esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results  
Our proposed three optimized deep learning models, OCNN, OANN, and OLSTM-based CKD prediction, are reported in this work. We examined each model's accuracy, sensitivity, recall, and \(F1\) score.  
In Table 3, the classification result of the proposed optimized convolution neural network (OCNN), optimized artificial neural network (OANN), and optimized long short-term memory (OLSTM) is explained. From optimized CNN, we obtained the highest accuracy of 98.75%. From the optimized ANN and optimized LSTM, we obtained 96.25% and 97.50% accuracy, respectively. The precision of OCNN is 96.55%, OANN is 90.32%, and OLSTM is 93.33%. When compared to existing classifiers, the results were also superior in terms of specificity, precision, and accuracy. The \(F1\) score of OCNN is 99%, OANN is 97%, and OLSTM is 98%. The deep learning models (OCNN, OANN, and OLSTM) gave the value of recall of 98%, 94%, and 96%, respectively.
## 3 Results and Discussion
### The Response of NEE and LE Fluxes to Extreme Precipitation  
Since the precipitation characteristics (type, intensity, duration, etc.) are drastically different between the warm and cold seasons, and thus may induce different responses in NEE and LE fluxes, we analyzed the warm and cold seasons separately. The identification of the periods was the same as in the previous section describing the influence of extreme temperatures. Two characteristics of the precipitation regime were used for the analysis: the daily precipitation amount, which characterizes the immediate effect of heavy rains on NEE and LE fluxes, and the API index, which captures the prolonged cumulative precipitation effect.
# Systematic Review on Human Skin-Compatible Wearable Photoplethysmography Sensors
## 2 Methodology  
The search strategy was developed according to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The systematic literature search was performed using the following five databases: PubMed, IEEE Xplore, Web of Science, Google Scholar, and Scopus. All databases were searched for the following terms:  
Group A terms: (PPG) OR (Photoplethysmography) OR (Photoplethysmogram) OR (plethysmogram) OR (optometric) OR (optoelectronic).  
Group B terms: (blood) OR (heart rate) OR (heart pressure) OR (oximeter) OR (hemoglobin).  
Group C terms: (flexible) OR (wearable) OR (portable) OR (stretchable) OR (attachable) OR (electronic skin).  
Group D terms: (organic) OR (inorganic) OR (hybrid).  
Group E terms: (reflection) OR (transmission) OR (non-invasive).  
Groups A, B, C, D, and E were combined. Recent studies published in English in the last five years are considered in this review. Two of the authors ([PERSON] and I.L.) independently searched the full text, excluding papers that did not meet the selection criteria and did not have full-text access.  
The search results are presented in Figure 1 and Table 1. A total of 639 papers were found in five databases. Duplicates and unrelated titles were excluded using EndNote, and 624 papers did not meet the inclusion criteria. After excluding irrelevant papers, 15 papers were selected for this study. The considered PPG devices were divided into four main groups according to fabrication materials: silicone, organic, inorganic, and hybrid.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Active Material**} & \multicolumn{2}{c}{**Bending**} & \multicolumn{2}{c}{**Photodethes**} \\  & & & **Radius** & **(mm)** & & **Response** (nm)** & **Ref** \\ \hline [PERSON] et al. & 2016 & Silicon PIN & - & Reflection & 350–1120 & [30] \\ [PERSON] et al. & 2017 & Silicon PIN & 5 & Reflection & 350–1120 & [31] \\ [PERSON] et al. & 2017 & Silicon & - & Reflection & 450–1000 & [32] \\ [PERSON] et al. & 2017 & GaAs & 2.4 & Reflection & 300–900 & [33] \\ [PERSON] et al. & 2017 & PbS QD & 0.035 & Transmission & 400–1100 & [34] \\ [PERSON] et al. & 2019 & Graphene/PbS QD & 16 & Reflection & 300–2000 & [35] \\ [PERSON] et al. & 2016 & P3 HT:PCBM & 0.1\(\div\) & Reflection & 350–800 & [36] \\ [PERSON] et al. & 2018 & PICP:PC\({}_{64}\)BM & 0.003\(\div\) & Transmission & 300–900 & [37] \\ [PERSON] et al. & 2018 & TMB:BB/PC\({}_{71}\)BM & \(\div\)100 & Reflection & 400–950 & [38] \\ [PERSON] et al. & 2019 & & \(\div\)50 & Reflection & 400–930 & [39] \\ [PERSON] et al. & 2017 & DPP-DTT:PCBM & 9\(\div\) & Reflection & - & [40] \\ [PERSON] et al. & 2018 & C\({}_{70}\):TAPC & 4.5 & Reflection & 350–750 & [41] \\ [PERSON] et al. & 2020 & PTB7-Th:PC\({}_{71}\)BM & 3.5 & Transmission & 400–800 & [42] \\ \hline \hline \end{tabular}
\end{table}
Table 1: State-of-art skin-compatible and wearable photoplethysmography (PPG) sensors.
