esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Processing
### Quality of GNSS Observations
LLI indicators show a temporary loss of signal coherence, which could be caused by interference, non-line-of sight conditions, or multipath, and they often produce cycle slips in the receiver. Cycle slips have a significant negative impact on GNSS positioning, but fortunately, they can be recovered during post-processing. The method used by RTKLIB compares the time series of carrier phase measurements from a GNSS receiver with the predicted values calculated from code and carrier phase measurements. The occurrence of a cycle slip is evident by a sudden change in the carrier phase observation, which does not match the predicted value. The software uses the carrier phase residuals or an advanced Kalman filter--the method used in our case--to estimate the carrier phase bias. When the problem is detected, the software adjusts the carrier phase prediction to account for the cycle deviation, thus improving the GNSS position accuracy. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Type of GNSS Device**} \\ \hline
**Parameters** & **Septentrio** & **u-blox** & **u-blox** & **Smartphone** \\
**AsteRx-U** & **ZED-F9P** & **ZED-F9R** & **Xiaomi Mi8** \\ \hline RMSE for multipath L1 (m) & 0.3554 & 0.5129 & 0.4848 & 0.7394 \\ RMSE for multipath L2 (m) & 0.4320 & 0.5292 & 0.5581 & - \\ RMSE for multipath L5 (m) & 0.3313 & - & - & 0.5515 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics for GNSS multipath on different frequencies for the receivers for the whole track.
## Methods and Materials  
### Photonic Band Structure of TM Modes in PhC NB Structure with a Horizontal Air Gap  
Firstly, the photonic band diagram of the TM mode in the PhC NB structure with a horizontal air gap was numerically calculated using the plane wave expansion method [25]. In this study, a free-standing silicon (\(n\)(Si) = 3.4) waveguide was used because it exhibited no optical absorption and a high refractive index of Si at the telecommunication wavelength, 1.55 \(\upmu\)m. The upper inset of Figure 1a shows the schematic of the PhC NB structure in which the circular holes were perforated periodically. To carry out the simulation, the width (\(w\)), thickness (\(t\)), and air gap (\(t_{g}\)) of the Si waveguide were set as 0.95\(a\), 1.2\(a\), and 0.15\(a\), respectively, where \(a\) is the lattice constant of the air holes. Figure 1a shows the photonic band diagram of the TM modes with \(r/a=0.45\) (the solid lines) and \(r/a=0.40\) (the dashed lines). The solid and dashed red lines indicate the first TM photonic bands at \(r/a=0.45\) and \(r/a=0.40\), respectively. As shown in the lower inset of Figure 1a, the first TM mode corresponds to the air gap mode because of the strongly localized electric field distribution at the air region. It can be observed that the normalized frequency of the first TM mode with \(r/a=0.45\) at the band edge (wavevector, \(k=\pi/a\)) is in the PBG of the TM mode with \(r/a=0.40\).  
To confine the light in the cavity of PhCs, the resonant frequency of the cavity mode should lie inside the frequency range of the PBG. Figure 1b shows the frequencies of the band edge modes as a function of \(r/a\). It can be observed that the frequencies of the TM band edge modes decrease as \(r/a\) increases. Therefore, to confine the light using the air gap mode, the surrounded air hole should be smaller than the air hole in the cavity region. In this paper, we used \(r/a=0.45\) at the center of the PhC NB cavity which was surrounded by air holes with \(r/a=0.405\) as the PBG mirror.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis  
The sharpness evaluation methods for no-reference images can be divided into four categories: spatial domain methods, spectral domain methods, learning methods and combination methods. The specific classification is shown in Figure 1.  
Figure 1: Image sharpness evaluation classification.
