esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Assessment of PT-JPL-Thermal vs. MOD16 ET in the Donana Region
Significantly higher correlation values were found in the summer months (i.e., July, August, and September) than in the period from October to March while the rest of the months did not show any significant correlation. Figure 8: PT-JPL-thermal vs. MOD16 multi-year average \(ET\) estimations (mm/day) in (**a**) the irrigated areas and (**b**) the natural ecosystems. Figure 9: Temporal correlation between PT-JPL-thermal and MOD16 (**a**) seasonal (\(n_{p}\) = 120, 120 months per pixel), (**b**) inter-annual (\(n_{p}\) = 10, 10 years per pixel). This information is complemented by Figure 11a,b, which show specific correlation values for each month and land cover class. A high significant correlation was found in the irrigated areas during the months of maximum water demand (Figure 11a). In the natural ecosystems, a non-significant correlation was found (Figure 11b). Particularly for the wetland, the correlation coefficient was \(\rho=0.07\) with \(p=0.25\). For the coniferous forests, the correlation coefficient was \(\rho=-0.04\) with \(p=0.86\). However, a significant correlation was observed in the shrubland during the spring and summer months (\(\rho>0.7\)). Figure 11: Correlation coefficients of the PT-JPL-thermal vs. MOD16 \(ET\) monthly patterns from 2003 to 2012 for: (**a**) the irrigated areas and (**b**) the natural ecosystems. Figure 10: Monthly \(ET\) (mm/month) from PT-JPL-thermal vs. MOD16 in the study region (irrigated areas plus natural ecosystems), \(n_{p}=12\),720 (1272 region-pixels in 10 years from 2003–2012).
In addition, MYB TFs can interact with other base helix-loop-helix (bHLH) TFs to form an MYB-bHLH complex to participate in the regulation of sesquiterpene biosynthesis [27; 28]. WRKY TFs, NaWRKY3, and NaWRKY6 participate in the regulation of the defensive terpene emission in _Nicotiana attenuata_[29]. In _Petunia \(\times\) hybrida_, R2R3-type MYB TFs, ODORANT1 (ODO1), EMISSION OF BENZENOIDS I (EBOI), and EMISSION OF BENZENOIDS II (EBOII) control the biosynthesis of phenylpropanoids/benzenoids by regulation of the shikimate pathway [30; 31]. Suppression of _PhODO1_ and _PhEBOII_ expression leads to a reduced amount of floral volatiles, through decreasing transcript level of many phenylpropanol/benzenoid genes such as _PhDAHPS_, _PhEPSPS_, _PhPAL_, _PhCM_, and _PhSAMS_[32]. In contrast, the MYB4 TF is a repressor of cinnate-4-hydroxylase from the phenylpropanol pathway in petunias [33]. For the biosynthetic pathway of the volatiles derived from fatty acids, studies on their transcriptional regulation have primarily focused on JA [34; 35]. Many TFs, including MYC, MYB, GAI, RGA, EIN3, EIL, ERF, and RGL1, have been demonstrated to be involved in the regulation of JA biosynthesis [23; 36]. _Anthurium andraemann_ is an important tropical and subtropical ornamental crop in the world market. The popularity of _A. anraemann_ is largely due to the exotic shapes and colors of the spathe, and the remarkable longevity of the plant's flowering period. The floral scent varies greatly among Anthurium cultivars. A survey of floral scent in 147 Anthurium species and hybrids showed that most plants emitted scents ranging from pleasant to unpleasant and from very weak to very strong [37]. The majority of Anthurium species that emit fragrance and volatile compounds do so at the pistillate stage of flower development, with the primary emitted compounds consisting of 1,8-cineole, \(\alpha\),\(\beta\) -pinene, sabinene, myrcene, and limonene, as well as some benzenoids [37]. To date, studies characterizing the components and formation mechanism of Anthurium floral scent are scarce.