esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 4 DVB-T2/C2 Demodulator Architecture
### Variable FFT Unit  
The Radix-2 algorithm is known for small quantization noise and various sizes of FFT can be easily implemented. The Radix-4 algorithm has bigger quantization noise than the Radix-2 algorithm, and different lengths of FFT with their parameters cannot be easily implemented. However, the Radix-4 algorithm has an advantage in high-speed operations with fewer complex multipliers compared to the Radix-2 algorithm. The Radix-2\({}^{2}\) algorithm implements the Radix-4 algorithm with Radix-2 blocks [11]. It implements FFT efficiently because it shares the benefit of the Radix-4 algorithm where a small number of multipliers are used, and also shares the benefits of the Radix-2 algorithm, such as small quantization noise and easy implementation.  
FFT architectures can be classified into two categories such as pipeline architectures and memory-based architectures [12; 13; 14]. Memory-based architectures take smaller H/W than pipeline architectures. However, memory-based architectures require longer latency and a faster operating clock than pipeline architectures. Depending on various FFT modes, the diversity of data rates in the FFT core complicates the interface circuits between functional blocks. Pipeline FFT architectures reduce the latency and the power consumption, which makes them the most appropriate for DVB-T2 and DVB-C2. Pipeline architectures are divided into a multipath delay commutator (MDC), a single-path delay feedback (SDF), and a single-path delay commutator (SDC) in accordance with the method of exchanging input and output data at each stage.  
DVB-T2 requires the FFT operation from 1K up to 32K. It is effective to use Radix-22 SDF FFT when the performance of quantization noise and the efficient implementation of H/W are considered. To support various FFT sizes from 1K up to 32K, the Radix-22 SDF architecture is used for 1K FFT and the Radix-2 SDF architecture is used for expandable parts. Figure 11 shows the block diagram of the variable FFT.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 2 Study Area and Data
### Leaf Area Index Satellite Products
#### 2.2.3 GEOV2 LAI  
The CGLS 1 km V2.0, here called GEOV2, LAI product derived from SPOT/VEGFATION (for the period from 1999 to May 2014) and PROBA-V (from May 2014 to present) observations is produced globally at 10 days temporal resolution under lat-lon projection at \(1/112^{\circ}\) spatial resolution [9]. The GEOV2 LAI product capitalizes on existing CYCLOPES V3.1 [48] and MODIS Collection 5 [45] products and the use of NNs. The inputs of the NNs are daily top of the canopy (TOC) reflectances in the red, NIR and SWIR spectral bands and the associated view and sun geometry. The daily LAI outputs of NNs are then filtered, smoothed and gap filled using dedicated temporal compositing techniques to ensure consistency and continuity of the LAI time course every 10 days [9]. The GEOV2 LAI product uses a climatological approach to fill missing data [49].
###### Abstract  
Ni wires, prepared through a hydrazine reduction, were exposed to external magnetic fields of different geometrical shape and configuration during the synthesis denoted as Ni-Non-Magnetic, Ni-Double, Ni-Single, Ni-Ring. Their effect on the wire morphology, magnetization and magnetic anisotropy was then investigated via various characterization techniques viz. X-ray diffraction (XRD), high-resolution field emission scanning electron microscopy (HR-FESEM), and vibrating sample magnetometer (VSM). The polycrystalline single phase of the Ni-wires with face centered cubic symmetry was confirmed through the analysis of XRD patterns. Analysis of HR-FESEM images revealed that the Ni-particles were aligned in form of wire-like morphology. The Ni-single sample formed the wires with minimum diameter compared to the parent sample. The magnetization measurements performed at 300 K and 50 K demonstrated the ferromagnetic behavior of all the samples. The room temperature saturation magnetization (M\({}_{\text{S}}\)) and anisotropy constant (K) of the Ni-wires were reduced upon providing the external field during synthesis. However, the low temperature (50 K) magnetization behavior was rather opposing, indicating enhanced values of M\({}_{\text{S}}\) and K. Among all, Ni-ring sample showed maximum anisotropy with a value of 3.84 \(\times\) 10\({}^{4}\) erg/cm\({}^{3}\) at 50 K. The ambiguous nature of the anisotropic constant and saturation magnetization ascribed partly to the variation in diameters of Ni-wires and to the intermittent spin-spin exchange interactions of unaligned/partially aligned particles during the synthesis. Briefly, in the present study, it was established that the morphology and magnetic anisotropy of the Ni-wires could be tailored through the external magnetic field assisted synthesis method.  
