esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{tabular}{p{113.0 pt} p{113.0 pt} p{113.0 pt} p{113.0 pt} p{113.0 pt}} \hline \hline
**Parameter Type** & **Parameters** & & **Prior to Dexamethasone, N = 28** & **Immediately after Completion of Dexamethasone, N = 25** & **\(p\) Value** \\ \hline \multirow{8}{*}{**Respiratory Parameters**} & & CMV & 7 (25) & 10 (40) & \\ \cline{3-6}  & Type of ventilatory support (\%) & HFOV & 4 (14.3) & 2 (8) & \\ \cline{3-6}  & & HFJV & 16 (57.1) & 1 (4) & \\ \cline{3-6}  & & Non-Invasive & 1 (3.6) & 12 (48) & \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{Mean airway pressure in cm H\({}_{2}\)O, mean (SD)} & 12.8 (2.46) & 11.38 (2.51) & 0.017 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{FiO\({}_{2}\) in \%, mean (SD)} & 48.52 (16.71) & 37.52 (14.88) & 0.003 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{PIP in cm H\({}_{2}\)O, mean (SD)} & 28.93 (6.38) & 21.33 (4.71) & \textless{}0.001 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{PEEP, cm H\({}_{2}\)O, mean (SD)} & 10.17 (2.18) & 9.15 (1.40) & 0.065 \\ \hline \multirow{8}{*}{LV FS (\%)} & & 41.36 (6.96) & 47.8 (14.72) & 0.062 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{LV EF (\%)} & 69.54 (9.82) & 70.33 (11.58) & 0.779 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{LVO (mL./kg/min)} & 271.45 (87.45) & 303.01 (135.11) & 0.137 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{LV VTI (cm)} & 10.5 (2.5) & 11.32 (4.12) & 0.196 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{LVIDd (cm)} & 1.28 (0.28) & 1.31 (0.33) & 0.395 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{LVIDs (cm)} & 0.76 (0.16) & 0.73 (0.19) & 0.381 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{LV posterior wall (cm)} & 0.24 (0.05) & 0.26 (0.04) & 0.166 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{Septal thickness (cm)} & 0.26 (0.07) & 0.27 (0.07) & 0.695 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{Relative wall thickness} & 0.41 (0.72) & 0.43 (0.11) & 0.667 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{LV \(S^{\prime}\) (cm/s)} & 4.77 (0.97) & 6.013 (1.41) & 0.006 * \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{RV S’ (cm/s)} & 7.18 (2.10) & 8.56 (2.24) & 0.05 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{Septum S’ (cm/s)} & 4.74 (1.01) & 5.57 (0.75) & 0.032 * \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{RVET (s)} & 0.18 (0.04) & 0.17 (0.03) & 0.672 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{PAAT (s)} & 0.06 (0.02) & 0.07 (0.02) & 0.152 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{PAAT: RVET ratio} & 0.36 (0.11) & 0.41 (0.13) & 0.106 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{RV VTI (cm)} & 9.81 (2.53) & 10.60 (3.58) & 0.358 \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{RV FAC in \%, mean (SD)} & 44.88 (5.85) & 49.71 (8.90) & 0.025 * \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{TAPSE (cm)} & 0.65 (0.12) & 0.73 (0.17) & 0.013 * \\ \cline{3-6}  & \multicolumn{2}{p{113.0 pt}}{PDA, n (\%)} & 17 (60.7) & 11 (39.3) & 0.453 \\ \hline \multicolumn{6}{p{113.0 pt}}{Eccentricity index in \%, mean (SD)} & 1.24 (0.31) & 1.09 (0.16) & 0.067 \\ \hline \hline \end{tabular}   
* *
## 2 Materials and Methods
### Study Organization  
The study was divided into two stages as shown in Table 3.  
