esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 4 Prediction Models for Thermal Conductivity and Other Properties of the NFs
### Viscosity  
The treatment of the viscosity variation in an NF can be considered similar to the effect in the viscosity of a solvent by adding particles of a solute. As in the conductivity study, there is a predominant model for the viscosity that was undergone to corrections over time. This model is designated by [PERSON] model, which in 1906, precisely studied the phenomenon of particle diffusion in a solute of a diluted solution. For that purpose, based in the Navier-Stokes equation for an incompressible flow, we can have Equation (16):  
\[\mu_{nf}=(1+\varepsilon\varphi)\mu_{bf} \tag{16}\]  
In this equation, \(\varphi\) corresponds to the used NP concentration and \(\mu_{fb}\) and \(\mu_{nf}\) to the NF viscosities and BF, respectively. We can observe the existence of the factor \(\varepsilon\) that  
Figure 6: Categories of prediction and regression machine learning algorithms (Adapted and abridged from [92].  
Figure 7: Schematic structure of the Artificial Neural Network (ANN) machine learning model topology (Adapted and abridged from [92]).  
will be representative of the type of particles in the solute. Considering them as rigid spheres, disregarding the interactions between them and with much bigger dimensions than the solvent particles, the factor will be 2.5, making Equation (17) valid for a certain concentration range:  
\[\mu_{nf}=(1+2.5\varphi)\mu_{bf}\quad,\ \ 0<\varphi<10\% \tag{17}\]  
As it has been discussed throughout this section, the interaction between the particles is a fundamental mechanism of the behavior of the NFs. In the latter, [PERSON] [97] has proposed a new equation that introduces a quadratic dependence with the volume fraction that allows a better representation of the particle interaction, as shown by Equation (18) and valid for the same interval:  
\[\mu_{nf}=\left(1+2.5\varphi+6.2\varphi^{2}\right)\mu_{bf}\quad,\ \ 0<\varphi<10\% \tag{18}\]  
Other models present alternative calculation methods to the [PERSON] deduction, as the case of [PERSON] [98], [PERSON], or [PERSON] in [97]. This model was presented by [PERSON] [99] and presents an exponential progression to explain the observed rise in viscosity, as it is reported in Equation (19):  
\[\mu_{nf}=\mu_{bf}e^{\frac{25\varphi}{1-k\varphi}} \tag{19}\]  
This factor \(k\) here introduced is designated by self-crowding effect, that better represents the interaction between particles, especially in greater concentrations, being the particle movement hampered by the existence of a greater number of neighbor particles, approximating their behavior to a solid. This factor \(k\) increases withparticle concentration, having the author estimated that the factor would belong in the range \(1.35<k<1.91\).
## 4 Results and Discussion
### Compressive Strength
The compressive strength  
Figure 9: Compressive strength results for specimens incorporating various dosages of rubber: (a) R1 (b) R5 (c) R10. reduction in the mixture incorporating the R1 and R10 rubber sizes was comparable (the difference in reduction was less than 2%) for all the tested dosages. The minimum reduction in compressive strength due to the addition of rubber was observed for the mixture incorporating R5 rubber compared to the R1 and R10 rubber sizes. For all the tested mixtures with various rubber sizes and their dosages, the compressive strength increased at later stages (Figure 9). For instance, the mixture incorporating 10% of the R5 rubber size showed approximately 16% and 23% higher compressive strengths at 28 and 56 days, respectively, compared to that of 14 days. The continuous hydration increased the strength of concrete with time [44]. The concrete specimens without rubber particles showed brittle failure. However, the concrete specimens incorporating rubber exhibited somewhat ductile behavior owing to the high deformability capacity of the used waste rubber. Diagonal failure patterns with small concrete fragments de-attaching from the surface were observed for the control specimen without rubber. On the other hand, transverse cracks along the longitudinal direction were observed in the concrete specimens incorporating rubber particles. Furthermore, no concrete chipping off near the failure was observed for the rubberized concrete, indicating the improved energy dissipation capacity. It should be noted that rubber particles are softer materials than the sand particles, leading to them exhibiting a flexible behavior under compression. Therefore, the concrete specimens with rubber particles exhibited a progressive and gradual failure [33].
