esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Finite Element Modeling and Validation
### Numerical Simulation Model
#### 2.2.2 Bond-Slip Model at PCM-Concrete Interface  
It can be seen from the test that the composite layer and concrete slip. Some specimens will be peeled off because of the large slip, so in the finite element modeling, the interaction between the concrete and composite layer is modeled by the bond-slip model proposed by  
Figure 3: Stress–strain relation curve.  
[PERSON] et al. [17]; the detailed calculation formula is shown in Equations (10)-(15); the relation curve is shown in Figure 4. In ABAQUS, cohesion contact is set on the interface between composite layer and concrete to represent the cohesion model. Interface shear stiffness \(K_{0}\), interface maximum shear stress \(\tau_{max}\), and interface limit slip \(S_{f}\) are the key parameters of the bond contact setting. After several trial calculations, this paper inputs \(K_{0}\) = 46 MPa, \(\tau_{max}\) = 1.4 MPa, and \(S_{f}\) = 0.354 mm.  
\[\tau=\left\{\begin{array}{ll}\tau_{max}s/s_{0}&(0<s\leq s_{0})\\ \tau_{max}\Big{(}s_{f}-s\Big{)}/\Big{(}s_{f}-s_{0}\Big{)}&\Big{(}s_{0}<s\leq s _{f}\Big{)}\\ 0&(s_{f}<s\Big{)}\end{array}\right. \tag{10}\]  
\[\tau_{max}=\alpha_{1}\beta_{w}f_{t} \tag{11}\]  
\[S_{0}=0.0195\beta_{w}f_{t} \tag{12}\]  
\[s_{f}=2G_{f}/\tau_{max} \tag{13}\]  
\[G_{f}=0.308\beta_{w}^{2}\sqrt{f_{t}} \tag{14}\]  
\[\beta_{w}=\sqrt{\frac{2-b_{f}/b_{c}}{1+b_{f}/b_{c}}} \tag{15}\]  
where \(\tau\) and \(s\) are local interfacial shear stress and relative slip, respectively; \(\tau_{max}\) and \(s_{0}\) are bond strength and their corresponding slip, respectively; \(s_{f}\) is corresponding slip when interfacial shear stress is 0; \(G_{f}\) is interfacial failure energy (fracture energy); \(f_{t}\) is tensile strength of concrete; \(\alpha_{1}\) is the finite element method to calculate the regression parameters; \(\beta_{w}\) is taken as the width influence coefficient; and \(b_{f}\) and \(b_{c}\) are the width of the FRP grid and the concrete section, respectively.
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 2 Study Area and Dataset
### Dataset  
In this study, a five-year temporal resolution landslide susceptibility database covering the period from 2000 to 2019 has been constructed. The landslide samples were sourced from the Hong Kong Spatial Data Sharing Platform. The original data are provided as geographic coordinates (latitude and longitude), accompanied by attribute information, including disaster ID, year of occurrence, and the number of individuals affected, as illustrated in Figure 2. Specifically, there were 1535 historical landslide points from 2000 to 2004, 3206 from 2005 to 2010, 781 from 2010 to 2014, and 1399 from 2015 to 2019. Using the landslide samples (historical landslide points) as spatial references, an equal number of random points were sampled outside a 5 km radius and defined as non-landslide samples. The landslide samples and non-landslide samples collectively compose the landslide dataset.  
Geospatial data relevant to landslide disasters within the study area were selected, including fixed factors such as elevation, slope, aspect, curvature, lithology, and topographic wetness index (TWI), as well as time-varying factors such as average rainfall, max normalized difference vegetation index (NDVI) values, and land cover [37]. Please refer to Figures 3-6 for further details. Administrative boundary data for the Hong Kong study area were sourced from the CSDI Geoportal [38]. Among these, elevation, slope, aspect, curvature, TWI, and lithology data serve as static landslide assessment factors, while average annual rainfall (AAR), average annual maximum NDVI (AMN), and annual land cover (LC) are considered dynamic landslide assessment factors. Elevation data were sourced from the geospatial data cloud [39]. Slope, aspect, and curvature data were derived from the elevation data. Lithology data were obtained from the Hong Kong Civil Engineering and Development Department [40], and the legend also referred to this website. The data format is in vector polygons, but in this study, they were rasterized. Hydrological analysis was conducted on elevation and slope data to generate landslide assessment factors such as TWI. Rainfall data were sourced from the geospatial resource data cloud (GRDC) [41]. NDVI data were obtained from the National Ecosystem Survey Data Center (NESDC) [42], and the data were stored after being multiplied by 10,000 [43]. Land cover data were  
Figure 1: Study area.  
