esa-sceva/satcom-chunk-collection huggingface dataset:
# Sustainability Audit of University Websites in Poland:
## 3 Materials and Methods
### Measurement Tools and Quality Indicators
\begin{table}
\begin{tabular}{c c c} \hline
**ID** & **Measurement Tool** & **Quality Indicators** \\ \hline
1 & Ecograder & Ecograder Score; Emissions per Page Load (grams of carbon dioxide, g); Page Assets (MB); Digital Carbon Rating (DCR) \\ \hline
2 & Website Carbon Calculator & Carbon rating; Emissions per Page Load (grams of carbon dioxide, g) \\ \hline
3 & Digital Beacon & Carbon Footprint, First Visit CO\({}_{2}\) (g); Size (MB) \\ \hline
4 & Kastor Green & Global Score; Ecodesign Score; Emissions per Page Load (grams of carbon dioxide, g) \\ \hline
5 & Green Web Audit Assistant & Ecol Impact Score \\ \hline
6 & Lighthouse & SEO Score Mobile \\ \hline
7 & RankMath SEO Analyzer & SEO Score \\ \hline  & (1) [[https://ecograder.com/](https://ecograder.com/)]([https://ecograder.com/](https://ecograder.com/)); (2) [[https://www.websitecarbon.com/](https://www.websitecarbon.com/)]([https://www.websitecarbon.com/](https://www.websitecarbon.com/)); (3) [[https://digitalbeacon.co/](https://digitalbeacon.co/)]([https://digitalbeacon.co/](https://digitalbeacon.co/)); (4) [[https://kastor.green](https://kastor.green)]([https://kastor.green](https://kastor.green)); (5) Custom GPT (ChatGPT); (6) Lighthouse plugin for Chrome; (7) [[https://rankmath.com/tools/seq-analyzer/](https://rankmath.com/tools/seq-analyzer/)]([https://rankmath.com/tools/seq-analyzer/](https://rankmath.com/tools/seq-analyzer/)) (accessed on 25 July 2025). \\ \end{tabular}
\end{table} 
Table 2: Green SEO audit tools and indicators. The Green Web Audit Assistant (GWA) has tools for searching for information, analysing code, and drafting technical documentation. Based on a website's URL, the GWA conducts a Green SEO audit and reports it with the EcoImpact Score as a grade from 0 to 100. This indicator is founded on four main components: performance (35%), carbon footprint estimate (35%), digital accessibility (15%), and sustainable SEO (15%). The EcoImpact Score is not a result of a laboratory measurement, but an estimation based on a heuristic analysis, public data, and a desk research analysis. The GPT agent was run using a normalised query template and unified auditing protocols for every website to ensure uniform and consistent results. The EcoImpact Score is not a result of a machine learning model but rather an aggregate of indicators fed by external sources. Therefore, the process yields stable and consistent results. Then, these indicators were juxtaposed with outcomes of a \"traditional SEO audit\" conducted with two tools: (1) Lighthouse (SEO Score Mobile) and (2) RankMath SEO Analyzer (SEO Score). All the indicators comprise a cohesive system for assessing the environmental impact of websites and their conformity with sustainable digital design principles, thereby supporting universities and public institutions in making informed design decisions.
