esa-sceva/satcom-chunk-collection huggingface dataset:
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 4 Results
### Tourism Sentiment Chain Generation and Scenario Validation
#### 4.4.1 Path Generation and Comprehensive Attractiveness Calculation Based on RW  
Adhering to the methodology detailed in Section 3.3.2 (1), each attraction is sequentially designated as the starting point with uniform initialization parameters (\(\alpha=\beta=\gamma=\frac{1}{3}\)). Utilizing the mean diameter of the spatial proximity and resonance correlation networks (set at 10) as the path length, 1000 random walk simulations are conducted to mitigate stochastic variability and ensure statistical significance. This process yielded 2577 Comprehensive Attractiveness Index (CAI) relationships across 178 attractions, as illustrated in Figure 11. Constructing a CAI association network with attractions as nodes and CAI as edge weights, we performed an exhaustive network characteristic analysis, summarized in Table 8.  
The CAI association network exhibits markedly higher connection density compared to the spatial proximity and resonance correlation networks, underscoring a richer and more diversified interconnection landscape among scenic spots. With a modularity value of 0.5540, the network maintains discernible community structures. The average betweenness centrality of 0.0106 indicates the absence of dominant hub nodes, suggesting a balanced  
Figure 11: Example of CAI association.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Metrics** & \multicolumn{3}{c}{**Evaluation Metrics**} \\ \hline Node count & 178 & Egde count & 2577 \\ Modularity & 0.5540 & Connectivity & Yes \\ Average degree centrality & 0.1636 & Average closeness centrality & 0.3476 \\ Average betweenness centrality & 0.0106 & Global clustering coefficient & 0.5232 \\ Power-law exponent of degree distribution & 0.4143 & Average shortest path length & 2.8344 \\ Network diameter & 8 & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Structural characteristics of CAI associaton network.  
nodal hierarchy and enhanced network resilience. The degree distribution power-law exponent of 0.4143 reveals the existence of a limited number of highly connected core nodes, confirming the network's scale-free nature without forming a fully connected graph.  
By synthesizing spatial proximity, resonance correlation, and thematic sentiment similarity, the CAI network effectively addresses the limitations inherent in single-dimensional frameworks. It preserves local clustering tendencies while ensuring path connectivity and mitigating excessive segregation. Consequently, this integrative approach enhances the comprehensiveness and coherence of TSC recommendations, providing a robust foundation for optimizing tourist experiences and resource allocation.
# _Helicobacter pylori_--The Bridge between Local and Systemic Inflammation in Children
## 3 Results  
Our study included 68 children divided according to the histopathological findings in three groups: 14 children with _H. pylori_ gastritis (group 1), 26 children with non-_H. pylori_ gastritis (group 2) and 28 children with no pathological findings--control group (group 3). We encountered a similar mean age between the 3 groups: 14.00 \(\pm\) 2.717 years for _H. pylori_ gastritis group, 13.50 \(\pm\) 2.875 years for non-_H. pylori_ gastritis group and 15.00 \(\pm\) 2.617 years for control group (\(p\) = 0.0678). We found no significant difference regarding the gender or the CRP among the three groups (\(p\) = 0.2159/\(p\) = 0.8096), but we found significant difference in terms of living area (\(p\) = 0.0404) (Table 1). Our study also pointed out that children with histologically proven _H. pylori_-gastritis are more commonly found to have positive immunoglobulin A (IgA) anti-_H. pylori_ (\(p\) = 0.0094) (Table 1). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{_H. pylori_ **Gastritis**} & \multicolumn{2}{c}{**Non-H. pylori** **Gastritis**} & \multicolumn{2}{c}{**Control Group (n = 28)**} \\  & **Group (n = 14)** & **Group (n = 26)** & **n (\%)** & **n (\%)** & **p Value** \\ \hline Age (years) & & 14.00 \(\pm\) 2.717 & 13.50 \(\pm\) 2.875 & 15.00 \(\pm\) 2.617 & 0.0678 \\ \hline Gender & Female & 7 (50.00\%) & 15 (57.69\%) & 21 (75.00\%) & 0.2159 \\ \cline{2-6}  & Male & 7 (50.00\%) & 11 (42.31\%) & 7 (25.00\%) & \\ \hline Area & Rural & 11 (78.57\%) & 11 (42.31\%) & 11 (39.29\%) & 0.0404 \\ \cline{2-6}  & Urban & 3 (21.43\%) & 15 (57.69\%) & 17 (60.71\%) & \\ \hline CRP & Negative & 5 (55.56\%) & 10 (58.82\%) & 14 (66.67\%) & 0.8096 \\ \cline{2-6}  & Positive & 4 (44.44\%) & 7 (41.18\%) & 7 (33.33\%) & \\ \hline IgA anti-_H. pylori_ & Positive & 5 (55.56\%) & 2 (12.50\%) & 1 (6.67\%) & 0.0094 \\ \cline{2-6}  & Negative & 4 (44.44\%) & 14 (87.50\%) & 14 (93.33\%) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of three groups according to age, gender, area and CRP. Regarding the assessed hematological, inflammatory and biochemical parameters, no significant differences were observed between the three groups, except for NLR (\(p\) = 0.0253) (Table 2). Applying the [PERSON]'s Multiple Comparison test, we noticed that NLR value was higher in \(H\). _pylori_-induced gastritis group versus non-\(H\). _pylori_ gastritis group (\(p\) = 0.0107) (Table 3 and Figure 1). Although we found no significant differences regarding mean corpuscular hemoglobin concentration (MCHC) among the three groups, when comparing the group of \(H\). _pylori_-induced gastritis group to non-\(H\). _pylori_ gastritis group, we noticed a significantly higher value of this parameters in \(H\). _pylori_-induced gastritis group (0.0499). Similarly, we also found a significantly higher value in non-\(H\). _pylori_ gastritis group when compared to control group (\(p\) = 0.0489) (Table 4 and Figure 2).