Ni-wires; shape of magnetic field; magnetic anisotropy; surface anisotropy +
Footnote †: journal: Physics Letters B
## 4 Simulation Results and Analysis  
In this section, simulation experiments and result analysis are used to verify the effectiveness of the designed NDI controller to ensure that the system can track the given command. Figure 5 is the LINK-BOX semi-physical simulation platform. Links-Box automatically converts the MATLAB simulation models to the embedded control prototype and supports engineering hardware to test the models. The physical device can be directly connected to the rapid prototyping simulator to dynamically verify controller performance. The features of the software package Links-RT are: (1) adapting to the models built in MATLAB; (2) providing input and output hardware to enable users to integrate the hardware environment into the simulation models; (3) automatic conversion of MATLAB model codes to VxWorks codes.  
The simulation conditions and partial control parameters are shown in Table 2. The simulation time is 20 s.
# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 5 Hybrid Edge-Cloud-Based Smart System for Chatter Detection and Suppression  
The complete solution required processing algorithms on both edge and cloud. Therefore, a hybrid edge and cloud platform was required to develop the smart system for chatter detection and suppression.  
Considering the tests carried out in Section 3, process damping and CSSV techniques were determined to be, in principle, the most appropriate techniques to remove chatter vibrations, since the ratio of the chatter frequency and the tooth passing frequency lay in Zone A (Figure 5; \(k\) = 207 for the portal lathe case) of the stability diagram, which is called the process damping zone [10; 18]. Both techniques were tested for the selected case to verify their effectiveness and learn about the vibration signature of the cutting process. The hybrid edge-cloud-based smart system for chatter detection and suppression was based on the computation of signals obtained by the accelerometers for chatter detection, and the smart combination of the aforementioned two techniques to suppress it.
## 4 Conclusions  
Training a CNN to classify a burn dataset is a difficult process because of the scarcity of images due several reasons such as privacy issues. For this reason, the concept of transfer learning, which involves reusing learnt weights of a CNN model trained on a different large dataset, can be used to solve another similar problem with deficient datasets. Transfer learning can used in two different ways: fine-tuning and feature extraction. The latter was used to extract off-the-shelf features, and DT, SVM, NB, LR, and KNN were trained independently to classify the features. Moreover, each classifier was trained using two different features extracted with the two different VGG16 layers.  
In this study, it was found that some classifiers performed better when features were extracted from a specific CNN layer while others returned poor performances. These performances were analysed using different performance evaluation measures such as accuracy, time, ROC curve, AUC, and PRC. Comparatively, NB and LR achieved better classification results with FC1 features, while DT, SVM, and KNN achieved good classification results with FC2 features. This was reflected in Table 1 for accuracy and in Figures 6 and 7.  
Furthermore, this study discovered a great correlation between accuracy and training time for each classifier. DT was more efficient in terms of accuracy and training time when trained with FC2 features, where it achieved 88.93% accuracy in 75.78 s compared to 88.86% accuracy in 104.75 s using FC1 features. SVM achieved 96.19% accuracy in 321.11 s using FC2 features compared to 95.83% accuracy in 323.99 s using FC1 features. KNN achieved 87.38% accuracy in 108.92 s using FC2 features compared to 82.44% accuracy in 117.09 s using FC1 features. On the other hand, the NB classifier recorded 92.32% accuracy in 2.91 s using FC1 features compared to 91.85% accuracy in 3.06 s using FC2 features, and LR achieved a classification accuracy of 96.07% in 26.10 s using FC1 features compared to 96.01% accuracy in 26.47 s using FC2 features. This study can be extended further with different pretrained CNN models, as well as using a multi-class classification problem, particularly for different degrees of burns.  
This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: PRC comparing performances of the classifiers using 4096 FC2 features of the VGG16 model.
