esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 4 Materials and Methods
### Methodological Innovations
#### 4.4.3 Sentiment Index and Illiquidity Index  
The Sentiment Index (SI) is constructed using t-SNE, which excels at capturing non-linear and complex relationships among features, offering superior performance over traditional linear methods like PCA. Figures 8 and 9 show the comparative distributions of SI produced by both PCA and t-SNE across different market classes (_Bear Market_, _Shock Market_, and _Bull Market_).  
The boxplots (Figure 8) and violin plots (Figure 9) highlight significant differences in the effectiveness of each technique. PCA results in compact, linear distributions, while t-SNE-derived SI shows wider and more detailed variations, especially within the _Shock Market_ and _Bull Market_ categories. This increased variability indicates that t-SNE captures intricate nonlinear patterns inherent in data--a critical aspect for financial markets known for their sudden changes and complex dynamics.  
Figure 8: Boxplots comparing Sentiment Index (SI) distributions generated by PCA and t-SNE for _Bear Market_, _Shock Market_, and _Bull Market_. Boxplots highlight median, quartile ranges, and outliers for both methods.  
Figure 9: Violin plots comparing Sentiment Index (SI) distributions generated by PCA and t-SNE for _Bear Market_, _Shock Market_, and _Bull Market_. Violin plots emphasize density and complexity of distributions, with t-SNE demonstrating superior performance in preserving nonlinear relationships.  
For example, during the _Bear Market_, the t-SNE-derived Sentiment Index (SI) reveals a higher concentration of positive values. This illustrates its capacity to maintain local proximities and detect nuanced sentiment-driven behaviors. Likewise, in the _Shock Market_, the broader distribution of SI highlights t-SNE's effectiveness at recognizing changing market conditions that linear methods like PCA often reduce to overly simplistic interpretations.  
This evidence highlights the effectiveness of t-SNE in building the Sentiment Index within this framework. By emphasizing nonlinear patterns, t-SNE effectively captures sentiment-driven insights that are often linked to sudden market shifts, thus boosting the model's predictive accuracy. Additionally, as described by [PERSON] (2002) [45] and [PERSON] (2023) [8], integrating the Illiquidity Index (ILLIQ) enriches this process by offering a fundamentalist viewpoint on liquidity conditions.
# Deep Learning for Predicting Traffic in V2X Networks
## 3 V2X Simulation  
To further model the V2X network in the smart city, the MATLAB computing platform was used. First, a mobility model for the V2X network was developed. Using a virtual mobility map, the ad hoc on-demand distance vector (AODV) routing protocol was created to investigate and evaluate its performance. The road network was developed through the creation of mobility maps that include basic entities such as city size, nodes, and RSUs (roadside units). The size of the city is needed to determine the boundaries in which the nodes of the V2X network move in random directions for the implementation of AODV. To implement the AODV routing protocol, the maximum size of the city and the number of nodes are required, and multiple RSUs must be set. If the size of the city is greater than the required simulation period, it is automatically enlarged. It is assumed that the size of the city is 100 \(\times\) 100 on the x-y axes. In the mobility model, the nodes on the city boundary can move along fixed routes in any direction.  
The simulation of a V2X in a smart city is shown in Figure 1. In Figure 1., the points represent individual nodes and locations of the RSU, characterized by identification numbers corresponding to the structure and configuration of the network. Nodes 20 and 70 serve as the model's starting point and closing point, respectively. The simulation module chooses the simulation's start and finish times and visualizes the network architecture. Nodes that move randomly can connect to other nodes that are far away from them due to the position of the RSU on the simulation map. The RSU makes it possible to connect to moving cars so that messages such as traffic reports and safety alerts can be transmitted.
