esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In vibration-based structural health monitoring, the highly accurate reference model derived through the FE model updating plays an important role. In the current case study, OMA was performed on a short-span skewed highway bridge by using a self-developed wireless acquisition system. Very accurate modal data obtained under the operational condition of the structure provides a reliable measure of the healthy state of the bridge. A refined FE model, built of solid elements, was successfully updated in reference to the experimental modal data by means of both manual tuning and the sensitivity-based model updating approach. The profound effects of the superelevation and boundary conditions to the dynamics characteristics of the skewed bridge were highlighted in the current study. In particular, the importance to remove idealization errors before parametric model updating is verified in obtaining the accurate reference FE model.  
Conceptualization, methodology, L.H., E.R., J.H.G.-P. and G.D.R.; validation, formal analysis, L.H., E.R. and J.H.G.-P.; investigation, resources, data curation, writing--original draft preparation, L.H., E.R., J.H.G.-P., G.C.M. and B.B.; writing--review and editing, L.H., E.R., and G.D.R.; project administration, J.H.G.-P.; funding acquisition, J.H.G.-P. and G.D.R. All authors have read and agreed to the published version of the manuscript.  
The financial supports from the Spanish Ministry of Science and Innovation, under Research Grant AMILCAR TEC2009-14595-C02-01 are acknowledged for the development of the wireless system and during the experimental campaign. Moreover, the first author needs to acknowledge the financial supports from the Fuzhou municipal city under the research project \"2017-G-67\" and those from the Fuzhou University under the research project \"XRC--1675\".  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Box Girder**} & \multicolumn{2}{c}{**Slab**} & \multicolumn{2}{c}{**Side Barrier**} \\ \hline  & **E\({}_{b}\) (MPa)** & **q (kg/m\({}^{3}\))** & **E\({}_{b}\) (MPa)** & **q (kg/m\({}^{3}\))** & **E\({}_{b}\) (MPa)** & **q (kg/m\({}^{3}\))** \\ \hline Initial & 32,373 & 2,500 & 32,373 & 2,500 & 16,187 & 2,500 \\ Updated & 33,069 & 2,418 & 35,371 & 2,835 & 18,208 & 3,283 \\ \(\Delta\theta\) & 2\% & \(-3\%\) & 9\% & 13\% & 12\% & 31\% \\ \hline \hline \end{tabular} Note: \(\Delta\theta=(\theta_{\text{updated}}-\theta_{\text{initial}})/\theta_{\text{ initial}}\).  
\end{table}
Table 4: Values of the initial and updated parameters \(\theta\).  
Figure 7: Plot of the vertical modal displacements along the transversal cross section. Note: The modal displacements are normalized to the maximum unity for the first thirty-three nodes.
# Anisotropic Effective Elastic Properties for Multi-Dimensional Fractured Models
## 2 Methodology
### Anisotropic Effective Elastic Properties for 3D Fractured Models
#### 2.2.2 Calculation of 3D Equivalent Stiffness Matrix  
We solve Equations (1) and (11) by applying the FEM to obtain the corresponding displacement field, which promotes the stress and strain components for every element to be obtained using Equations (3) and (4). Then, parameters allow us to estimate the corresponding average over the sample's volume:  
\[\overline{\sigma^{r}_{ij}}=\frac{1}{V}\int_{\Omega}\sigma^{r}_{ij}dV, \tag{25}\]  
And  
\[\overline{\sigma^{r}_{ij}}=\frac{1}{V}\int_{\Omega}\sigma^{r}_{ij}dV, \tag{26}\]  
where \(V\) is the volume of \(\Omega\), with \(r\) = 1-6 denoting the \(r\)th simulation oscillatory relaxation test described in Figure 3, respectively.  
