esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Response Surface Methodology  
RSM is a mathematical method commonly used to evaluate and establish a model for a dataset containing independent and dependent variables. In addition, RSM has been applied in multi-objective optimization models that try to accomplish desired objectives according to either predicted or input parameters [47]. Many models can be employed in RSM modeling to establish statistical connections between independent variables and the target variable. These consist of the historical data model, Box-Behnken, central composite design (CCD) model, etc. The number of variables and degree of variability determine the model type for analysis [47]. If the relationship between the input parameter and output is uncertain, a linear function could be constructed using the first-order expression, as given in Equation (1). Figure 3 illustrates the CCD framework according to four points, displaying factorial points (\(\pm\)1) and four central points describing (\(\pm\)\(\alpha\)) design points. The  
Figure 2: Systematic illustration of the mixing process and testing program.  
experimental dataset from thirteen (13) mixes was prepared with combination of three levels of variables (PU binder and NS) that were used to analyze the CCD model due to its flexibility for selecting \(\alpha\) (distance from design center to axial run); \(\alpha=1\) was implemented in this study [47]. According to Equations (1) and (2), the input parameters and output responses were statistically stated in a linear or polynomial form, respectively [47].  
\[r=\beta_{o}+\beta_{1}X_{1}+\beta_{2}X_{2}+\dots\ \beta_{n}X_{n}+\epsilon \tag{1}\]  
However, for non-linear interactions in the dataset, a higher-degree polynomial model, the second-order function stated in Equation (2), can be utilized:  
\[r=\beta_{o}+\sum_{i=1}^{k}\beta_{i}X_{i}+\sum_{i=1}^{k}\beta_{ii}X_{i}^{2}+\sum _{i<j}\beta_{ij}X_{i}X_{j}+\epsilon \tag{2}\]  
where \(r\) is the output, \(\beta_{o}\) is the r-intercept, in which \(X_{1}=X_{2}=0\), \(\beta_{1}\) is the coefficient of first input parameter, \(\beta_{2}\) is the coefficient of the second input parameter, \(X_{1}\) and \(X_{2}\) are the first and second input parameter coefficients, \(\epsilon\) represents the error, \(i\) an \(j\) are the linear and quadratic coefficient, and \(k\) is the variable number [47].  
The CCD was utilized for model analysis to optimize the response variable. Therefore, a PU binder content of 0%, 10%, 15%, or 25% was added to the mortar mixture based on the cement weight, and NS was also added to the mortar from 0 to 3% (intervals of 1%) by cement weight, and they were modeled as input parameters.
## 2 Materials and Methods
### Quality Assessment of Purified pDNA
#### 2.4.2 Proteins, Endotoxins, and Genomic DNA Quantification  
The total protein content in purified pDNA samples was determined using the micro-BCA (bicinchoninic acid) protein assay kit from Pierce (Rockford, IL, USA) following the manufacturer's recommendations. The calibration curve was performed with the standard bovine serum albumin (0.01-0.1 mg/mL) diluted in 10 mM Tris-HCl, pH 8.0 or in 10 mM of sodium citrate buffer, pH 5.0. To evaluate the endotoxins levels, a ToxiSensorTM Chromogenic Limulus Amoebocyte Lysate (LAL) kit (GenScript, New Jersey, USA) was applied, in accordance with the manufacturer's instructions. Briefly, a calibration curve was performed (from 0.005 to 0.1 EU/mL) using a stock solution of 8 EU/mL. To avoid the external endotoxin interference, all the samples were diluted or dissolved in non-pyrogenic water, which was also used as the blank. All the tubes and tips used to perform this analysis were endotoxin-free. The procedure was performed inside a laminar flow cabinet to assure aseptic conditions. Quantitative polymerase chain reaction (qPCR) was applied to quantify the amount of gDNA in the purified pDNA samples. To accomplish this analysis, an iQ5 Multicolor real-time PCR detection system (Bio-Rad) was used, as previously described [21]. To perform qPCR, primers able to amplify a 181-bp fragment of the 165 rRNA gene (sense (5'-ACACGGTCCA GAACTCCTACG-3') and antisense (5'-CCGGTGTCTTCTGCGGG ATACGTA-3')) were used. Moreover, for the gDNA quantification in lysate samples, a 100-fold dilution was prepared. The PCR amplicons were quantified by following the change in fluorescence of the DNA binding dye SYBR Green (Bio-Rad). Nonetheless, a calibration curve performed through consecutive dilutions of the purified _E. coli_ DH5 agDNA in the 0.005 to 50 ng/\(\upmu\)L range was obtained to measure the concentration of the gDNA obtained. Negative controls (no template) were run at the time of each analysis.
