esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 5 Prototype Fabrication and Measurements
### Radiation Pattern
Therefore, there is a 3.6 dB decrease in gain because of the absorption due to the finite conductivity of the human wrist, which in this case is emulated by IXB-063 phantom column. Moreover, a distortion of the radiation pattern can be noticed which is \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Measured** & **Simulated** & **\(\Delta\) (dB)** \\ \hline Peak Realized Gain (dBi) & \(-1.25\) & \(-2.37\) & \(1.12\) \\ Directivity (dBi) & \(1.78\) & \(3.15\) & \(1.37\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison between measured and simulated radiation characteristics of the antenna in free-space. Figure 12: Radiation pattern measurements: (**a**) measurement setup in free-space scenario; (**b**) measured 3D radiation pattern of the antenna system in free-space; (**c**) simulated 3D radiation pattern of the antenna system in free-space. due to the coupling of the radiator with the phantom column. It can be also noticed that the boresight direction is oriented to the rear-side of the antenna, which is due to the presence of the processing board as well as the ground plane and the coin cell battery that minimize the coupling between the antenna system and the phantom column. In Figure 13c the simulated 3D radiation pattern of the proposed antenna system on the phantom column IXB-063 scenario is reported. The simulated peak realized gain of the antenna results to be equal to \(-4.87\) dBi at 915 MHz. The simulated radiation pattern presents similar characteristics compared to the measured one, with boresight direction oriented to the rear-side of the antenna. Also, in this case slight differences can be noticed between measured and simulated radiation pattern due to the presence of the MAPS in the measurement system, which is not taken into account in 3D EM model used in simulation. In Table 8, a comparison between measured and simulated peak realized gain and directivity of the antenna on the phantom column IXB-063 is reported. A difference between measured and simulated peak realized gain of 1.22 dB and between measured and simulated directivity of 1.38 dB is observed. Also in this case, the difference in these values is mainly due to the fact that the losses associated with the SMA connector, the coaxial to CPGW transition and the matching network are not considered in 3D EM model. Figure 13: (**a**) antenna measurement setup on IXB-063 phantom column scenario; (**b**) measured 3D radiation pattern of the antenna system placed on IXB-063 phantom column; (**c**) simulated 3D radiation pattern of the antenna system placed on IXB-063 phantom column.
## 3 Results
### Identification of HIPS and ABS Using Stationary FTIR Spectrometer  
Figure 4 shows the FTIR analysis for ABS and HIPS plastics. The peaks located at 697 cm\({}^{-1}\), 758 cm\({}^{-1}\), 2849 cm\({}^{-1}\), 2920 cm\({}^{-1}\) and 3027 cm\({}^{-1}\) correspond to a single bond between carbon and hydrogen (C-H). The band wavelength at 1494 cm\({}^{-1}\) corresponds to single bonds between two carbon atoms (C-C). A characteristic bond can be distinguished between carbon and nitrogen (2237 cm\({}^{-1}\)). Vibrations of the methyl group are visible at a frequency of 1452 cm\({}^{-1}\). The peaks observed at 1602 cm\({}^{-1}\) and 1637 cm\({}^{-1}\) correspond to the double bond between two carbon atoms (C=C). Vibrations of the C=O groups are visible at approximately 1732 cm\({}^{-1}\). The spectrum shows vibrations at wavelengths of 758 cm\({}^{-1}\) and 966 cm\({}^{-1}\), corresponding to those of the =C-H group [16; 25; 26].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Mass** & **\% Mass** \\  & **[g]** & **[\%]** \\ \hline PS & 100.40 & 57.44 \\ ABS & 68.60 & 39.24 \\ PP & 4.80 & 2.75 \\ PA & 1.00 & 0.57 \\ \hline TOTAL & 174.80 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the test of the sample.  
Figure 3: Laboratory sample after the analysis with the NIR spectrometer.  
