esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Beach Profile Changes--Bulk Statistics  
The surveyed elevation along profiles, standard deviation and mean elevation calculated along each beach profile are shown in Figure 4.  
The mean beach profiles of Mari Ermi beach highlighted that the barrier is continuous from north to south. The northern sector of the barrier was more elevated than the central and southern sectors (about 3.3 m, 2.8 m and 2.8 m, respectively). Furthermore, the barrier was wider in the northern and central profiles in comparison to the southern one. The morphological variability was mainly located on the beachface area for northern and southern profiles, whereas along the central profile a peak of variability was found in the toe of the barrier (Figure 4a). Only for the southern profile, little morphological variability was found beyond the barrier (Figure 4a).  
The mean beach profile of Is Arutas beach highlighted the presence of a storm berm in the most elevated area (Figure 4b). The width of the subaerial beach was almost unchanged for all profiles and accounted for about 60 m. The morphological variability was mainly located on the beachface of the northern and southern profile, while in the central profile the most variable area was located at the foot of the storm berm (Figure 4b).  
The mean beach profiles on Maimoi beach showed that the width of the subaerial beach was about 40 m, and 30 m for the southern profile. The morphological variability involved the whole subaerial beach, from the toe of the dune up to the beachface where the highest variability was found (Figure 4c).  
The evolution of the beach profiles is shown in Figure 5. On Mari Ermi beach, the top of the barrier did not show major variations along the northern and central transects. On the other hand, during the winter the berm destruction and construction occurred on the beachface. In the southern transect little variability was found in the back barrier area for the same period (Figure 5a).  
Figure 4: A plot of profiles acquired along the following investigated beaches: (**a**) Mari Ermi; (**b**) Is Arutas; and (**c**) Maimoi. S = 23 September 2010, O = 22 October 2010, N = 5 November 2010, D = 14 December 2010, J1 = 4 January 2011, J2 = 17 January 2011, F = 7 February 2011, Ma = 4 March 2011, A = 14 April 2011, My = 13 May 2011. The continuous black line is the mean beach profile, the dashed black line is the value of standard deviation; N = north profile, C = center profile, S = south profile.  
On Is Arutas beach, the northern and southern profiles showed inverse behavior; when one advanced, the other retreated and vice versa (Figure 5b). This behavior was recorded during the winter, in the same period the berm developed in the central profile (Figure 5b).  
The evolution of the beach profiles on Maimoi beach is presented in Figure 5c. Along Maimoi beach the profiles showed the same behaviors with a generalized erosion along each profile and a lowering in the beach level by about 1 m during winter (Figure 5c). Subsequently, the subaerial beach increased in width and the topographic elevation of the beach profiles increased in height (Figure 5c).
# Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging
## 1 Introduction
Particular attention is paid to determining the minimum bit-width of wavelet filters coefficients, at which this noise does not have a significant impact on the 3D medical images DWT result (\(PSNR\geq 40\) dB for images with 8 BPC, for example), or does not affect it at all (\(PSNR=\infty\)). The values \(PSNR\geq 40\) dB describes the difference between the two images with 8 BPC almost imperceptible for human eyes [44; 45]. The value \(PSNR=\infty\) for identical images.
## 7 Discussion  
Based on the derived BER in Section 5, the simulation in Figure 5 shows the performance of DCSK systems with beta values of 128 and 256. The results show a close match to the derived BER from Equations (28) and (29), which proves that the proposed PDF and the theoretical BER can be used to describe the performance.  
Similarly, Figure 6 demonstrates how well QCSK systems perform with beta values of 128 and 256. The outcomes demonstrate a strong agreement with the derived BER from Equation (30), proving the applicability of both the theoretical BER and the suggested PDF in characterizing the performance.  
The outcome displayed in Figures 7 and 8 shows the polar-coded DCSK and QCSK systems' performance for various beta values and for different rates. Depending on the suggested coding rates, the improvement in the performance between the highest and the lowest rates was more than 5 dB for both modulations. In addition, comparing the results of the coded system and the uncoded systems illustrates the polar codes' ability to achieve performance improvements of more than 8 dB in the SNR at the lowest coding rate and 14 dB at the highest rate at BER = \(10^{-3}\), which can be a challenge in these types of channels.  
Figure 5: DCSK performance with a \(\mathrm{t}-\)distributed underwater channel compared to Equations (28) and (29) for \(\beta\) = 128 and 256.  
