esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Optimisation of Model Parameters over the Study Site  
Figure 8 shows the evolution of the RMSECV for a cross-validation of 2 blocks repeated 5 times with a N-PLS performed with the **X** cube and the **y** losses from the calibration set when varying the Gaussian filter width (\(P\)) and interval between dates (\(N\)). This procedure  
Figure 7: Workflow scheme of N-PLS application to the case study.  
led to an observation of the lowest error of cross-validation (RMSECV) at \(P\) = 30 and \(N\) = 15. \(N\) = 15 equates to \(J\) = 7, i.e., 7 dates over the study period, and resulted in a cube \(\underline{\mathbf{X}}\) of dimensions (107, 7, 12). Figure 8 highlights three situations in which the cross-validation error (RMSECV) increased when applied to these data, as follows: (i) when the Gaussian filter is too weak (\(P\) > 25); (ii) when the time step is short (\(N\) < 10 days); (iii) when the time step is long (\(N\) > 20 days).  
Using the optimised \(N\) and \(P\) values, Figure 9 presents the evolution of the cross-validation RMSEC and RMSECV against the number of latent variables used in the model. This figure highlighted the following classical phenomenon: a phase of a decrease in the RMSEC, which corresponded to an improvement of the explanatory value of the latent variables, then a phase of increase of the RMSECV (while the RMSEC continued to decrease), which corresponded to the overlearning phase. On the basis of this joint analysis, 5 latent variables were retained for the rest of the study.
## 4 Experimental Results
The noise effects on the mixed fringe patterns in the proposed method are also investigated. Here we consider the additive Gaussian noise with various power. Let the signal to noise ratio (SNR) be defined as  
\[\textbf{SNR}\ (\textbf{in}\ \textbf{dB})=10\ \textbf{log}_{10}\bigg{(}\frac{P_{s}}{P _{n}}\bigg{)} \tag{13}\]  
where \(P_{s}\) and \(P_{n}\) denote the signal power and noise power, respectively. Figure 11a-d show the noisy fringe patterns, which are the original fringe pattern in Figure 3a with additive Gaussian noise of SNR values 1, 3, 5, and 7 dB, respectively. Then, we test the proposed method with noisy fringe patterns of the SNR values from 1 to 10 dB. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Signal**} & **Position of Original** & **Position of Reconstructed** & **Difference** \\  & **Signals** & **Reconstructed Signals** & **Difference** \\ \hline (a) & (\(-0.7\), \(-0.5\)) & (\(-0.710\), \(-0.515\)) & (0.010, 0.015) \\ \hline (b) & (\(0.1\), \(-0.2\)) & (\(0.095\), \  
Table 6 shows the detected numbers of separated fringes and the corresponding CC values between the original and reconstructed fringe patterns under different SNR values of the noisy mixed fringe patterns. As shown in Table 6, the number of detected fringe patterns varies when the SNR values are within 1 and 7 dB. When the SNR values are 1, 2, 3, and 6 dB, some of the fringe patterns are not detected and are denoted as not available (NA) in the tables. On the other hand, some extra fringe pattern can be detected when the SNR value is 4 dB. When the SNR value is 5 dB, the CC values the fringe patterns No. 4 and No. 5 are \(-0.009\) and \(0.004\), respectively, which means that these two fringe patterns are not missing. Instead, two extra fringe patterns, at different locations, are detected. When the SNR value is 7 dB, the fringe number and the CC values are identical to the noiseless case. Note that the test results (both the fringe number and CC values) for SNR values higher than 7 dB are the same. Figure 11: The original fringe patterns in Figure 3a with additive Gaussian noise under different SNR values: (**a**) 1 dB; (**b**) 3 dB; (**c**) 5 dB; and (**d**) 7 dB. Figure 9: (**a**) An experimental optical fringe pattern and (**b**) its grayscale version.
