esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this study, experimental research using both ethylene glycol and phase change material (organic paraffine) as work fluid in PVT panels is analyzed. They concluded that using just PCM for cooling reduces the surface temperature of the PV cells inside the PVT panel by 11.05% compared to a conventional PVT unit. For this reason, electrical performance is 4.22% higher than that of a conventional PVT panel. Additionally, [PERSON] et al. [38] proposed the storage of a moderate amount of thermal energy inside PVT panels by including a PCM to reduce the possibility of freezing the working fluid under extreme ambient conditions. [PERSON] et al. [39] have simulated with a numerical model a hybrid PVT panel with TEGs. They have concluded that up to 10% of increment in the efficiency of the panel can be obtained in comparison with a standard PVT panel. Nevertheless, their paper does not include experimental data. In this study, the integration of TEGs modules into a real PVT hybrid panel has been carried out. The installation has been monitored to get experimental results. A 3D numerical model of conventional photovoltaics-thermal panel and PVT with TEG modules was also developed. The comparison between simulated and experimental results has been then made in order to validate the model simulation. The first tests show a concordance between the simulated and experimental results. Thus, they indicate the path to follow up to the definite design that allows an integrated PVT-TEG device with an additional performance that conventional PVT. The innovation presented in this paper is the unique combination of the experimental and computational results in a PVT panel with integrated thermoelectric generators inside it. After the validation with experimental results, detailed information from the numerical results obtained in the three-dimensional simulation allows a better understanding of the found inefficiencies. Thus, it gives light to provide new improvements to this integrated device.
## 2 Materials and Methods
### Corrosion Studies  
#### 2.4.1 Weight Loss Analysis  
Weight loss analysis is a common method of determining corrosion inhibition efficiency of any inhibitor. In this method, the metal sample was cut into 3 cm \(\times\) 1 cm \(\times\) 0.5 cm size, polished well to remove the unwanted metals present on the surface and cleaned with acetone to eliminate moisture and impurities, and the initial weight of each specimen was noted. The prepared metal specimens were kept in beakers with 100 mL HCl solution with and without inhibitor in such a way that the metal should not have physical contact with the inner surface of the beakers. The specimens were submerged in 1 M HCl at room temperature (303 \(\pm\) 1 K) for 3 h in beakers with different solutions--one was a blank solution without inhibitor and others were with inhibitors (methanolic ginseng fruit extract with 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1000 ppm). After 3 h of exposure in an acidic corrosive environment, the metal specimens were taken out and weighed after cleaning and drying as per the American Society for Testing and Materials (ASTM) specification. Loss in weight was calculated from the difference between initial and final weights for all the specimens. Then, the corrosion inhibition efficiency (IE %) was obtained from the following expression [46; 47; 48].  
\[IE\ (\%)=(W_{0}\ -\ W_{i})/(W_{0})\times 100 \tag{1}\]  
\[\theta=(IE\%)/100 \tag{2}\]  
where \(W_{0}\) is the loss in weight without inhibitor and \(W_{i}\) is the loss in weight with inhibitor.
## Materials and Methods
### Evaluation of Sustainability Indicators
#### 2.3.2 Atom Economy (_AE_) and _Atom Efficiency_  
Atom economy has been considered as a measure of environmental sustainability in minimizing the amount of theoretical waste [19] as shown in Equation (2). This concept was introduced by [PERSON] at Stanford University in 1991, and it depends on the molecular weights of the reactants and the final product. It was initially proposed to avoid relying only on the _yield_ of a product to measure its greenness. Unfortunately, the usefulness of _AE_ is limited since it only considers the stoichiometry of the reaction and does not take into account the _yield_ of the desired product [20]. The ideal atom economy for a chemical transformation is taken as the process where all reactant atoms are found in the desired product [19]. Since the catalyst concentration for biodiesel production was insignificant and consumed during the reaction, it is excluded from the _AE_ calculations. Similarly, atom efficiency (Equation (3)) is the product of atom economy and the obtained percentages _yield_ from the reaction. Atom economy provides the actual efficiency and utilization of atoms to produce useful products [1].  