As in the case of 2D, we use a second-order equivalent Voigt stiffness matrix \(\mathbf{M}\equiv\)\(M_{ij}\) to relate the averaging stress and strain components of 3D models. In the case of general anisotropy and under plane strain conditions, this relation is given by  
\[\left(\begin{array}{c}\overline{\sigma^{r}_{xx}}\\ \overline{\sigma^{r}_{yy}}\\ \overline{\sigma^{r}_{zz}}\\ \overline{\sigma^{r}_{yz}}\\ \overline{\sigma^{r}_{xy}}\end{array}\right)\equiv\left(\begin{array}{cccccc }M_{11}&M_{12}&M_{13}&M_{14}&M_{15}&M_{16}\\ M_{12}&M_{22}&M_{23}&M_{24}&M_{25}&M_{26}\\ M_{13}&M_{23}&M_{33}&M_{34}&M_{35}&M_{36}\\ M_{14}&M_{24}&M_{34}&M_{44}&M_{45}&M_{46}\\ M_{15}&M_{25}&M_{35}&M_{45}&M_{55}&M_{56}\\ M_{16}&M_{26}&M_{36}&M_{46}&M_{56}&M_{66}\endwhere \(M_{ij}\) is the equivalent stiffness components in the 3D media, and \(\overline{\sigma_{ij}^{r}}\) and \(\overline{\sigma_{ij}^{r}}\) are cells of the average stress and strain vector, respectively. In general, the matrix \(\mathbf{M}\) takes into account the highest degree of anisotropy for a 3D case. Like in the 2D case, we can obtain 21 unknown stiffness tensors by applying the least-squares procedure to minimize the following cost function \(F_{2}\) (\(\mathbf{M}\)),  
\[\frac{\partial F_{2}(\mathbf{M})}{\partial M_{ij}}=0. \tag{28}\]  
We can solve the linear system of twenty-one equations yielded by Equation (28) to obtain the equivalent stiffness matrix \(M_{ij}\), seeing Appendix B for more niceties.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Method
#### 3.2.4 Ship Sector  
A plume produced by a ship at a given moment will be displaced, over time, in the direction of the wind in the analyzed area. Having the wind information available, we would like to restrict the analysis to the part of the _ship plume image_, where the probability to find the plume of the ship is the highest. We performed the area restriction by defining an ROI of an analyzed ship, which we call a _ship sector_. The area of the _ship sector_ is determined on the basis of information about the ship's trajectory and the speed/direction of the local wind.  
As a starting point of the _ship sector's_ definition, we used the _wind-shifted ship track_, calculated as described in Section 3.2.1. We then calculated the _extreme wind-shifted tracks_ by adding the margin of wind-related uncertainty to either side of the _wind-shifted ship track_. The _extreme wind-shifted tracks_ delineate the borders of the _ship sector_, showing the extreme possible positions of the plume, taking the wind measurement uncertainty into account. The wind's uncertainty was assumed due to the limited spatial and temporal resolution of wind data [11], based on reported in several studies' [36; 37] measurement bias, as well  
Figure 5: Ship sector definition pipeline. (**a**) _Ship plume image_—the TROPOMI NO\({}_{2}\) signal for the area around the analyzed ship. Two ship plumes can be distinguished, but only one is of interest. (**b**) The NO\({}_{2}\) signal enhanced by [PERSON]’s \(I\) spatial auto-correlation statistic. (**c**) _Ship track_—estimated, based on AIS data records. The ship track is shown for the time period starting from 2 h before the satellite overpass until the moment of the satellite overpass. (**d**) _Wind-shifted ship track_—a ship track shifted in accordance with the speed and direction of the wind. The _wind-shifted ship track_ indicates the expected position of the ship plume. A black arrow indicates the wind direction. (**e**) _Extreme wind-shifted ship tracks_—calculated, based on wind information with assumed uncertainties; define the borders of the _ship sector_. (**f**) A resulting _ship sector_—an ROI of an analyzed ship. For all presented images, the size of the pixel is equal to \(4.2\times 5\,\mathrm{km}^{2}\).  
as assumptions of constant wind mentioned in Section 3.2.1. Illustrations of the _extreme wind-shifted tracks_ and the resulting _ship sector_ are shown in Figure 5e and 5f, respectively. As a result of the delineation of the _ship sector_ area, the plume should always lie within the _ship sector_ boundaries. Only pixels lying within the _ship sector_ were taken into consideration in further analysis. Parameters related to the _ship sector_ can be found in Table 1.