Figure 6: QCSK performance with a \(\mathrm{t}-\)distributed underwater channel compared to Equation (30) for \(\beta\) = 128 and 256.
## 4 Materials and Methods  
### Expression and Purification of hNudt16  
Human Nudt16 (hNudt16) WT and its mutants (E76Q, EE-QQ, EE-KK) were expressed and purified using an adapted protocol from [PERSON] et al. [38]. The protein was expressed in _E. coli_ Rosetta 2 (DE3) using the pET16b_Nudt16 vector. The construct contains four histidines, introduced just after the two C-terminal histidines of hNudt16, which resulted in a C-terminal 6 xHis-tag. Starter LB culture with 34 \(\mu\)g/mL chloramphenicolicol and 100 \(\mu\)g/mL ampicillin was inoculated using a stock of _E. coli_ Rosetta 2 (DE3) that had been transformed with the pET16b_Nudt16 vector and incubated at 37 \({}^{\circ}\)C overnight. Ten milliliters of starter culture was used to inoculate each 1 L of LB culture supplemented with 100 \(\mu\)g/mL ampicillin and 34 \(\mu\)g/mL chloramphenicolicol.  
_E. coli_ Rosetta 2 (DE3) cells were grown at 37 \({}^{\circ}\)C until the OD\({}_{600}\) reached a value in the range of 1-1.5. Then, protein expression was induced by the addition of IPTG (0.2 mM), and the cells were incubated overnight at 18 \({}^{\circ}\)C with 180 rpm. Cells were harvested by centrifugation at 7700 rpm, washed in PBS, and collected again by centrifugation at 6000 rpm.  
Next, the cell pellets were resuspended in a lysis buffer (20 mM HEPES-KOH pH 8.0, 50 mM NaCl, 300 mM urea, 10 mM imidazole, 10% glycerol, 1% Triton X-100, and 1 mM MgCl\({}_{2}\)) supplemented with lysozyme and 1000 U Benzonase or Viscolase, incubated on ice for 45 min, and disrupted by sonication. After centrifugation (25,000\(\times\)\(g\) for 55 min), the supernatant was filtered and incubated at 4 \({}^{\circ}\)C overnight with gentle stirring in the presence of 5 mL HIS-Select Nickel Affinity Gel (Sigma-Aldrich, Saint Louis, MO, USA) equilibrated with binding buffer. Ni beads with bound hNudt16 were loaded into a gravity column (Bio-Rad, Hercules, CA, USA) and washed with 10-fold the column volume of buffer containing 20 mM Tris-HCl (pH 8.0) and 300 mM NaCl. His-tagged hNudt16 protein was eluted with a linear gradient of imidazole (20 mM to 300 mM) in wash buffer. Fractions containing pure hNudt16 (determined by SDS-PAGE) were pooled, dialyzed overnight at 4 \({}^{\circ}\)C in 20 mM HEPES pH 7.9, 300 mM NaCl, 10 % glycerol, and 0.5 mM TCEP, flash frozen in liquid nitrogen, and stored at \(-\)80 \({}^{\circ}\)C.
# Mg,Sr-Cosubstituted Hydroxapatite with Improved Structural Properties
## 2 Materials and Methods
### The 3D Porous Scaffold of MgSr-HA  
Two types of porous architectures were used for the study: samples were either prepared using the replica method or by direct foaming. Porous scaffolds of Sr-HA were prepared using the replica method, as previously reported [23]. Briefly, a 15 vol% aqueous suspension of Sr-HA was prepared using 1.5 wt% of dispersant (Duramax D-3021). The suspension was then homogenized using zirconia balls for 4 h. Cellulose sponges (Sportex, Italy) were trimmed to the desired shape (about 15 mm diameter and height), soaked into the slurry, and left to dry for 24 h in the air. Sr-HA porous samples were then sintered at 1250 \({}^{\circ}\)C for 2 h using flowing air to promote the organic matrix elimination during the debonding step at 600 \({}^{\circ}\)C for 1 h.  
Porous scaffolds were also prepared by directly foaming a 60 wt% aqueous suspension of Sr-HA powder containing 1.5 wt% of Dolapix CA as dispersant and 1.4 wt% of foaming agent (Dermocin BS Conc). After casting and drying for 48 h, scaffolds were sintered at 1250 \({}^{\circ}\)C for 2 h in the air.  
