esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Challenges for a Future Imaging Radar for Space Observation
### The Motivation for Ground-Based High-Resolution Fully Polarimetric Space Imaging Systems
As a result, the interpretation of 2D radar images of a space object might be difficult, since scatterers which physically and spatially do not belong to each other appear at the same image position. Polarized radar images might help to solve this problem by separating these superimposed pixels in different polarization directions. Another prominent effect in imaging space objects is the occurrence of multipath reflections, e.g., from the satellite body itself or from mountings like optics of cameras. For instance, single or double reflections appear in different polarization regimes. Figure 14 shows exemplarily a space object which was observed in a bistatic polarized configuration by the Office national d'etudes et de recherches aerospatiales (ONERA) and Fraunhofer Institute for High Frequency Physics and Radar Techniques (FHR). One sees very clearly that different regions are prominently visible in both polarizations. This helps to investigate and to interpret the radar image much better than with just a single polarized ISAR image.
# Study on Asymmetric Support of Anchor Cable with C-Shaped Tube in Inclined Coal Seam Roadway
## 1 Introduction
[29] investigated the mechanical properties of Sumo and TG cable bolts under constant normal stiffness through pull-out tests. They also developed an analytical model that effectively predicts the load displacement behavior of anchor cables. [PERSON] et al. [30] conducted shear tests on anchor bolts and found that the shear resistance of the anchor bolts is influenced by the strength of the surrounding rock. [PERSON] et al. [31; 32; 33] claim that single and double shear beams are available to evaluate the anchor cable's shear ability and that the anchor cable's shear strength is correlated with its preload and installation angle. It is evident that there have been many advances in the study of inclined coal seam roadway supporting techniques, but the asymmetric deformation properties of the inclined coal seam roadway's surrounding rock are complicated, and there is a dearth of research on its asymmetric deformation process. Because of the inadequate shear strength of its free portion when an anchor cable support is being used, it cannot play a stronger supporting role. However, the anchor cable with a C-shaped tube (hereinafter referred to as the \"_ACC_\") developed by [PERSON] et al. [34; 35; 36] has the characteristics of high prestress and high shear performance, and is a new type of mining supporting component material. Currently, the _ACC_ has been applied in the engineering field, but there is little research on its mechanical properties and the numerical simulation of its engineering applications. Therefore, studying the support effect of the _ACC_ on inclined coal seam roadways has important reference value for the selection of support materials for similar roadways. The difference in shear performance between the _ACC_ and an anchor cable is first investigated in this paper using a double shear test. Then, based on the background of the 9-8032 roadway of an inclined coal seam in the Pangpangta Mine, the asymmetric deformation characteristics are analyzed based on a numerical simulation test. The supporting impact of the asymmetric support of the \(ACC\) on the roadway of an inclined coal seam is investigated in conjunction with the findings of the double shear test.
## 3 Results and Discussion  
### SLNs Production and Characteristics  
SLNs production was performed using the multiple emulsion (w/o/w) technique, as previously reported [4]. DLS analysis indicated that the particles size was under 150 nm, with a relatively narrow distribution (\(<\)0.25), as shown in Table 1. These SLN present relatively high positive ZP values (\(\sim\)+28 mV), which is achieved because of the use of cationic lipids, CTAB and DDAB as surfactants. The strategic use of CTAB and DDAB, aimed the improvement of the particles' stability as a result ofrepulsion between positive surface charges, which is responsible for a reduction in particle aggregation and posterior dynamic changes in particle size. The interaction between particles and cells is favored by the positive charges at particles surface, as cells present a negative surface charge, which is a point in favor to improve the delivery of encapsulated material [3]. But, on the other hand, the increased interaction of nanoparticles with cells also may alter the cell membrane stability, resulting in cytotoxicity. Indeed, a previous work demonstrated that CTAB, in a dose-dependent fashion, was an important contributor to cell cytotoxicity [4]. Laser diffraction results also showed that the mean particle size of these SLN was within the nanometer range, from 118 nm to 120 nm, as shown in Table 1. These results, i.e., surface charge and mean particle size, may contribute to improve the adhesion of SLNs to the cell membrane and their internalization to release the drug inside cells. These interactions may also interfere with cell metabolism and lead to toxicity. CTAB and DDAB have been selected as cationic surfactants for the production of CTAB-SLNs and DDAB-SLNs, respectively, as they are commonly used in the production of cationic nanoparticles.
# Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review
## 3 Review of DL and ML Safety Applications
### Shm
#### 3.1.2 Vibration-Based Damage Detection
Compared to the traditional method, which is costly and laborious, their proposed method could achieve a high detection accuracy with a low cost. Vibration-based methods integrated with DL have proven that they can perform well in damage detection, and the costs of these methods are relatively low. However, these methods still need much effort in data labeling, which is tedious. There is a novel application using an unsupervised learning method, which does not require data labeling. [PERSON] et al. [88] presented an unsupervised learning method called a sparse coding algorithm for SHM. Sparse coding was employed to learn the feature representations from unstructured vibration data to improve the performance of damage detection. Through simulation, different degrees of damage were conducted, and the results showed that the proposed method outperformed other ML methods, such as logistic regression and decision trees, with a precision of 98%. However, this application was only validated on the simulated model. As the performance on real-world structures has yet to be verified, this may become a notable topic for future research.
# Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review
## 9 Future Prospects  
The future prospects of IoT-based drug delivery systems are vast and promising. These systems can revolutionise the healthcare industry by improving patient outcomes and transforming healthcare delivery. With advancements in technology and the increasing demand for personalised medicine, IoT-based drug delivery can offer a tailored approach to treatment, resulting in enhanced patient care. The integration of IoT devices with drug delivery systems can provide real-time monitoring of patient health, enabling healthcare professionals to adjust treatments accordingly, leading to improved patient outcomes. Moreover, IoT-based drug delivery can also increase the efficiency and accuracy of drug administration, resulting in reduced medication errors and improved patient compliance.  
These systems can also facilitate remote monitoring of patients, enabling healthcare providers to track and monitor patients' health status from afar, making healthcare delivery more accessible and efficient. The proliferation of IoT devices and the growing demand for connected healthcare have opened up new opportunities for the development of innovative IoT-based drug delivery systems. These systems can leverage the power of machine learning and predictive analytics to offer real-time insights into patient health and optimise drug delivery, resulting in better patient outcomes. In addition, the deployment of IoT-based drug delivery systems can generate vast amounts of data, which can be leveraged to improve clinical research and drug development. These systems can facilitate the collection of patient data in real-time, allowing for the analysis of large datasets and the identification of trends and patterns that could inform drug development and research. Overall, the future prospects of IoT-based drug delivery systems are vast, and the integration of these systems with other emerging technologies, such as artificial intelligence and blockchain, can offer even more exciting opportunities for the healthcare industry. As technology continues to evolve and become more accessible, we can expect to see IoT-based drug delivery systems becoming more prevalent and transforming the way we deliver healthcare.
# A Review of Double-Walled and Triple-Walled Carbon Nanotube Synthesis and Applications
## 2 Synthesis and Characterization of DWNTs
### The Catalytic Chemical Vapor Deposition Method
#### 2.2.2 The Floating Catalyst Method  
In the case of the floating catalyst method, a metal catalyst precursor containing a solution is atomized by spraying or ultra-sonication, or the metal catalyst precursor powder is simply sublimated by heat, and then, the generated aerosol/vapor is transferred into the heated reaction zone by a carrier gas. The diameter and the number of walls of the resulting CNTs are strongly related to the composition of the precursor solution, the atomizing/vaporizing conditions and the reaction conditions, and the desired DWNTs are successfully prepared by optimizing these conditions.  