# Numerical and Experimental Investigation of Recycled Brick Coarse Aggregate Concrete
## 3 Results and Discussion
### Compressive Strength  
Figure 8 shows the concrete failure mode during the compression test with different RBCA contents. It can be observed that the integrity of the concrete gradually decreased after compression failure with increasing RBCA contents. Due to its low strength, the RBCA was destroyed during the concrete compression process. However, more aggregate damage occurs in recycled concrete with increased RBCA content, which reduces the integrity of RBCA concrete.  
Figure 6: Slump test of concrete: (a) Slump test, (b) Slump of recycled concrete with different red brick content.  
Figure 7: Apparent density of concrete experiment: (a) Apparent density test, (b) Apparent density of recycled concrete with different red brick contents.  
Figure 9 shows the RBCA concrete compressive strength test results for the five different aggregate replacement ratios. It can be observed that the concrete stress-strain relationship with different RBCA substitution rates has similar development patterns and shows apparent continuity and smoothness. The concrete compressive strength was decreased by 5.7%, 14%, 20.6%, and 28.6% when adding 25%, 50%, 75%, and 100% RBCA, respectively. Similarly, the concrete's peak strain increased from 2.5% to 15.0%, and the elastic modulus decreased from 10.4% to 38.5%, respectively. It can be observed that the maximum concrete compressive strength is close with RBCA contents from 0% to 50%, while the concrete compressive strength with RBCA contents of 75% and 100% decreased significantly. The RBCA concrete's peak compressive stress is always lower than ordinary concrete. Therefore, it can be concluded that increasing the RBCA content can reduce the strength and brittleness of concrete. The reason for this is that red brick aggregate is a high bibulous rate, low intensity, and porous material; these properties lead to the damage process of recycled concrete being different from that of traditional concrete. The damage process of recycled concrete occurs at the interface between the mortar and aggregate mortar as well as in the aggregate itself.  
Figure 8: Compression failure mode of RBCA concrete specimens: (**a**) RB-0, (**b**) RB-25, (**c**) RB-50, (**d**) RB-75, (**e**) RB-100.
# Rainfall Similarity Search Based on Deep Learning by Using Precipitation Images
## 4 Experiment and Result Analysis  
### Study Area and Data Preprocessing  
The Jialing River is the largest branch of the Yangtze River, which is about 1120 km long and covers an area of approximately 160,000 square kilometers within a geographical range of 29\({}^{\circ}\)17\({}^{\prime}\)30\({}^{\prime\prime}\) N-34\({}^{\circ}\)28\({}^{\prime}\)11\({}^{\prime\prime}\) N and 102\({}^{\circ}\)35\({}^{\prime}\)36\({}^{\prime\prime}\) E-109\({}^{\circ}\)01\({}^{\prime}\)08\({}^{\prime\prime}\) E (Figure 6). It belongs to the humid monsoon climate region, with an average annual precipitation of 931 mm. Innormal years, the floods caused by rainfall in the basin are mostly concentrated in July and August with the characteristics of rapid fluctuation, short duration, fast flow rate, and high flood peak, which pose a great threat to the life and property of the residents along the river.  
To verify the NDCG-IPSO method for image similarity search, daily precipitation images from Jialing Basin from 1 January 2010, to 12 December 2019, were used for training and validation. Therefore, the experiment chose 30 precipitation images with different rainfall grades (6 images of light rain, 9 images of moderate rain, and 15 images of heavy rain) as the query samples and 10 matching samples for each query sample from the historical precipitation image. Additionally, each matching sample was assigned a similarity score from 0 (totally dissimilar) to 2 (very similar) according to expert experience to measure how similar the query sample and the matched sample were. In the experiment, the query samples were divided into the training sample set and test sample set according to the ratio of 2:1.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 5 Conclusions  
This paper investigated six protection algorithms for MVDC grids integrating renewable energy sources (RESs) and electrolyzers. Various fault scenarios, including pole-to-pole and pole-to-ground faults with resistances of 0.01 \(\Omega\), 2 \(\Omega\), and 20 \(\Omega\), were simulated using PSCAD/EMTDC to assess the performance of each algorithm.  
The results indicate that the derivative-based algorithms (ROCOV, ROCOC, and LIVRD) perform better as fault resistance increases, providing faster detection times compared to the OC and UV algorithms, which experience significant delays under higher resistance conditions. The differential algorithm, although subject to communication delays, still meets the required DC protection requirements. Furthermore, the distance between the fault location and the measuring relay has a more significant impact on the detection time than variations in the fault resistance.  