## 2 Materials and Methods
### Gas Chromatography-Mass Spectrometry/Olfactometry  
Analyses of sample extracts via GC-MS/O were performed with a Trace GC Ultra and a Trace DSQ MS (both Thermo Fisher Scientific GmbH, Drei Reich, Germany) using DB-FFAP and DB-5 columns (cf. Section 2.4). Aliquots (2 \(\mu\)L) were injected by a multipurpose autosampler (MSP 2; Gerstel GmbH & Co. KG, Mulheim an der Ruhr, Germany) using the cold on-column technique (40 \({}^{\circ}\)C). After 2 min, the temperature was raised at 8 \({}^{\circ}\)C/min to 235 \({}^{\circ}\)C and held for 5 min for the DB-FFAP column, and to 250 \({}^{\circ}\)C and held for 10 min for the DB-5 column. The flow rate of the helium carrier gas was 2.3 mL/min. At the end of the capillary column, the effluent was split between an ODP and the MS using deactivated fused silica capillaries (0.5 m \(\times\) 0.2 mm). Mass spectra were generated in electron ionization (EI) full scan mode (_m/z_ range 35-250, 70 eV). Compound identification was made by comparing the odor qualities, mass spectra and RIs on the DB-FFAP and DB-5 capillary columns with those of the respective reference compounds.  
Quantitation of Selected Aroma Compounds by Stir-Bar Sorptive Extraction, Stabile Isotope Diluation Analysis and Gas Chromatography-Mass Spectrometry  
The gin samples were diluted individually 1:10 and 1:100 (v/v) in water, depending on the concentrations of the respective odorants, as estimated in a preliminary experiment. Subsequently, the isotopically labelled analogs (dissolved in 60% ethanol; amounts depending on the concentrations of the analytes) were added and the suspension was stirred at room temperature for 2 h. A Twister(r) (polydimethylsiloxane sorbent, 10 mm length, 0.5 mm coating thickness; Gerstel GmbH & Co. KG, Mulheim an der Ruhr, Germany) was added to the diluted sample and stirred at room temperature for 1 h before removal.  
The SBSE Twister(r) bar was placed in a thermo-desorption unit (TDU) liner. Desorption commenced at a temperature of 40 \({}^{\circ}\)C (initial time: 0.5 min), then increased to 250 \({}^{\circ}\)C at a rate of 120 \({}^{\circ}\)C/min, before being held at 250 \({}^{\circ}\)C for 5 min. The extracted volatiles were transferred to the cold-injection-system (CIS), cooled at -100 \({}^{\circ}\)C using liquid nitrogen, then transferred onto the GC system (cf. Section 2.6) by thermal desorption. After an initial 0.1 min at 40 \({}^{\circ}\)C, the oven temperature was raised at 12 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C and held for 10 min. The column flow of the helium carrier gas was adjusted to 50 mL/min. Mass spectra were generated in full scan mode (_m/z_ range 35-250, EI 70 eV).  
Response factors were calculated for each odor-active compound by analyzing binary mixtures of defined amounts of the labeled standard and the unlabeled analyte at three different mass ratios (1+3, 1+1, and 3+1 v/v; see Table S1 in Supplementary Material) under identical conditions (cf. Section 2.6). Data evaluation was performed using Thermo Xcalibur 2.2 SP1.48 (Thermo Fisher Scientific GmbH, Drei Reich, Germany).
