esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
In this study, the following parameters have been investigated:  
* Concrete grades: C20, C30, C40, and C50, with concrete compressive strength, f\({}_{\rm c}\), equal to 20 MPa, 30 MPa, 40 MPa, and 50 MPa, respectively;
* Steel grades: S200, S300, S400, and S500, with steel yield stress, f\({}_{\rm y}\), equal to 200 MPa, 300 MPa, 400 MPa, and 500 MPa, respectively;
* The diameter of the circular sections, D: 200 mm, 300 mm, 400 mm, 500 mm and 600 mm;
* The width of rectangular sections, B: 200 mm, 330 mm, 470 mm, and 600 mm;
* The height-to-width ratio of rectangular sections, H/B: 1.00 and 1.20;
* The diameter to thickness ratio of circular sections, D/t: 40, 55, 70, 85, and 100;
* The width to thickness ratio of rectangular sections, B/t: 40, 60, 80, and 100;
* The width thickness to height thickness ratio of rectangular sections, t\({}_{\rm f}\)/t\({}_{\rm w}\): 1.00 and 1.20;
* The member length to section diameter ratio, L/D: 3.0, 6.0, 9.0 and 12.0. Taking into account the aforementioned combinations of parameters for composite members with circular sections, suitable ones have (4 concrete grades) \(\times\) (4 steel grades) \(\times\) (5 diameters) \(\times\) (5 diameter-to-thickness ratios) \(\times\) (4 length-to-diameter ratios) = 1600 different models. Additionally, considering the aforementioned combinations of parameters for composite members with rectangular sections, suitable ones have (4 concrete grades) \(\times\) (4 steel grades) \(\times\) (4 widths) \(\times\) (2 height-to-width ratios) \(\times\) (4 width-to-thickness ratios) \(\times\) (2 width thickness to height thickness ratios) \(\times\) (4 length-to-diameter ratios) = 4096 different models. Therefore, the produced databank has 1600 + 4096 = 5696 finite element models, which have been examined under concentric or eccentric axial loads and pure flexural load.
# An Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines
## 5 Results  
### Signal #1  
An example of results is reported in Figure 4. The green line corresponds to the Instantaneous Spectral Entropy, defined at any timestep. The two horizontal dashed lines correspond to the upper and lower bounds of the Gaussian distribution fitted over the ISE values of the training set, that is to say, \(\text{ISE}(t)\equiv\mu_{b}+2\sigma_{b}\) and \(\text{ISE}(t)\equiv\mu_{b}-2\sigma_{b}\), where \(\mu_{b}\) and \(2\sigma_{b}\) correspond, in the same order, to the mean and standard deviation of the baseline tract, which is assumed to be almost stationary.  
However, it was verified that these two thresholds were not optimal for anomaly detection. Indeed, as it can be seen, the \(\text{ISE}(t)\) value is quite unstable and subject to strong, rapid fluctuations. For this reason, a moving mean (calculated over a sliding window of 10,000 timesteps, i.e., 0.84 s) was preferred as a more stable indicator. This is indicated by the thick black line. The area shaded in grey corresponds to its expected values in 'normal' conditions, defined (similarly as before) as all points in between \(\mu_{\text{moveman},b}-2\sigma_{\text{moveman},b}<\text{ISE}(t)<\mu_{\text{moveman },b}+2\sigma_{\text{moveman},b}\), i.e., with a 95.45% confidence of belonging to the same population as the training data points. One can see that, for the two test scenarios, the value of the moving average of \(\text{ISE}(t)\) generally deviated from the previously stationary conditions, trespassing the lower threshold. This can be used to perform automated and instantaneous fault detection.  
The results portrayed in Figure 4 focus on a single value of symmetry (\(\gamma=3\)) and varying \(\beta\). The effects of these two parameters have been thoroughly investigated. The findings will be discussed in the next subsection. However, the two points (1) and (2) highlighted above were encountered for any combination of \(\gamma\) and \(\beta\). This proves that the \(\text{ISE}(t)\), especially when smoothed via a moving average with a properly sized window  
Figure 3: Fast Fourier Transforms (FFTs) of the segments of each set included in signal #2. (**a**): training set; (**b**): validation set #1; (**c**): validation set #2; (**d**): test set #1; (**e**): test set #2.  
length, is, overall, effective and efficient as a time-dependent DSF, thus suitable for damage event detection.
