esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)
## 4 Advances in the Adsorbent Type Used in the Extraction Phase of the SPME Fiber
### SPME Based on Nanoparticles  
Nanoparticles are generally defined as being in the range of about 1-100 nanometers in diameter. Compared to bulk materials, their physical and chemical properties are different because they contain fewer atoms, with a larger percentage at or close to the surface. Definitions of nanoparticle size can differ, but when a property differs sufficiently (by a few percent) from the bulk material, a practical upper limit is attained [46].  
Researchers frequently use nanoparticles to clean and recover target analytes from intricate sample matrices. Their large surface area and strong affinity for particular analytes make them excellent sorbents that greatly improve the sensitivity and selectivity of analytical procedures. Furthermore, nanoparticles are prized for their remarkable adsorption characteristics, high preconcentration factors, reusability, and simplicity of surface modifications. These characteristics help improve the sample preparation procedure [47].  
Nanoparticles (NPs) are effective as adsorbents in various separation and preconcentration procedures, such as SPE, SPME, and \(\upmu\)-solid-phase extraction (\(\upmu\)SPE), making them crucial for environmental pollutant analysis. They are also used in chromatographic procedures such as GC and HPLC in which NP-coated columns act as stationary phases. NPs are preferred for extraction methods due to their large surface area and porosity, which  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Compound** & **Examples** & **SPME Coating Materials** & **Applications** & **Reference** \\ \hline Volatile Organic Compounds (VOCs) & Benzene, Toluene, Ethylbenzene, Xylenes (BTEX) & Carbon-ZrO\({}_{2}\) Nanocomposites, PDMS, MOFs & Environmental monitoring, water analysis & [38] \\ Polycyclic Aromatic Hydrocarbons (PAHs) & Naphthalene, Anthracene, Pyrene & Mesoporous Carbon, Carbon Nanotubes (CNTs) & Food safety, environmental analysis & [39] \\ Pesticides & Organochlorines, Organophosphates & 3D graphene-Iron Oxide & Food and water analysis & [40] \\ Nicotine and Metabolites & Nicotine, Cotinine, Nornicotine & Nitrogen-rich Porous Polymers & Neuroscience research, pharmacokinetics & [41] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classes of compounds analyzed using solid-phase microextraction based on nanomaterial coatings.  
improves extraction efficiency and greater extraction rates. Their superior mechanical, chemical, and thermal stabilities make them ideal for SPME coatings. Ongoing research focuses on producing cost-effective extractive materials that improve selectivity and capacity while reducing sample and solvent usage. Furthermore, these materials are sought out for adequate stability to allow reproducibility and reuse [48; 49; 50]. Recent reviews have focused on several types of nanoparticles and their applications in microextraction; we will also be focusing on the different types of nanoparticles used in SPME subsequently.
## 2 Basic Equations  
Following the three-phase-lag model [9; 13], the basic formulations in the dynamical model of the homogeneous isotropic and thermo-poro-elastic media without body forces and heating sources are written as follows:  
\[\mu u_{i,jj}+(\lambda+\mu)u_{j,ij}-R_{11}\Theta_{j}^{s}-R_{12}\Theta_{j,i}^{f} =\rho_{11}\frac{\partial^{2}u_{i}}{\partial^{2}}+\rho_{12}\frac{\partial^{2}U _{i}}{\partial^{2}}, \tag{1}\]\[RUJ_{,ijj}+Qu_{j,jj}-R_{21}\Theta^{s}_{j}-R_{22}\Theta^{f}_{,i}=\rho_{12}\frac{ \partial^{2}u_{i}}{\partial t^{2}}+\rho_{22}\frac{\partial^{2}U_{i}}{\partial t ^{2}}, \tag{2}\]  