## 2 Data and Methodology
### Verification of Simulation Results
#### 2.3.2 Verify Performance for WRF Model
The correlation coefficients for rainfall closely align with the published values. Thus, the calculation results \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{2}{c}{**R**} & \multicolumn{2}{c}{**MBE**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{2-7}  & **Precipitation** & **Wind Speed** & **Precipitation** & **Wind Speed** & **Precipitation** & **Wind Speed** \\ \hline Case1 & 0.27 & 0.51 & 0.01 & 1.21 & 1.41 & 1.71 \\ Case2 & 0.40 & 0.45 & 0.15 & 2.38 & 1.42 & 3.20 \\ Case3 & 0.51 & 0.54 & 0.10 & 1.32 & 1.07 & 1.80 \\ Case4 & 0.34 & 0.33 & 0.63 & 0.90 & 3.02 & 0.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of results of different schemes. Figure 6: Comparison of simulation results in Kangding. (**a**) Comparison of precipitation result; (**b**) comparison of wind speed result. obtained in this study can be utilized to assess the susceptibility of drifting snow, providing valuable insights. We performed linear fitting on the calculation results. The closer the points in the figure are to the diagonal line (the black dash line in figure), the closer the simulation results are to the real results. The brighter the color of the points, the more points fall in that area. Figure 7 shows the fitting results of temperature and wind speed calculation data and their changes over time. It can be found that the model can capture the temperature changes well. The simulated temperature of Kangding station is concentrated within 0 to 5 \({}^{\circ}\)C, and the temperature of Litang station is concentrated within \(-\)5 \({}^{\circ}\)C to 5 \({}^{\circ}\)C. The WRF simulation results of wind speed for both places are concentrated in 1-2 m/s. The simulation results of Litang station are higher than the actual recorded values; however, the WRF model accurately captured the change trend and extreme values of wind speed at both stations. Figure 8 shows the comparison between the rainfall calculation results and the actual results. The WRF model successfully captured the rainfall events. In summary, although there is some error between the data simulated by the WRF model and the actual data, the correlation of the simulation results is good, and it can accurately capture the change trend of the data, which also verifies the effectiveness of the simulation method.
## 2 Materials and Methods
### Statistical Analysis
#### 2.5.2 Reliability  
Within and between session reliability of the Enode was assessed using the standard error of measurement (SEM) and Interclass Correlation Coefficient (ICC; two-way random with absolute agreement), where the ICC was determined using MedCalc with SEM calculated in a custom spreadsheet. The ICCs were rated based on the guidelines suggested by [PERSON] and [PERSON] [31] using the 95% CI boundary, where descriptors of 'poor' (<0.5),'moderate' (0.5-0.75), 'good' (0.75-0.9), and 'excellent' (>0.9) were used. Once relative reliability was established, SEM was determined to assess absolute interunit reliability from the mean of each variable, where SEM was calculated as the product of the SD of the pooled mean values and the square root of \(1\) minus the ICC [32].  
\[\text{SEM}=\text{SDpool}\times\sqrt{1-ICC} \tag{6}\]  
Using the SEM, the smallest detectable difference (SDD) was calculated as  
\[\text{SDD}=\left(1.96\times\sqrt{2}\right)\times\text{SEM}. \tag{7}\]  
In practice, if the difference between two units \(\pm\) SDD is identified, this would indicate that a meaningful change outside of the error of the test-retest has occurred [33]. Hedges' g effect sizes were also calculated using a custom spreadsheet to analyze both within and between session differences of both the Enode and criterion. The effect size values and descriptors were interpreted using the conventions outlined by [PERSON] [34]: 'trivial' (<0.20),'small' (0.21-0.50),'moderate' (0.51-0.80), and 'large' (>0.80).
## 4 Discussion
### Limitations of This Study  
Since this study developed a prototype of the EMG-controlled system, at present, all the EMG signals need to be transmitted to the PC and then calculated to generate a certain angle value, which is then sent to the CPM device to drive the motor. This could be improved in the future, as a range of complicated processes could cause time delays when using that as a rehabilitation device. Additionally, the angle was calculated using a general root mean square equation. That equation could be improved for a more accurate angle. Further, this proposed device employed the Delsys Trigno(tm) system for the analogue signal source, and the latter could be replaced by a self-developed part in the future, although some issues exist. These limitations really indicate the research directions in the future.
# Dynamic Impedance Estimation: Challenges and Considerations
## 4 Dynamic Impedance Estimation
### Impedance Estimation at PCC  
The impedances in the Norton model of Figure 10 may be estimated using ICA [11; 17; 18; 41]. The main reason for focusing on this technique is that it is a promising technique to be extended to the more general estimation of the transfer function.  
The ICA's basic concept for impedance estimation in electrical systems consists of measurements made in the electrical system, identifying the individual harmonic sources, and the mixing matrix, which approximates the network's harmonic transfer matrix. Equation (5) represents the generic form of ICA. In this equation, \(\mathbf{x}\) is the measurement vector composed by currents and voltages at various points in the network, \(\mathbf{s}\) is the vector of the harmonic sources present in the system, but generally unknown, and \(\mathbf{A}\) is the mixing matrix.  
\[\mathbf{x}=\mathbf{As} \tag{5}\]  
To estimate impedances seen from the PCC, the measurement vector is composed of the current and voltage at PCC, taken a long time. The ICA technique then explores these signals' statistical nature to determine both the individual sources and the mixing matrix. Then the ICA identifies the inverse matrix of \(\mathbf{A}\), called decoupling matrix, as described in [11]  
\[\mathbf{\hat{s}}=\mathbf{H}\mathbf{x} \tag{6}\]  
where, \(\mathbf{\hat{s}}=\left[\begin{array}{cc}\hat{I}_{s}&\hat{I}_{L}\end{array} \right]^{T}\) is the vector of the estimated sources.  