Figure 4: Annual average rainfall dynamic landslide assessment factors: (**a**) 2000–2004; (**b**) 2005–2009; (**c**) 2010–2014; (**d**) 2015–2019.  
Figure 5: Annual max NDVI dynamic landslide assessment factors: (**a**) 2000–2004; (**b**) 2005–2009; (**c**) 2010–2014; (**d**) 2015–2019.
# Decentralized Coordination of DERs for Dynamic Economic Emission Dispatch
### Contributions  
This paper studies decentralized solutions to the DEED problem. The weight-sum method is adopted to formulate the problem, to investigate the trade-off between power generation cost and emission that aims at providing instructions to scientific decision making for power system operation. We first transform the DEED problem through a Lagrangian duality reformulation into an equivalent consensus optimization problem, where the multipliers are regarded as consensus variables. We then iterate them to the optimal solution using a distributed gradient descent algorithm. The optimal solution for the original variables is obtained through fully localized sub-optimization. Detailed contributions can be summarized as follows:* Compared with SED, DED, and EED problems, a more general DEED problem is studied, in which outputs of generators are subject to ramp constraints. In particular, transmission losses among grids are introduced to agree more with the real-world power system. Additionally, energy storage devices are considered to improve the reliability of the power supply with charging/discharging efficiency taken into account.
* With a rigorous conversion process, the formulated DEED problem is converted into an equivalent consensus optimization problem, which is applicable to be addressed by a majority of existing decentralized consensus-based algorithms [24; 31; 32; 33; 34; 35]. This result may facilitate the popularization of decentralized solutions to the DEED problem.
* A new decentralized algorithm is developed for solving the consensus optimization problem. Compared with existing common ADMM-based methods [27; 28; 29; 30], it reduces computational costs to some extent at each iteration because only one subproblem needs to be solved, and it is entirely localized. Especially for conventional quadratic cost functions, the proposed decentralized algorithm achieves a Q-linear convergence rate.
* Compared with decentralized methods [14; 15; 16; 17; 18; 19; 20; 21; 22; 26; 27], the proposed algorithm is more flexible in the choice of stepsizes since nonidentical stepsizes are adopted and an explicit upper bound for nonidentical stepsizes is provided. More notably, it allows asynchronous implementation that removes the global clock coordination and thus achieves fully decentralized realization.
## 2 Materials and Methods
### Remote Sensing Data  
#### 2.4.1 MODIS  
MODIS images for the product MOD09Q1 Version 6.1 were downloaded from the NASA Land Processes Distributed Active Archive Center (LP DAAC) webpage ([[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)) accessed on 10 September 2023). This product provides an estimate of the surface spectral reflectance for the Near Infrared (NIR) and Red (\(R\)) bands at a spatial resolution of 250 m and a temporal resolution of eight days. These bands were obtained for the calculation of the \(NDVI\) for the eddy covariance flux tower pixel from January 2018 to July 2020 (117 images), following Equation (5):  
\[NDVI=\frac{(NIR-R)}{(NIR+R)} \tag{5}\]  
Then, a Savitzky-Golay filter [53] was applied to reduce noise and smooth the time series using a second-order polynomial with a smoothing window of seven.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Source** & **Frequency** & \begin{tabular}{c} **Daily** \\ **Average** \\ \end{tabular} & \
\begin{tabular}{c} **5-Day** \\ **Average** \\ \end{tabular} &
\begin{tabular}{c} **8-Day** \\ **Average** \\ \end{tabular} \\ \hline GPP\_T & Flux tower & 30 min & Yes & Yes & Yes \\ T\({}_{\rm min}\) & Flux tower & 30 min & Yes & Yes & Yes \\ SWC & Flux tower & 30 min & Yes & Yes & Yes \\ PAR\({}_{\rm TOA}\) & Solar Radiation & daily & Yes & Yes & Yes \\ SENTINEL-2 NDVI & Sentinel-2 & 5-day & No & Yes & No \\ MODIS NDVI & MODIS & 8-day & No & No & Yes \\ MODIS\_GPP & MODIS & 8-day & No & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables obtained for the study from January 2018 to July 2020 in the Mediterranean grassland: Gross Primary Production obtained from the flux tower (GPP\({}_{\_}\)T). Minimum air temperature (T\({}_{\rm min}\)), Soil water content at 4 cm depth (SWC), Photosynthetically Active Radiation from the Top of the Atmosphere (PAR\({}_{\rm TOA}\)), NDVI obtained from Sentinel-2 (Sentinel-2 NDVI), NDVI obtained from MODIS (MODIS NDVI), Gross Primary Production derived from the MODIS product (MODIS\({}_{\_}\)GPP).  