\[AE=\frac{MW\ of\ product\ C}{MW\ of\ A+MW\ of\ B}\times 100 \tag{2}\]  
On the other hand, _atom efficiency_ provides a more concise solution because it also takes into account the production _yield_ percentage.  
\[Atom\ efficiency=AE\times\%\ yield \tag{3}\]
## 3 Materials and Methods
### Proposed Method
#### 3.3.2 Symmetric Projection Space Extractor
These mathematical constraints can be embodied in the loss function, for instance, through regularization terms such as L2 regularization or symmetry regularization. Through these constraints, the network can maximize privacy protection while retaining the structural information of the data. The application of the symmetric projection space extractor ensures that the data are both obfuscated for privacy protection and optimized for downstream tasks. By using nonlinear transformations, the extractor can efficiently reduce data dimensions while maintaining the critical features necessary for model training and inference. Furthermore, the introduction of symmetry constraints guarantees that data transformations do not disproportionately affect certain features, thus promoting better privacy protection and more balanced data representations. This design provides a key advantage in privacy-sensitive tasks, where ensuring that  
Figure 4: Architecture of symmetric projection space extractor. sensitive information is effectively obscured without compromising the utility of the data is paramount.
## 3 Probabilistic Forecasting of ALSC  
### Deterministic ALSC Problem  
The maximum load growing percentage \(\lambda\) can be used to quantify the ALSC. The \(\lambda\) is obtained by gradually increasing load until encountering an electrical constraint violation. The deterministic ALSC problem can be described mathematically as follows [3]:  
Objective:  
\[\max\lambda \tag{20}\]Subject to:  
\[\left\{\begin{array}{c}f(x)+\lambda b=0\\ U_{\rm{imin}}\leq U_{i}\leq U_{\rm{Imax}}\\ S_{ij}\leq S_{ij\rm{max}}\\ P_{Gi}\leq P_{Gi\rm{max}}\end{array}\right. \tag{21}\]  
where \(f(x)\) denotes the power flow equation under basic load; \(b\) denotes load growing vector; \(U_{i}\) is the voltage amplitude of bus \(i\); \(U_{\rm{imin}}\) and \(U_{\rm{imax}}\) are the minimum and the maximum voltage amplitude limitation of bus \(i\), respectively; \(S_{ij}\) is the apparent power of the branch between bus \(i\) and bus \(j\); \(S_{ij\rm{max}}\) is the maximum apparent power the branch can be transferred; \(P_{Gi}\) is the active power of the generator at bus \(i\); and \(P_{Gi\rm{max}}\) denotes the maximum power limitation of the generator at bus \(i\).
## 2 Materials and Methods  
### TDS-1 Mission and Datasets  
Spaceborne GNSS-R data from TDS-1 include three different data processing levels, e.g., Level 0 (L0), Level 1 (L1) and Level 2 (L2) [44]. L0 mainly contains the raw data, which are not available to the public except for some sample data. L1 includes L1a and L1b, which are the data converted from the L1a onboard processed DDMs and converted to NetCDF format. The L1b release includes the DDMs and metadata used in this study. Level 2 refers to the wind speed and mean square slope products. DDMs are generated by the Space GNSS Receiver Remote Sensing Instrument (SGR-ReSI) through cross-correlating scattered signals with code replicas generated locally for different time delays and Doppler shifts. When the reflection surface is smooth, most of the scattered power comes from the specular point, and very little from the glistening zone around the specular point [45]. Compared with the sea ice surface, the one of sea water yields a non-coherent reflection with more scattering in the delay and Doppler domains. This distinct characteristic in the spreading from sea ice and water provides the opportunity to monitor sea ice.  
