esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods
### Building Artificialized Patches to Monitor Land Conversion  
#### 4.2.1 Mathematical Morphology Operation: Closing  
The extent of geographical objects can be increased by applying a mathematical morphology operation called closing, which combines two basic operations called dilation and erosion [8, 37, 38, 39, 40]. In our case, the method used to produce an artificial patch is the successive application of the two transform operations to the artificial objects extracted from the satellite images. The successive execution of these operations enables the addition of elementary objects (from the same group) [40]. However, the dilation and erosion operations have opposite effects. The dilation effect first increases the size of building objects. The height and width of dilated components is then the respective sum of the heights and widths of the original components and of a structuring element. The erosion operation is used to shrink the resulting elements through dilation. The height and width of the eroded element is then the respective differences in the heights and widths of the original components and the structuring element. Therefore, the area eroded corresponds to the symmetrical element (or residue) of the dilation process [41].  
The structuring element is defined by its shape and size. The shape of the structuring element has an effect on the contours of the modified object; a square filter will highlight angular elements while a round filter will soften the contours [42]. In this study we applied a round structuring element in order to generate artificial patches with smooth contours (Figure 4).  
The size of the structuring element is linked to the distance between the buildings to be taken into account in order to decide if they belong, or not, to the same zone (which buildings are bounded within a single polygon?) The weaker the continuity criterion, the more fragmented the artificial patch, which therefore depends on the type of object under study and the accuracy of the data used.  
Figure 4: The mathematical morphology operation used to produce an artificial patch. The process combines two basic operations, dilation and erosion to merge separate objects.
## 3 Results
### Analysis of the Specific AR Training  
Descriptive data for the scores of the experimental and of the control groups in pre-test and post-test are presented in Table 3. Statistical analyses did not show significant effects of the type of training, whether AR-supported or conventional flight simulator training, either on trainees' performance, workload, situation awareness, emotion, or on their motivation.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Test** & \multicolumn{2}{c}{**Pretest**} & \multicolumn{2}{c}{**Posttest**} \\ \cline{2-5}
**Variable** & **Mean** & **SE** & **Mean** & **SE** \\ \hline
**Performance** & & & & \\ Objective deviation \({}^{*}\) & 7.71 & 0.22 & 4.24 & 0.31 \\ Deviation self-assessment \({}^{*}\) & 7.98 & 0.23 & 9.15 & 0.25 \\ Approach quality \({}^{*}\) & 158.79 & 11.47 & 261.86 & 9.22 \\ \hline
**Situation awareness SART** & & & & \\ Understanding \({}^{*}\) & 12.62 & 0.39 & 14.35 & 0.46 \\ Demands & 10.48 & 0.42 & 10.63 & 0.49 \\ Supply & 20.59 & 0.39 & 20.35 & 0.51 \\ Total SA score & 22.72 & 0.83 & 24.07 & 0.95 \\ \hline
**Workload NASA-TLX** & & & & \\ Workload & 10.83 & 0.66 & 10.02 & 0.6 \\ \hline
**PANAS** & & & & \\ Positive Emotion \({}^{*}\) & 36.33 & 0.75 & 35.49 & 0.97 \\ Negative Emotion \({}^{*}\) & 15.37 & 0.66 & 12.09 & 0.41 \\ \hline
**Motivation** & & & & \\ Challenge \({}^{*}\) & 17.87 & 0.46 & 16.57 & 0.53 \\ Interest & 24.47 & 0.45 & 24.16 & 0.55 \\ Success probability & 8.56 & 0.22 & 8.34 & 0.25 \\ Fear of failure \({}^{*}\) & 10.07 & 0.54 & 7.61 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive data for the pre-test and post-test across all groups. (\({}^{*}\)) indicates statistically significant variables.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Variable** & **Group** & \multicolumn{2}{c}{**Pretest**} & \multicolumn{2}{c}{**Posttest**} \\ \cline{3-6}
**Performance** & & **Mean** & **SE** & **Mean** & **SE** \\ \cline{2-6} Objective deviation & EG & 7.57 & 0.33 & 4.55 & 0.46 \\ Deviation self-assessment & EG & 8.17 & 0.34 & 9.01 & 0.37 \\  & CG & 7.78 & 0.30 & 9.29 & 0.33 \\ Approach quality & EG & 149.78 & 17.11 & 250.37 & 13.76 \\  & CG & 167.89 & 15.28 & 273.35 & 12.29 \\ \hline
**Situation awareness SART** & & & & \\ Understanding & EG & 12.75 & 0.55 & 14.29 & 0.66 \\  & CG & 12.49 & 0.54 & 14.42 & 0.63 \\ Demands & EG & 10.89 & 0.60 & 11.25 & 0.71 \\  & CG & 10.08 & 0.58 & 10.00 & 0.69 \\ Supply & EG & 20.29 & 0.56 & 19.79 & 0.73 \\  & CG & 20.89 & 0.55 & 20.91 & 0.71 \\ Total SA score & EG & 22.14 & 1.19 & 22.82 & 1.34 \\  & CG & 23.30 & 1.15 & 25.33 & 1.29 \\ \hline
**Workload NASA-TLX** & & & & & \\ Workload & EG & 11.32 & 0.95 & 9.79 & 0.86 \\  & CG & 10.34 & 0.92 & 10.25 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive data of the experimental group (EG) and control group (CG) for the pre-test and post-test.  
