esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Multiple Regression Analysis  
The multiple regression analysis included 813 participants because 631 individuals taking medications for hypertension, hyperlipidemia, or diabetes and 355 individuals that did not have all the data for variables listed below were excluded. SBP, BMI, \(\gamma\)-GTP, uric acid, GLU, and Ht were selected as explanatory variables. These models also included sex, age, and MRI machine as additional covariates. The explanatory variables of BMI (\(b=-0.28\), \(p<0.001\)), \(\gamma\)-GTP (\(b=-0.01\), \(p=0.16\)), and Glu (\(b=-0.02\), \(p=0.049\)) significantly predicted GM-BHQ (Table 3 and Figure 2). BMI exhibited the highest standardization coefficient among blood biomarkers, blood pressure, and obesity measurements.  
In the ad-hoc analyses for the younger and older group, BMI also showed significance and the highest standardization coefficient for both groups (Supplementary Material).  
Figure 2: Illustration of the results of the partial correlation and multiple regression analysis. Bold, black and red type denote the items showing significance in the partial correlation analysis without multiple comparison. The numbers and thickness of arrows show the standardization coefficient in the multiple regression analysis (dependent variable: GM-BHQ, explanatory variable: red bold).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(b\) & **Standard Error** & **Standardization Coefficient** & \(t\)**-Value** & \(p\) \\ \hline Systolic blood pressure & \(-0.01\) & \(0.01\) & \(-0.25\) & \(-0.13\) & \(0.194\) \\ Body mass index & \(-0.28\) & \(0.06\) & \(-0.93\) & \(-4.69\) & \(<\)0.001 \\ \(\gamma\)-glutamyltransferase (\(\gamma\)-GTP) & \(-0.01\) & \(0\) & \(-0.45\) & \(-2.41\) & \(0.016\) \\ Uric acid & \(-0.13\) & \(0.15\) & \(-0.18\) & \(-0.02\) & \(0.397\) \\ Fasting blood glucose & \(-0.02\) & \(0.01\) & \(-0.37\) & \(-1.97\) & \(0.049\) \\ Haematocrit & \(-0.01\) & \(0.05\) & \(-0.18\) & \(-0.847\) & \(0.901\) \\ \hline \end{tabular}
\end{table}
Table 3: Multiple regression analysis for predicting grey matter brain healthcare quotient.
## 3 Proposed System
### PMIPv6/ AAND--Various Multi-Homing Topologies
#### 3.4.2 Topology 2--PMIPv6/AAND  
Now the MN is linked to MAG1, and the CN to MAG2. The MN then shifts from MAG1 to MAG2. MAG1 is now known as P-MAG according to the CN. The P-MAG and the M-MAG are connected in Topology 2, to the same LMD. Now the packets are being moved via M-MAG from MN-CN. The handover between P-MAG and M-MAG occurs. The PBU list retains the P-MAG LL-ID using AAND fields when the MN changes its interface and allows for a successful mobility session. Figure 8 displays the PMIPv6/AAND Multihoming Topology 2.
# Rapid In-Field Volatile Sampling for Detection of _Botrytis cinerea_ Infection in Wine Grapes
## 2 Results and Discussion
### Correlation between Volatile Detection and B. cinerea Infection Severities
Apart from the correlations between the grape _B. cinerea_ infection levels and the detection of the volatile compounds, Figure 2 (left) demonstrates correlations among the metabolites. For example, the concentration of 3-octanone was positively correlated with the concentrations of 3-methyl-1-butanol, 1-octen-3-ol, and 1,5-dimethyltetalin. Eight-carbon oxylipin compounds such as 3-octanone and 1-octen-3-ol are common compounds related to fungal infection in grapes possessing a typical mushroom or earthy odours [31; 32; 33]. They are significant metabolites produced predominantly by fungi through the enzymatic oxidation and cleavage of linoleic acid by lipoxygenase and hydroperoxide [30] and could be considered as markers for fungal infection on grapes or other horticultural crops. SPME GC-MS was used for the analysis of the grape homogenates from the same sample set as a comparison and a benchmark method. Figure 2 (right) demonstrates the correlations between the LFD scores and the volatile metabolites detected using SPME GC-MS with the _p_-values for cross-correlation shown in Supplementary Table S3. Volatile metabolites, including 3-octanone, 3-octanol, 1-octen-3-ol, and 1-octen-3-one, were detected from _B. cinerea_ infected grapes, which supports the positive correlation by thermal desorption GC-MS for the detection of 3-octanone and 1-octen-3-ol. C6 compounds hexanal and 2-henenal were negatively correlated with the _B. cinerea_ grape infections and are important volatile components in grape skins derived from unsaturated fatty acids via the lipoxygenase hydroperoxide lyase enzymatic pathway, which is usually activated immediately following wounding [34; 35]. Different performance of headspace volatile sampling using SPME fibre and ZIF-8 may depend on the physical or chemical properties or the detection range of the absorbent materials.
