esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodological Framework
### Key Performance Indicators  
The evaluation of the kindergarten energy upgrade project is grounded on a robust framework of Key Performance Indicators (KPIs) designed to capture both the environmental and economic impacts of the energy upgrade. These KPIs align with the European Level(s) framework, ensuring compliance with standardized sustainability benchmarks. By focusing on critical aspects such as energy efficiency, carbon emissions, and financial performance, the KPIs provide a comprehensive methodology for assessing the renovation's outcomes over the building's lifecycle.  
An overview of the equations, variables, and methodologies for these KPIs is provided in Table 8, while a detailed explanation of the equations, variables, and methodologies is available in the Supplementary Materials.  
\begin{table}
\begin{tabular}{c c c c} \hline
**KPI Name** & **Equation** & **Units** & **Description** \\ \hline \multirow{6}{*}{Global Warming Potential (GWP)} & \multicolumn{2}{c}{Environmental KPIs} & \multirow{2}{*}{\(L_{\text{GWP}}: Total greenhouse gas emissions over the lifecycle} \\  & \(L_{\text{GWP}}=\frac{1}{\mu_{\text{GWP}}}\frac{1}{\mu_{\text{GWP}}}\) with & & \(L_{\text{GWP}}\): Building area with heating/cooling access \\  & \(L_{\text{GHG}}=L_{\text{GHG}}+\frac{N}{\mu_{\text{GHG}}}\left(Q_{\text{GHG}}^{ \text{G}}\right)\) & & \(0_{\text{GHG}}^{\text{G}}\): Operational GHG emissions of the building’s components in year \(i\): Infrastructure (embodied) GHG emissions. \\  & & & \(L_{\text{GHG}}\): Energy demand during infrastructure/construction phases \\ Primary Energy Demand (PED) & \(L_{\text{PE}}=l_{\text{PE}}+\sum\limits_{i=1}^{N}\left(Q_{\text{HE}}^{\text{G}}\right)\) & & \(0_{\text{PE}}^{\text{G}}\): Operational/maintenance energy demand during building’s use stage \\  & \multicolumn{2}{c}{Economic KPIs} & \multirow{2}{*}{\(l_{\text{G}}\): Infrastructure costs (CAFEX)} \\  & & & \\ Lifecycle Costs (LCC) & \(LCC=l_{\text{C}}+\sum\limits_{i=1}^{N}\left(Q_{\text{NIC}}^{\text{G}}\right)-V _{\text{R}}\) & EUR & \(0_{\text{NIC}}^{\text{G}}\): operational costs, including energy consumption and maintenance \\  & & & \(V_{\text{R}}\): Residual value of components at end of lifecycle \\  & & & \(L_{\text{C}}\): Infrastructure costs (CAFEX) \\ Whole Life Cost (WLC) & \(WLC=l_{\text{C}}+\sum\limits_{i=1}^{N}\left(Q_{\text{C}}^{\text{G}}\right)-V _{\text{R}}\) & EUR & \(0_{\text{C}}^{\text{G}}\): operational costs (energy consumption/ maintenance and operational \\  & & & \\ Renewable Energy System (RES) & & & \\ Payback Period & & & \\ \hline \multirow{6}{*}{Levelized Cost of Electricity (LCO)} & \multirow{2}{*}{\(LCOE=\frac{\sum\limits_{i=1}^{N}\frac{l_{\text{GCN}}^{\text{G}}}{L_{\text{GCN}}}+O_{\text{GCNCNNN}}^{ \text{G}}+O_{\text{GCNNN}}^{\text{G}}}{(1+\
u)^{\text{G}}}\)} & \multirow{2}{*}{EUR/AWh} & \multirow{2}{*}{\(L_{\text{GCN}}\): total energy simultaneously and operational} \\  & & & \\  & & & \\ \cline{1-1} \cline{4-4}  & & & \\ \cline{1-
# Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase
## 4 Simulation Setup
### Experimental Setup and Network Topology  
The simulation is performed with the Network Simulator 2 (ns-2) version 2.35. We used Fast TCP patch fast-tcp-ns2-v1_1c; developed at the University of Melbourne's Center for Ultra-Broadband Information Networks (CUBIN) [24] to implement our proposed algorithm as discussed in a previous section.