## 2 Regularized MoLE
### Regularization on Gates
In that case  
\[v_{pj}^{k+1}=\frac{S\Big{(}\sum_{i=1}^{n}r_{pi}x_{ij}(\hat{z}_{pi}^{k}-\hat{z} _{pi}^{j}),\lambda_{p}^{S}\alpha\Big{)}}{\sum_{i=1}^{n}r_{pi}^{t}x_{ij}^{2}+ \lambda_{p}^{S}(1-\alpha)}, \tag{20}\]  
where \(\hat{z}_{pi}^{j}=\sum_{l\
eq j}^{d}x_{il}v_{pl}\) is the fitted value of gate \(p\), without the contribution of variable \(j\). The major issue here is to find the most appropriate \(\lambda_{p}^{S}\) for each gate \(p\) and at each iteration of the IRLS algorithm, where the value of \(\lambda_{p}^{S}\) controls the sparsity of the solution. For such purpose, it is adopted the BIC, which measures the trade off between accuracy and complexity, and for the gates, it has the following format  
\[\text{BIC}^{S}(\lambda_{p}^{S},\alpha)=n_{p}^{S}\log\left(\sum_{i=1}^{n}r_{pi} \Big{(}z_{pi}-\mathbf{x}_{i}^{T}\mathbf{v}_{p}^{k}\Big{)}^{2}\right)+\log(n_{ p}^{S})\psi_{p}^{S}, \tag{21}\]  
where \(n_{p}^{S}=\left(\sum_{i}r_{pi}\right)\) is the number of effective samples of gate \(p\), and \(\psi_{p}^{S}\) is the degrees of freedom of expert \(p\) which is equal to the number of non zero elements in \(\mathbf{v}\). Thus, the selected value of \(\lambda_{p}^{S}\) is the one that minimizes the value of \(\text{BIC}^{S}(\lambda_{p}^{S},\alpha)\). The IRLS algorithm with EN penalty is described in Algorithm 2. ```
1:procedureIRLS-EN(\(\mathbf{v}_{p}^{\prime}\), \(\xi_{l}\),\(\mathbf{u}_{p}\),\(\mathbf{R}_{p}\),\(\lambda_{p}^{\delta}\),\(\alpha\) )
2: Initialize \(\hat{\mathbf{v}}_{p}^{0}\leftarrow\mathbf{v}_{p}^{\prime},\ k\gets 0\), \(\text{Done}\leftarrow\text{FALSE}\)
3:while not Done do
4:\(\hat{\mathbf{z}}_{p}^{k}\leftarrow\mathbf{X}\hat{\mathbf{v}}_{p}^{k}-\mathbf{R} _{p}^{-1}\mathbf{u}_{p}\)
5:\(\hat{\mathbf{v}}_{p}^{k+1}=\arg\min_{\hat{\mathbf{v}}_{p}}\frac{1}{2}\sum_{i=1 }^{n}r_{pi}(\hat{\mathbf{z}}_{pi}^{k}-\mathbf{x}_{i}^{T}\hat{\mathbf{v}}_{p}^{*})^{2 }+\lambda_{p}^{\delta}\Big{(}\alpha\sum_{i}|\hat{\mathbf{v}}_{pi}^{*}|^{2}+(1- \alpha)\sum_{i}|\hat{\mathbf{v}}_{pi}^{*}|\Big{)}\)
6:if\(||\hat{\mathbf{v}}_{p}^{k+1}-\hat{\mathbf{v}}_{p}^{k}||_{2}\leq\xi_{l}\)then
7:\(\text{Done}\)\(\leftarrow\)TRUE
8:endif
9:\(k\gets k+1\)
10:endwhile
11:return\(\mathbf{v}_{p}^{t+1}=\hat{\mathbf{v}}_{p}^{k}\)\(\triangleright\)\(t\): EM index; \(k\): IRLS index. 12:endprocedure