## 2 Materials and Methods  
### Reagents and Standards  
Cyanidin-3-glucose chloride, delphinidin-3,5-diglucoside chloride, delphinidin-3-galactoside chloride, petunidin-3-glucose chloride, malvidin-3-galactoside chloride, quercetin-3-glucose, and kaempfer-3-glucoside were purchased from PhytoLab (Vestenbergsgreuth, Germany). The remaining 31 analytical standards of the 38 phenolic compounds were supplied by Sigma-Aldrich (Milan, Italy). Individual stock solutions of each analyte, at a concentration of 1000 mg L\({}^{-1}\), were prepared by dissolving pure standards in HPLC-grade methanol and storing them in glass stoppered bottles at 4 \({}^{\circ}\)C, except for anthocyanins, which were stored at \(-15\)\({}^{\circ}\)C until analysis. Standard working solutionsat various concentrations were prepared daily by appropriate dilution of the stock solutions with HPLC-grade methanol. HPLC-grade methanol was supplied by Sigma-Aldrich (Milano, Italy). Deionized water (>18 M\(\Omega\) cm resistivity) was further purified using a Milli-Q SP Reagent Water System (Millipore, Bedford, MA, USA). All solvents and solutions were filtered through a 0.2 \(\upmu\)m polyamide filter from Sartorius Stedim (Goettingen, Germany). All chemicals and reagents were analytical grade. Before HPLC analysis, all samples were filtered with a Phenex(tm) RC 4 mm 0.2 \(\upmu\)m syringeless filter, Phenomenex (Castel Maggiore, BO, Italy). The alkane mixture (C\({}_{7}\)-C\({}_{30}\)) was purchased from Sigma-Aldrich (Milano, Italy). The Folin-Ciocolteu reagent, sodium carbonate (Na\({}_{2}\)CO\({}_{3}\)), gallic acid (C\({}_{7}\)H\({}_{6}\)O\({}_{5}\)), ferric chloride hexahydrate (FeCl\({}_{3}\)-6H\({}_{2}\)O), and Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid) were purchased from Sigma-Aldrich (St. Louis, MO, USA). The DPPH (2,2-diphenyl-1-picrylhydrazyl radical) was obtained from Glentham Life Sciences (Corsham, UK).
# The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services
## 3 The Next Generation PaaS: Architecture, Concepts, Processes, and Workflows
### Processes and Workflows  
The ability to deploy, in a modular way, both the platform and service components is one of the key features of NGPaaS. This allows the creation of an eco-system where telco operators can easily cooperate and integrate with multiple software vendors. By using an aligned descriptor format between the vendor and operator side, such as the RFB model, it becomes easier to share both service components and their related execution environments (PaaS). The processes defined in this section, enables the deployment of a customisable set of PaaSes and service components, empowering the support of many use-cases. The most critical processes between layers mentioned above are illustrated in Figure 5 and also analysed in Table 2.  
Figure 4: Example of RFB graph from the RDCL 3D tool.  
Figure 5: NGPaaS processes, to enable a customisable platform.  
Before requesting any platform or service deployment, (1) the required infrastructure for the VSP must be reserved by the NGPaaS operator. Once the appropriate infrastructure has been registered, then, (2) the VSP can request the deployment of the desired service. This is done by the composition of two RFB graphs, one for the underlying platform (if not already present) and one for the service itself. Once the RFB graphs have been composed then PaaS (3), or service workloads (4) can be orchestrated on their corresponding execution environments. A relatively similar workflow (5)-(8) is also available to software vendors through their dedicated Dev-for-Operations layer, which allows them (5)-(7) to test new software components that can be later (8) included as RFBs in the available RFB catalogue.
## 4 Analysis of Influencing Factors
### Influence of the Horizontal Bearing Stiffness
#### 4.5.1 Tunnel Horizontal Displacement  
Figure 18a depicts the tunnel's maximum horizontal displacement upon completion of foundation pit excavation under varying horizontal bearing stiffnesses. It is evident from the figure that the tunnel's horizontal displacement gradually decreases with increasing horizontal support stiffness; however, the reduction range varies. From three steel bracings to four steel bracings, the horizontal displacement is reduced by 11.06%, and from four steel bracings to five steel bracings, the horizontal displacement is reduced by 9.52%, indicating that increasing the number of horizontal bracings weakens the reduction in the tunnel  
Figure 17: Maximum structural stress of the tunnel for various insertion ratios.  
horizontal displacement. For the same number of bracings, from three concrete bracings to three steel bracings, the horizontal displacement is reduced by 2.35%; from four concrete bracings to four steel bracings, the horizontal displacement is reduced by 3.17%; from five concrete bracings to five steel bracings, the horizontal displacement is reduced by 4.47%; and the reduction effect is not as good as increasing the number of bracings. From three steel bracings to five concrete bracings, the tunnel's horizontal displacement is reduced by 19.53%. Therefore, changing the horizontal bearing stiffness has a certain effect on controlling the tunnel's horizontal displacement. The tunnel's horizontal displacement exceeds the warning value specified in the existing specifications with three bracings, whereas with four and five supports, it remains below the warning value.
