esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup  
For our experiments, microtools were fabricated by wire electro-discharge grinding (WEDG) [24; 25]. Figure 1a shows a WEDG system and a schematic of region A. PCD rods containing diamond grains of 10 \(\upmu\)m were used. A PCD rod of \(\omega\) 1 mm was clamped in a spindle and connected to a cathode as a workpiece. A brass wire was supported on a guide and connected to an anode as an electrode tool. The rod and wire were immersed in EDM oil. One hundred volts was applied between the rod and wire, and the rod was slowly fed in the z direction during drilling. The rod was shaped into a smaller rod of \(\phi\) 300 \(\upmu\)m by means of a few operations, including rough and finishing cuts [18]. The EDM conditions are summarized in Table 1.  
The PCD rod was used as a microtool for microdrilling. The EDMed surface of the tool is rough and covered with craters of several micrometers created by sparks, as shown in Figure 1b. The rough surface serves as an abrasive in the grinding process; numerous craters consisting of diamond grains act as cutting edges [22].  
Figure 2 shows a schematic of the microdrilling experiment. A PCD tool is fixed in a high-speed spindle mounted on the z-axis. The maximum rotational speed is 60,000 rpm. The fused silica used as a workpiece was placed on the dynamometer (9256C2, Kistler Instrumente AG, Winterthur, Switzerland), which was mounted on the x-y stage. Three components of drilling force were measured by a dynamometer in order to analyze drilling performances. The experiments were carried out with the drilling parameters of the axial feedrate ranging from 2 \(\upmu\)m/s to 30 \(\upmu\)m/s at a rotational speed of 50,000 rpm. The drilling conditions are summarized in Table 2. The machined holes and their surfaces were observed by means of scanning electron microscopy (SEM).  
\begin{table}
\begin{tabular}{l l} \hline
**Tool Electrode** & **Brass Wire** \\ \hline Workpiece & PCD (\(\phi\) 1 mm) \\  & Grain size 10 \(\upmu\)m \\ \hline Voltage (V) & 100 \\ \hline Capacitance (pF) & 5600\(\sim\)770,000 \\ \hline Dielectric fluid & EDM oil \\ \hline \end{tabular}
\end{table}
Table 1: EDM conditions.  
Figure 1: Microtool fabrication: (**a**) WEDG system and (**b**) fabricated microtool.  
Figure 2: Schematic illustration of the microdrilling experiment.
# Attenuating Oxidative Damage with Macelignan in Glutamate-Induced HT22 Hippocampal Cells
## 2 Materials and Methods
### Cell Viability Assay  
An EZ-Cytox test kit (DoGenbio, Seoul, Korea) was used to assess the viability of cells on the basis of the manufacturer's guidelines. In short, 96-well plates were seeded with HT22 cells at a density of 1 \(\times\) 10\({}^{4}\) cells/well. After being incubated for 24 h to adhere, the cells were only treated with either macelignan (1, 5, 10, or 20 uM) or NAC (a positive control; 0.5-2 mM) to measure the cytotoxic effect by the sample initially. Later, to identify the cell viability of macelignan in damaged HT22 cells, glutamate (5 mM) was added after a 2 h pretreatment of macelignan, and the cells underwent another 24 h incubation. After that, 10 uL of EZ-Cytox solution was added to the cells. An E-Max assay reader (Molecular Devices, USA) was used to obtain the absorbance at 450 nm. Changes in morphology were imaged using a fluorescence microscope (IX50; Olympus, Tokyo, Japan).  
Figure 1: Chemical structure (**a**) and DPPH scavenging activity of macelignan (**b**). Value represents the mean \(\pm\) S.D. (\(n\) = 4). Mac, macelignan.