As known from the literature, the ICA has scale indetermination, i.e., the sources are estimated with a scale factor,  
\[\left[\begin{array}{c}\hat{I}_{s}\\ \hat{I}_{L}\end{array}\right]=\left[\begin{array}{c}k_{1}\hat{I}_{s}\\ k_{2}\hat{I}_{L}\end{array}\right] \tag{7}\]  
Figure 10: Norton equivalent circuit for impedance estimation.  
Figure 9: A small section of a real transmission system.  
Taking (7) in (6) results,  
\[\left[\begin{array}{c}\hat{I}_{s}\\ \hat{I}_{L}\end{array}\right]=\left[\begin{array}{cc}\frac{h_{11}}{k_{1}}&\frac {h_{12}}{k_{1}}\\ \frac{h_{12}}{k_{2}}&\frac{h_{22}}{k_{2}}\end{array}\right]\left[\begin{array} []{c}\dot{V}_{\text{PCC}}\\ \dot{I}_{\text{PCC}}\end{array}\right] \tag{8}\]  
To overcome the scaling indetermination given in (8), consider the equation of the Norton equivalent circuit shown in Figure 10,  
\[\left[\begin{array}{c}\dot{V}_{\text{PCC}}\\ \dot{I}_{\text{PCC}}\end{array}\right]=\frac{1}{Z_{s}+Z_{L}}\left[\begin{array} []{cc}Z_{s}Z_{L}&Z_{s}Z_{L}\\ -Z_{s}&Z_{L}\end{array}\right]\left[\begin{array}{c}\dot{I}_{S}\\ \dot{I}_{L}\end{array}\right] \tag{9}\]  
Taking the inverse of (9) and comparing with (8), one arrives at the final Norton impedance estimation, without scale indetermination:  
\[Z_{s}=\frac{-h_{12}}{h_{11}} \tag{10}\]  
\[Z_{s}=\frac{-h_{22}}{h_{21}} \tag{11}\]
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 3 Methods
### Error Analysis  
Error analysis of the annual night-light images of Cambodia, pre-processed by denoising and other means, was conducted using version 1 of the 2015 Nightlight Image of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Year** & **Cloud-Free Months** \\ \hline
2012 & 12 \\
2013 & 1, 2, 3, 4, 10, 11, 12 \\
2014 & 1, 2, 3, 11, 12 \\
2015 & 1, 2, 3, 4, 5, 9, 11, 12 \\
2016 & 1, 2, 3, 4, 5, 11, 12 \\
2017 & 1, 2, 3, 4, 12 \\
2018 & 1, 2, 3, 4, 10, 11, 12 \\
2019 & 1, 2, 3, 4, 10, 11, 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cloud-free months in night-light images of Cambodia, 2012–2019.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Regression Models** & **Mathematical Equation** \\ \hline Linear & \(y=ax+b\) \\ Power & \(y=ax^{b}\) \\ Exponential & \(y=ae^{bx}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The three regression models used in this study.  
Year from the Payne Institute. This image has noise removed, including from auroras, fires, ships, and other temporary light sources. The results obtained in this work were compared with the total night-light values of the official processed data by using the relative error (\(RE\)) to measure the quality of image processing, according to the following equation:  
\[RE=\left|S_{p}-S_{a}\right|\div S_{p}\times 100\% \tag{2}\]  
where \(S_{p}\) denotes the total night-light value of the 2015 Cambodian regional image provided by the Payne Institute and \(S_{a}\) denotes that of the 2015 Cambodian regional images processed by the present study. The smaller the \(RE\), the closer the image is to the Payne Institute's data and the better the processing quality.