Figure 5 shows the FTIR analysis for the HIPS plastic. The peaks observed at 695 cm\({}^{-1}\), 754 cm\({}^{-1}\), 2848 cm\({}^{-1}\), 2920 cm\({}^{-1}\) and 3025 cm\({}^{-1}\) correspond to the single bond between carbon hydrogen atoms (C-H). Vibrations of the methyl group (-CH\({}_{2}\)) are visible at a frequency of 1452 cm\({}^{-1}\). The peaks located at 754 cm\({}^{-1}\) and 965 cm\({}^{-1}\) correspond to the double bond of carbon (=C-H). Vibrations of the C=C groups are visible at approximately 1601 cm\({}^{-1}\). The spectrum shows vibrations at a wavelength of 1493 cm\({}^{-1}\), corresponding to those of the C-C bond [16; 25; 26].  
Figure 4: FTIR spectrum of one of the tested ABS flakes.  
Figure 5: FTIR spectrum of one of the tested HIPS flakes.  
The microscopic images show uneven surfaces of the examined plastics, e.g., scratches and indentations, as a result of the grinding process of refrigerators from which the polymers for this research were obtained. We see no influence on the quality of polymer tiles obtained in this process. This can affect the results of the contact angles and thus their standard deviations. However, surface rolling can have a positive effect on the flotation process.
# Digital Twins in Solar Farms: An Approach through Time Series and Deep Learning
## 2 Background
### Deep Learning  
Within the field of DL [7], convolutional networks (CNNs) [8] have demonstrated their effectiveness in multiple problems like object recognition, image segmentation and classifying sequences. Other popular neural network architectures are the recurrent ones, like LSTM (long short-term memory) [9], which are able to learn patterns hidden in time series. Several studies have shown the efficacy of both in the detection of anomalies [10; 11].  
Another DL family widely used for time series is the autoencoders. These are deep neural networks that use a non-linear reduction of dimensionality to learn the representations of the data in an unsupervised way. In this way, the network can \"self-learn\" the optimal parameter representation that the data set represents. This functionality has found extensive use in different applications, one of them being anomaly detection [7]. By using normal data for training, the autoencoder can learn the correct representations of a system, minimizing the reconstruction error and, in turn, using it as an anomaly evaluation criterion. As a result, autoencoders are able to detect anomalies in a simple way.
## 4 Results and Discussion
### Gradual Change Behaviour of Mesoscopic Evaluation Indices for the Main Skeleton
#### 4.2.2 Gradual Change Trend of \(\overline{n}_{c}\) and C Value  
The C value and \(\overline{n}_{c}\) are obtained by contact analysis using iPas software and, thereafter, by statistical analysis of the data. For the three types of asphalt mixtures in the tests, the gradual change in the trends of \(\overline{n}_{c}\) and C value with loading time is shown in Figure 10.  
According to Figure 10, the following can be concluded:  
(1) The value of \(\overline{n}_{c}\) increases with the increase in loading time. When the asphalt mixture is loaded to the ultimate bearing capacity of the main skeleton, the \(\overline{n}_{c}\) values of SMA-13 and AC-13C approach 2 and 1.6, respectively. In the design process of DSAM, gradation should be optimised with the goal of increasing \(\overline{n}_{c}\) so as to enhance the main skeleton's load resistance.  