# Medical Application of Big Data: Between Systematic Review and Randomized Controlled Trials
## 3 Current Application Status of Big Data in Medical Health Area  
Big data has come to be increasingly recognized for its potential benefits in public health. In 2011, the UN (United Nations) declared the issues of 'NCD Crisis' and 'GOAL 25 by 25' [15]. NCD (Noncommunicable disease) refers to chronic diseases including cancer, cardiovascular disease, diabetes, dementia, and so on. These NCDs are increasingly being observed in the public health area compared to CD (communicable disease) due to the high prevalence and mortality of NCD, which is increasing with sharp speed even in proportion to the status of the aging society. The UN has declared that all efforts must be made to reduce health inequality in NCDs. Many research groups have now started conducting studies using big data to investigate the global burden of NCDs and inequality in NCDs. Moreover, the terminology 'Health care crisis' has appeared in recent days [16]. Korea's average growth rate in individuals' medical expenses is high among OECD (Organization for Economic Co-operation and Development) countries [17]. A similar concept as that which was investigated with evidence-based medicine is now being combined with research using big data.  
Aside from its important role in public health, big data is also being widely used in the era of the health care industry and the focus on profitmaking. Medical industrialization is evolving because the delivery of medical care in an analogue style is expeditiously changing into that in a digital style. Medical industrialization using big data has launched a new era including the development of diagnostic strategies or the development of specific target agents.  
Among the medical big data, the most recent one we encountered is the World Health Organization's (WHO) medical big data. Through the recent COVID-19 pandemic, the WHO's huge big data platform has been providing all of the information regarding the current status of the COVID-19 outbreak and the death rate in each country in real time. The WHO's World Health Data Hub is a comprehensive digital platform for global health data. It provides end-to-end solutions to collect, store, analyze, and share [18]. Not only COVID-19, but also various disease status including NCD were reported annually by WHO.  
With the utilization of big data, the most crucial issue is personal de-identification, which is closely related to ethical concerns. In the US, the health insurance portability and accountability act was enacted as a law in 1996 [19]. This mandates that personal information must be converted by personal de-identification, which is mainly performed by health care clearinghouses. To manage transparent big data, it is necessary to manage the government-oriented system.
# Universal Voltage Conveyor and its Novel Dual-Output Fully-Cascadable VM APF Application
###### Abstract  
This letter presents a novel realization of a voltage-mode (VM) first-order all-pass filter (APF) with attractive features. The proposed circuit employs a single readily available six-terminal active device called as universal voltage conveyor (UVC) and only grounded passive components, which predict its easy monolithic integration with desired circuit simplicity. The auxiliary voltage input (W) and output (ZP, ZN) terminals of the device fully ensure easy cascadability of VM APF, since the input and output terminal impedances are theoretically infinitely high and zero, respectively. Moreover, thanks to mutually inverse outputs of the UVC, the proposed filter simultaneously provides both inverting and non-inverting outputs from the same configuration. All of these features make the UVC a unique active device currently available in the literature. The behavior of the filter was experimentally measured using the readily available UVC-N1C 0520 chip, which was produced in cooperation with ON Semiconductor Czech Republic, Ltd.  
active circuit; all-pass filter; dual-output; fully-cascadable filter; universal voltage conveyor; UVC-N1C 0520; voltage-mode +
Footnote †: journal: Applied Sciences
## 3 Proposed Optimum Mother Wavelet Selection Method
### Determination of Decomposition Level  
In theory, after the \(j\)th scale decomposition, as long as the length of approximation coefficients is smaller than the length of wavelet filter, next scale decomposition can be performed. However, a too large decomposition level will increase the calculation complexity without significant improvement in the denoising effect. Thus, it is important to find a balance between the denoising effect and the decomposition level. The proper decomposition level J is essential for good denoising results.  
In traditional methods, the decomposition level is determined through trial and error or according to the signal frequency spectrum distribution before wavelet transform is performed on the noised signal. However, in scale dependent wavelet selection for denoising, the denoising effect changes with the selected wavelet at each scale; it is difficult to determine the decomposition level ahead of the wavelet transform. If the decomposition level is \(J\), the noise components in the detail coefficients at all scales will be eliminated, while the noise components in the approximation coefficients at the \(j\)th scale will be reserved. When the noise components are a tiny proportion of the approximation coefficients, the influence of the remaining noise on the denoising effect can be ignored. Given the approximation coefficients are of high SNR, then a further decomposition cannot visibly improve the denoising effect. Accordingly, this paper proposes a novel idea for decomposition level determination. After each decomposition, the energy composition of the approximation coefficients is analyzed to judge whether to perform the next scale decomposition; when it meets one of the following conditions, the decomposition stops.  