\[\left[K^{s}_{*}+(K^{s}+K^{s}_{*}\tau^{s}_{\theta})\frac{\partial}{\partial t}+K^{ s}\tau^{s}_{\theta}\frac{\partial^{2}}{\partial t^{2}}\right]\Theta^{s}_{ii}= \left(1+\tau^{s}_{q}\frac{\partial}{\partial t}\right)\left(F_{11}\frac{ \partial^{2}\Theta^{s}}{\partial t^{2}}+F_{12}\frac{\partial^{2}\Theta^{f}}{ \partial t^{2}}+T_{o}R_{11}\frac{\partial^{2}\epsilon_{kk}}{\partial t^{2}}+T _{o}R_{21}\frac{\partial^{2}\epsilon}{\partial t^{2}}\right), \tag{3}\]  
\[\left[K^{f}_{*}+\left(K^{f}+K^{f}_{*}\tau^{f}_{\theta}\right)\frac{\partial}{ \partial t}+K^{f}\tau^{f}_{\theta}\frac{\partial^{2}}{\partial t^{2}}\right] \Theta^{f}_{,ii}=\left(1+\tau^{f}_{q}\frac{\partial}{\partial t}\right)\left(F_ {21}\frac{\partial^{2}\Theta^{s}}{\partial t^{2}}+F_{22}\frac{\partial^{2} \Theta^{f}}{\partial t^{2}}+T_{o}R_{12}\frac{\partial^{2}\epsilon_{kk}}{ \partial t^{2}}+T_{o}R_{22}\frac{\partial^{2}\epsilon}{\partial t^{2}}\right). \tag{4}\]  
The constitutive equations  
\[\sigma_{ij}=2\mu e_{ij}+\left(\lambda\epsilon_{kk}+Q\epsilon-R_{11}\Theta^{s}- R_{12}\Theta^{f}\right)\delta_{ij}, \tag{5}\]  
\[\sigma=Q\epsilon_{kk}+R\epsilon-R_{21}\Theta^{s}-R_{22}\Theta^{f} \tag{6}\]  
\[e_{ij}=\frac{1}{2}\left(u_{i,j}+u_{j,i}\right),\;\epsilon=U_{i,i}. \tag{7}\]  
This model can be summarized as follows:  
(i) The three-phase-lag model is denoted by (TPL)  
\[0<\tau^{s}_{\theta}<\tau^{s}_{\theta}<\tau^{s}_{q}\text{ and }0<\tau^{f}_{ \theta}<\tau^{f}_{\theta}<\tau^{f}_{q}\] (ii) (GNIII) indicate the Green\(-\)Naghdi type III model  
\[\tau^{s}_{\theta}=\tau^{s}_{\theta}=\tau^{s}_{q}=\tau^{f}_{\theta}=\tau^{f}_{ \theta}=\tau^{f}_{q}=0\]  
where \(\tau^{s}_{\theta},\tau^{s}_{\theta},\tau^{s}_{q},\tau^{f}_{\theta},\tau^{f}_{q}\) are the solid and liquid phases' thermal delay times, respectively; \(K^{s}_{*}\) is the property of the solid material constant in theory; \(K^{f}_{*}\) is a property of the fluid material constant in theory; \(\rho^{s}\) is the solid-phase density per unit volume of bulk; \(K\) is the interphase heating conductions' interface coefficients; \(\rho^{f}\) is the density of liquid-phase per unit volume of bulk; \(\sigma\) is the normal stress of the liquid surface; \(\sigma_{ij}\) is the stress components of the solid surfaces; \(\lambda,\mu,\;R,\;Q\) are the poro-elastic materials coefficients; \(\alpha^{s},\;\alpha^{f}\) are the thermal expansion coefficients; \(\beta\) is the material porosity; \(T_{o}\) is the reference temperature; \(R_{11},\;R_{12},\;R_{21},\;R_{22}\) are the thermal and mixed coefficients; \(C^{s}_{E}\).
## 4 Valorization Technologies of Avocado-Derived Biomass through Obtaining Bioenergy and Biofuels  
In the orchards, after the avocado harvest, the trees are pruned, and it is necessary to correctly manage this waste. The biomass volume is very large and so the removal of all this material represents a lot of work and a high cost. One sustainable alternative is to use it for compost, although in most occasions these residues are burned. In the processing to obtain avocado oil, depleted pulp, peels, and stones are produced; the guacamole industry also generates peels and stones, and in all phases, a part of the fruit is discarded because it suffers some damage. These residues can be used as raw material in a biorefinery, taking advantage of their heat capacity and chemical composition, integrating the production of two or more bioproducts, which can be used in different industries, such as the energy, food, cosmetic, and pharmaceutical sectors.  