## 4 Simulation
### Power Fluctuation  
At t = 12 s, the fourth string experienced active power fluctuations, whose output drops from 250 MW to 100 MW. The system then recovered to 250 MW at t = 14.7 s, while the active power from the other wind power strings remained stable, as shown in Figure 11a. During the fluctuation, the DC voltages \(\rm{v_{out1}}\), \(\rm{v_{out2}}\), \(\rm{v_{out3}}\), and \(\rm{v_{out4}}\) in the MVDR remained unchanged, as shown in Figure 11b. The capacitor voltage in the MMC-HVDR stayed constant, as shown in Figure 11c. These data mean the control strategy shown in Figure 2b is effective. The output DC voltage \(\rm{v_{dc4}}\) in the HVDR initially decreased, gradually recovered, and increased back to its original level according to the power fluctuation, as shown in Figure 11d. These alterations are caused by the linearity exhibited in Equation (7). Meanwhile, the voltages in the other ports, \(\rm{V_{dc1}}\), \(\rm{V_{dc2}}\), and \(\rm{V_{dc3}}\), remained the same. The DC voltage in the HVDC line first decreased and then increased, with the DC current remaining constant, as shown in Figure 12. It can be concluded that the parallel-series topology is decoupled from other branches during power fluctuation.  
Figure 11: The waveforms for wind turbines. (**a**) Active power of wind turbine; (**b**) Output voltage of MVDR; (**c**) Average capacitor voltage of MMC; (**d**) Output voltage of HVDR.  
Figure 10: The waveforms for HVDC links. (**a**) The DC voltage of HVDC link; (**b**) The DC current of HVDC link.
## 2 Materials and Methods
### Study Design  
The 3D printed model and the chemically synthesised stones were used in the study. All RIRS procedures were performed under almost identical conditions using a 3D model and phosphate artificial stones of comparable size deposited in the renal pelvis of the model. The 3D printed pelvicalyceal system, together with the ureteropelvic junction, was sealed with hot melt adhesive after placing the stone in the renal pelvis. The average stone size was 471.6 mm\({}^{3}\) (the volume was calculated according to the formula of [PERSON] et al. A \(\times\) B \(\times\) C \(\times\) 0.524 [11]). The average dimensions of the stones were as follows: length 15 mm, width 12 mm, height 5 mm. The mean stone density in NCCT was 1021 HU  
Figure 2: IPP recording during the procedure using 12/14 Fr UAS (a), and 10/12 Fr UAS (b). The pressure values seen in the graphs are given in mmHg. The red arrows indicate when the hand pumping system for irrigation was used. UAS—ureteral access sheath; IPP—intrapelvic pressure.  
(620-1383, SD \(\pm\) 150). A flexible ureteroscope (Pusen, Zhuhai, China; PU3022A) was used by insertion through the 10/12 Fr (Flexor; Cook Medical; Bloomington, IN, USA) or 12/14 Fr (ReTrace; Coloplast, Fontenay-sous-Bois, France) ureteral access sheath, which was placed approximately 1 cm below the model ureteropelvic junction. The ureteral access sheath was attached to the model with a rubber connector. A holmium:YAG laser (Quanta System Cyber Ho 60 W; Samarate, Italy) with a 272 mm laser fibre (Quanta System; Samarate, Italy) was used to fragment the stones (Figure 3).  
Constant gravity-based irrigation was used, with a height of 50 cm above the model, and a hand pumping system if necessary. The hand pumping system was used on-demand during lithotripsy when reduced visibility made it difficult to continue the procedure. The operator has 15 years of experience in endourology, including 8 years of experience in RIRS. The laser energy and pulse frequency were used in 7 fixed settings. The laser power ranged from 12 to 25 watts. The pulse frequency was in the range of 10 to 50 Hz, while the energy was in the range of 0.3 to 2.5 joules. All procedures were carried out in dusting mode using the virtual basket option. Endourological materials, such as flexible ureteroscope, holmium laser fibre, and ureteral access sheath, were recycled and disinfected for study use. The PressureWire was placed in the model's renal pelvis. We started recording intrapelvic pressure measurements after filling the model with fluid shortly before beginning the lithotripsy. The baseline intrapelvic pressure in the model after fluid filling was 6.8 cmH\({}_{2}\)O.