In conclusion, this study identifies the most effective fault detection algorithms for MVDC systems and emphasizes the importance of considering factors such as the fault resistance and relay distance to the fault. It also highlights the limitations of certain algorithms, like OC, and stresses the need for continued research to improve protection systems in MVDC grids, particularly for large-scale, real-time applications.  
Conceptualization, A.B. and D.M.L.; methodology, A.B.; software, A.B.; validation, M.J.P.-M., P.E. and A.I.; formal analysis, A.B.; investigation, A.B.; resources, D.M.L.; data curation, A.B.; writing--original draft preparation, A.B.; writing--review and editing, M.J.P.-M., P.E., D.M.L. and A.I.; visualization, A.I.; supervision, P.E.; project administration, D.M.L.; funding acquisition, D.M.L. and P.E. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basque Government (GISEL research group IT1522-22 and Elkartek KK-2022/00039) and European Union--Next Generation EU (INVESTIGO program).  
Not applicable.  
The data presented in this study are available in reference number [25]. Which have been modified according to Section 3 in this document.  
The authors declare no conflict of interest.
## 2 Literature Review  
### System-of-Systems Architecture Selection  
The ISO/IEC/IEEE 42010:2011 (Systems and Software Engineering--Architecture Description) [6] defines architecture as \"fundamental concepts or properties of a system in its environment embodied in its elements, relationships, and in the principles of its design and evolution\". Researchers in the SoS domain [1; 7; 8] follow this definition and consider SoS architecture as an allocated architecture generated by allocating a particular functionality from a functional architecture to the physical resources of a physical architecture. In order to describe an architecture more comprehensively and formally, architecture frameworks have been developed in both the defense and industry sectors. Commonly used architecture frameworks include the Department of Defense Architecture Framework (DoDAF), Ministry of Defense Architectural Framework, the Open Group Architecture Framework, and the Unified Architecture Framework. Guided by the architecture frameworks, along with relevant decision support methods and tools, the output of architecture design becomes a set of description models (e.g., operational activity model, state-transition model) that illustrate the capability mission, operational tasks, services, physical systems, functions, and the inter-relationship among these elements. The variety of elements could yield a large number of alternatives. Selecting out the appropriate alternatives based on the mission requirements and all sorts of constraints is the key task of SoS architecture selection and associated methods can refer to the survey paper [1].
## 4 Advanced Identification of Expansion Gap Using Machine Learning
### Gap Distance Analysis Algorithm  
The texture of the pixels in the expansion gap area to be measured appears somewhat irregular depending on the gap, foreign matter, and type of expansion joint device. Texture irregularity is a factor that makes it difficult to distinguish pixels in the expansion gap area.  
The metal surface constituting the expansion joint device has a consistent texture compared to the gap region. This means that it is easier to extract the expansion joint device than the gap area. Therefore, to analyze the gap distance, the expansion joint device is extracted first, and the gap area is extracted again from the resulting image. Image segmentation using U-Net was applied to both area extraction processes (see Figure 13).  
Because the U-Net output represents the probability that each pixel is a gap area, binarization is performed by applying a threshold value of 0.5. When defining a binarization function as a response, the formula to search the x-coordinate of the minimum gap is as given below. Note that the same applies to rail-type expansion joint devices.  
\[\underset{1\leq x\leq 512}{arg\ min}\sum_{y=1}^{512}{response(x,y)} \tag{8}\]  
The input and output of the U-Net model have the size of 512 \(\times\) 512. By binarizing the final output probability map, we search for the x-coordinate with the smallest number of pixels with a value of 1. The number of pixels at the coordinates is the gap distance in pixels, and the actual gap distance is obtained by multiplying the distance value per pixel.  
\[(distance)=(\#\ of\ pixel)*\left(\frac{mm}{pix}\right) \tag{9}\]  
Figure 14 shows the pseudocode for obtaining the minimum gap distance from the output probability map of U-Net.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accuracy by Type of Expansion Joint (\%)** & **Precision** & **Recall** & **f1-Score** \\ \hline Positive (pixels of expansion joints) & 96.61 & 94.38 & 95.49 \\ \hline Negative (other pixels) & 99.23 & 99.55 & 99.39 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Gap identification accuracy by type of expansion joint (2017–2019).  
Figure 13: An approach for elaborate gap area extraction.