The product MOD17A2 HGF Version 6.1 Gross Primary Productivity (_GPP_) product is a cumulative 8-day composite of values with a 500-m pixel size [56]. This product was downloaded for the pixel of the flux tower with the AppEEARS Tool [57].
###### Abstract  
This study intended to investigate the long-term antibacterial effect, mechanical performance, and surface topography of new anticaries dental composites. While most artificial aging studies of dental resins lasted for 30-90 days, this study prolonged the water-aging to one year to be more clinically relevant. The base resin was loaded with dimethylaminobenzackadecyl methacrylate (DMAHDM) at 3 or 5 wt.% and nano-sized amorphous calcium phosphate (NACP) at 20 wt.%. Composites were subjected to one-year water storage and wear. Following water aging, samples were evaluated for flexural strength, elastic modulus, and microbiological assays. Biofilm plate counting method, metabolic assay, colorimetric quantification of lactic acid, and Baclight bacterial viability assay were measured after one year. Topography changes (ARa, ARq, ARv, ARt) were examined after wear and observed by scanning electron microscopy. Biofilm assays and topography changes data were analyzed via one-way ANOVA and Tukey's tests. Mechanical properties and normalized data were verified using a t-test. The flexural strength values for the formulations that contained 5% DMAHDM-20% NACP, 3% DMAHDM, and 5% DMAHDM were reduced significantly (\(p<0.05\)) in relation to the baseline but the values were still above the ISO standards. No significant differences were observed between the groups concerning the topography changes, except for the \(\Delta\)Rt, where there was a significant increase in the 5% DMAHDM-20% NACP group. All the groups demonstrated robust biofilm-inhibition, with slightly reduced antibacterial properties following water aging. The aged samples reduced the total microorganisms, total streptococci, and _mutans streptococci_ by 1.5 to 3-log, compared to the experimental control. The new formulations containing DMAHDM and NACP were able to sustain the antibacterial performance after one-year of aging. Mechanical properties and surface topography were slightly affected over time.  
amorphous calcium phosphate; antibacterial agents; biofilms; dental caries; surface roughness; surface topography +
Footnote †: journal: Applied Sci. 2021, 11, 3718. [[https://doi.org/10.3390/app11083718](https://doi.org/10.3390/app11083718)]([https://doi.org/10.3390/app11083718](https://doi.org/10.3390/app11083718))
# Time Domain Damage Identification Approach of the Newly Hollow Floor Structure with TRMD
## 2 Theoretical Formulation
### Sensitivity-Based Damage Identification  
Structural damages are identified by the sensitivity-based model updating procedure in this paper [19,20]. The objective function is represented as the difference between the measured response and the analytical response from the FE model as follows:  
\[\Delta\ddot{\mathbf{x}}=\ddot{\mathbf{x}}_{M}-\ddot{\mathbf{x}}_{A} \tag{12}\]  
where \(\ddot{\mathbf{x}}_{M}\) and \(\ddot{\mathbf{x}}_{A}\) indicate the measured acceleration and calculated acceleration of the finite element model, respectively. Then, the dynamic response sensitivity-based identification equation is expressed by a first-order Taylor series as follows [22]:  
\[\Delta\ddot{\mathbf{x}}=\mathbf{S}\Delta\alpha=\left[\frac{\partial\ddot{ \mathbf{x}}}{\partial\alpha_{1}},\frac{\partial\ddot{\mathbf{x}}}{\partial \alpha_{2}},\cdots,\frac{\partial\ddot{\mathbf{x}}}{\partial\alpha_{Ncal}} \right]\left\{\begin{array}{l}\Delta\alpha_{1}\\ \Delta\alpha_{2}\\ \vdots\\ \Delta\alpha_{Ncal}\end{array}\right\} \tag{13}\]  
where \(\Delta\ddot{\mathbf{x}}\) is the difference between the measured response and analytical response from the finite element model. \(\mathbf{S}\) is the sensitivity matrix, which represents the first derivative of the dynamic response with respect to the elemental stiffness variables \(\alpha\). If the TRMD controlled structure has local damage, the dynamic response of the controlled structure will change compared with the intact state. The dynamic response difference between the intact and damaged states of the controlled structure is taken as the objective function. During the updating process, the dynamic sensitivity matrix is recalculated based on the iteratively updated structural matrices to make the objective function value reach the specified tolerance. Therefore, local damage, which is expressed as the elemental stiffness variation in the TRMD controlled structure, can be obtained iteratively. The damage identification flow chart based on dynamic response sensitivity analysis for the controlled structure is shown in Figure 4. The proposed time domain damage identification method is promising because the dynamic response sensitivity matrix indicates the searching direction of the optimization process, which improves iteration efficiency and converges speed. In addition, a few measured responses of the controlled structure are needed to identify the location and extent of damage.
