esa-sceva/satcom-chunk-collection huggingface dataset:
# The Design and Investigation of Hybrid a Microfluidic Micromizer
## 2 Materials and Methods
### Numerical Scheme and Boundary Conditions  
In this work, numerical simulations of hybrid microfluidics micromixers are carried out, and governing equations are solved using the finite element method (FEM) in COMSOL Multiphysics. A detailed description of the numerical methodology is shown in Figure 4. There are three different schemes, such as the thermoviscous acoustic scheme that is used to solve acoustic-related equations, the laminar scheme that is used to solve fluid flow equations with laminar behaviour, and the transport-diluted species scheme that is used to determine the mixing performance of the system. The walls of the microchannel were assumed to be solid walls and stationery with no slip and zero velocity conditions. Thus, the flowing fluid at both inputs is assumed to be fully developed and uniform with a volumetric flow rate of 5 \(\mu\)L/min and an oscillation frequency of 15 KHz. Moreover, simulations of the optimum micromixer design are also carried out at different Reynolds numbers to examine the flow behaviour inside the microchamber. The pressure boundary condition is set to zero at both inlets and absolute pressure at the outlet. Similarly, the concentration of two different species was set at 1 mol/m\({}^{3}\) at one inlet and zero at another inlet. Table 2 lists the material properties of working fluids water and ethanol such as density, dynamic viscosity, specific heat capacity ratio, thermal expansion, thermal diffusivity, etc.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Water** & **Ethanol** \\ \hline Viscous dynamic viscosity & 890 \(\mu\)Pas & 1200 \(\mu\)Pas \\ Specific heat capacity & 4180 J/KgK & 2570 J/KgK \\ Density & 997 Kg/m\({}^{3}\) & 789 Kg/m\({}^{3}\) \\ Speed of sound & 1497 m/s & 1144 m/s \\ Compressibility & \(4.47\times 10^{-10}\) 1/Pa & \(1.1\times 10^{-9}\) 1/Pa \\ Specific heat capacity ratio & 1.012 & 1.13 \\ Thermal conductivity & 0.61 W/mK & 0.614 W/mK \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of water and ethanol.
# Operational Resilience Metrics for Complex Inter-Dependent Electrical Networks
## 5 Results
### Assessing the Impact of Improved Distribution Automation Systems (DAS) on the EDN Resilience
Then, it is worthwhile to compute the optimal development planning to maximize the network performances gain w.r.t the available budget. An optimization framework is going to be realized to support the electrical operator in the adoption of the best network development strategy. As a preliminary result, this work reports the _Pareto efficiency_ that can be computed assuming that the cost for the transformation of each line does not depend on the number of automatic switches present in the different lines. Figure 16 shows the cumulative delta curve obtained by the cumulative sum of the 4G impact gains, ordered in descending order. For example, transforming the first 20% of MV lines (i.e., these lines are the lines with the greatest gain factor) the 52% of theoretical gain can be reached. Figure 14: Automatic selection of a failure with the improved 4G DAS procedure. Figure 15: High concentration MV lines \(N-1\) analysis. The blue area represents the gain of performances adopting the 4G procedure for DAS. The lines are ordered from the line with the highest mean impact to the lowest. EDN Resilience vs. SCADA System Availability  
The improved resilience framework was used to analyze the resilience of the distribution grid as a function of the _SCADA system availability_. In this case, the objective is to analyze EDN performances considering problems on the tele-control system (S4 simulation in Table 5). These problems can have different causes as for example telecommunication network failures (i.e., in these cases the SCADA components are working properly but there is no communication between the control room and the EDN elements) or SCADA components failures. The electrical operator constantly monitors the availability of the network SCADA system. A _low SCADA system availability_ condition represents the case when the fraction of not-controllable SS is twice as much as in normal conditions (i.e., 1% with respect to the \"physiological\" 0.4%; see Table 2). The sensitivity of the resilience with respect to SCADA system availability is shown in Figure 17. For example, considering the 3% of tele-controllable SS as not tele-controllable, the resilience index decreases of \(\approx\)9%. In case of large telecommunication blackout (10% of SS tele-controllable with problems) the resilience decrease of \(\approx\)20%.
