esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Challenges and Solutions
### Polarization  
The polarization of electrical signals is another factor that affects the reliability of piezoresistivity performance during the electromechanical testing of multifunctional cementitious composites [18]. In cementitious materials, polarization occurs due to ion movement in the pore solution when using a DC measurement system [150]. To this end, it is imperative to consider a resting time before conducting electromechanical testing on self-sensing cementitious composites, as seen in Figure 19a,b. The testing should be started after reaching a stabilized condition, as seen in Figure 19c, where an increase in electrical resistance with time (polarization) occurred at the beginning of electromechanical experiments [86]. This effect decreases with time, indicating that before initiating electromechanical experiments using the DC measurement system, it is necessary to wait until a constant electrical resistance curve is reached over time. To mitigate polarization effects, various measures, including using a four-probe electrode configuration, using an AC measurement system, employing a high percentage of CNMs, and using embedded electrodes, have been explored [126]. For instance, Figure 19d illustrates that the rate of polarization effects decreases with an increasing concentration of CNTs, ranging from 0% to 0.5%, suggesting that optimizing the percentage of conductive filler is an effective solution to diminish polarization effects [189].  
Figure 18: Procedure of CVD technique and microstructure of graphene-coated quartz sand (**a**) Mixing of quartz sand with Phenolic resin and ethanol (**b**) Vacuum drying stage (**c**) Argon atmosphere for 30 min (**d**) Prepared graphene coated conductive aggregate [188], published by Elsevier (2024).
# Analysis of the Water Quality Improvement in Urban Stream Using MIKE 21 FM
## 2 Theoretical Background
### Introduction of the MIKE 21 FM Model  
To effectively analyze the water quality improvement based on the supply of the ozone-treated reclaimed wastewater in Gulpocheon, a numerical analysis model that can accurately simulate the actual flow of the stream and predict the water quality change was used in this study. A two-dimensional model was used to predict fluid flow accurately rather than the existing one-dimensional numerical analysis model typically used in the stream's hydraulic and water quality analyses.  
In more complex river systems, it is likely that a 1D model will deviate too far from reality, whereas a 2D model with horizontal dimensions predominating over vertical dimensions can lead to a more realistic description of the case. The evolution of numerical methods and the development of powerful computational tools, which facilitate the application of more complex approaches, have led to increasing use of 2D hydraulic models [27; 28].  
The water quality analysis models include QUAL2E, WASP series, EFDC, and MIKE 21 FM. Among these models, the MIKE 21 FM model is universally utilized both in and outside South Korea. Its chief advantage is that it can quickly analyze the hydraulic and water quality of the complicated streamflow under the unsteady flow [29; 30; 31].  
The MIKE 21 FM model is designed such that the hydraulic flow is analyzed using the continuity and momentum equations of the Hydrodynamic Module (HD) [31]. This HD module is the numerical analysis model that performs the water level and flow rate analyses in the stream and coastal areas.  
Furthermore, the MIKE 21 ECO Lab module analyzes water quality by connecting with the HD module and simulates various statuses of water quality reaction processes. The water quality model of MIKE 21 calculates simultaneous differential equations, which describe the physical, chemical, and biological interactions related to bacterial survival, oxygen conditions, and excess degree of nutrients, by the Standard Fourth-Order Runge-Kutta method or Euler Linear Integral method.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 11 Transmission Performance of HCFs  
As the commercial deployment of HCFs, with the exception of high-speed trading data links usually operating at a rate of 10 Gb/s [54], is yet to happen, we review the transmission experiments reported in literature and estimate the limits imposed by the fiber parameters.  
HCFs and cables with them are available in short lengths: 1-11 km [89; 111]; thus, transmission at longer distances is tested in recirculating the loop comprising length(s) of HCF and EFDA amplifier(s) compensating for the loss of fibers and passive components. In all experiments mentioned here, the operating wavelengths were within the 1550 nm window (C Band).
