esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR
## 1 Introduction
### Objectives of this Paper  
The main objective of this paper is to propose an imaging method that is able to efficiently focus a ground moving target with high quality, for airborne CSSAR with relatively high azimuth resolution. Based on the research gaps discussed in Section 1.1, studies are carried out to achieve the above objective from the following two aspects: (1) an accurate range equation model is proposed for airborne CSSAR ground moving target imaging; and (2) an efficient parameter searching strategy is proposed.  
In [25], an advanced hyperbolic range model was proposed for medium-Earth-orbit SAR stationary scene imaging. This range model is much more precise than the second-order Taylor-approximated range model. Inspired by this work, in this paper, a new range equation model is proposed for airborne CSSAR ground moving target imaging. The proposed range model has two main advantages: (1) its accuracy is higher than that of the second-order and third-order Taylor approximated range models; and (2) based on the proposed range model, an accurate analytical expression for the target's 2-D spectrum can be easily derived.  
After deriving the target's 2-D spectrum based on the proposed range model, an efficient imaging method is proposed in this paper. The proposed imaging method implements focusing process efficiently via a phase multiplication in the 2-D frequency domain. Moreover, to accomplish an efficient searching of the parameters of the proposed range model, the genetic algorithm (GA) is utilized. GA is a powerful parallel stochastic algorithm that searches the optimal solution based on the principles of natural selection and natural genetics [32; 33; 34; 35; 36; 37]. It is an efficient and robust optimization algorithm that can locate the global optimum in a multimodal landscape, and thus is utilized to reduce the computation load of the parameter searching involved in ground moving target imaging.  
The rest of this paper is organized as follows. The airborne CSSAR geometry and the proposed range model are presented in Section 2. In Section 3, the target's 2-D spectrum based on the proposed range model is derived. In Section 4, the proposed imaging method is described in detail. Numerical experiments are conducted in Section 5 to validate the proposed range model and the proposed imaging method. Finally, discussions and conclusions are made in Sections 6 and 7, respectively.
## 2 Experimental Section
### Synthesis of CNT-Metal Nanohybrids  
Carboxylated Multiwall Carbon Nanotubes (MWCNT-COOH) were synthesized following a previously published methodology [42]. Briefly, the CNTs were functionalized in a Microwave Accelerated Reaction System (Mode: CEM Mars, CEM Corporation, 3100 Smith Farm Road, Matthews, NC, USA) fitted with internal temperature and pressure controls. Pre-weighed amounts of purified MWCNT were treated with a mixture of concentrated H\({}_{2}\)SO\({}_{4}\) and HNO\({}_{3}\) solution under microwave radiation at 140 \({}^{\circ}\)C for 20 min. This led to the formation of carboxylic groups on the surface leading to high aqueous dispersibility. The resulting solid was filtered through a 10 \(\upmu\)m membrane filter, washed with water to a neutral pH and dried under vacuum at 80 \({}^{\circ}\)C to a constant weight.  
The MWCNT-COOH was used as the starting material to synthesize organic dispersible MWCNTs [43]. Pre-weighed amounts of MWCNT-COOH were mixed with thionylchloride (SOCI\({}_{2}\)) and dimethylformamide (DMF) and were subjected to microwave radiation at 70 \({}^{\circ}\)C for 20 min leading to formation of MWCNT-COCl. The product was filtered and washed with THF till and was then dried in a vacuum oven at room temperature for 12 h. MWCNT-COCl was then reacted with octadecylamine (ODA) under microwave radiation at 120 \({}^{\circ}\)C for 10 min. The product was filtered and washed with hot ethanol and dichloromethane and dried at room temperature under vacuum to obtain MWCNT-ODA. The products MWCNT-COOH and MWCNT-ODA were used to synthesize the palladium loaded functionalized CNT nanohybrids: p-MWCNT-Pd and np-MWCNT-Pd respectively. The former was expected to be more polar. The p-MWCNT-Pd and np-MWCNT-Pd were synthesized by direct chemical reduction of metal salts as previously published by our group [19]. Pre-weighed amounts of the selected CNTs (MWCNT-COOH or MWCNT-ODA) were added to the reaction chamber together with a palladium dichloride (PdCl\({}_{2}\))-ethanol mixture. The reactions were carried out under microwave radiation at 190 \({}^{\circ}\)C for 10 min. After cooling, the products were filtered, washed with 0.5 N hydrochloric acid and MilliQ water to a neutral pH. The products (p-MWCNT-Pd or np-MWCNT-Pd) were dried at room temperature in a vacuum oven to constant weight.  
