esa-sceva/satcom-chunk-collection huggingface dataset:
# Independent System Calibration of Sentinel-1B
## 7 Radiometric Calibration
### Absolute Radiometric Calibration
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**Co-Channels**} & \multicolumn{2}{c}{**Cross-Channels**} & \multicolumn{2}{c}{**All Channels**} \\  & \multicolumn{2}{c}{**(HH, VV)**} & \multicolumn{2}{c}{**(HV, VH)**} & \multicolumn{2}{c}{**(HH, HV, VV, VH)**} \\  & \(\mu\) **[dB]** & \(\sigma\) **[dB]** & \(\mu\) **[dB]** & \(\sigma\) **[dB]** & \(\mu\) **[dB]** & \(\sigma\) **[dB]** \\ \hline S1 & 0.39 & 0.45 & 0.49 & 0.35 & 0.44 & 0.41 \\ S2 & 1.13 & 0.23 & 1.12 & 0.18 & 1.13 & 0.21 \\ S5 & 0.07 & 0.25 & 0.10 & 0.43 & 0.08 & 0.31 \\ IW (1,2,3) & 1.40 & 0.23 & 1.17 & 0.33 & 1.31 & 0.29 \\ EM (2,3) & 1.40 & 0.30 & 1.54 & 0.14 & 1.45 & 0.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean value and standard deviation of the absolute calibration factor for the beams measured in-flight by separating co- and cross-channel results and summarizing all polarization channels. Figure 24: Absolute calibration factor for co-polarized channels using different targets (triangle: corner reflectors, square: transponders) acquired for different modes (blue: SM, red: IW, green: EW).
# An FPGA-Based Four-Channel 128k-Point FFT Processor Suitable for Spaceborne SAR
## 5 Conclusions  
In this paper, we proposed a high-speed four-channel 128k-point FFT processor suitable for FPGA implementation. The radix-2\({}^{3}\) algorithm was used to save multiplier resources, as well as the mixed-radix algorithm to achieve a four-channel parallel processing structure. Then, we adopted a SDF structure to realize pipeline processing. Thus, a fixed-point word length adjustment strategy was proposed to ensure the accuracy of data processing. The proposed FFT processor was implemented on the xc7 vx690 ffg1761-2 board. Experimental results showed that the proposed processor has high calculation accuracy compared with a single-precision floating-point number, with acceptable real-time performance. Finally, it has high efficiency in the occupation of hardware resources, compared with Xilinx IP and some previous work.  
Conceptualization, Y.L. and H.C.; methodology, Y.L. and Y.X.; software, Y.L., H.C., and Y.X.; validation, Y.L.; formal analysis, Y.L.; investigation, Y.L. and Y.X.; resources, H.C. and Y.X.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L., H.C., and Y.X.; supervision, Y.X.; project administration, H.C.; funding acquisition, H.C. and Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant no. 91738302.  
This work was supported by the Chang Jiang Scholars Program under grant T2012122 and the Hundred Leading Talent Project of Beijing Science and Technology under grant Z141101001514005.  
The authors declare no conflict of interest.
# Blockchain-Based Platform to Fight Disinformation Using Crowd Wisdom and Artificial Intelligence
## 4 Solution Description
### Trust and Security Framework
#### 4.6.3 Dishonest Behavior  
Malicious actors that gain in spreading disinformation might try to attack and defraud the news article assessment system of the platform protocol by using multiple blockchain accounts that assign high trust scores on articles that contain disinformation. To address this aspect, the system implements dynamic validator sets so that the probability of forming a majority around a particular news article is extremely low. Moreover, each eligible selected account that enters the new article assessment procedure stakes tokens as collateral. All tokens from participating accounts are locked into an article assessment pool, until the assessment period ends. Once the assessment period ends, the tokens from the pool are redistributed among participants based on their assessments. Therefore, malicious voters, that will likely be on the minority side, will lose tokens in favor of the accounts whose assessment is clustered near a particular trust score. This in turn acts as a big deterrent to dishonest behavior.
# Time Series Visualization and Forecasting from Australian Building and Construction Statistics
## 3 Methodology
### Time Series Forecasting
#### 3.2.1 Economic Features  
We obtain the building- and construction-related economic features from the ABS website economy section ([[https://www.abs.gov.au/statistics#economy](https://www.abs.gov.au/statistics#economy)]([https://www.abs.gov.au/statistics#economy](https://www.abs.gov.au/statistics#economy)), accessed on 14 February 2022). The purpose is to integrate information from different aspects, in order to guide the model to perform forecasting. We only keep state- and Australia-wide data, as we did for the building and construction data, as discussed in Section 3.1.1. There are a few more restrictions to help ensure the granularity of the economic data are identical with the building and construction data:  
* The data needs to be recorded quarterly, and the timestamp for each data point needs to be identical;
* The data sheet must include original data without any processing;
* The data must be state- or Australia-wide data after any processing.  
