esa-sceva/satcom-chunk-collection huggingface dataset:
# Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA
## 1 Introduction
These predictable king tide events can be estimated far in advance and make coordinating and mobilizing a volunteer effort to track their inundation extent easy, while maximizing the opportunity for local weather impacts to potentially amplify the inundation observed [20]. This manuscript describes methods employed at VIMS to disseminate automated inundation forecasts called Tidewatch Maps. The forecasts function as an operational flood forecast model, which leverages the open-source Semi-implicit Cross-scale Hydro-science Integrated System Model (SCHISM) to automatically compute storm tide simulations throughout the entire US East and Gulf Coasts (Figure 1). SCHISM then translates those water level outputs to 220 localized lidar-derived sub-grid modeled sub-basins ranging from 1 to 5 m resolution to calculate 36 1-h geospatial flood depth layers covering all of Tidewater Virginia. SCHISM and the Tidewatch Map currently download inputs and update mapping outputs twice daily, every 12 h. Reliable inundation prediction depends upon accurate simulation of large-scale inundation of the tidal long wave during a king tide to successfully propagate from the ocean, through the continental shelf, estuarine systems, into creeks, and ultimately city streets, and rigorous conservation of fluid momentum and mass as flood waters permeate the built environment. These Tidewatch forecast maps were benchmarked in Hampton Roads by \(>\)100,000 GPS-reported high water marks collected by citizen scientists during two king tide flooding events occurring in 2017 and 2018. The following sections highlight how coastal communities are being meaningfully engaged in coastal ocean observing mechanisms and the research efforts they support. What follows is a description of: (1) A citizen science flood mapping project called _Catch the King_ based in Hampton Roads, VA, (2) effective volunteer training methods using cell phones to provide meaningful GPS observations for effective model validation, (3) hydrodynamic modeling approaches used for expediently simulating and publicly mapping near-term inundation, along with (4) a summary of the results. The paper concludes with an identification of the modeling and monitoring challenges and potential solutions for modeling and citizen science efforts in the future.
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 4 Spatiotemporal Data Collection and Processing for Bridge Digital Twin
### The 2D Image Data Collection and Processing  
#### 4.1.1 The 2D Image Data Collection  
The accurate collection of 2D image data is essential for detecting surface deterioration in bridge structures, including cracks and spalling. In this study, unmanned aerial vehicles (UAVs), equipped with high-resolution cameras, were used to capture detailed images of the bridge surfaces. UAVs can ensure comprehensive coverage of all areas of the bridge, particularly offering considerable advantages in accessing regions that are difficult to reach by other means, such as the undersides of bridges and elevated sections. This capability is especially valuable for large-scale bridges, where traditional methods struggle to achieve comprehensive coverage of all areas. Moreover, this method ensures that the collected images are of high enough resolution to detect even fine surface cracks. The use of UAVs substantially reduces the time and labor typically required for manual inspection while improving the overall precision and efficiency of the data collection process. The 2D image data collection is shown in Figure 3.
## 3 Results
### Chemical Analysis of Dross Amount Formed on the Solder Bath Surface  
Two different samples were cast. During the cooling of the samples in the brass mould, there was a difference in the amount of oxides on their surface observed by the naked eye (Figure 12). The surface of the sample from the melt without the addition of phosphorus (Figure 12A) had a significantly higher amount of oxides in comparison with the surface of samples with the addition of phosphorus (Figure 12B). This significant difference is also visible in Figure 13. This visual difference refers to the ability of dross to be formed on the surface of both melts.  
Figure 11: Sand used as an interlayer in alloy melting—chemical composition of the surface, measured by semi-quantitative method.  
Figure 12: Visual differences between two samples: (**A**) without addition of phosphorus in the solder bath, (**B**) with addition of phosphorus in the solder bath.  
