esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Underwater Testing  
The developed robotic scanner system was tested in a shallow subsea location for the inspection of a flexible riser. Prior to the seawater trials, the system was subjected to several laboratory-based trials to verify the functionality.  
The robotic scanner system was managed from the control room on the surface through an umbilical link to power the subsea actuator units and the communication link, allowing the system to precisely control the scans at very low speeds required by the inspection system and allowing real-time monitoring and updates of system parameters. Figure 7 shows the snapshots from the underwater testing.  
The core functionality of the system was proven during the sea trials. This included satisfactory results from the inspection system at precisely controlled scanning speeds ranging from 0.03 to 1 rpm as was required by the inspection system. The supervisory control system performed without failures in functionality or loss of data transmission at 200 Mbps. The system therefore proved its potential for enhanced capability in monitoring and control of the actuators. The parameters that were used in the tests to command, monitor, and update the system status are listed in Table 2.  
The parameters were significant for control of the actuator and for the user to monitor the health status and updates of the motion parameters during runtime. A set of improvements were recognized to make the system better for use in harsh environments, such as physical ruggedization by incorporating safety measures on power inputs, alarming indicators, and a hardened peli-case for the topside unit. This also included safety measures, system operational modes, and indication of the operational mode during runtime to be implemented in the supervisory control.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Motion Parameters** & **Comm. Parameters** & **System Parameters** \\ \hline Mode (position/speed) & Node ID & Drive status \\ Position in encoder counts & Last error & Motor temperature \\ Target speed in encoder counts/sec & Error type & Controller temperature \\ Actual position feedback & Data validity & Drive current \\ Actual speed measurement & – & – \\ Motion status & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of actuator parameters for command and monitoring.  
Figure 7: (a) Robotic system being submerged around the riser; (b) system in operation underwater.
# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 4 Experiments
### Evaluation Metrics  
This paper used five evaluation indicators [17; 46] to evaluate the proposed method, including _precision_, _recall_, \(F\)1 score, detection rate \(P_{d}\), and false alarm rate \(F_{a}\). They are defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{15}\]  
\[Recall=\frac{TP}{TP+FN} \tag{16}\]  
\[F_{1}=\frac{2\cdot Precision\cdot Recall}{Precision+Recall} \tag{17}\]  
\[P_{d}=Recall \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Segment** & **Frames** & **Average SCR** & **SCR Variance** \\ \hline Data1 & 399 & 9.72 & 0.033 \\ Data2 & 599 & 4.34 & 0.220 \\ Data3 & 100 & 2.17 & 0.908 \\ Data4 & 399 & 3.75 & 3.646 \\ Data5 & 3000 & 5.45 & 1.285 \\ Data6 & 399 & 5.11 & 1.571 \\ Data7 & 399 & 6.33 & 20.316 \\ Data8 & 399 & 6.07 & 0.159 \\ Data9 & 399 & 6.29 & 17.086 \\ Data10 & 401 & 0.38 & 0.031 \\ Data11 & 745 & 2.88 & 2.148 \\ Data12 & 1500 & 5.20 & 2.226 \\ Data13 & 763 & 1.98 & 0.886 \\ Data14 & 1426 & 1.51 & 1.538 \\ Data15 & 751 & 3.42 & 0.965 \\ Data16 & 499 & 2.98 & 0.674 \\ Data17 & 500 & 1.09 & 0.353 \\ Data18 & 500 & 3.32 & 0.165 \\ Data19 & 1599 & 3.84 & 0.886 \\ Data20 & 400 & 3.01 & 1.485 \\ Data21 & 500 & 0.42 & 0.092 \\ Data22 & 500 & 2.20 & 0.150 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenarios of each data segment.  
Figure 7: Typical small target examples.  
\[F_{a}=1-Precision \tag{19}\]  
In the above formulas, \(TP\) represents the correct prediction, specifically referring to the number of predicted points within \(d\) pixels of the distance between the predicted point and the actual point. \(FP\) represents an incorrect prediction, specifically referring to the number of predicted points that do not have actual points within \(d\) pixels around the predicted point. \(FN\) represents the actual points that were missed, specifically referring to the number of actual points that have no predicted points within \(d\) pixels around the actual point. The value of \(d\) in this paper was set to 10. The _precision_ represents the ratio of the number of correctly predicted targets to the total number of predicted targets, the _recall_ rate represents the ratio of the number of correctly predicted targets to the total number of real targets, and the \(F_{1}\) score can comprehensively reflect the detection performance of the model.
