esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents research on the mass dispersion and adsorption of organics present in tap water on powdered activated carbon (PAC) in a two-layer filter column. The adsorption rate depends on the difference between the concentration of organics and the equilibrium concentration. In homogeneous flocculators with simultaneous adsorption on PAC, the concentration difference is lower than in a filter column with PAC. Therefore, the utilization of the PAC's adsorption capacity in filters is higher than in homogeneous flocculators. PAC is introduced into the upper anthracite layer of a filter bed, while the bottom layer is a sand layer, which protects the underdrain system from becoming clogged with PAC particles. The sorbent was introduced into the bed in the final phase of filter backwashing. The authors present a model of adsorption on PAC in a filter column. Both experiments and calculations confirmed a better utilization of PAC's adsorption capacity in the filter column compared to its utilization in a homogeneous flocculator. Three criterion equations were developed using dimensionless numbers, Re, \(Pe\) and \(Nu\), as well as two similarity moduli related to a sorbent apparent density and an adsorption coefficient. Additionally, a relationship between the Peclet number (\(Pe\)) and the Reynolds number (Re) as well as the similarity modulus for the sorbent apparent density were determined for the mass dispersion process. The relationship between the diffusive Nuselt number (\(Nu\)) and the Re number as well as the similarity modulus for the sorbent apparent density were determined for the parameter describing an adsorbate permeation rate across a water-sorbent interface. The impact of the Re number and the similarity modulus for the sorbent apparent density on the Henry constant was also investigated. The criterion equations can be used to determine the adsorption model parameters; they may be helpful in designing a filtration system supplemented with PAC. In the capillary velocity range \(V_{s}^{*}\in\) (0.15\(\cdot\)10\({}^{-2}\); 0.72\(\cdot\)10\({}^{-2}\)) m/s and with a change in the apparent density of the sorbent \(p_{sorb}\) from 3000 to 12,000 g PAC/m\({}^{3}\) of the bed, as a result of the experimental tests carried out, it was established that the actual coefficient of longitudinal dispersion \(D_{s}^{*}\) varied in the range of 0.16\(\cdot\)10\({}^{-4}\) to 2.03\(\cdot\)10\({}^{-4}\) m\({}^{2}\)/s, the product of the constant mass transfer rate and the specific outer surface of sorbent \(k\cdot a_{m}\) varied in the range of 2.23\(\cdot\)10\({}^{-7}\) to 1.70\(\cdot\)10\({}^{-6}\) (m/s)-(m\({}^{2}\)/g PAC), while the Henry constant \(\Gamma^{*}\) varied in the range of 7.24 to 44.20 1/m\({}^{3}\) of sorbent and the Henry constant \(\Gamma\) varied in the range of 0.0012 to 0.0019 m\({}^{3}\) of water/g PAC.  
adsorption; powdered activated carbon; filtration; water treatment; wastewater treatment; organic compounds; adsorption model; criterion equations +
Footnote †: journal: Applied Sciences
## 3 Results
### Grassland Cover Change Trend Analysis
#### 3.2.2 Grassland Change Matrix from 2018 to 2019  
Compared to 2018, only 15.03% of grassland shows an increase in both NPP and FVC in 2019, while 41.60% of the grassland area shows a decrease in NPP and FVC. The decreasing areas are mainly distributed in typical steppe zones including areas north of Xilin Gol, and west of Hulunbair and Ordos (see Figure 4). According to the meteorological data, annual average precipitation in IMAR was 346 mm in 2019, which is comparable to the precipitation of 369 mm in 2018. Nevertheless, there is high spatial variability throughout the year. In Xilin Gol league, where the majority of grasslands are typical grasslands, the precipitation was 261 mm during the grassland growing season in 2019, which was 89 mm less than the previous year (percentage of precipitation anomaly, 34%). The average  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Mean**} & \multicolumn{3}{c}{**Slope**} & \multicolumn{3}{c}{**CV**} \\ \cline{3-8}  & & + & \(-\) & **Mean** & **STD** & **Mean** & **STD** \\ \hline FVC & 0.48 & 52.71\% & 46.53\% & 0.001 & 0.009 & 0.22 & 0.18 \\ NPP & 124.83 & 50.43\% & 39.63\% & 10.45 & 35.72 & 0.14 & 0.06 \\ PNPP & 230.56 & 65.99\% & 30.12\% & 2.4 & 6.54 & 0.19 & 0.07 \\ HNPP & 166.94 & 63.32\% & 23.04\% & 2.57 & 6.21 & 0.26 & 0.43 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Slope and CV of FVC, NPP, PNPP, HNPP of grassland in IMAR from 2011 to 2019.  
