esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification and Isolation Techniques for Plant Growth Inhibitors in Rice
## 1 Introduction
In recent studies, polyphenolic and flavonoid glycosides from rice straw and leaves have been reported as having significant inhibitory effects on the seed germination and growth of paddy weeds comprising barnyard grass and pigweed [33]. Although research on allelochemicals has been performed since the 1960s, the identification and isolation of these compounds in rice have faced many challenges due to their complex properties, such as their solubility, volatility, and stability. Over the years, supercritical fluid extraction (SFE), ionic liquid extraction (ILE), solid-phase extraction, and column chromatography have been the most common methods used for the extraction and isolation of rice PGIs. Meanwhile, gas chromatography (GC), liquid chromatography (LC), and their improvements are essential techniques to determine the chemical constituents. The integration of GC or LC with mass spectrometry (MS), nuclear magnetic resonance (NMR), electron ionization mass spectrometry (EI/MS), infrared spectroscopy (IR), and X-ray crystallography made it convenient for the identification and quantification of rice allelochemicals [34; 35; 36; 24; 37]. This review paper aims to summarize major rice PGIs and assemble conventional and modern methodologies for the isolation and identification of PGIs in rice. In addition, the advantages of these techniques are discussed in order to support optimizing the efficient exploitation of rice PGIs.
# Infrared Imaging System with a Local Polarization Channel for Target Detection
## 4 Imaging Simulation Results
### Local Polarization Imaging
A linear polarizer and quarter-wave plate, which produced elliptically polarized light, were placed next to the central \"F\" to simulate the elliptical polarization characteristics of the FOI in the target scene, as shown in Figure 8; essentially, Area 1 was elliptically polarized. Four images were obtained on the detector with polarization information for the four polarization states 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\) by rotating the micro-polarizer in the imaging system four times, as shown in Figure 9. Area 1 was elliptically polarized, whereas the other areas were not. Therefore, when rotating the micro-polarizer during imaging, the light intensity of the central \"F\" in the image plane was lower than those of the other \"F\"s.  
In addition, by combining Equations (1), (2), and (4), we acquired the polarization images of the target scene shown in Figure 10. Figure 10a shows the Stokes vector element I, which was the total intensity of the light. As shown in Figure 10a, the magnification of the central \"F\" is twice that of the other \"F\"s, which also demonstrates a 2\(\times\) local magnification of the system. Figure 10b shows the Stokes vector element Q, and Figure 10c shows the AOP plot. These two figures show that only the enlarged central \"F\" was successfully imaged owing to the polarized property of the central \"F\" and the unpolarized property of the other \"F\"s. Thus, for the target with elliptical polarization characteristics, our system  
Figure 6: Detected images with polarization information for four polarization states of (**a**) 0\({}^{\circ}\), (**b**) 45\({}^{\circ}\), (**c**) 90\({}^{\circ}\), and (**d**) 135\({}^{\circ}\) when Area 1 was horizontally polarized. Figure 7: Polarization images of the target scene when Area \(1\) was horizontally polarized. (**a**) I, (**b**) Q, and (**c**) AOP. simultaneously captured images in the wide FOV and the local polarization images in the FOI.
## 4 Proposed Deep Learning-Based Patient Load Prediction Model  
The proposed deep learning-based system (Figure 3) assumes the existence of a health information exchange (HIE) system that allows health facilities to share relevant health information. HIE systems are typically categorized by how patient health information is stored and how the participants can access patient health information [78]. The common HIE models (from here on, we use the words model and system interchangeably) are:  
Decentralized model: In this model, each participating health facility controls its data separately in special \"edge servers\" at a unique location. Patient-specific data is only shared with other participants upon request. In a strictly decentralized model, every request for patient data must be made to every participating data source.  
Centralized model: Here participants agree to share data and the data is normalized in a common format and terminology and are housed together in a central data repository where they can be accessed and used by participants in line with agreed policies and procedures. More than one repository may exist for different kinds of data. This model may offer the best technical performance in terms of data availability and response time.  
Hybrid-federated model: This model is similar to the decentralized model, but it adds a \"record locator service\" to track patient movement.  
For our work, the required information to be shared by the health facilities is patient load information. Hence, the proposed model can suit any of the existing HIE models. However, in this work we propose two different system, a totally centralized system in which all control and computation tasks are handled by central controller and a decentralized control system.  
Figure 3: Proposed Deep Learning System.  