###### Abstract
In this regard, the research of novel methods capable of contributing to the solution of the previous points (i.e., time window, computational resources, and accuracy) is essential because it permits patients to go to the health center and take timely medical precautions. Due to the importance of proposing a new proficient method in forecasting an SCD with high accuracy and enough of a time window for predicting an SCD episode, this work investigates the capabilities of the empirical mode decomposition (EMD)-based methods combined with a CNN for the analysis of ECG signals with the object of forecasting an SCD episode 30 min prior to its onset. To perform this task, the EMD methods are utilized for decomposing the ECG signals in diverse frequency bands, namely intrinsic mode functions (IMFs), according to the frequential information contained in the signals. Next, the obtained frequency bands for each signal are combined to generate images. Finally, the obtained images are employed as inputs in a CNN for automatically forecasting an SCD event. It is essential to mention that diverse EMD-based methods, i.e., EMD, ensemble EMD (EEMD), and complete EEMD (CEEMD), are investigated to determine which one provides the most relevant features to be associated with an SCD episode; also, several CNN configurations (i.e., number of layers, number of filters, batch size, and epochs) are also investigated to obtain the highest accuracy with the least computing resources. The proposed method's effectiveness is tested with the data available on the MIT-BIH databases, which contain information about 20 patients with SCD [13] and 18 patients with a normal cardiac rhythm [19]. The results demonstrate that the features highlighted by EMD methods in combination with the CNN can be employed as a reliable tool for identifying suitable features in the ECG signals for its use in SCD forecast.
###### Abstract  
Spatially and temporally resolved aerosol data are essential for conducting air quality studies and assessing the health effects associated with exposure to air pollution. As these data are often expensive to acquire and time consuming to estimate, computationally efficient methods are desirable. When coarse-scale data or imagery are available, fine-scale data can be generated through downscaling methods. We developed an Artificial Neural Network Sequential Downscaling Method (ASDM) with Transfer Learning Enhancement (ASDMTE) to translate time-series data from coarse- to fine-scale while maintaining between-scale empirical associations as well as inherent within-scale correlations. Using assimilated aerosol optical depth (AOD) from the GEOS-5 Nature Run (GSNR) (2 years, daily, 7 km resolution) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) (20 years, daily, 50 km resolution), coupled with elevation (1 km resolution), we demonstrate the downscaling capability of ASDM and ASDMTE and compare their performances against a deep learning downscaling method, Super Resolution Deep Residual Network (SRDRN), and a traditional statistical downscaling framework called dissever ASDM/ASDMTE utilizes empirical between-scale associations, and accounts for within-scale temporal associations in the fine-scale data. In addition, within-scale temporal associations in the coarse-scale data are integrated into the ASDMTE model through the use of transfer learning to enhance downscaling performance. These features enable ASDM/ASDMTE to be trained on short periods of data yet achieve a good downscaling performance on a longer time-series. Among all the test sets, ASDM and ASDMTE had mean maximum image-wise \(R^{2}\) of 0.735 and 0.758, respectively, while SRDRN, dissever GAM and dissever LM had mean maximum image-wise \(R^{2}\) of 0.313, 0.106 and 0.095, respectively.  
downscaling; artificial neural network; transfer learning; deep learning; GSNR; MERRA-2 202213255. [[https://doi.org/10.3390/atmos13020255](https://doi.org/10.3390/atmos13020255)]([https://doi.org/10.3390/atmos13020255](https://doi.org/10.3390/atmos13020255))  
1
## 5 Conclusions  
In this work, an artificial viscosity term is added to the standard SPH algorithm in order to improve its accuracy in modelling sound propagation and interference. SPH forms of the continuity equation, the momentum equation and the equation of state are established firstly. Following this, a one-dimensional sound propagation model and a one-dimensional sound interference model are computed to evaluate the effects of the artificial viscosity term. A comparison between SPH results with and without artificial viscosity demonstrates that a more stable and smoother simulation result can be obtained by adding an artificial viscosity term to the standard SPH algorithm, which is due to effectively reducing the unphysical oscillations. Furthermore, analysis of numerical errors of sound pressure shows that the SPH simulation using the cubic spline kernel function can obtain more accurate results with the particle spacing being 0.04 m and the Courant number being 0.08. Parameter \(\alpha_{\Pi}\) has more obvious effects than \(\beta_{\Pi}\) on the simulation results, and the SPH algorithms of sound waves can obtain optimal results when \(\alpha_{\Pi}\) is in the region between 0.03 and 0.06, considering sound pressure error.