###### Abstract  
The accurate evaluation of the thermal performance of building envelope components (e.g., facade walls) is crucial for the reliable evaluation of their energy efficiency. There are several methods available to quantify their thermal resistance, such as analytical formulations (e.g., ISO 6946 simplified calculation method), numerical simulations (e.g., using finite element method), experimental measurements under lab-controlled conditions or in situ. Regarding measurements, when using the heat flow meter (HFM) method, very often, the measured value is based on surface conditions (e.g., temperature and heat flux), achieving in this way the so-called surface-to-surface or conductive thermal resistance (\(R_{cond}\)). When the building components are made of homogeneous layers, their \(R_{cond}\) values are constant, regardless of their internal and external surface boundary conditions. However, whenever this element is composed of inhomogeneous layers, such as in lightweight steel-framed (LSF) walls, their \(R_{cond}\) values are no longer constant, depending on their thermal surface resistance. In the literature, such systematic research into how these \(R_{cond}\) values vary is not available. In this study, the values of four LSF walls were computed, with different levels of thermal conductivity inhomogeneity, making use of four finite elements' numerical simulation tools. Six external thermal surface resistances (\(R_{\text{se}}\)) were modelled, ranging from 0.00 up to 0.20 m\({}^{2}\cdot\)K/W. The average temperature of the partition LSF walls is 15 \({}^{\circ}\)C, while for the facade LSF walls it is 10 \({}^{\circ}\)C. It was found that the accuracy values of all evaluated numerical software are very high and similar, the \(R_{cond}\) values being nearly constant for walls with homogeneous layers, as expected. However, the variation in the \(R_{cond}\) value depends on the level of inhomogeneity in the LSF wall layers, increasing up to 8%, i.e., +0.123 m\({}^{2}\cdot\)K/W, for the evaluated \(R_{\text{se}}\) values.  
lightweight steel-framed (LSF); conductive thermal resistance; computational tools; external surface resistances; level of inhomogeneity; walls' layers +
Footnote †: journal: Applied Sciences
# Development of Potato Mass Estimation System Based on Deep Learning
## 3 Results and Discussion  
### Implement Results of Potato Mass Estimation System  
The potato mass estimation system developed in the present study is implemented in C# language. The program is largely comprised of a main area, a sub-area, and a toolbar at the top, as shown in Figure 7. The main area is comprised of an image part (Figure 7a) where images can be identified in real time, and a recording part (Figure 7b), in which the work details are recorded in a table. The sub-area is divided into a real-time work area, a graph area, and a location information area. The real-time work area is configured to display the count and weight of the harvested potatoes per second and the cumulative count and mass in real time, the graph area is used to display the cumulative potato weight, and the location information area is designed to display the latitude, longitude, and time.  
Figure 7: Potato mass estimation system: (**a**) imaging part; (**b**) recording part.  
The camera calibration process that is carried out first before using the potato mass estimation system is configured to be carried out in a separate window using the button within the toolbar, as shown in Figure 8. The calibration window is largely comprised of a viewing area, a parameter input area, a calibration process area, a pixel calculation area, and an ROI (region of interest) setting area and it carries out distortion correction and then the matching of the pixel and the actual length after the parameter of the checkerboard is entered. Lastly, the system is configured such that the area in which the actual work is carried out is designated through the ROI setting area to reduce the computation load applied to the computer, and the final counting line is designated to adjust the point where the potato objects are counted. The calibration result is as shown in Figure 9.  
After the camera calibration process has been carried out, potato detection, tracking, and counting works are sequentially carried out using the detection start button in the toolbar area. The lengths of the major axis and the minor axis are calculated using the bounding box of the counted object, while the volume of the potato is calculated by applying the regression equation analyzed in Section 3.2.2. Then, the mass of the potato is estimated  
Figure 8: Camera calibration window: (**a**) viewing area; (**b**) parameter input area; (**c**) calibration process area; (**d**) pixel calculation area; (**e**) region of interest (ROI) setting area.  