The patient load at a health facility is influenced by the patient rate of arrival. We define the total patient load, \(PL_{total}\) to be:  
\[PL_{total} =\text{patients relayed from other hospitals}\] \[+\text{patients from the local catchment area (regular patient load)} \tag{6}\] \[=PL_{rel}+PL_{reg}.\]
## 3 Results
### Effect of PACT on Pi. aeruginosa Biofilm  
To evaluate the effect of PACT on biofilm, _P. aeruginosa_ PAO and ten clinical strains of _P. aeruginosa_ were selected on the basis of origin and biofilm production (strains no. 21, 50--lower respiratory tract and strong biofilm producer; strains no. 10, 69--lower respiratory tract and moderate biofilm producer; strains no. 3, 30, 49--chronic wound and strong biofilm producer; strains no. 6, 35, 73--chronic wound and moderate biofilm producer). First, extensive screening using standard strains of _P. aeruginosa_ (PAO, _P. aeruginosa_  
Figure 3: Values of reduction factors (R1 and R2) based on clinical strains of _P. aeruginosa_ treated with Pc+ at a concentration of 1.0 \(\times\) 10\({}^{-4}\) M in the dark (dark toxicity) and after irradiation with light (photocytotoxicity): (**A**) photocytotoxicity and toxicity—strains isolated from lower respiratory tract; (**B**) photocytotoxicity and toxicity—strains isolated from chronic wounds. Abbreviations: R1—photodynamic inactivation; R2—dark toxicity; R1 = Log L(-)Ps(-)—Log L(+)Ps(+); R2 = Log L(-)Ps(-)—Log L(-)Ps(+); L(-)Ps(-)—no irradiation, no Pc+ (negative control); L(-)Ps(+)—no irradiation, Pc+ (dark control); L(+)Ps(+)—irradiation, Pc+.  
ATCC 27853, _P. aeruginosa_ NCTC 6749) was carried out to determine the lowest Pc+ concentration, incubation and irradiation time (data not shown). For this study we chose Pc+ concentration of 1 \(\times\) 10\({}^{-4}\) M, 15 min of incubation and 20 min of irradiation time.  
As revealed in Figure 4, after single irradiation, a decrease of the number of bacteria in biofilm varied in the range of 1.3-4.2 log between tested strains. It is worth pointing out that the second treatment decreased the number of bacterial significantly, as a log reduction ranging from 3.4 to 5.5 log was noted. Boosted cell-death response was observed after the third irradiation, when the decrease in the number of viable bacterial cells was in the range 4.6-6.4 log. Scanning electron microscopic (SEM) images of a biofilm of _P. aeruginosa_ treated with Pc+ in the dark and after photodynamic therapy using Pc+ are shown in Figure 5. Specific changes in cellular morphology, including alterations in cellular size and structures, were noted.  
Figure 4: _Cont._  
Figure 4: Values of reduction factors (R) based on selected clinical strains of _P. aeruginosa_ biofilms treated with Pc+ at a concentration of \(1.0\times 10^{-4}\) M in the dark (dark toxicity) and after irradiation (photocytotoxicity): (**A**) photocytotoxic biofilm inactivation and toxicity—PAO and strains isolated from lower respiratory tract, strong biofilm producers; (**B**) photocytotoxic biofilm inactivation and toxicity—strains isolated from lower respiratory tract, moderate biofilm producers; (**C**) photocytotoxic biofilm inactivation and toxicity—strains isolated from chronic wound, strong biofilm producers; (**D**) photocytotoxic biofilm inactivation and dark toxicity—strains isolated from chronic wound, moderate biofilm producers.