## 3 Data and Empirical Results
### Empirical Results
#### 3.2.3 The Prediction of the SEZ's Economic Performance for the Next Five Years  
It is questionable whether economic models represent accurate forecasting techniques. To consider this issue, the statistical test of a model's forecast performance was technically conducted by dividing a given dataset into the in-sample period used for initial parameter estimation, and the out-of-sample period used for evaluating forecasting performance. Empirical evidence relying on out-of-sample forecast performance is more trustworthy and better reflects the information in real time than evidence based on in-sample performance due to the sensitivity of outliers and the process of discovering patterns in the datasets ([PERSON] and [PERSON], 2015). In this study, the in-sample data (1995-2011, roughly 70% of the data) were used to construct the estimation, while the out-of-sample data (2012-2016, the remaining 30%) were employed to produce the prediction. Afterwards, we forecasted the next five-year interval (2017-2021). The classical VAR versus Vine-COPAR models were compared using RMSE and MPE to evaluate their predictive power. Ultimately, the forecasts of the SEZ's economic performance for the next five years for the manufacturing, agriculture and service sectors were carried out. Figure 2 displays the GPP, FDI and TRADE performances throughout the 2017-2021 forecast period using D Vine-COPAR for the manufacturing sector, and C Vine-COPAR for the agriculture and service sectors. Interestingly, the FDI seemed to increase in value, especially in the manufacturing and service sectors, while TRADE stayed constant for the three sectors. The GPP and FDI flows exhibited high levels of volatility.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Manufacturing**} & \multicolumn{2}{c}{**Agriculture**} & \multicolumn{2}{c}{**Service**} \\ \hline \multirow{2}{*}{**Accuracy**} & **D-Vine** & **Classical** & **C-Vine** & **C-Vine** & **Classical** & **C-Vine** & **Classical** \\  & **COPAR** & **VAR** & **COPAR** & **VAR** & **COPAR** & **VAR** \\ \hline
**RMSE** & 0.679 & 0.685 & 0.998 & 1.024 & 1.140 & 1.145 \\ \hline
**MAE** & 0.480 & 0.482 & 1.106 & 1.219 & 0.808 & 0.829 \\ \hline \multirow{4}{*}{**RMSE**} & **BOOTSTRAP with** & **BOOTSTRAP with** & **BOOTSTRAP with** & **BOOTSTRAP with** & **CO-Vine** & **COPAR**1** \\  & **D-Vine COPAR**1** & **C-Vine COPAR**1** & **C-Vine COPAR**1** & **C-Vine COPAR**1** \\ \hline
**RMSE** & 0.674 & 0.992 & 1.134 \\ \hline \hline \end{tabular} Note: 1 A positive integer giving the number of bootstrap replicates requires R = 25 and the type of simulation is the fixed block length used in generating the replicate time series at l = 20.  
\end{table}
Table 2: Comparison of the predictive power of the CD-Vine COPAR, classical VAR and BOOTSTRAP with Vine COPAR.
###### Abstract  
The possibility of investigating the binding properties of the same molecularly imprinted polymer (MIP), most probably heterogeneous, at various concentration levels by different methods such as batch equilibration and sensing, is examined, considering two kinds of sensors, based respectively on electrochemical and surface plasmon resonance (SPR) transduction. As a proof of principle, the considered MIP was obtained by non-covalent molecular imprinting of 2-furaldehyde (2-FAL). It has been found that different concentration ranges of 2-FAL in aqueous matrices can be measured by the two sensing methods. The SPR sensor responds in a concentration range from 1 \(\times\) 10\({}^{-4}\) M down to about 1 \(\times\) 10\({}^{-7}\) M, while the electrochemical sensor from about 5 \(\times\) 10\({}^{-6}\) M up to about 9 \(\times\) 10\({}^{-3}\) M. The binding isotherms have been fit to the Langmuir adsorption model, in order to evaluate the association constant. Three kinds of sites with different affinity for 2-FAL have been detected. The sites at low affinity are similar to the interaction sites of the corresponding NIP since they have a similar association constant. This is near to the affinity evaluated by batch equilibration too. The same association constant has been evaluated in the same concentration range. The sensing methods have been demonstrated to be very convenient for the characterization of the binding properties of MIP in comparison to the batch equilibration, in terms of reproducibility and low amount of material required for the investigation.  
molecularly imprinted polymer; synthetic biomimetic receptors; 2-furaldehyde; binding isotherms; electrochemical sensing tools; surface plasmon resonance sensing tools +
Footnote †: journal: Sensors
_Article_  
**Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response**  
**[PERSON] 1,*\({}^{\copyright}\), [PERSON]. [PERSON] 1,*, [PERSON] 2, [PERSON] 1,3\({}^{\copyright}\) and [PERSON] 1,4,5**  
1 Centre for Mechanical and Aerospace Science and Technologies (C-MAST), University of Beira Interior, 6201-001 Covilha, Portugal; [EMAIL_ADDRESS] (J.C.O.M.) [EMAIL_ADDRESS] (J.P.S.C.)  
2 Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland; [EMAIL_ADDRESS]  
3 The Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP), University of Aveiro, Campus Universitario de Santiago, 3810-193 Aveiro, Portugal  
4 Institute for Systems and Computer Engineering, Technology and Science (INESC TEC) and the Faculty of Engineering of the University of Porto, 4200-465 Porto, Portugal  
5 Instituto de Engenharia de Sistemas e Computadores-Investigacao e Desenvolvimento (INESC-ID), Instituto Superior Tecnico, University of Lisbon, 1049-001 Lisbon, Portugal  
* Correspondence: [EMAIL_ADDRESS] (R.G.); [EMAIL_ADDRESS] (E.M.G.R.)  
