esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real Time and Lossless Encoding Scheme for Patch Electrocardiogram Monitors
###### Abstract  
Cardiovascular diseases are the leading cause of death worldwide. Due to advancements facilitating the integration of electric and adhesive technologies, long-term patch electrocardiogram (ECG) monitors (PEMs) are currently used to conduct daily continuous cardiac function assessments. This paper presents an ECG encoding scheme for joint lossless data compression and heartbeat detection to minimize the circuit footprint size and power consumption of a PEM. The proposed encoding scheme supports two operation modes: fixed-block mode and dynamic-block mode. Both modes compress ECG data losslessly, but only dynamic-block mode supports the heartbeat detection feature. The whole encoding scheme was implemented on a C-platform and tested with ECG data from MIT/BIH arrhythmia databases. A compression ratio of 2.1 could be achieved with a normal heartbeat. Dynamic-block mode provides heartbeat detection accuracy at a rate higher than 98%. Fixed-block mode was also implemented on the field-programmable gate array, and could be used as a chip for using analog-to-digital convertor-ready signals as an operation clock.  
electrocardiography (ECG); patch ECG monitor +
Footnote †: journal: Applied Sciences
# An Efficient Indoor Positioning Method with the External Distance Variation for Wireless Networks
###### Abstract  
This study strengthens the external distance variation for the indoor positioning performance. With the received signal strength (RSS) of the unknown node, a localization is performed to positioning its coordinates. The mean square error (MSE) of localization is deteriorated by the shadowing effect and the MSE depends on the location of reference nodes. Moreover, the minimum mean square error (MMSE) algorithm is also used with the RSS. The amount of variation in the distance between the reference point and the positioning node will also affect the accuracy. Therefore, this paper considers the distance between the reference point and the positioning node and also the distance variation between the reference points. MSE is used to estimate positioning performance and Monte Carlo is also used to simulate the average error of different shadowing and decay environments. When reference nodes have known distances, the distance is obtained separately and the estimated distances are identified by the MMSE method. In order to reduce the number of reference nodes and calculation cost, this paper uses adaptive reference node selection to improve the accuracy of positioning. Simulation results show that the external distance variation mechanism strengthens the indoor positioning performance. Moreover, this paper investigates the performance of several reference nodes (three, four, five, and six reference nodes) through 3D graphs to estimate the small range area. The differences are more clearly observed with fewer reference nodes and lower MSE. Finally, simulation results show that the MSE value of fixed three reference nodes is almost 100% better with external distance variation method compared to the random selected three reference nodes.  
Keywords: indoor positioning; reference point selection; strengthen external distance variation; distance variation; wireless networks +
Footnote †: journal: 2021
## 3 Results
### Volatile Profile
Considering the annotated compounds, 3-ethoxy-1-propanol, ethyl decanoate, 1-dodecanol, methyl 4-decenoate, (E)-nerolidol and ethyl 2-methylpropanoate (ethyl isobutyrate) were selected as the most discriminative metabolites of this fermentation strategy by displaying higher overall content. Meanwhile, _Sc_ and _Sc-Mp_ wines were separated in X-variate 2, that explained the 20% of the total variance of the samples (Figure 3). The volatile metabolites involved in such differentiation were pentyl-cyclopropane, isopentyl hexanoate and ethyl heptanoate, found in overall higher concentrations in _Sc-Mp_ wines, and 3-isopropyl-1-pentanol (3-ethyl-4-methylpentanol), ethyl trans-3-hexenoate, (E,E)-farnesol and 3-furaldehyde, that were found in higher contents in _Sc_ wines. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Analytical Platform** & **Model** & \
\begin{tabular}{c} **Mean Overall** \\ **BER (1)** \\ \end{tabular} & **Ncomp** & **Class** &