# Binaural Rendering with Measured Room Responses: First-Order Ambisonic Microphone vs. Dummy Head
## 2 Experiment I: Dynamic Rendering of Multiple-Orientation Binaural Signals
### Listening Experiment: Design  
We tested the LISHPh method rendering from MOBRIR resolutions of \(\Delta\phi=\{8,15,30,60\}^{\circ}\) and also included the broadband linearly interpolated ear signals for comparison. During the listening experiment, each listener was asked to (i) rate the spatial mapping (i.e., direction and distance) and timbre compared to a reference condition for static rendering (four different head orientations), and (ii) to rate the continuity or robustness when rendering dynamically, i.e., incorporating head movements of the listener.  
The 10 test participants (all male and at an age between 27 and 42) were asked to rate the overall difference between a reference (artifact-free linear interpolation with \(1^{\circ}\) resolution) and the  
Figure 2: Block diagram for BRIR selection, convolution, and continuous interpolation for one ear in a dynamic binaural rendering scenario.  
test signals on a continuous scale from _poor_ to _very good_. A hidden reference was used for screening of ratings, and thus the test procedure can be described as MUSHRA-like (multi stimulus with hidden reference and anchor [25]). The test signals were continuously looped, and participants were allowed to seamlessly switch between signals in real-time as often as desired.  
In terms of **timbre and spatialization**, we tested four different static head orientations \(\varphi=\{12,21,37,78\}^{\circ}\) (the sign of the orientation was randomly changed across participants) for a frontal source position with pink noise and music as source signal, respectively. The choice of the source position and orientations was met to make the experiment most sensitive to the expected interpolation artifacts: on the one hand, time-delays (phases or ITDs) are most head-orientation-dependent for predominantly frontal source positions, and, on the other hand, orientations were selected to enforce interpolation with \(0.2<\alpha<0.8\) for all MOBRIR sets under test.  
Testing the **continuity** involved a pink noise and a music signal played back over a virtual frontal (\(0^{\circ}\)) and lateral (\(90^{\circ}\)) loudspeaker. Here, listeners were asked to rotate their head between \(\phi=-45^{\circ}\dots 45^{\circ}\) and a check-box for automatic rotation with \(180^{\circ}/1\)s was included for fast movements.
## 2 Materials and Methods
### SERS Measurement  
The detection of thiophanate methyl by SERS effects was performed on an AuNR-coated glass substrate which acted as a SERS-based sensing platform. Scheme 1 illustrates the fabrication of the platform. The platform was prepared by firstly rinsing the glass substrate with a 3:1 (v/v) mixture of concentrated H\({}_{2}\)SO\({}_{4}\) and 30% H\({}_{2}\)O\({}_{2}\) for 15 min, subsequently washing it with DI water and drying in the air. A portion of AuNRs solution was gently dropped onto the clean substrate homogeneously, followed by drying it in the air. Next, 20 \(\mu\)L of thiophanate methyl solution (in DI water) was gently dropped onto the platform and subsequently dried in the air to be ready for SERS measurements. For comparison, Raman spectra of the clean substrate, AuNRs, and thiophanate methyl solution were also recorded.