# Visuo-Haptic Simulations to Understand the Dependence of Electric Forces on Distance
## 4 Data Analysis and Results
### Learning Gains
\[G_{rel}\ =\ \frac{Post-\ Pre}{Pre} \tag{7}\]  
In Figure 9, the average pre-test and average post-test grades for the five sections indicated in Table 2 are presented, considering only questions \(Q3\), \(Q4\), and \(Q5\) (Table 6), since for \(Q1\) and \(Q2\) the average leaning gains were zero. In this case, the average post-test is about 16 points higher than the average pre-test. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{_N = 111_} & \multicolumn{2}{c}{**Correct Answers Proportion**} & \multicolumn{2}{c}{**Gains**} & \multicolumn{2}{c}{_t-Test_} \\  & _Pre_ & _Post_ & \(G\) & _Grel_ & _p-Value_ \\ \hline \(Q1\) & \(1\) & \(1\) & \(0\) & - & - \\ \(Q2\) & \(1\) & \(1\) & \(0\) & - & - \\ \(Q3\) & \(0.847\) & \(0.802\) & \(-0.045\) & \(-0.053\)(*) & \(0.2952\) \\ \(Q4\) & \(0.441\) & \(0.631\) & \(0.190\) & \(0.340\) & \(0.0072\) \\ \(Q5\) & \(0.604\) & \(0.982\) & \(0.378\) & \(0.955\) & \(0.0000\) \\ \hline \hline \end{tabular} (*) Calculated with Equation (7).  
\end{table} 
Table 6: Question’s correct answers proportion, learning gains, and \(Pre\) vs _Post_ comparison test (\(N=111\)). Figure 8: Comparison of average pre-test and average post-test grades for the five studied sections, considering the five questions mentioned in Table 2, in a scale 0–100. In Figure 10, a comparison between the average pre-test and average post-test for the five sections indicated in Table 2, for each of the individual questions \(Q3\) (blue), \(Q4\) (orange), and \(Q5\) (green), is presented. As can be seen, for question \(Q3\) there is a slight decrease for sections EM1, EM3, and EF1, a slight increase for section EM4, and no change for section EM2. On the other hand, question \(Q4\) presents increments for all sections, particularly for EM1 and EM4, and no change for EF1. Finally, \(Q5\) shows important increments for all sections.
## 4 SSO Suppression Strategy for DFIG System
### Improved Controller Performance Analysis
Figure 6: Block diagram of the rotor current vector control embedded in the adaptive quasi-resonant and back-stepping controller. Figure 7 shows the Bode diagram of the frequency response of \(F_{1}(s)\). For the DFIG wind power system, the amplitude gain of \(F_{1}(s)\) at the sub-synchronous resonance frequency should be as small as possible; this allows the anti-disturbance ability of the sub-synchronous voltage component in the power grid to be improved. It is not difficult to see from Equation (25) that the influence of the sub-synchronous voltage \(U_{\text{sdq\_sub}}\) on the stator current \(I_{\text{sdq}}\) is affected by \(F_{1}(s)\): the smaller the amplitude of \(F_{1}(s)\) is, the smaller the interference of the sub-synchronous harmonic voltage with the DFIG system and the greater the robustness of the system. As can be seen from Figure 7, the amplitude gain in the improved controller at the resonant frequency is significantly smaller than the amplitude gain at other frequencies of the curve and, compared with when a resonant controller is not used, the amplitude gain at the resonance point is reduced by about 14 dB, which confirms that, in the case of sub-synchronous oscillation, a resonant controller can effectively reduce the effect of the sub-synchronous harmonic voltage on the influence of the generator stator current. Therefore, the proposed strategy based on adaptive quasi-resonant RSC backstepping control has a strong anti-disturbance capability with regard to sub-synchronous voltages, which can significantly enhance the robustness of the system. For the dynamic response of the current, it can be seen from Figure 8 that, when \(K_{\text{R}}\) or \(\omega_{\text{c}}\) increases, the amplitude of the rotor current of the DFIG control system decreases, resulting in a slower dynamic response speed in the current loop. As can be further seen in Figure 8, the rotor current may oscillate due to the appearance of a resonant peak and even cause the system to become unstable. Therefore, the choice of the resonance proportional coefficient \(K_{\text{R}}\) and the bandwidth \(\omega_{\text{c}}\) should be considered as a compromise.