## 13 Nanocellulose Aerogels for Electromagnetic Interference Shielding
It is worth mentioning that aerogels showcased more shielding performance than films for same compositions, which can be ascribed to the porosity and the multiple filler polymer interfaces within the porous structure that facilitate more internal scattering of microwaves. In situ coating of the CNF surface with intrinsically conducting polymers is yet another approach that can be utilized to fabricate high-performance microwave absorbers. This approach was utilized by [PERSON] et al. [157] for fabricating ultralight and ultra-fast heat-dissipating PANI/CNF aerogels with 95% microwave absorption ability in X-band frequencies. Initially, in situ electro-oxidative polymerization of the aniline monomer was carried out in CNF suspension to stabilize hydrogels, which were later freeze-dried at\(-80\)\({}^{\circ}\)C for 48 h to obtain PANI/CNF aerogels, as shown in Figure 17. These aerogels demonstrated a maximum EMI SE value of \(-32\) dB at 8.2 GHz and 5.0 mm thickness. It is noteworthy that owing to the very low density of 0.01925 g/cc, PANI/CNF showcased a high specific EMI SE value of \(\sim\)1667 dB.g\({}^{-1}\).cm\({}^{3}\) and has huge application potential in telecommunication, military and defense sectors [157]. These aerogels also depicted ultra-fast heat dissipation behavior when exposed to high-power EM radiation inside a microwave oven, and could readily absorb excess EM energy emanating from electronic devices such as a mobile phone. Figure 15: (**a**) Hierarchical structure of CNF and its preparation process. (**b**) Image of the CNF dispersion displaying the Tyndall effect. (**c**) AFM image of pristine CNFs. (**d**) TEM demonstrating good attraction and adhesion between the CNFs and AgNPs. Schematic representation of ice crystals’ growth during various freezing approaches of the CNF/AgNW dispersion and SEM images of the resultant aerogels: (**e**–**g**) bidirectional, (**h**–**j**) unidirectional and (**k**–**m**) uniform/random freezing. (**n**) Bendability, twistability and rollability of these ultralight aerogels. (**o**) Demonstration of large-area lamellar porous CNF/AgNW aerogels with varying densities (inset, a large-area aerogel held up by electrostatic force). Reproduced with permission from [155]. Copyright 2020, American Chemical Society.
###### Abstract  
This paper proposes new digital filter architecture based on a modified multiply-accumulate (MAC) unit architecture called truncated MAC (TMAC), with the aim of increasing the performance of digital filtering. This paper provides a theoretical analysis of the proposed TMAC units and their hardware simulation. Theoretical analysis demonstrated that replacing conventional MAC units with modified TMAC units, as the basis for the implementation of digital filters, can theoretically reduce the filtering time by 29.86%. Hardware simulation showed that TMAC units increased the performance of digital filters by up to 10.89% compared to digital filters using conventional MAC units, but were associated with increased hardware costs. The results of this research can be used in the theory of digital signal processing to solve practical problems such as noise reduction, amplification and suppression of the frequency spectrum, interpolation, decimation, equalization and many others.  
Keywords:digital signal processing; digital filter; multiply-accumulate unit +
Footnote †: journal: Applied
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms
### Deep Learning(DL) Methods
#### 4.10.3 Stacked Autoencoders  
A standard stacked autoencoder (SAE) is formed by stacking multiple autoencoders. The autoencoder can be learned by pretraining each layer before its successor using a back-propagation algorithm. At each layer, an autoencoder is used to obtain a set of features by jointly using an encoder and a decoder [157]. To prevent learning a trivial solution, stacked sparse autoencoders and stacked denoising autoencoders [158] have been applied to improve the SAEs' methods. SAE-based DL models have been applied to image fusion for multimodal medical image feature extraction. These extracted features can be used to design optimal fusion rules [159] and to obtain better fusion images. In this method, a multitask loss function related to image fusion quality is used to train the network.  
In summary, the clinical applications of the fusion methods are shown in Table 1.
## 2 Visualization of Hyperspectral Images
### Visualization on Google Android OS  
In the last few years, mobile devices, such as tablets, smartphones, _etc_. have increased their potentiality in terms of display resolution, processor capacity and speed. Often, built-in operating systems are specifically designed for these devices (for example Android, iOS, Symbian, _etc_.) and implement many advanced features. Other features are implemented by third-party applications (also called Apps) that can be installed. The potentiality of these devices prompted us to study this important segment.  
Therefore, we have realized a novel implementation of a tool for the visualization of hyperspectral images, similar to the one discussed above. This tool runs on Google Android OS (1.5 or later) ([14,15]). The tool reads the image on internal memory or on external data storage (if available), such as SD card, USB pen drive, _etc_.  
Figures 5 and 6 show respectively the AVIRIS Yellowstone Calibrated Scene 01 and Scene 18 produced by our tool on the SDK emulator ([16]) with screen resolution HVGA (480 \(\times\)320) in portrait mode (Figures 5a and 6a) and in landscape mode (Figures 5b and 6b).  
This tool might be helpful in many kinds of applications, as for example the visualization of a hyperspectral image directly on the field.  
Moreover, the portability of smartphone devices is certainly important in real application (for example in mineralogy, physics, _etc._).  