# A Continuous Monitoring and Reallocation Method for Successful Decisions in Change Management
## 4 Experiments and Results
### Second Experiment: Algorithm Implementation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Total Performance Improvement (\%)**} \\ \hline
**Repetitions** & **100** & **1.000** & **10.000** & **100.000** \\ \hline
0 & 0.00\% & 0.00\% & 0.00\% & 0.00\% \\
1 & 9.22\% & 9.14\% & 9.12\% & 9.13\% \\
2 & 35.81\% & 35.35\% & 35.22\% & 35.26\% \\
3 & 58.91\% & 58.31\% & 58.03\% & 58.09\% \\
4 & 75.26\% & 74.51\% & 74.26\% & 74.29\% \\
5 & 91.47\% & 90.54\% & 90.27\% & 90.30\% \\
6 & 109.51\% & 108.55\% & 108.18\% & 108.18\% \\ Time(\(t\)) & 7 & **124.17\%** & **122.85\%** & **122.36\%** & **122.39\%** \\
8 & **124.17\%** & **122.85\%** & **122.36\%** & **122.39\%** \\
9 & 112.31\% & 110.96\% & 110.59\% & 110.62\% \\
10 & 98.53\% & 97.36\% & 97.10\% & 97.09\% \\
11 & 83.73\% & 82.87\% & 82.62\% & 82.61\% \\
12 & 72.93\% & 72.29\% & 72.16\% & 72.15\% \\
13 & 64.34\% & 63.62\% & 63.64\% & 63.64\% \\
14 & 57.87\% & 57.46\% & 57.38\% & 57.41\% \\
15 & 53.09\% & 52.82\% & 52.72\% & 52.74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of Total Performance Improvement for 100, 1.000, 10.000, and 100.000 repetitions between the CoMoRe and CCMM methods for 100 workers and tasks (\(n=100\)). (\(n=10\)). This can be attributed to the larger set of workers and tasks, which provided more opportunities for optimal allocation, consequently leading to a substantial enhancement in overall performance, as evidenced in Tables 8 and 9. The CoMoRe algorithm is a computational approach that involves solving the HRAP through the utilization of the Hungarian algorithm. According to the literature, the Hungarian algorithm is known to exhibit a complexity of \(O(n^{3})\). In its remaining parts, the CoMoRe algorithm does not present a computational complexity of a higher order. More specifically, Table 10 presents the execution time of the Hungarian part of the algorithm, the time that corresponds to the remaining part, and the total execution time of the CoMoRe algorithm. It is evident from the data presented in Table 10 that the Hungarian part of the algorithm for a \(10\times 10\) matrix needs only 28.25 s, which is 3.6% of the total execution time, but for a \(100\times 100\) matrix, it needs 826.98 s, which is 46% of the total execution time. Therefore, the execution time of the Hungarian part increases by over 2800% as the matrix size increases from \(10\times 10\) to \(100\times 100\). The remaining part has a small increase of approximately 28.7%. The total time shows a modest increase of approximately 130%. In conclusion, while the Hungarian algorithm constitutes the primary computational issue, the overall complexity of the method remains manageable for moderate matrix sizes. The reported runtimes were measured when running the algorithm on a computer with the following characteristics: Intel(r) Core(tm) i7-8700 CPU @ 3.20 GHz, RAM 32.0 GB.