## 4 Simulation and Analysis of Magnetic Field Distribution
### Magnetic Field Generated by Sideband Current and Harmonic Current
#### 4.3.2 Odd Harmonic Current  
Other than the sideband currents at a frequency of 85 kHz, there are also non-negligible frequency doubling harmonics, including odd and even harmonics. The four power devices in inverter for  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Frequency** & **A2** & **A3** & **A4** & **B1** & **B2** & **B3** \\ \hline
84.8 kHz & 0.86 & 1.75 & 0.22 & 0.02 & 0.03 & 0.13 \\
85.2 kHz & 0.83 & 1.76 & 0.22 & 0.02 & 0.03 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Magnetic field strength at test points (A/m).  
Figure 17: Current spectrum of coupling coils: (**a**) current spectrum of primary coil; (**b**) current spectrum of secondary coil.  
WCS is controlled by PWM, and the output voltage is a pulse width modulated square wave. The large \(\mathrm{d}u/\mathrm{d}t\) and \(\mathrm{d}i/\mathrm{d}t\) can induce abundant harmonic currents in the primary and secondary current. Harmonics can not only bring serious EMI, but also affect the magnetic field distribution. The odd harmonic currents are large and the even harmonics are nearly zero; therefore, the influence of odd harmonics on the WCS is mainly considered here. The higher harmonics of coupling coils is gradually reduced, and the harmonic is negligible after the 5 th harmonic. Only 3 th and 5 th current harmonics are considered here at five cases for offset, as shown in Table 8.  
The LCC compensation topology can suppress the harmonics in a way, so the overall harmonic currents are small. The 3 rd harmonic current of the secondary coil is greater than that of the primary coil, and, as the offset increases, the primary harmonic current decreases at the beginning and then increases later. The odd harmonic currents are injected into the model when the coils are aligned, and a comparison of the magnetic field distribution of the 3 rd and 5 th harmonics with the fundamental magnetic field can be obtained, as shown in Figure 19.
# Review of Substrate Integrated Waveguide Circuits for Beam-Forming Networks Working in X-Band
## 3 Components for the Beam-Forming Network  
The beam-forming network (BFN) is a microwave network that processes the input signal in order to feed an array antenna in such a way to generate a given radiation pattern. BFNs can be very complicated, especially when the antenna elements must be fed by signals with different amplitudes and phases. Things become much worse when the same antenna must generate different beams, for example, with different main lobe directions. However, for the topic of antennas for DVB-S, the elements of the array are fed by signals of the same amplitude and phase, generating a broadside main lobe. This also maximizes the gain of the antenna. The drawback is that the side lobes are not at their minimum level. The main elements of a BFN are dividers, four-port couplers, magic tees, and six-port couplers. In the following, some structures are presented.
## 4 Technologies to Support Contextualization
### Sampling and Integrating Process Data
#### 4.2.1 Sampling Dynamic Data
Currently, an increasing amount of smart equipment is being developed [122; 142]. Such equipment can also provide information about its own state. For instance, a valve cannot only say whether it is open, but also indicate its degree of fouling. Edge computing can be combined with a hardware implementation of OPC UA to retain the benefits of semantics while using TSN to guarantee high-speed data-processing [135]. Regardless of where the computations are performed, they can provide operators with pre-processed data that are easier to interpret.
## 3 Results
### Identification of Rutin in D. racemosum Extracts via HPLC Analysis  
To identify the major bioactive constituents of DRE, HPLC analysis was performed using 18 standard reference compounds, including known flavonoids and phenolic compounds, at a detection wavelength of 360 nm. The chromatographic profile of the standard mixture showed distinct peaks corresponding to the individual compounds, including rutin. A comparison of the chromatograms of the crude extracts of \(D\). _racemosum_ and DRE revealed a prominent peak with the same retention time as that of the rutin standard, as indicated by the red marker. However, as shown in the overlaid HPLC chromatograms (Figure 6A), the intensity of the peak corresponding to rutin (retention time: 5.5 min) was relatively low in both the ethanol extract and ethyl acetate fraction of \(D\). _racemosum_, indicating that rutin is a minor component. This identification was based solely on retention time. This confirmed the presence of rutin, which was identified as one of the constituents present in the crude extracts of \(D\). _racemosum_ and DRE (Figure 6).  