# Automated Clinical Impression Generation for Medical Signal Data Searches
## 3 Experiments
### Data Description and Preprocessing  
The EEG data corpus collected by the Temple University Hospital (TUEG) [10] is utilized, which is publicly available ([[https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml](https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml)]([https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml](https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml)), accessed on 28 October 2022). The dataset comprises more than 30k clinical EEG recordings collected since 2002, organized by patient and corresponding session. For each session, there is a pair of an EEG recording and a physician report, which includes the patient description, clinical history, medications, and clinical impression. Since only the impression in the report is considered, sessions without impressions were removed.  
The average length of EEG recordings is around 30 min. Sessions in which the signal was longer than 30 min were removed. The raw EEG signals were recorded in the range of 250-1000 Hz and across various channels. Therefore, the signals were resampled at 200 Hz  
Figure 3: Diagram of the encoder–decoder architecture of the proposed method, using DeepSleepNet and a transformer, presented in this work.  
and utilized 19 channels: FP1, FP2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, CZ, A1, and A2. Additionally, bipolar montage was applied to the channels, as physicians consider it a clearer and more symmetric way to visualize and assess the signals [18]. The power line noise is considered as an artifact that exists in the frequency band around 50 Hz [19]. Therefore, the noise was removed using 4 th-order Butterworth filters, cutting off frequencies between 47.5 and 52.5 Hz and 57.5 and 62.5 Hz, as recommended in [20].  
In addition, as mentioned in Section 2.1, the signal is split into 30 s segments. Therefore, each signal can be divided into at most 60 segments. We divided the data into a training set (70%), validation set (15%), and test set (15%) based on patient. As a result, the obtained sets include 7917, 1685, and 1700 sessions from a total of 11,302 sessions for the training, validation, and test sets, respectively.
# Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale
## 2 Satellites Sensors and Data
### Brightness Temperature Datasets
#### 2.1.3 Ssmis  
The series of SSMIS are satellite-borne 24-channel PMW radiometers carried onboard the suite of near-polar orbiting satellites known as US Air Force DMSP. The first of these instruments was launched on DMSP-F16 platform on 18 October 2003. In common with its predecessor SSM/I, SSMIS employs conical-scanning geometry which offers the advantage that the polarization of the measured radiances is invariant across the scan [26]. In addition, the open design permits relatively large primary reflectors and consequently affords good horizontal resolution. SSMIS is the first conical scanner to be used for temperature sounding and combines a range of temperature sounding channels (50-63 GHz), humidity sounding channels (183 GHz), as well as a range of imaging channels (19-150 GHz). SSMIS instrument is flown onboard the DMSP F-16, F-17, F-18, and F19 satellites, whereas SSMIS onboard F-19 satellite stopped providing useful data in early 2016. Fully calibrated and accurately geolocated Level 1C version 05 TB data from F16, F17, and F-18 SSMIS sensors used in this study were obtained from the NASA website at [[https://disc.gsfc.nasa.gov/](https://disc.gsfc.nasa.gov/)]([https://disc.gsfc.nasa.gov/](https://disc.gsfc.nasa.gov/)) (accessed on 12 January 2021).
## 3 Water-Jet Propulsion Dynamics Modeling
### Geometric Models and Meshing
#### 3.1.2 Determining and Meshing the Flow Region  
In order to avoid the influence of boundary effects and to decrease the complexity of the mesh, a cylindrical computing domain with a length of 4L\({}_{\text{s}}\) and a diameter of 2L\({}_{\text{s}}\) were selected [23].  
Due to the larger size of the flow region and higher distortion degree of the screw mixed-flow pump, a hybrid mesh model of nanostructure and structure was used to divide the flow region of the pump and submersible, as shown in Figure 3a. For accuracy of the calculation results, the mesh of the flow region around the submersible was locally encrypted to capture vital information, as shown in Figure 3c. For the mesh dividing of the water-jet propulsion pump, we adopted a nonstructure mesh, as shown in Figure 3b.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **L\_/(mm)** & **R\_max/(mm)** & **L\_/(mm)** & **L\_w/(mm)** & **L\_h/(mm)** \\ \hline Value & 1500 & 136 & 247.5 & 746.5 & 76.66 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the submersible.  