\begin{tabular}{c} **Mean Class** \\ **Error (2)** \\ \end{tabular} & \(p\)**-Value (3)** \\ \hline GC-MS & Allvariables & 0.11 \(\pm\) 0.08 & 3 & _Sc-Td_ & 0.01 \(\pm\) 0.03 & BER: \(<\)0.001 \\  & & & _Sc-Mp_ & 0.15 \(\pm\) 0.16 & NMC: 0.024 \\  & & & _Sc_ & 0.18 \(\pm\) 0.17 & AUROC: 0.020 \\ \hline GC-MS & Variable reduction & 0.08 \(\pm\) 0.06 & 2 & _Sc-Td_ & 0.00 \(\pm\) 0.00 & BER: \(<\)0.001 \\  & & & _Sc-Mp_ & 0.12 \(\pm\) 0.11 & NMC: 0.007 \\ \hline LC-MS & Allvariables & 0.17 \(\pm\) 0.10 & 4 & _Sc-Td_ & 0.14 \(\pm\) 0.09 & BER: \(<\)0.001 \\  & & & _Sc-Mp_ & 0.07 \(\pm\) 0.13 & NMC: 0.120 \\  & & & _Sc_ & 0.29 \(\pm\) 0.21 & AUROC: 0.035 \\ \hline LC-MS & Variable reduction & 0.02 \(\pm\) 0.04 & 2 & _Sc-Td_ & 0.00 \(\pm\) 0.00 & BER: \(<\)0.001 \\  & & & _Sc-Mp_ & 0.05 \(\pm\) 0.11 & NMC: 0.027 \\  & & & _Sc_ & 0.00 \(\pm\) 0.02 & AUROC: 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model performance of the partial least squares discriminant analysis (PLS-DA) models built to discriminate yeast fermentation strategy and grape variety used to elaborate the rose wine samples. Figure 3: Graphical outputs of the partial least squares discriminant analysis (PLS-DA) performed to classify rose wines according to the fermentation strategy on the basis of their volatile profile. (**a**) Scores plot for the components 1 and 2; (**b**) and (**c**) loading contribution barplot on components 1 and 2. Colour indicates the class for which the compound has a maximal mean value. Bar length represents the multivariate regression coefficient with either a positive or negative sign for that particular feature of each component, i.e., the importance of each variable in the model. _Sc-Td_: wines fermented with sequential inoculation of _Torulaspora delbruechii_ and _Saccharomyces cerevisiae_. _Sc-Mp_: wines fermented with sequential inoculation of _Metchikovia pulcherrima_ and _Saccharomyces cerevisiae_. _Sc_: wines fermented with monoculture of _Saccharomyces cerevisiae_.
## 2 Distributed Energy Storage Aggregation Model Based on Inner Approximation of Minkowski Sum
### Characterization of Feasible Region for Individual Distributed Energy Storage Resources
\right]\right\} \tag{6}\]  
where \(\Omega_{\text{des}}^{r}\) and \(p_{\text{DES}}^{r}\) are the flexibility feasible domain and the power matrix during the scheduling period of the DES \(r\), respectively; \(A_{\text{DES}}^{r}\) and \(b_{\text{DES}}^{r}\) are defined as the inequality coefficient matrix and column vector, respectively, representing the feasible region of the \(r\)-th DES; \(E\cdot p_{\text{DES}}^{r}\leq b_{\text{DES}}^{r,\text{P}}\) denotes the power constraints, and \(A_{\text{DES}}^{r,\eta}\cdot p_{\text{DES}}^{r}\leq b_{\text{DES}}^{r,\text{E}}\) denotes the energy boundary constraints; \(E\) is the unit matrix, \(A_{\text{DES}}^{r,\eta}\) is determined by the charging and discharging efficiencies \(\eta_{\text{c}}^{r}\) and \(\eta_{d}^{r}\), \(b_{\text{DES}}^{r,\text{P}}\) is determined by the power boundaries of the DES \(r\), and \(b_{\text{DES}}^{r,\text{E}}\) is determined by the energy upper and lower bounds of the \(r\)-th DES \(E_{\text{DES,$max}}^{r}\) and \(E_{\text{DES,$min}}^{r}\). To improve the efficiency of optimization decisions, the aggregator aggregates the feasible regions of multiple individual DES resources, represented as \(\Omega_{\text{DES}}^{r}\), to form the flexibility feasible region of the cluster, represented as \(\Omega_{\text{agg}}\). This feasible region characterizes the adjustable power range of the DES aggregator during scheduling. The expression is shown in Equation (7):  
\[\Omega_{\text{agg}}=\Omega_{\text{des}}^{1}\oplus\ldots\oplus\Omega_{\text{ des}}^{N}=\left\{p\in\mathbb{R}^{N}\middle|p=\sum_{r=1}^{N}p_{\text{des}}^{r},p_{ \text{des}}^{r}\in\Omega_{\text{des}}^{r}\right\} \tag{7}\]where \(p\) represents the power of the aggregated cluster; \(\oplus\) denotes the Minkowski Sum operator. Through this aggregation, the cluster model replaces individual power variables with a single overall power variable, significantly reducing the computational burden for the aggregator in the decision-making process. However, resource aggregation and regulation often occur within a 24 h horizon, resulting in a high-dimensional feasible region, which introduces extremely high computational complexity for the Minkowski Sum. To address this issue, a DES aggregation method based on the Zonotope is proposed in this paper.
