esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Description of the Dataset  
#### 2.2.1 GRACE TWSA Dataset  
GRACE and GRACE-FO Level 1B are ranging measurements produced from the raw intersatellite microwave and laser ranging records. There are two fundamentally different GRACE and GRACE-FO Level 2 gravity field solutions, i.e., the spherical harmonics approach and the mass concentration approach, also known as mascon solution, each of which has advantages and limitations [15; 42]. Level 3 monthly Total Water Storage changes are computed as surface mass deviations relative to a baseline temporal average (usually  
Figure 1: Location map showing the division of Greece into Water Districts.  
2004-2009) and are known as Total Water Storage Anomalies (TWSA), using either the spherical harmonics or mascon solution. Although those two solutions are different in their nature, their outputs are not much different, and the choice of specific GRACE data is not critical [15]. The GRACE observations were used in the downscaling process as an ensemble mean from the three analysis centers, i.e., Jet Propulsion Laboratory (JPL), Center for Space Research at University of Texas, Austin (CSR) and GeoforschungsZentrum Potsdam (GFZ), with the most recent gravity fields, i.e., release 06 accessed from NASA's Physical Oceanography Distributed Active Archive Data Center ([[https://podaac-tools.jpl.nasa.gov/drive/files](https://podaac-tools.jpl.nasa.gov/drive/files)]([https://podaac-tools.jpl.nasa.gov/drive/files](https://podaac-tools.jpl.nasa.gov/drive/files)), accessed date 25 June 2021) [43]. Averaging of the three data centers' solutions is recommended as it has been found effective in reducing the noise in the gravity field solutions [44; 45]. The Level-3 GRACE data used herein are produced using Level-2 spherical harmonics as inputs [46], although the same approach can be applied for the mascon solution products. Error estimates provided along with the GRACE, and GRACE-FO data products were introduced in the analysis to estimate the level of uncertainty in the computed downscaled solution. The time step of GRACE TWSA retrievals is approximately 1 month, although successive records may cover longer or shorter time periods. In the present work, we developed our downscaling approach using only the GRACE-FO time series, starting from June 2018 until March 2021. GRACE-FO has no data gaps, which are frequently encountered during the last years of operation of GRACE, from 2011 until the end of mission, October 2017. GRACE data from 2005 to 2015 was subsequently used to test the methodology comparing the downscaled results to modeled TWSA values for that decade in two regions of Greece.
## 3 Results
### Comparison of Various Modified Chisholm Parameters
Figure 17 shows comparisons between the measured \(\Delta P_{\rm f}/\Delta L\) and the calculated frictional pressure drop \(\Delta P_{\rm f}\ {\rm cal}/\Delta L\) using [PERSON] and modified [PERSON]'s parameters; the result is shown in Figure 16:5 In Table 3, MD is the percentage mean deviation of the calculated frictional pressure drop from the experimental value. \(N\) is the number of data points. The mean deviation is defined as  
Figure 16:6The definition of the liquid limits or vapor Reynolds number \(Re_{\rm L}\)\(Re_{\rm G}\) between the laminar and turbulent is 2000. Of all 297 data points, 51 data points (17.2%) were laminar (liquid)-laminar (vapor); \(N=236\) data points (79.5%) were laminar (liquid)-turbulent (vapor); 4 data points (1.3%) were turbulent (liquid)-laminar (vapor); and 6 data points (2.0%) were turbulent (liquid)-turbulent (vapor). Figure 16:7 Comparison of calculated and measured frictional pressure drop gradients. [PERSON] et al. noted that Equation (13) is applicable for \(Re_{\rm L0}\) < 2200 and <1.0; however, at this time, all 297 data points are adapted. There are two applicable correlations suggested by [PERSON] et al., Equations (20) and (22). However, the calculation value from Equation (22) estimated less accurate 15% in whole comparing to Equation (20), so using the calculation from Equation (20) in Figure 16:8  
As several researchers have reported, [PERSON]'s parameter, which assigns a constant value by the flow regime, could not consider the effect of the differences in tube diameters, so the calculated values had little correspondence with the measured values. The calculated values were almost higher than the measured values. While [PERSON], [PERSON], and [PERSON] considered the effect of the differences in tube diameters, [PERSON] and [PERSON] had good prediction performances for the measured data. Notably, [PERSON], and [PERSON] made good predictions of the non-circular and circular tubes, respectively. As mentioned above, to consider the effect of the differences in tube diameters, the use of the hydraulic diameter \(D_{\rm h}\) and the Bond number Bo in modified [PERSON]'s parameter is shown to be beneficial in mini-/micro-tubes.
# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
## 6 Results and Discussion
### Performance of Throughput  
The outcomes of the throughout achievement for both recommended and present techniques are shown in Figure 5. The suggested ED-MORFO obtains superior outcomes in terms of throughput, compared to the existing MCH-EOR [24] and existing RDSAOA-EECP [25]. As ED-MORFO has a long network lifespan, the base station receives more data packets. Table 4 shows the performance analysis for throughput. Table 4 shows that the proposed ED-MORFO achieves a maximum throughput of 0.99 Mbps, whereas the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multicolumn{3}{c}{**Energy Consumption (J)**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Existing MCH-EOR [24]**} & \multicolumn{2}{c}{**Existing RDSAOA-EECP [25]**} & \multirow{2}{*}{**Proposed ED-MORFO**} \\ \hline
100 & 0.09 & 0.11 & 0.06 \\
200 & 0.23 & 0.25 & 0.14 \\
300 & 0.38 & 0.43 & 0.31 \\
400 & 0.51 & 0.49 & 0.42 \\
500 & 0.58 & 0.62 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of energy consumption.  
Figure 4: Performance of PDR for proposed EDMORFO and Existing MCH-EOR [24], RDSAOA-EECP [25].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multicolumn{3}{c}{**PDR (\%)**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Existing MCH-EOR [24]**} & \multicolumn{2}{c}{**Existing RDSAOA-EECP [25]**} & \multirow{2}{*}{**Proposed ED-MORFO**} \\ \hline
100 & 98.7 & 97.4 & 99.4 \\
200 & 98.1 & 96.5 & 98.9 \\
300 & 97.8 & 94.8 & 98.5 \\
400 & 96.2 & 92.6 & 97.8 \\
500 & 95.5 & 90.8 & 97.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performances of PDR.  
existing MCH-EOR [24] only manages 0.97 Mbps, and RDSAOA-EECP [25] only manages 0.94 Mbps.  
When compared to the existing approaches, the total simulation results show that the suggested ED-MORFO achieves better results in all node counts (100-500).
## 6 Conclusions  
The presented study focused on the optimization of the induction heating regime and proposed a suitable deformation (swaging) temperature for pre-sintered powder-based WNiCo tungsten heavy alloy workpieces. The numerical predictions and experimental investigations revealed that the optimum swaging regime consists of an induction heating time of 140 s and a deformation temperature of 900 \({}^{\circ}\)C. The lower heating temperature (700 \({}^{\circ}\)C) resulted in cracking during swaging, while the higher temperature (1100 \({}^{\circ}\)C) introduced surface oxidation and microstructure inhomogeneity. Swaging at 900 \({}^{\circ}\)C provided the workpieces with homogeneous circular shapes and oxidation-free surfaces. The rapid heating thus did not negatively affect the microstructure, while, on the other hand, it affected positively the surface quality of the swaged billets. The microstructures in the axial and subsurface regions of the piece swaged at 900 \({}^{\circ}\)C exhibited deformed tungsten agglomerates, which points to the effect of the shear strain involving the bulk of the swaged piece. Considering the fact that rotary swaging is an industrially applied technology following which no additional surface treatment in order to increase the surface quality is needed, the experiment proved the practical (industrial) applicability of the designed procedure.  