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 4 Results
### Calculation of the Harvested Areas  
The harvested area was calculated according to the dates of cells harvest completion. To do that, the determined harvest dates for each cell were divided into the sequences with a time lapse between the neighbouring dates not more than 30 days. The last dates of those sequences were considered the dates of cells harvest completion.  
The harvested areas are calculated for both analyzed algorithms. The optical algorithm parameters are as follows: \(\Delta NDVI=0.08\), \(NDVI_{prev}=0.3\), \(NDVI_{hav}=0.4\), \(\Delta T=40\) days, \(\mu=0.9\), and \(k=3\). The SAR-optical algorithm parameters are as follows: \(\varepsilon=0.05\), \(\theta=0.07\), \(NDVI_{hd}=0.4\), \(C_{hi}=0.5\), and \(\Delta T_{hi}=40\) days.  
Figure 11 and Table 3 represent the calculation results.  
Figure 11: The sugarcane areas harvested by month, identified by visual interpretation (lv), algorithm based on optical data (optic), algorithm based on Synthetic Aperture Radar (SAR) and optical data (sar): (**a**) absolute values; (**b**) accumulated values.  
Figure 10: Quality indicators of harvest dates identification by month: (**a**) distribution of cloud fraction of Sentinel-2 images by month; (**b**) distribution of the coinciding harvest dates (optical algorithm and visual interpretation), omitted harvest dates, and dates of false response (optical algorithm) by month.  
According to Table 3, it could be seen that the harvested areas evaluated by the results of the optical algorithm coincide with the harvested areas evaluated by a visual interpretation by 97%. For SAR + optical algorithm such coincidence is 90%.  
According to the Figure 11, it is quite clear that the combined algorithm determines the harvested areas worse than the algorithm based only on the optical data. That is due to a much longer interval between SAR images (12 days compared to 5 days for Sentinel-2); thus, the harvested area is recorded with time delays. The delays in identification of the harvest dates may be also explained by other field operations changing the distribution of reflectors and reducing the coherence. To estimate the effect of that factor, one needs more accurate data on the operations within the field. Finally, the greatest determination errors are possible due to the lack of one of the SAR images in November 2018.
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 3 Results
### Spacecraft Case
#### 3.1.1 Geometry and Materials  
The exact TANGO spacecraft topology is not accessible; nonetheless, a representative geometry made of multiple parts is created, and each part is assigned with an emissivity value. It is reported in Figure 8, and it is made of 205 solar cells, 5 panels, 5 antennas, 7 patch antennas, and 1 coil with 2 supports for a total of 225 parts. The emissivities of each part are reported in Table 1.
## 2 Methods
### Extracting a Noisy FECG  
In contrast to common ECG signal denoising, for extracting an FECG, there are not only various noises, but also the MECG existing in the AECG. That is, there are two quasi-periodic signals in the signal, which makes it difficult to extract the FECG.  
However, commonly, the FECG heart rate is between 120 and 180 bpm, and the MECG heart rate is between 60 and 100 bpm. The amplitude of the MECG is 5-10 times larger than that of the FECG [25; 26; 27]. Therefore, we think about the denoising in another way: For the MECG, the FECG can be treated as noise, and the MECG can be firstly extracted from the AECG based on the proposed method. Then, the MECG is subtracted from the AECG to get the FECG only mixed with noise, i.e., a noisy FECG.  
Before the MECG is extracted by the proposed method, the AECG is pretreated to eliminate power line interference and baseline drift. When calculating the instantaneous correlation coefficient, the high-frequency noise is properly filtered under the premise of ensuring the QRS shape.  
The extraction process of a noisy FECG is as follows:  
1. Remove power line interference and baseline wandering in the AECG.
2. Determine the reference heart rate of \(MECG\) using \(AECG\), and reconstruct the reference heart beat of the MECG using the dimensionless heart beat model.