# Design and Implementation of 24-GHz and 48-GHz VCOs Using Noise Filtering Technique in 90-nm CMOS
## 2 Design and Analysis
### The Proposed 24-GHz VCO  
The schematic of 24 GHz VCO with low phase noise is shown in Figure 2. This circuit is a voltage-controlled oscillator composed of CMOS architecture, \(M_{\text{N3}}\), \(M_{\text{N4}}\) and \(M_{\text{p1}}\), \(M_{\text{p2}}\) are the cross-couple pair, and the LC resonant cavity is composed of \(L_{1}\) and \(C_{\text{v1}}\). The capacitance change of the variable capacitor in the circuit mainly determines the overall tuning range of the VCO. However, for the circuit to oscillate stably, the negative impedance of the cross-coupled pair needs to offset the positive impedance of the resonant cavity. Therefore, the negative impedance of this voltage-controlled oscillator is created by the cross-coupling of the NMOS and PMOS to the transistor.  
The NMOS current mirror can use voltage to limit the size of the tail current, limiting the total power dissipated by the voltage-controlled oscillator. The channel width ratio of the transistors \(M_{\text{N1}}\) and \(M_{\text{N2}}\) is 16:1. However, the current mirror transistor in the tail current has a nonlinear effect. When the transistor operates in the nonlinear region, noise will be introduced. The second harmonic signal is mixed with the fundamental frequency signal, and the signals are multiplied to produce a signal whose frequency is close to the oscillation frequency, and this new signal will transfer the 2\(f_{0}\) noise to near the oscillation frequency, reducing the phase noise of the circuit [4]. Therefore, a set of LC filters with a  
Figure 1: Cross-section of a standard 90-nm CMOS process.  
resonant frequency at \(2f_{0}\) are added to the design of this section to filter the noise at \(2f_{0}\) through the capacitor to the ground. The inductor provides a high impedance of the circuit near the resonant frequency and blocks the passage of the \(2f_{0}\) signal. The function of the capacitor is to compose a resonant circuit with the inductor [5]. Finally, open-drain buffers \(M_{\text{N5}}\) and \(M_{\text{N6}}\) are used at the output, and bias Tee is used externally, which can be used to isolate RF and DC signals and enhance the output power.  
Figure 3 is the simulated output spectrum of the voltage-controlled oscillator after the implementation of the buffer amplifier. The output oscillation frequency is 23.63 GHz and the output power is 1.58 dBm when Vctrl is 0.6 V, while the frequency range is from 22.8-24.15 GHz, as shown in Figure 4, and the tuning range is 5.45%. Figure 5 shows the simulated results of phase noise after adding a noise filter circuit. The phase noise simulation result is \(-95.8\) dB\(\text{C}/\text{Hz}\) at a carrier offset of 1 MHz. Obviously, the phase noises are improved by about 8.3 dB by adding the noise filter circuit.  
Figure 3: Simulated output spectrum.  
Figure 2: Schematic of the proposed CMOS 24 GHz VCO.
## 2 Methodology
### Robustness Computation Method
#### 2.4.1 Introduction of the Auxiliary Function  
Before the introduction and definition of the auxiliary function, the parameters in the computation are explained as follows. The evaluation index is expressed as \(R(\textit{Cropping},\textit{S},\textit{L},\textit{N})\). Here \(S\) represents the cropped data percentage and the threshold value of \(\textit{BER}\) is set to \(r\). According to Principle 1, the total embedded watermark bits are \(L\times N\). From Principle 3 and Principle 4, the process of cropping images is randomly deleting certain number bits of watermark information. Based on principle 2, the operation of cropping \(S\) percentage of remote sensing image is equal to deleting \(S\) percentage of embedded watermark bits. To simplify the computation, the number of cropped watermark bits, denoted as \(D\), is calculated as the expected value, which is \(D=L\times N\times S\). According to the \(\textit{BER}\) calculation method and watermark synchronization, the watermark bit \(wm[i]\) will be properly extracted if any embedded watermark bits corresponding to \(i\) remain correct.  
The auxiliary function \(P(\textit{L},\textit{N},\textit{D})\) is then introduced as the count of all enumerations of any watermark bit is not ruined totally when deleting \(D\) watermark bits from watermark information length \(L\) embedded over \(N\) times. Figure 7 illustrates an example belonging to the auxiliary function \(P\), and Figure 8 shows an example not belonging to it as two watermark bits are totally cracked.  
Figure 7: An example belongs to enumerations of function \(P\).  