Besides that, we find many data sheets do not provide timestamps, so we could not align them with the building and construction time series. We ignore these data sheets directly. Similar to the building and construction data, the record's history is not in the same time range, and some data sheet only include very recent data. For example, the data sheets that start from 2010 leave only 50 data points, which is insufficient for training our forecasting model. So, we omit these data sheets.  
After manually filtering the data sheets, the remaining features include:  
* Residential property price indexes;
* Wage price indexes;State final demand;
* Selected living cost indexes;
* Producer price indexes.  
The residential property price indexes show the housing price changes over time and include the median price for different types of properties, so that each type of property can be set as a single feature. This feature can be strongly correlated with the building and construction data. The wage price index represents the residents' income changes over time. The other three features all show the change in the residents' consumption level (of all residents). These selected features are all in the form of a time series, so that they could be used as variants in the multivariate time series forecasting.
## 2 Modulation Transfer Function (MTF) Calculation Method
### Common Model to Calculate MTF for Any Image Motion  
In this section, we propose a general MTF expression for the mixed image motion of linear motion and sinusoidal motion. In order to obtain the analytical expression of MTF f or the mixed image motion of linear motion and sinusoidal motion, the summation of infinitely many terms involving Bessel function is introduced to replace the integral of trigonometric functions.  
The MTF calculation model is based on the assumption of an object with a sinusoidal luminance pattern. The intensity distribution of the imaging targets can be expressed as follows:  
\[\mathrm{i(x)}=\mathrm{I_{0}}+\mathrm{I_{m}}\mathrm{cos}\,2\pi\mathrm{f} \mathrm{x}, \tag{1}\]  
where \(\mathrm{I_{0}}\) is the direct current component of sinusoidal wave, \(\mathrm{I_{m}}\) is the amplitude of sinusoidal wave, f is the spatial frequency, and x represents the spatial coordinate. As \(\mathrm{I_{0}}\) and \(\mathrm{I_{m}}\) are constants, the MC can be expressed as follows:  
\[\mathrm{MC_{1}}=\frac{\mathrm{I_{m}}}{\mathrm{I_{0}}} \tag{2}\]  
To take image motion into account, the intensity distribution is described as follows:  
\[\mathrm{i(x,t)}=\mathrm{I_{0}}+\mathrm{I_{m}}\,\mathrm{cos}\,2\pi\mathrm{f} \mathrm{[x_{0}}+\mathrm{x(t)}], \tag{3}\]  
where \(\mathrm{x(t)}\) is the relative motion between the camera and the object during imaging and \(\mathrm{x_{0}}\) is the initial value of spatial coordinates. Considering the process of image acquisition of the optical remote-sensing sensors, the intensity distribution accepted by the sensors in the exposure time can be expressed as follows:  
\[\mathrm{i(x)}=\overline{\mathrm{i(x,t)}}=\frac{1}{\mathrm{t_{e}}}\int\limits _{0}^{\mathrm{t_{e}}}\mathrm{[I_{0}}+\mathrm{I_{m}}\,\mathrm{cos}\,2\pi \mathrm{f}\mathrm{[x_{0}}+\mathrm{x(t)}]\mathrm{]dt}, \tag{4}\]  
where \(\mathrm{t_{e}}\) is the exposure time. As \(\mathrm{x(t)}\) is the expression of the image motion, Equation (4) can be expressed as follows:  
\[\mathrm{i(x)}=\mathrm{I_{0}}+\mathrm{I_{m}}\,\sqrt{\mathrm{a^{2}}+\mathrm{b^{2 }}}\mathrm{cos}\,2\pi\mathrm{f}\mathrm{(x_{0}}+\varphi), \tag{5}\]  
where \(\varphi=\mathrm{arccos}(\frac{\mathrm{a}}{\sqrt{\mathrm{a^{2}}+\mathrm{b^{2} }}})\), \(\mathrm{a=}\frac{1}{\mathrm{t_{e}}}\int\limits_{0}^{\mathrm{t_{e}}}\mathrm{ sin}\,2\pi\mathrm{f}\mathrm{x(t)dt}\), and \(\mathrm{b=}\frac{1}{\mathrm{t_{e}}}\int\limits_{0}^{\mathrm{t_{e}}}\mathrm{ cos}\,2\pi\mathrm{f}\mathrm{x(t)dt}\). The MC of the intensity distribution accepted by sensors can be described as follows:  