Sulfur is used as an additive for the CNT synthesis to improve the yield of CNTs, as well as for controlling the number of walls. In the case of the floating catalyst method, it is not easy to introduce an oxide particle into the reaction zone in the gas phase; sulfur is commonly used as a sensitive agent for controlling the number of walls during DWNT synthesis. [PERSON] _et al_. [PERSON] _et al_. (2010) reported that when a small amount of sulfur (10 at % of Fe) is added into the precursor materials, mostly DWNTs (80%) were grown as the major CNT product, whereas SWNTs (95%) were mainly produced for the conditions without the sulfur additive. By increasing the concentration of sulfur, both the CNT diameter and number of layers increased. A growth mechanism for this route to CNT growth by the sulfur-assisted method was proposed. The nucleation of the CNT is activated at the location of the eutectic FeS-Fe on the surface of Fe nanoparticles (Figure 6j-l). Since by this floating catalyst method it is possible to supply catalyst precursor continuously, the continuous production of DWNTs can be achieved. Using this method, [PERSON] _et al_. [PERSON] _et al_. (2012) firstly reported the continuous production of SWNTs. Soon thereafter, the continuous production of DWNTs was also reported by optimizing the temperature, precursor composition (both of the metal catalyst precursor and the sulfur/sulfur-containing molecule), as well as the aerosol generation conditions and reaction conditions [PERSON] _et al_. (2012); [PERSON] _et al_. (2012); [PERSON] _et al_. (2012).
## 3 Mathematical Modeling and Design Methodology
### Modeling of a PV Array Output Power Generation
#### 3.1.2 SD Model  
The SD circuit model of a PV panel is shown in Figure 5. In the literature [2, 4, 21, 27, 29, 30], this is also called a five-parameter model. The model takes into account the series and leakage current to ground losses, which were not considered in the ISD model. They are represented by the series and shunt resistances \(R_{s}\) and \(R_{sh}\).  
The shunt resistance, \(R_{sh}\), is inversely proportional to the leakage current to the ground, and usually has a large value. Resistor \(R_{s}\) represents the series current losses and the internal resistance of the PV module and is, by design, very small. The PV cells are added together to form a PV module and then interconnected in parallel series to form a PV array [17, 31].  
This model has been used in grid-connected and standalone PV systems [17, 31]. This model is based on the well-known Shockley diode equation, which is also known as the five-parameter model. The relationship between the output current (I) and voltage (V) of the PV panel for a single unit, according to the SD model is expressed by (2) [6, 32]:  
\[I=I_{Ph}-I_{o}\left(e^{\left(\frac{V+IR_{s}}{\alpha_{\mathrm{T}}V_{\mathrm{T}} }\right)}-1\right)-\frac{V+IR_{s}}{R_{sh}} \tag{2}\]  
The five key parameters, \(I_{ph},~{}I_{o},~{}R_{s}~{},R_{sh}\), and \(V_{T}\), are given under standard test conditions (STCs) which are 1 kW/m\({}^{2}\) air mass 1.5 (AM1.5) and 25 \({}^{\circ}\)C. These parameters can be computed using analytical or numerical solutions, based on the specifications provided by the manufacturer. Further, \(I_{ph},~{}I_{o},V_{T}\), and \(I_{o_{stc}}\) are given in [6, 17, 33].  
However, the SD model neglects the recombination losses of the diode, which is a phenomenon that should be considered to accurately model a PV panel. The single diode model assumes a constant value for the ideality factor. The ideality factor is a measure of how closely the diode follows the ideal diode Equation of the diffusion process [27, 34]. It is usually considered as a function of the voltage across the device. Furthermore, at high voltage recombination in the device, electron-hole pairs are dominant at the surface of the bulk regions. This means that the ideality factor is close to one. However, at lower voltages, recombination in the junction dominates and the ideality factor approaches two [34]. The two exponential diodes model (i.e., TD model) takes into account the recombination phenomena and will be discussed in Section 3.1.3.  