Ion-exchange treatments were then explored: the sintered Sr-HA porous constructs were immersed in 1 and 2 M solutions of MgCl\({}_{2}\).6H\({}_{2}\)O for 1 and 5 days, with a material/solution ratio of 0.05 g/mL, rinsed with deionized water, dried, and subsequently analyzed. The ion-exchanged samples were correspondingly coded SM1d1, SM1d5, SM2d1, and SM2d5.
## 3 Results and Discussion
### Adsorption and Desorption Experiments
#### 3.2.5 Adsorption Isotherm  
The Langmuir isotherm model and Freundlich isotherm model were used to analyze the adsorption interaction between the adsorbate and adsorbent. The Langmuir isotherm model and Freundlich isotherm model are expressed by Equations (7) and (8).  
\[\frac{\mathrm{C_{e}}}{\mathrm{Q_{e}}}=\frac{1}{\mathrm{KQ_{m}}}+\frac{\mathrm{ C_{e}}}{\mathrm{Q_{m}}} \tag{7}\]  
Figure 6: Effect of type and concentration of eluent on the desorption efficiency.  
Figure 7: Adsorption isotherm of Cd(II) on Cd-I-MA-_co_-AN and NI-MA-_co_-AN.  
\[\text{lnQ}_{\text{e}}=\frac{1}{\text{n}}\text{lnC}_{\text{e}}+\text{lnK}_{\text{f}} \tag{8}\]  
where Q\({}_{\text{e}}\) denotes the adsorption capacity of the polymeric microsphere at equilibrium (mg g\({}^{-1}\)), C\({}_{\text{e}}\) denotes the equilibrium concentration of Cd(II) solution (mg L\({}^{-1}\)), Q\({}_{\text{m}}\) denotes the maximum adsorption capacity of the polymeric microsphere (mg g\({}^{-1}\)), K is the Langmuir constant, K\({}_{\text{f}}\) is the Freundlich constant and 1/n is the adsorption index [37].  
The linear spot and isotherm constant values of the Langmuir isotherm are presented in Figure 8 and Table 1. The values of K and R\({}^{2}\) of Cd-I-MA-_co_-AN are 3.732 \(\times\) 10\({}^{-3}\) and 0.7867, and the values of K and R\({}^{2}\) of NI-MA-_co_-AN are 8.819 \(\times\) 10\({}^{-3}\) and 0.7567. The Langmuir model shows a poor fitness of the adsorption isotherm. The linear spot and isotherm constant values of the Freundlich isotherm are presented in Figure 9 and Table 2. The values of R\({}^{2}\) and 1/n of Cd-I-MA-_co_-AN are 0.9564 and 0.959, and the values of R\({}^{2}\) and 1/n of NI-MA-_co_-AN are 0.9128 and 0.7545. The adsorption index of Cd-I-MA-_co_-AN is in the 0-1 range, proving that the adsorption reaction is favorable. Therefore, the Freundlich isotherm model evaluates the adsorption behavior well. Compared with the NI-MA-_co_-AN, the adsorption behavior of Cd-I-MA-_co_-AN was found to be better fitted with the Freundlich isotherm model. This indicates that the adsorbed sites on the surface of the imprinted polymeric microsphere are not energetically equivalent to cadmium ions and that the surface is heterogeneous [38].
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data  
[PERSON]  
1 Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China; [EMAIL_ADDRESS] (N.J.); [EMAIL_ADDRESS] (R.S.); [EMAIL_ADDRESS] (W.Z.)
* [2]University of Chinese Academy of Sciences, Beijing 100049, China
* [3]Shanxi Climate Center, Taiyuan 030002, China
* [4]Department of Plant and Soil Sciences, College of Agriculture, Food and Environment, University of Kentucky, KY 40506, USA; bo.tao@ukyedu (B.T.); [EMAIL_ADDRESS] (W.R.)
* [5]Institute of Dry Farming Engineering, Shanxi Agricultural University, Shanxi Taigu 030801, China; [EMAIL_ADDRESS]
* [6]National Meteorological Center, Beijing 100081, China; [EMAIL_ADDRESS]
* [7]School of Life Sciences, University of Technology Sydney, P.O. Box 123, Broadway NSW 2007, Australia
* [8]Correspondence: [PERSON]DDRESS]; Tel.: +61-2-9514-4142
# Lifetimes of Used Nuclear Fuel Containers Affected by
## 3 Sulphate-Reducing Bacteria Activity
### Suppressing Effect of Bentonite Dry Density  
Since the extreme pressure conditions inside the DGR differ from those on the experiments [18; 19], the reaction rate constant of Equation (8) is adjusted using the dry density of the bentonite as a suppressive factor because it determines the pore size of the clay and the swelling pressure which are two major factors limiting SRB activity.  