## 2 Materials and Methods
### Software and Model Evaluation  
The performances of PLS-DA and SVM models were evaluated by the classification accuracy which was calculated as the ratio of the number of correctly classified samples to the number of samples to be classified. Both PLS-DA and SVM models were conducted on MATLAB R2015b (The MathWorks, Matick, MA, USA).
# Microalgal Growth in Aquaculture Effluent: Coupling Biomass Valorisation with Nutrients Removal
## 3 Results and Discussion
### Wastewater Treatment
By analysing the performance metrics of the modified Gompertz model (R\({}^{2}\) and RMSE) used in this study, one can see that the obtained R\({}^{2}\) are all near one (\(\geq\)0.994), and the obtained RMSE values are low (0.013-1.926 mg L\({}^{-1}\)), demonstrating the adequacy of the model to accurately represent the experimental data. The NO\({}_{3}\)-N and PO\({}_{4}\)-P removal efficiencies obtained within this study regarding the aquaculture wastewater treatment, 98.0 \(\pm\) 0.1% and 92.7 \(\pm\) 0.1%, respectively, were similar to the literature values. [PERSON] et al. [35] used _Chlorella sorokiniana_, _S. obliquus_ and _Ankistrodesmus falcatus_ to treat aquaculture wastewater for 14 d and achieved NO\({}_{3}\)-N removal efficiencies from 75.8 to 80.9% and PO\({}_{4}\)-P removal efficiencies between 98.5 and 100%. [PERSON] et al. [36] also tested _C. sorokiniana_ in aquaculture wastewater treatment and obtained a NO\({}_{3}\)-N removal efficiency of 84.5% and a PO\({}_{4}\)-P removal efficiency of 73.4%. [PERSON] et al. [13] cultivated _Parachlorella kessleri_, _C. vulgaris_, _Scenedesmus quadrica_, _Chlorococcsp. and _S. obliquus_ in real aquaculture wastewater, after 5 d the NO\({}_{3}\)-N removal efficiency was 85.7-97.1% and the TP removal efficiency was 90.2-98.9%. The biomass yield coefficient was also calculated (Table 4). The values obtained for the positive control were statistically lower (\(p<0.05\)) than those obtained for AW. This means that the higher the biomass yield values, the higher the biomass productivity is for the same nutrient concentration. Comparing the yields between the different nutrients analysed, the yields regarding phosphorus are higher than the ones regarding nitrogen. Thus, it can be concluded that the nutrient that most impacted biomass production was nitrogen. For the same productivity, microalgae consumed more nitrogen than phosphorus. Figure 3: Experimental data and achieved modified Gompertz models (solid lines) for: (**a**) total nitrogen (TN); (**b**) nitrate-nitrogen (NO\({}_{3}\)-N); and (**c**) phosphate-phosphorus (PO\({}_{4}\)-P) concentrations in AW and C+ assays. The lowest EU legislation limits for treated urban wastewater discharged into aquatic bodies subject to eutrophication (10 mg\({}_{\text{N}}\) L\({}^{-1}\) and 1 mg\({}_{\text{P}}\) L\({}^{-1}\)) are represented by horizontal dashed lines.