Figure 6: Identification of the bioactive compounds in \(D\). _racemosum_ extracts via high-performance liquid chromatography (HPLC) analysis. HPLC was performed to identify the bioactive constituents  
of the crude extract and DRE using standard compounds. Chromatographic analysis was conducted at 360 nm. (**A**) HPLC chromatograms of (**top**) standard mixture, (**middle**) \(D\). _racemosum_ ethanol extract, and (**bottom**) ethyl acetate fraction. The x-axis represents the retention time (minutes), and the y-axis indicates absorbance. The following peaks in the standard mixture are labeled in blue and correspond to the numbered standard compounds: 2: Caffeic acid (3.4 min), 4: p-Cumaric acid (4.8 min), 5: Rutin (5.5 min), 9: Apigenin (10.8 min), 10: Kaempfer (11.0 min), 11: Hesperetin (11.3 min), 12: Chrysin (14.6 min), 13: Acacetin (14.8 min), 16: CAPE (15.6 min), and 18: Artepillin C (17.7 min). The retention time of peak 5 matches that of rutin; however, rutin is only a minor component in both the ethanol extract and DRE. This identification is based solely on retention time. The statement that rutin is a major active component has been revised to reflect that rutin is one of the identified compounds. The red arrows indicate that compound 5 has the same retention time. (**B**) Chemical structure of rutin, a flavonoid compound exhibiting antioxidant, anti-inflammatory, and anti-cancer activities.
# Acceptance Model for Mobile Building Information Modeling (BIM)
## 3 Exploring the Key Factors for Mobile BIM Acceptance  
### Key Factors for Mobile BIM Acceptance  
In this section, the exploratory factor analysis results are presented to establish a hypothesis for the mobile BIM acceptance model based on the survey results. The survey items comprise external variables that are expected to be significant to mobile BIM acceptance by referring to the TAM2 and IS success models, as well as previous studies related to the mobile TAM or BIM TAM. This study reviewed the relevant theories and literature reviews written in Korean and English related to the BIM TAM and mobile TAM that were published up until 2018 from around the world. Thus, the survey items are expected to be significant to construction practitioners worldwide. The representative literature is summarized in Table 2.  
Literature reviews on the BIM TAM or mobile TAM aim to improve the users' skills and increase the amount of information that is targeted. First, mobile capability is based on constant internet access, ease of communication, and convenience of portability. The literature reviews related to mobile TAM adopted the quality and usability of application functions as external variables and verified the relationships between variables by adopting the perceived usefulness, usability, and use as the internal variables. Next, BIM improves individual convenience and organizational convenience using visual objects that contain information. The literature reviews related to the BIM TAM adopted external variables, not only on the quality of the system, but also on the organizational factors. Therefore, the external variables adopted the information technology characteristics in literature reviews.  
Factors derived from literature reviews are summarized as follows: First, technology quality comprises key factors related to the performance of the tool that is being used, including system quality, information quality, service quality, and cost. Second, behavior control comprises key factors related to the environment in which the tool is expected to be used, including internal support, external support, internal pressure, and external pressure. Third, personal competency comprises key factors related  
Figure 2: IS success model (1992, 2003).  
to the individual's psychological state toward the tool that is to be used, including self-efficiency and personal innovativeness. Fourth, organization competency comprises key factors related to the group psychological state toward the tool that is to be used, including collective efficiency and organization innovativeness.
## 4 Conclusions  
A gas stream containing 10-15 vol % CO\({}_{2}\) has been treated in both a packed column and a membrane contactor set up, leading to the capture of the contaminant by means of a sodium hydroxide solution in order to finally obtain a valuable product (sodium carbonate) to be reused in the industry. The influence of the main operating parameters; i.e., concentration and flow rates of both the gas and the liquid phase, on the species evolution, the overall mass transfer coefficient and the CO\({}_{2}\) removal efficiency--was evaluated.  
Rapid exhaustion of the NaOH was found when the CO\({}_{2}\) was captured in the column, being faster when the sorbent concentration was smaller. When no more hydroxides exist, a secondary reaction between the carbonates and the contaminant produces bicarbonate. This becomes important because it opens the possibility to obtain different products and, therefore, further industrial applications will determine the components of interest. The evolution of the species in the membrane contactor followed the same trend.  