temperature was 14.5 \({}^{\circ}\)C, 1.2 \({}^{\circ}\)C higher than in the previous years. Less precipitation and higher temperatures in mid-season are the main factors affecting the formation and growth of forage production in Xilin Gol League in 2019 [42]. This phenomenon reveals the fact that areas with better precipitation conditions and grass production show a more obvious amplitude of vegetation change. On the other hand, the unpredictable annual meteorological conditions are a challenge for a precise livestock-forge balance policy, as well as effective implementation of SISGC. These projects need to set rules for the size of households' livestock according to the grassland condition of the exact current year.
###### Abstract  
In the current context of environmental concerns and the search for sustainable food solutions, this study investigated the valorization of _Luffa cylindrica_ seed press cake, a waste byproduct from oil extraction, as a functional ingredient for meat substitute formulations. The research systematically characterized the functional and bioactive properties of _L. cylindrica_ seed press cake powder (LP) and its blends with tapioca flour (TF) at ratios of 30-70%. Techno-functional analyses included: hydration properties (water holding capacity, water absorption capacity, water absorption index, water solubility index, swelling power, oil absorption capacity); rheological characteristics; bioactive profiling through antioxidant assays (DPPH, ABTS, FRAP); and reducing sugar content determination. Meat substitute formulations were developed using an LP30/TF70 blend combined with coral lentils, red beet powder, and water, followed by a sensory evaluation and storage stability assessment. Pure _L. cylindrica_ powder exhibited the highest water holding capacity (3.62 g H\({}_{2}\)O/g) and reducing sugar content (8.05 mg GE/g), while tapioca flour showed superior swelling properties. The blends demonstrated complementary functional characteristics, with the LP30/TF70 formulation selected for meat substitute development based on optimal textural properties. The sensory evaluation revealed significant gender differences in acceptance, with women rating the product substantially higher than men across all attributes. The study successfully demonstrated the feasibility of transforming agricultural waste into a valuable functional ingredient, contributing to sustainable food production and representing the first comprehensive evaluation of _L. cylindrica_ seed press cake for food applications. However, the study revealed limitations, including significant antioxidant loss during thermal processing (80-85% reduction); a preliminary sensory evaluation with limited participants showing gender-dependent acceptance; and a reliance on locally available tapioca flour, which may limit global applicability. Future research should focus on processing optimization to preserve bioactive compounds, comprehensive sensory studies with diverse populations, and an investigation of alternative starch sources to enhance the worldwide implementation of this valorization approach.  
**Keywords:**_Luffa cylindrica;_ **seed press cake; meat substitute; physicochemical properties; sensory analysis**
## 4 Discussion  
In this paper, we review the relevant literature on road traffic crashes combined with machine learning and summarizes and discusses various factors affecting the safety of road tunnels, including tunnel geometry, road surface condition, and traffic condition. The study also analyzed 38,786 traffic flow data and 200 accident data obtained from the Yingtian Tunnel in Nanjing, China, and was conducted on the same day. To account for missing traffic data, we combined the actual location of the tunnel flow logging equipment with the spatiotemporal relevance of the traffic data and designed a deep learning framework based on GCN-LSTM to predict the missing traffic flow values. We believe that our approach is more compelling and generalizable than traditional methods of using mean filling-in or direct deletion. This was followed by a combination of previous studies and correlation analysis to investigate factors affecting tunnel capacity and accident duration and visualization of the results using heat maps. Our study shows that seven independent variables--the type of accident, the time of the accident, the location of the accident, the proportion of heavy vehicles, the ratio of traffic during the accident to average traffic during the same month, if it was a peak-hour cycle, and weather--have a significant impact on tunnel capacity and accident duration, whereas other factors are insignificant. Thus, we confirm these seven variables as inputs to subsequent prediction models.  
Based on the above work, we built a prediction model of highway tunnel traffic accident severity using random forest and reverse propagation neural network, with time, tunnel environment, and traffic dynamics as input variables and tunnel traffic condition and accident duration as output variables. The results of this study showed that BP neural network and random forest model both performed well in predicting traffic conditions after a tunnel accident. Both models were run five times under the same experimental setting and hyperparameters. The BP neural network had an average prediction accuracy of 82.94%, standard deviation of 1.31; and the random forest model had an average prediction accuracy of 83.39% and a standard deviation of 0.81. The prediction accuracies of the stochastic forest model and the BP neural network are 75% and 60% in terms of accident duration, respectively, when we set the absolute error to less than 25 min.