3. Calculate the instantaneous correlation coefficient \(R_{A}(n)\).
4. Detect the R peak position of the MECG using \(R_{A}(n)\).
5. Extract the MECG using the simplified coherence average method and denote it by \(MECG_{c}\).
6. Obtain a noisy FECG by subtracting \(MECG_{c}\) from the AECG and denote it by \(FECG_{n}\).  
\[FECG_{n}(n)=AECG(n)-MECG_{c}(n) \tag{14}\]
## 4 Test Scenario: AC Signal Analysis of the Sum of 4 Sines with Arbitrary Frequencies
### Memory Usage  
In this section, we compare the memory usage required for the designed scenario. Goertzel and DTFT (without LUT) have the lowest memory consumption, as given in Table 9. Sorted from highest to lowest memory requirements, non-linear least squares sine  
Figure 6: Bump Chart of the AC signal analysis Methods Ordered by their speed in the Different Implementations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **STM32** & **Teensy 3.6** & **STM32** \\  & **(Native/Double)** & **(Teensyduino/Double)** & **(CMSIS/Float)** \\ \hline FFT & 99 ms & 76 ms & 8 ms \\ \hline Goertzel & 21 ms & 57 ms & 18 ms \\  & \^{}_^{}_^{}_{}_{_}{_}{_}{_}{_}{_}{_}{_}{_}{_}{_}{_}{_}{_}{}_{}_{}{_}{}_{}_{} _{}{}_{}_{}{}_{}{}_{}{}_{}{}_{}{}_{}{}_{}{}_{}{}  
fit requires the highest memory allocation, with around 55 k cases, then linear least-squares sine-fit with 49 k cases, then cross-correlation (with LUT variant) with 26 k cases, then DTFT (with LUT variant) with 16 k cases, then cross-correlation (without LUT) with 8 k cases, then FFT with 2 k to 4 k cases. On the other hand, Goertzel requires 28 cases which is 10 cases more than DTFT (without LUT variant). In most cases, the native embedded and CMSIS implementation requires the same number of cases, unless otherwise noted in Table 3. CMSIS operates on single-float precision, and therefore it requires half memory allocation space as the native embedded.
# Optimized Multiport DC/DC Converter for Vehicle Drivetrains: Topology and Design Optimization
## 3 Optimization Problem Formulation  
The optimization process aims at the optimal set of the inductor, the number of phases, and switching frequency to minimize three objective functions: the input current ripple (\(\Delta I_{in}\) as shown in Equation (4)), the total losses of the converter (\(P_{loss}\) as shown in Equation (20)), and the weight of all inductors (\(W_{zind}\) as shown in Equation (12)) that contributes critically to the whole weight of the converter. It is assumed that the weight of other components such as heat sink, IGBT modules, bus bar, and filter capacitor is fixed during the optimization process. The multi-objective optimization problem is mathematically presented in Equation (21).  
\[\mathbf{minimize}\left\{\begin{array}{c}\Delta\mathbf{I_{in}}(\mathbf{X})\\ \mathbf{W}_{\Sigma ind}(\mathbf{X})\\ \mathbf{P_{loss}}(\mathbf{X})\end{array}\right.\] \[s.t.\left\{\begin{array}{c}N_{ph\_min}\leq N_{ph}\leq N_{ph\_ max}\\ f_{sw\_min}\leq f_{sw}\leq f_{sw\_max}\\ \Delta I_{in_{BAT}}\leq 7.5\%_{0}I_{ing_{BAT}}\\ \Delta I_{in_{SC}}\leq 20\%_{0}I_{ing_{SC}}\\ W_{\Sigma ind}\leq 5\,\mathrm{kg}\\ P_{BCM}\leq 5\,\mathrm{kW}\end{array}\right. \tag{21}\]  
The design vector \(X\) containing three variables (continuous \(f_{sw}\), discrete \(N_{ph}\), and discontinuous Core index) must be found in the feasible solution space \(\Omega\) and subject to several constraints according to design specifications, physical limitation, and component safe operating areas. The minimum CCM power is added in the optimization routine to consider the negative effect of interleaving to ensure the Boundary Condition Mode (BCM) is at high power. In fact, as the current is split into several phases, the power at which the converter work in BCM is given by Equation (22):  
\[P_{BCM}=N_{ph}V_{in}\frac{\Delta I_{L}}{2} \tag{22}\]
# Predicting Ceramic Wool Diameter by Motor Frequency Using Improved BP Neural Network
## 6 Conclusions  
1. The measurement of fiber mean diameter was a very important issue. Theory analysis, simulation analysis, and experiment were used to form a better understanding of the nonlinear problem between motor frequency and fiber mean diameter. The approximate simulation of the fiber formation was discussed, and improved BP neural network based on PSO algorithm was developed to predict the ceramic mean diameter successfully.