Figure 9: Results before (**a**) and after (**b**) correction of fisheye distortion in the camera.  
by multiplying the calculated volume and the density, and the result is finally recorded in the table along with the locational information. Figure 10 shows the operation screen of the potato mass estimation system developed in the present study.
## 2 Electro-Optic Properties of Polymers  
### Second Order Nonlinear Susceptibility  
In many applications and in a more realistic description of the electromagnetic interaction, the response of a medium immersed in an electromagnetic field cannot be described as a linear response:  
\[P_{i}=\varepsilon_{0}\,\chi_{\bar{\imath}}\cdot E_{{}_{j}} \tag{1}\]  
(_i.e.,_ by considering that the dielectric susceptibility of the material is not dependent on the amplitude of the electric field). A more complete description of the electric polarization can be expressed in a general way as [17,21]:  
\[\vec{P}=\vec{f}(\vec{E}) \tag{2}\]  
Such dependence can be expanded in a power series of the electric field, and in a general case the electric polarization vector can be written as [17]:  
\[P_{i}=\varepsilon_{0}\left(\chi_{\bar{\imath}}^{(1)}E_{{}_{j}}+\chi_{\bar{\imath }\bar{\imath}}^{(2)}E_{{}_{j}}E_{{}_{k}}+\chi_{\bar{\imath}\bar{\imath}}^{(3)} E_{{}_{j}}E_{{}_{k}}E_{{}_{l}}+...\right) \tag{3}\]  
where the nonlinear susceptibilities \(\chi^{\rm(n)}\) are used (the \(\chi^{(1)}\) term is responsible for the linear refractive index). In centrosymmetric materials are present only the even term of Equation (3), otherwise in non-centrosymmetric materials the second order term \(\chi^{(2)}\) can be dominant with respect to the other nonlinear terms.  
We limit our attention only at the second order term that is responsible of the linear electro-optic (EO) effect (or Pockels effect) as well as other interesting phenomena and applications such as second harmonic generation (SHG), frequency mixing, photorefractivity [17,22].  
In Equation (3) if we consider that the driving field oscillates at one frequency \(\omega\), the only possible effects in the polarization term \(P\) are second harmonic generation (where \(P\) oscillates at \(2\omega\)) and optical rectification (where the solution with \(P\) that oscillates at \(\omega=0\) means that a static field results to be applied at the material when it is shined by the light).  
If the exciting field is composed by an electromagnetic field oscillating at \(\omega
## 4 Results and Discussion
### Discussion on Computational Complexities and Future Works  
In large-scale deployments, our model's computational efficiency becomes a crucial consideration, especially in high-demand settings like finance or healthcare. In our experiments, we used a multi-GPU setup to support model training and testing, which ensured that tasks like encryption and feature extraction were handled efficiently. However, in real-world production, where resources might be limited, this model's complexity could pose challenges. The skip attention mechanism and hash tree structure, though highly effective in enhancing privacy and feature extraction, introduce some computational overhead. Distributed or cloud computing could be leveraged to distribute tasks across nodes,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Encryption Method** & **PSNR (dB)** & **SSIM** \\ \hline Proposed Method & 28.5 & 0.72 \\ Standard Encryption (AES) [52] & 18.9 & 0.60 \\ Gaussian Noise Encryption [53] & 12.5 & 0.45 \\ Blur-Based Encryption [54] & 15.2 & 0.51 \\ Edge Masking Encryption [55] & 22.3 & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quantitative comparison of encryption methods using PSNR and SSIM metrics. Higher PSNR and SSIM values indicate better preservation of critical information while ensuring data security.  
thereby reducing the load on individual systems and improving scalability. Furthermore, selectively applying skip attention or using shallower hash trees in specific scenarios can help reduce computational demands without compromising overall performance.  
