esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mechanical Properties of Emulsions (Texture)  
Texture analysis is a method very commonly used in the cosmetics, pharmaceutical, and food industries. Texture analysis relies, among others, on the mechanical techniques for performing quantitative measurements of product characteristics. Mechanical tests cause deformation of the tested surface of the product and define its selected parameters, [54; 55] including hardness (maximum force at deformation), and adhesion or cohesion (maximum negative force recorded at product deformation).  
The values of hardness and adhesive force obtained for the analyzed hair conditioners are shown in Figure 6.  
For the reference hair conditioner (HC_0), the measured value of hardness was 8.5 g (Figure 6). For burdock powder, hardness varied within the range of 10.2 \(\div\) 12.45 g, while for the conditioners with added black radish powder, the parameter varied from 11.76 g to 14.8 g. Thus, the addition of burdock powder and black radish powder into the emulsions led to an increase in the measured parameter by up to nearly 35% compared to the emulsions without any added plant-derived powders.  
Figure 5: Yield point of analyzed hair conditioners.  
A similar tendency was observed for adhesion (Figure 6). Within a given series of conditioners containing a plant-derived powder, the adhesive force increased, reaching a value that was nearly 50% higher (\(-9.9\) g for HC_6) compared to the adhesive force measured for the baseline emulsion (\(-5.6\) g for HC_0).  
From the viewpoint of application properties, the values of hardness and adhesive force are thus advantageous, especially in terms of ease of dispensing a cosmetic product from its package. Since hair conditioners are usually packed in squeezable tubes or jars, these results indicate that the formulated hair conditioners would be easy to dispense from the packaging. Furthermore, the values of adhesive force recorded during the experiment show that users would have no difficulty spreading the product on their hair.  
[PERSON] et al. [48] evaluated the texture of emulsions containing, among other ingredients, xanthan gum and orange fibers as a function of the concentration of these additions in the emulsion formulation, and as a function of time. The authors proved that the hardness of the samples declined as a function of the decreasing percentage content of xanthan gum in the formulation, and rose as a function of the increasing orange fiber concentration to levels as high as 105 g. An analysis of the results of adhesive force measured in the prepared samples revealed an analogous trend. The values of adhesive force were as high as \(-77\) g, and increased with time. [PERSON] et al. [56] evaluated the effects of natural and synthetic polymers on the texture of formulated cosmetic emulsions, among other factors. The polysaccharides (e.g., carob, xanthan) used by the authors in the cosmetic emulsion formulations were shown to affect the values of emulsion hardness, which varied from 4 g to 6 g. The adhesive force measured in the studied creams was found to be in the range of \(-1.5\)-\(2.5\) g.
## 2 Materials and Methods
### Literature Search Strategy  
The literature search was conducted from February to July 2024 in three major global electronic bibliographic repositories: PubMed, Scopus, and Web of Science (WoS). To align with the topic and objectives of this review, the search query was designed so that it could identify relevant scientific articles focusing on three primary areas: (1) electrocardiogram; (2) normalization; and (3) risk indices serving as markers of cardiac anomalies or instabilities. The inclusion search terms or term roots (referred to as 'keywords' hereafter) based on these areas were as follows:  
1. ECG, EKG, electrocardiogram;
2. normaliz*;
3. alternans, risk, threat*, arrhythtim*, instability, abnorma*, anomal*, irregular*, vulnerable*, susceptib*.  
Specifically, the keywords 'ECG', 'EKG', and 'electrocardiogra*' were chosen to ensure the inclusion of all documents focusing on the ECG as the primary signal of interest. The keyword 'normaliz*' was chosen to capture all studies processing the ECG signal through any form of normalization. Lastly, the search query included the keywords 'alternans', 'risk', 'threat*, 'arrhythtim*', 'instability', 'abnorma*', 'anomal*', 'irregular*', 'vulnerab*', and'susceptibleb*' to encompass documents related to conditions of cardiac risk. All keywords within each search area were combined using the Boolean operator OR. Subsequently, the three resulting keyword combinations were further combined using the Boolean operator AND to formulate the final search query for each bibliographic repository. The search was limited to title and abstract fields concurrently. Automatic filters for language and publication access modality were applied, specifically restricting the search to English language and open-access documents. Moreover, documents were manually filtered according to the type; specifically, reviews, book chapters, and corrections of previous articles were excluded. No filters were imposed on the publication year. The same search query was entered into the advanced search tools of all considered bibliographic repositories, with syntax adjustments made to meet the specific requirements of each database. The specific query forms are detailed in Appendix B.