```  
**Algorithm 2** Iterative re-weighted least squares (IRLS) with elastic net (EN) regularization.
## 3 Results and Discussion
### Major Barriers and Policy Implications in India
#### 3.7.2 Technological Barriers in India  
There are numerous technological barriers against the deployment of CSP technology in India, such as lack of knowledge in operation and maintenance of the plant, lack of research and development, and complexities such as storage of energy, power, etc.; there is also a lack of knowledge among the local manufacturers with regard to the latest technology [82; 83]. Such impediments force entrepreneurs or investors to import from the international market. Therefore, the Ministry of Energy in India needs to create policies for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S. No** & **Barrier Category** & \multicolumn{2}{c}{**Major Barrier**} & \multicolumn{2}{c}{**Future Recommendations**} \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Financial Barriers} & (a) & Lack of local manufacturing units & (a) & Government should provide financial incentives, policies such as “Make in India” and “Niti \\  & & (b) & Financial institutes & & Aayog” should be strictly implemented \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{Technical Barriers} & (a) & Lack of skilled and operating & (a) & Initiate training from experts \\  & & & facilities & (b) & Utilize institutes and universities \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Infrastructural Barriers} & (a) & Non-availability good maps & (a) & Provide information to the \\  & & & & investors on potential areas \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{Lack of Social Awareness} & (a) & Unaware of benefits of power & (a) & Trainings and workshops need to \\  & & generation polices and renewable & & educate the public about the \\  & & energy projects & & benefits of renewable energy \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{Policy barriers} & (a) & Agreements and license will & (a) & Government should invite \\  & & & take time & (b) & Alter for existing energy policies \\ \cline{1-1}  & & & & (c) & Financial assistance for small \\ \hline \multirow{3}{*}{6} & \multirow{3}{*}{Special Status} & (a) & Lack of development from & (a) & Local politicians should engage \\ \cline{1-1}  & & various sectors & & with the central government for \\ \cline{1-1}  & & & & special status \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summarized barriers in the development of CSP technologies in India and list of recommendations.  
research and development and local manufacturers in order to improve energy storage in CSP plants. The government or project proponents initiate programs related to the CSP and TES technologies through experts from the field to educate the manufacturers and also local workers [84; 85; 86]. India is endowed with good researchers and scientists coming from reputable universities and institutes; however, the country imports most of its technical components from countries and regions such as the United States, Europe, China, Russia, and Israel.
# The Efficiency of Calcium Oxide on Microbial Self-Healing Activity in Alkali-Activated Slag (AAS)
## 2 Experimental Program  
### Materials  
Ground granulated slag was used as an AAS precursor. The chemical composition of slag was identified by utilizing X-ray fluorescence, as presented in Table 3. The finesses and the specific gravity of slag were 4088 cm\({}^{2}\)/g and 2.80, respectively. Sodium hydroxide (NaOH) flakes and sodium meta-silicate (Na\({}_{2}\)SiO\({}_{3}\)) were used to activate the AAS precursor. A NaOH solution was prepared 24 h before AAS mixing by adopting 37.2% sodium hydroxide flakes and 62.5% water (480 gm of NaOH was dissolved in 1 L of water to provide a solution with a molarity of 12). The Na\({}_{2}\)SiO\({}_{3}\) solution was composed of 27% sodium oxide, 30.01% silicon oxide, and 59.72% water. Calcium oxide was added in the form of powder with a percentage of 7% of the binder weight.  
prepared in a medium composed of 1% tryptone, 0.5% yeast extract, 1% NaCl, and 1.5% agar at 4 \({}^{\circ}\)C. Glassware and instrument sterilization was a crucial phase for preventing any contamination. For cell concentration preparation, a spectrophotometer was used to determine the optical bacteria density to develop a cell concentration of 10\({}^{5}\) cells/mL. The determinacy of subtilis absorbance was performed by a qualitative procedure involving the changes in OD600 (optical density measurement at 600 nm) of spore suspensions during germination. The solution of the bacteria was monitored every 24 h to obtain the OD600. After 48 h, the bacteria culture showed a 0.65 optical density that reached 1.1 OD600. At elevated pH (10.5), the growth profile of _Bacillus subtilis_ was the fastest, indicating the best alkali-resistant behavior. To convert the optical density into CFU/mL, sub-culturing of a pure bacterial strain into young exponential cells is performed.  
OD and CFU/mL are measured, and repeated measurements are performed until obtaining 0.1 OD and the corresponding CFU/mL. A linearized standard curve was plotted for conversion. The characteristics of pure culture for _Bacillus subtilis_ are given in Table 4.
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 5 Simulation and Results  
For the performance evaluation of the theoretical model created in the previous section, we used a scenario build in NS-3 to produce a two-level heterogeneous wireless network system. Therefore, simulation topology and numerical results are exhibited in the current section.