# Deep Eutectic Solvent Pretreatment and Green Separation of Lignocellulose
###### Abstract  
Plant-based waste biomass with lignocellulosic as an important component is produced in large quantities worldwide every year. The components of lignocellulosic that typically exhibit high utilization value include cellulose and hemicellulose, as well as pentoses and hexoses derived from their hydrolysis. As a pretreatment for the hydrolysis process, delignification is a pivotal step to enhance cellulose/hemicellulose accessibility and achieve high yields of fermentatable sugars. Additionally, deep eutectic solvents (DESs) are the most widely used solvents for delignification during biomass fractionation due to their clean and environmentally friendly attributes. DESs dissolve lignin by inducing a large amount of \(\beta\)-O-4 bond cleavage and partial carbon-carbon bond cleavage, retaining cellulose in the solid residue, while most of the hemicellulose is hydrolyzed in DES pretreatment. This article provides a comprehensive review of the influence of DESs in the lignocellulosic separation process. Key factors such as lignin removal rate, sugar conversion rate, and product chemical structure are critically reviewed to assess the feasibility of employing DESs for lignocellulosic separation.  
Keywords:Ignocellulose; deep eutectic solvents; lignin separation; sugar conversion rate; chemical structure +
Footnote †: journal: Applied Sciences
# Directional Suppression of Monotone Noises with A Parametric Array Loudspeaker
## 3 Realization of the PC-Powered ANC System  
The proposed ANC system is developed on a generic PC architecture utilizing standard PC-integrated hardware as the signal processing and noise control platform. The PC has a quad-core CPU (Core i7-7700, Intel, CA, USA) and 16 GB RAM. Other peripheral sensors and components are generic audio hardware such as microphones, sound cards, and audio power amplifiers (PAs). The closed-loop noise sampling hardware includes two condenser microphones (ECM8000, Behringer, Germany) with a high dynamic range as the reference and error sensors, respectively. A high-precision, fast-rate, multichannel sound card (UMC404 HD, Behringer, Germany) is used for signal digitizing. Both conventional VCL and bespoke PAL are tested and compared as secondary sources. A high-bit-depth oscilloscope provides real-time monitoring of the noise reduction results.  
Figure 2 shows the system diagram to implement the FxLMS algorithm for real-time ANC. The PC interfaces with the sound card to process the audio data input/output (I/O) streaming, as well as the main ANC computation. The I/O audio signals are streamed and buffered in segments by 2048 samples per frame through the USB interface from the sound card into the PC via standard audio driver types such as ASIO. The multichannel sound card digitizes the noise data at a bit rate of 44.1 kHz and a bit depth of 16 bit for fast-rate, high-precision sampling. Real-time audio data processing is implemented with a PC-based software environment to run the FxLMS algorithm by reading the data from the Audio Data Read module and provides the ANC signal to drive the secondary loudspeaker from the Audio Data Write module, as shown in the block diagram of Figure 2. The FxLMS computation and I/O streaming of the processed data are all executed in segments of the same length following the same sampling rate.  
According to the proposed FxLMS block diagram in Section 2, the first step is to estimate the secondary path following Figure 1b using white noise. The secondary path filter \(\hat{\mathbf{s}}(n)\) is recorded until the convergence error reaches \(-40\) dB. The FxLMS algorithm is then executed with the saved \(\hat{\mathbf{s}}(n)\) following the process in Figure 1a. When using PAL as the secondary source, the speaker instrumentation is more complex than the VCL case. The PAL hardware includes a bespoke PAL constructed with an array of transducers, which is powered by a PA module associated with a generic signal generator for DSB modulation of the actuation signals.  
Figure 2: System diagram of the PC-powered ANC system implemented with the FxLMS algorithm.
## 2 Study Area and Engineering Situation
### Geology and Hydrology of the Tunnel Site  
The geological environment of the Weilai Tunnel is complex. The tunnel site originates from the structural unit of the Xilin-Baise fault belt in the Youjiang regeneration trough of the Nanhua quasi-platform. Evident structural developments and large-scale sedimentary depressions are encountered in the tunnel site. Huge mountains, intersecting ridges, and valleys surround the tunnel site. Quaternary residual slope accumulation characterizes the site, and is mainly composed of argillaceous sandstone, sandstone, and mudstone. Nevertheless, argillaceous sandstone is the most predominant rock type in the tunnel alignment. The main lithology encountered along the tunnel route is schematized in Figure 2.  