## 1 Introduction
To improve the measurement stability and accuracy of the sensor, the white noise perturbations were introduced to effectively suppress the cavity mode noise. The performance of both measurement modes was analyzed in detail through experiments. The results demonstrated that the sensor exhibited excellent stability and high accuracy.
# A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump
###### Abstract  
An efficient Remaining Useful Life (_RUL_) prediction method is one of the most important features of a condition-based maintenance system. A running machine's _RUL_ prognosis in its real-time is a challenging task, especially when there is no historic failure data available for that particular machine. In this paper, an online _RUL_ of an in-operation industrial slurry pump having no historical failure data has been predicted. At first, the available raw vibration datasets were filtered out for valid datasets. The obtained valid datasets were utilized for constructing the Health Degradation Trends (HDTs) using principal component analysis and a moving average method. Then, a novel procedure for automatically selecting the HDT's data points for initiating the iteration process of prediction was formulated. Afterward, a hybrid deep LSTM model embedded with a smart learning rate mechanism was developed for estimating the online _RUL_ using the selected points of HDTs. The online _RUL_ prediction results produced by the developed model were quite satisfactory when they were compared with other online _RUL_ prediction methods.  
Remaining Useful Life; prediction; vibration data; LSTM modelling; learning rate +
Footnote †: 2022: 12, 4839. [[https://doi.org/10.3390/app12104839](https://doi.org/10.3390/app12104839)]([https://doi.org/10.3390/app12104839](https://doi.org/10.3390/app12104839))
## 4 Extraction Methods
### Extraction in Supercritical Fluids
#### 4.1.5 Environmental Issues and Perspectives  
Supercritical fluid extraction has numerous potential applications in tannin extraction, particularly in the food, nutraceuticalital, pharmaceutical, and polymer industries. For decades, SC-CO\({}_{2}\) has been used for the extraction of various botanicals, including commercial processes such as oils and nutraceuticalals extraction from marine and terrestrial biomass by Pharmalink Extracts Ltd. (Appleby, New Zealand) [94] and coffee bean decaffeination d by [PERSON] as described by [PERSON] [109]. The main advantages of this process are the ease of separation of extracted compounds via simple depressurization and the closed-loop system in terms of solvent recovery (Figure 4). Moreover, carbon dioxide is considered a non-toxic, inexpensive, chemically inert, and nonflammable solvent.  
Figure 4: Scheme of tannin extraction with SFE [110, 111].  
The increase in the environmental impact of SFE processes is primarily associated with using co-solvents--methanol and ethanol. Although water yields better results for the efficiency of tannins extraction, the lower boiling point of these alcohols makes them more advantageous when considering solvent evaporation from extracted products, lowering the energy demand of the process. However, the life cycle analyses (LCAs) of phenolic compounds extracted from bark spruce show that using ethanol as a co-solvent is the dominating contributor, exceeding 70%, in all studied environmental categories except ionizing radiation. This is due to ethanol loss in the residual biomass, for which the recovery approximates only 82-83% [112]. Integrating SFE extraction with bioethanol production in a biorefinery system may reduce its environmental impact [113].  