The CNT-NM hybrids were characterized using a scanning electron microscope (SEM, Zeiss, Oberkochen, Germany) fitted with an Energy Dispersive X-ray spectrometer (EDX, Zeiss, Oberkochen, Germany), Thermogravimetric analysis (TGA, Perkin Elmer Inc., Waltham, MA, USA), and Fourier Transform Infrared spectroscopy (FTIR, Perkin Elmer Inc., Waltham, MA, USA). SEM data was collected on a LEO 1530 VP Scanning Electron Microscope equipped with an energy-dispersive X-ray analyzer. TGA was performed using a Pyris 1 TGA (Perkin-Elmer Inc., Waltham, MA, USA). FTIR measurementswere carried out in purified Potassium bromide (KBr) pellets using a Perkin-Elmer (Spectrum One) instrument (Perkin Elmer Inc., Waltham, MA, USA).
# Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
## 3 Proposed Method
### Discrete Wavelet Transform  
The DWT is a process that quantifies how much energy is contained in specific frequency bands at particular times within a signal. The wavelet is a complex bounded signal, i.e., it integrates to zero and declines to zero amplitude at some distance. The decomposition of the signal creates a collection of daughter wavelets. The daughter wavelets are compressed or expanded versions of the mother wavelet, and each daughter wavelet extends across a different section of the original signal. Daughter wavelets are organized in hierarchies where wavelets in level \(n_{i}\) have half as many wavelets, with half the frequency and twice the length of the wavelets in level \(n_{i-1}\). The number of levels increases until there are as many wavelets as samples in the original signal. Each daughter wavelet in this collection is associated with a coefficient that relates it to the decomposed signal.  
DWT compression happens as the discretized signal is represented only with the coefficients of the daughter wavelets. The less representative coefficients, i.e., coefficients of wavelets that show a lower relation to the original signal, are filtered, thus resulting in a signal with a sparse representation. Filtering the coefficients can be achieved through various methods; we utilize thresholding and quantization in this paper.
# Designing and Managing a Smart Parking System Using Wireless Sensor Networks
## 2 Existing Smart Systems for Parking Management
### Mass Protocols
The RFID readers activate the passive RFID tag on the card of the driver entering or exiting or parking the car in the area and read the information of the tag, which contains a unique identification number, to transfer them to the database of the system via a multi-hop communication using Wi-Fi technology, to update the occupation states of the parking spaces in real-time. These mass protocols have improved delays in the delivery of the data to the base station and they downplayed the enormous energy consumption when transmitting this data compared to the linear protocols. However, these protocols are only applicable for car parks with a group or mass structure, which makes it possible to create clusters that are not useful for linear car parks of which they will create, on the one hand, a load imbalance between the sensor nodes and, on the other hand, minimize the lifetime of the entire network.
## 1 Introduction
A weighted Laplacian was incorporated in the PM model to perform the diffusion along the edge direction of the original image. [PERSON] and [PERSON] proposed a weighted and balanced anisotropic diffusion scheme for image denoising and restoration [17]. [PERSON] and [PERSON] presented a robust edge detector based on an anisotropic diffusion-driven process [18]. In this model, noise suppression and edge enhancement are achieved respectively by anisotropic diffusion and the edge-sensitive detector. [PERSON] et al. [19] contributed a new filter by introducing a semi-adaptive threshold to an anisotropic diffusion process. In detail, a Gaussian filter is applied to recognize corrupted pixels in noise-free pixels and then a semi-adaptive threshold function is introduced to the diffusion coefficient for noise removal and edge preservation. [PERSON] et al. found that the local probability value of image gray can be applied to adjust the indication degree of a low edge gradient in the feature space of a noisy image, similar to the action of an adaptive threshold parameter [20]. They proposed an improved anisotropic diffusion model which can adaptively adjust the implication of the image edge gradient in the diffusion process. More research studies about defect detection and image denoising related to other techniques refer to some recent papers [21; 22; 23; 24; 25]. Based on the above, it can be found most research focuses on designing novel diffusion patterns or using the observed information in new schemes. However, in complex scenes, inconsistent background and features should be properly treated for efficient defect detection. The gradient, Laplacian and some other geometric metrics are often used to describe the observed image features, and they are easily disturbed by an uneven background. In this paper, a novel hybrid energy based on L1 and L2 is introduced to a constrained optimization problem. Then the corresponding Euler-Lagrange equations are derived to form a nonlinear diffusion with a smoothed background estimation, which can remove the noise and preserve more details of the defect features. This paper is organized as follows. In Section 2, the basic fundamentals of defect detection and nonlinear diffusion are introduced. Then the proposed model based on a novel hybrid functional is introduced in Section 3; the algorithm and numerical scheme are also presented. Section 4 shows the experiments and results.