## 3 Results and Discussion
### Microstructure, Relative Density and Phase Analysis of the G/6061 Al Composite
In order to determine the interfacial reaction between the graphite and 6061 Al matrix, an observation was performed. The result shows that the interface is not clear when the temperature is raised to 650 \({}^{\circ}\)C, while there are many gray areas around the interface, some of which present to be regular shapes such as bar-type and hexagon, shown as region 1 in Figure 4e. Line scanning revealed that the regions appeared in the 6061 Al matrix on both sides of the graphite, consisting of C and Al, implying that Al\({}_{4}\)C\({}_{3}\) phase was formed. Figure 5 also demonstrates the formation of bar-type and hexagon Al\({}_{4}\)C\({}_{3}\) and nano-Al\({}_{4}\)C\({}_{3}\) in the G/6061 Al composite UHP sintered at 650 \({}^{\circ}\)C. However, UHP sintered at a temperature of 550 \({}^{\circ}\)C or below, the XRD pattern distinctly manifests that no Al\({}_{4}\)C\({}_{3}\) is visible, despite the content of graphite, as can be seen in Figure 6. It has been reported [10; 20] that Al\({}_{4}\)C\({}_{3}\) exists in Al/075-graphite composites sintered at 550 \({}^{\circ}\)C and hot extrud  
Figure 4: SEM micrograph of the composite **(b)** with 5 vol.% graphite sintered at 5.2 GPa and 550 \({}^{\circ}\)C and of that with 10 vol.% graphite under **(a)** 35 MPa and 520 \({}^{\circ}\)C, or **(c)** 5.2 GPa and 550 \({}^{\circ}\)C, **(d,e)** 5.2 GPa and 650 \({}^{\circ}\)C, and **(f)** EDS line scanning along the arrow in **(e)**. effect of graphite on the formation of Al\({}_{4}\)C\({}_{3}\) is deemed to be significant, in consistent with our finding. The reason is that the sintering temperature can seriously affect the formation of Al\({}_{4}\)C\({}_{3}\) and the UHP can also impede the formation of Al\({}_{4}\)C\({}_{3}\) to a certain extent. When the sintering temperature is 650 \({}^{\circ}\)C or higher, the XRD intensity of carbon is evidently decreased, while that of Al\({}_{4}\)C\({}_{3}\) is increased, shown in Figure 6. This variation also means that the addition of graphite may motivate the reaction of graphite and Al, leading to the formation of Al\({}_{4}\)C\({}_{3}\) at a high sintering temperature. At a high sintering temperature, the diffusion of elements and the combination between the graphite and Al matrix were also improved, thus porosities were easy to be reduced. Furthermore, the interplanar distance and lattice constants of Al and Al\({}_{4}\)C\({}_{3}\) were able to be decreased by the ultra-high pressure [21; 22], which was instrumental in improving relative density. Therefore, it is concluded that the high relative density of the UHP-sintered G/6061 Al should be attributed to the ultra-high sintering pressure and the high sintering temperature.
# Detachment Energy Evaluation in Nano-Particle Cleaning Using Lateral Force Microscopy
## 2 Measurement Method of Nano-Particle Detachment Energy Using Lateral Force Microscopy
### Measurand of the Force Required to Detach  
Silica standard particles with diameters of \(D\) = 52 \(\pm\) 3, 80 \(\pm\) 3, and 310 \(\pm\) 19 nm (nanoComposix NanoXactTM Silica Nanospheres, San Diego, CA, USA) were adhered to the oxide film surface after the suspension diluted in a pH10.5 solution had naturally dried. The probe was scanned on the surface in an LFM mode (XE-300P AFM, Park Systems, Suwon, Republic of Korea) within 30 min. The scanning speed was 1000 nm/s in the \(x\)-axis, and the sampling time \(\Delta t\) was \(1/256\) s. When scanning in the \(x\)-direction at any \(y\)-coordinate is completed, the probe moves slightly in the positive \(y\)-direction and starts scanning in the \(x\)-direction from the \(x\) = 0 nm point again. The typical results of the images showed the horizontal movement of the \(D\) = 80 nm silica particle as shown in Figure 3. It was clearly confirmed that the nano-particle was detached and moved by the probe pushing.  
Figure 3: \(D\) = 80 nm silica particle’s position and lateral force variation until detaching from the nano-particle.  
Figure 2: Measurement of lateral force variation during detachment of nano-particle.  
Figure 4 shows lateral forces before acting (Line \(y_{\text{n}}\)) and during acting on the SiO\({}_{2}\) particles (Line \(y_{\text{n+1}}\)-\(y_{\text{n+2}}\)) and after detaching (Line \(y_{\text{n+3}}\)). The timing of the nano-particle detachment was known as the lateral force maximum. The lateral force was converted from the measured voltage which indicated that the displacement of the laser spot moved on the position-sensitive detector when the cantilever was twisted. The resolution of measurable lateral force in this experiment was 2 nN (2\(\sigma\)) which calculated using the force variation during scanning on the oxide film in line \(y_{\text{n}}\). Subsequently, the work to detach \(W_{\text{Ifm}}\) was analyzed on the line \(y_{\text{n+2}}\) that the particle detached from on the surface.
## 4 Experimental Results and Discussion
### Vision Capabilities Simulations  
The developed networks were subsequently adopted for inference simulations in controlled environments and during various site visits in a vineyard lying on the hills near Treviso, Veneto, Italy (N 45 deg41 deg6.066'', E 12 deg34'15.697''). Since the neural networks' development took place during the late spring months (when a grapevine passes from flowering to budding), the simulations were carried out on faithful plastic replicas of dark red, light red and white fully ripe grape clusters (Figure 6).