The use of SC-CO\({}_{2}\) acting as a co-solvent with water or alcohols offers technical advantages. Its presence in the solvent mixture lowers the critical temperature and reduces energy consumption. Thus, supercritical fluid tannin extractions occur at temperatures similar to conventional extractions but in a significantly shorter time. Despite being an energy-demanding process due to solvent pressurization and heating, life-cycle analyses of various product extractions from different biomass resources have shown a reduced environmental burden in most categories compared to conventional solvent extraction [114, 115]. However, energy demand remains the most important factor in the environmental performance of the extraction processes. Its consumption can be limited by internal heat recovery and switching to renewable energy.  
The results of LCA studies, presented in the literature, were mostly obtained by analyzing processes performed on a laboratory scale. Improvements in process intensification and heat economy during scale-up can reduce the environmental impact of SFE processes. Therefore, a better understanding of the mechanisms, optimization, upscaling, and life cycle analyses is needed to increase the maturity of this environmentally friendly technology.
## 3 Results and Discussion
### Isotherm Models  
The adsorption phenomenon depicted an escalation for the initial 120 min after the commencement of the experiment. This was followed by a slow-down phase between 150 and 180 min as the system reached its equilibrium. The best-fit parameters for each adsorption model are listed in Table 2. While comparing with [PERSON] and [PERSON], it was seen that the best results were obtained in the Temkin model, suggesting that the adsorption of urea from wastewater followed the homogeneous or heterogeneous processes.
# Assessing the Seismic Demands on Non-Structural Components Attached to Reinforced Concrete Frames
###### Abstract
The structure's properties solely determine the seismic response of structures built on rock. The seismic response of a building is significantly affected by the interaction between the soil and the structure when it is built on soft soil. It can produce frequency shifts and amplitude alterations in the structure's floor response spectrum [27]. As a result, the effect of different soil types should be included while developing the structural model to simulate the seismic response realistically. In this research, an effort has been made to study the effect of various types of soil on the floor acceleration response of the regular and irregular building models. Two five-story reinforced concrete (RC) buildings are considered in this research. The NSC is modeled as an elastic SDOF system and attached to the RC building at a single point. The mass ratio (non-structural components mass to the primary structural mass) is assumed to be 0.001 for this investigation [8; 28]. The decoupling analysis of primary and non-structural components (i.e., neglecting the dynamic interaction that takes place between the supporting building and the NSC) is appropriate for lighter NSCs [29]. The primary goal of this research is to assess the elastic floor acceleration response of the regular building and buildings with mass irregularity on the lower story. Such mass irregularity exists when a specific floor is used for car parking, equipment storage, etc. The floor amplification factors, peak component acceleration, component dynamic amplification factors, and floor response spectra play an essential role in assessing seismic demands on NSCs. Therefore, all the specified factors and spectra are evaluated for building models under different soil types. The amplification factors are compared with those obtained from the code-based formulations. In the generation of FRS, component dynamic amplification factors play an important role as they reflect the amplification of NSCs. Therefore, in the current study, artificial neural networks (ANNs) are employed to develop the model to predict the component dynamic amplification factors. The created ANN model is used to generate a detailed design expression. The developed ANN-based expression is compared with the literature formulation. The effectiveness of the prediction model has also been assessed by comparing the component amplification factors computed from the ANN equation to those acquired through dynamic time history analysis of building models. The organization of the paper can be broken down into the following sections: Section 2 describes the modeling and analysis of considered building models. The overview of the FRS is discussed in Section 3. Section 4 gives the details of the ground motion for different soil types. Section 5 presents the results and discussion. Section 6 describes the ANN model and shows the procedure to develop the prediction expression, and concise conclusions are drawn in the last section (i.e., Section 7).