The contributions of the individual authors were the following: methodology, L.K., A.M. and Z.K.; numerical investigations, R.P., and M.M.; experimental validation, L.K., A.M. and Z.K.; microscopy investigation and evaluation, L.K.; writing--original draft preparation, L.K., A.M. and Z.K.; writing--review and editing, L.K., A.M., and Z.K.; project administration, A.M.; funding acquisition, A.M. and R.P. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support of the SP2020/34 project of specific research of VSB--Technical University of Ostrava, FV40286 TRIO programme by Ministry of Industry and Trade, as well as the project no. CZ.1.05/2.1.00/19.0387 \"Development of research and development basis of RMSTC\" within the frame of the operation program Research and Development for Innovations financed by the Structural Funds EU and from the state budget of the Czech Republic.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Floating Step-Down Converter with a Novel Lossless Snubber
## 6 Literature Comparison  
The circuit in reference [13] has an output power of 340 W at full load with an input voltage of 20 V to 60 V and an output voltage of 48.5 V, with a maximum efficiency of about 96.62%. The circuit in [14] has an output power of 220 W at full load with an input voltage of 48 V and an output voltage of 96 V, with a maximum efficiency of about 96.4 %. The circuit in [15] has an output power of 200 W at full load, an input voltage of 380 V, and an output voltage of 24 V, with a maximum efficiency of about 94.5%.  
References [13; 14; 15] all use lossless snubbers to achieve the near-ZVS or near-ZCS function. Table 3 compares seven items: (1) rated output power, (2) rated output voltage, (3) switching frequency, (4) the number of snubber elements, (5) whether the ZVS/ZCS function is available, (6) the peak efficiency, (7) the type of converter circuit, and (8) whether the snubber elements are located on the main power path. From Table 3, it can be seen that compared with references [13; 14; 15], the proposed circuit in this paper has the highest peak efficiency of 97.0%, the least number of snubber elements, and none of the snubber elements located on the main power path.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Items** & **Ref. No.** & **[13]** & **[14]** & **[15]** & **Proposed Circuit** \\ \hline Rated output power (W) & 340 & 220 & 200 & 270 \\ \hline Rated output voltage (V) & 48.5 & 96 & 24 & 135 \\ \hline Switching frequency (kHz) & 100 & 100 & 100 & 100 \\ \hline No. of snubber components (capacitor/inductor/diode) & \(1/1/2\) & \(2/1/2\) & \(2/2/4\) & \(1/1/2\) \\ \hline Turn ON/turn OFF & \(\times/\) ZVS & ZCS/ZVS & ZCS/ZVS & \(\times/\) ZVS \\ \hline Peak efficiency & 96.6\% & 96.4\% & 94.5\% & 97 \% \\ \hline Circuit type & Boost & Boost & Buck & Buck \\ \hline Snubber components on & & & & \\ main power path & No & Yes & Yes & No \\ \hline \end{tabular}
\end{table}
Table 3: Literature comparison.  
Figure 19: Efficiency comparison.
## 2 The Proposed Method
### Encryption  
The prepared message \(P\) is encrypted using a standard synchronous stream cypher and a shared key generating the encrypted message \(E=F(P)\) which is very similar to random data in distribution.
# A Three-Coil Constant Output Wireless Power Transfer System Based on Parity-Time Symmetry Theory
## 4 Experimental Verification
### Experimental Results
#### 4.3.4 Error Analysis  
The specific error between the experimental and theoretical data is attributed to various factors: (1) The asymmetric coupling coil design in this study resulted in deviations in coupling coefficients, yet the system retained PT symmetry. (2) The proposed multilayer coil internal resistance can only be controlled to a minimum in practice. (3) Tolerance in the coupling coil and compensation capacitor can shift the resonant frequency, while multiple parallel capacitors can resolve this and cause capacitor errors. (4) Experimental measurement equipment can also contribute to errors.
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 3 Results and Discussion
### Effectiveness of the GIN Message Aggregation Function Layers  
A basic hyperparameter of the GIN module is the number of layers of the internal MLP network (\(l_{GIN}\)). Figure 4 shows the system's behavior on the test set as a function of the MLP network depth (number of layers). For \(l_{GIN}=1\), GIN shows comparable performance to GraphSAGE (M1.1--Table 3), which is significantly lower than the best performing model, validating the limiting factor of shallow aggregation functions. However, as \(l_{GIN}\) increases beyond four layers, the \(F1_{micro}\) score displays a sharp decrease as it detects a higher percentage of existing interactions, combined with an increase in false positives, evidenced by the significant difference in the recall-precision curve slopes.  