## 2 Proposed System  
The workflow of human-machine interaction abstraction for agriculture proposed in this paper is shown in Figure 1, which aims to provide a friendly human-machine interaction for agricultural measurement and control systems. Step 1 describes the user inputting natural language commands by voice or text; Step 2 describes the process of unstructured natural language to structured intermediate language AOM; Step 3 describes the mapping of extracted key indicator information into the corresponding parameters to send to Raspberry Pi; Steps 4 and 5 describe the process of controlling field devices and debugging sensors, respectively. Step 6 describes the process of sending the data measured by the sensor back to the user. The proposed approach in this paper implements the transformation of a discrete, unstructured natural language into a structured intermediate language. We illustrate this process in terms of parameter passing. We enter the natural language command \"\(\
exists\)\" (\"Please help me turn on the air conditioner in the greenhouse and then turn off the sprinkler in the rice field.\"), after structuring, a structured AOM statement is generated: [{Action_1 : \"4 If\" (\"turn on\"). Object_1: \"4 If\" (\"greenhouse\"). Member_1: \"4 If\" (\"air conditioner\")},{Action_1 : \"4 If\" (\"turn off\"). Object_1: \"4 If\" (\"rice field\"). Member_1: \"4 If\" (\"sprinkler\")}. The keywords in the generated AOM statements are then mapped to the corresponding parameters and sent to the Raspberry Pi in the field, and the Raspberry Pi port recognizes the corresponding information to achieve the corresponding manipulation functions.  
Figure 1: Human-machine interaction abstraction workflow in agriculture.
# Investigation of Hydrogen Flux Influence on InGaP Layer and Device Uniformity
## 3 Results and Discussion
### Impact of Total H\({}_{2}\) Flux on the Uniformity of InGaP/GaAs HBTs
#### 3.2.1 Gummel Curve Measurements  
By examining the overlap of Gummel curves at five different points on each sample, we were able to gain insight into the uniformity of the epitaxial wafer. Figure 4 showcases this comparison across three samples, with the degree of curve overlap serving as a visual indicator of uniformity. Sample-2 stands out with the highest degree of overlap, suggesting exemplary uniformity. While Sample-1 also demonstrates commendable overlap, albeit with some variation, it indicates good overall uniformity. Sample-3, however, exhibits significant deviation, particularly in areas of high current density, hinting at potential inconsistencies in the epitaxial wafer's preparation process that could affect device performance stability. Although this visual assessment offers a preliminary view of uniformity, it lacks a concrete, quantitative benchmark. To refine our understanding of uniformity, we delved into analyzing critical parameters derived from the Gummel curve: the turn-on voltage (V\({}_{\text{on}}\)) and current gain (\(\beta\)). These parameters serve as vital measures of transistor performance, providing a more precise evaluation of the epitaxial wafer's uniformity.  
Figure 4: Comparative analysis of Gummel curves at five measurement points for the samples.  
Figure 3: Photograph of the InGaP/GaAs HBT Chip; the yellow regions (1–5) indicate the five measurement positions.  