## 2 Results and Discussion
### E-Nose and E-Tongue Analyses
(**a**) the radar map of the electronic nose under non-polar separation. (**b**) the radar map of the electronic nose under polar column separation. As shown in Table 3, the major taste components of the three durians were detected using the e-tongue, and the sweetness, sourness and umami tastes were detected, respectively. The e-tongue results showed that the three durians have differences regarding the perception of sweetness and umami. The greater the signal intensity value of the e-tongue, the stronger of corresponding sense of taste people feel. The critical value of sourness was \(-13\). Below this value sourness could not be sensed. In terms of sweetness, BT had the highest sweetness and MT had the lowest sweetness, which was consistent with the LC-MS data. The total sugar concentration of BT was 257.63 \(\upmu\)g/mg, and the total sugar concentration of MT was 138.73 \(\upmu\)g/mg. Considering that the sweetness of different sugars is different, the reason for this result may be that the sucrose content of the Black Thorn durian is much higher than that of the other two cultivars, which masks the sweetness effect. In terms of umami, MT had the highest umami, and BT had the lowest umami. It can be seen that the taste of durian varies according to origin and cultivar.
###### Abstract  
Interest in the use of organic electronic devices in radiation sensing applications has grown in recent years. The numerous device configurations (e.g., diodes, thin film transistors) and potential for improved tissue equivalence compared to their silicon-based analogues make them attractive candidates for various radiation dosimetry measurements. In this work, a variation of the organic thin film transistor (OTFT) is studied, in which a polymer electret is added. An OTFT electret design can be used in either a wired or wireless configuration for in vivo dosimetry with the possibility of real-time detection. The linearity, reproducibility, and dependence on energy of these devices were measured through exposure to 100 kVp photons from an orthovoltage treatment unit (Xstrahl 300) and 6 MV photons from a Varian TrueBeam medical linear accelerator. Prior to irradiation, all transistors were programmed with a \(-80\) V bias applied to the Gate electrode (Vg) for 3 s. In the wireless configuration, after each delivered dose, the transfer characteristic was scanned to readout the amount of erased charges by monitoring the drain current change. When the programmed charge was sufficiently depleted by radiation, transistors were reprogrammed for repeated use. The real-time readout in a wired configuration was performed by measuring the drain current with Vg = \(-15\) V; Vd = \(-15\) V. The 6 MV photon beam was turned on and off at different dose rates of 600, 400, 300, 200, and 60 cGy/min to quantify the sensitivity of the device to changes in dose rate. The wireless transistors showed a linear increase in current with increasing dose. The sensitivities for different energies were \(60\pm 5\) nA/Gy at 6 MV at a dose rate of 600 cGy/min and \(80\pm 10\) nA/Gy at 100 kVp at a dose rate of 200 cGy/min. The sensitivity of detectors tested in a wired configuration at Vd = \(-15\) V; Vg = \(-15\) V was 8.1 nA/s at a dose rate of 600 cGy/min. The principle of pentacene OTFTs with polymer electret as radiation detectors was demonstrated. Devices had excellent linearity, reproducibility, and were able to be reprogrammed for multiple uses as wireless detectors. The wired transistors demonstrated an effective response as real-time detectors.  
organic semiconductor detector; organic thin film transistors with polymer electret; radiation; radiation dosimetry
## 2 Standard Nondestructive Testing and Evaluation Techniques
### Dye Penetrant Testing  
One traditional NDT technique is dye penetrant testing, also called penetrant inspection (PI) or liquid penetrant testing (LPT). This technique is demonstrated in Figure 1. First, a dye or liquid penetrant is applied onto the surface of interest. The penetrant is then kept for sufficient time to allow for it to infiltrate into any defects present on the surface. After enough time has passed, the excess penetrant is removed, keeping only that which penetrated the defects. Next, a developer is applied to the specimen to make the defect with the penetrant more visible under normal lighting conditions or ultraviolet light [17].  
Given its simplicity, this technique is typically not researched much but is used to compare it with other methods. For example, [PERSON] et al. [18] conducted tests on shielded metal arc welding (SMAW) butt joints using dye penetrant (PT) and ultrasonic testing (UT). The tests were to distinguish the resulting discontinuity and determine acceptance criteria per the standards outlined by the American Society of Mechanical Engineers (ASME) [19]. Using this procedure, [18] used dye penetrant testing to yield indications of porosity defects on the surfaces of their investigated specimens, highlighting the effectiveness of PT in detecting surface-level defects compared to UT.
