esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 OMWW as a Source of Biopolymers and Bio-Energy Production
### Use of OMWW in Feed Formulation  
The use of agro-industrial by-products in animal feed can represent an economically and environmentally advantageous solution for the livestock sector, increasing its profitability and sustainability [100]. Olive oil by-products have been tested for the formulation of feed for lambs, pigs, and chickens by evaluating the antioxidant activity on animals and on final products. [PERSON] et al. [101] evaluated the effect of OMWW addition in a silage formulation for lambs, containing 52.5% of solids, 7.5% of OMWW, and 40% of water. The administration of OMWW-containing silage was found effective in improving animal welfare and productivity. Furthermore, several authors tested the effectiveness of a reduction in oxidative stress and in the stimulation of the immune response of the same extract for pigs. [PERSON] et al. [102] studied the antioxidant effect of the addition of 4% of OMWW (representing the retentate obtained by microfiltration) in silage. Piglets fed with the fortified formulation showed an increase in tested biomarkers (as total antioxidant capacity: TAC; glutathione: GSH; catalase activity: CAT; protein carbonyls: CARB; and reactive thiobarbituric acids: TBARS) in blood and tissues and a decrease in oxidative stress, with an overall increase in productivity. In addition, [PERSON] et al. [103] evaluated the antioxidant activity in piglets fed with phenol extracts, and results highlighted an increase in leukocytes and cyclooxygenase-2 (COX-2), known as markers of inflammation. [PERSON] et al. [104] repeated the test in chickens, highlighting markers of antioxidant activity with the same silage formulation proposed for piglet feeding. The results confirmed that such supplementation lowers the levels of lipid peroxidation and protein oxidation by increasing the total antioxidant capacity in plasma confirming that both OMWW and oil by-products (leaves and olive pomace) can be a viable alternatives to fortify animal feeds.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 3 Numerical Example
Some load cases present more localized hot spots, while others show larger thermal traces, probably depending on the nearness of the edges or thermal response heterogeneity of the composite. While temperature is a scalar quantity and has none of the directional complexity of strain, capturing peak values still requires optimal sensor placement and correct response characteristics due to the change in position of the maxima. Similarly, temperature peaks are not co-located across cases, just as the strain field, which highlights the importance of distributed sensors. The difference between these two types of fields supports the approach even more: optimized strain sensors must consider both spatial and angular considerations, while temperature sensor placement is based on accurate mapping of the scalar maxima. The FEM results shown in Figures 12 and 13 confirm that optimized sensor configurations based on field-aware criteria are necessary for robust structural monitoring in varying load conditions. Figure 12: Contour plots of equivalent elastic strain maps [mm/mm] for load cases 1 to 7, shown in subfigures (**a**–**g**), respectively.
## 4 Discussion
### LSI Method Generalization Analysis  
As described in Section 2, many test images (i.e., WV3-Tripoli, WV3-Rio and WV2-WDC) are employed to explore the validity of the proposed _LSI_ method. The _LSI_ method generalization is particularly analyzed with the overall accuracy measurement of shadow detection results for test images above (i.e., WV3-Tripoli, WV3-Rio and WV2-WDC), since the overall accuracy is the most powerful evidence for the shadow detection performance. Figure 11a-c respectively depict the overall accuracy measurements of shadow detection results of 16 test images of WV3-Tripoli, 16 test images of WV3-Rio and 16 test images of WV-WDC by the proposed _LSI_ method in various invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ).  
Figure 11: _LSI_ method generalization analysis. (**a**) WV3-Tripoli. (**b**) WV3-Rio. (**c**) WV2-WDC.  
As can be observed in Figure 11a-c, relatively high values of the overall accuracy measurement are acquired for most test images of WV3-Tripoli, WV3-Rio and WV2-WDC, which shows the good shadow detection ability of the proposed _LSI_ method for most test images of WV3-Tripoli, WV3-Rio and WV2-WDC. Additionally, stable and high values of the overall accuracy measurement are obtained for test images of WV3-Tripoli, WV3-Rio and WV2-WDC in HIS, HSV, CIELCh and YIQ spaces, although the _LSI_ method fails in detecting shadow in six test images of WV3-Rio in the YCbCr space. Through comparing the overall accuracy measurements for shadow detection results of test images of WV3-Tripoli, WV3-Rio and WV2-WDC, a conclusion can be drawn that the proposed _LSI_ method is able to further complete shadow detection tasks and delivers an excellent shadow detection performance for HSR multispectral satellite remote sensing images. Provided this situation, two test images of WV3-Tripoli are employed in this paper to specifically evaluate the shadow detection performance of the proposed _LSI_ method against other comparative shadow detection algorithms, as discussed in Section 2 previously.