## 4 Case Study
### Discussion
#### 4.3.3 Limitation of UX Quantification  
Basically, quantifying UX causes loss of information. In this case, user descriptions were transformed into numerical and relational information. With respect to the amount of information, a certain amount of information is unavoidably left out during transformation. As experiences of users are diverse, most UX research has the same limitation.  
Of course, even if the amount of information conveyed by a network analysis is small, that information seems to be highly relevant to decision making. Moreover, a value-structured method might be expected to lose less information than traditional usability or UX quantification methods in terms of enhancing the diversity of assessment dimensions.
# On the Needs and Requirements Arising from Connected and Automated Driving
## 4 Technical Requirements: from Automotive to Network Perspective  
Two types of requirements need to be considered when discussing connected automated driving: automotive requirements and network requirements. Automotive requirements stem from the use cases that need to be enabled and relate to various functional aspects of those use cases. The network requirements specify the required performances of the communication system, which shall be tightly coupled with the automotive requirements.  
In addition to the automotive and network requirements, there are other qualitative or functional requirements that are relevant for practical vehicular applications. Examples of such requirements are as follows:  
* Power consumption: represents the amount of power that a specific application consumes. Power consumption is generally not an issue for applications executed within vehicles, unlike the case of other types of mobile devices (e.g., mobile phones).
* Cost: in the case of vehicular applications, cost can be expressed in terms of the monetary cost or, alternatively, in terms of network resources that are required to execute the application.
* Safety: relates to the capability of vehicular applications to reduce the probability of hazardous events and the ability to bring the system always back to a failsafe state.
* Security: relates to the methods that are implemented in the application to prevent unauthorized users from accessing the application itself or the information generated by it.
* Privacy: relates to the methods implemented in the application to protect the sharing of user's private information. This includes, for example, preventing tracking the user real-time location and trajectory.  
In the following subsections, we first define the automotive and network KPIs; then, we discuss on the interrelations between them. Finally, we elaborate a requirement analysis for five particular use cases that have been studied in detail within the 5 GCAR project.
## 3 Experimental Results and Analysis
### Skeleton Curve  
Figure 9 shows the skeleton curves for each specimen. From Figure 9, it can be seen that:  
(1) The ultimate load-carrying capacity of specimen W-5 is the strongest and reaches the peak load first. The ultimate load-carrying capacity of specimen W-3 is the smallest.  
(2) The ultimate displacement of specimen W-2 is the largest and the falling section is gentler, which has better ductility and best meets the design requirements of structural seismic resistance.  
(3) Comparing the skeleton curves of W-1 and W-2, the deformation capacity and load-carrying capacity of the specimen were improved after adding 0.3% volume doping of fibers. But, comparing the skeleton curves of W-1 and W-3, the deformation capacity and load-carrying capacity of the members were decreased after adding 0.4% volume doping of fibers.
## 4 Simulation Results
### Analysis on Half-Intensity Radiation Angle  
As shown in Figure 13, we studied the effect of the half-intensity radiation angle \(\phi_{1/2}\) on the average system throughput. The proposed algorithm outperforms the RDR-PA method and the uniform method. The four curves follow the same trend and the average system throughput decreases as \(\phi_{1/2}\) increases. This is because according to Equation (1), the first derivative of Lambertian emission order \(m\) with respect to \(\phi_{1/2}\) satisfies \(m^{\prime}(\phi_{1/2})=-\ln 2\cdot\tan\phi_{1/2}/\ln^{2}(\cos\phi_{1/2})\), which is less than 0. Therefore, an increase in \(\phi_{1/2}\) will cause a decrease in \(m\) and a reduction in the resource utilization efficiency. Compared with the RDR-PA method, the performance improvement ratio is stable at 57%.  
