esa-sceva/satcom-chunk-collection huggingface dataset:
From these studies on adult seagrass, we know that it is possible that CO\({}_{2}\) enrichment could ameliorate or enhance the effects of warming in seagrass seedlings, but more studies are needed to provide a better understanding. _Enhalus acoroides_ is a common tropical Indo-Pacific foundation seagrass species widely distributed within the region. It is found from shallow intertidal to subtidal areas, being especially exposed to local and global disturbances, including climate change. _E. acoroides_ is the biggest seagrass species in terms of its architectural properties [26] with shoots up to 1 m long [27]. It shows both horizontal clonal growth and production of new plants from seeds that are genetically unique individuals (genets). Sexual reproduction is a major sink of resources in _E. acoroides_ compared to other co-habiting species [28; 29], and seeds are large and full of carbon reserves [18]. The photosynthetic process in _E. acoroides_ potentially has a very unique carbon concentrating mechanism more dependent on the uptake of CO\({}_{2}\) rather than HCO\({}_{3}\)[30] and is potentially C\({}_{1}\) limited at current concentrations [31]. As large seagrasses show low growth rates and biomass turnover [32], environmental changes could be detected more quickly in the seedling phase. Moreover, seedling development and growth are the most critical steps in seagrass survival [33] and could be more sensitive to changes than adult individuals from the same species. However, there is a lack of research conducted on responses of _E. acoroides_ to environmental changes, especially on seeds and seedlings [18; 29; 34; 35; 36; 37; 38; 39]. The understanding of seedling trait responses under different environmental conditions may help to understand the effect of those changes, but also enhance future restoration plans of these threatened ecosystems. The conservation and management of these ecosystems requires knowledge about how they perform at the individual plant level in both early and adult life stages. This study aims to measure the morphological, biochemical, and physiological trait responses of _E. acoroides_ in the seedling phase under an increased average temperature and increased CO\({}_{2}\) concentrations. We conducted a laboratory experiment using seedlings of _E. acoroides_ to test the hypothesis that seagrass seedling development would be enhanced (synergistic effect) by independent and combined increases in temperature and CO\({}_{2}\) concentrations in terms of growth and photosynthetic performance. The results of this study will help to understand the success of seedlings of _E. acoroides_ under extended exposure to high temperatures and increasing CO\({}_{2}\) concentrations, which are predicted to occur under global climate change scenarios.
## 2 System Models of Integrated Navigation
### Solar Doppler radial velocity measurement  
The radial velocity relative to the Sun and the target planetary object measured by the two spectrometers on board the probe is based on the Doppler shift principle, which can be directly used as navigation measurement information [33; 36]. According to the position vector \(\mathbf{r}_{\text{\emph{ps}}}\) and velocity vector \(\mathbf{v}_{\text{\emph{ps}}}\) of the probe, its radial velocity \(\mathbf{v}_{\text{\emph{ps}}}^{s}\) relative to the Sun can be determined as follows:  
\[\mathbf{v}_{\text{\emph{ps}}}^{s}=\frac{\mathbf{r}_{\text{\emph{ps}}}(t)\cdot\mathbf{v}_{ \text{\emph{ps}}}(t)}{\left|\mathbf{r}_{\text{\emph{ps}}}(t)\right|}+\mathbf{\omega}_ {1}(t) \tag{14}\]  
where \(\mathbf{\omega}_{1}(t)\) is the Doppler measurement noise of spectrometer 1 facing the Sun. Based on the above analysis, the Doppler radial velocity measurement model of the probe relative to Sun is described as follows  
\[\mathbf{Z}_{D}^{\text{\emph{S}}}(t)=\mathbf{h}_{D}^{\text{\emph{S}}}[\mathbf{X}(t),t]+ \mathbf{\omega}_{1}(t) \tag{15}\]  
The measurement equation of the Doppler radial velocity from the probe relative to the Sun is represented as follows:  
\[\mathbf{h}_{D}^{\text{\emph{S}}}[\mathbf{X}(t),t]=\frac{\mathbf{r}_{\text{\emph{psx}}} \cdot\mathbf{v}_{\text{\emph{psx}}}+\mathbf{r}_{\text{\emph{psy}}}\cdot\mathbf{v}_{\text {\emph{psy}}}+\mathbf{r}_{\text{\emph{psz}}}\cdot\mathbf{v}_{\text{\emph{psz}}}}{\sqrt {\mathbf{r}_{\text{\emph{psx}}}^{2}+\mathbf{r}_{\text{\emph{psy}}}^{2}+\mathbf{r}_{\text{ \emph{psz}}}^{2}}} \tag{16}\]  
The following expression can be used to represent the corresponding matrix of the observed values.  