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & \\  & **[PERSON] et al. [6]** & **[PERSON] et al. [5]** & **et al. [10]** & **0** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** & **1.0** \\ \hline cityscape & 13.58 & 22.74 & 19.88 & 12.89 & 14.35 & 16.08 & 18.12 & 20.46 & 22.66 & **23.26** & 21.60 & 19.20 & 16.98 & 15.06 \\  & 0.774 & 0.941 & 0.904 & 0.723 & 0.799 & 0.859 & 0.903 & 0.934 & 0.953 & **0.958** & 0.948 & 0.920 & 0.868 & 0.789 \\ \cline{2-13} crab & 12.10 & **27.19** & 12.99 & 15.29 & 16.28 & 17.37 & 18.59 & 19.95 & 21.43 & 22.97 & 24.32 & 25.01 & 24.64 & 22.64 \\  & 0.702 & **0.970** & 0.743 & 0.834 & 0.863 & 0.889 & 0.909 & 0.926 & 0.938 & 0.946 & 0.951 & 0.951 & 0.947 & 0.933 \\ \cline{2-13} coral reef & 11.74 & 19.78 & 18.07 & 16.25 & 17.22 & 18.26 & 19.38 & 20.53 & 21.65 & 22.58 & **23.11** & 23.04 & 22.38 & 20.83 \\  & 0.691 & 0.925 & 0.837 & 0.807 & 0.846 & 0.877 & 0.901 & 0.918 & 0.929 & 0.935 & **0.937** & 0.934 & 0.927 & 0.914 \\ \cline{2-13} landscape1 & 14.02 & **26.47** & 25.35 & 15.29 & 16.63 & 18.15 & 19.88 & 21.74 & 23.38 & 24.01 & 23.14 & 21.41 & 19.53 & 17.60 \\  & 0.667 & 0.936 & **0.937** & 0.81 & 0.852 & 0.882 & 0.902 & 0.916 & 0.923 & 0.925 & 0.919 & 0.904 & 0.874 & 0.824 \\ \cline{2-13} landscape2 & 14.63 & **23.60** & 18.02 & 15.97 & 17.23 & 18.57 & 19.88 & 20.90 & 21.25 & 20.74 & 19.61 & 18.23 & 16.84 & 15.54 \\  & 0.742 & **0.920** & 0.848 & 0.813 & 0.857 & 0.885 & 0.902 & 0.912 & 0.914 & 0.908 & 0.893 & 0.865 & 0.822 & 0.761 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation with PSNR and SSIM [24] for simulated haze images generated using nonuniform transmission map. First row is PSNR value, and second row is SSIM value in each cell. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & **[PERSON] et al. [6]** & **[PERSON] et al. [5]** & **[PERSON]** & **pixel-wise DCP** \\  & & **et al. [6]** & **et al. [5]** & **et al. [10]** & **w/o normalisation(\(\gamma\) = 0 )** \\ \hline uniform & Proposed & PSNR & Yes & Yes & Yes & Yes \\  & method(\(\gamma\) = 0.9) & SSIM & Yes & Yes & Yes & Yes \\ \hline non-uniform & Proposed & PSNR & Yes & No & No & Yes \\  & method(\(\gamma\) = 0.5) & SSIM & Yes & No & No & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Paired \(t\)-test results between proposed method and conventional methods. The statistically significant methods (p \(<\) 0.05) are indicated by “Yes” and others are indicated by “No”.
## 4 Conclusions  
The work demonstrated the performance of optical and laser techniques for the diagnostics of species (H\({}_{2}\)O, NO) and T in the pilot-scale processes, mimicking the LKAB burner system for pellet induration. The application of these techniques helped characterize the process behaviour for the three heating methods (FO, H\({}_{2}\), plasma) and detect and explain the process-related variations. The comparison of the TDL H\({}_{2}\)O measurements with the calculated H\({}_{2}\)O concentrations demonstrated the excellent agreement, better than 10%. The range of the applicability of the NO UV absorption method for simultaneous diagnostics of NO and T was increased by 200 K from 1350 to 1550 K. A very good agreement, better than 15%, between the NO measurements made using the UV NO and probe methods was found. The laser-based (TDL) and optical (UV NO absorption) temperatures were, in general, within 50 K.  
Experimental concept, A.S., C.F. and H.W.; experimental realization and data collection, A.S., C.F. and Y.O.; writing--original draft preparation, A.S.; writing--review and editing, A.S., C.F., Y.O. and H.W. All authors have read and agreed to the published version of the manuscript.  
This work has been conducted as part of the HYBRIT research project RP1. We gratefully acknowledge financial support from the Swedish Energy Agency through HYBRIT (Hydrogen Breakthrough Ironmaking Technology) and 50470-1 projects. HYBRIT is a joint initiative of the three companies SSAB, LKAB and Vattenfall with the aim of developing the world's first fossil-free ore-based steelmaking route.  
Not applicable.  