The cooling of the samples was followed by preparation for measuring the chemical composition using a digital optical emission spectrometer (Q4 TASMAN) with a CCD detector. Tolerances for the content range of individual elements were respected according to the J-STD-006C standard. The chemical composition of sample A can be seen in Table 4. In Table 5, where the measured chemical composition values for sample B are shown, the phosphorus content is 0.003%. At the beginning of melting, its content was higher (represented by 0.007% in the total batch volume). If we consider the properties of phosphorus at higher temperatures [16; 17], it can be assumed that, under the influence of temperature and melting time, a certain amount was burned out.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chemical Element** & **Content in Alloy [\%]** & **Chemical Element** & **Content in Alloy [\%]** \\ \hline Sn & 96.39 & As & 0.01 \\ \hline Ag & 2.9 & Ni & 0.001 \\ \hline Cu & 0.6 & Bi & 0.02 \\ \hline Pb & 0.02 & Cd & 0.000 \\ \hline Sb & 0.05 & Al & 0.001 \\ \hline Zn & 0.001 & In & 0.02 \\ \hline Fe & 0.01 & P & **0.000** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chemical composition of A sample—without addition of phosphorus in the solder bath.  
Figure 13: Visual differences between two samples: yellow tones of oxides on (**A**) sample surface, shine and glossy surface with significantly amount decrease of oxides on (**B**) sample surface.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chemical Element** & **Content in Alloy [\%]** & **Chemical Element** & **Content in Alloy [\%]** \\ \hline Sn & 96.42 & As & 0.002 \\ \hline Ag & 3.0 & Ni & 0.005 \\ \hline Cu & 0.4 & Bi & 0.004 \\ \hline Pb & 0.02 & Cd & 0.000 \\ \hline Sb & 0.1 & Al & 0.001 \\ \hline Zn & 0.001 & In & 0.02 \\ \hline Fe & 0.02 & P & **0.003** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Chemical composition of B sample—with addition of phosphorus in the solder bath.
## 3 Results and Discussions  
### Structural Analysis of TiO\({}_{2}\) P25  
This working group published the results of XRD patterns for TiO\({}_{2}\) P25 [26], according to the method of [PERSON] et al. [43]. In TiO\({}_{2}\) P25, in addition to anatase, Bragg positions of the anatase phase (JCPDS file 00-021-1272 of the ICDD database), and the rutile phase were found (JCPDS file 01-070-7347 of the ICDD database).  
Diffraction data were treated by the Warren-Averbach method [44]. It was possible to calculate crystallite size, average shape, the percentage of each phase, and the tensions caused by structural defects in TiO\({}_{2}\) P25 [26]. With this analysis, it was found that 85.27% of the photocatalyst was anatase and 14.73% was the rutile phase. The crystalline size was 20.97 nm for anatase and 33.96 nm for rutile, both presenting a spherical crystalline form [26].  
The Kubelka-Munk method served to handle absorbance spectra from photocatalysts. A graphic representation of \([\mathrm{F(R)}\hbar\upsilon]^{1/2}\) against \(\hbar\upsilon\) was obtained and allowed determination of the TiO\({}_{2}\) band gap.  
Figure 5 shows the graphs of the absorbance spectrum and the band-gap calculation for TiO\({}_{2}\) P25 performed with Origin Pro 2018 software version b9.5.0.193 (OriginLab, Northampton, MA, USA). A TiO\({}_{2}\) P25 band gap of 3.2 eV was determined, being a lower value than that for semiconductors obtained by the sol-gel method with titanium tetrabutoxide (TBT, 3.40 eV) and titanium tetraisopropoxide (TIPT, 3.38 eV) as precursors [30; 45]. These results point to easier activation of the photocatalyst TiO\({}_{2}\) P25 when exposed to solar  
radiation. Concerning band-gap estimation, the following recent references are also an alternative method on how to treat Tauc plot data to extract the band-gap energy [46; 47].