## 4 Physical Audio Features Extraction Techniques
### Frequency Domain Physical Features
#### 4.2.4 Tonality-Related Physical Frequency Features
Some approaches take into account only partial frequencies (like [PERSON] _et al._[94; 95]), while others also consider partial energies and bandwidths (see [PERSON] _et al._[96]). * **Harmonic-to-Noise Ratio:** Harmonic-to-noise Ratio (HNR) is computed as the relation between the energy of the harmonic part and the energy of the rest of the signal in decibels (dB) ([PERSON] [97]). Although HNR has been generally applied to analyze pathological voices (like in [PERSON] [49], or in [PERSON] _et al._[98]), it has also been applied in some music-related applications such as the characterization of growl and screaming singing styles, as in [PERSON] and [PERSON] [53]. * **MPEG-7 spectral timbral descriptors:** the MPEG-7 standard defines some features that are closely related to the harmonic structure of sounds, and are appropriate for discrimination of musical sounds: MPEG-7 harmonic spectral centroid (HSC) (the amplitude-weighted average of the harmonic frequencies, closely related to brightness and sharpness), MPEG-7 harmonic spectral deviation (HSD) (amplitude deviation of the harmonic peaks from their neighboring harmonic peaks, being minimum if all the harmonic partials have the same amplitude), MPEG-7 harmonic spectral spread (HSS) (the power-weighted root-mean-square deviation of the harmonic peaks from the HSC, related to harmonic bandwidths), and MPEG-7 harmonic spectral variation (HSV) (correlation of harmonic peak amplitudes in two adjacent frames, representing the harmonic variability over time). MPEG-7 spectral timbral descriptors have been employed for environmental sound recognition ([PERSON] and [PERSON] [47],[PERSON] and [PERSON] [48]). * **Jitter:** computes the cycle-to-cycle variations of the fundamental frequency ([PERSON] [49]), that is, the average absolute difference between consecutive periods of speech ([PERSON] _et al._[51]). Besides typically being applied to analyze pathological voices (like in [PERSON] [49], or in [PERSON] and [PERSON] [50]), it has also been applied to prototyping, classification and creation of musical sounds ([PERSON] [54]), improve speaker recognition ([PERSON] _et al._[51]), characterize growl and screaming singing styles ([PERSON] and [PERSON] [53]) or discriminate vocal and non-vocal regions from audio songs ([PERSON] and [PERSON] [52]), among others.
## 3 Results and Discussion
### Analysis of Volatile Compounds of Embryo Rice During Storage
#### 3.2.2 Analysis of HS-SPME-GC-MS of Embryo Rice During Storage
volatile compounds in the rice, but many compounds only appeared within 0 d, such as biphenyl (pleasant odor), fluorene (fruity flavor), and p-cymene (mild, pleasant, citrus). 2-Pentyflian (fruit flavor) is retained for the longest time and gradually fades with storage time [45]. The primary oxidation products impart a sweet or fruity aroma to rice during the oxidation process, but excessive production can also cause lipid oxidation and produce a rancid taste [46]. The presence of naphthalene in the storage process indicates that the embryo rice produces a tar taste. Toluene has a fragrant aroma similar to benzene and only appears in 240-300 d. Excessive toluene is toxic. It can be observed that aromatic hydrocarbons are less easily preserved and evaporate faster at 25 \({}^{\circ}\)C. Temperature can affect the retention time of aromatic hydrocarbons [47]. Alkylnes are unsaturated derivatives of alkanes and olefins, with two types detected. 2-Decyne exhibits a pungent odor during the later stages of storage. The volatile compounds in embryo rice during storage are rich in phenols and ketones, with 3 phenols and 5 ketones detected. Phenolic compounds can form a rice bran odor during the storage of embryo rice, affecting the flavor of the rice [48]. 2,4-Di-tert-butylphenol (phenolic flavor, stone carbon sour taste) and 2-methoxy-4-vinylphenol (sweet and spicy fruity aroma) appeared within 0-300 d during storage. Guaiacol (phenolic odor) only appeared in 300 d at 4 \({}^{\circ}\)C. Guaiacol and 2-methoxy-4-vinylphenol indirectly reflect the deterioration in embryo rice, forming a rice bran odor that affects the quality of food consumption. Research has found that volatile ketone compounds have a slightly fruity or sour-sweet taste. Acetone (mint scent) and 2-heptanone (fruity, spicy) appeared at 0 d, and acetone is a representative compound of aliphatic ketones. 3-Methyl-2-cyclohex-1-one (caramel odor), 4-methylophenone (acetophenone odor), and the 2-oxo-2-phenylethyl format appeared in the later stages of storage, with their contents continuously increasing. In addition to the volatile compounds mentioned above, embryo rice also contains acid and alcohol compounds which contribute to the complex aroma characteristics of embryo rice during storage. Acids are produced through lipid hydrolysis, and acetamide appears on day 60 with a rat odor. Most of the acid compounds appeared during late storage, which reflected the rancidity of the rice. However, hydrogen acid is limited by temperature and only appears at 25 \({}^{\circ}\)C. Alcohols are regarded as secondary products resulting from the oxidation of unsaturated fatty acids. These alcohols are formed through the further decomposition of aldehydes [49]. With the extension in storage time for embryo rice, 1,2-pentanediol and ethanol appeared [50].