\[\mathrm{MC_{2}}=\frac{\mathrm{I_{m}}\,\sqrt{\mathrm{a^{2}}+\mathrm{b^{2}}}}{ \mathrm{I_{0}}}. \tag{6}\]  
According to the definition of MTF, the MTF for the image motion can be expressed as follows:  
\[\mathrm{MTF=}\frac{\mathrm{MC_{2}}}{\mathrm{MC_{1}}}=\sqrt{\mathrm{a^{2}}+ \mathrm{b^{2}}}. \tag{7}\]  
Thus, the MTF for the image motion can be determined by the expression of the image motion, and is given by Equation (7). The MTF for the mixing of multiple forms of motions is calculated using Equation (7) in the following section.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 5 Commercial Case Studies
### DroneSmartX  
DroneSmartX, a UAV service provider, has created the UAV SmartHub, a system that allows users to connect to smart sensors on the ground and send data directly to the cloud platform. According to the company, headquartered at the University of Central Florida's business center in Kissimmee, Fla., the UAV SmartHub is connected to several types of commercial-grade drones. DroneSmartX claims that the device integrates UAV as a service with the Internet of things to make drones smarter [61].  
The platform gathers data from sources such as passive and active radio frequency identification (RFID), Bluetooth, GPS, and other sensors; processes the data; and delivers it back to the user through the cloud platform in real-time. Ideal applications include agriculture, energy, manufacturing, oil and gas, and inventory of warehouses. According to DroneSmartX, the SmartHub will add tags to thousands of items for inventory checks in minutes [61].
## 3 Results and Discussion
### Nanoparticles Characterization
The results obtained with samples 03 and 07 show that the greater amounts of surfactant favored the formation of smaller droplets containing monomer during the polymerization and led to the decrease of the nanoparticle size. To achieve a system suitable for enhanced oil recovery, the PSNP must present not only a nanometric size, but also a capacity to reduce IFT. Therefore, it was investigated which conditions lead to minimal IFT values and this was achieved by 03 and 07 samples whose PSNP resulted from 3 (_v/v_) % of DVB. The IFT reduction for the 07 sample was more pronounced probably due to a higher surfactant retention. Therefore, this condition of 3 (_v/v_) % of DVB was adopted for the subsequent experiments. The IFT reduction for the 07 sample was more pronounced probably due to a higher surfactant retention which is better discussed in Section 3.8. [PERSON] [45] showed this same tendency of particle size decrease at very low St-S content, followed by a sudden particle size increase. This behavior may be explained by a lower concentration of monomer molecules present in the micellar core since greater fractions of comonomers (St-S) were solubilized in the aqueous phase. The change, however, is due to a variation in the nucleation mechanism. Initially, when monomer species are almost completely hydrophobic, the major nucleation mechanism occurs in the micellar core [46]. Further increase of St-S concentration shifts the nucleation (locus of polymerization) to the aqueous medium, occurring homogeneous particle nucleation which causes mainly the growth of oligomers, that would be already stabilized by the emulsifier. Accordingly, the formed particles present greater sizes than the ones from micelles stabilized by the emulsifier [47]. Therefore, it was observed a minimal size of 66 nm for the sample produced with 0.012% of St-S and ten times the CMC of OADA. Due to the higher total particle surface area, which could be being stabilized by the sulfonated groups, the final particle size distribution was narrower and the polydispersity index was lower than 0.1, indicating that the nanoparticles were relatively monodisperse.