## 3 Results
### Spatial Consistency between MODIS GPP and GOSIF, NDVI, EVI, and NIRv during the 2019 Drought  
To further reveal the correlation between MODIS GPP and GOSIF, NDVI, EVI, and NIRv during the 2019 drought event, we analyzed the spatial consistency between GPP and GOSIF, NDVI, EVI, and NIRv (Figure 13), as well as the spatial consistency between standardized anomalies of GPP and GOSIF, NDVI, EVI, and NIRv (Table 2), respectively. As shown in Figure 13, the linear fitting R\({}^{2}\) value between MODIS GPP and GOSIF, NDVI, EVI, and NIRv were 0.742, 0.646, 0.664, and 0.674, respectively; all were positively correlated. Similar to the results in Figure 5, the relationship between MODIS GPP and SIF is also polynomial dependent, while that between GPP and NDVI, EVI, and NIRv were different. As displayed in Table 2, similar to Figure 13, the relationship between standardized anomalies of GPP and SIF, NDVI, EVI, and NIRv at pixel scale was also positively correlated during July-December. In July and October 2019, the NDVI performed better, with R-values of 0.157 and 0.451, respectively. While in other months, SIF performed better, with the linear fitting R-values of 0.341, 0.525, 0.368, and 0.367. The above phenomenon further verified the good spatial consistency between SIF and GPP compared with other VIs and the superior ability of SIF in capturing and quantifying drought-induced GPP losses.  
Figure 11: Variation of MODIS GPP from July to December in 2019. Note: The black curve denotes the multi-year monthly average of GPP during 2011–2020; the red curve denotes the monthly average GPP in 2019; the grey shaded areas denote the \(\pm 1\) standard deviation of the multi-year monthly averages.  
Figure 12: Spatial distributions of the standardized anomaly of GPP from July to December 2019.
# Antenna Arrays for Line-of-Sight Massive MIMO: Half Wavelength Is Not Enough  
[PERSON]  
1 DIEI andAmong the many aspects of massive MIMO systems, the optimization of the radiation system has been the object of relatively little research, and equispaced half-wavelength arrays are commonly considered for massive MIMO antennas. However, as pointed out above, massive MIMO antennas work in the near-field condition. This characteristic does not seem to have been exploited in the literature. A further point to be better explored is the reduction of the cost and the complexity of the overall antenna, which is critically related to the number of radiating elements.  
Accordingly, a fundamental consideration is now in order: is it possible to exploit this near-field behavior with a fixed complexity of the antenna in terms of the number of elements without affecting, or hopefully increasing, the performance? This paper will try to answer this question, showing that optimization of the antenna array using non-uniform arrays in the case of linear antennas and properly equispaced arrays in circular antennas can allow a significant improvement of the communication system performance.
## 3 Results  
### Characterization of PSS Using Infrared Spectroscopy  
We first demonstrated the use of DBD plasma to induce the polymerization of sodium 4-styrenesulfonate (NaSS). The resulting FTIR scans are shown in Figure 3.  
The primary focus of the IR spectra is the peaks at 985 \(\pm\) 5 cm\({}^{-1}\) and 910 \(\pm\) 5 cm\({}^{-1}\), which are the peaks associated with the C=C double bonds commonly observed in vinyl compounds. After DBD plasma polymerization, NaSS exhibits the complete disappearance of these peaks, indicating total polymerization of NaSS. This confirms the effectiveness of DBD plasma in inducing NaSS polymerization.  
The FTIR spectra of NaSS and PSS closely resemble those reported in the literature, with the major peaks at 985 cm\({}^{-1}\) and 910 cm\({}^{-1}\) indicating the completion of the polymerization process. A notable difference observed in our sample, compared to published spectra, is the presence of two distinct peaks in the 3400-3600 cm\({}^{-1}\) region for NaSS. This suggests the presence of water molecules in two forms: free water and water associated with NaSS via hydrogen bonding. This is not unusual, as varying amounts or interactions of water with the monomer can result in different peaks in this region.  
The plasma discharge on the substrate was assumed to be uniform based on prior studies involving DBD plasma. However, due to the NaSS deposition on the wafer surfaces via drop-casting, the resulting film is likely non-uniform. As this study serves as a preliminary investigation for subsequent research on polymerization within porous materials, we did not assess the film's uniformity.