## 4 Evolutionary Computation Technique and Parent Selection Methods  
### Grammatical Evolution  
GE [10] is a variation of GP that uses GAs to evolve variable-length binary strings, known as genotypes. These genotypes are mapped to programs or structures, referred to as phenotypes, in any language defined by a BNF grammar. This capability allows GE to evolve constructs in various programming languages.  
BNF is a notation used to describe context-free grammars. Figure 3 illustrates the GE mapping process in detail. BNF grammars consist of four key components:  
1. Production Rules (P): These define how a non-terminal symbol can be replaced with a combination of other symbols (terminals or non-terminals), guiding the generation of valid strings in the language defined by the grammar.
2. Terminals (T): These are the items that can appear in the final program and, in context-free grammars such as those used in this work, only on the Right-Hand Side (RHS) of a production rule and cannot be further expanded. Examples include symbols like '?' and '&' in the given grammar.
3. Non-Terminals (N): These are intermediate elements that are mapped by the production rules into members of the set T; these elements can appear on both the Left-Hand Side (LHS) and RHS of production rules and can be expanded into other non-terminals or terminals. For instance, \(<\) var \(>\) is a non-terminal in the example.
4. Start Symbol (S): This is a special non-terminal from which the derivation begins. It appears in the first production rule and is typically the leftmost non-terminal, such as \(<\) expr \(>\) in the first production rule shown in Figure 3.  
The genotype, a binary string, is divided into _codons_ and then converted into decimal integers. In the example shown in Figure 3, 8-bit codons of the genotype are used for this conversion. The first integer obtained is 40. The mapping process begins with the S from the first production rule (A). Given two possible RHS options in this rule, the selection is made using the modulus operation: the integer 40 modulo 2 equals 0, thus selecting the first option.  
Next, the selected option contains a non-terminal, expanding the leftmost non-terminal. The second integer, 118, determines the selection within the non-terminal \(<\) var \(>\), which has two options according to the third production rule (C). The operation 118 modulo 2 equals 0, so the first option, the variable 'x', is chosen. The subsequent non-terminal \(<\) op \(>\) has three options according to the second production rule (B). Using the third integer, 124, the selection is made by calculating 124 modulo 3, resulting in 1, thus choosing the second terminal, representing an OR operation. Finally, for the last non-terminal \(<\) var \(>\), the integer 137 modulo 2 results in 1, selecting the variable 'y'. The resulting expression, shown in Figure 3, is an OR operation between the inputs 'x' and 'y'. This example is adapted from [17], where a more detailed explanation can be found.
## 6 Conclusions  
In the present research, we investigated the slip effects on the steady flow and heat transfer of nanofluids over a porous sheet embedded in a Darcy-type porous medium. The viscosity and the thermal conductivity of the nanofluids were considered as linear functions of temperature, and wall slip conditions were employed in terms of shear stress. The governing system of equations was reduced to the ordinary differential equations by suitable similarity transformations, and the reduced system was solved numerically using the shooting method. The influence of key thermophysical parameters on the velocity and temperature profiles, as well as on the skin friction coefficient and Nusselt number were presented and discussed through graphs and tables. The present model exploited a number of simplifications in order to focus on the principal effects of permeability, variable viscosity, variable thermal conductivity, nanofluid volume concentration and slip parameters. In our work, we showed that the increase in the permeability of the porous medium, the viscosity of the nanofluids and the velocity slip parameter decreased the momentum and thermal boundary layer thickness and eventually increased the rate of heat transfer; whereas the opposite trend was observed for the increase in the thermal conductivity parameter. The present simplified model can be generalized to reveal the effects of effective dynamic viscosity on the slip flow and heat transfer of nanofluids for the viscosity models proposed by [PERSON] [45], [PERSON] [46] and [PERSON] [47]. Moreover, the analysis can be extended to include the results for different water-based nanofluids, and a comparison can be generated on the heat transfer characteristics of different nanofluids. Clearly, there is an opportunity to consider/extend this problem with non-Newtonian nanofluid models and to perform experimental work on these systems.  