\[\mathbf{H}_{D}^{\text{\emph{S}}}=\frac{\partial\mathbf{h}_{D}^{\text{\emph{S}}}[\mathbf{X} (t),t]}{\partial\mathbf{X}}=\left[\begin{array}{c}\frac{\mathbf{v}_{\text{\emph{ps}}} }{\left|\mathbf{r}_{\text{\emph{ps}}}\right|}\frac{\mathbf{r}_{\text{\emph{ps}}}\cdot \mathbf{v}_{\text{\emph{ps}}}}{\left|\mathbf{r}_{\text{\emph{ps}}}\right|}\\ \frac{\mathbf{r}_{\text{\emph{ps}}}^{2}}{\left|\mathbf{r}_{\text{\emph{ps}}}\right|} \end{array}\right] \tag{17}\]
## 3 Data Set Description
### Satellite Image  
Moderate-Resolution Imaging Spectroradiometer (MODIS) is an advanced sensor on the Terra and Aqua Spacecraft for gathering data through a broad spectrum of electromagnetic waves. Terra was the first satellite of the Earth Observing System (EOS) program and was launched on 18 December, 1999. It passes north to south over the equator in the morning. Aqua is the second EOS satellite which carries a MODIS sensor and passes south to north across the equator in the afternoon. Terra and Aqua MODIS cover the Earth's surface every one to two days. The sensors onboard these satellites measure 36 spectral bands from 0.405 \(\upmu\)m to 14.385 \(\upmu\)m. The data is released by different resolutions, i.e., 250 m (bands 1-2), 500 m (bands 3-7), and 1000 m (bands 8-36). The MODIS data is accessible at [[https://modis.gsfc.nasa.gov](https://modis.gsfc.nasa.gov)]([https://modis.gsfc.nasa.gov](https://modis.gsfc.nasa.gov)) and can be used for a significant number of applications in the land, atmosphere, and, ocean [38]. Figure 4 shows the false-color images of the Sistan and Baluchestan province (also regions of the Kerman and Hormozgan provinces) before the flood (A) and during the flood (B). The images were acquired by MODIS (bands 7-2-1) on 8 January and 13 January 2020. These images were used here for validation purposes.
# Research on Ecological Lawn Regulation and Storage System in Flight Area Based on Sponge Airport
## 4 Simulation Analysis of Design Results
### Simulation Analysis of Waterlogging Prevention  
The simulation shows that the addition of the ecological lawn regulation and storage system has a certain control effect on airport flood control and drainage. Under the precipitation scenarios with return periods of 5a, 10a, 20a, 30a, 50a and 100a, the total ponding volume of the traditional airport flight area lawn is 5890 m\({}^{3}\), 19,411 m\({}^{3}\), 41,757 m\({}^{3}\), 57,708 m\({}^{3}\), 80,572 m\({}^{3}\) and 115,568 m\({}^{3}\), respectively. With the addition of the ecological lawn regulation and storage system, the total ponding volume is 1494 m\({}^{3}\), 6578 m\({}^{3}\), 14,905 m\({}^{3}\), 20,691 m\({}^{3}\), 29,014 m\({}^{3}\) and 42,625 m\({}^{3}\), respectively. The reductions in rainwater ponding volume with different rainfall intensities are different, and the largest reduction is with the 5-year return period, reaching 74.63%. As the rainfall intensity increases, the reduction in ponding volume gradually decreases by 74.63%, 66.11%, 64.31%, 64.15%, 63.99% and 63.12%, respectively. Compared with that of the traditional airport flight area lawn, the average ponding duration with the addition of the ecological lawn regulation and storage system is reduced by 42.6 min at most (Table 2). The ecological lawn regulation and storage system is able to effectively reduce the total ponding volume and the ponding duration in the airport flight area, thus achieving the effect of alleviating waterlogging.  