In terms of computational complexity, the skip attention mechanism optimizes traditional self-attention by using skip connections to prioritize essential features and reduce redundant calculations, which is beneficial for complex or sequential data. However, it still requires significant memory and processing power due to multi-head attention computations, especially in large-scale or real-time settings. Similarly, the hash tree structure offers rapid integrity checks but becomes resource-intensive as data volume grows, particularly with frequent insertions or deletions. To manage these costs, strategies such as incremental hash updates, parallel processing, and dynamic tree structures can be employed to localize and streamline updates. Additionally, lightweight attention modules and model compression techniques like pruning or quantization further help in reducing the model's computational load, making it more viable for practical applications with resource limitations.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 1 Introduction
### Data Integrity  
Refers to the assurance that data remains unaltered, complete, and reliable throughout its transmission process. It ensures that data is not tampered with, modified, or corrupted during its exchange, maintaining its accuracy and trustworthiness. Integrity is measured using data conformity to defined protocols, information assurance principles, and standards [PERSON] et al. (2018). [PERSON], [PERSON], and [PERSON] et al. (2018) proposed a protocol to support data integrity, using location integrity and multicasting to secure data transmission.
# Data Matrix Based Low Cost Autonomous Detection of Medicine Packages
## 2 Proposed Methodology
### Data Matrix  
Data Matrix codes are two-dimensional symbols that hold a dense pattern of data with built-in error correction. Special scanners were designed to read them; however, high-resolution cameras can be used to acquire multiple Data Matrix images simultaneously. If the resolution is enough to identify each point of the Data Matrix, a library can be applied to decode its data. In the developed system, the \(libdmtx\) library is used to scan the Data Matrix codes in the medicines' boxes. \(libdmtx\) is an open-source software for reading and writing Data Matrix barcodes on Linux, Unix, OS X, Windows, and mobile devices. According to [26], several commercial scanners can be used. The software developer (for the hospital management) published the Data Formatting protocol and settings that should be used for the configuration of each matrix scanner (Datalog  
Figure 6: The start of the process order is made by a pushbutton: (**a**) green indicates that the system is available to start a new process; (**b**) red shows that the system is searching and decoding Data Matrix code.  
Figure 7: User interface showing the Data Matrix detection illustrated by the green squares.  
DS2278, Honeywell HH660, 1450G, and 7580G). Several validation tests were executed with different medicine boxes. For example, Figure 8a presents the acquired image and Figure 8b the respective \(libdmtx\) outputs that allowed to verify its operation (with the proper lighting system). Figure 8d exhibits the Data Matrix detected by the library, which are enclosed in green squares, and Figure 8e shows the extracted codes according to the defined protocol where each line corresponds to the decoded matrix code, as it can be read by a matrix code scanner, i.e., the output of the \(libdmtx\) library.
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.15. QLEEBDG and QLEEBDG-ADM  
In [38], the authors proposed using Q-Learning to solve the shortest-path approach. The rewards are calculated based on the successful transmission of data. For each successful data transmission, two rewards are awarded. The first award is awarded when an action is performed, and the second award is awarded based on future data transmission. Both data receiving and transmission become autonomous if the correct rewards are chosen. The network becomes energy efficient and has a longer lifespan. The network also detects transmission failure. After selecting the forwarding node, the agent receives the first reward, and the second reward is stored and distributed after the data is sent successfully. The data is re-transmitted via a different forwarding node if the sender does not receive an acknowledgment message due to transmission failure. The rewards decrease with the amount of re-transmission. The authors suggest using trained agents in the transmission range and the source to avoid void regions.  
* Comparison  
For the protocols discussed in the reinforcement learning section, QLEAR [35] is the base protocol that the others compare against. QLEARs reward point depends on the node selection, whereas QDRT [36] considers updates Q-table when a packet is successfully delivered, Ref. [37] updates the Q-table even when data goes missing. It goes into recovery mode and [38] awards the reward function in two steps; here, data transmissions become autonomous for the correct reward. If the sink node does not receive the data, the sender re-transmits it through a different route, and the q-tables are updated for the failed transmission to avoid the dead node. All three protocols show better energy efficiency, a successful packet delivery ratio, and network overhead than QLEAR. Table 7 illustrates the differences between the protocols.