In stage 1, the study was focused on testing the most effective OLR providing the highest organic compound removal and biogas production rate. The experiment was divided into five variants depending on the value of OLR calculated per volume of methanogenesis tank: variant 1, 4.0 g COD/L-d; variant 2, 6.0 g COD/L-d; variant 3, 8.0 g COD/L-d; variant 4, 10.0 g COD/L-d; and variant 5, 12.0 g COD/L-d. In each experimental variant, the reactor volume was replaced 20 times, and hence the duration of each variant and the hydraulic load of the reactor were different.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Unit** & **Value for AGS** & **Value for ASS** \\ \hline hydration & \% & 97.8 \(\pm\) 0.30 & 98.1 \(\pm\) 0.2 \\ total solids & g/L & 40.1 \(\pm\) 1.2 & 29.9 \(\pm\) 1.7 \\ mineral solids & g/L & 14.4 \(\pm\) 0.9 & 8.6 \(\pm\) 0.4 \\ volatile solids & g/L & 25.7 \(\pm\) 1.1 & 21.2 \(\pm\) 0.6 \\ filtrate COD & mg O\({}_{2}\)/L & 830 \(\pm\) 31.0 & 630 \(\pm\) 27.0 \\ filtrate TP & mg/L & 99.3 \(\pm\) 16.4 & 51.0 \(\pm\) 7.2 \\ filtrate TN & mg/L & 148 \(\pm\) 13.7 & 89.2 \(\pm\) 10.1 \\ pH & - & 7.49 \(\pm\) 0.13 & 7.27 \(\pm\) 0.12 \\ \hline \end{tabular}
\end{table}
Table 2: Characteristics of anaerobic granular sludge (AGS) and anaerobic suspended sludge (ASS) used for reactor inoculation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Unit** & **Value** \\ \hline COD & mg O\({}_{2}\)/L & 21,300 \(\pm\) 1750 \\ BOD\({}_{5}\) & mg O\({}_{2}\)/L & 14,290 \(\pm\) 1020 \\ TP & mg P/L & 112 \(\pm\) 31 \\ TN & mg N/L & 915 \(\pm\) 97 \\ total solids & mg/L & 1734 \(\pm\) 150 \\ pH & - & 7.01 \(\pm\) 0.31 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of dairy wastewater used in this study.  
In stage 2, the influence of the digestate liquid fraction recirculation on the efficiency of methane fermentation was examined. Four experimental variants differing in the degree of internal recirculation were tested: variant 1, 50%; variant 2, 100%; variant 3, 150%; and variant 4, 200%. As in stage 1, at OLR of 10 g COD/L-d (calculated per volume of methanogenesis tank), there was a significant decrease in AD efficiency; this OLR value was applied in stage 2.
# Hybrid Energy Routing Approach for Energy Internet
###### Abstract  
The Energy Internet (EI) has been proposed as an evolution of the power system in order to improve its efficiency in terms of energy generation, transmission and consumption. It aims to make the use of renewable energy effective. Herein, the energy router has been considered the crucial element that builds the net structure between the different EI components by connecting and controlling the bidirectional power and data flow. The increased use of renewable energy sources in EI has contributed to the creation of a new competitive energy trading market known as peer-to-peer energy trading, which enables each component to be part of the trading process. As a consequence, the concept of energy routing is increasingly relevant. In fact, there are three issues that need to be taken into account during the energy routing process: the subscriber matching, the energy-efficient path and the transmission scheduling. In this work, we first proposed a peer-to-peer energy trading scheme to ensure a controllable and reliable EI. Then, we introduced a new energy routing approach to address the three routing issues. A subscriber matching mechanism is designed to determine which producer/producers should be assigned for each consumer by optimizing the energy cost and transmission losses. This mechanism provides a solution for both mono and multi-source consumers. An improved ant colony optimization-based energy routing protocol was developed to determine a non-congestion minimum loss path. For the multi-source consumer case, an energy particle swarm optimization algorithm was proposed to choose a set of producers and to decide the amount of energy that should be collected from each producer to satisfy the consumer request. Finally, the performance of the proposed protocol, in terms of power losses, cost and computation time was compared to the best existing algorithms in the literature. Simulation results show the effectiveness of the proposed approach.  
energy cost; energy efficient path; Energy Internet; energy router; energy routing; P2P distributed energy trading; power loss; subscriber matching; transmission scheduling +
Footnote †: journal: Applied Energy Energy  
0021
## 5 Theoretical and bibliographical background
### Computing the clique minimal separators of a graph  
We first prove that for any graph \(G\) and any minimal triangulation \(H\) of \(G\), the clique minimal separators of \(G\) are exactly the minimal separators of \(H\) that are cliques in \(G\) (property 3.9).  
For this, it is sufficient to show that any clique of \(G\) has the same number of full components in \(G\) as in \(H\). This follows from the following property, which is an extension of Lemma 1 from [15].  