The best and the worst mass transfer coefficients and CO\({}_{2}\) removal efficiencies were found in the column and intermediate values corresponding to the membrane contactor were set up. Thus, the column offered some mass transfer coefficients one order of magnitude higher than in the membrane contactor, corresponding to the experiments where 0.5 and 1 mol L\({}^{-1}\) of NaOH, but the major part of the results shared the same coefficient value range (0.001-0.0065). Again, the composition of the sorbent implies the stronger effect on the characterization parameters. Moreover, when no more NaOH were in the process, the contaminant elimination decayed until 21%, far from the 97% reached with the highest hydroxide content.  
Regarding the flow rates of the fluids, the higher the absorbent velocity and the lower the gas fluent, the better the mass transfer. Nevertheless, the influence of this parameter may be negligible in comparison to the strength of the NaOH concentration effect.  
Results allow us to conclude that, firstly, the technical viability to capture CO\({}_{2}\) with the novelty offered by the membrane technology is a fact and, secondly, that it will become a powerful competitive alternative to traditional absorption, including an intensification factor of 5.38.  
[PERSON] and [PERSON] conceptualized this research work; [PERSON] carried out the investigation tasks and prepared the original draft. [PERSON] and [PERSON] wrote, reviewed and edited the manuscript. [PERSON] supervised the investigation and acquired the funding.  
[PERSON] would like to acknowledge the support of the research group \"Coefficient Processes for Sustainable Industrial Chemical and Biochemical Engineering\" in the department of Materials and process engineering (IMAP) of the Institute of Mechanics, Materials and Civil Engineering (iMMC), at the Universite catholique de Louvain. The authors acknowledge the financial support by the Fonds speciaux de recherche (FSR) of the Universite catholique de Louvain.  
The authors declare no conflict of interest.
# Smooth Information Criterion for Regularized Estimation of Item Response Models
## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Dev**} & \multirow{3}{*}{\(N\)} & \multicolumn{4}{c}{**Type-I Error Rate**} & \multicolumn{4}{c}{**Power Rate**} \\ \cline{3-10}  & & **AIC** & **SAIC** & **BIC** & **SBIC** & **AIC** & **SAIC** & **BIC** & **SBIC** \\ \hline \multirow{6}{*}{small} & \multicolumn{4}{c}{_Balanced deviations from the Rasch model_} \\  & 500 & **14.6** & 7.7 & 1.3 & 1.2 & **68.8** & **61.6** & **40.6** & **39.6** \\  & 1000 & **18.5** & 6.5 & 0.7 & 0.3 & **77.6** & 85.5 & **63.6** & **55.9** \\  & 2000 & **36.4** & 7.7 & 0.6 & 0.0 & **78.8** & 98.7 & **75.5** & **79.8** \\  & 500 & **11.4** & 6.6 & 1.3 & 1.2 & 97.9 & 95.8 & 93.8 & 93.6 \\  & 1000 & 9.3 & 6.6 & 0.6 & 0.2 & 99.7 & 99.8 & 99.4 & 98.9 \\  & 2000 & 4.4 & 7.4 & 0.1 & 0.0 & 100 & 100 & 100 & 100 \\ \hline \multirow{6}{*}{large} & \multicolumn{4}{c}{_Unbalanced deviations from the Rasch model_} \\  & 500 & **11.4** & 5.7 & 1.8 & 1.1 & 80.4 & **69.5** & **30.9** & **29.9** \\  & 1000 & **10.8** & 5.9 & 0.8 & 0.3 & 97.9 & 94.0 & **72.5** & **50.9** \\  & 2000 & 8.3 & 7.7 & 0.4 & 0.1 & 99.9 & 99.9 & 98.2 & 87.3 \\  & 500 & **13.7** & 5.9 & 1.2 & 1.0 & 100 & 100 & 99.9 & 99.9 \\  & 1000 & **12.1** & 5.8 & 0.7 & 0.2 & 100 & 100 & 100 & 100 \\  & 2000 & **10.9** & 7.3 & 0.4 & 0.0 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular} Note. Dev = size of deviation from the Rasch model; Type-I error rates larger than 10.0 and power rates smaller than 80.0 are printed in bold font.  