Data supporting the results of this work can be obtained from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
###### Abstract
The decline of these factors can considerably affect older adults' quality of life. Given the minimized sensor setting strategy and multiple-resident households of our pilot study, we focus on sleep and outing/visiting analysis as indicators of elder patients' health status in this paper. Sleep quantity and quality are well-recognized indicators of physical and mental wellness [15; 16]. We deploy a pressure-based bed sensor at the target subject's sleep side of the bed, to track personalized sleep activities. Loneliness and lack of social interaction is one of the major health issues associated with aging and thus, a serious concern in elderly care [17]. Outdoor activity and home visits can be used as reference of not only social activities, but also the capability of carrying out necessary outdoor-based household maintenance activities such as shopping, as well as to make sure that the necessary home-based medical or house-care related assistance have been delivered. Behavior patterns are extracted as groups of activity profiles that share similar features. We conduct both horizontal (across the cohort) analysis, and vertical (personalized behavior tracking over time) analysis. Specifically, user segmentation is conducted by grouping patients with similar behavior patterns across the cohort, resulting in 'precision cohorts. A user's change of routine can be observed by tracking the personalized activity patterns over time. Our objective is to provide a scalable generic solution for long-term practical usage to support independent living of senior adults with mild physical or cognitive impairments, regardless of the different number of residents and structures of the households. The system should be able to alleviate care provider workloads with reduced number of necessary home visits, and reduced medical emergencies by early detection of disease worsening. Our participant population consists of subjects with a mild post-stroke condition and other comorbidities. The sleep profile was used as the main daily activity indicator in our system, due to the sparse sensor setting and the multi-occupancy living condition, which can be enhanced when richer sensor setting becomes available. The paper is organized as follows. Section 2 reviews related research literature of binary-sensor-network-based AAL systems. Sections 3 and 4 introduce the sensor setting and the IT architecture of our system. The patient recruitment criteria and study statistics are explained in Section 5. Section 6 provides details of the methods applied for daily sleep profile extraction, sleep quality estimation, as well as sleep-based cohort analysis and longitudinal health-status tracking. Outing and visiting detection is introduced in Section 7. They are important physical and cognitive health-status indicators, thus, improving the personalized monitoring accuracy. Section 8 introduces and benchmarks the anomaly detection methods. Finally, concluding remarks are drawn in Section 9.
## 3 Results and Discussion
### Thermal Resistance  
Thermal resistance is the ratio of the temperature difference between the two faces of a heatsink to the heat flux. In heatsinks, the higher the thermal resistance, the lower is the heat dissipation and it has less ability to cool down power devices. Thermal resistance is defined as:  
\[R_{t}=\frac{(T_{max}-T_{min})}{qA_{h}} \tag{3}\]  
in which, \(T_{max}\) is maximum temperature, \(T_{min}\) is minimum temperature, \(q\) is the heat flux and \(A_{h}\) is the area of heater surface.  
Figure 8 shows that for both configurations the maximum temperature in the system is occurring in the third IGBT from the inlet, due to geometric position of this IGBT and also thermal coupling between the chips. As shown in Figure 9, minimum temperature of heatsink occurs at inlet area. At inlet area, the temperature of coolant is at its lowest value and this area is far from the hot spots. Therefore, minimum temperature of heatsink will be at inlet areas. In this study, the thermal resistance is calculated between the IGBT and the inlet area (maximum and minimum temperature).  
Figure 8: Temperature distribution from chips point of view at volumetric flow rate of 18 cm\({}^{3}\)s\({}^{-1}\) in (**a**) Offset strip fin design, (**b**) New offset design.  
Another point, which is worthy of mention in Figure 9b, is temperature profile in the area of U-turns. As the flow progresses in the channel, the temperature should increase. However, as it can be seen, the temperature is lower in the U-turn areas than fins before the U-turn areas. This decrease in the temperature is caused by the combinative effects generation of disturbance and recirculation areas along the curvy fins and creation of centrifugal forces in the U-turn (Section 3.1, Figure 7b,c).  
Figure 10 shows that the thermal resistance for both designs is decreasing by the increase of volumetric flow. It can be seen that the thermal resistance of the new offset design is lower than the conventional one (10-15%).  
Figure 10: Thermal resistance (\(R_{t}\)) in different volumetric flow rates (Q) for conventional offset strip fin design and new offset design.  
Figure 9: Temperature distribution from heatsink point of view at volumetric flow rate of 18 cm\({}^{3}\)s\({}^{-1}\) in (**a**) Offset strip fin design, (**b**) New offset design.