## 3 Results and Discussion
### Liquid Oxygen Compatibility of EP/BCI/BSEA Composites
#### 3.1.2 XPS Analysis before and after 98J LOT
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & **Cls** & \\ \cline{3-6} \multirow{2}{*}{**Sample**} & & & & **C-O-C/C-** & **C-C/C=C/** \\  & & & & & **C-N** & **C-H** \\ \hline Pure EP & Before & BE (eV) & — & 287.99 & 285.90 & 284.15 \\  & After & Area (\%) & — & 1.22 & 13.86 & 84.92 \\  & explosion & BE (eV) & 289.99 & 288.20 & 285.43 & 283.93 \\  & & Area (\%) & 2.46 & 3.36 & 24.80 & 69.38 \\ EP/BCI/BSEA-2 & Before & BE (eV) & — & 287.65 & 285.16 & 283.71 \\  & & Area (\%) & — & 2.84 & 21.37 & 75.79 \\  & & & BE (eV) & 290.40 & 287.70 & 285.45 & 284.02 \\ EP/BCI/BSEA-5 & Before & Area (\%) & 1.50 & 4.67 & 14.52 & 79.31 \\  & & & BE (eV) & — & 287.70 & 285.10 & 283.76 \\  & & Area (\%) & — & 2.44 & 25.23 & 72.33 \\  & & & BE (eV) & — & 287.35 & 285.42 & 283.98 \\  & & & Area (\%) & — & 6.47 & 15.51 & 78.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: C1s peak fitting results of pure EP and BSEA-modified EP. could release \(\cdot\)H at the same time and combine with \(\cdot\)H to form -COOH. Moreover, -COOH radicals could release -OH radicals to form C=O. Therefore, the generation of C=O bonds might be the key factor affecting the LOC of EP. Figure 5: O1s spectra of EP/BCI/BSEA-2 (**a**) before and (**a1**) after, EP/BCI/BSEA-5 (**b**) before and (**b1**) after the liquid oxygen compatibility test, P2p spectra of EP/BCI/BSEA-2 (**c**) before and (**c1**) after, EP/BCI/BSEA-5 (**d**) before and (**d1**) after the liquid oxygen compatibility test. The N1s fitting peaks before and after impact at around 398.88 eV, 400.08 eV and 405.78 eV were assigned to C-N, O=C-N and NO\({}_{3}\), as exhibited in Figure S1(b-d,b1-d1). The fitting peak of NO\({}_{3}\) only appeared after the impact of pure EP, while it did not appear after the impact of BSEA-modified EP, which indicated that the addition of BSEA could increase the antioxidant activity of EP, and thus improve the LOC of EP. As exhibited in Figure 5c,d, the P2p spectra of EP/BCI/BSEA-2 and EP/BCI/BSEA-5 could hardly be observed before impact. This is because of the low atomic mass fraction of the phosphorus element in the modified EP. Figure 5c1,d1 show that the P2p fitting peaks after impact at around 132.01 eV, 133.00 eV and 134.5 eV were ascribed to PO\({}_{3}\)/P\({}_{2}\)O\({}_{5}\), P=O and P-O-C, and the corresponding data are summarized in Table 4[2]. The results indicate that large numbers of PO- and HPO\({}_{2}\)- active free radicals were generated during the impact, and these phosphorus-containing radicals could capture the -OH, -H and -O high-energy free radicals, which could interrupt the chain decomposition of EP and inhibit incompatible reactions between EP and liquid oxygen, as shown in Figure 6[42; 43; 44].
# STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction
###### Abstract
Specifically, the periodic characteristics of traffic flow in daily changes and the heterogeneity of weekdays and weekends were considered during feature encoding, which were utilized as the input variables for traffic-flow prediction. In addition, a self-attention mechanism was introduced to capture the dynamic dependence of traffic flow on both the temporal and spatial dimensions. Furthermore, the gated mechanism was utilized to selectively extract the important features to further improve the prediction performance.