The results for trainees' AR immersion and their assessment of AR features are presented descriptively in Table 4. There were no significant gender differences.
###### Abstract  
To improve the tracking performance and robustness of traditional multi-motor speed cooperative control, this paper proposes a speed cooperative control method for multiple permanent magnet synchronous motors (multi-PMSMs) based on the fixed-time consensus protocol for multi-agent systems (MASs) combined with CSMC. Firstly, the speed regulation system of multi-PMSMs is regarded as a MAS. By designing a distributed consensus protocol based on an undirected communication topology, the system achieves fixed-time consensus convergence. Then, a terminal integral sliding mode observer (TISMO) is designed to estimate disturbances, and feedforward compensation is introduced into the consensus protocol to obtain the desired q-axis current. Furthermore, within the framework of the vector control speed cooperative system of PMSMs, a CSMC is designed to track the q-axis reference current. Meanwhile, the stability of the above controllers and observers is theoretically proven using the Lyapunov functions. Finally, comparative experiments are conducted on a multi-PMSM speed regulation experimental platform to verify the proposed control method against the traditional deviation coupling control (DCC) method. The results indicate that under the new control method proposed in this paper, the chattering phenomenon is reduced by about 2 r/min compared to the traditional DCC method. During sudden load and sudden relief load conditions, the speed fluctuation is reduced by approximately 4%, demonstrating good tracking performance and strong robustness.  
m 202413 4407 11 [1]School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China; [EMAIL_ADDRESS]  
1  
ultiple permanent magnet synchronous motors (multi-PMSMs); multi-agent system (MAS); fixed-time consensus; terminal integral sliding mode observer (TISMO); complementary sliding mode control (CSMC)
## 5 Experimental Results and Discussion
### Amplitude Mismatch with Differential Driven Mode  
Figure 8a shows the received signal spectra as a function of amplitude mismatch ratio after 20 km SSMF transmission. The bias voltage was fixed as 5.1 V. Here, amplitude ratio 0% (black curve) was equivalent to single-arm driven mode. As the amplitude ratio got smaller, the low-frequency region became lower, while the high-frequency region was slightly lifted. The tendency was similar to numerical results in Figure 3a. On the other hand, if the amplitude ratio was fixed at \(-15\%\), changing the bias voltage could also enlarge the bandwidth, as shown in Figure 3b. In comparison, amplitude ratio was more effective in terms of the spectrum.  
As shown in Figure 9a,b, the bias voltage and amplitude ratio were jointly optimized for the BER of 34 Gbaud PAM-6/8 signals after 20 km transmission, respectively. The BERs were obtained with VNE. It should be noted that the amplitude between \(-15\%\) and 0% was not swept, owing to the 75 mV minimum output limitation of the AWG. As mentioned in Section 3.2, a negative amplitude ratio would enhance the performance of fading frequency shifting, resulting in a smaller optimal bias voltage. Furthermore, the performance of 0% amplitude ratio was mainly limited by the high-frequency falling, while the performance of the \(-35\%\) amplitude ratio was degraded due to the low-frequency suppression. As a compromise, the \(-15\%\) amplitude ratio exhibited the best performance, and the BERs were further reduced from \(1.41\times 10^{-3}\) and \(1.37\times 10^{-2}\) to \(5.87\times 10^{-4}\) and \(8.84\times 10^{-3}\) compared with single-arm driven mode. Therefore, the amplitude ratio was kept as \(-15\%\) in Figure 9c-f.  