# Numerical Investigation of Thermal Efficiency of a Solar Cell
## 4 Conclusions  
The present work shows the validation of computational domain results with the experimental one for the PV system. It was seen that incident solar radiation, collector geometry, and cooling fluid and its configuration had a crucial role in solar-based power generation. The heat balance estimation was performed using the computational domain and our work concluded that uncertain heat loss was very low and validated the heat balance. The convective and radiative heat transfer through the PV cell and pipe surface were evaluated to estimate heat lost for different heat input. It was concluded that there is an optimum power input corresponding to \(Re=700\), which was required for cooling the PV cell. Beyond this value of power input, the cooling of the PV cell was impractical. The empirical relation between the thermal efficiency and \(Re\left(\eta_{Thermal}=0.002\times Re^{0.89}=f(Re)\right)\) evaluates the optimum power required for cooling to attain maximum efficiency. It was concluded that maximum electrical efficiency up to 20% can be achieved by the cooling of PV cells using water flowing under the panel when 40% of heat input was carried away by cooling water. Future research can validate the model for cooling fluids other than water.  
Conceptualization, E.A., M.N.A. and E.A.B.; Formal analysis, E.A., M.N.A. and E.A.B.; Funding acquisition, W.A.F.W.O.; Methodology, E.A., M.N.A. and E.A.B.; Project administration, W.A.F.W.O., E.A.B. and S.S.N.A.; Supervision, M.N.A. and E.A.B.; Writing--original draft, E.A.; Writing--review and editing, E.A., W.A.F.W.O. and S.S.N.A. All authors have read and agreed to the published version of the manuscript.  
This study was funded by Research Creativity and Management Office, Universiti Sains Malaysia. The authors would like to acknowledge the grant RUTop-Down 1001/PAERO/870052.  
Not Applicable.  
The authors declare no conflict of interest.  
Figure 10: Electrical efficiency of PV cell.
## 2 Methods
The \(l_{1}\) norm not only brings sparsity but also keeps the objective function convex, so that the \(l_{1}\) norm demands less computation time than the non-convex \(l_{0}\) problem [41]. Following the standard of Matlab [42], the factor \(\frac{1}{2n_{y}}\) adjusts the scale of \(l_{2}\) norm the residuum and the hyperparameter \(\lambda\) balances the weighting of \(l_{1}\) and \(l_{2}\) norms. A bigger value of \(\lambda\) leads to more sparsity in the optimized vector \(\xi_{i}\) and an undesired increase of Mean Square Error (MSE), and vice versa. Thus, an appropriate value of \(\lambda\) needs to be determined, so that the sparsity is promoted as much as possible without much increase of MSE. In order to select the hyperparameter \(\lambda\) automatically, cross-validation [43] and the 1 SE-principle [42] are used here. Cross-validation separates the whole data set into \(n_{cv}\) small sets. In the training process, \(n_{cv}-1\) sets of data are used for training and one for testing. Each small data set is selected as the test set in turn, so that the distribution of MSE can be obtained to evaluate the quality of fitting [43]. This distribution of MSE depends on the hyperparameter \(\lambda\) of LASSO, which can be calculated by running LASSO with different values of \(\lambda\). The biggest value of \(\lambda\), whose mean value of MSE after cross-validation within one Standard Error (SE) of minimum mean value of MSE of all experimented hyperparameters stays, is selected to be the hyperparameter of LASSO, because it tends to give a sparse coefficient vector without introducing too much error of fitting. This process is thus called the 1 SE-principle [42]. LASSO automates the construction of models with sparseness. However, the shrinkage of the estimated coefficients is not wanted, i.e., the absolute values of the coefficients are estimated to be smaller than the true values. To avoid this problem, the LS method after LASSO can be performed with selected candidate functions. This method is called LS-Post-LASSO (LSPL) [35]. The procedure of LSPL is shown in Figure 0(b).
## 3 Experiments and Results
### Classification and Regression Metrics
For the analysis of the performance of the regression machines, we will use the following scalar metrics: the Mean Squared Error (MSE), Mean Average Error (MAE), Pearson correlation coefficient (\(R^{2}\)) and the relative versions of MSE and MAE (MSER and MSER):  
\[\text{MSE}=\frac{1}{N}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{8}\]  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{9}\]  
\[R^{2}=\frac{\sum_{i=1}^{n}(y_{i}-\text{E}[y])(\hat{y}_{i}-\text{E}[\hat{y}])}{ \sqrt{\sum_{i=1}^{n}(y_{i}-\text{E}[y])^{2}}\sqrt{\sum_{i=1}^{n}(\hat{y}_{i}- \text{E}[\hat{y}])^{2}}} \tag{10}\]  
\[\text{MSER}=\frac{\frac{1}{N}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{(\hat{y})^ {2}} \tag{11}\]  
\[\text{MAER}=\frac{\frac{1}{N}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|}{\hat{y}}, \tag{12}\]  
where \(\hat{y}_{i}\) represents the predicted output of the regressor for the sample \(i\), and \(y_{i}\) is the real value or expected value for the sample \(i\). According to these metrics, a good performance of the regressor is demonstrated by MSE and MAE values close to zero, as well as \(R^{2}\) values close to 1. Thus, the \(R^{2}\) metric gives an idea of how correlated the regressor output and the real value are.