# Fundamental Spacetime Representations of Quantum Antenna Systems
## 6 Quantum Antenna Radiation Patterns: Basic Constructions  
### Introduction  
In contrast to classical antennas, q-antenna systems are intrinsically stochastic. The ultimate goal of a candidate q-antenna theory is to supply rules and guidelines for estimating _probabilities_ of potential detection (reception) processes ideally enacted somewhere within the near or far spacetime zones of the quantum sources. In conventional antenna theory, certain quantities are fundamental for analyzing and designing functioning actual devices able to radiate power in real-life settings. These often include directivity, gain (preferably with a measure of radiation efficiency), and the array factor [27, 33, 35]. As will be illustrated in this section, it turns out that in q-antennas, there also exist close counterparts for many of the conventional characterization measures/concepts already in use in -c-antennas. However, there are also fundamental differences between - and q-antennas in terms of the physical interpretation and meaning of the results thus obtained since, clearly, electromagnetic and quantum radiation are very distinct (though related) physical processes. For the purpose of constructing working definitions for directivity and gain functions suitable for q-antennas, we explore next some possible antenna radiation pattern constructions made available to us by the relativistic QFT approach proposed above.
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 4 Supervised Learning for Estimating the Bit Error Rate (BER)
### Estimation of Bit Error Rate (BER)  
In this step, we leverage the trained model to estimate the Bit Error Rate (BER) based on new data. Specifically, we feed 300 new samples into the model, which then generates an output corresponding to the estimated BER value.
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 5 Results and Discussion
### Key Findings and Discussion  
The experimental results demonstrate that Edge AI-based anomaly detection is a viable and efficient real-time smart home monitoring approach. While Isolation Forest (IF) proved to be a computationally lightweight and effective method for detecting outliers, it exhibited a higher false positive rate, occasionally misclassifying normal sensor readings as anomalies. In contrast, the LSTM Autoencoder (LSTM-AE) displayed greater sensitivity to sequential anomalies, successfully identifying time-dependent deviations while maintaining a lower false positive rate. This makes it particularly well-suited for applications where pattern recognition over time is essential, such as energy consumption monitoring or occupancy prediction.  
Despite its higher detection accuracy, the LSTM Autoencoder requires periodic retraining to address sensor data drift and environmental variations, ensuring that anomaly detection remains reliable over time. Based on typical smart home data dynamics, a weekly retraining schedule is a reasonable starting point, with adjustments made based on observed performance degradation. To reduce the frequency of full retraining cycles, adaptive learning techniques such as incremental learning can be leveraged, allowing models to gradually update as new data become available. An alternative approach is federated learning, where edge devices collaboratively refine model parameters without transmitting raw data to the cloud, thereby improving privacy and adaptability. By integrating these techniques, retraining can be optimized to balance model accuracy, computational efficiency, and data security.  
Future work will focus on adaptive learning strategies, incorporating federated learning and incremental updates to further enhance model generalization and long-term stability. A hybrid detection approach could also help balance computational efficiency and anomaly detection accuracy. A practical solution would involve using Isolation Forest  
Figure 4: Comparison of inference time and power consumption for Isolation Forest and LSTM Autoencoder models. While Isolation Forest exhibits faster inference (22 ms) and lower power consumption (2.8 W), the LSTM Autoencoder achieves more precise anomaly detection at the cost of increased computational load (35 ms, 4.2 W).  
as an initial anomaly filter, flagging suspicious readings, and subsequently applying the LSTM Autoencoder for secondary verification. This two-step process would reduce false positives while maintaining efficient real-time detection, making it an ideal strategy for resource-constrained edge deployments.
## 4 Discussion
### Physicochemical Quality Indices
#### 4.1.3 Peroxide Value  
The peroxide value (PV) is an indicator used for measuring the rancidity of fats and oils and also a criterion for predicting the quality and stability of oils [37]. The PVs for the oils analyzed were below 10 meqO\({}_{2}\)/kg oil, set by the Codex Standards for edible oils, except for walnut oil [18]. A low PV could indicate the presence of reduced amounts of hydroxy peroxides, and hence, less oxidation reactions during storage. Considering that most of the oils fell within the standard values, it is likely that the oxidation process in walnut oil may have a cause other than storage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Oil Samples**} & \multicolumn{4}{c}{**ILCR (\(\times\)10\({}^{-6}\))**} & \multicolumn{2}{c}{\(\sum\)**ILCR**} \\ \cline{2-6}  & **Cr** & **Cd** & **Pb** & **Ni** & \(\times\)**10\({}^{-6}\)** \\ \hline Sunflower & 1.78 & 1.35 & 2.27 & 1.79 & 7.19 \\ Grapeseed & 0.00 & 0.53 & 0.33 & 3.89 & 4.75 \\ Extra virgin olive & 5.88 & 0.13 & 0.78 & 3.59 & 10.38 \\ Organic extra virgin olive & 1.69 & 0.95 & 3.06 & 4.48 & 10.18 \\ Rapeseed & 3.21 & 0.67 & 1.85 & 2.10 & 7.83 \\ Blend & 11.07 & 0.53 & 3.00 & 1.79 & 16.39 \\ Walnut & 1.06 & 0.67 & 1.79 & 0.89 & 4.41 \\ Rice & 3.57 & 0.67 & 6.58 & 1.79 & 12.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The incremental lifetime cancer risks (ILCRs) calculated for heavy metals.