The TDS-1 satellite was launched in July 2014 and started its data collection from September 2014. As one of eight payloads onboard on TDS-1, the SGR-ReSI took measurements two days in an eight-day cycle until 2018. The SGR-ReSI was operated in full time mode (7/7 days) during its extension from February to December 2018. The TDS-1 data provide an intense coverage over most of Arctic and Antarctic regions as the satellite runs on a quasi-Sun synchronous orbit with an altitude of \(\sim\)635 km and an inclination of 98.4\({}^{\circ}\). The TDS-1 data are accessible on the Measurement of Earth Reflected Radio-navigation Signals by Satellite (MERRBys, www.merrbys.co.uk). The available DDMs from TDS-1 consists of 20 Doppler shift bins with an interval of 500 Hz and 128 delay bins with a resolution of 250 ns, which is the length of 0.25 C/A code chips. Figure 1 presents two different DDMs collected over sea water and ice, respectively. It is obvious that the spreading of DDM from sea ice is much less than that of sea water. The reflection of sea ice is more coherent than that of sea water, which results in more scattering in the delay and Doppler domains due to the presence of waves on the open water surface.
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 6 On-Field Measurement Campaigns
### Detection of an Interference from the Space  
On 17 May 2017, a CW interference on the L1 spectrum was detected by the researchers of the NavSAS group, analyzing the signal received from the ISMB rooftop antenna. Two spikes appeared at approximately \(\pm\)0.5 MHz from the L1 carrier frequency. The phenomenon happened during the afternoon (from about 1.00 p.m. UTC to 6.30 p.m. UTC) and repeated along consecutive days. To investigate the source of this interference, NGeneApp was used as portable RFI detector for dynamic observations (by foot and by car) around the ISMB premises as well as in some other areas of the city, far from the Institute. The spectra observed in all visited areas were similar, with the two spikes always appearing at the same frequency. This fact suggested the intuition that the interference was not a local effect, but something farther, probably originated in space.  
Furthermore, during the dynamic observations, when the receiver was moving around a building, the interference seemed to disappear each time the western part of the sky was blocked by the building, as illustrated in Figure 11 and better detailed in Figure 12. Considering the visible duration of the GPS satellites and the direction in the sky, the GPS SVN 71 (PRN 26) was first identified as the potential source of the interference. A dataset was then collected with NGeneApp for the post-processing. Figure 13 shows the correlation output of the GPS satellite SVN 71 performed by the NGeneApp. We can recognize that when the PRN 26 loses the tracking (see cursors info in Figure 13), the two spikes in the spectrum disappeared, as shown in Figure 12a,b at about 50 s and 294 s from the receiver start, respectively.  
However, the appearance of the interference did not perfectly match with the visibility of the SVN 71. This mismatch was then explained by the fact that the interfering signal did not come from the SVN 71, but from the non-operational GPS satellite SVN 49 which had a similar orbit to SVN 71. For more details about the analysis of the anomalous GPS signals reported from SVN 49, the interested reader can refer to [57; 58].  
Figure 11: The spectrum observed in the dynamic test.