1. The energy of approximation coefficients is smaller than 5% of wavelet domain total energy. The percentage of approximation coefficients energy \(p_{A_{j}}\) is computed as (15), where \(\sum\limits_{k}{{cA(j,k)}^{2}}\) is the energy of the approximation coefficients of \(j\)th scale, \(\sum\limits_{i}{\sum\limits_{k}{{cD(i,k)}^{2}}}\) is the total energy of detail coefficients from the first scale to the \(j\)th scale. The value of \(p_{A_{j}}\) gets smaller with the wavelet decomposition proceeding; this means that more levels of decomposition cannot improve the denoising effect significantly when \(p_{A_{j}}\) is very small.  
\[p_{A_{j}}=\frac{\sum\limits_{k}cA(j,k)^{2}}{\sum\limits_{k}cA(j,k)^{2}+\sum \limits_{i}\sum\limits_{k}cD(i,k)^{2}} \tag{15}\]
2. The SNR of approximation coefficients is larger than 20 dB. When the SNR of approximation coefficients is larger than 20 dB according to Equation (16), the energy of noise in the approximation coefficients is much smaller than the signal energy; a further decomposition can make very little contribution to the denoising effect at the cost of computation time. \[SNR_{A_{j}}=10\cdot\log_{10}(E_{AS}(j)/E_{AN}(j))\] (16)
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline
**Predictors** & **Model 1** & **Model 2** & **Model 3** \\  & **OR (95\% CI)** & **OR (95\% CI)** & **OR (95\% CI)** \\ \hline \multicolumn{4}{c}{**Boys**} \\ \hline Skips breakfast (no vs. yes) & 1.04 (0.99–1.08) & 1.02 (0.98–1.06) & 1.02 (0.98–1.06) \\ Eats a second fruit every day (no vs. yes) & 1.85 (1.79–1.91) & 1.81 (1.75–1.87) & 1.78 (1.72–1.84) \\ Consumes fish regularly (at least 2–3/week) (no vs. yes) & 0.95 (0.92–0.98) & 0.98 (0.95–0.99) & 1.01 (0.98–1.03) \\ Eats pulses >1/week (no vs. yes) & 1.54 (1.49–1.61) & 1.57 (1.51–1.63) & 1.54 (1.48–1.60) \\ Eats pasta or rice almost every day (no vs. yes) & 1.29 (1.25–1.33) & 1.29 (1.25–1.33) & 1.28 (1.24–1.33) \\ Consumes nuts regularly (at least 2–3/week) (no vs. yes) & 1.36 (1.31–1.40) & 1.34 (1.30–1.38) & 1.32 (1.28–1.36) \\ Uses olive oil at home (no vs. yes) & 0.85 (0.78–0.93) & 0.89 (0.80–1.00) & 0.93 (0.85–1.03) \\ Takes two yoghurts and/or some cheese daily (no vs. yes) & 1.26 (1.21–1.31) & 1.27 (1.22–1.32) & 1.27 (1.21–1.32) \\ Fast food consumption (\(<\)=1/week vs. >1/week) & 1.02 (0.98–1.06) & 0.96 (0.88–1.04) & 0.95 (0.95–1.01) \\ Takes sweets/candy several times every day (no v. yes) & 0.92 (0.88–0.96) & 0.94 (0.90–0.98) & 0.95 (0.91–0.99) \\ \hline \multicolumn{4}{c}{**Girls**} \\ \hline Skips breakfast(no vs. yes) & 1.02 (0.98–1.06) & 1.00 (0.97–1.05) & 0.99 (0.92–1.05) \\ Eats a second fruit every day (no vs. yes) & 1.83 (1.77–1.89) & 1.80 (1.73–1.85) & 1.76 (1.70–1.82) \\ Consumes fish regularly (at least 2–3/week) (no vs. yes) & 0.94 (0.91–0.97) & 0.97 (0.94–1.00) & 0.99 (0.96–1.02) \\ Eats pulses >1/week (no vs. yes) & 1.45 (1.40–1.49) & 1.46 (1.40–1.51) & 1.14 (1.39–1.49) \\ Eats pasta or rice almost every day (no vs. yes) & 1.20 (1.16–1.24) & 1.20 (1.16–1.24) & 1.19 (1.15–1.23) \\ Consumes nuts regularly (at least 2–3/week) (no vs. yes) & 1.35 (1.31–1.40) & 1.34 (1.30–1.38) & 1.32 (1.28–1.36) \\ Uses olive oil at home (no vs. yes) & 0.89 (0.81–0.98) & 0.91 (0.82–1.01) & 0.92 (0.84–1.01) \\ Takes two yoghurts and/or some cheese (40 g/daily) (no vs. yes) & 1.25 (1.19–1.29) & 1.25 (1.20–1.30) & 1.24 (1.20–1.28) \\ Fast food consumption (\(<\)=1/week vs. >1/week) & 0.98 (0.94–1.04) & 1.00 (0.95–1.05) & 0.99 (0.94–1.04) \\ Takes sweets/candy several times every day (no v. yes) & 0.86 (0.82–0.90) & 0.89 (0.85–0.94) & 0.90 (0.86–0.94) \\ \hline \end{tabular} Model 1: unadjusted; Model 2: adjusted for age, BMI, and waist circumference; Model 3: Model 2 + screen time, sleeping hours, and physical activity hours.  