(2) The C value decreases with the increase in loading time. The asphalt mixture is continuously compacted under the load action, the'suspended' coarse aggregates without contact points gradually come into contact with other coarse aggregates, and the quantity of coarse aggregates gradually decreases. The proportion of coarse aggregates with a coordination number of 0 to the total coarse aggregates gradually decreases from 30% before loading to approximately 7% at the end of the loading  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Coefficient** & \(\mathbf{y_{0}}\) & \(\mathbf{x_{c}}\) & \(\mathbf{W}\) & \(\mathbf{A}\) & \(\mathbf{R^{2}}\) \\ \hline \multirow{2}{*}{70\# asphalt AC-13C} & Before loading & \(-0.937\) & \(0.886\) & \(2.372\) & \(338.191\) & \(0.9717\) \\  & After 3-h loading & \(4.506\) & \(1.776\) & \(1.916\) & \(268.700\) & \(0.9962\) \\ \hline \multirow{2}{*}{Rock asphalt AC-13C} & Before loading & \(-1.091\) & \(1.124\) & \(2.182\) & \(313.786\) & \(0.9890\) \\  & After 3-h loading & \(3.897\) & \(1.685\) & \(1.870\) & \(266.512\) & \(0.9827\) \\ \hline \multirow{2}{*}{SMA-13} & Before loading & \(4.700\) & \(0.857\) & \(1.982\) & \(213.012\) & \(0.9829\) \\  & After 3-h loading & \(0.617\) & \(2.016\) & \(2.382\) & \(224.909\) & \(0.9154\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Coefficients of the Gaussian distribution function.  
Figure 9: Gaussian fitting curves. (**a**) Before loading; and (**b**) after 3-h loading.  
stage. In the design process of DSAM, a reasonable compaction standard should be selected to ensure the compaction power and to minimise the amount of'suspended' coarse aggregates.
###### Abstract  
Federated Learning (FL), a key paradigm in privacy-preserving and distributed machine learning (ML), enables collaborative model training across decentralized data sources without requiring raw data exchange. FL enables collaborative model training across decentralized data sources while preserving privacy. However, selecting appropriate clients remains a major challenge, especially in heterogeneous environments with diverse battery levels, privacy needs, and learning capacities. In this work, a centralized reward-based payoff strategy (RBPS) with cooperative intent is proposed for client selection. In RBPS, each client evaluates participation based on locally measured battery level, privacy requirement, and the model's accuracy in the current round computing a payoff from these factors and electing to participate if the payoff exceeds a predefined threshold. Participating clients then receive the updated global model. By jointly optimizing model accuracy, privacy preservation, and battery-level constraints, RBPS realizes a multi-objective selection mechanism. Under realistic simulations of client heterogeneity, RBPS yields more robust and efficient training compared to existing methods, confirming its suitability for deployment in resource-constrained FL settings. Experimental analysis demonstrates that RBPS offers significant advantages over state-of-the-art (SOA) client selection methods, particularly those relying on a single selection criterion such as accuracy, battery, or privacy alone. These one-dimensional approaches often lead to trade-offs where improvements in one aspect come at the cost of another. In contrast, RBPS leverages client heterogeneity not as a limitation, but as a strategic asset to maintain and balance all critical characteristics simultaneously. Rather than optimizing performance for a single device type or constraint, RBPS benefits from the diversity of heterogeneous clients, enabling improved accuracy, energy preservation, and privacy protection all at once. This is achieved by dynamically adapting the selection strategy to the strengths of different client profiles. Unlike homogeneous environments, where only one capability tends to dominate, RBPS ensures that no key property is sacrificed. RBPS thus aligns more closely with real-world FL deployments, where mixed-device participation is common and balanced optimization is essential.  
federated learning; client selection; game theory; cooperative learning; heterogeneous devices; reward-based payoff strategy +
Footnote †: journal: Applied sciences
## 4 Discussion
### Comparative Analysis of SWQI and CWQI in the Ribnica Reservoir  
The comparative evaluation of the Serbian Water Quality Index (SWQI) and the Canadian Water Quality Index (CWQI) provides an in-depth understanding of water quality conditions in the Ribnica Reservoir from diverse ecological and usage perspectives. Despite their common goal of comprehensively assessing water quality, these indices differ methodologically, thus offering complementary insights.  
Analysis using the SWQI indicated an overall enhancement in water quality from 2021 to 2022, characterized by increased oxygen saturation, lower biochemical oxygen demand (BOD), and improved or stable pH and electrical conductivity values. SWQI scores transitioned from primarily good and occasionally medium levels in 2021, to predominantly excellent and high-end good categories in 2022. These changes suggest favorable environmental management and positive hydrological conditions. However, depth-specific variations highlight persistent issues that may necessitate targeted interventions.  