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 3 Results and Discussion  
### Analysis of Climate Change Impacts  
The results from the analysis of climate change impacts was in the form of future daily rainfall, and maximum and minimum temperatures during 2021-2100 projected by CNRM-CM5, EC-EARTH, and NorESM1-M (note: multiple GCMs were employed to minimize the degree of uncertainty for a better representation and assessment of future climate changes, rather than rely on a single GCM as suggested by [PERSON] et al. [32]). Thereafter, the obtained future climate data from the three GCM models were averaged, in which the rainfall of the Lam Phaniang River Basin was then calculated by the Thiessen polygon method and contribution of every rain gauge was determined using ArcGIS 10.5. Obviously, Figure 5 indicates increasing trends in annual average values of rainfall, and both maximum and minimum temperatures, which were statistically significant at the P < 0.05 level. This is supported by the study of [PERSON] et al. [33], who reported the future increases in total rainfall, growing season temperature, and extreme maximum temperature in Northeast Thailand, compared to the baseline. In addition, the rates of their increases under RCP 8.5 were also found to be higher than RCP 4.5. In detail, for the 2021-2100 period against the baseline, the future annual maximum and minimum temperatures were found to be increased by 0.015 \({}^{\circ}\)C/year and 0.019 \({}^{\circ}\)C/year under RCP 4.5, and 0.034 \({}^{\circ}\)C/year and 0.044 \({}^{\circ}\)C/year under RCP 8.5, respectively. Under RCPs 4.5 and 8.5, on average in 2100, the increase in maximum temperature from the present situation (2020) would be up to 1.21 \({}^{\circ}\)C and 2.70 \({}^{\circ}\)C, respectively (Figure 5a), while the minimum temperature would also rise to about 1.50 \({}^{\circ}\)C and 3.48 \({}^{\circ}\)C, respectively, more than that of present (Figure 5b). The future annual average rainfall across the river basin was also projected to increase by between 1.41 mm (RCP 4.5) and 2.72 mm (RCP 8.5), as illustrated in Figure 5c. By 2100, the future annual rainfall projections under RCP 4.5 were expected to increase to about 113.02 mm and 217.47 mm for RCP 8.5 compared to the present year (2020).
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
## 4 Localization Schemes and MAC Considerations
### MAC Considerations
#### 4.3.2 Tdma  
In scheduled access approaches, nodes are not allowed to transmit unless they have received a medium resource, which is, for example, a time slot in the TDMA case or a frequency band when using FDMA schemes. Combining multiple of these MAC protocols is also possible and could result in larger supported users at the cost of an increased complexity. The main difference of a TDMA scheme with respect to a random access protocol like ALOHA, is that the transmission of every node is not random but based on scheduled access. Only the user who has gained the right to transmit can initiates the communication. In this way, collisions should be avoided. The most general TDMA approach is to divide the time in superframes, which in turn consist of different parts as shown in Figure 3. This figures summarizes information from multiple section of the ETSI EN 302 065 standard.  
The first slot (TDoA Sync) is used to synchronized the anchors nodes and therefore only present when adopting TDoA. The infrastructure nodes are also responsible to sequentially send UWB beacons to inform the mobile tags about the following slot schedule and to indicate the beginning of the contention access period (CAP). When a new tag joins the network, it requests a time slot during CAP using a random MAC protocol such as ALOHA. Once the scheduler assigns resources to a tag, the latter can communicate with the anchors without worrying about collisions with other nodes. This happens during the contention free period (CFP), which size is computed as follows.  
\[T_{CFP}=n_{resources}\cdot T_{packet} \tag{10}\]  
Consequently, to evaluate the maximum number of tags that can be accommodated with TDMA we can use (11).  
\[n_{resources}=\frac{T_{Superframe}-T_{CAP}-T_{Sync}-T_{beacon}}{T_{packet}} \tag{11}\]  
It is important to remember that using ALOHA during the CAP period, collisions may happen, which means that certain nodes would have to try to send more than one slot request, resulting in delayed access to the network and higher energy consumption.  
When TDMA is chosen in a TWR network, as it happens for ALOHA, more packets are exchanged and therefore the the slot size increases according to (9).