In Figure 14, the average ratio of satisfied terminals is shown as a function of \(\phi_{1/2}\).The performance decreases as \(\phi_{1/2}\) increases. When \(\phi_{1/2}\) increases from 50\({}^{\circ}\) to 70\({}^{\circ}\), the average ratio of satisfied terminals of the proposed algorithm decreases from 0.232 to 0.23 with a decrease ratio of only 1%. Furthermore, the performance of the RDR-PA method decreases from 0.208 to 0.14 with a decrease ratio of 32.9%. Compared with the RDR-PA method, the improvement ratio of the proposed algorithm increases from 11.4% to 64.6%, which indicates the proposed algorithm has a better QoS guarantee.  
Figure 14: The average ratio of satisfied terminals versus half-intensity radiation angle.  
Figure 13: The average system throughput versus half-intensity radiation angle.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture  
The security architecture in CPSs is commonly divided into three stages: protection, detection, and mitigation [28]. On the other hand, reliability and resilience ought to be considered throughout the entire smart grid framework. In this survey, these incidents are considered among all the aspects of cyber-physical attacks included in the previous section.  
In a similar context, developing a secure and resilient smart grid architecture can be classified into secure communication channels (protection, prior to an incident), state estimation (detection, during an incident), detecting malicious attacks (detection, during an incident), and contingency response (mitigation, after an incident).
# Removal of Cu(II) from Aqueous Solutions Using Amine-Doped Polyacrylonitrile Fibers
## 4 Conclusions  
PAN fibers modified with DETA were used as adsorbents for the removal of Cu(II) from aqueous solutions. The surface-modified PAN fibers that were treated with 85% DETA presented the highest Cu(II) adsorption capacity. The adsorption of Cu(II) followed the pseudo-second-order kinetic model, and the equilibrium data fit the Freundlich isotherm model, which indicated that the adsorption was a multilayer process that occurred on a heterogeneous surface. The maximum adsorption capacity of 87.77 mg/g was achieved at pH 4. The percent removal of Cu(II) was increased from 49.63% to 97.63% as the adsorbent dosage increased from 0.02 to 0.1 g. The surface-modified PAN fibers could be regenerated with NaOH solution. These results indicated that the surface-modified PAN fibers could be used as adsorbent for the removal of Cu(II) from aqueous solutions.  
**Author Contributions:** Conceptualization, methodology, formal analysis, writing--original draft preparation, K.T.A. and C.-G.L.; formal analysis, validation, visualization, S.-H.H.; conceptualization, conceptualization, supervision, writing--review and editing, S.-J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (Grant no. NRF-2017R1D1A1B03030649) and by the National Research Foundation (NRF) of Korea (Grant no. NRF-2018R1C1B5044937).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 9: Cu(II) adsorption dependence on number of regeneration cycles. Here, q denotes the adsorption capacity.
Without the use of a neutralizer, the spacecraft would build up a negative charge, causing ion back stream phenomena that would reduce the thrust and damage the satellite. A representation of the components and the working principle of a GIT is shown in Figure 1.  