An exponential functional form is selected to attenuate the reaction rate constant as experiments have shown that the swelling pressure of bentonite increases exponentially with respect to the dry density of the bentonite clay [11]. The exponential form developed must meet the following conditions  
* \(k_{A}(\rho_{R})=k_{\rho}\), i.e., the reaction constant under the baseline density of the experiment of Section 3.2 must equal the optimal reaction constant of Equation (8).
* \(k_{A}(\rho_{S})=0\), i.e., if the dry density equals the specific density of the bentonite clay \(\left(\rho_{S}=2760\left[\text{kg/m}^{3}\right]\right)\), then the reaction constant becomes zero as there is no pore space available for bacteria to remain active. The exponential form developed is \[k_{A}\left(\rho_{Dry}\right)=\left[\left(\frac{k_{\rho}}{C_{f}}+1\right)^{ \frac{\rho_{S}-\rho_{Dry}}{\rho_{S}-\rho_{0}}}-1\right]C_{f},\ \ \rho_{Dry}\in\left[\rho_{0},\ \rho_{S}\right]\] (10) where \(k_{A}\left(\rho_{Dry}\right)\) is the attenuated or adjusted rate, \(\rho_{Dry}\) is the dry density of the clay in [kg/m\({}^{3}\)] and \(C_{f}\) is a value at which the rate constant is taken to be zero (details on the derivation of this formula are presented in [21]). Sulphate to sulphide reduction rates estimated by [22] are used to determine the value of \(C_{f}\) that best approximate the sulphide production on those experiments.  
Therefore, an optimization model is created to find the optimal value of \(C_{f}\) matches the sulphide reduction presented in [22]. The model is the following  
\[\left\{\begin{array}{l}\underset{C_{f}}{\text{Min}}\sum\limits_{i}\left(k_{ D}{}^{(i)}-k_{A}{}^{(i)}\right)^{2}\\ s.t.\\ k_{A}{}^{(i)}=\left[\left(\frac{k_{\rho}}{C_{f}}+1\right)^{\frac{\rho_{S}-\rho_{ Dry}{}^{(i)}}{\rho_{S}-\rho_{0}}}-1\right]C_{f}.\end{array}\right. \tag{11}\]  
Gradient descent, which is an iterative approach to solve optimization problems, is used to find the optimal value for \(C_{f}\) yielding an optimal value of \(C_{f}{}^{*}=1.37\times 10^{-17}\left[1/s\right]\). The attenuated reaction rate constants are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Clay** & **Dry Density [kg/m\({}^{3}\)]** & **Rate Attenuated k\({}_{\text{A}}\) [1/s]** \\ \hline Gapfill & 1410 & \(7.33\times 10^{-11}\) \\ HCB & 1700 & \(3.19\times 10^{-12}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Attenuated reaction rate constant.
# Nutrition in Pediatric Intensive Care: A Narrative Review
## 2 Nutritional Status Assessment  
As previously described, a high prevalence of malnutrition can be diagnosed upon PICU admission [14; 15; 16; 17]. Due to the strong association of malnutrition (including obesity) with negative clinical outcomes, the early detailed assessment of nutritional status is recommended as a part of PICU admission [18]. Nutritional risk in critically ill (NUTRIC) patients [4; 19] and nutritional risk screening (NRS 2002) [4; 20] have been validated and adopted for adult patients. For pediatric patients, weight and height/length (in children \(<2\) years of age) and z score for body mass index (BMI) for age, mid upper-arm circumference or weight for age can be used [1; 18]. For children under 36 months, head circumference should also be monitored [18]. The anthropometric measurements should be performed repeatedly (according to the nutrition protocol) during the whole PICU stay to indirectly evaluate the effect of nutritional interventions. Besides a close physical examination, complete dietary history and functional status prior to PICU admission should be assessed [18]. Nutritional assessment generally requires special training, clinical expertise and more time to complete. Such a formal nutritional assessment typically requires a dietitian, nutrition specialist or other qualified health care provider. On the other hand, a quick way to assess nutritional status is nutrition screening tools, which are expected to be able to be administered quickly (i.e., less than 15 min) and do not require special training for the caregiver. The complete assessment can then be finished later, especially in high-risk patients. Several screening tools are available in the literature; however, none of them have been validated in the PICU [21; 22; 23; 24]. The Screening Tool for Risk of Impaired Nutritional Status and Growth (STRONGKids) appears to be a suitable admission screening test for malnutrition in PICU [25]; however, it is unable to evaluate children with abnormal anthropometrics [18]. A recent multicenter study of inpatients in 14 hospitals across 12 European countries found that three of these more commonly used screening tools showed a fair amount of variability in the classification of malnutrition risk, and each tool failed to identify a proportion of children with subnormal anthropometric measures [25]. Therefore, it seems reasonable to combine screening tools with anthropometric measurements and reassess them regularly during the PICU stay. Other possible screening methods for malnutrition are currently used mainly for research purposes and have not been validated, such as ultrasound, computed tomography (CT) and bioelectrical impedance [4; 26; 27; 28]. However, ultrasound muscle mass and density screening seem to be promising methods for the future due to their wide accessibility, minimal financial requirements and minimal staff's experience demands.