## 4 Case Study
### Recognition Result  
#### 4.2.1 Cold Wave Weather Identification Under Small Sample Conditions  
The results of each classification model are presented in Figure 12 as heat maps, which allow for an intuitive comparison of the classification performance. It can be seen that the recognition accuracy of all six classification models is generally high. The improved U-Net model proposed in this paper achieves the highest classification accuracy, followed by the random forest model and the LSTM model. However, while each classification model demonstrates high accuracy in recognizing non-cold wave events, there are significant differences in recognizing cold wave events. The individual recognition recall rates for cold wave events in the improved U-Net model, random forest model, and LSTM model are 0.667, 0.671, and 0.540, respectively, as shown in Table 4.  
As small-sample events, cold wave events accounts for only 3.8% of the total data over two years. This sample imbalance significantly affects the training process and classification performance of the model, particularly in the classification of cold wave event data. The lack of cold wave samples can lead to high overall classification accuracy. The number  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Random Forest & 0.9729 & 0.9734 & 0.6175 & 0.9731 \\ SVM & 0.9609 & 0.9436 & 0.0219 & 0.9434 \\ KNN & 0.9612 & 0.9514 & 0.2336 & 0.9542 \\ MLP & 0.9672 & 0.9611 & 0.5401 & 0.9620 \\ LSTM & 0.9706 & 0.9665 & 0.2189 & 0.9674 \\ U-Net & 0.9775 & 0.9789 & 0.6569 & 0.9780 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of classification results for different classification models.  
Figure 12: Classification results of different models.  
of cold wave samples in the test set is 137, and the recall rates for cold wave samples, as calculated by different models, are shown in Table 4. The recall rate reflects the model's ability to correctly identify real cold wave events.  
As shown in Table 4, the SVM model demonstrates extremely low recognition ability for cold wave weather, primarily due to the scarcity of cold wave samples, which prevents the model from fully learning the cold wave features during training.  
While all classification models exhibit relatively high accuracy, this elevated accuracy may lack practical significance. Due to the scarcity of cold wave samples, the models tend to misclassify cold wave events as normal weather. However, given the limited number of cold wave instances in the dataset, their misclassification has a minimal impact on the overall accuracy metric. Consequently, relying solely on accuracy metrics fails to reflect the model's true capability in effectively identifying real-world cold wave events. This result underscores the importance of feature enhancement and data augmentation for small-sample cold wave data and provides a research direction for improving model performance under imbalanced data conditions.
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 3 Results
### Monthly Comparisons between Basemap and Planet Fusion with PlanetScope  
When comparing each OFR surface area time series derived from Basemap and Planet Fusion with PlanetScope, for both datasets, most of the OFRs (63% and 61% for Basemap and Planet Fusion, respectively) showed good agreement with r\({}^{2}\geq\) 0.55, and 74% and 70% of the OFRs presented small uncertainties with MAPE <5% (Figure 6).  
The mean monthly percent error--calculated by comparing Basemap and Planet Fusion with PlanetScope--for Basemap and Planet Fusion varied between \(-\)2.45-1.48% and between \(-\)3.36%-1.66% for 0.1-5 ha, between \(-\)2.88-1.11% and between \(-\)3.56-0.51% for 5-10 ha, and between \(-\)2.23-0.53% and \(-\)3.13-0.76% for 10-30 ha. These values were stable throughout the year (Figure 7). The percent error variability decreased as the surface area observations increased in size, and the observations between 10 and 30 ha had the least variability. In addition, Planet Fusion presented smaller percent error variability when compared to Basemap for all size classes (Figure 7). The highest MAPE values for Basemap (4.73%) and Planet Fusion (5.80%) were found for observations between 0.1 and 5 ha, and the MAPE was <4.40% for all months for both Basemap and Planet Fusion for observations between 5 and 10 ha and 10 and 30 ha, respectively. This indicates that even when there are fewer PlanetScope images available to generate Basemap and Planet Fusion due to clouds, shadow, and haze, both products tend to have surface area uncertainties <5%.  
Figure 6: Frequency distribution of r\({}^{2}\) and MAPE calculated by comparing the OFR time series from Basemap and Planet Fusion with PlanetScope.