**Property 5.1**: _Let \(G=(V,E)\) be a graph, let \(H=(V,E+F)\) be a minimal triangulation of \(G\) and let \(S\) be a clique of \(G\). Then \(G(V-S)\) and \(H(V-S)\) have the same connected components, with the same neighborhoods._  
**Proof:** Let \(H^{\prime}\) be the graph defined by \(H^{\prime}=(V,E+F^{\prime})\), where \(F^{\prime}\) is the set of edges in \(F\) that are contained in \(N_{G}[C]\) for some connected component \(C\) of \(G(V-S)\). By definition of \(H^{\prime}\), \(G(V-S)\) and \(H^{\prime}(V-S)\) have the same connected components, with the same neighborhoods. Let us show that \(H^{\prime}\) is chordal. Let \(\mu\) be a cycle in \(H^{\prime}\) of length greater than 3, and let us show that \(\mu\) has a chord in \(H^{\prime}\). As \(H\) is chordal, \(\mu\) has a chord, say \(xy\), in \(H\). If \(xy\) is in \(H^{\prime}\) then it is a chord of \(\mu\) in \(H^{\prime}\). Otherwise there is a connected component \(C\) of \(G(V-S)\) such that \(x\) is in \(C\) and \(y\) is in \(V-N_{G}[C]\), or conversely. Hence there are two non-consecutive vertices \(w\) and \(z\) of \(\mu\) that are both in \(N_{G}(C)\), and therefore both in clique \(S\) of \(H^{\prime}\). It follows that \(wz\) is a chord of \(\mu\) in \(H^{\prime}\), which completes the proof that \(H^{\prime}\) is chordal. As \(G\subseteq H^{\prime}\subseteq H\), with \(H^{\prime}\) chordal and \(H\) a minimal triangulation of \(G\), \(H^{\prime}=H\), and therefore \(G(V-S)\) and \(H(V-S)\) have the same connected components, with the same neighborhoods. \(\Box\)  
**Corollary 5.2**: _(Property 3.9) Let \(G=(V,E)\) be a graph, let \(H=(V,E+F)\) be a minimal triangulation of \(G\). The clique minimal separators of \(G\) are exactly the minimal separators of \(H\) that are cliques in \(G\)._  
The fact that each minimal separator of a chordal graph \(H\) is generated by a vertex of \(H\) w.r.t. a peo of \(H\)[4] is well-known. A way of selecting the exact vertices that generate a minimal separator is discussed in [5].  
The following algorithm is based on the expanded version of MCS presented in [21] to generate the maximal cliques and a clique tree.  
This process is integrated in the implementation given in Section 4.
# Performance Evaluation of Recycled Asphalt
## 5 Affecting Factors on the Performance of Vibratory Compaction Specimens
### Effects of Curing Conditions  
During the curing process, the loss of water mass is shown in Figure 16.  
As can be seen from Figure 16, the water loss of emulsified asphalt mixture is very rapid in the first three days. The mass loss has rapidly decreased from about 1.5% to around 0.2%. After that, the change was relatively gentle, and the mass change in the 4 th-7 th day was about 0.1%. This is very similar to the regularity of strength growth. Therefore, the regulation of strength growth can be roughly judged by the loss of water, which also indirectly shows that the loss of water is an important factor in strength formation. In the curing process, the curing conditions (temperature, humidity) in the first few days are very important [41]. For hot mixed asphalt mixture, generally speaking, there is no need for excessive curing. In the cement emulsified asphalt recycled mixture, the loss needs a process, due to the existence of moisture, and water is an important factor in strength formation. In addition, as cement has the need for curing, curing conditions can be taken as one of the conditions to be considered.  
Under different curing conditions, the change rule of gross bulk density with curing time is shown in Figure 17.  
Figure 16: Mass loss of moisture content.  
Figure 17: Relationship between curing method, curing time-gross bulk density.  
As can be seen from Figure 17 that in the natural curing state, with the increase of curing time, the gross bulk density increases continuously. Within the first three days, the gross bulk density increases very fast, and gradually flattens after seven days. The regulation of accelerating curing is similar to that of natural curing. The gross bulk density of accelerated curing at 40 \({}^{\circ}\)C for two days is approximately equal to that of natural curing for three days, while the gross bulk density of accelerated curing at 40 \({}^{\circ}\)C for three days is approximately equal to that of natural curing for seven days. It can be seen from this that two days of accelerated curing at 40 \({}^{\circ}\)C is equivalent to three days of natural curing, and three days of accelerated curing at 40 \({}^{\circ}\)C is equivalent to seven days of natural curing. This provides time convenience for laboratory short-term indoor experiments.  
Under different curing conditions, the intensity changes with curing time, as shown in Figure 18.  