## 4 Proposed EHMFFL Algorithm
### Feature Selection Using PCC-GWO
#### 4.1.1 Calculating the Importance Score of Features Using PCC  
The PCC is a measure of the degree and direction of a relationship between two variables [38]. The PCC values vary from \(-1\) to \(+1\). A value of zero shows that there is no correlation between the two variables, while values near \(-1\) or \(+1\) suggest that there is a strong association between the two variables. The PCC is determined by:  
\[r_{x,y}=\frac{\sum{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt{\sum{(x_{i} -\overline{x})^{2}\sum{(y_{i}-\overline{y})^{2}}}}}, \tag{1}\]where \(\overline{x}\) and \(\overline{y}\) are the means of the two variables \(x\) and \(y\), respectively. Moreover, \(x_{i}\) denotes the \(i\)-th value of the variable \(x\), and \(y_{i}\) denotes the \(i\)-th value of the variable \(y\).  
By computing the correlation coefficient between each feature and the target variable, the method identifies the most informative features for an accurate classification. Then, by considering the correlation of each feature with respect to all the other features in the dataset, the method identifies redundant or highly correlated features that may not provide much additional information. The selection status of each feature is then determined based on a threshold value derived from its correlation coefficients. Finally, the GWO algorithm is used to repeat the selection process multiple times, and the feature subset with the highest fitness value is selected as the final solution. This method provides an effective way to identify and select the most valuable features in high-dimensional datasets, leading to improved predictive accuracy and better performance of machine learning models. The overall operation of PCC can be summarized as follows:  
1. The correlation coefficient of each feature \(i\) with the class is computed as \(CC_{i}\);
2. The correlation coefficient of each feature \(i\) in relation to the other features is calculated as \(CF_{i}\);
3. The importance score of each feature \(i\) can be calculated as \(IS_{i}=CC_{i}/CF_{i}\).  
Concerning the PCC, if the value of \(IS_{i}\) is greater than a specific threshold \(TH\) (\(IS_{i}>TH\)), the feature \(i\) is selected; otherwise, it is not chosen. However, in the proposed combined PCC-GWO algorithm, the importance scores obtained by the PCC are used to guide the search process of the GWO for achieving a better level of convergence.
# Analysis of the Working Performance of a Back-to-Back Geosynthetic-Reinforced Soil Wall
## 3 Results and Discussion
### Parametric Study on the Geogrid Length
#### 3.2.1 Post-Construction Deformation of the Wall  
The FEM results verified the previous research by [PERSON] and [PERSON] [30]; the maximum horizontal displacement for the first year with a 5 m long geogrid was 58.06 mm, where \(L/H\) = 0.57. It was larger than the lateral deformation limitation of 35.0 mm set by [PERSON] and [PERSON] [9]. Hence, the wall could be considered to be performing poorly or potentially unstable. A longer geogrid could lead to a smaller deformation as the maximum  
Figure 10: Comparison between the field test and simulated results on the geogrid strain. (**a**) 4.0 m left blocks; (**b**) 2.0 m right panel.  
Figure 9: Distribution of the geogrid strain with the time on-site. (**a**) 0 m high wall; (**b**) 2.4 m high wall; (**c**) 5.4 m high wall; (**d**) 7.5 m high wall.  
deformation for the reinforcement lengths of 6.0, 7.0, and 8.0 m were 24.38, 9.79, and 4.29 mm, respectively, on the left side wall (Figure 11). The maximum deformation of the right side wall decreased from 18.15 to 3.68 mm for the reinforcement length of 6.0 to 8.0 m, respectively. Overall, this indicated that, for reinforcements less than the required 0.7 \(H\), the BBGRSW could perform poorly.  
The stipulation from the railway department required more than the stability of the entire structure. The Chinese standard for high-speed railways states that the post-construction settlement must be less than 100 mm such that the railway tracks can remain functional. Therefore, the post-construction settlement on top of the wall required attention. For walls with a 5.0 to 8.0 m long geogrid, the maximum post-construction settlements on the top were 138.12 mm, 73.67 mm, 47.86 mm, and 34.30 mm. The FEM results indicated that the distance between the geogrid influenced the post-construction settlement as the settlement peaked with a reinforcement length of 5.0 m, which was approximately 390.23% of the baseline case settlement. It also verified the judgment based on the lateral deformation that the GRSW could perform poorly for reinforcement lengths less than 0.7 \(H\). In addition, the BBGRSW with the 8.0 m reinforcement was the only case in which the post-construction settlement was 96.9% of the baseline case. This indicated that the reinforcement length was a key factor in the settlement control.