In Figure 7, all the blocks represent extracted watermark bits, where the green one means that the remaining watermark bit has not changed after the attack. The red one represents the cropped bits. In this case, 30 bits have been cropped but watermark information can still be extracted correctly providing any column in the matrix has not been cropped totally. However, the situation in Figure 8 does not belong to function \(P\) as two watermark bits are both deleted, the watermark information extracted from this situation will lose the two bits permanently.
# Three-Dimensional Modeling of the Xichang Crust in Sichuan, China by Machine Learning
###### Abstract  
Seismicity and distribution of earthquakes can provide active fault structural information on the crust at a regional scale. The morphology of faults can be derived from the epicentral distribution of micro-earthquakes. In this study, we combined both the relocated earthquake catalogue and related preliminary geophysical information for 3D modeling of the crust in the Xichang area, Sichuan province, China. The fault morphology and deep crustal structure were automatically extracted by the machine learning approach, such as the supervised classification and cluster analysis methods. This new 3D crustal model includes the seismic velocity distribution, fault planes in 3D and 3D seismicity. There are many earthquake clusters located in the folded basement and low-velocity zone. Our model revealed the topological relation between the folded basement and faults. Our work show the crustal model derived is supported by the earthquake clusters which in turn controls the morphological characteristics of the crystalline basement in this area. Our use of machine learning techniques can not only be used to predict the refined fault geometry, but also to combine the seismic velocity structure with the known geological information. This 3D crustal model can also be used for geodynamic analysis and simulation of strong motionoseismic waves.  
fault morphology; seismic velocity structure; supervised classification; clustering analysis; geological modeling +
Footnote †: journal: Applied Sciences  
0000-0002-2300-2300-2300-2300-2300-2300-2300-2300-2300-2300-2300-2300-2300-2300-2300-2300-23000-23000-23000-23000-23000-23000-23000-23000-2  
are difficult to process the current observation data efficiently, and some Machine Learning (ML) algorithms have been widely used in Geoscience [20; 21]. For example, automatic detection of rupture using source location [22], earthquake relocation [23], automatic acquisition and application of source mechanism [24; 25], seismic probabilistic hazard analysis [26], fault geometry based on source information [27; 28; 29; 30], etc. In addition, 3D visualization technology has also been deeply applied in structural geology, fault plane fitting, and the 3D structure of the lithosphere [31]. However, relatively few studies on automatic modeling are based on multiple data [4; 5]. Therefore, we plan to construct a crustal framework by combining velocity structure and relocated earthquake catalogue and related geophysical prior information. Then we will refine the crustal medium structure by local refinement with a combination of fault model and source model. Finally, we integrate the multi-source model is by generating the crustal medium structure model, This geometric model will then be used for numerical simulation of seismic wave propagation.
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 6 Results
### Ber Cdf  
Figure 6 exhibits a CDF graph of the BER computed in the received signals. It can be observed that having a peak power of 6 W, the probabilities of receiving a BER under \(10^{-2}\) were remarkably high (more than 90%), but also a good performance to have a BER of \(10^{-3}\), which is over 70%. With regard to the 12 W test, the SC-FDE showed very good results, obtaining probabilities of over 80% for BER \(10^{-3}\) and more than 90% for BER under \(10^{-2}\). Finally, for the same test with a peak power of 25 W, it can be observed that SC-FDE outperformed the other models, having probabilities of almost 90% to have BER \(10^{-3}\) and around 95% for BER under \(10^{-2}\). As a first conclusion, it can be stated that the SC-FDE provides high yields for low power, especially around 12 W.  
Figure 5: NVIS link between LaSalle Campus, BCN and Cambrils, Tarragona. The highest peak is located at 573 m above sea level.
## 3 Research Method
### AHP Analysis  
Based on the sewer pipe data near road cave-ins in downtown Korea, a correlation analysis was conducted to analyze the relationship with road subsidence. The results showed that pipe type, pipe diameter, pipeline age (year of installation), and drainage type demonstrated high correlations with road subsidence. The buried depth was excluded from the evaluation because approximately 95% of the pipes were buried within a depth of 2 m, and the Pearson correlation coefficient between depth and road subsidence was low (0.2). In addition, the state and maintenance of the surrounding roads were added as items that can indirectly affect pipeline integrity. This inclusion of data considered the opinions of experts (25 university professors and geotechnical researchers). With regard to the detailed evaluation, each item was divided into three levels based on the sewer pipe data near the road subsidence locations analyzed above. Then, the risk was calculated by assigning weights to each item through AHP analysis.  