In general, the conversion of biomass into biofuel is accomplished by means of mechanical transformations, chemical transesterification, thermochemical transformations, and biological transformations (fermentation, anaerobic digestion, etc.). In general, for energy purposes, the initial humidity of the biomass must be considered, as commented on before. In this sense, in the energy production process from the dry biomass, the methods of direct combustion, pyrolysis, gasification, and transesterification to biodiesel can be used. Alternatively, when the generation of energy is from wet biomass, hydrothermal treatments, enzymatic hydrolysis, fermentation to bioethanol/biohydrogen/biobutanol, and anaerobic digestion can be used [35]. In this section, the potential that avocado biomass has to obtain bioenergy and biofuels is highlighted, giving an overview of the available processes, including those mentioned here (Figure 6). These technologies applied to avocado waste make it an alternative energy source to fossil fuels; also, greenhouse gas emissions can be reduced while, compared to other renewable resources, they do not compete with food production.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 3 Proposed Method
### Optimization Methods
#### 3.3.3 Moving Average Model  
The moving average model controls the amplitude of the variable update by constantly updating the decay rate, which makes the model update faster at the initial stage of training and slower when it is close to the optimal value. The decay rate and parameter update are shown in Equations (21) and (22).  
\[decay\_rate=\min\biggl{\{}init\_decay,\frac{1+num\_update}{10+num\_update} \biggr{\}}, \tag{21}\]  
\[shadow\_var=decay\times shadow\_var+(1-decay)\times var, \tag{22}\]  
where \(init\_decay\) is the initial decay rate, \(num\_update\) is the number of model updates, \(shadow\_var\) is the value of the variable after the update, and \(var\) is the variable to be updated.
# A Model Output Deep Learning Method for Grid Temperature Forecasts in Tianjin Area
## 5 Conclusions  
This paper proposes a temperature forecast model that is based on deep learning methods, called MODL. The novelty of MODL focuses on the application of advanced deep learning modules to improve the prediction accuracy of the global numerical model ECMWF. The principal work of this paper is the design and application of MODL model. Two MODL models with base skeleton structure are proposed, namely MODL-PLAIN and MODL-U. The MODL can deliver outputs (in the form of more accurate ECMWF prediction) in real-time, and it is easy to use by both professionals and basic end-users. Although the forecasting effect of MODL is affected by the input (ECMWF forecast data), the improvement effect of the MODL model on different dates and at different times is obvious.  
In summary, the MODL method is better than the univariate linear MOS method, the MOML method based random forest, and linear regression with a running period, and it has the ability to improve grid temperature forecast results in Tianjin area. Besides, MODL can be implemented in the weather consultation process as a post-processing method with high-grade application prospects and it can significantly reduce human resources expenditure in the weather consultation process. In terms of its practical applicability, it is not necessary to retrain the model when using the deep learning algorithm that is mentioned in this article to process data within two years. The prediction results can be quickly obtained. Our future research direction is to explore the application of different deep learning modules in the interpretability of weather forecast and the generalization ability of the model in different regions with different weather elements. In addition, finding the weather elements related to the 2 m air temperature and evaluating the model that is based on the sub-sampling results of these elements is also worthy of further study.  
Conceptualization, D.W.; Data curation, X.Y. and N.Z.; Formal analysis, N.Z. and D.W.; Funding acquisition, X.Y.; Investigation, K.C., P.W. and D.W.; Methodology, D.W.; Project administration, D.W.; Resources, P.W. and D.W.; Software, K.C.; Supervision, P.W.; Validation, X.Y. and N.Z.; Visualization, K.C.; Writing--original draft, K.C.; Writing--review & editing, D.W. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **MODL-PLAIN** & **MODL-U** \\ \hline
**storage space** & 172 KB & 690 KB \\
**memory requirements** & 3.9 GB & 6.5 GB \\
**FLOPs** & 0.558 GB & 1.354 GB \\
**parameter-number** & 0.041 MB & 0.1 MB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of evaluation indexes of different models.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant 61972282.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Bricks Using Clay Mixed with Powder and Ashes from Lignocellulosic Biomass: A Review