## 3 Results
### Antioxidant Activity Results  
Studies were done on the six laser irradiated extracts (Plal488, Plal514, Plal532, Plal552, Plal660, and Plal785) versus the corresponding control samples at 1 h (PlalC488, PlalC514, PlalC532, PlalC552, PlalC660, and PlalC785), and 2 h extraction time (PlalC) respectively, in comparison with a control negative sample (Control) in which the vegetal extract was replaced with distilled water. Figure 5a-f presents the results on the six test series in the \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Test Extract(\(\mu\)g/g Plan)** & **Control Extract** & **Laser Extract** & **PlalC/Plal488** & **Laser Extract** & **PlalC/Plal514** & **Laser Extract** & **PlalC/Plal532** & **PlalC/Plal532** \\  & **(PlalC)** & **Plal88** & **(C)** & **Plal514** & **(C)** & **Plal532** & **(C)** \\ \hline Total extractible/dry matter & 331,204 & 334,564 & +1.01 & 352,802 & +6.52 & 333,516 & +0.69 \\ Total phenolics/[GAE] & 6201 & 5986 & \(-\)3.47 & 5240 & \(-\)15.50 & 6220 & +0.31 \\ \hline
**Test Extract(\(\mu\)g/g Plan)** & **Control Extract** & **Laser Extract** & **PlalC/Plal552** & **(C)** & **Plal660** & **(C)** & **PlalC/Plal60** \\  & **(PlalC)** & **Plal552** & **(C)** & **Plal660** & **(C)** & **Plal785** & **(C)** \\ \hline Total extractible/dry matter & 331,204 & 352,793 & +6.52 & 331,195 & \(-\)0.01 & 324,110 & \(-\)2.14 \\ Total phenolics/[GAE] & 6201 & 6948 & +12.05 & 7162 & +15.50 & 7162 & +15.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Total extractible compounds and total phenolic compounds (\(\mu\)g/g plant, d.w., \(n=3\), mean value) in the aqueous extracts, non-irradiated extracts (PlalC) _versus_ irradiated extracts (Plal488, Plal514, Plal532, Plal552, Plal660, and Plal785). study: Figure 5a--series at 488 nm; Figure 5b--series at 514 nm; Figure 5c--series at 532 nm; Figure 5d--series at 552 nm; Figure 5e--series at 660 nm; Figure 5f--series at 785 nm. According to the data obtained, the control samples at 1 h (magenta line in series a. PlalC/488, b. PlalC/514, c. PlalC/532, d. PlalC/552, e. Plal/C660, f. PlalC/785), present similar antioxidant potency (\(\pm\)2% variation between the six series, see Table 3), and superior to that emphasized of the control sample at 2 h extraction time (red line, PlalC), suggesting 1 h extraction time as the best technological option. By comparison with the control extracts at 1 h, and 2 h extraction time, laser-assisted extracts at 488, 514, 552, and 785 nm (yellow line in series a.Plal488, b.Plal514, d.Plal552, and f.Plal785) emphasized the increase of the antioxidant potency at the beginning of the chemiluminescence reaction compared to both control series; yet, till the end of the reaction (60 s) the effects were similar to those of the control sample at 1 h, confirming activation, but transitory effects of laser irradiation on the test extracts; the activation effects, measured as an increase of the antioxidant activity, were estimated from 18 to 27% in comparison with control negative sample (which do not contain vegetal extract), and from 6 to 17% in comparison with control positive sample at 1 h.  
\begin{table}
# Study of RF Stray Currents in ITER Neutral Beam Test Facilities
###### Abstract  
The operation of SPIDER (Source for the Production of Ions of Deuterium Extracted from Radio-frequency plasma), full-scale prototype of ITER NBI (Neutral Beam Injector) radio-frequency ion source, pointed out deleterious effects caused by stray Radio-Frequency (RF) currents flowing in the electrical equipment not included in the RF power system. MITICA (Megavolt ITER Injector and Concept Advancement), the full-scale prototype of ITER NBI, is characterized by a similar design in terms of layout of the power supplies and connections to the beam source; thus, it is expected to be subject to the RF stray currents problem. SPIDER RF system is composed of four RF generators, four coaxial lines and four RF loads. Each RF generator is rated for operation at 200 kW in the frequency range 0.9 +- 1.1 MHz. The power is delivered to the four loads via as many RF coaxial lines, housed inside a multiconductor transmission line. Each load consists of a capacitive matching network and two plasma drivers in series. Due to the presence of stray connections at the generator and beam-source side (e.g., parasitic capacitances), unwanted RF currents can flow through alternative paths and negatively affect the components not intended for transmission of RF power, the output stages of power supplies and several diagnostics installed in the High-Voltage Deck (HVD) and at the beam source. This paper presents the development of a circuital model used to estimate the RF stray currents in SPIDER electrical system; the understanding of this phenomenon and the development of a model with predictive capabilities is fundamental for the assessment of the performance of both SPIDER and MITICA and, in general, of alternative RF system layouts with respect to the stray currents issue.  
ITER NBI; ion sources for fusion; NBTE; SPIDER; RF stray currents +
Footnote †: journal: Applied Sciences
## 5 Discussion  
### Evaluation of Training and Validation Performance  
As illustrated in Figures 5-8, the training and validation curves for the DSC criterion are presented as a function of the number of epochs on the Kvasir-SEG and CVC-ClinicDB datasets. Figures 5 and 6 correspond to the Kvasir-SEG dataset with 15% and 30% labeled images, respectively, while Figures 7 and 8 correspond to the CVC-ClinicDB dataset with 15% and 30% labeled images.  