As can be seen from Figure 18, the splitting strength increases with age. The dry and wet splitting strength of natural curing for 3-7 days increased rapidly, and then the growth was relatively flat, while the growth rate of dry and wet splitting strength of accelerated curing for 2-3 days was the same as that of natural curing for 3-7 days. Judging from the splitting strength, two days of accelerated curing at 40 \({}^{\circ}\)C is equivalent to three days of natural curing. Three days of 40 \({}^{\circ}\)C accelerated curing is equivalent to seven days of natural curing.
## 4 Experimental Results  
### Databases  
We evaluated the proposed multi-view method using the SCUT-FBP5500 benchmark dataset and performed a quantitative comparison of the results obtained with those obtained using a single-view approach only. The comparison is based on three measures: the Pearson correlation coefficient (PC), the mean absolute error (MAE) and the root mean square error (RMSE).  
The SCUT-FBP5500 contains 5500 frontal, unobstructed faces between the ages of 15 and 60 with neutral expressions. It can be divided into four racial and gender subgroups, including 2000 Asian females, 2000 Asian males, 750 Caucasian females and 750 Caucasian females.  
Figure 2: The framework of the proposed method. Different similarity graphs corresponding to the different descriptors are constructed. The correlation graph is constructed in the score space. All graphs are merged into a single graph. The FME method is repeatedly used to process the resulting fused graph and to refine the face beauty prediction for the unseen images in each iteration.  
males. Most of the SCUT-FBP5500 images were collected from the Internet. All images were labelled with beauty values in the range of [1; 5]. Some examples can be found in Figure 3.
## 3 Results
### FTIR Study  
The chemical structural nature of the S3 sample containing a Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\) lattice was identified by FTIR analysis. The FTIR spectrum measured in the range of 4000 to 400 cm\({}^{-1}\) is shown in Figure 3. (i) An extreme broad band detected in the 3650-3100 cm\({}^{-1}\) region and a small band of around 1677 cm\({}^{-1}\) may due to the stretching and bending vibrations of the adsorbed water molecules of the O-H surface hydroxyl groups [26]. Consequently, these bands validate which high humidity is absorbed by the nanocrystalline system with high surface-to-volume ratio [27]. (ii) The stretching vibration of O=C=O resulted in an IR band observed at 2352 cm\({}^{-1}\)[28]. During KBr palletization, the adsorption of CO\({}_{2}\) from the air on the surface of the as-synthesized material resulted in the presence of an IR band at 1411 cm\({}^{-1}\)[28]. Depending on the presence of carbon traces, the transmittance peak was observed due to C-OH stretching vibration at about 1147 cm\({}^{-1}\)[28]. (iii) It is possible to attribute the characteristic bands observed around 500 and 600 cm\({}^{-1}\) to the octahedral (B) and tetrahedral modes, respectively, in the spinel system [29]. Indeed, the 500 and 618 cm\({}^{-1}\) bands are related to the vibration of trivalent metal ions in B-sites and divalent metal ions in A-sites of the spinel Cu\({}_{1.5}\)Mn\({}_{1.5}\)O\({}_{4}\) lattice, respectively [30; 31].
# Characterization of Sorghum Processed through Dry Heat Treatment and Milling
## 2 Materials and Methods
### Methods
#### 2.2.4 Characterization of Optimal Samples  
Color Evaluation  
The color parameters of sorghum flour fractions were measured by reflectance using the CIE Lab system on a Konica Minolta CR-400 (Konica Minolta, Tokyo, Japan) device. The parameters recorded in triplicate were: \(L^{\star}\) (luminosity), \(\alpha^{\star}\) (describing red nuance if positive or green nuance if negative), and \(b^{\star}\) (describing yellow nuance if positive or blue nuance if negative).  
Starch Digestibility  
Starch fractions, such as rapidly digestible starch (RDS), slowly digestible starch (SDS), resistant starch (RS), and total digestible starch (TDS), and total starch contents of sorghum flour fractions were determined in triplicate following the international AOAC 2017.16 protocol using a Megazyme kit (K-DSTRS; Megazyme, Bray, Ireland). The principle consists of spectrophotometric measurement (at 510 nm) of the glucose released after sample digestion with \(\alpha\)-amylase and amyloglucosidase for 20 min (RDS), 120 min (SDS), or 240 min (RS, TDS) using GOPOD reagent.  
Total Polyphenols and Antiradical Activity  
The extract was prepared by mixing 1 g sorghum flour with methanol 99.9% (1:20 \(w/v\)), then the mix was sonicated at 50 \({}^{\circ}\)C, at 40 kHz for 30 min.  