## 6 Airtightness Sealing Performance and Failure Analysis  
### Air-Leakage Paths  
Theoretically, there are three possible air-leakage paths when sealing failure occurs at the gasket-in-groove, i.e., the upper gasket-gasket groove contact surface, the gasket-gasket contact surface, and the lower gasket-gasket groove contact surface. The FEA results in this study show that the air-leakage paths for the circumferential joint all are on the gasket-gasket contact surface, as shown in Figure 9. Only one case with an initial maximum contact pressure \(p_{imax}\) of 100.0 kPa is presented in Figure 9 due to the other cases having similar leakage paths. Previous studies have shown that fluid leakage occurs once the fluid pressure exceeds the maximum contact pressure of the contact surface [28; 29; 30]. Table 2 compares \(p_{imax}\) on possible air-leakage paths for the gasket-in-groove with different joint gaps. Findings from the study of the data indicate that \(p_{imax}\) at the gasket-gasket contact surface is the minimum in the three possible air-leakage paths for all cases, which reasonably explains that the gasket-gasket contact surface is the most vulnerable path for air-leakage when sealing failure occurs at the gasket-in-groove.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Possible Air-Leakage Paths**} & \multicolumn{5}{c}{**Joint Gap (mm)**} \\ \cline{2-6}  & **9.873** & **9.647** & **9.423** & **9.205** & **8.985** \\ \hline Gasket-Gasket surface & 20.0 & 40.0 & 60.0 & 80.0 & 100.0 \\ \hline Gasket-Groove surface & 21.6 & 43.4 & 65.7 & 85.1 & 112.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of initial maximum contact pressure for possible air-leakage paths of the gasket-in-groove (unit: kPa).  
Figure 8: The load–displacement relationship of the gasket-in-groove based on experiments.
# Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction
## 5 Results and Discussion
### Ablation Study Analysis  
The effectiveness of each component in our proposed architecture is validated through comprehensive ablation studies, as shown in Table 2. The base model using only EEG signals achieves 93.5% accuracy, comparable to existing single-modality approaches. The introduction of time series imaging significantly improves performance to 95.7%, demonstrating the value of our complementary visual representation strategy. The dual modality architecture further enhances accuracy to 97.2%, while the addition of the cross-attention mechanism yields the best performance of 98.7%. The ablation results also validate our knowledge distillation strategy. The student model without knowledge distillation achieves 94% accuracy, while the distilled version reaches 96.8%, demonstrating successful knowledge transfer from the teacher model while maintaining computational efficiency. This represents only a 1.9% accuracy drop from the teacher model while reducing the model size by 60%, making it highly suitable for deployment in resource-constrained environments.  
Feature visualization through UMAP dimensionality reduction (Figure 5) provides insights into the model's decision-making process. The clear separation between different seizure states in the feature space, which is particularly evident in the right portion of the plot, indicates that our dual-modality approach successfully learns discriminative representations.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Variant** & **Accuracy (\%)** & **Sensitivity (\%)** & **AUC (\%)** \\ \hline ADML (EEG time-series only) & 93.5 & 92.8 & 93.4 \\ ADML (MTF image only) & 92.9 & 92.6 & 92.2 \\ ADML (Dual-modal) & **98.7** & **98.3** & **98.8** \\ \hline ADML (Cross-attention fusion) & 97.2 & 96.8 & 97.1 \\ ADML (Gated fusion) & 95.7 & 95.1 & 95.6 \\ \hline Student Model (EEG time-series) & 94.2 & 93.8 & 94.1 \\ Student Model (MTF image) & 96.8 & 96.5 & 96.7 \\ \hline \hline \end{tabular} Note: Bold values indicate the best performance achieved for each evaluation metric.  
\end{table}
Table 2: Ablation study results on CHB-MIT.  
Figure 4: Feature correlations of test on Bonn.