## 4 Materials and Methods
### LAIR-1 Ig-Like Domain Expression and Purification  
The C2-type Ig-like domain sequence of LAIR-1 from residues 22 to 122 was inserted in a pET28a plasmid for bacterial expression with a polyhistidine tag at the C-terminal extremity to allow protein purification. Soluble LAIR-1 Ig-like domain was overexpressed by pET28a-LAIR-1 transformed _E. coli_ BL21(DE3) cells using an autoinduction protocol [44]. LAIR-1 Ig-like domain was purified in a two-step process. First, it was affinity purified on a nickel-loaded hitrap chelating HP column (GE Healthcare), washed by a 35 mM imidazole step followed a 35-500 mM imidazole gradient in 25 mM Tris, 150 mM NaCl (pH 8.2). Then a gel filtration step was performed using a Superdex 75 column (GE Healthcare) in 25 mM Tris, 150 mM NaCl (pH 8.2). The LAIR-1 mutants were expressed and purified with the same experimental procedures than for the wild-type. At each purification step, LAIR-1 concentration was estimated using an A\({}_{1\%,1\mathrm{cm}}\) of 10.3 and the molecular masses presented in Figure 1A.
## 2 Materials and Methods
### The Mathematical Model of a Squeeze Film Damper  
The controllable damping in the rotor supports can be achieved by applying magnetorheological squeeze film dampers. The principle of their work is similar to the function of normal squeeze film dampers, only they are lubricated by magnetorheological oil, the properties of which are controlled by a magnetic field. Figure 1: Frequency response characteristic. The radial magnetorheological damper (Figure 2) is a controllable damping element used for vibration attenuation of rotors. The damper consists of an inner and outer ring. The latter is fixed to the stationary part. The rotor journal is supported by a rolling element bearing inserted in the inner damper ring, which is coupled with the damper housing by a cage spring of a cylindrical form. The narrow gap between the rings is filled with a thin layer of magnetorheological oil. The stationary part is equipped with an electric coil that generates magnetic flux, passing through the damper body, a thin layer of the magnetorheological oil, and the inner damper ring that returns to the coil. The action of magnetic flux on the magnetorheological oil changes its character from a Newtonian-like liquid (no magnetic field) to a liquid with a yielding shear stress (presence of the magnetic field), the magnitude of which depends on magnetic induction of the magnetic field. In areas where the flow rate is low, the shear stress between two neighboring layers drops below the yielding shear stress and forms a core where the magnetorheological material behaves more similar to solid matter than liquid matter (But, the velocities of two neighboring layers are the same.). The velocity profile of the activated magnetorheological liquid flowing in a narrow gap or channel can be seen in Figure 3. The core is formed in the middle of the flow. Squeezing the oil film between the inner and outer damper rings because of the rotor vibration and magnitude of the yielding shear stress are the most significant factors that influence the damping effect. The developed mathematical model of the squeeze film damper is based on the classical theory of lubrication [37; 38], except the assumption for the lubricant. If the oil is not affected by a magnetic field, the lubricant is considered to be Newtonian liquid. If affected by a magnetic field, the oil is represented by bilinear (biviscous) material. Its flow curve is depicted in Figure 4. In addition, it is assumed that the geometric and design parameters make it possible to consider the damper as short and symmetric relative to the damper middle plane, which is perpendicular to the damper axis. In the area where  
Figure 3: The velocity profile of the activated magnetorheological liquid. Figure 2: Magnetorheological squeeze film damper. the width of the clearance between the rings rises because of the rotor vibration, the air is sucked in the damper from the neighborhood, and cavitation takes place there. Pressure in the cavitated regions is assumed to be constant and equal to the atmospheric one.
## 4 Conclusions  
In this study, the optical coupling effect dependent on the string spacing in the shingled module was studied by simulation and comparative analysis of the fabricated module. The efficiency of the module increased as the spacing between strings increased because of the enhanced optical coupling effect. For modules with string spacings of 0.2 cm, 0.5 cm and 1 cm, the efficiencies decreased by 3.07%, 2.05% and 1.63%, respectively, after the lamination of the strings. In contrast, when the string spacing was 2 cm, the efficiency increased by 1.18%, and when it was 4 cm, the efficiency increased by 2.87%. Based on the efficiency of 0.2 cm spacing, the optical coupling effect according to the string spacing was increased by 33.33%, 46.98, 67.01%, and 193.49% for spacings of 0.5 cm, 1 cm, 2 cm, and 4 cm, respectively. Furthermore, the coupling effect according to the structure of the different layers showed that using white EVA to reflect the incident light from the top layer resulted in an enhanced Pm loss 117.14% higher than that of using a UV-transparent EVA, which can be considered to increase the output of the module. Through our research, we have demonstrated that the amount of light reflected from the rear and reabsorbed by the module varies depending on the spacing between strings and the types of module material. However, increasing the spacing between strings in the same area has the advantage of enhancing string efficiency due to the increased reabsorption of light from the rear, but it also has the drawback of reducing the number of solar cells that can be installed in the module, leading to a decrease in overall performance. Therefore, to achieve the best performance, it is necessary to optimize the string spacing considering the area of the photovoltaic module. This optimization will be addressed in more detail in future research. As a result of the research, it is possible to explore ways to improve the output power of  
Figure 14: Output power loss comparison of before and after lamination process between the conventional EVA (UVT)—EVA (UVT)—white backsheet structure (**left**), EVA (UVT)—EVA (white)—white backsheet structure (**center**), and EVA (UVT)—EVA (UVT)—black backsheet structure (**right**).  