On the other hand, CWQI results revealed more nuanced water quality dynamics across different intended uses, highlighting risks not captured by the SWQI. CWQI scores varied significantly, ranging from poor to good, largely influenced by high concentrations of aluminum (Al), mercury (Hg), and reduced dissolved oxygen (DO). The assessment of water quality for drinking purposes showed mainly excellent outcomes, with slight decreases attributed to manganese (Mn) and iron (Fe). Importantly, CWQI identified substantial concerns regarding aquatic life, categorizing water quality primarily as poor to fair due to low dissolved oxygen levels and elevated concentrations of aluminum, chromium, and copper.  
This comparative evaluation underscores the utility of employing both indices simultaneously. While the SWQI effectively captures general physico-chemical properties and nutrient levels aligned with regional regulatory standards, the CWQI enriches the assessment by including broader ecological threats, such as heavy metal contamination, and suitability for specific uses (drinking water, aquatic life, and agriculture). This combined methodology provides a comprehensive overview, highlighting factors potentially overlooked when relying solely on one index.  
The integration of SWQI and CWQI assessments offers a robust and multifaceted approach for monitoring water quality in the Ribnica Reservoir. Continued application of both indices, supplemented by enhanced monitoring of microbiological and heavy metal parameters, is crucial for thorough water quality management and the protection of this ecologically valuable water body.
## 3 Case Study: Experimental Verification and Comparison Results
### Comparison Studies  
To evaluate the proposed method, the data from GA steel were set as the target domain, because it has the highest diagnosis difficulty. Among 360 target data collected from the GA steel welding under the same conditions, the randomly selected 180 data of 90 normal and 90 fault were used as target test data. The remaining 180 data were used for model training. For training the pre-trained model, 1440 data in the entire source domain were used. All result comparisons were derived by repeating the same model training process five times and calculating the arithmetic mean. In the following three comparison studies, we observed the model performances (i.e., prediction accuracy) depending on the employed (1) signal processing methods and learning algorithms, (2) the proposed method and the non-transfer learning method, (3) the instance optimization methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen** & **Electrode Force** & **Weld Current** & **Weld Time** & **Hold Time** & **Hold Time** \\ \hline Mild steel & 250 kgF & 5.0 kA & 0.167 s & 0.250 s & 0.250 s \\ GA steel & 385 kgF & 5.5 kA & 0.167 s & 0.250 s & 0.250 s \\ GI steel & 360 kgF & 6.0 kA & 0.167 s & 0.250 s & 0.251 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weld conditions for each specimen.  
Figure 5: Cross-section test images of the welded specimen: (**a**) normal state, (**b**) small nugget.
###### Abstract  
In this paper, we present a novel signal processing method for video synthetic aperture radar (ViSAR) systems, which are suitable for operation in unmanned aerial vehicle (UAV) environments. The technique improves aspects of the system's performance, such as the frame rate and image size of the synthetic aperture radar (SAR) video. The new ViSAR system is based on a frequency-modulated continuous wave (FMCW) SAR structure that is combined with multiple-input multiple-output (MIMO) technology, and multi-channel azimuth processing techniques. FMCW technology is advantageous for use in low cost, small size, and lightweight systems, like small UAVs. MIMO technology is utilized for increasing the equivalent number of receiving channels in the azimuthal direction, and reducing aperture size. This effective increase is achieved using a co-array concept by means of beat frequency division (BFD) FMCW. A multi-channel azimuth processing technique is used for improving the frame rate and image size of SAR video, by suppressing the azimuth ambiguities in the receiving channels. This paper also provides analyses of the frame rate and image size of SAR video of ViSAR systems. The performance of the proposed system is evaluated using an exemplary system. The results of analyses are presented, and their validity is verified using numerical simulations.  