\end{table} 
Table 6: Simulation Study 3: Type-I error rate and power rate for logarithm of item discriminations as a function of the sample size \(N\) and the size and the extent and direction of deviations from the Rasch model. parameters [21]. It can be advantageous to use a sufficiently small regularization parameter \(\lambda\) to ensure the empirical identifiability of the model but not to focus on effect selection if structural parameters are of interest [63]. In this sense, sparsity in effects is imposed in a defensive way. This research received no external funding. **Data Availability Statement**: Supplementary material for the simulation studies can be found at [[https://osf.io/ykew6](https://osf.io/ykew6)]([https://osf.io/ykew6](https://osf.io/ykew6)) (accessed on 2 April 2024). **Conflicts of Interest**: The authors declare no conflicts of interest.
## 4 Discussion
### Extended Yale B Database Analysis and Discussion  
Extended Yale B datasets include 38 target faces. As shown in Table 11, when BPLRC recognizes 10%, 20%, and 30% of face images, its number of categories where precision, recall, and F1 score are >0.7 is more than other methods. When BPLRC identifies 40% of the face image, its number of precision categories >0.7 is more than other methods, but the number of recall and F1 score categories > 0.7 is less than the Module LRC method. In fact, in face recognition, precision indicators are more important than recall and F1 Score. For example, when the precision value is too low in the family access control system, it is possible to identify strangers as family members. When the recall value is too low, even family members cannot be identified (though you can enter the house by inputting a password), and there will be no severe theft incidents. F1 score is the harmonic mean of two metrics, and simply provides a summary evaluation of model performance. According to the results in Table 11, the number of precision, recall, and F1 score categories equal to 0 is only 1 or 0; thus, the model's identification performance cannot be evaluated from the number of precision, recall, or F1 Score categories equal to 0. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Occlusion Test**} & \multirow{2}{*}{\
\begin{tabular}{c} **Other Measurement Model** \\ **Performance Indicators** \\ \end{tabular} } & \multicolumn{6}{c}{**Method**} \\ \cline{3-8}  & & **LRC** & **SRC** & **CRC** & **ESRC** & **Module LRC** & **BPLRC** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Scarf occlusion \\ \end{tabular} } & Precision \textgreater{} 0.7 & 6 & 24 & 38 & 4 & 77 & **86** \\  & Recall \textgreater{} 0.7 & 8 & 46 & 70 & 24 & 91 & **94** \\  & F1 Score \textgreater{} 0.7 & 5 & 33 & 53 & 6 & 90 & **93** \\  & Precision, recall, and F1 Score = 0 & 81 & 24 & 15 & 50 & 2 & **1** \\  & Precision \textgreater{} 0.7 & 37 & 27 & 25 & 13 & 72 & **75** \\ Sunglasses & Recall \textgreater{} 0.7 & 54 & 48 & 54 & 41 & 85 & **93** \\ occlusion & F1 Score \textgreater{} 0.7 & 42 & 30 & 43 & 23 & 89 & **94** \\  & Precision, recall, and F1 Score = 0 & 29 & 29 & 15 & 31 & 2 & **0** \\  & Precision \textgreater{} 0.7 & 4 & 15 & 34 & 1 & 93 & **96** \\  & Recall \textgreater{} 0.7 & 5 & 51 & 26 & 26 & 86 & **91** \\ Mixed occlusion & F1 Score \textgreater{} 0.7 & 45 & 20 & 69 & 1 & 93 & **93** \\  & Precision, recall, and F1 Score = 0 & 23 & 7 & 7 & 16 & 1 & **0** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The number of categories that identified performance indicators to an extent greater than 0.7 or single categories that were wrongly identified from AR datasets for different methods.
## Materials
### GPP Datasets
#### 2.2.1 MODIS  
The Moderate Resolution Imaging Spectroradiometer (MODIS), launched into space on the Terra satellite in 1999 and on the Aqua satellite in 2002, is designed to observe the Earth's surface, atmosphere, and oceans. One of the key functions of MODIS is to regularly measure the carbon dioxide uptake of the Earth's terrestrial vegetation through GPP products [51; 52] that are generated by calculating the daily net photosynthesis values of plants and combining these values over 8-day observation intervals throughout the year at 1 km (MOD17). The MODIS GPP algorithm, developed by Running et al. [53], is based on the Light Use Efficiency (LUE) concept originally proposed by Monteith [54] and depends on the type of vegetation in a specific area and the amount of sunlight absorbed for photosynthesis. The MODIS GPP data used in the study were provided by [55]. The flowchart for obtaining MODIS GPP values is given in Figure S1.