## 2 Materials and Methods
### Dimensions of Data Challenges for AI
#### 2.2.1 Dimension I: Data Quality
These methods aim to provide reasonable estimates of the missing values and ensure the completeness of the dataset. Feature Selection and Engineering: Feature selection and engineering play a crucial role in addressing data quality challenges, as they involve identifying relevant features and transforming raw data into a format suitable for analysis [64]. Techniques such as Recursive Feature Elimination (RFE), LASSO, and principal component analysis can be employed to reduce the dimensionality of the data and eliminate noise [65]. Moreover, domain knowledge can be leveraged to create new features that better capture the underlying patterns and relationships. Data Augmentation: Data augmentation techniques can be used to address the data quality challenges related to limited or unbalanced datasets. These techniques generate synthetic data samples by applying various transformations such as rotation, scaling, and flipping to the original data [66]. Data augmentation has been particularly successful in improving the performance of deep learning models in computer vision and natural language processing tasks [67]. Active Learning: Active learning is an approach that can help address data-quality challenges by guiding the data-collection process. In active learning, AI models iteratively select the most informative samples to be labeled and added to the training set, thereby reducing the amount of labeled data required and improving model performance [68]. Active learning has shown promise in various applications, including text classification and object recognition [69; 70]. Data Validation and Testing: Data validation involves checking data for accuracy and completeness, whereas testing involves assessing the performance of AI models using various metrics. These processes can help identify and address data quality issues that may affect the accuracy and effectiveness of AI models. Algorithmic Fairness: Algorithmic fairness involves ensuring that AI models are not biased towards specific groups or individuals. This can be achieved by carefully selecting training datasets and implementing algorithms designed to reduce bias. Data Bias Mitigation: Data bias can lead to inaccurate or unfair AI predictions and decisions. Mitigating data bias involves identifying and addressing bias in the training data through techniques such as dataset balancing, which involves adjusting the distribution of data to reduce bias. Continuous Monitoring and Maintenance: AI models need to be continuously monitored and maintained to ensure that they remain accurate and effective. This involves ongoing data-quality checks, updating models with new data, and retraining models as needed. Data Lineage: Data lineage involves tracking the history of the data and ensuring that it is used appropriately. This can help prevent issues such as data drift, where the quality of data changes over time.
## 4 Discussion  
The geometric characteristic evaluation showed the influence of the ball end mill used in the machining process. When comparing the shape deviations, comparable results were achieved. The constant Z strategy achieved the lowest shape deviations. When the roundness deviation was evaluated, the circular-pocket strategy obtained the highest measured values compared to the constant Z strategy, where the measured values were the lowest. Additionally, cylindricity deviation measurement showed a similarity between the circular-pocket strategy and constant stepover-pocket auto border. Similarly, the constant Z strategy achieved the lowest deviations of the cylindricity. When comparing the locations of the cylindricity measurements at different pocket heights, the lowest deviation was measured at the top of the pocket, and the highest deviation was at the bottom of the pocket.  
The experimental results show that it was possible to observe an effect of plowing in the surface texture obtained in the spiral strategy, where the cutting tool crushed the material at the tool center. For circular-pocket and constant stepover-pocket auto border strategies, there was minimal effect on the surface texture where circular paths could be observed. The surfaces after these strategies showed no signs of surface defects as in the spiral strategy, where tearing dimples or extrusion crack formation could be observed on the surface. Using the Constant Z strategy, a non-oriented surface texture was obtained in comparison with the circular-pocket and constant stepover-pocket auto border strategy. It can be concluded that there was an oscillation of the end of the cutter until the cutting edge had cut into the part. That means that the cutting by a ball end tool started at zero chip thickness and finished when it reached the minimum chip thickness. The spiral strategy did not perform well for the machining of the planar surfaces when the plowing effect was created. It can also be concluded that the spiral strategy is better for milling surfaces when the machined part includes a shallow region.  
3D maps of the extracted residual scallop height for each strategy were observed when examining the surface texture. Obtained result of the residual scallop height for the spiral strategy corresponded with the bottom flatness deviation shown in Figure 23b. In the constant Z strategy, a non-oriented surface was obtained. It can be concluded that the constant Z strategy is not suitable for a planar surface, although it allows for a planar surface machining function. As a result, it would be more suitable for free surface milling where the tool path follows the surface contour at different Z heights.  
The roughness parameter was the lowest for strategies with circular paths. Similar values were obtained for the strategies circular pocket and constant stepover-pocket auto border.