Through a detailed analysis of the Gummel curves, we derived crucial data on the turn-on voltage and current gain for each of the five measurement points across three samples, as detailed in Table 3. This analysis revealed a remarkable consistency in the turn-on voltage (V\({}_{\text{on}}\)) and current gain (\(\beta\)) values among the measurement points of Sample-2, indicating a uniform performance. Conversely, Sample-1 and Sample-3 displayed more varied data, with Sample-3 showing particularly pronounced disparities among its measurement points, suggesting potential uniformity issues in the epitaxial wafer preparation. To quantify these observations, we calculated the standard deviations of V\({}_{\text{on}}\) and \(\beta\) across the five measurement points for each sample, presented in Table 4. The findings show that Sample-2 exhibits the lowest standard deviation for both V\({}_{\text{on}}\) and \(\beta\), confirming its superior uniformity and consistency compared to Sample-1 and Sample-3. This quantitative approach underscores the effectiveness of Sample-2's epitaxial growth process in achieving high uniformity in transistor performance.
## 3 Nonthermal Methods
### Pulsed Electric Field (PEF)  
Pulsed electric field (PEF) is another nonthermal method used to control _L. monocytogenes_. This technique inactivates microorganisms by applying high-voltage electric field pulses (>18 kV/cm) for a short period. PEF disrupts the bacterial cell membrane, resulting in the destruction of the microorganism. Factors that influence the effectiveness of this method include electric field strength, temperature, and pH [71; 72]. One study showed that PEF is more effective at lower pH and higher electric field strengths [73]. For example, at pH 3.5, a treatment of 28 kV/cm for 400 s resulted in a 6 log10 reduction in _L. monocytogenes_ cells. PEF technology has been used in various food products, including milk, juices, and soups [22].
# A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
## 2 Related Works
### Classifiers
#### 2.2.4 Multi-Layer Perceptron  
Multi-Layer Perceptron (MLP) is the traditional basic neural network that learns a non-linear model to map training features X \((x_{1},x_{2},\ldots,x_{m})\) to output targets \(y\)[29]. The model comprises an input layer, one or more hidden layers, and an output layer. Each layer is made up of neurons that transform the value of the previous layer with a weighted linear summation \(w_{1}x_{1}+w_{2}x_{2}+\ldots+w_{m}x_{m}\) followed by a non-linear activation function \(g(\cdot):R\to R\).  
The MLP neural network has shown encouraging performances in recognizing facial expressions of emotion. [PERSON] and [PERSON] [33] proposed a system for classifying facial expressions using the Viola-Jones algorithm for face detection, the HOG descriptor for feature extraction, PCA for dimensionality reduction, and MLP for classification. They reported an average accuracy of 82.97% on the CK+ database when classifying eight basic expressions. [PERSON] et al. [34] developed a new constructive training algorithm for the Multi-Layer Perceptron (MLP) applied to FER. Unlike most traditional algorithms, which fix the structure of the neural network before training, the proposed constructive training algorithm learns the architecture of the network and performs the learning process simultaneously. The approach uses the Perceived Facial Images (PFI) in eight directions to extract features from a facial image, and the predicted expression is obtained by a fusion of the neural networks corresponding to the eight directions. The main drawback of MLP is that it is often time-consuming and tedious to determine a proper structure than can guarantee convergence and avoid over-fitting.
## 2 Pollutant Dispersion Models
### Gaussian-Type Model--Pasquill Formula  
The starting point for most models of dispersion of pollutants in the air is a differential equation describing turbulence diffusion [25] in the following form:  
\[\begin{array}{l}\frac{\partial\mathbb{C}}{\partial t}+u\frac{\partial \mathbb{C}}{\partial x}+v\frac{\partial\mathbb{C}}{\partial y}+w\frac{\partial \mathbb{C}}{\partial z}=\\ =\frac{\partial}{\partial x}\left(K_{x}\frac{\partial\mathbb{C}}{\partial x} \right)+\frac{\partial}{\partial y}\left(K_{y}\frac{\partial\mathbb{C}}{ \partial y}\right)+\frac{\partial}{\partial z}\left(K_{z}\frac{\partial \mathbb{C}}{\partial z}\right)+S_{c}\end{array} \tag{1}\]  
where:  
* \(C\)--concentration of pollution;
* \(t\)--time;
* \(u,v,w\)--wind speed vector components;
* \(x,y,z\)--coordinates of the location of a point in space;
* \(K_{x},K_{y},K_{z}\)--components of the atmosphere turbulence diffusion coefficient;
* \(S_{C}\)--term describing losses and sources of pollution in the atmosphere.  