Therefore, the intermediate values \(l_{GIN}=2\) and 3 are compared. The change in \(F1_{micro}\) is negligible, while the recall improvement and precision drop is approximately 4% when increasing the layer depth by one. However, at \(l_{GIN}=3\), a better compromise is made between the two metrics, with a difference of only 1.7%, compared to the corresponding 9.6% for \(l_{GIN}=2\). Moreover, considering the risk of not being able to detect an existinginteraction, the behavior of the system with the best recall is considered more appropriate for the current task.
## Materials
### LiDAR Data Simulation  
The DART (Discrete Anisotropic Radiative Transfer) model is a comprehensive physics-based 3-D model that can simulate the earth-atmosphere radiation interaction from visible to thermal infrared wavelengths [33; 34; 35; 36]. The DART model was developed at Center for the Study of the Biosphere from Space (CESBIO) in 1992 and patented in 2003. We acquired the free DART licenses (DART 5.6.0 V739) from Paul Sabatier University. The model can simulate the optical image and LiDAR data, including both airborne and terrestrial LiDAR data. The simulations even contain discrete return, waveform, and photo counting LiDAR data. We simulated the airborne waveform LiDAR data for the 3-D forest scene created by Onyx TreeMaker using the DART software. To explore the effects of the scanning angle, flying height, and pulse density on forest foliage profile retrieval, we simulated the airborne full-waveform LiDAR data under different conditions using the variable controlling method. The scanning angle was from 0\({}^{\circ}\) to 30\({}^{\circ}\) at an interval of 5\({}^{\circ}\), the flying height was set from 1000 m to 4000 m with an interval of 1000 m, and the pulse density was set from 0.25 pulse/m\({}^{2}\) to 8 pulses/m\({}^{2}\). The beam divergence was set as 0.15 mrad, so the footprint size was set from 0.15 m to 0.6 m with an interval of 0.15 m. The scanning angle is the angle from nadir. The parameters of all simulations are shown in Table 1. The simulated scan schematic is shown in Figure 3, where the green circles represent the trees, the blue arrow represents the flight route and direction, and the vertices of the green grid represent the centres of the footprints. Several examples of simulated single airborne LiDAR pulse data are shown in Figure 4.  
Figure 2: Simulated foliage profile.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Scanning Angle (\({}^{\circ}\))** & **Flying Altitude (m)** & **Pulse Density (Pulses/m\({}^{2}\))** \\ \hline  & 0 & & 0.25 \\  & 5 & 2000 & 1 \\  & 10 & 2000 & 4 \\ \(1\) & 15 & & 4 \\  & 20 & & \\  & 25 & 4000 & 1 \\  & 30 & & \\ \hline  & & 1000 & \\
2 & 20 & 2000 & 1 \\  & & 3000 & 1 \\  & & 4000 & \\ \hline  & & & 0.25 \\  & & & 0.5 \\
3 & 20 & 2000 & 2 \\  & & & 4 \\  & & & 8 \\ \hline \end{tabular}
\end{table}
Table 1: The airborne LiDAR data acquisition parameters of all simulations.  
Figure 4: Three examples (**a–c**) of simulated airborne LiDAR pulse data.  
Figure 3: Airborne LiDAR scan schematic (the scanning angle is 0\({}^{\circ}\), and the pulse density is 1 pulse/m\({}^{2}\)).
## 4 Discussion  
Using feed-forward neural networks on the combined values of MEG signal values and ST-FFT gave encouraging results, achieving a classification accuracy close to 92%. The one-dimensional convolution neural network (1D-CNN) with four layers achieved an accuracy of 90.4% but with significantly less training time compared to that of the FFNNs, with a computation cost reduction of up to 52%. This means that there is potentially significant room for improving the performance of the 1D-CNN model through a more thorough investigation and fine-tuning of the network's parameters (such as the number and size of convolutional layers, filter size at each layer, and the number and placement of pooling and ReLU layers).  
When introducing ANN model enhancement by utilizing pretrained FFNNs in various interconnections with untrained models, we obtained a slight optimization in the performance of the initial pretrained network by slightly higher than 1%. The significance of this optimization method lies in the ability to gradually build and train larger and slightly optimized models. It is a very small contribution, yet it can still benefit researchers with limited resources who wish to process a plethora of data with hidden information needed to be mined using various, even diverse, classification models.  