# A Review of CO\({}_{2}\) Laser-Mediated Therapy for Oral Mucosal Lesions
## 3 Results
### CO\({}_{2}\) Laser Treatment in the Case of Oral Erythroplakia  
[PERSON] et al. (2014) designed a study in order to analyze the outcomes of treatment in patients with oral erythroplakia. A total of 84 patients were enrolled in the research, all of them treated with CO\({}_{2}\) laser vaporization. The mean follow-up period was 46 months, and the postoperative recurrent rate was 14 out of 84 cases (16.7%). The authors concluded that laser surgery was effective in the case of oral erythroplakia, However, the postoperative recurrence seemed to be associated with the size of the area of the lesion (lesions larger than 80 mm\({}^{2}\) were a significant predictive factor of postoperative recurrence) ([PERSON] et al.'s studies (2014, 2015) used CO\({}_{2}\) laser treatment for patients with homogeneous leukoplakia, non-homogeneous leukoplakia, and erythroplakia. The mean age was 58 years and follow-ups extended to a mean period of 6.4 years. Moderate dysplasia developed in 42 patients, while severe dysplasia occurred in 18 patients. They concluded that the recurrence rate was 19.5% and malignant transformation took place in 10.4% of cases.  
It was noted that the recurrence rate seemed to depend on the degree of epithelial dysplasia, while smoking and alcohol consumption also played a significant part. Higher recurrence rates were observed in erythroplakia lesions and in cases of non-homogeneous leukoplakia.  
Another clinical study, published by [PERSON] et al. (2015), was performed on 30 patients with a diagnosis of potentially malignant lesions (leukoplakia, erythroplakia), in situ carcinomas, or T1/T2 squamous cell oral carcinoma. The outcomes of the CO\({}_{2}\) laser ablation were assessed, such as intraoperative blood loss, length of hospital stay, postoperative pain, time to re-epithelialization, pathological margins attained, and postoperative evolution, including functional outcome and recurrence rate after 1 year post-treatment. The authors concluded that CO\({}_{2}\) laser ablation was beneficial in the management of premalignant lesions as well as low-risk T1/T2 tumors of the oral cavity, with specific advantages related to rapid re-epithelialization and minimal intraoperative blood loss, minimum postoperative pain as well as minimum functional impairment. After 1 year post-treatment, none of the subjects developed local recurrence; however, one patient had lymph node recurrence.  
[PERSON] et al. (2019) published a systematic review with the aim of evaluating the outcomes of laser surgery over a wide range of lesions (oral leukoerythroplakia, oral leukoplakia, oral dysplasia, etc.) as well as to provide a solid and reproducible protocol for CO\({}_{2}\) laser surgery. The outcomes of their investigations were that CO\({}_{2}\) laser surgery is effective for a wide range of oral lesions ([PERSON] et al., 2019).
# Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review
## 3 Results
### Available Smart Home Technologies for CVD Management
#### 3.1.1 Sensor and Monitored Parameters  
The vital signs generally monitored to evaluate CVD patients' health are HR, BP, RR, SpO2, and body temperature [73]. Sensors recorded the participant's vital signs, and the readings were acquired either manually or automatically at normal home settings [67, 68, 69, 70, 71]. The sensors were vests [67] and attached to the body at different positions such as at the torso [68], arm, wrist, finger, and chest [69, 70]. The vital signs recorded by the sensors include HR [65, 66, 69], BP [67, 69, 70, 71], respiratory rate [67, 68, 69], blood glucose [70], and SpO2 [67, 69]. Furthermore, HR computation as a function of cardiac vibration extracted from the obtained ECG signals was performed [69]. Additionally, an SHTs system had non-wearable sensors such as weighing scales [67, 69, 70, 71].
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 6 Comparing MASU with the Existing Frameworks  
Although the literature reports a large number of pervasive data sensing applications and platforms that are suitable in particular application domains, there is a lack of transversal support for fully distributed and heterogeneous solutions that allow to implement pervasive sensing in variety of scenarios. Table 7 shows the similarities and differences between MASU and other sensing frameworks.  