2. A great relationship between the main motor frequency and the ceramic mean diameter was built which had been verified by practical examination. Compared to the nonlinear compensation model based on LSM, the mean measurement error  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Sample** & **Actual Mean Fiber** & **LSM Measuring** & **LSM Measuring** & **PSO-BP** & **PSO-BP** \\
**Number** & **Diameter/\(\upmu\)m** & **Results/\(\upmu\)m** & **Precision/\(\upmu\)** & **Measuring** & **Measuring** \\  & & & & & **Results/\(\upmu\)m** & **Precision/\%** \\ \hline
220.9 & 4.154 & 4.158 & 4.135 & 4.152 & 4.156 \\
235.2 & 4.023 & 4.025 & 3.994 & 4.031 & 4.024 \\
256.8 & 3.851 & 3.859 & 3.812 & 3.867 & 3.869 \\
267.1 & 3.731 & 3.719 & 3.687 & 3.721 & 3.716 \\
280.0 & 3.524 & 3.519 & 3.489 & 3.513 & 3.511 \\
309.4 & 3.347 & 3.345 & 3.337 & 3.342 & 3.343 \\
321.6 & 3.309 & 3.313 & 3.267 & 3.305 & 3.310 \\
334.4 & 3.252 & 3.245 & 3.226 & 3.243 & 3.245 \\
360.8 & 3.158 & 3.156 & 3.148 & 3.152 & 3.15 \\
378.5 & 3.176 & 3.181 & 3.160 & 3.172 & 3.174 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results comparison between LSM and PSO-BP model.  
Figure 9: Comparison of measured and model predicted value.  
of PSO-BP was 0.471% which was lower than that of LSM. The presented PSO-BP method was very valuable for predicting the wool diameter.
3. The neural networks could solve nonlinear problems successfully which was certified by the actual prediction of ceramic wool diameter. This shows a bright future in the subsequent wool production.  
Conceptualization, T.X.; methodology, T.X.; software, J.H.; validation, T.X., J.H., Y.L. and T.C.; formal analysis, T.X.; investigation, T.X.; resources, T.X.; data curation, J.H.; writing--original draft preparation, T.X.; writing--review and editing, J.H.; visualization, Y.L.; supervision, T.C.; project administration, T.X.; funding acquisition, T.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Science (Natural Science) Research Project of Higher Education of Jiangsu, grant number: 20 KJB560008 and the Introduced Talent Research Fund Project of Nanjing Vocational University of Industry Technology, grant number: 101050618 YK306.  
Ethical review and approval were waived for this study due to this study not involving humans or animals.  
Not applicable, for the study not involving humans.  
The authors declare no conflict of interest.
# A Method of Enhancing Silk Digital Printing Color Prediction through Pix2 Pix GAN-Based Approaches
## 2 Materials and Methods
### Database Construction  
The PANTONE TCX color blocks and printed silk twill and silk crepe satin color blocks were matched by PANTONE code to create a database for subsequent method development. A total of 5252 sets of matched data were archived with a corresponding CIELAB value and image from design PANTONE TCX color blocks and actual produced textiles. Data cleaning by removing irrelevant, duplicate, erroneous, and missing data and data normalization was implemented. All images were uniformly processed to 256 \(\times\) 256 pixels with mapping data to the [0, 1] interval (normalization). Data augmentation techniques were used to expand the dataset by rotating, cropping, and other methods to increase data diversity and robustness. Finally, data splitting was carried out by dividing the dataset into training and testing sets. The dataset contains 2626 pairs of corresponding images for 2 different fabrics, with 90% used for training and 10% for testing.
# Use of Binary Classification in Non-Invasive Load Monitoring
## 2 NILM--Key Elements
### Milestones  
The sensitive stages of the NILM are measurement, data preprocessing, including feature vector selection, model learning, model evaluation, and load identification. Their implementation is interconnected, and the way in which each of them is carried out can influence the results of the other stages. In addition, because the stages listed are very much determined by the analysis scheme adopted, we can analyze the steady state or transient events.  