\end{table} 
Table 3: Results (OR, 95%CI) from logistic regression models used to evaluate the association of children’s dietary habits with increased vegetable consumption (no vs. yes). both sexes. This model predicted correctly84% of consumers and 68% of non-consumers (i.e., how well the model predicts the group membership).
## 4 Results  
The initial selection will be made based on the value of the coefficient of determination R\({}^{2}\). As the following figure (Figure 7) shows, we have at our disposal the individual indicators of the model and the values of the coefficients.  
By gradually increasing the 'Number of terms', we compare the selected coefficient values. The resulting values are shown in Table 1.  
Based on the table, it is clear that the more we increase the \"degree\", the higher the value of the considered coefficient R\({}^{2}\), and the values of several indicators (SSE, RMSE, MSE, SMAPE) are lower. It is therefore possible to state that the model better represents the measured data. However, it is important to assess whether this additional complexity is justified. Will we achieve a significant improvement by increasing the complexity of the model?  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Number of Coefficients**} \\ \hline  & **4** & **5** & **6** & **7** \\ \hline R\({}^{2}\) & 0.855315 & 0.929122 & 0.942937 & 0.94663 \\ adjR\({}^{2}\) & 0.854916 & 0.928877 & 0.942700 & 0.946371 \\ SSE & 0.675565 & 0.330945 & 0.266441 & 0.249196 \\ RMSE & 0.021555 & 0.015087 & 0.013537 & 0.013091 \\ MSE & 0.000465 & 0.000228 & 0.000183 & 0.000171 \\ MAE & 0.005992 & 0.006701 & 0.004885 & 0.007245 \\ MAPE & 0.013936 & 0.014061 & 0.011719 & 0.013945 \\ SMAPE & 0.000442 & 0.000364 & 0.000343 & 0.000333 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of scenarios.  
Figure 7: Basic model in the Curve Fitting Toolbox environment.  
In this case, we can use the AIC (Akaike Information Criterion). The resulting values are shown in Table 2.  
While the R\({}^{2}\) value for the 'gauss7' model is higher than for 'gauss6', the AIC suggests that adding parameters beyond six does not significantly improve model quality.  
The following values also speak in favour of the gauss6 variant:  
* MAE--a lower value of this indicator means that the model is more accurate, because its predictions are closer to the actual values.
* MAPE expresses the error in the form of a percentage difference between the actual and predicted values, which allows easier comparison of different datasets or units. A lower value means a more accurate model.
# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 2 Materials and Methods
### Network Architecture  
We propose TSINet, a dual-branch network based on an encoder-decoder architecture, specifically designed to achieve efficient and accurate semantic and instance segmentation of tomato plant point clouds. As illustrated in Figure 3, the upper branch is responsible for semantic segmentation, while the lower branch handles instance segmentation. TSINet consists of three main components: a shared encoder, parallel decoders, and a dual attention-based feature enhancement module. The shared encoder is composed of stacked Geometry-Aware Adaptive Feature Extraction Blocks (GAFEBs), which progressively encode raw point cloud coordinates into hierarchical high-dimensional features. This enables effective extraction of both local geometric structures and global contextual information, providing unified feature representation for both segmentation tasks. The parallel decoder comprises two symmetric upsampling paths, which reconstruct semantic and instance features, respectively, and facilitate their fusion. To enhance the discriminative power and contextual awareness of the fused features, we introduce a dual attention module that combines spatial attention (SA) and channel attention (CA). The final semantic predictions are obtained via an argmax operation, while instance segmentation is achieved by applying the MeanShift clustering [25] algorithm to the instance-aware features.