## 4 Physical Audio Features Extraction Techniques
### Frequency Domain Physical Features
#### 4.2.5. Chroma-Related Physical Frequency Features  
Chroma is related to perception of pitch, in the sense that it is a complement of the tone height. In a musical context, two notes that are separated one or more octaves have the same chroma (e.g., C4 and C7 notes), and produce a similar effect on the human auditory perception.  
The following paragraphs describe chroma-related frequency features, which are basically computed from direct physical approaches:  
* **Chromagram:** also known as chroma-based feature, chromagram is a spectrum-based energy representation that takes into account the 12 pitch classes within an octave (corresponding to pitch classes in musical theory) ([PERSON] [99]), and it can be computed from a logarithmic STFT ([PERSON] and [PERSON] [100]). Then, it constitutes a very compact representation suited for musical and harmonic signals representation following a perceptual approach.
* **Chroma energy distribution normalized statistics:** or CENS for short, this feature was conceived for music similarity matching and has shown to be robust to tempo and timbre variations ([PERSON] _et al._[101]). Therefore, it can be used for identifying similarities between different interpretations of a given music piece.
## 2 Materials and Methods
### Characterization of Multilayer Sheets
#### 2.3.5 Tensile Tests  
The tensile properties of the samples were determined using a universal testing machine (Stable Micro System TA-XT plus, Haslemere, UK) following the standard method ASTM D882 [27]. To this end, the pre-conditioned samples, previously cut in dimensions of 25 mm \(\times\) 100 mm, were stretched by two grips, initially separated by 100 mm, at a crosshead speed of 50 mm\(\cdot\)min\({}^{-1}\) until breakage (model A/TG, Stable Micro System, Haslemere, UK). Stress (\(\sigma\)) versus strain (\(\epsilon\)) curves were obtained from the force-distance curves by considering sample dimensions and degree of deformation. The Young's modulus (E), stress at yield or elastic limit (\(\sigma_{\rm y}\)), and percentage of elongation at break (% \(\epsilon_{\rm b}\)) were obtained. Eight samples for each formulation were analyzed.
# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
## 2 Materials and Methods
### Data Used
#### 2.2.2 Ground Reference Data  
The sample data used in this research consist of two main components: the ground survey points and the portion labeled based on this visual interpretation of ultra-high resolution Google historical imagery. The sample points represent the crop attributes of the plot where the location is located. Ground sampling points are collected during various field activities to accurately record their location and attribute information. Based on the characteristics of real ground sample points, we create random points within Shandong. The random points are further interpreted by combining the existing field sample points  
Figure 1: The geographical location, city boundaries, topography in Shandong Province.  
and ultra-high-resolution images from Google Earth, resulting in a total of 938 sample point datasets, including 656 training samples and 282 validation samples (Figure 2).
# Energy Optimisation of Industrial Limestone Grinding Using ANN
## 7 Optimisation of the Grinding Process Using Artificial Neural Networks
### Design of a Hybrid Ensemble for Evaluation Indicators
#### 7.2.5 Evaluation of the Predictive Model for Indicators
For the \(S\) indicator, the model trained on the dataset from the industrial experiment performs the best. However, the prediction distributions of all models fall within the range of actual values, which is a positive signal, indicating prediction accuracy. The model's metrics are presented in Table 5.  
A comparison of results (Table 5) confirms that the hybrid ensemble model consistently outperforms the standalone ANN in terms of predictive accuracy across both energy indicators (S and W). The ensemble yields lower MAE, MSE, and RMSE values in most datasets, indicating a better fit and more reliable generalization capability. This advantage is particularly evident for the W indicator, where the ensemble achieves substantially higher \(R^{2}\) values (up to 0.54 for dataset A) compared to the ANN. The superior performance of the ensemble stems from the complementary strengths of the MLP and XGBoost components, enabling the model to capture both smooth nonlinearities and sharp threshold effects inherent in the grinding process. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{4}{c}{**S**} & \multicolumn{4}{c}{**W**} \\ \hline  & **MAE** & **MSE** & **RMSE** & \(R^{2}\) & **MAE** & **MSE** & **RMSE** & \(R^{2}\) \\ \hline A & 0.983 & 1.523 & 1.234 & 0.189 & 0.275 & 0.229 & 0.478 & 0.251 \\ Ensemble & **1.027** & **1.849** & **1.360** & **0.259** & **0.446** & **0.349** & **0.591** & **0.540** \\ \hline B & 1.451 & 3.972 & 1.993 & 0.433 & 0.687 & 0.907 & 0.952 & 0.399 \\ Ensemble & **1.349** & **3.380** & **1.839** & **0.518** & **0.654** & **0.794** & **0.891** & **0.473** \\ \hline C & 1.332 & 3.369 & 1.835 & 0.303 & 0.573 & 0.687 & 0.829 & 0.439 \\ Ensemble & **0.955** & **1.418** & **1.191** & **0.245** & **0.280** & **0.225** & **0.474** & **0.264** \\ \hline D & 1.284 & 3.153 & 1.776 & 0.347 & 0.563 & 0.691 & 0.831 & 0.435 \\ Ensemble & **1.203** & **2.642** & **1.626** & **0.453** & **0.562** & **0.627** & **0.792** & **0.488** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of metrics for ANN models vs. ensemble. Figure 11: Comparison of actual and predictive values of the \(W\)-index for A data. Figure 12: Comparison of actual and predictive values of the \(S\) and \(W\)-index for A data. Figure 10: Comparison of actual and predictive values of the \(S\)-index for A data.