Figure 8: (a) Received signal spectra versus amplitude ratio with 5.1 V bias voltage after 20 km SSMF transmission. (b) Received signal spectra versus bias voltage with amplitude ratio of \(-15\%\) after 20 km SSMF transmission.  
Figure 9c,d shows the ROP sensitivities of PAM-6 and PAM-8 signals after 20 km SSMF transmission, respectively. The ROP sensitivities could be improved to \(-\)4.3 dBm and \(-\)2.9 dBm at the 7% and 20% HD-FEC thresholds for PAM-6 and PAM-8 signals. Moreover, the ROP penalty at BTB were measured in Figure 9e,f. At 7% and 20% HD-FEC thresholds, there were 2.2 dB and 2.1 dB penalties for PAM-6 and PAM-8 signals, which was much larger than the single-arm driven mode. The reason is that part of amplitude modulation would become phase modulation when k < 0 and square-law detection at BTB loses the phase information.
## 1 Introduction
### Wavelet-Based Matched-Filter for the Inverse Radon Transform
The so-called centered (\(q=0\)) analyzing wavelets \(\psi_{j,0}(t)\) are centered on the time \(t_{j}^{0}=2^{j}\Bigl{(}t-\frac{1}{2F_{e}}\Bigr{)}\) and on the frequency \(v_{j}^{0}=\frac{2^{j+1}}{3}F_{e}\), with the bandwidth of \(\left[v_{j}^{1};\;v_{j}^{2}\right]=\left[\frac{2j}{3}F_{e};\frac{2^{j+2}}{3} F_{e}\right]\). For a signal \(s\) of sampled at frequency \(F_{e}\) with \(N\) samples, the only nonzero scales are the dilatation scales \(j\leq 0\). The wavelet coefficients are defined as the decomposition of the signal on dyadic functions, which are a convolution filters between the signal \(s(t)\) and the analyzing wavelets \(\psi_{j,0}(t)\):  
\[X_{j}(t)=\int_{-\infty}^{\infty}\psi_{j,0}(t)s(t)dt=\left[s(t)\;\otimes\; \psi_{j,0}(t)\right] \tag{17}\]  
If the signal \(s(t)\) is equal to \(\psi_{j}(t)\), which is the centered analyzing wavelet at the dilatation scale \(J\) (\(J\in\mathbb{Z}\)), the wavelet decomposition of this signal on the dyadic functions makes it possible to calculate the dyadic coefficients \(X_{j}\):  
\[X_{j}(t)=\psi_{j}(t)\;\otimes\;\psi_{j,0}(t)=\left\{ \begin{array}{c}1\text{ if }j=J\text{ and }q=0\\ 0\text{ elsewhere.}\end{array} \right. \tag{18}\]  
The properties of the wavelet decomposition are such that the coefficients \(X_{j}\) nullify everywhere except for \(j=J\). This equality makes it possible to adapt the discrete high-pass filtering of the Radon inversion to a wavelet-based matched filtering of the sinogram. The inversion of the data based on the Radon transform is done. The basic principles of the projection-like Radon-geometry algorithms applied for UST are well established for weakly variable media [7]. For imaging complete organs with hypo- and hyperechogenic structures, contrast imaging should be differentiated from obstacle imaging.
## 5 Brillouin Optical Correlation Domain Reflectometry  
As for the spontaneous Brillouin scattering caused along the fiber, distribution can also be measured by the correlation domain technique. This is called \"Brillouin optical correlation domain reflectometry (BOCDR)\" [9; 55; 79; 80; 81]. In this section, principle and performance improvement schemes for the BOCDR are described.
# Attenuation of Lightning-Induced Effects on Overhead Distribution Systems in Urban Areas
## 2 Lightning Details, FEM Model, and LIGHT-PESTO Code
### Lightning Details  
In the following, a typical first return stroke, whose time domain channel-base current (\(i(0,t)\)) is described by the well-known Heidler's function [16] is implemented. According  
Figure 1: Considered geometry in the presence of buildings.  
to the so-called engineering models, the propagation of the current along the channel is given by:  
\[i(z^{\prime},t)=i\left(0,t-\frac{z^{\prime}}{v}\right)P\left(z^{\prime}\right)u \left(t-\frac{z^{\prime}}{v}\right) \tag{1}\]  
where  
* \(v\) is the return stroke speed (assumed to be the half of the light speed)
* \(P(z^{\prime})\) is the attenuation function (assumed to be exponential, as dictated by the Modified Transmission Line Exponential (MTLE) model [17],
* \(u\) is the Heaviside function.