Figure 5: Equivalent circuit of a PV panel using the single diode (SD) model.
## 2 The Proposed SAR ADC
### Sampling Switch  
The sampling circuitry is located at the forefront of the entire SAR ADC, and its performance level directly determines the upper limit of performance that the ADC can achieve. In order to improve the performance of sampling switches and reduce the sampling errors, bootstrapped circuits are often used for sampling switches. Bootstrapped switches improve the on-resistance stability of sampling switches [15; 16]. The principle of a gate-voltage bootstrapped switch is to provide a constant gate source voltage to the MOS switch through the bootstrapped circuit when the switch is on, and the magnitude of this constant gate source voltage is close to the supply voltage.  
The bootstrap process consists of two phases, which are illustrated by the structure diagram shown in Figure 7. In the first stage, the sampling switch is reset when the sampling signal (Sample) is low and the CLK is high, with M1, M3, M4, and M9 being ON and M2, M5, M6, M7, and M8 being OFF. The point A and D voltages are low, and the point B and C voltages are high. In the second stage, the Sample is high; the sampling switch is closed; the CLK is low; M1, M3, M4, and M9 are OFF; M2, M5, M6, M7, and M8 are ON. The voltages at points A and D are high, and the voltages at points B and C are low. When the voltage at point A changes from ground to VIN, the voltage at point C changes from VDD to VDD + VIN, so the voltage at point D also becomes VDD + VIN.  
The output waveform of the input sine wave for a 100 khz clock signal is shown in Figure 8a, where it can be seen that a fixed difference between VIN and gate voltage is maintained at about VDD to ensure that the gate voltage follows the input variation. The FFT of the sampling switch is shown in Figure 8b; the SFDR and SNDR are 105.59 dB and 101.84 dB, respectively, and the ENOB is 16.39 bit, which meets the 10-bit SAR ADC requirements.  
Figure 7: Bootstrapped sampling switch.
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
## 7 Conclusions and Future Work  
In this paper, we study the coverage problem in hybrid WSNs and we give HCHA algorithm based on Delaunay to estimate as well as heal coverage holes in the monitoring area, besides we solve the boundary coverage holes estimation and consider multiple assisted nodes healing deployment strategy. In order to generalize problems, we also give the quantitative range of assisted nodes which is related to parameter \(k\), where \(k\) is the ration between \(R_{c}\) and \(R_{s}\).  
For the future work, we are prepared to research directional sensor networks (DSN) and how to enhance the coverage ratio with the random distribute sensors in the obstacles exist scenario. The coverage of DSN is related to node coordinate, \(R_{S}\), working direction and angle of view, which is different from Omnidirectional sensor network.  
This work is supported by the NSF of China under Grant No. 61772130, No. 61301118 and No. 71171045; the International S & T Cooperation Program of Shanghai Science and Technology Commission under Grant No. 15220710600; the Innovation Program of Shanghai Municipal Education Commission under Grant No. 14 YZ130; and the Fundamental Research Funds for the Central Universities. This paper is an extended version of our conference paper: Coverage Holes Detecting and Healing in Hybrid Wireless Networks [23].  
The work presented in this paper is a collaborative development by all of the authors. [PERSON] and [PERSON] contributed to the idea of the incentive mechanisms and designed the algorithms. [PERSON], [PERSON], and [PERSON] developed and analyzed the experimental results. All authors contributed to the organization of the paper including writing and proofreading.  
The authors declare no conflict of interest.