# An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods
## 5 The Spacing Judgement of Two Robotic Fishes
### The Phalanx Formation
#### 5.2.1 The Results Analysis  
Through the results in Figure 13 (Section 4), we found that the pressure fields on the adjacent side of the two fishes overlap greatly at a close spacing in the phalanx formation. \(p1_{iR}\) and \(p2_{iL}\) are more susceptible to spacing than pressures on the other side (\(p1_{iL}\) and \(p2_{iR}\)). Therefore, we calculated the kinematic pressures of the specific side of fish, which are \(p1_{iR}\) and \(p2_{iL}\). We found that \(p1_{iR}\) and \(p2_{iL}\) are symmetrical in a cycle of fish swimming, as shown in Figure 18. The data in Figure 18 are monitored at a suitable spacing (\(dy=0.4\ L\)), which can ensure the representativeness of the rules of data at different spacing. When the inlet flow velocity is 0, the interaction of fish body swing is large, thus the rules of the data are representative at different \(U\). Although there are some slight jitters in the data, \(p1_{iR}\) and \(p2_{iL}\) are approximately equal and we calculated the average of \(p1_{iR}\) and \(p2_{iL}\) as \(p_{i}^{\prime}\).  
In this work, the data we mainly used are monitored by sensors \(S_{0}\), \(S_{3}\) and \(S_{7}\). To save the cost of sensors placement, we give priority to the data at these positions. We used the data (\(p1_{0}\) and \(p2_{0}\)) monitored by \(S_{0}\), which are almost unaffected by spacing to predictive the inlet flow velocity for the phalanx formation. Thus, we consider the data monitored by \(S_{3}\) and \(S_{7}\) in this part. In the phalanx formation, we calculated \(p_{3}^{\prime}\) and \(p_{7}^{\prime}\) to discuss the relationship between the kinematic pressure and the spacing \(dy\). As shown in Figure 19, when the spacing \(dy\) is 0.2 \(L\), the kinematic pressures differ  
\begin{table}
\begin{tabular}{c c} \hline \(\Delta d\) & **Accuracy Rate** \\ \hline
0.3 L & 80\% \\
0.5 L & 84\% \\
0.7 L & 80\% \\
0.9 L & 84\% \\ \hline \end{tabular}
\end{table}
Table 9: The accuracy rate of the judgement models.  
greatly from that of other spacings at a certain \(U\). This is because the spacing is close, and the impact caused by the swing of two fishes is greater than that of large spacings. However, we found that \(p_{3}^{\prime}\) is monotonic with \(dy\) increasing at a certain \(U\) as shown in the left of Figure 19. Thus, we can use \(p_{3}^{\prime}\) to judge the spacing.
## 2 Materials and Methods
### Data Augmentation  
The dataset has come from different picture archiving and communication systems (PACS), which typically are inconsistent with some noises, and there are different orientations and variations depending upon the image modalities. This may hinder the generalizability of the deep learning model. To address this issue, the training dataset is prepared by applying various data augmentation methods. Translation and scaling help the model generalize across slight positional shifts and variations in verteb size that can occur in images as it is obtained from different PACS. Elastic transformation handles small deformations and replicates anatomical variations and minor deformations which may occur due to patient positioning during the scan. The rotation accounts for slight tilts and variations in the scan angles, and ensures that the model is not biased toward a fixed orientation. Lastly, contrast and brightness adjustment addresses the variations in light effect during scan to ensure the model remains invariant to contrast-related changes. A sample image from the non-fracture class along with the output of various symmetric and asymmetric augmentation strategies are displayed in Figure 3. The first image represents the original CT image from the non-fractured class, followed by the output of different operations, specifically, translation, zoom-in, elastic transformation, rotation, and contrast/brightness adjustment, respectively.  
Figure 3: Illustration of data augmentation. (**a**) Original image; and transformations applied: (**b**) Translation; (**c**) Zoomed-in/scaling; (**d**) Elastic transformation; (**e**) Rotation; and (**f**) Contrast/brightness enhancement.  
Figure 2: Histogram plot of a sample image from both classes: (**a**) non-fracture class and (**b**) fracture class.