Received: 9 February 2018; Accepted: 27 February 2018; Published: 9 March 2018  
**Abstract:** The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing grid. Therefore, new ways to reduce the consumption of energy are necessary to be implemented. When comparing an average house with an energy efficient house, it is possible to reduce annual energy bills up to 40%. Homeowners and tenants should consider developing an energy conservation plan in their homes. This is both an ecological and economically rational action. With this goal in mind, the need for the energy optimization arises. However, this has to be made by ensuring a fair level of comfort in the household, which in turn spawns a few control challenges. In this paper, the ON/OFF, proportional-integral-derivative (PID) and Model Predictive Control (MPC) control methods of an air conditioning (AC) of a room are compared. The model of the house of this study has a PV domestic generation. The recorded climacter data for this case study are for Evora, a pilot Portuguese city in an ongoing demand response (DR) project. Six Time-of-Use (ToU) electricity rates are studied and compared during a whole week of summer, typically with very high temperatures for this period of the year. The overall weekly expense of each studied tariff option is compared for every control method and in the end the optimal solution is reached.  
**Keywords:** energy optimization; model predictive control; home energy systems; photovoltaic microgeneration; demand response
## 4 Methods and Results
### \(\Delta\)logR Method
#### 4.1.2 Improved \(\Delta\)logR Method  
For source rocks in the same formation in practical working areas, \(R_{base}\) and \(\Delta t_{base}\) in the traditional \(\Delta\)logR formula (Equation (4)) can be viewed as constant; maturity of source rocks is basically consistent. The coefficient K can be gained through fitting of practical data; therefore, the original TOC formula can be simplified into:  
\[\text{TOC}=\text{a}\cdot\text{lgR}+\text{b}\cdot\Delta\text{t}+\text{c} \tag{5}\]  
where a, b and c can be gained through fitting of practical data of working areas.  
Sample data were brought into Equation (5) for parameter fitting using SPSS software. The TOC log interpretation formulas corresponding to different target formations are gained.  
\[\text{Ma 5: TOC}=0.323-0.107\text{ LgR}+0.0089\text{ }\Delta\text{t} \tag{6}\]  
\[\text{Ma 3: TOC}=-0.268-0.071\text{ LgR}+0.0043\text{ }\Delta\text{t} \tag{7}\]  
where R is the value of resistivity curve and \(\Delta\)t is the value of interval transit time.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **GR** & **AC** & **DEN** & **CNL** & **RT** & **TH** & **K** & **U** \\ \hline T & 8.9300 & 5.8818 & 8.5079 & 6.5592 & 3.9892 & 7.7918 & 7.9166 & 4.8520 \\ R & 0.5509 & 0.4144 & 0.5344 & 0.4484 & 0.3059 & 0.5058 & 0.5101 & 0.3579 \\ R\({}^{2}\) & 0.3035 & 0.1717 & 0.2856 & 0.2011 & 0.0936 & 0.2548 & 0.2602 & 0.1281 \\ \hline \end{tabular}
\end{table}
Table 1: Correlation analysis and significance test of each logging data.
## 3 Results
### Discussion of Results
#### 3.2.5 Parametric Study of the ANN-PSO Hybrid Model for Probability of Idle Servers
Future research should investigate the scalability of the ANN-PSO model for large-scale telemedicine networks with multiple service centers and servers, considering the interaction between various subsystems. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of** & **Swarm Population** & \multicolumn{2}{c}{**Acceleration Factor**} & \multirow{2}{*}{**Training \(R^{2}\)**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**Testing \(R^{2}\)**} \\
**Neurons** & **Size** & & & & & \\ \hline
10 & 10 & 1 & 2.75 & 0.6028 & 0.0017 & 0.2416 \\ \hline
10 & 20 & 1.5 & 2.50 & 0.7168 & 0.0012 & 0.0896 \\ \hline
10 & 50 & 1.5 & 2.50 & 0.7128 & 0.0012 & 0.2103 \\ \hline
10 & 100 & 1 & 2.75 & 0.7219 & 0.0012 & 0.0914 \\ \hline
10 & 2 One key limitation of this study is the use of simulated data generated via the Reveledu online queuing tool. Although input parameters were randomly sampled within defined ranges to ensure uniform coverage of the queuing system's parameter space, the artificial nature of the simulation may introduce underlying distributional biases that do not fully capture the complexity of real-world telemedicine systems. To mitigate this, the study generated 100 diverse scenarios and validated model performance on unseen test data to assess generalization. Nevertheless, future research should incorporate real patient data from operational telemedicine systems. This would enable additional validation of model predictions under real-world conditions and help compare model generalization across simulated vs. empirical environments. Collaborations with healthcare providers are planned to secure such datasets in the next phase of this research.
# Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method
## 2 Methods
### CFD Simulation  
#### 2.1.1 Numerical Model  
For simulating urban wind fields, the use of LES and DNS can yield better results [31]. However, due to their high computational costs, it is challenging to generate datasets for constructing urban wind fields. RANS has been widely applied for simulating the building wind field [28; 31], and the standard \(k-\varepsilon\) model has been proven to accurately simulate the flow field around buildings [50; 51]. Therefore, in this study, the wind fields around the buildings are obtained by solving the equations of the standard \(k-\varepsilon\) model. In the model, the turbulent dissipation rate is estimated as:Furthermore, turbulent viscosity \(\mu_{t}\) can be expressed as a function of \(k\) and \(\varepsilon\)  
\[\mu_{t}=\rho C_{\mu}\frac{k^{2}}{\varepsilon} \tag{2}\]  
where \(C_{\mu}\) is an empirical constant. In the standard \(k-\varepsilon\) model, \(k\) and \(\varepsilon\), are solved by the following transport equations  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{\partial x_ {i}}=\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{\mu_{t}}{\sigma_{k} }\right)\frac{\partial k}{\partial x_{j}}\right]+G_{k}+G_{b}-\rho\varepsilon -Y_{M}+S_{k} \tag{3}\]  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial(\rho ku_{i})}{ \partial x_{i}}=\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{\mu_{t}}{ \sigma_{\varepsilon}}\right)\frac{\partial\varepsilon}{\partial x_{j}} \right]+C_{1\varepsilon}\frac{\varepsilon}{\bar{k}}(G_{k}+C_{3\varepsilon}G_ {b})-C_{2\varepsilon}\rho\frac{\varepsilon^{2}}{\bar{k}}+S_{\varepsilon} \tag{4}\]  
where \(G_{k}\) is the former of turbulent kinetic energy \(k\) caused by mean velocity gradient, \(G_{b}\) is the term of turbulent kinetic energy \(k\) caused by buoyancy, and \(Y_{M}\) represents the fluctuation expansion in compressible turbulence. \(C_{1\varepsilon}\),\(C_{2\varepsilon}\), and \(C_{3\varepsilon}\) are empirical constants. \(\sigma_{k}\) and \(\sigma_{\varepsilon}\) are the Prandtl numbers corresponding to turbulent kinetic energy \(k\) and dissipation rate \(\varepsilon\), respectively. \(S_{k}\) and \(S_{\varepsilon}\) are user-defined source items. The coefficients are as follows:  
\[C_{1\varepsilon}=1.44,C_{2\varepsilon}=1.92,C_{\mu}=0.09 \tag{5}\]  
\[\sigma_{k}=1.0,\sigma_{\varepsilon k}=1.3 \tag{6}\]  
The equations above are numerically solved by the commercial CFD software ANSYS Fluent 2022R2.
## 4 Results
 \\ \hline
**Resnet18** & **26.83** & **30.0** \\ ResNet18 + SA & 33.56 & 42.3 \\ ResNet18 + BL & 31.88 & 40.2 \\ ResNet18 + BC & 27.68 & 34.9 \\ ResNet18 + SA + BL & 33.56 & 42.3 \\ ResNet18 + SA +BL + BC & 33.56 & 42.3 \\ Resnet34 & 31.88 & 40.2 \\ ResNet34 + SA & 33.56 & 42.3 \\ ResNet34 + BL & 33.  