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 3 Implementing Physics-Informed Surrogate Model  
Taking into account the taxonomy of Section 1.4 and the literature review of Section 2.1, a framework is presented in this chapter for the creation of PISMs. This stepwise framework is intended to help researchers identify and integrate physical considerations in the different stages of the model creation. The framework builds upon [PERSON]'s general framework for data-driven modelling [23] and the stepwise physics integration approach proposed by [PERSON] et al. [42]. The resulting stepwise strategies align with the four physics-integration strategies (ACT, LCT, FET, OCT) introduced in Section 1.4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Article**} & **Output of the Model** & **Model Algorithm** & **Physics-Informed Strategy** & **Implementation of the Physics-Informed Strategy** \\ \hline [PERSON] et al. [135] & Temperature field & Neural & \multirow{2}{*}{LCT} & Using loss terms from physics (from PDE) in combination with a data-driven loss term. \\ \hline [PERSON] et al. [136] & Pore pressure in the heated porous medium & Neural & ACT & Two coupled networks with loss terms defined by PDEs. \\ \hline [PERSON] et al. [137] & Temperature field & Neural & \multirow{2}{*}{LCT} & Loss terms evaluated divergence from PDEs. \\ \hline [PERSON] et al. [138] & Temperature field & Neural & \multirow{2}{*}{LCT} & Loss terms evaluated divergence from PDEs (just convection at the boundary). \\ \hline [PERSON] [133] & PDE solver & Neural & \multirow{2}{*}{LCT} & Creating loss terms linked to the residual of the physical equation. \\ \hline [PERSON] et al. [134] & PDE solver & Neural & \multirow{2}{*}{LCT} & Creating loss terms linked to the residual of the physical equation. \\ \hline [PERSON] et al. [14] & Temperature field & Neural & \multirow{2}{*}{LCT} & Loss terms from PDE. \\  & & network-based & ACT & Selecting activation functions and the configuration of the network. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Examples of physics-informed models in the heat transfer field.
## 3 Method
### Boundary Effect Model  
To understand the boundary effects that may be caused by geological formation changes on both sides of the survey line, this study explored the effects of the boundary effect on 2D ERT through the changes in the boundary medium using numerical simulations by assuming there was heterogeneous media at the boundary. This study explored the influence difference through a model comparison with a boundary effect and no boundary effect, as shown in Figures 3 and 4.  
The boundary effect was first calculated through the apparent resistivity profile of the model with a boundary medium by forward modeling. Then, we calculated the apparent resistivity by inverse calculation to obtain the resistivity profile of the model affected by the boundary medium. To understand the influence of the boundary effect, this study deleted the data points of the boundary medium in the apparent resistivity profile, and then through recalculation to obtain the new resistivity profile section, we compared the difference between the two. The deleted distances of the data points were 0 m, 2 m, 4 m, 6 m, 8 m, and 10 m from the boundary medium.  
Figure 4: 2.5D boundary effect model.  
Figure 3: 2D boundary effect model.  
This study explored the aspects of the resistivity ratio, electrode spacing, the influence distance of the boundary effect, medium depth, and medium size through parametric variation. In the model, it set that the resistivity of the background soil layer was R1, the resistivity of the boundary medium was \(R2\), the section size of the medium was \(dyp\times dzp=2\times 2\) m\({}^{2}\), the embedding depth was \(dep=2\) m, the spacing of the electrode rods was \(dx=2\), and the length of the survey line was \(L=50\) m. Table 2 is the explanatory table of the boundary effect model parameters.