* Bio-inspired routing protocols In the wireless network, solving many heuristic problems takes inspiration from nature. These are natural solutions that are known as biologically inspired routing protocols. The following section describes a few energy-efficient routing protocols used in UWSN.
## 3 Results and Discussion
### Potential Cytotoxicity Associated with the Use of Ozonation to Degrade PPCPs
#### 3.2.1 Effects of Single-Chemical Effluents on Cell Viability
Finally, toxicity was observed with exposure for 72 h to 0.25-1 mg/L of CBZ in the untreated effluent (Figure 2), with similar patterns found for shorter exposures (Figure S1). This evidence is apparently contrary to what would be expected from data available in the literature. [PERSON] et al. [45] reported no toxicity of 0.402 mg/L CBZ to HaCaT cells, despite cytotoxicity to rat brain microvascular endothelial cells (rCMECs) being found after exposure to 0.236 mg/L CBZ and reduced viability of oral keratinocyte cells (OKF6-TERT1) being observed for CBZ at 0.402 mg/L. The cytotoxicity was not alleviated by the ozonation treatment (Figure 2), although CBZ had been fully degraded [18]. The treated CBZ effluent contains BQM as highlighted in Section 3.1, which was reported to induce cytotoxicity in Chinese Hamster ovary fibroblast cells (CHO-K1), presumably due to the quinazoline within the compound molecular structure [46]. Overall, the cytotoxicity of the untreated and ozone-treated effluents is similar within each concentration. After a 72-h exposure period, the differences in the cell viability between untreated and ozone-treated samples were null or minor: for full-strength samples, the most pronounced difference was found for MP (24% improvement on cell viability). These data suggest that, despite yielding the full removal of the target compounds, ozonation was not remarkably effective on removing cytotoxicity to the HaCaT cells, even causing, in some cases, a slight increase in cytotoxicity (see, e.g., the data for the full-strength PP sample in Figure 2, where an 8% decrease in cell viability was observed).
## 3 Results and Discussion
### Analysis of Experimental Results
#### 3.4.2 Experimental Results of Model Loss Values  
Figures 5-8 show the changes in d_loss, d_loss_fake, d_loss_real, and g_loss during the training process of the model, showing the total loss value of the model, the loss of the reservoir fluid PVT analysis model, and the change trend of the loss value with the number of iterations, respectively.  
Figure 4: Comparison of the AUC results of multiple models.  
Figure 5 shows the change in the d_loss value during the training process, that is, the total loss value of the model. As the training progresses, the d_loss value gradually decreased, indicating that the overall performance of the model gradually improved.  
Figure 6 shows the change in the d_loss_fake value, which reflects the loss when the model generates fake data. The decreasing trend indicates that the model was gradually optimized in generating fake data with a distribution similar to the real data.  
Figure 5: Change in d_loss value during the training process.  
Figure 6: Change in d_loss_fake value during the training process.  
Figure 7 shows the change in the d_loss_real value, which represents the loss when the model processes real data. The decreasing trend indicates that the prediction ability of the model in processing real data was enhanced.  
Figure 8 shows the change in the g_loss value, that is, the loss value of the generator. The changing trend also indicates that the generator was continuously optimized during the training process to better generate samples close to the real data distribution.  
It can be clearly seen from the figures that as the training progressed, all loss values showed a steadily decreasing trend, especially as the total loss value gradually approached 0, indicating that the model continuously improved its analytical ability during the continuous learning and optimization process and gradually realized the prediction and analysis of reservoir fluid PVT, proving the effectiveness and stability of the enhanced ChebNet model in the high-pressure physical property analysis of reservoir fluid PVT.  
Figure 8: Change in g_loss Value during the training process.  
Figure 7: Change in d_loss_real value during the training process.