Groundwater inflows into the tunnel are mainly facilitated by the unfavorable geological conditions of the surrounding rocks, and the availability of groundwater in the existing aquifers. In fact, it should be noted that karst terrains exist in a relatively large scale in Guangxi province, which is located on the southeastern edge of the Yunnan-Guizhou Plateau. Typically, nearly 50% of groundwater inflows into rock tunnels are facilitated by karst terrains [16; 17]. The risk level of tunnel construction in Guangxi is increased by the ingress of groundwater into the host rocks.  
Figure 1: Location map of the Weilai Tunnel.
## 2 Conditioning Solutions  
Conditioning solutions, such as irrigating solutions in the endodontic treatment [13], induce morphological changes in the intra-radicular dentin through alterations in both superficial porosity and the diameter of the dental tubules [32]. These processes induce modification at collagen fibers of the dentinal wall; it also affects the calcium/phosphate ratio of the inorganic layer in the dentin, conditioning monomer penetration into the demineralized dentin [12]. These modifications can affect dentin hybridization by reducing the post-endodontic adhesive interface [13], provoking changes in adhesive system impregnation patterns, and reducing the infiltration into peritubular dentin resulting in restoration challenges for clinicians [14; 33].  
The use of different solutions has been implemented to improve dentin conditioning and an effective remove of the SSL throughout the space created for the fiber post; these solutions include acid solutions, that is, ethylenediaminetetraacetic acid (EDTA) [10; 25; 34], OA [25; 30; 34], maleic acid [20], and citric acid at several concentrations such as 10%, 20%, and 50% [9; 25]. Also, the use of several combined solutions allows a reduction of superficial tension, facilitating irrigants contact with dentinal walls, and enhancing SSL removal [35]; some examples are MTAD [34; 36; 37] and Q-mix [18; 24]. There activity is summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Conditioning Solution** & **Activity** \\ \hline \multicolumn{2}{c}{Acidic solutions} \\ \hline Ethylenediaminetetraacetic acid &
\begin{tabular}{c} Chelating agent [35; 38] \\ Removal of the SL [10; 11; 16; 22; 25; 34; 39] \\ \end{tabular} \\ \hline Orthophosphoric acid & Remove the SL and open the dentinal tubules [40] \\ \hline Citric acid & Demineralizing solution [18] \\ \hline Maleic acid & Demineralization of the intertubular dentin [18; 41] \\ \hline \end{tabular}
\end{table}
Table 1: Type of activity of conditioning solutions.
## 4 Discussion
### Constitutive Model Prediction  
Figure 8 shows the relationship between the damage amount and the strain. The damage evolution process of the rock samples under different strain rates shows significant three-stage characteristics, and there is a significant correspondence between the damage variables and the strain. By analyzing the damage evolution law of the rock samples during the loading process, the damage evolution process can be divided into the unchanged stage, the slow growth stage, and the accelerated growth stage. At 1 mm/min, the thresholds are a 0.38% strain between the unchanged and slow growth and a 0.72% strain between the slow growth and fast growth. At 3 mm/min, they are 0.32% and 0.54%. At 5 mm/min, they are 0.27% and 0.43%. At 7 mm/min, they are 0.26% and 0.40%, respectively. During the invariant stage, the internal structure of the rock sample is mainly characterized by the closure of the primary fractures. At this stage, dominated by the rearrangement of mineral particles and the compression of fractures, no secondary cracks occurred inside the rock samples, and the damage variables remained basically unchanged. After entering the slow growth stage, the microcracks stably expand along the mineral interface, and the damage variable grows slowly at a constant rate. When the strain increases to the yield strain, the secondary crack mesh shows bifurcation and fusion phenomena. The secondary crack propagation mode changes from steady state to unsteady state. The stress concentration effect at the tip of the secondary crack is significant, and the damage variable shows an  
Figure 7: Model rationality analysis.  
accelerating growth trend. Furthermore, it is not difficult to see that the greater the strain rate, the greater the increase rate of the rock sample damage variable, and the more severe the damage and destruction. The research results provide an important theoretical basis for the assessment of surrounding rock damage under different pressure rates of the mine roof.