## 2 Materials and Methods
### Filler  
In India, ordinary Portland cement (OPC) is the conventional mineral filler that is typically used in asphalt mixes for heavy-load road pavements. In this study, the effectiveness of silica fume (SF) as a mineral filler was evaluated by comparing it to OPC grade 43. According to the Indian Standard IS 8112-1989, OPC grade 43 must provide a designed strength of at least 43 MPa at 28 days.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Parameter** & **MoRTH Limits** & **Test Results** & **Test Method** \\ \hline Absolute viscosity (poises) at 60 \({}^{\circ}\)C & 2400–3600 & 2855 & IS 1206 (P-2) \\ Kinematic viscosity (cSt) at 135 \({}^{\circ}\)C & (Min) 350 & 392 & IS 1206 (P-3) \\ Flash point Cleveland open cup (\({}^{\circ}\)C) & (Min) 250 & 304 & IS 1448 (P-69) \\ Penetration (1/10 mm) at 25 \({}^{\circ}\)C, 100 gm, 5 s & (Min) 45 & 49 & IS 1203 \\ Sottering point (\({}^{\circ}\)C) & (Min) 47 & 48 & IS 1205 \\ Matter soluble in trichloroethylene (\% by mass) & (Min) 99 & 99.45 & IS 1216 \\ Viscosity ratio at 60 \({}^{\circ}\)C & (Max) 4.0 & 1.3 & IS 1206 (P-2) \\ Ductility (cm) at 25 \({}^{\circ}\)C after TFOT & (Min) 40 & 75 & IS 1208 \\ Specific gravity (g/cm\({}^{3}\)) & 0.97–1.02 & 0.987 & IS 1202 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of bitumen.  
Figure 1: Grain size distribution curve of aggregate as per MoRTH requirement for aggregate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Parameter** & **MoRTH Limits** & **Test Results** & **Test Method** \\ \hline Cleanliness (Dust) (\%) & (Max) 5 & 3 & IS 2386 Part I \\ Bulk specific gravity (g/cm\({}^{3}\)) & 2–3 & 2.68 & IS 2386 Part III \\ Percent wear by Los Angeles abrasion (\%) & (Max) 35 & 10.6 & IS 2386 Part IV \\ Soundness loss by sodium sulphate solution (\%) & (Max) 12 & 3.4 & IS 2386 Part V \\ Soundness loss by magnesium sulphate solution (\%) & (Max) 18 & 3.7 & IS 2386 Part V \\ Flakiness and elongation index (\%) & (Max) 35 & & IS 2386 Part I \\
20 mm & & 27.93 & \\
10 mm & & 32.13 & \\ Impact strength (\%) & (Max) 27 & & IS 2386 Part IV \\
20 mm & & 4.15 & \\
10 mm & & 5.91 & \\ Water absorption (\%) & (Max) 2 & 1.67 & IS 2386 Part III \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of aggregate.  
The processed silica fume used in the study was obtained in powder form and air-dried from Safew Tech system in Indore (India), while OPC was sourced from a local supplier. Both materials underwent assessment of their properties, including density, specific surface area, grain size distribution, methylene blue value analysis, mineralogical and chemical composition, German filler, and pH value. The German filler test, as prescribed by the National Asphalt Pavement Association (1995) [35], was used to determine the porosity of the fillers, which is inversely proportional to its German filler value. The silica fume grading curve was compared with the required size of mineral filler prescribed by MoRTH [34] and was found to lie within the recommended range for asphalt mixture (Table 3). Table 4 presents the various physical and chemical properties of silica fume.