Figure 19: Producers and Consumers CPU utilization when sharing GPS data periodically (low data transmission rate).  
Figure 20: CPU utilization of Producers, Consumers and Storages when sharing GPS data periodically with two Consumers.  
As shown in Table 7, these frameworks use some centralize component, do not support devices heterogeneity or a hybrid communication space (short and large range communication infrastructures). This is not surprising since they were proposed to support sensing in scenarios where no pervasiveness is required. In this sense, the MASU platform represents a step forward in that direction.
## 2 The Role of Cellulose Waste in Sustainable Construction
\begin{table}
\begin{tabular}{c c c c} \hline
**SRM** & **Application** & **Dosage** & **Reference** \\ \hline CNF & Portland cement paste & 1.5 wt\% of the cement weight & [34] \\ \hline RCF & Fiber cement board & From 10\% to 50\% of the total cellulose fiber content & [35] \\ \hline RCF & Cellulose nanocrystals cement paste & 0.2\% cellulose nanocrystals & [36] \\ \hline CNC & Cement paste & 0.2 vol-1.0 vol\% & [37] \\ \hline CF & Concrete & 1.0 kg/m\({}^{3}\) (0.09\%)-1.2 kg/m\({}^{3}\) (0.11\%)-1.5 kg/m\({}^{3}\) & [38] \\  & & (0.14\%)–1.8 kg/m\({}^{3}\) (0.16\%)-2.0 kg/m\({}^{3}\) (0.18\%) & [39] \\ \hline CF & Clay brick & 2.5–15 wt\% & [39] \\ \hline CNC & Concrete & 0.1 vol–0.2 vol\% & [40] \\ \hline MFC & Mortar and concrete & 0.4 to 4.5\% by weight of the cement & [41] \\ \hline CF & Reinforced concrete & 0.6–0.9–1.2–1.5 kg/cm\({}^{3}\) & [42] \\ \hline CNC/CNF & Cement & CNF 0.020–0.035–0.045–0.050–0.065–0.085–0.10–0.15 & \\  & CNC 0.020–0.035–0.045–0.050–0.065–0.085–0.10–0.15– & [43] \\  & & 0.20–0.40–0.60 wt\% & \\ \hline CF & Concrete & From 10 to 90 wt\% & [14] \\ \hline MCC/NFC & Cement pastes & MCC: 0.05–0.5–0.75–1.0\% & [44] \\  & & NFC: 0.05–0.5–1.0\% & \\ \hline CNC & Mortar and concrete mixes & 0.25–0.50–0.75–1.00 wt\% relative to the cement & [45] \\ \hline MCC/NFC & Cement pastes & 0.050–0.075–0.100\% by cement weight & [46] \\ \hline HF/JF & Cement composite (mortar and paste) & 0.25–0.5–1–2\% of the cement weight & [47] \\ \hline CF & Concrete & 0.5 vol\% & [48] \\ \hline CNF & Cement pastes and mortars & 0.3 wt-0.8 wt\% of cement mass & [49] \\ \hline CNF & Cement based systems & 0.1–0.2–0.3–0.4–0.5 (vol\%) & [50] \\ \hline MFC & Cement pastes & 0.3–0.6–1–2 wt.\% of the cement & [51] \\ \hline CNF & Cement composites & 1.5 kg/m\({}^{3}\) & [52] \\ \hline CF & Asphalt & 0.5\% & [53] \\ \hline CNF & Asphalt binders and mixes & 0.2–0.3–0.5–0.7\% by the weight of binder & [54] \\ \hline CF & As filler in hot mix asphalt & 0.25–0.5–0.75\% & [55] \\ \hline CF & SMA pavement & 1.0\% by weight of asphalt & [56] \\ \hline CF & Asphalt mixtures & 2.5–5.0–7.5–10.0\% by the weight of asphalt cement & [57] \\ \hline CNF & Additive in asphalt pavements. & 0.2–0.3–0.5–0.7\% by the weight of binder & [58] \\ \hline CF & Concrete & 1–2\% by the weight of cement & [59] \\ \hline KF & Cement composites & 1–1.5–2\% by mass & [60] \\ \hline KF & Cement pastes & 0.25–0.5\% by weight of cement & [61] \\ \hline HF & Cement based mortar & 0.5 wt–1 wt–1.5 wt\% on cement & [62] \\ \hline JF & Reinforced concrete & 5\% by cement mass (0.8\% content by volume & [63] \\  & & of concrete) & [63] \\ \hline HF & Concrete & 51.45–60.60–64.45 by \% volume & [64] \\ \hline \end{tabular}
\end{table} 
Table 1: Applications of cellulose waste in sustainable construction.