In the literature, there are many gridded ion thrusters, but, obviously, they do not cover all of the powers or thrusts that a generic space mission could require, particularly at very low powers (e.g., LUMIO [15] and M-ARGO [16] missions requirements). So, having a tool for the preliminary design of GITs could be very useful in the preliminary phase of a space mission design to have an idea of the performances and working parameters of this type of thruster (e.g., specific impulse) and, eventually, to exploit the data obtained from the tool as a starting point for the design of a specific thruster for that mission. The literature is lacking as regards tools for the preliminary design of gridded ion thrusters, but there are many analysis tools--analytic models describing the physics of GIT (e.g., [13; 17; 18; 19; 20; 21])--that can be used to build a preliminary design tool, as well as parametric design approaches for ion thrusters (e.g., [22]). One of the most complete models has been proposed by [PERSON] and [PERSON] [13], which takes into account ion confinement, primary electrons thermalization and confinement, the anode sheath, and the effects of the hollow cathode. This model calculates the performances and working parameters of the ion thruster starting from a known discharge geometry, magnetic field design, voltages, grid geometrical parameters, and cathode voltage drop and is valid for a ring cusp configuration of the magnetic field in the chamber. The purpose of this work has been to exploit analytic models, analysis tools, and data from known GITs belonging to the medium-low-power class (\(<\)5 kW i.e., the interest in miniaturizing electric engines has recently grown) to develop a preliminary design tool for gridded ion thrusters that takes the target thrust (or power) in terms of input and depends on the space mission requirements. Therefore, a simple tool characterized by low computational cost that is thus rapid and reliable has been successfully developed to design medium-low-power gridded ion thrusters. The following sections will describe the tool and will show its validation in an attempt to match known GIT data (e.g., NSTAR [23], XIPS [24; 25]); some of the results will be also compared with those obtained with a design tool that makes use of a simple macroscopic plasma-source simulation (SMPS) code and a genetic algorithm (GA) [26]; finally, it will be demonstrated that the tool can be used to design very low-power (100 W) thrusters (2 mN of thrust) that could enable new types of space missions (e.g., LUMIO [15] and M-ARGO [16]).
## 2 Methodology  
### Characteristics of the Research Objects  
The Kartuzy lake complex is situated in Kashubian Lakeland in the town of Kartuzy [25] (Figure 1).  
Mielenko (1.9 m) and Karczemne (3.2 m) are shallow, polymeric lakes that mix to the bottom throughout the year. The basins of both lakes are semicircular (DI--0.68, DI--0.62) and poorly seated in the ground, which is depicted by their relative depths: 0.0068 and 0.0050. The following two reservoirs, Klasztorne Male (20.0 m) and Klasztorne Duze (8.5 m) are classified as holomatic lakes that circulate twice a year. The basin of Klasztorne Male Lake, with a shape that resembles a paraboloid DI (depth indicator) (Table 1). In contrast, the basin of Lake Klasztorne Duze, which is shallow, even, and resembles a hemisphere (DI--0.59), is moderately seated in the ground, which is reflected in the value of relative depth at 0.0112 (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Mielenko** & **Karczemne** & \begin{tabular}{c} **Klasztorne** \\ **Male** \\ \end{tabular} & \
\begin{tabular}{c} **Klasztorne** \\ **Duze** \\ \end{tabular} &
\begin{tabular}{c} **Klasztorne** \\ **Duze** \\ \end{tabular} \\ \hline Geographical & 54°19′55′′ N & 54°19′42′′ N & 54°20′21′′ N & 54°20′52′′ N \\ coordinates & 18°10′55′′ E & 18°11′27′′ E & 18°11′35′′ E & 18°12′10′′ E \\ Height above & & & & \\ mean sea level & 204.0 & 207.3 & 203.0 & 202.3 \\ (m AMSL) & & & & \\ S (surphace) (ha) & 7.8 & 40.4 & 13.7 & 57.5 \\ D\({}_{\text{max}}\) (maximum  depth) (m) & 1.9 & 3.2 & 20.0 & 8.5 \\ D\({}_{\text{mean}}\) (mean  depth) (m) & 1.3 & 1.98 & 8.1 & 4.8 \\ D\({}_{\text{relative}}\) (relative  depth) & 0.0068 & 0.0050 & 0.054 & 0.0112 \\ DI (depth index) & 0.68 & 0.62 & 0.40 & 0.59 \\ V (volume) (m\({}^{3}\)) & 102,900 & 798,300 & 1,106,000 & 2,780,000 \\ L\({}_{\text{max}}\) (maximum  length) (m) & 460 & 1282 & 720 & 1320 \\ W\({}_{\text{max}}\) (maximum  width) (m) & 252 & 445 & 250 & 570 \\ EI (elongation  indicator) & 1.8 & 2.9 & 2.9 & 2.3 \\ Length of  shoreline (m) & 1314 & 3163 & 1850 & 4100 \\ SDI (shoreline  development  index) & 1.3 & 1.4 & 1.4 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Morphometric data of studied lakes [26].  
Figure 1: The position of Kartuzy lake complex in Poland and Europe.