esa-sceva/satcom-chunk-collection huggingface dataset:
Strength and Water Purification Properties of Environment-Friendly Construction Material Produced with the (D)PAOs and Zeolite  
[PERSON]  
1 Department of Construction Engineering Education, Chungnam National University, 99 Deahek-ro Yuseong-gu, Daejeon 34134, Korea 1  
[PERSON]  
2 Department of Civil Engineering, Daejeon University, 62 Daehak-ro Dong-gu, Daejeon 34520, Korea; 2  
[PERSON]  
3 Department of Convergence Systems Engineering, Daejeon University, 62 Daehak-ro Dong-gu, Daejeon 34520, Korea; 3  
Correspondence: [EMAIL_ADDRESS]; Tel.: +82-42-821-858232  
water, causing part of the aquatic ecosystem to be lacking in oxygen. Naturally, aquatic organisms decrease in number, thereby leading to the destruction of the ecosystem.  
In Korea, the number of commercial entities discharging wastewater has been increasing since 2004, and the volume of discharged wastewater has been increasing every year. As a country lacking water, Korea needs to gain reliable access to water resources. To this end, building wastewater treatment facilities is essential as the primary measure. Furthermore, additional chemical and physical treatment methods should be developed and applied to improve the water quality of rivers and streams that have already been contaminated [3,4].  
Research on the restoration of aquatic ecosystems, however, has focused almost exclusively on how to reduce contaminants or how to form natural rivers and streams. Few studies have been conducted on the restoration of contaminated rivers through water purification. Some studies are investigating ways of building river structures with porous concrete or using complex effective microorganisms, but their effects have turned out to be insignificant [5, 6, 7].  
Meanwhile, sewage treatment plants use phosphate accumulating organisms (PAOs) to eliminate biochemical oxygen demand (BOD), phosphorous, and nitrogen in anaerobic reactors, oxic reactors, and anoxic reactors. In the same manner, the application of PAOs to secondary concrete products and structures is expected to ensure outstanding water purification effects; little research, however, has been conducted on this approach [8, 9, 10].  
Against this background, the present study analyzed the mechanical properties of mortar mixed with both de-nitriying phosphate accumulating organisms ((D)PAOs) and zeolite based on the preliminary culture experiment of (D)PAOs in an effort to develop concrete products intended for ecological restoration that can be used for water purification of rivers and streams. Also, porous concrete was mixed with (D)PAOs and zeolite with optimal mixing ratios, and the strength and water purification characteristics of the developed products were analyzed.
# Investigation of the Behaviors of Methanol Spray Impingement and Wall Wetting
###### Abstract  
Port fuel injection is an important technical route in methanol engines. To obtain a theoretical basis for injector arrangement and injection strategy development in methanol engines, an optimal experimental platform based on diffuse back-illumination and the refractive index matching method (RIM) was designed and built in this study. The experiments on the behavior of low-pressure methanol spray-wall impingement and wall film were carried out and the influence of the three boundary conditions of spray distance (\(D_{imp}\)), wall temperature (\(T_{null}\)), and injection pressure (\(P_{inj}\)) were analyzed comprehensively. Results showed that with the increase of \(D_{imp}\), the overall shape of spray before impinging the wall changed from conical to cylindrical. The impinging spray height \(H_{i}\) and impinging spray width \(W_{i}\) increased with the decrease of \(D_{imp}\) and the increase of \(P_{inj}\). Adhesive fuel film mass \(M_{j}\) increased with the increase of \(D_{imp}\) due to the decrease of kinetic energy during wall impact. In addition, the increase of the wall temperature \(T_{null}\) reduced \(M_{j}\) due to evaporation, but when \(T_{null}\) reached 423 K, \(M_{j}\) rebounded due to the Leidenfrost effect. The results of this study are helpful to improve the accuracy of the numerical methanol engine model.  