## 4 Experimentations
### Clustering Performance Evaluation  
Assessing the performance of our clustering algorithm is more complex compared to classification. We have selected three evaluation metrics that do not rely on the absolute values of cluster labels, but rather evaluate the clustering based on the separation of similar data into groups akin to a set of ground truth classes. This approach allows us to evaluate the effectiveness of the clustering algorithm in capturing meaningful patterns and groupings within the data, irrespective of noise or non-standard cluster shapes. Moreover, our methodology accounts for the dynamic nature of clustering tasks, wherein clusters may evolve or merge over time, ensuring a comprehensive assessment of performance under real-world conditions.
## 5 Conclusions  
The aim of this study was to investigate the application of neural networks in the spatial analysis of binaural room impulse responses. The neural network was tested using binaural room impulse responses captured using two different binaural dummy heads. The neural network was shown to have no significant difference in accuracy when analysing the direct sound of the binaural room impulse response across the two binaural dummy heads, with 64.58% and 68.06% of the predictions being within \(\pm 5^{\circ}\) of the expected values for KEMAR and the KU100, respectively. However, upon presenting the NN with reflected components for analysis, the accuracy of the predictions was significantly reduced. The NN also generally produces more accurate results for reflected components of the binaural room impulse response captured with the KU100. Comparisons of the interaural cues for the direct sound and reflected components show a distinct blurring in the cues for the reflected components measured with KEMAR, which is present to a lesser extent for the KU100. This blurring could be a product of lower signal-to-noise ratios or misalignment in the measurement systems, leading to greater ambiguity in the measurements. The results presented in this paper show the potential of using this technique as a tool for analysing binaural room impulse responses, while indicating that further work is required to improve the robustness of the algorithm for analysing reflections and signals with lower signal-to-noise ratios. Further development of this algorithm will investigate the application of the neural network for elevation direction of arrival analysis and integration of the method with geometry inference and reflection back-propagation algorithms, allowing for analysis of a room's geometry and its affect on sounds played within it.  
Funding was provided by a UK Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Award, the Department of Electronic Engineering at the University of York, and in part by Digital Creativity Labs, EPSRC Grant Number: EP/M023265/1.  
[PERSON] developed the concepts, algorithms and experiments and wrote the paper. [PERSON] supervised the project and paper writing, providing input throughout the development process.  
The authors declare no conflict of interest  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DoA & Direction of arrival \\ ITD & Interaural time difference \\ ILD & Interaural level difference \\ HRIR & Head-related impulse responses \\ NN & Neural network \\ DNN & Deep neural networks \\ GMM & Gaussian mixture model \\ IACC & Interaural cross-correlation \\ FFT & Fast Fourier transform \\ \end{tabular}  
MCT Multi-conditional training ADAM Adaptive moment estimation BRIR Binaural room impulse responses SNR Signal-to-noise ratio ANOVA Analysis of variance
## 1 Introduction
### Overview of 6G Wireless Communication  
The technology of 6G is the type of next-generation wireless communication that is capable of overcoming the shortcomings of previous 5G/4G wireless communication systems [9]. An intelligent 6G wireless communication network with improved KPIs will be expected to be implemented between 2027 to 2030 [59]. At present, autonomous systems are very familiar in every element of life such as healthcare, transportation, industry, etc. Thus, a massive number of sensors have been introduced into power grids, cities, homes, industries, vehicles, offices, and other sectors for automated and smart-driven lifestyles [9]. So, 5G can provide seamless wireless connectivity for such an autonomous system [24; 60]. However, though 5G is already implemented in most countries, and it is going to be deployed all over the world within a few years, it cannot provide fully intelligent, reliable, and flexible communication with the required KPIs for the different applications of the smart grid [60; 61]. Therefore, future 6G wireless communication is a suitable solution for satisfying the various demands associated with smart grid communication networks. [62].  
Figure 2: The Conceptual model of the Smart Grid [5; 6].  
It must be noted that 6G can provide remarkable performance improvement in the case of data rate, jitter, latency, reliability, connection density, area traffic capacity, spectral efficiency, flexibility, and energy efficiency for the effective execution of the considered major smart grid applications [7; 62]. Moreover, 6G is a type of intelligent wireless communication that can provide vast possibilities, such as the augmentation of human intelligence, quality of experience, the Internet to everything, quality of life, etc. [63]. AI and 6G will create a revolution by transforming connected things into connected intelligence, which is appropriate for time-critical, secured, reliable, and adaptive smart grid applications [64].  