Based on the above process, the study proposes a method to evaluate the risk of road subsidence using six factors that are highly correlated with road subsidence. As shown in Equation (6), the evaluation method calculates the impact factor that each influencing factor contributes to road subsidence. The sum of the influencing factor multiplied by the impact factor represents the risk.  
\[Factor_{1}\times IF_{1}+Factor_{2}\times IF_{2}+Factor_{3}\times IF_{3}+\ldots =SRI_{s} \tag{6}\]  
where \(Factor_{x}\): factor influencing road subsidence; \(IF_{x}\): impact factor; \(SRI_{s}\): road subsidence risk around the sewer pipe.  
It is necessary to quantify the impact of each influence factor on the road subsidence evaluation score around the sewer pipe to evaluate the risk of road subsidence using sewer pipe information. To establish the impact factor, the Analytic Hierarchy Process (AHP), a multi-criteria decision-making (MCDM) technique, was used. The AHP was employed to stratify the decision-making problem and measure the relative importance of the alternatives in terms of each evaluation criterion, as well as the relative importance of the evaluation criteria by pairwise comparison [17]. The AHP is a method that can systematize the group decision-making of experts and is useful for determining the relative magnitude of decision items. It is suitable for predicting the risk of road subsidence because it can collect and analyze the opinions of experts through a structured questionnaire and calculate the aggregated weights.  
The AHP technique has been widely applied in research on management techniques and their application in MCDM [18]. The AHP is a logical and convenient decision-making method that has both objective and subjective evaluation factors. Therefore, it is widely used in decision-making in various fields of research [19; 20; 21]. The AHP has also been used in civil engineering research to determine the age of water pipes [22] and prioritize river water quality improvement [23]. It has also been used in disaster prediction to analyze landslide risk areas [24].
## 3 Comparison Study on Experimental and Numerical Results
### Comparison Study
According to the comparison between the numerical and laboratory experiments, it can be seen that the temperature variation can be generally modeled by the particle flow modeling method provided by this research. Although there are some differences owing to the different test environments in the numerical and physical conditions, the general variation trends of the temperature value and distribution are quite similar. Furthermore, the particle flow model can be used to carry out a series of consistent studies on the mechanism, considering different factors. This will be discussed in the next section.
# Industrial IoT Monitoring: Technologies and Architecture Proposal
## 3 WSN Diagnostic Tools
### Hardware Tools  
Finally, a source of faults that diagnostic tools also commonly address is hardware faults. Hardware faults may occur before and/or after WSN deployment, and are due to one or more of several reasons, such as: bad hardware design, external environment phenomena, aging, and extreme temperatures. Hardware diagnostic tools can be divided in two groups: (1) external-physical-tools that are used by developers and operators; and (2) on-node-tools that infer hardware faults using available hardware resources.  
The first group of tools consists of physical tools/equipment that operators and developers use to check hardware condition and the occurrence of faults. Tools like oscilloscopes, multimeters, and logic analysers are connected to the target system in order to check the condition of the electronics and the existence of faults. These tools are frequently used during the development phase, where developers search for electronics defects and for communication problems between hardware modules (e.g., using logic analysers [77]).  
On-node-tools use a different approach. This type of tools is designed during the hardware and/or firmware development phase, with the specific purpose of collecting state information from the different modules using on-node components. In [78], the authors use the ARM CoreSight technology to create a WDP (Watch Dog Processor), by polling the main processor memory variables and setting conditions on these variables. Also using the same technology, in [79], the authors extend Hardware in Loop (HIL) tests, by incorporating some metrics provided by the CoreSight technology. Moreover, in [80], the authors propose a technique to read the energy wasted in boards that use switching regulators. By collecting these metrics, operators and developers are able to detect hardware faults that occur during the deployment phase without using external tools.
# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 7 Results and Discussion
### Experiments and Relate Analysis of Image Quality Evaluation  
#### 7.2.1 Live IQA Database  
We use the LIVE IQA database2 [38] to test the performance of ROI-BRISQUE method. The database consists of 29 reference images and 779 distorted images spanning various distortion categories, JPEG compresses images (169 images), JPEG2000 compressed images (175 images), Gaussian blur (145 images) and Rayleigh fading channel (fast fading 145 images). Further, each distorted image has an associated difference mean opinion scores (DMOS), which represents the perceived quality of the image.  
Figure 11: Comparison of different visual saliency models. (**a**) Original image; (**b–d**) salient overlap maps of GBVS model, Itti model and improved Itti model respectively; (**f–h**) salient maps of GBVS model, Itti model and improved Itti model respectively; (**e**) Salient map of free looking task; (**i**) salient map of scoring task.  
Figure 12: Comparison of different visual saliency models. (**a**) Original image; (**b–d**) salient overlap maps of GBVS model, Itti model and improved Itti model respectively; (**f–h**) salient maps of GBVS model, Itti model and improved Itti model respectively; (**e**) Salient map of free looking task; (**i**) salient map of scoring task  
In order to calibrate the relationship between the extracted features and the ROI, as well as DMOS, the ROI-BRISQUE method requires two subsets of images. We randomly divide the LIVE IQA database2 into two non-overlapping sets \(-80\%\) training and \(20\%\) testing. We do this to make sure that the experiment results do not depend on features extracted from known distortion images, which can improve performance of the ROI-BRISQUE method. Further, in order to eliminate performance bias, we repeat this random \(80\%\) train and \(20\%\) test procedure \(1000\) times and compute the mean value of the performance. In the following text, the figures reported here are the median values of performance across these \(1000\) iterations.  
In this paper, we use four commonly used performance indexes to evaluate the IQA algorithms. They are Spearman's rank ordered correlation coefficient (SROCC), Kendall rank order correlation coefficient (KROCC), Pearson's linear correlation coefficient (PLCC) and Root Mean Squared Error (RMSE). The first two indexes can measure the prediction monotonicity of an IQA algorithm. The other two indexes are computed after passing the algorithmic value through a logistic nonlinearity as in [47].
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
###### Abstract  
Ship detection is an essential maritime security requirement. Current state-of-the-art synthetic aperture radar (SAR) based ship detection methods employ fully focused images. The time-consuming processing efforts required to generate these images make them generally unsuitable for real time applications. This paper proposes a novel real time oriented ship detection strategy applicable to range-compressed (RC) radar data acquired by an airborne radar sensor during linear, circular and arbitrary flight tracks. A constant false alarm rate (CFAR) detection threshold is computed in the range-Doppler domain using suitable distribution functions. Detection in range-Doppler has the advantage that principally even small ships with a low radar cross section (RCS) can be detected if they are moving fast enough so that the ship signals are shifted to the exo-clutter region. In order to determine a robust threshold, the ocean statistics have to be described accurately. Bright target peaks in the background ocean data bias the statistics and lead to an erroneous threshold. Therefore, an automatic ocean training data extraction procedure is proposed in the paper. It includes (1) a novel target pre-detection module that removes the bright peaks from the data already in time domain, (2) clutter normalization in the Doppler domain using the remaining samples, (3) ocean statistics estimation and (4) threshold computation. Various sea clutter models are investigated and analyzed in the paper for finding the most suitable models for the RC data. The robustness and applicability of the proposed method is validated using real linearly and circularly acquired radar data from DLR's (Deutsches Zentrum fur Luft- und Raumfahrt) airborne F-SAR system.  
s 2019 11 1270 10.3390/rs11111270 10.3390/rs11111270
## 3 Results and Discussion  
### Mesh Independence Verification  
The mesh independence is verified for the selection of time step and grid size, and the regenerator parameters used in the simulations are listed in Table 1. Firstly, the selection of the time step is verified. Simulations with different time steps are performed under the condition of the fixed parcel grids, and the effects of the time step on the results are compared. The proper time step is the one beyond which further refinement has little effect on the results. Then the selection of the size of gas parcels is verified through simulations with a fixed time step, but different initial sizes of gas parcels. The proper grid is obtained when further mesh refinement does not improve the simulation results.