video synthetic aperture radar; multiple-input multiple-output; multi-channel azimuth processing; frequency-modulated continuous waveform; beat frequency division; polar format algorithm +
Footnote †: journal:
# Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane
## 2 Hybrid Enhanced Polarization Image Clarity  
### Principle of Polarization Imaging  
The Stokes vector method is a commonly used method to describe polarized light. The Stokes vector has four components \(S=\left(I,Q,U,V\right)^{\mathsf{T}}\). It contains full polarization information of light. Among them, \(I\) is light intensity, \(Q\) and \(U\) are linear polarization information and \(V\) is circular polarization information. Aiming at the problem of weak and difficult measurements of circular polarization information in underwater measurement environments, In this paper, polarization imaging detection is based on linear polarization information. The linear polarization component of the Stokes vector can be expressed as:  
\[S=\left[\begin{array}{c}I\\ Q\\ U\end{array}\right]=\left[\begin{array}{c}1/2(I_{0}+I_{45}+I_{90}+I_{135})\\ I_{45}-I_{135}\\ I_{0}-I_{90}\end{array}\right] \tag{1}\]  
In Equation (1), \(I_{0}\), \(I_{45}\), \(I_{90}\) and \(I_{135}\) are polarization images with polarization directions of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\) and 135\({}^{\circ}\), respectively. According to the definition, \(DOP\) represents the ratio of linearly polarized light intensity to total light intensity, which can be calculated by Equation (2).  
\[DOLP=\frac{\sqrt{Q^{2}+U^{2}}}{I} \tag{2}\]  
The classical water degradation image-sharpening method has the following problems: the prior assumption of the underwater imaging model is the premise of image sharpening; image clarity is highly dependent on the accuracy of prior conditions. Therefore, the classical method is difficult to adapt to complex underwater scenes and its low robustness. In this paper, a hybrid enhanced water-descent polarization image-sharpening method is proposed. Figure 1 shows the algorithm flow. The pixel value is adjusted by analyzing the feature information of underwater image. Traditional avoidance methods are limited by complex prior conditions and cumbersome model parameter estimation. The algorithm implementation process is as follows:  
Firstly, according to the Stokes vector method, the total intensity image \(I\) of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\) and 135\({}^{\circ}\) polarization images is obtained using Equation (1).  
Secondly, an improved GrayWorld method combined with color compensation is established. The image attenuation difference caused by underwater propagation is compensated. Finally, the white balance image \(W\) is used as the guide image. By constructing a variational contrast and saturation enhancement algorithm, the image contrast and saturation are improved. The turbid appearance caused by underwater scattering is eliminated. After iterative calculation, a clear image \(D\) is finally obtained.
# Analysis of Lightweight Structure Mesh Topology of Geodesic Domes
###### Abstract
Additionally, research on the shaping and optimization of structures through the use of cross-section has a positive impact on the aspect of sustainable development [41; 42; 43]. Moreover, thanks to the use of advanced software, it allows the implementation of spectacular structures that become the showcase of a city or even a country. The available software allows you to design lightweight structures with maximum use and respect for materials, in line with the idea of sustainable development. This paper aims to present the mesh topology of shaping structural solutions in the form of geodesic domes while considering two different methods of creating. The base polyhedron for generating the analyzed domes is a regular octahedron, i.e., that is not commonly used when creating geodesic domes. Generally, the regular octahedron is more complicated in the process of creating domes but the shape is more effective than in the case of the dodecahedron, which is the biggest advantage of this structure. Until now, the octahedron was analyzed only by [PERSON] [44; 45; 46] and [PERSON] [47]. Based on this research, the paper presents in detail the process of creating the mesh topology of geodesic dome grids, using the example of two structures, a 4608-hedron and a 4704-hedron. Thus, the novelty of this paper is an implementation of the formulas and algorithms from geodesic domes based on the regular dodecahedron to the regular octahedron, which has not been presented so far. It should be emphasized that the dome shaped according to the first method is an easier solution due to the mesh topology of their forms. Moreover, the results obtained from the comparative static and strength analysis also indicate the advantage of this dome over the other construction solutions used. In addition, this research also shows that exciting structures can be made in the spirit of sustainable development.