Moreover, various novel technologies will be introduced in 6G wireless communication to effectively provide the required key performance indicators (KPIs) in terms of considered smart grid applications [1; 65]. Non-orthogonal multiple access (NOMA); energy harvesting (EH); wireless power transfer (WPT); a green IoT; massive multiple-input-multiple-output (mMIMO); mmWave; terahertz (THz); AI-based ultra-dense networks (UDNs); intelligent reflecting surface (IRS); beamforming; AI-based secured edge/cloud computing; and device-to-device (D2D) communication are all examples of novel technology that can be used with 6G wireless communication to provide significant performance improvement in the case of the considered KPIs for smart grid systems (e.g., data rate, energy efficiency, jitter, area traffic capacity, latency, connection density, spectral efficiency, and reliability). These are all illustrated in Table 1[1; 7; 63; 65].
## 6 Results and Discussion
### Model Implementation and Training Protocols
#### 6.2.6 Sensitivity Analysis  
In order to quantify the relative influence of each geomechanical variable on our ANN's uniaxial compressive strength (UCS) predictions, we conducted a sensitivity analysis using the partial derivatives (PD) method of [PERSON] et al. [53]. For each of the 30 independent network realizations, we computed the PD of the model output (predicted UCS) with respect to each input feature--water content, interconnected porosity, and real density--throughout all training runs. These derivatives were then averaged and normalized to yield percentage-based sensitivity scores, which are visualized in Figure 19. Interconnected porosity emerges as the dominant driver, accounting for 54.4% of the model's sensitivity, followed by water content at 30.9% and real density at 14.7%. This ranking underscores the preeminent role of porosity in controlling UCS within our ANN framework.
# Experimental Verification of Yam (_Dioscorea rotundata_) Drying with Solar Adsorption Drying
## 3 Results
### Vitamin C and Color of Dried Yam
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**METHOD/SAMPLE** & **L\({}^{*}\)** & **a\({}^{*}\)** & **b\({}^{*}\)** & **\(\Delta\)E** \\ \hline \multicolumn{4}{c}{’Pona’} \\ \hline Fresh & 81.83 a & \(-\)0.34 b & \(+\)14.36 a & \\ SAD & 76.87 c & \(-\)0.49 a & \(+\)7.55 c & 8.81 b \\ SD & 78.41 b & \(-\)0.22 c & \(+\)8.38 b & 7.30 c \\ OSD & 65.79 d & \(-\)0.06 d & \(+\)7.83 c & 17.50 a \\ \hline \multicolumn{4}{c}{’Liii’} \\ \hline Fresh & 83.69 a & \(-\)1.28 a & \(+\)14.34 a & \\ SAD & 76.01 b & \(-\)0.26 c & \(+\)5.75 c & 11.57 c \\ SD & 73.30 c & \(-\)0.33 b & \(+\)4.92 d & 14.06 b \\ OSD & 62.67 d & \(-\)0.35 b & \(+\)7.58 b & 22.10 a \\ \hline \multicolumn{4}{c}{’Dente’} \\ \hline Fresh & 83.68 a & \(-\)1.30 a & \(+\)14.98 a & \\ SAD & 76.22 b & \(-\)0.08 d & \(+\)6.08 d & 11.68 b \\ SD & 75.32 b & \(-\)0.14 c & \(+\)6.80 c & 11.75 b \\ OSD & 62.49 c & \(-\)0.74 b & \(+\)7.79 b & 22.38 a \\ \hline \hline \end{tabular} The values with the same superscripts in the same column for each cultivar are not significantly different at \(p<0.05\).  
\end{table} 
Table 4: Color measurement results for both fresh and dried yam of different varieties. \begin{table}
\begin{tabular}{c c} \hline \hline
**Drying Method** & **Vitamin C** \\ \hline \multicolumn{2}{c}{’Pona’} \\ \hline Fresh & 44.35 a \\ SAD & 6.83 b \\ SD & 7.06 b \\ OSD & 2.37 c \\ \hline \multicolumn{2}{c}{’Liii’} \\ \hline Fresh & 26.71 a \\ SAD & 4.77 b \\ SD & 4.99 b \\ OSD & 1.53 c \\ \hline \multicolumn{2}{c}{’Dente’} \\ \hline Fresh & 60.76 a \\ SAD & 6.85 c \\ SD & 7.77 b \\ OSD & 2.32 d \\ \hline \hline \end{tabular} The values with the same superscripts in the same column for each cultivar are not significantly different at \(p<0.05\).  