The built-in OS allows some features on touchscreen-based portable devices, such as gestures, pinch-to-zoom, _etc._, that enable the end user to facilitate use of the tool, without the use of external input peripherals (mouse, keyboards, _etc._).
# A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids
## 5 Conclusions  
The DP load demand predicting is already an essential part of DP vessel controllers, which the power demand from PMS for thrusting depends on weather conditions. As a result, unknown DP load demand in the future has predicted based on sea state by NARX-NN model. In this paper, we have implemented the concept of deep learning techniques, which we employed the NARX-NN as a recurrent network for DPS for operational planning, and economically dispatching between distributed generators. Hence, accurate DP load and power prediction for fixing the vessel position in this method can help the PMS to better performance in a difficult decision-making procedure for all DP vessels. The LM-NN based NARX has evaluated on real DP load profile in a PSV for thrusting the vessel position in various sea states. The proposed method confirms significant precision and defensibility in predicting technique compared with other conventional NN-time series structures. Moreover, we have trained the NARX base on the environmental disturbances as external dataset inputs to enhance the accuracy of the DP load predicting in different sea states. Furthermore, we have modernized the DP controller to forecast load by NARX-NN as a dynamic neural network, including recruited delay lines that they used for nonlinear filtering and prediction. This innovative method provides an integrated DP controller with PMS to increase the prediction accuracy of the DP load demand with the aim of enhancing operational planning in different sea conditions. In addition, three scenarios are considered for hourly, daily, and weekly load forecasting in different sea states. By using the environment disturbances dataset as external inputs in the NARX-NN, which effects on DP load demand, the proposed method can allow the PMS to increase the accuracy of short-term load prediction and operational planning for DP vessel power plant.  
Conceptualization, M.M. and C.-LS.; methodology, M.M., C.-L.S. and Y.T.; validation, M.M., C.-L.S. and Y.T.; formal analysis, M.M.; investigation, M.M. and Y.T.; resources, M.M. and P.X.; data curation, M.M.; writing original draft preparation, M.M., M.N.C. and C.-L.S.; writing-review and editing, M.M., N.B., C.-H.L. and C.-L.S.; visualization, J.C.V.; supervision, J.M.G.; project administration, J.M.G.; funding acquisition, J.M.G., C.-L.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by VILLUM FONDEN under the VILLUM Investigator Grant (no. 25920): Center for Research on Microgrids (CROM). The work of [PERSON] was funded by the Ministry of Science and Technology of Taiwan under Grant MOST 107-2221-E-992-073-MY3.  
The authors declare no conflict of interest.
## 4 Discussion
Although the differences between errors obtained with improved non-linear fitting, traditional non-linear and linear were not so big, they can still point to more proper fitting of the novel methodology. So, the parameters of adsorption isotherms obtained by improved non-linear fitting could be used to describe the adsorption. In addition, the errors for Langmuir, Dubinin-Radushkevich, and Hill isotherms were similar (a little bit lower for Langmuir, followed by Dubinin-Radushkevich and Hill), but we chose to use the parameters of all three isotherms for adsorption interpretation. Although the interpretation of isotherms and their parameters is theoretical, it can still give some insight into the adsorption process. Finally, according to adsorption results and properly fitted isotherms, and obtained adsorption parameter, the adsorption of quercetin derivatives onto \(\beta\)-glucan is described as a physical process, with Van der Waals forces and H bonds created between quercetin derivatives and \(\beta\)-glucan. The adsorption of quercetin-3-rhannoside and quercetin-3-glucoside is favorable, while quercetin-3-galactoside adsorbed in a somewhat lower amount. The spatial arrangements of OH groups on the quercetin derivative molecules affects adsorption. The code developed for improved non-linear fitting can be applied in many areas where the adsorption is studied. Adsorption is often used in studying environmental processes such as the adsorption of environmentally harmful gases [42; 43], the adsorption of substances from water and wastewaters [44; 45], the extraction of precious metals from electronic wastes [46], and adsorption-cooling systems [47]. The adsorption process is also important for new types of packaging like active packaging, where moisture is adsorbed by the active component situated within packaging material or in sachets inside packaging [48]. Studies of drug delivery complexes are often based on adsorption processes [49]. The widespread use of adsorption processes, in important areas such as ecology, packaging, environmental issues, and drug delivery, lends importance to newly developed code.