For total polyphenol content (TPC) determination, 0.2 mL extract was mixed with 2 mL Folin-Ciocalteu reagent and 1.8 mL sodium carbonate (7.5%). After resting at room temperature in the darkness for 30 min, the absorbance was read at 750 nm [27]. The calibration curve made with gallic acid had \(R^{2}\) = 0.99 and the equation y = 0.00949x + 0.02950.  
For antiradical activity (AA) of sorghum flour evaluation, the 2,2 diphenyl-1-picrylhydrazyl (DPPH) method was used. The extract (2 mL) was mixed with DPPH reagent (2 mL) and the absorbance read at 517 nm after 30 min of resting in the darkness at room temperature. The DPPH AA was calculated by using Equation (7).  
\[\text{DPPH AA }(\%)=\left(1-\frac{A_{sample}}{A_{blank}}\right)\cdot 100 \tag{7}\]  
Starch and Protein Molecular Characteristics  
Sorghum flour starch and protein characteristics were evaluated by ATR-FTIR analysis, on a Thermo Scientific Nicolet iS20 (Waltham, MA, USA) device. Three spectra were recorded in the range of 650 cm\({}^{-1}\) to 4000 cm\({}^{-1}\), with a resolution of 4 cm\({}^{-1}\) and 32 scans. After ATR correction of all spectra, the fractions of amide I (1652 cm\({}^{-1}\) for \(\alpha\)-helix structure and 1624 cm\({}^{-1}\) for \(\beta\)-sheets) and starch (1049 cm\({}^{-1}\) for crystalline area and 1022 cm\({}^{-1}\) for the amorphous region) were evaluated after applying Fourier self-deconvolution on the average spectra. The ratio of structures was determined by reporting the corresponding peak areas [28; 29]. Background was collected after each sorghum flour sample.
## 2 Study Site and Dataset Description
### Dataset Description  
#### 2.2.1 S1-Derived Soil Moisture Maps  
Sentinel-1 soil moisture maps (S1-SSM) at very high spatial resolution (up to a plot scale with a minimum area of 0.2 hectares) over our study area were analyzed. These soil moisture maps were operationally produced by coupling SAR data from both the S1A and S1B satellite constellations operating at the C-band (wavelength \(\sim\) 6 cm) and optical data from both the S2A and S2B satellite constellations [16]. To estimate the soil moisture over agricultural areas, [PERSON] et al. [16] inverted the Water Cloud Model (WCM) [17] combined with the Integral Equation Model (IEM) [18]. In their study, they used the WCM parameterized by [PERSON] et al. [19] for the C-band. The total backscattering coefficient in the WCM is considered the sum of the direct vegetation contribution and the soil contribution multiplied by the attenuation factor. Using a vegetation descriptor derived from optical images (Sentinel-2), the direct contribution of vegetation can be calculated. [PERSON] et al. [16] validated the proposed approach for the operational mapping of soil moisture over a study site in Occitanie in South France. They showed that the soil moisture in agricultural areas could be estimated with an accuracy of approximately 5 vol.%. Moreover, [PERSON] et al. evaluated SMOS, SMAP, ASCAT, and S1-SSM products [20], and revealed that the S1-derived soil moisture maps provide the most accurate estimation of SSM. The higher accuracy of estimated SSM moisture is probably due to the well-calibrated IEM combined with the well-parameterized WCM and the use of high spatial resolution (\(10\,\mathrm{m}\times 10\,\mathrm{m}\)) land cover maps derived from S2 images to eliminate SAR scattering from forest and urban areas. S1-SSM maps are produced for agricultural areas masking forest, urban, and high-slope areas (slope >20% calculated based on elevation data provided by the Shuttle Radar Topography Mission \"SRTM\" at 30 m \(\times\) 30 m spatial resolution). S1 soil moisture maps for the Occitanie region are available as open access data via the Theia French Land Data Center ([[http://www.theia-land.fr/en/thematic-products](http://www.theia-land.fr/en/thematic-products)]([http://www.theia-land.fr/en/thematic-products](http://www.theia-land.fr/en/thematic-products))). The SSM map at each date was generated using the ascending acquisition mode of the Sentinel-1 images (acquisition time \(\sim\)17:40 TU). Four maps are  
Figure 1: Annual cumulative precipitation calculated using Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) data over the study site (Occitanie region, South France) between 1 September 2016 and 31 August 2017. Black lines represent the department limits according to French administrative nomenclature.  
required to cover the region on a single date. Thus, a total of 250 soil moisture maps were downloaded to cover the whole region on the studied dates.