Figure 3: Computing domain and mesh dividing: (**a**) Computing domain; (**b**) Pump body; (**c**) Locally encryption mesh.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Q\_0/(m3/s)** & **H\_0/(m)** & **\(\eta_{0}\)/(\%)** & **n\_0/(rpm)** & **n\_s** \\ \hline Value & 0.00383 & 1.303 & 80 & 1500 & 277.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometric parameters of the screw mixed-flow pump.  
During the process of independence checking the grid, we found that the water-jet pump head error did not exceed 4.6%, and the efficiency error did not exceed 2.5% as the number of grid cells was more than 15.19 million. These errors were within the allowable range. When the number of grid cell was increased, it had little effect on improving the efficiency. Therefore, a mesh size of 15.19 million was selected for numerical simulations, as shown in Figure 4.
## 5 Simulation Analysis  
In this section, the performance of the proposed APC-EEDBR protocol is evaluated using the Aqua-Sim-NG [27] simulation platform, which was validated in a prior work by [PERSON] et al. [29], and compared with several representative protocols, including DBR [11], EDBR [12], EAVARP [30] and APCDBR [15]. To support dynamic transmission power adjustment during data forwarding, we extended and modified the physical layer module in AquaSim-NG, enabling a more accurate simulation of the adaptive power control mechanism under underwater communication conditions.
## 4 Case Study of Cardio-Vascular Disease Using Fuzzy Logic
### Experimental Outcomes  
Figure 11 demonstrates the rule editor in classification. The rule editor can be used to create rule statements based on the details of the output and input variables defined in the FIS Editor. It can also automatically select one item from each box of the input variable and one item from the output box of the connection item. The rule viewer can help the user to analyze the entire fuzzy analysis process by allowing them to look at the various membership functions' shapes, as shown in Figure 11.  
Table 2 shows the sample data collected from District Hospital Gariyaband, Chhattisgarh of 29 individuals, with input parameters including their age, BMI, cholesterol level, blood pressure, smoker, diabetes level, physical activity level, gender, and output parameter used for cardiovascular disease classification. To remedy the small data sample size, this research applied data augmentation techniques such as the Synthetic Minority Oversampling Technique (SMOTE) [74; 75]. SMOTE is specifically designed to tackle imbalanced datasets by generating synthetic samples for the minority class.  
Table 3 shows the detection performance of the popular soft computing models (which were top performers in the review). We implemented a number of popular classifiers on the cardiovascular data after applying data augmentation to mitigate the challenges of data imbalance and scarcity. In our experiments, the attention-based CNN outperformed other implemented classifiers. In the evaluation metric, it achieved a good level of accuracy, i.e., 99.53%, with a sensitivity of 0.99 and a specificity of 0.99. The experimental outcomes conformed to the survey review outcomes. The cardiovascular disease classification was based on the following criteria. Early stage: Individuals with one or more risk factors for cardiovascular disease, such as high blood pressure, high cholesterol, or smoking. Healthy: Individuals with no risk factors for cardiovascular disease. The cardiovascular disease classification was not a diagnosis; it was simply a way to identify individuals who may be  
Figure 11: Example graphical representation of the rule editor in expert systems.  
at an increased risk for developing cardiovascular disease. Individuals who are classified as early stage should talk to their doctor about ways to reduce their risk of developing cardiovascular disease.