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 4 Evaluation
### Analysis Based on the Number of Users  
The energy consumption, total execution cost, and delay of the application loop for 1 to 10 users is shown. Table 4 shows that the average energy consumption of the HHS algorithm is better than those of FCFS by 7.1%, concurrent by 28.6%, and DP by 2.7%. Based on Table 5, the average total execution cost of the HHS algorithm is better than those of FCFS by 53.48%, concurrent by 65.26%, and DP by 52.14%. In Table 6, the delay of HHS algorithm is better than those of FCFS by 6%, concurrent by 28%, and DP by 52.14%.  
In another comparison, the number of users in a fog-computing architecture is considered. The traditional and heuristic methods are presented as [13]. Figure 4a is based on energy consumption, so the HHS algorithm has the minimum energy consumption by \(1.43*10^{7}\). Figure 4b shows the total execution cost; that of the HHS algorithm is \(1.34*10^{6}\). Thus, it is better than the FCFS, concurrent, and DP algorithms. According to Figure 4c, the minimum delay of the HHS algorithm is 96 s; thus, the delay of the application loop is optimized by HHS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Value** & **FCFS** & **Concurrent** & **DP** & **HHS** \\ \hline Avg & 107 & 139 & 103 & 100 \\ Max & 107 & 155 & 106 & 103 \\ Min & 107 & 117 & 101 & 96 \\ SD & 16.8 & 11.4 & 1.52 & 2.61 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Application loop delay statistics for FCFS, Concurrent, DP, and HHS based on the number of users.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Value** & **FCFS** & **Concurrent** & **DP** & **HHS** \\ \hline Avg & 1.54 & 2.00 & 1.49 & 1.43 \\ Max & 1.54 & 2.15 & 1.52 & 1.43 \\ Min & 1.54 & 1.69 & 1.46 & 1.43 \\ SD & 1.97 & 1630 & 216 & 4.90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy consumption statistics of FCFS, Concurrent, DP, and HHS based on the number of users. Avg = \(Average*10^{-7}\), Max = \(Maximum*10^{-7}\), Min = \(Minimum*10^{-7}\), and SD = \(StandardDeviation*10^{-3}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Value** & **FCFS** & **Concurrent** & **DP** & **HHS** \\ \hline Avg & 2.89 & 3.87 & 2.81 & 1.34 \\ Max & 2.90 & 4.53 & 2.87 & 1.35 \\ Min & 2.89 & 3.18 & 2.74 & 1.33 \\ SD & 2.79 & 463 & 41.7 & 6.95 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Total execution cost statistics of FCFS, Concurrent, DP, and HHS based on the number of users. Avg = \(Average*10^{-6}\), Max = \(Maximum*10^{-6}\), Min = \(Minimum*10^{-6}\), and SD = \(StandardDeviation*10^{-3}\).
# Online Sparse DOA Estimation Based on Sub-Aperture Recursive LASSO for TDM-MIMO Radar
## 3 Results  
In the simulation and experimental verification, both the target and the platform are considered stationary. All of our simulations and experiments were conducted using a 64-bit MATLAB R2018a on a PC workstation with an Intel Core i5-9500 CPU, 3.0 GHz and 16 GB RAM. The proposed method is compared with the conventional DAS, IAA, and LASSO methods in terms of computational time and DOA accuracy. Because this study only concerns the angular estimation performance, the targets were set at the same distance. The root mean square error (RMSE) of the angular estimates is used for evaluating the performance of simulation results and is defined as  
\[\text{RMSE}=10\text{log}_{10}\sqrt{\frac{1}{P}\sum_{p=1}^{P}\left|\hat{\theta }_{p}-\theta_{p}\right|^{2}} \tag{28}\]  
where \(\theta_{p}\) denotes the true target value of the \(p\)-th target grid, and \(\hat{\theta}_{p}\) represents the estimate of the \(p\)-th target grid. The signal-to-noise ratio (SNR) is defined as  
\[\text{SNR}=10\text{log}_{10}\bigg{(}\frac{P_{s}}{\delta^{2}}\bigg{)} \tag{29}\]  
where \(P_{s}\) stands for the signal power, and \(\sigma^{2}\) is the variance in the additive Gaussian white noise.