# An NFC on Two-Coil WPT Link for Implantable Biomedical Sensors under Ultra-Weak Coupling
## 5 Experimental Results
### Tissue and Safety  
The in-body coil \(L_{\mathrm{i}}\) is sealed in silica gel and placed in tissue. The tissue increases its parasitic capacitance and decreases its self-resonated frequency and quality factor [26]. However, benefiting from the low frequency of the uplink carrier, the impact of the tissue is minor in our wireless link. As shown in Figure 16, the beef tissue induces negligible impact on the BER. According to the work in [27], the permittivity and the conductivity of the beef muscle tissue is close to those of the human muscle tissue when the frequency is about 2 MHz. As our in-body coil will be implanted around the eyeball, the tissues in the eyeball, especially the vitreous humor, need to be further studied. We simulate our inductive link where the in-body coil is sealed in silica gel with the detailed true-CAD human model in ANSYS HFSS [28], as shown in Figure 19a. The human model contains the tissues of skin, fat, muscle, eyeball and skull. The dielectric properties of the tissues are derived from [29]. Figure 19b shows the measured coupling coefficients when the in-body coil is in air or is covered by the beef tissue and the simulated coupling coefficients when the in-body coil is in air or is placed in the human body model over different coil separations. Figure 19b presents that the coupling coefficients with tissue are almost the same as the coupling coefficients measured/simulated in air. The tissues have negligible impacts on the coupling coefficients, because the low frequency magnetic field can pass through the tissues with little loss.  
Figure 18: The coupling coefficient and the power transfer efficiency versus (**a**) coil lateral misalignments and (**b**) coil angular misalignments when the coil separation \(d=20\) mm.  
Specific absorption rate (SAR) is an important metric to evaluate the radio frequency (RF) exposure safety on the human body. The ANSYS HFSS electromagnetic simulation suite was used to study the SAR. In the simulation results, the maximal local SAR in tissue is 1.7 \(\times\) 10\({}^{-3}\) W/kg in 10 g in our operating range. It is about three orders lower than the basic restriction 2 W/kg [30].  
In addition, power dissipated by the in-body coil and in-body circuits could also lead to the temperature increase in the human body. According to the comprehensive thermal analysis on an intraocular implant [31], our device with 5 mW power consumption may induce the maximum temperature increase of approximately 0.4 \({}^{\circ}\)C on the surface of the device. The temperature increase is below the guideline 1 \({}^{\circ}\)C in [30].
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## 4 Traffic Performance Analysis
### Bidirectional Communication  
#### 4.2.1 In EFA  
The number of network frames generated in the network when the source sends the first frame to the destination, denoted by \(nf_{EFA}^{1,send}\), is calculated as:  
\[nf_{EFA}^{1,send}=\sum_{i\in NR}2n_{i}-n_{d} \tag{11}\]  
The number of network frames generated in the network by the Locking message, denoted by \(nf_{EFA}^{lock}\), is calculated as:  
\[nf_{EFA}^{lock}=n_{d}+\sum_{i\in AQR}n_{i} \tag{12}\]  
After sending the Locking message, the destination DANH sends the first replied frame to the source DANH. The number of network frames generated in the network when the destination sends the first replied frame, denoted by \(nf_{EFA}^{1,reply}\), is calculated as:  
\[nf_{EFA}^{1,reply}=n_{d}+\sum_{i\in AQR}n_{i}+n_{s} \tag{13}\]  
The number of network frames generated in the network by sending the \(k\)th frame (\(k\geq 2\)), denoted by \(nf_{EFA}^{k,\ send}\), is calculated as:  
\[nf_{EFA}^{k,\ send}=n_{s}+\sum_{i\in AQR}n_{i}+n_{d} \tag{14}\]  
The number of network frames generated in the network by replying to the \(k\)th frame (\(k\geq 2\)), denoted by \(nf_{EFA}^{k,\ reply}\), is calculated as:  
\[nf_{EFA}^{k,\ reply}=n_{d}+\sum_{i\in AQR}n_{i}+n_{s} \tag{15}\]  
Generally, the number of network frames generated in the network when the source sends \(N\) unicast frames to the destination in bidirectional communication in EFA, denoted by \(nf_{EFA}^{bi}\), is calculated as:  
\[nf_{EFA}^{bi}=nf_{EFA}^{1,send}+nf_{EFA}^{lock}+nf_{EFA}^{1,reply}+(N-1)\Big{(} nf_{EFA}^{k,\ send}+nf_{EFA}^{k,\ reply}\Big{)} \tag{16}\]Therefore, \(nf_{EFA}^{bi}\) is calculated as:  
\[nf_{EFA}^{bi}=\left(\sum_{i\in NR}2n_{i}+n_{s}+n_{d}\right)+2(N-1)(n_{s}+n_{d})+ 2N\sum_{i\in AQR}n_{i} \tag{17}\]