\end{table} 
Table 3: Vitamin C content (mg/g dry matter) of fresh and dried cultivars of yam at different drying methods. The total color difference (\(\Delta\)E) is an indication of the variation of color of fresh and the dried yam samples. The variation in the total colour difference for all drying methods of cultivars were significant (\(p>0.05\)). The total color difference for all SAD dried samples were lower compared to OSD samples. For 'Pona' the SD samples had lower values which was largely contributed by the a* value, and for 'Dente' the color differences are comparable.
# Statistical Properties of SIS Processes with Heterogeneous Nodal Recovery Rates in Networks
## 3 Methodology
### Notation and Model
(**b**) The recovery process shows an infected node \(i\) recovering at a rate of \(\delta_{i}\). Using the approximation (5), the Markov differential Equation (3) turns out to be  
\[ \begin{split}\frac{dv_{i}(t)}{dt}&=-\delta_{i}v_{i}+ \sum_{j=1}^{N}a_{ij}\beta_{j}v_{j}(1-v_{i})\\ &=\sum_{j=1}^{N}a_{ij}\beta_{j}v_{j}-v_{i}\left(\sum_{j=1}^{N}a_{ ij}\beta_{j}v_{j}+\delta_{i}\right).\end{split}  \tag{6}\]  
The Markov differential Equations (3) and (6) were proposed and derived in ([19], (2) on p. 2). Employing the assumption of independence results in the overestimation of \(dv_{i}/dt\)[16]. Similarly, we can obtain the differential equations for all the nodal viral states:  
\[ \begin{split}\frac{dv_{1}(t)}{dt}&=\sum_{j=1}^{N} \beta_{j}a_{j1}v_{j}(t)-v_{1}(t)\left(\sum_{j=1}^{N}\beta_{j}a_{j1}v_{j}(t)+ \delta_{1}\right),\\ &\cdots\\ \frac{dv_{N}(t)}{dt}&=\sum_{j=1}^{N}\beta_{j}a_{jN}v_{j}(t )-v_{N}(t)\left(\sum_{j=1}^{N}\beta_{j}a_{jN}v_{j}(t)+\delta_{N}\right).\end{split}  \tag{7}\]  
The steady state of a system composed of the SIS process in a given finite-size network actually corresponds to the metastable state [16]. The steady state is denoted by \(v_{i\infty}\):  
\[\left.\frac{dv_{i}(t)}{dt}\right|_{t\rightarrow\infty}=0. \tag{8}\]  
For the steady-state regime, the differential Equation (6) for node \(i\)'s infection probability can be rewritten as  
\[\delta_{i}v_{i}=\sum_{j=1}^{N}a_{ij}\beta_{j}v_{j}(1-v_{i}). \tag{9}\]  
For the steady-state regime, applying some manipulations, the nodal infection probability (9) can be expressed as  
\[v_{i}=1-\frac{1}{1+\tau_{i}\sum_{j=1}^{N}a_{ji}v_{j}}. \tag{10}\]  
We also denote by \(y\) or \(y_{\infty}\) and \(I\) or \(I_{\infty}\) the metastable-state fraction of infected nodes and the metastable-state number of infected nodes, respectively. The mean value \(E[I]\) of the metastable-state number of infected nodes can be expressed as the sum of the expectation of the probability of all the nodes, according to the statistical property of the random variable following a binomial distribution composed of Bernoulli random variables. Thus, we arrive at  
\[E[I(t)]=\sum_{i=1}^{N}E[X_{i}]=\sum_{i=1}^{N}v_{i}. \tag{11}\]  
One exact expression of the variance \(\text{Var}[I(t)]\) of the metastable-state number \(I\) of infected nodes can be expressed as  
\[\text{Var}[I(t)]=\sum_{i=1}^{N}\text{Var}[X_{i}]+2\sum_{i=1}^{N}\sum_{j=1}^{i- 1}\text{Cov}\big{[}X_{i},X_{j}\big{]}, \tag{12}\]  
which was previously obtained in ([26], p. 30). Applying the assumption (5) of independence to the exact expression (12) of the metastable-state variance and substituting (2) into (12) yields  
\[\text{Var}[I(t)]=\sum_{i=1}^{N}\text{Var}[X_{i}]=\sum_{i=1}^{N}(1-v_{i})v_{i}. \tag{13}\]  
In other words, employing the independence approximation makes it possible to conveniently calculate the variance, but this method results in some deviations.