## 1 Introduction
In addition to background deposition of nutrients, local sources of nutrients to the atmosphere [9] can be also significant to dramatically increase atmospheric deposition of nutrients to the near-shore areas. This supports additional eutrophication (an increase in the input of organic matter to the system [10] and results in additional consumption of oxygen leading in hypoxia and even anoxia in coastal areas. By bringing new nutrients to the upper layers of the ocean, aerosol deposition influences ocean productivity, especially in waters with low nutrient and chlorophyll concentration [11]. [PERSON] et al. (2010) [11] have demonstrated that atmospheric nitrogen input can have a significant impact on marine biogeochemistry at the regional scale and this impact can be increased by human activities. At the same time, atmospheric inputs of Si and P can have only minimal impact on the productivity and biogeochemistry of marine ecosystems, since these inputs are usually quite small compared to the flux of these nutrients from below the euphotic zone. [PERSON] et al. (2017) [12] and [PERSON] et al. [13] have demonstrated that atmospheric deposition of nitrogen and dust phosphorus may potentially increase primary productivity over the Mediterranean Sea by 30-50% of the net primary production. Results of our studies [14; 15; 16] have revealed both inter- and intra-annual variations in the load of nutrients with atmospheric depositions. We have revealed intra-annual variations in the inorganic nitrogen volume-weighted mean concentration with increasedvalues in winter and a decrease from March to August. The inorganic phosphorus and silicon volume-weighted mean concentrations are usually higher for the warm period of the year [16]. The inter-annual variations are less regular but concentrations and loads of nutrients generally increase on the scale of years. The ratio of nutrients in atmospheric depositions is even more complex because the major sources of nitrogen, phosphorus and silicon are different. The ratio of nutrient concentrations in seawater may affect the elemental composition of particulate organic matter (POM) controlling phytoplankton population structure [17]. The main nutrients that cause eutrophication are nitrogen and phosphorus. Silicon is essential for the growth of diatoms. Anthropogenic activity has led to a sharp decrease in the silicon content in the Black Sea [18]. The effect of changes in the load and ration of nutrients with atmospheric depositions to the Black Sea remains unclear. This work aims to study the input of inorganic nitrogen, phosphorus, and silicon with atmospheric depositions to coastal area near Crimea, variations in this input on the time scale of months-to-years, drivers of these variations, the range of changes in the ratio of nutrients, and effects of atmospheric input of nutrients on primary production and potential oxygen consumption on a daily-time scale.
# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 4 Results and Discussion
### Cross-Regional Prediction Using Transfer Learning Based on Random Forest  
To improve the regional adaptability of the model, transfer learning (TL) was incorporated into the RF model. Firstly, the model was trained using data from Site01, and transferable features were extracted by applying it to samples from other regions. These features were then fused with the original input data of the target region to generate an augmented dataset. This dataset was re-divided into training and testing sets, and transfer learning was applied to retrain the model. As shown in Figures 8-10, the predicted values closely matched the measured data in each region, indicating that the model's prediction performance was significantly enhanced.  
Figure 6: Predicted and measured temperatures at Site02 using an RF model trained on Site01 data.  
To further validate the effectiveness of the transfer learning approach, data from the Site01 region were selected as the source-domain training set, while 30% of the samples from each target region were used as the testing set. The model's performance before and after optimization was compared, and the evaluation results are shown in Figure 11. As illustrated, the introduction of transfer learning significantly improved the prediction accuracy across all regions. For instance, in the Site02 region, the RMSE of the surface temperature prediction decreased from 7.83 to 3.78, the MAE dropped from 5.54 to 2.68, and \(R^{2}\) increased from 0.74 to 0.94. At Site03, the RMSE declined from 5.75 to 2.46, the MAE from 4.23 to 1.73, and \(R^{2}\) rose to 0.97. Similarly, at Site04, the RMSE was reduced from 6.48 to 3.03, the MAE from 5.08 to 2.28, and \(R^{2}\) improved significantly to 0.89. In addition, the prediction performance at different pavement depths (5 cm, 12 cm, and 30 cm) was also evaluated. Table 5 presents the corresponding improvements in evaluation metrics before and after optimization for each region. These results confirm that the TL-RF model effectively improves regional adaptability in pavement temperature prediction and demonstrates strong potential for broader cross-regional application.  
Figure 7: Model evaluation for temperature prediction in different regions using an RF model trained on Site01 data.  
Figure 8: Predicted and measured temperature at Site02.  
Figure 9: Predicted and measured temperature at Site03.