methanol engines; port fuel injection; spray impingement; wall wetting; refractive index matching +
Footnote †: journal: Applied Sciences
## 3 Results
### Chemical Volatile Composition of Methanolic Extracts  
The methanolic extracts were analyzed by direct injection in a GC-MS apparatus. In general, the number of compounds detected in the methanolic extract obtained from fresh infloreses (IME) was higher than that found in the extract obtained from the leaves (IME). Furthermore, no compound in common to the two extracts was detected (Table 3). The components 2-heptanol, 2-methyl- (42.7%) and 2,3-butanediol (24.6%) were the most abundant in IME, while fatty acid derivatives, such as 9,12,15-octadecrienal (47.6%), palmitic acid (23.2%) and 3,7,11,15-tetramethyl-2-hexadecen-1-ol (11.8%), were the most abundant components found in LME. Also interesting was the presence of two furan derivatives, such as _trans_-arbusculone (1.9%) and 2(5H)-furanone, 5,5-dimethyl- (8.7%) in IME sample. The chromatograms are shown in Figures S7 and S8. Mass spectra of 9,12,15-octadecrienal and 2-heptanol-2-methyl are shown in Figures S1 and S12.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\mathbf{N}^{\circ}\) & **COMPONENT 1** & **LRI 2** & **LRI 3** & **IAE** & **LAE** \\ \hline
1 & pentane, 1-methoxy- & 710 & 708 & - & 17.5 \(\pm\) 0.04 \\
2 & 1-butanol, 3-methyl- & 715 & 718 & - & 16.6 \(\pm\) 0.03 \\
3 & \(\beta\)-myrcene & 985 & 987 & 13.1 \(\pm\) 0.05 & 8.3 \(\pm\) 0.02 \\
4 & propanoic acid, 2-methyl-, & 995 & 996 & 8.9 \(\pm\) 0.03 & - \\
5 & linalool & 1089 & 1092 & 6.8 \(\pm\) 0.03 & 49.6 \(\pm\) 0.09 \\
6 & \(\beta\)-caryophyllene & 1442 & 1440 & 10.8 \(\pm\) 0.05 & - \\
7 & humulene & 1490 & 1499 & 41.6 \(\pm\) 0.08 & 8.0 \(\pm\) 0.03 \\
8 & \(\delta\)-cadinence & 1561 & 1538 & 4.4 \(\pm\) 0.02 & - \\
9 & humulene epoxide II & 1572 & 1570 * & 14.3 \(\pm\) 0.03 & - \\  & SUM & & & 99.9 & 100.0 \\  & Monoterpenoids & & & 19.9 & 57.9 \\  & Sesquiterpenoids & & & 71.1 & 8.0 \\  & Others & & & 8.9 & 34.1 \\ \end{tabular} \({}^{1}\) The components are reported according to their elution order on an apolar column; \({}^{2}\) Linear Retention Indices measured on an apolar column; \({}^{3}\) Linear Retention Indices from the literature; * Kovats Index; IAE: fresh infloreseses hops aqueous extract components; LAE.: fresh leaves hops aqueous extract components; not detected.  
\end{table}
Table 2: Chemical volatile composition (percentage mean value \(\pm\) standard deviation) of aqueous extracts from hops infloresescences and leaves, as determined by DI-SPME-GC-MS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\mathbf{N}^{\circ}\) & **COMPONENT 1** & **LRI 2** & **LRI 3** & **IME** & **LME** \\ \hline
1 & butanal, 3-methyl- & 648 & 651 & - & 2.5 \(\pm\) 0.06 \\
2 & propanoic acid, & & 762 & 765 & 3.9 \(\pm\) 0.03 & - \\
3 & 2-butanoli & 770 & 769 & 24.6 \(\pm\) 0.04 & - \\
4 & butanoic acid, & 851 & 857 & 7.1 \(\pm\) 0.02 & - \\ \end{tabular}
\end{table}
Table 3: Chemical volatile composition (percentage mean value \(\pm\) standard deviation) of methanolic extracts from hops infloresescences and leaves, as determined by GC-MS.
# Dynamic Response Analysis of Asphalt Pavement under Pavement-Unevenness Excitation
## 4 Numerical Results
### Impact of Vehicle Load  
Vehicle overload is one of the important causes of road-surface damage. The influence of vehicle load on road-surface mechanical response is discussed below. The change in the vehicle's body mass ratio \(\rm{m_{b}}\) is used to characterize the load change, with the default value of body mass \(\rm{mb}\) representing full load, 0.5 times of body mass \(\rm{mb}\) representing empty load, and 2.0 times of body mass \(\rm{m_{b}}\) representing overload. The mechanical response results at the bottom of the surface layer are shown in Figure 12.  