BIPV modules installed in limited spaces. Also, it is expected that the results can contribute to energy efficiency in buildings by enhancing the performance of BIPV modules.  
Conceptualization, J.L.; methodology, S.U. and J.K.; formal analysis, H.J.; investigation, S.U. and J.K.; writing--original draft preparation, H.J., J.K. and S.U.; writing--review and editing, J.L.; visualization, J.K. and S.U.; supervision, J.L. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data that support the findings of this study are available upon reasonable request.  
The authors declare no conflicts of interest.
# A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion
## 5 Discussion  
All the images in this paper were taken under low light conditions and all the experimental algorithms used were image enhancement algorithms. The proposed algorithm has the ability to process any single low-brightness image without the need for multiple lens images with different brightnesses of the same scene. In practical application, it can effectively solve the problem in which the fusion result is natural but not obvious, or in which its details are obvious, but the overall brightness is too bright. This makes the algorithm versatile and useful in cases where the fusion result needs to be improved in terms of natural visibility and overall brightness. By analyzing the model, we find that  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **DICM** & **LIME** & **VV** & **Avg.** \\ \hline Dong [13] & 4.313 & 2.601 & 3.597 & 3.504 \\ LIME [14] & 3.421 & 2.636 & 3.561 & 3.206 \\ MSR [12] & 3.346 & 2.452 & 3.629 & 3.142 \\ MF [17] & 3.506 & 2.825 & 3.234 & 3.188 \\ NPE [15] & 3.277 & 2.562 & 3.191 & 3.024 \\ SRIE [16] & 3.556 & 2.541 & 3.454 & 3.183 \\ Ours & **3.121** & **2.235** & **3.152** & **2.836** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the NIQE values of each algorithm in three datasets. The bold numbers indicate the best performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Input** & **Dong** & **LIME** & **MSR** & **MF** & **NPE** & **SRIE** & **Ours** \\ \hline bookshelf & 8.331 & 9.002 & 8.059 & 8.704 & 9.018 & 7.769 & **7.081** \\ cupboard & 11.372 & 10.927 & 10.998 & 12.932 & 11.566 & 10.224 & **10.128** \\ doll & 7.653 & 8.085 & 8.536 & 8.389 & 8.106 & 7.741 & **7.588** \\ classroom & 10.714 & 11.547 & 10.322 & 11.485 & 11.485 & 10.771 & **10.153** \\ swimming pool & 9.913 & 9.247 & 8.864 & 9.585 & 9.264 & 8.501 & **8.438** \\ hall & 10.703 & 11.119 & 9.788 & 11.919 & 10.272 & 9.922 & **9.673** \\ gym & 8.885 & 8.509 & 8.232 & 9.843 & 8.314 & 7.652 & **7.145** \\ wardrobe & 6.965 & 7.412 & 6.762 & 7.805 & 7.209 & 6.423 & **5.782** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative measurement results of NIQE. The bold numbers indicate the best performance.  
there are also some deficiencies. As shown in Figure 11, we adjust the parameter \(a\) while \(n\) is equal to four. It was observed that as the value of \(a\) increases, the details of the dark area become more pronounced, but the image is also prone to over-enhancement. Therefore, how to choose \(a\) is a problem that needs to be studied. We expect to solve this problem by adaptive selection of parameter \(a\) in the future.  
The method presented is simple and effective, but further research is needed to achieve real-time processing of low-light images and better application in areas such as nighttime image acquisition.