# Unveiling the Core Patterns of High-Latitude Electron Density Distribution at Swarm Altitude
###### Abstract  
The ionosphere has distinctive characteristics under different solar and geomagnetic conditions, as well as throughout the seasons, and has a direct impact on our technological life in terms of radio communication and satellite navigation systems. In the pursuit of developing highly accurate ionospheric models and/or improving existing ones, understanding the various physical mechanisms that influence electron density dynamics is critical. In this study, we apply the Multivariate Empirical Mode Decomposition (MEMD) method to the electron density distribution in the mid-to-high latitude (above 50\({}^{\circ}\) magnetic latitude) regions in order to identify the dominant scales at which these mechanisms operate. The data were collected by the Swarm mission in the Northern Hemisphere. MEMD allows us to separate the main intrinsic modes and assess their relative contributions to the original one, thereby identifying the most important modes and the spatial scales at which they exert influence. Our study spanned the period from 1 January 2016 to 31 December 2021, which was characterized by low solar activity levels. This choice allowed for a more focused investigation of other variables influencing electron density distribution under similar solar activity conditions. We specifically examined the variations of the resulting modes in relation to different seasons and geomagnetic activity conditions, providing valuable insights into the complex behavior of the ionosphere in response to various external factors.  
Multivariate Empirical Mode Decomposition; electron density distribution; Swarm satellites; high-latitude ionosphere; seasonal and geomagnetic variability 2023  
154550[[https://doi.org/10.3390/rs15184550](https://doi.org/10.3390/rs15184550)]([https://doi.org/10.3390/rs15184550](https://doi.org/10.3390/rs15184550))
# Economic Superiority of PIP Slip Joint Compared to Conventional Tubular Joints
###### Abstract  
This paper examines the costs associated with installing PIP (Pile-in-Pile) slip joints compared to traditional tubular joints, focusing on investment, installation processes, and long-term benefits. Previous studies have indicated that the structural performance of PIP slip joints is superior to that of traditional joints. By utilizing the frictional interfaces between conventional structural steel components and the simplest installation methods, PIP slip joints maximize structural integrity and ease of maintenance. As a result, they can lead to lower lifecycle costs, provided they are installed correctly. Quantitatively, the PIP slip joint achieved the highest internal rate of return (IRR) at 43.42%, the lowest Levelized Cost of Energy (LCOE) at 0.013589 EUR/kWh, and the shortest payback period at 2.92 years--outperforming grouted and bolted flange joints across all key financial metrics. The analysis also addresses logistical challenges and workforce requirements, highlighting that significant economic benefits can be realized when implemented appropriately. Furthermore, the PIP slip joint promotes sustainability goals by minimizing material usage, which ultimately leads to reduced carbon emissions through more efficient fabrication and installation, as well as enabling faster deployment. A comprehensive financial assessment of these joint systems in offshore wind monopiles reveals that PIP slip joints are the most cost-effective and financially advantageous option, outperforming key metrics like IRR, LCOE, and payback period due to lower initial investments and operational costs. As PIP slip joints yield a higher net present value (NPV), a shorter payback period, and a lower LCOE, they can enhance profitability and reduce financial risk, and are suitable for streamlined implementation. While grouted and bolted flange joints exhibit similar financial performance, PIP slip joints' minimal expenditure and consistent superiority make them the optimal choice for sustainable and economically viable offshore wind projects.  
PIP slip joints; lifecycle cost analysis; net present value; structural integrity; Levelized Cost of Energy; economic feasibility 2025  
4
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Multi-Scale Feature Adaptive Enhancement (MS-FAE) Module  
To address the issue of detail loss during the feature extraction of small objects, this section introduces the Multi-Scale Feature Adaptive Enhancement (MS-FAE) method. While this study employs YOLOv9 as the object detector, using only the deepest feature map (P2 layer) proves insufficient in preserving discriminative information for small objects. To resolve this limitation, we design the MS-FAE module as illustrated in Figure 2, which com  
Figure 1: Overall framework of the tracking model.  
prises the following three key components: (1) multi-scale feature pyramid fusion; (2) small object adaptive attention mechanism; and (3) precise feature mapping with dynamic anchor box compensation. By integrating feature representations from different scales and incorporating an adaptive attention mechanism, this method significantly enhances the tracker's capability to extract features from small objects.