## 5 Experimental Results and Analysis of the Prediction Model
### An Analysis of the Experimental Results  
In the final experiment, a strategy involving randomly initializing all model weights and training all layers from scratch was adopted, with the integration of the CBAM and Ranger optimizer. ResNet18 was selected as the pretrained model for transfer learning to ultimately perform microseismic magnitude classification and prediction. In this study, the allowable error thresholds were set at \(\pm\)0.2 and \(\pm\)0.3, and a total of 300 microseismic waveforms were used for prediction in the experiment. Detailed data from the six experiments are presented in Table 6.  
Among the schemes, the one designed in this study yielded 284 samples with errors within \(\pm\)0.3, corresponding to an accuracy rate of 94.7%; 261 samples with errors within \(\pm\)0.2, with an accuracy rate of 87%; and an experimental variance of 0.1362. Compared with the traditional magnitude prediction method using the CNN model, this scheme showed 70 more samples with errors within \(\pm\)0.3, 95 more samples with errors within \(\pm\)0.2, and a variance reduction of 0.8191, thus emerging as the top-performing approach among the six schemes.
# High-Temperature Liquid-Liquid Phase Transition in Glass-Forming Liquid Pd43 Ni20 Cu27P10
## 3 Results and Discussion
### AIMD Simulations
#### 3.2.1 Thermophysical Properties  
The temperature-dependent variations of density for liquid Pd\({}_{43}\)Ni\({}_{20}\)Cu\({}_{27}\)P\({}_{10}\) are plotted in Figure 3. A linear fit to AIMD results is applied to express the relation between temperature, \(T\), and density, \(\rho\) (Figure 3a). Clearly, the behavior of the density changes at a temperature \(T_{k1}\) of 890 K. The fitting equations are \(\rho=9.785-3.96464\times 10^{-4}T\) (\(T>T_{k1}\)) and \(\rho=10.052-6.7446\times 10^{-4}T\) (\(T<T_{k1}\)). The variation of the averaged enthalpy as a function of temperature is illustrated in Figure 3b. A kink is also seen at around \(T_{k2}=853\) K. At temperatures above \(T_{k2}\), enthalpy, \(H\), changes following \(H=-5.15098+1.17071\times 10^{-4}T\) and \(H=-5.21572+1.95746\times 10^{-4}T\) at temperatures below \(T_{k2}\). Between 600 K and 700 K, a kink can be obviously found if only the data below \(T_{k2}\) are utilized for analysis, and the kink temperature, \(T_{k3}\), is close to the glass transition temperature measured by FDSC. Hence, the temperature regime of the curve can be classified into three parts, separated by \(T_{k2}\) and \(T_{k3}\), namely, high-temperature liquid (HTL) regime, low-temperature liquid (LTL) regime, and metallic glass (MG) regime. The kink temperatures of density, \(T_{k1}\), and enthalpy, \(T_{k2}\), are quite close to the LLPT temperature measured by FDSC. From the continuous change of enthalpy and density, it can be concluded that the LLPT found in this work is a second-order transition, similar to that of Sn [8], in which a continuous change in the order parameter (a measurable quantity that characterizes the phase) is the characteristic as the system undergoes the transition.  