## Materials and Methods
### Study Sample
All sequences were aligned to find consensus or minimally conserved sequences. Three real-time polymerase chain reaction real-time polymerase reactions (RT-PCR) were performed on each sample. The first reaction measures total bacterial abundance using two degenerate primers and a probe corresponding to the highly conserved 16S ribosomal RNA gene sequence. The second reaction detected and quantified three red complex bacteria (_P. gingivalis, T. forysthia,_ and _T. denticola_) by multiplex RT-PCR. The third reaction detected and quantified _Aggregatibacter actinomycetemcomitans, Fusobacterium nucleatum, Echinococcus cordorens,_ and _Campylobacter rectus_. Oligonucleotide concentrations and RT-PCR conditions were optimized for sensitivity, specificity, and lack of inhibition by predetermined target numbers. Absolute quantitative analysis was performed using an Applied Biosystems 7500 Sequence Detection System (Thermo Fisher Scientific, Waltham, MA, USA). Amplification profiling was performed in two steps: 40 amplification cycles at 95 \({}^{\circ}\)C for 15 s and 57 \({}^{\circ}\)C for 60 s, after incubation at 95 \({}^{\circ}\)C for 10 min for polymerase activation. All these experiments were performed in controls without standards to eliminate reagent contamination. Plasmids containing synthetic DNA target sequences (Eurofin MWG Operon, Ebersberg, Germany) were used as standards for quantitative analysis. Standard curves for each target were performed in triplicate reactions using a mixture of equal amounts of plasmids diluted in series in the range of 101 to 107 copies. There was a linear relationship between the values of the threshold cycles plotted against the logarithm of the copy number at different dilution levels. The copy number of each plasmid preparation was estimated with a Thermo NanoDrop spectrophotometer (5800 ICP-OES, Agilent Technologies, Santa Clara, CA, USA). Absolute quantification of the total copy number of the bacterial genome in the sample allowed the calculation of the relative number of red complex species. To avoid contamination of the sample and the polymerase chain reaction, plasmid purification and handling were performed in a separate laboratory using appropriate pipettes.
## 3 Results and Discussion  
### Mud Forest Landscape Features and Evolution Stage  
According to the field investigation, it was found that the mud forest landscape in the study area is characterized by distinct stages, named infant stage, juvenile stage, youth stage and old stage. These four stages reflect to the germination, formation, development and extinction stages of the mud forest landscape (Figure 5).  
In the infant stage, because of surface erosion, small patches of cultivated soil are destroyed and the vegetation cannot grow. This exposed the loess-like soil directly (Figure 6a). First, the lack of cultivated soil layer makes terrain concave. Precipitation tends to accumulate there. Second, due to the lack of vegetation protection, the exposed loess-like soil is more conducive to rainfall infiltration. Precipitation penetrates along pores and vertical joints (Figure 5a). The eluviation makes the joints and fissures of loess-like soil enlarge. During long years of erosion, small cracks expand to form caves. Due to the special physical and chemical properties of loess-like soil, it is easy to disintegrate and dissolve when meeting water. This causes the soil layer to be vulnerable to subsurface erosion, thus expanding the cave range. Under the action of gravity, the top layer of soil collapses, creating holes (Figure 6b). With the continuation of the subsurface erosion, the sinkholes and the horizontal suffusion caves are developed. Some vertical sinkholes have also been formed (Figure 6c).  
Figure 5: Geomorphologic evolution and different periods of the mud forest landscapes: (**a**) the infant stage; (**b**) the juvenile stage; (**c**) the youth stage; and (**d**) the old stage.  
In the next juvenile stage, a large number of holes were formed. With the passage of time and the continuous occurrence of erosion, these holes expanded leading to a destruction of terraces integrity. Gradually, gullies of different sizes and shapes formed, showing accordingly the form of \"island\" (Figure 5b).  
With further development of dissolution and subsurface erosion, the ornamental value of the mud forest reaches its peak in the youth stage of landscape development. The solitary peaks are irregular and varied in shape. Some look like conical and humped forms (Figure 5c).  
After reaching the peak of mud forest landscape development, subsurface erosion continues to occur. However, continued subsurface erosion will begin to destroy the mud forest landscape. The mud forest landscape gradually loses its charm and looks like a series of small raised planes from a distance (Figure 5d). Although its form is unique, its ornamental value is far less than the youth stage of the mud forest landscape. This is the old stage of the mud forest landscape, it is also a stage of landscape degradation. At present, most of the mud forest landscape is in this stage.