Figure 5: Comparison of simulated runoff with different return periods.
## 2 Materials and Methods
### Printer Testing  
To evaluate the precision and accuracy of the printer (repeatability and fidelity to a specified CAD model, respectively), three Cellink Start hydrogel rectilinear line patterns consisting of five segments with varying distances (CAD model distances: 800, 1050 and 1300 \(\upmu\)m) were printed using both the BioVERVE bioprinter and a professional 3D Discovery bioprinting platform (RegenHU, CH). Conical 25G nozzles and a velocity of 1 mm/sec were used to print the hydrogel with both bioprinters. The \"UltiMaker Cura V4.13\" software was used to design the CAD model for BioVERVE, while a dedicated CAD software (BioCAD, RegenHU, CH) was used to design the 3D structures to be printed via RegenHU. The three rectilinear patterns were printed and measured for each line distance. After printing, constructs were observed under an optical microscope, and the distance between the centers of each line segment, measured in three points along the line, was measured. Three experimental replicates were analyzed for each rectilinear line pattern printed with each bioprinter. A Nikon Eclipse 80i microscope (Nikon Instruments Europe B.V., Italy) was used to take images and the Nikon NIS-Element BR software (Nikon Instruments Europe B.V., Italy) was used to measure distances between lines in the BioVERVE constructs, while a Nikon Eclipse E880 microscope (Nikon Instruments Europe B.V., Italy) was used to take images and the Nikon NIS-Element BR software (Nikon Instruments Europe B.V., Italy) was used to measure distances between lines in the RegenHU constructs.  
In the accuracy test, the mean percentage deviation (D%) of the line spacing distances of the printed model from the corresponding distances established in the CAD model was calculated as follows:  
\[\text{D}\%=(\text{Avg}\,(\text{d}_{\text{CAD}}-\text{d}_{\text{i}})/\text{d}_ {\text{CAD}})\times 100\]  
where \(\text{d}_{\text{CAD}}\) is the fixed distance in the specific CAD model and \(\text{d}_{\text{i}}\) represents the measured distances between the midpoints of the printed lines. The mean percentage deviations were compared using \(t\)-test for both bioprinters.
###### Abstract  
Hearing difficulty in noise can occur in 10-15% of listeners with typical hearing in the general population of the United States. Using one's smartphone as a remote microphone (RM) system with the AirPods Pro (AP) may be considered an assistive device given its wide availability and potentially lower price. To evaluate this possibility, the accuracy of voice-to-text transcription for sentences presented in noise was compared, when KEMAR wore an AP receiver connected to an iPhone set to function as an RM system, to the accuracy obtained when it wore a sophisticated Phonak Roger RM system. A ten-sentence list was presented for six technology arrangements at three signal-to-noise ratios (SNRs; +5, 0, and \(-\)5 dB) in two types of noise (speech-shaped and babble noise). Each sentence was transcribed by Otter AI to obtain an overall percent accuracy for each condition. At the most challenging SNR (\(-\)5 dB SNR) across both noise types, the Roger system and smartphone/AP set to noise cancelation mode showed significantly higher accuracy relative to the condition when the smartphone/AP was in transparency mode. However, the major limitation of Bluetooth signal delay when using the AP/smartphone system would require further investigation in real-world settings with human users.  
remote microphone system; voice-to-text transcription; AirPods Pro; typical hearing Article  
1
# Remote Sensing and Machine Learning for Safer Railways: A Review
## 3 Descriptive Analysis
### Distribution by Author Country  
Figure 4 shows the country of origin for the authors of the reviewed publications. To create this figure, the review identified the country of every author, based on their affiliations, and tallied their representation in the reviewed corpus of literature.  
