised classification and improves the interpretability of the model [16]. [PERSON] and [PERSON] invented a semi-supervised classification-based residential appliance annotator [16]. Only a small amount of data is labeled, and dynamic time warping is employed to calculate the distance between these unlabeled data and all the labeled data. [PERSON] et al. developed a semi-SVM-based load anomaly detector for detecting anomalies in home power usage [17]. By using the semi-supervised method to reduce the training cost of the method, this work also proposed a two-dimensional detection approach with improved monitoring performance with respect to electricity usage and the usage habits of home owners by providing detailed feedback. Semi-supervised classification-based anomaly detection methods not only reduce the training cost but also ensure a high detection accuracy; however, the limitations of such methods are as follows. First, the relationship between unlabeled data and labeled data needs to be known in advance. Second, compared with regression model-based methods, which can provide power consumption information to improve the energy efficiency of the system, the application of semi-supervised methods is limited. Table 1 exhibits the advantages and limitations of each method.
# Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms
## 3 Results and Discussion
### Maximum Crack Width Measurement
#### 3.2.1 Accuracy  
It can be observed from Table 3 that the method was capable of measuring crack widths as small as 0.85 mm, as observed with crack E. The overall performance of the method by calculating the mean absolute error (MAE) by taking a summation of the absolute errors and dividing it by the number of measured cracks. The MAE was found to be 0.26 mm; this means that, on average, our method will tend to measure crack widths to a value \(\pm\)0.26 mm away from the true value. [PERSON] et al. [2] also used lasers to measure maximum crack width and achieved results with errors of less than 1.5 mm. While [PERSON] et al. [4] had a relative error of between \(-\)13.27% and 24.01%. The results are within a reasonable error margin compared to previous studies.  
The smallest absolute error was observed for crack E, with a converted maximum crack width of 0.83 mm, which was an underestimation of 0.02 mm. Crack G, measured to be 5.57 mm instead of 5.00 mm, had the largest observed absolute error of 0.57 mm, which was an overestimation.
## 4 Experimental Design and Setup
### Prediction Accuracy Measures
#### 4.3.1 Performance Measures  
Several measures have been proposed in the literature to evaluate the accuracy of the prediction models in software engineering problems [50]. In this study, only one prediction accuracy measure, which is AUC, is used to evaluate and compare the performance of prediction models. AUC is based on the ROC (receiver operating characteristic) that graphs the true positive rate (TPR) on the y-axis against the false positive rate (FPR) on the x-axis at different threshold settings [51]. AUC ranges from 0 to 1, where the greater value indicates better results and 1 is the optimal result (a perfect classifier). Additionally, 0.5 indicates no discrimination, a value from 0.7 to 0.8 indicates an acceptable result, a value from 0.8 to 0.9 is recognized as excellent, and any values greater than 0.9 are considered outstanding results [52].  
According to a study published in [7], AUC was the principle and most frequently evaluation measure used for classification problems in software quality prediction. In addition, it is considered well-known and commonly employed in software maintainability prediction along with change-proneness prediction [13].  
Equation (4) calculates the value of \(AUC\), where i represents observations, \((1-\beta)\) represents the \(\text{TPR}=\frac{TP}{TP+FN}\), \(\alpha\) represents the \(\text{FPR}=\frac{FP}{FP+TN}\), and these values are extracted from the confusion matrix presented in Table 8.  
\[AUC=\sum_{i}\left\{(\ 1-\beta_{i}\ \cdot\Delta\alpha)+\frac{1}{2}[\Delta(1- \beta).\Delta\alpha]\right\} \tag{4}\]  
\(\Delta(1-\beta)\) indicates \((\ 1-\beta_{i}\ )-(\ 1-\beta_{i-1}\ )\), and \(\Delta\alpha\) indicates \(\alpha_{i}\ \ -\alpha_{i-1}\)