# Data-Driven Convolutional Model for Digital Color Image Demosaicing
## 3 Methods
### Full Reconstruction Network  
The Full Reconstruction network is a Fully Convolutional Neural Network that is a parallel composition of the two estimation networks for the Luminance and Chrominance channels. The network receives a 15 \(\times\) 15 \(\times\) 1 input patch as a Bayer patch and produces the estimated 16 \(\times\) 16 \(\times\) 3 RGB output patch. Since input Bayer patches have different configurations, the network converts the input 16 \(\times\) 16 \(\times\) 1 patch in three 15 \(\times\) 15 patches, filling the unavailable color information with zeros. This approach has been proposed in [7]  
Figure 8: Chrominance Reconstruction Network.  
Figure 7: Luminance Reconstruction Network.  
to simplify the alignment issues that arise from the possible Bayer configurations. The Full Reconstruction Network is responsible to keep track of the current configuration, and updates the patch conversion as it scans the overall image. This operation is graphically presented in Figure 9.  
In the final merging step, the output RGB image is obtained from the two output results as a simple pixel-wise summation (see Equation (8)).  
\[I_{est}(x,y)=\Phi_{est}(x,y)+\{\Psi_{est}(x,y)_{i}\},i\in\{R,G,B\} \tag{8}\]  
where \(\Phi_{est}\) is the estimated Luminance channel and \(\Psi_{est}\) is the estimated Chrominance three-dimensional vector.  
The final model is represented Figure 10 with the output of its internal intermediate steps.  
Figure 9: During the scanning of the whole input Bayer image, the network local receptive field corresponds to different Bayer configurations. This means that two input vectors may have values for two different color channels in the same positions. To solve this alignment issue, the network can internally convert the input vector in three \(15\times 15\) patches that are zero-filled when the relative color information is unavailable. The three converted patches are always aligned and can be processed by the two parallel networks. The Full Reconstruction Network is responsible to constantly keep track of the current scanning position and convert the input vector to the appropriate output based on the current Bayer configuration. To simplify readability, we depicted the conversion of a \(3\times 3\) input vector. The current implementation extends this idea to a \(15\times 15\) input vector, however the number of configurations encountered during the scanning process does not change.
# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 3 Results
### Comparison of CLUE-S and PLUS Simulation Results  
Figure 4 shows the simulation result map of the two models and the status map in 2020 under the scale of 30 m \(\times\) 30 m. Table 3 shows the area of each LU type obtained from the simulation result map. From Figure 4 and Table 3, the spatial location distribution and quantity of each LU type in the PLUS simulation map and the status map are more similar. The water area in the CLUE-S simulation is smaller than the actual status map, and areas that should be water are incorrectly modeled as farmland, as shown in local area 1 in Figure 4a. Figure 4b shows that there are waters in region 1. The PLUS simulation map indicates the presence of water at this location, suggesting that the PLUS model is better able to explore potential driving factors. In the north and south where the topography is flat,  
Figure 4: Comparison of simulation results of different models in 2020.  
the area of farmland in the CLUE-S and PLUS simulations is slightly larger than the status map, while the construction land is clustered toward the middle part, as shown in the local area 2 in Figure 4. There are fewer construction land patches in the CLUE-S simulation result map than in the PLUS simulation result map, indicating that the construction land is clustered more strongly in the CLUE-S simulation result map. Furthermore, it was found that the area of farmland and construction land in the CLUE-S simulation result map was slightly more than that in the PLUS simulation result map and the status map, and the area of forestland, grassland, and water was slightly less than that in the PLUS simulation map and the status map. As shown in local area 3 of Figure 4, both models failed to simulate a portion of grassland and forestland in the northwest. Additionally, the two models were most likely to differ where the edge of the LU type was excessive. The kappa coefficients of the CLUE-S and PLUS models were calculated to be 0.727 and 0.759, and the FOM values were 0.109 and 0.201. It is easy to know that the simulation accuracy of the PLUS model is better than that of the CLUE-S model at the same scale in the study area. The user's accuracy results of the two models are shown in Table 4, and it can be found that the accuracy of the PLUS model in simulating forestland and water is much improved than that of the CLUE-S model.