[PERSON], [PERSON] and [PERSON] contributed in the modeling and wrote the paper. [PERSON] performed the simulations and processed all figures. [PERSON] contributed to the idea and the organization of the research work.  
Figure 17: Temperature \(\theta(\eta)\) profiles for different values of blowing parameter \(S<0\).
## 3 Results and Discussion
### Histological Analysis--Hematoxylin-Eosin (X-E)
#### 3.4.1 Immunoreactivity for nNOS  
We found isolated expression of nNOS in SM cells in preparations incubated with 5-HT; in IQP-incubated preparations we found very low, barely noticeable expressions of nNOS in SM cells and low expression in the myenteric plexus located between the circular and longitudinal layers of the SM layers of the stomach. Using a combination of 5-HT and IQP, we found that the density and intensity of nNOS-positive cells increases significantly in the myenteric plexus and SM cells in the incubated preparations (Figure 4L) in comparison to controls (Figure 4D),G) and 5-HT (Figure 4E) and IQP (Figure 4I) substances alone. This increase is also confirmed by the statistical data processing (Figure 5).  
Figure 3: (**a**) Changes in the tonus of the SMPs caused by IQP in the presence of CCh (0.5 \(\upmu\)M) (\(p>0.05\); \(n=13\)); (**b**) bethanech (0.5 \(\upmu\)M) (\(p>0.05\); \(n=13\)). The “-” sign indicates the SM relaxation process. The solvent DMSO induced mild relaxation, the value of which was taken as the baseline for subsequent relaxation effects.  
Figure 4: Histologic and immunohistochemical results from SM of the stomach and myenteric plexus. (**A**) preparation incubated with 5-HT, H-E staining, \(\times\)20; (**B**) incubated with IQP, H-E staining, \(\times\)20; (**C**) incubated with 5-HT + IQP, H-E; staining, \(\times\)20; (**D**) incubated with 5-HT, control, \(\times\)20; (**E**) incubated with 5-HT, presence of slight eNOS expression of in myenteric plexus, \(\times\)20; (**F**) incubated with 5-HT, presence of slight nNOS expression of in myenteric plexus and neuronal branches between SM cells, \(\times\)20; (**G**) incubated with IQP, control, \(\times\)20; (**H**) incubated with IQP, lack of eNOS expression, \(\times\)20; (**I**) incubated with IQP, single cells are positive for nNOS in myenteric plexus and SM cells, \(\times\)20; (**J**) incubated with 5-HT + IQP, control, \(\times\)20; (**K**) incubated with 5-HT + IQP, presence of slight eNOS expression in myenteric plexus and SM cells, \(\times\)20; and (**L**) incubated with 5-HT + IQP, increased intensity and density in the nNOS manifestation in myenteric plexus and SM cells, \(\times\)20.  
Figure 5: Data from the measurement of the immunoreactivity of nNOS in circular and longitudinal SM cells and the myenteric plexus of the stomach are presented. A t-test was used. **_p_ \(<\) 0.05.
# Meteorological Conditions that Promote Persistent Contrails
## 2 Data and Methods
### ERA-5 Data  
For each selected aircraft position and time in the IAGOS/MOZAIC data set, the corresponding weather data from ECMWF's (European Centre for Medium-Range Weather Forecast) ERA-5 reanalysis [11; 12; 13] is retrieved and interpolated to the exact position and time. These data have been obtained in hourly resolution from the Copernicus Data Service [14]. Apart from the three radiation quantities and specific cloud ice-water content as in WGR21, to determine the iRF values, we retrieve the following thermodynamic and dynamic variables for pressure levels of 200, 225, 250, and 300 hPa: temperature, relative and absolute humidity, vertical velocity, divergence, relative vorticity, potential vorticity, and geopotential. Using pressure and temperatures on two adjacent levels, we later estimate a local lapse rate at the aircraft positions. The pressure range of 300 to 200 hPa covers approximately the flight levels of 300 to 390 hft.