## 3 Modeling of the VLC System and Its Components in the Indoor Environment
### Spectral Transmission Factor  
The spectral transmission factor \(T_{ij}(\lambda)\) accounts for the efficiency with which the mirror surface reflects light based on the wavelength \(\lambda\). This factor is crucial in VLC because the reflection efficiency can vary significantly with wavelength, affecting the received power.  
The spectral transmission factor can be modeled by using the optical properties and the coating of the mirror material. It can be expressed as follows [24]:  
\[T_{ij}(\lambda)=\exp(-\alpha(\lambda)d)R(\lambda), \tag{11}\]  
where \(\alpha(\lambda)\) is the material's absorption coefficient at wavelength \(\lambda\), \(d\) is the thickness of the mirror coating, and \(R(\lambda)\) is the reflectivity of the mirror material at wavelength \(\lambda\).  
The absorption coefficient \(\alpha(\lambda)\) describes how the mirror material absorbs light instead of reflecting it. This coefficient depends on the material and the wavelength of the incident light. Generally, it is defined as  
\[\alpha(\lambda)=\frac{4\pi k(\lambda)}{\lambda}, \tag{12}\]  
where \(k(\lambda)\) is the extinction coefficient, which varies with the wavelength.  
The reflectivity \(R(\lambda)\) of the mirror also varies with wavelength and can be obtained experimentally or through calculations based on the properties of the mirror material. Generally, for metals such as aluminum or silver, reflectivity is high across a broad range of wavelengths, but it may decrease at specific wavelengths.
## 2 The Model for Propagation  
### Dispersion Characteristics  
Dispersion is a phenomenon in which the propagation velocity (including group velocity and phase velocity) of ultrasonic waves in the medium varies with the frequency. Figure 1 shows the phase and group velocity dispersion curves for a cylindrical shell with an outer diameter of 1000 mm and a wall thickness of 2 mm. With reference to Figure 1, the large number of possible wave modes in a shell is illustrated in the dispersion curves, including longitudinal (L), flexural (F), and torsional (T) motions of the shell wall, which are labeled using the convention of Silk and Bainton [28]. Each mode wave is represented by an integer pair label, where the first integer gives the harmonic order of the circumferential change, and the second integer is the order list of each mode. Therefore, the mode wave with its first integer at zero is axisymmetric, while the mode wave with its first integer at one has a wave period of circumferential displacement and stress variation, etc. Dispersion curves provide useful information about dispersion characteristic of each mode, which is useful for the subsequent calculation of mode identification.  
Although AE signals have several frequencies and modes originating from fault sources, it should be analyzed in terms of each mode in the fundamental study involving the propagation mechanism. The particle only has circumferential displacement for the torsional mode. When the radius of the cylinder approaches infinity, the mode T(0,1) becomes the simple shear horizontal wave in the plate. There are three displacement components in the flexural mode, which are coupled with each other. The circumferential displacement of the particle in the longitudinal mode is zero, and in the limit of the radius of the cylinder approaching infinity, the longitudinal mode corresponds to all lamb waves in the plate. Among them, the L(0,1) mode dominated by radial displacement is similar to the A\({}_{0}\) plate mode at low frequencies, and the L(0,2) mode dominated by axial displacement is similar to the S\({}_{0}\) plate mode at low frequencies [29]. The majority of the work described in this paper have been carried out using the L(0,1) mode, which is equivalent to the A\({}_{0}\) Lamb wave due to the more sensitive A\({}_{0}\) mode for different damage types [30].