## 5 The Effect of Lignocellulosic Biomass Ash By-Products on Fired Bricks
### What Effect on Mechanical Properties?
Apart from the chemical and mineral compositions of soils and lignocellulosic ashes, a number of factors can influence the mechanical properties of the elaborated bricks, among which we can mention: molding technique, particle size, and during and firing process. For the molding technique, the forming pressure affects the green density of the samples, which influences the brick properties. It was observed that the higher the value of the forming pressure, the more improvement of the mechanical properties. However, this behavior ceases up to a certain limit of pressure beyond which no significant change could be observed. According to Table 2, the majority of studies do not provide pressure values [75]. Improved mechanical properties are also significantly influenced by the morphology and particle size of bricks. While finer particles create denser composites that offer better sintering and greater strength, larger particles restrict compaction within the bricks, leading to the formation of significant amounts of voids that degrade mechanical strength. Despite this characteristic, the majority of studies do not consider this influencing factor (Table 1). Using two studies by [60] and one by [61] as examples, the researchers used the same quantity, origin, and source of clay as well as the same source of ash. Figure 7 shows that the fly ash used to make fired bricks had greater mechanical strength than the bottom ash from olive pomace. This result could be explained by the fact that particles of fly ash were fine spherical powder assimilated to Additionally, the heating rate during the firing process has a significant impact on the compressive strength of fired bricks. A relatively high heating rate (\(>\)10 \({}^{\circ}\)C/min) tends to form a heterogeneous microstructure, which has a severely detrimental impact on the brick's strength [76].
# Whispered Speech Conversion Based on the Inversion of Mel Frequency Cepstral Coefficient Features
## 1 Introduction
proposed to build a continuous probability model for the spectral envelope of whispered speech using a probability-weighted GMM [24] to obtain the mapping relations of channel parameters between the whispered speech and the corresponding normal speech. The probability-weighted GMM can theoretically obtain a reasonable mapping relation between the source and target feature vectors, but it is still difficult for GMM to build a model because of the many dimensions of the spectral envelope. Therefore, it is important to choose speech features when converting whispered speech using GMM. Compared with other feature parameters, the Mel frequency cepstral coefficient (MFCC) simulates the hearing characteristics of the human ear. [PERSON] et al. used MFCC feature inversion to synthesize speech, and the perceptual evaluation of speech quality (PESQ) of the synthesized speech was over 3.5 [25]. To consider the sparseness of speech, in this study, we used the L1/2 algorithm to reconstruct speech based on the research by [PERSON] et al., which improved the reconstruction effectiveness. In this paper, we report a method for converting whispered speech to normal speech based on MFCC and GMM. In the model establishment stage, we extracted the MFCC parameters of each frame of whispered speech and reference normal speech from the parallel corpus; we then used the GMM to establish the joint probability distribution between the frame feature parameters. In the conversion stage, we first input the frame feature parameters of whispered speech into the model. After estimating the feature parameters of normal speech, we used the MFCC feature parameter inversion method to directly reconstruct normal speech. Compared with existing GMM-based whispered speech conversion methods, the GMM developed in this study took into account the correlation between adjacent frames of speech, and the proposed method does not require fundamental frequency estimation when reconstructing speech.
## 4 Experimental Results and Discussions
### DenStream  
Figure 16 shows the clustering of DenStream algorithm with the Horseshoe, T, Moon, and Three Spirals datasets. In the Horseshoe dataset, two macroclusters were found with the settings of \(\epsilon=0.01\), \(\lambda=0.001\), \(\beta=0.35\) and \(\mu=6\). In the T dataset, DenStream identified one macrocluster using the settings of \(\epsilon=0.01\), \(\lambda=0.001\), \(\beta=0.35\) and \(\mu=3\). In the Moon dataset, two macroclusters were found with the settings of \(\epsilon=0.01\), \(\lambda=0.001\), \(\beta=0.35\) and \(\mu=3\). The algorithm obtained maximum ARI in the previous clustering. However, DenStream could not find the clusters in the Three Spirals dataset correctly. For the Three Spirals dataset, the best settings were \(\epsilon=2.5\), \(\lambda=0.01\), \(\beta=0.35\) and \(\mu=3\), which resulted in 21 macroclusters and \(ARI=0.20\). Just like the other algorithms, an exhaustive search to the best parameters was performed, as illustrated in Figure 17.  
The performance of the algorithm is shown in Figure 17, where the Epsilon (\(\epsilon\)) and Lambda (\(\lambda\)) are hyperparameters more critical to the best ARI metric value. In general, the intervals with Epsilon equal to 0.01 and Lambda between 0.001 and 0.01 expressed excellent results, except for Figure 17d. Despite the exhaustive search for the best hyperparameters, it was impossible to find high values for the ARI metric for the Three Spirals dataset, as shown Figure 17d. However, the big problem in DenStream learning is offline. That is, the final macroclusters are found after when all samples have arrived in the algorithm.  