## 4 Fragility Analysis Based on IDA Method
### Intensity Index and Ground Motion Amplitude Modulation Rule  
To obtain the complete dynamic response of the structure from elastic to elastoplastic to collapse, it is necessary to modulate the amplitude of each selected ground motion record into different levels. Various indices are used to indicate the intensity of ground motion. For instance, GB/T50011-2010 [42] employs PGA as an index, while FEMA P695 [40] uses the elastic acceleration spectrum \(\mathrm{S_{a}}\) based on the fundamental period of the structure. Additionally, peak ground velocity (PGV) can serve as an amplitude modulation index. Studies have shown that \(\mathrm{S_{a}}\) is more relevant and applicable as a strength index compared with PGA [44; 45]. Therefore, this paper adopts \(\mathrm{S_{a}}\) as the intensity measure (IM) for the amplitude modulation of ground motion records, with the fundamental period T being 2.5 s for both isolation structures.  
[PERSON] et al. summarized three amplitude modulation methods suitable for IDA [46; 47]. This study employs the equal-step ground motion recording method, with an intensity difference of 0.5 g and a peak ratio of 1:1:0.65 for the X, Y, and \(Z\) three-component acceleration recordings.
## 3 Results and Analysis
### Spectral Indicators and Estimation Models Based on the Original Spectrum  
#### 3.4.1 Identification of Sensitive Bands and Construction of Vegetation Indices  
The reduced sampling method was adopted for the systematic quantification of all possible two-band combinations of hyperspectral indices within the full wavelength range (350-2500 nm) [24]. This enabled us to evaluate the relationships between chlorophyll and NDSI, RSI, DSI and SASI from 350 nm to 2500 nm at 10 nm intervals. According to changing values of R\({}^{2}\), we obtained the contour map for the R\({}^{2}\) values of chlorophyll and NDSI, RSI, DSI and SASI across the full wavelength region, based on the original spectral reflectance. The contour map was then used to identify the sensitive spectral range with a relatively greater R\({}^{2}\). The \"hotspots\" of high correlation coefficients between chlorophyll and NDSI, RSI, DSI and SASI were found to be located in the VIS and NIR bands. The full-wavelength R\({}^{2}\) contour map showed the sensitive wavelength ranges for chlorophyll based on the different indices: NDSI, 515-525 nm and 525-530 nm; RSI, 730-760 nm and 550-590 nm; DSI, 720-780 nm and 570-640 nm; and SASI, 680-780 nm and 560-660 nm. After linear regression analysis, R\({}^{2}\) values were mostly greater than 0.65 and the values for RMSE based on the NDSI, RSI, DSI and SASI estimation models were all lower than 2.04 in the two spectral ranges of VIS and NIR; this indicated that the vegetation index constructed by these bands had a good correlation with the chlorophyll value for peanut.  
In addition, accurate sampling of these sensitive spectral regions yielded more detailed contour maps of R\({}^{2}\) values for chlorophyll and either NDSI, RSI, DSI or SASI at 1 nm intervals. The optimum vegetation indices were obtained from the R\({}^{2}\) and RMSE values: NDSI group, NDSI (R\({}_{520}\), R\({}_{528}\)) (R\({}^{2}\) = 0.66); RSI group, RSI (R\({}_{748}\), R\({}_{561}\)) (R\({}^{2}\) = 0.65); DSI group, DSI (R\({}_{758}\), R\({}_{602}\)) (R\({}^{2}\) = 0.68); SASI group, SASI (R\({}_{753}\), R\({}_{624}\)) (R\({}^{2}\) = 0.69) (Figure 6).  
Figure 6: _Cont._