## 4 Improvement of the Thermal Stability of the Femtosecond Laser Chromatic Confocal Probe

Table 3: Summary of the contribution of each factor on the overall thermal stability of the setup. Experiments were then carried out to verify the improvement of the thermal stability of the second prototype chromatic confocal probe in the same manner as the first prototype one described in the previous section. Three repetitive experiments were carried out. Figure 12 shows one of the experimental results. A mean value of \(d\lambda/dT\) was evaluated to be 7.9 nm/\({}^{\circ}\)C. Therefore, from Equation (7), the thermal stability of the overall second prototype measurement setup for axial displacement measurement (\(dZ/dT\))\({}_{\text{Overall}}\) was evaluated as (\(dZ/dT\))\({}_{\text{Overall}}\) = (7.9 nm/\({}^{\circ}\)C)-(\(-255\) nm/nm) = \(-2.01\) m/\({}^{\circ}\)C. It should be noted that this result contains the contribution of the thermal expansion of the mounting plate. To evaluate the thermal instability of the probe, the contribution of the mounting plate is estimated in theory. Figure 13 shows the schematic of the chromatic confocal lens and the target sample in the second prototype measurement setup. The mounting plate is made of aluminum having a linear thermal expansion coefficient of \(23.1\times 10^{-6}\)\({}^{\circ}\)C\({}^{-1}\)[34]. Regarding a length of the mounting plate (175 mm), from Equation (12), the contribution of the mounting plate is calculated as (\(dZ/dT\))\({}_{\text{Mounting plate}}\) = (\(23.1\times 10^{-6}\)\({}^{\circ}\)C\({}^{-1}\))-(175/2 mm) = \(-2.02\) m/\({}^{\circ}\)C. Considering the result of the above theoretical calculation, the thermal stability of the second prototype chromatic confocal probe is evaluated as follows:  
\[\begin{array}{ll}(dZ/dT)_{\text{Probe}}&\approx(dZ/dT)_{\text{Overall}}-(dZ/dT )_{\text{Mounting plate}}\\ &=(-2.01\text{
## 4 Results and Discussion
### Spray Height  
As can be seen from the Figure 6, the spray height is on the rise when the injection time is increasing.  
At the time of 0.8 ms, the image of the diesel spray impinging on the wall surface was observed. The beginning of the observed H is 3.33 mm, increasing steadily from 0.8 ms to 1.4 ms, and the increasing rate was 5.55 m/s. The results of the experimental results were close to the experimental results, and the mean error was 2.8%.  
In the period of 1.5 ms to 2.1 ms, due to the strong entrainment of diesel spray and air, H increased slowly. On account of the injection pressure is high and the diesel atomization effect is good, that the particle size of the spray droplets is small, the interaction between the spray droplets group and the surrounding air is strong. The center of the gas spray has a greater momentum, the spray gas will be around the outer entrainment. After the wall hit the droplet was significantly broken, the occurrence of outward rebound or splash. Due to the influence of air resistance and friction, the rate of fortifying of spray height is decrease with the increase of spraying time. At this stage, the change of H is small, the growth rate is more and more slow, the average growth rate is 1.5 m/s, and the error of the calculation result and the experimental result is in the vicinity of 2.0%.  
After 2.2 ms, spray height continued increasing from 8.4 mm to 10.2 mm, the increasing rate is 2.9 m/s. The error between calculation and experiment is 3.2%. Mainly due to the entrainment between the spray oil bundles and the environmental medium during the free movement, result in a part of oil mist was pushed by the droplet of the splashing wall but did not come into contact with the wall and the kinetic energy loss is small, resulting in a higher wall spray height. It is mainly due to the influence of air resistance and wall friction on the spray contact with the wall after diffusion and evaporation, which interferes the nearby gas molecules with it [21].  
Figure 5: Experimental extraction of R, H, and \(\alpha\).
## 4 Conclusions  
In this paper, a new prototype is introduced to acquire endoscopic NBI videos and multispectral videos during in vivo stomach exploration. The prototype is used to acquire spectra on 16 patients. For each of these patients, 200 spectra are selected, leading to a dataset of 3200 spectra. Spectra obtained with multispectral cameras are processed with a classification pipeline containing a wavelength reduction algorithm, the computation of three types of features (reduced bands, subtractions and divisions) and a leave one patient out classification with an SVM classifier with a linear kernel. The classifier can recognize a healthy stomach wall from chronic gastritis and intestinal metaplasia with a good precision of 80%. Moreover, two exciting wavelength ranges are identified in the red and near-infrared ranges: [745, 755 nm] and [780, 840 nm]. To the best of our knowledge, the analysis of this ensemble of three types of tissues (i.e., chronic gastritis, intestinal metaplasia, and healthy) has never been considered in a previous paper.  
These promising results must be confirmed on a larger dataset. The prototype can also be improved to have better quality for the images and, thus, to be able to exploit textural information caught by the multispectral cameras. Finally, unmixing algorithms can be used to get information on the relative concentration of chromophores and thus have a better understanding of the biological changes that appear with pre-cancerous lesions.  
Investigation, A.K.; Methodology, A.K.; Project administration, F.M.; Resources, D.L. and T.B.; Supervision, Y.B., F.M. and D.L.; Writing--original draft, A.K.; Writing--review & editing, A.K., Y.B. and F.M. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the French Research National Agency (ANR) program EMMIE under the grant agreement 15-CE17-0015  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Antenna Array with BM Network
Despite this limitation, the proposed design remains highly effective for specific underground applications where fixed beam angles are sufficient, and its low power consumption and robust performance make it well-suited for improving communication systems in underground mining environments [112].