# Flexible Rectenna on an Eco-Friendly Substrate for Application in Next-Generation IoT Devices
## 2 Development Approach: Design Goals, Materials, and Methods
### Materials  
We selected eco-friendly materials from renewable sources (natural rubber for the polymer matrix) and agricultural by-products (rice husk ash for the filler) to fabricate the flexible substrate. The natural rubber (STR-10) was used as the polymer matrix due to its high elasticity, moisture, chemical, and dust resistance [26]. Furthermore, the low cost and ease of processing of natural rubber, combined with its renewable origin, make it suitable for flexible electronics, protective coatings, tires, and even medical devices [27], which will facilitate the seamless integration of the rectenna. As a filler in the natural rubber composite, we used the by-product of rice milling--rice husk ash (RHA)--due to its potential as a renewable alternative to conventional fillers (carbon black and commercial silica) [28] and its low cost. Additionally, using RHA as a filler enhances natural rubber's mechanical properties, thermal stability, and aging resistance, making it suitable for applications that require flexible substrates, such as flexible wearable antennas [29]. For the rectenna elements, we chose brass foil because it offers excellent electrical conductivity and flexibility. Moreover, brass can be qualified as an eco-friendly material because it is easily recyclable without compromising its fundamental properties [30].
# Droplets Transmission Mechanism in a Commercial Wide-Body Aircraft Cabin
## 4 Conclusions  
The COVID-19 pandemic has severely affected the air transport industry, forcing airlines to pay attention to the ventilation system in order to ensure the safety of passengers and crewmembers. In this study, the infection risk of passengers in a twin-aisle wide-body aircraft under three traditional ventilation systems was studied, and a new personalized exhaust system was proposed and investigated.  
The conclusions arising from this study are the follows:  
1. The droplets transmission mechanism and the infection risk of passengers are closely related to the type of ventilation system used in the aircraft cabin and the location of the infectious source.
2. Under the same ventilation system, when passenger C is the infectious source, the cabin environment is at greatest risk compared with other infectious sources.
3. The infection risk of mixing ventilation is greater than that of displacement ventilation. Therefore, from the perspective of epidemic prevention, the cabin air flow should be organized as direct as possible, and the passenger breathing area should avoid the air flow pathways between the passenger's mouth and the air outlet as far as possible.  
Figure 10: Overall cabin infection risk of different the infectious sources under different ventilation systems.  
The personalized ventilation system proposed in this study can effectively reduce the infection risk of the cabin, the infection risk of passengers is reduced by up to 27%.  
Conceptualization, B.K., H.S. and Y.J.; methodology, B.K. and H.S.; software, B.K.; validation, B.K., H.S. and Y.J.; investigation, B.K.; resources, Y.Z. and M.C.; data curation, B.K.; writing--original draft preparation, B.K.; writing--review and editing, Y.Z., M.C., H.S. and Y.J.; visualization, B.K.; supervision, H.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### XRD Examination  
In order to analyze the hydration products of the mortar specimens, mortar specimens were subjected to XRD analysis at different ages. Figure 6 presents the results. In addition to calcium hydroxide (Ca(OH)\({}_{2}\)) and quartz (SiO\({}_{2}\)), ettrigite (AFt) is also present, which is the product of internal sulfate attack. This provides evidence that SO\({}_{4}\)\({}^{2-}\) from lithium mica slag reacted with the hydration products. The types of corrosion byproducts generated during the erosion process of sulfates depend on the concentration of SO\({}_{4}\)\({}^{2-}\). According to the dissolution of SO\({}_{4}\)\({}^{2-}\) from lithium mica slag, the concentration of SO\({}_{4}\)\({}^{2-}\) was below 1000 mg/L, which demonstrates that the corrosion byproduct is AFt rather than gypsum. During this process, Ca\({}^{2+}\) in the mortar specimen reacted with SO\({}_{4}\)\({}^{2-}\) and Al\({}^{3+}\) to form AFt. The chemical reaction equation is as follows:  
\[\mathrm{Al_{2}(SO_{4})\cdot 18H_{2}O+6 Ca(OH)}_{2}\rightarrow\ 3 CaO\cdot \mathrm{Al_{2}O_{3}\cdot 3 CaSO_{4}\cdot 32H_{2}O} \tag{5}\]  
Figure 5: The expansions for mortar specimens with different lithium mica slag contents at different ages.