# Experimental Demonstration of Single-Channel EEG Signal Using 32 x 32 Pixel OLED Screen and Camera
## 5 Experiments and Results
### Experimental Set Up and Hardware Description
bits obtained after signal processing were uploaded to the microcontroller-embedded system using C programming code and MBED software. The uploaded bits were sent through a switch board or potentiometer to the OLED screen. The switch board acted as a bridge between the microcontroller and the OLED screen. The voltage regulator was designed using KICAD software and the LM series 741 op-amp was used due to simplicity and being readily available. The voltage regulator was used to regulate the dual supply voltage to 3.3 V for the evaluation board of the OLED screen, through Drain Supply (VDD). Hence, in a single frame of the OLED screen, with pixel size of 32, a total of 30 symbols or data bits could be transmitted. The information bits were converted into an image and transmitted over the optical channel, captured by the camera at the receiver section, and then followed by offline processing in MATLAB. The transmitted bits go through the optical channel, received by a camera in video form; thereafter, image processing in MATLAB was conducted to determine BER and system reliability. The camera parameters are listed in Table 3. The length of the video captured by a camera is given by  
\[\text{Length of video}=\frac{\text{Total number of bits to be transmitted}}{\text{number of bits that can be transmitted in }\text{ sin gle frame}}\]  
For example, for 2.8 kbps data rate and 30 symbols, or bits per frame, the length of the video required will be  
\[\text{Length of video}=\frac{2.8\text{ kbps}}{30}=93.33\text{ s }or\text{ }1.56\text{ min}\]  
For the image processing and to detect the border for image detection, matrix formed of rows and columns of \(4\times 5\) was used instead of \(5\times 6\). Therefore, in this experimental work, the useful bits transmitted per single frame of \(32\times 32\) pixel size were counted as 20, comprising of four rows and five columns, Hence, the length of video required is given by  
\[\text{Length of video}=\frac{2.8\text{ kbps}}{20}=140\text{ s }or\text{ }2.3\text{ min}\]  
The length of video is useful in determining the exact time to stop the video, which helps in saving both time and memory. The 2.3 min video could transmit a bit rate of 2.8 kbps without any additional infrastructure, as LED lights are installed, and without hardware modifications by using a camera as the receiver, unlike VLC systems deploying photodiodes as the receiver.
## 2 Materials and Methods  
The overview of the research workflow for this study includes all key steps, beginning with the background and a literature review based on the Environmental Kuznets Curve theory, as discussed by [PERSON] [21] and [PERSON] and [PERSON] [25]. This theory serves as the main framework for this study. Next, the data and methodology are established by identifying stationary variables, followed by conducting spatial analysis tests to determine the most appropriate spatial econometric model.  
In this context, we use spatial econometrics to address spatial interaction (spatial autocorrelation) and spatial structure (spatial heterogeneity) in regression models for panel data. This method incorporates both spatial (geographical) and time dimensions. The focus on location and spatial interaction has recently gained prominence not only in applied but also in theoretical econometrics, particularly in models such as the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Dynamic Panel Model (SDPD) [51]. After identifying stationary variables, serial correlation, and multicollinearity problems, we conduct a Spatial Correlation Test (LM-test) to determine whether the SLM or SEM model is more appropriate. Additionally, the Hausman test is used to decide whether a fixed-effects or random-effects model should be employed. Once the appropriate model type (SLM or SEM) is identified, it is integrated into the Spatial Dynamic Panel Model (SDPD) framework for estimating the variables. This study adopts the Spatial Dynamic Panel Instrumental Variables Model (SDPD IV), which provides consistent and accurate estimates, particularly when the sample size is large. The selection of instrumental variables is validated using the two-stage least squares (2 SLS) method. This approach ultimately aims to identify the most appropriate model for estimating the relationships among variables. Furthermore, a Generalized Additive Model (GAM) analysis is employed as a supplementary method to enhance policy-relevant insights, similar to the role of hot spot analysis. Finally, all analytical results are synthesized to provide policy recommendations. These steps are illustrated in Figure 8.  
Based on previous studies [17, 35, 36, 37, 40, 41, 43], Equation (1) illustrates the functional relationship between energy consumption per capita (_Energycons_pc_) and the variables grounded in the EKC theory, as identified through the literature review summarized in Table 2. In addition, this study incorporates a road density variable derived from satellite-based data to generate meaningful insights.