# Intelligent Measurement of Coal Moisture Based on Microwave Spectrum via Distance-Weighted kNN
## 2 Materials and Methods
### Nested Cross-Validation Method  
Feature selection can affect the accuracy of machine learning models. For the DW-kNN algorithm, the nearest neighbor number \(k\) is a fundamental factor affecting the accuracy. Under a small \(k\) value, the DW-kNN algorithm was equivalent to the prediction with a small number of training samples, and only a small part of useful data could be captured, which could lead to the high overall complexity of the model and overfitting. On the contrary, under a larger \(k\) value, the training samples far from the sample to be tested could also be processed by the model, leading to a wrong prediction [36; 37]. Therefore, choosing an appropriate \(k\) value is crucial for achieving the high prediction performance of a model.  
The NCV is a well-known method for estimating the generalization ability of a model, especially when the amount of data is limited [38]. In the field of classification and regression analysis, the NCV has been used to select data features and adjust model hyperparameters to avoid model overfitting [39; 40]. The NCV typically includes two types of loops: an inner loop, which is used to optimize hyperparameters of a model, and an outer loop, which is used to evaluate model performances using unprecedented test data. The schematic diagram of the NCV is shown in Figure 4.  
The dataset was divided into six outer folds, each of which was retained for outer testing, while the remaining five folds were used in the inner loop as an outer training set. In the inner loop, the five folds were used as a validation set in turn, and the remaining data were used for inner training. The hyperparameters were constant in each inner cross  
Figure 4: The schematic diagram of the Nested Cross-validation method: the inner loop is used to optimize hyperparameters of the model and the outer loop is used to evaluate model performances using unprecedented test data, and D1–D6 represent the divided 6 groups of data respectively.  
validation and were evaluated by the average \(R^{2}\) obtained from the validation set. The hyperparameters corresponding to the best prediction effect of the model in the inner loop were selected to test the test set. After repeating the above loops, six groups of outer loop results were obtained, and the performance of the model was evaluated by the average indices.
# Flexible Antennas: A Review
## 2 Materials for Flexible Antennas
### Conductive Materials  
In wireless applications, the realization of conductive patterns with superior electrical conductivity is essential for ensuring high gain, efficiency, and bandwidth. Additionally, resistance to degradation due to mechanical deformation is another desired feature for the conductive material. Nanoparticle (NP) inks (i.e., silver and copper) are often preferred for fabricating flexible antennas due to their high electrical conductivity. Silver-nanoparticle ink edges over copper nanoparticles due to their low rate of oxide formation [21]. Very few investigations have been observed for flexible antennas based on copper-based nanoparticles [22]. Besides nanoparticles, electro-textile materials like Ni/Ag-plated, Flectron (copper-coated nylon fabric), and nonwoven conductive fabrics (NWCFs) are generally used in flexible antennas. Various types of textile and non-textile conductive materials for developing flexible antennas have been reviewed in an earlier article [14]. Adhesive copper [14], copper tapes [23], and copper cladding [24] have been reported in the development of flexible antennas.  
Figure 3 shows the images of antennas fabricating with different conducting materials. Conductive polymers like polyaniline (PANI) [25], polypyrrole (PPy) [26], and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) [27] seem to be promising materials for flexible and wearable antennas. The low conductivity of conductive polymers was improved by adding carbon nanotubes [28], graphene [29], and carbon nanoparticles [30] (Figure 3). Flexible antennas using graphene are promising due to their decent electrical conductivity and excellent mechanical properties. Graphene paper [31], graphene nanoflake ink [32; 33], graphene oxide ink [34], and graphene nanoparticle ink [35] have been used in prior studies for fabricating flexible antennas. The performance of flexible antennas relies heavily on the fabricated conducting traces with high deformation sustainability while maintaining electrical conductivity [36]. For accommodating mechanical strain and deformation without deteriorating the performance of the antennas, different stretchable conductive materials exploit doping to improve their conductivity. Some of the examples include silver nanowire embedded silicone [37], silver loaded fluorine rubber [38], carbon nanotubes (CNT)-based conductive polymers [28; 39], liquid metals in the stretchable substrate [40], and use of stretchable fabric itself [41]. Table 1 lists the different conductive materials used in the fabrication of a flexible antenna along with their conductivity values.  
Figure 3: Schematic of antennas with different conducting materials. (**a**) Polymer ultra-wideband antenna using poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) [42], (**b**) platinum-decorated carbon nanoparticle/polyaniline hybrid paste for flexible wideband dipole tag-antenna [30], and (**c**) a stretchable microstrip patch antenna composed of nanowire (AgNW)/polydimethylsiloxane (PDMS) flexible conductor [37].