## 6 Conclusions  
In this paper, an interference-aware subcarrier allocation algorithm for mMTC is proposed for the subcarrier allocation problem of D2D communication in mMTC scenarios. Initially, we establish a model maximizing the number of mMTC pairs accessed. When carrying out subcarrier allocation, we calculate the maximum interference that each subcarrier can sustain. Then we determine the mMTC pair with the least interference to the subcarrier and estimate the access property according to whether  
Figure 5: Number of mMTC pairs accessed versus different mMTC device transmit powers.  
Figure 6: Number of mMTC pairs accessed versus different number of mMTC devices.  
the QoS of the mMTC pair is satisfied. When the accumulated interference caused by the mMTC pair is greater than the maximum interference limit that the subcarrier can sustain, the subcarrier will be not be accessible to other mMTC pairs anymore. Simulation results demonstrated the effectiveness of the proposed algorithm.  
[PERSON] designed the algorithm, performed the theoretical analysis, and wrote the manuscript. [PERSON] and [PERSON] implemented the simulation and contributed to the manuscript preparation. [PERSON] analyzed the data. [PERSON] and [PERSON] contributed to polishing the revised manuscript and provided suggestions on simulation evaluation.  
This work was supported by the Science and Technology Project of Xuzhou (KC18105 KC18068), the National Natural Science Foundation of China (61771417 51734009 51804304), the National Key Research and Development Program (2016 YFC0801403), and the Fundamental Research and Development Foundation of Jiangsu Province.  
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
# Deep Learning-Based Method for Classification and Ripeness Assessment of Fruits and Vegetables
## 3 Materials and Methods
### Analyze  
In the fruit and vegetable classification dataset, images that do not have representative characteristics or contain some other types of information were removed, which significantly improved the model's performance. The analytical method used is the 80-10-10 method, where 80% of the data is used for training, 10% for testing, and 10% for validation. That is, for classification, out of the total number of images, 2688 images were used for training, 168 for testing, and 168 for validation. On the other hand, for ripeness, 4080 images were used for training, 255 for testing, and 255 for validation. In addition, data augmentation is performed for each of the domains.  
To run the models, ablation tests were used to understand the contribution of each component of the model. These components are the hyperparameters, such as the optimizer, the number of epochs, and the architecture. All the tests are shown in the Results section. To perform these tests, first a model was trained with a configuration, then the metrics were analyzed to decide which hyperparameter had to be changed to obtain better performance. After identifying the components that could be changed or removed, the model was trained again to finally evaluate the new metrics. This process was repeated several times until the performance was better than the previous one.
## 2 Tucker Model  
This section first presents a brief overview of the Tucker model, and then focuses on the Tucker-2 model used in this work. For an \(N\)th-order tensor \(\underline{\mathbf{T}}\in\mathbb{C}^{I_{1}\times\cdots\times I_{N}}\), a Tucker-\(N\) model or Tucker model is defined in the following scalar form as [26]:  
\[\begin{split} t_{i_{1},\ldots,i_{N}}&=\sum_{r_{1}=1 }^{R_{1}}\cdots\sum_{r_{N}=1}^{R_{N}}g_{r_{1},\ldots,r_{N}}\left(a_{i_{1},r_{1 }}^{(1)}\times\cdots\times a_{i_{N  
This model is called Tucker-2 model or Tucker-(2, 3) model, and is widely applied in data analysis and parameter estimation [4]. \(\mathbf{A}^{(1)}\) and \(\mathbf{A}^{(2)}\) are the two loading matrices, and \(\underline{\mathbf{G}}\) is the core tensor. In the same way, such a model can be written in terms of mode-\(n\) product as:  
\[\begin{split}\underline{\mathbf{T}}&=\underline{ \mathbf{G}}\times_{1}\mathbf{A}^{(1)}\times_{2}\mathbf{A}^{(2)}\times_{3} \mathbf{I}_{3}\\ &=\underline{\mathbf{G}}\times_{n=1}^{2}\mathbf{A}^{(n)}.\end{split} \tag{5}\]