The measurement and associated data acquisition constitute the first stage of the NILM. The frequency at which the measurements are carried out determines the type and range of the parameters that can be extracted from them [3; 4; 5]. In the case of low-frequency measurements, we must limit ourselves to basic quantities, such as the active power, reactive power, effective current, and power factor. In the case of the data obtained from high-frequency measurements, the harmonics, THD (total harmonic distortion), CF (crest factor), etc., can also be used. In recent years, the popularity of methods that use  
Figure 1: Schematic of the measurements according to the NILM concept (**a**) and the ILM concept (**b**).  
these types of measurements has increased [4; 5; 6; 7; 8]. In practice, low-frequency measurements are mainly used for event analysis.  
Feature vector selection aims to select from the collected measurements and their derived magnitudes (computationally determined) those that have the best information properties in the context of device identification. The selection of signatures is often expert-driven and based on experience, but studies using computer-based methods for this purpose are increasingly emerging [7; 8; 9; 10]. A comprehensive overview of the features used for identification can be found in the work of [PERSON] and colleagues [5]. Importantly, feature selection, by eliminating features that do not carry information, results in a reduction in the dimensions of the input data, which benefits the identification process.  
The identification of electrical devices for monitoring is carried out via artificial intelligence methods such as artificial neural networks, including deep networks [4; 6; 7; 8; 11; 12; 13; 14]; rule-based methods such as decision trees or random forests [15; 16; 17]; k-nearest neighbors methods (kNN) [4; 15; 18]; Bayes classifiers and hidden Markov chains [4]; optimization methods [19]; and hybrid methods [20; 21; 22; 23]. In recent years, methods using deep learning networks have become very popular. Their advantage is very good identification results, but the need to use very large data repositories for their learning is a major obstacle. Consequently, most authors base their research on the same data available in public repositories.
## 3 An Improved Objective Function Based on SVDM
### Selection of Key Parameters in Improved Objective Function
#### 3.3.3 Selection of the Weights of the Frequency and Mode Shape Terms  
The objective function of Equation (8) includes two parts: the part \(\beta_{\varphi}(\mathbf{\mu})\) related to the mode shape and the part \(\beta_{\omega}(\mathbf{\mu})\) related to the natural frequencies, which is as shown in Equation (9). The theoretical range of the mode shape part \((1-MAC(\mathbf{H}_{i}(\mathbf{\mu},\overline{\omega_{i}}),\overline{\mathbf{\varphi}_{i}}))\) is the interval [0,1]. The minimum value of the \(h_{ai}^{2}(\mathbf{\mu},\overline{\omega_{i}})\) part is zero, while the maximum value is related to the factors such as excitation, structural characteristics and damping. Therefore, the magnitude of the two parts of Equation (9) may vary greatly. In order to ensure their balanced influence on the objective function, the modal shape weight \(\alpha_{\varphi}\) and the frequency weight \(\alpha_{\omega}\) should be taken reasonably. The trial estimation method is adopted to determine the weights: Firstly, one substructure is selected, indexed by \(j\), as if only the \(j\)-th substructure was damaged, and a damage factor \(\mu_{j}\in[0.3,1]\) is assumed. Then the mode shape part \(\beta_{\varphi}(\mathbf{\mu})\) and the frequency part \(\beta_{\omega}(\mathbf{\mu})\) of the objective function can be calculated by Equation (9). Denote the maximum values of \(\beta_{\varphi}(\mathbf{\mu})\) and \(\beta_{\omega}(\mathbf{\mu})\) by \(\beta_{\varphi,\max}\) and \(\beta_{\omega,\max}\), respectively. Finally, the weight coefficients are calculated as \(\alpha_{\varphi}=1/\beta_{\varphi,\max}\), \(\alpha_{\omega}=1/\beta_{\omega,\max}\), which is to ensure that the numerical ranges of the two parts are as close as possible.  
\[\left\{\begin{array}{l}\beta_{\varphi}(\mathbf{\mu})=\sum\limits_{i=1}^{n_{ \varphi}}(1-MAC(\mathbf{H}_{i}(\mathbf{\mu},\overline{\omega_{i}}),\overline{\mathbf{ \varphi}_{i}}))\\ \beta_{\omega}(\mathbf{\mu})=\sum\limits_{i=1}^{n_{\omega}}\sum\limits_{\alpha=1} ^{n_{s}}|h_{ai}(\mathbf{\mu},\overline{\omega_{i}})|^{2}\end{array}\right. \tag{9}\]