# Formation of Conductive Networks in Polysulfone Filled with Graphite-Derived Materials
## 3 Results and Discussion  
### Structure of Filler Powders  
Morphology, Microstructure, and Particle Size Distribution  
Natural graphite particles have a layered structure with carbon atoms organized in a honeycomb lattice and a plane spacing of 0.35 nm. SEM examination reveals a range of graphite flake sizes. Graphite particles are generally prismatic platelets, with well-defined basal planes and edge diameters. The graphite sheets appear to be big sheets with smooth and resilient surfaces and particle sizes of up to 180 \(\upmu\)m. Natural graphite has flat particles with an average size of about 200 \(\times\) 500 \(\upmu\)m and a thickness of about 15-20 \(\upmu\)m, and, as can be seen in Figure 2a,b, natural graphite particles consist of highly oriented thin 2D graphene-like sheets packed in packs. This structure facilitates their intercalation when producing particles of thermally expanded graphite, which thus proceeds in two stages: splitting the packs into individual plates and splitting the plates into few-layer graphene. Due to the lamellar shape of the particles, the bulk density of natural graphite is not high, and it was 0.464 g/cm\({}^{3}\) and the tapping density was 0.584 g/cm\({}^{3}\).  
Figure 1: Figure showing the direction of heat according to graphite sheets of PSU-based composite filled with NG and NG + CB.  
A fine structure is observed in the artificial graphite particles, formed at the stage of obtaining the \"green billet\" due to the flow of coal tar pitch filled with coke particles during the pressing process. The AG particles seemed to be a random aggregation of microscopic particles in a spherical shape, Figure 2c,d. Magnitudes of bulk and tapping densities were 0.797 and 0.952 g/cm\({}^{3}\), respectively.  
We employed carbon fillers of various sizes to produce very efficient conductive structures. Carbon black particles are aggregates bonded by low molecular weight polymer and van der Waltz forces, with typical sizes ranging from 1 to 15 \(\upmu\)m. Figure 2e,f shows such a structure provides excellent dispersion of CB into individual particles in diverse solvents, polymers, and rubbers, including those with sizes less than 0.1 \(\upmu\)m. Bulk and tapping densities were 0.352 and 0.390 g/cm\({}^{3}\).
# A New Windage Loss Model for S-CO\({}_{2}\) Turbomachinery Design
## 5 Summary Conclusions  
The existing windage loss models found in the open literature are mostly based on air or liquid experiments and can have substantial errors for predicting the S-CO\({}_{2}\) windage loss in turbomachinery. It is shown that the major source of error is due to the assumption of uniform properties and not considering the thermodynamic state change due to viscous heating. The viscous heating via windage loss should be correctly reflected to estimate windage loss, since the small temperature change due to viscous heating can result in substantial density change from inlet to outlet in S-CO\({}_{2}\) turbomachinery. In order to reflect the physics correctly, the windage loss test under the CO\({}_{2}\) condition is designed and performed in this research.  
Experimental data are newly obtained from an S-CO\({}_{2}\) windage loss test to evaluate the existing windage loss models under S-CO\({}_{2}\) conditions. The newly obtained windage loss data are compared with the existing models. The comparison shows that the existing models do not perform satisfactorily to predict the windage loss under the S-CO\({}_{2}\) conditions. Therefore, a new windage loss model is suggested by modifying the best pre-existing model with the inlet Taylor number and inlet specific heat ratio. These two added variables are selected since these variables correlate with the newly obtained data quite well. The new model shows a satisfactory performance, and therefore, it is recommended to be used for designing an S-CO\({}_{2}\) turbomachine used in the S-CO\({}_{2}\) power cycle in the future.  
Conceptualization, D.K. and J.I.L.; data curation, D.K.; formal analysis, D.K. and J.I.L.; investigation, D.K., Y.J., I.W.S. and J.I.L.; methodology, D.K. and J.I.L.; software, D.K.; validation, D.K. and J.I.L.; writing--original draft, D.K. and J.I.L.; writing--review and editing, D.K. and J.I.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Challengeable Future Defense Technology Research and Development Program (912767601) of the Agency for Defense Development in 2023, grant number UC190029 RD.  
Not applicable.  
Figure 15: Measured windage loss vs. calculated windage loss from the second new model for the superheated gas phase.