As stated in the introduction of our work, MEG, while a highly powerful tool, is a relatively new and expensive method with limited clinical application. MEG signal samples, especially those related to specific categories of diseases such as epilepsy, are scarce. Neurologists are often unfamiliar with interpreting MEG signals and therefore hesitate to incorporate them into clinical practice. Our objective was to evaluate basic artificial neural network (ANN) models for the classification of epileptic signals, compare these basic models with slightly more advanced ones, and ultimately test a technique for optimizing ANNs using pretrained models. By presenting our results, we believe our work makes a significant contribution to the development of an automated tool for the classification of epileptic MEG signals. Such a tool could help increase the recognition and broader adoption of MEG, thereby aiding in the training of practicing neurologists.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 2 Overview of V2X Protocols  
This section provides a detailed overview of the protocols proposed by the international research community for V2X communication.
## 1 Introduction
### Status of Production Time Estimation by Machine Learning Applications in SMEs  
SMEs often specialize in the custom manufacturing of various products for different customers. The product specification is provided by technical drawings and, for complex products, by a bill of materials. Product types may be repetitive, or they may be one-off jobs. SMEs often have Enterprise Resource Planning (ERP) and shop floor data collection systems. However, these systems only serve as databases for corporate accounting purposes. Job planning is performed in the form of projects, and the scheduling of jobs to resources is often performed operationally. Production time estimation is often based on rough estimates using descriptive analytics or expert knowledge due to the high variability of products.  
ML is part of Data Mining (DM) [61], where data are extracted from companies' databases. The Cross Industry Standard Process for DM (CISPDM) is a standard DM process that consists of six stages: business understanding, data understanding, data preparation, modeling, evaluation, and implementation [49; 64].  
As presented, there is a strong foundation for using ML applications to estimate production time. However, the general implementation of ML in SMEs is insufficient [65]. This is also confirmed by a study of 18 companies [66], which shows that ML methods are increasingly being adopted by companies but that SMEs face challenges in implementing them compared to larger companies. A study of 60 companies in the engineering, construction, automotive, and electrical industries [67] found that the perceived importance of ML, willingness to pay, and readiness to perform data management are key factors influencing the likelihood of ML adoption in SMEs.  
However, ML can reduce production times and scrap, improve resource utilization, and derive patterns from data [68]. Therefore, our research proposes a methodology that considers the receptivity factors of SMEs towards DM and ML for production time estimation.
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 2 Hydrogel Materials
### Natural Hydrogels
#### 2.2.1 Chitosan
Chitosan has been increasingly utilized in drug delivery systems for oral, ocular, transdermal, and injectable formulations, where their biodegradability and mucoadhesive properties promote drug retention and absorption over extensive periods [71; 72; 75; 77; 78]. The main challenges of chitosan-based hydrogel are the limited aqueous solubility and low mechanical strength. Chitosan quickly loses solubility as the pH rises from acidic conditions and becomes practically insoluble at neutral and basic pH environments, which restricts their utilities in physiological applications [68,77]. An alternative approach is to decrease the average molecular weight of chitosan, which is generally over 100 kDa when obtained from chitin, via hydrolysis or oxidative reactions; however, this pathway still poses critical challenges like limited controllability over the final molecular weight and scalability [78, 79, 80, 81].
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 5 Summary of Research Findings and Practical Implications
### Limitations  
While this study provides valuable insights into the performance of MANET routing protocols in heterogeneous networks, it is important to acknowledge its limitations. First, our simulations were conducted with a relatively small number of nodes (two PDAs, five laptops, and five mobile phones). The scalability of DYMO and other protocols in larger networks (e.g., 1000+ nodes) remains untested and warrants further investigation. Second, real-world factors such as urban Wi-Fi congestion, signal interference, and energy constraints were not fully accounted for in our simulations. Moreover, our study focused on small to medium-sized networks, so the scalability of DYMO in large-scale deployments (e.g., smart cities with 500+ IoT devices) remains an open question. Future work should evaluate the protocol performance in such scenarios to assess its suitability for real-world applications. These factors could significantly impact the protocol performance in practical deployments. Finally, this study focused on a specific set of mobility and transmission parameters, which may not fully capture the variability of real-world scenarios. Future work should explore these limitations to provide a more comprehensive understanding of the protocol performance in large-scale, real-world environments.