# Learning Weighted Forest and Similar Structure for Image Super Resolution
## 4 The Proposed Super Resolution Method
### Similar Structure Clustering and Low Rank Constraint  
It is well known that nature images contain many local similar structures. In order to express the inherent geometry structures well, we propose to partition the obtained HR image patches into several clusters so that each cluster has similar structures. For this purpose, we exploit a structural clustering conception enlightened by [36] and further implement a low rank constraint. The feature of local image patch is represented by normalized pixel intensity. For the obtained HR image Y, let \(y_{h}^{i}\) express image patch vector in the \(i\)-\(th\) two dimensional position. A multi-cluster union consisting of all feature vectors is represented as follows:  
\[Y=\mathop{\cup}\limits_{k=1}^{C}\left\{y_{h}^{i}\middle|i\in\Omega_{k}\right\} \tag{10}\]  
where C is cluster number, and \(\Omega_{k}\) denotes the index set _in k-th_ cluster. The k-means clustering operates quickly, and is able to accurately partition the initial HR patches to appropriate subsets, so k-meansclustering is adopted to realize clustering. In the process of clustering, we utilize \(l_{2}\)-norm as distance metric and minimize inter-cluster variance to partition the obtained HR patches into multiple clusters.  
\[J=\sum_{k=1}^{C}\sum_{i\in\Omega k}\
orm{\widetilde{y_{h}^{i}}-\overline{y_{(k)}} }_{2}^{2} \tag{11}\]  
where \(\widetilde{y_{h}^{i}}\) denotes normalized vector, and \(\overline{y_{(k)}}\) denotes mean vector for the _k-th_ cluster. In our research, set C = 12, and the initial HR patches are partitioned into 12 clusters by 20 iterative operation.  
The above clustering algorithm can partition the obtained HR image patches with similar structures into the same cluster. \(F_{k}Y\) is defined as a matrix consisting of image patch vectors in the _k-th_ cluster.  
Since the image patches in the same cluster have a high degree of similarity, it can be considered that the matrix formed by each set of vectorized image patches is approximated as a low rank matrix, which is described as below:  
\[F_{k}Y=A_{k}+E_{k} \tag{12}\]  
where \(A_{k}\) is low rank matrix, and \(E_{k}\) is error matrix. Because of the difficulty in solving a low rank matrix, the problem is transformed into a kernel norm minimization problem for approximate solution, which is expressed as follows:  
\[Y=\underset{Y}{\text{argmin}}\
orm{F_{k}Y-A_{k}}_{F}^{2}+\lambda\
orm{A_{k}}_{*} \tag{13}\]  
where \(\
orm{\cdot}_{*}\) denotes the kernel norm. Equation (13) indicates a high similarity for patches in the _k-th_ cluster.
# A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios
## 3 Method Details  
### Overall Design  
Our stave-aware OMR method is the process of extracting metadata, such as the position and type, from the musical symbols in score photos to reassemble the sequence of notes in text format. Specifically, we preprocess the image and then utilize YOLOX-S to detect the position of the musical staff. Then, we employ the FCOS model to detect the positions of the notes and utilize a classification network to classify the duration of notes within their respective segments. Finally, we employ a well-designed notation assembly module to reassemble the notes. The stave-aware OMR method consists of two stages: symbol detection and symbol assembly. The structure is shown in Figure 1.  
1. _Symbol detection._ In this stage, we detect symbols, which affect the pitch of notes, in the music sheet such as note heads, clefs, key signatures, and accidentals. Simultaneously, the detection of staff lines and the segmentation of notes are also conducted. The result will be utilized for recognizing the pitch and type of the notes.
2. _Symbol assembly._ This stage includes pitch assembly and duration assembly. In the pitch assembly, information about the type and position of accidentals, and the position of note heads and staff lines are fused. In duration assembly, we combine information about note head duration and note duration to obtain note type. Note head duration refers to the type of note heads through an object detection network, whereas note duration is determined by the stem type and whether it is dotted or not through a classification network.  
Figure 1: The structure of our two-stage OMR method.
# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 3 Results and Discussion
### Pixel Scale
Additionally, the root mean square error (RMSE) and mean absolute error (MAE) values show that the GPM-F product performs well with the lowest RMSE and MAE values at all temporal scales. GPM-L and GPM-E products also outperform the ERA5 model in terms of error metrics. The ERA5 product consistently ranks last in terms of error and bias across all temporal scales (Figure 4). These findings suggest that the GPM products, particularly GPM-F, demonstrate better performance in terms of bias, correlation, and error metrics compared to the ERA5 model. However, it is important to consider the specific context, dataset, and evaluation criteria when interpreting and generalizing these results. The coefficient of determination (\(R^{2}\)) is a measure of how well the regression model fits the observed data. The confidence intervals, depicted by shaded regions, provide a range of potential values for both the slope and intercept of the regression line. These intervals help visualize the uncertainty associated with these parameters. The provided results indicate that at the daily scale, the GPM-F, GPM-L, and GPM-E models have relatively low \(R^{2}\) values, suggesting that these models are not very effective in explaining the variance of the observed data at this time scale. In contrast, the ERA5 model has a higher \(R^{2}\) value, indicating a better correspondence between the observed data and the linear regression model (Figure 5). However, at the monthly and annual scales, all four products show higher \(R^{2}\) values, indicating that they are more effective in explaining the variance of the observed data. The GPM-L, GPM-E, and GPM-F models demonstrate \(R^{2}\) values above 0.5 on the monthly scale, with the highest value of 0.87 for GPM-F. Similarly, at the annual scale, all \(R^{2}\) values are relatively high, with values ranging from 0.49 to 0.86. These results suggest that the products have a better ability to explain the variance of the observed data at the monthly and annual scales compared to the daily scale (Figures 6 and 7). Moreover, the results indicate that the improvement in \(R^{2}\) is more significant at the monthly scale than at other temporal scales, the ERA5 and GPM-F models exhibit good fits to the observed data. The performance of GPM-L and GPM-E is relatively acceptable at the monthly and annual scales but weaker at the daily scale.
## 7 New Approaches to Monitoring and Optimisation of Microalgae Cultivation Systems
### Metaheuristic-Based Predictions  
Modern technologies for monitoring the cultivation of microalgae increasingly use predictive algorithms based on metaheuristic methods to optimise growth conditions and predict biomass dynamics and metabolite accumulation. Metaheuristics such as evolutionary algorithms, particle swarm optimisation (PSO), and simulated annealing (SA) enable the modelling of complex, non-linear biological processes under variable cultivation conditions.  
One of the most important applications of metaheuristic methods in the cultivation of microalgae is the prediction of biomass growth based on multidimensional environmental data, including light intensity, temperature, pH, and nutrient availability. Models based on genetic algorithms (GA) enable the calibration of mathematical models describing microalgae growth and allow accurate prediction of cell proliferation rates under different cultivation conditions [242]. The optimisation of cultivation parameters using metaheuristics is also applied to increase the production yield of secondary metabolites such as lipids, pigments, and polysaccharides. PSO and SA algorithms are used to determine optimal environmental parameters that maximise the content of desired products in the microalgal biomass. Studies indicate that these methods significantly improve the efficiency of biodiesel production by precisely regulating the nitrogen and phosphorus content in the culture medium [243].  
In the context of in situ monitoring, metaheuristic algorithms play a crucial role in analysing microscopic images and spectral data that enable automatic classification of microalgae cells based on their morphology and biochemical composition. The use of ant colony optimisation (ACO) algorithms and artificial neural networks optimised by genetic algorithms enables the effective detection of anomalies within microalgae populations and the prediction of environmental stress effects based on image analysis [244]. Another important application of metaheuristics in microalgae monitoring is the prediction of metabolic responses to stress factors. Hybrid models combining evolutionary algorithms with machine learning methods such as random forest (RF) and artificial neural networks (ANN) facilitate the development of accurate predictive models describing the effects of environmental changes on the expression of genes related to lipid and carbohydrate metabolism [245].  
The application of metaheuristic methods in the monitoring of microalgae culture opens up new possibilities for the optimisation and automation of biotechnological processes. The integration of predictive algorithms into real-time cultivation control systems enables a significant improvement in the efficiency of biomass production and increases process stability. The further development of these methods, especially in combination with the increasing amount of data from spectroscopic and microscopic imaging methods, can contribute to even more precise bioprocess control and improved commercial microalgae production.