China dominated with 167 authors, followed by the United States with 26 and Sweden with 19. The Netherlands and Brazil were close behind, each with authors contributing to 11 and 8 publications, respectively. Authors from the other 13 countries represented only 18% of the total authorship. China and the United States alone accounted for 68% of the authorship with China dominating at 60%.
## 3 Results
### Corrosion Investigations  
#### 3.2.1 Potentiodynamic Polarization  
The results of the potentiodynamic polarization (PDP) measurement are shown in Figure 3, as well as the determined corrosion current densities \(\mathrm{i_{corr}}\) and estimated corrosion rates \(\mathrm{\dot{m}_{corr,e}}\) in Table 2. The current density \(\mathrm{\dot{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{  \mathrm{ {   }}}}}}}}}}}}}}\) is plotted over the potential E versus an Ag/AgCl electrode. Qualitatively, the three measurements performed are comparable, whereas the open circuit potential (OCP) varies slightly by 80 mV. Since the following tests are carried out with a galvanostatic polarization, slight fluctuations in the potential are not crucial. Close to the OCP, the anodic branch seems to flatten out but then changes to a higher slope accompanied by increased noise. As the potential increases further, the active area flattens out. Due to the asymmetry of both branches, the corrosion current densities were estimated by Tafel extrapolation of the cathodic branch. The values are used to estimate the corrosion rate applying [PERSON]'s law. The mean corrosion current density and corrosion rate are \(\mathrm{i_{corr,mean}}\) = 16.48 \(\pm\) 6.38 \(\upmu\)A cm\({}^{-2}\) and \(\mathrm{\dot{m}_{corr,mean}}\) = (6.55 \(\pm\) 2.53) \(\cdot\) 10\({}^{1}\) mg cm\({}^{-2}\) a\({}^{-1}\) and exhibit the typical high standard deviations (SD) for this testing type. Taking the anodic branch into account, it allows an estimation of the application range for galvanostatic anodic polarization. For the following measurements, 0.5, 1, 2, and 20 mA cm\({}^{-2}\) are used.  
Figure 2: Results of microstructural investigations on Mg WE43: (**a**) SEM image of cross section; (**b**) SEM image for EDS mapping of (**c**) Mg; (**d**) Y; (**e**) Nd; and (**f**) Zr.
## 2 DPS Modeling of LEO-SIN
### Performance Analysis
#### 3.3.4 ACF Analysis after Error Compensations by CPF  
We further analyze the performance of the RMEDS-SP improved by error compensations by CPF. The conditions and indicators of the analysis would be the same. The results of ACF of RMEDS-SP and after error compensations by CPF are shown in Figure 12.  
The number of sinusoids is set as \(N_{1}=20,N_{2}=21,K=4\). As Figure 12 shows, the ACF would diverge with the simulation error increasing when the relative time is more than \(\tau_{max}\) under this condition. We can figure out that the original simulation error of ACF improved by CPF can be further compensated, which is more similar to the referenced model. According to Equation (32), we can obtain \(\overline{MSE}\) results of RMEDS-SP and after error compensations, as Table 3 shows.  
As Table 3 shows, after error compensations by CPF, the MSE of DPS is 0.00293, that of only RMEDS-SP is 0.00394. There is about a 25.63% performance improvement.  
The corresponding DPS of a different model is operated by the Fourier transform of the ACF, which is shown in Figure 13. The proposed RMEDS-SP after error compensations by CPF can improve the similarity between the model and referenced model. The MSE of the DPS of the two models is 12.85 and 10.47. We can figure out that there is an 18.52% performance improvement after error compensations by CPF.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **MSE** \\ \hline \multirow{3}{*}{RMEDS-SP} & \(N_{1}=20\), & \multirow{3}{*}{0.00394} \\  & \(N_{2}=21\), & \\  & \(K=4\) & \\ \hline \multirow{3}{*}{RMEDS-SP after error compensations by CPF} & \(N_{1}=20\), & \multirow{3}{*}{0.00293} \\  & \(N_{2}=21\), & \\ \cline{1-1} \cline{3-3}  & \(K=4\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: ACF analysis after error compensations by CPF.  