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 2 Methodology
### Model Development
#### 2.3.4 Mathematical Model Radial Bias Network (RBF)  
A type of neural network known as Radial Basis Function (RBF) algorithms is very good at simulating the nonlinear relationships present in energy demand forecasting. They enable the network to recognize intricate patterns in input by using radial basis functions as activation functions, usually Gaussian functions that react to the distance from a center point. RBF networks can learn from a variety of input elements in the context of energy demand, with the underlying mathematical relationships described by Equations (29)-(32). These elements include historical consumption data, seasonal effects, temperature fluctuations, and economic indicators, which are all further illustrated in Table 5 [48].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Equation** & **NO. Equation** \\ \hline Input Variables & \(X=[X_{1},X_{2},X_{3},X_{4},X_{5}]\) & (23) \\ Model Equation & \(\hat{Y}=\sum_{k=1}^{K}f_{k}(X)\) & (24) \\ Objective Function & \(\mathcal{L}=\sum_{i=1}^{N}\text{Loss}\left(\gamma_{i},\hat{Y}_{i}\right)+ \sum_{k=1}^{K}\Omega(f_{k})\) & (25) \\ Regularization Term & \(\Omega(f_{k})=\gamma T+\frac{1}{2}\lambda\sum_{i=1}^{T}w_{j}^{2}\) & (26) \\ Tree Splitting Gain & (27) \\ Final Prediction & (28) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mathematical model for XGBoost.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Equation** & **NO. Equation** \\ \hline Input Variables & \(X=[X_{1},X_{2},X_{3},X_{4},X_{5}]\) & (17) \\ Ensemble & \(\hat{Y}=\frac{1}{N}\sum_{i=1}^{N}f_{i}(X)\) & (18) \\ Prediction & Each tree \(f_{i}(X)\) is constructed using & \\ Tree Structure & random samples of features & \\  & and instances & \\ Node Splitting & \(\text{arg max}\ _{j\in J}(\text{Gain}\ (j))\) & (19) \\ Leaf Prediction & \(\hat{Y}_{\text{leaf}}=\frac{1}{m}\sum_{j=1}^{m}Y_{j}\) & (20) \\ Feature & Importance \((X_{k})=\frac{1}{N}\sum_{i=1}^{N}\text{Gain}\ (X_{k})\) & (21) \\ Error Estimation & OOB Error \(=\frac{1}{N}\sum_{i=1}^{N}\mathbb{I}(Y_{i}\
eq\hat{Y}_{i})\) & (22) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mathematical model for RF.
# GPU Algorithms for Structured Sparse Matrix Multiplication with Diagonal Storage Schemes +
## 7 Experimental Results
### Banded Matrix Multiplication  
We employed nine different problem dimensions \(n=1024\), 2048, 3072, 4096, 5120, 6144, 7168, 8192, 9216 with four different bandwidths \(5,10,15,20\). Figure 6 displays the running times of sequential (CPU) and parallel (GPU) implementations. It is clear that the GPU implementation yields significant acceleration over the sequential version, giving the highest acceleration factor with the largest matrix dimension. More importantly, the rate of improvement scales nicely with the band size and matrix dimension. To investigate the scaling further, we conduct separate experiments with a fixed matrix dimension while varying the band size. The comparative running times of the experiment are displayed in Figure 7. The GPU speedup obtained vs. CPU, order of improvement, is quite evident and scales well with the increasing band size.
## 4 Experiments and Results
### Experimental Methodology
#### 4.1.4 Experimental Testbed Configuration  
Table 1 shows the hardware/software configurations of our experiments. To emulate local node collaboration, for the edge and fog layers, we set up a compute cluster of compute units in each layer using LAN. The cloud layer was an EMR cluster. On the cluster in each computing layer, we deployed the big data processing platforms to facilitate collaborative processing and distributed computing tasks. Our primary focus in this paper is to analyze and compare the performance of existing big data processing platforms across edge, fog, and cloud computing layers. We carefully selected widely used, existing, off-the-shelf platforms for the following three data processing paradigms: Apache Spark for batch processing, Apache Flink for stream processing, and Apache OpenWhisk for function processing. For batch processing, the ability of Spark to distribute computations across a cluster of nodes enhances its suitability for decentralized computing scenarios, facilitating parallel processing and optimizing performance, aligning with the distinctive characteristics of edge/fog computing layers. Flink's distributed stream processing capa  
bilities contribute to scalability, allowing it to effectively harness the collaborative power of nodes within the edge and fog clusters. The lightweight nature of Apache Flink aligns with the resource constraints often present in edge devices, making it an optimal choice for deployment and resource-efficient stream processing. The selection of Apache OpenWhisk for function processing in edge and fog computing layers is underpinned by its serverless architecture and suitability for the execution of small, event-driven functions in resource-constrained environments. It is one of the most widely used and popular open-source serverless platforms. These platforms are deployed across edge, fog, and cloud computing layers, and our experiments are designed to reflect real-world scenarios. There were some differences in the platforms' software versions, as we chose the latest available versions in the fog and cloud environments at the time of the experiment and manuscript preparation. As these were minor variations, we assumed that there was no impact on the comparative performance of the platforms.  