The training process was designed to run for up to 300 epochs, incorporating an early-stopping strategy that halted training if the model showed no improvement over 50 continuous epochs on the validation set. This strategy was crucial in preventing overfitting by stopping the training once the model's performance plateaued, ensuring the model did not learn noise from the training data. By stopping early, we achieved a model that balanced underfitting and overfitting, performing well on unseen data.  
Figure 6: Training and validation curves for the DSC criterion as a function of the number of epochs on Kvasir-SEG with 30% labeled images. Dark blue denotes the training curve, while orange highlights the validation curve.  
Figure 7: Training and validation curves for the DSC criterion as a function of the number of epochs on CVC-ClinicDB with 15% labeled images. Dark blue denotes the training curve, while orange highlights the validation curve.  
For the Kvasir-SEG dataset, as shown in Figure 5, the maximum number of training epochs was 216, with a maximum validation DSC of 89.41%, while Figure 6 shows 139 epochs with a maximum validation DSC of 90.43%. For the CVC-ClinicDB dataset, Figure 7 indicates 195 epochs with a maximum validation DSC of 89.78%, and Figure 8 shows 157 epochs with a maximum validation DSC of 91.96%. This evidence suggests that the chosen maximum training epoch parameter in our experiments is well-suited.  
The performance dynamics of the segmentation model vary considerably depending on the quantity of labeled data available. As the proportion of labeled data increased, the segmentation model's performance on the validation set improved, as demonstrated by higher DSC scores. Furthermore, it is evident that with more labeled data, the required number of training epochs decreased substantially. This indicates that as the amount of labeled data grows, the model converges more rapidly, leading to earlier termination of training and a reduction in the number of iterations. These findings highlight the critical role of labeled data in developing effective semi-supervised segmentation models and demonstrate the benefits of leveraging additional labeled data to achieve better results.
## 2 Background
### Image Dehazing Using CNN Architectures  
Image dehazing is a highly desired task in various image processing and computer vision applications that aims to lessen haze effects in a hazy input image. Following the simplified hazy image degradation model, to achieve a reliable haze removal system, two significant estimates are required: transmission map (\(t\)) and atmospheric light (\(AL\)).  
Lately, innovative image haze removal methods based on deep learning techniques have been proposed achieving outstanding results. In [26], [PERSON] et al. proposed a multi-scale DNN for image haze removal, and the mapping between the hazy input image and its conforming transmission map is learned. This method incorporates the prediction of the transmission map and its refined one. [PERSON] et al. [28] designed an end-to-end system (DehazeNet) for generating transmission maps of hazy images. This proposed dehazing model employs hazy images as an input and builds their transmission maps, and then restores clear images by using the atmospheric scattering model. A ranking convolutional neural network (Ranking-CNN) [29] has been presented to recover clear images from hazy ones. The proposed architecture employs conventional CNN with the insertion of a new ranking layer to well-capture both the statistical and structural characteristics of the hazy image together.  
The above-mentioned learning-based dehazing methods can generate clear images very close to ground truth images, which proves their robustness and performance. However, most of the deep learning-based approaches still need farther improvements because of limitations that explained below:  
* Most deep learning-based approaches for image dehazing ignore the accurate estimation of atmospheric light [20; 26; 28] and set its value empirically, which leads to inaccurate dehazing results because the simplified physical model requires the estimation of both atmospheric light and transmission map.
* The inefficiency presented in [PERSON] et al.'s method [29], as mentioned in their conclusion, is because of the redundant computations caused by the additional layer.
* The majority of deep learning-based dehazing models lack the structural integrity of the original scene. This trouble produces impractical results far from clear ground truth images when compared to using a structural similarity metric.  
Overcoming the above limitations, and benefiting from the effectiveness of CNNs and DNNs architectures, we propose a new robust multi-scale cascade CNN-based dehazing method. Firstly, we introduce a new accurate atmospheric light estimation algorithm based on image blurriness map. Then,we design a cascaded multi-scale CNN model that consists of two sub-CMCNNs: \(\text{CMCNN}_{t}\), serves to produce rough transmission maps from hazy input images and \(\text{CMCNN}_{t}\) represents a transmission map refinement operator that eliminates halo effects and discontinuities. The detail of the proposed method is explained in the next section.