Figure 12: ACF of RMEDS-SP and after error compensations.
# Uncertainty Analysis of Multisource Land Cover Products in China
## 1 Introduction
Some scholars have realized these issues, and the uncertainty of land cover products at the regional scale has begun to be explored. [PERSON] et al. studied the consistency of GlobeLand30 and FROMGLC in the Beijing-Tianjin-Hebei region of China, and their differences in land cover types and landscape pattern indexes were compared and analyzed [25]. A single resolution was selected in their study, only two sets of products were selected, and the area differences of the land cover types in the two types of products were directly compared, without focusing on spatial differences between the products. [PERSON] et al. explored five sets of land use models and found that different regions were very inconsistent and had high uncertainty [26]. A method to resolve these differences was proposed using high-resolution land cover datasets [27]. [PERSON] et al. studied the consistency of five global land cover datasets in China. The five datasets were compared in pairs. The results showed that the forest classification differed significantly, and the consistency was the lowest [28]. These studies mainly focused on comparisons between qualitative products, they rarely involved quantitative products and semiquantitative products, and they did not involve the impact of resolution on the uncertainty of land cover products. The uniqueness of this study was that it considered three completely different types of land cover products, including qualitative products, semiquantitative products, and quantitative products. It explored the impact of resolution on the uncertainty of land cover products. Land cover products have a wide range of sources and different classification methods. There may be differences when the same area is described by different products, and there is high uncertainty in the land cover types in areas with large differences. Before these land cover products with different resolutions are used by researchers, the uncertainty of various products must be evaluated. That is, the difference in the area and the uncertainty of the change must be determined with different spatial resolutions [29]. Therefore, this study used three types of land cover data, and the methods of maximum area aggregation,deviation analysis, and least squares regression were used to analyze the difference in the areas and spatiotemporal changes of forest and nontree vegetation of MCD12, ESA CCI, and VCF land cover classification products. China was chosen as the study area due to its large variations in land cover, the junction of vegetation cover types, and a large number of mixed pixels, making it an ideal study area. When different products and resolutions are selected by scholars for research, our research helps reduce the uncertainty of multisource land cover products.
## 3 Computational Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Instance**} & \multirow{2}{*}{**BFS (60 s)**} & \multicolumn{4}{c}{**MST2 (60 s)**} & \multicolumn{4}{c}{**\(r\)-Flip (60 s)**} \\ \cline{3-8}  & & **OFV** & **TB (s)** & **OFV (\(r=1\))** & **TB (s)** & **OFV (\(r=2\))** & **TB (s)** \\ \hline p6000\_1 & 11,384,976 & 11,377,315 & 42 & 11,384,976 & 12 & 11,384,976 & 5 \\ p6000\_2 & 14,333,855 & 14,330,032 & 39 & 14,333,855 & 27 & 14,333,767 & 14 \\ p6000\_3 & 16,132,915 & 16,122,333 & 51 & 16,130,731 & 24 & 16,132,915 & 48 \\ p7000\_1 & 14,477,949 & 14,467,157 & 56 & 14,477,949 & 41 & 14,476,263 & 21 \\ p7000\_2 & 18,249,948 & 18,238,729 & 55 & 18,249,948 & 47 & 18,246,895 & 47 \\ p7000\_3 & 204,464,077 & 20,431,354 & 59 & 20,446,407 & 15 & 20,446,407 & 12 \\ p8000\_1 & 17,340,538 & 17,326,259 & 47 & 17,340,538 & 26 & 17,340,538 & 35 \\ p8000\_2 & 22,208,986 & 22,180,465 & 55 & 22,208,986 & 54 & 22,208,683 & 53 \\ p8000\_3 & 24,670,258 & 24,647,248 & 56 & 24,670,258 & 43 & 24,669,351 & 50 \\ p3000\_1 & 127,252,438 & 126,732,48,3 & 60 & 127,252,438 & 58 & 127,219,336 & 60 \\ p3000\_2 & 158,384,175 & 157,481,366 & 69 & 158,384,175 & 59 & 158,339,497 & 60 \\ p3000\_3 & 179,103,085 & 178,093,109 & 89 & 179,103,085 & 58 & 179,029,747 & 54 \\ \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Instance**} & \multirow{2}{*}{**BFS (600 s)**} & \multicolumn{4}{c}{**MST2 (600 s)**} & \multicolumn{4}{c}{**\(r\)-Flip (600 s)**} \\ \cline{3-8}  & & **OFV** & **TB (s)** & **OFV (\(r=1\))** & **TB (s)** & **OFV (\(r=2\))** & **TB (s)** \\ \hline p3000\_1 & 3,931,583 & 3,931,583 & 11 & 3,931,583 & 5 & 3,93  
The \(r\)-flip strategy can be embedded in other local search heuristics as an improvement procedure. Clever implementation of the \(r\)-flip strategy can reduce the computing time and improve the solution quality. We reported the time and solutions out of 10 runs for each instance. The time deviation and solution deviation of 10 runs were computed with the short CPU time limits due to the computing resources available to this study. The observed results show the effectiveness of the employed methodology in finding the best solutions within very competitive CPU time limits. This suggests the possibility of using alternative stopping criteria as a potential improvement in the future. Also, the application of an efficient strategy in evaluating flip moves has the potential to further improve the algorithm performance, as suggested by [PERSON] et al. [12]. The \(r\)-flip method we developed presents itself as a compelling alternative for quantum computing solvers, which are capable of efficiently handling QUBO problems as large as 5000 by 5000 variables due to the design of quantum processors. The advantages of the \(r\)-flip method become particularly pronounced when dealing with substantial, complex problem instances, showcasing superior computing performance. In essence, our method emerges as a favorable choice for addressing large-scale, real-world challenges with efficacy and computational prowess.
# A Lightweight Method for Detecting Sewer Defects Based on Improved YOLOv5
## 3 Experimental Results and Analysis
### Experimental Environment and Configuration
As the attention mechanism is added, the model can pay more attention to the feature information that is useful for defect detection during feature fusion, and suppress the influence of noise and other interfering information; the average recall rate and F1 value of the model are increased by 3.65% and 2%, respectively. From Table 4, it can be seen that due to the addition of the attention mechanism, FPS on the GPU has been slightly reduced, but the introduction of the Ghost module has increased the detection speed of the model on the CPU. Although the Ghost module with the CA mechanism sacrifices some detection accuracy, it greatly reduces the model volume and calculation power, and ultimately improves the model's detection accuracy while significantly reducing the required calculation power, making it more suitable for pipeline defect detection tasks on small mobile devices. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**AP50/\%**} & \multicolumn{5}{c}{**Recall/\%**} & \multicolumn{5}{c}{**FPS**} \\ \cline{2-10}  & **Crack** & **Deposition** & **Root** & **Stagger** & **Crack** & **Deposition** & **Root** & **Stagger** & **/(Frames/s)** \\ \hline YOLOv5 & 74.26 & 89.21 & 90.68 & 91.17 & 61.82 & 80.95 & 90.24 & 90.67 & 5.5 \\ YOLOv5-G & 67.21 & 93.72 & 89.27 & 94.05 & 58.90 & 87.95 & 79.27 & 93.15 & 10.3 \\ YOLOv5-GB & 81.44 & 93.01 & 85.05 & 93.17 & 67.12 & 85.54 & 71.95 & 94.52 & 8.4 \\ YOLOv5-GBC & 82.20 & 94.85 & 81.14 & 90.64 & 68.49 & 93.98 & 76.83 & 90.41 & 9.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AP, Recall and FPS of different models for each defect.