# Assessment Framework for the Maintainability of Sewer Pipeline Systems
## 5 Study Findings
### Ranking Defects via the Risk Index
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & P1 & 0.789 & 27.843 & 2 & & \\ \cline{2-7} D10 & & P2 & 0.833 & 29.412 & 1 & 0.708 & 6.971 \\ \cline{2-7}  & & P3 & 0.722 & 25.49 & 3 & & \\ \cline{2-7}  & & P4 & 0.489 & 17.255 & 4 & & \\ \hline  & & P1 & 0.722 & 28.384 & 2 & & \\ \cline{2-7} D11 & & P2 & 0.756 & 29.694 & 1 & 0.636 & 6.26 \\ \cline{2-7}  & & P3 & 0.622 & 24.454 & 3 & & \\ \cline{2-7}  & & P4 & 0.444 & 17.467 & 4 & & \\ \hline  & & P1 & 0.7 & 26.695 & 2 & & \\ \cline{2-7}  & & P2 & 0.722 & 27.542 & 1 & & \\ \cline{2-7} D12 & & P3 & 0.689 & 26.271 & 3 & 0.656 & 6.452 \\ \cline{2-7}  & & P4 & 0.511 & 19.492 & 4 & & \\ \hline  & & P1 & 0.767 & 28.87 & 1 & & \\ \cline{2-7} D13 & & P2 & 0.689 & 25.941 & 2 & & \\ \cline{2-7}  & & P3 & 0.689 & 25.941 & 3 & 0.664 & 6.534 \\ \cline{2-7}  & & P4 & 0.511 & 19.247 & 4 & & \\ \hline  & & P1 & 0.711 & 25.6 & 2 & & \\ \cline{2-7}  & & P2 & 0.744 & 26.8 & 1 & & \\ \cline{2-7}  & & P3 & 0.644 & 23.2 & 4 & 0.694 & 6.834 \\ \cline{2-7}  & & P4 & 0.678 & 24.4 & 3 & & \\ \hline  & & P1 & 0.711 & 23.97 & 3 & & \\ \cline{2-7}  & & P2 & 0.878 & 29.588 & 1 & & \\ \cline{2-7}  & & P3 & 0.8 & 26.966 & 2 & & \\ \cline{2-7}  & & P4 & 0.578 & 19.476 & 4 & & \\ \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Failure** & **Defect Category** & **Defect** & \multicolumn{3}{c}{**Probability**} & **Impact** & **RI** & **Ranking** \\ \hline  & & **Code** & & **RII** & **Weighted Mean \%** & **RII** & **Weighted Mean \%** & \\ \cline{3-8}  & & & D1 & 0.6 & 5.96 & 0.672 & 6.616 & 0.403 & 12 \\ \cline{3-8}  & & D2 & 0.567 & 5.629 & 0.608 & 5.987 & 0.345 & 15 \\ \cline{3-8} Pipe collapse & Structural defects & & D3 & 0.667 & 6.623 & 0.692 & 6.807 & 0.462 & 8 \\ \cline{3-8}  & & D4 & 0.678 & 6.733 & 0.692 & 6.807 & 0.469 & 6 \\ \cline{3-8}  & & D5 & 0.6 & 5.96 & 0.664 & 6.534 & 0.398 & 13 \\ \cline{3-8}  & & D6 & 0.733 & 7.285 & 0.686 & 6.752 & 0.503 & 4 \\ \hline  & & D7 & 0.744 & 7.395 & 0.708 & 6.971 & 0.527 & 2 \\ \cline{3-8} Sewer system & & & D8 & 0.756 & 7.506 & 0.675 & 6.643 & 0.51 & 3 \\ \cline{3-8} overflow (SSO) & Hydraulic defects & & D9 & 0.578 & 5.74 & 0.664 & 6.534 & 0.384 & 14 \\ \cline{3-8}  & & D10 & 0.756 & 7.506 & 0.708 & 6.971 & 0.535 & 1 \\ \hline \end{tabular}
\end{table} 
Table 7: The ranking of defects based on probability and impact. Lastly, the results of the preceding steps were combined and summarized in Table 8. Table 8 also provides a ranking of the contributing factors for each defect. For instance, considering defect D12 (root intrusion), the contributing factors are ranked as follows: first C17 (sewer maintenance practices--improper root removal), then C16 (structural defects of pipes), followed by C15 (incorrectly constructed joints), and finally C14 (inappropriate choice of joints). A summary of the complete findings can be found in Table 8. It is worth noting that the weighted mean %, along with the rank, are empty in Table 8 for defects with one contributing factor.