The solution to Equation (1) can be made using different numerical or analytical methods, but the analytical solution requires a number of simplifications. One of the most frequently used ways of dealing with the discussed problem is to apply the Gaussianformula concerning the propagation of pollution emitted by point-type sources, taking into account the negligence of both spatial and temporal variability of meteorological parameters.  
\[C_{x,y,z}=\frac{E}{2\pi UL_{sr}\sigma_{y}\sigma_{z}}\times e^{\left(-\frac{y^{2 }}{2\sigma_{y}^{2}}\right)}\times\left[e^{\left(-\frac{(z-H_{e})^{2}}{2\sigma_{ z}^{2}}\right)}+e^{\left(-\frac{(z+H_{e})^{2}}{2\sigma_{z}^{2}}\right)}\right] \tag{2}\]  
where: \(C(x,y,z)\)--concentration of the pollutant at the point(\(x,y,z\)). An extremely important transformation is the introduction of empirical dependence of plume dispersion coefficients determining its distance from the source (2). The obtained relation is described in the literature under the name of Pasquill formula [25; 28]. Use in analyses of the dispersion of pollutants in the air in accordance with this principle is optimal if the average wind speed, \(U_{sr}\), is greater than \(1\frac{m}{s}\). In order to show how the distribution of a plume of pollutants for a point source with continuous emissions fixed over time is analysed, the diagram presented in the following Figure 2 is used.  
In Poland, according to the Regulation of the Ministry of the Environment from 5 th December 2002, the Gaussian model--the Pasquill formula--is used to determine the state of air pollution for existing or planned sources [26].
## 3 Results and Discussion
### Determination of DPPH Radical Scavenging Activity  
According to Figure 13, USeNPs could be considered a moderate antioxidant. [PERSON] et al. [66] studied the DPPH scavenging activity of SeNPs capped with Sargassum fusiforme polysaccharides and found that these NPs showed the lowest DPPH free radical scavenging ability. This extract's DPPH scavenging activity and its inhibition (%) were found to change in a concentration-dependent manner, and that concurred with [PERSON] et al. [67], who found that DPPH scavenging activity increased linearly with increasing concentrations of SeNPs because of the accessibility of more interaction sites on SeNPs. Despite this, its efficacy did not exceed vitamin C (reference drug) in its antioxidant properties. [PERSON] [68] proved that reductions are generally responsible for reducing properties, and [PERSON] et al. [69], [PERSON] et al. [70], [PERSON] et al. [71], and [PERSON] et al. [72] found that the antioxidants of some seaweed components may occur by donating electrons and reacting with free radicals to convert them into more stable products and by ending the free radical chain reaction. These strong antioxidant activities boost the seaweeds' overall anticancer activity [73].
# All-Optical Modulation and Ultrafast Switching in MWIR with Sub-Wavelength Structured Silicon
## 6 Conclusions  
In conclusion, this work investigates the performance of the all-optical modulation in the MWIR using an optical membrane made of silicon. The investigation explored the development of the reflectance spectra prior to, during and after excitation from an ultrashort laser pulse as a function of time. We demonstrated the spectra change under different pump fluences and observed the transition of optical response from semiconductor to metal in the monitored spectral region. The investigation allowed the construction of a robust optical model describing the evolution of the reflected signal as a function of time and signal wavelength for the different pumping fluences. Using this model, we optimised the performance to achieve the fastest time response and the highest contrast of the modulation for 4 \(\upmu\)m signal. We have shown that it is possible to achieve the modulation of 30% of the reflected signal with the extremely fast response time of 0.55 ps. Such a fast response is possible because the modulation does not rely on the carrier decay, but rather rests on the properties that relate the reflectance and the development of the effective dielectric function in time. An important point is that the low effective dielectric function of the membrane can be altered more easily by optical pumping compared to the bulk counterparts to achieve the modulation condition as \(\epsilon_{eff}\to 1\).  
Conceptualization, A.K.; Methodology, R.W.; Software, J.C.; Validation, R.W., J.C. and D.C.; Formal Analysis, R.W.; Writing--Original Draft Preparation, R.W.; Writing--Review & Editing, A.K., J.C. and D.C.; Supervision, A.K.; Funding Acquisition, A.K. EPSRCU.K., QuantIC.  
The authors acknowledge financial support provided by The Quantum Enhanced Imaging Hub (QuantIC) and EPSRC U.K.  
The authors declare no conflict of interest.