## 5 Conclusions  
Aiming at the highly nonlinearity and multiscale property of MI-EEG, MFE is introduced and improved to measure its complexity. Especially with the parameters' independent optimization strategy, all the parameters of MFE are optimized for each scale factor in sequence. So, the MFE of each coarse-grained MI-EEG on a different scale factor is calculated by using different parameter values. This makes IMFE a more accurate multiscale analysis method. It would be helpful to discover the nature of a nonlinear signal in more detail. The improved MFE is applied to the feature extraction of MI-EEG, and results in relatively higher classification accuracy compared with the exiting nonlinear dynamic methods and conventional time, frequency, or spatial domain analysis methods. The statistical results of a paired \(t\)-test further illustrate that IMFE has significant advantages over MFE. These lay the foundation for expanding the application of nonlinear dynamic methods in EEG or even other bioelectrical signals. However, IMFE requires relatively more computation time than some other methods. This is mainly due to the exponential fuzzy membership function in MFE. We will solve that problem by simplifying the fuzzy membership function and improving programming skills in future work.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(t\)-test & \multicolumn{3}{c}{**Channel**} \\ \cline{3-5}  & & **C3** & **Cx** & **C4** \\ \hline \multirow{3}{*}{\(\tau=2\)} & Null hypothesis rejection & True & True & True \\  & \(p\) value & 0.0037 & 2.4309 \(\times 10^{-4}\) & 7.0122 \(\times 10^{-6}\) \\  & Test statistic & 2.7161 & 3.5727 & 4.5037 \\ \hline \multirow{3}{*}{\(\tau=3\)} & Null hypothesis rejection & True & True & True \\  & \(p\) value & 7.0122 \(\times 10^{-6}\) & 0.0169 & 1.4949 \(\times 10^{-8}\) \\  & Test statistic & 4.5037 & 2.1425 & 5.8743 \\ \hline \multirow{3}{*}{\(\tau=4\)} & Null hypothesis rejection & True & True & True \\  & \(p\) value & 1.3387 \(\times 10^{-6}\) & 0.0497 & 5.3877 \(\times 10^{-17}\) \\ \cline{1-1}  & Test statistic & 4.8958 & 1.6591 & 9.4577 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Paired \(t\)-test results.  
**Acknowledgments:** This work was financially supported by the National Natural Science Foundation of China (No. 81471770), the Natural Science Foundation of Beijing (No. 7132021), and the Integrated Promotion Project of Beijing University of Technology. We would like to thank all of the people who have given us helpful suggestions and advice. The authors are obliged to the anonymous referees for carefully looking over the details and for useful comments that improved this paper.  
**Author Contributions:** [PERSON] conceived the study; [PERSON] and [PERSON] conducted the experiments and analyzed the results; [PERSON] wrote the manuscript; [PERSON], [PERSON] and [PERSON] helped revise the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design and Evaluation of Flexible Support Based on Space Mirror
## 4 Analysis and Evaluation
### Error Analysis  
The data results for the simulation analysis, with core parameters as the calculation of 1 g gravity load, forced displacement (5 \(\upmu\)m) load, 16-24 \({}^{\circ}\)C temperature load, axial gradient 2 \({}^{\circ}\)C load, and radial gradient 2 \({}^{\circ}\)C load, are illustrated in Tables 6-8. We substituted the figures into Equation (3) to obtain the average value of RMS for the surface shape of the mirror component [19].  
\[\begin{array}{l}\sqrt{X^{2}+Y^{2}+t_{(20\pm 4^{\circ})}^{2}+S_{( forced\ displacement)}^{2}+t_{(axial\ or\ radial\ 2\ ^{\circ}\text{C})}^{2}}\\ =\sqrt{0.009^{2}+0.009^{2}+0.01^{2}+0.001^{2}+0.008^{2}}\\ =0.018\lambda\end{array} \tag{4}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Working Condition** & **Temperature Load** & **Axial Temperature** & **Radial Temperature** \\  & **(20 \(\pm\) 4 \({}^{\circ}\)C)** & **Gradient Load (4 \({}^{\circ}\)C)** & **Gradient Load (4 \({}^{\circ}\)C)** \\ \hline \hline \end{tabular}
\end{table}
Table 7: RMS of mirror shape error under different Temperature Load.  
Figure 8: Schematic of forced displacement contact surface.  
The average value of the root-mean-square of the mirror shape is calculated to be 0.018\(\lambda\).