Figure 12 shows the variation and distribution of \(\sigma_{xx}\), \(\sigma_{zz}\), \(\varepsilon_{xx}\), and \(\varepsilon_{zz}\) at the bottom of layers with different loads. The increase in vehicle load will increase the mean value and peak value of stress and strain at the bottom of the surface layer. From no-load to overload, the peak value of \(\sigma_{xx}\) increases from 0.00871 MPa to 0.0217 MPa. The peak value of \(\sigma_{zz}\) increases from 0.0288 MPa to 0.0719 MPa, \(\varepsilon_{xx}\) peak increases from 0.00737 to 0.0184, and the peak value of \(\varepsilon_{zz}\) increases by 1.5 times from 0.0171 to 0.0425. Overload results in high stress and strain at the bottom of the surface, which affects the service life of the pavement.  
Figure 11: Influence of speed on strain at bottom of surface layer. (**a**) Bottom of the surface layer \(\sigma_{zz}\); (**b**) bottom of the surface layer \(\sigma_{xx}\); (**c**) bottom of the surface layer \(\varepsilon_{xx}\); (**d**) bottom of the surface layer \(\varepsilon_{zz}\).
# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
## 2 Background
### Clinical Text Summarization
#### 2.1.1 Extractive Text Summarization  
Extractive text summarization involves selecting a few relevant sentences from the original document to create a concise summary based on their relevance, importance, or saliency to the overall meaning of the text. Extractive summarization methods can be categorized into various approaches, each with distinct techniques and objectives [11; 12]. Concept-based extractive summarization methods focus on extracting concepts from the text using external knowledge bases to calculate the importance of the sentence. Graph-based methods use sentence-based graphs for document representation and to rank sentences. Topic-based summarization methods identify the main subject of the document using techniques like TF-IDF. Clustering-based extractive summarization methods aim to identify the most central and important sentences in a cluster, as they include the important information related to the main subject; one well-known summarizer based on this method is BERTSummarizer, which employs the BERT (bidirectional-encoder transformer) [13] model to encode the input text and generate sentence embeddings. Despite offering speed and simplicity, extractive summarization can lead to redundancies and longer sentences, underscoring the necessity to refine the summaries.
# Characterization and Optimization of a Conical Corona Reactor for Seed Treatment of Rapeseed
## 3 Results and Discussion
### Discharge Characterization
Figure 4a shows a majority of N\({}_{2}\) second positive system bands between 300 nm and 450 nm. It also exhibited a weak NO(\(\gamma\)) emission between 230 nm and 280 nm and emission bands of OH at 308 nm, as depicted in Figure 4b. Figure 4c exhibits argon lines emitted between 690 nm and 930 nm. A small peak corresponding to atomic oxygen at 777 nm could also be observed. The excited oxygen and nitrogen species identified by OES can be directly related to the generation of reactive oxygen and nitrogen species (RONS) in the plasma region, which are important for several plasma processing techniques, including the field of plasma agriculture [35]. The identified excited species by OES are summarized in Figure 4d according to the relative intensity values for the different applied voltage duty cycles. It can be noticed that the OH band was the most intense from all analyzed species, followed by N\({}_{2}\), O, and NO. When increasing the voltage duty cycle, the intensity of those  
Figure 2: Power density values of the funnel reactor: (**a**) while varying the applied voltage with 100% voltage duty cycle and (**b**) when varying duty cycle and keeping the applied voltage at 20 kV p-p.  
species increased almost linearly. Even though the intensity values cannot be directly associated with the species concentration [36], they can be used as an indication. Thus, Figure 4d suggests that the generation of especially reactive oxygen species (ROS) was high using this reactor. Some ROS, such as the hydroxyl radical (OH) and singlet oxygen (O), are associated with fast surface modification of seeds leading to an increase in wettability [35]. As investigated in previous work using a DBD plasma source operating with argon and helium, a ROS-rich environment can be relevant for treatments of seeds, accelerating the germination process [18]. Moreover, wheat and barley displayed chemical modification of the carryopses surface, with altered surface wettability and water uptake [19]. The use of argon as a carrier gas facilitates plasma generation and leads to the production of ROS. An intensification of ROS generation could be easily obtained by introducing a small percentage of air to the carrier gas. Once, the use of pure air required operation using even higher voltage values. On the one hand, the conical corona reactor operating with argon used in the present study was a laboratory model and could only be used for the treatment of small numbers of seeds. On the other hand, the use of argon makes it difficult to apply this plasma source on an industrial scale and the conical configuration does not facilitate its upscaling. Therefore, this reactor configuration can only be used for small applications and basic science investigations. Figure 4: _Cont._  
Figure 3: Temperature raised up to 5 min measured inside the reactor at the bottom central part of the glass Petri dish. A maximum error of \(\pm\)0.5 \({}^{\circ}\)C for all data was considered.