would not be used in a workflow for lesion detection, or in clinical practice, as it aims at improving performance in areas that result in no additional value in CAD or diagnosis. The performance metrics of the refined masks should then be interpreted as directly correlated with the actual capability of models to segment the chest cavity. The application of the refinement algorithm led to considerable improvements in the similarity coefficients, but with a slight increase in inference times (Tables 4 and 5). The best agreement between model segmentation and manual segmentation was again found for the ResNet34 model with self-attention, bottlenecked connection and blurring mechanism model configuration. It achieved a DSC of 0.9612 and JSC of 0.9789, with inference times of 98.99 ms/image. The worst agreement between model segmentation and manual segmentation was found for the ResNet50 with self-attention model configuration, with a DSC of 0.9465 and JSC of 0.9708, with inference times of 261.7 ms/image. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Configuration** & **DSC \(\pm\) SD (n = 406)** & **JSC \(\pm\) SD (n = 406)** \\ \hline Resnet18 & 0.9750 \(\pm\) 0.0451 & 0.9552 \(\pm\) 0.0667 \\ ResNet18 + SA & 0.9731 \(\pm\) 0.0449 & 0.9552 \(\pm\) 0.0669 \\ ResNet18 + BL & 0.9739 \(\pm\) 0.0447 & 0.9536 \(\pm\) 0.0701 \\ ResNet18 + BC & 0.9717 \(\pm\) 0.0450 & 0.9497 \(\pm\) 0.0670 \\ ResNet18 + SA + BL & 0.9751 \(\pm\) 0.0448 & 0.9556 \(\pm\) 0.0665 \\ ResNet18 + SA + BL + BC & 0.9737 \(\pm\) 0.0447 & 0.9533 \(\pm\) 0.0674 \\ Resnet34 & 0.9744 \(\pm\) 0.0420 & 0.9541 \(\pm\) 0.0682 \\ ResNet34 + SA & 0.9734 \(\pm\) 0.0419 & 0.9527 \(\pm\) 0.0628 \\ ResNet34 + BL & 0.9698 \(\pm\) 0.0417 & 0.9512 \(\pm\) 0.0642 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Similarity coefficients for all models after refinement algorithm. The similarity coefficients are represented as mean \(\pm\) standard deviation. The best performing model was the ResNet34 model with a self-attention layer, bottleneck connection and blurring mechanism, which is highlighted in bold. Abbreviations: BC—bottleneck connection, BL—blurring mechanism, DSC—DSC Similarity Coefficient, JSC—Jaccard Similarity Coefficient, SA—self-attention layer. Figure 6: Schematic of the solution. The input image is inputted in a deep learning model, which outputs a generated mask. A refinement algorithm then removes the bottom part of the mask. The segmentation mask is then applied to the input image.
## 2 EPDM Tensile Specim and Inverse Method
### Finite Element Analysis  
Figure 5 shows the finite element model of the tensile specimen. The elements were created in a three-dimensional form, and their overall average volume was set to 1.5 mm\({}^{3}\). The maximum volume of the element was 3.0 mm\({}^{3}\) and the minimum volume of the element was set to 1.0 mm\({}^{3}\). The elements are all hexagonal. To reduce the hourglass phenomena that cause errors in stress calculations, all elements were considered with eight integral points in the calculation process. The tensile specimen model was analyzed using LS-Dyna's explicit solver under the same conditions as shown in Figure 2. In the FEA results, the x-axis values are the maximum displacement in the longitudinal (\(l_{0}\)) direction, and the y-axis values are the average values of the forces generated at the nodes of the grip portion. As shown in Figure 6, the FEA results differed considerably from the actual experimental results. For the EPDM physical properties, [PERSON]'s incompressible model shown in Figure 4 was used.  
In the FEA, the numerical analysis was repeated until a suitable Ogden coefficient, wherein the mesh did not behave unstably, was derived.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Value** & **Symbol** & **Value** \\ \hline \(a_{1}\) & \(-\)10.88 & \(\mu_{1}\) & \(-\)56.93 \\ \(a_{2}\) & 3.376 & \(\mu_{2}\) & 0.5681 \\ \(a_{3}\) & \(-\)10.88 & \(\mu_{3}\) & 56.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Curve fitting results for [PERSON]’s incompressible model.  
Figure 5: Finite element model for tensile specimen of ethylene propylene diene monomer (EPDM) 60 material.  
Figure 6: Experimental data expressed as points and finite element analysis (FEA) results expressed as curves.  
The fact that the experimental results are similar to the [PERSON] model derived through numerical analysis does not guarantee the accuracy of FEA. This is demonstrated in Figure 6. The Ogden coefficients derived in Figure 4 were inadequate to represent the actual physical quantities.  
The results of the analysis seem to apply the general property polynomial rather than Equation (7). However, in Figure 4, the range of \(\lambda\) values between 1 and 1.1 show a similar trend. Therefore, it can be estimated that the numerically fit physical properties have occurred in the process of being applied to the finite element analysis.  
Although the results of the analysis are somewhat inaccurate, the process of deriving the physical properties as shown in Table 2 is meaningful in finding the initial values of the physical properties. The results of FEA can be approximated to the experimental results by changing the physical property coefficients shown in Table 2. This method is called the inverse method.