###### Abstract  
Ultrasound (US) is a non-thermal food processing method that can be used as a pre-treatment or integrated during drying to enhance mass transfer by inducing cavitation and forming microchannels in plant tissue. Thus, this study investigated the combined effect of ultrasound pre-treatment (21 kHz; 180 W; 10 min, 20 min, 30 min) and the subsequent hybrid drying process--ultrasound-assisted hot-air drying (temperature of 70 \({}^{\circ}\)C, frequency of 36 kHz; ultrasound power of 120 W, 160 W, 200 W)--on the drying kinetics and quality attributes of dried Gloster apples. The experimental design was optimized using the response surface methodology (RSM). The effects of ultrasound parameters on drying time, dry matter content, water activity, rehydration and hygroscopic properties, color change, textural properties, content of vitamin C, polyphenols and flavonoids, and antioxidant activity were evaluated. Among the analyzed variants, the most effective combinations were longer US duration (30 min) with lower US power (120 W) or shorter US duration (10 min) with higher US power (200 W). To obtain dried material with the most desirable rehydration and hygroscopic properties, a US power in the range of 120-160 W, preceded by a US pre-treatment lasting 20 min, should be selected. Conversely, optimizing the content of bioactive components would involve choosing the longest US treatment time and medium to high ultrasonic power during drying. These results provide actionable insights for the industry to tailor drying parameters based on the desired product attributes.  
vitamin C; bioactive compounds; color; rehydration rate; hygroscopic properties; texture; structure +
Footnote †: journal: Applied sciences
## 6 Performance Evaluation
### Simulation Results
#### 6.2.1 Sensitivity to the Number of Queries  
In the simulation, an increasing number of queries with period range 100 uniformly requested data transmissions from 50 nodes. Figure 12 is the graph of average energy consumption values for each node in a sensor network to the number of total queries.  
The graph indicates  
Figure 14(a,b) are the graphs for network lifetime and the NLR against the number of queries. As expected, network lifetime is inversely proportional to the number of queries and the NLR is proportional to the number of total queries.  
Figure 14: Sensitivity of the **(a)** Network Lifetime and **(b)** NLR to the number of queries.  
Figure 13: Sensitivity of the EER to the number of queries.
# The Use of Chalcogenite as a Biosorption Bed in the Treatment of Groundwater
## 4 Discussion of Results
The deposits are not subjected to a desorption process, but only to a periodic rinsing to remove accumulated sediments. The sorption properties of chalcedonite and the ability to retain the water microorganisms that cause the biofilm formed on its surface takes over the sorbent functions and uses the retained substances for the development and multiplication of bacterial cells. The creation of biofilms and the formation of manganese oxide coatings on the surface of chalcedonite causes the deposit to work continuously (does not run out). For that very reason, the [PERSON] model commonly applied to describe the continuous column system [30] may not be used. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Water I** & **Water II** & **Water III** \\ \cline{2-4}  & **Beds Pre-activated** & **Bed without Pre-activation** \\ \hline The amount of nitrogen removed & & & \\ \(\Delta\) N-NH\({}_{4}\)\({}^{*}\) [g N-NH\({}_{4}\)\({}^{*}\)/m\({}^{3}\)] & 0–3.70 & 0.1–3.5 & 1.34–3.00 \\ \hline Amount of nitrite nitrogen formed & & & \\ \(\Delta\) N-NO\({}_{2}\)\({}^{-}\) [g N-NO\({}_{2}\)\({}^{-}\)/m\({}^{3}\)] & & & \\ \hline Amount of nitrate nitrogen formed & & & \\ \(\Delta\) N-NO\({}_{3}\)\({}^{-}\) [g N-NO\({}_{3}\)\({}^{-}\)/m\({}^{3}\)] & & & \\ \hline Oxygen consumption \(\Delta\) O\({}_{2}\) [g O\({}_{2}\)/m\({}^{3}\)] & 0–4.06 & 0.52 & 4.12–8.43 \\ \hline Efficiency of N-NH\({}_{4}\)\({}^{*}\) removal [\%] & 0–100 & 2.85–100 & 78–96 \\ \hline Efficiency of Fe removal [\%] & 85–100 & 93–100 & 93–100 \\ \hline \multirow{2}{*}{Efficiency of Mn removal [\%]} & (–34)–97\% & & \multirow{2}{*}{} & from 28 days 0, \\  & from 57 days, & & & washing out Mn to the \\ \multirow{2}{*}{Efficiency of Mn to} & washing out Mn to & & & filtrate \\  & the filtrate & & & \\ \hline Time to obtain a normative value & & & \\ (0.39 g N-NH\({}_{4}\)\({}^{*}\)/m\({}^{3}\)) [day] & 23 & 23 & 14 \\ \hline Oxygen consumption per 1g of nitrogen [g O\({}_{2}\)/1 g N] & 2.02 & 2.43 & 3.20 \\ \hline The start time of the I phase of nitrification [day] & 31 & 1 & 7 \\ \hline The start time of the II phase of nitrification [day] & 51 & 24 & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of parameters in the biofiltration process on chalcedonite beds.