## 3 Results
### Assessment of Nickel Oxide Nanoparticles Cytotoxicity
aureus_ started to decrease on days 28 th and 21 st in the control and cheese containing the yoghourt starter culture, respectively. Further, the concentration of the organism decreased gradually to 3.2 log CFU/g and 2.5 log CFU/g at the end of the experiment (5 weeks) in such groups, respectively. On the other hand, NiO NPs prevent the increase of _S. aureus_ from the first step of manufacture (5.3 log CFU/g) with undetectable levels on the 21 st day of cheese ripening. Moreover, there was a significant reduction (\(p<0.05\)) in the mean count of _S. aureus_ strains  
Figure 4: Total bacterial counts, _S. aureus_ survival and pH evolution during Domiati cheese ripening for 35d storage after the addition of a yoghourt starter culture. \(\bullet\) Observed growth of total bacterial counts (dashed line corresponds to the fitted Baranyi model); \(\square\) Observed survival of _S. aureus_ (solid line corresponds to the fitted Weibull model); \(\times\) pH evolution. Figure 5: Total bacterial counts, _S. aureus_ survival and pH evolution during Domiati cheese ripening for 35d storage after the addition of nickel oxide nanoparticles (NiO NPs). \(\bullet\) Observed growth of total bacterial counts (dashed line corresponds to the fitted Baranyi model); \(\square\) Observed survival of _S. aureus_ (solid line corresponds to the fitted Weibull model); \(\times\) pH evolution. in presence of NiO NPs in comparison to the control during the manufacturing and ripening of Domiati cheese (Figure 6). In a modeling approach, the data of APC from the current study were further analyzed using the Baranyi model. For the fitted treatments, there was good agreement between predicted and observed values as seen in Table 1 (R2 = 0.996, 0.934 and 0.850 for control, yoghourt starter culture and NiO NPs cheese samples, respectively). No significant differences were obtained between the estimated \(\mu_{\text{max}}\) (\(p\geq 0.05\)), though it was slightly higher in control samples than in cheeses with yoghourt starter culture or NiO NPs (Table 1). The Weibull inactivation model was used to examine the survival of _S. aureus_ in control and yoghourt starter culture Domiati cheese. Estimated parameters are presented in Table 2. In both cases, the fitted models reflected adequately the convexity due to the initial resistance of _S. aureus_ population followed by a decay period (Figures 3 and 4). Significant differences were obtained between the kinetic parameters of the Weibull models (\(p<0.05\)) being the \(\delta\) parameter (first-decimal reduction time) higher for control samples than for the cheeses with yoghourt starter culture (Table 2). While in NiO NPs cheese, a linear inactivation pattern was shown for _S. aureus_ with a mean inactivation rate (k\({}_{\text{max}}\)) of 0.3 log CFU/g (Table 2).
## 3 Results and Discussion
### Forced Ignition Process  
The forced ignition process was completed by high voltage discharge. Accurate timing control was needed to avoid discharging strong light in order to capture the ignition flame at the initial time. Time-resolved sequences OH-PLIF images are shown in Figure 2. The dynamic change process of the flame structure is recorded. At the initial time (t = 0 ms), the electric spark creates a transient flame core in the mixture of oil and gas; then, the fire kernel develops and propagates. A weak flame front below the flame core exists. It's worth noting that the flame surface does not develop directly from the flame core, and the transfer of heat and mass should contribute to the evolution. This phenomenon may be affected by ignition energy [21]. From the appearance of the flame, it is similar to a turbulent premixed flame, so it can be speculated that although the combustion process of heavy oil is extremely complex, involving two-phase combustion, the ignition process can be viewed as a gas combustion process, i.e., belonging to homogeneous combustion, of which similar studies can be done. The wrinkled structure of the flame surface can be used as an indicator to reflect the interaction between turbulence and flame. It is of great significance for enhancing material transport and intensifying combustion [22; 23], so tracing the path of the structure development can help designers to better understand combustion conditions. The displacement vector diagram of the wrinkled structure and flame surface are shown in Figure 5. It can be seen that the wrinkled structure develops downward, namely in deviation from direction of airflow. The wrinkled structure can be used as the leading edge of a brush [24], which largely reflects the combustion trend. What is more, the displacement velocity along the direction of the flow gets gradually smaller, but the vertical velocity does the opposite. This indicates that the center of combustion is not along the direction of gas flow, but has a certain angle with it.  