# Multidisciplinary Study of Mud Emissions Following the 2016 Norcia Earthquake
## 3 Methodology
### Rheology  
The rheological parameters, namely, the Bingham yield stress and the viscosity, were determined using an Anton Paar Physica MCR 301 rheometer equipped with an air-bearing system. A vane rotor system ST22-6V-16--with 6 vanes of 22 mm diameter and 16 mm length immersed in a cylindrical cup (CC27; 27 mm in diameter) [50; 51]--was used for fluid suspensions (AM1, AM2, AM4, AM5, AM6, and AM7), and a parallel plate system (PP25; diameter of 25 mm and a distance between the 2 plates of 1.7 mm for AM3) was used for very viscous suspensions. The particular advantages of the vane geometry are its simplicity of fabrication, ease of cleaning, and, more than anything else, its elimination of serious wall slip effects. These geometric characteristics are very useful for the measurement of the flow properties of non-Newtonian liquids.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**EM** & **EC** & **pH\({}_{\text{Ca}}\)** & **F\({}^{-}\)** & **Cl\({}^{-}\)** & **Br\({}^{-}\)** & **NO\({}_{3}\)\({}^{-}\)** & **SO\({}_{4}\)\({}^{2-}\)** & **Alkalinity** & **Na\({}^{+}\)** & **K\({}^{+}\)** & **Mg\({}^{2+}\)** & **Ca\({}^{2+}\)** \\  & **mS/cm** & & **(meq/L)** & **(meq/L)** & **(meq/L)** & **(meq/L)** & **(meq/L)** & **(meq/L)** & **(meq/L)** & **(meq/L)** & **(meq/L)** & **(meq/L)** & **(meq/L)** \\ \hline
1 & 2.23 & 8.1 & 0.061 & 0.76 & - & - & 8.03 & 11.5 & 18.96 & 1.06 & 0.16 & 0.29 \\
3 & 2.45 & 8.1 & 0.102 & 0.5 & - & 0.7 We used the rheometer in rotational mode for all 7 suspensions. The flow curves of all suspensions were carried out using the controlled shear stress (CSS) method: Steps of constant stresses were applied long enough to reach a steady apparent viscosity with time. For each step of stress, the steady state of viscosity and shear rate was reached [52]. The values of apparent viscosity and the shear rate were calculated by averaging their values reached at steady state for each stress step. Therefore, we could achieve the flow curves with shear stress and shear rate and with apparent viscosity and shear rate for all suspensions.  
A fluid-saturated sand can liquefy in response to a sudden shock or earthquake under a shear rate \(\dot{\gamma}\)  that can be approximated by peak ground velocity (hereafter PGV) using the empirical relationship \(\gamma\sim\frac{PGV}{V_{\mathrm{s}}}\)[53]. Using spectral velocity (\(v_{\mathrm{s}}\)) expected values of 800 and 1800 m/s [38] for the source area of the collected sample, we calculated that the studied materials underwent a shear rate between 3.4\(\cdot\)10\({}^{-5}\) and 7.8\(\cdot\)10\({}^{-3}\) s\({}^{-1}\). This range of \(\dot{\gamma}\)  is within the shear rate obtained under the experimentally applied shear rate (10\({}^{-3}\)-10\({}^{-7}\) s\({}^{-1}\)).
## 2 Background  
Corrosion is an electrochemical process where pipe wall materials are eroded from one location and deposited into another (Figure 1). There are different types of pipeline corrosion which include uniform or general, galvanic, crevice, pitting, and microbial [10]. Pipeline corrosion can be prevented by different methods, such as selecting corrosion-resistive material, layering external protective coats, and applying cathodic protection [11]. Various mitigation methods can be employed when corrosion occurs to minimize the impact and extend the service life of pipelines. These mitigation operations include pigging (maintenance operation using pipeline integrity gauge), adding corrosion inhibitor chemicals, drying, adding biocide, and applying coats and cathodic protection [12]. The selection of these methods depends on factors like cost, location of the corrosion defect, type of corrosion, and severity of the damage.  
The corrosion maintenance can be implemented following a three-step procedure [13]. First, the latest state of the pipeline is examined through inspection or flow monitoring methods. This can be accomplished using ILI, holiday detection, above-ground monitoring, remote monitoring, hydrostatic testing, and below-ground inspection [12]. The growth of defects due to corrosion is estimated using stochastic or deterministic methods in the second step of the procedure. Deterministic methods include mathematical formulation such as single-value, linear, and nonlinear functions, whereas stochastic methods adopt MP, the time-dependent/independent generalized extreme value distribution, the Gamma process, and the Monte-Carlo simulation [14]. In the final step, maintenance operation parameters are optimized to exploit maintenance resources and the remaining service life of a pipeline.  
Figure 1: Corrosion prevention and maintenance process flow.
# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## 2 Background and Related Works
### Convolutional Neural Networks  
CNN is an artificial neural network architecture widely used in deep learning for image processing. It analyzes images through artificial neurons placed in three dimensions, called channels: height, width and depth [12]. It is specialized to detect and classify images and extract their features, such as corners and edges. For example, its ability to recognize objects allows the detection of tumors, in the medical field, and of obstacles, in the field of autonomous car driving. A convolutional neural network has a structure consisting of multiple levels of feature detectors:  
* Convolutional layer;
* Non-linear activation functions;
* Pooling;
* Fully connected network (optional);
* Dropout layer (optional).  
Our proposal makes use of the results presented in the pioneering paper [4], in which the author proposes a particular CNN, called U-Net, to solve the problem of automatic semantic segmentation of biomedical images. While a CNN is implemented in order to learn the feature map of an image and exploit it to create a more nuanced mapping by converting the image itself into a vector, when segmenting an image it is also necessary to reconstruct the image from this vector obtained. This task is particularly difficult as it is more complex to convert a vector to an image rather than the other way around. The basic idea of a U-Net is to take advantage of the functionality mapping, learned during the conversion of an image into a vector, and use it to reconstruct the output image. In this way, the integrity of the image structure is preserved and distortion is greatly reduced.  
The resulting structure includes elements typical of deep learning tools, such as convolutional layers and max pooling. Figure 1 shows the structure of the U-Net, with the parameters used in this work. Details about the configuration that has been actually used in this work are given in Section 3.2.
## 3 Results and Discussion
### Porous Carbons Characterization
#### 3.1.1 N\({}_{2}\) Adsorption-Desorption Isotherms and Pore Size Distribution
The micropore size distribution displays that both carbons presented a well-developed microporous structure with a dominance of pore widths between 0.6 and 0.7 nm. Between 0.7 and 2.0 nm, the pore size distribution is similar for both carbons, although it seems that CC carbon is richer in micropores with this size range. The mesopore size distribution from the BJH model shows that both carbons also show a broad distribution of mesopores, but ACK carbon presents mainly small mesopores (<3.0 nm) while CC carbon has a predominance of mesopores with pore widths between 3.0 and 4.5 nm. Figure 1: Micropore size distribution (DFT adsorption model for carbon slit-shaped pores) (**left**) and mesopore size distribution (BJH method) (**right**) of the carbon samples.
## 1 Introduction
Therefore, a possible solution to improve the mechanical properties of FCs is to use additives that reduce the water/cement (w/c) ratio such as plasticizers and superplasticizers [20; 21]. Another important factor that is directly related to the compressive strength of FC is the amount of foam added. [PERSON] et al. [22] observed that by increasing the amount of foam in the mortar the compressive strength decreases. [PERSON] et al. [23] evaluated the effect of curing conditions, cement type, foaming agent, dry density, and presence of a superplasticizer on the development of the 28-day compressive strength, and concluded that all factors had a significant influence on the compressive strength; for example, the presence of the superplasticizer associated with the cellophane curing condition has led to an increase in the compressive strength as compared to the case without a superplasticizer. The highest strength values were measured for specimens that were wrapped within a cellophane sheet during the curing time, whereas the worst compressive strengths were observed in specimens cured in air; the different nature of the foaming agents contributes to a different arrangement of the void in the specimen microstructure and, consequently, changes to the resistance strength. According to the same authors, another significant factor is the water/cement ratio: when it is increased, the compressive strength decreases. [PERSON] [24] also evaluated three types of foam agents (one protein-based and two synthetic) on mechanical properties and thermal conductivity, and found that the foam agent type changes the amount and distribution of pores in the concrete structure, thereby changing the compressive strength and the thermal resistance. The optimization of trace elements in FC mixtures should also encompass aspects related to the durability of this type of concrete. In general, concretes with less permeability are less prone to deterioration. One of the factors that controls the permeability of concrete is the cement used. Cements containing pozzolanic additions result in more durable concretes [25; 26]. In view of the above, it can be observed that several works were and are being developed using CCW to substitute for a natural aggregate (river sand) in the production of civil construction materials (concrete, mortar, etc. ); however, the authors did not find studies that relate the use of CCW (in total substitution to river sand) to cement type and different additives in foamed concrete. In addition, the CCW from Passo Fundo, a city in the south of Brazil, has not been fully studied yet and, consequently, we have not verified the possibility of its reuse. Therefore, the present paper sets out to evaluate the influences of different types of cement, the amount of foam, and different types of additive on the properties of foamed concrete in order to develop a foamed concrete using CCW to totally replace natural sand.