Figure 2: FDSC curves of Pd\({}_{43}\)Ni\({}_{20}\)Cu\({}_{27}\)P\({}_{10}\) at different cooling rates. The gray dashed line represents the trend of anomalous endothermic peaks.
# Advances in Tissue Engineering for Disc Repair
## 2 Biological Approaches  
### Molecular Therapies  
#### 2.1.1 Growth Factors  
In the early stages of IVDD, growth factor injection may rebalance the anabolic and catabolic pathways in the degenerative cascade [16]. Degenerated discs have been reported to be repaired by intradiscal injection of growth factors, including insulin-like growth factor-1(IGF-1), epidermal growth factor (EGF), basic fibroblast growth factor (bFGF or FGF2), transforming growth factor-\(\beta\) (TGF-\(\beta\)), bone morphogenetic protein-2 (BMP-2), BMP-7 (osteogenic protein-1; OP-1), and growth and differentiation factor-5 (GDF-5) by promoting cell proliferation and matrix synthesis in experimental models [17; 18; 19]. The mitogenic potential of human NP and AF cells can be stimulated by platelet-derived growth factor (PDGF), bFGF, and IGF-1 [20; 21]. PDGF, a known angiogenic growth factor, has been shown to inhibit IVD cell apoptosis and promote anabolic gene expression [22]. In several animal experiments, intradiscal injection of BMP-7 has shown improvements in the disc height and NP proteoglycan content [23; 24]. GDF-5, also known as BMP-14, is another anabolic protein that promotes cell proliferation and proteoglycan synthesis in degenerated discs [18]. Similar to BMP-7, many animal studies of intradiscal injection of GDF-5 have shown improved disc height, cell proliferation, and matrix synthesis [25; 26]. Despite their efficacy, there are many concerns surrounding the clinical use of recombinant growth factors due to their short half-life, limited stability, high cost, and problems associated with binding large molecules to polymers [27]. Thus, the right carrier is a matter to consider. In recent years, the use of biodegradable microspheres for controlled local drug delivery has become a valuable approach to overcome the drawbacks of growth factors. [PERSON] et al. demonstrated that injection of GDF-5 loaded into poly(lactic-co-glycolic acid) (PLGA) microspheres could improve regenerative efficacy of GDF-5 in a rat model [28].
## 3 Development and Validation of CFD/CSD Coupled Solvers
### Verification of the CFD/CSD Co-Simulation
The nodes of the structural finite element model include external surface nodes and internal structural nodes. The information exchange of aerodynamic force and displacement is realized between the FEM nodes on the outer surface and the aerodynamic grid through interpolation, while the internal nodes of the structure do not participate in the above exchange. However, since both outer surface nodes and inner structural nodes participate in the solution of structural dynamics, the two types of nodes need to be distinguished in the solution process of co-simulation: only outer surface nodes among structural nodes are extracted and used in the CSD/CFD interface interpolation calculation, while all structural nodes are used in the solution of structural dynamics. The comparison between the flutter boundary predicted by the CFD/CSD program developed in this paper and the experimental results at each Mach number is shown in Figure 6. It can be seen that the difference between the simulation results and the experimental results is small, which proves the validity of the CFD/CSD calculation in this paper. Figure 7 shows two states of whether the wing structure reaches the flutter boundary or not when the Mach number is 0.687. Figure 7a indicates that the wing structure does not reach the flutter boundary, and 13(b) indicates that the wing structure reaches the flutter boundary. Figure 6: The flutter boundary calculated in this paper are compared with the experimental results [31]. Figure 7: Time-domain responses of AGARD 445.6 wing at Mach 0.678: (**a**) FSI = 0.407; (**b**) FSI = 0.417.