Figure 16: Clustering using DenStream algorithm. (**a**) Clustering with the Horseshoe dataset; (**b**) Clustering with the T dataset; (**c**) Clustering with the Moon dataset; (**d**) Clustering with the Three Spirals dataset.
# Catalytic Degradation of 4-Ethylpyridine in Water by Heterogeneous Photo-Fenton Process
## 3 Results and Discussions
### Detailed Ontvestigations of the Degradation of 4 EP by the Heterogeneous Ohoto-Fenton Process
The mineralization of organic pollutants containing heteroatoms involves the release of inorganic ions. To obtain more details about the degradation of 4 EP by the heterogeneous photo-Fenton oxidation, additional chemical analysis (ion chromatography) was conducted. Figure 5d shows the change of the concentration of nitrates, nitrites, and ammonium ions with time during the treatment of 100 mg/L 4 EP by the heterogeneous photo-Fenton process under optimal conditions. The concentration of ammonium increased linearly from the beginning of the treatment to reach a plateau after 60 min. Only small amounts of nitrate and nitrite ions (\(<\)0.2 mg/L) were detected. This result indicates that the organic nitrogen contained in 4 EP was released stoichiometrically as ammonium ions. Based on HPLC, ion chromatographic, and TOC analyses, a simple mechanistic scheme can be proposed for the degradation of 4 EP by the heterogeneous photo-Fenton process (see Figure 6). 4 EP degradation begins by a rapid release of ethyl group as formic acid and its substitution by hydroxyl radical to form 4-pyridone and 4-hydroxypyridine (tautomeric forms). These intermediates undergo oxidative opening of pyridine ring by hydroxyl radicals to form aliphatic carboxylic acids (oxalic and formic acids) and release ammonium ions. Finally, the carboxylic acids end to mineralize into CO\({}_{2}\) and H\({}_{2}\)O in the final stages of the treatment.
## 1 Introduction
Urban growth is a dynamic process that brings about significant changes in various parameters, shaping the overall landscape and functionality of a city. As cities expand, several key parameters undergo transformations, influencing the urban environment and its sustainability. The growth of urbanization and industrialization both swallow up arable land and influence environmental factors in cities, including air pollution, energy consumption, waste production, etc. [26; 27; 28]. The interaction between LULC changes and city parameters plays a crucial role in understanding the evolving nature of cities. As urbanization progresses, shifts in population dynamics, land consumption, and resource utilization significantly impact the urban fabric. There is a complex relationship between LULC changes and various city parameters, such as population, climate change, inequality, poverty, and other key indicators [29; 30]. Understanding these changes is crucial for effective urban planning, resource management, and environmental sustainability. Recent studies show that classification processes were carried out with satellite images and various methodologies in order to extract detailed information about the Earth surface. Earth surface classes were produced to give information about LULC with different nomenclatures and scales [2; 31; 32]. Nowadays, the Urban Atlas and CORINE, which are the most used methodologies in urban planning and LULC nomenclature, provide information about the Earth's surface at different levels and scales by classifying satellite images according to their own methodology. Additionally, these data are updated in certain years and are available to the public as an open data source [14; 33; 34; 35; 36]. Gaziantep is the city with the ninth highest population in Turkey according to population density as a metropolitan municipality [37], and which constantly receives immigrants due to its geographical location and developing industrial facilities. Thus, the aim of this study was to determine the LULC changes in Gaziantep with Urban Atlas data, which were produced over four years by researchers and the European Environment Agencyproject. Urban Atlas maps for the years 2010 and 2015 using remote sensing data were produced for the scope of this LULC study for Gaziantep. Meanwhile, the European Space Agency (ESA) carried out the production of maps for the years 2012 and 2018 within the framework of Copernicus, the European Earth Observation Programme. Areal values of the LULC classes belonging to the Urban Atlas nomenclature were calculated and compared over four years. The size and direction of urbanization were determined in the city center. Further, the environmental, demographic, and statistical data of the city along with spatial changes, which were collected from official sources related to the city, were examined. The aim of this study is to demonstrate the relationship between LULC changes and different parameters within the city, highlighting the impact of urban development on various types of parameters in the city.