As the turbulent burning velocity is defined according to different flame types [25], to be more intuitive, the displacement velocity of it is used to describe the combustion rate. In Figure 6, the displacement velocity of the wrinkled structure and flame surface were extracted. It can be seen that the velocity gradually decreased. In addition, the former is slightly larger than the latter. The reason  
Figure 4: Flame shape in combustion.  
for this might be that the position of the wrinkled structure burns more intensely than elsewhere, showing that the displacement speed is larger [25; 26]. It also implies that the wrinkled structure plays a role in intensifying combustion.
## 2 Methodology
### Component Interaction  
Figure 5 depicts the simulation interaction, where three essential conditions must be met to accurately replicate the dynamics of the interaction. These conditions involve the type of interaction, selection of components, and their properties, each significantly influencing the contact behavior among the four involved parts. Firstly, the interaction type condition, universally set to \"contact\" for all parts, defines the nature of their interaction. This selection ensures the faithful representation of physical contact and collision effects during system operation. Secondly, the component condition necessitates that all four parts are designated as contacts with each other, essential for simulating their interactions and computing realistic contact forces for analysis. Finally, the properties condition specifies a coefficient of friction of 0.15, crucial for determining the frictional characteristics between the material pairs in contact. This coefficient directly affects the resistance to relative motion between components, essential for comprehending system performance under varying loading conditions. By meeting these three conditions in the simulation setup, a robust and realistic portrayal of contact interactions among the four parts is achieved, enhancing the accuracy of analyzing the lead screw system's behavior and performance across different operational scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dimension** & **0.25 mm** & **0.30 mm** & **0.35 mm** & **0.40 mm** \\ \hline Torque & 1.0055 N m & 1.0205 N m & 1.0356 N m & 1.0507 N m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculated results for torque.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bolt/Nut Materials** & **Lubricant** & **Coefficient of Friction** \\ \hline Steel/Bronze & None added & 0.15 \\ Titanium & Molybdenum disulfide grease & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Friction coefficient for steel and titanium [15].
# Recurrent Deep Learning for Beam Pattern Synthesis in Optimized Antenna Arrays
## 3 Numerical Experiments
### Runtime Analysis  
Runtime analysis is an essential part of evaluating algorithms and systems, as it provides insights into the actual time efficiency of a solution under practical conditions. In this analysis, we focused on the execution time of both the training and inference stages rather than theoretical time complexity, which refers to how the execution time scales with the size of the input \(n\). For the beam pattern synthesis of uniform linear arrays, the LSTM network took \(7.991\) min for the training stage on average; however, this is not critical, as training is typically performed offline. On the other hand, the trained network required \(0.072\) s to process the \(92\) test patterns on average. This demonstrates that, once trained, the network can generate beam patterns efficiently in real-time scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Triangular Pattern** & \multicolumn{3}{c}{**Square Pattern**} & \multicolumn{3}{c}{**Test Pattern 1**} & \multicolumn{3}{c}{**Test Pattern 2**} \\
**SLL** & \(\mathbf{D}\) **(dB)** & **SLL** & \(\mathbf{D}\) **(dB)** & **SLL** & \(\mathbf{D}\) **(dB)** & **SLL** & \(\mathbf{D}\) **(dB)** & \(\mathbf{D}\) **(dB)** \\ \hline \(-11.83\) & \(8.10\) & \(-12.15\) & \(7.81\) & \(-12.25\) & \(7.60\) & \(-12.24\) & \(8.58\) \\ \hline \end{tabular}
\end{table}
Table 3: Sidelobe level and directivity (output data) of the LSTM-RNN.