To simulate a distributed computing environment that reflects real-world conditions, a four-node cluster provides a balance between resource availability and complexity, allowing us to observe the scalability and performance of both Apache Spark and Apache Flink in a multinode setup and enabling us to assess the parallel processing capabilities of these big data processing frameworks, providing valuable insights into their behavior in a distributed computing environment representative of practical deployment scenarios. For the function processing platform, the standalone setup in our experiments is driven by the nature of function processing, where a single function is the primary unit of computation, and parallelization is not applicable.
# A Review on Two-Phase Volumetric Expanders and Their Applications
## 7 Two-Phase Piston Expander  
### Working Principle of Two-Phase Piston Expander  
In piston expanders, the head of a movable element (piston) and the surface of the stator (cylinder) delimit the variable volume of the chamber. Piston expanders can be reciprocating, rolling, and free.  
In reciprocating machines, a crankshaft converts the motion of the piston from a linear motion to a rotative one. These machines can be axial and radial depending on the arrangement of the axes of the cylinders. In axial machines (Figure 15a), the axes of the cylinders are parallel, while in the radial machines (Figure 15b), the axes are arranged in a radial configuration.  
However, reciprocating piston expanders suffer some issues in terms of vibrations and noise that can be mitigated by selecting a low rotation speed and balancing weights [12].  
Conversely, rolling piston systems employ a circular piston that moves over the surface of the cylinder to drive a shaft. This operation principle requires that the piston be assembled eccentrically within the cylinder to torque the shaft.  
In these systems, the working chamber is a volume delimited by a part of the surfaces of the cylinder, piston, and a sliding vane that is in contact with it (Figure 16). The sizing of this working chamber depends on the length of the sliding vane, the piston and cylinder radii, and the eccentricity of the piston. An improvement in the rolling piston leads to the swing piston machine in which the vane and the piston constitute a rigid body because they are integrated together, thus, resulting in a reduction in leaks and friction.  
Figure 16: Principle of operation of a rolling (**left**) and swing (**right**) piston expander.  
Figure 15: Two-phase reciprocating piston expander, with the principle of operation for an axial device (**a**) and a radial one (**b**) [45].  
A common aspect of reciprocating and rolling machines is the need for a crankshaft to drive a shaft. However, the crankshaft requires bearings that cause frictional losses, thus, reducing the mechanical efficiency of the machine, especially at small sizes [70]. A strategy that overcomes this issue is to implement free-piston architecture by suppressing the crankshaft system and the corresponding friction losses (Figure 17). The result is a compact crank-less machine whose advantages are compactness, leakage reduction, lower friction, and simplicity.  
Several systems based on free-piston architecture have been proposed to recover energy in the literature. However, the common aspect relies on valves that admit and discharge the working fluid to drive the motion of a piston between two endpoints of the stator. The piston drives electrical actuators by membranes or a linear generator by a piston rod during its motion.
## 1 Introduction
Then, the sparse rainfall distribution obtained by rainfall detection network is reconstructed by using ordinary Kriging interpolation (OK) and radial basis function (RBF) neural network. As a result, the continuous distribution of rainfall fields in the area is obtained. Finally, the method is verified by the hybrid rain cell (HYCELL) model and the real rainfall fields. The purpose of this work is to preliminarily verify the feasibility of using ESLs combined with HMLs to retrieve rainfall field and to provide reference for future applications. In this article, the major contributions are as follows. 1. A method for detecting rainfall by combining multiple sources of microwave links is proposed. We built a rainfall detection network for retrieving rainfall fields in combination with ESLs and HMLs, and we validated the significant potential of the method to retrieve high-precision rainfall fields using the HYCELL model and real rainfall fields. 2. The OK algorithm and RBF neural network are applied to the joint network to retrieve rainfall fields. The results indicate that the joint networks of ESLs and HMLs based on the OK algorithm and RBF neural network can both retrieve the distribution characteristics of rainfall accurately. Moreover, the overall performance of the RBF neural network is better than that of the OK algorithm.